0230201709 India's New Economy

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India’s New Economy

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Also by Jati Sengupta:

Jati Sengupta (author)INDIA’S ECONOMIC GROWTHA Strategy for the New Economy

Jati Sengupta (author)COMPETITION AND GROWTHInnovations 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 ANALYSISTechniques of Evaluation of Productivity of Firms in a Growing Economy

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India’s New Economy

Industry Efficiency and Growth

Jati SenguptaProfessor of Economics, University of California, Santa Barbara, California, USA

and

Chiranjib NeogiAssociate Scientist, Economic Research Unit, Indian Statistical Institute, Calcutta, India

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© Jati Sengupta and Chiranjib Neogi 2009

All rights reserved. No reproduction, copy or transmission of thispublication may be made without written permission.

No portion of this publication may be reproduced, copied or transmittedsave with written permission or in accordance with the provisions of theCopyright, Designs and Patents Act 1988, or under the terms of any licencepermitting 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 publicationmay be liable to criminal prosecution and civil claims for damages.

The authors have asserted their rights to be identifiedas the authors of this work in accordance with the Copyright, Designsand Patents Act 1988.

First published 2009 byPALGRAVE 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.

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Palgrave Macmillan is the global academic imprint of the above companiesand 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 hardbackISBN-10: 0 230 20170 9 hardback

This book is printed on paper suitable for recycling and made from fullymanaged and sustained forest sources. Logging, pulping and manufacturingprocesses are expected to conform to the environmental regulations of thecountry of origin

A catalogue record for this book is available from the British Library.

Library of Congress Cataloging-in-Publication DataSengupta, Jatikumar.

India’s new economy : industry efficiency and growth / by JatiSengupta 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 2008338.0954—dc22

2008030363

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Printed and bound in Great Britain byCPI Antony Rowe, Chippenham and Eastbourne

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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

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List of Tables

1.1 Impact of R&D spending on growth efficiency based onthe DEA model 7

1.2 Impact of R&D inputs for DEA efficient firms 81.3 Number of foreign collaborations in electronics industry

by type of collaboration 91.4 Cost of technology import as a proportion of sales for

different product groups (in %) 91.5 Inter-state variations in electronics output 101.6 Export performance by major product groups 111.7 Pattern of software exports from India and its

competitors (1990) 131.8 Effects of knowledge and other explanatory variables on

R&D intensity 171.9 Unweighted average customs duty rates (%) 211.10 Performance of the five largest IT service providing

firms in India 221.11 R&D distribution by industry (%) 241.12 R&D footprints of the top ten global R&D spenders, 2004 251.13 Percentage growth of R&D spending 1999–2004 252.1 Number of ‘births’ relative to the total number of

businesses (%) 332.2 Entry and exit rates (%) in Dutch manufacturing 342.3 Explaining entry rates and market share turbulence in

terms of industrial growth rates, scale economies andHerfindahl index 35

2.4 Growth of output and inputs in the total manufacturingsector of the USA, Japan and Korea (1975–90, %) 42

2.5 Average annual rates of growth of total and partial factorproductivity in the total manufacturing sector of the US,Japan and Korea (1975–90, %) 42

2.6 Sources of output growth for the total manufacturingsectors of the USa, Japan and Korea (1975–90, %) 43

2.7 Internal rates of return on net investment in physicaland R&D capital (in percentage) 43

2.8 Decomposition of average annual TFP growth rates (%) 442.9 Effect of R&D investment on firm performance 53

viii

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List of Tables ix

3.1 Growth rate of output of industries during 1973–4 to1997–8 61

3.2 Percentage share of output in Indian industries 623.3 Percentage share of value added in Indian industries 643.4 Percentage of share of export of major items groups 823.5 Export performance of the industries 863.6 Imported input intensity of selected commodities 873.7 Growth of TFP and labor productivity over three

subperiods 893.8 Sources of TFP growth 913.9 Import tariff rates of selected commodities 953.10 Test of changes of competition in selected industries 974.1 Radial labor efficiency measure (θ) (pre-reform era) 1114.2 Radial labor efficiency (θ) (post-reform era) 1124.3 Scale elasticity β1 = 1/b1 of banks in India 1174.4 Sources of growth efficiency 1244.5 Output trends over time (�y(t) = a0 + a1y(t)) 1244.6 Level efficiency versus growth efficiency 1254.7 Annual average levels of output per hour, investment

per hour and R&D per hour in manufacturing (1990–8)at 1995 prices 126

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

4.9 The regression results over 1990–8 (fixed effects model) 1274.10 Economic growth indicators in Taiwan 1314.11 Estimates of the ratio FK/GM , 1967–87 1325.1 Summary statistics of technical efficiencies of Indian

industries with fixed rankings: time varying(Cobb–Douglas) model 141

5.2 Summary statistics of technical efficiencies of Indianindustries with variable rankings: time varying(Cobb–Douglas) model 142

5.3 Average efficiencies of manufacturing units in the Indiantextiles industry 154

5.4 Average scale of operation and efficiency ofmanufacturing units in the textiles industry in India 156

5.5 Ownership-wise efficiency of manufacturing units intextiles industries in India 158

5.6 Estimates of regression parameters of total efficiencyvariations 161

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x List of Tables

5.7 Average efficiencies of manufacturing units in the Indianleather industry 162

5.8 Average scale of operation and efficiency ofmanufacturing units in the leather industry in India 164

5.9 Ownership-wise efficiency of manufacturing units in theleather industry in India 167

5.10 Estimates of regression parameters of total efficiencyvariations 170

5.11 Average efficiency of the textiles industry 1755.12 Average efficiency of the electronics industry 1775.13 Ownership wise average efficiency of the textiles industry 1785.14 Ownership wise average efficiency of the electronics

industry 1795.15 State-wise average efficiency of the textiles industry 1805.16 State-wise average efficiency of the electronics industry 1825.17 Distribution of outputs of the textiles industry 1845.18 Distribution of outputs of the electronics industry 1855.19 Percentage of underutilization of labor in the electronics

industry 1865.20 Percentage of underutilization of capital in the

electronics industry 1875.21 Average efficiency of the computer industry 1886.1 Features of commercial banking 2016.2 Summary of the banking sector (billion rupees) 2026.3 Selected banking indicators 2036.4 Output-oriented technical and scale efficiency of banks

in India 2156.5 Technically efficient banks by ownership and by year 2186.6 Input congestion in Indian commercial banks 2216.7 Labor congestion in Indian commercial banks 2256.8 Cost efficiency of Indian commercial banks (cost

function DEA approach) 2296.9 Sources of variation in cost efficiency 2336.10 Minimum average cost of efficient banks (in rupees) 2336.11 Optimum output calculated from the cost function

(100,000 rupees) 2356.12 Test of Arrow’s learning by doing 2366.13 Allocative efficiency of Indian commercial banks 238

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List of Figures

3.1 Share of output 633.2 Share of value added 653.3 Index of structural change 663.4 Aggregate TFP indices for traditional, modern and total

industries 733.5 Weighted TFP indices of traditional, modern and all

industries 743.6 Wage differential components of traditional, modern and

all industries 753.7 Rent differential components of traditional, modern and

all industries 753.8 Relative TFP indices of traditional and modern to total

industry 763.9 Relative weighted TFP indices of traditional and modern

to all industries 773.10 Relative wage differential components of traditional and

modern to all industries 783.11 Relative rent differential components of traditional and

modern to all industries 783.12 Trends in exports of selected commodities 836.1 Technical efficiency of Indian commercial banks 2166.2 Percentage of banks with labor congestion 2286.3 Cost efficiency of Indian commercial banks 2326.4 Allocative efficiency of Indian commercial banks 239

xi

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Preface

This volume attempts to understand India’s New Economy in recentyears: its strength, weakness and economic potential. The new economycomprises three key areas of growth: the IT (information technology) sec-tor, the export trade with its externality effects and the financial sectorwith banking reforms.

Over the past two decades the IT and communication sector has grownmost rapidly in India. Software development, electronic communicationand telephone services have undergone a rapid surge and various tech-nologies are merging. The overall impact of all these trends has been asteep rise in exports of IT products and related services. We have provideda critical analysis of these trends and assess their strength, weakness andpotential. Trade and policy reforms in recent years have helped removemany bottlenecks and constraints on free market paradigms but stillmuch more liberalization is needed if the global opportunities for com-petitive efficiency and competitive advantage are to be exploited to thefullest extent. The experiences of most rapid growth in the South EastAsian countries labelled the newly industrializing countries (NICs) pro-vide a unique growth model for India. How could these NICs, whichinclude Hong Kong (China), South Korea, Taiwan and Singapore, growso quickly over the past two decades, exceeding an average growth innational income of 7 to 8 per cent per year? By a systematic changein fostering free markets and global trade, adopting new technologiesand improving them. Joint ventures, providing incentives for steppingup exports and fostering complementary investments in sectors directlyand indirectly linked with the IT and communication sector are somekey strategies adopted by the NICs in their growth model. India can notonly adopt this growth model but improve on it substantially. This isdue to the potential of the New Economy in India. Learning by doing,investment in knowledge capital and the pool of managerial talent pro-vide the unique catalysts for India. Sustaining a high rate of growthin the global framework requires an efficient banking sector and soundmonetary policy.

This volume analyses the above three key areas of growth of the neweconomy. Comparing the productivity and efficiency of some key indus-tries like textile, leather, electronics and computer-related products andevaluating the performance of the export sector provide the insight for

xii

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Preface xiii

understanding the new economy. The efficiency and growth of the bank-ing sector, comprising both private and nationalized banks, are criticallyanalysed.

We are grateful to Professor Amitava Sen for his useful commentsand suggestions on the chapters ‘Industrial Productivity in the NewEconomy’ and ‘Efficiency of Selected Manufacturing Industries’.

Jati SenguptaChiranjib Neogi

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1The New Knowledge Economy andIndia’s Growth

What is the knowledge economy? What are its characteristics and howdoes it affect India’s growth? These are the basic questions to be askedwhen one attempts to assess India’s economic growth today and tomor-row. The knowledge economy has three fundamental characteristics:knowledge capital, competitive efficiency and open trade based on com-parative advantage theory. Knowledge capital may take several forms,e.g. (a) software development, (b) blueprints and designs, (c) R&D knowl-edge as innovations and (d) human capital and skill in adopting newtechnology from abroad and improving it. Competitive efficiency refersto the market process by which entrepreneurs compete to exploit knowl-edge capital to improve their profitability. The profit incentives and opencompetition activate the output and market enhancing effects throughcost efficiency due to economies of scale and of scope. Openness intrade involves competition to improve domestic efficiency, the adop-tion of leading edge technology and the exploitation of human capitaland knowledge spillover from the international field. The outward ori-entation of an open economy allows India to exploit the benefits of thecomparative advantage principle, where the IT (information technology)and ICT (information and communication technology) sectors play adominant role. The dynamic comparative advantage principle suggeststhat India has to restructure its export trade in the IT sector so that itcan act as a catalyst, improving the productivity of other domestic sec-tors through diffusion and market expansion. In China and Taiwan thisforce has played a dominant role in raising national growth rates andrevitalizing rural and agricultural sectors.

In the high-tech industries of today, investments in knowledge capitalhave played a crucial role as engines of growth. Many of the subsectors of

1

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2 India’s New Economy

the ICT sector specializing in software services and managerial skills, inthe area of international ‘outsourcing’, are highly labor-intensive. Theyexploit the spillover benefits of global R&D and innovation technol-ogy. The evolution of other modern industries has ushered in a newparadigm, affected by innovations in product design and productionprocesses. On the one hand these innovations have helped the lead-ing firms to grow at a rapid rate and enhance their core competenceand managerial efficiency. Innovations in R&D and knowledge capitalmay take many forms but in a broad sense they involve developing newprocesses and new products and services and improving the borrowedtechnology and services. Several dynamic features of this innovationprocess are important for industry growth in today’s world. First, R&Dexpenditures not only generate new knowledge and new informationabout the latest technical processes and products but also enhance thefirm’s ability to assimilate, exploit and improve the existing ‘knowl-edge capital’ and information base. We have to note, however, thatmost of the industry-wide R&D expenditures are devoted to productimprovement and enhancement of the quality of existing services, suchas better software and better networking. According to McGraw-Hillannual industry surveys for the past ten years the bulk of R&D spend-ing (around 80 percent) is devoted to improving products, services andexisting designs. Thus only a very small part of R&D expenditure iscommitted to the search for breakthrough innovations emphasized inthe Schumpeterian model. The overall impact of R&D and expenditureson knowledge capital is to enhance the firm’s ability to assimilate andimprove the existing various technology processes. For example, Cohenand Levinthal (1989) have shown in their extensive empirical study thatthe major reason why firms have invested in R&D in the semiconductorindustry is because it provides an in-house technology capability thatcould keep these firms on the leading edge of the latest technology andthereby facilitate the assimilation of new technology developed else-where. A second aspect of R&D spending within a firm is its externality orspillover effect, involving knowledge diffusion to other firms, and veryoften this knowledge innovation finds new applications both locally andglobally, thereby stimulating further innovative activity in other firms,e.g. the software industry and outsourcing services. Finally, the possibil-ity of collaboration in R&D and research networking or joint venturesincreases the incentive of firms to invest more, resulting in more industryinvestment in R&D and knowledge capital. In the absence of such collab-oration, both implicit and explicit, the competing firms may not investenough, since most innovation benefits cannot be totally internalized or

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The New Knowledge Economy and India’s Growth 3

appropriated by the innovator, e.g. his competitors will copy theinvention and thus ‘free ride’ without paying for it.

Two types of theoretical models explaining the impact of knowledgecapital on long-run economic growth of an economy are available inmodern growth theory. One is the macrodynamic growth model due toRomer (1986) and Lucas (1993), which emphasizes knowledge capital asan input in the production function that has increasing marginal pro-ductivity and a spillover effect in the form of externalities. The second isa microdynamic model of industry growth fostered by increasing returnsassociated with R&D expenditures and other forms of knowledge capi-tal. This model has been explored by Sengupta (2004) to explain industryevolution and market growth associated with the entry of new firms andthe expansion of increased market share through mergers, alliances andglobalization of demand.

The competitive growth equilibrium model due to Romer has twoimportant characteristics that are useful for a discussion of long-termgrowth prospects for India. First, it is based on the production functionY = F(k(t), K(t)), which exhibits global increasing marginal productivityof knowledge capital. Here ki(t) = k(t) is the knowledge capital for firmi = 1, 2, . . . , N where firms are assumed to be identical for simplicity and

K(t) =N∑

i=1

ki(t)

is the aggregate stock of knowledge for the whole economy. The produc-tion function is assumed to be concave in k(t), exhibiting diminishingmarginal productivity for any fixed value of K(t). Romer has shown theexistence of an equilibrium growth solution of this competitive model,where the three basic elements of externalities, increasing returns in theproduction of total output due to K(t) and decreasing returns (due to k(t))in the production of new knowledge combine to produce a computablemodel of long run growth. This model is capable of explaining historicallong-run growth in the absence of state intervention. Second, the welfareimplications of this long-run growth model are very important in that itprovides a framework for optimal government policymaking. Here eachfirm recognizes the private return to knowledge capital through ∂F/∂k(t)but neglects the productivity impact of the change in aggregate knowl-edge capital denoted by ∂F/∂K(t). Hence the amount of consumption atany point in time is too high in competitive equilibrium and the amountof research too low. Thus any government policy that shifts the allocation

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of current goods away from consumption toward research or knowledgecapital will be welfare improving. The appropriate tax-subsidy measuremay thus be employed by the government to achieve Pareto-optimalimprovements in resource allocation and income generation.

The Lucas model is very similar to Romer’s approach. It views totalcapital in two parts: human capital allocated to current production andthat allocated to skill acquisition or schooling. If u is the fraction allo-cated to current production and v is the productivity parameter, thenthe two basic equations of the Lucas model are

Y = AKα(uH)1−αHγa

H = v(1 − u)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 thephysical capital stock, which evolves over time according to the stan-dard 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. Notethat in this model there are constant returns to scale to the stock ofhuman capital. Thus some allocations may yield high external bene-fits and growth in production and wages, while others may not. If u∗

is the optimal allocation of an individual’s time between current pro-duction and education (research) then it can be shown that the steadystate growth rate g equals v(1 − u∗), implying that education and researchknowledge can augment the long-run growth rate. Another feature ofthe spillover effect on technology is that it yields a strong connectionbetween rapid productivity growth and openness in trade. Thus coun-tries opening up could take advantage of the spillover technology byusing strong increasing returns to scale to augment the output growthand export expansion and diversification.

Our objective here is threefold. First, we discuss the structure andgrowth of the ICT and IT sectors in India in the light of the findings ofthe modern theory of long-term economic growth. Second, we attemptto evaluate the role of R&D investments and the learning by doing effectsof India’s investment in the computer industry in its software and hard-ware developments and the electronic industry in India. Finally, theimplications of global markets and the future prospects for the com-puter industry and the knowledge economy are discussed in the light ofcurrent economic policy in India.

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1 The IT sector and the world perspective

The IT sector comprises several industries, such as computers, electron-ics, semiconductors, communications and both software and hardwaredevelopments. The world perspective is most important for the IT sectorfor several reasons. First, the international spillover aspect of R&D invest-ments in these sectors is very critical in augmenting the national growthrate. 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 thegrowth 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 modelusing pooled cross-section data for a sample of 21 OECD countries withfive observations for each country over 1965–90 at five-year intervals.The estimated results with t-statistics in parentheses are as follows:

Y/Y = 0.19(2.5)

H/H + 0.54 L/L + 0.27(2.6)

K/K + 0.08(2.2)

R/R + exp(−0.36t)(1.1)

D

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 forhuman capital. Note that the estimated coefficients of human capital,physical capital and R&D expenditure are positive and statistically sig-nificant. The estimates show a strong positive correlation between thegrowth of R&D expenditure and the growth of the GDP variable Y . Thissuggests that not only would the introduction of a subsidy to the R&Dinvestment encourage innovation activity but so would the introductionof a subsidy to physical capital production. This physical capital subsidypositively affects the long-run growth rate by providing the incentivesto increase the variety of capital goods.

Second, the impact of R&D inputs on long-run growth of outputsoccurs at various sectoral levels. The learning effect is especially impor-tant in knowledge diffusion and intersectoral transfers. If s denotes theknowledge of an agent and S economy-wide knowledge, then the agent’sgrowth 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 pro-ducer, with s as the efficiency of the producer, a his output of capital

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goods and A the economy-wide growth investment in the capital-usingindustries. Then Arrow’s learning by doing can be represented as

s/s = δ1/θθs(t)1−1/θA(t)

where θ > 0 and s = S, since it is assumed that all capital goods producersare equally efficient. Here the learning effects of A are like a public good,i.e. learning knowledge produced collectively by all the capital goodsfirms jointly with their output of capital goods. In Romer’s model learn-ing comes through research. There are invention costs but no adoptioncosts. The output of research is designs. Here if we denote the cumulativenumber 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 andthe population is fixed, then S = ns and hence

s(t)/s(t) = δnasγ−1

Thus if we double the population, we raise the growth rate at each dateby a factor of two. This type of research model finds empirical supportfrom cross-sectional firm data as firms that perform R&D generate morepatents and their productivity is higher.

Third, the world perspective in high-tech industries today has beensignificantly influenced by the growth of the computer industry andits impact on communication and other industries. India’s IT sector isheavily impacted by such developments in the world computer indus-try. R&D investment by firms not only generate new knowledge abouttechnical processes and products but also enhance the firm’s capabilityin improving the stock of existing knowledge capital. Sengupta (2004)has applied a two-stage model of this improvement process in US com-puter industry. In the first stage the efficient levels of R&D inputs aredetermined by an efficiency model known as data envelopment anal-ysis (DEA), and in the second stage we estimate by a regression modelthe impact of R&D spending on total sales. Our empirical applicationis based on Standard and Poor’s Compustat data on 40 firms over the16-year period 1985–2000. The data set includes such well known firmsas Apple, Compaq, Dell, IBM, HP, Hitachi and Toshiba. Denoting theefficiency score by θ (θ = 1 denoting highest efficiency) and the marginal

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Table 1.1 Impact of R&D spending on growth efficiency based on the DEAmodel

1985–9 1990–4 1995–2000

θ β θ β θ β

Dell 1.00 2.71 1.00 0.15 0.75 0.08Compaq 0.97 0.03 1.00 0.002 0.95 0.0001HP 1.00 1.89 0.93 0.10 0.88 0.002Sun 1.00 0.001 1.00 0.13 0.97 1.79Toshiba 0.93 1.56 1.00 0.13 0.97 1.78Silicon Graphics 0.99 0.02 0.95 1.41 0.87 0.001Sequent 0.72 0.80 0.92 0.001 0.84 0.002Hitachi 0.88 0.07 0.98 0.21 0.55 0.001Apple 1.00 1.21 0.87 0.92 0.68 0.001Data General 0.90 0.92 0.62 0.54 0.81 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 shownin Table 1.1.

Now we consider a regression approach to specify the impact of R&Dinputs on output. With net sales as proxy output (y) and x1, x2, x3 asthree inputs comprising R&D spending, net capital expenditure and alldirect 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 percent respectively. This uses a slightly reduced sample set. When theregressions are run separately for the DEA efficient and inefficient firms,the coefficient for R&D inputs is about 12 per cent higher for the effi-cient firms, while the other coefficients are about the same. When eachvariable 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 contributionto output (or sales), in both the level form and the incremental form.

When we consider the R&D efficient firms only and several subperiodsthe regression results consistently show the dominant role of the R&Dinput in its contribution to sales (Table 1.2). The adjusted R2 is very high

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Table 1.2 Impact of R&D inputs for DEA efficient firms

Intercept x1 x2 x3 R2

1985–8 767.5 6.95∗∗ 1.38∗∗ 0.49 0.8281993–6 −146.6 2.54∗∗ −0.09 1.35∗∗ 0.8281997–2000 −239.9 4.00∗∗ −0.15 1.19∗∗ 0.9951985–2000 8.62 4.29 0.11∗ 1.08∗∗ 0.996

and the t-values for R&D expenditure are significant at the 1 per centlevel. The elasticity of output with respect to R&D expenses estimated atthe 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 com-puter industry in India. One is the emphasis on R&D investments andspending on knowledge capital. Second, the industry has to capturethe complementarity in productivity growth for other inputs and otherindustries.

Now consider the role of knowledge capital, which may take differ-ent forms, e.g. R&D spending, computer software and communicationsequipment, much like the Schumpeterian concept of innovations. Therole of IT capital in augmenting overall productivity growth has beenexamined in the current literature in terms of total factor productiv-ity (TFP) growth. Thus Jorgensen and Stiroh (2000) identify IT capitalwith computer hardware and software and communications equipment.They find that from 1973–90 to 1995–98 the contribution of IT capital toaggregate growth doubles and the productivity growth triples. Oliner andSichel (2000), using the same definition of IT capital and a somewhat nar-rower definition of output, find a similar increase in productivity growth.More recently, Hall (2000) argued that the spread of IT investment wasassociated with a new type of capital, e-capital for short, and this e-capitalis not measured by the standard National Income Accounting. He findsthat with e-capital the contribution of other inputs and TFP to outputgrowth is substantially reduced, e.g. without e-capital TFPG accounts forabout 40 per cent of total output growth, whereas with e-capital the com-bined contribution of e-capital and TFP accounts for about 75 per centof total output growth and most of it is due to e-capital.

The Indian perspective on the new industries based on knowledgecapital may be analyzed in terms of three components: the electronicsindustry, the software industry and the ICT sector industries. Growth inthese industries is basically conditioned by core managerial competencein facing the challenge of world competition. Technology import and

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

Technical Financial Design Total Collaboration in electronicsas % of total collaboration

1980 40 13 6 59 11.21985 140 46 24 210 20.21990 40 35 20 95 15.31991 61 41 10 112 11.5

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

Consumer Computers Software Communications Otherelectronics equipment

1989 0.0 0.03 0.04 0.09 0.191991 0.58 0.48 0.10 0.25 0.291993 0.27 0.25 0.11 0.41 0.741995 0.04 0.39 1.36 0.96 0.651996 0.18 0.16 0.13 0.22 0.41

R&D investment provide two critical measures of future growth in theseindustries. Development of the electronics industry during the 1970swas oriented toward indigenous development of technology and there-fore dependence on the import of technology was minimal. Estimates byJoseph (2004) and Agarwal (1985) suggest a slow upward trend in foreigncollaborations (Table 1.3).

Statistical data on the cost of technology imports as a proportion ofsales in the 1990s tend to suggest that with the liberalization of govern-ment policies and the opening up of the electronics industry, companiesstarted spending more on foreign technology. Estimates based on theElectronics Commission data reported by Joseph (2004) are shown inTable 1.4.

The trend for the computer and software sectors steadily rose from1997 to 2005. Two comments are in order for the electronics industry.First, the central government from the beginning put more emphasison domestic technology and import substitution. Unlike in Taiwan andChina, the electronics industry in India has not taken full advantage ofjoint ventures and technical collaborations to start new industries or ini-tiate new entry into this growing market. The public sector units played

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a major role in augmenting the output of electronics during 1970s to1990s. However, on a global perspective it is still very small in absoluteterms. For example, BEL and ITI spent on average nearly 7 per cent oftheir sales on R&D in 1983, amounting to about US$12 and 14 millionrespectively, whereas Goldstar, a South Korean company, spent morethan $20 million. The private sector’s role in R&D is also not very promis-ing. This is in sharp contrast with the record of performance of Taiwan,China, South Korea and Singapore. The private sector accounts for lessthan 15 per cent of the national R&D expenditure in India, as against30 per cent in the successful NICs (newly industrializing countries) ofSoutheast Asia. Since 1973 the Indian government has introduced liber-alized import facilities for equipment and raw materials for firms within-house R&D units registered with DSIR (Department of Scientific andIndustrial Research). Yet the record is unpromising so far. In 1995 therewere about 158 registered electronics R&D units in the private sector,accounting for about 7 per cent of total manufacturing units. In termsof employment generation the rough estimates by Joseph (2004) basedon ASI (annual survey of industry data on the number of employeesper gross investment capital) show that it is nearly two and a half timeshigher than that of the textiles industry, six times higher than that ofnon-ferrous metals and nearly ten times higher than that of the chemicalindustry. Yet the growth of electronics output has not been very satisfac-tory compared to the record of the NICs in Asia. This situation has beencompounded by the fact that some regions and states in India have con-sistently failed to participate in this industry to any significant degree.The estimates by Joseph (2004) of the state’s share of electronics produc-tion are shown in Table 1.5. The trend in export performance by majorproduct groups shows the annual compound growth rates shown inTable 1.6.

Table 1.5 Inter-state variations in electronics output

1971 1981 1990 1996

Karnataka 50.3 20.71 – –Maharashtra 25.5 23.77 16.55 17.30Uttar Pradesh 0.7 10.98 19.23 19.57Kerala 0.4 2.50 3.00 3.70West Bengal 5.0 5.01 3.38 3.32Bihar 0.03 0.12 0.22 0.18Gujarat 0.4 3.49 3.89 3.82

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It is clear that in the electronics field, the electronics capital goodssector accounts for the largest share, followed by electronic intermediatesand electronic consumer goods. While the NICs in Asia have moved awayduring the past decade from low technology products such as radios andTVs to medium and high technology items, India has not been ableso far to exploit the new opportunities. One silver lining for India isthe performance record of the computer software sector. For the mostrecent period (2000–05) the annual compound growth rate in exportsfor 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 softwareindustry in terms of two criteria. One is in terms of the ratio of R&D topretax profits and the other in terms of a competency index computedin two rounds of investigations in 1999 and 2002 on the basis of 11 rep-resentative software firms. These software firms include both Indian andforeign firms. In terms of R&D ratio he found that all firms reporting avalue greater than one (with a maximum of three) are from the USA, suchas Hewlett Packard, Texas Instruments and Microsoft. The Indian firmsin both the private and public sectors showed a value of 0.8 or less. Interms of the competency index, which is defined as the sum of fourcompetencies – product competence, project competence, skill levelcompetence of employees and competence in terms of formal trainingexperiences – most firms did not exhibit very high levels of training com-petency and skill competency. Banerjee (2004) also found a low level of‘switching competency’, which refers to the ease and flexibility in switch-ing from one product or service or mode of production to another. Thesoftware industry in India has to adopt a forward-looking innovativestrategy.

Sengupta (2003a) has discussed the importance of some dynamicstrategies in the Indian software industry based on three Cs: competitiveadvantage, comparative advantage and core competence in knowledge

Table 1.6 Export performance by major product groups

Electronic Electronic Electronic Software Totalconsumer capital intermediatesgoods goods

1981–8 11.85 45.35 21.62 40.08 26.641988–94 20.35 6.81 −2.09 32.78 15.341981–94 15.70 26.08 10.03 36.67 21.29

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accumulation, application and diffusion. The use of knowledge indifferent forms, e.g. learning skills, importing and improving newinnovations and diffusing knowledge externalities to take advantage ofeconomies of scale, has been strongly emphasized by Arrow (2000) as amajor source of dynamic growth as follows:

Every country or firm must have education and training in technol-ogy and science, even if the research is not on a par with that beingconducted elsewhere. Knowledge cannot be absorbed unless someknowledge is already possessed.

Countries and firms must be open to new ideas and see that ideasare diffused. This point strongly argues for freedom of entry, evenwhen it seems to forego economies of scale. (Arrow, 2000, p. 19)

Arrow’s remarks emphasize the importance of learning by doing in accu-mulating and improving knowledge capital and especially knowledgediffusion. Knowledge diffusion helps other sectors grow and improvetheir productivity. These diffusion effects are called externalities andspillovers, since the individual firms do not have to pay for them. Thisprovides a critical source for increasing returns to scale and the open-ness of global trade implies that these scale economies can be profitablyexploited so as to augment industry growth and expansion. Nachum(2002) analyzed the outward FDI (foreign direct investment) data fromthe USA for the years 1989–98 and found two most important explana-tory variables in the ‘innovation capabilities’ of firms and ‘flexibilitywith networking’. The role of increasing returns industries was found tobe much more important than that of the diminishing returns industries.

Software exports from India take three forms: (a) the export of soft-ware services through consultancy, (b) the support of software packagingdeveloped abroad and (c) electronic bookkeeping and data entry. Allthese forms of software exports are highly labor-intensive and Indiafaces stiff competition from six countries identified by the World Bank(Table 1.7). It is clear that India’s competitors rely more on software pack-age development and India’s export pattern exposes its vulnerability.Over the past decade India’s dependence on software package exports hasbeen less than 3 per cent, whereas even China and Mexico performedmuch better at more than 28 per cent. It is clear that Indian softwaredevelopers must forge a more dynamic global strategy in shifting tosoftware package development exports. This also calls for developingan effective system of alliances with US and Japanese counterparts sothat appropriate market niches can be set up and exploited. The most

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Table 1.7 Pattern of software exports from India and its competi-tors (1990)

Proportion of exports (%)

Software Software Data entry andservices packages bookkeeping

India 90 5 5China 17 56 27Singapore 25 58 17Ireland 65 21 14Mexico 53 32 15Philippines 39 20 41

important example to follow here is provided by Taiwan, which hasattempted to exploit the licensed clone market in different niches of thecomputer software market by entering into alliances and joint ventureswith other international firms. Moreover, Taiwan has offered some mildselective inducement of up to 2.5 per cent subsidy. This fosters active par-ticipation of small electronic and software firms into the R&D networkof government-affiliated laboratories.

Finally, India needs to develop core competence in managerial skillsin order to face international competition most successfully. In manyways ‘the coordination failures’ have created strong bottlenecks to fastergrowth in the modern technology-intensive industries such as electron-ics, computers and telecommunications. The Economist (London, 3–9June 2006) points out several distortions and bottlenecks associatedwith coordination failures. While the ‘license raj’ has been substantiallyreduced at the central government level, it still survives at the state level,with a pervasive ‘inspector raj’ imposing heavy transaction costs on firmsand new companies. Some parts (states) of the country deter investmentbecause they are so badly governed. The indirect tax system also detersany new investment. A 2002 study found that India’s combined indi-rect tax (e.g. import duties, excises, sales taxes and octroi) accounts fornearly one-half of a price disadvantage of roughly 39 per cent sufferedby 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 moreindustries are taken off a list of those “reserved” for small companies, apolicy that has prevented many firms from achieving the economies ofscale they need to compete internationally.’

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2 R&D investments and their impact

Innovations take many forms but in a broad sense they involve develop-ing new processes, new products and new organizational improvements.R&D investment plays an active role in innovations in new processesand in new products and services. Several dynamic features of R&Dinvestment by firms are important for selection and industry evolution.First, R&D expenditure not only generates new knowledge and informa-tion about new technical processes and products, but also enhances thefirm’s ability to assimilate, exploit and improve existing information andhence existing ‘knowledge capital’. Enhancing this ability to assimilateand improve existing information affects the learning process within anindustry, which has cumulative impact on the industry evolution. Forexample, Cohen and Levinthal (1989) have argued that one of the mainreasons why firms invested in R&D in the semiconductor industry is thatit provides an in-house technical capability that could keep these firmson the leading edge of the latest technology and thereby facilitate theassimilation of new technology developed elsewhere.

A second aspect of R&D investment within a firm is its spillover effectwithin an industry. R&D yields externalities in the sense that knowledgeacquired in one firm spills over to other firms and very often knowledgespread in this way finds new applications both locally and globally andthereby stimulates further innovative activity in other firms.

Finally, the possibility of implicit or explicit collaboration in R&D net-working or joint ventures increases the incentive of firms to invest. Thismay encourage more industry R&D investment in equilibrium. In theabsence of collaboration the competing firms may not invest enough,since innovations cannot be appropriated by the inventor, e.g. his com-petitors will copy the invention and thus ‘free ride’ without paying forit. Thus the basic reason for the success of joint R&D ventures is thatexternalities or spillovers are internalized, thus eliminating free rides.

We consider first the empirical basis of R&D innovations in modernindustries and then its implications for selection and industry evolu-tion. Cohen and Levinthal (1989) have made an important contributionin this area by analyzing the two faces of R&D investment in terms ofspillover and externality. One impact of R&D spillovers emphasized byNelson, Arrow and others is that they diminish firms’ incentive to investin R&D and related production. The other impact discussed by Cohenand Levinthal emphasizes the point that spillovers may encourageequilibrium industry R&D investment, since the firm’s R&D invest-ment develops its ability to exploit knowledge from the environment,

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i.e. develops its ‘absorptive’ capacity or learning by which a firm canacquire outside knowledge. Thus a significant benefit of a firm’s R&Dinvestment is its contribution to the intra-industry knowledge base andlearning, by which externality and spillovers may help firms developnew products and/or new processes.

The model developed by Cohen and Levinthal starts with the firm’sstock of knowledge, denotes the addition to the firm’s stock of tech-nological and scientific knowledge by zi and assumes that zi increasesthe firm’s gross earnings πi but at a diminishing rate. The relationshipdetermining zi is assumed to be of the form

zi = Mi + γi

⎛⎝θ

∑j �=i

Mj + T

⎞⎠, 0 ≤ γi ≤ 1 (1.1)

where Mi is the firm’s R&D investment, γi is the fraction of intra-industryknowledge that the firm is able to exploit, θ is the degree of intra-industryspillover of research knowledge. Mj represents other firms’ ( j �= i) R&Dinvestments that contribute to zi and θ denotes the degree to which theresearch effort of one firm may spill over to a pool of knowledge poten-tially available to all other firms, e.g. θ = 1 implies that all the benefitsof one firm’s research accrue to the research pool potentially availableto all other firms, whereas θ = 0 implies that the research benefits areexclusively 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 out-side knowledge, i.e. its complexity, ease of transferability and link withthe existing industry-specific knowledge. Clearly the composite variableβ will differ from one industry to another, e.g. in the pharmaceuticalindustry it may involve a lot of experimentation, long gestation peri-ods and the complexity of the marketing process for new drugs, whereasfor the computer industry it may involve software experimentation andthe ease of application in multiple situations. It is assumed that thecomposite variable β denoting ‘ease of learning’ is such that a higherlevel indicates that the firm’s ability to assimilate outside knowledge ismore dependent on its own R&D expenditure. Thus it is assumed thatincreasing β increases the marginal effect of R&D on the firm’s absorptivecapacity but diminishes the level of absorptive capacity.

Cohen and Levinthal evaluate the effects of increasing the explanatoryvariables such as β, θ and T on the equilibrium value of firm’s R&D

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investment denoted by M∗, where it is derived from maximizing πi withrespect to Mi as

R = MC = 1 (1.2)

where MC is the marginal cost of R&D expenditure equal to one and Ris marginal return given by

R = πizi

⎡⎣1 + γMi

⎛⎝θ

∑j �=i

Mj + T

⎞⎠

⎤⎦ + θ

∑j �=i

γjπizj

(1.3)

where the subscripts denote partial derivatives. On solving equations(1.2) and (1.3) simultaneously one obtains the equilibrium value of eachfirm’s R&D denoted by M∗.

The impact on M∗ of the explanatory variables β, θ and T are derived as:

sign(∂M∗/∂β) = sign[πi

ziγMβ(θ(n − 1)M + T) + θ(n − 1)

∂γ

∂βπi

zi

](1.4)

sign(∂M∗/∂θ) = sign[πi

ziγM(n − 1)M + (n − 1)γπi

zi

](1.5)

sign(∂M∗/∂T) = sign[γMπizi

+ (πizizj

+ (n − 1)πizizj

γ(1 + γM T)] (1.6)

The first term on the right hand side of equation (1.4) shows that a higherβ induces the firm with more incentives to conduct R&D, because itsown R&D has become more critical to assimilating its rivals’ spilloversθ(n − 1)M and the extra-industry knowledge T . The second term shows adecline in rivals’ absorptive capacity (n − 1)γ as β increases. As a result therival competitors are less able to exploit the firm’s spillover. Due to boththese effects the payoffs to the firm’s R&D increases and ceteris paribusmore R&D investment is induced.

The effect of θ on M∗ is ambiguous, due to two offsetting effects: thebenefit to the firm of increasing its absorptive capacity denoted by thefirst term and the loss associated with the diminished appropriability ofrents denoted by the second term on the right hand side of equation (1.5).Note, however, that the desire to assimilate knowledge generated byother 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 ofexternal knowledge. With γM = 0, i.e. zero endogenous absorptive capac-ity, the sign (∂M∗/∂T) is negative, since a higher T merely substitutes forthe firm’s own R&D, i.e. πi

zizj< 0.

Cohen and Levinthal estimate by regression (OLS, GLS and Tobit) mod-els the effects of the knowledge inputs and other industry characteristics

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Table 1.8 Effects of knowledge and other explanatory variables on R&Dintensity

OLS GLS Tobit

1 Technological opportunity(a) Appropriability (1 – θ) 0.396∗ 0.360∗∗ 0.260

(0.156) (0.104) (0.161)(b) Usertech 0.387∗∗ 0.409∗∗ 0.510∗∗

(0.099) (0.070) (0.166)(c) Univtech 0.346∗∗ 0.245∗∗ 0.321∗

(0.128) (0.089) (0.147)(d) Govtech 0.252∗ 0.170∗ 0.200∗

(0.100) (0.076) (0.100)2 Basic science research

(a) Biology 0.176 0.042 0.159(0.096) (0.057) (0.116)

(b) Chemistry 0.195∗∗ 0.095 0.149(0.071) (0.050) (0.078)

(c) Physics 0.189 0.037 0.156(0.109) (0.082) (0.109)

3 Applied science research(a) Computer science 0.336∗∗ 0.157 0.446∗∗

(0.123) (0.093) (0.121)(b) Material science −0.005 −0.028 0.231∗

(0.121) (0.089) (0.116)4 New plant 0.055∗∗ 0.041∗∗ 0.042∗∗

(0.008) (0.006) (0.007)5 Elasticity of

(a) Price −0.180∗∗ −0.071 −0.147∗(0.061) (0.044) (0.060)

(b) Income 1.062∗∗ 0.638∗∗ 1.145∗∗(0.170) (0.136) (0.180)

R2 0.278 – –

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

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

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asked to rate on a seven-point scale the effectiveness of different methodsused by firms to protect the competitive advantages of new products andnew processes. For a line of business, appropriability is then defined asthe maximum score. Thus if appropriability increases the spillover leveldeclines and hence R&D intensity increases. The new plant variable isused to reflect the relative maturity of an industry’s technology, i.e. itmeasures the percentage of an industry’s plant and equipment installedwithin the five years preceding 1977 as reported in the FTC’s data set.Industry demand conditions are represented by the price and incomeelasticity measures.

The explanatory variables T and β are measured indirectly for the sur-vey data. The level of extra-industry knowledge T is measured by fivesources, of which three are reported in Table 1.8: downstream users ofindustry’s products (usertech), government agencies and research labora-tories (govtech) and university research (univtech). The proxy variablesused for β in Table 1.8 represent cumulativeness and the targeted qual-ity of knowledge, which are field-specific; hence research in basic andapplied sciences is reported here, e.g. the characteristic that distinguishesthe basic from the applied sciences is the degree to which research resultsare targeted to the specific needs of firms, where basic science is lesstargeted than the applied. Hence the β value associated with basic sci-ence research is higher than that associated with applied science. As aresult the coefficient values of the technological opportunity variablesassociated with the basic sciences should exceed those of the appliedsciences. The estimates in Table 1.8 show that except in computer sci-ence the coefficients are uniformly greater for the basic sciences. Theexception of computer science may also be due to the rapid advance insoftware and process development, where basic and applied knowledgeare intermingled.

Some broad conclusions emerge from the estimates reported inTable 1.8. First, the results reject across all three estimation methods thehypothesis that the effects on R&D spending of basic and applied scienceare equal. This means that the role of learning differs significantly acrossfields in terms of cumulativeness, targetedness and the pace of advance,which affect the influence of technological opportunity on R&D spend-ing. Second, increasing the technological opportunity through the lesstargeted basic sciences evokes more R&D spending than does increas-ing the technological opportunity through applied sciences. Finally, theOLS and GLS estimates of the coefficient of appropriability are posi-tive and significant, implying that spillovers have a net negative effecton R&D.

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Next we consider an application in the world computer industry,which has witnessed rapid technological changes in recent years inboth hardware and software R&D. Recent empirical studies have foundcost-reducing effects of rapid technological progress to be substantial inmost technology-intensive industries of today, such as microelectronics,telecommunication and computers. Two types of productivity growthare associated with such technological progress: the scale economieseffect and the shift of the production and cost frontiers. There also existsubstantial improvements in the quality of inputs and outputs. The con-tribution of R&D expenditure has played a significant role here. Thisrole involves learning in different forms that help improve productiveefficiency of firms. One may classify learning into two broad types: oneassociated 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. Thesecond measure is cumulative experience embodied in strategic inputssuch as R&D investments in Arrow’s learning by doing models. Finally,the experience in ‘knowledge capital’ available to a firm due to spilloverfrom other firms may be embodied in the cost function through theresearch inputs.

Unlike the regression approach of Cohen and Levinthal we nowdevelop and apply a nonparametric and semiparametric model of pro-duction and cost efficiency involving R&D expenditure and its learningeffects. These nonparametric models do not use any specific form ofthe cost or production function; they are based on the observed levelsof inputs, outputs and their growth over time. Using these models wedetermine the number of computer firms that are efficient or are not.Then we run a regression of the dependent variable log output = y onthe three independent variables: log R&D (x1), log plant and equipment(x2) and log cost of goods sold (x3) with a dummy variable D for eachcoefficient, where D = 1 for the efficient firms and zero otherwise. Thedetails are described by Sengupta (2004). The results are as follows:

1987 − 98 y = 1.199∗∗ + 0.162∗∗ x1 + 0.065∗D x1 + 0.009 x2 − 0.034D x2

+ 0.743∗∗ x3 + 0.034∗ Dx3 (R2 = 0.996)

1991 y = 1.214∗∗ + 0.262∗∗ x1 − 0.075 x2 + 0.791∗∗ x3 (R2 = 0.998)

D significant for x1 and x3 only

1998 y = 0.925∗∗ + 0.140∗ x1 + 0.015 x2 + 0.0842∗∗ x3 (R2 = 0.998)

D significant for x1 and x3 only

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Clearly R&D expenditures have played a most dynamic role in the pro-ductivity growth of the efficient firms in the computer industry and thistrend 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 andSahoo (2006) elsewhere, where a set of 17 companies out of a larger set of45 is selected from the Compustat database from Standard and Poor. Theshare 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 emergefrom these results. One is that the number of firms on the cost-efficiencyfrontier is about one-third and these firms are invariably efficient in usingtheir R&D inputs. Second, both the efficiency score and the inputs helpthe firms improve their cost-efficiency and hence improve their marketshare.

For the Indian economy the major implications are clear. There shouldbe increasing emphasis on R&D investment and spending on learningby doing in both computers and pharmaceutical industries, if India isto face the challenges in the competitive world markets. We apply DEAmodels of efficiency to four types of industries in India – leather, textiles,computer-related products and electronic equipment – and analyze theirgrowth characteristics and prospects.

3 Growth of markets and policy reforms

India’s knowledge economy depends very critically on the trends in inter-national markets for software and IT-related services. The export marketsalso depend on the various policy measures adopted by the governmentfor liberalized trade policies. We discuss in this section three types ofstrategies related to trade policies, IT-related services and innovationtrends in IT service markets.

Although import licensing has been abolished, high import tariffs posea key constraint on better industrial performance and competitiveness.The tariff reductions program was rapid until the mid-1990s but the pro-cess has slowed down. Compared to China and South Korea the tariffsare much higher in India (Table 1.9).

While the government policy on foreign direct investment (FDI) hasbeen liberalized, it is still not allowed in selected sectors such as telecoms,insurance and IT-related services. As of 2006 the government has movedto liberalize investment in the housing construction sector and also the

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Table 1.9 Unweighted average customs duty rates (%)

All goods Agricultural Manufacturinggoods goods

India2003 35.0 47.1 33.32004 32.7 46.8 30.7

China 2000 16.3 16.5 16.2S. Korea 2000 12.7 47.9 6.6

Source: World Bank Report (2003).

transportation sector. Recent moves by the Reserve Bank of India to allowrepatriation of profits in dollar terms from housing investment by non-resident Indians and private FDI in major airports have contributed toimprove the private capital markets. However, the role of both centraland state bureaucracies is still pervasive in creating factor market dis-tortions through corruption and delay in the speed of liberalization.Thus according to the Global Competitiveness Report (2003) India ranksseventy-third out of a total of 75 countries analyzed, with China’s rankbeing twenty-third in the overall degree of competitiveness. The WorldBank Report (2003) has noted that setting up a business in India requiresten permits but in China only six, and the median time to get approval is90 days in India as against 30 days in China. This report also found thatmanagers of industries in India spend about 16 per cent of their timedealing with the government bureaucracy, compared with 9 per cent inChina and 11 per cent in Latin America. Moreover, the proportion offirms making illegal and/or irregular payments in India is about 90 percent. This is almost twice that of Malaysia.

It is clear that more transparency in trade and investment policiestoward both direct and foreign investment is needed so as to foster agrowth perspective for the industrial sector. The IT sector providing var-ious types of anchored services, such as industrial consultancy, softwaremarket services and business process outsourcing (BPO), is now beingactively helped by the liberalized government policies. Policy reformshave been very supportive of the recent growth trends in this sector.Stiffler (2006) has analyzed the AMR research survey for this sector show-ing 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 empha-sized. First, financial services have dominated the growth structure.

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Table 1.10 Performance of the five largest IT service providing firms in India

Company Quarter Revenue Employees Income perreported ($m) (no.) employee ($)

Cognizant 30 June 2006 337 31,000 8,026Tata Consultancy 30 June 2006 896 71,190 12,024(TCS)

Infosys 30 June 2006 660 58,409 13,560Wipro 30 June 2006 682 55,000 11,127Satyam 30 June 2006 323 30,000 8,267Comparison firmsAccenture 31 May 2006 4,408 133,000 20,602IBM (Services arm) 30 June 2006 11,894 200,000 23,880

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

Telecommunications, the retail sector and financial services have playeda key role for TCS, Wipro and Satyam. Healthcare, life services andfinancial service consultancy have been a major share of Cognizant’sgrowth. Second, infrastructure management of various forms is increas-ingly gaining ground in the new business strategies followed by threeleading companies. Increasing investment in this new category of busi-ness management is highlighted by HCL Technologies, Wipro, TCS andCognizant. For Infosys packaged implementation testing, BPO and cus-tom application development provide the most bright spots of growth.Finally, employment growth in this sector has been very high. For exam-ple, in 2006 Cognizant hired more than 6,600 employees. This impliesa multiplier effect of hundreds of other functional industry and domainspecialists. Satyam and Infosys made a concerted effort to hire increas-ingly more senior and/or non-Indian nationals on their payrolls. Allthese leading companies grow between 30 and 40 per cent and add thou-sands of employees annually. Among their major concerns are attrition,how to control costs and the smoothness with which they can maintaina healthy utilization rate, especially as larger deal sizes start to competewith the dynamics of growth.

The most important development in the IT services sector and soft-ware development in India is the increasing emphasis on R&D methods,where India’s skilled personnel can be most productively utilized. Jointventures, effective collaboration with leading edge firms abroad and thecontribution of venture capitalists from Silicon Valley (some of these areNRIs) all play a very dynamic role with a huge growth potential.

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Innovations take many forms but in a broad sense they involve devel-oping new processes, new products and new organizational improve-ments. R&D investment plays an active role in innovations in newprocesses and in new products and services. Several dynamic featuresof R&D investment by firms are important for selection and industryevolution. First, R&D expenditure not only generates new knowledgeand information about new technical processes and products, but alsoenhances the firm’s ability to assimilate, exploit and improve existinginformation and hence existing ‘knowledge capital’. Enhancing this abil-ity to assimilate and improve existing information affects the learningprocess within an industry, which has a cumulative impact on the indus-try’s evolution. For example, Cohen and Levinthal (1989) have arguedthat one of the main reasons why firms invested in R&D in the semi-conductor industry was that it provides an in-house technical capabilitythat could keep these firms on the leading edge of the latest technol-ogy and thereby facilitate the assimilation of new technology developedelsewhere.

A second aspect of R&D investment within a firm is its spillover effectwithin an industry. R&D yields externalities in the sense that knowledgeacquired in one firm spills over to other firms and very often knowledgespread in this way finds new applications both locally and globally andthereby stimulates further innovative activity in other firms.

Finally, the possibility of implicit or explicit collaboration in R&D net-working or joint ventures increases the incentive of firms to invest. Thismay encourage more industry R&D investment in equilibrium. In theabsence of collaboration the competing firms may not invest enough,since innovations cannot be appropriated by the inventor, e.g. his com-petitors will copy the invention and thus ‘free ride’ without paying forit. Thus the basic reason for the success of joint R&D ventures is thatexternalities or spillovers are internalized, thus eliminating free rides.

Most of the leading software companies in the USA have opened upjoint R&D centers in India. European firms are also getting involved. InSeptember 2006, IBS (Intelligent Business Systems), a leading softwaresolutions provider in the UK, announced plans to set up an R&D cen-ter in India to focus primarily on leveraging the growth potential in theworld’s fastest growing technology market. This appears to be the mostopportune moment for more economic policy reform so that the dif-fusion of gains in the IT sector to other sectors may be activated. Oneaspect of this reform should be to attempt to provide incentives throughsubsidies so that small and medium-sized firms could carry on the dif-fusion process, as in Taiwan. Taiwan’s experience is most significant for

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Table 1.11 R&D distribution by industry (%)

Telecom 2 Technology 8Aerospace 3 Auto 18Consumer goods 4 Computing & electronics 25Industrials 5 Health 21Software 5 Others 2Chemicals & energy 7

Total = US$384 billion.

the Indian economy because it has diffused the learning by doing processrelated to R&D and software development throughout the economy, sothat the gains are more equally shared. India has to realize that it cansuccessfully develop its talent pool in the knowledge economy by a sig-nificant margin. Compared to China, South Korea and Taiwan, it hasmore potential capability and core competence. But the needed policyreform is to speed up investment spending in R&D in the IT sector, bothpublic and private. The managerial challenge is much more focused onthe private sector, since it can reap the gains more directly by raising theexport drive to more diversified products in the value chain. According toestimates by Lall (1999), the average real rate of growth of in-house R&Dexpenditure during the post-reform period 1992–95 was 5.05 per centper year in the public and 10.28 per cent in the private sector. But in thecrisis year 1991–92 it was 1.81 and 0.91 per cent respectively. By compar-ison, the private sector figures for the same period of 1992–95 exceed 10per cent for both Taiwan and South Korea. Other NICs in Southeast Asiaand Japan have similar trends. However Lall’s estimates mainly relate tothe manufacturing sector and do not incorporate the recent upsurge ininvestment activity in software development and other IT-related R&Dactivities in the form of innovations.

Economic policy reforms aimed at innovations in the IT sector musthave a long-run vision of eight to ten years and any future five-yearplans must follow the competitive ladder in a world perspective. Severalfeatures of this reform policy may be emphasized here. First, India hasto know that it is facing intense international competition in this area.Hence it must adopt forward looking strategies. It is instructive here tolook at the survey report of the Booz Allen Hamilton Global Innovation1000 plan, discussed in some detail by Clark (2006). This report foundthe profile of R&D spending distribution in the world (2004) shown inTable 1.11.

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

Company Home Global R&D operations Newest R&Dcountry locations

Microsoft USA Half of its 6 major R&D labs Indiaare located in UK,China & India

Pfizer USA Half of its 10 major R&D Chinalabs are located outside US

Ford USA Mostly in US GermanyDaimler-Chrysler Germany Half of its 10 R&D centers Japan & China

are located in India,Japan & China

Toyota Japan 6 of the 7 R&D centers are Thailandoutside Japan

GM USA Mostly design centers Germany &Sweden

Siemens Germany Only 25 out of 150 R&D China, India &centers in Germany Russia

Matsushita Japan Has 10% R&D centers in US, ChinaElectric UK, Malaysia & China

IBM USA Half of R&D centers are Indialocated in Japan, China,India & Israel

Johnson & USA Located in USA, UK & China USA (California)Johnson

Table 1.13 Percentage growthof R&D spending 1999–2004

North America 6.6Europe 6.2Japan 4.8China and India 21.1Rest of the world 36.7Average 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 ofR&D in the computing and healthcare fields. The R&D footprints of thetop ten Global R&D are given in Table 1.12. In terms of growth in R&Dspending for the period 1999–2004, the record of India and China ismost impressive (Table 1.13).

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A second key feature of the innovation process that India needs to fol-low successfully is that it requires an exceptional level of cross-functionalcooperation among R&D, marketing, sales, service and manufacturing.And failure to forge effective collaboration can have a devastating impacton the success of the innovation process. The stages of the successfulinnovation process are:

1 Ideation. Customer insights from marketing, sales and service teamsare essential for identifying attractive opportunities for new prod-ucts and service. This is especially true for innovative softwaredevelopment.

2 Project selection. The current trends in market growth in the inter-national field must be constantly utilized so that the R&D team canidentify projects that are most likely to gain marketplace success.

3 Development and commercialization. The product or service can suc-ceed only if there is effective collaboration between R&D, marketing,manufacturing, sales and service.

The report of the Booz Allen Hamilton Global Innovation 1000 iden-tified four key elements in successful and effective innovations asfollows:

1 Align the innovation strategy with corporate strategy. In India thisis the most important challenge for top managers, since there is atendency to downgrade the contributions of new innovations.

2 Make the right bets. Any project selection should be evaluated inthe context of both customer needs and development costs. The eco-nomic evaluation of potential projects must be made on a meticulousbasis and followed up on a quarterly basis.

3 Manage the pipeline with speed and efficiency. The key emphasisshould be on core competence and efficiency.

4 Strive to maintain organizational efficiency. Companies should askthemselves: are incentives in place to reward desired performance andspeed up implementation efficiency? They should develop clear chan-nels for sharing knowledge about innovation and productivity. Thespillover effects of innovations should be internalized as far as possibleso that an ‘innovation culture’ can be fostered.

Since 2000 the Indian economy has picked up the speed of economicgrowth with its annual GDP growth changing from less than 4 per cent

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to more than 9 per cent in 2006. Outsourcing, telecoms and financialservices are fueling much of the current rapid economic growth butIndia’s manufacturing industry still suffers from low investment, weakinfrastructure and competition from manufacturers in China, Malaysia,Taiwan and other parts of Asia. The Indian government has reiterated itsdecision to establish the country as a reliable source of manufacturing,software development and IT research, coaxing technology giants likeMicrosoft, Intel, Oracle and Cisco Systems to invest more in India. Asof September 2006, Cisco Systems, Oracle and IBM have declared theirfuture plans to relocate some of their top international and US execu-tives to India. In their vision, India and China will lead the technologyworld order in the next decade and hence these companies plan to playan effective networking role.

India and China, with their fast growing markets and rapidly expand-ing innovation capabilities, are emerging to redefine the global order forthe high-tech industry. But instead of throwing up barriers and view-ing India and China as competitive threats, US technology vendors arebuilding global high-tech innovation networks: multipolar network sys-tems that exploit the huge markets and the growing talent pools in Indiaand China. It is useful here to analyze Forrester’s Business Technograph-ics survey data. In the 703 Asia Pacific (APAC) firms that respondedto Forrester’s 2006 survey, 60 were Indian enterprises and governmentagencies. Although the number of respondents is too low for statisticalreliability, more than half of the respondents came from the manufac-turing sector, giving us a view into business investment plans in this lesssizable segment of the Indian economy. From the perspective of steppingup the diffusion of IT innovation this is very important. Several featurescome out very clearly in this survey data as follows.

1 Forty-one per cent of Indian respondents foresaw a challenging yearin 2006, with stiff competition from China and other Southeast Asiancountries. About 21 per cent believed the 2006 outlook to be very goodfor their manufacturing industry and 24 per cent more somewhatchallenging. This compares favorably with 15 and 17 per cent forthe USA and Europe, where manufacturing companies considered theoutlook in 2006 to be very good.

2 About 68 per cent or more responded that their IT budgets wouldincrease in 2006 and thereafter. The corresponding figures for APACoverall, North America and Europe were 47, 42 and 29 per centrespectively.

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3 Half of Indian manufacturing enterprises (51 per cent) planned toincrease their hardware budget in 2006. A majority of IT managingdirectors in India ranked security, disaster recovery and consolidat-ing their IT infrastructure at the top of their IT agenda for 2006 andthereafter.

4 The percentage response to the question ‘Which of the following ini-tiatives are likely to be your IT organization’s major business themefor 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 inno-

vations, 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 onthe priority list of IT plans in the manufacturing sector in India.

One has to observe also the increase in effective collaboration of Indianfirms with the leading giants like Hewlett-Packard (HP/Compaq) andMicrosoft. Forrester’s survey data (2006) show that about 31 per centof Indian IT enterprises plan to increase their spending with HP for com-puting hardware, and 39 per cent of Indian firms planned to increasetheir IT spending on software development with Microsoft. In 2006Microsoft announced plans to invest more than $1.5 billion in India forR&D, marketing and education to foster growth in the Indian market forits software products. Other winners in this software market expansioninclude SAP and Oracle/People Soft, with 14 and 12 per cent of Indianfirms respectively.

A few remarks on the recent trends in the Indian software marketare in order, since software services are most labor-intensive and alsodiffusion-intensive. They can be easily decentralized, e.g. financial ser-vices, publishing, communication networks. Kumar and Jetharandani(2005) have discussed the trend in this market. Their key findings are asfollows:

1 India is the fourth largest software market in the APAC region(excluding Japan), with about 9.5 per cent of the regional market.

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2 India’s software market is among the fastest growing in the APACregion, with an expected compound annual growth rate (CAGR) of15 per cent through 2008.

3 The biggest areas for growth are in banks, government services atthe federal and state levels, telecommunications, manufacturing andsmall and midsized businesses.

Along with the software market, India has a great potential in the BPO(business process outsourcing) market. Forrester, who pioneered the Busi-ness Technographic Survey, visited India in 2005 and interviewed anumber of prominent BPO suppliers in the voice, transaction-processingand knowledge-processing segments. He found that this offshore IndianBPO market, despite its relative youth, continues to exhibit dynamism.Martorelli (2005) has noted that the Indian BPO market is undergoinga dramatic shift in its service mix. It was not long ago that the voice-based call center applications were the major source of the Indian BPOmarket’s meteoric rise. It still accounts for slightly more than 60 per centof the $3.5 billion offshore industry in India according to NASSCOM(National Association of Software and Service Companies). But the lead-ing companies have performed a dynamic shift, emphasizing more andmore the transaction-processing and knowledge-processing opportuni-ties. This diversification would enhance business growth and meet thechallenge from other Asian competitors, including IBM and Accentureoperations in India.

In conclusion we may point out that the new knowledge economyin India provides a vibrant sector of growth. It shows a dynamism andchallenge: the dynamism due to world competition and innovation andthe challenge due to intensifying the need to improve core competenceand economic efficiency in the IT sector. This market-based philosophyof sustaining growth through improving economic efficiency has beensuccinctly put forward by Baumol (2002) in his technology-consortiummodel, where the cost of not joining a technology and innovation con-sortium is very high for each member not joining. The advantage ofjoining is growth through substantial scale economies, dynamic com-parative advantages and learning by doing. India has all the potential tobecome a leading partner in this innovation consortium.

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2India’s Industry Growth: ItsStructure and Potential

India’s industry is at a crossroads today. Its IT sector is booming. Its globalmarkets are expanding. The manufacturing and service sectors are bear-ing the impact of IT expansion. Yet the traditional industries andagriculture are yet to mirror the overall growth. The rural sector hasreaped little or no benefit from the high growth rate of national income(exceeding 8 per cent). Goldman and Sachs predicted in February 2007that this high growth rate is likely to continue for the next three decadesor more. However, India has to implement some more appropriate toopenness in world trade and competitive efficiency and transparency.Three types of strategies are needed in particular. One is to develop andexpand the incentive system for export-sensitive industries so that theycan compete more easily in the world markets. On the domestic frontnew markets have to be fostered and developed. Second, the IT sec-tor, with its software network and various ‘outsourcing’ services soldabroad, needs to adopt strategies that will sustain and improve compet-itive and comparative advantage in the world market. As in the otherNICs (newly industrializing countries) of Southeast Asia, also knownas growth miracle countries, India’s IT sector has to actively pursue astrategy of technology diffusion to other sectors, such as manufacturingand services like retailing, real estate development and rural develop-ment. Finally, India has to look for new market entries on both domesticand international fronts. Expanding export markets and developing newindustries for the domestic economy are very critical to maintaining thehigh growth rate of the Indian economy achieved so far. The growthimpact of spillover technology and externality effects have been stronglyemphasized by modern growth theorists like Lucas (1993), Romer (1990)and others.

Our object here is threefold. First, we discuss some models that are rel-evant for industry dynamics and growth. This is followed by the theory

30

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India’s Industry Growth: Its Structure and Potential 31

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

1 Industry dynamics and growth

Industry growth in India can be analyzed from several perspectives, ofwhich three are most important from a dynamic theoretical viewpoint.First, we may analyze the models of technological change in relationto capital investment and discuss their lessons for industrial growth inIndia. The Solow model assumed technological change as exogenous andconcluded that the long-run growth of steady-state income depends onlyon exogenous technological change. In modern endogenous growthmodels, technological change may take several forms, such as Arrow’slearning by doing or cumulative experience, skill acquisition througheducation and research and spillover technology. Most of these formsof technological change generate induced investment and the diffu-sion of new technology. The broad concept of Schumpeterian innovationsuccinctly conveys these features of new ideas, new products and servicesand new institutional forms. The demand pull perspective emphasizes therelative importance of market demand growth on the supply of knowl-edge and innovations. In his classic study of the invention and diffusionof hybrid corn, Griliches (1957) first showed empirically the role ofdemand in determining the timing, location and diffusion of invention.Schmookler (1966) in a massive study of US patent statistics showed thatwhen investment rose, capital goods innovations also rose; when invest-ment fell, the flow of patent applications also declined. Modern growththeory emphasizes two main channels of inducing growth through R&Dexpenditure, which is the core component of innovation or technolog-ical change. One is its impact on the range of available products andservices, and the other is its impact on the stock of knowledge availablefor R&D. Helpman (2004) has discussed the role of endogenous R&Dinvestments in improving the industrial productivity of a developingcountry participating in world markets. Two impacts are distinguished.One is the market size effect. Access to a larger world market raises theprofitability of inventive activities and encourages investment in moreR&D and knowledge creation. The second is the competition effect. Ithas two sides. On the negative side it may hurt profits, because for-eign competitors are more efficient than domestic firms. On the positiveside, however, open competition may induce the technological leadersto forge ahead. The NICs of Southeast Asia and Japan have adopted this

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positive side of competitive efficiency and the openness in trade hashelped these countries raise their industrial growth rate at a faster speed.

Second, the industrial structure of any country is characterized byhigh degrees of diversity. Whereas some industries such as chemicals,oil refining and iron and steel, comprise only a small number of largeenterprises that are hardly ever challenged by entering firms (i.e. newentries), others, such as textiles, leather goods and wooden and profes-sional services, comprise a large population of small firms, where newentries and exits occur very frequently. Models of entry and market evo-lution attempt to explain why firms and industries grow or decline. Theempirical experiences of the industrial countries offer important lessonsfor India.

Some useful empirical models may be discussed in this connection.One is the study of the manufacturing sector in the UK for 1980–90by Lansbury and Mayes (1996), who noted that the competitive processinvolves not just the development of existing firms but new entrants whochallenge the incumbents. The productivity of most new entrants washigher than that of the sample as a whole for most of the years from1980 to 1990. The new entrants can be totally new firms, new lines ofbusiness for existing firms or new plants for existing firms in the sameindustry. Table 2.1 exhibits the pattern of entry indicated by ‘births’ overthe years 1981–90 for 20 branches of the manufacturing sector.

The high entry industries include ‘extraction of minerals’, motor vehi-cles and parts, and rubber and plastics. Textiles, leather goods, metalmanufacturing and food products comprise some of the important indus-tries where birth rates are low. One would expect a greater volatility (orchurning) in entry rates in an industry that is more competitive. Like-wise, low churning is likely to be a feature of industries that are notreadily contestable due to high entry costs or large fixed costs. However,the product cycle affects these results to a large extent. A similar studywas made by Veloce and Zellner (1985) of the Canadian household furni-ture industry using annual data over 1957–87. The econometric estimateof the entry equation was as follows:

� ln Nt = 0.37(0.13)

+ 0.85(0.14)

� ln Nt−1 − 0.53(0.15)

� ln Nt−2 + 0.16(0.0005)

t

− 0.20(0.08)

ln rt−1 − 0.43 × 10−8

(0.18×10−8)St−1

R2 = adjusted R2 = 0.7389

where the standard errors are in parentheses. Here Nt = number of estab-lishments in the industry, rt = real rate of interest measured by the

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Table 2.1 Number of ‘births’ relative to the total number of businesses (%)

1981 1983 1985 1987 1988 1989 1990

1 Metal manufacturing 3.96 2.97 5.41 11.43 9.44 4.76 3.962 Minerals extraction 4.29 2.45 7.88 22.22 22.39 3.58 10.853 Non-metallic minerals 4.96 3.75 6.87 11.46 9.02 8.69 7.664 Chemicals 3.22 4.21 5.06 8.83 6.73 9.56 7.575 Manmade fiber 0.0 6.67 3.33 – 5.88 9.38 –6 Metal goods 2.86 2.24 4.13 7.04 6.62 8.54 6.227 Mechanical eng. 3.55 3.45 5.08 5.53 7.78 9.43 7.818 Data processing equip. 18.18 20.86 9.59 29.38 15.07 14.13 16.059 Electrical eng. 4.59 3.06 5.31 7.92 4.86 9.65 8.65

10 Motor vehicles & parts 3.17 3.42 2.64 9.07 5.91 4.95 12.3211 Other transport equip. 2.34 3.92 5.90 6.02 4.35 6.04 11.7112 Instrument eng. 7.74 4.53 3.40 12.70 10.37 5.74 10.2213 Food products 4.64 3.86 3.94 8.26 11.05 10.83 6.3814 Textiles 2.71 2.16 3.50 4.26 3.72 4.79 3.3515 Leather goods 1.83 2.55 4.08 2.94 8.94 3.33 2.7216 Footwear & clothing 3.05 3.33 4.50 7.36 4.80 7.54 5.0317 Wooden furniture 3.38 4.03 6.63 10.73 8.32 10.14 7.2118 Paper products 3.99 4.30 4.85 10.41 8.98 9.57 7.0819 Rubber & plastics 3.05 3.53 4.44 9.59 9.01 11.46 6.6720 Other manufacturing 4.15 3.67 3.41 17.45 10.04 12.46 7.51

Total manufacturing 3.88 3.60 4.90 8.89 7.97 8.97 7.28

difference between the nominal rate on ten-year Canadian industrialbonds and the Canadian CPI, t = time in years indicating trends andSt = total sales. It is clear that this furniture industry is characterized bya positive time trend and a high positive impact of the first order laggedterm � 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) isdropped, the resulting equation becomes

Constant � ln Nt−1 � ln Nt−2 t ln rt−1 R2

0.29 0.89 −0.52 0.009 −0.205 0.663(0.14) (0.16) (0.17) (0.004) (0.095)

The results confirm a positive trend effect and a negative effect of realinterest rate (lagged).

A more comprehensive analysis of industry dynamics and the role oftechnological progress has been attempted by van Dijk (2002) for theDutch manufacturing sector. He raised some fundamental theoreticalissues about this dynamic behavior and tested detailed empirical data

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Table 2.2 Entry and exit rates (%) in Dutch manufacturing

Year Entry rate (%) Exit rate (%)

Firms Employment Firms Employment

1978 – – 6.67 3.141979 8.39 3.07 5.41 2.301980 5.82 1.93 7.14 2.681981 4.95 1.88 7.85 3.261982 5.73 2.25 8.83 4.631983 9.73 4.91 7.20 3.031984 6.09 2.39 4.93 2.201985 5.88 2.34 4.34 1.841986 4.89 1.72 3.99 1.621987 6.06 2.13 4.09 2.021988 6.14 2.05 4.15 1.981989 6.54 2.32 4.91 2.321990 5.17 2.41 6.74 3.421991 9.36 3.47 7.06 3.251992 7.27 2.96 – –

Mean 6.52 2.56 5.95 2.69

on 106 industries in the Dutch manufacturing sector between 1978 and1992. The SM (Statistics Netherlands) database was used and it capturesall firms with more than 20 employees that have been active in the man-ufacturing sector. In total there were 10,246 firms in the data set, ofwhich 2,558 firms were present throughout the period 1978–92. Thesecontinuing firms captured on average 53.5 and 52.4 per cent of totalmanufacturing employment and value of output respectively. Given thehigh hazard rates of entrants the long-run or cumulative impact of entrymay be less substantial. However, as Baldwin (1995) shows for the Cana-dian manufacturing sector, the accumulation of entry over 1970–81 wasof considerable magnitude. The number of entrants alive and active in1981 equaled 35.5 per cent of the 1970 firm population, while theirnumber 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 correla-tion between entry and exit rates. For example, when cumulative entryand exit rates are considered, the correlation is found to be 0.23. Thisincreases to 0.52 when cumulative sales entry and exit rates are consid-ered. Indirectly this implies a significant turbulence or churning effect(volatility). Hence van Dijk undertook a detailed regression analysis to

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Table 2.3 Explaining entry rates and market share turbulence in termsof industrial growth rates, scale economies and Herfindahl index

Annual entry rates Cumulative entry rates Marketshare

Firms Sales Firms Sales turbulence

Constant 0.118∗∗∗ 0.119∗∗∗ 1.120∗∗∗ 1.054∗∗∗ 0.027(0.054)

Profit margin −0.421∗∗∗ −0.299∗∗∗ −1.881∗∗∗ −2.030∗∗∗ 0.059(0.256)

Industrial growth 0.020∗∗∗ 0.011∗∗∗ 0.133∗∗∗ 0.116∗∗∗ 0.002rate (0.009)

Investment 0.278∗ 0.153 0.855 0.657 −0.141margin (−0.349)

Median firm size −0.013∗∗ −0.015∗∗∗ −0.159∗∗∗ −0.152∗∗∗ 0.037∗∗(scale economies) (0.013)

Herfindahl index 0.263∗∗∗ −0.011 0.307∗∗∗ −0.249∗ −0.166∗∗Adjusted R2 0.653 0.210 0.490 0.516 0.075

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

explain the annual entry rates and market share turbulence in terms ofseveral explanatory variables, i.e. profit margin, industrial growth rate,investment margin, median firm size and Herfindahl index. The regres-sion results are reported in Table 2.3. Several implications have to benoted here. First, earlier research on the determinants of gross entryshowed that profitability does not seem to have a significant effect oninviting entry. This is at odds with the standard models of the entryprocess, where high profits seem to attract profit-seeking entrants. Notethat we obtain a negative impact of average profit margins, but this maylargely reflect the negative impact of scale economies, for which theproxy 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 posi-tive effect on market turbulence among the incumbent firms and theHerfindahl index (concentration in the industry) has a significant nega-tive impact on entry rates. This implies that higher concentration tendsto reduce the entry of new firms in the industry.

van Dijk has also discussed the role of technological competitivenessof a firm or industry in inducing or deterring entry. The technologicalcompetitiveness TCi of a firm i is assumed to depend on the intrinsic

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quality Qτ of the product technology it is applying and the total shareTτ 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 net-work externalities on the demand side, i.e. the higher α is, the more thetotal market share of a technology determines technological competi-tiveness. This type of technology-based entry model may be understoodas a process by using the Schumpeterian concept of ‘creative destruc-tion’. 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 lev-els Qτ such that QK > QK−1 > QK−2 > · · · > Q1. Further, there is a classof old technologies with τ = 0 that have an intrinsic quality level Q0.At some interval a pioneering entrant or incumbent brings out a newintrinsically better product technology. This causes all technologies todrop one level in their intrinsic quality. Hence the newly introducedtechnology becomes K (i.e. the technology with the highest quality levelQK), and τ = 1 becomes part of the class of old technologies (τ = 0) anddegrades to the intrinsic quality level Q0. Shy (1996) has related thistechnological competitiveness process to consumer dynamics within anoverlapping generation model, where the generation of entering con-sumers chooses whether to purchase a certain product based on an oldtechnology or whether to purchase the product based on a new tech-nology with a higher quality. Then the size of the network of the newtechnology is the sum of the population size of the young generationand a certain proportion of the old generation of users. This percentageis determined by the degree of compatibility between the old and thenew technology. Hence the higher the compatibility the larger the net-work size associated with the new technology. Shy (1996) has shownthat a decrease in the degree of compatibility between new and oldtechnologies will increase the duration of each technology. The moreconsumers value quality and network size as substitutes rather than com-plements, the more the frequency of technology adoption and the lowerthe duration of each adopted technology. van Dijk performed a series ofsimulations under different technological regimes represented by vari-ous technological competitiveness and spillover conditions. In generalhe found a smaller, more concentrated population of firms when cumu-lativeness (or technological competitiveness) conditions are high. This

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provided significant entry barriers. Widespread spillover conditions, onthe other hand, led to a higher number of new entrant firms and lowerlevels of concentration.

We may now summarize the three broad lessons from the above empir-ical dynamics of the entry and market evolution process, which can beprofitably used in the industry growth framework in India in its man-ufacturing and skill-intensive service sectors. First, successful trading inthe world market for manufactured goods demands excellence in tech-nological competitiveness. Maintaining and improving cost efficiency atthe firm level are most important. Second, R&D investments have to beso planned that quality ladders and product diversity are paid increasedattention. The success of the Japanese automakers in the US marketamply demonstrates the value of this insight. Finally, the degree of sub-stitutability of old and new technologies (or processes) must be carefullyanalyzed in the various product-based R&D investments so that a smoothtransition can be attempted from the old to the new. This applies to allthose branches of manufacturing and skill-intensive services that have tocompete 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 combineto produce output Y as

Y = Kα(ALY )1−α, 0 < α < 1 (2.1)

For a given level of technology represented by A this production functionexhibits constant returns to scale in the two inputs K and LY . However,when research ideas represented by A are also an input into the pro-duction function, this yields increasing returns. In this model A(t) isthe stock of knowledge or the number of research ideas that have beeninvented over the course of history up to time t. Then the growth ofR&D ideas can be represented by

A = δLA (2.2)

where dot denotes the time derivative. It says thatA is equal to the num-ber of researchers LA multiplied by their productivity δ. The productivity

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parameter δ may be a constant or may increase over time. This meansone can write it as

δ = δAφ (2.3)

where δ and φ are constants. If φ > 0 then it implies that the productivityof research increases with the stock of ideas that have already been dis-covered. In this model total laborforce L = LY + LA is allocated betweenproducing output (LY ) and producing research activities (LA). In a simplecase Romer assumed φ = 1, so that we obtain

A = δLAA, δ = δA

But this specification rules out increasing returns to research inputs(R&D) or positive knowledge spillovers. But empirically this assumptionφ = 1 fails to hold; it is strongly rejected by the empirical trends in growthrates of the USA and other industrial economies.

If gA is the growth rate of A (i.e. gA =A/A) and r the rate of return onR&D capital, then the proportion pR = LA/L of people engaged in R&Dcan be expressed as

pR = 1/[

1 + r − nαgA

], L/L = n (2.4)

This shows that the faster the economy grows (i.e. the higher is gA), thehigher the fraction of population engaged in research. The higher thediscount rate that applies to current profits to get the present discountedvalue (r – n), the lower the fraction working in research. It is also clearfrom equation (2.4) that a permanent increase in the R&D share pA

increases the level of technology permanently. This is the level effect ofthe Solow model but applied to technology represented by R&D term A.

Unlike Romer, Lucas introduced the R&D spillover effect, where R&Din one industry generates induced investment or more physical capitalaccumulation in other industries. In other words, the productive con-tribution of increased physical capital already incorporates some of theinduced investment due to the spillover effect of R&D technology.

The nonrival nature of the R&D input generates increasing returnsto scale and this may help industries grow faster, if they are effectivelyusing R&D investments. Modeling the optimal time path R(t) of theR&D capital may be easily done for a firm investing in R&D. The model

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maximizes a discounted profit function � by choosing its R&D capitalstock R = R(t) and investment u = u(t).

max � =∫ ∞

0exp(−ρt)[r(R) − c(u)]dt (2.5)

subject to R = u − δR

where u is investment in R&D and δ is the constant rate of depreciation.Here r(R) is the revenue function and c(u) specifies the investment costsof R&D capital. If investments are irreversible then we would have u ≥ 0.Now we assume that the revenue function r(R) is convex and the costfunction c(u) also convex. Assuming a quadratic form

r(R) = aR + bR2, c(u) = c1u + c2u2

where all parameters a, b, c1 and c2 are positive, the current valueHamiltonian H takes the form

H = aR + bR2 − c1u − c2u2 = q (u − δR)

By the maximum principle one obtains

∂H/∂u = 0, i.e. u = (q − c1)/2c2

Hence for u > 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 − a2c2

− bc2

R

The steady state has the equilibrium values R and u as follows

R = (1/2){c1(ρ + δ) − a}/{b − c2δ(ρ + δ)}u = δR = (δ/2){c1(ρ + δ) − a}/{b − c2δ(ρ + δ)}

Thus the steady state level R of R&D or knowledge capital decreases if theparameters a and b of the revenue function increase. The investment u insteady state rises, however, with increases in the parameters c1, c2 of the

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investment cost function. Also, u rises for any decrease in the parametersa and b of the revenue function. As in the Romer model, any permanentincrease in the parameters c1, c2 of the investment cost will increase thesteady state level of the knowledge capital R.

An interesting case of this model arises when the following condi-tions 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 dynamicsystem 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 ispositive. On the stable trajectory we can only consider the case λ1, whichresults in convergence to the steady state. The complete optimal pathsmay then be written as

R(t) = R + (R0 − R) exp (λ1t)

u(t) = u + (R0 − R)(δ + λ1) exp (λ1t)

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 rev-

enue function r(R) is convex but more nonlinear than a quadratic, thenthis may lead to multiple steady state equilibria and also path-dependentequilibria. 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 capitalstock as R(t) = R0exp(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 modelmost of the R&D stock is a fixed cost that generates increasing returns toscale and a very fast rate of growth.

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Griliches (1998) undertook a careful analysis of the contribution ofR&D capital to productivity in the US manufacturing sector, includingboth R&D and non-R&D firms over 1982–87, with 676 sample values.He found a strong relationship between TFP (total factor productiv-ity) growth and privately financed R&D expenditure intensity. For theperiod 1959–78 he found that own R&D had a relatively large and sig-nificant rate of return of the order of 0.30 and that it did not declinesignificantly between 1959–68 and 1974–78. For the HPAE (high per-formance Asian economies) countries, Nadiri and Kim (1996) estimatedtranslog cost functions with four inputs (labor, materials, physical cap-ital and R&D capital) and compared productivity in the USA, Japanand South Korea over the period 1975–90. His estimates are reported inTables 2.4 to 2.6.

Several comments are in order. First, R&D investment has been a sig-nificant contributor to growth of output and productivity in the USAand Japan, but not in Korea. But more recently the contribution of R&Dhas been rapidly rising and the contribution of R&D in Korean man-ufacturing in absolute terms is not very far from that of the USA andJapan. The net rates of return to both physical and R&D capital in theKorean manufacturing sector have been very impressive (see Table 2.7).Second, one may also note that the conventional TFP measure is not avery appropriate measure of technical change when perfect competitiondoes not prevail or when economies of scale are present. The empiri-cal estimates here suggest that the rate of technical change measured bythe decline in costs over time is rather small, being about 0.3–0.5 percent. Finally, Nadiri and Kim decomposed TFP growth into five com-ponents: scale effect, disequilibrium effect, R&D effect, pure technicalchange effect and mark-up effect. The most important contributor to TFPgrowth is found to be the scale effect. This effect is responsible respec-tively for about 35, 38 and 30 per cent of traditional TFP growth in theUS, Japanese and Korean manufacturing sectors. The contribution of themark-up is unusually high in Korea (1.9 per cent). This is mainly due tothe 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 theperiod 1963–85 to estimate the contribution of two forms of interna-tional spillover effects: direct and indirect. Here Y is output, v is a vectorof variable factor demands, K is physical capital and S is a vector of R&D

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14/10/200816:35

Page42

42Table 2.4 Growth of output and inputs in the total manufacturing sector of the USA, Japan and Korea (1975–90, %)

Output Labor Materials Physical capital R&D capital

USA Japan Korea USA Japan Korea USA Japan Korea USA Japan Korea USA Japan Korea

1975–80 1.0 3.5 13.4 −0.2 −0.6 8.2 0.8 2.7 12.2 3.7 3.6 18.5 0.5 6.8 35.31981–85 1.6 4.0 10.1 −1.2 1.3 3.7 0.7 2.3 8.6 2.0 6.0 8.4 3.1 9.3 28.21986–90 3.3 4.5 15.2 −0.2 0.3 2.6 3.2 5.8 14.8 1.5 7.0 15.1 3.9 9.0 25.11975–90 1.9 4.0 12.9 −0.5 0.3 5.0 1.5 3.2 11.9 2.5 5.4 14.3 2.4 8.3 29.9

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

TFP Labor productivity Materials productivity Physical capital R&D capitalproductivity productivity

USA Japan Korea USA Japan Korea USA Japan Korea USA Japan Korea USA Japan Korea

1975–80 0.08 0.99 0.35 1.25 4.07 5.19 0.30 0.76 1.21 −2.58 −0.08 −5.07 0.59 −3.28 −21.91981–90 0.77 0.51 1.80 3.22 3.44 9.51 0.49 0.76 0.93 0.74 −2.23 0.92 −1.05 −4.92 −14.01975–90 0.51 0.69 1.26 2.48 3.68 7.89 0.42 0.76 1.04 −0.51 −1.43 −1.32 −0.44 −4.31 −17.0

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

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

Period Gross Labor Materials Physical R&D Technical Residualoutput effect effect capital effect change

effect

USA1975–80 1.09 −0.03 10.47 2.53 0.21 0.46 −1.091981–90 2.47 −0.13 1.55 0.18 0.18 0.39 0.301975–90 1.95 −0.09 1.20 0.24 0.12 0.55 −0.06Japan1975–80 3.47 −0.11 2.32 0.30 0.12 0.75 0.081981–90 4.27 0.15 3.32 0.60 0.21 0.36 −0.371975–90 3.97 0.05 2.95 0.49 0.18 0.50 −0.20South Korea1975–80 13.39 0.99 10.47 2.53 0.02 0.46 −1.091981–90 12.66 0.34 9.62 1.58 0.18 0.25 0.021975–90 12.93 0.59 9.94 1.94 0.12 0.33 0.68

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

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

Year Physical capital R&D capital

USA Japan Korea USA Japan Korea

1980–90 10.63 7.69 17.84 12.39 11.73 19.421980 11.30 9.27 17.55 14.16 12.01 31.461985 11.74 7.96 15.06 11.56 12.31 18.941990 9.63 9.33 22.78 11.11 15.60 23.88

spillovers, which in a bilateral production model is the R&D capital fromthe other country. Table 2.8 presents their estimates.

The contribution of spillover effects is estimated through the respectiveinput output ratios as

∂(vt/Yt )/∂St−1 = φ + γ(Kt−1/Yt−1)′ζ + γ(�Kt/Yt )′µ(

∂(Kt/Yt )∂St−1

)

where prime denotes transpose. The direct spillover effect on variablefactor demands is measured by φ, the indirect effects are measured byall capital inputs ζ and net investment through µ. The international

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Table 2.8 Decomposition of average annual TFP growth rates (%)

TFPG Scale Physical Adjustment Spillover Spillovercapital cost (direct) (indirect)

USA1963–67 0.953 0.802 4.353 −3.861 0.175 −0.5161968–73 2.413 0.369 2.556 1.081 0.534 −2.1271974–79 −0.396 −0.314 1.956 −0.180 0.405 −2.2671980–85 −2.413 0.116 0.809 0.127 0.632 −4.0971963–85 0.104 0.219 2.334 −0.571 −0.448 −2.326

Japan1963–67 1.749 −0.144 3.997 −1.685 1.136 −1.5551968–73 2.289 0.640 3.239 −1.830 1.125 −0.8851974–79 2.279 0.312 0.646 0.936 1.122 −0.7371980–85 1.394 − 1.118 −2.243 3.967 −1.4481963–85 1.935 0.217 2.174 −1.185 1.868 −1.139

R&D spillovers reduced the labor–output and physical capital–outputratios for both US and Japanese manufacturing sectors. Their estimatesshow that in the USA labor and physical capital output ratios declinedby 0.02 per cent but in Japan these ratios declined by 3.5 and 0.13 percent respectively. Thus the effects from US R&D capital were signifi-cantly greater for Japan than the effects arising from Japanese generatedspillovers. Table 2.8 shows that the direct effect from Japanese R&D capi-tal contributed about 20 per cent to US productivity growth over the twodecades from the mid-sixties to the mid-eighties. Over the same periodthe US effect accounted for around 60 per cent of Japanese TFP growthin its R&D-intensive manufacturing sector.

Clearly this has two important lessons for India’s growth structureanalysis. One is to capture the benefits of US spillover technology inthose parts of the manufacturing sector that are likely to be R&D inten-sive in the near future. Joint ventures, inviting US investment andincreasing cooperation in the software R&D field should be activelypursued by both private and government policies. Second, indirectspillover effects through input cost reductions in sectors of the over-all economy other than the IT sector should be built into the plannedstrategy for accelerating the overall growth rate of R&D-intensive andnon R&D-based industries.

In Schumpeter’s innovation process, imitation and improvementrather than inventions can also provide a source of growth through cre-ative destruction. Thus Japanese firms have historically gained time and

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cost advantages in imitation due to their acquisition of the know-how offoreign competitors. Most comparative advantages come not from inter-nal technology or new software products but from external technologybought or copied from competitors. According to a recent report of theUS National Academy of Sciences, in nearly 300 cases of research linksbetween US and Japanese companies more than 90 per cent involveda transfer of US technology to Japan. In Japan firms take about 25 percent less time and spend about 50 per cent less money in carrying out aninnovation because of their use of external borrowed technology ratherthan in-house invention. Moreover, this is valid for almost all industriesin Japan. The noted management scientist Peter Drucker observed thespecific comparative advantages of the imitation process. The followerallows the first mover to test the waters. It learns from the innovator’smistakes. The follower can also take advantage of subsequent product orprocess innovations, such as more powerful computers or chips, whilethe first mover may be locked into the technology at the start, because oflarge sunk costs in research and manufacturing. It is not just technologythat can be imitated but also services and professional skills. For exam-ple, even as Dell Computer was cloning the technology of the IBM PCand offering PCs at a lower price with better customer service, other com-petitors were imitating Dell’s approach to direct marketing. Clearly Indiacan develop its own efficiency in imitating new technology and soft-ware development due to its talent pool of scientists and engineers, andthereby improve its IT market share in the world. This policy frameworkhas the added support of the modern theory of dynamic comparativeadvantage. For example, Kemp and Okawa (1995) have shown that fortwo free-trading countries under any Hicksian type, technical progress(i.e. Solow’s A in the production function Y = AKαL1−α) in one countrynecessarily benefits the other country if preferences in the progressivecountry are homothetic. This Hicksian proposition also holds even ifone 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 itinvolves collaboration in R&D and FDI with US and other global partners.The private sector has a special role in adopting an effective imitationand improvement policy. The spillover benefits of international R&Dinvestment need to be captured by the business leaders in India. Var-ious management and engineering institutes and research institutionshave a direct catalytic role to play here. On the domestic front, diffusionmust involve induced investment and incentive promotion, so that fac-tors are reallocated to more productive subsectors and regions. Further,

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R&D investments must be optimally used in the private and publicsectors. It is instructive here to compare the various policy incentivesadopted by Singapore and its comparative R&D indicators. This showsthe benefits of agglomeration arising from the presence of a pool ofsuppliers. The benefits of agglomeration effects have been strongest inSingapore, followed by Thailand and Malaysia. Firms have benefitedfrom supplier proximity in their ability to meeting changing demandquickly through a shortened supply chain.

3 Strategy for global competition

In global markets today competitive efficiency holds the key to suc-cess. Three important aspects of this efficiency have to be noted. Themost important aspect of competitiveness is national productivity andespecially the productivity of those sectors like IT and software profes-sional services. Michael Porter (1990) investigated for four years whynations gain competitive advantage, studying the ten important coun-tries Denmark, Germany, Italy, Japan, South Korea, Singapore, Sweden,Switzerland, the UK and the USA, and reached three important con-clusions. First, sustained productivity growth at the industry and firmlevels requires that an economy continually upgrade itself. A country’sgrowing firms must also develop the capability to compete in more andmore sophisticated industry segments, where productivity and overallefficiency are higher. At the same time an upgrading economy is onethat develops the capability of competitive success in entirely new andsophisticated industries. Doing so absorbs human resources released inthe process of improving productivity in existing fields. This is especiallyrelevant for the IT sector in India, which has achieved significant suc-cess in global markets in recent times. India is now the fourth largestsoftware market in the Asia Pacific region, claiming 9.5 per cent of thetotal regional software market. Its software market is among the fastestgrowing in the Asia Pacific region, with an expected compound annualgrowth rate (CAGR) of 15 per cent through 2008. The biggest opportuni-ties for growth are in banks, government agencies, telecommunications,manufacturing and small and midsized businesses.

The second feature of competitive advantage principle is emphasizedby its dynamic aspects. International trade allows a country to raiseits productivity by eliminating the need to produce all goods and ser-vices within the country itself. A country can therefore specialize inthose industries and segments in which its firms are relatively moreproductive and import those goods and services where its firms are

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less productive than the foreign competitors. Foreign direct investmentand the establishment of foreign subsidiaries by a country’s firms canraise national productivity, provided they involve shifting less produc-tive activities to other countries. A country’s firms can thus increaseexports and earn profits from abroad, which boost national income.Finally, competitive advantage basically involves contributing to thevalue chain, i.e. increasing the contribution to buyer value. Core strat-egy guides the way a firm performs individual activities and organizes itsvalue chain. Note that firms can gain competitive advantage from con-ceiving new ways to conduct their business activities, employing newprocedures, new technologies or different inputs. For instance, Makitaof Japan emerged as a leading competitor in power tools, because it wasthe first to employ new and less expensive materials and to producestandardized models in a single plant. A firm’s value chain is connectedby interdependent linkages. These linkages often create cost economies.For example, a more sophisticated product design and a more thoroughinspection can reduce after-sale service costs. A company can also cre-ate competitive advantage by optimizing or coordinating the variouslinks to the value chain. Frequent and timely deliveries by suppliers,a practice now widely termed kanban after its Japanese innovators, canlower a firm’s handling costs and reduce the required levels of inventory.These costs emphasize what are called economies of scope in managerialeconomies. Scope is important because it shapes the nature of a firm’sactivities 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 nar-row target segment, where a firm, for instance, can tailor each of itsactivities to the segment’s needs and achieve lower costs. A second wayis to take advantage of agglomeration effects by sharing activities acrossindustry segments. For example, Japanese consumer electronic producerssuch as Sony, Matsushita and Toshiba reap great economies of scope fromcompeting in related industries such as TV sets, audio equipment andVCRs. These firms use the same international market networks to takeadvantage of common product and process technologies and employjoint purchasing and some collaborative research activities.

Finally, one must note that governments cannot create competitiveindustries, only firms and industries can. Governments can only shapeor influence the institutional structure and the industrial environmentsurrounding firms. The best example is the role of the Japanese govern-ment, which is currently being followed by Taiwan, South Korea andSingapore. The Japanese government encourages early demand, devel-ops cooperation policies for adopting frontier technologies and speeds

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up the process of upgrading and new innovations. Thus as broad nationalpolicy governments should play an active and direct role in those areasof business where externalities cause firms to underinvest. The lessonsfor the IT-based sectors in manufacturing and skilled services in Indiaare clear. Government policy should attempt to lay the foundation forupgrading competitive advantage in India’s IT industry and manufac-turing sector. As Porter (1990, p. 622) has stressed very strongly, ‘Thehighest-order advantages associated with high levels of productivity arethose that accrue from a steadily rising level of technology, a stream ofnew models, investments in building close customer relationships andeconomies of such growing out of a global market presence.’

The three principles of competitive advantage that we have discussedare summarized by three Cs: cost efficiency, comparative advantage andcore competence. Sengupta (2005) has discussed the role of these princi-ples in India’s economic growth. Four basic elements of core competenceare: learn, coordinate, integrate and innovate. Core competence has beendefined by Prahalad and Hamel (1994) as collective learning in the orga-nization, especially learning how to coordinate diverse production skillsand multiple streams of technologies. An example is provided by the pat-tern of software exports from India relative to other Asian countries. Itis clear that India’s software competitors rely more on software packagesthan services. Since software package exports produce more stable andgreater earnings than software services, India’s skewed pattern of soft-ware exports exposes its vulnerability in the future. The need for optimaldiversification and for exploring scope economies through coordinationand core competence is very clear.

The sources of competitive advantage for India’s exports in IT-basedproducts and services need a more detailed analysis, since this advantagehas a significant multiplier effect on the growth of the whole economy.Three phases can be easily identified. In the early stages of growth of theIT sector the relative advantage of skilled labor and the high internationaldemand for IT services provide the basic source of growth. The secondstage is marked by ‘new factors’ such as firm-specific capabilities. Thethird phase, which is now starting, is to meet the competitive challengeby exploiting agglomeration economies and developing new product-mixes through optimal use of R&D investments and learning by doing.

Software and IT services accounted for 1.98 per cent of India’s GDPin 2002 and are expected to reach 7.7 per cent by the estimates ofNASSCOM. The contribution to employment is less marked but it isrising over time. In 1996 exports (in million $) were 1085, with employ-ment of 160,000 and total revenues of $1766 million. In 2000 the figures

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were 4500, 320,000 and 5600 respectively. Noyelle (1990) has recentlycomputed the growth of computer software and computer services infive Asian countries: India (1989), Singapore (1990), Hong Kong (1990),South Korea (1990) and the Philippines (1989). He mentioned theadvanced stage of development of Singapore’s software industry, compa-rable to those of the USA and Japan; Singapore attracts foreign computerprofessionals mostly from Malaysia, China and India and a number ofcompanies use overseas subcontractors from Malaysia, China and thePhilippines to compensate for local strategies. The latter emphasizes alevel of sophistication in the area of project development managementthat is very rarely found among firms from other developing countriesof Asia. It is clear that India’s computer software industry has primarilyfocused on the development of tools and professional services for thesoftware industry. India has been a net exporter of human capital forseveral decades. Arora et al. (2004) estimate that Indians account for avery large fraction, perhaps about 40 per cent, of the H1-B work per-mit visas issued by the US government. Their estimates of comparativelabor costs for computer services show that the costs of a developmentprogrammer and network analyst in India are 19.51 per cent and 28.58per cent of those of their US counterparts respectively. However, thissituation is likely to change over the next decade, since other countriesare quickly catching up. Note that the widest wage gap concerns thelower end of the skill spectrum (test engineers) but India’s wage gap withthe USA and UK is at all levels of qualification. Openness in trade hashelped India’s growth in this sector in two ways: through internationalmobility of skilled personnel and the network of linkages with formerexpatriates from India, who are largely concentrated in Silicon Valley,and access to the professional services and subsidiaries of VC (venturecapital) firms from the USA and other industrial countries. For example,in the second quarter of 2006 IBM and Accenture generated employmentof 200,000 and 133,000 with income per employee $23,880 and $20,602respectively. Of all the computer services, the financial services sectordominates the growth of the industry in India and this is certainly goingto continue for the next decade.

Of all the HPAEs the Taiwan model offers very important lessons forIndia’s development of the IT sector. The production of IT output fueledTaiwan’s impressive economic growth in the past two decades. Its IT out-put grew from less than US$100 million in 1980 to more than $5 billionin 1989 and grew over 20 per cent annually in the 1990s, when its GDPgrowth was about 6–7 per cent. In 1999 it reached $21 billion and ifTaiwanese manufacturing in China is included, the total IT output was

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more than $35 billion in 1999. How did this small island of 24 millionsurpass other Asian economies as well as more advanced economies inthe global technology competition? Saxenian (2004) has discussed insome detail the major forces that made Taiwan a major global center ofelectronic systems design, manufacturing and logistics. One basic indi-cator of Taiwan’s technological achievements is its ranking among USpatent recipients: in 1980 it ranked twenty-first, by 1990 it reachedeleventh and in 1995 it ranked seventh. Today Taiwan receives morepatents per capita than the other Asian NICs and ranks ahead of all theG7 countries except the USA and Japan. Four major factors are identifiedby Saxenian (2004) as behind the rapid growth in Taiwan’s IT sector. First,there were the contributions of two separate clusters of entrepreneur-ship, comprising dozens of small firms and start-up companies: one inthe Taipei area cloning PCs and components, building on the skills andinfrastructure of multinational corporations in the earlier decade; theother in the Hsinchu Science Park, which spun out of the government-funded semiconductor research institute. Many state policies in the1980s, such as the emphasis in science and technology-based industry,the rapid transfer of public research to the private sector and the creationof a domestic VC industry, were influenced by the Silicon Valley model.The government offered a 20 per cent tax reduction to all investors in VCfunds that were targeted to strategic technology-intensive and research-intensive segments. Second, the agglomeration and scale effects flowedfrom the large infusion of entrepreneurial and managerial resources fromthe USA, which provided important linkages to technology and marketsin Silicon Valley. These forces were instrumental in shifting Taiwan tothe technological frontier in the manufacturing of ICs (integrated cir-cuits), PCs and related components. By 1999 Taiwan had 153 VC firmswith an investment total of $1.08 billion in IT-related businesses, whichalong with Israel made Taiwan the largest in the world after SiliconValley. The Hsinchu region, like Silicon Valley, provides for Taiwan anindustrial environment in which small companies can grow large, whilestill remaining a part of this decentralized infrastructure of the region.One has to emphasize that the Hsinchu industrial system constitutedan almost complete component design and manufacturing supply chainfor IT services, although it still depends on outside providers of high-endmicroprocessors, hard disk drives, specialized memory chips etc. Third,Saxenian points out that the Hsinchu region exemplifies the dynamicrole of Marshallian external economies, both physical and financial, inwhich the localization of skills and specialized know-how helps generatecost reductions for individual firms and increasing return for the region

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as a whole. Taiwan did not adopt the high-volume assembly strategy oflarge vertically integrated Korean conglomerates. Instead it developed anextensive supplier and subcontracting infrastructure, which produces anongoing stream of innovation-intensive SMEs (small and medium-sizedenterprises). Finally, the comparative advantage benefits of the Hsinchumodel suggest that the social and entrepreneurial structure of a techni-cal community is fundamental to the organization of IT production atboth the global and local levels. Saxenian concludes with a statementfrom Fred Chang, the CEO of Windbond North America: ‘The best wayto start a technology company today is to take the best from each region,combining Taiwan’s financial and manufacturing strength with SiliconValley’s engineering and technical skill.’

In India Bangalore provides an IT cluster. But it needs to develop clus-ters in other cities; for, example, the Salt Lake IT complex in Kolkata andIT centers in Hyderabad were set up over a decade ago but they have notbeen successful in developing effective linkages with Silicon Valley expa-triates from India. There is an imperative need for an active state policywith incentives and grants, so that these clusters can provide a completerange of IT-related services, which have significant scale economies andagglomeration benefits for these regions as a whole.

One common criticism of the rapid growth of India’s IT sector in recenttimes is that it creates a chasm between income per worker in the pro-gressive IT sector and that in the rest of the economy. This dualisticstructure creates an asymmetry in growth rates between the ‘old econ-omy’ and the so-called ‘new economy’. Two types of policy measuresare relevant here. One is to promote the process of technology diffusionacross the country, as we have observed in the Taiwan model, whichstimulated the diffusion process through subcontracting IT jobs to SMEs.China favored town and village enterprises (TVEs) in its IT policy. Fiscalincentives and direct government assistance may be needed in the ini-tial stage. Second, the rapid productivity growth in the IT sector coupledwith an increase in national spending on secondary and tertiary edu-cation and training in skill acquisition may help speed up the processof factor allocation from other sectors to the IT sector. This happenedin the HPAEs when they achieved a high rate of growth. These coun-tries achieved technological advance and diffusion through five broadeconomic policies:

1 Human capital deepening.2 Creation of publicly financed research centers and institutes.3 Fiscal incentives for private R&D activities.

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4 Economic incentives for information technology and its decentrali-zation.

5 Technology transfer arrangements through FDI in technology-intensive industries.

The dynamic role of R&D investment and expenditure on innovations inthe Schumpeterian sense is of special importance for continuing the sus-tained growth in the future for the IT sector in India. There is a record ofR&D investment in the NICs of Asia and their openness to foreign trade.The two are closely interrelated through (FDI) and joint ventures. TheR&D indicators show the NICs in Asia to be mostly ahead of the ASEAN-4. Korea, Taiwan and now Singapore invest proportionately more R&Dthan several middle-income OECD countries. Patent activity is more vig-orous in the NICs than the ASEAN-4 and these economies rate morehighly according to a broad estimate of technology capabilities proxiedby the technology index. In terms of openness to international tradethe NICs of Asia fare much better, although countries like Malaysia arecatching up. India’s performance here is not any better than the ASEAN-4countries.

Sengupta (2005) reviewed the comparative performance of India inthe context of NICs of Asia, using data on an R&D index comprisingseveral components, such as high-technology exports as a proportion ofmanufacturing export, the number of scientists and engineers in R&Das a percentage of GDP and average annual number of patents. Selectedrankings 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. TheTAI (technology achievement index) aims to capture how well a countryis creating diffusing technology and building a human skill base. Thiscomposite index measures observed achievements. It is not a measure ofwhich country is leading in global technology development but focuseson how well the country as a whole is participating in creating and usingtechnology. This composite index TAI is a weighted combination (equalweights) of four components: (a) technology creation measured by thenumber of patents granted to residents; (b) diffusion of recent innova-tions as measured by the number of interested hosts per capita and theshare of high- and medium-technology exports in total goods exports;(c) diffusion of old innovations measured by telephones per capita andelectricity consumption per capita; and (d) human skills measured bymean years of schooling and the gross tertiary science enrollment ratio.Note that in terms of TAI India ranks lower than the Philippines, China

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and Indonesia. India’s technology creation is negligible. This is mainlydue to the very low R&D spending in both private and public sectors. Astudy by the World Bank (Yusuf et al., 2004) estimated the impact of R&Don firm productivity based on the World Bank survey data of 1826 firmsdistributed 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 economiesthe sample of firms comprises ten industries: with five manufacturingand five services. Note that excluding the Philippines and Malaysia theNICs in Asia achieved a very high level of R&D spending in business as apercentage of total R&D spending. For instance, the figures for 2001 esti-mated by the UNDP are 84 per cent for Korea, 62.5 per cent for Singaporeand 76.4 per cent for Indonesia.

The equation for estimating the impact of R&D on firm performancewas taken in its reduced form as:

ln π = α0 + α1 ln K + α2 ln L + δ ln Rn∑

i=1

βizi

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&Dpersonnel over 1998–2000 and zi are the different dummy variables fordifferent metropolitan areas. Clearly the estimates in Table 2.9 showing

Table 2.9 Effect of R&D investment on firm performance

Productivity (ln(A)) Profit (ln(profit))

VariableConstant 3.094 (7.67) 1.897 (3.51)ln K 0.373 (8.01) 0.484 (7.91)ln L 0.270 (3.71) 0.139 (1.48)ln R 0.325 (6.05) 0.276 (3.85)

Dummy variablesSeoul (Korea) 5.753 (9.43) 4.742 (5.09)Tianjin (China) −0.020 0.031 (0.09)Shanghai (China) 0.831 (4.81) 0.804 (3.45)Guangzhou (China) 0.525 (2.90) 0.456 (1.85)Chengdu (China) −0.058 (−0.34) 0.068 (0.29)Adjusted R2 0.688 0.580Sample size 408 359

Note: t-values are in parentheses.

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elasticities of productivity and profitability with respect to R&D person-nel are statistically significant. The R&D personnel in Seoul, Shanghaiand Guangzhou exhibit the highest impact of productivity and prof-itability. The coefficient estimates for the dummy variables associatedwith the metropolitan clusters lend strong support to the view thattechnology clusters improve the performance of the R&D variable.

Among the cluster attributes that enhance the effectiveness the follow-ing are most important: concentration of IT investments, extensive R&Dnetwork relationships and wide coverage of complementary services andsegments.

The impact of the growth of the IT sector on other sectors of the Indianeconomy is most important for two reasons. One is the mechanism ofthe so-called Verdoorn law, named after P. J. Verdoorn (1949), who founda strong empirical relationship between productivity and output growthin a cross section of industries. This is particularly true for the IT sectortoday. The second reason is that the high productivity growth in the ITsector may raise wage rates in this sector, which then attracts labor fromother sectors. The first mechanism was extensively used by Kaldor (1967)to explain the process of industrialization, where the manufacturing sec-tor pulls up other sectors. Two mechanisms are at work. First, the growthrate of productivity in manufacturing (here we would think of it as theIT sector) increases with the rate of growth of GDP. Second, employmentgrowth in manufacturing (i.e. the IT sector) tends to increase the rateof productivity growth in other sectors. This follows due to diminishingreturns to labor in other sectors and the absorption of surplus labor fromthese sectors. In Kaldor’s (1967) original analysis of cross section dataon 12 developed countries from 1953 to 1964 the estimated Verdoornrelationship is found to be

gP = 1.035 + 0.484 gM(0.070)

; R2 = 0.826

where gP and gM are respectively the rates of growth of labor produc-tivity and production in manufacturing and the standard error is givenin parentheses. The value of the Verdoorn coefficient (0.484) impliesthat each additional percentage point in the growth of output is associ-ated with a 0.50 per cent increase in employment and a 0.50 per centincrease in the growth of productivity. For the Indian data on the IT sec-tor over the period 1990–2000 the Verdoorn coefficient is of the order0.502, implying significant increasing returns to scale for the IT sector,which may help speed up the rate of industrialization and factor reallo-cation from other sectors. As Ros (2000) has interpreted it, the Verdoorn

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coefficient can be derived from a log linear production function as

M = AKa+u L1−a (2.10)

where M may be viewed as the output of the IT sector, with K and L aslabor and capital. Taking logs and differentiating with respect to timegives

gM = β + (a + u)gK + (1 − a)gL

where gx = x/x and β =A/A and dot denotes time derivative. LetgP = gM − gL be the growth of labor productivity in the IT sector. Thenone can easily derive

gP = (1 + u)−1[(a + u)gK + ugM ] (2.11)

If we assume a constant capital output ratio so that gM = gK , then thisreduces to

gP = (1 − a)−1[β + ugM ] (2.12)

which shows that the Verdoorn coefficient u/(1 − a) is determined in thesteady state by scale economies. A positive and less than unity Verdoorncoefficient 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 aVerdoorn coefficient of the order of 0.5 would mean an increasing returnsparameter 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. cumu-lative experience) and the relation (2.11) implies that the productivitygrowth that is the key to increasing the competitiveness of India’s ITsector 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 cre-ate competitive advantage and increasing productivity by perceiving ordiscovering new and better ways to compete in the industry and bring-ing them to market. As Porter (1990) emphasized, the most importantreason why competitive advantage is sustained is constant improvementand upgrading. Hence the need for R&D and innovation efficiency.

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3Industrial Productivity inthe New Economy

1 Introduction

In post-independent planning in India emphasis was given to inward-oriented growth and a protected home market for the development of theeconomy. This resulted in a sinking industrial economy in particular andan economy in general of no return zone in the eighties. India embarkedupon a ‘New Economic Policy’ to revive the economy from its dismalstate. The main features of the so-called ‘New Economic Policy’ are:(a) a gradual process of easing out government control through industrialderegulation; and (b) opening up the channels for greater connectivitywith the international market. The experiences of East Asian countrieshad emboldened the policymakers to adopt the new economic policy.Subsequently, the debacle of some South American and African countriesfollowing their pursuit of the policy of liberalization created some fearamong the general public of a higher dependency on imports and greaterindebtedness on the external front. Contrary to expectations, the pro-pagandists welcomed the ‘New Economic Policy’ because they thoughtthat this policy would correct the deficiencies that were inherent in theearlier strategy of bureaucratic control.

There is a massive literature on the relationship between trade liber-alization and growth through increasing the productivity and efficiencyof both traditional and modern industries. Trade liberalization in par-ticular implies the reduction or removal of quantitative restrictionson imports and the lowering of tariff rates. Import liberalization andremoval of quantitative restrictions in particular would not only inducemore efficient allocation of investment along the line of a country’scomparative advantage but also eliminate costs associated with intrusivebureaucracies and wasteful rent-seeking special interest groups. Further-more, it would make the economy more competitive.

56

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Industrial Productivity in the New Economy 57

Liberal economists often blame the public sector for the inefficientuse of resources but empirical research shows that a competitive envi-ronment is more important for ‘allocative efficiency’ than ownershipper se. Technical and allocative inefficiencies are not confined to thepublic sector, they pervade the entire economy. The research on theproductivity of industries in India and abroad shows that a competi-tive environment is crucial for enhancing the efficiency of resource usethrough technical upgrading and allocation in the economy. It is rec-ognized that the protective trade policies during the plan period of the30 years before 1990 were the major impediment to higher productivity,efficient use of scarce resources and more utilization of resources with alow opportunity cost. Thus to increase the productivity and efficiencyof the economy liberalized trade policies have been taken in order toopen the economy to the international market. As a policy, the rates ofeffective protection for the inefficient domestic manufacturing sector arereduced by lowering tariff rates, and more uniform rates across industriesare gradually being implemented to provide a level playing field for allindustries.

In this chapter we discuss some issues related to the linkage betweentrade liberalization and the productivity growth of Indian industries. Theissue of welfare gain due to the less monopolistic structure of the indus-tries after liberalization is taken into consideration. It is expected thatafter the liberalization policies have been implemented there will be arise in the productivity and efficiency of industries because the indus-tries will be more competitive than before and trade volume will alsorise. But there is a two-way relation between competitiveness and exportpromotion. It is said that the interaction between international tradeand long-run output and productivity growth is less understood in tradeand/or liberalization literature. It is interesting to examine whether grow-ing trade leads to faster productivity growth or the other way round.Some attempts are made to analyze the cause and effect nexus betweenthese two variables.

The organization of the chapter is as follows. In section 2 there isa brief discussion of the issue of industrial policies taken by the gov-ernment of India during the pre- and post-liberalization periods. Weanalyze the pattern of structural changes in Indian industries during therecent period in section 3. A decomposition of total factor productivityfor modern and traditional sectors of Indian industries and total man-ufacturing sector as a whole is carried out in section 4. Section 5 dealswith the performance of the export sector as a whole during 1987–88 to1999–2000 in terms of growth rate and the share of exports in total

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exports. The productivity growth in terms of the TFPG of a few industriesis measured and the causality between TFPG and export growth is dis-cussed in this section. A microlevel analysis of productivity, competitionand 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 thechapter in section 7.

2 A brief review of Indian industrial policy

The strategy for industrial development in India was initiated during theSecond Five Year Plan (1956–61), based on the Mahalanobish Model.However, some policies regarding self-reliance and licensing schemeswere adopted under the Industrial Development and Regulation Act(IDRA) of 1951. But the main thrust for overall development throughindustrialization was given in the Second Plan model. Heavy and basicindustries were in the public sector, and a regulated private sector wasgiven charge of consumer good industries. So, the plan was implementedunder the framework of a mixed economy, where both the public andprivate sectors had a role in industrial development. But the policy ofindustrialization in India was marked by frequent changes in objectivesand 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 wasused to regulate foreign investment in India, created a highly protectedindustrial regime, where there was no significant role for internal com-petition or for any strict planned implementation of overall industrialdevelopment. By the second half of the seventies, it was realized that thelicensing system and the regulatory policies were detrimental to indus-trial development in India instead of being stimulants. At that timeseveral committees and commissions were set up to review the differ-ent aspects of industrial and trade policies. Among the committees andcommissions, which submitted their reports at the end of that decadeor at the beginning of the eighties, the noted ones are the AlexanderCommittee (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 anddelicensing were implemented to increase the pace of industrial devel-opment. According to Ahluwalia (1991), due to these policies therewas a turnaround in the productivity of Indian industries during the1980s. But this hypothesis was rejected by many authors on the ground

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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 significantupward trend of productivity and efficiency during the 1980s.

Policymakers felt that the slower and inefficient growth experiencedby India during the past 40 years was the result of the tight regulatorysystem for the industrial and foreign trade sectors. These policies ledto an economy of subsidies and inefficiencies in India. The new eco-nomic policy (NEP), of which the New Industrial Policy (NIP) of 1991(Sandesara, 1991; Subrahmanian, 1991; Patel, 1992) is the most impor-tant part, was launched against this background. The NIP of 1991 wasa major part of the broad structural adjustment program implementedduring the nineties in India. It was set in motion with the objectiveof transforming the policy of planning to a policy of regulated marketeconomy.

Liberalization is a process of economic policy changes specifically initi-ated from 1991 as declared state policy. It has its own economic, politicaland international compulsions. Indian economic reforms in their cur-rent form had been initiated with the help of financial support fromthe International Monetary Fund (IMF) and the World Bank, and lateralso from the Asian Development Bank (ADB). Hence, these reformshave involved a set of conditionalities mutually agreed upon betweenthe government of India and the multilateral institutions. When thecrisis reached a peak in 1991, the IMF extended an 18-month balanceof payment assistance program of US$2.2 billion to India, covering aninitial period up to March 1993. This reform package covered the areas ofmacroeconomic stabilization policies and structural adjustment policies.

Some major policy changes, which are called economic reforms orliberalization, can be mentioned as follows. The broad policy mea-sures are:

1 Macro economic stabilization measures, which include (a) manage-ment of balance of payment crisis, (b) fiscal deficit management, and(c) monetary policy correctives.

2 Sectoral structural adjustment reforms, which include (a) tradepolicy (and associated policy) reforms, (b) industrial policy reforms,(c) policy reforms relating to public sector, (d) policies for attract-ing foreign direct investment (including NRIs), technology andequity participation, (e) administrative reform for faster investmentapprovals through the Reserve Bank of India, (f) tax structure reform,(g) tariff reform for both capital goods and consumer goods,

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(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 thepublic distribution scheme (PDS) and the like.

The NIP of 1991 effected some very fundamental policy changes, suchas near abolition of licensing, easing of the rigors of MRTP and FERA, areduced list of industries to be reserved for the public sector, automaticapproval of foreign technology agreements and private investment ininfrastructure. Some other important policy changes are freer import ofcapital goods, transport subsidies for backward areas and promotion ofFDI and NRI investment. The sole objective of these highly liberalizedpolicy measures was to enhance the productivity and efficiency in Indianindustries 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 needfor the introduction of a new economic policy in India in the year1991 and the process of liberalization started thereafter. However, thepattern of Indian industries started changing long before 1991. Thischanging pattern of industrial structure may have some role in explain-ing the subsequent period productivity growth of industrial sector as awhole. Growth of new industries does not merely increase productivitybut also changes the distribution of inputs and outputs across sectors.Before going on to analyze the productivity differentials among the dif-ferent sectors we first try to understand the structural changes of Indianindustries during the period 1973–74 to 1999–2000. A comparison of theshares of different industries in terms of value added, output, employ-ment and number of factories in each industry will give us greater insightinto 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 beenmoving quickly towards technology-intensive manufacturing industriesfrom traditional and less productive industries. Attempts have beenmade to understand the structural changes in terms of a few parame-ters, namely output, value added, employment and number of firms ineach industry. The data for the industries have been collected from theAnnual Survey of Industries (ASI) from 1973–74 to 1999–2000. Figuresfor these four indicators are collected for 18 two-digit industries and

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Table 3.1 Growth rate of output of industriesduring 1973–4 to 1997–8

Growth rate (%)

Traditional sectorFood 6.75Beverage 5.52Textile 5.71Wood 1.88Paper 5.97Leather 8.29Nonmetallic 7.97Basic metals 6.98Metal products 6.11Construction etc. 9.97

Modern sectorReady made garments 11.61Basic chemicals 10.88Rubber & plastics 7.13Machinery 7.08Electrical & electronics 8.18Transport equipment 8.40Other manufacturing 14.19

the shares of each industry are calculated for all the years mentionedabove.

Looking at the growth rate of output in real terms it has been foundthat there is a phenomenal growth of output across all the industriesduring the period. However, Table 3.1 suggests that the industries in themodern sector show better performance in terms of growth of outputcompared to traditional sectors’ industries. Other manufacturing indus-try, which includes medical and photographic instruments, jewelry,related articles, watches and clocks etc., shows the highest growth amongall industries. The ready made garments and basic chemicals industriesin the modern sector show a growth of around 11 per cent. The growthrates of industries in the traditional sector cluster around 6 per cent perannum, with a maximum of 9.97 per cent in the construction industryand a low of 1.8 per cent in the wood industry. Thus, due to wide vari-ation in growth rates the structure of Indian industry is changing overtime. We now examine the nature of the structural changes in Indianindustry during the post- and preliberalization period.

Table 3.2 and the corresponding graph (Figure 3.1) show the percent-age share of total industrial output of some major industrial groups from1973–74 to 1999–2000. The shares of the traditional industries, namely

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62Table 3.2 Percentage share of output in Indian industries

Year Industry codes

20–21 22 23–25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 &above

1973–74 14.09 2.54 19.13 0.79 0.42 3.05 0.77 12.29 5.05 3.02 10.79 2.16 5.25 6.07 6.63 0.58 7.361974–75 16.81 2.41 16.81 1.13 0.61 3.31 0.77 12.68 6.95 2.68 10.10 2.62 5.69 5.31 5.21 0.66 6.241975–76 15.87 2.63 16.80 1.18 0.42 1.73 0.82 13.91 7.66 3.42 5.42 2.72 5.89 6.29 5.51 0.70 9.021976–77 15.58 2.61 14.88 1.33 0.52 2.77 0.99 12.52 8.00 2.92 10.62 2.45 5.92 5.63 4.82 0.62 7.811977–78 16.36 2.56 15.75 1.40 0.55 2.75 0.86 13.01 8.43 2.90 8.84 2.47 5.70 5.46 4.43 0.75 7.791978–79 15.33 2.38 15.27 1.40 0.53 2.54 1.01 12.88 7.87 2.76 11.05 2.20 5.61 5.30 4.80 0.76 8.321979–80 14.32 1.98 14.27 1.33 0.53 2.76 1.19 13.15 8.84 2.71 11.35 2.51 5.68 5.70 5.32 0.77 7.571980–81 13.00 1.81 13.23 1.39 0.53 2.77 0.86 13.21 10.00 2.74 11.68 2.27 5.85 5.90 5.50 0.79 8.481981–82 13.18 1.87 11.88 1.33 0.46 2.80 0.81 13.31 10.41 2.81 12.51 2.25 5.78 5.33 5.77 0.64 8.881982–83 13.82 1.65 11.29 1.27 0.45 2.54 0.73 12.78 11.77 3.18 12.40 2.05 5.59 5.51 5.54 0.67 8.771983–84 13.84 1.89 11.48 1.11 0.44 2.65 0.72 13.37 10.44 3.41 11.53 1.98 5.73 5.06 5.36 0.71 10.281984–85 13.14 1.89 11.56 1.22 0.43 2.92 0.81 13.30 10.61 3.67 12.18 2.06 5.63 5.27 5.52 0.71 9.071985–86 12.58 1.82 11.01 1.26 0.39 2.68 0.83 13.40 11.92 3.78 11.99 1.97 5.77 5.30 5.33 0.82 9.151986–87 12.78 1.88 9.99 1.23 0.41 2.83 0.77 13.59 11.53 3.60 11.93 1.75 5.34 5.27 5.71 0.73 10.651987–88 13.48 1.85 9.43 1.38 0.41 2.80 0.94 13.36 11.02 3.43 11.54 2.04 5.45 5.95 5.55 0.82 10.571988–89 13.18 1.87 9.07 1.35 0.44 2.59 0.94 12.84 11.71 3.47 12.69 2.18 5.24 6.24 5.96 0.73 9.511989–90 14.22 1.85 9.78 1.59 0.36 2.75 0.98 12.65 9.79 3.37 12.33 2.14 5.47 6.22 5.77 0.86 0.41 9.461990–91 13.56 1.98 9.67 1.58 0.33 2.73 1.07 12.25 10.91 3.41 12.53 2.16 5.43 6.15 5.87 0.81 0.45 9.101991–92 14.51 2.08 9.65 1.79 0.30 2.86 1.06 13.20 8.01 4.06 12.05 2.21 5.48 6.35 5.33 0.92 0.52 9.631992–93 13.53 1.98 9.20 1.74 0.29 2.65 0.94 13.62 8.73 3.55 12.60 1.96 5.20 6.29 5.55 1.07 0.53 10.571993–94 13.24 1.95 9.55 2.25 0.32 2.70 1.12 13.21 9.23 3.29 11.13 2.24 4.77 5.51 5.45 1.29 0.56 12.181994–95 13.12 1.91 9.83 2.34 0.29 2.73 1.08 13.14 8.83 3.25 11.21 2.08 4.68 6.46 6.10 1.27 0.50 11.181995–96 12.30 1.54 8.88 2.25 0.25 2.88 0.88 13.33 9.01 3.29 11.42 2.23 5.24 6.04 7.11 1.31 0.45 11.601996–97 12.87 1.51 8.91 2.01 0.28 2.51 0.88 14.05 9.91 3.57 10.31 2.25 5.34 5.57 6.46 1.31 0.82 11.431997–98 14.47 2.07 10.69 2.47 0.28 2.60 1.04 16.42 9.53 3.50 13.01 2.57 5.02 6.71 6.88 1.54 1.201998–99 16.43 2.41 8.52 2.75 0.36 2.54 1.03 17.39 9.59 3.22 10.79 2.52 7.14 5.96 5.24 1.92 2.191999–00 15.47 2.45 8.03 3.03 0.44 2.65 0.95 17.07 9.99 3.70 10.94 2.20 5.25 5.39 7.34 2.41 2.70

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0

5

10

15

20

2519

73–7

419

74–7

519

75–7

619

76–7

719

77–7

819

78–7

919

79–8

019

80–8

119

81–8

219

82–8

319

83–8

419

84–8

519

85–8

619

86–8

719

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819

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919

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019

90–9

119

91–9

219

92–9

319

93–9

419

94–9

519

95–9

619

96–9

719

97–9

819

98–9

919

99–0

0

Year

Sha

res

(%)

20–21 22 23–24–25 26 27 28 29 30 31

32 33 34 35 36 37 38 39 40 & above

Figure 3.1 Share of output

food products and textiles, declined over time. The basic chemical indus-try’s share of total industrial output showed an upward trend during theperiod and became more prominent after 1990. Rubber and plastic andbasic metals showed an upward trend during the initial phase of theperiod. However, the shares of these industries either fell or remainedstagnant during the later phase of the period of study. Interestingly, theshare of construction, repair and other services (industry code 40 andabove) showed an upward trend during the period, rising from 7.35 percent in 1973–74 to 11.43 per cent in 1996–97, with a maximum of 12.17per cent in 1993–94. This rise in the share may be due to an increase ofdevelopmental activities and supply to the growing ancillary industries.

Among industries with a lower share of total output, electrical machin-ery, transport equipment and ready made garments showed a mildupward trend during the period. Most of the industries that showedeither no trend or downward trends during 1973–74 to 1999–2000belonged to the traditional sector.

The figures for percentage of share of value added explained by theindustries in total show a similar trend to that found in the figures forshares in value of output (Table 3.3 and Figure 3.2). The figures for the

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64Table 3.3 Percentage share of value added in Indian industries

Year Industry codes

20–21 22 23–25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 &above

1973–74 6.16 1.97 22.96 0.62 0.44 3.97 0.46 11.96 3.14 3.00 9.71 2.49 6.24 7.00 7.38 0.84 11.661974–75 7.25 3.17 19.35 0.82 0.64 4.95 0.58 13.07 4.04 2.89 10.57 2.81 7.02 6.37 6.54 0.97 8.961975–76 6.51 2.80 17.10 0.92 0.51 2.62 0.47 14.06 4.00 3.99 5.38 3.12 7.70 8.23 7.12 1.06 14.401976–77 7.58 3.27 14.88 0.97 0.57 3.82 0.62 12.19 5.17 3.32 11.00 2.76 7.84 6.50 6.39 0.88 12.241977–78 8.26 2.21 16.35 1.05 0.63 3.85 0.57 12.61 5.30 3.71 7.63 2.77 7.59 6.51 6.15 1.06 13.761978–79 6.59 2.57 16.97 1.14 0.56 3.34 0.57 13.59 3.97 3.11 10.02 2.40 6.95 6.01 6.20 0.92 15.071979–80 6.69 2.10 18.24 0.98 0.59 3.70 0.66 12.73 4.31 3.17 9.48 2.92 6.96 6.41 6.73 0.99 13.361980–81 5.82 1.90 16.51 0.98 0.53 3.57 0.56 11.90 4.41 3.32 9.99 2.73 7.31 6.86 7.04 1.06 15.501981–82 6.45 1.74 13.22 1.01 0.50 3.69 0.51 11.89 4.13 3.32 12.06 2.51 7.21 6.18 7.59 0.86 17.131982–83 7.02 1.55 11.39 0.97 0.46 2.92 0.50 12.09 5.63 4.37 13.53 2.25 7.04 7.08 7.74 0.98 14.481983–84 8.29 1.68 11.72 0.88 0.54 2.78 0.56 12.81 3.10 4.19 10.25 2.26 7.03 6.50 6.97 1.02 19.431984–85 7.89 2.20 11.61 1.29 0.50 3.53 0.67 12.30 4.95 4.77 8.91 2.35 7.70 8.19 7.35 1.19 14.591985–86 7.87 2.00 11.16 0.99 0.43 2.81 0.58 12.85 5.14 4.95 10.44 2.33 8.25 6.58 6.75 1.71 15.121986–87 7.64 2.31 11.03 1.02 0.41 3.11 0.53 11.92 7.47 3.95 8.59 2.06 6.87 6.44 7.42 1.20 18.041987–88 7.67 2.40 9.58 1.21 0.43 3.05 0.67 13.19 7.88 3.93 9.07 2.52 6.87 7.66 6.64 1.16 16.081988–89 7.90 2.46 8.43 1.33 0.39 2.61 0.61 12.25 11.70 3.55 11.35 2.72 5.86 7.33 6.44 0.96 14.111989–90 9.15 2.40 10.36 1.56 0.31 3.21 0.71 12.05 6.82 3.75 9.99 2.27 6.49 7.56 6.30 1.08 0.94 15.031990–91 7.52 2.50 10.37 1.71 0.37 3.11 0.85 11.82 7.08 4.37 10.89 2.09 6.30 7.29 7.01 0.87 1.13 14.731991–92 8.15 2.96 8.98 2.09 0.35 3.38 1.01 12.89 6.02 5.98 7.45 2.43 6.85 8.25 6.74 1.23 1.26 13.971992–93 6.80 2.56 7.65 1.83 0.30 2.95 0.85 15.09 7.45 3.69 9.03 1.89 6.05 7.78 5.87 1.13 1.28 17.791993–94 7.73 2.38 8.96 2.92 0.33 3.22 1.15 15.42 7.39 3.45 9.24 2.12 5.39 6.06 5.40 1.81 1.22 15.821994–95 8.36 2.46 8.89 2.83 0.28 3.14 0.77 14.92 6.63 3.51 9.93 2.04 5.15 8.07 5.74 1.30 1.11 14.871995–96 6.61 1.91 6.46 2.31 0.24 3.34 0.65 17.01 6.62 4.14 10.01 2.34 6.06 6.55 7.70 1.39 1.01 15.631996–97 6.00 2.42 7.78 2.09 0.31 0.91 0.51 16.89 8.42 5.33 10.99 2.42 6.35 6.01 6.93 1.36 0.95 14.311997–98 9.14 3.03 8.84 2.47 0.29 2.77 0.89 18.22 6.07 4.39 15.66 2.44 6.32 7.93 7.83 1.85 1.871998–99 9.86 3.10 7.09 2.99 0.35 2.53 0.82 23.84 6.93 3.25 11.99 2.53 8.17 7.29 5.88 2.06 1.311999–00 9.14 4.16 6.15 3.27 0.58 2.86 0.89 22.91 7.16 4.69 11.92 2.56 6.68 5.73 7.56 2.62 1.12

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0

5

10

15

20

25

3019

73–7

419

74–7

519

75–7

619

76–7

719

77–7

819

78–7

919

79–8

019

80–8

119

81–8

219

82–8

319

83–8

419

84–8

519

85–8

619

86–8

719

87–8

819

88–8

919

89–9

019

90–9

119

91–9

219

92–9

319

93–9

419

94–9

519

95–9

619

96–9

719

97–9

819

98–9

919

99–0

0

Year

Sha

res

(%)

20–21 22 23–24–25 26 27 28 29 30 31

32 33 34 35 36 37 38 39 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 forthree industries. Basic chemicals and ready made garments show a clearupward trend in share of number of employees in total during the entireperiod, while cotton and jute textiles show a significant fall in share.The shares in terms of number of factories of these industries are almoststagnant during the period. This indicates that changes observed amongthe industries in terms of value of output or value added were due to thechanges in scale of operation of the firms.

Measuring the changes of industrial structure

The structure of manufacturing industries of India has been changingover time and there is a swing from the traditional industries to modernindustries. To obtain the trajectory of the path of the changing structurean index was constructed following the line of Van Ark et al. (1999) forintercountry comparison of structural change. The idea of this index isto construct a vector constituted of the value added share of all branchesin aggregate manufacturing for two time points. In this analysis onetime point is the terminal year and the other point is any year within

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0.75

0.8

0.85

0.9

0.95

1

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

Year

Inde

x

NF

EMP

Output

NVA

Figure 3.3 Index of structural change

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

I tT =

m∑j=1

STj St

j

√m∑

j=1(ST

j )2m∑

j=1(St

j )2

Where t is any time point within the period and T is the end point. Stj

is the share of jth industry at year t, STj is the corresponding share at

the terminal year and m is the number of industries. The index variesbetween zero and one and it will be low in the case of lower dissimi-larity between the terminal year and the observed year. If the two timepoints have the same production structure the vectors will coincide andthe index will take a value of one. In contrast, if the composition of theindustries in terms of their shares is different, i.e. the vectors are orthog-onal to each other, the index will take a value of zero. The indices of allthe four indicators – value of output, value added, number of employeesand number of firms – are given in Figure 3.3. The figure shows that a

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definite and significant structural change occurred in Indian industriesduring this period. The changes are more prominent in terms of share ofvalue added. The structural change is also visible in terms of other threeindices. However, the change in terms of share of number of firms ineach 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 behindsustainable economic development of a country. There are basically tworeasons for examining the growth pattern of productivity of countries.First, it is the only plausible route to increase the standard of livingby raising real purchasing power (Krugman,1994). Second, productiv-ity growth also raises the competitiveness of a country by reducing thecost, and thus, ceteris paribus, the offer price in international markets.But one can argue in another way that liberalization raises the produc-tivity of a country through competitiveness. So, when a county opensup its trade, the natural force of competition will raise the productivityof the country and also the quality of product at a lower cost. It is alsoargued that unless there is an increase in productivity, openness cannotraise the standard of living. Naturally, increasing productivity growth isa target of the policies of many developing countries.

Total factor productivity (TFP) growth is now recognized as the mostcomprehensive measure of the productivity and also as a major con-tributor to economic growth of many economies of today’s world. Anygrowth of output may be due to the increase in the use of input and theimprovement in the productivity of factors used. In empirical economicstechnological progress and TFP are used interchangeably. However, thereis a basic difference between these two terms. Any measure of tech-nological progress shows the effect of improvement in knowledge ofproduction procedure, while TFP captures not only the effect of puretechnical progress but also the overall effect of changes in the allocationof factors used on the efficiency of the factors used in production. In asingle homogeneous input–output model the measure of TFP is thereforestraightforward; it is equal to the difference between the rate of outputgrowth and the rate of input growth. But in multifactor and multiprod-uct systems where there could be changes in allocation of pattern, theestimation of TFP will be rather complicated. The most common measurein this context is factor productivity. But the problem with this measureis that the changes in partial productivity depend upon the use of otherfactors. This problem of productivity can be resolved by the analysis of

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TFPG, which separates out the effect of an increase in the use of inputsfrom the other factors that influence the growth of output. TFPG encom-passes not only the effect of technical progress but also the effect of anincrease in the efficiency with which resources are used.

In less developed countries, where resources (particularly capital) arescarce, inputs must be used efficiently to generate enough surplus forthe development of the country. Since any measure of TFPG estimatesthe extent of efficient use of resources, the study of TFP in a developingcountry is of great importance for an understanding of the exact pictureof technological development in that country.

Method of decomposition

A decomposition of aggregate TFPG is proposed here that consistsof the following three parts: (1) a weighted average of industry-wise/region-wise TFPG; (2) the effect of distribution of investment acrossindustries/regions on the aggregate TFPG; and (3) the effect of reallo-cation of inputs across industries/regions on the aggregate TFPG. Theprocedure of decomposition of TFPG is as follows.1

Let us consider an industry consisting of several subgroups of indus-tries. Suppose the net output (V) of the industry is measured at constantprices and that there are two inputs of production, capital (K) andlabor (L). For any time period (t) we have the following identities:

V(t) ≡∑

i

Vi(t) (3.1)

i.e. aggregate net output is the sum total of industry-wise net outputs.Similarly

K(t) ≡∑

i

Ki(t) (3.2)

and

L(t) ≡∑

i

Li(t) (3.3)

where both K and L are measured in real terms, The industry specificTFPGs 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 withrespect to capital and labor in the ith industry in the time period

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t respectively. Analogously, for the aggregate industry, the TFPG isgiven 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) =∑

i

λi(t)d log Vi(t) (3.6)

d log K(t) =∑

i

λKi(t)d log Ki(t) (3.7)

d log L(t) =∑

i

λLi(t)d log Li(t) (3.8)

and ηK(t) and ηL(t) are defined as

ηK(t) =∑

i

λi(t)ηKi(t) (3.9)

ηL(t) =∑

i

λi(t)ηLi(t) (3.10)

where λi(t), λKi(t) and λLi(t) denote the share of ith industry in theaggregate 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) −∑

i

πLi(t)d log Li(t) (3.11)

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 into 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 onthe aggregate TFPG. Similarly, the third component (

∑i πLi(t)d log Li(t))

measures the effect of distribution of changes in the labor input acrossindustry on the aggregate TFPG.

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While the first component of the aggregate TFPG break up has a sim-ple and straightforward interpretation, the other two components needsome explanations in terms of πKi(t) and πLi(t). From their definitionin (3.12) and (3.13), it should be apparent that these quantities repre-sent comparative returns to capital and labor respectively in an industry.Under the competitive assumption ηK(t) and ηL(t) are the shares of cap-ital and labor inputs in aggregate net output and ηKi(t) and ηLi(t) are thecorresponding 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 laborengaged in the ith industry, if these inputs received the average rentaland 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 cap-ital and labor engaged in the ith industry respectively if these are paidthe rental and wage rate of the specific industry. So, πKi(t) and πLi(t) mea-sure the differences, if any, in the earning of total capital and total labor,respectively, in the ith industry arising out of interindustry differencesin capital and labor productivity (assuming that these productivities arereflected in the corresponding factor returns).

The interpretation of the proposed decomposition of the aggregateTFPG would perhaps explain its empirical relevance. It would be usefulto see

• how important are the interindustry movements of investment andlabor in explaining the aggregate TFPG;

• whether the patterns of intertemporal movements of investment andlabor are consistent with efficiency (i.e. whether investment and labormove towards industries that yield greater return on their inputs);

• whether the patterns of intertemporal movement of aggregate TFPGand its components are different for different industry groups.

It may be noted that TFPG defined in (3.5) above is based on a logarithmicdifference, i.e. φt = log TFP − log TFPt−1, if one considers discrete changeover time. Thus,

eφ1 = TFP1

TFP0

so that a time series of indices of TFP would be

Iφ(0, t) = e

t∑s=1

φs(3.14)

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where Iφ(0,0) = 1, i.e. this series of TFP indices provides a comparisonof TFP level for the tth year with that of the base year t = 0. For thepurpose of analysis, such time series indices are constructed based onthe 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 allindustries together and for groups of industries separately. These timeseries have been used to compare temporal movements of each indexseparately across industries.

To examine if the industry-specific indices show any tendency to con-verge or diverge from the corresponding all-industry indices, another setof 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 yearzero taken as base) for the ith industry and all industry respectively. Thenthe index

I∗φi(0, t) = Iφi(0, t)

Iφ(0, t)(3.15)

should give an idea whether the industry-specific indices of aggregateTFP or its components follow the same pattern of movements as that ofthe corresponding all-industry index. Thus if I∗

φi(0,t) declines (rises) overtime, this should mean that the index for the industry has changed less(more) compared to the index of all industry; otherwise the time seriesof a relative index will show movement around the value of unity.

The decomposition proposed clearly shows that aggregate TFPG maybe considerably affected by the way the distribution of the factors of pro-duction, i.e. capital and labor, changes over time across the constituentregions/industries. The empirical results show that the factor distributioncomponent (particularly, the rental differential component) of aggregateTFPG could be important as far as the difference between the aggregateTFPG and weighted TFPG is concerned.

Data and computations

The present study is primarily based on the data published in the AnnualSurvey of Industries (ASI) on 17 two-digit industries over the periodfrom 1973–74 to 1997–98 (the names of the industries and the codes aregiven in Appendix 3.1). The data on value added, capital, labor, wagesand salaries for each of the 17 industries over this period were collectedfrom the office of CSO, Government of India. The other data relatingto price indices have been collected from various issues of economicsurveys published by the Government of India, Annual publications

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of the Reserve Bank of India and a book entitled India Data Base: TheEconomy, by H. L. Chandok and Policy Group. The figures for the grossstock of capital were calculated using the perpetual inventory accumu-lation (PIA) method from the net capital stock available from ASI. The17 two-digit industries are subdivided into two major industrial groups,namely the modern sector and the traditional sector. The modern sec-tor comprises seven industries: manufacturing of ready made garments;basic chemicals; rubber and plastics; machinery and machine tools;electrical machineries; transport equipment; and other manufacturingindustries. 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, foreach year we have computed the weighted average of the industry-levelTFPGs, and subsequently the components relating to capital and labourchanges following the given formulae (3.4) to (3.13). To obtain the valuesof 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 dlogK(t) ≡ log Kt − log Kt−1, where Kt denotes the value of the variable K attime t.

The values of ηL(t) ≡ [(ηLt + ηLt−1)/2] were calculated by taking ηLt asshare of total wages and salaries in the gross value added, and for thereturn on capital we used ηK(t) ≡ 1 − ηL. Capital and labor shares havebeen calculated for each industriy as well as for total industry.

Empirical results

Let us first examine the over time movements of aggregate and weightedTFP indices of three categories: modern industries; traditional industries;and all industries. Then the movements of wage differential and rent dif-ferential components are analyzed. All these analysis are done in termsof the graphs of the movements of the indices presented in Figures 3.4to 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 tothe late seventies the TFPG indices fell for all the three industry groups.During later years the TFPG indices show a generally rising trend exceptin the last year. This finding of TFP growth indicates that a turnaroundof Indian manufacturing industries from the industrial stagnation of thesixties and seventies took place during the mid-eighties. This result isalso found in other studies on Indian industrial growth (Ahluwalia, 1985,

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0.7

0.75

0.8

0.85

0.9

0.95

1

1.05

1.1

1.15

1.2

1973 1978 1983 1988 1993Year

Inde

x

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 ofTFP growth between the modern and traditional sector industries. How-ever, the level of TFPG of the modern sector is lower than that of thetraditional sector during the period. One of the main reasons behindthe low level of TFP in the modern sector is the inflexibility of laborlaws 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 manufacturingresults in faster capital deepening and lower aggregate TFP growth thanwould have prevailed under flexible labour laws, though productivitycontinues to increase’ (Virmani, 2006). The reason behind the low TFPGof modern sector may be the time period covered in this study. Duringthat period the contributions of industries like computer and electronicproducts and information technology were not prominent in total indus-trial output. The other reason may be that the contribution of R&D tothe TFP growth of the modern sector cannot be incorporated due to alack of information.

The index corresponding to weighted average TFPG in Figure 3.5 showsa similar trend to that in aggregate TFPG. However, the curves are closer

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0.8000

0.9000

1.0000

1.1000

1.2000

1.3000

1.4000

1.5000

1973 1978 1983 1988 1993Year

TF

P in

dice

s

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 differ-ence in the pattern of TFPG between modern and traditional industries.Figure 3.6 shows the indices of wage differential components of tradi-tional, 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 indicesand in the traditional sector the trend is more prominent than in theother two sectors. For the modern sector until 1988 the curve is at levelone and after that there is a sudden jump. This upward trend of theindices possibly indicates suboptimality in the allocation of labor amongthe industries, particularly in the traditional sector. However, this featureis less prominent in the modern sector. This is an expected result as thewage differentials in traditional industries are much higher compared tomodern industries and the allocation of labor in traditional sectors is notalways made on the basis of efficiency. On the other hand, laborers inmodern sector industries are more homogeneous in nature and supposedto be better utilized.

Figure 3.7 shows the indices of rent differential components of thesethree types of industries. In this case we have found a declining trendof the indices over this period. This pattern of movement indicates thatthe allocation of investments among the industries has been made in

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75

0.99

1.01

1

1.02

1.03

1.04

1.05

1.06

1.07

1973 1978 1983 1988 1993Year

Indi

ces

Wage Diff-All Wage Diff-Trad

Wage Diff-Mod

Figure 3.6 Wage differential components of traditional, modern and allindustries

0.82

0.88

0.86

0.84

0.9

0.94

0.92

0.96

0.98

1

1.02

1973 1978 1983 1988 1993Year

Indi

ces

Rent Diff-All Rent Diff-Trad

Rent Diff-Mod

Figure 3.7 Rent differential components of traditional, modern and allindustries

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0.8

0.9

0.85

0.95

1

1.05

1.1

1973 1978 1983 1988 1993Year

Rel

ativ

e in

dice

s

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. Thisis expected, since capital is homogeneous and there are hardly any rentdifferentials among the industries. Moreover, capital is more mobile thanlabor and should move according to changes of the rate of return oncapital 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 anupward or downward trend of any of these relative indices for a sectorindicates a tendency of divergence away from the all-industry pattern.Thus, suppose the relative index for rental differential for a sector showsa downward trend; this should mean that compared to all-industry thissector experiences a better allocation of investment among its industries.Let us now consider Figure 3.8, which shows the movements of the rela-tive indices of the aggregate TFPG. The relative index for the traditionalsector shows an upward movement in the initial years but then divergesfrom the all-industry level in a downward direction. On the other hand,the index for the modern sector shows an opposite movement to thetraditional sector index. This pattern of movement may be due to thegreater weight of the modern sector compared to the traditional sectorin changing the pattern of the TFPG index for the all-industry total.

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0.9000

1.0200

1.0000

0.9800

0.9600

0.9400

0.9200

1.0400

1.0600

1.0800

1.1000

1973 1978 1983 1988 1993Year

Rel

ativ

e in

dice

s

Weighted-trad-® Weighted-mod-®

Figure 3.9 Relative weighted TFP indices of traditional and modern to allindustries

In Figure 3.9 a similar trend is observed in the relative indices of theweighted TFPG for these two sectors.

Figure 3.10 gives the graph for the relative index of the wage differen-tial component of the aggregate TFPG. Both curves show a rising trendduring the period. However, the relative index for the modern sectorstarts from below one, crosses one in the year 1988 and then divergesfrom one. On the other hand, the index of the traditional sector alwaysdiverges away from the all-industry level. Thus the movement of thecurves indicate that in both sectors there was some misallocation of laboremployment compared to all-industry level and the problem becomesacute in the recent period.

The graph for the relative index of the rent differential component ofthe aggregate TFPG is shown in Figure 3.11. Here also, both curves showa rising trend during the period and move with similar trends duringthe period. Thus the curves indicate that both sectors misallocated someinvestment compared to the all-industry level during the period of study.

Thus, the analysis of TFPG shows that there is a definite upswing inthe trend of aggregate TFPG during the post-liberalization period. How-ever, this change was started much before 1991 and there is hardly anydifference between the modern and traditional sectors in the trends of

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78

1.0100

1.0000

0.9900

0.9800

0.9700

1.0200

1.0300

1.0400

1.0500

1973 1978 1983 1988 1993Year

Indi

ces

Wage Diff-Trad Wage Diff-Mod

Figure 3.10 Relative wage differential components of traditional and modern toall industries

1.025

1.02

1.015

1.01

1.005

1

0.995

1.03

1.035

1.04

1.045

1973 1978 1983 1988 1993Year

Indi

ces

Rent Diff-Trad Rent Diff-Mod

Figure 3.11 Relative rent differential components of traditional and modern toall industries

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TFPG. The pattern of intertemporal movements of labor are inconsis-tent with efficiency and this phenomenon is true for both traditionaland modern sectors. The pattern of intertemporal movements of invest-ment as evident from the figures moved in the right direction duringthe period. Finally, aggregate TFP growth in India was mostly due to theintrabranch effect of TFP growth. A similar result was found by Timmer(1999) in his analysis of structural change and productivity growth inIndian industries.

5 Total factor productivity growth in export-orientedindustries

Development economists often argue that a protected trade regimereduces the efficiency and productivity of the industrial sector. First, aprotected home market allows domestic producers to enjoy monopolypower and excess profits. Consequently these firms fail to achieve bothscale efficiency and technical efficiency. Second, a protected marketattracts inefficient small firms to operate, causing an increase of aver-age cost. Thus these two intra-industry effects of protectionism are amore important source of welfare loss than the traditional compara-tive advantage effect. The earliest arguments for gains from trade arebased on the concept of comparative advantages from proper allocationof resources. The recent emphasis is given on the improvement of effi-ciency and productivity through competition due to openness of trade.However, the impact of trade policies in long-run growth is ultimately anempirical question. In developing countries where oligopolistic behaviorof the firms in small domestic markets is more likely, few studies linktrade reform with increased competition. Trade liberalization will bringadditional welfare gain by reducing the dead weight losses created bydomestic monopolies and oligopolies, by increasing competition andreducing price and marginal cost markups. The empirical evidence showsthat import penetration through liberal trade policies lowers price–cost margins in several developing countries. Research with developedcountry data suggests a negative relation between import penetrationand reported price–cost margins (Domowitz et al., 1988; Roberts andTybout, 1991). Thus studies on the linkage between trade reform andproductivity suggest that the debate is still unresolved and the impact oftrade policies on long-run growth is ultimately an empirical question.

The role of trade in promoting economic well-being has a long tradi-tion in the trade literature, but the interaction between internationaltrade and productivity movement is less understood. Recent works

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in the growth literature outline a variety of mechanisms by whichincreased trade might affect aggregate productivity growth (Grossmanand Helpman, 1991; Rivera-Batiz and Romer, 1991; Romer, 1994;Feenstra, 1996). The major issue of these studies is the possibility oftransfer of knowledge and ideas across the countries and the possibil-ity that countries with lower productivity might catch up to the leadingcountries. At the same time there is a possibility that faster productivitygrowth allows firms or industries to increase the flow of exports. Tyboutet 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 presenceof these entry costs, only relatively productive firms will choose to paythe cost and enter the foreign market. Thus the implied relationshipbetween exporting and productivity is positive in a cross section of firmsor industries.

Bhagwati (1988) argues that although the logic for the success of anexport promotion strategy is based on economies of scale, no empiricalsupport for this is available in a developing country. The lack of anyconclusive evidence of the linkage between trade reform and competi-tion, as well as productivity, is very natural due to the non-availabilityof sufficient data before and after liberalization and also due to the lackof suitable econometric methods.

This study discusses some issues regarding trade liberalization andthe productivity growth of Indian industries. It is expected that afterthe implementation of liberalization policies there will be a rise in theproductivity and efficiency of industries, the industries will be more com-petitive than before and trade volume will rise. But there is a two-wayrelation between competitiveness and volume of trade. It has been saidthat the interaction between international trade and long-run outputand productivity growth is less understood in the trade and/or liberal-ization literature. It is interesting to examine whether growing trade leadsto faster productivity growth or the other way round.

In this section we analyze the effect of an increase in total volumeof international trade on industrial performance. We test whether inter-national trade, in the form of exports, has any effect on productivitygrowth within the industries.

Data

The major impediment to empirical analysis is the non-availability ofa consistent series of comparable data over a good period of time. Thebasic data on trade are collected from various issues of trade statisticsbrochures and books published by the Director General of Commerce

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and Intelligent Service (DGCIS). A consistent series of data for majortrade groups in India is available from 1987–88 to 1999–2000. Data ontrade have also been collected from the International Trade Statistics pub-lished by the UNO. These data have been supplemented by data takenfrom various issues of Statistical Abstract, published by the Central Statis-tical Organization. Statistics on tariff rates on different commodities havebeen collected from various issues of Custom Tariff in India, compiled byR. K. Jain and published by Centax Publication Pvt Ltd, New Delhi.

The Central Statistical Organization publishes data on manufacturingindustries in India. These data are available in annual issues of the AnnualSurvey of Industries. But these data are at an aggregate level, with four-digitclassifications of industries. Recently, they have started selling firm/unitlevel data on manufacturing industries. These data are available continu-ously from 1980–81 to 1998–99 except for the single year 1995–96. Thesefirm-level data have been collected for a few selected industries for thepurpose of the analysis. Industry-level data for a few selected industriesfor the years 1974–75 to 1998–99 have been collected from differentissues of the Annual Survey of Industries. Data have also been collectedfrom various issues of the Reserve Bank of India Bulletin.

Export performance of Indian industry during 1987–88 to1999–2000

It has been argued that in recent years the Indian export scenario haschanged due to a greater openness in international trade in India. Wehave analyzed the change in trade pattern of India during the recent past.We consider here only the export performances of different commod-ity groups in India during 1987–88 to 1999–2000. Trade liberalizationincludes both export and import liberalization. Export promotion poli-cies are taken in terms of indirect and direct subsidies. There are at leastfour types of indirect subsides: (a) exemptions or concessional tariffs onraw materials/inputs; (b) access to special import licenses for restrictedinputs; (c) concessional income tax provisions traditionally applied toexports (export earnings are tax exempted); (d) export financing at con-cessional rates. There are also some direct subsidies to exports and variouspolicies are taken to promote foreign direct investment in some indus-tries. Import liberalization policies give a better scope for industrialiststo import inputs at a lower cost than before. As a consequence therewill be a chance of producing goods at a lower cost than before usingboth imported inputs and imported technology, and goods can be madeavailable at a lower price. As a result, the export-oriented industries are

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Table 3.4 Percentage of share of export of major items groups

Items 1987–8 1999–2000

Agriculture 23 18Traditional industries 52 58Modern industries 14 20Minerals 5 1Ores 4 2Others 2 1

expected to increase their exports by lowering the offer price in theinternational market.

This study has been conducted on the basis of the data collected fromDGCIS. First, these export figures are converted into real terms by usingthe unit value index of export. Since the data period is not long enoughfor any rigorous testing using sophisticated time series models, we haverelied on some simple techniques to establish the changing pattern ofexports in India during this period.

Table 3.4 shows the overall changes in real values of exports (deflatedby unit value index) during 1987–88 to 1999–2000. The export items areclassified into six broad areas: agricultural goods; manufacturing goods(traditional); manufacturing goods (modern); ores; minerals and min-eral oils; others. Exports of agricultural items decreased from 23 percent in 1987–88 to 18 per cent in 1999–2000. At the same time therewas a corresponding rise in exports of modern items, from 14 to 20 percent during the same period. The proportion of exports of ores fell from4 per cent in 1987–88 to 2 per cent in 1999–2000 and that of miner-als including oil fell from 5 to 1 per cent during the same period. Thusit can be said that there was a definite shift of exports from agricul-tural and primary commodities to manufacturing commodities duringthe decade after liberalization. There was also a rise in manufacturingitems in the modern sector, including chemicals, machinery, electronicgoods, computer software, transport equipment and sport goods. Thusa change in trade pattern in terms of exports is observed during thepost-liberalization period. A detailed breakdown of the growth patternof exports can be studied to identify the export items that registered abetter 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) showsthat most of the industries had rising exports during the period.The movement of individual commodity groups can be divided into sixcategories on the basis of growth rates and their share in total exports.

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0

100,000

200,000

300,000

400,000

500,000

600,000

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

Years

Exp

ort

(Rs

00,0

00)

Leather Gems and jewelry Drugs and pharmaceuticals

Machinery and instrument 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 estimatedfor comparison.

First, items are grouped according to the growth rates over the entireperiod and the share of exports during the period. The commoditygroups that had a very high share of total exports and also showed highgrowth rates over the period were gems and jewelry, ready made gar-ments (cotton) including accessories, and cotton yarn fabrics made upsetc. Gems and jewelry shows the highest proportion of exports for theentire period at around 15 per cent of total exports, and this group alsoshows a high growth of exports during this period, at around 4 per centper annum.

The second group includes items that show very high growth rates butmake up a lesser proportion of total exports. Primary and semi-finishediron and steel registered a maximum growth rate of about 14 per centper annum but the item accounts for only 0.1 per cent of total exports

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in 1987–88. However, the proportion of exports had increased to about2 per cent in the year 1999–2000. Electronic goods showed a growth rateof about 6 per cent per annum and the share in total exports rose from1.1 to 1.7 per cent during the period. Other items in this group includeinorganic chemicals and plastic linoleum.

A third group of items can be categorized, which show a high growthrate but have very little share in exports. Woolen yarn and fabrics madeups falls in this category, which shows a growth rate of about 10 per centper 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 thiscategory, showing a high growth rate of 11 per cent per annum butaccounting for only 0.03 per cent in 1987–88, rising to 0.1 per cent in1999–2000.

The fourth group comprises industries that register low growth ratesbut a high proportion of total exports. Leather manufacture is one suchindustry, which registered a growth of only 0.29 per cent per annum buta high share of 7.4 per cent in 1987–88. But the share fell to 2.6 per centin 1999–2000.

In the fifth group the items show both low growth rates and a lowshare of total exports. The items in this group are numerous, includingpaints, enamel, varnish etc., tobacco manufactured and machine tools.The final group of items shows very poor export performance during theperiod of our study.

The standard statistical test to find any major changes in the move-ment of exporst after liberalization using dummy variable in the timeseries analysis failed to provide statistically significant results for most ofthe industries. Average annual growth rates have been calculated for twosubperiods, one from 1987–88 to 1992–93 and another from 1993–94to 1999–2000. The comparison of growth rates during the two subperi-ods indicates that 18 out of 43 items show higher growth rates duringthe post-liberalization period. To rank the industries considering bothparameters, i.e. growth of exports and share of exports of each industry,we calculated an index for each parameter. The index is similar to thedevelopment index. Each index is added to find out the rank of eachindustry. Since for both parameters a high value of the index means ahigh rank one can add the two indices to make a composite index andorder the industries according to that value. The index of each parameteris calculated in the following way.

IG = (GOBS − GMIN )GMAX − GMIN

, 0 ≤ IG ≤ 1

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where, GOBS is the observed growth rate of a particular industry,GMIN

and GMAX are the minimum and maximum values of growth among theindustries. A similar index (IS) is calculated for the share parameter ofthe industries. Ranking of industries on the basis of the combinationof these two indices in Table 3.5 shows that gems and jewelry has thehighest rank. The other industries that show high ranks in this compos-ite index and also belong to the upper rung of the table if consideredseparately for the parameters are transport equipment, drugs and phar-maceuticals, machinery, and iron and steel. It can be seen from Table 3.6that some of the industries that performed better in terms of exports havehigh values of ‘imported-input’ intensity. But some industries (leather,textile-garments, transport and iron and steel) show a high growth ofexports although the percentage of imported input in these industries arecomparatively low. Thus the hypothesis that the industries that showedbetter export growth during the past decade necessarily benefited fromthe import of inputs is rejected. There are other factors, such as FDI, thatalso play an important role in export growth.

Linkage between total factor productivity growth and exportperformance in selected industries

According to several economists TFP growth is the only source of long-run development in any country. TFP measures the efficiency of inputuse in a production process. The growth of an economy is determinedby the rate of expansion of its productive resources through capital for-mation and TFP growth. Differences in TFP growth rates between sectorsare crucial determinants of evolution in the long run (Nishimizu andRobinson, 1984). Thus TFP growth is one major policy issue for both thedeveloped 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 growthand changes in policy on protection or whether there is any relationbetween TFP growth and fiscal incentives to the industries to accelerateactivities.

The most significant stylized fact of the empirical literature on TFPgrowth is the importance of TFPG in contributing at least 50 per centof growth in output. This literature also suggests that there is a posi-tive relationship between productivity change and the rate of growth ofoutput. Now the question is how trade policies affect TFP growth. It hasbeen argued that an implicit mechanism is competition, forcing domes-tic industries to adopt new technologies, to reduce ‘X-inefficiencies’ andto reduce cost through increasing productivity and efficiency. The most

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86

Table 3.5 Export performance of the industries

Commodities GR Share Combined(1987–99) 1999–2000 rank

Gems & jewelry 4.225 18.189 1Cotton yarn, fabrics, made ups etc. 6.892 10.210 2Primary & semi-finished iron & steel 14.220 1.770 3Other cotton incl. accessories 4.722 8.055 4Manmade fabrics, made ups 9.277 2.681 5Plastic & linoleum products 9.960 1.690 6Computer software 11.021 0.111 7Drugs, pharmacuticals & fine chemicals 6.819 4.671 8Woolen yarn, fabrics, made ups etc. 10.028 0.165 9Inorganic/organic/agro chemicals 7.853 1.782 10Other chemical & allied products 8.611 0.898 11Manmade fibers 6.697 2.443 12Transport equipments 6.052 2.671 13Glass/glassware/ceramics/reftrs/cement 7.872 0.588 14Iron & steel bars/rods etc. 7.595 0.249 15Machinery and instruments 4.372 3.901 16Paper/wood products 7.173 0.499 17Electronic goods 5.869 1.651 18Manufactures of metals 5.399 1.958 19Handcrafts (excl. handmade carpets) 5.551 1.713 20Dyes/intermediates & coar tar 5.389 1.645 21Rubber manufactured products 6.113 0.748 22Processed minerals 5.865 0.672 23Processed fruits & juices 5.202 0.304 24Carpets (excl. silk) manmade 4.266 1.203 25Coir & coir manufacture 5.118 0.118 26Paints, enamels, varnishes etc. 3.601 0.443 27Natural silk yarn, fabrics, made up 3.078 0.786 28Carpets (excl. silk) millmade 3.407 0.272 29Other commodities 2.637 1.144 30Sports goods 2.692 0.162 31Other textile materials 2.466 0.325 32Ferrous alloys 2.474 0.144 33Leather manufacture 0.297 2.581 34Wool 2.193 0.428 35Residual engineering items 2.433 0.084 36Cosmetics, toiletries etc. 1.576 0.547 37Machine tools 1.743 0.231 38Jute manufacture excl. floor covering −0.470 0.294 39Non–ferrous metals −1.735 0.071 40

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Table 3.6 Imported input intensity of selected commodities

Sl. number Commodity Imported input intensity (%)

1 Electronic equipment (incl. TV) 33.91102 Machinery 30.34113 Drugs and medicines 15.96574 Miscellaneous manufacturing 1.26775 Transport 10.42426 Iron & steel 10.08027 Leather 5.17148 Textile garments 3.9076

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

common way to increase competition is to open domestic industries tothe international market. Thus export expansion and import liberaliza-tion are the two major policies for opening the market. One argumentof gains from trade is based on the concept of allocative efficiency, i.e.trade liberalization will help allocate resources according to a country’scomparative advantage. But the other argument is that in an imperfectlycompetitive market, trade reforms increase competition. While a policyfor increasing imports may restrict the market for domestic goods, it alsoincreases competition and hence induces greater efficiency. An exportpromotion policy may affect competition in both ways. On the onehand, if firms increase innovative and productive activities in order toenter foreign markets then high exports as a reward for this activity maylead to healthy competition. On the other hand, excessive export subsi-dies may distort incentives and lead to increased inefficiency. Thus it isimportant to focus on the impact of trade policies on the TFPG and toexamine the causal relation between TFP and trade policies.

Methods and data

In this study we measure TFP growth and its changes during the post-liberalization period for some selected manufacturing industries thataccording to our analysis show comparatively better performance interms of export during 1975–1998. We also try to understand the factorsbehind the changes in TFPG during the same period. The selected indus-tries are: leather; transport equipment; drugs and pharmaceuticals; gemsand jewelry; machinery; and iron and steel. Data relating to productionfor these industries were collected from the Annual Survey of Industries.Export data for these industries were collected from various issues of the

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Reserve Bank of India Bulletin. Price indices to deflate the output of theseindustries were collected from issues of Statistical Abstract India, pub-lished by CSO. The consumer price index for industrial workers was alsotaken from publications of CSO. Unit value index is used to deflate theexport figures for different items, and is taken from the publications ofDGCIS.

Two different measures were used to estimate the TFP of these selectedindustries. The first method is a standard translog index with value addedas a measure of output, and two inputs are labor and capital. Under thistranslog index the equation to estimate TFPG is:

� log TFPt = � log Yt − (Vk(� log Kt ) + VL(� log Lt ))

where

V (.) = V(.)t − V(.)t−1

2

represent the corresponding factor shares of inputs and Y represents thevalues added.

The second method is based on an output function where instead ofvalue added the value of gross output is taken to estimate the TFPG andthere are three inputs: labor, capital and materials. The functional formof the measure is:

� log TFPt = � log Qt − (Vk(� log Kt ) + VL(� log Lt ) + Vm(� log Mt ))

Where Q represents the gross value of output, M represents the materialinput and Vm represents the share of that input.

Both the value added and the gross output are deflated by the respec-tive price indices to get the real values. Total number of persons employedis taken as measure of labor input. Share of labor in output is taken as theproportion of wages to total output or gross value added depending onthe model specification. Wage is deflated by the consumer price indexof industrial workers to get the real value. It is well known that the mea-surement of capital is always a difficult task for any empirical analysisof production. Gross fixed capital stock is calculated using the perpetualinventory accumulation method.2 The time series of materials is deflatedby 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

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Table 3.7 Growth of TFP and labor productivityover three subperiods

Year TFP-VA LPT

Leather1976–80 −7.86 −4.181981–90 1.96 6.051991–97 2.14 9.85

Transport1976–80 −5.56 1.791981–90 3.42 7.661991–97 1.69 7.39

Medical1976–80 −6.55 −6.761981–90 1.58 29.131991–97 5.32 10.52

Gems and jewelry1976–80 3.08 13.371981–90 1.90 30.721991–97 11.59 24.66

Machinery1976–80 0.55 3.591981–90 −0.17 4.991991–97 0.48 6.56

Iron and steel1976–80 −3.42 0.661981–90 −0.66 5.401991–97 5.76 13.40

transport; and iron and steel), were estimated over the years 1975–76to 1997–98. Table 3.7 presents the growth rates of TFP measured usingvalue added method and the corresponding growth rates of labor pro-ductivity in the three subperiods 1976–80, 1980–90 and 1991–99. It canbe seen from the table that per annum growth rates of TFP measured withthe single-deflated value added method during 1990–91 to 1997–98 arehigher in all the industries, except transport equipment, compared to theprevious decade. Estimates of TFP with the value added function showthat the growth rates of leather, transport equipment, drugs and phar-maceuticals and iron and steel were at a minimum during 1975–76 to1979–80 among the three subperiods. In the other two industries theywere marginally higher than the figures for 1980–81 to 1989–90.

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The findings of Goldar and Kumari (2002), Trivedi et al. (2000) andSrivastava (2000) suggest that TFP growth rates of ‘textiles, leather, trans-port, chemical products, and metal and metal products rose during the1990s. Thus from these studies we find a broad agreement about themovement of TFPG during two subperiods of the 1980s and 1990s.

Now the question is whether these upward movements in TFPG inthe industries are the outcome of the policies taken for liberalization orsimply an effect of output growth. In the next subsection an attempt ismade to understand this phenomenon using econometric model.

Sources of TFP growth

The analysis to identify the forces behind the TFP growth is based ona regression model with time series cross section pooled data of fiveindustries, each for 21 years. For each industry the TFPG for each yearis calculated and the corresponding variables are taken to obtain theestimates. The basic model is

TFPGit = α + β1OG + β3ERP + β4EER + EXPGR + ε

TFPGit indicates annual growth rates of total factor productivity in indus-try i at time t. OG represents the annual growth rate of output (real terms)for the corresponding year. ERP represents the effective rate of protectionaccorded by tariff to industry i in year t. EER is the real effective exchangerate in year t. EXPGR represents the average annual export growth duringthis year. A detailed description of data for the variables is presented inAppendix 3.2.

Results of the regression analysis are given in Table 3.8. The depen-dent variable here is TFP growth and the independent variables are logof output, log of exports, log of square of exports, effective rate of pro-tection and time. It has been observed that exports play an importantrole in explaining the TFPG. Log of square of exports is included to checkthe nonlinearity of the variable. The coefficient of this variable becomesstatistically significant, which indicates that the higher the growth ofexports the higher will be the TFP growth of Indian industry. The coef-ficient of effective rate of protection indicates that the lower the ratethe higher will be the TFP growth. However, the value is not statisticallysignificant at the 10 per cent level.

6 Market power and productivity: firm-level study

In many studies an imperfectly competitive home market has been citedas a justification for the introduction of a policy of trade liberalization.

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Table 3.8 Sources of TFP growth

Variable Pooled estimatecoefficient

C 3.132604(0.2101)

Log output 0.705246(0.7577)

Log export −5.19282(−1.7209)

Log export-square 0.568645(1.9743)

ERP −0.01118(−0.7342)

Time −0.01513(−0.4752)

Adjusted R2 0.053132Durbin–Watson stat. 2.343931

Dependent variable: TFPG.

In a protected market dominated by few domestic firms, trade reformincreases competition. Grossman and Helpman (1990) suggest that tradeliberalization not only generates a one-time increase in growth throughbetter allocation of resources but also affects long-run growth by accel-erating technological change. But they also argue that trade reform willaccelerate growth only if the allocation of resources is made in the properdirection.

The impact of trade reform on long-run growth is ultimately an empir-ical question. There have been some good efforts to find the correlationbetween trade reform and productivity growth. However, the empiricalresults from microlevel studies are still inconclusive. In developing coun-tries where the prevalence of oligopolistic markets is more likely there is alack of conclusive evidence of linkage between trade reform and produc-tivity growth (Bhagwati, 1988 ; Nishimizu and Page, 1990; Tybout, 1992;Harrison 1994). One reason behind the inconclusive relation betweentrade reform and productivity growth is the measurement of produc-tivity. Solow-type measurement of TFP is based on the assumption ofperfect competition with the factor shares being distributed accordingto the law of factor price equalization. However, in a developing econ-omy where the small domestic market is dominated by very few firms theassumption of perfect competition cannot be justified. A shift in trade

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policies may alter the level of competition and that in turn affects thecomposition of factor shares.

The potential biases in assuming perfect competition in the estimationof TFP have long been recognized. In a recent study by Harrison (1994) anattempt was made to correct these biases in the estimation of TFP growth.The chapter is based on an extension of the methodology pioneered byHall (1988) and Domowitz et al. (1988) on aggregate data.

In this study an attempt has been made to analyze the relation betweenproductivity growth and changes in market power using a similar modelwith firm-level data. Krishna and Mitra (1998) attempted to study thisrelationship with CMIE data for the period 1986–93. This study is, how-ever, different in certain ways. First, the data in this study are takenfrom unit-level information on selected industries supplied by the AnnualSurvey of Industries. Second, the period that this study covers differs fromthat 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 attime 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. Thecorresponding factor shares are defined by w, r and m respectively.

The partial derivative of Y with respect to labor can be written as

∂Yijt

∂Lijt= µw

p, where µ = 1

/[1 + s · 1

e

]

Similar results can be obtained for other factors of production

∂Yijt

∂Kijt= µ

rp

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and

∂Yijt

∂Mijt= µ

np

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 equationis obtained:

d log Yijt = µj

[wLijt

pYijt· d log Lijt + rKLijt

pYijt· d log Kijt + nMijt

pYijt· d log Mijt

]

+dAjt

Ajt+ dfit

fit(3.17)

Let α = wLpY , αk = rK

pY , am = nMpY and α + αk + αm = 1

under constant return to scale. If we introduce imperfect competitionand variable return to scale then

α + αk + αm = β

µj

where β is the return to scale parameter, or

µjαk = β − µjα − µjαm

Now from equation (3.17)

d log Yijt − d log Kijt = µj[α · d log Lijt + αm · d log Mijt ]

+ [β − µjαj − µjαm − 1]d log Kijt

+ dAjt

Ajt+ dfit

fit(3.18)

Let = log LK , m = log M

K , y = log YK

Then rearranging the terms of equation (3.18)

dyijt = µj[αid + αmdm] + (β − 1)d log Kijt + dAjt

Ajt+ dfit

fit(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.

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If we ignore the firm specific effect, i.e. ∂fit/fit = 0, and constant returnto scale, i.e. β = 1, then

dy − αd + αmdm = φ = (µ − 1)(αd + αmdm) + dAjt

Ajt

where φ = observed productivity change and dAjt/Ajt = true productivitychange.

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 andm are rising (falling), then dAjt/Ajt is over (under) estimated. Second, theestimate of changes in the trend rate of growth of productivity will beincorrect. This bias in the estimate of the change in productivity occursdue to change in the expected value of µ before and after reform. It isexpected that after trade reform the price cost margin will fall to unityand the measured productivity will be equal to the true productivitydAjt/Ajt .

Estimation of the model

To estimate the effects of changes of trade reform on the market power offirms and to see the effect of changes in productivity, equation (3.19) ismodified to allow for a change in markup by firms after reform. Changein firm behavior is captured by introducing a slope dummy to the term[αldl + αmdm] in equation (3.19). To capture the overall shift in the pro-ductivity level after trade reform an intercept dummy is introduced inthe model. The form of the function to be estimated is then

dyijt = β1jdxijt + β2j[Ddx]ijt + β3jD + β4jdKijt + dAjt

Ajt+ ε (3.20)

where, dx = (αldl + αmdm), 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 ofproductivity growth for the industry j. This rate will then be captured bythe coefficient of the intercept term.

If trade reform leads to the firms in the industry becoming more com-petitive than before the expected sign of the coefficient of the slopedummy should be negative, which reflects the fall in markups when

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firms are exposed to the international market. If there is any shift inthe overall productivity the coefficient of intercept dummy should bepositive. The coefficient β4 is equal to the scale parameter β minus one.

The non-availability of panel data forced us to estimate the model withthe ordinary least squares method. Regression models have been esti-mated for each industry group and the two sets of regression equationscan be written as: dy = β0 + β1D + β2dx + β3Ddx + ε for the model withoutscale parameter and dy = β0 + β1D + β2dx + β3Ddxβ4dk + ε for the modelwith scale parameter.

Results

Data for this analysis of this part were collected from the Annual Surveyof Industries firm- or unit-level data supplied on demand in electronicmedia. Data were collected for seven selected industries (selection onthe basis of export performance the period of study): manufacturingof all types of textile garments and clothing accessories (265); manu-facturing of leather footwear (291); manufacturing of drugs, medicinesand allied products (304); manufacturing of semi-finished iron and steelproducts (331); manufacturing of television receivers, apparatus for radiobroadcasting etc. (366); computer and computer software (367); andmanufacturing of jewelry and related articles (383). Data were collectedfor the years 1980–81 to 1997–8, barring the year 1996–97.3

Before we analyze the change in the general level of productivity andthe markup of firms due to import liberalization it may be useful to takea look at the standard rate of import duty at two time points, i.e. 1987–88and 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 1998–9

30 Pharmaceuticals 100 3042 Articles of leather 100 4061 Articles of apparel and clothing 100 4062 Clothing accessories 100 4071 Natural and cultured pearls and 100 30

precious stones73 Articles of iron and steel 100–300 2084 Calculators, data processing and 100–200 10–40

other office machines

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

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all the selected industries fell drastically during the period of study. Theimport duties in 1987–88 were in most cases higher than or equal to100 per cent, while the rates comes down to a range of 10–40 per cent in1998–99. Thus there was a major change in import liberalization througha reduction in tariff rates after the policy of liberalization taken in 1991.

Estimations of changes in productivity and markup are computed forthe equations based on the assumptions on variable return to scale. Anordinary least squares technique is applied to estimate the coefficient ofthe equation. The first estimation is based on the assumption of vari-able return to scale. The estimation of level of markup and changes ofmarkup after liberalization is presented in Table 3.10. β2 denotes the levelof markup of firms while β3 denotes the changes in markup of firms.Figures for four industries (manufacturing of leather footwear, manufac-turing of drugs and medicines, manufacturing of television receivers andcomputer and computer software) showed a decrease in markup. How-ever, the coefficient is statistically significant only in manufacturing ofleather footwear. In three industries (textile garments, manufacturingof 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 fromTable 3.10 that in five industries out of seven the markup is greater thanor equal to one. It is interesting to note that manufacturing of televi-sion receivers and manufacturing of jewelry and related articles behavedcompetitively even before reform. This result is quite natural since theseindustries largely comprise small units and the firms have little controlover price movement. But during post-liberalization markup in at leastthree industries fall below one. This is not unlikely, since industries dur-ing the adjustment period of post-liberalization may encounter loss fora short period (Levinsohn, 1993).

This result is somewhat different from that of the study by Krishna andMitra (1998). They found a decline in markup in three industries out ofthe four they studied during the post-liberalization period. However, theindustries they examined and the period of study are different from ours.

β1 indicates the changes in productivity during the post-liberalizationperiod. The figures show that productivity has fallen in all the industriesbar one. However, only in two industries (manufacturing of drugs andmedicine and manufacturing of semi-finished iron and steel products) isthe coefficient statistically significant. Since productivity is consideredas procyclical, the statistically significant increase in productivity maynot be found during the first phase of trade reform. However, in the

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97

Table 3.10 Test of changes of competition in selected industries

Industry Coefficient

β0 β1 β2 β3 β4 R2

Textile garments (265) −0.0205 −0.0268 1.0799 0.6561 −0.0046 0.37(−0.39) (−0.46) (4.57) (2.3) (−0.04)

Mfg of leather footware (291) 0.0094 −0.0148 1.3899 −0.7819 −0.0581 0.68(0.44) (−0.48) (12.11) (−5.66) (−0.76)

Mfg of drugs & medicine (304) 0.0219 −0.0908 0.9703 −0.4081 −0.1885 0.16(0.54) (−1.87) (3.67) (−1.36) (−1.3)

Mfg of semi-finished iron and steel (331) 0.0111 −0.1122 1.0961 0.5010 −0.0505 0.72(1.3) (−5.01) (28.69) (4.84) (−1.29)

Mfg of television recievers etc. (366) 0.0167 0.1845 0.0624 −0.0675 0.0591 0.05(0.27) (2.14) (1.71) (−0.53) (0.22)

Computer & computer software (367) −0.0553 −0.0966 1.4576 −0.5054 0.1264 0.2(−0.66) (−0.74) (2.97) (−0.74) (0.33)

Mfg of jewelry & related articles (383) 0.0149 −0.0699 0.6434 0.4389 0.1479 0.76(0.19) (−0.87) (6.46) (2.75) (0.47)

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

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analysis of industry-specific TFP a mild increase in TFPG was foundin those industries. But the analysis in the previous section is basedon industry-level data and the methodologies are different in thesetwo analyses. This decline in the productivity of industries in thepost-liberalization period is also found in a study by Balakrishnan andPushpangandan (2000). They took five industry groups, namely machin-ery, transport equipment and parts, textiles, textile products, andchemicals. Most of these industries are common to the two studies. Theirestimated coefficient of the time dummy of the intercept indicates noimprovement in productivity in the post-reform period.

7 Concluding remarks

The inward-oriented policies of 40 years after independence pushed theeconomy to a no-return zone. The concept of increasing efficiency andproductivity through outward-oriented policies was neglected and neverbecame a major policy issue before 1990. Only at the end of eightieswhen the Indian economy was almost on the verge of collapse were thepolicymakers bound to implement reform policies to revive the econ-omy. In 1991 the government had undertaken the policy of liberalizationunder the guidance of Finance Minister Professor Monmohan Singh. Thisreform package included many policies and trade liberalization was oneof them.

It was expected that after trade liberalization through export pro-motion (abolition of quotas) and the reduction or abolition of importtariffs industries could increase their productivity and efficiency throughhealthy competition among the firms in the industries. The industrieswould obtain the import component of input use more easily and at acheaper price than before to help firms to produce better quality prod-ucts at a comparatively lower cost. Due to various export promotionpolicies the firms within the export-intensive industries were likely toincrease their exports. Thus it was expected that there would be a rise inexports during the period of post-liberalization. On the other hand, tradecan spur innovation by enhancing industrial learning, since it facilitatesinternational exchanges of technical innovation and improves the effi-ciency of firms. However, the direction of the effect of trade liberalizationon the productivity and efficiency of firms is ambiguous and purely anempirical question.

Before analyzing the productivity and efficiency of export-orientedindustries we discussed the structural changes in and total factor pro-ductivity growth of the traditional and modern sectors during 1973–74

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to 1999–2000. It has been observed that a definite structural change inIndian industries has occurred in favour of modern industries with moresophisticated technology during this period. The analysis of the TFPGof these two sectors suggests that the patterns of movement of TFPG forboth sectors are similar but the level of TFPG of the modern sector indus-tries is low compared to that of traditional industries. The aggregate TFPgrowth in India was mostly due to the intrabranch effect of TFP growth.

The study also sought to understand if there was any rise in exportsduring the post-liberalization period in India that can be explained asan effect of liberalization, and whether there was any link between thisrise in exports and increases in productivity. In other words, could theexport-oriented industries, using both imported inputs and importedtechnology, lower costs and increase their exports by lowering prices onthe international market.

It has been observed from the trade data for the period 1987–88 to1999–2000 that almost all the industries registered a growth in exports.However, the ranks of the industries according to their share in totalexport did not change dramatically during this period. The industriesthat performed better during this period and had a large share of exportsto total were textile industries and drugs and pharmaceuticals. Some ofthe industries studied here, however, registered lower growth during theliberalization period but the export shares of these industries were highcompared to those of other industries.

Some of the top ranking industries in terms of growth and export sharewere taken for analysis of the linkage between productivity and tradereform. It was found that this group comprises both traditional and mod-ern industries. The import content of input use of these industries differsconsiderably. Growths of productivity of these industries were estimatedto 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 efficiencyto compete in the international market and may increase export while onthe other hand excessive export subsidy may distort incentive and leadsto higher inefficiency. TFPG is one of the measures of efficiency and theresults suggest that efficiencies in the selected industries (ASI factory sec-tor industry-level data) increased during the post-liberalization periodbut the difference between the two regimes is not statistically signifi-cant. The TFPG is positively related with the exports and competitivenesscaptured by the effective rate of protection.

It has been argued that the standard estimates of TFPG models of theSolow type are based on the assumption of perfect competition and give

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biased estimates of TFP growth. Particularly in a less developed economylike India, the market is characteristically oligopolistic and the policyof liberalization is expected to change the character of the market tonearly perfect competition. A model based on the work of Hall (1988)and Domowitz et al. (1988) was estimated with firm-level data to calcu-late the TFP growth that is free from such bias. Changes in markup wereestimated for selected industries using firm-level data that indicate thechanges in the level of competition. The results suggest that competi-tiveness increased in four industries out of seven during this period, andin most of these industries productivity was declining, which are verysimilar to findings from other research.

The industry-level estimates of TFPG, however, differ from the firm-level estimates. This is not unnatural because the estimation procedure isdifferent and the TFPG estimates with firm-level data are corrected fromtheir potential bias of taking the assumption of perfect competition inthe Solow model.

The basic argument in favor of conducting such studies is that aftermore than ten years of implementation of reform policies there is nomarked improvement in India’s industrial scene and international trade.Foreign multinational companies are still hesitant about investing ina big way and foreign direct investments are not coming in enoughquantiy in the priority sectors. The productivity and efficiency of indus-tries are still far behind those of any advanced country. It is true thatIndia is at the recipient end and it is in its interest to make the envi-ronment conducive for foreign investment. Although at a lower scale,there is some evidence of FDI and technological collaborations duringthe post-liberalization period. Import tariffs have been lowered drasti-cally across the board. Some positive efforts in terms of giving subsidies toexport-intensive industries and tax reductions on export earnings havebeen made to boost export growth. The industries whose import con-tent of input is higher compared to others are expected to benefit fromthe reduction of import tariffs and can produce more efficiently. Theincreasing incidence of technological collaboration helps the produc-tion of higher quality goods at a lower price by avoiding the sunk costof R&D. For all these reasons it is expected that there will be a rise inproductivity and competitiveness in the industries and those that takethe advantage of liberalization can compete in the international marketwith 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 duringthe post-liberalization phase. (b) Productivity and efficiency are not the

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

Appendix 3.1 Industrial classification

Table 3.A1

Code Description

Traditional industries21 Food products22 Beverages23 Manufacture of cotton textiles24 Manufacture of wool, silk and synthetic fibers25 Jute textiles27 Wood products28 Paper29 Leather30 Nonmetallic minerals33 Basic metals34 Manufacture of metal products97 Construction etc.

Modern sector26 Manufacture of ready made garments30 Basic chemicals31 Rubber and plastics35 Manufacturing and machinery36 Electrical and electronics37 Transport equipment38 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 1992and 0 for the other years.

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Effective rate of protection is defined as the percentage excess ofdomestic value added introduced because of tariffs and other tradebarriers:

ERP = [(VAd − VAw)/VAw] × 100 (A3.1)

where VAd = value added at the domestic price, VAw = value added in theabsence of domestic tariffs.

This ratio measures the distortions introduced due to both tariff andnontariff barriers on input price, as well as the final output prices, andtherefore measures the true level of protection as compared to worldprices.

The ERP used in this study has been taken from the tariff basesestimates of ERP. It is written as

ERPj =(tj −

∑aijtj

)/(1 −

∑aij

)(A3.2)

where aij is the free trade input coefficient per unit of output. Effectiverates of protection are thus an increasing function of output tariffs anddecreasing function of input tariff. In the tariff-based approach, ERPs aremeasured using published tariff rates. The advantage of using nominal orpublished rates is that they contain information about the formal (poten-tial) protective structure adopted by the government. The ERPs have beencalculated according to equation (A3.2) using the input–output coeffi-cients estimated by the CSO and published tariff rates. The value addedis calculated as the returns to the primary factors directly involved inproductive activity. This is done by subtracting costs of the trade inputsused directly in production from the value of output. Nontraded inputsare treated as part of the primary sector production, thus overestimatingthe true value added.

Real effective exchange rate (EER)

The nominal exchange rate is defined as the relative price of domes-tic currency in terms of foreign currency. The real exchange rate (EER)is usually defined as the nominal exchange rate adjusted by domesticlocal-currency prices relative to foreign local currency. It is real becauseit adjusts for the relative inflation rates in the domestic economy andforeign economies. It is effective because it is constructed as a weighedaverage 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

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proxy for a country’s degree of competitiveness, while depreciation inEER 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 =n∏

i=1

[(eeI

) (PPI

)]wi

where e = exchange rate of rupee against a numeraire (SDRs) in indexform (1985 = 100); eI = exchange rate of currency i against the numeraire(SDRs) in index form (1985 = 100); e/eI = exchange rate of rupee againstcurrency I in an indexform (1985 = 100); P = India’s wholesale priceindex (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 isIndia’s bilateral trade (export plus imports) with country I in the baseperiod.

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4Industry Efficiency Analysis

The characterization and estimation of productive efficiency of an indus-try have followed three stages of development over the past decade. Oneis the parametric theory, whereby a production or cost frontier is esti-mated by assuming a composed error model with two components oferror: one measuring technical inefficiency, the other indicating purelyrandom 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 theoryby stipulating that a given firm (or decision-making unit (DMU)) is notefficient in producing its outputs from given inputs, if it can be shownthat some other DMU or combination of DMUs can produce more ofsome outputs without utilizing more of any input. This DEA techniqueis sometimes called nonparametric or semiparametric, since it does notpostulate any functional form of the production or cost frontier. In orderto obtain reliable estimates of the production frontier, one may adoptsmoothing methods and outlier rejection techniques for the observeddata on inputs and outputs and then apply the DEA method to esti-mate the production or cost frontiers. The third approach to industryefficiency analysis is designed to improve the efficiency scores of theDEA model by incorporating various methods of error reduction, e.g.the bootstrap methods rescale the individual efficiency scores using aver-age efficiencies calculated from different subsets of the data. Anotherapproach is to apply the method of least sum of absolute errors (LAV) tothe production or cost function and derive the estimates of the respectivefrontiers.

We discuss these recent developments, emphasizing only the mostpractical techniques that can be easily applied to industry data on inputsand outputs. Some of these methods are applied to estimate productive

104

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Industry Efficiency Analysis 105

efficiencies in selected industries, such as leather, textiles, computerproducts and electronics. The efficiency of the banking sector in India isalso discussed.

1 Econometric estimation of productivity

The composed error model may be simply written as a productionfunction

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

εj = yj − gj(xj, α)

into its two separate components, where the nonnegative error compo-nent uj measures technical inefficiency in the sense that it measures theshortfall of actual or observed output from its maximum value g(xj, α).Another important econometric issue with this model (4.1) is how toderive statistically consistent estimates of α when we have panel datacomprising both time series and cross sectional data for n firms.

For the first issue Jondrow et al. (1982) developed a firm-specificmethod (JLMS technique) of estimating technical inefficiency. Thismakes it directly comparable to the DEA linear program (LP) model,which computes technical inefficiency for each observation. Theirmethod explains the theorem that the conditional distribution of ugiven ε is that of a normal distribution N(µ∗, σ2∗ ), where σ2 = σ2

u + σ2v ,

u∗ = −σ2uε/σ2, σ2∗ = σ2

uσ2v /σ2 and it is assumed that each vj, which is

assumed to be symmetrically distributed, is normal N(0, σ2v ) and that

uj is distributed as the absolute value of a normal variable N(0, σ2u ). By

using this theorem one can obtain by the ML method a point estimate ofthe nonsymmetric component u by using the mean E(u|ε) or the modeM(u|ε) of the conditional distribution where

E(u|ε) = µ∗ + Kσ∗; K = f (−µ∗/σ∗)/{

1 − F(

−µ∗σ∗

)}

M(u|ε) ={

−ε(σ2u/σ2) if ε ≤ 0

0, if ε > 0

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where f (·) and F(·) is the standard normal density and its cumulative dis-tribution respectively. Thus by replacing µ∗, σ∗ by their sample estimatesone can estimate the conditional mean E(u|ε). Battese and Coelli (1991)proposed a simple model to estimate the time behavior of technical inef-ficiencies. This model applicable to a panel data framework representstechnical inefficiency as

ujt = {exp[−η(t − T)}uj j = 1, 2, . . . , n; t = 1, 2, . . . , T . (4.2)

where uit ∼ N+(µ, σ2), with N+ denoting half normal distribution, andη is a parameter (or a vector of parameters) to be estimated. Underthis formulation technical inefficiencies prior to time T depend on theparameter η. As t → T , however, ujt tends to uT . Thus technical ineffi-ciency 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 thedistance 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 ordi-nary last squares (COLS) first developed by Richmond (1974). On usinga Cobb–Douglas production function

ln yjt = (α0 − µ) +m∑

i=1

αi ln xijt + vjt − (uj − µ) (4.3)

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

with E(uj) = µ > 0 and vjt following a symmetric distribution like thenormal, we may treat εjt = vjt − (uj − µ) as the disturbance term whereuj ∼ iid(µ, σ2

u ) and is assumed to be independent of vjt. In this frame-work Schmidt and Sickles (1984) have shown that we can directly applyOLS (ordinary least squares) to this equation (4.3). These OLS estimatesof α0 = α0 − µ and α = (α1, α2, . . . , αm) will be statistically consistent asn → ∞, though not for T → ∞ for fixed n, if the effects uj are uncor-related with the regressors xjt . Under these circumstances one can applythe generalized least squares (GLS) as in the panel data literature, i.e. GLSestimates of α0 and α are based on the consistent estimates of σ2

u and σ2v

when they are unknown, but the consistent estimation of σ2u 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 thatone can recover the individual firm-specific intercept term α0j = α0 − uj

from the estimated residual.

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Developments in panel data analysis of technical inefficiency havealso allowed the introduction of policy variables that may affect techni-cal inefficiency. Thus Battese and Coelli (1991) have used the followingstructure 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 byg = g(w, z, y; α) where w is the vector of input prices, y is output, z isthe external policy variable and α is the parameter vector. Here v ∼ iidN(0, σ2

v ) captures the effect of random noise, u ≥ 0 captures the effect ofcost inefficiency and the parameter α is to be estimated along with thevector δ of parameter of the policy variable z. The component yjt capturesthe effect of technical inefficiency, which has a symmetric componentδ′zjt associated with the exogenous variables and a random componentζjt which is assumed to be normally distributed as N(0, σ2

ζ ) with the distri-bution of ζjt being bounded below by the variable truncation point −δ′zjt .Once this model is specified as (4.4), the technology parameter and theinefficiency parameters are estimated by the ML techniques. Inefficiencyestimates of individual firms at different time points are obtained as usualby the JLMS technique mentioned before.

2 Data envelopment analysis

The DEA models analyze firm-specific economic efficiency by a sequenceof LP models, one for each firm, to compare its relative efficiencyamong the set of all other firms in the industry. The DEA efficiencymodels have several interesting features, which have fostered numerousapplications in several disciplines, e.g. management science, operationsresearch and production theory in microeconomies. The first feature isthe specification and estimation of a multi-output multi-input produc-tion frontier, when there is no information about the output and inputprices. The multi-output case cannot be easily estimated by the econo-metric method. Second, both discretionary and nondiscretionary inputs

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may be used in the calculation and both radial and nonradial measuresof efficiency may be computed. In the radial case one explores if equipro-portionate reduction of inputs or outputs can be made without loweringoutputs or increasing inputs. Third, if the market price data for the inputsand output are available, one can easily estimate the cost frontier, overallcost efficiency and what is known as allocative efficiency. The dynamicextension of these cost efficiency models may also be carried out in anintertemporal framework. Sengupta and Sahoo (2006) have developeddifferent types of DEA models to evaluate the productivity of firms inIndia in its pre- and post-liberalization phases. Both production andcost frontier models have been developed and empirically applied to theIndian banking industry and the insurance industry in order to studythe extent of scale economies or diseconomies and capacity utilization.

In this section we discuss two examples of the DEA approach, onefor the production frontier and the other for the cost frontier. Then weconsider several extensions of the cost efficiency approach, including itsdynamic extensions. For surveys of the DEA approach one may refer toseveral recent publications such as Cooper et al. (2004) and Sengupta andSahoo (2006).

As an example of the production frontier estimation we start with thestochastic framework of a production function with one output (y) andm 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 formmay be written as

yj = β0 +M∑

I=1

βixij + uj, j ∈ In (4.5)

where it is usually required that the parameters βi are nonnegative withthe intercept term β0 free of sign. The error term uj ≥ 0 is nonnegativesince the optimal output y∗

1 = β0 + ∑i βixij is greater than or equal to

observed output. Timmer (1971) used this formulation to minimize thesum of absolute values of errors |uj| under the constraints uj ≥ 0. Thisleads to the linear programming (LP) model in vector matrix notation:

minβ∈C(β)

g = β′x (4.6)

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

j=1

xij

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It is assumed that the vector β incorporates the intercept term β0 of unre-stricted sign. Instead of minimizing the mean g as above Farrell (1957)adopted a two-step approach of estimating a frontier. In the first stepeach observed unit (e.g. firm k) is tested for efficiency by running an LPmodel

min uk = Xkβ − 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 frontiery = y∗ = Xkβ

∗, i.e. the optimal slack variable s∗k = Xkβ

∗ − yk is zero. If unitk is relatively inefficient, then yk < y∗

k , where asterisk denotes the optimallevels of output, i.e. observed output is less than the optimal output. Atthe second step one varies k in the index set In and runs n LP modelsof the form (4.7) and determines two subsets S1 and S2 of efficient andinefficient units respectively. These two steps comprise the formulationof the DEA model.

The dual of the LP formulation (4.7) with some transformation maybe written as the input-oriented DEA model, also known as the BCC(Banker, Charness and Cooper) model:

min θ, s.t. (4.8)

n∑j=1

Xjλj ≤ θXk;n∑

j=1

yjλj ≥ yk;n∑

j=1

λj = 1

λJ ≥ 0, J ∈ In; θ scalar

This standard BCC model measures the potential for equiproportionatereduction in all inputs where Xj is the input vector for each j ∈ In. If theinput price vector (q) is available from market data, then the input cost(IC) minimization may be used as the optimality criterion. This yieldsthe DEA model defining allocative efficiency:

min IC = q′x, s.t. (4.9)

∑Xjλj ≤ x; c

n∑j=1

yjλj ≥ yk;n∑

j=1

λj = 1, λj ≥ 0, j ∈ In

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The case of many outputs would generalize the output constraint as

n∑j=1

yrjλj ≥ yr ; r ∈ Is

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. Incase the output price data are also available one could reformulate theobjective function of (4.9) as profits (r):

max π = p′y − q′X, s.t.

∑Xjλj ≤ x; �

jYjλj ≥ y;

∑λ ≥ 0##24

This single LP model then characterizes the optimal input and outputbundle for the profit frontier.

Ray (2005) applied the radial efficiency model (4.8) with θ for the laborinputs only, for Indian manufacturing data for the years 1986–2000, with1986–91 as pre-reform data and the rest as post-reform. Interstate dataare used, with the aggregate output of manufacturing treated as a quan-tity index. Two types of labor input are distinguished: production laborand nonproduction labor (i.e. administrative and managerial). The otherinputs used are fuels (i.e. energy cost) and capital measured by the bookvalue 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 arelative inefficiency measure the results for the pre-reform (1986–91) andpost-reform (1991–2000) periods are reported in Tables 4.1 and 4.2. AsRay notes, this radial labor efficiency measure understates the extent ofsurplus labor. For example, consider the case of Kerala (KE) in the year1986–87. The radial labor efficiency measure is 0.813. This implies thatit is possible to reduce the numbers of both production and nonproduc-tion workers by 18.644 per cent. But this does not exhaust the potentialfor reducing all surplus labor, since the model does not allow for any sub-stitution between different types of inputs. Table 4.1 shows very clearlythat West Bengal (WB) exhibits the highest incidence of surplus laborin the pre-reform period (average θ∗ = 64.5 per cent) and this trend hascontinued over the post-reform era (average θ∗ = 53.9 per cent). In thismodel the effect of policy measures can be easily evaluated provided wehave data on such measures, e.g. if zj is a specific policy measure like

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Table 4.1 Radial labor efficiency measure (θ) (pre-reform era)

State 1986–7 1987–8 1988–9 1989–90 1990–1 Average

AN 1 1 1 1 1 1AP 1 1 1 1 1 1AS 1 1 0.882 1 1 0.976BI 1 1 1 1 1 1CH 1 1 1 1 1 1DE 1 1 1 1 1 1GO 1 1 1 1 1 1GU 1 1 1 0.956 1 0.991HA 0.743 0.740 0.782 0.808 0.830 0.781HP 1 1 1 1 1 1JK 0.690 1 0.762 0.945 1 0.879KA 1 1 1 1 0.780 0.956KE 0.813 1 0.841 1 0.924 0.916MH 1 1 1 1 1 1MP 1 1 1 0.883 0.859 0.948OR 0.933 0.811 1 1 0.862 0.921PO 1 0.728 0.727 0.699 0.745 0.780PU 0.934 0.898 0.836 0.908 0.854 0.886RA 1 0.762 0.844 0.808 0.866 0.856TN 0.962 0.882 0.970 1 0.945 0.952UP 0.844 0.787 0.789 0.852 0.867 0.828WB 0.605 1 0.538 0.527 0.555 0.645

additional clearness allowance (state level) for each worker, then onecan add the constraint

n∑j=1

zjλj ≤ zk

in order to evaluate the relative incidence of such a constraint. Note,however, that the critical shortage of nonlabor inputs such as energyand the inadequacy of infrastructure-related inputs not included heremay also be responsible for the existence of surplus labor in Indianmanufacturing.

An important characteristic of the DEA model (4.8) is that it appliesthe 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 thedistribution of the error term uj for minimizing, where each sample isassumed to provide a modal estimate. Timmer used the mean g = β′x

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Table 4.2 Radial labor efficiency (θ) (post-reform era)

State 1991–2 1993–4 1995–6 1997–8 1999–2000 Average

AN 1 0.316 1 1 1 0.924AP 1 1 1 1 0.775 0.947AS 1 0.845 0.842 1 0.925 0.907BI 1 1 1 1 1 1CH 1 1 1 1 1 1DE 1 1 1 1 1 1GO 1 1 1 1 1 1GU 0.910 0.874 0.918 1 0.938 0.954HA 0.739 0.598 0.621 0.805 0.746 0.758HP 1 1 1 0.554 0.929 0.943JK 1 1 1 1 0.823 0.943KA 1 0.675 1 0.682 0.664 0.772KE 0.984 0.749 0.946 0.932 1 0.941MH 1 1 1 0.899 1 0.989MP 0.852 1 1 1 1 0.945OR 1 0.712 1 1 1 0.945PO 0.759 0.702 0.679 1 1 0.850PU 0.802 0.663 0.735 0.713 0.865 0.767RA 0.952 0.822 0.985 0.911 1 0.936TN 1 0.992 1 0.874 0.914 0.964UP 1 0.809 0.807 0.863 0.741 0.853WB 0.575 0.526 0.553 0.566 0.491 0.539

instead of the mode for minimizing errors because he was interested inan average estimate of technical efficiency.

Note, however, that the LAV method is a special case of the Lp-normestimation. The LP norm minimizes the loss function

n∑j=1

L(εj), L(t) = |t |p

which is closely related to the nonparametric Huber estimate (seeSengupta, 1990) often used in nonparametric statistical theory, where

L(t) ={

t2/2 for |t | ≤ kk|t | − k2/2 for |t | > k

}

Three values of p = 1, 2, ∞ are most important in applied work. TheL1 estimate minimizes the sum of absolute values of residuals (the

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

µ(θ) = maxj∈In

∣∣εj(β)/rj∣∣

attains its minimum value, where the range of each εj(β) is assumed tobe ±rj. It is well known that if the errors εj have two-sided exponentialdensity, then the ML estimate yields the LAV estimate as in the DEAmethod.

One point has to be stressed about the ML methods above based onthe 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 ofthe basic regularity conditions that make ML estimators consistent andasymptotically efficient. Greene (1990) has shown that these desirableasymptotic properties of ML estimators could still hold if the errors fol-low the gamma distribution. Since the exponential density could be agood approximation to gamma density, the linear DEA model could bejustified in this framework as a method of estimation of the stochasticfrontier.

Another simple approach is to interpret the production frontier in theform of a COLS model as

yj = α0 +m∑

i=1

αixij + ej, ej = µ − uj, α0 = α0 − µ (4.10)

where E(uj) = µ > 0 and Eej = µ = E(uj) = 0. Here the new error term ej haszero mean and satisfies the usual regularity conditions (like fixed meanand variance) except normality. When µ is known the usual ML esti-mators 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 beendeveloped and applied empirically so as to obtain the bootstrappedefficiency estimates. These bootstrap techniques derive bias-correctedefficiency scores when we have DEA models for groups and hierarchies.These techniques rescale the individual DEA efficiency scores using aver-age efficiencies calculated from different subsets of the data. Staat (2002)has applied this bootstrap technique of bias correction of DEA efficiencyscores in order to identify the true differences in efficiency. To illustratethis method we start with the production set X = {x, y)|x can produce

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output y} and the input requirement set X(y) = {x|(x, y) ∈ X}. The usualassumptions are that the set X(y) is convex for all output y and nonzeroinputs x are required for nonzero outputs y. Strong disposability of x andy are also assumed. The efficient boundary of the input requirement setX(y) denoted as ∂X(y) has to be estimated. For any sample of observationsS = {(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

θk = min {θ|yk ≤∑

λjyj; θxk ≥∑

λjxj > 0;n∑

j=1

λj = 1,

λj ≥ 0, j = 1, . . . , n)} (4.11)

The weights λj are identical with the BCC type input-based DEAmodel. These are assigned to efficient firms on the production frontierand the condition

∑λj = 1 allows variable returns to scale. In order to

gain information on the sampling properties of the bootstrap estimatesθk, we generate a number of pseudo samples drawn from the originalDEA scores. The estimates θk and the bootstrap estimates θ∗

k are related as

[(θk − θk)|S ] is approximated by [θ∗k − θk|S∗]

The bias of the DEA estimator ES(θk) − θk may then be written as ES

(θ∗k ) − θ∗

k . Hence the bias-corrected estimates θk can be obtained byapplying the correction

θk = θk − biask = 2θk − θ∗k where θ∗

k = (1/R)R∑

k=1

θ∗k

Simar and Wilson (1998) have proved that this bias correction willremedy the problems with comparing average DEA inefficiencies fromsamples of different sizes. The vector θ∗

k of smoothed efficiency mea-sures is essentially generated from the R pseudo samples. The economist’sobjection is that this method is very artificial and dependent on randomdrawings of pseudosamples. Bootstrap methods can also be applied to theregression model (4.10) and the various applied techniques are discussedby Sengupta (1989).

Like the production frontier a cost frontier may be estimated by theDEA approach. A cost frontier has a number of advantages. First, vari-able and fixed costs may be separated by estimation. For the single output

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(w composite output as weighted combination of several outputs) case,average cost may be directly calculated from the total cost frontier. Inthe case of the quadratic cost frontier the average cost may then be min-imized so as to obtain the minimum efficient scale of output. Second,the multi-output allocation of total cost between several outputs may beeasily determined from the linear cost frontier.

An input-oriented cost frontier model may be set up in a DEAframework as

min θ, s.t .n∑

j=1

λjCj ≤ θCh, �λjyj ≥ yh (4.12)

n∑j=1

y2j λj ≥ y2

h ,n∑

j=1

λj = 1, λj ≥ 0

If the firm h is on the cost frontier (θ∗ = 1.0), then

C∗h = γ0 + γ1yh + γ2y2

h

If γ0 is positive, then the average cost (ACh) may be minimized so asto obtain the MSE level of output y∗

h = (γ0/γ2)1/2 with the minimum ACgiven by c∗

h = C∗h/yh = 2(γ0γ2)1/2 + γ1.

A dynamic cost frontier may also be easily formulated in the DEAmodel, as shown by Sengupta (2004a). Consider, for example, a partialform of the translog cost function by omitting the input prices

ln TC = b0 + b1 ln y + b2t

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 pro-gresses if b2 < 0. The reciprocal of the parameter b1 measures the degree ofreturns to scale or scale elasticity (e.g. b1 < 1 indicates increasing returnsto scale).

The cost frontier models above may be easily set up as a DEA model

min θ, s.t.n∑

j=1

Cjλj ≤ θCh,∑

j

yjλjyj ≥ yh

∑λj ≤ 1, λj ≥ 0

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where Cj = ln TCj and yj = ln yj. If firm j is level efficient, then its costfrontier is

Cj = (α/β)yj − bt

where the Lagrangian function is

L = −θ + β

⎛⎝θCh −

∑j

Cjλj

⎞⎠ + α

(�yjλj − yh

) + b

⎛⎝t −

n∑j=1

λjt

⎞⎠

By imposing the condition∑

λjt = t the optimal value of b may be madefree 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 characterizedby a similar model as follows

min φ(t), s.t.

n∑j=1

Cj(t)λj(t) ≤ φ(t)Ch(t)

n∑j=1

yj(t)λj(t) ≥ yh(t)

∑j

λj(t) ≤ 1, λj(t) ≥ 0, j = 1, 2, . . . , n

where Cj(t) = �Cj(t)/Cj(t), yj(t) = �yj(t)/yj(t). For long-run costs five-yearaverages of growth of output and costs may be considered. Now thedynamic 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 andoutput yj follow a geometric random walk process, so that the first differ-ences 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 bestructurally stable, e.g. OLS estimates (or COLS method) may be valid.Furthermore, this dynamic cost efficiency model (4.14) characterizes theintertemporal growth frontier specified by {φ∗(t), λ∗(t), C∗

j (t) as time tevolves over every five years, for example.

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Table 4.3 Scale elasticity β1 = 1/b1 of banks in India

Type of bank 1997–8 1999–2001

A B A B(two (composite (two (compositeoutputs) output) outputs) output)

Nationalized 0.769 0.907 0.843 0.809Private 1.173 1.564 1.050 1.034Foreign 1277 19.212 1.201 1.345

Sengupta and Sahoo (2006) have applied the quadratic cost frontiermodels (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 (investmentsand performing loan assets). A case of composite output and total costis also considered by taking a weighted combination of the two out-puts with weights taken as the respective revenue shares. The results areshown in Table 4.3.

It is clear that in both subperiods the nationalized banks exhibitdecreasing returns to scale measured by β1 = 1/b1. The time trend, how-ever, indicates that the banks are more and more exploiting their returnsto scale situation, thus improving their efficiency. Sengupta and Sahoo(2006) have also measured the trend of capacity utilization and the extentof economies of scope of Indian banks. Their findings show that in mostcases the prevalent situation is the underutilization of existing capacity,which has resulted in large cost inefficiency. Policymakers in India haveto play a much more dynamic role than before to improve the overalleconomic 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 pro-foundly affected by innovations in different forms such as new productdesigns and new software developments. R&D spending captures thekey elements of the dynamic innovation process. Several features ofR&D investment by firms are important in the dynamic evolution of anindustry. First, R&D spending not only generates new knowledge abouttechnical processes and products but also enhances the firm’s capability

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to improve the stock of existing ‘knowledge capital’. This is the process oflearning that has cumulative impact on industry growth. Second, growthof R&D spending helps in expanding the growth of sales or demandthrough new product variety and quality improvements. This has oftenbeen called economies of scale in demand in the modern theory of hyper-competition analyzed by Sengupta (2004b). Third, the R&D investmentwithin a firm has a spillover effect in the industry as a whole. This isbecause R&D spending yields externalities in the sense that knowledgeacquired by one firm spills over to other firms and very often knowl-edge spread in this way finds new applications both locally and globallyand thereby stimulates further innovative activity and R&D intensity inother firms.

Our objective here is to incorporate R&D investment into the DEAefficiency models and thereby show its impact on market demand andefficiency. Three types of R&D models are developed here for empiricaland theoretical applications. One emphasizes the cost-reducing impactof R&D inputs. This may be related to the learning by doing implicationsof knowledge capital. Second, the impact on output growth throughincreases in R&D spending is formalized through a growth efficiencymodel. Here a distinction is drawn between level and growth efficiency,where the former specifies a static production frontier and the lattera dynamic frontier. Finally, the market structure implications of R&Dspending are analyzed in a Cournot-type industry, where R&D spendingis used as a marketing strategy just like advertisement.

Denote average cost by cj/yj where total cost cj excludes R&D costsdenoted by rj. Then we set up the DEA model with radial efficiencyscores θ.

min θ, s.t.

n∑j=1

cjλj ≤ θch,∑

j

rjλj ≤ rh

∑j

r2j λj = r2

h ,∑

yjλj ≥ yh (4.15)

∑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

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average cost frontier:

c∗j = β∗

0 − β∗2rj + β∗

3r2j + α∗yj (4.16)

since β∗1 = 1.0 if θ∗ > 0. Thus if R&D spending rj rises, average cost cj falls

if 2β∗3rj < β∗

2. If we replace rj by cumulative R&D knowledge capital Rj asin the learning by doing model, where Rj is cumulative experience, thenthe AC frontier (4.2) becomes

c∗j = β∗

0 − β∗2Rj + β∗

3R2j + α∗yj (4.17)

As long as the coefficient β∗3 is positive, rj may also be optimally chosen

as r∗ if we extend the objective function in (4.15) as min θ + r and replacerh by r. In this case we obtain the optimal value of R&D spending r∗ as

r∗ = (2β∗

3

)−1 (1 + β∗

2

)(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

c∗j = β∗

0 −m∑

i=1

β∗zirij +

m∑i=1

β3ir2ij +

s∑k=1

α∗kykj

Second, we may formulate the model in terms of total costs rather thanaverage costs as

min θ, s.t.∑cjλj ≤ θch,

∑j

rjλj ≤ rh

∑j

r2j λj = r2

h ,∑

j

yjλj ≥ yh, �y2j λj ≥ y2

h

∑λh = 1, λ ≥ 0

In this case the total cost frontier becomes

c∗j = β∗

0 − β∗2rj + β∗

3r2j + α∗

1yj + α∗2y2

j

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where α∗1, α∗

2 ≥ 0. If the intercept term β∗0 is positive, then average cost

for the jth efficient unit can be reduced to

ACj = (β∗

0/yj) + α∗

1 + α∗2yj +

(β∗

3r2j − β∗

2rj

)/yj

On setting its derivative e to zero we obtain the optimal level of outputy∗

j for fixed levels of rj as

y∗j =

[(β∗

0 + β∗3r2

j − rj

)/α∗

2

]1/2(4.19)

If research costs rj are already included in total costs, then the optimallevel of efficient output y∗

j in (4.19) reduces to

y∗j = (

β∗0/α2

)1/2 (4.20)

The associated value of minimum AC then becomes

ACmin = α∗1 + 2

√β∗

0α∗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 comprehensiveand structural than the more traditional productive scale size (MPSS) usedin DEA models. Flaherty (1980) discussed noncooperative game theorymodels where firms employ cost-reducing investments over time in orderto attain an optimal dynamic growth path.

Now consider a second type of model where growth efficiency isconsidered. Several types of models of growth efficiency frontier andtheir comparison with level efficiency have been discussed by Sengupta(2003b). Here we consider a firm j producing a single composite outputyj with m inputs xij by means of a log-linear production function

yj = β0

m∏i=1

eBi xβiij ; j = 1, 2, ..., N (4.21)

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

Yj ≤m∑

i=0

biXij +m∑

i=1

φiXi (4.22)

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

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eBi = φiXi, Xij = xij/xij, Yj = yj/yj, Xi =

N∑j=1

xij

N∑j=1

xij

Dot denotes time derivative.Note that b0 denotes here technical progress representing innovation

efficiency or productivity growth (i.e. Solow-residual) and φi denotes theinput-specific industry efficiency parameter.

We now consider how to test the relative efficiency of each firm k inan industry of n firms. One solves the following LP model:

min Ck =m∑

i=0

(bjXik + φiXi), s.t.

m∑i=0

(biXij + φiXi) ≥Yj; j = 1, 2, . . . , n (4.23)

b0 free in sign, b1, b2, . . . , bm ≥ 0, φi ≥ 0Let b∗, φ∗ be the optimal solutions for the observed input–output data

set Xij, Xi and yj, j = 1, 2, . . . , n with all slack variables zero. Then firm k isgrowth efficient if

Yk = b∗0 +

m∑i=1

(b∗

i Xik + φ∗i Xi

)(4.24a)

If, however, we have

b∗0 +

m∑i=1

(b∗

i Xik + φ∗i Xi

)> Yk (4.24b)

then the kth firm is not growth efficient, since the observed output Yk isless than the optimal output

Y∗k = b∗

0 +m∑

i=1

(b∗

i Xik + φ∗i Xi

)

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,

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one efficient and thus satisfying (4.24a) and the other inefficient, sat-isfying (4.24b). Second, if the input–output data set is available overtime, one could estimate the parameters b∗

0(t), φ∗j (t) and b∗

i (t) for allt = 1, 2, . . . , T . The output efficiency scores ε∗

k(t) = Yk(t)/Y∗k (t) can also

be computed for the efficient and inefficient units. Third, if the innova-tion efficiency is not input-specific, i.e. eBi = φt, then one could combinethe two measures of dynamic efficiency as b∗

0 + φ∗ = b∗0, say, represent-

ing innovation and access efficiency. In this case the dual problem forequation (4.7) can be simply formulated as

max µ, s.t.N∑

j=1

Xijλj ≤ Xik; i = 0, 1, 2, . . . , m

N∑j=1

Yjλj ≥ µYk;N∑

j=1

λj = 1, λj ≥ 0 (4.25)

An input-based efficiency model can be similarly specified as

min θ, s.t .N∑

j=1

Xijλj ≤ θXik, i = 0, 1, . . . , m (4.26)

N∑j=1

Yjλj ≥ Yk;∑

λj = 1, λj ≥ 0

If the optimal values µ∗ and θ∗ are unity, then the unit k is growthefficient; 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 andoutputs for each firm are available. Since some of the inputs are servicesof capital inputs, their impact on supply side economies of scale can becaptured by the sum of the respective coefficients of production.

Finally, we note that the growth efficiency models can be comparedwith the static model for testing the level efficiency of firm k. Forinstance, the models analogous to (4.23) and (4.26) would appear asfollows:

min Ck = β0 +m∑

i=1

{βi ln xik + φi ln xi} where xi =N∑

j=1

xij

s.t. β0 +m∑

i=1

{βi ln xij + φi ln xi} ≥ yj (4.27)

β0 free in sign, βi, φi ≥ 0; j = 1, 2, . . . , N

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and

min θ, s.t.

N∑j=1

xijλj ≤ θxik (4.28)

N∑j=1

yjλj ≥ yk;∑

λj = 1, λj ≥ 0

The time series values of efficiency scores θ∗(t) of level efficiency maythen be compared with those θ∗(t) of growth efficiency defined by theLP model (4.26). If innovation and access efficiency by R&D spendingare the most dominant characteristics of firms on the leading edge of thegrowth frontier, this would be captured more strongly by the dynamicefficiency scores θ∗(t) and their trend over time.

We now consider an empirical application to the computer indus-try based on Standard and Poor’s Compustat data, where on economicgrounds a set of 40 firms (companies) in the computer industry overthe 16-year period 1984–99 were selected by way of illustrating the con-cepts of dynamic efficiency analyzed above. The companies includedhere comprise such well known firms as Apple, Compaq, Dell, IBM andHP and lesser known firms such as AST Research, Pyramid Tech, Toshiba,NBI and Commodore. Due to a variety of differentiated products, a com-posite output represented by total sales revenue is used as the singleoutput (yj) for each company. Ten inputs are selected from the Compus-tat Database, representing both financially related input variables, suchas manufacturing costs and marketing costs, and ‘net capital employed’at the end of the reporting period, including input variables such as work-ing capital, plant and equipment and other fixed assets. We use a proxyvariable (x10) for all nondiscretionary inputs represented by advertisingexpenditures by the competing firms. Three inputs in manufacturingcosts are x1 for raw material costs, x2 for direct labor and x3 for over-head 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 netplant and equipment and x9 for other fixed assets. Finally, x10 represents aproxy variable for competitive pressure exerted by competitors on a givenfirm j. Thus we have used empirical data on 40 firms, each producingone output (yj) with ten inputs (xij; i = 1, 2, . . . , 10 and j = 1, 2, . . . , 40).

Three types of empirical applications are discussed here. The firstcharacterizes the two subsets of efficient (N1) and inefficient (N2) firms

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Table 4.4 Sources of growth efficiency

Technical R&D Plant & equipment Marketprogress (%) efficiency (%) efficiency pressureb0 b5 b8 b10

Efficient firms 35 39 21 21(N1 = 12)

Inefficient firms 12 13 18 19(N2 = 28)

Table 4.5 Output trends over time (�y(t) = a0 + a1y(t))

a0 a1 a2 R2

Efficient firms (N1 = 12) −0.602 0.019* – 0.961Inefficient firms (N2 = 28) – 0.009* −0.004 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 themedian 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 standsout most clearly in the estimates of Table 4.4. Dynamic efficiency in theform of technical progress and R&D efficiency explain the major shareof growth efficiency of the efficient firms. Since these two sources ofefficiency are good proxy variables for innovation and access efficiency,it is clear that hypercompetition accentuates the divergence of less effi-cient firms from the cutting edge growth frontier. The market pressurecoefficient (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 ineffi-cient firms. Furthermore, the inefficient firms exhibit a logistic trend,with the rate of growth declining at a slow rate. The latter aspect mayreflect a tendency to exit from the industry. Table 4.6 compares the twotypes of efficiency: level efficiency and growth efficiency. The efficientfirms reveal a much stronger showing in terms of growth efficiency thanlevel efficiency. This implies that in the computer industry, it is morerelevant to apply a dynamic production frontier involving the growth ofvarious inputs and output.

The role of R&D investment in raising industrial productivity is mostsignificant in other industries like pharmaceuticals, computer products

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Table 4.6 Level efficiency versus growth efficiency

Median score Mean deviation Coefficient ofvariation

θ∗

θ∗

θ∗ θ∗

θ∗ θ∗

Efficient firms 0.951 0.982 0.105 0.043 0.457 0.231Inefficient firms 0.895 0.891 0.101 0.014 0.356 0.247

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 signifi-cant t-values at the 1% level.

and services. Sengupta and Sahoo (2006) have discussed the level effi-ciency and growth efficiency of the US pharmaceutical industry over theperiod 1980–2000. Their results show that the companies that are leadersin growth efficiency show a very high elasticity of output with respectto R&D spending. Sources of growth efficiency in terms of Solow-typetechnical progress and R&D efficiency are shown in Table 4.7.

This suggests the growing need in India to increase R&D investmentsin computer-related industries, pharmaceuticals and other technology-intensive industries in the manufacturing, transport and service sectors.This would improve the long-run comparative and competitive advan-tage of India in world trade. The common belief that increased laborproductivity due to cost-reducing R&D investments hampers the growthof employment in manufacturing and IT-dependent industries may nothold in the long run. Corley et al. (2002) examined empirical data atfiner levels of aggregation (e.g. industry and firm levels) and foundthat for many high-growth manufacturing industries in the USA andEU (European Union) increases in productivity have been accompaniedby increases and not decreases in employment.

These high-growth and high-productivity industries in the EU andUSA (which include software manufacture, electronics and technology-intensive manufacturing) are generally characterized by high levels ofinvestment, in the form not only of physical capital but also of R&D.R&D investments create new products and processes emphasized bySchumpeterian innovations and also enable workers to absorb these newprocesses and products so that the learning by doing effects generate

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Table 4.7 Annual average levels of output per hour, investment per hour andR&D per hour in manufacturing (1990–8) at 1995 prices

Output per hour Investment per hour R&D per hour

CanadaLow tech 21.0 2.80 0.15High tech 24.9 3.45 1.69Total 22.9 3.12 0.91

DenmarkLow tech 25.1 4.24 0.20High tech 27.4 4.75 2.12Total 26.3 4.50 1.20

FranceLow tech 27.1 4.22 0.71High tech 32.9 4.85 3.97Total 30.3 4.57 2.48

GermanyLow tech 20.7 3.64 0.18High tech 25.8 3.81 2.69Total 24.1 3.75 1.82

ItalyLow tech 29.4 5.39 0.05High tech 29.5 5.69 1.24Total 29.4 5.56 0.71

UKLow tech 23.1 2.82 0.15High tech 26.2 3.48 2.24Total 24.9 3.2 1.34

USALow tech 27.6 2.91 0.31High tech 35.5 4.68 4.21Total 32.3 3.86 2.62

See Corley et al. (2002) for details.

cumulative effects. Table 4.7 reports the contributions of R&D per hourfor 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 ofthe regression equation

LPi(1994–8) = b0 + b1(RD/L)1990–3 + b2(I/L)1994–8 (4.29)

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Table 4.8 The elasticities of R&D per work hour (the regression of labor produc-tivity on I/L, RD/L and HK (1994–8) for the EU and USA)

Industry Standardized coefficients (elasticities)

Constant I/L RD/L HK N Adj. R2

High tech 8.25 0.47 0.30** 0.16 80 0.346(1.33) (5.04) (3.16) (1.65)

Low tech 8.17 0.91** 0.09 0.16 40 0.759(1.94) (9.51) (0.82) (1.33)

Total 8.06 0.54** 0.34** 0.14* 120 0.452(1.83) (7.67) (4.83) (2.01)

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 arestandardized 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 0.409** 0.152** 0.934Low tech 0.469** 0.055 0.983Total 0.421** 0.151** 0.943

where LPi(1994–8) is the level of labor productivity in industry i averagedover four years (1994–8); (RD/L)1990–3 is the R&D spending per workerin industry i lagged four years 1990–3; (I/L)1994–8 is gross fixed capitalformation 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 thewhole-economy level averaged over 1994–8.

When this model (4.29) is re-estimated using a fixed effects modelwith dummy variables to capture the country effects the results improvein terms of adjusted R2 as shown in Table 4.9. Note that R&D invest-ment has a statistically significant effect on productivity only in thehigh-tech industries. These are precisely the industries that competein the international field with a dynamic comparative advantage.As Table 4.7 shows, in terms of R&D per hour, high-tech industries in theUSA and UK score about 14 times higher or more than the low-tech indus-tries. A major strength of the US economy is the dynamic competitiveadvantage that enables the manufacturing sector to increase productiv-ity while simultaneously increasing employment. If the EU countries and

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India have to close the competition gap with the USA as measured byincome per capita, they must increase productivity while maintainingor increasing employment levels.

Modern theories of growth developed by Romer (1986, 1990), Lucas(1993) and others have emphasized the endogenous forces in the formof knowledge capital. Knowledge capital, unlike physical capital, isassumed to be an input in the aggregate production function showingincreasing marginal productivity. To the extent that knowledge capitalmay be viewed as external to the firm but internal to the industry, thecompetitive market model may still apply, but with endogenous techno-logical change. In contrast to the models based on diminishing returns,growth rates in this new class of endogenous models can be increasingover time. In such a framework the effects of small random disturbances(i.e. shocks) can be applied by the actions of private business, and largecountries may grow faster than small ones if they can keep up theirdynamic efficiency over time. Romer (1986) and Lucas (1993) have pro-vided long-run empirical evidence in support of this type of endogenousgrowth 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 standpointtwo types of answers have been offered. One is managerial, the othereconomic. 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 thebasic source of growth. Economic efficiency of both physical and humancapital, including innovations through R&D, has been stressed by themodern theory of endogenous growth.

Core competence rather than market power has been identified byPrahalad and Hamel (1994) as the basic cornerstone of success in themodern hypercompetitive world. Core competence has been defined asthe collective learning of the organization, especially learning how tocoordinate diverse production skills and integrate multiple streams oftechnologies. Four basic elements of core competence are: learn fromown and outside research, coordinate, integrate so as to reduce unitcosts and innovate so as to gain market share through price and costreductions.

A company’s own R&D expenditures help reduce its long-run unit costsand also yield spillover externalities. These spillovers yield increasingreturns to scale as discussed before. Now we consider a dynamic modelof industry evolution, where R&D investments tend to reduce unit costsand hence profitability. This profitability induces new entries and also

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increased market share for incumbent firms that succeed in followingthe cost efficiency frontier.

From the macroeconomic perspective the spillover technology andexternalities of R&D investments play a critical role in the endogenousgrowth model due to Lucas (1993). While emphasizing the point thatAsian growth miracles cannot generally be explained by physical cap-ital accumulation alone, he discussed the productivity-enhancing roleof human capital accumulation at school and on the job. This rate ofexpansion in knowledge in both forms transforms Solow’s level effectinto a growth effect. This knowledge capital is a nonrival input withother inputs such as labor and physical capital, since it has strongcomplementarities with other inputs. An important dimension of thelearning by doing impact of spillover technology is that for such learn-ing to continue on a long-run sustained basis, the workers, managers andentrepreneurs must work continually to improve the technology throughwhat Grossman and Helpman (1991b) called the ‘quality ladder effect’.Finally, the spillover technology is closely associated with Schumpeterianinnovation in its many forms, e.g. R&D spending, new processes, newproducts and services and new markets and networks. When the fruitsof research are allowed to be exploited more openly and broadly by freeglobal trade, then such trade generates a scale effect, helping to speedup the growth of trading countries. In many ways the spillover technol-ogy allows dynamic externalities, which generate dynamic gains fromtrade. Thus declining computer prices and improved technology-basedinputs have helped the NICs (newly industrializing countries) in Asiaand China to reap the benefits of spillover technology. As an exampleof the productivity-augmenting impact of externalities consider the pro-duction 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 threerival inputs, such as physical capital, energy and materials, and N isa proxy for nonrival input measured by labor employed in the exportsector only. Two asterisks denote significant values of t at the 1 per centlevel. Clearly the nonrival input (N) has a significant effect in terms ofincreasing returns, implying that a 10 per cent increase in N generates a15 per cent increase in total manufacturing output. When our attention

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is shifted to the IT-related sector specializing in high-tech goods andservices, 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 sectorhave been diffused through other sectors at a rapid rate. Indirectly ithas helped to maintain a more or less equitable distribution of incomethrough gains from global trade. Gort and Konakayama (1982) andSengupta (2004b) have analyzed dynamic models of diffusion in the pro-duction of an innovation that aptly describe the growth of the electronicsindustry 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(q∗

it ) (4.30)

where Eit is net entry in industry i (either the existing or the new indus-try) at time t, nit equals the actual number of producers in industry iat t, n∗

it equals the expected cost-minimizing number of producers, andthe total cost function TC(·) relates to the expected equilibrium outputq∗

it of the industry. As the cost efficiency improves in the industry i, ittends to generate the situation n∗

it > ni,t–1, resulting in an increase in netentry. Likewise net exits increase when nn,t–1 > n∗

it . Gort and Konakayamaapplied the simple model (4.30) to manufacturing data (1947, 1954,1958, 1963, 1967 and 1972) for seven firms in the USA and foundthat the phenomenon of diffusion of an innovation measured by thenumber of patents is very well explained by this model, when n∗

it isrelated to other explanatory variables such as technical change, dynamicadjustment 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 summarizedin Table 4.10. This table shows the rapid growth of the export sector andthe strong emphasis the state has put on expanding education at theprimary and secondary levels. Encouraging foreign direct investmentfrom the USA and EU also helped utilize the R&D externalities in high-tech products such as electronics and computer products and services.

As Table 4.10 shows, IT products and telecommunications equip-ment grew at an average annual rate of around 15.1 and 14.0 percent respectively. The Taiwanese Council for Economic Planning andDevelopment prepared a ten-year plan (1980–89) that set specific tar-gets for R&D expenditures. More recently both the government andprivate entrepreneurs have pursued an international technology pol-icy by cutting costs through productive efficiency and transferring theresults of research in government laboratories to the private sector. In

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Table 4.10 Economic growth indicators in Taiwan

1995 1998 2000

Export/GDP (%) 42.03 41.32 47.66Gross investment/GDP 24.93 24.72 22.57Export growth rate (%) 20.0 −9.42 21.98Literacy rate (%) 21.33 −8.53 26.49Secondary school enrollment rate (%) 95.93 97.21 99.61Higher education enrollment rate (%) 45.32 51.06 60.85Output of electronics and IT sectorTotal (US$ bill) 15.4 – 69.8

(1990) (2005)Information products 6.9 – 35

(1990) (2005)Consumer electronics 2.3 7.0

(1990) (2005)

Source: Sengupta (2004b).

terms of complementary human capital accumulation, a deliberate pol-icy of sending trainees abroad and inviting foreign collaborators withlucrative incentives was deliberately pursued. All these show the impor-tance of learning spillover technology, which was utilized by Taiwan ata rapid rate.

Two types of industry efficiency analysis are important for the Indianeconomy in the post-reform period. One is to track down the effectof human capital and actively pursue a policy of expanding secondarytechnical and general education. This may be easily done through theapplication of the DEA models discussed above. Second, the externalitiesof human capital have to be captured and utilized in a systematic fash-ion. Taking the second policy first, consider a two-sector model with twooutputs: X for the export sector (mainly technology-intensive productsand 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(KM , LM , HM ; X)

Three inputs for each sector are physical capital (K), labor (L) and humancapital (H) respectively. Time differentiation yields

�X = GK�K∗ + GL�L∗ + gM�M + GH�H∗�M = FK�KM + FL�LM + FX�X + FH�HM

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Table 4.11 Estimates of the ratio FK/GM , 1967–87

Japan Korea Taiwan

FX 0.29 0.99 0.18GM 0.16 0.32 0.20FX/GM 1.8 3.1 9.1

where the subscripts on F and G denote the marginal productivities of therespective inputs in the two sectors, i.e. FK/GM of marginal productivitiesof 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 theperiod reported by Sengupta (1998) are shown in Table 4.11. Note thatSouth Korea and Taiwan have a far greater degree of export externalitythan Japan. More recently this degree of externality has increased fortwo reasons. One is the increase in Taiwan’s share of world exports andthe second is the rise in income elasticity of demand for world exportproducts and services from Taiwan and China.

A second method of analyzing the growth effect due to externalities ofR&D investment and human capital is to adopt a dynamic version of theDEA model we have discussed before. Consider the Lucas-type growthmodel

Y = Kα (uHL)β Hγ

E

H = HY + HE, HY = uH (4.32)

H = (1 − u)vH

Here Solow-type technology parameter A is represented by the propor-tion of human capital (H) in the form of skill HY = uH used in currentoutput (A = uH). Total human capital (H) is composed of that allocated tocurrent output (HY ) and that comprising externalities (HE), which maybe captured by the dummy variable of exports of technology-intensiveproducts. Growth of human capital H/H equals the product of the pro-portion 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 posi-tive, the human capital grows at a constant exponential rate. Coupledwith increasing returns to scale (i.e. α + β + γ > 1) this may lead to highrates of growth in both the short and the long run. Since u is a policy vari-able, human capital can be made to grow at a higher rate by increasing(1 − u) as a government policy on expenditure on education.

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Denoting by z = z/z for z as output Y and xi/xi for the four inputsK, H , L, u, 1 − u, the DEA model for any industry j can be written as

min θ, s.t.

n∑y=1

xijλj ≤ θxih,n∑

J=1

yjλj ≥ yh, �λj = 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 ofinputs. Growth of output Y/Y = z follows from the production functionin (4.32) as

YY

= α(K/K) + β1(u/u + H/H) + β2(L/L}+ γ{( − u)/(1 − u)/(1 − u) + H/H}

i.e. z = αx1 + β1(x4 + x2) + β2x3 + γ{x5 + x2}= a1x1 + a2x2 + a3x3 + a4x4 + a5x5 + a0

Clearly the firms that are most successful in the export market and alsomost skill-intensive will contribute most to growth of output. Studies ofindustry efficiency analysis in this framework would afford more insightinto the industries that are on the leading edge of the international pro-duction and hence cost frontier. The experience of the miracle growthcountries of Southeast Asia provides ample proof in this regard.

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5Efficiency Analysis of SelectedManufacturing Industries

1 Introduction

The appropriateness of technologies that should be used in industriesin developed countries is a major research area in development eco-nomics. Empirical research by economists in the developed countrieshas firmly established and made us aware of the role of R&D in fosteringtechnological advances in industry, which in turn help achieve fasterproductivity growth in their countries.1 The less developed countriesoften bothered little about the long-run cost of the outright introduc-tion of advanced capital-intensive technology available to them, and didnot pay any heed to their domestic factor endowment and the efficiencyof resource use. Increasing interest in advanced technology under theprevailing institutional frameworks stems, among other things, mainlyfrom three important considerations. First, given the scarcity of capital,to get rid of the short-run cost of R&D and related uncertainties in devel-oping appropriate technology, outright import of foreign technology isconsidered to be the better option. Second, by doing this, the LDCs havebeen able to introduce wide varieties of new products for their rising mid-dle class. Third, with an increasing reliance on capital-intensive methodsthe producers have been able to bypass to some extent labor troubles inorganized sectors of these industries.

One of the important features of the Indian industrial change fromthe fifties to the sixties was the relatively increasing emphasis on for-eign capital, particularly in the capital good sector. The devaluationepisode of the mid-sixties was supposed to work in favor of the inflowof foreign capital. Thereafter import-substituting industrialization wasthe major policy to check the burden of foreign capital outflow andto develop indigenous technology. But the seventies saw the beginning

134

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Efficiency Analysis of Selected Manufacturing Industries 135

of indiscriminate imports of foreign technology not only in the capitalgoods sector but also in other sectors. The sheltered market phenomenonwas the main feature of trade liberalization policy until recently. TheEXIM policy, expansion of the open general list and the New Indus-trial Policy of 1991 were expected to change the industrial scenario ofIndia and lead to economic liberalization. The increasing diminutionof industrial efficiency during the seventies and eighties was very muchevident in the Seventh Five-Year Plan document, which laid tremendousemphasis on the ‘Sunrise’ industries. These industries are telecommuni-cation, computers, microelectronics, ceramics and biotechnology. It wasproposed to attain self-sustaining industrial growth and technologicaldevelopment through, among other things, the adoption of promotionalmeasures to raise the productivity and efficiency of Indian manufac-turing industries. It also explicitly mentioned that the protection frominternational competition found in the earlier semi-insular phase gaverise to high manufacturing costs, which inhibited expansion in thedomestic market and rapid export development. The main thrust wasto build a conducive environment to encourage and promote greaterefficiency, higher productivity and faster industrial growth.

There is no denying the fact that the question of efficiency is inextri-cably related to the appropriateness of the chosen technology, and moreoften than not the fault may lie in the institutional preparedness for sci-entific management that is necessary for the smooth functioning of thenew technology. The important source of growth and development inan economy is the efficient use of existing resources. In India, awarenessof the efficient use of existing resources through appropriate adjustmentpolicy variables was sadly lacking during the early phase of industrial-ization. In fact, the cost of neglect of efficiency had pushed India to ano-return zone before1991, and the recent liberalization process soughtto correct that inefficient regime.

For the purpose of examining the impact of liberalization on theperformance of Indian industries we have selected three manufactur-ing industries: textiles, leather and electronics. Textiles and leather aretraditional industries, while electronics, which includes computer man-ufacturing, belongs to the modern sector. Primary data for the analysisare the unit/firm-level information collected from the Annual Survey ofIndustries (ASI), Government of India.

The rest of the chapter proceeds as follows. The efficiency of manufac-turing sectors in India during the pre-liberalization period is analyzed insection 2. Section 3 deals with the analysis of efficiencies in two tradi-tional industries during the post-liberalization period. A comparison of

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performance in the pre- and post-liberalization periods is made for thetextile and electronic industries in section 4. A nonradial efficiency mea-sure of the computer industry is also carried out in this section. Section5 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 gov-ernment of India after Independence were as inevitable as the NewIndustrial Policy of 1991 and subsequent liberalization packages.2 Ina large number of papers attempts have been made to evaluate thestrengths and weaknesses of these liberalized policy measures (see, forexample, Sandesara, 1991; Subrahmanian, 1991; Patel, 1992; Neogiand Ghosh, 1998; Ray, 2005). But very few empirical studies have beencarried out to investigate the intertemporal efficiency movements andinter-industry efficiency variations in India on which the new policypackage is based or to assess industrial performance before liberalization.First, one has to be sure whether efficiencies in Indian industries havebeen falling over time since the industrial globalization program wasundertaken. Although neither the policymakers nor the industrialistsreally do know the actual outcome of opening Indian industries to inter-national competition, the experience of such drastic policy changes indifferent NICs is mixed. Since our economy is characterized by an acutescarcity of capital, it cannot afford to use the scarce factors inefficiently inthe name of industrial modernization. More often than not high outputgrowth does not necessarily mean efficient utilization of resources.

The purpose of this study is to reveal intertemporal efficiency varia-tions in 35 use-based industrial groups, covering the entire organizedmanufacturing sector, over the period 1974–75 to 1987–88. We alsoinvestigate interindustry variations in efficiency. Finally, we identify thesupply-side factors responsible for technical efficiencies for cross sec-tional time series pooled data. The methodology applied is a time varyingversion of the frontier production function (FPF) approach to measuringefficiency with both fixed and variable rankings.

Some justification is needed here about the selection of the period ofour study. The initial years covered industries under serious autarky andprotection and as we move towards the end of our study industries startedbecoming freer than before, although some protection elements were stillpresent. Hence, the main presumption upon which the liberalizationpolicy package is based, namely increasing inefficiency supposedly as a

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result of state control, subsidy and protection from foreign competition,can be appropriately tested from the analysis of industries during theperiod.

Methods of measuring efficiency

In this section we estimate efficiencies using the parametric techniques.The stochastic frontier model is a major improvement over the earlierdeterministic models and probabilistic models in the sense that it makesa clear distinction between the so-called white noise and inefficiency assuch. Aigner et al. (1977) and Meeusen and Broeck (1977) proposed thisstochastic model with the idea that the error term is composed of twoparts 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 therandom effect of measurement error and exogenous shock, while tech-nical efficiency relating to stochastic frontier is captured by Ui, whichare assumed to be a nonnegative truncation of the N(0, σ2) 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 correspondingfrontier output, given the level of inputs. Thus, technical efficiency ofthe ith firm in the context of the stochastic model is the same expressionas in the case of deterministic model,

TE = Yi

Y∗i

= f (Xi, β)e(Vi−Ui)

f (Xi, β)eVi= e−Ui

The above model considers only the cross sectional observations of firms.But one problem with this cross sectional data in measuring efficiencyis that TE cannot be separated from firm-specific effect, which may notbe 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 = ηitUi = {e[−η(t−T)}Ui

t ∈ τ(i); i = 1, 2, . . . , N (5.3)

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where Vit are i.i.d with N(0, σ2v ). The Ui are also assumed to be i.i.d and

a nonnegative truncation of the distribution N(µ, σ2) and τ(i) repre-sents the set of Ti time periods among the T periods involved for whichobservations for the ith firm are obtained.

One of the main shortcomings in this approach is that the ranks ofthe firms in terms of efficiencies remain unchanged over time and therelationship is constrained to be monotonic over time. In Cornwell et al.(1990) the model efficiency measurement focuses on the cross sectionalvariations over firms and it also allows efficiency to vary over time. Thismay be considered as an improvement over the fixed ranking modelon theoretical virtues. This is done by introducing a flexible function oftime into the production function, with coefficients varying across firms.This function represents productivity growth that varies over firms, andit implies that the levels of efficiency for each firm vary over time.

The basic model is

Yit = αi + X′itβ + 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 oftime with parameters that vary over firms. We have taken a quadraticfunction of time following Cornwell et al. (1990):

αit = θi1 + θi2t + θi3t2

Then the model can be written as

Yit = X′itβ + W ′

itδi + Vit , (5.5)

where

Wit = [1, t, t2] and δi = [θi1, θi2, θi3]

Naturally, this model allows for time-varying efficiency with variablerankings of the firms. By applying this model we have relaxed theassumption of fixity of rankings of the previous model and at the sametime retained the advantage of using panel data.

In our stochastic model, we have estimated the time-varying modelusing both Cobb–Douglas (C–D) and translog specifications. The gen-eral form of the time-varying model was discussed earlier. Here we haveestimated the FPF using stochastic models and we have taken the C–D

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and translog production functions. The forms of the cross sectional C–Dand 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 allemployees respectively. The multiplicative error term is

e−u = Yf (X)

(5.8)

Hence, e−u must lie between zero and unity. And, naturally, u takes valuesbetween 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 sta-tistical noise and the other for the firm- or industry-specific effect. Theerror term now becomes:

e(Vit −Uit ) (5.9)

The exponential specification of the behavior of the firm or industryeffect over time is a rigid parameterization. It implies that TE of the firmor industry

TEit = e−Uit (5.10)

is a double exponential function of time (see equation 5.3) for a givenfirm or industry (see Battese and Coelli, 1991).

The estimation of the stochastic frontier function (time-varyingmodel) and the prediction of corresponding technical efficiencies of theindustries in question over time are calculated here using a computerprogram developed by Coelli (1991).

Insofar as the Cornwell et al. (1990) model is concerned, time-varying firm productivity and technical efficiency are estimated from theresiduals based on the within estimated of the C–D production function.

log Yit = αit + βk log K + β1 log L + vit , (5.11)

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

at = maxj(ajt ) and uit = αt − αit

Thus the relative efficiency levels of the industries at time t can be derivedfrom the estimates of uit .3

A very brief review of the studies on technical efficiency of Indianindustries during the pre-liberalization period may be brought into focushere. There is a dearth of studies relating to efficiency in Indian industry,particularly during the pre-liberalization period. However, the followingworks are worth noting here: Little et al. (1987), Bhavani (1991) andSingh (1991). Among these, the first two studies are related to small-scale 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 TFPand concluded that there was evidence of declining efficiency in Indianindustry in recent years. We analyze intertemporal efficiency variationsacross industries in order to understand the dynamics of the industrialprocess 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, byH. L. Chandhok and The Policy Group. As noted earlier, our period ofstudy refers to the years 1974–75 to 1987–88.

The gross measure of value added is obtained by adding the net valueadded and depreciation of the corresponding years as given in the ASI.Capital is taken as gross fixed capital stock estimated by the perpetualinventory accumulation method (see Appendix 5.1). Labor is representedby total number of persons employed. We have taken both total workersand total employees as given in the ASI. The difference between the two,according to the ASI, are taken to be skilled laborers. Total emolumentgiven in the ASI measures the return to labor.

These ASI figures, all given in nominal terms, are converted intoreal value using suitable deflators. Gross value added is deflated by the

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commodity-specific wholesale price indices taken from India Database.Estimated gross fixed capital stock is deflated by wholesale price indicesof machinery, machine tools and parts. Real wages are obtained by deflat-ing the total emoluments by the corresponding consumer price indicesfor industrial laborers taken from Indian Labor Journal. The figures arethe total of all firms in each group. We have divided these by the cor-responding number of firms in each group, thereby reducing them intoaverage firm figures for each industry group.4

Empirical analysis

We have estimated time-varying technical efficiencies for 35 broad indus-trial groups for two models as defined earlier. The hypothesis regardingthe distribution of the random variables associated with Vit and Uit sug-gests that traditional average production function is not an adequaterepresentation of our set of panel data. Further, since only the hypothesisµ = 0 is accepted and η = 0 is rejected the model is proved to be timevariant and the distribution of industry effect is half-normal. Hence, thehypothesis of time-invariant technical efficiency in Indian industry isrejected.

Technical efficiencies (TE) of the fixed ranking model with the spec-ification of half-normal distribution of the error term with a C–Dspecification are estimated for 35 industries for four different years. Table5.1 gives the summary statistics of the values of efficiencies for theseyears. First, the table reveals that the average values of TEs for theseindustries have registered downward trends over the period of our study.Second, there are considerable variations of TEs across industries. Giventhe assumption that industry effect changes exponentially over timewith η < 0, the TEs decrease at an increasing rate. It is, therefore, expectedthat the predicted TEs diverge over time. Our result shows that the coef-ficient of variations of efficiencies increases from 0.38 in 1974–75 to 0.47in 1987–88.

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

Statistics 1974–5 1978–9 1983–4 1987–8

Average 0.5783 0.5571 0.5313 0.5086Max 0.9796 0.9780 0.9757 0.9737Min 0.1210 0.1015 0.0798 0.0647Coefficient of variation 0.3823 0.4084 0.4411 0.4734

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Among the 35 industries, 16 remained above the yearly mean effi-ciency throughout the period. It has been observed that most of theseindustries come from the consumer goods sector; only three come fromthe capital goods sector. Those from the capital goods sector are (a)medical, scientific, photographic and optical instruments, (b) electricalmachinery, apparatus, appliances, supplies and parts and (c) machinery,machine tools and parts. Among the 16 industries 13 comprise con-sumer good industries, and drugs and medicines achieved the highestefficiency, 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 remainedthe same over the entire period. In order to permit different rankingsof the industry in terms of efficiency at different time points, we haveapplied the Cornwell et al. (1990) model to the same set of data for 14consecutive years. As mentioned earlier, we have used a C–D produc-tion function and estimated the parameters by within estimators. Thevalues of the coefficients of log(K) and log(L) and the corresponding tstatistics (in parentheses) are 0.4809 (19.89) and 0.6497 (12.14) respec-tively, and the value of R2 is 0.59. The F-test of the ratios of restrictedand unrestricted production function reveals that the sum of the coef-ficients is not significantly different from unity at the 99 per cent level.The relative efficiencies have been derived from our estimates for the 35industries over 14 years. The summary of the values of TEs at four differ-ent time points (1974–75, 1978–79, 1983–84 and 1987–88) are presentedin Table 5.2.

The important findings from the efficiencies and rankings on thebasis of variable ranking model are as follows. The values in Table 5.2indicate that the average values of efficiency declined and the dispersionof efficiency among the industries increased over the period. Out of 35industries, the efficiencies of 26 industries fell substantially over time.

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

Statistics 1974–5 1978–9 1983–4 1987–8

Average 0.4148 0.4732 0.4714 0.3774Max 1.0000 1.0000 1.0000 1.0000Min 0.0922 0.1262 0.1088 0.0581Coefficient of variation 0.4292 0.4054 0.4683 0.5783

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The industries with substantial declines in efficiency were perfumes, cost-metics etc., matches, gas and steam, pulp, paper containers and board,rubber, plastics, petroleum and coal products, etc. Industries with risingefficiencies were footwear from vulcanized and moulded rubber, radio,TV, tape recorder, telephone etc., medical, surgical, scientific, photo-graphic and optical instruments etc. The industries that substantiallyimproved their rankings from the first to last year come from the mod-ern sector, e.g. medical, surgical, scientific, photographic and opticalinstruments, electrical apparatus, appliances etc., radio, TV, tape recorderetc., motorcycles, scooters and parts etc. In terms of rankings, the sharpdeterioration occurred mainly in the traditional industries.

There are wide variations in the predicted TEs of the industries. Wenow examine some economic factors – basically internal to the indus-tries – that can be used to explain these efficiency variations.5 Sufficeit to say that there are other important factors that are external to theindustry, namely demand forces, technology, information quality, rateof tariff protection, degree of competition, management quality, govern-ment 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 quitea large percentage of variations in efficiencies being explained.

We have considered labor productivity, skill, real wages, profit, cap-ital intensity, capital utilization and industry dummy as independentvariables and TEs estimated from the variable ranking model as thedependent variable.6 All variables except the dummy are transformedinto logarithmic forms. All the factors except real wages appear to behighly significant in all cases. The regressions are quite satisfactory, asevident from the high values of R2 (ranging from 0.7758 to 0.7994).

Since the variables are in log form, the coefficients represent the corre-sponding elasticity estimates. It has been found that labor productivity,skill capacity utilization and profit play a positive role in enhancing theefficiency of industries. The coefficients of industry dummy indicate thatconsumer goods industries are in general more efficient than capital andintermediate goods industries. This finding might have been influencedby the rising demand for consumer durables in recent years in India,which we could not incorporate in our analysis. Capital intensity, quitecontrary to general beliefs, here shows a negative relationship with TEs.Coondoo et al. (1993) show that capital coefficients (K/Y and K/L) havebeen rising at very high rates uniformly in Indian industries irrespec-tive of their technological status. This phenomenon is accompanied byinefficient use of resources and the rise in capital coefficients was not the

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result of a technological upgrading of industries. Moreover, since capitalintensity is found to be negatively related with efficiency and given thatthe public sector is plagued by huge subsidies and employment obliga-tions, this negativity may be taken as a proxy for public sector dominancein Indian manufacturing industry. (Some of the results are discussed inNeogi 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 twotraditional industries, namely textiles and leather. First, we examine thelevels of technical efficiency of individual firms from the Indian textilegarments industry (NIC code 235) using establishment-level data fromthe ASI covering the period 1989–90 through 1997–98. Then, we under-take a similar exercise for the leather industry (NIC code 291) for the sameperiod. This allows us to examine how the levels of technical efficiencyhave changed in the post-reform years.

There are several reasons why the textiles industry deserves specialattention. In the first place, as one of the most important nondurableconsumer goods, textiles account for 14 per cent of the total industrialproduction 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 eco-nomic position in the international market. An added, and in some waysmore important, point to note in this context is that the multifiber agree-ment (MFA) that permitted countries to impose bilateral export quotasin textiles formally ended on 1 January 2005. While opening up of theUS and EU markets can be a golden opportunity for Indian exporters tomake inroads in these markets, they are equally exposed to the risk ofbeing marginalized in the face of severe competition from China andother exporting countries unless they can retain and enhance their costcompetitiveness. An audit of the levels of technical efficiency along withan analysis of the determinants of efficiency is, therefore, of interest toboth academics and policy.

While the competitive position of Indian textiles in the export markethas attracted considerable attention, there is little in the existing liter-ature that addresses the question of productivity and/or efficiency inthe industry from the perspective of the technology. In a notable excep-tion Parmar and Singh (2003) estimated a stochastic frontier production

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function using firm-level data for 694 companies from the textile indus-try. Their sample covered the period 1989–99 and the input–outputquantity data were constructed from annual financial statements of thecompanies. Output was measured by value added,8 while labor, capitaland material inputs were measured, respectively, by the wage bill, thesum of interest, repair and replacement costs of machinery, and materialexpenses (including energy costs). They found that the mean level oftechnical efficiency for the entire sample was 0.55. Across groups, aver-age efficiency was the highest at 0.58 for firms in the medium asset sizegroup (100–500 million rupees).

Leather is another important traditional industry and has a significantrole in the Indian economy for its massive potential of employmentgeneration and exports earnings. There has been an increasing emphasison its planned development for efficient use of raw materials and formaximizing the returns from both domestic and export markets. Theindustry has been changing its strategy from a mere exporter of rawmaterials to an exporter of high-value finished products. During the pastfew decades the home market for Indian leather goods also expanded ata moderate pace. The policies taken by the government of India since1973 have been instrumental in the development of the leather industryin general. During the phase of globalization of the Indian economy after1991, the industry is poised for further growth to achieve a greater sharein global trade.

According to an EXIM bank report in 2004–05, the industry recordeda 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 highof 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 restric-tions on semi-finished leather. Total leather and leather manufacturesexports stood at Rs.102,860 million in 2004–05. Leather footwear is thelargest component of leather exports, with a share of 26 per cent. fromUS$27 billion in 2000 to nearly US$34 billion in 2004. India’s majorcompetitor in the world market of leather goods is China. Particularlyin leather finished goods, China’s export share is much higher than thatof India. However, India has some distinct advantages in the produc-tion of leather goods in terms of scale of production. Since the Indianleather industry has its advantage of raw material and labor resources,Indian leather exporters should pay greater heed to marketing to increasetheir share, which should be consistent with their inherent strength andpotential. But this has to be done against many constrains imposed bythe developed countries of the West. Some of the major issues that affect

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the sector are cost escalation and environmental problems. The modern-ization of existing technology and the efficient use of resources are alsovery 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 gen-eration. Direct and indirect employment in the industry is around2 million. Skilled and semi-skilled workers constitute nearly 50 per centof the total workforce. In recent years the government of India hasannounced various policies to make the leather industry more productiveand competitive in the world market.9

The present study extends the literature on measurement of efficiencyin the Indian textiles industry in a number of important ways. First, weemploy the nonparametric method of data envelopment analysis (DEA)instead of stochastic frontier analysis (SFA), where an explicit specifica-tion of a parametric production function is required. Even in this strandof the literature, our study differs from other DEA applications in that weobtain nonradial Pareto–Koopmans measures of efficiency instead of theradial measures that are either input- or output-oriented. Ours is a gen-eralized measure of overall efficiency that simultaneously incorporatesboth unrealized potential increase in the output and feasible reductionin any individual input. Our overall efficiency measure can be decom-posed into input-oriented and output-oriented components. Moreover,we are able to assess, for each individual input, the proportionate reduc-tion possible without any increase in any other input or a decrease inthe level of the output.

Second, we propose a new method of explaining the variation intechnical efficiency across observed firms (accommodating the fact thattechnical efficiency cannot exceed unity) without resorting to a Tobitanalysis.

Finally, we use establishment-level data from the ASI to measure inputsand outputs. This is a distinct improvement over studies that constructthe relevant variables from company-level financial data. This is espe-cially true for companies that produce and sell products that oftencorrespond to widely different industrial classification codes.

After the enactment of deregulation policies the industries had to findtheir ways to survive in the market by increasing efficiency and pro-ductivity. India is a typical example of a nation where each state hassome special characteristics that influence the growth and performanceof industries in different ways. Further, the spread of type of industries ineach state is not similar. Each state has own industrial policy, and thoughthere is a broad agreement in the policies of the states their approaches

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are not always the same. As a result the growth and performance ofindustries in the different states do not always move in the same direc-tion. Since the efficiency and productivity of the industries also dependson the labor laws and the government’s attitude towards implementa-tion of labor laws, the liberal states suffer from the inefficient use oflabor in industries. The performance of production units also dependson the organization and ownership type. It is a common belief that pub-lic sector industries in general are inefficient compared to the privatesector industries. But inefficiencies are not confined to the public sector.Some recent studies argue that inefficiency is an all pervasive phenom-ena even in developed countries and efforts should be taken to increasethe efficiency of production units by the appropriate use of inputs in theproduction process.

The main advantage of using firm-level data is that the informationloss will be much less compared to aggregate data. Since the informa-tion is available for each unit of the industry and information about thelocation and ownership type of each unit is also available the analysis iscarried out in the following areas:

1 The overall efficiency trend of the industry during the period.2 An input-specific analysis of efficiency of each group of firms.3 A state-specific analysis of efficiency.4 An ownership-type specific analysis of efficiency.5 The forces behind the efficiency variation of production units.

In this study an effort has been made to understand the nature ofinefficiency in the textiles and garments industry in India during thepost-liberalization period. The study is based on unit- or firm-level infor-mation on production in the industry, and this is thought to be the firstattempt to measure the efficiency of each input separately and of outputusing firm-level data.

The section is divided into the following subsections. The next sub-section deals with data and methodology. Empirical analyses are donein the following subsection and concluding remarks are made in the lastsubsection.

Data and methodology

Data for the analysis of efficiency were collected from the Industrialwing of the Central Statistical Organization, Government of India. Veryrecently they have released unit-level data for different industrial codes.For the purpose of analysis we collected unit-level data on the textiles

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industry (NIC 265) and leather industry (NIC 291) for the period 1989–90to 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 thesecodes the location of the units can be identified. In some states firms withNIC code 265 and 291 are not present and in some states the numbersof units are very small compared to other states. From the distributionof the number of units in each state the major textile producing stateswere selected for this analysis. For each unit total production in valueterms was taken as a measure of output. Average of opening and closingstock of capital was taken as a measure of capital. Total employment wasdivided into production worker and nonproduction worker and the pro-portion of nonworkers to workers was considered as the skill factor of aunit. Fuel and materials consumed are other two inputs of production.Since efficiency is a relative concept and we have taken the cross sec-tional data to measure the efficiency of units the values are not deflatedby 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 outputbundle. Unlike parametric models, the non-parametric approach DEAdoes not specify the production possibility set explicitly. Instead, itonly 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 effi-ciencies from the production possibility set defined in (5.12). One isinput-oriented and the other is output-oriented.

The input-oriented DEA (BCC) model can be written as

min θ, s.t.∑λjyj ≥ y0

∑λjxj ≤ θx0 (5.13)

∑λj = 1

λj ≥ 0, j = 1, 2, . . . , N

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The output-oriented VRS model can be written as

max φ, s.t.∑λjyj ≥ φy0

∑λjxj ≤ x0 (5.14)

∑λj = 1

λj ≥ 0, j = 1, 2, . . . , N

Both of these measures are radial measures because all inputs are con-tracted or all outputs can be expanded by the same proportion. However,due to the presence of slack some input combinations are inefficientbecause one can produce the target output from a smaller amount of atleast one input. If reduction of any input in the input set causes the out-put level to be infeasible we can call the part of the isoquant as efficientsubset of the isoquant.

In a similar fashion we can define the efficient subset of output iso-quant of input combination when no output slack is present in theoutput isoquant. The radial measure of output-oriented technical effi-ciency does not reflect the unutilized potential for increasing any outputdue to the presence of slack. On the other hand, the nonradial mea-sure takes account of this output slack while estimating the technicalefficiencies of DMUs.

The problem of slacks in any optimal solution of a radial DEA modelarises because we seek to expand all outputs or contract all inputs by thesame proportion. In nonradial models, one allows the individual outputsto increase or the inputs to decrease at different rates. Färe and Lovell(1978) introduced the following output-oriented, nonradial measure oftechnical efficiency, which they called the Russell measure:10

RMy(x0, y0) = 1ρy

, (5.15)

where ρy = max 1m

∑r

φr

s.t.∑

j

λjyrj ≥ φryr0; r = 1, 2, . . . , m;

∑j

λjxij ≤ xi0; I = 1, 2, . . . , n;

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∑j

λj = 1; λj ≥ 0; j = 1, 2, . . . , N.

When output slacks do exist at the optimal solution of a radial DEAmodel, the nonradial Russell measure falls below the conventional mea-sure obtained from an output-oriented BCC model. That is, because theradial projection is always a feasible point for this problem, ρy ≥ φ∗.Hence, the nonradial Russell measure of technical efficiency neverexceeds the corresponding radial measure.

The analogous input-oriented nonradial measure of technical effi-ciency is:11

RMx(x0, y0) = ρx, (5.16)

where ρx = min 1n

∑i

θi

s.t.∑

j

λjyrj ≥ yr0; r = 1, 2, . . . , m;

∑j

λjxij ≤ θixi0; i = 1, 2, . . . , n;

∑j

λj = 1; λj ≥ 0; j = 1, 2, . . . , N.

The optimal solution projects the observed input bundle x0 onto thebundle x∗ = (θ∗

1x10, θ∗2x20, . . . , θ∗

nxn0) in the efficient subset of the isoquantof the output y012 (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 mea-sure of technical efficiency of a firm that uses input x but produces outputy ≤ f (x). In stochastic frontier analysis (SFA), one specifies an explicit forof the function f (x) but allows the frontier to move up or down due torandom shocks. Thus a stochastic frontier may be conceptualized as

y∗ = g(x; v) = f (x)ev (5.18)

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where v may be either positive or negative. The actual output relates tothe 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 variableand a log-linear or a log-quadratic function of x with a composite errorterm appended to it on the right hand side. Typically, one assumes theusual N(0,σ2

v ) as the probability distribution of v and a truncated Normaldistribution N+(µ, σ2

u ) for the one-sided error, u. When µ equals zero, weget the familiar half-normal distribution.

In data envelopment analysis (DEA), one the other hand, one con-structs a nonparametric graph of the technology as the upper boundaryof the free disposal convex-hull of the observed input–output combi-nations. The piece-wise linear function is treated as the nonparametricfrontier 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 ran-dom noise alongside inefficiency, the DEA efficiency score has to be seenas drawn from the truncated form of the distribution of the compositeerror distribution. This has prompted many researchers to specify a Tobitmodel explaining variation in measured efficiency scores across firms interms of observable heterogeneity. There are two major problems withthis approach – one is conceptual and the other is practical. First, there isno obvious censoring mechanism that results in zero values of ε (alterna-tively values of φ equal to one). Thus, applying the Tobit model appearsto be an ad hoc correction of the problem. Second, even after the Tobitregression has been estimated, it is not clear how one can extract the pureefficiency component purged of the systematic factors affecting the DEAscores from the results. In this section, we take a different approach and

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model the efficiency score obtained from any given sample as a randomvariable 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); σ2v and u ∼ N+(0,σ2

u ) Clearly, φ ≥ 1 for all values of(z, u, v). We can easily write v as

v = µ(z) + η where η ∼ N(0, σ2v ). (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 estimatedusing standard maximum likelihood procedures. There is one com-plicating problem, however. Whenever the value of φ equals unity,the dependent variable is undefined. A possible way to overcome thisproblem is to replace ln (φ − 1) by its Box–Cox transformation

(ϕ − 1)λ−1 − 1λ − 1

= limλ→1

ln (ϕ − 1).

Thus, the model becomes

q ≡ 1 − (ϕ − 1)λ−1

λ − 1= µ(z) + v − u.

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 outputsare taken into account in measuring efficiency. In a nonradial measure

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one can obtain the efficiency of each input and output separately. In thisanalysis we have one output and four inputs. Thus in a nonradial mea-sure of efficiency we can estimate the efficiencies of each input separatelyand find out the relative importance of each input in total efficiency. Effi-ciencies of individual units are estimated for each year and for each unitseven different efficiencies are calculated. The efficiencies are for produc-tion worker, nonproduction worker, capital, fuel, materials, total inputand total output.

The textiles industry

All-India efficiency Table 5.3 describes the all-India average efficiencyof the textiles industry as a whole for the years 1989–90 to 1997–98.Efficiency figures for production workers suggest that there was a risingtrend during the initial period of liberalization and a sharp fall in theyear 1994–95. After that efficiency rose but did not reach the peak level.The efficiency of nonproduction workers, however, fell slowly duringthe period but not without fluctuations. Efficiency figures for capitalalso show a fluctuating trend during the period, with a maximum valueof 0.8129 in the year 1992–93 but a sharp fall to only 0.3673 in thefollowing year. Then in subsequent years the figure rose to a moderatevalue of 0.6052. Efficiencies for fuel and materials showed a similar trendduring the period and the efficiency of materials also reached a peak inthe year 1992–93. Total input efficiency figures show that it reached apeak in 1990–91 then fell slightly to 0.7942 in 1992–93. In the followingyear there was a sharp drop in efficiency to 0.5057. During the laterperiod efficiency rose but it was still well below the level of earlier years.Output efficiency, however, remained almost stagnant during the periodof study around 0.7.

This account of efficiency indicates that liberalization affected thetextile industry as a whole adversely and there was no sign of improve-ment during the period. Efficiencies are now classified into two sets. Inthe first set units are grouped according to state codes. There are 33 statesfor which data have been collected. However, units are not available in allthe states. We have taken six major textile-manufacturing states, namelyDelhi, Gujarat, Karnataka, Maharashtra, Tamil Nadu and Uttar Pradeshfor our analysis, and average efficiencies of the state and their coefficientsof variations were calculated for all the years of the study. The secondset of units are classified according to the ownership pattern of eachunit. There are six type of ownership defined by CSO, namely whollycentral government, wholly state and local government, central and

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Table 5.3 Average efficiencies of manufacturing units in the Indian textiles industry

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

Production worker 0.7062 0.7509 0.7670 0.7826 0.4846 0.6521 0.5707 0.5925Nonproduction worker 0.8258 0.7070 0.6417 0.6533 0.6269 0.5127 0.6257 0.6760Capital 0.7946 0.7853 0.6564 0.8129 0.3982 0.6014 0.5050 0.6052Fuel 0.7527 0.8775 0.3958 0.7873 0.3673 0.6214 0.4714 0.4937Materials 0.9024 0.9373 0.6422 0.9350 0.6516 0.9676 0.4725 0.8239Total input 0.7964 0.8116 0.6206 0.7942 0.5057 0.6710 0.5291 0.6383Total output 0.6877 0.6949 0.6845 0.6335 0.7577 0.7291 0.6969 0.7987

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state government and/or local government jointly, joint sector public,joint sector private and wholly private. In most of the years the units ofall these types of ownership are available for the textiles industry. For allthese groups average efficiencies are calculated for each of the years ofstudy.

State-specific analysis First we concentrate our analysis on state specificaverage efficiencies (Table 5.4). It is found from the figures that in termsof efficiency of production workers Delhi registered the highest rank formost of the years among the six states. In most of the states the averageefficiency fell in the later years of the study after a rise for the early lib-eralization period. In terms of efficiency of nonproduction workers theaverage value for Uttar Pradesh is highest for three different years. Effi-ciencies for later years are small compared to the earlier years. Efficiencyhad a rising trend in the last two years for all these states except Gujarat.

Efficiency of capital input figures show that the magnitude and trendsare similar for all the states. There was an initial rise in the value and inthe year 1993–94 there was a sharp fall in efficiency. Again there was arising trend during the later years of the study.

Total input efficiency figures for the selected states show a slightupward trend up to 1992–93 with a fall in 1991–92. Then there was asharp fall in efficiency in 1993–94 and a downward trend in efficiencyduring the later years of the study. The magnitude of the efficiency showsno remarkable difference among the states. However, the figures forDelhi, Maharashtra and Uttar Pradesh are slightly higher than those forthe other states.

The output efficiency figures of the states indicate a rising trend duringthe period after liberalization but not without fluctuations. Compari-son of the efficiency level among the states indicates that Delhi andMaharashtra are in the upper tier, while the other states are almost inthe same position.

To summarize, both the input and output efficiencies of the textilesindustry in most of the states in India registered a fall in the later yearsafter a rise in the initial years of liberalization. The basic advantage of thenonradial measure is that efficiencies for each input can be estimated sep-arately. It has been found from this analysis that those states which showa better utilization of production workers are not capable of maintaininghigher efficiency utilization of nonproduction workers. In terms of inputand output efficiencies the ranks of the states for the years are similar.As expected, Delhi, Maharashtra and Gujarat, as major textile-producingstates, perform better than other states.

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156Table 5.4 Average scale of operation and efficiency of manufacturing units in the textiles industry in India

Delhi Gujarat Karnataka Maharashtra Tamil Nadu Uttar Pradesh

Average output1989–90 34,733,000 35,130,930 19,394,520 16,406,630 14,341,100 17,792,5901990–1 39,417,990 35,748,780 21,752,020 17,073,930 13,251,480 16,946,4801991–2 39,366,170 38,526,760 23,242,120 20,374,590 18,442,780 20,441,9801992–3 49,140,710 36,961,720 24,976,590 25,335,200 20,329,490 24,991,6501993–4 85,333,180 42,776,470 43,137,180 91,470,480 46,837,140 28,596,0401994–5 83,378,860 38,966,490 42,116,650 75,729,660 53,638,360 46,502,2101996–7 133,016,500 151,919,500 44,166,910 94,640,260 75,111,820 75,375,0301997–8 186,114,500 152,812,000 109,830,100 193,389,400 207,041,500 206,436,600

Efficiency of production workers1989–90 0.7723 0.5592 0.5493 0.8336 0.6202 0.82681990–1 0.8961 0.6082 0.5734 0.8840 0.6214 0.83341991–2 0.8924 0.6659 0.6325 0.7823 0.7286 0.76361992–3 0.9166 0.7865 0.6446 0.8817 0.6296 0.91701993–4 0.5623 0.4982 0.3336 0.5247 0.3759 0.57731994–5 0.7145 0.6492 0.5810 0.7140 0.5444 0.70091996–7 0.6435 0.5877 0.5013 0.6379 0.5184 0.58121997–8 0.6908 0.6208 0.6158 0.6534 0.4453 0.5298

Efficiency of nonproduction workers1989–90 0.7794 0.8326 0.7605 0.8869 0.8311 0.84721990–1 0.7113 0.7809 0.6557 0.7617 0.6569 0.78741991–2 0.5296 0.5864 0.6598 0.6512 0.7488 0.72391992–3 0.5851 0.6789 0.6295 0.7532 0.6502 0.67011993–4 0.6323 0.6040 0.5663 0.6566 0.5749 0.71061994–5 0.5570 0.4750 0.4487 0.4971 0.4679 0.58631996–7 0.5780 0.7728 0.6001 0.6756 0.6461 0.53531997–8 0.6712 0.7291 0.6899 0.6843 0.6794 0.6361

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Efficiency of capital input1989–90 0.7760 0.8298 0.8282 0.8458 0.7171 0.77941990–1 0.8330 0.7732 0.8075 0.7992 0.7465 0.66991991–2 0.5877 0.6306 0.7409 0.6448 0.7266 0.55731992–3 0.8507 0.8615 0.8053 0.8230 0.8252 0.67361993–4 0.4251 0.3633 0.3362 0.4054 0.4087 0.34361994–5 0.6763 0.5760 0.5823 0.6099 0.5699 0.55311996–7 0.4284 0.5268 0.5208 0.5468 0.5382 0.46411997–8 0.5149 0.6636 0.6960 0.5440 0.6360 0.5525

Total input efficiency1989–90 0.7843 0.7445 0.7532 0.8494 0.7719 0.81451990–1 0.8453 0.7804 0.7625 0.8548 0.7816 0.81081991–2 0.5543 0.5625 0.6238 0.6383 0.6728 0.64211992–3 0.8101 0.7842 0.7579 0.8344 0.7798 0.78071993–4 0.5313 0.5041 0.4248 0.5091 0.4764 0.56091994–5 0.7028 0.6528 0.6532 0.6658 0.6381 0.67641996–7 0.4833 0.6123 0.5357 0.5673 0.5314 0.47461997–8 0.6283 0.6477 0.6911 0.6375 0.6063 0.5842

Total output efficiency1989–90 0.8261 0.6756 0.7318 0.7414 0.5785 0.61921990–1 0.8432 0.6381 0.7021 0.7219 0.6011 0.69151991–2 0.8402 0.6625 0.6661 0.6672 0.6254 0.60731992–3 0.7937 0.6561 0.6149 0.6489 0.5446 0.60921993–4 0.8165 0.7109 0.8023 0.7471 0.7209 0.67861994–5 0.8543 0.7001 0.6166 0.7198 0.7040 0.66831996–7 0.7899 0.5667 0.5972 0.7425 0.6946 0.72151997–8 0.8701 0.7765 0.7307 0.8390 0.8132 0.8151

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Ownership-specific analysis It has often been claimed that manufactur-ing units in the private sector are more efficient than public sectorenterprises. The units producing textiles in selected states in India wereclassified in terms of ownership and a comparison of efficiencies is dis-cussed in this section. Table 5.5 presents the values of efficiencies ofinputs and outputs of units for different categories. The output effi-ciency figures indicate that the efficiencies in the central governmentunits were higher than those in the private sector industries except in

Table 5.5 Ownership-wise efficiency of manufacturing units in textiles industriesin India

Central State Central–state Joint–public Joint–private Private

Efficiency of production workers1989–90 0.7911 0.5670 0.2292 0.9412 0.6720 0.70771990–1 0.7292 0.5232 1.0000 0.6535 0.8435 0.75361991–2 0.7036 0.8373 0.7453 0.8721 0.6606 0.76611992–3 1.0000 0.8224 0.7888 1.0000 1.0000 0.78071993–4 0.8226 0.4143 0.4805 0.3969 0.48471994–5 0.5927 0.0785 0.6883 0.65291996–7 1.0000 0.7733 0.5686 0.6059 0.8887 0.56781997–8 1.0000 0.7060 1.0000 0.5256 1.0000 0.5855Average 0.8638 0.6545 0.6114 0.7104 0.8441 0.6624

Efficiency of nonproduction workers1989–90 0.9669 0.8231 0.1061 1.0000 0.9147 0.82511990–1 0.9033 0.3609 0.9423 0.6541 0.6037 0.71051991–2 0.6581 0.3002 0.6968 0.5222 0.6353 0.64561992–3 0.6406 0.5295 0.7896 0.4345 0.2327 0.65391993–4 0.8779 0.8013 0.4420 0.6619 0.62571994–5 0.8073 0.3797 0.7384 0.50921996–7 0.9777 0.8735 0.2469 0.9009 0.5220 0.62261997–8 1.0000 1.0000 0.5358 0.7412 0.6429 0.6718Average 0.8606 0.6870 0.5174 0.7067 0.5919 0.6581

Capital efficiency1989–90 0.9048 0.7036 0.3429 1.0000 0.7546 0.79531990–1 0.8041 0.7044 0.7342 0.8650 0.9983 0.78321991–2 0.8710 0.8398 0.5390 0.7481 1.0000 0.65221992–3 0.6101 0.7280 0.8521 1.0000 1.0000 0.81251993–4 1.0000 0.7349 0.3192 0.5344 0.39251994–5 0.9183 0.2028 0.7902 0.59821996–7 1.0000 1.0000 0.2327 0.6763 0.4254 0.50021997–8 1.0000 1.0000 0.8052 0.2923 0.2578 0.6030Average 0.8843 0.8286 0.5035 0.7383 0.7394 0.6421

(Continued)

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Table 5.5 (Continued)

Central State Central–state Joint–public Joint–private Private

Total input efficiency1989–90 0.8941 0.8002 0.2919 0.9784 0.7770 0.79591990–1 0.8685 0.6964 0.8831 0.8008 0.8690 0.81201991–2 0.7092 0.6847 0.6353 0.7327 0.6437 0.61831992–3 0.8198 0.7850 0.8337 0.8793 0.8420 0.79341993–4 0.9401 0.7622 0.4477 0.5327 0.50201994–5 0.8468 0.3017 0.8434 0.66901996–7 0.8363 0.8289 0.2710 0.7602 0.4984 0.52561997–8 1.0000 0.8948 0.8110 0.5774 0.6149 0.6333Average 0.8669 0.7874 0.5594 0.7631 0.7075 0.6687

Output efficiency1989–90 1.0000 0.3124 1.0000 0.2662 0.2576 0.69351990–1 0.7488 0.2885 0.7844 0.6899 0.7801 0.69861991–2 0.7242 0.4081 1.0000 0.4841 0.5450 0.68781992–3 1.0000 0.3146 0.6167 0.3997 1.0000 0.63481993–4 0.6790 0.3492 0.8731 0.6992 0.76141994–5 0.7963 1.0000 0.3494 0.73071996–7 1.0000 0.6906 1.0000 0.6054 1.0000 0.69571997–8 1.0000 0.7868 0.7874 0.9006 1.0000 0.7974Average 0.8789 0.4933 0.8827 0.5493 0.7638 0.7125

one year. The state government units and the units belonging to jointsectors show a poor performance in terms of output-oriented efficiency.The efficiencies of the units belonging to central government and theprivate sector show a rising trend during the post-liberalization period.However, the units in other groups do not show any clear tend of outputefficiency.

Contrary to the general belief, it has been found that the input-oriented efficiencies in the private sector were much lower than those ofthe state run units during the period of study. Joint public sector unitsalso performed better than private sector units in terms of input effi-ciency. There was a faint declining trend of efficiencies of units in allthe ownership types during this period. The figures for efficiencies ofindividual inputs suggest that for both production and non-productionworkers the private sector units were less efficient than the central andstate run units except in a years when the private sector units were betterthan state run units. Thus, this analysis reveals that in terms of input-oriented efficiency the manufacturing units belong to the private sectorperformed worse than those in the state managed sector.

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Factors behind efficiency variations The performances of the units of thetextile industry in some major states and in different ownerships havebeen discussed in the previous subsections. Now some explanation isneeded for the variability of the values of efficiency, and we attemptto uncover the factors responsible for this variation among states andownership groups.

Regression analyses are carried out to discover the factors responsiblefor the variability of efficiency, where efficiencies are taken as dependentvariables and variables such as age of the unit, scale of operation, skill,state dummy, ownership dummy and time dummy for reform are takenas independent variables. As stated, a Box–Cox transformation of theefficiency values is needed instead of standard logarithm transformationin order to avoid the problem with efficiency values that are equal tounity. The analysis is of the total efficiency of the units. The results ofthe regressions are presented in Table 5.6. In the first regression totalefficiency is taken as the dependent variable and 1993 onwards is takenas the post-reform period. Scale of operation (logarithm of output), skill,state dummy for Delhi and the reform dummy are found to be statisti-cally significant. The second regression, changing the reform dummy to1994, indicates that only three variables, i.e. scale of operation, reformdummy and the state dummy for Delhi, are statistically significant. How-ever, the reform dummy is not highly significant. The values of adjustedR2 are not however very high.

The leather industry

Table 5.7 shows the all-India average efficiency of the leather industryas a whole for the years 1989–90 to 1997–98. The figures indicate thatthe efficiency of nonproduction workers was better than that of pro-duction workers and for both efficiencies no significant trend can beobserved over the period of analysis. Efficiencies of capital, fuel andmaterials show higher average values compared to labor efficiency butagain the efficiencies show no trend over the period of analysis. Oneinteresting point is that in the year 1996–97 there was a major fall in theefficiencies of all the inputs. Output efficiency showed a slight upwardtrend during this period of post-liberalization. It rose from 0.8444 in1989–90 to 0.9249 in 1996–97 but then there was a marginal fall in1997–98.

State-specific analysis As in our previous analysis of the textiles indus-try we have taken six major leather-producing states from the 33 statesappearing in the list. The states are Haryana, Karnataka, Maharashtra,

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Table 5.6 Estimates of regression parameters of total efficiency variations

Variable Regression I Regression II

C −116.8108 −120.1982Age 0.0057 0.0045

(0.748492) (0.5963)Log y 9.2934 9.7366

(31.29111)∗∗ (32.7342)∗∗Joint 15.0354 15.5592

(1.475779) (1.5194)Private 10.3415 11.6625

(0.986568) (1.1071)Public 17.3816 18.9887

(1.504937) (1.6360)Skill 2.0393 1.0917

(2.57808)∗∗ (1.3840)Karnataka 2.1473 2.3300

(0.927612) (1.0011)Delhi 10.3913 10.1227

(4.543483)∗∗ (4.4017)∗∗Gujarat −2.2197 −3.3195

(−0.661021) (−0.9836)Maharashtra 3.5810 2.5860

(1.515877) (1.0898)Tamil Nadu 1.4335 1.2685

(0.624264) (0.5496)Uttar Pradesh 1.1105 0.3465

(0.348878) (0.1083)West Bengal 5.0079 4.1880

(0.824846) (0.6863)TD93 9.9561 –

(7.923802)∗∗TD94 – 2.2082

(1.7516)R2 0.1942 0.1859Adjusted R2 0.1922 0.1839Durbin–Watson statistic 1.9341 1.9164

Note: Dependent variable: total efficiency. Included observations: 5829. ∗ indicatecoefficients 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 ofaverage values of output of these states that the average scale of opera-tion in Tamil Nadu is highest. However, the other states do not lag farbehind and the figures are very close to each other.

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Table 5.7 Average efficiencies of manufacturing units in the Indian leather industry

Year 1989–90 1990–91 1991–92 1992–93 1993–94 1994–95 1996–97 1997–98

Production workers 0.6801 0.7273 0.6469 0.7911 0.6552 0.7082 0.4656 0.7215Nonproduction workers 0.6921 0.7632 0.7347 0.7626 0.8390 0.5203 0.4869 0.7671Capital 0.6043 0.6173 0.6202 0.7129 0.7006 0.6687 0.5692 0.4678Fuel 0.8431 0.8197 0.8107 0.8021 0.6601 0.6449 0.2871 0.6935Materials 0.9895 0.9803 0.9769 0.9729 0.8059 0.7803 0.4792 0.9794Total input 0.7618 0.7816 0.7949 0.8083 0.7322 0.6645 0.4576 0.7259Total output 0.8444 0.8318 0.8243 0.8167 0.7370 0.8136 0.9249 0.9133

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Table 5.8 shows the values of average efficiency for different inputs andoutput for these six states for all the years of analysis. The values of effi-ciency for production workers indicate that the efficiency of productionworkers in Tamil Nadu was comparatively low, while that in Delhi washigher compared to other states. However, the figures do not show anytrend over the years for the states we have chosen for analysis. The val-ues of efficiency for nonproduction workers indicate that Haryana andKarnataka were in a much better position compared to the other states,while Tamil Nadu, Uttar Pradesh and Delhi registered lower values thanthe others and remained at the same level of average efficiency over theseyears.

Figures for capital input efficiency for these states show that Delhi andUttar Pradesh registered lower values of efficiency compared to the otherstates. But the figures for all the states do not indicate any significanttrend over this period.

Figures for average efficiency of material input over the years give thehighest values for all the states compared to all the input efficiencies.Maharashtra showed the best performance in terms of material inputefficiency, where for most years the firms were 100 per cent efficient.The figures for total input efficiency, however, do not show any markeddifference among the states.

Figures for average efficiency of output indicate that the performanceof firms in Delhi was better than that of the other states, averag-ing about 0.9, with a significant fall to 0.62 in 1996–97. Karnataka,Maharashtra, Tamil Nadu and Uttar Pradesh registered almost the samelevel of efficiency over these years, averaging about 0.85. The average effi-ciency of Haryana was, however, slightly lower than those of the otherstates.

Ownership-specific analysis Table 5.9 presents the values for efficienciesof different inputs and outputs for three categories of ownership. Theaverage figures for output efficiency of the leather industry during theperiod 1989–90 to 1997–98 reveal that firms belonging to the privatesector and state governments’ firms did better than central governmentorganizations. The average input efficiency figures for these three types offirms indicate that firms within the purview of state government did bet-ter than both private and central government organizations. The inputefficiencies of these three types of firms did not show any trend duringthis period. However, figures for output efficiency show a mild increasingtrend over the period.

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164Table 5.8 Average scale of operation and efficiency of manufacturing units in the leather industry in India

Haryana Karnataka Maharashtra Tamil Nadu Uttar Pradesh Delhi

Average output1989–90 33,660,178 18,941,788 37,827,036 41,792,521 10,303,635 35,883,5281990–1 78,216,270 25,189,328 56,359,748 60,794,204 40,734,315 37,643,2781991–2 73,561,566 29,837,334 41,326,796 72,541,945 41,812,776 42,474,1351992–3 56,363,316 16,895,854 37,782,037 75,627,333 35,412,561 64,756,3691993–4 55,905,672 197,516,366 74,681,553 121,193,034 63,412,758 78,512,1571994–5 51,763,653 23,536,130 31,749,146 124,357,585 85,979,635 198,457,1311996–7 182,519,937 75,719,160 143,904,160 143,567,758 94,212,210 133,364,2791997–8 118,054,604 691,751,671 135,974,102 190,147,295 189,277,793 132,271,075

Efficiency of production workers1989–90 0.7388 0.9213 0.7320 0.5619 0.6491 0.85991990–1 0.7911 0.7829 0.7797 0.6224 0.7483 0.91161991–2 0.6996 0.6213 0.8883 0.5810 0.6928 0.68011992–3 1.0000 0.8385 0.9484 0.6799 0.7264 0.90191993–4 0.8871 0.7320 0.6462 0.5794 0.6883 0.86301994–5 0.7494 0.7510 0.6937 0.6303 0.7014 0.66781996–7 0.4236 0.4883 0.4607 0.3913 0.4650 0.80921997–8 0.8792 1.0000 0.8494 0.6549 0.7648 0.5816

Efficiency of nonproduction workers1989–90 0.8598 0.7775 0.6858 0.6927 0.6138 0.67321990–1 0.8225 0.8840 0.7454 0.7489 0.7199 0.73611991–2 0.8410 0.6536 0.8127 0.8116 0.7319 0.52601992–3 0.8032 0.8885 0.8450 0.7921 0.6725 0.82691993–4 0.8163 0.9260 0.8651 0.8034 0.8825 0.90761994–5 0.4829 0.6613 0.5985 0.4653 0.4803 0.50641996–7 0.3756 0.5481 0.4586 0.4698 0.4400 0.65261997–8 0.6431 1.0000 0.3928 0.7988 0.7473 0.2983

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Efficiency of capital inputs1989–90 0.5459 0.6603 0.6471 0.5796 0.6335 0.63271990–1 0.8972 0.5507 0.6725 0.5857 0.6289 0.50981991–2 1.0000 0.5548 0.8609 0.5989 0.6488 0.43681992–3 0.6108 0.6271 0.8040 0.7372 0.6634 0.56071993–4 0.8349 0.7977 0.5914 0.7005 0.6565 0.94431994–5 0.6194 0.7591 0.7164 0.6469 0.6793 0.70221996–7 0.4029 0.6005 0.4190 0.5667 0.5591 0.87971997–8 0.5272 1.0000 0.3942 0.3969 0.4084 0.1711

Efficiency of fuel inputs1989–90 0.8011 0.9808 0.8621 0.8535 0.7227 0.91311990–1 0.8893 0.8542 0.7912 0.8770 0.7718 0.77551991–2 0.4890 0.8758 0.9249 0.8073 0.7228 0.78371992–3 0.6424 0.8450 0.8860 0.8426 0.6279 0.71231993–4 0.7409 0.7056 0.6076 0.6224 0.7156 0.78431994–5 0.5624 0.6020 0.5953 0.6981 0.5625 0.79621996–7 0.2746 0.2293 0.2196 0.2608 0.2805 0.40021997–8 0.7061 1.0000 0.7701 0.6905 0.5543 1.0000

Efficiency of material inputs1989–90 1.0000 1.0000 1.0000 0.9805 0.9825 1.00001990–1 1.0000 0.9964 1.0000 0.9894 0.9664 0.99371991–2 0.9732 0.8809 1.0000 0.9806 0.9838 0.99091992–3 0.9781 0.9328 1.0000 0.9781 0.9662 0.90361993–4 0.9072 0.8690 0.8007 0.8180 0.7765 0.91171994–5 0.6979 0.8765 0.7818 0.7222 0.8083 0.63221996–7 0.5529 0.4149 0.5913 0.4725 0.3742 0.61171997–8 1.0000 1.0000 1.0000 0.9751 0.9747 1.0000

(Continued)

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166Table 5.8 (Continued)

Haryana Karnataka Maharashtra Tamil Nadu Uttar Pradesh Delhi

Total input efficiency1989–90 0.7891 0.8680 0.7854 0.7336 0.7240 0.81581990–1 0.8800 0.8136 0.7978 0.7647 0.7670 0.78531991–2 0.8435 0.8200 0.8472 0.7853 0.7492 0.78321992–3 0.8069 0.8264 0.8967 0.8060 0.7313 0.78111993–4 0.8373 0.8061 0.7022 0.7047 0.7439 0.88221994–5 0.6224 0.7300 0.6771 0.6326 0.6463 0.66101996–7 0.4059 0.4562 0.4299 0.4322 0.4238 0.67071997–8 0.7511 1.0000 0.6813 0.7033 0.6899 0.6102

Total output efficiency1989–90 0.7038 0.8986 0.8910 0.8533 0.8560 0.91051990–1 0.8615 0.7307 0.8455 0.8486 0.8664 0.87181991–2 0.7345 0.7660 0.8561 0.8575 0.8554 0.93591992–3 0.6075 0.8014 0.8667 0.8664 0.8445 1.00001993–4 0.7384 0.6482 0.7519 0.7286 0.8201 0.84721994–5 0.8761 0.7774 0.8676 0.8398 0.8019 1.00001996–7 0.8439 0.8186 1.0000 0.9351 0.9506 0.62811997–8 0.9331 1.0000 0.8522 0.8796 0.9690 1.0000

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167

Table 5.9 Ownership-wise efficiency of manufacturing units inthe leather industry in India

Central State Private

Total input efficiency1989–90 0.6290 0.7802 0.76121990–1 0.7705 0.7664 0.78621991–2 0.6886 0.7538 0.76171992–3 0.6705 0.8020 0.81231993–4 0.2440 0.8786 0.72821994–5 0.6166 0.8364 0.65861996–7 0.5539 0.6990 0.44751997–8 0.8272 0.9029 0.7123Average 0.6250 0.8024 0.7085

Total output efficiency1989–90 0.8419 0.7758 0.84881990–1 0.7096 0.7256 0.84171991–2 0.6935 0.7911 0.83031992–3 0.6775 0.8566 0.81881993–4 0.0126 0.7683 0.73881994–5 0.5354 0.9366 0.80971996–7 1.0000 1.0000 0.92121997–8 1.0000 0.8524 0.9142Average 0.6838 0.8383 0.8404

Fuel efficiency1989–90 0.7896 0.8043 0.84481990–1 1.0000 0.7832 0.81911991–2 0.7644 0.9521 0.80461992–3 0.7270 0.8348 0.80181993–4 0.0246 0.9928 0.64591994–5 0.5165 0.7553 0.64031996–7 0.5000 0.4983 0.27681997–8 1.0000 0.8812 0.6779Average 0.6653 0.8127 0.6889

Capital efficiency1989–90 0.3903 0.8705 0.58611990–1 0.6010 0.9088 0.60461991–2 0.5287 0.7979 0.61661992–3 0.4857 0.7737 0.71541993–4 0.5000 0.9081 0.69151994–5 0.5262 0.7903 0.66701996–7 0.5077 0.8122 0.56081997–8 0.7665 0.8304 0.4283Average 0.5383 0.8365 0.6088

(Continued)

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Central State Private

Efficiency of production workers1989–90 0.5031 0.7068 0.67771990–1 0.6382 0.6276 0.73971991–2 0.5577 0.5970 0.64951992–3 0.5748 0.8816 0.79161993–4 0.0212 0.7582 0.65441994–5 0.5341 0.8324 0.70491996–7 0.5067 0.6659 0.45761997–8 1.0000 0.8834 0.7123Average 0.5420 0.7441 0.6735

Efficiency of nonproduction workers1989–90 0.5483 0.5296 0.70641990–1 0.7031 0.5320 0.78651991–2 0.5922 0.4304 0.76071992–3 0.5649 0.5749 0.77841993–4 0.1741 0.8573 0.84611994–5 0.5062 0.8309 0.50811996–7 0.5116 0.7546 0.47651997–8 0.3696 0.9194 0.7655Average 0.4963 0.6786 0.7035

Efficiency of material inputs1989–90 0.9135 0.9897 0.99071990–1 0.9101 0.9806 0.98101991–2 1.0000 0.9916 0.97711992–3 1.0000 0.9448 0.97411993–4 0.5000 0.8763 0.80291994–5 1.0000 0.9731 0.77261996–7 0.7434 0.7642 0.46561997–8 1.0000 1.0000 0.9774Average 0.8834 0.9400 0.8677

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

The average efficiency of production workers was highest in purelystate government firms, while the rank of average efficiency of firmsin the private sector came second among these three types of owner-ship. However, the figures for the average efficiency of nonproductionworker indicate that firms in the private sector were doing better than theother two sectors. For both the efficiencies of production and nonpro-duction workers central government organizations showed the lowest

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figures compared to the others. The efficiencies of production and non-production workers in the state-controlled firms showed a mild upwardtrend during 1989–90 to 1997–98.

The average efficiencies of other inputs for the firms under these threecategories show similar pattern. For all the input-specific efficienciesstate-run organizations performed much better than the other two typesof firms. However, there were no clear trends of efficiencies of inputsduring this period.

Factors behind efficiency variations The performances in terms of variousefficiency indicators of firms belonging to different states and differenttypes of ownership have been discussed. Now a regression analysis iscarried out to uncover the factors responsible for the variation in effi-ciencies of firms. The dependent variable here is the transformed valuesof total efficiency (the purpose and method of transformation are dis-cussed above). The independent variables for the regression are age of theunit, scale of operation, skill, state dummy, ownership dummy and timedummy for reform. We have taken 1993 as the break point to understandthe effect of liberalization on efficiency.

The results presented in Table 5.10 suggest that scale of operation has asignificant positive effect on the variation of efficiency. The coefficient ofdummy variable indicates that efficiency went up during the later phaseof liberalization. The coefficient of skill is positive, but not statisticallysignificant. The result suggests that there is no marked difference in thevalues of efficiencies among the major leather-producing states in India.

4 Comparison of efficiency in the post- andpre-liberalization periods

Liberalization is a process of economic policy changes specifically ini-tiated from 1991 as declared state policy. It had its own economic,political and international compulsions. Indian economic reforms hadbeen initiated with the help of financial support from the InternationalMonetary Fund and the World Bank and later from the Asian Devel-opment Bank. Hence, these reforms have been involved with a set ofconditions mutually agreed upon between the government of India andthe multilateral institutions. It was believed that during the pre-reformyears productivity in most of the industries became one of the lowest byinternational standards. It was also argued that macroeconomic imbal-ances and microeconomic inefficiencies had fed one another in a highlycomplex manner.

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Table 5.10 Estimates of regression parameters oftotal efficiency variations

Variable Coefficient

C −479.958Age 0.12707

(0.945628)Log y 67.35051

(12.65468)∗Private −27.1022

(−0.593352)Maharashtra −81.5084

(−1.025743)Tamil Nadu −37.791

(−0.920261)Uttar Pradesh −41.7784

(−0.722294)Skill 37.94028

(1.129931)Year D 160.1062

(5.909711)∗R2 0.16863Adjusted R2 0.163297S.E. of regression 428.147

Note: Dependent variable: total efficiency. Included obser-vations: 1256. ∗ Indicates coefficients are significant at the5% level. Figures in parentheses are t-statistics.

The New Industrial Policy of July 1991 laid down some very fundamen-tal policy changes, such as abolition of licensing, easing of the rigors ofMRTP and FERA, freer imports of capital goods and liberal policy mea-sures for attracting investment in new technology-intensive industries.The sole objective of these highly liberalized policy measures, with whichwe are concerned here, was to enhance the productivity and efficiencyof Indian industries by creating a competitive environment.

It has been observed that the industrial composition in India has beenchanging over time. Particularly after the New Economic Policy of 1991and the opening up of the economy to the world market, the growthof industries has taken place through natural market competition andthe industries with high inefficiency and low productivity have foundit hard to survive in the market. But the Indian industrial composi-tion is characterized by the coexistence of a traditional labour-intensive

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manufacturing sector and a capital-intensive modern sector. In this sec-tion we study the performance of firms in two leading industries inIndia, namely textiles and electronics. The computer industry is includedin electronics and we analyze the efficiency of this industry separatelyduring the post-reform period.

We have already discussed the importance of the textiles industry inIndia. Now we discuss the growth and importance of the electronicsindustry, whose emergence is comparatively new in India. The industrialcomposition of India, as in many developed and developing countries,has been changing with the advent of new technologies and the chang-ing pattern of mass consumption demand. Changes in the structureof industries in developed countries mainly come from the introduc-tion of new technology, while in most of the developing countries thechanges are driven by the demand for modern consumption and capi-tal goods. The technologies of production of such goods are primarilyimported from the advanced countries of the West. Thus the changesin developing countries take place with a lag from those in developedcountries. According to the Technology Information, Forecasting andAssessment Council (TIFAC) India will gain enormously from the useof computers in manufacturing processes (India 2020: A Vision for theNew Millennium, 1998). There are many success stories where India hasgained by using computers combined with highly skilled manpower inboth manufacturing and service sectors. Thus the need for computerand similar electronic processing units creates a boom for computersand related electronic industries. We know that in India the IT andIT-enabled services (ITES) sectors are hugely successful. India is now apreferred destination for the production of electronic goods. To achievethis, the Ministry of IT recently announced a comprehensive policy, themuch-awaited semiconductor policy, which offers both pre-operativeand post-operative benefits and aims to attract foreign investment inthis industry. Demand for personal computers, laptops and peripheralsis increasing at a high pace.

The electronics industry as a whole has been one of the fastest growingindustries right from its inception. With an increasing middle incomegroup population the potential consumer demand for consumer elec-tronics ranging from televisions to laptop computers is almost unlimited,and hence a strong growth performance could be expected. The start ofthis industry dates back to the early 1960s. Electronics was primarilyfocused on the field of communication systems for radios, telephony,telegraphy and television broadcast. Until the 1980s the electronic sec-tor was government owned. From there on the growth of the electronic

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industry took off due to economic changes resulting in the globalizationof the economy. Due to the rise in income level demand for various typesof durable consumer products from the electronics industry is increas-ing day by day. The electronics industry recorded a very high growth bythe 1990s. The liberalization of the economy in 1991 opened up manyavenues to generate demand for electronic goods from every corner oflife. Thus, due to its pervasive applicability, the electronics industry isstrongly linked with the macroeconomic conditions of India.

This section has the following subsections. First, we provide a descrip-tion of the data and methodology. A comparison of efficiencies of thetextiles and electronics industries in the pre- and post-liberalization erasand a nonradial analysis of the computer industries during the postlib-eralization period are then presented, together with some remarks onthese issues.

Data and methodology

As before, data for the analysis of efficiency were collected from theIndustrial wing of the Central Statistical Organization, Government ofIndia. For the purpose of analysis we collected unit-level data on thetextiles and electronics industries for the period 1980–81 to 2002–03, bar-ring 1995–96, for which data have not been published. For the computerindustry in particular we have taken the data for the years 1989–90 to1997–98. The detailed industrial codes are given in Appendix 5.3. Theseunit-level data have a state code and from these codes the location ofunits can be identified. In some states firms within these industries arenot found and in some states the number of units is very low comparedto other states. From the distribution of the number of units in eachstate the major textile producing states were selected for this analysis.Similarly, for the electronics industry we selected a few major states forthe purpose of analysis. For each unit total production in value termsis taken as a measure of output. Average of opening and closing stockof capital is taken as a measure of capital. Total employment is dividedinto production workers and nonproduction workers and the proportionof nonproduction to production workers is considered as the skill factorof a unit. Fuel and materials consumed are other two inputs of produc-tion. The values of outputs and inputs are deflated by the correspondingprice indices.

The efficiencies of the firms in each industry and for each year werecalculated using both parametric and nonparametric methods of estima-tion. First, we estimated the efficiencies with the corrected OLS (COLS)method. The COLS method, first noted by Richmond (1974), is based on

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the OLS result. Let us consider a simple C–D production function in itslinear 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, itsatisfies all the usual ideal conditions except normality. Therefore, theequation may be estimated by OLS to obtain the BLUE of (α0 − µ) andof αis. It was observed by Richmond (1974) that the mean and varianceof one-sided disturbance terms are both equal to µ. Now, if a specificdistribution is assumed for u and if the parameters of this distribution canbe derived from its higher order central moments, then we can estimatethese parameters consistently from the moments of the residuals. Sinceµ is a function of these parameters, it too can be estimated consistentlyand the estimate can be used to correct the OLS constant term, which isa consistent estimate of (α0 − µ).

The difficulty with this technique is that even after correcting the con-stant term, some residuals may have the wrong signs. To manage thisproblem the parameters of the above equation should first be estimatedby OLS and then the constant term corrected not by the above tech-nique but by shifting it up until no residual is positive and at least oneis zero. We have used this method to estimate the frontier productionfunction for each of the years. Then the efficiency is calculated as theratio between the observed and frontier output for each industry.

Here we have estimated the FPF using stochastic models and we havetaken the C–D production functions. The forms of the cross sectionalC–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 L2represent productive and nonproductive workers respectively. The mul-tiplicative error term is

e−u = Yf (X)

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

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The nonparametric DEA measure of efficiency is done for the set offirms for each year and for each industry. The simplest radial measure ofoutput oriented technical efficiency can be obtained from the solutionof an LP model and can be written as:

max φ, s.t.N∑

j=1

µjykj ≥ φykt , k = 1, 2, . . . , m (5.28)

n∑j=1

µjxkj ≤ xlt , l = 1, 2, . . . , n

µ ≥ 0

where y is the output bundle and x is the input bundle. The scale of oper-ation for this model is considered as constant return to scale (CRS). Forthe model with variable return to scale (VRS) we have to include anotherconstraint

∑µ = 1 to get the values of µ and the efficiency parameter .

In the analysis of the firms in the computer industry we estimated thenonradial efficiency of inputs. We have already discussed the concept ofnonradial efficiency. The underutilizations of inputs are calculated fromthe nonradial model. The optimum use of each input is calculated bymultiplying the efficiency score of each input with the correspondingobserved value of input. Now the simple arithmetic (X – X∗)/X∗ givesthe measure of input utilization, where X and X∗ represent the observedand optimum values of input.

Instead of measuring the input efficiency with a nonradial measurewe have used a radial input-oriented BCC-DEA model for subvector effi-ciency for the analysis of state-specific input utilization in the electronicsindustry during the post-liberalization period (Ray, 2004).

The model can be written as follows:

min θ, s.t.∑

λjyj ≥ y0 (5.29)

∑λjL1j ≤ θL10

∑λjL2j ≤ θL20

∑λjKj ≤ K0

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∑λj = 1

λj ≥ 0, j = 1, 2, . . . , N

Now the expected optimal values of θ will be less than or equal toone. The production frontier is taken here as a log-linear model andthe values of inputs and output are transformed into log values. Theproportion of underutilization is calculated as (1 – θ)/θ. The proportionsof underutilization of labor and capital are estimated from the state-leveldata for the electronics industry.

Empirical analysis

First we try to get some idea about the changes in the efficiency of tex-tiles and electronics – one from the traditional and the other from themodern 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 period1980–1 0.0626 0.1561 0.3181 0.6237 0.58201981–2 0.0623 0.1117 0.1634 0.7368 0.49051982–3 0.0861 0.1536 0.2791 0.6569 0.55001983–4 0.0146 0.0371 0.1171 0.5006 0.48991984–5 0.0405 0.0926 0.1551 0.6943 0.49361985–6 0.0235 0.1070 0.1615 0.7058 0.49371986–7 0.0624 0.1201 0.2035 0.6633 0.51821987–8 0.0791 0.1426 0.2205 0.7040 0.51071988–9 0.0642 0.1435 0.2104 0.7678 0.50011989–90 0.0173 0.0526 0.1157 0.6286 0.51841990–1 0.0078 0.1326 0.2116 0.7202 0.4997Average 0.0473 0.1136 0.1960 0.6729 0.5133

Efficiency during the post-liberalization period1991–2 0.0372 0.1003 0.2239 0.5757 0.50351992–3 0.0459 0.0915 0.1443 0.7128 0.45941993–4 0.0392 0.0736 0.1378 0.6435 0.33811994–5 0.0761 0.1295 0.2241 0.6282 0.33941996–7 0.0152 0.0838 0.1384 0.6815 0.33681997–8 0.0512 0.1439 0.1902 0.8343 0.36541998–9 0.0472 0.0890 0.1525 0.6563 0.37411999–2000 0.0172 0.0759 0.2119 0.4397 0.37192000–1 0.0293 0.1062 0.1949 0.5791 0.37202001–2 0.0927 0.1503 0.2276 0.7249 0.38712002–3 0.0170 0.1301 0.1749 0.8092 0.3650Average 0.0426 0.1067 0.1837 0.6623 0.3830

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the values for five types of efficiencies in the textiles industry during thisperiod. The average efficiency during the post-liberalization period waslow compared to that in the pre-liberalization period. However, the fallsin the values are not significant and no trends of the values during theperiod are observed. If we consider the values of efficiencies estimatedusing the stochastic frontier model, a major fall in the level of efficien-cies is observed during the post-liberalization period. There is a suddenfall in efficiency in the year 1993–94, just after the year when the liber-alization policy was undertaken by the government of India. The valuerose again during the latter period but it never reached the level of pre-liberalization period. This fall in the early phase of liberalization may bedue to structural adjustment problems in the industries having to copewith the policy of liberalization.

Table 5.12 presents the values of efficiencies in the electronics industryduring the same period. The values of efficiencies show similar fea-tures to those in the textiles industry. The levels of efficiencies in thepost-liberalization period were low compared to those during the pre-liberalization period. Similarly to the textiles industry, the values ofefficiencies do not show any trend and fluctuate during this period.

The ownership-specific output-oriented efficiencies for six differentyears are shown in Table 5.13. The values of efficiency for the textilesindustry indicate that firms belonging to central government organi-zations did better than in privately owned firms. This feature is moreprominent in the efficiencies estimated with the DEA model and simi-lar to our earlier results using the input-oriented nonradial measure. Onthe other hand, efficiencies in the electronics industry (Table 5.14) showthat the firms in the private sector are more efficient than those in othertypes of ownership. The levels of efficiencies for both industries in thepost-liberalization years are comparatively lower than those in the yearsbefore liberalization.

The state-specific efficiencies in the textiles industry along with theirranks for six discrete years are given in Table 5.15. We have consideredhere the DEA efficiency with VRS and efficiency measured by stochasticfrontier analysis. We have already noted that efficiencies fell over timebut not without fluctuations. The same feature is followed here for all thestates we considered in the analysis. The ranks of the states in terms oftwo types of efficiencies changed over this period. States that registeredhigh ranks for most of the years were Haryana, Maharashtra and Panjab.The second group of states, which registered high rankings for some ofthe years, are Gujarat, Rajasthan and Goa.

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Table 5.12 Average efficiency of the electronics industry

Year COLS CRS VRS Scale Stochastic

Efficiency during the pre-liberalization period1980–1 0.0778 0.1990 0.2433 0.8561 0.51251981–2 0.0720 0.1765 0.2298 0.8394 0.53621982–3 0.0947 0.1640 0.2235 0.8225 0.56041983–4 0.1065 0.1803 0.2378 0.8346 0.48051984–5 0.0431 0.1145 0.1826 0.7588 0.51291985–6 0.0642 0.1231 0.1668 0.8414 0.55611986–7 0.0672 0.1195 0.1801 0.7776 0.64041987–8 0.1119 0.1728 0.2190 0.8649 0.45021988–9 0.0339 0.1983 0.2416 0.8508 0.54261989–90 0.0507 0.0910 0.1682 0.7564 0.99291990–1 0.0718 0.1186 0.1715 0.7874 0.4295Average 0.0722 0.1507 0.2058 0.8173 0.5649

Efficiency during the post-liberalization period

1991–2 0.0557 0.1078 0.1644 0.7829 0.51471992–3 0.0544 0.1155 0.1828 0.7828 0.51801993–4 0.0602 0.1273 0.2293 0.6729 0.44291994–5 0.0685 0.1207 0.1670 0.8283 0.43391996–7 0.0465 0.0968 0.1316 0.8664 0.46381997–8 0.0595 0.1115 0.1790 0.7066 0.38741998–9 0.0512 0.1171 0.2609 0.4947 0.34851999–2000 0.0711 0.1538 0.2334 0.7166 0.50462000–1 0.0768 0.1827 0.2421 0.7987 0.37852001–2 0.0546 0.0867 0.1457 0.7680 0.40302002–3 0.0363 0.0804 0.1577 0.6070 0.4040Average 0.0577 0.1182 0.1904 0.7295 0.4363

Table 5.16 shows the state-specific vales of efficiencies for the samediscrete point for the electronics industry. The ranks of the states do notshow any consistency of values from which a cluster of states can beformed according to their efficiency. Goa and Uttar Pradesh are the twostates that show high rankings in terms of efficiency for most of the yearswe have examined.

It has been argued that efficient firms in an industry are more homoge-neous in terms of output and input structure. We examined this featureof firms belonging to the textiles and electronics industry for some dis-crete time point. Table 5.17 gives the distribution of outputs of the textileindustry for both efficient and inefficient firms measured by the output-oriented BCC model. It can be seen from the table that the efficient firms

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Table 5.13 Ownership wise average efficiency of the textiles industry

1980–1 1985–6 1990–1 1994–5 1999–2000 2002–3

VRS efficiencyWholly central 0.3376 0.1232 0.2588 0.3987 0.1017 0.0748government

Wholly state and/or 0.3017 0.1122 0.2431 0.4546 0.1384 0.1167local government

Central and state 0.3777 0.1676 0.1966 0.4112 0.0834 0.0492government jointly

Joint sector public 0.2669 0.0992 0.2368 0.5288 0.1534 0.1499Joint sector private 0.4588 0.1392 0.2466 0.5010 0.2569 0.1227Wholly private 0.3257 0.1133 0.2070 0.6579 0.2240 0.1829ownership

Stochastic efficiencyWholly central 0.5367 0.4471 0.4446 0.2079 0.1741 0.2630government


Central and state 0.5838 0.5004 0.4499 0.1687 0.1994 0.1633government jointly


are comparatively bigger than, the inefficient firms. Again, the other val-ues of distribution of output of the firms indicate that the efficient firmsin the textile industry are less dispersed compared to the inefficient firms.The percentage of efficient firms in the textiles industry averaged aboutaround 2 per cent of the total firms in that year.

A similar exercise was carried out with the firms in the electronicsindustry. The results in Table 5.18 indicate that the character of the firmsin the electronics industry is the same as we found in the analysis of thetextiles industry. The efficient firms are larger in size and more homoge-neous in output size. The percentage of efficient firms in the total wasaround 4 per cent over this period of study.

We now deal with the analysis of underutilization of inputs, namelylabor and capital, in electronics industries in different states during thepostliberalization period. It is expected that due to the enactment ofthe policy of liberalization competition among the states in attracting

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Table 5.14 Ownership wise average efficiency of the electronics industry

1980–1 1985–6 1990–1 1994–5 1999–2000 2002–3

VRS efficiencyWholly central 0.2985 0.2022 0.3358 0.3843 0.4196 0.2164government


Central and state 0.2382 0.1626 0.2268 0.0368 0.0480 NAgovernment jointly


Stochastic efficiencyWholly central 0.5463 0.4644 0.3625 0.3845 0.5017 0.3690government


Central and state 0.5120 0.5599 0.5216 0.3763 0.4146 NAgovernment jointly


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

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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 efficiencyAndhra Pradesh 0.2671 11 0.1180 11 0.1843 13 0.2171 9 0.2268 7 0.1579 10Chandigarh 0.2558 12 0.0949 16 0.2547 4 0.1726 17 0.1543 14 0.0000 18Delhi 0.2201 15 0.1573 5 0.2365 5 0.1836 15 0.2402 6 0.0720 16Goa 0.3749 3 0.2054 1 0.2256 8 0.1576 18 0.1824 11 0.1828 7Gujarat 0.1780 16 0.1265 10 0.2101 11 0.2079 11 0.1785 12 0.1492 11Haryana 0.3515 4 0.1085 14 0.3470 1 0.3793 1 0.2522 3 0.3318 1Himachal Pradesh 0.3892 2 0.1355 8 0.2610 2 0.3120 2 0.0853 18 0.0170 17Karnataka 0.2816 9 0.1129 13 0.2105 10 0.2159 10 0.1519 15 0.0991 14Kerala 0.3135 8 0.1046 15 0.1731 16 0.1943 14 0.1212 17 0.1046 13Madhya Pradesh 0.2445 13 0.1366 7 0.1638 17 0.2368 5 0.2499 4 0.2128 5Maharashtra 0.6160 1 0.1895 3 0.2266 7 0.2567 4 0.2084 8 0.1350 12Orissa 0.3185 7 0.0813 18 0.1091 18 0.1735 16 0.1699 13 0.2251 3Pondicherry 0.2782 10 0.0824 17 0.2135 9 0.2340 6 0.1251 16 0.1806 8Punjab 0.3320 6 0.1667 4 0.2576 3 0.2310 8 0.2546 2 0.2443 2Rajasthan 0.3363 5 0.2006 2 0.2295 6 0.2324 7 0.2459 5 0.2164 4Tamil Nadu 0.2431 14 0.1266 9 0.2025 12 0.2572 3 0.2058 10 0.1654 9Uttar Pradesh NA 0.1153 12 0.1766 14 0.1999 12 0.2065 9 0.1991 6West Bengal NA 0.1436 6 0.1763 15 0.1985 13 0.3255 1 0.0982 15

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Stochastic efficiencyAndhra Pradesh 0.5347 14 0.4468 10 0.4604 12 0.3306 9 0.4035 5 0.3492 7Chandigarh 0.5380 12 0.4431 11 0.5384 5 0.1602 18 0.2019 17 0.0002 18Delhi 0.5406 11 0.4841 7 0.5264 6 0.3054 12 0.5147 1 0.2215 16Goa 0.5968 6 0.5341 3 0.5166 8 0.2528 15 0.3568 10 0.3434 8Gujarat 0.5497 10 0.4852 6 0.5220 7 0.3298 10 0.3722 9 0.2920 12Haryana 0.5592 8 0.5668 1 0.5662 1 0.4506 2 0.3964 6 0.4716 1Himachal Pradesh 0.6073 4 0.4115 16 0.5386 4 0.4677 1 0.1786 18 0.0743 17Karnataka 0.5216 15 0.4268 13 0.4532 13 0.3224 11 0.2867 14 0.2409 15Kerala 0.5662 7 0.4411 12 0.4144 15 0.2828 14 0.2612 15 0.2860 13Madhya Pradesh 0.5534 9 0.4202 14 0.4135 16 0.3456 8 0.4066 4 0.4019 5Maharashtra 0.6412 2 0.5192 5 0.5112 9 0.3630 7 0.3390 11 0.2940 11Orissa 0.6575 1 0.3347 18 0.3482 18 0.1990 17 0.2875 13 0.2969 10Pondicherry 0.6261 3 0.3709 17 0.5022 10 0.3713 5 0.2595 16 0.4037 4Punjab 0.6058 5 0.5315 4 0.5605 2 0.3706 6 0.4315 2 0.4136 2Rajasthan 0.5349 13 0.5604 2 0.5558 3 0.3800 4 0.4138 3 0.3698 6Tamil Nadu 0.5013 16 0.4630 8 0.5020 11 0.3917 3 0.3896 7 0.4057 3Uttar Pradesh NA 0.4521 9 0.4433 14 0.3019 13 0.3222 12 0.3344 9West Bengal NA 0.4131 15 0.4100 17 0.2396 16 0.3724 8 0.2837 14

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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 efficiencyAndhra Pradesh 0.1771 15 0.1613 10 0.1355 10 0.1061 8 0.1973 14 0.3217 1Chandigarh 0.3053 4 0.0707 17 0.0758 18 0.0838 5 0.2840 4 0.1561 9Delhi 0.2670 8 0.1705 8 0.1715 7 0.1871 12 0.2909 2 0.1465 10Goa 1.0000 1 0.1147 15 0.4408 1 0.1623 11 0.1531 17 0.0815 15Gujarat 0.1916 13 0.1397 13 0.2091 4 0.0907 7 0.2183 10 0.2240 6Haryana 0.2457 9 0.1846 6 0.1496 9 0.0710 3 0.2906 3 0.1614 8Himachal Pradesh 0.3420 3 0.0650 18 0.0915 17 0.3403 18 0.2822 5 0.2307 4Karnataka 0.1947 12 0.1504 11 0.1855 5 0.2359 15 0.1947 15 0.0330 18Kerala 0.2158 11 0.1320 14 0.1032 14 0.0732 4 0.2097 12 0.2474 3Madhya Pradesh 0.4535 2 0.0721 16 0.0949 16 0.1937 13 0.3492 1 0.0873 14Maharashtra 0.2407 10 0.1452 12 0.2175 3 0.1619 10 0.2011 13 0.2302 5Orissa NA 0.2625 1 0.1008 15 0.0567 2 0.2650 8 0.1348 11Pondicherry 0.0690 17 0.1800 7 0.1087 13 0.0373 1 0.1808 16 0.1840 7Punjab 0.1320 16 0.2126 4 0.1720 6 0.1101 9 0.2543 9 0.1089 12Rajasthan 0.1780 14 0.2106 5 0.1327 11 0.3171 17 0.2799 6 0.1051 13Tamil Nadu 0.2752 7 0.1682 9 0.1299 12 0.0844 6 0.1365 18 0.2911 2Uttar Pradesh 0.2914 5 0.2175 3 0.1590 8 0.2083 14 0.2693 7 0.0460 17West Bengal 0.2811 6 0.2306 2 0.2189 2 0.2412 16 0.2159 11 0.0622 16

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Stochastic efficiencyAndhra Pradesh 0.4867 10 0.5018 16 0.3427 17 0.3643 15 0.4341 18 0.3947 10Chandigarh 0.5176 8 0.5405 10 0.3998 10 0.4555 7 0.5126 7 0.3743 13Delhi 0.5554 5 0.6059 2 0.4446 6 0.4674 5 0.5685 1 0.4669 3Goa 0.7230 1 0.5706 5 0.5434 1 0.5585 1 0.5091 9 0.3507 14Gujarat 0.4414 15 0.5343 13 0.4439 7 0.3965 13 0.5097 8 0.3131 17Haryana 0.5875 4 0.5635 8 0.3710 14 0.4026 10 0.5171 4 0.4167 8Himachal Pradesh 0.7134 2 0.4642 17 0.3467 15 0.4603 6 0.5210 3 0.3872 12Karnataka 0.4860 11 0.5306 14 0.4497 4 0.4738 3 0.4967 10 0.3884 11Kerala 0.4827 12 0.5211 15 0.3849 11 0.3603 16 0.4865 12 0.3371 16Madhya Pradesh 0.5937 3 0.4600 18 0.3437 16 0.4212 9 0.5561 2 0.4553 4Maharashtra 0.5287 6 0.5592 9 0.4779 2 0.4380 8 0.4874 11 0.3976 9Orissa NA 0.5399 11 0.3363 18 0.3320 18 0.4489 16 0.2429 18Pondicherry 0.4324 16 0.7085 1 0.4760 3 0.3489 17 0.4828 14 0.5382 1Punjab 0.4637 14 0.5638 7 0.3770 13 0.3974 12 0.5135 6 0.4187 7Rajasthan 0.5092 9 0.5780 3 0.4189 9 0.4692 4 0.4783 15 0.4475 5Tamil Nadu 0.5247 7 0.5350 12 0.3840 12 0.3791 14 0.4849 13 0.3501 15Uttar Pradesh 0.4698 13 0.5701 6 0.4471 5 0.4839 2 0.5148 5 0.4669 2West Bengal 0.4301 17 0.5709 4 0.4310 8 0.4007 11 0.4430 17 0.4235 6

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184Table 5.17 Distribution of outputs of the textiles industry

1980–1 1985–6 1990–1 1994–5 1999–2000 2002–3

Efficient firms with the VRS modelMean 329,544,687 458,576,158 489,664,597 513,41,621 1,293,103,408 588,288,879Standard deviation 557,158,260 997,943,363 1,027,248,735 929,691,320 2,402,210,547 884,414,079Coefficient of var. 1.6907 2.1762 2.0979 1.8106 1.8577 1.5034Kurtosis 6.3311 5.1799 9.1982 8.1652 10.9598 4.0432Skewness 2.4771 2.4217 2.9732 2.7995 3.1187 2.0861Per cent efficient firms 2.9851 1.0572 1.3872 1.6360 1.7718 2.1292

Inefficient firms with the VRS modelMean 75,736,558 59,569,803 85,369,242 131,235,421 214,450,370 134,219,526Standard deviation 146,703,610 144,756,215 201,638,384 245,547,595 387,151,125 247,870,681Coefficient of var. 1.9370 2.4300 2.3620 1.8710 1.8053 1.8468Kurtosis 28.8181 51.2391 67.5237 52.4200 31.1260 31.1940Skewness 4.5006 5.8220 6.6764 5.8107 4.4795 4.6485

Efficient firms with the CRS modelMean 31,866,892 59,448,567 82,258,524 151,308,613 138,166,285 857,927,594Standard deviation 33,830,666 131,143,522 82,383,530 137,268,797 117,958,092 798,890,706Coefficient of var. 1.0616 2.2060 1.0015 0.9072 0.8537 0.9312Kurtosis 0.4474 12.4967 1.4658 −1.9970 0.0434 3.3929Skewness 1.3393 3.5083 1.3459 0.1384 0.5477 1.7727Per cent efficient firms 0.8641 0.4739 0.3567 0.7157 0.4961 0.9226

Inefficient firms with the CRS modelMean 83,761,340 63,808,909 91,008,993 137,389,310 234,037,489 137,238,061Standard deviation 179,273,305 180,686,838 238,196,290 275,282,803 516,658,566 266,468,671Coefficient of var. 2.1403 2.8317 2.6173 2.0037 2.2076 1.9417Kurtosis 49.9816 128.1408 106.2992 62.0546 147.4829 40.7390Skewness 5.7393 8.9903 8.3319 6.4379 9.1689 5.2566

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Table 5.18 Distribution of outputs of the electronics industry

1980–1 1985–6 1990–1 1994–5 1999–2000 2002–3

Efficient firms with the VRS modelMean 187,682,327 349,459,540 379,989,486 1,021,525,376 1,599,839,705 3,218,784,817Standard deviation 379,019,078 853,288,203 754,139,437 1,722,556,636 3,223,916,200 6,252,669,603Coefficient of var. 2.01947 2.44174 1.98463 1.68626 2.01515 1.94256Kurtosis 9.96985 10.88709 8.29137 6.23371 12.91167 4.57479Skewness 3.03101 3.22783 2.81430 2.37633 3.40118 2.30479Per cent efficient firms 4.05405 3.56201 3.61068 3.70370 6.73759 4.42708

Inefficient firms with the VRS modelMean 22,118,745 33,824,649 52,530,772 91,787,405 214,347,640 215,508,299Standard deviation 71,961,027 95,500,027 140,813,908 192,997,520 518,049,107 576,865,139Coefficient of var. 3.2534 2.8234 2.6806 2.1027 2.4169 2.6768Kurtosis 65.0695 41.9031 40.1876 32.8277 18.2624 35.7436Skewness 7.2417 5.7215 5.6724 4.8695 4.0557 5.5069

Efficient firms with the CRS modelMean 111,543,674 71,173,320 411,373,503 726,231,295 2,668,542,584 797,957,898Standard deviation 151,375,642 80,134,557 616,527,894 637,659,365 4,736,818,040 1,116,421,077Coefficient of var. 1.3571 1.1259 1.4987 0.8780 1.7751 1.3991Kurtosis 1.2889 −0.5618 5.4766 −1.2596 6.0486 4.1930Skewness 1.5616 1.0915 2.2932 0.7074 2.4033 2.0336Per cent efficient firms 2.1236 0.9235 1.4129 1.1696 2.8369 1.3021

Inefficient firms with the CRS modelMean 27,036,223 44,824,251 59,381,036 119,121,445 238,766,580 342,535,879Standard deviation 106,022,751 193,697,817 191,126,601 405,909,374 569,241,370 1,532,596,540Coefficient of var. 3.9215 4.3213 3.2186 3.4075 2.3841 4.4743Kurtosis 112.8982 214.8952 117.8810 152.1718 15.1164 125.2082Skewness 9.3965 12.8300 9.0563 10.8043 3.7630 10.4020

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Table 5.19 Percentage of underutilization of labor in the electronics industry

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

Andhra Pradesh 53 47 58 32 32 10 25 34 28 65 49 25 38Chandigarh 3 0 3 0 0 8 23 13 21 0 26 23 10Delhi 23 0 0 0 8 15 14 10 0 14 16 14 10Goa 0 0 0 0 0 0 0 0 3 0 13 0 1Gujarat 17 1 25 18 18 21 21 20 14 36 38 21 21Haryana 17 28 32 31 18 13 15 15 7 26 26 15 20Himachal 4 9 3 0 0 19 20 8 9 26 14 20 11Pradesh

Karnataka 10 8 17 14 14 31 28 32 22 42 40 28 24Kerala 17 27 37 35 27 20 26 30 18 40 35 26 28Madhya 23 18 21 26 20 27 20 29 23 41 44 20 26Pradesh

Maharashtra 17 3 23 35 27 27 24 27 18 32 37 24 25Orissa 38 28 49 43 41 16 0 0 27Punjab 35 30 30 41 32 0 0 0 0 0 0 0 14Rajasthan 50 32 44 33 27 37 0 48 30 42 44 37 35Tamil Nadu 10 5 10 26 29 19 37 53 19 39 35 23 25Uttar Pradesh 7 7 24 15 15 35 23 23 18 32 35 22 21West Bengal 29 27 21 21 26 24 22 32 33 33 39 21 27Average 21 16 23 22 20 19 19 23 16 29 29 19 21

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Table 5.20 Percentage of underutilization of capital in the electronics industry

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

Andhra 7 5 5 5 3 11 20 12 2 5 15 6 8Pradesh

Chandigarh 6 0 0 0 0 6 0 6 4 0 18 11 4Delhi 1 0 0 0 1 3 17 5 0 5 13 0 4Goa 0 0 0 0 0 0 9 0 1 0 12 0 2Gujarat 6 2 9 11 13 15 11 9 7 12 23 14 11Haryana 8 6 8 10 4 3 8 9 4 5 19 7 8Himachal 17 12 10 0 0 15 0 0 8 12 18 12 9Pradesh

Karnataka 5 2 6 3 1 9 6 5 4 7 17 8 6Kerala 7 6 8 8 6 9 10 3 3 5 15 7 7Madhya 16 15 17 11 7 10 2 8 8 9 19 9 11Pradesh

Maharashtra 3 0 6 7 3 9 8 9 6 8 14 8 7Orissa 13 13 14 13 11 4 0 0 0 0 0 0 6Punjab 12 8 11 11 5 0 3 0 0 0 0 0 4Rajasthan 8 5 6 8 3 13 11 11 10 13 24 12 10Tamil Nadu 9 4 6 6 5 6 17 12 9 10 20 11 10Uttar Pradesh 5 2 7 3 1 8 7 6 5 6 16 6 6West Bengal 5 4 5 8 11 5 0 7 12 11 21 11 8Average 7 5 7 6 4 7 8 6 5 7 15 7 7

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Table 5.21 Average efficiency of the computer industry

Total input Production Nonproduction Capitalefficiency workers workers

1989–90 0.446 0.517 0.452 0.3691990–1 0.379 0.413 0.385 0.3391991–2 0.404 0.329 0.504 0.3781992–3 0.333 0.427 0.315 0.2581993–4 0.391 0.451 0.399 0.3221994–5 0.287 0.290 0.271 0.3011996–7 0.280 0.297 0.334 0.2111997–8 0.288 0.370 0.310 0.185

Percentage of efficient firms1989–90 18.182 21.591 19.318 20.4551990–1 13.000 18.000 15.000 14.0001991–2 15.596 18.349 22.018 14.6791992–3 9.483 14.655 11.207 9.4831993–4 15.741 16.667 18.519 17.5931994–5 11.236 12.360 12.360 13.4831996–7 11.268 11.268 16.901 11.2681997–8 14.286 15.873 17.460 14.286

The figures for the state-specific average utilization of capital duringthe period indicate that Goa, Delhi, Chandigarh, Panjab, Uttar Pradeshand Karnataka did better in terms of efficient utilization of capital dur-ing the period. The ranking of the states remains similar to that in theutilization of labor. However, the utilization of capital was more efficientthan the utilization of labor for all the states and the variation in the per-centage of utilization of capital among the states was small compared tothat in labor utilization. The year-wise average ranking of all the statesagain does not indicate any improvement in the utilization of capitalduring the post-liberalization period.

Finally, we estimated a nonradial efficiency of inputs for only com-puter manufacturing firms during 1989–90 to 1997–98. Contrary to thegeneral belief, the input efficiency of this industry also fell during thepost-liberalization period. Table 5.21 shows that the efficiency of twotypes of workers and capital fell over this period. The percentage figuresof efficient firms in terms of all types of input uses also fell during theperiod of post-liberalization, but not without fluctuations.


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

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(fixed ranking) model is used to test statistically the trends in efficiency.Relaxing the assumption of fixed ranking, a variable-ranking model ofCornwell et al. (1990) has also been applied to the same set of data. Theseanalyses confirmed the decreasing efficiency of Indian industries from1974–75 to 1987–88. Third, there are substantial variations in techni-cal efficiencies across industries. But here again, the relative rankings ofthe industries are similar in all these estimates. Finally, the analysis ofthe sources of efficiency variations suggests that among the economicfactors that are basically internal to the industries, skill, labor productiv-ity, profit, capital utilization and industry dummy contribute a majorpositive part in explaining the variations in efficiencies. Contrary togeneral belief, capital intensity plays a significantly negative role inexplaining this variation. This may be due to the problems relating to def-inition of capital, shortage of working capital and inappropriate choiceof technology.

If we compare the efficiency rankings of the industries, it is found thatmost of the consumer goods industries registered higher efficiency rank-ings relative to capital goods and intermediate goods industries duringthis period. Interestingly enough, most of these industries emerged inthe economies of the NICs in the seventies. NICs like India have tried tofocus on these commodities from the late seventies onwards. In India,this new set of commodities has found renewed importance during theSeventh Five Year Plan as sunrise industries.

Thus, the main presumption on which the liberalization policy wasimplemented, namely that Indian industries were increasingly becominginefficient over the period, is supported by our analysis. But the expec-tation that globalization would cure this inefficient industrial regimecannot be so easily affirmed. The analyses that follow are carried outto examine the effects of industrial liberalization on the performance ofIndian industries.

There is a mild upward trend of efficiencies, estimated using the non-radial measure, in both the industries, after a fall in the values duringthe early phase of liberalization. It has been seen in this analysis that inthe textiles industry states that show the better utilization of productionworkers are not capable of maintaining a higher efficiency utilization ofnonproduction workers. However, the rankings of the states in terms ofboth production and nonproductions worker are almost the same in theleather industry. But for Delhi the efficiency of non-production workersis lower than that of production workers. In terms of input and outputefficiencies the ranks of the states for the years are similar. As expected,Delhi, Maharashtra and Gujarat as major textile-producing states per-form better than other states. The output efficiencies of Delhi and Tamil

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Nadu in the leather industry are higher than those of the other states.In terms of input-oriented efficiency manufacturing units belonging tothe private sector perform worse than those in the state-managed sectorfor the textiles industry. The input efficiencies of the leather industry,however, indicate that privately run and state government run organi-zations perform better than the central government organizations. Inexplaining the total efficiency of the textiles industry skill plays a sig-nificant positive role, while it has a negative effect in explaining thevariation in efficiency of production workers. Scale of operation plays asignificant positive role in explaining the variation of efficiencies in theleather industry. There is no significant difference in the level of totalefficiencies 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 Indiado not show any significant improvement during the post-liberalizationperiod as envisaged by the policymakers. In terms of input utilizationit has been found that some states with a smaller geographic area, suchas Goa, Pondicherry, Haryana and Delhi, perform better than the largerstates. The average capital utilization of firms in all the states is higherthan labor utilization and the variation among the states in capitalutilization is also low compared to that in labour utilization. A radialmeasure of input efficiency in the computer hardware industry indicatesthat the efficiencies of all type of inputs fell during the post-liberalizationperiod.

Appendix 5.1

It is well known that there is no unanimously accepted method of mea-suring capital stock. There are both theoretical and empirical problemsin measuring capital over time. The main problem is to judge whetherone should take the gross fixed capital stock (GFCS) or net fixed capitalstock (NFCS) as the best measure of capital input required for measuringefficiency and productivity. Most economists generally prefer the GFCSto NFCS for the purpose of production function and related issues. Thereare two reasons. First, as pointed out by Leontief (1953), ‘Use of depreci-ated coefficients implies that capital stock decreases in efficiency in exactrelation to depreciation charge’, whereas ‘most available evidence indi-cates that this is not a reliable assumption’. The other reservation abouttaking the NFCS is an empirical limitation. It is argued that the availableestimates of depreciation are based on either tax-based accounting or a

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certain rule of thumb. Naturally, it is preferable to work with gross fixedcapital stock.

We have estimated the gross fixed capital stock using the perpetualinventory accumulation (PIA) method. GFCS up to 1971 (for which dataseries are available consistently without any break) was calculated usingthe figure of GFCS for the benchmark year 1964 taken from the studyof Hashim and Dadi (1973). We took the NFCS and the depreciationfor those years from ASI census sector at the four-digit level and addedthem up to the two-digit level according to our requirements. Then thegross:net ratio for 1971 was used in 1974 to calculate the GFCS at 1974assuming that the ratio did not change significantly. And for the remain-ing years GFCS was calculated using PIA. We have assumed that thegross:net ratios for the census and sample sectors are the same for eachindustry group. Moreover, since we have estimated the gross:net ratiofrom Hashim–Dadi estimates of GFCS at the benchmark year, we haveextended those two-digit industries into our three-digit classificationwhenever required.

Appendix 5.2

NIC 265: Manufacture of all types of textile garments

1 Manufacture of custom made wearing apparel.2 Manufacture of ready made garments – hand printed.3 Manufacture of ready made garments – hand embroidered.4 Manufacture of ready made garments – other than (2) and (3).

NIC 291: Manufacture of foot wares

1 Manufacture of leather shoes.2 Manufacture of leather cum rubber/plastic/cloth shoes.3 Manufacture of leather sandals and chappals.4 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.

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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 andparts.

2 Manufacturing of television receivers, apparatus for radio broadcast-ing, radio telephony, video recording/reproducing, record/cassetteplayers and others.

3 Manufacture of computers and computer-based systems.4 Manufacture of electronic valves, tubes and other electronic

components.

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6The Performance of the BankingSector in the New Economy

1 Introduction

The primary aim of liberalization is to strengthen the market mecha-nism by eliminating relative price distortion to achieve efficiency andgrowth, and to improve the performance of deregulated industry byencouraging competition. The presence of fixed transaction costs asso-ciated with every transaction will encourage borrowers and depositorsto form a coalition to share the burden of transaction costs. A coali-tion of large number of investors will be able to invest in less liquid butmore profitable securities to fulfill the individual investor’s liquidity-intact demands for the appropriate size of the coalition. The bankingsector of an economy plays a significant role in the financial life of theeconomy by functioning, particularly, as a financial intermediary, offer-ing access to a payment system, transforming assets, managing risks,processing information and monitoring borrowers. As the banking sec-tor performs the task of intermediation efficiently, the cost of loanablefunds reduces and that encourages investment expenditure, resulting ina potential increase in the rate of economic growth. Improvements incompetition and proficiency allocate resources efficiently for the ben-efit of the economy by reducing the prices of services extended to itscustomers.

India initiated reform in the banking sector as an important counter-part of broad economic reform in 1991. Most of the commercial bankswere under the control of the overregulated and overadministered publicsector. Keeping pace with global changes in banking liberalization, Indiaresorted to liberalization and deregulated the banking sector to copewith the ongoing reforms in real sectors. The reform measures aimedto strengthen prudential norms relating to income recognition, asset

193

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classification, provisioning for bad and doubtful debts and capital ade-quacy for the banking system. Other reforms included relaxation of theadministered structure of interest rates, and a gradual reduction of SLR(statutory liquidity ratio) and CRR (cash reserve ratio). The new policyprovided licenses for the entry of new banks in the private sector, allow-ing private sector banks to access the capital market to augment theircapital base, the establishment of debt recovery tribunals for the pur-pose of helping the banking system to recover its debts and the settingup of an ombudsman to resolve customer grievances.

The nationalization of banks helped to diversify credit access for smallindustries and farmers across the country. The concept of governmentintervention in the process of development emerges in the context ofmarket failure. This leads to a need to keep interest rates lower than mar-ket clearing levels. The focus of the financial institutions should be onsocial priorities instead of profit maximization, and deliberate credit allo-cation for development purposes instead of a market-driven process. Theinstitutions successfully achieved their deposit mobilization objective byexpanding branch networks in all parts of the country. Bank credit wasan extremely scarce commodity. Policymakers formulated guidelines forcredit rationing so that, based on production requirements, credit wasavailable to unit. The aim was to reduce the misuse and diversification ofcredit in nonapproved investments on the part of resource users. Creditflow to large industries was regulated and rationed. Social planners triedto adopt measures to address the discrimination against small industriesand farmers in the share of scarce credit resources to fulfill the objec-tive of development with equity. Interest rates on deposits and advanceswere highly regulated. So briefly we can conclude that allocations ofloans through the market mechanism were either very weak or absent.The concept of price competition lies outside the purview of bankingactivities, particularly for public sector banks. However, the expectationwas that in the regime of regulated interest rates by the state, banks couldcompete with each other for scarce savings by providing better conve-nience to customers, with more branches or more employees per officeor per geographic area of operation. This is clearly an underutilization ofphysical and human capital.

The performances of firms are often measured in terms of their effi-ciency. Conventional wisdom holds that a policy of deregulation alwaysimproves efficiency. In this chapter, we investigate the performanceof commercial banks in terms of technical efficiency, which measuresmanagerial performance against the improved resources managementpractices of recent years. The technical efficiency of a firm can be

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The Performance of the Banking Sector in the New Economy 195

measured in two ways: input-oriented measures, i.e. comparison of theobserved level of inputs with the minimum level of inputs that couldproduce the observed level of output; and output-oriented measures fora given firm as the ratio of outputs of the firm under consideration usingthe same input vector to the outputs as a fully efficient firm. Output-oriented pure technical efficiency for a given firm is defined as the ratioof output of a firm using the same input vector to output of the firmoperating on constant returns to scale technology using the same inputvector. Output scale efficiency compares the efficiency scores of a firmunder CRS and VRS assumptions. It captures whether the firm operatesat the optimum size or right size. We have not seen any major study thatinvestigated the scale efficiency aspect of Indian commercial banks. Theconventional technique is to measure it from cost function or from costfrontier. Nevertheless, this process poses problems when prices are dif-ficult to estimate or unavailable. Efforts have been made to investigatethe technical and scale efficiency scores of Indian commercial banks notfrom a cost angle but from a production perspective.

The second objective of this chapter is to investigate the nature of inputcongestion in Indian commercial banks of the three ownership types.For the analysis, first the presence of input congestions in the banks isestablished and then the sources of input congestions are identified. Thecomparison of input congestions among the three types of banks revealssome interesting features of Indian commercial banks in terms of theirinefficiency.

Third, the results for the input congestions suggest an analysis of theproblem of input mix in explaining the inefficiency of Indian commer-cial banks. To understand the problem of input mix we have estimatedthe allocative efficiency of banks using the standard cost frontier model.In the absence of market prices of inputs some derived prices can beestimated for the inputs of banking operations. Those prices, along withthe corresponding inputs, are often used to estimate the allocative effi-ciency of banks. The alternative way to find out the cost efficiency ofbanks is to estimate cost frontier using total cost incurred for a givenoutput, defined in terms of advances, investment etc. of banks. This isbasically a cost function approach from production economics. Indiancommercial banking is particularly interesting in the sense that it con-sists of different ownership forms: state-owned, privately owned andforeign-owned banks. The coexistence of public, private and foreignbanks gives us a good opportunity to investigate possible relation-ships between the efficiency scores and ownership forms of commercialbanks.

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The organization of the chapter is as follows. Section 2 presents anoverview of the Indian commercial banking sector. In section 3 wepresent a review of the literature, and section 4 describes the method-ology used in our study. Section 5 deals with the input and outputset specification and the data source. Section 6 analyzes the empiricalfindings 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 largenumbers of institutions providing finance to different sectors of the econ-omy. During the five year plans, the RBI (Central Bank in India) and thegovernment nurtured and encouraged commercial banks through vari-ous financial incentives and other supportive programs to provide cheapfinance to encourage industries to implement the import substitutiongrowth model adopted by the planning commission of India. There wasa significance presence of foreign banks as well as domestic banks. Thecommercial banks comprise foreign banks operating in India, public-and private-sector Indian banks and regional rural banks (RRBs). Therewere two nationalizations of banks in India, one in 1969 and the otherin 1980. The activities of private-sector and foreign banks were restrictedthrough branch licensing and entry regulation norms. The nationaliza-tion of banks provided an impetus to change and gave a new orientationto the system as a whole. It allowed nationalized banks to spread theiractivities in rural and semi-urban areas to mobilize deposits and extendedcredit, which integrated the barter economy into mainstream financialactivities. Deposit mobilization and lending were the main objectives ofthe public-sector banks (PSBs) in India. Deposit mobilization in each yearwas the performance indicator for bank officials. Profitability and effi-ciency had an insignificant role in evaluation of performance. Depositsize was the only measuring rod to judge the performance of employ-ees as well as the balance sheet of every bank. The share of advances tothe priority sector increased considerably following the nationalizationof major banks. RBI has heavily regulated market entry or exit, capitaladequacy, reserve and liquidity requirements, asset portfolio allocation,number of branches, deposit insurance and interest rates on deposits andloans.

RBI has set the target of 40 per cent of net bank credit going to the prior-ity sector for Indian commercial banks (both public-sector and domesticprivate-sector banks) and 32 per cent for foreign private banks operatingin India.

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The overregulated and overadministered polices eroded the capitalbase of most of the public-sector banks and recapitalization of 19 nation-alized banks was carried out by the government through budgetaryprovision during the recent period. The government also provided thebanks with money towards writing down the capital base for adjustmentof their losses.

But acute problems arose in the productivity, efficiency and profitabil-ity front of the commercial banks. The policy of directed investment inthe form of high SLR and CRR, directed credit programs, extra admin-istrative interference in credit decision making, high operating costs,regulated interest rates, a nontransparent accounting system coupledwith the nonexistence of operational flexibility, internal autonomy andthe absence of competition contaminated the health of the commercialbanks and threatened their future survival.

Financial sector reforms became inevitable to cope with the ongoingreforms of the real sector coupled with the deterioration of the bank-ing sector health and introduction of BIS capital adequacy norms. Thegovernment has paid attention to recapitalization of public sector banksthrough the provision of budgetary support and resource mobilizationfrom the capital market.

The Committee on Financial Systems (GOI, 1991), with the objec-tive of fabricating an efficient, prudent and internationally competitivesystem, suggested a more market-friendly blueprint for first-generationreforms of the financial sector. Liberal policies aimed to increase mar-ket competition among banks to augment efficiency and productivity,and allowed the management of individual banks to make independentdecisions about input–output and prices. The Committee on BankingSector Reforms (GOI, 1998) suggested a road map for second-generationreforms to keep pace with the liberalization of financial sectors in otherparts 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 banksto determine lending and deposit rates.

3 Competition was infused through the operation of new private-sectorbanks and a more liberal entry regime for foreign banks.

4 A set of microprudential measures to impart greater strength to thebanking system and to ensure safety and soundness, with the avowedobjective of moving towards international best practices (capital

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adequacy norms, exposure limits, recognition rules for NPAs, pro-visioning 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 competenot only with local private-sector banks but also with foreign banks.Hardly any Indian bank can compete globally in the international mar-ket. 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 largepublic-sector banks to create large banks that measure up to globalstandards.

Although the reform was initiated in 1991, the transformation intoa fully price competitive setup was not effective until 1994. The entryrestrictions in the banking market on new private sector banks werediluted to accelerate competition provided they fulfilled certain crite-ria: a start-up capital requirement of Rs 1000 million, consecutive netprofit records for three years, a capital adequacy ratio of 8 per cent anda net NPA rate of less than 15 per cent.

It was mandatory on the part of commercial banks to get a licensefrom RBI to open new branches until 1992. RBI withdrew the practice ofbranch licensing and gave greater freedom to banks to rationalize theirexisting branch network to relocate branches and establish extensioncounters provided they attain the revised capital adequacy norms andprudential accounting system.

Foreign banks operating in India have gained the freedom to open newbranches, provided they also fulfill the norms set for the entry of newbanks. Foreign banks are also permitted to collaborate with new private-sector banks. Foreign equity in private banks is permissible. This allowsjoint ventures between local banks and foreign banks in the businessof nonbank financial services. The basic tenet of these polices is thewithdrawal of government intervention in the financial system by wayof ceilings in interest rates or direction of credit allocation and increasedfreedom of entry in the sector.

The efficiency and progress of the financial sector depends on port-folio management of assets, information acquisition and the stockof skilled human resources. Public-sector banks now enjoy greaterautonomy to recruit skilled and specialized human resources from theopen market with market-ruled remunerations to cope with the new

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technological and business challenges of the new and emerging bankingactivities.

The aim of the measures of liberalization in India from 1991 onwardswas to make the banking sector strong, efficient, functionally diverseand competitive, coupled with the preservation of safety and soundness.Indian banks have a limited lending exposure to sensitive sectors suchas equity trading, real estate business etc. Large holdings of governmentbonds have kept credit risk at a lower level. There is strict control overoff-balance sheet activities. Reforms provided greater operational flex-ibility and functional autonomy to boost efficiency, productivity andprofit. Foreign banks’ control of the banking sector’s assets and presencein India are insignificant. The entry of foreign banks will depend onthe structure, strength and competitive environment of domestic bankstogether with the regulatory framework. Foreign banks, with better tech-nology and knowledge in derivative trading, trade finance etc., can leadIndian domestic banks to concentrate expertise in these areas effectively.

The operational flexibility and functional autonomy of PSBs will def-initely improve due to partial privatization. The government diluted itsholding stake of equity to 51 per cent. It has further proposed reducing itsholding to a minimum of 33 per cent on a case-by-case basis. The entryof new private banks and foreign banks will promote competitiveness byintroducing new products and better technology.

The committee has undertaken deregulation to encourage competitionto increase productivity and efficiency. The banks, guided by the prin-ciple of the free market, are likely to change their product mix, clientmix and geographic areas of activity by executing appropriate humanresource management given the technological constraints. The banksmay opt for more risky assets to earn higher expected returns on assets.Banks are likely to shift higher funding costs and interest rate risk toborrowers. The synergic effect of deregulation-induced competition willlead to a higher level of efficiency, better resource allocation, innovationof products and progress in technology.

In 1997, RBI constituted a Committee on Capital Account Convert-ibility (CAC) under the Chairmanship of S. S. Tarapore. The road mapof CAC depends on fiscal consolidation, mandated inflation targetingand strengthening of the financial system. It recommended a number ofliberalization measures to provide operational flexibility and autonomyin the financial sector in order to promote efficiency, productivity andprofit. It advocates that the banking sector should fix targets of 5 and 3per cent for gross NPA as a percentage of total advances and cash reserveratio respectively.

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The Reserve Bank of India in 2006 appointed a high-level Committeeon Fuller Capital Account Convertibility (FCAC), also under ChairmanS. S. Tarapore. Restructuring the banking sector by providing appropriatesafeguards is necessary as the economy moves to a more open position onthe external front. As the economy integrates with the global system, thebanking sector will also integrate with rest of the world. The banks willbe exposed to greater volatility of markets in the FCAC regime. The FCACregime requires commercial banks’ involvement in multidimensionaloperations in situations of large inflows and outflows of capital. There-fore, it demands efficient management of exchange rate risk. The otherrisk elements (counterparty credit risk, transfer risk, legal risk and risk inderivative trading) are more prominent in the FCAC regime than now.The committee has recommended more liberal policies in conformitywith the earlier recommendations of the Narasihmam I and NarasihmamII committee reports.

A close look in Table 6.1 reveals that the Indian commercial bankingsector witnessed a phenomenal improvement in activities by coveringurban and semi-urban areas. The population served per branch rosemarginally due to high population growth. There was a growth indeposits, advances and priority sector lending. Evidence of the domi-nance of public sector banks in major banking activities is observed fromTable 6.2. Deregulation of interest rates on deposits and advances, cou-pled with the lowering of bank rate, SLR and CRR, have helped to lowerthe cost of deposits, return on advances and net interest margin (Tables6.2 and 6.3).

3 Review of the literature

Many studies have been organized to evaluate the performance of thebanking sector in developed countries (Ferrier and Lovell, 1990; Noulas,1997; Daniels and Tirtiroglu, 1998). Despite the fact that deregulationpolicies are aimed at increasing efficiency and productivity, the directlinkage between performance and deregulation may not always be uni-directional. One can see this from the experience of several countries. USbanks experienced no change in efficiency (Bauer et al., 1993; Elyasianiand Mehdian, 1995) but a reduction in productivity (Humphrey, 1993;Humphrey and Pully, (1997). Spain experienced an outcome similar tothe USA (Lozano, 1995; Grifell-Tataji and Lovell, 1997; Kumbhankaret al., 2001). Japan experienced little effect of deregulation on efficiency(Fukuyama, 1995). Norway, Portugal and Turkey experienced improved

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Table 6.1 Features of commercial banking

Indicator 1998 2006 % of growth

No. of commercial banks 300 222 −26.00Scheduled commercial banks 299 218 −27.09Number of offices of scheduled 64,218 69,471 8.18commercial banks in IndiaRural 32,878 30,579 −6.99Semi-urban 13,980 15,556 11.27Urban 9,597 12,032 25.37

Metropolitan 7,763 11,304 45.61Deposits of scheduled commercial 59,848.5 210,904.9 25.240banks in India (Rs million)

Credit of scheduled commercial 32,407.9 150,707.7 36.503banks in India (Rs million)

Per capita deposits of scheduled 6,170 19,276 212.41commercial banks (Rs)

Per capita credit of scheduled 3,356 13,774 310.43commercial banks (Rs)

Deposits of scheduled commercial 47.3 73.8 56.03banks as percentage of nationalincome (at current prices)

Scheduled commercial banks’ 1,089.05 5,467.74 4.0207advances to priority sector(Rs million)

Share of priority sector advances 34.6 37.2 7.51in total credit of scheduledcommercial banks (%)

Share of priority sector advances 36.1 38.2 5.82in total non-food credit ofscheduled commercial banks (%)

Credit deposit ratio 54.2 71.5 31.92Investment deposit ratio 36.5 35.5 −2.74Cash deposit ratio 10.2 6.6 −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 inthe Taiwanese banking system after deregulation. Leightner and Lovell(1998) reported that deregulation led to a significant improvementin efficiency for Thai banks. Gilbert and Wilson (1998) reported thatKorean banks improved their efficiency and productivity due to privati-zation and deregulation.

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Table 6.2 Summary of the banking sector (billion rupees)

1990–1 1995–6 2002–3 2004–5

PSB Pvt For. PSB Pvt For. PSB Pvt For. PSB Pvt. For.

No. of banks 28 25 23 27 35 29 27 29 38 28 29 31Total deposit 2,087 94 85 3,908 361 306 10,794 2,069 693 14,207 3,146 865Total credit 1,306 50 51 2,075 219 225 5,493 1,377 522 8,093 2,211 753Total income 240 104 15 539 72 75 1,285 316 121 1411 326 130Total profit 5 0.3 2 3 5 7 122 29 18 158 35 20

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

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Table 6.3 Selected banking indicators

Year Bank rate SLR CRR Cost of Return on Netdeposits advances interest

margin

1992 12.00 38.50 15.00 6.50 14.70 3.801993 12.00 37.80 14.50 7.40 17.20 3.001994 12.00 34.80 14.50 6.70 13.10 3.201995 12.00 31.50 15.00 5.80 10.90 3.201996 12.00 25.00 13.50 7.00 12.90 3.401997 11.00 25.00 10.00 7.80 14.60 3.301998 9.00 25.00 10.30 7.60 13.00 3.001999 7.00 25.00 10.50 8.00 12.70 2.702000 8.00 25.00 5.50 7.40 11.50 2.802001 7.00 25.00 8.00 7.20 11.10 2.802002 6.50 25.00 5.50 7.00 10.50 2.602003 6.25 25.00 4.75 6.40 9.90 2.702004 6.00 25.00 4.50 4.90 8.10 2.902005 6.00 25.00 5.00 4.20 7.20 2.90

Source: RBI.

Very few studies have been conducted to evaluate the performanceof the banking sector in developing countries like India. However,Bhattacharyya et al. (1997) studied the pre-deregulation period andreported that foreign-owned banks in India were somewhat more effi-cient than privately owned domestic banks but government-ownedbanks were more efficient than either. Saha and Ravishankar (2000)estimated technical efficiency and reported technical efficiency scoresranging between 0.58 and 0.74 with a mean score of 0.69 in the year1995 for Indian public-sector banks. Rammohan (2002, 2003) exam-ined financial indicators for Indian commercial banks and reported animprovement in performance. Rammohan and Ray (2004) concludedthat public-sector banks were better than private-sector banks on revenuemaximization efficiency but between public sector banks and foreignbanks the efficiency difference was not significant. Bhaumik and Dimova(2004) concluded that deregulation had helped public-sector banks toreduce the gap in performance that existed between them and pri-vate banks. Das et al. (2005) found that median efficiency scores ingeneral and of bigger banks in particular have improved during the post-liberalization period. Berger and Humphrey (1997) stated that industrycondition prior to deregulation and other interactions might intervene inguiding the direction of productivity and efficiency change. Kumbhakar

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and Sarkar (2005) analyzed the performance of Indian commercial banksduring the post-deregulation period. They examined the pattern ofchanges in efficiency using econometric methods. They found cost inef-ficiency of Indian banks during the post-liberalization period, and notedthat private banks, on average, were more cost efficient than public-sector banks. Sahoo et al. (2007) estimated the cost efficiency using aDEA model and found that public-sector banks were less efficient thanprivate-sector banks.

4 Methodology

Technical efficiency

The discussion in this section provides a very brief introduction toefficiency measurement. The measurement of firm efficiency has beendiscussed by Farrell (1957), Lovell (1993) and Färe et al. (1994). How-ever, modern efficiency measurement begins with Farrell (1957), whodealt with firms using multiple inputs for the first time. In paramet-ric models, one specifies the functional form of the production frontierand estimates the parameters using inputs and output. Efficiency deriveddepends on the appropriateness of the functional form assumed. Weestimate efficiency scores using data envelopment analysis (DEA), anonparametric approach that computes ‘best practice’ efficient fron-tiers based on convex combinations of firms in the industry. It is analternative mathematical programming based nonparametric approachto measuring efficiency rather than a conventional regression analysis.DEA involves the use of linear programming methods to construct a non-parametric piece-wise surface (or frontier) over data. Efficiency measuresare then calculated relative to this frontier. One can calculate the effi-ciency of a firm in terms of how far it is from the frontier. This providesa nonparametric alternative to parametric frontier production functiontechniques, i.e. no functional specification of production technology isrequired (Charnes et al. (CCR), 1978; Banker et al. (BCC), 1984). How-ever, the main limitation of this approach is that it eliminates randomerror. Banker (1993) showed that while the DEA estimator is biasedfor finite samples, the biasedness no longer exists for large samples.Therefore, the DEA estimator is asymptotically consistent. The technicalefficiency of a firm can be measured in two ways.First, there are input-oriented measures, i.e. comparison of the observed level of inputs withthe minimum level of input that could produce the observed of output.Output-oriented technical efficiency for a given firm is defined as the

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ratio of output of the firm under consideration using the same inputvector to the output a fully efficient firm.

In DEA, a benchmark technology is constructed from the observedinput–output bundles of the DMUs in the sample on the basis of somegeneral assumptions about the production technology without speci-fying a functional form. All observed input–output combinations arefeasible. An input–output bundle (x, y) is feasible when the output bundley can be produced from the input bundle x. If we have a sample of N firmsfrom an industry producing m outputs from n inputs, xj = (x1j, x2j, . . . , xnj)is the input bundle of firm j, ( j = 1, 2, . . . , N) and yj = (y1j, y2j, . . . , ymj) isthe observed output bundle. Then each (xj, yj) is a feasible input–outputbundle.

The production possibility set is convex, i.e. if two feasible input–output bundles (XB, yA) and (XB, YB) are considered, the weightedaverage input–output bundle (x′, y′), where x′ = λXA + (1 − λ)XB andy′ = λyB + (1 − λ)yB, 0 ≤ λ ≤ 1, is also feasible.

Inputs are freely disposable, i.e. if (x0, y0) is feasible, then for any x ≥ x0,(x, y0) is also feasible.

Outputs are freely disposable, i.e. if (x0, y0) is feasible, then for anyy ≤ y0, (x0, y) is also feasible.

In the CCR model an additional assumption holds, i.e. the assumptionof 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 theobserved input–output bundles (S).

S = {(x, y) : x ≥∑

λjxj, y ≤∑

λjyj; λj ≥ 0, j = 1, 2, . . . , N}

Under CRS, the output-oriented technical efficiency of a firm t producingoutput vector yt from input vector xt is 1/�∗, where

�∗ = max � : (xt , �yt ) ∈ S

This problem may be converted to a linear programming problem andthen our problem becomes

max �, s.t.∑λjymj ≥ �ymt , m = 1, 2, . . . , m

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∑λjxnj ≤ xnt , n = 1, 2, . . . , n

� free λj ≥ 0, j = 1, 2, . . . , N.

However, firms may face economies of scale or diseconomies of scaledue to market conditions, fiscal crises and regulatory polices. Farrelland Fieldhouse (1962) proposed transformation of data to include non-constant returns to scale. Forsund and Hjalmarsson (1979) decomposedFarrell’s measure of efficiency into scale efficiency and pure-technicalefficiency for the parametric production frontier. Banker et al. (1984)utilized Frisch’s (1965) concept of technically optimal production scaleefficiency. The VRS production possibility set (SV ) is the smallest setcontaining the free disposal convex hull of the observed input–outputbundles.

The production possibility set under VRS is

S = {(x, y) : x ≥∑

λjxj, y ≤∑

λjyj;∑

λj = 1; λj ≥ 0, j = 1, 2, . . . , N}

Under VRS, the output-oriented technical efficiency of a firm t producingoutput vector yt from input vector xt is 1/�∗, where

�∗ = max � : (xt , �yt ) ∈ SV

This problem is converted to a linear programming problem and thenour problem becomes

max �, s.t.∑λjymj ≥ �ymt , m = 1, 2, . . . , m

∑λjxnj ≤ xnt , n = 1, 2, . . . , n

� free λj ≥ 0, j = 1, 2, . . . , N.

One can determine the reference frontier points by imposing the sum ofweights of λ for the solutions of frontiers. No restriction is imposed onthe 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 technologyset and the CRS frontier is the largest; the NIRS technology set lies inbetween (Ray, 2004).

Output-oriented technical efficiency measures the ratio of theobserved level of outputs of a firm to maximal feasible outputs from

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a given input set. Output-oriented pure technical efficiency for a givenfirm is defined as the ratio of output of a firm using the same inputvector to the output of the firm operating on constant returns to scaletechnology using the same input vector. Scale efficiency of banks is animportant concept and different from technical efficiency measures. Wecan obtain scale efficiency for each firm by assuming that technology isVRS. The firm is scale inefficient if the CRS and VRS technical efficiencyscores of a particular firm differ. On the other hand, scale efficiency com-pares the efficiency scores of a firm under VRS and CRS assumptions. Itis the ratio of average productivity of a firm operating at the projectedpoint on the VRS frontier to the average productivity of the point oper-ating at the point of optimal scale. Scale efficiency arises when the firmis operating on either decreasing or increasing returns to scale. Calcu-lations of scale efficiency measures are relevant when frontier exhibitsvariable returns to scale. It assumes that the CRS frontier generates themost optimal scale. Scale efficiency shows how close the observed firm isto the most productive scale size. A firm is scale inefficient if it operatesbeyond the most productive scale size, i.e. when the firm is experiencingdecreasing returns to scale or if it fails to appropriate the full advan-tage of increasing returns to scale. Output scale efficiency compares theefficiency scores of a fully efficient firm under CRS and VRS assump-tions. 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 becauseof nonoptimal productive scale size. It remains silent about the returnsto scale.

Increasing returns to scale hold when technical efficiency scores withreference to NIRS and CRS are equal but different from the VRS frontier.On the other hand, decreasing returns to scale hold if technical efficiencyscores with reference to NIRS and VRS are equal but different from CRS.Technical efficiency scores with reference to NIRS, VRS and CRS are equalwhen the firm is scale efficient.

Measure of congestion

Färe et al. (1994) made a distinction between strong and weak disposabil-ity and strong disposability. Strong disposability of inputs implies thatif any input combination X0 can produce an output combination Y0

then any X ≥ X0 can also produce Y0. Similarly, strong disposability ofoutput implies that if X0 can produce Y0 then any output combinationY ≤ Y0 can be produced by X0. Weak disposability, on the other hand,implies that if all inputs are increased proportionately from X0 thenthe new input combination can also produce Y0. In weak disposability

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if any input is decreased there will be a reduction of at least oneoutput.

Now, the congestion of input can be defined as a situation when reduc-tion in one or more inputs can increase at least one output or increase ofany one or more inputs causes a reduction in one or more outputs. Thestandard BCC model of input efficiency under strong disposability canbe written as

min θS, s.t.

n∑j=1

λjxij ≤ θSxio, i = 1, 2, . . . , m

n∑j=1

λjyij ≥ yio, r = 1, 2, . . . , k

n∑j=1

λj = 1, j = 1, 2, . . . , n

λj ≥ 0

If we assume weak disposability of inputs the model can be written as

min θW , s.t.

n∑j=1

λjxij ≤ θWxio, i = 1, 2, . . . , m

n∑j=1

λjyij ≥ yio, r = 1, 2, . . . , k

n∑j=1

λj = 1, j = 1, 2, . . . , n

λj ≥ 0

The basic difference between these two models is that the input inequali-ties are changed into input equalities. The input congestion measure canthen be defined as η = θS/θW . Now, θS must be greater than θW becausethe latter is associated with equalities. If the value of η = 1 then the inputsare not congested and if η < 1 then we can say the input congestion ispresent in the production unit.

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Now, this measure does not reveal the source of congestion, i.e. whichspecific input or input bundle is responsible for the input congestion.Färe et al. (1994) suggested a method for identifying the sources of con-gestion. The inputs are arbitrarily partitioned into two groups XS and XW ,where the first subvector is treated as freely or strongly disposable andthe latter is treated as weakly disposable. The corresponding DEA modelfor identifying the inputs responsible for congestion can be written as

min θ, s.t.

n∑j=1

λjxSij ≤ θxS

io, i = 1, 2, . . . , S

n∑j=1

λjxWij = θxW

io , i = S + 1, . . . , m

n∑j=1

λjyij ≥ yio, r = 1, 2, . . . , k

n∑j=1

λj = 1, j = 1, 2, . . . , n

λj ≥ 0

If there exists input congestion, i.e. η < 1 and the optimal value of θS = θ∗,as obtained from the optimization of the model for subvectors associatedwith weak disposability, then it can be said that input congestion is dueto the specific input group.

Cost efficiency

In an input-oriented DEA measure of efficiency all the inputs are tocontract in the same proportion (radial measure) to reach the frontierwithout reducing any output. The prices of the inputs are not consid-ered in measuring the technical efficiency of the DMUs. However, it isoften necessary to consider the total cost of the inputs that are con-tracted in the same proportion to measure the efficiency of firms. Whenthe prices of the inputs are available, the firms may be interested in min-imizing the cost for a given level of output. Thus, for minimization ofcost the inputs are to be contracted at different proportions according tothe values of inputs. If we do not consider any underlying cost functionof the firm a cost frontier may be estimated by the DEA approach, similarto the production frontier DEA model. The DEA approach of measuring

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cost efficiency is an alternative to standard econometric modeling evenwhen prices exist (Ray, 2004). In a DEA model a firm is said to be costefficient if no other input bundle can produce the given level of outputat a lower cost than the optimum level.

Cost efficiency can be decomposed into technical efficiency and alloca-tive efficiency. Allocative inefficiency of any DMU comes from theselection of inappropriate input mix. This concept of allocative efficiencycan be illustrated graphically.

Let us say for simplicity that the production unit produces a singleoutput using two inputs and the production process follows a constantreturn to scale. It is also assumed that the efficient production functionis known.

If the objective of a producer is to minimize the wastage of input usethe performance of the production unit can be measured in terms oftechnical efficiency/inefficiency. On the other hand, if the objective of aproduction unit is to minimize cost for a given level of output or maxi-mization of profit by allocating inputs and outputs then the performanceof 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 ofVRS can be written as follows:

min∑i=1

woi xi, s.t.

n∑j=1

λjxij ≤ xio, i = 1, 2, . . . , m

n∑j=1

λjyij ≥ yio, r = 1, 2, . . . , k

n∑j=1

λj = 1, j = 1, 2, . . . , n

λj ≥ 0

The optimal solution of this linear programming model gives the costminimizing input bundle and the objective function value shows theminimum cost. The optimal input bundle will lie in the efficient subsetof the isoquant for the target output bundle. From this cost efficiencymodel we are interested in estimating the allocative efficiency of banks.

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It has already been explained that the ratio of cost efficiency to technicalefficiency will give the estimate of allocative efficiency.

In a cost function approach an input-oriented cost frontier DEA modelmay be set up in a DEA framework as

min θ, s.t.

n∑j=1

λjCj ≤ θCh, �λjyj ≥ yh

n∑j=1

y2j λj ≥ y2

h ,n∑

j=1

λj = 1, λj ≥ 0

The objective function value gives the minimum cost for given values ofoutput.

If firm h is on the cost frontier (θ∗ = 1.0), then

C∗h = γ0 + γ1yh + γ2y2

h

If γ0 is positive, then the average cost (ACh) may be minimized so as toobtain the MSE level of output y∗

h = (γ0/γ2)1/2, with the minimum ACgiven by c∗

h = C∗h/yh = 2(γ0γ2)1/2 + γ1.

We examine the performance of Indian commercial banks during therecent period after liberalization from the following indicators using theabove 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 andoutput set used to measure different efficiency scores of Indian com-mercial banks. In the banking literature, there is no consensus on thespecification of banks’ outputs and inputs. Banks as financial firms pro-vide a variety of services, loans, deposits, safe deposits, box rentals,mutual funds sales, foreign exchange transactions etc. For Indian sched-uled commercial banks, recent years have witnessed sweeping changesin the regulatory environment, a huge growth of off-balance sheet riskmanagement financial instruments, the introduction of e-commerce

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and online banking and significant financial industry consolidation. Allthese have resulted in an expansion of the domain of financial servicesprovided by commercial banks. For example, banks are now engaged insecurities-related services such as underwriting and mutual fund sales.

We can witness a wide variety of input and output specifications acrossthe studies using DEA to measure efficiency scores of financial intermedi-aries. There are two broad approaches, namely the production approachand the intermediation approach. Before we discuss these two broadapproaches and some other approaches, let us mention some problemsthose arise when the input–output specification of the banking sectoris concerned. There is no consensus among economists regarding thespecification 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 therole of the producer of deposits and loans as outputs, using capital andlabor as inputs, all measured in terms of number of transactions peraccount. The main problem regarding this approach is that it ignoresinterest receipts and payments of banks and the magnitude of individualtransactions. However, this approach may be of interest to evaluate theoperating efficiency of the bank. We are aware of a few studies in thisarea, including Sherman and Gold (1985) and Ferrier and Lovell (1990),who used the production approach.

The other broad and more frequently used approach to specifythe input and output set of commercial banks is the intermediationapproach, which assumes that the role of the banking sector is recon-ciling 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 includesboth operating and interest expenditure, to measure both the technicaland economic efficiency of a financial organization.

Some economists do not use either of the above-mentionedapproaches directly and specify the inputs and outputs used in theirstudy on some other consideration. Some researchers argue for themeasurement of efficiency as directly as possible, i.e. management’s suc-cess in controlling costs and generating revenues (that is, x efficiency).According to this approach, two inputs, interest expenditure and non-interest expenditure, and two outputs, interest income and noninterestincome, are taken. Sathye (2001) adopted this approach to measure theefficiency of Australian commercial banks.

Now we come to another interesting point and an issue of long-lasting debate in this literature. In the banking literature, there have

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always been differences of opinion regarding the specification of theinput–output set. Nevertheless, major disagreement concentrates onwhether one should treat deposits as an input or an output. In India,commercial banks work primarily as financial intermediaries with theobjective of collecting deposits. In India, until the eighties, banks wereoperating in a protective environment characterized by administeredinterest rates. India initiated a reform process to improve the produc-tivity and efficiency of the financial system. The reform included agradual deregulation of interest rates. However, the role of deposits asthe traditional main source of funds is still prevalent in the liberalizedperiod.

In India, commercial banks act mainly as financial intermediaries.Indian commercial banks collect deposits, give loans and invest primar-ily in government securities and other securities as well. In our study,we have used the intermediation approach. We have used data from Sta-tistical Tables Relating to Banks of India and Reports on Trends and Progressof Banking in India (both RBI publications). We investigated a sampleof 68 commercial banks (27 public-sector banks,1 28 domestic private-sector banks and 13 foreign banks) for a period of nine years (1996–97to 2004–05), for which a consistent series of data is available.

Since there are alternative measures of input and output for studyingthe performance of banks, the selection of inputs and outputs dependson the purpose of the study. Problem loans have been included in theoutput set in some studies of the efficiency of banks. Berg et al. (1992)included nonperforming loans in the nonparametric approach to effi-ciency study. Hughes and Mester (1993) and Mester (1996, 1997) appliedproblem loans in the parametric estimation of efficiency of commercialbanks. Das et al. (2005) used earning assets instead of total advancesin their analysis of Indian commercial banks. In our study, the threeinputs are: net worth of banks, that is the sum of capital and reservesand surpluses; number of employees; and loanable funds comprisingboth deposit and borrowing. In our analysis, we consider three outputs:net performing loans (advances in the terminology of Indian commercialbanks); investment; and noninterest income. We also consider assets netof fixed assets as a composite measure of output. We deflate both outputand input data by a single price index (wholesale price index). The priceof inputs is average staff cost as wages of labor, interest paid on depositper one rupee of deposit as price of deposit, and nonlabor operationalcost per rupee of fixed asset as price of capital. The sum of operatingexpenditure and interest expenditure is considered as a single measureof cost.

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6 Empirical analysis

Technical efficiency of Indian commercial banks

The main objective of this section is to measure the efficiency of theIndian commercial banks and analyze the possible differences betweenthe efficiency of banks of three ownership forms. The common argu-ment is that public-sector banks lack the ability to influence incentivesto reduce costs, so that private-sector banks are more productively effi-cient than public-sector ones. We examine the influence of ownershipon the efficiency of Indian commercial banks.

In this section, we report the performance of Indian commercial banksaccording to ownership, as well as taking all of them together. The resultsare calculated from the solution of DEA for each year separately for eachbank. The measure of technical efficiency calculates the proportionateincrease in output that can be achieved if the bank operates on the effi-cient frontier. As we mentioned earlier, the TE score can be decomposedinto pure technical efficiency (PTE) and scale efficiency (SE), these mea-sures will lie between zero and one. Table 6.4 reports the mean valuesof technical efficiency of all banks for all years studied. There exists nouniform pattern of movement of technical efficiency scores for all banksor for banks with different ownership. Contrary to the general beliefthe output-oriented average technical efficiency with the VRS specifi-cation for public-sector banks was greater than that of private-sectorbanks. Managerial efficiencies in the public-sector banks were high anddeclined marginally during the period, but not without fluctuations. Thetechnical efficiencies of foreign banks showed higher values comparedto the other two types of bank. Average values of PTE from the CRSspecification show that the difference between private-sector and public-sector banks was negligible. From a comparison of the scale efficienciesbetween public- and private-sector banks it can be argued that private-sector banks were more scale efficient than public-sector banks. Thereexists no uniform pattern of movement of scale efficiency scores eithertype of banks. However, it seems that in the initial years both the public-and private-sector banks were weak in respect of size and now they areachieving the optimum scale size, and getting more efficient than before.Figure 6.1 shows the differences in the level of technical efficiencies ofIndian commercial banks for the years 1996–97 to 2004–05.

The CRS frontier generates the most optimal scale. It captures whetherthe firm operates at the optimum or right size. The firm is scale inefficientif technical efficiency with the CRS specification of that firm is less thanthat with the VRS specification. Banks operating either at the DRS or at

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215

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

Average efficiency % of banks

CRS VRS Scale DRS IRS Scale efficient

(a) Public-sector banks1996–7 0.8629 0.9698 0.8894 77.78 0.00 22.221997–8 0.9000 0.9770 0.9207 74.07 0.00 25.931998–9 0.8409 0.9646 0.8728 85.19 0.00 14.811999–2000 0.8660 0.9587 0.9031 81.48 0.00 18.522000–1 0.8329 0.9410 0.8841 92.59 0.00 7.412001–2 0.8431 0.9116 0.9226 81.48 0.00 18.522002–3 0.8861 0.9346 0.9472 85.19 0.00 14.812003–4 0.8974 0.9453 0.9497 74.07 0.00 25.932004–5 0.8529 0.9253 0.9204 85.19 0.00 14.81

(b) Private-sector banks1996–7 0.8452 0.9044 0.9339 67.86 14.29 17.861997–8 0.8912 0.9229 0.9659 57.14 14.29 28.571998–9 0.8609 0.8811 0.9787 28.57 42.86 28.571999–2000 0.8703 0.8966 0.9717 17.86 53.57 28.572000–1 0.8316 0.8710 0.9571 42.86 42.86 14.292001–2 0.8317 0.8517 0.9763 10.71 60.71 28.572002–3 0.8452 0.8685 0.9736 28.57 53.57 17.862003–4 0.8686 0.8830 0.9838 21.43 50.00 28.572004–5 0.7960 0.8306 0.9616 30.77 46.15 23.08

(c) Foreign banks1996–7 0.9456 0.9618 0.9825 15.38 15.38 69.231997–8 0.9515 0.9832 0.9681 23.08 7.69 69.231998–9 0.8562 0.9232 0.9279 15.38 38.46 46.151999–2000 0.9409 0.9651 0.9742 15.38 23.08 61.542000–1 0.8971 0.9465 0.9455 7.69 30.77 61.542001–2 0.8414 0.9002 0.9352 15.38 46.15 38.462002–3 0.9294 0.9527 0.9715 7.69 23.08 69.232003–4 0.8850 0.9262 0.9518 7.69 30.77 61.542004–5 0.8423 0.9040 0.9300 7.69 38.46 53.85

(d) All banks1996–7 0.871 0.941 0.926 61.76 8.82 29.411997–8 0.906 0.956 0.948 57.35 7.35 35.291998–9 0.852 0.922 0.927 48.53 25.00 26.471999–2000 0.882 0.934 0.945 42.65 26.47 30.882000–1 0.845 0.913 0.926 55.88 23.53 20.592001–2 0.838 0.885 0.947 39.71 33.82 26.472002–3 0.878 0.911 0.963 47.06 26.47 26.472003–4 0.883 0.916 0.964 39.71 26.47 33.822004–5 0.822 0.879 0.937 48.48 25.76 25.76

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0.8200

0.8400

0.8600

0.8800

0.9000

0.9200

0.9400

0.9600

0.9800

1.0000

1997 1998 1999 2000 2001 2002 2003 2004 2005

Year

Effi

cien

cy

Public Private Foreign Total

Figure 6.1 Technical efficiency of Indian commercial banks

the IRS segment of the production frontier indicate a nonoptimal outputvector. Returns to scale are defined by the change in outputs resultingfrom a change in inputs. Increasing returns to scale arise when outputincreases more than the proportionate increase in inputs. Decreasingreturns signify a less than proportionate increase in outputs because ofthe increase in inputs. On the other hand, constant returns to scale occurwhen a proportionate increase in inputs is exactly equal to a proportion-ate increase in outputs. The percentage of banks with the optimum sizevaried from 35 to 21 per cent in our study period and did not show anyspecific trend. The percentage of private banks with optimum size wit-nessed a slight downward trend during the later period. Around 60 percent 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 sawIRS in their operation except in the first two years. An insignificant orzero percentage of public-sector banks recorded IRS in their operationduring our period of investigation. Nevertheless, except in a few yearsa good number of private-sector banks recorded IRS in their operation.Therefore, there is a huge potential to exploit economies of scale. Thepercentage of foreign banks experiencing IRS remained more or less sta-ble at between 15 and 46 per cent, except in 1997–98. The percentage

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of Indian commercial banks suffering from DRS lay between 39 and 60per cent. The percentage of Indian public-sector commercial banks suf-fering from DRS was acutely severe. The percentage always settled above70 per cent and even reached nearly 90 per cent. This confirms that scaleinefficiency in public-sector banks is the outcome of their excessive size.Table 6.4 shows no consistency of private-sector banks operating on theDRS segment of the frontier. The percentage varied from as low as 10to as high as 68 per cent. However, the proportion of foreign banksfacing DRS remained low throughout this period of investigation. Thegovernment of India’s future agenda to consolidate large public-sectorbanks to create larger banks is likely to aggravate scale inefficiency due totheir consistently predominant presence in the DRS segment of the fron-tier. Great efforts to make them scale efficient by adopting appropriatepolicies 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 banksreveal that foreign banks were more efficient than the banks in other twotypes of ownership. The percentage of efficient banks in the public sectorwas higher than that in the private sector for almost all the years of thestudy. The State Bank of India was efficient in all the years among thebanks in the public sector. Some public-sector banks, such as the StateBank of Mysore and the Indian Overseas Bank, were efficient for mostof the years considered in the study. Private-sector banks that were effi-cient in most of the years included HDFC, ICICI, IDBI and the CatholicSyrian Bank. However, most of the banks in the private sector did notreach the frontier level during the years 1996–97 to 2004–05. On theother hand, most of the foreign banks were efficient during the periodof study.

Input congestion in Indian banks

Table 6.6 shows the banks with input congestion during 1996–97 to2004–05. We can see that most of the public sector banks had inputcongestion during this period. There was no sign of a reduction in thenumber of banks with input congestion during the period. However,some of the banks in the public sector showed no congestion at all overthe entire period. These were the State Bank of India and the Bank ofBaroda. Six banks out of 27 in the public sector showed input conges-tion in only one or two years during the entire period. The banks that wefound to be input congested in most of the years were the Indian Bank,the State Bank of Saurashtra, the State Bank of Patiala, the SyndicateBank and the State Bank of Bikaner and Jaipur.

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218Table 6.5 Technically efficient banks by ownership and by year

Efficient bank

1996–7 1997–8 1998–9 1999–2000 2000–1 2001–2 2002–3 2003–4 2004–5

Public-sector banksState Bank of India × × × × × × × × ×State Bank of Bikaner And Jaipur × ×State Bank of Hyderabad × × × × × ×State Bank of Indore × × × × ×State Bank of Mysore × × × × × × ×State Bank of PatialaState Bank of SaurashtraState 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 BankIndian 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 48.15 55.56 55.56 48.15 37.04 25.93 37.04 37.04 37.04

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219Private-sector banksBank of Punjab ×Bank of Rajasthan ×Bharat Overseas BankCatholic Syrian Bank × × × × × × ×Centurion Bank ×City Union BankDevelopment Credit Bank ×Dhanalakshmi BankFederal Bank × × ×Ganesh Bank of Kurundwad × × × × × × × × ×Global Trust Bank × × × × × ×Hdfc Bank × × × × × ×Icici Bank × × × × × × ×Idbi Bank × × × × × × × ×Indusind Bank × × × × × × × × ×Jammu & Kashmir BankKarnataka BankKarur Vysya BankLakshmi Vilas BankLord Krishna Bank × ×Nainital Bank × ×Ratnakar Bank ×Sangli BankSbi Commercial & Intl BankSouth Indian Bank ×Tamilnad MercantileUnited Western Bank × ×Uti Bank × × × × × × × ×

% efficient private banks 32.14 39.29 32.14 32.14 25.00 21.43 25.00 35.71 25.00

(Continued)

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Efficient bank

1996–7 1997–8 1998–9 1999–2000 2000–1 2001–2 2002–3 2003–4 2004–5

Foreign banksAbn 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 76.92 92.31 61.54 76.92 69.23 61.54 76.92 69.23 69.23

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221Table 6.6 Input congestion in Indian commercial banks

Input congestion Years withcongestion

1997 1998 1999 2000 2001 2002 2003 2004 2005

Public-sector banksState Bank of India 0State Bank of Bikaner and Jaipur × × × × × × 6State Bank of Hyderabad × 1State Bank of Indore × × 2State Bank of Mysore × 1State Bank of Patiala × × × × × × × 7State Bank of Saurashtra × × × × × × × × 8State Bank of Travancore × 1Allahabad Bank × × × × × 5Andhra Bank × × × × 4Bank of Baroda × × × × × 5Bank of India 0Bank of Maharashtra × × × × 4Canara Bank × × × × × 5Central Bank of India × × × × 4Corporation Bank × × × × 4Dena Bank × 1Indian Bank × × × × × × × × × 9Indian Overseas Bank × × 2Oriental Bank of Commerce × × × 3Punjab National Bank × × × 3Punjab and Sind Bank × × × × × 5Syndicate Bank × × × × × × × 7Uco Bank × × × 3Union Bank of India × × × × 4United Bank of India × × × × × 5Vijaya Bank × × × × × 5

% public banks with input congestion 29.6 40.7 40.7 22.2 51.9 59.3 44.4 59.3 37

(Continued)

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Input congestion Years withcongestion

1997 1998 1999 2000 2001 2002 2003 2004 2005

Private-sector banksBank of Punjab 0Bank of Rajasthan × × × 3Bharat Overseas Bank × 1Catholic Syrian Bank × × 2Centurion Bank × × 2City Union Bank × × 2Development Credit Bank Ltd × × 2Dhanalakshmi Bank × × 2Federal Bank × × 2Ganesh Bank of Kurundwad 0Global Trust Bank × × 2Hdfc Bank × 1Icici Bank 0Idbi Bank × 1Indusind Bank 0Jammu & Kashmir Bank × × × 3Karnataka Bank × 1Karur Vysya Bank × × × × × 5Lakshmi Vilas Bank × × × × × 5Lord Krishna Bank × × 2Nainital Bank × × × × × × 6Ratnakar Bank × × × × 4

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Sangli Bank × × × × × × × × × 9Sbi Commercial & Intl Bank × × × × 4South Indian Bank × × × 3Tamilnad Mercantile × × × × × 5United Western Bank × × × 3Uti Bank 0

% private banks with input 21.4 10.7 32.1 42.9 14.3 28.6 25 39.3 35.7congestion

Foreign banksAbn Amro Bank 0Abu Dhabi Commercial Bank 0American Express Bank 0Bank of Bahrain & Kuwait 0Bank of Ceylon × × 2Bank of Nova Scotia 0Bank of Tokyo Mitsubishi × × × × × 5Barclays Bank 0Cho Hung Bank 0DBS Bank × × × 3Dresdner Bank × × 2Société Generale × 1Sonali Bank 0

% foreign banks with input 0 0 23.1 7.69 15.4 23.1 7.69 15.4 7.69congestion% all banks with input 20.6 20.6 33.8 27.9 29.4 39.7 29.4 42.6 30.9congestion

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Input congestion was less prominent in the private sector banks. Itis evident from Table 6.6 that the percentage of banks with congestionwas lower in most of the years compared to the public-sector banks. Thenonexistence of input congestion is discernible for four banks duringthe period of study: the Bank of Punjab, the Ganesh Bank of Kurund-wad, ICICI Bank and UTI Bank. For most of the banks, input congestionwas not found for more than three years out of the nine years in theliberalized era.

Table 6.6 show that the percentages of foreign banks with input con-gestion were lower than those of the banks in the other two sectors inall the years studied. The foreign banks, as expected, showed very littleor no input congestion during the period. The table reveals that Bank ofTokyo Mitsubishi was the only bank to show input congestion in mostof the years, while most of the banks showed no congestion during theentire period.

To discover the sources of congestion we divided the inputs into twogroups. Labor is associated with weak disposability and other inputsare associated with strong disposability. Thus from the DEA model forsubvector input and the DEA model with strong disposability of allinputs, as defined earlier, we can identify the banks with labor inputcongestion. Table 6.7 lists the banks with labour congestion duringthe period 1996–97 to 2004–05. The comparison of the percentage ofbanks with labor congestion among the three groups of banks revealsthat foreign banks had either no congestion or very little labor conges-tion during the period of study. On the other hand, the percentage ofpublic-sector banks with labor congestion varied from 7.4 per cent inthe year 2000 to a high of 44.4 per cent in the year 2002. Figure 6.2depicts the comparison of level of congestion in terms of percentageof banks with labor congestion among the three ownership types. Thebanks that showed labor congestion for most of the years were the Bankof Saurashtra and the Bank of Patiala. Banks showing labor congestionfor more than 50 per cent of the years were the Bank of Bikaner andJaipur, the Syndicate Bank and the Vijaya Bank. Among the private-sectorbanks only one bank showed labor congestion for most of the years, theSangli Bank. The labor congestion in other banks was evenly dispersedand ten banks in this sector showed no labor congestion during thisperiod.

Thus, as expected, labor congestion was high in the public-sector banksand there was much less congestion in the private-sector banks. Again,the foreign banks had very little or no labor congestion during the periodof study.

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225Table 6.7 Labor congestion in Indian commercial banks

Labor congestion Years withcongestion

1997 1998 1999 2000 2001 2002 2003 2004 2005

Public-sector banksState Bank of India 0State Bank of Bikaner and Jaipur × × × × × 5State Bank of Hyderabad × 1State Bank of Indore × × 2State Bank of Mysore × 1State Bank of Patiala × × × × × × × 7State Bank of Saurashtra × × × × × × × × 8State Bank of Travancore 0Allahabad Bank × × × 3Andhra Bank × × × 3Bank of Baroda × 1Bank of India 0Bank of Maharashtra × × 2Canara Bank × 1Central Bank of India × × × × 4Corporation Bank × × × 3Dena Bank × 1Indian Bank × 1Indian Overseas Bank × 1Oriental Bank of Commerce 0Punjab National Bank × × 2Punjab and Sind Bank × × × 3Syndicate Bank × × × × × 5Uco Bank 0Union Bank of India 0United Bank of India × × × × 4Vijaya Bank × × × × × 5

% public banks with labor 22.2 33.3 14.8 7.41 29.6 44.4 37 25.9 18.5congestion

(Continued)

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Labor congestion Years withcongestion

1997 1998 1999 2000 2001 2002 2003 2004 2005

Private-sector banksBank of Punjab 0Bank of Rajasthan × × × 3Bharat Overseas Bank × 1Catholic Syrian Bank × × 2Centurion Bank 0City Union Bank × × 2Development Credit Bank Ltd × 1Dhanalakshmi Bank × 1Federal Bank 0Ganesh Bank of Kurundwad 0Global Trust Bank 0Hdfc Bank 0Icici Bank 0Idbi Bank 0Indusind Bank 0Jammu & Kashmir Bank × 1Karnataka Bank × 1Karur Vysya Bank × × × × × 5Lakshmi Vilas Bank × × × × × 5Lord Krishna Bank × 1Nainital Bank × × × × × 5Ratnakar Bank × × × 3

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Sangli Bank × × × × × × × 7Sbi Commercial & Intl Bank × × 2South Indian Bank × 1Tamilnad Mercantile × × × × 4United Western Bank × × 2Uti Bank 0

% private banks with labor 7.14 7.14 32.1 3.57 10.7 21.4 25 35.7 25congestion

Foreign banksAbn Amro Bank 0Abu Dhabi Commercial Bank 0American Express Bank 0Bank of Bahrain & Kuwait 0Bank of Ceylon 0Bank of Nova Scotia 0Bank of Tokyo Mitsubishi × × × 3Barclays Bank 0Cho Hung Bank 0DBS Bank 0Dresdner Bank 0Société Generale 0Sonali Bank 0

% foreign banks with labor 0 0 7.69 0 0 7.69 0 7.69 0congestion% all banks with labor 11.8 16.2 20.6 4.41 16.2 27.9 25 26.5 17.6congestion

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0

10

5

15

20

25

30

35

40

45

50

1997 1998 1999 2000 2001 2002 2003 2004 2005

Year

Per

cent

age


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 termsof cost efficiencies. Here, as already noted, we use the cost functionDEA approach to estimate the optimum cost and the corresponding costefficiency of banks for all the years. The average efficiency level of thepublic-sector banks was over 70 per cent for most of the years. The num-ber of banks whose cost and output vectors lay on frontier was, however,not significant compared to the total number of banks and the percent-age ranged from a low of 7 to a high of 26 per cent in 1997–98. Theprivate-sector banks show a lower level of efficiency than the public-sector banks (see Figure 6.3). This result is different from the efficienciesobtained in studies by Kumbhakar and Sarkar (2005) and Sahoo et al.(2007). They found that efficiencies in the private sector-banks werehigher than in the public sector banks. However, their period, outputspecification and methods of analysis were different from those of thepresent study. The value ranged between 40 and 80 per cent, and for mostof the years the value was lower than that of public-sector banks. Thenumbers of efficient banks were very low compared to the other two sec-tors. Foreign banks performed much better than the banks in other twotypes of ownership. The percentage of efficient banks in this ownership

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229Table 6.8 Cost efficiency of Indian commercial banks (cost function DEA approach)

1997 1998 1999 2000 2001 2002 2003 2004 2005

Public-sector banksState Bank of India 1 1 1 1 1 1 1 1 1State Bank of Bikaner and Jaipur 0.5766 0.8824 0.7957 0.7329 0.6035 0.4745 0.7077 0.6155 0.6196State Bank of Hyderabad 0.5532 0.9730 0.9248 0.8831 0.8916 0.5596 0.8783 0.8230 0.7669State Bank of Indore 0.5049 0.8854 0.7871 0.7795 0.6918 0.5620 0.7301 0.6041 0.6371State Bank of Mysore 0.5105 0.8122 0.7095 0.6793 0.5820 0.3291 0.6008 0.5492 0.5775State Bank of Patiala 0.5216 0.8732 0.8767 0.8278 0.8204 0.4014 0.7442 0.7553 0.8006State Bank of Saurashtra 0.5795 0.8721 0.7556 0.6715 0.5934 0.3299 0.6947 0.6755 0.7029State Bank of Travancore 0.4715 0.8317 0.7781 0.6960 0.6667 0.3217 0.6934 0.7330 0.7000Allahabad Bank 0.4775 0.9949 0.7939 0.7631 0.6823 0.4625 0.5959 0.6405 0.7454Andhra Bank 0.4794 0.8900 0.8035 0.8066 0.8414 0.3335 0.6864 0.5363 0.7800Bank of Baroda 0.6515 0.9346 0.9002 0.9201 0.8601 0.7229 0.8184 0.9813 0.8553Bank of India 0.7931 1 1 0.9965 1 0.8742 1 1 0.7898Bank of Maharashtra 0.5120 0.9568 0.8004 0.6818 0.7782 0.3527 0.7624 0.7262 0.7028Canara Bank 0.5256 1 0.8967 1 0.9844 1 0.8893 1 0.8295Central Bank of India 0.5348 1 0.8381 0.8801 0.8440 0.4764 0.7613 0.8474 0.8146Corporation Bank 0.5433 1 0.8876 0.8783 0.7728 0.5922 0.7491 0.6089 0.6908Dena Bank 0.4820 0.9209 0.7381 0.7011 0.5547 0.5000 0.6169 0.6052 0.6412Indian Bank 0.4026 0.8101 0.6362 0.6747 0.6973 0.6502 0.7490 0.6708 0.7792Indian Overseas Bank 0.4252 0.8325 0.7131 0.6895 0.7173 0.5771 0.7228 0.6715 0.6315Oriental Bank of Commerce 0.5172 1 0.7702 0.9036 0.8294 0.5762 0.6885 0.7078 0.6963Punjab National Bank 1 0.8666 0.6887 0.5951 0.5890 0.3033 0.5834 0.4852 0.5844Punjab and Sind Bank 0.1293 0.9972 0.8987 0.9063 0.9039 0.6560 0.8775 0.9296 0.8948Syndicate Bank 0.5152 0.9146 0.7721 0.7693 0.6554 0.2705 0.6250 0.6716 0.6628Uco Bank 0.3601 0.8975 0.5173 0.5255 0.5387 0.5701 0.4786 0.5064 0.5141Union Bank of India 0.7295 1 1 1 1 0.6837 1 1 1United Bank of India 0.5217 0.9328 0.8352 0.8576 0.8633 0.4261 0.6276 0.5608 0.5421Vijaya Bank 0.4666 0.8990 0.7066 0.5769 0.6303 0.3229 0.6910 0.6951 0.7731

Mean efficiency of public banks 0.5476 0.9251 0.8083 0.7925 0.7627 0.5307 0.7397 0.7259 0.7308% efficient public banks 7.41 25.93 11.11 11.11 11.11 7.41 11.11 14.81 7.41

(Continued)

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1997 1998 1999 2000 2001 2002 2003 2004 2005

Private-sector banksBank of Punjab 0.8341 0.8574 0.8587 0.6964 0.5398 0.3059 0.5322 0.3221 0.2554Bank of Rajasthan 0.4481 0.6692 0.5650 0.4970 0.4836 0.2496 0.6922 0.8288 0.6578Bharat Overseas Bank 0.4820 0.6598 0.6194 0.5853 0.6067 0.3716 0.6816 0.3141 0.2725Catholic Syrian Bank 0.4234 0.6331 0.5244 0.4985 0.4606 0.3301 0.5741 0.3989 0.2607Centurion Bank 0.7931 0.7731 0.5048 0.6474 0.3947 0.1871 0.3268 0.1587 0.2710City Union Bank 0.5736 0.7121 0.6458 0.6612 0.6058 0.3576 0.6342 0.3909 0.2599Development Credit Bank Ltd 0.6011 1 0.6964 0.8493 0.6323 0.3761 0.6191 0.4383 0.3914Dhanalakshmi Bank 0.4432 0.6611 0.5996 0.5960 0.5379 0.3495 0.5995 0.2762 0.1629Federal Bank 0.5620 0.9459 0.7428 0.6055 0.7383 0.3351 0.6068 0.5192 0.5839Ganesh Bank of Kurundwad 0.4731 0.5619 0.5536 0.5089 0.4577 0.2552 0.5309 0.4454 0.4036Global Trust Bank 0.7828 0.9584 1 1 0.6099 0.3090 0.4982 0.2883Hdfc Bank 0.7311 1 1 1 0.9435 0.542301 0.9332 1 0.8714Icici Bank 0.7870 1 0.9283 0.8025 1 1 1 1 1Idbi Bank 1 1 0.8791 0.5924 0.4724 0.3667 0.6978 0.7540 1Indusind Bank 0.7001 1 0.7457 0.8761 0.8102 0.4949 0.8047 0.6384 0.5043Jammu & Kashmir Bank 0.5696 0.9591 0.8510 0.6758 0.8267 0.3612 0.7128 0.7157 0.7456Karnataka Bank 0.5232 0.8036 0.6944 0.5657 0.5665 0.3954 0.6738 0.5919 0.6411Karur Vysya Bank 0.5460 0.8142 0.6999 0.6110 0.6482 0.3567 0.7268 0.4128 0.4090Lakshmi Vilas Bank 0.5272 0.7604 0.6646 0.7776 0.6761 0.3790 0.6502 0.3325 0.2546Lord Krishna Bank 0.5165 0.5650 0.4526 0.6170 0.5119 0.4205 0.5992 0.3504 0.1837Nainital Bank 0.4704 0.6369 0.6522 0.5104 0.1601 0.4172 0.5908 0.3716 0.2571Ratnakar Bank 0.4444 0.6082 0.5470 0.4860 0.4573 0.3897 0.5443 0.2674 0.2306

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Sangli Bank 0.4460 0.7385 0.6647 0.5490 0.4222 0.3549 0.6594 0.4988Sbi Commercial & Intl Bank 0.3631 0.7270 0.5765 0.7929 0.4847 0.3008 0.4615 0.4064 0.4345South Indian Bank 0.4181 0.7119 0.6404 0.6009 0.5786 0.3443 0.5949 0.5470 0.4520Tamilnad Mercantile 0.7123 0.7884 0.7379 0.6213 0.5586 0.3327 0.6296 0.4447 0.4674United Western Bank 0.7368 0.9630 0.7967 0.9342 0.7278 0.3750 0.6535 0.4589 0.3715Uti Bank 0.5130 1 0.9523 0.9771 0.7436 0.9735 0.6654 0.5447 0.8611

Mean efficiency of private banks 0.5865 0.8039 0.7069 0.6834 0.5948 0.4011 0.6391 0.4899 0.4693% efficient private banks 3.57 21.43 7.14 7.14 3.57 3.57 3.57 7.14 7.14

Foreign banksAbn Amro Bank 1 1 1 1 0.7342 0.4130 0.7808 0.5758 1Abu Dhabi Commercial Bank 0.4104 0.6648 0.6764 0.4705 1 0.4559 0.8009 0.2946 0.2106American Express Bank 1 1 0.9897 1 0.44879 0.5348 0.4848 0.1933 0.3691Bank of Bahrain & Kuwait 0.4565 0.5378 1 0.5410 0.5690 0.3656 0.6360 0.1099 0.0804Bank of Ceylon 1 1 1 1 0.9700 0.5740 0.9993 0.2271 0.4496Bank of Nova Scotia 0.7429 1 0.9407 1 0.9899 0.4408 0.7320 1 1Bank of Tokyo Mitsubishi 0.7565 0.7638 0.1809 0.6500 0.7274 0.4285 0.5864 0.2257 0.2059Barclays Bank 0.5380 0.8459 0.5719 0.4960 0.6418 0.8442 1 0.9114 1Cho Hung Bank 1 0.9618 0.4894 1 1 1 1 1 1DBS Bank 0.3295 0.7241 0.7325 0.9512 0.5759 0.4091 0.7853 0.1788 0.8499Dresdner Bank 0.2981 0.5643 0.5412 0.5576 1 1 1 1 1Société Generale 0.4048 0.6529 0.4970 0.5738 0.3838 0.2460 0.7296 0.7176 1Sonali Bank 1 1 1 1 1 1 1 0.4215 0.3377

Mean efficiency of foreign banks 0.6874 0.8243 0.7400 0.7877 0.7724 0.5932 0.8104 0.5274 0.6541% efficient foreign banks 38.46 38.46 30.76 46.15 30.76 23.07 30.76 23.07 46.15Mean efficiency of all banks 0.5903 0.8559 0.7535 0.7466 0.6954 0.4893 0.7118 0.5908 0.6127% efficient banks 11.76 26.47 13.23 16.17 11.76 8.82 11.76 13.23 14.70

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0.4

0.5

0.6

0.7

0.8

0.9

1

1997 1998 1999 2000 2001 2002 2003 2004 2005

Year

Effi

cien

cy


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 duringthe period, lying between 0.49 and 0.85 during the period of study. Thepercentage of efficient banks in total was around 12 per cent in all theyears. Thus, the results suggest that public-sectors banks did better thanprivate sector banks. However, the levels of efficiency of foreign bankswere much better than those of the banks in the other two sectors.

There were huge variations in the level of efficiencies among the banksand among the types of ownership. We now examine the sources ofvariation in efficiencies among the banks. For this we depend on a verysimple test using the ordinary least squares method. The independentvariables considered here are assets (proxy of size), capital:labour ratio(proxy of technology) and ownership dummy. We have used the crosssectional time series pooled data to run the regression. Table 6.9 gives thevalues of the coefficient, the corresponding t-statistics and the value ofadjusted R2. The coefficients of assets and capital:labor ratio are positiveand statistically significant. The coefficient of private dummy is negativeand statistically significant, while that of foreign dummy is positive butnot statistically significant. The result suggests that size and technologyplay a significant role in enhancing the values of efficiency of banks in

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Table 6.9 Sources of variation in cost efficiency

Variable Coefficient

Intercept 0.758020Asset 2.13869 × 10−8

(3.6884)PVTD −0.078854

(−2.0253)FD 0.059825

(1.0326)Labor:capital ratio −0.358578

(−1.9904)Adjusted R2 0.3389D.F. 63

Table 6.10 Minimum average cost of efficientbanks (in rupees)

Year Minimumaverage cost

1997 0.045201998 0.053351999 0.062092000 0.033782001 0.039692002 0.021842003 0.054532004 0.012342005 0.01195

India. Contrary to the general belief, efficiencies in private-sector bankswere significantly lower than in public-sector banks.

One can estimate the minimum values of average costs from the aver-age costs of the efficient banks in each year. Table 6.10 gives the valuesof optimum average costs of the banks during 1996–97 to 2004–05. Thefigures indicate a downward trend over the period, which suggests thatbanks became more competitive than before in terms of cost efficientuse of inputs.

We have already mentioned in the methodology section that one canestimate the optimum level of output that minimizes the average cost

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for both efficient and inefficient banks from the regression analysis ofcost function. Table 6.11 describes the values of optimum output levelfor both efficient and inefficient banks for all the years of our study.However, in some cases the values of optimum output calculated usingthis method are not economically meaningful due to negative values. Ithas been observed that for all the comparable years the values of opti-mum output in efficient banks were higher than those in the inefficientbanks. This result is meaningful in the sense that the inefficient banksmay have still some scope to increase their scale of operation comparedto the efficient banks. The values also show an increasing trend over thisperiod of analysis.

To measure the economies due to the learning by doing model of Arrowthe following regression model is estimated for each year for the banks ofdifferent ownership types and for the efficient banks only. The functioncan be written as log AC = f ( log CO, log W), where AC represents theaverage cost, CO represents the average cumulative output over threeyears and W represents the labor cost. Total cost is taken as the sumof operating expenditure and interest expenditure. Output is defined astotal assets net of fixed assets. Now if the coefficient of cumulative outputis less than one then the economies of scale due to learning by doing areprevalent in the banks under study. If the coefficient is greater than onethen we have diseconomies due to learning by doing.

Table 6.12 gives the results of the estimates of the coefficients of theindependent variables of the aforesaid cost function for different types ofbanks. The values of the coefficients of log- cumulative output (L-CO) areall less than one and negative for the efficient banks. However, some ofthe values are not statistically significant. The values of the coefficient ofL-CO for all banks together are less than one and statistically significantfor all the years except 2002–03. Thus we can say that experience ishelping the banks in reducing costs. Now the same exercise has beencarried out separately for three types of banks in terms of ownership.It was found that the coefficients of L-CO for the private- and public-sector banks were less than one and statistically significant for most ofthe years, while the coefficients were not statistically significant for theforeign banks even when the values were less than one. Thus we canconclude that the private- and public-sector banks in India used theirexperience to reduce the costs of operation. Foreign banks, on the otherhand, pay little heed to past experiences since in most cases they operateat optimum average cost.

Finally, we estimated the allocative efficiency of Indian commercialbanks during this period. Table 6.13 gives the values of average

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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 banksOptimum output 2,414,825 NA NA 1,846,758 2,564,869 3,291,361 2,935,931 5,797,294 3,730,793Adjusted R2 0.9878 0.9989 0.9969 0.9986 0.9989 0.9774 0.9957 0.9959 0.9932

Inefficient banksOptimum output 393,087 596,643 NA 324,864 NA NA 2,751,969 3,225,458 2,428,917Adjusted R2 0.8140 0.9989 0.9718 0.9387 0.9774 0.9603 0.9696 0.9724 0.9587

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Table 6.12 Test of Arrow’s learning by doing

Coefficients

Efficient banksIntercept −1.776 −1.146 −2.313 −2.267 −1.235 −2.199 0.171L-labor cost 0.066 0.104 0.097 0.098 0.229 0.059 0.308

(1.310) (1.789) (1.570) (1.870) (2.080) (1.070) (2.087)L-cumulative output −0.078 −0.143 −0.063 −0.069 −0.220 −0.054 −0.368

(−1.315) (−2.074) (−0.879) (−1.155) (−1.917) (−0.935) (−2.437)Adjusted R2 −0.0063 0.0746 0.0934 0.1130 0.1043 −0.0316 0.1315Observations 38 32 32 26 21 27 29

All banksIntercept −1.7880 −1.0545 −1.8344 −1.6616 −1.4151 −2.0992 −0.1575L-labor cost 0.0547 0.0913 0.1017 0.0988 0.1571 0.0509 0.1565


(−1.833) (−2.295) (−1.883) (−2.300) (−2.688) (−1.415) (−2.786)Adjusted R2 0.0201 0.0644 0.0626 0.0606 0.0894 0.0001 0.1657Observations 68 68 68 68 68 68 68

Public banksIntercept −1.5610 −1.1913 −1.1522 −0.9785 0.3891 −0.0414 0.0151L-labor cost 0.0881 0.1570 0.1503 0.1870 0.3135 0.3638 0.3434

(2.037) (1.415) (1.578) (2.117) (3.538) (3.282) (3.589)

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L-cumulative output −0.1089 −0.1752 −0.1739 −0.2090 −0.3738 −0.3823 −0.3812(−2.507) (−1.592) (−1.827) (−2.336) (−4.153) (−3.438) (−3.848)

Adjusted R2 0.1711 0.0287 0.0615 0.1219 0.3914 0.2741 0.3301Observations 27 27 27 27 27 27 27

Private banksIntercept −1.5962 −1.3905 −1.0681 −0.3779 −0.3077 −1.9553 −1.6197L-labor cost 0.0472 0.0135 0.0992 0.0474 0.0843 −0.0051 0.0301

(1.265) (0.325) (2.528) (0.761) (0.905) (−0.084) (0.474)L-cumulative output −0.0800 −0.0756 −0.1503 −0.1705 −0.2004 −0.0356 −0.0895

(−1.922) (−1.812) (−3.924) (−3.198) (−2.597) (−0.671) (−1.706)Adjusted R2 0.0591 0.0950 0.3323 0.3334 0.2717 0.0336 0.2126Observations 28 28 28 28 28 28 28

Foreign banksIntercept −2.0232 0.1255 −3.5435 −2.7084 −2.6657 −2.5368 2.9356L-labor cost 0.2669 0.5994 0.2622 0.1111 0.1223 0.0862 0.1880


(−0.983) (−1.920) (−0.092) (−0.285) (−0.310) (−0.452) (−1.643)Adjusted R2 0.2991 0.3243 0.3107 −0.0080 −0.0900 −0.0541 0.1950Observations 13 13 13 13 13 13 13

Note: Dependent variable: log(average cost). Figures in parentheses are t-statistics.

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Table 6.13 Allocative efficiency of Indian commercial banks

1997 1998 1999 2000 2001 2002 2003 2004 2005

Public-sector banksMean efficiency 0.9229 0.9363 0.9437 0.9332 0.9380 0.9222 0.9469 0.9324 0.9387% efficient banks 29.63 29.63 33.33 22.22 22.22 22.22 29.63 22.22 18.52

Private-sector banksMean efficiency 0.8812 0.9083 0.8931 0.8422 0.8664 0.9127 0.8664 0.8522 0.8741% efficient banks 3.57 3.57 0.00 3.57 0.00 10.71 3.57 3.57 7.14

Foreign banksMean efficiency 0.9435 0.9454 0.9311 0.8673 0.9403 0.9331 0.9024 0.8772 0.8745% efficient banks 53.85 61.54 53.85 46.15 69.23 61.54 61.54 61.54 53.85

All banksMean efficiency 0.91 0.926 0.92 0.883 0.909 0.92 0.905 0.889 0.901% efficient banks 18.60 19.77 18.60 15.12 17.44 19.77 19.77 17.44 16.28

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0.82

0.84

0.88

0.86

0.90

0.92

0.94

0.96

1997 1998 1999 2000 2001 2002 2003 2004 2005

Year

Effi

cien

cy


Figure 6.4 Allocative efficiency of Indian commercial banks

efficiencies of the banks in three types of ownership patterns and for allbanks together. The pattern of the differences among these three owner-ships was similar to what was observed for the other efficiencies but thelevel of efficiencies in all the cases was higher than that of the other twotypes of efficiencies. The percentages of efficient banks in the private sec-tor were very low compared to those of the banks in the other two typesof ownership. Thus the allocation of inputs is very poor in private-sectorbanks compared to the banks in the other two sectors. Figure 6.4 showsthe trend of average values of allocative efficiencies of banks of the threeownership types.


In our study, we have calculated the technical and scale efficiency scoresof Indian commercial banks using DEA, and tried to analyze how theefficiency scores across banks of different ownership pattern vary. Aver-age technical efficiency in public-sector banks was greater than that inprivate-sector banks. There exists no uniform pattern of movement ofscale efficiency scores. SE scores for private-sector banks for the years2003 and 2004 showed statistically significant improvement. It seems

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that, in the initial years, the private banks were weak with respect to sizeand they have been achieving the optimum scale size during the recentperiod. However, this point needs more information and in-depth study.

The percentage of Indian public-sector commercial banks sufferingfrom diminishing returns to scale (DRS) was acutely severe. This con-firms that scale inefficiency in public-sector banks is the outcome of theirexcessive size. However, the proportion of foreign banks that sufferedfrom DRS remained low throughout this period of investigation. The gov-ernment of India’s future agenda to restructure large public-sector banksby merging them to create a few very large banks is likely to aggravatescale inefficiency due to their consistently predominant presence in theDRS segment of the frontier. The top priority before consolidation shouldbe to make them scale efficient by adopting appropriate policies. Ourfindings, on the other hand, suggest consolidating public-sector bankswith private-sector banks because the latter category of banks recordedincreasing returns to scale (IRS) in their operation. Therefore, judiciouspolicy demands a strategy to encourage consolidation of private- andpublic-sector banks to take advantage of the huge potential to make themefficient by exploiting economies of scale.

The presence of input congestion in public- and private-sector bankssuggests that if a portion of inputs were withdrawn from the process,banks could achieve a higher level of output or no reduction of outputdue to the withdrawal of inputs. The public-sector banks show high laborcongestion during the period, higher than private and foreign banks.This feature supports the notion that public-sector banks suffer fromoverstaffing. It was expected that after liberalization the banks could usethe optimum level of staff to become more efficient and competitivein the market. However, this is not observed even after more than fif-teen years of liberalization and this phenomenon persists due to politicalpressure on the government and pressure from the labor unions on themanagement of banks. It is also argued that since public-sector bankshave some social obligation they have to absorb more labor than thebanks in other two sectors.

Results on cost and allocvative efficiencies suggest that the patternof difference among the types of ownership is similar to that of tech-nical efficiencies of banks. The analysis of the sources of variation inefficiency reveals that size and technology have a positive impact onthe variation of cost efficiencies. Cost efficiencies in private sector banksare significantly lower than in the public sector banks. The efficienciesof foreign banks are greater than those of public-sector banks; the dif-ference is, however, not statistically significant. Our study reveals that

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Indian banks take their input from past experience to become more effi-cient and productive. In our study, we find that liberalization has madethe Indian banking sector more competitive even though the marketshare of public-sector banks is still very high. Indian public-sector banksperformed better in all respects compared to private-sector banks. There-fore, judicious policy demands strategies to make public-sector banksmore aggressive to take advantage of the huge potential before them.

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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 isto find out the benchmark year’s gross capital stock. In order to construct thegross fixed capital stock it is assumed that the value of finished equipment ofa balanced age composition would be exactly half of the value of equipmentwhen it was new. Hence in this study, twice the book value of the base year istaken 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 typesof 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 capitalstock is calculated using the following method.

KCGn = KON

1 (1 + α)n + (1 + α)n−1I1 + (1 + α)n−2I2 + · · · + (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 tothe difference between the initial year of production and the first year of thepresent data for the individual firm. Now substituting the initial year openingstock in the above formula we can write

KCGn =

[KON

n −n∑

i=1

Ii

](1 + α)n + (1 + α)n−1I1 + (1 + α)n−2I2 + · · · + (I + α)In−1 + In

Since the difference between the year of initial production of the firm and thefirst year of the present data is not large enough and also due to nonavailabilityof the investment figures of the earlier years it is assumed that Ii = 0 for theearlier years. The last equation then boils down to

KCGn = [KON

n ](1 + α)n

Chapter 5

1. A non-conventional approach to the choice of technology by long periods oftrial and error can be found in Ishikawa (1981). Moreover numerous attemptshave been made to measure the contribution of R&D to productivity growth(e.g. Griliches, 1980; Nadiri, 1980).

242

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Notes 243

2. The salient features of the New Economic Policy package may be outlinedas follows: (a) to allow direct foreign capital in industries, trading compa-nies and banking up to 51 per cent of the share capital (in some cases 100per cent); (b) automatic clearance for capital goods imports; (c) automaticapproval of foreign technology agreements in high priority areas (includingsmall sectors); (d) the setting up of a Foreign Investment Promotion Boardto negotiate with MNCs and grant single-point clearance; (e) private sec-tor banking by public limited companies with upgraded technology – bothdomestic and foreign; (f) the abolition of all but a few industrial licensea, theabolition of MRTP and FERA provisions, the closing down of chronically sickpublic 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, whichseem to affect the analysis. But since we have converted each group into anaverage 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 pro-cedure to obtain consistent parameter estimates and identify determinantsof technical efficiency.

6. The factors explaining the variations of efficiency are defined as fol-lows. 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 cap-ital; CINT = capital intensity = fixed capital/employees; CAPU = capitalutilization = 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 exist-

ing technology. Concessions in duties on imported machinery and chemicalswere announced. Integrated tanneries were delisensed. Regulations limitingseveral types of leather goods to the small-scale sector were removed. Freeexport of raw hides and skins, semi-finished and finished leather and leatherproducts was allowed.

10. Färe and Lovell allow individual components of the input or output bundleto take zero values. They define the indicator variables δr that take the value0 if output r is 0, and 1 otherwise. Their objective function is

ρy =∑

φr∑δr

Throughout the present analysis, we assume that all inputs and outputs arestrictly positive. The range adjusted measure (RAM) introduced by Cooperet al. (1999) can accommodate zero inputs or outputs unless the relevantinput/output is constant across observations. If the φrs are not restricted tobeing greater than or equal to unity, some outputs may actually be reducedwhile others are increased.

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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 thatsynthesizes the best features of the conventional radial Debreu–Farrell mea-sure and the nonradial Russell measure. In the input-oriented case, thisextended measure emerges by first projecting an observed input bundle x0

radially onto the isoquant of the corresponding output bundle. Once oneachieves this proportional scaling (by the factor θ), one projects any inputslack present in this bundle θx0 further onto the efficient subset of the iso-quant by solving the nonradial problem for RMx(θx0, y0). When no inputslack exists in the radial projection of the observed input bundle, no furtheradjustment need occur so that the radial and nonradial measures coincide.

12. In an alternative approach, Torgersen et al. (1996) adjust the efficient radialprojection of the output bundle for slacks in individual outputs to obtain anonradial projection onto the efficient subset of the output isoquant. Insteadof a summary measure of efficiency combining the radial expansion fac-tor 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 ofEconomics, University of Kalyani.

1. Data for two private-sector banks for 2004–5 are not available. Therefore, inour final year of investigation we have 26 private-sector banks.

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Index

Note: Page numbers in italics refer to tables.

Accenture 49agglomeration strategies 46, 47agriculture 30

exports 82, 82Ahluwalia, I. 58–9, 140Aigner, D.J. et al 137Alexander Committee (1978) 58allocative 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 49Arrow, K.J. 6, 12, 55, 234, 236–7Asia Pacific (APAC) firms 27Asian Development Bank (ADB) 59,

169

Baldwin, J. 34Banerjee, P. 11Bangalore 51Banker, R.D. 194, 206banking sector 193–241

aims of liberalization measures199

allocative efficiency of 195, 234,238, 239, 239, 240

changes in 211–12cost efficiency of 228, 229–31,

232–3, 232, 240and credit 194and deposits 196, 213deregulation and productivity

200–1features of commercial 201influence 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

measuring of efficiency 194–5,204–11

minimum average cost of efficient233, 233

nationalization of 196optimum output calculated from

cost function 233–4, 235overview of Indian 196–200ownership forms 195problems arising in 197reforms initiated 193–4, 197–8review of literature evaluating

performance of 200–4role in economy 193and scale efficiency 214, 215, 216,

240scale elasticity 117, 117selected indicators 203sources of variations in levels of

efficiency 232–3, 233, 240summary of 202technical efficiency of 214–17,

215, 216test of Arrow’s learning by doing

234, 236–7Battese, G.E. 106, 107, 137Baumol, W.J. 29BCC (Banker, Charness and Cooper)

model 109–10, 114, 208BEL 10Berg, S.A. et al 213Berger, A.N. 203Bernstein, J. 41Bhagwati, J. 80Bhattacharya, A. 203Bhaumik, S.K. 203Bhavani, T.A. 140bootstrap technique 113–14Booz Allen Hamilton Global

Innovation 1000 pan 24, 26Box-Cox model 150–2, 160

255

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256 Index

BPO (business process outsourcing)29

Broeck, J. 137Business Technographic Survey 29

Capital Account Convertibility (CAC)199

capital intensity and efficiency143–4

capital stock measuring of 190–1cash reserve ratio see CRRCCR model 205Central Bank in India see RBICentral Statistical Organization (CSO)

81, 88, 147Chandok, H.L. 72Chang, Fred 51chemical industry

output 61, 61, 62value added 65

China 1, 27, 51Cisco Systems 27Coelli, T.J. 106, 107, 137Cognizant 22, 22Cohen, W. 2, 14, 15–17, 23collaboration and R&D investment

14, 23, 45COLS (corrected ordinary last squares)

method 106, 113, 172–3Committee on Banking Sector

Reforms 197Committee on Financial Systems

197comparative advantage 1, 30, 45, 48,

79, 125competency index 11competition 31, 85, 87competitive advantage 30, 55, 125

features of 46, 47and government’s role 47–8sources of in IT sector 48–52three Cs of 47–8and value chain 47

competitive growth equilibriummodel 3–4

composed error model 104, 105–7Compustat Database 123computer industry 6–8, 123–5, 171

average efficiency of 188, 188level efficiency versus growth

efficiency 124, 125output growth 124, 125and R&D 19–20sources of growth efficiency 124,

124spending on foreign technology 9see also software industry

congestion measurement 217–19constant return to scale see CRSconstruction industry 61, 61core competence 49, 128Corley, M. et al 125Cornwell, C. et al 138, 139, 142, 189corrected ordinary last squares see

COLS methodcost efficiency 49

of banking sector 228, 229–31,232–3, 232, 240

measuring 209–11cost frontier model 108, 114–17,

119–20, 195cost function DEA approach 228‘creative destruction’ 36, 44credit 194CRR (cash reserve ratio) 194, 205CRS (constant return to scale) 195,

205, 206, 207cumulative experience 59Custom Tariff in India 81

Dagli Committee (1979) 58Daimler-Chrysler 25Das, B.D. et al 203, 213DEA (data envelopment analysis) 6,

104, 107–17, 131, 132, 204,205, 209–10

bootstrap models 113–14and cost frontier estimation

114–17, 119–20, 195features 107–8input-oriented (BCC) model

109–10, 148and LAV method 111–12and measurement of efficiency in

textiles 146measuring of cost effectiveness

209–10

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Index 257

output-oriented VRS model 149and production frontier estimation

108–14radial efficiency model 110–11,

111, 112, 148–50, 174, 209debt recovery tribunals 194decision-making unit (DMU) 104,

205, 210Dell 45Dimova, R. 203Director General of Commerce and

Intelligent Service (DGCIS)80–1, 82

disposability, weak and strong 207–8DRS 214, 217Drucker, Peter 45Dutch manufacturing 33–5, 34, 35dynamic comparative advantage 45dynamic production frontier 118,

124

e-capital 8economic growth 26–7economic policy reforms 23–4economies of scale 118economies of scope 47–9Economist 13EER (real effective exchange rate)

102–3efficiency 134–92

and appropriateness of chosentechnology 134, 135

comparison of in post- andpre-liberalization periods169–88

economic factors explainingvariations in technical 143

in leather industry 144–7, 160–9,see also leather industry

of manufacturing industries inpre-liberalization period136–7, 141–4

technical see technical efficiencyin textile industry 144–7, 153–60

see also textile industrysee also industry efficiency analysis

efficiency, measuring of 137–40,194–5, 202–11

and Box-Cox model 150–2, 160congestion 207–11cost 209–11radial/nonradial measures 110–11,

111, 112, 148–50scale 207stochastic frontier model 137–9,

146, 173, 176technical 194–5, 204–5

electronics industry 8, 9–11, 135, 171development of 171–2employment generation 10export performance 10, 11, 84foreign collaborations 9, 9growth of 171inter-state variations in output 10,

10pre- and post-liberalization

comparisonaverage efficiency 176, 177distribution of outputs 178, 185ownership-specific 176, 179state-specific 177, 182–3

R&D investment 10semiconductor policy 171technology import 9, 9underutilization of labor and capital

in post-liberalization period178–9, 186–7, 188

see also software sectoremployment

changes in industry 65growth of in IT sector 22and productivity 125, 127–8

endogenous growth models 128–9entry rates

in Dutch manufacturing 34–6, 34,35

and technological competiveness35–7

exports 30, 81–5linkage between total factor

productivity growth and85–90, 99

percentage share of major itemsgroups 82, 82

ranking of industries 85, 86yearly value of selected commodity

items 82–4, 93

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258 Index

externalities 12, 118 see alsospillover effect

Färe, R. 106, 149, 204, 207, 209Farrell, M.J. 109, 137, 204Fieldhouse, M. 206financial services 21–2, 27, 49Flaherty, M. 120food industry 61, 63Ford 25foreign direct investment (FDI)

20–1, 47, 100Foreign Exchange Regulation Act

(FERA) (1973) 58, 60, 170Forrester, J. 27–8, 29Forsund, F.R. 206Freire-Seren, M. 5Frisch, R. 206frontier production function (FPF)

approach 136–9, 204Fuller Capital Account Convertibility

200

game theory 120GDP 26–7, 50generalized least squares (GLS) 106Gilbert, R.A. 201global competition, strategy for

46–55Global Competitiveness Report 21globalization 198GM 25Goldman and Sachs 30Goldstar 10Gort, M. 130Greenbaum, S.I. 212Greene, W.H. 113Grillches, Z. 31, 41gross fixed capital stock (GFCS)

190–1Grossman, G.M. 91, 129growth efficiency models 118, 120–3growth miracle countries 30

Hamel, G. 48, 128Harrison, A.E. 92HDFC 217, 219Helpman, E. 31, 91, 129

Herfindal index 35Hewlett-Packard 28Hjalmarsson, L. 206HPAEs (high performance Asian

economies) 41, 51Hsinchu Science Park (Taiwan) 54human capital methods of analysing

growth effects due to 131–3Humphrey, D.B. 203

IBM 25, 27, 49IBS (Intelligent Business Systems) 23imitation process 44–5imports 85, 87

liberalization 56tariffs 20, 21, 95–6, 95, 100

Indian Labour Journal 140industrial classification 101Industrial Development and

Regulation Act (IDRA)(1951) 58

industrial policy review of Indian58–60

industry(ies) 30–55, 76–103change in shares among 60–5decomposition of aggregate TFPG

67–79dynamics and growth 31–7efficiency analysis of selected

manufacturing 134–92employment changes 65export performance 81–5linkage between liberalization and

productivity 56–103output changes 61, 62, 63, 63R&D efficiency and growth of

117–33R&D investment and technology

diffusion 37–46reasons for progress and

evolvement 128–9and rent differentials 74, 75, 76,

77, 78strategies needed for 30strategy for global competition

46–55structural changes 65–8, 66, 98–9and technological change 31–2

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Index 259

total factor productivity growth inexport-oriented 79–90, 99

value added changes 63, 64, 65wage differentials 74, 75, 78

industry efficiency analysis 104–33and composed error model 104,

105–7and data environmental analysis

(DEA) 104, 107–17Infosys Technologies 22innovations 2, 117

economic policy reforms aimed at24–9

key elements in successful 26and R&D investment 23stages of successful process 26

input-oriented measures 195, 204BCC model 109–10, 148

Intel 27intermediation approach and

output-input set of banks 212International Monetary Fund (IMF)

59, 169iron and steel

estimation of changes inproductivity and markup96, 97

TFP growth 89IT sector 30

criticism of rapid growth of India’s51

employment growth 22impact of growth of on other sectors

of Indian economy 54–5importance of R&D to sustained

growth in 22–4, 51–4performance growth of largest firms

21–2, 22policy measures to ensure

continued growth 51and policy reform 21sources of competitive advantage

48–50success of India in global markets

46technology diffusion strategy 30and world perspective 5–13

ITI 10

Jain, R.K. 81Japan

banking 200decomposition of TFP growth rates

44export externality 132, 132growth of output and productivity

in manufacturing sector 41,42, 43

and imitation 44–5rates of return in physical and R&D

capital 43role of government in creating

competitive industries 47–8transfer of US technology to 45

Jethanandani, J. 28jewellery see gems/jewelleryJLMS technique 105Johnson & Johnson 25joint ventures 23, 44Jondrow, J. et al 105Jorgensen, D.W. 8

Kaldor, N. 54kanban 47Kemp, M.C. 45Kerala (KE) 110Kim, S. 41knowledge capital 1–3, 8, 118,

128, 129knowledge economy 1, 20, 29knowledge transfer 80Konakayama 130Korea

banking system 201growth of output and productivity


rates of return in physical and R&Dcapital 43

Krishna, P. 92Kumar, P. 28Kumbhakar, S.C. 203–4

labor inputs radial labor efficiencymeasure 110–11, 111, 112

labor productivity effect of R&D on127, 127

Lall, S. 24

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260 Index

Lansbury, M. 32LAV (least sum of absolute errors)

method 104, 111–13learning 48, 128, 129learning by doing model 6, 12, 118,

234, 236–7least sum of absolute errors see LAVleather industry 135, 144–5, 160–9

all-India efficiency analysis 160,162

development of 145efficiency in post-liberalization era

144–5, 160–9, 190and employment 146exports 84, 145factors behind efficiency variations

169, 170issues affecting 145–6ownership-specific analysis 163,

167–8, 168–9state-specific analysis 160–1, 163,

164–6TFP growth 89

Leightner, P.S. 201Leontief, W. 190less developed countries (LDCs) 134level efficiency 118, 120, 122, 124,

125Levinthal, D. 2, 14, 15–17, 23liberalization 9, 59–60 see also trade

liberalizationliberalization dummy 101–2licensing 60, 170Lindley, J.T. 212Little, I.M.D. 140Lovell, C.A.K. 149, 201, 204Lucas model 4, 132Lucas, R.E. 3, 38, 128, 129

McGraw-Hill 2Mahalanobish Model 64Makita 47Malaysia industrial policy support

46, 47manufacturing industry 27, 28, 30

efficiency of in pre- liberalizationperiod 136–44

and output 61

and TFPG 72, 73see also leather industry; textiles

industryMartorelli, W. 29Matsushita 25, 49maximum likelihood see MLMayes, D. 32medical

estimation of changes inproductivity and markup 96,97

TFP growth 89Meeusen, W. 137Microsoft 25, 27, 28minimum productive scale size

(MPSS) 120minimum efficient scale (MES) 120Mitra, D. 92ML (maximum likelihood) methods

104, 105, 107, 113Mohnen, P. 41MRTP 58, 60, 170multifiber agreement (MFA) 144

Nachum, N. 12Nadiri, M. 41NASSCOM (National Association of

Software and ServiceCompanies) 29

National Accounts Statistics 140Neoji, C. 59, 136, 152, 242net fixed capital stock (NFCS) 190,

191New Economic Policy (NEP) 56, 59New Industrial Policy (NIP) (1991)

59, 135, 136, 170NICs 30, 31–2, 129, 189

openness in foreign trade 56, 56R&D investment 55–6

nonradial efficiency 148–50, 174Noyelle, T. 49

Okawa, M. 45optimal time path model 38–40Oracle 27, 28ordinary least squares (OLS) 106, 232organization theory 128output changes in industries’ 61, 62,

63, 63

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Index 261

output-oriented measures 149, 195,204–5, 206–7

outsourcing 27

parametric models 104, 137, 204Pareto efficiency 104Parmar, R. 144–5patents 52perpetual inventory accumulation

(PIA) method 72, 191pharmaceutical industry (US)

impact of R&D 20level and growth efficiency 125

Policy Group 72Porter, Michael 46, 48, 55Prahalad, C.K. 48, 128production approach and

output-input set of banks 212production frontier estimation

108–14productivity 46, 56–103

analysis of differentials amongdifferent sectors 79–90

decomposition of aggregate TFPG67–79

and employment 125, 128interaction between international

trade and 79–80as major force behind sustainable

economic development 67and market power 90–98reduction of by protectionism 79TFPG in export-oriented industries

79–90, 99and trade liberalization 56–103ways to raise 46, 47

public/private sectorleather industry efficiency

comparisons 163, 167–8,168–9

textile industry efficiencycomparisons 158–9, 158–9

quadratic cost frontier models 117‘quality ladder effect’ 129

R&D (research and development) 2,6, 14–20, 31

collaboration in 14, 23, 45and computer industry 19–20cost-reducing impact of inputs

118–20dynamic features of 14effect of on labor productivity

126, 127effects of knowledge and other

explanatory variables onintensity of 16–18, 17

features that are important todynamic evolution of industry117–8

footprints of top ten globalspenders on 25

impact of on firm performance53–4, 53

impact of on growth efficiency6–7, 7, 8

impact of on knowledge capital 2,14

impact of on long-run growth ofinputs 5–6

impact on output growth 118,120–2

impact on productivity in high-techindustries 127

impact on US pharmaceuticalindustry 20

impact on world computer industry19–20

importance of to IT sector’s growth22–4, 51–4

and industry growth 117–33market structure implications 118methods of analysing growth effects

due to 131–3and NICs of Asia 52percentage growth of global

spending 25role of in improving industrial

productivity 31–2spending on 10spillover effect see spillover effectsunk cost of 80, 100and technology diffusion 37–46world distribution by industry

24–5, 24

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262 Index

radial efficiency model 110–11, 111,112, 148–50, 174, 209

Rajadhyakha Committee (1980) 58Rammohan, T.T. 203Ravishankar, T.S. 203Ray, S.C. 110, 203RBI (Central Bank in India) 196,

198, 199ready made garments industry 61,

63, 65, 83real effective exchange rate (EER)

102–3rent differentials 74, 75, 76, 77, 78research and development see R&DReserve Bank of India 21, 200Reserve Bank of India Bulletin 81, 88Richmond, J. 106, 172, 173Romer model 3–4, 6, 37–8, 40Romer, P.M. 3, 6, 128Russell measure 149–50

Saha, A. 203Sahoo, B. 108, 117Salt Lake IT complex (Kolkata) 51SAP 28Satyam Computers 22, 22Saxenian 50scale efficiency 207Schmidt, P. 106Schmookler, J. 31Schumpeter 44Sealey, C.W. 212Second Five Year Plan (1956-61) 58semiconductor industry 2, 14, 23,

171Sengupta, J.K. 3, 6, 11–12, 48, 52,

108, 114, 115, 117, 118, 120,129, 130

Seventh Five Year Plan 135, 189Shy, O. 36Sickles, R.C. 106Siemens 25Silicon Valley 50Simar, L. 114Singapore

industrial policy support 46software industry 49

Singh, J. 140

Singh, Professor Monmohan 98Singh, S.K. 144–5SLR (statutory liquidity ratio) 194SMEs (small and medium-sized

enterprises) 51software sector 1, 8, 11–13, 49

and competency index 11contribution to India’s GDP 48dynamic strategies in 11–12export patterns 12, 13, 48, 84impact of openness in trade on

growth of 49and knowledge diffusion 12need for development in core

competence in managerialskills 13

and R&D 11recent trends in 28–9spending on foreign technology 9success of India in global market

46Solow model 31, 38Sony 47South Korea 129–30

export externality 132, 132Spain, banking 200spillover effect 2, 4, 5, 12, 14–17, 23,

30, 38, 45, 118, 129–31, 41,43–4, 44

Staat, M. 103State Bank of England 198Statistical Abstract 81, 88statutory liquidity ratio (SLR) 194Stiroh, K.J. 8stochastic frontier analysis (SFA)

137–9, 146, 173, 176subsidies 23, 81, 87, 99, 100‘Sunrise’ industries 135

Taiwan 1, 13, 23–4, 130banking system 201economic growth indicators 130,

131export externality 132, 132growth of IT sector 49–51as US patent recipient 50utilization of learning spillover

technology 131

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Index 263

utilization of R&D externalities130

Taiwanese Council for EconomicPlanning and Development130

Tandon Committee (1980) 58Tarapore, S.S. 199, 200tariffs 20, 21, 56, 57, 95–6, 95, 100Tata Consultancy (TCS) 22, 22taxation system 13technical efficiency

of banks 214–7, 215, 216of Indian industries 141–2, 141,

142measuring of 194–5, 204–7

technological change 31–2technological competitiveness and

entry rates 35–7technological progress and total factor

productivity 67technology

compatibility between old and new36–7

cost of imports of as proportion ofsales 9, 9

diffusion 30, 37–46, 51–2efficiency and appropriateness of

chosen 134, 135technology achievement index (TAI)

52Technology Information, Forecasting

and Assessment Council(TIFAC) 171

technology-consortium model 28textiles industry 135

all-India efficiency 153, 154,155

competitive position of 144efficiency in post- liberalization era

144–5, 147–60, 189factors behind efficiency variations

160, 161output 61, 63, 65ownership-specific efficiency

158–9, 158–9pre- and post-liberalization

comparisonaverage efficiency 175–6, 175

distribution of outputs 177–8,184

ownership-specific 176, 178state-specific 176, 180–1

state-specific average efficiencies155, 156–7

TFPG (total factor productivitygrowth) 8, 41, 42

decomposition of aggregate 67–79in export-oriented industries

79–90, 99linkage with export performance

85–90, 99Thailand industrial policy support

46Timmer, C.P. 111–12Timmer, M.P. 79Tobit model 151Toshiba 47total factor productivity growth see

TFPGToyota 25trade liberalization 56, 57, 89, 98–9

and export performance 81–5impact of on industry efficiency

169–88, 181–90impact of market changes on

productivity 90–98, 99–100impact of on TFPG 85–90, 99and industrial productivity

56–103and sheltered market phenomenon

134–5trade policies 20–9translog cost function 115Tybout, J. et al 80

UK manufacturing sector 32, 33United States

banking 200contribution of R&D capital to

manufacturing sector 41decomposition of TFP growth

rates 44economy 128growth of output and productivity


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264 Index

rates of return in physical and R&Dcapital 43

value added changes in industries’63, 64, 65

value chain 47Van Ark, B. et al 65van Dijk, M. 33, 34–5, 35–6variable return to scale (VRS) 174,

195, 206variable-ranking model 189

Veloce, W. 32Verdoorn, P.J. 54–5

wage differentials 74, 75, 78Wilson, P. 114Wilson, P.W. 201Wipro 22, 22World Bank 21, 53, 59,

169

Zellner, A. 32

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