Loan quality, ownership and efficiency of Indian banks: a bootstrap truncated regression approach.
Sathye, Suneeta ; Sathye, Milind
Abstract
Many prior studies on Indian banking efficiency have typically
regressed nonparametric estimates of production efficiency on
environmental variables in a two-stage process. However, Simar and
Wilson (2007, 2011) have demonstrated that the studies that use such
conventional approaches are invalid due to complicated and unknown
serial correlation among estimated efficiencies. Using the data
envelopment analysis bootstrap procedure suggested by these authors, for
the first time, we analyse the technical efficiency of Indian banks and
regress the bootstrap scores on a set of environmental variables using a
truncated regression. Banks that are on efficiency frontier as per
conventional analysis are actually away from the frontier when bootstrap
scores are used. Contrary to many prior studies, state ownership was
found to have significant negative impact on efficiency.
Key words: Indian banks, efficiency, truncated regression,
bootstrap
1. INTRODUCTION
The objectives of this study are (a) to assess the production
efficiency of Indian banks using the bootstrap approach to data
envelopment analysis and (b) to examine the impact of loan quality and
ownership on bias-corrected bootstrap efficiency scores. It explores
these issues by addressing five related questions:
(i) What is the production efficiency of Indian banks using the
unbiased bootstrap approach?
(ii) What is the effect of state ownership on bank inefficiency?
(iii) What is the effect of bank soundness on bank efficiency?
(iv) What is the effect of size on bank efficiency?
(v) What is the effect of loan quality on bank efficiency?
The paper also considers whether there was a change in bootstrap
efficiency scores of Indian banks during the three periods: pre-GFC,
during the GFC and post GFC.
The immediate motivation for the paper is the passage in December
2012 of the Banking Liberalisation Bill in the Indian Parliament that
raises the foreign investment limits in Indian banks to 26 per cent from
the present 10 per cent and liberalizes the licensing regime for banks
(FT 2012). The liberalization is intended to improve the efficiency of
the banking system, which is tipped to become the third largest in the
world, next only to China and the United States, by 2025 (FT 2012).
Further, the extant studies on Indian banking efficiency have used the
nonparametric data envelopment analysis and the two-stage regression
approach without bootstrapping the efficiency scores. The Reserve Bank
of India (2008), for example, found that 17 out of 81 banks were on the
efficiency frontier using the data envelopment approach. However, the
efficiency scores were not bootstrapped. Simar and Wilson (2007) have
demonstrated that these studies are invalid due to complicated and
unknown serial correlation among estimated efficiencies.
Further, Dyson and Shale (2010), state that the true efficient
frontier lies within the confidence limits that are produced by
bootstrap procedures. This removes the main drawback that statistical
inference can't be conducted with DEA efficiency scores (Halkos and
Tzeremes, 2010).
Using the data envelopment analysis bootstrap procedure suggested
by these authors, for the first time, we analyze the technical
efficiency of Indian banks and regress the bootstrap scores on a set of
environmental variables using a truncated regression. Second, while the
banking sector in many countries of the developed world faced enormous
problems of financial stress and sustainability, the Indian banking
sector came out of the global financial crisis (GFC) relatively
unscathed. Barr et al. (2000) found that banks with higher efficiency
are more likely to survive than those with relatively low scores.
Consequently, an examination of the efficiency of Indian banks post-GFC
becomes important. Podpiera and Cihak (2005) stated that regular
screening of banking efficiency is important, as it can serve as an
early warning system. Third, The Economist (2012) stated that the Indian
banking system runs the "risk of Spanish disease" and that
"India has a bigger bad-debt problem than the rather stable level
of banks' official 'non-performing" loans suggests."
The magnitude of the impact of such non-performing loans (loan quality)
on banking efficiency is also an issue that we examine in this paper.
Fourth, few studies on Indian banking efficiency have examined the
impact of ownership and credit risk (loan quality) together on
production efficiency in second-stage regression. Where they have, it is
either multiple regression or tobit regression that has been used on
non-bootstrapped efficiency scores instead of truncated regression as
suggested by Simar and Wilson (2007). Finally, the results would be of
interest to researchers in emerging economies like China, Brazil, Russia
and other developing countries where banks continue to be publicly
owned. Fry (1995) states that a key stylised fact about developing
countries is financial intermediation is mostly carried by commercial
banks rather than by financial markets. Ataullah and Le (2006) emphasize
that it is vital for governments in developing countries to create an
environment that enhances commercial banking efficiency for overall
economic growth.
Furthermore, as stated by Simar and Wilson (2007), the procedure
ensures the efficient estimation of the second stage estimators, a
property which is not guaranteed with alternative methods. The use of
truncated regression enables us to obtain more reliable evidence (Barros
and Garcia-del-Barrio, 2011).
The study proceeds as follows. Section 2 provides a background of
the Indian banking system in brief, section 3 reviews prior studies,
section 4 provides data and analysis and section 5 provides results.
Conclusions of the study are presented in section 6.
2. OVERVIEW OF THE INDIAN BANKING SYSTEM
India has a massive banking system that caters to the financial
needs of over 1 billion people. At the top of the banking system is the
Reserve Bank of India, which is the central bank of the country.
Commercial banks are the major type of financial intermediary and
consist of 26 public sector, 22 private sector and 41 foreign banks (see
Table 1). Besides the commercial banks, cooperative banks, which are
also state-partnered institutions, mainly cater to the needs of the
rural sector. As per RBI (RTPB 2012, Table IV.1), total assets of the
Indian banking sector are Rs 82,994 billion with total deposits of Rs
64,537 billion and total advances of Rs 50,746 billion (INR55/USD). The
return on assets of 1.08 (2012) is comparable with that of other
countries of the world. The figure for the net non-performing assets as
a percentage of net advances at 1.4 does, however, indicate an area of
concern, given the report in The Economist cited above that a huge
amount of restructured loans are not included in the ratio.
3. PRIOR STUDIES
Studying banking efficiency is important. Fiordelisi et al. (2010)
found that reduction in efficiency increases banks' future risks
and indicated bad management. For measuring bank efficiency, the
frontier analysis approach is increasingly being used. The approach
consists of separating institutions that are performing poorly as
compared to those that are performing well using a particular standard.
The separation is achieved either by applying the non-parametric or
parametric frontier method. The parametric approach includes stochastic
frontier analysis, the free disposal hull, thick frontier and the
distribution-free approaches (DFAs), while the non-parametric approach
is data envelopment analysis (DEA) (Molyneux et al. 1996).
Though many empirical studies have examined banking efficiency over
the years, few have used the bootstrap DEA procedures. Consequently, the
results obtained through the use of conventional DEA would need to
undergo renewed scrutiny. Matthews et al. (2009) examined the Malmquist
productivity (not efficiency) and non-performing loans in Chinese banks
using bootstrap procedures. Curi et al. (2013) examined foreign-bank
bootstrap DEA efficiency in Luxemburg. Barros and Assaf (2011) and
Halkos and Tzeremes (2013) examined bootstrap efficiency of Japanese and
Greek banks respectively. Chortareas et al. (2013) used bootstrap DEA to
examine banking efficiency in the European Union.
As can be seen from the above the bootstrap DEA efficiency studies
in banking have largely been confined to developed countries. Examining
banking efficiency using bootstrap is important in the context of
developing countries since they generally have predominance of public
sector banks which are typically known to be saddled with inefficiency
(Fry, 1995; Ataullah and He, 2006). Governments in these countries need
an efficient banking sector for promoting growth. The starting point of
this is to accurately assess the banking efficiency in these countries.
We address this gap in the literature using data of Indian banks.
Bhattacharya et al.'s paper (1997) was the first to apply
frontier analysis (both DEA and stochastic frontier analysis) to assess
the efficiency of 86 Indian banks during the early liberalization period
(1986-1991). The study found publicly-owned banks to be most efficient,
followed by foreign banks and privately-owned banks. Das (1997) used DEA
to examine the efficiency of 65 Indian commercial banks for the year
1995 and compared their technical and allocative efficiency and found
the former to be more efficient. Mukherjee et al. (2002) examined the
technical efficiency of 68 commercial banks for the period 1996-1999.
The findings were similar to those of the Bhattacharya et al. (1997)
study that is, the publicly-owned banks were found to be more efficient
than both private and foreign banks. Sathye (2003) examined the impact
of ownership on Indian banking efficiency using DEA and found that
publicly-owned banks were more efficient than foreign banks and
privately-owned banks. Ram Mohan and Roy (2004) also found
publicly-owned banks to be more efficient than privately-owned banks,
but foreign banks had caught up with them over the years.
Das and Ghosh (2009: 193) examined the banking efficiency during
19922002, and found that 'medium-sized public sector banks
performed reasonably well and are more likely to operate at higher
levels of technical efficiency. A close relationship is observed between
efficiency and soundness as determined by bank's capital adequacy
ratio. The empirical results also show that technically more efficient
banks are those that have, on an average, less nonperforming
loans'. Ghosh (2009) studied the cost and profit efficiency of
Indian banks during the period 1992-2004. The study found that big
state-owned banks performed well in terms of efficiency, and a close
relationship was found between efficiency and soundness as determined by
a bank's capital adequacy ratio. Ray and Das (2010) examined the
cost and profit efficiency of Indian commercial banks during a 7year
period beginning 199697. The study found that publicly-owned banks were
more profit efficient than were privately-owned banks. Gulati and Kumar
(2008) found that when non-traditional activities were accounted for in
the output specification, the foreign banks appeared to be more
efficient than were public and private sector banks. Kaur and Kaur
(2010) used DEA to examine the impact of mergers on the cost efficiency
of Indian banks during the period 1990-91 to 2007-08. These authors
found that the average cost efficiency of publicly-owned banks was lower
than that of privately-owned banks. Dwivedi and Charyulu (2011) studied
banking efficiency for the 5-year period 2005-2006 to 2009-2010 and
found that new privately-owned banks and foreign banks were more
efficient than were publicly-owned banks.
Our study makes several new contributions. None of the above
studies have used bootstrap DEA scores. As already stated by Simar and
Wilson (2007), conventional DEA approach, that is, without bootstrap can
give misleading results. Thereafter we examine how ownership, size,
soundness, loan quality variables impact on the bootstrap DEA efficiency
scores and thus provide robust analysis of Indian banking efficiency as
compared to prior studies. In particular, we examine the important issue
of efficiency differences resulting from foreign vs domestic ownership
as well as public vs private ownership and contribute to the literature
discussing this theme. We examine whether, on average, these ownership
types have positive or negative effects on bank efficiency. Furthermore,
we analyse Indian banking efficiency in a period subsequent to what
prior studies have analysed especially the post GFC years.
4. DATA AND METHOD
The data for the study were drawn from the Reserve Bank of India
(RBI) publication A Profile of Banks available online and refer to the
5-year period 20072008 to 2011-12.
Table 1 presents the total number of banks in various categories,
and, of these, the number included in our sample. The banks for which
data as required for the study were not available were excluded from the
sample.
DEA Efficiency Calculation
DEA is a linear programming technique initially developed by
Charnes, Cooper and Rhodes (1978) to evaluate the efficiency of public
sector non-profit organizations. It involves calculation of relative
efficiency scores of decision-making units (DMUs) in the sample. The
DMUs could be banks or branches of banks. A major advantage of DEA is
the identification of peers with which the efficiency could be compared.
For choosing the inputs and outputs to be used in DEA analysis, two
major approaches, the production approach and the intermediation
approach, are prevalent. The production approach involves use of
physical inputs and outputs and relevant processes. The intermediation
approach is commonly used and has some variants. The asset approach
involves use of labour and capital as inputs and loans as output (Sealy
and Lindley 1977). Under the user cost approach, the outputs are where
the financial returns on an asset exceed the opportunity cost of the
funds and the financial costs of a liability are less than the
opportunity cost. If it is vice versa, it is treated as inputs (Hancock
1985). The value-added approach considers as outputs those assets or
liabilities that contribute to bank value added that is, business
associated with the consumption of real resources (Berger et al. 1987).
According to Jemric and Vujcic (2002), yet another popular approach is
the operating or income-based approach. In this approach, interest
income and non-interest income are considered as outputs, and interest
expenses and non-interest expenses are considered as inputs. The
approach has been used in many prior studies for example, Leightner and
Lovell (1998), Avkiran (1999), Sathye (2003) and Das and Ghosh (2006).
In the present study, we use this approach.
Charnes et al. (1976) described the original DEA model as follows.
There are N units producing J outputs, with I inputs. Efficiency is
measured by maximising the ratio weighted outputs to weighted inputs for
that unit under following constraints:
Max [e.sup.o] = [J.summation over (j=1)] [u.sup.o.sub.j]
[y.sup.o.sub.j]/[I.summation over (i=1)] [v.sup.o.sub.i] [x.sup.o.sub.i]
(1)
Subject to
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]
[y.sup.n.sub.j], [y.sup.n.sub.j] represent the outputs and inputs
of the nth unit called a decision making unit (DMU) and [v.sup.o.sub.i],
[u.sup.o.sub.j] are the variable weights. These are determined by
solving problem (1).
Given the difficulty in solving the above non-linear problem the
objective function is transformed into a linear one as follows:
Max [h.sup.o] = [J.summation over (j=1)] [u.sup.o.sub.j]
[y.sup.o.sub.j] (2)
Subject to
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII].
The variables in (2) above are the same as in equation (1) above.
Readers interested in knowing the details of the DEA procedure are
advised to refer to the original paper by Charnes et al. (1976).
In the present study, we use the variable returns to scale (an
input-oriented model) to compute technical efficiency. It shows the
extent to which the output could be enhanced by each of the banks in the
sample with the existing inputs.
The Bootstrap Approach
Simar and Wilson (1998, 1999, 2007) stated that the DEA scores
calculated above have strong association with each other and using them
in second-stage regression may be inappropriate. The scores are relative
and not absolute, and as these are calculated and not estimated, it is
difficult to obtain statistical properties of DEA. Consequently, these
authors proposed a double bootstrap procedure that enables computation
of confidence intervals and standard errors for the DEA scores. This
computer-based method draws from the theory of re-sampling original data
to assign statistical properties to it and also enables accounting for
the impact of environmental variables on efficiency (Simar and Wilson
2007). These authors also did not consider the use of ordinary least
squares to estimate the relationship between DEA scores and
environmental variables as appropriate since regression assumption of no
auto-correlation and absence of multi-collinearity get violated, and
they suggested instead the use of truncated regression. The procedure
for the bootstrap method has been described in detail by Simar and
Wilson (2007) and is not repeated here. Descriptive statistics of inputs
and outputs used in DEA are presented in Table 2.
Descriptive statistics of the bias-corrected scores for the years
2007-08 to 2011-12 are presented in the table below:
It will be seen from the above that outputs could be increased on
average for all banks by approximately 12 per cent (2007-08), 16 per
cent (2008-09), 19 per cent (2009-10), 13 per cent (2010-11) and 16 per
cent (2011-12). As can be seen, the post-crisis average efficiency of
banks has deteriorated. In 2007-08, there was scope to increase
efficiency by 12 per cent, while in the year 2011-12, a 16 per cent
increase in efficiency could be achieved with the given inputs.
Truncated Regression
The environmental variables that we use are drawn from relevant
theory and prior empirical studies. We use the following model to assess
the link between environmental variables and the bootstrap efficiency
score.
[[theta].sub.i], = [[beta].sub.i] + [[beta].sub.1][ForOwn.sub.i] +
[[beta].sub.1][Soundness.sub.i] + [[beta].sub.3][StateOwn.sub.i] +
[[beta].sub.4][LoanQuality.sub.i] + [[beta].sub.5][Size.sub.i] +
[[epsilon].sub.i],
When bootstrap procedure followed by truncated regression is used
the issue of 'separability condition' becomes relevant. Simar
and Wilson (2011:207) say that by separability they 'mean that the
support of the output variables does not depend on the environmental
variables in Z' where Z refers to environmental covariates. As the
data generation process (DGP) used in our study corresponds to DGP 2
described by the above authors, separability is a reasonable assumption
in this study.
For Own: Foreign versus domestic ownership is a binary variable.
Foreign ownership equals 1, while domestic ownership equals zero. It is
intended to detect the influence of foreign ownership on technical
efficiency. The variable could have either negative or positive
influence on efficiency. Studies such as that of Williams and Strum
(2007) found foreign banks more efficient than domestic banks; however,
in the Indian context barring Dwivedi and Charyulu (2011), other studies
found domestic banks to be more efficient than foreign banks.
Consequently, we do not postulate an a priori sign for this variable.
Soundness: The soundness of a bank depends upon the capital it
holds vis-avis risk-weighted assets. We use the capital-to-risk-assets
ratio (CRAR) as a measure of soundness. Many prior studies such as those
of Das and Ghosh (2006) and Ghosh (2009) have used CRAR as a measure of
soundness. Better capitalized banks are expected to be more efficient
because of their ability to attract more business. Fiordilisi et al.
(2010) stated that higher capital levels positively impact efficiency.
Das and Ghosh (2006) stated that financial soundness reduces
uncertainties and systematic risk and thus contribute to lowering
inefficiency. Consequently, we expect a positive relationship a priori
between soundness and technical efficiency.
State Own: State versus private ownership is a binary variable.
State ownership equals 1 while non-state ownership equals zero. It is
intended to detect the influence of state ownership on technical
efficiency. Most prior studies in Indian banking have found that
state-owned banks are more efficient than are other banking groups;
however, recent study by Dwivedi and Charyulu (2011) already cited
above, finds that the case is otherwise. Consequently, we do not
postulate an a priori sign for this variable.
Loan Quality: Similar to prior studies (such as Das and Ghosh
[2006] and Ghosh [2009]), we capture loan quality by the ratio of
non-performing loans to net advances. Inadequate loan monitoring and bad
debt control can lead to lesser interest income. Further, bad loans
require higher supervision and monitoring, which increase operational
expenses. The combined effect would be lower efficiency. This is
consistent with the bad management hypothesis of Berger and DeYoung
(1997). We expect that a priori this variable will have a negative sign
as non-performing loans increase, efficiency will lower.
Size: As per public choice theory and principal agent framework,
different types of ownership impact on efficiency differently. 'The
theoretical argument is straightforward: a lack of capital market
discipline weakens owners' control over management, enabling the
latter to pursue their own interests, and giving fewer incentives to be
efficient' (Das and Ghosh, 2006). Prior studies have used assets,
deposits, advances, number of ATMs, number of employees, number of
branch offices as measures of size. We use number of employees as a
measure of size.
As stated by Keuleneer and Leszczynska (2011) 'Size is claimed
by many to bring economies of scale and cost reductions almost per
definition.' In the banking context, studies in the US indicate
that economies of scale appear in small banks but not in the large banks
(Short, 1979; Miller and Noulas, 1996). However, studies such as Sun and
Chang (2011) have found that size impacts efficiency negatively. We
postulate a negative sign for this variable a priori.
To run the truncated regression, data from a total of 293 banks
were used, and the procedure suggested by Simar and Wilson (2007) was
deployed to obtain the following results.
5. RESULTS AND DISCUSSION
Table 4 provides for the year 2012 (for other years, data are
available on request from the authors) output-oriented DEA bootstrap
scores and the raw scores. It indicates the extent to which output could
be increased with the current input levels. The details of the variable
returns to scale (VRS) technical efficiency scores of each of the 68
banks in the sample together with the bias-corrected efficiency scores,
the extent of bias and the lower and upper confidence levels are
presented.
Following Simar and Wilson (1999), we used 2,000 bootstrap
replications (B=2,000). According to these authors, this should provide
an adequate coverage of the confidence intervals.
It will be noticed that although 24 out of 68 banks are on the
frontier with a score of 1 (when raw efficiency is computed), after the
bias is corrected, even these banks have inefficiencies and their output
could be increased. For example, in the case of first bank--AB bank--the
efficiency could be improved by 9.84 per cent. The bias-corrected column
shows that few banks are quite close to the frontier but are not exactly
on the frontier, suggesting that there is scope to further increase
output with the same inputs. The information could be useful to bank
managements to take appropriate strategic actions.
Foreign ownership was found to have significant negative
association with efficiency. Berger et al. (2009, 2010) in the context
of Chinese banks found that foreign banks were most efficient. In the
Indian context, Mohan and Ray (2004) and Das et al. (2005) found that
foreign-owned and state-owned banks were not significantly different in
efficiency. Our study, however, shows that foreign-owned banks had
significantly lower efficiency. Thus, foreign banks in these two
countries show divergent results with respect to efficiency. Berger et
al. (2009) study, however, refers to the 1994-2003, that is, the pre-GFC
period. Our finding is in line with that of Lensink et al. (2008), who
found that foreign ownership negatively affects bank efficiency.
However, in countries with good governance, this negative effect is less
pronounced. Chen et al. (2013) state that efficiency flows through two
channels, that is, the monitoring channel and the information channel
reducing issues associated with agency problem and information
asymmetry. However, corporate governance in Indian banks is wanting. A
senior official of RBI recently stated 'sserious lapses observed in
governance framework during the crisis, tilted the balance in favour of
more rigorous regulation' (Sinha, 2013).
This could be because many foreign banks suffered badly during the
GFC, and their income deteriorated also due to high bad debt provision.
Many foreign banks curtailed operations due to problems in their home
countries.
The soundness variable is not found to be significant. Earlier
studies have found that well capitalized banks are more efficient.
During the GFC, most banks were required to beef up capital given the
rising bad debts; consequently, it appears that the normal salutary
effect that increased capital may have on efficiency through increased
business and increased income is not seen. The additional capital was to
provide a buffer against bad debts rather than for expanding business
because of the unusual circumstances through which the banks were
passing.
The state ownership variable shows a negative significant result.
This is particularly interesting. Many prior studies of Indian banks
have found the relationship to be positive for medium-sized state-owned
banks (Kumbhakar and Sarkar 2005; Das and Ghosh 2006; Chatterjee 2006).
However, our results are consistent with recent studies such as those by
Kaur and Kaur (2010) and Dwivedi and Charyulu (2011), who found that
state-owned banks were less efficient than were non-state-owned banks.
Lensink et al. (2008:841) also found that 'state-owned banks are,
in general, less efficient than non-state owned banks'. It appears
that after the GFC, the foreign and private sector banks have
considerably improved their efficiency vis-a-vis the state-owned banks.
Further, given the archaic labour laws in India, it may be hard for
state-owned banks to take drastic measures such as curtailing staff,
which could be possible for private sector and foreign banks.
The loan quality variable, which measures credit risk, has
significant negative association with technical efficiency. Higher
credit risk (non-performing loans) implies lower interest income and
higher operational expenses in loan collections and monitoring. The
result is consistent with prior work of Berger and Mester (1997), who
found that poor management of credit portfolio, has an unfavorable
impact on efficiency. Ghosh (2009) also found similar results for Indian
banks.
Size has a significant negative influence on efficiency. As the
number of employees increases, the operational costs, which include
salary and wages, would increase, and unless it is compensated by
increased income through increased business, the efficiency would be
negatively impacted. The result is similar to those of prior studies
such as Sun and Chang (2011), Ghosh (2009) and Das and Ghosh (2006).
These studies found that large banks are less efficient than are small
banks.
Next we examine if there was significant difference in bootstrap
banking efficiency in the pre -GFC years, during GFC years and between
GFC years and post GFC years. The impact of GFC (using a decline in GDP
as the criteria to define GFC years) on the GDP was felt by India in the
years 2008-09 and 2009-2010. The GDP growth rate which showed a rising
trend prior to these years showed a sharp decline and stagnancy in these
years. Thereafter, the GDP rose significantly in 2010-11 (over 10 per
cent from 7 per cent in earlier years) but again sharply declined in
2011-12 to the GFC years' level.
We use Kruskal-Wallis test to assess whether there was significant
difference in banking efficiency in the above periods. The computation
returned chi square value of 4.889 (with 1 d.f.) and probability of
0.027. This demonstrates that the GFC years did affect banking
efficiency. While comparing GFC years with the post-GFC years, an
interesting result can be found. When we compare the efficiency scores
GFC years with 2010-11 (GDP rose significantly exceeding 10 per cent),
we get the chi square value of 5.378 (with 1 d.f.) and a probability of
0.020 indicating that post GFC year there was significant difference in
efficiency. However, if we use the year 2011-12 data as well, then the
results are not significant. This was because in 2011-12, India's
GDP sharply declined to GFC years' level. Overall, when we compare
pre-GFC year with post-GFC years, no significant difference in banking
efficiency was noticed.
As indicated by Podpiera and Cihak (2005), regular screening of
banking efficiency is important as it can serve as early warning system.
Indian banking efficiency does show signs of stress in the post GFC
years. It appears that the Government of India too is concerned about
the inefficiencies that plague the Indian banking system and has taken
policy measures like the recent Banking Reforms Bill 2012, which raises
limit on foreign capital to 26 per cent from 10 per cent. Hopefully,
these measures would help improve Indian banking efficiency over the
years. Berger et al. (2009) observation in the context of Chinese
banking could be equally applicable to Indian situation. 'The
"real" reward of such reforms may be continued economic growth
because an open and flexible banking environment not only provides more
credit, but a better allocation of credit, funding more positive net
present value projects that contribute to economic growth'.
5. CONCLUSION
In the current study, we provide a bootstrap efficiency analysis of
Indian banks for the 5-year period from 2007-08 to 2011-12 using Simar
and Wilson's (2007) method. Such an analysis is being done for a
developing country for the first time, to our knowledge. These authors
have already demonstrated that DEA analysis using conventional methods
(non-bootstrap) may not provide reliable results. In the current paper,
we not only rectify the situation in the Indian banking context but also
provide results from truncated regression, which has not been employed
in prior studies on Indian banking. Consequently, our study provides
more appropriate analysis of Indian banking efficiency than found in
studies hitherto.
Interestingly, contrary to prior studies, we find that state
ownership has a negative impact on efficiency in the Indian context.
Similarly, foreign ownership also was found to have negative influence
on efficiency. The bad management hypothesis finds support, as the loan
quality variable was found to have significant negative impact on
efficiency.
The bootstrap scores suggest that there is room for expanding
output with current input levels by banks. It is hoped that the study
would provide an impetus for similar studies that use bootstrap
efficiency analysis and second-stage truncated regression so as to draw
valid conclusions.
Besides the above conceptual contributions, the study is expected
to help bank managements in further improving efficiency by suitable
strategic actions such as reducing inputs or making better use of inputs
and to policy makers to continue with banking reforms agenda. Regulatory
authorities in developing countries may also like to consider conducting
similar studies of banks in their respective countries so that
efficiency is accurately measured so as to draw valid conclusions for
policy actions.
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SUNEETA SATHYE AND MILIND SATHYE *
* Faculty of Business, Government and Law, University of Canberra,
ACT 2601 (Australia)
** Professor of Banking and Finance, Faculty of Business,
Government and Law, University of Canberra, ACT 2601 (Australia),
E-Milind.Sathye@canberra.edu.au
Table 1
Distribution of banks in India and banks in the sample
2007-08 2008-09 2009-10
Foreign banks
total 28 31 32
in the sample 9 14 17
Private sector banks
total 23 22 22
in the sample 19 19 19
Public sector banks
total 28 27 27
in the sample 26 26 26
Total 79 80 81
in the sample 54 59 62
2010-11 2011-12 Total
Foreign banks
total 34 40
in the sample 14 14
Private sector banks
total 21 20
in the sample 19 19
Public sector banks
total 26 26
in the sample 26 26
Total 81 86 407
in the sample 59 59 293
Table 2
Descriptive statistics of input and outputs used for DEA
Year 1 Variable Obs. Mean Std. Dev.
2007-08
Interest expenses 54 36785.56 54625.04
Non-interest expenses 54 13313.89 20515.99
Interest income 54 54028.81 79550.46
Non- Interest income 54 10247.56 16724.3
2008-09
Interest expenses 59 43938.03 66450.71
Non-interest expenses 59 15080.78 23701.45
Interest income 59 64497.95 96153.47
Non- Interest income 59 12699 20665.51
2009-10
Interest expenses 62 43255.61 68959.56
Non-interest expenses 62 15827.39 27866.65
Interest income 62 65902.77 102390.4
Non- Interest income 62 12537.65 22054.29
2010-11
Interest expenses 59 50393.9 73581.3
Non-interest expenses 59 20619.08 32746.39
Interest income 59 82555.22 120504.3
Non- Interest income 59 13183.97 23115.34
2011-12
Interest expenses 59 110027 158398.5
Non-interest expenses 59 14207.14 22714.62
Interest income 59 72571.31 98984.41
Non- Interest income 59 22960.72 37077.77
Year 1 Variable Min Max
2007-08
Interest expenses 4 319291
Non-interest expenses 27 126086
Interest income 45 489503
Non- Interest income 36 88108
2008-09
Interest expenses 6 429153
Non-interest expenses 34 156487
Interest income 14 637884
Non- Interest income 46 126908
2009-10
Interest expenses 4 473225
Non-interest expenses 40 203187
Interest income 11 709939
Non- Interest income 1010 149682
2010-11
Interest expenses 7 488680
Non-interest expenses 43 230154
Interest income 12 813944
Non- Interest income 35 158246
2011-12
Interest expenses 23.298 1065215
Non-interest expenses 995.57 143514.5
Interest income 8.479 632303.7
Non- Interest income 49.522 260689.9
Table 3
Descriptive statistics of bias-corrected efficiency scores
Year Banks Mean Std. Dev. Min Max
2007-08 54 1.124906 0.097194 1.0212 1.523
2008-09 59 1.158169 0.109709 1.0286 1.5036
2009-10 62 1.193563 0.2211 1.0178 2.1587
2010-11 59 1.125178 0.095275 1.0194 1.4239
2011-12 59 1.159812 0.150925 1.0346 1.8622
Table 4
Bootstrap efficiency scores of banks in the sample
for the year 2011-12
S. No. Bank Tech. eff. Bias
VRS corrected
1 AB bank 1 1.0984
2 Abu Dhabi 1.4213 1.459
3 American 1 1.101
4 Antwerp 1.4903 1.5698
5 Bank of America 1 1.0572
6 Bank of Bahrain and Kuwait 1.348 1.393
7 Bank of Ceylon 1 1.0874
8 BNP Paribas 1.6843 1.7566
9 Citibank 1 1.0853
10 Credit Agricole 1 1.0946
11 Credit Suisse 1 1.0995
12 DBS Bank 1.2615 1.3044
13 Deutsche 1 1.0969
14 JPMorgan 1 1.09
15 Krung Thai 1 1.0943
16 Mizuho 1 1.1001
17 Oman 1 1.0928
18 Shinhan 1.0323 1.0627
19 Societe Generale 1.4571 1.4976
20 Sonali 1 1.1025
21 Standard Chartered 1 1.0968
22 State Bank of Mauritius 1.0866 1.1358
23 Bank of Nova Scotia 1 1.0995
24 Bank of Tokyo 1.0562 1.1086
25 HSBC 1 1.0758
26 Royal Bank of Scotland 1.1808 1.2325
27 City Union 1.1911 1.227
28 ING Vyasya 1.3272 1.3612
29 TMB 1.2209 1.2605
30 Federal 1.0513 1.0635
31 Jammu Kashmir 1.008 1.019
32 Karnataka 1.2324 1.2542
33 Karur 1.1419 1.1641
34 Lakshmi Vilas 1.3519 1.3903
35 Nainital 1.4008 1.4515
36 Ratnakar 1.7611 1.8344
37 South Indian 1.1435 1.1589
38 HDFC 1 1.06
39 ICICI 1 1.099
40 IndusInd 1.2055 1.2431
41 Kotak Mahindra 1.1774 1.2104
42 Yes Bank 1.0538 1.0913
43 Allahabad 1.0233 1.0393
44 Andhra 1 1.0127
45 Bank of Baroda 1 1.041
46 Bank of India 1.0424 1.0833
47 Bank of Maharashtra 1.1258 1.1424
48 Canara Bank 1 1.0539
49 CBI 1.1286 1.1513
50 Corporation Bank 1.0161 1.0576
51 Dena Bank 1.0666 1.077
52 Indian Bank 1.0111 1.0278
53 Indian Overseas Bank 1.0769 1.0975
54 Oriental 1.0216 1.0454
55 PSB 1.1643 1.1764
56 PNB 1 1.0438
57 Syndicate 1.0452 1.0616
58 UCO 1.0097 1.0344
59 Union 1.0514 1.0774
60 United 1.0655 1.0769
61 Vijaya 1.0786 1.099
62 SBI 1 1.0959
63 SBBJ 1.1048 1.1195
64 SB Hyderabad 1.0248 1.0391
65 SB Mysore 1.1615 1.1787
66 SB Patiala 1.0634 1.0765
67 SB Travancore 1.1297 1.1419
68 IDBI 1 1.0897
S. No. BIAS SD lower upper
1 -0.0984 0.0096 1.0022 1.3816
2 -0.0377 0.0005 1.4254 1.5132
3 -0.101 0.0101 1.0033 1.3926
4 -0.0795 0.0028 1.494 1.6963
5 -0.0572 0.0011 1.0022 1.1272
6 -0.045 0.0008 1.3513 1.4637
7 -0.0874 0.0057 1.0025 1.2627
8 -0.0723 0.0023 1.6887 1.8726
9 -0.0853 0.0047 1.0024 1.2274
10 -0.0946 0.007 1.0026 1.2813
11 -0.0995 0.0095 1.0027 1.3737
12 -0.0429 0.0011 1.265 1.3879
13 -0.0969 0.009 1.0019 1.3395
14 -0.09 0.0059 1.0028 1.2505
15 -0.0943 0.0079 1.0022 1.3048
16 -0.1001 0.0097 1.0033 1.385
17 -0.0928 0.0073 1.0025 1.289
18 -0.0304 0.0003 1.0353 1.104
19 -0.0405 0.0006 1.4606 1.5613
20 -0.1025 0.0104 1.0024 1.3914
21 -0.0968 0.0094 1.0024 1.3569
22 -0.0492 0.0011 1.0897 1.2244
23 -0.0995 0.0092 1.0027 1.3562
24 -0.0524 0.0016 1.0595 1.2083
25 -0.0758 0.0035 1.0026 1.2229
26 -0.0517 0.0012 1.1845 1.3196
27 -0.0359 0.0003 1.1939 1.2712
28 -0.034 0.0003 1.3303 1.4009
29 -0.0396 0.0004 1.2241 1.3046
30 -0.0122 3.7903 1.0535 1.0775
31 -0.011 3.4933 1.0095 1.0322
32 -0.0218 0.0001 1.235 1.2808
33 -0.0222 0.0001 1.1448 1.1905
34 -0.0384 0.0004 1.3549 1.4388
35 -0.0507 0.0009 1.4051 1.5232
36 -0.0733 0.0018 1.7658 1.9283
37 -0.0154 7.9498 1.1455 1.1792
38 -0.06 0.002 1.003 1.177
39 -0.099 0.0093 1.0035 1.3598
40 -0.0376 0.0003 1.2087 1.2856
41 -0.033 0.0003 1.1803 1.2458
42 -0.0375 0.0007 1.0558 1.1663
43 -0.016 9.055 1.0253 1.0618
44 -0.0127 4.451 1.0019 1.0282
45 -0.041 0.0005 1.0028 1.0916
46 -0.0409 0.0005 1.0453 1.1369
47 -0.0166 7.6105 1.128 1.1616
48 -0.0539 0.0017 1.0027 1.1621
49 -0.0227 0.0001 1.1305 1.1822
50 -0.0415 0.0012 1.0187 1.156
51 -0.0104 3.4057 1.0681 1.09
52 -0.0167 8.3148 1.0134 1.0483
53 -0.0206 0.0001 1.0796 1.1244
54 -0.0238 0.0002 1.0242 1.0849
55 -0.0121 4.6666 1.1663 1.1924
56 -0.0438 0.0009 1.0027 1.1244
57 -0.0164 0.0001 1.0469 1.0869
58 -0.0247 0.0003 1.0122 1.0801
59 -0.026 0.0002 1.0531 1.1129
60 -0.0114 3.542 1.0678 1.0909
61 -0.0204 0.0001 1.081 1.1352
62 -0.0959 0.0095 1.0022 1.3924
63 -0.0147 5.8062 1.1074 1.137
64 -0.0143 4.9514 1.0275 1.0554
65 -0.0172 7.1753 1.1644 1.1968
66 -0.0131 4.7428 1.0658 1.0925
67 -0.0122 3.9716 1.132 1.156
68 -0.0897 0.0071 1.0028 1.2974
Table 5
Truncated regression results
Variable Co-eff Std Error p-value
Foreign-owned=1 -0.06093 0.024 0.01
Soundness -1.7E-05 0.001 0.99
State-owned=1 -0.09497 0.019 0.00
Loan Quality -0.0148 0.006 0.02
Size (staff numbers) -5.93E-07 3.06E-07 0.05
Constant 1.201007 0.023 0.00
Log likelihood: 168.47 (Prob > chi square = 0.000)
No. of observations=293, LL=0, Wald chi2(5) = 42.66
