Market structure and concentration in Islamic and conventional banking
Ali CHKIR* & Amira HAKIM**
Abstract
This paper investigates the market structure and the degree of concentration of Islamic banking
industry. This paper reports an empirical assessment of competitive conditions among the Arab
GCC banking industry during the years of 2005-2010. We opt for a methodology as proposed by
Panzar and Rosse based on a nonstructural estimation of the H-statistic and the most frequently
applied measures of concentration k-bank concentration ratio (CRk), Herfindahl–Hirschman Index
(HHI), and the measure of entropy. The results show that conventional banking industry is more
concentrated than the Islamic banking industry. However the measure of entropy confirms that the
market structure of Islamic and conventional banking is almost similar with reference to total
deposits and totals loans. The empirical evidence reveals that the banking industry in general and
the conventional banking industry in the region operate under perfect competition; the prevailing
market structure in Islamic banking industry is mostly monopoly.
Keywords: Islamic banking – concentration- market structure- GCC countries- Panzar-Rosse
Approach
1
Introduction
In the context of the financial globalization, Islamic finance seems to be a strategic issue to
guard against economic crises and practice of our ethical values. Disclosure of Islamic banks in the
banking sector has changed the structure of the banking market. This is a question mark for the
economic policy makers. In the same context of financial globalization and the emergence of new
technologies of information and communication, the banking industry is sustained by a process of
banking and financial consolidation observed in the worldwide sphere. These worked embrittlement
of the barriers to entry of new competitors and an intensification of competition. The study of
market structure in the banking industry is one of the controversial subjects which surmount a
review of extensive literature treating the subject in several dimensions namely a bank, a country
and a geographic agglomeration. The study suggests that the competitive situation emphasize
economic concentration for its interdependence with the competitive power of the sector. The study
of market structure and the competitive power of the banking industry is an interest in economic
policy for its impact on the economy. Several recent studies focus on the competitive structure of
the banking industry. However, questions remain on the concentration and conditions of
competition that Islamic banks are thriving in an economic environment characterized by the
hegemony of conventional banks. Several recent studies focus on the competitive structure of the
banking industry. However, questions remain on the concentration and conditions of competition
that Islamic banks are thriving in an economic environment characterized by the hegemony of
conventional banks.
This study provides an overview of the tools at hand to investigate competitive conditions and to
calculate the degree of concentration in banking market. To this purpose, we apply a methodology
first proposed by Rosse and Panzar to a panel of banks. Section 2 illustrates the Rosse–Panzar test,
with particular reference to the theoretical framework, the empirical implications and the existing
studies employing this methodology. Section 3 describes our sample used to conduct this study.
2
Section 4 presents an overview of market concentration measures, and compares measures of
concentration between Islamic and conventional banks. Section 5 presents and discusses our
application to the GCC banking system. Some conclusions are given in the last section.
1- The Panzar and Rosse approach: theory
Panzar-Rosse approach provides an assessment of the market structure and competitive conditions
through the income elasticity in relation to input prices. H-statistic calculated derived from the
estimation of a production function in its reduced form defined by the sum of the income elasticities
to variations in input prices. Panzar-Rosse model is a non-structural approach is part of the new
theory of industrial organization. This approach emphasizes the impact of prices of production
factors on the income of the banking firm to determine the nature of competition in banking systems,
which can serve as a measure of competitive behaviour of banks. This is an approach that provides
a measure of market power of a bank and the structure of the banking market. The Panzar-Rosse
approach is based on a number of assumptions including:





The cost structure is homogeneous.
The production function is Cobb-Douglass standard.
The elasticity of demand relative to the price is higher than unity.
The banks operate in their long-term equilibrium.
Banks' performance is influenced by the actions of other banks (except in the case of monopoly
market structure).
The Panzar-Rosse approach has undergone several deepening conducted by contemporary
economists. They have added other assumptions that are crucial to apply this approach to banking.
And Gelos and Roldos (2002) added two fundamental assumptions. On the one hand banks are
firms mono produced in which labor, capital and funds are considered as inputs. On the other hand
input prices are assumed to be not associated with high quality services. In contrast, prices could
imply high incomes that will biased the value of H-statistic. Panzar-Rosse have drawn a test of
competition based on the estimation of income function in its reduced form defined by the sum of
elasticities bank revenue compared to variations in input prices that allows assessment of the
situation market for each banking system.
3
The test is derived from a general banking market model, which determines equilibrium output and
the equilibrium number of banks, by maximising profits at the bank level and the industry level.
This implies, first, that bank i maximises its profits, where marginal revenue equals marginal cost:
𝑹∗𝒊 (𝒙∗ , 𝒏∗ , 𝒛∗ ) − 𝑪∗𝒊 (𝒙∗ , 𝒘∗ , 𝒕∗ ) = 𝟎
Ri : equilibrium value of revenue of bank i
Ci : costs of bank i (the prime denoting marginal)
xi : the output of bank i
n: the number of banks
wi : a vector of m factor input prices of bank i,
zi: is a vector of exogenous variables that shift the banks revenue function
ti: a vector of exogenous variables that shift the banks cost function.
Market power is measured by the extent to which a change in factor input prices ( 𝝏𝑾𝒌𝒊 ) is
reflected in the equilibrium revenues (𝝏𝑹∗𝒊 ) earned by bank i. Panzar and Rosse define a measure of
competition H as the sum of the elasticities of the reduced-form revenues with respect to factor
prices:
𝒎
𝑯 = ∑(𝝏𝑹∗𝒊 /𝝏𝑾𝒌𝒊 )(𝑾𝒌𝒊 /𝑹∗𝒊 )
𝒌=𝟏
The strongest assumption of the model is that banks operate in their long-run equilibrium. This test
is derived from a general equilibrium model that determines the market equilibrium maximizing
profits at each bank in the market. More precisely bank i maximizes its profit when its marginal
revenue equals its marginal cost. The condition of long run equilibrium is maintained on the entire
market.
𝑹∗𝒊 (𝒙∗ , 𝒏∗ , 𝒛∗ ) − 𝑪∗𝒊 (𝒙∗ , 𝒘∗ , 𝒕∗ ) = 𝟎
The calculation of the statistic H is defined by the sum of elasticities income of bank compared to
variations in input prices. This statistic reflects an assessment of the degree of competition in the
banking market.
𝒎
𝑯 = ∑(𝝏𝑹∗𝒊 /𝝏𝑾𝒌𝒊 )(𝑾𝒌𝒊 /𝑹∗𝒊 )
𝒌=𝟏
4
Table-1 Discriminatory power of H- Statistic
Values of H
Competitive environment
H< 0
Monopoly equilibrium: each bank operates independently as under monopoly
profit maximisation conditions (H is a decreasing function of the perceived
demand elasticity) or perfect cartel.
0<H<1
Monopolistic competition free entry equilibrium
(H is an increasing function of the perceived demand elasticity).
H =1
Perfect competition. Free entry equilibrium with full efficient
capacity utilisation.
2- literature review
The aim of this paper is to measure the competitiveness of banking in Islamic banks testing the
conditions of competition in the banking sector. To do this, the literature on the nature of
competition in the banking industry is divided into two streams which are of structural approaches
and non-structural approaches. Structural approaches analyze the market structure. The most
popular is the current "Structure-Conduct-Performance" which is developed by Mason (1939) and
further refined by Bain (1951). This approach requires that the market structure affects the behavior
of firms in a given industry and this in turn affects performance. Actually the market organization
can influence the nature of competition and price formation. However, this model has weaknesses
in the theoretical and empirical framework. There is also the theory of structure-efficiency which
has been developed by Dermetz (1973) and Petzman (1977). This model is the result of a change in
the latter approach by announcing that this positive relationship is not the result of market power
but high efficiency of firms with market shares of the most important. This model states that
efficient firms decrease in size and market share due to their ability to generate more profits which
5
leads to market concentration. The structural approach is a non formal measure of competition.
However, it is difficult to identify the most suitable structural model.
Non structural models capable of measuring the competition will compensate the shortcomings of
theoretical and empirical evidence deficiencies of structural models. Among these models the
method of Panzar Rosse (1987) and the method of Bresnahan and etLau (1982). This model is based
on the theory of industrial organization. This model was based on the model developed by Iwata
(1974) they have solved the problem of econometric identification. The model maximizes the profit
of banks in oligopoly. This is a static model that determines in the short term the market power. The
conjunctural variation of parameters is determined by the simultaneous estimation of the supply
side and the demand based on time series data for the banking industry studied. Panzar Rosse model
(1987) is derived from the literature of the new theory of industrial organization. Panzar Rosse
model (1987) is derived from the literature of the new theory of industrial organization. Panzar and
Rosse suggests a measure of market power and the conditions of competition in the sector from the
impact of prices of production factors on the revenues of firms in this sector. They developed an
empirical test of competition called the "H-statistic" by estimating the revenue function in its
reduced form defined by the sum of the elasticities of production in relation to price variations of
production factors to assess the structure banking market. The method of Lerner (1934) also comes
from the theory of industrial organization. It measures the market power depending on the elasticity
of demand with respect to price. More specifically when the elasticity is higher over the market
price, it is close to marginal cost. This measure suggests the extent of monopoly power of a firm in
the industry to which it belongs through its rate of profit: the difference between the selling price
and the marginal cost of production. The index takes the value zero if the firm operates in a
condition of perfect competition and the value 1 if it operates in a monopoly situation.
The measure of competition in the banking industry is likely to be evaluated by several methods
which differ according to the origin of the theory: the theory of industrial organization and the new
6
theory of industrial organization. To perform an empirical explanation consistent with the proposed
issue taking into account the characteristics of banks in each country, the structural approach is
most suitable. Without being exhaustive, we will limit the Panzar and Rosse approach to study
competition in the banking sector. Studies dealing with this subject in the case of conventional
banks based on the method of PanzaretRosse are numerous (Bang Nam Jeon et al. (2011). Rima
Turk Ariss (2009), and Eduardo Levy Yeyatt Ale Jundro Micco (2007), Kent Mathews et al. (2007).
Saeed al-Muharem et al. (2006) ...). They are configured with different characteristics and in
different countries. However, the literature on the comparison between competitiveness and the
concentration of Islamic banks and conventional is too limited. Recent studies deal with the
conditions of competitiveness for Islamic and conventional banks in the banking market and the
differences in profitability. In the same lineage, Laurent Weill (2010) and Rima Turk Ariss (2010)
compare the market power of Islamic banks compared to conventional banks in the case of MENA
countries. Finally, Muhamed-Zulkhibri et al. (2007) study the market structure of Islamic banking
in Malaysia and assess the degree of competition in the sector. Most of the work dealing with the
problem of measuring the competitiveness of banking in Islamic banks have looked to the MENA
countries. Paper the most recent being that of Rima Turk Ariss (2010). It is based on a sample of 58
Islamic banks and conventional banks 192 for the period 2000-2006 for 13 countries belong to the
MENA. The author evaluated the competitive conditions of Islamic banks and conventional banks
based on indicators of market power. The model used is the Panzar-Rosse model. The application of
this model implies that the monopolistic competition market structure is described by Islamic and
conventional banks. The ratios of concentrations are three times higher for Islamic banks. The
Lerner index indicates that Islamic banks have market power higher than their peer (conventional
banks). The regression results show that Islamic banks do not get higher returns compared to
conventional banks.
7
Laurent Weill (2010) conducts his analysis of purchasing power by a comparison between Islamic
banks and conventional banks in the case of MENA countries. The paper deals 1166 135 Islamic
banks and conventional banks for the period 2000-2007. The models used are Panzar-Rosse model
and approach to industrial organization model based on the SCP (Structure-Conduct-Performance).
Comparing the Lerner index for Islamic banks and conventional banks, the basic conclusion is the
absence of a significant difference in terms of market power between Islamic and conventional
banks. Regression of this index and the comparison states suggests that the market power of Islamic
banks is lower than the market power of banks. Estimation of the model Panzar-Rosse tends to
confirm our conclusion. Muhamed Abdul-Zulkhibri Majidet Fadzlan Sufian (2007) discuss the
market structure of Islamic banking in Malaysia and analyzes the degree of competitiveness. The
model used is the Panzar-Rosse highlighting 17 Islamic banks for the period 2001-2005. The
application of this model to the case of Malaysia confirms previous studies in the case of Malaysia
and in other developing countries. The market structure of the Islamic banking system is described
by monopolistic competition.
Table 2- the Panzar-Rosse application in previous studies
Authors
Period
Countries
Results
Bang Nam Jeon et al (2011)
(1997-2008)
17 countries of Asia
and Latin America
Monopolistically competitive
Rima Turk Ariss(2009)
(2000-2006)
monopolistic competition
Mahmoodul Hasan Khan (2009)
(1997-2007)
MENA countries
Pakistan
Kent Mathews et al (2007)
(1988-2004)
Saeed Al-Muharem et al (2006)
(1993_2002)
Great Brittan
GCC countries
monopolistic competition
monopolistic competition
Kuwait, Saudi Arabia, UAE: perfect competition
Bahrain, Qatar, Oman: monopolistic competition
EMamatzukis et al (2005)
(1998-2002)
Coccoresel (2004)
(1997-1999)
Jaccob A.Bikker et Katharina Haaf
(1988-1998)
8 South East Europe
countries
Italy
23 countries
monopolistic competition
monopolistic competition
monopolistic competition
(2002)
Phil Molyneux et al (1994)
(1986-1989)
Germany, France
Italy, Spain, UK
8
France, Germany, Spain, UK: monopolistic
competition
Italy : monopoly
Table 3- the Panzar-Rosse Application in Islamic Banks- Previous studies
Authors
Rima Turk Ariss(2010)
Period
(2000-2006)
Muhamed Abdul-Zulkhibri Majid (2001-2005)
&Fadzlan Sufian (2007)
Laurent Weill
(2010)
(2000-2007)
Countries
Results
MENA countries
Islamic banking industry and conventional banking
58 Islamic Banks
industry operates under monopolistic competition.
192 conventional banks
Islamic Banks are less competitive than
conventional banks
Malaysia
17 Islamic Banks
monopolistic competition
MENA countries :
135 Islamic Bank and
1166 conventional Banks
No significant difference of market power
between Islamic banks and conventional banks.
the Rosse-Panzar model confirms that Islamic
banks are no less competitive than conventional
banks.
3- Data description
We use annual bank-level balance sheet and income statement data retrieved from the BankScope
database to estimate the degree of banking competition and foreign. Our data set
covers a total of 6 emerging economies from GCC for the period 2005-2010. This period was
chosen for reasons related to the significant economic and crises experienced during this period.
The final sample includes 15 Islamic and 47 conventional banks operating in 6 different countries.
It is important to mark the structure of banks in our sample which is similar to the functional,
institutional, and legal one. Our sample is homogeneous it probably makes more reliable estimates.
The table 3 lists the number of banks and observations in each country. The figures shows that the
number of Islamic banks operating in countries of the sample is very small compared to the number
of conventional banks, which share of conventional banks in the sample is about 76 ℅. This shows
that the conventional banking industry has a hegemonic character and the Islamic banking industry
is still in its embryonic stage.
Table4: Sample size of the Islamic and conventional banks data sets, 2005-2010.
Countries
SAUDI ARABIA
QATAR
U.A.E
Kuweit
Jordan
Bahrain
Total
Islamic Banks
Number of Banks
Number of
Observations
3
18
2
12
3
18
2
12
2
12
3
18
15
90
9
Conventional Banks
Number of Banks
Number of observations
6
36
4
24
11
66
8
48
9
54
9
54
47
282
4- Banking Market Concentration: an analysis of concentration measures
The description of the market structure by the number of banks is a relatively limited .It ignores the
distribution of banks in a given market. As the concentration indices, weighted by market share,
taking into account both the number of banks and distribution, they are used as a proxy for the
intensity of competition in the banking market. We used the ratio of concentration of n-banks,
particularly the ratio of the three largest banks and the ratio of the five largest banks, the
Herfindahl-Hirschmann, and the entropy measure by referring to the total of debt, total assets and
total deposits.There is a need to investigate comparative measures of bank concentration. We first
turn to the definition and discussion of three concentration indices.
n-bank concentration ratio
Simplicity and limited data requirements make the k bank concentration ratio one of the most
frequently used measures of concentration in the empirical literature. The N bank concentration
measure is simply the ratio between the assets of the N largest banks within a country divided by
the total assets in the banking system at a given point in time. Giving equal emphasis to the n
leading banks, but neglecting the many small banks in the market. There is no rule for the
determination of the value of 𝐶𝑛 .
𝑪𝒏 = ∑𝒏𝒊=𝟏 𝒒𝒊 ⁄𝑸 = ∑𝒏𝒊=𝟏 𝑺𝒊
We present the concentration index of three and five banking firms by reference to total assets, total
loans and total deposits as measures of the bank size. The calculation of the concentration ratios in
the market shows that there are some banks that dominate the market in the case of Islamic banks
and conventional banks. The concentration of conventional banks is two times higher than the
concentration of Islamic banks. This result is the same in the case of the concentration ratios of
three and five banking firms by reference to total assets. However, if one refers to the total deposits,
the concentration of Islamic banks and conventional banks are almost similar for the period 20052008. While for the period 2009-2010, we found that the concentration of conventional banks is
10
galloping in 2009. It decreased in 2010 but it is still too high compared to Islamic banks. The
decrease and the evolution towards higher are explained by the subprime crisis which has been
sustained by all countries of the world including the Gulf countries which have been affected
despite the high concentration of Islamic banks in this agglomeration.
C3 (Assets)
C5 (Assets)
0.4
0.3
0.2
0.3
Islamic
0.1
Convention
al
0
2004 2006 2008 2010 2012
0.8
C3 (deposits)
0.2
Islamic
0.1
Conventional
0
2004 2006 2008 2010 2012
0.6
0.6
0.4
Islamic
0.4
0.2
Conventi
onal
0.2
0
2004
C5 (deposits)
0.8
2006
2008
2010
2012
Islamic
0
2004
2006
2008
2010
2012
Source : authors' calculations
Herfindahl-Hirschman Index:
The HHI is the sum of the squared bank market shares. The Herfindahl-Hirschman index stresses
the importance of larger banks by assigning them a greater weight than smaller banks, and it
incorporates each bank individually, so that arbitrary cut-offs and insensitivity to the share
distribution are avoided.
𝒒
𝟐
𝒊
𝑰𝑯𝑯 = ∑𝑵
𝒊=𝟏 ( 𝑸 )
The Herfindahl-Hirschmann shows the unequal size of the firm. In terms of total assets and total
loans, the size of firms in the conventional banking industry is unequal. But compared against the
Islamic banking industry, the size of conventional banks is even more unequal than Islamic banks
11
for the entire period. With reference to total deposits, the variation of the Herfindahl-Hirschmann,
the variation of the inequality of size compared to conventional banking Islamic banking is
marginal for the period 2005-2008. In 2009, the Herfindahl-Hirschmann is almost zero in the case
of Islamic banks, while there is an increase in this index in the case of conventional banks. This
growth continued in 2010 for both conventional and Islamic banks.
IHH (Assets)
0.05
0.15
Index HirschmanHerfindahl (Deposits)
0.04
0.1
0.03
Islamique
Islamic
0.02
conventionnell
e
0.05
0.01
0
2004 2006 2008 2010 2012
Conventional
0
2004
Evolution of the concentration of
conventional banks
0.35
0.8
0.3
0.6
0.25
C3
(deposits)
0.4
C5
(deposits)
0.2
0
2004
IHH
(deposits)
2006
2008
2010
2006
2008
2010
2012
Evolution of the concentration of
Islamic banks
0.2
C3
(deposits)
0.15
0.1
C5
(deposits)
0.05
0
2004
2012
2006
2008
2010
2012
Source : authors' calculations
The ratios of concentrations C3etetC5 in the case of Islamic banks are higher for three periods than
conventional banks and the Herfindahl-Hirschmann is six times higher than conventional banking
industry.
Entropy measure
The index ranges between 0 and
, and is therefore not restricted to [0, 1], as most of the other
measures of concentration presented above. The value of the Entropy varies inversely to the degree
12
of concentration. It approaches zero if the underlying market is monopoly and reaches its highest
value, E = log n , when market shares of all banks are equal and market concentration is lowest. The
Entropy measure takes the form:
𝒏
𝒏
𝑬 = − ∑ 𝒔𝒊 𝐥𝐨𝐠 𝟐 𝒔𝒊 = −(𝟏⁄𝐥𝐧 𝟐) ∑ 𝐬𝐢 𝐥𝐧 𝒔𝒊
𝒊=𝟏
𝒊=𝟏
The entropy measure determines the unequal distribution of firms in the market. Its values are
between 0 and
with n is the number of firms in the market. In the case of our sample, the
values of the entropy must be between 0 and
0 and
for the Islamic banking market, and is between
for the case of conventional banking market. With reference to total assets, the
concentration is low in the conventional banking industry and market shares of all firms tend to be
equal. The market is so competitive. However in the case of Islamic banks, the value of the entropy
tends to 0. So the market is monopolistic. The structure of conventional and Islamic banking market
is almost similar with reference to total deposits and total loans.
E (Assets)
E (Deposits)
5
4
4
3
3
Islamic
2
1
0
2004
2
Islamic
1
Conventional
Conventional
2006
2008
2010
0
2004
2012
2006
2008
2010
2012
E (Loans)
5
4
3
Islamic
2
Conventional
1
0
2004
2006
2008
2010
2012
Source : authors' calculations
13
5- Evaluation Method of the H- Statistic
Presentation of the model
We proceed in our study of the model with the presentation which is the fundamental step in our
empirical study. The PR methodology allows for the calculation of a measure of market structure,
the H-statistic, as the sum of the elasticities of total revenues of the bank with respect to its input
prices. We estimate equations banking income which they are explained by the price of production
factors and bank-specific factors that have an impact on the long run equilibrium.
The production function is Cobb-Douglas type:
TRit= β (WL,it) β1 (WF,it)β2 (WK,it)β3 (Y1,it)γ1 (Y1,it)γ2 eεit
The logarithm of this function is as follows:
ln (TRit) = β + β1 ln(WL,it)+ β2 ln(WF,it) + β3 ln(WK,it) + γ1 ln(Y1,it) + γ2 ln(Y2,it) + εit
On the basis of the theoretical assumptions of the model Panzar Rosse we estimate two linear
equations by changing the dependent variable for each regression, all other things being equal. The
first postulate is that we will refer to the intermediation approach, which states that any banking
entity uses inputs in its interim financial function. The second calls for the estimation of the
function of total income including interest and non-interest view that growth in other income
excluding interest, such as commissions and capital gains is important. So according to theoretical
foundations and the work of Molyneux, Thornton, and Lloyd-Williams (1996), Gelos and Roldos
(2004) Classens and Leaven (2004), and Rima Turk (2010), the first regression is as follows:
ln (TRit)= β +β1ln(WL,it) +β2ln(WF,it) +β3ln(WK,it) +γ1ln(Y1,it) +γ2ln(Y2,it) +εit
(1)
We will test later the equilibrium of long-term referring to the third theoretical postulate. This
assumes that all banks in the industry are in their equilibrium of long-term. With reference to
Nathan and Neave (1989) and Shaffer (1982), we tested the hypothesis using the following model:
14
ln (ROAit)= β +β1ln(WL,it) +β2ln(WF,it) +β3ln(WK,it) +γ1ln(Y1,it) +γ2ln(Y2,it) +εit
E denotes the statistical equilibrium that it
(2)
is equal to the sum of input price elasticities with
respect to income. If the value of E is different from zero then the market is not in equilibrium. In
fact, long-term ROA do not relate to the price of inputs.
TRit is the total income of bank i at time t. Modernization of the banking sector was followed by
the increase in the share of interest income in the total income of the bank. However, with the
emergence of new banking products, the share of non-interest income occupies an important part in
the total income of the firm. Total revenue is measured by the ratio of total interest income and noninterest relative to total assets.
WL,it
is the cost of labor. This variable is represented by the ratio of personnel expenses to total
assets. Personal expenses include salaries and wages.
WF,it is the cost of funds. This variable is represented by the ratio of interest expense to total
deposits. Interest expense includes interest paid on deposits and interest paid on loans. As for the
funds, they are made by customer deposits, interbank deposits, subordinated debt and other longterm debts.
WK,it is the cost of fixed capital. This variable comprises general and administrative costs,
physical depreciation, depreciation of investment securities, and other overheads, This variable is
represented by the ratio of other operating and administrative expenses of total assets.
Y1,it this variable takes into account the specific risk to the bank. It is measured by the ratio of total
net loans and total assets. It reflects both the credit risk and interest rate risk. If the ratio is high it
indicates the relative illiquidity of the bank and the t limited ability to grant long-term credits, and
the effects of loan quality, loan loss reserve was netted out from total loans. However, it is
positively correlated with income.
Y2,it : equity is an indicator of the ability of funding. It is expected to have a positive effect on
income. if the bank has more equity it has the ability to give more credit and receive more revenue.
This variable is measured by the ratio of total equity to total assets. The latter is used as a rough
indicator of the size of the bank account for economies of scale. The influence of this variable is
indeterminate because all positive influences on income can be offset by the banks of large sizes
because they are able to diversify and spread the risk of their business.
Empirical illustration
We estimated the steps outlined above based on the same data set. The estimates were made by the
Winrats program. The procedure was, first, to estimate the model for the whole sample with no
distinction between Islamic and conventional banks. Was appointed associate model (1) where the
endogenous variable is the total revenue. Thereafter it was entitled to two estimates for the case of
Islamic and conventional banks which case the endogenous variable is the total revenue. Statistics
H is defined as the sum of elasticities bank revenue to changes in input prices. So the results of the
15
estimation of the banking income allow for the determination of market structure in the banking
industry for each category. The H-statistic is written as follows:
𝑯 = ∑𝟑𝒊=𝟏 𝜷𝒊 +∑𝟐𝒊=𝟏 𝜸𝒊
Table 5- the results of H – statistic
All Banks
Islamic Banks
Conventional Banks
WL
0.1526253
-0.463053
-0.1801023
WF
1.7361353
0.00000
-0.1745021
WK
0.0270870
0.00000
-0.2890106
Y1
0.0047361
0.00000
-0.0000157
Y2
0.9061875
-0.00000
6.0046954
H-statistic
2.8267712
-0.463053
5.3610647
Appendix 2 shows the H-statistic for each industry derived from individual regressions using bank
effects (fixed or random depending on the results of the Hausman test), and the H-statistic
calculated from a pooled regression on all countries estimated with country and bank random
effects.
According to the results, table 7, the average level of H-statistic is positive and varies between
5.3610647 and 2.8267712 in the case of the banking industry in general and the case of the
conventional banking industry in particular. These values are well above zero rows. So the
regression results are too consistent with the case of the perfect competition. So the perfect
competition seems to describe the competitive behavior of the banking industry better than in
general and conventional banking industry in particular in the case of the Gulf countries. However
in the case of Islamic banks, the value of H-statistic is above zero which is of the order of -0.463053.
So the regression results are too consistent with the case of monopoly. The market structure of the
Islamic banking industry in the Gulf countries is described by a monopoly. This result is different
from the work of Rima Turk (2010) which suggests that monopolistic competition is the best
description of the market structure for Islamic and conventional banks. Further to the decomposition
16
of elasticities of various production factors shown in the table above, we noticed that the principal
price of production factors inducing the amplitude of H-statistic is the price of the fund for the case
of the banking industry in general. This can be justified by the nature of the activity of banks which
is essentially the management of funds and diversification of risk. This is very consistent with the
intermediation approach.
In the case of banking industry in general the price of labor is an important way on the value of Hstatistic. This is explained by the fact that conventional banks in the course hold a competitive
advantage over its competitors in research and develop and recruit qualified personnel. the case of
the banking industry in general and the case of the conventional banking industry in particular,
perfect competition is mainly explained by the homogeneity of products and services offered by
Islamic banks. There is a standardized level of conventional financial products. The innovation
process in Islamic banks is static. There must be a dynamic level of the innovation process. More
specifically banking industry in GCC countries must invest more in research and development and
training of these employees. The H-statistic calculated that Islamic banking industry in the GCC
region generally operates under conditions of monopoly. The monopoly is explained by the very
high concentration of the banking sector. In fact it can occupy a larger market share of the global
market in which competition is increasing in this case beyond national borders to international ones.
Islamic banks are still in the embryonic stage, even in a community of Muslim countries where
Islamic finance has seen its real growth.
Table 8: the results of Equilibrium tests
E= -0.1315917
E= 6.894867
E= 0.510937
Model Agregate
Islamic Model
Conventional Model
The results of equilibrium tests for all markets shown in Table-8 indicate that all countries are not in
an equilibrium state. The calculated E-statistic is not significantly different from 0 for all banking
17
industry considered and for conventional banking industry. This implies that the banking market is
in long-run equilibrium. However, the calculated E-statistic is significantly different for Zero for all
Islamic banking industry. Therefore we can think of the banking sector in each economy as not
being in long-run equilibrium.
Conclusion
This article sought to assess competitive conditions and concentration in the GCC banking industry
as many as 6 countries over 6 years (2005–2010). Our stating idea was to detect the level of
penetration of Islamic banks in the country with a majority Muslim population and to study the
evolution of the phenomenon of economic concentration of Islamic banks in the banking industry in
general.
The Panzar–Rosse approach has been applied to obtain a measure of competitive conditions in to
GCC countries over a time span of 6 years. The empirical evidence indicates that GCC banks in
general and conventional banks earn revenues as if they were under conditions of perfect
competition. Therefore, the recent tendency to concentration (usually considered with worry from a
theoretical point of view) appears not to have had a significant impact on the conduct of the firms
operating in the GCC banking industry. The results for GCC countries also suggest this conclusion,
although the data do not represent long-run equilibrium values. Concentration in the banking
markets of GCC countries was measured using various k-bank concentration ratios, the Herfindahl
index, and the entropy measure. The results of the calculation of concentration ratios show that
conventional banks have a very high degree of concentration in the market. While the concentration
of Islamic banks is even lower than their pair (conventional banks). This sort of conclusion may
help policy makers in determining future measures aimed at improving efficiency of these banking
systems. The study of concentration and competitiveness of Islamic banks appears a necessity, or
even an emergency to meet the challenges of the banking industry. The penetration of Islamic banks
is unavoidable to go to meet the challenges and overcome moments of crisis suffered in the context
18
of financial globalization phase. Our findings suggest that the banking market in the GCC countries
tends to success to confront the forces of international banking competition by developing a
competitive structure.
Acknowledgements
The authors are grateful to three anonymous referees for helpful comments and advice. Naturally all
remaining errors are ours.
19
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Appendix 1
3-Banks Index Concentration
2005
2006
2007
2008
2009
2010
C3 (deposits)
Islamic
Conventional
0,10258194
0,19192997
0,09902329
0,15251665
0,18211511
0,31249836
0,26664465
0,31559097
0,01120973
0,71636996
0,07455837
0,44766648
C3 (Loan)
Islamic
Conventional
0,15059951
0,18424247
0,13278345
0,19352794
0,13828704
0,18844455
0,15025185
0,20114203
0,15677682
0,22061411
0,16050524
0,23326031
C3 (Assets)
Islamic
Conventional
0,1129183
0,19099889
0,11281041
0,18501362
0,11541345
0,1824176
0,12688453
0,18631007
0,13536846
0,20708316
0,14034673
0,21486276
5-Banks concentration Index
2005
2006
2007
2008
2009
2010
C5 (deposits)
Islamic
Conventional
0,10258194
0,28200397
0,09902329
0,24019802
0,18211511
0,37802686
0,26664465
0,27738147
0,01120973
0,72340958
0,07455837
0,51289813
C5 (Loans)
Islamic
Conventional
0,18520836
0,29032098
0,17199042
0,30438236
0,18338152
0,29861883
0,1894472
0,30440873
0,19516895
0,31905024
0,20069041
0,34210945
C5 (Assets)
Islamic
Conventional
0,1129183
0,29837198
0,11281041
0,28725493
0,11541345
0,28918337
0,12688453
0,30138002
0,13536846
0,32943033
0,14034673
0,33597143
Herfindal-Hischman Index
2005
2006
2007
2008
2009
2010
IHH (deposits)
Islamic
Conventional
0,00592987
0,03667525
0,02202562
0,04521244
0,01904352
0,06841192
0,04310182
0,06793099
6,0942E-05
0,12540462
0,00304982
0,14253137
IHH (Loans)
Islamic
Conventional
0,01273151
0,04567499
0,00825766
0,04249717
0,00850456
0,04199886
0,00919863
0,04364158
0,00998077
0,04568467
0,01055759
0,035667
IHH (Assets)
Islamic
Conventional
0,00643596
0,03951757
0,00601749
0,03908138
0,00609025
0,03897188
0,00707457
0,04021875
0,00766132
0,04238889
0,00836749
0,04282191
Entropy Measure
2005
2006
2007
2008
2009
2010
E (deposits)
Islamic
Conventional
1,12230001
3,73357113
1,45107695
3,51219762
1,25170961
3,35993166
1,40087111
3,27868161
0,19846123
1,17597851
0,95000593
3,25244806
E (Loans)
Islamic
Conventional
1,05215042
3,79248679
1,02303977
4,173252
1,07760014
3,80787182
1,0961167
3,46781421
1,12081822
3,67130495
1,16018733
3,61773464
23
E (Assets)
Islamic
Conventional
0,98284437
4,05240982
0,99781594
4,03056934
1,02611664
3,99218103
1,06087546
3,92137074
1,08676396
3,84675464
1,17558971
3,76905624
Appendix 2
Appendix 2-1Panel regression results of competitive condition for total
revenue ( Model Agregate Banking industry -GCC countries)
Panel Regression - Estimation by Random Effects
Dependent Variable Y
Panel(6) of Annual Data From 1//2005:01 To 62//2010:01
Usable Observations 371 Degrees of Freedom 365
Total Observations 372 Skipped/Missing
1
Centered R**2 0.999716 R Bar **2 0.999712
Uncentered R**2 0.999931 T x R**2 370.974
Mean of Dependent Variable 31.417789757
Std Error of Dependent Variable 17.873447208
Standard Error of Estimate
0.303191936
Sum of Squared Residuals
33.552752677
Variable
Coeff
Std Error
T-Stat
Signif
*******************************************************************************
1. WL
0.1526253 0.1073310 1.42201 0.15502441
2. WF
1.7361353 5.3914985
0.32201 0.74744246
3. WK
0.0270870 0.7364978 0.03678 0.97066189
4. Y1
0.0047361 0.0276241 0.17145 0.86387287
5. Y2
0.9061875 12.4614713 0.07272 0.94202962
6. Constant
-275.2022614 215.6067826 -1.27641 0.20181127
Appendix 2-2 Panel regression results of competitive condition for total revenue
( Conventional banking Industry -GCC countries)
Panel Regression - Estimation by Random Effects
Dependent Variable Y
Panel(6) of Annual Data From 1//2005:01 To 47//2010:01
Usable Observations 281 Degrees of Freedom 275
Total Observations 282 Skipped/Missing
1
Centered R**2 0.999283 R Bar **2 0.999270
Uncentered R**2 0.999849 T x R**2 280.958
Mean of Dependent Variable 33.451957295
Std Error of Dependent Variable 17.299628549
Standard Error of Estimate
0.467487948
Sum of Squared Residuals
60.099869913
Variable
Coeff
Std Error
T-Stat
Signif
*******************************************************************************
1. WL
-0.1801023 0.1363672 -1.32072 0.18659603
2. WF
-0.1745021 7.2434657 -0.02409 0.98078005
3. WK
-0.2890106 0.7638188 -0.37838 0.70515143
4. Y1
-0.0000157 0.0304842 -5.15536e-04 0.99958866
5. Y2
6.0046954 29.2948438 0.20497 0.83759206
6. Constant
394.9140193 273.9479347 1.44157 0.14942486
24
Appendix 2-3 Panel regression results of competitive condition for total revenue
(Islamic Banking Industry- GCC countries)
Panel Regression - Estimation by Random Effects
Dependent Variable Y
Panel(6) of Annual Data From 1//2005:01 To 15//2010:01
Usable Observations 89 Degrees of Freedom 85
Total Observations 90 Skipped/Missing
1
Centered R**2 0.990460 R Bar **2 0.990124
Uncentered R**2 0.996701 T x R**2 88.706
Mean of Dependent Variable 24.752808989
Std Error of Dependent Variable 18.097137455
Standard Error of Estimate
1.798487169
Sum of Squared Residuals
274.93726828
Variable
Coeff
Std Error T-Stat Signif
*******************************************************************************
1. WL
-0.463053 0.290946 -1.59155 0.11148692
2. WF
0.000000 0.000000 0.00000 0.00000000
3. WK
0.000000 0.000000 0.36653 0.71396837
4. Y1
0.000000 0.000000 0.00000 0.00000000
5. Y2
-0.000000 0.000000 -0.07296 0.94183429
6. Constant
953.764967 584.408137 1.63202 0.10267556
Appendix 3
The H statistic is calculated as the sum of the input price elasticities given by:
H=𝜷𝟏 +𝜷𝟐 +𝜷𝟑 +𝜸𝟏 +𝜸𝟐
Similarly the long-run equilibrium equation (2) was modified in the same way as
above and described below. The long-run equilibrium test is given as:
E=𝜷𝟏 +𝜷𝟐 +𝜷𝟑 +𝜸𝟏 +𝜸𝟐
25