Proceedings of 10th Annual London Business Research Conference
10 - 11 August 2015, Imperial College, London, UK, ISBN: 978-1-922069-81-8
Cost Efficiency in Emerging Economies: Evidence from the
MENA Banking Sectors
Idries Mohammad Al-Jarrah and Khalid Shams Al-Abdulqader
This study utilizes the Translog stochastic frontier model to estimate cost efficiency levels for
banking sectors in the Middle East and North Africa (MENA) countries over the 2007-2013
period. The stake of the banks under study in terms of their assets holdings in the banking
sectors of MENA countries is about 80 percent, comprising a sample of 1308 observations over
the study period.
The estimated cost efficiency averaged around 77 percent for the banks in MENA region with
slight changes to this score over the period 2007-2013. This suggests that the same level of
output could be produced with approximately 77% of current inputs if banks under were operating
on the most efficient frontier. The results show that the banks operating in the Gulf Cooperation
Council (GCC) countries are the most efficient while those operating in countries that have
witnessed political instability are the least. The results also show that the efficiency scores for
commercial and Islamic banks are similar while those for Investment are less. Besides, the
efficiency scores are positively correlated with the bank size, the relative concentration in the
market, the measure of asset quality, and with the bank capital adequacy ratio. In addition, the
recent financial crisis seems to have slight impact on the observed efficiency scores of the banks
under study.
Finally, though many of non-oil producing countries under study have implemented programs of
financial reforms to enhance the performance of their banking sectors, these may still need
further reforms to catch-up with the level of efficiency observed in oil-producing countries.
Keywords: Cost efficiency, Translog Form, Stochastic Frontier, Intermediation Approach
JEL Classifications: G21, G28, G32, C52, F23
1. Introduction
The aim of this study is to investigate the efficiency levels in the banking sectors of MENA
countries over the 2007-2013 period. The empirical evidence on bank efficiency aims to
highlight the features associated with the major events that faced the banking sectors in
these countries including the global financial crisis in 2009 and onward, high instability in
oil prices and observing the role of economic development and financial reforms that
have taken place in many of these countries over the last decade.
At the beginning, it is worth saying that the financial sectors in developing countries,
including those in the MENA region have traditionally been characterised by relatively
high levels of government controls where regulatory authorities maintained a protected
banking environment that inhibited competition. However, market conditions in banking
have undergone extensive changes over the last two decades or so. On the demand
side, customer preferences have changed substantially, becoming more sophisticated
and price conscious. On the supply side, the globalisation of financial markets has been
______________________________________________________________________
Prof. Idries Mohammad Al-Jarrah, Qatar University, Doha 2713 Qatar, email: Idries@qu.edu.qa, Work Tel.:
+974 44036491
Dr. Khalid Shams Al-Abdulqader, Qatar University, Doha 2713 Qatar, email: Khld-shams@qu.edu.qa, Work
Tel.: +974-44035088
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Proceedings of 10th Annual London Business Research Conference
10 - 11 August 2015, Imperial College, London, UK, ISBN: 978-1-922069-81-8
accompanied by governmental deregulation, financial innovation and automation. As
such, banks are now faced with competition from banks and non-bank institutions, and
this also accentuates competition within the banking and financial services sector overall.
To assist banks in confronting the new challenge, various MENA countries have passed a
substantial body of legislation (over the last few years) aimed at liberalising their financial
systems through the reduction of direct government control, at the same time, it is
associated with upgrades of prudential regulations (Al-Jarrah, 2003).
In this context, among the recent studies that addressed the impact of financial
deregulation on bank efficiency is that of Kumar (2013) who analyses the trends of cost
efficiency across Indian public sector banks (PSBs) during the post-deregulation period
from 1992/93 to 2007/2008. The results reveal substantial cost inefficiencies of about
25.6% for Indian PSBs and the analysis shows the presence of convergence in cost
efficiency levels during the post-deregulation years.
As for the more broad studies that addressed the efficiency in emerging countries,
Oluitan (2014) used the translog functional form to examine the cost efficiency of banking
sectors in forty seven African countries over ten years period. The estimated levels of
inefficiency range from about 10 to 26 percent for the financial sectors under study. The
author argues that much of the inefficiency for countries under study are attributed to
poor intermediation and low skilled staff in the banking sectors under study.
Given the observed inextricable link between financial liberalisation and efficiencies, it is
therefore interesting to highlight the impact of economic and financial reforms in various
MENA countries on the efficiency levels of the financial institutions operating in these
countries. Furthermore, our study period embraces the global financial crisis in 2007 and
on and high instability in oil prices; and we expect these events to have impact on the
performance of the banking sectors in various MENA countries.
On the other hand, despite the extensive literature that has examined productive
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Proceedings of 10th Annual London Business Research Conference
10 - 11 August 2015, Imperial College, London, UK, ISBN: 978-1-922069-81-8
efficiency, especially in the US banking system and other European markets, empirical
research on financial sectors in developing countries, including MENA region, is much
less. Besides, there are other motives for examining efficiency levels in MENA banking
systems. First, such a study should help assessing the impact of the economic and
financial reforms that have taken place in the countries under study. Furthermore, this
study aims to provide empirical evidence about efficiency differences across various
MENA banking industries (and across various types of financial institutions operating in
these countries such as commercial, investment and Islamic banks).
In addition to this introduction, this study is structured into five sections. Section 2
describes features of the banking sectors in MENA countries. Section 3 presents the
study sample and methodology. Section 4 presents the empirical results. Finally, Section
5 summarizes the findings and gives the conclusion.
2. Features of the Banking Sectors in MENA countries
The total assets of the banking sectors in MENA region has grown from about $1,510
billion in 2007 to about $2,327 billion in 2013; with an annual growth of almost 8 percent
over this period. The largest banking sector in the region are those of Saudi Arabia,
United Arab Emirates (UAE), Iran and Kuwait whose total shares of the region banking
sectors is about 58 percent over the study period.
The banking sectors in MENA region are generally characterized by high concentration,
in terms of the share of the largest three banks of total assets in their respective banking
sectors. The 3-firm concentration ratio (3-CR) averaged 56 percent for MENA banking
sectors, with insignificant changes for this share over the period of 2007-2013 (Table 1).
On the other hand, the individual bank market share (MS) in its respective market
averaged about 8 percent over the study, with insignificant changes to this share over
time (Table 1). Both the 3-CR ratio and the market share indicate that the MENA banking
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Proceedings of 10th Annual London Business Research Conference
10 - 11 August 2015, Imperial College, London, UK, ISBN: 978-1-922069-81-8
sectors are rather concentrated markets and this might affect negatively the
competitiveness and the performance of the banking sectors in these countries.
As for the quality of loan portfolios, as measured by the ratio loan-loss reserves as a
percent of total loans, in the MENA banking sectors, this ratio averages around 6 percent
with high dissimilarities among the banks over the study (Table 1). On the other hand, the
capital adequacy ratio, measured by the ratio of equity capital as percent of total assets,
this ratio average 13 percent for the countries in the region (Table 1). Both the asset
quality and capital adequacy ratios show high dissimilarities for banks operating in MENA
region countries. Thus, the ability of the MENA banking sectors to stand potential
negative shocks that might strike these sectors will not be symmetrical.
As for the efficiency in utilization of assets of banking sectors in MENA region, in term of
the return on assets (ROA), the banking sectors of GCC countries and the banking sector
of Algeria seem to be the most efficient given the return on asset in these countries
average more than 1.5 percent over the study period (Table 1). As for the profitability of
the MENA region banking sectors, as measured by return on equity (ROE), the ROE has
witnessed some decline from around 16 percent in 2007 to about 12 percent in 2013
(Table 1). Both the ROA and ROE ratios show clearly that the profitability of banking
sectors vary substantially based on the geographical location; both indicates that the
most profitable sectors are those of GCC countries over the study.
As for the distribution of banks according to specialization in MENA countries, it is clear
that the commercial banks dominate the banking sectors, as measured by the ratio of
their asset holding as percent of the total banking sector assets in their respective
sectors. The share of the commercial banks exceeds 90 percent in many countries over
the period 2007 to 2013. On the other hand, the Islamic banks dominate the banking
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Proceedings of 10th Annual London Business Research Conference
10 - 11 August 2015, Imperial College, London, UK, ISBN: 978-1-922069-81-8
sector in Iran as it seems to be not allowed for other non-Islamic based banks to conduct
business there. Finally, the investment seem to have relatively substantial role in some
MENA banking sectors including those of Malta, Kuwait, Palestine, Oman and Jordan
where the share of these banks, in terms of the assets holdings in their respective
banking sectors, exceeded 5 percent.
To conclude, this section shows that the size of the banking sectors in various MENA
countries vary considerably and the banking sectors of oil-producing GCC countries, Iran
and Egypt are the largest. The banking sectors in MENA countries region seem to be
highly concentrated as measured by the individual bank market share and by the 3-CR in
these markets which signal worries about the competitiveness of these sectors . The
asset quality of the banking sectors in MENA region countries vary substantially and this
applies to the observed capital adequacy ratios in these countries over the period 20072013. The profitability measures display different trends at country level, as measured by
ROA, ROE and NIM and again the banks operating in GCC countries seem to be the
most profitable.
3. The Study Sample and Methodology
This study employs the Translog functional form to derive measures of cost efficiency for
banking sectors operating in MENA region countries over the 2007-2013 period. The
stochastic frontier methodology was proposed by Gallant (1981, 1982), discussed later by
Elbadawi et al. (1983), Chalfant and Gallant (1985), Eastwood and Gallant (1991) and
applied to the analysis of bank cost efficiency by Spong et al. (1995), Mitchell and
Onvural (1996) and Berger et al. (1997), among many others. The stochastic cost
function for a sample of N firms can be written as:
lnTCi = lnTC(yi ,wi ,di ;B)+ui +vi, ,
i = 1, ..., N,
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Proceedings of 10th Annual London Business Research Conference
10 - 11 August 2015, Imperial College, London, UK, ISBN: 978-1-922069-81-8
where TCi is the observed cost of bank i, y i is a vector of output, wi is a vector of input
prices, di a vector of control variables that include firm-specific (and country-specific
variables). B is a vector of parameters to be estimated and vi is a two-sided error term
representing the statistical noise. u it represents non-negative variables that account for
inefficiency and are assumed to be independently and identically distributed (iid) as
truncations at zero of the N( it d,  2uit ) distribution; where mi =  di where di are the control
variables and  is a vector of their parameters. In the light of these concerns, the Translog
functional form for the cost function has been specified as:

2
2
1 2 2
   B ln( w / w ) ln(w j / w3 ) 
ln(C / w )     B ln( w / w )    ln y 
3
i
i 3
k
k 2 
ij
i 3

i 1
k 1
i=1 j=1

9
3
3
2
3


1
     ln y ln y      ln( w / w ) ln( y )    di  uit  vit
k
m
ik
i 3
k
2  k  1 m  1 km
i 1
 i  1k  1
where lnTC is the natural logarithm of bank’s total costs (operating and financial); ln yi is
the natural logarithm of bank outputs (i.e. loans and other earnings assets); ln wi is the
natural logarithm of ith input prices (i.e. interest rate, wage rate and cost of financial
capital); di are set of control variables and
 ,  ,  , , and  are coefficients to be
estimated.
Before running the maximum-likelihood to get estimate for parameters in the Translog
stochastic frontier, we employed panel Ordinary Least Squares (OLS) regression with
various combination of control variables to check the need to add these variables in final
model that employed to derive efficiency measures. All the specified models, based on Ftest that enables comparing various nested models’ specifications, Wald test that check
for restricting the coefficients to zero and that likelihood ratio that allow examining the
redundancy in the specified model indicate that the model should include all the control
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Proceedings of 10th Annual London Business Research Conference
10 - 11 August 2015, Imperial College, London, UK, ISBN: 978-1-922069-81-8
variables (only the best specified model is reported, Table 2).
The sample of this study comprises data for banks operating in the financial systems of
MENA countries over the 2007-2013. The share of the banks included in our study in
terms of their assets holdings in their respective banking sectors is about 80 percent over
the period 2007-2013 (Table 3).
This study employs the intermediation approach for defining bank inputs and outputs. The
bank inputs include deposits, labour and financial capital (k) while the output are loans
(y1) and other earning assets (y2), measured in millions of US dollars. In line with Aly et
al. (1990), the cost of deposit (w1) is derived by taking the total interest expense on
various types of deposits and other banks borrowing as percent of total deposits. As staff
numbers were not available for the banks in the sample, the cost of Labour (w2) is
measured by personnel expenses as a percent of total earning assets minus loans (given
loans are used in calculation of w1). The cost of financial capital (w3) is measured by the
return on equity (ROE). The description, means, standards of deviation of the input and
output variables are reported in table 4. In addition to the input and output variables, we
include nine control variables to the cost function to rule out the effect of other factors that
might explain the observed dissimilarities in efficiency scores for the banks under study.
These variables include measures for asset quality (S), scale economies (SE), financial
capital (k), liquidity risk (L), 3-firm concentration ratio (3-CR), market share (MS), bank
specialization (Sp), time trend (T) and a country indicator variable (Cn) (Table 5 shows
descriptive statistics of the control variables).
4. Study Results
The cost efficiency scores for banks in the MENA countries based on our sample
averaged around 77 percent over the 2007-2013 period, with slight changes to this score
over the study period (Table 6). This efficiency level suggests that the same level of
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Proceedings of 10th Annual London Business Research Conference
10 - 11 August 2015, Imperial College, London, UK, ISBN: 978-1-922069-81-8
output could be produced with approximately 77% of current inputs if banks under study
were operating on the most efficient frontier. This level of inefficiency is somewhat higher
than the range of 10-15% for the 130 studies surveyed by Berger and Humphrey (1997)
and Berger and DeYoung (1997). These inefficiency scores are somehow also
comparable to the level of inefficiency observed in European studies including Carbo et
al.’s (2000) whose findings for a sample of banks, from twelve countries, show mean cost
inefficiency of around 22 % for the period 1989 to 1996. Finally, these levels of efficiency
are comparable to more recent studies of Kumar (2013) on Indian banking sectors and
Oluitan (2014) on African banking sectors.
As for the efficiency scores based on the geographical location, the banks operating in
Gulf Cooperation Council (GCC) countries seem to be the most cost efficient as the
efficiency scores for these countries ranges from about 83 percent in UAE to 99 percent
in Qatar, on average, over the study period. On the other hand, the least efficient banks
are those operating in countries that have witnessed or witnessing political instability
including those of Lebanon, Yemen, Palestine and Syria. The trend of cost efficiency
scores over time for the countries included seem to be highly correlated, which most
probably attributed to adverse impact of latest financial crisis. For instance, the efficiency
scores for the banks operating in GCC countries have witnessed identical drift over time
in a form of slight deterioration for their efficiency scores over the study period.
As for the cost efficiency scores based on specialization, the efficiency scores for both
commercial and Islamic banks are somehow analogous while the efficiency scores for
Investment banks are slightly less. Thus, though it has been argued that the Islamic
banks have cost advantage over other banks given part of the sources of funds for these
banks carry no cost, our results do not find rich evidence that support this argument. This
result may be attributed to intense competition between commercial and Islamic banks to
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Proceedings of 10th Annual London Business Research Conference
10 - 11 August 2015, Imperial College, London, UK, ISBN: 978-1-922069-81-8
attain greater market share in the market.
As for the impact of capital adequacy ratio, as measured by the bank owners’ equity as
percent of its total assets, on the estimated efficiency scores, we observed that the
efficiency scores are highly correlated with capital adequacy ratios. Specifically, the
efficiency scores ranged from 68 percent for relatively undercapitalized banks with capital
adequacy ratio of less than 6 percent to reach 80 percent for banks with capital adequacy
ratio that ranges between 10 to 25 percent.
The efficiency scores based on bank size, as measured by the total asset holdings, Table
10 shows that efficiency scores are positively correlated with bank size. The efficiency
scores ranged from 68 percent for small banks whose assets holdings are less than $1
billion to 82 percent for banks whose asset holdings are $25 billion or more. Thus, large
banks seem to be enjoying higher levels of efficiency, most probably due to the
concurrent scale economies, market power and other favourable features including the
ability of these banks to utilize more efficient technology with less cost, the ability of these
banks to set up more specialized staff for the most profitable activities and the ability of
these banks to provide better quality output and therefore charge higher prices.
As for the impact of bank’s market share, as measured by the share of individual bank of
total assets in the respective market, on the observed efficiency scores. Our results show
that the banks with larger market shares are relatively more cost efficient than their
counterparties banks. This finding supports our argument that large banks enjoy
economies of scale and market power over other banks in their respective market. The
movements in trends of cost efficiency for the banks under study seem to be highly
correlated over time; as the negative or positive shocks appear to affect banks of various
sizes in a similar way.
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Proceedings of 10th Annual London Business Research Conference
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On the other hand, the efficiency scores based on the share of the largest three banks of
total assets (3-CR) in their respective banking market, the results show that the efficiency
scores is positively associated with the increase in the concentration but this relationship
does not hold for the most concentrated sectors in the study where the share of the
largest three banks exceeds 70 percent. Thus, the increase in the concentration seems to
be advantageous for most banks in the MENA region, probably due to the market power
enjoyed by the largest banks operating in these markets.
As for the impact of asset quality (measured by the ratio of loan reserve as percent of
total loans - LLR ratio) on the efficiency scores for the banks under study, the results
show that the efficiency scores are positively correlated with better asset quality.
Specifically, the efficiency scores range from 66 percent for banks with the lowest asset
quality (highest LLR ratio) to about 79 percent for the banks with the highest asset
quality.
Finally, as for the impact of liquidity ratio, as measured by the ratio of cash and cash due
with other banks as percent of total assets, on the efficiency scores, the results show that
more liquid banks are relatively less cost efficient.
To sum up, the average cost efficiency is 77 percent for the MENA countries over the
2007-2013 period, with slight decline for this score over time. The efficiency scores for
both commercial and Islamic banks are comparable and better than that of the
Investment. The efficiency scores for oil-producing countries mainly the GCC countries
are better than the efficiency scores observed in the other countries especially the
countries that have witnessed or witnessing political and economic instability including
Palestine, Yemen and Syria. The observed efficiency scores for the banks in the MENA
are highly correlated with the market share, the concentration in the market and the bank
size. Furthermore, the efficiency scores for overcapitalized banks with low ratio of loan10
Proceedings of 10th Annual London Business Research Conference
10 - 11 August 2015, Imperial College, London, UK, ISBN: 978-1-922069-81-8
loss reserves to total assets are much better than the scores observed for
undercapitalized banks with inferior asset quality. Finally, the liquidity is found to be
negatively correlated with efficiency scores for the banks under study.
Thus, while many of non-oil producing countries under study have implemented programs
of financial and economic reforms with the aim of upgrading the performance of their
banking sector in the economic development, these may still need further geared reforms
to catch-up with the level of efficiency observed in relatively rich oil-producing countries
including those of GCC countries. We argue that the observed cost efficiency scores are
negatively correlated with political instability, where the latter has heavily (and negatively)
affected the performance of the banks in these countries. In this regard, the political
conflicts in various countries including Libya, Syria, Yemen and Iraq might contribute to
the observed level of efficiency for the banks operating in these countries. Furthermore,
the instability in the oil prices has negative impact on the observed efficiency levels in
some oil-importing countries including those of Jordan, Morocco, Egypt, Tunis and
Lebanon. On the other side of MENA countries, the move to create single GCC market
may positively help the banks operating in these countries, in a similar style to that of
countries in European Monetary Union (EMU) which intensified the competition and result
in a better observed efficiency for the banks in these countries (see European
Commission (1997)). We expect that the operation of a single banking market in the GCC
to force greater merger and acquisition activities in the financial sector that could result in
the realization of larger economies and greater efficiency for their banking and financial
systems. Finally, we observed that the efficiency scores for banks under study are slightly
affected by the latest financial crisis, in general.
5. Conclusion
This study employs the cost function with stochastic frontier to estimate the cost efficiency
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Proceedings of 10th Annual London Business Research Conference
10 - 11 August 2015, Imperial College, London, UK, ISBN: 978-1-922069-81-8
levels for banking sectors operating in MENA region countries over the period 2007-2013.
The share of the banks included in the study in terms of their assets holdings in their
respective banking sectors is about 80 percent, comprising an unbalanced sample of
1308 observations over the same period. The intermediation approach is used for
defining bank inputs and outputs. The bank inputs include deposits, labour and financial
capital (k) while the output are loans (y1) and other earning assets (y2), measured in
millions of US dollars. In addition to the input and output variables, we include a nine
control variables to the cost function to rule out the effect of other factors that might
explain the observed differences among efficiency scores for the banks under study.
The maximum likelihood estimates for the parameters of the Translog function form that
include control variables is estimated using Battese and Coelli’s (1995) model. Before
deriving the maximum-likelihood estimate for parameters in the Translog stochastic
frontier, we employed panel OLS regression with various combinations of control
variables to check the necessity to add these variables in our well-specified model that
used to derive efficiency measures. The various specified models we estimated, based
mainly on Wald test and the likelihood ratio indicate that the best specified model is the
one that include all the control variables.
The cost efficiency scores for banks in the MENA countries based on our sample
averaged around 77 percent over the 2007-2013 period, with slight changes to this score
over the study period. This suggests that the same level of output could be produced with
approximately 77% of current inputs if banks under study were operating on the most
efficient frontier. This level of inefficiency is somewhat higher than the range of 10-15%
for the 130 studies surveyed by Berger and Humphrey (1997) and Berger and DeYoung
(1997). These inefficiency scores are somehow also comparable to the level of
inefficiency observed in European studies including Carbo et al.’s (2000) whose findings
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Proceedings of 10th Annual London Business Research Conference
10 - 11 August 2015, Imperial College, London, UK, ISBN: 978-1-922069-81-8
for a sample of banks, from twelve countries, show mean cost inefficiency of around 22 %
for the period 1989 to 1996. Furthermore, our inefficiency results for banking sectors in
MENA region countries are comparable with more recent studies including those of
Kumar (2013) on Indian banks and Oluitan (2014) on African banking sectors.
As for the efficiency scores based on the geographical location, the banks operating in
Gulf Cooperation Council (GCC) countries seem to be the most cost efficient while the
least efficient banks are those operating in countries that have witnessed or witnessing
political instability. As for the cost efficiency scores based on specialization, the efficiency
scores for both commercial and Islamic banks are analogous while the efficiency scores
for Investment are less. This result may be attributed to intense competition between
commercial and Islamic banks.
The derived efficiency scores are found to be positively correlated with the bank’s size;
the latter reflect the ability of relatively large banks to benefit from the concurrent scale
economies, market power and other favourable features including the ability to utilize
more efficient technology with less cost. The results also show that the efficiency scores
is positively associated with the increase in the bank’s market share and the degree of
concentration in the market; probably due to the market power enjoyed by the largest
banks operating in these markets. The observed efficiency scores are found to be
positively correlated with superior asset quality but it were negatively correlated with the
increase in the bank’s liquidity. Furthermore, the efficiency scores for overcapitalized
banks with low ratio of loan-loss reserves to total assets are much better than the scores
observed for undercapitalized banks with inferior asset quality.
To sum up, the efficiency scores for oil-producing countries mainly the GCC countries are
better than the efficiency scores observed in the other countries especially the MENA
countries that have witnessed or witnessing political and economic instability.
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Proceedings of 10th Annual London Business Research Conference
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Furthermore, while many of non-oil producing countries under study have implemented
programs of financial and economic reforms with the aim of upgrading the performance of
their banking sector and their roles in the economic development, these may still need
further geared reforms to catch-up with the level of efficiency observed in relatively rich
oil-producing countries. The move to create single GCC market may positively help the
banks operating in these countries, in a similar style to that of countries in European
Monetary Union (EMU) which intensified the competition and result in a better observed
efficiency for the banks in these countries (European Commission (1997)). Finally, we
argue that the observed efficiency scores are slightly affected by the latest financial crisis
but are heavily affected by the political instability in various MENA region countries under
study.
References
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Proceedings of 10th Annual London Business Research Conference
10 - 11 August 2015, Imperial College, London, UK, ISBN: 978-1-922069-81-8
Commercial Banks’, ‘Federal Reserve Board, Washington, D.C.’, (WP 8/1997).
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Appendix
Table 1: The Financial Indicators of the MENA Banking Sectors over 2007-2013, on Average
Country/ Year
3-CR**
MS**
LLR/
Equity/A ROA**
ROE**
NIM**
**
**
Loans
ssets
Algeria
0.70
0.07
0.01
0.19
0.020
0.13
0.046
Bahrain
0.51
0.07
0.03
0.14
0.014
0.12
0.024
Egypt
0.52
0.04
0.13
0.10
0.013
0.15
0.032
Iran
0.56
0.11
0.02
0.12
0.012
0.13
0.015
Iraq*
0.78
0.04
0.07
0.28
0.038
0.15
0.026
Jordan
0.68
0.07
0.05
0.14
0.013
0.1
0.034
Kuwait
0.51
0.10
0.05
0.11
0.015
0.12
0.027
Lebanon
0.44
0.04
0.08
0.08
0.009
0.13
0.024
Libya*
0.63
0.19
0.07
0.11
0.008
0.10
0.038
Malta
0.70
0.16
0.02
0.09
0.011
0.13
0.024
Morocco
0.57
0.09
0.03
0.09
0.014
0.16
0.029
Oman
0.64
0.14
0.05
0.12
0.019
0.17
0.035
Palestine
0.87
0.23
0.02
0.16
0.010
0.08
0.047
Qatar
0.68
0.11
0.02
0.18
0.025
0.15
0.03
Saudi
0.44
0.08
0.03
0.16
0.019
0.14
0.031
Syria
0.55
0.15
0.07
0.09
0.007
0.09
0.032
Tunis
0.37
0.06
0.10
0.13
0.013
0.12
0.025
UAE
0.49
0.05
0.05
0.16
0.020
0.13
0.035
Yemen
0.71
0.22
0.28
0.09
0.012
0.13
0.042
Average
0.56
0.08
0.06
0.13
0.015
0.13
0.03
Source: Adapted from Bankscope – Bureau van Dijk, World Banking Information Source.
*The size of the sample drawn from Libya and Iraq is less than 70 percent, in their respective market and
thus these will be excluded from our discussion as the samples might not be representative.
The 3-CR is the 3-firm concentration ratio as measured by the share of the largest three banks in the total
assets of the banking sector in the respective market, MS is the share of individual bank, on average, in the
total assets of the banking sector in the respective market, LLR/Loans is the loan-loss reserve as percent of
total loans in the respective market, Equity/Assets is the ratio of total owners’ equity as percent of total
assets in the respective market, ROA is the average ratio of net income as percent of total owners’ equity in
the respective market, ROE is the ratio of net income as percent of total owners’ equity in the respective
market, and NIM is the net interest margin as measured by the ratio of net interest income as percent of
total earning assets in the respective market.
16
Proceedings of 10th Annual London Business Research Conference
10 - 11 August 2015, Imperial College, London, UK, ISBN: 978-1-922069-81-8
Table 2:Maximum Likelihood parameter estimates and Ordinary Least Squares for the Study Model
for MENA banks using Translog functional form
Maximum Likelihood Parameter Estimates
Panel OLS Estimates
Symbol (in
Variables (all are
coefficient
t-ratio Coefficient P-value
the model)
logged)
(0.00)
(0.01)
10.83
0.24

lny1
0.52
6.65
0.63
0.00 ***

lny2
0.62
7.32
1.61
0.00 ***

Lnw1/w3
(0.18)
(1.64)
4.46
0.00 ***

lnw2/w3
0.67
5.79
(1.11)
0.32

lny1lny1
0.06
12.48
(5.97)
0.00 ***

lny1lny2
(0.12)
(11.57)
5.04
0.00 ***

lny1lnw1/w3
0.02
1.64
(0.28)
0.01 ***

lny1lnw2/w3
0.00
0.17
0.01
0.96

lny2lny2
0.06
10.57
0.17
0.16

lny2lnw1/w3
0.04
3.17
(0.43)
0.00 ***

lny2lnw2/w3
0.00
0.06
0.10
0.34

lnw1/3lnw1/w3
(0.08)
(15.47)
0.42
0.01 ***

lnw1/w3lnw2/w3
0.00
5.87
(0.22)
0.12

lnw2/w3lnw2/w3
0.00
1.98
(0.49)
0.00 ***

(LLR/ Loans) (S)
0.93
6.21
0.00
0.00 ***

(Liquid asset/
1.88
16.99
(0.00)
0.01 ***
Asset) (L)

(Ln(TA)) (SE)
0.01
3.25
(3.15)
0.06 *

(OE/TA)(K)
(0.60)
(4.76)
1.27
0.46

Specialization
(Sp)
(0.00)
(0.03)
(3.85)
0.00 ***

3-FCR
(0.30)
(3.11)
11.07
0.00 ***

MS
(0.24)
(1.73)
(10.26)
0.00 ***

time
trend
(T)
(0.03)
(5.40)
0.63
0.00 ***

Country (Cn)
0.02
5.94
11.56
0.00 ***

sigma-square
0.13
24.50
gamma
0.00
0.01
Sigma-squared
0.00
R-squared
0.99
Sigma-squared (v)
0.13
Adjusted R-squared
0.99
Lamda
0.00
S.E. of regression
5.18
Contribution of the inefficiency
0.00
Sum squared resid
34,481
effect/ T. variance
Log likelihood function
(535.71)
Log likelihood
(3,996)
[this statistic has a mixed chi-squared distribution]
F-statistic
7,475
Prob(F-statistic)
0.00
Source: Authors’ own estimation.
*, **, and *** means significant at 10%, 5% and 10% respectively.
Table 3: Share of the Banks in their respective markets according to specialization
Country/
Size of Sample (% of No. of Obs. Over Commercial
Islamic
17
Investment
Proceedings of 10th Annual London Business Research Conference
10 - 11 August 2015, Imperial College, London, UK, ISBN: 978-1-922069-81-8
Year
T. Assets)
2007-13
banks
banks
banks
Algeria
0.83
81
1.00
0.00
0.00
Bahrain
0.59
61
0.74
0.26
0.00
Egypt
0.86
134
0.96
0.03
0.01
Iran
0.97
63
0.00
1.00
0.00
Iraq*
0.21
40
0.39
0.17
0.44
Jordan
0.99
102
0.86
0.07
0.07
Kuwait
0.81
55
0.64
0.25
0.11
Lebanon
0.9
159
0.99
0.00
0.01
Libya*
0.38
14
1.00
0.00
0.00
Malta
0.75
33
0.42
0.00
0.58
Morocco
0.84
67
0.99
0.00
0.01
Oman
0.86
42
0.92
0.00
0.08
Palestine
0.98
30
0.69
0.23
0.08
Qatar
0.99
65
0.82
0.18
0.00
Saudi
0.96
81
0.83
0.17
0.00
Syria
0.77
36
0.84
0.16
0.00
Tunis
0.78
99
0.94
0.02
0.04
UAE
0.93
123
0.84
0.16
0.00
Yemen
0.72
23
0.45
0.55
0.00
Average
0.80
1308
Source: Adapted from Bankscope – Bureau van Dijk, World Banking Information Source
*The size of the sample drawn from Libya and Iraq is less than 70 percent, in their respective market and
thus these will be excluded from our discussion as the samples might not be representative.
Table 4: Descriptive statistics of the total costs, inputs and outputs for banks in MENA region over
2007-2013
Variables
Description
Mean St. Dev. Min.
Max.
TC ($Millions)
Total cost of banks includes
314
547
0 6,285
both operating and financial
costs (US$ millions).
Bank Output
y1 Loans ($Millions)
The US $ value of total
5,876
9,745
0 85,360
aggregate loans (all types of
loans) (US$ millions).
y2 Other Earning Assets ($Millions) The US $ value of other earning
3,380
5,222
1 37,780
assets (short-term investment,
equity and other investment and
public sector securities (US$
millions)).
Cost of bank Inputs
w1 cost of deposits
Cost of customer deposits (%)
0.07
0.55 0.00 18.39
(total interest expense/ total
customer deposits (demand,
saving and time deposits)).
w2 cost of labor
Price of labour (%) ((total
0.01
0.02 0.00
0.36
personnel expense/(Earning
Assets - Loans).
w3 Cost of Financial capital
Price of financial capital (%)
0.16
0.67 0.00 22.19
(Net Income/ Owners’ Equity).
Source: Adapted from Bankscope – Bureau van Dijk, World Banking Information Source
18
Proceedings of 10th Annual London Business Research Conference
10 - 11 August 2015, Imperial College, London, UK, ISBN: 978-1-922069-81-8
Table 5: Descriptive statistics of the control variables for banks in MENA countries over 2007-2013
Control Variables
Description
Mean
Standard Minimum Maximum
Deviation
Asset Quality (S)
The ratio of loan loss reserves as
0.06
0.10
0.00
0.87
percent of total loans (LLR ratio)
Scale Economies
The value of total assets
10,824
16,086
30
121,837
(TA, US$ Millions)
(SE)
Capital Adequacy
The ratio of owners' equity as
0.13
0.08
-0.01
0.90
(%) (K)
percent of total assets
Liquidity Risk (L)
The ratio of cash and due with
0.12
0.14
0.00
0.95
other financial institutions as
percent of total assets
The 3-Firm
the largest 3 banks total assets of
0.56
0.13
0.21
1.00
Concentration Ratio /Total assets of all banks in the
(3-CR)
bank country for the respective
years
Market share (MS)
Bank assets market share (%) for
0.08
0.10
0.00
0.74
each year
Specialization (Sp)
Bank specialization; Commercial,
1.3
0.6
1.0
3.0
Islamic and Investment
Time trend (T)
Time trend for the years of the
4.0
2.0
1.0
7.0
study period
Country code (Cn)
Indicator variable for the
9.3
4.9
1.0
19.0
countries included in the study
Source: Adapted from Bankscope – Bureau van Dijk, World Banking Information Source
Table 6: Efficiency Scores for MENA Countries over 2007-2013
19
Proceedings of 10th Annual London Business Research Conference
10 - 11 August 2015, Imperial College, London, UK, ISBN: 978-1-922069-81-8
Country/ Year
Qatar
2007
1.00
2008
1.00
2009
0.97
2010
1.00
2011
0.99
2012
0.99
2013
0.97
Average
0.99
No. of Obs.
65
Bahrain
0.92
0.92
0.94
0.94
0.92
0.94
0.94
0.93
61
Oman
0.92
0.96
0.98
0.87
0.94
0.86
0.85
0.91
42
Kuwait
0.89
0.86
0.84
0.82
0.85
0.88
0.86
0.86
55
Saudi
0.85
0.81
0.84
0.87
0.85
0.86
0.83
0.85
81
Morocco
0.91
0.83
0.85
0.87
0.83
0.77
0.73
0.83
67
UAE
0.83
0.83
0.81
0.83
0.85
0.83
0.81
0.83
123
Jordan
0.86
0.84
0.79
0.85
0.82
0.80
0.77
0.82
102
Malta
0.85
0.77
0.81
0.89
0.81
0.81
0.81
0.82
33
Algeria
0.79
0.81
0.77
0.82
0.79
0.79
0.78
0.79
81
Egypt
0.79
0.79
0.72
0.76
0.75
0.76
0.73
0.76
134
Tunis
0.74
0.78
0.74
0.75
0.69
0.71
0.67
0.73
99
Iran
0.74
0.73
0.72
0.70
0.69
0.72
0.66
0.71
63
Palestine
0.75
0.77
0.70
0.54
0.73
0.65
0.73
0.69
30
Lebanon
0.65
0.64
0.67
0.68
0.66
0.60
0.58
0.64
159
Yemen
0.72
0.67
0.58
0.61
0.56
0.53
0.63
0.61
23
Syria
0.54
0.53
0.55
0.58
0.66
0.58
0.49
0.56
36
Iraq*
0.29
0.41
0.44
0.41
0.38
0.40
0.45
0.40
40
Libya*
0.22
0.24
0.23
0.49
0.52
0.50
0.25
0.35
14
Average
0.78
0.77
0.76
0.78
0.77
0.76
0.74
0.77
1308
Source: Authors’ own estimation
*The size of the sample drawn from Libya and Iraq is less than 70 percent, in their respective market
and thus these will be excluded from our discussion as the samples might not be representative.
20