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R&D and Technological Learning in Indian Industry: EconometricEstimation of the Research Production FunctionAmit S. Ray; Saradindu Bhaduri

Online publication date: 19 August 2010

To cite this Article Ray, Amit S. and Bhaduri, Saradindu(2001) 'R&D and Technological Learning in Indian Industry:Econometric Estimation of the Research Production Function', Oxford Development Studies, 29: 2, 155 171

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Oxford Development Studies, Vol. 29, No. 2, 2001

R&D and Technological Learning in Indian Industry:

Econometric Estimation of the Research Production

Function

AMIT S. RAY & SARADINDU BHADURI

ABSTRACT Estimation of research production functions has produced rich and useful results

for developed countries in the past. This paper makes a pioneering attempt to estimate the same

in the context of a less-developed country (LDC) (India). The objective is to examine the

process of technology generation and learning in Indian industry. The existing literature

recognizes two principal characteristics of technological activities in LDCs. First, their R&D

effort is geared towards minor as opposed to major innovations. Second, technological

learning constitutes an integral part of their research thrust. This paper attempts to capture

these characteristics in a rigorous econometric framework by estimating a comprehensive

research production function incorporating the role of learning. We use Indian rm-level

in-house R&D data for two sectors: pharmaceuticals and electronics. Our study not onlycaptures the role of learning in determining research effort and research output, but also

re-examines some of the existing hypotheses relating to the effects of rm size, technology import

and ownership. We nd that the two sectors display two distinct learning trajectories, but in

both cases learning proves to be crucially important in technology generation.

1. Introduction

Much of the theoretical literature on technology and R&D evolved against the back-

drop of capital-rich developed economies. In this literature less-developed countries(LDCs) are portrayed as mere recipients of old technologies from the industrialized

countries in the mature phase of the product cycle. Challenging this paradigm, the

importance of technological activities in LDCs started gaining ground in the 1960s and

1970s, particularly after the emergence of Japan as a major technological power.

Economists recognized that LDCs may carry out independent research activities,

according to their economic environment and priorities. These ideas crystallized in the

conceptual framework offered by development economists such as Nelson, Katz, Lall,

Bell and others. All of them recognized two principal characteristics of technological

activities in LDCs. First, their R&D effort is geared towards minor as opposed tomajor innovations. Second, technological learning, in some form or other, constitutes

an integral part of their research thrust. Unfortunately, however, the existing empirical

literature in this area has not captured these LDC characteristics in a rigorous

Amit S. Ray and Saradindu Bhaduri, School of International St udies, Jawaharlal Nehru University, New Delhi

110067, India.

We are grateful to the Department of Science and Technology, Government of India, for a research grant.

ISSN 1360-0818 print/ISSN 1469-9966 online/01/020155-17 2001 Internationa l Development Centre, OxfordDOI: 10.1080/13600810120059306

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156 A. S. Ray & S. Bhaduri

econometric framework. None of the studies, for instance, estimated a research pro-

duction function for LDCs, let alone a comprehensive one incorporating the role of

learning.1

This paper attempts to ll this gap in the literature and focuses on in-house R&D

of Indian enterprises in order to understand the process of technology generation and

learning in Indian industry. Section 2 presents an analytical framework and arrives at

a research production function incorporating the role of learning. Section 3 outlines the

econometric model and Section 4 presents the results of the econometric estimation.

Section 5 synthesizes and concludes.

2. Analytical Framework

Industrial R&D is often viewed as a production process where research inputs such as

R&D spending (equipment, manpower, etc.) are transformed into research outputs

such as invention, innovation and diffusion. The R&D production function, however,

is not a simple mapping of research inputs into research outputs. Rather, it encom-passes a complex set of factors evolving from the large body of theoretical literature on

technology. The Schumpeterian hypothesis as well as the later neo-classical models

(Arrow, 1962a; DasguptaStiglitz, 1980, etc.) considered a wide array of theoretical

determinants of the nature and direction of R&D activity, much of it revolving around

market structure variables. In this theoretical tradition, however, technological progress

is identied with major breakthroughs in science and technology resulting in a shift of

the frontier.2 As a result, the important contribution to technical progress made in

diffusion, adaptation and application of new technologies, which are particularly

important in the context of LDCs, has remained under-emphasized. However, theevolutionary models of technological progress (Nelson & Winter, 1982; Mowery &

Rosenberg, 1989) are perhaps the only theoretical constructs that consider minor, as

opposed to major, innovations to be the more likely and more conventional research

output of any R&D programme. These models have a broader perspective on technol-

ogy dened as a set of linked capabilities based on different types of knowledge: formal

and informal (i.e. tacit or experimental). Indeed, the evolutionary models characteriza-

tion of technical change as a tacit, path-dependent and non-linear movement

makes technological progress similar to the process of technological catch-up com-

monly observed in many LDCs.

Lall (1987) observed that considering technological progress only as a movement

of the frontier is a highly simplied neo-classical view because major technological

innovations are not the only, perhaps not even the main, source of productivity

improvement in the history of industrial development and minor changes to given

technologiesto equipment, materials, processes and designsare vital and continu-

ous source of productivity gain in practically every industry. Therefore, one can argue

in line with Bell (1984) that technological effort should ideally be viewed as conscious

use of technological information and the accumulation of technological knowledge,

together with other resources, to choose, assimilate and adapt existing technology

and/or to create new technology. This is what reects technological capability of anLDC, dened as the capacity to select, absorb, assimilate, adapt, imitate and perhaps

improve given (imported) technologies.3 Several case studies (country level, industry

level and rm level) conrm that the creation of such indigenous technological

capabilities requires conscious technological effort and risky investments in R&D.4

Accordingly, one must broaden the denition of technological output in the context

of a research production function of an LDC. R&D units in LDCs need not come up



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R&D and Technological Learning 157

Figure 1. The R&D production function: a schematic framework.

with very different products or processes but may still be acknowledged as an innovator,

albeit of minor rather than major innovations. Katz (1984) further extended the

coverage of technological output by including not only adaptation and assimilation to

be a part of innovative process but also changes in market structure and organizational

planning as an outcome of their technological effort.

Another departure point for LDCs with respect to their technological activities is,

perhaps, the absence of the so-called technology shelf5 which generally implies higher

search cost for LDC entrepreneurs. They are engaged in two kinds of researchactivities: to nd the best (most suitable) technology among an existing set and to

achieve new technologies. Learning, thus, becomes a most essential component of

technological activities in developing countries. As Nelson (1987) puts it: To the

extent that technology is not well understood, sharply dened invention possibility sets

are misleading concepts and interaction between learning through R&D and learning

through experience is an essential part of the invention process.

2.1 The Research Production Function

Given the idiosyncrasies of technological activities of developing countries, we posit a

research production function for our analysis in the form of a schematic framework

(Figure 1). R&D inputs and outputs are both endogenously determined in this

framework in a recursive structure. Apart from the conventional rm-size effects of

R&D, which has been extensively researched in the context of developed as well as

developing countries, our framework considers further determinants like technology

imports, ownership and, above all, learning of different kinds.

2.2 Firm sizeIn order to test the Schumpeterian hypothesis, most of the studies have focused on

the likely impact of the size of a rm on its research effort, assuming market structure

to be exogenous and independent of a rms R&D decision. It is argued that large rms

are better qualied or perhaps more eager to undertake R&D than smaller rms for the

following reasons. First, R&D is characterized by increasing returns to scale which a

large rm can exploit better. Second, since R&D activity involves a high level of risk



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that is difcult to eliminate with insurance (for reasons of moral hazard), large rms

may be more willing to take these risks as they can be diversied over a wider range of

product lines. Third, the production pattern in a large rm is more systematic and

routinized, which makes it easier for them to implement a new innovation.

The results obtained are diverse and the sizeR&D relationship remains inconclus-

ive. Among the Indian studies, while Goldar & Ranganathan (1997) obtained a linearly

positive effect of rm size on research effort of a rm, Katrak (1985, 1990) concluded

that R&D effort increases with rm size, but less than proportionately. We will examine

this relationship (linear or non-linear) and explore the existence of an optimum rm

size, if any, with respect to research effort.6

2.3 Imported Technology and R&D Effort: Complement or Substitute?

This is an issue specic to a typical LDC rm. Import of technology is likely to enhance

in-house R&D if it is adaptive and absorptive in nature.7 Along this line, Kumar (1987)

argues that technology import through FDI may be followed by less in-house R&Dcompared with technology import through licensing by non-afliate rms, as the latter

may be more willing to absorb, assimilate and adapt the imported technology.

On the other hand, if the rms R&D activity is geared towards import substitution

(essentially substituting for the imported technical know-how as well as intermediate

inputs as argued by Dore (1984), Lall (1984, 1987) and Desai (1984) for Indian rms),

we may expect a negative relationship between technology import and R&D.

In fact the latter argument will hold particularly for disembodied technology

imports, while the former may be relevant for import of embodied technology. Accord-

ingly, we examine the effect of both imported capital goods (embodied technology) andimported disembodied technology on research effort. The latter represents the direct

import of technical know-how, which reduces the necessity of rms research activity.

Import of capital goods, on the other hand, promotes R&D as purchased machines are

to be adapted in domestic environment for protable functioning. We thus hypothesize

a negative relationship of R&D with import of disembodied technology but a positive

one with imported capital goods.

Implicit in the hypothesis posited above is the presumption that technology import

decisions are exogenous to R&D decisions of rms. This could appear to be suspect.8

We justify this presumption on the grounds that ours is a cross-sectional study for a

given point in time. Any technology import/collaboration agreement is made for a

period of at least 34 years (if not longer), while the bulk of R&D projects are of much

shorter duration in India. Therefore, we can reasonably argue that while deciding about

R&D at a given point in time, the rm considers its technology imports as fait accompli.

2.4 Ownership

In the standard neo-classical production theory, ownership per se is not expected to play

any role in the day-to-day operation of a rm, because every rm is hypothesized to be

a prot maximizer. But unlike developed economies, LDCs are characterized by thepresence of rms with different ownership categories, with diverse levels of technologi-

cal as well as nancial capabilities. Accordingly, we hypothesize that research thrust

would also vary among rms belonging to different ownership groups and the differen-

tial effects of ownership are expected to play an important role in the process of

technology generation in LDCs.

In our study, we have distinguished between three types of ownership structures:



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public enterprises, private Indian enterprises and foreign multinationals. It has been

suggested that since technology is readily available to a multi-national corporation

(MNC) (from their parent bodies), their research thrust would be simpler and less than

that of a domestic private enterprise. In the Indian context, Goldar & Ranganathan

(1997) found a signicant positive impact of foreign ownership on R&D intensity.

Rays (1998) results show that though MNCs employ fewer research personnel, they

are more productive in converting the inputs into R&D outputs.

The theoretical literature is less precise about the effect of government ownership on

R&D activity. It is difcult to address this issue because the prot maximization

principle may not play a decisive role in determining R&D behaviour of public sector

rms. Indeed, public sector rms can afford to stay out of equilibrium longer compared

with a private rm and therefore can undertake activities that are erratic (non prot

maximizing).9 Goldar & Ranganathan (1997) found a positive impact of a public sector

dummy on R&D intensity of Indian rms. Our study covers the pharmaceutical and

electronics sectors. Both are among the more R&D-aggressive industries receiving

special attention from the government from time to time for the development of thesesectors. It is, therefore, difcult to predict an exact a priori impact of ownership on

R&D.

2.5 Learning

There has been little explicit theorization of the role of learning in the research

production process. Arrow (1962b) is perhaps the only theoretical construct introduc-

ing the concept of learning by doing in the neo-classical theoretical literature, butthere is little discussion even in that article regarding the nature of the process

involved.10 In the context of developing countries, Bell (1984) distinguished between

two dimensions of the learning process: (1) doing based learning; and (2) learning

by training or learning by hiring or learning by searching or spillover.

Both types of learning are equally important in the research production process in

an LDC. Learning by doing, for instance, may not result in a research outcome which

is altogether new (major innovation), but it certainly contributes to acquisition of

technological capability (absorptive, adaptive) and the consequent minor changes or

inventing around, which is crucially important in LDCs. We also expect that rms

with longer experience will spend more on R&D. The justication comes from an

evolutionary framework, where rms that are successful in research continue with

their research activity and enlarge their R&D outt. Learning through experience also

raises the efciency with which R&D inputs are converted into outputs. It thus

has a positive impact on the amount of technological output by raising the marginal

productivity of R&D inputs.11 We therefore expect that ceteris paribus rms with longer

history of learning (or with more experience) would produce more research output.

With regard to the role of learning through spillover, the neo-classical literature is

less precise as it assumes instantaneous diffusion.12 However, later developments

recognized diffusion as a complex process requiring explicit effort and investment.13

This is true even for acquiring knowledge freely available in the public domain.

Spillovers would then enter the research production process in a signicant way. It

would augment technological output in the same manner as learning by doing, but its

impact on research effort is less obvious. We dene two distinct sources of spillover:

national and international, both of which could act as important inputs into the

research production function.14



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3. Econometric Specication

We specify the following econometric models for estimation.

RS5Xb1 u, (1)

where RS is an n3 1 vector denoting R&D effort for n number of rms, X is an n3 k

matrix consisting of k explanatory variables, b is the coefcient matrix of order k3 1and u is the matrix of error terms of the order n3 1.

TQ5Zg1 v, (2)

where TQ is the n3 1 vector denoting R&D output for n number of rms, Z is an n3 k

matrix consisting of k explanatory variables, g is the coefcient matrix of order k3 1

and v is the matrix of error terms of the order n3 1.The variables RS, X, TQ and Z are

described below.

3.1 The Data and Variables

For this cross-sectional econometric analysis, we select two industry categories: phar-

maceutical and electronics rms with 71 and 52 observations, respectively. The sample

is constructed by merging a corporate database (PROWESS) supplied by the Centre for

the Monitoring of the Indian Economy (CMIE) with the rm-level R&D data from

NSTMIS division of the Department of Science & Technology (DST), Government of

India. Although DST maintains a time series, we could obtain the data only for

199495, which included information on R&D expenditure for the latest 3 years. In the

pharmaceutical sample we have 13 foreign rms and the others are domestic rms. In

the electronics and electrical sample, there are eight foreign rms, nine public sector

rms and 35 private (Indian) rms.

R&D input (RS). We have used R&D stock as the measure of research input.15 Stock

is constructed assuming a 15% depreciation rate for both industries.16 We had the

gures for R&D expenditure only for a period of 3 years. Our R&D stock measure thus

covers the period 1992/93 to 1994/95.

R&D output (TQ). TQ is a summation of various technological outputs produced andreported by Indian enterprises. The variable includes the number of product, process,

import substitutes and design prototypes developed by a rm. It also includes the

number of publications of papers, books and technical reports and consultancy services

provided by the enterprise. Data for two consecutive years 1993/94 and 1994/95 have

been aggregated to rule out the possibility of systematic errors or year-to-year

uctuations.

Learning. Learning is believed to operate in two ways: (a) learning through experience;

and (b) learning through interaction (spillover). Learning through experience (LE) hasbeen measured by the age of the R&D unit of a rm.17 To measure the effect of learning

on the efciency of the research production process, one can look at the interaction

effect of R&D stock and experience (age of R&D unit) by constructing a variable

(RSAGE).

Spillover has been identied in the empirical literature as the stock of knowledge

transmitted to a rm from sources extraneous to its R&D outt. Accordingly measures



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of spillover pool have been incorporated as input into a rms research production

function.18 Such measures have been conceptualized either on the basis of the total

stock of knowledge pool (created by R&D of all other rms) or on the basis of

specic sources of spillovers like patent applications. These measures also implicitly

assume that spillover is available to a rm automatically.

Our measure of learning through interaction captures spillover in a more general

sense. We look at the participation (attendance) of rms in R&D-related national and

international seminars and training programmes to capture the extent of benets

received by a rm from the common stock of knowledge pool. This knowledge pool

contains broad and overall developments in the relevant elds of research rather than

specic information about particular innovations and patents. Patent data exclude all

research that is either unsuccessful or not patented, but may generate considerable

knowledge base. It is in this sense that we call our measure a more general index of

spillover. Access to this generalized spillover pool will vary from rm to rm depending

on their attendance. Our measure, therefore, captures this inter-rm variation better

and more directly than commonly used measures (e.g. Basant & Fikkert, 1993) ofspillover pools, which capture industry-level variations better than rm-level variations.

We have two different indices of spillover: national or domestic spillover (NSP) and

international spillover (ISP). The former contains the number of various national-level

training programmes and seminars attended by a rm. The latter counts the number of

international training programmes and seminars that a rm has attended during the

years 1993/94 and 1994/95. The square of these two variables (NSP2 and ISP2) would

take care of possible non-linear effects and would enable us to determine an optimum

spillover level, if it exists.19

Ownership. The effect of ownership has been captured through two dummy variables.

The dummy showing the effect of foreign ownership (FD) takes the value one for

multinational rms and zero for Indian (both public and private) rms. Likewise, the

dummy capturing the impact of private versus public ownership (PD) takes the value

one for private rms (both domestic and MNCs) and zero for Indian public sector

enterprises.

Size. The effect of size is measured by the annual sales turnover of rms (S) for the year1994/95. The square of the sales gures (S2) will be used to represent the non-linear

effect of size on R&D effort.

Technology import. The import of embodied technology is captured by the value of

imported capital goods (ME). Disembodied technology import is measured by royalty

payments (MD).

3.2 The Models

From the foregoing discussion we now list the models which we attempt to estimate

using tools of applied econometrics:

(1) RSi5 a01 a1 Si1 a2S2i1 a3LEi1 a4MEi1 a5MDi1 a6FDi1 a7PDi1 u1i.

(2) TQi5b01 b1RSi1 b2LEi1 b3NSPi1 b4NSP2i1 b5ISPi1 b6ISP

2i1 b7FDi1 b8PDi

1 u2i.



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Table 1. Pharmaceutical: correlation matrix of

the independent variables of model 1

Variables S FD ME MD

S 1

FD 0.25 1ME 0.52 20.1 1

MD 0.47 0.03 0.28 1

3.3 Estimation Method

The above models represent a set of recursive simultaneous equations, and therefore

can be estimated individually, using classical least squares.20 Model 1 is estimated by

ordinary least squares. To test for the presence of heteroscedasticity we use the

CookWeisberg (1983) test.

21

In case of heteroscedastic error structure we use robustestimation correcting for the standard errors and signicance levels of the coefcients.

We also check for the presence of multicollinearity by looking at the pairwise corre-

lation coefcient matrix of the independent variables.

In our attempt to estimate the research production function (model 2) we notice

that the research output TQ can only take non-negative values with a signicant

proportion of zeros. The dependent variable in this model is therefore (left)- censoredat

zero. We therefore use the Tobit estimation procedure to estimate the model.

4. Results and Analysis

4.1 The Pharmaceutical Industry

Model 1 (Tables 1 and 2). There is clear evidence that initially R&D effort increases with

rm size but at a decreasing rate and then falls after attaining an optimum rm size

(calculated to be sales level of rupees 550.55 crores (crore5 ten millions)).22 Foreign

rms R&D effort appears to be signicantly less than that of Indian rms. It is further

observed that import of embodied technology promotes R&D while import of disem-

bodied technology reduces R&D effort.

Model 2 (Tables 3 and 4). We included the national and international spillover variables

separately to avoid possible multicollinearity problems. The principal factors that

appear as statistically signicant determinants of research output are the learning

variables.

Learning through experience does not appear to have any signicant impact on the

research production function (RPF) on its own. For that matter, we nd that even the

key input of R&D effort (RS) does not always explain variations in research output. But

interestingly, the interaction term (RSAGE) appears positive and signicant. An older

rm uses one unit of R&D input much more efciently than a newer rm, which is lessexperienced in R&D.

There is clear evidence of an inverse U-shaped impact of spillover on total output,

although the quadratic effect of national spillover is weak. The optimum (satiation)

point is reached earlier for ISP compared with NSP. This may be explained as follows.

International spillover exposes a rm to the state of the art on the global frontier. This

is important for LDC manufacturers but in a limited manner as they are not engaged



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Table 2. Pharmaceutical: estimation of model 1 (dependent

variable is RS

Independent

variables Equation (1)a Equation 1(a)(robust)

Constant2

21434.65**2

21434.65**(2 2.357) (2 2.43)

S 722.03*** 722.0281***

(7.598) (5.118)

S2 2 0.656*** 2 0.656***

(2 5.117) (2 3.948)

LE 175.614 175.6135

(0.483) (0.469)

FD 2 32213.2** 2 32213.2**

(2 2.494) (2 2.145)

ME 13686.11*** 13686.11***

(3.489) (2.889)

MD 2 22568.85*** 2 22568.85***

(2 3.577) (2 3.115)

Adj R2 0.68 0.7

F-statistic 25.77*** 10.31***

No. of 71 71

observations

aCookWeisberg test for homoscedasticity was rejected at the 1% level of

signicance.

*Signicant at the 10% level, **5% level and ***1% level.

in R&D to push the frontiers of global technology. Rather, most of their R&D activities

are adaptive in nature and outputs are often in the form of minor changes. Therefore,

their exposure to international spillover will have a steeper slope, but reach an optimum

early. National spillover, on the other hand, exposes them to research of similar

adaptive nature. Therefore, although the marginal gains from NSP may be lower than

ISP initially, it continues to remain positive for a larger amount of spillover compared

with ISP.

4.2 The Electronics Industry

Model 1 (Tables 5 and 6). When we estimated the model with the complete sample, the

coefcient for the private ownership dummy (PD) appeared signicantly negative,

indicating that private sector rms spend less than the public sector rms on R&D.

Does this mean that public sector rms are more research-oriented than private rms?

Table 3. Pharmaceutical: correlation matrix of the inde-

pendent variables of model 2

RDS FD LE NSP ISP

RS 1

FD 0.019 1

LE 0.17 0.193 1

NSP 0.293 0.049 20.043 1

ISP 0.257 0.093 0.178 0.431 1



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Table 4. Pharmaceutical: Tobit estimation of model 2 (dependent variable is TQ)

Independent

variables Equation (2) Equation (3) Equation (4) Equation (5)

Constant 2 4.618 2 2.774 2.307 2.12

(2 0.996) (2 0.871) (0.544) (0.686)

RS 4.23 102 5 6.753 102 5*

(1.193) (1.943)

LE 0.148 20.547

(0.822) (20.309)

RSAGE 3.033 102 6*** 2.173 102 6*

(3.099) (1.713)

FD 2 5.302 2 3.814 25.563 25.76

(2 0.827) (2 0.678) (20.914) (20.951)

NSP 1.903*** 1.952***

(3.2) (3.579)

NSP2 2 0.193 2 0.021*

(2 1.422) (2 1.667)

ISP 6.515*** 7.148***

(3.202) (3.527)

ISP2 20.293* 20.366**

(21.867) (2.291)

c2 statistic 27.12*** 32.99*** 27.53*** 26.78***

No. of observations 57 57 71 71


This sounds unrealistic, especially in the face of the alleged inefciencies of the public

sector rms. Of course, this could be due to the fact that their decision-making process

is often guided by considerations other than prot maximization. Overall policy thrust

can prompt them to spend more on R&D, although this is not a viable and sustainable

proposition in the long run. We therefore repeated the same regression taking private

rms only as reported in Table 6. The pair-wise correlation matrix reported in Table

51 displays signicant mutual correlation among S, FD, ME and MD.23

We correct for this multicollinearity by constructing a principal component for the

four variables, S, FD, ME and MD (PCP). Our principal component is the weighted

average of all (four) individual components with weights equal to the percentage ofvariations explained by them.

PCP and LE appear with signicant coefcients. The positive coefcient of PCP

implies that the combined effect of large size, foreign ownership and higher imported

technology (embodied and disembodied) raises R&D effort. LE displays signicantly

Table 5. Electronics: correlation matrix of the independent

variables of model 1 (private rms)

S LE FD ID ME MD

S 1

LE 0.45 1

FD 0.43 0.074 1

ID 0.035 2 0.303 0.192 1

ME 0.66 0.158 0.507 0.148 1

MD 0.35 0.083 0.228 0.23 0.273 1



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Table 6. Electronics: estimation of model 1 with principal

components (private rms only)

Equation (1a)

Independent (robust

variable Equation(1)a estimation)

Constant 6058.75 6058.75

(0.409) (0.588)

LE 1173.459* 1173.459***

(1.882) (3.588)

PD

ID 2318 2 318

(2 0.028) (0.976)

PCP 25480.93*** 25480.93***

(3.986) (3.669)

Adjusted R2 0.37 0.42

F-statistic 9.00*** 14.95***

No. of

observations 42 42

aCookWeisberg test for h omoscedasticity was rejected at the 1% level of

signicance.

*signicant at the 10% level, **5% level and ***1% level.

positive impact on the RS, signifying that rms with more experience of research spend

more on R&D.

Model 2 (Tables 79). The primary input to R&D production function, research effort

(RS) has a negative and signicant impact on the research output. PD is shown to have

a positive impact on the amount of research output produced, suggesting that private

rms produce more output relative to public sector rms. These two results demand

further explanation.

Positive PD in this model together with the negative sign of PD in the earlier model

should imply that private sector rms spend less on R&D but spend it more efciently

than public sector rms. Therefore, one may reasonably suggest that the negative effect

of RSon TQ is due to the large and unproductive R&D expenditure of the public sector

rms. In fact, when we carry out a separate regression taking only private rms, RS

becomes insignicant (equation (3) in Table 9).24

LE is positive and signicant in both the samples. With regard to spillover, both

NSP and ISP and their square terms appear statistically signicant with the expected

Table 7. Electronics: correlation matrix (all rms) of the independent variables of

model 2

RS LE FD PD ID NSP ISP

RS 1

LE 0.352 1

FD 0.181 0.081 1

PD 2 0.289 0.07 0.195 1

ID 0.179 20.268 0.133 2 0.213 1

NSP 0.258 20.043 0.016 2 0.474 20.085 1

ISP 0.123 0.131 2 0.107 2 0.13 20.263 0.092 1



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Table 8. Electronics: correlation matrix of the indepen-

dent variables of model 2 (for private rms)

RS LE FD NSP ISP

RS 1

LE 0.372 1FD 0.39 0.074 1

NSP 0.555 0.305 0.394 1

ISP 0.021 0.11 2 0.085 0.045 1

signs. When we carry out the same regression with only private rms, the results are

slightly different (see equation (3) in Table 9). NSP variables become insignicant. The

coefcient of FD is seen to display a statistically signicant negative impact on amount

of output. However, there is a possible multicollinearity problem in our estimation of

model 2 for private rms. The relevant correlation matrix (see Table 8) of the

explanatory variables this time shows strong positive correlation between RS and NSP.

We therefore construct a principal component for these two variables ( PCSP).25

Revised estimates of equation (3a) show that the coefcient of PCSP is positive and

signicant. FD remains negative and signicant showing that foreign MNCs produce

less research output than the private Indian rms. ISP and ISP2 are signicant with

positive and negative signs respectively, conrming the existence of an optimum level

of international spillover.

Table 9. Electronics: Tobit estimation of model 2 (dependent variable is TQ)

Independen t vari ables Eq uation (2 for Eq uat ion (3) (for Equati on (3a)(for

all rms) private rms) private rms)

Constant 2 78.795** 2 18.544 0.731**

(2 2.666) (2 1.314) (0.048)

PCSP 29.23***

(4.714)

RS 2 0.00049*** 2 1.413 1025

(2 3.175) (2 0.067)

LE 2.022*** 1.18* 1.062 (1.393)

(3.094) (1.66)

FD 2 10.144 2 32.402* 2 46.08**

(2 0.64) (2 1.739) (22.245)

PD 48.764*

(1.818)

NSP 1.312*** 0.797

(4.269) (1.202)

NSP2 2 0.002*** 0.002

(2 4.065) (0.387)

ISP 8.435** 8.341** 7.834*

(2.524) (2.15) (1.978)

ISP2 2 0.164** 2 0.166* 20.156*

(2 2.234) (2 1.95) (21.798)

c2 statistic 34.98*** 28.09*** 21.19***

No. of observation 51 42 33




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5. Synthesis and Conclusion

Our econometric analysis presents new and interesting insights into the process of

technology generation in Indian industry by estimating a comprehensive research

production function for two industries: pharmaceuticals and electronics. We have

analysed four specic determinants of technology generation, namely, rm size, tech-

nology import, ownership and learning.With regard to rm size, we nd that for both sectors larger rms, in general, spend

more on R&D, perhaps due to their liquidity and scale economy advantages.26 In the

pharmaceutical sector, however, R&D effort increases less than proportionately with

rm size, and tapers off beyond an optimum level. If R&D in this sector is primarily

business-driven reverse engineering to come up with non-infringing processes to cap-

ture newer markets, then very large rms already enjoying large market share and

diversied product portfolios may have less incentive to spend on R&D.

In the pharmaceutical industry, import of embodied technology promotes domestic

R&D effort. In this sector import of capital equipment for production often demandsgreater adaptive R&D to meet the requirements of changing process parameters.

Import of quality control equipment could also promote greater R&D in order to

conform to better quality precision. However, the import of disembodied technology

(licensing) substitutes for in-house R&D as it reduces the need for reverse engineering.

On the issue of ownership effect on R&D, we nd inter-industry differences. In the

pharmaceutical industry MNCs are found to spend less on R&D than Indian rms.

However, no signicant difference exists in terms of the R&D output they produce.

This in a sense may imply that pharmaceutical MNCs are more efcient than the

domestic rms in R&D activities.

For the electronics industry, domestic private rms are seen to spend less on R&D

but produce more research output than public sector rms. The impact of foreign

ownership per se on R&D effort could not be detected due to problems of multi-

collinearity, but MNCs appear to produce less research output than Indian rms. It is

thus evident that the Indian private rms are more active and productive in R&D in the

electronics sector.

Perhaps the most important nding of our study relates to the role of learning,

which has been conceptualized in the literature as a key driving force behind technology

generation in LDCs. Our results reveal learning to be the most important determinant

of research production process. In fact, for both industries, research effort on its ownfails to explain variations in research output. Only the learning variables come up as

signicant determinants of the research production function.

Learning through experience enters the research production function for both

sectors, although the way in which it augments research output differs across the two

industries. In the pharmaceutical sector, it enters interactively with research effort,

implying that rms with older R&D outts spend on R&D more efciently. In other

words, experience-based learning augments the efciency of R&D effort in the pharma-

ceutical sector, which is mainly reverse engineering (through trial and error). Research

experience helps the rm to decode the technology faster, reducing its cost of trial anderror and thereby making its R&D effort more efcient.

In the electronics sector, on the other hand, learning through experience enters the

production function as an independent input. Given that the electronics industry in

India is driven by the so-called screw-driver technology, simple experience-based

knowledge (of assembling) proves to be important in the R&D process.

Equally important in the research production process is the learning through



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interaction or spillover. The effect of spillover on research output appears to be

non-linear. In both industries there is evidence of an optimum level of spillover

(national as well as international).

To summarize, in this paper we have made a clear distinction between R&D inputs

and R&D outputs in a research production function framework to understand the

process of technology generation in Indian industry. We nd that the conventional

determinants of R&D, like rm size, technology import or ownership, appear signicant

only in explaining R&D effort in line with existing empirical studies. However, when we

seek to explain the variations in research output, none of these factors, not even

research effort on its own, appear to be statistically signicant. Here, in fact, learning,

both experience based as well as interaction (or spillover) based, proves to be the only

important determinant of the research production process. In some cases, learning also

augments the efciency of research effort in producing research output. We therefore

conclude that technological learning proves to be the most important determinant of

technology generation in Indian industry.

Notes

1. Such exercises have produced rich and useful results for developed industrialized nations.

See Kamien & Schwartz (1975) and Cohen & Levin (1989) for comprehensive surveys of

this empirical literature.

2. See, for instance Schumpeter (1934, 1939). Note that Rosenberg (1976) has strongly

criticized the Schumpeterian usage of the term innovation on four grounds: (1) We

conne our thinking about innovations to characteristics which are likely to be true only of

major innovations, (2) we focus disproportionately upon discontinuities and neglect continu-

ities in the innovative process, (3) we attach excessive importance to the role of scienticknowledge and insufcient importance to engineering and other lower forms of knowledge,

and (4) we attach excessive signicance to early stages in the process of invention and neglect

the crucial later stages.

3. According to Enos (1991) there are three fundamental components of technological capa-

bility: individuals with inclination and skills, institutions (rms) assembling these skills and

know-how and a common purpose/objective driving the rst two.

4. See Lall (1984) for India, Westphal et al. (1984) for Korea, Dahlman (1984) for Brazil, for

instance.

5. See Ranis (1990) and Nelson (1987).

6. Although there are empirical studies relating rm size with R&D output, we hypothesize that

the size effect is limited to research effort only since it is not theoretically well established whyresearch output should vary with rm size, ceteris paribus.

7. This is shown by Odagiri (1983) for non-innovating Japanese rms and Braga & Wilmore

(1991) for Brazilian rms.

8. Indeed, Basant (1993, 1998) considered technology purchase and i ndigenous R&D as two

simultaneously determined decisions to examine the mutual relationship between the two

decisions but fails to nd any complementarity.

9. See Katz (1987).

10. Nelson (1987, p. 81).

11. This is in line with the timecost trade-off analysis by Scherer (1967) showing that

curtailment of learning period makes the research production process less efcient by

reducing the scope of trial and error.

12. If at all, spillover was believed to have an adverse effect on the incentive to innovate. See

Spence (1984).

13. See, for instance, Cohen & Levinthal (1989).

14. The theoretical literature is less precise about the pattern of learning of both types (through

experience or through spillovers). It is evident from several empirical studies (Katz (1987) for

Latin American rms, Lall (1984) for Indian rms, Jomo et al. (1999) for Malaysian rms)

that the learning pattern as well as its importance varies from industry to industry and

according to ownership structure.



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15. The use of current R&D spending has been criticized on the grounds that it shows capital

expensed, not the capital capitalized in current accounting rule.

16. Griliches (1979) and Basant (1993) also assumed the same rate of depreciation.

17. Lall (1983) and Goldar & Ranganathan (1997) used age of rm for supposedly a similar

purpose.

18. See, for instance, Griliches (1979) and Jaffe (1986). It may be noted that the Indian studies

on spillover do not look at this aspect. Their focus has been on the relationship (or trade-off)

between spillover and own R&D.19. One can argue that seminars and training programmes attended by a rm may not be

exogenous as it might depend on the kind of research a rm wants to carry out and the extent

of R&D expenditure it intends to incur. But after all the seminars and training programmes

are organized by others, not by the rms themselves. Moreover, due to problems of

asymmetric information and associated moral hazard, organizers are unlikely to provide full

information regarding the scope and benets of the programmes. It is therefore unrealistic to

assume that rms will be able to exercise an effective choice regarding their participation in

a particular seminar or training programme. Therefore, it may be reasonably assumed to be

exogenously determined depending on availability.

20. If R&D teams which are more successful in terms of their research outputs are allowed larger

R&D budgets, we may have a causality problem of TQ determining RS, resulting in acollapse of the recursive structure of our model. But in reality, TQ may determine RS at best

with a lag, i.e. RSt,5f (TQt2 1). Therefore, in a cross-sectional model, RSt, becomes

exogenously determined, given the realized value of TQt21.

21. The test statistic is dened as Var(u)5 s2 * eXt , c2, where C is the set of explanatory

variables. It tests the null hypothesis H0: t50 against the alternative hypotheses H1: t10. If

H0 (homoscedasticity) is rejected for the given sample and at the appropriate level of

signicance.

22. A look at the data set reveals that 95% of the rms lie to the left of the above-mentioned

optimum level. Therefore, most of the rms have not yet attained the optimum size.

23. We also notice that LE is correlated with S, but not with any of the other variables, and

therefore not included in the principal component.24. The negative impact (though sometime insignicant) of the key input RS in the production

process may evoke many questions, as economic theory does not permit negative marginal

impact of inputs in any production process. But since this is a research production function,

where output may be realized with a lag (if it is of a complex nature), it may be possible that

our cross-section study does not capture the complete research process. Moreover, one may

also note that public sector rms are not always guided by the prot maximization principle

and therefore some erratic behaviour may be sustained for a longer period. Bureaucratic

control over the public sector rms often leads to delays in the decision-making process.

Often a necessary decision to purchase a technology is taken only when a substantial amount

is already spent on developing the product indigenously. All these may lead to over-spending

with no commensurate output. These are mere conjectures, which can be conrmed onlythrough extensive case studies of public sector rms.

25. This limits the scope of our study as the marginal impact of a prime input RS cannot be

detected.

26. In the electronics sector, since size correlates with foreign ownership and technology imports,

we have a combined effect of all these on R&D effort through a principal component.

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