Firm Size and R&D in Indian Industry

7/29/2019 Firm Size and R&D in Indian Industry

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Scalability versus flexibility: firm size and R&D in Indian

industry

Sumit K. Majumdar

Published online: 29 December 2009 Springer Science+Business Media, LLC 2009

Abstract This article has examined the impact of firm size on R&D spending for a panel

of several thousand Indian firms, for a period of seven years from 19992000 to 2005

2006. The average levels of R&D spending are low but for firms that do undertake R&D

the average levels of R&D spending are much higher. The results of the analysis for all the

manufacturing sector firms have shown that larger firm size is associated with a higher

probability of R&D spending. In non-linear estimation the squared term is negative

denoting that after a particular threshold firm size has no effect on R&D spending. Whenonly the R&D spending firms are evaluated then size has a mild impact on R&D spending

and in a non-linear framework the effect of size disappears signifying that both the rela-

tively smaller and larger firm alike seem to be motivated in building capabilities in the

post-liberalization period of the Indian economy.

Keywords Firm size R&D Indian manufacturing industry

1 Introduction

The issue of whether larger or smaller firms engage in relatively greater amounts of

innovative activity has generated a large literature since it is these innovative activities that

provide the foundations for a countrys economic growth. Firms provide the engine of

innovation in economies and R&D efforts lead to innovation outcomes. While India is a

large and very important economy today, not much as yet is known about Indian firms

behavior and performance and certainly the relationship between firm size and the

undertaking of innovative activities has never been explored for India.

This article reports the results of a study that specifically explores the relationship

between firm size and R&D spending in India. Based on a large proprietary dataset onIndian firms, the relative importance of firm size as a determinant of R&D spending is

evaluated for a large panel of manufacturing sector firms over a 7 year period from

J Technol Transf (2011) 36:101116

DOI 10.1007/s10961-009-9147-x


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19992000 to 20052006. The time span straddles the important recent period in the

history of the Indian economy.1

2 The firm size and R&D relationship

The famous assertion by Schumpeter (1942) that the degree of innovation is positively

correlated with firm size, and associated market power, has generated a large literature. At

the heart of the relationship between firms size and positive innovation has been a sca-

lability assumption. Schumpeters own assertion was that a large firm needed the short run

protection to provide enough market power that would provide the incentive to undertake

R&D. Large firms would expect to receive larger gains to innovations and their market

share would provide protection against imitators. Without this incentive, large firms would

be less likely to invest in innovative activities and there would be no economy wide

innovation. Conversely, small firms would be lacking the ability to spend efficiently on

R&D because it would be too hazardous in competitive environments.

The resources availability and scaling argument that Schumpeter (1942) put forth

revolved around larger firms greater abilities to spend more amounts of money on R&D, a

theme echoed by Galbraith (1952). The Arrow (1962) extension of the theme revolved

around underinvestment in R&D by smaller firms because of risk aversion, financial

weaknesses of the smaller firms and an inability to fully enjoy the returns to R&D. Since

then, important developments in this capabilities based argument have been advanced,

especially by Cohen et al. (1987). Because of capital market imperfections, large firms

have superior access to funds for R&D projects, a vital consideration since R&D involvessignificant start-up costs and scale and scope economies.

Then, complementarities between R&D and activities such as marketing, sales, and

distribution can be better evolved within large firms. In large firms, returns to process

innovations are also higher as reductions to production costs can be proportionately larger

(Bozeman and Link 1983; Cohen and Klepper 1996). Finally, large firms can spread the

risks of R&D by diversifying and can invest in risky projects with higher returns

(Holmstrom 1989). All of the above arguments are now standard in the literature and

repeated often.

In spite of these assertions, the empirical literature has noted the opposite effect. The

early work by Hamberg (1964), Scherer (1965), Comanor (1967), Mansfield (1968) andLink (1980) found no evidence of R&D increasing with firm size, while Grabowski ( 1968)

did find the R&D and size relationship to be positive but only for the chemicals and

pharmaceuticals sectors. These early studies used R&D expenditures or R&D employment

as measures of innovative activity. See Kamien and Schwartz (1982) and Symeonidis

(1996) for reviews. A possible reason for these findings has been that small firms display

the flexibility (Carlsson 1989) to react to rapid market changes and, thus, can launch

relevant research and innovation programs more quickly.

Since then, one stream of work, such as Bound et al. (1984), Pavitt et al. (1987), Acs

and Audretsch (1991) and Cohen and Klepper (1996), using alternative measures ofinnovation such as patents, has established the negative relationship between size and

R&D, or innovative activity. Such a finding is also established by Lichtenberg and Siegel

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(1991) who found no systematic relationship between firm size and returns to R&D.2 There

is a smaller subset of the literature, however, specifically the work by Acs and Audretsch

(1987) and Klepper and Simons (1997), that finds the size and R&D relationship to be

positive in some industries.

Klepper and Simons (1997) make a life cycle argument in suggesting that the rela-tionship between size and R&D, or innovative activity, is a function of the stage of the life

cycle an industry or an economy is at. In the early stages of the life cycle, as there is a race

by firms to acquire capabilities and competencies, it the skillful firms that can enhance

sales rapidly so that the typical large fixed costs associated with R&D projects can be

covered by a rising sales volume. These firms can also trigger the scale and scope econ-

omies in the production of innovations and are in a better position to exploit unforeseen

innovations. Thus, the larger firms might be able to engage in relatively greater amounts of

R&D activities expenditures in the early phases of an economys industrial development.

Additionally, the resource-based view of the firm suggests that the basis for competitive

advantage is firm resources (Wernerfelt 1984). These tangible and intangible physical,

human, and organizational resources encompass the scope of firm activities, assets,

capabilities and knowledge. Furthermore, according to the dynamic capability approach, an

extension of the resource based view (Bowman and Ambrosini 2003), through learning,

coordination, and configuration processes (Teece et al. 1997), firms can create, extend,

integrate, and recombine resources based on market preferences (Eisenhardt and Martin

2000). Thus, the resource-based view suggests that a firms combination of resources and

capabilities is associated with competitive advantage, performance and growth (Meyer

et al. 2009; Newbert 2008).

The notion that firm size is related to possessing different kinds of resources has supportin the management literature regarding the different characteristics of large and small

firms. Dean et al. (1998) suggest that firms of different sizes have different resources and

capabilities, which can influence their strategies. Small firms have resources and capa-

bilities that enable them to be flexible, make fast decisions, innovation, and respond

quickly to industry changes (Chen and Hambrick 1995; Dean et al. 1998).

Such firms may be better able to leverage knowledge and experience for innovation

compared to large firms (Leiblein and Madsen 2009) which are more formal in approach

and bureaucratic (Daft 1986; Hitt et al. 1990; Hodge and Anthony 1991). Such formal-

ization minimizes firms ability to adapt and innovate (Hitt et al. 1990; Knight and

Cavusgil 2004).Larger firms, however, have more access to resources (Bonaccorsi 1992; Dickson et al.

2006), more slack in their resources (Singh 1990), and better capabilities to achieve scale

economies (Hambrick et al. 1982; Helpman and Krugman 1985) and market share (Dean

et al. 1998). They may, thus, be more capable of engaging in formal research and

development activities, and have more human and financial resources to implement these

(Aragon-Correa et al. 2008; Svetlicic et al. 2007).

Such resources can allow them to be eventually successful at entering multiple markets

(Calof 1994). Thus, larger firms may be suited to perform a greater breadth of routine

2 They also noted that previous studies of the private, or firm-level, returns to R&D had been based on

incomplete and imprecise measures of productivity and showed how Census-Bureau, plant level data, the

Scalability versus flexibility 103


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activities, whereas smaller firms, due to their innovative capabilities and flexible organi-

zational processes, yet with limited access to financial and human resources, may tend to

focus on performing a few activities that require innovation and flexibility. In general,

consistent with the above arguments, it is expected that the majority of larger firms would

be less flexible and adaptable, and more resource endowed, than smaller firms.

3 Expectations for Indian firms

The analysis covers an important 7 year period in the economic history of India from

19992000 to 20052006. Since 1991, the Indian industrial sector has been subject to

reforms. These reforms ended industrial licensing, brought in competition and made Indian

markets contestable.

The enhancement of market contestability changes individual psychology (Ellerman

1985) and sets in motion entrepreneurial experimentation (Eliasson 1991). As new

entrepreneurs enter an industry, many firms find that their resources and capabilities are

often better matched for the exploitation of several growth opportunities. There is a race

for growth by firms that have been held back from exploiting opportunities for a

generation.

Corporate growth requires capabilities to be built and one way to do so would be to

invest in R&D. One of the characteristics of Indian firms and entrepreneurs, after the 1991

liberalization, now almost two decades later, was a recovery of self-confidence for the

enterprising class, letting go of the mental path-dependencies of the control era (Guha

2007; Khilnani 1999), and a large surge of entrepreneurship (Luce 2006; Majumdar 2007).This surge of entrepreneurship will have influenced significant investments in firm level

capability building.

In such a milieu, what would the expected size and R&D spending relationship be? As

the environment opens up one would expect the smaller and more flexible firms to be

leaping ahead nimbly, and the relationship would be negative. The larger firms, caught up

with the baggage of the past, would possibly find it harder to immediately adjust to the new

thinking and would not start incurring substantial R&D expenditures right away. Never-

theless, after a period of time when the liberalization would have proceeded the larger

firms would be able to create a more substantial base of capabilities and incur R&D

expenditures so as to exploit the scalability features associated with size. Thus, if one wereto evaluate the relationship several years after the liberalization moves were introduced,

one would observe a positive relationship.

4 Analysis

4.1 Data

To test the relationship between firm size and R&D spending in Indian industry, datadrawn from the Reserve Bank of India database on financial accounts of non-government

public limited companies were used. The choice of the data was driven by two important

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the balance sheets, profit and loss accounts and annual reports of the companies. Aggre-

gates based on these accounts inform policy and have been used for the compilation of

national accounts. They have also been used for estimating the growth and performance of

the real sector of the economy. The data relate to companies that are public limited,

according to the definitions of the Companies Act, 1956, and some of these may be listedon stock exchanges. The Reserve Bank of India also collects similar data on private limited

companies, as defined in the Companies Act, 1956, but these data are never released to

outsiders.

The data are widely perceived to have representative coverage of most sub-segments of

the Indian corporate sector. The RBI public limited company data represents approxi-

mately 85% of the paid-up capital of 86 3-digit industries (Feinberg and Majumdar 2001;

Majumdar 2009). The consistent coverage over a long period has contributed to database

quality. Additionally, the data are standardized into a common format across companies

and time to maintain consistency.

While the data are proprietary, the Reserve Bank of India database has been commonly

used for empirical work related to policy on the Indian corporate sector by the various

government bodies that have reported on policy matters. Private use of it is rare. It was

important that the coverage be not only representative of the population in each year, but

that it was consistent over the long period of time covered in the study. Second, it was

necessary to use a database taking adequate care of changes in accounting norms over this

period.

To construct the panel, data on an unbalanced number of manufacturing sector firms for

the period 19992000 to 20052006 were used. Between 1,600 and 3,000 companies are

surveyed each year. While the RBI has systematically collected data on large publiclimited firms, its coverage of the smaller public limited companies is, however, somewhat

sporadic and sketchy.

Entries and exits in and out of the sample are the smaller firms that may not submit data

rather than actual entries and exits. The total number of firm-year observations over the

years was 10,454 for the manufacturing sector firms. The industry categories that these

firms belonged to in the manufacturing sector were furniture, transport, motor, medical,

communications, electrical equipment, office equipment, machine tools, metals fabrication,

ferrous metals, ceramics, rubber, chemicals, printing, paper, wood, leather, apparel, tex-

tiles, tobacco and food.

The Reserve Bank of India database included several diversified firms. However, profitsand other financial characteristics for the different business units of these firms were not

separately recorded in the data base. State owned enterprises and privately held limited

companies were excluded from the sample. This limitation effectively excluded the vast

number of information technology companies in India as most of them are organized as

private limited companies or as unincorporated enterprises. Very few of them are actually

public limited companies. Those few that are have been included in the database.

Further, the analyses were confined specifically to the manufacturing sector. The effect

of the business cycle and institutional factors such as credit availability, impact of fiscal

policy and fluctuations in interest and exchange rates would be similar for public limitedfirms in the manufacturing sector.



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be included in the database. As such, this might be the most comprehensive reporting of

R&D data available for Indian firms. The dependent variable was firms ratio of research

and development expenditures to sales (R&D) and the primary explanatory variable was

the size variable (Size) measured in the standard way as the natural log of total assets.

The mean of the R&D variable over the years studied is given in Table 1. For all of themanufacturing sector firms the average spending is just over 0.20% of sales. This is an

extraordinarily low proportion given the importance of R&D in modern economies. This

figure emerges after considering all firms, but there are many observations that will have

incurred zero R&D values because of no spending at all on research activities.

For the positive R&D spending observations, the average is much larger and has ranged

from over 0.30% in 19992000 and has increased to about 1.00% of sales by 20052006.

Thus, there is a minority of firms that have started undertaking R&D activities in the post-

liberalization period and this rate of spending has kept increasing. This is a welcome trend.

These trends are further brought out in Fig. 1. While, for the manufacturing sector as a

whole in India, research spending may have been low as well as flat, a matter of

Table 1 Mean of the R&D variable

Year All manufacturing firms Positive R&D spending firms

Observations R&D ratio (%) Observations R&D ratio (%)

19992000 1,424 0.216 460 0.329

20002001 1,412 0.257 462 0.785

20012002 1,466 0.225 445 0.740

20022003 1,462 0.210 437 0.702

20032004 1,596 0.232 467 0.792

20042005 1,590 0.276 404 1.085

20052006 1,504 0.218 330 0.976

0.216

0.257

0.225 0.210.232

0.276

0.218

0.329

0.7850.74

0.702

0.792

1.085

0.976

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

1.1

1.2

R&Dt

o

SalesPercentage

All Firms Positive R&D Firms

106 S. K. Majumdar


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considerable concern, for the smaller sub-set of firms that do undertake research their R&D

spending rates have been clearly and significantly rising over time. As is usual in almost all

economies, the size distribution firms shows that it is a handful of firms that behave

innovatively, with a vast majority if firms in Indian industry being much less than inno-

vative. A few firms in a sector drive growth forward. This is a standard feature of thedistribution of the behavior of firms in an economy.

Additional control variables are necessary in the estimation. There is a substantial

literature on the determinants of firms R&D spending. Almost all of it relates to developed

economies and there is hardly any literature for emerging economies. There are the

important classic pieces in the genre (Grabowski 1968; Mansfield 1964) as well as rela-

tively more contemporary work (Bhagat and Welch 1995; Cohen 1995; Majumdar 2009).

The additional variables chosen for this analysis are based on the important variables that

have been observed and identified.

A variable (Capital Intensity) measured as the ratio of net fixed assets to total assets

accounted for several industry specific effects that would impact on R&D expenditures.

Such industry effects would affect spending and capital investment patterns. Next, the ratio

of firms imports to sales (Imports) was introduced as a control variable since firms that

acquired, presumed, better quality inputs, whether materials or other items, from overseas

would be able to generate innovations more productively.

Since R&D activities very extensively depend on high quality human capital within

firms, as the outcomes of R&D efforts are primarily embodied within the personnel who

conduct research, the relative amount of spending on human capital denotes payment for

higher quality of personnel and this can affect R&D spending. A variable (Employee Costs)

measured as the ratio of employment costs to total costs captured the extent of relativespending on human capital by firms.

An important set of considerations is the amount of finance available to firms to finance

R&D, and on this topic a large literature has emerged (Hall 2002; Hao and Jaffe 1993;

Harhoff 1998; Himmelberg and Petersen 1994; Hoshi et al. 1991; Hubbard 1998). Vari-

ables important in this literature, measuring firms leverage (Debt Equity) and the amount

of funds retained internally after profits for investment purposes (Retention Rate) were

included in the estimation. A third variable was the ratio of cash to total assets (Cash

Assets) since a greater amount of cash would permit undertaking of activities such as

research and development.

5 Results

5.1 Primary probit estimation results for all manufacturing sector firms

Several analyses are carried out. The first set of results relates to all the manufacturing

sector firms. These results, without controlling for industry effects, are given in Table 2.

Because there are several observations with absolutely no R&D spending, the appropriate

estimation technique to use is a discrete choice model such as a probit model. The resultsof this discrete choice estimation are given in Table 2.

As the results in Table 2 show, the size coefficient is positive and highly significant. A



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When a non-linear approach to account for size is used, since the relationship between

firms size and undertaking R&D activity can be U-shaped, the size variable stays positive

and significant but the size squared variable is negative and significant. This result points to

the existence of diminishing returns in R&D activities, and that above a certain sizethreshold the incentives to undertake R&D diminish. Such a finding is quite in consonance

with the organizational diseconomies argument that is prevalent in the literature.

Since the firms have belonged of variety of industry segments within the manufacturing

sector, such controls have been appropriate. The results, with industry effects introduced,

are given in Table 3. The results persist when industry effects are added in the estimation.

In fact, the coefficient estimates are both somewhat larger in magnitude and the standard

errors are somewhat reduced as well after the introduction of industry controls.

The several industry categories controlled for were furniture, transport, motor, medical,

communications, electrical equipment, office equipment, machine tools, metals fabrication,

ferrous metals, ceramics, rubber, chemicals, printing, paper, wood, leather, apparel, tex-tiles, tobacco and food. The effect of these industry related controls have been useful in

making the results sharper.

Table 4 lists results when an alternative measure of firms size is used. Instead of the log

of total assets, the log of net fixed assets is used to measure size. This measure eliminates

the current assets, working capital and other financial investments that form a major part of

companies asset structure from the computation of firm size. The results, estimated after

both without and with industry effects, are similar in magnitude and significance to those

that are reported in Tables 2 and 3. Hence, the results of the R&D and size relationship stay

robust to alternative size specifications.

5.2 Generalized estimating equation results for all manufacturing sector firms

Table 2 Probit regression results for all manufacturing sector firms

Dependent variable: R&D intensity

Without industry effects controlled for

Coefficient (standard error) t Statistic Coefficient (standard error) t Statistic

Constant -10.263 (0.474) 21.65*** -19.889 (2.420) 8.22***

Size 0.664 (0.035) 18.80*** 2.104 (0.348) 6.04***

Size2

-0.053 (0.12) 4.26***

Capital intensity -0.011 (0.002) 4.76*** -0.010 (0.002) 4.61***

Imports -0.005 (0.002) 2.41** -0.005 (0.002) 2.37**

Employee costs 0.039 (0.005) 8.44*** 0.039 (0.005) 8.04***

Debt equity -0.004 (0.005) 0.83 0.000 (0.000) 0.00

Retention rate 0.000 (0.000) 0.80 0.000 (0.000) 0.67

Cash assets -0.003 (0.005) 0.62 -0.000 (0.005) 0.11

Log likelihood -3843.93*** -3834.47***

N 10,452 10,452

*** p\0.01, ** p\0.05, * p\0.10. Standard errors are in parentheses

108 S. K. Majumdar


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Table 3 Probit regression results for all manufacturing sector firms with industry effects controlled for


Industry effects controlled fora


Constant -12.268 (0.667) 18.39*** -24.484 (2.261) 10.83***

Size 0.637 (0.038) 16.54*** 2.411 (0.314) 7.67***

Size2

-0.065 (0.011) 5.75***

Capital intensity -0.008 (0.003) 2.44** -0.009 (0.002) 3.80***

Imports -0.003 (0.002) 1.60* -0.003 (0.002) 1.79*

Employee costs 0.030 (0.007) 4.33*** 0.033 (0.005) 5.78***

Debt equity 0.001 (0.003) 0.38 0.004 (0.004) 1.07

Retention rate 0.000 (0.000) 0.38 0.000 (0.000) 0.47

Cash assets -0.003 (0.004) 0.57 -0.002 (0.004) 0.41

Log likelihood -3707.32*** -3694.26***

N 10,452 10,452

*** p\0.01, ** p\0.05, * p\0.10. Standard errors are in parenthesesa The industry categories controlled for were furniture, transport, motor, medical, communications, elec-

trical equipment, office equipment, machine tools, metals fabrication, ferrous metals, ceramics, rubber,

chemicals, printing, paper, wood, leather, apparel, textiles, tobacco and food; industry coefficients not

displayed

Table 4 Probit regression results for all manufacturing sector firms with an alternative size measure


Without industry effects controlled for With industry effects controlled for


Constant -9.211 (0.415) 22.17*** -9.780 (0.595) 16.43***

Size (alternate measure) 0.688 (0.035) 19.51*** 0.659 (0.034) 18.89***

Capital intensity -0.030 (0.002) 11.41*** -0.026 (0.002) 9.90***

Imports -0.002 (0.002) 1.05 -0.004 (0.002) 1.74**

Employee costs 0.036 (0.005) 7.27*** 0.035 (0.005) 6.82***

Debt equity -0.005 (0.004) 1.34* 0.000 (0.004) 0.09

Retention rate 0.000 (0.000) 0.77 0.000 (0.000) 0.72

Cash assets 0.001 (0.005) 0.12 -0.001 (0.005) 0.17

Log likelihood -3812.02*** -3688.82***

N 10,452 10,452

The industry categories controlled for were furniture, transport, motor, medical, communications, electrical

equipment, office equipment, machine tools, metals fabrication, ferrous metals, ceramics, rubber, chemicals,

printing, paper, wood, leather, apparel, textiles, tobacco and food; industry coefficients not displayed

*** p\0.01, ** p\0.05, * p\0.10. Standard errors are in parentheses



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Continuous or categorical data can be collected under these designs as repeated mea-

sures. This is an important consideration in R&D related work since there will be several

instances where there are no R&D values for many of the firms, and many other instances

where the values can be a continuous variable. The dependent variables can be from many

different distributions, including normal, binomial, and Poisson.Using quasi likelihood estimation, termed marginal models because the mean response

depends on the covariates of interest and not on any random effects or previous responses,

the generalized estimating equation approach helps in tackling these distributional heter-

ogeneities. It permits the estimation of more efficient and unbiased regression parameters

(Hardin and Hilbe 2003).

The coefficients have the same interpretation as in related estimation approaches. They

measure differences in the response for a unit change in the predictor, averaged over the

whole sample. Generalized estimating equation models are suitable when the correlation is

of no substantive interest. They provide consistent estimates of the parameters, and con-

sistent estimates of the standard errors, using a robust estimator even if the correlation

matrix is incorrectly specified.

Alternative results, based on the use of generalized estimating equation models are

presented in Table 5. The estimates for the Size variable are consistent with those esti-

mated using the probit models, as reported in Tables 2 and 3, when the Size variable is

dealt with in linear terms, and the significance levels remain equally high, with the stan-

dard errors being actually substantially lower under the generalized estimating equation

specification. Hence, the results stay robust to an alternative specification.

5.3 Dynamic panel data estimates for manufacturing sector firms with positive R&D

An important issue in R&D analysis is to take into account only those firms that have

conducted some R&D, because most firms will incur no R&D spending at all in their lives

Table 5 Additional generalized estimating equation (GEE) results for all manufacturing sector firms


Model (A): without industry effects Model (B): with industry effectsa


Constant -6.856 (0.113) 60.46*** -6.296 (0.344) 18.28***

Size 0.367 (0.006) 57.79*** 0.195 (0.006) 34.92***

Capital intensity -0.008 (0.000) 34.34*** 0.001 (0.000) 5.96***

Imports 0.027 (0.000) 60.37*** 0.023 (0.001) 39.54***

Employee costs 0.019 (0.000) 24.87*** 0.022 (0.001) 22.17***

Debt equity -0.021 (0.006) 35.57*** 0.000 (0.000) 0.71

Retention rate 0.000 (0.000) 1.85** 0.000 (0.000) 1.81**

Cash assets -0.010 (0.001) 11.93*** -0.002 (0.000) 2.91**

Log likelihood -3913.51*** -3559.62***

N 10,452 10,452

110 S. K. Majumdar


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and including them in regression models can be potentially misleading. Thus, the approach

to account for such a contingency is to perform the estimation with only observations that

have positive R&D values. This helps to evaluate the R&D and size relationship specifi-

cally for those observations where there has been R&D spending.

Since the R&D values are now all non-zero, a probit model will not work and analternative specification such as dynamic panel data estimation is required. The type of

dynamic panel data regression considered has the following general form:

yit yi;t1 a byi;t1 X0itd kt uit; or equivalently

yit a $byi;t1 X

0itd kt uit; i 1; . . .;N; t 2; . . .; T 1

where y is the logarithm of the dependent variable, i is an firm, tis a period of time which is

a year, $b is a scalar ( b = b ? 1), X0 represents the set of explanatory variables 1 9 K

and d is K9 1; kt is the time-specific effect; uit= li ? tit, where li is the unobservable

firm-specific effect and tit is the an error term.The presence of firm-level heterogeneity in panel data models with lagged dependent

variables tend generates biased and inconsistent estimates if the time dimensions of the

panel are fixed and not of very substantial length (Nickell 1981; Judson and Owen 1999).

Thus, a generalized method of moments (GMM) estimator is appropriate. Nevertheless,

two problems exist with the dynamic panel regression in (1). First, the lagged dependent

variable as a regressor leads to autocorrelation; second, firm-specific effects characterize

inherent heterogeneity (Baltagi 2008).

As yit is a function ofli, thus yi,t-1 would also be a function ofli. Hence, yi,t-1, which is

a right-hand side regressor, will be correlated with the error term. This yields biased and

inconsistent OLS estimators even if the tit are not serially correlated. The initial step is to

first-difference (1) in order to eliminate the individual effects (Arellano and Bond 1991).

This procedure yields

yit yi;t1 $b yi;t1 yi;t2

X0it X

0i;t1

d kt kt1 tit ti;t1

: 2

This method of eliminating firm-specificity, however, introduces another issue. The

first-differencing causes the new error term Dtit= tit- ti,t-1 to be correlated with the

lagged dependent variable, Dyi,t-1 = yi,t-1 - yi,t-2. This correlation, combined with the

potential endogeneity of the explanatory variables, leads to the consideration of the use of

instrumental variables as suggested by Arellano and Bond (1991), under the assumptions

that tit is not serially correlated and with the moment restrictions E [yi,t-sDtit] = 0 for

t= 1,T, and s C 2.

For instance, for equation Dyi3 = dDyi2 ? Dti3, the instrument available is yi1; for

Dyi4 = dDyi3 ? Dti4, the instruments available are yi1, yi2, and so on. If the regressors in

Xit are endogenous, in the sense that E [Xittis] = 0 for s[ t and = 0 otherwise, the

moment conditions E[Xi,t-sDtit] = 0 for t= 1,T, and s C 2 are available. The estimator

that uses those moment conditions is known as the difference estimator (Arellano 2003;

Baltagi 2008).

The validity of the instruments is tested by means of the Sargan test of over-identifyingrestrictions. The Sargan test is distributed as v2 with (J- K) degrees of freedom, J being

the n mber of instr ments and K the n mber of regressors The n ll h pothesis is that the



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correlation in the differenced residuals. For the GMM estimator to be valid, the null

hypothesis for both tests, denoted by high p-values, is accepted.

The results of the estimation for the positive R&D spending observations are given in

Table 6. Two sets of results, with size specified in linear and non-linear forms are given.

For both results the Sargan test does not reject the null hypothesis related to instrument

validity. Second-order correlation is also not present in the error term, pointing to con-

sistency of the estimators. Where the size variable is treated as linear then the coefficientestimate for size is positive and significant. When, however, the size variable is introduced

in its non-linear specification, both the size and the size squared variables are non-sig-

nificant, denoting that the size and R&D relationship does not actually hold for firms that

actually undertake some R&D activities.

6 Discussion

The matter for disquiet is the very low levels of R&D actually still being undertaken in

India if the entire manufacturing sector is evaluated. R&D spending has been associated

with industrial capability development, and economic growth. See Aghion and Howitt

Table 6 Dynamic panel data regression results of the R&D and size relationship for manufacturing sector

firms with positive R&D expenditures


Model (A) Model (B)


Constant -0.017 (0.037) 0.45 -0.015 (0.039) 0.40

R&Dt-1 0.039 (0.071) 0.55 0.042 (0.072) 0.58

Size 0.534 (0.327) 1.63** 0.999 (2.747) 0.36

Size2

-0.017 (0.097) 0.17

Capital intensity 0.012 (0.009) 1.31* 0.012 (0.009) 1.31*

Imports -0.005 (0.004) 1.01 -0.005 (0.004) 1.01

Employee costs -0.006 (0.018) 0.30 -0.006 (0.018) 0.30

Debt equity -0.011 (0.016) 0.66 -0.010 (0.016) 0.62

Retention rate -0.000 (0.000) 1.29* -0.000 (0.000) 1.29*

Cash assets 0.013 (0.013) 1.04 0.013 (0.012) 1.04

v2 7.85** 7.76**

N 1,050a 1,050a

Sargan v2 21.14 (0.10) 21.24 (0.10)

AR(1) -10.66 (0.00) -10.61 (0.00)

AR(2) 0.65 (0.52) 0.69 (0.49)

*** p\0.01, ** p\0.05, * p\0.10. Standard errors in parenthesesa

Total number of observations is 3,004 and after introducing lags results in 1,050 observations

112 S. K. Majumdar


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Lichtenberg (1984). The outcomes of such low spending levels will have influenced

retardation in Indias industrial progress.

Prior to the liberalization of the economy in 1991, in the 1980s and earlier, R&D

activities were practically zero. Even in the twenty-first century, the R&D to sales ratio of

India firms is less than one-fifth that of firms from OECD or G8 countries. Therefore, animportant conclusion is that Indian firms have to increase their R&D expenditures by an

order of magnitude if they are to benefit from income streams that emanate from such

research and capability enhancing activities. If they do so, the positive benefits can be

substantial.

The results, which are robust to the inclusion of important variables, capturing firm

effects considered important in the literature, are of considerable significance. The mag-

nitude of impact of the size variable has been substantial in influencing probability levels

of firms undertaking research efforts. This relationship is established when all of the

manufacturing sector firms are evaluated, including those that do not undertake any

research activities. At this point in India, scalability matters. Risks associated with R&D

activities may be too great, so that only larger firms engage in it.

If a non-linear relationship is evaluated, then the primary term is positive. A relatively

larger firm has a greater probability of engaging in research. This is not an exceptional

finding since there will be a minimum size threshold above which only will firms engage in

research. The squared term is negative. This denotes that after a particular size threshold

there are diminishing returns to scale and research spending drops.

The other result established is that for the firms that have undertaken R&D, if a linear

relationship is evaluated larger size leads to mildly higher levels of R&D activities, relative

to other firms, but if a non-linear relationship is evaluated then the size variable has noeffect. Given a minimum size for firms to engage in research activities, once firms that

actually undertake research are reviewed, both the relatively smaller firms and the larger

firms engage in enhancing capabilities.

These are welcome symptoms of corporate development in the post-liberalization

Indian economy. The creation of a knowledge base, domestically in India, can lead to the

leverage of such knowledge capabilities in domestic and overseas markets. This is an

important consideration, since the globalization process for Indian industry is just under

way. As both the smaller and larger firms that undertake research enhance their capabil-

ities, so that both scalability and flexibility become important drivers of the R&D process

in India, the possibilities for Indian firms to gain shares in domestic and global marketsbecomes high.

7 Conclusion

Based on a panel of several thousand Indian manufacturing firms for a latest period of

7 years, resulting in over ten thousand firm-year observations, this article has examined the

impact of firm size on R&D spending. The results of the analysis show that larger firm size

results in probability of enhanced R&D spending. When the firms that do undertake R&Dactivities are assessed, then firm size is mildly related to research efforts and a non-linear

relationship shows that the size effect disappears. Thus, among the manufacturing firms



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Firm Size and R&D in Indian Industry