Analysis of Competitiveness in Qatar Banking

8/9/2019 Analysis of Competitiveness in Qatar Banking

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Int. J. Business Innovation and Research, Vol. X, No. Y, XXXX

Copyright © 200X Inderscience Enterprises Ltd.

Analysis of competitiveness in Qatar bankingindustry

Saeed Al-Muharrami

Sultan Qaboos University,

PO Box 20, Al-Khod 123,

Sultanate of Oman

Fax: 00-(968) 24414043

E-mail: [email protected]

Abstract: This study evaluates the monopoly power of Qatar banking industry

during the period 1993 to 2002. The sample consists of panel of 60observations using the ‘ H-statistic’ by Panzar and Rosse and investigates themarket structure using the most frequently applied measures of concentrationk-bank concentration ratio and Herfindahl–Hirschman index. Both of theconcentration indices indicate that the Qatar had a ‘very concentrated market’.The Panzar and Rosse ‘ H-statistic’ suggests that Qatar banks operate undermonopolistic competition. Qatar Central Bank should be very cautious ingranting mergers among banks, in particular among large ‘core’ banks.

Keywords: k-bank concentration ratio; Herfindahl–Hirschman index; H-statistic; Panzar and Rosse; Qatar.

Reference to this paper should be made as follows: Al-Muharrami, S. (200X)‘Analysis of competitiveness in Qatar banking industry’, Int. J. Business Innovation and Research, Vol. X, No. Y, pp.xxx–xxx.

Biographical note: Saeed Al-Muharrami is an Assistant Professor of bankingand finance at Sultan Qaboos University. He received his BSc in 1988 fromUniversity of Arizona, USA, MBA in 1994 from Oregon State University,USA, and PhD in 2005 from Cardiff University, UK. His areas of interestare banking market structure, competitiveness, efficiency, productivity, performance, capital structure and budgeting. He has published several papersin journals and conferences in these fields.

1 Introduction

Commercial banks performance is determined by the market structure in which theyoperate. Perfect competition is known to be an idealistic market structure that secures

socially just and efficient outcomes. On the other hand, pure monopoly causes

inefficiency of resources, inequality of income distribution and net social welfare loss.

Monopoly is therefore viewed by societies as bad situation that requires government

intervention for correction through different schemes of regulation. In reality, there is a

spectrum of market structures that contains a variety of structures ranging from perfect

competition to pure monopoly and in many cases, decision makers face a grey area of

market structures where it is difficult to determine the deviation from the competitive

norm and to what extent the situation may justify regulatory action.


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Even though Qatar Central bank issued a law stopping foreign banks to enter Qatar

since the mid-1970s, yet there are foreign banks existed even before when this law wasapplied. The Qatar banking system currently includes 14 commercial banks, seven

national and seven foreign, as well as one specialised bank (Qatar Central Bank, 2000).1

Many studies on bank market structure, market power and competitive conditions

have been conducted but, to-date, a wide gap exists and few have referred to banks in

Qatar. This study is motivated by the researcher’s aim to fill the gap and also the fact that

commercial banks play a vital role in the economy. Evaluating their overall performance

and monitoring their financial condition is important to depositors, owners, potential

investors, managers and, of course, regulators. In addition to examining the theoretical

aspects of market power and competitive conditions, this study finding may assist and

guide policy makers and regulatory authorities in ways to minimise inefficiency in the

banking sector in order to realise a number of benefits. As banks become more profitable,

investors expect higher dividends because of increased profitability; investor confidenceis boosted, thereby attracting more capital in addition to increased internally generated

retained reserves, thus boosting capital accumulation. This increases the safety and

soundness of banks and hence, the stability of the financial system which means a

reduction in the risk of bank failures and the pertinent costs.

This study is trying to answer the following two questions: Should concentration

in the Qatar banking industry cause a big concern? And, under which competitive

condition did the Qatar banks gain their total revenue and make profit during the period

1993 to 2002? Therefore, the two aims of this paper are: first, to investigate the market

structure of Qatar banking industry using the most frequently applied measures of

concentration k-bank Concentration Ratio (CR K ) and Herfindahl–Hirschman Index (HHI)

The second aim is to evaluate the competitive conditions of the Qatar banking industry

using the ‘ H-statistic’ by Panzar and Rosse during the period 1993 to 2002.

The organisation of the paper is as follows. Section 2 presents the background and the

growth of the banking sector in Qatar. Section 3 summarises the literature review while

Section 4 presents the methodology and data. Section 5 describes the empirical model by

Panzar and Rosse. Section 6 shows the results and practical implications. Section 7

summarises the paper with the concluding remark.

2 Development and growth of the banking sector in Qatar

Prior to commercial export of Qatar’s oil, Qatar did not have any banking entities

practising banking activities (Qatar Monetary Agency, 1992). The first ever bank in Qatar

was established in 1950, when the Eastern Bank (known today as the ANZ Standard

Chartered Bank) established its Qatar branch after Qatar’s oil exports commenced inDecember 1949. In 1954 and 1956, the British Bank of the Middle East (known today as

the HSBC bank) and the Ottoman Bank (currently known as the Grindlays Bank),

respectively, opened their Qatar branches. Two Arab banks were also established later:

the Arab Bank Limited in 1957 and the Intra Bank (known later as Almashreq Bank) in

1960. Until the mid-1960s, foreign bank branches dominated banking activities, until

Qatar established its first national bank (known as the Qatar National Bank) in 1965 with

joint venture capital shared equally between the Government of Qatar and the public.

The economic expansion in Qatar attracted more foreign banks; thus, in the second half

of the 1960s, the government authorised four new foreign banks.


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Analysis of competitiveness in Qatar banking industry

Qatar established in 1973 the country’s central bank known as the QMA, which

is later called the Qatar Central Bank (QCB). The QMA regulates banking credit andfinances, issues currency, and manages the foreign reserves necessary to support the

Qatari Rial. One of the first steps taken by the QMA was to restrict the licensing of new

bank establishments or branch openings of foreign banks. The oil boom started in 1973,

promoting economic growth, and this resulted in an expansion of the banking sector as

three national banks were established during the latter part of the 1970s. Furthermore,

another two national banks were added to the banking structure during the 1980s.

However, one foreign bank, the Qatar branch of Al-Mashrek Bank – headquartered in

Beirut – was closed and put into liquidation in 1989 (Qatar Monetary Agency, 1992).

Table 1 shows the growth of the total assets of Qatar local commercial and Islamic

banks. The total assets had increased by 118% over the 10 years period where the growth

rates annually ranged from 3% as a minimum to 12% as a maximum.

Table 1 Total assets of Qatar banks (in QR)

Year QNB CMBQ DB ABQ QISB QIISB Total assets % Growth

2002 31,055.9 6141.1 7413.8 2155.6 5126.5 3242.9 55,135.8 12%

2001 28,390.7 5208.5 6505.2 2115.4 4415.1 2699.9 49,334.8 12%

2000 24,621.4 5065.6 5511.6 2622.5 4059.1 2097.9 43,978.1 8%

1999 22,356.2 4641.1 5064.4 2667.2 3983.4 1840.0 40,552.3 12%

1998 19,487.3 4387.5 4545.3 2373.2 3823.3 1624.6 36,241.2 11%

1997 18,297.7 3563.3 4122.1 2021.7 3360.9 1311.9 32,677.6 12%

1996 16,284.2 3221.7 3771.0 1805.0 3035.3 1095.7 29,212.9 3%

1995 17,224.5 2657.0 3389.9 1494.2 2831.3 864.8 28,461.7 8%

1994 15,824.0 2283.0 2980.0 1313.0 3190.4 841.0 26,431.4 5%1993 15,449.0 1920.0 2849.0 1245.0 3045.1 760.5 25,268.6

Source: Compiled by the author from banks’ annual reports

Qatar banks have expanded their branch networks considerably, from 38 branches at the

end of 1993 to 71 at the end of 2002 as shown in Table 2. So, is this an acceptable

number of branches? Therefore, one of the aims of this study is to investigate whether

Qatar is under or over branched.

Table 2 Branches of banks in Qatar

Year/Name QNB CMBQ DB ABQ QISB QIISB Total

2002 22 15 13 7 8 6 71

2001 23 15 13 8 8 6 73

2000 21 15 13 8 8 6 71

1999 18 10 11 8 8 6 61

1998 18 9 9 7 7 4 54

1997 18 8 9 4 7 3 49

1996 15 7 9 3 7 3 44

1995 15 6 8 3 6 3 41

1994 14 5 8 3 5 3 38

1993 14 5 7 3 5 4 38


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3 Literature review

The first application of monopoly power test has been made by Rosse and Panzar (1977),

who employed a cross section of data in order to estimate the H-statistic for the

newspaper firms in the local media markets. In the banking industry, there has been

growing attention toward the application of the Panzar–Rosse methodology.

Shaffer (1982), in his pioneering study on New York banks, observed monopolistic

competition. For Canadian banks, Nathan and Neave (1989) found perfect competition

for 1982 and monopolistic competition for 1983 to 1984. Molyneux et al. (1996) revealed

perfect collusion for Japan.

Molyneux et al. (1994) tested the P–R statistic on a sample of French, German,

Italian, Spanish and British banks for the period 1986 to 1989 in order to assess the

competitive conditions in major EC banking markets. They obtain values for H which

is not significantly different from zero and from unity for France, Germany (except for1987), Spain and the UK, thus pointing to monopolistic competition. The H-statistic

for Italy during 1987 to 1989 is negative and significantly different from zero; hence it

was not possible to reject the hypotheses of monopoly.

Coccorese (1998), however, who also intends to evaluate the degree of competition in

the Italian banking sector, obtains significantly non-negative values for H . H was also

significantly different from unity, except in 1992 and 1994. Vesala (1995) applies the

model to the Finnish banking industry (1985 to 1992) to test for competition and market

power in the Finnish banking sector. His estimates of H were always positive, but

significantly different from zero and from unity only in 1989 and 1990. For Switzerland,

Rime (1999) observed monopolistic competition. Hondroyiannis et al. (1999) also

observed monopolistic competition for Greece banks. Bikker and Groeneveld (2000)

determine the competitive structure of the whole EU banking industry. The estimated

values for the H-statistic lie between two-thirds and one in most countries. The

hypothesis 0 = H is rejected for all countries, whereas 1 = H cannot be rejected for

Belgium and Greece at the 95% confidence level.

De Brandt and Davis (2000) investigate banking markets in France, Germany and

Italy within groups of large and small banks. Aiming to assess the effects of EMU on

market conditions, they obtain estimates of H , which are significantly different from zero

and from unity for large banks in all three countries. The H-statistics estimated for the

sample with small banks indicate monopolistic competition in Italy, and monopoly power

in France and Germany. Bikker and Haaf (2002) consider banks in 23 OECD countries

and investigate small, medium-sized and large banks separately. This P–R analysis finds

monopolistic competition virtually everywhere, although perfect competition cannot be

rejected for some market segments. For Germany, Hempell (2002) observed monopolistic

competition for the period 1993 to 1998. Coccorese (2004) also observed monopolisticcompetition for Italian banks for the period 1997 to 1999.

Al-Muharrami et al. (2006) evaluate the monopoly power of GCC banks over

10 years period, 1993 to 2002, using the ‘ H-statistic’ by Panzar and Rosse. The results

show that banks in Kuwait, Saudi Arabia and UAE operate under perfect competition;

banks in Bahrain and Qatar operate under conditions of monopolistic competition; and

they were unable to reject monopolistic competition for the banking market in Oman.

Gunalp and Celik (2006) employed the Panzar–Rosse H-statistic to assess the

competitive environment of the Turkish banking industry over the period 1990 to 2000.

The results indicated that for the period under consideration, bank revenues behaved as if


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they were earned under conditions of monopolistic competition. Therefore, the observed

high profitability of the Turkish banking sector was not an indication of an increase inmonopoly power.

Finally, Yildirim (2007) examines the evolution of competitive conditions in the

banking industries of 14 Central and Eastern European (CEE) transition economies for

the period 1993 to 2000. The results of the competition analysis suggest that the banking

markets of CEE countries cannot be characterised by the bipolar cases of either perfect

competition or monopoly over 1993 to 2000 except for Macedonia and Slovakia.

4 Methodology and data

As mentioned in the introduction that this study investigates the market structure of Qatar

banking industry and it evaluates the monopoly power of banks during the period 1993 to2002. Therefore, it uses the most frequently applied measures of concentration CR k and

HHI and evaluates the monopoly power of banks over the 10 years period using the

‘ H-statistic’ by Panzar and Rosse. The following subsections discuss the methodology

using these approaches.

4.1 Measuring market structure

There are a number of measures of concentration that have been used in banking studies.

Hall and Tideman (1967) suggested a list of six desirable properties of measures of

concentration. These are:

1 a concentration index should be a one-dimensional measure

2 concentration in an industry should be independent of the size of that industry

3 concentration should increase if the share of any firm is increased at the expense of

a smaller firm

4 if all firms are divided into K equal parts then the concentration index should be

reduced by a proportion 1/ K

5 if all firms are divided into N equal parts then the concentration should be

a decreasing function of N

6 a concentration measure should be between zero and one.

In a review of 73 US Structure–Conduct–Performance studies from 1961 to 1991,

Molyneux et al. (1996) report that in 37 studies, the three-bank deposits concentration

measure was used. The second most frequently used is the HHI (HHI – 18 studies)followed by the number of firms in the market. Following the steps of these measures and

due to the limited number of banks in Qatar, this paper uses the highest two and three

bank deposits as well as HHI for deposits as a measure of market structure.

4.1.1 The k bank concentration ratio

Simplicity and limited data requirements make the k bank CR K one of the most

frequently used measures of concentration in the empirical literature. Summing only the

market shares of the k largest banks in the market, it takes the form:


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CR k =∑=

k

i MS

1

ι ,

where MS is the market share of the ith firm and k is the number of the biggest banks in

the market. The index gives equal emphasis to the k leading banks, but neglects the many

small banks in the market. There is no rule for the determination of the value of k , so that

the number of banks included in the concentration index is a somewhat arbitrary decision.

The CR K may be considered as one point on the concentration curve and it is a

one-dimensional measure ranging between zero and unity. The index approaches zero for

an infinite number of equally sized banks (given that the k chosen for the calculation of

the CR is comparatively small when compared to the total number of banks) and it equals

unity if the banks included in the calculation of the CR make up the entire industry.

4.1.2 The Herfindahl–Hirschman Index

Policy makers in the US Department of Justice have, for many years, published formal

guidelines that identify structural changes resulting from mergers that are likely to cause

the Department to challenge a merger. Since 1982, the Department has based its merger

guidelines on the HHI of concentration. This measure, which is also used by bank

regulatory agencies, is calculated by squaring the market share of each firm competing in

a defined geographic banking market and then summing the squares. The HHI can range

from zero in a market having an infinite number of firms to 10,000 in a market having

just one firm (with a 100% market share).

According to the current screening guidelines in USA, the banking industry is

regarded to be a competitive market if the HHI is less than 1000, a somewhat

concentrated market if the HHI lies between 1000 and 1800 and a very concentrated

market if HHI is more than 1800. If the post-merger market HHI is lower than

1800 points, and the increase in the index from the pre-merger situation is less than

200 points, the merger is presumed to have no anti-competitive effects and is approved

by the regulators. Should these threshold values be exceeded, the regulators will check

for the existence of potential mitigating factors. If the mitigating factors are not enough to

justify the merger, the regulators may require the divestiture of some branches and

offices, in order to bring the CR to or below the threshold level. If divestiture would not

accomplish this goal, the merger application is denied (Rhoades, 1993).

The HHI index was developed independently by the economists A.O. Hirschman

(in 1945) and O.C. Herfindahl (in 1950) (Rhoades, 1993). The HHI is a static measure

and, therefore, gauges market concentration at a single point in time. Algebraically, it can

be depicted as:

2

1

,HHI (MS )n

ii=

=∑

where MS is the market share of the ith firm and n is number of firms in the market.

The HHI 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.


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4.2 Measuring the competitive condition

The view on the relationship between competition and market structure is based on the

traditional monopoly power hypothesis, which suggests that more concentrated markets

tend to be more collusive, generating market power which allows banks to earnmonopolistic profits by offering lower deposit rates and charging higher loan rates.

These arguments ‘Structural Models’ are challenged by other theoretical approaches.

In reaction to the theoretical and empirical deficiencies of the Structural Models,

‘Non-Structural Models’ of competitive behaviour have been developed. These new

empirical industrial organisation approaches such as the Iwata model, the Bresnahan

model and the Panzar and Rosse model measure competition, and emphasise the analysis

of the competitive conduct of banks without using explicit information about the structure

of the market.

This study employs one of the ‘Non-Structural Model’ approach suggested by Rosse

and Panzar (1977) and Panzar and Rosse (1982, 1987), so called ‘ H-statistic’, which has

been widely employed for the examination of the competitive structure of the banking

industry in various countries, in order to investigate the market structure of Qatar banking

industry during the period 1993 to 2002. Furthermore, it evaluates whether monopoly

power of banks has been indeed increased along with the increased market concentration

for this period.

The method developed by Panzar and Rosse (1987) determines the competitive

behaviour of banks on the basis of the comparative static properties of reduced-form

revenue equations based on cross-section data. Panzar and Rosse show that if their

method is to yield plausible results, banks need to have operated in a long-term

equilibrium (i.e. the number of banks needs to be endogenous to the model) while the

performance of banks needs to be influenced by the actions of other market participants.

Furthermore, the model assumes a price elasticity of demand, e, greater than unity, anda homogeneous cost structure. To obtain the equilibrium output and the equilibrium

number of banks, profits are maximised at the bank as well as the industry level.

Few assumptions need to be made to apply this model in this study. First, one needs

to assume that banks can be treated as single product firms (De Bandt and Davis, 2000);

consistent with the intermediation approach to banking, banks are viewed as producing

intermediation services using labour, physical capital and financial capital as inputs.

Second, one needs to assume that higher input prices are not correlated with higher

quality services that generate higher revenues, because such a correlation could bias

the computed H-statistic. This means, however, that if one rejects the hypothesis of

a contestable/competitive market, this bias cannot be too large (Molyneux et al., 1996).

Third, one needs to be observing banks in long-run equilibrium.

4.3 The data

The data is obtained from financial statements of banks, on their web pages on the

internet, annual central bank reports and from the Fitch-IBCA Ltd. Bankscope CD-Rom.

This study covers six banks privately held and domestically owned that are fully licensed

commercial. These are: Qatar National Bank (QNB), Commercial Bank of Qatar

(CMBQ), Doha Bank (DB), Al-Ahli Bank of Qatar (ABQ), Qatar Islamic Bank (QISB)

and Qatar International Islamic Bank (QIISB). The period sample covers is from 1993

to 2002. The final sample consists of panel of 60 bank-year observations. The sample of


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60 observations is very similar to the sample size used in previous studies of banking.

For example, Nathan and Neave (1989) used samples of 39 observations on Canadiantrust companies and 33 observations on mortgage companies; Shaffer (1993) used

25 observations on Canadian banks; and Shaffer and DiSalvo (1994) used samples of

36 and 44 observations on duopoly banks in alternate specifications.

5 The empirical model

Following Shaffer (1982, 1985), Nathan and Neave (1989), Molyneux et al. (1994) and

Hondroyiannis et al. (1999), the paper estimates the following bank revenue equation (1)

in which revenue is explained by factor prices and other bank-specific variables

that affect long-run equilibrium bank revenues for Qatar banks during the period 1993

to 2002.Ln TREV = + ( ln PL + ln PK + ln PF)

0 1 2 3

+ ln RISKAST + ln ASSET + ln BR.54 6

α α α α

α α α (1)

The justification for using the log-linear form, typically to improve the regression’s

goodness of fit and may reduce simultaneity bias (De Bandt and Davis, 2000). Molyneux

et al. (1996) found that a log-linear revenue equation gave similar results as a more

flexible translog equation. The revenue equation in the Panzar–Rosse model is interpreted

as a reduced form rather than a structural equation.

In long-run equilibrium, rates of return should be uncorrelated with input prices. To

test if the banking market is in long-run equilibrium the paper also estimates an auxiliary

equation (2), which tests for the equality of risk-adjusted rates of return across banks.

Ln (ROA +1) = + ( ln PL + ln PK + ln PF)0 1 2 3

+ ln RISKAST + ln ASSET + ln BR,54 6

β β β β

β β β (2)

where

Ln Natural logarithm

TREV Total revenue to total assets

ROA Net profits to total assets

PL Personnel expenses to employees (unit price of labour)

PK Capital expenses to fixed assets (unit price of capital)

PF Ratio of annual interest expenses to own funds (unit price of funds)

RISKAST Provisions to total assets

ASSET Bank total assets

BR Number of branches of each bank to the total number of branches

of the whole banking system

The dependent variable total revenue to total assets, total bank revenue variable (TREV)

is used since it reflects the banking market forces. According to Coccorese (1998), the


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nature of the estimation of the H-statistic means that we are especially interested in

understanding how the total revenue reacts to variations in the cost figures and for thisreason the dependent variable is given by the sum of all the revenues, including the

interest revenues. So, in line with Nathan and Neave (1989), Molyneux et al. (1996),

Coccorese (1998, 2004), Hondroyiannis et al. (1999), De Bandt and Davis (2000), and

Bikker and Haff (2002), this study uses the ratio of total revenue to total assets to be the

dependent variable in measuring the competitive conditions.

While the independent variables include bank-specific and market-specific variables

similar to those used in other studies (Nathan and Neave, 1989; Molyneux et al., 1994,

Hondroyiannis et al., 1999). Unlike previous studies which rely on a simple cross-

sectional estimation, the current study investigates the competitive conditions in the Qatar

banking system by using pooled estimation with fixed effects using 10 years pooled data.

As explained by Gelos and Roldos (2004), this approach has various advantages. First, by

including bank fixed effects, we can control for unobserved heterogeneity – this isimportant since the regressions are otherwise likely to suffer from omitted variable

problems. All bank-specific, non-time-varying determinants of revenues, not explicitly

addressed in the regression specification, are captured by the fixed effects. Second, as

noted above, panel estimation allows us to obtain more reliable estimates by observing

the behaviour of banks over time and testing for changes in the coefficients.

5.1 The H-statistic for testing competitive conditions

The nature of estimation of the H-statistic means that we are especially interested

in understanding how total revenues react to variations in the cost figures. PL, PK and

PF are the unit prices of the inputs of the banks: labour, capital and funds or proxies of

these prices. In the notation of equation (1), the H-statistic reads as (α1 + α

2 + α

3).

5.2 The H-statistic for testing equilibrium

Finally, PL, PK and PF are the unit prices of the inputs of the banks: labour, capital and

funds or proxies of these prices. In the notation of equation (2), the H-statistic reads as

( β 1 + β 2 + β 3). The empirical test for equilibrium is justified on the grounds that

competitive capital markets will equalise risk-adjusted rate of returns across banks such

that, in equilibrium, rates of return should not be correlated statistically with input prices.

The long-run equilibrium test is carried out using the H-statistic also, in which case it

measures the sum of elasticity of Return on Assets (ROA) with respect to input prices.

Note that in the equilibrium tests the dependent variable in the revenue equations is the

ROA and not the TREV as in the competitive position tests. Values of the H -statistic

equal to zero would indicate equilibrium and values less than zero disequilibrium.However, if the sample is not in long-run equilibrium, it is true that H < 0 no longer

proves monopoly, but it remains true that H > 0 disproves monopoly or conjectural

variation short-run oligopoly (Shaffer, 1985).

To verify that input prices are not correlated with industry returns, the paper regresses

the ratio ROA as the dependent variable. Because ROA can take on small negative

values, following Claessens and Laeven (2004) and Utrero-Gonzalez (2004), this study

computes the dependent variable as ln (ROA+1), where ROA is the unadjusted return on

assets. The long-run equilibrium test measures the sum of the elasticity of return on assets


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with respect to input prices. If the H-statistic ( β 1+ β 2 + β 3) = 0, this implies that the

banking market is in long-run equilibrium. If rejected, the market is assumed not to be inequilibrium. It should be noted that equilibrium does not mean that competitive

conditions are not allowed to change during the sample period. It only implies that

changes in banking are taken as gradual. Table 3 reports in brief the H-statistic values for

the different interpretations of the Rosse–Panzar ‘ H-statistic’.

Table 3 Discriminatory power of H

Values of H Competitive environment test

H ≤ 0 Monopoly equilibrium: each bank operates independently as undermonopoly profit maximisation conditions (H is a decreasing function of the perceived demand elasticity) or perfect cartel.

0


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Table 4 Trends in concentration in deposits and loans market

Trends in concentration in deposits Trends in concentration in loans

Year CR2 (%) CR3 (%) HHI CR2 (%) CR3 (%)

2002 70 80 3565 70 80

2001 71 81 3687 70 80

2000 68 79 3470 67 78

1999 67 78 3380 69 80

1998 66 78 3239 71 81

1997 68 79 3398 72 83

1996 67 79 3314 70 81

1995 73 83 3995 72 83

1994 62 77 2845 72 82

1993 65 80 3028 73 84

Source: Calculated by the author from banks annual reports

6.2 Regression results

The equilibrium test and the competitive position tests for the pooled data are reported

in Table 5. For both models, this study performed a variety of tests to check for

serial correlation, normality of the residuals and heteroscedasticity and for the functional

form. All tests confirm the good fit of the models. Most of the estimated coefficients

are statistically significant, while there is no evidence of multicollinearity among the

independent variables. All tests confirm the good fit of the models. The estimated

regression equation explained 85% in the TREV equation.In the TREV equation, the coefficients of the unit price of capital, unit price of fund

and the unit price of labour have significant positive signs at 5%, 1% and 10%,

respectively, indicating the direct effect of unit price of capital and unit price of labour in

the total revenue. The sign of the RISKAST variable is positive and statistically

significant at 10%; indicating that banks with higher provisions to assets in their balance

sheet would generate higher revenues per Qatari Rial of assets. The coefficient of the

ASSET variable is positive and statistically significant at 10%. This suggests that size, in

terms of assets, lead to higher total revenue per Rial of asset implying that larger banks

seem to be more efficient compared to smaller banks. The coefficient of the variable

depicting size effects in terms of branches, BR, was negative and statistically

insignificant; suggesting that banks with greater number of branches may generate lower

revenues per branch. This indicates also that Qatar is over branched.In the TREV equation, in accordance with the actual estimated value of H from the

estimated regression equations, suggest that the H-statistic value is positive and

statistically equal to 0.63 for the period 1993 to 2002. The Panzar and Rosse ‘ H-statistic’

suggests that Qatar banks operate under monopolistic competition. So this at least implies

that bank revenues in those years appear to be earned in a monopolistic competition.

To assess the long-run equilibrium the ROA equation is estimated. The signs of the

regression coefficients of the unit price of capital, labour and funds are mixed and

statistically insignificant. H-statistic value is statistically equal to zero for the period 1993

to 2002.


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Table 5 Regression results: data covering the period 1993–2002

Variable LnTREV Ln (1+ROA)

Intercept –2.168

(–2.731)***

–0.090

(–2.025)**

LnPK 0.076

(2.030)**

0.0004

(0.183)

LnPF 0.406

(10.977)***

–0.0008

(–0.374)

LnPL 0.146

(1.929)*

–0.002

(–0.546)

LnASSET 0.117

(1.799)*

0.008

(2.069)**LnBR –0.082

(–0.988)

–0.005

(–1.109)*

LnRISKAST 0.031

(1.923)*

–0.002

(–2.098)**

Adjusted R-squared 0.85 0.23

H-Value 0.63 0.00

Test result Monopolistic competition Equilibrium

Number of observation 60 60

The values in parentheses are the t-statistics.

* Significant at 10%.

** Significant at 5%.

*** Significant at 1%.

7 Concluding remarks

This paper investigated the market structure of Qatar banking industry using the most

frequently applied measures of concentration k-bank CR k and HHI and evaluated the

monopoly power of banks during the period 1993 to 2002 using the ‘ H-statistic’ by

Panzar and Rosse.

Both of the concentration indices indicate that Qatar banking industry had a very

concentrated market. Both the concentration indices indicate that the country is not

moving toward a better position in terms of the market concentration.The Panzar and Rosse ‘ H-statistic’ suggests that banks in Qatar operate under

monopolistic competition. The estimated value of H-statistic is equivalent to 0.63 during

the sample period leading us to conclude that banks earned their revenues and made a

profit in the condition of monopolistic competition.

On the basis of these findings, it is safe to conclude that Qatar banking industry is

highly concentrated and the concentration in general should cause a big concern since

the concentration indices indicate stability to increase in concentration over the 10 years.

In addition, the results suggest that Qatar Central Bank should be very cautious in


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granting mergers among banks because such move will lead to higher concentration.

The results also suggest that Qatar Central Bank should stop granting banks to opennew branches because the country is over branched.

Acknowledgements

I am grateful to the two anonymous referees and the editor for the helpful comments and

advice. Naturally, all remaining errors are mine.

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Note

1 Among the national banks, two are Islamic. One of the foreign banks, the Grindlays bank Ltd.,changed into a national bank by 1st August 2000. The specialised bank is Qatar IndustrialDevelopment Bank, initiated in 1997 to provide loans to small and medium-sizedmanufacturing firms.

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Analysis of Competitiveness in Qatar Banking