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Efficiency of Private Banks in India--A Critical Inquiry.


In the last three decades, it is observed that efficiency studies have been very popular in both developed and developing economies with a difference in focus. The studies in the former look at the impact of structural changes, policy changes and competition on efficiency. The studies in the latter countries, on the other hand, focus on the impact of reforms, privatization of banking sector and entry of foreign banks on overall banking efficiency. One possible reason behind the popularity of banking and insurance efficiency focused studies is the rapid pace of reforms in the sectors that is bringing about a dynamic change therein. The other reason is the fast stride in terms of financial and non-financial technological advancements. It is pointed by researchers that since the beginning of 1990s, there have been many studies on the financial sector (Favero and Papi, 1995; Kaparakis et al., 1994). Moreover, the concept of efficiency has attracted the interest of researchers across all countries since organizational efficiency has a serious impact on the development of an economy. It is remarked that efficiency has a positive effect on profitability, fund mobilization, better pricing and a safety net with a larger capital buffer (Berger, Hunter and Timme, 1993). In efficiency studies covering the financial sector, the scope of investigation has varied widely ranging from branch efficiency, productive efficiency, frontier efficiency to technical and cost efficiency. In recent years, the focus of efficiency has further expanded to areas like revenue efficiency and profit efficiency. The present also focuses on technical efficiency and identifies its linkage with the firm size.

The empirical study is divided into five sections. Section 1 introduces the topic to the readers. Section 2 covers the different studies already carried out in this field. Section 3 elaborates the objectives of the study, hypothesis to be tested and the research design. Section 4 gives the details about the findings and the last section, Section 5 gives the concluding remarks.

2. Literature Review

A summary of the different researches made with regard to performance assessment in banking is given. It is true that banking efficiency studies have been popular in developed economies like US and European countries. Some of the mentionable researches done include the contribution made by Aly et al. (1990), Favero and Papi (1995), Lang and Welzel (1996), Barr et al.(1999), Barr et al. (1999); Mertens and Urga (2001), Angelidis and Lyroudi (2006), Brissimis et al. (2006), Yildirim and Philippatos (2007) and Aydin et al. (2009).

It is also a matter of fact that in Asia also, several studies have been covered, some of which include Chu and Lim, 1998; Karim, 2001; Rezvanian and Mehdian, 2002, Hasan and Marton (2003), Altunbas et al., 2007; Kumar and Gulati, 2008, Tahir et al., 2009 and Usman et al., 2010. At the same time, one has to admire that banking in India is no less popular as a research area. The study of Indian banking industry is an old story. What has changed over the years is the method that is used to evaluate the performance of institutions. Initially, the investigators used banking indicators to carry out growth/trend analysis, profitability and productivity analysis as evident in the works of Subrahmanyam (1993), Subramaniam and Swamy (1994), Swamy and Subramaniam (1994), Das (1999) and others.

A categorization of the evaluation techniques for performance assessment shows the different methods used like composite index--Das (1999) and Hansda (1995), regression analysis--Gourlay et al. (2006), taxonomic study--Subramanyam and Swamy (1994), indicator-based study--Angadi and Devraj (1983), sequential decomposition model--Das (1999), use of index number method--Sarkar and Das (1997) among few others. The introduction of DEA application in banking was made by Sherman and Gold (1985) after which the tool is being used with basic and modified versions of the traditional model. Some of the remarkable studies that used this method include Noulas and Katkar (1996), Das (1999), Saha and Ravisankar (2000), Mukherjee et al. (2002), Sathye (2003 and 2005), Mohan and Ray (2004) and Sinha and Moses (2004). Bhaumik et al (1991) compared the efficiency of public and private sector banks for the period 1995-96 to 2000-01. Like Swamy and Subramanyam (1994) who compared the performance of different public sector banks, many similar studies were carried out. The recent studies that applied DEA include Bhattacharya et al. (1997), Sinha and Khan (2014), Ghosh et. al (2014) and Gayval and Bajaj (2015) among others.

In the context of India, the coverage of these studies include the effect of foreign banks' entry, impact of liberalization and banking sector reforms, operational issues of banking, the effect of non-performing loans on the performance of banking sector and a few others. There are also a few studies that aim to study the impact of regulatory reforms on banking sector and a few look into the efficiency performance of various banks. Among the noticeable contributions in this regard, we have Bhattacharya et al. (1997), Shanmugam and Lakshmanasamy (2001), Mukherjee et al. (2002), Kumbhakar and Sarkar (2002), Debasiah (2006), Sanjeev (2006), Ray (2007), Gupta, Doshit and Chinubhai (2008), Kumar and Gulati (2008), Bala and Kumar (2011) Dhanapal (2012) and Kaur and Gupta (2015).

Research Gap:

The researcher has not come across any study on the banking industry of India that analyses the private sector exclusively (belonging to the same strategic group) and tests whether there is any statistical difference between the banks of different sizes. The research also points out the potential scope for improvement for the different banks. Moreover, the enquiry into the issue of alpha convergence makes this study unique among the many already covered.

3. Objectives and Research Design

3.1 Objectives of the Study

The objectives of the study are:

i. To find out the technical efficiency of the banks.

ii. To find out significant difference in technical efficiency, if any, among the banks of different sizes.

iii. To test for c--convergence in efficiency during the period.

3.2 Research Design

The present study has manifold objectives. The research focuses on leading fifteen private sector banks, selected on the basis of business criterion (in descending order) which are studied based on a twelve-year study (2005-2016) using secondary data collected from the Capitaline database.

The efficiency analysis is done using Data Envelopment Approach (DEA), a non-parametric tool. The advantages of this method have been cited by various authors Thanassoulis, 1993; Yang, 2009; Favero and Papi, 1995 who mention the lack of requirement of assumption about the functional form of error terms, ability to handle multiple inputs and outputs and ability to track and trace efficiency changes over time. Sufian (2007) further adds that DEA permits the choice of any kind of input and output, regardless of the units in which they are measured. The basic concept of DEA is that all the units together generate an envelope (or frontier) against which the efficiency score is computed. If an organization lies on that envelope, it is considered to be efficient with a relative efficiency score of one. Any organization that does not lie on the frontier is considered to be inefficient with a score of less than one and it has a radial distance from the 'best practice' frontier. Thus, no matter, what approach is taken by the researcher, the aim is to position the organization on the frontier either by minimizing inputs or maximizing outputs.

Though DEA is popular among researchers, there is a controversy regarding certain aspects as discussed below:

a) Whether to use input-based model or output-based?

The input-based model aims to minimize the usage of 'inputs' keeping the output unchanged, whereas, the output-based model aims to maximize the outputs, keeping the inputs unchanged. In efficiency literature, however, the application of output-based study is more popular. The researcher uses the output-based study since even today, the focus remains strong on financial performance and numbers of the asset/income side as it not only presents a strong balance sheet, but also acts in favour of bank managers who get higher incentives and promotion scopes.

b) Which returns to scale to consider for analysis?

There are two options like constant returns to scale and variable returns to scale. The former considers that proportionate change in the output and input is in the same ratio, whereas the latter considers that the change in output factor is not exactly the same as change in inputs. Banker et al. (1984) point out that in the presence of imperfect competition and constraint of resources (including finance), it is not possible for organizations to operate at the optimal scale (i.e, constant returns to scale). Hence, the variable return to scale assumption is used.

The present investigation applies the output-oriented, BCC model proposed by Banker, Charnes and Cooper (1984) to arrive at efficiency scores.

Choice of Input and Output Variables

There is lack of consensus regarding the choice of input and output variables (Berger and Humphrey, 1997). In this regard, Sarkis and Weinrach (2001) mentions that the data set should be non-negative for outputs, strictly positive for the inputs and test of 'isotonicity' (for checking positive correlation between inputs and outputs) is to be satisfied. Moreover, it is seen that because DEA is sensitive to variables selection, if more variables are added, it is likely that some inefficient DMUs might become efficient (Smith, 1997). Thus, a reasonable number of input and output variables are to be considered. Hence, the following two thumb rules, given by Cooper et. al. (2007) obtained in an article by Bala and Kumar (2011), are taken into consideration:

* n [greater than or equal to] p x q, where n is the number of DMUs, p is the number of inputs and q is the number of outputs

* r = 3 (p+q), where r is the total number of observations.

It is again pertinent to mention that the two most common approaches used in efficiency studies are production approach and intermediation approach (Sealey and Lindley, 1977). The former considers financial institutions as production units generating loans and deposits, whereas the latter considers banks to be intermediaries transferring funds from one hand to another. With regard to banking studies, Berger and Humphrey (1997) comments that the productions approach is appropriate for efficiency measurement at the branch level but not for a financial institution as a whole. Hence, the intermediation approach is chosen for analysis purpose. Accordingly, the inputs used are capital and deposits, whereas the two outputs are investments and loans/advances.

4. Analysis and Findings

4.1 Test for Isotonicity

The isotonicity test result is checked to look into the relationship between inputs and outputs. The Pearson's correlation coefficients are as follows:

[, investment] = 0.800, [, advance = 0.742, [r.sub.deposit, investment] = = 0.966 and [r.sub.deposit, advance] = 0.931, all of which are significant at 1% level. Since the correlation coefficients are significantly positive, it implies that inputs and outputs are positively correlated. Thus, the basic condition for applying DEA is satisfied.

4.2 Technical Efficiency Results

In table I, the technical efficiency results of the different banks under variable returns to scale (VRS) assumption is given. A cross-sectional look at the figures over the years shows the overall consistency of private banks. Except, a limited few, most of them lie close to the efficient frontier as evident from the scores being close to one (i.e, 100 Percent efficiency). With respect to the performance of banks, it is evident that mean performance of HDFC Bank, ICICI Bank and Kotak Mahindra Bank is one. In other words, for all the years of study, these three banks have led from the front and have displayed the 'best practice' for the industry. Of the remaining banks, more than 50 Percent (seven out of twelve) achieved more than 90 Percent efficiency which is really commendable. In other words, these banks have produced around 90 Percent or more of their outputs (compared to the benchmark) keeping the inputs constant. Similar finding has been obtained in the studies of Sinha and Khan (2014), Kaur and Gupta (2015) where they pooled the data for the public and private sector banks. Among the sample banks, IndusInd Bank which has a reasonably good average efficiency of 80.5 percent is the worst performer. It clearly points to the cut-throat competition in the private sector.

The mean efficiency in none of the years during the study period is less than 90% which needs appreciation. Even the financial crisis of 2008 failed to impact the performance of these banks. The year 2014 is the best one with the highest average score.

The following table (No. II) is an outcome of the technical efficiency results. It is very relevant since it points to the improvement scope remaining with the private sector banks in order to be on the efficient frontier (envelope of the frontier). The values in the table are computed as

Improvement scope = (1/Technical efficiency) - 1

From the formulae, it is obvious that higher is the efficiency score, lower is the scope for improvement and logically so as it will be closer to the efficient frontier. For example, in cases where the score is zero, it implies that these banks are already on the frontier in those years and hence potential improvement is nil. Similarly, for bank 8, in the year 2007, the improvement scope is 46.41 Percent and likewise. In other words, if the improvement is to such an extent, the bank can reach the frontier boundary. For leading banks like HDFC, ICICI and Kotak Mahindra, the improvement scope is nil.

4.3 Comparing Groups of Banks based on the Efficiency Score

The sample fifteen banks have been divided into three groups, large-sized, medium-sized and small-sized which are denoted by 3, 2 and 1 respectively. This grouping is made after the computation of three quartiles, viz. first (Q1), second (Q2) and third (Q3) based on the quantum of business. The banks with business below the second quartile fall into the 'small-sized' group, those between Q2 and Q3 fall into the 'medium-sized' category and those exceeding Q3 into the 'large-sized' category.

In this sub-section, the researcher investigates whether there is any significant difference among the three groups of banks in terms of their technical efficiency. The test for normality shows the data for all the efficiency scores to be non-normal as evident from the Shapiro Walk test. Thus, the Kruskal-Wallis test, a nonparametric test is applied to statistically check the difference in efficiency score of the three groups. Since the p-value is 0.000, the assumption of 'no-difference' gets rejected. It means a significant difference exists among the three groups of banks. A more stringent test to look for differences in the mean efficiency score is the Welch test that compares populations having unequal variances. The test statistic is 52.363 which is significant at 1% level, thereby proving the existence of significant difference among the three groups. In order to get a more specific result by a pair-wise comparison, the Scheffe test is applied, as the sample sizes vary. This test also rejects the null hypothesis at 1% level for all the pair-wise combinations.

4.4 Convergence in Technical Efficiency

The concept of convergence is quite commonly used to determine whether there is tendency for the efficiency of banks to converge. In other words, the aim of such an application is to test whether the banks having low efficiency towards the beginning are able to catch-up with the efficient banks towards the end of the study period (Sala-i-Martin and Xavier, 1996). This subsection looks into the O--convergence aspect to check whether the width of distribution of efficiency scores tend to narrow down over time. In simple words, it tests whether the gap in the efficiency scores tend to taper across a set of banks during the period. To check for [sigma]-convergence, the following regression / trend equation is run:

ln (S[D.sub.t]) = [alpha] + [sigma]. [tau] + [[epsilon].sub.t],

where, SD stands for standard deviation of the efficiency score across cross-sectional data, a is the constant and t is the trend variable. In place of standard deviation, some researchers use the coefficient of variation (Das, 2016). If the equation obtained gives a negative and significant value of c, it is considered to be conclusive evidence about the presence of such convergence. The result of the trend equation shows the F-statistic to be insignificant at even 10% level. Though, slope in the trend line equation is (-) 0.232, it is insignificant with the t-statistic as (-) 1.757. Hence, there is a lack of convergence of efficiency among the Indian private banks. In other words, the lowly efficient banks have not been able to catch-up with the efficient banks.

5. Conclusion

This study is an interesting one and it focuses on the efficiency aspect of the top fifteen private banks in India for the period 2005-2016. The application of output-oriented Data Envelopment Approach (DEA) is made under the condition of variable returns to scale. The results show the absolute dominance of leading private banks, viz. HDFC Bank, ICICI Bank and Kotak Mahindra Bank over their peers. These three banks have consistently remained on the efficient frontier, thereby setting a benchmark for others. IndusInd Bank, the Mumbai-based new generation bank is the poorest performer among the entire sample set. However, it is worthy to note that in terms of the trend in efficiency score during the period, the sector has performed commendably well with an average efficiency exceeding 90 percent in all the years. Among the sample banks, there is enough scope for improvement for banks like IndusInd Bank, Karnataka Bank and South Indian Bank. For IndusInd bank, the performance has however improved during the recent years. Furthermore, with regard to difference in the efficiency score based on size, it is observed that there is a significant difference among the three categories of banks. In fact, there is total heterogeneity among the banks in terms of their efficiency score. The study concludes that the private banking sector is dominated by the brand-rich new generation banks like HDFC Bank, ICICI Bank and Kotak Mahindra Bank which have been very aggressive players in the banking space. The findings of the study corroborate the resilience of the financial system of the country.


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Abhijit Sinha

Assistant Professor (Stage 3), Department of Commerce Vidyasagar University, West Bengal.
Table I
Technical Efficiency Results

Bank                   2005     2006     2007     2008     2009

Axis Bank             0.892    1.000    1.000    0.976    0.990
City Union Bank       0.920    1.000    1.000    1.000    1.000
DCB Bank              0.938    0.908    0.739    0.753    0.730
Dhanlaxmi Bank        0.789    0.879    1.000    1.000    1.000
Federal Bank          1.000    0.906    1.000    0.865    0.846
HDFC Bank             1.000    1.000    1.000    1.000    1.000
ICICI Bank            1.000    1.000    1.000    1.000    1.000
IndusInd Bank         0.742    0.774    0.683    0.720    0.688
Karnataka Bank        0.807    0.861    0.866    0.827    1.000
Karur Vysya Bank      1.000    1.000    0.977    1.000    0.975
Kotak Mahindra Bank   1.000    1.000    1.000    1.000    1.000
Lakshmi Vilas Bank    1.000    1.000    0.957    0.962    1.000
RBL Bank              1.000    1.000    1.000    1.000    1.000
South Indian Bank     0.883    0.802    0.809    0.822    0.795
Yes Bank              1.000    1.000    0.779    0.756    0.804
Average               0.931    0.942    0.921    0.912    0.922

Bank                   2010     2011     2012     2013     2014

Axis Bank             1.000    1.000    1.000    1.000    1.000
City Union Bank       1.000    1.000    1.000    1.000    1.000
DCB Bank              0.855    0.912    0.914    1.000    1.000
Dhanlaxmi Bank        1.000    0.946    1.000    1.000    1.000
Federal Bank          0.908    0.869    0.869    0.869    0.943
HDFC Bank             1.000    1.000    1.000    1.000    1.000
ICICI Bank            1.000    1.000    1.000    1.000    1.000
IndusInd Bank         0.866    0.765    0.818    0.848    0.918
Karnataka Bank        0.840    0.837    0.746    0.768    0.806
Karur Vysya Bank      1.000    0.880    0.870    0.907    0.931
Kotak Mahindra Bank   0.980    1.000    1.000    0.990    0.910
Lakshmi Vilas Bank    0.876    0.959    0.914    0.966    0.946
RBL Bank              1.000    1.000    1.000    1.000    1.000
South Indian Bank     0.858    0.833    0.884    0.825    0.869
Yes Bank              0.932    0.786    0.934    1.000    1.000
Average               0.941    0.919    0.930    0.945    0.955

Bank                   2015     2016     Avg.

Axis Bank             1.000    1.000    0.988
City Union Bank       1.000    1.000    0.993
DCB Bank              1.000    1.000    0.896
Dhanlaxmi Bank        1.000    1.000    0.968
Federal Bank          0.812    0.746    0.886
HDFC Bank             1.000    1.000    1.000
ICICI Bank            1.000    1.000    1.000
IndusInd Bank         0.894    0.940    0.805
Karnataka Bank        0.787    0.853    0.833
Karur Vysya Bank      0.904    0.868    0.943
Kotak Mahindra Bank   0.868    0.890    0.970
Lakshmi Vilas Bank    0.905    0.915    0.950
RBL Bank              1.000    1.000    1.000
South Indian Bank     0.801    0.826    0.834
Yes Bank              1.000    1.000    0.916
Average               0.931    0.936

Source: Computation by the author

Table II
Efficiency Improvement Scope

Bank                    2005     2006     2007     2008     2009

Axis Bank              0.120    0.000    0.000    0.025    0.010
City Union Bank        0.087    0.000    0.000    0.000    0.000
DCB Bank               0.066    0.102    0.353    0.327    0.371
Dhanlaxmi Bank         0.267    0.137    0.000    0.000    0.000
Federal Bank           0.000    0.104    0.000    0.155    0.182
HDFC Bank              0.000    0.000    0.000    0.000    0.000
ICICI Bank             0.000    0.000    0.000    0.000    0.000
IndusInd Bank          0.348    0.292    0.465    0.389    0.454
Karnataka Bank         0.239    0.161    0.155    0.209    0.000
Karur Vysya Bank       0.000    0.000    0.024    0.000    0.026
Kotak Mahindra Bank    0.000    0.000    0.000    0.000    0.000
Lakshmi Vilas Bank     0.000    0.000    0.045    0.040    0.000
RBL Bank               0.000    0.000    0.000    0.000    0.000
South Indian Bank      0.133    0.247    0.237    0.216    0.258
Yes Bank               0.000    0.000    0.283    0.323    0.244

Bank                    2010     2011     2012     2013     2014

Axis Bank              0.000    0.000    0.000    0.000    0.000
City Union Bank        0.000    0.000    0.000    0.000    0.000
DCB Bank               0.170    0.097    0.094    0.000    0.000
Dhanlaxmi Bank         0.000    0.057    0.000    0.000    0.000
Federal Bank           0.102    0.151    0.150    0.151    0.060
HDFC Bank              0.000    0.000    0.000    0.000    0.000
ICICI Bank             0.000    0.000    0.000    0.000    0.000
IndusInd Bank          0.155    0.307    0.222    0.179    0.090
Karnataka Bank         0.191    0.195    0.341    0.302    0.241
Karur Vysya Bank       0.000    0.136    0.150    0.103    0.075
Kotak Mahindra Bank    0.021    0.000    0.000    0.011    0.099
Lakshmi Vilas Bank     0.141    0.042    0.094    0.035    0.057
RBL Bank               0.000    0.000    0.000    0.000    0.000
South Indian Bank      0.166    0.201    0.132    0.213    0.150
Yes Bank               0.073    0.272    0.070    0.000    0.000

Bank                    2015     2016

Axis Bank              0.000    0.000
City Union Bank        0.000    0.000
DCB Bank               0.000    0.000
Dhanlaxmi Bank         0.000    0.000
Federal Bank           0.232    0.340
HDFC Bank              0.000    0.000
ICICI Bank             0.000    0.000
IndusInd Bank          0.119    0.064
Karnataka Bank         0.270    0.172
Karur Vysya Bank       0.106    0.152
Kotak Mahindra Bank    0.152    0.124
Lakshmi Vilas Bank     0.105    0.093
RBL Bank               0.000    0.000
South Indian Bank      0.249    0.211
Yes Bank               0.000    0.000
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Author:Sinha, Abhijit
Article Type:Report
Geographic Code:9INDI
Date:Oct 1, 2018
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