A study into the efficiency of Indian banking sector and its determinants.
A look at the growth in the various sectors of the Indian economy shows that the banking sector has proved to be one of the most stable sectors. The growth has been backed by such a strong base that the industry proved to be resilient even during the recent crisis period. Data compiled by the RBI for the period 1990 to 2007 shows that different performance parameters exceeded a CAGR of 15 percent. During the first decade of the present century, viz. from 2001 to 2010, the revenues have increased from US$11.8 bn to US$ 46.9 bn. This growth rate reflects the inherent strength that the sector has which is mainly due to the highly regulated banking and the consistent growth of the country's economy.
During the recent years of the economic slowdown after the US subprime mortgage crisis, the banking sector has come up with new policy initiatives like the savings rate deregulation, mandatory provision coverage ratio of 70 percent, relaxation for the expansion of branches/ offices to the non-tier I cities etc. In this backdrop, the present study aims to find out the critical factors that affect the efficiency of the Indian banking sector. The findings will definitely help to provide guidance to the strategy makers to decide ways to improve performance of the financial institutions.
The extract of the different literatures that have been covered are cited below:
Ahmad and Noov (2011): The study focused on 78 Islamic banks operating in 25 countries covering the data period from 2002 to 2009. The efficiency analysis is done using input-oriented DEA under the VRS assumption. The overall efficiency results showed that there were ups and downs in the mean efficiency during each year. The overall average during the period was found to be 66 percent. Furthermore, the analysis to determine the factors affecting overall efficiency revealed that of the bank-specific factors, operating expenses to total assets ratio, equity to total assets ratio and non-performing loans to total loans affected positively (significant at 1 percent level) but the size of the bank showed positive coefficient (significant at 5 percent level). On the other hand, loan intensity (measured by loan to total assets ratio) showed a negative coefficient (significant at 1 percent level).
Ataullah, Cockerill and Le (2004): The authors looked into the effect of financial liberalization on the efficiency of Commercial Banks in India and Pakistan. Their study period was 1988 to 1998 which was divided into three sub-periods to capture the effect of reforms on the banking efficiency. The researchers computed the technical efficiency scores on the basis of loan based model and income based model. They found that in both the countries, scale efficiency was the major factor for low overall technical efficiency (OTE) and at the same time pointed out that though the efficiency scores were quite low under both the models, there was a sign of improvement. Furthermore, their analysis revealed that the rate of improvement for the private sector exceeded that of the public sector. Moreover, they found that size did not significantly affect efficiency, in contrast to what was inferred in earlier studies.
Bala and Kumar (2011): The researchers analyzed the efficiency of public sector banks (PSBs) in India. The results with respect to technical efficiency (TE) under CCR model showed an average of 87 percent for the 27 PSBs. Of the total sample, only one-third attained a score of one. The range for TE scores for the remaining banks was found to be in the range of 55.3 percent to 99.9 percent. Since, several banks attained a score of one, the super-efficiency model was run. The ultimate ranking showed that the top three banks in terms of efficiency were IDBI Bank, Corporation Bank and Indian Bank. Leading banks like the SBI, Punjab National Bank and Canara Bank secured ranks of 10, 8 and 22 respectively.
Chhikara and Bhatia (2012): The authors made a study on the efficiency level of 28 foreign banks operating in India considering data for the year 2010-11. They looked into the scores in terms of the three efficiency aspect viz., technical, pure technical and scale. 15 banks were found to be perfectly efficient with a score of 100 percent on all counts. The mean scores of technical efficiency (TE), pure technical efficiency (PTE) and scale efficiency (SE) were found to be 91.0 percent, 93.8 percent and 96.9 percent respectively. In order to clearly identify the superior banks among those which obtained 100 percent efficiency score, the Anderson and Peterson's super-efficiency approach was applied. It revealed that Citi Bank, HSBC and Standard Chartered were the top three banks in terms of efficiency.
Das and Ghosh (2010): The researchers looked into the profit efficiencies of the Indian commercial banks using DEA during the period 1992-2004 which coincides with the post-reform period. Furthermore, they investigated into the possible factors that affected efficiency. Results of their study revealed that the range of cost efficiency values remained between 85.66 percent to 96.09 percent. On the other hand, the profit efficiency figures during the period fell in the range of 40.04 percent to 70.63 percent. With reference to the regression results, for different models, different determinants were identified.
Dhanapal (2012): The researcher investigated into the efficiency level of 21 public sector banks covering the data period from 2006-07 to 2010-11. In addition to this, it analyzed the determinants of profitability of the sector as a whole. With regard to the second objective, the author found that the most significant factors that affected profitability in a positive way were return on assets and NPA to total assets. On the other hand, NPA to Net advances had a significant negative effect on the dependent variable. With regard to the efficiency scores, it was observed that the efficient units were Andhra Bank, Indian Bank, Oriental Bank of Commerce, Punjab & Sind Bank, Vijaya Bank and IDBI Bank. Furthermore, 50 percent of the small-sized banks and 66 percent of the large-sized banks were identified to be efficient.
Goel and Bajpai (2013): The researchers in their article looked into the effect of global recession on the Indian banking sector. They tested the hypothesis that the effect of recession was not significant. They made their study on the different categories of banks which included the SBI and its associates, nationalized banks, private banks and foreign banks considering data for the period 2006-09. The analysis was made on financial data covering aspects like Operating profit to total assets, Return on assets, Profit per Employee, Capital Adequacy, CASA Deposit Ratio, Business per Employee, Credit Deposit Ratio and the Investment Deposit Ratio. The results depicted that the sector was not significantly affected by the slowdown.
Gupta, Doshit and Chinubhai (2008): The researchers studied 56 banks over a period of five years from 19992003. To determine the relative efficiency level, they considered the asset weighted score. Their analysis revealed that the average efficiency of all banks increased by 2.4 percent during the period. The state bank group proved to be the most efficient followed by private banks. Hence, the other nationalized banks proved to be the least efficient. In determination of the factors that contributed to efficiency of the banks, the significant ones were operating profit per total asset and capital adequacy ratio. Size, NPA level and business per employee were found to have no significant effect on the banks.
Nigmonov (2010): The study investigated into the efficiency aspect of the banks in Uzbekistan considering the three input-three output DEA model on the basis of three years' data from 2004 to 2006. The efficiency results showed that in all the three years, only Galla Bank and Uzbekistan KDB maintained 100 percent efficiency level. The overall technical efficiency during the different years was: 87 percent in 2005 and 83 percent in 2006. Of the entire sample selected, Samarkand bank was least efficient. With regard to the effect of size and ownership on the efficiency of banks, it was found that though there was significant difference in the scores on the basis of size, there was an insignificant difference in terms of ownership.
Paciouras (2007): The study made an in-depth analysis to study the impact of regulatory and supervisory requirements on the overall efficiency of banks by considering data of 715 banks spread across 95 countries. To be specific, the researcher studied the impact of capital adequacy requirements, information disclosure requirements, restriction on banking activities, deposit insurance scheme, disciplinary power of authorities etc. on efficiency. Some of the important results are as follows: capital stringency, private monitoring and increasing disciplinary power were positively related with efficiency. Moreover, the analysis showed that a few insignificant factors were: activity restriction and entry requirement parameter. On the other hand, the significant factors affecting efficiency included size, equity to total assets, diversification effect, stock market capitalization to GDP ratio etc.
Souza et. al (2003): The researchers in their article made an in-depth study into the efficiency of Brazilian banks by applying the output-oriented DEA approach. Moreover, they identified certain critical factors that significantly affected the efficiency scores. They revealed that only bank type and origin played an important role. The other factors like bank nature, bank control and non-performing loans did not significantly affect the performance levels. Furthermore, their analysis proved that the domestic banks performed better than the foreign banks.
Research Gap: From our study of the above literatures, we find that the efficiency analysis has studied different aspects of the financial institutions. Though most of the articles have focused on the efficiency results, there are quite a number of research papers where the main focus is to identify the determinants of efficiency (or inefficiency). There are a few studies on a similar area but the time period considered is quite low. Moreover, after the financial turmoil that affected the globe, it is relevant to study the affecting factors very seriously. The present study considers the major players in the Indian banking industry and tries to arrive at the efficiency results and looks for the endogenous factors affecting efficiency. Hence, this is an important research study which can act as a guideline for bank managers at the time of strategic decision-making. This has become more important presently, when the RBI is guiding the sector with stricter guidelines in lending and deposit rates, cash reserve ratio, repo rates, reverse repo rates etc.
Objectives of the Study
This research article aims at meeting the following two objectives:
(i) To assess efficiency level of the industry and the two sectors, and
(ii) To identify the endogenous factors that significantly affects the overall (or technical) efficiency level of the Indian Banking Sector, private as well as public.
In order to meet its objectives, the research design is made accordingly and is elaborated below:
Sample of the study:
In choosing the desired sample, purposive sampling method is followed. The main reason is to consider such number of banks which is representative of the population. On the basis of data given in the Capitaline, we have chosen sixteen Scheduled Commercial Banks of India comprising eight each from the public and private sector. The sample is chosen on the basis of market capitalization in descending order (as on 12/4/2013). In other words, the sample consists of larger-sized banks which are representative of the industry: the market capitalization of the sample public sector and private sector banks is around 80 percent and 90 percent of the sectoral capitalization respectively.
Data source and period: The present study is based on secondary data collected from the Capitaline database package over the period 2004-05 to 2011-12.
The research paper objectives are met by applying the two-stage DEA (Data Envelopment Analysis) model. In the first stage, to meet the first objective, DEA is applied which gives a comprehensive picture relating to different aspects of efficiency viz. technical, pure technical and scale. For the latter part, in the second stage, Tobit regression is applied (instead of OLS because the dependent variable is censored from 0 to 1), considering technical (overall) efficiency as the endogenous variable and certain bank-specific factors as exogenous.
In the case of DEA, it is important to mention here that since the result of DEA depends on a number of inputs and outputs, the following two thumb rules, given by Cooper et. al. (2007) are considered:
* n [greater than or equal to] p x q, where n is the number of decision-making units (DMUs), p is the number of inputs and q is the number of outputs, and
* r = 3 (p+q), where r is the total number of observations.
After a thorough review of literature, the following two inputs and outputs are considered:
Input variables: Deposits and Assets
Output variables: Loans and Advances and Investments
The nominal data of these have been deflated to the base year 2004-05 using GDP Deflators. To test the relationship between the inputs and outputs before applying DEA, the isotonicity test was carried out which gave us favorable results.
Analysis and Findings
The following points, divided into two parts, summarize the results of the study. In the first part, the researchers discuss about the efficiency results, whereas in the second part, the determinants of efficiency are identified.
Efficiency Results using Data Envelopment Approach
In this paragraph, the researchers applied the DEA methodology considering all the sixteen banks in the sample.
This is done to arrive at the relative efficiency scores of the individual insurers which help to identify not only the position of individual banks, but also the mean result. Three different aspects are considered, viz. overall (total) efficiency, pure technical efficiency and scale efficiency. The technical efficiency pertains to the overall (total) efficiency attained by the private and public players. Pure technical efficiency, on the other hand, relates to the efficiency arising out of managerial decisions and sound resource allocation. The scale efficiency reflects the efficiency arising due to the extent of utilization which is computed as shown below:
Scale Efficiency = Technical Efficiency CCR / Pure Technical Efficiency BCC The charts below summarize the industry results in the three efficiency forms.
(a) Overall Efficiency
The trend in the movement of the overall efficiency is depicted in chart 1 below.
It is observed in Chart 1 above, that in terms of technical efficiency, there is a sign of improvement not only in the industry, but also in both the sectors. In terms of the industry trend, it is clear that there is substantial improvement from 74 percent in 2004-05 to 87 percent in 2011-12 with a consistency in improvement. If we look at the sectoral trend, it is observed that the enhancement in the case of private sector banks is much more compared to the public sector counterpart. In case of the former, there has been an uptrend from around 60 percent level to almost 90 percent efficiency in contrast to the stagnancy observed in the latter case. However, the public sector banks reflected a greater consistency than the private one. But, it is worthy to note that the private players have overtaken the public competitors in the last five years.
(b) Pure Technical Efficiency
This aspect covers one component of the total (or overall) efficiency. It highlights the strength of the organization in terms of managerial skill and resource allocation soundness.
In terms of pure technical efficiency (refer to chart 2), the industry scores reflect that the trend is overall encouraging. It is observed that the public sector shows a comfortable average in all the years with the scores lying in a small range, thereby showing high consistency. On the other hand, the private players show a dramatic improvement in the later years with the recent scores touching 90 percent levels and also exceeding that percentage in a few years. In all the three sub-units, the efficiency scores lie at a commendable level. Hence, the pure technical inefficiency is quite low leaving less scope for improvement.
(c) Scale Efficiency
This is the second component of total efficiency which throws light on the extent of scale utilization. Higher is the score, better is the size utilization, thereby pointing to its closeness to the minimum point on the average cost curve. A scale efficiency score of one reflects operation at constant returns to scale, whereas a score of less than one implies either decreasing or increasing returns to scale. The industry and sectoral results are given in Chart no. 3.
The efficiency results by applying DEA point to the overall high scale efficiency; the score exceeding 90 percent in all years of the study. The overall impression in this case is the same as observed in the other two cases: the private players surpassing the public sector in the later years of the study period. The overall efficiency in the industry is at a comfortable level. Moreover, the private sector has shown signs of improvement during the period in comparison to the consistency seen in the case of the public sector banks.
It may therefore be mentioned that though the public sector has reflected consistency, it is the private players that are showing a drastic improvement in all fields of efficiency. Thus, the situation in the industry in totality and the two sectors, in particular, has shown tremendous strength even during the recent years of crisis.
Investigation into the Determinants of Bank Efficiency:
For finding the efficiency determinants for Indian banks, the researchers have run Tobit regression model due to the censored nature of the dependent variable. It has been first applied by considering the data for all banks (private as well as public). Then, the same regression is applied to the private and public banks separately to identify variables that explain the efficiency of each sector. In our study, the Tobit model works well and it proves itself as a good fitting model as envisaged from the value %2 statistic of likelihood estimates.
The different independent endogenous bank-specific variables considered for the model are as follows:
Size, which is represented by the natural log of total assets
CDR which stands for the credit-deposit ratio
ROA stands for return on assets, representing the profitability aspect
BPE stands for business per employee, representing staff productivity
LOANQLT stands for the quality of business represented by percentage of Net NPA
LIQTY stands for liquidity represented by the ratio of liquid assets to total assets
LADVTA stands for the ratio of loans and advances to total assets, representing loan intensity.
The results of Tobit regression are given below.
Regression Results for the Banking Sector.
The results of tobit regression is depicted in table-I.
By considering all the selected banks (public as well as private) into a single pack, variables like size, credit-deposit ratio, return on assets and advances to total assets ratio are found to be significant; the first two at 1 percent and the other two at 5 percent level of significance (refer to table 1). Loan quality, staff productivity and liquidity are found to be insignificant for estimating the efficiency of Indian banks. Out of the chosen variables, loan intensity is found to be the most efficient predictor, though the relationship is negative. A possible reason behind the sign is the compromise on quality in this era of competitive pressure. Thus, the banks must give their maximum effort to minimize the ratio for improving their efficiency. The second powerful explanatory variable is found to be the traditional return on assets ratio, which represents the efficiency with which the assets are being put to use. For improving efficiency, banks should be careful for improving their return on assets.
Regression Results for the Private Sector Banks.
The results of tobit regression is depicted in table-II.
When Tobit is applied on private banks only, four variables are found to be significant. The factors are: size and credit-deposit ratio (at 1 percent), return on asset (at 10 percent) and loan intensity (at 1 percent level of significance). In this case also, loans and advances to total assets ratio is found to be the most efficient predictor of bank efficiency and with a negative co-efficient. The second powerful explanatory variable also remains the same.
Regression Results for the Public Sector Banks.
The results of tobit regression is depicted in Table-III.
In the case of public sector banks (refer to table-III), the picture is somewhat different. Unlike private banks, return on asset ratio is found to be insignificant for capturing their efficiency. The four significant explanatory variables are size, credit- deposit ratio, asset liquidity and loan intensity (all are significant at 5 percent level). Of these, loan intensity and liquidity are two most important determinants of efficiency for the public sector banks. High negative coefficient is found for liquidity, which suggests that the Indian public banks should reduce the percentage of liquid assets in their balance sheet in order to improve their efficiency. Similar comment goes for the advances to total assets ratio.
On the basis of above analysis, we find that the overall strength of the Indian banking industry is at a commendable level, be it any form of efficiency. The industry has proved to be really tough even in the horizon of the global financial crisis to which India could not remain unscathed. The credit for this resilience goes mainly to the RBI which brings about strict regulations from time to time and also imposes several regulatory requirements. This has helped in bringing stability to the industry, thereby leading to no severe impact on the banks during the recent economic and financial turmoil. Regarding the study of explanatory variables for capturing the efficiency of Indian banks, loan intensity has proved itself as the most significant and powerful predictor, though its coefficient is negative. A reason could be the shifting priority from business quality to business quantity. Thus, the Indian banks, private as well as public must devote serious efforts to the quality of advances in relation to their total assets. For public sector banks, liquidity stands in the way of improving their efficiency.
Hopefully, the Indian banks will think about the outcomes of this study seriously, so that they can maintain and improve their efficiency and face the global challenges even in the situation of tight liquidity in the industry.
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Assistant Professor, Department of Commerce with Farm Management, Vidyasagar University, West Bengal.
Tagar Lal Khan
Associate Professor, Department of Commerce with Farm Management, Vidyasagar University, West Bengal.
Table--I Result of Tobit Regression Considering the Banking Industry Tobit regression Log Likelihood = 39.688909 te Coef. Std. Err. t size .0488957 .0123177 3.97 cdr .005671 .0012967 4.37 roa .0766327 .0369024 2.08 bpe -.0043977 .0031425 -1.40 loanqlt .0222522 .0229309 0.97 liqty -.3672309 .6648319 -0.55 ladvta -.7641738 .3123897 -2.45 _cons .2572317 .2105837 1.22 /sigma .13431 .0097971 Tobit regression Number of obs = 128 LR chi 2(7) = 53.82 Prob > chi 2 = 0.0000 Log Likelihood Pseudo R2 = -2.1053 = 39 te P > [absolute [95% Conf. value of (t)] Interval] size 0.000 .0245096 .0732818 cdr 0.000 .0031038 .0082382 roa 0.040 .0035746 .1496907 bpe 0.164 -.010619 .0018236 loanqlt 0.334 -.0231455 .0676499 liqty 0.582 -1.683441 .9489793 ladvta 0.016 -1.382632 -.1457159 _cons 0.224 -.1596742 .6741376 /sigma .1149141 .1537058 Source: Computed by the authors Table--II Result of Tobit Regression for Private Sector Banks Tobit regression Log Likelihood = 22.616576 te Coef. Std. Err. t size -.0737755 .0208731 -3.53 cdr .0102275 .0020817 4.91 roa .0814064 .0462941 1.76 bpe .0090034 .0060232 1.49 loanqlt -.0415178 .0291473 -1.42 Liqty -.3827407 .8915333 -0.43 ladvta -1.913625 .4595835 -4.16 _cons 1.920158 .3381247 5.68 /sigma .1046908 .0120621 Tobit regression Number of obs = 64 LR chi 2(7) = 48.02 Prob > chi = 0.0000 Log Likelihood Pseudo R2 = 17.2318 = 22 te P> [absolute [95% Conf. value of (t)] Interval] size 0.001 -.115573 -.0319779 cdr 0.000 .0060589 .014396 roa 0.084 -.0112959 .1741088 bpe 0.140 -.0030579 .0210646 loanqlt 0.160 -.0998842 .0168486 Liqty 0.669 -2.168005 1.402524 ladvta 0.000 -2.833925 -.9933252 _cons 0.000 1.243075 .2.597241 /sigma .0805368 .1288447 Source: Computed by the authors Table--III Result of Tobit Regression for Public Sector Banks Tobit regression Log Likelihood = 41.494553 te Coef. Std. Err. t size .0497975 .0214081 2.33 cdr .0085293 .0040713 2.09 roa .0270615 .0419672 0.64 bpe -0025884 .0028748 0.90 loanqlt .0092199 .0211969 0.43 Liqty -.982319 .5004636 -1.96 ladvta -1.154653 .4957587 -2.33 _cons .430649 .2575529 1.67 /sigma .0720181 .0081819 Tobit regression Number of obs = 63 LRchi 2(7) = 51.57 Prob > chi = 0.0000 Log Likelihood Pseudo R2 = -1.6414 = 41 te P> [absolute [95% Conf. value of (t)] Interval] size 0.024 .006912 .092683 cdr 0.041 .0003735 .0166852 roa 0.522 -.0570089 .1111319 bpe 0.372 -.0031706 .0083474 loanqlt 0.665 -.0332426 .0516824 Liqty 0.055 -1.984868 .02023 ladvta 0.023 -2.147777 -.1615294 _cons 0.100 -.0852915 .9465896 /sigma .0556277 .0884085 Source: Computed by the authors Chart--1 Technical Efficiency Summary 2004-05 2005-06 2006-07 2007-08 PSBs 88.45 88.23 87.77 83.34 PrSBs 59.81 69.59 79.54 86.54 Industry 74.13 73.49 83.66 84.95 2008-09 2009-10 2010-11 2011-12 PSBs 76.12 85.47 84.45 84.31 PrSBs 84.3 90.31 88.11 89.15 Industry 80.21 87.89 86.29 86.73 Source: Computed by the authors Chart--2 Pure Technical Efficiency Summary 2004-05 2005-06 2006-07 2007-08 PSBs 88.88 88.33 31.26 86.02 PrSBs 71.8 78.22 89.32 39.74 Industry 80.35 77.03 90.30 87.89 2008-09 2009-10 2010-11 2011-12 PSBs 83.32 88.22 89.18 88.52 PrSBs 89.64 91.8 89.62 93.92 Industry 86.43 90.01 89.41 91.22 Source: Computed by the authors Chart 3 Scale Efficiency Summary 2004-05 2005-06 2006-07 2007-08 PSBs 99.47 99.88 95.31 96.57 PrSBs 87.64 90.7 89.95 96.5 Industry 93.51 89.86 92.88 96.54 2008-09 2009-10 2010-11 2011-12 PSBs 91.1 96.95 94.6 95.15 PrSBs 94.17 98.35 98.23 94.98 Industry 92.64 97.65 96.42 95.07 Source: Computed by the authors
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|Author:||Sinha, Abhijit; Khan, Tagar Lal|
|Date:||Apr 1, 2014|
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