Capital requirements, the cost of financial intermediation and bank risk-taking: Empirical evidence from Bangladesh

Accepted Manuscript Title: Capital Requirements, the Cost of Financial Intermediation and Risk-Taking: Empirical Evidence from Bangladesh Authors: Mohammed Mizanur Rahmana, Changjun Zheng, Badar Nadeem Ashraf, Mohammad Morshedur Rahman PII: DOI: Reference:

S0275-5319(17)30312-4 http://dx.doi.org/doi:10.1016/j.ribaf.2017.07.119 RIBAF 809

To appear in:

Research in International Business and Finance

Received date: Accepted date:

6-5-2017 5-7-2017

Please cite this article as: Rahmana, Mohammed Mizanur, Zheng, Changjun, Ashraf, Badar Nadeem, Rahman, Mohammad Morshedur, Capital Requirements, the Cost of Financial Intermediation and Risk-Taking: Empirical Evidence from Bangladesh.Research in International Business and Finance http://dx.doi.org/10.1016/j.ribaf.2017.07.119 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Title: Capital Requirements, the Cost of Financial Intermediation and Risk-Taking: Empirical Evidence from Bangladesh Mohammed Mizanur Rahmana*, Changjun Zhenga, Badar Nadeem Ashrafb, and Mohammad Morshedur Rahmanc a

School of Management, Huazhong University of Science and Technology, Wuhan (430074), Hubei, China b International School, East China Jiao Tong University, Nanchang (330013), Jiangxi, China c School of Business, Department of AIS, University of Chittagong, Chittagong, Bangladesh

*Corresponding author. E-mail: [email protected], [email protected], Address: Room#530, School of Management, Huazhong University of Science and Technology, Wuhan, Hubei, P.R.China. Phone number: 0086-13125100498.

Abstract Using two-step Generalized Methods of Moments (GMM) estimation, we investigate the effects of capital regulations on the cost of financial intermediation and banks’ risk-taking by employing a panel data of 32 Bangladeshi commercial banks over the period of 2000 to 2014. Higher capital adequacy ratio requires more regulatory capital in banks. The result is a higher the cost of intermediation and lower banks’ risktaking. The results hold when we use equity to total assets ratio as an alternative measure of bank capital. We also observe that switching from BASEL I to BASEL II has no measurable impact on the cost of financial intermediation and bank risk-taking in Bangladesh. Findings also reveal that some factors have reduced the cost of intermediation of banks: an increase in management efficiency, reserve and income diversification and a reduction in financial intermediation. Surprisingly, banking monopoly and GDP growth has no measurable impact on the cost of intermediation and risk-taking. Results also show that, increase risk-weighted assets and liabilities in the assets structure enhance the risk-seeking attitude of the banks’. Finally, the results support the Central Bank’s initiative to enforce capital regulations to ensure the stability and competitiveness of the banking sector in Bangladesh. JEL Classifications: G21, G32, C23 Keywords: Cost of intermediation; risk-taking; capital regulation; GMM estimation

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1. Introduction In this paper, we empirically examine the impact of capital requirements ratios on bank cost of intermediation and risk taking using a rich dataset of listed commercial banks of Bangladesh. To reveal these, two questions need to be addressed: Does cost of intermediation1 vary with the regulatory capital requirement? Do capital regulations have a substantial impact on banks’ risk-taking behavior? Our specific goal is to answer these two questions. In this context, capital regulations have a significant key role to make this sector more competitive through reducing the intermediation cost with lower bank risk-taking. Nowadays, banking capital requirement set with according to the Basel Accord guidelines. Setup of the minimum regulatory capital and liquidity, which are required by the banks for recovering unexpected losses, is the primary objective of the Basel. Basel regulation was firstly introduced in 1988 by the Basel Committee on Banking Supervision (BCBS). Basel-I specifically focused on banks credit risk and set of minimum capital requirements to the bank assets portfolio risk. Later it has been modified several times with situation demanded. For example, in 1996 Basel-I Accord was revised to incorporate the premium for bank market risk into risk-based capital requirements. In 2004, BCBS’s introduced Basel-II Accord to overcome the shortfalls of Basel-1 mainly to integrate the operational risk into regulatory restriction structure in addition to credit and market risks. Likewise, Basel-II also integrates supervisory review and market discipline instruments. Most of the world economy has been applied Basel II regulatory standard for their banking sectors, and it became a complete set of guideline after global financial crisis smash in 2007-2008. Anyway, Basel I and II is not free of criticism, it have been largely criticized on two grounds: first, there is the probability of regulatory capital arbitrage through financial innovations such as securitization (Jones, 2000); and secondly, regulatory restrictions are pro-cyclical and can increase the intensity of economic cycles(Jokipii and Milne, 2011). Recent crisis and aftermath debates regarding how to ensure banking system stability, capital regulation still play a focusing role. Later in September 2010, the Basel Committee on Banking Supervision declared the Basel-III Accord. Basel III is a complete package of regulatory guidelines of banking sector all over the world. Updated Accord assimilated both the quantity and the quality of regulatory capital. Since 1996, the Bangladesh Bank (BB)2 has adopted regulatory capital restrictions for Bangladeshi banks in line with the directions of Basel Accords. BB has revised regulatory capital restrictions time-to-time to update it according to the amendments in Basel Accords. In this direction, BB has recently provided a road map to implement Basel III capital accord (BRPD Circular No. 7, March 2014) to make this sector more competitive as well as to increase the credibility worldwide. Prior literature argued that the capital regulation is expected to reduce the probability of future banking crises (Angelini et al., 2015), even though the regulation is not free of criticisms. Thus the nexus between capital regulation, the cost of bank credit and risk-taking is under severe argument. The supporters argue that new regulation would have a substantial positive impact on banks interest margin. Corporate finance theory of capital structure suggests that bank equity is an expensive source of funding and a percentage increase in equity increases the overall weighted average cost of capital 1

Bernanke (1983) defines the cost of intermediation as the cost of channeling funds from the ultimate savers/lenders into the hand of good borrowers, which includes screening, monitoring, accounting costs, and expected losses by bad borrowers. 2 Central Bank of Bangladesh Page 2 of 33

(WACC) for the banks. Thus, this cost ultimately bear by the borrowers that turn into higher interest rates on loans. Based on this theory, if all is equal, the higher capital ratio would translate into increased cost of intermediation. For example, (IIF, 2011) represents over 400 financial institutions across the world projected that the price of credit in the United States would be almost 5 percentage points higher as a result of the regulatory changes proposed by Basel III. Miles et al. (2013) find that the changes in capital may affect financial activity through their effect on the cost of financial intermediation. Similarly, an increase in capital requirements involves banks to substitute equity for deposit financing, reducing into shareholder’s surplus. The reduction in surpluses enhances the likelihood of loss, imposing a rise in the cost of intermediation to maintain profitability. In the line of this hypothesis is the empirical indication showing a significant impact on interest margins in response to higher capital holdings and the share of total assets seized by banks(Naceur and Kandil, 2009). The above channels of debate justify our findings. The opponents argue that the impact of new regulation would be small. These groups of debate consider another phase of capital structure theory, the bankruptcy costs, and argue that shareholders may require a lower return on equity for investing well-capitalized banks(Baker and Wurgler, 2015), since bettercapitalized banks are less likely to default, due to which shareholders may decrease required return on equity. With more capital, banks should at least in principle become safer; therefore, the cost of funding could lessen as a consequence of greater capital levels. We can also explain the impact of higher capital on the cost of intermediation with capital buffers. Banks usually hold more capital than the minimum level, and a further increase of capital would not affect by the banks due to capital buffers. Prior literature also argues that the banks could respond to a tightening in capital requirements by partially cutting their capital buffers (Slovik and Cournède, 2011). There is another reason to prove that the higher capital requirements would have fewer effects on the cost of intermediation as banks would keep higher capital to get better credit ratings and a good share price in the stock market. Based on this debate, we raise the question ‘how the implementation of stringent capital requirements has affected the cost of financial intermediation in Bangladesh?’ Likewise, the existing literature predicts that the effect of increased capital requirements on bank risktaking is controversial.VanHoose (2007) argue that banks could reduce their asset portfolio risk to response higher risk-based capital requirements. However, another aspect of studies proposes that banks could induce assets portfolio risk to balance the adverse effect of stringent capital requirements on bank leverage and profitability.Gonzalez (2005) argue that a more relax regulatory system may create either a less stable or a more stable banking systems. Relaxing borders on banking activities may inspire bank risk-taking by growing a bank’s range of activities. However, relaxing restrictions may also explore opportunities for bank diversification, and thereby reduce risk-taking. Based on these two contrary options, interested groups (i.e., regulators, bankers, and academicians) would want to examine bank risktaking incentives because the ultimate consequence of regulatory restrictions is under severe debate. Another concern with this empirical literature is that it mostly has been conducted on developed countries’ banking sectors. Further, many troubled banks during the global financial crisis were conforming to minimum capital requirements shortly before and even during the crisis (Demirgüç-Kunt and Detragiache, 2011) that also raise questions about the effectiveness of stringent capital requirements. Based on above background, it is problematic to assume the impact of the regulatory restrictions on bank risk-taking in Bangladesh and it is an open empirical question that deserves attention. Moreover, due to

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fewer chances of financial innovation in the underdevelopment capital market like Bangladesh, the capital regulation would have a more visible effect on bank risk-taking. Thus, our second research question is ‘what is the impact of stringent capital requirements on the bank risk-taking in Bangladesh?’ To answer these two issues, we use a panel dataset of 32 Bangladeshi banks over the period from 2000 to 2014. By employing a dynamic panel generalized method of moments (GMM) estimator, we find robust evidence that higher regulatory capital ratios induce the cost of financial intermediation and reduce banks risk-taking. We apply several robustness tests to confirm these results. This study contributes to the literature in at least five ways: First, this study is the first that examines the impact of capital regulation on the banks’ cost of financial intermediation and risk-taking. The banking sector of Bangladesh has undertaken through several capital regulation reforms in the last two decades and is an ideal laboratory to inspect our hypothesis. The consistent growth of last decades deserve special attention as an emerging economy and findings reported here can be generalized to other developing and emerging economies with the similar economic condition. Moreover, we select Bangladesh as a geographical sample because in our 15 years sample period consists the reflection of Basel II (2007) and Basel III (2014) implementation as a whole. Second, this study examines the impact of capital requirements on the cost of financial intermediation and complements the recent research. Alonside, Naceur and Kandil (2009) discusses the Egyptian bank and find a positive association, Soedarmono and Tarazi (2013) consider publicly traded banks in Asia also find the positive association and Maudos and Solís (2009) examine Mexican banks find a positive relationship between equity capital ratio and net interest margin. Besides, Afzal and Mirza (2012) consider the Pakistani banks find a negative correlation with contrary of our findings. Third, we examine the impact of capital regulation on bank risk-taking and complement the studies such(Ashraf et al., 2016) examine Pakistani banks find the negative association and (Lee and Hsieh, 2013) consider the Asian banking included Bangladesh find a negative relationship between capital regulation and risk-taking. Besides, Laeven and Levine (2009) study 48 countries including 296 banks found a positive association in this direction. Fourth, most of the previous studies focus mainly on the relationship between capital and risk, yet rarely on the relationship between capital and cost of intermediation. This paper examines capital, risk, and cost of intermediation simultaneously. Last but not least, from the methodological view, this study is the complete set of estimation technique including high power two-step GMM estimation technique and simple OLS estimation technique. Regressions results prove that data also seems to warrant it. The remainder of the paper is structured as follows. Section 2 reviews the theoretical also empirical literature dealing with the effects of capital requirements on bank’s cost of intermediation and risk, Section 3 present scenario of capital regulation in Bangladesh, Section 4 the sample, models, and methodology, Results of

regression analysis and robust are discussed in section 5, and finally, conclusions and policy directives for developing countries are presented in section 6. Page 4 of 33

2. Review of Related Papers and Hypothesis Nowadays, in the banking sector, there are some questions a buzz word. Can regulatory capital requirement prevent the bank from taking excessive risk? Does higher level of capital structure ensure the lower cost of intermediation? Answer of these questions helps the policy maker and potential investors to stay on the right track. The prior literature suggests that financial systems in developing countries typically show significantly higher and persistent interest rates spreads compare to develop countries (Barajas et al., 1999, Chirwa and Mlachila, 2004, Hesse, 2007). They argued that’s happened because of two reasons: either financial underdevelopment or institutional backwardness. Naceur and Kandil (2009) Consider the data from 1989-2004 with 28 banks from Egypt using GMM and panel data estimators find that higher capital adequacy requirements increase the cost of intermediation because the excessive regulation may influence the spread positively in the line with our findings. Also, suggest that higher net interest margin deemed to reflect higher profitability in banking instead of higher intermediation cost charged to borrowers. A review of the literature regarding bank capital regulation in contemporary banking theory (Santos, 2001) found that the net interest margin and overhead cost may increase with the result of increasing regulatory pressure on bank activities. The excessive capital requirement is pressure on bank shareholders and managers to make necessary capital intact; to make that prescribed reserve money the bank should enhance their cost of lending. As consequence of the increasing credit cost, the cost of intermediation will boost up. When an increase in the cost of intermediation is followed by a decrease in bank’s earnings or profitability(Soedarmono et al., 2010). For instance, an increase in the cost of intermediation may due to an increase in monitoring costs borne by managers who dominate banks3. Therefore, to measure the bank inefficiency cost of intermediation is an important tool(Demirgüç-Kunt and Huizinga, 1999, DemirgucKunt et al., 2003). Poghosyan (2013) conclude using bank-level evidence on 359 commercial banks in 48 LICs and 2535 commercial banks in 67 EMs for the period 1996–2010 and their empirical findings is higher intermediation costs exist low-income countries (LICs) about emerging market (EM) country. Since the regulatory capital requirement and banking competition are relatively elevated in the emerging market economy rather than lower income countries in the world. By the way, Soedarmono and Tarazi (2015) use competition-stability hypothesis and found higher bank competition leads to higher economic growth because banks with greater market power willing to charge higher loan prices which may increase the cost of financial intermediation (Degryse and Ongena, 2005). Thus enhancing the regulatory requirement is to create level playing field for all competitors ensures the reduction of bank risk-taking behavior and strengthens financial intermediation. Using (Ho and Saunders, 1981) dealership model adding with some macro, industry and bank specific variables in four South Asian countries (Bangladesh, India, Nepal and Pakistan) (Islam and Nishiyama, 2015) found that net interest margins have a positive relationship with liquidity and equity positions. Besides, required reserve and operating expenses to total asset ratios, the relative size of the banks, market power, and economic growth affect inversely. But in their paper, they consider net interest income over total asset is rather earning asset as the variable of NIM. Net interest income over earning asset is the more careful NIM calculation (Naceur and Kandil, 2009) that we have consider. 3

Coleman et al. (2006) consider that banks with superior monitoring efforts are able to charge a higher cost of intermediation.Chen et al. (2000) also highlight the positive link between monitoring activities and loan spreads in the U.S. branches of Japanese banks. Page 5 of 33

Not all researchers agree that capital regulation has had significant effects on bank lending. Jackson et al. (1999) analyze some prior literature and investigate how capital adequacy rules influence actual capital ratios; for instance (Jacques and Nigro, 1997, Rime, 2001, Shrieves and Dahl, 1992). In the case of strict capital requirement banks mainly response to reducing lending and that there is little conclusive evidence that capital regulation has influence banks to maintain higher capital-to-assets ratios than they otherwise would choose if unregulated(Jackson et al., 1999). Based on the above debate we develop the following hypothesis. H11: Capital regulation has a positive significance effect on the cost of intermediation. A lot of studies has found on that dimension like capital regulations and risk-taking. The regulatory capital requirement discourages banks from taking excessive risk to become safe from insolvency (Homölle, 2004). For proper application of the regulatory guideline by the regulator, the verification of the relationship between capital regulation and risk is one of the important issues at this moment (Lee and Hsieh, 2013). It is deemed that higher capital requirement has a favorable impact on risk of the banking industry (Lee and Chih, 2013), but the empirical findings are mixed. Some studies show that there is a positive relationship between risk and capital (Altunbas et al., 2007, Blum, 1999, Gonzalez, 2005, Jokipii and Milne, 2011, Laeven and Levine, 2009, Pettway, 1976, Rime, 2001), which refers to “regulatory hypothesis.” On the contrary, some studies find a negative relationship between risk and capital (Agoraki et al., 2011, Deelchand and Padgett, 2009, Jacques and Nigro, 1997, Jahankhani and Lynge, 1979, Lee and Hsieh, 2013, Lee and Chih, 2013) which refers to the “moral hazard hypothesis”. Mixed results also found in some studies, for instance: (Calem and Rob, 1999, Iwatsubo, 2007, Aggarwal and Jacques, 1998). Some studies show no relation, for example: (Aggarwal and Jacques, 2001, Hussain and Hassan, 2005, Guidara et al., 2013). Van Roy (2008) found weakly capitalized banks making a quick response to capital regulation, while capital regulation did not change the behavior of well capitalized U.S. banks. Market discipline is the valuable tool for capital build-up. Their study was based on G-10 countries after Basel 1 implication in 1988. Bougatef and Mgadmi (2016) reveals that prudential regulation has no significant impact on bank risk-taking behavior considering 24 banks operating in MINA countries. Thus the justification of capital regulation impact on risk in banking industry positive, negative and no relation have been found. Thus our hypothesis possesses the following. H12: There is a significant negative relation between capital regulation and risk

3. Capital Regulation in Bangladesh Basel regulation was introduced firstly in Bangladesh1996 (globally 1988). That’s primary task to ensure the two principle aims: Banks have an adequate level of capital and creating an equal playing field in competitive perspective. Due to some limitation of Basel I, later in 2007 Basel II was adopted in Bangladeshi Banking sector which was released by the Basel Committee in 2004 and should be implemented from year end 2006. At present all banks are operated in Bangladesh according to Basel II regulations, (Circular No. 14 of BRPD) was made on December 30, 2007, about the roadmap and action plan about Basel II in Bangladesh. In Table-1 shows four types of banks operated in Bangladesh, State-

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owned Commercial Banks (SCBs), State-owned Development Financial Institutions (DFIs), Private Commercial Banks (PCBs), and Foreign Commercial Banks (FCBs).

Addressing Bangladesh Bank annual report, we comprehend that on 31 December 2014 in aggregate, the State-owned Commercial Banks (SCBs), Development Financing Institutions (DFIs), Private Commercial Banks (PCBs) and Foreign Commercial Banks (FCBs) maintained CAR of 8.3, -17.3, 12.50, and 22.60 percent respectively. But only 6 banks; individually, 2 SCBs, 2 PCBs, and 2 DFIs did not uphold the minimum required CAR. Considering our sample banks, above figure-1 the CAR of the banking industry was 12.13 percent at the end of December 2014 as against 11.58 percent of 2013. The principal reason for an upsurge in CAR in 2014 was the enactment of newly revised policy on loan rescheduling (BRPD Circular no.15/2013). Moreover, Bangladesh Bank provides instruction will be adopted in a phased manner starting from the January 2015, with full implementation of capital ratios from the beginning of 2019. As per Table 2 below, Common Equity Tier 1 of at least 4.5% of the total RWA, Tier 1 capital will be at least 6.0% of the total RWA, Minimum CAR of 10% of the total RWA, Additional Tier 1 capital can be admitted maximum up to 1.5% of the total RWA or 33.33% of Common Equity Tier 1(CET1), whichever is higher. Tier 2 Capitals can be admitted maximum up to 4.0% of the total RWA or 88.89% of CET1, whichever is greater. In addition to minimum CAR, Capital Conservation Buffer (CCB) of 2.5% of the total RWA is being introduced which will be maintained in the form of CET1. 4. The Sample, Models, and Methodology

4.1. The Sample and Time Frame The data used to estimate the models is taken from the Bureau Van Dijk’s Bank Scope database, using consolidated financial statement and the audited annual report collected from Dhaka Stock Exchange (DSE). The others which are not listed received from the bank directly. That happens because Bureau Van Dijk’s Bank Scope database information presentation system is different in some cases, say in regard to total assets Bankscope database presented as total bank assets minus non-performing loan equal to total real assets, whereas banks audited annual report include the non-performing loan with total asset and the Bank Scope database is full of missing data like n.a.That’s why we emphasis on the annual report of the bank. The sample contains 32 banks observed over the period 2000–2014. Our sample is an unbalance panel comprising 414 observations from the Banks’. The macroeconomic data were collected from the World Bank World Development Indicators (WDI)4. The below Table 3 is a detail description of our variables employed in this study. 4.2. Variables Definition (i) Main variables The cost of intermediation variable is proxied by two alternative measures: the ratio of net interest revenue over average interest-bearing assets (nim1) and the ratio of net interest income over average total assets (nim2). Its need to be mentioned that, for calculating average total assets and average total earning assets the base year is not averaged for every 32 banks, because of the unavailability of previous year data. 4

See more at http://data.worldbank.org/indicator

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Based on recent literature (Laeven and Levine, 2009, Ashraf et al., 2016), we measure bank risk-taking with the bank Z-scores. Z-score = (ROA+CAR)/σ(ROA), where ROA is equal to annual return on average assets before taxes, CAR is equal to annual equity to total assets ratio, and σ(ROA) is equal to standard deviation of annual values of return(pre-tax profit) on average assets calculated over individual sample bank study periods starting in 2000 and ending in 2014. (zscore) measures the number of standard deviations from the mean value by which return has to fall to deplete all shareholders’ capital. Greater values of (zscore) indicate higher bank stability. Further, as (zscore) is a highly skewed risk measure, therefore, considering studies mentioned above, we take the log of (zscores). For brevity, we name it (zscore) throughout rest of this paper. We engaged three measures of capital regulation. The first is a continuous measure of the ratio of regulatory capital to total risk-weighted assets (car). Soedarmono and Tarazi (2015) consider the banks’ total capital adequacy ratio (car) because bank capital ratios confessedly affect lending behavior which may lead to “capital crunch” problems (Bernanke et al., 1991, Peek and Rosengren, 1995). Banks attempt to accommodate the capital requirement by raising the contribution of shareholders or decreasing assets, mainly risk-weighted assets(Naceur and Kandil, 2009). Requirements of Basel accord gives a proper guideline for maintaining optimum capital adequacy ratio, where excess ratio may deal with idle money or liquidity shortage, the shortage is a signal for excessive risk-weighted assets in the operational process. The second is a continuous measure of the ratio of shareholders equity over the total asset (oetta). This variable also proxied as risk aversion(Poghosyan, 2013) higher ratio implying increased risk aversion for banks. The relation with the cost of intermediation with this variable is ambiguous. First, well-capitalized banks may be perceived as relatively safe by depositors, which would reduce their funding costs and boost margins. On the other hand, higher risk aversion may stimulate banks to assign their funds to less risky undertakings with low returns, resulting in lower margins(Poghosyan, 2013) which do a puzzle for the decision maker. Higher the ratio lower the risk and likely to increase the bank net interest margin because of a decline in funding cost since higher (oetta) can reduce bank default risk which in turn decreases funding costs(Demirguc-Kunt et al., 2003) Third, to test the effects of the Basel II application over time, we incorporate a dummy variable (capdummy) that takes a zero value before the change in capital regulation up to 2006 and one after that. If the effects of capital regulation on the cost of intermediation and risk-taking persist over time, we expect a statistically significant coefficient on this dummy variable

(ii) Bank level variables:

In our study we found auto-correlations with the variables, we need to employ lagged for one year for (nim1) and (zscore) both because today’s banking practices are influenced by the trend of previous periods. As this consequence, banks are adjusted their risk and capital based on last year risk and capital levels(Zhang et al., 2008). In the same direction, banks are adjusted their net interest margin and capital based on last year margin and capital levels. Because banks with a small degree of margin have a tendency to increase their margin, hence the relationship between net margin and lagged margin is expected to be positive. In the same way, banks are also changed their portfolio risk in the current period. Banks is Page 8 of 33

having higher risk level push them to decrease their portfolio risk; therefore a negative relationship between risk and lagged risk is expected. Management efficiency (maneff): the ratio of earning assets to total assets. The higher the ratio, the greater management efficiency is, and we expect a lower cost of intermediation. Alternatively, as managers strive for more earnings, it is likely that they would increase the cost of intermediation and reduce risk, which would enhance profits. That implies the management can employ the asset in a proper way to generate earnings. The more the earning asset on total asset more the efficiency in management is. By the way, Casu and Girardone (2004) find out that, “the most cost-efficient banking groups seem to be also the least profitable,” p. 693. For this, a negative sign is expected. Reserves (rsvs): Natural logarithm of banks reserves at the central bank. Higher reserves may influence a reduction in the cost of intermediation to push out excess reserves and increase profits. Alternatively, larger reserves may induce an increase in the cost of intermediation to make up for excess reserves and generate more earnings(Naceur and Kandil, 2009). By the same way, the higher reserve might motivate management to take excessive risk to mitigate with excess reserves. Thus we expect negative with bank cost and positive with risk-taking. Risk-weighted assets to total assets (rwata): As per Basel Accord II, risk-weighted assets to total assets

(rwata) are also an important determinant of capital adequacy ratio where risk-weighted assets calculated as total assets minus loans and advances to banks, government securities at market value, and cash. A Higher ratio indicates the higher requirement of (car) as increasing the overall risk(Gropp and Heider, 2007). Lower the ratio higher the capital adequacy which implies assets are associated with lower risk vice-versa. In this study, we expect positive sign between Risk-weighted assets to total assets ratio and risktaking of the bank.

Leverage (lev): Total liabilities over total assets also denote the risk-taking behavior of the bank. Fewer shareholders equity in capital structure related to the risk-seeking attitude of the management. Higher the leverage ratio means employed more debt in the capital structure. Excessive debt remains in the capital structure is the sign of extreme risk-taking behavior of the bank. We expect a positive relation between leverage ratio and bank risk-taking. Financial Intermediation (imed): Competitive environment decreases spread (Haruna, 2013). Also proxied by liquidity (Naceur and Kandil, 2009). Higher figures denote lower liquidity greater risk. (imed) measures the risk of not having sufficient reserve of cash to cope with the withdrawal of deposits. Moreover, as higher (imed) is associated with more lending activities, the higher loan-to-deposit ratio is expected to increase the intermediation cost because banks tend to offset higher monitoring costs related to lending activities(Soedarmono and Tarazi, 2013). Thus we expect positive with the cost of intermediation and negative with bank risk-taking. Income Diversification (id): Calculated as noninterest income over total operating income. How much revenues generated from other activities except interest investment. We expect this variable has significant negative relation with the cost of intermediation. Higher the noninterest income over total income may reduce the cost of intermediation because it releases the pressure on interest income. For this, a negative sign is expected. (iii) Industry-specific variable: Page 9 of 33

Market power (conc): The size of banks’ assets in the three largest banks to total assets. The higher the concentration ratio, lower the competition which generates more monopoly power there is in the banking system, enabling banks to increase the intermediation cost and produce more profits (Naceur and Kandil, 2009). Likewise, higher bank concentration ratio is expected to increase the bank intermediation cost because banks in concentrated markets can charge higher lending rates (Demirguc-Kunt et al., 2003). (iv)Macroeconomic variable:

Growth in GDP (gdp): At the time of expansion of economy when (gdp) growth is booming trend, borrowers need adequate funds from banks regarding debt but during a recession, demand for debt drops. Banks needs to extend their credit service to poor debtors, resulting NPLs moving beyond of control. As a result cost of intermediation and bank risk-taking affected in both ways. Demirgüç-Kunt and Huizinga (1999) and Tarus et al. (2012) studied the relationship between economic growth and cost of financial intermediation and found a negative correlation. Therefore we expect the statistically significant impact on the cost of intermediation and bank risk-taking. 4.3. Empirical Methodology

This study we apply the two-step dynamic panel data approach suggested by Arellano and Bover (1995) and Blundell and Bond (2000), further this evidence also uses dynamic panel GMM technique to address potential endogeneity, heteroskedasticity, and autocorrelation problems within the variables (Doytch and Uctum, 2011). Linear GMM estimators have one- and two-step variants. The two-step estimator that we use is typically more efficient than the one-step estimator, particularly for the system GMM. The system GMM is essentially an extension of the standard GMM developed by (Arellano and Bond, 1991).The system GMM estimator delivers for a more flexible variance-covariance structure under the moment conditions. There are two unlike estimators for the dynamic panel models: (i) the difference panel estimator eliminates a possible source of omitted variable bias in the estimation, and (ii) the system panel model estimator combines the regression difference with the regression in levels in order to condense the potential biases and imprecision associated with the difference estimator. Besides, standard GMM only consider the first difference of each variable in the regressions, while the lagged levels of explanatory variables are used as instruments. The use of the lagged levels as instruments may be inappropriate, particularly when variables are close to a random in nature. Also, Baltagi (2008) proves that the system GMM produces more efficient and precise estimates than the Standard GMM. Further, This study employs (Windmeijer, 2005) finite-sample correction to report standard errors of the two-step estimation, devoid of which those standard errors tend to be severely downward biased. The standard errors for the regression coefficients are clustered at the bank level to control for the dependence of errors for a given bank over time. The dynamic panel model technique – the GMM model, is particularly well-suited to handling small macro groups with endogenous variables and is also supportive in amending the bias induced by mislaid variables in cross-sectional estimates and the discrepancy caused by endogeneity. It is rather suitable that the dynamic GMM technique at the same time permits us to control for the endogeneity bias made by reverse causality running from dependent and explanatory variables based on past theoretical study. Similarly, In a dynamic panel setting with unobserved fixed effects and with endogeneity between dependent and independent variables, system GMM estimator of (Arellano and Bover, 1995) and(Blundell and Bond, 2000) delivers reliable estimates and is considered logical. Hence, this study estimated with two-step system Page 10 of 33

GMM estimator. To govern the endogenous bank-level variables, we follow the Durbin-Wu-Hausmann test. We treat (zscore) and capital requirements (car,oetta) as endogenous for the cost of intermediation, besides (nim1) and capital requirements (car,oetta) as endogenous for bank risk-taking, and use their one period lag values together with a lag of dependent variable as instruments. Additionally, the results of diagnostic statistics for the dynamic panel system GMM are consistent with the assumptions of our econometric model. In particular, the coefficients for second-order autocorrelation, AR (2) in the first-differenced residuals are insignificant. The Sargan test of over-identifying restrictions, which examines whether the instruments as a group appear exogenous, shows insignificant results, endorsing that the instruments are exogenous. The number of instruments (13 to 14 in all Model of the cost of intermediation and risk-taking, 28 to 29 after considering sample year dummies) is not too high as compared to the number of banks (32), and therefore we can assume that the issue of instruments proliferation is not undermining the validity of our results. Moreover, we have performed the LM serial correlation test and White heteroskedasticity test. The null of LM and white test is there is no such in the model, and we reject the null across all models in our study. That leads us to run the regression with system GMM (heteroskedasticity robust standard error) to ensure the trustworthiness of our findings. Finally, we have tested the Hausman fixed/random effect, and we accept the null of random effect exist in our model. We also perform an analysis of non-stationary (Augmented Dickey Fuller-Fisher type) of our individual variables: see Table-A1. 4.4. Specified Models

In this evidence we explain the cost of intermediation and risk-taking in the banking system in Bangladesh, using an empirical model that includes a measure of capital regulations plus some the other main determinants. The main variables and the Banks’ internal and external determinants chosen to gauge the cost of intermediation and risk-taking are shown in Table 3. The empirical model specification is as follows: B

X ij,t = C + δX ij,t−1 + ∑ βb Yitb + β1 Market powerj, t + β2 GDP growthj, t +∈it

(1)

b=1

Here Xij,t−1 is the one period lagged cost of intermediation and risk-taking, c a constant term, δ the speed of adjustment to equilibrium, Yit with superscripts b denote bank-specific determinants, and εit is the disturbance. The different variables employed in the cost of intermediation are management efficiency (maneff) and income diversification (id), whereas risk-weighted asset to total asset (rwata) and leverage (lev) used in risk equation only, rest are same. We use three alternative measures of capital regulations a summary of the empirical models is as follows: Model 1 with the CAR: B

X ij,t = C + δX ij,t−1 + 𝜆CAR𝑖, 𝑡 + ∑ βb Yitb + β1 Market powerj, t + β2 GDP growthj, t +∈it b=1

Here CAR is equal eligible regulatory capital over total risk-weighted assets.

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(2)

Model 2 with the OETTA: B

X ij,t = C + δX ij,t−1 + 𝜆OETTA𝑖, 𝑡 + ∑ βb Yitb + β1 Market powerj, t + β2 GDP growthj, t +∈it

(3)

b=1

Where OETTA is equal to equity over total assets, Model 3 with the dummy variable: B

X ij,t = C + δX ij,t−1 + 𝜆CAP − DUMMY𝑖, 𝑡 + ∑ βb Yitb + β1 Market powerj, t + β2 GDP growthj, t +∈it

(4)

b=1

Here CAP-DUMMY is a dummy variable that takes 1 in the current year 2007 and subsequent years following the implementation of Basel II and 0 before.

5. Empirical Results 5.1: Summary Statistics and Correlation Matrix.

The cost of intermediation, (nim1) means 3% where the minimum is negative and maximum 7%. In this context, there is some banks are not able to cover their deposit interest by the lending rates, that’s why the deviation is also high. Banks’ log of default risk means 2.85 which represent the banks are far from insolvency risk. More the (zscore) more the stability in default risks. Our study is unbalanced panel because of unavailability of state-owned banks early periods annual reports. Average capital adequacy ratio is 10.90%, which is higher the minimum requirement according to Basel II. Owners’ equity to total assets (oetta) mean shows 7%, but it is surprising that here minimum ratio is near about negative. We have deleted some observation with negative (oetta), as banks with negative (oetta) may not fully operate in the market because they may require assistance from bank regulators or the lender of last resort(Soedarmono and Tarazi, 2013). Total liability over total assets is the rest portion of owners’ equity to the total asset. Here shows that the average of this two is 100%, which proves our collected data are putted correctly. Notes: Total number of observations is 414. *, **, *** indicate significance at p< .10, p< .05 and p< .01 (2-tailed) respectively. Table 5 shows the highest correlation is between (car) and (oetta) (Pearson’s correlation = 0.68) but this two variable is an alternative measure for capital regulation. So, the issue of multicollinearity does not pose a challenge for our study.5

5

Gujarati (2007) indicate that multicollinearity is a serious problem if correlation coefficient between two independent variables is above 0.80, which is not the case here.

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5.2. The Determinants of the Cost of Intermediation

Table-6 is a snapshot of accumulated result for every individual measure of the cost of intermediation that we have to employ on of the following: capital adequacy ratio, equity to total assets ratio and capital dummy variables for regulations. In this study, we use simultaneous equation modeling to justify our topics; we want to find out the regulatory capital impact on the cost of intermediation and risk taking, concerning the endogeneity issue results do not appear the association between risk and banks cost. Besides, regarding the (cap-dummy) is a proxy of capital regulation in model 3 both6 measure risk and cost of intermediation we found a significant relationship.The result implies that after Basel II applications (nim1) and (zscore) are positively related with one another, the subsequent increasing cost of intermediation, raising (zscore) means reducing default risk. Model robustness check also provides the same outcome. The lagged dependent variable measures the degree of persistence exist in the model. The lagged dependent variable is statistically significant across all models, indicating a high level of persistence characterizing cost of intermediation and justifying the auto-correlation presents in the model. As our expectation, we find positive lagged relationship. These findings are also apparent in (Naceur and Kandil, 2009). Capital adequacy ratio has a positive and statistically significant effect, increasing the cost of intermediation contrary to the findings of previous studies (Afzal and Mirza, 2012). As expected, banks increase the cost of intermediation to make up for a higher capital adequacy ratio. The capital variable (equity/assets) has a positive and statistically significant on the cost of intermediation. Banks are merely tried to raise the cost of intermediation to make up for a higher risk to shareholders. This finding is by those of (Kosmidou et al., 2005, Maudos and Solís, 2009, Naceur and Kandil, 2009, Soedarmono and Tarazi, 2013) indicating that healthy capitalized Bangladeshi banks face lower costs of going bankrupt. That might facilitate a reduction in the cost of funding resulting generate more profit. Meanwhile, our findings are contrary to the results of(Poghosyan, 2013). Beyond our expectation, we find there is an insignificant relation between capital dummy and net interest margin. That happened because may be one reason Basel II application from 2007 1st January was voluntary, not mandatory in Bangladeshi banking sector, whereas it becomes mandatory on 1st January 2010. So Basel II implementation has no significant impact on banks cost of intermediation in the case of the capital dummy as a proxy for capital regulations. Management efficiency has a negative and meaningful effect on interest margins all the models, indicating higher the efficiency of the management team lower the cost of intermediation, but contrary to the findings of (Naceur and Kandil, 2009). Reserves have a statistically significant adverse effect on the cost of intermediation in all models. Thereby, commercial banks are reluctant to hold more reserve with the central bank. More reserve with central bank reduce the cost of intermediation, contrary to the findings of (Naceur and Kandil, 2009).

6

please go through 3rd model of table-6,7

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Financial intermediation has a positive and statistically significant effect on the cost of intermediation, as evidence in models 1, 2 and 3 in Table 6. The ratio of net loans to customer deposit (imed) is statistically significant and positively related to the profit generating ability through increasing intermediation cost of the bank, by our expectations and some earlier studies (Islam and Nishiyama, 2015, Naceur and Kandil, 2009). In generally banks receive lower returns on holding excess cash or securities; they face a competitive market for deposits (Demirgüç-Kunt et al., 2003). But adverse with the findings of (Haruna, 2013) As we expected income diversification has a statistically significant adverse effect on the cost of intermediation in all models. That makes us clear that the more non-interest income on total operating income reduce the interest margin because total operating income is the combination of interest income and non-interest income. Generate more non-interest income reduce the pressure of making more cost of intermediation. None of the coefficients of industry and macroeconomic variables like market power (conc) and growth in GDP (gdp) explains the cost of intermediation significantly except concentration is significant in model 2 at 10% level. We retain these two variables in our model because it has a measurable effect as an instrumental variable in the model. 5.3. The Determinants of Bank Risk-Taking

To makes end the analysis regarding the consequences of capital regulations, we study determinants of banks’ risk-taking, as measured by the (zscore) = log (CAR+ROA)/s(ROA). Be remembering that, increasing (zscore) resulting reduce risk. Table 7 summarizes the results of the model explaining different measures of capital regulations using dynamic panel estimation. As explained earlier in table-6, we did not find any significant relationship between (zscore) and (nim1) except (cap-dummy) as a measure of capital regulation. The measures (car) and (oetta) as a proxy of capital regulation, the cost of intermediation has no significant impact on banks’ risk-taking, while after Basel II application in the banking sector (zscore) and (nim1) are positive significantly correlated, ensuring the increasing cost of intermediation reducing bank risk-taking. The lagged dependent variable measures the degree of auto-correlation exist in the model. The lagged dependent variable is statistically significant across all models, indicating a high level of persistence characterizing the banks’ risk-taking. Capital adequacy ratio (car) has a positive and statistically significant effect, raising the (zscore) resulting reduce the risk-taking behavior of the bank. The more imposing regulatory capital requirements demotivate the banks’ to take excessive risk. As our expectation, there is a negative relation between capital regulation (car) and risk-taking. These findings are also apparent in (Soedarmono and Tarazi, 2015, Hussain and Hassan, 2005). But contrary to the results of previous studies (Bichsel and Blum, 2004, Lin et al., 2013) using OLS and 3SLS respectively. Equity to assets ratio (oetta) has a positive and statistically significant effect, reducing the risk-seeking attitudes of the banks. Higher the ratio lowers the debt in the capital structure; well-capitalized banks’ tends to seek lower risk to generate sustainable profit for the shareholders. As our deemed, capital regulation (oetta) has a significant negative relation with bank risk-taking. These findings also supported Page 14 of 33

by (Zheng, 2015, Bougatef and Mgadmi, 2016), they use loan loss provision to total loan and nonperforming loan to total loan as a proxy for risk. In model 3 capital dummy (cap-dummy) have no significant impact on banks’ risk-taking. That means switching from Basel I to Basel II has no measurable impact on the risk-taking behavior in Bangladeshi banking sector. The probable reason we have already explained in table-6 model 3. None of the coefficients of reserve, financial intermediation and GDP growth explains the risk-taking significantly, except leverage (negative) and financial intermediation (positive) substantially affect the risk-taking in model 3, when (cap-dummy) as a measure of capital regulation. Risk-weighted assets to total assets (rwata) and total liability over the total asset (lev) have a highly positive significant effect on the risk-taking behavior of the banks’. More the ratio more risk-weighted assets in the assets structure, which resulting in a provide warning to the management for taking unnecessary risk. Similarly, more debt in the capital structure induces the risk-taking of banks, also proxied as debt ratio by (Gonzalez, 2005) and found a similar association. 5.4. Robustness Check: Inclusion of Time Dummies with Alternative Dependent Variable

Table 8 and 9 presents the robust result of our baseline equations (2)-(4) of the cost of intermediation and risk-taking. We conducted the robustness check for our all models by using the net interest margin (nim2) as an alternative measure of net interest margin (nim1) and replacing the macroeconomic variable with time dummies to control the year fixed effects. (nim2) has been calculated as the ratio of net interest income over average total assets of the banks (Naceur and Kandil, 2009). However, using (nim2) as an alternative measurement of net interest income, we found no change in signs and no significant change in values of the coefficients of the explanatory variables. The reported estimation results, presented in Table 8 and 9 compare to Table 6 and 7, ensure that the results obtained from equation (2)-(4) remain fitted. 5.5. Sensitivity Analysis: Pooled Panel OLS Regression

We performed several robustness tests to confirm the main specification. First, we justify our primary result with alternative dependent variables proxies along with sample period dummies. Second, we reestimated our baseline result of all six models (Table 6 & 7) with pooled panel OLS regression and report the results in Table10 & 11. As shown, the results remained consistent, confirming that our results are not biased due to use high powered estimation techniques system GMM. These robustness tests show that our main results are robust and coherent. In sum, our results confirm that the cost of intermediation of commercial banks in Bangladesh has increased in response to more stringent capital adequacy requirements. Likewise, the bank’s risk-taking reduces in response to the regulatory capital requirement. Overall, the findings of this study are consistent and dependable to use for policy making, citation for the future research and finally for bank management itself.

6. Summary and conclusions This study investigates the effects of capital regulations on banks’ cost of intermediation and risk-taking in Bangladesh. Two measures regarding the cost of intermediation and risk-taking are under investigation: the cost of intermediation and banks’ risk-taking, as measured by net interest margin (nim1) and (zscore). Page 15 of 33

We did not find any significant relationship between the cost of intermediation and risk-taking, but after Basel II application in this sector, this two dependent variables proxy is positive significantly affected by one another. We investigate the effect of capital regulations in three dimensions. First, we test the effect of the capital adequacy ratio of the cost of intermediation and risk-taking. Second, we examine the effect of the ratio of capital to total assets on the cost of intermediation and risk-taking. Third, we introduce a dummy variable that captures the structural break marking the introduction of Basel II application in this sector. The results provide a strong evidence of the effects of capital regulations on the cost of intermediation and banks’ risk-taking. As the capital adequacy ratio increase the solid capital in the structure, banks increase the cost of intermediation, reducing the risk-seeking behavior of the banks. Meanwhile, equity to assets ratio internalizes the risk for shareholders; banks increase the cost of intermediation, which provides the reluctant attitudes regarding higher risk-taking for the banks. Whereas, capital dummy means, switching from Basel I to Basel II has no substantial impact on the cost of intermediation and risk-taking both, but our two dependent variables proxy are positive significantly affected after Basel II application. In addition to the above effects, the empirical estimation discloses interesting evidence about the effects of banking-specific, industry-specific and macroeconomic variables on the cost of intermediation of banks in Bangladesh. Management efficiency, reserve, and income diversification resulted in reducing the cost of intermediation, whereas higher financial intermediation ratio increases the cost of intermediation. None of the industry and macroeconomic variables as market power (conc) and growth in GDP (gdp) explains the cost of intermediation significantly except concentration is significant in model 2 at 10% level. The results are robust, regardless of the definition net interest margins and estimation technique. Rest of the above findings, the empirical estimation discloses remarkable evidence about the effects of banking-specific variables on the risk-taking of banks in Bangladesh. Risk-weighted assets to total assets (rwata) and liability over total assets (lev) have a significant positive relation with the risk-taking. More the ratio more risk-weighted assets and liabilities compare to assets in the assets structure, causing induces risk. None of the coefficients of the reserve, financial intermediation and GDP growth explains the risktaking significantly, except financial intermediation significantly negative affect the risk-taking in model 3, when (cap-dummy) as a measure of capital regulation. Overall, the results point to the necessity of capital regulation to the intermediation cost of banks and financial stability in Bangladesh. Moreover, the state of the economy is a major factor that determines the intermediation cost of the banking industry. Financial consistency could be at risk as a result of shocks grafting on the economic system, disappear proper policy adjustments to cover the effects of these shocks. Banks set the cost of intermediation in an attempt to increase profitability and maintain stability, provide excess liquidity and restricted to restrain demand for credit. The application of regulatory policy, the result identifies the necessity of regulatory restrictions to enhance the reliability and credibility of the banking sector to general public and regulators as well. The regulatory reforms should aim at ensuring more competition in the banking sector to ensure that performance indicators are similar measurable standard with the optimal practices of the intermediation function that provide financial consistency over time. And support the “public view” of the hypothesis. The results raise an admonitory flag: state policies that rely excessively on direct government regulation and supervision of Page 16 of 33

bank activities should in parallel incentives care for private agents to promote banking stability with lower risk, intermediation performance, and development. This study suggests the regulatory authority comply the banking supervision with Basel-III as early as possible. Regarding the direction of future research, we suggest incorporating some other explanatory variables including capital buffer, taxation, and information asymmetry as well compare with the other South Asian countries.

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Reg.cap year mean %

14 12 10

11.28 9.37 9.48 9.5

10.5 10.24 10.62

11.69 11.31 11.79

10.3

11.56

10.37

11.58 12.13

8 6 4 2 0 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

. Figure-1: Annual average capital adequacy ratios of all sample banks

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Table: 1 Capital to RWA (in percentage) by Types of Banks Types of 2000 bank

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

SCBs

4.4

4.24

4.1

4.3

4.1

-0.4

1.1

7.9

6.9

9.0

8.9

11.7

8.1

10.8

8.3

DFIs

3.2

3.93

6.9

7.7

9.1

-7.5

-6.7

-5.5

-5.3

0.4

-7.3

-4.5

-7.8

-9.7

-17.3

PCBs

10.9

9.85

9.7

10.5

10.3

9.1

9.8

10.6

11.4

12.1

10.1

11.5

11.4

12.6

12.5

FCBs

18.4

16.84

21.4

22.9

24.2

26.0

22.7

22.7

24.0

28.1

15.6

21.0

20.6

20.2

22.6

Total

6.7

6.7

7.5

8.4

7.3

5.6

6.7

9.6

10.1

11.6

9.3

11.4

10.5

11.5

11.3

Source: Annual Report 2013-2014, Bangladesh Bank

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Table 2: Phase-in Arrangement of Minimum Capital Requirements Minimum Common Equity Tier 1 Capital Ratio Capital Conservation Buffer Minimum CET1 plus Capital Conservation Buffer Minimum Tier 1 Capital Ratio Minimum Total Capital Ratio Minimum Total Capital plus Capital Conservation Buffer

2015 4.50% 4. 5% 5.50% 10.00% 10.00%

2016 4.50% 0.625% 5.125% 5.50% 10.00% 10.625%

Source: Bangladesh Bank guidelines on risk-based capital adequacy 2014.

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2017 4.50% 1.25% 5.75% 6.00% 10.00% 11.25%

2018 4.50% 1.875% 5.75% 6.00% 10.00% 11.875%

2019 4.50% 2.50% 7.00% 6.00% 10.00% 12.50%

Table-3: Description of the Variables Variables Dependent Variables Cost of intermediation

Symbol

Description

Sources of Variable

nim1

The ratio of net interest revenue over average total interest-bearing assets The ratio of net interest income over average total assets Equals[log [(ROA + CAR)/ σ(ROA)], where ROA and CAR are an annual return on average assets before annual taxes and equity to total assets ratios, respectively. σ(ROA) is the standard deviation of annual values of return on average assets before taxes calculated over individual banks sample period rolling window. Higher values of (zscore) represent lower bank risk-taking and vice versa.

(Aysen Doyran, 2013, Naceur and Kandil, 2009) (Islam and Nishiyama, 2015, Naceur and Kandil, 2009) (Ashraf, 2015)

Regulatory capital to risk weighted assets, i.e. Capital Adequacy Ratio (CAR) The ratio of shareholders equity to total assets.

(Soedarmono and Tarazi, 2015)

Cap-dummy is a dummy variable that takes 1 in the current year and subsequent years following the implementation of Basel II and 0 before The ratio of earning assets to total assets. The higher the ratio, the greater management efficiency is Log of banks’ reserves at the central bank

Author’s calculation

Ratio of risk-weighted assets to total assets

Author’s Idea

Calculated as total liabilities over total assets

(Gonzalez, 2005)

Calculated total loan over total deposit.

(Islam and Nishiyama, 2015)

Calculated as noninterest income over total operating income

Author’s calculation

The size of banks’ assets in the three largest banks to total assets.

(Islam and Nishiyama, 2015)

Annual growth in real gross domestic product

(Islam and Nishiyama, 2015)

nim2 Risk

zscore

Bank independent control variables Capital Requirements car7

Management Efficiency Reserves Risk-weighted assets to total assets Leverage

Financial Intermediation Income Diversification

oetta6 capdummy maneff rsvs rwata lev imed id

Industry-specific Variable Market power conc Macro-economic variable Growth in GDP

gdp

7

(Bougatef and Mgadmi, 2016)

(Naceur and Kandil, 2009)

(Naceur and Kandil, 2009)

: This study we use capital requirements ratios instead of world bank survey question (Barth et al., 2013) of banking regulation and supervision mainly for two reasons, 1) to examine the effect of regulatory requirements for individual banks and years nexus of our sample period 2) Survey question data provide a yearly index for a country which may not deliver the clear picture for single country study.

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Table-4: Descriptive Statistics Variables Dependent variables nim1 zscore Independent variables Bank specific car % (Capital) oetta (Capital) cap-dummy (Capital) maneff rsvs rwata lev imed id conc gdp Source: Authors’ calculation

Mean

Std. Deviation Minimum

Maximum

N

.03 2.85

.01 0.67

-.02 -1.57

.07 4.03

414 414

10.90 .07 .61 .88 8.03 .71 .93 .82 .57 .31 5.83

1.06 .03 .49 .05 1.27 .18 .07 .12 .17 .04 .82

1.58 .02 .00 .66 4.63 .07 .05 .08 .12 .24 3.83

24.17 .15 1.00 .99 11.03 1.27 1.13 1.54 1.72 .38 7.06

414 414 414 414 414 414 414 414 414 414 414

Table-5: Correlation variable s

nim1

car

oetta

nim1

1.00

zscore

.366***

1.00

car oetta

.282*** .385***

.500*** .355***

1.00 .684***

capdum maneff

1.00

.147***

.044

.167***

.410***

1.00

-.058

.126***

.024

.006

rsvs

-.074

.078

.045

.151***

.275*** .451***

rwata lev

.316*** .215*** .396***

.052 -.116** .245***

.024 .291*** .152***

.573*** .441*** .360***

.428*** .231*** .127***

.324*** .028

.173*** .015

-.044

.214***

.054

gdp

.117**

.027

.093*

.140*** .145*** .193***

.048

conc

.780*** .007

.393***

.170***

imed Id

zscore

capd

maneff

rsvs

rwata

lev

1.00 .312*** .462***

1.00

imed

id

conc

gdp

1.00 .504*** .076 -.008

1.00

.229***

.136*** .139***

.053 -.051

.212*** .210***

Source: authors’ calculation

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.142*** .154*** .207***

.165*** .067

1.00 1.00

.041

.297*** .025

.033

1.00

-.050

.139***

.001

.194***

1.0 0

Table-6: Determinants of Cost of Intermediation a Regressors Intercept zscore nim1t-1 car oetta cap-dummy maneff rsvs imed id conc gdp Adjusted R2 Sargan test (P-value) AR(1) (p-value) AR(2) (p-value) No of Instruments Observations

nim1(car) 0.045***(5.67) 1.12E-05(0.38) 0.460***(20.96) 0.00036***(2.62)

nim1(oetta) 0.048***(5.70) 2.38E-05(0.94) 0.443***(19.54)

nim1(cap-dummy) 0.040***(4.12) 6.21E-05***(4.10) 0.496***(25.22)

0.048***(4.52) -0.031***(-5.48) -1.07E-7***(-3.44) .0088***(4.12) -0.035***(-9.44) 0.015(1.28) 0.00040(0.99) 79.94% 21.35(0.19) -5.21(0.00) -0.87(0.43) 13 414

-0.031***(-6.13) -1.27E-07***(-3.33) 0.0058***(2.73) -0.036***(-9.68) 0.023*(1.77) 0.00021(0.46) 80.76% 20.12(0.28) -4.52(0.00) -1.02(0.31) 13 414

a Notes:

-0.00078(-0.58) -0.029***(-5.56) -1.05E-07***(-2.86) 0.0091***(4.29) -0.033***(-9.24) 0.025(1.62) 0.00056(0.97) 79.90% 21.16(0.21) -6.02(0.00) -0.49(0.66) 13 414

Dependent variable is (nim1).The estimation method is the two-step GMM dynamic panel estimator. ***, ** and* indicate significance at the 1%, 5% and 10% levels, respectively. Heteroskedasticity-robust t-statistics are in parentheses. The null hypothesis of the Sargan test is that the instruments used are not correlated with residuals (over-identifying restrictions). Arellano–Bond order 1 (2) are tests for first (second) order correlation, asymptotically N (0,1). These test the first-differenced residuals in the system GMM estimation.

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Table-7: Determinants of Bank Risk-Taking b Regressors Intercept nim1 zscoret-1 car oetta cap-dummy rsvs rwata lev imed gdp Adjusted R2 Sargan test (P-value) AR(1) (p-value) AR(2) (p-value) No of Instruments Observations

zscore(car) 4.564***(3.15) -10.33(-1.01) 0.881***(40.18) 0.559***(4.91)

zscore(oetta) 3.663***(3.23) -4.247(-0.43) 0.899***(38.61)

zscore(cap-dummy) 7.434***(3.03) 32.86***(2.89) 0.915***(47.17)

52.73***(7.11) 2.36E-05(0.78) -3.670***(-3.73) -2.63*(-1.51) 1.223(1.49) 0.0020(0.49) 89.78% 16.39(0.37) -6.95(0.00) -1.05(0.28) 13 414

2.24E-05(0.68) -8.363***(-4.91) -3.69**(-1.97) 1.190(1.25) 0.0037(0.74) 89% 17.05(0.28) -5.04(0.00) -1.44(0.19) 13 414

b Notes:

0.637(1.17) 1.34E-05(0.46) -6.765***(-5.27) -5.060**(-2.56) 2.935***(2.73) 0.00076(0.89) 88.17% 16.85(0.32) -6.40(0.00) -1.29(0.21) 14 414

Dependent variable is (zscore).The estimation method is the two-step GMM dynamic panel estimator. ***, ** and* indicate significance at the 1%, 5% and 10% levels, respectively. Heteroskedasticity-robust t-statistics are in parentheses. The null hypothesis of the Sargan test is that the instruments used are not correlated with residuals (over-identifying restrictions). Arellano–Bond order 1 (2) are tests for first (second) order correlation, asymptotically N (0,1). These test the first-differenced residuals in the system GMM estimation.

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Table-8: Determinants of Cost of Intermediation c Regressors Intercept zscore nim2t-1 car oetta cap-dummy maneff rsvs imed id conc time-dummies Adjusted R2 Sargan test (P-value) AR(1) (p-value) AR(2) (p-value) No of Instruments Observations

nim2(car) 0.026***(4.13) 3.77E-06(0.15) 0.468***(20.64) 0.00032***(2.81)

nim2(oetta) 0.028***(4.21) 1.88E-05(0.89) 0.450***(18.45)

nim2(cap-dummy) 0.022***(2.85) 4.90E-05***(3.77) 0.507***(24.63)

0.042***(4.48) -0.012***(-2.76) -6.94E-08***(-2.68) 0.0077***(3.99) -0.029***(-8.58) 0.013(1.20) yes 78.81% 19.25(0.15) -4.31(0.00) -0.53(0.81) 28 414

-0.013***(-2.92) -8.82E-08***(-2.69) 0.0052***(2.71) -0.030***(-8.90) 0.020*(1.67) yes 79.56% 17.49(0.23) -3.93(0.00) -1.44(0.19) 28 414

c Notes:

-0.00087(-0.73) -0.012***(-2.86) -6.53E-08**(-2.10) 0.0080***(4.20) -0.027***(-8.73) 0.023(1.63) yes 78.72% 20.11(0.19) -4.06(0.00) -0.41(0.74) 28 414

Dependent variable is (nim1).The estimation method is the two-step GMM dynamic panel estimator. ***, ** and* indicate significance at the 1%, 5% and 10% levels, respectively. Heteroskedasticity-robust t-statistics are in parentheses. The null hypothesis of the Sargan test is that the instruments used are not correlated with residuals (over-identifying restrictions). Arellano–Bond order 1 (2) are tests for first (second) order correlation, asymptotically N (0,1). These test the first-differenced residuals in the system GMM estimation.

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Table-9: Determinants of Bank Risk-Taking d Regressors Intercept nim2 zscoret-1 car oetta cap-dummy rsvs rwata lev imed time-dummies Adjusted R2 Sargan test (P-value) AR(1) (p-value) AR(2) (p-value) No of Instruments Observations

zscore(car) 4.965***(2.98) -14.00(-1.11) 0.882***(41.15) 0.560***(4.81)

zscore(oetta) 3.579***(3.15) -4.901(-0.42) 0.900***(39.08)

zscore(cap-dummy) 7.025***(3.19) 40.31***(2.70) 0.914***(46.41)

52.45***(6.98) 2.24E-05(0.76) -3.597***(-3.66) -3.29**(-1.98) 1.258(1.51) yes 89.77% 13.23(0.29) -5.84(0.00) -0.85(0.43) 28 414

2.21E-05(0.67) -8.268***(-4.88) -4.05***(-2.17) 1.174(1.22) yes 89% 13.50(0.19) -3.99(0.00) -1.96(0.14) 28 414

d Notes:

0.639(1.13) 1.24E-05(0.42) -6.745***(-5.13) -4.700***(-2.60) 2.915***(2.66) yes 88.16% 14.48(0.26) -5.32(0.00) -1.01(0.32) 29 414

Dependent variable is (zscore).The estimation method is the two-step GMM dynamic panel estimator. ***, ** and* indicate significance at the 1%, 5% and 10% levels, respectively. Heteroskedasticity-robust t-statistics are in parentheses. The null hypothesis of the Sargan test is that the instruments used are not correlated with residuals (over-identifying restrictions). Arellano–Bond order 1 (2) are tests for first (second) order correlation, asymptotically N (0,1). These test the first-differenced residuals in the system GMM estimation.

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Table-10: Determinants of Cost of Intermediation e Regressors zscore car

nim1(car) .0000816 ** (.0000403) .0003824 *** (.0001149)

oetta

nim1(oetta) .0000661* (.000037)

.1046109 *** (.0129258)

cap-dummy maneff rsvs imed id conc gdp constant R-squared F-statistics (P-value) Observations

-.0374965 *** (.0105291) -7.77e-08* (4.67e-08) .0179921*** (.0034258) -.0506169 *** (.0030973) .0091181 (.0113227) .0011059 *** (.0004265) .0605991*** .0097969 68.09% 74.41(0.00) 414

-.0375714 *** (.0097266) -1.24e-07 *** (4.43e-08) .0103724 *** (.0029888) -.0512723*** (.0029463) .0194794 * (.0110513) .0008742 ** (.0004032) .0628474 *** (.0092957) 71.8% 94.65(0.00) 414

e

nim1(cap-dummy) .0001451 *** (.0000383)

.4397201 (.9304004) -.0367753 *** (.0092883) -2.08e-07 *** (5.16e-08) .0155562 *** (.003295) -.0507639 *** (.0033123) -.0129976 (.0124158) .0003163 (.0004795) .0744815 *** (.0094078) 68.90% 68.45(0.00) 414

Notes: Dependent variable is (nim1).The estimation method is the pooled panel OLS regression. ***, ** and* indicate significance at the 1%, 5% and 10% levels, respectively. Numbers in parentheses are standard error. The standard errors for the regression coefficients are clustered at the bank level to control for the dependence of errors for a given bank over time.

Page 31 of 33

Table-11: Determinants of Bank Risk-Taking f Regressors nim1 car

zscore(car) 20.89371*** (4.089895) 1.236655 *** (.1925296)

oetta

zscore(oetta) 24.03836 *** (4.309817) 11.90516 *** (2.105584) 11.90516 *** (2.105584)

cap-dummy rsvs rwata lev imed gdp constant R-squared F-statistics (P-value) Observations

5.277652 (3.322015) -.0001758* .0000897 -10.54749 *** (2.347357) 14.13118 *** (4.202179) -.7734589 (.5468869) -13.06542*** (4.566273) 51.55% 21.46(0.00) 414

.0001514 (.0000969) -1.738678 *** (.3574038) -6.95413 *** (1.780162) 15.16711 *** (4.827063) -.6307703 (.5676911) 15.37108*** (4.341682) 59.97% 21.29(0.00) 414

f

zscore(cap-dummy) 29.02641*** (4.434331) .6561581 (1.290404)

-.6561581 (1.290404) .0002226 ** (.000099) -9.486933*** (3.650965) -6.821599*** (1.052349) 17.45222 *** (5.258293) -.3484803 (.6181831) 12.10243*** (1.164858) 68.13% 20.25(0.00) 414

Notes: Dependent variable is (zscore).The estimation method is the pooled panel OLS regression. ***, ** and* indicate significance at the 1%, 5% and 10% levels, respectively. Numbers in parentheses are standard error. The standard errors for the regression coefficients are clustered at the bank level to control for the dependence of errors for a given bank over time.

Page 32 of 33

Table-A1: Test of Non-Stationary Variables Cost of intermediation(nim1) Cost of intermediation(nim2) Risk (zscore) Capital Requirements (car) Capital Requirements (oetta) Management Efficiency (maneff) Reserves (rsvs)

χ2 p-Value 92.64 0.0044 97.19 0.0017 117.75 0.0000 131.54 0.0000 70.24 0.0379 109.26 0.0000 100.85 0.0000 Risk-weighted assets to total assets (rwata) 91.62 0.0053 Leverage (lev) 77.00 0.0488 Financial Intermediation (imed) 116.81 0.0000 Income Diversification (id) 128.82 0.0000 Market power (conc) 119.44 0.0000 Growth in GDP (gdp) 131.04 0.0000 Note: The table shows the augmented dickey fuller test-fisher type( which does not require a panel to be balanced) results where the null of non-stationarity have been rejected for all the variables at 5% level of significance.

Page 33 of 33