Article
ISSN: 2348-3784
Institutional Reforms and Export Efficiency of Indian Pharmaceutical
Industry – A Comparative Analysis of Transitory-TRIPS and
Post-TRIPS Periods
Satyanarayana Rentala, Byram Anand and Majid Shaban
Digital Object Identifier: 10.23837/tbr/2017/v5/n1/149499
Abstract
The impact of institutional reforms on the performance of various industries in many emerging economies
had been a growing area of research in the recent times. In this context, we investigate the influence of
institutional reforms on the export efficiency of Indian pharmaceutical industry after India became a
signatory to the provisions of World Trade Organisation (WTO) from 1st January, 1995. India had been
given a transition period of 10 years till 31st December, 2004 to fully comply with Trade Related
Intellectual Property Rights (TRIPS) as per the provisions of WTO agreement. Accordingly, India has
completely transitioned to a product-patent regime from a process-patent regime effective from 1st
January, 2005. Many researchers and industry professionals of the Indian pharmaceutical industry
postulated that the institutional reforms would have a negative effect on the growth prospects of the
industry. Contrary to the predictions, Indian pharmaceutical industry has capitalized on the export
opportunities in various developed and emerging economies in the world. In this backdrop, we measure the
export efficiency of Indian pharmaceutical industry during transitory-TRIPS (1995-2004) and post-TRIPS
(2005-2014) periods using data envelopment analysis (DEA). The analysis of our research indicates that
theexport efficiency of theIndianpharmaceutical industrywas higher in thepost-TRIPSperiod.
Key Words:Export efficiency,Indianpharmaceutical industry,Institutionalreforms,Post-TRIPS,
Transitory-TRIPS
Introduction
The primary focus of many studies in strategic management research pertains to measuring corporate
performance in terms of financial measures alone. In this process, earlier research neglected the
significance
of
efficiency
measurement
in
determining
corporate
performance
(Chen,
Delmas
&
Lieberman, 2015). Measuring efficiency using frontier methodologies like data envelopment analysis
(DEA) and stochastic frontier analysis (SFA) can help to bridge this gap (Chen, Delmas & Lieberman,
2015).
Dr. Satyanarayana Rentala, Program Manager - South Zone, Piramal Foundation for Education Leadership, A-56,
Panchsheel Enclave, New Delhi - 110017, India. Phone: +91 73392 17534, Email: rentsatya@gmail.com
(Corresponding author)
Dr. Byram Anand Assistant Professor, Department of Management, Pondicherry University,
Karaikal Campus, Karaikal – 609 605, India
Dr. Majid Shaban Lecturer (Contractual), Department of Commerce, Government Degree College, Budgam
(Jammu & Kashmir) – 191 111, India
Institutional Reforms and Export Efficiency of Indian Pharmaceutical
35
Industry – A Comparative Analysis of Transitory-TRIPS and Post-TRIPS Periods
Though measuring efficiency of firms in different industries has earlier been attempted, very few
studies
(Pusnik,
2010;
Saranga,
2007)
have considered
export
efficiency
as
a measure of
firm
performance. In this research we attempt to contribute to this nascent area of research in the context of
emerging economies by comparing the export efficiency of Indian pharmaceutical industry (IPI) in two
different time periods of institutional reforms – transitory-TRIPS period (1995-2004) and post-TRIPS
period
(2005-2014).
Some
of
the earlier
studies
have analysed
the export
efficiency
of
Indian
pharmaceutical firms either during the transitory-TRIPS period (1995-2004) or during post-TRIPS
period (2005-2014). The unique contribution of our research lies in the fact that it analyses and
compares the export efficiency of IPI across two different periods and discusses how the export
efficiency ofthe industryvaried during transitory-TRIPS andpost-TRIPS periods.
In this research, we have made an attempt to examine the export efficiency of the IPI during the
transitory-TRIPS and post-TRIPS periods using Data Envelopment Analysis (DEA). Very few earlier
studies examined the export efficiency of firms in the context of various nations and their constituent
industries. Saranga (2007) studied the export efficiency of Indian pharmaceutical firms during the
transitory-TRIPS
period. Naude and Serumaga-Zake (2003) investigated the export efficiency of
multiple South African industries. Pusnik (2010) examined the export efficiency of various Slovenian
industries.
In view of the variables considered in the earlier studies, we measured export efficiency by taking
export sales as output variable in this study. We have used R&D expenses, import of raw materials,
compensation paid to employees and marketing expenses as input variables for employing DEA. We
investigated export efficiency through calculation of Constant Returns to Scale Efficiency (CRSTE) and
Variable Returns to Scale Efficiency (VRSTE) and Scale Efficiency (CRSTE/VRSTE) during transitory-
TRIPSandpost-TRIPS periods.
Export efficiency is measured by using data envelopment analysis (DEA). DEA has received increasing
importance as a tool for evaluating and improving the performance of manufacturing and service
operations (Talluri, 2000). It has been extensively applied in performance evaluation and benchmarking
of schools, hospitals, bank branches, production plants, etc. (Charnes, Cooper, Lewin & Seiford, 1994).
DEA
is
a
multi-factor
productivity
analysis
model
for
measuring
the
relative
efficiencies
of
a
homogenous set of decision making units (DMUs). Charnes, Cooper and Rhodes (1978) coined the term
dataenvelopmentanalysis(DEA) by proposingan input orientation with constant returns toscale(CRS)
model. Banker,Charnesand Cooper(1984) proposed the variable returnstoscale(VRS) model.
As mentioned earlier, we measured export efficiency by taking export sales as output. Research and
development (R&D) expenses, import of raw materials expenses, compensation paid to employees and
marketing expenses are taken as inputs. Using data envelopment analysis, we measured export
efficiency through calculation of CRSTE (constant returns to scale technical efficiency) and VRSTE
(variable returns to scale technical) efficiency. Additionally Scale Efficiency (CRSTE/VRSTE) was
measuredfor the sample firmsduring transitory-TRIPS andpost-TRIPS periods.
TheoreticalFramework, ModelSpecification andReview ofLiterature
TheoreticalFramework
Data Envelopment Analysis (DEA) is a relatively new “data oriented” approach for evaluating the
performance of a set of peer entities called Decision Making Units (DMUs) which convert multiple
inputs into multiple outputs. The definition of a DMU is generic and flexible. Recent years have seen a
TSM Business Review, Vol. 5, No. 1, June 2017
k
k
36
Institutional Reforms and Export Efficiency of Indian Pharmaceutical Industry – A
Comparative Analysis of Transitory-TRIPS and Post-TRIPS Periods
great variety of applications of DEA for use in evaluating the performances of many different kinds of
entitiesengagedin manydifferentactivitiesin manydifferent contextsin many differentcountries.
DEA has been used in many disciplines to evaluate the performance of entities such as operations
research,
management
control
systems,
organization
theory,
strategic
management,
economics,
accounting
&
finance,
human
resource
management
and
public
administration
including
the
performance of countriesandregions(Rouse, 1997). Becauseit requires veryfew assumptions,DEA has
also opened up possibilities for use in cases which have been resistant to other approaches because of
the complex (often unknown) nature of the relations between the multiple inputs and multiple outputs
involved in DMUs.
Data envelopment analysis (DEA) is a mathematical method based on production theory and the
principles of linear programming. DEA was initiated in 1978 when Charnes, Cooper and Rhodes (1978)
demonstrated how to change a fractional linear measure of efficiency into a linear programming (LP)
format. As a result, decision- making units (DMUs) could be assessed on the basis of multiple inputs and
outputs, even if the production function was unknown. It enables one to assess how efficiently a firm,
organization, agency, or such other unit uses the resources available inputs to generate a set of outputs
relative to otherunits in the dataset(Ramanathan2003; Silkman1986).
This non-parametric approach solves an LP formulation per DMU and the weights assigned to each
linear aggregation are the results of the corresponding LP. The weights are chosen so as to show the
specific DMU in as positive a light as possible, under the restriction that no other DMU, given the same
weights,is more than100% efficient.
Since DEA in its present form was first introduced in 1978, researchers in a number of fields have
quickly recognized that it is an excellent and easily used methodology for modelling operational
processes for performance evaluations. DEA’s empirical orientation and the absence of a need for the
numerous a priori assumptions that accompany other approaches (such as standard forms of statistical
regression analysis) have resulted in its use in a number of studies involving efficient frontier
estimationin the governmentaland non-profitsector, in the regulated sector,andin the privatesector.
In their originating study, Charnes, Cooper and Rhodes (1978) described DEA as a ‘mathematical
programming model applied to observational data [that] provides a new way of obtaining empirical
estimates of relations - such as the production functions and/or efficient production possibility surfaces –
that arecornerstones of moderneconomics’.
ModelSpecification
Data envelopment analysis (DEA) is a non-parametric tool because it requires no assumption on the
shape or parameters of the underlying production function. DEA is a linear programming technique
based on the pioneering work of Farrell’s efficiency measure (1957), to measure the different efficiency
of decision-making units (DMUs). Assuming the number of DMUs is s and each DMU uses m inputs and
produces n outputs. Let DMUk be one of s decision units, 1 ≤ k≤ s. There are m inputs which are marked
with Xi (i = 1, ..., m), and n outputs marked with Yj (j = 1,...., n). The efficiency equals the total outputs
divide by totalinputs. The efficiency ofDMUk can bedefined asfollows:
TSM Business Review, Vol. 5, No. 1, June 2017