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

Analysis of competitiveness in Qatar banking industry 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 60 observations using the ‘H-statistic’ by Panzar and Rosse and investigates the market structure using the most frequently applied measures of concentration k-bank concentration ratio and Herfindahl–Hirschman index. Both of the concentration indices indicate that the Qatar had a ‘very concentrated market’. The Panzar and Rosse ‘H-statistic’ suggests that Qatar banks operate under monopolistic competition. Qatar Central Bank should be very cautious in granting mergers among banks, in particular among large ‘core’ banks. Keywords: k-bank concentration H-statistic; Panzar and Rosse; Qatar.

ratio;

Herfindahl–Hirschman

index;

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 banking and finance at Sultan Qaboos University. He received his BSc in 1988 from University of Arizona, USA, MBA in 1994 from Oregon State University, USA, and PhD in 2005 from Cardiff University, UK. His areas of interest are banking market structure, competitiveness, efficiency, productivity, performance, capital structure and budgeting. He has published several papers in journals and conferences in these fields.

1

Introduction

Commercial banks performance is determined by the market structure in which they operate. 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. Copyright © 200X Inderscience Enterprises Ltd.

S. Al-Muharrami 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 was applied. 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 confidence is 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 (CRK) 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 in December 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.

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 and finances, 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% 5%

1994

15,824.0

2283.0

2980.0

1313.0

3190.4

841.0

26,431.4

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 Year/Name

Branches of banks in Qatar 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

S. Al-Muharrami

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 for 1987), 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 monopolistic competition 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

Analysis of competitiveness in Qatar banking industry 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 in monopoly 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 to 2002. Therefore, it uses the most frequently applied measures of concentration CRk 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 CRK 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:

S. Al-Muharrami k

CRk =

∑ MS

ι

,

i =1

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 CRK 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: n

HHI = ∑ (MSi )2 , i=1

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.

Analysis of competitiveness in Qatar banking industry

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 earn monopolistic 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, and a 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

S. Al-Muharrami 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 Canadian trust 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 = α + (α ln PL + α ln PK + α ln PF) 0 1 2 3 + α ln RISKAST + α5ln ASSET + α ln BR. 4 6

(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) = β + (β ln PL + β ln PK + β ln PF) 0 1 2 3 + β ln RISKAST + β5ln ASSET + β ln BR, 4 6

(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

Analysis of competitiveness in Qatar banking industry 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 this reason 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 crosssectional 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 is important 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 + α2 + α3).

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 (β1 + β2 + β3). 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

S. Al-Muharrami with respect to input prices. If the H-statistic (β1+β2 +β3) = 0, this implies that the banking market is in long-run equilibrium. If rejected, the market is assumed not to be in equilibrium. 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 under monopoly profit maximisation conditions (H is a decreasing function of the perceived demand elasticity) or perfect cartel.

0