Proceedings of 11th Asian Business Research Conference
26-27 December, 2014, BIAM Foundation, Dhaka, Bangladesh, ISBN: 978-1-922069-68-9
Comparative Technical Efficiency of the Bangladesh Banking
Industry
Abdus Samad
This paper estimates the technical efficiency of the banking industry in Bangladesh during
2010. Applying the time invariant stochastic frontier function the paper finds that the mean
technical efficiency of the Bangladesh banking industry was 69.5 percent. The paper also
finds that 63 percent of the total banks operate above the mean efficiency level. The
estimates of comparative efficiencies for the five groups of nationalized banks, private
domestic banks, foreign banks, specialized banks, and nonscheduled banks find that the
mean efficiency of private domestic banks is 72 percent and it ranks the highest in
efficiency level. The nationalized banks’ mean is 70 percent and it ranks second in
efficiency. Foreign banks have the lowest efficiency in loans and advances.
JEL Classification: G20, G21 and C33
Keywords: Bangladesh, Efficiency, Banking Industry, Stochastic Frontier.
I. Introduction
Banks operate in asymmetric information which leads to adverse selection and moral
hazard. Given asymmetric information, some banks perform well above the average efficiency
level while other performs below the average efficiency. Determining bank efficiency and relative
efficiency is a key element in a competitive market for improving the bank efficiency level. The
allocation of bank scarce resources for the improvement of its efficiency is worthwhile only when
its current efficiency level is known. So, the study of the estimates of efficiency level is important.
The study of efficiency is also important for bank customers, bank managements, and
bank regulators in determining a bank position relative to its peer group.
Since Bangladesh opened its liberalization policy in 1982, there are now fifty-one banks
operating in Bangladesh. Among the fifty-one banks, there are thirty domestic banks, eleven
foreign banks, four specialized banks, and four non-scheduled banks, in addition to four
nationalized banks. It is important to know the average efficiency level of the Bangladesh
banking industry as well as the five groups’ relative efficiency position before making any
resource allocation for their improvement.
The Bangladesh government laid great emphasis on efficient performance. In its effort,
the government formed a national commission on money, banking, and credit in 1986 for the
efficient operation and management of the banking system.
____________________________________________________________________________
Abdus Samad, Ph.D., Associate Professor, Department of Finance and Economics, Utah Valley University, 800 W.
University
PKY,
Orem,
UT
84058,
USA,
Phone:
801-863-8368,
Fax:
801-863-7218,
Proceedings of 11th Asian Business Research Conference
26-27 December, 2014, BIAM Foundation, Dhaka, Bangladesh, ISBN: 978-1-922069-68-9
A current survey of literature shows no record of the comparative efficiency study for the
Bangladesh banking industry since the last study of Samad (2007) for the period of 2000. The
importance of the estimates of efficiency and the absence of the comparative efficiency study in the
Bangladesh banking industry constitutes the key motivating factor for this study.
The paper is organized as: Section II provides a brief survey of literature. Data, methodology and
the description of mode are discussed in Section III. Empirical results and conclusions are provided in
Section IV.
II.
Survey of Literature
Bank efficiency studies are large; however, a large volume of these studies focused on the
banks of developed countries, USA and Europe in particular. Berger and Humphrey (1997), Berger and
Mester (1997) and Bar et al (1999).There are few bank efficiency studies for less developed countries.
Samad (2009) estimated the technical efficiencies of the Bangladesh banking industries during
the year 2000 and found that the average efficiency of banks was 69.6 and the average efficiency lies
between 12.7 and 94.7 during 2000. This study is different from the previous studies with respect to
outputs and input. Compared to loans as output, deposits and employee expenses as inputs in my
previous studies, this study uses labor, capital, and deposits as inputs for loans and advances as
outputs.
Samad (2010) estimated the technical efficiency of just one bank, the Grameen Bank, the symbol
of micro-financing founded by Nobel Laurate, Professor Younus.
There are several studies in India. Some of these studies used bank financial indicators for
measuring bank efficiency. Rammohan and Roy (2004) and Sarkar et al (1998) used financial indicators
for measuring bank efficiency. Rammohan and Roy found that public sector banks are more efficient
than the private sector banks. In Kumbhakar and Sarkar’s (2003) cost efficiency approach for measuring
bank efficiency, they found that private sector banks improved their performance compared to public
sector banks.
There are other studies in India that used different approaches for measuring bank efficiency.
Saha and Ravishankar (2000), Bhattacharyya et al (1997), and Sanjeev (2006) measured the efficiency
Proceedings of 11th Asian Business Research Conference
26-27 December, 2014, BIAM Foundation, Dhaka, Bangladesh, ISBN: 978-1-922069-68-9
of Indian Banks using the DEA approach. Bhattacharyya et al (1997) found that the public sector banks
were the best performing banks in India during the late 1980s and early 1990s. Shanmugam and Das
(2004) used stochastic frontier analysis (SFA) for measuring technical efficiencies of Indian commercial
banks. They found that the state bank group and foreign banks were more efficient than their
counterparts during 1992-1999.
Andries and Cocris (2010) analyzed the comparative efficiency of the main banks in Romania, the
Czech Republic, and Hungary during the 2000-2006 period using both data envelopment analysis (DEA)
and stochastic frontier approach (SFA). They found that the banks in these three countries operate at a
low level of technical efficiency.
III
Methodology
This paper employs the time invariant model of stochastic frontier production function for the
measuring technical efficiencies (TE) of banks. The actual production function of a bank can be written
as:
Qit = f(Xit, β) exp(-uit);
uit
;
i= 1, 2, ----n,
t = 1, 2, -------T
(1)
Where Qit = actual output of sample bank i in period t; Xit is a vector of inputs used by bank, and β
is a vector of parameters
Uit is one sided (non-negative) residual. If the performance of a bank is inefficient, its actual output
is less than its potential output. (In other words, if a bank is efficient, its output is equal to its potential
output). Therefore, TE is the ratio of the actual output Qit to potential output of a bank in period t as:
TEi =
exp( xiB  ui
Qit
=
= exp(-ui).
exp( xiB )
exp( xiB )
(2)
When the residual term (uit) is zero, the bank produces the potential output and the bank is fully
TE. When (uit) >0, the bank produces less than its potential output. If the bank is less than full TE, it
operates below the production frontier. Thus, uit is a bank’s TE and it is inversely (negatively) related.
Proceedings of 11th Asian Business Research Conference
26-27 December, 2014, BIAM Foundation, Dhaka, Bangladesh, ISBN: 978-1-922069-68-9
For capturing the effects of omitted variables (i.e. measurement errors), a random error,
it
is
included in (1) which gives:
Qit = f(Xit, β) exp( it-uit)
Where
I
(3)
is a random error and is assumed to be iid (independent and identically distributed) as N(0,σv2)
and independent of ui which represents technical efficiency/inefficiency.
Battese and Coelli (1992), proposed a model in which the parameters of technical inefficiency
(uith) are estimated with the method of maximum likelihood and can be written as:
Uith = {exp[ - (t – T)]}ui
ui
‘s
(4)
are non-negative random variable and ui
( ,
),
independently and identically distributed as truncated normal with mean
Where
=
). The value of =
it
N(
).
and variance,
That is, ui is
.
. It is the estimate of the ratio of standard deviation of
inefficiency to the standard deviation of random error. It explains the variation of inefficiency as a
percentage of error term.
= explains inefficiency as a percentage of total deviation of output.
Whether there is no technical inefficiency component in the model, it is tested by the null
hypothesis H0:
= 0 against the alternative hypothesis, Ha:
> 0. It is tested by standard LR.
Data and Model
The measure of bank output (production) is controversial in banking literature mainly because a
bank provides a variety of services such as issue of deposits, loans, and discounts. Hence, there is no
consensus over what banks produce (i.e. output) and what are banks’ inputs (i.e. resources) used to
produce outputs. There are two approaches which most researchers use in banking studies. They are
Proceedings of 11th Asian Business Research Conference
26-27 December, 2014, BIAM Foundation, Dhaka, Bangladesh, ISBN: 978-1-922069-68-9
either the production approach or the intermediate approach. Berger, et al (1992) provides detailed
discussions about it. According to the intermediate approach, a bank collects deposits with labor and
capital and intermediate these resources into loans, other assets, and incomes. Deposit is, thus,
considered as input and loans and advances, and investments are defined as output. According to the
production approach, banks use labor and capital including other financial inputs to produce outputs. The
outputs are services that a bank provides to the account holders of deposits and advances (Ferrier and
Lovell, 1990). Thus, production approach measures outputs by the number of accounts (deposits and
advances) that a bank generates with cost. Since the numbers of accounts are hardly available,
intermediate approach is widely used.
This paper uses the intermediate approach in determining bank inputs and outputs. That is, loans
and advances are used as a banks’ output (Q). These outputs are generated by applying three inputs
such as deposits, labor, and capital. The total value of a bank’s capital is estimated as total assets minus
total liabilities and is denoted as capital (K). The total full-time and part-time number of employees is
considered as labor (L). Demand deposits and fixed deposits are considered as deposits (D).
Data of all variables such as loan and advances (Q), deposits (D), total employee (L), and capital
(K) are obtained from Banks and Financial Institutions Activities 2010, Finance Department, Finance
Ministry, and Peoples’ Republic of Bangaldesh,
This paper estimates the following Cobb-Douglas production function as:
ln(Qit) = β0 +β1ln(Kit) + β2ln(L it) + Vit –Uit
(5)
Where Q is the output represented by total loans and investments (LNADV), K is capital, L is
labor, and ln is a natural log of all variables.
Proceedings of 11th Asian Business Research Conference
26-27 December, 2014, BIAM Foundation, Dhaka, Bangladesh, ISBN: 978-1-922069-68-9
IV
Empirical Results
Result of the ML estimation for equation (7) is presented in Table 1.
Stochastic frontier model
Number of obs =
Wald chi2(3)
Log likelihood = -26.272368
=
51
285.43
Prob > chi2
=
0.0000
-----------------------------------------------------------------------------LogLNADV |
Coef. Std. Err.
z
P>|z|
[95% Conf. Interval]
-------------+---------------------------------------------------------------LogDep |
.040128
9.73 0.000
.3118576
.4691564
LogK | .0938308 .0330951
2.84 0.005
.0289655
.158696
4.64 0.000
.1513291
.3725844
8.81 0.000
3.200311
5.031349
LogL
.390507
| .2619567 .0564437
_cons |
4.11583 .4671101
-------------+---------------------------------------------------------------/lnsig2v | -2.657581 .5317582
-5.00 0.000
-3.699808 -1.615354
/lnsig2u | -1.292396
-2.63 0.009
-2.256629 -.3281619
.491965
-------------+----------------------------------------------------------------
λ
.2647974 .0704041
.1572523
.4458928
.5240345 .1289033
.3235781
.8486733
.3447298 .1127478
.1237483
.5657113
1.979002 .1845487
1.617293
2.34071
-----------------------------------------------------------------------------Likelihood-ratio test of sigma_u=0: chibar2(01) = 1.41 Prob>=chibar2 = 0.108
All inputs—deposits, capital and labor—have a positive impact on production as shown in Table 1.
The low p value, 0.000, 0.005, and 0.000 for the coefficients of deposits, capital, and labor suggest that
they are significant factors for bank production. Among the inputs, deposits and labor are dominant
Proceedings of 11th Asian Business Research Conference
26-27 December, 2014, BIAM Foundation, Dhaka, Bangladesh, ISBN: 978-1-922069-68-9
factors for the production of loans and advances. The positive coefficient for deposit, capital, and labor
indicates they affect production of loans and advances positively.
The variance of inefficiency, σ2=0.52, in Table 1, indicates that 52 percent of the variation of bank
output is due to technical inefficiency and is statistically significant.
The variance of random error measured by σ2v= 0.26 indicates that 26 percent of variation of
output is due to model’s measurement error and is statistically insignificant.
A test on the significance of technical inefficiency has been done. A test on the significance of
technical inefficiency measured by random variable, ut, is obtained from LR ratio of σu. The LR value has
approximately 𝜒2 distribution with parameter shown in Table 1. LR = -26.27 and is significant. The
significant is provided by the probability of Wald 𝜒2> is 0.0000. This means that the inefficiency effect as
measured by uit is significantly different from zero. Thus, H0: σ 2ui=0 is rejected which suggests Ha:
σ 2ui>0.
Table
Descriptive Statistics of Estimated Efficiency of Bangladesh Banking Industry
Observation Mean
Median
Maximum
Minimum
S.D.
51
0.720
0.925
0.268
0.14
0.695
Jarque
Bera
15.40
2
Probility
0.000
Table 2 shows that the average technical efficiency of the Bangladesh banking industry is 69.5 percent
with a median efficiency of 72 percent. The result is consistent with the result of the previous study
(Samad, 2009. The range of estimated efficiency—the difference between the maximum and minimum
efficiencies—is 0.657 and is very high. The 0.000 probability of Jarque-Bera suggests that the distribution
of efficiency is normally distributed.
Table
Frequency Distribution of Estimated Efficiencies of Banking Industry
Range
efficiencies
0.0 – 0.3
0.3 – 0.5
of Frequency
1
5
Relative Frequency
Cumulative Relative Frequency
0.019
0.098
0.01
0.117
3
Proceedings of 11th Asian Business Research Conference
26-27 December, 2014, BIAM Foundation, Dhaka, Bangladesh, ISBN: 978-1-922069-68-9
0.5 – 0.7
0.7 – 0.9
0.9 and above
Total
13
30
2
51
0.255
0.588
0.04
1.00
0.372
0.96
100
Table 3 shows that 37.2 percent of the total banks of Bangladesh operated below the industry mean
efficiency of 70 percent and 63 percent of banks in Bangladesh operated above the banking industry
average during 2010. The results are very much in consistence with the findings of the previous study
(Samad, 2009)
Table
Comparative Technical Efficiency of Bangladesh Banks
Bank Group
Nationalized Banks
Private Banks
Foreign Banks
Specialized Banks
Non-scheduled Banks
Mean
0.70
0.72
0.59
0.60
0.62
Median
0.71
0.72
0.60
0.62
0.62
4
Maximum
0.80
0.83
0.82
0.75
0.91
Minimum
0.60
0.39
0.26
0.55
0.35
S.D.
0.08
0.09
0.20
0.09
0.28
Table 4 shows that among the four groups of banks, the mean efficiency of private banks is 72 percent
and it is the highest level of efficiency in Bangladesh. The nationalized banks rank second with an
average efficiency level of 70 percent. The foreign banks have the lowest level of efficiency in loans and
advances production. The mean efficiency of foreign banks is 59 percent.
Conclusions:
The technical efficiency of the Bangladesh banking industry is estimated during 2010 using the
stochastic frontier production function. The estimates find that the mean efficiency of the banking industry
is 69.5 percent. About 37 percent of the total banks operate below the mean efficiency level and 63
percent of Bangladesh banks operate above the mean efficiency level. Results are consistant with the
findings of the previous study (Samad, 2009)
Among the four groups of banks, the efficiency level of private commercial banks is the highest.
Private bank mean efficiency is 72 percent. Nationalized banks stand second. Their efficiency level is 70
percent. The lowest efficiency level is observed for foreign banks. The foreign bank efficiency level is 59
percent.
References
Berg, S.A., Forsund, F., Hjalmarsson, L. and Suominen, M. 1993. ―Banking efficiency in Nordic
countries‖, Journal of Banking and Finance, 17, 371-388.
Proceedings of 11th Asian Business Research Conference
26-27 December, 2014, BIAM Foundation, Dhaka, Bangladesh, ISBN: 978-1-922069-68-9
Samad, Abdus. 2009. ―Measurement of Inefficiencies in Bangladesh Banking Industry Using Stochastic
Frontier Production Function‖ Global Journal of Business and Research
3 (1,) 41-48.
Samad, Abdus (2010). “Estimate of Production efficiency: Evidence from Grameen Bank Microfinancing‖
Global Review of Business and Economic Research, 6 (2), 183-189
Ram Mohan, T.T., Roy, S.C. 2004. ―Comparing performance of Public and Private Sector banks: A
Revenue Maximization Efficiency Approach‖, Economic and Political Weekly, 39,(12), 37-48?.
Sarkar, J., Sarkar, S. and Bhaumik, S.K. 1998. ―Does ownership always matter? Evidence from Indian
banking Industry‖, Journal of comparative economics, 26, 213-27?.
Kumbhakar, S.C., and Sarkar, S. 2003. ―Deregulation, Ownership, and Productivity Growth in the banking
Industry: Evidence from India‖, Journal of Money, Credit and banking,35 (3), 403-424.
Saha, S., and Ravishankar,T.S. 2000. ―Rating of Indian commercial Banks: A DEA approach‖, European
Journal of Operations Research, 124, 312 -320?.
Bhattacharyya, A., Lovell, C.A.K., Sahay, P. 1997. ―The Impact of Liberalization on Productive Efficiency
of Indian Commercial Banks‖, European Journal of Operations Research, 98, 113 -127?.
Sanjeev, G. M. 2006. ―Data Envelopment Analysis (DEA) for measuring Technical Efficiency of Banks‖,
Journal of Business Perspective, 10 (1), 97- 113?.
Shanmugam, K.R., and Das, A. 2004. ―Efficiency of Indian Commercial Banks during the reform period‖,
Applied Financial Economics, 14 (June), 681-686.