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Foreign firms, technology transfer and knowledgespillovers to Indian manufacturing firms: astochastic frontier analysisVinish KathuriaPublished online: 04 Oct 2010.

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Foreign Wrms, technology transfer and

knowledge spillovers to Indian

manufacturing Wrms: a stochastic

frontier analysis

VINISH KATHURIA

Gujarat Institute of Development Research (GIDR), Near Gota Char Rasta, P.O. High

Court, Gota, Ahmedabad ± 380 060, India

E-mail: vinish67@yahoo.com and gidr@ad1.vsnl.net.in

This paper uses techniques from a stochastic production frontier (i.e., the best prac-

tice technology used in the industry vis-aÁ -vis average practised technology) and panel

data literature to test for the spillover hypothesis that `presence of foreign-owned

® rms and disembodied technology import in a sector leads to higher productivity

growth for domestic ® rms’ . The study uses panel data for 368 medium and large-

sized Indian manufacturing ® rms for the period 1975± 1976 to 1988± 1989. The resultsindicate that there exists positive spillovers from the presence of foreign-owned ® rms

but the nature and type of spillovers vary depending upon the industries to which the

® rms’ belong. There exist signi® cant positive spillovers for the domestic ® rms belong-

ing to the scienti® c’ subgroup provided the ® rms themselves possess signi® cantR&D capabilities. However, for the `non-scienti ® c’ subgroup presence of foreign

® rms itself forces the local ® rms to be more productive by inducing greater competi-

tion. However, the results change marginally when the initial level of productivity

(i.e. the technology-gap) is considered.

I . INTRODUCTION

A complete view of the process of technology transfer (to

the developing countries) recognizes the fact that ® rms

experience production externalities from technological spill-

overs within an industry as well as across industries. The

empirical relevance of the spillover argument is concep-

tually related to the transfer of nonconventional factors

of production, including technology, management skills,

and motivation between foreign and domestically owned® rms. The literature on technological spillovers however,

has spawned from two distinct but not entirely mutuallyexclusive sources ± knowledge spillovers from the presence

(or entry) of foreign ® rms and spillovers from disembodied

technology import (i.e., spillovers from technology brought

in through licensing agreements).1

The ® rst source of knowledge spillovers, i.e., the eŒect of

presence of foreign ® rms2 has been extensively studied in

the literature. The results of previous studies however, have

been inconclusive in terms of the overall direction and

magnitude of spillovers. The studies by Caves (1974) on

Australia, Globerman (1979) on Canada, Blomstrom and

Persson (1983) on Mexico and Nadiri (1991) on several

OECD countries have found either positive or weak posi-

tive spillover impacts of foreign presence on the productiv-

ity of local ® rms. On the other hand, studies by Cantwell(1989) on several European countries, Haddad and

Harrison (1993) on Morocco, Aitken and Harrison

Applied Economics ISSN 0003± 6846 print/ISSN 1466± 4283 online # 2001 Taylor & Francis Ltd

http://www.tandf.co.uk/journals

Applied Economics, 2001, 33, 625 ± 642

625

1The diŒerent channels through which spillovers may occur are labour/employee mobility, demonstration (or contagion eŒect) and

through competition.2In the present paper, the terms foreign ® rms …FDI† and multinational corporations (MNCs) have been used interchangeably.

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(1994) on Venezuela and Kokko et al. (1996) on Uruguay

have found either negative or no impact of foreign presence

on the productivity of domestic ® rms.While a part of the contradictory ® ndings can be

explained by the diŒerences in methodology and data avail-

ability, it is also possible that there are systematic diŒer-

ences in spillovers across countries and industries. Kokko

(1994) in his study on Mexican manufacturing industry

argues that positive spillovers are less likely in industries

with highly diŒerentiated products and large economies of

scale ( technological enclaves’ ). Another explanation given

in the literature for negative or insigni® cant spillovers

is that ® rms’ bene® t in only those sectors where thetechnology-gap ’ between foreign ® rms and domestic

® rms is relatively small. Some studies that have found

negative or insigni® cant spillovers at the aggregate level,

found positive and signi® cant spillovers in low-tech sectors

(where the technology-gap between the foreign ® rms and

domestic ® rms is small) when they bifurcated the sample

into high-tech and low-tech sectors3 (see, for example,

Cantwell, 1989; Haddad and Harrison, 1993; Aitken and

Harrison, 1994; Kokko et al., 1996).The role of second source i.e., disembodied technology

import, in knowledge spillovers for developing countries is

as relevant (if not more) as the R&D spillovers to devel-

oped countries. This is due to the fact that for developing

countries, R&D is mainly of an adaptive nature rather than

fundamental research and since the late 1960s most of the

developing countries have relied extensively on technology

import through arm’s length transaction. However, empiri-cal work on this source of spillovers is barely a few years

old and most of the work has been con® ned to India only.4

The present paper contributes to the existing literature in

three ways: (1) it employs a diŒerent but more rigorous

methodology, namely the stochastic production frontier

(SPF) methodology; (2) it includes both the sources of spill-

overs in the analysis; (3) the study looks into the spillover

eŒect on the multifactor productivity as against the partial

measure of productivity (i.e., the labour or capital produc-tivity) that has been used in most of the previous studies on

the spillover impact of MNCs.5 The study uses panel data

for Indian manufacturing ® rms for a period of 14 years

from 1975± 1976 to 1988± 1989.

The results indicate that there exists positive spillovers

from the presence of foreign-owned ® rms, but the nature

and type of spillovers vary depending upon the industries

to which the ® rms’ belong. There exist signi® cant positive

spillovers for the domestic ® rms belonging to the scienti® c’

subgroup provided the ® rms themselves possess signi® cant

R&D capabilities. However, for the `nonscienti ® c’ sub-

group, the presence of foreign ® rms itself forces the local® rms to be more productive by inducing greater competi-

tion (or through demonstration eŒect).

Results change marginally when the initial level of pro-

ductivity (i.e., technology-gap) is considered. The results

indicate that domestic ® rms in the scienti® c’ sectors tendto learn from the foreign ® rms’ presence but the gain is

more for the ® rms that are relatively away from the e� -

ciency frontier (i.e., gap is large) and having signi® cant

R&D capabilities. On the other hand, results show that

for `nonscienti® c’ domestic ® rms, after their initial level

of productivity is accounted for, it is ® rms which are closerto the frontier that tend to gain more in terms of produc-

tivity improvement.

The paper is organized into ® ve sections. Section II dis-

cusses the framework and the model used in the estimation.

Measurement of the variables and data sources used in thestudy are discussed in Section III. Section IV presents

statistical results of the model and Section V gives a brief

comparison with previous work. Section VI concludes the

paper by summarizing the results and discussing the scope

for further research in the area.

II . MODEL FORMULATION AND

ESTIMATION TECHNIQUES

If the knowledge embodied in a foreign ® rm or the new

technology brought through arm’s length transaction dif-

fuses to domestic ® rms or alternatively if the presence (or

entry) of a foreign ® rm induces signi® cant competitive

pressure, forcing local ® rms to manage their resourcesmore e� ciently to stay in competition, one can ® nd the

evidence (of spillovers) in the form of increased productiv-

ity levels of local ® rms in the sectors associated with sig-

ni® cant foreign presence and technology import. Thus, the

present study tests the hypothesis that larger is the pres-ence of foreign ® rms and disembodied technology import

in the industry, faster is the productivity growth of domes-

tic ® rms’ .6

Spillover eVects of technology transfer on productivitygrowth

The testing of the above hypothesis involves use of two

stages. In the ® rst stage, data on inputs and output are

employed to generate a vector of ® rm-speci® c time-

variant’ productive e� ciency indices for each industry

626 V. Kathuria

3The only deviation is the study by Blomstrom and WolŒ(1994) on the Mexican manufacturing sector. They ® nd that spillovers tend to

grow with the size of the `technology-gap’ between the foreign and domestic ® rms.4

See, for example, Ferrantino (1992), Raut (1995), Haksar (1995) and Basant and Fikkert (1996).5

See, for example, Caves (1974), Globerman (1979), Blomstrom and Persson (1983), Kokko (1994), Kokko et al. (1996) etc.6

The hypothesis assumes that MNCs or technology-recipient ® rms are more productive and local ® rms have a lot to learn from them.

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separately using techniques from the SPF and panel data

literature. As shown below, the productivity indices are

essentially the remainder from a regression of value

added on labour and capital inputs. In the second stage,

these productivity scores are used as a dependent variable

to regress against a set of explanatory variables including

the spillover variables. In the e� ciency frontier literature,

introduced ® rst by Farell in 1957, the production function

f …x† de® nes the maximum possible output a ® rm can pro-

duce given input bundle x, representing the best-practice or

frontier technology in the industry. If a ® rm employs a

larger bundle of inputs than the minimum required to

obtain actual output then it would be deemed as technically

or productively ine� cient. Once the best practice or fron-

tier production is estimated, an e� ciency index for a ® rm

can be derived from the deviation of its actual output from

the frontier.

Until recently, most studies on e� ciency frontiers have

been cross-sectional requiring strong distributional

assumptions about the technical e� ciency (e.g., half nor-

mal or gamma etc.) in order to separate technical e� ciency

from random noise (see for example, Jondrow et al., 1982;

Battese and Coelli, 1988). It is not clear how robust one’s

results are to these assumptions (Schmidt and Sickles,

1984, p. 367). Another di� culty in estimating the cross-

sectional stochastic frontier is that the technical e� ciency

of a ® rm can be estimated, but not consistently. The vari-

ance of the distribution of technical e� ciency conditional

on the whole error term does not vanish even when the

sample size increases (see Jondrow et al., 1982 for detail).

Besides, the studies have also assumed that productive e� -

ciency is independent of factor inputs. The assumption of

independence of factor inputs can be a potential source of

error in the estimation. For instance, if a ® rm knows its

level of technical ine� ciency, it would try to alter its input

choices accordingly.

Availability of panel data facilitates estimation of con-

sistent and robust ® rm speci® c productive e� ciency indices

obviating the need for all these assumptions (see Schmidt

and Sickles, 1984 for details). However, the model by

Schmidt and Sickles assumes time-invariant’ technical

ine� ciency (i.e., the e� ciency of a ® rm is ® xed over

time). The assumption of this time-invariant’ ® rm speci® c

e� ciency is very strong and is more di� cult to justify when

the time period of the study becomes larger. For instance,

in a technologically dynamic or growing industry ± viz.,

machine tools or software industry, ® rms’ technical e� -

ciency would continue to change commensurating with

their eŒorts and investment in human and physical capital

etc. Alternatively, one would expect that managers learn

from their previous experience in the production processand so their technical ine� ciency should fall over time. The

assumption of time-invariant’ ® rm speci® c e� ciency was

® rst relaxed by Cornwell et al. (1990). The present study

uses the Cornwell et al. model to estimate the time-variant

® rm-speci® c’ productive e� ciency.We begin with a production function with value-added

Y, being a function of two inputs, labour …L† and capital

…K†:7

Yijt ˆ AijtF…Lijt; Kijt† …1†

where Aijt is the time-varying productivity level of the ® rm

which is also diŒerent across ® rms. For an industry j, the

empirical model can be written as:

yijt ˆ ¬jt ‡ ­ 0j xijt ‡ vijt ¡ uijt

i ˆ 1; 2; . . . ; N and t ˆ 1; 2; . . . ; T for industry j …2†

where i indexes ® rms and t indexes time periods. yijt is the

output in logarithms (for ® rm i in industry j at time t) and

xijt is a vector of k inputs (i.e., labour and capital) in loga-rithms. vijt is the usual normally distributed statistical noise

with null mean and standard error …¼v† accounting for

random disturbances, measurement errors and various fac-

tors that are out of the control of ® rms such as luck,

weather, machine breakdown, input supply breakdownetc. uijt…uijt 5 0† represents productive ine� ciency of the

® rm which is not only diŒerent across ® rms, but also

time variant. The productive or technical ine� ciency, uijt

implies that the output in an industry j must lie on or belowthe frontier, ajt ‡ ­ 0

j xijt ‡ vijt. Although uijt is unobserved

by the econometrician, its permanency would lead us to

expect ® rms to observe uijt and take the level of uijt into

account while choosing their inputs. For example, if this is

interpreted as managerial ine� ciency, it is reasonable to

assume that the realization of these is known to managers

and would aŒect their choice of inputs. This violates theassumption of the linear model of uncorrelatedness of

regressors with the error term making the estimation

inconsistent. However, including the ® rm-speci® c eŒects,

uijt as regressors (i.e., the ® xed-eŒect model) rather than

relegating them to the error term removes any biases thatwould have resulted from correlation between uijt and the

regressors.

It should also be pointed out that even though ® xed

eŒect removes the correlation between the regressors and

uijt, it is possible that ® rms also observe the period-speci ® cshocks, vijt before making their input choice at time t. If so,

Foreign Wrms, technology transfer and spillovers 627

7As `materials’ inputs can also be used ine� ciently, gross output serves as a better indicator for dependent variable. However, when

annual reports data are used (as in the present case), the high correlation across plants between gross output and materials inputs violatesthe assumption of prior choice of inputs and renders the production functions’ coe� cients unstable and frequently unsatisfactory (Caveset al., 1992). To avoid this bias, value added has been taken as a dependent variable. It needs to be mentioned further that use of value-added as the dependent variable assumes separability between materials and labour and capital inputs.

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one would expect the input choices at time t to be functions

of vijt thus, introducing correlation between the regressorsand the error term. It is customary in the literature to

assume that the inputs at time t are chosen before vijt is

observed by the ® rm or that the ® rm never observes vijt,

making the regressors at time t independent of this com-

ponent of the error term. The assumption however, seemsquite valid at least in the Indian context as the Government

of India (GOI) as part of its system of industrial planning

regulated ® rms’ physical capital stocks, rationed the for-

eign exchange needed to import raw materials and inter-

mediate inputs, restricted foreign technology purchases,

rationed scarce domestic inputs, and made it di� cult for® rms to adjust their labour forces. Hence the ability of

domestic ® rms to adjust their input levels to respond to

period speci® c shocks, vijt would have been far below

than that which could have occurred in a more market-

oriented economy.Thus, the model can be rewritten as

yijt ˆ ¬ijt ‡ ­ 0j xijt ‡ vijt …3†

where ¬ijt…ˆ ¬jt ¡ uijt† is the time-variant productivity level

of a ® rm. This change in productivity level over time of the® rm could be due to the accumulated learning experience,

capacity utilization, the eŒect of competition or any other

factor. The feature can be realized by introducing a ¯ exible

(e.g. quadratic) function of time into the production func-

tion with coe� cients varying over ® rms (Cornwell et al.,

1990, p. 186),8 i.e.,

¬ijt ˆ ³i1 ‡ ³¤i2t ‡ ³¤

i3t2 ˆ W 0

t ³ij …4†

where W 0t ˆ …1; t; t2† and ³ij ˆ …³ij1; ³ij2; ³ij3† 0.

Cornwell et al. (1990) have suggested that withoutimposing a structure on the error term and without assum-

ing uncorrelatedness of the determinants of the technical

ine� ciency with the regressors, performing least squares on

the deviation form model (i.e., within estimation) would

give consistent and e� cient estimates of ­­ 0. The `within

estimator’ is an instrument variable estimator with instru-ments given by MQ ˆ ‰I ¡ Q…Q 0Q†¡1Q 0Š where, I is the

identity matrix and Q ˆ diag…Wi†t, i ˆ 1; 2; . . . ; N with

NT £ NL dimensions. The within estimator of ­ would

be ­­ 0 ˆ …X 0MQX†¡1X 0MQy (ibid., p. 188).

After obtaining ­­ 0j , the residuals …Yijt ¡ ­­ 0

j Xijt† give thejoint estimate of the productive e� ciency and the error

term, i.e., ¬ijt ‡ vijt. In order to separate out the error

term and derive ® rm-speci® c time-variant’ productive e� -

ciency, the residuals for a ® rm are regressed on W 0t ³ij. The

® tted values from this regression give T estimated produc-

tivity measures for a ® rm i in sector j for T periods, i.e., ¬¬ij1,

¬¬ij2, ¬¬ij3; . . . ; ¬¬ijT . The estimates of ¬¬ijt would be consistent

as T becomes larger (Cornwell and Schmidt, 1996, p. 866).

The change in productivity level or productivity growth of

the ® rm would be d¬¬ijt…ˆ ¬¬ijt ¡ ¬¬ijt¡1†.To test for the hypothesis that presence of a foreign ® rm

(denoted by Spil1) or disembodied technology import in

the sector (denoted by Spil2) leads to increased productiv-

ity growth, the model9 would be:

d¬¬ijt ˆ f ……Spil1†jt¡1; …Spil2†jt¡1† …5†10

Further, spillovers do not arise instantaneously, but pro-

pagate through some lag mechanism. The identi® cation of

this lag is a matter of debate (Griliches, 1979). In the pres-

ent study, a simple one-year lag has been used for both the

spillover variables.11 Though knowledge (or technology)may be a public good to a certain extent, it would still

need R&D investment on the part of the ® rms to decodify

and exploit any spilled knowledge. Wang and Blomstrom

(1992) in their model to ascertain the conditions entailing

for maximum spillover bene® ts also conclude that theextent of spillovers is not determined by the degree of for-

eign presence alone. Instead, the results are related to ® rm’s

investment decisions. The more the local ® rm invests in

learning, the more potential spillovers it is able to absorb.

628 V. Kathuria

8Though, the intercept for each ® rm is quadratic in time but the form of the quadratic varies across ® rms. However, the maximal(frontier) intercept is not necessarily quadratic, and therefore neither is the pattern of productive (in)e� ciency for a given ® rm.9The assumption that spillovers captured by each domestically owned ® rm in an industry is directly proportional to the amount ofsubsidiary production/sales in the industry and available foreign technical capital stock in the sector embodies the notion that spillovershave a `public-good’ nature, i.e. consumption by one ® rm does not reduce the amount available for other ® rms.10

There can be other spillover variables also like knowledge spillovers from domestic R&D or from domestic technology transfer thatcan have impact on productivity growth. There are a number of reasons for not considering them in the present case. First is the objectiveof the study, where focus is to capture the spillovers associated with only foreign technology transfer. Another reason is that suchspillovers would be minuscule in the Indian case as the private sector in India engages in little basic research ± most private R&D activityis of an adaptive nature (Desai, 1984; Lall, 1987). And moreover, since 1959, of the total outlay on R&D, the private sector hasaccounted for only 10% (Haksar, 1995). Further, it may be best to ignore reported domestic technology payments as they are frequentlya means which ® rms use to transfer funds to other ® rms in the same industrial conglomerate, thereby evading India’s restrictions on® rm’s holding equity in multiple other ® rms (Basant and Fikkert, 1996, p. 186). Multicollinearity between foreign and domestic spillovervariables is the last reason that prevents other spillover variables from being included.11

Most of the studies done to estimate the productivity enhancing eŒect of R&D spillovers have also used one-year lag. See Griliches(1992) for a review of those studies. Haksar (1995) and Basant and Fikkert (1996) have also used the one-year lag. JaŒe (1986) justifyingthe use of the one year lag, argues that the stock of R&D ± a measure of R&D spillovers in any given period ± is just the expenditure inthat period on R&D as even with a lag structure most of the weight falls on contemporaneous R&D (p. 988).

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Thus, in the present case, a ® rm that engages in R&D

activities would tend to bene® t more from these knowledgespillovers. Two interaction terms ± one with Spil1 and

other with Spil2 (i.e., Spil1*R&D and Spil2*R&D) ± are

employed to capture the possible eŒect of the role of

R&D investment in absorbing the spillovers.

Thus the model to be estimated becomes,

d¬¬ijt ˆ f …Spil1jt¡1; Spil2jt¡1; …Spil1jt¡1 ¤ R&Dijt¡1†;

…Spil2jt¡1 ¤ R&Dijt¡1†† …6†

Other determinants of productivity growth

Productivity improvement in economic activities is gener-

ally regarded as a consequence of two diŒerent factors.

While the adoption of technical innovation in processes

and products (i.e., the technological progress) pushes the`potential frontier of production’ upward, productivity

improvement also takes place from a ® rm’s own eŒorts in

the form of improved production with the given inputs and

available technology (e.g., learning by doing, learning by

using etc.). The various determinants of productivity

growth however, can be classi® ed into three groups(Fecher and Perelman, 1992, p. 474). The ® rst group

includes competitive conditions in the industry as well as

competitiveness of the ® rm. The industry competitiveness is

re¯ ected in its openness in terms of presence (or entry) of

foreign ® rms in the sector (i.e., Spil1), whereas outward

orientation of the ® rm may indicate the competitivenessof the ® rm. The second group captures the innovations

arising from both ± ® rm’ s own R&D eŒorts or innovation

at the international level (i.e., the technological spillovers,

Spil2). The third group includes production characteristics

such as the ownership in the ® rm, the rate of output growthleading to improved capacity utilization or the extent of

capital formation. The remaining section discusses the vari-

ous controlling variables that have been used in the studyand how they aŒect the productivity growth at the ® rm

level.

Competitiveness

Competitiveness of a ® rm is re¯ ected in its ability to export

(and to meet import competition).12 This is because the

world markets outside the domestic market bring domestic

producers into competition with a large, shifting and un-familiar group of foreign rivals. Thus, the greater the abil-

ity of a ® rm to export, the more is its international

competitiveness and hence, the higher is its productivity

growth rate. Further, by expanding the market through

international trade, ® rms can achieve strong economies ofscale, which can keep production costs down and increase

productivity (Pratten, 1988). As the study covers the period

in which strategy of Indian industrialization has predomi-

nantly been of import-substitution (IS) resulting in the

® rms to be more inward-oriented, any diversi ® cation intointernational markets should have resulted in higher pro-

ductivity growth (and competitiveness) of the ® rm.13

Export intensity …EXP† of the ® rm has been used as a vari-

able accounting for its outward orientation.

Innovation

From an empirical point of view, it is generally accepted

that a main factor in productivity growth is the R&D activ-ities (Griliches, 1979). The concept refers to the amount of

technology created by a ® rm i.e., its `own technology base’

…R&D†14 as opposed to its use of imitated technology’ (i.e.,

the technological spillovers, Spil2).

Another source of productivity gains is the learningcurve, which depends on the skills honed, and equipment

Foreign Wrms, technology transfer and spillovers 629

12International competition through imports can promote productivity growth by inducing changes in capital expenditure, workforce

structure and utilization, and the distribution of product varieties and production scales in the competing domestic industry. The presentstudy covers the period, when the import was highly regulated rendering this means of productivity growth ineŒective. Hence, importintensity at the industry level has not been considered in the analysis.13

The results need to be viewed with caution as a recent study by Rao (1994) has found that the exports of many large companies andforeign owned companies in India are either items traded by them or they are exporting to (erstwhile) USSR and the East Europeancountries (because of the then prevailing rupee trade agreement between these countries and India). An earlier study by Goyal et al.(1991) using daily trade register …DTR† data also suggests that the export basket of most of the large ® rms comprises goods notmanufactured by them. This implies that under such circumstances larger export intensity may not necessarily re¯ ect the competitivenessof the ® rm.14

It needs to be mentioned that investment in R&D (or any other investment) is motivated by future pro® t expectation. Thus, anadjustment cost approach in an intertemporal cost minimizing framework would be appropriate in order to look into the R&Dinvestment. I am extremely thankful to the reviewer for pointing this out. However, the contribution of R&D investment of a ® rm toits productivity growth depends on the motive for investing in R&D. When a ® rm invests in R&D to take advantage of the ® scalincentives (like accelerated depreciation, easy import, tax rebate etc.), the investments need not result in productivity enhancement. TheR&D policy in India from 1973 onwards till the late 1980s (incidentally, this also forms the period of analysis of this study) was such thatbesides tax and ® scal bene® ts, the ® rms were also given bene® t of liberalized import for purchase of raw-materials, equipment, com-ponents etc. The investment in R&D was not driven by future pro® t expectations rather than by the current ® scal bene® ts a ® rm is goingto enjoy for its investment in R&D activities. For every rupee spent on R&D, a ® rm was entitled to receive even more than one rupee inthe form of ® scal and other bene® ts. Further, once a ® rm got a recognized R&D unit status from the GOI, most of the technology importor capital goods import became highly subsidized for it. Thus the motive for R&D may not be to maximize the future earnings, but to

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and procedures tuned as a result of accumulation of pro-

duction experience and/or time. The basic predictor of thelearning curve is the age of plant and machinery …AGE†and the nature of the industry. For instance, other things

being equal, learning appears to accumulate faster in indus-

tries that require large-scale units and put substantial cost

penalties on small-scale. Similarly in process industries, thelearning curve may be steeper than in the product indus-

tries and the lumpiness of capital may not make it feasible

for a ® rm to scrap the capital of not so recent vintage or

retro® t the recent innovations in plant and machinery

(Rosenberg, 1994).

Many important productivity-raising innovations takeplace outside the country and diŒuse to domestic producers

through various channels, including imported capital

goods, licensed technologies, and intangible ¯ ow of non-

proprietary information. Thus, one can expect a positive

association between technology import and productivitygrowth. To control for the productivity enhancement eŒect

of these variables, capital goods import intensity …CGImp†and a stock of foreign disembodied technical capital …FT†have been included.

Production structures

A rapid growth in output re¯ ects signi® cant increase indemand, which in turn puts pressure on the existing ca-

pacity facilitating better capacity utilization.15 Similarly,

market growth, according to `Verdoorn’s law’ bestirs pro-

ductivity gains by inducing more investment in innovation,

promoting economies of scale, spreading ® xed costs, allow-ing a ® ner division of labour, and so forth (Bairam, 1987).

In these circumstances, output growth should always aŒect

productivity change positively. The present study uses

growth in output …OUTGRT† as a variable to account

for increase in demand and hence leading to improvedproductivity.16

A ® rm’s productivity growth is also aŒected by capital

formation. As much new technology is embodied in new

vintages of capital, an increase in capital formation can

speed the rate of introduction of new technology and there-

fore, increases the productivity level. However, similar toR&D, this process may take some time before resulting in

the productivity gain. As a result, the ® rm may appear

ine� cient in the short term. To control for this pro-

ductivity enhancing eŒect of capital formation, ® xedinvestment …FINV† by the ® rm has been taken as an

explanatory variable.

Lastly, the association between foreign ownership

…FEqty† of a ® rm and its productivity growth should

show a positive relationship as collaborations and partner-ships can be a vehicle for new organizational learning,

helping ® rms to reorganize dysfunctional routines and

preventing strategic blind spots (Teece et al., 1997).

Alternatively, a ® rm, which already has foreign tie-up

would be closer to the e� ciency frontier as it has an access

to a host of tangible and intangible assets of the parent ® rm± viz., technical know-how, marketing and managerial

skills, reputation etc. Thus, any scope for the (e� cient)

foreign-owned ® rm to experience faster productivity

growth is relatively less as compared to (ine� cient) domes-

tic ® rms (which are away from the frontier) unless thereexist strong international competitive pressures. Given the

coverage of the present study, when such exposure to inter-

national competition was nonexistent, it is likely that the

growth of domestic ® rms may be higher than that of

foreign-owned ® rms.Thus, the ® nal model to be estimated would be

d¬¬ijt ˆ f …Spil1jt¡1; Spil2jt¡1; …Spil1jt¡1 ¤ R&Dijt¡1†;

…Spil2jt¡1 ¤ R&Dijt¡1†; FEqtyijt; FTijt¡1;

AGEijt; EXPijt; CGImpijt¡1; R&Dijt¡1;

OUTGRTijt¡1; FINVijt¡1†

‡ Industry dummies ‡ error term …7†

By de® nition the spillovers or externalities are the bene® ts

accruing to the producers because of market activity of

other producers. Thus, the relevance of spillovers is maxi-mum for the ® rms that have no or very little foreign equity

participation. These ® rms are termed as non-FDI ® rms in

the present analysis. The exact de® nition of non-FDI ® rms

has been given in the next section.

The above model (Equation 7) has been estimated

mainly for non-FDI ® rms. As discussed in Section I thatone explanation given for spillovers not coming out signif-

icant in some of the studies is the extent of technology-gap ’

630 V. Kathuria

extract present bene® ts. This seems to be partly re¯ ected in the data also as of the 192 sample ® rms that had GOI recognized R&D units,only 115 spent on R&D activities during the whole period from 1975± 1976 to 1988± 1989. Thus, the study does not foresee theapplicability of the adjustment cost approach for R&D investment. However, one can argue that in such circumstances, will theR&D be still eŒective in absorbing the spillovers. This argument is partly countered by using the R&D stock variable instead ofR&D intensity of the ® rm. A ® rm, which is continuously spending on R&D activities, may have acquired some technological capabilities,thereby enhancing its spillover absorption potential.15

A sustained increase in demand may also result in insu� cient capacity in the long run unless the ® rm invests to increase the capacity.16

While the data always seem to con® rm this relationship. Caves and Barton (1990) argue that the relation can be spurious for tworeasons. First, the rapid productivity growth lowers the product’ s relative price and increases the quantity bought, so causation can runin either direction. Secondly, the change in output is one component of the measurement of productivity changes, so that measurementerrors generate a spurious positive relation (p. 137).

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between domestic and foreign ® rms in the sector. In the

next step, the model has been re-estimated to test for thetechnology-gap or catch-up hypothesis.

As mentioned earlier, the model is estimated in two

stages. In stage one, the SPF for each industry is estimated

separately employing the methodology given by Cornwell

et al. (1990). The second stage involves estimation ofEquation 7 to see the impact of spillovers and other ® rm

speci® c variables in aŒecting the productivity growth. The

® nal model (Equation 7) is linear in form and has been

estimated by pooling the observations for all the years

for all the ® rms for all the industries.

Pooling being a methodological issue, there exist twotechniques to pool the data depending upon the structure

of the error term ± error component and the Kmenta type

(Baltagi, 1996, p. 295). The error component model (ECM)

is more popular speci® cation in panel data, but it assumes

homoscedastic disturbances, whereas the alternative tech-nique is based on the fact that cross-section are hetero-

scedastic (Kmenta, 1986, p. 618).17 Given the nature of

the data, which consist of ® rms of diŒerent sizes, existence

of heteroscedasticity cannot be ruled out.

The Hausman test ± which is generally used to choosebetween random eŒects and ® xed eŒects model ± cannot be

used to distinguish the two models as it tests the orthogon-

ality of the disturbances with the explanatory variables and

in both the cases the hypothesis is true by construction.

However, the Bartlett Test18 for homoscedasticity can dis-

tinguish between these two disturbance structure (Baltagi,1996, p. 298). If the disturbances are heteroscedastic, the

disturbances cannot be of the usual error-component type,

and hence the Kmenta model need to be employed.

II I . DATA DESCRIPTION AND VARIABLE

CONSTRUCTION

Data description

The estimation of the above equations involves a great deal

of data construction and data cleaning work. The analysis

however, has been con® ned to the manufacturing sector

only. The data have been provided by Institute for

Studies in Industrial Development (ISID), Delhi, India.

The data set consisted of 413 ® rms belonging to 34, 3-

digit industries for the period 1975± 1976 to 1988± 1989.

The GOI, as part of its industrial policy reserved several

products exclusively for small sectors such as tobacco,

leather and leather products, matches etc. Taking a cue

from this, ® rms belonging to these sectors are taken out

for the ® nal analysis. Furthermore, in consonance with the

objective of the study i.e., testing for presence of intrain-

dustry spillovers, all those industries, where there is no

foreign-owned ® rm, are also taken out of the sample

(e.g., steel, cement etc.). Thus, the ® nal analysis involves

the use of annual report data for 368 medium and large-

sized ® rms having a paid-up capital of more than Rs. 10

million belonging to 26, 3-digit level manufacturing indus-

tries.

Of the 368 ® rms for which analysis has been done, nearly

two-® fths of ® rms (i.e., 150 ® rms)19 have a foreign equity

participation of more than 25% or more during the study

period. In terms of representation, the foreign ® rms in the

sample account for nearly 66% of the total foreign ® rms

registered in India is on 1980± 1981. However, in terms of

the market share, foreign ® rms in the sample account for

more than 90% of the total sales of the registered foreign

® rms’ during the study period (RBI Bulletins, various

years). The sample ® rms cover a wide range of industires:

8.4% of the observations come from automobile, 20.7%

from nonelectrical machinery, 14.7% from electrical mach-

inery, 14.4% from metals, 7.9% from drugs and pharma-

ceuticals, 24.2% from chemicals, 5.4% from paper and paper

products, and 4.3% from rubber and plastic products.

Variable construction

All the variables are measured in constant 1975± 1976 prices

in order to ensure that price changes over time do not

distort the estimations.20 Gross value-added has been

used as a measure of output.21 The output is de¯ ated by

Foreign Wrms, technology transfer and spillovers 631

17The other major diŒerence between the two techniques is how serial correlation is modelled. In the usual ECM, the serial correlation is

constant across time, whereas it decays over time with the Kmenta technique. Refer Baltagi (1996, pp. 295± 6) for detailed descriptionabout ECMs and the distinction between the two techniques.18 The generic name of this test is likelihood ratio’ test. This test is based on the idea that if the null hypothesis is true, the value ofmaximum likelihood obtained under the assumption of homoscedasticity should not diŒer signi® cantly from that obtained under theassumption of possible heteroscedasticity (Kmenta, 1986: p. 297). The test statistics is distributed as À2

with N ¡ 1 degrees of freedom,where N is the number of cross-section units.19 In the case of three ® rms the foreign equity participation fell below 25% during the study period, but to avoid any possible loss ofdegrees of freedom, these ® rms have been retained in the FDI group only.20

All the input and output variables used in the SPF, are in log values and have been calculated using three years moving averagemethod. The three-year moving average has been carried out to smooth out uneven ¯ uctuations in output (i.e., the value added) as valueadded ® gures show wide variation even in two consecutive years.21

In the case of eight ® rms, a small positive value close to zero has been assigned to the observations, which have their three-year movingaverage value for value added to be negative. This has been done to facilitate logarithmic transformation of the dependent variable forthe estimation of the SPF.

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3-digit industry speci® c, wholesale price de¯ ators as

obtained from the index numbers of wholesale prices inIndia (Chandhok et al., 1990). Labour input has been

obtained by dividing the total wage bill of a ® rm by the

corresponding 3-digit level industry wage rate (as calcu-

lated from various issues of Annual Survey of Industries,

ASI). The average wage rate of the industry has been com-puted by dividing each industry’s total emoluments to the

workers by the total man-days worked.

A net capital stock series is generated as a measure of

capital input of the ® rm. The capital stock as reported in

the annual report is at its purchase prices (i.e., the historical

cost of the capital). Following Hulten (1990), the perpetualinventory method is used to compute the capital stock at

constant prices. As discussed in detail in the Appendix, the

reported capital stock is calculated at constant 1975± 1976

prices using a depreciation rate of 6% per annum.

The annual report of a ® rm reports the yearly expendi-ture on disembodied technology import, i.e., expenditure

on foreign patents, royalties, technical and consultancy fees

etc. To the extent that technological inputs are durable they

must be measured as stocks. Typically, the stock ® gure is

arrived at by adjusting for `obsolescence’ as opposed to the`depreciation’ of physical capital. Thus, these ¯ ows are

used to calculate a foreign knowledge stock …FT1† of

each ® rm using a perpetual inventory method (see

Appendix). Similarly, based on the past expenditures

incurred by the ® rm on R&D activities, own technical capi-

tal stock variable …R&D† has been also constructed usingperpetual inventory method (see Appendix).

Firm-speciWc variables22

Foreign-owned Wrm (FEqty) ± deWnition. In India, Reserve

Bank of India (RBI), the main controlling bank de® nesforeign controlled rupee companies (FCRCs), as joint

stock companies registered in India in which 25% or

more of equity capital is held abroad by a foreign com-

pany or its nominee or 40% is held outside India. The

present study also uses the same cut-oŒ to de® ne aforeign-owned ® rm.23

Spillover variables. There are two possible approaches to

the construction of spillover’ variables: (a) a symmetric’approach ± where every ® rm in an industry or subindus-

try is treated equally, and all R&D within the industry

or foreign disembodied technical capital stock or some

alternative classi® cation scheme is aggregated with equal

weight; and (b) an `asymmetric’ approach ± where everypossible pair of ® rms, or industries or countries is treated

separately, and the relevant stock of spillovers for the

receiving’ unit is constructed speci® cally for it, using its

`distance’ from the various spilling units as a weight.

Studies quantifying R&D spillovers have mainly usedeither R&D expenditures of other ® rms or patents regis-

tered as the proxy for the aggregate knowledge stocks

(see Griliches, 1992 for review of the various approaches).

The present study, however, constructs two spillovers

variables employing the symmetric approach. The ® rst

spillovers variable (Spil1) i.e., the spillovers due to thepresence of foreign ® rms in the sector has been con-

structed as a ratio of sales share of foreign-owned ® rms

to the total industry sales. The other spillovers variable,

Spil2 has been constructed as foreign disembodied techni-

cal capital stock in the sector to the total industry sales.Table 1 gives a brief description of measurement of vari-

ous spillover and ® rm speci® c variables and their expected

sign as used in the study. All variables are in rupees (Rs.)

terms and are proxies/surrogates for the variables in ques-

tion. The use of proxies is warranted by the fact that thevariables like spillovers or own technological eŒorts or

learning from disembodied technology import etc. are not

easily quanti® able. Table 2 gives the descriptive statistics of

both the spillover variables and the interaction terms.

IV. ESTIMATION RESULTS

Spillover eVects of technology transfer on productivity

growth

The notion of knowledge spillovers from technology trans-

fer assumes relevance only if foreign-owned or technology

recipient ® rms are more productive, i.e., they are closer to

the e� ciency frontier and thus, paving scope for diŒusionof knowledge to the local ® rms. The estimated results show

that of the 26 industries, only in 13 industries foreign ® rms

or technology recipient ® rms are at or close to the e� ciency

frontier. This implies that only in these 13 sectors one can

expect knowledge spillovers. Thus, the model (Equation 7)

has been estimated for these 13 sectors only. The resultsfurther show that of these 13 sectors, the local ® rms

showed increase in productivity growth in nine sectors,

whereas the foreign ® rms productivity improved only in

632 V. Kathuria

22It is to be noted that wherever industry sales have been used in the calculation of variables, like spillover variables etc., it is industry

sales of the sample ® rms not the total industry sales.23

The foreign ownership is more-or-less a static variable as equity ownership remains the same for years together unless there is somelegal enforcement or some drastic change in the host country or parent ® rm’s policy. The time period covered in the present study falls inbetween two major policy changes i.e., the enactment of Foreign Exchange Regulation Act (FERA) in 1973 and liberalization of Indianindustry in the 1990s. As a result, there is not much change in foreign-equity ownership for most of the ® rms for most of the years,thereby indicating the static nature of the variable.

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seven sectors. Table 3 gives the productivity growth of two

groups of ® rms. From the table it is clear that the sectors

where foreign ® rms were leader, the ® rms in general have

experienced productivity growth.

Before estimating Equation 7 to quantify the spillovers,the Bartlett test is used to check for homoscedasticity, i.e.,

which model would be applicable in the second stage. The

test statistic is distributed as À2 with …N ¡ 1† degrees of

freedom. The observed value of the test statistic is

2468.62 for a degrees of freedom of 212. This implies

that the homoscedasticity hypothesis is decisively rejectedat 1% level of signi® cance. Thus, pooling by the Kmenta

model is appropriate. Table 4 gives the results of Equation

7 for the sectors, where foreign ® rms are closer to the e� -

ciency frontier.

Table 4 has been divided into four columns. Column 1

reports the results for all the ® rms in these 13 industries. As

mentioned earlier, the main emphasis of the present studyis to see the impact on the non-FDI ® rms. Columns 2 to 4

tabulate the results for the non-FDI ® rms. Before going

into the details of the results, it needs to be mentioned

that the two spillovers variables as used in the analysis

capture the two diŒerent modes of learning from two dif-ferent, but not entirely mutually exclusive sources; they

have some degree of overlap. Thus the variables are neither

complementary in nature nor do they perfectly substitute

each other. Further, in some of the estimations, only one of

the spillover variables (or interaction terms) is reported;

this is necessitated by the severe multicollinearity between

the spillover variables (and/or interaction terms) in the

data. The variable reported in such cases captures the eŒectof both the spillover variables and/or the interaction terms.

All Wrms

Rows 1 and 2 of Table 4 report the coe� cients of spillover

variables. The statistical signi® cance and sign of Spil2 vari-

able (row 2, column 1) suggests that there exist positive

spillovers from the available foreign technical capitalstock in the sector. This implies that the larger the disem-

bodied technology import in the sector, the faster would be

the learning and hence, the higher would be the productiv-

ity growth of the ® rms. The other spillover variable, Spil1,

has a negative sign, though it is not signi® cantly diŒerentfrom zero in statistical terms. The two interaction terms,

Interact1 and Interact2, that look into the possible comple-

mentarities between spilled knowledge and R&D are also

not signi® cantly diŒerent from zero in statistical terms.

Rows 5 to 10 give coe� cients of ® rm speci® c variables.

The positive sign and signi® cance level of FEqty variable(row 5, column 1) indicates that though foreign ® rms are

closer to the e� ciency frontier, they are growing faster.

Foreign Wrms, technology transfer and spillovers 633

Table 1. Description of spillover and Wrm speciWc variables

Variable Description of variable Expected sign

Spil1 Spillover variable 1; Share of sales of foreign ® rms to the total industry sales.* ‡Spil2 Spillover variable 2; Foreign disembodied technical capital stock of rest of the ® rms in the ‡

industry to the total industry sales.Interact1 Interaction term 1 5 Spil1 * R&D. ‡Interact2 Interaction term 2 5 Spil2 * R&D. ‡FEqty FEqty ˆ 1, if the foreign equity participation in the ® rm is 25% or more during the study ‡=¡

period; and 0, otherwise.FT Foreign disembodied technical capital stock of the ® rm (FT1) as a ratio of total capital stock ‡

of the ® rm.CGImp Capital goods import intensity; ratio of capital goods import to the annual sales turnover of the ® rm. ‡AGE Learning curve; ratio of accumulated depreciation to the value of total plant, machinery ‡

and equipment.R&D R&D stock of the ® rm. ‡EXP Export intensity; exports as a ratio of total sales turnover. ‡OUTGRT Change in demand; percentage change in output of the ® rm. ‡FINV Fixed investment; net capital formation in a year by the ® rm. 1 /7GAP Technology-gap; diŒerence in ® rm’s productivity level from the most e� cient ® rm (MEF) in 1 /7

the industry (i.e., ¬¬jt…max† ¡ ¬¬ijt†:

Note: * For FDI ® rms, the variable would be the sales share of rest of the foreign-owned ® rms to the total industry sales.

Table 2. Descriptive statistics of spillover variables

Variable Mean (1) Std. deviation (2) Minimum (3) Maximum (4) N (5)

Spil1(% ) 54.464 19.758 12.84 94.55 1188Spil2 (% ) 0.942 0.855 0.00 6.477 1188Ineract1 15 973 48 055 0.00 0.38 £10

61188

Interact2 191.86 657.88 0.00 6116.9 1188

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Given their high initial productivity levels, there exist two

possibilities, which may be facilitating them to grow faster.

First, if there exist strong international competitive press-

ure, it may force them to be more productive. Second, if the

level of interaction or association of the foreign-owned

® rms with their parent ® rms is high, this would enable

foreign subsidiaries to adopt and absorb the innovation

occurring at the parent level. As argued earlier, the period

of analysis encompasses the years, when India pursued IS

industrialization, thereby precluding the ® rst possibility as

there was minimal international pressure for the ® rms to

improve their productivity levels. Given the level of tech-

nology import of FDI ® rms (indirectly related to the extent

of tie-up with the parent ® rm), which is signi® cantly higher

than their domestic counterparts, it is very much likely that

the ® rms would have kept themselves abreast with the pro-

ductivity raising innovations occurring at the international

level. The data show that during the study period foreign-

owned ® rms had a foreign technical capital stock of nearly

1.25 times that of the domestic ® rms and had an R&D

stock of more than 2.35 times than that of domestic ® rms.

Among other ® rm speci® c variables, only capital goods

import intensity …CGImp† and ® xed investment by the ® rm…FINV† seems to have positive eŒect on the productivity

growth. However, none of the other ® rm speci® c variables

like vintage eŒect …AGE†, Export intensity …EXP† and own

foreign disembodied technology import …FT † are signi® -

cantly diŒerent from zero in statistical terms.

Non-FDI Wrms only

Column 2 reports the results for only `non-FDI’ ® rms for

the sectors where foreign-owned ® rm is the leader’ .24 The

results in terms of sign and signi® cance level remain same

for the `non-FDI’ ® rms (column 2). The sign and signi® -cance of the variable, Spil2 indicates the presence of positive

634 V. Kathuria

Table 3. Productivity growth over the period

¢…¬†77¡88 If ¢…¬† > 0Frontier ® rm

Industry All ® rms Non-FDI ® rms FDI ® rms All ® rms Non-FDI ® rms in the sectorSno. (1) (2) (3) (4) (5) (6) (7)

1 Automobiles 0.1026 0.0459 0.2350 1 1 F2 Automotive components 70.1040 70.3573 0.1262 F3 Electric cables 1.0948 1.2786 0.2680 1 14 Dry cells and batteries 0.3346 0.4190 0.3135 1 1 F5 Electrical machinery and 0.2100 0.1151 0.3555 1 1 F

equipment6 Machine tools 0.5706 0.6302 0.4960 1 1 F7 Textile machinery 71.2916 71.2743 71.36108 Non-electrical 70.0841 0.1234 70.1982 1 F

machinery9 Steel tubes and pipes 70.2386 70.1944 70.3490

10 Steel wire ropes 70.3574 70.1935 71.013011 Steel forging 70.4008 70.4475 0.114012 Foundries and 0.7473 0.8634 70.1820 1 1

engineering works13 Metal products 0.7432 0.9426 0.3943 1 1 F14 Chemical fertilizers 0.1437 0.2257 70.3480 1 1 F15 Dyes and dyestuŒs 70.9841 71.0868 70.7787 F16 Man-made ® bres 0.5306 0.4200 1.0835 1 117 Plastic raw materials 70.1868 0.0688 70.6980 118 Basic industrial chemicals 70.5564 70.6296 70.426319 Drugs and pharma 0.1380 0.2225 0.0935 1 120 Paints and varnishes 0.2653 0.2844 0.2462 1 1 F21 Soaps and toiletries 70.2582 70.2044 70.2941 F22 Tyres/tubes 70.1308 70.2900 0.108023 Rubber products 70.0538 70.0130 70.0945 F24 Paper and pulp 71.2310 71.2803 70.541025 Paper products 0.3638 0.4498 0.0200 1 126 Plastic products 0.3351 0.4288 70.2270 1 1 F

27 All industries 70.0115 0.0210 70.1022 13 1528 Industries with FDI leader 0.068 0.120 0.017 929 Industries with non-FDI 70.150 70.077 70.222 6

leader

24The calculated value of À2

test statistics for non-FDI ® rms is 1126.38 for degrees of freedom of 107 against the tabulated value of 143at 1% signi® cance level. This again rejects the homoscedasticity hypothesis, suggesting the applicability of the Kmenta model.

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spillovers from the sectoral level of disembodied technical

import. The other spillover variable, i.e., the eŒect of pres-

ence of foreign ® rms, though has a positive coe� cient, it is

not statistically diŒerent from zero. Similarly, the two inter-

action terms, Interact1 and Interact2 to explain the role of

R&D in absorbing spillovers have still not attained signi® -

cance in statistical terms.The ® rm speci® c variables as reported in rows 6 to 10 of

column 2, suggest that non-FDI ® rms are experiencingproductivity gains from their embodied technology import

…CGImp† and from increased investment in plant and capi-

tal …FINV †. However, the other ® rm speci® c variables such

as learning from export markets …EXP†, or the increased

capacity utilization or scale as proxied by the change in

demand …OUTGRT† or the learning curve …AGE†, do not

attain signi® cance level. The disembodied foreign technical

capital stock …FT †, though comes out to be positive, is not

statistically diŒerent from zero.

The above results partially validate the hypothesis of

presence of knowledge spillovers for non-FDI ® rms but

only from the level of disembodied technology import in

the sector. However, the presence of foreign ® rms as such

does not seem to have any eŒect on the productivity

growth of local ® rms. Similarly, the investment by local

® rm in R&D and other learning activities is not facilitating

any spillovers. One possible explanation for no spillovers

from the presence of foreign ® rms can be the characteristics

of the sample ® rms as the sample includes all the ® rms from

all the sectors irrespective of their technological require-

ments. There are sectors like drugs and pharmaceuticals,

chemicals etc. where a strong technological base is needed

in the form of greater skill content and the R&D activities

not only to carry out the production activities but also to

facilitate any spillovers. Alternatively, there are sectors likepaper, metal products etc. requiring fewer capabilities on

the part of the ® rm to absorb the spillovers. Thus, to gainsome insights into the nature and eŒect of spillovers i.e.,

which sectors entail larger bene® t, the sample is bifurcated

into two broad subgroups ± scienti® c’ and `nonscienti ® c’

subgroups. The scienti® c’ subgroup consists of ® rms in the

drugs and pharmaceuticals, chemicals, electronics indus-

tries etc. and the `nonscienti® c’ subgroup comprises ® rms

Foreign Wrms, technology transfer and spillovers 635

Table 4. Knowledge spillovers estimates for diVerent group of Wrms ( for sectors having foreign-owned or technology-recipient Wrm as `most-eYcient’ Wrm)

All ® rms (FDI and Scienti® c non- Non-scienti®non-FDI ® rms) Non-Fdi ® rms FDI ® rms non-FDI ® rmsCoe� cient

aCoe� cient

aCoe� cient

aCoe� cient

a

Variable (1) (2) (3) (4)

1 Spil1 70.38514 £ 10¡4

0.315 £ 10¡4 70.00267*** 0.00079***

(70.696) (0.3054) (711.996) (4.67)2 Spil2 0.00416*** 0.00679*** 0.004093 70.03***

(2.43) (2.651) (1.387) (74.79)3 Interact1 ˆ Spil1 £ R&D 0.1569 £ 10

¡70.1095 £ 10

¡70.1015 £ 10

¡6*** 70.1924 £ 10

¡6

(0.665) (0.655) (4.56) (71.264)4 Interact2 ˆ Spil2 £ R&D 70.8106 £ 10

¡70.3017 £ 10-5 ±

b±

b

(70.072) (1.4132)5 FEqty 0.00894** ± ± ±

(2.094)6 FT 70.000219 0.000437 70.001014 0.00296***

(70.789) (0.9687) (71.163) (3.175)7 CGImp 70.419 £ 10¡3*** 0.000854*** 70.471 £ 10¡3 0.416 £ 10¡3***

(3.635) (5.697) (70.725) (2.949)8 EXP 0.712 £ 10¡4 0.000135 70.00037* 0.000154

(0.4975) (0.8244) (71.746) (0.44)9 AGE 70.000123 0.00078 0.00983** 70.0011

(70.1208) (0.3933) (2.406) (70.23)10 OUTGRT 70.1315 £ 10¡5 70.839 £ 10¡5 0.1465 £ 10¡4 70.33 £ 10¡5

(70.5012) (70.1758) (0.697) (70.698)11 FINV 0.5028 £ 10

¡4* 0.134 £ 10

¡3** 0.155 £ 10

¡30.1184 £ 10

¡3*

(1.7224) (2.335) (1.0253) (1.632)12 Constant 70.01464*** 70.02398*** 0.05773*** 0.00465

(73.4588) (73.792) (3.962) (0.4729)13 Adj. R-sq. 0.0362 0.0557 0.7738 0.092214 F 3.295 6.306 180.96 6.48915 N 2343 1188 539 649

Notes: R&D has been found highly correlated with Interact1 and Interact2 and hence has been dropped from the estimations.a

Figures in parentheses are t-ratios and ***,**,* are signi® cance levels at 1% , 5% and 10% respectively.b Interact2 is found to be highly correlated with Interact1, hence has been dropped from the estimations.

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in the automobiles, nonelectrical machinery, metal prod-

ucts etc.25 The bifurcation of the sample also allows oneto see whether any technology diŒusion’ has taken place or

if it is more of `competitive eŒect (or demonstration eŒect)’

that has resulted in spillovers. This is due to the fact that

in the `nonscienti® c’ sectors, where the technological

innovation is product centred, ® rms may learn by reverseengineering (demonstration eŒect). Furthermore, the

level of technological requirement is itself not being very

high in the `nonscienti ® c’ subgroup, it may force local ® rms

to be more in direct competition with the foreign-owned

® rms (competitive eŒect). On the other hand, in the scien-

ti® c’ subgroup, where the innovations are process oriented,the learning for the local ® rms would be mainly through

technology diŒusion. It is generally argued that a weak

patent regime in India enhanced the imitation potential

of foreign technologies in the scienti® c sectors (Basant,

1997, p. 1697). This implies that spillovers may be feasibleespecially for ® rms belonging to the scienti® c sectors.

Columns 3 and 4 of Table 4 report the results for both

the sub-groups of ® rms.

ScientiWc and nonscientiWc non-FDI Wrms

The results suggest that presence of foreign ® rms in the

sectors has a varied nature of spillovers depending upon

the sectors to which the ® rms belong. The extent of spil-

lovers vary depending upon the nature of industry, i.e., ifthe ® rms belong to the `nonscienti ® c’ subgroup, there exist

signi® cant knowledge spillovers for local ® rms from the

presence of foreign-owned ® rms (row 1, column 4). But

for the ® rms belonging to the scienti® c’ subgroup the pres-

ence of foreign ® rms in itself has a negative impact.

However, the scienti® c’ domestic ® rms that engage inR&D activities tend to gain from the spilled foreign knowl-

edge (rows 1 and 3, column 3). This implies that knowledge

embodied in a foreign ® rm can diŒuse to local ® rms pro-

vided the local ® rms invest in R&D, i.e., the ® rms need to

have su� cient own technological capabilities in the form ofR&D stock to decodify it and the ® rms should belong to

scienti® c’ subgroup. This con® rms the ® ndings of Fikkert

(1994) and Basant and Fikkert (1996) that foreign techno-

logical spillovers are complemented by ® rms’ R&D. Based

on the coe� cient, it appears that a ® rm having an average

R&D stock for the average share of foreign ® rms’ sales in

the sector at the mean productivity growth would experi-

ence 4% higher productivity growth than a ® rm having no

R&D stock. However, presence of available foreign tech-

nical capital stock in the sector does not propel the local

® rms to grow faster in the scienti® c’ subgroup, but in the

`nonscienti® c’ subgroup it tends to have negative impact on

the productivity growth.

Rows 6 to 10 of Table 4 report coe� cients of other

controlling variables of productivity growth. Results indi-

cate that for the `nonscienti ® c’ non-FDI ® rms, technology

import in either forms viz., disembodied …FT† or embodied

…CGImp† facilitates higher productivity growth. Results

also suggest that a ® rm that has invested in capital forma-

tion …FINV† would grow faster in the `nonscienti® c’ sec-

tors. However, in the case of the scienti® c’ subgroup

excepting EXP and AGE, none of the ® rm-speci® c vari-

ables attain statistical signi® cance.

Row 9 gives the coe� cient of the learning curve, AGE.

The variable has a signi® cantly positive sign for the scien-

ti® c’ domestic ® rms. This implies that in the scienti® c’

subgroup, where most of the industries are process

oriented, the use of plant and machinery of older vintage

has facilitated signi® cant learning eŒect leading to higher

productivity growth (i.e., learning by using). However, for

® rms in the `nonscienti® c’ sectors, the variable is not sig-

ni® cantly diŒerent from zero in statistical terms.

The most counter-intuitive result is the negative but

highly signi® cant sign of export orientation for the scien-

ti® c’ non-FDI ® rms implying that productivity growth of

scienti® c’ domestic ® rms would be low if they tend to

export. The only plausible explanation can be given by

looking into the direction and composition of exports of

these ® rms. If ® rms are exporting to (erstwhile) Eastern

block countries because of prevailing rupee-trade agree-

ment at that time and items exported do not represent

the commodity basket of the ® rm, a large export intensity

will only result in increased pro® tability rather improved

e� ciency.26 The lack of data however, restrains to look

into the direction and composition of exports aspect but

the data show that average pro® tability of the scienti® c

export-oriented’ non-FDI ® rms during the study period is

signi® cantly higher compared to that of `non-export

636 V. Kathuria

25Griliches and Mairesse (1984), Basant and Fikkert (1996) also have used the same groupings in their analysis.

26 A recent study on Czechoslovakia (Brada et al., 1997) that looked into the eŒect of export activity on its productive e� ciency arguedthat the exports to the USSR impaired the technical e� ciency of East European enterprises. The lack of technical progress in the (former)USSR and the predominance of seller market led East European suppliers to produce obsolescent products, characteristics that werewasteful of inputs. Their hypothesis is partly validated by the empirical results as they ® nd signi® cantly negative impact of exports on thee� ciency for some of the industries. Another recent study for Taiwan has also argued that the trade with technologically advancedcountries determine the level of technology from which the developing country can learn (Chuang, 1996). Thus, in the present case, if thebreak-up of ® rm’ s exports to Western Europe and Eastern Europe is known, it would lend support to the conjecture. However, such abreak-up is not available.

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oriented scienti® c’ domestic ® rms. This partially validates

our conjecture.27

Thus, the above set of results indicates that presence of

foreign-owned ® rms in the sector has varied spillover

impacts depending upon the nature of the sector. For

® rms belonging to the scienti® c’ subgroup, the spilled

knowledge can be decodi® ed provided the ® rm has su� -

cient technical base or capabilities in the form of own R&D

stock to put ideas (as gathered from outside) into practice.

In other words, there exists complementarity between ideas

absorbed from the presence of foreign ® rms and own

research activities. Thus, the results validate the hypothesis

given by Wang and Blomstrom (1992) . However, in the

`nonscienti® c sector’ , where the technological requirement

is low and the embodied technology in the foreign ® rms

itself is not very complex, the `competitive’ pressure from

the foreign ® rms or the `demonstration eŒect’ may have

facilitated the local ® rms to learn and be more productive.

Catching-up or technology-gap hypothesis

The customary approach in the literature on technology

diŒusion is that the diŒusion of technology or knowledge

follows a logistic curve. This implies that the ® rms, whichare at a lower level of e� ciency, can gain the most in terms

of productivity enhancement.28 However, this view is not

universally accepted. A ® rm that is less technically e� cient

(but economically viable) may not experience signi® cant

productivity gains for a number of reasons. Such a ® rmmay have a higher threshold for its perception of threats

as well as opportunities, hence a lower level of aspirations.

Similarly, the repertory of routines available for (re)com-

Foreign Wrms, technology transfer and spillovers 637

Table 5. Testing for catch-up (or technology-gap) hypothesis

Scienti® c non-FDI ® rms Non-scienti® c non-FDI ® rmsVariable Coe� cient

a(1) Coe� cient

a(2)

1 Spil1 70.00197*** 0.704 £ 10¡3

***

(78.409) (3.2836)

2 Spil2 0.00524** 70.03098***

(2.0465) (74.63)

3 Interact1 ˆ Spil1 £ R&D 0.966 £10¡7*** 0.78 £ 10¡8

(2.344) (0.059)

4 Interact2 ˆ Spil2 £ R&D ±b

±b

5 FEqty ± ±

6 FT 70.0001 0.00364***

(70.098) (3.375)

7 CGImp 70.5002 £ 10¡4 0.602 £ 10¡3***

(70.773) (4.595)

8 EXP 70.0001 0.721 £ 10¡4

(70.4257) (0.198)

9 AGE 0.00345 70.0013

(0.8115) (70.233)

10 OUTGRT 70.394 £ 10¡5 70.342 £ 10¡5

(70.183) (70.689)

11 FINV 0.000137 0.0001

(0.889) (1.427)

12 GAP 0.0281*** 70.0492***

(7.106) (78.602)

13 Constant 0.08*** 70.0378***

(6.432) (72.584)

14 Adj. R-sq. 0.5245 0.1966

15 F 52.94 14.197

16 N 539 649

Notes: R&D has been found highly correlated with Interact1 and Interact2 and hence has been dropped from the estimations.a Figures in parentheses are t-ratios and ***,**,* are signi® cance levels at 1% , 5% and 10% respectively.b

Interact 2 is found to be highly correlated with Interact 1, hence has been dropped from the estimations.

27Recently, some studies have stressed on the importance of a threshold level’ of exports before exports show any positive association

with the productivity (see, for example, Raut, 1992).28

A number of empirical studies of this process have been carried out (see, for example, Abramovitz, 1986; Dollar and WolŒ, 1988).

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bining into new and more reproductive con® guration may

itself be limited to an ine� cient ® rm (Caves and Barton,

1990).29 Thus, it is di� cult to predict which of these factors

in¯ uence more. In order to test for catch-up hypothesis, a

technology-gap variable …GAP† has been used in the analy-

sis. The variable has been de® ned as the diŒerence between

productivity of the most e� cient ® rm in the sector and the

own productivity of the ® rm. Table 5 reports the results for

both the scienti® c’ and `nonscienti® c’ subgroup of ® rms.

Column 1 gives the results for ® rms belonging to scien-

ti® c’ industries. The sign and signi® cance of the ® rst spill-

over variable i.e., presence of foreign ® rms in the sector

…Spil1† and interaction term …Interact1† do not change

after introducing the gap variable …GAP†. Thus, the results

indicate that there exist positive spillovers from foreign

® rms’ presence to the domestic ® rms belonging to scienti-

® c’ industries provided the domestic ® rms have signi® cant

technological or R&D capabilities. The other ® rm speci® c

variables ± EXP and AGE however, lose signi® cance after

the introduction of GAP. The GAP variable itself has come

out to be signi® cantly positive implying that the further the

® rm is from the technological frontier, the more is the

learning opportunities for it and hence the higher is the

productivity growth, provided the ® rm operates in indus-

tries requiring signi® cant level of technological and produc-

tion requirement. Thus, the results validate the previous

® ndings of logistic path (S-curve) of technology diŒusion

for the scienti® c’ subgroup.

In the case of the `nonscienti® c’ subgroup also, the sig-

ni® cance of spillover variables do not change with the

introduction of the GAP variable. Based on the sign and

signi® cance of the coe� cients, results indicate that besides

the `knowledge spillovers’ from foreign-owned ® rms, the

other variables that have impact in enhancing productivity

of this group of ® rms are the technology import in either

form, i.e., disembodied …FT† and embodied …CGImp†. The

GAP variable is negative and highly signi® cant suggesting

that a ® rm, which is away from the frontier, will not grow

faster despite the fact that it has signi® cant scope of learn-

ing. As argued earlier, the pursuit of innovations and pro-

ductivity raising opportunities for a laggard ® rm would

depend on the action taken by a rival ® rm and the per-

ceived threats and opportunities they reveal. Given the

level of disembodied technology import and R&D activ-

ities, which is not only signi® cantly lower for this group of

® rms but also relatively less varying across ® rms, precludes

the possibility of perceived threat or productivity raising

investments by the other ® rms.

The above results indicate positive spillovers from the

presence of foreign-owned ® rms for both groups of ® rms,

but under diŒerent conditions. The varying nature of spill-

overs seems to have stemmed from the diŒering character-

istics of the sectors. For the scienti® c’ non-FDI ® rms, there

are positive spillovers only for the ® rms that have signi® -

cant technological capabilities, whereas for the `nonscien-

ti® c’ ® rms the spillovers are not related to the ® rm’s

commitment to research activities. The spillovers for the

`nonscienti® c’ ® rms seem to be due to `competitive press-

ure’ (or `demonstration eŒect’), whereas for the scienti® c’

subgroup they are due to technology diŒusion’.

V. COMPARISON WITH RELATED STUDIES

As mentioned earlier, though a number of studies have

been done to estimate knowledge spillovers from foreign-

owned ® rms, they are not comparable as they diŒer not

only in terms of the data but also in the methodology

adopted. The only study that comes closer to the presentwork is the one by Haddad and Harrison (1993) for

Morocco. However, the models used in testing the twospillover hypotheses in their study suŒer from an inherent

contradiction. In their ® rst hypothesis, they test that pres-

ence of foreign-owned ® rms leads to reduced dispersion in

e� ciency in the sector under the assumption that `multi-

factor productivity of the ® rm is time-invariant’ (p. 73)30

and in their second hypothesis, using an alternative model

for the same period, they look for the eŒect of foreign ® rms

on the productivity growth. The results thus, diŒer accord-

ingly.

However, in the methodology used by Haddad and

Harrison to test for the spillover eŒect on productivity

growth,31 it is assumed that all the ® rms irrespective of

their industry a� liations experience the same set of factor

use and conditions (i.e., employ similar technology) in their

production function. Given the nature and type of indus-

tries where factor and skill requirements may be so diverse

(e.g., drugs and pharmaceuticals, electrical machinery etc.),

the assumption seems untenable. In the present analysis,

the Chow test rejects the null that all the ® rms even in the

broad subgroup ( scienti ® c’ and `nonscienti® c’ ) experience

same set of factor use and conditions.

638 V. Kathuria

29These ideas can be found in the Carnegie School, Leibenstein, Nelson and Winter and others. The important works in the area are

Cyert and George (1969), Bower (1970) and Nelson and Winter (1982). According to the Carnegie School, if ® rms are operating withconsiderable ignorance of the possible opportunity sets open to them, their pursuit of innovation and productivity raising opportunitieswould depend in part on actions taken by rival ® rms and the perceived threats and opportunities they reveal (Caves and Barton, 1990).30

The study by Haddad and Harrison (1993), which uses panel data from 1985 to 1989, employs Schmidt and Sickles’ (1984) model andestimates `time-invariant’ productive e� ciency of the ® rm.31

Refer, Kathuria (1998, pp. 25± 6) for the methodology and detailed comparison.

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The other important drawback with Haddad and

Harrison’ s study is the exclusion of an important sourceof knowledge spillovers i.e., spillovers from disembodied

foreign technical purchase. The studies by Haksar (1995)

and Basant and Fikkert (1996) have found signi® cant

returns to this spillover source for Indian manufacturing

® rms and strong complementarities between technologicalspillovers and a ® rm’s own R&D eŒorts. The entirely dif-

ferent methodologies refrain one from comparison of the

results. However, despite the limitation, the present study

also ® nds complementarity between spilled knowledge and

own R&D especially for the scienti® c’ ® rms.

VI. CONCLUDING REMARKS

This paper uses techniques from the stochastic produc-

tion frontier and panel data literature to test a spillover

hypothesis that `presence of foreign-owned ® rms and dis-embodied technology import in the sector leads to higher

productivity growth for the domestically owned ® rms’ . The

above hypothesis assumed that foreign-owned ® rms are

more e� cient. On comparing the e� ciency of two groups

of ® rms, results indicated that it was only in 13 of the total

26 sectors, FDI ® rms were closer to the frontier. Thisimplies spillovers should have occurred in these 13 sectors

only.

Results for these 13 sectors indicated that the channel

and type of spillovers depended on the nature of the sectors

to which the ® rms belonged. The technology diŒused tolocal ® rms from the presence of foreign-owned ® rms pro-

vided the ® rms themselves engage in R&D activities to

decodify the spilled knowledge. But it was mainly ® rms

in the scienti® c’ subgroup for which the complementarities

between knowledge embodied in the foreign-owned ® rmsand own R&D, was validated. However for the `nonscien-

ti® c’ subgroup, the competitive eŒect (or demonstration

eŒect) of presence of foreign-owned ® rms in the sector

seemed to have forced local ® rms to be more productive.

The other source of knowledge spillover, i.e., the disem-

bodied technology import in the sector attained variedsign and signi® cance in the two subgroups of ® rms.

From a policy perspective, results do suggest knowledge

spillovers from the presence of foreign-owned ® rms to the

domestic ® rms belonging to the scienti® c’ subsectors pro-

vided the ® rms’ engage in R&D activities. Thus, the con-clusion of most of the previous studies carried out for India

that knowledge spillovers and R&D are complementary

in the Indian case, is con® rmed only for the scienti® c’

subgroup. However, the reason for negative spillovers

from the disembodied technology import for the `nonscien-ti® c’ subgroup can be the regulated policy regime which

desisted the ® rms to import the technology freely in dis-

embodied and embodied form etc. in order to actualize the

bene® ts of any spilled knowledge.32

The new Industrial policy announced in July 1991 recog-nized these bottlenecks or lacunae in the earlier industrial

and trade policy and now welcomes the foreign investment

and technical collaboration lock, stock and barrel. Thus, it

would be interesting to see how the ® rms are now perform-

ing in terms of productivity level and productivity growth

when most of the regulations have either been lifted orslashed.

ACKNOWLEDGEMENTS

I am grateful to the Institude for Studies in Industrial

Development (ISID), Delhi, India for permitting me to

use the data. A substantial part of this paper was draftedduring my Ph.D. internship at the United Nations

University/Institute for New Technologies (UNU/

INTECH), Maastricht, The Netherlands. I acknowledge

with thanks the ® nancial and other support I received

from UNU/INTECH. I bene® ted a great deal from the

discussions I had with Dr Nagesh Kumar of the UNU/INTECH (now with RIS, Delhi) and Professor Ed

Steinmueller of the MERIT, Maastricht (now with

SPRU, Sussex) on this paper and wish to thank both of

them. The author thanks Dr Subir Gokarn, Dr Subrata

Sarkar and Dr Veena Mishra for their useful commentsand suggestions. I also thank the anonoymous referee(s)

for the valuable comments. The usual disclaimer applies.

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APPENDIX

Construction of Wrms’ physical capital stocks 33

A net capital stock series is generated as a measure of

capital input of the ® rm. As the capital stock reported inthe annual report is at their purchase prices (i.e., the his-

torical cost of the capital). In order to generate a capital

stock series, this reported capital stock needs to be brought

at constant 1975± 1976 prices. Firms also report accumu-

lated depreciation in their annual reports. Using this avail-

able information for 1975± 1976 (the ® rst year of the dataset) and in absence of the knowledge of exact age distri-

bution of the capital assets for a particular ® rm as of 1975±

1976, average age …AA† of each ® rm’s capital stock has

been calculated as per the following formula. Generally,

for accounting purposes it is assumed that full depreciation

of capital stock takes 16 years implying that under theassumption of straight-line depreciation method capital

depreciates at a rate of 6% per annum. Thus, average

age of the ® rm would be,

AA ˆ …AD75¡76=GC75¡76† ¤ 16 …A 1†

This average age, AA is used to construct a price de¯ ator of

capital …DCi† for each ® rm’ s capital stock in order to

de¯ ate from the year 1975± 1976 ± AA to the year 1975±

1976. Thus, the capital stock of each ® rm at current prices

would be,

NC75¡76 ˆ …GC75¡76=DCi† ¤ …1 ¡ 0:06†AA …A 2†

Similarly, net capital stock for 1976± 1977 at 1975± 1976

prices would be

NC76¡77 ˆ…NC75¡76†¤…1¡0:06†‡…NI76¡77†=PC76¡77 …A 3†

where NI76¡77 is the net investment in 1976± 1977 and

PC76¡77 is the price de¯ ator for the year 1976± 1977.Same formula is used to get the series till the terminal

year of the study, i.e., 1988± 1989.

Construction of Wrms’ foreign disembodied technical capitalstocks (FT1)

To measure disembodied technology as purchased from

foreign countries through expenditure on foreign patents,

royalties, technical and consultancy fees in the form oflump-sum payments etc., a foreign purchased technical

capital stock …FT1† has been calculated. Perpetual inven-

tory method (as given below) is used to construct the

knowledge stocks generated from technology purchase.

FT1i;t ˆ …1 ¡ ¯†FT1i;t¡1 ‡ TPi;t¡1 …A 4†

where TP is the ® rm’s expenditure on technology pur-

chased in the form of licences from foreign countries and

¯ is the rate of depreciation of the technical knowledge.

Pakes and Schankerman (1984) argue that the rate of

obsolescence of knowledge capital must be higher than

that of physical capital because of depreciation through

obsolescence, not only because new knowledge replacesold knowledge, but because the appropriability of knowl-

edge decreases as the diŒusion of that knowledge takes

place with the passage of time. In the present case, a depre-

ciation rate of 15% has been assumed, similar to the one

assumed in several studies (see, for example, Hall and

Mairesse, 1992; Basant and Fikkert, 1996). As the USAis the largest seller of technology to India, to bring the

technology purchase expenditure at constant 1975± 1976

prices, rupee± US dollar exchange rate at the 1975± 1976

exchange rate has been used to de¯ ate these expenditures.

Foreign Wrms, technology transfer and spillovers 641

33The procedure adopted to calculate capital stock, own and foreign technical capital stock is somewhat identical to what has been

followed by Basant and Fikkert (1996) in their analysis.

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To compute knowledge stock for the initial year, the fol-

lowing procedure is adopted. Department of Science andTechnology documents (DST, Research and Development

Statisticts) suggest that payments stream for a foreign

licensing contract lasts for four years. This implies that

for a depreciation rate of 15% , any agreement signed

before 1967± 1968 would have become obsolete by 1975±1976, the ® rst year of the study period. Based on this infor-

mation, those sample ® rms are identi® ed that have gone for

technology transfer in any given year during the period

1967± 1968 to 1974± 1975 (source: IIC, 1982). Then, using

® rm level information for the ® rst year of the study period,

i.e., 1975± 1976, the average ratio of technology purchaseexpenditures to sales is computed for 3-digit industry

groups. These ratios are multiplied by each ® rm sales in

1975± 1976 to get a rough estimate of payment for tech-

nology per year for any year during 1967± 1968 to 1974±

1975, the years in which ® rm is known to have purchasedtechnology as determined above. These payment streams

are then discounted using the above formula to generate

the stock for the initial year.

Construction of R&D stock (i.e., Wrms’ own technicalcapital stock)

Information about past R&D investments of a ® rm can be

used to approximate the technical capital or knowledge

generated by it. Perpetual inventory method is used to con-

struct the own technology stock of the ® rm.

R&Di;t ˆ …1 ¡ ¯†R&Di;t¡1 ‡ RDPi;t¡1 …A 5†

where RDPi;t¡1 is the expenditure on research and devel-

opment at time t ¡ 1 and ¯ is the rate of depreciation to

technical knowledge. Following earlier studies and abovediscussion, present study also uses a depreciation rate of

15% . These R&D expenditures have been de¯ ated using a

weighted average of the wage and capital investment pricede¯ ators.

As most ® rm’ s did not do R&D prior to 1975± 1976, the

® rst year of our data, one can assume that the ® rms which

have not reported any R&D in the ® rst year of our data

have not engaged in any R&D activities in preceding per-iods also. Thus, the initial period R&D stock for these

® rms would be zero. But to calculate the initial year

R&D stock for the ® rms which have reported R&D in

the ® rst year of our data we need to know ± the number

of years since when the ® rms have been doing R&D (i.e.,

the age of their R&D unit), the rate of growth of R&Dexpenditures in such units and the rate of depreciation of

R&D stock. The calculations indicate that real R&D

expenditures per R&D unit (as recognized by the

Department of Science and Technology) in the pre-1975

period has grown at about 5% a year in India and theestimated average age of the R&D units recognized by

DST in 1975, the initial year of our data has been 4.9

years. Based on the information one can assume that the

® rms reporting R&D expenditures in the initial year of the

data has been doing R&D for ® ve years prior to that year.Thus, the 1975 stock of R&D would be

R&Di;1975 ˆ RDP0 ‡ …1 ¡ ¯†RDP¡1 ‡ …1 ¡ ¯†RDP¡2

‡ …1 ¡ ¯†RDP¡3 ‡ …1 ¡ ¯†RDP¡4 …A 6†

For a depreciation rate of 15% and the assumed R&D

growth of 5% , the ® nal equation to estimate the initial

year of R&D stock would be

R&Di;1975 ˆ RDP0

X4

sˆ0

‰…1 ¡ 0:15†=…1 ‡ 0:5†Šs( )

…A 7†

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