Proceedings of 27th International Business Research Conference
12 - 13 June 2014, Ryerson University, Toronto, Canada, ISBN: 978-1-922069-53-5
Technical Efficiency of Commercial Banks in India
Harjinder Singh
Present study examines empirical findings pertaining to technical efficiency of
commercial banks in India for the period 2001 to2011. To calculate technical
efficiency, a non-parametric data envelopment analysis approach has been applied.
Technical efficiency has been calculated for all the commercial banks using two
models based on different variables. According to empirical findings there is
inefficiency to the tune of 7.6 percent as depicted in model I and 18.6 percent as per
model II.This suggests that banks can on an average minimize their costs by
eliminating the elements of inefficiencies with the best practices and still produce the
same level of output. Policy implications of the results is that although efficiency of
banks have improved during the reform period, but still there are certain key areas of
weakness which need immediate redress by the policy makers. Inefficient use of
scarce resources and managerial irregularities are found to be the major cause of
concern in emerging technical inefficiency among commercial banks in India.
Keywords: DEA, Technical Efficiency, Pure Technical Efficiency, Scale Efficiency.
JEL Classification: G21, D24
Introduction
Banking sector plays an important role in the financial system of emerging market
economies, including India. It continues to be the centre of attention for academic and
policy makers alike. The performance of the banking sector has repercussions across
the length and breadth of the economy. Issues of productivity and efficiency have been
at the centre stage of discussions in recent years. Nowhere is this truer than the
financial sector which is perceived to be the brain of the economy (Stiglitz 1998). Even
within the financial sector, given the dominance of bank-based financial systems in
most emerging markets including ours and the systematic importance of banks in the
financial system. Not surprisingly therefore, performance of the banking sector has
repercussions across length and breadth of the economy. The quality of functioning of
the financial sector can be expected to affect the functioning and productivity of all
sectors of the economy. Efficient financial intermediation should help in improving
economy wide resource allocation thereby productivity growth all round. The efficient
intermediation of funds from savers to users enables the application of available
resources to their most productive uses. The more efficient a financial system is in such
resource generation and its allocation, the greater is its contribution to productivity and
economic growth (Mohan, 2006)
____________________________________________________
Dr. Harjinder Singh, Department Of Economics, Post Graduate Government College, Sector-46,
Chandigarh 160047, India, Fax No. +91172 2678022 (office),
E mail: harrythandi@hotmail.com,
Proceedings of 27th International Business Research Conference
12 - 13 June 2014, Ryerson University, Toronto, Canada, ISBN: 978-1-922069-53-5
Measurement of efficiency and productivity of the banking sector is done by
using accounting measures or economic measures. However there are limitations of
using accounting measures because the choice of a single ratio does not provide
precise information about various dimensions of the performance of a bank which uses
multiple inputs to produce multiple outputs. Moreover, these measures also do not
differentiate between efficiency and productivity clearly. This problem is better
addressed through economic measures that capture all aspects of banking operations
in a single measure.
Review of Literature
Initially, efficiency studies were confined to detection of scale (size) and scope
economies (product mix) under the implicit assumption that banks are technically and
allocatively efficient (Berger and Humphrey, 1997). However with banking structure
undergoing overwhelming changes in the event of financial globalization and evolution
of new products, a broad consensus evolved that difference in technical efficiency
cannot be attributed completely to incorrect scale and scope of output (Berger and
Humphrey, 1997). Similarly with regard to impact of deregulation on bank efficiency
there is no consensus. A study based on separate panels of 88 Spanish banks and 55
savings banks covering a period of 1985-91 highlights the point that deregulation was
associated with a decrease in relative cost efficiency for commercial banks but no
change for savings banks. A study on Turkish Commercial banks experienced scale
inefficiency during the period of deregulation. On the other hand a couple of studies on
the Indian banking sector show that deregulation which permitted diversification in fee
based activities and relaxed norms for branching has contributed to the efficiency and
productivity growth (Bhattacharya, Bhattacharya and Kumbhakar, 1997).
Some studies have documented that cost efficiency can particularly enhance
safety and soundness of the financial system. It has been demonstrated empirically
how bank failure can be predicted using efficiency scores obtained from DEA as a
proxy of management quality. It was found that banks receiving high efficiency scores
are much more likely to survive than banks which have relatively low scores (Barr and
Siems, 1996).
Research Methodology
Among the several techniques for economic measures, the data envelopment
analysis (DEA) approach used in this chapter has several advantages over others.
Firstly it provides bank level efficiency score. Secondly it does not require a prior
specification about the underlying technologies. Under the DEA approach, a best
practice frontier which represents optimal utilization level of resources is prepared and
efficiency of banks is measured relative to that best frontier (bench mark). If a bank lies
on the frontier, it is referred to as an efficient bank otherwise it is termed as less efficient
bank. More away the bank is from the frontier, the less efficient it is. Since, in practice,
the true ideal technology is not observable, the DEA analysis attempts to define the
feasible technology frontier.
Data envelopment analysis (DEA) is a non-parametric method of measuring
efficiency of a decision making unit (DMU) such as bank/firm. DEA employs
Proceedings of 27th International Business Research Conference
12 - 13 June 2014, Ryerson University, Toronto, Canada, ISBN: 978-1-922069-53-5
mathematical programming to construct a best practice frontier from the observed data
and to measure efficiency relative to the constructed frontier. The DEA frontier is
formed by connecting the set of best practice observations (the piecewise linear
combination).Thus the DEA efficiency score for a DMU or bank is not defined by an
absolute standard but is defined relative to other banks. The best way to explain DEA is
by way of ratio form. For each DMU, we would like to obtain a measure of the ratio of all
outputs (y) over all inputs (x) such as u’y/v’x, where u is output weight and v is input
weight. To select the optimal weights, we specify the mathematical programming
problem:
Max (u’y/v’x)
S.t. u’y/v’x 1, J= 1, 2 ----N
u, v  0
This involves finding values for u and v, such that the efficiency measure of the
ith DMU is maximized, subject to the constraint that all efficiency measures must be
less than or equal to one.
The DEA was first introduced into operations research literature by Charnes,
Cooper and Rhodes (CCR model, European Journal of Operations Research: 1978).
(Ray, 2004) Although the original CCR model was applicable to only technologies
characterized by constant returns to scale, Banker, Charnes and Cooper (BCC)
(Management Science, 1984) extended the CCR model to accommodate technologies
that exhibit variable returns to scale.
Although prime facie, DEA appears to be relatively new methodology, the
intellectual underpinnings of DEA in economics date back to the 1950’s. Debreau
(1951) defined the coefficient of resource utilization as a measure of technical efficiency
for the economy as a whole and any deviation of this measure from unity was
interpreted as a dead weight loss suffered by society due to inefficient utilization of
resources. Farrell (1957) constructed a linear programming model using actual inputoutput data of sample of firms. He approximated the underlying production possibility
set of convex hull of a cone containing observed input-output bundles. In other words,
non-parametric analysis of efficiency using linear programming in economics bears a
long history prior to the formal introduction of DEA into the literature.
The present study has been devoted to analyse different measures of efficiency
to have a comprehensive view of banks performance. Therefore, the overall efficiency
(x-efficiency) takes into account the combined effect of allocative efficiency (AE) and
technical efficiency (TE). Technical efficiency further takes into consideration the
influence of scale efficiency (SE) and pure technical efficiency (PTE).
Input and Out Specifications
There are two approaches which are used for the specifications of inputs and outputs
I;e production approach and intermediation approach.in the present study
intermediation approach has been used as followed by most of the researchers using
dea approach.within the intermediation approach the exact set of inputs and output
variables depend upon the availability of data.DEA is an approach which is sensitive to
the choice of input-output variables.this is the strength of the technique since it reveals
which of the the input variables need to be closely monitored by bank management to
improve efficiency.
Proceedings of 27th International Business Research Conference
12 - 13 June 2014, Ryerson University, Toronto, Canada, ISBN: 978-1-922069-53-5
Specifications of Model 1 and Model Ii
INPUTS
INTEREST EXPENSES
DEPOSITS
NON-INTEREST
STAFF MEMBERS
EXPENSES
OUTPUTS
INTEREST INCOME
LOANS
NON-INTEREST INCOME
NON-INTEREST INCOME
Model 1 is an income based model whereas Model 11 is a loan based model. These
two models have been used to show that how efficiency scores differ when inputs and
outputs are changed.
Sources of Data;
The data for the study relates to Indian commercial banks operating in india which
includes public sector banks, private sector banks and foreign banks. Data for the
purpose has been extracted from the websites of Reserve Bank of India and Indian
Bank Association for the period 2001 to 2011.
In the present study DEAP version 2.1 of DEA computer programme developed by Tim
Coelli has been used to calculate technical efficiency.
Measurement of Technical Effciency Using Dea
The technical efficiency of a firm refers to its success/failure in transforming its
inputs into outputs. It is a relative concept as its measurement requires a standard of
performance against which the success or failure of the firm is assessed. Broadly
speaking, the contemporary empirical studies employ parametric or non-parametric
frontier techniques to measure the efficiency of firms relative to an estimated ‘best
practice’ frontier that represents the optimum utilization of resources.
The parametric approaches usually involve econometric estimation of a
prescribed function. In contrast, non-parametric DEA does not require the specification
of a particular functional form for the frontier. Instead the production frontier is
constructed through a piecewise linear combination of the actual input-output
correspondence set that envelops the input-output correspondence of all the firms in
the sample. Hence efficiency measurement is not contaminated by a possible
misspecification of the production function (Bauer et al., 1998). The main weakness of
the DEA is that the measurement error and statistical noise are assumed to be nonexistent (Berger and Mester, 1997; Yildrim, 2002).
DEA generalizes the Farrell’s (1957) technical efficiency measures to the
multiple-inputs and multiple outputs case. DEA involves the use of linear programming
methods to construct a non-parametric piecewise surface (frontier) over the data.
Efficiency measures are then calculated relative to this surface. Comprehensive review
of the methodology is presented in Seiford and Thrall (1990. Using this frontier DEA
computes a maximal performance measure for each DMU relative to that of all other
DMU’s. The DMU’s that lie on the frontier are the best practice units and retain a value
Proceedings of 27th International Business Research Conference
12 - 13 June 2014, Ryerson University, Toronto, Canada, ISBN: 978-1-922069-53-5
of one; those enveloped by the external surface are scaled against a convex
combination of the DMU’s on the frontier facet closest to it and have values somewhere
between 0 and one.
Several mathematical programming DEA models have been proposed in the
literature. In the present study, we use the CCR (named after its developers, Charnes,
Cooper and Rhodes, 1978) and BCC (named after its developers, Banker, Charnes and
Cooper, 1984) models to obtain efficiency measures corresponding to the assumptions
of CRS and VRS, respectively. The efficiency measures obtained from CCR model are
popularly known as overall technical efficiency (OTE) scores and are confounded by
scale efficiencies. The efficiency measures obtained from BCC model are popularly
known as pure technical efficiency (PTE) scores and devoid of scale efficiency (SE)
effects. Scale efficiency (SE) for each DMU can be obtained by a ratio of OTE score to
PTE score (i.e. SE= OTE/PTE).
Empirical Findings
The empirical observations obtained through Charnes, Cooper and Rhodes
(CCR) and Banker, Charnes, Cooper (BCC) model have been presented in this
chapter. The empirical results illustrate and analyse inter-temporal, comparison of CRS
and VRS technical efficiency and its components among commercial banks in India for
the period 2001 to 2011. An attempt has been made to explore the causes of
inefficiency among Commercial banks in India
.
Technical Efficiency Scores and Its Components
Empirical results have been presented in the Table 1.1 and 1.2. It has been
represented in the graphs 1.1 and 1.2. It presents average TE scores and its
component pertaining to various sub-periods. In this analysis two models have been
used to obtain observations of the Technical efficiency scores of commercial banks in
India. Model-I is an income based model in which interest
Proceedings of 27th International Business Research Conference
12 - 13 June 2014, Ryerson University, Toronto, Canada, ISBN: 978-1-922069-53-5
Table 1.1
Temporal pattern of average technical efficiency and its components among commercial banks in India-2001
to 2011 (Model-I).
Year
Technical Efficiency
Pure Technical
Scale Efficiency
(CRS)
Efficiency (VRS)
2001
0.930
0.955
0.973
2002
0.954
0.969
0.984
2003
0.940
0.965
0.974
2004
0.920
0.964
0.956
2005
0.917
0.955
0.960
2006
0.933
0.961
0.965
2007
0.948
0.967
0.980
2008
0.910
0.956
0.951
2009
0.915
0.957
0.956
2010
0.926
0.952
0.972
2011
0.874
0.948
0.921
Mean
0.924
0.959
0.962
Proceedings of 27th International Business Research Conference
12 - 13 June 2014, Ryerson University, Toronto, Canada, ISBN: 978-1-922069-53-5
Table 1.2
Temporal pattern of average technical efficiency and its components among commercial banks in India -2001
to 2011 (Model-II)
Year
Technical Efficiency
Pure Technical
(CRS)
Efficiency (VRS)
2001
0.842
0.917
0.918
2002
0.857
0.910
0.941
2003
0.735
0.835
0.880
2004
0.779
0.867
0.898
2005
0.796
0.884
0.900
2006
0.815
0.887
0.918
2007
0.844
0.913
0.924
2008
0.829
0.894
0.927
2009
0.841
0.887
0.958
2010
0.775
0.864
0.896
2011
0.844
0.914
0.923
Mean
81.4
0.887
0.916
103
Scale Efficiency
Proceedings of 27th International Business Research Conference
12 - 13 June 2014, Ryerson University, Toronto, Canada, ISBN: 978-1-922069-53-5
Figure 1.1
Average technical efficiency and its sources (Model-I)
Figure 1.2
Average technical efficiency and its sources (Model II)
Expenses and operating expenses have been used as inputs and Interest
income and non-interest income as outputs. In the model-II which is a loan
based model deposits and employees have been used as inputs to obtain
output in the form of loans and non-interest income.
However the present study is undertaken during the post-reforms period i.e.
2001 to 2011. It has been observed that TE under CRS as per model-I shows an
Proceedings of 27th International Business Research Conference
12 - 13 June 2014, Ryerson University, Toronto, Canada, ISBN: 978-1-922069-53-5
efficiency score of 92.4 percent for the entire banking industry. The maximum TE
score observed during this period is 95.4 percent in 2011. The empirical findings
have observed average TE score 81.4 percent as per model-II. The average TE
under CRS has not improved and there is only insignificant improvement from 84.2
percent in 2001 to 84.4 period in 2011. Technical efficiency score of 81.4 percent
(model II) suggest technical inefficiency of commercial banks to the tune of 18.6
percent. In other words, it can be said that banks can, on an average, curtail their
expenditure on labour and loanable funds by at least 18.6 percent with the help of
best practices without altering the level of outputs. Alternatively, the banks have
scope of producing 1.23 times (i.e. 1/0.214) as much outputs from the same level of
inputs.
The average VRS TE score for the banking industry is 98.3 percent (model-I)
as depicted in Table 1.1 and Figure 1.1 and 88.7 percent (model II) as depicted in
Table 1.2 and Figure 1.2. The lower average TE score under both CRS and VRS
could be attributed the large number of NPA’s in the banking industry.
It should be noted here that the figures reported in Table 1.1 should not be
compared across years as DEA measures relative efficiency and not absolute
efficiency. A higher value of average efficiency does not imply higher average
performance compared to the performance with respect to lower average efficiency.
The values reported in this study are high as compared to the average efficiency
reported in similar studies of banks in India and other countries. The difference in the
estimated efficiency between studies can be due to many reasons: Variation in (i) the
best practice frontier, (ii) input-output variables set used in the DEA model, (iii) time
period captured in the analysis and (iv) characteristics of the sampled banks
(Galagedera and Edirisuriya, 2003).
However the results obtained in this study are consistent with the studies
undertaken by Bhattacharya, Lovell and Sahay (1997) and Sathye (2001). The study
undertaken by Bhattacharya et al examined the productive efficiency of Indian
commercial banks during 1986-91 and reported that the annual average efficiency of
p banks averaged between 0.82 and 0.90 . Sathye (2001) considered two sets of
input-output variables thereby estimating efficiency with two DEA models and
reported that in one model the mean efficiency is 0.83 and in other the mean
efficiency is 0.62 highlighting the possibility of obtaining different efficiency estimates
when alternative sets of inputs are used.
As depicted in table 1.1 and 1.2 average VRS TE i.e. Pure technical efficiency
(PTE) score of commercial banks in India has worked out to be 95.9 percent
(model-I) and 88.7 percent (model-II). In the model 1I value of VRS TE has
fluctuated between 83.5 percent in 2003 to 91.7 percent in 2001. This in turn implies
that average pure technical inefficiency (PTIE) to the tune of about 11.3 percent in
the study period is due to the inappropriate management practices in converting
critical inputs into outputs. On the other hand average scale efficiency score of
commercial banks has noticed to be 96.2 percent (model-I) and 91.6 percent (modelII) varying from 92.1 percent in 2011 to 98.4 percent in 2002 and 88 percent in 2003
and 95.8 percent in2009 for model I and model II respectively. This in turn implies
that average scale inefficiency to the tune of about 3.8 percent (model I) and 8.4
percent (model-II) is due to choice of scale of operation. These findings suggest that
managerial irregularities have played a key role in emerging technical inefficiencies
among commercial banks in India. Empirical findings are consistent with a study
Proceedings of 27th International Business Research Conference
12 - 13 June 2014, Ryerson University, Toronto, Canada, ISBN: 978-1-922069-53-5
conducted by Zhao et al (2008). In view of the empirical findings it is suggested that
working management of banks should be made more effective.
Conclusion
In the present study, an effort has been made to present empirical findings pertaining
to commercial banks in India for the period 2001 to 2011. To calculate technical
efficiency, a non-parametric data envelopment analysis approach has been applied.
Firstly technical efficiency has been calculated for all the commercial banks using
two models based on different variables. According to empirical findings there is
inefficiency to the tune of 7.6 percent as depicted in model I and 18.6 percent as per
model II. This suggests that, banks can, on an average, minimize their costs by
eliminating the elements of inefficiencies with the best practices and still produce the
same level of output.
In sum, the policy implication of the aforementioned results is that although efficiency
of banks have improved during reform period, but still there are certain key areas of
weaknesses which need quick or immediate redresses by the policy makers. Accor
ding to empirical findings the inefficient use of scarce resources and managerial
irregularities are found to be the major cause of concern in emerging technical
inefficiency among commercial Banks in India. Lastly contrary to the general belief
public sector banks have performed well as compared to their private counterparts.
The policy of privatization of banking sector needs to be revised in the light of these
findings.
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