Technology, Firm Sizeand Export Behaviourin Developing Countries:The Case of Indian Enterprises

By Nagesh Kumar and N.S. Siddharthan

UNU/INTECH Working Paper No. 9September 1993

CONTENTS

1. Introduction 1

2. Technology and Exports 3

2.1 The Literature 3

2.2 Technology and Trade Behaviour in Developing Countries 4

3. Hypotheses 7

3.1 Technology 7

3.2 Firm Size 7

3.3 Advertising and Promotion 8

3.4 Capital Intensity 8

3.5 MNE Association 8

3.6 Policy Factors 9

4. Empirical Analysis 11

4.1 Sample, Data and Period 11

4.2 Empirical Findings 12

5. Concluding Remarks 15

Endnotes 17

Annex 19

References 25

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1. INTRODUCTION

A growing body of literature has addressed itself to an analysis of export performance ofenterprises in industrialised and developing countries. The neo-factor endowment andneo-technology theories of international trade have generally provided the theoreticalframework for most of these studies. As the predictions emanating from these theoriesare about industry characteristics, most of the studies have been made in an inter-industrycontext. A great variation in export performance across firms within an industry suggeststhat firm characteristics also play an important role in explaining it. The studies analyzingexport performance using firm level observations have generally focused on an exami-nation of the role of firm-size variable. Little attention has been paid to the examinationof the potential role played by different aspects of firm behaviour including technologicalactivities.

This paper analyzes the interfirm variation in export behaviour in 13 Indian manufactur-ing industries using panel data for 640 firms for the period 1987/88 to 1989/90. The tenetsof neo-technology theories are adapted for explaining the trade behaviour of developingcountry enterprises. It is argued that the technology factor could be important forexplaining the export performance of Indian enterprises in the case of low and mediumtechnology industries. This contention is then put to empirical verification. The analysisis conducted in the framework of Tobit models in view of the fact that a large number ofIndian enterprises do not export and hence the dependent variable has a zero value inmany cases. It examines in particular the role of firm size, in-house innovation andtechnology imports, relative advertising and capital intensity of operations, controllingaffiliation with multinational enterprises (MNEs), and export incentives and concessionsoffered by the Indian government.

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2. TECHNOLOGY AND EXPORTS

2.1 The Literature

The recent theoretical and empirical literature on international trade has emphasised thecontribution of technology and skills to countries’ relative competitiveness. The neo-technology theory, in particular, highlights the role of technology gap in determining acountry’s international trade pattern [Posner 1961; Vernon 1966; Hufbauer 1966; Krug-man 1979]. Among the empirical studies, Gruber, Mehta and Vernon [1967] have foundthe ’technology factor’ to be important in explaining international trade and haveobserved that U.S. industries associated with a relatively high ’research effort’ also tendto export a relatively high proportion of their output. Caves et al. [1980] found net exportsof Canadian industries to be significantly related to the industry’s R&D intensity. Soete[1981, 1987] in his studies of 40 industries found OECD countries’ export performanceto be a function of their share of patents. Sveikauskus [1983] found technology to be amore important factor in explaining U.S. competitiveness than skill and capital intensity.Hughes [1986] found the export intensity of U.K. industries to be significantly related totheir R&D and skill intensities and inversely with average R&D intensity of industry inU.S., France, Germany and Japan. In the same study, the export intensity of Germanindustries was also found to be related to their R&D intensity. In a more recent studyprobing the role of technology, van Hulst, Mulder and Soete [1991] found the patternsof export specialisation to be quite similar to those of technological specialisation in thecase of Germany, Sweden and the Netherlands. Cotsomitis et al.[1991] using a technologystock variable and time series data, however, found technology gap theory unable topredict the direction of high technology trade. Verspagen and Wakelin [1993] in a recentstudy of 22 sectors in 9 OECD countries found technology (either R&D or patents) andlabour costs to be important influences on trade. Siddharthan and Kumar [1990] foundR&D intensity to be an important variable in explaining the inter-industry pattern ofintra-firm trade of U.S. MNEs.

Few studies have examined the role of technology in developing countries’ trade.Dasgupta and Siddharthan [1985] have found that Indian exports consist largely ofstandardised goods with a low skill and technological content. In a study explainingexport performance of 100 engineering and 45 chemical firms, Lall [1986] found R&Dexpenditure (and not its intensity) to be significant with a negative sign in the case ofengineering firms but with a positive sign in the case of chemical firms. Royalty paymentsproxying the extent of technology imports and skill intensity had an insignificantcoefficient throughout. Kumar [1990, chap.6] in a study of 43 Indian industries found thetechnology variable (capturing intensity of R&D and technology imports) to be notsignificant in explaining export performance. Willmore [1992] in a study of 3764exporters and 2826 importers in Brazil found the existence of R&D to be significant inexplaining neither the probability of export nor the export performance of exporters. The

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average wage employed as a proxy of skill intensity turned out with a negative sign butnot significantly different from zero in explaining the probability of export but wassignificant with the same sign in explaining export performance of exporters.

2.2 Technology and Trade Behaviour in Developing Countries

The empirical literature reviewed above generally confirms the importance of technologyin explaining the trade performance of industrialised countries. In the case of developingcountries, however, the neo-technology models have had a limited success in explainingexport performance. The limitation of neo-technology models in explaining trade ofdeveloping countries is now widely recognised. This is because early versions of thesemodels were constructed with the purpose of explaining trade among countries at a similardevelopment level [Goglio, 1993]. Krugman [1979] in his version of the technology gapmodel brought in the possibility of technology diffusion to explain the North - Southtrade. New technology is primarily created in the North but soon gets diffused to theSouth. The gap between creation and diffusion creates the possibility of trade. Therefore,this model assumes the existence of certain imitative, adaptive or absorptive capabilityin the South to allow for diffusion of technological innovations. There is, however, a greatvariation across the developing countries in terms of local technological capability forimitation, adaptation or absorption. Even within a developing country, industry firmsgenerally differ vastly in terms of extent of technological capability. Hence, it may notbe possible to deduce a relationship at the industry level.

Developing countries are likely to be in an apparently disadvantageous position in theexport of high technology or knowledge intensive goods. The bulk of the R&D activityin most developing countries with the exception of newly industrialising economies suchas South Korea, is adaptive rather than creative in nature. In high technology industries,the competitive advantage is determined by product innovations which are not the focusof technological activity of developing country enterprises. The product life cycles inthese industries are usually short making it difficult to achieve a competitive advantageon the basis of imitation. By the time such product technology diffuses to developingcountries it may already be outdated.

The entry of developing country enterprises into international markets for high technol-ogy goods is also deterred by other barriers such as vertical integration and geographicaldiversification. Manufacture of high technology goods contains a significant element ofproprietary and firm specific knowledge (hence these are referred to as S-products byAharoni and Hirsch, 1993). Marketing of many such products is also associated by arange of highly specialised services such as instruction, installation, repairs etc. whichdepend on proprietary knowledge originating with the manufacturer of the goods. Thefirm specific nature of the knowledge involved in the product, process and associatedservices make unbundling difficult. Hence, these industries are dominated by verticallyintegrated firms that undertake R&D, manufacturing, distribution and servicing in-houseor through associated organizations [Aharoni and Hirsch, 1993]. The importance ofvertical integration thus acts as an entry barrier in these industries.

Furthermore, exporters of high technology products generally have to organize to provideproduct specific services such as instruction, installation, repairs, maintenance etc. in thepotential markets abroad. It could either be done through an unaffiliated licensee orthrough an affiliate such as a subsidiary or a branch. If the services are provided through

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unaffiliated licensees the transaction or governance costs are high because of the risk oflosing proprietary knowledge and need of supervision of quality standards [Dunning,1993; Kumar 1990]. The transaction costs can be minimized if the services are providedby own affiliates. Multinational enterprises, therefore, enjoy an inherent competitiveadvantage in international markets for high technology goods because of their in-houseability to provide associated services at geographically diverse locations and savings ontransactions costs.

To sum up the above discussion, developing country enterprises are unlikely to achievecompetitive advantage on the basis of their own technological activities in high technol-ogy industries because of their inability to compete through product innovations, shorterproduct life cycles, firm specific nature of the knowledge and hence significant economiesof vertical integration and geographical diversification. The industrialised country enter-prises many of which are multinational in terms of coverage of their operations andintegrated vertically to handle all the tasks related to production and marketing of hightechnology products reap these economies. The imitative capability that technology gapmodels assume, therefore, is expected to provide a competitive edge to developingcountry products only in low or medium technology industries. This contention is alsoconsistent with the product cycle theory of Vernon which predicts exports from devel-oping countries to take place in maturing phases of a product’s life when competitiveadvantage is determined more by factor costs rather than by innovation. That propositioncan be generalised to hold good for industries that have reached a relative maturity interms of technological opportunities viz. low and medium technology industries.

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3. HYPOTHESES

The export behaviour of an enterprise can be influenced by a number of other factorsbesides technology. A growing volume of literature has debated the role of firm size indetermining export performance. In addition, other aspects of firm behaviour such asadvertising and promotion and capital intensity of operations, and affiliation withinternational chains of companies are likely to influence export behaviour. The exportperformance of enterprises is also likely to be determined by government policy factorssuch as incentives in the form of export linked imports etc., tax concessions and exportobligations. The factors potentially influencing the export behaviour of Indian enterprisesconsidered in this study are identified below along with predictions.

3.1 Technology

As discussed above, technology factor is expected to be important in explaining inter-firmvariation in export behaviour in the case of developing countries such as India in low andmedium technology industries only. Technology factor will be captured with the help oftwo variables viz. in-house R&D activity : RDS, and skill intensity of operations: SKIL,as a proxy of potential of informal R&D. Indian firms widely resort to importing to fulfiltheir technology requirements. Hence, a measure of import of technology: TECHIM willbe included to examine its role in strengthening export competitiveness1.

3.2 Firm Size

The export marketing literature has treated firm-size as a proxy of firm resources that areconsidered important for venturing in the international markets. The industrial organisa-tion literature has posited a positive role of firm size in view of economies of scale inproduction and marketing. The empirical findings on the relationship, however, have beenmixed [Bonaccorsi 1992, for a recent survey]. Lall [1986] found firm size to be significantwith a positive sign in case of engineering firms but insignificant for chemical firms.Patibandla [1988] in the case of Indian engineering industry, and Bonaccorsi [1992] inthe case of Italian industry found small and medium firms to be quite active in exports.Kumar [1990, chap. 6] found the firm size - export performance relationship to besignificant with a positive sign in the case of foreign controlled firms but insignificant inthe case of local firms in Indian manufacturing industries. Willmore [1992] in hisBrazilian study found firm size and its quadratic term to be significant with positive andnegative signs respectively in explaining the probability of exporting and an oppositeresult while explaining export performance of exporters.

The mixed findings about firm size - export behaviour relationship for Indian enterprisescould possibly be due to non-linearity. Exporting may be beyond the capacity of toosmall enterprises. On the other hand very large oligopolistic enterprises enjoying captive

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access to protected domestic market as was prevailing in India in the late 1980s may notfeel compelled to export. Hence, an inverted-U shaped relationship is possible. This willbe examined by including along with firm size (SIZE), its quadratic term.

3.3 Advertising and Promotion

Generally enterprises from developing countries compete in international markets on thebasis of price cutting and focus on the lower end of the markets or sell to multinationalbuyer groups. This is because they have no resources to engage themselves in thenon-price rivalry with multinational enterprises. Nevertheless, enterprises with advertis-ing and promotion activity are likely to do better in international markets than others.This is because in the process of building brands and trade names Indian enterprises arelikely to become conscious of the need to maintain quality. The quality consciousness isan important requisite for a sustained export effort. Hence, a positive relationship isexpected between advertisement intensity (ADS) and export behaviour.

3.4 Capital Intensity

A higher capital intensity of operations (e.g. a higher extent of automation) is unlikely togive the firm a competitive advantage in a developing country setting as in India withlabour abundance and relative scarcity of capital. Hence, an inverse relationship is positedbetween capital intensity of operations (CAPINT) and export behaviour.

3.5 MNE Association

Firms having formal links with multinational enterprises in the form of controllingownership stakes are generally considered to be better placed to tap international marketsthan Indian owned firms in view of their captive access to the information and marketingnetworks of their parent enterprises [see Dunning, 1993, for a recent survey of empiricalliterature]. As argued earlier, MNEs also enjoy economies of vertical integration andgeographical diversity that impede the entry of developing country firms in high technol-ogy industries. Given their strategic inclination to export from their host countries, MNEaffiliates are likely to do better than local enterprises in terms of export behaviour. MostMNE affiliates in India, however, were set up primarily to tap the local market in responseto the import substitution programme [Kumar, 1987]. Kumar [1990] in his study of 43Indian industries did not find a significant difference in the export performance of foreigncontrolled and local firms. Nonetheless, a dummy variable separating all the foreigncontrolled firms (DFOR) will be included to examine the influence of MNE associationon export behaviour.

3.6 Policy Factors

In order to promote Indian exports of manufactures, the Indian government providescertain incentives to enterprises. The structure of the export incentive system hasundergone a dramatic change since 1991 when the government embarked on extensivereforms and liberalisation of the Indian economy. In the late 1980s, to which theobservations of the present study relate, cash compensatory support (CCS), importreplenishment licences (REP), and corporate tax concessions had been the main incen-

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tives granted to exporting firms. Furthermore, under the Foreign Exchange RegulationAct (FERA) of 1973 which had been effective all through the 1980s, a general ceiling of40 percent was imposed on the level of foreign ownership in an Indian company [Kumar,1990, chap. 1]. Exceptions to this general limit were permitted for companies acceptinga significant export commitment. The available data base allows us to examine theinfluence of REP, tax incentives and export commitments accepted by Indian affiliatesof MNEs in exchange for allowing over 40 percent equity stake on the export behaviourof Indian enterprises. Hence, we limit the discussion to the proxies constructed to capturetheir effect.

a. Import replenishment The REP licences were issued by the government to cover the imports of raw materials,components and spares by exporters which were otherwise restricted by the importlicensing policy. An exporter not needing to import could transfer the REP licences toothers at a premium which used to be considerable because of import restrictions. Thissystem provided pecuniary incentive to exporters by way of premium. On the other hand,it encouraged the firms heavily dependent on import of raw materials etc. to export partof their output in order to earn REP licences which would have to be procured at apremium otherwise. Hence, we expect import dependence (IMPDEP) of firms to affecttheir export behaviour favourably.

b. Tax ConcessionsUnder the income tax law, the exporting firms can claim proportionate reduction in theircorporate income tax liability. Therefore, this policy is likely to prompt the profitablefirms to diversify into exporting in order to reduce their tax liability. Hence, a positiverelationship is predicted between profitability (PROF) and export behaviour. Such arelationship is expected also because only profitable firms are likely to venture intointernational markets in view of the higher degree of risk and uncertainty involved2.

c. Export CommitmentsAs indicated above, export commitments were generally accepted by Indian affiliates ofMNEs wishing to retain more than 40 percent foreign ownership as part of the implemen-tation of the provisions of the FERA. This provision was designed to exploit theadvantages of multinational enterprises in international markets for promotion of Indianexports. Enterprises with export commitments are expected to be more active on theexport front than those without such obligations. Hence, a positive relationship isexpected between a dummy variable identifying firms with export commitments (DEX-COMT) and export behaviour.

Thus the export behaviour of firms within an industry can be explained in the frameworkof the following Tobit model [see Maddala, 1983 for a discussion of Tobit models]:

EXP = f (SIZE, SIZE2 , RDS, SKIL, TECHIM, ADS, COR, DFOR, IMPDEP,PROF, DEXCOMT)

........ if EXP 0; and

EXP = 0; otherwise.

where EXP measures export to sales ratio.

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4. EMPIRICAL ANALYSIS

This section will present the sample and data employed for estimating the above Tobitmodel and will discuss the empirical findings.

4.1 Sample, Data and Period

The study uses the data tapes compiled by the Reserve Bank of India as a part of theirregular surveys of Finances of Large Public Limited Companies in the Private Sector.The data base contains panel data for 640 corporations for the 1987-88 to 1989-90 period.Each of the companies included in the data base had been assigned a three digit industrycode referring to the industry accounting for over half of the turnover of the company.Companies engaged in non-manufacturing activities e.g. plantations, mining, trading andother services were dropped from the sample for the present study. Among the manufac-turing industries, some regrouping of industries was found to be necessary and industrieshaving a small number of firms had to be dropped in order to ensure adequate degrees offreedom. The final coverage was restricted to 13 industries which in all accounted for406 companies.

Table 1 lists the industries and with the respective number of firms included in sampleof the study. The sample industries have been classified according to relative technologyintensity using UNCTAD [1987] classification. According to this classification, fiveindustries viz. food processing, textiles, fabricated metal products, cement and structuralclay products and paper are classified as low technology industries. Transport equipment,manmade fibres and plastic raw materials, rubber products and toiletries, basic industrialchemicals and dye stuffs, and other chemical products including toiletries are classifiedas medium technology industries. Electrical and non-electrical machinery, and drugs andpharmaceutical are classified as high technology industries.

The table also provides the mean export intensity and the coefficient of variation for thesample industries. From it, one notes a considerable interfirm variation in export intensityin the sample industries. It is particularly high in low and high technology industries. Themedium technology industries have a relatively lower coefficient of variation with theexception of manmade fibres industry.

The observations for three years viz. 1987/8, 1988/9 and 1989/90 were pooled for theempirical exercise. Major decisions had been taken to liberalise the foreign trade regimeand deregulate the Indian industry during 1985/6. Since July 1991 the Indian governmentembarked on a more comprehensive reform of the policy to improve the competitivenessof the Indian industry. In the reference years for the study, however, no major policydecisions were taken. Though the years covered for the study have been the ones with arelatively stable policy regime, year dummies for the years 1989 and 1990 (DYR89,

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DYR90) have been introduced to control for any temporal changes. The variable meas-urements have been provided in Annex 1.

4.2 Empirical Findings

As indicated earlier, not all sample firms exported. Hence, for a number of observationsthe dependent variable had a zero value. Therefore, Tobit models are used and areestimated by maximum likelihood method3. DEXCOMT identifying firms with more than40 percent foreign ownership is a subset of DFOR, another dummy variable indicatingforeign stake of 25 percent or more. These can not be tried together as the matrix becomessingular. Hence, two versions of the model are estimated for each industry so that eitherDFOR or DEXCOMT is included at a time. The estimated Tobit models for each industryare presented in Table 2. In what follows the findings of these estimations are discussed.

The main variable capturing technology factor i.e. RDS is statistically significant withpositive sign in four industries viz. transport equipment and manmade fibres (at 5 percentor a higher level), and paper and rubber products (at 10 percent level). SKIL is significantwith a positive sign in the case of food processing and transport equipment industry. Allthese industries are those classified under low or medium technology industries. Thesevariables are insignificant in all the three high technology industries except for a 10percent level of significance achieved by SKIL in the case of non-electrical machineryindustry. These results, therefore, tend to support the prediction that in developingcounties like India, a firm’s innovative activity does contribute to export competitivenessin low and medium technology industries. In high technology industries, it is beyond thecapacity of the developing country enterprises to achieve export competitiveness on thebasis of their R&D activity.

The insignificance of either R&D or skill variable in textiles, fabricated metal products,cement and structural clay products, and basic industrial chemicals and the negative signin the case of other chemical products and toiletries, however, need explanation. Intextiles and cement industries, innovative activity in India is concentrated at equipment(e.g. spindles, looms, plants) manufacturers and industry or government sponsored R&Dorganisations e.g. Ahmedabad Textile Industry Research Institute (ATIRA) and CementResearch Institute respectively. The R&D activity in the Indian chemical industries hasgenerally been geared towards process adaptations to accommodate locally available rawmaterials and feed stocks or development of indigenous processes for known chemicalcompounds [see Desai, 1980, for an analysis of nature of R&D activity of Indianenterprises]. These adaptations could be cost saving in general but might deter the entryof enterprise in the export markets because of product patents.

Technology imports variable, TECHIM, is significant with a positive sign in the case ofpaper products, rubber products, non-electrical machinery (at 10 percent level) andelectrical machinery industries only. The latter two are engineering industries and areclassified as high technology industries. Thus there appears to be some evidence of Indianengineering enterprises breaking into international markets with the help of foreigncollaborations or technology imports. In these industries, technology imports usuallycomprise imports of product designs which are usually transferred in subcontractingagreements. TECHIM is insignificant in all medium and low technology industries exceptpaper and rubber products. In medium and low technology industries, technology importsalone appear unlikely to give the Indian enterprises a competitive advantage in export

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markets. A successful export effort in these industries would require technology imports,if at all, complemented by in-house technological activity. The significant negativecoefficient of TECHIM in the case of cement industry probably highlights the fact thatimported processes for manufacture of cement incorporating a considerable extent ofautomation are not cost effective given the Indian factor endowments.

Firm size comes up with a positive sign in most cases and is statistically significant inseven industries (viz. textiles, cement, paper, manmade fibres, rubber tyres, electrical andnon-electrical machinery). In all these seven industries SIZE2 also is significant with anegative sign. This suggests that firm size -- export behaviour relationship in Indianindustry is predominantly inverted-U shaped. Therefore, very large firms enjoying anoligopolistic hold in the domestic market are generally less inclined to export than otherfirms. In five industries viz., basic industrial chemicals, drugs and pharmaceutical, foodprocessing, metal products, and transport equipment, firm size variables generally havesimilar signs but are not significantly different from zero because of lack of a dominantpattern. In the case of other chemical products, SIZE is significant with a negative signand SIZE2 is significant with a positive sign suggesting a U-shaped relationship. In thisindustry, therefore, a certain minimum threshold size should be reached before anenterprise becomes an effective exporter.

Advertising intensity of firms is found to be exerting a favourable and statisticallysignificant influence on their export behaviour in five industries viz., fabricated metalproducts, paper, transport equipment, rubber products, and drugs and pharmaceutical.Generally, exporters from developing countries like India concentrate on the pricecompetitive lower end of the market. The evidence nevertheless seems to suggest thatenterprises that build up some goodwill for their brand or trade names through advertisingand sales promotion at home or abroad are likely to do better than others in export marketsespecially in the above industries. Only in the case of textile industry is ADS significantwith a negative sign which is counter intuitive. One possible explanation for this couldbe that advertising and establishing brands in the textile industry generates so muchdemand in the local market that firms hardly have any surpluses or inclination left forexports. However, more probing is needed into the peculiarities of the industry to find anexplanation.

The measure of capital intensity, COR, is significant with the hypothesized negative signin six industries; food processing, cement, paper, rubber products, industrial and otherchemicals. All these industries happen to be low and medium technology industries. Onthe other hand in two of the three high technology industries viz., electrical engineeringand drugs and pharmaceutical, COR is significant with a positive sign. This resultsuggests that while a higher degree of capital intensity of operations (or automation) doesnot give a competitive advantage to exporting firms in low and medium technologyindustries, it is desirable for breaking into export markets in high technology industries.In other words, enterprises employing labour intensive processes have an edge over thosewith more automated production in low and medium technology industries apparentlybecause of low wages prevailing in the country. In the high technology industries labourintensive processes appear to be inefficient despite low wages. This finding is ofconsiderable policy interest and warrants more detailed further work.

The dummy variable identifying controlling foreign stake, DFOR, has a positive sign inmost cases but does not achieve statistical significance except in the case of non-electrical

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machinery industry. It suggests that the export behaviour of multinational affiliates andother Indian enterprises is not significantly different in most cases. This finding isconsistent with that of Kumar [1990] for 43 Indian industries for a three year periodending 1980-81. Thus in an economy with a large protected market such as India, MNEslike local firms find an inward-oriented market strategy to be a far more attractive optionthan exporting.

IMPDEP has a significant coefficient with a positive sign in the case of six industriesviz., textiles, fabricated metal products, cement, manmade fibres, non-electrical machin-ery and drugs and pharmaceutical. It would appear to suggest that compulsion to earnimport replenishment licences has prompted firms heavily dependent on imports of rawmaterials, components etc. to export part of their output. In the basic industrial chemicalsindustry, IMPDEP is significant with a negative sign. It implies that in this particularindustry, import dependent firms are unlikely to achieve export competitiveness.

PROF behaves rather poorly and is significant with the hypothesized positive sign in thecase of just four industries viz., cement, transport equipment, non-electrical equipmentand drugs and pharmaceutical (only in equation 13.b). It appears that corporate taxconcessions for exporting firms have not had much success in prompting profitable firmsto diversify into exporting. In the case of basic industrial chemicals in fact, PROF issignificant with a negative sign. It indicates a lower inclination of profitable chemicalindustry firms to export. Again a need for more detailed work is indicated to find anexplanation for such a behaviour.

Out of ten industries where DEXCOMT was tried, it was significant in five industrieswith a positive sign and with a negative sign in one i.e. fabricated metal products industry.The industries for which DEXCOMT has a positive and significant coefficient are: paper,transport equipment, rubber products, non-electrical machinery, and drugs and pharma-ceutical. This suggests that the Indian government’s policy of allowing higher foreignequity in exchange for accepting export commitments has met with some success inprompting these firms to export. This finding is of interest in the context of the currentdebate on the desirability of trade related performance requirements (TRIMs) [seeUNCTC/UNCTAD, 1991, for a review]. It appears from the present finding that devel-oping countries, especially those with large home markets, can use export obligations tosuccessfully tap resources of MNEs for expanding their manufactured exports which isa high priority development objective for most developing countries.

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5. CONCLUDING REMARKS

This paper has analyzed interfirm variation in export behaviour of Indian enterprises in13 manufacturing industries with data for a three year panel, 1988-90. The neo-technol-ogy theories have been adapted for explaining the export behaviour of developing countryenterprises. It is argued that technological activities of enterprises from developingcountries like India would be important in explaining their export performance only inlow and medium technology industries. These enterprises are unlikely to achieve com-petitive advantage on the basis of their own technological effort in high technologyindustries because of their inability to compete through product innovations, shorter lifecycles of products, firm specific nature of knowledge and hence significant economiesof vertical integration and geographical diversification.

The estimated Tobit models uphold our prediction that in developing countries like India,a firm’s technological activity favourably influences its export behaviour only in mediumand low technology industries. In high technology industries Indian firms are unable toenter the export markets on the strength of their own technological activity. Indianengineering enterprises seem to have made a breakthrough in export markets with thehelp of imports of technology and designs. In low and medium technology industries,however, technology imports do not appear to provide a competitive advantage to Indianfirms. A successful export effort in these industries would require technology imports, ifat all, complemented by in-house technological effort.

The firm size - export behaviour relationship in Indian industries is predominantlyinverted U-shaped. This is because very large enterprises enjoying an oligopolistic holdover the protected domestic market are generally less inclined to export than other firms.Building brands and trade marks with advertising generally helps in export markets eventhough Indian firms may be vying for the price competitive lower ends of the market.Given India’s factor endowments, a higher degree of capital intensity of operations (orautomation) does not give a competitive advantage to exporting firms in low and mediumtechnology industries. In high technology industries, however, capital intensity appearsto favourably affect export behaviour. MNE affiliates in general do not exhibit asignificantly different export behaviour from other firms. Therefore, in economies witha large protected market as in India, MNEs, like local firms, tend to focus on the domesticmarket.

The different government policy measures for encouraging Indian enterprises to exporthave had varying levels of success in achieving their objectives. The compulsion to earnimport licences in the earlier trade policy regime seems to have prompted importdependent firms to diversify into exports. The tax concessions granted to exporters appearto have had a limited success in encouraging profitable firms to export. The governmentpolicy of allowing higher foreign equity to MNEs in their Indian affiliates in exchangefor accepting export commitments seems to have been successful in general in promoting

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exports. It would suggest that negotiation with MNEs regarding export obligations at thetime of entry can prove to be an effective means, especially for countries with large homemarkets, of tapping their marketing advantages in international markets for expandingmanufactured exports from the host countries.

Finally, the divergent performance of technology and capital intensity variables in highand lower technology industries is of considerable policy interest. In low and mediumtechnology industries, export competitiveness is to be obtained on the basis of indigenoustechnological effort and through use of labour intensive production processes. In the hightechnology industries, on the other hand, imports of technology and thus affiliation withMNEs, a higher degree of automation and modernisation appear to be important forbreaking into the international markets. In view of this the domestic resource cost forpromoting exports of high technology industries is likely to be higher than that for exportsof low and medium technology industries in a country like India. Another implication ofthe findings is that export promotion policies need to be made more specific to industrieskeeping in mind their peculiarities.

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ENDNOTES

1. The exporting enterprises may require a higher level of technological effort than those focusing on thedomestic market. In that case the export behaviour - technology relationship could be subject tosimultaneity problem. Such a bias, if it exists, can be taken care of in the framework of a simultaneousequation model where both export behaviour and RDS are specified as endogenous variables. Since alarge proportion of Indian enterprises do not report R&D activity, the equation explaining variation inRDS also will have to be specified as a Tobit model. The software resources presently available, however,do not allow estimation of a simultaneous equation system containing more than one Tobit specification.Hence, it has not been possible to verify or correct for the simultaneity problem with respect to export -R&D relationship.

2. The profitability of export operations may be different from that of local sales. Hence, the profitability- export behaviour relationship too may be subject to simultaneity bias. In the present case, however, thisproblem is unlikely to be significant as exports constitute only a marginal proportion of the total turnoverof sample firms.

3. The Tobit models have been estimated using William Greene’s LIMDEP.

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ANNEX 1

Measurements of Variables

i EXP : exports to sales ratio.

ii RDS : R&D expenditure to sales ratio.

iii SKIL : proportion of high income employees (viz., those drawingover Rs. 6000 p.m. or more) in total wage bill.

iv TECHIM : technology payments abroad to sales ratio.

v SIZE : net sales of the company.

vi ADS : advertisement expenditure to sales ratio.

vii COR : gross fixed assets to sales ratio.

viii PROF : profits before tax to sales ratio.

ix IMPDEP : total value of imports as a proportion of sales.

x DFOR : a dummy variable, taking value 1 if the company is twentyfive percent or more foreign owned and 0 otherwise.

xi DEXCOMT : a dummy variable taking value 1 if the company is morethan forty percent foreign owned and 0 otherwise.

xii DYR89 : a dummy variable taking value 1 for observations relatingto the year 1988/9 and 0 otherwise.

xiii DYR90 : a dummy variable taking value 1 for observations relatingto the year 1989/90 and 0 otherwise.

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Table 1.: Intra-industry Variation in Export Intensity in Indian Industries

Industry GroupNo. Of Firms

Export Intensity(mean)

Coeff. of Variation

%

Low Technology Industries

1. Food processing 29 0.04063 263.46

2. Textiles 68 0.06343 145.10

3. Fabricated metal products 53 0.03652 389.72

4. Cement and structural clay products 29 0.00748 219.40

5. Paper 27 0.00298 273.48

Medium Technology Industries

6. Transport equipment 35 0.02618 153.37

7. Manmade fibres and plastic raw materials 17 0.02376 232.26

8. Rubber products including tyres 15 0.03298 118.49

9. Basic industrial chemicals and dyestuffs 38 0.07483 167.10

10. Other chemical products and toiletries 29 0.09866 186.53

High Technology Industries

11. Electrical machinery 25 0.03595 215.31

12. Non-electrical machinery 57 0.07166 210.13

13. Drugs and pharmaceuticals 34 0.07449 212.20

Notes :a) The technology classification of industry is according to UNCTAD [1987].b) Mean and variation are across pooled samples of firms for the years 1987/8 to 1989/90.

20

TABLE 2: ESTIMATED TOBIT MODELS EXPLAINING EXPORT BEHAVIOUR OF INDIAN FIRMS

S.No Constant SIZE SIZE2 RDS TECHIM SKIL ADS COR IMPDEP PROF DFOR DEXCOMT L.L. Sigma N

LOW TECHNOLOGY INDUSTRIES

1. Food Processing

a) -.93E-2(.139)

-.16E-6(1.041)

.54E-13(.866)

117.84(.677)

-.129(.032)

.906(2.794)

-.17E-1(.010)

-.139(1.661)

-.215 (.436)

-.54E-1(.123)

.59E-3 (.004)

-10.6 .173 (7.9)

87

2. Textiles

a) .10E-1(.444)

.47E-7 (2.91)

-.37E-14(2.732)

15.702(.773)

-1.285(.466)

-.227(1.032)

-2.19(1.529)

.24E-1(.982)

.265 (2.724)

.16E-1(.226)

.49E-1(1.225)

64.1 .112(16.2)

204

b) .91E-2(.390)

.49E-7(2.994)

-.39E-14(2.891)

14.334(.706)

-.990(.360)

-.200(.910)

-2.35 (1.649)

.27E-1(1.095)

.275(2.834)

.33E-1(.464)

.125(1.098)

63.9 .113(16.2)

204

3. Fabricated Metal Products

a) -.111(3.005)

.25E-7(.529)

-.40E-14(.324)

-.251(.021)

.203(.117)

-.33E-1(.318)

1.78(2.751)

.28E-1 (.788)

.430 (6.047)

.134 (1.107)

-.29E-1(.577)

-1.4 .143(12.1)

159

b) -.116(3.088)

.28E-7(.598)

-.50E-14 (.401)

1.603(.134)

.260(.147)

.77E-2(.072)

1.681 (2.577)

.31E-1(.846)

.432 (6.060)

.147 (1.189)

-.99E-1(1.474)

-.464 .143(12.0)

159

4. Cement and Structural Clay Products

a) -.52E-2(.471)

.41E-7(5.977)

-.42E-14(4.249)

-1.158 (.808)

-2.853 (2.192)

.32E-1(.112)

-.436 (.625)

-.21E-1(3.404)

.113 (2.357)

.77E-1(2.115)

-.16E-3 (.013)

74.1 .436(7.8)

87

5. Paper and Products

a) -.20E-1(2.341)

.20E-7 (2.473)

-.27E-14 (1.856)

3.537 (1.332)

1.685 (1.455)

.37E-1(1.045)

4.245 (2.642)

-.91E-2 (1.315)

.267(.817)

-.53E-2 (.188)

.74E-2(1.112)

46.9 .98E-2 (5.6)

81

b) -.19E-1(2.304)

.18E-7 (2.227)

-.24E-14(1.631)

3.604 (1.380)

1.778 (1.543)

.29E-1 (.814)

3.788 (2.260)

-.77E-2 (1.105)

.24E-1(.778)

-.31E-2(.108)

.11E-1 (1.451)

47.5 .97E-2(5.6)

81

TABLE 2 (contd.)

S.No Constant SIZE SIZE2 RDS TECHIM SKIL ADS COR IMPDEP PROF DFOR DEXCOMT L.L. Sigma N

MEDIUM TECHNOLOGY INDUSTRIES

6. Transport Equipment

a) .22E-2(.138)

.29E-8(.626)

-.42E-17 (.012)

2.388 (2.202)

.374 (.678)

.64E-1(1.711)

.767 (1.423)

-.12E-1(.802)

-.20E-1(.377)

.147(1.885)

.37E-2(.276)

109.1 .455 (11.9)

105

b) .54E-3 (.035)

.36E-8(.810)

-.19E-16 (.056)

2.693 (2.779)

.284 (.522)

.36E-1(.940)

.731 (.139)

-.10E-1 (.658)

-.15E-1 (.292)

.129 (1.757)

.35E-1(2.045)

111.1 .446 (11.9)

105

7. Manmade Fibres and Plastic Raw Materials

a) -.114(3.069)

.50E-7(2.170)

-.54E-14 (1.523)

20.447 (2.988)

-.150 (.100)

.19E-1 (.468)

-.619(.085)

-.24E-1 (.925)

.452 (4.556)

.198 (1.242)

.17E-1 (.543)

38.6 .49E-1 (7.2)

51

8. Rubber Products including Tyres

a) -.20E-1 (2.341)

.20E-7 (2.473)

-.27E-14 (1.856)

3.537 (1.332)

1.685(1.455)

.37E-1 (1.045)

4.244(2.642)

-.91E-2 (1.315)

.26E-1 (.817)

-.53E-2 (.188)

.74E-2 (1.112)

46.9 .98E-2 (5.6)

81

b) -.19E-1(2.304)

.18E-7(2.227)

-.24E-14 (1.631)

3.604 (1.380)

1.777 (1.543)

.29E-1 (.814)

3.788 (2.260)

-.77E-2 (1.105)

.25E-1 (.778)

-.31E-2(.108)

.11E-1 (1.451)

47.52 .97E-2(5.6)

81

9. Basic Industrial Chemicals and Dyestuffs

a) .205(3.685)

.63E-8(.082)

.33E-14 (.121)

2.185(.764)

-3.604(.987)

.60E-1(.766)

-1.819 (.489)

-.83E-1(2.534)

-.401(2.526)

-.682 (3.236)

-.48E-1 (1.113)

16.3 .149 (12.1)

114

b) .192 (3.440)

.46E-8(.059)

.33E-14 (.120)

-.179 (.058)

-2.63(.740)

.31E-1(.410)

-1.907 (.512)

-.78E-1(2.378)

-.388 (2.448)

-.717 (3.440)

.65E-1 (1.075)

16.3 .149(12.1)

114

10. Other Chemical Products and Toiletries

a) .276(2.855)

-.11E-6(1.759)

.11E-13(1.675)

-20.748(2.215)

5.051(.684)

-.15E-1(.068)

1.686(1.103)

-.318(3.092)

.123(.412)

-.67E-1(.315)

-.61E-1(.866)

-4.2 .207(10.8)

87

b) .272(2.880)

-.12E-6(1.853)

.13E-13 (1.881)

-20.037 (2.189)

4.419 (.595)

-.50E-1(.228)

1.392 (.921)

-.328 (3.128)

.232 (.797)

-.60E-1(.279)

-.148 (1.201)

-3.8 .207(10.9)

87

TABLE 2 (contd.)

S.No Constant SIZE SIZE2 RDS TECHIM SKIL ADS COR IMPDEP PROF DFOR DEXCOMT L.L. Sigma N

HIGH TECHNOLOGY INDUSTRIES

11. Electrical Machinery

a) -.28E-1(.795)

.15E-6(1.922)

-.69E-13(1.567)

-1.584(.674)

1.357 (2.693)

.19E-1(.272)

.83E-1 (.052)

.34E-1(1.836)

-.30E-1(.352)

-.60E-2 (.101)

.28E-1(1.144)

45.1 .714 (9.5)

75

b) -.17E-1(.513)

.14E-6(1.739)

-.67E-13 (1.453)

-.709(.316)

1.305 (2.617)

.22E-1 (.327)

-.448(.289)

.29E-1(1.637)

-.13E-1(.159)

-.70E-2 (.119)

-.27E-3(.006)

44.4 .712(9.5)

75

12. Non-electrical Machinery

a) -.93E-1(2.882)

.54E-7(2.094)

-.91E-14 (2.299)

.671(.145)

1.195 (1.309)

.102 (1.614)

1.573(.900)

-.90E-2(.423)

.875(9.573)

.133(1.429)

.49E-1 (1.993)

66.0 .123(16.1)

171

b) -.99E-1(3.154)

.57E-7 (2.265)

-.96E-14(2.489)

1.432(.318)

1.336 (1.504)

.79E-1(1.298)

1.970(1.165)

-.23E-2(.113)

.878 (9.932)

.142 (1.618)

.95E-1 (3.143)

69.7 .120(16.2)

171

13. Drugs and Pharmaceuticals

a) -.147(4.077)

.66E-7(.998)

-.20E-13(.733)

.456 (.171)

1.580 (.350)

.94E-2 (.232)

.577(1.775)

.71E-1 (1.399)

1.048 (14.909)

.277(1.150)

.12E-1(.534)

104.3 .79E-1(13.9)

102

b) -.147(4.169)

.67E-7(1.043)

-.19E-13(.716)

.335 (.131)

.271(.064)

-.12E-2(.031)

.513(1.570)

.69E-1 (1.410)

1.052 (15.103)

.351(1.457)

.30E-1(1.437)

105.25 .79E-1(13.9)

102

Notes:i) t-values have been given in parentheses. ii) Coefficients printed in bold are significant at 0.05 or higher level; those italisized are significant at 0.10 level.iii)L.L. is log likelihood. iv) Coefficients of year dummies were never significant and are not reported for want of space. v) Equation b) is not reported for industries where DEXCOMT had a zero value for all the observations.

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