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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

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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

[email protected]

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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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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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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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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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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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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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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