deshbhagatuniversity.indeshbhagatuniversity.in/Journalupload/5145cf3f-1251-4951... · 2018-09-12 · International Journal of Business Management & Research EDITORIAL BOARD Patron

International Journal of Business Management & Research EDITORIAL BOARD

Patron Editor-in-Chief

Dr. Zora Singh Dr. Payal Bassi Chancellor, Associate Director

Desh Bhagat University University School of Management

Desh Bhagat University

ASSOCIATE EDITORS

Dr. Rajni Saluja Mr. Rajinder Kumar Associate Professor, Assistant Professor

University School of Management, University School of Management

Desh Bhagat University Desh Bhagat University

ADIVSORY BOARD

Prof. (Dr.) R.K Uppal Prof. (Dr.)DeepakTandon Professor, Professor of Finance,

Department of Economics, Lal Bahadhur Institute of Management & Technology,

DAV College, Malout, Punjab New Delhi

Prof. (Dr.) BishnuPriya Mishra Prof. (Dr.) Pardeep Singh Walia Professor of Finance, Professor, Department of Commerce,

Uttkal University, Bhubaneswar, Post Graduate Government College for Girls,

Odisha Chandigarh

Prof. (Dr.) Navkiranjit Kaur Dhaliwal Professor, Department of Commerce,

Punjabi University, Patiala

Contents Page

1. Internet Based E-Banking Services and Bankers’ Perspective – An Indian

Experience

Rajinder Kumar Uppal 5

2. A Study of the Determinants of Foreign Direct Investment Inflows into

BRICS Economies

Payal Bassi & Rajni Saluja 20

3. Integrated Sustainable Business Model: A case of Bhutan Chamber of

Commerce and Industry

K. B. Singh,Namgay Dorji & Sangay Dorji 29

4. Assessing the Financial Efficiency in Indian Pharmaceutical Industry: An

Application of Data Envelopment Analysis

Jatin Goyal & Harpreet Kaur 47

5. A Study on the Impact of FII, FDI and GDs on GDP of India

Dr.R. Venkataraman & Thilak Venkatesan 63

6. A Comparative Study of Non-Performing Assets in Scheduled Commercial

Banks during Pre SARFAESI Period and Post SARFAESI Period

Munish Gupta & Naresh Malhotra 78

7. A Causal Relationship between Agricultural Production and Exports: An

Impact on Indian Economy

Waseem Ahmad Khan & Aditi Agrawal 91

8. Growth and Performance of the Education Sector and Economy in Haryana

Niyati Chaudhary 105

9. A Study of Businessman’s Perception towards Online Promotional Tools

Suman Kumari & Sonia 116

10. Recruitment and Selection Policies and Practices in Indian Commercial Banks

Simarpreet Kaur 126

11. Training and Development Practices in Private Sector Banks

Jaspreet Kaur & Payal Bassi 137

International Journal of Business Management & Research- A Bi-Annual UGC-Approved Journal (ISSN 2249-2143)

IJBMR, Vol. 7, Issue 2, July-Dec, 2017 Page 5

Internet Based E-Banking Services and Bankers’ Perspective

- An Indian Experience

Rajinder Kumar Uppal, Ph.D., D.Litt Head, Dept. of Economics, D.A.V. College, Malout (Punjab)

Abstract

The paper is an attempt to study the perceptions of bankers for internet based e-banking services

related with important issues like collaborative culture, training & development, knowledge

management. The perceptions of employees experienced in e-banking system are surveyed where the

study covers 60 employees of e-banks located in selected districts of Punjab during the first half of

July, 2017. The study concludes that there exists collaborative culture and employees are satisfied

with the working of e-channels and training programmes organized by e-banks, but not much satisfied

with the knowledge management and behavioural aspects of e-banking. The employees experienced

some frustration and a major problem of lack of knowledge of e-channels and their operating ways

while dealing with these channels, are the most prevalent ones.

Keywords: E-services in Banks, Policy Recommendations and Future Areas of Research

Introduction

The process of globalization has affected

each and every aspect of life where

technology has become the forerunner of

this dynamic change. In this changing

scenario, banking sector is no an

exception. Banking sector is passing

through a crucial transformation stage

where all vistas of working are changing at

a fast pace and technology is the most

dominating factor which helped the banks

to have a mix of knowledge with

innovative products/services to win the

competitive market. Prior to the electronic

era, the whole business was done manually

while only a little bit business was done

through computers, but now-days, every

transaction is made electronically through

various e-channels like, ATMs,

Credit/Debit Cards, Smart Cards, I-

Banking, M-Banking, Tele-Banking, EFTs

etc, which is also known as e-channels of

banks. These e-channels are becoming

more popular among the entire banks

world over where in India only foreign

banks, new private sector banks and a few

of public sector banks are fully

computerized and delivering services to

their customers electronically. Public

sector banks are also entering this e-age

banking but at very low speed.

Technology has enabled the banks to scale

borders, change strategic behaviour and

thus bring about new possibilities (Mittal



& Sanjay, 2007), It helped the banks to

improve their efficiency and save the time

& cost. Transactions through e-channels

cost much less to the banks than the

customers reaching the banks and doing

the transactions.

For customers, it offers 7 days X 24 hours

service irrespective to their locations.

Hence, it helps the banks to retain the

customers by providing better services.

But our public sector banks are still in race

to catch the new Information Technology.

In this respect, they are facing threat and

also motivation from new private sector

banks and foreign banks. The Information

Technology is not an option now, but it is

necessary even for their survival.

If computerization has today become a

byword in banking, its sustained growth is

wholly due to its role as an enabler in the

smooth and efficient conduct of a whole

gamut of banking practices (Shastri,2003).

Technology has played a significant role in

improving the efficiency of the financial

system in the recent years (Report on

Trend and Progress of Banking in India,

2005-06). Technology will play a catalyst

role in future (Nair, 2004). It will play a

critical role in the years to come by

providing better customer services (Uppal

& Kaur 2006). The several innovative

Information Technology based services

such as ATMs, EFT, anywhere anytime

banking, smart cards, net-banking etc are

no longer alien concepts to Indian banking

customers (Rangarajan, 2000). Indian

banking is fastly moving towards

Information Technology (Uppal & Kaur,

2007). The cost of the average payment

transactions on internet is minimum(Deva,

2007).

Whether the banks are public owned or

private is not the matter of concern, the

main thing is the success of every business

depends upon its employees. The

motivational aspects assume greater

significance in the present environment,

particularly in PSBs (Loganathan, 2006-

07). Now the working culture has been

changed totally i.e. from manual work to

computerized where the burden of paper

work and delivery time is reduced,

database management is improved with

lesser strain of work load. The employees

feel free to provide services through e-

channels and can spend their saved time on

other important activities. If the employees

are not satisfied from their job, working

conditions, work culture, management etc.

they can never make the customers

satisfied with better quality services. A big

question to be answered for all the banks is

how to manage human resources so that

optimum production in terms of best

services to customers can be get along

with the fulfillment of their individual



goals too. Apart from attracting new

customers, business organizations these

days realize the importance of retaining the

existence customers (Sudesh, 2007). There

is need to analyze the perceptions of bank

employees regarding their working

through e-channels in this new electronic

era. Some questions arisen from the above

discussion are:

1. Whether the bank employees are

satisfied by working through e-

channels or not and to what extent

they are satisfied or dis-satisfied?

2. Have they any problem in dealing

through e-channels, if so, what

type of problems they are facing

and how to solve these problems?

3. By working with which type of

banks either traditional or e-

banking, they are more satisfied?

In this paper, an attempt is made to

examine the perceptions of bank

employees dealing with internet based e-

banking services so that an analysis can be

made to find out the problems faced by

bank employees so that an appropriate

solution for their efficient performance can

be searched out.

Organization of the Paper

The whole paper is divided into six parts.

After brief introduction about the study,

section II review some studies related to

technology and banking sector. Section III

describes the objectives and methodology.

Section IV exhibits the results of the

survey. Section V suggests some policy

recommendations to make the e-banking

services efficient and popular among the

employees where last part concludes the

paper.

Review of Literature

In the past, some studies have been

conducted to study the impact of

information technology on banking sector,

these are:

Kumar, M. (2007) studied the impact of IT

on stock markets. The Information

Technology communication channels

provide universal connectivity and

ultimately it helps in increasing the

efficiency and productivity in all sectors of

the business.

Mittal, R.K. &Dhingra, S. (2007) studied

the role of technology in banking sector.

They analyzed investment scenario in

technology in Indian banks but this study

was related to the time period before the

Information Technology Act and at that

time technology in Indian banks was very

low. But both the researchers nicely

presented their views.

Padhy, K.C. (2007) studied the impact of

technology development in the banking

system and he also highlights the future of

banking sector. The core competencies

will provide comparative advantages.



T.M. Bhasin (2004) analyzed the e-

governance in Indian financial sector. The

paper concludes that e-governance has

paramount significance in transformation

of Indian financial sector.

Uppal, R.K. & Kaur, R. (2007) studied the

impact of Information Technology on

various parameters of bank performance

and concluded that Indian banking

industry is fastly moving towards IT. The

future of e-channels is very bright.

Uppal, R.K. (2006) studied the survival

factors of Public sector banks in the post

LPG era and Information Technology is

the major factor which affects the

efficiency of banks. The researcher

suggested new competitive strategies to

develop bank efficiency

Although abundant of literature is

available related to Information

Technology in banking, but a very few

studies are related to current data. The

present study is based on current data

related to e-channel of banks and it will be

an addition to the present literature in this

areas.

Objectives

To study and analyze satisfaction

level of bank employees working

through e-channels.

To study and examine the

effectiveness of culture, behaviour

and knowledge management etc.

To examine the problems faced by

the employees, if any, while

dealing with e-channels and to

suggests some measures to solve

these problems.

Methodology

This survey is conducted to examine the

perceptions of bank employees providing

e-banking services. The methodology

adopted for this study was based on

primary data collected through well-

defined and well-structured questionnaire.

The study was based on a sample of 60

employees working with e-channels and

having experience in dealing with

customers through e-channels. The survey

was conducted in the first half July, 2017

in different cities of Punjab as Ludhiana,

Bathinda, Jalandhar, Patiala and Sangrur.

A sample size of only 60 bank employees

taken due to shortage of time, finance and

the employees of only e-banks having

experience in dealing with e-channels were

surveyed for the study.

Data was analyzed with the help of

percentage method; ranking and weighted

average score (WAS) methods. The

respondents were asked to respond on a

five-point scale i.e. strongly agree, agree,

undecided, disagree, strongly disagree

regarding various statements. Weights of

2, 1, 0, -1, -2 were assigned to these scales

respectively for calculating the weighted



average score. On the other hand for the

purpose of ranking, the following step-by-

step methodology has been followed:

First Step: Firstly, in respect of each

aspect (Collaborative Culture, Training &

Development, Knowledge Management

etc.) the number of times a factor occupied

the 1st, 2

nd, …. Nth ranks were computed

in terms of frequency.

Second Step: Weights were assigned to

each rank in the descending order. For

example in collaborative culture aspect,

there were three factors with three ranks,

weightage pattern was as follows: 1st rank

was assigned with 3 weight, 2nd

rank with

2 weight and 3rd

rank with 1 weight.

Third Step: The sum of the above given

weights, for all the ranks, were calculated

which was denoted in the tables as total

score.

Fourth Step: Overall ranks were assigned

on the basis of total score values for each

factor calculated in the above step.

Limitation

The main limitation of the present study is

that a few bank employees were not

interested to properly fill up the

questionnaire either due to lack of time or

lesser interest.

Findings of the Study

(A) Socio-Economic Background of the

Respondents: From the survey, it is

evident that out of 60 respondents the

majority of the respondents i.e. 32 pc

were under the age of 26 years and 45

pc respondents were those having

income above 2 lakhs whereas 42 pc

have annual income between 1 to 2

lakhs. From 60 respondents, 82 pc

were male members where only 18 pc

were females that indicate the

women’s employment in banks is still

low. 58 pc were highly qualified with

master degrees where only 5 pc were

school pass outs. 42 pc of the

respondents have been employed in the

banks for less than 3 years where 40 pc

employed for more than 7 years at their

jobs. From total sample, 55 pc were

posted at manager level where 17 pc

were clerks. Overall, majority of the

respondents were well qualified males,

with rich experience and income.

Table:1 (a) Socio-Economic Background of the Respondents

Age Annual Income Educational Qualification

Range Responses %age Range

(Lakhs) Responses %age Range Responses %age

Less

than 26 19 32

Less

than 1 8 13

High

School 3 5

26 to

35 16 27 1 to 2 25 42

Bachelor

Degree 22 37



36 to

45 11 18

2 &

above 27 45

Master

Degree 35 58

Above

45 14 23

Doctorate

Degree 0 0

Source: Computed from Data Collected through Survey

Table: 1 (b)Socio-Economic Background of the Respondents

Job Duration Category of Job Range (Years) Responses Percentage Category Responses Percentage

Less than 3 25 42 Manager 33 55 3 to 4 6 10 Executives 17 28 5 to 6 5 3

Clerks 10 17 Above 6 24 40

Fig. 1 to Fig. 4 Shows Employees Profile

(B) Bankers’ Perspective regarding

Internet-based E-banking

Services

Table – 2 shows the responses

regarding collaborative culture aspect

of e-banking services where majority

of the respondents have opinion that e-

Age Variations

Less than 26

26 to 35

36 to 45

Above 45

Annual Income

1

2

3

4

Experince (In Years)

Less than 3

3 to 4

5 to 6

Above 6

Category of Job

1

2

3

4



banks helped to communicate

efficiency with peers as awarded with

first rank and on the five point likert

scale, the factors, enhanced the

collaborative culture and helped to

communicate with peers were

significantly contributing towards

collaborative culture in e-banks as

there WAS was above 1.

Table 2 Responses Regarding “Collaborative Culture Factor” in e-banks

E-banks have: Overall

Ranking SA A UD DA SDA WAS

(i) brought about group cohesiveness 3 22 23 4 10 1 0.92 (ii) enhanced the collaborative culture 2 17 33 7 3 0 1.07 (iii) helped to communicate efficiency with

peers 1 18 34 6 2 0 1.13


Note: SA- Strongly Agree, A- Agree, UD- Undecided, DA- Disagree & SDA- Strongly Disagree

Table- 3 shows the responses regarding

behavioural factors where majority of the

respondents (24 respondents) out of 60

awarded the factor e-bank have helped to

do routine work more efficiently with first

rank where increase interest in work was at

second position. On the rating scale too,

the respondents were strongly agreed that

e-banks helped to do routine work more

efficiently as its WAS was above 1

whereas other factors were not much

significant as there WAS was below 1 that

means employees experienced as e-banks

haven’t reduced work stress, confusions

and they were not satisfied with their jobs

too.

Table 3 Reponses Regarding “Behavioural Factor” in e-banks



(i) helped in reducing work stress 4 24 23 1 8 4 0.92

(ii) helped in reducing chaos and

confusions 5 17 23 10 10 0 0.78

(iii) helped to do routine work more

efficiently 1 30 28 2 0 0 1.47

(iv) increased interest in work 2 14 33 8 4 1 0.92

(v) increased level of motivation 3 15 28 8 9 0 0.82

(vi) increased level of job satisfaction 6 17 28 4 8 3 0.80

Table 4 exhibits the responses regarding

Training & Development where all the

factors like training enhanced confidence,

help to work more efficiently etc. have got

WAS more than 1 hence, all were

contributing significantly in training and

development policies of e-banks.



Table 4Responses Regarding “Training and Development Factor” in e-banks



(i) enhanced the skills 3 27 28 1 4 0 1.30 (ii) increased the confidence level through

training 1 27 28 1 4 0 1.30

(iii) increased effectiveness at job due to

training 2 36 20 2 2 0 1.50

(iv) organized training programmes to match

changing technical skills 4 17 33 9 1 0 1.10

(v) provided adequate training in handling e-

banks services 5 23 27 5 4 1 1.12

On the basis of ranking method, factor that

training enhanced the confidence was

awarded with first rank and increased

effectiveness at the job was awarded with

second rank. We may conclude that

employees were satisfied with the training

programmes organized by the e-banks to

improve their efficiency and confidence in

working.

Table 5 examines the responses regarding

Knowledge Management where majority

of the respondents were strongly agree

with the only statement that e-banks have

empowered with better access to

information as its WAS was above but

others were not having significant

contribution towards knowledge

management as their WAS was below 1.

On the basis of ranking too, the same

statement was awarded with first rank and

others like control over work, enhanced

creativity were at second and third

positions respectively. Hence, it is

concluded that knowledge management

was still not according to the desired level.

Table 5 Responses Regarding “Knowledge Management Factor” in e-banks


Ranking SA A

UD DA SDA WAS

(i) empowered with better access to

information 1 40 17 1 2 0 1.58

(ii) empowered with more control over

work 2 19 27 2 9 3 0.83

(iii) enhanced creativity 6 12 23 9 12 4 0.45

(iv) empowered to solve problems 5 14 27 4 10 5 0.58

(v) enhanced capacity to contribute in

research & development activities 4 9 37 13 0 1 0.88

(vi) increased involvement in decision –

making 3 14 30 4 8 4 0.70

(vii) magnified abilities to think and articulate

thoughts 7 7 31 9 5 8 0.40




Table – 6 examines that to what extent the

respondents were satisfied with the ways

of working through e-channels. It is

evident that 36 out of 60 respondents were

satisfied where only 15 were highly

satisfied with the ways of working through

e-channels. Overall its WAS was above 1

i.e. 1.07 hence, it can be said that

employees were satisfied by working

through e-channels.

Table 6 Responses Regarding the Satisfaction Level among the Bank Employees Regarding

the Way of Working through e-channels

Highly

Satisfied Satisfied Undecided Dissatisfied

Highly

Dissatisfied WAS

15 36 7 2 0 1.07 Source: Computed from Data Collected through Survey

Table 7 examines that out of 60

respondents, 41 were agreed with the

statement that with the downsizing of

employees efficiency has increased but

overall, its WAS was below 1 and hence,

we can say that all the employees were not

experienced with that downsizing

increased the efficiency.

Table 7Responses regarding the statement, “There is a downsizing of employees due to the

emerging technology but efficiency in terms of productivity has increased.”

Strongly Agree Agree Undecided Disagree Strongly Disagree WAS 12 41 0 5 2 0.93


Table 8 exhibits the responses regarding

various negative effects of e-banking

system. Only 32 pc of the total

respondents felt that there was no

frustration while dealing electronically but

others found that they get frustrated some

times. Majority of the respondents were in

favour that e-banks have increased their

work-efficiency whereas 32 pc feels little

strain due to e-banking system and 22 pc

experienced very much strain.

Table 8 Responses Regarding the Negative Effects of e-channels

Effects of e-channels Very

Much Some

What A

Little Very

Little Not at

all (i) frustration in getting work done

electronically 7

(11.67) 12

(20.00) 8

(13.33) 14

(23.33) 19

(31.67) (ii) increased work efficiency but reduced

personal efficiency 15

(25.00) 13

(21.67) 14

(23.33) 11

(18.33) 7

(11.67) (iii) strain, if any, due to e-banking as

compared to manual banking 13

(21.67) 10

(16.67) 10

(16.67) 19

(31.67) 8

(13.33) Source: Computed from Data Collected through Survey

Table 9 tested the responses for problems

faced by the respondents while dealing

with customers electronically. It is evident

that only three problems were the most



dominating problems as there WAS is

above 1 these were illiteracy, increasing

expectations of the customers and lack of

knowledge regarding how to use/operate

various e-channels, while others like lack

of trust and problem of security etc. were

not much important in the opinion of

majority of the respondents.

Table 9Responses Regarding the Problems Face by the Employees while dealing through e-

channels

Problems SA A UD DA SDA WAS (i) illiteracy 32 20 4 4 0 1.33 (ii) increasing expectations of customers 19 36 3 2 0 1.20 (iii) lack of trust 8 33 7 11 1 0.60

(iv) lack of knowledge regarding how to

use/operate 30 26 3 1 0 1.42

(v) problem of security 24 20 6 7 3 0.83 (vi) resist to change 13 30 9 7 1 0.78 (vii) unawareness among the customers 26 25 4 3 2 0.12


Table 10 examines that how many

problems the respondents face when they

work through e-channels. It is evident that

50 pc of the respondents experienced that

the problem of lack of knowledge about

these channels was interrupting the work

to a large extent while 35 pc experienced

its effect to some extent. Lack of proper

training was also affecting the work very

much in the opinion of 43 pc respondents,

and they experienced that this problem was

a major obstacle and obsolete technology

etc. were the other ones affecting the

working of bank employees negatively.

Table 10Response-s Regarding the Difficulties Faced by the Employees to Work with e-channels

Difficulties Very Much Some What A Little Very Little Not at all

(i) Lack of knowledge 30

(50.00) 21

(35.00) 4

(6.67) 4

(6.67) 1

(1.67) (ii) lack of proper

training 26

(43.33) 24

(40.00) 5

(8.33) 3

(5.00) 2

(3.33) (iii) obsolete technology 14

(23.33) 25

(41.67) 9

(15.00) 6

(10.00) 6

(10.00) (iv) technology up

gradation 13

(21.67) 27

(45.00) 15

(25.00) 4

(6.67) 1

(1.67) (v) technical bottlenecks 20

(33.33) 23

(38.33) 11

(18.33) 4

(6.67) 2

(3.33) Note: Values in the parenthesis show percentage of responses

Glaring Issues

1. Behavioural aspect towards e-

banking is not developed to the

desired extent.

2. Knowledge management is also not

developed properly.

3. Frustration and strain is still

prevalent among the employees



due to technical faults or poor

network etc.

4. E-channels are less popular mainly

because of problems of illiteracy,

increasing expectations of

customers, lack of security and

knowledge, lack of proper training

etc.

Policy Recommendations

E-banking is a major issue for all the banks

especially in the current transformation

era. Therefore, special care is needed to

manage these services efficiently to make

the bank employees satisfied with the

working conditions, culture etc. so that

they can further provide best services to

the valuable customers. As we all know

that satisfied customer is an asset for the

banks and the whole prestige of an

organization is attached with the working

of employees in a manner that how they

make their customers delighted. Due to e-

banking system, work culture is totally

changed and there are some problems due

to which employees feel uncomfortable to

work electronically. Hence, there is a need

to solve these problems with effective

implementation of some practical

strategies to make e-banking more popular

and friendly among the employees. In this

context, below some suggestions are given

in the light of deficiencies experienced

during this survey.

Teamwork is a need of the hour, so

create collaborative culture for

work by motivating the employees

to work together. For this purpose,

organize the people in different

task groups with specified targets

and time period, it will definitely

result in collaborative work culture

and help in timely achievement of

the desired goals.

Effective training especially on the

job, should be given to all the

employees engaged in e-banking

system and are in need to work

efficiently so that their stress and

confusions can be eliminated.

To access training needs, it will be

more effective to fix meeting for

every last day of the month to

listen their problems, confusions. It

will help to eliminate their

frustration due to some difficulties

and technical problems occurred

during their working hours.

Research and development is an

important task to grow and lead in

today’s competitive market. So

every bank should establish

separate department by involving

all the employees with creative

brains and by welcoming their

suggestions to motivate them,



which will help to provide

innovative services to the

customers.

Make all the employees up to date

by providing entire current

information for any aspect of

banking services and products to

develop knowledge management

concept.

As downsizing have negative and

wrong concept in the minds of

employees, make it clear and

implement in renewed ways and

effectively, so that productivity

cannot be affected negatively.

From the survey it is observed that

the problem of lack of knowledge

regarding new channels and how to

operate and use these channels is a

major bottleneck in the way of

progress of e-banking channels. So

firstly trained the employees about

each and every new concept of e-

banking system only then they can

provide right information to the

customers to make them aware

about these e-channels. Customers

prefer to know anything better on

the counter, so arrange demo for

how to use e-channels at the

counter rather through

advertisements in newspapers and

television etc. More particularly,

separate cell should be established

for the queries of the customers,

which will be more helpful for

awareness about this new system

among the masses.

Every bank should establish

separate HRD department, which

can control all the aspects/issues,

related to human resources. It is

necessary, because now it has

become a continuous and full time

process to create, develop and then

maintain the human resources in a

way to make them more efficient

and adaptable to the changing

environment.

Future Areas of Research

1. Comprehensive study is required to

know the employee’s satisfaction

level in the e-banking working

environment as compared to that of

traditional banking at bank group

level and at individual bank level.

2. Comparative study to examine the

perceptions of employees of e-

banks in rural areas as compared to

that of urban areas especially the

problems they generally face while

working through computers and

proving customer services

electronically.



3. In-depth survey of the e-banking

related all aspects adopted by the

various banks and to what extent

these are efficient.

Conclusion

From the survey, it is concluded that

although the employees are satisfied with

the working of e-channels, but still they

are facing some problems. To make the

employees more efficient and getting their

services to the desired goals, it is necessary

to make them satisfied with their jobs by

providing proper training, friendly work

environment, collaborative culture and up

to date knowledge about all customer’s

demands/queries. e-banking is at infant

stage in Indian banking industry but have

enveloped the whole banking industry into

a single net. Hence, there is a need to

improve and make some effective and

practical efforts to bring the banks out of

the wood.

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A Study of the Determinants of Foreign Direct Investment Inflows into

BRICS Economies

Payal Bassi* Rajni Saluja** *Associate Director, University School of Management, Desh Bhagat University, MandiGobindgarh

**Associate Professor, University School of Management, Desh Bhagat University,

MandiGobindgarh

Abstract Foreign Direct Investment has become sine-quo-non for the economic development of both developed

and developing countries. FDI is defined as a non-debt financial capital. FDI is also described as

“investment into the business of a country by a company in another country”. The BRICS economies

have been identified as some of the fastest growing countries and the engines of the global recovery

process which underscores the changed role of these economies. BRICS have the potential to evolve

into a powerful economic bloc. This study intends to evaluate the trends and patterns of FDI Inflows

into BRICS. The relationship between FDI Inflows and its selected determinants are examined. The

study is based on secondary time series data collected for ten years ranging from FY 2005-06 to FY

2014-15. GDP, Financial Position, Exchange rate, Trade openness etc. are the variables taken as the

determinants of FDI Inflows. Data is analyzed by using correlation analysis, linear regression analysis

and compounded annual growth rate. Significant relationship is found between selected variables and

FDI Inflows and these variables are positively correlated. Equations were formulated using the

regression analysis and they were found to be good fit to predict the FDI Inflows. Policy makers

should make concerted efforts to improve these variables under study which will result in increased

foreign capital inflow.

Keywords: FDI Inflows, Determinants, BRICS Economies

1. Introduction

Foreign Direct Investment has become

sine-quo-non for the economic

development of both developed and

developing countries. FDI is defined as a

non-debt financial capital. FDI is also

described as “investment into the business

of a country by a company in another

country”. FDI has proven to be an ‘engine

of growth’ of a country in the modern era.

FDI is a key element in this rapidly

evolving international globalization. FDI

provides a means for creating direct, stable

and long-lasting links between economies.

The present study aims to study the trends

and patterns of FDI Inflows into BRICS

economies and examine the determinants

of FDI flows.

1.1 Determinants of FDI

The determinant varies from one

country to another due their

unique characteristics and

opportunities for potential

investors. In specific determinants

of FDI in India are as:

StablePolicies: India stable

economic and socio policies have



attracted investors across border.

Investors prefer countries with

stable economic policies. If

government makes changes in

policies which will have effect on

the business,the business requires

a lot of funds to be deployed and

any change in policy against the

investor will have a negative

effect.

EconomicFactors: Different

economic factors encourage

inward FDI. These include

interest loans, tax breaks, grants,

subsidies and removal of

restrictions and limitation. The

government of India has given

many tax exemption and

subsidies to foreign investors who

would help in developing the

economy.

Cheap Labour: There is

abundant labour available in India

in terms of skilled and unskilled

human resources. Foreign

investors will take advantage of

difference in cost of labour as we

have cheap and skilled labours.

For example foreign firms have

invested in BPO’ in India which

required skilled labour and we

have been providing the same.

Basic Infrastructure: India

though is a developing country, it

has developed special economic

zones where there have focused

to build required infrastructure

such as roads, effective

transportation and registered

carrier departure worldwide,

Information and communication

network / technology, powers,

financial institutions and legal

system and other basic amenities

which are must for success of

business. A sound legal system

and modern infrastructure

supporting an efficient

distribution of goods and services

in host country.

Unexplored Markets: In India,

there is large scope for investors

because there is a large section of

markets have not explored or

unutilized. In India, there is

enormous potential customer

market with large middle class

income group who would be

target group for new markets. For

example BPO was one sector

where investors had large scope

exploring markets where service

was provided with just a call,

with almost customer satisfaction.



Availability of Natural

Resources: As we know that

India has large volume of natural

resources such as coal, iron ore,

natural gas etc. If natural

resources are available, they can

be used in production process or

for extraction of mines by foreign

investors.

2.0 Concept of BRICS

The acronym was coined by Jim O’

Neil in a 2001 paper entitled, ‘Building

Better Global Economic BRICS’.

BRICS originally BRIC before the

inclusion of South Africa in 2010 is the

title of an association of emerging

national economies: Brazil, Russia,

India, China and South Africa. With

the exception of Russia, the BRICS

members are all developing

industrialized countries but they are

distinguished by their fast growing

economies and significant influence on

regional and economic affairs.

Table 2.1: General Profile of BRICS Nations

Profile/Country Brazil Russia India China South

Africa

Area of territory

(1000 sq km.)

8516 17125 3287 9600 1221

Capital city Brasilla Moscow New Delhi Beijing Pretoria

Mid-Year Population

(Million persons)

204 146 1254 1371 55

Population Density

(Persons per sq. km.)

24.0 8.6 396 143 44.2

National Currency Real- R$ Rouble- Rub Rupee- INR Renminbi- RMB Rand-ZAR

Source: BRICS Joint Statistical Publication, 2016

Table 2.1 highlights general

information of BRICS countries.

Population density is highest in India

that is 396 persons per sq. km.

Table 2.2: Economic & Social Indicators of BRICS Nations

Indicators/Country Brazil Russia India China South Africa

Population (Mid-Year)Million Persons 204 146 1254 1371 55.0

Male Population (%) 49.4 46.3 51.8 51.2 26.9

Female Population (%) 50.6 53.7 48.2 48.8 28.1

Labour Force share (%) 66.5 52.5 39.5 56.3 38.4

Unemployment Rate (%) 6.9 5.6 2.2 4.1 25.3

GDP (at current prices)Billion US$ 1772 1332 2035 11006 313

Per capita GDP

(at current prices/US$)

8668 9098 1586 8027 6483

Source: BRICS Joint Statistical Publication, 2016



Table 2.2 indicates Economic and

Social Indicators of BRICS Nations.

Female population is less as compared

to male population in India and China

that is 48.2 percent and 48.8 percent

respectively. It is opposite in case of

Brazil, Russia and South Africa.

Unemployment rate is lowest in India

that is 2.2 percent and highest in

Russia that is 25.3 percent. Per capita

GDP at current prices in terms of US$

is lowest in India that is 1586 as

compared to other BRICS economies.

3.0 Objectives of the Study

To study the trend and pattern of

Foreign Direct Investment Inflows

into BRICS Nations

To examine the influence of Gross

Domestic Product (GDP) , Trade

Openness (TO), Share of External

Debt as percentage of GDP

(EXDGDP), Annual Average

Exchange rate (EXR), Foreign

Exchange Reserves as percentage

of GDP (RESGDP)

4.0 Research Hypothesis

To fulfil the objectives of this study the

following hypothesis have been set:

H01: There is no significant relationship

between FDI Inflows and its determinants

in terms of proxy variables (Gross

Domestic Product, Trade Openness, Share

of External Debt, Annual Average

Exchange rate, Foreign Exchange

Reserves as percentage of GDP) in BRICS

Nations

H11: There is significant relationship

between FDI Inflows and its determinants

in terms of proxy variables (Gross

Domestic Product, Trade Openness, Share

of External Debt, Annual Average

Exchange rate, Foreign Exchange

Reserves as percentage of GDP) in BRICS

Nations.

5.0 Research Methodology

5.1 Research Design:The study is

descriptive and analytical as it aims to

study the relationship between the selected

variables and FDI Inflows in BRICS.

5.2 Sources of Data: The data for this

study has been collected from various

secondary sources like BRICS Joint

Statistical Publication, World

Development Indicators Report and other

online publications. National and

International Journals related to Foreign

Direct Investment and BRICS has also

been referred to.

5.3 Statistical tools used:Descriptive

Statistics, Simple Growth Rate, Compound



Annual Growth Rate, Correlation

Analysis, Regression Analysis

5.4 Period of the Study:The study is

conducted for a period of ten financial

years starting from 2005-2006 to 2015-

2016.

5.5 Variables used in the study:

Gross Domestic Product

(GDP)

Trade Openness (TO)

Annual Average Exchange

Rate (EXR)

Share of External Debt as

percentage of GDP (EXDGDP)

Foreign Exchange Reserves

as percentage of GDP (RESGDP)

6.1: FDI Inflows in BRICS Nations

Table 6.1: FDI Inflows in BRICS Nations

(Million US$)

Year/Natio

n

Brazil Russia India China South Africa

Value GR Value GR Value GR Value GR Value GR

2006

19418 - - - 22826 - 63021 - 312 -

2007

44579 1.2

9

55874 - 34843 0.5

3

74768 0.1

9

6530 19.

9

2008

50716 0.1

3

74783 0.3

4

41873 0.2

0

92395 0.2

4

9220 0.4

1

2009

31481 -

0.3

7

36583 -

0.5

1

37745 -

0.0

9

90033 -

0.0

3

7535 -

0.1

8

2010

88452 1.8

0

43168 0.1

8

34847 -

0.0

8

105735 0.1

7

3635 -

0.5

2

2011

10158 -

0.8

8

55084 0,2

7

46556 0.3

4

116011 0.0

9

4248 0.1

7

2012

86607 7.5

3

50588 -

0.0

8

34298 -

0.2

6

111716 -

0.0

4

4559 0.0

7

2013

69181 -

0.2

0

69219 0.3

7

36046 0.0

5

117586 0.0

5

8304 0.8

2

2014

96895 0.4

0

22891 -

0.6

7

45148 0.2

5

119562 0.0

2

5775 -

0.3

0

2015

75075 -

0.2

3

- - 55457 0.2

2

126267 0.0

6

1774 -

0.6

9

Mean

57256.2 51023.7

5

38963.

9

101709.

4

5189.2

SD

28954.1

2

15801.3

8

8381.5

4

19850.5

9

2697.0

8

CV

0.506 0.310 0.215 0.195 0.520



CAGR 14.48 -8.54 9.28 7.20 18.98 Source: Calculated values

6 Analysis & Interpretation

From the table 6.1, it is seen that there is

no particular pattern found in FDI Inflows.

There is a mixed trend in FDI Inflows.

Compound Annual Growth Rate is

calculated to be 14.48 percent for Brazil,

9.28 percent for India, 7.20 percent for

China, 18.98 percent for South Africa for

the period under study. This means the

FDI inflows have increased on an average

of 14.48 percent, 9.28 percent. 7.20

percent, 18.98 percent year after year for

ten years respectively for these nations.

The compound annual growth rate is

negative in case of Russia that is – 8.54

percent indicating negative increase in FDI

Inflows over a period of ten years.

6.2: To examine the influence of Gross

Domestic Product (GDP), Trade

Openness (TO), Share of External Debt

as percentage of GDP (EXDGDP),

Annual Average Exchange Rate (EXR),

Foreign Exchange Reserves as

percentage of GDP (RESGDP)

Table 6.2.1 shows correlation coefficients

of FDI and its determinants for BRICS

Nations. In case of Brazil there is moderate

relationship between FDI and GDP, FDI

and RESGDP.

6.2.1: Correlation Analysis: For the purpose of testing hypothesis, correlation analysis has been used.

Table 6.2.1: Correlation Coefficients of FDI and its Determinants for BRICS Nations

Determinants/

Nations

Brazil Russia India China South Africa

FDI P

value

FDI P

value

FDI P

value

FDI P

value

FDI P

value

GDP

.743 .014 -

.238

.570 .679 .031 .948 .000 -

0.15

.967

TO

-

.008

.982 .687 .060 .165 .671 -

.331

.385 .109 .765

EXR

.202 .577 -

.641

.087 .528 .117 -

.938

.000 -

.100

.783

EXGDP

-

.119

.744 -

.554

.155 .599 .067 .129 .722 .278 .468

RESGDP

.653 .041 .297 .475 -

.208

.563 .238 .508 .268 .454

Source: Calculated Values

6.2.2 Regression Analysis: To further verify the relationship and to predict the FDI Inflows,

regression analysis has been used.

Table 6.2.2 (a): Regression Equations of FDI and its Determinants for BRICS Nations (Brazil,

Russia & India)

Nation Brazil Russia India

Dependent

Variable/

Equation R

Square

Equation R

Square

Equation R

Square



Independent

Variable

FDI -3520.115+52.392

(GDP)

.552 64169.046-

13.682

(GDP)

.057 23192.237+120

(GDP)

.460

FDI 67875.640-4.184

(TO)

.000 -15578.912

+ 70.672

(TO)

.472 36478.464 +

101.781 (TO)

.000

FDI 46981.381+.8649.473

(EXR)

.041 88524.844-

1126.582

(EXR)

.411 8011.109

+622.667 (EXR)

.279

FDI 87189.347-1512.937

(EXDGDP)

.014 120670.565-

2049.943

(EXDGDP)

.307 -

1761.291+2040.30

(EXDGDP)

.359

FDI -31190.125 + 4.745

(RESGDP)

.427 54152.970-

.027

(RESGDP)

.096 45873.554-

30.831(RESGDP)

0.43


Table 6.2.2 (b): Regression Equations of FDI and its Determinants for BRICS Nations (China &

South Africa)

Nation China South Africa

Dependent

Variable/

Independent

Variable

Equation R

Square

Equation R

Square

FDI 46486.001+8.404(GDP)

.899 5362.804- 8.26 (GDP) .000

FDI 223877.658-235.857(TO)

.232 4027.079+ 1.129 (TO) .006

FDI 341949.500-36072.087(EXR)

.880 6110.699- 100.85

(EXR)

.010

FDI 91433.829+970.309

(EXDGDP)

.057 1872.779+ 121.265

(EXDGDP)

.077

FDI 60350.009+.997(RESGDP)

.017 -2073.95 +.359

(RESGDP)

.072


Regression Equations evolved in table

6.2.2 (a) and table 6.2.2 (b) are of good fit

and the R square values seems to be

significant in explaining the variations in

the dependent variable FDI. Using the

above equations, FDI Inflows can be

predicted with the help of independent

variables.

6.2.3: Multiple Regression Analysis: To get multiple regression equation of FDI and its

determinants for BRICS Nations



Table 6.2.3: Multiple Regression Equation of FDI and its Determinants for BRICS Nations

Dependent

Variable/Nation/

Independent Variable

Equation R

Square

FDI (Brazil) -270745.387+78.464 (GDP)+ 341.159 (TO)- 18576.522 (EXR)

+ 7535.639 (EXDGDP) + 2.256 (RESGDP)

.866

FDI (Russia) -132661.850+ 87.052 (GDP) + 124.219 (TO)- 2001.024 (EXR)

+ 1388.176 (EXDGDP) + .021 (RESGDP)

.917

FDI (India) -185688.25 +197 (GDP) +12117.008 (TO) + 2541.829 (EXR) –

1668.513 (EXDGDP) +133.486 (RESGDP)

.962

FDI (China) 234381.279 + 4.648 (GDP) – 24.315 (TO) – 19484.513 (EXR)

– 767.161(EXDGDP) -.307 (RESGDP)

.966

FDI (South Africa) -69923.638- 163.858 (GDP)- 6.006 (TO) + 4275.275 (EXR) +

544.802 (EXDGDP) + 3.008 (RESGDP)

.841


Table 6.2.3 shows multiple regression

equations for FDI & its determinants for

BRICS Nations. The value of R Square in

case of Brazil is .866 which means that

independent variables GDP, TO, EXR,

EXDGDP, RESGDP can explain 86.6

percent of the variations in the dependent

variable which is FDI Inflows. From the

given equation, FDI Inflows can be

predicted with the help of GDP, TO, EXR,

EXDGDP, RESGDP. In case of Russia,

value of R square is .917 which indicates

that selected independent variables can

explain 91.7 percent of the dependent

variable. On the same lines, 96.2 percent,

96.6 percent, 84.1 percent of the variations

in the dependent variable that is FDI

Inflows in India, China, South Africa is

explained by independent variables on

basis value of R square of India (.962),

China (.966) & South Africa (.841)

respectively. From the above equations,

FDI can be predicted with help of gross

domestic product, trade openness, annual

average exchange rate, external debt as

percentage of GDP and foreign exchange

reserves as percentage of GDP.

7.0 Conclusions

Foreign capital is sine-quo-non for the

development of emerging economies.

Policy makers should make concerted

efforts to improve these variables under

study which will result in increased foreign

capital inflow.

References

Agrawal, Gaurav (2015). Foreign Direct

Investment and Economic in BRICS

Economies: A Panel Data Analysis,

Journal of Economics, Business and



Management, 3(4):421-424.

http://www.joebm.com/papers/221-

W00050.pdf

Himachalapathy, R. & V, Kavya A Study

on the determinants of Foreign Direct

InvestmentInflowsintoIndia.http://www.sje

c.edu.in/pdf/Determinants%20of%20Forei

gn%20Direct.pdf

Narender& Devi Shilpi (2015) Foreign

Direct Investment and Growth in BRICS

Countries: A Review.International

Journal of Science and Research (IJSR),

4(4):1932-1934.

https://www.ijsr.net/archive/v4i4/SUB153

542.pdf

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BRICS Economic Growth, Elsevier

Procedia Economics and Finance,32:981-

985.

http://www.sciencedirect.com/science/artic

le/pii/S2212567115015579

Ranjan,Vinit& Agrawal, Gaurav (2011).

FDI Inflow Determinants in BRIC

Countries: A Panel Data Analysis.

International Business Research, 4(4):255-

263.

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20Ranjan_2011.pdf

http://www.business-and-

management.org/library/2010/5_3--1--13

Vijayakumar,Sridharan,Rao.pdf

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Integrated Sustainable Business Model: A case of Bhutan Chamber of

Commerce and Industry

K. B. Singh* Namgay Dorji ** Sangay Dorji**

*Colombo Plan Faculty (on deputation through MEA, Govt. of India) Gaeddu College of Business

Studies, Royal University of Bhutan, Gedu, Chukha (Dist.), Bhutan

**Associate Lecturer, Gaeddu College of Business Studies, Royal University of Bhutan, Bhutan

Abstract

For a not-for-profit organization such as chamber of commerce, revenue depends mostly on the

quality of services rendered to its members. Sustainability of any organization depends on the amount

of revenue it generates. This exploratory research aims to address the issue of financial resource

constraint faced by Bhutan Chamber of Commerce and Industry (BCCI) and ultimately develop a

revenue model for the organization. At present BCCI depends mostly on the grants from the

government and membership fees. We develop and test a revenue model for BCCI which will help it

in identifying the quality and quantity aspects of the existing services. This model named as

Integrated Sustainable Business Model will help organization in crafting strategies for different

services based on the category they fall into. Need, Content, Cost, Delivery and Evaluation

(NCCDE) parameters are used to measures the quality index of the services. Analysis of data

indicates in the prescription of a range of services which the organization should offer and charge in

order to increase its revenue which will lead make it sustainable.

Keywords: Integrated Sustainability Business Model, Quality- Quantity Index, Quality-Quantity

Table, Quality-Quantity Matrix

Introduction

Bhutan Chamber of Commerce and

Industry (BCCI) is a not-for-profit making

service oriented organization with business

community members all over Bhutan. It

was established in 1980 under the Royal

Command of His Majesty, the fourth

DrukGyelpo Jigme SingyeWangchuk

(Bhutan Chamber of Commerce &

Industry, 2013). BCCI secretariat located

in the capital city Thimphu is mainly

supported by three departments namely -

General Affairs Department, Business

Support Department and Research and

Policy Department. As of now it has six

regional offices located across the country

(Bhutan Chamber of Commerce &

Industry, 2013).

The organization being the apex-body for

private sector in Bhutan, it plays crucial

role in economic development of the

nation. It has a role in creating conducive

environment for the growth and

development of the private sector. So,



BCCI also being the facilitator for

business development in the nation acts as

a bridge between the private sector and

government. The organization also

provides advocacy service and lobby

facilities with effective business support

services delivery system.

As recognized by business enterprises in

different nations, BCCI also considers

genuine need for a revenue model since

long. According to Zott, Amit & Massa

(2011) revenue model plays a central role

in explaining firm performance and hence

its profitability. Such model can also help

in key decision making for both new as

well as existing organizations. The authors

mention that once the recourses are in

place the effect of the external

environmental factors will be at lesser risk

and can be helpful in the analysis and

improvement of the model for better

results.

BCCI aims to render effective services to

the private sector enterprises. However,

with the limited manpower and financial

constraints, it is unable to render attractive

business development services as

expected. Further, it has not been able to

diversify its services due to so many

inherent constraints such as limited

government grant, non-existence of pay for

services attitude amongst Bhutanese

private sector members and free services

rendered to the government and other

agencies.

In this backdrop we intend to develop a

Revenue Model for BCCI. The model is

expected to indicate the index of both

quality and quantity of all the existing

services of the organization. In addition,

the model will help in categorizing the

services in different group of matrices

which can be used by BCCI to generate

adequate revenue by providing quality

services and satisfaction to its members.

Literature Review

In the following paragraph we present

review of previous studies on revenue,

quality, 3P’s and chamber of commerce.

The focus is on the quality concept of the

services relating to satisfaction,

profitability, revenue growth and cost

reduction.

Review on Importance of Revenue for the

Organization

Literature on revenue model is very

limited. Yet, there are a few papers which

has examined the determinants of the

profits of various organizations.

According to Shunlong,

Xiaodong&Lingying (2013) non-profit

intermediary organizations’ revenues were

originally from membership dues,

government funding, social donations,

income from paid services and the



operations of assets. Revenue to any

business or organization is like air, food

and water to mankind. Without revenue, a

business is as lifeless as plant without

water (Mullin and Komisar, 2009). In this

line revenue means money that the

organization receives from its customers in

return for whatever it offers and revenue is

life blood of any organizations.

Review on Quality of Services and

Customer Satisfaction

Many studies have found that the quality

of products and services are the most

important aspect of revenue. Similarly,

costs were found to be detrimental factor

to the amount of income firms generate

(Shah, 2009). On the quality aspect of

goods and services, Jacobson and Aaker

(1987) found the quality to have positive

influence on the market share. Similarly,

Homburg, Koschate and Hoyer (2005)

have stated that customers will pay more if

they are satisfied with the quality of the

services. In other words, customers who

are satisfied with high quality have a

higher perception of the value of firm’s

offerings and will be more loyal to the firm

for the long period of time. This is also

found to lead the spread of the word

through word-of-mouth that helps the firm

to advertise its quality of services offered.

Alike, Rust, Moorman and Dickson,

(2002) reaffirmed that quality

improvements result in increased customer

satisfaction, which will result in improved

efficiency, dependability and reliability,

which in turn will reduce costs through

efficiencies in the process and also

increase revenue.

Moreover, Rosenfeld (2009) and Scheeres

(2010) have found that effort to improve

quality always results in reduced costs due

to increased efficiencies, and therefore,

increasing the prospects of revenue growth

and profitability. Hence, offering quality

services stems as the main role of the

business organization. According to Shah

(2009) Chambers can improve the quality

of the services by listening to their

members, competitors’ members and their

own employees. This allows management

to identify the most important inputs that

have the largest impact on perceptions of

quality by the consumers and allocate

resources accordingly.

In addition to the quality of services

offered, Shah (2009) also highlighted the

importance of costs in understanding and

estimating the profits of an organization.

He recommends alternatives of means to

reduce the cost. Jhon (2013) also

suggested various institutes to develop

strategies to increase number of sources of

revenue and cost saving measures.

However, Rust, Zeithaml and Lemon

(2000) emphasized that if firm attempts to



reduce costs through layoffs and reduction

in other benefits, then it will reduce

employee morale, which in turn result in

lower customer service, customer loyalty

and lower quantity of services.

But for service oriented organizations like

chambers of commerce, the number of

times the services are offered also have

significant impact on the revenue it

generates. As Nilson et al., (2001) stated,

focus on the product and service attributes

lead to higher sales and hence, the

profitability of the firm.

Review on the Concept of 3 P’s

The term ‘triple bottom line’ which is

often called as 3P’s was initially coined by

Elkington in 1994 to be used as a

framework for measuring and reporting

performance against economic, social and

environmental parameters (Onyali, 2014).

The sustainability of any business in the

era of contemporary world depends on the

integration of three performance areas

consist of economic, social and

environmental parameter (Onyali, 2014).

The researcher has found that the market

share of corporations could be improved

by implementing triple bottom line

accounting methodologies, by providing

management with information needed for

preparing social and environmental reports

useful for stakeholder communication.

There is also a transformation in societal

focus from mere profit to environmental

focus as bigger picture to see the business

impact on the world around the people

(Onyali, 2014). So it is viewed as a

necessary practice for the survival of the

modern corporations.

Review on Chamber of Commerce

Chambers of Commerce have played

major role in economic development in

many countries. In particular, the Chamber

as an intermediary service organization

between government and business, its

importance was genuinely felt in the more

developed and integrated market economy.

Likewise, the functions becoming more

and more diverse and the market becoming

ever complex it has increased the size of

chambers of commerce in many countries

around the globe (Shunlong et al., 2013).

The authors have emphasized the

importance of clear and concrete mission

for the chambers. Those that do not have

clear stated missions are found to lose their

direction in the developmental processes.

On this front BCCI is considered to have

clear mission of promoting private sectors

of the nation, established under the Royal

Command of His Majesty the Fourth

DrukGyalpo in 1980 (Bhutan Chamber of

Commerce & Industry, 2013). One

success factor of any organization is the

existence and composition of professional

team. However, Shunlong et al., noted that



many chambers of commerce lack

innovative and creative management team.

The authors further exerted the reasons of

failure of some chambers to poor

management personnel to execute the

policies and systems. When it comes to

BCCI, the chamber has good number of

qualified managerial team and over the

years has provided business trainings and

conducted several related workshops to

further enhance the professional

development of its staff.

The absence of law also has been

hindering the normal functioning of the

chambers. For example, carrying out daily

activities and dealing with the government,

chamber of commerce and other social

organizations were hindered without a

proper law to refer (Shunlong et al., 2013).

The author also observes that a close

communication and co-operation between

the chamber of commerce with the

regional permanent joint offices plays an

important role in developing the regional

economic activities, and ultimately the

development of national economy. In a

similar line, BCCI established five

regional offices across the country so that

its services can be provided in a better and

effective manner.

In addition, Noel and Luckett (2014)

suggested that the customers in long-term

relationship should experience three

benefits besides its services, i.e. a)

Confidence benefits, b) Social benefits and

c) Special treatment benefits. These are

some of the important factors that directly

affect the sustainability of the chamber of

commerce.

Research Methodology

Review of literature indicates that there is

no information available on how similar

problems or research issues were solved in

the past elsewhere and specially in Bhutan.

This preliminary work has been done to

gain familiarity with the phenomena in the

situation and to develop a business

sustainable integrated model for BCCI. So,

this study is an explorative in nature. The

main objective of the study is to develop a

Revenue Model for BCCI. We aim to

develop Integrated Sustainable Business

Model to helps the organization in

identifying the quality and quantity aspects

of the existing services. This will help the

organization in crafting strategies for

different services based on the category

they fall. After development of the said

model we test the model through the

information gathered from mainly primary

source of data as well as secondary source.

Primary data are collected through

interviews and structured questionnaire.

The secondary data collected from website

of BCCI, ministry of finance, RMA (the

central bank of Bhutan) and various



business reports and articles published by

BCCI.

Using purposive sampling method, a

sample size of 33 respondents working in

different regional offices of BCCI

weregiven structured questionnaire. The

sample distribution is indicated in the table

below:

The survey covers sector associations and

all six regional offices of BCCI in Bhutan

namely: Thimphu, Phuentsholing,

SamdrupJonkhar, Bumthang, Mongar and

Gelephu. The size of the sample was taken

based on the number of members of the

organization.

The information related to the services

provided by BCCI is analyzed by using

Microsoft Excel Spreadsheet Applications

to assess the relationship between the

variables. The quality index and quantity

index are calculated by using formula

based on five parameters (NCCDE) along

with the given rating scales. Further, the

percentage analysis, graphical analysis and

measure of center tendency analysis tools

were used.

Data Analysis and Findings

In this section we first discuss the business

model along with 3 P’s model and

Integrated Sustainable Business Model

supported by Quality - Quantity Index.

Business Model

In general, the net income of BCCI

depends on the revenue from services

offered to members and the costs incurred

on rendering those services. The revenue

from services depends on the quality and

quantity of services offered by BCCI. On

the other hand, the quality and number of

times the services offered are the two main

determinants of costs. Change in quality

will change the net income and also

change in quantity will have an impact on

the net income. Similarly the income

generated from the services rendered will

be used for the development of business.

The social development will depend upon

the growth of business development. So,

Distribution of sample

Total Region I Region

II Region

III Region

IV Region

V Region

VI Sector

Association

Population 7 2 4 2 2 6 10 33

% 21 6 12 6 6 18 30 100



there is relationship between the revenue

of BCCI and quality and quantity of the

services.

Figure 1: Business Model

(Source: Author’s analysis)

3P’s Model

Presently societies are moving towards

environmental longevity and businesses

are looking beyond the picture of financial

performance. There is a philosophy of

business that addresses all the issues

encompass the topic related to People,

Planet and Profit (3P’s). So, the conscious

awareness has led to the concept of

Sustainable Development with the help of

3P’s. Similarly the BCCI being the apex

body of all the business of the nation, it is

not the exception to this concept. For

BCCI the term ‘Profit’ can be replaced by

‘Net Income’ and the income can be used

for rendering services for members’

Business Development (People). If there is

business development through the services

of BCCI, there will be Social Development

(Planet) and this will ensure the

sustainability of BCCI through the income

generation from members.

Figure 2: 3 P’s Model



(Source: Elkington, 1995)

Integrated Sustainable Business Model

The business model and the 3P’s model

explained earlier can be integrated to

develop Integrated Sustainable Business

Model. This model depicts in a holistic

view in generating net income from the

members. It takes into account of revenue

and cost on the basis of quality and

quantity of services. Further, the diagram

shows that the revenue generated on the

basis of quality and quantity of services

will be going back to the members in the

form of different services for the

development of business. In turn the

business development will ensure the

development of society. So, the societal

development will ensure the sustainability

of BCCI in generating revenue.

Figure 3: Integrated Sustainable Business Model

(Source: Author’s Analysis)

Pathway towards Sustainable Revenue

Model (Quality - Quantity Matrix)

This model will be used to determine the

quantity and quality of existing services of

BCCI. It will help the organization in

categorizing its services under the four

categories: Low Quality - Low Quantity,

Low Quality - High Quantity, High

Quality - Low Quantity and High Quality -

High Quantity.

The services of BCCI that fall in the

category other than the quadrant High

Quality - High Quantity will need some

strategies to improve either in quality or



quantity or both parameters. The positively

slope line known as 2Q – Unitary Curve

shows that the services fall on the line are

in equal index of quality and Quantity. The

services that fall above the line indicate

Figure 4: Quality - Quantity Matrix

(Source: Author’s Analysis)

that those services that will have greater

quality than quantity and the quotient of

the index will be always greater than one.

On the contrary, the services that fall

below the linewill have the lower quality

index as compared to quantity index and

will have always negative quotient. The

ultimate aim of the organization is that the

services those fall away from the 2Q-

Unitary Curve should move closer either

increasing the shortfall in quality or

quantity index and should move towards

the higher level through the line that shows

the pathways toward the sustainable

development.

Decomposition of 2Q index

2Q = N

L

Q

Q where, QL is the Quality Index

and QN is the Quantity Index.

The Solution can be of any three different

cases:

Case I: 2Q > 1, if QL > QN,

Case II: 2Q < 1, if QL < QN,

Case III: 2Q = 1, if QL = QN.

For example, let a service fall on different

cases;

In Case I: QL > QN, So let QL=25 and

QN=20, which gives 2Q= 1.25 (lies below

the 2Q-Unitary curve),

In Case II: QL < QN, let QL=20 and QN=25,

which gives 2Q= 0.80 (lies above the 2Q-

Unitary curve),

In Case III: QL = QN, Where QL=25 and

QN=25, which gives 2Q= 1 (lies on

Unitary Value).

Therefore, the result should be:

Case I: need to increase the quantity to

reach at unitary curve,



Case II: need to increase the quality to

reach at unitary curve,

Case III: for sustainability there should be

an upward drift of QL and QN at same

proportion, remaining along 2Q-Unitary

curve but to higher levels.

Quality Index (QL)

The quality index is measured for every

service of BCCI with the help parameters

like Need (N), Content (CN), Cost (CS),

Delivery (D), and Evaluation (E). It is

calculated by summation of all the

parameters for every service individually.

Quantity Index(QN)

The quantity index can be calculated with

the help of Service Coverage Ratio (SCR)

and Service Frequnecy Ratio (SFR).

Further, the SCR can be calculated on the

basis of members, sector specific and

general (irrespective of members). The

SCR on the basis of member can be

calculated by number of members

availling services divided by total number

of members. Similarly, SCR on the sector

specific can be calculated by sector

specific members availing services

devided by total number of members in

the sepecific sectors. However, on the

basis of the relevancy and convinent of

information, ‘need’ for the service by the

member is taken in place of SCR.

membersnoTotal

servicesavailingmemberofNoSCR

.

.

Similarly, SFR can be calculated on the

basis of number of times services delivered

divided by target frequency to be provided.

As SCR has been taken base on the

relevancy, ‘delivery’ of the different

services is taken as SFR.

deliveredbetoservicesoffrequencyetT

deliveredservicestimesofNoSFR

arg

.

The quantity index can be calculated by

SCR multiplied by SFR. Therefore,

quantity index for particular service is the

product of need and delivery of the

particular services.

SFRxSCRQN

In sum, Quality – Quantity of the services

will be supported by Quality – Quantity

Index for different services. The Quality –

Quantity Table for different services will

be also shown. Finally the existing

services will be categorized under the

given matrix based on the score of Quality

- Quantity Index along with Quality –

Quantity Index Table.

We now analyze the data collected from

the respondents and present those on the

model discussed above. First the quality

index of the services is discussed of the

services provided by BCCI such as

business facialitation servicesnetwork and

linkages,training service, providing

arbitration and mediation services on

request,advocacy services and support to



CSMEs services. Need, Content, Cost,

Delivery and Evaluation (NCCDE)

parameters are used to measures the

quality index of these services.Quantity

index is presented next for all these

services followed by quality-quantity

table, where we present the scores

calculated from the responses obtained.

Finally we present quality - quantity

matrix of these services and its analysis in

the last paragraph.

1. Quality Index of the Services

Business Facialitation Services

Figure 5: Quality Index for Business Facilitations Services

Business facilitations services includes the

services related to the promotion of trade

and investment mission, business referals/

matchmaking, information collection and

decimination of business information and

opportunity and issuance of

recommendation letter, token, certificate to

the members. The overall quality index

(calculated as explained in the first

section) for the services is 52.62%. Figure

5 above shows that the need of the service

for members is quite significance. It is

followed by the content of the service with

12.22. This indicates that there is relevacy,

adequacy and completeness of the material

in the service. However, in terms of

evaluation, the parameter of quality index

scores the least, indicates that the

organization is poor in taking feedback for

their services.

Network and Linkages

Network and linkages service includes

networking amongst business and

institutional networking. Figure 6 indicates

the service is highly required by the

business members with of 16.03.

However, the overall quality index is

52.24. This is due to low score in other

parameters like cost, delivery and

evaluation of the service.

15.42

12.58

8.26 9.12

7.74 0.00

5.00

10.00

15.00

20.00Need

Content

CostDelivery

Evaluation



Figure 65: Network and Linkages

Training (in & ex-country) Service

Figure 7: Training (In & Ex-Country) Service

Training Services includes training for the

members both within the country and

abroad. This service is mostly a free or

gratis service for the members and for the

training outside countries are charged at

very minimal cost. Figure 7 above

indicates that the service is highly required

by the business members with the score of

15.52. However, the overall quality index

is lowered to 55.94 due low score in other

parameters like cost, delivery and

evaluation of the service.

Providing Arbitration and Mediation

Services on Request

This service is an on-request service and

provides services to the members like

mediation or intervention on tax issues,

dispute settlement between the business



members, intervention between local

business and business counterparts abroad.

Figure 8 above indicates that these

Figure 86: Providing Arbitration and Mediation Services on Request

services are highly required by the

business members with a score of 14.87.

The overall quality index is 49.64. Like

other services it is a free or gratis service

for the members.

Figure 9: Quality Index for Advocacy Services

Advocacy Services

The advocacy service is generally

provided to the members and the figure 9

depicts that the service is highly required

by the business members with a score of

16.06. However, the overall quality index

is 57.21. The score in other parameters like



cost, delivery and evaluation of the service are quite low.

Figure 10: Quality Index for Support to CSMEs Services

Support to CSMEs Services

Support to Cottage, Small and Medium

Enterprises (CSMEs) service includes

economic resource inventorying in each

region, micro financing to micro small and

cottage enterprises, identification of

project ideas and guiding in preparing

feasibility reports and facilations,

conducting basic enterpreneurship

promotion or managerial training. Figure

10 indicates that these the service is highly

required by the business members with a

score of 15.94 in need factor. The overall

quality index is to 56.11. However, the

other remaining parameters are quite low.

2. Quantity Index

Figure 11 shows the quantity index of the

six services of BCCI. In overall the

quantity index (calculated as explained in

the first section) of all the serrvices of

BCCI are quite low. The quantity index of

the services ranges from 29.66% to

40.69%. The services of support to

CSMEs stands the highest followed by

advocacy services with 39.40%. The

services on providing arbitration and

medition service has the lowest score in

the index.

We now present the quality-quantity table,

where the scores have been calculated as

explained in the first section of this part.



Figure 17: Quantity Index for Six Major Services

4. Quality – Quantity Index Table

Table 1: Quality - Quantity Table of Services

Services Quality

Index

Quantity

Index

Quality Quantity

Organizing trade fairs & exhibitions and coordinating

member’s participation in overseas fairs and exhibitions.

53.09 35.70 HIGH LOW

Hosting visiting delegations as arranging meetings between

visiting trade delegations and members and vice-versa

52.85 35.90 HIGH LOW

Processing of business enquiries and redirecting these to

relevant members for their interests through relevant mode

of communication such as email, letter, website, phone call,

SMS, media, etc.

53.33 35.49 HIGH LOW

Organization of business matchmaking activities by

initiating buyer-Seller-Meets (BSM) and other support

measures.

52.55 34.56 HIGH LOW

Arranging B2B meetings. 50.91 33.28 HIGH LOW

BCCI collects information through published reports,

government bodies and even embassies abroad, subscription

to the internet and information providers, exchange with

51.27 33.52 HIGH LOW



other chambers in the network, etc.

Dissemination of government circulars, notifications and

announcements pertaining to the private sectors

53.27 34.49 HIGH LOW

Information dissemination on the training opportunities

related to private and corporate sectors

52.00 32.65 HIGH LOW

Dissemination of information on the trade fairs and

exhibitions within and abroad

55.70 39.54 HIGH LOW

Issuance of visa recommendation letter 55.09 37.09 HIGH LOW

Issuance of school certificate recommendation 51.27 32.83 HIGH LOW

Issuance of apple, orange, mushroom &Cordycept token

number for exporter.

50.61 30.00 HIGH LOW

Issuance of general recommendation. 50.73 30.04 HIGH LOW

Networking amongst business 54.48 35.40 HIGH LOW

Institutional Networking 53.27 36.74 HIGH LOW

Training (in & ex-country) services 51.21 32.48 HIGH LOW

BCCI provide mediation or intervention on Tax issues

between Government and private sectors

53.70 35.54 HIGH LOW

Settle commercial dispute between business members 46.30 24.81 LOW LOW

Intervention and settle commercial dispute between local

business and business counter parts abroad.

48.91 29.04 LOW LOW

Advocacy of services 57.21 39.91 HIGH LOW

Economic resource inventorying in each district 55.94 41.46 HIGH LOW

Micro Financing to Micro Small and Cottage Enterprises 59.09 45.09 HIGH LOW

Identification of project ideas; /Guidance in preparing

feasibility reports and facilitation thereafter

54.24 37.29 HIGH LOW

Conducting basic entrepreneurship promotion/managerial

training.

55.15 38.88 HIGH LOW

(Author’s calculation)

4. Quality - Quantity Matrix of Services

Analysis of the scores indicate that almost

all the services of BCCI that fall in the

category of Low Quantity- High Quality

matrix except the services on providing

arbitration and mediation service on

request falls under the category of Low

Quantity- Low Quality. However, the

services that fall under the high quality

index have also just crossed the marginal

point with highest quality index of 59.09

in the service of micro financing to micro



small and cottage enterprises. Figure 12

depicts that none of the services fall at the

2Q – Unitary Curve and the quantity index

of all the services are low as compare to

quality index. So all the services fall below

the 2Q – Unitary Curve and indicates

shortfall in quantity index.

Figure 18: Quality - Quantity Matrix for the Existing Services

Conclusion

BCCI acts as an apex body of the private

sectors in representing the interest and

views of the business community to the

government and vice versa. It plays an

important role in the economic

development of the country. We have

developed the Self-Generated Revenue

Model along with 3Ps model and finally,

the Integrated Sustainable Business Model

supported by Quality - Quantity Matrix.

Need, Content, Cost, Delivery and

Evaluation (NCCDE) parameters are used

to measures the quality index of the

services. Similarly, the multiple of service

coverage ratio and service frequency ratio

is taken to measure the quantity of the

services. The matrix helps the chamber in

identifying the services under different

categories. Categorically, the organization

is required craft the strategies related to

either quality or quantity.



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Assessing the Financial Efficiency in Indian Pharmaceutical Industry: An


Jatin Goyal* Harpreet Kaur** *Senior Research Fellow, University School of Applied Management,Punjabi University,

Patiala (Punjab)

**Associate Professor, Department of Distance Education,Punjabi University, Patiala

(Punjab)

Abstract

Indian pharmaceutical industry, which accounts for approximately 2.4 percent of the global

pharmaceutical industry in value terms and 10 percent in volume terms, is now in the bust phase due

to high competition and challenging price environment. Most of the investors experienced to taste

bitterness in earnings of the industry in the recent past which is now impacting the sentiments of the

sector for the long-term. In the wake of above issues, it is an imperative task to figure out the financial

efficiency levels in the Indian pharmaceutical industry. The present study attempts to carry out an in

depth analysis into the financial efficiency levels of 91 companies based on cross-sectional data of

2015-16 using DEA approach. The DEA results highlight that the level of financial inefficiency in

Indian pharmaceutical industry is a whopping 30.54 percent. Out of this scale size and managerial

incapacity are almost equal contributors of inefficiency. Therefore, there is a huge scope for

improvement in financial efficiency in the industry. The findings hold an important place in the wake

of the overwhelming contribution of Indian pharmaceutical industry to India’s economy and the need

for maximizing the shareholder’s value so as to make it attractive for the investors globally.

Keywords:India, Pharmaceutical Industry, DEA, Financial Efficiency.

1. Introduction

The pharmaceutical industry in

India has developed rapidly after the

economic liberalization. Firms in the

industry have undergone series of changes

right from licensing, regulation and

process patent to delicensing, deregulation

and product patent. The players in

pharmaceutical industry of India are facing

severe competition both on domestic as

well as global front. However, despite of

huge competition, the Indian

pharmaceutical industry is one of the most

dynamic and growth oriented industries of

India. Where most of the developing

counties still rely heavily on imports of

pharmaceutical products, India is one

amongst the few exporting countries which

is capable of producing a wide range of

Active Pharmaceutical Ingredients (APIs).

The underlying strength of Indian

pharmaceutical industry is its generic

drugs segment which contributes to 70

percent of total market share in terms of

revenue and is armed with domestic

production processes that has made the

country a leading producer of low-cost

medicines in the world. Further, various



international companies associated with

this sector have also stimulated, assisted

and spearheaded its dynamic development

and helped to put India on the

pharmaceutical map of the world.

In spite of the long development

and high cost of drug research, pharma

companies are undeniably more profitable

than companies of any other industry.

However, things have changed in recent

years. The so-called defensive

pharmaceutical sector is on the way to the

ventilator. The financial performance of

Indian pharmaceutical industry is getting

degraded day by day. Indian

pharmaceutical companies used to be the

main players to get approvals from United

States Food and Drug Administration

(USFDA). But the market is now getting

crowded. Nearly, a third of approvals have

been given to players from outside

traditional markets. Companies from

Turkey, New Zealand, Taiwan and even

Bangladesh have now got clearance to sell

products in Unites States. Further, lower

number of buyers and increasing number

of new entrant tilt the balance of

negotiations in the hands of the buyers,

hitting profit margins badly.

Market often makes it a boom or

bust play. In pharma's case, the valuation

multiple i.e., the price earning (P/E) ratio,

which measures how expensive the field

is, shows that prices have outstripped

earnings far too much. So far in the

calendar year 2017, the Nifty Pharma

index has underperformed with a fall of

nearly 6%, as compared to a rally of

around 15% in the Nifty 50 and the S&P

BSE Sensex. Where the overall market is

in boom phase, the pharma sector is in the

bust phase. Most of the investors

continued to taste bitterness in earnings

due to high competition and challenging

price environment, which is impacting the

sentiments of the sector for the long-term.

For export oriented Indian pharmaceutical

companies, there are certain speed

breakers on the road due to the stringent

quality and compliance issues of United

States Food and Drug Administration

(USFDA). Further, due to massive loss of

income and sales as a result of patent

expirations of blockbuster drugs, even the

big pharma companies are becoming

dinosaurs for investors. Despite scientific

advances and favorable demographics, the

industry suffers from long lead times to get

its products through the R&D, regulatory

maze and on sale. Usage of more generic

medicines and price regulations of

National Pharmaceutical Pricing Authority

(NPPA) are amongst the few other reasons

due to which pharma stocks are declining

and also approaching to its 52 week low.

All these turbulences contend that pharma



stock prices aren't going to head up in any

meaningful way, any time soon. This made

the pharma sector investors little scared

and unhappy.

In this scenario, the moot questions

in every investor’s mind are such that - are

there any chances of recovery? If the

fundamentals of the individual businesses

are still strong? Despite being suffering

from market-driven conditions, can

companies sustain or create more

shareholder value with the existing

resources? If the answer to all these

questions is yes, how much chances of

improvement are there?

Keeping all these questions in

mind, this paper attempts to measure the

financial efficiency of the Indian

pharmaceutical industry considering the

shareholder value maximization as one of

the important parameters. The study will

offer the direction for improvement of

financial efficiency of Indian

pharmaceutical companies along with

some important policy implications. In

order to achieve the above mentioned

objective, a non-parametric linear

programming based frontier technique

named data envelopment analysis (DEA)

has been utilized due to its capability of

taking multiple inputs and outputs

simultaneously for calculating the relative

efficiency and come up with a scalar

measure of overall performance for easier

decision making. DEA has been widely

used and accepted as methodology for

performance evaluation and

benchmarking. The basic concept of

directing methodology at frontiers rather

than central tendencies such as statistical

regression, gives DEA an advantage over

traditional methods. DEA is capable of

identifying relationships among entities

that traditional methods are not able to

identify. It quantifies relations of entities

in a direct manner without requiring

several assumptions or variations on data

sets.

The rest of paper is organized as

follows. In Section 2, we provide a brief

review of the related studies on the subject

matter. In Section 3, the methodological

framework, data sources, sample selection

and details of variables taken in this study

are outlined. Section 4 presents the

empirical findings of the DEA models

employed in this study. The final section

concludes the paper by providing some

useful policy implications.

2. Review of Literature

In this section, we discuss some

reviews of the related literature concerning

this study given as follows:

González & Gascón (2004) analyzed the

efficiency and productivity growth of 80

pharmaceutical companies of Spain



between 1994 to 2000. The results of the

study suggested that the contribution of

technical change to productivity growth

was negligible. The poor result of R&D

activities hindered the efficiency and

growth of Spanish pharmaceutical

industry. The study concluded that there is

a need to intensify the R&D efforts and

expansion of production possibilities to

develop high margin and patented

products.

Saranga & Phani (2004) applied DEA on

a sample of 44 Indian pharmaceutical

companies for the period of 1992-2002 to

look at the internal efficiencies of

pharmaceutical companies. Technical and

scale efficiencies were computed using the

CCR and BCC models. The results of

DEA were analyzed along with their

Compounded Annual Growth Rate

(CAGR) to check whether internal

efficiencies, size and growth rate are

related or not. Findings showed that the

size of a company has no influence on the

internal efficiencies scores. However,

efficiency scores and growth rates were

found to be positively related except for a

few companies.

Hashimoto & Haneda (2008) measured

the R&D efficiency of 10 Japanese firms

for the period of 1982-2001 using DEA

based Malmquist productivity index. The

results showed that innovation of R&D

technology had not taken place so much

for decade 1983–1992 and Japanese

pharmaceutical industry experienced a

great R&D efficiency loss in year 1992 to

50 percent. Although, the firms had

continued to increase R&D expenditure

every year, yet the R&D efficiency

showed no significant improvement over

time.

Tripathy et al. (2009) examined the levels

and determinants of firm’s efficiency using

firm-level data of 90 Indian

pharmaceutical firms for the years 2001-02

to 2007-08. A two stage DEA model,

considering one output variable and three

input variables was applied to compute the

technical efficiency scores. The results

showed that the performance of a large

number of sample firms was sub-optimal

and with the introduction of product

patents, the pharmaceutical industry has

become more competitive. To become

efficient, the firms need to reduce their

inputs to attain a given level of output.

Wang et al. (2011) gauged the efficiency

of 12 Taiwanese pharmaceutical

companies using grey relational analysis

coupled with DEA based Malmquist

analysis. The study primarily focused on

how to utilize intellectual capital more

efficiently in order to strengthen the

competitiveness of enterprises. The results

indicated that the companies in the



intellectual capital management, still have

great room for improvement and need to

reduce waste of input resources, to

enhance the intellectual capital

management performance.

In sum, a careful screening of the available

literature reveals that most of the studies

have been conducted outside India. Few

studies that have been conducted for

Indian pharmaceutical industry are prior to

the global recession of 2008 and focused

only on operational parameters. After

2008, major structural changes have taken

place at national and global level. The

environment in which companies are

operating now is not same as before.

Therefore, keeping this in mind, the

present study seeks to fill such gaps and

intends to enrich the available literature

concerning with the measurement of

financial efficiency of Indian

pharmaceutical industry using DEA

methodology.

3. Methodological Framework

3.1 Concept and Measurement of

Technical Efficiency

The literature on the measurement

of efficiency begins with Farrell (1957)

who drew upon the work of Debreu (1951)

and Koopmans (1951) to consider the

technical efficiency measure in a single-

output and single-input situation. Farrell

proposed that the efficiency of a firm

consists of two components viz.

technicalefficiency, which reflects the

ability of a firm to obtain maximal output

from a given set of inputs, and

allocativeefficiency, which reflects the

ability of a firm to use the inputs in

optimal proportions, given their respective

prices and the production technology.

These two measurements are then

combined to provide a measure of total

economic efficiency. The measure of the

allocative efficiency requires the

information on both output and input

prices data. Because India's economy is

still under the process of transformation to

a planned economy, the complete and

authentic price data is not yet available for

Indian pharmaceutical industry. For this

reason the analysis in this paper will

concentrate on the parameters of technical

efficiency alone. Since the technical

efficiency essentially measures the gap

between the possible outputs, or the best

practice and actual outputs of a firm, it

demonstrates the extent to which the

observed firms’ performance approaches

its potential or the so-called ‘best practice’

standard.

3.2 The DEA Approach − CCR and

BCC Models

DEA was originally developed in

the late 70's to provide a linear

programming based mathematical



technique for measuring the efficiency of a

set of decision-making units (DMUs).

Since the inception of DEA methodology,

numerous mathematical programming

models have been proposed in DEA

literature (See Charnes et al., 2013; Zhu,

2014). The first seminal paper introducing

DEA was given by Charnes et al. (1978),

which got recognized after their names as

CCR (Charnes, Cooper and Rhodes)

model. CCR model uses the optimization

method of mathematical programming to

generalize the Farrell’s (1957) single-

output and single-input technical

efficiency measure to the multiple-output

and multiple-input situation by

constructing a single ‘virtual’ output to a

single ‘virtual’ input relative efficiency

measure. The DEA technique is non-

parametric in the sense that it is entirely

based on the observed input-output data to

estimate the efficient production frontier in

a piecewise linear fashion. The purpose of

DEA is to construct a non-parametric

envelopment frontier over the data points

such that all observed points lie on or

below the production frontier and then to

determine if the DMU under consideration

is technically efficient or not. Because

DEA calculations are generated from

actual observed data for each DMU, they

produce only relative efficiency measures.

The relative efficiency of each DMU is

calculated in relation to all the other

DMUs, using the actual observed values

for the outputs and inputs of each DMU.

CCR model was further expanded

by Banker, Charnes and Cooper (1984)

which later on got recognition as BCC

model. The basic difference between CCR

and BCC model is that the former has an

assumption that all firms operate at

constant returns to scale, while the latter

accounts for variable returns to scale. Both

these models are further divided into two

orientations namely input and output

orientation. The input orientated model is

the method that seeks to measure technical

efficiency as a proportional reduction in

input usage, with output levels held

constant. On the contrary the output

orientation model seeks to measure

technical efficiency as a proportional

increase in output production, with input

levels held fixed (Coelli et al., 2005).

Since in Indian pharmaceutical industry,

the major concern is shareholder value

maximization. So in this case, an output

orientation is more appropriate.

An intuitive way to comprehend

DEA is via the ratio form. For each DMU,

we would like to obtain a measure of the

ratio of all outputs over all inputs. To

illustrate the CCR model, consider 𝑛

DMUs, 𝑗 = 1,2, … . . , 𝑛. The units are



homogeneous with the same types of

inputs and outputs. Assume there are 𝑚

inputs, 𝑖 = 1,2, … . . , 𝑚 and 𝑠 outputs,

𝑟 = 1,2, … . . , 𝑠. Let 𝑥𝑖𝑗 and 𝑦𝑟𝑗 denote,

respectively, the input and output vectors

for the 𝑗𝑡ℎ DMU. Thus, 𝑥𝑖𝑗 is a (𝑚 × 1)

column vector and 𝑦𝑟𝑗 is a (𝑠 × 1) column

vector. Moreover, 𝑋 = (𝑥1, 𝑥2, … . . , 𝑥𝑛)is

the (𝑚 × 𝑛) input matrix and 𝑌 =

(𝑦1, 𝑦2, … . . , 𝑦𝑛) is the (𝑠 × 𝑛) output

matrix. The CCR model assigns weights to

each input and output, and then assesses

the efficiency of a given DMU by the ratio

of the aggregate weighted output to the

aggregate weighted input. The weights

assigned must be non-negative. Also, they

must restrict each DMU from receiving a

ratio (of the weighted output to the

weighted input) that is greater than 1.

Mathematically, when evaluating the

efficiency of the DMU 𝑘, we solve for the

following linear programming problem

(LPP):

𝑢𝑇𝑦𝑘

𝑣𝑇𝑥𝑘{𝑢,𝑣}

𝑀𝑎𝑥𝑖𝑚𝑖𝑧𝑒

[1]

𝑆𝑢𝑏𝑗𝑒𝑐𝑡 𝑡𝑜: 𝑢𝑇𝑦𝑗

𝑣𝑇𝑥𝑗≤ 1

𝑗 = 1,2, … . . , 𝑛

𝑢, 𝑣 ≥ 0

Where 𝑢 is the (𝑠 × 1) vector of

output weights and 𝑣 is the (𝑚 × 1) vector

of input weights. 𝑇 denotes the matrix

transpose operator. Thus, 𝑢 and 𝑣 are

chosen to maximize the efficiency measure

of the DMU 𝑘 subject to the constraints

that the efficiency levels of all units must

be less than or equal to 1.

One problem with this particular

ratio formulation is that it has an infinite

number of solutions. To generate a unique

solution, an additional constraint 𝑣𝑇𝑥𝑘 =

1 is imposed. The maximization problem

then becomes:

𝑢𝑇𝑦𝑘{𝑢,𝑣}𝑀𝑎𝑥𝑖𝑚𝑖𝑧𝑒 [2]

𝑆𝑢𝑏𝑗𝑒𝑐𝑡 𝑡𝑜: 𝑣𝑇𝑥𝑘 = 1

𝑢𝑇𝑦𝑗 − 𝑣𝑇𝑥𝑗 ≤ 0

𝑗 = 1,2, … . . , 𝑛

𝑢, 𝑣 ≥ 0

The duality problem to output-oriented

CCR model can be written as follows:

𝑀𝑎𝑥𝑖𝑚𝑖𝑧𝑒 𝜓𝑘 [3]

𝑆𝑢𝑏𝑗𝑒𝑐𝑡 𝑡𝑜: ∑ 𝜆𝑗

𝑁

𝑗=1

𝑥𝑖𝑗 ≤ 𝑥𝑖𝑘

∑ 𝜆𝑗

𝑁

𝑗=1

𝑦𝑟𝑗 ≥ 𝜓𝑘𝑦𝑟𝑘

𝜆𝑗 ≥ 0

Where, 𝜆 is a (𝑛 × 1) column

vector; 𝜓 is a scalar; 𝑖 = 1,2, … . . , 𝑚

(Counter for inputs); 𝑟 = 1,2, … . . , 𝑠

(Counter for outputs); 𝑗 = 1,2, … . . , 𝑛

(Counter for companies); 𝑥𝑖𝑗 = amount of

input 𝑖 used by DMU 𝑗; 𝑦𝑟𝑗 = amount of

output 𝑟 produced by DMU 𝑗; and 𝑘



represents the DMU whose efficiency is to

be evaluated.

Let 𝜓𝑘∗ and is the solution to (3)

then obviously 𝜓𝑘∗ ≥ 1. According to the

Farrell's definition (1957), if 𝜓𝑘∗ = 1, it

indicates a CCR technically efficient

DMU, if 𝜓𝑘∗ > 1, it indicates CCR

technically inefficient. Here it is

worthwhile to note that the above linear

programming problem must be solved 𝑛

times, once for each DMU in the sample.

A value of 𝜓 is then obtained for each

DMU. We denote 𝑇𝐸𝐶𝑅𝑆 = 1/𝜓𝑘 = 𝜃, the

overall technical efficiency (OTE) score

measured by the output oriented CCR

method.

The CCR model is based on the

assumption of constant returns to scale.

Given this assumption, the size of the

DMU is not considered to be relevant in

assessing the relative efficiency. This

means that even small DMUs can produce

at the same level parallel to large DMUs.

However, this assumption is not

appropriate in developing economies

where economies/dis-economies of scale

could set in. In fact, not all DMUs always

operate at an optimal scale. Imperfect

competition, constraints on finance, etc.

may cause a DMU to be not operating at

optimal scale (Coelli et al., 2005).

Therefore, a less restrictive VRS frontier

can be constructed where Overall

Technical Efficiency (OTE) can be

decomposed into pure technical efficiency

(PTE) and scale efficiency (SE). The VRS

model incorporates the dual of CRS

model, with an extra convexity constraint

∑ 𝜆𝑗 = 1𝑁𝑗=1 into problem, which

essentially ensures that an inefficient

DMU is only benchmarked against DMU

of similar size.

The duality problem to output oriented

BCC model can be written as follows:

𝑀𝑎𝑥𝑖𝑚𝑖𝑧𝑒 = 𝜇𝑘 [4]

𝑆𝑢𝑏𝑗𝑒𝑐𝑡 𝑡𝑜: ∑ 𝜆𝑗

𝑁

𝑗=1

𝑥𝑖𝑗 ≤ 𝑥𝑖𝑘

∑ 𝜆𝑗

𝑁

𝑗=1

𝑦𝑟𝑗 ≥ 𝜇𝑘𝑦𝑟𝑘

∑ 𝜆𝑗 = 1

𝑁

𝑗=1

𝜆𝑗 ≥ 0

We denote 𝑇𝐸𝑉𝑅𝑆 = 1𝜇𝑘

⁄ = 𝞿, the

pure technical efficiency (PTE) score

measured by the output oriented BCC

method. It is worthwhile to mention that

BCC model measures the PTE, whereas

CCR model measures both PTE and SE.

Clearly, 𝑇𝐸𝐶𝑅𝑆 ≤ 𝑇𝐸𝑉𝑅𝑆, hence by using

𝑇𝐸𝐶𝑅𝑆𝑘 and 𝑇𝐸𝑉𝑅𝑆

𝑘 measures, we derive a

measure of SE as a ratio of 𝑇𝐸𝐶𝑅𝑆𝑘 to

𝑇𝐸𝑉𝑅𝑆𝑘 given as:



𝑆𝐸𝑘 = 𝛿𝑘 = 𝑇𝐸𝐶𝑅𝑆

𝑘

𝑇𝐸𝑉𝑅𝑆𝑘⁄ =

𝜓𝑘𝜑𝑘

⁄ =

𝑂𝑇𝐸𝑃𝑇𝐸⁄ [5]

The idea of looking at scale

efficiency is appealing because it provides

a measure of what could be gained by

adjusting the size of the firm (Bogetoft &

Otto, 2010). Banker et al. (1984)

introduced the concept of Most Productive

Scale Size (MPSS) to define the level of

operations that maximizes the efficiency of

a DMU. In short run, a DMU may either

operate at DRS or IRS, nevertheless in the

long run, it will move to CRS by becoming

larger or smaller as a result of changing its

operating strategy in terms of scaling up or

scaling down to survive in a competitive

market.

3.3 Data and Sample

In this study, the analysis is based

on cross-sectional data of 91 Indian

pharmaceutical companies for the year

2015-16. All the data relating to selected

input and output variables have been

extracted from the Prowess database of

Centre for Monitoring Indian Economy

(CMIE). Initially, we got the data of 93

pharmaceutical companies. In order to

detect the potential outliers from the

sample we then applied the method

suggested by Bogetoft & Otto (2015). In

this process, 2 companies were turned out

to be outlier. The removal of outliers

provided us with a more representative

frontier. We used software R1 to perform

the empirical analysis.

3.4 Selection of Input and Output

Variables

The selection of inputs and outputs is

one the most crucial exercises of DEA

analysis. However, there are no specific

rules defined for the selection of input and

output variables, generally the inputs are

defined as resources utilized by the DMU

and outputs as the benefits generated.

Since an organization’s performance is a

complex phenomenon requiring more than

a single criterion, recent studies have

argued that a multi-factor performance

measurement model may be used (Zhu,

2000). Indeed, an accurate selection of the

indicators, which are best adapted to the

objectives of the analysis, is critical to the

relevance and usefulness of the results.

The foremost task for the computation of

technical efficiency using DEA is to

specify a set of input & output variables.

Since an organization’s performance is a

complex phenomenon requiring more than

a single criterion, recent studies have

argued that a multi-factor performance

measurement model may be used (Zhu,

2000). So far our choice of input and

1Benchmarking, ucminf and lpSolveAPI

packages.



output variables is concerned, we referred

to various natural choices amongst various

researchers (See Kakani et al., 2001;

Tehrani et al., 2012; Dastgir et al., 2012)

In the present study, our choice of

inputs is governed by the fact that three

major elements of financial performance

viz. Liquidity, Solvency and Profitability

have been considered. Two key ratios for

each Indicator have been taken. The final

input variables which have been

considered are (i) Current Ratio, (ii) Quick

Ratio, (iii) Debt-equity Ratio, (iv) Interest

Coverage Ratio, (v) Return-on-Assets and

(vi) Return-on-Equity.

While making the choice of output

variables, we found Tobin’s Q ratio and

market value to book value ratio as widely

accepted proxies for measuring firm value

amongst various researchers. (See

Wernerfelt& Montgomery, 1988; Beaver

& Ryan, 1993; Fama& French 1995;

Kakani et al., 2001).Likewise, following

the same pattern, we used (i) Tobin’s Q

Ratio and (ii) Market Value to Book Value

Ratio as two outputs.

The size of the sample utilized in the

present study is consistent with the various

rules of thumb available in the DEA

literature. Cooper, Seiford, and Tone

(2007) provides two such rules that

together can be expressed as: 𝑛 ≥ {𝑚 ×

𝑠}or 𝑛 ≥ {3(𝑚 + 𝑠)}, ∀ 𝑛 = number of

DMUs, 𝑚 = number of inputs, 𝑠 =number

of outputs. The first rule of thumb states

that sample size should be greater than

equal to product of inputs and outputs.

While the second rule states that number

of observation in the data set should be at

least three times the sum of number of

input and output variables. Given 𝑚 = 6

and 𝑠 = 2 in our study, the sample size

𝑛 = 91 used in the present study exceeds

the desirable size as suggested by the

above mentioned rules of thumb to obtain

sufficient discriminatory power.

4. Empirical Findings

In this section, the efficiency

results obtained through output-oriented

CCR and BCC models have been

presented and discussed. Table 1 presents

the descriptive statistics and frequency

distribution of overall technical efficiency

(OTE) scores of all the 91 Indian

pharmaceutical companies for the year

2015-16 obtained by running output

oriented CCR model. We find that the

mean of OTE scores has turned out to be

0.6946 indicating that on an average the

companies in Indian pharmaceutical

industry have overall technical inefficiency

(OTIE) of about 30.54 percent. The

perusal of the Table 1 further tells that out

of 91 pharmaceutical companies included



in the sample, only 24 companies have

been found to be relatively efficient with

OTE score equal to one. It represents that

26.37 percent companies set an example of

best-practice by defining the efficient

frontier. The practices of these companies

must be imitated by the inefficient

companies to improve their score of OTE.

It clearly dictates that there is a huge scope

of more value creation for the investors of

Indian pharmaceutical industry.

Table 1: Frequency Distribution and Descriptive Statistics of Overall Technical Efficiency

(OTE) Scores of Indian Pharmaceutical Industry

Frequency Distribution

OTE Scores Range No. of Companies Percentage

OTE < 0.4 15 16.48

0.4 ≤ OTE <0.5 11 12.09

0.5 ≤ OTE <0.6 14 15.38

0.6 ≤ OTE <0.7 5 5.49

0.7 ≤ OTE <0.8 5 5.49

0.8 ≤ OTE <0.9 10 10.99

0.9 ≤ OTE <1 7 7.69

OTE = 1 24 26.37

Total 91 100.00

Descriptive Statistics

Minimum First

Quartile Mean Median

Third Quartile

Maximum Standard Deviation

0.1789 0.4748 0.6946 0.7125 1.0000 1.0000 0.2645 Source: Authors’ calculations.

Decomposition of Overall Technical Efficiency

As stated earlier, the OTE scores obtained

through CCR model can be decomposed

into two mutually exclusive non-additive

components viz. pure technical efficiency

(PTE) and scale efficiency (SE).

Recall,𝑆𝐸 = 𝑂𝑇𝐸 𝑃𝑇𝐸⁄ i.e. 𝑂𝑇𝐸 =

𝑃𝑇𝐸 × 𝑆𝐸. It can be done by using the

BCC model upon the same data. If there is

a difference in scores for a particular

DMU, it indicates that there exists scale

inefficiency (SIE). In DEA literature, the

DMUs getting OTE scores equal to 1 are

referred to as ‘globally technical efficient’

and DMUs getting PTE scores equal to 1

but OTE scores not equal to 1 are called

‘locally technical efficient’.

Table 2 provides the descriptive

statistics and frequency distribution of



PTE scores of Indian pharmaceutical

companies. The mean value of PTE scores

has turned out to be 0.8396 indicating that

the extent of pure technical inefficiency

(PTIE) in the Indian pharmaceutical

industry is to the tune of about 16.04

percent. Only 50 pharmaceutical

companies out of 91 (i.e. 54.95 percent)

have acquired the status of locally

technical efficient since they attained PTE

score equal to 1. Out of these 50

pharmaceutical companies, 24

pharmaceutical companies are also

relatively efficient under CRS with OTE

score equal to 1 i.e. they are globally as

well as locally technical efficient. Further,

for remaining 26 pharmaceutical

companies it may be stated that they are

locally technical efficient but globally

inefficient.

Table 2: Frequency Distribution and Descriptive Statistics of Pure Technical Efficiency (PTE)

Scores of Indian Pharmaceutical Industry


PTE Scores Range No. of Companies Percentage

PTE < 0.4 9 9.89

0.4 ≤ PTE <0.5 4 4.40

0.5 ≤ PTE <0.6 5 5.49

0.6 ≤ PTE <0.7 3 3.30

0.7 ≤ PTE <0.8 7 7.69

0.8 ≤ PTE <0.9 4 4.40

0.9 ≤ PTE <1 9 9.89

PTE = 1 50 54.95

Total 91 100.00


Minimum First


Third Quartile



Table 3: Frequency Distribution and Descriptive Statistics of Scale Efficiency (SE) Scores of

Indian Pharmaceutical Industry


SE Scores Range No. of Companies Percentage

SE < 0.4 5 5.49

0.4 ≤ SE <0.5 4 4.40

0.5 ≤ SE <0.6 6 6.59

0.6 ≤ SE <0.7 7 7.69

0.7 ≤ SE <0.8 6 6.59

0.8 ≤ SE <0.9 11 12.09



0.9 ≤ SE <1 28 30.77

SE = 1 24 26.37

Total 91 100.00


Minimum First


Third Quartile



Table 3 provides the descriptive

statistics and frequency distribution of SE

scores of Indian pharmaceutical

companies. The value of SE scores = 1

implies that the particular DMU is

operating at MPSS i.e. optimal scale size.

On the contrary, a value of SE scores ≠ 1

implies that company is experiencing

inefficiency because it is not operating at

its optimal scale size. For our analysis, the

mean value of SE scores has turned out to

be 0.8399 indicating that the average level

of SIE in the Indian pharmaceutical

industry is about 16.01 percent. Given

PTIE = 16.04 percent, this fact reveals that

scale size and managerial incapacity are

almost equal contributors of OTIE. The

perusal of the Table 3 further tells that out

of 91 pharmaceutical companies included

in the sample, only 24 companies (i.e.

26.37 percent) have attained SE score

equal to 1 and are operating at MPSS.

Thus, it portrays that the remaining 67

pharmaceutical companies (i.e. 73.63

percent) are operating with some degree of

SIE, albeit of different magnitude.

5. Conclusions

In today’s competitive business

environment, efficiency measurement is

receiving increased attention from policy

makers in all sectors of the economy. In

this study, an attempt has been made to

measure the financial efficiency of the

Indian pharmaceutical industry using

cross-sectional data of 91 pharmaceutical

companies for the year 2015-16. We

applied two widely used DEA models viz.

CCR and BCC to calculate the best

practice frontier and estimates of technical

efficiency scores based on selected

financial parameters. The empirical results

indicate that overall technical efficiency

(OTE) scores for the Indian

pharmaceutical companies range from

0.1789 to 1, with mean value of 0.6946. It

implies that on an average the companies

in Indian pharmaceutical industry have the

potential to increase their outputs by about

30.54 percent to using the same level of

inputs. Since we have taken two important

parameters of share value maximization as

output variables in this model, it can be

inferred that Indian pharmaceutical



companies have huge potential to improve

the shareholder value by using the same

resources as before.

The decomposition of the OTE

scores into two mutually exclusive non-

additive components viz. pure technical

efficiency (PTE) and scale efficiency (SE)

reveals that 16.04 percentage points of

30.54 percent of overall technical

inefficiency (OTIE) as identified by CCR

model are primarily attributed to

managerial inefficiency. The PTE scores

for the Indian pharmaceutical companies

range from 0.1998 to 1, with mean value

of 0.8396. Out of 50 efficient

pharmaceutical companies under BCC

model, 24 companies have also been found

to be relatively efficient under CCR model

with OTE score equal to 1 indicating that

they are globally as well as locally

technical efficient. For remaining 26

companies, it may be stated that OTIE in

these companies is caused not due to

managerial incapability to organize the

resources but rather inappropriate choice

of the scale size. For our analysis, it has

been observed that SE scores range from a

minimum of 0.2445 to a maximum of 1.

The mean value of SE scores has turned

out to be 0.8399 indicating that the average

level of scale inefficiency (SIE) in the

Indian pharmaceutical industry is about

16.01 percent.

In sum, DEA results clearly witness

that there exists a substantial room for the

improvement of financial efficiency in

Indian pharmaceutical industry. Given the

importance of this industry for the Indian

economy, it is imperative that efforts

should be taken to increase the efficiency

of companies whose performance is sub-

optimal. There is a need to take concrete

steps to eliminate the managerial

inefficiencies in the process of resource

utilization and correcting the scale of

operations. Looking carefully into the root

causes of inefficiency can help the Indian

pharmaceutical industry to create more

value for its shareholders. Although, there

is a need to improve the regulatory

policies, especially in the area of patent

and price control, however, in order to

boost the financial efficiency still there are

untapped opportunities available within the

companies internally. Fundamentals of the

individual businesses are still strong and

there is need to use the limited resources

wisely.

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A Study on the Impact of FII, FDIand GDs on GDP of India

R. Venkataraman* Thilak Venkatesan**

*Professor, Presidency College, Bangalore.

**Research Scholar, Bharathiar University, Coimbatore.

Abstract

Gross Domestic Product (GDP) is the broadest quantitative measure of a nation's total economic

activity. GDP represents the monetary value of all goods and services produced within a nation's

geographic borders over a specified period of time. A country’s financial health and growth in the

global economy is measured with the help of this macroeconomic factor. In the recent times, India’s

GDP has developed immensely emphasizing the country as one of the most promising emerging

economy. India remains the fastest growing country across the world with an estimated GDP growth

of 7.5% compared to global GDP of 2.5% in the current year. The economic theories on growth,

state’s investment and savings are the most significant factors contributing to a higher growth. This

investment can be broadly classified into domestic savings & foreign capital aiding the growth. In this

context the study was focused to understand the relationship among various investments and savings

augmenting the GDP growth. The data for the analysis was secondary, collected from the RBI

Bulletin. Econometric tools such as ADF test, vector auto regression & Granger causality test were

used for the analysis. FII was found stationary at level and was dropped from the analysis; the

remaining factors were used to fit a ARDL Model. The Granger causality test as well proved a

unidirectional relationship from FDI and GDS to GDP.

Keywords: Investment, Savings, GDP, Econometric Model & Causality

1. Introduction

The growth of Indian economy is

significantly large compared to the other

Asian peers & the emerging countries. The

increase in GDP is driven by various

factors including the consumption,

investments, government expenditure,

exports, and imports and so on. Foreign

investment acts as a catalyst in aiding the

GDP growth. The government has taken

various initiatives in the recent years

including the increase in FDI limits,

attracting more foreign capital to achieve a

higher GDP growth.

Business Standard reported “India’s

growth trajectory over the last decade has

thrown up a direct link between capital

flows and GDP expansion. While domestic

consumption is a big growth booster,

nearly 20 per cent of the country’s growth

has been fueled by capital flows — both

portfolio and foreign direct investment

(FDI).”



The research was initiated to find and to fit

a model between GDP, FII, FDI and GDS.

The focus of the paper is to substantiate

which form of investment yields a higher

GDP growth. The tools used for the

analysis were ADF, VAR, ARDL and

Granger causality test. A step by step

approach of econometric tools using e-

views was followed.

2. Review of literature

Shrivastav (2013) examined that the

investments in Indian market was

attributed to institutional investors among

whom foreign investors areof primary

importance. The analysis focused to check

whether foreign investors (FII) direct the

Indian stock market. The study examined

whether market movement can be

explained by these investors and their

impact on the stock markets. The short-

term nature of FII had bidirectional

causation with the returns of other

domestic financial markets such as

money markets, stock markets and

foreign exchange markets. The author

observed a positive correlation between

the FII investments and returns of



SensexNifty. The various sectoral indices

were as well studied for their relationship.

Menani (2013)comparedFDI and FII as

drivers of growth for Indian economy. Her

studies proved that there is unidirectional

causality from FII towards GDP at lag 1

and causality from GDP to FII at lag 2.

Her studies compared both FII & FDI, and

since both provide impetus to growth, she

suggested FDI to be encouraged as it

provides a long term framework unlike FII

which remains a short term phenomenon.

Malhotra (2014) analyzed the impact of

FDI on the Indian economy, challenges to

particularly after two decades of economic

reforms, and the challenges to implement

reforms post globalization. The research

analyzed the FDI inflow patterns to

evaluate the key factors determining FDI

flows. The research found that there has

been a positive impact of the FDI inflows

on the economic growth and the FDI flows

supplements the shortfall of the domestic

capital.

Mehta (2014)analyzed the causal

relationship between real gross domestic

product (GDP) and real gross domestic

saving (GDS) in India. The focus of the

paper was to assess the direction of

causality between saving and economic

growth.The tools used were Granger-

causality technique to analyze the causal

relationship during the period 1951- 2011.

The granger causality test revealed that

there is no evidence of causality in any

direction between per capita GDP.

Abdu (2015)studied the impact of

savings, foreign aid on growth in India for

the period 1981 to 2011 and concluded

that the factors are positively co-integrated

and exhibit stable long run equilibrium.

His studies suggested utilizing aid for

productive sectors and implements poverty

reduction policies.

3. Statement of the Research Problem

The growth of India’s GDP has

largely depended on the domestic

consumption, followed by the foreign

flows. Among the foreign flows,

foreign direct investments would

significantly aid in creating

employment, increasing standard of

living and thereby act as a multiplier

to a consistent growth story, whereas

foreign institutional investments are

more volatile in nature to add

constructively to higher growth. The

purpose of the study is to evaluate the

impact of GDS, FII, FDI on the GDP

of India and model the factors using

VAR to test the linear

interdependency among the variables.

ARDL model wasused to find the

long-term relationship among the

multiple variables and finding out the



significant determinant of the

affecting factor to Indian GDP.

3.1 Objectives of the Study

1. To study the impact of foreign

institutional investmenton

gross domestic product.

2. To study the impact of foreign

direct investment on gross

domestic product.

3. To study the impact of gross

domestic savings on gross

domestic product.

3.2 Database&Methodology

The data for the research was

collected through secondary sources

mainly from Reserve Bank of India

publications that is RBI Bulletin.

The time period for the study is

15years from 2000 to 2015. E-Views

version 7.2 was used to analyse the

data.

3.2.1 Augmented dickey fuller test- unit

root:

A series is said to be (weakly or

covariance) stationary if the mean

and autocovariances of the series do

not depend on time. Any series that

is not stationary is said to be non

stationary. ADF test can be specified

with no drift and no trend; with trend

and no drift; lastly with both trend

and drift as follows.

∆Yt = δ Yt −1 + ∑αi ∆Yt

−1 +Ut No

drift, no intercept

∆Yt = β 0 + δ Yt −1 + ∑αi

∆Yt −1 +Ut

Intercept, no drift

term

∆Yt = β 0 + β1t + δ Yt −1 +

∑αi ∆Yt −1 +Ut With

intercept and trend

The test specify the Null hypothesis (

H0 ) as that the time series has unit

root, thus the time series is non-

stationary against the Alternative

Hypothesis ( H1 ) that the time series

has no unit root, thus a stationary

time series:

H0: Time series has a unit

root (δ = 1)

H1: Time series has no unit

root (δ ≠1)

3.2.2 Vector Auto Regression

Vector Auto Regression is an

economic model used to capture the

linear interdependencies among

multiple times series of data. Vector

auto regression is used to interpret

the univariate autoregressive model

by allowing for more than one

evolving variable. Vector auto

regression calculated with estimates



in this project gives an equation

which is used in solving ARDL

model. The structural approach to

simultaneous equations modeling

uses economic theory to describe the

relationships between several

variables of interest.

3.2.3 Normality Test

An informal approach to testing

normality is of comparing a

histogram of the sample data to a

normal probability curve. The

empirical distribution of histogram

data should be resembled normally

distributed. It is difficult to analyze

the distribution if the sample is

small. In regressing the data for

smaller sample one might proceed

against the qualities of normal

distribution with the same mean.

3.2.4 Breush- Godfrey Serial

Correlation

The Breusch-Godfrey serial

correlation LM test is a

autocorrelation in the errors in the

regression model. It makes use of the

residuals from the model being

considered in a regression analysis,

and the test statistic is derived from

the above test. The test also specifies

about the null hypothesis that there is

no serial correlation of any order up

to the p value.

3.2.5 Breusch-Pagan-Godfrey for

Heteroskedasticity

Breusch-Pagan-Godfrey test was

developed in the year 1979which is

used for heteroskedasticity for a

linear regression model. It tests

whether the estimated variance of

the residuals from a regression are

dependent on the values of the

independent variables. In that case it

means it has heteroskedasticity. In

other words heteroskedasticity

means that the variables are scattered

and does not have a linearity which

is not favorable for the analysis.

3.2.6 Stability Test (CUSUM TEST)

The CUSUM test (Brown, Durbin,

and Evans, 1975) is based on the

cumulative sum of the recursive

residuals. This option plots the

cumulative sum together with the

5% critical lines. The test finds

parameter instability if the

cumulative sum goes outside the

area between the two critical lines.

3.2.7 VAR Granger causality test

The Granger causality test is a

statistical hypothesis test for

determining whether one time series

is useful in forecasting another.

Granger causality is a statistical

concept of causality that is based on

prediction. According to Granger



causality, if a signal X1 "Granger-

causes" (or "G-causes") a signal X2,

then past values of X1 should

contain information that helps

predict X2 above and beyond the

information contained in past values

of X2 alone.

3.2.8 Auto Regressive Distributive Lag

Model

The test is used for finding out the

long term relationship among the

variables and finding out the

significant determinants ofGross

Domestic Product.

4. Data analysis and interpretation

The data was collected from the RBI

Bulletin and the data was differenced to

obtain stationarity. The Gross domestic

product was considered as dependent

variable,foreign institutional investments,

foreign direct investment and gross

domestic savingswere independent

variables.

Table 1: GDP at Factor Cost, FII, FDI & GDS

Source: dbie.rbi.org.in

The stationary was observed at the first

difference of GDP (gross domestic

product). It was observed that the

probability value was 0.0006 which is less

than 0.05 inferring that the data is

stationary.

Year GDP at Factor Cost FII FDI GDS

2000 18642.28 1329 10,733 4329.468

2001 19726.05 2293 18,654 4874.588

2002 20482.9 527 12,871 4916.977

2003 22227.6 7769 10,064 5657.718

2004 23887.69 8599 14,653 7333.365

2005 26161.02 9929 24,584 8249.81

2006 28711.2 7011 56,390 9392.25

2007 31297.18 24448 98,642 10647.23

2008 33393.74 -16553 1,42,829 10171.2

2009 45160.72 17910 1,23,120 13963.35

2010 49185.31 37985 97,320 15818.34

2011 52475.28 2168.26 1,65,146 16627.09

2012 54821.12 30110.74 1,21,907 16412.86

2013 57417.9 7027.23 1,47,518 16837.36

2014 98270.89 38008.27 1,89,107 28785.55

2015 51597.57 8443.898 1,91,063



Table 2: ADF: GDP (gross domestic product at factor cost)

Null Hypothesis: D(GDP) has a unit root

Exogenous: Constant, Linear Trend

Lag Length: 3 (Automatic - based on SIC, maxlag=3)

t-Statistic Prob.*

Augmented Dickey-Fuller test statistic -7.499550 0.0006

Test critical values: 1% level -5.124875

5% level -3.933364

10% level -3.420030

*MacKinnon (1996) one-sided p-values.

Table 3: ADF test: FDI (foreign direct investment) Null Hypothesis: D(FDI) has a unit root

Exogenous: Constant


t-Statistic Prob.*


Test critical

values: 1% level -4.004425

5% level -3.098896

10% level -2.690439


The stationarity was obtained at the first

difference of FDI (foreign direct

investment). It was observed that the

probability value was 0.0032 which is less

than 0.05 inferring that the data is

stationary.

Table 4:ADF test:GDS (gross domestic saving)

Null Hypothesis: D(GDS) has a unit root

Exogenous: Constant, Linear Trend


t-Statistic Prob.*



5% level -4.008157

10% level -3.460791




The stationarity was obtained at the first

difference of GDS (Gross domestic

savings). It was observed that the p –Value

was 0.0005 which is less than 0.05

inferring that the data is stationary. The

same can as well be observed by the t-

statistic of 8.070145.

Table 5: ADF : FII (Foreign institutional investments) Null Hypothesis: FII has a unit root

Exogenous: Constant


t-Statistic Prob.*



5% level -3.081002

10% level -2.681330


The stationarity was obtained at the level

of FII (foreign institutional investment). It

was observed that the probability value

was 0.0019 which is less than 0.05

inferring that the data is stationary. The

same can as well be observed by the t-

statistic of 4.868828.

Vector Auto Regression Analysis

The vector auto regression (VAR) is an

econometric model used to capture the

linear interdependencies among multiple

time series. VAR models generalize the

univariate autoregressive model (AR

model) by allowing for more than one

evolving variable. All variables in a VAR

are treated symmetrically in a structural

sense (although the estimated quantitative

response coefficients will not in general be

the same); each variable has an equation

explaining its evolution based on its own

lags and the lags of the other model

variables. VAR modeling does not require

as much knowledge about the forces

influencing a variable as do structural

models with simultaneous equations: The

only prior knowledge required is a list of

variables which can be hypothesized to

affect each other inter temporally.

https://en.wikipedia.org/wiki/Econometric_model

https://en.wikipedia.org/wiki/Time_series

https://en.wikipedia.org/wiki/Autoregressive_model

https://en.wikipedia.org/wiki/Lag_operator

https://en.wikipedia.org/wiki/Structural_equation_modeling

https://en.wikipedia.org/wiki/Structural_equation_modeling

https://en.wikipedia.org/wiki/Simultaneous_equations_model



Table 6: Vector Auto regression Estimates

Date: 05/03/16 Time: 15:29

Sample (adjusted): 2003 2014

Included observations: 12 after adjustments

Standard errors in ( ) & t-statistics in [ ]

DGDP DFDI DGDS

DGDP(-1) 1.274379 -14.84190 0.462883

(2.48260) (9.51833) (0.74297)

[ 0.51332] [-1.55930] [ 0.62302]

DGDP(-2) 3.815938 11.89551 0.918278

(2.69895) (10.3478) (0.80772)

[ 1.41386] [ 1.14957] [ 1.13688]

DFDI(-1) 0.082174 -0.082867 0.011296

(0.11363) (0.43566) (0.03401)

[ 0.72317] [-0.19021] [ 0.33218]

DFDI(-2) -0.158232 0.239423 -0.054845

(0.12222) (0.46858) (0.03658)

[-1.29469] [ 0.51095] [-1.49947]

DGDS(-1) -6.908431 22.65152 -2.099964

(5.15954) (19.7818) (1.54410)

[-1.33896] [ 1.14507] [-1.35999]

DGDS(-2) -12.05358 -19.73790 -3.332423

(6.61636) (25.3673) (1.98008)

[-1.82178] [-0.77809] [-1.68297]

C 10582.69 21406.31 3602.450

(5480.09) (21010.8) (1640.03)

[ 1.93112] [ 1.01883] [ 2.19657]

R-squared 0.704614 0.511979 0.700954

Adj. R-squared 0.350150 -0.073646 0.342098

Sum sq. resids 4.05E+08 5.95E+09 36270798

S.E. equation 8999.725 34505.12 2693.355

F-statistic 1.987832 0.874244 1.953302

Log likelihood -121.0338 -137.1608 -106.5570

Akaike AIC 21.33897 24.02680 18.92616

Schwarz SC 21.62184 24.30966 19.20902

Mean dependent 6482.333 14686.33 1989.048

S.D. dependent 11164.08 33300.66 3320.572

Determinant resid covariance (dof adj.) 2.76E+22

Determinant resid covariance 1.99E+21

Log likelihood -345.3458

Akaike information criterion 61.05763

Schwarz criterion 61.90622



Equation derived from the VAR Estimates:

DGDP = C(1)*DGDP(-1) + C(2)*DGDP(-

2) + C(3)*DFDI(-1) + C(4)*DFDI(-2) +

C(5)*DGDS(-1) + C(6)*DGDS(-2) + C(7)

ARDL: Autoregressive Distributive Lag

model:

The test is used for finding out the long

term relationship among the variables and

finding out the significant determinants

affecting GDP. The model equation used

at the beginning of the approach consisting

of all the variables is:

DGDP = C(1)*DGDP(-1) + C(2)*DGDP(-

2) + C(3)*DFDI(-1) + C(4)*DFDI(-2) +

C(5)*DGDS(-1) + C(6)*DGDS(-2) + C(7)

DFDI = C(8)*DGDP(-1) + C(9)*DGDP(-

2) + C(10)*DFDI(-1) + C(11)*DFDI(-2) +

C(12)*DGDS(-1) + C(13)*DGDS(-2) +

C(14)

DGDS = C(15)*DGDP(-1) +

C(16)*DGDP(-2) + C(17)*DFDI(-1) +

C(18)*DFDI(-2) + C(19)*DGDS(-1) +

C(20)*DGDS(-2) + C(21)

Table 7: Dependent Variable: DGDP

ARDL Model: Method: Least Squares

Date: 05/03/16 Time: 15:36

Sample (adjusted): 2003 2015

Included observations: 13 after adjustments

DGDP = C(1)*DGDP(-1) + C(2)*DGDP(-2) + C(3)*DFDI(-1) + C(4)*DFDI(-2)

+ C(5)*DGDS(-1) + C(6)*DGDS(-2) + C(7)

Coefficient Std. Error t-Statistic Prob.

C(1) 0.068364 1.405292 0.048647 0.9628

C(2) 4.794228 2.046467 2.342685 0.0576

C(3) 0.097732 0.104701 0.933442 0.3866

C(4) 0.124705 0.103091 -1.20966 0.2719

C(5) 5.670292 4.481677 -1.265217 0.2527

C(6) 14.48063 4.983009 -2.906001 0.0271

C(7) 12073.02 4632.743 2.60602 0.0403

R-squared 0.890742 Mean dependent var 2393.43

Adjusted R-squared 0.781485 S.D. dependent var 18209.8

S.E. of regression 8512.3 Akaike info criterion 21.2401

Sum squared resid 4.35E+08 Schwarz criterion 21.5443

Log likelihood -131.0609 Hannan-Quinn criter. 21.1776

F-statistic 8.15268 Durbin-Watson stat 1.77957

Prob(F-statistic) 0.010998



The ARDL Model, R square is .89 which

translates to 89% prediction of the

dependent variable. The F-statistic being

0.01 less than 0.05% suggest that the

overall model has greater predictive

power. The tests for residual diagnostics

are tabled below.

Normality test:

Normality tests are used to determine if the

data arenormally distributed.The results of

the normality test are given below.

Graph 1 & Table 8: Result of normality test

The value of Jarque-Berastatistics, is more

than 0.05 that is 0.886170. The P-value

evidences that the data is normally

distributed. The Null hypothesis being that

the data is not normally distributed which

is being rejected according to the P-Value.

Test for Serial Correlation

The test was performed to check the

relationship between a given variable and

itself over various time intervals. Serial

correlations are often found in repeating

patterns when the current value of a

variable effects its future value.

Table 9: Serial Correlation test

Date: 05/03/16 Time: 21:22

Sample: 2003 2015

Included observations: 13

Autocorrelation Partial Correlation AC PAC Q-Stat Prob

. | . | . | . | 1 0.069 0.069 0.0780 0.780

.***| . | ****| . | 2 -0.469 -0.476 3.9792 0.137

0

1

2

3

4

-10000 -5000 0 5000 10000

Series: ResidualsSample 2003 2015Observations 13

Mean -2.92e-12Median 22.94577Maximum 11425.52Minimum -9793.514Std. Dev. 6019.105Skewness 0.247791Kurtosis 2.552116

Jarque-Bera 0.241693Probability 0.886170

https://en.wikipedia.org/wiki/Normal_distribution



From the above result of the serial

correlation, the probability is more than

0.05 or 5% which is 0.780 and therefore

thetest suggests that there is no serial

correlation in the model.

Heteroskedasticity Test

One of the key assumptions of regression

is that the variance of the errors is constant

across observations. If the errors have

constant variance, the errors are called

homoscedastic. Typically, residuals are

assessed this assumption

Table 10: Heteroskedasticity Test: Breusch-Pagan-Godfrey

F-statistic 2.041765 Prob. F(6,6) 0.2031

Obs*R-squared 8.726166 Prob. Chi-Square(6) 0.1896

Scaled explained SS 1.442558 Prob. Chi-Square(6) 0.9632

From the above table, the probability of

the chi square with the observed R square

is more than 0.05 or 5% thus the model

proves that there is no heteroskedasticity.

Graph 2: Stability test:

From the above graph, the blue line of the

data is within the 5% significance. This

refers that the data in the model using

ARDL model is stable.

Table 11: VAR Granger Causality

Date: 05/03/16 Time: 15:48

Sample: 2000 2015

Included observations: 12

-8

-6

-4

-2

0

2

4

6

8

2010 2011 2012 2013 2014 2015

CUSUM 5% Significance



Dependent variable: DGDP

Excluded Chi-sq df Prob.

DFDI 4.709301 2 0.0949

DGDS 8.346115 2 0.0154

All 11.85425 4 0.0185

Dependent variable: DFDI

Excluded Chi-sq df Prob.

DGDP 2.492986 2 0.2875

DGDS 1.436596 2 0.4876

All 3.622755 4 0.4595

It was observed that theDFDI has a

probability of 9.49% (Less than 10% level)

and DGDS has a probability on 1.54%

(Less than 5% level) in table 10. It is

concluded that GDS Granger Causes GDP

at 5% level and FDI Granger Causes GDP

at 10% level. The same relationship is

checked for a two way relationship. It was

observed that the relation is only one way.

5. Suggestions

The unidirectional causality was

significant in the VAR causality test. GDS

was found significant at 5% level were as

FDI was found significant only at 10%

level. GDS was found to have a higher

impact over GDP compared to FDI. Hence

the policy makers are suggested to

incentivize the domestic savings as well

along with priority to foreign direct

investments to encourage higher GDP

growth.

6. Implications

The research paper proves that there exists

a strong relationship between GDS and

GDP, which was significant at 5% level

and between FDI and GDP at 10% level.

The various initiatives taken by the policy

makers to increase FDI augers well to

increase the GDP growth. The policy

makers can provide incentives to increase



domestic savings which would contribute a

higher GDP growth. The various schemes

of increase the domestic savings can be

relooked to facilitate a higher savings.

7. Conclusion

The research demonstrates that Gross

domestic savings leads to an increase in

the GDP with 2 lags. It is observed from

the ARDL linear estimates (Table 6). The

relationship using Granger causality was

found unidirectional. The various tests for

normality, serial correlation

&heteroskedasticity proved residuals to be

free from all the criteria’s. The ARDL

model fit estimates a 89% accuracy (R2

value) with the model fit F-value (0.01).

The data suggests that a higher growth of

GDP can be achieved by increasing the

domestic savings.

8. Limitations and Scope for further

research

The data collected was limited to 4

variables- GDP, GDS, FII & FDI. The

other forms of investment such as portfolio

investments can also be considered to give

a clear picture. FII proved stationary al

level leading to rejection of the variable

from the model. The other theoretical

models of savings can as well be tested to

obtain reliable estimates. The model can

be extended to a higher time period; the

study was performed using 15 years data

i.e. from 2000 to 2015. GDP data used for

the analysis was at factor cost, other

substitutes can yield a different dimension

to the model.

References

Abdu, Murtala(2015).Impact of savings,

foreign aid on growth in India, Retrieved

from

http://www.globalbizresearch.org/Chennai

_Symposium/conference/pdf/C541.pdf

Gujarati, Damodar N. (2009). Basic

Econometrics, Tata Mc-Graw hill,492-

499.

Handbook of statistics on Indian economy,

Retrieved from,

http://dbie.rbi.org.in/DBIE/

dbie.rbi?site=publications

Koop, Gary (2005). Analysis of economic

data, John Wiley & Sons, 121-133.

Malhotra, Bhavya(2014). Foreign direct

investment: Impact on Indian economy,

Retrieved from

http://www.ripublication.com/gjbmit/

gjbmitv4n1_03.pdf

Mehta, Sachin N. & Rami, Gaurang D.

(2014).Causal relationship between

savings and economic growth in

India,Retrieved from

https://www.academia.edu/5903697/

CAUSAL_RELATIONSHIP_BETWEEN_S

AVINGS_AND_ECONOMIC_GROWTH_I

N_INDIA?auto=download.

http://www.globalbizresearch.org/Chennai_Symposium/conference/pdf/C541.pdf

http://www.globalbizresearch.org/Chennai_Symposium/conference/pdf/C541.pdf

http://dbie.rbi.org.in/DBIE/%20dbie.rbi?site=publications

http://dbie.rbi.org.in/DBIE/%20dbie.rbi?site=publications

http://www.ripublication.com/gjbmit/%20gjbmitv4n1_03.pdf

http://www.ripublication.com/gjbmit/%20gjbmitv4n1_03.pdf

https://www.academia.edu/5903697/



Menani, Shikha(2013).FDI and FII as

drivers of growth for Indian economy: A

Comparison, Retrieved from

www.ijird.com/index.php/ijird/article/view

File/44248/35762

Shrivastav, Anubha(2013).Influence of FII

flows on Indian stock market, Retrieved

fromhttp://accman.in/images/feb13/Shriva

stav%20A.pdfhttp://www.business-

standard.com/article/opinion/indian-gdp-

growth-largely-depends-on-capital-flows-

111121400005_1.html

http://accman.in/images/feb13/Shrivastav%20A.pdf

http://accman.in/images/feb13/Shrivastav%20A.pdf



A Comparative Study of Non-Performing Assets in Scheduled Commercial

Banks during Pre SARFAESI Period and Post SARFAESI Period Munish Gupta

* Naresh Malhotra**

*Assistant Professor, Department of Commerce, St.Soldier College(Co- Ed), Jalandhar

**Professor, Department of Commerce, Doaba College Jalandhar

Abstract

Non-performing assets have adversely affected the profitability of all scheduled commercial banks of

India. Almost all banks whether it is public sector bank, new private sector bank or old private sector

bank in India, are affected with this death worthy disease. Government of India has enacted many

legislatures to recover the dead amount of loan advanced by banks from time to time. However,

SARFAESI act is major enactment that has been introduced in 2002. In this research paper, an attempt is

made to analyze the status of non-performing assets in sector wise banks before and after enactment of

SARFAESI Act 2002.

Keywords:Gross NPA, Net NPA, Pre SARFAESI, Post SARFAESI, Public sector Banks, New Private

Sector Banks, Old Private sector Banks.

Introduction

Banking sector in India is going through a

transformation since era of the beginning

liberalization. Interest rate has declined

considerably. The performance of banks has

improved slightly over time. However,

public sector banks are doing the worst

among all banks. The banking sector as a

whole especially the public sector banks still

suffer from considerable Non-performing

Assets. The growing NPAs have been

reeling under high level of bad debts. But

the situation has improved over time. In the

recent past, the bank regulators have

introduced a number of measures to link the

regulation of commercial banks to the level

of risk and financial liability of these banks

(Aspal&Malothra 2012). New legal

developments like the Securitization and

Reconstruction of Financial Assets and

Enforcement of Security Interest

(SARFAESI) Act provide new option to

banks in their struggle against non-

performing assets.

Non-Performing Assets

An asset is classified as non-performing

asset (NPA) if the borrower does not pay

dues in the form of principal and interest for

a period of 180 days. However, with effect



from March 2004, default status would be

given to a borrower if dues were not paid for

90 days.

If any advance or credit facilities granted by

a bank or the financial institution to a

borrower become non performing, then the

bank have to treat all the advances/credit

facilities granted to that borrower as non

performing without having any regard to the

fact there may still exist certain

advances/credit facilities having performing

status.

With a view to moving toward international

best practices and to ensure greater

transparency, it has been decided to adopt

the ’90 days overdue’ norms for

identification of non-performing assets,

from the year ending 31 March 2004. Thus

with effect from March 2004, a non-

performing asset (NPA) shall be a loan or an

advance where:

1. Interest and/or installment of

principal remain overdue for a

period of more than 90 days in

respect of a Term Loan.

2. The account remains ‘out of order

for more than 90 days, in respect of

an overdraft/cash credit.

3. The bill remains overdue for a

period of more than 90 days in the

case of bill purchased and

discounted.

4. Interest and/or installment of

principal remains overdue for two

harvest seasons but for a period not

exceeding two half years in the case

of an advance granted for

agriculture purpose and

5. Any amount to be received remains

overdue for a period of more than

90days in respect of other accounts.

Classification of assets

From the Reserve Bank of India definition

of Non-Performing Asset, assets are

categorized as follows.

1) Standard Assets: Assets which do not

disclose any problem or once which carry

only the normal risk to be classified as

standard.

2) Sub- Standard assets: It is one which has

been classified as Non-performing asset for

a period not exceeding 12 months. With

effect from 31 March 2005 a substandard

asset is one which has remained NPA for a

period less than or equal to one year (12

months) Thus the earlier period of 18

months has been reduced to 12 months.



3) Doubtful Assets: A doubtful asset is one,

which remains substandard for a period of

12 months. With effect from 31 March

2005, an asset is classified as doubtful, if it

has remained substandard for period of 12

months. Thus, the earlier period of 18

months has now reduced to 12 months.

4) Loss assets: Assets where the losses are

confirmed or these are considered as

uncorrectable are categorized as loss assets.

These are the assets where the bank or

external auditors or Reserve Bank of India,

inspectors has identified loss but the amount

has not written off, wholly or partly.


Menon (2015)The level of NPA in private

sector banks is lower than their nationalized

counterparts. This could be due to better

credit standards maintained by these private

players. However, the authorities should

ensure that the interest of customers is

protected by the banks and they are not

exceeding the limits, so as to reduce the

NPA levels

Roy (2014) The alarming thing is that all the

developed and developing countries have

already managed to curb the NPA level from

the high of 2008-09 at the time of global

recession, where it is still rising in India.

Mukund (2011) found that the cases get

delayed inordinately in a Debt Recovery

Tribunal much against the spirit and motive

of its very establishment. Banks have

expressed their dissatisfaction with the

system that was instituted to ensure speedy

recovery.

Objectives of the Study

1. To review the sector wise NPA

position of scheduled commercial

banks in pre –SARFAESI period and

post SARFAESI period.

2. To assess the comparative position

of NPAs of scheduled commercial

banks in pre –SARFAESI period and

post SARFAESI period.

Sources of Data

The data collected is mainly secondary in

nature. The sources of data for this paper

include the literature published by Indian

Banking Association and Reserve Bank of

India, various magazines, Journals, Books

dealing with the current banking scenario

and research papers.


Research design used to carry out this study

is descriptive research because it deals with

statistical data and the main aim of the

report is to review the NPA position of



public sector banks, new private sector

banks and old private sector banks during

pre –SARFAESI Act 2002 and post

SARFAESI Act 2002. An attempt has been

made to analyse the sector wise magnitude

of non-performing assets of banks under

study. The study is done on the basis of data

for the period of 20 years that has been

segregated in two phases i.e Phase-I for 7

years (1996-97 to 2002-03) during Pre

SARFAESI Act and Phase-II for 13 years

(2003-04 to 2015-16) during Post

SARFAESI Act. The sample size consists of

10 Public Sector Banks 5 New Private

Sector Banks and 5 old Private Sector

Banks. The scope of the study is limited to

the analysis of NPAs of Sector wise selected

scheduled commercial Banks in two phases.

It examines comparative analysis of Gross

NPA ratio, Net NPA ratio and rank them as

per mean during PRE SARFAESI period

and POST SARFAESI period. The data has

been analyzed using percentage method, and

selected statistical tools such as mean,

compound annual growth rate and ranking.

Data is presented with the help of tables,

charts etc.

TABLE 1: List of Banks Taken for Study

SR.NO. NAME OF THE BANK TYPE OF BANK Abbreviations

1. STATE BANK OF INDIA Public Sector Bank SBI

2. STATE BANK OF PATIALA Public Sector Bank SBOP

3. ALLAHABAD BANK Public Sector Bank AB

4. BANK OF INDIA Public Sector Bank BOI

5. CANARA BANK Public Sector Bank CB

6. CENTRAL BANK OF INDIA Public Sector Bank CBI

7. INDIAN BANK Public Sector Bank IB

8. PUNJAB AND SINDH BANK Public Sector Bank PSB

9. PUNJAB NATIONAL BANK Public Sector Bank PNB

10. UCO BANK Public Sector Bank UB

11. HDFC BANK New Private Sector Bank HDFC

12. ICICI BANK New Private Sector Bank ICICI

13. AXIS BANK(UTI BANK) New Private Sector Bank AXIS

14. INDUSIND BANK New Private Sector Bank ISB

15. DEVLOPMENT CREDIT BANK New Private Sector Bank DCB



16. CITY UNION BANK Old Private Sector Bank CUB

17. DHANLAKSHMI BANK Old Private Sector Bank DB

18. FEDERAL BANK Old Private Sector Bank FB

19. JAMMU AND KASMIR BANK Old Private Sector Bank JKB

20. KARNATAKA BANK Old Private Sector Bank KB

Table 2 shows the gross NPA ratio of sector

wise scheduled commercial banks during

PRE SARFAESI Period with significant

statistics like mean, growth rate of NPA’s

through CAGR. From the above table it is

seen that gross NPA of public sector banks

is in the downward trend with varying

growth. The compound annual growth rate

of public sector banks under study is in the

range of -18.61% to -5.20%. The gross NPA

of New private sector banks is having the

upward trend with varying growth. The

compound annual growth rate of New

private sector banks under study is in very

high range of -20.31% to 28.20%. In the

same way, the gross NPA of old private

sector banks is having generally the upward

trend with varying growth. The compound

annual growth rate of old private sector

banks under study is in the range of -5.11%

to 11.80%. As per the mean, which is the

representative of data in the group, banks are

ranked in ascending order, which interpret

the gross NPA that better the performance,

lower the ratio. From the above table it is

extracted that HDFC Bank is ranked first as

it was able to manage lowest means GNPA

ratio of 2.35%, followed by ICICI Bank at

second position with mean GNPA ratio of

5.11%. Indian Bank and Punjab and Sind

Bank have got lowest rank of 20 with a

mean ratio of 28.79% and 19 with a mean

ratio of 21.67% respectively followed by

Allahabad Bank of 18th rank with GNPA

ratio of 19.22%.

Similar trend has been shown by net NPA

ratio in Table - 3 during PRE SARFAESI

Act period. From the above table it is

extracted that HDFC Bank is ranked first as

it was able to manage lowest means NNPA

ratio of 0.63%, followed by ICICI Bank at

second position with mean NNPA ratio of

2.88% and third rank achieve by Jammu and

Kashmir Bank with mean GNPA ratio of

3.36%. Indian Bank and Allahabad Bank

have got lowest rank of 20 with a mean ratio

of 16.32% and 19 with a mean ratio of

12.02% respectively followed by Punjab and

Sind Bank at 18th

Rank with 11.09%.



Analysis and Interpretation

Table 2: Sector wise Gross NPA ratio and Ranks of Individual Banks (PRE-SARFAESI Act 2002)

Sec

tors

Ban

ks

1996

-97

1997

-98

1998

-99

1999

-00

2000

-01

2001

-02

2002

-03

Mea

n

SD

Ran

k

CA

GR

Publi

c S

ecto

r B

ank

SBI 16.02 14.14 15.56 14.25 12.93 11.95 9.34 13.46 2.29 14 -8.60%

SBP 11.32 11.88 13.98 10.99 9.66 6.94 4.80 9.94 3.12 8 -13.32%

AB 23.93 23.18 20.09 19.07 17.66 16.94 13.65 19.22 3.59 18 -8.93%

BOI 11.78 11.55 11.87 12.90 10.25 9.37 8.55 10.90 1.55 10 -5.20%

CB 20.26 18.69 18.32 10.42 7.48 6.22 5.96 12.48 6.38 12 -18.45%

CBI 25.00 20.47 17.41 16.63 16.06 14.70 13.06 17.62 3.99 17 -10.26%

IB 39.12 38.96 38.70 32.77 21.76 17.86 12.39 28.79 11.27 20 -17.44%

PSB 30.71 26.79 23.01 15.27 18.45 18.19 19.25 21.67 5.46 19 -7.49%

PNB 16.31 14.50 14.12 13.19 11.71 11.38 11.58 13.26 1.84 13 -5.55%

UCO 28.35 24.04 22.55 18.79 11.64 9.59 8.24 17.60 7.86 16 -18.61%

Old

Pri

vat

e

Sec

tor

Ban

ks AXIS 4.33 7.15 7.86 5.47 4.64 5.18 3.16 5.40 1.63 3 -5.11%

CUB 8.50 11.03 12.02 12.40 13.69 13.20 12.11 11.85 1.71 11 6.08%

DCB 8.09 7.03 6.25 7.40 7.84 9.29 9.56 7.92 1.19 6 2.82%

DB 6.75 15.68 18.80 14.58 14.77 15.29 13.18 14.15 3.69 15 11.80%

FB 7.00 7.34 10.93 11.75 12.84 11.88 8.21 9.99 2.41 9 2.69%

New

Pri

vat

e

Sec

tor

Ban

ks HDFC 0.50 3.04 1.65 3.07 2.81 3.18 2.22 2.35 0.99 1 28.20%

ICICI 2.24 1.93 4.72 2.54 5.42 10.23 8.72 5.11 3.28 2 25.42%

ISB 2.74 5.33 10.08 7.14 6.13 7.41 4.94 6.25 2.30 4 10.32%

JKB 12.14 9.40 7.90 6.52 4.97 3.62 3.11 6.81 3.26 5 -20.31%

KB 4.47 4.98 8.01 8.82 10.58 10.43 12.99 8.61 3.08 7 19.46%



Table 3: Sector wise Net NPA ratio and Ranks of Individual Banks (PRE-SARFAESI Act 2002)

Sec

tors

Ban

ks

1996

-97

1997

-98

1998

-99

1999

-00

2000

-01

2001

-02

2002

-03

Mea

n

SD

Ran

k

CA

GR

Publi

c S

ecto

r B

ank

SBI 7.30 6.07 7.18 6.41 6.03 5.63 4.50 6.16 0.95 10 -7.75%

SBP 5.88 7.04 8.23 6.09 4.92 2.94 1.49 5.23 2.34 6 -20.45%

AB 14.84 15.09 12.54 12.24 11.23 11.09 7.08 12.02 2.70 19 -11.60%

BOI 6.93 7.34 7.28 7.55 6.72 6.02 5.37 6.74 0.79 11 -4.16%

CB 9.32 7.52 7.09 5.20 4.84 3.89 3.59 5.92 2.11 8 -14.70%

CBI 14.40 12.21 9.79 9.84 9.72 7.98 7.02 10.14 2.49 16 -11.29%

IB 25.24 26.01 21.67 16.80 10.06 8.28 6.15 16.32 8.26 20 -20.97%

PSB 12.04 10.84 10.48 9.39 12.27 11.70 10.89 11.09 1.00 18 -1.66%

PNB 10.38 9.57 8.96 8.52 6.69 5.32 3.86 7.61 2.39 14 -15.20%

UCO 13.73 11.14 10.83 8.75 6.35 5.45 4.36 8.66 3.43 15 -17.40%

Old

Pri

vat

e

Sec

tor

Ban

ks AXIS 3.66 5.63 6.32 4.71 3.43 2.74 2.39 4.13 1.47 4 -6.86%

CUB 5.30 7.54 7.96 7.26 8.20 8.22 8.21 7.53 1.05 13 7.57%

DCB 5.93 5.02 4.79 5.86 6.12 6.47 7.76 5.99 0.98 9 4.58%

DB 4.51 11.01 12.33 11.08 11.34 11.66 9.25 10.17 2.67 17 12.72%

FB 7.16 5.28 7.53 8.56 10.08 8.60 4.95 7.45 1.85 12 -5.97%

New

Pri

vat

e

Sec

tor

Ban

ks HDFC 0.01 1.24 1.08 0.77 0.45 0.50 0.37 0.63 0.43 1 82.54%

ICICI 1.73 1.14 2.88 1.53 2.19 5.48 5.21 2.88 1.77 2 20.17%

ISB 2.08 3.96 7.20 5.98 5.25 6.59 4.25 5.04 1.76 5 12.65%

JKB 6.03 4.57 3.79 3.22 2.45 1.88 1.58 3.36 1.58 3 -20.01%

KB 3.12 3.06 4.99 5.73 6.93 5.90 7.36 5.30 1.70 7 15.38%



Table 4 shows the gross NPA ratio of

sector wise scheduled commercial banks

during POST SARFAESI period with

significant statistics like mean, growth rate

of NPA’s through CAGR. From the above

table it is seen that gross NPA of public

sector banks is generally in the upward

trend with varying growth. The compound

annual growth rate of public sector banks

under study is in the range of -8.23% to

7.27%. The gross NPA of New private

sector banks is having the mixed trend

with varying growth. The compound

annual growth rate of New private sector

banks under study is in range of -10.51%

to 8.75%. In the same way, the gross NPA

of old private sector banks is having

generally the downward trend with varying

growth. The compound annual growth rate

of old private sector banks under study is

in the range of -13.14% to -4.25%. As per

the mean, which is the representative of

data in the group, banks are ranked in

ascending order, which interpret the gross

NPA that better the performance, lower the

ratio. From the above table it is extracted

that HDFC Bank is ranked first as it was

able to manage lowest means GNPA ratio

of 1.29%, followed by Axis Bank at

second position with mean GNPA ratio of

1.46% and third rank achieve by Indusind

Bank.Bank with mean GNPA ratio of

1.88%. Development Credit Bank and

Central Bank of India have got lowest rank

of 20 with a mean ratio of 6.15% and 19

with a mean ratio of 5.97% respectively

followed by Punjab and Sind Bank of 18th

rank with GNPA ratio of 5.51%.

Similar trend has been shown by net NPA

ratio in Table - 4.104 during POST

SARFAESI Act period. From the above

table it is extracted that HDFC Bank is

ranked first as it was able to manage

lowest means NNPA ratio of 0.31%,

followed by AXIS Bank at second position

with mean NNPA ratio of 0.63% and third

rank achieve by Federal Bank with mean

GNPA ratio of 0.98%. UCO Bank and

Central Bank of India have got lowest rank

of 20 with a mean ratio of 2.91% and 19

with a mean ratio of 2.89% respectively

followed by Punjab and Sind

Bank at 18th

Rank with 2.87%



Table 4: Sector wise Gross NPA ratio and Ranks of Individual Banks (POST-SARFAESI Act 2002)

Sectors Ban

ks

2003

-04

2004

-05

2005

-06

2006

-07

2007

-08

2008

-09

2009

-10

2010

-11

2011

-12

2012

-13

2013

-14

2014

-15

2015

-16

Mea

n

SD

Ran

k

CA

GR

Publi

c S

ecto

r B

ank

SBI 7.75 5.96 3.90 2.92 3.04 2.98 3.28 3.48 4.90 4.75 4.95 4.25 6.50 4.51 1.50 14 -1.46%

SBP 3.71 4.13 2.40 1.80 1.42 1.30 2.14 2.60 2.94 3.25 4.44 5.41 7.87 3.34 1.83 8 6.47%

AB 8.66 5.80 3.90 2.61 2.01 1.80 1.71 1.80 1.91 3.92 5.72 5.46 9.75 4.23 2.70 12 0.99%

BOI 7.86 5.45 3.70 2.42 1.68 1.70 1.64 2.64 2.90 2.99 3.24 5.39 13.06 4.21 3.21 11 4.32%

CB 6.33 3.89 2.30 1.51 1.31 1.60 1.53 1.47 1.75 2.57 2.49 3.89 9.40 3.08 2.37 6 3.35%

CBI 12.55 9.50 6.80 4.81 3.16 2.70 2.32 1.82 4.83 4.80 6.27 6.09 11.95 5.97 3.48 19 -0.41%

IB 7.99 4.19 2.90 1.85 1.21 0.90 0.76 0.99 1.94 3.33 3.67 4.40 6.65 3.14 2.26 7 -1.52%

PSB 18.16 18.16 9.60 2.43 0.74 0.70 0.63 0.99 1.64 2.96 4.41 4.76 6.48 5.51 6.20 18 -8.23%

PNB 9.35 5.96 4.10 3.45 2.74 1.80 1.71 1.79 3.15 4.27 5.25 6.55 12.90 4.85 3.27 15 2.72%

UCO 6.93 4.96 3.30 3.17 2.97 2.20 2.15 3.32 3.73 5.42 4.32 6.76 16.09 5.02 3.67 17 7.27%

Old

Pri

vat

e

Sec

tor

Ban

ks AXIS 2.88 1.98 1.70 1.13 0.83 1.10 1.39 1.28 1.18 1.19 1.29 1.36 1.71 1.46 0.52 2 -4.25%

CUB 10.36 5.89 4.30 2.58 1.81 1.80 1.36 1.21 1.01 1.13 1.81 1.86 2.41 2.89 2.64 4 -11.44%

DCB 8.19 14.19 15.00 5.14 1.55 8.80 8.68 5.86 4.40 3.18 1.68 1.76 1.51 6.15 4.61 20 -13.14%

DB 11.43 8.51 6.70 5.06 2.95 2.00 1.53 0.74 1.18 4.81 5.99 6.70 6.36 4.92 3.17 16 -4.77%

FB 7.44 7.29 4.60 2.95 2.42 2.60 2.97 3.49 3.35 3.44 2.46 2.03 2.83 3.68 1.75 9 -7.74%

New

Pri

vat

e

Sec

tor

Ban

ks HDFC 1.86 1.69 1.40 1.39 1.42 2.00 1.44 1.06 0.95 0.85 0.91 0.89 0.92 1.29 0.39 1 -5.70%

ICICI 4.70 4.27 1.50 2.08 3.30 4.30 6.52 5.80 4.83 3.22 3.02 3.78 5.82 4.09 1.48 10 1.80%

ISB 3.30 3.53 2.90 3.07 3.04 1.60 1.23 1.01 0.98 1.02 1.12 0.81 0.87 1.88 1.08 3 -10.51%

JKB 3.04 2.72 2.50 2.89 2.53 2.60 1.97 1.95 1.54 1.59 1.06 5.96 8.32 2.97 2.00 5 8.75%

KB 11.93 7.58 5.10 3.95 3.42 3.70 3.73 3.97 3.26 2.51 2.92 2.95 3.44 4.50 2.57 13 -9.84%



Table – 5 Sector wise Net NPA ratio and Ranks of Individual Banks (POST-SARFAESI Act 2002)

Sectors Ban

ks

2003

-04

2004

-05

2005

-06

2006

-07

2007

-08

2008

-09

2009

-10

2010

-11

2011

-12

2012

-13

2013

-14

2014

-15

2015

-16

Mea

n

SD

Ran

k

CA

GR

Publi

c S

ecto

r B

ank

SBI 3.48 2.65 1.88 1.56 1.78 1.79 1.72 1.63 1.82 2.10 2.57 2.12 3.81 2.22 0.72 14 0.76%

SBP 1.36 1.23 0.99 0.83 0.60 0.60 1.04 1.21 1.35 1.62 3.17 3.88 3.98 1.68 1.19 9 9.36%

AB 2.37 1.28 0.84 1.07 0.80 0.72 0.66 0.79 0.98 3.19 4.15 3.99 6.76 2.12 1.89 13 9.13%

BOI 4.50 2.80 1.49 0.95 0.52 0.44 1.31 0.91 1.47 2.06 2.00 3.36 7.79 2.28 2.02 15 4.68%

CB 2.89 1.88 1.12 0.94 0.84 1.09 1.06 1.10 1.46 2.18 1.98 2.65 6.42 1.97 1.50 12 6.88%

CBI 5.57 2.98 2.59 1.70 1.45 1.24 0.69 0.65 3.09 2.90 3.75 3.61 7.36 2.89 1.93 19 2.35%

IB 2.71 1.35 0.79 0.35 0.24 0.18 0.23 0.53 1.33 2.26 2.26 2.50 4.20 1.46 1.25 6 3.72%

PSB 9.62 8.11 2.43 0.66 0.37 0.32 0.36 0.56 1.19 2.16 3.35 3.55 4.62 2.87 3.02 18 -5.93%

PNB 0.98 0.20 0.29 0.76 0.64 0.17 0.53 0.85 1.52 2.35 2.85 4.06 8.61 1.83 2.35 10 19.85%

UCO 3.65 2.93 2.10 2.14 1.98 1.18 1.17 1.84 1.96 3.17 2.38 4.30 9.09 2.91 2.07 20 7.90%

Old

Pri

vat

e

Sec

tor

Ban

ks AXIS 1.29 1.39 0.98 0.72 0.42 0.40 0.40 0.29 0.27 0.36 0.44 0.46 0.74 0.63 0.38 2 -4.53%

CUB 6.37 3.37 1.95 1.09 0.98 1.08 0.58 0.52 0.44 0.63 1.23 1.30 1.53 1.62 1.62 8 -11.21%

DCB 4.84 6.34 4.50 1.64 0.66 3.88 3.11 0.96 0.57 0.75 0.91 1.01 0.75 2.30 1.98 16 -14.39%

DB 6.68 3.92 2.82 1.75 0.88 0.88 0.84 0.30 0.66 3.36 3.80 3.29 2.78 2.46 1.81 17 -7.05%

FB 2.89 2.21 0.95 0.44 0.23 0.30 0.48 0.60 0.53 0.98 0.74 0.73 1.64 0.98 0.80 3 -4.61%

New

Pri

vat

e

Sec

tor

Ban

ks HDFC 0.16 0.24 0.44 0.43 0.47 0.63 0.31 0.19 0.18 0.20 0.27 0.25 0.28 0.31 0.14 1 4.77%

ICICI 2.21 1.65 0.72 1.02 1.55 2.09 2.12 1.11 0.73 0.77 0.97 1.61 2.98 1.50 0.70 7 2.52%

ISB 2.72 2.71 2.09 2.47 2.27 1.14 0.50 0.28 0.27 0.31 0.33 0.31 0.36 1.21 1.06 5 -15.51%

JKB 1.48 1.41 0.92 1.13 1.07 1.38 0.28 0.20 0.15 0.14 0.22 2.77 4.31 1.19 1.20 4 9.32%

KB 4.98 2.29 1.18 1.22 0.98 0.98 1.31 1.62 2.11 1.51 1.91 1.98 2.35 1.88 1.05 11 -6.07%



Table 6: Composite Ranks of Scheduled Commercial Banks

Pre SARFAESI Act 2002 (1996-97 to 2002-03)

Post SARFAESI Act 2002 (2003-04 to 2015-16)

Ba

nk

Ranks as per

GNPA

Ranks as per

NNPA

Average

Co

mp

osi

te R

an

ks Ranks as

per GNPA Ranks as

per NNPA Average

Co

mp

osi

te R

an

ks

SBI 14 10 12 12 14 14 14 15 SBP 8 6 7 6 8 9 8.5 8 AB 18 19 18.5 18 12 13 12.5 12 BOI 10 11 10.5 10 11 15 13 14 CB 12 8 10 9 6 12 9 10 CBI 17 16 16.5 17 19 19 19 20 IB 20 20 20 20 7 6 6.5 7

PSB 19 18 18.5 18 18 18 18 17 PNB 13 14 13.5 14 15 10 12.5 12 UCO 16 15 15.5 15 17 20 18.5 19 AXIS 3 4 3.5 3 2 2 2 2 CUB 11 13 12 12 4 8 6 5 DCB 6 9 7.5 8 20 16 18 17 DB 15 17 16 16 16 17 16.5 16 FB 9 12 10.5 10 9 3 6 5

HDFC 1 1 1 1 1 1 1 1 ICICI 2 2 2 2 10 7 8.5 8 ISB 4 5 4.5 5 3 5 4 3 JKB 5 3 4 4 5 4 4.5 4 KB 7 7 7 6 13 11 12 11

Table 6 shows the composite rank of each

bank, this is computed by averaging the

ranks of banks as per GNPA and NNPA.

This reason behind this is that average

performance in each will determine

goodness in performance of bank to curb

the nonperforming assets during PRE

SARFAESI period and during POST

SARFAESI period. In above concluding

table, final ranks are assigned to banks is

based on the average of earlier two ranks.

It can be seen that in PRE SARFAESI

period, HDFC Bank has been proved as

best performer and ranked first followed

by ICICI Bank at second position. Third

rank achieved by Axis Bank and 4th

rank is

attained by Jammu and Kashmir Bank.

Indian Bank has lowest rank of 20.

Allahabad Bank and Punjab and Sind

Bank have same ranking of 18 followed by

Central Bank of India with 17th

rank.

Conclusion

The NPAs have always created a big

problem for the banks in India. It is just

not only problem for the banks but for the

economy too. Profitability of banks is

adversely affected due to growth in non-

performing assets. It is very important for

banking sector to curb non- performing



assets maintain profitability and survival in

long run. Results of above study has

enlighten the sector wise level of

nonperforming assets of different

scheduled banks and relation between

different banks in the level of

nonperforming assets in two phases i.e

PRE SARFAESI period and POST

SARFAESI period. It is found that during

PRE SARFAESI period, level of gross and

net NPA ratio of public sector banks is on

an average in downward trend. On the

other hand, new and old private sector

have on an average upward trend of non-

performing assets on the contrary, during

POST SARFAESI period, public sector

banks and have shown ineffectiveness of

SARFAESI Act, 2002 to curb NPAs.

Whereas, new and old private sector banks

have shown better management to curb

NPAs. However, Indian Bank has

slippages during the period of study in

controlling of NPAs in the early years of

the decade. (Selvarajana&Vadivalagan

2013).

References

Antony, Valasamma (2004). Non-

Performing Assets – A menace to

the Banking Industry. Southern

Economist, January: 20-23.

Aspal, Parvesh Kumar & Malhotra,

Naresh (2012). Performance

Appraisal of Indian Public Sector

Banks. Proceedings of 19th

International Business Research

Conference ,2012.

Balasubramaniam, C.S. (2001).

Non-performing assets and

profitability of commercial banks

in India: assessment and emerging

issues. Abhinav Journal, 1(7).

ISSN 2277-1166.

Bardia, S.C. (2004). Credit

Efficiency in Banks: A

Comparative Study, The ICFAI

University Press, August.

Kakker, Rajendra (2004). NPA

Management – Role of Asset

Reconstruction Companies. IBA

Bulletin, 4(11): 17

Kaveri V.S. (2001). Prevention of

NPAs– Suggested strategies. IBA

Bulletin, August.

Karunakar, M., Vasuk, K.

&Saravanam, S. (2008). Are non -

Performing Assets Gloomy or

Greedy from Indian Perspective?

Research Journal of Social

Sciences, 3: 4-12.

Murthy, C.R.K.(2000-2001).

Branch Level Management of Non

Performing Assets: Part III –

Effective Management of Civil

Litigation. Vinimaya, XXI(2):5-11.



Reserve Bank of India, Statistical

Tables Relating to Banks (Annual

Issues) (Various Issues)

Reserve Bank of India, Report on

Trend and Publications, New Delhi

of Banking in India (Annual

Issues) (Various Issues)

Roy, Saikat Ghosh (2014).

Determinants of Non-Performing

Assets in India - Panel Regression.

Eurasian Journal of Economics and

Finance, 2(3): 69-78.

Satyanarayana, K.

&Subrahmanyam, G. (2000).

Anatomy of NPAs of Commercial

Banks. Applied Finance, 6(3, July).

Selvarajan, B. &Vadivalagan, G.

(2013). A Study on Management of

Non-Performing Assets in Priority

Sector reference to Indian Bank

and Public Sector Banks (PSBs),

Global Journal of Management and

Business Research, 13(1).

Taori, K.J.(2000). Problems and

Issues relating to Management of

Non Performing Assets of Banks in

India. The Journal of Indian

Institute of Bankers, 2(April June).

Unny, Mukund P. (2011). A Study

on the Effectiveness of Remedies

Available For Banks in a Debt

Recovery Tribunal - A Case Study

on Ernakulam DRT. Working

Paper Series, Centre for Public

Policy Research.



A Causal Relationship between Agricultural Production and Exports: An

Impact on Indian Economy

Waseem Ahmad Khan Aditi Agrawal

Department of Economics, Aligarh Muslim University, Aligarh

Abstract In this paper, we analyze the impact of agricultural exports and agricultural production on Indian

economy, this paper also analyzes the effect of Agricultural production on GDP. Moreover it analyzes

the causal relationship between agriculture exports, agricultural production and GDP. Variable which

we have taken to fulfill our objective are AP (Agricultural production), AXp (Agricultural Export),

GDP (Gross Domestic Product) in which GDP is dependent variable and AP, AXp are independent

variables. In order to attain our objective, we will undertake certain methodology in which we will use

pair wise granger causality and also use the regression model to find out the impact between the

variable. This paper is divided into three parts - first part of the study tries to show the trend between

the variable and use CAGR to find out the volume or magnitude between the periods of the study.

Secondly, with the help of pair wise granger causality test, we try to show the causal relationship

between the variables in order to perform the regression model. Finally, at the end of our study, we

have used the regression model to explore the impact of agricultural export on Indian economy.

Keywords: Agricultural production, agricultural exports, India’s GDP.

Introduction

We live in a country where a large portion

of the population reside in rural areas and

agriculture employs 60% of the Indian

population thus agriculture accounts for

substantial share in production as well as

exports. In 2005, 70% of all production in

India was located in rural areas and

keeping this in mind, it is obvious to pose

the question that how the conditions in the

rural areas affect firms, particularly the

production part of the firms which thereby

affect the export sector of the economy.

One should know that the most important

and fundamental aim of the developing

countries is rapid economic growth and

development and exports are considered as

one of the most important tool for attaining

economic growth. Indian agriculture has

greatly contributed to foreign trade even in

its traditional form. Agricultural products

have been facing stiff competition from

Asian countries for long time. Due to

globalization and liberalized regime, this

competition is likely to increase further

and new initiatives in agriculture

development shall have to meet the

emerging challenges. The performance of

agriculture after amalgamation with the

world markets is linked to the success of

exports. In its bid to increase overall

exports, the government of India has

decided to achieve this objective by giving

a push to production and export of



agricultural commodities. After more than

two decades of liberalization it is quite

appropriate to talk about the impact of

liberalization on Indian Agriculture

production, exports and India’s GDP. The

available literature reveals that economic

environment in India has undergone

qualitative changes as the ‘import

substitution inward oriented development

strategy’ has been replaced with ‘export

promotion outward oriented strategy’ with

implementation of economic reforms in

agriculture sector. The outward orientation

of the economy including that of the

agricultural sector leads to higher growth

of the economy. On the superiority of the

export-promotion strategy over inward

looking strategies, the reforms initiated in

1991 facilitated higher exports of a number

of commodities. The growth rate of

agricultural-export has accelerated from

11.9 percent per annum in 1980s to 18.6

percent during first half of 1990s. While it

seemed to be strong initially, there was a

significant slowdown in the exports after

1995. During 1996-2000, agricultural

exports have in fact shown a negative

growth. There is a marked decline in the

percentage share of agricultural exports to

total exports during1996-97 periods.

However in 1996-97 agricultural export of

India amounted to 20.40 percent of total

exports, in 2000-01, it decline to 14.43 per

cent, which further fell to 10.47 percent in

2010-11. During the past five years,

agricultural sector has seen a lot of growth

and advancement in terms of increased

productivity of food grain, oilseeds, cash

crops, fruits, vegetables, dairy products

etc. India has emerged as the highest

producer of milk in the world and second

highest in terms of fruits and vegetables.

This paper is divided into three sections.

Section I is attributed towards showing the

trend and growth pattern of agriculture

production and agriculture exports with the

help of CAGR. It will help us to show

whether the growth taking place in the

agriculture sector has been positive or

negative by taking into account the annual

and compound annual growth rates.

Section II empirically tests the causal

relationship between the three variables,

viz, Agriculture Production (AP),

Agriculture Exports (AXp) and India’s

GDP using pair wise Granger causality

test, thereby proving the causal

relationship between the above mentioned

variables. Test has been conducted

between AP and AXp, AXp and India’s

GDP, and AP and India’s GDP. Section III

is dedicated towards defining the impact of

the two independent variables i.e. AP and

AXp on the dependent variable i.e. GDP of

India with the help of multipleregression

conducted separately on the variables. The



future growth in agriculture must come

from viz.

New technologies which are not

only “cost effective” but also “in

compliance” with natural climatic

regime of the country;

Technologies pertinent to rain-fed

areas specifically;

Sustained genetic improvements

for better seeds and yields;

Data improvements for better

research and sustainable planning;

Bridging the gap between facts and

tradition and

Proficient management practices

and sustainable use of natural

resources.

Objectives of the Study

To find out the trend and

magnitude of agriculture

productivity and agriculture

exports.

To explore the causal relationship

between the variables viz,

agriculture production, agriculture

exports and the GDP of India.

To find out the impact of

agriculture production and

agriculture exports on Indian

economy.

Hypothesis of the Study

H0: There is no significant impact

of agriculture exports on India’s

GDP.

H0: There is no significant impact

of agriculture production on India’s

GDP.

Data and Methodology

For conducting the study the variable

which are included are AP (Agriculture

Production), AXp (Agricultural exports)

and GDP (Gross Domestic Product) where

GDP is dependent variable, AP and AXp

are independent variables. In the above

variables we will examine the impact of

agriculture Production and exports on

GDP which is a dependent variable. Data

taken for the study would be secondary for

time period 23 years from 1991 to 2014. In

methodology we will use pair wise granger

causality test and keeping in view the

nature of variables the model estimation

would be done. By using e-views and

SPSS software, the study will analyze the

impact of Agricultural Exports and

Production on GDP.



Table 1: Trend and Growth pattern of Agriculture Production, Agriculture Exports and India’s

GDP

(Rs. in crores)

YEAR GDP (at

Factor cost)

Agriculture

Production

Annual

Growth

Rate (%)

Agriculture

Exports

Annual

Growth

Rate (%)

1991-92 1367171 390201 - 7838.04 -

1992-93 1440503 416153 6.65 9040.30 15.33

1993-94 1522343 429981 3.32 12586.55 39.22

1994-95 1619694 450258 4.71 13222.76 5.05

1995-96 1737740 447127 -0.69 20397.74 54.26

1996-97 1876319 491484 9.92 24161.29 18.45

1997-98 1957031 478933 -2.55 24832.45 2.77

1998-99 2087827 509203 6.32 25510.64 2.73

1999-00 2254942 522795 2.66 25313.66 -0.77

2000-01 2348481 522755 -0.007 28657.37 13.20

2001-02 2474962 554157 6.00 29728.61 3.73

2002-03 2570935 517559 -6.60 34653.94 16.57

2003-04 2775749 564391 9.04 36415.48 5.08

2004-05 2971464 565426 0.18 41602.65 14.24

2005-06 3253073 594487 5.13 49216.96 18.30

2006-07 3564364 619190 4.15 62411.42 26.80

2007-08 3896636 655080 5.79 79039.52 26.64

2008-09 4158676 655689 0.09 85551.67 8.23

2009-10 4516071 660987 0.80 89341.5 4.42

2010-11 4918533 717814 8.59 117483.6 31.49

2011-12 5247530 753832 5.01 187609.3 59.68

2012-13 5482111 764510 1.41 232041.1 23.68

2013-14 5741791 800548 4.71 268469.1 15.69

CAGR 6.74%

3.32%

17.42%

Source: Central Statistical Organization: Advance Estimate, Directorate General Of Commercial Intelligence and Statistics, Ministry of

Commerce Kolkata

The table given above shows the growth

pattern of our variables. An increasing

trend in all the variables is quite evident,

though the rate of growth may differ. As

far as GDP and agriculture production is

concerned, both of them have seen an



average rise of 6.7% and 3.3%

respectively. The slow growth in

agriculture production may be attributed to

the negligence of our policy makers

towards this sector. Some years have even

seen negative growth which is due to the

various factors underlying the production

process mainly the traditional processes

undertaken by the farmers which still

occupies the major portion of this sector.

But if we have a look at export growth, it

has experienced a high jump of more than

17% in the post-reform era. The reason for

such a boost in exports is quite obvious-

opening up of the economy. Adoption of

the New Economic Policy has given a

boost to our export industries which has

directly influenced our foreign exchange

reserve, thus making way for our country’s

growth and development.

Chart 1: Trend of GDP and Agricultural Exports

Chart 1 given above shows the trend line

of Agriculture exports and GDP. The data

values have been converted into log values

in order to make the growth pattern clearly

visible in the diagram. The positive

relationship can be clearly seen between

the variables which suggest that an

increasing amount of Agriculture exports

is giving way for country’s growth in the

form of rising GDP. One more inference

can be drawn from the graph that the two

lines areslowly and steadily converging in

which exports are growing at a higher rate

as compared toGDP. So, this paper could

1

10

100

1000

10000

100000

1000000

10000000

GDP at fc

Agricultural exports



Chart 2: Trend of GDPand Agricultural Production

Chart 3: Trend of Agriculture Production and Agriculture Exports

serve as a base for other researchers to

conduct future forecasting and find out

whether this convergence continues or

drifts apart. Another point to be mentioned

here is that GDP is more or less growing at

a constant rate with a nice smooth slope,

whereas, if we focus on our exports, the

growth there have been quite rough with

1

10

100

1000

10000

100000

1000000

10000000

GDP at fc

Agriculture Production

1

10

100

1000

10000

100000

1000000

AgricultureProduction

Agriculturalexports



few ups and down but still has made its

way up. Next, in chart 2, we have tried to

portray the relationship between GDP and

Agriculture Production. The two variables

are again sharing the positive relationship

with huge gap in the growth rate and the

gap has been persistent throughout the

period of study. Where GDP has shown an

average growth of almost 7%, agriculture

production grew at less than 4%, infact

some years have even seen negative

growth. This is a matter of concern that

despite agriculture being the dominant

sector of Indian economy, still it bears

such sluggish growth rate, reason being the

ignorance of the policy makers towards

this sector. In our opinion, the problem

behind such issue may be giving extra

importance to the industrial sector and

developing it at the cost of our very own

agriculture sector. Industrialization is, no

doubt, an important tool for development

but we should not ignore other sectors in

this race. Lastly, chart 3 depicts again a

positive relationship between Agriculture

production and Agriculture Exports.

Whereas AP have seen a nominal growth,

AXp have grown rapidly with a growth

rate as high as 17%. As we all know, we

are discussing post-reform era, therefore

such high growth is understandable. Indian

economy, after undertaking economic

reforms, has seen an upward swing in not

only agriculture exports but overall exports

of India has also increased to a large

extent. As we can see in the graph, the

huge gap between the two variables has

been converging very rapidly. This implies

that the reform which on one hand has

elevated the agriculture exports has not

been of much importance for agriculture

production.

A Causal Relationship between

the Variables There are different types of variables

which inter connectedly shows the impact

on the Indian economy. Some of the

variable has the bilateral relationship

whereas some shows the uni directional

relationship, in this study we have tried to

find out that whether the directional

relationship between the variables exist or

not and either they have one sided

relationship or double sided. This part of

the study tries to show the relationship

between the variables that we are using in

this paper. To find out the causal

relationship, we are using three variables

in which our first relationship is between

the annual agriculture production and

gross domestic product, second between

the annual agricultural exports and gross

domestic product and lastly shows the

relationship between the agriculture

production and agricultural exports.



Actually this part of our paper tries to

show the impact of these two independent

variables on gross domestic product but it

is important to know how these variables

are inter-related or we can say that whether

our study is going in the right direction or

not that will be defined by the use of pair

wise granger causality test. The

relationship between the variable can

easily be seen from the diagram given

below:

Chart 1: Relationship between the Variables

With this chart we can easily show the

relationship between the variable that have

been taken from the Economy. With the

given set of data the relationship between

the variables is quite clearly visible. There

is the impact of agricultural exports on

GDP which can be clearly seen from the

gear diagram that increase in the

agricultural export leads to the increase in

the GDP whereas according to our result,

the GDP in itself is not capable to gear the

agricultural exports in the Indian

Economy. By the gear diagram we can

effortlessly analyze that both agriculture

production and exports seems to gear or

say circulate the GDP.

Pair-wise Granger Causality Test

Granger Causality has been conducted to

see whether one time series such as

variable X is useful for forecasting another

variable Y or not. This research will see

the causality relationship between Exports

AXP with GDP. Secondly research will

analyze the causality relationship between

Agriculture Production with GDP and

thirdly our research will see the causality



relationship between Agriculture

Production with Agricultural Exports.

Enders have Suggested granger causality

test in order to understand that whether the

lag value of one variable cause another

variable or not. If there are two equation

models X and Y having p lags, x is

granger cause y if the whole co efficient is

not equal to zero. Generally the pair wise

granger causality test model in the form of

X and Y are:

Xt= β0 + β1Yt – i+ β2Xt – j + u1t

Yt= β0 + β1Yt – I + β2Xt – j + u2t

Here we assume that X and Y variables are

stationary and we also suppose that the

disturbance of U1t and U2t are

uncorrelated. The null hypothesis of

Granger causality can be expressed as:

H0: Y does not Granger, cause, X and vice

versa.

Table 2:Result From Pair-Wise Granger Causality Test with Lags 1

Null Hypothesis: Obs F-Statistic P-value

AXP does not Granger Cause AP

AP does not Granger Cause AXP

22 0.86389

1.23125

0.364

0.281

GDP does not Granger Cause AP

AP does not Granger Cause GDP

22 17.4342

0.92533

0.000

0.348

GDP does not Granger Cause AXP

AXP does not Granger Cause GDP

22 2.75658

23.2400

0.113

0.000

Table 3: Result From Pair-Wise Granger Causality Test with Lags 2

Null Hypothesis: Obs F-Statistic P-value

AXP does not Granger Cause AP

AP does not Granger Cause AXP

21 0.06611

2.02573

0.9363

0.1644

GDP does not Granger Cause AP

AP does not Granger Cause GDP

21 7.42402

8.21293

0.0052

0.0035

GDP does not Granger Cause AXP

AXP does not Granger Cause GDP

21 3.23556

6.12380

0.0661

0.0106

Result of the granger causality test has

been judged under the 5% level of

significance, it means that if the result is

less than the 5% level of significance it

will lead to rejection of the null hypothesis

whereas if our result comes out to be

greater than 5% we will accept null

hypothesis. Now, we have two results of



granger causality test- first one with lag 1

and second one with lags 2. Actually we

have an opportunity to conduct the test up

to four lags because Akaike Information

Criterion and Schwartz Information

Criterion suggest us to perform the test

within four lags, so our both result is

correct but to perform the regression test

we have to choose one and which one is

best for our analysis depends on the how

much relationship we have found to be

significant.

The result from the lag 1 shows that most

of the null hypothesis seems to be accepted

or we can also say that p-value is not

significant with 5% level of significance

thats why we are not considering this

result and the result from the lag 2 are as

follows:

AXp(Agriculture exports)

Probability value is 0.9363 which

is greater than significant value so

null hypothesis is accepted and we

may conclude that Agriculture

exports does not have affect on the

Agriculture production.

AP(Agricultural production) P-

value is 0.1644 which is greater

than significant value so null

hypothesis is accepted and we may

conclude that agriculture

production is not a granger cause of

agricultural exports.

GDP (Gross Domestic Product) P-

value is 0.0052 which is less than

significant value so null hypothesis

is rejected and conclude that GDP

affect agriculture production.

AP(Agricultural production)P-

value is 0.0035 which is less than


is rejected and can be concluded

that agriculture production affect

the GDP

GDP(Gross Domestic Product)P-

value is 0.0661 which is greater

than significant value so null

hypothesis is accepted and we may

conclude that GDP does not affect

the agricultural exports

AXp (Agricultural exports) P-value

is 0.0106which is less than


is rejected and we may concluded

that agricultural exports affect

GDP. If Agricultural exports

increase it will have an impact on

GDP, Causing increase or decrease

in the GDP.

The final result from the pair wise granger

causality test shows that there is an impact

of Agriculture Production and Agricultural



Exports on India’s GDP within the 5%

level of significance.

Impact of Agriculture Production and

Agriculture Exports on GDP

sith the result from the Pair wise granger

Causality test we may say that there is the

relationship between the variable and we

are now able to perform the multiple

regression to show the impact of AP and

AXp on GDP.

Model,

lnGDP = β0 + β1 lnAXp + β2 lnAP +

u1

Where,

lnAP = Natural Log of Agriculture

Production

lnAXp = Natural Log of Agricultural

Exports

lnGDP = Natural Log of Gross Domestic

Product

U1 and U2 = Error terms

And Coefficient of variable is β0, β1 and β2

Result from Step-wise Regression

Stepwise regression is a semi-automated

process of building a model by

successively adding or removing variables

based on the t-statistics or f statistics of

their estimated coefficients.

Table 4: Variables Entered/Removed

Model Variables Entered Variables Removed Method

1 Lnaxp . Stepwise (Criteria: Probability-of-F-to-enter <= .050,

Probability-of-F-to-remove >= .100).

2 Lnap . Stepwise (Criteria: Probability-of-F-to-enter <= .050,

Probability-of-F-to-remove >= .100).

Dependent Variable: lngdp

Table 5: Coefficients

Model Unstandardized Coefficients

Standardized

Coefficients t Sig.

B Std. Error Beta

1 (Constant) 9.975 .197 50.632 .000

lnaxp .457 .018 .983 24.723 .000

2

(Constant) 12.151 .642 18.914 .000

lnaxp .507 .021 1.091 24.499 .000

lnap -.206 .059 -.156 -3.496 .002


Table 6: Excluded Variables

Model Beta In t Sig. Partial

Correlation

Collinearity Statistics

Tolerance VIF Minimum

Tolerance

1 Lnap -.156a -3.496 .002 -.616 .520 1.925 .520

Predictors in the Model: (Constant), lnaxp




In this study we use stepwise multiple

regression method to find out the

independent variables that have the most

significant impact on the dependent

variable. The time series data that we have

used in this regression model have been

converted into natural log so as to make

the series stationary or normal. From table

4 we can see that both of our independent

variable comes under the entered variable

with the consideration that f value should

be less than 0.05(5%). In table 5 there are

two models that show the significance

level of the coefficients, so the result by

the step wise regression tries to explore the

model that is best fitted to our study. Both

the variable that we have taken shows the

significance level under the 5%, which

means our model is best fitted but if we

have a look at the coefficients values of

agriculture production and agricultural

exports that is -0.206 and 0.507

respectively, we can say that 1 percent

increase in the India’s Agricultural exports

will leads to change in GDP by 0.507.

According to the coefficient and

significance level this variable clearly

shows the impact on GDP whereas the

coefficient of agricultural production

shows negative impact on the GDP, this

means that if there is 1 percent increase in

the agriculture production it will lead to

change in the GDP by -0.206.But if we see

from the first part of our study, the data

clearly shows positive trend thus we

cannot say that the increase in the

agriculture production will lead to negative

impact on GDP according to the data. It

means that there is a problem in the model

that we have not clearly seen. Further the

table 6 shows the excluded variable in the

model, so with the help of stepwise

regression model we easily find out the

variable that we exclude from the model.

Now, if we compare the result of pair-wise

granger causality test and step-wise

regression model of the same variable then

we can easily analyze that why we use

regression after the use of granger

causality test because granger causality

test shows the relationship between the

variable whereas the step-wise-regression

model shows the cause and effect that how

much the dependent variable is affected by

the independent variable and excluding the

least effective independent variable from

the model. At the end we can say that by

the use of stepwise regression model there

is the relationship between the variables

that we consider in our study and reached

to the conclusion that our pre assume null

hypothesis are rejected under the 5 percent



level of significance, it means that there is

the impact of both the variable on GDP.

Conclusion

The above parts of the study tried to

explore the impact of agriculture

production and exports on Indian

Economy. The first part of the paper shows

that there is the relationship between the

variables which we can see from the

diagram, the direction of independent

variables is same as the direction of the

dependent variable which reveals some

type of relationship between the variables

in the diagram, so it means that there is the

impact of independent variables on GDP.

In second part of the study we found that

there is the bilateral relationship between

the agriculture production and GDP

whereas the relationship between the

agricultural exports and GDP is uni-

directional and we could not find any

relationship between the independent

variables, which means that by the use of

pair-wise granger causality test we are able

to say that, there exist some type of

relationship in between the dependent and

independent variables in the study. Third

part of the study also shows the significant

impact of the both independent variables

on the GDP by the use of regression model

but with the use of stepwise regression

model we are also able to find out the

excluded or entered variable. At the end,

we can say that there is the impact of

agriculture production and exports on

Indian economy.

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Agricultural Production Growth: Case

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Growth and Performance of the Education Sectorand Economy in

Haryana

Niyati Chaudhary

Senior Research fellow, IMSAR, Maharishi Dayanand University, Rohtak

Abstract

Haryana has seen a remarkable change in last few decades. Government of India has done so many

efforts in this field so that the aim of inclusive growth and more access to education can be achieved

very soon by it. In India, literacy rate increased from 18.3% in 1950-51 to 74.04% in 2010-11, it is a

great success of the government. Haryana has shown a diverse image when compared with its

neighboring states and India as a whole. The main objective of this paper is to study the developments

in Haryana in context of growth of literacy rate, education, state economy, primary, secondary sector

and tertiary sector. This research article is descriptive in nature. It is primarily based on secondary

data collected from various sources like national reports and economic surveys, websites etc.

Descriptive statistical tools like bar graphs, linear charts, etc. have been used for interpretation of the

data.

Keywords: Education Sector, Haryana, Economy, Literacy Rate.

Introduction

After the reorganization of the Punjab

state, on 1st November 1966 Haryana

came into existence as a new state.

Haryana is one of the few states in the

country where males are more than

females. As per 2011 census Haryana's

population was about 2.53 crores, literacy

rate was 76.6 %, sex ratio of 877 females

per 1000 males. 71 % of its population

living in V

villages. The State has 21 administrative

districts. In Haryana literacy rate increased

considerably. Haryana had finished

tremendous development in economy.

State government wants more revenue for

economic development. Many agendas and

planning were done in this regard. Tourism

forms a part of such agendas.Surajkund,

Kartik and Geeta Jayanti festivals,

development of Kurukshetra and Morni

Hills are contribute considerably to the

State’s economy. Government of Haryana

did many efforts for the growth and

development of the economy. A great

success for the Indian government in the

literacy rate from 18.3% in 1950-51 to

74.04% in 2010-11. By enhancing

education status the standard of living of

people will improve and also solve the

problem of poverty and unemployment,

social equality, equal income distribution.

Education adds to the individual

development as well as economy

development. Haryana GDP has shown

higher growth in comparison to the



national GDP's growth. Haryana economy

is shifting from the primary to secondary

and teritary sector.

Government Initiatives

In the area of education, government of

India has been taken many steps for

improving the quality of education and

more capacity in higher and technical

education. Engagement of private sector in

education is great initiative of the

government. Initiatives taken by

government for improvement of education

in Haryana as follows:

Enactment of Law University: In

2012 there was amendment in The

National Law University, Haryana

Act. Many universities were

proposed to establish in various

cities of Haryana.

Enactment of Anti Ragging Act,

2012: THE HARYANA

PROHIBITION OF RAGGING IN

EDUCATIONAL INSTITUTION

ACT, 2012 is mandatory to follow.

Various rules has been mode for

sopping ragging. Anti-ragging

committee need to establish in each

college, university and school for

safety of students. Strict

punishment is applicable for the

culprit.

Establishment of Private

Universities: In Haryana 14

universities have been set up.

University, AMITY university, O.P

Jindal Global University, Baba

Mast Nath University, Ansal

University, ManavRachna

University, Jagganath University,

GD Goenka etc. These universities

will help the Haryana government

to achieve their objective of

improving quality of education and

development of education level.

EDUSAT PROJECT: This

project has objective of

development of education by

providing education through

satellite. In Haryana, 63

government colleges and 3 private

aided colleges has been

implemented this project. Many

students get benefit of this project.


Kalirajan (2004) analyzed the pattern of

the 15 major states in India for getting

facts of economic growth. He found

different growth pattern among all these

states. Only seven states which are

industry-oriented states showed a

consistent increase in growth. He found a

significant relationship with the GDP

growth rates and increase in investment



and growth in the secondary sector. Diaz-

Bautista (2003) examined the relationship

between industrial growth and overall

economic performance in the Mexican

economy by using technique of co-

integration and Granger causality. He

found a long run relationship between

industrial sector and overall economy. He

concluded that industries are engines of

growth. Laitner (2000) analyzed the

economy and its sector. He mentioned that

economy consist of mainly two sectors

which are agricultural and manufacturing

sector. Land is vital determinant for the

agricultural sector while capital is

important factor for the manufacturing

sector. He found the share of agriculture in

total GDP tends to zero and the share of

manufacturing touches to unity

Linden and Mahmood (2007)

studied the relationship of between sector

shares (agriculture, manufacturing and

services) and economic growth of the 15

Schengen countries for the time period

1970 to 2004. He stated that there is bi-

directional relationship between services-

share growth and the growth rate of real

per capita GDP. He confirmed that there

exists relationship between the growth rate

of real per capita GDP and service sector.

Fisher (1939) conducted a study in which

division of sectors were done. He divided

the sectors as per the hierarchy of needs. In

primary sector those goods which satisfy

basic needs are included, in secondary

sector standardized products such as

manufacturing and in the tertiary sector

new products are embraced. Fisher (1952)

studied that these three sectors are

associated with an rising income elasticity

of demand for their particular products.

Wang and Li (2010) conducted a study to

find the relationship between services

industry and economic growth in China.

They found a Granger causality and long-

term stable equilibrium relationship

between the services industry and

economic growth. They stated that the

development of the service sector plays an

significant position in economic growth in

China.

Zakaria&Yusoff, (2011) mentioned

that the quality of the educations depends

on the good infrastructure, the syllabus,

resources and teaching process. The found

six factors which effects students'

satisfaction for their education such as

lecture and ancillary factors, facilitating

process, and explicit and implicit services.

Ashraf & Ibrahim, (2009) stated that by

changing the method of teaching and

learning and assessment methods ,

upgrading the professional knowledge and

skills, improving the broader educational,

administrative and resource environments ,

the quality of education in universities will



be improved.Farukyet. al, (2012)

investigated the factors which are affecting

the quality education in the private

universities. They mentioned faculty

credentials, students’ personal

development and safety measurement’,

academic and supportive facilities, and

social status are the important determinant

.Sass (2003) stated that a main hitch are

the methods of training of higher

education's are not up to the mark and

workforce are not having appropriate level

of education.

Objectives and Research methodology

The main objective of this paper is to

study the developments in Haryana in

context of growth of literacy rate,

education, state economy, primary,

secondary sector and tertiary sector. This

research article is descriptive in nature. It

is primarily based on secondary data

collected from various sources like

national reports and economic surveys,

websites etc. Descriptive statistical tools

like bar graphs, linear charts, etc. have

been used for interpretation of the data.

Analysis and Findings

In this section, literacy rate and education

level has been analyzed .Growth in

number of institutions in state has been

studied.

Table 1: Growth of Literacy rate in Haryana

Year National Haryana Haryana Males Haryana females

1981 43.57 37.13 48.2 22.3

1991 52.21 55.85 67.85 40.94

2001 64.84 67.91 78.5 55.7

2011 74.2 72.99 80.89 64.64

Source: Census of India, 2011

Literacy rate in Haryana showed a

tremendous growth. Haryana's males are

more educated than Haryana's females.

The 2001 census evidenced literacy rates

of 67.91 per cent, as compared to 55.85

per cent in 1991 and it increase to 72.99%

in 2011. In 2001, the male literacy rate was

78.5per cent which was 48.2 per cent in

1981 as against it, the female literacy rate

was 55.7per cent which was just 22.3 per

cent in 1981 but it rose to64.64 % in 2011.

The female literacy in Haryana has

developed at more rapidly rate than male

literacy over the last three decades (chart

1and table 1).



Chart 1: Growth of Literacy rate in Haryana

Table 2:Progression of Education at various levels in Haryana

Levels Number in Lakhs. Primary 22.0 Middle 12.4 High/ Senior secondary 9.7

Higher education 3.5 Source: Haryana Statistical Abstract 2011-12

Table 2 showed more progress at primary

level. Approx. 22 lakhs institutions were

opened. Higher education is at low level.

More outlet of students in higher level of

education due to limited access in rural

areas and poor quality of colleges in

Haryana. There was demand supply gap in

the number of institutions at higher level

education.

Chart 2 depicted in 2011-12 there was

3400 approx. primary institutions was

increased. Middle school was double in

2011-12. There was tremendous growth in

higher education .In 2011-12, number of

higher education enhanced 4 times in

comparison to 2000-01. This is due to

many initiatives have been taken by

government in the area of higher

education. many private and government

universities have been established.



Chart 2:Growth of Educational Institutes in Haryana

Source: Haryana Statistical Abstract 2011-12

Table 3: Number of institutions in various categories

Category of Institutions Number of Institutions

Engineering Degree

159

Diploma

187

MBA 171

Degree Pharmacy

33

Source:http://techeduhry.nic.in/present_status.pdf

There are many MBA and diploma

colleges while pharmacy degree colleges

were less. This shows students are having

first choice towards commerce and less

preference towards pharmacy (Table no

3).Engineering colleges are 159 predicts

students are willing to get more technical

knowledge and government of Haryana

also taking so many steps for improving

technical skills in students.

Table 4: Number of institutions in various universities and colleges

Category Number of Institutions

Universities, Research Institutes, Institutes of

National Importance

24 (IIM-Rohtak, NIT Kurukshetra)

Arts and Science Colleges

192

Teacher Training Colleges

472

Other

1

Source: Haryana Higher education Commission



Haryana at present has about 24

universities out of which 9 universities are

public. Haryana is home to a number of

renowned private universities in the

country like Amity University, O.P. Jindal

Global University, K. Mangalam

University and G.D. Goenka University.

Many teaching training colleges are in

Haryana approx. 500. Government had

been set up many training institutions for

developing better teaching skills in

students. Many well established research

institutions were set up for promoting

more research development in state.

Table 5: Growth of the Haryana Economic Performance

Year Primary sector Secondary sector Tertiary sector 2006-07 22 32 47 2007-08 20 31 49 2008-09 20 30 50 2009-10 17 30 53 2010-11 17 30 54 2011-12 17 29 55 Source: Haryana Economic Survey

Chart 3: Growth of the Haryana Economic Performance

Territory sector has performed very well in

all over the period. Its share in economic

growth has been increased year by year. In

Haryana, primary and secondary sector

contributed less in the overall growth.

After 2008-2009, tertiary sector was

contributed more than 50% in economic

growth. Gradually contribution of primary

and secondary sector was declined in over

the period. Almost in all period

contribution of primary sector was less

than 20 % in the overall economic

growth.(Table no 5.)



Table 6:Economic performance of key districts in Haryana

Districts Primary sector

(%) Secondary sector

(%) Tertiarysector

(%) GDDP

Ambala 16 18 66 5,52,846 Karnal 33 25 42 5,53,750 Hisar 26 41 33 6,21,994 Panipat 11 26 62 7,23,461 Fariabad 9 38 53 13,12,893 Gurgaon 3 42 55 20,03,146 Source: Planning Commission, State wise District Domestic Product Report

Table 6 conveys that Ambala district

contribute 66% in territory sector which is

highest among all districts. Among all

districts Karnal contribute more than 30

percent in primary sector. Gurgaon district

had more than 50 percent contribution in

territory sector while it had only 3% in

primary sector. This indicates agriculture

are very less developed on the other side

Auto and IT industries are very much in

numbers in this area and also it had highest

GDDP in comparison to other districts.

Panipat had 62% in territory sector which

shows more textile and refinery industries

contribute to the tertiary sector.

Table 7:Key industrial activity in both large scale industries segment and small scale segment

for major industrial districts of Haryana

District

Contribution of

District contribution

to overall

state manufacturing

output (in %)

Potential Sectors for large scale industrial

Growth

Gurgaon

34.61 Food, Auto, Textile, IT,

Faridabad

17.62 Auto, footwear, machinery

Rewari

7.88 Auto industry, electronics, food processing,

mineral processing, pharmaceuticals, metal

based

Hisar

7.09 Textile, metal, food processing

Sonipat

4.15 Food processing, books, leather, metal, auto and

dairy

Jhajjar

4.11 Leather, ceramics, paper, metal

Panipat

3.95 Oil, fertilizers, textiles

Source: Development Commissioner Ministry of Micro, Small and Medium Enterprises



Faridabad and Gurgaon districts contribution

by mostly auto industries, oil and Refineries

are mainly contributed industries in

Panipatgrowth while Rewari, Hisar and

Sonipat had mainly contribution of food

processing industries.

Chart 4: Contribution of districts in state manufacturing output.

Gurgaon contributes more than 30 percent in

the state manufacturing growth and followed

by Faridabad. Sonipat, Jhajjar and Panipat

contributed less than 5 percent in the state

manufacturing growth (Chart 4).

Conclusions

Haryana's males are more educated

than Haryana's females while the

female literacy in Haryana has

developed at more rapidly rate than

male literacy over the last three

decades. The government needs to

adopt a focused approach to carry the

female literacy levels at par with the

male literacy level

Higher education is at low level.

There was demand supply gap in the

number of institutions at higher level

education.

There was tremendous growth in

higher education .In 2011-12,

number of higher education

enhanced 4 times in comparison to

2000-01.



There are many MBA and diploma

colleges while pharmacy degree

colleges were less. This shows

students are having first choice

towards commerce and less

preference towards pharmacy.

Haryana at present has about 24

universities out of which 9

universities are public. Many well

established research institutions were

set up for promoting more research

development in state.

Tertiary sector has performed very

well in all over the period. Its share

in economic growth has been

increased year by year. Almost in all

period contribution of primary sector

was less than 20 % in the overall

economic growth.

Gurgaon had 55 % and Panipat had

62% contribution in tertiary sector

Refernces

Ashraf, M.A., Ibrahim, Y. &Joarder, M.H.R.

(2009). Quality Education Management at

Private Universities in Bangladesh: An

Exploratory

Study.JurnalPendidikdanPendidikan,

24:17–32.

Diaz-Bautista, Alejandro. (2003). Mexico's

industrial engine of growth:

Cointegrationand causality.

RevistaMomentoEconomico, 126, 34 - 41.

Faruky, K. N. B., Uddin, A. & Hossain, T.

(2012). Students’ satisfaction: A study

among private university students of

Bangladesh.World Journal of Social

Sciences, 2(4):138-149.

Fisher, A.G.B. (1939).Production, primary,

secondary and tertiary.The Economic

Record, 15: 24–38.

Fisher, A.G.B. (1952).A note on tertiary

production.Economic Journal, 62: 820–834.

Kalirajan, K. (2004). Economic reform and

the transmission of growth impulses across

Indian states.International Journal of Social

Economics, 31(5/6):623-636.

http://dx.doi.org/10.1108/030682904105294

34

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A Study of Businessman’s Perception towards Online Promotional Tools

Suman Kumari & Sonia

Assistant Professor, Dept. of Business Administration, Ch. Devilal University, Sirsa

Abstract

The internet has dramatically changed the face of marketing. Also the advancement in information

technologies has changed the way of communications between consumers and companies. So, emergence

of internet technology has created a plenty of opportunities for marketer in virtual environment to carry

on their businesses. Now a day’s online promotional tools are becoming more and more popular among

companies around the world, as they discovered the benefits of promoting their product or services

online. These tools are not only convenient for customers but also convenient for businessmen. Online

promotional tools are one of the emerging tools in virtual environment. This paper intends to study

perception of businessmen towards various online promotional tools. This paper is based on primary data

collected from a sample of 245 respondents from Delhi/NCR through a well-structured questionnaire.

Exploratory Factor Analysis (EFA) is conducted using SPSS version 20 to study perception of

businessmen towards various online promotional tools. Major findings of the study revealed that

businessmen are appreciative of online promotional tools. They have overall positive attitude towards

these tools. These promotional tools are perceived to be convenient, credible, appropriate and reliable by

businessmen for promoting their product and services.

Introduction

Internet has shown the potential of growing

explosively outside the national boundaries.

So with the beginning of new millennium,

marketers are experiencing the most

dynamic and revolutionary changes in the

history of marketing. These changes are

being driven by advancement in technology

and developments that have led to

remarkable growth of communication

through interactive media, mainly the

internet. Interactive media allow for a back-

and-forth flow of information where users

can contribute and modify the form and

content of the information they receive in

real time. Information and services that are

provided through online communication

channels can be pulled by users as required

rather than pushed too concerned and

unconcerned stakeholders. Due to this

characteristics and the large number of

users, internet has become even more

powerful than traditional communication

channels such as TV, magazine and radio.

While the internet is changing the ways

companies design and implements their

entire business and marketing strategies, it is

also affecting their marketing

communications programs. The emergence

of internet technology has created a plenty



of opportunities for marketer and all which

are involved in vertical environment to carry

on their business. With the rapid growth of

the Internet and the globalization of the

world, companies have accepted and

adopted new information and

communication technologies in performing

their activities. In today's technological

society, the use of the internet has become

essential for companies functioning in

highly complicated markets. Internet as a

promotional tool provides important

opportunities for companies to search and

adopt pioneering practices in to address the

increasing demands of customers.

So whether a company is just starting out or

has been in business for years online

promotion is one of the emerging tools in

marketing. Several online promotional tools

are used by companies to deliver the

promotional message to target customers.

Each online promotional tool put in a

different way to reach customers and attain

communication objectives. In the fiercely

competitive world of marketing, the hard

truth is that being good isn’t good enough.

So to make greater promotional impact

businessmen have to maximize online

promotion effectiveness. Every businessman

has to use optimum mix of various online

promotional tools and techniques to enhance

the online experience of customers

irrespective of type of business.


One of the advantages of internet is that it

enables businesses to reach a worldwide

customer population, so that customers can

search, select, and purchase products and

services from businesses around the world

(Kailani& Kumar, 2011). Internet

facilitated users to pay lower transaction

costs and it provided an easy way of access

on information and details. It also provided

more alternatives and competitive prices

about the products or services rather than

traditional environment (Chun and Kim,

2005). All businesses need to communicate

to the customers what they have to offer

(Jobber and Lancaster, 2006). Internet is not

only a space to promote the company and

its products, but also an interactive

communication tool to engage the

customer, meet their needs and encourage

them for repeat purchase (Constantinides,

2002). Promotion is one of the key factors

in marketing mix and plays an important

role in marketing success. Promotion is the

way of communication between product and

customers which influence their buying



decision (Kotler & Armstrong, 2010). The

astounding growth of the internet with its

unique capabilities has gained the attention

of the marketing community, Bush et al.,

(2000). Despite, being a new platform for

buying and selling, internet has also become

a new intermediary for the companies to

promote their businesses. Khan and

Mahapatra (2009) stated that technology

plays a critical role in improving the quality

of services provided by the business units.

These technologies are a valuable

complement to traditional marketing

methods whatever the size of a company or

business is. Thompson (2005) concluded

that the growth of Internet technology has

enormous potential as it reduces the costs of

product and service delivery and extends

geographical boundaries in bringing buyers

and sellers together. Ruckman (2012)

suggested that Internet research becomes an

increasingly important tool during the

purchasing process. Internet development

has led to new changes in businesses and

created an interactive and social

communication platform for companies to

interact with customers. Huang (2010)

expressed that internet has changed the way

of business and created new marketplace

where companies and customers come

together and create communication with

each other more efficiently. Similar to this

view, Pries et al., (2006) stated that internet

facilitated easy access to a global

marketplace where information of products,

prices and distribution are equal for all.

Internet is a vital medium of communication

(Caride and Senra, 2005), but it should be

kept in mind that communication in digital

environments has unique characteristics

(Wind and Mahajan, 2001). Li and Bernoff

(2008) discussed that new internet

technological developments enabled new

ways of marketing communication, to gather

customer opinions and experiences about

products and services. Shih and Hu (2008)

observed that Internet is an important

channel for companies and it should be

properly used by marketing departments to

attract new customers and retain the existing

ones. They concluded that if companies

expect to get good return from their e-

commerce companies and online efforts,

they must design their marketing activities

in such a way that they should be able to

reach new customers and retain existing

ones by providing good online customer

service. Similar to this view Furrer and

Sudharsan (2001) analysed how internet can

be used as a marketing tool and exposed that



internet is a formidable tool for marketing

which offers many opportunities to the

marketer’s. Hamid (2008) also affirmed that

internet offers many opportunities for

companies and it can be a useful platform

for their marketing activities, such as

spreading information, attracting new

customers, retaining existing ones and

improving relationship with existing

customers by online customer relationship

management. Therefore it is necessary for

the companies to adopt internet as a part of

their marketing communications programs

in their marketing strategies.

Rationale of the study

After the internet came into existence, it had

huge impact on the way organizations were

doing their business. Also it has

dramatically changed the face of marketing.

With the beginning of World Wide Web

(www), it has transformed the businesses

and commercial organizations and new

dimensions have begun in the online

markets across the world. In today’s

technological environment, internet has

become a new intermediary for companies

to promote their businesses. Online

promotional tools are one of the emerging

tools in virtual environment. These tools

allow businessmen to offer unlimited range

of products and services to all consumers

from around the world at any point of time.

While online promotional tools strategies

are used by many businesses, however the

effectiveness of these methods being used

can be debated. Several online promotional

tools are used by companies to deliver the

promotional message to target customers.

Each online promotional tool put in a

different way to reach customers and attain

communication objectives. Every

businessman has to use optimum mix of

various online promotional tools and

techniques to enhance the online experience

of customers irrespective of type of

business. So the present study aims to study

perception of businessmen towards different

online promotional tools.

Objectives of the study

The present study intends to know about the

perception of businessmen towards various

online promotional tools.

Research methodology

Research Design: The present work is an

exploratory study that aims to know

businessmen’s perception towards online

promotional tools.



Sampling size and Design: The sampling frame comprises of online businessmen

from Delhi/NCR. Data was collected from a

sample of 300 respondents, out of which 55

were rejected due to half-filled or unfilled

responses.

Sources of Data Collection: Both primary

and secondary data is used in present study.

Primary data is collected through

observation and awell-structured

questionnaire. 5-point Likert scale is used to

indicate responses where 1 stood for

strongly disagree and 5 stood for strongly

agree. Secondary data is collected from

various search engines, websites, books and

articles.

Tools of Data Analysis: The

quantitative data was analyzed by using

factor analysis through SPSS version 20.

Result and Discussions

To study the perception of customers

towards online promotional tools 29

statements are used which are highlighted in

table given below 5.1. 5-point Likert scale is

used to indicate responses where 1 stands

for strongly disagree and 5 stand for

strongly agree.

Reliability of the construct: Reliability of

test refers to the degree to which a test is

consistent and stable in measuring what it is

intended to measure. The most widely used

reliability coefficient is Cronbach’s alpha

which can range from 0 to 1, with higher

figures indicating a better reliability. The

reliability of this construct is 0.895 which

indicates data is highly reliable.

Kaiser-Meyer-Olkin (KMO) measure of

sampling adequacy is used to test the

sampling adequacy for factor analysis. The

value of KMO ranges from 0 to 1 and the

values above 0.50 are acceptable (Hair et al.,

2005). The value is 0.885, which is an

excellent value and indicates that the sample

is very good enough for sampling (KMO

&Barlett’s Test table given below). Barlett

Test of Sphericity is used to test correlations

among variables and overall significance of

correlation matrices by providing support

for the validity of factor analysis of the data.

Results indicate that overall correlations are

significant at the .01 level.

Table 1 KMO and Bartlett's test

Kaiser-Meyer-Olkin Measure of Sampling Adequacy. 0.885

Bartlett's Test of Sphericity Approx. Chi-Square 4159.855

Df 406



Sig. .000

Source: Primary data

Exploratory factor analysis (EFA) is

conducted to study the perception of

businessmen towards of online promotional

tools. Principal component method is used

to find out the major factors of

consideration. Factors with eigenvalues

greater than one are considered significant

and retained for analysis. Further, the

varimax rotation method is used. In addition

to this, factors are assigned ranking on the

basis of the overall mean value of each

factor. Five factors are extracted in the study

which explains 63% of total variance. Table

2 reveals overall results of factor analysis.

1. Convenience

2. Credibility

3. Reliability

4. Appropriateness

5.Unpleasant/Annoyance

Factors Statements Loadings Eigen

values Mean SD Factor

Mean Factor

Rank

Convenience

Online promotional tools

are informative

0.561 3.88 0.953


are convenient to use

0.728

1.627

3.87 0.948

3.83

1


are useful

0.751 3.88

0.881


are entertaining

0.621 3.69 0.975


are time saving

0.615 3.84 0.974

Credibility


are credible

0.747

1.054

3.67 0.897

3.72

2 Online promotional

tools are convincing 0.658 3.78 0.886

Online promotional

tools are believable 0.708 3.71 1.001

Reliability

Online promotional

tools are reliable 0.639

5.319

3.62 0.940

Online promotional

tools are attractive 0.661 3.80 0.893

Online promotional tools are trustworthy

0.690 3.51 0.944



Online promotional tools are

valuable source of information

0.710 3.63 0.960 3.65 3


easy accessible 0.687 3.69 0.916


provide real time benefits 0.591 3.62 0.940

Appropriateness


easy to manage 0.610

2.004

3.60 0.994

3.59

4


creative 0.639 3.66 0.861


a reference for purchase 0.660 3.62 0.914


best tool of promotion 0.767 3.68 0.909


appropriate according to needs

0.774 3.51 0.833


enjoyable 0.663 3.51 0.952

Unpleasant/

Annoyance


annoying 0.700

8.316

3.08 1.055

3.11

5


disruptive 0.836 3.10 1.043


objectionable 0.824 3.14 1.155


easy to ignore 0.733 3.24 1.144


time consuming 0.821 3.24 1.227


boring 0.850 3.09 1.243


deceptive 0.785 3.10 1.166


wastage of time 0.792 2.96 1.262

Source: Primary data



Table 2 discloses the value of loading,

mean and SDs of variables, value of mean

of factors and ranking of factors on the

basis of above discussed mean values. It is

noticed that factor convenience having the

highest mean value (3.83), falls in the first

rank. The factors credibility and reliability

obtained second and third rank with mean

values of 3.72 and 3.65 respectively.

Factor unpleasant with lowest mean (3.11)

has obtained last rank. The above said

rankings assigned to the factors are based

on the concept that as themean value of

factors decreases, the corresponding values

of their rank increases.

Conclusions and Suggestions

It is concluded from the study that

businessmen are appreciative of online

promotional tools. They have overall

positive attitude towards these tools. In the

era of internet technology, the online

promotional tools are very effective in

reaching out to the target audience. They

are perceived to be credible, appropriate,

reliable convenient and trustworthy by

businessmen. These are one of the best

tools of promotion in today’s technological

environment. No matter what type of

business a businessmen have, online

promotion is likely to be at the heart of

their promotional strategy. The online

promotion provides valuable information

on the product purchased, special

discounts and coupon available on other

goods and services. Online promotional

tools are useful for businessmen because

these provide good quality of information

for customer. With a good quality of

promotional campaign, businessmen can

tailor their online promotional tools

techniques to their target audience,

ensuring that their product or service will

meet their eye in a timely and concentrated

manner. Online promotional tools are

considered as truthful and believable by

businessmen. Easy accessibility of online

promotional tools made these very popular

with businessmen and customers.

Businesses are open for business 24 hours

a day, 7 days a week without the constraint

of opening or closing hours. As these tools

are easily accessible, so these tools provide

real time benefits for businessmen. In

today’s technological environment it is

nearly impossible for a business to be

successful without using online

promotional tools to compete against

thousands of companies going online

every day. Even though these tools are

perceived as time saving by businessmen

but sometimes these may be unpleasant.

Though, users sometimes find the online

promotional tools to be annoying,

deceptive and boring, yet they are

convincing.



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Recruitment and Selection Policies and Practices in Indian Commercial

Banks

Simarpreet Kaur

Vocational Lecturer: Govt. Sen. Sec. School, BadshapurKaleki, Patiala.

Abstract

At present, Indian commercial banks cannot claim to have a proper human resource planning system

that captures the type of people it requires, the level at which they are required and clearly defined

roles for everyone and now it has become essential to design the recruitment process carefully and to

adopt the effective measures to acquire the best talent in the banking sector. The present research

based on qualitative as well as quantitative approaches proposes to make a comparative analysis of

policies and practices relating to employees’ recruitment and selection in public and private sector

commercial banks. It has been traced that well defined recruitment and selection system is followed

and line manager and HR managers participate. A comprehensive selection process is used before

rendering a decision, unbiased tests and interviewing techniques are used, attitude and desire to work

in a team and individual as a criterion used in recruitment and selection process in Indian commercial

banks. Moreover, public sector banks by following the private sector banks’ philosophy of growth

have exploited productivity enhancement for growth so far, but now they need to induct new talent in

large numbers to maintain growth. Recruitment machinery is required to attract talent (as against

evaluate applicants) and to retain them through well planned HRM practices.

Key words: HR, HRM, Commercial Banks, Recruitment and Selection, Processes, Practices

Policies.

Introduction

With the emergence of improved

technologies and global competitive

environment, upgrading the work methods,

work norms, improving technical and

managerial skills and employees’

satisfaction, have become the need of the

hour, both in manufacturing and services

sectors. There is a mounting pressure on

Indian commercial banks to provide cost

effective and cost efficient quality services

in the fast changing competitive

environment. Almost three decades after

the economic liberalisation process began,

a vibrant banking sector powered by both

improved-efficiency public sector banks

and growth-hungry private ones emerged

on the economic scene. Indian banks

making available the number of

instruments and services to both the retail

and corporate clients globally and the

levels of technology involved in managing

these products have become pure science

oriented over the last 30 years.

Concept

The term ‘recruitment’ applies to

the process of attracting potential

employees to the organization or company.

It is a systematic means of finding and

inducting available manpower to apply to

the company or enterprise for employment.

Since it involves the process of searching



for prospective employees, it is concerned

with the range of sources of supply of

labour or personnel, and of recruitment

practice and techniques. The process of

identification of different sources of

human resources is known as recruitment.

It is a linking activity that brings together

those offering job and the job seekers.

Recruitment precedes the selection

process, i.e., selecting the right person for

various positions in the organisation. It is a

positive process as it attracts suitable

applicants to apply for the available jobs.

Research Objectives

The present research is proposes to

make a comparative analysis of policies

and practices relating to employees’

recruitment and selection. The broad

objectives of the proposed study are:

1. To study recruitment and selection

policies, practices and trends in public and

private sector commercial banks in India.

2. To suggest ways to improve recruitment

policies and practices in Indian

commercial banks.

In order to achieve the

specific objectives, qualitative as well as

quantitative approaches have been

followed for the purpose of this research

work.

Sampling Design

The sampling design of the study is

based on multi-stage stratified purposive

sampling technique.As such, out of the

whole country the State of Punjab has been

chosen as the first step. In Punjab,

choosing the public sector and private

sector banks is the next stage of sampling;

and selecting the four sample banks out of

the total public and private sector

commercial banks is the third step of

sampling;and the selection of sample

employee respondents has been done at the

4th

stage of sampling.

The universe of the study is all

public and private sector commercial

banks operating in India, but due to non-

feasibility and time constraint, the scope of

the study has been restricted only to four

commercial banks operating in the state of

Punjab. The criterion adopted for the

selection of private sector banks was their

year of incorporation and size of their

market share. The banks selected as

sample units for the present study are

listed as under:



Table 1

Banks Selected as Sample Units

S. No. Public Sector Banks Private Sector Banks

1. State Bank of Patiala HDFC Bank

2. Punjab National Bank Axis Bank

Total 02 02

The respondent employees have

been taken from all the categories of

Workmen; Clerical or Award staff. In

order to examine and compare the

employees' perceptions in relation to the

recruitment and selection policies and

practices, mainly primary data has been

used. A structured questionnaire was

framed and administered on the sample

employees. Of the two public sector banks

under study, 100 employees were

randomly selected. Similarly, 100

employees were randomly selected from

the two selected private sector banks.

The primary data was drawn from

the respondent employees working in

different branches, service offices, training

centres, specialized branches and offices,

regional offices and head offices of both

public and private sector commercial

banks situated in the state of Punjab. As

many as 232 employees working in the

selected banks were approached for the

purpose of required data. The response

percentage in the case of employees is

86.21 per cent. Their response was found

to be complete in all respects for the

analysis. The responses to the questions on

recruitment and selection practices have

been measured on a five-point Likert scale.

Various statistical tools have been used to

analyse the collected data.

Recruitment Practices of Employees in

banks

1. Award Staff is recruited through

campus recruitments, recruitment

facilitators, recruitment agencies,

outsourcing agents, through references and

through advertisements on the websites

and in newspapers.

2. Special Assistants are promoted

from within as per vacancies. Most of the

employees are requisitioned locally

through Employment Agents; and are

recruited and selected by Branch Managers

with the support of HR Department

specialists. For the selection, objective

type written tests are conducted followed

by on line or face to face interviews.

Candidate’s actual performance in

interview and his communication skills are

considered for selection.



3. Armed Guards are recruited

locally through District Sainik Welfare

Boards or security service providers.

Recruitment of Officers

All appointments and promotions

in the officer grade are made by the

Management through a Competent

Authority in accordance with the policy or

guidelines, laid down in this regard by the

Board of Directors. All appointments are

made first in the minimum of pay scale to

which the appointment is made. An officer

who has rendered continuous temporary

service in the bank prior to his

appointment against a permanent vacancy,

the provisions regarding the period

required to be spent on probation may be

waived at the discretion of the Appointing

Authority to the extent of the period of

such temporary service. An officer directly

recruited in the bank is confirmed, if in the

opinion of the Competent Authority,

his/her conduct and performance has been

satisfactory during the period of probation

including the extended period.

A core area like Manpower

Planning has not received serious attention

in the banking sector so far. Manpower

assessments are made on the basis of

branch activity analysis and productivity

norms. Manpower planning in each bank is

kept subordinate to guidelines issued by

the Government of India and RBI. The

recruitment exercise carried out today in

many public sector banks also does not

reckon skill and competency requirements.

Moreover, large scale Core Banking

Services implementation at branch level

has made no significant difference in the

realignment of manpower in public sector

banks as compared to the private sector

commercial banks. Lack of proper human

resource planning has also resulted in wide

variance in staff ratios across public sector

commercial banks as well as in many of

the private sector commercial banks. At

present, banks cannot claim to have a

proper human resource planning system

that captures the type of people it requires,

the level at which they are required and

clearly defined roles for everyone.

As such, it has become essential to

design the recruitment process carefully

and to adopt the effective measures to

acquire the best talent in the banking

sector. Similarly, campus recruitment is

not just about approaching any Institution

or University, but also aims at creating a

pool of suitable candidates and

interviewing them equally in order to fill a

large number of anticipated openings. A

major concern before public sector banks

is to replace a large number of employees,

who are to retire during the coming years.

Table 2 carries the response data

regarding human resource (recruitment and



selection) policies and practices being

followed by the public and private sector

commercial banks in terms of form and

contents, and systems and procedures

through ‘content analysis’. The table

explains as to whether these policies and

practices are framed and exercised fully or

partially in these commercial banks.

Table 2: Comparative Analysis of Human Resource (Recruitment and Selection)

Policies and Practices in Public and Private Sector Commercial Banks

S.

No. Policies and Practices Public Sector Banks Private Sector Banks

Form

and

Contents

Systems

and

Procedures

Form and

Contents Systems and Procedures

1. HRM practices are standardized Yes Partial Partial Partial

2. Staff strength is balanced No Yes Yes Yes

3. Recruitment and selection policies:

Well-defined Yes Yes No No

4. Analysis of positions and

requirements is made before

recruitment process starts

Yes Yes Yes No

5. Defined mode of recruitment Yes Yes Yes No

6. Line managers and HR managers

participate in recruitment & selection Yes Yes Yes Yes

7. Valid and standardized recruitment

tests Yes Yes Yes Partial

8. Comprehensive selection process

before rendering a decision Yes Yes Yes Partial

Earlier recruitment in public sector

banks was made through Banking

Selection and Recruitment Board (BSRB).

The high standards of recruitment set by

the Board helped the public sector banks to

get quality staff. However, after

liberalization, BSRB was scrapped and

banks started recruiting the staff at their

whims and fancies. Some grave

irregularities came to light in the

recruitment process of some public sector

banks, and in some cases the seniority of

the officers working in these banks was

ignored at the time of their promotion.

After dismantling the common

recruitment board for the industry, banks

were allowed to recruit employees at their

own levels even for the senior positions,

based on their own requirements. The

recruitments were done according to the

business strategies and ratified by the

board which had Government nominees.



And, as such, no senseless recruitment was

reported as done by the commercial banks,

but, this led to cut-throat competition for

talent search and poaching of each other’s

employees. After a while, the Government

started feeling concerned about the large

recruitment programmes which were

carried out by some of the public sector

banks. The question that why the banks

needed so much staff despite

computerization and implementation of

technology, was still an unsolved mystery.

However, a few private banks

remained cautious about their expansion

programmes, and the recruitment outlook

of the public sector banks started

becoming healthy due to significant

retirements and rural expansion plans.

Some of the private banks that have grown

their business aggressively, riding on the

world’s second fastest growing economy,

started shrinking their balance-sheets,

while public sector banks began to expand

by opening new branches across the

country. Moreover, new recruitments

started keeping pace with the banks’

expansion plans. The banks were adopting

fast-track promotions to fill-in all

management gaps. Another reason behind

the sudden spurt in recruitment was the

fact that unlike private banks, public sector

banks cannot outsource many activities,

including sourcing loans. Now, apart from

recruiting through a normal process of

written examinations and interviews,

banks are also going for campus

recruitments and outright poaching and

sometimes from fellow public sector

banks. Despite low salary, many private

sector employees are now approaching

public sector banks looking for job

security.

The public sector banks now have

started recruitments through IBPS which is

an autonomous body to recruit the required

manpower in the public sector banks. The

private sector banks make selections of

employees and officers directly or through

the recruiting agencies and consultants as

per their requirements.

Methods of Selection

Table 3 highlights sector-wise

analysis of employees’ perceptions

regarding the methods of selection in both

the public and private sector banks.

An analysis of the table presents that 86

(86.0 per cent) of the respondent

employees in public sector commercial

banks have been selected through written

test followed by interview. And 12 (12.0

per cent) of them have been selected

onmerit of qualifying the test andwritten

examination, while only 1



Table 3: Sector-wise Analysis of Employees’ Perceptions Regarding Methods of Selection

Method of Recruitment Public Sector

Banks Private Sector

Banks Total of Public and

Private Sector Banks

Merit of qualifying test/ exam. 12 (12.0%) 15 (15.0%) 27 (13.0%)

Written test followed by interview 86 (86.0%) 55 (55.0%) 141 (70.5%)

Only written test 01 (01.0%) 03 (03.0%) 04 (02.0%)

Only interview 01 (01.0%) 20 (20.0%) 21 (10.5%)

Direct appointment by head of the bank 00 (00.0%) 07 (07.0%) 07 (03.5%)

Total 100 (100.0%) 100 (100.0%) 200 (100.0%)

Chi-square value 32.339*

*Significant at 1 per cent level

(1.0 per cent) employees in public sector

banks admitted that they have been

selected through written test, while another

1 (1.0 per cent) have been selected through

interview only. However, in the private

sector commercial banks majority of the

respondents i.e., 55 (55.0 per cent) have

been recruited through written test

followed by interview; and 20 (20.0 per

cent) have been selected through only

interview. Similarly, 15 (15.0 per cent), 7

(7.0 per cent) and 3 (3.0 per cent)

respondent employees belonging to the

private sector banks admitted that they

have been selected on the basis of merit of

qualifying test and written examination,

direct appointment by head of the bank

and only by written test respectively. The

p-value 0.000 exhibits that there exists a

highly significant difference between the

responses of the respondent employees in

this regard.

Employees’ Perceptions Regarding

Recruitment and Selection Policy

Table 4 carries the response data

regarding the recruitment and selection

policy of the banks under study.

Table 4: Sector-wise Analysis of Employees’ Perceptions Regarding Recruitment and

Selection Policies in Commercial banks

Policy Level Public Sector Banks Private Sector Banks Total of Public and Private

Sector Banks

Good 00 (00.0%) 03 (3.0%) 03 (01.5%)

Bad 00 (00.0%) 01 (1.0%) 01 (00.5%)

Very bad 65 (65.0%) 60 (60.0%) 125 (62.5%)



Worst 35 (35.0%) 36 (36.0%) 71 (35.5%)

Total 100 (100.0%) 100 (100.0%) 200 (100.0%)

Chi-square value 4.214

The above table exhibits that 65

(65 per cent) employees from the public

sector banks hold a very bad opinion about

bank’s recruitment policy, while the

remaining 35 (35.0 per cent) of the

respondents have the worst to say about it.

There is none in the good and bad

categories to give the response in this

regard. On the other hand, 60 (60.0 per

cent) of the employees from the private

sector commercial banks hold a very bad

opinion and 36 (36.0 per cent) of them

have the worst opinion about the

recruitment and selection policy of these

banks, while only 3 (3.0 per cent) and 1

(1.0 per cent) of the bank employees

respectively hold a good and bad opinion

in this regard. The Pearson’s Chi-square

value shows that there is an insignificant

gap between the responses of employees

from the both the public and private sector

banks regarding their recruitment and

selection policy.

Findings and suggestions

The public sector banks have

gained market share over the last decade

by following their counterpart private

sector banks in terms of product

innovation, marketing and implementation

of technology but still fail to attract the

right talent for specialized services such as

treasury and risk management and other

specialized areas. They had not been

focusing on recruitment and selection

policies and practices, career planning,

training and development, performance-

linked compensations, succession planning

and grooming of leaders, a contingent of

contented workforce over the past as

expected. Similar problems in the private

sector banks rarely prevailed because of

their clear, long term vision and well

defined sound planning in this regard and

their recruitments and selections at all

levels have always been need based.

The staff strength of the public

sector banks had gone down during the

period under study due to retirements and

voluntary retirement schemes, but that of

the private sector banks has gone up

significantly. Another major concern

before the public sector banks is the large

number of employees who are to retire in

the near future. The practice of recruiting

the employees directly, even at the senior

levels, by the banks at their own levels

resulted in cut-throat competition for talent

and poaching of each other’s employees



within the banking sector. Some of the

private banks that have aggressively

enhanced their business, riding on the

world’s second fastest growing economy,

are now shrinking their businesses while

the public sector banks are expanding by

opening new branches across the country.

Similar problems in the private sector

banks never prevail because of their clear

long term vision and well defined sound

planning in this regard and their

recruitments and selections at all levels are

always need based.

A well-defined recruitment and

selection system is followed, line manager

and HR managers participate,

comprehensive selection process is used

before rendering a decision, unbiased tests

and interviewing techniques are used,

attitude and desire to work in a team and

individual as a criterion used in

recruitment and selection process in their

banks with t-test indicating insignificant

differences in the opinions on these

statements and further the statement that

the comprehensive selection process is

used before rendering a decision, with an

insignificant difference in opinions, the

respondent officers of both the public and

the private sector banks revealed that they

‘agree’ on these statements.

As far as the use of recruitment and

selection system is concerned, a well-

defined recruitment and selection system is

followed in the banks and their banks

preferably use attitude and desire to work

in a team and individual as a criterion in

employees’ selection while the public

sector banks are facing a crunch of

manpower and they need to use the retired

people as they could be useful in brand

building efforts, or perhaps, in bank’s

financial inclusion initiatives.

1. With the aim to meet the global

standards and to remain

competitive, both the public and

private sector banks should recruit

specialists in various fields such as

Treasury Management, Credit,

Risk Management, IT related

services, HRM, etc. in keeping

with the segmentation and product

innovation. 2. Public sector banks

by following the private sector

banks’ philosophy of growth have

exploited productivity

enhancement for growth so far, but

now they need to induct new talent

in large numbers to maintain

growth. Recruitment machinery is

required to attract talent (as against

evaluate applicants) and to retain

them through well planned HRM

practices. Banks also need to

explicitly tackle the generation gap.



3. Further, this needs to be integrated

with the Business Plan and strategy

of the bank. Another key problem

area for the public sector banks is

that the kind of talent they

require. Banks need to acquire

people with the right kind of talent

out of same limited talent pool that

will be targeted by the public and

private banks, Financial

Institutions, Insurance, Telecom

and other industries which are on

fast growth track and in need of

talented manpower.

References:

Armstrong, M (2006). A Handbook

of Human Resource Management

Practice, Tenth Edition, Kogan

Page Publishing, London, p. 264

Arora, P.N., Arora, Sumeet, and

Arora, S. (2010). Comprehensive

Statistical Methods, S. Chand &

Company limited, New Delhi.

Bajaj, K. K (2000). E-Commerce

Issues in the Emerging Hi-Tech.

Banking Environment. The Journal

of the Indian Institution of

Bankers. Jan-March.

Bhatia, and Batra

(2001).Human Resource

Development, A Book Edited

by Bhatia and Batra, Deep And

Deep Publishers, New Delhi.

Chatterjee, S.R (2006).

Perspectives of Human Resource

Management,The Asia Pacific,

PEARSON Prentice-Hall,

Malaysia, pp. 41-62.

Dessler, G.; and Berkkey, B.

(2009), Human Resource

Management. Eleventh Edition,

Prentice Hall, PEARSON, Delhi, p.

299.’

Gupta, M.; and Goswami, S.

(1986), “Profitability and Planning

in Banks: Establishment Cos and

Staff Strength”, A Paper Presented

at the Bank Economist Meet, pp.

92-100.

Kumar, Satish (1996). A Critical

Study of Human Resource

Development in Co-operative

Banks of Himachal Pradesh. A

Doctoral Thesis Submitted to

Himachal Pradesh University,

Shimla.

Mishra, Kavita (2002). A Ph.D.

Thesis, A Study of Human

Resource Management, Submitted

to Kurukshetra University,

Kurukshetra.

Shikha, N. Khera (2011), “Human

Resource Practices and their



Impacton Employee Productivity:

A Perceptual Analysis of Private,

Public and Foreign Bank

Employees in India.

Valerie, Anderson (2011),

Research Methods in Human

Resource Management, Second

Edition, Universities Press (India)

Private Limited, pp. 50.



Training and Development Practices in Private Sector Banks

Jaspreet Kaur* Payal Bassi**

Research Scholar, Department of Commerce, Punjabi University, Patiala.

Associate Direcor, Desh Bhagat University, MandiGobindgarh.

Abstract

Human resources are the vital resources for any organization. With the introduction of

liberalization, privatization and globalization there is keen competition to survive. Training

and development is very necessary to cope with dynamic environment. Banks are the nerve

system of any economy. So training and development programmes are very important for

banks. The aim of this paper is to study the training and development practices of Indian

public sector banks and their impact on performance of employees.

Key word: Human resources, Training and Development, Employees, Banks.

Introduction

The core strength of India is its human

resource. The prosperity of a nation

depends on the proper development and

utilization of its human resources, as all

other resources can be generated by a well-

motivated human resource. Organizational

augmentation, alteration and success

ultimately depend on the actions of human

resources. The global economy has

endangered the survival of every

organization and particularly those who

wish to gain a competitive advantage. The

competitive advantage may be a daydream

in the absence of superior quality products,

which are the responsibility of well-trained

employees. No organization can get a

candidate who precisely matches with the

job and the organization requirements

hence, training is imperative to develop the

employee and make him suitable to the

job. The purpose of training new

employees is to develop the basic skills

they need to perform their jobs. So that

organization requirements to provide

opportunities for the continuous

development of employees not only in

their jobs but also to develop their

capabilities for other jobs for which they

might late be considered.

Banks play an imperative role for

the development of economy. They are the

foremost participants of financial system.

The tremendous management of the banks

improves the economic affluence. This

depends upon the actions of the resources

of banks. There are two types of resources

i.e. human and non-human. The human

resources are the active factors of

production. The production of non-human



resources is depending upon the skill of

human resources, how they make use of

them to get maximum output. For the

banks it is very important to train and

develop its human resources with

advancement of technology. There is rising

pressure on Indian commercial banks to

provide cost effective quality services in

fast changing competitive environment.

Training and Development policies and

practices affect the productivity of human

resources. This is very important to match

the right person for the right job to

accomplish certain predetermined

objectives. Training, as used in this

perspective, refers to attainment of skills

and information directly required for the

performance of a specific role. It includes

on-the-job training, workshops, seminars

and conference. Manpower development

generally refers to job enrichment that has

an intrinsic mechanism to motivate an

employee to accept and play challenging

organizational task.

Definition of Training and Development

According to the Michel Armstrong,

“Training is systematic development of the

knowledge, skills and attitudes required by

an individual to perform adequately a

given task or job”.

According to the Edwin B Flippo,

“Training is the act of increasing

knowledge and skills of an employee for

doing a particular job.”

Dale S Beach defined “Training is usually

considered as the organized procedure by

which people gain knowledge and increase

skill for a definite purpose”

Literature Review

Abdullah (2009) studied the

challenges to the effective management of

HR Training &Development activities in

manufacturing firms in Malaysia It was

found that there are three major challenges

to effective management of HR Training &

Development. These include a shortage of

intellectual HRD professionals to manage

HR T&D activities, coping with demand

for knowledge workers and fostering

learning& development in the workplace.

He suggested that relevant and appropriate

policies and procedures can be developed

and implemented for effective

management of HR T&D.

Afaq et.al. (2011) in their research

paper examined the relationship between

training courses and employee

performance at the Pearl Continental (PC)

Hotel, Karachi. They found that there is a

significant relationship between the two

variables; revealing that the employees

who have taken trainings were more

capable in performing different task and

vice versa. They recommended that the



prevalent problems of service delivery can

be overcome by properly conducting needs

assessment, design, development and

delivery of training programs

Okereke and Beatrice Nnenna

(2011) in their research examined the

perception and relevance of influence of

training and manpower development on

employee performance. They found that

manpower development influenced job

performance, but the influence of type of

training on job performance was

inconclusive. Those that had training and

those exposed to manpower development

had high job performance as against their

counterparts with no training and

manpower development.

Karim et. al. (2012) evaluated how

training refers to the acquisitions of

knowledge, skill and attitudes. It was

found that training helps employees to get

a clear view of their job. It increased the

efficiency and ability of employees to

perform their job. On-the-job as well as

off-the-job training are equally important.

Job satisfaction level was high in trained

employees than those who did not receive

training.

Sowjanya and Rajashekar (2012)

studied the existing T & D policies of the

sample companies and opinions of the

employees on the effectiveness of training

programs conducted by the organization.

The study found that organizing a

significant number of training programs

for the employees are very vital in order to

enhance the capability level and the skill

set. The performances of employees in the

respective departments are directly

proportionate to the number of training

programs attended. They recommended

that training programs should be

conducted on a regular.

Akilandeswari and Jayalakshmi

(2014) in their research paper studied the

training and development programmes of

banks and their effectiveness for

employees to discharge their duties. It was

found that the public and private sector

banks use training and development

practices. It increased the skill if

employees in discharging their duties.

Banks provided training programmes to

enhance the knowledge and skills of

employees to satisfy the customers.

Laxmanrao (2015) studied the

training and development programmes of

public sector banks and their impact on

banks. The researcher found that there is

adoption of appropriate training and

development programmes. Growth of

banking sector in India is the outcome of

skilled manpower which is the result of

training and development practices.



Research Objectives

1. To study training and

development programmes in selected

public sector banks for their employees.

2. To study the impact of training

and development programmes on

performance of employees in selected

public sector banks.


The primary data was collected

through questionnaire from 100 employees

of Private sector banks like Axis bank,

ICICI bank, HDFC bank and Kotak

Mahindra bank. Present study is limited to

selected public sector banks and

boundaries of Ludhiana district only.

Data Analysis

Table No.1: Training needs assessed before training is imparted

Opinion % Response of Employees

Strongly Agree 70

Agree 18

Indifferent 04

Disagree 05

Strongly Disagree 03

Total 100

Above table describes that

majority of 70 % of the employees are

strongly agreed with the statement

thattraining needs assessed before training

is imparted. 18% of the employees are

agreed with the statement thattraining

needs assessed before training is imparted.

4% of the employees are indifferent with

the statement thattraining needs assessed

before training is imparted.5% of the

employees are disagreed with the

statement thattraining needs assessed

before training is imparted. 3% of the

employees are strongly disagreed with the

statement thattraining needs assessed

before training is imparted.

Table No.2: Training programmes are planned in the organized way


Strongly Agree 68

Agree 23

Indifferent 7

Disagree 2

Strongly Disagree 0

Total 100



The above table highlights that majority of

68% employees are strongly agreed with

the statement that training programmes are

planned in the organized way. 23%

employees are agreed with the statement

that training programmes are planned in

the organized way. 7% employees are

indifferent with the statement that training

programmes are planned in the organized

way. 2% employees are disagreed with the

statement that training programmes are

planned in the organized way. 0%

employees are strongly disagreed with the

statement that training programmes are

planned in the organized way.

Table No. 3: Employees are made aware about the objectives of the training before attending it


Strongly Agree 72

Agree 20

Indifferent 5

Disagree 2

Strongly Disagree 1

Total 100

The above table exhibits that 72%

employees strongly agreed with the

statement that employees are made aware

about the objectives of the training before

attending it. 20% employees agreed with

the statement that employees are made

aware about the objectives of the training

before attending it. 5% employees are

indifferent with the statement that

employees are made aware about the

objectives of the training before attending

it. 2% employees disagreed with the

statement that employees are made aware

about the objectives of the training before

attending it.1% employees strongly

disagreed with the statement that



it.

Table No. 4: The present training system is adequate to meet the job requirement


Strongly Agree 60

Agree 20

Indifferent 10

Disagree 5

Strongly Disagree 5

Total 100



The above table shows that 60% of

employees strongly agreed with the

statement that the present training system

is adequate to meet the job requirement.

20% of employees agreed with the



10% of employees are indifferent with the



5% of employees disagreed with the



5% of employees strongly disagreed with

the statement that the present training

system is adequate to meet the job

requirement.

Table No. 5: Training programme contents are relevance to trainee’s current job


Strongly Agree 67

Agree 20

Indifferent 15

Disagree 5

Strongly Disagree 3

Total 100

As per the table 5 majority of 67%

employees strongly agreed that training

programme contents are relevance to

trainee’s current job.20% employees

agreed that training programme contents

are relevance to trainee’s current job. 15%

employees are neutral that training


trainee’s current job.5% employees

disagreed that training programme

contents are relevance to trainee’s current

job. 3% employees are strongly disagreed

that training programme contents are

relevance to trainee’s current job.

Table No. 6: Training needs is identified through a formal performance appraisal mechanism


Strongly Agree 60

Agree 16

Indifferent 5

Disagree 9

Strongly Disagree 10

Total 100



The above table shows that 60% of

employees strongly agreed that training

needs is identified through a formal

performance appraisal mechanism. 16%

agreed that training needs is identified

through a formal performance appraisal

mechanism. 5% indifferent that training

needs is identified through a formal

performance appraisal mechanism. 9%

disagreed that training needs is identified

through a formal performance appraisal

mechanism. 10% strongly disagreed that

training needs is identified through a

formal performance appraisal mechanism.

Table No. 7: There are formal training evaluation methods to assess the effectiveness of the

training


Strongly Agree 54

Agree 30

Indifferent 10

Disagree 3

Strongly Disagree 3

Total 100

Table 7 exhibits that 54% employees

strongly agreed that there are formal

training evaluation methods to assess the

effectiveness of the training. 30%

employees strongly agreed that there are

formal training evaluation methods to

assess the effectiveness of the training.

10% employees strongly agreed that there

are formal training evaluation methods to

assess the effectiveness of the training. 3%

employees strongly agreed that there are

formal training evaluation methods to


3% employees strongly agreed that there

are formal training evaluation methods to


Table No. 8: T&D have resulted in higher employee performance in our Bank


Strongly Agree 65

Agree 20

Indifferent 8

Disagree 5

Strongly Disagree 2

Total 100



The data shown above reveals that 65%

employees strongly agreed that t&d have

resulted in higher employee performance

in our Bank. 20% employees strongly

agreed that t&d have resulted in higher

employee performance in our Bank.8%



in our Bank. 5% employees strongly

agreed that t&d have resulted in higher

employee performance in our Bank. 2%



in our Bank.

Table No. 9: Training programmes are able to bring improvement in knowledge about the job

of employees


Strongly Agree 52

Agree 30

Indifferent 12

Disagree 4

Strongly Disagree 2

Total 100

The above table exhibits that

majority of 52% employees are strongly

agreed that training programmes are able

to bring improvement in knowledge about

the job of employees. 30% employees are

agreed that training programmes are able

to bring improvement in knowledge about

the job of employees. 12% employees are

indifferent that training programmes are

able to bring improvement in knowledge

about the job of employees. 4%

employees disagreed that training

programmes are able to bring

improvement in knowledge about the job

of employees. 2% employees are strongly

disagreed that training programmes are

able to bring improvement in knowledge

about the job of employees.

Table No. 10:Training helps in increase the efficiency of employees


Strongly Agree 82

Agree 17

Indifferent 0

Disagree 1

Strongly Disagree 0

Total 100



As is evident from the above tablethat

majority of 82% employees strongly

agreed that training helps in increase the

efficiency of employees. 17% employees

agreed that training helps in increase the

efficiency of employees. 0% employees

indifferent that training helps in increase

the efficiency of employees. 1%

employees disagreed that training helps in

increase the efficiency of employees. No

employee is strongly disagreed that

training helps in increase the efficiency of

employees.

Table No. 11: Training helps in acquiring skills for the next higher job


Strongly Agree 72

Agree 15

Indifferent 7

Disagree 4

Strongly Disagree 2

Total 100

The above table depicts that majority of 72

% employees strongly agreed that training

helps in acquiring skills for the next higher

job. 15% employees agreed that training

helps in acquiring skills for the next higher

job. 7% employees are indifferent that

training helps in acquiring skills for the

next higher job. 4% employees disagreed

that training helps in acquiring skills for

the next higher job. 2% employees

strongly disagreed that training helps in

acquiring skills for the next higher job.

Table No. 12:Training helps in more job satisfaction


Strongly Agree 75

Agree 20

Indifferent 4

Disagree 1

Strongly Disagree 0

Total 100

The above table reveals that majority of

75% employees strongly agreed

thattraining helps in more job satisfaction.

20% employees strongly agreed



thattraining helps in more job satisfaction.

4% employees strongly agreed thattraining

helps in more job satisfaction. 1%

employees strongly agreed thattraining

helps in more job satisfaction. 0%

employees strongly agreed thattraining

helps in more job satisfaction.

Findings

Training is an important function of

management as it contributes to the

development of human resources. They

need continuous development because

technology is developing continuously and

at a fast rate. The above study reveals the

facts that majority of employees opined

that training needs assessed before training

is imparted. Highest employees happy

about training programmes are planned in

the organized way. Great majority of



it. Most of employees find that the present

training system is adequate to meet the job

requirement. Large no. of employees

shown satisfaction towards training


trainee’s current job. Good no. of

employees satisfied that training needs is

identified through a formal performance

appraisal mechanism. Greater part of

employees pleased with formal training

evaluation methods to assess the

effectiveness of the training. Great no. of

employees agreed that T&D have resulted

in higher employee performance. A large

no. of employees shown satisfaction

towards training programmes is able to

bring improvement in knowledge about the

job of employees. All most all employees

satisfy with the statement that training

helps in increase the efficiency of

employees. A large amount of employees

discover training helps in acquiring skills

for the next higher job. High majority

employees find that training helps in more

job satisfaction.

Conclusion

The overall opinion about the training

conducted by the selected private sector

banks among the employees is very good

and effective, it is very much helpful to

improve the performance of individual and

the organizational growth too and they are

satisfied with the training programmes

provided by banks.

References

Books

Jyothi P. and Venkatesh D.N.

(2006), “Human Resource

Management” Oxford University

Press.

Raj Aparna (2011), “Training and

Development” Kalyani Publisher.



Rao P.L. (2004), “Human Resource

Management” Excel Publishing

House.

Journals

Abdullah Haslinda (2009), “Major

Challenges to the Effective

Management of Human Resources

Training and Development”,The

Journal of International Social

Research, Vol.2/8 Summer 2009.

AfaqUlFakhar, RoRosman bin Md.

Yusoff, Anwar Khan, Kamran

Azamand ThukimanKassim

(2011), “Employees’ Training and

Performance Relationship in

Hospitality Sector A Case of Pearl

Continental Hotel, Karachi,

Pakistan”, International Review of

Business Research Papers, Vol. 7,

No. 3, pp.149-158.

Akilandeswari and Jayalakshmi

(2014), “A study on effectiveness

of Training in Indian Banks”,

International Journal of Recent

Advances in Organisational

Behaviour and Decision Sciences

(IJRAOB), Vol.1, No.1, p.p.85-99.

Karim Mohammed Rejoul, Huda

KaziNazmul and Khan Rehnuma

Sultana (2012), “Significance of

Training and Post Training

Evaluation for Employee

Effectiveness: An Empirical Study

on Sainsbury’s Supermarket Ltd,

UK”, International journal Of

Business and Management, Vol.7,

No. 18, pp. 141-148.

LaxmanraonSontakkeRajratna

(2015), “An analytical study on

Training and Development

Practices in Public Sector Banks”,

Indian streams research journal”

vol.4, Issue -2,p.p.1-5.

OkerekeHukwunenyeIheanacho

and Beatrice NnennaIgboke (2011),

“Training, manpower development

and job performance: Perception

and relevance among civil servants

in Ebonyi State, Nigeria”, Journal

of Economics and International

Finance, Vol.3(6), pp. 399-406.

Sowajanya G. and Rajasekher M.

(2012), “Effectiveness of

Employees’ Training and

Development in Manufacturing

Industries (A Case Study of East

Godavari District, Andhra

Pradesh)”, Global Research

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