The impact of bank competition and concentration on bank risk-taking behavior and stability: Evidence from GCC countries

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The impact of bank competition and concentration on bank risktaking behavior and stability: Evidence from GCC countries Abdulazeez Y.H. Saif-Alyousfia,b, , Asish Sahac, Rohani Md-Rusa ⁎

a b c

School of Economics, Finance and Banking, Universiti Utara Malaysia, Kedah, Malaysia Department of Finance and Banking, Faculty of Administration Sciences, Taiz University, Taiz, Yemen Department of Finance, FLAME School of Business, FLAME University, Pune, India

ARTICLE INFO

ABSTRACT

JEL codes: C58 E5 G01 G18 G21 G28

This paper presents a comprehensive assessment of the impact of competition on bank fragility pre and post financial crisis period in the GCC banking market as measured by bank risk-taking behavior and bank stability during the period 1998–2016. Our results indicate that a higher level of bank competition and the greater degree of concentration adds to financial fragility. The findings further shows that during the 2008 crisis, lower bank competition maintain the stability of GCC banks. We also find that lower level of competition and lower concentration in the banking market increases the risk-taking behavior of the low capitalized, low liquid and small banks which add to fragility in the banking system. Our findings suggest that countries with greater capital stringency, greater supervisory power, greater market discipline, and private monitoring, with explicit deposit insurance schemes, higher shareholder protection, and higher legal efficiency decrease banks’ risk-taking and increase their stability. We also find that greater regulatory restrictions and higher creditor protection decrease banks’ stability and increase risk in concerned countries. We find support for both competition-fragility and competition-stability hypotheses in the GCC banking market. The results also confirm that the use of a single measure of competition is insufficient to assess the role of competition in banking stability.

Keywords: Bank competition and concentration Moral hazard Bank stability Regulation Global financial crisis Bank characteristics

1. Introduction The forces of globalization, financial innovation and advances in information technology during the decade of the 1970’s and 1980 s strengthened the wave of deregulation across the financial markets. This, in turn, led banks to pursue aggressive business strategies to strengthen their bottom-line. It is argued in the literature that the resultant heightened level of risk in the balance sheet of banks is the main factor behind the global financial crisis in 2008. This prompted regulators to rethink their stance and explore ways and means to ensure financial stability by striking a balance between apparently conflicting forces of competition and concentration in the financial markets. There is, however, an ongoing debate amongst the policy planners, regulators and in the academic world whether banking competition has positive or negative contribution to stability (Anzoategui, Pería, & Melecky, 2012; Apergis, 2015; Jiménez, Lopez, & Saurina, 2013; Lee, Yang, & Chang, 2014; Martinez-miera and Repullo, 2010; Mirzaei, Liu, & Moore, 2013; Tan, 2017). As per traditional perception, there exists a trade-off between economic efficiency and stability in the banking system of any country. It is argued that a competitive banking system is more efficient and hence fosters banking stability. However, it is also

Corresponding author. E-mail addresses: [email protected], [email protected], [email protected] (A.Y.H. Saif-Alyousfi), [email protected] (A. Saha), [email protected] (R. Md-Rus). ⁎

https://doi.org/10.1016/j.najef.2018.10.015 Received 1 June 2018; Received in revised form 20 October 2018; Accepted 29 October 2018 1062-9408/ © 2018 Elsevier Inc. All rights reserved.

Please cite this article as: Saif-Alyousfi, A.Y.H., North American Journal of Economics and Finance, https://doi.org/10.1016/j.najef.2018.10.015

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argued that market power is necessary to ensure stability in the banking system. Whether there exists such a trade-off is therefore unclear. The structure–conduct–performance hypothesis stresses that banks in the highly concentrated market are less competitive, which leads to collusive behavior of banks to generate unusual returns. The charter value view of competition proposes that there could be significant stability costs of competition given the fact that in a highly competitive marketplace which results in pressure on profit due to the reduction in margin, banks tend to take on excessive risk (Allen and Gale, 2004; Marcus, 1984). This results in greater fragility (Hellmann, Murdock, & Stiglitz, 2000). It is argued by Keeley (1990) that banks with market power seek higher rents and as a result, they have higher charter values. He has also argued that the rise in competition results in a decline in charter value which entices increased risk-taking by banks. The Report on Bank Competition and Financial Stability (OECD, 2011) argues that competition amongst banks contributes to instabilities and has resulted in banking problems in many countries. On the other hand, it is also argued in the literature that banking fragility may also be the result of the absence of competition. Boyd and De Nicoló (2005) argue that banks with more market power are likely to charge higher interest rates to borrowing firms which in turn induce them to take higher risk resulting in increased fragility of the financial system. Goodhart Charles (2011) argued that the structure of banking has no relationship with the financial crisis. Anginer, Demirguc-Kunt, and Zhu (2014) argue that faced with the higher level of competition, banks tend to take diversified risks reducing the financial fragility of the banking system. In general, most previous studies have been conducted in the context of developed countries and provided support to the competition fragility-hypothesis. It is argued that in less competitive markets increase in competition may foster risk-shifting behavior and help improve efficiency supporting the competition-stability hypothesis. However, it is not clear whether banks assume a lower level of risk in a concentrated market or whether there exists opposite association between concentration and competition. GCC, despite being a key economic block, no comprehensive assessment of the relevance of competition-stability or competition-fragility hypothesis in the context of the GCC banking sector has so far been reported in the literature. We investigate the GCC countries for several other reasons as well. First, credit growth in the GCC region has been more moderate, less volatile and less risky. However, more recently, the GCC countries have experienced rapid and more volatile credit growth rates, which may raise concerns about the stability of the financial system, and especially the fact that higher credit growth is often followed by the financial crisis (Crowley, 2008). Second, as a bridge between developed and developing countries in Europe, Asia, and Africa, the GCC region attracts investors and bankers worldwide. This strategic position makes the GCC countries more susceptible to political instability and thus economic and financial vulnerability. Third, in practice, the last 30 years have seen a significant structural change in the GCC financial markets. In particular, policies of financial liberalization and financial restructuring were implemented with the goal of enhancing competitiveness in the banking sector. Fourth, though the banking sector of the GCC region is roughly 10% of the US banking sector in terms of asset size, banks in the GCC countries is relatively young, with the oldest banks dating back to no earlier than the 1950s. Although the majority is privately owned, the public sector continues to have a prominent role in the banking industry GCC countries. Private sector ownership of financial institutions also tends to be concentrated in a few shareholders which reduce the threat of corporate control. The pattern of ownership concentration is, however, not similar across all GCC countries. Government ownership holding is more prominent in the case of Saudi Arabia, Qatar, and the UAE, but less pronounced for Kuwait, Bahrain, and Oman. The level of foreign shareholding is a significant factor in the Saudi Arabian banking sector through the legacy of enforced “Saudization” of the original foreign banks operating in the Kingdom. The level of local shareholding is more significant in Kuwait and Bahrain. Foreign shareholding in Bahraini institutions mostly reflects inter-GCC ownership. The shareholding structure with significant government influence/control and individual/family management may result in higher risk-aversion among financial institutions in GCC countries. Fifth, the global trend towards consolidation has so far by-passed the Gulf. However, with World Trade Organization (WTO) liberalization planned for the near future, it is quite probable that the current fragmented banking sector will be unable to face the onslaught when markets do eventually open up and banks start reconsidering their competitive strategies. It is therefore relevant to assess the competitive structure in the GCC banking sector and to also evaluate the performance of the commercial banks in the GCC economies. It is also relevant to assess what effect did the recent policy changes of the governments in the GCC economies have on the risk-taking behavior and stability of banks? Did the global financial crisis affect the performance of banks in those economies? As of now, only a few studies have been reported in the literature for the GCC economies (Al-muharrami, Matthews, & Khabari, 2006; Ashraf, Ramady, & Albinali, 2016; Saif-Alyousfi, Saha, & Md-Rus, 2018). Al-muharrami et al. (2006) have used unadjusted return on assets with the H-statistic proposed by Panzar and Rosse to estimate the monopoly power of GCC banks over the sample period 1993–2002. Ashraf et al. (2016) examine the relationship between HHI as a measure of ownership concentration and Z-score as a proxy of the financial stability of financial institutions in GCC countries 2000–2011. In our study, we use data from 1998 to 2016 to cover the period during which the GCC countries have experienced significant changes in competitive conditions because of mergers and acquisitions, deregulation, international financial integration, and privatization. The economies have also witnessed the phase of the global financial crisis during the study period. We also aim at assessing the impact of both competition and concentration on bank risk-taking behavior and bank stability for listed commercial banks in GCC stock markets. We use two structural (HHI and five larger banks concentration ratio) and two non-structural indices (Boone indicator and Lerner index) to estimate the level of competition in the respective economies thereby our study does not suffer from the choice of the single measure of competition (Fu, Lin, & Molyneux, 2014; Khan, Ahmed, & Gee, 2016). World Bank Data suggests that concentration in the banking sector of GCC economies is very high compared to other Asian countries and some other developed economies. The average assets of the five larger GCC commercial banks (CR5) account for 92.75% (95.87% for Bahrain, 100% for each Kuwait and Oman, 99.09% for Qatar, and 79.55% and 82.01% for Saudi Arabia and UAE respectively) of total assets, an increase of 2.48% during the post-financial crisis 2010–2016 compared to the crisis period 2007–2009. In comparison, the said ratio in other Asian countries was 82.39% in China, 39.76% in India, 58.03% in Japan, 78.28% 2

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in Malaysia, 68.5% in Thailand and 57.83% in Indonesia. The ratio was 47.42%, 78.28%, and 67.13% in the USA, UK, and Turkey respectively. The overall NPL ratio, which according to Brownbridge (1998) and Kammer (2013) is an important indicator of the risktaking indicator, in the GCC banking sector is high when compared to a group of selected emerging and developed countries as well as the average NPL ratio of the global banks during the period of 2005–2016. As per the World Bank data, the average NPL ratio to total loans in the banking sector of UAE and Kuwait (6.01% and 5.8%) was almost double than the average NPL ratio of the global banks (3.01%). The said ratio was 4.6% in Bahrain and 3.6% in Oman during the same period. However, it may be pointed out here that the asset classification norm is more conservative in Qatar, and Saudi Arabia, and less so in the UAE, Kuwait, Bahrain and Oman. In our empirical analysis, we examine the impact of competition on bank risk-taking behavior and financial stability for six GCC countries over the period 1998–2016. Our paper contributes to the literature in several important ways. First, our study has focused on analyzing both bank risk-taking behavior and financial stability. We use four proxies of bank risk-taking behavior: standard deviation of return on assets (SDROA), the standard deviation of return on equity (SDROE), NPL, and loan loss provisions (LLP). In addition, we use two measures for bank stability: Z-score based on ROA (ZROA) and ROE (ZROE). Second, we also compare the results that we obtain by using both structural approach (CR5 and HHI) and non-structural approach (Lerner index and Boone indicator) to bank competition. To the best of our knowledge, until now, only Schaeck and Cihak (2014) and Kasman and Kasman (2015) have used Boone indicator to investigate the relationship between competition and stability. Third, our study is the first study that investigates and compares the impact of bank competition on bank risk-taking behavior and financial stability during pre and post global financial crisis period. Fourth, our study is the first of its kind that analyses and compares the extent of banks' response to competition based on their characteristics of financial strength such as capitalization, liquidity, and size. Fifth, we also divide the analysis based on a subsample of banks with different level of capitalization, liquidity, and size before, during and after the global financial crisis. Sixth, we also replace conventional Lerner index by efficiency-adjusted Lerner index and a quadratic term for the Lerner index as measures of competition in the banking market. Seventh, we consider two variables for ownership, four variables on regulations and four environmental (institutional) developments, including the existence of deposit insurance. Finally, the interaction between market power and regulatory and environmental variables are also included in the model. To address the potential endogeneity problems among risk and competition, we adopt a Generalized Method of Moments (GMM) estimator as in Fu et al. (2014), Kasman and Kasman (2015) and Khan et al. (2016). We also incorporate a range of robustness tests using various model specifications. Overall our results indicate that higher bank competition/lower market power and higher concentration add to financial fragility (increase the risk and reduce the financial stability of banks). Furthermore, our findings also show that during 2008 global financial crisis, greater market power/lower competition in the GCC banking market might have contributed to the reduction of moral hazard and maintaining the stability of banks. We also find that higher market power/lower level of competition and lower concentration in the banking market increase the bank risk-taking behavior and decrease the stability of low capitalized, low liquid and small banks, in contrast to the highly capitalized, highly liquid and large banks. Our findings on banks in the GCC banking sector support both the views of competition-fragility and competition-stability. We find that GCC countries with greater capital stringency, greater supervisory power, greater market discipline, and private monitoring, with explicit deposit insurance schemes, higher shareholder protection, and higher legal efficiency decrease banks’ risk-taking and increase their stability. We also find that greater regulatory restrictions and higher creditor protection decrease banks’ stability and increase risk in the concerned countries. Our results also confirm that the use of a single measure of competition is insufficient to decipher its role in relation to banking stability. The remainder of the paper is organized as follows. Section 2 reviews the related literature on the banking risk and competition. Section 3 describes the research model and the parameters that we use in our analysis. Section 4 illustrates the methodology and the sample used. Section 5 exhibits our empirical findings, and Section 6 concludes the paper. 2. Related literature The theoretical and empirical literature on the relationship between bank competition and stability produce contradictory evidence. In this paper, we intend to validate two hypotheses in the context of the GCC banking sector. The first hypothesis suggests that competition in the banking sector leads to instability. To the contrary, the second hypothesis suggests that there exist a positive relationship between competition and stability. The competition-fragility hypothesis, which is also called as franchise value paradigm, was proposed by Marcus (1984) and Keeley (1990). The main argument in this hypothesis is that higher bank competition increases the risk-taking incentives of banks. Marcus (1984) indicates that higher competition results in a reduction in franchise value of banks which prompts them to adopt more risky strategies. Dermine (1984) find that market power has a negative and significant effect on bank credit risk. Using the preference model, Keeley (1990) find that a decrease in franchise value leads to increased risk-taking by banks. Rhoades and Eutz (1982) examines the effect of competition, measured by three-bank deposit concentration, on bank risk-taking behavior for the USA banks and find that increase in bank concentration leads to a decrease in bank-risk-taking behavior (returns volatility). Broecker (1990) also provides evidence to support the hypothesis of “franchise value” by showing that the association between the market-power of banks and credit quality is negative and significant. Demsetz, Saidenberg, and Strahan (1996) observe that USA banks with higher market power have stronger solvency ratio and carry lower risk in their asset book. In a dynamic model of imperfect competition, Matutes and Vives (1996, 2000) find that higher market power reduces the probability of bank default. In a moral hazard dynamic model, Hellmann et al. (2000) stress that bank competition has a negative impact on the prudence in risk-taking by banks. In addition, Yeyati and Micco (2007) in 8 Latin American countries, Nicolò and Loukoianova (2007) in133 non-industrialized economies, Berger, Klapper, and Turk-Ariss (2009) in 23 developed economies, Uhde and Heimeshoff (2009) in 25 EU countries, and Ariss (2010) in 60 3

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developing countries, find support for the traditional view on “competition-fragility”: banks with higher market power are less risky. In the case of Turkish banks over the period 2002–2012, Kasman and Kasman (2015) use Lerner index and Boone indicator as measures of the competition and Z-score and NPLs as proxies for bank stability in Turkish banks during 2002–2012. Their results provide support for the competition-fragility view, where they find that competition is positively associated with Z-score but negatively associated with the NPL. The findings also suggest that higher concentration has a negative impact on the Z-score but positive impact on the NPLs. The competition-stability hypothesis was proposed by Chan and Greenbaum (1986). The authors argue that the quality of screening loan requests and hence the quality of its loans portfolio depend upon the surplus that results from such a screening process. They also argue that the said surplus decreases with the increase in the level of competition in the market and results in a decline in the quality of their loan assets. Mishkin (1999) suggest that banks in a highly concentrated (lower competition) market usually get public support and guarantees, which may result in increased risk-taking (lower stability) by banks with resultant moral hazard problem. Caminal and Matutes (2002) argue that in less competitive market banks tend to exercise lower credit rationing and extend larger loans that precipitate bank failures. In their dynamic model of imperfect competition, Repullo (2004) finds that in markets with imperfect competition, the intermediation margin of banks would be small which would result in a fall in the franchise value of banks and prompt them to carry risky activities. Boyd, Nicolò, and Jalal (2006) provide evidence that supports the “competitionstability” hypothesis. For the US and an international bank sample, they show that a higher degree of bank competition is not necessarily associated with an increase in the probability of bank failures. Soedarmono, Machrouh, and Tarazi (2011, 2013) find that the insolvency risk is higher in Asian countries with a higher degree of market power which is related to higher capital ratios, higher insolvency risk, and higher volatility of bank returns. In addition, Fu et al. (2014) examine the trade-off between financial stability and competition in 14 Asia Pacific countries from 2003 to 2010. The authors conclude that higher concentration increases financial fragility and in addition, lower pricing power results in greater exposure to bank risk. The empirical literature on the relationship between competition and stability has also produced mixed results. In their study of 69 countries over the period 1980–1997, Beck, Demirgu-Kunt, and Levine (2006) find that economies with less market concentration are less likely to suffer from crises. Schaeck, Cihak, and Wolfe (2009) conclude that if executed properly, policies fostering bank competition enhances banking stability. Berger et al. (2009) highlight that the “competition-stability” hypothesis and the “franchise value” hypothesis need not be viewed as opposing propositions. Based on a sample of 8235 banks in 23 developed economies, their empirical results suggest that a higher degree of bank market power is associated with a decrease in bank insolvency risk and hence, highlighting the “franchise value” hypothesis. On the other hand, a higher degree of bank market power is associated with an increase in NPLs, supporting the “competition-stability” hypothesis. The first finding is due to an increase in bank capital ratios when bank market power increases. Boyd, Nicoló, and Jalal (2009, 2010) find that intensification of competition results in a reduction in borrower risk and a corresponding rise in the ratio of loan to total asset and a reduction in the probability of bank failure. Zhao, Casu, and Ferrari (2010) find that deregulation enhances the performance of Indian banks and encourages competition in the lending market despite stricter prudential norms. Tabak, Gomes, and Medeiros (2015) indicate that higher market power of Brazilian banks is negatively associated with overall bank risk-taking behavior. The results further suggest that banks with higher levels of capital and lower market power tend to take higher levels of risk. By using the two-step system GMM estimator, Khan et al. (2016) find that Lerner index, HHI, and CR5 indicate that lower competition/higher market power decrease the effectiveness of monetary policy transmission through bank's loan, while the Boone indicator shows the opposite. There is, therefore, conflicting views in the literature regarding the association between competition, concentration, bank risk-taking behavior, and bank stability. Using a sample of 543 banks operating in 13 Central and East European (CEE) countries over the 1998–2005 period, Agoraki, Delis, and Pasiouras (2011) find that capital requirements reduce risk in general, but for banks’ with higher market power this effect is significantly weaker or can be reversed. In the case of GCC economies over the period 1993–2002, Al-muharrami et al. (2006) find that the banking markets in Saudi Arabia, Kuwait, and UAE are moderately concentrated, while the banking markets in Bahrain, Qatar, and Oman are highly concentrated. Their results suggest that there exist perfect competition in the banking market in Saudi Arabia, Kuwait, and UAE; whereas there is monopolistic competition in Bahrain, Qatar, and Oman. Using a sample of 125 financial firms from GCC economies over the period 2000–2011, Ashraf et al. (2016) find that irrespective of the nature of shareholding, greater ownership concentration (HHI) is positively related to insolvency risk. 3. Data, variables and descriptive statistics 3.1. Data We collect the data of listed commercial banks in GCC economies for the period 1998 to 2016 which covers the period of the global financial crisis during 2008–2009 and create the sample of unbalanced panel data from Bankscope Fitch IBCA database. We follow Maghyereh and Awartani (2014) and Ashraf et al. (2016) and focus only on the listed commercial bank in GCC stock exchanges as they face similar regulatory requirements across these economies. The distributions of sample banks across the economies are UAE (20), Bahrain (13), Saudi Arabia (11), Kuwait (10), Oman (8), and Qatar (8). Moreover, the data available for the selected banks are adequate to estimate Lerner Index and Boone indicator as measures of bank competition in the GCC banking Sector. Data for the macroeconomic factors are collected from the World Bank’s World Development Indicators (WDI). Data for ownership variables and regulatory indices are collected from the World Bank database on “Bank Regulation and Supervision” developed by Barth, Caprio, and Levine (2001a, 2001b) and updated by Barth, Caprio, and Levine (2006, 2008) and Cihák, Demirgüc-Kunt, 4

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Martínez Soledad, and Mohseni-Cheraghlou (2012). Data for the environmental variables are obtained from the World Bank database developed by La Porta, Lopez-de-Silanes, Shleifer, and Vishny (1998) and Demirgüç-kunt, Karacaovali, and Laeven (2005). 3.2. Concentration and competition measures In this paper we use four proxies to measure competition in the banking market. Two structural ratios used are the concentration ratio of the top five banks (CR5) and the Herfindahl Hirschman Index (HHI). The two non-structural ratios are the Lerner index and Boone indicator. Berger et al. (2009) argue that concentration and competition could coexist and can simultaneously induce stability or fragility. Claessens and Laeven (2004) argue that concentration measures (CR5 and HHI) are not ideal measures of competition and are challenged by the idea of market contestability. It is argued that the behavior of banks is conditioned by the threat of new entry and exit. They are forced to behave competitively even in a concentrated market where there is a low barrier for the new entrants and exit by the inefficient ones. In order to overcome the challenges 3associated with measuring competition in the traditional approach, the recent research works in the area of bank competition is focused on measuring the market power directly by assessing the pricing behavior of banks using indices like Panzar-Rosse H-statistic, Lerner index, and Boone indicator. One can find support for both “competition-stability” and “competition-fragility” in the banking markets in countries like GCC. In Gulf countries, the level of competition is high and therefore a rise in competition can erode the margin of the banks, increasing the probability of default (Allen and Gale, 2004). Thus, we expect that higher levels of competition can have positive effects in terms of risk-shifting behavior. Boyd and Nicoló (2005) suggest that a reduction in lending rates to borrowers reduces the loan’s probability of default. We feel that initially risk decreases but at some point, higher competition leads to reduction in loan rates and reduces banks’ interest income from non-defaulting a loan which is used as a buffer for loan losses. Hence, the relationship between competition and concentration with bank risk and stability might be ambiguous. 3.3. Bank competition measures 3.3.1. Lerner index Our paper uses the Lerner index as a measure of market power or competition. This index has been used by many researchers including (Agoraki et al., 2011; Ariss, 2010; Chen and Liao, 2011; Fiordelisi and Mare, 2014; Fu et al., 2014; Kasman and Kasman, 2015; Soedarmono, Machrouh, & Tarazi, 2013; Tan, 2016). The Lerner index of banking market may range between ‘less than zero’ to ‘one’: the value of ‘zero’, ‘one’ and ‘less than one’ would imply perfect completion, monopoly, and a non-optimal state (where banks price their product below their marginal cost) respectively. The Lerner index is calculated as the difference between the price and marginal costs of total assets of a bank as a percentage of the price of its total assets and can be written as follows:

LERNER=

P-MC P

(1)

where p is the average price of a bank which is calculated as the ratio of total revenue (interest and non-interest income) to total assets. MC is the marginal cost of total assets that is estimated on the basis of a translog cost function with one output and three input prices. Following Chen and Liao, (2011), Soedarmono et al. (2013), Fu et al. (2014), Fiordelisi and Mare (2014), and Tan (2016), the cost function is estimated as follows:

lnCostit =

0

+

1ln

Q it +

1 2

2

ln Qit2 +

3

3 kt

ln Wk,it +

k=1

3 k

k-1

3

ln Qit ln Wk,it +

ln Wk,it ln Wj,it + k=1 j=1

it

(2)

where, COST is total cost of bank (interest expenses plus non-interest expenses) for bank i at time t. Qit represents a proxy for bank output or total assets. Wk,it represent three input prices. W1,it, W2,it and W3,it indicate the input prices of labor, funds, and fixed capital, respectively. The input prices are defined as: (W1,it) – personnel costs/total assets; (W2,it) – interest expenses/total deposits; and (W3,it) – other operating and administrative expenses/total assets.

MC TAit =

Costit Qit

3 1

+

2

ln Qit +

ln Wk,it

(3)

k=1

The Lerner index is averaged over time for each bank ‘i’ for inclusion in the regression model. The data for this measure of competition is at the aggregate level in any country and are estimated country-by-country and year-by-year. 3.3.2. Boone indicator Boone indicator (Boone, 2008) is used in estimating the level of competition in any market. The main idea of the Boone indicator is that the more efficient banks improve their market share and their earnings at the expense of less efficient banks. The higher is the extent of competition in the market, more pronounce would be the effect on the inefficient banks. Following Khan et al. (2016) we estimate the Boone indicator by using the following model:

ln(Profit)i =

+ ln(Marginal Costs)i +

(4)

i

The β indicates the Boone indicator. The more is the negative value of the Boone indicator, higher is the level of competition in the market because of higher reallocation effect. However, a positive value is feasible and it would mean that the marginal costs of 5

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banks are higher and they will earn more profit. A higher positive value would imply that the collusion amongst banks is high or they are competing on quality. The Boone indicator overcomes the weakness of concentration measures like CR5 or HHI which aims to assess competitiveness by investigating levels of concentration in the banking sector: high level of concentration need not necessarily imply lack of competition. Contrary to market concentration measures, the Boone indicator assesses the interaction between banks by focusing on their behavior. On the other hand, the computation of Panzar-Rosse H-statistic to analyze the level of competition in the banking market requires some restrictive assumption like the presence of long-term equilibrium (Kasman and Kasman, 2015) which cannot be realized in our study because of the entry and exit from the market (Claessens and Laeven, 2004). It is also a static measure (Tan, 2016). The Boone indicator is innovative in the sense that it not only enables the assessment of competition at the aggregate level but also at the level of product markets as well as the types of banks like the commercial banks, cooperative banks, and investment banks. Unlike H-statistics model, the Boone indicator requires relatively small data and it allows estimation of competition on an annual basis (van Leuvensteijn, Bikker, van Rixtel, & Sørensen, 2011) and at the same time, it has a sound technical basis. 3.3.3. Static measures 3.3.3.1. Herfindhal Hirschman index (HHI). Following Chen and Liao, (2011), Fiordelisi and Mare (2014) and Kasman and Kasman (2015), we use three alternative measures of HHI: HHI of total assets, HHI of total loans, and HHI of total deposits. We define the HHI as: n

(MSi)2

HHI= i=1

where MS i MSi is the market share in terms of total assets, total deposits, or total loans of a bank and n is the number of banks in the market. The HHI is estimated through the sum of the squared market shares of banks in the relevant market. The lower the number of banks in the market and the more variance is there in terms of their size, the larger the value of HHI. Al-muharrami et al. (2006) and Tan (2016) suggest that HHI attaches more importance to the larger banks. 3.3.3.2. Concentration ratio. The concentration ratio shows the degree of competition in the banking sector. Following the study of Kasman and Kasman (2015) and Khan et al. (2016), we compute the concentration ratio as the ratio of the assets, loans, or deposits of the five largest banks divided by the total assets, loans, or deposits of the banking sector in each country (CR5). 3.4. Bank risk measures In this paper, we use two different indicators for ‘risk exposure’ as dependent variables: bank risk-taking and stability. These variables have been extensively used in banking risk literature (e.g., Agoraki et al., 2011; Soedarmono et al., 2011, 2013; Lee and Hsieh, 2013; Kasman and Kasman, 2015; Tabak et al., 2015; Ashraf et al., 2016). We consider four dependent variables to measure ‘bank risk-taking’: SDROA, SDROE, NPL and LLP. To measure ‘bank stability’, we use two types of Z-score measures: based on ROA represented by ZROA, and ROE represented by ZROE. SDROA, SDROE, ZROA and ZROE are used to compare the results with the findings reported in the literature, while the inclusion of LLP and NPL as the risk indicators follows the study of Kasman and Kasman (2015) and Tan (2016). A higher Z-score indicates that there are higher stability and lower risk. However, Fang, Hasan, and Marton (2011) argue that the potential stability of banks cannot be necessarily reflected the Z-score. Given the economic and regulatory conditions deviation from the bank’s current stability and the maximum stability must be considered. Thus, we use LLP and NPL as dependent variables to measure the bank risk. Kasman and Kasman (2015) also argue that credit risk (NPL) is the main source of banking risk; the inability of banks to control the rise in NPL may result in banking failures. Furthermore, a higher LLP suggests that the bank has a higher risk. Empirical investigations indicate that an increase in risk exposure leads to increase in the probability of bank defaults. In GCC banking sectors, all the banks are required to hold more than enough LLP to improve the risk management; the extent of LLP is set at the beginning of the year. Therefore, the NPL and LLP are important macro-prudential indicators that need to be monitored by the regulators to evaluate the stability of banking systems. In literature, the NPL and LLP are usually used to measure the loan quality of banks and are used to measure bank risk-taking behavior as we do in this study. In line with the authors, in our paper we use three-years rolling time, from t to t-2, to calculate the SDROA. SDROE is also calculated on the basis of a three-year rolling window. LLP is computed by the ratio of LLP to total loans. The ratio of NPL to gross loans is used to measure the NPL. ZROA suggests the number of standard deviations that the ROA of banks have to fall below its forecasted value before its equity is depleted. Therefore, the higher the value of ZROA the lower is the default probability of banks. For a bank i and time t, ZROA is computed as follows:

ZROAit=

ROAit+ EQTAit SDROAit

where ROA is return to total assets, EQTA is equity to total assets. Similarly, the ZROE is computed as follow: 6

North American Journal of Economics and Finance xxx (xxxx) xxx–xxx

A.Y.H. Saif-Alyousfi et al.

ZROE it=

1+ ROEit SDROE it

where ROE is return on equity. 3.5. Control variables measures Following Soedarmono et al. (2011, 2013), Lee and Hsieh (2013), Fu et al. (2014), and Kasman and Kasman (2015), we also introduce a range of control variables like bank’s size (SIZE), non-interest revenue to total revenue (NIR), cost-income-ratio (COST), equity to total assets (EQTA), and liquidity risk (LIQ) which are bank-specific factors. SIZE is measured by the logarithm of its total assets. The NIR is used to measure the diversification of income. We consider also the COST measured by the ratio of operating expenses to total income, the EQTA is the ratio of equity to total assets, and LIQ is measured by the ratio of loans to total assets. For country-specific control variables, business cycle conditions are controlled by introducing the annual growth rate of real GDP and the inflation rate (INF). Apart from the macroeconomic variables, we also use foreign and government ownership as potential determinants of bank competition. Foreign ownership is defined as the percentage of foreign-owned banks in terms of total industry assets, and public ownership as the percentage of publicly owned banks in terms of total industry assets. We also include four variables each to capture the effect of regulations and environmental (institutional) developments following Agoraki et al. (2011) and Lee and Hsieh (2013). The regulatory variables included in our study are Capital requirements index (CAPRI), Supervisory power index (SPRI), Activity restrictions index (ACTRI), and Market discipline index (MDPI). CAPRI is an index of capital requirements that account for both initial and overall capital stringency. It is calculated by considering the sources of funds used as capital and by taking into account various issues that emerge during the calculation of the capital-to-assets ratio. The index takes values between 0 and 8, with higher values indicating greater capital stringency. SPRI measures the power of supervisory agencies, indicating the extent to which these authorities can take specific actions against bank management and directors, shareholders, and bank auditors. This index takes values between 0 and 14 with higher values indicating more SPRI. ACTRI is a proxy for the level of restrictions on banks’ activities in each country. It is determined by considering whether participation in securities, insurance, and real estate activities and ownership of non-financial firms are unrestricted, permitted, restricted, or prohibited. ACTRI takes values between 4 and 16, with higher values indicating higher restrictions. Finally, MDPI is an indicator of market discipline and shows the degree to which banks are forced to disclose accurate information to the public and whether there are incentives to increase market discipline. This index ranges between 0 and 8 with higher values indicating greater MDPI. The four environmental (institutional) developments variables included in our study are Deposit insurance explicit (DEPI), Shareholder protection (SHPI), Creditor protection (CRPI), and Legal efficiency (LEEI). According to Demirgüç-kunt et al. (2005) and Lee and Hsieh (2013), under the explicit deposit insurance schemes banks have more incentives for risk-taking. Deposit insurance is a dummy variable that takes a value of 1 if a country has explicit deposit insurance and a value of 0 otherwise. The last three variables are proxies for corporate governance. SHPI and CRPI range from 0 to 5, with higher scores for higher protection, while LEEI may take the multiple values based on the efficacy of the rule of law and the efficiency of the judicial system. 3.6. Descriptive statistics Table 1 presents the variables used in this study, their definition, and the descriptive statistics for all banks in GCC banking market while Table 2 presents the statistics for each country. The mean values of competition and concentration measures provide an image of the structure of banking market in the sample countries. Qatar has a higher concentration of banking market with a mean value of 424.57 and 86.16 for HHI and CR5 respectively. The Boone indicator (−0.01) displays a lower competition level in Qatar. However, opposite is true with HHI and CR5. The Lerner index (0.47) for Qatar is not the largest in the GCC region, suggesting that large market concentration does not necessarily be associated with larger market power. Bank concentration in the region is the lowest Saudi Arabia with mean values of 27.59 and 39.194 for HHI and CR5 respectively. However, the mean value of Lerner index (0.51) for Saudi Arabia is higher than that in Qatar which supports the view that concentration may not reflect market power. For other GCC countries, the mean value of HHI for Bahrain, Kuwait, Oman, and the UAE are 190.83, 117.56, 100.71, and 36.48 and the value of CR5 is 70.48, 64.05, 62.41, and 49.20 for CR5 respectively. The mean values of Lerner index are 0.39, 0.54, 0.41, and 0.46 for these countries. Saudi Arabia, Kuwait, and UAE have a similar value for the Boone indicator (−0.04), which is the lowest value in the region, suggesting that competition in these banking markets are higher compared to other GCC countries. However, Bahrain has the highest mean value of Boone indicator (0.05), that indicating either the competition is least in Bahrain or banks are competing in terms of their quality in a highly concentrated market in the country. Our findings suggest that it is difficult to arrive at a consistent interpretation of the extent of competition in the GCC banking markets as the indicators provide different results on a standalone basis. In terms of banks’ risk measured by SDROA, SDROE, NPL, and LLP, the table shows that Bahrain commercial banks are riskier as the SDROA, SDROE, NPL, and LLP are 2.69%, 9.33%, 13.41%, and 1.49% respectively. UAE banks, in comparison, are less risky (0.51%, 3.12%, 5.87%, and 0.97% respectively) and are more stable in terms of ZROA and ZROE (58.29% and 12.51% respectively) than other GCC countries. In terms of the size of banks, there is not much of difference across the GCC: it ranged between 14.67 for Bahrain to 16.70 for Saudi Arabia. Oman banks have the least income diversification (NIR is the lowest 25.79%), while Bahrain banks are more engaged in nontraditional activities (NIR is the highest 53.34%). Furthermore, Oman the highest mean value of cost to income ratio or COST 7

86.90 1381.95 8.21 1.73 1.14

Interest expenses + non-interest expenses (million USD) A mount of total assets (million USD) Ratio of interest expenses to total deposits Ratio of personnel expenses to total assets Ratio of other non-interest expenses to total assets

The Boone indicator is measured from a profitability model. ln (Profit )i = + ln (MarginalCosts)i + i , the larger the effect (i.e., the larger the β in absolute value), the higher the competition.

8 16.51 12.19

65.72 73.80

Annual GDP growth rate

Annual inflation rate

Macroeconomic factors Real GDP growth (GDP)

Inflation Rate (INF)

3.16

5.34

54.17

44.92 17.08

Calculated as operating expenses to total income Calculated as equity to total assets

Calculated as loans to total assets

35.08

3.40

4.70

21.59

43.05 11.42

28.56

1.45

459.19 16.60

174.65 59.43

15.66

355.09 398.48

0.04

0.09

142.23 2523.15 14.38 1.25 2.93

3.54

18.81

99.91

99.86

3737.88 99.34

3508.17 3496.07

0.16

0.62

761.41 12311.74 112.32 7.93 18.73

23.54

Max

−4.86

−7.07

0.27

9.77 −15.69

15.05

26.17

95.53

613.43 99.27

−437.93 225.00

10.92

50.64

33.03

0.00 27.04

0.00 0.00

−0.08

0.25

0.04 0.13 0.27 0.42 −4.73

0.42

Std. Dev. Min

117.22 144.85

−0.02

Calculated as non-interest revenue to total revenue

Liquidity (LIQ)

Non-Interest revenue (NIR) Cost-income-ratio (COST) Equity to total assets (EQTA)

Control variables Bank-specific characteristics Bank size (SIZE) The natural logarithm of the total assets value

Measures of concentration HHI (total assets) Sum of the squared each bank's total assets to total assets of banking sector HHI (total deposits) Sum of the squared each bank's total deposits to total deposit of banking sector HHI (total loans) Sum of the squared each bank's total loans to total loans of banking sector CR5 (total assets) Calculated by the ratio of the total assets of the five largest banks divided by the total assets of all banks operating in the market. CR5 (total deposits) Calculated by dividing the total deposits of the five largest banks with the total deposits of all banks operating in the market CR5 (total loans) Calculated by dividing the total loans of the five largest banks with the total loans of all banks operating in the market

Boone indicator

Measures of competition Lerner index Calculated on the difference between a bank's price revenue and marginal 0.46 costs divided by a bank's price revenue, higher values suggesting less competition in the banking sector.

6.23

Ratio of total revenue to total assets

Bank’s Price Translog specification Total cost Total assets Price of fund Price of labor Price of capital

Mean

Definition

Variable

Table 1 Variables definition and descriptive statistics

−

+

−

− +

−

+

+/−

+/−

+/− +/−

+/− +/−

+/−

+/−

Bankscope Bankscope Bankscope Bankscope Bankscope Bankscope Bankscope

Sources

Fu et al. (2014), and Kasman and Kasman (2015) Tan (2016) and Chen and Liao (2011)

Lee and Hsieh (2013) and Lee et al. (2014) Lee et al. (2014) and Chen and Liao (2011) Chen and Liao (2011) Fu et al. (2014), Kasman and Kasman (2015), and Lee et al. (2014) Kasman and Kasman (2015) and Lee et al. (2014)

Fu et al. (2014), Lee et al. (2014), Kasman and Kasman (2015) and Khan et al. (2016) Fu et al. (2014), Lee et al. (2014), Kasman and Kasman (2015) and Khan et al. (2016)

WDI

WDI

(continued on next page)

Bankscope

Bankscope Bankscope

Bankscope

Bankscope

Calculated by authors

Calculated by authors

Calculated by authors Calculated by authors

Calculated by authors Calculated by authors

Soedarmono et al. Calculated by authors (2013), Fu et al. (2014), Kasman and Kasman (2015) and Tan (2016) Fu et al. (2014), Kasman Calculated by authors and Kasman (2015) and Khan et al. (2016)

Fu et al. (2014), Lee et al. (2014), Kasman and Kasman (2015) and Khan et al. (2016)

Expected sign References with Z-Score

A.Y.H. Saif-Alyousfi et al.

North American Journal of Economics and Finance xxx (xxxx) xxx–xxx

Supervisory power index (SPRI)

Regulatory variables Capital requirements index (CAPRI)

17.16

20.52

Mean

This variable is determined by adding 1 if the answer is yes to questions 6.66 1–6 and 0 otherwise, while the opposite occurs in the case of questions 7 and 8 (i.e. yes = 0, no = ). (1) Is the minimum required capital asset ratio risk-weighted in line with Basle guidelines? (2) Does the ratio vary with market risk? (3–5) Before minimum capital adequacy is determined, which of the following are deducted from the book value of capital: (a) market value of loan losses not realized in accounting books? (b) Unrealized losses in securities portfolios? (c) Unrealized foreign exchange losses? (6) Are the sources of funds to be used as capital verified by the regulatory/supervisory authorities? (7) Can the initial or subsequent injections of capital be done with assets other than cash or government securities? (8) Can initial disbursement of capital be done with borrowed funds? 12.15 This variable is determined by adding 1 if the answer is yes and 0 otherwise, for each one of the following fourteen questions: (1) does the supervisory agency have the right to meet with external auditors to discuss their report without the approval of the bank? (2) Are auditors required by law to communicate directly to the supervisory agency any presumed involvement of bank directors or senior managers in illicit activities, fraud, or insider abuse? (3) Can supervisors take legal action against external auditors for negligence? (4) Can the supervisory authorities force a bank to change its internal organizational structure? (5) Are off-balance sheet items disclosed to supervisors? (6) Can the supervisory agency order the bank’s directors or management to constitute provisions to cover actual or potential losses? (7) Can the supervisory agency suspend director’s decision to distribute dividends? (8) Can the supervisory agency suspend director’s decision to distribute bonuses? (9) Can the supervisory agency suspend director’s decision to distribute management fees? (10) Can the supervisory agency supersede bank shareholder rights and declare bank insolvent? (11) Does banking law allow supervisory agency or any other government agency (other than court) to suspend some or all ownership rights of a problem bank? (12) Regarding bank restructuring and reorganization, can the supervisory agency or any other government agency (other than court) supersede shareholder rights? (13) Regarding bank restructuring & reorganization, can supervisory agency or any other government agency (other than court) remove and replace management? (14) Regarding bank restructuring & reorganization, can supervisory agency or any other government agency (other than court) remove and replace directors?

% of government owned banks in terms of total industry assets

% of foreign-owned banks in terms of total industry assets

Ownership variables Foreign ownership (FOREIGN)

Government ownership (GOVERNMENT)

Definition

Variable

Table 1 (continued)

1.78

1.55

16.4

27.89

9

3

0

0

Std. Dev. Min

14.5

9

46

87

Max

+

+

−

+

Bank Regulation and Supervision Database, World Bank; Barth et al. (2001a, 2001b, 2006, 2008) Bank Regulation and Supervision Database, World Bank; Barth et al. (2001a, 2001b, 2006, 2008)

Sources

9

(continued on next page)

Agoraki et al. (2011) and Bank Regulation and Lee and Hsieh (2013) Supervision Database, World Bank; Barth et al. (2001a, 2001b, 2006, 2008) and Cihák et al. (2012)

Agoraki et al. (2011) and Bank Regulation and Lee and Hsieh (2013) Supervision Database, World Bank; Barth et al. (2001a, 2001b, 2006, 2008) and Cihák et al. (2012)

Agoraki et al. (2011)

Agoraki et al. (2011)

Expected sign References with Z-Score

A.Y.H. Saif-Alyousfi et al.

North American Journal of Economics and Finance xxx (xxxx) xxx–xxx

Definition

10

0.46 0.34 0.39 4.97

0.3 2.63 3.94 44.43

2.80 32.71 10.84 3.82 66.96 19.96

0.98

3.01

0.99 6.86 6.55 0.97 53.08 10.24

2.11

0.00 0.01 0.04 −56.60 −1.81 −3.14

40

3

0 1.91

1.83

6

Std. Dev. Min

9.98

Mean

32.62 561.54 99.51 58.50 338.24 204.74

50

5

1 3.02

4.5

13

Max

-

+

+ +

+

+

Sources

Lee and Hsieh (2013)

Lee and Hsieh (2013)

Lee and Hsieh (2013) Lee andHsieh (2013)

Bankscope Bankscope Bankscope Bankscope Bankscope Bankscope

La Porta et al. (1998)

La Porta et al. (1998)

Demirgüç-kunt et al. (2005) La Porta et al. (1998)

Agoraki et al. (2011) and Bank Regulation and Lee and Hsieh (2013) Supervision Database, World Bank; Barth et al. (2001a, 2001b, 2006, 2008) and Cihák et al. (2012)

Agoraki et al. (2011) and Bank Regulation and Lee and Hsieh (2013) Supervision Database, World Bank; Barth et al. (2001a, 2001b, 2006, 2008) and Cihák et al. (2012)

Expected sign References with Z-Score

Note: Table shows the variables used in this study along with their definition and descriptive statistics for a full sample of GCC banking market. Data is winsorized at 1% and 99% percentiles to control for outlier.

Dependent Variables (Bank risk and stability) SDROA Standard deviation of ROA from a three-period rolling window SDROE Standard deviation of ROE from a three-period rolling window NPL Calculated as loan loss provision to total loans LLP Calculated as non-performing loans to total loans ZROA Z-score based on ROA from a three-period rolling window ZROE Z-score based on ROE from a three-period rolling window

The score for this variable is determined on the basis of the level of regulatory restrictiveness for bank participation in: (1) securities activities (2) insurance activities (3) real estate activities (4) bank ownership of nonfinancial firms. These activities can be unrestricted, permitted; restricted or prohibited that are assigned the values of 1–4, respectively. We use an overall index by calculating the average value over the four categories. This variable is determined by adding 1 if the answer is yes to questions Market discipline index (MDPI) 1–7 and 0 otherwise, while the opposite occurs in the case of questions 8 and 9 (i.e. yes = 0, no = 1). (1) Is subordinated debt allowable (or required) as part of capital? (2) Are financial institutions required to produce consolidated accounts covering all bank and any non-bank financial subsidiaries? (3) Are off-balance sheet items disclosed to public? (4) Must banks disclose their risk management procedures to public? (5) Are directors legally liable for erroneous/misleading information? (6) Do regulations require credit ratings for commercial banks? (7) Is an external audit by certified/licensed auditor a compulsory obligation for banks? (8) Does accrued, though unpaid interest/principal enter the income statement while loan is non-performing? (9) Is there an explicit deposit insurance protection system? Environmental (Institutional development) variables Deposit insurance (DEPI) Deposit insurance explicit = 1; otherwise 0. Shareholder protection Shareholder protection; index scales range from 0 to 5, with higher scores (SHPI) for higher protection Creditor protection (CRPI) Creditor protection; index scales range from 0 to 5, with higher scores for higher protection Legal efficiency (LEEI) Legal efficiency is the multiple values for the rule of law and the efficiency of the judicial system, with higher scores for higher legal efficiency.

Activity restrictions index (ACTRI)

Variable

Table 1 (continued)

A.Y.H. Saif-Alyousfi et al.

North American Journal of Economics and Finance xxx (xxxx) xxx–xxx

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A.Y.H. Saif-Alyousfi et al.

(65.72%), while it is the lowest for Qatar (31.58). Bank capitalization ranges between 13.89% for Qatar to 20.87% for Bahrain. Oman banks are less liquid with the highest mean value of 67.84% for LIQ while banks in Bahrain are more liquid with the lowest mean value of LIQ of 34.75%. The overall economic outlook does not vary much across the countries, with GDP growth ranging from 3.50% for Oman to 11.17% for Qatar and inflation ranging from 1.75% for Bahrain to 4.08% for UAE. Table 3 displays the correlation coefficients between the parameters in this study. As shown in Table 3, the correlation between the independent variables is < 0.90, suggesting the absence of the problem of multicollinearity. Table 2 Country-wise descriptive statistics Variable

Bahrain

Kuwait

Oman

Mean

Std. Dev.

Min

Max

Mean

Std. Dev.

Min

Max

Mean

Std. Dev.

Min

Max

SDROA SDROE NPL LLP ZROA ZROE Lerner index Boone indicator HHI CR5 SIZE NIR COST EQTA LIQ GDP INF FOREIGN GOVERNMENT CAPRI SPRI ACTRI MDPI DEPI SHPI CRPI LEEI N

2.69 9.33 13.41 1.49 42.94 5.45 0.39 0.05 190.83 70.48 14.67 53.34 63.75 20.87 34.75 4.88 1.75 71.70 1.26 6.31 12.81 8.88 3.56 1 2.5 4 40 208

5.57 15.49 20.99 8.53 64.46 8.41 0.07 0.04 305.20 10.17 1.63 56.53 64.45 15.49 21.84 1.78 1.51 21.84 1.25 1.76 1.43 1.32 0.27 0 0 0 0

0.00 0.17 0.64 −56.60 −3.07 −3.14 0.25 −0.01 0.11 47.88 10.92 −437.93 20.62 −15.69 0.27 2.10 −1.21 28.04 0 3 11 7 3.17 1 2.5 4 40

32.62 85.78 99.51 58.50 338.24 49.30 0.49 0.16 1239.97 88.53 17.34 225.00 613.43 91.46 72.54 8.29 3.53 87 3.7 8 14.5 10 3.83 1 2.5 4 40

0.65 6.28 5.31 1.21 7.67 1.83 0.54 −0.04 117.56 64.05 16.07 30.37 38.24 13.89 54.46 4.38 3.48 0 6.75 7.69 10.50 8.00 3.43 0 2.75 4 40 160

0.91 81.44 4.88 1.28 29.67 10.50 0.06 0.02 170.14 6.62 1.09 15.84 21.46 7.20 18.98 5.82 2.33 0 5.97 1.11 0.50 1.81 1.12 0 0 0 0

0.00 0.01 0.04 −0.37 −1.14 −2.88 0.41 −0.08 0.03 55.76 12.99 −59.73 12.20 0.77 4.51 −7.08 0.89 0 0 6 10 6 1.83 0 2.75 4 40

5.69 561.54 30.33 8.77 336.66 195.39 0.62 −0.02 1023.08 80.66 18.17 66.66 191.07 79.93 95.53 17.32 10.58 0 12 9 11 10 4.5 0 2.75 4 40

1.03 4.49 6.16 0.63 8.60 1.44 0.41 −0.02 100.71 62.41 14.77 25.79 65.72 19.48 67.84 3.50 2.41 21.93 5.63 6.63 13.13 11.94 2.84 1 2.35 4 40 128

3.24 6.17 7.15 1.32 34.14 9.44 0.04 0.02 289.12 2.87 1.21 11.62 87.22 19.62 16.78 2.97 3.16 9.19 4.98 0.93 0.90 0.66 0.59 0 0 0 0

0.04 0.07 0.12 −1.45 0 −2.84 0.31 −0.03 0.01 57.64 11.17 4.17 23.75 7.45 3.97 −2.67 −1.20 11.1 0 6 12 11 2.00 1 2.35 4 40

24.39 35.86 39.48 10.23 305.62 194.45 0.44 0.04 2056.11 66.22 17.30 99.72 574.95 99.27 85.79 8.20 12.09 30 10 8 14.5 13 3.58 1 2.35 4 40

Variable

Qatar

SDROA SDROE NPL LLP ZROA ZROE Lerner index Boone indicator HHI CR5 SIZE NIR COST EQTA LIQ GDP INF FOREIGN GOVERNMENT CAPRI SPRI ACTRI MDPI DEPI SHPI

Saudi Arabia

UAE

Mean

Std. Dev.

Min

Max

Mean

Std. Dev.

Min

Max

Mean

Std. Dev.

Min

Max

0.49 4.02 4.63 0.54 12.77 2.74 0.47 −0.01 424.57 86.13 15.98 30.59 31.58 16.00 52.69 11.17 4.04 6.80 31.11 6.94 11.25 9.56 2.96 0 1.91

0.59 6.76 8.40 1.06 41.87 12.03 0.10 0.02 852.38 12.00 1.21 12.69 11.80 6.37 20.13 6.97 5.47 4.25 12.22 1.94 1.72 2.35 0.97 0 0

0.00 0.11 0.05 −0.95 0 −1.40 0.25 −0.03 4.59 65.38 13.27 6.53 11.91 7.01 5.11 3.72 −4.86 3.7 20.4 4 9 6 2 0 1.91

3.44 42.16 47.54 8.20 335.78 175.77 0.60 0.08 3508.17 99.34 18.81 73.34 80.34 50.62 84.33 26.17 15.05 14.9 46 9 13 12 3.94 0 1.91

0.57 3.48 2.93 0.67 21.72 3.76 0.51 −0.04 27.59 39.19 16.70 32.07 40.24 15.17 52.64 5.07 2.70 12.49 16.49 4.56 13.44 11.56 3.37 0 2.68

0.84 3.37 3.63 0.60 51.86 10.73 0.08 0.01 34.27 11.22 0.89 12.49 17.06 10.80 17.58 2.88 2.82 6.82 7.97 0.50 0.50 0.50 1.07 0 0

0.01 0.02 0.04 −0.29 0 −1.10 0.31 −0.07 0.30 27.04 14.13 0.89 17.74 7.85 4.26 0.13 −1.13 0 0 4 13 11 1.83 0 2.68

6.12 21.47 27.46 3.00 343.40 104.75 0.59 −0.02 211.48 65.03 18.25 100.00 160.00 98.93 87.90 9.96 9.87 20.7 21.4 5 14 12 4.33 0 2.68

0.51 3.12 5.87 0.97 58.29 12.51 0.46 −0.04 36.48 49.20 15.70 32.20 37.11 16.83 63.03 4.66 4.08 6.86 32.09 7.44 11.81 10.19 2.33 0 3.02

0.60 3.40 5.03 0.91 66.56 22.51 0.08 0.01 67.52 6.06 1.33 12.66 14.62 6.32 17.54 3.89 3.64 11.64 15.68 0.43 2.15 2.15 0.87 0 0

0.01 0.07 0.16 −0.62 0 −1.37 0.30 −0.05 0.08 42.36 13.03 −20.96 9.77 6.34 2.12 −5.24 0.66 0 0 7 9 8 1.83 0 3.02

4.10 19.24 26.58 6.83 325.97 204.74 0.59 −0.02 330.39 59.00 18.52 77.53 155.65 52.03 94.32 10.85 12.25 27 41.5 8 14 13 3.83 0 3.02

11

(continued on next page)

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A.Y.H. Saif-Alyousfi et al.

Table 2 (continued) Variable

Bahrain

CRPI LEEI N

Kuwait

Oman

Mean

Std. Dev.

Min

Max

Mean

Std. Dev.

Min

Max

Mean

Std. Dev.

Min

Max

4 40 128

0 0

4 40

4 40

3.63 50 176

0.93 0

3 50

5 50

4 50 320

0 0

4 50

4 50

Note: Table reports descriptive statistics for each country in the sample. Each column in the table shows mean value, standard deviation, minimum value, maximum value for Standard deviation of ROA (SDROA), Standard deviation of ROE (SDROE), non-performing loans (NPL), loan loss provision (LLP), Z-score index based on ROA (ZROA), and Z-score index based on ROE, Lerner Index, Boone indicator, Herfindahl–Hirschman Index (HHI), 5-bank concentration ratio (CR5), bank size or natural log of total assets(SIZE), non-Interest revenue (NIR), cost-income-ratio (COST), equity to total assets (EQTA), loan to total assets as a measured of liquidity (LIQ), annual GDP growth rate (GDP), annual inflation rate (INF), foreign ownership (FOREIGN), government ownership (GOVERNMENT), capital requirements index (CAPRI), supervisory power index (SPRI), activity restrictions index (ACTRI), market discipline index (MDPI), deposit insurance (DEPI), shareholder protection (SHPI), creditor protection (CRPI), and legal efficiency (LEEI). The last row reports the number of observations for each country.

4. Methodology To examine the impact of bank competition on bank risk-taking behavior, we follow Soedarmono et al. (2011, 2013), Beck, Jonghe, and Schepens (2013), Lee and Hsieh (2013), Fu et al. (2014), and Kasman and Kasman (2015), who argue that in studies of panel data, a dynamic model should be used to estimate the time persistence in the bank risk. Thus, the equation of a dynamic linear model (1): J

RISKit =

0

+

1 RISK it 1

+

2 LERNER

(L)it +

3 BOONE

(L)it +

4 HHI

(L)it +

5 CR5

(L)it +

J 6 BANit

j=1 J

+

J 8 Ownershipit

j=1

+

J 9 Regulationit

j =1

+

+

7 MACROit j=1

J 10 Environmentalit

j =1

+

11 L

× Regulationit

j=1

J 12 L

+

× Environmentalit +

it

(5)

j=1

RISKit RISKit is the risk-taking of bank i over the period t and is proxied by six different parameters SDROA, SDROE, LLP, NPL, ZROA and ZROE. β0 is a constant term; LERNERit LERNERit and BOONE it BOONEit are the Lerner index and Boone indicator respectively used as measures of bank competition in country i at time t; HHIit CR5it HHIit is Herfindhal Hirschman index of banks’ assets, deposits, or loans in country i at time t; CR5it CR5it is the concentration ratio of five largest banks assets in a country i at time t; BANitBANit , MACROit, Ownershipit, Regulationit, and Environmentalit, are the bank-specific characteristics, macroeconomic factors, ownership variables, regulation variables, and environmental variables as a control variables in a country i at time t; and it ∊it is the error term. Previous studies in banking literature such as Al-muharrami et al. (2006), Chen and Liao (2011), and Tabak et al., (2015) examine their empirical models by traditional estimators of panel data: pooled-OLS, and random effect and fixed effect. However, Baltagi (2008) stress that random effect and fixed effect are biased estimators in a dynamic model of panel data. He also argues that pooled OLS is biased and inconsistent even if εit are not serially correlated. Therefore, in the current study, we use the estimation of generalized method of the moments (GMM), which helps to solve the endogeneity problem between the bank competition measures and bank risk measures, capitalization levels, as well as loan risk. For example, well-capitalized banks may be able to merge with other banks and extend their market power. Rapid growth in loans may result in the increase in the bank's assets and hence its size. However, capitalization may also change with changes in the size of banks. Furthermore, the lagged bank risk is important because it is possible that banks with high risk in the past year are more likely to face financial trouble in the next year (bank risk-taking behavior is highly persistent). GMM addresses the endogeneity problem, unobserved heterogeneity, and the dependent variable persistence (Fu et al., 2014; Kasman and Kasman, 2015; Lee and Hsieh, 2013). It uses lagged values of independent and dependent variables in variance instruments. Moreover, GMM is more efficient than 2SLS because it accounts for heteroskedasticity (Hall, 2005; Lee, Liang, Lin, & Yang, 2015). There are two types of GMM estimator which are used to estimate dynamic panels: First, the ‘difference GMM estimator’ developed by Arellano and Bond (1991), and second, the ‘system GMM estimator’ developed by Arellano and Bover (1995). In the difference GMM, the data is first-differenced to eliminate fixed effects, while in system GMM, data is estimated simultaneously in differences and levels. Blundell and Bond (1998, 2000) argue that the system GMM performs better than the difference GMM because it is more robust to capture efficiency gains and may reduce the finite sample bias. Sarafidis, Yamagata, and Robertson (2009) also argue that the system GMM may probably deal serial correlation better in unbalanced panel data. Therefore, this study prefers the system GMM due to the fact that the system GMM estimator addresses the unit root property problem and provides more accurate findings (Bond, 2002). 12

North American Journal of Economics and Finance xxx (xxxx) xxx–xxx

A.Y.H. Saif-Alyousfi et al.

System GMM estimator has the one-step and the two-step alternative. For the system GMM, Lee and Hsieh (2013) stress that the two-step GMM estimator is usually more efficient than the one-step GMM estimator because it is more robust to the weak instruments problem. Moreover, Windmeijer (2005) presents a corrected variance estimate for the two-step estimator which, according to him, enables more accurate inferences than that of the one-step estimator due to lower bias and standard errors. For these reasons and in line with the previous study in banking risk of Lee and Hsieh (2013), and Kasman and Kasman (2015), we use the two-step system GMM estimator with Windmeijer (2005) corrected standard error to conduct the analysis. As the number of years (t) in our study are 18 years, the use of GMM estimator of Blundell and Bond (1998) and Arellano and Bover (1995) is justifiable. The validity of the instruments is examined via Sargan-test of overidentifying restrictions. To test the autocorrelation, the Arellano-Bond test for autocorrelation is used. The presence of first-order autocorrelation, AR (1), does not imply that the estimates are inconsistent. The presence of second-order serial correlation, AR (2), would render the GMM estimator inconsistent and this is the most important test. For this reason, the result for AR (2) should not reject the null hypothesis to ensure the consistency of the GMM estimator. We assess our model during the time period 1998–2016. 5. Empirical results 5.1. Overall results In Table 4 we report the empirical results of bank risk-taking behavior (Eq.-5) using the two-step system GMM dynamic estimator using SDROA, SDROE, NPL, LLP, ZROA and ZROE as the dependent variables. We estimate four regressions for each dependent variable. In each regression, we use the measure of bank competition. As shown in the table, the Wald-test displays the joint significance of the variables and the Sargan-test shows that there is no evidence of over-identification restrictions. Although the analysis indicates that the first-order autocorrelation is present, however, this does not mean that the assessments are inconsistent. The estimation is inconsistent if the second-order autocorrelation is present (Arellano and Bond, 1991), but the same is rejected by the test for AR (2). Hence, all GMM estimations are valid. In all estimations, the lagged endogenous variables (Lagt-1) are significant and positive with parameter values almost 0.67 for SDROA, 0.77 for SDROE, 0.25 for NPL, 0.37 for LLP, 0.19 ZROA and 0.38 for ZROE, confirming that there exists a high degree of persistence in the bank risk-taking. The first column of each dependent variable in Table 4 shows the impact of market power measured by Lerner index together with some bank-specific factors on bank risk-taking. The results show that the coefficient of market power (Lerner index) is negative and significant with bank risk measures (SDROA, SDROE, NPL, and LLP), but positive and significant with bank stability measures (ZROA and ZROE), suggesting more market power leads to decrease the bank risk-taking behavior and increase the stability (decrease the insolvency risk). The second column of each dependent variable in Table 4 presents the estimation results using the Boone indicator as the measure of bank competition. The findings indicate that the impact of Boone indicator on SDROA, SDROE, NPL, and LLP as proxies of bank risk is significantly negative, while it is significantly positive with ZROA and ZROE as measures of bank stability. As the higher negative values of the Boone indicator imply less competition and vice-versa, our findings suggest that competition lead to an increase in the risk-taking behavior of GCC banks and decrease banking stability. The significant and positive relationship between the Boone indicator and bank stability (ZROA and ZROE) supports the negative effect of competition on bank stability. These findings are in line with the results of Agoraki et al. (2011) in Central and Eastern European banks, Fu et al. (2014) in the Asia Pacific, and Kasman and Kasman (2015) in Turkey. However, their findings are not in valence with the results of Soedarmono et al. (2011, 2013) in 12 Asian economies, Liu, Molyneux, and Nguyen (2012) for South East Asian commercial banks, and Yeyati and Micco (2007) in 8 Latin American countries. As shown in column 3 and 4 of Table 4, the effect of market concentration measured by HHI and CR5 on bank risk measures (SDROA, SDROE, NPL, and LLP) are positive and significant, indicating that banks in more concentrated markets take higher risk. Furthermore, the findings show that the relationship of HHI and CR5 with ZROA and ZROE are negative and significant, confirming that in GCC economies with higher banking concentration are not stable. Ashraf et al. (2016) examine HHI with Z-score and find similar results for GCC banks. In general, our results support the view that higher banking concentration and lower pricing power results in bank fragility. In all regressions, the coefficient of the bank size (SIZE) is negative and significantly related to bank risk measures, while it is positively and significantly related to bank stability, implying that larger banks are more stable and less risky. Non-interest revenue (NIR) has a positive relationship with bank risk and has a negative relationship with stability in all the regressions, suggesting that banks that more engaged in non-traditional activities are riskier and less stable. The positive relationship of the cost-to-income ratio (COST) on SDROA, SDROE, NPL, LLP and negative relationship with ZROE, suggests that the inefficiency in managing cost make banks riskier and less stable. In all estimations, the relationship between capitalization (EQTA) and bank risk measures are negative and significant, whereas it is positive and significant with bank stability, implying that banks with higher capitalization have lower risk and are more stable. Meanwhile, the coefficient of bank liquidity (LIQ) measured by loans-total assets ratio bears a negative and significant relationship with bank risk-taking and shows a positive and significant relationship with bank stability indicating that higher degree of loan exposure (lower liquidity) decrease the bank risk-taking and enhance stability. These reflect that GCC banks have the ability to manage and monitor their loans portfolio well which results in higher stability. The coefficient of the GDP growth rate on different risk-taking variables is negative and significant, while its coefficient on stability measures is positive and significant, meaning higher economic growth rate decreases the risk and boost banks’ stability. In another word, banks accumulate risks more rapidly in economically bad times and some of these risks materialize as asset quality deteriorates during subsequent recessions. 13

14

1 .392* .614* .545* −.111* −.118* −.122* −.260* .077* 0.042* −.366* .344* .362* −.277* −.290* −0.060* 0.029* 0.298* −0.154* 0.003* 0.067* −0.049* 0.123* 0.253* −0.071* −0.010* −0.154*

SDROA SDROE NPL LLP ZROA ZROE Lerner Boone HHI CR5 SIZE NIR COST EQTA LIQ GDP INF FORE GOVE CAPRI SPRI ACTRI MDPI DEPI SHPI CRPI LEEI

14

1 −.160* 0.054* −0.006

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27

No

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

1 −0.049 0.122*

15

1

No

Table 3 Correlation matrix.

1 0.240*

16

1 .227* .289* −.062* −.078* −0.002* −.060* 0.034* 0.019* −.070* .088* .070* −0.017* −0.044* −0.109* 0.045* 0.063* −0.066* 0.043* −0.043* −0.027* 0.080* 0.047* −0.01* 0.001* −0.103*

2

1

17

1 .510* −.084* −.104* −.217* −.266* .084* .183* −.413* .323* .420* −0.037* −.335* −0.063* 0.168* 0.203* −0.162* −0.068* 0.115* −0.060* 0.095* 0.251* −0.032* 0.004* −0.151*

3

18

1 −.044* −.062* −0.053* −.247* 0.053* 0.041* −.128* .108* .218* −0.013* −.189* −0.072* 0.056* 0.035* −0.014* 0.009* −0.011* 0.029* 0.024* 0.038* 0.025* 0.001* −0.026*

4

19

1 .173* .126* .042* −.050* −.035* 0.061* −.064* −.044 .016* .072* 0.003* −0.069* −0.029* 0.063* 0.076* −0.050* −0.040* −0.073* −0.059* 0.047* 0.080* 0.048*

5

20

1 .197* .092* −.110* −.035* .169* −.084* −.107* .075* .072* 0.018* −0.082* −0.095* 0.033* 0.049* −0.079* 0.065* −0.046* −0.099* 0.010* 0.109* 0.062*

6

21

1 −.397* −.078* −.295* .422* −.169* −.111* −.120* .161* 0.020 0.004 −0.294* 0.294* 0.213* −0.476* 0.038* −0.076* −0.472* 0.144* 0.062 0.182*

7

22

1 0.035 .306* −.249* .284* .209* .093* −.318* −0.050 −0.220* 0.549* −0.253* −0.013* 0.081* −0.017* 0.120* 0.387* −0.413* 0.192* −0.494*

8

23

1 .362* .246* −.073* −.067* −.123* 0.04 0.127* −0.004 −0.036* 0.036* −0.017* −0.002* −0.057 0.025* 0.029* −0.303* 0.029* −0.222*

9

24

1 −.220* .075* 0.053 0.025 −.175* 0.219* −0.161* 0.166* −0.080* 0.058 −0.164* −0.260* 0.091* 0.315* −0.365* 0.127* −0.541*

10

25

1 −.259* −.322* −.491* .162* −0.035 0.112* −0.321* 0.280* 0.052* −0.319* 0.201* −0.155* −0.417* 0.085* 0.009* 0.250*

11

27

1 .335* −.205* −0.089* −0.098* 0.231* −0.163* 0.023* 0.083* 0.011* 0.036* 0.286* −0.068* 0.013* −0.145*

13

(continued on next page)

26

1 .208* .199* −.411* 0.055* 0.026 0.269* −0.097* −0.099* 0.118* −0.098* 0.096* 0.195* −0.027* 0.029* −0.096*

12

A.Y.H. Saif-Alyousfi et al.

North American Journal of Economics and Finance xxx (xxxx) xxx–xxx

0.165* −0.066* −0.005* 0.087* −0.006* 0.015* 0.184* −0.043* 0.022* −0.068*

18 19 20 21 22 23 24 25 26 27

−0.345* 0.256* 0.161* −0.090* 0.120* −0.275* −0.236* 0.170* 0.125* 0.225*

15

0.020* 0.129* −0.147* 0.042* −0.244* 0.034* −0.137* −0.313* 0.003* −0.101*

16 −0.089* 0.320* 0.104* −0.051 −0.159* −0.172* −0.223* 0.070* 0.071* 0.113*

17 1 −0.399* −0.011* 0.260* −0.209* 0.297* 0.556* −0.219* 0.048* −0.373*

18 1 0.166* −0.279* 0.215* −0.393* −0.569* 0.193* 0.057 0.511*

19

1 −0.528* −0.071* −0.231* −0.097* 0.140* 0.274* −0.141*

20

1 −0.135* 0.197* 0.287* −0.048* −0.171* 0.119*

21

1 −0.063* 0.020* 0.001* −0.061* 0.295*

22

1 0.184* −0.206* −0.446* −0.280*

23

1 −0.354* 0.098* −0.384*

24

1 −0.023* 0.507*

25

1 −0.169*

26

1

27

Note: Table reports descriptive statistics for each country in the sample. Each column in the table shows mean value, standard deviation, minimum value, maximum value for Standard deviation of ROA (SDROA), Standard deviation of ROE (SDROE), non-performing loans(NPL), loan loss provision (LLP), Z-score index based on ROA (ZROA), and Z-score index based on ROE, Lerner Index, Boone indicator, Herfindahl-Hirschman Index (HHI), 5-bank concentration ratio (CR5), bank size or natural log of total assets(SIZE), non-Interest revenue (NIR), cost-income-ratio (COST), equity to total assets (EQTA), and loan to total assets as a measured of liquidity (LIQ), annual GDP growth rate (GDP), annual inflation rate (INF), foreign ownership (FOREIGN), government ownership (GOVERNMENT), capital requirements index (CAPRI), supervisory power index (SPRI), activity restrictions index (ACTRI), market discipline index (MDPI), deposit insurance (DEPI), shareholder protection (SHPI), creditor protection (CRPI), and legal efficiency (LEEI). * Indicate the significance of correlation at 1%.

14

No

Table 3 (continued)

A.Y.H. Saif-Alyousfi et al.

North American Journal of Economics and Finance xxx (xxxx) xxx–xxx

15

t-1

16

t-1

Boone indicator

Lerner index

Lag

No. observations No. banks Wald-test Sargan P value AR(1) P value AR(2) P value

Country dummy Time dummy Intercept

INF

GDP

LIQ

EQTA

COST

NIR

SIZE

CR5

HHI

Boone indicator

Lerner index

Lag

.361*** (.001) −.289** (.132)

−22.130*** (5.196)

.370*** (.026)

.371*** (.0008)

3 .383*** (.0005)

4

−1.43*** (.078) .365*** (.081) .073*** (.005) −.324*** (.065) −.087*** (.008) −.978*** (.009) 1.008*** (.021) Yes Yes 61.65** (30.83) 993 70 683.6*** 0.9996 0.2918 0.3156

.782*** (.031) −36.81*** (.611)

.297*** (.002) 60.72*** (9.472)

1

2

.021** (.009) −.299** (.131) .017*** (.005) .017*** (.003) −.055*** (.016) −.022*** (.005) −.043*** (.002) .069*** (.003) Yes Yes 7.022** (2.376) 997 70 1124.3*** 1.0000 0.0937 0.5553

.654*** (.027)

1

−.324** (.135) .020*** (.005) .018*** (.003) −.061*** (.017) −.022*** (.005) −.051*** (.002) .074*** (.005) Yes Yes 4.712** (2.279) 997 70 1064.5*** 1.0000 0.0943 0.5700

.002** (.0001)

.651*** (.028)

ZROA

−2.315*** (.138) .017*** (.005) .012*** (.003) −.051*** (.017) −.014*** (.005) −.032*** (.002) .083*** (.002) Yes Yes 5.929*** (1.334) 993 70 1072.9*** 0.9991 0.0960 0.3933

−18.08*** (.133)

.683*** (.029)

LLP

−2.301*** (.146) .016*** (.005) .017*** (.003) −.053*** (.017) −.019*** (.005) −.045*** (.001) .090*** (.002) Yes Yes 5.779*** (1.442) 993 70 998.3*** 0.9993 0.0999 0.5457

.692*** (.029) −.409*** (.029)

4

1

3

1

2

SDROE

SDROA

196.61*** (24.871)

.185*** (.001)

2

−2.372*** (.006) .360*** (.078) .038*** (.005) −.339*** (.055) −.074*** (.007) −.901*** (.006) 1.198*** (.015) Yes Yes 61.31* (32.85) 993 70 788.6*** 0.9995 0.2885 0.3119

−115.41*** (1.444)

.769*** (.028)

2

Table 4 Estimation results of bank risk-taking behavior equation with the whole sample period (1998–2016)

.131*** (.001)

3

−2.465*** (.035) .353*** (.078) .072*** (.005) −.342*** (.057) −.095*** (.007) −1.037*** (.009) 1.354*** (.0128) Yes Yes 61.09* (31.79) 997 70 853.7*** 1.0000 0.2919 0.3163

.005*** (.0001)

.765*** (.027)

3

.137*** (.0004)

4

.147*** (.001) −2.522*** (.041) .341*** (.001) .078*** (.003) −.326*** (.004) −.091*** (.003) −.992*** (.007) 1.067*** (.019) Yes Yes 68.705*** (.681) 997 70 901.9*** 0.9999 0.2917 0.3157

.763*** (.0001)

4

.346*** (.001) 47.53*** (.775)

1

ZROE

−4.514*** (.048) .048*** (.001) .031*** (.0002) −.029*** (.001) −.046*** (.0006) −.181*** (.002) .356*** (.0028) Yes Yes 78.49*** (.787) 1001 70 500.6*** 0.9994 0.0376 0.3286

.258*** (.003) −4.791*** (.211)

1

NPL

54.89*** (2.366)

.399*** (.002)

2

−4.552*** (.024) .083*** (.001) .011*** (.0002) −.063*** (.001) −.041*** (.001) −.162*** (.002) .251*** (.003) Yes Yes 77.92*** (.284) 1001 70 577.7*** 0.9990 0.0171 0.3884

−79.35*** (.727)

.229*** (.003)

2

.395*** (.002)

4

.008*** (.0007) −3.947*** (.036) .051*** (.001) .033*** (.0002) −.026*** (.004) −.048*** (.002) −.195*** (.005) .280*** (.006) Yes Yes 67.53*** (.535) 1010 70 546.2*** 1.0000 0.0282 0.3271

.276*** (.001)

4

(continued on next page)

.360*** (.001)

3

−4.340*** (.030) .044*** (.001) .035*** (.0002) −.013*** (.002) −.056*** (.001) −.229*** (.007) .265*** (0.004) Yes Yes 74.39*** (.435) 1010 70 562*** 1.0000 0.0345 0.2996

.003*** (.0009)

.245*** (.002)

3

A.Y.H. Saif-Alyousfi et al.

North American Journal of Economics and Finance xxx (xxxx) xxx–xxx

17

4

−.641*** (.010) .049*** (.0003) .037*** (.0001) −.067*** (.0005) −.013*** (.0005) −.0318*** (.003) .0188*** (.003) Yes Yes 12.85*** (.162) 1001 70 375.5*** 0.9988 0.0568 0.4273

−.787*** (.016) .045*** (.008) .030*** (.004) −.077*** (.024) −.006*** (.0008) −.018*** (.002) .043*** (.002) Yes Yes 15.23*** (3.534 1001 70 426.8*** 0.9984 0.0597 0.4787

−.753*** (.010) .048*** (.0006) .038*** (.0001) −.067*** (.001) −.009*** (.0008) −.022*** (.002) .042*** (.002) Yes Yes 14.36*** (.185) 1010 70 428.6*** 1.0000 0.0620 0.4275

.001*** (.0002) .012** (.001) −.680*** (.011) .044*** (.0002) .037*** (.0001) −.061*** (.0005) −.011*** (.0004) −.029*** (.0017) .023*** (.0029) Yes Yes 12.94*** (.149) 1010 70 449.7*** 1.0000 0.0691 0.3949 15.939*** (1.249) −.940*** (.082) −.005 (.018) 2.533*** (.166) .393*** (.022) .134*** (.013) −.432*** (.025) Yes Yes −45.44*** (3.684) 993 70 720.4*** 0.9998 0.0069 0.2686

1

3

1

2

SDROE

SDROA

16.032*** (1.025) −1.216*** (.091) −.016 (.012) 1.470*** (.205) .602*** (.032) .194*** (.006) −.782*** (.012) Yes Yes −92.56*** (13.95) 993 70 330.8*** 0.9972 0.0098 0.2212

2

39.055*** (.682) −1.154*** (.028) .013 (.021) 1.908*** (.074) 1.409*** (.032) .0794*** (.007) −.842*** (.013) Yes Yes −44.98*** (1.539) 997 70 330.6*** 1.0000 0.0917 0.5367

−.056*** (.004)

3

−2.513*** (.028) 36.028*** (.817) −.978*** (.070) −.026 (.020) 1.685*** (.065) 1.366*** (.034) .129*** (.009) −.693*** (.011) Yes Yes −54.0*** (3.728) 997 70 400.5*** 1.0000 0.0916 0.4750

4

.676*** (.059) −.077*** (.006) −.012*** (.004) .118*** (.015) .064*** (.005) .444*** (.066) −7.839*** (.189) Yes Yes −21.20*** (1.493) 993 70 1480.1*** 0.9991 0.0103 0.5672

1

NPL

2.515*** (.037) −.077*** (.003) −.005* (.002) .179*** (.013) .082*** (.004) 1.078*** (.110) −11.18*** (.129) Yes Yes −27.22*** (.537) 993 70 1501.9*** 0.9990 0.0064 0.9052

2

3.405*** (.098) −.081*** (.008) −.014*** (.007) .157*** (.013) .065*** (.003) .516*** (.193) −12.08*** (.260) Yes Yes −42.90*** (1.884) 997 70 800.5*** 1.0000 0.0053 0.9133

−.006*** (.0002)

3

−.510*** (.007) 3.467*** (.101) −.039*** (.004) −.007)** (.003 .064*** (.011) .057*** (.004) .866*** (.0932) −11.07*** (.210) Yes Yes −72.99*** (1.945) 997 70 1160.9*** 1.0000 0.0032 0.7932

4

Note: Table reports estimation results for the impact of bank competition on bank risk-taking behavior for overall period (2000-2016) using two-step system GMM dynamic panel model by Arellano and Bover (1995). The dependent variables are the standard deviation of ROA (SDROA), Standard deviation of ROE (SDROE) calculated from a three-period based rolling window, non-performing loans to total loans (NPL), loan loss provision to total loans (LLP), Z-score index based on ROA (ZROA), and Z-score index based on ROE. Lerner Index, Boone indicator, Herfindahl–Hirschman Index (HHI), and 5bank concentration ratio (CR5) are the market power indexes. SIZE is the logarithm of total assets. NIR refer to income diversification of banks measured by non-Interest revenue to total revenue. COST is the ratio of operating expenses to total income. EQTA refers to the bank capitalization calculated as the ratio of total equity to total assets. LIQ refer to bank liquidity calculated by the ratio of total loans to total assets.GDP is annual GDP growth rate. INF IS annual inflation rate. The Sargan test is the test for over-identifying restrictions in GMM estimation. AB test AR(1) and AR(2) refer to the Arellano–Bond test that average autocovariance in residuals of order 1 resp. of order 2 is 0 (H0: no autocorrelation). The standard errors are in parentheses. *** Significance at the 1% level, ** Significance at the 5% level,* Significance at the 10% level.

No. observations No. banks Wald-test Sargan P value AR(1) P value AR(2) P value

Country dummy Time dummy Intercept

INF

GDP

LIQ

EQTA

COST

NIR

SIZE

CR5

HHI

Table 4 (continued)

A.Y.H. Saif-Alyousfi et al.

North American Journal of Economics and Finance xxx (xxxx) xxx–xxx

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A.Y.H. Saif-Alyousfi et al.

Furthermore, the effects of inflation on bank risk and stability are positive and negative respectively; higher rate of inflation is associated with higher risk and lower stability in matured banks. This means that economic uncertainty caused by inflation stimulates banks to restrict credit. The coefficients on time dummy variables (not shown in the table) are significant but signs are varied. For the years 2007, 2008 and 2009, the coefficients are positive and significant with bank risk and negative and significant with bank stability, indicating that risk of GCC banks is higher and the stability is lower during the recent global financial crisis 2008 compared to other years. 5.2. Pre, during and post global financial crisis periods During the phase of economic slowdown arising out of financial crisis, banks in general, tend to reduce their lending activities and increase the capital position to strengthen their ability to absorb possible shocks arising out of crisis and/or to strengthen their competitive position. Mishkin (1999) argue that large decline in the capital ratios during a financial crisis, which adversely affects their franchise value, may encourage banks to imprudent lending decisions during the crisis period. Given the importance of franchise value of banks, we have also analysed whether market power or market concentration in the GCC banking sector during the financial crisis period have any moderating effect on the issue of moral hazard by engaging into imprudent lending activities by banks. We have conducted the analysis by dividing the sample into 3 sets: subsample for the pre-financial crisis period (1998–2006), subsample period for the period of financial crisis (2007–2009), and the subsample for post-financial crisis period (2010–2016). The Definition of the financial crisis period adopted in our study is based on prior studies (Fu et al., 2014; Kasman and Kasman, 2015; Khan et al., 2016; Soedarmono et al., 2013). In the 3 subsamples, we have introduced time fixed effects and country fixed effects. The mean values of main variables over every subsample period are presented in Table 5. The mean values of bank risk measured by SDROA, SDROE, and LLP during the financial crisis are significantly high, while the mean values of bank stability proxied by ZROA and ZROE during the financial crisis period are significantly low. These indicate that GCC listed commercial banks have been adversely affected by the global financial crisis, which made them riskier and less stable as compared to their position during the pre and post-financial crisis periods. However, the mean NPL is noticeably low during the crisis period compared to pre and post-crisis period, which may be due to a decline in the lending activities (loan growth is 22.5% during the financial crisis compared to 24.2% and 25.9% over pre and post-crisis period respectively) and loan write-offs by GCC banks at that time Among others, Khan et al. (2016) also notes that Asian banks witnessed a decline in their lending activities during the period of financial crisis. Unlike the Boone indicator, the mean values of market power and concentration indicators show a decline during the financial crisis period and a sharp increase in the post-crisis period. The sharp and significant increase in Boone indicator suggests that the level of competition in banking market continues to decline. The profile of Boone indicator reflects that there is a decline in the competitive situation in the GCC banking market during and after the financial crisis. We have used the two-step system GMM to analyze the sample groups of pre-crisis (1998–2006) and post-crisis (2010–2016). However, for the sample group of financial crisis period (2007–2009), we apply the Least Square Dummy Variable (LSDV) model to avoid the problem of small sample size. The estimation results for the 3 subsamples: 1998–2006 (pre-crisis), 2007–2009 (during the crisis), and 2010–2016 (post-crisis) are reported in Tables 6–8 respectively. Results from Tables 6 and 8 suggest that market structure indicators and control variables in pre and post-crisis period are similar to the findings of our overall findings as has been reported in Table 4. However, Table 7 shows that during crisis period 2007–2009, higher Lerner index (higher market power) and higher Boone indicators (lower competition) in banking market have a negative impact on bank risk-taking (SDROA, SDROE, NPL, and LLP) as well as bank stability (ZROA and ZROE). In contrast, we also find that higher market concentration (HHI and CR5) has a positive significant effect on bank risk-taking measures and bank stability measures. The level of significance of market power, competition, and concentrations with the measures of stability is, however, significant only at the level of 10%. Overall, banks in less competitive GCC markets carry a lower risk of insolvency (still stable) during the financial crisis period 2007–2009. This may due to the fact that 2008 financial crisis has indirectly affected GCC banks. Our main results suggest that bank risk-taking behavior and stability did not change significantly during the Table 5 Mean values of variables in subsamples. Variable

Subsample 1998–2006

Subsample 2007–2009

Subsample 2010–2016

SDROA SDROE NPL LLP ZROA ZROE Lerner index Boone indicator HHI CR5

0.813809 4.55443 8.84892 0.847492 41.88182 8.715275 0.431735 −0.0221302 155.476 61.6940

1.47618 12.6590 4.83549 1.26146 36.98464 4.825798 0.428911 −0.0109342 93.1731 55.5814

0.930743 6.26294 5.39336 0.945122 75.01142 14.72832 0.529755 −0.00867350 97.8435 58.7159

Note: Table displays the mean values of bank risk-taking and market power indicators for three subsamples. Subsample 2007–2009 represents the global financial crisis period. 18

t-1

19

Boone indicator

Lerner index

Lag t-1

No. observations No. banks Wald-test Sargan P value AR(1) P value AR(2) P value

Country dummy Time dummy Intercept

INF

GDP

LIQ

EQTA

COST

NIR

SIZE

CR5

HHI

Boone indicator

Lerner index

Lag

.218*** (.018) −2.675*** (.432)

−16.46*** (1.771)

.168*** (.019)

.139*** (.005)

3 .147*** (.009)

4

−.301* (.159) .023*** (.006) .015** (.006) .003 (.021) −.012** (.006) −.0055 (.0200) .262*** (.0617) Yes Yes −2.78 (2.10) 363 57 902.2*** 0.3326 0.0159 0.1981

.636*** (.011) −6.342** (1.654)

.144*** (.016) 38.18*** (7.615)

1

2

.008*** (.002) −.148*** (.054) .017*** (.002) .011*** (.003) .001 (.003) .002 (.002) −.0105*** (.0029) .0195 (.014) Yes Yes 1.960** (.901) 367 57 418.9*** 0.1765 0.0632 0.9170

.532*** (.012)

1

−.004 (.055) .018*** (.002) .011*** (.002) −.015* (.008) .002 (.002 −.0121*** (.002) .0014 (.0121) Yes Yes −.570 (.928) 367 57 1325*** 0.2338 0.0744 0.9310

.001*** (.0002)

.537*** (.013)

ZROA

−.749*** (.116) .019*** (.007) .005** (.002) −.038*** (.008) .0004 (.002) −.00615** (.003) .0097 (.009) Yes Yes 10.52*** (1.838) 363 57 302.9*** 0.1504 0.3087 0.7382

−5.490*** (1.217)

.619*** (.037)

LLP

−.008 (.054) .014*** (.002) .013*** (.002) −.013*** (.003) −.012** (.005) −.0138*** (.003) .0429*** (.011) Yes Yes −1.31 (.894) 363 57 918.4*** 0.1375 0.0779 0.9306

.560*** (.009) −2.361*** (.175)

4

1

3

1

2

SDROE

SDROA

49.99*** (7.390)

.121*** (.014)

2

−.379** (.156) .022*** (.006) .014** (.006) −.035* (.018) −.017* (.009) −.0020 (.023) .285*** (.069) Yes Yes −3.95* (2.10) 363 57 802.2*** 0.2920 0.0161 0.1890

−5.764** (2.630)

.642*** (.013)

2

Table 6 Estimation results of bank risk-taking behavior equation before the global financial crisis period (1998–2006).

.095*** (.013)

3

.219 (.221) .014** (.007) .017** (.005) .019 (.017) −.014** (.007) −.0166 (.0178) .310*** (.058) Yes Yes −.654 (3.74) 367 57 789.9*** 0.2180 0.0196 0.1793

.057*** (.001)

.651*** (.012)

3

.131*** (.013)

4

.041** (.016) −.773** (.256) .030** (.007) .012** (.005) −.037* (.019) −.020** (.009) −.0139 (.0199) .312*** (.0562) Yes Yes −1.65 (4.83) 367 57 660.6*** 0.2122 0.0182 0.2007

.641*** (.017)

4

.281*** (.013) 10.83*** (2.749)

1

ZROE

−12.86*** (1.009) .248*** (.026) .176*** (.037) −.596*** (.125) −.022** (.010) −.165*** (.043) .629*** (.187) Yes Yes 218.26*** (16.84) 371 61 1055.6*** 0.1299 0.0701 0.5075

.580*** (.048) −31.29*** (4.162)

1

NPL

58.015** (25.739)

.295*** (.013)

2

−11.04*** (1.440) .243*** (.030) .190*** (.036) −.391*** (.104) −.029** (.012) −.309*** (.0449) 1.009*** (.205) Yes Yes 172.58*** (22.51) 371 61 1439.5*** 0.1751 0.0256 0.2845

−56.79*** (11.30)

.573*** (.057)

2

.307*** (.013)

4

.104** (.043) −13.23*** (1.246) .225*** (.030) .206*** (.035) −.537*** (.109) −.020* (.010) −.234*** (.0473) .807*** (.210) Yes Yes 212.92*** (20.89) 380 61 1201.6*** 0.1051 0.0435 0.3369

.587*** (.046)

4

(continued on next page)

.253*** (.013)

3

−14.52*** (.996) .315*** (.034) .291*** (.031) −.370*** (.061) −.030** (.012) −.313*** (.0417) 1.061*** (.241) Yes Yes 220.82*** (15.69) 380 61 1118.2*** 0.1586 0.0687 0.3317

.006*** (.001)

.716*** (.033)

3

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North American Journal of Economics and Finance xxx (xxxx) xxx–xxx

20

4

−.991*** (.075) .014*** (.002) .033*** (.005) −.001 (.007) −.002 (.003) −.003 (.0048) .060*** (.020) Yes Yes 17.80*** (1.45) 371 61 849.8*** 0.2920 0.1070 0.6959

−.667*** (.056) .017*** (.002) .038*** (.003) .002 (.006) −.005* (.003) −.0180*** (.00572) .0334 (.0227) Yes Yes 12.24 (.918) 371 61 1047.5*** 0.1924 0.1222 0.4591

−.892*** (.053) .023*** (.001) .010*** (.003) −.013** (.006) −.005** (.002) −.00588 (.00495) .0244 (.0218) Yes Yes 13.83*** (.786) 380 61 1506.7*** 0.2186 0.1283 0.3683

.009** (.004) .0004 (.002) −.778*** (.066) .019*** (.001) .012*** (.003) −.003 (.005) −.006*** (.002) −.0034 (.004) .032 (.021) Yes Yes 12.56*** (1.060) 380 61 1060.4*** 0.3354 0.1214 0.3488 60.51*** (8.740) −.599* (.331) −.018 (.290) .445 (.703) .204** (.108) .396 (.697) −12.2*** (1.24) Yes Yes 1128.9*** (157.82) 363 57 2371.7*** 0.5716 0.2025 0.6319

1

3

1

2

SDROE

SDROA

27.77*** (7.821) −.493* (.298) −.068 (.347) .307 (.675) 2.161** (.178) 1.876** (.776) −5.617*** (1.517) Yes Yes 498.84*** (130.36) 363 57 1898.3*** 0.4492 0.1979 0.4030

2

43.29*** (7.475) −.675** (.292) .039 (.274) .474 (.784) 2.132** (.182) .387 (.646) −7.824*** (2.320) Yes Yes 729.34*** (131.21) 367 57 1649.8*** 0.4332 0.1967 0.4426

−.045*** (.006)

3

−.456** (.155) 45.01*** (7.474) −.516** (.219) .126 (.281) .161 (.772) .295** (.101) 1.267* (.710) −10.31*** (1.857) Yes Yes 775.18** (129.12) 367 57 1596.5*** 0.4657 0.1976 0.5124

4

5.942*** (.748) −.067** (.027) −.076*** (.021) .159*** (.044) .043** (.015) .190 (.151) −.468** (.237) Yes Yes 96.71*** (10.730) 363 57 1406*** 0.1251 0.0193 0.3298

1

NPL

5.171*** (.649) −.021 (.028) −.079*** (.022) .147*** (.046) .049** (.024) .274** (.133) −1.072*** (.262) Yes Yes 88.94*** (10.28) 363 57 602.1*** 0.1897 0.0237 0.3227

2

4.518*** (.875) −.053* (.028) −.156*** (.036) .124*** (.056) .035** (.014) .00993 (.112) −.161 (.236) Yes Yes 78.70*** (14.46) 367 57 1642.5*** 0.1905 0.0101 0.3309

−.003* (.001)

3

−.034** (.012) 5.060*** (.813) −.042* (.022) −.045** (.014) .124** (.050) .049** (.024) .251* (.143) -.531** (.241) Yes Yes 86.95*** (12.44) 367 57 1288.3*** 0.2279 0.0137 0.3098

4

Note: Table reports estimation results for the impact of bank competition on bank risk-taking behavior in the pre-global financial crisis period (2000–2006) using two-step system GMM dynamic panel model by Arellano and Bover (1995). The dependent variables are the standard deviation of ROA (SDROA), Standard deviation of ROE (SDROE) calculated from a three-period based rolling window, nonperforming loans to total loans (NPL), loan loss provision to total loans (LLP), Z-score index based on ROA (ZROA), and Z-score index based on ROE. Lerner Index, Boone indicator, Herfindahl-Hirschman Index (HHI), and 5-bank concentration ratio (CR5) are the market power indexes. SIZE is the logarithm of total assets. NIR refer to income diversification of banks measured by non-Interest revenue to total revenue. COST is the ratio of operating expenses to total income. EQTA refers to the bank capitalization calculated as the ratio of total equity to total assets. LIQ refer to bank liquidity calculated by the ratio of total loans to total assets. GDP is annual GDP growth rate. INF IS annual inflation rate. The Sargan test is the test for over-identifying restrictions in GMM estimation. AB test AR(1) and AR(2) refer to the Arellano–Bond test that average autocovariance in residuals of order 1 resp. of order 2 is 0 (H0: no autocorrelation). The standard errors are in parentheses. *** Significance at the 1% level, ** Significance at the 5% level. * Significance at the 10% level.

No. observations No. banks Wald-test Sargan P value AR(1) P value AR(2) P value

Country dummy Time dummy Intercept

INF

GDP

LIQ

EQTA

COST

NIR

SIZE

CR5

HHI

Table 6 (continued)

A.Y.H. Saif-Alyousfi et al.

North American Journal of Economics and Finance xxx (xxxx) xxx–xxx

t-1

21

t-1

HHI

Boone indicator

Lerner index

Lag

No. observations No. banks F-statistic R-square

Country dummy Time dummy Intercept

INF

GDP

LIQ

EQTA

COST

NIR

SIZE

CR5

HHI

Boone indicator

Lerner index

Lag

−.390** (.179) −19.449** (7.777)

−16.645** (7.619)

−.411** (.200)

.031** (.011)

−.534** (.243)

3 −.414** (.214)

4

−27.162** (11.264) −.603** (.235) .025 (.136) −1.771*** (.256) −.018 (.013) −13.66** (5.01) 3.729** (1.230 Yes Yes 48.47** (15.73) 210 70 12.41** 0.31

.982*** (.017) −36.330** (12.789)

.633*** (.172) −73.105* (36.252)

1

2

.117** (.057) −4.388*** (.973) −.031** (.015) .014** (.005) −.335*** (.082) .036** (.010) −.0677* (.0384) .0542*** (.014) Yes Yes 74.46*** (15.67) 210 70 17.86*** 0.38

.195** (.095)

1

−7.409*** (1.362) −.029* (.015) .016** (.003) −.334*** (.081) .032** (.015) −.402** (.151) −.0707*** (.025) Yes Yes 126.09*** (22.19) 210 70 20.59*** 0.47

.012** (.006)

.236** (.099)

ZROA

−4.306*** (.977) −.026* (.014) .013** (.006) −.302*** (.079) .031** (.014) −.0337** (.014) .0259** (.0‘103) Yes Yes 75.50*** (16.01) 210 70 17.72*** 0.38

−3.152** (1.911)

.124** (.065)

LLP

−5.111*** (.974) −.028** (.014) .012** (.005) −.317*** (.075) .039** (.013) −.05226*** (.018) .0807** (.036) Yes Yes 82.05*** (15.42) 210 70 19.65*** 0.44

.132** (.069) −14.508*** (4.955)

4

1

3

1

2

SDROE

SDROA

−64.649* (37.661)

.594*** (.195)

2

−29.261** (11.695) −.628** (.231) .016 (.134) −1.422*** (.272) −.012 (.220) −3.679** (1.244) .861** (.305) Yes Yes 52.88** (25.02) 210 70 12.69*** 0.32

−14.80** (5.94)

.998*** (.034)

2

Table 7 Estimation results of bank risk-taking behavior equation during the global financial crisis period (2007–2009).

.079* (.040)

708** (.359)

3

−51.148** (23.314) −.725** (.259) .001 (.163) −1.791*** (.384) −.033 (.261) −33.642** (1.366) 2.458** (1.076) Yes Yes 88.28** (37.62) 210 70 12.66*** 0.33

.252** (.117)

.991*** (.018)

3

.621*** (.173)

4

.531** (.226) −26.751** (11.755) −.587*** (.139) .036 (.134) −2.017*** (.338) −.041 (.223) −3.253** (1.288) 2.405** (.950) Yes Yes 46.48 (25.63) 210 70 12.44*** 0.31

.999*** (.025)

4

.374** (.162) −16.401* (8.06)

1

ZROE

−4.363*** (1.362) −.079** (.039) .058** (.019) −.621*** (.176) .074** (.031) −1.694** (.802) .750** (.314) Yes Yes 38.63** (15.59) 210 70 15.03*** 0.32

1.158** (.454) −43.67*** (11.699)

1

NPL

−15.605* (7.799)

.338** (.143)

2

−1.520** (.691) −.002 (.031) .031** (.015) −.408*** (.112) .063** (.028) −.489** (.222) .337** (.121) Yes Yes 33.12** (15.70) 210 70 13.41*** 0.35

−35.16** (12.813)

.389** (.195)

2

.471*** (.127)

4

.186** (.073) −1.919** (.367) −.081* (.041) .034** (.019) −.677*** (.196) .087** (.032) −.0330** (.0143) .213 (.258) Yes Yes 36.88 (38.52) 210 70 12.9*** 0.33

2.067*** (.525)

4

(continued on next page)

.027* (.014)

.896** (.404)

3

−1.168** (.669) −.089* (.045) .054** (.024) −.655*** (.203) .098** (.038) −.489** (.200) .0553*** (.019) Yes Yes −40.25** (18.46) 210 70 12.69*** 0.33

.099** (.035)

3.175*** (.702)

3

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22

4

−3.615** (1.483) −.051** (.020) .042*** (.013) −.253** (.105) .044** (.021) −.0795 (.065) .568*** (.139) Yes Yes 53.923** (23.528) 210 70 16.94*** 0.35

−2.896** (1.433) −.045*** (.015) .045*** (.013) −.220** (.107) .046** (.021) −.151*** (.0143) .137*** (.0735) Yes Yes 50.84** (24.01) 210 70 16.07*** 0.33

−3.221 (2.065) −.041** (.015) .061*** (.015) −.277** (.115) .051** (.024) −.0722*** (.0115) .0882*** (.0147) Yes Yes 66.60* (33.77) 210 70 15.93*** 0.34

.190** (.088) −2.772* (1.464) −.031** (.012) .047*** (.013) −.298** (.116) .066*** (.016) −.173** (.0197) .0308** (.0141) Yes Yes 44.41* (23.65) 210 70 15.88*** 0.33 10.916** (4.177) .040** (.019) −.046 (.127) 2.430** (1.171) −.395** (.171) .377** (.111) −.420** (.206) Yes Yes −78.83 (72.49) 210 70 13.26*** 0.26

1

3

1

2

SDROE

SDROA

13.951** (6.665) .057*** (.015) −.037 (.126) 1.953** (.589) −.489** (.206) .866*** (.220) −.376** (.126) Yes Yes −20.32 (24.16) 210 70 13.47*** 0.27

2

18.238** (8.949) .089** (.042) −.054 (.132) 3.814*** (1.124) −.053** (.021) .556** (.272) −.320* (.182) Yes Yes −26.48 (30.54) 210 70 14.26*** 0.31

3 .089* (.045) 9.937** (4.715) .051** (.022) −.053 (.125) 2.217*** (.249) −.038** (.018) .361** (.153) −.227* (.131) Yes Yes −199.67* (109.88) 210 70 13.24*** 0.26

4

.991** (.470) .061** (.028) −.031** (.016) −.003 (.152) −.001 (.027) 1.950*** (.109) −.480* (.249) Yes Yes 5.88 (31.32) 210 70 14.34*** 0.31

1

NPL

.991** (.452) .071** (.029) −.027** (.011) −.018 (.158) .043* (.027) 1.379*** (.013) −.222 (.343) Yes Yes 16.36 (31.97) 210 70 13.60*** 0.27

2

2.482** (.878) .079** (.032) −.026** (.012) −.059 (.1708) −.067** (.032) 2.089*** (.278) −.834** (.407) Yes Yes −2.36 (46.86) 210 70 13.59*** 0.28

3

.226* (.114) 1.832* (.935) .060** (.029) −.027** (.011) −.062 (.164) −.057** (.027) 1.482*** (.490) −.552* (.307) Yes Yes 15.95 (31.14) 210 70 13.92*** 0.29

4

Note: Table reports estimation results for the impact of bank competition on bank risk-taking behavior during the global financial crisis period (2007–2009) using Least Square Dummy Variable (LSDV) technique. The dependent variables are the standard deviation of ROA (SDROA), Standard deviation of ROE (SDROE) calculated from a three-period based rolling window, non-performing loans to total loans (NPL), loan loss provision to total loans (LLP), Z-score index based on ROA (ZROA), and Z-score index based on ROE. Lerner Index, Boone indicator, Herfindahl–Hirschman Index (HHI), and 5-bank concentration ratio (CR5) are the market power indexes. SIZE is the logarithm of total assets. NIR refer to income diversification of banks measured by non-Interest revenue to total revenue. COST is the ratio of operating expenses to total income. EQTA refers to the bank capitalization calculated as the ratio of total equity to total assets. LIQ refer to bank liquidity calculated by the ratio of total loans to total assets. GDP is annual GDP growth rate. INF IS annual inflation rate. The standard errors are in parentheses. *** Significance at the 1% level, ** Significance at the 5% level, * Significance at the 10% level.

No. observations No. banks F-statistic R-square

Country dummy Time dummy Intercept

INF

GDP

LIQ

EQTA

COST

NIR

SIZE

CR5

Table 7 (continued)

A.Y.H. Saif-Alyousfi et al.

North American Journal of Economics and Finance xxx (xxxx) xxx–xxx

t-1

23

Boone indicator

Lerner index

Lag t-1

No. observations No. banks Wald-test Sargan P value AR(1) P value AR(2) P value

Country dummy Time dummy Intercept

INF

GDP

LIQ

EQTA

COST

NIR

SIZE

CR5

HHI

Boone indicator

Lerner index

Lag

.495*** (.059) −6.676** (2.636)

−12.956*** (.828)

.499*** (.061)

.374*** (.041)

3 .434*** (.007)

4

−3.324*** (.206) .184*** (.029) .053** (.023) −.698*** (.067) −.019* (.010) −.159*** (.039) .366** (.165) Yes Yes 65.77*** (7.073) 420 70 2070.2*** 0.6723 0.0340 0.7792

.040*** (.005) −3.977** (1.971)

.278*** (.056) 1.169*** (.109)

1

2

.006*** (.001) −.468*** (.032) .054*** (.010) .006*** (.001) −.024*** (.006) −.017*** (.004) −.039*** (0.01) .050** (.021) Yes Yes 8.18*** (.593) 420 70 3486.5*** 0.1373 0.0520 0.1938

.029*** (.006)

1

−.624*** (.076) .058*** (.012) .018*** (.004) −.134*** (.037) −.024*** (.009) −.0458*** (.012) .0632* (.0326) Yes Yes 11.19*** (1.296) 420 70 2017.2*** 0.1252 0.0917 0.2666

.013** (.005)

.029*** (.006)

ZROA

−.538*** (.117) .037*** (.004) .244*** (.017) −.263*** (.052) −.013*** (.007) −.051** (.021) .114** (.0489) Yes Yes 10.55*** (1.861) 420 70 6497.1*** 0.2556 0.0011 0.3156

−51.17*** (9.82)

.528*** (.084)

LLP

−.8993*** (.063) .054*** (.012) .047** (.019) −.175*** (.054) −.021** (.009) −.094*** (.021) .034** (.011) Yes Yes 16.06*** (1.67) 420 70 7759.7*** 0.5399 0.0091 0.3322

.402*** (.043) −.515** (.256)

4

1

3

1

2

SDROE

SDROA

9.127*** (2.907)

.141*** (.024)

2

−3.11*** (.283) .061** (.022) .123*** (.047) −.607*** (.074) −.022** (.010) −.177*** (.0344) .494*** (.138) Yes Yes 60.14*** (4.504) 420 70 2017.7*** 0.6837 0.0681 0.7692

−6.875** (2.21)

.045*** (.007)

2

Table 8 Estimation results of bank risk-taking behavior equation after the global financial crisis period (2010–2016).

.158*** (.017)

3

−3.894*** (.244) .141*** (.012) .120*** (.022) −.735*** (.044) −.018*** (.001) −.163*** (.0308) .180** (.0991) Yes Yes 67.18*** (3.714) 420 70 968.8*** 0.3448 0.0310 0.2786

.005** (.002)

.039*** (.005)

3

.146*** (.017)

4

.005** (.002) −2.717*** (.289) .098*** (.012) .101*** (.026) −.539*** (.043) −.006 (.008) −.0880*** (.0199) .259** (.105) Yes Yes 48.74*** (4.469) 420 70 1432.8*** 0.4314 0.1008 0.1627

.047*** (.005)

4

.487*** (.101) 15.72** (5.28)

1

ZROE

−3.532*** (.535) .024** (.011) .039** (.018) −.564*** (.057) −.081*** (.016) −.0767** (.0367) .0332*** (.012) Yes Yes 15.23* (8.151) 420 70 2191*** 0.2629 0.0746 0.1406

.762*** (.071) −12.75*** (2.347)

1

NPL

39.82*** (3.766)

.485*** (.132)

2

−5.191*** (.025) .025*** (.005) .077** (.032) −.418*** (.076) −.014** (.005) −.114** (.0479) .0271** (.011) Yes Yes −23.42** (7.71) 420 70 1840.1*** 0.7683 0.0420 0.6664

−89.020** (42.083)

.945*** (.098)

2

.370*** (.053)

3

.513*** (.109)

4

−.0534* (.0316) .0909** (.0397) Yes Yes 30.10*** (.022) 420 70 1070.7*** 0.2139 0.0354 0.1736

.009** (.004) −1.821*** (.517) .085*** (.020) .076*** (.018) −.144** (.060) −.073***(.013)

.313*** (.059)

4

(continued on next page)

−3.065*** (.917) .081*** (.021) .044** (.021) −.171*** (.050) −.033** (.013) −.0859** (.0335) .148* (.0794) Yes Yes 54.63*** (16.33) 420 70 1090.5*** 0.3227 0.0652 0.2021

.009** (.003)

.268*** (.063)

3

A.Y.H. Saif-Alyousfi et al.

North American Journal of Economics and Finance xxx (xxxx) xxx–xxx

24

4

−.0951** (.0453) .0476*** (.013) Yes Yes −10.43** (4.976) 420 70 484.1*** 0.7808 0.2984 0.3038

.117 (1.033) .104*** (.013) .049*** (.012) −.284*** (.048) .004(.009)

−.212 (.829) .100*** (.020) .031*** (.008) −.389** (.154) .003 (.005) −.0478** (.0209) .0583*** (.016) Yes Yes 5.74** (2.72) 420 70 718.6*** 0.8454 0.2843 0.3125

−2.325** (.966) .046*** (.010) .032** (.012) −.165** (.072) 003 (.004) −.0391** (.0150) .0736** (.0278) Yes Yes −2.64 (8.57) 420 70 1626.7*** 0.7337 0.2903 0.3182

.003** (.001) .003** (.001) .036 (.259) .095*** (.015) .058*** (.010) −.300*** (.028) .005 (.004) −.0387* (.0220) .118** (.0550) Yes Yes 4.786** (1.453) 420 70 2263.9*** 0.1399 0.2709 0.3130 10.53** (4.738) −.468 (2.164) −.332 (1.228) 3.654** (1.520) 1.167** (.528) .415** (.135) −1.942*** (1.123) Yes Yes −14.86** (7.200) 420 70 195.8*** 0.1673 0.0010 0.2731

1

3

1

2

SDROE

SDROA

11.626*** (3.065) −.481 (1.451) −.0303 (.908) 4.307*** (1.204) 1.262** (.512) .698** (.240) −1.530*** (1.031) Yes Yes −16.94 (6.984) 420 70 92.75*** 0.1479 0.0343 0.1533

2

3.831** (1.859) −.621 (1.559) −1.237 (.860) 1.081** (.506) 1.735*** (.506) .474* (.249) −.929** (.453) Yes Yes −3.02 (5.202) 420 70 325*** 0.1259 0.0320 0.8788

−.115** (.056)

3

−.072** (.029) 5.057** (2.504) −.656 (1.137) −1.177 (.781) 6.336*** (1.818) 1.910** (.365) .359* (.190) −1.317* (.754) Yes Yes −6.83 (4.140) 420 70 212.1*** 0.1535 0.0410 0.9766

4

5.989*** (1.038) −.017 (.076) −.195 (.210) .432** (.208) .275** (.112) 30.14* (15.56) −6.851** (2.381) Yes Yes −69.10** (24.10) 420 70 67.6*** 0.8257 0.0411 0.2299

1

NPL

5.679*** (1.464) −.091 (.098) −.104 (.109) .721*** (.169) .259** (.127) 2.811* (1.762) −3.955** (1.714) Yes Yes −79.15*** (22.25) 420 70 59.88*** 0.6299 0.0267 0.3624

2

5.693*** (1.247) −.010 (.111) −.141 (.098) .295** (.144) .314** (.182) 3.017* (1.637) −5.983** (2.080) Yes Yes 78.63** (33.92) 420 70 405.2*** 0.4122 0.0141 0.7275

−.021*** (.004)

3

−.695** (.299) 8.395*** (2.751) −.094 (.100) −.113 (.111) .691** (.266) .262** (.104) 1.039* (.720) −8.292** (3.909) Yes Yes −65.21* (33.12) 420 70 138.07*** 0.5479 0.0194 0.5958

4

Note: Table reports estimation results for the impact of bank competition on bank risk-taking behavior in the post-global financial crisis period (2010–2016) using two-step system GMM dynamic panel model by Arellano and Bover (1995). The dependent variables are the standard deviation of ROA (SDROA), Standard deviation of ROE (SDROE) calculated from a three-period based rolling window, nonperforming loans to total loans (NPL), loan loss provision to total loans (LLP), Z-score index based on ROA (ZROA), and Z-score index based on ROE. Lerner Index, Boone indicator, Herfindahl-Hirschman Index (HHI), and 5-bank concentration ratio (CR5) are the market power indexes. SIZE is the logarithm of total assets. NIR refer to income diversification of banks measured by non-Interest revenue to total revenue. COST is the ratio of operating expenses to total income. EQTA refers to the bank capitalization calculated as the ratio of total equity to total assets. LIQ refer to bank liquidity calculated by the ratio of total loans to total assets. GDP is annual GDP growth rate. INF IS annual inflation rate. The Sargan test is the test for over-identifying restrictions in GMM estimation. AB test AR(1) and AR(2) refer to the Arellano-Bond test that average autocovariance in residuals of order 1 resp. of order 2 is 0 (H0: no autocorrelation). The standard errors are in parentheses. *** Significance at the 1% level, ** Significance at the 5% level, * Significance at the 10% level.

No. observations No. banks Wald-test Sargan P value AR(1) P value AR(2) P value

Country dummy Time dummy Intercept

INF

GDP

LIQ

EQTA

COST

NIR

SIZE

CR5

HHI

Table 8 (continued)

A.Y.H. Saif-Alyousfi et al.

North American Journal of Economics and Finance xxx (xxxx) xxx–xxx

North American Journal of Economics and Finance xxx (xxxx) xxx–xxx

A.Y.H. Saif-Alyousfi et al.

entire period 1998 to 2016 including the period of global financial crisis. Furthermore, Table 7 shows that, during the financial crisis period, non-interest revenue (NIR) has a negative impact on bank risktaking (SDROA, SDROE, NPL, and LLP) and a positive impact on bank stability (ZROA and ZROE). In contrast, loan total assets ratio (LIQ) has a positive effect on bank risk and a negative impact on the bank stability. These suggest that during the financial crisis, diversification into non-interest income bearing activities by banks in GCC has reduced instability in the banking system. The coefficients of time dummy variables (2007, 2008 and 2009) in Table 7 are significantly positive with bank risk-taking behavior but significantly negative with stability, suggesting that bank risk-taking behavior was higher during the financial crisis but the stability was low. 5.3. Bank characteristics and competition To examine the existence of any cross-sectional heterogeneity in the association between competition, concentration, risk, and bank stability, we examine whether our results hold for too-big-to- fail banks, highly capitalized banks, and highly liquid banks. The first posit is that a too-big-to-fail bank makes the effect of competition on bank risk and stability less important. It can be argued that larger banks exist in several markets in different countries. They engage in nontraditional activities and tend to have higher profits, less risky and are more stable. Higher capital requirements can also reduce agency cost between bank owners and depositors, resulting in better performance (lower risk and higher stability). Apart from mandating the need for the capital of good quality and capital buffers, capital buffers, Basel III introduces a new framework for liquidity standards to hedge liquidity risks that “establishes minimum levels of liquidity for internationally active banks. Therefore, we divide the banks based on the level of liquidity to determine whether the results hold for highly liquid banks. Notably, Khan et al. (2016) find that banks with individual characteristics such as large size, high capitalization, and high liquidity respond differently to monetary policy shocks. For these reasons, in this section, we analyze whether banks with individual characteristics like high liquidity, high capitalization, and large size may respond differently to the market competition. To study these possibilities, we split the data into two subsamples based on liquidity, capitalization, and size. For each country in every year, we classify the banks as follow: banks with a value (capitalization, size, and liquidity) higher than the country average are classified as high capitalization, large size, and high liquidity. While banks with a value less than or equal to the country average are classified as low capitalization, small size, and low liquidity. We then investigate the response of banks' stability and risk to changes in the bank characteristics in all the subsamples. The findings of these analyses are recorded in Tables 9–14 and show that all GMM estimations are valid. The coefficient on bank competition (Lerner index and Boone indicators) and market concentration (HHI and CR5) are significant for all the bank categories but the signs are different. Tables 9 and 10 reports the results of banks with high and low capitalizations, Tables 11 and 12 show the findings for banks with high and low liquidity, while the results for banks with large and small size are reported in Tables 13 and 14 respectively. Tables 9, 11 and 13 show that higher market power (higher Lerner index), lower competition (higher Boone indicator) and lower concentration (HHI and CR5) in the banking market decrease the risk and enhance the stability of banks that are highly capitalized, highly liquid and have a large size. These findings are similar to the results of the main estimation in Table 4. However, the findings in the case of low capitalized, low liquid and small banks are the opposite as reported in Tables 10, 12 and 14. Specifically, Lerner index and Boone indicator are positively related to bank risk-taking behavior (SDROA, SDROE, NPL, and LLP), while they are negatively associated with bank stability (ZROA and ZROE). However, market concentration parameters (HHI and CR5) are negatively related to bank risk-taking behavior and positively related to bank stability. These indicate that higher market power, lower level of competition and lower concentration in the banking market increase the risk and decrease the stability of low capitalized, low liquid, and small banks. Such results could be due to the fact that, during a financial crisis, banks with low capitalization, low liquidity, and small size could face a huge decline in their capital ratios which may prompt them to engage in imprudent lending decisions and hence the moral hazard. Moreover, in a highly competitive market, banks with low capitalization, low liquidity, and small size may fade out. It may be mentioned in this context that with the removal of restriction on foreign ownership and ease of entry of foreign banks in the GCC economies over the last decade, low capitalized, low liquid and small size banks might have found it difficult to meet the competitive challenges posed by foreign banks by virtue of their superior technology, better competency and experience, and stronger capital base which has, in turn, affected the stability of this group. Wu, Chen, Jeon, and Wang (2017) in their study of domestic banks in 35 emerging markets conclude that presence of foreign banks increases the risk profile of domestic banks. However, in contrast to the large, highly capitalized, and highly liquid banks in GCC economies, non-interest revenue and costincome ratio of low capitalized, low liquid and small banks have negative association with Bank risk-taking behavior (SDROA, SDROE, NPL, and LLP) and positive association with bank stability (ZROA and ZROE). These imply that the banks in this group have been able to remain stable by not diversifying into non-interest revenue generating activities and by controlling cost. However, loan to total assets ratio (LIQ) is found to be negatively related to the stability (ZROA and ZROE) of low capitalized, low liquid and small banks. This indicates that banks in this group do not have sufficient capacity to manage and monitor the loans which result in instability. 5.4. Robustness tests To confirm the robustness of our results, we carry out several robustness checks. First, in order to check as to whether the study results suffer from any sample bias, we rerun the analysis by excluding Bahrain banks (which accounts for 28% of the sample 25

t-1

26

Boone indicator

Lerner index

Lag t-1

No. observations No. banks Wald-test Sargan P value AR(1) P value AR(2) P value

Country dummy Time dummy Intercept

INF

GDP

LIQ

EQTA

COST

NIR

SIZE

CR5

HHI

Boone indicator

Lerner index

Lag

.395*** (.007) −2.621** (1.186)

−37.651*** (9.417)

.420*** (.014)

.379*** (.007)

3 .396*** (.012)

4

−.922** (.433) .052*** (.003) .028*** (.001) −.037** (.014) −.057*** (.011) −.283*** (.0204) .141*** (.0409) Yes Yes −9.41 (7.52) 544 37 515.6*** 1.0000 0.0929 0.3894

.811*** (.006) −2.312*** (.409)

.090** (.038) 49.72*** (7.620)

1

2

.008*** (.002) −.265*** (.042) .014*** (.002) .009*** (.0005) −.054*** (.008) −.021*** (.004) −.0416*** (.00778) .0755*** (.0174) Yes Yes 6.879** (2.389) 515 37 154.2*** 1.0000 0.1206 0.4738

.616*** (.008)

1

−.332*** (.023) .006*** (.001) .010*** (.0004) −.052*** (.006) −.026*** (.001) −.0433*** (.009) .0531*** (.0160) Yes Yes 9.600** (3.753) 515 37 159.8*** 1.0000 0.1209 0.4706

.006*** (.001)

.608*** (.007)

ZROA

−.417*** (.063) .009*** (.002) .004*** (.0004) −.051*** (.009) −.021*** (.002) −.0224*** (.0039) .0960*** (.0127) Yes Yes 6.929*** (1.520) 544 37 1310*** 1.0000 0.1214 0.3823

−13.288*** (1.874)

.591*** (.007)

LLP

−.396*** (.102) .009*** (.002) .005*** (.0004) −.038*** (.009) −.017*** (.004) −.0334*** (.009) .0538*** (.019) Yes Yes 4.821*** (.779) 544 37 830.5*** 1.0000 0.1233 0.4307

.588*** (.008) −3.127*** (.453)

4

1

3

1

2

SDROE

SDROA

92.51*** (23.151)

.072** (.027)

2

−.885*** (.274) .059*** (.004) .023*** (.002) −.050*** (.014) −.053*** (.008) −.230*** (.0329) .144*** (.0420) Yes Yes −10.14** (4.92) 544 37 751.9*** 1.0000 0.0840 0.3237

−40.728*** (7.477)

.829*** (.005)

2

.064*** (.013)

3

−1.687*** (.467) .067*** (.005) .031*** (.002) −.014*** (.002) −.046*** (.011) −.354*** (.0251) .150*** (.0453) Yes Yes −32.46*** (9.14) 515 37 355.3*** 1.0000 0.1019 0.4232

.007*** (.001)

.836*** (.005)

3

Table 9 Estimation results of bank risk-taking behavior equation for banks with high capitalization (high equity to total assets)

.116*** (.034)

4

.071*** (.009) −1.279*** (.236) .055*** (.002) .028*** (.002) −.042*** (.011) −.046*** (.007) −.252*** (.0281) .0575** (.0263) Yes Yes −14.41*** (4.43) 515 37 658.9*** 1.0000 0.0864 0.3640

.821*** (.006)

4

.275*** (.006) 14.984** (6.672)

1

ZROE

−2.801*** (.367) .019*** (.002) .011*** (.0008) −.282*** (.028) −.002 (.003) −.0682*** (.0113) .505*** (.0225) Yes Yes 35.89*** (6.99) 535 37 2370*** 1.0000 0.2530 0.6993

−.493*** (.023) −16.78*** (1.524)

1

NPL

98.17*** (16.741)

.350*** (.002)

2

−2.574*** (.435) .021*** (.002) .008*** (.0007) −.323*** (.012) −.006** (.003) −.0382*** (.00797) .290*** (.0207) Yes Yes 38.239*** (6.859) 535 37 133.7*** 1.0000 0.2033 0.5581

−4.768*** (.380)

−.395*** (.009)

2

.342*** (.002)

4

.164*** (.009) −2.309*** (.667) .039*** (.003) .003*** (.0006) −.325*** (.013) −.008** (.003) −.0798*** (.0150) .356*** (.0323) Yes Yes 49.711*** (9.806) 511 37 41.9*** 0.1764 0.4469 0.1764

−.187*** (.004)

4

(continued on next page)

.283*** (.002)

3

−3.518*** (.339) .043*** (.001) .007*** (.001) −.387*** (.019) −.030*** (.003) −.184*** (.0222) .457*** (.0227) Yes Yes 58.51*** (4.56) 511 37 60.7*** 1.0000 0.1619 0.3643

.003** (.001)

−.214*** (.013)

3

A.Y.H. Saif-Alyousfi et al.

North American Journal of Economics and Finance xxx (xxxx) xxx–xxx

27

4

−.180** (.088) .046*** (.006) .019*** (.002) −.096*** (.010) −.003 (.003) −.0139 (.0153) .0778*** (.0296) Yes Yes 14.00** (5.04) 535 37 22.8*** 1.0000 0.0858 0.6609

−.954*** (.290) .025*** (.007) .011*** (.002) −.078*** (.012) −.030** (.011) −.0313* (.0169) .0229 (.018) Yes Yes 33.27** (13.26) 535 37 13.9*** 1.0000 0.0880 0.6887

−1.404*** (.313) .046*** (.002) .018*** (.0007) −.077*** (.006) −.005** (.002) −.0134* (.00692) .0015 (.021) Yes Yes 23.69** (11.22) 511 37 25.6*** 1.0000 0.1014 0.6666

.004** (.001) .003* (.001) −.361*** (.107) 049*** (.003) .019*** (.005) −.090*** (.007) −.041*** (.013) −0.000208 (0.0147) .00733 (.0193) Yes Yes 61.85*** (13.49) 511 37 27.9*** 1.0000 0.0999 0.5663 39.752*** (13.231) −.955*** (.274) −.044 (.113) 2.662** (1.223) .759** (.300) .0702*** (.0236) −.543*** (.0411) Yes Yes −302.40** (105.10) 544 37 17.2*** 1.0000 0.0045 0.3964

1

3

1

2

SDROE

SDROA

23.337*** (7.901) −.555*** (.133) −.040 (.119) 1.203* (.976) .693*** (.225) .124*** (.0265) −.636*** (.0606) Yes Yes −281.24** (130.96) 544 37 133.4*** 1.0000 0.0030 0.7033

2

30.998*** (1.264) −.445*** (.145) −.084 (.066) .190*** (.052) 1.107*** (.209) .0592*** (.0147) −.533*** (.0367) Yes Yes −198.34*** (24.98) 515 37 428.2*** 1.0000 0.0028 0.3206

−.444** (.220)

3

−2.514*** (.506) 23.580*** (9.963) −4.148*** (.227) −.0002 (.082) 3.147** (1.637) 1.195*** (.182) .120*** (.0390) −.757*** (.0654) Yes Yes -414.52*** (130.37) 515 37 36.1*** 1.0000 0.0044 0.9095

4

3.511*** (.147) −.013** (.006) −.010 (.006) .090** (.042) .015** (.007) 5.573*** (.296) −12.55*** (.422) Yes Yes 28.39*** (2.38) 544 37 59.2*** 1.0000 0.1731 0.6280

1

NPL

1.005*** (.337) −.047*** (.008) −.011 (.006) .030** (.013) .069*** (.011) 5.402*** (.455) −13.94*** (1.289) Yes Yes 22.239*** (4.761) 544 37 150.3*** 1.0000 0.1529 0.5693

2

1.265*** (.192) −.045*** (.007) −.004 (.003) .039*** (.010) .031*** (.006) 5.132*** (.239) −16.93*** (1.045) Yes Yes 27.638*** (3.193) 515 37 885.7*** 1.0000 0.1800 0.6784

−.032*** (.001)

3

−.084*** (.015) 1.173*** (.120) −.041*** (.005) −.002 (.008) .033*** (.008) .048*** (.005) 5.498*** (.144) −12.77*** (1.194) Yes Yes 26.19*** (2.57) 515 37 864.1*** 1.0000 0.1333 0.5903

4

Note: Table reports estimation results for the impact of bank competition on bank risk-taking behavior for banks with high capitalization using two-step system GMM dynamic panel model by Arellano and Bover (1995). The dependent variables are the standard deviation of ROA (SDROA), Standard deviation of ROE (SDROE) calculated from a three-period based rolling window, non-performing loans to total loans (NPL), loan loss provision to total loans (LLP), Z-score index based on ROA (ZROA), and Z-score index based on ROE. Lerner Index, Boone indicator, Herfindahl–Hirschman Index (HHI), and 5bank concentration ratio (CR5) are the market power indexes. SIZE is the logarithm of total assets. NIR refer to income diversification of banks measured by non-Interest revenue to total revenue. COST is the ratio of operating expenses to total income. EQTA refers to the bank capitalization calculated as the ratio of total equity to total assets. LIQ refer to bank liquidity calculated by the ratio of total loans to total assets. GDP is annual GDP growth rate. INF IS annual inflation rate. The Sargan test is the test for over-identifying restrictions in GMM estimation. AB test AR(1) and AR(2) refer to the Arellano–Bond test that average autocovariance in residuals of order 1 resp. of order 2 is 0 (H0: no autocorrelation). The standard errors are in parentheses. *** Significance at the 1% level, ** Significance at the 5% level, * Significance at the 10% level.

No. observations No. banks Wald-test Sargan P value AR(1) P value AR(2) P value

Country dummy Time dummy Intercept

INF

GDP

LIQ

EQTA

COST

NIR

SIZE

CR5

HHI

Table 9 (continued)

A.Y.H. Saif-Alyousfi et al.

North American Journal of Economics and Finance xxx (xxxx) xxx–xxx

t-1

28

Boone indicator

Lerner index

Lag t-1

No. observations No. banks Wald-test Sargan P value AR(1) P value AR(2) P value

Country dummy Time dummy Intercept

INF

GDP

LIQ

EQTA

COST

NIR

SIZE

CR5

HHI

Boone indicator

Lerner index

Lag

.372*** (.014) 2.687*** (.332)

8.280*** (1.921)

.376*** (.022)

.882*** (.009)

3 .389*** (.022)

4

−3.283*** (.622) −1.243*** (.036) .−372*** (.027) −1.929*** (.180) −.091*** (.016) −1.042*** (.0582) 2.305*** (.0943) Yes Yes 164.01*** (11.88) 449 33 8080*** 1.0000 0.3094 0.3192

.748*** (.001) 54.006***(5.239)

.237*** (.018) −49.98*** (7.017)

1

2

−.007*** (.001) −.183*** (.019) −.004*** (.001) −.003** (.001) −.023*** (.003) .0005 (.0004) −.0045*** (.0018) .0284*** (.00357) Yes Yes 2.983*** (.277) 482 33 745.4*** 1.0000 0.0117 0.6663

.606*** (.013)

1

−.125*** (.019) −.004*** (.001) −.003** (.001) −.028*** (.003) −.005** (.0004) −.00381* (.0022) .0359*** (.00294) Yes Yes 1.599*** (.269) 482 33 343.3*** 1.0000 0.0125 0.6577

−.001*** (.0001)

.634*** (.010)

ZROA

−.144*** (.018) −.003** (.001) −.0003 (.0007) −.028*** (.003) −.0008** (.0003) −.0050*** (.00147) .0334*** (.00219) Yes Yes 1.988*** (.289) 449 33 389.1*** 1.0000 0.0116 0.4056

5.779*** (.682)

.644*** (.009)

LLP

−.152*** (.019) −.003** (.001) −.004*** (.0007) −.036*** (.003) −.001*** (.0004) −.0024* (.0014) .0312*** (.0031) Yes Yes 2.560*** (.254) 449 33 313.8*** 1.0000 0.0134 0.4998

.638*** (.010) 2.747*** (.118)

4

1

3

1

2

SDROE

SDROA

−178.6*** (12.322)

.130*** (.005)

2

−4.700*** (.728) −1.248*** (.024) −.180*** (.042) −2.446*** (.191) −.051** (.022) −1.099*** (.0438) 2.249*** (.111) Yes Yes 154.46*** (14.19) 449 33 17200*** 1.0000 0.3047 0.3239

150.14*** (28.306)

.737*** (.001)

2

.147*** (.005)

3

−3.707*** (.716) −1.089*** (.034) −.132*** (.024) −2.409*** (.1803) −.060*** (.018) −1.082*** (.0341) 2.189*** (.0894) Yes Yes 137.47*** (14.21) 482 33 14700*** 1.0000 0.3245 0.3069

−.014*** (.003)

.735*** (.001)

3

Table 10 Estimation results of bank risk-taking behavior equation for banks with low capitalization (low equity to total assets)

.142*** (.003)

4

−.378*** (.010) −5.415*** (.742) −1.170*** (.029) −.123*** (.037) −2.463*** (.129) −.054** (.023) −1.026*** (.0532) 1.959*** (.129) Yes Yes 186.46*** (13.73) 482 33 4970*** 1.0000 0.3040 0.3227

.734*** (.001)

4

.203*** (.011) −45.69*** (5.460)

1

ZROE

−.953*** (.282) −.075*** (.005) −.040*** (.008) −.091*** (.015) −.010*** (.003) −.214*** (.00843) .152*** (.0150) Yes Yes −9.84** (4.64) 466 33 629.7*** 1.0000 0.0036 0.9909

.921*** (.022) 6.542*** (1.426)

1

NPL

−79.96*** (1.446)

.310*** (.016)

2

−.218** (.111) −.055*** (.004) −.057*** (.005) −.109*** (.022) −.017*** (.003) −.239*** (.00712) .0542*** (.0149) Yes Yes −8.67*** (2.104) 466 33 2093.7*** 1.0000 0.0021 0.9612

15.03*** (4.334)

.854*** (.016)

2

.288*** (.011)

4

−.006* (.003) −.378*** (.107) −.063*** (.002) −.060*** (.005) −.117*** (.017) −.006** (.001) −.236*** (.0111) .0519*** (.0066) Yes Yes −4.44*** (1.64) 499 33 635.3*** 1.0000 0.0017 0.9032

.885*** (.008)

4

(continued on next page)

.301*** (.016)

3

−.417*** (.072) −.065*** (.003) −.054*** (.005) −.090*** (.019) −.008*** (.002) −.229*** (.0130) .0463*** (.0106) Yes Yes −5.493*** (1.171) 499 33 800.2*** 1.0000 0.0019 0.9682

−.003* (.001)

.882*** (.009)

3

A.Y.H. Saif-Alyousfi et al.

North American Journal of Economics and Finance xxx (xxxx) xxx–xxx

29

4

−.171*** (.040) −.033*** (.002) −.026*** (.002) −.094*** (.005) −.005*** (.001) −.0419*** (.00418) .00452 (.00623) Yes Yes 5.946*** (.788) 466 33 91.2*** 1.0000 0.0124 0.9810

−.049** (.024) −.029*** (.002) −.023*** (.001) −.074*** (.009) −.004*** (.001) −.0301*** (.00308) .00686 (.00551) Yes Yes 4.279*** (1.051) 466 33 48.3*** 1.0000 0.0093 0.8249

−.417*** (.072) −.064*** (.003) −.054*** (.005) −.090*** (.018) −.008*** (.002) −.0350*** (.00453) .00948 (.00629) Yes Yes -5.493*** (1.171) 499 33 800.2*** 1.0000 0.0019 0.9682

−.003** (.001) −.003** (.001) −.086** (.042) −.031*** (.002) −.017*** (.001) −.082*** (.008) −.004*** (.001) −.0327*** (.00484) .0181*** (.00292) Yes Yes 4.88*** (.761) 499 33 15.9*** 1.0000 0.0095 0.9491 61.222*** (8.008) .792** (.378) 5.395*** (.813) 2.516 (3.058) −.363*** (.112) .266*** (.0300) −.597*** (.0513) Yes Yes −109.2*** (20.25) 449 33 30.2*** 1.0000 0.0403 0.1850

1

3

1

2

SDROE

SDROA

64.933*** (15.862) .387** (.166) 3.683** (.996) 1.198 (4.123) −.305** (.129) .156*** (.0553) −.860*** (.0900) Yes Yes −99.69*** (29.25) 449 33 33.5*** 1.0000 0.0220 0.1509

2

72.606*** (10.962) 1.452*** (.122) 2.664** (.813) 3.811 (4.272) −.687*** (.155) .0571 (.0795) −1.115*** (.162) Yes Yes −115.6*** (18.19) 482 33 46.2*** 1.0000 0.2191 0.8539

.063*** (.015)

3

2.500*** (.260) 62.727*** (10.257) .912*** (.150) 1.591** (.409) 5.769 (4.814) −.574*** (.171) .0898*** (.0288) −.950*** (.132) Yes Yes −98.88*** (20.48) 482 33 26.5*** 1.0000 0.2177 0.8505

4

6.861*** (.565) .288*** (.031) .372** (.132) .304 (.251) −.158*** (.014) 4.034*** (.662) −3.759*** (1.333) Yes Yes −102.4*** (8.809) 449 33 75.9*** 1.0000 0.0123 0.1085

1

NPL

7.337*** (1.211) .184*** (.027) .089** (.035) .341 (.202) −.157*** (.024) 0.624 (.558) −8.483*** (1.065) Yes Yes −94.66*** (17.51) 449 33 98.5*** 1.0000 0.0088 0.2067

2

8.649*** (.794) .066** (.032) .097** (.041) .392 (.430) −.079*** (.025) .746 (1.027) −10.31*** (1.429) Yes Yes −122.5*** (15.65) 482 33 19.9*** 1.0000 0.0149 0.5059

.009*** (.002)

3

.669*** (.083) 10.187*** (.589) .071** (.034) .166** (.054) .057 (.460) −.068*** (.017) 2.957** (1.482) −7.679*** (1.792) Yes Yes -6.09*** (.716) 482 33 34.3*** 1.0000 0.0124 0.4236

4

Note: Table reports estimation results for the impact of bank competition on bank risk-taking behavior for banks with low capitalization using two-step system GMM dynamic panel model by Arellano and Bover (1995). The dependent variables are the standard deviation of ROA (SDROA), Standard deviation of ROE (SDROE) calculated from a three-period based rolling window, non-performing loans to total loans (NPL), loan loss provision to total loans (LLP), Z-score index based on ROA (ZROA), and Z-score index based on ROE. Lerner Index, Boone indicator, Herfindahl–Hirschman Index (HHI), and 5bank concentration ratio (CR5) are the market power indexes. SIZE is the logarithm of total assets. NIR refer to income diversification of banks measured by non-Interest revenue to total revenue. COST is the ratio of operating expenses to total income. EQTA refers to the bank capitalization calculated as the ratio of total equity to total assets. LIQ refer to bank liquidity calculated by the ratio of total loans to total assets. GDP is annual GDP growth rate. INF IS annual inflation rate. The Sargan test is the test for over-identifying restrictions in GMM estimation. AB test AR(1) and AR(2) refer to the Arellano–Bond test that average autocovariance in residuals of order 1 resp. of order 2 is 0 (H0: no autocorrelation). The standard errors are in parentheses. *** Significance at the 1% level, ** Significance at the 5% level, * Significance at the 10% level.

No. observations No. banks Wald-test Sargan P value AR(1) P value AR(2) P value

Country dummy Time dummy Intercept

INF

GDP

LIQ

EQTA

COST

NIR

SIZE

CR5

HHI

Table 10 (continued)

A.Y.H. Saif-Alyousfi et al.

North American Journal of Economics and Finance xxx (xxxx) xxx–xxx

t-1

30

Boone indicator

Lerner index

Lag t-1

No. observations No. banks Wald-test Sargan P value AR(1) P value AR(2) P value

Country dummy Time dummy Intercept

INF

GDP

LIQ

EQTA

COST

NIR

SIZE

CR5

HHI

Boone indicator

Lerner index

Lag

.248*** (.004) −8.358*** (1.905)

−26.768*** (5.137)

.261*** (.007)

.257*** (.003)

3 .260*** (.001)

4

−2.768*** (.189) .016*** (.004) .033*** (.0009) −.136*** (.022) −.072*** (.007) −.258*** 0.0356) .438*** (.0612) Yes Yes 41.28*** (3.225) 590 42 45300*** 1.0000 0.1731 0.3510

.524*** (.008) −7.938*** (2.081)

.151*** (.009) 41.43*** (6.85)

1

2

.005*** (.001) −.665*** (.078) .021*** (.001) .013*** (.0002) −.021*** (.004) −.025*** (.002) −.0525*** (.00826) .118*** (.0108) Yes Yes 9.99*** (1.20) 604 42 613.2 1.0000 0.2298 0.5812

.428*** (.011)

1

−.787*** (.108) .021*** (.001) .013*** (.0003) −.016*** (.005) −.023*** (.002) −.0598*** (.00957) .0989*** (.0124) Yes Yes 12.29*** (1.73) 594 42 977.5*** 1.0000 0.2430 0.5790

.0007*** (.0001)

.415*** (.012)

ZROA

−.664*** (.047) .017*** (.001) .008*** (.0003) −.00001 (.002) −.013*** (.0009) −.000428 (.00660) .112*** (.0117) Yes Yes 10.88*** (.667) 594 42 6648.1*** 1.0000 0.2401 0.4623

−24.088*** (1.883)

.497*** (.005)

LLP

−.860*** (.026) .014*** (.001) .013*** (.0003) −.020*** (.004) −.018*** (.001) −.0317*** (.00542) .0694*** (.0166) Yes Yes 13.627*** (.504) 590 42 5505.7*** 1.0000 0.2474 0.5331

.477*** (.004) −.992** (.457)

4

1

3

1

2

SDROE

SDROA

96.33*** (2.506)

.250*** (.024)

2

−1.731*** (.204) .022*** (.003) .024*** (.001) −.117*** (.019) −.046*** (.008) −0.117*** (0.0269) .346*** (.0389) Yes Yes 28.52*** (3.29) 594 42 557.7*** 1.0000 0.1598 0.3118

−64.635*** (3.715)

.547*** (.006)

2

Table 11 Estimation results of bank risk-taking behavior equation for high liquidity banks (low loans to total assets)

.252*** (.005)

3

−2.439*** (.367) .028*** (.003) .035*** (.001) −.091*** (.016) −.047*** (.003) −.299*** (0.0543) .291*** (.0435) Yes Yes 39.43*** (5.54) 594 42 1896.5*** 1.0000 0.1806 0.4000

.016*** (.002)

.509*** (.011)

3

.274*** (.008)

4

.021** (.007) −2.282*** (.163) .027*** (.002) .034*** (.001) −.121*** (.019) −.054*** (.005) −.297*** (.0250) .354*** (.0389) Yes Yes 34.51*** (2.17) 604 42 1945.4*** 1.0000 0.1665 0.3658

.528*** (.006)

4

.317*** (.015) 14.751** (5.303)

1

ZROE

−7.154*** (.261) .082*** (.010) .032*** (.002) −.050*** .017) −.059*** (.014) −.180*** (.0382) .230*** (.0566) Yes Yes 119.11*** (4.74) 585 42 736.1*** 1.0000 0.1710 0.2657

−.134*** (.016) −2.090*** (.389)

1

NPL

73.21*** (12.587)

.388*** (.020)

2

−6.861*** (.423) .133*** (.015) .010*** (.002) −.106*** (.024) −.053*** (.007) −.141*** (.0275) .224*** (.0230) Yes Yes 117.93*** (6.793) 590 42 1781.1*** 1.0000 0.1451 0.2802

−126.3*** (12.719)

−.191*** (.009)

2

.583*** (.076)

4

.028*** (.006) −5.790*** (.323) .104*** (.008) .039*** (.003) −.059*** (.016) −.061*** (.011) −.181*** (.0377) .330*** (.0386) Yes Yes 95.51*** (5.370) 603 42 926.9*** 1.0000 0.1489 0.2585

−.081*** (.028)

4

(continued on next page)

.489*** (.026)

3

−6.307*** (.519) .115*** (.007) .034*** (.002) −.057*** (.021) −.069*** (.012) −.159*** (.0338) .247*** (.0610) Yes Yes 106.61*** (8.414) 590 42 384.4*** 1.0000 0.1516 0.2261

.019** (.008)

−.103*** (.030)

3

A.Y.H. Saif-Alyousfi et al.

North American Journal of Economics and Finance xxx (xxxx) xxx–xxx

31

4

−1.032*** (.255) .051*** (.003) .032*** (.001) −.071*** (.017) −.011*** (.003) −.0211 (.0381) .187*** (.0646) Yes Yes 6.561 (4.589) 598 42 861.6*** 1.0000 0.1097 0.5669

−.946*** (.094) .056*** (.002) .024*** (.001) −.116*** (.007) −.009*** (.003) −.0786*** (.0232) .0352* (.0194) Yes Yes 18.554*** (1.406) 602 42 1910*** 1.0000 0.1099 0.6411

−1.009*** (.074) .054*** (.001) .028*** (.0002) −.101*** (.006) −.009*** (.002) −.0485*** (.0176) .0494*** (.0176) Yes Yes 18.478*** (1.087) 602 42 559.1*** 1.0000 0.1095 0.6292

.004*** (.001) .019*** (.001) −.822*** (.111) .053*** (.002) .029*** (.0004) −.083*** (.007) −.006* (.003) −.0433** (.0211) .0761*** (.0158) Yes Yes 16.476*** (1.655) 614 42 1050*** 1.0000 0.1163 0.5556 78.345*** (17.247) −.993*** (.299) −.180 (.117) 8.648** (4.132) .323*** (.108) .0851 (.0562) −.242* (.141) Yes Yes −96.25** (31.40) 590 42 1596.6*** 1.0000 0.0218 0.1493

1

3

1

2

SDROE

SDROA

32.702*** (8.576) −.694*** (.198) −.056 (.061) 1.115** (.542) .376*** (.072) .0392 (.0718) −.433*** (.0701) Yes Yes −27.87* (13.91) 594 42 870.7*** 1.0000 0.0224 0.1636

2

26.817*** (1.506) −.408** (.200) −.025 (.126) .828*** (.242) .508*** (.046) .435*** (.0546) −.786*** (.0892) Yes Yes −330.48*** (25.02) 594 42 411.8*** 1.0000 0.0106 0.2735

−.063** (.027)

3

−.168*** (.020) 24.124*** (1.997) −.507** (.200) −.0007 (.152) 1.556*** (.213) .338*** (.075) .445*** (.0669) −.0544 (.195) Yes Yes −307.29*** (40.190) 604 42 505.4*** 1.0000 0.0098 0.2725

4

2.739*** (.643) −.023*** (.005) −.008** (.004) .024 (.055) .057*** (.012) .140 (1.650) −7.460** (3.216) Yes Yes −42.41*** (9.426) 591 42 584.3*** 1.0000 0.0333 0.9661

1

NPL

1.839*** (.543) −.027* (.015) −.016*** (.005) .065 (.053) .038*** (.009) 3.222 (1.985) −12.01*** (1.121) Yes Yes −21.97*** (8.235) 595 42 501.1*** 1.0000 0.0345 0.8445

2

3.490*** (.844) −.057* (.029) −.010 (.038) .107 (.098) .119*** (.009) 3.155** (1.299) −9.327*** (2.442) Yes Yes −63.62*** (14.503) 594 42 1559.5*** 1.0000 0.1804 0.7037

−126*** (.002)

3

−.717*** (.150) 8.909*** (2.691) −.055*** (.019) −.006 (.021) .032 (.240) .120*** (.017) 1.172 (1.515) −12.90*** (1.125) Yes Yes −184.3*** (40.04) 605 42 396.1*** 1.0000 0.2097 0.2336

4

Note: Table reports estimation results for the impact of bank competition on bank risk-taking behavior for high banks liquidity using two-step system GMM dynamic panel model by Arellano and Bover (1995). The dependent variables are the standard deviation of ROA (SDROA), Standard deviation of ROE (SDROE) calculated from a three-period based rolling window, non-performing loans to total loans (NPL), loan loss provision to total loans (LLP), Z-score index based on ROA (ZROA), and Z-score index based on ROE. Lerner Index, Boone indicator, Herfindahl–Hirschman Index (HHI), and 5-bank concentration ratio (CR5) are the market power indexes. SIZE is the logarithm of total assets. NIR refer to income diversification of banks measured by non-Interest revenue to total revenue. COST is the ratio of operating expenses to total income. EQTA refers to the bank capitalization calculated as the ratio of total equity to total assets. LIQ refer to bank liquidity calculated by the ratio of total loans to total assets. GDP is annual GDP growth rate. INF IS annual inflation rate. The Sargan test is the test for over-identifying restrictions in GMM estimation. AB test AR(1) and AR(2) refer to the Arellano–Bond test that average autocovariance in residuals of order 1 resp. of order 2 is 0 (H0: no autocorrelation). The standard errors are in parentheses. *** Significance at the 1% level, ** Significance at the 5% level, * Significance at the 10% level.

No. observations No. banks Wald-test Sargan P value AR(1) P value AR(2) P value

Country dummy Time dummy Intercept

INF

GDP

LIQ

EQTA

COST

NIR

SIZE

CR5

HHI

Table 11 (continued)

A.Y.H. Saif-Alyousfi et al.

North American Journal of Economics and Finance xxx (xxxx) xxx–xxx

t-1

32

Boone indicator

Lerner index

Lag t-1

No. observations No. banks Wald-test Sargan P value AR(1) P value AR(2) P value

Country dummy Time dummy Intercept

INF

GDP

LIQ

EQTA

COST

NIR

SIZE

CR5

HHI

Boone indicator

Lerner index

Lag

.757*** (.003) 2.196*** (.334)

23.521*** (2.592)

.743*** (.005)

.770*** (.002)

3 .763*** (.003)

4

−2.544*** (.266) −1.205*** (.015) −.086*** (.012) −.444*** (.048) −.049*** (.016) −1.479*** (.0176) 1.189*** (.0450) Yes Yes 99.515*** (4.186) 403 28 2420*** 1.0000 0.2980 0.3162

.744*** (.0003) 29.895*** (2.004)

.067*** (.008) −40.92*** (3.275)

1

2

−.006*** (.001) −.101*** (.021) −.002** (.001) −.004*** (.001) −.022*** (.003) −.013*** (.001) −.0511*** (.00267) .0551*** (.00414) Yes Yes .076** (.038) 393 28 2160*** 1.0000 0.2451 0.2270

.805*** (.003)

1

−.105*** (.030) −.003** (.001) −.002** (.001) −.039*** (.003) −.011*** (.001) −.0589*** (.00301) .0632*** (.00451) Yes Yes −.241*** (.451) 404 28 237.7*** 1.0000 0.2586 0.3329

−.003** (.001)

.802*** (.003)

ZROA

−.096*** (.019) −.002*** (.0007) −.007*** (.001) −.023*** (.002) −.013*** (.001) −.0481*** (.00314) .0645*** (.00324) Yes Yes −.050** (.027) 399 28 481.8*** 1.0000 0.2448 0.1925

1.649*** (.678)

.808*** (.002)

LLP

−.029** (.013) −.002* (.001) −.008*** (.0007) −.024*** (.002) −.015*** (.001) −.0523*** (.00247) .0744*** (.00392) Yes Yes .490** (.247) 403 28 3660*** 1.0000 0.2424 0.2412

.810*** (.002) 1.807*** (.110)

4

1

3

1

2

SDROE

SDROA

−74.39*** (16.782)

.076*** (.003)

2

−3.646*** (.361) −1.223*** (.009) −.090*** (.013) −.468*** (.038) −.080*** (.014) −1.359*** (.0320) 2.053*** (.0621) Yes Yes 104.28*** (5.84) 399 28 16400*** 1.0000 0.2988 0.3242

57.413*** (11.311)

.739*** (.0004)

2

Table 12 Estimation results of bank risk-taking behavior equation for low liquidity banks (high loans to total assets).

.091*** (.001)

3

−2.413*** (.478) −1.471*** (.023) −.061** (.026) −.822*** (.042) −.057*** (.014) −1.507*** (.0339) 2.342*** (.0973) Yes Yes 101.96*** (7.835) 404 28 5040*** 1.0000 0.2920 0.3289

−.010*** (.001)

.745*** (.0002)

3

.092*** (.001)

4

−.183*** (.005) −3.744*** (.308) −1.099*** (.007) −.134*** (.011) −.453*** (.037) −.049*** (.013) −1.338*** (.0240) 1.778*** (.0763) Yes Yes 109.81*** (4.80) 393 28 23800*** 1.0000 0.2991 0.3202

.735*** (.0003)

4

.299*** (.003) −50.108*** (2.623)

1

ZROE

−.588*** (.064) −.046*** (.001) −.027*** (.002) −.120*** (.006) −.023*** (.001) −.116*** (.0035) .275*** (.011) Yes Yes 14.99*** (1.220) 417 28 2040*** 1.0000 0.0421 0.7790

.747*** (.002) 2.187*** (.396)

1

NPL

−135.55*** (8.052)

.377*** (.005)

2

−1.114*** (.056) −.017*** (.004) −.023*** (.003) −.010*** (.006) −.022*** (.003) −.105*** (.0037) .142*** (.0116) Yes Yes 22.319*** (1.309) 412 28 1830*** 1.0000 0.0246 0.6539

52.484*** (2.042)

.709*** (.003)

2

.345*** (.004)

4

−.021*** (.001) −1.192*** (.063) −.055*** (.001) −.032*** (.001) −.143*** (.005) −.028*** (.001) −.132*** (.00410) .178*** (.0068) Yes Yes 23.662*** (.953) 408 28 1850*** 1.0000 0.0317 0.9676

.674*** (.001)

4

(continued on next page)

.319*** (.004)

3

−1.258*** (.086) −.056*** (.002) −.087*** (.003) −.180*** (.004) −.028*** (.002) −.148*** (.00365) .122*** (.00722) Yes Yes 24.194*** (1.479) 420 28 1430*** 1.0000 0.0376 0.9227

−.0016*** (.0005)

.644*** (.002)

3

A.Y.H. Saif-Alyousfi et al.

North American Journal of Economics and Finance xxx (xxxx) xxx–xxx

33

4

−.016*** (.007) −.020*** (.003) −.013*** (.001) −.048*** (.012) −.028*** (.001) −.0813*** (.00912) .0511*** (.0072) Yes Yes 2.930** (1.188) 404 28 554.3*** 1.0000 0.0811 0.2600

−.045*** (.003) −.011*** (.003) −.016*** (.001) −.056*** (.013) −.020*** (.002) −.0574*** (.00710) .0746*** (.00689) Yes Yes 2.514*** (.713) 400 28 177.1*** 1.0000 0.0951 0.2456

−.033*** (.003) −.024*** (.003) −.046*** (.002) −.037*** (.007) −.024*** (.001) −.0969*** (.00553) .0674*** (.00843) Yes Yes 2.879*** (.576) 409 28 1050*** 1.0000 0.0829 0.3248

−.0007*** (.0001) −.011*** (.002) −.158*** (.039) −.018*** (.002) −.012*** (.0009) −.038*** (.006) −.023*** (.001) −.0912*** (.00506) .0565*** (.00549) Yes Yes −1.309** (.621) 396 28 90.5*** 1.0000 0.0774 0.2751 48.721*** (5.133) 2.001*** (.264) .364* (.193) 2.745*** (.810) −1.746*** (.129) .222*** (.0205) −.273*** (.0573) Yes Yes −750.18*** (96.86) 403 28 229.8*** 1.0000 0.0695 0.1768

1

3

1

2

SDROE

SDROA

51.648*** (3.783) 1.201*** (.211) .641*** (.058) 3.727*** (.599) −1.602*** (.135) .222*** (.0379) −.791*** (.0448) Yes Yes −650.30*** (74.84) 399 28 255.1*** 1.0000 0.2300 0.1287

2

59.367*** (5.089) .785** (.328) .845** (.396) 4.424*** (.952) −1.941*** (.216) .0993*** (.0205) −.634*** (.0673) Yes Yes −804.61*** (95.35) 404 28 244.7*** 1.0000 0.2228 0.1116

.084*** (.005)

3

2.303*** (.295) 46.521*** (7.077) 1.301*** (.298) .688*** (.131) .622 (1.457) −1.961*** (.238) .137*** (.0386) −.853*** (.0472) Yes Yes −641.5*** (146.72) 393 28 240.4*** 1.0000 0.2232 0.1612

4

1.040*** (.299) .155*** (.020) .062*** (.026) .222*** (.042) −.155*** (.012) 0.444 (.355) −3.958*** (.729) Yes Yes −3.81* (1.67) 403 28 208.8*** 1.0000 0.0241 0.6036

1

NPL

1.242*** (.452) .075** (.030) .050 (.042) .263*** (.039) −.132*** (.016) .0840 (.503) −7.106*** (.851) Yes Yes −.678*** (8.40) 399 28 305.1*** 1.0000 0.0126 0.8540

2

2.781*** (.424) .158*** (.038) .110*** (.031) .260*** (.057) −.156*** (.028) .175 (.735) −8.406*** (.699) Yes Yes −19.48*** (9.495) 403 28 242.8*** 1.0000 0.0081 0.8411

.006*** (.001)

3

.128*** (.030) 2.441*** (.430) .056** (.020) .096** (.045) .199*** (.036) −.142*** (.020) .261 (.343) −7.150*** (.890) Yes Yes -20.716** (9.784) 393 28 227.4*** 1.0000 0.0056 0.7443

4

Note: Table reports estimation results for the impact of bank competition on bank risk-taking behavior for low banks liquidity using two-step system GMM dynamic panel model by Arellano and Bover (1995). The dependent variables are the standard deviation of ROA (SDROA), Standard deviation of ROE (SDROE) calculated from a three-period based rolling window, non-performing loans to total loans (NPL), loan loss provision to total loans (LLP), Z-score index based on ROA (ZROA), and Z-score index based on ROE. Lerner Index, Boone indicator, Herfindahl–Hirschman Index (HHI), and 5-bank concentration ratio (CR5) are the market power indexes. SIZE is the logarithm of total assets. NIR refer to income diversification of banks measured by non-Interest revenue to total revenue. COST is the ratio of operating expenses to total income. EQTA refers to the bank capitalization calculated as the ratio of total equity to total assets. LIQ refer to bank liquidity calculated by the ratio of total loans to total assets. GDP is annual GDP growth rate. INF IS annual inflation rate. The Sargan test is the test for over-identifying restrictions in GMM estimation. AB test AR(1) and AR(2) refer to the Arellano–Bond test that average autocovariance in residuals of order 1 resp. of order 2 is 0 (H0: no autocorrelation). The standard errors are in parentheses. *** Significance at the 1% level, ** Significance at the 5% level, * Significance at the 10% level.

No. observations No. banks Wald-test Sargan P value AR(1) P value AR(2) P value

Country dummy Time dummy Intercept

INF

GDP

LIQ

EQTA

COST

NIR

SIZE

CR5

HHI

Table 12 (continued)

A.Y.H. Saif-Alyousfi et al.

North American Journal of Economics and Finance xxx (xxxx) xxx–xxx

t-1

34

Boone indicator

Lerner index

Lag t-1

No. observations No. banks Wald-test Sargan P value AR(1) P value AR(2) P value

Country dummy Time dummy Intercept

INF

GDP

LIQ

EQTA

COST

NIR

SIZE

CR5

HHI

Boone indicator

Lerner index

Lag

.369*** (.023) −1.721*** (.202)

−8.706*** (1.425)

.353*** (.018)

.396*** (.013)

3 .384 (.012)

4

−3.168** (.924) 1.345*** (.028) .203*** (.069) −2.634*** (.197) −.082*** (.028) −1.210*** (.0583) 2.462*** (.191) Yes Yes 92.265*** (18.335) 555 38 22600*** 1.0000 0.2994 0.3175

.737*** .001) −28.123*** (4.215)

.155*** (.010) 126.51*** (9.449)

1

2

.004*** (.0007) −.059*** (.015) .006*** (.0007) .005*** (.001) −.006*** (.001) −.0004 (.0003) −.0122*** (.00163) .0331*** (.00306) Yes Yes 1.338*** (.295) 568 38 407.2*** 1.0000 0.0113 0.1149

.636*** (.005)

1

−.058** (.023) .007*** (.001) .005*** (.001) −.00003 (.004) −.0007* (.0003) −.0117*** (.00186) .0351*** (.00323) Yes Yes 1.157*** (.321) 578 38 240.7*** 1.0000 0.0135 0.1150

.0003*** (.0001)

.634*** (.012)

ZROA

−.057*** (.018) .006*** (.0009) .007*** (.001) −.032*** (.006) −.0010** (.0004) −.0121*** (.00179) .0325*** (.00352) Yes Yes 1.024*** (.353) 562 38 194.5*** 1.0000 0.0152 0.1160

−.601*** (.134)

.639*** (.008)

LLP

−.055*** (.014) .005*** (.0004) .007*** (.001) −.012** (.005) −.0009* (.0004) −.0154*** (.00161) .0423*** (.00316) Yes Yes .779*** (.260) 555 38 444.4*** 1.0000 0.0176 0.1287

.640*** (.008) −.315** (.128)

4

1

3

1

2

SDROE

SDROA

Table 13 Estimation results of bank risk-taking behavior equation for large size banks.

197.52** (24.957)

.108*** (.004)

2

−1.331 (1.247) 1.405*** (.032) .487*** (.060) −2.713** (.179) −.058*** (.028) −1.198*** (.0630) 2.352*** (.120) Yes Yes 84.732*** (22.328) 562 38 9190*** 1.0000 0.2940 0.3136

−93.71*** (7.180)

.733*** (.001)

2

.122*** (.003)

3

−1.706** (.851) 1.381*** (.031) .152*** (.056) −2.620*** (.200) −.035*** (.016) −.987*** (.0545) 2.242*** (.219) Yes Yes 105.56*** (15.670) 578 38 9810*** 1.0000 0.3025 0.3302

.011*** (.001)

.730*** (.0007)

3

.127*** (.003)

4

.217*** (.011) −1.773* (.926) 1.267*** (.034) .371*** (.061) −2.510*** (.151) −.058*** (.020) −1.107*** (.0608) 2.234*** (.130) Yes Yes 105.44*** (17.25) 568 38 3910*** 1.0000 0.2982 0.3241

.731*** (.001)

4

.392*** (.008) 27.81*** (4.702)

1

ZROE

−1.025*** (.129) .049*** (.005) .010 (.008) −.126*** (.018) −.008*** (.001) −.187*** (.0140) .171*** (.00987) Yes Yes −9.42*** (2.27) 572 38 397.1*** 1.0000 0.0075 0.4648

.884*** (.009) −6.674*** (.655)

1

NPL

126.2*** (9.629)

.376*** (.011)

2

−.443*** (.052) .041*** (.004) .015* (.008) −.132*** (.010) −.006*** (.001) −.194*** (.00893) .0583*** (.00925) Yes Yes −3.92*** (.79) 580 38 2465.9*** 1.0000 0.0047 0.4634

−8.890*** (1.182)

.831*** (.005)

2

.330*** (.032)

4

.021*** (.003) −.742*** (.090) .035*** (.004) .017** (.005) −.121*** (.013) −.005*** (.001) −.192*** (.00842) .0422*** (.00908) Yes Yes −10.68*** (1.75) 589 38 1115.6*** 1.0000 0.3517 0.4016

.877*** (.014)

4

(continued on next page)

.319*** (.015)

3

−.546*** (.066) .044*** (.004) .016*** (.006) −.124*** (.016) −.007*** (.002) −.190*** (.0133) .0422*** (.00954) Yes Yes −6.17*** (1.30) 598 38 862.2*** 1.0000 0.0039 0.4038

.0013* (.0007)

.844*** (.006)

3

A.Y.H. Saif-Alyousfi et al.

North American Journal of Economics and Finance xxx (xxxx) xxx–xxx

35

4

−.057 (.045) .027*** (.002) −.005 (.005) −.075*** (.009) −.0022*** (.0008) −.0461*** (.00533) .00475 (.00700) Yes Yes 1.353** (.628) 559 38 489.1*** 1.0000 0.0111 0.2303

−.017 (.043) .019*** (.002) −.004 (.003) −.058*** (.008) −.001 (.001) −.0309*** (.00291) .00196 (.00531) Yes Yes 2.475*** (.742) 566 38 715.7*** 1.0000 0.0110 0.3248

−.071 (.044) .023*** (.002) .0003 (.004) −.062*** (.007) −.002** (.0008) −.0394*** (.00411) .00397 (.00856) Yes Yes .782 (.608) 584 38 133.6*** 1.0000 0.0090 0.2396

.0003** (.0001) .010*** (.001) .008 (.031) .019*** (.003) −.003 (.002) −.055*** (.009) −.0018** (.0008) −.0366*** (.00469) .000677 (.00714) Yes Yes 1.168*** (.397) 574 38 6596.5*** 1.0000 0.0106 0.2626 36.152*** (.506) −1.430*** (.279) −4.085*** (.416) 3.953* (1.999) .968*** (.098) .108*** (.0322) −.371*** (.0884) Yes Yes −785.32*** (112.42) 555 38 316.4*** 1.0000 0.0154 0.1992

1

3

1

2

SDROE

SDROA

37.668*** (9.030) −.925*** (.208) −3.627*** (.697) 5.282** (2.518) .884*** (.088) .145*** (.0387) −.569*** (.148) Yes Yes −590.43*** (209.10) 562 38 429.9*** 1.0000 0.2052 0.3359

2

52.736*** (18.298) −1.002*** (.298) −3.820*** (.732) 5.456*** (1.658) .841*** (.116) .0913** (.0418) −.968*** (.0948) Yes Yes −895.78*** (313.050) 578 38 1170.8*** 1.0000 0.2010 0.3336

−.031* (.016)

3

−.650** (.257) 51.120*** (7.910) −1.599*** (.272) −4.061*** (.445) 2.928* (1.746) .751*** (.089) .0590 (.0432) −.362*** (.138) Yes Yes −797.37*** (148.182) 568 38 859.1*** 1.0000 0.2034 0.3886

4

6.168*** (.285) −.251*** (.023) −.186*** (.039) .513*** (.147) .151*** (.016) .319 (.438) −8.075*** (1.007) Yes Yes −89.34*** (5.026) 556 38 5037.8*** 1.0000 0.0109 0.1122

1

NPL

7.721*** (1.417) −.096** (.040) −.099 (.063) .967*** (.146) .195*** (.020) .626*** (.219) −11.55*** (.967) Yes Yes −110.5*** (24.069) 563 38 608.2*** 1.0000 0.0094 0.1288

2

8.596*** (1.001) −.126*** (.039) −.260*** (.079) 1.158*** (.138) .151*** (.017) 2.218* (1.318) −11.30*** (1.000) Yes Yes −129.7*** (18.57) 578 38 566.87 1.0000 0.0172 0.3776

−.003** (.001)

3

−.744*** (.095) 7.036*** (1.047) −.143** (.051) −.121* (.066) −.084 (.154) .135*** (.025) 2.365*** (.709) −11.61*** (.802) Yes Yes −144.7*** (17.05) 569 38 113.8*** 1.0000 0.0149 0.3727

4

Note: Table reports estimation results for the impact of bank competition on bank risk-taking behavior for large size banks using two-step system GMM dynamic panel model by Arellano and Bover (1995). The dependent variables are the standard deviation of ROA (SDROA), Standard deviation of ROE (SDROE) calculated from a three-period based rolling window, non-performing loans to total loans (NPL), loan loss provision to total loans (LLP), Z-score index based on ROA (ZROA), and Z-score index based on ROE. Lerner Index, Boone indicator, Herfindahl–Hirschman Index (HHI), and 5-bank concentration ratio (CR5) are the market power indexes. SIZE is the logarithm of total assets. NIR refer to income diversification of banks measured by non-Interest revenue to total revenue. COST is the ratio of operating expenses to total income. EQTA refers to the bank capitalization calculated as the ratio of total equity to total assets. LIQ refer to bank liquidity calculated by the ratio of total loans to total assets. GDP is annual GDP growth rate. INF IS annual inflation rate. The Sargan test is the test for over-identifying restrictions in GMM estimation. AB test AR(1) and AR(2) refer to the Arellano–Bond test that average autocovariance in residuals of order 1 resp. of order 2 is 0 (H0: no autocorrelation). The standard errors are in parentheses. *** Significance at the 1% level, ** Significance at the 5% level, * Significance at the 10% level.

No. observations No. banks Wald-test Sargan P value AR(1) P value AR(2) P value

Country dummy Time dummy Intercept

INF

GDP

LIQ

EQTA

COST

NIR

SIZE

CR5

HHI

Table 13 (continued)

A.Y.H. Saif-Alyousfi et al.

North American Journal of Economics and Finance xxx (xxxx) xxx–xxx

t-1

36

t-1

Boone indicator

Lerner index

Lag

No. observations No. banks Wald-test Sargan P value AR(1) P value AR(2) P value

Country dummy Time dummy Intercept

INF

GDP

LIQ

EQTA

COST

NIR

SIZE

CR5

HHI

Boone indicator

Lerner index

Lag

.351*** (.004) 2.888*** (.687)

26.361*** (1.692)

.352*** (.005)

.366*** (.004)

3 .371*** (.005)

4

−1.850*** (.167) −.041*** (.004) −.035*** (.002) −.092*** (.016) −.069*** (.022) −.132*** (.0329) .0198 (.0528) Yes Yes 31.36*** (2.24) 438 32 290.85*** 1.0000 0.0818 0.4386

.867*** (.007) 3.671*** (.350)

.060*** (.008) −86.36*** (3.675)

1

2

−.020*** (.002) −.812 (.054) −.015*** (.001) −.012*** (.001) −.067*** (.007) −.039*** (.003) −.0420*** (.00638) .0691*** (.0138) Yes Yes 16.02*** (.690) 429 32 3944.7*** 1.0000 0.1114 0.5375

.587*** (.006)

1

−.568*** (.037) −.014*** (.002) −.014*** (.001) −.063*** (.008) −.051*** (.003) −.0573*** (.00649) .0847*** (.0166) Yes Yes 11.73*** (.511) 420 32 4723.2*** 1.0000 0.5645 0.1059

−.005*** (.001)

.579*** (.007)

ZROA

−.772*** (.069) −.020*** (.001) −.010*** (.0007) −.080*** (.005) −.030*** (.003) −.0329*** (.00467) .102*** (.0106) Yes Yes 13.937*** (1.007) 431 32 7699.1*** 1.0000 0.1133 0.4722

10.578*** (2.325)

.622*** (.006)

LLP

−.871*** (.087) −.021*** (.003) −.012*** (.0008) −.078*** (.008) −.038*** (.005) −.0379*** (.00576) .0527*** (.0181) Yes Yes 14.244*** (1.404) 438 32 2123.3*** 1.0000 0.1164 0.5653

.618*** (.007) 2.601*** (.596)

4

1

3

1

2

SDROE

SDROA

Table 14 Estimation results of bank risk-taking behavior equation for small size banks

−66.83*** (12.44)

.065*** (.013)

2

−1.497*** (.368) −.029*** (004) −.024*** (.003) −.161*** (.023) −.095*** (.006) −.151*** (.0311) .103*** (.0199) Yes Yes 32.04*** (5.34) 431 32 11700*** 1.0000 0.0598 0.3907

50.856*** (12.641)

.866*** (.008)

2

.073*** (.006)

3

−1.580*** (.352) −.036*** (.004) −.043*** (.002) −.109*** (.019) −.096*** (.011) −.204*** (.0210) .0492* (.0257) Yes Yes 31.525*** (4.912) 420 32 4841.6*** 1.0000 0.0714 0.5979

−.069** (.032)

.851*** (.006)

3

.097*** (.011)

4

−.039*** (.009) −1.247*** (.355) −.029*** (.005) −.043*** (.002) −.107*** (.012) −.113*** (.006) −.184*** (.0280) .0269 (.0334) Yes Yes 28.884*** (5.509) 429 32 1314.8*** 1.0000 0.0718 0.5912

.847*** (.007)

4

.217*** (.012) −38.48*** (1.907)

1

ZROE

−6.730*** (.270) −.037*** (.007) −.019*** (.001) −.267*** (.006) −.127*** (.018) −.0271** (.0127) .633*** (.0329) Yes Yes 121.04*** (4.40) 430 32 1484.4*** 1.0000 0.1345 0.3095

.218*** (.013) 9.848*** (1.245)

1

NPL

−71.33*** (17.950)

.241*** (.003)

2

−7.196*** (.298) −.086*** (.008) −.014*** (.002) −.266*** (.036) −.093*** (.011) −.0109 (.0136) .332*** (.0230) Yes Yes 124.83*** (5.81) 422 32 3424.4*** 1.0000 0.1108 0.4302

156.8*** (9.171)

.130*** (.017)

2

.261*** (.004)

4

−.021** (.007) −6.283*** (.380) −.048*** (.003) −.026*** (.001) −.248*** (.014) −.143*** (.010) −.0660*** (.0186) .395*** (.0360) Yes Yes 109.03*** (6.554) 422 32 563.6*** 1.0000 0.1339 0.3241

.203*** (.014)

4

(continued on next page)

.244*** (.002)

3

−7.570*** (.394) −.029*** (.004) −.020*** (.001) −.307*** (.016) −.126*** (.033) −.0707*** (.0178) .416*** (.0464) Yes Yes 132.14*** (6.438) 412 32 934.5*** 1.0000 0.1400 0.2492

−.029** (.010)

.139*** (.010)

3

A.Y.H. Saif-Alyousfi et al.

North American Journal of Economics and Finance xxx (xxxx) xxx–xxx

37

4

−.588** (.248) −.047*** (.004) −.029*** (.001) −.087*** (.005) −.027*** (.004) −.00489 (.00898) .0246 (.0195) Yes Yes 14.38*** (3.38) 443 32 1030.3*** 1.0000 0.0812 0.4883

−.726*** (.127) −.049*** (.001) −.024*** (.001) −.095*** (.003) −.017*** (.003) −.0136 (.0105) .0283*** (.00792) Yes Yes 15.37*** (1.66) 436 32 218.6*** 1.0000 0.0802 0.5170

−.428*** (.113) −.048*** (.003) −.032*** (.001) −.085*** (.002) −.032*** (.003) −.0135*** (.00338) .00962 (.0101) Yes Yes 10.34*** (1.80) 427 32 1974.9*** 1.0000 0.0763 0.4773

−.020*** (.003) −.013*** (.003) −.698*** (.131) −.045*** (.001) −.030*** (.001) −.074*** (.003) −.020*** (.003) −.00165 (.00469) .00635 (.00932) Yes Yes 13.082*** (1.749) 436 32 477.0*** 1.0000 0.0878 0.4491 22.402*** (6.529) .101** (.049) .164** (.058) 2.479** (.912) −.258*** (.063) .0214 (.0358) −.267*** (.0681) Yes Yes −403.99*** (101.60) 438 32 162.9*** 1.0000 0.0148 0.1066

1

3

1

2

SDROE

SDROA

57.302*** (9.285) .143 (.179) .119** (.056) 2.101*** (.725) −.056 (.094) .00904 (.0257) −.701*** (.0345) Yes Yes −188.26 (150.28) 431 32 332.02 1.0000 0.0143 0.1054

2

24.932*** (3.706) .169*** (.027) .121 (.093) .121 (.099) −.796*** (.101) .242*** (.0366) −.863*** (.0635) Yes Yes −248.28*** (58.99) 420 32 1320*** 1.0000 0.0096 0.2464

.443*** (.078)

3

3.197*** (.270) 32.192*** (3.873) .235*** (.085) .138*** (.033) .882*** (.203) −.796*** (.065) .0664** (.0309) −.863*** (.117) Yes Yes −560.04*** (60.18) 429 32 1279.7*** 1.0000 0.0033 0.1827

4

3.698*** (.557) .042*** (.007) .019** (.005) .097** (.039) −.149*** (.008) 1.833*** (.204) −7.657*** (.423) Yes Yes 36.387*** (8.619) 438 32 2379*** 1.0000 0.1237 0.4578

1

NPL

2.227*** (.162) .015 (.015) .014** (.005) .012 (.026) −.131*** (.009) 1.872*** (.248) −8.433*** (.593) Yes Yes 34.402*** (2.319) 431 32 525.5*** 1.0000 0.1156 0.2895

2

4.559*** (.382) .014** (.005) .019*** (.005) .244*** (.037) −.076*** (.009) .508 (.676) −11.43*** (.688) Yes Yes 79.87*** (5.86) 419 32 2208.4*** 1.0000 0.1259 0.3937

.252*** (.015)

3

.120*** (.029) 1.909*** (.353) .025*** (.006) .005 (.003) .091*** (.021) −.078*** (.007) 1.717*** (.223) −8.653*** (.606) Yes Yes 41.06*** (3.93) 429 32 1547.9*** 1.0000 0.1100 0.3228

4

Note: Table reports estimation results for the impact of bank competition on bank risk-taking behavior for small size banks using two-step system GMM dynamic panel model by Arellano and Bover (1995). The dependent variables are the standard deviation of ROA (SDROA), Standard deviation of ROE (SDROE) calculated from a three-period based rolling window, non-performing loans to total loans (NPL), loan loss provision to total loans (LLP), Z-score index based on ROA (ZROA), and Z-score index based on ROE. Lerner Index, Boone indicator, Herfindahl–Hirschman Index (HHI), and 5-bank concentration ratio (CR5) are the market power indexes. SIZE is the logarithm of total assets. NIR refer to income diversification of banks measured by non-Interest revenue to total revenue. COST is the ratio of operating expenses to total income. EQTA refers to the bank capitalization calculated as the ratio of total equity to total assets. LIQ refer to bank liquidity calculated by the ratio of total loans to total assets. GDP is annual GDP growth rate. INF IS annual inflation rate. The Sargan test is the test for over-identifying restrictions in GMM estimation. AB test AR(1) and AR(2) refer to the Arellano–Bond test that average autocovariance in residuals of order 1 resp. of order 2 is 0 (H0: no autocorrelation). The standard errors are in parentheses. *** Significance at the 1% level, ** Significance at the 5% level, * Significance at the 10% level.

No. observations No. banks Wald-test Sargan P value AR(1) P value AR(2) P value

Country dummy Time dummy Intercept

INF

GDP

LIQ

EQTA

COST

NIR

SIZE

CR5

HHI

Table 14 (continued)

A.Y.H. Saif-Alyousfi et al.

North American Journal of Economics and Finance xxx (xxxx) xxx–xxx

North American Journal of Economics and Finance xxx (xxxx) xxx–xxx

A.Y.H. Saif-Alyousfi et al.

Table 15 Estimation results of bank risk-taking behavior equation with the whole sample period (1998-2016): Controlling for the ownership variables, regulation variables, environmental variables and interaction terms Dependent variable: Bank risk-taking behavior (SDROA) Panel A

L.SDROAt-1 Lerner index (L) Boon indicator (L)

Panel B

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

0.622*** (0.00116) −0.456** (0.225)

0.626*** (0.00151)

0.593*** (0.00190)

0.596*** (0.00173)

0.639*** (0.00258) −0.527** (0.221)

0.638*** (0.00149)

0.608*** (0.00196)

0.623*** (0.00195)

HHI (L)

−14.93*** (0.311)

(0.000111)

CR5 (L) SIZE NIR COST EQTA LNTA GDP INF FOREIGN GOVERNMENT CAPRI SPRI ACTRI MDPI DEPI

0.00181***

−0.0340* (0.0175) 0.0115*** (0.000387) 0.0144*** (0.000150) −0.0623*** (0.00168) −0.0168***

(0.000696) −0.0671*** (0.00291) 0.0768*** (0.00390) 0.0660*** (0.000815) −0.0209*** (0.00126) −0.292*** (0.0179) −0.137*** (0.0137) 0.135*** (0.00759) −0.0546*** (0.0162)

−0.131*** (0.0171) 0.00825*** (0.000312) 0.0116*** (0.000167) −0.0486*** (0.000933) −0.00977*** (0.000599) −0.0563*** (0.00190) 0.0857*** (0.00252) 0.0593*** (0.00111) −0.0207*** (0.00145) −0.255*** (0.0116) −0.00540 (0.00915) 0.140*** (0.00589) −0.0933*** (0.0151)

0.0168*** (0.000883) −0.0879*** (0.0191) 0.0102*** (0.000522) 0.0143*** (0.000236) −0.0552*** (0.00167) −0.0150***

(0.000788) −0.0733*** (0.00302) 0.0746*** (0.00383) 0.0712*** (0.000909) −0.0232*** (0.00151) −0.504*** (0.0169) −0.241*** (0.0140) 0.144*** (0.0103) −0.0150 (0.0124)

5.565*** (0.332) 993 70 1500***

2.972*** (0.310) 993 70 865***

5.624*** (0.371) 997 70 360***

−0.184*** (0.0137) 0.00790*** (0.000268) 0.0144*** (0.000121) −0.0539*** (0.00149) −0.0123***

−16.39*** (0.537)

−0.00879*** (0.00114)

−0.167*** (0.0303) 0.0077*** (0.000397) 0.0134*** (0.000163) −0.0488*** (0.00181) −0.0109***

−0.165*** (0.0251) 0.0082*** (0.000371) 0.0106*** (0.000262) −0.0471*** (0.00195) −0.0081***

−0.173*** (0.0340) 0.0103*** (0.000406) 0.0150*** (0.000156) −0.0594*** (0.00177) −0.0159***

0.0122*** (0.00121) −0.167*** (0.0351) 0.0087*** (0.000580) 0.0138*** (0.000281) −0.0486*** (0.00274) −0.0136***

(0.000844) −0.0643*** (0.00311) 0.0727*** (0.00310) 0.0683*** (0.00110) −0.0250*** (0.00125) −0.470*** (0.0191) −0.204*** (0.0162) 0.137*** (0.00736) −0.0170 (0.0198)

(0.000730) −0.0657*** (0.00345) 0.0671*** (0.00527) 0.0746*** (0.00212) −0.0267*** (0.00185) −0.334*** (0.0254) −0.0977*** (0.0222) 0.208*** (0.00933) −0.177*** (0.0381) −5.407*** (0.449) −0.306 (0.640) 0.262*** (0.0712) −0.507*** (0.0459)

(0.000662) −0.0578*** (0.00311) 0.0697*** (0.00568) 0.0771*** (0.00160) −0.0315*** (0.00241) −0.327*** (0.0164) 0.000304 (0.0127) 0.215*** (0.00947) −0.270*** (0.0288) −5.546*** (0.357) −0.670 (0.624) 0.371*** (0.0683) −0.424*** (0.0381)

(0.000730) −0.0807*** (0.00384) 0.0717*** (0.00479) 0.0925*** (0.00298) −0.0387*** (0.00169) −0.543*** (0.0237) −0.222*** (0.0298) 0.241*** (0.0164) −0.220*** (0.0477) −5.409*** (0.451) −5.237*** (0.503) 0.284*** (0.0874) −0.382*** (0.0416)

(0.000806) −0.0741*** (0.00396) 0.0639*** (0.00499) 0.0844*** (0.00280) −0.0352*** (0.00192) −0.525*** (0.0301) −0.177*** (0.0254) 0.246*** (0.0122) −0.207*** (0.0429) −8.555*** (0.434) −5.770*** (0.802) 0.234*** (0.0760) −0.807*** (0.0563)

6.878*** (0.361) 997 70 117***

28.99*** (2.511) 993 70 339***

23.72*** (1.845) 993 70 363***

13.33*** (1.875) 997 70 894***

31.46*** (1.785) 997 70 367***

SHPI CRPI LEEI L* CAPRI L* SPRI L* ACTRI L* SHPI L* CRPI L* LEEI Intercept No. observations No. banks Wald−test

38

(continued on next page)

North American Journal of Economics and Finance xxx (xxxx) xxx–xxx

A.Y.H. Saif-Alyousfi et al.

Table 15 (continued) Dependent variable: Bank risk-taking behavior (SDROA) Panel A

Sargan P value AR(1) P value AR(2) P value

Panel B

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

0.9993 0.0998 0.5306

0.9994 0.0979 0.3920

1.0000 0.0933 0.5814

1.0000 0.0947 0.5554

0.9992 0.0991 0.5200

0.9994 0.0975 0.3791

1.0000 0.0934 0.5727

1.0000 0.0945 0.5411

Dependent variable: Bank risk-taking behavior (SDROA) Panel C

L.SDROAt-1 Lerner index (L) Boon indicator (L)

Panel D

(9)

(10)

(11)

(12)

(13)

(14)

(15)

(16)

0.621*** (0.00140) −35.93*** (3.757)

0.623*** (0.00168)

0.593*** (0.00184)

0.593*** (0.00178)

0.623*** (0.00269) −19.84** (9.453)

0.617*** (0.00231)

0.610*** (0.00287)

0.621*** (0.00234)

HHI (L)

−3.698** (1.21)

CR5 (L)

0.0026*** (0.000835)

−15.9** (6.75)

0.0154** (0.00708)

SIZE

−0.0389*

−0.353***

−0.0188

0.0718*** (0.0191) −0.126***

NIR

(0.0222) 0.0078***

(0.0227) 0.0087***

(0.0198) 0.0113***

(0.0193) 0.0104***

(0.0393) 0.00845***

(0.0379) 0.0051***

(0.0388) 0.00981***

(0.000320)

(0.000558)

(0.000706)

(0.000431)

(0.000554)

(0.000493)

(0.000679)

COST

0.0139*** (0.000165)

0.0116*** (0.000200)

0.0143*** (0.000213)

0.0142*** (0.000266)

0.0125*** (0.000243)

0.0115*** (0.000280)

0.0150*** (0.000237)

EQTA

−0.0544***

−0.0551***

−0.0614***

−0.0563***

−0.0477***

−0.0525***

−0.0594***

LNTA

(0.00170) −0.0132***

(0.00125) −0.0098***

(0.00176) −0.0166***

(0.00181) −0.0148***

(0.00275) −0.0117***

(0.00257) −0.0085***

(0.00227) −0.0144***

(0.000927)

(0.000647)

(0.000582)

(0.000927)

(0.00110)

(0.000997)

(0.00120)

−0.0551***

−0.0603***

−0.0737***

−0.0648***

−0.0552***

−0.0476***

−0.0784***

GOVERNMENT

(0.00247) 0.0590*** (0.00458) 0.0689*** (0.000830) −0.0285***

(0.00346) 0.0844*** (0.00378) 0.0511*** (0.000925) −0.0139***

(0.00275) 0.0754*** (0.00404) 0.0715*** (0.000999) −0.0244***

(0.00272) 0.0704*** (0.00358) 0.0686*** (0.00124) −0.0241***

(0.00436) 0.0336*** (0.00458) 0.0940*** (0.00296) −0.0365***

(0.00473) 0.0239*** (0.00640) 0.0575*** (0.00382) −0.000845

(0.00504) 0.0719*** (0.00529) 0.0904*** (0.00339) −0.0384***

CAPRI

(0.00138) −1.309***

(0.00188) −0.287***

(0.00159) −0.511***

(0.00222) −0.137

(0.00299) −1.988***

(0.00260) −0.198***

(0.00302) −0.551***

(0.109) −1.126*** (0.0675) 0.464*** (0.0592) −0.0455**

(0.0200) −0.227*** (0.00890) 0.235*** (0.0106) −0.159***

(0.0190) −0.249*** (0.0164) 0.149*** (0.0123) −0.0137

(0.0944) 0.122** (0.0516) 0.150*** (0.0443) −0.0464**

(0.246) −1.404*** (0.114) 0.444*** (0.110) −0.243***

(0.0244) −0.259*** (0.0226) 0.380*** (0.0236) −0.471***

(0.0391) −0.216*** (0.0333) 0.248*** (0.0216) −0.232***

(0.0187)

(0.0228)

(0.0172)

(0.0225)

(0.0361) −5.333***

(0.0616) −3.833***

(0.0546) −5.420***

SHPI

(0.609) −1.797

(0.424) −3.759***

(0.524) −5.881***

CRPI

(1.281) 0.0985

(1.200) 0.548

(0.758) 0.345***

GDP INF FOREIGN

SPRI ACTRI MDPI DEPI

39

−0.0208

−0.415***

−0.158***

0.0864* (0.0472) −0.207*** (0.0449) 0.00847*** (0.000611) 0.0138*** (0.000339) −0.0496*** (0.00283) −0.0129*** (0.000993) −0.0710*** (0.00552) 0.0478*** (0.00820) 0.0766*** (0.00275) −0.0348*** (0.00332) −0.581*** (0.164) −0.0773 (0.118) 0.586*** (0.0767) −0.138*** (0.0418) −8.285*** (0.526) −10.12*** (1.326) 1.164*

(continued on next page)

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A.Y.H. Saif-Alyousfi et al.

Table 15 (continued) Dependent variable: Bank risk-taking behavior (SDROA) Panel C (9)

Panel D (10)

(11)

(12)

LEEI L* CAPRI L* SPRI L* ACTRI L* SHPI

2.148*** (0.222) 2.177*** (0.159) −0.564***

1.640*** (0.524) 4.665*** (0.628) −5.907***

8.67e−05 (5.83e−05) 0.0018*** (6.36e−05) −4.82e−05

0.0045*** (0.00107) 0.0048*** (0.000805) −0.000141

(0.129)

(0.358)

(5.85e−05)

(0.000627)

L* CRPI L* LEEI Intercept No. observations No. banks Wald−test Sargan P value AR(1) P value AR(2) P value

19.30*** (1.867) 993 70 1470*** 0.9999 0.1045 0.5375

8.813*** (0.355) 993 70 279*** 0.9992 0.0940 0.3738

5.513*** (0.388) 997 70 323*** 1.0000 0.0935 0.5829

1.099 (1.648) 997 70 150*** 1.0000 0.0943 0.5481

(13)

(14)

(15)

(16)

(1.019) −0.0661

(0.354) −0.745***

(0.0751) −0.419***

(0.119) 3.235*** (0.478) 2.938*** (0.234) −0.103

(0.0759) 2.058** (0.812) 3.475*** (0.970) −8.804***

(0.0484) 0.000158 (0.000130) 0.000175** (6.98e−05) −0.000146

(0.241) 3.868 (2.431) −0.176

(0.512) 78.62*** (11.16) −0.907

(1.768) 1.028*** (0.179) 30.79*** (5.712) 993 70 492*** 0.9999 0.1010 0.5112

(13.71) 8.995*** (0.903) 34.75*** (2.401) 993 70 105*** 0.9999 0.1025 0.3738

(0.000115) 0.0018*** (0.000286) −0.00442** (0.00216) 6.10e−05 (0.000237) 13.32*** (2.228) 997 70 385*** 1.0000 0.0947 0.5698

(0.634) −1.045*** (0.101) 0.00197 (0.00224) 0.000902 (0.00187) −0.0053*** (0.00118) 0.0394*** (0.0139) −0.0302 (0.0187) 0.00222 (0.00191) 31.12*** (4.458) 997 70 1430*** 1.0000 0.0944 0.5318

Dependent variable: Bank stability (ZROA) Panel A

L.ZROAt−1 Lerner index (L) Boon indicator (L)

Panel B

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

0.137*** (0.00114) 36.9*** (2.58)

0.117*** (0.00299)

0.121*** (0.00248)

0.123*** (0.00130)

0.300*** (0.00658) 234.3*** (33.18)

0.203*** (0.00664)

0.162*** (0.00540)

0.165*** (0.00555)

HHI (L)

41.09*** (7.97)

CR5 (L) SIZE NIR COST EQTA LNTA GDP INF FOREIGN GOVERNMENT CAPRI SPRI

20.54*** (1.359) −0.687*** (0.0891) 0.00248 (0.0457) 2.129*** (0.130) 1.105*** (0.0571) 0.606*** (0.136) −4.819*** (0.517) −0.434** (0.194) 2.745*** (0.179) 5.754** (2.864) 26.04***

6.987*** (2.372) −0.498*** (0.104) −0.0175 (0.0682) 1.793*** (0.532) 1.049*** (0.0452) 1.314*** (0.241) −8.539*** (0.459) −0.371*** (0.110) 1.818*** (0.117) 8.283** (4.199) 30.95***

−0.0697*** (0.00555) 7.071*** (1.764) −0.319*** (0.113) −0.171 (0.122) 1.087*** (0.168) 1.021*** (0.109) 1.077*** (0.218) −9.611*** (0.411) −0.322** (0.134) 1.854*** (0.152) 8.050** (3.161) 38.03***

−0.932*** (0.129) 3.755* (2.282) −0.338*** (0.0585) −0.109 (0.0701) 1.108*** (0.192) 1.195*** (0.115) 1.388*** (0.265) −9.029*** (0.501) −0.105 (0.202) 1.876*** (0.463) 7.749 (6.460) 33.59***

40

12.75*** (3.610) −0.688*** (0.164) 0.0338 (0.0542) 1.801*** (0.610) 0.446*** (0.0685) 0.979*** (0.171) −3.136*** (0.388) −3.028*** (0.974) 1.389** (0.543) 10.29** (5.083) 29.69***

158.7* (88.24)

−8.840* (4.888) −0.495*** (0.173) −0.0107 (0.0578) 0.567 (0.388) 0.548*** (0.0616) 2.419*** (0.253) −7.239*** (0.240) −1.051 (1.169) 0.537 (0.626) 20.93*** (6.386) 19.18*

−0.241*** (0.0144) 0.116 (8.528) −0.212 (0.169) −0.0320 (0.0837) 0.626 (1.043) 0.975*** (0.105) 2.351*** (0.396) −8.189*** (0.764) −0.346 (0.912) −0.711 (0.534) 4.676 (7.602) 42.97***

−1.360*** (0.224) −0.750 (4.841) −0.363*** (0.137) −0.0784 (0.0687) 0.129 (0.701) 1.029*** (0.0868) 2.840*** (0.342) −7.602*** (0.618) −1.362 (1.538) 0.444 (0.850) 7.541 (6.570) 27.24***

(continued on next page)

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A.Y.H. Saif-Alyousfi et al.

Table 15 (continued) Dependent variable: Bank stability (ZROA) Panel A

ACTRI MDPI DEPI

Panel B

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(2.579) −18.71*** (1.258) 21.68*** (2.571)

(1.652) −15.71*** (0.964) 17.29*** (4.151)

(2.397) −15.28*** (1.400) 12.74*** (3.253)

(4.724) −14.25*** (2.075) 15.64*** (3.947)

(5.957) −31.12*** (5.644) 19.20 (14.12) 542.6*** (72.25) 817.1*** (74.48) −37.38*** (14.12) 14.41** (6.746)

(10.03) −14.81* (8.213) 6.547 (15.36) 656.2*** (105.1) 507.3*** (72.30) −18.36 (23.82) 53.22*** (6.289)

(5.927) −19.79*** (4.278) 7.123 (9.510) 805.8*** (82.09) 954.0*** (109.5) −20.24 (18.19) 47.79*** (7.282)

(7.594) −15.14** (7.357) 7.086 (13.99) 621.8*** (119.1) 653.7*** (73.75) −7.339 (28.27) 32.44*** (6.953)

304.7*** (63.81) 993 70 508*** 0.9999 0.0062 0.2302

707.9*** (39.37) 993 70 3260*** 0.9999 0.1903 0.2208

780.7*** (72.27) 997 70 205*** 1.0000 0.1875 0.2545

741.0*** (145.8) 997 70 158*** 1.0000 0.1886 0.2313

−2,329*** (198.3) 993 70 921*** 0.9999 0.0054 0.3487

−3,358*** (252.7) 993 70 1820*** 1.0000 0.1878 0.2162

−4,101*** (320.4) 997 70 1130*** 1.0000 0.1946 0.6602

−2,849*** (215.7) 997 70 291*** 1.0000 0.1937 0.6828

SHPI CRPI LEEI L* CAPRI L* SPRI L* ACTRI L* SHPI L* CRPI L* LEEI Intercept No. observations No. banks Wald-test Sargan P value AR(1) P value AR(2) P value

Dependent variable: Bank stability (ZROA) Panel C

L.ZROAt−1 Lerner index (L) Boon indicator (L)

Panel D

(9)

(10)

(11)

(12)

(13)

(14)

(15)

(16)

0.322*** (0.00981) 3,272*** (1,230)

0.166*** (0.00345)

0.153*** (0.00323)

0.161*** (0.00404)

0.271*** (0.0146) 9,229** (4,481)

0.126*** (0.00573)

0.142*** (0.00935)

0.167*** (0.00641)

HHI (L)

8.475* (4.226)

CR5 (L) SIZE NIR COST EQTA LNTA GDP

26.92*** (7.627) −0.744*** (0.118) −0.0788 (0.0541) 1.252** (0.591) 0.512*** (0.0616) 4.277*** (0.250)

10.54*** (4.003) −0.851*** (0.146) −0.0571 (0.0726) 1.444* (0.797) 0.496*** (0.0600) 1.320*** (0.381)

−0.585*** (0.0597)

4.739 (6.889) −0.458*** (0.140) −0.0968 (0.102) 1.164 (1.176) 0.742*** (0.126) 1.330*** (0.297)

15.414* (8.497)

−20.12*** (5.014) 13.49*** (3.827) −0.180 (0.147) −0.0847 (0.0901) −0.701 (0.465) 0.674*** (0.0943) 1.799*** (0.247)

41

12.16 (8.894) −0.409* (0.228) −0.139** (0.0657) 3.429** (1.525) 0.321*** (0.0927) 0.275 (0.680)

29.96*** (6.720) −0.124 (0.203) −0.0181 (0.104) 1.222* (0.720) 0.841*** (0.142) 2.266*** (0.626)

−4.474** (2.042)

19.12** (7.835) −0.0475 (0.208) −0.123 (0.0923) −−0.998 (1.248) 0.973*** (0.135) 3.534*** (0.577)

−56.57*** (21.22) 21.27 (20.78) −0.0585 (0.186) −0.0106 (0.0905) 1.721 (2.740) 0.967*** (0.156) 2.638*** (0.685)

(continued on next page)

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A.Y.H. Saif-Alyousfi et al.

Table 15 (continued) Dependent variable: Bank stability (ZROA) Panel C

Panel D

(9)

(10)

(11)

(12)

(13)

(14)

(15)

(16)

−2.399***

−7.532***

−10.20***

−9.288***

−0.0207

−5.744***

−10.32***

(0.405) −1.215*** (0.247) 0.303 (0.459) 138.4*** (34.13) 34.40* (18.80) −74.17*** (15.17) 28.18*** (5.568)

(0.297) −1.586*** (0.269) 1.085*** (0.288) 0.313 (4.732) 21.03*** (5.421) −14.38*** (3.052) 18.00*** (4.705)

(0.609) −0.643** (0.282) 1.663*** (0.293) 2.448 (5.218) 33.41*** (5.467) −10.90*** (4.011) 4.535 (5.271)

(0.437) −0.711* (0.366) 1.110*** (0.197) 95.66*** (20.59) 20.72 (16.82) 7.700 (12.56) 12.67** (5.597)

−303.0*** (74.50) −25.26 (42.39) 109.4*** (32.93)

−309.0 (312.0) −81.52 (157.5) 345.1*** (103.9)

−0.0144*** (0.00460) −0.0192*** (0.00408) 0.0210*** (0.00418)

−1.269*** (0.271) −0.730*** (0.230) 0.202 (0.162)

Intercept

2,628***

747.9***

599.1***

−593.5

(0.656) −1.833 (1.431) 0.764 (1.044) 210.9* (111.1) 134.0* (70.70) −95.35* (49.26) 28.52 (30.03) 436.4*** (138.9) 312.7 (340.8) −445.1 (372.6) 4.699 (22.87) −483.8** (225.3) −232.0 (143.1) 131.7 (86.43) −663.4 (630.6) 666.2 (619.4) −62.97 (46.56) 1,663

(0.975) −1.227 (1.165) 2.125** (0.896) 0.196 (7.152) 20.79*** (6.724) −22.28*** (6.892) 7.808 (11.02) 567.7*** (90.58) 543.6*** (111.8) −153.6** (68.17) 39.62*** (8.210) −102.6 (393.4) −152.4 (281.5) 87.87 (210.0) −3,388 (2,765) 6,532*** (2,483) −7.341 (128.4) −2,580***

(0.914) −1.900 (2.683) 0.753 (2.005) 11.28 (10.04) 23.77** (10.22) −0.723 (8.394) 21.71 (19.82) 1,053*** (161.2) 1,348*** (168.1) −19.18 (39.82) 43.68*** (10.61) −0.00520 (0.0227) −0.0262*** (0.0101) 0.0592*** (0.0137) −0.652*** (0.160) 0.482 (0.483) −0.0507* (0.0293) −5,814***

No. observations No. banks Wald-test Sargan P value AR(1) P value AR(2) P value

(589.0) 993 70 263*** 1.0000 0.0040 0.2741

(113.1) 993 70 908*** 0.9998 0.1916 0.1169

(105.5) 997 70 488*** 1.0000 0.2010 0.8630

(383.1) 997 70 534*** 1.0000 0.1943 0.7551

(2,089) 993 70 311*** 1.0000 0.0098 0.4274

(346.9) 993 70 1190*** 1.0000 0.1911 0.0249

(401.9) 997 70 2170*** 1.0000 0.2023 0.6579

−9.196*** (1.547) −1.597 (2.196) 1.143 (1.640) 123.3* (69.89) 37.80 (48.01) 35.84 (26.89) 24.44 (26.14) 743.5*** (165.0) 1,647*** (402.7) −119.6 (140.8) 9.710 (35.34) −1.416 (1.067) −0.922 (0.762) 0.624 (0.485) −12.06** (5.243) 2.456 (3.586) −0.285 (0.647) −7,438*** (1,276) 997 70 472*** 1.0000 0.1903 0.5350

INF FOREIGN GOVERNMENT CAPRI SPRI ACTRI MDPI DEPI SHPI CRPI LEEI L* CAPRI L* SPRI L* ACTRI L* SHPI L* CRPI L* LEEI

Note: Table reports estimation results for the impact of bank competition on bank risk-taking behavior and stability for overall period (1998-2016) after controlling for the ownership variables, regulation variables, environmental variables and Interaction terms using two-step system GMM dynamic panel model by Arellano and Bover (1995). The dependent variables are the standard deviation of ROA (SDROA) calculated from a threeperiod based rolling window, and Z-score index based on ROA (ZROA. Lerner Index, Boone indicator, Herfindahl–Hirschman Index (HHI), and 5bank concentration ratio (CR5) are the market power indexes. SIZE is the logarithm of total assets. NIR refer to income diversification of banks measured by non-Interest revenue to total revenue. COST is the ratio of operating expenses to total income. EQTA refers to the bank capitalization calculated as the ratio of total equity to total assets. LIQ refer to bank liquidity calculated by the ratio of total loans to total assets. GDP is annual GDP growth rate. INF is annual inflation rate. Ownership variables: FOREIGN is foreign ownership, GOVERNMENT is government ownership. Regulation variables: CAPRI refer to capital requirements index, SPRI refer to supervisory power index, ACTRI refer to activity restrictions index, MDPI refer to market discipline index. Environmental variables: DEPI refer to deposit insurance, SHPI refer to shareholder protection, CRPI refer to creditor protection, LEEI refer to legal efficiency. L* CAPRI, L* SPRI, L* ACTRI, L* SHPI, L* CRPI, and L* LEEI refers to the interaction between market competition measures with capital requirements index, supervisory power index, activity restrictions index, shareholder protection, creditor protection, and legal efficiency respectively. The Sargan test is the test for over-identifying restrictions in GMM estimation. AB test AR(1) and AR(2) refer to the Arellano–Bond test that average autocovariance in residuals of order 1 resp. of order 2 is 0 (H0: no autocorrelation). The standard errors are in parentheses. *** Significance at the 1% level, ** Significance at the 5% level,* Significance at the 10% level.

42

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A.Y.H. Saif-Alyousfi et al.

population) from the dataset in the first stage; then we exclude UAE banks (which accounts for 19% of the sample population) in the second stage. In the third stage, we exclude both Bahraini and UAE banks and carry-out our analysis. Our main findings remain unchanged despite the removal of a large sample of observation. Second, to check whether variations in the macroeconomic and the financial environment explain stability and risk exposures of banks, we included stock market capitalization and domestic credit to private sector as a percent of GDP as control variables in our analysis. We also incorporate bank level fixed effects variables as dummies in all the regressions. Inclusions of these new parameters do not alter our main results. Third, following Ariss (2010) and Fu et al. (2014), we replace conventional Lerner index by efficiency-adjusted Lerner index as a measure of competition in the banking market in all regressions. Our main results remain consistent. Fourth, to capture a possible non-linear relationship among competition with bank risk and stability, we follow Soedarmono et al. (2013), Fu et al. (2014), and Kasman and Kasman (2015), and replace Lerner index by a quadratic term for the Lerner index in all our regressions. Our findings also remain unchanged in this robustness check. Fifth, we also consider the following modifications of our empirical model. We consider two variables for ownership (foreign and government), four variables on regulations (capital requirements index (CAPRI), supervisory power index (SPRI), activity restrictions index (ACTRI), and market discipline index (MDPI)) and three for environmental and institutional developments (Shareholder protection (SHPI), Creditor protection (CRPI), Legal efficiency (LEEI)), including the existence of Deposit insurance (DEPI). In addition to the variables of ownership, regulations and environmental, we also include the interaction between the measures of bank competition and concentration with the regulatory variables as well as environmental variables to examine whether the total effect of regulations can change the sign depending on the value of market power. Table 15 presents the estimation results after implementing the ownership variables, regulatory factors, and environmental variables. Compared with the benchmark results in Table 4, the relationship between bank competition (Lerner index and Boone indicator) and concentration (HHI and CR5) with risk-taking (SDROA, SDROE, NPL, and LLP) or stability (ZROA and ZROE) presents the identical direction. This implies that there is a significantly negative relationship between the Lerner index and Boon indicator with bank risk-taking behavior and a positive relationship between the Lerner index and Boon indicator with bank stability. At the same time, HHI and CR5 have a positive and significant impact on bank risk-taking behavior and a negative impact on bank stability. Bank ownership appears to be another important parameter, a higher presence of foreign (state-owned) banks in the market results in higher (lower) risk-taking. These findings are not consistent with the literature that suggests a number of benefits from the entry of foreign banks in developing markets as well as the negative impact of state-owned banks on the banking stability (Agoraki et al., 2011). Nevertheless, taking regulatory and environmental variables into consideration decrease (increase) the positive persistence of risk-taking (stability). Our results suggest that countries with greater capital stringency, a higher supervisory power, a higher degree of market discipline and private monitoring, under explicit deposit insurance schemes, higher shareholder protection, or higher legal efficiency decrease banks’ risk-taking and increase their stability. On the other hand, banks’ risk (stability) will increase (decrease) for countries with greater regulatory restrictions on commercial bank activities or higher creditor protection. This implies that countries with greater regulatory restrictions and higher creditor protection decrease banks’ stability, but at the same time increase their risk. Notably, in the regressions that include the interaction between market power and regulatory and environmental variables, interaction between market power and the capital requirements index (L*CAPRI), market power and supervisory power index (L*SPRI), market power and shareholder protection (L* SHPI), and market power and legal efficiency (L*LEEI) enters with a positive (negative) and significant coefficient with risk-taking (stability). These indicate that CAPRI, SPRI, SHPI, and LEEI have an independent effect on bank risk and stability. However, these impact decreases for banks with greater market power. The interaction between market power and the capital requirements index (L*ACTRI) and market power and creditor protection (L*CRPI) enters with a negative (positive) and significant coefficient with risk-taking (stability), indicating that activity restrictions and creditor protection increase (decrease) the risk-taking (stability) of banks with low market power. In other words, activity restrictions and creditor protection limit the risktaking of banks with high market power, thus enhancing bank soundness. These results in line with the findings of Agoraki et al. (2011) who argue that as the integration of financial services gets restricted, banks focus on the loan market to replace the forgone non-interest income. However, due to the increased competition, banks with low market power in lending may view the financing of risky borrowers as the only way to attract customers and increase their market share. Overall, it seems that ignoring the interactions between regulations and market power will lead to erroneous inference about the impact of regulations on both risk and stability of banks. Sixth, this study takes into account the analysis based on a subsample of banks with different level of capitalization, liquidity, and size before, during and after the global financial crisis (see Table 16). Table 16 shows that our findings remain similar to the main results that reported in Tables 10–14. 6. Conclusions In this paper, we examine the association between the competition and bank risk-taking behavior and stability using both bank risk-taking behavior (SDROA, SDROE, NPL, and LLP) and bank stability (ZROA and ZROE) measures. We use data of 70 commercial banks from 6 GCC economies over the 1998–2016 periods. We incorporate both the competition (Boone indicator and Lerner index) and the concentration (HHI and CR5) measures in 4 separate models for each dependent variable to determine their impact on bank risk-taking behavior and stability. We also estimate the impact of market power in the banking industry in the GCC economies during pre and post global financial crisis periods to account for the changes in the market structure on bank risk-taking and stability. Furthermore, we analyze the banks' response to competition based on their characteristics of financial strength (capitalization, liquidity, and size). We also divide the analysis based on a subsample of banks with different level of capitalization, liquidity, and size before, during and after the global financial crisis. To investigate the robustness of the empirical results, we also consider the 43

44

8.305*** (0.281) −0.0018*** (3.39e−05)

(low equity to total assets) 0.687*** 0.682*** (0.00696) (0.00312)

Panel D: Banks with low liquidity (high loans to total assets) L.SDROAt-1 0.572*** 0.519*** 0.480*** (0.00926) (0.00710) (0.00881) Lerner index 1.591*** (0.0931) Boone indicator 6.726*** (0.290) HHI −0.0019*** (7.73e−05)

Panel C: Banks with high liquidity (low loans to total assets) L.SDROAt-1 0.662*** 0.649*** 0.626*** (0.000861) (0.00121) (0.00152) Lerner index −0.511*** (0.0601) Boone indicator −18.80*** (0.0333) HHI 0.0035*** (0.000468) CR5

CR5

HHI

Panel B: Banks with low capitalization L.SDROAt-1 0.782*** (0.00557) Lerner index 1.690*** (0.112) Boone indicator

Panel A: Banks with high capitalization (high equity to total assets) L.SDROAt-1 0.551*** 0.534*** 0.531*** (0.000452) (0.000797) (0.000580) Lerner index −2.651*** (0.0235) Boone indicator −3.718*** (0.0648) HHI 0.00181** (0.000889) CR5

0.477*** (0.0111)

0.0531*** (0.00231)

0.598*** (0.00259)

−0.0310*** (0.00508)

0.546*** (0.0135)

0.0628*** (0.000239)

0.482*** (0.000144)

4

0.434 (1.285) 11.25* (6.716)

1.262*** (0.321) −12.84** (5.450)

0.834*** (0.137) 2.710** (0.917)

1.722*** (0.432) −24.30** (10.82)

1.174** (0.430)

0.541 (1.091)

−18.59* (9.63)

1.214*** (0.356)

3.668** (1.715)

0.783*** (0.105)

−10.96** (4.98)

1.844*** (0.302)

2

−0.00476 (0.00947)

0.622 (1.114)

0.0362* (0.0195)

1.240*** (0.361)

0.00443** (0.00220)

0.776*** (0.133)

0.0756* (0.0409)

1.519*** (0.445)

3

1

3

1

2

During the financial crisis: 2007–2009

Before the financial crisis: 1998–2006

Dependent variable: Bank risk-taking behavior (SDROA)

0.446 (1.197)

0.197* (0.101)

1.196*** (0.321)

0.0972*** (0.0222)

0.806*** (0.0880)

0.325** (0.121)

1.588*** (0.416)

4

0.546*** (0.00330) 4.878*** (1.214)

0.270*** (0.00555) −21.93*** (0.665)

0.275*** (0.0380) 3.262*** (0.487)

0.507*** (0.00563) −25.85*** (1.360)

1

14.66*** (1.437)

0.592*** (0.00169)

−67.79*** (4.130)

0.306*** (0.00535)

17.50*** (3.263)

0.180*** (0.0259)

−53.7*** (5.851)

0.529*** (0.00468)

2

0.574*** (0.00110)

0.0206*** (0.00111)

0.269*** (0.00284)

0.00320*** (0.000641)

0.330*** (0.0140)

0.0409*** (0.00176)

0.472*** (0.00201)

4

(continued on next page)

−0.000653** (0.000300)

0.575*** (0.00132)

0.00059* (0.000318)

0.285*** (0.000380)

−9.65e−05 (0.000158)

0.317*** (0.0149)

0.0064*** (0.00182)

0.488*** (0.000877)

3

After the financial crisis: 2010–2016

Table 16 Estimation results of bank risk-taking behavior equation on subsample of banks with different level of capitalization, liquidity and size before, during and after financial crisis.

A.Y.H. Saif-Alyousfi et al.

North American Journal of Economics and Finance xxx (xxxx) xxx–xxx

45

CR5

HHI

3.296*** (0.0359)

0.568*** (0.000877)

−2.420*** (0.166)

0.448*** (0.00642)

−0.0054*** (0.000524)

0.582*** (0.000807)

0.0011*** (5.64e−05)

0.433*** (0.00356)

−0.0198 (0.161)

1.048* (0.539)

0.00554*** (0.00166)

0.833*** (0.0687)

3

76.3*** (3.70) −0.416*** (0.0284)

(high equity to total assets) 0.0292*** 0.0312*** (0.000202) (1.79e−05)

2

−1.401*** (0.0782)

0.0352*** (4.12e−05)

0.567*** (0.185) 24.41** (10.5)

47.7*** (12.6)

0.533*** (0.118)

2

−0.584* (0.329)

0.124*** (0.0165)

3

1

9.402* (5.76)

1.525*** (0.448)

−5.444*** (2.010)

0.849*** (0.0214)

1

1.537*** (0.404) 23.85** (11.45)

0.895*** (0.0352) −3.480** (1.471)

3

During the financial crisis: 2007–2009 4

−0.0436*** (0.000592)

0.555*** (0.000440)

0.0104*** (0.00199)

0.438*** (0.00572)

−0.0049*** (0.000734)

2

Before the financial crisis: 1998–2006

Dependent variable: Bank stability (ZROA)

Panel A: Banks with high capitalization L.ZROAt-1 0.0321*** (0.000372) Lerner index 79.19*** (0.482) Boone indicator

CR5

HHI

Panel F: Banks with small size L.SDROAt-1 0.588*** (0.000982) Lerner index 2.773*** (0.0370) Boone indicator

CR5

HHI

Panel E: Banks with large size L.SDROAt-1 0.540*** (0.00492) Lerner index −1.662*** (0.0949) Boone indicator

CR5

4

1

3

1

2

During the financial crisis: 2007–2009

Before the financial crisis: 1998–2006

Dependent variable: Bank risk-taking behavior (SDROA)

Table 16 (continued)

−0.813** (0.243)

0.566*** (0.187)

4

−0.236* (0.130)

1.496*** (0.406)

0.0250* (0.0147)

0.865*** (0.0217)

−0.0207* (0.0136)

4

64.7*** (6.363)

0.525*** (0.00507)

−2.929** (1.096)

0.298*** (0.0405)

2

−0.025*** (0.00626)

0.482*** (0.000848)

6.92e−05 (6.35e−05)

0.289*** (0.0229)

3

0.600*** (0.0261) 20.5*** (211.1)

1

3.432** (1.534)

0.584*** (0.0444)

2

−0.818** (0.326)

0.583*** (0.0109)

4

−0.0343*** (0.00172)

0.468*** (0.00150)

0.000788* (0.000449)

0.294*** (0.0183)

−0.000740* (0.000413)

4

(continued on next page)

−0.383*** (0.0672)

0.554*** (0.0100)

3

After the financial crisis: 2010–2016

0.498*** (0.00600) 28.77*** (1.692)

0.244*** (0.0342) −2.119** (0.803)

1

After the financial crisis: 2010–2016

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North American Journal of Economics and Finance xxx (xxxx) xxx–xxx

−21.81*** (6.15) 0.0234*** (0.00160)

(low equity to total assets) 0.0548*** 0.104*** (0.00248) (0.00207)

46

CR5

HHI

Panel E: Banks with large size L.ZROAt-1 0.120*** (0.00332) Lerner index 49.54*** (5.408) Boone indicator

19.71*** (1.119)

0.0480*** (0.00408)

−0.0262*** (0.00207)

0.0866*** (0.00309)

Panel D: Banks with low liquidity (high loans to total assets) L.ZROAt-1 0.0433*** 0.0215*** 0.0350*** (0.00117) (0.000227) (0.000236) Lerner index −54.44*** (2.249) Boone indicator −3.138*** (0.6134) HHI 0.0653*** (0.000858) CR5

Panel C: Banks with high liquidity (low loans to total assets) L.ZROAt-1 0.391*** 0.317*** 0.359*** (0.00424) (0.00209) (0.00635) Lerner index 158.9*** (2.378) Boone indicator 298.8*** (19.07) HHI −0.0525*** (0.00493) CR5

CR5

HHI

4

−0.845*** (0.133)

0.0909*** (0.00357)

2.918*** (0.0567)

0.0350*** (0.000614)

−0.302*** (0.0540)

0.329*** (0.00569)

2.563*** (0.0763)

0.0654*** (0.00194)

1.065** (0.498) 67.54*** (10.1)

0.257*** (0.0661) −53.1* (29.32)

1.022 (0.782) 11.181** (5.108)

0.358* (0.204) −71.89*** (5.44)

25.9* (14.0)

0.830* (0.424)

−18.95* (9.54)

0.268*** (0.0712)

2.328* (1.207)

0.816 (0.810)

−88.021*** (21.0)

0.361* (0.207)

2

−0.782*** (0.114)

0.818* (0.430)

0.194* (0.103)

0.508 (0.492)

−0.355* (0.244)

0.594 (0.571)

1.109** (0.0977)

0.424* (0.256)

3

1

3

1

2

During the financial crisis: 2007–2009

Before the financial crisis: 1998–2006

Dependent variable: Bank stability (ZROA)

Panel B: Banks with low capitalization L.ZROAt-1 0.135*** (0.00253) Lerner index −3.153** (1.52) Boone indicator

Table 16 (continued)

−1.276*** (0.140)

0.847* (0.442)

0.492* (0.293)

0.262*** (0.0623)

−2.383* (1.260)

0.719 (0.695)

1.269** (0.994)

0.349* (0.193)

4

0.220*** (0.0304) 95.5** (46.6)

0.203*** (0.0222) −71.00*** (20.1)

0.401*** (0.00986) 11.009** (4.549)

0.367*** (0.0310) −47.15** (22.4)

1

21.857*** (5.326)

0.126*** (0.0386)

−21.046*** (4.053)

0.0737** (0.0294)

5.559** (2.067)

0.436*** (0.0231)

−19.307*** (3.501)

0.152*** (0.0280)

2

−0.392 (0.813)

0.134*** (0.00900)

1.293*** (0.474)

0.122*** (0.00863)

−0.616** (0.303)

0.391*** (0.0131)

2.788*** (0.588)

0.132*** (0.00836)

4

(continued on next page)

−0.0613* (0.0384)

0.130*** (0.00727)

0.212** (0.0746)

0.117*** (0.00736)

−0.167*** (0.0494)

0.394*** (0.00412)

0.103 (0.0799)

0.134*** (0.00826)

3

After the financial crisis: 2010–2016

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North American Journal of Economics and Finance xxx (xxxx) xxx–xxx

−87.05*** (8.064)

0.0314*** (0.000114)

1.271*** (0.273)

0.0367*** (0.000122)

0.159*** (0.0215)

0.0379*** (3.94e−05)

0.240*** (0.0394) −8.152* (4.681) −28.632** (12.711)

0.254*** (0.0370)

2

0.0306 (0.842)

−0.237 (0.696)

3

2.253** (1.059)

0.289*** (0.0420) −74.34*** (15.86)

4 0.532*** (0.0357)

1

−3.642** (1.822)

0.533*** (0.0454)

2

1.999*** (0.617)

0.508*** (0.0138)

3

After the financial crisis: 2010–2016

2.428*** (0.348)

0.530*** (0.0124)

4

Note: Table reports estimation results for the impact of bank competition on bank risk-taking behavior for banks with different level of capitalization, liquidity and size before, during and after financial crisis. To analyses the data before and after financial crisis periods, the two-step system GMM dynamic panel model by Arellano and Bover (1995) is used, while Least Square Dummy Variable (LSDV) technique is used to analyse the data of the global financial crisis period (2007–2009). The dependent variables are the standard deviation of ROA (SDROA) calculated from a three-period based rolling window, and Z-score index based on ROA (ZROA. Lerner Index, Boone indicator, Herfindahl–Hirschman Index (HHI), and 5-bank concentration ratio (CR5) are the market power indexes. SIZE is the logarithm of total assets. NIR refer to income diversification of banks measured by non-Interest revenue to total revenue. COST is the ratio of operating expenses to total income. EQTA refers to the bank capitalization calculated as the ratio of total equity to total assets. LIQ refer to bank liquidity calculated by the ratio of total loans to total assets. GDP is annual GDP growth rate. INF IS annual inflation rate. Ownership variables: FOREIGN is foreign ownership, GOVERNMENT is government ownership. Regulation variables: CAPRI refer to capital requirements index, SPRI refer to supervisory power index, ACTRI refer to activity restrictions index, MDPI refer to market discipline index. Environmental variables: DEPI refer to deposit insurance, SHPI refer to shareholder protection, CRPI refer to creditor protection, LEEI refer to legal efficiency. L* CAPRI, L* SPRI, L* ACTRI, L* SHPI, L* CRPI, and L* LEEI refers to the interaction between market competition measures with capital requirements index, supervisory power index, activity restrictions index, shareholder protection, creditor protection, and legal efficiency respectively. The Sargan test is the test for over-identifying restrictions in GMM estimation. AB test AR(1) and AR(2) refer to the Arellano–Bond test that average autocovariance in residuals of order 1 resp. of order 2 is 0 (H0: no autocorrelation). The standard errors are in parentheses. *** Significance at the 1% level, ** Significance at the 5% level,* Significance at the 10% level.

CR5

HHI

4

1

3

1

2

During the financial crisis: 2007–2009

Before the financial crisis: 1998–2006

Dependent variable: Bank stability (ZROA)

Panel F: Banks with small size L.ZROAt-1 0.0335*** (0.000200) Lerner index −14.8*** (0.596) Boone indicator

Table 16 (continued)

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North American Journal of Economics and Finance xxx (xxxx) xxx–xxx

47

North American Journal of Economics and Finance xxx (xxxx) xxx–xxx

A.Y.H. Saif-Alyousfi et al.

following modifications of our empirical model. First, we replace the conventional Lerner index by efficiency-adjusted Lerner index and a quadratic term for the Lerner index as measures of competition in the banking market. Second, we consider two variables for ownership, four variables on regulations and four variables for environmental (institutional) developments, including the existence of deposit insurance. Third, the interaction between market power and regulatory and environmental variables are also included. Our empirical results indicate that both Lerner index and Boone indicator have a negative and significant effect on all bank risktaking (SDROA, SDROA, NPL, and LLP) and significant positive effect on bank stability (ZROA and ZROA). These imply that a higher degree of market power/ and lower competition in the banking market decreases the bank risk-taking behavior and improves bank stability. However, we find that bank concentration (HHI and CR5) is positively and significantly associated with bank risk-taking behavior and negatively and significantly related to bank stability. Our results suggest that banks in a higher concentrated market take greater risk and less stable. Overall, our results indicate that banks are less risky and more stable in a less competitive market with greater market power and in a market with lower concentration. We also find that greater market power/lower competition in the banking market contribute to the reduction of moral hazard and maintain the stability of banks during the 2008 global financial crisis that has affected GCC banking sectors. We find that higher market power, lower level of competition and lower concentration in the banking market increase the bank risk-taking and decrease the stability of low capitalized, low liquid and small banks in contrast to the highly capitalized, highly liquid and large banks. In sum, our analyses not only highlight that the association of bank competition measures with individual risk-taking are negative and significant but also show that bank concentration measures are positively and significantly associated with bank fragility. Furthermore, bank competition measures are positively and significantly related to bank stability while concentration measures are negatively associated with it. Our findings support both the theories of competition-fragility and competition-stability in the context of the GCC banking markets. Our results suggest that consistent with the traditional “competition-fragility” view, banks with a higher degree of market power also have less overall risk exposure. However, the data also provides some support for of the “competitionstability” view – that market power does increase loan risk in these countries. This risk may be offset in part by higher equity capital ratios. We confirm with robustness tests that our results for regulatory quality and environmental (institutional) developments are independent of differences in risk and stability. Our results also confirm that the use of a single measure of competition (e.g., bank concentration) is insufficient to identify its role because it might give misleading results. Our findings highlight various policy implications in the GCC economies: First, policy planners may encourage mergers between banks with low capitalization, low liquidity, and small size with banks of medium capitalization, medium liquidity, and medium-size banks to improve stability. However, regulators should simultaneously be watchful while evaluating and approving merger proposals to prevent excessive concentration. Second, they should foster the development of sound credit culture in banks to avoid exuberance in their lending behavior resulting in banking instability arising out of shocks like the global financial crisis. Third, regulators in these economies may like to foster financial innovation with the corresponding strengthening of the risk management culture and architecture in banks to improve the efficiency of resource allocation within an economy. Fourth, the banking supervision mechanism may be made more robust to pre-empt excessive risk-taking by banks. Fifth, our results suggest that banking sector in these countries, with explicit deposit insurance, are more stable. However, international experience also indicates that deposit insurance schemes appear to increase moral hazard and risk shifting behavior so any policy moves to increase coverage should be treated with caution as this could have the unintended consequence of boosting risk as opposed to promoting stability. Sixth, our findings suggest that the banking market in GCC has yet to attain appropriate sophistication in competitive structure to face the onslaught of the forces of banking competition in the era of globalization. Banks in these economies are found to be more stable and less risky when the environment is less competitive. A policy framework for the careful surveillance of developments like international financial integration, free entry of foreign banks, privatization and deregulation, and banking consolidations that affect the competitive conditions in the banking industry needs to be in place. Competition policies may act as a catalyst to support financial stability and create the appropriate conditions for stable financial markets. The global financial crisis has stressed the importance of reconsidering the role of competition policy and competition agencies in these markets. Our findings underline the importance of regulatory policies and market structure for stability. Apart from a direct effect of these policies and market structure on the risk-taking incentives of banks, they also have an indirect effect by dampening or exacerbating the effect of competition on banks’ riskiness. In effect, a deeper insight into the competition policies in the banking sector in the GCC economies is the need of the hour. Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi.org/10.1016/j.najef.2018.10.015. References Agoraki, M. K., Delis, M. D., & Pasiouras, F. (2011). Regulations, competition and bank risk-taking in transition countries. Journal of Financial Stability, 7(1), 38–48. Allen, F., & Gale, D. (2004). Competition and financial stability. Journal of Money, Credit and Banking, 36(3), 453–480. Al-muharrami, S., Matthews, K., & Khabari, Y. (2006). Market structure and competitive conditions in the Arab GCC banking system. Journal of Banking & Finance, 30(30), 3487–3501. Anginer, D., Demirguc-Kunt, A., & Zhu, M. (2014). How does competition affect bank systemic risk? Journal of Financial Intermediation, 23(1), 1–26. Anzoategui, D., Pería, M. S. M., & Melecky, M. (2012). Bank competition in Russia: An examination at different levels of aggregation. Emerging Markets Review, 13(1), 42–57. Apergis, N. (2015). Competition in the banking sector: New evidence from a panel of emerging market economies and the financial crisis. Emerging Markets Review, 25, 154–162. Arellano, M., & Bond, S. (1991). Some tests of specification for panel Carlo application to data: Evidence and an employment equations. The Review of Economic Studies,

48

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A.Y.H. Saif-Alyousfi et al.

58(2), 277–297. Arellano, M., & Bover, O. (1995). Another look at the instrumental variable estimation of error-components models. Journal of Econometrics, 68(1), 29–51. Ariss, R. T. (2010). On the implications of market power in banking: Evidence from developing countries. Journal of Banking and Finance, 34(4), 765–775. Ashraf, D., Ramady, M., & Albinali, K. (2016). Financial fragility of banks, ownership structure and income diversification: Empirical evidence from the GCC region. Research in International Business and Finance, 38(4), 56–68. Baltagi, B. (2008). Econometric analysis of panel data. John Wiley & Sons. Barth, J. R., Caprio, G., & Levine, R. (2001a). Banking systems around the globe: Do regulation and ownership affect performance and stability? In F. S. Mishkin (Ed.). Prudential supervision: What works and what doesn’t. University of Chicago Press. Barth, J. R., Caprio, G., & Levine, R. (2001b). The regulation and supervision of banks around the world: A new database. In R. E. Litan, & R. Herring (Eds.). Integrating emerging market countries into the global financial system (pp. 183–240). Brookings-Wharton Papers in Financial Services, Brooking Institution Press. Barth, J. R., Caprio, G., & Levine, R. (2006). Rethinking bank regulation: Till angels govern. Cambridge University Press. Barth, J. R., Caprio, G., & Levine, R. (2008). Bank regulations are changing: But for better or worse? Comparative Economic Studies, 50(4), 537–563. Beck, T., Demirgu-Kunt, A., & Levine, R. (2006). Bank concentration, competition, and crises: First results. Journal of Banking and Finance, 30(5), 1581–1603. Beck, T., Jonghe, O. De, & Schepens, G. (2013). Bank competition and stability: Cross-country heterogeneity. Journal of Financial Intermediation, 22(2), 218–244. Berger, A. N., Klapper, L. F., & Turk-Ariss, R. (2009). Bank competition and financial stability. Journal of Financial Services Research, 35(2), 99–118. Blundell, R., & Bond, S. (1998). Initial conditions and moment restrictions in dynamic panel data models. Journal of Econometrics, 87(1), 115–143. Blundell, R., & Bond, S. (2000). GMM estimation with persistent panel data: An application to production functions. Econometric Reviews, 19(3), 321–340. Bond, S. R. (2002). Dynamic panel data models: A guide to micro data methods and practice. Portuguese Economic Journal, 1(2), 141–162. Boone, J. (2008). A new way to measure competition. The Economic Journal, 118(531), 1245–1261. Boyd, J., Nicoló, G. De, & Jalal, A. M. (2010). Bank competition, asset allocations and risk of failure: An empirical investigation. CESifo Working Paper, (No. 3198). Boyd, J. H., & Nicoló, G. De. (2005). The theory of bank risk taking and competition revisited. The Journal of Finance, 60(3), 1329–1343. Boyd, J., Nicolò, G. De, & Jalal, A. Al. (2006). Bank risk taking and competition revisited: New theory and new evidence. International Monetary Fund, 6(297). Boyd, J., Nicoló, G. De, & Jalal, A. M. (2009). Bank competition, risk and asset allocations. International Monetary Fund, No, 9–143. Broecker, T. (1990). Credit-worthiness tests and interbank competition. Journal of the Econometric Society, 2, 429–452. Brownbridge, M. (1998). The causes of financial distress in local banks in Africa and implications for prudential policy. UNCTAD Discussion Papers, 132. Caminal, R., & Matutes, C. (2002). Market power and banking failures. International Journal of Industrial Organization, 20(9), 1341–1361. Chan, Y.-S., & Greenbaum, S. I. (1986). Information reusability, competition and bank asset quality. Journal of Banking & Finance, 10(2), 243–253. Chen, S.-H., & Liao, C.-C. (2011). Are foreign banks more profitable than domestic banks? Home- and host-country effects of banking market structure, governance, and supervision. Journal of Banking & Finance, 35(4), 819–839. Cihák, M., Demirgüc-Kunt, A., Martínez Soledad, M., & Mohseni-Cheraghlou, A. (2012). Bank regulation and supervision around the world: a crisis update. World Bank Policy Research Working Papers, 6286(12), 410–425. Claessens, S., & Laeven, L. (2004). What drives bank competition? Some international evidence. Journal of Money, Credit and Banking, 36(3), 563–583. Crowley, J. (2008). Credit growth in the Middle East, North Africa, and Central Asia Region. IMF Working Papers, (WP/08/184), 1–60. Demirgüç-kunt, A., Karacaovali, B., & Laeven, L. (2005). Deposit insurance around the world : A comprehensive database. World Bank Policy Research Working Paper No. 3628, 1(1). Demsetz, R. S., Saidenberg, M. R., & Strahan, P. (1996). Banks with something to lose: The disciplinary role of franchise value. Economic Policy Review, 2(2), 1–14. Dermine, J. (1984). Deposit rates, credit rates and bank capital: The Klein-Monti model revisited. Journal of Banking and Finance, 10(1), 99–114. Fang, Y., Hasan, I., & Marton, K. (2011). Market reforms, legal changes and bank risk-taking – Evidence from transition economies. Bank of Finland Research Discussion (Papers 7). Fiordelisi, F., & Mare, D. S. (2014). Competition and financial stability in European cooperative banks. Journal of International Money and Finance, 45, 1–16. Fu (Maggie), X., Lin (Rebecca), Y., & Molyneux, P. (2014). Bank competition and financial stability in Asia Pacific. Journal of Banking & Finance, 38, 64–77. Goodhart Charles A. E. (2011) Not far enough: recommendations of the UK’s Independent Commission on Banking. Hall, A. R. (2005). Generalized method of moments. Oxford University Press. Hellmann, T. F., Murdock, K. C., & Stiglitz, J. E. (2000). Liberalization, moral hazard in banking, and prudential regulation: Are capital requirements enough? American Economic Review, 90(1), 147–165. Jiménez, G., Lopez, J. A., & Saurina, J. (2013). How does competition affect bank risk-taking? Journal of Financial Stability, 9(2), 185–195. Kammer, A. (2013). United Arab Emirates selected issues and statistical appendix. International Monetary Fund, 27(13), 1–56. Kasman, S., & Kasman, A. (2015). Bank competition, concentration and financial stability in the Turkish banking industry. Economic Systems, 39(3), 502–517. Keeley, M. C. (1990). Deposit insurance, risk, and market power in banking. The American Economic Review, 80(5), 1183–1200. Khan, H. H., Ahmed, R. B., & Gee, C. S. (2016). Bank competition and monetary policy transmission through the bank lending channel: Evidence from ASEAN. International Review of Economics and Finance, 44, 19–39. La Porta, R., Lopez-de-Silanes, F., Shleifer, A., & Vishny, R. W. (1998). Law and finance. Journal of Political Economy, 106(6), 1113–1155. Lee, C.-C., & Hsieh, M.-F. (2013). The impact of bank capital on profitability and risk in Asian banking. Journal of International Money and Finance, 32(2), 251–281. Lee, C.-F., Liang, W., Lin, F.-L., & Yang, Y. (2015). Applications of simultaneous equations in finance research: Method and empirical results. Review of Quantitative Finance and Accounting, 47(4), 943–971. Lee, C., Yang, S., & Chang, C. (2014). Non-interest income, profitability, and risk in banking industry: A cross-country analysis. North American Journal of Economics and Finance, 27, 48–67. Liu, H., Molyneux, P., & Nguyen, L. H. (2012). Competition and risk in the South East Asian commercial. Applied Economics, 44(28), 3627–3644. Maghyereh, A. I., & Awartani, B. (2014). The effect of market structure, regulation, and risk on banks efficiency: Evidence from the Gulf cooperation council countries. Journal of Economic Studies, 41(3), 405–430. Marcus, A. J. (1984). Deregulation and bank financial policy. Journal of Banking and Finance, 8(4), 557–565. Martinez-miera, D., & Repullo, R. (2010). Does competition reduce the risk of bank failure? The Review of Financial Studies, 23(10), 3638–3664. Matutes, C., & Vives, X. (1996). Competition for deposits, fragility, and insurance. Journal of Financial Intermediation, 5(2), 184–216. Matutes, C., & Vives, X. (2000). Imperfect competition, risk taking, and regulation in banking. European Economic Review, 44(1), 1–34. Mirzaei, A., Liu, G., & Moore, T. (2013). Does market structure matter on banks’ profitability and stability? Emerging versus advanced economies. Journal of Banking & Finance, 37(8), 2920–2937. Mishkin, F. S. (1999). Financial consolidation: Dangers and opportunities. Journal of Banking & Finance, 23(2), 675–691. Nicolò, G. De, & Loukoianova, E. (2007). Bank ownership, market structure and risk. International Monetary Fund, 7(215), 1–44. OECD, 2011. Bank competition and financial stability. < http://www.oecd.org > . Panzar. Repullo, R. (2004). Capital requirements, market power, and risk-taking in banking. Journal of Financial Intermediation, 13(2), 156–182. Rhoades, S. A., & Eutz, R. D. (1982). Market power and firm risk: A test of the ‘quiet life’ hypothesis. Journal of Monetary Economics, 9(1), 73–85. Saif-Alyousfi, A. Y. H., Saha, A., & Md-Rus, R. (2018). Impact of oil and gas price shocks on the non-performing loans of banks in an oil and gas-rich economy: Evidence from Qatar. International Journal of Bank Marketing, 36(3), 529–556. Sarafidis, V., Yamagata, T., & Robertson, D. (2009). A test of cross section dependence for a linear dynamic panel model with regressors. Journal of Econometrics, 148(2), 149–161. Schaeck, K., & Cihak, M. (2014). Competition, efficiency, and stability. Financial Management, 43(1), 215–241. Schaeck, K., Cihak, M., & Wolfe, S. (2009). Are competitive banking systems more stable? Journal of Money, Credit and Banking, 4(41), 711–734. Soedarmono, W., Machrouh, F., & Tarazi, A. (2011). Bank market power, economic growth and financial stability: Evidence from Asian banks. Journal of Asian Economics, 22(6), 460–470. Soedarmono, W., Machrouh, F., & Tarazi, A. (2013). Bank competition, crisis and risk taking: Evidence from emerging markets in Asia. Journal of International Financial Markets, Institutions and Money, 23, 196–221. Tabak, B. M., Gomes, G. M. R., & Medeiros, M. (2015). The impact of market power at bank level in risk-taking: The Brazilian case. International Review of Financial Analysis, 40, 154–165.

49

North American Journal of Economics and Finance xxx (xxxx) xxx–xxx

A.Y.H. Saif-Alyousfi et al.

Tan, Y. (2016). The impacts of risk and competition on bank profitability in China. Journal of International Financial Markets, Institutions and Money, 40, 85–110. Tan, Y. (2017). The impacts of competition and shadow banking on profitability: Evidence from the Chinese banking industry. North American Journal of Economics and Finance, 42, 89–106. Uhde, A., & Heimeshoff, U. (2009). Consolidation in banking and financial stability in Europe: Empirical evidence. Journal of Banking and Finance, 33(7), 1299–1311. van Leuvensteijn, M., Bikker, J. A., van Rixtel, A. A. R. J. M., & Sørensen, C. K. (2011). A new approach to measuring competition in the loan markets of the euro area. Applied Economics, 43(1), 3155–3167. Windmeijer, F. (2005). A finite sample correction for the variance of linear efficient two-step GMM estimators. Journal of Econometrics, 126(1), 25–51. Wu, J., Chen, M., Jeon, B. N., & Wang, R. (2017). Does foreign bank penetration affect the risk of domestic banks? Evidence from emerging economies. Journal of Financial Stability, 31, 45–61. Yeyati, E. L., & Micco, A. (2007). Concentration and foreign penetration in Latin American banking sectors: Impact on competition and risk. Journal of Banking and Finance, 31(6), 1633–1647. Zhao, T., Casu, B., & Ferrari, A. (2010). The impact of regulatory reforms on cost structure, ownership and competition in Indian banking. Journal of Banking and Finance, 34(1), 246–254.

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