Proceedings of 23rd International Business Research Conference
18 - 20 November, 2013, Marriott Hotel, Melbourne, Australia, ISBN: 978-1-922069-36-8
Efficiency of Kuwaiti Banks: A Comprehensive
X-Efficiency Framework
Jamal Al-Khasawneh, Khiyar Abdullah Khiyar and
Mohammed Z. Shariff
This study tracks the nonparametric X-efficiency based measures and
compares the efficiency dynamics of conventional and Islamic Kuwaiti
banks over the period 1996-2010. The findings show that, in terms of
all efficiency measures, Kuwaiti banks kept losing efficiency: a trend
that started in 2004 and worsened after the 2008 economic crisis.
However, two efficiency stages can be noted: The first trend is
indicated for the period 1996-2003, during which conventional banks
had clear efficiency superiority over Islamic banks. The second trend,
from 2004 until 2010, shows Islamic banks having superiority over
conventional banks. This superiority of Islamic banks was mainly due
to the loss of efficiency in conventional banks and not to any gained
efficiency in Islamic banks.
1. Introduction
The Kuwaiti banking system witnessed structural economic shocks during
the last two decades. The Iraqi invasion of Kuwait in 1990 and the consequent
exponential increase in the defense bill in the 1990s, combined with relatively low
oil revenues, made the Kuwaiti economy go on a slow-down phase. However,
the government‟s unlimited support in the aftermath of the first Gulf War was able
to re-establish confidence in the financial system, and banks started showing
healthy operating signs and positive profitability four years after the liberation of
Kuwait. By 2003, luck had coincided with luck; clouds totally cleared when
national security concerns became a thing of the past after the collapse of the
hostile regime in Iraq and the record increase in oil prices. The period of 20032008 represents a booming modern era in the economic history of Kuwait and
the Gulf Cooperation Council (GCC) countries. According to the 2004 Financial
Sector Assessment Report, issued by the World Bank, Kuwait had made an
impressive recovery from the economic damage caused by the 1990-91 war; oil
production and exports had been restored, and fiscal and external current
account balances since the mid-1990s had reached surpluses to the order of
20% of GDP. The World Bank‟s report also indicated that by the end of 2002, the
total assets of financial institutions amounted to more than 200% of nominal
GDP, while stock market capitalization reached about 100%.
______________________________________
Jamal Al-Khasawneh, Department of Economics and Finance, Gulf University for Science and
Technology (GUST), Email: Alkhasawneh.j@gust.edu.kw,
Khiyar Abdullah Khiyar, Department of Economics and Finance, Gulf University for Science and
Technology (GUST), Email: Abdalla.K@gust.edu.kw,
Mohammed Z. Shariff, Department of Economics and Finance, Gulf University for Science and
Technology (GUST), Email: shariff.m@gust.edu.kw,
Proceedings of 23rd International Business Research Conference
18 - 20 November, 2013, Marriott Hotel, Melbourne, Australia, ISBN: 978-1-922069-36-8
The institutional structure of the banking and financial system in the state
of Kuwait consists of units subject to the Central Bank of Kuwait‟s supervision
and oversight. These units consist of local banks (commercial, specialized and
Islamic), investment companies (conventional and Islamic), exchange
companies, and investment funds. The term “local banks”, as used by the
Central Bank of Kuwait (CBK), encompasses the group of fourteen commercial
banks registered with it. Of these, eleven are Kuwaiti banks and the rest are
branches of foreign banks. By the end of 2010, of the eleven Kuwaiti banks, five
were conventional commercial banks, five were Islamic commercial banks, and
one was specialized, the Industrial Bank of Kuwait. Table (1) shows the number
of financial institutions and their classifications. However, Al-Hassan el al.
indicate that the banking industry is highly concentrated, with the top three banks
having 63% of the market share, and the top five banks having 81%. They also
indicate that the banking sector is largely domestically owned, due to entry
barriers and licensing restrictions for foreign banks, including GCC banks.
Table 1: Number of Kuwaiti Financial Institutions and their Classifications
Type
Total No.
Conventional
Islamic
Commercial Banks
Investment Companies
Investment Funds
Total
10
100
112
222
5
46
57
108
5
54
55
114
The latest shock came from outside Kuwait‟s borders; this time the 2008
worldwide economic crisis exposed the Kuwaiti economy as well as the banking
system to unexpected potential losses. Given the unique economic history of the
state of Kuwait, this paper examines cost, revenue, and profit in the state of
Kuwait during the period 1996-2010. Data Envelopment Analysis (DEA) is used
to compute absolute efficiencies, which can be used to characterize the efficieny
changes in Kuwaiti banks. We believe that the study of the dynamic changes in
efficiencies, after critical political and economic shocks in this country, is missing
in the literature. Furthermore, there is a unique phenomenon in Kuwait where
some conventional banks conventional banks where some conventional banks
did convert and others are in the process of conversion into Islamic banks
2. Literrature Review
In this section, we present some of the relevant literature related to the GCC
region and Middle East and North Africa countries MENA . Darrat et al. (2003)
found that Kuwaiti banks fail to optimally utilize a significant proportion of their
resources. The sources of bank inefficiency appear to be both allocative
(regulatory) and technical (managerial) in nature. The results also indicate that
larger banks in Kuwait are less efficient than smaller ones, although all banks
have improved their efficiency levels and experienced some gains in productivity,
indicating that the loss of efficiency is due mainly to pricing decisions. Hassan et
Proceedings of 23rd International Business Research Conference
18 - 20 November, 2013, Marriott Hotel, Melbourne, Australia, ISBN: 978-1-922069-36-8
al. (2004) investigated the efficiency of the Bahraini banking sector. Their
findings indicate that the cost inefficiency is due mainly to technical inefficiency
rather than allocative inefficiency, while the major source of the total technical
inefficiency for Bahraini banks is pure technical inefficiency (input waste) and not
scale inefficiency. Maghyereh (2004) investigated the effect of financial
liberalization on Jordanian commercial banks‟ efficiency. The findings indicate
high technical efficiency across Jordanian banks and that larger banks have
lower scale efficiency and higher pure technical efficiency than small and
medium banks. Jordanian banks were investigated further by Bdour et al. (2008),
who indicated that, with minor exceptions, the X-efficiency scores of most of the
banks showed a consistent increase after the implementation of the liberalization
policy. Ariss (2008) investigated the evolution of bank efficiency in the Lebanon
subsequent to a period of deregulation liberalization. The findings indicated
positive cost-efficiency improvement with higher efficiency improvement in
consolidating banks. Gattoufi et al. (2009) analyzed the performance of Oman's
banking sector, using panel data for the period of 2000-2005. The findings show
that Omani banks have high pure technical efficiency and that local commercial
banks outperform their foreign counterparts. Srairi (2009) indicated that banks in
the Gulf region are relatively more efficient at generating profits than at
controlling costs, and that conventional banks are more efficient than Islamic
banks. Furthermore, the findings demonstrate a consistent increase in profit
efficiency since 2000, while showing decreasing cost efficiency since 2004.
Al-Delaim et al. (2006) employed DEA to measure the relative cost efficiency of
24 Islamic banks from 14 countries. Overall findings indicate that most Islamic
banking institutions are efficient and the rest are on the way to improving their
efficiency. Kabir (2006) investigated the efficiency of Islamic banks around the
world. The findings indicated that, on average, Islamic banks are less efficient
than conventional banks in terms of all efficiency measures. Using DEA, Fadzlan
et al. (2007) analyzed the comparative performance of foreign and domestic
Islamic banks in Malaysia. The authors found that foreign banks exhibit higher
technical efficiency compared to domestic Islamic banks. Furthermore, the
results show that technically more efficient banks are larger in size, have greater
loans intensity, and on average have less non-performing loans. In a study
carried out by Kamaruddin et al. (2008), the cost and profit efficiency of fullyfledged Islamic banks and the Islamic window operations of domestic and foreign
banks in Malaysia were analyzed using DEA. The findings indicated that Islamic
windows outperform Islamic banks in the term of cost efficiency and profitability.
Ariss (2010) indicated that Islamic banks are more efficient in terms of financing
activities but still less competitive (they have a small market share) than
conventional banks. In another study carried out by Abdul-Majid et al. (2010), it
was found that Islamic banks in different countries had no advantage over other
conventional banks, as Islamic banks suffered poor allocative efficiency, which is
a determinant or subcomponent of cost efficiency.
Proceedings of 23rd International Business Research Conference
18 - 20 November, 2013, Marriott Hotel, Melbourne, Australia, ISBN: 978-1-922069-36-8
3. Methodology
3.1. The Non-parametric Data Envelopment Analysis
Charnes, Cooper, and Rhodes (1978) coined the term „data envelopment
analysis‟ (DEA). There has since been a multitude of works that have applied
and extended the DEA methodology. DEA constructs a frontier based on the
sample data rather than using an assumed production function. This nonparametric approach shows how a particular decision making unit (DMU)
operates relative to other DMUs by providing a benchmark for the best practice
technology based on the DMUs in the sample. Because DEA makes no
assumptions about inefficiency distributions, it is subject to data problems and
inaccuracies created by accounting rules (Isik, 2000). However, DEA works
better than the parametric approach when the sample size is small.
Following Rangan et al. (1988), Berger et al. (1992), Elyasiani and
Mehdian (1992), Fare et al. (1994), Grabowaski et al. (1993), Leightner and
Lovell (1998), Wheelcock and Wilson (1995), Isik and Kabir (2003), and others,
DEA is used in this paper to measure U.S. banks‟ efficiency scores. This choice
is motivated by the small sample size during some years of our data set. Some
other reasons for this choice are: 1) most studies that have used both Stochastic
Frontier Approach (SFA) and DEA have found that both approaches preserve the
efficiency ranking of the DMUs (see Isik and Kabir, 2002, 2003; and Al-Sharkas
et al. (2008). Since the purpose of this paper is to use the efficiency scores to
rank merging banks according to their efficiency characteristics, we use DEA
rather than SFA; 2) the non-parametric DEA is the better choice when the
industry has experienced a series of reforms and/or shocks because we can
assume variable returns to scale (which is not an option in SFA); and finally, and
most importantly, 3) under DEA, profit efficiency scores can be broken down into
more basic components (cost efficiency, revenue efficiency, etc.). Farrell (1957)
decomposes the overall cost efficiency (CE) of each DMU into two components:
a) technical efficiency (TE), which shows the ability of a DMU to achieve the
maximum output for a given production set; and b) allocative efficiency (AE),
which shows management‟s ability to construct an optimal product mix, given
their respective prices which is related mainly to the regulatory environment or
macroeconomic conditions (Lovell, 1993). Assuming constant returns to scale
(CRS), CE is decomposed as follows:
CE = TE  AE
(1)
Banker et al. (1984) proposed the variable returns to scale frontier (VRS),
in which the frontier changes over time due to technological progress, financial
crises, higher industry concentration due to mergers and acquisitions, and
financial deregulation (Isik and Kabir, 2003). However, Banker et al. further
decompose TE into two components: a) pure technical efficiency (PTE), which
indicates the proportional reduction in input usage if inputs are not wasted; and
b) scale efficiency (SE), which represents the proportional output reduction if the
bank achieves CRS. So, equation 1a can be re-written as follows:
Proceedings of 23rd International Business Research Conference
18 - 20 November, 2013, Marriott Hotel, Melbourne, Australia, ISBN: 978-1-922069-36-8
CE = PTE  SE  AE
(2)
As shown previously, cost efficiency can be estimated by summing input
prices rather than output quantities. Consider n DMUs, where each DMU uses m
inputs to produce s outputs. The general form of the cost minimization problem is
then:
m
min  p i x i*
i 1
s.t.
n

j 1
j
x ij  x i*
(3)
n

j 1
i  1,2,........, m;
j
y rj  y r
r  1,2,........, s;
 j , x i*  0

j
1
Assuming (VRS).
where pi is a vector of input prices for the j-th DMU and xi* is the cost
minimization vector of input quantities for the j-th DMU, given the input prices and
the output levels.
The first constraint places a restriction on the input side, requiring the use
of inputs in a linear combination at the efficient frontier to be less than or equal to
the use of the inputs by the i-th bank. The second constraint shows that the
observed outputs of DMUj must be less than or equal to a linear combination of
outputs, xi* , of the DMUs forming the efficient frontier. The third constraint
assures the feasibility of the solution. The fourth constraint imposes the VRS
assumption. The only way to derive a more cost efficient DMU is by getting it
closer to the efficient frontier. This can be achieved by using input equal to X 
rather than X 1 , holding the output fixed (the bold horizontal arrow shows this
choice). Finally, the cost efficiency of the each DMU can be obtained as follows:
m
p x
i 1
m
i
*
i
p x
i 1
i

M inimum virtual cost
 1,
Observed cost
(4)
i
where the cost efficiency value will be equal to one for the DMUs that lie on the
efficient frontier. The cost efficiency scores take values in the range (0,1).
We can also obtain the allocative efficiency using equation (1a) as follows:
CE
CE  TE  AE  AE 
TE
Proceedings of 23rd International Business Research Conference
18 - 20 November, 2013, Marriott Hotel, Melbourne, Australia, ISBN: 978-1-922069-36-8
3.2. Estimation of Revenue Efficiency
Using the same considerations as in the previous section, we can obtain
the revenue efficiency (RE) scores for each DMU. The revenue maximization
problem maximizes the vector of output quantities, y * , in the first step. Then, the
revenue-maximizing problem is calculated as follows:
s
max  q r y r*
r 1
s.t.
n

j 1
j
x ij  x i
j
y rj  y
(5)
n

j 1
i  1,2,........, m;
r  1,2,........, s;
*
r
 j , y i*  0
n

j 1
j
1
Assuming (VRS).
where q r is a vector of output prices for the j-th DMU, and y r* is the maximization
vector of output quantities of the DMUs forming the efficient frontier. The first
constraint indicates that the use of the inputs in a linear combination of efficient
DMUs must be less than or equal to the use of inputs of the j-th DMU. The
second constraint shows that the observed outputs of the j-th DMU must be less
than or equal to the linear combination of the DMUs forming the efficient frontier.
The last two constraints are well defined in the previous section. After solving
the above problem, we can obtain RE as follows:
s
RE 
q
r 1
s
q
r 1
r
yr
(6)
r
y
*
s
s
where
q
r 1
r
y r is the observed/actual revenue of the DMU, and
q
r 1
r
y * is the
virtual efficiency profit that could be achieved if the DMU were situated on the
efficient frontier. The value of the profit efficiency scores will always fall in the
range (0,1).
3.3. Estimation of Profit Efficiency
Summing the cost and revenue efficiencies generates the profit efficiency
(PE) concept, which seeks to minimize costs and maximize revenue
simultaneously. Unlike cost and revenue efficiencies, PE is obtained by allowing
inputs and outputs to vary. The profit maximization problem can be described as
follows:
Proceedings of 23rd International Business Research Conference
18 - 20 November, 2013, Marriott Hotel, Melbourne, Australia, ISBN: 978-1-922069-36-8
s
m
r 1
i 1
max  q r y r*   pi xi*
s.t.
n

j 1
j
xij  xi*
i  1,2,........, m;
j
y rj  y r*
r  1,2,........, s;
n

j 1
(7)
xi*  xi , y r*  y r
j  0
n

j 1
j
1
Assuming (VRS).
where the first constraint indicates that the use of the inputs in a linear
combination of efficient DMUs must be less than or equal to the use of inputs of
the j-th DMU. The second constraint shows that the observed outputs of the j-th
DMU must be less than or equal to the linear combination of the DMUs forming
the efficient frontier. However, the two constraints in this problem are solved
simultaneously. The third constraint is imposed to assure that the revenue
maximization and cost minimization are both achieved. This constraint requires
that the inputs of the j-th DMU must be greater than or equal to the output of the
DMUs on the efficient frontier, and it indicates that the output of the j-th DMU
must be less than or equal to the outputs of the DMUs on the efficient frontier.
This constraint is important because it is possible to maximize profit efficiency by
minimizing costs only. In this case, profit maximization will be equivalent to cost
minimization. The same argument is valid for the revenue efficiency. Finally, the
profit efficiency can be obtained using the following equation:
s
q
r 1
s
q
r 1
m
s
where
q
r 1
r
m
r
y r   p i xi
r
y   pi x
i 1
m
*
r
i 1
,
(8)
*
i
y r   p i x i represents the observed profitability of DMUi. This value
i 1
could be negative for DMUs with losses.
s
m
r 1
i 1
 q r y r*   pi xi* , on the other hand,
represents the virtual profitability that could be achieved if the DMU is located on
the efficient frontier. Accordingly, the profit efficiency values must lie in the range
( ,1) .
Proceedings of 23rd International Business Research Conference
18 - 20 November, 2013, Marriott Hotel, Melbourne, Australia, ISBN: 978-1-922069-36-8
4. Data, Sample Characteristics, and Variable Defination
DEA needs a set of inputs and outputs in order to measure efficiency, and
therefore, relative productivity. Accordingly, we model commercial banks as
multi-product firms, producing two outputs and employing two inputs. All
variables are measured in millions of U.S. dollars except prices, which are
measured as ratios. The outputs include (1) net loans; and (2) other earning
assets, which consist of loans to special sectors, interbank loans, and investment
securities (treasuries and other securities). All output prices are estimated as
proxies. These are calculated as follows: 1) the price of loans is defined as total
interest income to net loans; and 2) the price of other operating income is defined
as other operating income to other earning assets weighted by the proportion of
other earning assets over the total of other earning assets plus off-balance-sheet
items.
Inputs include (1) personnel expenses; and (2) loanable funds, which are
defined as the sum of demand and time deposits and non-deposit funds as of the
end of the respective year. Also, input prices are estimated as proxies. The
price of labor is calculated as personnel expenses over total assets. The price of
capital is calculated as non-interest expense over total assets. Finally, the price
of funds is calculated as total interest expense over loanable funds.
To examine the X-efficiency scores of Kuwaiti banks, we manually
gathered data for the years 1996-2010; all accounting data were obtained from
the annual reports of each bank as posted on each single bank‟s website.
Furthermore, we compared these data to the data posted by the Kuwaiti Central
Bank, where we found a full match between the two sources.
Tables (2) and (3) show the summary statistics for the total input and
output variables of sample banks. Table (2) shows the absolute dollar value of
input variables. To make the comparison easier, we calculated the percentage
increase of each variable as of 1996 and 2010. The results are shown in panel B
of Table (2). Table (2) shows that personnel expenses in 2010 increased by
0.766% from those in 1996, while its price increased very marginally by 0.26%.
More importantly, deposits increased 963% with a decrease in interest paid of
0.79%. Table (3), which represents outputs and their prices, shows that loans
increased by 622%, which is basically less than the percentage increase of
deposits, where it accounts only for two thirds of deposits. These idle deposits
could be a possible source of inefficiency for Kuwaiti banks if no other investment
avenues are used. However, interest earned decreased by almost 1% from its
2010 figure. Comparing the general downward interest trend, it can be said that
the interest margin decreased by 0.2%. Accordingly, Kuwaiti banks increased
their other earning assets by 450%; those earnings show an encouraging
increasing return of around 0.75%.
Proceedings of 23rd International Business Research Conference
18 - 20 November, 2013, Marriott Hotel, Melbourne, Australia, ISBN: 978-1-922069-36-8
5. Revenue, Cost and Profit Efficiencies Results
5.1. Revenue Efficiency Results
Table (4) represents the average revenue scores and their descriptions:
conventional, Islamic, and the whole sample. Table (4) shows that revenue
efficiency was in a state of consistent increase for the period 1996-2004, with a
clear advantage for conventional banks, whose efficiency scores were 5% to
10% higher than those of Islamic banks. Starting from 2005, efficiency scores
converted backwards with all Kuwaiti banks‟ inefficiencies increasing significantly
with a marginal superiority given to Islamic banks. Comparing the efficiency
scores of 2002 with those of 2009, we can indicate -9%, -20%, and 11%
efficiency changes for all, conventional, and Islamic banks respectively. Year
2010 looks totally different, and we can indicate 11%, 45%, and -32% efficiency
changes from 2009 for all, conventional, and Islamic banks respectively. Such a
significant efficiency change can be explained by three possible reasons: Firstly,
Islamic banks usually invest in longer-term investments than conventional banks,
especially in real estate. Accordingly, it is expected that Islamic banks‟ losses
may take a longer time to be actualized/re-evaluated. The second reason is the
number of Islamic banks which have only recently converted to Islamic banking
and, therefore, have limited experience in Islamic banks‟ investment opportunity
sets and, consequently, a limited ability to operate successful Islamic funds.
Thirdly, the Kuwaiti government supported the banking system after the
economic crisis by issuing government bonds and treasury bills, a choice Islamic
banks could not accept.
5.2. Cost Efficiency Results
Table (5) represents the cost-efficiency scores of Kuwaiti banks. We can
see that there was a general trend of efficiency loss over time for all Kuwaiti
banks, with two distinctive stages to be noted: 1997-2003 and 2004-2010. In the
first stage, conventional banks achieved a higher efficiency score than Islamic
banks. In the second stage, however, we can see that, with the exception of
2007, Islamic banks outperformed conventional banks. This superiority of Islamic
banks was mainly due to the loss of efficiency in conventional banks and not to
any gained efficiency in Islamic banks.
5.3. Profit Efficiency Results
Table (6) represents the profit efficiency scores of our sample banks.
Profit efficiency sums up both revenue and cost efficiencies. We can still see the
previously indicated trends in the last two sections. The period of 1996-2003
shows conventional banks having a clear superiority over Islamic banks; they
scored about 85%, while Islamic banks achieved 60%. For 2004-2010, the whole
scene looks different; Islamic banks achieved higher average efficiency scores of
Proceedings of 23rd International Business Research Conference
18 - 20 November, 2013, Marriott Hotel, Melbourne, Australia, ISBN: 978-1-922069-36-8
73%, with conventional banks scoring 57%. Overall, the profit efficiency trend of
Islamic banks looks less volatile than that of conventional banks.
6. Conclusions
Given the uniqueness of the Kuwaiti banking system, and the economic history
of the state of Kuwait, this paper examines cost, revenue, and profit in the state
of Kuwait during the period 1996-2010. Data Envelopment Analysis (DEA) is
used to compute absolute efficiencies. The results of all efficiency measures
show that there was a general trend of efficiency loss over time for all Kuwaiti
banks, with two distinctive stages to be noted: 1997-2003 and 2004-2010. In
1997-2003, conventional banks maintained a higher efficiency score than Islamic
banks. The second stage witnessed serious inefficiencies increase of
conventional banks and a slight efficiencies increase of Islamic banks, putting
Islamic banks on the lead for the latest stage. Overall, Islamic banks‟ cost,
revenue, and profit efficiency performance look more stable than that of
conventional banks.
Proceedings of 23rd International Business Research Conference
18 - 20 November, 2013, Marriott Hotel, Melbourne, Australia, ISBN: 978-1-922069-36-8
Table 2: Banks’ Inputs and Inputs’ Prices
1996
Inputs
Average(Whole Sample)
Max
Min
Stdev
Skewness
Kurtosis
Input Prices
2003
2010
Growth 2010-1996
Per Exp
Tdep
Per Exp
Tdep
Per Exp
Tdep
Per Exp
Tdep
10.32
47.2
4.2
14.05
2.83
8.24
928.11
3275.9
141.6
947.16
2.24
5.82
17.6
31.5
9.3
9
0.7
-1.04
1833.65
3222.6
935.2
876.38
0.63
-0.26
89.38
285.33
12.95
38.57
1.55
1.18
9867.33
26157
177.18
3920.39
1.14
0
7.66
5.05
2.08
9.63
6.98
0.25
0.26
-0.79
1996
Per Exp
Tdep
Average(Whole Sample)
0.006
0.083
Max
0.011
0.147
Min
0.006
0.072
Stdev
0.002
0.034
Skewness
2.71
1.02
Kurtosis
7.707
0.424
Per Exp: Personnel expenses, and Tdep: Total deposits.
2003
Per Exp
0.004
0.006
0.003
0.001
-0.055
-1.726
Tdep
0.035
0.079
0.02
0.022
2.188
4.954
2010
Per Exp
0.007
0.009
0.005
0.001
0.137
-0.94
Tdep
0.018
0.038
0.004
0.009
1.022
2.377
Proceedings of 23rd International Business Research Conference
18 - 20 November, 2013, Marriott Hotel, Melbourne, Australia, ISBN: 978-1-922069-36-8
Table 3: Banks’ Outputs and Outputs’ Prices
1996
Outputs
Average(Whole Sample)
Max
Min
Stdev
Skewness
Kurtosis
NetL
710.14
3649.2
183.2
1130.11
2.74
7.76
NetL
1318.08
2478.4
445.8
741.07
0.5
-0.18
1996
Output Prices
Average(Whole Sample)
Max
Min
Stdev
Skewness
Kurtosis
OEA
1262.3
6611
182
2026.95
2.88
8.47
2003
NetL
0.128
1.974
0.145
0.596
2.972
8.876
OEA
0.018
0.055
0.013
0.015
2.296
5.836
NetL: Net Loans, OEA: Other Earning Assets.
OEA
5120.875
14051
2014.25
1902.18
1.78
2.78
2010
NetL
6937.225
19633.33
826.575
2471.3
1.29
1.12
2003
NetL
0.152
0.54
0.062
0.19
2.437
5.952
OEA
0.019
0.024
0.012
0.005
-0.325
-1.862
OEA
2643.7
8944.15
195
1230.34
1.52
1.23
2010
NetL
0.065
0.093
0.051
0.014
1.572
1.727
OEA
0.071
0.354
0.003
0.115
2.273
5.213
Growth 2010-1996
NetL
6.21
OEA
4.50
Growth 2010-1996
NetL
-0.977
OEA
0.747
Proceedings of 23rd International Business Research Conference
18 - 20 November, 2013, Marriott Hotel, Melbourne, Australia, ISBN: 978-1-922069-36-8
Table 4: The Nonparametric Revenue Efficiency of Kuwaiti Banks (1996-2010).
Average(Whole Sample)
Max
Min
Stdev
Skewness
Kurtosis
Average(Non Islamic
Banks)
Max
Min
Stdev
Skewness
Kurtosis
Average(Islamic Banks)
Max
Min
Stdev
1996
0.70
1.00
0.53
0.20
1.04
-0.14
1997
0.76
1.00
0.63
0.13
1.34
1.93
1998
0.79
1.00
0.67
0.14
1.05
-1.00
1999
0.82
1.00
0.70
0.13
0.85
-1.40
2000
0.82
1.00
0.72
0.11
0.99
-0.32
2001
0.84
1.00
0.72
0.10
0.34
-1.31
2002
0.87
1.00
0.68
0.14
-0.60
-1.68
2003
0.92
1.00
0.76
0.12
-0.98
-1.81
2004
0.71
1.00
0.41
0.23
-0.16
-2.06
2005
0.62
0.84
0.49
0.13
0.72
-1.00
2006
0.66
1.00
0.46
0.24
0.23
-1.12
2007
0.72
1.00
0.27
0.25
-0.42
-0.48
2008
0.63
1.00
0.26
0.26
0.03
-1.12
2009
0.61
1.00
0.33
0.26
0.26
-1.68
2010
0.82
1.00
0.34
0.24
-1.30
0.66
0.70
1.00
0.53
0.20
1.04
-0.14
0.77
1.00
0.63
0.13
1.11
1.55
0.67
0.80
1.00
0.67
0.16
0.90
-1.89
0.76
0.83
1.00
0.70
0.14
0.57
-2.12
0.71
0.83
1.00
0.72
0.11
0.74
-0.85
0.73
0.87
1.00
0.75
0.09
0.23
-1.45
0.72
0.90
1.00
0.68
0.13
-1.30
0.91
0.71
0.96
1.00
0.78
0.10
-2.24
5.00
0.76
0.69
1.00
0.41
0.25
0.15
-2.47
0.82
0.65
0.84
0.50
0.14
0.29
-1.95
0.53
0.58
0.46
0.13
0.67
1.00
0.52
0.26
0.22
-1.22
0.64
0.82
0.46
0.25
0.70
1.00
0.27
0.28
-0.40
-0.21
0.77
1.00
0.51
0.24
0.66
1.00
0.26
0.25
-0.43
1.26
0.57
0.95
0.32
0.34
0.50
0.83
0.33
0.22
1.05
-0.61
0.88
1.00
0.76
0.17
0.95
1.00
0.77
0.09
-2.30
5.35
0.56
0.81
0.34
0.23
The revenue efficiency scores presented in this table are obtained by the following equation:
s
RE 
q
r 1
s
q
r 1
r
yr
s
where
r
y*
q r is the price outputs and y r is the quantity of output r.  q r y r is the observed/actual revenue of the DMU and
r 1
s
q
r 1
r
y * is the virtual efficiency profit that could be achieved if the DMU lies on the efficient frontier. The profit efficiency scores take values in the
range (0, 1).
Proceedings of 23rd International Business Research Conference
18 - 20 November, 2013, Marriott Hotel, Melbourne, Australia, ISBN: 978-1-922069-36-8
Table 5: The Nonparametric Cost Efficiency of Kuwaiti Banks (1996-2010).
Average(Whole Sample)
Max
Min
Stdev
Skewness
Kurtosis
Number Of banks
Average(Non Islamic
Banks)
Max
Min
Stdev
Skewness
Kurtosis
Number of Banks
Average(Islamic Banks)
Max
Min
Stdev
Number of Banks
1996
0.84
0.93
0.64
0.12
-1.58
2.30
5
1997
0.81
0.98
0.67
0.13
0.09
-1.98
7
1998
0.85
1.00
0.71
0.11
0.20
-1.66
7
1999
0.88
1.00
0.66
0.12
-1.09
1.24
7
2000
0.83
0.99
0.66
0.12
-0.36
-0.69
7
2001
0.80
1.00
0.59
0.15
-0.22
-1.37
7
2002
0.80
1.00
0.55
0.16
0.07
-0.87
7
2003
0.86
1.00
0.66
0.16
-0.45
-2.28
7
2004
0.63
1.00
0.30
0.27
0.07
-1.77
7
2005
0.57
0.76
0.37
0.15
0.04
-1.65
8
2006
0.59
1.00
0.44
0.23
0.75
-0.48
8
2007
0.56
1.00
0.28
0.23
1.11
0.61
9
2008
0.53
0.82
0.28
0.20
0.31
-1.01
9
2009
0.61
1.00
0.36
0.25
0.91
-0.84
9
2010
0.60
1.00
0.42
0.17
1.90
4.16
9
0.84
0.93
0.64
0.12
-1.58
2.30
5
0.83
0.98
0.67
0.12
-0.30
-1.61
6
0.68
0.87
1.00
0.75
0.10
0.08
-1.97
6
0.71
0.92
1.00
0.83
0.07
-0.09
-2.26
6
0.66
0.86
0.99
0.69
0.10
-0.58
1.44
6
0.66
0.84
1.00
0.69
0.12
-0.27
-1.43
6
0.59
0.84
1.00
0.70
0.14
0.47
-2.22
6
0.55
0.90
1.00
0.70
0.13
-1.11
-0.43
6
0.66
0.60
1.00
0.30
0.28
0.48
-1.60
6
0.81
0
1
1
1
1
1
1
1
1
0.52
0.76
0.37
0.14
0.80
-0.26
6
0.71
0.77
0.45
0.02
2
0.58
1.00
0.44
0.24
0.97
-0.03
6
0.61
0.77
0.45
0.23
2
0.59
1.00
0.28
0.28
0.67
-1.00
6
0.49
0.60
0.42
0.09
3
0.58
0.82
0.28
0.22
-0.05
-1.22
6
0.45
0.62
0.30
0.16
3
0.55
0.92
0.36
0.22
1.63
2.87
6
0.78
1.00
0.56
0.31
3
0.61
1.00
0.42
0.20
1.93
4.32
6
0.58
0.71
0.49
0.12
3
The cost efficiency scores presented in this table are obtained by the following equation:
m
CE 
px
i 1
m
*
i i
px
i 1

M inimum virtual cost
 1 where p i is the price of input i, x * is the optimal input quantity and x i is the actual inputs quantity.
Observed cost
i i
The cost efficiency score will be one for DMUs on the efficient frontier. The cost efficiency scores take values in the range (0,1).
Proceedings of 23rd International Business Research Conference
18 - 20 November, 2013, Marriott Hotel, Melbourne, Australia, ISBN: 978-1-922069-36-8
Table 6: The Nonparametric Profit Efficiency of Kuwaiti Banks (1996-2010).
Average(Whole Sample)
Max
Min
Stdev
Skewness
Kurtosis
Average(Non Islamic
Banks)
Max
Min
Stdev
Skewness
Kurtosis
Average(Islamic Banks)
Max
Min
Stdev
1996
0.65
1.00
0.20
0.35
-0.20
-1.90
1997
0.64
1.00
0.33
0.27
0.56
-1.38
1998
0.71
1.00
0.36
0.29
-0.03
-2.28
1999
0.80
1.00
0.51
0.22
-0.38
-2.29
2000
0.71
1.00
0.49
0.19
0.59
-0.94
2001
0.78
1.00
0.55
0.20
-0.09
-2.30
2002
0.79
1.00
0.46
0.22
-0.35
-1.50
2003
0.94
1.00
0.66
0.14
-2.45
6.00
2004
0.60
1.00
0.22
0.29
-0.13
-1.72
2005
0.49
0.77
0.32
0.14
1.08
1.56
2006
0.60
1.00
0.42
0.27
0.48
-0.57
2007
0.67
1.00
0.15
0.30
-0.34
-0.75
2008
0.54
1.00
0.09
0.33
0.14
-0.99
2009
0.53
1.00
0.11
0.40
0.12
-2.12
2010
0.83
1.00
0.32
0.26
-1.27
0.18
0.65
1.00
0.20
0.35
-0.20
-1.90
0.66
1.00
0.33
0.29
0.31
-2.05
0.53
0.72
1.00
0.36
0.32
-0.17
-2.92
0.65
0.84
1.00
0.51
0.21
-0.98
-1.04
0.58
0.72
1.00
0.49
0.20
0.27
-1.40
0.60
0.81
1.00
0.55
0.19
-0.58
-1.71
0.58
0.82
1.00
0.46
0.22
-0.92
-0.12
0.59
1.00
1.00
1.00
0.00
-2.24
5.00
0.66
0.58
1.00
0.22
0.31
0.18
-2.06
0.74
0.49
0.77
0.32
0.16
1.23
1.99
0.50
0.58
0.58
0.24
0.59
1.00
0.42
0.31
0.55
-1.57
0.62
0.66
0.58
0.06
0.62
1.00
0.15
0.33
0.04
-0.90
0.79
1.00
0.54
0.23
0.57
1.00
0.09
0.29
-0.36
1.98
0.47
1.00
0.15
0.46
0.40
1.00
0.11
0.38
1.26
0.59
0.87
1.00
0.73
0.19
0.94
1.00
0.68
0.13
-2.27
5.20
0.61
1.00
0.32
0.35
The profit efficiency scores presented in this table are obtained by the following equation:
s
q
r 1
s
q
r 1
m
r
r
y r   p i xi
i 1
m
y r*   pi xi*
s
where
q
r 1
m
r
y r   p i x i is the observed profitability of the banki and
i 1
s
q
r 1
m
r
y r*   p i x i* is the virtual profitability that
i 1
i 1
could be achieved if the DMU is located on the efficient frontier. The profit efficiency score takes values in the range
( ,1) .
Proceedings of 23rd International Business Research Conference
18 - 20 November, 2013, Marriott Hotel, Melbourne, Australia, ISBN: 978-1-922069-36-8
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