Evidence from China

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International Review of Financial Analysis 86 (2023) 102501

Available online 11 January 2023

Overlapping membership between risk management committee and audit committee and bank risk-taking: Evidence from China

Bin Yan Ding, Feng Wei

^*^,¹

School of Economics and Business Administration, Chongqing University, Chongqing 400-044, PR China

A R T I C L E I N F O JEL classification:

G30 G34 G21 M4 Keywords:

Bank risk-taking

Risk management committee Audit committee

Overlapping membership

A B S T R A C T

Although overlapping membership between risk management committee and audit committee is prevalent in banks’ boards, the existing literature focuses on the impact of a single board committee on bank risk-taking.

Using a sample of Chinese listed banks from 2007 to 2020, we examine whether and how overlapping membership between risk management committee and audit committee influences bank risk-taking. The results show that overlapping membership between risk management committee and audit committee reduces bank risk- taking. Furthermore, the risk-averse role of overlapping membership between risk management committee and audit committee is stronger in banks with weaker monitoring intensity and higher information acquisition costs. When exploring the potential channels of monitoring and information, we find that overlapping membership between risk management committee and audit committee helps reduce executive earnings management and make conservative interbank liability decisions. Finally, compared with other overlapping member characteristics, the role of overlapping risk management committee chair and financial experts in reducing bank risk- taking is more evident.

1. Introduction

The 2008 global financial crisis has largely been attributed to banks’

excessive risk-taking. Since then, improving corporate governance mechanisms to reduce bank risk-taking has attracted extensive attention from countries and academics (Ballester, Gonzalez-Urteaga, & Martinez, 2020; de Haan & Vlahu, 2016; Hopt, 2021; John, De Masi, & Paci, 2016). Compared to other countries, China is a typical bank-based financing country in which banks are the dominant part of the financial system and financing source (Wang, Liu, & Luo, 2020). Thus, excess risk-taking in Chinese banks produces high negative externalities and systemic financial risk. At the Fifth National Financial Work Conference in 2017, Xi Jinping stated that preventing systemic financial risk was an eternal theme of financial work. However, Chinese banks tend to take excessive risks owing to the weak monitoring intensity and complex business structures (Jiang, Liu, Lobo, & Xu, 2019; Zhu & Yang, 2016).

The board is an integral part of the corporate governance mechanisms (Jensen & Meckling, 1976), delegating risk-related monitoring and advising duties to two active board committees, namely the risk management committee (hereafter, RMC) and audit committee (hereafter,

AC) (Stulz, Tompkins, Williamson, & Ye, 2021). However, existing studies focus on the impact of a single board committee on bank risk- taking, with mixed findings (Aljughaiman & Salama, 2019; García- S´anchez, García-Meca, & Cuadrado-Ballesteros, 2017; Hines & Peters, 2015; Iselin, 2020). The main reason for the mixed findings may be that the usage of board committees separates information and damages communication between committee members and other directors (Adams, Ragunathan, & Tumarkin, 2021), thus undermining the board’s functioning (Malenko, 2014).

As the board assigns limited directors to multiple committees, overlapping membership between RMC and AC (hereafter, OMRA) is prevalent and plays an integral role in shaping board committee decisions (Chen & Wu, 2016). Numerous studies have focused on the governance role of overlapping membership on board committees and found competing effects, namely knowledge sharing and information dissemination (Al-Dhamari, Alquhaif, & Al-Gamrh, 2020; Faleye, Hoi- tash, & Hoitash, 2011) and busyness costs (Laux & Laux, 2009; Liao &

Hsu, 2013; Wan-Hussin, Fitri, & Salim, 2021). However, most of the above studies univocally focus on the monitoring role of overlapping membership between AC and compensation committee of non-financial

* Corresponding author.

E-mail addresses: [email protected] (B.Y. Ding), [email protected] (F. Wei).

1 Present/permanent address: School of Economics and Business Administration, Chongqing University, Chongqing, PR China Contents lists available at ScienceDirect

International Review of Financial Analysis

journal homepage: www.elsevier.com/locate/irfa

https://doi.org/10.1016/j.irfa.2023.102501

Received 5 June 2022; Received in revised form 24 August 2022; Accepted 9 January 2023

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firms in developed countries. To the best of our knowledge, Hines, Masli, Mauldin, and Peters (2015) and Tao and Hutchinson (2013) are the two studies investigating the role of overlapping membership on board committees in financial firms. However, although Hines et al. (2015) point out the information spillover effect of OMRA, they examine the U.

S. listed banks and the role of OMRA in bank audit pricing. Tao and Hutchinson (2013) is the only study to examine the monitoring role of overlapping membership between RMC and compensation committee in the risk-taking of Australian financial firms, but they focus on market risk and find an insignificant relationship between the two. Given that the monitoring role of overlapping membership on board committees differs depending on national legal systems (Sassen, Stoffel, Behrmann, Ceschinski, & Doan, 2018) and advising on risk decisions is also an essential board function (Stulz et al., 2021), we extend this avenue of research by investigating whether and how OMRA influences the risk- taking of Chinese banks.

We argue that OMRA may be particularly important in mitigating the risk-taking of Chinese banks for the following reasons. First, OMRA helps RMC and AC strengthen board monitoring intensity to weaken executives’ risk-taking. Because banks with centralized state ownership and implicit government guarantees do not fully bear the consequences of the risks taken by them, managers are more likely to pursue political promotion at the cost of bank risks (Zhu & Yang, 2016). OMRA helps RMC and AC gain more expertise in financial reporting and risk appetite to reduce executive moral hazard. Second, OMRA helps RMC obtain more information to assist the board in making conservative risk decisions. The complexity and opacity of the banking business aggravate the problem of information asymmetry. Understanding financial information helps RMC better predict a bank’s financial uncertainty and perceive decision risks. Although OMRA may also have overloading costs, its knowledge and information spillover effects dominate its busyness costs in banks characterized by weak monitoring and opaque information.

China provides a rich and natural setting to pursue our research objectives for the following reasons. First, OMRA is prevalent in Chinese banks’ boards. In 2016, the Comprehensive Risk Management Guide- lines for Banking Financial Institutions issued by the China Banking Regulatory Commission (hereafter, CBRC) stated that banks should establish communication channels between RMC and other board committees to enhance information sharing. The Corporate Governance Standards for Banking and Insurance Institutions issued in 2021 proposed that banks should form AC to monitor risks related to financial reporting and RMC to monitor and advise on the overall risks. Thus, the interaction between AC and RMC is a determinant of bank risk-taking.

According to our statistics, in 2020, 29.74% of RMC members held AC positions within the boards of Chinese listed banks. Second, the Chinese government and banks attach great importance to risk-taking. In the context of China’s economic transition, preventing financial systemic risks plays a crucial role in facilitating banks to serve the real economy and deepening the financial supply-side structural reform. However, the Chinese government controls and implicitly guarantees almost all banks (Deng, He, Kong, & Zhang, 2019), making them “too big to fail” and “too interconnected to fail” (Hopt, 2021). This unique setting in China mo- tivates us to investigate the role of OMRA in bank risk-taking. Our findings for Chinese banks can be generalized to other emerging markets.

Based on a sample of Chinese listed banks covering 2007 to 2020, we explore the relationship between OMRA and bank risk-taking. Our results show that OMRA reduces bank risk-taking. We also perform several endogenous analyses, including the two-stage least squares method (hereafter, 2SLS) and the change-on-change method to support the robustness of our findings. In addition, because the bank’s characteristics affect the governance efficiency of overlapping membership on board committees, we further explore the heterogeneity of the risk- averse effect of OMRA based on monitoring intensity and information acquisition cost. The results show a stronger risk-averse role of OMRA in

banks with weaker monitoring intensity and higher information acquisition costs. Next, we explore the potential mechanism and find the role of OMRA in assisting the board in reducing executive earnings management and making conservative interbank liability decisions. Finally, we examine the role of overlapping member characteristics in bank risk- taking and find that, compared with other overlapping member characteristics, the overlapping RMC chair and finance experts are more effective in reducing bank risk-taking.

Our study makes several contributions to the literature. First, we expand the research on corporate governance mechanisms and bank risk-taking. RMC and AC are crucial for the board to perform risk-related monitoring and advising duties. However, existing literature explores the impact of RMC and AC on bank risk-taking, ignoring the interaction between RMC and AC. Although Hines et al. (2015) find that OMRA reduces bank audit pricing, they focus on the U.S. sample and fail to explore the impact on bank risk-taking. To the best of our knowledge, this is the first study to examine the risk-averse role of OMRA in Chinese banks.

Second, our study enriches the literature on the governance efficiency of overlapping membership on board committees. Existing studies largely focus on the monitoring role of overlapping membership between AC and compensation committee on non-financial firms in developed countries and find mixed results. Compared to non-financial firms, banks are more likely to take excessive risks because of their higher moral hazard and information asymmetry. However, few studies have focused on the role of overlapping membership on board committees in bank risk-taking. Only Tao and Hutchinson (2013) find that overlapping membership between RMC and compensation committee has an insignificant effect on the market risk of Australian financial firms. In this study, we find that OMRA reduces the risk-taking of Chi- nese banks, thereby complementing empirical evidence of the knowledge and information spillover effects of overlapping membership on board committees.

Third, our study further reveals the potential mechanism of OMRA on bank risk-taking. Existing studies point out the information spillover effect of OMRA (Hines et al., 2015) but fail to empirically test the potential mechanism. Based on the channels of monitoring and information, we further explore the role of OMRA in executive earnings management and interbank liabilities and analyze the heterogeneity of the relationship between OMRA and bank risk-taking from the perspective of monitoring intensity, information acquisition cost, and overlapping member characteristics.

The remainder of this paper is organized as follows. Section 2 presents the literature review and research hypothesis. Section 3 undertakes the research design. Section 4 presents the empirical results. Section 5 reports the mechanism test. Section 6 is the further analysis. Section 7 concludes the paper.

2. Literature and hypothesis

2.1. Corporate governance mechanism and bank risk-taking

The corporate governance mechanism plays a crucial role in reducing bank risk-taking. Existing studies focus on the impact of corporate governance mechanisms such as executive compensation, ownership structure, and board characteristics on bank risk-taking, finding that weak monitoring and opaque information are the main drivers of bank risk-taking (Boubakri, El Ghoul, Guedhami, & Hossain, 2020; Dong, Girardone, & Kuo, 2017; Jiang et al., 2019; Khatib, Abdullah, Elamer, & Abueid, 2021; Zhu & Yang, 2016). The bank’s board performs monitoring and advising functions in risk management and delegates these responsibilities to the RMC and AC. Specifically, bank’s AC reviews financial reporting and internal controls, selects auditors, and monitors the risk related to financial reporting. Sun and Liu (2014) find a negative impact of AC effectiveness on the risk-taking of U.

S. banks. García-S´anchez et al. (2017) adopt the sample of multinational

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banks and verify the supervisory advantage hypothesis of financial experts on AC. Thus, bank’s AC plays an essential role in risk monitoring (Hines et al., 2015). However, as banking activities become more complex, AC with more financial experts lacks risk expertise to identify overall risks (Vidyadhar & Hovey, 2011).

To complement the above deficiencies of AC, bank’s board estab- lishes RMC to ensure that the professional team devotes sufficient time and resources to monitoring and advising on overall risk activities (Stulz et al., 2021). Prior research investigating the effect of RMC on bank risk- taking has reached ambiguous conclusions. Some studies examine the role of establishing RMC and find a strong risk-governance effect. For example, Iselin (2020) finds that establishing the RMC reduces the banks’ regulatory risk. Kamiya, Kang, Kim, Milidonis, and Stulz (2021) argue that banks with RMC are less likely to experience cyberattacks.

However, other studies have raised doubts regarding the effectiveness of RMC formation. Hines and Peters (2015) conclude that voluntary RMC plays a symbolic role and has no substantive effect on bank risk management. Using bank and interview data from the U.S., Stulz et al.

(2021) further confirm that the existence of RMC fails to reduce bank risk-taking. With the increasing prevalence of RMC in banks, studies have focused on the role of RMC characteristics in risk management.

Some studies regard RMC characteristics as an integral part of risk governance mechanisms, and verify the negative impact of risk governance mechanisms on bank risk-taking (Aljughaiman & Salama, 2019).

However, Tao and Hutchinson (2013) find a positive relationship between the composition of RMC and the risk-taking of Australian financial firms.

In sum, scholars have paid extensive attention to the impact of corporate governance mechanisms on bank risk-taking. RMC and AC assist the board in monitoring and advising on risk behaviors. However, existing literature focuses on the role of a single board committee in bank risk-taking and provides inconsistent evidence. The establishment of multiple board committees helps a single board committee carry out specific responsibilities but may result in information separation costs among board committees (Chen & Wu, 2016). Therefore, we investigate the risk-averse role of interaction between RMC and AC to expand the research on corporate governance mechanisms and bank risk-taking.

2.2. Governance efficiency of overlapping membership on board committees

Overlapping membership facilitates communication and coordina- tion among board committees. With the increasing prevalence of overlapping membership on board committees, assigning directors to multiple committees affects the governance efficiency of the board and committees. Since 2010, scholars have shifted their attention from the board committee characteristics, such as size, independence, meeting frequency, and financial expertise, to overlapping membership (Sassen et al., 2018). However, existing literature investigating the governance efficiency of overlapping membership on board committees focuses on overlapping membership between AC and compensation committee of non-financial firms in developed countries and supports competing theoretical perspectives of knowledge and information spillover and busyness effects.

On the one hand, overlapping membership between AC and compensation committee helps AC obtain more knowledge and information about compensation policies and earnings management and enhance the board monitoring, thus improving financial reporting quality (Chandar, Chang, & Zheng, 2012; Faleye et al., 2011; Habib &

Bhuiyan, 2016; Velte, 2017) and reducing the cost of debt (Al-Dhamari et al., 2020) and audit fees (Kalelkar, 2017; Mendez, Pathan, & Garcia, 2015). In addition, overlapping membership also helps the compensation committee obtain the financial reporting information and design more effective CEO pay contracts, thus reducing total CEO compensation (Brandes, Dharwadkar, & Suh, 2016; Mendez et al., 2015; Pathan, Wong, Benson, & Smith, 2017), equity-based incentives (Chang, Luo, &

Sun, 2012), excess compensation (Faleye et al., 2011), and the use of earnings metrics in compensation (Carter, Lynch, & Martin, 2022).

On the other hand, a larger number of overlapping members between AC and compensation committee may weaken the monitoring effectiveness of AC owing to excessive workload, thus improving earnings management (Chang et al., 2012; Laux & Laux, 2009; Liao & Hsu, 2013) and reducing internal and external audit quality (Karim, Robin, & Suh, 2015; Wan-Hussin et al., 2021). Consistent with the busyness hypothesis, overlapping members are more likely to dilute the functions of the compensation committee and thus weaken executive incentive compensation (Hoitash & Hoitash, 2009; Laux & Laux, 2009; Liao &

Hsu, 2013).

In addition, few scholars have examined the role of overlapping membership among other board committees of non-financial firms. For example, Peters and Romi (2013) find that overlapping membership between environmental committee and AC improves environmental information disclosure. Similarly, Lee (2020) documents that a high proportion of directors serving on both monitoring and advising committees improves the board’s advisory performance and fails to deteri- orate its monitoring roles. Gai, Cheng, and Wu (2021) conclude that after experiencing a peer restatement shock, boards with overlapping membership between AC and nominating committee are more likely to appoint experienced directors to reduce future financial restatement risk.

Although the corporate governance mechanisms of banks differ from those of non-financial firms (de Haan & Vlahu, 2016), only Tao and Hutchinson (2013) and Hines et al. (2015) investigate the governance outcomes of overlapping membership on board committees in financial firms. These studies focus on financial firms in developed countries and the monitoring role of overlapping membership on board committees.

However, little research has investigated whether and how OMRA influences the risk-taking of Chinese banks.

2.3. OMRA and bank risk-taking

We expect OMRA to be associated with risk aversion because it mitigates information asymmetry and agency problems for Chinese banks through monitoring and information channels. With respect to the monitoring channel, OMRA helps RMC and AC enhance the board monitoring of executives’ risk behaviors. Chinese banks are largely government-dominated, allowing executives to pursue extra risk for their political careers at the expense of debtholders (Zhu & Yang, 2016).

Moreover, the serious soft budget constraints and implicit government guarantees in Chinese banks allow executives to take fewer risks, aggravating their moral hazard (Fang, Lee, Chung, Lee, & Wang, 2020).

Pathan et al. (2017) indicate that overlapping directors use spillover knowledge gained from other board committees to enhance their monitoring intensity. Overlapping directors with expertise in audit and internal control help RMC better grasp operational activities to identify cover-ups in executives’ risk-taking motives, thereby reducing addi- tional monitoring costs for RMC (Stulz et al., 2021). Tao and Hutchinson (2013) argue that overlapping membership between RMC and compensation committee allows the compensation committee to set compensation packages in line with banks’ risk appetites, thereby better monitoring and managing excessive risk. Likewise, overlapping directors with risk expertise help AC better monitor internal controls and select auditors to reduce bank risk-taking.

With respect to the information channel, OMRA helps RMC assist the board in making more conservative risk decisions. In Chinese banks with complex and opaque business structures (de Haan & Vlahu, 2016), boards rely on operational information provided by executives and information sharing among committee members to advise on risk decisions. However, executives are reluctant to share information that helps directors advise because directors may also use this information to monitor (Laux & Laux, 2009). Furthermore, the separation of tasks caused by multiple board committees exacerbates communication issues

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between committee members and other directors (Chen & Wu, 2016).

Overlapping members on RMC with more information about financial reporting and risk appetite are more likely to perceive high risk in strategic decisions and assist the board in making more conservative decisions. Additionally, members serving on multiple committees within the board may be more influential because they tend to participate in more board decisions and develop a network of stakeholders who support their initiatives (Hines et al., 2015). Consistent with this view, OMRA helps board reduce CEO’s bargaining power and acquire more information (Lee, 2020), thus facilitating consensus on conservative strategies at board meetings. Therefore, overlapping members between RMC and AC are better monitors and advisors owing to their information and knowledge. These considerations lead to the following hypothesis:

H1: There is a negative association between OMRA and bank risk- taking.

Kolev, Wangrow, Barker, and Schepker (2019) point out that board committee governance efficiency depends on firm characteristics. To better understand the potential mechanism by which OMRA influences bank risk-taking, we follow Lee (2020) and further explore whether the effect of OMRA on bank risk-taking varies across monitoring intensity and information acquisition cost. Consistent with the substitution theory, if OMRA improves the effectiveness of board risk management through monitoring and information channels, we predict that banks’ boards are more likely to design a high level of OMRA to compensate for the weaker monitoring intensity and higher information acquisition costs.

2.4. Role of monitoring intensity

As executors of risk decisions, executives with weaker monitoring have stronger incentives to take excessive risk. Effective internal monitoring mechanisms reduce executive moral hazard. Thus, if OMRA enhances the board’s risk monitoring of executives, the negative relationship between OMRA and bank risk-taking becomes more evident in banks with weaker monitoring intensity. In China, measures of monitoring intensity may be given by deferral pay and critical mass female directors. These two variables are simultaneously considered because of weak external monitoring resulting from serious political controls and implicit guarantees. Specifically, deferral pay facilitates banks to align the interests of executives and debtholders (Deng, Ge, & He, 2021), and restrain executives’ risk-taking behaviors (Deng et al., 2019). According to the critical mass theory, critical mass female directors reduce bank earnings management (Fan, Jiang, Zhang, & Zhou, 2019) and pay attention to executives’ risk-taking behaviors (Abou-El-Sood, 2021).

Therefore, we propose the following hypotheses:

H2a: In banks without deferral pay, the negative relationship between OMRA and bank risk-taking is more evident.

H2b: In banks without critical mass female directors, the negative relationship between OMRA and bank risk-taking is more evident.

2.5. Role of information acquisition cost

The information acquisition cost is a determinant for banks to improve their risk decision-making ability. More information acquired by the board helps make more conservative risk decisions. Thus, if OMRA helps the board acquire more information about risk decisions, we predict that the negative relationship between OMRA and bank risk- taking is more evident in banks with higher information acquisition costs. In China, measures of information acquisition cost may be given by the types of bank listings and capital regulatory pressure. These two variables are simultaneously considered because of the complex business structure and opaque information of Chinese banks. Specifically, compared with banks listed only in mainland China, cross-listed banks in mainland China and Hong Kong are subject to a dual regulatory environment, resulting in stricter information disclosure and lower information acquisition costs. In addition, banks under stronger capital

regulation pressure tend to improve their capital adequacy ratio, pru- dently invest in risky assets (Wang, Zhang, & Wang, 2022), and disclose detailed risk information (Hirtle, Kovner, & Plosser, 2020). However, banks with less capital regulatory pressure are more likely to aggres- sively allocate risky assets (Abou-El-Sood, 2021), and manipulate earnings information to avoid regulatory interventions (Cheng, War- field, & Ye, 2011). This leads to the following hypotheses.

H3a: In banks listed only in mainland China, the negative relationship between OMRA and bank risk-taking is more evident.

H3b: In banks with less capital regulatory pressure, the negative relationship between OMRA and bank risk-taking is more evident.

3. Research design

3.1. Sample selection and data sources

We select Chinese A-share listed banks from 2007 to 2020 as the initial sample and remove bank-year observations with missing values.

Finally, we obtain unbalanced panel data of 36 listed banks, including 5 large state-owned commercial banks, 10 joint-stock commercial banks, 13 city commercial banks, and 8 rural commercial banks, with a total of 281 bank-year observations. We start our sample in 2007 for two reasons. First, the China Securities Regulatory Commission revised the Annual Standards for Information Disclosure Contents and Formats of Publicly Issued Securities Companies No. 2 in 2007 and required listed companies to disclose board committee members in annual reports.

Second, Chinese listed banks implemented new accounting standards in 2007. We hand-collect data on RMC and AC based on board committee information disclosed in annual reports and board meeting resolutions.

Other data are from CSMAR and WIND databases.

3.2. Variables

3.2.1. Explained variable

Existing literature often uses the ratio of risk weighted assets, Z- score, asset return volatility, and non-performing loan ratio to measure bank risk-taking. Following Dong et al. (2017) and Wang et al. (2022), we select the non-performing loan ratio (NPL) to measure the risk-taking of Chinese banks. The main reasons for this are as follows. First, because banks are the main financing resources in China, credit risk is the main driver of banks destabilization. Second, earnings indicators may result from financial manipulations. Third, Chinese banks are controlled by central or local governments and bear a low bankruptcy risk (Deng et al., 2019). Thus, NPL measures bank risk-taking better in China. The NPL is calculated as the ratio of non-performing loans to total loans. A higher NPL indicates higher levels of bank risk-taking.

3.2.2. Explanatory variable

Following Hines et al. (2015) and Velte (2017), we adopt the proportion of the number of RMC members who also sit on AC to the total number of RMC members to measure the degree of OMRA (Overlap).

We identify board committees as follows: Given that the Commercial Banking Law of the People’s Republic of China does not mandate banks to establish separate RMC and AC, we identify them according to the actual responsibilities of board committees disclosed in annual reports.

Specifically, we use risk as a keyword to search for separate and merged RMC and ultimately identify RMC based on risk monitoring and advisory responsibilities. Similarly, we use audit as a keyword search and further identify separate and merged AC according to financial reporting monitoring responsibilities. To test the robustness of our model, we exclude the sample with merged RMC or AC.

Next, we identify board committee members as follows. We extract the members who attend committee meetings in the current year by reading the committee information disclosed in the annual reports and board meeting resolutions. Owing to the resignation or appointment of members during the year, we identify the members for the given year

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based on June. Directors appointed by the committee from January to June and resigned from July to December are regarded as committee members in the given year. However, directors appointed by the committee from July to December are regarded as members of the following year, and those resigned by the committee from January to June are excluded in the current year.

3.2.3. Monitoring intensity

We construct the following two proxies to measure monitoring intensity: deferred pay (Dpay) and critical mass female directors (Critical).

Dpay is a dummy variable that equals one if the deferred pay policy is effectively implemented and zero otherwise. Owing to the delayed effect of policy implementation, the effect of the deferred pay policy begins the year after the issuing policy year (Deng et al., 2019). Furthermore, following Fan et al. (2019), we measure Critical as a dummy variable that equals one if critical mass (three or more) of female directors exists and zero otherwise.

3.2.4. Information acquisition cost

Following existing literature (Deng et al., 2021; Wang et al., 2022), we measure the information acquisition cost from two aspects: bank listing type (List) and capital regulatory pressure (Car). List equals one if banks are cross-listed in mainland China and Hong Kong and zero otherwise. Car equals one if the capital adequacy ratio is higher than the sample mean and zero otherwise.

3.2.5. Control variables

We control for factors that may affect bank risk-taking. Following Zhang and Wu (2020), we first control for bank financial indicators, including asset size (Size), return on assets (Roa), non-interest income (Nir), operating efficiency (Cti), liquidity (Ldr), and listing years (Fir- mage). Second, because effective corporate governance mechanisms reduce bank risk-taking (Dong et al., 2017), we control for the following governance indicators: ownership concentration (Topone), CEO duality (Dual), board independence (Indrate), board size (Bdsize), board dili- gence (Bdmmet), and audit quality (Big4). Given that the demand for corporate governance strength also prompts overlapping membership (Liao & Hsu, 2013), controlling for the above governance variables may alleviate the concern of potential omitted variables. Third, we control for macroeconomic indicator, namely the real regional GDP growth rate (GDPg), to account for the local economic environment (Dong et al., 2017). Finally, we also include bank and year fixed effects to address issues related to omitted variables. All continuous variables are win- sorized at the 1st and 99th percentiles to avoid the undue influence of extreme observations. Detailed definitions of all the variables are shown in Table 1.

3.3. Model

Following Zhang and Wu (2020), we design the regression model to test our hypotheses.

NPLit=β₀+β₁Overlap_it+∑

Controlsit+ui+vt+εit (1) where NPL denotes the ratio of non-performing loans to total loans.

Overlap is the proportion of the number of RMC members who also sit on AC to the total number of RMC members. Overlap is the core variable of interest, and we expect β1 to be negative. Controls, μ, υ, and ε refer to control variables (Size, Roa, Nir, Cti, Ldr, Firmage, Topone, Dual, Indrate, Bdsize, Bdmeet, Big4, GDPg), bank fixed effect, year fixed effect, and random disturbance term, respectively.

4. Results

4.1. Descriptive statistics

Table 2 presents descriptive statistics for the main variables. We find that the mean value of NPL (0.0130) is less than the median value (0.0134), indicating that most Chinese listed banks have high non- performing loan ratios. Overlap averages out at around 29.69% for the banks in our sample and peaks at approximately 80%, indicating a high degree of OMRA in Chinese listed banks. This situation may be attributed to the Comprehensive Risk Management Guidelines for Banking Financial Institutions issued by the CBRC in 2016. Nevertheless, the mean value of Overlap (0.2969) is larger than the median value (0.2857), indicating that more than half of Chinese listed banks have a low degree of OMRA. In addition, the mean value of Dpay is 0.6975, suggesting that 69.75% of Chinese listed banks effectively implement the deferred pay policy. The mean value of Critical is 0.4021, indicating that 40.21% of the banks have three or more female directors. The mean values of List (0.4413) and Car (0.4555) show that approximately 45% of Chinese listed banks have lower information acquisition costs.

4.2. Correlation analysis

Table 3 lists the Pearson correlation coefficients of the main variables. The correlation coefficients of the control variables are less than 0.5, indicating that the problem of multicollinearity is weak in the model Table 1

Variable definitions.

Type Abbreviation Measurement

Explained variable NPL Ratio of non-performing loans to total loans Explanatory

variable Overlap Proportion of the number of RMC members who also sit on AC to the total number of RMC members

Monitoring intensity

Dpay Dummy variable that equals one if the

deferred pay is effectively implemented and zero otherwise

Critical Dummy variable that equals one if critical mass (three or more) of female directors exists and zero otherwise

Information acquisition cost

List Dummy variable that equals one if banks are cross-listed in mainland China and Hong Kong and zero otherwise

Car Dummy variable that equals one if capital adequacy ratio is higher than the sample mean and zero otherwise

Control variables

Size Natural logarithm of total bank assets Roa Ratio of net profit to assets

Nir Ratio of the non-interest net income to the operating income

Cti Ratio of the operating and administrative expenses to the operating income Ldr Ratio of total loans to total deposits Firmage Natural logarithm of one plus the number of

years a bank has been listed Topone Proportion of shares held by the largest

shareholder

Dual Dummy variable that equals one if chair serves as CEO and zero otherwise Indrate Percentage of independent directors on the

board of directors

Bdsize Natural logarithm of the total number of directors

Bdmeet Natural logarithm of the number of board meetings

Big4 Dummy variable that equals one if auditor is Big Four and zero otherwise

GDPg

Large state-owned and joint-stock commercial banks use the real GDP growth rate of country and other banks use the real GDP growth rate of the province where they are registered

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(1). We further provide variance inflation factors (VIF) to examine the multicollinearity issue. The results show that the maximum value of the VIF is 4.25 (less than the critical value of 10), again indicating that our model does not have a serious multicollinearity problem. In addition, the correlation coefficient between Overlap and NPL is negative but nonsignificant, suggesting no correlation between OMRA and bank risk- taking when other factors are not considered. Therefore, we adopt regression analysis to verify the causal relationship between the two.

4.3. OMRA and bank risk-taking

Columns 1–4 of Table 4 report the results of the model (1). In column 1, we do not include bank financial and governance characteristics and macroeconomic indicator but add bank and year fixed effects. Column 2 includes bank financial characteristics and fixed effects. Column 3 includes bank financial and governance characteristics and fixed effects.

Column 4 includes bank financial and governance characteristics, macroeconomic indicator, and fixed effects. The results show that the coefficients of Overlap are significantly negative at the 1% level, suggesting that a high degree of OMRA reduces the risk-taking of Chinese banks. These results support H1 and suggest that in-depth collaboration between RMC and AC is conducive to reducing bank risk-taking, thus expanding the conclusion of Hines et al. (2015). In addition, our results support the view of Tao and Hutchinson (2013). They argue that cooperation between RMC and other committees is beneficial for financial firms. The reason may be that overlapping directors with rich knowledge and information about financial reporting and risk appetite help the board better monitor executive moral hazard and advise on risk Table 2

Descriptive statistics.

N Mean Sd P50 Min Max

NPL 281 0.0130 0.0045 0.0134 0.0042 0.0265

Overlap 281 0.2969 0.2089 0.2857 0 0.8000

Dpay 281 0.6975 0.4602 1 0 1

Critical 281 0.4021 0.4912 0 0 1

List 281 0.4413 0.4974 0 0 1

Car 281 0.4555 0.4989 0 0 1

Size 281 28.4000 1.5090 28.4900 25.2600 30.9700

Roa 281 0.0100 0.0022 0.0100 0.0046 0.0148

Nir 281 0.2196 0.0914 0.2093 0.0582 0.4463

Cti 281 0.3082 0.0520 0.3025 0.2052 0.4626

Ldr 281 0.7492 0.1187 0.7364 0.4743 1.1120

Firmage 281 1.7740 0.9036 1.9460 0 3.3320

Topone 281 0.2745 0.1821 0.2000 0.0436 0.6760

Dual 281 0.0783 0.2691 0 0 1

Indrate 281 0.3650 0.0457 0.3571 0.2000 0.5000

Bdsize 281 2.6740 0.1703 2.7080 2.1970 2.9440

Bdmeet 281 2.2840 0.3419 2.3030 1.3860 3.1350

Big4 281 0.8790 0.3267 1 0 1

GDPg 281 0.0753 0.0255 0.0715 0.0170 0.1423

This table provides descriptive statistics for the main variables. NPL is the ratio of non-performing loans to total loans. Overlap is the proportion of the number of RMC members who also sit on AC to the total number of RMC members. Dpay equals one if the deferred pay is effectively implemented and zero otherwise.

Critical equals one if critical mass (three or more) of female directors exists and zero otherwise. List equals one if banks are cross-listed in mainland China and Hong Kong and zero otherwise. Car equals one if the capital adequacy ratio is higher than the sample mean and zero otherwise. Size is the natural logarithm of the total bank assets. Roa is the ratio of net profit to assets. Nir is the ratio of non- interest net income to the operating income. Cti is the ratio of operating and administrative expenses to the operating income. Ldr is the ratio of total loans to total deposits. Firmage is the natural logarithm of one plus the number of years a bank has been listed. Topone is the proportion of shares held by the largest shareholder. Dual equals one if the chair serves as CEO and zero otherwise.

Indrate is the percentage of independent directors. Bdsize is the natural logarithm of the total number of directors. Bdmeet is the natural logarithm of the number of board meetings. Big4 equals one if the auditor is Big Four and zero otherwise.

GDPg is the real regional GDP growth rate.

Table 3 Correlation analysis. VIF NPL Overlap Dpay Critical List Car Size Roa Nir Cti Ldr Firmage Topone Dual Indrate Bdsize Bdmeet Big4 Overlap 1.34 −0.001 1.000 Dpay 1.84 0.088 −0.008 1.000 Critical 1.18 −0.009 −0.015 −0.108* 1.000 List 2.71 0.221*** 0.221*** 0.070 0.017 1.000 Car 1.29 0.014 0.095 0.073 0.008 0.166*** 1.000 Size 4.25 0.158*** 0.291*** 0.202*** −0.045 0.671*** 0.027 1.000 Roa 1.82 −0.375*** 0.044 −0.129** −0.040 0.275*** 0.256*** 0.240*** 1.000 Nir 2.24 0.280*** 0.051 0.479*** −0.032 0.331*** −0.006 0.483*** −0.096 1.000 Cti 1.95 −0.216*** −0.041 −0.311*** 0.249*** 0.014 −0.135** −0.248*** −0.040 −0.361*** 1.000 Ldr 1.97 0.213*** 0.070 0.204*** 0.036 0.080 −0.189*** 0.242*** −0.329*** 0.479*** −0.208*** 1.000 Firmage 2.20 −0.122** 0.092 0.266*** 0.052 0.124** −0.146** 0.494*** 0.049 0.466*** −0.117* 0.456*** 1.000 Topone 2.71 0.111* 0.429*** 0.012 0.157*** 0.603*** 0.067 0.643*** 0.180*** 0.194*** 0.008 0.021 0.160*** 1.000 Dual 1.11 0.101* −0.034 0.134** −0.104* 0.034 0.079 0.036 −0.036 0.078 −0.053 0.076 −0.024 −0.012 1.000 Indrate 1.24 −0.007 −0.035 0.217*** 0.025 −0.024 −0.004 0.134** −0.123** 0.093 −0.163*** 0.190*** 0.175*** 0.097 0.133** 1.000 Bdsize 1.64 −0.270*** −0.084 −0.308*** 0.045 0.063 −0.009 0.069 0.242*** −0.068 0.293*** 0.039 0.133** −0.135** −0.173*** −0.227*** 1.000 Bdmeet 1.26 0.083 0.191*** 0.079 −0.040 0.354*** −0.038 0.324*** 0.086 0.221*** −0.046 0.140** 0.078 0.286*** 0.036 0.093 −0.011 1.000 Big4 1.56 −0.010 0.223*** 0.041 −0.119** 0.330*** −0.055 0.495*** 0.142** 0.308*** −0.273*** 0.133** 0.333*** 0.308*** 0.068 0.022 0.029 0.254*** 1.000 GDPg 2.25 −0.113* 0.029 −0.528*** 0.067 0.009 −0.024 −0.134** 0.403*** −0.395*** 0.407*** −0.446*** −0.263*** 0.045 −0.139** −0.300*** 0.297*** −0.098* −0.134** This table reports Pearson correlation coefficients of the main variables. NPL is the ratio of non-performing loans to total loans. Overlap is the proportion of the number of RMC members who also sit on AC to the total number of RMC members. Dpay equals one if the deferred pay is effectively implemented and zero otherwise. Critical equals one if critical mass (three or more) of female directors exists and zero otherwise. List equals one if banks are cross-listed in mainland China and Hong Kong and zero otherwise. Car equals one if the capital adequacy ratio is higher than the sample mean and zero otherwise. Size is the natural logarithm of total bank assets. Roa is the ratio of net profit to assets. Nir is the ratio of non-interest net income to the operating income. Cti is the ratio of operating and administrative expenses to the operating income. Ldr is the ratio of total loans to total deposits. Firmage is the natural logarithm of one plus the number of years a bank has been listed. Topone is the proportion of shares held by the largest shareholder. Dual equals one if the chair serves as CEO and zero otherwise. Indrate is the percentage of independent directors. Bdsize is the natural logarithm of the total number of directors. Bdmeet is the natural logarithm of the number of board meetings. Big4 equals one if the auditor is Big Four and zero otherwise. GDPg is the real regional GDP growth rate. ***, **, and * indicate significance at 1%, 5%, and 10%, respectively.

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decisions, thus reducing bank risk-taking.

With regards to the control variables, the coefficients of Size, Nir, Ldr, and Big4 are significantly positive, indicating that banks with more assets, higher non-interest income, higher liquidity, and higher audit quality take more risks. However, the coefficients of Roa, Cti, and Fir- mage are significantly negative, suggesting that bank risk-taking declines as profitability, operational efficiency, and listing years increase. In addition, the coefficient of GDPg is significantly positive, indicating that bank risk-taking is positively correlated with the level of regional economic development. Overall, the results of the control variables are in line with theoretical expectations and consistent with most previous studies (Dong et al., 2017; Wang et al., 2022; Zhang & Wu, 2020).

4.4. Endogeneity tests

As with other studies on overlapping membership on board committees (Carter et al., 2022; Lee, 2020), our study is not exempt from potential endogeneity issues. First, bank risk-taking may be determined by unobserved variables that also affect OMRA. For example, directors’

financial expertise determines whether they hold multiple committee positions (Carter et al., 2022) and implement bank risk-taking (García- S´anchez et al., 2017). Second, reverse causality may also drive our results. Banks with higher risk-taking are less likely to attract high-quality talents, leading to a higher degree of overlapping membership on board committees (Liao & Hsu, 2013). Therefore, we use several methods to alleviate these problems.

First, following Zhang, Cai, Dickinson, and Kutan (2016), we intro- duce the instrumental variable (IV) for Overlap and use 2SLS to mitigate reverse causality bias. For large state-owned and joint-stock commercial banks, IV is measured by the average Overlap of all banks in this group.

For city commercial banks and rural commercial banks, we measure IV as the average Overlap of all banks in the province where the bank is located. Due to competition and learning effects among banks in the region (Devenow & Welch, 1996), banks may imitate the corporate governance mechanisms of other banks in the region. Meanwhile, the OMRA of other banks in the region is not directly related to bank risk- taking. If such a relationship exists, it also affects bank risk-taking mainly by influencing bank committee characteristics. Thus, both the relevance and exclusion conditions of instrumental variable are satis- fied. The results of the first stage are reported in column 1 of Table 5.

The results reject the weak instrumental variable hypothesis and the under-identification hypothesis, and show that the coefficient of IV is significantly positive. The second-stage results in column 2 show that the coefficient of Overlap is still significantly negative, indicating that the baseline results are robust.

Second, due to continuous bank risk-taking, changes in bank risk- taking better reflect the incremental value of OMRA. Thus, following Deng et al. (2019), we use the change-on-change method to mitigate the potential bias caused by time-invariant factors. Column 3 of Table 5 reports the results and shows that the changes in OMRA are negatively correlated with changes in bank risk-taking.

4.5. Robustness check 4.5.1. Alternative proxies

First, we use non-performing loan provision coverage (Cover) as a substitute variable for NPL. Cover is the ratio of loan loss reserves to non- performing loans and is positively correlated with the cautious expec- tation of banks’ credit risk. Second, we adopt the proportion of the number of RMC members who also sit on AC to the total number of RMC and AC members (Overlap1) as an alternative indicator for Overlap.

Overlap1 indicates the degree of interpenetration between RMC and AC.

Third, because AC and RMC consist of independent and non- independent directors, we also measure OMRA as the proportion of independent directors (non-independent directors) who both sit on AC and RMC to the total number of independent directors (non-independent directors) on RMC, namely Overlapind (Overlapexd). The results in columns 1–4 of Table 6 show that the relationship between OMRA and bank risk-taking remains significantly negative after replacing the key indicators.

4.5.2. Competing interpretations

Some banks may manage risk activities well without OMRA because their boards have stronger monitors and more financial experts. Thus, RMC and AC are likely to assist the board in monitoring and advising on risk decisions regardless of OMRA. To mitigate these competing expla- nations, we perform the following tests. First, in terms of talent supply, our results may be caused by the strong monitors on RMC and AC. Di- rectors who perform better monitoring duties are more likely to serve on multiple committees (Lee, 2020). Following Gorshunov, Armenakis, Harris, and Walker (2021) and Trinh, Aljughaiman, and Cao (2020), we adopt four requisite qualifications to measure strong monitors: gender, independence, multiple directorships, and financial expertise. Directors who sit on other boards are defined as those with multiple directorships.

That is, directors are classified as strong monitors if they are women, Table 4

Effect of OMRA on bank risk-taking.

(1) NPL (2) NPL (3) NPL (4) NPL

Overlap −0.0062*** −0.0047*** − 0.0049*** −0.0048***

(0.0012) (0.0011) (0.0011) (0.0011)

Size 0.0033*** 0.0024** 0.0027**

(0.0010) (0.0012) (0.0012)

Roa −0.4773*** − 0.5069*** −0.5201***

(0.1421) (0.1448) (0.1445)

Nir 0.0101*** 0.0107*** 0.0095***

(0.0030) (0.0030) (0.0031)

Cti −0.0278*** − 0.0267*** −0.0269***

(0.0064) (0.0065) (0.0065)

Ldr 0.0121*** 0.0121*** 0.0121***

(0.0026) (0.0027) (0.0026)

Firmage −0.0024*** − 0.0025*** −0.0025***

(0.0005) (0.0006) (0.0006)

Topone 0.0014 0.0010

(0.0036) (0.0036)

Dual 0.0006 0.0006

(0.0006) (0.0006)

Indrate − 0.0014 −0.0012

(0.0036) (0.0036)

Bdsize − 0.0011 −0.0013

(0.0014) (0.0014)

Bdmeet − 0.0005 −0.0004

(0.0006) (0.0006)

Big4 0.0015** 0.0015**

(0.0007) (0.0007)

GDPg 0.0635*

(0.0380)

Year YES YES YES YES

Bank YES YES YES YES

Constant 0.0197*** −0.0630** − 0.0348 −0.0535

(0.0008) (0.0289) (0.0325) (0.0342)

N 281 281 281 281

R2 0.6297 0.7517 0.7612 0.7642

This table provides reports the results of the model (1). NPL is the ratio of non- performing loans to total loans. Overlap is the proportion of the number of RMC members who also sit on AC to the total number of RMC members. Size is the natural logarithm of total bank assets. Roa is the ratio of net profit to assets. Nir is the ratio of non-interest net income to the operating income. Cti is the ratio of operating and administrative expenses to the operating income. Ldr is the ratio of total loans to total deposits. Firmage is the natural logarithm of one plus the number of years a bank has been listed. Topone is the proportion of shares held by the largest shareholder. Dual equals one if the chair serves as CEO and zero otherwise. Indrate is the percentage of independent directors. Bdsize is the natural logarithm of the total number of directors. Bdmeet is the natural logarithm of the number of board meetings. Big4 equals one if the auditor is Big Four and zero otherwise. GDPg is the real regional GDP growth rate. Standard errors are reported in brackets. ***, **, and * indicate significance at 1%, 5%, and 10%, respectively.

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independent, serve on multiple boards, and have financial expertise. In the model (1), we add two indicators (Rcquality and Acquality) to measure the proportion of monitoring directors on RMC and AC, respectively. If these attributes drive the baseline results, their coefficients should be highly significant. The results in column 5 of Table 6 show that the coefficient of Overlap is still significantly negative, and those of Rcquality and Acquality are negative but nonsignificant.

Second, in terms of high monitoring and advising needs, our results may be due to financial experts rather than overlapping members on RMC. AC members monitor the financial reporting process and possess the financial expertise necessary to monitor complex banks. García- S´anchez et al. (2017) find that financial experts on AC have monitoring

advantages in reducing bank risk-taking. Therefore, RMC members with financial expertise better understand financial reporting risks and improve the quality of risk management. To mitigate the above alternative interpretation, we follow Carter et al. (2022) and control for the proportion of financial experts on RMC (Rcexpert). Column 6 of Table 6 reports the results and shows that the coefficient of Overlap is still significantly negative and that of Rcexpert is negative but nonsignificant.

Taken together, we rule out the possibility that it is the supply of monitoring talents and the demand for financial experts, rather than the knowledge and information spillover effects of OMRA, that is associated with lower risk-taking in Chinese banks.

4.5.3. Test of nonlinear relation

Apart from knowledge and information spillover effects, overlapping directors with multiple responsibilities may be too busy to improve corporate governance efficiency (Chandar et al., 2012). Thus, there may be a potential nonlinear U-shaped relationship between OMRA and bank risk-taking. In other words, the risk-averse effect increases with OMRA when OMRA is low; however, when the members of RMC and AC overlap highly or even completely, they suffer more from overloading, thus deteriorating risk management. Following Pathan et al. (2017), we add the quadratic term of Overlap (Overlap2) to the model (1). The results in column 7 of Table 6 show that the coefficient of Overlap2 is negative but nonsignificant, suggesting no nonlinear relationship between OMRA and risk-taking of Chinese banks. The results further indicate that the knowledge and information spillover effects of OMRA are dominant in Chinese banks.

4.5.4. Constrained sample

We adjust the sample range to mitigate the influence of sample factors on our results. The Corporate Governance Standards for Banking and Insurance Institutions issued by the China Banking and Insurance Regulatory Commission in 2021 proposed that the board should establish separate or merged committees. However, merged RMC or AC may increase information spillover and busyness effects of OMRA because overlapping directors perform multiple tasks beyond risk monitoring and advising. To reduce the impact of committee formation on our results, we remove samples with merged RMC or AC and estimate the model (1). The results in column 8 of Table 6 show that the coefficient of OMRA is still significantly negative. Additionally, bank decisions were highly uncertain and ambiguous during the 2008 global financial crisis.

To mitigate the impact of the 2008 global financial crisis on our results, we delete samples from 2007 to 2008 and estimate the model (1). The results in column 9 of Table 6 show that the coefficient of OMRA is still significantly negative.

4.5.5. Placebo test

To further address the concern of omitted variables, we follow Cor- naggia and Li (2019) and perform the placebo test. Specifically, we first conduct a random assignment on Overlap in our sample to obtain a random sample. Then, we regress the model (1) for this random sample and record the T value of Overlap. Finally, we repeat 500 times according to the above ideas and use all the T value distributions of Overlap for the placebo test. If the omitted variables have no effect on our results, the above T values of Overlap are concentrated near zero. Otherwise, our findings are more likely to be pseudo-correlation caused by accidental factors. As shown in Fig. 1, the T value of most random sampling results is near zero, and no T value is to the left of the T value (− 4.31) of the baseline result. These results further support the robustness of the baseline results.

4.6. Effect of monitoring intensity

We classify our sample into stronger and weaker monitoring intensity based on Dpay and Critical. Table 7 reports the results for the subsamples. Specifically, the results in columns 1–2 show that the Table 5

Results of endogeneity.

2SLS Change-on-change

(1) Overlap (2) NPL (3) NPL

Overlap −0.0020** −0.0059***

(0.0010) (0.0021)

Size −0.2354*** −0.0040* 0.0025**

(0.0602) (0.0020) (0.0012)

Roa 5.6436 −0.3011** −0.5113***

(7.5998) (0.1375) (0.1374)

Nir −0.1444 −0.0011 0.0096***

(0.1651) (0.0025) (0.0029)

Cti 0.3502 0.0010 −0.0260***

(0.3412) (0.0063) (0.0063)

Ldr 0.0356 −0.0012 0.0121***

(0.1391) (0.0033) (0.0025)

Firmage −0.0226 −0.0008 −0.0025***

(0.0295) (0.0012) (0.0005)

Topone −0.2774 −0.0042 0.0007

(0.1910) (0.0038) (0.0035)

Dual −0.0232 0.0004 0.0006

(0.0302) (0.0003) (0.0005)

Indrate −0.3380* 0.0014 −0.0019

(0.1866) (0.0022) (0.0036)

Bdsize 0.0099 0.0003 −0.0012

(0.0727) (0.0008) (0.0013)

Bdmeet 0.0347 −0.0005 −0.0004

(0.0290) (0.0004) (0.0005)

Big4 0.0979*** 0.0006 0.0016**

(0.0376) (0.0007) (0.0007)

GDPg −0.4590 0.0232 0.0616*

(2.0039) (0.0293) (0.0361)

IV 0.9620***

(0.1140)

Year YES YES YES

Bank YES YES YES

Constant 6.2657*** 0.0003

(1.7573) (0.0017)

N 281 244 280

R2 0.3920 0.6430 0.7632

C-D 71.158

LM 60.291***

This table reports the results of the endogeneity tests. In column 1, C–D is the weak instrumental variable test and LM is the under-recognition test. IV is the mean value of Overlap for all banks located in the region where the bank is located. NPL is the ratio of non-performing loans to total loans. Overlap is the proportion of the number of RMC members who also sit on AC to the total number of RMC members. Size is the natural logarithm of total bank assets. Roa is the ratio of net profit to assets. Nir is the ratio of non-interest net income to the operating income. Cti is the ratio of operating and administrative expenses to the operating income. Ldr is the ratio of total loans to total deposits. Firmage is the natural logarithm of one plus the number of years a bank has been listed. Topone is the proportion of shares held by the largest shareholder. Dual equals one if the chair serves as CEO and zero otherwise. Indrate is the percentage of independent directors. Bdsize is the natural logarithm of the total number of directors. Bdmeet is the natural logarithm of the number of board meetings. Big4 equals one if the auditor is Big Four and zero otherwise. GDPg is the real regional GDP growth rate. In column 3, the variables represent the respective changes. Standard errors are reported in brackets. ***, **, and * indicate significance at 1%, 5%, and 10%, respectively.