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International Journal of Engineering, Applied and Management Sciences Paradigms, Vol. 44, Issue 01
Publishing Month: February 2017
An Indexed and Referred Journal
ISSN (Online): 2320-6608
www.ijeam.com
IJEAM
www.ijeam.com
7
An Assessment of Performance of Indian Software
Industry during 2000-01 to 2014-15 using Data
Envelopment Analysis
Prosenjit Das
PhD Scholar, Department of Economics, University of Kalyani, Nadia, West Bengal, India
Publishing Date: February 14, 2017
Abstract This study is an attempt to assess the performance of
Indian software industry during 2000-01 to 2014-15.
The empirical study consists of two stages. In the first-
stage, data envelopment analysis technique is applied
to evaluate the performance in terms of technical
efficiency. In the second-stage, a random-effect Tobit
model is employed to investigate the determinants of
the technical efficiency of software industry. For this
purpose, firm level secondary data is collected from
CMIE-prowess online database. The DEA results
found that pure technical (or managerial) inefficiency
is the primary reason for overall inefficiency of this
industry. Decreasing returns to scale (DRS)
technology is found to be predominant in this industry
during the study period, which indicates over-
utilization of resources. The input-specific efficiency
analysis reveals that a major part of overall input
inefficiency occurs due to inefficient use of net fixed
assets. The regression results show that profitability,
size, market concentration and net exports have
positive impact on the performance of this industry.
On an average, public limited and non-group software
firms are found to be more efficient than their private
and group counterparts, respectively. It is evident from
the regression analysis that the aftermath of the US
subprime crisis has been negative on the performance
of the Indian software industry. In policy front, the
study suggests to formulate segment-specific policies
rather than a common policy for the industry. The
telecommunication policy and existing tax system
should be rationalized further to ease of doing
business in India.
Keywords: Indian Software Industry, Data
Envelopment Analysis, Technical Efficiency,
Pareto-Koopmans Efficiency, Random-Effect
Tobit Model.
1. Introduction
India’s software industry experienced a
phenomenal growth during the liberalization
period, precisely, after 1991. Before 1991, there
was no such development and growth occurred
in this industry mainly due to India’s inward
looking policies, adverse tax structure and
numerous bureaucratic barriers. Basically, the
liberalization reform policies of 1991 laid the
foundation of this industry’s growth. The
establishment of Software Technology Parks
(STPs) in India in 1991 had also given impetus
to the growth of software industry by providing
various tax sops, single-window clearances,
among other facilities. Moreover, 1991’s export-
oriented policy helped to enhance the growth of
software exports considerably. One of the main
reasons for this remarkable achievement of
India’s software industry is the abundant supply
of IT-skilled and English speaking workforce. In
fact, in the world market, India’s comparative
advantage in software export primarily comes
from the availability of the large pool of cheap
IT-workers. Indian Diaspora has played an
important role in establishing India’s software
industry’s reputation in abroad, especially in the
US. A number of People of Indian Origin (PIO)
in US have helped to speed up the process of
body shopping, by which a significant number of
Indian IT-professionals not only got chance to
work in US but also it helped to enrich the
liaison between Indian and the US software
firms over time.
During recent years, it has been observed that the
sustainability of the performance of India’s
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International Journal of Engineering, Applied and Management Sciences Paradigms, Vol. 44, Issue 01
Publishing Month: February 2017
An Indexed and Referred Journal
ISSN (Online): 2320-6608
www.ijeam.com
IJEAM
www.ijeam.com
8
software industry becomes critical in the
backdrop of various global phenomena. For
instance, India is no longer be the low-cost
destination for outsourcing IT-services as some
other countries, such as China, Philippines,
Mexico, Canada have emerged as low-cost IT-
service providers. On the other hand, during the
US subprime crisis that occurred in 2007-08, a
considerable portion of India’s software export
to the US was declined. Further, the prevalence
of exchange rate volatility also adversely affects
the export revenue earnings of Indian software
exporting companies. Therefore, to retain India’s
favorable global position in software export and
outsourcing business, it is imperative to maintain
a sustainable and robust performance of its
software industry over time.
In this background, the present study intends to
examine the following objectives:
the trend of performance of the Indian
software industry during the study period
decomposition of overall efficiency into
managerial and scale efficiencies
the contribution of individual inputs to
the performance of the industry
determination of the impact of various
environmental factors on the
performance of the industry
In this study, Data Envelopment Analysis (DEA)
technique has been incorporated to assess the
performance of Indian software industry in terms
of technical efficiency during 2000-01 o 2014-
15. In this context, two radial DEA models,
namely, CCR and BCC have been used to
evaluate technical efficiency under constant
returns to scale (CRS) and variable returns to
scale (VRS) technologies, respectively. To deal
with the problems of input and/or output slack
present in the optimal solutions of CCR and
BCC models, a non-radial Pareto-Koopmans
(PK) model has been used to evaluate technical
efficiency. Further, a random-effect Tobit model
has been employed to investigate the
determinants of technical efficiency of the
software industry.
This paper is divided into five sections. Section-
1 contained introduction and objectives of the
paper. Section-2 provides an overview of the
Indian software industry. Setion-3 presents a
brief review of literature. Section-4 deals with
methodology and data. Section-5 analyses
empirical findings and section-6 concludes the
paper with some policy prescriptions and scope
for future research.
2. An Overview of the Indian Software
Industry
The software industry formally came into
existence in India since the foundation of Tata
Consultancy Services (TCS), a subsidiary of
Tata group, in late 1960s. Within a few years of
its inception, TCS started body shopping, where
software professionals from India were being
sent to the US for providing onsite client support
for American MNCs. The opportunity for body
shopping primarily came from the increasing
shortage of computer professionals and engineers
in the US. More often, a significant number of
IT-professionals who were sent to the US for
onsite work support extended their stay in the US
by engaging themselves in other IT-assignments
and further training. Over time, a large number
of Indian Diaspora formed in the US and this
helped the emergence of body shopping in a
considerable manner. Moreover, the abundance
of low-cost Indian software professionals, liberal
immigration policy of the US and recurrent
scarcity of computer engineers in the west gave
impetus to the growth of body shopping. During
late 1980s, the US clients started offshoring
software related jobs, such as software
development, maintenance etc. to India. As the
Indian software industry matured and started
gaining foreign clients’ confidence over time,
Indian software companies began to work
offshore rather sending engineers overseas. The
foundation of National Association of Software
and Services Companies (NASSCOM) during
1988 played a pivotal role in endorsing the brand
image of Indian software industry in
international arena. Since uninterrupted supply
of electricity and internet facility are two
essential prerequisites for offshoring business,
the Indian government has supported the
industry by creating Software Technology Parks,
opening up the software market and developing
telecommunication facilities, such as high-speed
broadband network nationwide. Moreover,
Indian government’s various policy decisions,
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International Journal of Engineering, Applied and Management Sciences Paradigms, Vol. 44, Issue 01
Publishing Month: February 2017
An Indexed and Referred Journal
ISSN (Online): 2320-6608
www.ijeam.com
IJEAM
www.ijeam.com
9
like increasing the limit of FDI in software
industry, rationalization of import duties, tariffs
and taxes, etc. had become boon for growth of
this industry. On the other hand, the
establishment of some pioneer technical and
management institutions, like IITs and IIMs is
another reason of this sector’s persistent
development process. These institutions have
been providing the finest human capitals such as
engineers, programmers and management
professionals to this industry. Since the last
decade, Indian software industry has been
dedicated to provide high-end services to the
foreign as well as domestic clients. To move up
the value chain, Indian software firms have been
widening their product at par the world standard.
For instance, most of the top firms of this
industry are now entering to product-oriented
business, such as hardware and other high-end
Research and Development (R&D) segments.
The Indian IT industry is broadly segregated into
three segments, namely, software, hardware and
ITeS-BPO. In our present study, we contain our
analysis in software segment only. In India, the
software segment is more robust than the
hardware segment. Presently, the ITeS-BPO
segment is the fastest growing segment of this
industry. Software firms’ revenue comes from
mainly two sources, viz. exports and domestic
market. A major portion of revenue comes from
exporting software services. The software firms
are mainly associated with the development and
maintenance of software products. Coding,
testing, designing, installation of software etc.
are the other activities of the software firms.
3. A Brief Review of Literature
Chaudhuri (2016) examined the impact of
economic liberalization on the efficiency and
technical progress of Indian hardware
manufacturing firms during 2002-03 to 2009-10.
Data was taken from CMIE-Prowess database.
The study used stochastic frontier analysis to
estimate the technical efficiency by incorporating
a translog type production function. The results
found that there was an improvement in technical
progress but the mean technical efficiency has
declined during the study period. The
relationship between firm-size and technical
efficiency was found to be positive. Moreover,
foreign hardware firms were found to be
technically more efficient than their domestic
counterparts. Sahoo (2016) investigated relative
efficiency of Indian software companies during
1999-2008 by using Input-oriented DEA
technique. Firm level data was collected from
CMIE prowess database. The paper used sales as
output variable, and expenditure on computers
and electronic equipments, power, fuel and water
charges, employment, and operating expenditure
as input variables. The average overall technical
efficiency (OTE) and pure technical efficiency
(PTE) scores were found to be 0.477 and 0.654,
respectively. The study found that the average
scale efficiency of Indian software companies
has been decreasing over the years during the
study period. The second-stage regression
analysis revealed that the overall technical
efficiency of the private Indian software
companies was better than their foreign
counterparts and group-owned companies. Goh
(2015) applied output-oriented CCR and BCC
models to evaluate technical efficiency of 32
Korea Securities Dealers Automated Quotation
(KOSDAQ) IT service companies during 2013.
To measure efficiency, assets and capital were
considered as inputs whereas sales and operating
profits were selected for outputs. A super-
efficiency model was also used to rank efficient
firms. The results revealed that the average CCR
and BCC (or PTE) efficiency were 67.05% and
77.35%, respectively. The scale efficiency was
found to be 86.15%. PTE was appeared to be the
primary contributor to the overall technical
efficiency. This study suggested inefficient
companies to reduce over-invested inputs In
order to be efficient. Sahoo (2013) estimated the
total factor productivity of Indian software
companies by using Malmquist productivity
index during 1999 to 2008. The study found that
on an average, the Indian software industry
experienced productivity gain by 0.4 percent
during the study period. The study also revealed
that older companies experienced better TFP
growth than their newer counterparts. The
Indian-owned companies were found to be more
productive than the group-owned companies.
The second-stage regression results found
negative and statistically significant impact of
initial overall efficiency on TFP growth. Finally,
the role of R&D was found to be minimal in
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International Journal of Engineering, Applied and Management Sciences Paradigms, Vol. 44, Issue 01
Publishing Month: February 2017
An Indexed and Referred Journal
ISSN (Online): 2320-6608
www.ijeam.com
IJEAM
www.ijeam.com
10
enhancing productivity of Indian software
companies. Bhattacharjee (2012) analyzed the
sustainability of performance of Kolkata’s
Software Technology Park (STP)’s IT-ITeS
firms. For the study, data was collected for the
period of 15 years, viz. 1993-94 to 2007-08 from
the STP, Kolkata. The study employed an
output-oriented model under VRS technology to
evaluate the technical efficiency. To evaluate
DEA scores, total revenue was considered as
output variable whereas employment and capital
stocks were taken as input variables. The results
showed that on an average, the technical
efficiency of IT-ITeS firms was declined over
the study period. An OLS regression model was
constructed to assess the determinants of
technical efficiency. For this purpose, net foreign
exchange earnings and the international
orientation (the ratio of foreign exchange
outflow to the total cost) were considered as
independent variables and the technical
efficiency scores as dependent variable. Both the
coefficients of the independent variables were
found to be positive and statistically significant.
It implies that the higher the forex earning and
the higher the global orientation, the higher the
performance of the IT-ITeS industry. Chen et al.
(2011) used DEA method to calculate the
managerial, technical and scale efficiencies for
73 listed Chinese Information Technology (IT)
companies during 2005 to 2007. This paper also
employed Malmquist Productivity Index (MPI)
to evaluate Total Factor Productivity (TFP) and
its sources. To compute technical efficiency
scores; intangible assets, fixed assets, number of
employees and operating costs were taken as
input variables and net profit, annual sales and
were considered as output variables. The DEA
results revealed that on an average, the Chinese
IT sector was 6.8 percent technically inefficient
and 5.1 percent managerially inefficient during
the study period. The TFP analysis showed no
considerable progress in TFP during the study
period. Finally, the efficiency convergence
analysis indicated an incidence of substantial
technical diffusion along with a decline in the
technical convergence from 2005 to 2007. The
study recommended that the IT-companies
should invest in R&D activities and improve
intellectual capital for achieving competitive
advantages and improvement in performance.
Mathur (2007) assessed technical efficiency of
92 Indian software firms for the year 2005-06.
For this purpose, input-oriented DEA technique
was applied. For the study, secondary data was
collected from CMIE Prowess online database.
To evaluate technical efficiency, sales and net
exports were taken as output variables and total
costs, number of employees and years in
business were considered as input variables. A
tobit model was applied in the second stage to
investigate he determinants of the technical
efficiency. The tobit regression results revealed
that firm size and net exports have positive and
statistically significant impact on the technical
efficiency. On the other hand, the impact of total
cost on technical efficiency was found to be
negative and statistically significant. Finally, the
coefficients of number of employees and years in
business variables were found to be statistically
insignificant. The study also evaluated technical
efficiency of 12 selected countries. The results
showed that Taiwan was the best performer (with
a score of 1) and India was the worst performer
with a score of 0.72). India got lowest DEA
score mainly because of its poor
telecommunication infrastructure.
4. Methodology and Data
4.1. Methodology
4.1.1. Theoretical Framework of DEA
Technique
DEA is a non-parametric, deterministic
mathematical programming technique and it
measures efficiency of a DMU1 with respect to
the ‘best-practice’ production frontier. Basically,
this technique evaluates relative efficiency scores
with respect to the efficient DMUs. The
advantage of DEA over other techniques is that it
does not require any functional specification of
the production function. Moreover, this method
can evaluate efficiency score under multi input-
multi output framework. The DEA technique
was initially suggested by Farrell (1957). He
applied the principle of frontier analysis for a
1 In DEA literature, a Decision Making Unit (DMU) is
defined as a production unit which transforms input(s)
into output(s). In the present study, DMUs refer to the
software firms.
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International Journal of Engineering, Applied and Management Sciences Paradigms, Vol. 44, Issue 01
Publishing Month: February 2017
An Indexed and Referred Journal
ISSN (Online): 2320-6608
www.ijeam.com
IJEAM
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11
DMU’s efficiency calculation. Charnes et al.
(1978) combined Farrell’s concept as a non-
parametric analysis and constructed the DEA
technique mathematically by using linear
programming model (popularly known as CCR
model) under CRS technology. Since the
introduction of CCR model, DEA was started
gaining popularity among the researchers. The
CCR efficiency score is also known as the
overall technical efficiency. Banker et al. (1984)
introduced a DEA model under the assumption
of variable returns to scale (VRS) technology by
incorporating a convexity condition into the
CCR liner programming model. This is known as
BCC model. BCC efficiency score is known as
pure technical efficiency or managerial
efficiency.
The BCC measure of TE, also known as pure TE
(PTE), considers the managerial efficiency only.
On the other hand, the CCR measure of TE, also
known as overall TE (OTE), takes care of
managerial efficiency as well as the scale
efficiency. The CCR measure of TE can be
decomposed as follows:
OTE = PTE * SE ……(1)
Hence, the scale efficiency (SE) can be defined
as the ratio of OTE and PTE.
i.e., SE = ; 0 < SE ≤ 1
Whereas the managerial efficiency (or PTE) is
associated with the performance of a DMU’s
management in transforming inputs into outputs,
the measure of scale efficiency indicates whether
the DMU operates at optimal scale or not. In this
context, the SE score would be equal to 1 if the
DMU under evaluation operates at the most
productive scale size (MPSS); otherwise SE
would be less than 1. Moreover, it can be said
that the SE will be equal to 1 only when the
technology of the DMU exhibits CRS. For any
non-CRS technology, the SE would be less than
one, which means the DMU under evaluation
would be scale inefficient. When a DMU
operates at MPSS i.e., SE = 1, that further
implies OTE = PTE. In this case, both CCR and
BCC scores would be equal to one and the DMU
will be called efficient.
Although the CCR and BCC models have gained
popularity in the empirical studies of evaluating
technical efficiency, these models suffer from
some limitations. Since both these CCR and
BCC models are associated with radial (or
proportional) measure of efficiency, one of the
main limitations is the non-consideration of non-
radial output and input slacks. To overcome this
problem, this study uses a Pareto-Koopmans
measure of non-radial (non-oriented) model to
evaluate efficiency scores of software firms. In
addition to the non-radial efficiency score, the
present study also evaluates efficiency scores on
the basis of output-oriented CCR and BCC
models for more comprehensive analysis. It is to
mention that the technical efficiency score lies
between 0 and 1 for all those three models.
We consider there are N numbers of DMUs,
which are producing m outputs from n inputs.
For DMU ‘j’, the observed input bundle is: xj =
(x1j, x2j,…,xnj) and the output bundle is: yt = (y1j,
y2j,…, ymj).
The production possibility set (S) under CRS
technology can be defined as
S = {(x, y): x ≥ λjxj, y ≤ λjyj; λj ≥ 0, (j = 1, 2….
,N)}
The output-oriented CCR LPP model for DMU
‘t’ can be stated as follows:
Max θ
Subject to,
r = 1, 2, … m;
i = 1, 2, … n;
θ free; λj ≥ 0 j = 1, 2, … N
The optimal solution of the CCR model may
be presented as (θ*; . The
resulting output-oriented technical efficiency
score of DMU ‘t’ under CCR model may be
denoted as
The production possibility set (I) under VRS
technology can be defined as:
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International Journal of Engineering, Applied and Management Sciences Paradigms, Vol. 44, Issue 01
Publishing Month: February 2017
An Indexed and Referred Journal
ISSN (Online): 2320-6608
www.ijeam.com
IJEAM
www.ijeam.com
12
I = {(x, y): x ≥ λjxj, y ≤ λjyj; ∑λj = 1; λj ≥ 0, (j =
1, 2…. ,N)}
The BCC-DEA model can be constructed by
inserting an additional constraint in the above
CCR-DEA linear programming problem. This
additional constraint is also known as the
convexity restriction, viz. =1, which
captures the essence of variable returns to scale
(VRS) technology. Under the BCC model,
the production possibility set ‘I’ is constituted by
a convex hull. The output-oriented BCC model
for DMU ‘t’ can be stated as follows:
Max φ
Subject to,
r = 1, 2, … m;
i = 1, 2, … n;
=1
free; λj ≥ 0 j = 1, 2, … N
The optimal solution of the BCC model can be
represented as (φ*; . The
resulting output-oriented technical efficiency
score of DMU ‘t’ under BCC model may be
denoted as
In the CCR model, if θ*=1, then the DMU under
evaluation is called efficient. On the other hand,
if θ*>1, the DMU under evaluation is said to be
inefficient. The same explanation is applicable
for the BCC model.
Both the CCR and BCC output-oriented models
are the measures of radial efficiency. It means
these models only consider proportional increase
in output given the inputs. But in reality, there
exists input/output slacks, which are not being
captured by the conventional radial models (viz.
BCC and CCR models). The reason behind the
existence of slacks in a radial DEA model is the
expansion of all output or reduction of all inputs
by equal proportion. In this context, it is to
mention that the notion of technical efficiency is
closely associated with the concept of Pareto
optimality (Ray, 2004). An input-output
combination is said to be Pareto-Koopmans
efficient if and only if (1) it is not feasible to
increase at least one output without raising any
input and/or decreasing any other output; and (2)
it is not feasible to decrease at least one input
without reducing any output and/or raising any
other input. It is clear from this discussion that
radial projection of an observed input-output
bundle under CCR and BCC models does not
satisfy the condition of Pareto optimality. On the
other hand, it implies that in case of CCR and
BCC models, input and/or output slacks might
present at the optimal solution. To overcome this
shortcoming of CCR and BCC models, we
introduce a non-radial Pareto-Koopmans
measure of technical efficiency proposed by
Pastor et al. (1999). This non-radial measure
provides a non-oriented efficiency score unlike
radial measures which is either input or output-
oriented (Ray et al. 2007). Since this model has
no orientation, it incorporates possible increase
in output as well as viable reduction in inputs
simultaneously. The corresponding LP problem
for DMU ‘t’ can be given as under:
Min Γ =
Subject to,
r = 1, 2… m;
i = 1, 2…n;
r = 1, 2… m;
i = 1, 2… n;
j = 1, 2… N
Where, θ = (θ1, θ2, …, θn) and φ = (φ1, φ2, …,
φm).
The optimal solution of the above LP problem
can be represented as (θ*; φ*; .
The resulting Pareto-Koopmans (PK) measure of
technical efficiency score of DMU‘t’ can be
represented as
Where, is the input-specific
component and ) is the output-specific
component of efficiency. Like the radial models,
the PK-efficiency score also lies between the
interval (0, 1].
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International Journal of Engineering, Applied and Management Sciences Paradigms, Vol. 44, Issue 01
Publishing Month: February 2017
An Indexed and Referred Journal
ISSN (Online): 2320-6608
www.ijeam.com
IJEAM
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13
4.1.2. Second-stage Regression Analysis
In the second-stage of our empirical analysis, we
carry out a random-effect tobit regression model
to investigate the determinants of technical
efficiency of software firms. Since the dependent
variable (i.e., technical efficiency score)
censored between 0 and 1, the application of
OLS method might generate biased and
inconsistent estimates of regression parameters.
To overcome this problem, we apply the
random-effect tobit model.
The random effects Tobit model can be
formulated as under:
yit* = β′xit + uit i = 1, 2,…, N
t = 1, 2,…, Ti
uit = vi + εit vi ~ IID(0, )
and εit ~ IID(0, )
yit =
Where, IID implies independently and
identically distributed; yit denotes the PK-
efficiency score, yit* denotes the latent or
unobserved variable, ‘β’ denotes the vector of
unknown regression parameters, xi denotes the
vector of regressors. uit is an composite error
term that consists of two elements, namely, vi
and εit. vi captures the time-invariant individual
random effects; and εit is the time-varying
idiosyncratic random error component.
4.2 Data Description
The study is based on secondary data. Data
extracted from CMIE Prowess online database
on software firms during the period 20002 to
2014. Sales is chosen as output variable, whereas
net fixed assets, wages and salaries, and
operating expenses are chosen as input variables
for conducting efficiency analysis.
2 CMIE Prowess provides data for financial year (FY) rather than calendar year. Therefore, in this study, the years indicate
FY. For instance, any data for the year 2000 actually refer to
the data for the FY 2000, i.e., from April 2000 to March 2001. For notational simplicity, we have used 2000 instead of
2000-01. The same interpretation is applicable for other years
considered in the paper.
Table 1: Input and output variables3 for DEA
Input Output
1. Salaries and
wages
1. Sales
2. Net fixed assets
3. Operating
expenses
Output Variable
Sales: Empirical studies pertaining to the
assessment of technical efficiency of Indian IT
industry using DEA are found to be considered
sales as one of the output variable (Mathur 2007;
Sahoo 2011; sahoo 2013; Bhattacharjee 2012).
Since, one of the prime objective of the present
study is to examine how software firms
efficiently produces their output given the inputs,
sales would be the most appropriate output
variable.
Input Variables
Salaries and Wages: The choice of salaries
and wages as one of the input variable is
consistent with prior studies of this genre
(Bhattacharya 2010; Dastidar 2004; Mahajan et
al. 2014). Wages and salaries data consists of
total annual expenses incurred by a software firm
on all employees.
Net Fixed Assets: A significant number of
studies (Mogha et al. 2012; Zhang et al. 2012;
Subramanyam et al. 2008; Ahuja et al. 1995)
considered either gross fixed assets or net fixed
assets as an input variable in evaluation of
technical efficiency of various industries by
using DEA. The consideration of gross fixed
assets as an input could be problematic as it does
not consider depreciation of fixed assets. On the
other hand, net fixed assets of a company
comprises of the all the fixed assets such as land,
buildings, equipment, machinery, etc. less the
accumulated Depreciation. Hence, NFA takes
care of the presence of depreciation of the fixed
assets. For a software firm, net fixed assets
mainly comprise of the costs of installation and
3 Inputs and output data are measured in rupees millions.
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maintenance of servers, desktop terminals,
networking apparatuses, VSAT terminals etc.
Operating Expenses: We have taken
operating expenses as another input variable for
the estimation of relative efficiency scores.
Operating expenses of a firm includes day to day
expenses like wages and salaries of its
employees, research and development (R&D)
expenses, inventory cost, insurance and
marketing costs etc. Operating expenses is
considered as one the important input variable in
the production process (Sahoo 2011, Chen et al.
2011,). Since wages and salaries is already
included in our efficiency analysis as an input
variable, we have subtracted wages and salaries
from the operating expenses and the resulting
variable is considered as an input variable in our
DEA models. This input variable captures costs
of core operations incurred by a firm on a daily
basis. This is the cost that managers always
desire to minimize without compromising the
quality of its output.
4.2.1 Variable Measurement for Second-
stage Regression
In the second stage regression analysis, the
efficiency scores obtained from the first stage
DEA analysis are regressed on various external
environmental variables such as experience in
business, size, managerial quality, market
concentration, profit rate, returns to scale, effect
of US subprime crisis, net export, group dummy
and ownership dummy. A brief description of
independent variables is given below.
‘Size’ variable is measured as the natural
logarithm of sales. We have converted the
nominal values of sales into real values by using
GDP deflator prior to its logarithmic
transformation. ‘Experience in business’ is
measured as natural logarithm of years in
business. ‘Managerial quality’ is constructed as
the ratio of operating expenses to total assets.
Management of a firm always aims to improve
total assets without increasing the operating
expenses or at least proportionately (das et al.
2007). It is worthwhile to mention that the
managerial quality and the ratio of operating
expenses to total assets are inversely related.
Therefore, it can be stated that as the ratio of
operating expenses to total assets rises, the
managerial quality decreases and vice versa. It is
generally believed that there is a positive
relationship between technical efficiency and
managerial quality. In this study, we would like
to examine whether there exist any significant
relationship between technical efficiency and
managerial quality of software companies.
Market concentration is evaluated on the basis of
Herfindahl-Hirschman index (HHI). ‘Profit rate’
variable is measured as the ratio of profit to
sales. ‘Net export’ is measured as the ratio of net
export to sales. ‘Return to scale’ dummies are
incorporated to examine which software firms
are more efficient among IRS, CRS and DRS
technologies. To examine the impact of US
subprime crisis, we use time dummies.
Ownership dummy is introduced to examine the
difference in average efficiency between public
and private limited software firms. Finally, to
observe whether the group or non-group firms
are more efficient, we have considered the group
dummy variable.
We summarize the variable description in the
following table 2:
Table 2: Description of Variables for Second-
Stage Regressions
Variable Measurement
Dependent variable: DEA efficiency scores of software firms
obtained from first stage
Independent variables
1. Size
Natural logarithm of real sale
2. Experience in
business Natural logarithm of years in business
3. Managerial
quality Ratio of operating expenses to total
assets
4. Market
concentration Herfindahl-Hirschman index
5. Profit rate
Ratio of profit to sales
6. Net export
(export - import)/sales
7. Returns to
scale (Dummy)
a. CRS dummy =1, if the firm
exhibits CRS
=0, otherwise
b. DRS dummy =1, if the firm
exhibits DRS
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= 0, otherwise
8. US subprime
crisis
(Dummy) =1, for the years
2008 to 2014
=0, otherwise
9. Ownership
(Dummy) =1, if the firm is a
public limited company
= 0, otherwise
10. Group
(Dummy) =1, if the firm is a group company
=0, otherwise
Source: Author’s construction
In the present study; a random-effect panel Tobit
model is considered to investigate the influence
of environmental control variables on the
technical efficiency of Indian software industry.
5. Results and Discussions
5.1. Analysis of First-stage DEA Results:
In this section, we analyze the trend of average
efficiency of Indian software industry during the
study period. The efficiency scores are obtained
from three different DEA models, viz. CCR,
BCC and PK. Table 2 shows the mean CCR,
BCC, PK and scale efficiency (SE) scores during
2000 to 2014. Figure 1 is the graphical depiction
of table 3.
Table 3: Pure Technical Efficiency (BCC),
Overall Technical Efficiency (CCR), Pareto-
Koopmans (PK) Efficiency and Scale
Efficiency (SE) for Indian Software Industry
Year BCC
(PTE)
CCR
(OTE)
PK SE
2000 0.811 0.764 0.770 0.942
2001 0.818 0.782 0.774 0.956
2002 0.807 0.758 0.767 0.939
2003 0.783 0.712 0.728 0.909
2004 0.820 0.742 0.780 0.905
2005 0.847 0.756 0.796 0.893
2006 0.825 0.721 0.765 0.874
2007 0.831 0.757 0.771 0.911
2008 0.800 0.707 0.737 0.884
2009 0.810 0.721 0.726 0.891
2010 0.803 0.633 0.734 0.788
2011 0.826 0.750 0.759 0.908
2012 0.821 0.762 0.750 0.928
2013 0.810 0.720 0.733 0.889
2014 0.780 0.700 0.747 0.897
Mean 0.813 0.732 0.756 0.901
Source: Author’s calculations
The findings in Table 3 reveal that average PTE
and PK scores were highest during the year
2005, viz. 0.847 and 0.796, respectively. On the
other hand, average OTE and SE scores were
highest during 2001, viz. 0.782 and 0.956,
respectively. The average PTE was lowest during
2014, viz. 0.780. Average CCR and SE were
lowest during 2010, viz. 0.780 and 0.788,
respectively. The average PK efficiency was
least during 2009, viz. 0.726. The mean PTE,
OTE, PK and SE during 2000-14 were found to
be 0.813, 0.732, 0.756 and 0.901, respectively.
Figure 1: Year-wise Mean Technical
Efficiency of Indian Software Industry during
2000 to 2014
To examine the relative contribution of Pure
Technical Inefficiency4 (PTIE) and Scale
Inefficiency (SIE) in Overall Technical
Inefficiency (OTIE), we have calculated PTIE,
SIE and OTIE and presented in table 4 below.
Figure 2 (column chart) is the pictorial depiction
of table 4.
4 PTIE = (1-PTE)*100, the same calculation is applicable for
other inefficiency scores.
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Table 4: Decomposition of OTIE into PTIE
and SIE (in percentage)
Year OTIE PTIE SIE
2000 23.64 18.91 5.84
2001 21.82 18.22 4.40
2002 24.17 19.28 6.06
2003 28.83 21.71 9.09
2004 25.85 18.04 9.52
2005 24.35 15.28 10.71
2006 27.85 17.49 12.56
2007 24.35 16.95 8.91
2008 29.34 20.04 11.63
2009 27.88 19.02 10.94
2010 36.74 19.67 21.25
2011 25.03 17.42 9.22
2012 23.83 17.91 7.21
2013 28.00 19.00 11.11
2014 30.00 22.00 10.26 Source: Author’s calculations
Figure 2: Decomposition of Overall Technical
Inefficiency (OTIE) into Pure Technical
Inefficiency (PTIE) and Scale Inefficiency
(SIE)
From figure 2, it can be inferred that the PTIE is
the main contributor to the OTIE, rather than
SIE. Therefore, it can be said that the primary
source of OTIE is the managerial
underperformance rather than inappropriate scale
size. Here, managerial underperformance or
inefficiency is related to the BCC efficiency.
Managerial inefficiency occurs when the
management of a firm fails to transform input (s)
into output (s) optimally.
Table 5: Year-wise number and percentage of
software firms on the basis of Returns to Scale
(RTS)
Year
No. of Firms Total
no. of
firms
% of firms
CRS IRS DRS CRS IRS DRS
2000 12 40 6 58 21 69 10
2001 13 43 5 61 21 70 8
2002 9 39 14 62 15 63 23
2003 7 24 37 68 10 35 54
2004 9 17 44 70 13 24 63
2005 12 24 47 83 14 29 57
2006 10 36 51 97 10 37 53
2007 13 41 44 98 13 42 45
2008 12 37 61 110 11 34 55
2009 15 37 60 112 13 33 54
2010 10 21 83 114 9 18 73
2011 15 27 73 115 13 23 63
2012 13 37 72 122 11 30 59
2013 11 40 74 125 9 32 59
2014 10 36 86 132 8 27 65
Source: Author’s Calculations
Table 5 shows the number and percentage shares
of Software firms exhibiting CRS, IRS and DRS
over the study period. Figure 3 graphically
represents the percentage shares of CRS, IRS
and DRS firms during 2000 to 2014. During the
initial years of study (viz. 2000, 2001 and 2002),
firms exhibiting IRS technology were
dominating the industry. During the later period
(from 2003 to 2014), DRS firms consistently
dominated the industry. Moreover, it can be said
that most of the Indian software firms are now
over utilizing its resources to produce their
output.
Figure 3: Year-wise Percentage Share of
Firms Exhibiting CRS, IRS and DRS
Technologies
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Table 6: Decomposition of Pareto-Koopmans
(PK) Efficiency over the Study Period
Year
Efficie
ncy of
Wages
&
salarie
s
Efficie
ncy of
Opera
ting
expens
es
Efficie
ncy of
Net
fixed
assets
Overal
l Input
efficie
ncy
Output
efficienc
y
PK
efficie
ncy
2000 0.937 0.984 0.843 0.921 0.833 0.770
2001 0.987 0.997 0.778 0.921 0.837 0.774
2002 0.979 0.992 0.753 0.908 0.843 0.767
2003 0.988 0.964 0.744 0.899 0.807 0.728
2004 0.960 0.993 0.786 0.913 0.850 0.780
2005 0.973 0.986 0.747 0.902 0.880 0.796
2006 0.939 0.990 0.754 0.894 0.855 0.765
2007 0.924 0.997 0.729 0.883 0.874 0.771
2008 0.902 0.996 0.732 0.877 0.835 0.737
2009 0.919 0.994 0.670 0.861 0.841 0.726
2010 0.899 0.996 0.715 0.870 0.841 0.734
2011 0.945 0.995 0.663 0.868 0.867 0.759
2012 0.950 0.991 0.668 0.870 0.861 0.750
2013 0.945 0.994 0.723 0.887 0.826 0.733
2014 0.943 0.994 0.666 0.868 0.859 0.747
Source: Author’s calculations.
Table 6 represents the input-specific efficiency
scores along with the overall input efficiency,
output efficiency and PK efficiency scores over
the study period. Figure 4 portrays the average
input-specific efficiency as well as overall input
efficiency over time. It is revealed that the
performance of the input ‘net fixed asset’ is the
worst. On the other hand, the input ‘operating
expenses’ performed best during the study
period. The performance of the input wages and
salaries were in between. Subsequently, it can
also be seen that the mean efficiency of net fixed
assets experienced a declining trend over the
study period.
Figure 4: Year-wise Trends of Input-specific
Efficiencies and Overall Input Efficiency of
Indian Software Industry
5.2. Analysis of Second-stage Regression
Results
The pre-regression diagnostic checking found
that there exist high correlation between the size
and DRS dummy and between profit rate and
managerial quality variables. Therefore, to
alleviate the problem of multi co-linearity, we
have estimated two regression models. In model-
I, we exclude managerial quality and RTS
dummies and in model -II, we exclude size and
profit rate as independent variables.
The regression results are summarized in the
following table 7. The regression coefficients of
size, profit rate, HHI, CRS dummy, ownership
dummy and net export are found to be positive
and statistically significant. On the other hand,
the coefficient of managerial quality (MQ), DRS
dummy, crisis dummy and group dummy are
found to be negative and statistically significant.
The coefficient of experience is found to be
positive but statistically insignificant. The values
of Wald chi-square are found to be statistically
significant for both the models. This implies that
both the models are overall significant.
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Table 7: Summary of the Second Stage
Regression Result
Regression results of Random-effects Tobit model
Dependent
variable
Pareto-Koopmans efficiency
score
Coefficients
Independent
variables
Model I Model II
Size 0.0201** -
Profit rate 0.1819*** -
HHI 0.0076*** 0.0063**
MQ - -0.1436***
CRS Dummy - 0.2006***
DRS Dummy - -0.0845***
Crisis dummy -0.1034*** -0.0459***
Ownership
Dummy
0.0759* 0.01529*
Group Dummy -0.2075** -0.0207*
Experience 0.0321 0.0141
Net export 0.0103* 0.0105*
Constant 0.4163*** 0.9412***
No. of observation 1427 1427
Wald χ2 201.25 320.39
Prob > Wald χ2 0.000 0.000
Log likelihood 53.21 86.37
*, **, *** => significant at 10%, 5% and 1%,
respectively
Source: Author’s calculations
The positive sign of the coefficient of the
variable ‘size’ indicates that the higher the size
of the firm, the higher the technical efficiency.
Since size is measured by taking the natural log
of sales, it can be inferred that on an average, a
firm with higher sales is technically more
efficient. On the other hand, it can also be said
that the larger the revenue of a firm, the greater
the specialization and differentiation, as a result,
the higher the efficiency. Herfindahl-
Hirschman Index (HHI) measures the extent of
market concentration. Both the models give a
positive and statistically significant coefficient of
HHI. This implies that the higher the market
concentration, the higher the technical efficiency
of the software firms. Theoretically, it is known
that the as market concentration falls,
competition among firms rises and hence
efficiency also rises. But our empirical finding
indicates the opposite. One of the reasonable
justifications would be rooted in the efficient
structure hypothesis. According to this
hypothesis, a low cost structure firm can increase
profits by lowering prices and raising market
share. Hence, higher efficiency in production
process leads to higher profit, which ultimately
raises market concentration. Therefore, a positive
relationship between efficiency and market
concentration can be explained by this
hypothesis. Our empirical result also supports
this. Profit rate measures the profitability of the
firm. The regression coefficient of this variable
is positive and significant at 1% level (model-I).
It can be inferred that there exists a positive and
statistically significant relationship between
profitability and technical efficiency of the
software firms over the period of study. It can
also be concluded that more profitable firms are
more efficient in transforming inputs into output.
Managerial Quality (MQ) has measured as the
ratio of operating expenses to the total assets
(Das et al. 2007). The coefficient of this variable
is found to be negative and significant at 1%
level (model-II). This indicates a positive
association between managerial quality and
technical efficiency of software firms over the
study period. The sign of the coefficient of MQ
is negative here. It implies that as the operating
expenses increases more proportionately to the
total assets, the managerial quality falls, and
consequently, the efficiency falls. Basically,
superior management of firm leads to reduction
in the operational expenses relative to the total
assets. Consequently, the negative sign of the
coefficient of MQ justifies the expected relation
between MQ and efficiency. We can conclude
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that on an average, the management of a
software firm was able to contain the day to day
operating costs without compromising the
efficiency of the production process during the
study period. The coefficient of net export is
found to be significant at the 10% level in both
the models. We can infer that efficiency of a firm
increases with higher foreign exchange earnings
and export orientation. The coefficients of both
CRS and DRS dummies are found to be
statistically significant at 1% level. The sign of
the coefficient of CRS dummy is positive
whereas it is negative for the DRS dummy. In
the regression analysis, IRS firms were
considered as benchmark. We can say that the
firms which exhibit CRS technology performed
better than the benchmark IRS firms. On the
other hand, the negative coefficient of DRS
dummy implies that the firms which exhibit DRS
technology poorly performed compared to the
benchmark IRS firms. To investigate the impact
of 2008’s subprime crisis in the US on the
performance of Indian ITeS industry, we
introduced a dummy, namely, crisis dummy.
The period 2000 to 2007 was taken as
benchmark. The regression coefficient of this
dummy is found to be negative and significant at
1% level in both the models. It can be said that
Indian software industry has performed less
efficiently after the US subprime crisis period
(i.e., during 2008 to 2014) as compared to the
benchmark period (i.e., the pre-crisis period). We
have introduced the ownership dummy to
examine whether the public Ltd. firms or the
private Ltd. firms performed better during the
study period. The coefficient of this variable is
found to be positive and statistically significant
at 10% level across both the models. It may be
inferred as a conclusion that the public Ltd. IT
firms perform better than their private
counterpart during the study period. Finally, the
coefficient of group dummy variable is found to
be negative and statistically significant in both
the models. It implies that the non-group (which
is considered as benchmark) firms have
performed better than their group counterparts
during the study period.
6. Conclusions
This paper tried to assess the performance of
Indian software industry during 2000 to 2014. In
the first-stage, the performance was evaluated in
terms of technical efficiency by using a non-
parametric technique, namely, data envelopment
analysis. Further, in the second-stage, the
determinants of technical efficiency were
investigated by employing random-effect tobit
model. The DEA results revealed that the
primary source of overall technical inefficiency
was the managerial inefficiency. It has also been
found that the firms exhibiting DRS technology
dominated the industry for most of the study
period. The input-specific efficiency analysis
showed that the input ‘net fixed assets’ was the
worst performer whereas the input ‘operating
expenses’ was the best performer during the
study period. The second-stage regression
analysis revealed that firm size and profitability
have positive impact on efficiency. On the other
hand, the negative sign of the coefficient of the
crisis dummy indicates that on an average, the
performance of the Indian software industry
declined during the post US-subprime crisis
period. Finally, the public limited and non-group
software firms appeared to be more efficient than
their private limited and group counterparts.
6.1. Policy Prescriptions
On the basis of the findings of the study, the
following policy prescriptions could be
incorporated by the policymakers and
stakeholders for further development of the
Indian software industry:
Since the study indicates a decline in
average performance of the industry after
2008’s subprime crisis in the US, both the
government and software firms’ managers
should give emphasis on diversifying the
market for the software products and
services. As the software services are mainly
exported to the developed countries, a
significant portion of revenue earning
depends on the mercy of global scenarios.
To mitigate such uncertainty, software firms
should try to increase their footprint in
domestic software market. To create a
vibrant domestic market for software
services, government should improve the
telecommunication facilities alongwith
rationalization of tax system by expediting
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the implementation of Goods and Services
Tax (GST).
The efficiency level of various categories of
the software firms (such as public limited,
private limited, group and non-group) was
found to be different in this study. This
implies that the policymakers should devise
segment-specific policies instead of a
common policy for the development of this
industry.
During the study, it was noticed that for
most of study period, the firms which
exhibit DRS technology, were dominating
the industry. It implies that on an average,
the Indian software industry over utilizes
resources. In this context, policymakers
should formulate policy in such a way that
ensures optimal use of resources. On the
other hand, the managers of the software
firms should incorporate relevant policies to
ensure optimal resource utilization.
The input-specific efficiency analysis
revealed that net fixed asset was the worst
performer as an input variable. In this
regard, the stakeholders of the software
industry should use this input more
judiciously in future. It is also found that
managerial underperformance is the main
reason for the overall inefficiency of the
software industry during the study period.
Therefore, it is recommended to improve the
managerial performance so that the inputs
could be transformed into output more
efficiently in future.
Since human capital is one the major inputs
for software industry, a continuous supply of
skilled software engineers and management
professionals is critical for further
development of this industry. In this context,
the government should generate more
technology and management-based
educational institutions nationwide to meet
the increasing demands for high quality,
skilled software professionals. Besides, the
quality of some existing educational
Institutes and Universities needs to be
enhanced as per the international standard.
6.2. Scope for Future Research
For future research, the present study can be
extended in the following ways:
In future research, one could apply the
stochastic-DEA technique to
incorporate the random error term in
evaluation technical efficiency of
software firms. Moreover, the input and
output-specific slacks might be
measured to analyze the sources of
inefficiency over time.
In second-stage regression analysis,
variables such as R&D investment,
advertising and marketing costs,
expenditure on imported capital goods
might be incorporated to examine their
influence on technical efficiency.
Further, the total factor productivity of
the software industry could be assessed
by applying DEA-based productivity
indices, such as Malmquist index,
Luenberger index etc.
List of Abbreviations
BCC Banker Charnes Cooper model
CCR Charnes Cooper Rhodes model
CMIE Center for monitoring Indian economy
CRS Constant returns to scale
DEA Data envelopment analysis
DRS Decreasing returns to scale
FDI Foreign direct investment
GST Goods and services tax
IRS Increasing returns to scale
IIM Indian Institute of Management
IIT Indian Institute of Technology
IT Information technology
ITeS Information technology-enabled services
MNCs Multinational companies
PIO People of Indian origin
PK Pareto-Koopmans model
R&D Research and development
STP Software technology park
US United States
VRS Variable returns to scale
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