Measuring the Efficiency of Pharmaceutical Firms in India:
An Application of Data Envelopment Analysis and Tobit Estimation
Ishita G. Tripathy, Surendra S. Yadav and Seema Sharma
Abstract
The pharmaceutical industry of India has been identified as one of the main drivers of India's
recent high export-led growth and an employment generator possessing enormous positive externalities.
This paper examines the levels and determinants of efficiency of firms of this vital sector of the Indian
economy by using firm-level data. For this purpose, a two stage data envelopment analysis has been used.

In the first stage, technical efficiency analysis of 90 sample firms has been undertaken. One
output, viz. sales of the sample firms for the years 2001-02 to 2007-08, and three inputs; viz. (i) Raw
material cost; (ii) Cost of salaries and wages; and (iii) Cost of advertising and marketing; have been
considered. In the second stage, the efficiency scores obtained from the first stage are regressed on external
environmental factors like the age of the firms, export of goods, import of capital goods, profit rate, R&D
intensity, ownership, patent regime and foreign direct investment using a censored regression model, viz.
Tobit model.

The efficiency analysis reveals that during the period of the study the performance of a large
number of sample firms was sub-optimal, ranging between 68% and 78%. Almost throughout the study
period, the average efficiency of the R&D-intensive firms is higher than that of non-R&D firms and thedifference between the two is statistically significant. The Malmquist Productivity Index indicates that the
total factor productivity of the sample firms has remained at the same level during the period of study. The
important determinants of pharmaceutical firms’ efficiency are the new patent regime, export of goods,
presence of foreign direct investment, the profitability of firms and R&D intensity.

Keywords: Efficiency, pharmaceutical firms in India, Data Envelopment Analysis, Tobit Analysis
JEL Classification: D24, C14

1.

Introduction
The pharmaceutical industry of India has been identified as one of the main

drivers of the high export-led growth of India during 2003-04 to 2007-08 (GOI, 2008)
and an employment generator, possessing enormous positive externalities (GOI, 2009).
The industry ranks third worldwide, accounting for 10% of the world’s production by
volume and fourteenth globally, constituting around 1.5% in terms of value (GOI, 2009).
It meets about 70% of the domestic requirements of bulk drugs1 and almost the entire
domestic requirement of formulations2. Besides, the industry exports both bulk drugs aswell as formulations.

The extant vitality of the pharmaceutical industry in India has been earned over
the years primarily because of strategic policies of Government of India (GOI) including
a process patent regime, deliberately initiated under the Indian Patents Act, 1970.
Between 1970 and 2005 the Indian pharmaceutical firms successfully competed with
Multinational Corporations (MNCs) both in India and abroad and the industry progressedfrom an importer of Active Pharmaceutical Ingredient (API) to a competitive
manufacturer of bulk drugs and formulations.

Concurrently, the dominance ofmonopolistic MNCs in the Indian pharmaceutical industry transformed into theflourishing of domestic oligopolies. The key features of the four-phased evolution of thepharmaceutical industry of India have been summarized in Table 1.

Phase
I
II
III
IV

Table 1: Key Features of the Evolution of the Pharmaceutical Industry of India
PeriodKey Features
Up to 1970
TNC Domination. Bulk drugs and API imported.
1970 to 1995
Indian Patents Act, 1970 enacted. Growth of indigenous firms.
1995 to 2005
WTO came into effect. Compulsory to introduce product patents by 2005.
Since 2005
Shift from process-based patenting to product patents. Focus on R&D.

Up to 1970, the pharmaceutical industry of India was dominated by MNCs which
imported most of the bulk drugs, from their parent companies abroad and sold theformulations in India.

The Indian Patents Act, 1970 introduced patents on the

manufacturing process and not on the end-product (Jha, 2007; Chittoor et al., 2008). In
2

1995, when the World Trade Organisation came into being, India being one of its founder
members automatically became a signatory of the Trade-Related Intellectual Property
Rights agreement and was under compulsion to introduce a product patent regime by2005 (EXIM, 2007). During the process patent regime (1970-2005) the pharmaceuticalindustry of India grew not only in terms of the number of producers, but also in terms ofcatering to the domestic market requirements. At present, there are around 300 medium
and large pharmaceutical firms in India, including both domestic and MNCs and thenumber of small firms is estimated to be around 10,000 (GOI, 2009).

In the year 2005, a new scenario emerged in the Indian pharmaceutical sector.
The 35-year old process patent regime was replaced by the more rigorous product patent
regime. Thus, post-2005, the importance of research and development (R&D) activities
for the Indian pharmaceutical industry has gone up, with a number of firms setting up
their own R&D units (Jha, 2007), and collaborating with research laboratories (FICCI,2005). The focus of pharmaceutical firms has come to be governed by the size of theiroperations, with large firms emphasizing on discovery and development of new drugs;
medium firms stressing on producing generics3 and small firms opting for contract
manufacturing (Rao, 2008).

R&D comprises the search for various novel pathways and the development ofexpertise which facilitate faster product development. On the one hand, it generates newtechnologies and, on the other, it enhances a firm’s ability to exploit existing technology(Cohen and Levinthal, 1989).

The importance of R&D for a technology-intensive

industry like the pharmaceuticals cannot be over-emphasised. However, despite Indian
pharmaceuticals enjoying cost advantages in the form of Indian tax rebates on R&D
expenditure, and drugs developed using indigenous R&D being exempted from pricecontrols in India, the industry has a low average R&D intensity, (the ratio of expenditure
on R&D activities to total sales) of only 3.5% (ISID, 2007), as compared to 8-10% in
USA (Congressional Budget Office (CBO), 2006) and 10-12% in Europe (Sharp andPatel, 1996).

3

The competitive advantage that Indian pharmaceutical firms had in the global
markets from 1970 to 2005 is due to a variety of factors.

The scenario for thepharmaceutical sector has changed significantly in recent years due to two major factors;
one, the introduction of the new patent regime on January 1, 2005, and the second factor
is the current economic crisis, which has engulfed the entire world and has resulted ininter alia shrinking global demand for exports and stifled rates of growth. The global
pharmaceutical industry has not been unscathed by the on-going global crisis. In thiscontext, it is important to understand whether the internal efficiencies of individual
pharmaceutical firms have undergone any change. The utilization of resources available
to the firms of the pharmaceutical sector warrants a probe.

This paper examines the levels of efficiency of pharmaceutical firms in India anddiscusses the determinants of efficiency. The remainder of the paper is divided into fivesections. The literature review has been presented in section 2. Section 3 delineates theframework used for measuring efficiency, i.e., Data Envelopment Analysis (DEA) andTobit estimation and discusses the research methodology which has been adopted for the
analysis. The results of DEA and Tobit are presented in section 4. Drawing on the
results, section 5 gives the concluding remarks, including prescribing some policy
options. The last section dwells on the limitations of the study and enumerates thepossible areas of further research.

2.

Literature Review
The two principal methods of studying comparative efficiency are parametric andnon-parametric methods. Stochastic Frontier Analysis (SFA) is a parametric method
which determines comparative efficiency levels by hypothesising a functional form. Data
Envelopment Analysis (DEA) is a non-parametric method which employs mathematicalprogramming (linear programming model) (Coelli et al. 1998). The popularity of DEA
rests on its capability to consider multiple inputs and outputs for calculating relative
efficiency. DEA comes up with a single scalar value as a measure of efficiency and does
not require any specification of functional forms as is required under SFA.
4

2.1

Data Envelopment Analysis

DEA is a linear programming model used to measure technical efficiency. It
comes up with a single scalar value as a measure of efficiency. Efficiency of any firm
can be defined in terms of either output maximization for a set of inputs or input
minimization for a given output. In DEA, relative efficiencies of a set of decision-making
units (DMUs) are calculated. Each DMU is assigned the highest possible efficiency scoreby optimally weighing the inputs and outputs. DEA constructs an efficient frontiercomposed of those firms that consume as little input as possible while producing as muchoutput as possible. Those firms that comprise the frontier are efficient, while those firms
below the efficient frontier are inefficient. For every inefficient DMU, DEA identifies aset of corresponding benchmark efficient units (Coelli et al. 1998).

Researchers have used DEA to measure the performance of firms, especially inthe banking (Jackson and Fethi, 2000; Mukherjee, et al., 2002; Mostafa, 2007; Delis andPapanikolaou, 2009) and health care sectors (Chilingerian, 1995, Luoma, et al., 1998,
Akazili, et al., 2008; Kirigia et al., 2008). Some researchers have studied thepharmaceutical industry also (Feroz, et al., 2008; Hashimoto and Haneda, 2008; Saranga
and Phani, 2009). Besides comparing individual firms, DEA has been used to compare
efficiencies of nations too (Tan, et al., 2007; Sharma and Thomas, 2008) (Table 2).

Table 2: Select Studies on Efficiency using DEA
RegionAuthor (Year)
Banks
Pharmaceuticals
Jackson & Fethi (2000)
TurkeyFeroz et al. (2008)
Mukherjee et al. (2002)
India
Hashimoto and Haneda (2008)
Mostafa (2007)
Arab world
Saranga and Phani (2009)
Delis and Papanikolaou (2009)
European Union Other Sectors
Fethi et al. (2000)
Health Centres
Chilingerian (1995)
USA
Chen et al. (2004)
Luoma et al. (1998)
Finland
Leachman et al. (2005)
Akazili et al. (2008)
Ghana
Wang and Huang (2007)
Kirigia et al. (2008)
Seychelles
Tan et al. (2007)
Sharma and Thomas (2008)
Hwang and Oh (2008)
Hsu and Hsueh (2009)
Author (Year)

5

Region
USA
Japan
India
EuropeTaiwan
MNCs
30 countries
12 countries
22 countries
Korea

TaiwanFeroz, et al. (2008) have demonstrated the usefulness of DEA in performancemeasurement in the US pharmaceutical industry and have shown the applicability of
DEA in arriving at an unbiased account of relative performance in a set of companies.
Applying DEA, Hashimoto and Haneda (2008) observed that the R&D efficiency ofJapanese pharmaceutical industry has worsened throughout the decade 1983-92. In the
Indian context, after controlling for firm size and initial efficiency levels, Saranga andPhani (2009) found that in the era prior to the introduction of the product patent regime,
higher R&D investments in pharmaceutical firms translated into higher efficiencies.

2.2

Two-stage DEA

Apart from measuring the performance of firms, empirical studies have also been
carried out to study the determinants of efficiency. In this context, a two-stage DEA has
been employed by a number of researchers. This entails obtaining DEA efficiency scoresin the first stage. In the next stage the efficiency scores are used as the dependent
variable, which is regressed on the external environmental factors to determine whatcauses differences in efficiency levels across the DMUs under study.

Chilingerian (1995) analyzed both technical and scale efficiency using DEA and amulti-factor Tobit model to study the variables which were associated with higher levelsof performance of physicians. The study revealed that a substantial amount of money
could be saved if every physician practised medicine as efficiently as the most competent
physicians. Jackson and Fethi (2000) investigated the performance of Turkishcommercial banks using DEA. Using a Tobit model they identified the variables which
explained the efficiency of some banks as the size of the bank, the number of branches,
profitability, ownership and capital adequacy ratio. They found that larger and more
profitable banks are more likely to operate at higher levels of technical efficiency.

A study conducted by Luoma, et al. (1998) to examine the productive efficiency
of Finnish health centres applied DEA and Tobit analysis to evaluate how various
economic, structural and demographic factors affect inefficiency. The results indicated6that a higher level of central government grants and a higher taxable income per
inhabitant are predictors of inefficiency.

Using data from eight major automobile

manufacturers, Leachman, et al. (2005) adopted a two-stage DEA to examine the
manufacturing performance and showed that a strong R&D commitment and ability tocompress production time can explain differences in manufacturing performance.

A two-stage methodology was followed by Delis and Papanikolaou (2009) as wellto analyze the efficiency of ten banks operating in the European Union. They found thatbank size, industry concentration and the investment environment had a positive impact
on the efficiency of banks. Hwang and Oh (2008) measured the performance of Korean
software firms. With efficiency measured by using DEA, they used a Tobit regression toinvestigate whether the presence of Intellectual Property Rights (IPR) have a stronger
effect on efficiency. Their results indicated that the average efficiency of software firms
which possess any kind of software IPR was higher than that of firms not having them.
Fethi, et al. (2000) used a two-stage DEA application to assess the efficiency of European
airlines. Their empirical findings confirmed that concentration and subsidy policies havea negative impact on the efficiency of European airlines.

2.3

Three-stage DEA

Wang and Huang (2007) used a three-stage DEA to evaluate the relative
efficiency of R&D activities across 30 countries. In the second stage, the input slacks
obtained from the first stage of DEA were used as dependent variables in the Tobit
regression. The estimated coefficients from the Tobit regression were used to predict the
total input slack for each input and for each country. The empirical analysis indicatedthat less than one-half of the countries are fully efficient in R&D activities, while morethan two-thirds were at the stage of increasing returns to scale. A three-stage DEA has
been adopted by Hsu and Hsueh (2009) as well to assess the relative efficiency of
government-sponsored R&D projects in Taiwan.

Firm size and the ratio of public

subsidy to R&D budget of the recipient firm were the determinants of technicalefficiency of these projects.
7

2.4

Determinants of efficiency of firms

A review of the literature throws light on the factors which have been considered
as determinants of efficiency of firms. Amongst the determinants of the performance offirms, Fethi, et al. (2000) and Delis and Papanikolaou (2009) considered liberalisation as
one of the explanatory factors.

The impact of foreign ownership on the level of efficiency was considered by
Delis and Papanikolaou (2009), Jackson and Fethi (2000) and Fethi, et al. (2000). To
determine the efficiency of banks, another independent variable which was considered istheir profitability (Jackson and Fethi, 2000).

The age of the firm can also be a determinant of its efficiency level (Hwang andOh, 2008). In his study of the clinical efficiency of 36 physicians in a single hospital,
Chilingerian (1995) employing a two-stage DEA, took the age of the physicians as one of
the independent variables in the Tobit model.

Technological knowledge is important for a firm to attain and sustain its
competitive advantage (Narasimha, et al., 2003). Leachman, et al. (2005) considered
R&D intensity (ratio of expenditure on R&D and sales) as one of the explanatory
variables determining the level of efficiency of manufacturing performance. Hwang andOh (2008) took R&D intensity as one of the determinants of Korean software firms.
Some empirical studies found that the long-run performance of firms depends on thefirm-specific advantages such as R&D (Gregory and McCorriston, 2005).

From the literature review it emerges that DEA has been used extensively to study
the efficiency of firms of various sectors. However, for the Indian pharmaceutical sector,
very few studies have analysed the levels and determinants of efficiency of the firms,
especially in the post-2005 era.

Moreover, the number of studies comparing theefficiency of R&D-intensive and non-R&D pharmaceutical firms in India, areconspicuously limited. The current study aims to cover these gaps.
8

3.

Research Methodology
This research study uses a two-stage DEA to achieve its research objectives. In

the first stage, a set of observed inputs and outputs is used to derive efficiency scores for
all the sample pharmaceutical firms. The first stage analysis using DEA is elaborated in
section 3.1. In the second stage, the efficiency estimates obtained are regressed on
factors which influence efficiency. For this purpose, a censored regression, Tobit, hasbeen used. This is discussed in section 3.2.

3.1

Data Envelopment Analysis

3.1.1

DEA framework
In this study, an input orientation as opposed to an output orientation has been

adopted. While the former seeks to minimize the usage of inputs given a fixed level of
output, the latter maximizes the level of output for a given level of inputs. Since the
DMUs are in control of the inputs which they use, the usage of an input-orientation wasdeemed appropriate here.

The efficiency score depends on how well the DMU is

performing vis-à-vis other firms. Under DEA, the Constant Returns to Scale (CRS)
model states that the optimal mix of inputs and outputs is independent of the firm’s scaleof operation. Following the notations used by Coelli (1996), the objective of CRS is:

Maximiseuv(u′yi/ v′xi)

Subject tou′yj/ v′xj <=1(1)

j = 1, 2, 3, …, N
whereu, v >=0
N = number of DMUs

K = number of inputs

M = number of outputsthxi = vector of inputs for i DMU

yi = vector of outputs for ith DMU

X = K X N input matrix for N DMUs

Y = M X N output matrix for N DMUs

v = K X 1 vector of input weights

u = M X 1 vector of output weights

9

The constant returns to scale (CRS) DEA model states that the optimal mix of
inputs and outputs is independent of the firm’s scale of operation, which implies that a
proportionate increase in the inputs results in the same proportionate increase in the
output. The objective function specified in (1) involves finding values for u and v, so thatthe efficiency of the ith DMU is maximized, subject to the constraint that all efficiency
measures must be less than or equal to 1. The above model is non-linear in nature andhas infinite number of solutions. Since linear programming cannot handle fractions, theabove formulation needs to be transformed in such a way that the denominator of theobjective function is limited and maximization of the numerator is allowed. For thispurpose, an additional constraint needs to be added. Thus, the above non-linear modeltransforms into the following linear model.

Maximiseµ ν(µ′yi)

Subject toν′xi =1(2)µ′ yj - ν′xj <= 0
j = 1, 2, 3, …, Nµ, ν >=0

where the notation changes from u to µ and from v to ν representing the transformation.

To solve the Linear Programming specified in (2) a dual of the primal can beformulated in the following form:
Minimiseθ, λ θ(3)Subject to – yi + Yλ >= 0
θxi – Xλ >= 0,
λ >=0where θ is a scalar and is the efficiency score of the ith DMU.

λ is a N X 1 vector.

If θ = 1, it indicates a technically efficient DMU. The linear programming
mentioned in (3) will be solved N number of times, once for each DMU, providing avalue of θ for each DMU. The CRS assumption is appropriate in cases where all DMUs
operate at an optimal scale. However, there might exist constraints on DMUs which do
not allow them to operate at the optimal scale. Using CRS for such DMUs will yield10

Technical Efficiency (TE) scores, which are affected by Scale Efficiencies (SE).
Therefore, one needs to use the Varying Returns to Scale (VRS) model of DEA. VRS
implies that an increase in inputs may result in either more or less than proportionate
increase in the output. The VRS model incorporates the dual of CRS model, with an
extra convexity constraint on λ.

Minimiseθ, λ θ(4)Subject to – yi + Yλ >= 0
θxi – Xλ >= 0,N 1′λ = 1
λ >=0where N 1 is a N X 1 vector of ones.

A Malmquist DEA, which is an application of DEA to a panel data to calculate
indices of total factor productivity change (a productivity measure involving all factors of
production), technological change, technical efficiency change and scale efficiencychange, has been used in this paper. The Malmquist productivity index takes a value ofmore than one, if there is productivity growth. In case the index is equal to one, there isstagnation. If there is productivity decline, the index will take a value of less than one.

The input-oriented Malmquist productivity index or TFP growth can be definedusing the technology of period t or period t+1. Therefore, it is defined as the geometricmeans of the index of the periods t and t+1. It is estimated as the ratios of distancefunctions of observations from the frontier.
1/2

M(q, xt, qt+1, xt+1) =dt+1 (qt+1, xt+1)
dt (qt+1, xt+1)-------------------- X -------------------dt+1 (qt+1, xt+1)
dt (qt, xt)

(5)

where
qt, is the output vector of period t
xt, is the input vector of period t

qt+1, is the output vector of period t+1
xt+1, is the input vector of period t+1

d is the input distance function11

Equation (5) can be rewritten as:
1/2

M(q, xt, qt+1, xt+1) =dt+1 (qt+1, xt+1)-------------------dt (qt, xt)

dt (qt+1, xt+1)-------------------- X
dt+1 (qt+1, xt+1)

dt (qt, xt)----------dt+1 (qt, xt)(6)

The ratio outside the brackets in equation (5) calculates the technical efficiencychange between period t and t+1. The remaining part of the TFP index measures thetechnological change.
3.1.2

Data used for DEA
From the population of 300 large pharmaceutical firms in India, including both

domestic and MNCs, a sample of 90 firms4 having an annual sales of more than Rs. 1
crore during the year 2007-08 was selected. These firms together comprised 87% of thetotal sales of all the firms in the population during 2007-08. All the data relating to the
inputs and output for the years 2001-02 to 2007-08 was culled out from the Prowess
database of Centre for Monitoring Indian Economy (CMIE).

3.1.3

Selection of Input and Output

While making use of the DEA application, one needs to be careful about the
choice of inputs and outputs. The efficiency scores could be very sensitive to changes inthe data and depend heavily on the number and type of input and output factors
considered. In this study, akin to the study by Saranga and Phani (2009), one output andthree inputs have been considered (Table 3).

Table 3: Inputs and Output Used for DEA
Output / Inputs
Mean Standard Maximum Minimum
Deviation
Sales (Rs. Crore)
355.5
632.2
4,295.2
0.03
Raw Material cost (Rs. Crore)
169.5
290.6
2,178.3
0.08
Cost of salaries and wages (Rs. Crore)
51.2
121.8
1,187.3
0.05
Cost of Advertising and Marketing (Rs. Crore)
36.4
77.3
695.5
0.01

12

The total sales of the sample firms for the years 2001-02 to 2007-08 have beentaken as the output for DEA. The choice of inputs is governed by the fact that the three
inputs together constitute a substantial part of the total operating costs (more than twothirds) which the pharmaceutical firms incur in their effort to produce the output, viz.
sales of the firms. The three inputs which have been considered are:(i)

Raw material cost which includes the cost of all raw materials, spares,
packaging, purchase of finished goods, printing, stationery and outsourced
manufacturing jobs. This accounts for 48% of the total cost incurred by
the 90 sample firms during the period under study. Over the period of thestudy, material cost rose by 146%.

(ii)

Cost of salaries and wages comprises 10% of the total cost incurred bysample firms. This is inclusive of remuneration in all forms made to theemployees, viz. salaries, wages, bonus ex gratia, contribution to Provident
Fund, outsourced professional jobs and directors’ fees. Between 2001-02
and 2007-08, the manpower cost increased by 150%.

(iii)

Cost of Advertising and Marketing which includes the cost of advertising,marketing, distribution, travel and communication, account for 10% of thetotal cost of the sample firms. During the study period, marketing cost
hiked up by 122%.

Mostafa (2007) highlighted that in case the minimum number of firms chosen for
DEA is more than three times the total number of inputs and outputs, the chances of an
inefficient firm being declared efficient reduces. In this study, there being three inputs
and one output, the minimum requirement for the number of sample firms for using DEA
is 12. Another condition that is required for DEA is that the function relating inputs andoutputs should possess the monotonicity property, which essentially means that anincrease in the inputs will increase the output. This relation is observed in the inputs andoutputs which have been considered for analysis in the study. The third condition that all
inputs and outputs should be positive is also satisfied. The input and output data has beenused for computing VRS technical efficiency (VRS TE) scores of the firms using DEA
software, version 2.1 (DEAP 2.1).
13

3.2

Tobit Analysis

3.2.1

Model

Once the relative efficiencies have been calculated, the determinants of the DEA
efficiency scores can be investigated into. It is customary to regress the DEA efficiency
scores on the relevant control variables (Luoma et al., 1998; Fethi, et al., 2000;
Chilingerian, 1995; Hwang and Oh, 2008). Since the DEA efficiency score lies in theinterval 0 and 1, the dependent variable is ‘a limited dependent variable’. Therefore, it is
apt to use the Tobit model, which is a censored regression model, applicable in cases
where the dependent variable is constrained in some way. The Tobit model may be
defined as:

y=

y

*

y* ;

0 <= y* <=10;y* < 0;

1;

*

1< y

= βxi

+ εt

(7)

εt ~ i e N(0, σ2)where y is the DEA VRS TE score.
y* is a latent (unobservable) variable.

β is the vector of unknown parameters which determines the relationship between
the independent variables and the latent variable.
xi is the vector of explanatory variables, which are discussed in section 3.2.2.

3.2.2

Variables

Taking into account the characteristics of the Indian pharmaceutical industry andfollowing the literature, the explanatory variables which are considered in the Tobit
model to estimate the factors which determine efficiency of pharmaceutical firms areownership, new patent regime, age, export of goods, FDI, import of capital goods,
profitability and R&D intensity.

Both the dependent and independent variables are14

elaborated in Table 4. The panel data for the independent variables for the years 2001-02
to 2007-08 for all the 90 firms was taken from CMIE’s Prowess database.

Table 4: Description of Variables used for Tobit Estimation
Variables
Description
Hypothesis
Dependent Variable : DEA efficiency scores of firms obtained from stage I of DEA
Independent Variables:
1. Ownership (Dummy)
Odum = 1, if domestic ownership;
Foreignownership= 0, otherwise
increases efficiency.
2. Regime (Dummy)
Rdum= 1 for the years 2004-05 to 2007-08 New patent regime= 0 for the years 2001-02 to 2003-04 increases efficiency.
3. AgeA is the age of the firm.
+
4. Export of Goods
X is the absolute amount of exports.
+
5. FDI (Dummy)
FDIdum= 1, if firms receive FDI,
Presenceof
FDI= 0, otherwiseenhances efficiency.
6. Capital Imports
M is the amount of capital imports.
+
7. Profit Rate
P is the ratio of profits to sales.
+
8. R&D intensity
RDI is the ratio of R&D to sales.
+
Note: A ‘+’ sign indicates a positive influence of the explanatory variable on the dependent
variable.

Ownership:

The management decides the magnitude and combination of inputs which

go into producing an output. Thus, ownership of the firms is an important variable.

New Patent Regime: To retain their competitiveness in the new patent regime
introduced in the year 2005, it is assumed that the pharmaceutical firms would have tostep up their efficiency levels. Thus, it is postulated that the stricter new regime would
lead to a higher level of efficiency.

Age: With age, firms gain the experience needed to be technically efficient. Therefore, it
is hypothesized that the older firms have a higher level of efficiency.

Export of Goods: The pharmaceutical industry of India is looked upon as one of thedrivers of India’s export led growth. It is, thus, hypothesized that the efficiency of apharmaceutical firm may be a fall-out of its level of exports.

15

Foreign Direct Investment: The pharmaceutical firms of India have been recipients of
FDI flows since the opening up of the economy in 1991. It is postulated that FDI inflows
increase the efficiency of firms.

Import of capital goods: The magnitude of capital goods imported by pharmaceuticalfirms has been taken as an explanatory variable, with the expectation that imports would
enhance the efficiency of these firms.

Profitability: It is hypothesized that higher profits would enable a firm to overcome its
shortcomings in terms of utilization of available resources.
R&D intensity: It is postulated that the investments made by pharmaceutical firms in
R&D may enhance their performance in terms of efficiency.
Thus, the Tobit model used in this study may be specified as:
y* = α + β1Odum + β2Rdum + β3A + β4X + β5FDIdum + β6M + β7P + β8RDI + εt4.

Empirical Results

4.1

Results of DEA(8)

The results obtained by running DEA are presented in two parts. The first part,elaborated in section 4.1.1 presents the results of Malmquist DEA for all the 90 firms forthe years 2001-02 to 2007-08. The second part is discussed in section 4.1.2 and provides
results of Malmquist DEA run separately for the two groups of firms, viz. R&D intensive
and non-R&D firms.

16

4.1.1

DEA Results for All 90 Firms

Frequency Distribution
The yearly analysis reveals that 70 (78%), 68 (76%), 61 (68%), 64 (71%), 65(72%), 70 (78%) and 67 (74%) firms operated inefficiently in the years 2001-02, 200203, 2003-04, 2004-05, 2005-06, 2006-07 and 2007-08, respectively (Table 5). Thesefirms needed to reduce their inputs to attain a given level of output to become efficient.

VRS TE<.5.5 to .7
.7 to less than 1
1
Total

Table 5: Frequency Distribution of VRS TE
2001-02 2002-03 2003-04 2004-05 2005-06
4
2
3
3
5
22
18
16
16
15
44
48
42
47
45
20
22
29
26
25
90
90
90
90
90

2006-07
3
13
54
20
90

2007-08
12
18
37
23
90

Average Efficiencies
Table 6 gives the summarized form of the average efficiency figures of all firms.
The average VRS TE for the firms was 0.79, 0.85, 0.86, 0.84, 0.83, 0.84 and 0.78 for
2001-02 to 2007-08. Had the firms been efficient, they could have reduced their inputs
by 21%, 15%, 14%, 16%, 17%, 16% and 22%, respectively, for the given level of output.
Table 6: Average VRS TE of Sample Firms
Description of Firms
2001-02
2002-03
2003-04 2004-05 2005-06
All firms (90)
Mean
0.79
0.85
0.86
0.84
0.83
Standard Deviation
0.18
0.16
0.16
0.16
0.18
Maximum
1.00
1.00
1.00
1.00
1.00
Minimum
0.03
0.04
0.04
0.05
0.02
R&D intensive firms (55)
Mean
0.82
0.84
0.88
0.85
0.86
Standard Deviation
0.15
0.13
0.13
0.14
0.14
Maximum
1.00
1.00
1.00
1.00
1.00
Minimum
0.03
0.04
0.04
0.06
0.03
Non-R&D firms (35)
Mean
0.76
0.85
0.84
0.83
0.78
Standard Deviation
0.22
0.21
0.21
0.20
0.22
Maximum
1.00
1.00
1.00
1.00
1.00
Minimum
0.50
0.52
0.48
0.48
0.41
Note: Figures in brackets are the number of firms in each category.

17

2006-072007-08

0.84
0.16
1.00
0.02

0.78
0.22
1.00
0.02

0.84
0.12
1.00
0.03

0.81
0.18
1.00
0.02

0.83
0.21
1.00
0.52

0.73
0.27
1.00
0.32

Efficiency of R&D intensive firms vis-à-vis non-R&D firms

The VRS TE of firms categorized by R&D intensity over the period 2001-02 to
2007-08 is depicted in Figure 1. Except for 2002-03, when the mean VRS TE of nonR&D firms was marginally higher than that of R&D intensive firms, in all other years
R&D intensive firms were more efficient. Throughout the study period, non-R&D firms
show more variability in performance than the R&D intensive firms (Table 6).

The null hypothesis stating that the mean technical efficiency of R&D firms andnon-R&D firms is not different, was tested using a one-tailed t-statistic. Since thecalculated t-value is greater than the probability value of a two-tailed t-test, the null
hypothesis was rejected. Therefore, it may be concluded that the difference between themean VRS TE of R&D firms and that of non R&D intensive is statistically significant.

Malmquist Productivity Index

For Malmquist productivity index, the first year of the study period, viz. 2001-02,
has been taken as the technology reference period. Over the period 2001-02 to 2007-08,
the average total factor productivity of the sample firms has remained at the same level
18

(Table 7). While efficiency change (CRS TE) regressed by 6%, pure efficiency change by
1% (VRS TE) and scale efficiency by 5%, technological change gained by 6% over thesame period. Efficiency change (0.94) is a product of the pure efficiency change (0.99)
and scale efficiency change (0.95). The technological change of 1.06 indicates thatduring the period under study, there has been a 6% technical progress.

Table 7: Summary Results of Malmquist DEA for all 90 Firms
Year
Efficiency
Technological
Pure
Scale
Malmquist
Change*ChangeEfficiency Efficiency
Index of Total
Change**ChangeFactorProductivity
Change
2003
1.16
0.85
1.08
1.08
0.98
2004
0.95
1.06
1.02
0.93
1.01
2005
0.98
1.01
0.98
1.01
0.99
2006
0.89
1.10
0.98
0.91
0.98
2007
1.10
0.87
1.01
1.09
0.96
2008
0.65
1.63
0.90
0.72
1.06
Average
0.94
1.06
0.99
0.95
1.00
*: Technical Efficiency change relative to CRS technology**: Pure Technical Efficiency Change relative to VRS Technology

Having established in Table 6 that for R&D-intensive firms the efficiency level ishigher and having rejected the hypothesis that the mean VRS TE of R&D firms is equal
to that of non-R&D firms, the firms were categorized into two groups according to thepresence or absence of R&D activities. Thereafter, a Malmquist DEA was run for both
the groups separately for the years 2001-02 to 2007-08. The results are presented inTables 8 and 9. It may be noted that these results yield the efficiency levels of the firms
within their respective groups and their individual efficiency levels cannot be comparedacross groups. The efficiency of a firm in the total sample set of 90 firms would be
different from that in the groups categorized as per the presence or absence of R&D.

4.1.2

DEA Results for the Group of R&D-intensive firms

Between 2001-02 and 2007-08, the number of R&D intensive firms which are
efficient has gone up from 13 to 17, after having reached a peak of 21 efficient firms in19

2004-05, the year in which the patent regime changed (Table 8). The mean VRS TE of
inefficient firms has declined from 0.76 in 2001-02 to 0.74 in 2007-08, although in someyears in between, the mean had crossed 0.80 and in 2002-03 it had reached 0.85.

Description of Firms
All firms (55 firms)
MeanStandard Deviation
Efficient TE=1
Number of Firms
Inefficient
Number of Firms
MeanStandard Deviation

4.1.3

Table 8: Average VRS TE of R&D intensive Firms
2001-02 2002-03 2003-04 2004-05 2005-06

2006-072007-08

0.82
0.15

0.85
0.13

0.88
0.13

0.86
0.13

0.89
0.10

0.86
0.12

0.82
0.17

13

12

17

21

19

15

17

42
0.76
0.13

43
0.85
0.13

38
0.83
0.12

34
0.78
0.10

36
0.84
0.08

40
0.80
0.10

38
0.74
0.14

DEA Results for the Group of Non-R&D firms

The number of efficient firms which are non-R&D has nearly doubled between
2001-02 and 2007-08, from 8 to 15 (Table 9). However, the mean VRS TE of inefficientfirms over the same period has gone down from 0.71 to 0.56, although the standard
deviation has remained the same.

Table 9: Average VRS TE of non R&D Firms
Description of Firms
2001- 2002- 2003- 2004- 200502
03
04
05
06
All firms (35 firms)
Mean
0.77
0.86
0.84
0.86
0.82
Standard Deviation
0.22
0.21
0.21
0.19
0.22
Efficient TE=1
Number of Firms
8
12
14
11
13
Inefficient
Number of Firms
27
23
21
24
22
Mean
0.71
0.77
0.74
0.80
0.70
Standard Deviation
0.21
0.22
0.22
0.21
0.22

20

200607200708

0.82
0.21

0.76
0.27

11

15

24
0.74
0.21

20
0.56
0.21

4.2

Results of Tobit Analysis

After obtaining the DEA scores, the next step is to regress through the Tobit
model these scores on variables which are considered as determinants of efficiency. The
results of the Tobit estimation using the STATA software are presented in Table 10.
Four models have been presented in the table. For each of the four models used, the Prob
> χ2 is zero, implying that the set of independent variables considered together
satisfactorily explain the variations in the dependent variable.

Table 10: Results of Tobit Estimation
Model I
Model II
Model III2
3
4
VRS TEVRS TEVRS TE(All Firms)(R&D) 1(R&D) 2
Independent Variables
CoefficientsOdum
0.8827573
0.0332265
0.0328079
Rdum
0.0241874*
0.0067488*
0.0082413*
A-0.0006655-0.0002829*-0.0002879*
X
0.0002565**
0.0007917*
0.0008107*
FDIdum
0.0141862*-0.0157963-0.0152869
M-0.0000237
0.0001723
0.0001667
P
0.0000265*
0.0058958**
0.0059263**
RDI-0.0000129
0.0000142*
Constant
0.8600945**
0.8505905**
0.8495158**
No. of observations
630 = 90 X 7
385 = 55 X 7
385 = 55 X 7
LR χ2
58.74
42.68
42.57
2
Prob > χ
0.00000.00000.0000Log likelihood-94.75-11.87-11.93*: Significant at 10% level; **: Significant at 1% level.
Variables
1
Dependent Variable

Model IV
5
VRS TE(Non R&D)

0.0322066*-0.0115441**
0.0001028
0.4696286**
0.0001114*
0.0000272
1.086546**
245 = 35 X 7
63.90
0.0000-80.70Column 2 of Table 10 presents results of the Tobit model wherein the VRS TE
scores obtained by running DEA on all 90 firms has been taken as the dependent variable.
It is observed that the coefficients of Rdum, X, FDIdum and P are significant and bear the
expected positive sign. The new patent regime, the level of exports, the presence of FDI
and profitability positively influences efficiency. The signs of the coefficients of othervariables, A, M and RDI, are negative, contrary to expectations. But these are notsignificant.

21

In Model II, results of which are presented in column 3 of Table 10, the VRS TE
obtained by using Malmquist DEA on R&D intensive firms has been taken as thedependent variable. The variables whose coefficients are observed to be significant are
Rdum, A, X, P and RDI. In the new patent regime, the efficiency of firms has increased.
The results imply that younger the firm, higher is its efficiency. More profitable firms
are more efficient. A higher level of outward orientation of firms in the form of exports
increases their efficiency.

The intensity of R&D expenditure has a positive andsignificant impact on the firms’ efficiency.

To facilitate comparison between the efficiency levels of R&D-intensive firms
and non-R&D firms, another model has been adopted and the results are provided incolumn 4 of Table 10. In this model too, the dependent variable is the VRS TE of R&D
intensive firms. The difference between the models presented in columns 3 and 4 is the
non-inclusion of the R&D variable in the latter. The coefficients of the variables, Rdum,
A, X and P, which were significant in Model II, continue to be significant in Model III.

In Model IV, the VRS TE derived by utilizing Malmquist DEA on non-R&D
intensive firms is the dependent variable. Since all the non-R&D firms are domesticallyowned, the variable Odum, has not been considered as an explanatory variable here.
Unlike the models for R&D-intensive firms, in this model the coefficient of FDIdum andM, in addition to Rdum and A, are found to be positive and significant. This implies thatfor non-R&D firms, the presence of FDI, the level of capital imports, the new patent
regime and the age of the firm have a positive influence on the firm’s level of efficiency.

5.

Implications and Conclusion

The first stage of DEA model of this study finds that the performance of a large
number of sample firms was sub-optimal, ranging between 68% and 78%. To become
efficient, these firms need to reduce their inputs to attain a given level of output. Further,
R&D intensive firms are more efficient than non-R&D firms. Given the importance of22the sector for the Indian economy, it is important that efforts be taken to increase theefficiency of the firms whose performance is sub-optimal.

The second stage of the analysis involving a Tobit estimation identifies theimportant determinants of the efficiency of pharmaceutical firms as the introduction of
the new patent regime, export of goods, inflow of FDI and the profitability of firms. For
R&D intensive firms, in particular, their R&D intensity adds to their efficiency. This issimilar to the findings of Saranga and Phani (2009). For non-R&D firms, the import ofcapital goods increases their technical efficiency.

The findings hold important managerial and policy implications. With the onsetof the product patent regime, the management of pharmaceutical firms has become more
conscious of the survival and competitiveness of their firms. As indicated by the second
stage of analysis, most parameters which affect a pharmaceutical firm’s efficiency are
dependent on its outward orientation like exports, imports and FDI. Therefore, theintegration of the Indian economy with the rest of the world has an important influence
on a firm’s internal efficiency. The export of goods may require meeting stringent
standards set by the importing countries, which mandates the efficient utilization ofavailable resources. Pharmaceutical firms may like to enhance their efficiency throughaugmentation of their outward orientation. Non-R&D firms which do not have their own
R&D units, tend to import foreign capital goods to enhance their performance. Thus, theimport of foreign capital goods acts as a substitute of local R&D activities.

In terms of policy implications for the Government, the empirical results yieldthat the availability of foreign funds in the form of FDI has proved to be beneficial for thefirms. In this context, GOI has been providing alluring opportunities to foreign investors.
Attracting further funds from abroad may prove to be advantageous for thepharmaceutical industry. The GOI’s role in encouraging pharmaceutical firms to take up
R&D activities has been overwhelmingly positive (GOI, 2009). Various tax exemptions
for R&D-intensive firms are some of the steps taken by the GOI. The importance of
encouraging pharmaceutical firms to invest in R&D assumes importance not only
23

because R&D is considered the backbone of the pharmaceutical industry (EXIM, 2007),
but also because results of this study indicate that R&D intensity has a positive andsignificant influence on the firms’ efficiency.

Despite the progress made by the pharmaceutical industry in recent years, thesector has not been left unscathed by the current global economic crisis. A shrinking
demand for exports from the destination countries, along with a fluctuating Rupee vis-àvis the US Dollar, can adversely affect the growth prospects of the pharmaceutical sector.
The findings of this study corroborate the fact that the Indian pharmaceutical sector isheavily integrated with the global economy. The determinants which affect efficiency
are primarily dependent on the world economic order. Therefore, the task for GOI in
promoting this sector and creating an environment where the internal efficiencies can be
maintained will definitely be a challenging one in the near future.

A limitation of this study is that it has used data up to 2007-08, i.e. prior to the
spread of the global economic crisis to the developing world. It would be interesting to
see the levels and determinants of efficiency by including data on the period which has
been affected by the crisis. Also possible is a study employing the three-stage DEA
model, where the estimated coefficients from the Tobit regressions can be used to predict
the total input slack for each input.

End Notes

1.
2.
3.
4.

Bulk drug production involves the production of the active ingredients present in
the drug, called the Active Pharmaceutical Ingredient (API).
Formulation production involves the processing of bulk drugs into finisheddosage forms such as tablets, capsules, injections, ointments, syrups, etc.
Off-patented drugs.
Includes only those firms for which the full set of observations during the sample
period was available because DEA requires balanced datasets.

24

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