Competitive Advantages and Performance of Banks in India in Post reform Period:
Application of Diamond Theory
By
Dr. Medha Tapiawala
Introduction
The competitiveness is shown by the efficiency of domestic resources in an economy. The
evolution of growth is experienced by the improvement in efficiency of the resources over the
period of time. Every country target towards the better efficiency, productivity. This should result
into optimal and competitive use of the resources available. The process of development leading
to institutional changes have also experienced the problem of commons (Hardin 1968). Thus, in
the recent times the efficient use of available resources should be prioritise for achievement of the
targeted development.
Competitiveness defined:
The competitiveness is indicated by the rate of growth of a particular industry or by the numbers
of innovations leading to betterment of life or use of updated technology in various sectors. There
are different definitions of competitiveness (Porter,1988)
Porter’s diamond model was manly for measuring competitiveness of nations on different edges
of diamond as shown in his paper on Competitive advantages of Nation (1990). The
competitiveness of a nation depends on industries up gradation and challenges. The countries
progress due to such industries which show their competitiveness due to innovation and pressure
of successful survival. The national, as well as local prosperity is not inherited – it has to be created
(Porter; 1998, p.155). This prosperity depends on the industry’s ability to perpetually innovate and
upgrade itself, and that is possible solely by means of increase in productivity – in all areas of
economic activity. The diamond as the entire system comprises key factors which lead to
competitive success.
Some organizations view competitiveness as the ability to persuade customers to choose their
offerings over alternatives while others view competitiveness as the ability to improve
continuously process capabilities of resources. Since resources are interlinked, it is difficult to
quantify the use of resources (Feurer & Chaharbagh 1994). Hence, in the process of development
not only the competitiveness of the institutions is important but the sustainability of
competitiveness of the institutions is equally important.
Competitiveness ties directly to economic performance and encompasses the full range of factors
that shape national prosperity, and especially the influence of public policy and business practice.
Foundational competitiveness is defined as the expected level of output per working-age individual
given the overall quality of a country as a place to do business. This definition goes beyond the
expected level of productivity per employed worker, because prosperity is ultimately rooted in the
ability to both achieve high productivity as well as mobilize a high share of the available
workforce. (Delgado, Ketels, Porter & Stern, 2012)
The competitiveness of resources used in the process of production, hence is difficult to separate
and measure. The national gain is dependent on complementarity of the resources used in sectors
as well as intra sectors.
India, being the upcoming power, the competitiveness must be improved. There are some studies
to check the competitiveness in various sectors like Retail organised sector (Pawar, Veer, 2013)
Software industry (Howard 2008). The diamond theory is applied to check the competitiveness in
retail sector shows underutilised capacity of India’s capabilities. Sardy and Fetscherin (2009)
checked the competitiveness of automobile sector in China , India And south Korea. The result
indicated that China is the most competitive and S. Korea is the next rival.
The competitiveness of resources leading to competitiveness of a firm using it result in increasing
competition among the firms in an industry. If we combine the horizontal and vertical integration
within and across the sectors, which can give overall competitiveness of the economy.
This paper is trying to indicate that the micro competitiveness of an industry can bring about the
better efficiency and competitiveness for an economy as a whole. Porter in his paper on Diamond
theory, has indicated competitiveness across industries and also nations with the help of forming
a link which is framed into a diamond shaped chart
Porter’s Diamond Chart
The idea of competitiveness is still at the distance for many upcoming nations. But, the idea seems
to be very attractive for any country considered as an upcoming power like BRIC nations. India,
has an advantage of good going of service sector which is dragging the overall growth for the
nation.
Literature review
The efficiency, performance, competitiveness is measured by many thinkers by using different
methods. The competitiveness of the banks depends mainly on their productivity and efficiency.
Though both the terms are used synonyms of each other they are not treated so technically,
productivity is a cardinal measure whereas efficiency is an ordinal measure. But, the
competitiveness is the combination of both (Tapiawala, 2009). The measurement of efficiency also
is now converted in cardinal units when technical efficiency is measured (Koopman, 1951). )
technical efficiency is a physical input output vector, where it is technologically impossible to
increase any output without simultaneously reducing inputs.
Further Farrell (1957) decomposed the efficiency in allocative and technical efficiency by
developing production frontier approach. The approach was non-parametric approach. Aigner et.
al (1977) extended it to stochastic frontier model. Kumbhkar & Sarkar (2001) continue with
somewhat similar approach. They divided Total efficiency in allocative efficiency and technical
efficiency. Allocative efficiency arises when the producer fails to use inputs in such a way that
cost is minimized i.e. some inputs are overused or underused.
Ramnathan (2003) indicated that the measured efficiency of production unit (affirm or plant)
is generally interpreted as the difference between its observed input and output level and
corresponding optimal output.
India’s story
Competitiveness of three main sectors in India is shown by their contribution to the GDP.
Though majority of the population is employed in Agricultural sector, the tertiary sector is
contributing highest compared to other sectors. Though it is not the perfect criteria to judge the
leading sector the leading sector is judged on the past relative growth that has supported the current
contribution of the sector to the GDP and the contribution of the sector to aggregate growth. It is
observed that Indian growth has always been service led (Nadkarni, 2009). Service sector is
comprised of (i) Trade, hotels and restaurants, (ii) Transport, storage and communication, (iii)
Banking and Insurance and Real Estate and Business Services, (iv) Community, Social and
Personal services. It was argued that the rapid rate of growth of service sector was dominated by
information and communication technology in the beginning of reforms period (Joshi, 2007)
In the market economies, banks are considered as one of the vital veins of the financial
market. The more competitive the banking sector is the better economic position of the country.
Without strong, stable, reliable, and efficient banking sector, competitiveness of the economy and
the country is impossible to attain. Especially, in the bank based economy like India, if the
economic growth engine has to churn out a powerful performance, banks would, necessarily, have
to be the prime mover. Unlike in many countries, in India there is still a potential and scope to
expand credit.
Here, the competitiveness of banks is measured on the following edges –called diamond edges
(Porter, 1978)I.
The factoral determinants of the banks are taken as the employees of the banks and
branch-office network of the bank.
II.
Demand condition of the banks is shown by the demand of banking services mainly as
primary service of accepting deposit, giving loans.
III.
Firm strategy, structure, and rivalry for banks is taken in terms of intra banking
competition for earning a better market share of total income of the banks
IV.
Related and supporting institutions of the banks leads to the business of other financial
institutions.
Banks, as the financing agent of the economic and developmental activities have an important role
in promoting overall sustainable development. The efficiency of banks is shown by their ability in
transforming its resources to output by making its best allocation, which is essential for the growth
of an economy. Hence, they need to be competitive in this sense.
Indian Banking system is comprising of mixed pattern where there are public sector owned banks
private sector owned banks including scheduled and non-scheduled banks, co-operative banks and
foreign management owned banks etc. During last few decades, the environment under which
Indian banking sector has been operating, witnessed remarkable changes. After the reforms
implemented in 1990, the role and scope of service sector has expanded widely. Hence, the need
for the supply of financial services have also been increasing. The banking sector in India has
undergone changes as it has accommodated other players also. But only having more players does
not make banking more competitive. One inevitable outcome of the changing banking structure
will be that the industry would have more players which compels bank to be efficient and
competitive. But, simply by bringing in more players would not make the banking industry more
competitive. In India the concentration rate also is not very high for banking sector. Concentration
rate is shown by top three banks in total banking assets which is just under 30%. Whereas, in Japan
it is 75%, in China, Brazil and U.K. it is 50% and in U.S it is 35% (Chakravarti, 2014).
It is observed that out of total credit issued by various institutions in India in the year 2010, 60%
is issued by public sector banks(27), private sector commercial banks(22) have given 16.4%,
foreign banks share is 7.2%, RRB(86) contributed only 2.3%,urban(1721) and rural(96061) cooperative banks have shared 9.2% , other financial institutions contribution is 3.5% and NBFI has
contributed 1.5%.This shows that the public sector commercial banks dominates in issuing of
credit to the economy.
Review of literature for banking competitiveness
Competitiveness of the banks depends on the productivity and the efficiency of the banks. Data
envelopment analysis used by Aly et.al (1990) to measure overall inefficiency. Stochastic cost
frontier production function used by Ferrier and Lovell (1990). It is used to measure overall
inefficiency. They have compared the inefficiency by comparing DEA and SFA. Wheelock and
Wilson (1995) have compared the efficiency measures, especially focusing on Non-parametric
technique, of different banks on the basis of difference in the produced of the banks. Kruger (2001),
Senia (2003) have also shown the techniques of measuring banking productivity.
Bandopadhyay (1996) examined the concept of cost responsiveness to measure the efficiency of
the banks in India. On the other hand Naulas and Ketkar (1996) have used the technique of DEA
to measure the banking productivity. Hansda (1999), Sarkar (2004), Sensarma (2005) have also
measured the efficiency of banks in India using various methods.
Here, the overall competitiveness of the banks groups is measured by applying a technique useful
to cheque the position of each group out of the market share of various diamond edges applied to
the banks group.
Research methodology:
The structural change in competitiveness of Banks (group wise) is measured in India by using the
Diamond Edges of competitiveness for banks as mentioned above. Here, the data is taken for 11
years from 2002 to 2013
In the first edge of the factoral determinants of the banks, the expenses made on employees of
the banks and on branch-office network of the groups of bank having its correlation with the profits
earned by the groups of banks is measured. These factors are selected as the independent variables
and it is checked whether their impact on profit is significant or not.
In the next edge of the competitiveness, Demand conditions of the banks is shown by the demand
of banking services mainly as primary service of accepting deposit, giving loans. Here, number of
accounts of the groups of the banks and how far there correlation with the profits of banks is
significant is to be checked.
Another edge of the bank’s competitiveness is shown by Firm strategy, and regulatory structure
for banks is taken in terms of intra group banking competition for earning a better market share in
total profit in response to the policy rates of the banks.
Finally, the edge to show the competitiveness of banks group is shown by market share of NPA
of each group of the banks affecting the business of other institutions.
Due to limitations of data the last two edges are modified and the competitiveness of banks is
measured here by taking NPA s of the banks as the level of NPA shows the capability of banks to
face the competition in survival. NPA s are inversely related to the profits. Hence if banks can
make provision for NPAs the survival and keep NPAs under control then banks can be competitive,
Good management of banks is shown by the behaviours of the banks in given policy rates which
are common for all banks. Hence, the ability to maintain the profitability in such conditions is also
taken as a measure of competitiveness of banks.
The model
Here the edges are framed on the line of Porter’s model with some modification due to the
constraints of availability of data. While applying the model to the service industry like banking,
the compatibility of banks is measured as shown in following equation
𝜋𝑖 = 𝑎 + 𝛽𝑖1 (𝑤𝑖 ) + 𝛽𝑖 2 (𝑅𝑖 ) + 𝛽𝑖3 (𝐴𝑖 ) + 𝛽𝑖4 (𝐼𝑖 ) + 𝛽𝑖4 (𝑁𝑖 ) + ∑𝐷𝑖 + 𝑢𝑖
1
∑𝐷𝑖 = 𝐷1 + 𝐷2 + 𝐷3 + 𝐷4
Where 𝜋𝑖 shows profit of ith group of bank.
𝛽𝑖1is the coefficient of factor determinants like 𝑤𝑖 which is payment and provision for employees
and 𝑅𝑖 is the expenses incurred on offices and branches.
𝛽𝑖2is the coefficient of 2nd edge of ith group of the banks indicating 𝐴𝑖 as demand for the banks
services. Here, the demand for bank services is shown by the number of accounts of deposits and
lending handled by the banks. 𝐴𝑖 indicates number of deposit accounts
𝛽𝑖3 (𝐼𝑖 ) Indicates number of accounts of loans of ith group of the bank.
𝛽𝑖4 (𝑁𝑖 ) Shows that NPA of ith group of banks affecting the profit.
∑𝐷i are dummy variables used in LSDV.
Since the policy rates for all banks are almost same, (repo rate, SLR, etc.) its effect on the profit
of groups of banks is not included in the above model.
The above mentioned model shows that the groups of banks are efficient in their profit earning
WRT the independent variables mentioned. The results are drawn by running a least square dummy
variable method to reduce the effect of unobserved variables. Here fixed effect panel data is used.
Conclusion and Results
Following table shows coefficient matrix of variaous edges across bank groups
State Bank and Nationalised
Scheduled
Assciates
Commercial
Bank
Foreign Banks
Banks
factoral
1.00
1.13
1.46
1.37
1.87
1.76
-12.25
2.5
0.26
-0.75
-0.10
-0.52
determinants
Demand
determinants
NPA
Note : scheduled commercial banks include old private sector banks
The result of the competitiveness of banks in terms of elasticity of factors, demand elasticity and
influence of changing rates of NPAs on rates of profits earned by the respective groups of banks
is measured.
Here, the multiple regression is run by using LSDV method which gives fixed effect on the
assumption that the slopes and the corresponding intercepts of the groups of banks. The data table
is given in appendix. The data is processed to measure the rate of growth of the variables. Hence,
the data is converted into market share of each group and taken the logarithm values. The
regression result shows that the model satisfy the goodness of fit.
Here, Nationalised Banks seems to be more competent in terms of factor determinants with highest
coefficient of 1.53. Foreign sector banks shows better competitiveness in demand determination.
In terms of NPA also the SBI are more competitive. The fourth edge regarding the policy rate and
the competitiveness of the banks could not be measured as the rates are common for all.
Future scope and limitations
The results are measured over groups of banks. Hence, there is a scope to check the
competitiveness in the group over intra banks to know about the competitiveness of each bank at
micro level. The method of fixed effect leads to control the effects of unobserved independent
variables. But in the future by extending the numbers of variables the study can comprehensively
made to gather the information which can help for banks too.
The study was constrained also due to type of industry chosen. Here, the attempt is made to PPLY
the Diamond theory which is used for measuring the competitiveness of industries producing
tangible units. The output of Banks is to provide financial services. Hence, here the profits of the
banks is selected as an outcome of commercial services provided by banks. The study is useful to
understand the insight of policy changes on the basis of the edges of Diamond model as shown
here.
Apendix
Results of the model
Adjusted R
Model
1
R
.999a
R
Squareb
.997
Square
std. Error of the Estimate
.994 .02083
Coefficientsa,b
Standardized
Unstandardized Coefficients
Model
1
B
Coefficients
Std. Error
Beta
t
Sig.
d1
-.599
.492
-1.069
-1.217
.235
d2
-.886
.348
-1.581
-2.545
.018
d3
.018
.068
.033
.268
.791
d4
.410
.218
.732
1.884
.072
d1wg
1.259
1.095
.650
1.150
.262
d1exfa
-.250
.197
-.095
-1.270
.216
d1dpst
.756
1.572
.316
.481
.635
d1loan
1.123
.449
.479
2.501
.020
d1npa
.268
.460
.132
.583
.565
d2wg
1.330
.454
1.188
2.930
.007
d2exfa
.203
.076
.125
2.664
.014
d2dpst
2.632
.752
2.465
3.501
.002
d2loan
-.970
.349
-.735
-2.782
.010
d2npa
-.756
.209
-.670
-3.620
.001
d3wg
1.464
.280
.389
5.227
.000
d3exfa
.027
.080
.023
.331
.743
d3dpst
-13.097
10.664
-.167
-1.228
.231
d3loan
.744
.377
.141
1.975
.060
d3npa
-.103
.267
-.033
-.385
.703
d4wg
1.722
.669
.225
2.573
.017
d4exfa
-.358
.352
-.054
-1.018
.319
d4dpst
-2.802
1.854
-.506
-1.511
.144
d4loan
-.298
.133
-.140
-2.239
.035
d4npa
-.520
.424
-.054
-1.226
.232
a. Dependent Variable: pr
b. Linear Regression through the Origin
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