India's New Economy: Industry Efficiency and Growth

India’s New Economy Also by Jati Sengupta: Jati Sengupta (author) INDIA’S ECONOMIC GROWTH A Strategy for the New Econ...

Author: Jati K. Sengupta | Chiranjib Neogi

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India’s New Economy

Also by Jati Sengupta: Jati Sengupta (author) INDIA’S ECONOMIC GROWTH A Strategy for the New Economy

Jati Sengupta (author) COMPETITION AND GROWTH Innovations and Selection in Industry Evolution

Jati Sengupta (author) DYNAMICS OF ENTRY AND MARKET EVOLUTION

Jati Sengupta and Biresh Sahoo (authors) EFFICIENCY MODELS IN DATA ENVELOPMENT ANALYSIS Techniques of Evaluation of Productivity of Firms in a Growing Economy

India’s New Economy Industry Efficiency and Growth

Jati Sengupta Professor of Economics, University of California, Santa Barbara, California, USA

and Chiranjib Neogi Associate Scientist, Economic Research Unit, Indian Statistical Institute, Calcutta, India

© Jati Sengupta and Chiranjib Neogi 2009 All rights reserved. No reproduction, copy or transmission of this publication may be made without written permission. No portion of this publication may be reproduced, copied or transmitted save with written permission or in accordance with the provisions of the Copyright, Designs and Patents Act 1988, or under the terms of any licence permitting limited copying issued by the Copyright Licensing Agency, Saffron House, 6–10 Kirby Street, London EC1N 8TS. Any person who does any unauthorized act in relation to this publication may be liable to criminal prosecution and civil claims for damages. The authors have asserted their rights to be identified as the authors of this work in accordance with the Copyright, Designs and Patents Act 1988. First published 2009 by PALGRAVE MACMILLAN Palgrave Macmillan in the UK is an imprint of Macmillan Publishers Limited, registered in England, company number 785998, of Houndmills, Basingstoke, Hampshire RG21 6XS. Palgrave Macmillan in the US is a division of St Martin’s Press LLC, 175 Fifth Avenue, New York, NY 10010. Palgrave Macmillan is the global academic imprint of the above companies and has companies and representatives throughout the world. Palgrave® and Macmillan® are registered trademarks in the United States, the United Kingdom, Europe and other countries. ISBN-13: 978 0 230 20170 5 ISBN-10: 0 230 20170 9

hardback hardback

This book is printed on paper suitable for recycling and made from fully managed and sustained forest sources. Logging, pulping and manufacturing processes are expected to conform to the environmental regulations of the country of origin A catalogue record for this book is available from the British Library. Library of Congress Cataloging-in-Publication Data Sengupta, Jatikumar. India’s new economy : industry efficiency and growth / by Jati Sengupta and Chiranjib Neogi. p. cm. Includes bibliographical references and index. ISBN 978–0–230–20170–5 (alk. paper) 1. High technology industries—India. 2. Finance—India. 3. India—Commerce. 4. India—Economic policy—1991– I. Neogi, Chiranjib. II. Title. HC440.H53S46 2008 338.0954—dc22 2008030363 10 9 8 7 6 5 4 3 2 1 18 17 16 15 14 13 12 11 10 09 Printed and bound in Great Britain by CPI Antony Rowe, Chippenham and Eastbourne

To Jayen, Aria, Shiven and Myra and Archisman

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Contents List of Tables

viii

List of Figures

xi

Preface

xii

1

The New Knowledge Economy and India’s Growth

1

2

India’s Industry Growth: Its Structure and Potential

30

3

Industrial Productivity in the New Economy

56

4

Industry Efficiency Analysis

104

5

Efficiency Analysis of Selected Manufacturing Industries

134

6

The Performance of the Banking Sector in the New Economy

193

Notes

242

References

245

Index

255

vii

List of Tables 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 1.10 1.11 1.12 1.13 2.1 2.2 2.3

2.4 2.5

2.6 2.7 2.8 2.9

Impact of R&D spending on growth efficiency based on the DEA model Impact of R&D inputs for DEA efficient firms Number of foreign collaborations in electronics industry by type of collaboration Cost of technology import as a proportion of sales for different product groups (in %) Inter-state variations in electronics output Export performance by major product groups Pattern of software exports from India and its competitors (1990) Effects of knowledge and other explanatory variables on R&D intensity Unweighted average customs duty rates (%) Performance of the five largest IT service providing firms in India R&D distribution by industry (%) R&D footprints of the top ten global R&D spenders, 2004 Percentage growth of R&D spending 1999–2004 Number of ‘births’ relative to the total number of businesses (%) Entry and exit rates (%) in Dutch manufacturing Explaining entry rates and market share turbulence in terms of industrial growth rates, scale economies and Herfindahl index Growth of output and inputs in the total manufacturing sector of the USA, Japan and Korea (1975–90, %) Average annual rates of growth of total and partial factor productivity in the total manufacturing sector of the US, Japan and Korea (1975–90, %) Sources of output growth for the total manufacturing sectors of the USa, Japan and Korea (1975–90, %) Internal rates of return on net investment in physical and R&D capital (in percentage) Decomposition of average annual TFP growth rates (%) Effect of R&D investment on firm performance viii

7 8 9 9 10 11 13 17 21 22 24 25 25 33 34

35 42

42 43 43 44 53

List of Tables ix

3.1

Growth rate of output of industries during 1973–4 to 1997–8 3.2 Percentage share of output in Indian industries 3.3 Percentage share of value added in Indian industries 3.4 Percentage of share of export of major items groups 3.5 Export performance of the industries 3.6 Imported input intensity of selected commodities 3.7 Growth of TFP and labor productivity over three subperiods 3.8 Sources of TFP growth 3.9 Import tariff rates of selected commodities 3.10 Test of changes of competition in selected industries 4.1 Radial labor efficiency measure (θ) (pre-reform era) 4.2 Radial labor efficiency (θ) (post-reform era) 4.3 Scale elasticity β1 = 1/b1 of banks in India 4.4 Sources of growth efficiency 4.5 Output trends over time (y(t) = a0 + a1 y(t)) 4.6 Level efficiency versus growth efficiency 4.7 Annual average levels of output per hour, investment per hour and R&D per hour in manufacturing (1990–8) at 1995 prices 4.8 The elasticities of R&D per work hour (the regression of labor productivity on I/L, RD/L and HK (1994–8) for the EU and USA) 4.9 The regression results over 1990–8 (fixed effects model) 4.10 Economic growth indicators in Taiwan 4.11 Estimates of the ratio FK /GM , 1967–87 5.1 Summary statistics of technical efficiencies of Indian industries with fixed rankings: time varying (Cobb–Douglas) model 5.2 Summary statistics of technical efficiencies of Indian industries with variable rankings: time varying (Cobb–Douglas) model 5.3 Average efficiencies of manufacturing units in the Indian textiles industry 5.4 Average scale of operation and efficiency of manufacturing units in the textiles industry in India 5.5 Ownership-wise efficiency of manufacturing units in textiles industries in India 5.6 Estimates of regression parameters of total efficiency variations

61 62 64 82 86 87 89 91 95 97 111 112 117 124 124 125

126

127 127 131 132

141

142 154 156 158 161

x

List of Tables

5.7 5.8 5.9 5.10 5.11 5.12 5.13 5.14 5.15 5.16 5.17 5.18 5.19 5.20 5.21 6.1 6.2 6.3 6.4 6.5 6.6 6.7 6.8 6.9 6.10 6.11 6.12 6.13

Average efficiencies of manufacturing units in the Indian leather industry Average scale of operation and efficiency of manufacturing units in the leather industry in India Ownership-wise efficiency of manufacturing units in the leather industry in India Estimates of regression parameters of total efficiency variations Average efficiency of the textiles industry Average efficiency of the electronics industry Ownership wise average efficiency of the textiles industry Ownership wise average efficiency of the electronics industry State-wise average efficiency of the textiles industry State-wise average efficiency of the electronics industry Distribution of outputs of the textiles industry Distribution of outputs of the electronics industry Percentage of underutilization of labor in the electronics industry Percentage of underutilization of capital in the electronics industry Average efficiency of the computer industry Features of commercial banking Summary of the banking sector (billion rupees) Selected banking indicators Output-oriented technical and scale efficiency of banks in India Technically efficient banks by ownership and by year Input congestion in Indian commercial banks Labor congestion in Indian commercial banks Cost efficiency of Indian commercial banks (cost function DEA approach) Sources of variation in cost efficiency Minimum average cost of efficient banks (in rupees) Optimum output calculated from the cost function (100,000 rupees) Test of Arrow’s learning by doing Allocative efficiency of Indian commercial banks

162 164 167 170 175 177 178 179 180 182 184 185 186 187 188 201 202 203 215 218 221 225 229 233 233 235 236 238

List of Figures 3.1 3.2 3.3 3.4

Share of output Share of value added Index of structural change Aggregate TFP indices for traditional, modern and total industries 3.5 Weighted TFP indices of traditional, modern and all industries 3.6 Wage differential components of traditional, modern and all industries 3.7 Rent differential components of traditional, modern and all industries 3.8 Relative TFP indices of traditional and modern to total industry 3.9 Relative weighted TFP indices of traditional and modern to all industries 3.10 Relative wage differential components of traditional and modern to all industries 3.11 Relative rent differential components of traditional and modern to all industries 3.12 Trends in exports of selected commodities 6.1 Technical efficiency of Indian commercial banks 6.2 Percentage of banks with labor congestion 6.3 Cost efficiency of Indian commercial banks 6.4 Allocative efficiency of Indian commercial banks

xi

63 65 66 73 74 75 75 76 77 78 78 83 216 228 232 239

Preface This volume attempts to understand India’s New Economy in recent years: its strength, weakness and economic potential. The new economy comprises three key areas of growth: the IT (information technology) sector, the export trade with its externality effects and the financial sector with banking reforms. Over the past two decades the IT and communication sector has grown most rapidly in India. Software development, electronic communication and telephone services have undergone a rapid surge and various technologies are merging. The overall impact of all these trends has been a steep rise in exports of IT products and related services. We have provided a critical analysis of these trends and assess their strength, weakness and potential. Trade and policy reforms in recent years have helped remove many bottlenecks and constraints on free market paradigms but still much more liberalization is needed if the global opportunities for competitive efficiency and competitive advantage are to be exploited to the fullest extent. The experiences of most rapid growth in the South East Asian countries labelled the newly industrializing countries (NICs) provide a unique growth model for India. How could these NICs, which include Hong Kong (China), South Korea, Taiwan and Singapore, grow so quickly over the past two decades, exceeding an average growth in national income of 7 to 8 per cent per year? By a systematic change in fostering free markets and global trade, adopting new technologies and improving them. Joint ventures, providing incentives for stepping up exports and fostering complementary investments in sectors directly and indirectly linked with the IT and communication sector are some key strategies adopted by the NICs in their growth model. India can not only adopt this growth model but improve on it substantially. This is due to the potential of the New Economy in India. Learning by doing, investment in knowledge capital and the pool of managerial talent provide the unique catalysts for India. Sustaining a high rate of growth in the global framework requires an efficient banking sector and sound monetary policy. This volume analyses the above three key areas of growth of the new economy. Comparing the productivity and efficiency of some key industries like textile, leather, electronics and computer-related products and evaluating the performance of the export sector provide the insight for xii

Preface xiii

understanding the new economy. The efficiency and growth of the banking sector, comprising both private and nationalized banks, are critically analysed. We are grateful to Professor Amitava Sen for his useful comments and suggestions on the chapters ‘Industrial Productivity in the New Economy’ and ‘Efficiency of Selected Manufacturing Industries’. Jati Sengupta Chiranjib Neogi

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1 The New Knowledge Economy and India’s Growth

What is the knowledge economy? What are its characteristics and how does it affect India’s growth? These are the basic questions to be asked when one attempts to assess India’s economic growth today and tomorrow. The knowledge economy has three fundamental characteristics: knowledge capital, competitive efficiency and open trade based on comparative advantage theory. Knowledge capital may take several forms, e.g. (a) software development, (b) blueprints and designs, (c) R&D knowledge as innovations and (d) human capital and skill in adopting new technology from abroad and improving it. Competitive efficiency refers to the market process by which entrepreneurs compete to exploit knowledge capital to improve their profitability. The profit incentives and open competition activate the output and market enhancing effects through cost efficiency due to economies of scale and of scope. Openness in trade involves competition to improve domestic efficiency, the adoption of leading edge technology and the exploitation of human capital and knowledge spillover from the international field. The outward orientation of an open economy allows India to exploit the benefits of the comparative advantage principle, where the IT (information technology) and ICT (information and communication technology) sectors play a dominant role. The dynamic comparative advantage principle suggests that India has to restructure its export trade in the IT sector so that it can act as a catalyst, improving the productivity of other domestic sectors through diffusion and market expansion. In China and Taiwan this force has played a dominant role in raising national growth rates and revitalizing rural and agricultural sectors. In the high-tech industries of today, investments in knowledge capital have played a crucial role as engines of growth. Many of the subsectors of

1

2 India’s New Economy

the ICT sector specializing in software services and managerial skills, in the area of international ‘outsourcing’, are highly labor-intensive. They exploit the spillover benefits of global R&D and innovation technology. The evolution of other modern industries has ushered in a new paradigm, affected by innovations in product design and production processes. On the one hand these innovations have helped the leading firms to grow at a rapid rate and enhance their core competence and managerial efficiency. Innovations in R&D and knowledge capital may take many forms but in a broad sense they involve developing new processes and new products and services and improving the borrowed technology and services. Several dynamic features of this innovation process are important for industry growth in today’s world. First, R&D expenditures not only generate new knowledge and new information about the latest technical processes and products but also enhance the firm’s ability to assimilate, exploit and improve the existing ‘knowledge capital’ and information base. We have to note, however, that most of the industry-wide R&D expenditures are devoted to product improvement and enhancement of the quality of existing services, such as better software and better networking. According to McGraw-Hill annual industry surveys for the past ten years the bulk of R&D spending (around 80 percent) is devoted to improving products, services and existing designs. Thus only a very small part of R&D expenditure is committed to the search for breakthrough innovations emphasized in the Schumpeterian model. The overall impact of R&D and expenditures on knowledge capital is to enhance the firm’s ability to assimilate and improve the existing various technology processes. For example, Cohen and Levinthal (1989) have shown in their extensive empirical study that the major reason why firms have invested in R&D in the semiconductor industry is because it provides an in-house technology capability that could keep these firms on the leading edge of the latest technology and thereby facilitate the assimilation of new technology developed elsewhere. A second aspect of R&D spending within a firm is its externality or spillover effect, involving knowledge diffusion to other firms, and very often this knowledge innovation finds new applications both locally and globally, thereby stimulating further innovative activity in other firms, e.g. the software industry and outsourcing services. Finally, the possibility of collaboration in R&D and research networking or joint ventures increases the incentive of firms to invest more, resulting in more industry investment in R&D and knowledge capital. In the absence of such collaboration, both implicit and explicit, the competing firms may not invest enough, since most innovation benefits cannot be totally internalized or


3

appropriated by the innovator, e.g. his competitors will copy the invention and thus ‘free ride’ without paying for it. Two types of theoretical models explaining the impact of knowledge capital on long-run economic growth of an economy are available in modern growth theory. One is the macrodynamic growth model due to Romer (1986) and Lucas (1993), which emphasizes knowledge capital as an input in the production function that has increasing marginal productivity and a spillover effect in the form of externalities. The second is a microdynamic model of industry growth fostered by increasing returns associated with R&D expenditures and other forms of knowledge capital. This model has been explored by Sengupta (2004) to explain industry evolution and market growth associated with the entry of new firms and the expansion of increased market share through mergers, alliances and globalization of demand. The competitive growth equilibrium model due to Romer has two important characteristics that are useful for a discussion of long-term growth prospects for India. First, it is based on the production function Y = F(k(t), K(t)), which exhibits global increasing marginal productivity of knowledge capital. Here ki (t) = k(t) is the knowledge capital for firm i = 1, 2, . . . , N where firms are assumed to be identical for simplicity and

K(t) =

N

ki (t)

i=1

is the aggregate stock of knowledge for the whole economy. The production function is assumed to be concave in k(t), exhibiting diminishing marginal productivity for any fixed value of K(t). Romer has shown the existence of an equilibrium growth solution of this competitive model, where the three basic elements of externalities, increasing returns in the production of total output due to K(t) and decreasing returns (due to k(t)) in the production of new knowledge combine to produce a computable model of long run growth. This model is capable of explaining historical long-run growth in the absence of state intervention. Second, the welfare implications of this long-run growth model are very important in that it provides a framework for optimal government policymaking. Here each firm recognizes the private return to knowledge capital through ∂F/∂k(t) but neglects the productivity impact of the change in aggregate knowledge capital denoted by ∂F/∂K(t). Hence the amount of consumption at any point in time is too high in competitive equilibrium and the amount of research too low. Thus any government policy that shifts the allocation


of current goods away from consumption toward research or knowledge capital will be welfare improving. The appropriate tax-subsidy measure may thus be employed by the government to achieve Pareto-optimal improvements in resource allocation and income generation. The Lucas model is very similar to Romer’s approach. It views total capital in two parts: human capital allocated to current production and that allocated to skill acquisition or schooling. If u is the fraction allocated to current production and v is the productivity parameter, then the two basic equations of the Lucas model are γ

Y = AK α (uH)1−α Ha ˙ = v(1 − u)H, H

v>0

The second equation spells out how current schooling or research time (1 − u) affects the accumulation of human capital. Here K denotes the physical capital stock, which evolves over time according to the standard Solow model, i.e. K˙ = Y − C, with C as total current consumption. Ha denotes the part of human capital that has a spillover effect. Note that in this model there are constant returns to scale to the stock of human capital. Thus some allocations may yield high external benefits and growth in production and wages, while others may not. If u∗ is the optimal allocation of an individual’s time between current production and education (research) then it can be shown that the steady state growth rate g equals v(1 − u∗ ), implying that education and research knowledge can augment the long-run growth rate. Another feature of the spillover effect on technology is that it yields a strong connection between rapid productivity growth and openness in trade. Thus countries opening up could take advantage of the spillover technology by using strong increasing returns to scale to augment the output growth and export expansion and diversification. Our objective here is threefold. First, we discuss the structure and growth of the ICT and IT sectors in India in the light of the findings of the modern theory of long-term economic growth. Second, we attempt to evaluate the role of R&D investments and the learning by doing effects of India’s investment in the computer industry in its software and hardware developments and the electronic industry in India. Finally, the implications of global markets and the future prospects for the computer industry and the knowledge economy are discussed in the light of current economic policy in India.


5

1 The IT sector and the world perspective The IT sector comprises several industries, such as computers, electronics, semiconductors, communications and both software and hardware developments. The world perspective is most important for the IT sector for several reasons. First, the international spillover aspect of R&D investments in these sectors is very critical in augmenting the national growth rate. If human capital in the form of knowledge capital is denoted by H, labor force by L and the stock of ideas by R, then one could estimate the growth of national output (Y) in terms of the growth of H, L, R and K, where K is physical capital. Freire-Seren (2001) estimated such a model using pooled cross-section data for a sample of 21 OECD countries with five observations for each country over 1965–90 at five-year intervals. The estimated results with t-statistics in parentheses are as follows: ˙ ˙ ˙ + 0.27 K/K ˙ ˙ + exp(−0.36t)D Y/Y = 0.19 H/H + 0.54 L/L + 0.08 R/R (2.5)

(2.6)

(2.2)

(1.1)

where D are dummy variables. Here the information about total R&D expenditure comes from OECD statistics and the estimated years of schooling is used as a proxy for human capital. Note that the estimated coefficients of human capital, physical capital and R&D expenditure are positive and statistically significant. The estimates show a strong positive correlation between the growth of R&D expenditure and the growth of the GDP variable Y. This suggests that not only would the introduction of a subsidy to the R&D investment encourage innovation activity but so would the introduction of a subsidy to physical capital production. This physical capital subsidy positively affects the long-run growth rate by providing the incentives to increase the variety of capital goods. Second, the impact of R&D inputs on long-run growth of outputs occurs at various sectoral levels. The learning effect is especially important in knowledge diffusion and intersectoral transfers. If s denotes the knowledge of an agent and S economy-wide knowledge, then the agent’s growth of knowledge can be viewed as ds(t)/s(t) = s˙ = F(s(t), a(t), S(t), A(t)) where a is the policy action to accumulate and disseminate knowledge. Jovanovic (1997) has used this formulation for the capital goods producer, with s as the efficiency of the producer, a his output of capital


goods and A the economy-wide growth investment in the capital-using industries. Then Arrow’s learning by doing can be represented as s˙ /s = δ1/θ θs(t)1−1/θ A(t) where θ > 0 and s = S, since it is assumed that all capital goods producers are equally efficient. Here the learning effects of A are like a public good, i.e. learning knowledge produced collectively by all the capital goods firms jointly with their output of capital goods. In Romer’s model learning comes through research. There are invention costs but no adoption costs. The output of research is designs. Here if we denote the cumulative number of designs, e.g. software, by s and researchers’ labor input by a, then the growth equation of knowledge capital becomes s˙ (t) = ds(t)/dt = δSγ (t)a(t) Romer assumed γ = 1. If n is the equilibrium number of researchers and the population is fixed, then S˙ = n˙s and hence s˙ (t)/s(t) = δnasγ−1 Thus if we double the population, we raise the growth rate at each date by a factor of two. This type of research model finds empirical support from cross-sectional firm data as firms that perform R&D generate more patents and their productivity is higher. Third, the world perspective in high-tech industries today has been significantly influenced by the growth of the computer industry and its impact on communication and other industries. India’s IT sector is heavily impacted by such developments in the world computer industry. R&D investment by firms not only generate new knowledge about technical processes and products but also enhance the firm’s capability in improving the stock of existing knowledge capital. Sengupta (2004) has applied a two-stage model of this improvement process in US computer industry. In the first stage the efficient levels of R&D inputs are determined by an efficiency model known as data envelopment analysis (DEA), and in the second stage we estimate by a regression model the impact of R&D spending on total sales. Our empirical application is based on Standard and Poor’s Compustat data on 40 firms over the 16-year period 1985–2000. The data set includes such well known firms as Apple, Compaq, Dell, IBM, HP, Hitachi and Toshiba. Denoting the efficiency score by θ (θ = 1 denoting highest efficiency) and the marginal


7

Table 1.1 Impact of R&D spending on growth efficiency based on the DEA model 1985–9

Dell Compaq HP Sun Toshiba Silicon Graphics Sequent Hitachi Apple Data General

1990–4

1995–2000

θ

β

θ

β

θ

1.00 0.97 1.00 1.00 0.93 0.99 0.72 0.88 1.00 0.90

2.71 0.03 1.89 0.001 1.56 0.02 0.80 0.07 1.21 0.92

1.00 1.00 0.93 1.00 1.00 0.95 0.92 0.98 0.87 0.62

0.15 0.002 0.10 0.13 0.13 1.41 0.001 0.21 0.92 0.54

0.75 0.95 0.88 0.97 0.97 0.87 0.84 0.55 0.68 0.81

β 0.08 0.0001 0.002 1.79 1.78 0.001 0.002 0.001 0.001 0.65

impact of the growth of R&D spending on the growth of output by β, the results for selected firms averaged over three subperiods are shown in Table 1.1. Now we consider a regression approach to specify the impact of R&D inputs on output. With net sales as proxy output (y) and x1 , x2 , x3 as three inputs comprising R&D spending, net capital expenditure and all direct production inputs, we obtain y = 70.8∗ + 0.621∗∗ x1 + 0.291∗∗ x2 + 1.17∗ x3

R2 = 0.981

where one and two asterisks denote significant t-values at 5 and 1 per cent respectively. This uses a slightly reduced sample set. When the regressions are run separately for the DEA efficient and inefficient firms, the coefficient for R&D inputs is about 12 per cent higher for the efficient firms, while the other coefficients are about the same. When each variable is taken in incremental form we obtain the result y = −6.41 + 0.65∗∗ x1 + 1.05∗∗ x2 + 1.17∗∗ x3

R2 = 0.994

It is clear that the R&D variable has the highest marginal contribution to output (or sales), in both the level form and the incremental form. When we consider the R&D efficient firms only and several subperiods the regression results consistently show the dominant role of the R&D input in its contribution to sales (Table 1.2). The adjusted R2 is very high

8 India’s New Economy Table 1.2

1985–8 1993–6 1997–2000 1985–2000

Impact of R&D inputs for DEA efficient firms 2

Intercept

x1

x2

x3

R

767.5 −146.6 −239.9 8.62

6.95∗∗ 2.54∗∗ 4.00∗∗ 4.29

1.38∗∗ −0.09 −0.15 0.11∗

0.49 1.35∗∗ 1.19∗∗ 1.08∗∗

0.828 0.828 0.995 0.996

and the t-values for R&D expenditure are significant at the 1 per cent level. The elasticity of output with respect to R&D expenses estimated at the mean level comes out to 0.799 in 1985–88 and 0.421 in 1985–2000. These results have two important lessons for the growth of the computer industry in India. One is the emphasis on R&D investments and spending on knowledge capital. Second, the industry has to capture the complementarity in productivity growth for other inputs and other industries. Now consider the role of knowledge capital, which may take different forms, e.g. R&D spending, computer software and communications equipment, much like the Schumpeterian concept of innovations. The role of IT capital in augmenting overall productivity growth has been examined in the current literature in terms of total factor productivity (TFP) growth. Thus Jorgensen and Stiroh (2000) identify IT capital with computer hardware and software and communications equipment. They find that from 1973–90 to 1995–98 the contribution of IT capital to aggregate growth doubles and the productivity growth triples. Oliner and Sichel (2000), using the same definition of IT capital and a somewhat narrower definition of output, find a similar increase in productivity growth. More recently, Hall (2000) argued that the spread of IT investment was associated with a new type of capital, e-capital for short, and this e-capital is not measured by the standard National Income Accounting. He finds that with e-capital the contribution of other inputs and TFP to output growth is substantially reduced, e.g. without e-capital TFPG accounts for about 40 per cent of total output growth, whereas with e-capital the combined contribution of e-capital and TFP accounts for about 75 per cent of total output growth and most of it is due to e-capital. The Indian perspective on the new industries based on knowledge capital may be analyzed in terms of three components: the electronics industry, the software industry and the ICT sector industries. Growth in these industries is basically conditioned by core managerial competence in facing the challenge of world competition. Technology import and


9

Table 1.3 Number of foreign collaborations in electronics industry by type of collaboration

1980 1985 1990 1991

Technical

Financial

Design

Total

Collaboration in electronics as % of total collaboration

40 140 40 61

13 46 35 41

6 24 20 10

59 210 95 112

11.2 20.2 15.3 11.5

Table 1.4 Cost of technology import as a proportion of sales for different product groups (in %)

1989 1991 1993 1995 1996

Consumer electronics

Computers

Software

Communications

Other equipment

0.0 0.58 0.27 0.04 0.18

0.03 0.48 0.25 0.39 0.16

0.04 0.10 0.11 1.36 0.13

0.09 0.25 0.41 0.96 0.22

0.19 0.29 0.74 0.65 0.41

R&D investment provide two critical measures of future growth in these industries. Development of the electronics industry during the 1970s was oriented toward indigenous development of technology and therefore dependence on the import of technology was minimal. Estimates by Joseph (2004) and Agarwal (1985) suggest a slow upward trend in foreign collaborations (Table 1.3). Statistical data on the cost of technology imports as a proportion of sales in the 1990s tend to suggest that with the liberalization of government policies and the opening up of the electronics industry, companies started spending more on foreign technology. Estimates based on the Electronics Commission data reported by Joseph (2004) are shown in Table 1.4. The trend for the computer and software sectors steadily rose from 1997 to 2005. Two comments are in order for the electronics industry. First, the central government from the beginning put more emphasis on domestic technology and import substitution. Unlike in Taiwan and China, the electronics industry in India has not taken full advantage of joint ventures and technical collaborations to start new industries or initiate new entry into this growing market. The public sector units played


a major role in augmenting the output of electronics during 1970s to 1990s. However, on a global perspective it is still very small in absolute terms. For example, BEL and ITI spent on average nearly 7 per cent of their sales on R&D in 1983, amounting to about US$12 and 14 million respectively, whereas Goldstar, a South Korean company, spent more than $20 million. The private sector’s role in R&D is also not very promising. This is in sharp contrast with the record of performance of Taiwan, China, South Korea and Singapore. The private sector accounts for less than 15 per cent of the national R&D expenditure in India, as against 30 per cent in the successful NICs (newly industrializing countries) of Southeast Asia. Since 1973 the Indian government has introduced liberalized import facilities for equipment and raw materials for firms with in-house R&D units registered with DSIR (Department of Scientific and Industrial Research). Yet the record is unpromising so far. In 1995 there were about 158 registered electronics R&D units in the private sector, accounting for about 7 per cent of total manufacturing units. In terms of employment generation the rough estimates by Joseph (2004) based on ASI (annual survey of industry data on the number of employees per gross investment capital) show that it is nearly two and a half times higher than that of the textiles industry, six times higher than that of non-ferrous metals and nearly ten times higher than that of the chemical industry. Yet the growth of electronics output has not been very satisfactory compared to the record of the NICs in Asia. This situation has been compounded by the fact that some regions and states in India have consistently failed to participate in this industry to any significant degree. The estimates by Joseph (2004) of the state’s share of electronics production are shown in Table 1.5. The trend in export performance by major product groups shows the annual compound growth rates shown in Table 1.6.

Table 1.5

Inter-state variations in electronics output

Karnataka Maharashtra Uttar Pradesh Kerala West Bengal Bihar Gujarat

1971

1981

1990

1996

50.3 25.5 0.7 0.4 5.0 0.03 0.4

20.71 23.77 10.98 2.50 5.01 0.12 3.49

– 16.55 19.23 3.00 3.38 0.22 3.89

– 17.30 19.57 3.70 3.32 0.18 3.82


11

It is clear that in the electronics field, the electronics capital goods sector accounts for the largest share, followed by electronic intermediates and electronic consumer goods. While the NICs in Asia have moved away during the past decade from low technology products such as radios and TVs to medium and high technology items, India has not been able so far to exploit the new opportunities. One silver lining for India is the performance record of the computer software sector. For the most recent period (2000–05) the annual compound growth rate in exports for software has exceeded 48 per cent and the trend is still increasing. The software subsector needs some detailed discussion. Banerjee (2004) has studied the importance of knowledge wealth in the Indian software industry in terms of two criteria. One is in terms of the ratio of R&D to pretax profits and the other in terms of a competency index computed in two rounds of investigations in 1999 and 2002 on the basis of 11 representative software firms. These software firms include both Indian and foreign firms. In terms of R&D ratio he found that all firms reporting a value greater than one (with a maximum of three) are from the USA, such as Hewlett Packard, Texas Instruments and Microsoft. The Indian firms in both the private and public sectors showed a value of 0.8 or less. In terms of the competency index, which is defined as the sum of four competencies – product competence, project competence, skill level competence of employees and competence in terms of formal training experiences – most firms did not exhibit very high levels of training competency and skill competency. Banerjee (2004) also found a low level of ‘switching competency’, which refers to the ease and flexibility in switching from one product or service or mode of production to another. The software industry in India has to adopt a forward-looking innovative strategy. Sengupta (2003a) has discussed the importance of some dynamic strategies in the Indian software industry based on three Cs: competitive advantage, comparative advantage and core competence in knowledge

Table 1.6 Export performance by major product groups

1981–8 1988–94 1981–94

Electronic consumer goods

Electronic capital goods

Electronic intermediates

Software

Total

11.85 20.35 15.70

45.35 6.81 26.08

21.62 −2.09 10.03

40.08 32.78 36.67

26.64 15.34 21.29


accumulation, application and diffusion. The use of knowledge in different forms, e.g. learning skills, importing and improving new innovations and diffusing knowledge externalities to take advantage of economies of scale, has been strongly emphasized by Arrow (2000) as a major source of dynamic growth as follows: Every country or firm must have education and training in technology and science, even if the research is not on a par with that being conducted elsewhere. Knowledge cannot be absorbed unless some knowledge is already possessed. Countries and firms must be open to new ideas and see that ideas are diffused. This point strongly argues for freedom of entry, even when it seems to forego economies of scale. (Arrow, 2000, p. 19) Arrow’s remarks emphasize the importance of learning by doing in accumulating and improving knowledge capital and especially knowledge diffusion. Knowledge diffusion helps other sectors grow and improve their productivity. These diffusion effects are called externalities and spillovers, since the individual firms do not have to pay for them. This provides a critical source for increasing returns to scale and the openness of global trade implies that these scale economies can be profitably exploited so as to augment industry growth and expansion. Nachum (2002) analyzed the outward FDI (foreign direct investment) data from the USA for the years 1989–98 and found two most important explanatory variables in the ‘innovation capabilities’ of firms and ‘flexibility with networking’. The role of increasing returns industries was found to be much more important than that of the diminishing returns industries. Software exports from India take three forms: (a) the export of software services through consultancy, (b) the support of software packaging developed abroad and (c) electronic bookkeeping and data entry. All these forms of software exports are highly labor-intensive and India faces stiff competition from six countries identified by the World Bank (Table 1.7). It is clear that India’s competitors rely more on software package development and India’s export pattern exposes its vulnerability. Over the past decade India’s dependence on software package exports has been less than 3 per cent, whereas even China and Mexico performed much better at more than 28 per cent. It is clear that Indian software developers must forge a more dynamic global strategy in shifting to software package development exports. This also calls for developing an effective system of alliances with US and Japanese counterparts so that appropriate market niches can be set up and exploited. The most


13

Table 1.7 Pattern of software exports from India and its competitors (1990) Proportion of exports (%)

India China Singapore Ireland Mexico Philippines

Software services

Software packages

Data entry and bookkeeping

90 17 25 65 53 39

5 56 58 21 32 20

5 27 17 14 15 41

important example to follow here is provided by Taiwan, which has attempted to exploit the licensed clone market in different niches of the computer software market by entering into alliances and joint ventures with other international firms. Moreover, Taiwan has offered some mild selective inducement of up to 2.5 per cent subsidy. This fosters active participation of small electronic and software firms into the R&D network of government-affiliated laboratories. Finally, India needs to develop core competence in managerial skills in order to face international competition most successfully. In many ways ‘the coordination failures’ have created strong bottlenecks to faster growth in the modern technology-intensive industries such as electronics, computers and telecommunications. The Economist (London, 3–9 June 2006) points out several distortions and bottlenecks associated with coordination failures. While the ‘license raj’ has been substantially reduced at the central government level, it still survives at the state level, with a pervasive ‘inspector raj’ imposing heavy transaction costs on firms and new companies. Some parts (states) of the country deter investment because they are so badly governed. The indirect tax system also deters any new investment. A 2002 study found that India’s combined indirect tax (e.g. import duties, excises, sales taxes and octroi) accounts for nearly one-half of a price disadvantage of roughly 39 per cent suffered by manufacturers compared with their Chinese counterparts. The message is clear: India needs to speed up liberalization further. As The Economist concludes: ‘In every annual budget for example more industries are taken off a list of those “reserved” for small companies, a policy that has prevented many firms from achieving the economies of scale they need to compete internationally.’


2 R&D investments and their impact Innovations take many forms but in a broad sense they involve developing new processes, new products and new organizational improvements. R&D investment plays an active role in innovations in new processes and in new products and services. Several dynamic features of R&D investment by firms are important for selection and industry evolution. First, R&D expenditure not only generates new knowledge and information about new technical processes and products, but also enhances the firm’s ability to assimilate, exploit and improve existing information and hence existing ‘knowledge capital’. Enhancing this ability to assimilate and improve existing information affects the learning process within an industry, which has cumulative impact on the industry evolution. For example, Cohen and Levinthal (1989) have argued that one of the main reasons why firms invested in R&D in the semiconductor industry is that it provides an in-house technical capability that could keep these firms on the leading edge of the latest technology and thereby facilitate the assimilation of new technology developed elsewhere. A second aspect of R&D investment within a firm is its spillover effect within an industry. R&D yields externalities in the sense that knowledge acquired in one firm spills over to other firms and very often knowledge spread in this way finds new applications both locally and globally and thereby stimulates further innovative activity in other firms. Finally, the possibility of implicit or explicit collaboration in R&D networking or joint ventures increases the incentive of firms to invest. This may encourage more industry R&D investment in equilibrium. In the absence of collaboration the competing firms may not invest enough, since innovations cannot be appropriated by the inventor, e.g. his competitors will copy the invention and thus ‘free ride’ without paying for it. Thus the basic reason for the success of joint R&D ventures is that externalities or spillovers are internalized, thus eliminating free rides. We consider first the empirical basis of R&D innovations in modern industries and then its implications for selection and industry evolution. Cohen and Levinthal (1989) have made an important contribution in this area by analyzing the two faces of R&D investment in terms of spillover and externality. One impact of R&D spillovers emphasized by Nelson, Arrow and others is that they diminish firms’ incentive to invest in R&D and related production. The other impact discussed by Cohen and Levinthal emphasizes the point that spillovers may encourage equilibrium industry R&D investment, since the firm’s R&D investment develops its ability to exploit knowledge from the environment,


15

i.e. develops its ‘absorptive’ capacity or learning by which a firm can acquire outside knowledge. Thus a significant benefit of a firm’s R&D investment is its contribution to the intra-industry knowledge base and learning, by which externality and spillovers may help firms develop new products and/or new processes. The model developed by Cohen and Levinthal starts with the firm’s stock of knowledge, denotes the addition to the firm’s stock of technological and scientific knowledge by zi and assumes that zi increases the firm’s gross earnings πi but at a diminishing rate. The relationship determining zi is assumed to be of the form ⎛ zi = Mi + γi ⎝θ

⎞ Mj + T ⎠,

0 ≤ γi ≤ 1

(1.1)

j =i

where Mi is the firm’s R&D investment, γi is the fraction of intra-industry knowledge that the firm is able to exploit, θ is the degree of intra-industry spillover of research knowledge. Mj represents other firms’ ( j = i) R&D investments that contribute to zi and θ denotes the degree to which the research effort of one firm may spill over to a pool of knowledge potentially available to all other firms, e.g. θ = 1 implies that all the benefits of one firm’s research accrue to the research pool potentially available to all other firms, whereas θ = 0 implies that the research benefits are exclusively appropriated by the firm conducting the research. It is assumed that γi = γi (Mi , β) depends on both Mi (the firm’s R&D) and β, where β is a composite variable reflecting the characteristics of outside knowledge, i.e. its complexity, ease of transferability and link with the existing industry-specific knowledge. Clearly the composite variable β will differ from one industry to another, e.g. in the pharmaceutical industry it may involve a lot of experimentation, long gestation periods and the complexity of the marketing process for new drugs, whereas for the computer industry it may involve software experimentation and the ease of application in multiple situations. It is assumed that the composite variable β denoting ‘ease of learning’ is such that a higher level indicates that the firm’s ability to assimilate outside knowledge is more dependent on its own R&D expenditure. Thus it is assumed that increasing β increases the marginal effect of R&D on the firm’s absorptive capacity but diminishes the level of absorptive capacity. Cohen and Levinthal evaluate the effects of increasing the explanatory variables such as β, θ and T on the equilibrium value of firm’s R&D


investment denoted by M ∗ , where it is derived from maximizing πi with respect to Mi as R = MC = 1

(1.2)

where MC is the marginal cost of R&D expenditure equal to one and R is marginal return given by ⎡ ⎞⎤ ⎛ (1.3) Mj + T ⎠⎦ + θ γj πzi j R = πzi i ⎣1 + γMi ⎝θ j=i

j=i

where the subscripts denote partial derivatives. On solving equations (1.2) and (1.3) simultaneously one obtains the equilibrium value of each firm’s R&D denoted by M ∗ . The impact on M ∗ of the explanatory variables β, θ and T are derived as:

∂γ sign(∂M ∗ /∂β) = sign πzi i γMβ(θ(n − 1)M + T ) + θ(n − 1) πzi i (1.4) ∂β i

∗ i (1.5) sign(∂M /∂θ) = sign πzi γM(n − 1)M + (n − 1)γπzi sign(∂M ∗ /∂T ) = sign[γM πzi i + (πzi i zj + (n − 1)πzi i zj γ(1 + γM T )]

(1.6)

The first term on the right hand side of equation (1.4) shows that a higher β induces the firm with more incentives to conduct R&D, because its own R&D has become more critical to assimilating its rivals’ spillovers θ(n − 1)M and the extra-industry knowledge T . The second term shows a decline in rivals’ absorptive capacity (n − 1)γ as β increases. As a result the rival competitors are less able to exploit the firm’s spillover. Due to both these effects the payoffs to the firm’s R&D increases and ceteris paribus more R&D investment is induced. The effect of θ on M ∗ is ambiguous, due to two offsetting effects: the benefit to the firm of increasing its absorptive capacity denoted by the first term and the loss associated with the diminished appropriability of rents denoted by the second term on the right hand side of equation (1.5). Note, however, that the desire to assimilate knowledge generated by other firms provides a positive incentive to invest in R&D as θ increases. The relation (1.5) shows that with an endogenous absorptive capacity, the firm has a positive incentive to invest in R&D to exploit the pool of external knowledge. With γM = 0, i.e. zero endogenous absorptive capacity, the sign (∂M ∗ /∂T ) is negative, since a higher T merely substitutes for the firm’s own R&D, i.e. πzi i zj < 0. Cohen and Levinthal estimate by regression (OLS, GLS and Tobit) models the effects of the knowledge inputs and other industry characteristics

The New Knowledge Economy and India’s Growth Table 1.8 intensity

Effects of knowledge and other explanatory variables on R&D

1 Technological opportunity (a) Appropriability (1 – θ) (b) Usertech (c) Univtech (d) Govtech 2 Basic science research (a) Biology (b) Chemistry (c) Physics 3 Applied science research (a) Computer science (b) Material science 4 New plant 5 Elasticity of (a) Price (b) Income R2

17

OLS

GLS

Tobit

0.396∗ (0.156) 0.387∗∗ (0.099) 0.346∗∗ (0.128) 0.252∗ (0.100)

0.360∗∗ (0.104) 0.409∗∗ (0.070) 0.245∗∗ (0.089) 0.170∗ (0.076)

0.260 (0.161) 0.510∗∗ (0.166) 0.321∗ (0.147) 0.200∗ (0.100)

0.176 (0.096) 0.195∗∗ (0.071) 0.189 (0.109)

0.042 (0.057) 0.095 (0.050) 0.037 (0.082)

0.159 (0.116) 0.149 (0.078) 0.156 (0.109)

0.336∗∗ (0.123) −0.005 (0.121) 0.055∗∗ (0.008)

0.157 (0.093) −0.028 (0.089) 0.041∗∗ (0.006)

0.446∗∗ (0.121) 0.231∗ (0.116) 0.042∗∗ (0.007)

−0.180∗∗ (0.061) 1.062∗∗ (0.170) 0.278

−0.071 (0.044) 0.638∗∗ (0.136) –

−0.147∗ (0.060) 1.145∗∗ (0.180) –

Note: Only a selected set of regression coefficient estimates are given here with standard errors in parentheses. One and two asterisks denote significant values of t tests at 5 and 1 per cent respectively.

on unit R&D expenditure (intensity) of business units. The sample data included R&D-performing business units consisting of 1302 units representing 297 firms in 151 lines of business in the US manufacturing sector over the period 1975–77. The empirical data were obtained from the FTC’s (Federal Trade Commission) Line of Business Program and the survey data collected by Levin et al. (1987). A set of estimates of selected regression coefficients is reproduced in Table 1.8. Appropriability here is defined as follows: the respondents in Levin et al.’s (1987) survey were


asked to rate on a seven-point scale the effectiveness of different methods used by firms to protect the competitive advantages of new products and new processes. For a line of business, appropriability is then defined as the maximum score. Thus if appropriability increases the spillover level declines and hence R&D intensity increases. The new plant variable is used to reflect the relative maturity of an industry’s technology, i.e. it measures the percentage of an industry’s plant and equipment installed within the five years preceding 1977 as reported in the FTC’s data set. Industry demand conditions are represented by the price and income elasticity measures. The explanatory variables T and β are measured indirectly for the survey data. The level of extra-industry knowledge T is measured by five sources, of which three are reported in Table 1.8: downstream users of industry’s products (usertech), government agencies and research laboratories (govtech) and university research (univtech). The proxy variables used for β in Table 1.8 represent cumulativeness and the targeted quality of knowledge, which are field-specific; hence research in basic and applied sciences is reported here, e.g. the characteristic that distinguishes the basic from the applied sciences is the degree to which research results are targeted to the specific needs of firms, where basic science is less targeted than the applied. Hence the β value associated with basic science research is higher than that associated with applied science. As a result the coefficient values of the technological opportunity variables associated with the basic sciences should exceed those of the applied sciences. The estimates in Table 1.8 show that except in computer science the coefficients are uniformly greater for the basic sciences. The exception of computer science may also be due to the rapid advance in software and process development, where basic and applied knowledge are intermingled. Some broad conclusions emerge from the estimates reported in Table 1.8. First, the results reject across all three estimation methods the hypothesis that the effects on R&D spending of basic and applied science are equal. This means that the role of learning differs significantly across fields in terms of cumulativeness, targetedness and the pace of advance, which affect the influence of technological opportunity on R&D spending. Second, increasing the technological opportunity through the less targeted basic sciences evokes more R&D spending than does increasing the technological opportunity through applied sciences. Finally, the OLS and GLS estimates of the coefficient of appropriability are positive and significant, implying that spillovers have a net negative effect on R&D.


19

Next we consider an application in the world computer industry, which has witnessed rapid technological changes in recent years in both hardware and software R&D. Recent empirical studies have found cost-reducing effects of rapid technological progress to be substantial in most technology-intensive industries of today, such as microelectronics, telecommunication and computers. Two types of productivity growth are associated with such technological progress: the scale economies effect and the shift of the production and cost frontiers. There also exist substantial improvements in the quality of inputs and outputs. The contribution of R&D expenditure has played a significant role here. This role involves learning in different forms that help improve productive efficiency of firms. One may classify learning into two broad types: one associated with technological capital and the other with human capital. Three types of measures of learning are in general use in the literature. One is the cumulative experience embodied in cumulative output. The second measure is cumulative experience embodied in strategic inputs such as R&D investments in Arrow’s learning by doing models. Finally, the experience in ‘knowledge capital’ available to a firm due to spillover from other firms may be embodied in the cost function through the research inputs. Unlike the regression approach of Cohen and Levinthal we now develop and apply a nonparametric and semiparametric model of production and cost efficiency involving R&D expenditure and its learning effects. These nonparametric models do not use any specific form of the cost or production function; they are based on the observed levels of inputs, outputs and their growth over time. Using these models we determine the number of computer firms that are efficient or are not. Then we run a regression of the dependent variable log output = yˆ on the three independent variables: log R&D (ˆx1 ), log plant and equipment (ˆx2 ) and log cost of goods sold (ˆx3 ) with a dummy variable D for each coefficient, where D = 1 for the efficient firms and zero otherwise. The details are described by Sengupta (2004). The results are as follows: 1987 − 98 yˆ = 1.199∗∗ + 0.162∗∗ xˆ 1 + 0.065∗ D xˆ 1 + 0.009 xˆ 2 − 0.034D xˆ 2 + 0.743∗∗ xˆ 3 + 0.034∗ Dxˆ 3 1991

yˆ = 1.214∗∗ + 0.262∗∗ xˆ 1 − 0.075 xˆ 2 + 0.791∗∗ xˆ 3

(R2 = 0.996) (R2 = 0.998)

D significant for xˆ 1 and xˆ 3 only 1998

yˆ = 0.925∗∗ + 0.140∗ xˆ 1 + 0.015 xˆ 2 + 0.0842∗∗ xˆ 3 D significant for xˆ 1 and xˆ 3 only

(R2 = 0.998)


Clearly R&D expenditures have played a most dynamic role in the productivity growth of the efficient firms in the computer industry and this trend is likely to continue in the future. We adopt a similar approach for the US pharmaceutical industry (1981–2000) to evaluate the impact of R&D investments on output (i.e. sales) for the efficient firms. These results are discussed by Sengupta and Sahoo (2006) elsewhere, where a set of 17 companies out of a larger set of 45 is selected from the Compustat database from Standard and Poor. The share of R&D in total costs is quite important for these 17 companies, which include well known companies such as Merck, Ely Lily, Pfizer, Bausch and Lomb and Glaxo Smith Kline. Two important points emerge from these results. One is that the number of firms on the cost-efficiency frontier is about one-third and these firms are invariably efficient in using their R&D inputs. Second, both the efficiency score and the inputs help the firms improve their cost-efficiency and hence improve their market share. For the Indian economy the major implications are clear. There should be increasing emphasis on R&D investment and spending on learning by doing in both computers and pharmaceutical industries, if India is to face the challenges in the competitive world markets. We apply DEA models of efficiency to four types of industries in India – leather, textiles, computer-related products and electronic equipment – and analyze their growth characteristics and prospects.

3 Growth of markets and policy reforms India’s knowledge economy depends very critically on the trends in international markets for software and IT-related services. The export markets also depend on the various policy measures adopted by the government for liberalized trade policies. We discuss in this section three types of strategies related to trade policies, IT-related services and innovation trends in IT service markets. Although import licensing has been abolished, high import tariffs pose a key constraint on better industrial performance and competitiveness. The tariff reductions program was rapid until the mid-1990s but the process has slowed down. Compared to China and South Korea the tariffs are much higher in India (Table 1.9). While the government policy on foreign direct investment (FDI) has been liberalized, it is still not allowed in selected sectors such as telecoms, insurance and IT-related services. As of 2006 the government has moved to liberalize investment in the housing construction sector and also the


21

Table 1.9 Unweighted average customs duty rates (%)

India 2003 2004 China 2000 S. Korea 2000

All goods

Agricultural goods

Manufacturing goods

35.0 32.7 16.3 12.7

47.1 46.8 16.5 47.9

33.3 30.7 16.2 6.6

Source: World Bank Report (2003).

transportation sector. Recent moves by the Reserve Bank of India to allow repatriation of profits in dollar terms from housing investment by nonresident Indians and private FDI in major airports have contributed to improve the private capital markets. However, the role of both central and state bureaucracies is still pervasive in creating factor market distortions through corruption and delay in the speed of liberalization. Thus according to the Global Competitiveness Report (2003) India ranks seventy-third out of a total of 75 countries analyzed, with China’s rank being twenty-third in the overall degree of competitiveness. The World Bank Report (2003) has noted that setting up a business in India requires ten permits but in China only six, and the median time to get approval is 90 days in India as against 30 days in China. This report also found that managers of industries in India spend about 16 per cent of their time dealing with the government bureaucracy, compared with 9 per cent in China and 11 per cent in Latin America. Moreover, the proportion of firms making illegal and/or irregular payments in India is about 90 per cent. This is almost twice that of Malaysia. It is clear that more transparency in trade and investment policies toward both direct and foreign investment is needed so as to foster a growth perspective for the industrial sector. The IT sector providing various types of anchored services, such as industrial consultancy, software market services and business process outsourcing (BPO), is now being actively helped by the liberalized government policies. Policy reforms have been very supportive of the recent growth trends in this sector. Stiffler (2006) has analyzed the AMR research survey for this sector showing the high-level financial results for the five largest firms in India (Table 1.10). Several aspects of this impressive growth trend need to be emphasized. First, financial services have dominated the growth structure.

22 India’s New Economy Table 1.10 Performance of the five largest IT service providing firms in India Company

Quarter reported

Cognizant Tata Consultancy (TCS) Infosys Wipro Satyam Comparison firms Accenture IBM (Services arm)

30 June 2006 30 June 2006

Revenue ($m)

Employees (no.)

Income per employee ($)

337 896

31,000 71,190

8,026 12,024

30 June 2006 30 June 2006 30 June 2006

660 682 323

58,409 55,000 30,000

13,560 11,127 8,267

31 May 2006 30 June 2006

4,408 11,894

133,000 200,000

20,602 23,880

Source: AMR research report. Accenture and IBM are listed as competitive points of comparison.

Telecommunications, the retail sector and financial services have played a key role for TCS, Wipro and Satyam. Healthcare, life services and financial service consultancy have been a major share of Cognizant’s growth. Second, infrastructure management of various forms is increasingly gaining ground in the new business strategies followed by three leading companies. Increasing investment in this new category of business management is highlighted by HCL Technologies, Wipro, TCS and Cognizant. For Infosys packaged implementation testing, BPO and custom application development provide the most bright spots of growth. Finally, employment growth in this sector has been very high. For example, in 2006 Cognizant hired more than 6,600 employees. This implies a multiplier effect of hundreds of other functional industry and domain specialists. Satyam and Infosys made a concerted effort to hire increasingly more senior and/or non-Indian nationals on their payrolls. All these leading companies grow between 30 and 40 per cent and add thousands of employees annually. Among their major concerns are attrition, how to control costs and the smoothness with which they can maintain a healthy utilization rate, especially as larger deal sizes start to compete with the dynamics of growth. The most important development in the IT services sector and software development in India is the increasing emphasis on R&D methods, where India’s skilled personnel can be most productively utilized. Joint ventures, effective collaboration with leading edge firms abroad and the contribution of venture capitalists from Silicon Valley (some of these are NRIs) all play a very dynamic role with a huge growth potential.


23

Innovations take many forms but in a broad sense they involve developing new processes, new products and new organizational improvements. R&D investment plays an active role in innovations in new processes and in new products and services. Several dynamic features of R&D investment by firms are important for selection and industry evolution. First, R&D expenditure not only generates new knowledge and information about new technical processes and products, but also enhances the firm’s ability to assimilate, exploit and improve existing information and hence existing ‘knowledge capital’. Enhancing this ability to assimilate and improve existing information affects the learning process within an industry, which has a cumulative impact on the industry’s evolution. For example, Cohen and Levinthal (1989) have argued that one of the main reasons why firms invested in R&D in the semiconductor industry was that it provides an in-house technical capability that could keep these firms on the leading edge of the latest technology and thereby facilitate the assimilation of new technology developed elsewhere. A second aspect of R&D investment within a firm is its spillover effect within an industry. R&D yields externalities in the sense that knowledge acquired in one firm spills over to other firms and very often knowledge spread in this way finds new applications both locally and globally and thereby stimulates further innovative activity in other firms. Finally, the possibility of implicit or explicit collaboration in R&D networking or joint ventures increases the incentive of firms to invest. This may encourage more industry R&D investment in equilibrium. In the absence of collaboration the competing firms may not invest enough, since innovations cannot be appropriated by the inventor, e.g. his competitors will copy the invention and thus ‘free ride’ without paying for it. Thus the basic reason for the success of joint R&D ventures is that externalities or spillovers are internalized, thus eliminating free rides. Most of the leading software companies in the USA have opened up joint R&D centers in India. European firms are also getting involved. In September 2006, IBS (Intelligent Business Systems), a leading software solutions provider in the UK, announced plans to set up an R&D center in India to focus primarily on leveraging the growth potential in the world’s fastest growing technology market. This appears to be the most opportune moment for more economic policy reform so that the diffusion of gains in the IT sector to other sectors may be activated. One aspect of this reform should be to attempt to provide incentives through subsidies so that small and medium-sized firms could carry on the diffusion process, as in Taiwan. Taiwan’s experience is most significant for


R&D distribution by industry (%)

Telecom Aerospace Consumer goods Industrials Software Chemicals & energy

2 3 4 5 5 7

Technology Auto Computing & electronics Health Others

8 18 25 21 2

Total = US$384 billion.

the Indian economy because it has diffused the learning by doing process related to R&D and software development throughout the economy, so that the gains are more equally shared. India has to realize that it can successfully develop its talent pool in the knowledge economy by a significant margin. Compared to China, South Korea and Taiwan, it has more potential capability and core competence. But the needed policy reform is to speed up investment spending in R&D in the IT sector, both public and private. The managerial challenge is much more focused on the private sector, since it can reap the gains more directly by raising the export drive to more diversified products in the value chain. According to estimates by Lall (1999), the average real rate of growth of in-house R&D expenditure during the post-reform period 1992–95 was 5.05 per cent per year in the public and 10.28 per cent in the private sector. But in the crisis year 1991–92 it was 1.81 and 0.91 per cent respectively. By comparison, the private sector figures for the same period of 1992–95 exceed 10 per cent for both Taiwan and South Korea. Other NICs in Southeast Asia and Japan have similar trends. However Lall’s estimates mainly relate to the manufacturing sector and do not incorporate the recent upsurge in investment activity in software development and other IT-related R&D activities in the form of innovations. Economic policy reforms aimed at innovations in the IT sector must have a long-run vision of eight to ten years and any future five-year plans must follow the competitive ladder in a world perspective. Several features of this reform policy may be emphasized here. First, India has to know that it is facing intense international competition in this area. Hence it must adopt forward looking strategies. It is instructive here to look at the survey report of the Booz Allen Hamilton Global Innovation 1000 plan, discussed in some detail by Clark (2006). This report found the profile of R&D spending distribution in the world (2004) shown in Table 1.11.


25

Table 1.12 R&D footprints of the top ten global R&D spenders, 2004 Company

Home country

Global R&D operations

Newest R&D locations

Microsoft

USA

India

Pfizer

USA

Ford Daimler-Chrysler

USA Germany

Toyota

Japan

GM

USA

Half of its 6 major R&D labs are located in UK, China & India Half of its 10 major R&D labs are located outside US Mostly in US Half of its 10 R&D centers are located in India, Japan & China 6 of the 7 R&D centers are outside Japan Mostly design centers

Siemens

Germany

Matsushita Electric IBM

Japan

Johnson & Johnson

USA

USA

Only 25 out of 150 R&D centers in Germany Has 10% R&D centers in US, UK, Malaysia & China Half of R&D centers are located in Japan, China, India & Israel Located in USA, UK & China

China Germany Japan & China

Thailand Germany & Sweden China, India & Russia China India

USA (California)

Table 1.13 Percentage growth of R&D spending 1999–2004 North America Europe Japan China and India Rest of the world Average

6.6 6.2 4.8 21.1 36.7 6.5

It is clear that R&D spending is heavily concentrated in the computing, health and automobile sectors. India has a huge potential for growth of R&D in the computing and healthcare fields. The R&D footprints of the top ten Global R&D are given in Table 1.12. In terms of growth in R&D spending for the period 1999–2004, the record of India and China is most impressive (Table 1.13).


A second key feature of the innovation process that India needs to follow successfully is that it requires an exceptional level of cross-functional cooperation among R&D, marketing, sales, service and manufacturing. And failure to forge effective collaboration can have a devastating impact on the success of the innovation process. The stages of the successful innovation process are: 1 Ideation. Customer insights from marketing, sales and service teams are essential for identifying attractive opportunities for new products and service. This is especially true for innovative software development. 2 Project selection. The current trends in market growth in the international field must be constantly utilized so that the R&D team can identify projects that are most likely to gain marketplace success. 3 Development and commercialization. The product or service can succeed only if there is effective collaboration between R&D, marketing, manufacturing, sales and service. The report of the Booz Allen Hamilton Global Innovation 1000 identified four key elements in successful and effective innovations as follows: 1 Align the innovation strategy with corporate strategy. In India this is the most important challenge for top managers, since there is a tendency to downgrade the contributions of new innovations. 2 Make the right bets. Any project selection should be evaluated in the context of both customer needs and development costs. The economic evaluation of potential projects must be made on a meticulous basis and followed up on a quarterly basis. 3 Manage the pipeline with speed and efficiency. The key emphasis should be on core competence and efficiency. 4 Strive to maintain organizational efficiency. Companies should ask themselves: are incentives in place to reward desired performance and speed up implementation efficiency? They should develop clear channels for sharing knowledge about innovation and productivity. The spillover effects of innovations should be internalized as far as possible so that an ‘innovation culture’ can be fostered. Since 2000 the Indian economy has picked up the speed of economic growth with its annual GDP growth changing from less than 4 per cent


27

to more than 9 per cent in 2006. Outsourcing, telecoms and financial services are fueling much of the current rapid economic growth but India’s manufacturing industry still suffers from low investment, weak infrastructure and competition from manufacturers in China, Malaysia, Taiwan and other parts of Asia. The Indian government has reiterated its decision to establish the country as a reliable source of manufacturing, software development and IT research, coaxing technology giants like Microsoft, Intel, Oracle and Cisco Systems to invest more in India. As of September 2006, Cisco Systems, Oracle and IBM have declared their future plans to relocate some of their top international and US executives to India. In their vision, India and China will lead the technology world order in the next decade and hence these companies plan to play an effective networking role. India and China, with their fast growing markets and rapidly expanding innovation capabilities, are emerging to redefine the global order for the high-tech industry. But instead of throwing up barriers and viewing India and China as competitive threats, US technology vendors are building global high-tech innovation networks: multipolar network systems that exploit the huge markets and the growing talent pools in India and China. It is useful here to analyze Forrester’s Business Technographics survey data. In the 703 Asia Pacific (APAC) firms that responded to Forrester’s 2006 survey, 60 were Indian enterprises and government agencies. Although the number of respondents is too low for statistical reliability, more than half of the respondents came from the manufacturing sector, giving us a view into business investment plans in this less sizable segment of the Indian economy. From the perspective of stepping up the diffusion of IT innovation this is very important. Several features come out very clearly in this survey data as follows. 1 Forty-one per cent of Indian respondents foresaw a challenging year in 2006, with stiff competition from China and other Southeast Asian countries. About 21 per cent believed the 2006 outlook to be very good for their manufacturing industry and 24 per cent more somewhat challenging. This compares favorably with 15 and 17 per cent for the USA and Europe, where manufacturing companies considered the outlook in 2006 to be very good. 2 About 68 per cent or more responded that their IT budgets would increase in 2006 and thereafter. The corresponding figures for APAC overall, North America and Europe were 47, 42 and 29 per cent respectively.


3 Half of Indian manufacturing enterprises (51 per cent) planned to increase their hardware budget in 2006. A majority of IT managing directors in India ranked security, disaster recovery and consolidating their IT infrastructure at the top of their IT agenda for 2006 and thereafter. 4 The percentage response to the question ‘Which of the following initiatives are likely to be your IT organization’s major business theme for 2006’ was as follows: – improving IT efficiency, 50 per cent – expanding the business value of information assets, 41 per cent – increasing the impact of IT on business performance, 32 per cent – improving the long term strategy for IT services, 35 per cent – marketing the IT department within the company, 35 per cent – improving IT development by adopting new processes and innovations, 18 per cent – increasing the scope of IT services in various divisions of the company, 26 per cent. It is clear that IT consolidation and the drive for efficiency rank high on the priority list of IT plans in the manufacturing sector in India. One has to observe also the increase in effective collaboration of Indian firms with the leading giants like Hewlett-Packard (HP/Compaq) and Microsoft. Forrester’s survey data (2006) show that about 31 per cent of Indian IT enterprises plan to increase their spending with HP for computing hardware, and 39 per cent of Indian firms planned to increase their IT spending on software development with Microsoft. In 2006 Microsoft announced plans to invest more than $1.5 billion in India for R&D, marketing and education to foster growth in the Indian market for its software products. Other winners in this software market expansion include SAP and Oracle/People Soft, with 14 and 12 per cent of Indian firms respectively. A few remarks on the recent trends in the Indian software market are in order, since software services are most labor-intensive and also diffusion-intensive. They can be easily decentralized, e.g. financial services, publishing, communication networks. Kumar and Jetharandani (2005) have discussed the trend in this market. Their key findings are as follows: 1 India is the fourth largest software market in the APAC region (excluding Japan), with about 9.5 per cent of the regional market.


29

2 India’s software market is among the fastest growing in the APAC region, with an expected compound annual growth rate (CAGR) of 15 per cent through 2008. 3 The biggest areas for growth are in banks, government services at the federal and state levels, telecommunications, manufacturing and small and midsized businesses. Along with the software market, India has a great potential in the BPO (business process outsourcing) market. Forrester, who pioneered the Business Technographic Survey, visited India in 2005 and interviewed a number of prominent BPO suppliers in the voice, transaction-processing and knowledge-processing segments. He found that this offshore Indian BPO market, despite its relative youth, continues to exhibit dynamism. Martorelli (2005) has noted that the Indian BPO market is undergoing a dramatic shift in its service mix. It was not long ago that the voicebased call center applications were the major source of the Indian BPO market’s meteoric rise. It still accounts for slightly more than 60 per cent of the $3.5 billion offshore industry in India according to NASSCOM (National Association of Software and Service Companies). But the leading companies have performed a dynamic shift, emphasizing more and more the transaction-processing and knowledge-processing opportunities. This diversification would enhance business growth and meet the challenge from other Asian competitors, including IBM and Accenture operations in India. In conclusion we may point out that the new knowledge economy in India provides a vibrant sector of growth. It shows a dynamism and challenge: the dynamism due to world competition and innovation and the challenge due to intensifying the need to improve core competence and economic efficiency in the IT sector. This market-based philosophy of sustaining growth through improving economic efficiency has been succinctly put forward by Baumol (2002) in his technology-consortium model, where the cost of not joining a technology and innovation consortium is very high for each member not joining. The advantage of joining is growth through substantial scale economies, dynamic comparative advantages and learning by doing. India has all the potential to become a leading partner in this innovation consortium.

2 India’s Industry Growth: Its Structure and Potential

India’s industry is at a crossroads today. Its IT sector is booming. Its global markets are expanding. The manufacturing and service sectors are bearing the impact of IT expansion. Yet the traditional industries and agriculture are yet to mirror the overall growth. The rural sector has reaped little or no benefit from the high growth rate of national income (exceeding 8 per cent). Goldman and Sachs predicted in February 2007 that this high growth rate is likely to continue for the next three decades or more. However, India has to implement some more appropriate to openness in world trade and competitive efficiency and transparency. Three types of strategies are needed in particular. One is to develop and expand the incentive system for export-sensitive industries so that they can compete more easily in the world markets. On the domestic front new markets have to be fostered and developed. Second, the IT sector, with its software network and various ‘outsourcing’ services sold abroad, needs to adopt strategies that will sustain and improve competitive and comparative advantage in the world market. As in the other NICs (newly industrializing countries) of Southeast Asia, also known as growth miracle countries, India’s IT sector has to actively pursue a strategy of technology diffusion to other sectors, such as manufacturing and services like retailing, real estate development and rural development. Finally, India has to look for new market entries on both domestic and international fronts. Expanding export markets and developing new industries for the domestic economy are very critical to maintaining the high growth rate of the Indian economy achieved so far. The growth impact of spillover technology and externality effects have been strongly emphasized by modern growth theorists like Lucas (1993), Romer (1990) and others. Our object here is threefold. First, we discuss some models that are relevant for industry dynamics and growth. This is followed by the theory 30


31

of technology diffusion and how India can learn from it. Finally, we discuss some strategic models for expansion of IT-sensitive sectors, such as exports, the IT sector and computer-related services.

1 Industry dynamics and growth Industry growth in India can be analyzed from several perspectives, of which three are most important from a dynamic theoretical viewpoint. First, we may analyze the models of technological change in relation to capital investment and discuss their lessons for industrial growth in India. The Solow model assumed technological change as exogenous and concluded that the long-run growth of steady-state income depends only on exogenous technological change. In modern endogenous growth models, technological change may take several forms, such as Arrow’s learning by doing or cumulative experience, skill acquisition through education and research and spillover technology. Most of these forms of technological change generate induced investment and the diffusion of new technology. The broad concept of Schumpeterian innovation succinctly conveys these features of new ideas, new products and services and new institutional forms. The demand pull perspective emphasizes the relative importance of market demand growth on the supply of knowledge and innovations. In his classic study of the invention and diffusion of hybrid corn, Griliches (1957) first showed empirically the role of demand in determining the timing, location and diffusion of invention. Schmookler (1966) in a massive study of US patent statistics showed that when investment rose, capital goods innovations also rose; when investment fell, the flow of patent applications also declined. Modern growth theory emphasizes two main channels of inducing growth through R&D expenditure, which is the core component of innovation or technological change. One is its impact on the range of available products and services, and the other is its impact on the stock of knowledge available for R&D. Helpman (2004) has discussed the role of endogenous R&D investments in improving the industrial productivity of a developing country participating in world markets. Two impacts are distinguished. One is the market size effect. Access to a larger world market raises the profitability of inventive activities and encourages investment in more R&D and knowledge creation. The second is the competition effect. It has two sides. On the negative side it may hurt profits, because foreign competitors are more efficient than domestic firms. On the positive side, however, open competition may induce the technological leaders to forge ahead. The NICs of Southeast Asia and Japan have adopted this


positive side of competitive efficiency and the openness in trade has helped these countries raise their industrial growth rate at a faster speed. Second, the industrial structure of any country is characterized by high degrees of diversity. Whereas some industries such as chemicals, oil refining and iron and steel, comprise only a small number of large enterprises that are hardly ever challenged by entering firms (i.e. new entries), others, such as textiles, leather goods and wooden and professional services, comprise a large population of small firms, where new entries and exits occur very frequently. Models of entry and market evolution attempt to explain why firms and industries grow or decline. The empirical experiences of the industrial countries offer important lessons for India. Some useful empirical models may be discussed in this connection. One is the study of the manufacturing sector in the UK for 1980–90 by Lansbury and Mayes (1996), who noted that the competitive process involves not just the development of existing firms but new entrants who challenge the incumbents. The productivity of most new entrants was higher than that of the sample as a whole for most of the years from 1980 to 1990. The new entrants can be totally new firms, new lines of business for existing firms or new plants for existing firms in the same industry. Table 2.1 exhibits the pattern of entry indicated by ‘births’ over the years 1981–90 for 20 branches of the manufacturing sector. The high entry industries include ‘extraction of minerals’, motor vehicles and parts, and rubber and plastics. Textiles, leather goods, metal manufacturing and food products comprise some of the important industries where birth rates are low. One would expect a greater volatility (or churning) in entry rates in an industry that is more competitive. Likewise, low churning is likely to be a feature of industries that are not readily contestable due to high entry costs or large fixed costs. However, the product cycle affects these results to a large extent. A similar study was made by Veloce and Zellner (1985) of the Canadian household furniture industry using annual data over 1957–87. The econometric estimate of the entry equation was as follows: ln Nt = 0.37 + 0.85 ln Nt−1 − 0.53 ln Nt−2 + 0.16 t (0.13)

(0.14)

(0.15)

− 0.20 ln rt−1 − 0.43 × 10 (0.08)

−8

(0.18×10−8 )

(0.0005)

St−1

2

R = adjusted R2 = 0.7389 where the standard errors are in parentheses. Here Nt = number of establishments in the industry, rt = real rate of interest measured by the

India’s Industry Growth: Its Structure and Potential Table 2.1

33

Number of ‘births’ relative to the total number of businesses (%)

1 Metal manufacturing 2 Minerals extraction 3 Non-metallic minerals 4 Chemicals 5 Manmade fiber 6 Metal goods 7 Mechanical eng. 8 Data processing equip. 9 Electrical eng. 10 Motor vehicles & parts 11 Other transport equip. 12 Instrument eng. 13 Food products 14 Textiles 15 Leather goods 16 Footwear & clothing 17 Wooden furniture 18 Paper products 19 Rubber & plastics 20 Other manufacturing Total manufacturing

1981

1983

1985

1987

1988

1989

1990

3.96 4.29 4.96 3.22 0.0 2.86 3.55 18.18 4.59 3.17 2.34 7.74 4.64 2.71 1.83 3.05 3.38 3.99 3.05 4.15

2.97 2.45 3.75 4.21 6.67 2.24 3.45 20.86 3.06 3.42 3.92 4.53 3.86 2.16 2.55 3.33 4.03 4.30 3.53 3.67

5.41 7.88 6.87 5.06 3.33 4.13 5.08 9.59 5.31 2.64 5.90 3.40 3.94 3.50 4.08 4.50 6.63 4.85 4.44 3.41

11.43 22.22 11.46 8.83 – 7.04 5.53 29.38 7.92 9.07 6.02 12.70 8.26 4.26 2.94 7.36 10.73 10.41 9.59 17.45

9.44 22.39 9.02 6.73 5.88 6.62 7.78 15.07 4.86 5.91 4.35 10.37 11.05 3.72 8.94 4.80 8.32 8.98 9.01 10.04

4.76 3.58 8.69 9.56 9.38 8.54 9.43 14.13 9.65 4.95 6.04 5.74 10.83 4.79 3.33 7.54 10.14 9.57 11.46 12.46

3.96 10.85 7.66 7.57 – 6.22 7.81 16.05 8.65 12.32 11.71 10.22 6.38 3.35 2.72 5.03 7.21 7.08 6.67 7.51

3.88

3.60

4.90

8.89

7.97

8.97

7.28

difference between the nominal rate on ten-year Canadian industrial bonds and the Canadian CPI, t = time in years indicating trends and St = total sales. It is clear that this furniture industry is characterized by a positive time trend and a high positive impact of the first order lagged term ln Nt−1 . The second order effect (i.e. −0.53) is, however, negative, implying volatility or churning effect. When the sales variable (St−1 ) is dropped, the resulting equation becomes Constant ln Nt−1 0.29 0.89 (0.14) (0.16)

ln Nt−2 −0.52 (0.17)

t 0.009 (0.004)

2

ln rt−1 R −0.205 0.663 (0.095)

The results confirm a positive trend effect and a negative effect of real interest rate (lagged). A more comprehensive analysis of industry dynamics and the role of technological progress has been attempted by van Dijk (2002) for the Dutch manufacturing sector. He raised some fundamental theoretical issues about this dynamic behavior and tested detailed empirical data

34 India’s New Economy Table 2.2 Entry and exit rates (%) in Dutch manufacturing Year

Entry rate (%)

Exit rate (%)

Firms

Employment

Firms

Employment

1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992

– 8.39 5.82 4.95 5.73 9.73 6.09 5.88 4.89 6.06 6.14 6.54 5.17 9.36 7.27

– 3.07 1.93 1.88 2.25 4.91 2.39 2.34 1.72 2.13 2.05 2.32 2.41 3.47 2.96

6.67 5.41 7.14 7.85 8.83 7.20 4.93 4.34 3.99 4.09 4.15 4.91 6.74 7.06 –

3.14 2.30 2.68 3.26 4.63 3.03 2.20 1.84 1.62 2.02 1.98 2.32 3.42 3.25 –

Mean

6.52

2.56

5.95

2.69

on 106 industries in the Dutch manufacturing sector between 1978 and 1992. The SM (Statistics Netherlands) database was used and it captures all firms with more than 20 employees that have been active in the manufacturing sector. In total there were 10,246 firms in the data set, of which 2,558 firms were present throughout the period 1978–92. These continuing firms captured on average 53.5 and 52.4 per cent of total manufacturing employment and value of output respectively. Given the high hazard rates of entrants the long-run or cumulative impact of entry may be less substantial. However, as Baldwin (1995) shows for the Canadian manufacturing sector, the accumulation of entry over 1970–81 was of considerable magnitude. The number of entrants alive and active in 1981 equaled 35.5 per cent of the 1970 firm population, while their number of employees equaled 10.9 per cent of total employment in 1970. It is clear from Table 2.2 that there exists a strong positive correlation between entry and exit rates. For example, when cumulative entry and exit rates are considered, the correlation is found to be 0.23. This increases to 0.52 when cumulative sales entry and exit rates are considered. Indirectly this implies a significant turbulence or churning effect (volatility). Hence van Dijk undertook a detailed regression analysis to


35

Table 2.3 Explaining entry rates and market share turbulence in terms of industrial growth rates, scale economies and Herfindahl index Annual entry rates Firms

Sales

0.118∗∗∗

0.119∗∗∗

Cumulative entry rates Market share Firms Sales turbulence 1.120∗∗∗

1.054∗∗∗

−0.421∗∗∗ −0.299∗∗∗ −1.881∗∗∗

−2.030∗∗∗

Industrial growth 0.020∗∗∗ 0.011∗∗∗ 0.133∗∗∗ rate Investment 0.278∗ 0.153 0.855 margin Median firm size −0.013∗∗ −0.015∗∗∗ −0.159∗∗∗ (scale economies) Herfindahl index 0.263∗∗∗ −0.011 0.307∗∗∗ Adjusted R2 0.653 0.210 0.490

0.116∗∗∗

Constant Profit margin

0.657 −0.152∗∗∗ −0.249∗ 0.516

0.027 (0.054) 0.059 (0.256) 0.002 (0.009) −0.141 (−0.349) 0.037∗∗ (0.013) −0.166∗∗ 0.075

Note: Standard errors in parentheses. One, two and three asterisks denote significant estimates at 10, 5 and 1 per cent levels respectively.

explain the annual entry rates and market share turbulence in terms of several explanatory variables, i.e. profit margin, industrial growth rate, investment margin, median firm size and Herfindahl index. The regression results are reported in Table 2.3. Several implications have to be noted here. First, earlier research on the determinants of gross entry showed that profitability does not seem to have a significant effect on inviting entry. This is at odds with the standard models of the entry process, where high profits seem to attract profit-seeking entrants. Note that we obtain a negative impact of average profit margins, but this may largely reflect the negative impact of scale economies, for which the proxy variable median size firm in an industry has been used. Second, both industrial growth rates and investment margin have positive effects. Third, only median firm size (a proxy for scale economies) has a positive effect on market turbulence among the incumbent firms and the Herfindahl index (concentration in the industry) has a significant negative impact on entry rates. This implies that higher concentration tends to reduce the entry of new firms in the industry. van Dijk has also discussed the role of technological competitiveness of a firm or industry in inducing or deterring entry. The technological competitiveness TCi of a firm i is assumed to depend on the intrinsic


quality Qτ of the product technology it is applying and the total share Tτ of this technology in the industry as TCi = αTτ + (1 − α)Qτ where 0 < α < 1 and Q0 ≤ Qτ ≤ 1. The parameter α determines the network externalities on the demand side, i.e. the higher α is, the more the total market share of a technology determines technological competitiveness. This type of technology-based entry model may be understood as a process by using the Schumpeterian concept of ‘creative destruction’. Assume that at every period t, K product technologies are available. At birth every firm starts with an endowed technology τ(τ = 1, 2, . . . , K) such that the probability of receiving a given technology is equal to 1/K. These technologies are ranked according to their intrinsic quality levels Qτ such that Q K > Q K−1 > Q K−2 > · · · > Q1 . Further, there is a class of old technologies with τ = 0 that have an intrinsic quality level Q0 . At some interval a pioneering entrant or incumbent brings out a new intrinsically better product technology. This causes all technologies to drop one level in their intrinsic quality. Hence the newly introduced technology becomes K (i.e. the technology with the highest quality level Q K ), and τ = 1 becomes part of the class of old technologies (τ = 0) and degrades to the intrinsic quality level Q0 . Shy (1996) has related this technological competitiveness process to consumer dynamics within an overlapping generation model, where the generation of entering consumers chooses whether to purchase a certain product based on an old technology or whether to purchase the product based on a new technology with a higher quality. Then the size of the network of the new technology is the sum of the population size of the young generation and a certain proportion of the old generation of users. This percentage is determined by the degree of compatibility between the old and the new technology. Hence the higher the compatibility the larger the network size associated with the new technology. Shy (1996) has shown that a decrease in the degree of compatibility between new and old technologies will increase the duration of each technology. The more consumers value quality and network size as substitutes rather than complements, the more the frequency of technology adoption and the lower the duration of each adopted technology. van Dijk performed a series of simulations under different technological regimes represented by various technological competitiveness and spillover conditions. In general he found a smaller, more concentrated population of firms when cumulativeness (or technological competitiveness) conditions are high. This


37

provided significant entry barriers. Widespread spillover conditions, on the other hand, led to a higher number of new entrant firms and lower levels of concentration. We may now summarize the three broad lessons from the above empirical dynamics of the entry and market evolution process, which can be profitably used in the industry growth framework in India in its manufacturing and skill-intensive service sectors. First, successful trading in the world market for manufactured goods demands excellence in technological competitiveness. Maintaining and improving cost efficiency at the firm level are most important. Second, R&D investments have to be so planned that quality ladders and product diversity are paid increased attention. The success of the Japanese automakers in the US market amply demonstrates the value of this insight. Finally, the degree of substitutability of old and new technologies (or processes) must be carefully analyzed in the various product-based R&D investments so that a smooth transition can be attempted from the old to the new. This applies to all those branches of manufacturing and skill-intensive services that have to compete in the world market today.

2 R&D investments and technology diffusion The Romer model specifies the aggregate production function in Cobb– Douglas form, where the physical capital stock K and labor LY combine to produce output Y as Y = K α (ALY )1−α ,

0 0 we obtain q > c1 > 0. The adjoint equation becomes q˙ = (ρ + δ)q − a − 2bR This yields u˙ = (ρ + δ)u +

(ρ + δ)c1 − a b − R 2c2 c2

The steady state has the equilibrium values R and u as follows R = (1/2){c1 (ρ + δ) − a}/{b − c2 δ(ρ + δ)} u = δR = (δ/2){c1 (ρ + δ) − a}/{b − c2 δ(ρ + δ)} Thus the steady state level R of R&D or knowledge capital decreases if the parameters a and b of the revenue function increase. The investment u in steady state rises, however, with increases in the parameters c1 , c2 of the


investment cost function. Also, u rises for any decrease in the parameters a and b of the revenue function. As in the Romer model, any permanent increase in the parameters c1 , c2 of the investment cost will increase the steady state level of the knowledge capital R. An interesting case of this model arises when the following conditions hold c1 (ρ + δ) − a < 0 and b − c2 (ρ + δ) < 0

(2.6)

In this case we have the steady state as a saddle point equilibrium, where the two eigenvalues associated with the Jacobian of the dynamic system are λ1 , λ2 = (1/2)[ρ ∓ {(ρ + 2δ)2 − 4b/c2 }1/2 ]

(2.7)

Clearly from equation (2.6) it follows that λ1 is negative, while λ2 is positive. On the stable trajectory we can only consider the case λ1 , which results in convergence to the steady state. The complete optimal paths may then be written as R(t) = R + (R0 − R) exp (λ1 t) u(t) = u + (R0 − R)(δ + λ1 ) exp (λ1 t) where R0 is the initial value of the R&D capital stock. Note that as t → ∞, both R(t) and u(t) converge to their steady states. Two points must be noted about this optimal R&D model. If the revenue function r(R) is convex but more nonlinear than a quadratic, then this may lead to multiple steady state equilibria and also path-dependent equilibria. Second, if we apply a proportional investment policy u(t) = (δ + h)R(t) for all t

(2.8)

instead of an optimal control policy, we obtain the path of R&D capital stock as R(t) = R0 exp(ht) and the profit function as π(R0 ) = R0 (ρ − h)−1 {a − c1 (δ + h)} + R20 (ρ − 2h)−1 {b − c2 (δ + h)2 }

(2.9)

provided that 2h < ρ. If b > c2 (δ + h)2 then profit π(R0 ) is unbounded. Hence competitive equilibrium may not exist. In Romer’s growth model most of the R&D stock is a fixed cost that generates increasing returns to scale and a very fast rate of growth.


41

Griliches (1998) undertook a careful analysis of the contribution of R&D capital to productivity in the US manufacturing sector, including both R&D and non-R&D firms over 1982–87, with 676 sample values. He found a strong relationship between TFP (total factor productivity) growth and privately financed R&D expenditure intensity. For the period 1959–78 he found that own R&D had a relatively large and significant rate of return of the order of 0.30 and that it did not decline significantly between 1959–68 and 1974–78. For the HPAE (high performance Asian economies) countries, Nadiri and Kim (1996) estimated translog cost functions with four inputs (labor, materials, physical capital and R&D capital) and compared productivity in the USA, Japan and South Korea over the period 1975–90. His estimates are reported in Tables 2.4 to 2.6. Several comments are in order. First, R&D investment has been a significant contributor to growth of output and productivity in the USA and Japan, but not in Korea. But more recently the contribution of R&D has been rapidly rising and the contribution of R&D in Korean manufacturing in absolute terms is not very far from that of the USA and Japan. The net rates of return to both physical and R&D capital in the Korean manufacturing sector have been very impressive (see Table 2.7). Second, one may also note that the conventional TFP measure is not a very appropriate measure of technical change when perfect competition does not prevail or when economies of scale are present. The empirical estimates here suggest that the rate of technical change measured by the decline in costs over time is rather small, being about 0.3–0.5 per cent. Finally, Nadiri and Kim decomposed TFP growth into five components: scale effect, disequilibrium effect, R&D effect, pure technical change effect and mark-up effect. The most important contributor to TFP growth is found to be the scale effect. This effect is responsible respectively for about 35, 38 and 30 per cent of traditional TFP growth in the US, Japanese and Korean manufacturing sectors. The contribution of the mark-up is unusually high in Korea (1.9 per cent). This is mainly due to the extremely high rate of output growth. Bernstein and Mohnen (1994) used a production function Yt = F(vt , Kt−1 , Kt , St−1 ) and the associated dual cost function for the USA and Japan over the period 1963–85 to estimate the contribution of two forms of international spillover effects: direct and indirect. Here Y is output, v is a vector of variable factor demands, K is physical capital and S is a vector of R&D

Growth of output and inputs in the total manufacturing sector of the USA, Japan and Korea (1975–90, %) Output

1975–80 1981–85 1986–90 1975–90

42

Table 2.4

Labor

Materials

Physical capital

R&D capital

USA

Japan

Korea

USA

Japan

Korea

USA

Japan

Korea

USA

Japan

Korea

USA

Japan

Korea

1.0 1.6 3.3 1.9

3.5 4.0 4.5 4.0

13.4 10.1 15.2 12.9

−0.2 −1.2 −0.2 −0.5

−0.6 1.3 0.3 0.3

8.2 3.7 2.6 5.0

0.8 0.7 3.2 1.5

2.7 2.3 5.8 3.2

12.2 8.6 14.8 11.9

3.7 2.0 1.5 2.5

3.6 6.0 7.0 5.4

18.5 8.4 15.1 14.3

0.5 3.1 3.9 2.4

6.8 9.3 9.0 8.3

35.3 28.2 25.1 29.9

Table 2.5 Average annual rates of growth of total and partial factor productivity in the total manufacturing sector of the US, Japan and Korea (1975–90, %) TFP

1975–80 1981–90 1975–90

Labor productivity

Materials productivity

Physical capital productivity

R&D capital productivity

USA

Japan

Korea

USA

Japan

Korea

USA

Japan

Korea

USA

Japan

Korea

USA

Japan

Korea

0.08 0.77 0.51

0.99 0.51 0.69

0.35 1.80 1.26

1.25 3.22 2.48

4.07 3.44 3.68

5.19 9.51 7.89

0.30 0.49 0.42

0.76 0.76 0.76

1.21 0.93 1.04

−2.58 0.74 −0.51

−0.08 −2.23 −1.43

−5.07 0.92 −1.32

0.59 −1.05 −0.44

−3.28 −4.92 −4.31

−21.9 −14.0 −17.0

Note: TFP growth was calculated as a Tornquist index approximation with total cost shares of inputs as weights.


43

Table 2.6 Sources of output growth for the total manufacturing sectors of the USA, Japan and Korea (1975–90, %) Period

Gross Labor Materials Physical output effect effect capital effect

USA 1975–80 1.09 1981–90 2.47 1975–90 1.95 Japan 1975–80 3.47 1981–90 4.27 1975–90 3.97 South Korea 1975–80 13.39 1981–90 12.66 1975–90 12.93

R&D Technical Residual effect change

−0.03 −0.13 −0.09

10.47 1.55 1.20

2.53 0.18 0.24

0.21 0.18 0.12

0.46 0.39 0.55

−1.09 0.30 −0.06

−0.11 0.15 0.05

2.32 3.32 2.95

0.30 0.60 0.49

0.12 0.21 0.18

0.75 0.36 0.50

0.08 −0.37 −0.20

0.99 0.34 0.59

10.47 9.62 9.94

2.53 1.58 1.94

0.02 0.18 0.12

0.46 0.25 0.33

−1.09 0.02 0.68

Note: The four input effects are calculated as the growth rate of each input weighted by its output elasticity.

Table 2.7 Internal rates of return on net investment in physical and R&D capital (in percentage) Year

1980–90 1980 1985 1990

Physical capital

R&D capital

USA

Japan

Korea

USA

Japan

Korea

10.63 11.30 11.74 9.63

7.69 9.27 7.96 9.33

17.84 17.55 15.06 22.78

12.39 14.16 11.56 11.11

11.73 12.01 12.31 15.60

19.42 31.46 18.94 23.88

spillovers, which in a bilateral production model is the R&D capital from the other country. Table 2.8 presents their estimates. The contribution of spillover effects is estimated through the respective input output ratios as ∂(Kt /Yt ) ∂(vt /Yt )/∂St−1 = φ + γ(Kt−1 /Yt−1 ) ζ + γ(Kt /Yt ) µ ∂St−1 where prime denotes transpose. The direct spillover effect on variable factor demands is measured by φ, the indirect effects are measured by all capital inputs ζ and net investment through µ. The international


Decomposition of average annual TFP growth rates (%) TFPG

Scale

Physical capital

Adjustment cost

Spillover (direct)

Spillover (indirect)

USA 1963–67 1968–73 1974–79 1980–85 1963–85

0.953 2.413 −0.396 −2.413 0.104

0.802 0.369 −0.314 0.116 0.219

4.353 2.556 1.956 0.809 2.334

−3.861 1.081 −0.180 0.127 −0.571

0.175 0.534 0.405 0.632 −0.448

−0.516 −2.127 −2.267 −4.097 −2.326

Japan 1963–67 1968–73 1974–79 1980–85 1963–85

1.749 2.289 2.279 1.394 1.935

−0.144 0.640 0.312 − 0.217

3.997 3.239 0.646 1.118 2.174

−1.685 −1.830 0.936 −2.243 −1.185

1.136 1.125 1.122 3.967 1.868

−1.555 −0.885 −0.737 −1.448 −1.139

R&D spillovers reduced the labor–output and physical capital–output ratios for both US and Japanese manufacturing sectors. Their estimates show that in the USA labor and physical capital output ratios declined by 0.02 per cent but in Japan these ratios declined by 3.5 and 0.13 per cent respectively. Thus the effects from US R&D capital were significantly greater for Japan than the effects arising from Japanese generated spillovers. Table 2.8 shows that the direct effect from Japanese R&D capital contributed about 20 per cent to US productivity growth over the two decades from the mid-sixties to the mid-eighties. Over the same period the US effect accounted for around 60 per cent of Japanese TFP growth in its R&D-intensive manufacturing sector. Clearly this has two important lessons for India’s growth structure analysis. One is to capture the benefits of US spillover technology in those parts of the manufacturing sector that are likely to be R&D intensive in the near future. Joint ventures, inviting US investment and increasing cooperation in the software R&D field should be actively pursued by both private and government policies. Second, indirect spillover effects through input cost reductions in sectors of the overall economy other than the IT sector should be built into the planned strategy for accelerating the overall growth rate of R&D-intensive and non R&D-based industries. In Schumpeter’s innovation process, imitation and improvement rather than inventions can also provide a source of growth through creative destruction. Thus Japanese firms have historically gained time and


45

cost advantages in imitation due to their acquisition of the know-how of foreign competitors. Most comparative advantages come not from internal technology or new software products but from external technology bought or copied from competitors. According to a recent report of the US National Academy of Sciences, in nearly 300 cases of research links between US and Japanese companies more than 90 per cent involved a transfer of US technology to Japan. In Japan firms take about 25 per cent less time and spend about 50 per cent less money in carrying out an innovation because of their use of external borrowed technology rather than in-house invention. Moreover, this is valid for almost all industries in Japan. The noted management scientist Peter Drucker observed the specific comparative advantages of the imitation process. The follower allows the first mover to test the waters. It learns from the innovator’s mistakes. The follower can also take advantage of subsequent product or process innovations, such as more powerful computers or chips, while the first mover may be locked into the technology at the start, because of large sunk costs in research and manufacturing. It is not just technology that can be imitated but also services and professional skills. For example, even as Dell Computer was cloning the technology of the IBM PC and offering PCs at a lower price with better customer service, other competitors were imitating Dell’s approach to direct marketing. Clearly India can develop its own efficiency in imitating new technology and software development due to its talent pool of scientists and engineers, and thereby improve its IT market share in the world. This policy framework has the added support of the modern theory of dynamic comparative advantage. For example, Kemp and Okawa (1995) have shown that for two free-trading countries under any Hicksian type, technical progress (i.e. Solow’s A in the production function Y = AK α L1−α ) in one country necessarily benefits the other country if preferences in the progressive country are homothetic. This Hicksian proposition also holds even if one of the two industries is oligopolistic. Thus India needs to adopt an active policy of technology diffusion, when technology is viewed in the broadest sense. Internationally it involves collaboration in R&D and FDI with US and other global partners. The private sector has a special role in adopting an effective imitation and improvement policy. The spillover benefits of international R&D investment need to be captured by the business leaders in India. Various management and engineering institutes and research institutions have a direct catalytic role to play here. On the domestic front, diffusion must involve induced investment and incentive promotion, so that factors are reallocated to more productive subsectors and regions. Further,


R&D investments must be optimally used in the private and public sectors. It is instructive here to compare the various policy incentives adopted by Singapore and its comparative R&D indicators. This shows the benefits of agglomeration arising from the presence of a pool of suppliers. The benefits of agglomeration effects have been strongest in Singapore, followed by Thailand and Malaysia. Firms have benefited from supplier proximity in their ability to meeting changing demand quickly through a shortened supply chain.

3 Strategy for global competition In global markets today competitive efficiency holds the key to success. Three important aspects of this efficiency have to be noted. The most important aspect of competitiveness is national productivity and especially the productivity of those sectors like IT and software professional services. Michael Porter (1990) investigated for four years why nations gain competitive advantage, studying the ten important countries Denmark, Germany, Italy, Japan, South Korea, Singapore, Sweden, Switzerland, the UK and the USA, and reached three important conclusions. First, sustained productivity growth at the industry and firm levels requires that an economy continually upgrade itself. A country’s growing firms must also develop the capability to compete in more and more sophisticated industry segments, where productivity and overall efficiency are higher. At the same time an upgrading economy is one that develops the capability of competitive success in entirely new and sophisticated industries. Doing so absorbs human resources released in the process of improving productivity in existing fields. This is especially relevant for the IT sector in India, which has achieved significant success in global markets in recent times. India is now the fourth largest software market in the Asia Pacific region, claiming 9.5 per cent of the total regional software market. Its software market is among the fastest growing in the Asia Pacific region, with an expected compound annual growth rate (CAGR) of 15 per cent through 2008. The biggest opportunities for growth are in banks, government agencies, telecommunications, manufacturing and small and midsized businesses. The second feature of competitive advantage principle is emphasized by its dynamic aspects. International trade allows a country to raise its productivity by eliminating the need to produce all goods and services within the country itself. A country can therefore specialize in those industries and segments in which its firms are relatively more productive and import those goods and services where its firms are


47

less productive than the foreign competitors. Foreign direct investment and the establishment of foreign subsidiaries by a country’s firms can raise national productivity, provided they involve shifting less productive activities to other countries. A country’s firms can thus increase exports and earn profits from abroad, which boost national income. Finally, competitive advantage basically involves contributing to the value chain, i.e. increasing the contribution to buyer value. Core strategy guides the way a firm performs individual activities and organizes its value chain. Note that firms can gain competitive advantage from conceiving new ways to conduct their business activities, employing new procedures, new technologies or different inputs. For instance, Makita of Japan emerged as a leading competitor in power tools, because it was the first to employ new and less expensive materials and to produce standardized models in a single plant. A firm’s value chain is connected by interdependent linkages. These linkages often create cost economies. For example, a more sophisticated product design and a more thorough inspection can reduce after-sale service costs. A company can also create competitive advantage by optimizing or coordinating the various links to the value chain. Frequent and timely deliveries by suppliers, a practice now widely termed kanban after its Japanese innovators, can lower a firm’s handling costs and reduce the required levels of inventory. These costs emphasize what are called economies of scope in managerial economies. Scope is important because it shapes the nature of a firm’s activities and the way their contribution to the value chain is realized. There are two ways in which this can happen. One is by selecting a narrow target segment, where a firm, for instance, can tailor each of its activities to the segment’s needs and achieve lower costs. A second way is to take advantage of agglomeration effects by sharing activities across industry segments. For example, Japanese consumer electronic producers such as Sony, Matsushita and Toshiba reap great economies of scope from competing in related industries such as TV sets, audio equipment and VCRs. These firms use the same international market networks to take advantage of common product and process technologies and employ joint purchasing and some collaborative research activities. Finally, one must note that governments cannot create competitive industries, only firms and industries can. Governments can only shape or influence the institutional structure and the industrial environment surrounding firms. The best example is the role of the Japanese government, which is currently being followed by Taiwan, South Korea and Singapore. The Japanese government encourages early demand, develops cooperation policies for adopting frontier technologies and speeds


up the process of upgrading and new innovations. Thus as broad national policy governments should play an active and direct role in those areas of business where externalities cause firms to underinvest. The lessons for the IT-based sectors in manufacturing and skilled services in India are clear. Government policy should attempt to lay the foundation for upgrading competitive advantage in India’s IT industry and manufacturing sector. As Porter (1990, p. 622) has stressed very strongly, ‘The highest-order advantages associated with high levels of productivity are those that accrue from a steadily rising level of technology, a stream of new models, investments in building close customer relationships and economies of such growing out of a global market presence.’ The three principles of competitive advantage that we have discussed are summarized by three Cs: cost efficiency, comparative advantage and core competence. Sengupta (2005) has discussed the role of these principles in India’s economic growth. Four basic elements of core competence are: learn, coordinate, integrate and innovate. Core competence has been defined by Prahalad and Hamel (1994) as collective learning in the organization, especially learning how to coordinate diverse production skills and multiple streams of technologies. An example is provided by the pattern of software exports from India relative to other Asian countries. It is clear that India’s software competitors rely more on software packages than services. Since software package exports produce more stable and greater earnings than software services, India’s skewed pattern of software exports exposes its vulnerability in the future. The need for optimal diversification and for exploring scope economies through coordination and core competence is very clear. The sources of competitive advantage for India’s exports in IT-based products and services need a more detailed analysis, since this advantage has a significant multiplier effect on the growth of the whole economy. Three phases can be easily identified. In the early stages of growth of the IT sector the relative advantage of skilled labor and the high international demand for IT services provide the basic source of growth. The second stage is marked by ‘new factors’ such as firm-specific capabilities. The third phase, which is now starting, is to meet the competitive challenge by exploiting agglomeration economies and developing new productmixes through optimal use of R&D investments and learning by doing. Software and IT services accounted for 1.98 per cent of India’s GDP in 2002 and are expected to reach 7.7 per cent by the estimates of NASSCOM. The contribution to employment is less marked but it is rising over time. In 1996 exports (in million $) were 1085, with employment of 160,000 and total revenues of $1766 million. In 2000 the figures


49

were 4500, 320,000 and 5600 respectively. Noyelle (1990) has recently computed the growth of computer software and computer services in five Asian countries: India (1989), Singapore (1990), Hong Kong (1990), South Korea (1990) and the Philippines (1989). He mentioned the advanced stage of development of Singapore’s software industry, comparable to those of the USA and Japan; Singapore attracts foreign computer professionals mostly from Malaysia, China and India and a number of companies use overseas subcontractors from Malaysia, China and the Philippines to compensate for local strategies. The latter emphasizes a level of sophistication in the area of project development management that is very rarely found among firms from other developing countries of Asia. It is clear that India’s computer software industry has primarily focused on the development of tools and professional services for the software industry. India has been a net exporter of human capital for several decades. Arora et al. (2004) estimate that Indians account for a very large fraction, perhaps about 40 per cent, of the H1-B work permit visas issued by the US government. Their estimates of comparative labor costs for computer services show that the costs of a development programmer and network analyst in India are 19.51 per cent and 28.58 per cent of those of their US counterparts respectively. However, this situation is likely to change over the next decade, since other countries are quickly catching up. Note that the widest wage gap concerns the lower end of the skill spectrum (test engineers) but India’s wage gap with the USA and UK is at all levels of qualification. Openness in trade has helped India’s growth in this sector in two ways: through international mobility of skilled personnel and the network of linkages with former expatriates from India, who are largely concentrated in Silicon Valley, and access to the professional services and subsidiaries of VC (venture capital) firms from the USA and other industrial countries. For example, in the second quarter of 2006 IBM and Accenture generated employment of 200,000 and 133,000 with income per employee $23,880 and $20,602 respectively. Of all the computer services, the financial services sector dominates the growth of the industry in India and this is certainly going to continue for the next decade. Of all the HPAEs the Taiwan model offers very important lessons for India’s development of the IT sector. The production of IT output fueled Taiwan’s impressive economic growth in the past two decades. Its IT output grew from less than US$100 million in 1980 to more than $5 billion in 1989 and grew over 20 per cent annually in the 1990s, when its GDP growth was about 6–7 per cent. In 1999 it reached $21 billion and if Taiwanese manufacturing in China is included, the total IT output was


more than $35 billion in 1999. How did this small island of 24 million surpass other Asian economies as well as more advanced economies in the global technology competition? Saxenian (2004) has discussed in some detail the major forces that made Taiwan a major global center of electronic systems design, manufacturing and logistics. One basic indicator of Taiwan’s technological achievements is its ranking among US patent recipients: in 1980 it ranked twenty-first, by 1990 it reached eleventh and in 1995 it ranked seventh. Today Taiwan receives more patents per capita than the other Asian NICs and ranks ahead of all the G7 countries except the USA and Japan. Four major factors are identified by Saxenian (2004) as behind the rapid growth in Taiwan’s IT sector. First, there were the contributions of two separate clusters of entrepreneurship, comprising dozens of small firms and start-up companies: one in the Taipei area cloning PCs and components, building on the skills and infrastructure of multinational corporations in the earlier decade; the other in the Hsinchu Science Park, which spun out of the governmentfunded semiconductor research institute. Many state policies in the 1980s, such as the emphasis in science and technology-based industry, the rapid transfer of public research to the private sector and the creation of a domestic VC industry, were influenced by the Silicon Valley model. The government offered a 20 per cent tax reduction to all investors in VC funds that were targeted to strategic technology-intensive and researchintensive segments. Second, the agglomeration and scale effects flowed from the large infusion of entrepreneurial and managerial resources from the USA, which provided important linkages to technology and markets in Silicon Valley. These forces were instrumental in shifting Taiwan to the technological frontier in the manufacturing of ICs (integrated circuits), PCs and related components. By 1999 Taiwan had 153 VC firms with an investment total of $1.08 billion in IT-related businesses, which along with Israel made Taiwan the largest in the world after Silicon Valley. The Hsinchu region, like Silicon Valley, provides for Taiwan an industrial environment in which small companies can grow large, while still remaining a part of this decentralized infrastructure of the region. One has to emphasize that the Hsinchu industrial system constituted an almost complete component design and manufacturing supply chain for IT services, although it still depends on outside providers of high-end microprocessors, hard disk drives, specialized memory chips etc. Third, Saxenian points out that the Hsinchu region exemplifies the dynamic role of Marshallian external economies, both physical and financial, in which the localization of skills and specialized know-how helps generate cost reductions for individual firms and increasing return for the region


51

as a whole. Taiwan did not adopt the high-volume assembly strategy of large vertically integrated Korean conglomerates. Instead it developed an extensive supplier and subcontracting infrastructure, which produces an ongoing stream of innovation-intensive SMEs (small and medium-sized enterprises). Finally, the comparative advantage benefits of the Hsinchu model suggest that the social and entrepreneurial structure of a technical community is fundamental to the organization of IT production at both the global and local levels. Saxenian concludes with a statement from Fred Chang, the CEO of Windbond North America: ‘The best way to start a technology company today is to take the best from each region, combining Taiwan’s financial and manufacturing strength with Silicon Valley’s engineering and technical skill.’ In India Bangalore provides an IT cluster. But it needs to develop clusters in other cities; for, example, the Salt Lake IT complex in Kolkata and IT centers in Hyderabad were set up over a decade ago but they have not been successful in developing effective linkages with Silicon Valley expatriates from India. There is an imperative need for an active state policy with incentives and grants, so that these clusters can provide a complete range of IT-related services, which have significant scale economies and agglomeration benefits for these regions as a whole. One common criticism of the rapid growth of India’s IT sector in recent times is that it creates a chasm between income per worker in the progressive IT sector and that in the rest of the economy. This dualistic structure creates an asymmetry in growth rates between the ‘old economy’ and the so-called ‘new economy’. Two types of policy measures are relevant here. One is to promote the process of technology diffusion across the country, as we have observed in the Taiwan model, which stimulated the diffusion process through subcontracting IT jobs to SMEs. China favored town and village enterprises (TVEs) in its IT policy. Fiscal incentives and direct government assistance may be needed in the initial stage. Second, the rapid productivity growth in the IT sector coupled with an increase in national spending on secondary and tertiary education and training in skill acquisition may help speed up the process of factor allocation from other sectors to the IT sector. This happened in the HPAEs when they achieved a high rate of growth. These countries achieved technological advance and diffusion through five broad economic policies: 1 Human capital deepening. 2 Creation of publicly financed research centers and institutes. 3 Fiscal incentives for private R&D activities.


4 Economic incentives for information technology and its decentralization. 5 Technology transfer arrangements through FDI in technologyintensive industries. The dynamic role of R&D investment and expenditure on innovations in the Schumpeterian sense is of special importance for continuing the sustained growth in the future for the IT sector in India. There is a record of R&D investment in the NICs of Asia and their openness to foreign trade. The two are closely interrelated through (FDI) and joint ventures. The R&D indicators show the NICs in Asia to be mostly ahead of the ASEAN4. Korea, Taiwan and now Singapore invest proportionately more R&D than several middle-income OECD countries. Patent activity is more vigorous in the NICs than the ASEAN-4 and these economies rate more highly according to a broad estimate of technology capabilities proxied by the technology index. In terms of openness to international trade the NICs of Asia fare much better, although countries like Malaysia are catching up. India’s performance here is not any better than the ASEAN-4 countries. Sengupta (2005) reviewed the comparative performance of India in the context of NICs of Asia, using data on an R&D index comprising several components, such as high-technology exports as a proportion of manufacturing export, the number of scientists and engineers in R&D as a percentage of GDP and average annual number of patents. Selected rankings are as follows: Japan (1), the USA (3), Singapore (6), Korea (13), Malaysia (16), China (20), India (22). Although this index is very rough, it shows one thing: how far India has to improve on the R&D front. The TAI (technology achievement index) aims to capture how well a country is creating diffusing technology and building a human skill base. This composite index measures observed achievements. It is not a measure of which country is leading in global technology development but focuses on how well the country as a whole is participating in creating and using technology. This composite index TAI is a weighted combination (equal weights) of four components: (a) technology creation measured by the number of patents granted to residents; (b) diffusion of recent innovations as measured by the number of interested hosts per capita and the share of high- and medium-technology exports in total goods exports; (c) diffusion of old innovations measured by telephones per capita and electricity consumption per capita; and (d) human skills measured by mean years of schooling and the gross tertiary science enrollment ratio. Note that in terms of TAI India ranks lower than the Philippines, China


53

and Indonesia. India’s technology creation is negligible. This is mainly due to the very low R&D spending in both private and public sectors. A study by the World Bank (Yusuf et al., 2004) estimated the impact of R&D on firm productivity based on the World Bank survey data of 1826 firms distributed across eleven cities: Bangkok, Jakarta, Juala Lumpur, Manila, Seoul, Singapore and five Chinese cities, including Beijing, Guangzhou, Shanghai and Tianjin. For each of these eleven metropolitan economies the sample of firms comprises ten industries: with five manufacturing and five services. Note that excluding the Philippines and Malaysia the NICs in Asia achieved a very high level of R&D spending in business as a percentage of total R&D spending. For instance, the figures for 2001 estimated by the UNDP are 84 per cent for Korea, 62.5 per cent for Singapore and 76.4 per cent for Indonesia. The equation for estimating the impact of R&D on firm performance was taken in its reduced form as: n ln π = α0 + α1 ln K + α2 ln L + δ ln R βi zi i=1

where π is firm performance measured as either value added or profit, K and L are physical capital and labor, R is the average number of R&D personnel over 1998–2000 and zi are the different dummy variables for different metropolitan areas. Clearly the estimates in Table 2.9 showing

Table 2.9 Effect of R&D investment on firm performance

Variable Constant ln K ln L ln R Dummy variables Seoul (Korea) Tianjin (China) Shanghai (China) Guangzhou (China) Chengdu (China) Adjusted R2 Sample size Note: t -values are in parentheses.

Productivity (ln(A))

Profit (ln(profit))

3.094 (7.67) 0.373 (8.01) 0.270 (3.71) 0.325 (6.05)

1.897 (3.51) 0.484 (7.91) 0.139 (1.48) 0.276 (3.85)

5.753 (9.43) −0.020 0.831 (4.81) 0.525 (2.90) −0.058 (−0.34) 0.688 408

4.742 (5.09) 0.031 (0.09) 0.804 (3.45) 0.456 (1.85) 0.068 (0.29) 0.580 359


elasticities of productivity and profitability with respect to R&D personnel are statistically significant. The R&D personnel in Seoul, Shanghai and Guangzhou exhibit the highest impact of productivity and profitability. The coefficient estimates for the dummy variables associated with the metropolitan clusters lend strong support to the view that technology clusters improve the performance of the R&D variable. Among the cluster attributes that enhance the effectiveness the following are most important: concentration of IT investments, extensive R&D network relationships and wide coverage of complementary services and segments. The impact of the growth of the IT sector on other sectors of the Indian economy is most important for two reasons. One is the mechanism of the so-called Verdoorn law, named after P. J. Verdoorn (1949), who found a strong empirical relationship between productivity and output growth in a cross section of industries. This is particularly true for the IT sector today. The second reason is that the high productivity growth in the IT sector may raise wage rates in this sector, which then attracts labor from other sectors. The first mechanism was extensively used by Kaldor (1967) to explain the process of industrialization, where the manufacturing sector pulls up other sectors. Two mechanisms are at work. First, the growth rate of productivity in manufacturing (here we would think of it as the IT sector) increases with the rate of growth of GDP. Second, employment growth in manufacturing (i.e. the IT sector) tends to increase the rate of productivity growth in other sectors. This follows due to diminishing returns to labor in other sectors and the absorption of surplus labor from these sectors. In Kaldor’s (1967) original analysis of cross section data on 12 developed countries from 1953 to 1964 the estimated Verdoorn relationship is found to be gP = 1.035 + 0.484 gM ;

R2 = 0.826

(0.070)

where gP and gM are respectively the rates of growth of labor productivity and production in manufacturing and the standard error is given in parentheses. The value of the Verdoorn coefficient (0.484) implies that each additional percentage point in the growth of output is associated with a 0.50 per cent increase in employment and a 0.50 per cent increase in the growth of productivity. For the Indian data on the IT sector over the period 1990–2000 the Verdoorn coefficient is of the order 0.502, implying significant increasing returns to scale for the IT sector, which may help speed up the rate of industrialization and factor reallocation from other sectors. As Ros (2000) has interpreted it, the Verdoorn


55

coefficient can be derived from a log linear production function as M = AK a+u L1−a

(2.10)

where M may be viewed as the output of the IT sector, with K and L as labor and capital. Taking logs and differentiating with respect to time gives gM = β + (a + u)gK + (1 − a)gL ˙ where gx = x˙ /x and β =A/A and dot denotes time derivative. Let gP = gM − gL be the growth of labor productivity in the IT sector. Then one can easily derive gP = (1 + u)−1 [(a + u)gK + ug M ]

(2.11)

If we assume a constant capital output ratio so that gM = gK , then this reduces to gP = (1 − a)−1 [β + ugM ]

(2.12)

which shows that the Verdoorn coefficient u/(1 − a) is determined in the steady state by scale economies. A positive and less than unity Verdoorn coefficient implies that u is positive (i.e. increasing returns to scale) and a + u < 1 (i.e. diminishing returns to capital). Thus with a = 1/3 a Verdoorn coefficient of the order of 0.5 would mean an increasing returns parameter of 1/3. Note that the parameter u in the production function (2.10) may be interpreted as Arrow’s learning by doing effect (i.e. cumulative experience) and the relation (2.11) implies that the productivity growth that is the key to increasing the competitiveness of India’s IT sector depends on both growth of capital and output in the IT sector. Thus the major challenge for the IT enterprises in India today is to create competitive advantage and increasing productivity by perceiving or discovering new and better ways to compete in the industry and bringing them to market. As Porter (1990) emphasized, the most important reason why competitive advantage is sustained is constant improvement and upgrading. Hence the need for R&D and innovation efficiency.

3 Industrial Productivity in the New Economy

1 Introduction In post-independent planning in India emphasis was given to inwardoriented growth and a protected home market for the development of the economy. This resulted in a sinking industrial economy in particular and an economy in general of no return zone in the eighties. India embarked upon a ‘New Economic Policy’ to revive the economy from its dismal state. The main features of the so-called ‘New Economic Policy’ are: (a) a gradual process of easing out government control through industrial deregulation; and (b) opening up the channels for greater connectivity with the international market. The experiences of East Asian countries had emboldened the policymakers to adopt the new economic policy. Subsequently, the debacle of some South American and African countries following their pursuit of the policy of liberalization created some fear among the general public of a higher dependency on imports and greater indebtedness on the external front. Contrary to expectations, the propagandists welcomed the ‘New Economic Policy’ because they thought that this policy would correct the deficiencies that were inherent in the earlier strategy of bureaucratic control. There is a massive literature on the relationship between trade liberalization and growth through increasing the productivity and efficiency of both traditional and modern industries. Trade liberalization in particular implies the reduction or removal of quantitative restrictions on imports and the lowering of tariff rates. Import liberalization and removal of quantitative restrictions in particular would not only induce more efficient allocation of investment along the line of a country’s comparative advantage but also eliminate costs associated with intrusive bureaucracies and wasteful rent-seeking special interest groups. Furthermore, it would make the economy more competitive. 56

Industrial Productivity in the New Economy 57

Liberal economists often blame the public sector for the inefficient use of resources but empirical research shows that a competitive environment is more important for ‘allocative efficiency’ than ownership per se. Technical and allocative inefficiencies are not confined to the public sector, they pervade the entire economy. The research on the productivity of industries in India and abroad shows that a competitive environment is crucial for enhancing the efficiency of resource use through technical upgrading and allocation in the economy. It is recognized that the protective trade policies during the plan period of the 30 years before 1990 were the major impediment to higher productivity, efficient use of scarce resources and more utilization of resources with a low opportunity cost. Thus to increase the productivity and efficiency of the economy liberalized trade policies have been taken in order to open the economy to the international market. As a policy, the rates of effective protection for the inefficient domestic manufacturing sector are reduced by lowering tariff rates, and more uniform rates across industries are gradually being implemented to provide a level playing field for all industries. In this chapter we discuss some issues related to the linkage between trade liberalization and the productivity growth of Indian industries. The issue of welfare gain due to the less monopolistic structure of the industries after liberalization is taken into consideration. It is expected that after the liberalization policies have been implemented there will be a rise in the productivity and efficiency of industries because the industries will be more competitive than before and trade volume will also rise. But there is a two-way relation between competitiveness and export promotion. It is said that the interaction between international trade and long-run output and productivity growth is less understood in trade and/or liberalization literature. It is interesting to examine whether growing trade leads to faster productivity growth or the other way round. Some attempts are made to analyze the cause and effect nexus between these two variables. The organization of the chapter is as follows. In section 2 there is a brief discussion of the issue of industrial policies taken by the government of India during the pre- and post-liberalization periods. We analyze the pattern of structural changes in Indian industries during the recent period in section 3. A decomposition of total factor productivity for modern and traditional sectors of Indian industries and total manufacturing sector as a whole is carried out in section 4. Section 5 deals with the performance of the export sector as a whole during 1987–88 to 1999–2000 in terms of growth rate and the share of exports in total


exports. The productivity growth in terms of the TFPG of a few industries is measured and the causality between TFPG and export growth is discussed in this section. A microlevel analysis of productivity, competition and trade reform is carried out using the firm/unit level data in section 6. Finally, some concluding remarks are made on the major findings of the chapter in section 7.

2 A brief review of Indian industrial policy The strategy for industrial development in India was initiated during the Second Five Year Plan (1956–61), based on the Mahalanobish Model. However, some policies regarding self-reliance and licensing schemes were adopted under the Industrial Development and Regulation Act (IDRA) of 1951. But the main thrust for overall development through industrialization was given in the Second Plan model. Heavy and basic industries were in the public sector, and a regulated private sector was given charge of consumer good industries. So, the plan was implemented under the framework of a mixed economy, where both the public and private sectors had a role in industrial development. But the policy of industrialization in India was marked by frequent changes in objectives and policy instruments. In spite of that, some acts, such as the MRTPA (1970), which was enacted to control concentration of economic power, and the Foreign Exchange Regulation Act (FERA) of 1973, which was used to regulate foreign investment in India, created a highly protected industrial regime, where there was no significant role for internal competition or for any strict planned implementation of overall industrial development. By the second half of the seventies, it was realized that the licensing system and the regulatory policies were detrimental to industrial development in India instead of being stimulants. At that time several committees and commissions were set up to review the different aspects of industrial and trade policies. Among the committees and commissions, which submitted their reports at the end of that decade or at the beginning of the eighties, the noted ones are the Alexander Committee (1978), the Dagli Committee (1979), the Tandon Committee (1980) and the Rajadhyakha Committee (1980). On the recommendation of these commissions and committees, some changes in the policy measures in terms of deregulation and delicensing were implemented to increase the pace of industrial development. According to Ahluwalia (1991), due to these policies there was a turnaround in the productivity of Indian industries during the 1980s. But this hypothesis was rejected by many authors on the ground


that her analysis suffers from inaccurate measurement of value added (Balakrishnan and Pushpangadan, 1994). Others (Ghosh and Neogi, 1993; Neogi and Ghosh, 1994) showed that there was no significant upward trend of productivity and efficiency during the 1980s. Policymakers felt that the slower and inefficient growth experienced by India during the past 40 years was the result of the tight regulatory system for the industrial and foreign trade sectors. These policies led to an economy of subsidies and inefficiencies in India. The new economic policy (NEP), of which the New Industrial Policy (NIP) of 1991 (Sandesara, 1991; Subrahmanian, 1991; Patel, 1992) is the most important part, was launched against this background. The NIP of 1991 was a major part of the broad structural adjustment program implemented during the nineties in India. It was set in motion with the objective of transforming the policy of planning to a policy of regulated market economy. Liberalization is a process of economic policy changes specifically initiated from 1991 as declared state policy. It has its own economic, political and international compulsions. Indian economic reforms in their current form had been initiated with the help of financial support from the International Monetary Fund (IMF) and the World Bank, and later also from the Asian Development Bank (ADB). Hence, these reforms have involved a set of conditionalities mutually agreed upon between the government of India and the multilateral institutions. When the crisis reached a peak in 1991, the IMF extended an 18-month balance of payment assistance program of US$2.2 billion to India, covering an initial period up to March 1993. This reform package covered the areas of macroeconomic stabilization policies and structural adjustment policies. Some major policy changes, which are called economic reforms or liberalization, can be mentioned as follows. The broad policy measures are: 1 Macro economic stabilization measures, which include (a) management of balance of payment crisis, (b) fiscal deficit management, and (c) monetary policy correctives. 2 Sectoral structural adjustment reforms, which include (a) trade policy (and associated policy) reforms, (b) industrial policy reforms, (c) policy reforms relating to public sector, (d) policies for attracting foreign direct investment (including NRIs), technology and equity participation, (e) administrative reform for faster investment approvals through the Reserve Bank of India, (f) tax structure reform, (g) tariff reform for both capital goods and consumer goods,


(h) financial sector reforms, (i) reform in the civil aviation sector, and (j) reform in agriculture-related items. 3 Measure to share social cost of reforms, which include reform of the public distribution scheme (PDS) and the like. The NIP of 1991 effected some very fundamental policy changes, such as near abolition of licensing, easing of the rigors of MRTP and FERA, a reduced list of industries to be reserved for the public sector, automatic approval of foreign technology agreements and private investment in infrastructure. Some other important policy changes are freer import of capital goods, transport subsidies for backward areas and promotion of FDI and NRI investment. The sole objective of these highly liberalized policy measures was to enhance the productivity and efficiency in Indian industries by creating a competitive environment.

3 Changing pattern of industries in recent years It was mentioned in the previous section that there was an urgent need for the introduction of a new economic policy in India in the year 1991 and the process of liberalization started thereafter. However, the pattern of Indian industries started changing long before 1991. This changing pattern of industrial structure may have some role in explaining the subsequent period productivity growth of industrial sector as a whole. Growth of new industries does not merely increase productivity but also changes the distribution of inputs and outputs across sectors. Before going on to analyze the productivity differentials among the different sectors we first try to understand the structural changes of Indian industries during the period 1973–74 to 1999–2000. A comparison of the shares of different industries in terms of value added, output, employment and number of factories in each industry will give us greater insight into the nature of the structural transformation of Indian industry. Change in shares among industries The structure of Indian industries during the past thirty years has been moving quickly towards technology-intensive manufacturing industries from traditional and less productive industries. Attempts have been made to understand the structural changes in terms of a few parameters, namely output, value added, employment and number of firms in each industry. The data for the industries have been collected from the Annual Survey of Industries (ASI) from 1973–74 to 1999–2000. Figures for these four indicators are collected for 18 two-digit industries and

Industrial Productivity in the New Economy 61 Table 3.1 Growth rate of output of industries during 1973–4 to 1997–8 Growth rate (%) Traditional sector Food Beverage Textile Wood Paper Leather Nonmetallic Basic metals Metal products Construction etc. Modern sector Ready made garments Basic chemicals Rubber & plastics Machinery Electrical & electronics Transport equipment Other manufacturing

6.75 5.52 5.71 1.88 5.97 8.29 7.97 6.98 6.11 9.97 11.61 10.88 7.13 7.08 8.18 8.40 14.19

the shares of each industry are calculated for all the years mentioned above. Looking at the growth rate of output in real terms it has been found that there is a phenomenal growth of output across all the industries during the period. However, Table 3.1 suggests that the industries in the modern sector show better performance in terms of growth of output compared to traditional sectors’ industries. Other manufacturing industry, which includes medical and photographic instruments, jewelry, related articles, watches and clocks etc., shows the highest growth among all industries. The ready made garments and basic chemicals industries in the modern sector show a growth of around 11 per cent. The growth rates of industries in the traditional sector cluster around 6 per cent per annum, with a maximum of 9.97 per cent in the construction industry and a low of 1.8 per cent in the wood industry. Thus, due to wide variation in growth rates the structure of Indian industry is changing over time. We now examine the nature of the structural changes in Indian industry during the post- and preliberalization period. Table 3.2 and the corresponding graph (Figure 3.1) show the percentage share of total industrial output of some major industrial groups from 1973–74 to 1999–2000. The shares of the traditional industries, namely

Table 3.2

Percentage share of output in Indian industries

Year

Industry codes 62

1973–74 1974–75 1975–76 1976–77 1977–78 1978–79 1979–80 1980–81 1981–82 1982–83 1983–84 1984–85 1985–86 1986–87 1987–88 1988–89 1989–90 1990–91 1991–92 1992–93 1993–94 1994–95 1995–96 1996–97 1997–98 1998–99 1999–00

20–21

22

23–25

26

27

28

29

30

31

32

33

34

35

36

37

38

14.09 16.81 15.87 15.58 16.36 15.33 14.32 13.00 13.18 13.82 13.84 13.14 12.58 12.78 13.48 13.18 14.22 13.56 14.51 13.53 13.24 13.12 12.30 12.87 14.47 16.43 15.47

2.54 2.41 2.63 2.61 2.56 2.38 1.98 1.81 1.87 1.65 1.89 1.89 1.82 1.88 1.85 1.87 1.85 1.98 2.08 1.98 1.95 1.91 1.54 1.51 2.07 2.41 2.45

19.13 16.81 16.80 14.88 15.75 15.27 14.27 13.23 11.88 11.29 11.48 11.56 11.01 9.99 9.43 9.07 9.78 9.67 9.65 9.20 9.55 9.83 8.88 8.91 10.69 8.52 8.03

0.79 1.13 1.18 1.33 1.40 1.40 1.33 1.39 1.33 1.27 1.11 1.22 1.26 1.23 1.38 1.35 1.59 1.58 1.79 1.74 2.25 2.34 2.25 2.01 2.47 2.75 3.03

0.42 0.61 0.42 0.52 0.55 0.53 0.53 0.53 0.46 0.45 0.44 0.43 0.39 0.41 0.41 0.44 0.36 0.33 0.30 0.29 0.32 0.29 0.25 0.28 0.28 0.36 0.44

3.05 3.31 1.73 2.77 2.75 2.54 2.76 2.77 2.80 2.54 2.65 2.92 2.68 2.83 2.80 2.59 2.75 2.73 2.86 2.65 2.70 2.73 2.88 2.51 2.60 2.54 2.65

0.77 0.77 0.82 0.99 0.86 1.01 1.19 0.86 0.81 0.73 0.72 0.81 0.83 0.77 0.94 0.94 0.98 1.07 1.06 0.94 1.12 1.08 0.88 0.88 1.04 1.03 0.95

12.29 12.68 13.91 12.52 13.01 12.88 13.15 13.21 13.31 12.78 13.37 13.30 13.40 13.59 13.36 12.84 12.65 12.25 13.20 13.62 13.21 13.14 13.33 14.05 16.42 17.39 17.07

5.05 6.95 7.66 8.00 8.43 7.87 8.84 10.00 10.41 11.77 10.44 10.61 11.92 11.53 11.02 11.71 9.79 10.91 8.01 8.73 9.23 8.83 9.01 9.91 9.53 9.59 9.99

3.02 2.68 3.42 2.92 2.90 2.76 2.71 2.74 2.81 3.18 3.41 3.67 3.78 3.60 3.43 3.47 3.37 3.41 4.06 3.55 3.29 3.25 3.29 3.57 3.50 3.22 3.70

10.79 10.10 5.42 10.62 8.84 11.05 11.35 11.68 12.51 12.40 11.53 12.18 11.99 11.93 11.54 12.69 12.33 12.53 12.05 12.60 11.13 11.21 11.42 10.31 13.01 10.79 10.94

2.16 2.62 2.72 2.45 2.47 2.20 2.51 2.27 2.25 2.05 1.98 2.06 1.97 1.75 2.04 2.18 2.14 2.16 2.21 1.96 2.24 2.08 2.23 2.25 2.57 2.52 2.20

5.25 5.69 5.89 5.92 5.70 5.61 5.68 5.85 5.78 5.59 5.73 5.63 5.77 5.34 5.45 5.24 5.47 5.43 5.48 5.20 4.77 4.68 5.24 5.34 5.02 7.14 5.25

6.07 5.31 6.29 5.63 5.46 5.30 5.70 5.90 5.33 5.51 5.06 5.27 5.30 5.27 5.95 6.24 6.22 6.15 6.35 6.29 5.51 6.46 6.04 5.57 6.71 5.96 5.39

6.63 5.21 5.51 4.82 4.43 4.80 5.32 5.50 5.77 5.54 5.36 5.52 5.33 5.71 5.55 5.96 5.77 5.87 5.33 5.55 5.45 6.10 7.11 6.46 6.88 5.24 7.34

0.58 0.66 0.70 0.62 0.75 0.76 0.77 0.79 0.64 0.67 0.71 0.71 0.82 0.73 0.82 0.73 0.86 0.81 0.92 1.07 1.29 1.27 1.31 1.31 1.54 1.92 2.41

39

0.41 0.45 0.52 0.53 0.56 0.50 0.45 0.82 1.20

40 & above 7.36 6.24 9.02 7.81 7.79 8.32 7.57 8.48 8.88 8.77 10.28 9.07 9.15 10.65 10.57 9.51 9.46 9.10 9.63 10.57 12.18 11.18 11.60 11.43 2.19 2.70

Industrial Productivity in the New Economy 63 25

Shares (%)

20

15

10

5

1973–74 1974–75 1975–76 1976–77 1977–78 1978–79 1979–80 1980–81 1981–82 1982–83 1983–84 1984–85 1985–86 1986–87 1987–88 1988–89 1989–90 1990–91 1991–92 1992–93 1993–94 1994–95 1995–96 1996–97 1997–98 1998–99 1999–00

0

Year 20–21 32

22 33

23–24–25 34

35

26 36

27 37

28 38

29 39

30

31

40 & above

Figure 3.1 Share of output

food products and textiles, declined over time. The basic chemical industry’s share of total industrial output showed an upward trend during the period and became more prominent after 1990. Rubber and plastic and basic metals showed an upward trend during the initial phase of the period. However, the shares of these industries either fell or remained stagnant during the later phase of the period of study. Interestingly, the share of construction, repair and other services (industry code 40 and above) showed an upward trend during the period, rising from 7.35 per cent in 1973–74 to 11.43 per cent in 1996–97, with a maximum of 12.17 per cent in 1993–94. This rise in the share may be due to an increase of developmental activities and supply to the growing ancillary industries. Among industries with a lower share of total output, electrical machinery, transport equipment and ready made garments showed a mild upward trend during the period. Most of the industries that showed either no trend or downward trends during 1973–74 to 1999–2000 belonged to the traditional sector. The figures for percentage of share of value added explained by the industries in total show a similar trend to that found in the figures for shares in value of output (Table 3.3 and Figure 3.2). The figures for the

Table 3.3

Percentage share of value added in Indian industries

Year

Industry codes 64

1973–74 1974–75 1975–76 1976–77 1977–78 1978–79 1979–80 1980–81 1981–82 1982–83 1983–84 1984–85 1985–86 1986–87 1987–88 1988–89 1989–90 1990–91 1991–92 1992–93 1993–94 1994–95 1995–96 1996–97 1997–98 1998–99 1999–00

20–21

22

23–25

26

27

28

29

6.16 7.25 6.51 7.58 8.26 6.59 6.69 5.82 6.45 7.02 8.29 7.89 7.87 7.64 7.67 7.90 9.15 7.52 8.15 6.80 7.73 8.36 6.61 6.00 9.14 9.86 9.14

1.97 3.17 2.80 3.27 2.21 2.57 2.10 1.90 1.74 1.55 1.68 2.20 2.00 2.31 2.40 2.46 2.40 2.50 2.96 2.56 2.38 2.46 1.91 2.42 3.03 3.10 4.16

22.96 19.35 17.10 14.88 16.35 16.97 18.24 16.51 13.22 11.39 11.72 11.61 11.16 11.03 9.58 8.43 10.36 10.37 8.98 7.65 8.96 8.89 6.46 7.78 8.84 7.09 6.15

0.62 0.82 0.92 0.97 1.05 1.14 0.98 0.98 1.01 0.97 0.88 1.29 0.99 1.02 1.21 1.33 1.56 1.71 2.09 1.83 2.92 2.83 2.31 2.09 2.47 2.99 3.27

0.44 0.64 0.51 0.57 0.63 0.56 0.59 0.53 0.50 0.46 0.54 0.50 0.43 0.41 0.43 0.39 0.31 0.37 0.35 0.30 0.33 0.28 0.24 0.31 0.29 0.35 0.58

3.97 4.95 2.62 3.82 3.85 3.34 3.70 3.57 3.69 2.92 2.78 3.53 2.81 3.11 3.05 2.61 3.21 3.11 3.38 2.95 3.22 3.14 3.34 0.91 2.77 2.53 2.86

0.46 0.58 0.47 0.62 0.57 0.57 0.66 0.56 0.51 0.50 0.56 0.67 0.58 0.53 0.67 0.61 0.71 0.85 1.01 0.85 1.15 0.77 0.65 0.51 0.89 0.82 0.89

30

31

32

33

34

35

36

37

38

39

11.96 3.14 3.00 9.71 2.49 6.24 7.00 7.38 0.84 13.07 4.04 2.89 10.57 2.81 7.02 6.37 6.54 0.97 14.06 4.00 3.99 5.38 3.12 7.70 8.23 7.12 1.06 12.19 5.17 3.32 11.00 2.76 7.84 6.50 6.39 0.88 12.61 5.30 3.71 7.63 2.77 7.59 6.51 6.15 1.06 13.59 3.97 3.11 10.02 2.40 6.95 6.01 6.20 0.92 12.73 4.31 3.17 9.48 2.92 6.96 6.41 6.73 0.99 11.90 4.41 3.32 9.99 2.73 7.31 6.86 7.04 1.06 11.89 4.13 3.32 12.06 2.51 7.21 6.18 7.59 0.86 12.09 5.63 4.37 13.53 2.25 7.04 7.08 7.74 0.98 12.81 3.10 4.19 10.25 2.26 7.03 6.50 6.97 1.02 12.30 4.95 4.77 8.91 2.35 7.70 8.19 7.35 1.19 12.85 5.14 4.95 10.44 2.33 8.25 6.58 6.75 1.71 11.92 7.47 3.95 8.59 2.06 6.87 6.44 7.42 1.20 13.19 7.88 3.93 9.07 2.52 6.87 7.66 6.64 1.16 12.25 11.70 3.55 11.35 2.72 5.86 7.33 6.44 0.96 12.05 6.82 3.75 9.99 2.27 6.49 7.56 6.30 1.08 0.94 11.82 7.08 4.37 10.89 2.09 6.30 7.29 7.01 0.87 1.13 12.89 6.02 5.98 7.45 2.43 6.85 8.25 6.74 1.23 1.26 15.09 7.45 3.69 9.03 1.89 6.05 7.78 5.87 1.13 1.28 15.42 7.39 3.45 9.24 2.12 5.39 6.06 5.40 1.81 1.22 14.92 6.63 3.51 9.93 2.04 5.15 8.07 5.74 1.30 1.11 17.01 6.62 4.14 10.01 2.34 6.06 6.55 7.70 1.39 1.01 16.89 8.42 5.33 10.99 2.42 6.35 6.01 6.93 1.36 0.95 18.22 6.07 4.39 15.66 2.44 6.32 7.93 7.83 1.85 1.87 23.84 6.93 3.25 11.99 2.53 8.17 7.29 5.88 2.06 22.91 7.16 4.69 11.92 2.56 6.68 5.73 7.56 2.62

40 & above 11.66 8.96 14.40 12.24 13.76 15.07 13.36 15.50 17.13 14.48 19.43 14.59 15.12 18.04 16.08 14.11 15.03 14.73 13.97 17.79 15.82 14.87 15.63 14.31 1.31 1.12

Industrial Productivity in the New Economy 65 30

25

Shares (%)

20

15

10

5

1973–74 1974–75 1975–76 1976–77 1977–78 1978–79 1979–80 1980–81 1981–82 1982–83 1983–84 1984–85 1985–86 1986–87 1987–88 1988–89 1989–90 1990–91 1991–92 1992–93 1993–94 1994–95 1995–96 1996–97 1997–98 1998–99 1999–00

0

Year 20–21

22

23–24–25

32

33

34

35

26 36

27

28

37

38

29 39

30

31

40 & above

Figure 3.2 Share of value added

percentage of shares of number of employees explained by each industry, however, show no significant movement during this period except for three industries. Basic chemicals and ready made garments show a clear upward trend in share of number of employees in total during the entire period, while cotton and jute textiles show a significant fall in share. The shares in terms of number of factories of these industries are almost stagnant during the period. This indicates that changes observed among the industries in terms of value of output or value added were due to the changes in scale of operation of the firms. Measuring the changes of industrial structure The structure of manufacturing industries of India has been changing over time and there is a swing from the traditional industries to modern industries. To obtain the trajectory of the path of the changing structure an index was constructed following the line of Van Ark et al. (1999) for intercountry comparison of structural change. The idea of this index is to construct a vector constituted of the value added share of all branches in aggregate manufacturing for two time points. In this analysis one time point is the terminal year and the other point is any year within


1

0.95

Index

0.9

0.85 NF EMP

0.8

Output NVA

1973–74 1974–75 1975–76 1976–77 1977–78 1978–79 1979–80 1980–81 1981–82 1982–83 1983–84 1984–85 1985–86 1986–87 1987–88 1988–89 1989–90 1990–91 1991–92 1992–93 1993–94 1994–95 1995–96 1996–97 1997–98

0.75

Year Figure 3.3 Index of structural change

the period. For each time point the shares are represented by one single vector. The index is defined as m

I tT =

j=1 m

j=1

SjT Sjt

(SjT )2

m j=1

(Sjt )2

Where t is any time point within the period and T is the end point. Sjt is the share of jth industry at year t, SjT is the corresponding share at the terminal year and m is the number of industries. The index varies between zero and one and it will be low in the case of lower dissimilarity between the terminal year and the observed year. If the two time points have the same production structure the vectors will coincide and the index will take a value of one. In contrast, if the composition of the industries in terms of their shares is different, i.e. the vectors are orthogonal to each other, the index will take a value of zero. The indices of all the four indicators – value of output, value added, number of employees and number of firms – are given in Figure 3.3. The figure shows that a


definite and significant structural change occurred in Indian industries during this period. The changes are more prominent in terms of share of value added. The structural change is also visible in terms of other three indices. However, the change in terms of share of number of firms in each industry is very mild compared to the other indices.

4 Decomposition of aggregate TFPG It has been argued that productivity growth is the major force behind sustainable economic development of a country. There are basically two reasons for examining the growth pattern of productivity of countries. First, it is the only plausible route to increase the standard of living by raising real purchasing power (Krugman,1994). Second, productivity growth also raises the competitiveness of a country by reducing the cost, and thus, ceteris paribus, the offer price in international markets. But one can argue in another way that liberalization raises the productivity of a country through competitiveness. So, when a county opens up its trade, the natural force of competition will raise the productivity of the country and also the quality of product at a lower cost. It is also argued that unless there is an increase in productivity, openness cannot raise the standard of living. Naturally, increasing productivity growth is a target of the policies of many developing countries. Total factor productivity (TFP) growth is now recognized as the most comprehensive measure of the productivity and also as a major contributor to economic growth of many economies of today’s world. Any growth of output may be due to the increase in the use of input and the improvement in the productivity of factors used. In empirical economics technological progress and TFP are used interchangeably. However, there is a basic difference between these two terms. Any measure of technological progress shows the effect of improvement in knowledge of production procedure, while TFP captures not only the effect of pure technical progress but also the overall effect of changes in the allocation of factors used on the efficiency of the factors used in production. In a single homogeneous input–output model the measure of TFP is therefore straightforward; it is equal to the difference between the rate of output growth and the rate of input growth. But in multifactor and multiproduct systems where there could be changes in allocation of pattern, the estimation of TFP will be rather complicated. The most common measure in this context is factor productivity. But the problem with this measure is that the changes in partial productivity depend upon the use of other factors. This problem of productivity can be resolved by the analysis of


TFPG, which separates out the effect of an increase in the use of inputs from the other factors that influence the growth of output. TFPG encompasses not only the effect of technical progress but also the effect of an increase in the efficiency with which resources are used. In less developed countries, where resources (particularly capital) are scarce, inputs must be used efficiently to generate enough surplus for the development of the country. Since any measure of TFPG estimates the extent of efficient use of resources, the study of TFP in a developing country is of great importance for an understanding of the exact picture of technological development in that country. Method of decomposition A decomposition of aggregate TFPG is proposed here that consists of the following three parts: (1) a weighted average of industrywise/region-wise TFPG; (2) the effect of distribution of investment across industries/regions on the aggregate TFPG; and (3) the effect of reallocation of inputs across industries/regions on the aggregate TFPG. The procedure of decomposition of TFPG is as follows.1 Let us consider an industry consisting of several subgroups of industries. Suppose the net output (V) of the industry is measured at constant prices and that there are two inputs of production, capital (K) and labor (L). For any time period (t) we have the following identities: V(t) ≡

Vi (t)

(3.1)

i

i.e. aggregate net output is the sum total of industry-wise net outputs. Similarly K(t) ≡ Ki (t) (3.2) i

and L(t) ≡

Li (t)

(3.3)

i

where both K and L are measured in real terms, The industry specific TFPGs are defined as φi (t) = d log Vi (t) − ηki (t)d log Ki (t) − ηLi (t)d log Li (t)

(3.4)

where φi (t), ηKi (t) and ηLi (t) denote the TFPG, elasticity of output with respect to capital and labor in the ith industry in the time period


t respectively. Analogously, for the aggregate industry, the TFPG is given by φ(t) = d log V (t) − ηk (t)d log K(t) − ηL (t)d log L(t)

(3.5)

where d log V(t) =

λi (t)d log Vi (t)

(3.6)

λKi (t)d log Ki (t)

(3.7)

λLi (t)d log Li (t)

(3.8)

i

d log K(t) =

i

d log L(t) =

i

and ηK (t) and ηL (t) are defined as ηK (t) =

λi (t)ηKi (t)

(3.9)

λi (t)ηLi (t)

(3.10)

i

ηL (t) =

i

where λi (t), λKi (t) and λLi (t) denote the share of ith industry in the aggregate net output, capital and labor in time period t respectively. Substituting (3.4) and (3.6) to (3.10) in (3.5) and rearranging, we have φ(t) =

i

λi (t)φi (t) −

i

πKi (t)d log Ki (t) −

πLi (t)d log Li (t)

(3.11)

i

where πKi (t) = ηK (t)λKi (t) − ηKi (t)λi (t)

(3.12)

πLi (t) = ηL (t)λLi (t) − ηLi (t)λi (t)

(3.13)

Relation (3.11) provides the required decomposition of aggregate TFPG in to three components. The first component ( i λi (t)φi (t)) is the weighted average of industry specific TFPGs, the weights being the industry specific shares in net output. The second component ( i πKi (t)d log Ki (t)) represents the effect of allocation of investment across industries on the aggregate TFPG. Similarly, the third component ( i πLi (t)d log Li (t)) measures the effect of distribution of changes in the labor input across industry on the aggregate TFPG.


While the first component of the aggregate TFPG break up has a simple and straightforward interpretation, the other two components need some explanations in terms of πKi (t) and πLi (t). From their definition in (3.12) and (3.13), it should be apparent that these quantities represent comparative returns to capital and labor respectively in an industry. Under the competitive assumption ηK (t) and ηL (t) are the shares of capital and labor inputs in aggregate net output and ηKi (t) and ηLi (t) are the corresponding shares in the ith industry. Thus ηK (t)λKi (t) and ηL (t)λLi (t) measure the shares in the aggregate net output of capital and labor engaged in the ith industry, if these inputs received the average rental and average wage rate of the aggregate industry. ηKi (t)λi (t) and ηLi (t)λi (t), on the other hand, measure the share in the aggregate net output of capital and labor engaged in the ith industry respectively if these are paid the rental and wage rate of the specific industry. So, πKi (t) and πLi (t) measure the differences, if any, in the earning of total capital and total labor, respectively, in the ith industry arising out of interindustry differences in capital and labor productivity (assuming that these productivities are reflected in the corresponding factor returns). The interpretation of the proposed decomposition of the aggregate TFPG would perhaps explain its empirical relevance. It would be useful to see • how important are the interindustry movements of investment and

labor in explaining the aggregate TFPG; • whether the patterns of intertemporal movements of investment and

labor are consistent with efficiency (i.e. whether investment and labor move towards industries that yield greater return on their inputs); • whether the patterns of intertemporal movement of aggregate TFPG and its components are different for different industry groups. It may be noted that TFPG defined in (3.5) above is based on a logarithmic difference, i.e. φt = log TFP − log TFPt−1 , if one considers discrete change over time. Thus, eφ1 =

TFP1 TFP0

so that a time series of indices of TFP would be t

Iφ (0, t) = e

s=1

φs

(3.14)


where Iφ (0,0) = 1, i.e. this series of TFP indices provides a comparison of TFP level for the tth year with that of the base year t = 0. For the purpose of analysis, such time series indices are constructed based on the aggregate TFPG and its three components, i.e. the weighted aver age of TFPG ( i λi (t)φ(t)), investment allocation component ( i πKi (t)d log Ki (t)) and labor allocation component ( i πLi (t)d log Li (t)), for all industries together and for groups of industries separately. These time series have been used to compare temporal movements of each index separately across industries. To examine if the industry-specific indices show any tendency to converge or diverge from the corresponding all-industry indices, another set of indices are constructed, which can be described as follows. Suppose, Iφi (0,t) and Iφ (0,t) are the indices for aggregate TFP for year t (with year zero taken as base) for the ith industry and all industry respectively. Then the index ∗ Iφi (0, t) =

Iφi (0, t) Iφ (0, t)

(3.15)

should give an idea whether the industry-specific indices of aggregate TFP or its components follow the same pattern of movements as that of ∗ the corresponding all-industry index. Thus if Iφi (0,t) declines (rises) over time, this should mean that the index for the industry has changed less (more) compared to the index of all industry; otherwise the time series of a relative index will show movement around the value of unity. The decomposition proposed clearly shows that aggregate TFPG may be considerably affected by the way the distribution of the factors of production, i.e. capital and labor, changes over time across the constituent regions/industries. The empirical results show that the factor distribution component (particularly, the rental differential component) of aggregate TFPG could be important as far as the difference between the aggregate TFPG and weighted TFPG is concerned. Data and computations The present study is primarily based on the data published in the Annual Survey of Industries (ASI) on 17 two-digit industries over the period from 1973–74 to 1997–98 (the names of the industries and the codes are given in Appendix 3.1). The data on value added, capital, labor, wages and salaries for each of the 17 industries over this period were collected from the office of CSO, Government of India. The other data relating to price indices have been collected from various issues of economic surveys published by the Government of India, Annual publications


of the Reserve Bank of India and a book entitled India Data Base: The Economy, by H. L. Chandok and Policy Group. The figures for the gross stock of capital were calculated using the perpetual inventory accumulation (PIA) method from the net capital stock available from ASI. The 17 two-digit industries are subdivided into two major industrial groups, namely the modern sector and the traditional sector. The modern sector comprises seven industries: manufacturing of ready made garments; basic chemicals; rubber and plastics; machinery and machine tools; electrical machineries; transport equipment; and other manufacturing industries. The traditional sector comprises ten industries: food products; beverages, tobacco etc.; textiles (cotton and jute); wood products; paper; leather; non-metallic mineral products; basic metals; metal products; and construction etc. As far as the computation of the aggregate TFPG is concerned, for each year we have computed the weighted average of the industry-level TFPGs, and subsequently the components relating to capital and labour changes following the given formulae (3.4) to (3.13). To obtain the values of the variables for discrete time points in growth equations (for example, wage rates) we have used the average of two consecutive time points. In the case of change of a variable, say dlog K(t), we have used dlog K(t) ≡ log Kt − log Kt−1 , where Kt denotes the value of the variable K at time t. The values of ηL (t) ≡ [(ηLt + ηLt−1 )/2] were calculated by taking ηLt as share of total wages and salaries in the gross value added, and for the return on capital we used ηK (t) ≡ 1 − ηL . Capital and labor shares have been calculated for each industriy as well as for total industry. Empirical results Let us first examine the over time movements of aggregate and weighted TFP indices of three categories: modern industries; traditional industries; and all industries. Then the movements of wage differential and rent differential components are analyzed. All these analysis are done in terms of the graphs of the movements of the indices presented in Figures 3.4 to 3.11. Aggregate TFP indices for three categories of industries (modern, traditional and all) are depicted in Figure 3.4. It can be seen that up to the late seventies the TFPG indices fell for all the three industry groups. During later years the TFPG indices show a generally rising trend except in the last year. This finding of TFP growth indicates that a turnaround of Indian manufacturing industries from the industrial stagnation of the sixties and seventies took place during the mid-eighties. This result is also found in other studies on Indian industrial growth (Ahluwalia, 1985,


1.2 1.15 1.1 1.05 Index

1 0.95 0.9 0.85 0.8 0.75 0.7 1973

1978

1983

1988

1993

Year TFPG-Index_Agg.-Total

TFPG-Index_Agg.-Trad

TFPG-Index_Agg.-Mod Figure 3.4 Aggregate TFP indices for traditional, modern and total industries

1991). It may be noted that there is no marked difference in the trend of TFP growth between the modern and traditional sector industries. However, the level of TFPG of the modern sector is lower than that of the traditional sector during the period. One of the main reasons behind the low level of TFP in the modern sector is the inflexibility of labor laws that discourage the hiring of semi-skilled labour in modern industry (and services) and encourage the adoption of labor-saving technology. ‘The adoption of capital intensive technology in modern manufacturing results in faster capital deepening and lower aggregate TFP growth than would have prevailed under flexible labour laws, though productivity continues to increase’ (Virmani, 2006). The reason behind the low TFPG of modern sector may be the time period covered in this study. During that period the contributions of industries like computer and electronic products and information technology were not prominent in total industrial output. The other reason may be that the contribution of R&D to the TFP growth of the modern sector cannot be incorporated due to a lack of information. The index corresponding to weighted average TFPG in Figure 3.5 shows a similar trend to that in aggregate TFPG. However, the curves are closer


1.5000 1.4000

TFP indices

1.3000 1.2000 1.1000 1.0000 0.9000 0.8000 1973

1978

1983

1988

1993

Year Weighted-all

Weighted-trad

Weighted-mod Figure 3.5 Weighted TFP indices of traditional, modern and all industries

and the fluctuation around the growth is smaller, and there is no difference in the pattern of TFPG between modern and traditional industries. Figure 3.6 shows the indices of wage differential components of traditional, modern and total industries in Indian during 1973–74 to 1997–98. It can be seen that there are mild upward trends of the all three indices and in the traditional sector the trend is more prominent than in the other two sectors. For the modern sector until 1988 the curve is at level one and after that there is a sudden jump. This upward trend of the indices possibly indicates suboptimality in the allocation of labor among the industries, particularly in the traditional sector. However, this feature is less prominent in the modern sector. This is an expected result as the wage differentials in traditional industries are much higher compared to modern industries and the allocation of labor in traditional sectors is not always made on the basis of efficiency. On the other hand, laborers in modern sector industries are more homogeneous in nature and supposed to be better utilized. Figure 3.7 shows the indices of rent differential components of these three types of industries. In this case we have found a declining trend of the indices over this period. This pattern of movement indicates that the allocation of investments among the industries has been made in

75

1.07 1.06 1.05

Indices

1.04 1.03 1.02 1.01 1 0.99 1973

1978

1983

1988

1993

Year Wage Diff-All

Wage Diff-Trad

Wage Diff-Mod Figure 3.6 Wage differential components of traditional, modern and all industries

1.02 1 0.98

Indices

0.96 0.94 0.92 0.9 0.88 0.86 0.84 0.82 1973

1978

1983

1988

1993

Year Rent Diff-All

Rent Diff-Trad

Rent Diff-Mod Figure 3.7 Rent differential components of traditional, industries

modern and all


1.1

Relative indices

1.05 1 0.95 0.9 0.85 0.8 1973

1978

1983

1988

1993

Year TFPG-index_Agg.-Trad

TFPG-index_Agg.-Mod

Figure 3.8 Relative TFP indices of traditional and modern to total industry

the right direction according to the efficiency of the investment. This is expected, since capital is homogeneous and there are hardly any rent differentials among the industries. Moreover, capital is more mobile than labor and should move according to changes of the rate of return on capital among the industries. Figures 3.8 to Figure 3.11 present the time series of the relative indices (i.e. index of a particular sector relative to that of all industries together) for aggregate TFPG and its three components. It may be noted that an upward or downward trend of any of these relative indices for a sector indicates a tendency of divergence away from the all-industry pattern. Thus, suppose the relative index for rental differential for a sector shows a downward trend; this should mean that compared to all-industry this sector experiences a better allocation of investment among its industries. Let us now consider Figure 3.8, which shows the movements of the relative indices of the aggregate TFPG. The relative index for the traditional sector shows an upward movement in the initial years but then diverges from the all-industry level in a downward direction. On the other hand, the index for the modern sector shows an opposite movement to the traditional sector index. This pattern of movement may be due to the greater weight of the modern sector compared to the traditional sector in changing the pattern of the TFPG index for the all-industry total.


1.1000 1.0800

Relative indices

1.0600 1.0400 1.0200 1.0000 0.9800 0.9600 0.9400 0.9200 0.9000 1973

1978

1983

1988

1993

Year Weighted-trad-®

Weighted-mod-®

Figure 3.9 Relative weighted TFP indices of traditional and modern to all industries

In Figure 3.9 a similar trend is observed in the relative indices of the weighted TFPG for these two sectors. Figure 3.10 gives the graph for the relative index of the wage differential component of the aggregate TFPG. Both curves show a rising trend during the period. However, the relative index for the modern sector starts from below one, crosses one in the year 1988 and then diverges from one. On the other hand, the index of the traditional sector always diverges away from the all-industry level. Thus the movement of the curves indicate that in both sectors there was some misallocation of labor employment compared to all-industry level and the problem becomes acute in the recent period. The graph for the relative index of the rent differential component of the aggregate TFPG is shown in Figure 3.11. Here also, both curves show a rising trend during the period and move with similar trends during the period. Thus the curves indicate that both sectors misallocated some investment compared to the all-industry level during the period of study. Thus, the analysis of TFPG shows that there is a definite upswing in the trend of aggregate TFPG during the post-liberalization period. However, this change was started much before 1991 and there is hardly any difference between the modern and traditional sectors in the trends of

78

1.0500 1.0400 1.0300

Indices

1.0200 1.0100 1.0000 0.9900 0.9800 0.9700 1973

1978

1983

1988

1993

Year Wage Diff-Trad

Wage Diff-Mod

Figure 3.10 Relative wage differential components of traditional and modern to all industries

1.045 1.04 1.035

Indices

1.03 1.025 1.02 1.015 1.01 1.005 1 0.995 1973

1978

1983

1988

1993

Year Rent Diff-Trad

Rent Diff-Mod

Figure 3.11 Relative rent differential components of traditional and modern to all industries


TFPG. The pattern of intertemporal movements of labor are inconsistent with efficiency and this phenomenon is true for both traditional and modern sectors. The pattern of intertemporal movements of investment as evident from the figures moved in the right direction during the period. Finally, aggregate TFP growth in India was mostly due to the intrabranch effect of TFP growth. A similar result was found by Timmer (1999) in his analysis of structural change and productivity growth in Indian industries.

5 Total factor productivity growth in export-oriented industries Development economists often argue that a protected trade regime reduces the efficiency and productivity of the industrial sector. First, a protected home market allows domestic producers to enjoy monopoly power and excess profits. Consequently these firms fail to achieve both scale efficiency and technical efficiency. Second, a protected market attracts inefficient small firms to operate, causing an increase of average cost. Thus these two intra-industry effects of protectionism are a more important source of welfare loss than the traditional comparative advantage effect. The earliest arguments for gains from trade are based on the concept of comparative advantages from proper allocation of resources. The recent emphasis is given on the improvement of efficiency and productivity through competition due to openness of trade. However, the impact of trade policies in long-run growth is ultimately an empirical question. In developing countries where oligopolistic behavior of the firms in small domestic markets is more likely, few studies link trade reform with increased competition. Trade liberalization will bring additional welfare gain by reducing the dead weight losses created by domestic monopolies and oligopolies, by increasing competition and reducing price and marginal cost markups. The empirical evidence shows that import penetration through liberal trade policies lowers price– cost margins in several developing countries. Research with developed country data suggests a negative relation between import penetration and reported price–cost margins (Domowitz et al., 1988; Roberts and Tybout, 1991). Thus studies on the linkage between trade reform and productivity suggest that the debate is still unresolved and the impact of trade policies on long-run growth is ultimately an empirical question. The role of trade in promoting economic well-being has a long tradition in the trade literature, but the interaction between international trade and productivity movement is less understood. Recent works


in the growth literature outline a variety of mechanisms by which increased trade might affect aggregate productivity growth (Grossman and Helpman, 1991; Rivera-Batiz and Romer, 1991; Romer, 1994; Feenstra, 1996). The major issue of these studies is the possibility of transfer of knowledge and ideas across the countries and the possibility that countries with lower productivity might catch up to the leading countries. At the same time there is a possibility that faster productivity growth allows firms or industries to increase the flow of exports. Tybout et al. (1997) developed a model of exporting with sunk cost (cost of R&D) of entry and tested it on a sample of Colombian firms. In the presence of these entry costs, only relatively productive firms will choose to pay the cost and enter the foreign market. Thus the implied relationship between exporting and productivity is positive in a cross section of firms or industries. Bhagwati (1988) argues that although the logic for the success of an export promotion strategy is based on economies of scale, no empirical support for this is available in a developing country. The lack of any conclusive evidence of the linkage between trade reform and competition, as well as productivity, is very natural due to the non-availability of sufficient data before and after liberalization and also due to the lack of suitable econometric methods. This study discusses some issues regarding trade liberalization and the productivity growth of Indian industries. It is expected that after the implementation of liberalization policies there will be a rise in the productivity and efficiency of industries, the industries will be more competitive than before and trade volume will rise. But there is a two-way relation between competitiveness and volume of trade. It has been said that the interaction between international trade and long-run output and productivity growth is less understood in the trade and/or liberalization literature. It is interesting to examine whether growing trade leads to faster productivity growth or the other way round. In this section we analyze the effect of an increase in total volume of international trade on industrial performance. We test whether international trade, in the form of exports, has any effect on productivity growth within the industries. Data The major impediment to empirical analysis is the non-availability of a consistent series of comparable data over a good period of time. The basic data on trade are collected from various issues of trade statistics brochures and books published by the Director General of Commerce


and Intelligent Service (DGCIS). A consistent series of data for major trade groups in India is available from 1987–88 to 1999–2000. Data on trade have also been collected from the International Trade Statistics published by the UNO. These data have been supplemented by data taken from various issues of Statistical Abstract, published by the Central Statistical Organization. Statistics on tariff rates on different commodities have been collected from various issues of Custom Tariff in India, compiled by R. K. Jain and published by Centax Publication Pvt Ltd, New Delhi. The Central Statistical Organization publishes data on manufacturing industries in India. These data are available in annual issues of the Annual Survey of Industries. But these data are at an aggregate level, with four-digit classifications of industries. Recently, they have started selling firm/unit level data on manufacturing industries. These data are available continuously from 1980–81 to 1998–99 except for the single year 1995–96. These firm-level data have been collected for a few selected industries for the purpose of the analysis. Industry-level data for a few selected industries for the years 1974–75 to 1998–99 have been collected from different issues of the Annual Survey of Industries. Data have also been collected from various issues of the Reserve Bank of India Bulletin.

Export performance of Indian industry during 1987–88 to 1999–2000 It has been argued that in recent years the Indian export scenario has changed due to a greater openness in international trade in India. We have analyzed the change in trade pattern of India during the recent past. We consider here only the export performances of different commodity groups in India during 1987–88 to 1999–2000. Trade liberalization includes both export and import liberalization. Export promotion policies are taken in terms of indirect and direct subsidies. There are at least four types of indirect subsides: (a) exemptions or concessional tariffs on raw materials/inputs; (b) access to special import licenses for restricted inputs; (c) concessional income tax provisions traditionally applied to exports (export earnings are tax exempted); (d) export financing at concessional rates. There are also some direct subsidies to exports and various policies are taken to promote foreign direct investment in some industries. Import liberalization policies give a better scope for industrialists to import inputs at a lower cost than before. As a consequence there will be a chance of producing goods at a lower cost than before using both imported inputs and imported technology, and goods can be made available at a lower price. As a result, the export-oriented industries are


Percentage of share of export of major items groups

Items Agriculture Traditional industries Modern industries Minerals Ores Others

1987–8

1999–2000

23 52 14 5 4 2

18 58 20 1 2 1

expected to increase their exports by lowering the offer price in the international market. This study has been conducted on the basis of the data collected from DGCIS. First, these export figures are converted into real terms by using the unit value index of export. Since the data period is not long enough for any rigorous testing using sophisticated time series models, we have relied on some simple techniques to establish the changing pattern of exports in India during this period. Table 3.4 shows the overall changes in real values of exports (deflated by unit value index) during 1987–88 to 1999–2000. The export items are classified into six broad areas: agricultural goods; manufacturing goods (traditional); manufacturing goods (modern); ores; minerals and mineral oils; others. Exports of agricultural items decreased from 23 per cent in 1987–88 to 18 per cent in 1999–2000. At the same time there was a corresponding rise in exports of modern items, from 14 to 20 per cent during the same period. The proportion of exports of ores fell from 4 per cent in 1987–88 to 2 per cent in 1999–2000 and that of minerals including oil fell from 5 to 1 per cent during the same period. Thus it can be said that there was a definite shift of exports from agricultural and primary commodities to manufacturing commodities during the decade after liberalization. There was also a rise in manufacturing items in the modern sector, including chemicals, machinery, electronic goods, computer software, transport equipment and sport goods. Thus a change in trade pattern in terms of exports is observed during the post-liberalization period. A detailed breakdown of the growth pattern of exports can be studied to identify the export items that registered a better performance during the post-liberalization era. The graphical representation of yearly value of exports (in real terms) of some major commodity items over the period (Figure 3.12) shows that most of the industries had rising exports during the period. The movement of individual commodity groups can be divided into six categories on the basis of growth rates and their share in total exports.


600,000

Export (Rs 00,000)

500,000

400,000

300,000

200,000

100,000

1999–00

1998–99

1997–98

1996–97

1995–96

1994–95

1993–94

1992–93

1991–92

1990–91

1989–90

1988–89

1987–88

0

Years Leather

Gems and jewelry

Machinery and instrument

Drugs and pharmaceuticals Transport instruments

Iron, steel etc.

Figure 3.12 Trends in exports of selected commodities

Overall growth during the whole period and annual average growth rates (from discrete year to year growth rates) for two subperiods are estimated for comparison. First, items are grouped according to the growth rates over the entire period and the share of exports during the period. The commodity groups that had a very high share of total exports and also showed high growth rates over the period were gems and jewelry, ready made garments (cotton) including accessories, and cotton yarn fabrics made ups etc. Gems and jewelry shows the highest proportion of exports for the entire period at around 15 per cent of total exports, and this group also shows a high growth of exports during this period, at around 4 per cent per annum. The second group includes items that show very high growth rates but make up a lesser proportion of total exports. Primary and semi-finished iron and steel registered a maximum growth rate of about 14 per cent per annum but the item accounts for only 0.1 per cent of total exports


in 1987–88. However, the proportion of exports had increased to about 2 per cent in the year 1999–2000. Electronic goods showed a growth rate of about 6 per cent per annum and the share in total exports rose from 1.1 to 1.7 per cent during the period. Other items in this group include inorganic chemicals and plastic linoleum. A third group of items can be categorized, which show a high growth rate but have very little share in exports. Woolen yarn and fabrics made ups falls in this category, which shows a growth rate of about 10 per cent per annum but with a share of exports less than 0.1 per cent in 1987–88, rising to about 1 per cent in 1999–2000. Computer software falls in this category, showing a high growth rate of 11 per cent per annum but accounting for only 0.03 per cent in 1987–88, rising to 0.1 per cent in 1999–2000. The fourth group comprises industries that register low growth rates but a high proportion of total exports. Leather manufacture is one such industry, which registered a growth of only 0.29 per cent per annum but a high share of 7.4 per cent in 1987–88. But the share fell to 2.6 per cent in 1999–2000. In the fifth group the items show both low growth rates and a low share of total exports. The items in this group are numerous, including paints, enamel, varnish etc., tobacco manufactured and machine tools. The final group of items shows very poor export performance during the period of our study. The standard statistical test to find any major changes in the movement of exporst after liberalization using dummy variable in the time series analysis failed to provide statistically significant results for most of the industries. Average annual growth rates have been calculated for two subperiods, one from 1987–88 to 1992–93 and another from 1993–94 to 1999–2000. The comparison of growth rates during the two subperiods indicates that 18 out of 43 items show higher growth rates during the post-liberalization period. To rank the industries considering both parameters, i.e. growth of exports and share of exports of each industry, we calculated an index for each parameter. The index is similar to the development index. Each index is added to find out the rank of each industry. Since for both parameters a high value of the index means a high rank one can add the two indices to make a composite index and order the industries according to that value. The index of each parameter is calculated in the following way. IG =

(GOBS − GMIN ) , GMAX − GMIN

0 ≤ IG ≤ 1


where, GOBS is the observed growth rate of a particular industry,GMIN and GMAX are the minimum and maximum values of growth among the industries. A similar index (IS ) is calculated for the share parameter of the industries. Ranking of industries on the basis of the combination of these two indices in Table 3.5 shows that gems and jewelry has the highest rank. The other industries that show high ranks in this composite index and also belong to the upper rung of the table if considered separately for the parameters are transport equipment, drugs and pharmaceuticals, machinery, and iron and steel. It can be seen from Table 3.6 that some of the industries that performed better in terms of exports have high values of ‘imported-input’ intensity. But some industries (leather, textile-garments, transport and iron and steel) show a high growth of exports although the percentage of imported input in these industries are comparatively low. Thus the hypothesis that the industries that showed better export growth during the past decade necessarily benefited from the import of inputs is rejected. There are other factors, such as FDI, that also play an important role in export growth. Linkage between total factor productivity growth and export performance in selected industries According to several economists TFP growth is the only source of longrun development in any country. TFP measures the efficiency of input use in a production process. The growth of an economy is determined by the rate of expansion of its productive resources through capital formation and TFP growth. Differences in TFP growth rates between sectors are crucial determinants of evolution in the long run (Nishimizu and Robinson, 1984). Thus TFP growth is one major policy issue for both the developed and developing worlds in their long-run growth planning. The issue of policy relevance has to deal with the sources of TFP growth. It can be checked whether there exists any relation between TFP growth and changes in policy on protection or whether there is any relation between TFP growth and fiscal incentives to the industries to accelerate activities. The most significant stylized fact of the empirical literature on TFP growth is the importance of TFPG in contributing at least 50 per cent of growth in output. This literature also suggests that there is a positive relationship between productivity change and the rate of growth of output. Now the question is how trade policies affect TFP growth. It has been argued that an implicit mechanism is competition, forcing domestic industries to adopt new technologies, to reduce ‘X-inefficiencies’ and to reduce cost through increasing productivity and efficiency. The most

86 Table 3.5

Export performance of the industries

Commodities

Gems & jewelry Cotton yarn, fabrics, made ups etc. Primary & semi-finished iron & steel Other cotton incl. accessories Manmade fabrics, made ups Plastic & linoleum products Computer software Drugs, pharmacuticals & fine chemicals Woolen yarn, fabrics, made ups etc. Inorganic/organic/agro chemicals Other chemical & allied products Manmade fibers Transport equipments Glass/glassware/ceramics/reftrs/cement Iron & steel bars/rods etc. Machinery and instruments Paper/wood products Electronic goods Manufactures of metals Handcrafts (excl. handmade carpets) Dyes/intermediates & coar tar Rubber manufactured products Processed minerals Processed fruits & juices Carpets (excl. silk) manmade Coir & coir manufacture Paints, enamels, varnishes etc. Natural silk yarn, fabrics, made up Carpets (excl. silk) millmade Other commodities Sports goods Other textile materials Ferrous alloys Leather manufacture Wool Residual engineering items Cosmetics, toiletries etc. Machine tools Jute manufacture excl. floor covering Non–ferrous metals

GR (1987–99)

Share 1999–2000

Combined rank

4.225 6.892 14.220 4.722 9.277 9.960 11.021 6.819 10.028 7.853 8.611 6.697 6.052 7.872 7.595 4.372 7.173 5.869 5.399 5.551 5.389 6.113 5.865 5.202 4.266 5.118 3.601 3.078 3.407 2.637 2.692 2.466 2.474 0.297 2.193 2.433 1.576 1.743 −0.470 −1.735

18.189 10.210 1.770 8.055 2.681 1.690 0.111 4.671 0.165 1.782 0.898 2.443 2.671 0.588 0.249 3.901 0.499 1.651 1.958 1.713 1.645 0.748 0.672 0.304 1.203 0.118 0.443 0.786 0.272 1.144 0.162 0.325 0.144 2.581 0.428 0.084 0.547 0.231 0.294 0.071

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40

Industrial Productivity in the New Economy 87 Table 3.6 Imported input intensity of selected commodities Sl. number

Commodity

1 2 3 4 5 6 7 8

Electronic equipment (incl. TV) Machinery Drugs and medicines Miscellaneous manufacturing Transport Iron & steel Leather Textile garments

Imported input intensity (%) 33.9110 30.3411 15.9657 1.2677 10.4242 10.0802 5.1714 3.9076

Note: Maximum import intensity 67% in Petroleum. Next is 34% in Other non-electrical machinery in item 2. Minimum is 0%.

common way to increase competition is to open domestic industries to the international market. Thus export expansion and import liberalization are the two major policies for opening the market. One argument of gains from trade is based on the concept of allocative efficiency, i.e. trade liberalization will help allocate resources according to a country’s comparative advantage. But the other argument is that in an imperfectly competitive market, trade reforms increase competition. While a policy for increasing imports may restrict the market for domestic goods, it also increases competition and hence induces greater efficiency. An export promotion policy may affect competition in both ways. On the one hand, if firms increase innovative and productive activities in order to enter foreign markets then high exports as a reward for this activity may lead to healthy competition. On the other hand, excessive export subsidies may distort incentives and lead to increased inefficiency. Thus it is important to focus on the impact of trade policies on the TFPG and to examine the causal relation between TFP and trade policies. Methods and data In this study we measure TFP growth and its changes during the postliberalization period for some selected manufacturing industries that according to our analysis show comparatively better performance in terms of export during 1975–1998. We also try to understand the factors behind the changes in TFPG during the same period. The selected industries are: leather; transport equipment; drugs and pharmaceuticals; gems and jewelry; machinery; and iron and steel. Data relating to production for these industries were collected from the Annual Survey of Industries. Export data for these industries were collected from various issues of the


Reserve Bank of India Bulletin. Price indices to deflate the output of these industries were collected from issues of Statistical Abstract India, published by CSO. The consumer price index for industrial workers was also taken from publications of CSO. Unit value index is used to deflate the export figures for different items, and is taken from the publications of DGCIS. Two different measures were used to estimate the TFP of these selected industries. The first method is a standard translog index with value added as a measure of output, and two inputs are labor and capital. Under this translog index the equation to estimate TFPG is: log TFPt = log Yt − (V k ( log Kt ) + V L ( log Lt )) where V (.) =

V(.)t − V(.)t−1 2

represent the corresponding factor shares of inputs and Y represents the values added. The second method is based on an output function where instead of value added the value of gross output is taken to estimate the TFPG and there are three inputs: labor, capital and materials. The functional form of the measure is: log TFPt = log Qt − (V k ( log Kt ) + V L ( log Lt ) + V m ( log Mt )) Where Q represents the gross value of output, M represents the material input and V m represents the share of that input. Both the value added and the gross output are deflated by the respective price indices to get the real values. Total number of persons employed is taken as measure of labor input. Share of labor in output is taken as the proportion of wages to total output or gross value added depending on the model specification. Wage is deflated by the consumer price index of industrial workers to get the real value. It is well known that the measurement of capital is always a difficult task for any empirical analysis of production. Gross fixed capital stock is calculated using the perpetual inventory accumulation method.2 The time series of materials is deflated by the price index of the corresponding industry group. Empirical analysis The growth rate of TFP in six industries (leather; transport equipment; drugs and pharmaceuticals; gems and jewelry; machinery other than

Industrial Productivity in the New Economy 89 Table 3.7 Growth of TFP and labor productivity over three subperiods Year

TFP-VA

LPT

Leather 1976–80 1981–90 1991–97

−7.86 1.96 2.14

−4.18 6.05 9.85

Transport 1976–80 1981–90 1991–97

−5.56 3.42 1.69

1.79 7.66 7.39

Medical 1976–80 1981–90 1991–97

−6.55 1.58 5.32

−6.76 29.13 10.52

Gems and jewelry 1976–80 1981–90 1991–97

3.08 1.90 11.59

13.37 30.72 24.66

Machinery 1976–80 1981–90 1991–97

0.55 −0.17 0.48

3.59 4.99 6.56

Iron and steel 1976–80 1981–90 1991–97

−3.42 −0.66 5.76

0.66 5.40 13.40

transport; and iron and steel), were estimated over the years 1975–76 to 1997–98. Table 3.7 presents the growth rates of TFP measured using value added method and the corresponding growth rates of labor productivity in the three subperiods 1976–80, 1980–90 and 1991–99. It can be seen from the table that per annum growth rates of TFP measured with the single-deflated value added method during 1990–91 to 1997–98 are higher in all the industries, except transport equipment, compared to the previous decade. Estimates of TFP with the value added function show that the growth rates of leather, transport equipment, drugs and pharmaceuticals and iron and steel were at a minimum during 1975–76 to 1979–80 among the three subperiods. In the other two industries they were marginally higher than the figures for 1980–81 to 1989–90.


The findings of Goldar and Kumari (2002), Trivedi et al. (2000) and Srivastava (2000) suggest that TFP growth rates of ‘textiles, leather, transport, chemical products, and metal and metal products rose during the 1990s. Thus from these studies we find a broad agreement about the movement of TFPG during two subperiods of the 1980s and 1990s. Now the question is whether these upward movements in TFPG in the industries are the outcome of the policies taken for liberalization or simply an effect of output growth. In the next subsection an attempt is made to understand this phenomenon using econometric model. Sources of TFP growth The analysis to identify the forces behind the TFP growth is based on a regression model with time series cross section pooled data of five industries, each for 21 years. For each industry the TFPG for each year is calculated and the corresponding variables are taken to obtain the estimates. The basic model is TFPGit = α + β1 OG + β3 ERP + β4 EER + EXPGR + ε TFPGit indicates annual growth rates of total factor productivity in industry i at time t. OG represents the annual growth rate of output (real terms) for the corresponding year. ERP represents the effective rate of protection accorded by tariff to industry i in year t. EER is the real effective exchange rate in year t. EXPGR represents the average annual export growth during this year. A detailed description of data for the variables is presented in Appendix 3.2. Results of the regression analysis are given in Table 3.8. The dependent variable here is TFP growth and the independent variables are log of output, log of exports, log of square of exports, effective rate of protection and time. It has been observed that exports play an important role in explaining the TFPG. Log of square of exports is included to check the nonlinearity of the variable. The coefficient of this variable becomes statistically significant, which indicates that the higher the growth of exports the higher will be the TFP growth of Indian industry. The coefficient of effective rate of protection indicates that the lower the rate the higher will be the TFP growth. However, the value is not statistically significant at the 10 per cent level.

6 Market power and productivity: firm-level study In many studies an imperfectly competitive home market has been cited as a justification for the introduction of a policy of trade liberalization.

Industrial Productivity in the New Economy 91 Table 3.8 Sources of TFP growth Variable

C Log output Log export Log export-square ERP Time Adjusted R2 Durbin–Watson stat.

Pooled estimate coefficient 3.132604 (0.2101) 0.705246 (0.7577) −5.19282 (−1.7209) 0.568645 (1.9743) −0.01118 (−0.7342) −0.01513 (−0.4752) 0.053132 2.343931

Dependent variable: TFPG.

In a protected market dominated by few domestic firms, trade reform increases competition. Grossman and Helpman (1990) suggest that trade liberalization not only generates a one-time increase in growth through better allocation of resources but also affects long-run growth by accelerating technological change. But they also argue that trade reform will accelerate growth only if the allocation of resources is made in the proper direction. The impact of trade reform on long-run growth is ultimately an empirical question. There have been some good efforts to find the correlation between trade reform and productivity growth. However, the empirical results from microlevel studies are still inconclusive. In developing countries where the prevalence of oligopolistic markets is more likely there is a lack of conclusive evidence of linkage between trade reform and productivity growth (Bhagwati, 1988 ; Nishimizu and Page, 1990; Tybout, 1992; Harrison 1994). One reason behind the inconclusive relation between trade reform and productivity growth is the measurement of productivity. Solow-type measurement of TFP is based on the assumption of perfect competition with the factor shares being distributed according to the law of factor price equalization. However, in a developing economy where the small domestic market is dominated by very few firms the assumption of perfect competition cannot be justified. A shift in trade


policies may alter the level of competition and that in turn affects the composition of factor shares. The potential biases in assuming perfect competition in the estimation of TFP have long been recognized. In a recent study by Harrison (1994) an attempt was made to correct these biases in the estimation of TFP growth. The chapter is based on an extension of the methodology pioneered by Hall (1988) and Domowitz et al. (1988) on aggregate data. In this study an attempt has been made to analyze the relation between productivity growth and changes in market power using a similar model with firm-level data. Krishna and Mitra (1998) attempted to study this relationship with CMIE data for the period 1986–93. This study is, however, different in certain ways. First, the data in this study are taken from unit-level information on selected industries supplied by the Annual Survey of Industries. Second, the period that this study covers differs from that of their study. Methodology The basic framework is the extension of Hall (1988) and Domowitz et al. (1988) and the model by Harrison (1994). Let us start with a production function for firm i in industry j and at time t: Yijt = Ajt fit g(Lijt , Kijt , Mijt )

(3.16)

A profit function is then defined as πi,j,t = (pY − wL − rK − nM)ijt P = p(Yj ); and Yj = Yij L, K and M represent labor, capital and materials respectively. The corresponding factor shares are defined by w, r and m respectively. The partial derivative of Y with respect to labor can be written as

∂Yijt µw , where µ = 1 = ∂Lijt p

1+s·

1 e

Similar results can be obtained for other factors of production ∂Yijt r =µ p ∂Kijt


and ∂Yijt n =µ ∂Mijt p where µ represents the mark up of firms. Taking the total differential of equation (3.16) and substituting the values of partial derivatives after dividing by yijt the following equation is obtained: d log Yijt = µj +

wLijt nMijt rKLijt · d log Lijt + · d log Kijt + · d log Mijt pYijt pYijt pYijt

dAjt dfit + Ajt fit

(3.17)

rK Let α = wL , αk = pY , am = nM and α + αk + αm = 1 pY pY under constant return to scale. If we introduce imperfect competition and variable return to scale then

α + αk + αm =

β µj

where β is the return to scale parameter, or µj αk = β − µj α − µj αm Now from equation (3.17) d log Yijt − d log Kijt = µj [α · d log Lijt + αm · d log Mijt ] + [β − µj αj − µj αm − 1]d log Kijt +

dAjt dfit + Ajt fit

(3.18)

Let = log KL , m = log M , y = log YK K Then rearranging the terms of equation (3.18) dyijt = µj [αi d + αm dm] + (β − 1)d log Kijt +

dAjt dfit + fit Ajt

(3.19)

where µ, the markup, is the coefficient of the changes in L/K and M/K, weighted by their respective share of output.


If we ignore the firm specific effect, i.e. ∂fit /fit = 0, and constant return to scale, i.e. β = 1, then dy − α d + αm dm = φ = (µ − 1)(α d + αm dm) +

dAjt Ajt

where φ = observed productivity change and dAjt /Ajt = true productivity change. Under perfect competition, µ = 1 and φ =

dAjt Ajt

and the Solow measure of productivity becomes unbiased. If µ is greater than one there are two possible sources of bias. First, we may get bias in estimating the rate of productivity change dAjt /Ajt . If l and m are rising (falling), then dAjt /Ajt is over (under) estimated. Second, the estimate of changes in the trend rate of growth of productivity will be incorrect. This bias in the estimate of the change in productivity occurs due to change in the expected value of µ before and after reform. It is expected that after trade reform the price cost margin will fall to unity and the measured productivity will be equal to the true productivity dAjt /Ajt . Estimation of the model To estimate the effects of changes of trade reform on the market power of firms and to see the effect of changes in productivity, equation (3.19) is modified to allow for a change in markup by firms after reform. Change in firm behavior is captured by introducing a slope dummy to the term [αl dl + αm dm] in equation (3.19). To capture the overall shift in the productivity level after trade reform an intercept dummy is introduced in the model. The form of the function to be estimated is then dyijt = β1j dxijt + β2j [Ddx]ijt + β3j D + β4j dKijt +

dAjt +ε Ajt

(3.20)

where, dx = (αl dl + αm dm), β1j = µj , β2j = coefficient of slope dummy, β3j = coefficient of intercept dummy and β4j = β − 1 in equation (3.19). The productivity term dAjt /Ajt can be thought of as the average rate of productivity growth for the industry j. This rate will then be captured by the coefficient of the intercept term. If trade reform leads to the firms in the industry becoming more competitive than before the expected sign of the coefficient of the slope dummy should be negative, which reflects the fall in markups when


firms are exposed to the international market. If there is any shift in the overall productivity the coefficient of intercept dummy should be positive. The coefficient β4 is equal to the scale parameter β minus one. The non-availability of panel data forced us to estimate the model with the ordinary least squares method. Regression models have been estimated for each industry group and the two sets of regression equations can be written as: dy = β0 + β1 D + β2 dx + β3 Ddx + ε for the model without scale parameter and dy = β0 + β1 D + β2 dx + β3 Ddxβ4 dk + ε for the model with scale parameter. Results Data for this analysis of this part were collected from the Annual Survey of Industries firm- or unit-level data supplied on demand in electronic media. Data were collected for seven selected industries (selection on the basis of export performance the period of study): manufacturing of all types of textile garments and clothing accessories (265); manufacturing of leather footwear (291); manufacturing of drugs, medicines and allied products (304); manufacturing of semi-finished iron and steel products (331); manufacturing of television receivers, apparatus for radio broadcasting etc. (366); computer and computer software (367); and manufacturing of jewelry and related articles (383). Data were collected for the years 1980–81 to 1997–8, barring the year 1996–97.3 Before we analyze the change in the general level of productivity and the markup of firms due to import liberalization it may be useful to take a look at the standard rate of import duty at two time points, i.e. 1987–88 and 1998–99. We can see from Table 3.9 that the import tariff rates for Table 3.9 Import tariff rates of selected commodities Code

Items

Standard rate of duty (%) 1987–8

30 42 61 62 71 73 84

Pharmaceuticals Articles of leather Articles of apparel and clothing Clothing accessories Natural and cultured pearls and precious stones Articles of iron and steel Calculators, data processing and other office machines

Source: R. K. Jain, Customs Tariffs of India.

1998–9

100 100 100 100 100

30 40 40 40 30

100–300 100–200

20 10–40


all the selected industries fell drastically during the period of study. The import duties in 1987–88 were in most cases higher than or equal to 100 per cent, while the rates comes down to a range of 10–40 per cent in 1998–99. Thus there was a major change in import liberalization through a reduction in tariff rates after the policy of liberalization taken in 1991. Estimations of changes in productivity and markup are computed for the equations based on the assumptions on variable return to scale. An ordinary least squares technique is applied to estimate the coefficient of the equation. The first estimation is based on the assumption of variable return to scale. The estimation of level of markup and changes of markup after liberalization is presented in Table 3.10. β2 denotes the level of markup of firms while β3 denotes the changes in markup of firms. Figures for four industries (manufacturing of leather footwear, manufacturing of drugs and medicines, manufacturing of television receivers and computer and computer software) showed a decrease in markup. However, the coefficient is statistically significant only in manufacturing of leather footwear. In three industries (textile garments, manufacturing of semi-finished iron and steel products, and manufacturing of jewelry) there is a significant rise in markup. β2 shows the level of markup before liberalization. It is evident from Table 3.10 that in five industries out of seven the markup is greater than or equal to one. It is interesting to note that manufacturing of television receivers and manufacturing of jewelry and related articles behaved competitively even before reform. This result is quite natural since these industries largely comprise small units and the firms have little control over price movement. But during post-liberalization markup in at least three industries fall below one. This is not unlikely, since industries during the adjustment period of post-liberalization may encounter loss for a short period (Levinsohn, 1993). This result is somewhat different from that of the study by Krishna and Mitra (1998). They found a decline in markup in three industries out of the four they studied during the post-liberalization period. However, the industries they examined and the period of study are different from ours. β1 indicates the changes in productivity during the post-liberalization period. The figures show that productivity has fallen in all the industries bar one. However, only in two industries (manufacturing of drugs and medicine and manufacturing of semi-finished iron and steel products) is the coefficient statistically significant. Since productivity is considered as procyclical, the statistically significant increase in productivity may not be found during the first phase of trade reform. However, in the

Table 3.10 Test of changes of competition in selected industries Industry

Textile garments (265) Mfg of leather footware (291) Mfg of drugs & medicine (304) Mfg of semi-finished iron and steel (331) Mfg of television recievers etc. (366) Computer & computer software (367) Mfg of jewelry & related articles (383)

Coefficient 2

β0

β1

β2

β3

β4

R

−0.0205 (−0.39) 0.0094 (0.44) 0.0219 (0.54) 0.0111 (1.3) 0.0167 (0.27) −0.0553 (−0.66) 0.0149 (0.19)

−0.0268 (−0.46) −0.0148 (−0.48) −0.0908 (−1.87) −0.1122 (−5.01) 0.1845 (2.14) −0.0966 (−0.74) −0.0699 (−0.87)

1.0799 (4.57) 1.3899 (12.11) 0.9703 (3.67) 1.0961 (28.69) 0.0624 (1.71) 1.4576 (2.97) 0.6434 (6.46)

0.6561 (2.3) −0.7819 (−5.66) −0.4081 (−1.36) 0.5010 (4.84) −0.0675 (−0.53) −0.5054 (−0.74) 0.4389 (2.75)

−0.0046 (−0.04) −0.0581 (−0.76) −0.1885 (−1.3) −0.0505 (−1.29) 0.0591 (0.22) 0.1264 (0.33) 0.1479 (0.47)

0.37 0.68 0.16 0.72 0.05 0.2 0.76

Dependent variable: dy . Figures in parentheses are t -statistics.

97


analysis of industry-specific TFP a mild increase in TFPG was found in those industries. But the analysis in the previous section is based on industry-level data and the methodologies are different in these two analyses. This decline in the productivity of industries in the post-liberalization period is also found in a study by Balakrishnan and Pushpangandan (2000). They took five industry groups, namely machinery, transport equipment and parts, textiles, textile products, and chemicals. Most of these industries are common to the two studies. Their estimated coefficient of the time dummy of the intercept indicates no improvement in productivity in the post-reform period.

7 Concluding remarks The inward-oriented policies of 40 years after independence pushed the economy to a no-return zone. The concept of increasing efficiency and productivity through outward-oriented policies was neglected and never became a major policy issue before 1990. Only at the end of eighties when the Indian economy was almost on the verge of collapse were the policymakers bound to implement reform policies to revive the economy. In 1991 the government had undertaken the policy of liberalization under the guidance of Finance Minister Professor Monmohan Singh. This reform package included many policies and trade liberalization was one of them. It was expected that after trade liberalization through export promotion (abolition of quotas) and the reduction or abolition of import tariffs industries could increase their productivity and efficiency through healthy competition among the firms in the industries. The industries would obtain the import component of input use more easily and at a cheaper price than before to help firms to produce better quality products at a comparatively lower cost. Due to various export promotion policies the firms within the export-intensive industries were likely to increase their exports. Thus it was expected that there would be a rise in exports during the period of post-liberalization. On the other hand, trade can spur innovation by enhancing industrial learning, since it facilitates international exchanges of technical innovation and improves the efficiency of firms. However, the direction of the effect of trade liberalization on the productivity and efficiency of firms is ambiguous and purely an empirical question. Before analyzing the productivity and efficiency of export-oriented industries we discussed the structural changes in and total factor productivity growth of the traditional and modern sectors during 1973–74


to 1999–2000. It has been observed that a definite structural change in Indian industries has occurred in favour of modern industries with more sophisticated technology during this period. The analysis of the TFPG of these two sectors suggests that the patterns of movement of TFPG for both sectors are similar but the level of TFPG of the modern sector industries is low compared to that of traditional industries. The aggregate TFP growth in India was mostly due to the intrabranch effect of TFP growth. The study also sought to understand if there was any rise in exports during the post-liberalization period in India that can be explained as an effect of liberalization, and whether there was any link between this rise in exports and increases in productivity. In other words, could the export-oriented industries, using both imported inputs and imported technology, lower costs and increase their exports by lowering prices on the international market. It has been observed from the trade data for the period 1987–88 to 1999–2000 that almost all the industries registered a growth in exports. However, the ranks of the industries according to their share in total export did not change dramatically during this period. The industries that performed better during this period and had a large share of exports to total were textile industries and drugs and pharmaceuticals. Some of the industries studied here, however, registered lower growth during the liberalization period but the export shares of these industries were high compared to those of other industries. Some of the top ranking industries in terms of growth and export share were taken for analysis of the linkage between productivity and trade reform. It was found that this group comprises both traditional and modern industries. The import content of input use of these industries differs considerably. Growths of productivity of these industries were estimated to analyze if the growths were due to liberalization or some other features. It was noted earlier that an export promotion policy (subsidization) may affect competition in both ways. Firms may increase their efficiency to compete in the international market and may increase export while on the other hand excessive export subsidy may distort incentive and leads to higher inefficiency. TFPG is one of the measures of efficiency and the results suggest that efficiencies in the selected industries (ASI factory sector industry-level data) increased during the post-liberalization period but the difference between the two regimes is not statistically significant. The TFPG is positively related with the exports and competitiveness captured by the effective rate of protection. It has been argued that the standard estimates of TFPG models of the Solow type are based on the assumption of perfect competition and give


biased estimates of TFP growth. Particularly in a less developed economy like India, the market is characteristically oligopolistic and the policy of liberalization is expected to change the character of the market to nearly perfect competition. A model based on the work of Hall (1988) and Domowitz et al. (1988) was estimated with firm-level data to calculate the TFP growth that is free from such bias. Changes in markup were estimated for selected industries using firm-level data that indicate the changes in the level of competition. The results suggest that competitiveness increased in four industries out of seven during this period, and in most of these industries productivity was declining, which are very similar to findings from other research. The industry-level estimates of TFPG, however, differ from the firmlevel estimates. This is not unnatural because the estimation procedure is different and the TFPG estimates with firm-level data are corrected from their potential bias of taking the assumption of perfect competition in the Solow model. The basic argument in favor of conducting such studies is that after more than ten years of implementation of reform policies there is no marked improvement in India’s industrial scene and international trade. Foreign multinational companies are still hesitant about investing in a big way and foreign direct investments are not coming in enough quantiy in the priority sectors. The productivity and efficiency of industries are still far behind those of any advanced country. It is true that India is at the recipient end and it is in its interest to make the environment conducive for foreign investment. Although at a lower scale, there is some evidence of FDI and technological collaborations during the post-liberalization period. Import tariffs have been lowered drastically across the board. Some positive efforts in terms of giving subsidies to export-intensive industries and tax reductions on export earnings have been made to boost export growth. The industries whose import content of input is higher compared to others are expected to benefit from the reduction of import tariffs and can produce more efficiently. The increasing incidence of technological collaboration helps the production of higher quality goods at a lower price by avoiding the sunk cost of R&D. For all these reasons it is expected that there will be a rise in productivity and competitiveness in the industries and those that take the advantage of liberalization can compete in the international market with better quality goods at a competitive price. The findings of this study are important in a number of ways. (a) It reveals the nature of changes in export patterns of industries during the post-liberalization phase. (b) Productivity and efficiency are not the


prime considerations of export performance after liberalization. (c) There is no marked evidence of falling markup or a rise in competitiveness in the firm-level data. (d) This study reveals that it is difficult to give any general conclusion about the effect of liberalization on productivity and welfare from any partial study with a few types of industries. The results of earlier studies differ considerably because the sets of industries or the methods of estimation differ.

Appendix 3.1 Industrial classification Table 3.A1 Code

Description

Traditional industries 21 Food products 22 Beverages 23 Manufacture of cotton textiles 24 Manufacture of wool, silk and synthetic fibers 25 Jute textiles 27 Wood products 28 Paper 29 Leather 30 Nonmetallic minerals 33 Basic metals 34 Manufacture of metal products 97 Construction etc. Modern sector 26 30 31 35 36 37 38

Manufacture of ready made garments Basic chemicals Rubber and plastics Manufacturing and machinery Electrical and electronics Transport equipment Other manufacturing industries

Note: NIC 23 + 24 + 25 = textiles industry.

Appendix 3.2 Measurement of effective rate of protection, effective real exchange rate and liberalization dummy LIBDUM is the liberalization dummy. LIBDUM = 1 for the years after 1992 and 0 for the other years.


Effective rate of protection is defined as the percentage excess of domestic value added introduced because of tariffs and other trade barriers: ERP = [(VAd − VAw )/VAw ] × 100

(A3.1)

where VAd = value added at the domestic price, VAw = value added in the absence of domestic tariffs. This ratio measures the distortions introduced due to both tariff and nontariff barriers on input price, as well as the final output prices, and therefore measures the true level of protection as compared to world prices. The ERP used in this study has been taken from the tariff bases estimates of ERP. It is written as ERPj = tj − aij tj / 1 − aij

(A3.2)

where aij is the free trade input coefficient per unit of output. Effective rates of protection are thus an increasing function of output tariffs and decreasing function of input tariff. In the tariff-based approach, ERPs are measured using published tariff rates. The advantage of using nominal or published rates is that they contain information about the formal (potential) protective structure adopted by the government. The ERPs have been calculated according to equation (A3.2) using the input–output coefficients estimated by the CSO and published tariff rates. The value added is calculated as the returns to the primary factors directly involved in productive activity. This is done by subtracting costs of the trade inputs used directly in production from the value of output. Nontraded inputs are treated as part of the primary sector production, thus overestimating the true value added. Real effective exchange rate (EER) The nominal exchange rate is defined as the relative price of domestic currency in terms of foreign currency. The real exchange rate (EER) is usually defined as the nominal exchange rate adjusted by domestic local-currency prices relative to foreign local currency. It is real because it adjusts for the relative inflation rates in the domestic economy and foreign economies. It is effective because it is constructed as a weighed average of the exchange rates relative to the country’s trading partners. The weights are based on the trade flow in the base year. The EER is


proxy for a country’s degree of competitiveness, while depreciation in EER leads to an increase in competitiveness. The values of EER are taken from the Reserve Bank of India Bulletins. The formula used for the computation of EER may be written as: EER =

wi n

e P i=1

eI

PI

where e = exchange rate of rupee against a numeraire (SDRs) in index form (1985 = 100); eI = exchange rate of currency i against the numeraire (SDRs) in index form (1985 = 100); e/eI = exchange rate of rupee against currency I in an indexform (1985 = 100); P = India’s wholesale price index (1985 = 100); PI = consumer price index of country I (1985 = 100); Wi = weight attached to country or currency I in the index [ wi = 1]; and N = number of countries or currencies in the index other than India. The index constructed by the RBI is based on exchange rates vis-à-vis 36 countries. The weights wi are computed as wi = Xi / Xi , where Xi is India’s bilateral trade (export plus imports) with country I in the base period.

4 Industry Efficiency Analysis

The characterization and estimation of productive efficiency of an industry have followed three stages of development over the past decade. One is the parametric theory, whereby a production or cost frontier is estimated by assuming a composed error model with two components of error: one measuring technical inefficiency, the other indicating purely random components. The method of nonlinear maximum likelihood (ML) is then applied. The second is the data envelopment analysis (DEA), which employs the basic notion of Pareto efficiency of economic theory by stipulating that a given firm (or decision-making unit (DMU)) is not efficient in producing its outputs from given inputs, if it can be shown that some other DMU or combination of DMUs can produce more of some outputs without utilizing more of any input. This DEA technique is sometimes called nonparametric or semiparametric, since it does not postulate any functional form of the production or cost frontier. In order to obtain reliable estimates of the production frontier, one may adopt smoothing methods and outlier rejection techniques for the observed data on inputs and outputs and then apply the DEA method to estimate the production or cost frontiers. The third approach to industry efficiency analysis is designed to improve the efficiency scores of the DEA model by incorporating various methods of error reduction, e.g. the bootstrap methods rescale the individual efficiency scores using average efficiencies calculated from different subsets of the data. Another approach is to apply the method of least sum of absolute errors (LAV) to the production or cost function and derive the estimates of the respective frontiers. We discuss these recent developments, emphasizing only the most practical techniques that can be easily applied to industry data on inputs and outputs. Some of these methods are applied to estimate productive 104

Industry Efficiency Analysis 105

efficiencies in selected industries, such as leather, textiles, computer products and electronics. The efficiency of the banking sector in India is also discussed.

1 Econometric estimation of productivity The composed error model may be simply written as a production function yj = g(xj , α) + εj , where εj = vj − uj ;

uj ≥ 0, j = 1, 2, . . . , n

(4.1)

where yj is output for observation j, xj is a vector of inputs, α = (α0 , α1 , . . . , αm ) is a vector of parameters and the composed error is εj . The first issue with this model is how to decompose the estimate εˆ j = yj − gj (xj , α) ˆ into its two separate components, where the nonnegative error component uj measures technical inefficiency in the sense that it measures the shortfall of actual or observed output from its maximum value g(xj , α). Another important econometric issue with this model (4.1) is how to derive statistically consistent estimates of α when we have panel data comprising both time series and cross sectional data for n firms. For the first issue Jondrow et al. (1982) developed a firm-specific method ( JLMS technique) of estimating technical inefficiency. This makes it directly comparable to the DEA linear program (LP) model, which computes technical inefficiency for each observation. Their method explains the theorem that the conditional distribution of u given ε is that of a normal distribution N(µ∗ , σ∗2 ), where σ 2 = σu2 + σv2 , u∗ = −σu2 ε/σ 2 , σ∗2 = σu2 σv2 /σ 2 and it is assumed that each vj , which is assumed to be symmetrically distributed, is normal N(0, σv2 ) and that uj is distributed as the absolute value of a normal variable N(0, σu2 ). By using this theorem one can obtain by the ML method a point estimate of the nonsymmetric component u by using the mean E(u|ε) or the mode M(u|ε) of the conditional distribution where E(u|ε) = µ∗ + Kσ∗ ; M(u|ε) =

−ε(σu2 /σ 2 ) 0,

µ∗ K = f (−µ∗ /σ∗ ) 1 − F − σ∗ if ε ≤ 0 if ε > 0


where f (·) and F(·) is the standard normal density and its cumulative distribution respectively. Thus by replacing µ∗ , σ∗ by their sample estimates ˆ one can estimate the conditional mean E(u|ε). Battese and Coelli (1991) proposed a simple model to estimate the time behavior of technical inefficiencies. This model applicable to a panel data framework represents technical inefficiency as ujt = {exp[−η(t − T )}uj

j = 1, 2, . . . , n;

t = 1, 2, . . . , T .

(4.2)

where uit ∼ N + (µ, σ 2 ), with N + denoting half normal distribution, and η is a parameter (or a vector of parameters) to be estimated. Under this formulation technical inefficiencies prior to time T depend on the parameter η. As t → T , however, ujt tends to uT . Thus technical inefficiency in period T can be viewed as the reference or benchmark point. If η is positive, then exp{–η(t − T )} exceeds one and increases with the distance of period t from the last period T . Thus when η is positive, technical inefficiencies fall (increase) over time. For panel data one can apply very easily the method of corrected ordinary last squares (COLS) first developed by Richmond (1974). On using a Cobb–Douglas production function ln yjt = (α0 − µ) +

m

αi ln xijt + vjt − (uj − µ)

(4.3)

i=1

j = 1, 2, . . . , n; t = 1, 2, . . . , T with E(uj ) = µ > 0 and vjt following a symmetric distribution like the normal, we may treat εjt = vjt − (uj − µ) as the disturbance term where uj ∼ iid(µ, σu2 ) and is assumed to be independent of vj t. In this framework Schmidt and Sickles (1984) have shown that we can directly apply OLS (ordinary least squares) to this equation (4.3). These OLS estimates of αˆ 0 = α0 − µ and αˆ = (αˆ 1 , αˆ 2 , . . . , αm ) will be statistically consistent as n → ∞, though not for T → ∞ for fixed n, if the effects uj are uncorrelated with the regressors xjt . Under these circumstances one can apply the generalized least squares (GLS) as in the panel data literature, i.e. GLS estimates of α0 and α are based on the consistent estimates of σu2 and σv2 when they are unknown, but the consistent estimation of σu2 requires that n → ∞. Thus the strongest (weakest) case for GLS occurs when n is large (small) but T small (large). One advantage of this GLS procedure is that one can recover the individual firm-specific intercept term α0j = α0 − uj from the estimated residual.


Developments in panel data analysis of technical inefficiency have also allowed the introduction of policy variables that may affect technical inefficiency. Thus Battese and Coelli (1991) have used the following structure in a cost frontier context c = g(w, z, y; α) exp(v + u) i.e. in a log linear form ln cjt = ln g + vjt + ujt ;

ujt ≥ 0

(4.4)

ujt = δ zjt + ζjt Here c is the minimal cost with a deterministic component given by g = g(w, z, y; α) where w is the vector of input prices, y is output, z is the external policy variable and α is the parameter vector. Here v ∼ iid N(0, σv2 ) captures the effect of random noise, u ≥ 0 captures the effect of cost inefficiency and the parameter α is to be estimated along with the vector δ of parameter of the policy variable z. The component yjt captures the effect of technical inefficiency, which has a symmetric component δ zjt associated with the exogenous variables and a random component ζjt which is assumed to be normally distributed as N(0, σζ2 ) with the distribution of ζjt being bounded below by the variable truncation point −δ zjt . Once this model is specified as (4.4), the technology parameter and the inefficiency parameters are estimated by the ML techniques. Inefficiency estimates of individual firms at different time points are obtained as usual by the JLMS technique mentioned before.

2 Data envelopment analysis The DEA models analyze firm-specific economic efficiency by a sequence of LP models, one for each firm, to compare its relative efficiency among the set of all other firms in the industry. The DEA efficiency models have several interesting features, which have fostered numerous applications in several disciplines, e.g. management science, operations research and production theory in microeconomies. The first feature is the specification and estimation of a multi-output multi-input production frontier, when there is no information about the output and input prices. The multi-output case cannot be easily estimated by the econometric method. Second, both discretionary and nondiscretionary inputs


may be used in the calculation and both radial and nonradial measures of efficiency may be computed. In the radial case one explores if equiproportionate reduction of inputs or outputs can be made without lowering outputs or increasing inputs. Third, if the market price data for the inputs and output are available, one can easily estimate the cost frontier, overall cost efficiency and what is known as allocative efficiency. The dynamic extension of these cost efficiency models may also be carried out in an intertemporal framework. Sengupta and Sahoo (2006) have developed different types of DEA models to evaluate the productivity of firms in India in its pre- and post-liberalization phases. Both production and cost frontier models have been developed and empirically applied to the Indian banking industry and the insurance industry in order to study the extent of scale economies or diseconomies and capacity utilization. In this section we discuss two examples of the DEA approach, one for the production frontier and the other for the cost frontier. Then we consider several extensions of the cost efficiency approach, including its dynamic extensions. For surveys of the DEA approach one may refer to several recent publications such as Cooper et al. (2004) and Sengupta and Sahoo (2006). As an example of the production frontier estimation we start with the stochastic framework of a production function with one output ( y) and m inputs (xi ), where there are n firms in the industry. Let Ik = (1, 2, . . . , k) denote the set of k firms. Then the production function in a linear form may be written as

yj = β0 +

M

βi xij + uj ,

j ∈ In

(4.5)

I=1

where it is usually required that the parameters βi are nonnegative with the intercept term β0 free of sign. The error term uj ≥ 0 is nonnegative since the optimal output y1∗ = β0 + i βi xij is greater than or equal to observed output. Timmer (1971) used this formulation to minimize the sum of absolute values of errors |uj | under the constraints uj ≥ 0. This leads to the linear programming (LP) model in vector matrix notation: min g = β x

(4.6)

β∈C(β)

where C(β) = {β|u = Xβ − y ≥ 0; β ≥ 0} x = (xi ), xi = (1/n)

n j=1

xij


It is assumed that the vector β incorporates the intercept term β0 of unrestricted sign. Instead of minimizing the mean g as above Farrell (1957) adopted a two-step approach of estimating a frontier. In the first step each observed unit (e.g. firm k) is tested for efficiency by running an LP model min uk = Xk β − yk

(4.7)

subject to (s.t.) y ≤ Xβ,

β≥0

Here prime denotes transpose and Xk is the input vector for the kth firm. Clearly the unit k is relatively efficient if it is on the production frontier y = y ∗ = Xk β∗ , i.e. the optimal slack variable s∗k = Xk β∗ − yk is zero. If unit k is relatively inefficient, then yk < yk∗ , where asterisk denotes the optimal levels of output, i.e. observed output is less than the optimal output. At the second step one varies k in the index set In and runs n LP models of the form (4.7) and determines two subsets S1 and S2 of efficient and inefficient units respectively. These two steps comprise the formulation of the DEA model. The dual of the LP formulation (4.7) with some transformation may be written as the input-oriented DEA model, also known as the BCC (Banker, Charness and Cooper) model: min θ, s.t. n

(4.8)

Xj λj ≤ θXk ;

j=1

n

yj λj ≥ yk ;

j=1

n

λj = 1

j=1

λJ ≥ 0, J ∈ In ; θ scalar This standard BCC model measures the potential for equiproportionate reduction in all inputs where Xj is the input vector for each j ∈ In . If the input price vector (q) is available from market data, then the input cost (IC) minimization may be used as the optimality criterion. This yields the DEA model defining allocative efficiency: min IC = q x, s.t.

Xj λj ≤ x;

c

n j=1

(4.9) yj λj ≥ yk ;

n j=1

λj = 1, λj ≥ 0, j ∈ In


The case of many outputs would generalize the output constraint as n

yrj λj ≥ yr ;

r ∈ Is

j=1

with s outputs. Note that given the input, output and input price data, this model has only one stage to identify the efficient subset S1 . In case the output price data are also available one could reformulate the objective function of (4.9) as profits (r): max π = p y − q X, s.t.

Xj λj ≤ x;

Yj λj ≥ y; j

λ ≥ 0##24

This single LP model then characterizes the optimal input and output bundle for the profit frontier. Ray (2005) applied the radial efficiency model (4.8) with θ for the labor inputs only, for Indian manufacturing data for the years 1986–2000, with 1986–91 as pre-reform data and the rest as post-reform. Interstate data are used, with the aggregate output of manufacturing treated as a quantity index. Two types of labor input are distinguished: production labor and nonproduction labor (i.e. administrative and managerial). The other inputs used are fuels (i.e. energy cost) and capital measured by the book value of fixed assets deflated by the price index of new capital equipment. With θ ∗ = 1.0 as the measure of 100 per cent efficiency and θ ∗ < 1.0 as a relative inefficiency measure the results for the pre-reform (1986–91) and post-reform (1991–2000) periods are reported in Tables 4.1 and 4.2. As Ray notes, this radial labor efficiency measure understates the extent of surplus labor. For example, consider the case of Kerala (KE) in the year 1986–87. The radial labor efficiency measure is 0.813. This implies that it is possible to reduce the numbers of both production and nonproduction workers by 18.644 per cent. But this does not exhaust the potential for reducing all surplus labor, since the model does not allow for any substitution between different types of inputs. Table 4.1 shows very clearly that West Bengal (WB) exhibits the highest incidence of surplus labor in the pre-reform period (average θ ∗ = 64.5 per cent) and this trend has continued over the post-reform era (average θ ∗ = 53.9 per cent). In this model the effect of policy measures can be easily evaluated provided we have data on such measures, e.g. if zj is a specific policy measure like

Industry Efficiency Analysis 111 Table 4.1 State AN AP AS BI CH DE GO GU HA HP JK KA KE MH MP OR PO PU RA TN UP WB

Radial labor efficiency measure (θ) (pre-reform era) 1986–7

1987–8

1988–9

1989–90

1990–1

Average

1 1 1 1 1 1 1 1 0.743 1 0.690 1 0.813 1 1 0.933 1 0.934 1 0.962 0.844 0.605

1 1 1 1 1 1 1 1 0.740 1 1 1 1 1 1 0.811 0.728 0.898 0.762 0.882 0.787 1

1 1 0.882 1 1 1 1 1 0.782 1 0.762 1 0.841 1 1 1 0.727 0.836 0.844 0.970 0.789 0.538

1 1 1 1 1 1 1 0.956 0.808 1 0.945 1 1 1 0.883 1 0.699 0.908 0.808 1 0.852 0.527

1 1 1 1 1 1 1 1 0.830 1 1 0.780 0.924 1 0.859 0.862 0.745 0.854 0.866 0.945 0.867 0.555

1 1 0.976 1 1 1 1 0.991 0.781 1 0.879 0.956 0.916 1 0.948 0.921 0.780 0.886 0.856 0.952 0.828 0.645

additional clearness allowance (state level) for each worker, then one can add the constraint n

zj λj ≤ zk

j=1

in order to evaluate the relative incidence of such a constraint. Note, however, that the critical shortage of nonlabor inputs such as energy and the inadequacy of infrastructure-related inputs not included here may also be responsible for the existence of surplus labor in Indian manufacturing. An important characteristic of the DEA model (4.8) is that it applies the LAV method for each sample k with the input output vectors (Xk , yk ). One interpretation of this is that the model uses the mode of the distribution of the error term uj for minimizing, where each sample is assumed to provide a modal estimate. Timmer used the mean g = β x

112 India’s New Economy Table 4.2 State AN AP AS BI CH DE GO GU HA HP JK KA KE MH MP OR PO PU RA TN UP WB

Radial labor efficiency (θ) (post-reform era) 1991–2

1993–4

1995–6

1997–8

1999–2000

Average

1 1 1 1 1 1 1 0.910 0.739 1 1 1 0.984 1 0.852 1 0.759 0.802 0.952 1 1 0.575

0.316 1 0.845 1 1 1 1 0.874 0.598 1 1 0.675 0.749 1 1 0.712 0.702 0.663 0.822 0.992 0.809 0.526

1 1 0.842 1 1 1 1 0.918 0.621 1 1 1 0.946 1 1 1 0.679 0.735 0.985 1 0.807 0.553

1 1 1 1 1 1 1 1 0.805 0.554 1 0.682 0.932 0.899 1 1 1 0.713 0.911 0.874 0.863 0.566

1 0.775 0.925 1 1 1 1 0.938 0.746 0.929 0.823 0.664 1 1 1 1 1 0.865 1 0.914 0.741 0.491

0.924 0.947 0.907 1 1 1 1 0.954 0.758 0.943 0.943 0.772 0.941 0.989 0.945 0.945 0.850 0.767 0.936 0.964 0.853 0.539

instead of the mode for minimizing errors because he was interested in an average estimate of technical efficiency. Note, however, that the LAV method is a special case of the Lp -norm estimation. The LP norm minimizes the loss function n

L(εj ),

L(t) = |t|p

j=1

which is closely related to the nonparametric Huber estimate (see Sengupta, 1990) often used in nonparametric statistical theory, where L(t) =

t 2 /2 k|t| − k2 /2

for |t| ≤ k for |t| > k

Three values of p = 1, 2, ∞ are most important in applied work. The L1 estimate minimizes the sum of absolute values of residuals (the


LAV method). The L2 method is the OLS procedure. The L∞ estimate (minimax estimate) finds the value of β for which

µ(θ) = max εj (β)/rj

j∈In

attains its minimum value, where the range of each εj (β) is assumed to be ±rj . It is well known that if the errors εj have two-sided exponential density, then the ML estimate yields the LAV estimate as in the DEA method. One point has to be stressed about the ML methods above based on the LAV norm L1 . The range of the dependent variable (i.e. output here) depends on the parameters (β) to be estimated and this violates one of the basic regularity conditions that make ML estimators consistent and asymptotically efficient. Greene (1990) has shown that these desirable asymptotic properties of ML estimators could still hold if the errors follow the gamma distribution. Since the exponential density could be a good approximation to gamma density, the linear DEA model could be justified in this framework as a method of estimation of the stochastic frontier. Another simple approach is to interpret the production frontier in the form of a COLS model as yj = α˜ 0 +

m

αi xij + ej ,

ej = µ − uj ,

α˜ 0 = α0 − µ

(4.10)

i=1

where E(uj ) = µ > 0 and Eej = µ = E(uj ) = 0. Here the new error term ej has zero mean and satisfies the usual regularity conditions (like fixed mean and variance) except normality. When µ is known the usual ML estimators can be applied for deriving asymptotically consistent estimates. When µ is not known, the moments of the OLS residuals from equation (4.10) can be utilized to derive consistent estimators of µ and α0 , αi . Nonparametric estimates of the parameters of a DEA model have been developed and applied empirically so as to obtain the bootstrapped efficiency estimates. These bootstrap techniques derive bias-corrected efficiency scores when we have DEA models for groups and hierarchies. These techniques rescale the individual DEA efficiency scores using average efficiencies calculated from different subsets of the data. Staat (2002) has applied this bootstrap technique of bias correction of DEA efficiency scores in order to identify the true differences in efficiency. To illustrate this method we start with the production set X = {x, y)|x can produce


output y} and the input requirement set X(y) = {x|(x, y) ∈ X}. The usual assumptions are that the set X(y) is convex for all output y and nonzero inputs x are required for nonzero outputs y. Strong disposability of x and y are also assumed. The efficient boundary of the input requirement set X(y) denoted as ∂X(y) has to be estimated. For any sample of observations S = {(xj , yj )|j = 1, 2, . . . , n} the sample estimates of ∂X(y) ∂X(y) = {x|x ∈ X(y);

θx ∈ / X(y), 0 < θ < 1}

are obtained by solving θˆ k = min {θ|yk ≤

λj yj ;

θxk ≥

λj xj > 0;

n

λj = 1,

j=1

λj ≥ 0, j = 1, . . . , n)}

(4.11)

The weights λj are identical with the BCC type input-based DEA model. These are assigned to efficient firms on the production frontier and the condition λj = 1 allows variable returns to scale. In order to gain information on the sampling properties of the bootstrap estimates θˆ k , we generate a number of pseudo samples drawn from the original DEA scores. The estimates θˆ k and the bootstrap estimates θˆ k∗ are related as [(θˆ k − θk )|S ] is approximated by [θˆ k∗ − θˆ k |S∗ ] The bias of the DEA estimator ES (θˆ k ) − θ k may then be written as ES (θˆ k∗ ) − θˆ k∗ . Hence the bias-corrected estimates θˆ k can be obtained by applying the correction R ˆ k = 2θˆ k − θˆ k∗ where θˆ k∗ = (1/R) θˆ k = θˆ k − bias θˆ k∗ k=1

Simar and Wilson (1998) have proved that this bias correction will remedy the problems with comparing average DEA inefficiencies from samples of different sizes. The vector θˆ k∗ of smoothed efficiency measures is essentially generated from the R pseudo samples. The economist’s objection is that this method is very artificial and dependent on random drawings of pseudosamples. Bootstrap methods can also be applied to the regression model (4.10) and the various applied techniques are discussed by Sengupta (1989). Like the production frontier a cost frontier may be estimated by the DEA approach. A cost frontier has a number of advantages. First, variable and fixed costs may be separated by estimation. For the single output


(w composite output as weighted combination of several outputs) case, average cost may be directly calculated from the total cost frontier. In the case of the quadratic cost frontier the average cost may then be minimized so as to obtain the minimum efficient scale of output. Second, the multi-output allocation of total cost between several outputs may be easily determined from the linear cost frontier. An input-oriented cost frontier model may be set up in a DEA framework as n min θ, s.t. λj Cj ≤ θCh ,

λj yj ≥ yh

(4.12)

j=1 n

yj2 λj ≥ yh2 ,

j=1

n

λj = 1, λj ≥ 0

j=1

If the firm h is on the cost frontier (θ ∗ = 1.0), then Ch∗ = γ0 + γ1 yh + γ2 yh2 If γ0 is positive, then the average cost (ACh ) may be minimized so as to obtain the MSE level of output yh∗ = (γ0 /γ2 )1/2 with the minimum AC given by ch∗ = Ch∗ /yh = 2(γ0 γ2 )1/2 + γ1 . A dynamic cost frontier may also be easily formulated in the DEA model, as shown by Sengupta (2004a). Consider, for example, a partial form of the translog cost function by omitting the input prices ln TC = b0 + b1 ln y + b2 t Its time derivative yields TC/TC = b1 (y/y) + b2

(4.13)

Here time t is used as a proxy for technology change, e.g. technology progresses if b2 < 0. The reciprocal of the parameter b1 measures the degree of returns to scale or scale elasticity (e.g. b1 < 1 indicates increasing returns to scale). The cost frontier models above may be easily set up as a DEA model n ˆ h, ˆ j λj ≤ θ C min θ, s.t. C

j=1

λj ≤ 1, λj ≥ 0

j

yˆ j λj yj ≥ yˆ h


ˆ j = ln TCj and yˆ j = ln yj . If firm j is level efficient, then its cost where C frontier is ˆ j = (α/β)ˆyj − bt C where the Lagrangian function is ⎛ ˆh − L = −θ + β ⎝θ C

⎞

⎛

ˆ j λj ⎠ + α ˆyj λj − yˆ h + b ⎝t − C

j

n

⎞ λj t ⎠

j=1

By imposing the condition λj t = t the optimal value of b may be made free in sign, so that technology regress can also be measured, i.e. b < 0 (regress) and b > 0 (progress). As against the level efficiency, growth efficiency may be characterized by a similar model as follows min φ(t), s.t. n

˜ h (t) ˜ j (t)λj (t) ≤ φ(t)C C

j=1 n

y˜ j (t)λj (t) ≥ y˜ h (t)

j=1

λj (t) ≤ 1, λj (t) ≥ 0,

j = 1, 2, . . . , n

j

˜ j (t) = Cj (t)/Cj (t), yˆ j (t) = yj (t)/yj (t). For long-run costs five-year where C averages of growth of output and costs may be considered. Now the dynamic cost frontier for firm j takes the form Cj (t)/Cj (t) = (α/β)(yj (t)/yj (t)) − b = b1 (yj (t)/yj (t)/yj (t) + b2 (4.14) where b1 = α/β and b2 = −b. Note that if over time total cost TCj and output yj follow a geometric random walk process, so that the first differences of lnTC and lny are stationary, then the growth efficiency model, i.e. the dynamic cost frontier (4.14) has parameters that are known to be structurally stable, e.g. OLS estimates (or COLS method) may be valid. Furthermore, this dynamic cost efficiency model (4.14) characterizes the ˜ ∗ (t) as time t intertemporal growth frontier specified by {φ∗ (t), λ∗ (t), C j evolves over every five years, for example.

Industry Efficiency Analysis 117 Table 4.3

Scale elasticity β1 = 1/b1 of banks in India

Type of bank

Nationalized Private Foreign

1997–8

1999–2001

A (two outputs)

B (composite output)

A (two outputs)

B (composite output)

0.769 1.173 1277

0.907 1.564 19.212

0.843 1.050 1.201

0.809 1.034 1.345

Sengupta and Sahoo (2006) have applied the quadratic cost frontier models (4.12) to the banking sector in India over the period 1997–2001, covering 75 commercial banks. Three input costs are considered here (borrowed funds, labor and fixed assets) and two outputs (investments and performing loan assets). A case of composite output and total cost is also considered by taking a weighted combination of the two outputs with weights taken as the respective revenue shares. The results are shown in Table 4.3. It is clear that in both subperiods the nationalized banks exhibit decreasing returns to scale measured by β1 = 1/b1 . The time trend, however, indicates that the banks are more and more exploiting their returns to scale situation, thus improving their efficiency. Sengupta and Sahoo (2006) have also measured the trend of capacity utilization and the extent of economies of scope of Indian banks. Their findings show that in most cases the prevalent situation is the underutilization of existing capacity, which has resulted in large cost inefficiency. Policymakers in India have to play a much more dynamic role than before to improve the overall economic growth of the banking sector and hence the whole economy.

3 R&D efficiency and industry growth The evolution of high-tech industries in modern times has been profoundly affected by innovations in different forms such as new product designs and new software developments. R&D spending captures the key elements of the dynamic innovation process. Several features of R&D investment by firms are important in the dynamic evolution of an industry. First, R&D spending not only generates new knowledge about technical processes and products but also enhances the firm’s capability


to improve the stock of existing ‘knowledge capital’. This is the process of learning that has cumulative impact on industry growth. Second, growth of R&D spending helps in expanding the growth of sales or demand through new product variety and quality improvements. This has often been called economies of scale in demand in the modern theory of hypercompetition analyzed by Sengupta (2004b). Third, the R&D investment within a firm has a spillover effect in the industry as a whole. This is because R&D spending yields externalities in the sense that knowledge acquired by one firm spills over to other firms and very often knowledge spread in this way finds new applications both locally and globally and thereby stimulates further innovative activity and R&D intensity in other firms. Our objective here is to incorporate R&D investment into the DEA efficiency models and thereby show its impact on market demand and efficiency. Three types of R&D models are developed here for empirical and theoretical applications. One emphasizes the cost-reducing impact of R&D inputs. This may be related to the learning by doing implications of knowledge capital. Second, the impact on output growth through increases in R&D spending is formalized through a growth efficiency model. Here a distinction is drawn between level and growth efficiency, where the former specifies a static production frontier and the latter a dynamic frontier. Finally, the market structure implications of R&D spending are analyzed in a Cournot-type industry, where R&D spending is used as a marketing strategy just like advertisement. Denote average cost by cj /yj where total cost cj excludes R&D costs denoted by rj . Then we set up the DEA model with radial efficiency scores θ.

min θ, s.t. n

cj λj ≤ θch ,

j=1

rj λj ≤ rh

j

rj2 λj = rh2 ,

yj λj ≥ yh

(4.15)

j

λj = 1, λj ≥ 0,

j ∈ In = (1, 2, . . . , n)

On using dual variables β1 , β2 , β3 , α, β0 and solving the LP model (4.15) we obtain for an efficient firm j, θ ∗ = 1.0 and all slacks zero, the following


average cost frontier: cj∗ = β0∗ − β2∗ rj + β3∗ rj2 + α∗ yj

(4.16)

since β1∗ = 1.0 if θ ∗ > 0. Thus if R&D spending rj rises, average cost cj falls if 2β3∗ rj < β2∗ . If we replace rj by cumulative R&D knowledge capital Rj as in the learning by doing model, where Rj is cumulative experience, then the AC frontier (4.2) becomes cj∗ = β0∗ − β2∗ Rj + β3∗ R2j + α∗ yj

(4.17)

As long as the coefficient β3∗ is positive, rj may also be optimally chosen as r ∗ if we extend the objective function in (4.15) as min θ + r and replace rh by r. In this case we obtain the optimal value of R&D spending r ∗ as −1 1 + β2∗ r ∗ = 2β3∗

(4.18)

A similar result follows when we use the cumulative R&D spending Rj or R. Two simple extensions of the cost frontier model (4.15) can be derived. One is to extend the case to multiple outputs and multiple R&D inputs. We have to replace single output yj to ykj , k ∈ Im with m research inputs. Then the AC frontier (4.16) would appear as cj∗ = β0∗ −

m

∗ βzi rij +

i=1

m

β3i rij2 +

i=1

s

α∗k ykj

k=1

Second, we may formulate the model in terms of total costs rather than average costs as min θ, s.t. rj λj ≤ rh cj λj ≤ θch , j

j

rj2 λj = rh2 ,

yj λj ≥ yh , yj2 λj ≥ yh2

j

λh = 1, λ ≥ 0

In this case the total cost frontier becomes cj∗ = β0∗ − β2∗ rj + β3∗ rj2 + α∗1 yj + α∗2 yj2


where α∗1 , α∗2 ≥ 0. If the intercept term β0∗ is positive, then average cost for the jth efficient unit can be reduced to ACj = β0∗ /yj + α∗1 + α∗2 yj + β3∗ rj2 − β2∗ rj /yj On setting its derivative e to zero we obtain the optimal level of output yj∗ for fixed levels of rj as yj∗ =

1/2 β0∗ + β3∗ rj2 − rj /α∗2

(4.19)

If research costs rj are already included in total costs, then the optimal level of efficient output yj∗ in (4.19) reduces to 1/2 yj∗ = β0∗ /α2

(4.20)

The associated value of minimum AC then becomes ACmin = α∗1 + 2 β0∗ α∗2 This level of cost ACmin may be used to define minimum efficient scale (MES) of efficient firm j. Note that this measure is more comprehensive and structural than the more traditional productive scale size (MPSS) used in DEA models. Flaherty (1980) discussed noncooperative game theory models where firms employ cost-reducing investments over time in order to attain an optimal dynamic growth path. Now consider a second type of model where growth efficiency is considered. Several types of models of growth efficiency frontier and their comparison with level efficiency have been discussed by Sengupta (2003b). Here we consider a firm j producing a single composite output yj with m inputs xij by means of a log-linear production function yj = β0

m

β

eBi xiji ;

j = 1, 2, ..., N

(4.21)

i=1

where the term eBi represents the industry effect or a proxy for the share in total industry R&D. On taking logs and time derivatives of both sides of equation (4.21) one can then easily derive the production frontier Yj ≤

m i=0

bi X ij +

m

î φi X

i=1

where bi = βi , b0 = β˙ 0 /β0 , Xoj = 1, all j = 1, 2, . . . , N

(4.22)

Industry Efficiency Analysis 121 N

e

Bi

î = ˆ i , Xij = x˙ ij /xij , Yj = y˙ j /yj , X = φi X

x˙ ij

j=1 N

xij

j=1

Dot denotes time derivative. Note that b0 denotes here technical progress representing innovation efficiency or productivity growth (i.e. Solow-residual) and φi denotes the input-specific industry efficiency parameter. We now consider how to test the relative efficiency of each firm k in an industry of n firms. One solves the following LP model: min Ck =

m

ˆ i ), s.t. (bj Xik + φi X

i=0 m

ˆ i ) ≥Yj ; (bi Xij + φi X

j = 1, 2, . . . , n

(4.23)

i=0

b0 free in sign, b1 , b2 , . . . , bm ≥ 0, φi ≥ 0 Let b∗ , φ∗ be the optimal solutions for the observed input–output data ˆ i and yj , j = 1, 2, . . . , n with all slack variables zero. Then firm k is set Xij , X growth efficient if Yk = b0∗ +

m

î bi∗ Xik + φi∗ X

(4.24a)

i=1

If, however, we have b0∗ +

m

ˆ i > Yk bi∗ Xik + φi∗ X

(4.24b)

i=1

then the kth firm is not growth efficient, since the observed output Yk is less than the optimal output Yk∗ = b0∗ +

m

î bi∗ Xik + φi∗ X

i=1

Note that this nonparametric method has several flexible features. First, on varying k over 1, 2, . . . , n one could group the firms into two subsets,


one efficient and thus satisfying (4.24a) and the other inefficient, satisfying (4.24b). Second, if the input–output data set is available over time, one could estimate the parameters b0∗ (t), φj∗ (t) and bi∗ (t) for all t = 1, 2, . . . , T . The output efficiency scores ε∗k (t) = Yk (t)/Yk∗ (t) can also be computed for the efficient and inefficient units. Third, if the innovation efficiency is not input-specific, i.e. eBi = φt, then one could combine the two measures of dynamic efficiency as b0∗ + φ∗ = b˜ 0∗ , say, representing innovation and access efficiency. In this case the dual problem for equation (4.7) can be simply formulated as N max µ, s.t. Xij λj ≤ Xik ;

i = 0, 1, 2, . . . , m

j=1 N

Yj λj ≥ µYk ;

j=1

N

λj = 1, λj ≥ 0

(4.25)

j=1

An input-based efficiency model can be similarly specified as N min θ, s.t. Xij λj ≤ θXik ,

i = 0, 1, . . . , m

(4.26)

j=1 N

Yj λj ≥ Yk ;

λj = 1, λj ≥ 0

j=1

If the optimal values µ∗ and θ ∗ are unity, then the unit k is growth efficient; otherwise it is inefficient. As before the efficiency scores µ∗ (t), θ ∗ (t) can be computed over time if the time series data on inputs and outputs for each firm are available. Since some of the inputs are services of capital inputs, their impact on supply side economies of scale can be captured by the sum of the respective coefficients of production. Finally, we note that the growth efficiency models can be compared with the static model for testing the level efficiency of firm k. For instance, the models analogous to (4.23) and (4.26) would appear as follows: min Ck = β˜ 0 +

m

{β˜ i ln xik + φ˜ i ln xi } where xi =

i=1

s.t. β˜ 0 +

m

{β˜ i ln xij + φ˜ i ln xi } ≥ yj

i=1

β˜ 0 free in sign, β˜ i , φ˜ i ≥ 0; j = 1, 2, . . . , N

N

xij

j=1

(4.27)


and min θ˜ , s.t. N

xij λ˜ j ≤ θ˜ xik

(4.28)

j=1 N

yj λ˜ j ≥ yk ;

λ˜ j = 1, λ˜ j ≥ 0

j=1

The time series values of efficiency scores θ˜ ∗ (t) of level efficiency may then be compared with those θ ∗ (t) of growth efficiency defined by the LP model (4.26). If innovation and access efficiency by R&D spending are the most dominant characteristics of firms on the leading edge of the growth frontier, this would be captured more strongly by the dynamic efficiency scores θ ∗ (t) and their trend over time. We now consider an empirical application to the computer industry based on Standard and Poor’s Compustat data, where on economic grounds a set of 40 firms (companies) in the computer industry over the 16-year period 1984–99 were selected by way of illustrating the concepts of dynamic efficiency analyzed above. The companies included here comprise such well known firms as Apple, Compaq, Dell, IBM and HP and lesser known firms such as AST Research, Pyramid Tech, Toshiba, NBI and Commodore. Due to a variety of differentiated products, a composite output represented by total sales revenue is used as the single output ( yj ) for each company. Ten inputs are selected from the Compustat Database, representing both financially related input variables, such as manufacturing costs and marketing costs, and ‘net capital employed’ at the end of the reporting period, including input variables such as working capital, plant and equipment and other fixed assets. We use a proxy variable (x10 ) for all nondiscretionary inputs represented by advertising expenditures by the competing firms. Three inputs in manufacturing costs are x1 for raw material costs, x2 for direct labor and x3 for overhead expenses. Three inputs for marketing costs are x4 for advertising, x5 for R&D expenses and x6 for other selling and administrative expenses. Net capital employed in dollars includes x7 for working capital, x8 for net plant and equipment and x9 for other fixed assets. Finally, x10 represents a proxy variable for competitive pressure exerted by competitors on a given firm j. Thus we have used empirical data on 40 firms, each producing one output ( yj ) with ten inputs (xij ; i = 1, 2, . . . , 10 and j = 1, 2, . . . , 40). Three types of empirical applications are discussed here. The first characterizes the two subsets of efficient (N1 ) and inefficient (N2 ) firms


Sources of growth efficiency Technical R&D Plant & equipment Market progress (%) efficiency (%) efficiency pressure b0 b5 b8 b10

Efficient firms (N1 = 12) Inefficient firms (N2 = 28)

Table 4.5

35

39

21

21

12

13

18

19

Output trends over time (y(t) = a0 + a1 y(t))

Efficient firms (N1 = 12) Inefficient firms (N2 = 28)

a0

a1

a2

R2

−0.602 –

0.019* 0.009*

– −0.004

0.961 0.954

Note: Asterisk denotes significant t at 5% and a2 is the coefficient for a logistic trend.

where N = N1 + N2 = 40. Since efficiency varies over time we consider the ∗ median efficiency level θ over the period 1984–98 and N1 includes all ∗ firms with efficiency level θ ∗k higher than θ . Likewise for the level effi∗ ciency score θ˜ (t) when we apply the LP model (4.28). One point stands out most clearly in the estimates of Table 4.4. Dynamic efficiency in the form of technical progress and R&D efficiency explain the major share of growth efficiency of the efficient firms. Since these two sources of efficiency are good proxy variables for innovation and access efficiency, it is clear that hypercompetition accentuates the divergence of less efficient firms from the cutting edge growth frontier. The market pressure coefficient (b10 ) is also very important. Table 4.5 shows the output growth of efficient and inefficient firms. The growth-efficient firms exhibit much faster growth than the inefficient firms. Furthermore, the inefficient firms exhibit a logistic trend, with the rate of growth declining at a slow rate. The latter aspect may reflect a tendency to exit from the industry. Table 4.6 compares the two types of efficiency: level efficiency and growth efficiency. The efficient firms reveal a much stronger showing in terms of growth efficiency than level efficiency. This implies that in the computer industry, it is more relevant to apply a dynamic production frontier involving the growth of various inputs and output. The role of R&D investment in raising industrial productivity is most significant in other industries like pharmaceuticals, computer products

Industry Efficiency Analysis 125 Table 4.6

Level efficiency versus growth efficiency Median score ∗ θ˜

Efficient firms Inefficient firms

0.951 0.895

Mean deviation

Coefficient of variation

∗

θ∗

θ

∗

θ∗

θ

0.982 0.891

0.105 0.101

0.043 0.014

0.457 0.356

0.231 0.247

θ

∗

∗ . Efficient firms: θ˜ t∗ = 0.013 + 0.957∗∗ θ˜ t−1 ∗ . Inefficient firms: θ˜ t∗ = 0.028 + 0.867∗∗ θ˜ t−1 Efficient firms: θ *(t) = 0.003 + 0.978∗∗ θ ∗ (t − 1). Inefficient firms: θ ∗ (t) = 0.012 + 0.879∗∗ θ ∗ (t − 1). Note: θ˜ t∗ = level efficiency score; θ ∗ (t) = growth efficiency score. Two asterisks denote significant t -values at the 1% level.

and services. Sengupta and Sahoo (2006) have discussed the level efficiency and growth efficiency of the US pharmaceutical industry over the period 1980–2000. Their results show that the companies that are leaders in growth efficiency show a very high elasticity of output with respect to R&D spending. Sources of growth efficiency in terms of Solow-type technical progress and R&D efficiency are shown in Table 4.7. This suggests the growing need in India to increase R&D investments in computer-related industries, pharmaceuticals and other technologyintensive industries in the manufacturing, transport and service sectors. This would improve the long-run comparative and competitive advantage of India in world trade. The common belief that increased labor productivity due to cost-reducing R&D investments hampers the growth of employment in manufacturing and IT-dependent industries may not hold in the long run. Corley et al. (2002) examined empirical data at finer levels of aggregation (e.g. industry and firm levels) and found that for many high-growth manufacturing industries in the USA and EU (European Union) increases in productivity have been accompanied by increases and not decreases in employment. These high-growth and high-productivity industries in the EU and USA (which include software manufacture, electronics and technologyintensive manufacturing) are generally characterized by high levels of investment, in the form not only of physical capital but also of R&D. R&D investments create new products and processes emphasized by Schumpeterian innovations and also enable workers to absorb these new processes and products so that the learning by doing effects generate

126 India’s New Economy Table 4.7 Annual average levels of output per hour, investment per hour and R&D per hour in manufacturing (1990–8) at 1995 prices Output per hour

Investment per hour

R&D per hour

Canada Low tech High tech Total

21.0 24.9 22.9

2.80 3.45 3.12

0.15 1.69 0.91

Denmark Low tech High tech Total

25.1 27.4 26.3

4.24 4.75 4.50

0.20 2.12 1.20

France Low tech High tech Total

27.1 32.9 30.3

4.22 4.85 4.57

0.71 3.97 2.48

Germany Low tech High tech Total

20.7 25.8 24.1

3.64 3.81 3.75

0.18 2.69 1.82

Italy Low tech High tech Total

29.4 29.5 29.4

5.39 5.69 5.56

0.05 1.24 0.71

UK Low tech High tech Total

23.1 26.2 24.9

2.82 3.48 3.2

0.15 2.24 1.34

USA Low tech High tech Total

27.6 35.5 32.3

2.91 4.68 3.86

0.31 4.21 2.62

See Corley et al. (2002) for details.

cumulative effects. Table 4.7 reports the contributions of R&D per hour for several industrialized countries over the period 1990–98. Table 4.8 summarizes the effect of tangible (physical) and intangible (R&D and human capital) investment on labor productivity in terms of the regression equation LPi(1994–8) = b0 + b1 (RD/L)1990–3 + b2 (I/L)1994–8

(4.29)

Industry Efficiency Analysis 127 Table 4.8 The elasticities of R&D per work hour (the regression of labor productivity on I/L, RD/L and HK (1994–8) for the EU and USA) Industry

High tech Low tech Total

Standardized coefficients (elasticities) Constant

I/L

RD/L

HK

8.25 (1.33) 8.17 (1.94) 8.06 (1.83)

0.47 (5.04) 0.91** (9.51) 0.54** (7.67)

0.30** (3.16) 0.09 (0.82) 0.34** (4.83)

0.16 (1.65) 0.16 (1.33) 0.14* (2.01)

N

Adj. R2

80

0.346

40

0.759

120

0.452

Note: t -ratios are in parentheses; one and two asterisks denote significance at 5 and 1% respectively. N is the number of observations. All coefficients of explanatory variables are standardized and represent elasticities.

Table 4.9 The regression results over 1990–8 (fixed effects model) Industry

I/L

(RD/L)t−u

Adj. R2

High tech Low tech Total

0.409** 0.469** 0.421**

0.152** 0.055 0.151**

0.934 0.983 0.943

where LPi(1994–8) is the level of labor productivity in industry i averaged over four years (1994–8); (RD/L)1990–3 is the R&D spending per worker in industry i lagged four years 1990–3; (I/L)1994–8 is gross fixed capital formation per worker in industry i averaged over the four years 1994–8; HK is the share of R&D scientists and engineers in the labor force at the whole-economy level averaged over 1994–8. When this model (4.29) is re-estimated using a fixed effects model with dummy variables to capture the country effects the results improve in terms of adjusted R2 as shown in Table 4.9. Note that R&D investment has a statistically significant effect on productivity only in the high-tech industries. These are precisely the industries that compete in the international field with a dynamic comparative advantage. As Table 4.7 shows, in terms of R&D per hour, high-tech industries in the USA and UK score about 14 times higher or more than the low-tech industries. A major strength of the US economy is the dynamic competitive advantage that enables the manufacturing sector to increase productivity while simultaneously increasing employment. If the EU countries and


India have to close the competition gap with the USA as measured by income per capita, they must increase productivity while maintaining or increasing employment levels. Modern theories of growth developed by Romer (1986, 1990), Lucas (1993) and others have emphasized the endogenous forces in the form of knowledge capital. Knowledge capital, unlike physical capital, is assumed to be an input in the aggregate production function showing increasing marginal productivity. To the extent that knowledge capital may be viewed as external to the firm but internal to the industry, the competitive market model may still apply, but with endogenous technological change. In contrast to the models based on diminishing returns, growth rates in this new class of endogenous models can be increasing over time. In such a framework the effects of small random disturbances (i.e. shocks) can be applied by the actions of private business, and large countries may grow faster than small ones if they can keep up their dynamic efficiency over time. Romer (1986) and Lucas (1993) have provided long-run empirical evidence in support of this type of endogenous growth theory. At the microeconomic level we have to ask: what makes a firm grow? What causes an industry to evolve and progress? From a broad standpoint two types of answers have been offered. One is managerial, the other economic. The managerial perspective is based on organization theory, which focuses on cost competence as the primary source of growth. The economic perspective emphasizes productivity and efficiency as the basic source of growth. Economic efficiency of both physical and human capital, including innovations through R&D, has been stressed by the modern theory of endogenous growth. Core competence rather than market power has been identified by Prahalad and Hamel (1994) as the basic cornerstone of success in the modern hypercompetitive world. Core competence has been defined as the collective learning of the organization, especially learning how to coordinate diverse production skills and integrate multiple streams of technologies. Four basic elements of core competence are: learn from own and outside research, coordinate, integrate so as to reduce unit costs and innovate so as to gain market share through price and cost reductions. A company’s own R&D expenditures help reduce its long-run unit costs and also yield spillover externalities. These spillovers yield increasing returns to scale as discussed before. Now we consider a dynamic model of industry evolution, where R&D investments tend to reduce unit costs and hence profitability. This profitability induces new entries and also


increased market share for incumbent firms that succeed in following the cost efficiency frontier. From the macroeconomic perspective the spillover technology and externalities of R&D investments play a critical role in the endogenous growth model due to Lucas (1993). While emphasizing the point that Asian growth miracles cannot generally be explained by physical capital accumulation alone, he discussed the productivity-enhancing role of human capital accumulation at school and on the job. This rate of expansion in knowledge in both forms transforms Solow’s level effect into a growth effect. This knowledge capital is a nonrival input with other inputs such as labor and physical capital, since it has strong complementarities with other inputs. An important dimension of the learning by doing impact of spillover technology is that for such learning to continue on a long-run sustained basis, the workers, managers and entrepreneurs must work continually to improve the technology through what Grossman and Helpman (1991b) called the ‘quality ladder effect’. Finally, the spillover technology is closely associated with Schumpeterian innovation in its many forms, e.g. R&D spending, new processes, new products and services and new markets and networks. When the fruits of research are allowed to be exploited more openly and broadly by free global trade, then such trade generates a scale effect, helping to speed up the growth of trading countries. In many ways the spillover technology allows dynamic externalities, which generate dynamic gains from trade. Thus declining computer prices and improved technology-based inputs have helped the NICs (newly industrializing countries) in Asia and China to reap the benefits of spillover technology. As an example of the productivity-augmenting impact of externalities consider the production function estimates of the manufacturing sector in South Korea (1985–94) reported by Sengupta (2005). ln Y = 4.92∗∗ − 0.47 ln R1 + 0.16 ln R2 − 0.57 ln R3 + 1.51∗∗ ln N(adj R2 = 0.96) Here Y is total manufacturing output, R1 through R3 measure the three rival inputs, such as physical capital, energy and materials, and N is a proxy for nonrival input measured by labor employed in the export sector only. Two asterisks denote significant values of t at the 1 per cent level. Clearly the nonrival input (N) has a significant effect in terms of increasing returns, implying that a 10 per cent increase in N generates a 15 per cent increase in total manufacturing output. When our attention


is shifted to the IT-related sector specializing in high-tech goods and services, the impact of nonrival inputs (N) is much higher than 1.51. The experience of Taiwan among the growth miracle countries (i.e. NICs) in Asia is more striking. The scale economies of the IT sector have been diffused through other sectors at a rapid rate. Indirectly it has helped to maintain a more or less equitable distribution of income through gains from global trade. Gort and Konakayama (1982) and Sengupta (2004b) have analyzed dynamic models of diffusion in the production of an innovation that aptly describe the growth of the electronics industry and the IT sector in Taiwan. This diffusion model is of the form Eit = α(n∗it − ni,t−1 ), 0 < α < 1 n∗it = TC(qit∗ )

(4.30)

where Eit is net entry in industry i (either the existing or the new industry) at time t, nit equals the actual number of producers in industry i at t, n∗it equals the expected cost-minimizing number of producers, and the total cost function TC(·) relates to the expected equilibrium output qit∗ of the industry. As the cost efficiency improves in the industry i, it tends to generate the situation n∗it > ni,t–1 , resulting in an increase in net entry. Likewise net exits increase when nn,t–1 > n∗it . Gort and Konakayama applied the simple model (4.30) to manufacturing data (1947, 1954, 1958, 1963, 1967 and 1972) for seven firms in the USA and found that the phenomenon of diffusion of an innovation measured by the number of patents is very well explained by this model, when n∗it is related to other explanatory variables such as technical change, dynamic adjustment costs and the growth of transferable accumulated experience, presumably through the transfer of personnel from existing to new firms. The experience of Taiwan over the period 1995–2000 is summarized in Table 4.10. This table shows the rapid growth of the export sector and the strong emphasis the state has put on expanding education at the primary and secondary levels. Encouraging foreign direct investment from the USA and EU also helped utilize the R&D externalities in hightech products such as electronics and computer products and services. As Table 4.10 shows, IT products and telecommunications equipment grew at an average annual rate of around 15.1 and 14.0 per cent respectively. The Taiwanese Council for Economic Planning and Development prepared a ten-year plan (1980–89) that set specific targets for R&D expenditures. More recently both the government and private entrepreneurs have pursued an international technology policy by cutting costs through productive efficiency and transferring the results of research in government laboratories to the private sector. In

Industry Efficiency Analysis 131 Table 4.10 Economic growth indicators in Taiwan

Export/GDP (%) Gross investment/GDP Export growth rate (%) Literacy rate (%) Secondary school enrollment rate (%) Higher education enrollment rate (%) Output of electronics and IT sector Total (US$ bill) Information products Consumer electronics

1995

1998

2000

42.03 24.93 20.0 21.33 95.93 45.32

41.32 24.72 −9.42 −8.53 97.21 51.06

47.66 22.57 21.98 26.49 99.61 60.85

15.4 (1990) 6.9 (1990) 2.3 (1990)

–

69.8 (2005) 35 (2005) 7.0 (2005)

–

Source: Sengupta (2004b).

terms of complementary human capital accumulation, a deliberate policy of sending trainees abroad and inviting foreign collaborators with lucrative incentives was deliberately pursued. All these show the importance of learning spillover technology, which was utilized by Taiwan at a rapid rate. Two types of industry efficiency analysis are important for the Indian economy in the post-reform period. One is to track down the effect of human capital and actively pursue a policy of expanding secondary technical and general education. This may be easily done through the application of the DEA models discussed above. Second, the externalities of human capital have to be captured and utilized in a systematic fashion. Taking the second policy first, consider a two-sector model with two outputs: X for the export sector (mainly technology-intensive products and services) and M for the domestic sector, with Y as national output. Y =X+M X = G(K ∗ , L∗ , H ∗ ; M)

(4.31)

M = F(K M , LM , H M ; X) Three inputs for each sector are physical capital (K), labor (L) and human capital (H) respectively. Time differentiation yields X = GK K ∗ + GL L∗ + gM M + GH H∗ M = FK K M + FL LM + FX X + FH H M

132 India’s New Economy Table 4.11 Estimates of the ratio FK /GM , 1967–87

FX GM FX /GM

Japan

Korea

Taiwan

0.29 0.16 1.8

0.99 0.32 3.1

0.18 0.20 9.1

where the subscripts on F and G denote the marginal productivities of the respective inputs in the two sectors, i.e. FK /GM of marginal productivities of the two sectors may then be used as a measure of export externality. The estimates of this ratio for Japan, South Korea and Taiwan for the period reported by Sengupta (1998) are shown in Table 4.11. Note that South Korea and Taiwan have a far greater degree of export externality than Japan. More recently this degree of externality has increased for two reasons. One is the increase in Taiwan’s share of world exports and the second is the rise in income elasticity of demand for world export products and services from Taiwan and China. A second method of analyzing the growth effect due to externalities of R&D investment and human capital is to adopt a dynamic version of the DEA model we have discussed before. Consider the Lucas-type growth model γ

Y = K α (uHL)β HE

H = HY + HE , HY = uH

(4.32)

˙ = (1 − u)vH H Here Solow-type technology parameter A is represented by the proportion of human capital (H) in the form of skill HY = uH used in current output (A = uH). Total human capital (H) is composed of that allocated to current output (HY ) and that comprising externalities (HE ), which may be captured by the dummy variable of exports of technology-intensive ˙ products. Growth of human capital H/H equals the product of the proportion of human capital allocated to R&D and learning by doing (1 − u) and its average productivity (v). Clearly when the term (1 − u)v is positive, the human capital grows at a constant exponential rate. Coupled with increasing returns to scale (i.e. α + β + γ > 1) this may lead to high rates of growth in both the short and the long run. Since u is a policy variable, human capital can be made to grow at a higher rate by increasing (1 − u) as a government policy on expenditure on education.


Denoting by z˜ = z˙ /z for z as output Y and x˜ i /xi for the four inputs K, H, L, u, 1 − u, the DEA model for any industry j can be written as min θ, s.t. n

x˜ ij λj ≤ θ x˜ ih ,

y=1

n

y˜ j λj ≥ y˜ h , λj = 1

J=1

λj ≥ 0,

j ∈ In ,

i ∈ Im = {1, 2, . . . , m)

Here n is the number of firms in the given industry and m is the number of ˙ inputs. Growth of output Y/Y = z˜ follows from the production function in (4.32) as Y˙ ˙ ˙ ˙ ˙ + H/H) + β2 (L/L} = α(K/K) + β1 (u/u Y ˙ ˙ ˙ + γ{( − u)/(1 − u)/(1 − u) + H/H} i.e.

z˜ = α˜x1 + β1 (x˜ 4 + x˜ 2 ) + β2 x˜ 3 + γ{˜x5 + x˜ 2 } = a1 x˜ 1 + a2 x˜ 2 + a3 x˜ 3 + a4 x˜ 4 + a5 x˜ 5 + a0

Clearly the firms that are most successful in the export market and also most skill-intensive will contribute most to growth of output. Studies of industry efficiency analysis in this framework would afford more insight into the industries that are on the leading edge of the international production and hence cost frontier. The experience of the miracle growth countries of Southeast Asia provides ample proof in this regard.

5 Efficiency Analysis of Selected Manufacturing Industries

1 Introduction The appropriateness of technologies that should be used in industries in developed countries is a major research area in development economics. Empirical research by economists in the developed countries has firmly established and made us aware of the role of R&D in fostering technological advances in industry, which in turn help achieve faster productivity growth in their countries.1 The less developed countries often bothered little about the long-run cost of the outright introduction of advanced capital-intensive technology available to them, and did not pay any heed to their domestic factor endowment and the efficiency of resource use. Increasing interest in advanced technology under the prevailing institutional frameworks stems, among other things, mainly from three important considerations. First, given the scarcity of capital, to get rid of the short-run cost of R&D and related uncertainties in developing appropriate technology, outright import of foreign technology is considered to be the better option. Second, by doing this, the LDCs have been able to introduce wide varieties of new products for their rising middle class. Third, with an increasing reliance on capital-intensive methods the producers have been able to bypass to some extent labor troubles in organized sectors of these industries. One of the important features of the Indian industrial change from the fifties to the sixties was the relatively increasing emphasis on foreign capital, particularly in the capital good sector. The devaluation episode of the mid-sixties was supposed to work in favor of the inflow of foreign capital. Thereafter import-substituting industrialization was the major policy to check the burden of foreign capital outflow and to develop indigenous technology. But the seventies saw the beginning 134

Efficiency Analysis of Selected Manufacturing Industries 135

of indiscriminate imports of foreign technology not only in the capital goods sector but also in other sectors. The sheltered market phenomenon was the main feature of trade liberalization policy until recently. The EXIM policy, expansion of the open general list and the New Industrial Policy of 1991 were expected to change the industrial scenario of India and lead to economic liberalization. The increasing diminution of industrial efficiency during the seventies and eighties was very much evident in the Seventh Five-Year Plan document, which laid tremendous emphasis on the ‘Sunrise’ industries. These industries are telecommunication, computers, microelectronics, ceramics and biotechnology. It was proposed to attain self-sustaining industrial growth and technological development through, among other things, the adoption of promotional measures to raise the productivity and efficiency of Indian manufacturing industries. It also explicitly mentioned that the protection from international competition found in the earlier semi-insular phase gave rise to high manufacturing costs, which inhibited expansion in the domestic market and rapid export development. The main thrust was to build a conducive environment to encourage and promote greater efficiency, higher productivity and faster industrial growth. There is no denying the fact that the question of efficiency is inextricably related to the appropriateness of the chosen technology, and more often than not the fault may lie in the institutional preparedness for scientific management that is necessary for the smooth functioning of the new technology. The important source of growth and development in an economy is the efficient use of existing resources. In India, awareness of the efficient use of existing resources through appropriate adjustment policy variables was sadly lacking during the early phase of industrialization. In fact, the cost of neglect of efficiency had pushed India to a no-return zone before1991, and the recent liberalization process sought to correct that inefficient regime. For the purpose of examining the impact of liberalization on the performance of Indian industries we have selected three manufacturing industries: textiles, leather and electronics. Textiles and leather are traditional industries, while electronics, which includes computer manufacturing, belongs to the modern sector. Primary data for the analysis are the unit/firm-level information collected from the Annual Survey of Industries (ASI), Government of India. The rest of the chapter proceeds as follows. The efficiency of manufacturing sectors in India during the pre-liberalization period is analyzed in section 2. Section 3 deals with the analysis of efficiencies in two traditional industries during the post-liberalization period. A comparison of


performance in the pre- and post-liberalization periods is made for the textile and electronic industries in section 4. A nonradial efficiency measure of the computer industry is also carried out in this section. Section 5 concludes the chapter.

2 Efficiency of manufacturing industries in India: analysis of the pre-liberalization period It has been argued that very few economic policies pursued by the government of India after Independence were as inevitable as the New Industrial Policy of 1991 and subsequent liberalization packages.2 In a large number of papers attempts have been made to evaluate the strengths and weaknesses of these liberalized policy measures (see, for example, Sandesara, 1991; Subrahmanian, 1991; Patel, 1992; Neogi and Ghosh, 1998; Ray, 2005). But very few empirical studies have been carried out to investigate the intertemporal efficiency movements and inter-industry efficiency variations in India on which the new policy package is based or to assess industrial performance before liberalization. First, one has to be sure whether efficiencies in Indian industries have been falling over time since the industrial globalization program was undertaken. Although neither the policymakers nor the industrialists really do know the actual outcome of opening Indian industries to international competition, the experience of such drastic policy changes in different NICs is mixed. Since our economy is characterized by an acute scarcity of capital, it cannot afford to use the scarce factors inefficiently in the name of industrial modernization. More often than not high output growth does not necessarily mean efficient utilization of resources. The purpose of this study is to reveal intertemporal efficiency variations in 35 use-based industrial groups, covering the entire organized manufacturing sector, over the period 1974–75 to 1987–88. We also investigate interindustry variations in efficiency. Finally, we identify the supply-side factors responsible for technical efficiencies for cross sectional time series pooled data. The methodology applied is a time varying version of the frontier production function (FPF) approach to measuring efficiency with both fixed and variable rankings. Some justification is needed here about the selection of the period of our study. The initial years covered industries under serious autarky and protection and as we move towards the end of our study industries started becoming freer than before, although some protection elements were still present. Hence, the main presumption upon which the liberalization policy package is based, namely increasing inefficiency supposedly as a


result of state control, subsidy and protection from foreign competition, can be appropriately tested from the analysis of industries during the period. Methods of measuring efficiency In this section we estimate efficiencies using the parametric techniques. The stochastic frontier model is a major improvement over the earlier deterministic models and probabilistic models in the sense that it makes a clear distinction between the so-called white noise and inefficiency as such. Aigner et al. (1977) and Meeusen and Broeck (1977) proposed this stochastic model with the idea that the error term is composed of two parts and the form of the function is Yi = f (Xi , β)e(Vi −Ui ) ,

i = 1, 2, . . . , n

(5.1)

The random error Vi has some symmetric distribution to capture the random effect of measurement error and exogenous shock, while technical efficiency relating to stochastic frontier is captured by Ui , which are assumed to be a nonnegative truncation of the N(0, σ 2 ) distribution, e.g. a half-normal distribution, or an exponential distribution. Most of the models employ measures of efficiency derived from Farrell (1957) in terms of the ratio of observed output to the corresponding frontier output, given the level of inputs. Thus, technical efficiency of the ith firm in the context of the stochastic model is the same expression as in the case of deterministic model, TE =

Yi f (Xi , β)e(Vi −Ui ) = e−Ui ∗ = Yi f (Xi , β)eVi

The above model considers only the cross sectional observations of firms. But one problem with this cross sectional data in measuring efficiency is that TE cannot be separated from firm-specific effect, which may not be related to TE. This problem can be avoided if panel data are available. The time-varying model of Battese and Coelli (1991) is defined by Yit = f (Xit , β)e(Vit −Uit )

(5.2)

and Uit = ηit Ui = {e[−η(t−T ) }Ui t ∈ τ(i);

i = 1, 2, . . . , N

(5.3)


where Vit are i.i.d with N(0, σv2 ). The Ui are also assumed to be i.i.d and a nonnegative truncation of the distribution N(µ, σ 2 ) and τ(i) represents the set of Ti time periods among the T periods involved for which observations for the ith firm are obtained. One of the main shortcomings in this approach is that the ranks of the firms in terms of efficiencies remain unchanged over time and the relationship is constrained to be monotonic over time. In Cornwell et al. (1990) the model efficiency measurement focuses on the cross sectional variations over firms and it also allows efficiency to vary over time. This may be considered as an improvement over the fixed ranking model on theoretical virtues. This is done by introducing a flexible function of time into the production function, with coefficients varying across firms. This function represents productivity growth that varies over firms, and it implies that the levels of efficiency for each firm vary over time. The basic model is Yit = αi + Xit β + vit

(5.4)

where the symbols have their usual meanings and αi is the firm effect. The firm effect is then replaced by a flexible parameterized function of time with parameters that vary over firms. We have taken a quadratic function of time following Cornwell et al. (1990): αit = θi1 + θi2 t + θi3 t 2 Then the model can be written as Yit = Xit β + Wit δi + Vit ,

(5.5)

where Wit = [1, t, t 2 ]

and

δi = [θi1 , θi2 , θi3 ]

Naturally, this model allows for time-varying efficiency with variable rankings of the firms. By applying this model we have relaxed the assumption of fixity of rankings of the previous model and at the same time retained the advantage of using panel data. In our stochastic model, we have estimated the time-varying model using both Cobb–Douglas (C–D) and translog specifications. The general form of the time-varying model was discussed earlier. Here we have estimated the FPF using stochastic models and we have taken the C–D


and translog production functions. The forms of the cross sectional C–D and translog production functions are respectively as follows: log Yi = α0 + βK log Ki + βL log Li − u

(5.6)

and log Yi = α0 + βK log Ki + βL log Li + (1/2)βKK ( log Ki )2 + (1/2)βLL ( log Li )2 +βKL log Ki log Li − u,

i = 1, . . . , n

(5.7)

where K and L represent gross fixed capital stock (GFCS) and all employees respectively. The multiplicative error term is e−u =

Y f (X)

(5.8)

Hence, e−u must lie between zero and unity. And, naturally, u takes values between zero and infinity. Since we have used panel data, a time dimension is added to the model. The corresponding error term is subdivided into two parts: one for statistical noise and the other for the firm- or industry-specific effect. The error term now becomes: e(Vit −Uit )

(5.9)

The exponential specification of the behavior of the firm or industry effect over time is a rigid parameterization. It implies that TE of the firm or industry TEit = e−Uit

(5.10)

is a double exponential function of time (see equation 5.3) for a given firm or industry (see Battese and Coelli, 1991). The estimation of the stochastic frontier function (time-varying model) and the prediction of corresponding technical efficiencies of the industries in question over time are calculated here using a computer program developed by Coelli (1991). Insofar as the Cornwell et al. (1990) model is concerned, timevarying firm productivity and technical efficiency are estimated from the residuals based on the within estimated of the C–D production function. log Yit = αit + βk log K + β1 log L + vit ,

(5.11)


where Y = value added (real), K = GFCS (real) and L = all employees. In order to estimate δi of equation (5.5) the residuals for industry i are regressed on Wit . The fitted values of these regressions provide an estimate of αit . The estimate of the frontier intercept at time t and the industry-specific level of TE for the ith industry at time t are respectively aˆ t = maxj (aˆ jt )

and

uˆ it = αˆ t − αˆ it

Thus the relative efficiency levels of the industries at time t can be derived from the estimates of uit .3 A very brief review of the studies on technical efficiency of Indian industries during the pre-liberalization period may be brought into focus here. There is a dearth of studies relating to efficiency in Indian industry, particularly during the pre-liberalization period. However, the following works are worth noting here: Little et al. (1987), Bhavani (1991) and Singh (1991). Among these, the first two studies are related to smallscale industries, while the last one considers only the power sector. They have considered only cross sectional data. In addition to these, Ahluwalia (1985) tried to infer efficiency from the growth rate of TFP and concluded that there was evidence of declining efficiency in Indian industry in recent years. We analyze intertemporal efficiency variations across industries in order to understand the dynamics of the industrial process in India during the seventies and eighties. Data The basic data for our study were collected from three principal sources: the Annual Survey of Industries (ASI), National Accounts Statistics (NAS) and the Indian Labour Journal, all published by the Government of India. This data set is supplemented by India Database and The Economy, by H. L. Chandhok and The Policy Group. As noted earlier, our period of study refers to the years 1974–75 to 1987–88. The gross measure of value added is obtained by adding the net value added and depreciation of the corresponding years as given in the ASI. Capital is taken as gross fixed capital stock estimated by the perpetual inventory accumulation method (see Appendix 5.1). Labor is represented by total number of persons employed. We have taken both total workers and total employees as given in the ASI. The difference between the two, according to the ASI, are taken to be skilled laborers. Total emolument given in the ASI measures the return to labor. These ASI figures, all given in nominal terms, are converted into real value using suitable deflators. Gross value added is deflated by the


commodity-specific wholesale price indices taken from India Database. Estimated gross fixed capital stock is deflated by wholesale price indices of machinery, machine tools and parts. Real wages are obtained by deflating the total emoluments by the corresponding consumer price indices for industrial laborers taken from Indian Labor Journal. The figures are the total of all firms in each group. We have divided these by the corresponding number of firms in each group, thereby reducing them into average firm figures for each industry group.4 Empirical analysis We have estimated time-varying technical efficiencies for 35 broad industrial groups for two models as defined earlier. The hypothesis regarding the distribution of the random variables associated with Vit and Uit suggests that traditional average production function is not an adequate representation of our set of panel data. Further, since only the hypothesis µ = 0 is accepted and η = 0 is rejected the model is proved to be time variant and the distribution of industry effect is half-normal. Hence, the hypothesis of time-invariant technical efficiency in Indian industry is rejected. Technical efficiencies (TE) of the fixed ranking model with the specification of half-normal distribution of the error term with a C–D specification are estimated for 35 industries for four different years. Table 5.1 gives the summary statistics of the values of efficiencies for these years. First, the table reveals that the average values of TEs for these industries have registered downward trends over the period of our study. Second, there are considerable variations of TEs across industries. Given the assumption that industry effect changes exponentially over time with η < 0, the TEs decrease at an increasing rate. It is, therefore, expected that the predicted TEs diverge over time. Our result shows that the coefficient of variations of efficiencies increases from 0.38 in 1974–75 to 0.47 in 1987–88.

Table 5.1 Summary statistics of technical efficiencies of Indian industries with fixed rankings: time varying (Cobb–Douglas) model Statistics

1974–5

1978–9

1983–4

1987–8

Average Max Min Coefficient of variation

0.5783 0.9796 0.1210 0.3823

0.5571 0.9780 0.1015 0.4084

0.5313 0.9757 0.0798 0.4411

0.5086 0.9737 0.0647 0.4734


Among the 35 industries, 16 remained above the yearly mean efficiency throughout the period. It has been observed that most of these industries come from the consumer goods sector; only three come from the capital goods sector. Those from the capital goods sector are (a) medical, scientific, photographic and optical instruments, (b) electrical machinery, apparatus, appliances, supplies and parts and (c) machinery, machine tools and parts. Among the 16 industries 13 comprise consumer good industries, and drugs and medicines achieved the highest efficiency, which ranged from 0.9796 in 1974–75 to 0.9737 in 1987–88. In order of rankings, perfumes, cosmetics etc. stood third, while radio, TV, tape etc. stood fourth. According to the above model, the rankings of the industries remained the same over the entire period. In order to permit different rankings of the industry in terms of efficiency at different time points, we have applied the Cornwell et al. (1990) model to the same set of data for 14 consecutive years. As mentioned earlier, we have used a C–D production function and estimated the parameters by within estimators. The values of the coefficients of log(K) and log(L) and the corresponding t statistics (in parentheses) are 0.4809 (19.89) and 0.6497 (12.14) respectively, and the value of R¯ 2 is 0.59. The F-test of the ratios of restricted and unrestricted production function reveals that the sum of the coefficients is not significantly different from unity at the 99 per cent level. The relative efficiencies have been derived from our estimates for the 35 industries over 14 years. The summary of the values of TEs at four different time points (1974–75, 1978–79, 1983–84 and 1987–88) are presented in Table 5.2. The important findings from the efficiencies and rankings on the basis of variable ranking model are as follows. The values in Table 5.2 indicate that the average values of efficiency declined and the dispersion of efficiency among the industries increased over the period. Out of 35 industries, the efficiencies of 26 industries fell substantially over time.

Table 5.2 Summary statistics of technical efficiencies of Indian industries with variable rankings: time varying (Cobb–Douglas) model Statistics

1974–5

1978–9

1983–4

1987–8

Average Max Min Coefficient of variation

0.4148 1.0000 0.0922 0.4292

0.4732 1.0000 0.1262 0.4054

0.4714 1.0000 0.1088 0.4683

0.3774 1.0000 0.0581 0.5783


The industries with substantial declines in efficiency were perfumes, costmetics etc., matches, gas and steam, pulp, paper containers and board, rubber, plastics, petroleum and coal products, etc. Industries with rising efficiencies were footwear from vulcanized and moulded rubber, radio, TV, tape recorder, telephone etc., medical, surgical, scientific, photographic and optical instruments etc. The industries that substantially improved their rankings from the first to last year come from the modern sector, e.g. medical, surgical, scientific, photographic and optical instruments, electrical apparatus, appliances etc., radio, TV, tape recorder etc., motorcycles, scooters and parts etc. In terms of rankings, the sharp deterioration occurred mainly in the traditional industries. There are wide variations in the predicted TEs of the industries. We now examine some economic factors – basically internal to the industries – that can be used to explain these efficiency variations.5 Suffice it to say that there are other important factors that are external to the industry, namely demand forces, technology, information quality, rate of tariff protection, degree of competition, management quality, government policy and the like (see Clague, 1970; White, 1978; Patel, 1992). The nonavailability of data restricts us to using only the internal factors. However, even with these internal factors we have come out with quite a large percentage of variations in efficiencies being explained. We have considered labor productivity, skill, real wages, profit, capital intensity, capital utilization and industry dummy as independent variables and TEs estimated from the variable ranking model as the dependent variable.6 All variables except the dummy are transformed into logarithmic forms. All the factors except real wages appear to be highly significant in all cases. The regressions are quite satisfactory, as evident from the high values of R¯ 2 (ranging from 0.7758 to 0.7994). Since the variables are in log form, the coefficients represent the corresponding elasticity estimates. It has been found that labor productivity, skill capacity utilization and profit play a positive role in enhancing the efficiency of industries. The coefficients of industry dummy indicate that consumer goods industries are in general more efficient than capital and intermediate goods industries. This finding might have been influenced by the rising demand for consumer durables in recent years in India, which we could not incorporate in our analysis. Capital intensity, quite contrary to general beliefs, here shows a negative relationship with TEs. Coondoo et al. (1993) show that capital coefficients (K/Y and K/L) have been rising at very high rates uniformly in Indian industries irrespective of their technological status. This phenomenon is accompanied by inefficient use of resources and the rise in capital coefficients was not the


result of a technological upgrading of industries. Moreover, since capital intensity is found to be negatively related with efficiency and given that the public sector is plagued by huge subsidies and employment obligations, this negativity may be taken as a proxy for public sector dominance in Indian manufacturing industry. (Some of the results are discussed in Neogi and Ghosh, 1994.)

3 Efficiency in the Indian textiles and leather industries: post-liberalization scenario In this section we analyze the pattern of changes in efficiency of two traditional industries, namely textiles and leather. First, we examine the levels of technical efficiency of individual firms from the Indian textile garments industry (NIC code 235) using establishment-level data from the ASI covering the period 1989–90 through 1997–98. Then, we undertake a similar exercise for the leather industry (NIC code 291) for the same period. This allows us to examine how the levels of technical efficiency have changed in the post-reform years. There are several reasons why the textiles industry deserves special attention. In the first place, as one of the most important nondurable consumer goods, textiles account for 14 per cent of the total industrial production and 18 per cent of the total employment in industry in India. Moreover, 27 per cent of India’s export earnings are from textiles.7 Thus, efficiency in the textiles industry is of special importance for India’s economic position in the international market. An added, and in some ways more important, point to note in this context is that the multifiber agreement (MFA) that permitted countries to impose bilateral export quotas in textiles formally ended on 1 January 2005. While opening up of the US and EU markets can be a golden opportunity for Indian exporters to make inroads in these markets, they are equally exposed to the risk of being marginalized in the face of severe competition from China and other exporting countries unless they can retain and enhance their cost competitiveness. An audit of the levels of technical efficiency along with an analysis of the determinants of efficiency is, therefore, of interest to both academics and policy. While the competitive position of Indian textiles in the export market has attracted considerable attention, there is little in the existing literature that addresses the question of productivity and/or efficiency in the industry from the perspective of the technology. In a notable exception Parmar and Singh (2003) estimated a stochastic frontier production


function using firm-level data for 694 companies from the textile industry. Their sample covered the period 1989–99 and the input–output quantity data were constructed from annual financial statements of the companies. Output was measured by value added,8 while labor, capital and material inputs were measured, respectively, by the wage bill, the sum of interest, repair and replacement costs of machinery, and material expenses (including energy costs). They found that the mean level of technical efficiency for the entire sample was 0.55. Across groups, average efficiency was the highest at 0.58 for firms in the medium asset size group (100–500 million rupees). Leather is another important traditional industry and has a significant role in the Indian economy for its massive potential of employment generation and exports earnings. There has been an increasing emphasis on its planned development for efficient use of raw materials and for maximizing the returns from both domestic and export markets. The industry has been changing its strategy from a mere exporter of raw materials to an exporter of high-value finished products. During the past few decades the home market for Indian leather goods also expanded at a moderate pace. The policies taken by the government of India since 1973 have been instrumental in the development of the leather industry in general. During the phase of globalization of the Indian economy after 1991, the industry is poised for further growth to achieve a greater share in global trade. According to an EXIM bank report in 2004–05, the industry recorded a 5.8 per cent export growth to reach a level of US$2.3 billion. However, its share in total exports has declined in percentage terms from a high of 7.99 per cent in 1990–91 to 2.89 per cent in 2004–05. From 1991–92, India has been exporting only finished leather because of export restrictions on semi-finished leather. Total leather and leather manufactures exports stood at Rs.102,860 million in 2004–05. Leather footwear is the largest component of leather exports, with a share of 26 per cent. from US$27 billion in 2000 to nearly US$34 billion in 2004. India’s major competitor in the world market of leather goods is China. Particularly in leather finished goods, China’s export share is much higher than that of India. However, India has some distinct advantages in the production of leather goods in terms of scale of production. Since the Indian leather industry has its advantage of raw material and labor resources, Indian leather exporters should pay greater heed to marketing to increase their share, which should be consistent with their inherent strength and potential. But this has to be done against many constrains imposed by the developed countries of the West. Some of the major issues that affect


the sector are cost escalation and environmental problems. The modernization of existing technology and the efficient use of resources are also very important issues in the competitive world market. Since most of the industrial units belong to the small-scale sector, the leather industry has tremendous potential for employment generation. Direct and indirect employment in the industry is around 2 million. Skilled and semi-skilled workers constitute nearly 50 per cent of the total workforce. In recent years the government of India has announced various policies to make the leather industry more productive and competitive in the world market.9 The present study extends the literature on measurement of efficiency in the Indian textiles industry in a number of important ways. First, we employ the nonparametric method of data envelopment analysis (DEA) instead of stochastic frontier analysis (SFA), where an explicit specification of a parametric production function is required. Even in this strand of the literature, our study differs from other DEA applications in that we obtain nonradial Pareto–Koopmans measures of efficiency instead of the radial measures that are either input- or output-oriented. Ours is a generalized measure of overall efficiency that simultaneously incorporates both unrealized potential increase in the output and feasible reduction in any individual input. Our overall efficiency measure can be decomposed into input-oriented and output-oriented components. Moreover, we are able to assess, for each individual input, the proportionate reduction possible without any increase in any other input or a decrease in the level of the output. Second, we propose a new method of explaining the variation in technical efficiency across observed firms (accommodating the fact that technical efficiency cannot exceed unity) without resorting to a Tobit analysis. Finally, we use establishment-level data from the ASI to measure inputs and outputs. This is a distinct improvement over studies that construct the relevant variables from company-level financial data. This is especially true for companies that produce and sell products that often correspond to widely different industrial classification codes. After the enactment of deregulation policies the industries had to find their ways to survive in the market by increasing efficiency and productivity. India is a typical example of a nation where each state has some special characteristics that influence the growth and performance of industries in different ways. Further, the spread of type of industries in each state is not similar. Each state has own industrial policy, and though there is a broad agreement in the policies of the states their approaches


are not always the same. As a result the growth and performance of industries in the different states do not always move in the same direction. Since the efficiency and productivity of the industries also depends on the labor laws and the government’s attitude towards implementation of labor laws, the liberal states suffer from the inefficient use of labor in industries. The performance of production units also depends on the organization and ownership type. It is a common belief that public sector industries in general are inefficient compared to the private sector industries. But inefficiencies are not confined to the public sector. Some recent studies argue that inefficiency is an all pervasive phenomena even in developed countries and efforts should be taken to increase the efficiency of production units by the appropriate use of inputs in the production process. The main advantage of using firm-level data is that the information loss will be much less compared to aggregate data. Since the information is available for each unit of the industry and information about the location and ownership type of each unit is also available the analysis is carried out in the following areas: 1 2 3 4 5

The overall efficiency trend of the industry during the period. An input-specific analysis of efficiency of each group of firms. A state-specific analysis of efficiency. An ownership-type specific analysis of efficiency. The forces behind the efficiency variation of production units.

In this study an effort has been made to understand the nature of inefficiency in the textiles and garments industry in India during the post-liberalization period. The study is based on unit- or firm-level information on production in the industry, and this is thought to be the first attempt to measure the efficiency of each input separately and of output using firm-level data. The section is divided into the following subsections. The next subsection deals with data and methodology. Empirical analyses are done in the following subsection and concluding remarks are made in the last subsection. Data and methodology Data for the analysis of efficiency were collected from the Industrial wing of the Central Statistical Organization, Government of India. Very recently they have released unit-level data for different industrial codes. For the purpose of analysis we collected unit-level data on the textiles


industry (NIC 265) and leather industry (NIC 291) for the period 1989–90 to 1997–98, barring one year, 1995–96, for which data are not published (see Appendix 5.2). These unit-level data have a state code and from these codes the location of the units can be identified. In some states firms with NIC code 265 and 291 are not present and in some states the numbers of units are very small compared to other states. From the distribution of the number of units in each state the major textile producing states were selected for this analysis. For each unit total production in value terms was taken as a measure of output. Average of opening and closing stock of capital was taken as a measure of capital. Total employment was divided into production worker and nonproduction worker and the proportion of nonworkers to workers was considered as the skill factor of a unit. Fuel and materials consumed are other two inputs of production. Since efficiency is a relative concept and we have taken the cross sectional data to measure the efficiency of units the values are not deflated by any price index. Radial and nonradial measures of technical efficiency Consider the production possibility set: T = {(x, y) : y can be produced from x}

(5.12)

where x is an n-element input bundle and y is an m-element output bundle. Unlike parametric models, the non-parametric approach DEA does not specify the production possibility set explicitly. Instead, it only assumes that: (a) all observed input–output bundles are feasible; (b) inputs are freely disposable; (c) outputs are freely disposable; and (d) the production possibility set is convex. There are two alternative approaches in DEA to estimate the efficiencies from the production possibility set defined in (5.12). One is input-oriented and the other is output-oriented. The input-oriented DEA (BCC) model can be written as min θ, s.t. λj yj ≥ y 0 λj xj ≤ θx0 λj = 1 λj ≥ 0, j = 1, 2, . . . , N

(5.13)


The output-oriented VRS model can be written as max φ, s.t. λj yj ≥ φy 0 λj xj ≤ x0 λj = 1

(5.14)

λj ≥ 0, j = 1, 2, . . . , N Both of these measures are radial measures because all inputs are contracted or all outputs can be expanded by the same proportion. However, due to the presence of slack some input combinations are inefficient because one can produce the target output from a smaller amount of at least one input. If reduction of any input in the input set causes the output level to be infeasible we can call the part of the isoquant as efficient subset of the isoquant. In a similar fashion we can define the efficient subset of output isoquant of input combination when no output slack is present in the output isoquant. The radial measure of output-oriented technical efficiency does not reflect the unutilized potential for increasing any output due to the presence of slack. On the other hand, the nonradial measure takes account of this output slack while estimating the technical efficiencies of DMUs. The problem of slacks in any optimal solution of a radial DEA model arises because we seek to expand all outputs or contract all inputs by the same proportion. In nonradial models, one allows the individual outputs to increase or the inputs to decrease at different rates. Färe and Lovell (1978) introduced the following output-oriented, nonradial measure of technical efficiency, which they called the Russell measure:10 RMy (x0 , y 0 ) = where ρy = max

1 , ρy

1 φr m r

s.t.

λj yrj ≥ φr yr0 ;

r = 1, 2, . . . , m;

j

j

λj xij ≤ xi0 ;

I = 1, 2, . . . , n;

(5.15)


λj = 1;

λj ≥ 0; j = 1, 2, . . . , N.

j

When output slacks do exist at the optimal solution of a radial DEA model, the nonradial Russell measure falls below the conventional measure obtained from an output-oriented BCC model. That is, because the radial projection is always a feasible point for this problem, ρy ≥ φ∗ . Hence, the nonradial Russell measure of technical efficiency never exceeds the corresponding radial measure. The analogous input-oriented nonradial measure of technical efficiency is:11 RMx (x0 , y 0 ) = ρx , where ρx = min 1n

(5.16)

θi

i

s.t.

λj yrj ≥ yr0 ;

r = 1, 2, . . . , m;

j

λj xij ≤ θi xi0 ;

i = 1, 2, . . . , n;

j

λj = 1;

λj ≥ 0; j = 1, 2, . . . , N.

j

The optimal solution projects the observed input bundle x0 onto the bundle x∗ = (θ1∗ x10 , θ2∗ x20 , . . . , θn∗ xn0 ) in the efficient subset of the isoquant of the output y 012 (Ray, 2004). Box–Cox model for explaining efficiency Consider a semi-parametric stochastic frontier y = f (x)τ

(5.17)

where x is an n-vector of inputs, y is a scalar output, and τ ∈ (0,1) is a measure of technical efficiency of a firm that uses input x but produces output y ≤ f (x). In stochastic frontier analysis (SFA), one specifies an explicit for of the function f (x) but allows the frontier to move up or down due to random shocks. Thus a stochastic frontier may be conceptualized as y ∗ = g(x; v) = f (x)ev

(5.18)


where v may be either positive or negative. The actual output relates to the stochastic frontier as y ∗ = g(x, v)e−u = f (x)ev−u ;

u≥0

(5.19)

Thus, τ = e−u ≤ 1 is a measure of the firm’s technical efficiency. In SFA, it is customary to specify the natural log of y as the dependent variable and a log-linear or a log-quadratic function of x with a composite error term appended to it on the right hand side. Typically, one assumes the usual N(0,σv2 ) as the probability distribution of v and a truncated Normal distribution N + (µ, σu2 ) for the one-sided error, u. When µ equals zero, we get the familiar half-normal distribution. In data envelopment analysis (DEA), one the other hand, one constructs a nonparametric graph of the technology as the upper boundary of the free disposal convex-hull of the observed input–output combinations. The piece-wise linear function is treated as the nonparametric frontier h(x) and the observed output from the input x relates to it as y = h(x)/φ;

φ≥1

(5.20)

Alternatively, h(x) = y;

φ≥y

(5.21)

Hence, we can write the model as y = h(x) − ε;

ε≥0

(5.22)

As can be seen, y ∗ = h(x) is a deterministic frontier. Even if we allow random noise alongside inefficiency, the DEA efficiency score has to be seen as drawn from the truncated form of the distribution of the composite error distribution. This has prompted many researchers to specify a Tobit model explaining variation in measured efficiency scores across firms in terms of observable heterogeneity. There are two major problems with this approach – one is conceptual and the other is practical. First, there is no obvious censoring mechanism that results in zero values of ε (alternatively values of φ equal to one). Thus, applying the Tobit model appears to be an ad hoc correction of the problem. Second, even after the Tobit regression has been estimated, it is not clear how one can extract the pure efficiency component purged of the systematic factors affecting the DEA scores from the results. In this section, we take a different approach and


model the efficiency score obtained from any given sample as a random variable with a naturally one-sided distribution (Ray and Neogi, 2007). Consider the model φ = 1 + e−w ;

w = v − u;

(5.23)

where v ∼ N(µ(z); σv2 and u ∼ N + (0,σu2 ) Clearly, φ ≥ 1 for all values of (z, u, v). We can easily write v as v = µ(z) + η

where η ∼ N(0, σv2 ).

(5.24)

Combining (5.22) and (5.23) we get − ln (φ − 1) = µ(z) + η − u.

(5.25)

This is a straightforward composite error model that can be estimated using standard maximum likelihood procedures. There is one complicating problem, however. Whenever the value of φ equals unity, the dependent variable is undefined. A possible way to overcome this problem is to replace ln (φ − 1) by its Box–Cox transformation (ϕ − 1)λ−1 − 1 = lim ln (ϕ − 1). λ→1 λ−1 Thus, the model becomes q≡

1 − (ϕ − 1)λ−1 = µ(z) + v − u. λ−1

Writing µ(z) = Xβ we finally arrive at the model q = Xβ + v − u.

Empirical analysis We have shown that in a nonradial measure slacks of inputs and outputs are taken into account in measuring efficiency. In a nonradial measure


one can obtain the efficiency of each input and output separately. In this analysis we have one output and four inputs. Thus in a nonradial measure of efficiency we can estimate the efficiencies of each input separately and find out the relative importance of each input in total efficiency. Efficiencies of individual units are estimated for each year and for each unit seven different efficiencies are calculated. The efficiencies are for production worker, nonproduction worker, capital, fuel, materials, total input and total output. The textiles industry All-India efficiency Table 5.3 describes the all-India average efficiency of the textiles industry as a whole for the years 1989–90 to 1997–98. Efficiency figures for production workers suggest that there was a rising trend during the initial period of liberalization and a sharp fall in the year 1994–95. After that efficiency rose but did not reach the peak level. The efficiency of nonproduction workers, however, fell slowly during the period but not without fluctuations. Efficiency figures for capital also show a fluctuating trend during the period, with a maximum value of 0.8129 in the year 1992–93 but a sharp fall to only 0.3673 in the following year. Then in subsequent years the figure rose to a moderate value of 0.6052. Efficiencies for fuel and materials showed a similar trend during the period and the efficiency of materials also reached a peak in the year 1992–93. Total input efficiency figures show that it reached a peak in 1990–91 then fell slightly to 0.7942 in 1992–93. In the following year there was a sharp drop in efficiency to 0.5057. During the later period efficiency rose but it was still well below the level of earlier years. Output efficiency, however, remained almost stagnant during the period of study around 0.7. This account of efficiency indicates that liberalization affected the textile industry as a whole adversely and there was no sign of improvement during the period. Efficiencies are now classified into two sets. In the first set units are grouped according to state codes. There are 33 states for which data have been collected. However, units are not available in all the states. We have taken six major textile-manufacturing states, namely Delhi, Gujarat, Karnataka, Maharashtra, Tamil Nadu and Uttar Pradesh for our analysis, and average efficiencies of the state and their coefficients of variations were calculated for all the years of the study. The second set of units are classified according to the ownership pattern of each unit. There are six type of ownership defined by CSO, namely wholly central government, wholly state and local government, central and

154

Table 5.3

Average efficiencies of manufacturing units in the Indian textiles industry

Year Production worker Nonproduction worker Capital Fuel Materials Total input Total output

1989–90

1990–1

1991–2

1992–3

1993–4

1994–5

1996–7

1997–8

0.7062 0.8258 0.7946 0.7527 0.9024 0.7964 0.6877

0.7509 0.7070 0.7853 0.8775 0.9373 0.8116 0.6949

0.7670 0.6417 0.6564 0.3958 0.6422 0.6206 0.6845

0.7826 0.6533 0.8129 0.7873 0.9350 0.7942 0.6335

0.4846 0.6269 0.3982 0.3673 0.6516 0.5057 0.7577

0.6521 0.5127 0.6014 0.6214 0.9676 0.6710 0.7291

0.5707 0.6257 0.5050 0.4714 0.4725 0.5291 0.6969

0.5925 0.6760 0.6052 0.4937 0.8239 0.6383 0.7987


state government and/or local government jointly, joint sector public, joint sector private and wholly private. In most of the years the units of all these types of ownership are available for the textiles industry. For all these groups average efficiencies are calculated for each of the years of study. State-specific analysis First we concentrate our analysis on state specific average efficiencies (Table 5.4). It is found from the figures that in terms of efficiency of production workers Delhi registered the highest rank for most of the years among the six states. In most of the states the average efficiency fell in the later years of the study after a rise for the early liberalization period. In terms of efficiency of nonproduction workers the average value for Uttar Pradesh is highest for three different years. Efficiencies for later years are small compared to the earlier years. Efficiency had a rising trend in the last two years for all these states except Gujarat. Efficiency of capital input figures show that the magnitude and trends are similar for all the states. There was an initial rise in the value and in the year 1993–94 there was a sharp fall in efficiency. Again there was a rising trend during the later years of the study. Total input efficiency figures for the selected states show a slight upward trend up to 1992–93 with a fall in 1991–92. Then there was a sharp fall in efficiency in 1993–94 and a downward trend in efficiency during the later years of the study. The magnitude of the efficiency shows no remarkable difference among the states. However, the figures for Delhi, Maharashtra and Uttar Pradesh are slightly higher than those for the other states. The output efficiency figures of the states indicate a rising trend during the period after liberalization but not without fluctuations. Comparison of the efficiency level among the states indicates that Delhi and Maharashtra are in the upper tier, while the other states are almost in the same position. To summarize, both the input and output efficiencies of the textiles industry in most of the states in India registered a fall in the later years after a rise in the initial years of liberalization. The basic advantage of the nonradial measure is that efficiencies for each input can be estimated separately. It has been found from this analysis that those states which show a better utilization of production workers are not capable of maintaining higher efficiency utilization of nonproduction workers. In terms of input and output efficiencies the ranks of the states for the years are similar. As expected, Delhi, Maharashtra and Gujarat, as major textile-producing states, perform better than other states.

Table 5.4

Average scale of operation and efficiency of manufacturing units in the textiles industry in India Gujarat

Karnataka

Maharashtra

Tamil Nadu

Uttar Pradesh

34,733,000 39,417,990 39,366,170 49,140,710 85,333,180 83,378,860 133,016,500 186,114,500

35,130,930 35,748,780 38,526,760 36,961,720 42,776,470 38,966,490 151,919,500 152,812,000

19,394,520 21,752,020 23,242,120 24,976,590 43,137,180 42,116,650 44,166,910 109,830,100

16,406,630 17,073,930 20,374,590 25,335,200 91,470,480 75,729,660 94,640,260 193,389,400

14,341,100 13,251,480 18,442,780 20,329,490 46,837,140 53,638,360 75,111,820 207,041,500

17,792,590 16,946,480 20,441,980 24,991,650 28,596,040 46,502,210 75,375,030 206,436,600

Efficiency of production workers 1989–90 0.7723 1990–1 0.8961 1991–2 0.8924 1992–3 0.9166 1993–4 0.5623 1994–5 0.7145 1996–7 0.6435 1997–8 0.6908

0.5592 0.6082 0.6659 0.7865 0.4982 0.6492 0.5877 0.6208

0.5493 0.5734 0.6325 0.6446 0.3336 0.5810 0.5013 0.6158

0.8336 0.8840 0.7823 0.8817 0.5247 0.7140 0.6379 0.6534

0.6202 0.6214 0.7286 0.6296 0.3759 0.5444 0.5184 0.4453

0.8268 0.8334 0.7636 0.9170 0.5773 0.7009 0.5812 0.5298

Efficiency of nonproduction workers 1989–90 0.7794 1990–1 0.7113 1991–2 0.5296 1992–3 0.5851 1993–4 0.6323 1994–5 0.5570 1996–7 0.5780 1997–8 0.6712

0.8326 0.7809 0.5864 0.6789 0.6040 0.4750 0.7728 0.7291

0.7605 0.6557 0.6598 0.6295 0.5663 0.4487 0.6001 0.6899

0.8869 0.7617 0.6512 0.7532 0.6566 0.4971 0.6756 0.6843

0.8311 0.6569 0.7488 0.6502 0.5749 0.4679 0.6461 0.6794

0.8472 0.7874 0.7239 0.6701 0.7106 0.5863 0.5353 0.6361

Average output 1989–90 1990–1 1991–2 1992–3 1993–4 1994–5 1996–7 1997–8

156

Delhi

0.8298 0.7732 0.6306 0.8615 0.3633 0.5760 0.5268 0.6636

0.8282 0.8075 0.7409 0.8053 0.3362 0.5823 0.5208 0.6960

0.8458 0.7992 0.6448 0.8230 0.4054 0.6099 0.5468 0.5440

0.7171 0.7465 0.7266 0.8252 0.4087 0.5699 0.5382 0.6360

0.7794 0.6699 0.5573 0.6736 0.3436 0.5531 0.4641 0.5525

Total input efficiency 1989–90 0.7843 1990–1 0.8453 1991–2 0.5543 1992–3 0.8101 1993–4 0.5313 1994–5 0.7028 1996–7 0.4833 1997–8 0.6283

0.7445 0.7804 0.5625 0.7842 0.5041 0.6528 0.6123 0.6477

0.7532 0.7625 0.6238 0.7579 0.4248 0.6532 0.5357 0.6911

0.8494 0.8548 0.6383 0.8344 0.5091 0.6658 0.5673 0.6375

0.7719 0.7816 0.6728 0.7798 0.4764 0.6381 0.5314 0.6063

0.8145 0.8108 0.6421 0.7807 0.5609 0.6764 0.4746 0.5842

Total output efficiency 1989–90 0.8261 1990–1 0.8432 1991–2 0.8402 1992–3 0.7937 1993–4 0.8165 1994–5 0.8543 1996–7 0.7899 1997–8 0.8701

0.6756 0.6381 0.6625 0.6561 0.7109 0.7001 0.5667 0.7765

0.7318 0.7021 0.6661 0.6149 0.8023 0.6166 0.5972 0.7307

0.7414 0.7219 0.6672 0.6489 0.7471 0.7198 0.7425 0.8390

0.5785 0.6011 0.6254 0.5446 0.7209 0.7040 0.6946 0.8132

0.6192 0.6915 0.6073 0.6092 0.6786 0.6683 0.7215 0.8151

157

Efficiency of capital input 1989–90 0.7760 1990–1 0.8330 1991–2 0.5877 1992–3 0.8507 1993–4 0.4251 1994–5 0.6763 1996–7 0.4284 1997–8 0.5149

158

Ownership-specific analysis It has often been claimed that manufacturing units in the private sector are more efficient than public sector enterprises. The units producing textiles in selected states in India were classified in terms of ownership and a comparison of efficiencies is discussed in this section. Table 5.5 presents the values of efficiencies of inputs and outputs of units for different categories. The output efficiency figures indicate that the efficiencies in the central government units were higher than those in the private sector industries except in

Table 5.5 in India

Ownership-wise efficiency of manufacturing units in textiles industries

Central

State

Central–state Joint–public Joint–private Private

Efficiency of production workers 1989–90 0.7911 0.5670 1990–1 0.7292 0.5232 1991–2 0.7036 0.8373 1992–3 1.0000 0.8224 1993–4 0.8226 0.4143 1994–5 0.5927 1996–7 1.0000 0.7733 1997–8 1.0000 0.7060 Average 0.8638 0.6545

0.2292 1.0000 0.7453 0.7888 0.4805 0.0785 0.5686 1.0000 0.6114

0.9412 0.6535 0.8721 1.0000 0.3969 0.6883 0.6059 0.5256 0.7104

0.6720 0.8435 0.6606 1.0000

Efficiency of nonproduction workers 1989–90 0.9669 0.8231 0.1061 1990–1 0.9033 0.3609 0.9423 1991–2 0.6581 0.3002 0.6968 1992–3 0.6406 0.5295 0.7896 1993–4 0.8779 0.8013 0.4420 1994–5 0.8073 0.3797 1996–7 0.9777 0.8735 0.2469 1997–8 1.0000 1.0000 0.5358 Average 0.8606 0.6870 0.5174

1.0000 0.6541 0.5222 0.4345 0.6619 0.7384 0.9009 0.7412 0.7067

0.9147 0.6037 0.6353 0.2327

Capital efficiency 1989–90 0.9048 1990–1 0.8041 1991–2 0.8710 1992–3 0.6101 1993–4 1.0000 1994–5 1996–7 1.0000 1997–8 1.0000 Average 0.8843

1.0000 0.8650 0.7481 1.0000 0.5344 0.7902 0.6763 0.2923 0.7383

0.7546 0.9983 1.0000 1.0000

0.7036 0.7044 0.8398 0.7280 0.7349 0.9183 1.0000 1.0000 0.8286

0.3429 0.7342 0.5390 0.8521 0.3192 0.2028 0.2327 0.8052 0.5035

0.8887 1.0000 0.8441

0.5220 0.6429 0.5919

0.4254 0.2578 0.7394

0.7077 0.7536 0.7661 0.7807 0.4847 0.6529 0.5678 0.5855 0.6624 0.8251 0.7105 0.6456 0.6539 0.6257 0.5092 0.6226 0.6718 0.6581 0.7953 0.7832 0.6522 0.8125 0.3925 0.5982 0.5002 0.6030 0.6421 (Continued)

Efficiency Analysis of Selected Manufacturing Industries 159 Table 5.5 (Continued) Central

State

Central–state Joint–public Joint–private Private

Total input efficiency 1989–90 0.8941 0.8002 1990–1 0.8685 0.6964 1991–2 0.7092 0.6847 1992–3 0.8198 0.7850 1993–4 0.9401 0.7622 1994–5 0.8468 1996–7 0.8363 0.8289 1997–8 1.0000 0.8948 Average 0.8669 0.7874

0.2919 0.8831 0.6353 0.8337 0.4477 0.3017 0.2710 0.8110 0.5594

0.9784 0.8008 0.7327 0.8793 0.5327 0.8434 0.7602 0.5774 0.7631

0.7770 0.8690 0.6437 0.8420

Output efficiency 1989–90 1.0000 1990–1 0.7488 1991–2 0.7242 1992–3 1.0000 1993–4 0.6790 1994–5 1996–7 1.0000 1997–8 1.0000 Average 0.8789

1.0000 0.7844 1.0000 0.6167 0.8731 1.0000 1.0000 0.7874 0.8827

0.2662 0.6899 0.4841 0.3997 0.6992 0.3494 0.6054 0.9006 0.5493

0.2576 0.7801 0.5450 1.0000

0.3124 0.2885 0.4081 0.3146 0.3492 0.7963 0.6906 0.7868 0.4933

0.4984 0.6149 0.7075

1.0000 1.0000 0.7638

0.7959 0.8120 0.6183 0.7934 0.5020 0.6690 0.5256 0.6333 0.6687 0.6935 0.6986 0.6878 0.6348 0.7614 0.7307 0.6957 0.7974 0.7125

one year. The state government units and the units belonging to joint sectors show a poor performance in terms of output-oriented efficiency. The efficiencies of the units belonging to central government and the private sector show a rising trend during the post-liberalization period. However, the units in other groups do not show any clear tend of output efficiency. Contrary to the general belief, it has been found that the inputoriented efficiencies in the private sector were much lower than those of the state run units during the period of study. Joint public sector units also performed better than private sector units in terms of input efficiency. There was a faint declining trend of efficiencies of units in all the ownership types during this period. The figures for efficiencies of individual inputs suggest that for both production and non-production workers the private sector units were less efficient than the central and state run units except in a years when the private sector units were better than state run units. Thus, this analysis reveals that in terms of inputoriented efficiency the manufacturing units belong to the private sector performed worse than those in the state managed sector.


Factors behind efficiency variations The performances of the units of the textile industry in some major states and in different ownerships have been discussed in the previous subsections. Now some explanation is needed for the variability of the values of efficiency, and we attempt to uncover the factors responsible for this variation among states and ownership groups. Regression analyses are carried out to discover the factors responsible for the variability of efficiency, where efficiencies are taken as dependent variables and variables such as age of the unit, scale of operation, skill, state dummy, ownership dummy and time dummy for reform are taken as independent variables. As stated, a Box–Cox transformation of the efficiency values is needed instead of standard logarithm transformation in order to avoid the problem with efficiency values that are equal to unity. The analysis is of the total efficiency of the units. The results of the regressions are presented in Table 5.6. In the first regression total efficiency is taken as the dependent variable and 1993 onwards is taken as the post-reform period. Scale of operation (logarithm of output), skill, state dummy for Delhi and the reform dummy are found to be statistically significant. The second regression, changing the reform dummy to 1994, indicates that only three variables, i.e. scale of operation, reform dummy and the state dummy for Delhi, are statistically significant. However, the reform dummy is not highly significant. The values of adjusted R2 are not however very high. The leather industry Table 5.7 shows the all-India average efficiency of the leather industry as a whole for the years 1989–90 to 1997–98. The figures indicate that the efficiency of nonproduction workers was better than that of production workers and for both efficiencies no significant trend can be observed over the period of analysis. Efficiencies of capital, fuel and materials show higher average values compared to labor efficiency but again the efficiencies show no trend over the period of analysis. One interesting point is that in the year 1996–97 there was a major fall in the efficiencies of all the inputs. Output efficiency showed a slight upward trend during this period of post-liberalization. It rose from 0.8444 in 1989–90 to 0.9249 in 1996–97 but then there was a marginal fall in 1997–98. State-specific analysis As in our previous analysis of the textiles industry we have taken six major leather-producing states from the 33 states appearing in the list. The states are Haryana, Karnataka, Maharashtra,

Efficiency Analysis of Selected Manufacturing Industries 161 Table 5.6 Estimates of regression parameters of total efficiency variations Variable

Regression I

Regression II

C Age

−116.8108 0.0057 (0.748492) 9.2934 (31.29111)∗∗ 15.0354 (1.475779) 10.3415 (0.986568) 17.3816 (1.504937) 2.0393 (2.57808)∗∗ 2.1473 (0.927612) 10.3913 (4.543483)∗∗ −2.2197 (−0.661021) 3.5810 (1.515877) 1.4335 (0.624264) 1.1105 (0.348878) 5.0079 (0.824846) 9.9561 (7.923802)∗∗ –

−120.1982 0.0045 (0.5963) 9.7366 (32.7342)∗∗ 15.5592 (1.5194) 11.6625 (1.1071) 18.9887 (1.6360) 1.0917 (1.3840) 2.3300 (1.0011) 10.1227 (4.4017)∗∗ −3.3195 (−0.9836) 2.5860 (1.0898) 1.2685 (0.5496) 0.3465 (0.1083) 4.1880 (0.6863) –

Log y Joint Private Public Skill Karnataka Delhi Gujarat Maharashtra Tamil Nadu Uttar Pradesh West Bengal TD93 TD94 R2 Adjusted R2 Durbin–Watson statistic

0.1942 0.1922 1.9341

2.2082 (1.7516) 0.1859 0.1839 1.9164

Note: Dependent variable: total efficiency. Included observations: 5829. ∗ indicate coefficients are significant at the 5% level. Figures in parentheses are t -statistics.

Tamil Nadu, Uttar Pradesh and Delhi. It can be seen from the figures of average values of output of these states that the average scale of operation in Tamil Nadu is highest. However, the other states do not lag far behind and the figures are very close to each other.

162

Table 5.7

Average efficiencies of manufacturing units in the Indian leather industry

Year Production workers Nonproduction workers Capital Fuel Materials Total input Total output

1989–90

1990–91

1991–92

1992–93

1993–94

1994–95

1996–97

1997–98

0.6801 0.6921 0.6043 0.8431 0.9895 0.7618 0.8444

0.7273 0.7632 0.6173 0.8197 0.9803 0.7816 0.8318

0.6469 0.7347 0.6202 0.8107 0.9769 0.7949 0.8243

0.7911 0.7626 0.7129 0.8021 0.9729 0.8083 0.8167

0.6552 0.8390 0.7006 0.6601 0.8059 0.7322 0.7370

0.7082 0.5203 0.6687 0.6449 0.7803 0.6645 0.8136

0.4656 0.4869 0.5692 0.2871 0.4792 0.4576 0.9249

0.7215 0.7671 0.4678 0.6935 0.9794 0.7259 0.9133


Table 5.8 shows the values of average efficiency for different inputs and output for these six states for all the years of analysis. The values of efficiency for production workers indicate that the efficiency of production workers in Tamil Nadu was comparatively low, while that in Delhi was higher compared to other states. However, the figures do not show any trend over the years for the states we have chosen for analysis. The values of efficiency for nonproduction workers indicate that Haryana and Karnataka were in a much better position compared to the other states, while Tamil Nadu, Uttar Pradesh and Delhi registered lower values than the others and remained at the same level of average efficiency over these years. Figures for capital input efficiency for these states show that Delhi and Uttar Pradesh registered lower values of efficiency compared to the other states. But the figures for all the states do not indicate any significant trend over this period. Figures for average efficiency of material input over the years give the highest values for all the states compared to all the input efficiencies. Maharashtra showed the best performance in terms of material input efficiency, where for most years the firms were 100 per cent efficient. The figures for total input efficiency, however, do not show any marked difference among the states. Figures for average efficiency of output indicate that the performance of firms in Delhi was better than that of the other states, averaging about 0.9, with a significant fall to 0.62 in 1996–97. Karnataka, Maharashtra, Tamil Nadu and Uttar Pradesh registered almost the same level of efficiency over these years, averaging about 0.85. The average efficiency of Haryana was, however, slightly lower than those of the other states. Ownership-specific analysis Table 5.9 presents the values for efficiencies of different inputs and outputs for three categories of ownership. The average figures for output efficiency of the leather industry during the period 1989–90 to 1997–98 reveal that firms belonging to the private sector and state governments’ firms did better than central government organizations. The average input efficiency figures for these three types of firms indicate that firms within the purview of state government did better than both private and central government organizations. The input efficiencies of these three types of firms did not show any trend during this period. However, figures for output efficiency show a mild increasing trend over the period.

Table 5.8

Average scale of operation and efficiency of manufacturing units in the leather industry in India Karnataka

Maharashtra

Tamil Nadu

Uttar Pradesh

Delhi

33,660,178 78,216,270 73,561,566 56,363,316 55,905,672 51,763,653 182,519,937 118,054,604

18,941,788 25,189,328 29,837,334 16,895,854 197,516,366 23,536,130 75,719,160 691,751,671

37,827,036 56,359,748 41,326,796 37,782,037 74,681,553 31,749,146 143,904,160 135,974,102

41,792,521 60,794,204 72,541,945 75,627,333 121,193,034 124,357,585 143,567,758 190,147,295

10,303,635 40,734,315 41,812,776 35,412,561 63,412,758 85,979,635 94,212,210 189,277,793

35,883,528 37,643,278 42,474,135 64,756,369 78,512,157 198,457,131 133,364,279 132,271,075

Efficiency of production workers 1989–90 0.7388 1990–1 0.7911 1991–2 0.6996 1992–3 1.0000 1993–4 0.8871 1994–5 0.7494 1996–7 0.4236 1997–8 0.8792

0.9213 0.7829 0.6213 0.8385 0.7320 0.7510 0.4883 1.0000

0.7320 0.7797 0.8883 0.9484 0.6462 0.6937 0.4607 0.8494

0.5619 0.6224 0.5810 0.6799 0.5794 0.6303 0.3913 0.6549

0.6491 0.7483 0.6928 0.7264 0.6883 0.7014 0.4650 0.7648

0.8599 0.9116 0.6801 0.9019 0.8630 0.6678 0.8092 0.5816

Efficiency of nonproduction workers 1989–90 0.8598 1990–1 0.8225 1991–2 0.8410 1992–3 0.8032 1993–4 0.8163 1994–5 0.4829 1996–7 0.3756 1997–8 0.6431

0.7775 0.8840 0.6536 0.8885 0.9260 0.6613 0.5481 1.0000

0.6858 0.7454 0.8127 0.8450 0.8651 0.5985 0.4586 0.3928

0.6927 0.7489 0.8116 0.7921 0.8034 0.4653 0.4698 0.7988

0.6138 0.7199 0.7319 0.6725 0.8825 0.4803 0.4400 0.7473

0.6732 0.7361 0.5260 0.8269 0.9076 0.5064 0.6526 0.2983

Average output 1989–90 1990–1 1991–2 1992–3 1993–4 1994–5 1996–7 1997–8

164

Haryana

Efficiency of capital inputs 1989–90 0.5459 1990–1 0.8972 1991–2 1.0000 1992–3 0.6108 1993–4 0.8349 1994–5 0.6194 1996–7 0.4029 1997–8 0.5272

0.6603 0.5507 0.5548 0.6271 0.7977 0.7591 0.6005 1.0000

0.6471 0.6725 0.8609 0.8040 0.5914 0.7164 0.4190 0.3942

0.5796 0.5857 0.5989 0.7372 0.7005 0.6469 0.5667 0.3969

0.6335 0.6289 0.6488 0.6634 0.6565 0.6793 0.5591 0.4084

0.6327 0.5098 0.4368 0.5607 0.9443 0.7022 0.8797 0.1711

Efficiency of fuel inputs 1989–90 0.8011 1990–1 0.8893 1991–2 0.4890 1992–3 0.6424 1993–4 0.7409 1994–5 0.5624 1996–7 0.2746 1997–8 0.7061

0.9808 0.8542 0.8758 0.8450 0.7056 0.6020 0.2293 1.0000

0.8621 0.7912 0.9249 0.8860 0.6076 0.5953 0.2196 0.7701

0.8535 0.8770 0.8073 0.8426 0.6224 0.6981 0.2608 0.6905

0.7227 0.7718 0.7228 0.6279 0.7156 0.5625 0.2805 0.5543

0.9131 0.7755 0.7837 0.7123 0.7843 0.7962 0.4002 1.0000

Efficiency of material inputs 1989–90 1.0000 1990–1 1.0000 1991–2 0.9732 1992–3 0.9781 1993–4 0.9072 1994–5 0.6979 1996–7 0.5529 1997–8 1.0000

1.0000 0.9964 0.8809 0.9328 0.8690 0.8765 0.4149 1.0000

1.0000 1.0000 1.0000 1.0000 0.8007 0.7818 0.5913 1.0000

0.9805 0.9894 0.9806 0.9781 0.8180 0.7222 0.4725 0.9751

0.9825 0.9664 0.9838 0.9662 0.7765 0.8083 0.3742 0.9747

1.0000 0.9937 0.9909 0.9036 0.9117 0.6322 0.6117 1.0000 165

(Continued)

166

Table 5.8

(Continued) Haryana

Karnataka

Maharashtra

Tamil Nadu

Uttar Pradesh

Delhi

Total input efficiency 1989–90 0.7891 1990–1 0.8800 1991–2 0.8435 1992–3 0.8069 1993–4 0.8373 1994–5 0.6224 1996–7 0.4059 1997–8 0.7511

0.8680 0.8136 0.8200 0.8264 0.8061 0.7300 0.4562 1.0000

0.7854 0.7978 0.8472 0.8967 0.7022 0.6771 0.4299 0.6813

0.7336 0.7647 0.7853 0.8060 0.7047 0.6326 0.4322 0.7033

0.7240 0.7670 0.7492 0.7313 0.7439 0.6463 0.4238 0.6899

0.8158 0.7853 0.7832 0.7811 0.8822 0.6610 0.6707 0.6102

Total output efficiency 1989–90 0.7038 1990–1 0.8615 1991–2 0.7345 1992–3 0.6075 1993–4 0.7384 1994–5 0.8761 1996–7 0.8439 1997–8 0.9331

0.8986 0.7307 0.7660 0.8014 0.6482 0.7774 0.8186 1.0000

0.8910 0.8455 0.8561 0.8667 0.7519 0.8676 1.0000 0.8522

0.8533 0.8486 0.8575 0.8664 0.7286 0.8398 0.9351 0.8796

0.8560 0.8664 0.8554 0.8445 0.8201 0.8019 0.9506 0.9690

0.9105 0.8718 0.9359 1.0000 0.8472 1.0000 0.6281 1.0000

167 Table 5.9 Ownership-wise efficiency of manufacturing units in the leather industry in India Central

State

Private

0.6290 0.7705 0.6886 0.6705 0.2440 0.6166 0.5539 0.8272 0.6250

0.7802 0.7664 0.7538 0.8020 0.8786 0.8364 0.6990 0.9029 0.8024

0.7612 0.7862 0.7617 0.8123 0.7282 0.6586 0.4475 0.7123 0.7085

Total output efficiency 1989–90 0.8419 1990–1 0.7096 1991–2 0.6935 1992–3 0.6775 1993–4 0.0126 1994–5 0.5354 1996–7 1.0000 1997–8 1.0000 Average 0.6838

0.7758 0.7256 0.7911 0.8566 0.7683 0.9366 1.0000 0.8524 0.8383

0.8488 0.8417 0.8303 0.8188 0.7388 0.8097 0.9212 0.9142 0.8404

Fuel efficiency 1989–90 1990–1 1991–2 1992–3 1993–4 1994–5 1996–7 1997–8 Average

0.7896 1.0000 0.7644 0.7270 0.0246 0.5165 0.5000 1.0000 0.6653

0.8043 0.7832 0.9521 0.8348 0.9928 0.7553 0.4983 0.8812 0.8127

0.8448 0.8191 0.8046 0.8018 0.6459 0.6403 0.2768 0.6779 0.6889

Capital efficiency 1989–90 1990–1 1991–2 1992–3 1993–4 1994–5 1996–7 1997–8 Average

0.3903 0.6010 0.5287 0.4857 0.5000 0.5262 0.5077 0.7665 0.5383

0.8705 0.9088 0.7979 0.7737 0.9081 0.7903 0.8122 0.8304 0.8365

0.5861 0.6046 0.6166 0.7154 0.6915 0.6670 0.5608 0.4283 0.6088

Total input efficiency 1989–90 1990–1 1991–2 1992–3 1993–4 1994–5 1996–7 1997–8 Average

(Continued)


(Continued) Central

State

Private

Efficiency of production workers 1989–90 0.5031 1990–1 0.6382 1991–2 0.5577 1992–3 0.5748 1993–4 0.0212 1994–5 0.5341 1996–7 0.5067 1997–8 1.0000 Average 0.5420

0.7068 0.6276 0.5970 0.8816 0.7582 0.8324 0.6659 0.8834 0.7441

0.6777 0.7397 0.6495 0.7916 0.6544 0.7049 0.4576 0.7123 0.6735

Efficiency of nonproduction workers 1989–90 0.5483 1990–1 0.7031 1991–2 0.5922 1992–3 0.5649 1993–4 0.1741 1994–5 0.5062 1996–7 0.5116 1997–8 0.3696 Average 0.4963

0.5296 0.5320 0.4304 0.5749 0.8573 0.8309 0.7546 0.9194 0.6786

0.7064 0.7865 0.7607 0.7784 0.8461 0.5081 0.4765 0.7655 0.7035

Efficiency of material inputs 1989–90 0.9135 1990–1 0.9101 1991–2 1.0000 1992–3 1.0000 1993–4 0.5000 1994–5 1.0000 1996–7 0.7434 1997–8 1.0000 Average 0.8834

0.9897 0.9806 0.9916 0.9448 0.8763 0.9731 0.7642 1.0000 0.9400

0.9907 0.9810 0.9771 0.9741 0.8029 0.7726 0.4656 0.9774 0.8677

Note: Central, wholly central government; State: wholly state government; Private: wholly private ownership.

The average efficiency of production workers was highest in purely state government firms, while the rank of average efficiency of firms in the private sector came second among these three types of ownership. However, the figures for the average efficiency of nonproduction worker indicate that firms in the private sector were doing better than the other two sectors. For both the efficiencies of production and nonproduction workers central government organizations showed the lowest


figures compared to the others. The efficiencies of production and nonproduction workers in the state-controlled firms showed a mild upward trend during 1989–90 to 1997–98. The average efficiencies of other inputs for the firms under these three categories show similar pattern. For all the input-specific efficiencies state-run organizations performed much better than the other two types of firms. However, there were no clear trends of efficiencies of inputs during this period. Factors behind efficiency variations The performances in terms of various efficiency indicators of firms belonging to different states and different types of ownership have been discussed. Now a regression analysis is carried out to uncover the factors responsible for the variation in efficiencies of firms. The dependent variable here is the transformed values of total efficiency (the purpose and method of transformation are discussed above). The independent variables for the regression are age of the unit, scale of operation, skill, state dummy, ownership dummy and time dummy for reform. We have taken 1993 as the break point to understand the effect of liberalization on efficiency. The results presented in Table 5.10 suggest that scale of operation has a significant positive effect on the variation of efficiency. The coefficient of dummy variable indicates that efficiency went up during the later phase of liberalization. The coefficient of skill is positive, but not statistically significant. The result suggests that there is no marked difference in the values of efficiencies among the major leather-producing states in India.

4 Comparison of efficiency in the post- and pre-liberalization periods Liberalization is a process of economic policy changes specifically initiated from 1991 as declared state policy. It had its own economic, political and international compulsions. Indian economic reforms had been initiated with the help of financial support from the International Monetary Fund and the World Bank and later from the Asian Development Bank. Hence, these reforms have been involved with a set of conditions mutually agreed upon between the government of India and the multilateral institutions. It was believed that during the pre-reform years productivity in most of the industries became one of the lowest by international standards. It was also argued that macroeconomic imbalances and microeconomic inefficiencies had fed one another in a highly complex manner.

170 India’s New Economy Table 5.10 Estimates of regression parameters of total efficiency variations Variable

Coefficient

C Age

−479.958 0.12707 (0.945628) 67.35051 (12.65468)∗ −27.1022 (−0.593352) −81.5084 (−1.025743) −37.791 (−0.920261) −41.7784 (−0.722294) 37.94028 (1.129931) 160.1062 (5.909711)∗ 0.16863 0.163297 428.147

Log y Private Maharashtra Tamil Nadu Uttar Pradesh Skill Year D R2 Adjusted R2 S.E. of regression

Note: Dependent variable: total efficiency. Included observations: 1256. ∗ Indicates coefficients are significant at the 5% level. Figures in parentheses are t -statistics.

The New Industrial Policy of July 1991 laid down some very fundamental policy changes, such as abolition of licensing, easing of the rigors of MRTP and FERA, freer imports of capital goods and liberal policy measures for attracting investment in new technology-intensive industries. The sole objective of these highly liberalized policy measures, with which we are concerned here, was to enhance the productivity and efficiency of Indian industries by creating a competitive environment. It has been observed that the industrial composition in India has been changing over time. Particularly after the New Economic Policy of 1991 and the opening up of the economy to the world market, the growth of industries has taken place through natural market competition and the industries with high inefficiency and low productivity have found it hard to survive in the market. But the Indian industrial composition is characterized by the coexistence of a traditional labour-intensive


manufacturing sector and a capital-intensive modern sector. In this section we study the performance of firms in two leading industries in India, namely textiles and electronics. The computer industry is included in electronics and we analyze the efficiency of this industry separately during the post-reform period. We have already discussed the importance of the textiles industry in India. Now we discuss the growth and importance of the electronics industry, whose emergence is comparatively new in India. The industrial composition of India, as in many developed and developing countries, has been changing with the advent of new technologies and the changing pattern of mass consumption demand. Changes in the structure of industries in developed countries mainly come from the introduction of new technology, while in most of the developing countries the changes are driven by the demand for modern consumption and capital goods. The technologies of production of such goods are primarily imported from the advanced countries of the West. Thus the changes in developing countries take place with a lag from those in developed countries. According to the Technology Information, Forecasting and Assessment Council (TIFAC) India will gain enormously from the use of computers in manufacturing processes (India 2020: A Vision for the New Millennium, 1998). There are many success stories where India has gained by using computers combined with highly skilled manpower in both manufacturing and service sectors. Thus the need for computer and similar electronic processing units creates a boom for computers and related electronic industries. We know that in India the IT and IT-enabled services (ITES) sectors are hugely successful. India is now a preferred destination for the production of electronic goods. To achieve this, the Ministry of IT recently announced a comprehensive policy, the much-awaited semiconductor policy, which offers both pre-operative and post-operative benefits and aims to attract foreign investment in this industry. Demand for personal computers, laptops and peripherals is increasing at a high pace. The electronics industry as a whole has been one of the fastest growing industries right from its inception. With an increasing middle income group population the potential consumer demand for consumer electronics ranging from televisions to laptop computers is almost unlimited, and hence a strong growth performance could be expected. The start of this industry dates back to the early 1960s. Electronics was primarily focused on the field of communication systems for radios, telephony, telegraphy and television broadcast. Until the 1980s the electronic sector was government owned. From there on the growth of the electronic


industry took off due to economic changes resulting in the globalization of the economy. Due to the rise in income level demand for various types of durable consumer products from the electronics industry is increasing day by day. The electronics industry recorded a very high growth by the 1990s. The liberalization of the economy in 1991 opened up many avenues to generate demand for electronic goods from every corner of life. Thus, due to its pervasive applicability, the electronics industry is strongly linked with the macroeconomic conditions of India. This section has the following subsections. First, we provide a description of the data and methodology. A comparison of efficiencies of the textiles and electronics industries in the pre- and post-liberalization eras and a nonradial analysis of the computer industries during the postliberalization period are then presented, together with some remarks on these issues. Data and methodology As before, data for the analysis of efficiency were collected from the Industrial wing of the Central Statistical Organization, Government of India. For the purpose of analysis we collected unit-level data on the textiles and electronics industries for the period 1980–81 to 2002–03, barring 1995–96, for which data have not been published. For the computer industry in particular we have taken the data for the years 1989–90 to 1997–98. The detailed industrial codes are given in Appendix 5.3. These unit-level data have a state code and from these codes the location of units can be identified. In some states firms within these industries are not found and in some states the number of units is very low compared to other states. From the distribution of the number of units in each state the major textile producing states were selected for this analysis. Similarly, for the electronics industry we selected a few major states for the purpose of analysis. For each unit total production in value terms is taken as a measure of output. Average of opening and closing stock of capital is taken as a measure of capital. Total employment is divided into production workers and nonproduction workers and the proportion of nonproduction to production workers is considered as the skill factor of a unit. Fuel and materials consumed are other two inputs of production. The values of outputs and inputs are deflated by the corresponding price indices. The efficiencies of the firms in each industry and for each year were calculated using both parametric and nonparametric methods of estimation. First, we estimated the efficiencies with the corrected OLS (COLS) method. The COLS method, first noted by Richmond (1974), is based on


the OLS result. Let us consider a simple C–D production function in its linear form: log Y = α0 + αi log Xi − u (5.26) Let µ be the mean of u; the equation can be written as log Y = (α0 − µ) +

αi log Xi − (u − µ)

Since the distribution specification of the error term is half-normal, it satisfies all the usual ideal conditions except normality. Therefore, the equation may be estimated by OLS to obtain the BLUE of (α0 − µ) and of αi s. It was observed by Richmond (1974) that the mean and variance of one-sided disturbance terms are both equal to µ. Now, if a specific distribution is assumed for u and if the parameters of this distribution can be derived from its higher order central moments, then we can estimate these parameters consistently from the moments of the residuals. Since µ is a function of these parameters, it too can be estimated consistently and the estimate can be used to correct the OLS constant term, which is a consistent estimate of (α0 − µ). The difficulty with this technique is that even after correcting the constant term, some residuals may have the wrong signs. To manage this problem the parameters of the above equation should first be estimated by OLS and then the constant term corrected not by the above technique but by shifting it up until no residual is positive and at least one is zero. We have used this method to estimate the frontier production function for each of the years. Then the efficiency is calculated as the ratio between the observed and frontier output for each industry. Here we have estimated the FPF using stochastic models and we have taken the C–D production functions. The forms of the cross sectional C–D production functions are respectively as follows: log Yi = α0 + βK log Ki + βL log L1i + log L2i − u

(5.27)

where K represents gross fixed capital stock (GFCS) and L1 and L2 represent productive and nonproductive workers respectively. The multiplicative error term is e−u =

Y f (X)

Hence, e−u must lie between zero and unity and gives the measure of efficiency.


The nonparametric DEA measure of efficiency is done for the set of firms for each year and for each industry. The simplest radial measure of output oriented technical efficiency can be obtained from the solution of an LP model and can be written as: max φ, s.t. N

µj ykj ≥ φykt ,

k = 1, 2, . . . , m

(5.28)

j=1 n

µj xkj ≤ xlt ,

l = 1, 2, . . . , n

j=1

µ≥0 where y is the output bundle and x is the input bundle. The scale of operation for this model is considered as constant return to scale (CRS). For the model with variable return to scale (VRS) we have to include another constraint µ = 1 to get the values of µ and the efficiency parameter . In the analysis of the firms in the computer industry we estimated the nonradial efficiency of inputs. We have already discussed the concept of nonradial efficiency. The underutilizations of inputs are calculated from the nonradial model. The optimum use of each input is calculated by multiplying the efficiency score of each input with the corresponding observed value of input. Now the simple arithmetic (X – X ∗ )/X ∗ gives the measure of input utilization, where X and X ∗ represent the observed and optimum values of input. Instead of measuring the input efficiency with a nonradial measure we have used a radial input-oriented BCC-DEA model for subvector efficiency for the analysis of state-specific input utilization in the electronics industry during the post-liberalization period (Ray, 2004). The model can be written as follows: min θ, s.t. λj yj ≥ y 0

λj L1j ≤ θL10 λj L2j ≤ θL20 λj Kj ≤ K 0

(5.29)


λj ≥ 0,

λj = 1

j = 1, 2, . . . , N

Now the expected optimal values of θ will be less than or equal to one. The production frontier is taken here as a log-linear model and the values of inputs and output are transformed into log values. The proportion of underutilization is calculated as (1 – θ)/θ. The proportions of underutilization of labor and capital are estimated from the state-level data for the electronics industry. Empirical analysis First we try to get some idea about the changes in the efficiency of textiles and electronics – one from the traditional and the other from the modern sector – during the period 1980–81 to 2002–03. Table 5.11 shows Table 5.11 Average efficiency of the textiles industry Year

COLS

CRS

VRS

Scale

Stochastic

Efficiency during the pre-liberalization period 1980–1 0.0626 0.1561 1981–2 0.0623 0.1117 1982–3 0.0861 0.1536 1983–4 0.0146 0.0371 1984–5 0.0405 0.0926 1985–6 0.0235 0.1070 1986–7 0.0624 0.1201 1987–8 0.0791 0.1426 1988–9 0.0642 0.1435 1989–90 0.0173 0.0526 1990–1 0.0078 0.1326 Average 0.0473 0.1136

0.3181 0.1634 0.2791 0.1171 0.1551 0.1615 0.2035 0.2205 0.2104 0.1157 0.2116 0.1960

0.6237 0.7368 0.6569 0.5006 0.6943 0.7058 0.6633 0.7040 0.7678 0.6286 0.7202 0.6729

0.5820 0.4905 0.5500 0.4899 0.4936 0.4937 0.5182 0.5107 0.5001 0.5184 0.4997 0.5133

Efficiency during the post-liberalization period 1991–2 0.0372 0.1003 1992–3 0.0459 0.0915 1993–4 0.0392 0.0736 1994–5 0.0761 0.1295 1996–7 0.0152 0.0838 1997–8 0.0512 0.1439 1998–9 0.0472 0.0890 1999–2000 0.0172 0.0759 2000–1 0.0293 0.1062 2001–2 0.0927 0.1503 2002–3 0.0170 0.1301 Average 0.0426 0.1067

0.2239 0.1443 0.1378 0.2241 0.1384 0.1902 0.1525 0.2119 0.1949 0.2276 0.1749 0.1837

0.5757 0.7128 0.6435 0.6282 0.6815 0.8343 0.6563 0.4397 0.5791 0.7249 0.8092 0.6623

0.5035 0.4594 0.3381 0.3394 0.3368 0.3654 0.3741 0.3719 0.3720 0.3871 0.3650 0.3830


the values for five types of efficiencies in the textiles industry during this period. The average efficiency during the post-liberalization period was low compared to that in the pre-liberalization period. However, the falls in the values are not significant and no trends of the values during the period are observed. If we consider the values of efficiencies estimated using the stochastic frontier model, a major fall in the level of efficiencies is observed during the post-liberalization period. There is a sudden fall in efficiency in the year 1993–94, just after the year when the liberalization policy was undertaken by the government of India. The value rose again during the latter period but it never reached the level of preliberalization period. This fall in the early phase of liberalization may be due to structural adjustment problems in the industries having to cope with the policy of liberalization. Table 5.12 presents the values of efficiencies in the electronics industry during the same period. The values of efficiencies show similar features to those in the textiles industry. The levels of efficiencies in the post-liberalization period were low compared to those during the preliberalization period. Similarly to the textiles industry, the values of efficiencies do not show any trend and fluctuate during this period. The ownership-specific output-oriented efficiencies for six different years are shown in Table 5.13. The values of efficiency for the textiles industry indicate that firms belonging to central government organizations did better than in privately owned firms. This feature is more prominent in the efficiencies estimated with the DEA model and similar to our earlier results using the input-oriented nonradial measure. On the other hand, efficiencies in the electronics industry (Table 5.14) show that the firms in the private sector are more efficient than those in other types of ownership. The levels of efficiencies for both industries in the post-liberalization years are comparatively lower than those in the years before liberalization. The state-specific efficiencies in the textiles industry along with their ranks for six discrete years are given in Table 5.15. We have considered here the DEA efficiency with VRS and efficiency measured by stochastic frontier analysis. We have already noted that efficiencies fell over time but not without fluctuations. The same feature is followed here for all the states we considered in the analysis. The ranks of the states in terms of two types of efficiencies changed over this period. States that registered high ranks for most of the years were Haryana, Maharashtra and Panjab. The second group of states, which registered high rankings for some of the years, are Gujarat, Rajasthan and Goa.

Efficiency Analysis of Selected Manufacturing Industries 177 Table 5.12 Average efficiency of the electronics industry Year

COLS

CRS

Efficiency during the pre-liberalization period 1980–1 0.0778 0.1990 1981–2 0.0720 0.1765 1982–3 0.0947 0.1640 1983–4 0.1065 0.1803 1984–5 0.0431 0.1145 1985–6 0.0642 0.1231 1986–7 0.0672 0.1195 1987–8 0.1119 0.1728 1988–9 0.0339 0.1983 1989–90 0.0507 0.0910 1990–1 0.0718 0.1186 Average 0.0722 0.1507

VRS

Scale

Stochastic

0.2433 0.2298 0.2235 0.2378 0.1826 0.1668 0.1801 0.2190 0.2416 0.1682 0.1715 0.2058

0.8561 0.8394 0.8225 0.8346 0.7588 0.8414 0.7776 0.8649 0.8508 0.7564 0.7874 0.8173

0.5125 0.5362 0.5604 0.4805 0.5129 0.5561 0.6404 0.4502 0.5426 0.9929 0.4295 0.5649

0.1644 0.1828 0.2293 0.1670 0.1316 0.1790 0.2609 0.2334 0.2421 0.1457 0.1577 0.1904

0.7829 0.7828 0.6729 0.8283 0.8664 0.7066 0.4947 0.7166 0.7987 0.7680 0.6070 0.7295

0.5147 0.5180 0.4429 0.4339 0.4638 0.3874 0.3485 0.5046 0.3785 0.4030 0.4040 0.4363

Efficiency during the post-liberalization period 1991–2 1992–3 1993–4 1994–5 1996–7 1997–8 1998–9 1999–2000 2000–1 2001–2 2002–3 Average

0.0557 0.0544 0.0602 0.0685 0.0465 0.0595 0.0512 0.0711 0.0768 0.0546 0.0363 0.0577

0.1078 0.1155 0.1273 0.1207 0.0968 0.1115 0.1171 0.1538 0.1827 0.0867 0.0804 0.1182

Table 5.16 shows the state-specific vales of efficiencies for the same discrete point for the electronics industry. The ranks of the states do not show any consistency of values from which a cluster of states can be formed according to their efficiency. Goa and Uttar Pradesh are the two states that show high rankings in terms of efficiency for most of the years we have examined. It has been argued that efficient firms in an industry are more homogeneous in terms of output and input structure. We examined this feature of firms belonging to the textiles and electronics industry for some discrete time point. Table 5.17 gives the distribution of outputs of the textile industry for both efficient and inefficient firms measured by the outputoriented BCC model. It can be seen from the table that the efficient firms

178 India’s New Economy Table 5.13 Ownership wise average efficiency of the textiles industry 1980–1 1985–6 1990–1 1994–5 1999–2000 2002–3 VRS efficiency Wholly central government Wholly state and/or local government Central and state government jointly Joint sector public Joint sector private Wholly private ownership Stochastic efficiency Wholly central government Wholly state and/or local government Central and state government jointly Joint sector public Joint sector private Wholly private ownership

0.3376

0.1232

0.2588

0.3987

0.1017

0.0748

0.3017

0.1122

0.2431

0.4546

0.1384

0.1167

0.3777

0.1676

0.1966

0.4112

0.0834

0.0492

0.2669 0.4588 0.3257

0.0992 0.1392 0.1133

0.2368 0.2466 0.2070

0.5288 0.5010 0.6579

0.1534 0.2569 0.2240

0.1499 0.1227 0.1829

0.5367

0.4471

0.4446

0.2079

0.1741

0.2630

0.5164

0.4628

0.4280

0.3013

0.2435

0.3229

0.5838

0.5004

0.4499

0.1687

0.1994

0.1633

0.4736 0.6183 0.5678

0.3886 0.4445 0.4440

0.4715 0.4708 0.5068

0.3460 0.3330 0.3495

0.2700 0.3478 0.3957

0.3142 0.3032 0.3742

are comparatively bigger than, the inefficient firms. Again, the other values of distribution of output of the firms indicate that the efficient firms in the textile industry are less dispersed compared to the inefficient firms. The percentage of efficient firms in the textiles industry averaged about around 2 per cent of the total firms in that year. A similar exercise was carried out with the firms in the electronics industry. The results in Table 5.18 indicate that the character of the firms in the electronics industry is the same as we found in the analysis of the textiles industry. The efficient firms are larger in size and more homogeneous in output size. The percentage of efficient firms in the total was around 4 per cent over this period of study. We now deal with the analysis of underutilization of inputs, namely labor and capital, in electronics industries in different states during the postliberalization period. It is expected that due to the enactment of the policy of liberalization competition among the states in attracting

Efficiency Analysis of Selected Manufacturing Industries 179 Table 5.14 Ownership wise average efficiency of the electronics industry 1980–1 1985–6 1990–1 1994–5 1999–2000 2002–3 VRS efficiency Wholly central government Wholly state and/or local government Central and state government jointly Joint sector public Joint sector private Wholly private ownership Stochastic efficiency Wholly central government Wholly state and/or local government Central and state government jointly Joint sector public Joint sector private Wholly private ownership

0.2985

0.2022

0.3358

0.3843

0.4196

0.2164

0.2410

0.1678

0.1116

0.0689

0.0598

0.0598

0.2382

0.1626

0.2268

0.0368

0.0480

NA

0.1750 0.4400 0.2153

0.2883 0.1003 0.3086

0.1481 0.1646 0.1729

0.0691 0.0479 0.1753

0.2191 0.2633 0.2347

0.3277 0.0920 0.1572

0.5463

0.4644

0.3625

0.3845

0.5017

0.3690

0.4587

0.5537

0.3388

0.2785

0.3430

0.2614

0.5120

0.5599

0.5216

0.3763

0.4146

NA

0.6325 0.6301 0.4436

0.5214 0.5465 0.5382

0.3813 0.3705 0.4357

0.3638 0.3765 0.4444

0.4208 0.5106 0.5120

0.4366 0.3737 0.4088

both domestic and foreign investment would have increased. Naturally, efficiency in terms of utilization of resources would increase in the manufacturing sector. We estimated the percentage of underutilization of labor and capital in the electronics industry using the subsector efficiency model as described above. The figures in Tables 5.19 and 5.20 show the percentage of underutilization of labor and capital respectively. The figures for underutilization of labor show that Goa, Chandigarh, Delhi, Himachal Pradesh and Panjab were in the upper tier of the ranking in better utilization of labor. Goa topped the ranking in terms of the average utilization of resources over the period 1990–91 to 2002–03. Chandigarh and Delhi came next in terms of efficient utilization of labor. Low ranking states in terms of utilization of labor are Andhra Pradesh, Rajasthan, Kerala and West Bengal. The year-wise average ranking of all the states does not indicate any improvement in the utilization of labour in the electronics industry during the post-liberalization period.

180

Table 5.15 State-wise average efficiency of the textiles industry State

1980–1

Rank

1985–6

Rank

1990–1

Rank

1994–5

Rank

1999–2000

Rank

2002–3

Rank

VRS efficiency Andhra Pradesh Chandigarh Delhi Goa Gujarat Haryana Himachal Pradesh Karnataka Kerala Madhya Pradesh Maharashtra Orissa Pondicherry Punjab Rajasthan Tamil Nadu Uttar Pradesh West Bengal

0.2671 0.2558 0.2201 0.3749 0.1780 0.3515 0.3892 0.2816 0.3135 0.2445 0.6160 0.3185 0.2782 0.3320 0.3363 0.2431 NA NA

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

0.1180 0.0949 0.1573 0.2054 0.1265 0.1085 0.1355 0.1129 0.1046 0.1366 0.1895 0.0813 0.0824 0.1667 0.2006 0.1266 0.1153 0.1436

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

0.1843 0.2547 0.2365 0.2256 0.2101 0.3470 0.2610 0.2105 0.1731 0.1638 0.2266 0.1091 0.2135 0.2576 0.2295 0.2025 0.1766 0.1763

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

0.2171 0.1726 0.1836 0.1576 0.2079 0.3793 0.3120 0.2159 0.1943 0.2368 0.2567 0.1735 0.2340 0.2310 0.2324 0.2572 0.1999 0.1985

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

0.2268 0.1543 0.2402 0.1824 0.1785 0.2522 0.0853 0.1519 0.1212 0.2499 0.2084 0.1699 0.1251 0.2546 0.2459 0.2058 0.2065 0.3255

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

0.1579 0.0000 0.0720 0.1828 0.1492 0.3318 0.0170 0.0991 0.1046 0.2128 0.1350 0.2251 0.1806 0.2443 0.2164 0.1654 0.1991 0.0982

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

Stochastic efficiency Andhra Pradesh Chandigarh Delhi Goa Gujarat Haryana Himachal Pradesh Karnataka Kerala Madhya Pradesh Maharashtra Orissa Pondicherry Punjab Rajasthan Tamil Nadu Uttar Pradesh West Bengal

0.5347 0.5380 0.5406 0.5968 0.5497 0.5592 0.6073 0.5216 0.5662 0.5534 0.6412 0.6575 0.6261 0.6058 0.5349 0.5013 NA NA

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

0.4468 0.4431 0.4841 0.5341 0.4852 0.5668 0.4115 0.4268 0.4411 0.4202 0.5192 0.3347 0.3709 0.5315 0.5604 0.4630 0.4521 0.4131

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

0.4604 0.5384 0.5264 0.5166 0.5220 0.5662 0.5386 0.4532 0.4144 0.4135 0.5112 0.3482 0.5022 0.5605 0.5558 0.5020 0.4433 0.4100

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

0.3306 0.1602 0.3054 0.2528 0.3298 0.4506 0.4677 0.3224 0.2828 0.3456 0.3630 0.1990 0.3713 0.3706 0.3800 0.3917 0.3019 0.2396

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

0.4035 0.2019 0.5147 0.3568 0.3722 0.3964 0.1786 0.2867 0.2612 0.4066 0.3390 0.2875 0.2595 0.4315 0.4138 0.3896 0.3222 0.3724

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

0.3492 0.0002 0.2215 0.3434 0.2920 0.4716 0.0743 0.2409 0.2860 0.4019 0.2940 0.2969 0.4037 0.4136 0.3698 0.4057 0.3344 0.2837

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

181

182

Table 5.16 State-wise average efficiency of the electronics industry State

1980–1

Rank

1985–6

Rank

1990–1

Rank

1994–5

Rank

1999–2000

Rank

2002–3

Rank

VRS efficiency Andhra Pradesh Chandigarh Delhi Goa Gujarat Haryana Himachal Pradesh Karnataka Kerala Madhya Pradesh Maharashtra Orissa Pondicherry Punjab Rajasthan Tamil Nadu Uttar Pradesh West Bengal

0.1771 0.3053 0.2670 1.0000 0.1916 0.2457 0.3420 0.1947 0.2158 0.4535 0.2407 NA 0.0690 0.1320 0.1780 0.2752 0.2914 0.2811

15 4 8 1 13 9 3 12 11 2 10

0.1613 0.0707 0.1705 0.1147 0.1397 0.1846 0.0650 0.1504 0.1320 0.0721 0.1452 0.2625 0.1800 0.2126 0.2106 0.1682 0.2175 0.2306

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

0.1355 0.0758 0.1715 0.4408 0.2091 0.1496 0.0915 0.1855 0.1032 0.0949 0.2175 0.1008 0.1087 0.1720 0.1327 0.1299 0.1590 0.2189

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

0.1061 0.0838 0.1871 0.1623 0.0907 0.0710 0.3403 0.2359 0.0732 0.1937 0.1619 0.0567 0.0373 0.1101 0.3171 0.0844 0.2083 0.2412

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

0.1973 0.2840 0.2909 0.1531 0.2183 0.2906 0.2822 0.1947 0.2097 0.3492 0.2011 0.2650 0.1808 0.2543 0.2799 0.1365 0.2693 0.2159

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

0.3217 0.1561 0.1465 0.0815 0.2240 0.1614 0.2307 0.0330 0.2474 0.0873 0.2302 0.1348 0.1840 0.1089 0.1051 0.2911 0.0460 0.0622

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

17 16 14 7 5 6

Stochastic efficiency Andhra Pradesh Chandigarh Delhi Goa Gujarat Haryana Himachal Pradesh Karnataka Kerala Madhya Pradesh Maharashtra Orissa Pondicherry Punjab Rajasthan Tamil Nadu Uttar Pradesh West Bengal

0.4867 0.5176 0.5554 0.7230 0.4414 0.5875 0.7134 0.4860 0.4827 0.5937 0.5287 NA 0.4324 0.4637 0.5092 0.5247 0.4698 0.4301

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

0.5018 0.5405 0.6059 0.5706 0.5343 0.5635 0.4642 0.5306 0.5211 0.4600 0.5592 0.5399 0.7085 0.5638 0.5780 0.5350 0.5701 0.5709

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

0.3427 0.3998 0.4446 0.5434 0.4439 0.3710 0.3467 0.4497 0.3849 0.3437 0.4779 0.3363 0.4760 0.3770 0.4189 0.3840 0.4471 0.4310

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

0.3643 0.4555 0.4674 0.5585 0.3965 0.4026 0.4603 0.4738 0.3603 0.4212 0.4380 0.3320 0.3489 0.3974 0.4692 0.3791 0.4839 0.4007

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

0.4341 0.5126 0.5685 0.5091 0.5097 0.5171 0.5210 0.4967 0.4865 0.5561 0.4874 0.4489 0.4828 0.5135 0.4783 0.4849 0.5148 0.4430

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

0.3947 0.3743 0.4669 0.3507 0.3131 0.4167 0.3872 0.3884 0.3371 0.4553 0.3976 0.2429 0.5382 0.4187 0.4475 0.3501 0.4669 0.4235

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

183

Table 5.17 Distribution of outputs of the textiles industry 1985–6

1990–1

1994–5

1999–2000

2002–3

Efficient firms with the VRS model Mean 329,544,687 Standard deviation 557,158,260 Coefficient of var. 1.6907 Kurtosis 6.3311 Skewness 2.4771 Per cent efficient firms 2.9851

458,576,158 997,943,363 2.1762 5.1799 2.4217 1.0572

489,664,597 1,027,248,735 2.0979 9.1982 2.9732 1.3872

513,41,621 929,691,320 1.8106 8.1652 2.7995 1.6360

1,293,103,408 2,402,210,547 1.8577 10.9598 3.1187 1.7718

588,288,879 884,414,079 1.5034 4.0432 2.0861 2.1292

Inefficient firms with the VRS model Mean 75,736,558 Standard deviation 146,703,610 Coefficient of var. 1.9370 Kurtosis 28.8181 Skewness 4.5006

59,569,803 144,756,215 2.4300 51.2391 5.8220

85,369,242 201,638,384 2.3620 67.5237 6.6764

131,235,421 245,547,595 1.8710 52.4200 5.8107

214,450,370 387,151,125 1.8053 31.1260 4.4795

134,219,526 247,870,681 1.8468 31.1940 4.6485

Efficient firms with the CRS model Mean 31,866,892 Standard deviation 33,830,666 Coefficient of var. 1.0616 Kurtosis 0.4474 Skewness 1.3393 Per cent efficient firms 0.8641

59,448,567 131,143,522 2.2060 12.4967 3.5083 0.4739

82,258,524 82,383,530 1.0015 1.4658 1.3459 0.3567

151,308,613 137,268,797 0.9072 −1.9970 0.1384 0.7157

138,166,285 117,958,092 0.8537 0.0434 0.5477 0.4961

857,927,594 798,890,706 0.9312 3.3929 1.7727 0.9226

Inefficient firms with the CRS model Mean 83,761,340 Standard deviation 179,273,305 Coefficient of var. 2.1403 Kurtosis 49.9816 Skewness 5.7393

63,808,909 180,686,838 2.8317 128.1408 8.9903

91,008,993 238,196,290 2.6173 106.2992 8.3319

137,389,310 275,282,803 2.0037 62.0546 6.4379

234,037,489 516,658,566 2.2076 147.4829 9.1689

137,238,061 266,468,671 1.9417 40.7390 5.2566

184

1980–1

Table 5.18 Distribution of outputs of the electronics industry 1980–1

1990–1

1994–5

1999–2000

2002–3

Efficient firms with the VRS model Mean 187,682,327 Standard deviation 379,019,078 Coefficient of var. 2.01947 Kurtosis 9.96985 Skewness 3.03101 Per cent efficient firms 4.05405

349,459,540 853,288,203 2.44174 10.88709 3.22783 3.56201

379,989,486 754,139,437 1.98463 8.29137 2.81430 3.61068

1,021,525,376 1,722,556,636 1.68626 6.23371 2.37633 3.70370

1,599,839,705 3,223,916,200 2.01515 12.91167 3.40118 6.73759

3,218,784,817 6,252,669,603 1.94256 4.57479 2.30479 4.42708

Inefficient firms with the VRS model Mean 22,118,745 Standard deviation 71,961,027 Coefficient of var. 3.2534 Kurtosis 65.0695 Skewness 7.2417

33,824,649 95,500,027 2.8234 41.9031 5.7215

52,530,772 140,813,908 2.6806 40.1876 5.6724

91,787,405 192,997,520 2.1027 32.8277 4.8695

214,347,640 518,049,107 2.4169 18.2624 4.0557

215,508,299 576,865,139 2.6768 35.7436 5.5069

Efficient firms with the CRS model Mean 111,543,674 Standard deviation 151,375,642 Coefficient of var. 1.3571 Kurtosis 1.2889 Skewness 1.5616 Per cent efficient firms 2.1236

71,173,320 80,134,557 1.1259 −0.5618 1.0915 0.9235

411,373,503 616,527,894 1.4987 5.4766 2.2932 1.4129

726,231,295 637,659,365 0.8780 −1.2596 0.7074 1.1696

2,668,542,584 4,736,818,040 1.7751 6.0486 2.4033 2.8369

797,957,898 1,116,421,077 1.3991 4.1930 2.0336 1.3021

Inefficient firms with the CRS model Mean 27,036,223 Standard deviation 106,022,751 Coefficient of var. 3.9215 Kurtosis 112.8982 Skewness 9.3965

44,824,251 193,697,817 4.3213 214.8952 12.8300

59,381,036 191,126,601 3.2186 117.8810 9.0563

119,121,445 405,909,374 3.4075 152.1718 10.8043

238,766,580 569,241,370 2.3841 15.1164 3.7630

342,535,879 1,532,596,540 4.4743 125.2082 10.4020

185

1985–6

186

Table 5.19 Percentage of underutilization of labor in the electronics industry State

Andhra Pradesh Chandigarh Delhi Goa Gujarat Haryana Himachal Pradesh Karnataka Kerala Madhya Pradesh Maharashtra Orissa Punjab Rajasthan Tamil Nadu Uttar Pradesh West Bengal Average

1990–1 1991–2 1992–3 1993–4 1994–5 1996–7 1997–8 1998–9 1999–2000 2000–1 2001–2 2002–3 State average 53 3 23 0 17 17 4

47 0 0 0 1 28 9

58 3 0 0 25 32 3

32 0 0 0 18 31 0

32 0 8 0 18 18 0

10 8 15 0 21 13 19

25 23 14 0 21 15 20

34 13 10 0 20 15 8

28 21 0 3 14 7 9

65 0 14 0 36 26 26

49 26 16 13 38 26 14

25 23 14 0 21 15 20

38 10 10 1 21 20 11

10 17 23

8 27 18

17 37 21

14 35 26

14 27 20

31 20 27

28 26 20

32 30 29

22 18 23

42 40 41

40 35 44

28 26 20

24 28 26

17 38 35 50 10 7 29 21

3 28 30 32 5 7 27 16

23 49 30 44 10 24 21 23

35 43 41 33 26 15 21 22

27 41 32 27 29 15 26 20

27 16 0 37 19 35 24 19

24

27

18

32

0 0 37 23 22 19

0 48 53 23 32 23

0 30 19 18 33 16

0 42 39 32 33 29

37 0 0 44 35 35 39 29

24 0 0 37 23 22 21 19

25 27 14 35 25 21 27 21

Table 5.20 Percentage of underutilization of capital in the electronics industry State

Andhra Pradesh Chandigarh Delhi Goa Gujarat Haryana Himachal Pradesh Karnataka Kerala Madhya Pradesh Maharashtra Orissa Punjab Rajasthan Tamil Nadu Uttar Pradesh West Bengal Average

1990–1 1991–2 1992–3 1993–4 1994–5 1996–7 1997–8 1998–9 1999–2000 2000–1 2001–2 2002–3 State average 7

5

5

5

3

11

20

12

2

5

15

6

8

6 1 0 6 8 17

0 0 0 2 6 12

0 0 0 9 8 10

0 0 0 11 10 0

0 1 0 13 4 0

6 3 0 15 3 15

0 17 9 11 8 0

6 5 0 9 9 0

4 0 1 7 4 8

0 5 0 12 5 12

18 13 12 23 19 18

11 0 0 14 7 12

4 4 2 11 8 9

5 7 16

2 6 15

6 8 17

3 8 11

1 6 7

9 9 10

6 10 2

5 3 8

4 3 8

7 5 9

17 15 19

8 7 9

6 7 11

3 13 12 8 9 5 5 7

0 13 8 5 4 2 4 5

6 14 11 6 6 7 5 7

7 13 11 8 6 3 8 6

3 11 5 3 5 1 11 4

9 4 0 13 6 8 5 7

8 0 3 11 17 7 0 8

9 0 0 11 12 6 7 6

6 0 0 10 9 5 12 5

8 0 0 13 10 6 11 7

14 0 0 24 20 16 21 15

8 0 0 12 11 6 11 7

7 6 4 10 10 6 8 7 187

188 India’s New Economy Table 5.21 Average efficiency of the computer industry

1989–90 1990–1 1991–2 1992–3 1993–4 1994–5 1996–7 1997–8

Total input efficiency

Production workers

Nonproduction workers

Capital

0.446 0.379 0.404 0.333 0.391 0.287 0.280 0.288

0.517 0.413 0.329 0.427 0.451 0.290 0.297 0.370

0.452 0.385 0.504 0.315 0.399 0.271 0.334 0.310

0.369 0.339 0.378 0.258 0.322 0.301 0.211 0.185

21.591 18.000 18.349 14.655 16.667 12.360 11.268 15.873

19.318 15.000 22.018 11.207 18.519 12.360 16.901 17.460

20.455 14.000 14.679 9.483 17.593 13.483 11.268 14.286

Percentage of efficient firms 1989–90 18.182 1990–1 13.000 1991–2 15.596 1992–3 9.483 1993–4 15.741 1994–5 11.236 1996–7 11.268 1997–8 14.286

The figures for the state-specific average utilization of capital during the period indicate that Goa, Delhi, Chandigarh, Panjab, Uttar Pradesh and Karnataka did better in terms of efficient utilization of capital during the period. The ranking of the states remains similar to that in the utilization of labor. However, the utilization of capital was more efficient than the utilization of labor for all the states and the variation in the percentage of utilization of capital among the states was small compared to that in labor utilization. The year-wise average ranking of all the states again does not indicate any improvement in the utilization of capital during the post-liberalization period. Finally, we estimated a nonradial efficiency of inputs for only computer manufacturing firms during 1989–90 to 1997–98. Contrary to the general belief, the input efficiency of this industry also fell during the post-liberalization period. Table 5.21 shows that the efficiency of two types of workers and capital fell over this period. The percentage figures of efficient firms in terms of all types of input uses also fell during the period of post-liberalization, but not without fluctuations.

5 Concluding remarks The major findings regarding the performance of Indian industries during 1974–75 to 1987–88 are summarized below. First, a time-varying


(fixed ranking) model is used to test statistically the trends in efficiency. Relaxing the assumption of fixed ranking, a variable-ranking model of Cornwell et al. (1990) has also been applied to the same set of data. These analyses confirmed the decreasing efficiency of Indian industries from 1974–75 to 1987–88. Third, there are substantial variations in technical efficiencies across industries. But here again, the relative rankings of the industries are similar in all these estimates. Finally, the analysis of the sources of efficiency variations suggests that among the economic factors that are basically internal to the industries, skill, labor productivity, profit, capital utilization and industry dummy contribute a major positive part in explaining the variations in efficiencies. Contrary to general belief, capital intensity plays a significantly negative role in explaining this variation. This may be due to the problems relating to definition of capital, shortage of working capital and inappropriate choice of technology. If we compare the efficiency rankings of the industries, it is found that most of the consumer goods industries registered higher efficiency rankings relative to capital goods and intermediate goods industries during this period. Interestingly enough, most of these industries emerged in the economies of the NICs in the seventies. NICs like India have tried to focus on these commodities from the late seventies onwards. In India, this new set of commodities has found renewed importance during the Seventh Five Year Plan as sunrise industries. Thus, the main presumption on which the liberalization policy was implemented, namely that Indian industries were increasingly becoming inefficient over the period, is supported by our analysis. But the expectation that globalization would cure this inefficient industrial regime cannot be so easily affirmed. The analyses that follow are carried out to examine the effects of industrial liberalization on the performance of Indian industries. There is a mild upward trend of efficiencies, estimated using the nonradial measure, in both the industries, after a fall in the values during the early phase of liberalization. It has been seen in this analysis that in the textiles industry states that show the better utilization of production workers are not capable of maintaining a higher efficiency utilization of nonproduction workers. However, the rankings of the states in terms of both production and nonproductions worker are almost the same in the leather industry. But for Delhi the efficiency of non-production workers is lower than that of production workers. In terms of input and output efficiencies the ranks of the states for the years are similar. As expected, Delhi, Maharashtra and Gujarat as major textile-producing states perform better than other states. The output efficiencies of Delhi and Tamil


Nadu in the leather industry are higher than those of the other states. In terms of input-oriented efficiency manufacturing units belonging to the private sector perform worse than those in the state-managed sector for the textiles industry. The input efficiencies of the leather industry, however, indicate that privately run and state government run organizations perform better than the central government organizations. In explaining the total efficiency of the textiles industry skill plays a significant positive role, while it has a negative effect in explaining the variation in efficiency of production workers. Scale of operation plays a significant positive role in explaining the variation of efficiencies in the leather industry. There is no significant difference in the level of total efficiencies among the states in either industry. The major findings of our last section can be summarized as follows. The efficiency of industries in the modern and traditional sectors in India do not show any significant improvement during the post-liberalization period as envisaged by the policymakers. In terms of input utilization it has been found that some states with a smaller geographic area, such as Goa, Pondicherry, Haryana and Delhi, perform better than the larger states. The average capital utilization of firms in all the states is higher than labor utilization and the variation among the states in capital utilization is also low compared to that in labour utilization. A radial measure of input efficiency in the computer hardware industry indicates that the efficiencies of all type of inputs fell during the post-liberalization period.

Appendix 5.1 It is well known that there is no unanimously accepted method of measuring capital stock. There are both theoretical and empirical problems in measuring capital over time. The main problem is to judge whether one should take the gross fixed capital stock (GFCS) or net fixed capital stock (NFCS) as the best measure of capital input required for measuring efficiency and productivity. Most economists generally prefer the GFCS to NFCS for the purpose of production function and related issues. There are two reasons. First, as pointed out by Leontief (1953), ‘Use of depreciated coefficients implies that capital stock decreases in efficiency in exact relation to depreciation charge’, whereas ‘most available evidence indicates that this is not a reliable assumption’. The other reservation about taking the NFCS is an empirical limitation. It is argued that the available estimates of depreciation are based on either tax-based accounting or a


certain rule of thumb. Naturally, it is preferable to work with gross fixed capital stock. We have estimated the gross fixed capital stock using the perpetual inventory accumulation (PIA) method. GFCS up to 1971 (for which data series are available consistently without any break) was calculated using the figure of GFCS for the benchmark year 1964 taken from the study of Hashim and Dadi (1973). We took the NFCS and the depreciation for those years from ASI census sector at the four-digit level and added them up to the two-digit level according to our requirements. Then the gross:net ratio for 1971 was used in 1974 to calculate the GFCS at 1974 assuming that the ratio did not change significantly. And for the remaining years GFCS was calculated using PIA. We have assumed that the gross:net ratios for the census and sample sectors are the same for each industry group. Moreover, since we have estimated the gross:net ratio from Hashim–Dadi estimates of GFCS at the benchmark year, we have extended those two-digit industries into our three-digit classification whenever required.

Appendix 5.2 NIC 265: Manufacture of all types of textile garments 1 2 3 4

Manufacture of custom made wearing apparel. Manufacture of ready made garments – hand printed. Manufacture of ready made garments – hand embroidered. Manufacture of ready made garments – other than (2) and (3).

NIC 291: Manufacture of foot wares 1 2 3 4

Manufacture of leather shoes. Manufacture of leather cum rubber/plastic/cloth shoes. Manufacture of leather sandals and chappals. Manufacture of leather cum rubber/plastic/cloth sandals and chappals.

Appendix 5.3 Textiles 1 Weaving and finishing of cotton textiles on power looms. 2 Cotton spinning and weaving and processing mills. 3 Bleaching, dyeing and printing of cotton textile.


4 Spinning, weaving and processing of man made fibres. 5 Bleaching, dyeing and printing of artificial/synthetic textile fabrics. Electronics 1 Manufacture of office computing and accounting machinery and parts. 2 Manufacturing of television receivers, apparatus for radio broadcasting, radio telephony, video recording/reproducing, record/cassette players and others. 3 Manufacture of computers and computer-based systems. 4 Manufacture of electronic valves, tubes and other electronic components.

6 The Performance of the Banking Sector in the New Economy

1 Introduction The primary aim of liberalization is to strengthen the market mechanism by eliminating relative price distortion to achieve efficiency and growth, and to improve the performance of deregulated industry by encouraging competition. The presence of fixed transaction costs associated with every transaction will encourage borrowers and depositors to form a coalition to share the burden of transaction costs. A coalition of large number of investors will be able to invest in less liquid but more profitable securities to fulfill the individual investor’s liquidityintact demands for the appropriate size of the coalition. The banking sector of an economy plays a significant role in the financial life of the economy by functioning, particularly, as a financial intermediary, offering access to a payment system, transforming assets, managing risks, processing information and monitoring borrowers. As the banking sector performs the task of intermediation efficiently, the cost of loanable funds reduces and that encourages investment expenditure, resulting in a potential increase in the rate of economic growth. Improvements in competition and proficiency allocate resources efficiently for the benefit of the economy by reducing the prices of services extended to its customers. India initiated reform in the banking sector as an important counterpart of broad economic reform in 1991. Most of the commercial banks were under the control of the overregulated and overadministered public sector. Keeping pace with global changes in banking liberalization, India resorted to liberalization and deregulated the banking sector to cope with the ongoing reforms in real sectors. The reform measures aimed to strengthen prudential norms relating to income recognition, asset 193


classification, provisioning for bad and doubtful debts and capital adequacy for the banking system. Other reforms included relaxation of the administered structure of interest rates, and a gradual reduction of SLR (statutory liquidity ratio) and CRR (cash reserve ratio). The new policy provided licenses for the entry of new banks in the private sector, allowing private sector banks to access the capital market to augment their capital base, the establishment of debt recovery tribunals for the purpose of helping the banking system to recover its debts and the setting up of an ombudsman to resolve customer grievances. The nationalization of banks helped to diversify credit access for small industries and farmers across the country. The concept of government intervention in the process of development emerges in the context of market failure. This leads to a need to keep interest rates lower than market clearing levels. The focus of the financial institutions should be on social priorities instead of profit maximization, and deliberate credit allocation for development purposes instead of a market-driven process. The institutions successfully achieved their deposit mobilization objective by expanding branch networks in all parts of the country. Bank credit was an extremely scarce commodity. Policymakers formulated guidelines for credit rationing so that, based on production requirements, credit was available to unit. The aim was to reduce the misuse and diversification of credit in nonapproved investments on the part of resource users. Credit flow to large industries was regulated and rationed. Social planners tried to adopt measures to address the discrimination against small industries and farmers in the share of scarce credit resources to fulfill the objective of development with equity. Interest rates on deposits and advances were highly regulated. So briefly we can conclude that allocations of loans through the market mechanism were either very weak or absent. The concept of price competition lies outside the purview of banking activities, particularly for public sector banks. However, the expectation was that in the regime of regulated interest rates by the state, banks could compete with each other for scarce savings by providing better convenience to customers, with more branches or more employees per office or per geographic area of operation. This is clearly an underutilization of physical and human capital. The performances of firms are often measured in terms of their efficiency. Conventional wisdom holds that a policy of deregulation always improves efficiency. In this chapter, we investigate the performance of commercial banks in terms of technical efficiency, which measures managerial performance against the improved resources management practices of recent years. The technical efficiency of a firm can be

The Performance of the Banking Sector in the New Economy 195

measured in two ways: input-oriented measures, i.e. comparison of the observed level of inputs with the minimum level of inputs that could produce the observed level of output; and output-oriented measures for a given firm as the ratio of outputs of the firm under consideration using the same input vector to the outputs as a fully efficient firm. Outputoriented pure technical efficiency for a given firm is defined as the ratio of output of a firm using the same input vector to output of the firm operating on constant returns to scale technology using the same input vector. Output scale efficiency compares the efficiency scores of a firm under CRS and VRS assumptions. It captures whether the firm operates at the optimum size or right size. We have not seen any major study that investigated the scale efficiency aspect of Indian commercial banks. The conventional technique is to measure it from cost function or from cost frontier. Nevertheless, this process poses problems when prices are difficult to estimate or unavailable. Efforts have been made to investigate the technical and scale efficiency scores of Indian commercial banks not from a cost angle but from a production perspective. The second objective of this chapter is to investigate the nature of input congestion in Indian commercial banks of the three ownership types. For the analysis, first the presence of input congestions in the banks is established and then the sources of input congestions are identified. The comparison of input congestions among the three types of banks reveals some interesting features of Indian commercial banks in terms of their inefficiency. Third, the results for the input congestions suggest an analysis of the problem of input mix in explaining the inefficiency of Indian commercial banks. To understand the problem of input mix we have estimated the allocative efficiency of banks using the standard cost frontier model. In the absence of market prices of inputs some derived prices can be estimated for the inputs of banking operations. Those prices, along with the corresponding inputs, are often used to estimate the allocative efficiency of banks. The alternative way to find out the cost efficiency of banks is to estimate cost frontier using total cost incurred for a given output, defined in terms of advances, investment etc. of banks. This is basically a cost function approach from production economics. Indian commercial banking is particularly interesting in the sense that it consists of different ownership forms: state-owned, privately owned and foreign-owned banks. The coexistence of public, private and foreign banks gives us a good opportunity to investigate possible relationships between the efficiency scores and ownership forms of commercial banks.


The organization of the chapter is as follows. Section 2 presents an overview of the Indian commercial banking sector. In section 3 we present a review of the literature, and section 4 describes the methodology used in our study. Section 5 deals with the input and output set specification and the data source. Section 6 analyzes the empirical findings of our study. Some concluding remarks are made in section 7.

2 Overview of Indian commercial banks In the post-independence period, India witnessed the emergence of large numbers of institutions providing finance to different sectors of the economy. During the five year plans, the RBI (Central Bank in India) and the government nurtured and encouraged commercial banks through various financial incentives and other supportive programs to provide cheap finance to encourage industries to implement the import substitution growth model adopted by the planning commission of India. There was a significance presence of foreign banks as well as domestic banks. The commercial banks comprise foreign banks operating in India, publicand private-sector Indian banks and regional rural banks (RRBs). There were two nationalizations of banks in India, one in 1969 and the other in 1980. The activities of private-sector and foreign banks were restricted through branch licensing and entry regulation norms. The nationalization of banks provided an impetus to change and gave a new orientation to the system as a whole. It allowed nationalized banks to spread their activities in rural and semi-urban areas to mobilize deposits and extended credit, which integrated the barter economy into mainstream financial activities. Deposit mobilization and lending were the main objectives of the public-sector banks (PSBs) in India. Deposit mobilization in each year was the performance indicator for bank officials. Profitability and efficiency had an insignificant role in evaluation of performance. Deposit size was the only measuring rod to judge the performance of employees as well as the balance sheet of every bank. The share of advances to the priority sector increased considerably following the nationalization of major banks. RBI has heavily regulated market entry or exit, capital adequacy, reserve and liquidity requirements, asset portfolio allocation, number of branches, deposit insurance and interest rates on deposits and loans. RBI has set the target of 40 per cent of net bank credit going to the priority sector for Indian commercial banks (both public-sector and domestic private-sector banks) and 32 per cent for foreign private banks operating in India.


The overregulated and overadministered polices eroded the capital base of most of the public-sector banks and recapitalization of 19 nationalized banks was carried out by the government through budgetary provision during the recent period. The government also provided the banks with money towards writing down the capital base for adjustment of their losses. But acute problems arose in the productivity, efficiency and profitability front of the commercial banks. The policy of directed investment in the form of high SLR and CRR, directed credit programs, extra administrative interference in credit decision making, high operating costs, regulated interest rates, a nontransparent accounting system coupled with the nonexistence of operational flexibility, internal autonomy and the absence of competition contaminated the health of the commercial banks and threatened their future survival. Financial sector reforms became inevitable to cope with the ongoing reforms of the real sector coupled with the deterioration of the banking sector health and introduction of BIS capital adequacy norms. The government has paid attention to recapitalization of public sector banks through the provision of budgetary support and resource mobilization from the capital market. The Committee on Financial Systems (GOI, 1991), with the objective of fabricating an efficient, prudent and internationally competitive system, suggested a more market-friendly blueprint for first-generation reforms of the financial sector. Liberal policies aimed to increase market competition among banks to augment efficiency and productivity, and allowed the management of individual banks to make independent decisions about input–output and prices. The Committee on Banking Sector Reforms (GOI, 1998) suggested a road map for second-generation reforms to keep pace with the liberalization of financial sectors in other parts of the world. The other notable banking sector reforms were: 1 Reduction in financial regulation through statutory pre-emption, while stepping up prudential regulation. 2 Abolishion of the administered interest rate regime, allowing banks to determine lending and deposit rates. 3 Competition was infused through the operation of new private-sector banks and a more liberal entry regime for foreign banks. 4 A set of microprudential measures to impart greater strength to the banking system and to ensure safety and soundness, with the avowed objective of moving towards international best practices (capital


adequacy norms, exposure limits, recognition rules for NPAs, provisioning norms, accounting rules, valuation norms etc.). 5 Measures to broaden the ownership base of PSBs. 6 Greater levels of transparency and standards of disclosure. 7 Ratification of the legal structure to strengthen banks’ position in the areas of loan and default loan. Globalization has challenged Indian public-sector banks to compete not only with local private-sector banks but also with foreign banks. Hardly any Indian bank can compete globally in the international market. The State Bank of India ranks 82 in S&P’s list of 300 top banks. The government of India has the future agenda of consolidating large public-sector banks to create large banks that measure up to global standards. Although the reform was initiated in 1991, the transformation into a fully price competitive setup was not effective until 1994. The entry restrictions in the banking market on new private sector banks were diluted to accelerate competition provided they fulfilled certain criteria: a start-up capital requirement of Rs 1000 million, consecutive net profit records for three years, a capital adequacy ratio of 8 per cent and a net NPA rate of less than 15 per cent. It was mandatory on the part of commercial banks to get a license from RBI to open new branches until 1992. RBI withdrew the practice of branch licensing and gave greater freedom to banks to rationalize their existing branch network to relocate branches and establish extension counters provided they attain the revised capital adequacy norms and prudential accounting system. Foreign banks operating in India have gained the freedom to open new branches, provided they also fulfill the norms set for the entry of new banks. Foreign banks are also permitted to collaborate with new privatesector banks. Foreign equity in private banks is permissible. This allows joint ventures between local banks and foreign banks in the business of nonbank financial services. The basic tenet of these polices is the withdrawal of government intervention in the financial system by way of ceilings in interest rates or direction of credit allocation and increased freedom of entry in the sector. The efficiency and progress of the financial sector depends on portfolio management of assets, information acquisition and the stock of skilled human resources. Public-sector banks now enjoy greater autonomy to recruit skilled and specialized human resources from the open market with market-ruled remunerations to cope with the new


technological and business challenges of the new and emerging banking activities. The aim of the measures of liberalization in India from 1991 onwards was to make the banking sector strong, efficient, functionally diverse and competitive, coupled with the preservation of safety and soundness. Indian banks have a limited lending exposure to sensitive sectors such as equity trading, real estate business etc. Large holdings of government bonds have kept credit risk at a lower level. There is strict control over off-balance sheet activities. Reforms provided greater operational flexibility and functional autonomy to boost efficiency, productivity and profit. Foreign banks’ control of the banking sector’s assets and presence in India are insignificant. The entry of foreign banks will depend on the structure, strength and competitive environment of domestic banks together with the regulatory framework. Foreign banks, with better technology and knowledge in derivative trading, trade finance etc., can lead Indian domestic banks to concentrate expertise in these areas effectively. The operational flexibility and functional autonomy of PSBs will definitely improve due to partial privatization. The government diluted its holding stake of equity to 51 per cent. It has further proposed reducing its holding to a minimum of 33 per cent on a case-by-case basis. The entry of new private banks and foreign banks will promote competitiveness by introducing new products and better technology. The committee has undertaken deregulation to encourage competition to increase productivity and efficiency. The banks, guided by the principle of the free market, are likely to change their product mix, client mix and geographic areas of activity by executing appropriate human resource management given the technological constraints. The banks may opt for more risky assets to earn higher expected returns on assets. Banks are likely to shift higher funding costs and interest rate risk to borrowers. The synergic effect of deregulation-induced competition will lead to a higher level of efficiency, better resource allocation, innovation of products and progress in technology. In 1997, RBI constituted a Committee on Capital Account Convertibility (CAC) under the Chairmanship of S. S. Tarapore. The road map of CAC depends on fiscal consolidation, mandated inflation targeting and strengthening of the financial system. It recommended a number of liberalization measures to provide operational flexibility and autonomy in the financial sector in order to promote efficiency, productivity and profit. It advocates that the banking sector should fix targets of 5 and 3 per cent for gross NPA as a percentage of total advances and cash reserve ratio respectively.


The Reserve Bank of India in 2006 appointed a high-level Committee on Fuller Capital Account Convertibility (FCAC), also under Chairman S. S. Tarapore. Restructuring the banking sector by providing appropriate safeguards is necessary as the economy moves to a more open position on the external front. As the economy integrates with the global system, the banking sector will also integrate with rest of the world. The banks will be exposed to greater volatility of markets in the FCAC regime. The FCAC regime requires commercial banks’ involvement in multidimensional operations in situations of large inflows and outflows of capital. Therefore, it demands efficient management of exchange rate risk. The other risk elements (counterparty credit risk, transfer risk, legal risk and risk in derivative trading) are more prominent in the FCAC regime than now. The committee has recommended more liberal policies in conformity with the earlier recommendations of the Narasihmam I and Narasihmam II committee reports. A close look in Table 6.1 reveals that the Indian commercial banking sector witnessed a phenomenal improvement in activities by covering urban and semi-urban areas. The population served per branch rose marginally due to high population growth. There was a growth in deposits, advances and priority sector lending. Evidence of the dominance of public sector banks in major banking activities is observed from Table 6.2. Deregulation of interest rates on deposits and advances, coupled with the lowering of bank rate, SLR and CRR, have helped to lower the cost of deposits, return on advances and net interest margin (Tables 6.2 and 6.3).

3 Review of the literature Many studies have been organized to evaluate the performance of the banking sector in developed countries (Ferrier and Lovell, 1990; Noulas, 1997; Daniels and Tirtiroglu, 1998). Despite the fact that deregulation policies are aimed at increasing efficiency and productivity, the direct linkage between performance and deregulation may not always be unidirectional. One can see this from the experience of several countries. US banks experienced no change in efficiency (Bauer et al., 1993; Elyasiani and Mehdian, 1995) but a reduction in productivity (Humphrey, 1993; Humphrey and Pully, (1997). Spain experienced an outcome similar to the USA (Lozano, 1995; Grifell-Tataji and Lovell, 1997; Kumbhankar et al., 2001). Japan experienced little effect of deregulation on efficiency (Fukuyama, 1995). Norway, Portugal and Turkey experienced improved

The Performance of the Banking Sector in the New Economy 201 Table 6.1

Features of commercial banking

Indicator No. of commercial banks Scheduled commercial banks Number of offices of scheduled commercial banks in India Rural Semi-urban Urban Metropolitan Deposits of scheduled commercial banks in India (Rs million) Credit of scheduled commercial banks in India (Rs million) Per capita deposits of scheduled commercial banks (Rs) Per capita credit of scheduled commercial banks (Rs) Deposits of scheduled commercial banks as percentage of national income (at current prices) Scheduled commercial banks’ advances to priority sector (Rs million) Share of priority sector advances in total credit of scheduled commercial banks (%) Share of priority sector advances in total non-food credit of scheduled commercial banks (%) Credit deposit ratio Investment deposit ratio Cash deposit ratio

1998 300 299 64,218

2006 222 218 69,471

% of growth −26.00 −27.09 8.18

32,878 13,980 9,597 7,763 59,848.5

30,579 15,556 12,032 11,304 210,904.9

−6.99 11.27 25.37 45.61 25.240

32,407.9

150,707.7

36.503

6,170

19,276

212.41

3,356

13,774

310.43

47.3

1,089.05

73.8

5,467.74

56.03

4.0207

34.6

37.2

7.51

36.1

38.2

5.82

54.2 36.5 10.2

71.5 35.5 6.6

31.92 −2.74 −35.29

Source: RBI.

efficiency and productivity (Berg et al., 1992; Zaim, 1995; Ana Canhota, 2003; Isik and Hassan, 2003). Shyu (1998) found improved efficiency in the Taiwanese banking system after deregulation. Leightner and Lovell (1998) reported that deregulation led to a significant improvement in efficiency for Thai banks. Gilbert and Wilson (1998) reported that Korean banks improved their efficiency and productivity due to privatization and deregulation.

202

Table 6.2

Summary of the banking sector (billion rupees) 1990–1

No. of banks Total deposit Total credit Total income Total profit

1995–6

2002–3

2004–5

PSB

Pvt

For.

PSB

Pvt

For.

PSB

Pvt

For.

PSB

Pvt.

For.

28 2,087 1,306 240 5

25 94 50 104 0.3

23 85 51 15 2

27 3,908 2,075 539 3

35 361 219 72 5

29 306 225 75 7

27 10,794 5,493 1,285 122

29 2,069 1,377 316 29

38 693 522 121 18

28 14,207 8,093 1411 158

29 3,146 2,211 326 35

31 865 753 130 20

PSB, public sector banks; pvt, private sector banks; for., foreign banks. Source: RBI.

The Performance of the Banking Sector in the New Economy 203 Table 6.3

Selected banking indicators

Year

Bank rate

SLR

CRR

Cost of deposits

Return on advances

Net interest margin

1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005

12.00 12.00 12.00 12.00 12.00 11.00 9.00 7.00 8.00 7.00 6.50 6.25 6.00 6.00

38.50 37.80 34.80 31.50 25.00 25.00 25.00 25.00 25.00 25.00 25.00 25.00 25.00 25.00

15.00 14.50 14.50 15.00 13.50 10.00 10.30 10.50 5.50 8.00 5.50 4.75 4.50 5.00

6.50 7.40 6.70 5.80 7.00 7.80 7.60 8.00 7.40 7.20 7.00 6.40 4.90 4.20

14.70 17.20 13.10 10.90 12.90 14.60 13.00 12.70 11.50 11.10 10.50 9.90 8.10 7.20

3.80 3.00 3.20 3.20 3.40 3.30 3.00 2.70 2.80 2.80 2.60 2.70 2.90 2.90

Source: RBI.

Very few studies have been conducted to evaluate the performance of the banking sector in developing countries like India. However, Bhattacharyya et al. (1997) studied the pre-deregulation period and reported that foreign-owned banks in India were somewhat more efficient than privately owned domestic banks but government-owned banks were more efficient than either. Saha and Ravishankar (2000) estimated technical efficiency and reported technical efficiency scores ranging between 0.58 and 0.74 with a mean score of 0.69 in the year 1995 for Indian public-sector banks. Rammohan (2002, 2003) examined financial indicators for Indian commercial banks and reported an improvement in performance. Rammohan and Ray (2004) concluded that public-sector banks were better than private-sector banks on revenue maximization efficiency but between public sector banks and foreign banks the efficiency difference was not significant. Bhaumik and Dimova (2004) concluded that deregulation had helped public-sector banks to reduce the gap in performance that existed between them and private banks. Das et al. (2005) found that median efficiency scores in general and of bigger banks in particular have improved during the postliberalization period. Berger and Humphrey (1997) stated that industry condition prior to deregulation and other interactions might intervene in guiding the direction of productivity and efficiency change. Kumbhakar


and Sarkar (2005) analyzed the performance of Indian commercial banks during the post-deregulation period. They examined the pattern of changes in efficiency using econometric methods. They found cost inefficiency of Indian banks during the post-liberalization period, and noted that private banks, on average, were more cost efficient than publicsector banks. Sahoo et al. (2007) estimated the cost efficiency using a DEA model and found that public-sector banks were less efficient than private-sector banks.

4 Methodology Technical efficiency The discussion in this section provides a very brief introduction to efficiency measurement. The measurement of firm efficiency has been discussed by Farrell (1957), Lovell (1993) and Färe et al. (1994). However, modern efficiency measurement begins with Farrell (1957), who dealt with firms using multiple inputs for the first time. In parametric models, one specifies the functional form of the production frontier and estimates the parameters using inputs and output. Efficiency derived depends on the appropriateness of the functional form assumed. We estimate efficiency scores using data envelopment analysis (DEA), a nonparametric approach that computes ‘best practice’ efficient frontiers based on convex combinations of firms in the industry. It is an alternative mathematical programming based nonparametric approach to measuring efficiency rather than a conventional regression analysis. DEA involves the use of linear programming methods to construct a nonparametric piece-wise surface (or frontier) over data. Efficiency measures are then calculated relative to this frontier. One can calculate the efficiency of a firm in terms of how far it is from the frontier. This provides a nonparametric alternative to parametric frontier production function techniques, i.e. no functional specification of production technology is required (Charnes et al. (CCR), 1978; Banker et al. (BCC), 1984). However, the main limitation of this approach is that it eliminates random error. Banker (1993) showed that while the DEA estimator is biased for finite samples, the biasedness no longer exists for large samples. Therefore, the DEA estimator is asymptotically consistent. The technical efficiency of a firm can be measured in two ways.First, there are inputoriented measures, i.e. comparison of the observed level of inputs with the minimum level of input that could produce the observed of output. Output-oriented technical efficiency for a given firm is defined as the


ratio of output of the firm under consideration using the same input vector to the output a fully efficient firm. In DEA, a benchmark technology is constructed from the observed input–output bundles of the DMUs in the sample on the basis of some general assumptions about the production technology without specifying a functional form. All observed input–output combinations are feasible. An input–output bundle (x, y) is feasible when the output bundle y can be produced from the input bundle x. If we have a sample of N firms from an industry producing m outputs from n inputs, xj = (x1j , x2j , . . . , xnj ) is the input bundle of firm j, ( j = 1, 2, . . . , N) and y j = ( y1j , y2j , . . . , ymj ) is the observed output bundle. Then each (xj , y j ) is a feasible input–output bundle. The production possibility set is convex, i.e. if two feasible input– output bundles (X B , y A ) and (X B , Y B ) are considered, the weighted average input–output bundle (x , y ), where x = λX A + (1 − λ)X B and y = λy B + (1 − λ)y B , 0 ≤ λ ≤ 1, is also feasible. Inputs are freely disposable, i.e. if (x0 , y 0 ) is feasible, then for any x ≥ x0 , (x, y 0 ) is also feasible. Outputs are freely disposable, i.e. if (x0 , y 0 ) is feasible, then for any y ≤ y 0 , (x0 , y) is also feasible. In the CCR model an additional assumption holds, i.e. the assumption of constant return to scale. If (x, y) is feasible, then for any k ≥ 0, (kx, ky) is also feasible. Under CRS, the conical hull constitutes the production possibility set; it is the smallest cone containing the free disposal convex hull of the observed input–output bundles (S). S = {(x, y) : x ≥

λj xj , y ≤

λj y j ; λj ≥ 0, j = 1, 2, . . . , N}

Under CRS, the output-oriented technical efficiency of a firm t producing output vector y t from input vector xt is 1/∗ , where ∗ = max : (xt , y t ) ∈ S This problem may be converted to a linear programming problem and then our problem becomes max , s.t. λj ymj ≥ ymt ,

m = 1, 2, . . . , m


λj xnj ≤ xnt ,

free λj ≥ 0,

n = 1, 2, . . . , n j = 1, 2, . . . , N.

However, firms may face economies of scale or diseconomies of scale due to market conditions, fiscal crises and regulatory polices. Farrell and Fieldhouse (1962) proposed transformation of data to include nonconstant returns to scale. Forsund and Hjalmarsson (1979) decomposed Farrell’s measure of efficiency into scale efficiency and pure-technical efficiency for the parametric production frontier. Banker et al. (1984) utilized Frisch’s (1965) concept of technically optimal production scale efficiency. The VRS production possibility set (SV ) is the smallest set containing the free disposal convex hull of the observed input–output bundles. The production possibility set under VRS is S = {(x, y) : x ≥

λj xj , y ≤

λj y j ;

λj = 1; λj ≥ 0, j = 1, 2, . . . , N}

Under VRS, the output-oriented technical efficiency of a firm t producing output vector y t from input vector xt is 1/∗ , where ∗ = max : (xt , y t ) ∈ SV This problem is converted to a linear programming problem and then our problem becomes max , s.t. λj ymj ≥ ymt , m = 1, 2, . . . , m λj xnj ≤ xnt , n = 1, 2, . . . , n free λj ≥ 0,

j = 1, 2, . . . , N.

One can determine the reference frontier points by imposing the sum of weights of λ for the solutions of frontiers. No restriction is imposed on the sum of λ under CRS. Under VRS, the sum equals unity. Under NIRS (nonincreasing returns to scale), the sum is less than or equal to one ( λj ≤ 1). So, the VRS frontier is the smallest production technology set and the CRS frontier is the largest; the NIRS technology set lies in between (Ray, 2004). Output-oriented technical efficiency measures the ratio of the observed level of outputs of a firm to maximal feasible outputs from


a given input set. Output-oriented pure technical efficiency for a given firm is defined as the ratio of output of a firm using the same input vector to the output of the firm operating on constant returns to scale technology using the same input vector. Scale efficiency of banks is an important concept and different from technical efficiency measures. We can obtain scale efficiency for each firm by assuming that technology is VRS. The firm is scale inefficient if the CRS and VRS technical efficiency scores of a particular firm differ. On the other hand, scale efficiency compares the efficiency scores of a firm under VRS and CRS assumptions. It is the ratio of average productivity of a firm operating at the projected point on the VRS frontier to the average productivity of the point operating at the point of optimal scale. Scale efficiency arises when the firm is operating on either decreasing or increasing returns to scale. Calculations of scale efficiency measures are relevant when frontier exhibits variable returns to scale. It assumes that the CRS frontier generates the most optimal scale. Scale efficiency shows how close the observed firm is to the most productive scale size. A firm is scale inefficient if it operates beyond the most productive scale size, i.e. when the firm is experiencing decreasing returns to scale or if it fails to appropriate the full advantage of increasing returns to scale. Output scale efficiency compares the efficiency scores of a fully efficient firm under CRS and VRS assumptions. It captures whether the firm operates at the optimum or right size. Scale efficiency is less than one at all points on the VRS frontier because of nonoptimal productive scale size. It remains silent about the returns to scale. Increasing returns to scale hold when technical efficiency scores with reference to NIRS and CRS are equal but different from the VRS frontier. On the other hand, decreasing returns to scale hold if technical efficiency scores with reference to NIRS and VRS are equal but different from CRS. Technical efficiency scores with reference to NIRS, VRS and CRS are equal when the firm is scale efficient. Measure of congestion Färe et al. (1994) made a distinction between strong and weak disposability and strong disposability. Strong disposability of inputs implies that if any input combination X 0 can produce an output combination Y 0 then any X ≥ X 0 can also produce Y 0 . Similarly, strong disposability of output implies that if X 0 can produce Y 0 then any output combination Y ≤ Y 0 can be produced by X 0 . Weak disposability, on the other hand, implies that if all inputs are increased proportionately from X 0 then the new input combination can also produce Y 0 . In weak disposability


if any input is decreased there will be a reduction of at least one output. Now, the congestion of input can be defined as a situation when reduction in one or more inputs can increase at least one output or increase of any one or more inputs causes a reduction in one or more outputs. The standard BCC model of input efficiency under strong disposability can be written as min θ S , s.t. n

λj xij ≤ θ S xio ,

i = 1, 2, . . . , m

j=1 n

λj yij ≥ yio ,

r = 1, 2, . . . , k

j=1 n

λj = 1,

j = 1, 2, . . . , n

j=1

λj ≥ 0 If we assume weak disposability of inputs the model can be written as min θ W , s.t. n

λj xij ≤ θ W xio ,

i = 1, 2, . . . , m

j=1 n

λj yij ≥ yio ,

r = 1, 2, . . . , k

j=1 n

λj = 1,

j = 1, 2, . . . , n

j=1

λj ≥ 0 The basic difference between these two models is that the input inequalities are changed into input equalities. The input congestion measure can then be defined as η = θ S /θ W . Now, θ S must be greater than θ W because the latter is associated with equalities. If the value of η = 1 then the inputs are not congested and if η < 1 then we can say the input congestion is present in the production unit.


Now, this measure does not reveal the source of congestion, i.e. which specific input or input bundle is responsible for the input congestion. Färe et al. (1994) suggested a method for identifying the sources of congestion. The inputs are arbitrarily partitioned into two groups X S and X W , where the first subvector is treated as freely or strongly disposable and the latter is treated as weakly disposable. The corresponding DEA model for identifying the inputs responsible for congestion can be written as min θ˜ , s.t. n

λj xSij ≤ θ˜ xSio ,

i = 1, 2, . . . , S

j=1 n

˜ W λj xW ij = θ xio ,

i = S + 1, . . . , m

j=1 n

λj yij ≥ yio ,

r = 1, 2, . . . , k

j=1 n

λj = 1,

j = 1, 2, . . . , n

j=1

λj ≥ 0 If there exists input congestion, i.e. η < 1 and the optimal value of θ S = θ˜ ∗ , as obtained from the optimization of the model for subvectors associated with weak disposability, then it can be said that input congestion is due to the specific input group. Cost efficiency In an input-oriented DEA measure of efficiency all the inputs are to contract in the same proportion (radial measure) to reach the frontier without reducing any output. The prices of the inputs are not considered in measuring the technical efficiency of the DMUs. However, it is often necessary to consider the total cost of the inputs that are contracted in the same proportion to measure the efficiency of firms. When the prices of the inputs are available, the firms may be interested in minimizing the cost for a given level of output. Thus, for minimization of cost the inputs are to be contracted at different proportions according to the values of inputs. If we do not consider any underlying cost function of the firm a cost frontier may be estimated by the DEA approach, similar to the production frontier DEA model. The DEA approach of measuring


cost efficiency is an alternative to standard econometric modeling even when prices exist (Ray, 2004). In a DEA model a firm is said to be cost efficient if no other input bundle can produce the given level of output at a lower cost than the optimum level. Cost efficiency can be decomposed into technical efficiency and allocative efficiency. Allocative inefficiency of any DMU comes from the selection of inappropriate input mix. This concept of allocative efficiency can be illustrated graphically. Let us say for simplicity that the production unit produces a single output using two inputs and the production process follows a constant return to scale. It is also assumed that the efficient production function is known. If the objective of a producer is to minimize the wastage of input use the performance of the production unit can be measured in terms of technical efficiency/inefficiency. On the other hand, if the objective of a production unit is to minimize cost for a given level of output or maximization of profit by allocating inputs and outputs then the performance of the production unit can be defined in terms of economic efficiency. Now if the target output bundle is y0 and the input price vector is w0 then the DEA formulation of cost efficiency under the assumption of VRS can be written as follows: min

woi x˜ i , s.t.

i=1 n

λj xij ≤ x˜ io ,

i = 1, 2, . . . , m

λj yij ≥ yio ,

r = 1, 2, . . . , k

j=1 n j=1 n

λj = 1,

j = 1, 2, . . . , n

j=1

λj ≥ 0 The optimal solution of this linear programming model gives the cost minimizing input bundle and the objective function value shows the minimum cost. The optimal input bundle will lie in the efficient subset of the isoquant for the target output bundle. From this cost efficiency model we are interested in estimating the allocative efficiency of banks.


It has already been explained that the ratio of cost efficiency to technical efficiency will give the estimate of allocative efficiency. In a cost function approach an input-oriented cost frontier DEA model may be set up in a DEA framework as min θ, s.t. n

λj Cj ≤ θCh , λj yj ≥ yh

j=1 n j=1

yj2 λj ≥ yh2 ,

n

λj = 1,

λj ≥ 0

j=1

The objective function value gives the minimum cost for given values of output. If firm h is on the cost frontier (θ ∗ = 1.0), then Ch∗ = γ0 + γ1 yh + γ2 yh2 If γ0 is positive, then the average cost (ACh ) may be minimized so as to obtain the MSE level of output yh∗ = (γ0 /γ2 )1/2 , with the minimum AC given by ch∗ = Ch∗ /yh = 2(γ0 γ2 )1/2 + γ1 . We examine the performance of Indian commercial banks during the recent period after liberalization from the following indicators using the above methodologies. 1 Technical efficiency, scale efficiency and pure technical efficiency. 2 Total input congestion and labor congestion. 3 Cost and allocative efficiencies.

5 Input–output set of banks used in the analysis In this section, we deal with the problem of selection of the input and output set used to measure different efficiency scores of Indian commercial banks. In the banking literature, there is no consensus on the specification of banks’ outputs and inputs. Banks as financial firms provide a variety of services, loans, deposits, safe deposits, box rentals, mutual funds sales, foreign exchange transactions etc. For Indian scheduled commercial banks, recent years have witnessed sweeping changes in the regulatory environment, a huge growth of off-balance sheet risk management financial instruments, the introduction of e-commerce


and online banking and significant financial industry consolidation. All these have resulted in an expansion of the domain of financial services provided by commercial banks. For example, banks are now engaged in securities-related services such as underwriting and mutual fund sales. We can witness a wide variety of input and output specifications across the studies using DEA to measure efficiency scores of financial intermediaries. There are two broad approaches, namely the production approach and the intermediation approach. Before we discuss these two broad approaches and some other approaches, let us mention some problems those arise when the input–output specification of the banking sector is concerned. There is no consensus among economists regarding the specification of physical inputs and outputs of commercial banks. Now we come to the two broad approaches and give a brief description. In the production approach, banks are implicitly assumed to perform the role of the producer of deposits and loans as outputs, using capital and labor as inputs, all measured in terms of number of transactions per account. The main problem regarding this approach is that it ignores interest receipts and payments of banks and the magnitude of individual transactions. However, this approach may be of interest to evaluate the operating efficiency of the bank. We are aware of a few studies in this area, including Sherman and Gold (1985) and Ferrier and Lovell (1990), who used the production approach. The other broad and more frequently used approach to specify the input and output set of commercial banks is the intermediation approach, which assumes that the role of the banking sector is reconciling savers and lenders in the economy. Sealey and Lindley (1977) first used the intermediation approach to analyze financial institutions. Greenbaum (1967) used the intermediation approach, which includes both operating and interest expenditure, to measure both the technical and economic efficiency of a financial organization. Some economists do not use either of the above-mentioned approaches directly and specify the inputs and outputs used in their study on some other consideration. Some researchers argue for the measurement of efficiency as directly as possible, i.e. management’s success in controlling costs and generating revenues (that is, x efficiency). According to this approach, two inputs, interest expenditure and noninterest expenditure, and two outputs, interest income and noninterest income, are taken. Sathye (2001) adopted this approach to measure the efficiency of Australian commercial banks. Now we come to another interesting point and an issue of longlasting debate in this literature. In the banking literature, there have


always been differences of opinion regarding the specification of the input–output set. Nevertheless, major disagreement concentrates on whether one should treat deposits as an input or an output. In India, commercial banks work primarily as financial intermediaries with the objective of collecting deposits. In India, until the eighties, banks were operating in a protective environment characterized by administered interest rates. India initiated a reform process to improve the productivity and efficiency of the financial system. The reform included a gradual deregulation of interest rates. However, the role of deposits as the traditional main source of funds is still prevalent in the liberalized period. In India, commercial banks act mainly as financial intermediaries. Indian commercial banks collect deposits, give loans and invest primarily in government securities and other securities as well. In our study, we have used the intermediation approach. We have used data from Statistical Tables Relating to Banks of India and Reports on Trends and Progress of Banking in India (both RBI publications). We investigated a sample of 68 commercial banks (27 public-sector banks,1 28 domestic privatesector banks and 13 foreign banks) for a period of nine years (1996–97 to 2004–05), for which a consistent series of data is available. Since there are alternative measures of input and output for studying the performance of banks, the selection of inputs and outputs depends on the purpose of the study. Problem loans have been included in the output set in some studies of the efficiency of banks. Berg et al. (1992) included nonperforming loans in the nonparametric approach to efficiency study. Hughes and Mester (1993) and Mester (1996, 1997) applied problem loans in the parametric estimation of efficiency of commercial banks. Das et al. (2005) used earning assets instead of total advances in their analysis of Indian commercial banks. In our study, the three inputs are: net worth of banks, that is the sum of capital and reserves and surpluses; number of employees; and loanable funds comprising both deposit and borrowing. In our analysis, we consider three outputs: net performing loans (advances in the terminology of Indian commercial banks); investment; and noninterest income. We also consider assets net of fixed assets as a composite measure of output. We deflate both output and input data by a single price index (wholesale price index). The price of inputs is average staff cost as wages of labor, interest paid on deposit per one rupee of deposit as price of deposit, and nonlabor operational cost per rupee of fixed asset as price of capital. The sum of operating expenditure and interest expenditure is considered as a single measure of cost.


6 Empirical analysis Technical efficiency of Indian commercial banks The main objective of this section is to measure the efficiency of the Indian commercial banks and analyze the possible differences between the efficiency of banks of three ownership forms. The common argument is that public-sector banks lack the ability to influence incentives to reduce costs, so that private-sector banks are more productively efficient than public-sector ones. We examine the influence of ownership on the efficiency of Indian commercial banks. In this section, we report the performance of Indian commercial banks according to ownership, as well as taking all of them together. The results are calculated from the solution of DEA for each year separately for each bank. The measure of technical efficiency calculates the proportionate increase in output that can be achieved if the bank operates on the efficient frontier. As we mentioned earlier, the TE score can be decomposed into pure technical efficiency (PTE) and scale efficiency (SE), these measures will lie between zero and one. Table 6.4 reports the mean values of technical efficiency of all banks for all years studied. There exists no uniform pattern of movement of technical efficiency scores for all banks or for banks with different ownership. Contrary to the general belief the output-oriented average technical efficiency with the VRS specification for public-sector banks was greater than that of private-sector banks. Managerial efficiencies in the public-sector banks were high and declined marginally during the period, but not without fluctuations. The technical efficiencies of foreign banks showed higher values compared to the other two types of bank. Average values of PTE from the CRS specification show that the difference between private-sector and publicsector banks was negligible. From a comparison of the scale efficiencies between public- and private-sector banks it can be argued that privatesector banks were more scale efficient than public-sector banks. There exists no uniform pattern of movement of scale efficiency scores either type of banks. However, it seems that in the initial years both the publicand private-sector banks were weak in respect of size and now they are achieving the optimum scale size, and getting more efficient than before. Figure 6.1 shows the differences in the level of technical efficiencies of Indian commercial banks for the years 1996–97 to 2004–05. The CRS frontier generates the most optimal scale. It captures whether the firm operates at the optimum or right size. The firm is scale inefficient if technical efficiency with the CRS specification of that firm is less than that with the VRS specification. Banks operating either at the DRS or at

215 Table 6.4 Output-oriented technical and scale efficiency of banks in India Average efficiency CRS

% of banks

VRS

Scale

DRS

IRS

Scale efficient

(a) Public-sector banks 1996–7 0.8629 1997–8 0.9000 1998–9 0.8409 1999–2000 0.8660 2000–1 0.8329 2001–2 0.8431 2002–3 0.8861 2003–4 0.8974 2004–5 0.8529

0.9698 0.9770 0.9646 0.9587 0.9410 0.9116 0.9346 0.9453 0.9253

0.8894 0.9207 0.8728 0.9031 0.8841 0.9226 0.9472 0.9497 0.9204

77.78 74.07 85.19 81.48 92.59 81.48 85.19 74.07 85.19

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

22.22 25.93 14.81 18.52 7.41 18.52 14.81 25.93 14.81

(b) Private-sector banks 1996–7 0.8452 1997–8 0.8912 1998–9 0.8609 1999–2000 0.8703 2000–1 0.8316 2001–2 0.8317 2002–3 0.8452 2003–4 0.8686 2004–5 0.7960

0.9044 0.9229 0.8811 0.8966 0.8710 0.8517 0.8685 0.8830 0.8306

0.9339 0.9659 0.9787 0.9717 0.9571 0.9763 0.9736 0.9838 0.9616

67.86 57.14 28.57 17.86 42.86 10.71 28.57 21.43 30.77

14.29 14.29 42.86 53.57 42.86 60.71 53.57 50.00 46.15

17.86 28.57 28.57 28.57 14.29 28.57 17.86 28.57 23.08

(c) Foreign banks 1996–7 0.9456 1997–8 0.9515 1998–9 0.8562 1999–2000 0.9409 2000–1 0.8971 2001–2 0.8414 2002–3 0.9294 2003–4 0.8850 2004–5 0.8423

0.9618 0.9832 0.9232 0.9651 0.9465 0.9002 0.9527 0.9262 0.9040

0.9825 0.9681 0.9279 0.9742 0.9455 0.9352 0.9715 0.9518 0.9300

15.38 23.08 15.38 15.38 7.69 15.38 7.69 7.69 7.69

15.38 7.69 38.46 23.08 30.77 46.15 23.08 30.77 38.46

69.23 69.23 46.15 61.54 61.54 38.46 69.23 61.54 53.85

(d) All banks 1996–7 1997–8 1998–9 1999–2000 2000–1 2001–2 2002–3 2003–4 2004–5

0.941 0.956 0.922 0.934 0.913 0.885 0.911 0.916 0.879

0.926 0.948 0.927 0.945 0.926 0.947 0.963 0.964 0.937

61.76 57.35 48.53 42.65 55.88 39.71 47.06 39.71 48.48

8.82 7.35 25.00 26.47 23.53 33.82 26.47 26.47 25.76

29.41 35.29 26.47 30.88 20.59 26.47 26.47 33.82 25.76

0.871 0.906 0.852 0.882 0.845 0.838 0.878 0.883 0.822


1.0000 0.9800 0.9600

Efficiency

0.9400 0.9200 0.9000 0.8800 0.8600 0.8400 0.8200 1997

1998

1999 Public

2000

2001 Year

Private

2002 Foreign

2003

2004

2005

Total

Figure 6.1 Technical efficiency of Indian commercial banks

the IRS segment of the production frontier indicate a nonoptimal output vector. Returns to scale are defined by the change in outputs resulting from a change in inputs. Increasing returns to scale arise when output increases more than the proportionate increase in inputs. Decreasing returns signify a less than proportionate increase in outputs because of the increase in inputs. On the other hand, constant returns to scale occur when a proportionate increase in inputs is exactly equal to a proportionate increase in outputs. The percentage of banks with the optimum size varied from 35 to 21 per cent in our study period and did not show any specific trend. The percentage of private banks with optimum size witnessed a slight downward trend during the later period. Around 60 per cent of foreign banks achieved the optimum size in all years except three. Table 6.4 shows that about 25 per cent of Indian commercial banks saw IRS in their operation except in the first two years. An insignificant or zero percentage of public-sector banks recorded IRS in their operation during our period of investigation. Nevertheless, except in a few years a good number of private-sector banks recorded IRS in their operation. Therefore, there is a huge potential to exploit economies of scale. The percentage of foreign banks experiencing IRS remained more or less stable at between 15 and 46 per cent, except in 1997–98. The percentage


of Indian commercial banks suffering from DRS lay between 39 and 60 per cent. The percentage of Indian public-sector commercial banks suffering from DRS was acutely severe. The percentage always settled above 70 per cent and even reached nearly 90 per cent. This confirms that scale inefficiency in public-sector banks is the outcome of their excessive size. Table 6.4 shows no consistency of private-sector banks operating on the DRS segment of the frontier. The percentage varied from as low as 10 to as high as 68 per cent. However, the proportion of foreign banks facing DRS remained low throughout this period of investigation. The government of India’s future agenda to consolidate large public-sector banks to create larger banks is likely to aggravate scale inefficiency due to their consistently predominant presence in the DRS segment of the frontier. Great efforts to make them scale efficient by adopting appropriate policies should be the top priority before consolidation. Table 6.5 shows the banks that were efficient during the period 1996– 97 to 2004–05. The ownership-wise percentage figures of efficient banks reveal that foreign banks were more efficient than the banks in other two types of ownership. The percentage of efficient banks in the public sector was higher than that in the private sector for almost all the years of the study. The State Bank of India was efficient in all the years among the banks in the public sector. Some public-sector banks, such as the State Bank of Mysore and the Indian Overseas Bank, were efficient for most of the years considered in the study. Private-sector banks that were efficient in most of the years included HDFC, ICICI, IDBI and the Catholic Syrian Bank. However, most of the banks in the private sector did not reach the frontier level during the years 1996–97 to 2004–05. On the other hand, most of the foreign banks were efficient during the period of study. Input congestion in Indian banks Table 6.6 shows the banks with input congestion during 1996–97 to 2004–05. We can see that most of the public sector banks had input congestion during this period. There was no sign of a reduction in the number of banks with input congestion during the period. However, some of the banks in the public sector showed no congestion at all over the entire period. These were the State Bank of India and the Bank of Baroda. Six banks out of 27 in the public sector showed input congestion in only one or two years during the entire period. The banks that we found to be input congested in most of the years were the Indian Bank, the State Bank of Saurashtra, the State Bank of Patiala, the Syndicate Bank and the State Bank of Bikaner and Jaipur.

Table 6.5

Technically efficient banks by ownership and by year Efficient bank 1997–8

1998–9

1999–2000

2000–1

2001–2

2002–3

2003–4

2004–5

× ×

× × × ×

×

×

×

×

×

×

×

× × ×

× × ×

× × ×

× ×

×

×

×

×

×

×

×

×

×

×

× × ×

× ×

×

×

×

× ×

× ×

× × × ×

× × × × × × × × 48.15

× ×

× × × × ×

×

× × ×

× ×

× ×

× ×

×

× ×

×

× ×

× × ×

× ×

×

× ×

×

× ×

×

×

55.56

55.56

48.15

37.04

×

25.93

× ×

×

×

×

×

× ×

37.04

37.04

37.04

218

Public-sector banks State Bank of India State Bank of Bikaner And Jaipur State Bank of Hyderabad State Bank of Indore State Bank of Mysore State Bank of Patiala State Bank of Saurashtra State Bank of Travancore Allahabad Bank Andhra Bank Bank of Baroda Bank of India Bank of Maharashtra Canara Bank Central Bank of India Corporation Bank Dena Bank Indian Bank Indian Overseas Bank Oriental Bank of Commerce Punjab National Bank Punjab and Sind Bank Syndicate Bank Uco Bank Union Bank of India United Bank of India Vijaya Bank % efficient public banks

1996–7

× ×

×

× ×

× ×

×

×

× ×

× × × ×

× × × ×

×

× ×

× × ×

× × × × × × ×

× × × × × ×

×

×

× × × ×

× ×

× × ×

× × ×

× × × × × ×

× × × ×

× ×

×

32.14

× 39.29

× 32.14

× 32.14

× 25.00

× 21.43

× 25.00

× × 35.71

× × 25.00

(Continued)

219

Private-sector banks Bank of Punjab Bank of Rajasthan Bharat Overseas Bank Catholic Syrian Bank Centurion Bank City Union Bank Development Credit Bank Dhanalakshmi Bank Federal Bank Ganesh Bank of Kurundwad Global Trust Bank Hdfc Bank Icici Bank Idbi Bank Indusind Bank Jammu & Kashmir Bank Karnataka Bank Karur Vysya Bank Lakshmi Vilas Bank Lord Krishna Bank Nainital Bank Ratnakar Bank Sangli Bank Sbi Commercial & Intl Bank South Indian Bank Tamilnad Mercantile United Western Bank Uti Bank % efficient private banks

220

Table 6.5

(Continued) Efficient bank

Foreign banks Abn Amro Bank Abu Dhabi Commercial Bank American Express Bank Bank of Bahrain & Kuwait Bank of Ceylon Bank of Nova Scotia Bank of Tokyo Mitsubishi Barclays Bank Cho Hung Bank DBS Bank Dresdner Bank Société Generale Sonali Bank % efficient foreign banks

1996–7

1997–8

1998–9

1999–2000

2000–1

2001–2

2002–3

2003–4

2004–5

× × ×

× × × × × ×

× × ×

× × ×

× × ×

× ×

× × ×

× × ×

× × ×

× ×

× ×

×

×

×

×

× ×

× ×

× × ×

× × × ×

× × × ×

× ×

× ×

× 61.54

× × × 69.23

×

× 69.23

× × × × × × 76.92

× × × × × × × 76.92

× × × × × × 92.31

× 61.54

× × 76.92

× 69.23

Table 6.6

Input congestion in Indian commercial banks Years with congestion

Input congestion 1997

× ×

× × ×

× ×

×

×

× ×

×

× × 29.6

1999

× × × × ×

×

×

×

× × 40.7

2001

2002

2003

2004

2005

×

×

× ×

×

×

×

× ×

× ×

× ×

×

×

×

×

× × ×

× ×

×

×

2000

× × 40.7

× × × ×

×

22.2

× × × × × × 51.9

× × × ×

×

× × × × ×

× × 59.3

×

×

× ×

×

×

×

× × ×

× × ×

× ×

× ×

×

× ×

× × × 59.3

× ×

× × × ×

× ×

44.4

0 6 1 2 1 7 8 1 5 4 5 0 4 5 4 4 1 9 2 3 3 5 7 3 4 5 5

37 (Continued)

221

Public-sector banks State Bank of India State Bank of Bikaner and Jaipur State Bank of Hyderabad State Bank of Indore State Bank of Mysore State Bank of Patiala State Bank of Saurashtra State Bank of Travancore Allahabad Bank Andhra Bank Bank of Baroda Bank of India Bank of Maharashtra Canara Bank Central Bank of India Corporation Bank Dena Bank Indian Bank Indian Overseas Bank Oriental Bank of Commerce Punjab National Bank Punjab and Sind Bank Syndicate Bank Uco Bank Union Bank of India United Bank of India Vijaya Bank % public banks with input congestion

1998

(Continued)

222

Table 6.6

Years with congestion

Input congestion 1997 Private-sector banks Bank of Punjab Bank of Rajasthan Bharat Overseas Bank Catholic Syrian Bank Centurion Bank City Union Bank Development Credit Bank Ltd Dhanalakshmi Bank Federal Bank Ganesh Bank of Kurundwad Global Trust Bank Hdfc Bank Icici Bank Idbi Bank Indusind Bank Jammu & Kashmir Bank Karnataka Bank Karur Vysya Bank Lakshmi Vilas Bank Lord Krishna Bank Nainital Bank Ratnakar Bank

1998

1999

2000

2001

×

×

×

×

×

2002

2003

×

×

×

2005

× ×

×

×

× ×

×

×

×

2004

×

×

× ×

× × × ×

× × ×

×

× ×

× ×

×

×

×

× × × × × ×

× × × ×

0 3 1 2 2 2 2 2 2 0 2 1 0 1 0 3 1 5 5 2 6 4

Sangli Bank Sbi Commercial & Intl Bank South Indian Bank Tamilnad Mercantile United Western Bank Uti Bank % private banks with input congestion Foreign banks Abn Amro Bank Abu Dhabi Commercial Bank American Express Bank Bank of Bahrain & Kuwait Bank of Ceylon Bank of Nova Scotia Bank of Tokyo Mitsubishi Barclays Bank Cho Hung Bank DBS Bank Dresdner Bank Société Generale Sonali Bank % foreign banks with input congestion % all banks with input congestion

× ×

× ×

× 21.4

10.7

× × ×

×

×

× × × × ×

32.1

42.9

14.3

× × ×

×

×

×

×

×

× ×

×

×

×

28.6

25

39.3

35.7

×

×

×

×

×

× ×

×

×

0

0

23.1

7.69

15.4

23.1

7.69

15.4

7.69

20.6

20.6

33.8

27.9

29.4

39.7

29.4

42.6

30.9

9 4 3 5 3 0

0 0 0 0 2 0 5 0 0 3 2 1 0

223


Input congestion was less prominent in the private sector banks. It is evident from Table 6.6 that the percentage of banks with congestion was lower in most of the years compared to the public-sector banks. The nonexistence of input congestion is discernible for four banks during the period of study: the Bank of Punjab, the Ganesh Bank of Kurundwad, ICICI Bank and UTI Bank. For most of the banks, input congestion was not found for more than three years out of the nine years in the liberalized era. Table 6.6 show that the percentages of foreign banks with input congestion were lower than those of the banks in the other two sectors in all the years studied. The foreign banks, as expected, showed very little or no input congestion during the period. The table reveals that Bank of Tokyo Mitsubishi was the only bank to show input congestion in most of the years, while most of the banks showed no congestion during the entire period. To discover the sources of congestion we divided the inputs into two groups. Labor is associated with weak disposability and other inputs are associated with strong disposability. Thus from the DEA model for subvector input and the DEA model with strong disposability of all inputs, as defined earlier, we can identify the banks with labor input congestion. Table 6.7 lists the banks with labour congestion during the period 1996–97 to 2004–05. The comparison of the percentage of banks with labor congestion among the three groups of banks reveals that foreign banks had either no congestion or very little labor congestion during the period of study. On the other hand, the percentage of public-sector banks with labor congestion varied from 7.4 per cent in the year 2000 to a high of 44.4 per cent in the year 2002. Figure 6.2 depicts the comparison of level of congestion in terms of percentage of banks with labor congestion among the three ownership types. The banks that showed labor congestion for most of the years were the Bank of Saurashtra and the Bank of Patiala. Banks showing labor congestion for more than 50 per cent of the years were the Bank of Bikaner and Jaipur, the Syndicate Bank and the Vijaya Bank. Among the private-sector banks only one bank showed labor congestion for most of the years, the Sangli Bank. The labor congestion in other banks was evenly dispersed and ten banks in this sector showed no labor congestion during this period. Thus, as expected, labor congestion was high in the public-sector banks and there was much less congestion in the private-sector banks. Again, the foreign banks had very little or no labor congestion during the period of study.

Table 6.7

Labor congestion in Indian commercial banks Years with congestion

Labor congestion 1997

×

1999

×

× × ×

× ×

× ×

×

×

×

×

2000

× ×

2001

2002

2003

2004

2005

×

× ×

×

×

×

× ×

× ×

× ×

×

×

×

×

×

×

× × ×

× × ×

×

×

×

×

× ×

× ×

× 29.6

× × 44.4

×

22.2

× 33.3

7.41

× × ×

×

× 14.8

× ×

37

×

× × 25.9

×

0 5 1 2 1 7 8 0 3 3 1 0 2 1 4 3 1 1 1 0 2 3 5 0 0 4 5

18.5

(Continued)

225

Public-sector banks State Bank of India State Bank of Bikaner and Jaipur State Bank of Hyderabad State Bank of Indore State Bank of Mysore State Bank of Patiala State Bank of Saurashtra State Bank of Travancore Allahabad Bank Andhra Bank Bank of Baroda Bank of India Bank of Maharashtra Canara Bank Central Bank of India Corporation Bank Dena Bank Indian Bank Indian Overseas Bank Oriental Bank of Commerce Punjab National Bank Punjab and Sind Bank Syndicate Bank Uco Bank Union Bank of India United Bank of India Vijaya Bank % public banks with labor congestion

1998

(Continued)

226

Table 6.7

Years with congestion

Labor congestion 1997 Private-sector banks Bank of Punjab Bank of Rajasthan Bharat Overseas Bank Catholic Syrian Bank Centurion Bank City Union Bank Development Credit Bank Ltd Dhanalakshmi Bank Federal Bank Ganesh Bank of Kurundwad Global Trust Bank Hdfc Bank Icici Bank Idbi Bank Indusind Bank Jammu & Kashmir Bank Karnataka Bank Karur Vysya Bank Lakshmi Vilas Bank Lord Krishna Bank Nainital Bank Ratnakar Bank

1998

1999

2000

2001

×

×

2002

2003

×

×

×

× × ×

2004

× ×

×

×

× ×

× ×

×

×

×

2005

×

×

× × × × × ×

× × × ×

0 3 1 2 0 2 1 1 0 0 0 0 0 0 0 1 1 5 5 1 5 3

Sangli Bank Sbi Commercial & Intl Bank South Indian Bank Tamilnad Mercantile United Western Bank Uti Bank % private banks with labor congestion Foreign banks Abn Amro Bank Abu Dhabi Commercial Bank American Express Bank Bank of Bahrain & Kuwait Bank of Ceylon Bank of Nova Scotia Bank of Tokyo Mitsubishi Barclays Bank Cho Hung Bank DBS Bank Dresdner Bank Société Generale Sonali Bank % foreign banks with labor congestion % all banks with labor congestion

×

×

× 7.14

× × ×

×

×

× 7.14

32.1

3.57

10.7

×

×

×

×

×

×

×

×

21.4

25

35.7

25

×

7 2 1 4 2 0

0 0 0 0 0 0 3 0 0 0 0 0 0

×

0

0

7.69

0

0

7.69

0

7.69

0

11.8

16.2

20.6

4.41

16.2

27.9

25

26.5

17.6

227


50 45 40

Percentage

35 30 25 20 15 10 5 0 1997

1998

1999 Public

2000

2001 Year

Private

2002 Foreign

2003

2004

2005

Total

Figure 6.2 Percentage of banks with labor congestion

Cost and allocative efficiency of Indian commercial banks Table 6.8 describes the performance of Indian commercial banks in terms of cost efficiencies. Here, as already noted, we use the cost function DEA approach to estimate the optimum cost and the corresponding cost efficiency of banks for all the years. The average efficiency level of the public-sector banks was over 70 per cent for most of the years. The number of banks whose cost and output vectors lay on frontier was, however, not significant compared to the total number of banks and the percentage ranged from a low of 7 to a high of 26 per cent in 1997–98. The private-sector banks show a lower level of efficiency than the publicsector banks (see Figure 6.3). This result is different from the efficiencies obtained in studies by Kumbhakar and Sarkar (2005) and Sahoo et al. (2007). They found that efficiencies in the private sector-banks were higher than in the public sector banks. However, their period, output specification and methods of analysis were different from those of the present study. The value ranged between 40 and 80 per cent, and for most of the years the value was lower than that of public-sector banks. The numbers of efficient banks were very low compared to the other two sectors. Foreign banks performed much better than the banks in other two types of ownership. The percentage of efficient banks in this ownership

Table 6.8

Cost efficiency of Indian commercial banks (cost function DEA approach) 1998

1999

2000

2001

2002

2003

2004

2005

1 0.5766 0.5532 0.5049 0.5105 0.5216 0.5795 0.4715 0.4775 0.4794 0.6515 0.7931 0.5120 0.5256 0.5348 0.5433 0.4820 0.4026 0.4252 0.5172 1 0.1293 0.5152 0.3601 0.7295 0.5217 0.4666 0.5476 7.41

1 0.8824 0.9730 0.8854 0.8122 0.8732 0.8721 0.8317 0.9949 0.8900 0.9346 1 0.9568 1 1 1 0.9209 0.8101 0.8325 1 0.8666 0.9972 0.9146 0.8975 1 0.9328 0.8990 0.9251 25.93

1 0.7957 0.9248 0.7871 0.7095 0.8767 0.7556 0.7781 0.7939 0.8035 0.9002 1 0.8004 0.8967 0.8381 0.8876 0.7381 0.6362 0.7131 0.7702 0.6887 0.8987 0.7721 0.5173 1 0.8352 0.7066 0.8083 11.11

1 0.7329 0.8831 0.7795 0.6793 0.8278 0.6715 0.6960 0.7631 0.8066 0.9201 0.9965 0.6818 1 0.8801 0.8783 0.7011 0.6747 0.6895 0.9036 0.5951 0.9063 0.7693 0.5255 1 0.8576 0.5769 0.7925 11.11

1 0.6035 0.8916 0.6918 0.5820 0.8204 0.5934 0.6667 0.6823 0.8414 0.8601 1 0.7782 0.9844 0.8440 0.7728 0.5547 0.6973 0.7173 0.8294 0.5890 0.9039 0.6554 0.5387 1 0.8633 0.6303 0.7627 11.11

1 0.4745 0.5596 0.5620 0.3291 0.4014 0.3299 0.3217 0.4625 0.3335 0.7229 0.8742 0.3527 1 0.4764 0.5922 0.5000 0.6502 0.5771 0.5762 0.3033 0.6560 0.2705 0.5701 0.6837 0.4261 0.3229 0.5307 7.41

1 0.7077 0.8783 0.7301 0.6008 0.7442 0.6947 0.6934 0.5959 0.6864 0.8184 1 0.7624 0.8893 0.7613 0.7491 0.6169 0.7490 0.7228 0.6885 0.5834 0.8775 0.6250 0.4786 1 0.6276 0.6910 0.7397 11.11

1 0.6155 0.8230 0.6041 0.5492 0.7553 0.6755 0.7330 0.6405 0.5363 0.9813 1 0.7262 1 0.8474 0.6089 0.6052 0.6708 0.6715 0.7078 0.4852 0.9296 0.6716 0.5064 1 0.5608 0.6951 0.7259 14.81

1 0.6196 0.7669 0.6371 0.5775 0.8006 0.7029 0.7000 0.7454 0.7800 0.8553 0.7898 0.7028 0.8295 0.8146 0.6908 0.6412 0.7792 0.6315 0.6963 0.5844 0.8948 0.6628 0.5141 1 0.5421 0.7731 0.7308 7.41

(Continued)

229

Public-sector banks State Bank of India State Bank of Bikaner and Jaipur State Bank of Hyderabad State Bank of Indore State Bank of Mysore State Bank of Patiala State Bank of Saurashtra State Bank of Travancore Allahabad Bank Andhra Bank Bank of Baroda Bank of India Bank of Maharashtra Canara Bank Central Bank of India Corporation Bank Dena Bank Indian Bank Indian Overseas Bank Oriental Bank of Commerce Punjab National Bank Punjab and Sind Bank Syndicate Bank Uco Bank Union Bank of India United Bank of India Vijaya Bank Mean efficiency of public banks % efficient public banks

1997

230

Table 6.8

(Continued)

Private-sector banks Bank of Punjab Bank of Rajasthan Bharat Overseas Bank Catholic Syrian Bank Centurion Bank City Union Bank Development Credit Bank Ltd Dhanalakshmi Bank Federal Bank Ganesh Bank of Kurundwad Global Trust Bank Hdfc Bank Icici Bank Idbi Bank Indusind Bank Jammu & Kashmir Bank Karnataka Bank Karur Vysya Bank Lakshmi Vilas Bank Lord Krishna Bank Nainital Bank Ratnakar Bank

1997

1998

1999

2000

2001

2002

2003

2004

2005

0.8341 0.4481 0.4820 0.4234 0.7931 0.5736 0.6011 0.4432 0.5620 0.4731 0.7828 0.7311 0.7870 1 0.7001 0.5696 0.5232 0.5460 0.5272 0.5165 0.4704 0.4444

0.8574 0.6692 0.6598 0.6331 0.7731 0.7121 1 0.6611 0.9459 0.5619 0.9584 1 1 1 1 0.9591 0.8036 0.8142 0.7604 0.5650 0.6369 0.6082

0.8587 0.5650 0.6194 0.5244 0.5048 0.6458 0.6964 0.5996 0.7428 0.5536 1 1 0.9283 0.8791 0.7457 0.8510 0.6944 0.6999 0.6646 0.4526 0.6522 0.5470

0.6964 0.4970 0.5853 0.4985 0.6474 0.6612 0.8493 0.5960 0.6055 0.5089 1 1 0.8025 0.5924 0.8761 0.6758 0.5657 0.6110 0.7776 0.6170 0.5104 0.4860

0.5398 0.4836 0.6067 0.4606 0.3947 0.6058 0.6323 0.5379 0.7383 0.4577 0.6099 0.9435 1 0.4724 0.8102 0.8267 0.5665 0.6482 0.6761 0.5119 0.1601 0.4573

0.3059 0.2496 0.3716 0.3301 0.1871 0.3576 0.3761 0.3495 0.3351 0.2552 0.3090 0.542301 1 0.3667 0.4949 0.3612 0.3954 0.3567 0.3790 0.4205 0.4172 0.3897

0.5322 0.6922 0.6816 0.5741 0.3268 0.6342 0.6191 0.5995 0.6068 0.5309 0.4982 0.9332 1 0.6978 0.8047 0.7128 0.6738 0.7268 0.6502 0.5992 0.5908 0.5443

0.3221 0.8288 0.3141 0.3989 0.1587 0.3909 0.4383 0.2762 0.5192 0.4454 0.2883 1 1 0.7540 0.6384 0.7157 0.5919 0.4128 0.3325 0.3504 0.3716 0.2674

0.2554 0.6578 0.2725 0.2607 0.2710 0.2599 0.3914 0.1629 0.5839 0.4036 0.8714 1 1 0.5043 0.7456 0.6411 0.4090 0.2546 0.1837 0.2571 0.2306

Sangli Bank Sbi Commercial & Intl Bank South Indian Bank Tamilnad Mercantile United Western Bank Uti Bank Mean efficiency of private banks % efficient private banks

0.4460 0.3631 0.4181 0.7123 0.7368 0.5130 0.5865 3.57

0.7385 0.7270 0.7119 0.7884 0.9630 1 0.8039 21.43

0.6647 0.5765 0.6404 0.7379 0.7967 0.9523 0.7069 7.14

0.5490 0.7929 0.6009 0.6213 0.9342 0.9771 0.6834 7.14

0.4222 0.4847 0.5786 0.5586 0.7278 0.7436 0.5948 3.57

Foreign banks Abn Amro Bank Abu Dhabi Commercial Bank American Express Bank Bank of Bahrain & Kuwait Bank of Ceylon Bank of Nova Scotia Bank of Tokyo Mitsubishi Barclays Bank Cho Hung Bank DBS Bank Dresdner Bank Société Generale Sonali Bank Mean efficiency of foreign banks % efficient foreign banks Mean efficiency of all banks % efficient banks

1 0.4104 1 0.4565 1 0.7429 0.7565 0.5380 1 0.3295 0.2981 0.4048 1 0.6874 38.46 0.5903 11.76

1 0.6648 1 0.5378 1 1 0.7638 0.8459 0.9618 0.7241 0.5643 0.6529 1 0.8243 38.46 0.8559 26.47

1 0.6764 0.9897 1 1 0.9407 0.1809 0.5719 0.4894 0.7325 0.5412 0.4970 1 0.7400 30.76 0.7535 13.23

1 0.7342 0.4705 1 1 0.44879 0.5410 0.5690 1 0.9700 1 0.9899 0.6500 0.7274 0.4960 0.6418 1 1 0.9512 0.5759 0.5576 1 0.5738 0.3838 1 1 0.7877 0.7724 46.15 30.76 0.7466 0.6954 16.17 11.76

0.3549 0.3008 0.3443 0.3327 0.3750 0.9735 0.4011 3.57

0.6594 0.4615 0.5949 0.6296 0.6535 0.6654 0.6391 3.57

0.4988 0.4064 0.5470 0.4447 0.4589 0.5447 0.4899 7.14

0.4345 0.4520 0.4674 0.3715 0.8611 0.4693 7.14

0.4130 0.4559 0.5348 0.3656 0.5740 0.4408 0.4285 0.8442 1 0.4091 1 0.2460 1 0.5932 23.07 0.4893 8.82

0.7808 0.8009 0.4848 0.6360 0.9993 0.7320 0.5864 1 1 0.7853 1 0.7296 1 0.8104 30.76 0.7118 11.76

0.5758 0.2946 0.1933 0.1099 0.2271 1 0.2257 0.9114 1 0.1788 1 0.7176 0.4215 0.5274 23.07 0.5908 13.23

1 0.2106 0.3691 0.0804 0.4496 1 0.2059 1 1 0.8499 1 1 0.3377 0.6541 46.15 0.6127 14.70

231


1 0.9

Efficiency

0.8 0.7 0.6 0.5 0.4 1997

1998

1999 Public

2000

2001 Year

Private

2002 Foreign

2003

2004

2005

Total

Figure 6.3 Cost efficiency of Indian commercial banks

type was around 30 per cent of the total number of banks in this sector. The mean efficiency level of all the banks did not show any trend during the period, lying between 0.49 and 0.85 during the period of study. The percentage of efficient banks in total was around 12 per cent in all the years. Thus, the results suggest that public-sectors banks did better than private sector banks. However, the levels of efficiency of foreign banks were much better than those of the banks in the other two sectors. There were huge variations in the level of efficiencies among the banks and among the types of ownership. We now examine the sources of variation in efficiencies among the banks. For this we depend on a very simple test using the ordinary least squares method. The independent variables considered here are assets (proxy of size), capital:labour ratio (proxy of technology) and ownership dummy. We have used the cross sectional time series pooled data to run the regression. Table 6.9 gives the values of the coefficient, the corresponding t-statistics and the value of adjusted R2 . The coefficients of assets and capital:labor ratio are positive and statistically significant. The coefficient of private dummy is negative and statistically significant, while that of foreign dummy is positive but not statistically significant. The result suggests that size and technology play a significant role in enhancing the values of efficiency of banks in

The Performance of the Banking Sector in the New Economy 233 Table 6.9 Sources of variation in cost efficiency Variable

Coefficient

Intercept Asset

0.758020 2.13869 × 10−8 (3.6884) −0.078854 (−2.0253) 0.059825 (1.0326) −0.358578 (−1.9904) 0.3389 63

PVTD FD Labor:capital ratio Adjusted R2 D.F.

Table 6.10 Minimum average cost of efficient banks (in rupees) Year

Minimum average cost

1997 1998 1999 2000 2001 2002 2003 2004 2005

0.04520 0.05335 0.06209 0.03378 0.03969 0.02184 0.05453 0.01234 0.01195

India. Contrary to the general belief, efficiencies in private-sector banks were significantly lower than in public-sector banks. One can estimate the minimum values of average costs from the average costs of the efficient banks in each year. Table 6.10 gives the values of optimum average costs of the banks during 1996–97 to 2004–05. The figures indicate a downward trend over the period, which suggests that banks became more competitive than before in terms of cost efficient use of inputs. We have already mentioned in the methodology section that one can estimate the optimum level of output that minimizes the average cost


for both efficient and inefficient banks from the regression analysis of cost function. Table 6.11 describes the values of optimum output level for both efficient and inefficient banks for all the years of our study. However, in some cases the values of optimum output calculated using this method are not economically meaningful due to negative values. It has been observed that for all the comparable years the values of optimum output in efficient banks were higher than those in the inefficient banks. This result is meaningful in the sense that the inefficient banks may have still some scope to increase their scale of operation compared to the efficient banks. The values also show an increasing trend over this period of analysis. To measure the economies due to the learning by doing model of Arrow the following regression model is estimated for each year for the banks of different ownership types and for the efficient banks only. The function can be written as log AC = f ( log CO, log W), where AC represents the average cost, CO represents the average cumulative output over three years and W represents the labor cost. Total cost is taken as the sum of operating expenditure and interest expenditure. Output is defined as total assets net of fixed assets. Now if the coefficient of cumulative output is less than one then the economies of scale due to learning by doing are prevalent in the banks under study. If the coefficient is greater than one then we have diseconomies due to learning by doing. Table 6.12 gives the results of the estimates of the coefficients of the independent variables of the aforesaid cost function for different types of banks. The values of the coefficients of log- cumulative output (L-CO) are all less than one and negative for the efficient banks. However, some of the values are not statistically significant. The values of the coefficient of L-CO for all banks together are less than one and statistically significant for all the years except 2002–03. Thus we can say that experience is helping the banks in reducing costs. Now the same exercise has been carried out separately for three types of banks in terms of ownership. It was found that the coefficients of L-CO for the private- and publicsector banks were less than one and statistically significant for most of the years, while the coefficients were not statistically significant for the foreign banks even when the values were less than one. Thus we can conclude that the private- and public-sector banks in India used their experience to reduce the costs of operation. Foreign banks, on the other hand, pay little heed to past experiences since in most cases they operate at optimum average cost. Finally, we estimated the allocative efficiency of Indian commercial banks during this period. Table 6.13 gives the values of average

Table 6.11 Optimum output calculated from the cost function (100,000 rupees) Independent variable

1997

1998

1999

2000

2001

2002

2003

2004

2005

Efficient banks Optimum output Adjusted R2

2,414,825 0.9878

NA 0.9989

NA 0.9969

1,846,758 0.9986

2,564,869 0.9989

3,291,361 0.9774

2,935,931 0.9957

5,797,294 0.9959

3,730,793 0.9932

Inefficient banks Optimum output Adjusted R2

393,087 0.8140

596,643 0.9989

NA 0.9718

324,864 0.9387

NA 0.9774

NA 0.9603

2,751,969 0.9696

3,225,458 0.9724

2,428,917 0.9587

235

236

Table 6.12 Test of Arrow’s learning by doing Coefficients Efficient banks Intercept L-labor cost L-cumulative output Adjusted R2 Observations All banks Intercept L-labor cost L-cumulative output Adjusted R2 Observations Public banks Intercept L-labor cost

−1.776 0.066 (1.310) −0.078 (−1.315) −0.0063 38

−1.146 0.104 (1.789) −0.143 (−2.074) 0.0746 32

−2.313 0.097 (1.570) −0.063 (−0.879) 0.0934 32

−2.267 0.098 (1.870) −0.069 (−1.155) 0.1130 26

−1.235 0.229 (2.080) −0.220 (−1.917) 0.1043 21

−2.199 0.059 (1.070) −0.054 (−0.935) −0.0316 27

0.171 0.308 (2.087) −0.368 (−2.437) 0.1315 29

−1.7880 0.0547 (1.699) −0.0711 (−1.833) 0.0201 68

−1.0545 0.0913 (1.770) −0.1408 (−2.295) 0.0644 68

−1.8344 0.1017 (2.373) −0.0964 (−1.883) 0.0626 68

−1.6616 0.0988 (2.513) −0.1078 (−2.300) 0.0606 68

−1.4151 0.1571 (2.922) −0.1610 (−2.688) 0.0894 68

−2.0992 0.0509 (1.349) −0.0580 (−1.415) 0.0001 68

−0.1575 0.1565 (1.791) −0.2595 (−2.786) 0.1657 68

−1.5610 0.0881 (2.037)

−1.1913 0.1570 (1.415)

−1.1522 0.1503 (1.578)

−0.9785 0.1870 (2.117)

0.3891 0.3135 (3.538)

−0.0414 0.3638 (3.282)

0.0151 0.3434 (3.589)

L-cumulative output Adjusted R2 Observations Private banks Intercept L-labor cost L-cumulative output Adjusted R2 Observations Foreign banks Intercept L-labor cost L-cumulative output Adjusted R2 Observations

−0.1089 (−2.507) 0.1711 27

−0.1752 (−1.592) 0.0287 27

−0.1739 (−1.827) 0.0615 27

−0.2090 (−2.336) 0.1219 27

−0.3738 (−4.153) 0.3914 27

−0.3823 (−3.438) 0.2741 27

−0.3812 (−3.848) 0.3301 27

−1.5962 0.0472 (1.265) −0.0800 (−1.922) 0.0591 28

−1.3905 0.0135 (0.325) −0.0756 (−1.812) 0.0950 28

−1.0681 0.0992 (2.528) −0.1503 (−3.924) 0.3323 28

−0.3779 0.0474 (0.761) −0.1705 (−3.198) 0.3334 28

−0.3077 0.0843 (0.905) −0.2004 (−2.597) 0.2717 28

−1.9553 −0.0051 (−0.084) −0.0356 (−0.671) 0.0336 28

−1.6197 0.0301 (0.474) −0.0895 (−1.706) 0.2126 28

−2.0232 0.2669 (1.963) −0.1385 (−0.983) 0.2991 13

0.1255 0.5994 (2.650) −0.4512 (−1.920) 0.3243 13

−3.5435 0.2622 (1.483) −0.0166 (−0.092) 0.3107 13

−2.7084 0.1111 (0.898) −0.0390 (−0.285) −0.0080 13

−2.6657 0.1223 (0.736) −0.0536 (−0.310) −0.0900 13

−2.5368 0.0862 (0.945) −0.0430 (−0.452) −0.0541 13

2.9356 0.1880 (0.618) −0.5329 (−1.643) 0.1950 13

Note: Dependent variable: log(average cost). Figures in parentheses are t -statistics.

237

238

Table 6.13 Allocative efficiency of Indian commercial banks 1997

1998

1999

2000

2001

2002

2003

2004

2005

Public-sector banks Mean efficiency % efficient banks

0.9229 29.63

0.9363 29.63

0.9437 33.33

0.9332 22.22

0.9380 22.22

0.9222 22.22

0.9469 29.63

0.9324 22.22

0.9387 18.52

Private-sector banks Mean efficiency % efficient banks

0.8812 3.57

0.9083 3.57

0.8931 0.00

0.8422 3.57

0.8664 0.00

0.9127 10.71

0.8664 3.57

0.8522 3.57

0.8741 7.14

Foreign banks Mean efficiency % efficient banks

0.9435 53.85

0.9454 61.54

0.9311 53.85

0.8673 46.15

0.9403 69.23

0.9331 61.54

0.9024 61.54

0.8772 61.54

0.8745 53.85

All banks Mean efficiency % efficient banks

0.91 18.60

0.926 19.77

0.92 18.60

0.883 15.12

0.909 17.44

0.92 19.77

0.905 19.77

0.889 17.44

0.901 16.28


0.96 0.94

Efficiency

0.92 0.90 0.88 0.86 0.84 0.82 1997

1998

1999 Public

2000

2001 Year

Private

2002 Foreign

2003

2004

2005

Total

Figure 6.4 Allocative efficiency of Indian commercial banks

efficiencies of the banks in three types of ownership patterns and for all banks together. The pattern of the differences among these three ownerships was similar to what was observed for the other efficiencies but the level of efficiencies in all the cases was higher than that of the other two types of efficiencies. The percentages of efficient banks in the private sector were very low compared to those of the banks in the other two types of ownership. Thus the allocation of inputs is very poor in private-sector banks compared to the banks in the other two sectors. Figure 6.4 shows the trend of average values of allocative efficiencies of banks of the three ownership types.

7 Concluding remarks In our study, we have calculated the technical and scale efficiency scores of Indian commercial banks using DEA, and tried to analyze how the efficiency scores across banks of different ownership pattern vary. Average technical efficiency in public-sector banks was greater than that in private-sector banks. There exists no uniform pattern of movement of scale efficiency scores. SE scores for private-sector banks for the years 2003 and 2004 showed statistically significant improvement. It seems


that, in the initial years, the private banks were weak with respect to size and they have been achieving the optimum scale size during the recent period. However, this point needs more information and in-depth study. The percentage of Indian public-sector commercial banks suffering from diminishing returns to scale (DRS) was acutely severe. This confirms that scale inefficiency in public-sector banks is the outcome of their excessive size. However, the proportion of foreign banks that suffered from DRS remained low throughout this period of investigation. The government of India’s future agenda to restructure large public-sector banks by merging them to create a few very large banks is likely to aggravate scale inefficiency due to their consistently predominant presence in the DRS segment of the frontier. The top priority before consolidation should be to make them scale efficient by adopting appropriate policies. Our findings, on the other hand, suggest consolidating public-sector banks with private-sector banks because the latter category of banks recorded increasing returns to scale (IRS) in their operation. Therefore, judicious policy demands a strategy to encourage consolidation of private- and public-sector banks to take advantage of the huge potential to make them efficient by exploiting economies of scale. The presence of input congestion in public- and private-sector banks suggests that if a portion of inputs were withdrawn from the process, banks could achieve a higher level of output or no reduction of output due to the withdrawal of inputs. The public-sector banks show high labor congestion during the period, higher than private and foreign banks. This feature supports the notion that public-sector banks suffer from overstaffing. It was expected that after liberalization the banks could use the optimum level of staff to become more efficient and competitive in the market. However, this is not observed even after more than fifteen years of liberalization and this phenomenon persists due to political pressure on the government and pressure from the labor unions on the management of banks. It is also argued that since public-sector banks have some social obligation they have to absorb more labor than the banks in other two sectors. Results on cost and allocvative efficiencies suggest that the pattern of difference among the types of ownership is similar to that of technical efficiencies of banks. The analysis of the sources of variation in efficiency reveals that size and technology have a positive impact on the variation of cost efficiencies. Cost efficiencies in private sector banks are significantly lower than in the public sector banks. The efficiencies of foreign banks are greater than those of public-sector banks; the difference is, however, not statistically significant. Our study reveals that


Indian banks take their input from past experience to become more efficient and productive. In our study, we find that liberalization has made the Indian banking sector more competitive even though the market share of public-sector banks is still very high. Indian public-sector banks performed better in all respects compared to private-sector banks. Therefore, judicious policy demands strategies to make public-sector banks more aggressive to take advantage of the huge potential before them.

Notes Chapter 3 1. This method of decomposition of TFP was developed by Coondoo and Neogi (1998). 2. The major problem in obtaining estimates of the gross fixed capital stock is to find out the benchmark year’s gross capital stock. In order to construct the gross fixed capital stock it is assumed that the value of finished equipment of a balanced age composition would be exactly half of the value of equipment when it was new. Hence in this study, twice the book value of the base year is taken as a rough estimate of the replacement value of fixed capital stock. 3. Data for the firm-level study were collected from ASI, CSO. There are two types of assets given in the firm-level data: (a) opening net stock of assets and (b) closing net stock of asset. With these data on net stock a series of gross capital stock is calculated using the following method. KnCG = K1ON (1 + α)n + (1 + α)n−1 I1 + (1 + α)n−2 I2 + · · · + (1 + α)In−1 + In where α is the rate of depreciation, and I represents the investment. The assumption taken in the construction of this formula is that at the initial year opening net stock is equal to opening gross asset. n is equal to the difference between the initial year of production and the first year of the present data for the individual firm. Now substituting the initial year opening stock in the above formula we can write KnCG = KnON −

n

Ii (1 + α)n + (1 + α)n−1 I1 + (1 + α)n−2 I2 + · · · + (I + α)In−1 + In

i=1

Since the difference between the year of initial production of the firm and the first year of the present data is not large enough and also due to nonavailability of the investment figures of the earlier years it is assumed that Ii = 0 for the earlier years. The last equation then boils down to KnCG = [KnON ](1 + α)n

Chapter 5 1. A non-conventional approach to the choice of technology by long periods of trial and error can be found in Ishikawa (1981). Moreover numerous attempts have been made to measure the contribution of R&D to productivity growth (e.g. Griliches, 1980; Nadiri, 1980). 242

Notes

243

2. The salient features of the New Economic Policy package may be outlined as follows: (a) to allow direct foreign capital in industries, trading companies and banking up to 51 per cent of the share capital (in some cases 100 per cent); (b) automatic clearance for capital goods imports; (c) automatic approval of foreign technology agreements in high priority areas (including small sectors); (d) the setting up of a Foreign Investment Promotion Board to negotiate with MNCs and grant single-point clearance; (e) private sector banking by public limited companies with upgraded technology – both domestic and foreign; (f) the abolition of all but a few industrial licensea, the abolition of MRTP and FERA provisions, the closing down of chronically sick public sector enterprises and the like. 3. For details of this approach see Schmidt and Sickles (1984) and Cornwell et al. (1990). 4. There are wide variations in the number of firms in each of the groups, which seem to affect the analysis. But since we have converted each group into an average firm figure, the analysis has not been affected by varying weights. (On the issue of average firm figures see Laumas and Williams, 1981.) 5. Kumbhakar et al. (1991) developed (and applied) a single-step maximum procedure to obtain consistent parameter estimates and identify determinants of technical efficiency. 6. The factors explaining the variations of efficiency are defined as follows. LPT = labor productivity = value added/number of employees; SKILL = (employees − workers)/employees; WAGE = (total emoluments − total wage)/ (all employees − all workers); PROFIT = (value added − wage bill)/fixed capital; CINT = capital intensity = fixed capital/employees; CAPU = capital utilization = working capital/fixed capital; DUMMY = industry dummy. 7. Statistics relate to the year 2000–1. 8. Using value added to measure output while including materials and energy among inputs is inappropriate. 9. Policies were implemented for the modernization and improvement of existing technology. Concessions in duties on imported machinery and chemicals were announced. Integrated tanneries were delisensed. Regulations limiting several types of leather goods to the small-scale sector were removed. Free export of raw hides and skins, semi-finished and finished leather and leather products was allowed. 10. Färe and Lovell allow individual components of the input or output bundle to take zero values. They define the indicator variables δr that take the value 0 if output r is 0, and 1 otherwise. Their objective function is φr ρy = δr Throughout the present analysis, we assume that all inputs and outputs are strictly positive. The range adjusted measure (RAM) introduced by Cooper et al. (1999) can accommodate zero inputs or outputs unless the relevant input/output is constant across observations. If the φr s are not restricted to being greater than or equal to unity, some outputs may actually be reduced while others are increased.

244 Notes 11. See Russell (1985) for a number of limitations of this nonradial measure. Zieschang (1984) proposes a two-step ‘Russell–extended-Farrell’ measure that synthesizes the best features of the conventional radial Debreu–Farrell measure and the nonradial Russell measure. In the input-oriented case, this extended measure emerges by first projecting an observed input bundle x0 radially onto the isoquant of the corresponding output bundle. Once one achieves this proportional scaling (by the factor θ), one projects any input slack present in this bundle θx0 further onto the efficient subset of the isoquant by solving the nonradial problem for RMx (θx0 , y 0 ). When no input slack exists in the radial projection of the observed input bundle, no further adjustment need occur so that the radial and nonradial measures coincide. 12. In an alternative approach, Torgersen et al. (1996) adjust the efficient radial projection of the output bundle for slacks in individual outputs to obtain a nonradial projection onto the efficient subset of the output isoquant. Instead of a summary measure of efficiency combining the radial expansion factor with the slacks, they report the potential output quantities individually, reflecting the output-specific efficiency levels.

Chapter 6 This chapter is written jointly with Professor Nitish Datta, Department of Economics, University of Kalyani. 1. Data for two private-sector banks for 2004–5 are not available. Therefore, in our final year of investigation we have 26 private-sector banks.

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Index Note: Page numbers in italics refer to tables.

measuring of efficiency 194–5, 204–11 minimum average cost of efficient 233, 233 nationalization of 196 optimum output calculated from cost function 233–4, 235 overview of Indian 196–200 ownership forms 195 problems arising in 197 reforms initiated 193–4, 197–8 review of literature evaluating performance of 200–4 role in economy 193 and scale efficiency 214, 215, 216, 240 scale elasticity 117, 117 selected indicators 203 sources of variations in levels of efficiency 232–3, 233, 240 summary of 202 technical efficiency of 214–17, 215, 216 test of Arrow’s learning by doing 234, 236–7 Battese, G.E. 106, 107, 137 Baumol, W.J. 29 BCC (Banker, Charness and Cooper) model 109–10, 114, 208 BEL 10 Berg, S.A. et al 213 Berger, A.N. 203 Bernstein, J. 41 Bhagwati, J. 80 Bhattacharya, A. 203 Bhaumik, S.K. 203 Bhavani, T.A. 140 bootstrap technique 113–14 Booz Allen Hamilton Global Innovation 1000 pan 24, 26 Box-Cox model 150–2, 160

Accenture 49 agglomeration strategies 46, 47 agriculture 30 exports 82, 82 Ahluwalia, I. 58–9, 140 Aigner, D.J. et al 137 Alexander Committee (1978) 58 allocative efficiency 57, 108, 200–1 and banking sector 195, 234, 238, 239, 239, 240 Annual Survey of Industries 60, 71, 81, 87, 92, 95, 135, 140 Arora, A. et al 49 Arrow, K.J. 6, 12, 55, 234, 236–7 Asia Pacific (APAC) firms 27 Asian Development Bank (ADB) 59, 169 Baldwin, J. 34 Banerjee, P. 11 Bangalore 51 Banker, R.D. 194, 206 banking sector 193–241 aims of liberalization measures 199 allocative efficiency of 195, 234, 238, 239, 239, 240 changes in 211–12 cost efficiency of 228, 229–31, 232–3, 232, 240 and credit 194 and deposits 196, 213 deregulation and productivity 200–1 features of commercial 201 influence of ownership on efficiency 214, 217, 218–20, 224, 228, 239–40 input congestion 195, 217, 221–2, 224, 240 labor congestion 224, 225–7, 228, 240 255

256 Index BPO (business process outsourcing) 29 Broeck, J. 137 Business Technographic Survey 29 Capital Account Convertibility (CAC) 199 capital intensity and efficiency 143–4 capital stock measuring of 190–1 cash reserve ratio see CRR CCR model 205 Central Bank in India see RBI Central Statistical Organization (CSO) 81, 88, 147 Chandok, H.L. 72 Chang, Fred 51 chemical industry output 61, 61, 62 value added 65 China 1, 27, 51 Cisco Systems 27 Coelli, T.J. 106, 107, 137 Cognizant 22, 22 Cohen, W. 2, 14, 15–17, 23 collaboration and R&D investment 14, 23, 45 COLS (corrected ordinary last squares) method 106, 113, 172–3 Committee on Banking Sector Reforms 197 Committee on Financial Systems 197 comparative advantage 1, 30, 45, 48, 79, 125 competency index 11 competition 31, 85, 87 competitive advantage 30, 55, 125 features of 46, 47 and government’s role 47–8 sources of in IT sector 48–52 three Cs of 47–8 and value chain 47 competitive growth equilibrium model 3–4 composed error model 104, 105–7 Compustat Database 123 computer industry 6–8, 123–5, 171

average efficiency of 188, 188 level efficiency versus growth efficiency 124, 125 output growth 124, 125 and R&D 19–20 sources of growth efficiency 124, 124 spending on foreign technology 9 see also software industry congestion measurement 217–19 constant return to scale see CRS construction industry 61, 61 core competence 49, 128 Corley, M. et al 125 Cornwell, C. et al 138, 139, 142, 189 corrected ordinary last squares see COLS method cost efficiency 49 of banking sector 228, 229–31, 232–3, 232, 240 measuring 209–11 cost frontier model 108, 114–17, 119–20, 195 cost function DEA approach 228 ‘creative destruction’ 36, 44 credit 194 CRR (cash reserve ratio) 194, 205 CRS (constant return to scale) 195, 205, 206, 207 cumulative experience 59 Custom Tariff in India 81 Dagli Committee (1979) 58 Daimler-Chrysler 25 Das, B.D. et al 203, 213 DEA (data envelopment analysis) 6, 104, 107–17, 131, 132, 204, 205, 209–10 bootstrap models 113–14 and cost frontier estimation 114–17, 119–20, 195 features 107–8 input-oriented (BCC) model 109–10, 148 and LAV method 111–12 and measurement of efficiency in textiles 146 measuring of cost effectiveness 209–10

Index output-oriented VRS model 149 and production frontier estimation 108–14 radial efficiency model 110–11, 111, 112, 148–50, 174, 209 debt recovery tribunals 194 decision-making unit (DMU) 104, 205, 210 Dell 45 Dimova, R. 203 Director General of Commerce and Intelligent Service (DGCIS) 80–1, 82 disposability, weak and strong 207–8 DRS 214, 217 Drucker, Peter 45 Dutch manufacturing 33–5, 34, 35 dynamic comparative advantage 45 dynamic production frontier 118, 124 e-capital 8 economic growth 26–7 economic policy reforms 23–4 economies of scale 118 economies of scope 47–9 Economist 13 EER (real effective exchange rate) 102–3 efficiency 134–92 and appropriateness of chosen technology 134, 135 comparison of in post- and pre-liberalization periods 169–88 economic factors explaining variations in technical 143 in leather industry 144–7, 160–9, see also leather industry of manufacturing industries in pre-liberalization period 136–7, 141–4 technical see technical efficiency in textile industry 144–7, 153–60 see also textile industry see also industry efficiency analysis efficiency, measuring of 137–40, 194–5, 202–11

257

and Box-Cox model 150–2, 160 congestion 207–11 cost 209–11 radial/nonradial measures 110–11, 111, 112, 148–50 scale 207 stochastic frontier model 137–9, 146, 173, 176 technical 194–5, 204–5 electronics industry 8, 9–11, 135, 171 development of 171–2 employment generation 10 export performance 10, 11, 84 foreign collaborations 9, 9 growth of 171 inter-state variations in output 10, 10 pre- and post-liberalization comparison average efficiency 176, 177 distribution of outputs 178, 185 ownership-specific 176, 179 state-specific 177, 182–3 R&D investment 10 semiconductor policy 171 technology import 9, 9 underutilization of labor and capital in post-liberalization period 178–9, 186–7, 188 see also software sector employment changes in industry 65 growth of in IT sector 22 and productivity 125, 127–8 endogenous growth models 128–9 entry rates in Dutch manufacturing 34–6, 34, 35 and technological competiveness 35–7 exports 30, 81–5 linkage between total factor productivity growth and 85–90, 99 percentage share of major items groups 82, 82 ranking of industries 85, 86 yearly value of selected commodity items 82–4, 93

258 Index externalities 12, 118 see also spillover effect Färe, R. 106, 149, 204, 207, 209 Farrell, M.J. 109, 137, 204 Fieldhouse, M. 206 financial services 21–2, 27, 49 Flaherty, M. 120 food industry 61, 63 Ford 25 foreign direct investment (FDI) 20–1, 47, 100 Foreign Exchange Regulation Act (FERA) (1973) 58, 60, 170 Forrester, J. 27–8, 29 Forsund, F.R. 206 Freire-Seren, M. 5 Frisch, R. 206 frontier production function (FPF) approach 136–9, 204 Fuller Capital Account Convertibility 200 game theory 120 GDP 26–7, 50 generalized least squares (GLS) 106 Gilbert, R.A. 201 global competition, strategy for 46–55 Global Competitiveness Report 21 globalization 198 GM 25 Goldman and Sachs 30 Goldstar 10 Gort, M. 130 Greenbaum, S.I. 212 Greene, W.H. 113 Grillches, Z. 31, 41 gross fixed capital stock (GFCS) 190–1 Grossman, G.M. 91, 129 growth efficiency models 118, 120–3 growth miracle countries 30 Hamel, G. 48, 128 Harrison, A.E. 92 HDFC 217, 219 Helpman, E. 31, 91, 129

Herfindal index 35 Hewlett-Packard 28 Hjalmarsson, L. 206 HPAEs (high performance Asian economies) 41, 51 Hsinchu Science Park (Taiwan) 54 human capital methods of analysing growth effects due to 131–3 Humphrey, D.B. 203 IBM 25, 27, 49 IBS (Intelligent Business Systems) 23 imitation process 44–5 imports 85, 87 liberalization 56 tariffs 20, 21, 95–6, 95, 100 Indian Labour Journal 140 industrial classification 101 Industrial Development and Regulation Act (IDRA) (1951) 58 industrial policy review of Indian 58–60 industry(ies) 30–55, 76–103 change in shares among 60–5 decomposition of aggregate TFPG 67–79 dynamics and growth 31–7 efficiency analysis of selected manufacturing 134–92 employment changes 65 export performance 81–5 linkage between liberalization and productivity 56–103 output changes 61, 62, 63, 63 R&D efficiency and growth of 117–33 R&D investment and technology diffusion 37–46 reasons for progress and evolvement 128–9 and rent differentials 74, 75, 76, 77, 78 strategies needed for 30 strategy for global competition 46–55 structural changes 65–8, 66, 98–9 and technological change 31–2

Index total factor productivity growth in export-oriented 79–90, 99 value added changes 63, 64, 65 wage differentials 74, 75, 78 industry efficiency analysis 104–33 and composed error model 104, 105–7 and data environmental analysis (DEA) 104, 107–17 Infosys Technologies 22 innovations 2, 117 economic policy reforms aimed at 24–9 key elements in successful 26 and R&D investment 23 stages of successful process 26 input-oriented measures 195, 204 BCC model 109–10, 148 Intel 27 intermediation approach and output-input set of banks 212 International Monetary Fund (IMF) 59, 169 iron and steel estimation of changes in productivity and markup 96, 97 TFP growth 89 IT sector 30 criticism of rapid growth of India’s 51 employment growth 22 impact of growth of on other sectors of Indian economy 54–5 importance of R&D to sustained growth in 22–4, 51–4 performance growth of largest firms 21–2, 22 policy measures to ensure continued growth 51 and policy reform 21 sources of competitive advantage 48–50 success of India in global markets 46 technology diffusion strategy 30 and world perspective 5–13 ITI 10

259

Jain, R.K. 81 Japan banking 200 decomposition of TFP growth rates 44 export externality 132, 132 growth of output and productivity in manufacturing sector 41, 42, 43 and imitation 44–5 rates of return in physical and R&D capital 43 role of government in creating competitive industries 47–8 transfer of US technology to 45 Jethanandani, J. 28 jewellery see gems/jewellery JLMS technique 105 Johnson & Johnson 25 joint ventures 23, 44 Jondrow, J. et al 105 Jorgensen, D.W. 8 Kaldor, N. 54 kanban 47 Kemp, M.C. 45 Kerala (KE) 110 Kim, S. 41 knowledge capital 1–3, 8, 118, 128, 129 knowledge economy 1, 20, 29 knowledge transfer 80 Konakayama 130 Korea banking system 201 growth of output and productivity in manufacturing sector 41, 42, 43 rates of return in physical and R&D capital 43 Krishna, P. 92 Kumar, P. 28 Kumbhakar, S.C. 203–4 labor inputs radial labor efficiency measure 110–11, 111, 112 labor productivity effect of R&D on 127, 127 Lall, S. 24

260 Index Lansbury, M. 32 LAV (least sum of absolute errors) method 104, 111–13 learning 48, 128, 129 learning by doing model 6, 12, 118, 234, 236–7 least sum of absolute errors see LAV leather industry 135, 144–5, 160–9 all-India efficiency analysis 160, 162 development of 145 efficiency in post-liberalization era 144–5, 160–9, 190 and employment 146 exports 84, 145 factors behind efficiency variations 169, 170 issues affecting 145–6 ownership-specific analysis 163, 167–8, 168–9 state-specific analysis 160–1, 163, 164–6 TFP growth 89 Leightner, P.S. 201 Leontief, W. 190 less developed countries (LDCs) 134 level efficiency 118, 120, 122, 124, 125 Levinthal, D. 2, 14, 15–17, 23 liberalization 9, 59–60 see also trade liberalization liberalization dummy 101–2 licensing 60, 170 Lindley, J.T. 212 Little, I.M.D. 140 Lovell, C.A.K. 149, 201, 204 Lucas model 4, 132 Lucas, R.E. 3, 38, 128, 129 McGraw-Hill 2 Mahalanobish Model 64 Makita 47 Malaysia industrial policy support 46, 47 manufacturing industry 27, 28, 30 efficiency of in pre- liberalization period 136–44 and output 61

and TFPG 72, 73 see also leather industry; textiles industry Martorelli, W. 29 Matsushita 25, 49 maximum likelihood see ML Mayes, D. 32 medical estimation of changes in productivity and markup 96, 97 TFP growth 89 Meeusen, W. 137 Microsoft 25, 27, 28 minimum productive scale size (MPSS) 120 minimum efficient scale (MES) 120 Mitra, D. 92 ML (maximum likelihood) methods 104, 105, 107, 113 Mohnen, P. 41 MRTP 58, 60, 170 multifiber agreement (MFA) 144 Nachum, N. 12 Nadiri, M. 41 NASSCOM (National Association of Software and Service Companies) 29 National Accounts Statistics 140 Neoji, C. 59, 136, 152, 242 net fixed capital stock (NFCS) 190, 191 New Economic Policy (NEP) 56, 59 New Industrial Policy (NIP) (1991) 59, 135, 136, 170 NICs 30, 31–2, 129, 189 openness in foreign trade 56, 56 R&D investment 55–6 nonradial efficiency 148–50, 174 Noyelle, T. 49 Okawa, M. 45 optimal time path model 38–40 Oracle 27, 28 ordinary least squares (OLS) 106, 232 organization theory 128 output changes in industries’ 61, 62, 63, 63

Index output-oriented measures 149, 195, 204–5, 206–7 outsourcing 27 parametric models 104, 137, 204 Pareto efficiency 104 Parmar, R. 144–5 patents 52 perpetual inventory accumulation (PIA) method 72, 191 pharmaceutical industry (US) impact of R&D 20 level and growth efficiency 125 Policy Group 72 Porter, Michael 46, 48, 55 Prahalad, C.K. 48, 128 production approach and output-input set of banks 212 production frontier estimation 108–14 productivity 46, 56–103 analysis of differentials among different sectors 79–90 decomposition of aggregate TFPG 67–79 and employment 125, 128 interaction between international trade and 79–80 as major force behind sustainable economic development 67 and market power 90–98 reduction of by protectionism 79 TFPG in export-oriented industries 79–90, 99 and trade liberalization 56–103 ways to raise 46, 47 public/private sector leather industry efficiency comparisons 163, 167–8, 168–9 textile industry efficiency comparisons 158–9, 158–9 quadratic cost frontier models ‘quality ladder effect’ 129

117

R&D (research and development) 6, 14–20, 31

2,

261

collaboration in 14, 23, 45 and computer industry 19–20 cost-reducing impact of inputs 118–20 dynamic features of 14 effect of on labor productivity 126, 127 effects of knowledge and other explanatory variables on intensity of 16–18, 17 features that are important to dynamic evolution of industry 117–8 footprints of top ten global spenders on 25 impact of on firm performance 53–4, 53 impact of on growth efficiency 6–7, 7, 8 impact of on knowledge capital 2, 14 impact of on long-run growth of inputs 5–6 impact on output growth 118, 120–2 impact on productivity in high-tech industries 127 impact on US pharmaceutical industry 20 impact on world computer industry 19–20 importance of to IT sector’s growth 22–4, 51–4 and industry growth 117–33 market structure implications 118 methods of analysing growth effects due to 131–3 and NICs of Asia 52 percentage growth of global spending 25 role of in improving industrial productivity 31–2 spending on 10 spillover effect see spillover effect sunk cost of 80, 100 and technology diffusion 37–46 world distribution by industry 24–5, 24

262 Index radial efficiency model 110–11, 111, 112, 148–50, 174, 209 Rajadhyakha Committee (1980) 58 Rammohan, T.T. 203 Ravishankar, T.S. 203 Ray, S.C. 110, 203 RBI (Central Bank in India) 196, 198, 199 ready made garments industry 61, 63, 65, 83 real effective exchange rate (EER) 102–3 rent differentials 74, 75, 76, 77, 78 research and development see R&D Reserve Bank of India 21, 200 Reserve Bank of India Bulletin 81, 88 Richmond, J. 106, 172, 173 Romer model 3–4, 6, 37–8, 40 Romer, P.M. 3, 6, 128 Russell measure 149–50 Saha, A. 203 Sahoo, B. 108, 117 Salt Lake IT complex (Kolkata) 51 SAP 28 Satyam Computers 22, 22 Saxenian 50 scale efficiency 207 Schmidt, P. 106 Schmookler, J. 31 Schumpeter 44 Sealey, C.W. 212 Second Five Year Plan (1956-61) 58 semiconductor industry 2, 14, 23, 171 Sengupta, J.K. 3, 6, 11–12, 48, 52, 108, 114, 115, 117, 118, 120, 129, 130 Seventh Five Year Plan 135, 189 Shy, O. 36 Sickles, R.C. 106 Siemens 25 Silicon Valley 50 Simar, L. 114 Singapore industrial policy support 46 software industry 49 Singh, J. 140

Singh, Professor Monmohan 98 Singh, S.K. 144–5 SLR (statutory liquidity ratio) 194 SMEs (small and medium-sized enterprises) 51 software sector 1, 8, 11–13, 49 and competency index 11 contribution to India’s GDP 48 dynamic strategies in 11–12 export patterns 12, 13, 48, 84 impact of openness in trade on growth of 49 and knowledge diffusion 12 need for development in core competence in managerial skills 13 and R&D 11 recent trends in 28–9 spending on foreign technology 9 success of India in global market 46 Solow model 31, 38 Sony 47 South Korea 129–30 export externality 132, 132 Spain, banking 200 spillover effect 2, 4, 5, 12, 14–17, 23, 30, 38, 45, 118, 129–31, 41, 43–4, 44 Staat, M. 103 State Bank of England 198 Statistical Abstract 81, 88 statutory liquidity ratio (SLR) 194 Stiroh, K.J. 8 stochastic frontier analysis (SFA) 137–9, 146, 173, 176 subsidies 23, 81, 87, 99, 100 ‘Sunrise’ industries 135 Taiwan 1, 13, 23–4, 130 banking system 201 economic growth indicators 130, 131 export externality 132, 132 growth of IT sector 49–51 as US patent recipient 50 utilization of learning spillover technology 131

Index utilization of R&D externalities 130 Taiwanese Council for Economic Planning and Development 130 Tandon Committee (1980) 58 Tarapore, S.S. 199, 200 tariffs 20, 21, 56, 57, 95–6, 95, 100 Tata Consultancy (TCS) 22, 22 taxation system 13 technical efficiency of banks 214–7, 215, 216 of Indian industries 141–2, 141, 142 measuring of 194–5, 204–7 technological change 31–2 technological competitiveness and entry rates 35–7 technological progress and total factor productivity 67 technology compatibility between old and new 36–7 cost of imports of as proportion of sales 9, 9 diffusion 30, 37–46, 51–2 efficiency and appropriateness of chosen 134, 135 technology achievement index (TAI) 52 Technology Information, Forecasting and Assessment Council (TIFAC) 171 technology-consortium model 28 textiles industry 135 all-India efficiency 153, 154, 155 competitive position of 144 efficiency in post- liberalization era 144–5, 147–60, 189 factors behind efficiency variations 160, 161 output 61, 63, 65 ownership-specific efficiency 158–9, 158–9 pre- and post-liberalization comparison average efficiency 175–6, 175

263

distribution of outputs 177–8, 184 ownership-specific 176, 178 state-specific 176, 180–1 state-specific average efficiencies 155, 156–7 TFPG (total factor productivity growth) 8, 41, 42 decomposition of aggregate 67–79 in export-oriented industries 79–90, 99 linkage with export performance 85–90, 99 Thailand industrial policy support 46 Timmer, C.P. 111–12 Timmer, M.P. 79 Tobit model 151 Toshiba 47 total factor productivity growth see TFPG Toyota 25 trade liberalization 56, 57, 89, 98–9 and export performance 81–5 impact of on industry efficiency 169–88, 181–90 impact of market changes on productivity 90–98, 99–100 impact of on TFPG 85–90, 99 and industrial productivity 56–103 and sheltered market phenomenon 134–5 trade policies 20–9 translog cost function 115 Tybout, J. et al 80 UK manufacturing sector 32, 33 United States banking 200 contribution of R&D capital to manufacturing sector 41 decomposition of TFP growth rates 44 economy 128 growth of output and productivity in manufacturing sector 41, 42, 43

264 Index rates of return in physical and R&D capital 43 value added changes in industries’ 63, 64, 65 value chain 47 Van Ark, B. et al 65 van Dijk, M. 33, 34–5, 35–6 variable return to scale (VRS) 174, 195, 206 variable-ranking model 189

Veloce, W. 32 Verdoorn, P.J. 54–5 wage differentials 74, 75, 78 Wilson, P. 114 Wilson, P.W. 201 Wipro 22, 22 World Bank 21, 53, 59, 169 Zellner, A.

32

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