Rookie Independent Directors and Corporate Policies: Evidence from China

Abstract

In this study, we investigate how corporate policies are influenced by the presence of rookie independent directors (RIDs). We hypothesize that RIDs, due to their inexperience, impact corporate policies in ways that may amplify agency problems. Specifically, firms with RIDs demonstrate higher investment in R&D and capital expenditure, increased leverage (both short- and long-term), enhanced liquidity (cash holdings and working capital), and elevated risk-taking, while their presence leads to a conservative payout policy. Using a sample of Chinese-listed firms from 2008 to 2022, our findings confirm these predictions. Additional analyses reveal that RIDs’ effects are more pronounced in high-CEO-power environments, where their limited governance capabilities may align with managerial interests, exacerbating financial risks. This study contributes to the corporate governance literature by integrating upper echelon and agency theories, shedding light on the dual-edged role of RIDs in shaping corporate outcomes.

1. Introduction

Independent directors are a central component of corporate governance because they are expected to monitor management, protect shareholder interests, and constrain opportunistic corporate decisions. Prior research shows that the effectiveness of independent directors depends not only on formal independence but also on director-specific characteristics such as experience, reputation, and the ability to challenge management (Bryan & Mason, 2020; Ding et al., 2025; Fei, 2022; X. Li et al., 2024). Among these characteristics, tenure is especially important because board experience shapes directors’ understanding of firm-specific issues, their confidence in the boardroom, and their ability to participate meaningfully in strategic decision-making. Studies have shown that longer tenure of decision makers leads to favorable corporate outcomes (Liu et al., 2022; Patro et al., 2018; Zhou et al., 2020). On the other hand, directors or managers with shorter tenures can have negative or insignificant effects on corporate policies. For example, a CEO with shorter tenure leads to negative real earnings management (Geertsema et al., 2020). Similarly, directors with short tenures bring information asymmetry (Cheong et al., 2022). In terms of the corporate governance literature, the directors who serve on a company board for less than three years are called rookie directors (J. Chen et al., 2022). This study aims to investigate the effect of rookie independent directors (hereafter RIDs) on corporate policies.

Examining RIDs is important for more than reasons of novelty. Corporate policies such as investment, financing, liquidity management, payout, and risk-taking are among the most consequential choices firms make because they shape growth opportunities, financial flexibility, agency costs, and shareholder value (Bernile et al., 2018; Jebran et al., 2022). If rookie independent directors are less effective monitors, their presence may allow greater managerial discretion over these decisions (Bin Khidmat et al., 2024). At the same time, newer directors may also bring fresh perspectives and support growth-oriented initiatives (Ullah et al., 2024a). This makes RIDs an important setting for understanding how board composition translates into real corporate outcomes. More broadly, board characteristics can influence both policy choices and the risk that firms ultimately bear, rather than affecting governance through a single channel alone (Bernile et al., 2018).

The governance implications of RIDs can be understood through the joint lens of agency theory and upper echelons theory. Agency theory suggests that effective monitoring is necessary to align managerial actions with shareholder interests (Fama & Jensen, 1983; Jensen & Meckling, 1976). Rookie independent directors may be weaker monitors because they possess limited board experience, lower reputation capital, and greater dependence on insiders for reappointment, all of which can reduce their willingness or ability to challenge management effectively (J. Chen et al., 2022; Levit & Malenko, 2016; Lin et al., 2016). On the other hand, upper echelons theory offers a complementary perspective by emphasizing that the background and experience of decision-makers shape organizational outcomes (Hambrick & Mason, 1984). From this perspective, rookie directors may bring fresh perspectives and openness to change, but their limited exposure to boardroom dynamics may also constrain their effectiveness when firms face complex policy decisions (Cao et al., 2023; Z. Chen & Keefe, 2020).

Although prior research has increasingly examined the consequences of rookie independent directors, the existing evidence remains fragmented and outcome-specific. Recent studies link RIDs to corporate fraud, firm performance, audit fees, dividends, compensation gaps, cash holdings, and agency-related outcomes (Bai & Yu, 2022; Bin Khidmat et al., 2024; Cao et al., 2023; J. Chen et al., 2022; Z. Chen & Keefe, 2020; Ullah et al., 2024a). However, this literature still does not provide a unified understanding of whether RIDs systematically shape a broader set of core corporate policies through a common governance mechanism. This omission is important because investment, financing, liquidity, payout, and risk-taking decisions are the main channels through which board monitoring affects firm behavior and long-term value. By examining these policy domains together, this study moves beyond isolated outcome-based evidence and offers a more integrated view of how rookie independent directors influence corporate decision-making. This broader framing is in line with work showing that board characteristics should be studied not only through single outcomes but also through the way they jointly shape corporate policies (Bernile et al., 2018; Jebran et al., 2022).

The choice of China further strengthens the contribution of this study. Most existing research on board dynamics and corporate governance has focused on developed economies, whereas China provides a particularly informative setting for examining the governance effects of rookie independent directors (Beji et al., 2021; Lu & Wang, 2021; Van Hoang et al., 2021). China combines the scale and economic importance of a major capital market with institutional conditions that make board monitoring especially consequential. In particular, weaker legal enforcement, underdeveloped governance mechanisms, and limited shareholder protection increase the scope for managerial opportunism and make differences in director effectiveness more visible (Huang et al., 2023; F. Jiang & Kim, 2024). This setting is therefore well-suited for identifying whether the limited tenure, lower reputation capital, and weaker boardroom influence of RIDs translate into meaningful differences in corporate policies. Moreover, as the world’s second-largest economy, China’s unique blend of rapid economic growth and institutional constraints provides a rich context for investigating the governance implications of RIDs. Managerial behavior in Chinese firms, influenced by these systemic factors, further accentuates the need for research in this area.

Using a large sample of Chinese A-share firms listed on the Shanghai and Shenzhen stock exchanges from 2009 to 2022, this study finds that the presence of RIDs is associated with significant changes in corporate policies. Specifically, firms with more RIDs exhibit higher investment intensity, greater reliance on debt financing, higher liquidity retention, lower payout, and greater risk-taking. Additional analyses further show that these effects are stronger in firms with powerful CEOs and are also associated with higher earnings management and stock price crash risk. These findings suggest that rookie independent directors, because of their limited monitoring effectiveness, may allow managerial choices that intensify agency problems.

This study makes three contributions. First, it extends the growing literature on rookie independent directors by moving beyond isolated outcome-based evidence and showing that rookie status is a governance-relevant board attribute with systematic implications for major corporate policy choices. Second, it contributes to theory by linking agency theory with upper echelons theory to explain why rookie independent directors may influence firm behavior. This perspective helps explain how director inexperience, lower reputation value, and dependence on insiders interact to affect policy outcomes. Third, by focusing on Chinese-listed firms, this study provides evidence from an emerging-market setting where differences in the experience profile of independent directors are likely to have stronger effects on firm behavior because external governance institutions are less effective in constraining managerial opportunism.

The remainder of the paper is organized as follows. Section 2 reviews the relevant literature and develops the hypotheses. Section 3 describes the sample, variables, and empirical methodology. Section 4 presents empirical findings and additional analyses. Section 5 concludes.

2. Literature Review and Hypothesis Development

2.1. Theoretical Background on RIDs

2.1.1. Agency Theory and RIDs

Agency theory provides the main framework for understanding why rookie independent directors may affect corporate policies. The separation of ownership and control creates incentives for managers to pursue private benefits that may not align with shareholder interests, making board monitoring a key internal governance mechanism (Fama & Jensen, 1983; Jensen & Meckling, 1976). Independent directors are expected to constrain opportunistic managerial behavior, improve decision quality, and protect outside investors. The effectiveness of this role, however, depends on whether directors possess sufficient information, authority, and incentives to challenge management.

Rookie independent directors may be weaker monitors because they have limited board experience, lower reputation capital, and less influence in the boardroom. Their short tenure may reduce their ability to understand firm-specific operations, interpret complex managerial proposals, and identify opportunistic behavior. They may also rely more heavily on insiders for information and future board retention, which can weaken their practical independence even when they satisfy formal independence requirements (J. Chen et al., 2022; Levit & Malenko, 2016; Lin et al., 2016). In emerging-market settings, where internal governance mechanisms often operate under greater constraints, these limitations may become even more important. From this perspective, rookie status is a governance-relevant attribute because it may weaken board monitoring and increase managerial discretion over major corporate decisions.

2.1.2. Upper Echelons Theory and RIDs

Upper echelons theory offers a complementary perspective by emphasizing that organizational outcomes reflect the background, experiences, and cognitive frames of key decision-makers (Hambrick & Mason, 1984). Applied to boards, this view suggests that director experience can shape how information is interpreted, how risks are evaluated, and how strategic alternatives are assessed. From this perspective, rookie independent directors may differ from more experienced directors not only in their monitoring strength but also in the way they approach strategic choices.

This perspective offers a broader interpretation of RIDs. On the one hand, newer directors may bring fresh perspectives, current knowledge, and greater openness to innovation or strategic renewal (Z. Chen & Keefe, 2020; Ullah et al., 2023). On the other hand, limited tenure may reduce their confidence and firm-specific understanding. It may also weaken their influence in the boardroom. As a result, they may be less able to contribute effectively when firms face complex policy decisions (Bin Khidmat et al., 2024). In this sense, upper echelons theory does not replace the agency perspective in this study but complements it by showing that director inexperience may affect both monitoring and decision style. The key empirical question is therefore whether the potential benefits of freshness and strategic openness outweigh the costs of weaker oversight. In the Chinese context, where governance constraints are weaker and insider influence is stronger, the weaker-monitoring channel is expected to dominate on average.

2.2. Empirical Evidence

Independent directors are expected to strengthen corporate governance by monitoring management, reducing agency conflicts, and improving the quality of strategic decision-making. However, prior research shows that independent directors are not a homogeneous group. Their effectiveness depends not only on formal independence but also on attributes such as tenure, reputation, expertise, and boardroom influence (Bryan & Mason, 2020; Ding et al., 2025; Fei, 2022; X. Li et al., 2024). Among these attributes, director experience is especially important because it shapes how effectively directors understand firm-specific issues, question management, and participate in major corporate decisions. For this reason, recent research has increasingly emphasized the role of board experience and director tenure in explaining differences in board effectiveness and firm outcomes (Asad et al., 2024; Gao & Huang, 2024; James et al., 2021; Patro et al., 2018).

A growing literature from developed markets generally suggests that directors’ experience improves monitoring and advisory effectiveness. Experienced directors possess stronger firm-specific knowledge, greater confidence in challenging management, and higher influence in board deliberations, which can improve oversight and decision quality (Brown et al., 2017; N. Li & Wahid, 2018; Livnat et al., 2021; Mooney et al., 2021). Related work also shows that board experience and expertise influence investment choices, innovation, and firm value, suggesting that director characteristics matter not only for compliance and monitoring but also for strategic corporate decisions (Bravo & Reguera-Alvarado, 2018; S. Kang et al., 2018; Kor, 2006). These studies imply that director tenure and experience should be treated as important determinants of board effectiveness rather than as background descriptors.

Evidence from emerging markets points to an even stronger role for directors’ experience, but for somewhat different reasons. In such settings, weaker legal enforcement, more concentrated ownership, and stronger insider influence often reduce the practical independence of outside directors and make board monitoring more difficult (Khan et al., 2024; Khosa, 2017; Luo & Liu, 2023). As a result, directors with limited tenure may face greater informational disadvantages and stronger pressure to align with insiders. This suggests that director inexperience may be especially costly in emerging markets because external governance mechanisms are less able to offset weak board oversight. The Chinese setting is particularly informative in this regard, as listed firms operate in an environment where managerial discretion can be substantial, and the effectiveness of independent directors may vary considerably with their experience and boardroom influence (W. Jiang et al., 2015; Y. Li et al., 2021).

Within this broader literature, recent studies have begun to examine rookie independent directors more directly. This emerging evidence links RIDs to corporate fraud, audit fees, dividend outcomes, agency problems, cash holdings, and other governance-related outcomes, suggesting that rookie status is not a trivial board characteristic (Bai & Yu, 2022; Bin Khidmat et al., 2024; Cao et al., 2023; J. Chen et al., 2022; Z. Chen & Keefe, 2020; Ullah et al., 2024a). At the same time, the existing evidence remains largely fragmented, with most studies focusing on single outcomes rather than examining whether RIDs systematically shape a broader set of core corporate policies. This leaves an important gap in understanding how director inexperience affects firm behavior across multiple policy domains.

The present study also relates to a broader stream of research showing that board characteristics can shape several corporate policies simultaneously. Bernile et al. (2018), for example, show that board diversity influences financial policies, innovation efficiency, policy persistence, and firm risk jointly, rather than through a single isolated outcome. Similarly, Jebran et al. (2022) highlight that board-related characteristics and governance conditions can affect firm-level policy and risk outcomes in connected ways. Therefore, the effect of rookie independent directors should not be assessed only through one governance outcome but through the wider set of policy choices through which boards influence firm behavior.

2.3. Hypothesis Development

The literature reviewed above suggests that the effect of rookie independent directors on corporate policies is not mechanically one-sided. Studies from developed markets generally show that directors’ experience improves monitoring, advising, and decision quality. In emerging markets, director inexperience may be more costly because legal protection is weaker and insider influence is stronger (Brown et al., 2017; Khan et al., 2024; Livnat et al., 2021; Mooney et al., 2021). At the same time, broader board research indicates that board characteristics may influence multiple corporate policies simultaneously and may shape both policy choices and firm risk (Bernile et al., 2018; Jebran et al., 2022). Against this background, the hypotheses developed below distinguish between two competing possibilities: rookie independent directors may contribute fresh perspectives and support strategic renewal, but they may also weaken monitoring and allow greater managerial discretion. Given the institutional environment in China, the latter effect is expected to dominate.

2.3.1. RIDs and Investment Policies

Investment policy is one of the clearest domains in which the potential benefits and costs of RIDs coexist. On the one hand, upper echelons theory suggests that newer directors may contribute fresh perspectives and encourage strategic renewal, especially in areas such as research and development and other long-term growth initiatives (Hambrick & Mason, 1984). RIDs may infuse boards with diverse viewpoints and contemporary knowledge that are valuable for innovation and long-term strategy (Pang et al., 2020). More broadly, board-composition research shows that some board characteristics support higher R&D investment and better innovation efficiency. This suggests that some forms of real risk-taking may be productive rather than reckless (Bernile et al., 2018).

On the other hand, agency theory suggests that investment decisions are particularly vulnerable to weak monitoring. Managers may use R&D, capital expenditures, or acquisitions to pursue empire-building, prestige, or other private benefits rather than shareholder value maximization (Jensen & Meckling, 1976). This concern is likely to be more pronounced when directors lack experience, firm-specific knowledge, and boardroom authority needed to critically assess complex investment proposals (Tran Phuong et al., 2022). RIDs may rely more heavily on insider support and may therefore be less able to resist managerial influence, particularly in settings characterized by weaker governance (Bin Khidmat et al., 2024; J. Chen et al., 2022; Levit & Malenko, 2016). Thus, although RIDs may occasionally support value-enhancing innovation, the dominant expectation in the Chinese setting is that weaker monitoring will permit more aggressive investment behavior.

Hypothesis 1.

Rookie independent directors are positively associated with corporate investment policies.

2.3.2. RIDs and Financing Policies

The relation between directors’ tenure and financing policy is also theoretically mixed. Debt can discipline managers by reducing free cash flow, but it can also facilitate overexpansion when boards fail to monitor financing decisions effectively (Myers, 1977). From a more favorable perspective, newer directors may support external financing when it enables growth opportunities, operational expansion, or strategic repositioning. If RIDs are more open to new initiatives, they may be willing to approve borrowing that supports long-term investments. Prior research further indicates that board characteristics can systematically shape financial policy choices, highlighting the importance of linking board composition to financing behavior rather than assuming neutrality (Bernile et al., 2018; Jebran et al., 2022).

However, the agency-based concern is stronger in the present context. Financing decisions require directors to evaluate debt capacity, repayment discipline, and the broader risk implications of leverage. Longer-tenured directors are typically better positioned to do so because they possess greater institutional knowledge and experience (Brown et al., 2017; Mooney et al., 2021). By contrast, RIDs may lack the expertise and confidence needed to scrutinize debt-related proposals, making them more likely to defer to CEOs or senior board members (J. Chen et al., 2022). Such deference may result in excessive or suboptimal borrowing, thereby increasing agency costs and financial risk (Bin Khidmat et al., 2024; Owusu et al., 2022). In a setting where external governance constraints are weaker, this monitoring weakness is likely to dominate any strategic-growth motive.

Hypothesis 2.

Rookie independent directors are positively associated with debt financing.

2.3.3. RIDs and Liquidity Policies

Higher cash holdings and stronger working capital positions may reflect prudence, financial flexibility, and preparation for uncertainty (Baum et al., 2012). Because newer directors may be cautious when operating in unfamiliar board environments, RIDs may support larger liquidity buffers to reduce perceived downside risk (Ullah et al., 2024b). This interpretation is broadly consistent with upper echelons theory, which suggests that directors’ experiences and cognitive frames shape how they respond to uncertainty (Hambrick & Mason, 1984).

At the same time, agency theory emphasizes that liquid assets are especially susceptible to managerial discretion. Managers may hoard cash to avoid capital market scrutiny, preserve discretionary resources, and pursue projects that do not maximize shareholder value (Jebran et al., 2022; Manoel & Moraes, 2022). Independent directors are expected to constrain such behavior by ensuring that retained resources are justified and efficiently deployed (Bryan & Mason, 2020). Yet, RIDs may be less effective in this role because their short tenure, limited authority, and weaker familiarity with firm-specific details reduce their ability to detect and challenge opportunistic behavior (J. Chen et al., 2022). In addition, less experienced directors may adopt overly cautious financial strategies and favor cash preservation, which can itself lead to inefficient resource allocation (J.-K. Kang et al., 2016; Ullah et al., 2024b). Taken together, these arguments imply that although higher liquidity may partly reflect prudence, in the Chinese context, it is more likely to reflect weaker monitoring over internal funds.

Hypothesis 3.

Rookie independent directors are positively associated with liquidity policies.

2.3.4. RIDs and Payout Policies

The effect of RIDs on payout policy is similarly ambiguous. On the one hand, dividends can serve as a governance mechanism by reducing free cash flow under managerial control and by signaling financial discipline to outside investors (Butt et al., 2022). RIDs are often described as ambitious and eager to establish credibility, which may induce them to participate actively in board decisions and support shareholder-oriented policies (Z. Chen & Keefe, 2020). Related evidence also suggests that managerial ability is associated with higher dividend payments (Hou et al., 2025). This line of reasoning implies that RIDs could, in principle, strengthen payout discipline rather than weaken it.

On the other hand, the limited experience of RIDs can hinder their ability to govern effectively, especially in complex corporate environments. Their lack of familiarity with intricate organizational dynamics may make it difficult for them to challenge opportunistic managers or assert independent judgment. Research has shown that a higher proportion of RIDs on boards can be associated with weakened governance and even increased corporate fraud (Bai & Yu, 2022; J. Chen et al., 2022). This raises concerns that RIDs may inadvertently enable self-serving managerial behaviors, such as hoarding resources or diverting funds for personal gains, at the expense of shareholder payouts.

In that sense, the same factors that weaken RID monitoring in investment and financing decisions should also make them less likely to enforce payout discipline. Given the Chinese institutional setting, where shareholder protection is comparatively weaker, the retention motive is expected to dominate.

Hypothesis 4.

Rookie independent directors are negatively associated with payout policies.

2.3.5. RIDs and Risk-Taking

The final hypothesis examines the influence of rookie independent directors on their firm’s risk-taking. RIDs may significantly influence their firms’ risk-taking behavior, largely due to their limited experience and unique motivations. Studies suggest that managers are more inclined to take excessive risks when the potential for personal gain outweighs the consequences of loss (Wu & Tu, 2007). RIDs, characterized by short tenure and lower reputation capital, may struggle to effectively monitor and curb such behavior, particularly in the case of opportunistic CEOs (Bin Khidmat et al., 2024).

The reputation–liquidation theory posits that RIDs, motivated by career concerns, may avoid challenging management to maintain insider support, inadvertently enabling excessive risk-taking (Levit & Malenko, 2016). The lack of robust oversight by RIDs allows CEOs to pursue risky strategies that may inflate short-term performance metrics, benefiting their compensation but potentially harming shareholders in the long term (J. Chen et al., 2022). Inexperienced directors often lack the confidence and authority to counter senior executives, creating a governance gap that encourages speculative investments, over-leveraging, and volatile financial practices (Bai & Yu, 2022). This can lead to outcomes such as stock price crashes or financial instability (Çolak & Korkeamäki, 2021). While RIDs may promote innovative strategies that involve calculated risks, their inexperience often tilts the balance towards excessive risk-taking. We propose the following hypothesis:

Hypothesis 5.

Rookie independent directors are positively associated with risk-taking.

3. Methods

3.1. Sample

To test the proposed hypotheses, we utilize data from Chinese A-share firms listed on the Shanghai and Shenzhen stock exchanges between 2009 and 2022. The selected period begins in 2009 because R&D data, a key variable in our study, is not available prior to this year. Additionally, the global financial crisis of 2007–2008 significantly affected economic conditions, and choosing 2009 as the starting point helps minimize potential distortions caused by this event.

We follow prior studies in the Chinese context, and we have collected the required data from the China Stock Market and Accounting Research (CSMAR) database (Bai & Yu, 2022; T. Chen, 2015; Z. Chen & Keefe, 2020; Firth et al., 2016; Hai et al., 2018). To prepare the dataset, we first remove observations with missing values for all dependent, independent, moderation, and control variables to ensure the completeness of the data. We excluded firms in the financial sector because their distinct functions and reporting standards make them unsuitable for comparison with firms in other industries. To address potential outliers that could skew the results, all continuous variables are winsorized at the 1% level. After applying these steps, the final sample consists of 32,456 firm-year observations, providing a robust dataset to test the hypotheses and examine the role of rookie independent directors in influencing corporate policies.

3.2. Variables Measurement

3.2.1. Corporate Policies

The dependent variables in this study comprehensively cover critical areas of corporate decision-making, covering investment, financing, liquidity, payout, and risk-taking policies. Research and Development (R&D) expenditures, Merger and Acquisition (M&A), and Capital Expenditures (CAPEX) serve as measures of corporate investment policies, reflecting a firm’s commitment to innovation and long-term growth strategies. Previous studies on the interaction between TMT attributes and firms’ investment policies have also used the same proxies to measure investment. For instance, Kor (2006) emphasizes how top management teams affect R&D strategies. Similarly, a study by Ramírez et al. (2022) showed how ownership structure affects the investment decisions using CAPEX as a proxy. Additionally, M&A has been used as a proxy for investment policy to investigate the effect of policy uncertainty on mergers and acquisitions (Bonaime et al., 2018).

In the realm of financing policies, short-term financing (STD) and long-term financing (LTD) are included to capture the firm’s capital structure choices. These variables, as discussed by Myers (1977), are essential in understanding a firm’s leverage preferences and financial stability. On the liquidity front, cash holdings (Cash) and working capital (WC) are crucial metrics, with Arslan-Ayaydin et al. (2014) demonstrating the strategic importance of cash reserves in mitigating operational risks and maintaining flexibility during economic uncertainties. Working capital efficiency, as explored by Gill and Biger (2013), reflects the firm’s ability to manage its short-term assets and liabilities effectively.

Payout and risk-related variables further enrich the analysis. Dividend ratio (DPR) and changes in dividends (∆Div) capture the firm’s shareholder return policies, with studies like Tripathy et al. (2021) highlighting the role of dividends in signaling financial health. Meanwhile, volatility of return (Vol-Ret) and volatility of ROE (Vol-ROE) provide a lens to assess the firm’s risk profile, as suggested by Fama and French (1992), who link governance quality to risk-taking behaviors. By integrating these dependent variables, this study aligns with a robust body of literature, providing a nuanced understanding of how RIDs influence diverse corporate policy areas.

3.2.2. RIDs

Building on prior research, we categorized independent directors into two groups: rookie independent directors and seasoned independent directors (Bai & Yu, 2022; Z. Chen & Keefe, 2020; Ullah et al., 2024a). RIDs are defined as independent directors with three or fewer years of board-level experience, while seasoned independent directors have more than three years of such experience. The three-year threshold aligns with earlier studies conducted in the Chinese context (J. Chen et al., 2022; Z. Chen & Keefe, 2020), which highlighted this period as critical for differentiating between inexperienced and experienced board members.

To classify directors, we use data from the China Stock Market and Accounting Research (CSMAR) database and track board appointments from 1999 onward. This allows us to calculate the tenure of each independent director serving on a board in a given year. Following the prior literature, an independent director is classified as a rookie independent director (RID) if their board tenure is less than three years. We then measure RIDs in two ways. RID1 is the proportion of rookie independent directors among all independent directors on the board in year t. RID2 is a dummy variable equal to 1 if rookie independent directors account for at least 50% of the independent directors on the board in year t, and 0 if otherwise.

3.2.3. Control Variables

We included several control variables to account for board-related attributes, ownership structures, and firm-specific characteristics, ensuring the robustness of our analysis. First, we controlled board size, measured as the total number of directors on the board, and board independence, defined as the proportion of independent directors to total board members (J. Chen et al., 2022; Luo & Liu, 2023). These variables are crucial for capturing the impact of board composition on governance effectiveness. Second, we controlled ownership structures by incorporating state ownership (a dummy variable equal to 1 if the organization is state-owned and 0 if otherwise) and institutional ownership (the shareholding ratio of institutional investors). These variables reflect the influence of ownership types on corporate policies, as state-owned firms may have different governance dynamics compared to privately owned firms, while institutional investors often play an active role in monitoring managerial actions (Conyon & He, 2011; Hai et al., 2018).

Finally, we included firm-specific variables such as firm size (measured as the natural logarithm of total assets), firm age (number of years since the organization’s founding), profitability (ratio of net income to total assets), Tobin’s Q (ratio of market value to book value of total assets), and sales growth (percentage change in sales from year t − 1 to year t). These factors capture variations in firm characteristics that can influence corporate policies and governance outcomes (Firth et al., 2016; Jebran et al., 2022).

3.2.4. CEO Power

The CEO power variable is a construct that is not directly observable. The literature offers various indicators to measure. Consistent with prior studies, we employ the CEO’s pay slice (CPS) as a proxy for CEO power, given its objectivity and ability to capture the CEO’s influence within the top management team studies (Bebchuk et al., 2011; Usman et al., 2018). CPS is calculated as the CEO’s total compensation divided by the aggregate compensation of the five highest-paid executives, including the CEO. Following this, we built a dummy variable that takes the value of 1 if the resulting value is greater than the median CPS value.

3.2.5. Stock Price Crash Risk

Grounded on the expanded market model, we used two measures of stock price crash risk (Al Mamun et al., 2020; Garg et al., 2022; Jebran et al., 2022; X. Li et al., 2017). Based on the firm-specific week return, the expanded market model regression is written as:

$$Rit=\mathsf{\alpha }\mathrm{i}+\mathsf{\beta }1\mathrm{i} \mathrm{r}\mathrm{m},\mathrm{t}-2+\mathsf{\beta }2\mathrm{i} \mathrm{r}\mathrm{m},\mathrm{t}-1+\mathsf{\beta }3\mathrm{i} \mathrm{r}\mathrm{m},\mathrm{t}+\mathsf{\beta }4\mathrm{i} \mathrm{r}\mathrm{m},\mathrm{t}+1+\mathsf{\beta }5\mathrm{i} \mathrm{r}\mathrm{m},\mathrm{t}+2+\mathsf{\epsilon }\mathrm{i},\mathrm{t}$$

(1)

where Rit represents stock return (i) in the week (t), while rm, t signifies value-weighted market index return in week t.

Non-synchronous trading is corrected by taking the lag and the lead value of the market return. The first measure of crash risk is calculated by taking the natural logarithm of one plus the residual returns (1 + εi, t) from Equation (1) above. Using the values of W_it from Equation (1), crash risk is calculated as the negative conditional return skewness (NSKEW). The NSKEW is derived by taking the negative third moment of the firm-specific weekly returns divided by the standard deviation of the firm’s weekly returns raised to the third power as:

$$\mathrm{N}\mathrm{S}\mathrm{K}\mathrm{E}\mathrm{W}\mathrm{i},\mathrm{t}=[-{\left(n-1\right)}^{3∕2}\sum {\left(Wi t\right)}^3]/\left[\left(n-1\right)\left(n-2\right){{\left((\sum Wi t\right)}^3)}^{3/2}\right]$$

(2)

In Equation (2) above, n is the number of trading weeks on stock i in year t. The higher the value of NSKEW, the higher the crash risk.

3.2.6. Earnings Management

To examine whether the effect of RIDs on corporate policies differs across firms with high and low earnings management, we employ the Modified Jones Model proposed by Dechow et al. (1995), which measure discretionary accruals. The Modified Jones Model is widely used in the literature for detecting earnings management, as it adjusts for changes in revenues and accounts for the discretionary nature of accruals (Aljifri & Elrazaz, 2024; Amara et al., 2025; Farah Freihat et al., 2025). Recognizing the limitations of traditional models, we control firm performance using the approach proposed by Kothari et al. (2005), who argue that failing to account for performance can result in model misspecification and heteroskedasticity. Accordingly, we include return on assets (ROA) as a control variable in the estimation process to improve the robustness of our earnings management measure. We further classify firms into high and low earnings management groups based on the median discretionary accruals value derived from the two above-mentioned proxies of discretionary accruals. This classification enables us to analyze the differential impact of RIDs on corporate policies under varying levels of earnings management.

3.3. Empirical Model

The model is estimated separately for each corporate policy category as follows:

Investment policies

$${\mathit{Investment} \mathit{Policy}}_{\mathrm{i}, +1} = {\beta }_0 + {\beta }_1 {\mathit{RID}}_{\mathrm{i},\mathrm{t}} + \Sigma {\beta }_{\mathrm{k}} {\mathit{Controls}}_{\mathrm{i},\mathrm{t}} + {\epsilon }_{\mathrm{i},\mathrm{t}}$$

(3)

where Investment Policy denotes R&D, M&A, and CAPEX.

Financing policies

$${\mathit{Financing} \mathit{Policy}}_{\mathrm{i}, +1} = {\beta }_0 + {\beta }_1 {\mathit{RID}}_{\mathrm{i},\mathrm{t}} + \Sigma {\beta }_{\mathrm{k}} {\mathit{Controls}}_{\mathrm{i},\mathrm{t}} + {\epsilon }_{\mathrm{i},\mathrm{t}}$$

(4)

where Financing Policy denotes STD and LTD.

Liquidity policies

$${\mathit{Liquidity} \mathit{Policy}}_{\mathrm{i}, +1} = {\beta }_0 + {\beta }_1 {\mathit{RID}}_{\mathrm{i},\mathrm{t}} + \Sigma {\beta }_{\mathrm{k}} {\mathit{Controls}}_{\mathrm{i},\mathrm{t}} + {\epsilon }_{\mathrm{i},\mathrm{t}}$$

(5)

where Liquidity Policy denotes Cash and WC.

Payout policies

$${\mathit{Payout} \mathit{Policy}}_{\mathrm{i}, +1} = {\beta }_0 + {\beta }_1 {\mathit{RID}}_{\mathrm{i},\mathrm{t}} + \Sigma {\beta }_{\mathrm{k}} {\mathit{Controls}}_{\mathrm{i},\mathrm{t}} + {\epsilon }_{\mathrm{i},\mathrm{t}}$$

(6)

where Payout Policy denotes Div-dummy, DPR, and ΔDividends.

Risk-taking

$${\mathit{Risk} \mathit{Policy}}_{\mathrm{i}, \mathrm{t}+1} = {\beta }_0 + {\beta }_1 {\mathit{RID}}_{\mathrm{i},\mathrm{t}} + \Sigma {\beta }_{\mathrm{k}} {\mathit{Controls}}_{\mathrm{i},\mathrm{t}} + {\epsilon }_{\mathrm{i},\mathrm{t}}$$

(7)

where Risk Policy denotes Vol-Ret and Vol-ROE.

In all specifications, RID and the control variables are measured at year t, while the dependent variables are measured at year t + 1. Detailed definitions of all variables are provided in Appendix A.

We employ ordinary least squares (OLS) to estimate all models except the payout models, because the payout variables are left-censored. For payout policies, we apply the Tobit method following the approach of Ullah et al. (2024a). Industry and year fixed-effects are included across all model specifications to control unobserved heterogeneity, and all independent and control variables are lagged by one year (t − 1) to examine causal relationships and mitigate endogeneity concerns (Dass et al., 2014). To address issues of autocorrelation and heteroscedasticity, we use robust standard errors clustered at the firm level (Ullah et al., 2024a). Furthermore, to minimize the impact of outliers, all continuous variables are winsorized at the 1st and 99th percentiles. This approach ensures the robustness and reliability of the results, aligning with methodologies used in prior studies. We also checked multicollinearity among the explanatory variables. The unreported diagnostics indicate that multicollinearity is not a serious concern in our models.

4. Empirical Findings

4.1. Descriptive Statistics

Table 1. Descriptive Statistics.

Variables	Obs.	Mean	STD	Min	p25	p50	p75	Max
R&D	32,456	0.019	0.021	0	0.001	0.015	0.028	0.1
M&A	32,456	0.045	0.08	0.0001	0.001	0.015	0.045	0.2
CAPEX	32,456	0.089	0.098	0.01	0.026	0.06	0.118	0.3
STD	32,456	0.362	0.18	0.1	0.22	0.34	0.472	0.7
LTD	32,456	0.081	0.092	0.01	0.014	0.042	0.122	0.25
Cash	32,456	0.11	0.115	0	0.002	0.09	0.165	0.4
WC	32,456	0.195	0.24	0.01	0.045	0.192	0.35	0.8
Div-dummy	32,456	0.45	0.5	0	0.11	0.15	0.28	1
DPR	32,456	0.026	0.038	0	0	0.012	0.03	0.15
∆Div	32,456	0.007	0.009	0	0.0007	0.004	0.008	0.05
Vol-Ret	32,456	0.13	0.055	0.05	0.1	0.125	0.155	0.3
Vol-ROE	32,456	0.06	0.09	0.01	0.02	0.035	0.055	0.2
RID1	32,456	0.282	0.283	0	0	0.25	0.429	1
RID2	32,456	0.184	0.387	0	0	0	0	1
BS	32,456	8.7	1.75	5	7	9	10	15
BI	32,456	0.37	0.055	0.2	0.33	0.333	0.43	0.5
Lev	32,456	0.44	0.23	0.1	0.28	0.43	0.59	0.9
SO	32,456	0.37	0.485	0	0	0	1	1
Size	32,456	22.83	1.09	20.64	21.72	22.36	23.03	24.01
Growth	32,456	0.19	0.43	−0.2	−0.005	0.12	0.28	1.5
TQ	32,456	2.1	1.6	0.8	1.2	1.5	2.2	5
FA	32,456	2.798	0.51	1.05	1.30	1.96	2.41	3.02

This table reports descriptive statistics for all variables used in the analysis. Corporate policy variables include investment policies (R&D, M&A, and CAPEX), financing policies (STD and LTD), liquidity policies (Cash and WC), payout policies (Div-dummy, DPR, and ΔDiv), and risk-taking measures (Vol-Ret and Vol-ROE). RID1 is the proportion of rookie independent directors among all independent directors on the board, while RID2 is a dummy variable equal to 1 if rookie independent directors constitute at least 50% of the independent directors on the board, and 0 if otherwise. Detailed variable definitions are provided in Appendix A. The sample consists of Chinese A-share listed firms over the period 2009 to 2022.

provides descriptive statistics for 32,456 firm-year observations. Rookie independent directors are well-represented, with RID1 averaging 28.2% (SD = 28.3%) and RID2 indicating that 18.4% of firms have the most rookie directors. These findings align with studies highlighting the governance challenges posed by inexperienced board members (Bai & Yu, 2022; Z. Chen & Keefe, 2020).

Investment policies reveal an average R&D expenditure of 1.9% (SD = 2.1%), M&A activity at 4.5% (SD = 8%), and capital expenditure at 8.9% (SD = 9.8%). Financing policies show short-term financing averaging 36.2% (SD = 18%) and long-term financing at 8.1% (SD = 9.2%). Liquidity policies include cash holdings of 11% (SD = 11.5%) and working capital of 19.5% (SD = 24%). Dividend policies show a mean dividend ratio of 2.6% (SD = 3.8%) and small dividend changes averaging 0.7% (SD = 0.9%). Risk-taking metrics, such as stock return and ROE volatility, average 13% and 6%, respectively. Control variables provide further insights into the sample. Board size averages 8.7 members (SD = 1.75), with an average board independence of 37% (SD = 5.5%). Leverage is relatively high, with a mean of 44% (SD = 23%), while state ownership accounts for 37% of the sample. Firms have an average age of 16.4 years (2.79 log value), with growth averaging 19% and Tobin’s Q at 2.1, reflecting a mixture of mature and developing firms.

4.2. Main Findings

4.2.1. Effect of RIDs on Investment Policies

Table 2. RIDs and Investment Policies.

Variable	R&D	M&A	CAPEX	R&D	M&A	CAPEX
RID1	0.0042 ***	0.0051 **	0.0047 *
	(3.207)	(2.176)	(1.863)
RID2				0.0028 ***	0.0032 **	0.0039 *
				(3.012)	(2.987)	(1.542)
BS	0.0035 ***	0.0041	0.0052 ***	0.0035	0.004 *	0.0051 ***
	(4.785)	(1.879)	(5.008)	(1.837)	(1.732)	(5.31)
BI	0.006 **	0.007 ***	0.007 ***	0.005	0.0074	0.008 ***
	(2.601)	(4.585)	(5.433)	(1.986)	(2.98)	(5.461)
Lev	0.0042 ***	0.0047	0.0051 *	0.0041 ***	0.0049 ***	0.0056 ***
	(6.9876)	(1.8765)	(1.5432)	(−6.9876)	(−7.5432)	(−7.5432)
SO	0.0032 **	0.0044	0.0056 ***	0.0036	0.0043	0.0051 ***
	(2.457)	(1.835)	(6.042)	(1.134)	(5.952)	(6.91)
Size	−0.0058 ***	−0.0061	−0.0075	−0.0050 **	−0.0063 *	−0.0071
	(−6.274)	(−1.749)	(−1.195)	(−2.263)	(−1.405)	(−1.943)
Growth	0.0064	0.0042 *	0.004	0.0042 ***	0.0049 ***	0.0058
	(1.876)	(1.754)	(1.204)	(4.836)	(5.654)	(6.164)
TQ	−0.0062 ***	−0.0068	−0.0028 **	−0.00617 ***	−0.0073	−0.0081 ***
	(−7.203)	(−1.118)	(−2.429)	(−7.475)	(−1.961)	(−8.011)
FA	−0.0047 ***	−0.0031 **	−0.0044 *	−0.0043	−0.0051 ***	−0.0059 ***
	(−4.507)	(−2.114)	(−1.839)	(−1.577)	(−5.531)	(−6.572)
Industry	Yes	Yes	Yes	Yes	Yes	Yes
Year	Yes	Yes	Yes	Yes	Yes	Yes
Obs.	32,456	32,456	32,456	32,456	32,456	32,456
Adjusted R2	0.3912	0.0876	0.1123	0.4054	0.0923	0.1231

This table reports the effect of rookie independent directors on investment policies. The dependent variables are R&D, M&A, and CAPEX. Columns 1 to 3 use RID1, while Columns 4 to 6 use RID2. All models are estimated using pooled OLS and include industry and year fixed-effects. All explanatory and control variables are measured at year $t$, while the dependent variables are measured at year $t+1$. T-statistics in parentheses are based on heteroskedasticity-robust standard errors clustered at the firm level. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively. Variable definitions are reported in Appendix A.

reports the effects of rookie independent directors on investment policies measured by R&D, M&A, and CAPEX. The coefficients on both RID1 and RID2 are positive across all three specifications, with the strongest and most consistent effects observed for R&D. These findings support agency theory. Rookie independent directors may be less effective in constraining managerial investment discretion. This may reflect their limited experience, lower reputation capital, and greater dependence on insiders (J. Chen et al., 2022; Jensen & Meckling, 1976; Levit & Malenko, 2016). In such circumstances, managers may face fewer obstacles in pursuing visible and expansionary projects, including innovation spending, acquisitions, and capital expenditures. At the same time, the result is also compatible with the complementary upper echelons perspective that newer directors may be more receptive to growth-oriented or innovation-related initiatives. This interpretation is in line with prior work suggesting that less experienced or newer directors may support strategic renewal, but may also lack the monitoring capacity needed to distinguish efficiently between value-enhancing investment and managerial overinvestment (Bai & Yu, 2022; Pang et al., 2020).

The economic magnitude of the results is also meaningful. One percentage-point increase in RID1 is associated with an increase of approximately 0.00004 in R&D, 0.00005 in M&A, and 0.00005 in CAPEX. Relative to the sample means of 0.019, 0.045, and 0.089, these correspond to increases of about 0.22%, 0.11%, and 0.05%, respectively. For RID2, moving from boards without a rookie majority to boards with a rookie majority is associated with increases of 0.0028 in R&D, 0.0032 in M&A, and 0.0039 in CAPEX. These results show that the effect of RIDs is not only statistically significant but also economically relevant, with the largest relative effect observed for R&D. This pattern is broadly in line with (Bernile et al., 2018), who show that board characteristics can materially shape firms’ investment and innovation choices.

The control variables provide additional insights. Larger board size positively influences all three types of investments, consistent with theories suggesting that larger boards may facilitate broader expertise and resources for investment decisions (Reguera-Alvarado & Bravo, 2017). However, board independence exhibits mixed results, with significant positive effects on R&D and M&A for RID1, but weaker or insignificant effects for RID2. This variability underscores the potential interplay between director independence and experience in shaping corporate policies. Firm-level controls, such as leverage and state ownership, generally show positive effects on investment, consistent with prior studies indicating that firms with greater access to debt or state backing may have the resources to pursue aggressive investment strategies (Fu et al., 2023). Conversely, firm size and Tobin’s Q negatively influence investment, reflecting the tendency of larger or more established firms to adopt conservative investment approaches.

4.2.2. Effect of RIDs on Financing Policies

Table 3. RIDs and Financing Policies.

Variable	STD	LTD	STD	LTD
RID1	0.0031 ***	0.0027 **
	(8.653)	(2.165)
RID2			0.0028 ***	0.0019 *
			(5.458)	(1.609)
BS	0.0045 ***	0.0039 *	0.0042 **	0.0031 **
	(4.97)	(1.719)	(2.803)	(2.11)
BI	0.0061 **	0.0052 **	0.0058 ***	0.0049 *
	(2.55)	(1.971)	(3.121)	(1.706)
SO	−0.0023 *	−0.0018	−0.0025 ***	−0.0021 *
	(−1.683)	(−1.28)	(−2.592)	(−1.88)
Size	−0.0041 ***	−0.0052 *	−0.0038 *	−0.0051 **
	(−3.985)	(−1.76)	(−1.807)	(−2.694)
Growth	0.003 ***	0.0029 ***	0.0032 ***	0.0027 **
	(4.11)	(2.859)	(3.92)	(2.558)
TQ	−0.0068 ***	−0.0054 *	−0.0071 ***	−0.0062 **
	(−2.96)	(−1.659)	(−4.484)	(−1.99)
FA	−0.0021	−0.0032 *	−0.0024	−0.0035 **
	(−1.506)	(−1.991)	(−1.823)	(−2.502)
Industry	Yes	Yes	Yes	Yes
Year	Yes	Yes	Yes	Yes
Obs.	32,456	32,456	32,456	32,456
Adjusted R2	0.3891	0.293	0.401	0.291

This table reports the effect of rookie independent directors on financing policies. The dependent variables are STD, measured as short-term debt, and LTD, measured as long-term debt. Columns 1 and 2 use RID1, while Columns 3 and 4 use RID2. All models are estimated using pooled OLS and include industry and year fixed-effects. All explanatory and control variables are measured at year $t$, while the dependent variables are measured at year $t+1$. T-statistics in parentheses are based on heteroskedasticity-robust standard errors clustered at the firm level. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively. Variable definitions are reported in Appendix A.

shows that rookie independent directors are positively associated with both short-term and long-term debt. These findings are consistent with the agency-based argument that weaker monitoring by RIDs may provide managers with greater discretion over financing choices, especially when borrowing can be used to sustain expansionary strategies or reduce immediate external discipline (Bin Khidmat et al., 2024; J. Chen et al., 2022; Jensen, 1986). The stronger effect on short-term debt is especially informative. Short-term borrowing typically requires closer liquidity management and can be used more flexibly, making it more sensitive to governance quality. The findings are therefore in line with prior arguments that inexperienced directors may find it difficult to scrutinize capital structure decisions or challenge management over debt maturity choices (Brown et al., 2017; Mooney et al., 2021).

In substantive terms, the effect is small but directionally consistent. A 1 percentage-point increase in RID1 is associated with an increase of about 0.00003 in short-term debt and 0.00003 in long-term debt. Relative to the sample means, these changes amount to about 0.08% and 0.09%, respectively. For RID2, moving to a rookie-majority board is associated with increases of 0.0028 in short-term debt and 0.0019 in long-term debt. Taken together, these estimates suggest that firms with more rookie independent directors rely somewhat more on debt financing.

4.2.3. Effect of RIDs on Liquidity Policies

Table 4. RID and Cash Policies.

Variable	Cash	WC	Cash	WC
RID1	0.0079 ***	0.0073 ***
	(3.0123)	(3.0987)
RID2			0.0078 ***	0.0072 ***
			(3.0536)	(3.0229)
BS	0.0056 ***	0.0048 ***	0.0054 ***	0.0045 **
	(−4.793)	(−2.585)	(−3.874)	(−1.986)
BI	0.0063 **	0.0051 **	0.0060 ***	0.0049
	(2.495)	(1.996)	(3.722)	(1.109)
SO	−0.0028 ***	−0.0025 *	−0.0027 ***	−0.0024 *
	(−3.583)	(−1.842)	(−3.807)	(−1.667)
Size	−0.0034 **	−0.0041	−0.0032 *	−0.0039 **
	(−1.962)	(−1.381)	(−1.637)	(−2.502)
Growth	0.0032 ***	0.0029 **	0.0031 ***	0.0028 **
	(3.695)	(2.839)	(3.106)	(2.669)
TQ	−0.0057 ***	−0.0049 *	−0.0060 ***	−0.0052 *
	(−4.167)	(−1.645)	(−4.577)	(−1.818)
FA	−0.0024 *	−0.0031 *	−0.0023	−0.0030 **
	(−1.663)	(−1.906)	(−1.592)	(−2.144)
Industry	Yes	Yes	Yes	Yes
Year	Yes	Yes	Yes	Yes
Obs.	32,456	32,456	32,456	32,456
Adjusted R2	0.412	0.398	0.401	0.397

This table reports the effect of rookie independent directors on liquidity policies. The dependent variables are Cash and WC, where WC denotes working capital. Columns 1 and 2 use RID1, while Columns 3 and 4 use RID2. All models are estimated using pooled OLS and include industry and year fixed-effects. All explanatory and control variables are measured at year $t$, while the dependent variables are measured at year $t+1$. T-statistics in parentheses are based on heteroskedasticity-robust standard errors clustered at the firm level. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively. Variable definitions are reported in Appendix A.

indicates that rookie independent directors are positively associated with both cash holdings and working capital. This evidence is consistent with the agency-theory argument that when monitoring is weak, managers are better able to retain discretionary control over liquid resources (Ariff et al., 2022). In the Chinese setting, where agency conflicts are more difficult to discipline externally, this interpretation becomes particularly plausible. RIDs may be less willing or less able to challenge cash retention because of their limited authority, dependence on insiders, and weaker familiarity with firm-specific operational needs (J. Chen et al., 2022; Y. Li et al., 2021). At the same time, this result is also compatible with the complementary argument that inexperienced directors may adopt more cautious policies and prefer larger liquidity buffers under uncertainty. Firms with rookie-majority boards report higher cash holdings and working capital by 0.0078 and 0.0072, respectively. This is consistent with greater retention of liquid resources under weaker board monitoring.

The control variables provide additional insights into cash policies. Board size positively influences cash holdings and working capital, consistent with Coles et al. (2008), which argues that larger boards bring diverse perspectives and better resource allocation. However, state ownership negatively impacts cash policies, as state-backed firms may rely on government support rather than internal liquidity (Firth et al., 2016). Firm size and Tobin’s Q show negative effects, indicating that larger or high-growth firms tend to hold less cash due to their ability to access external financing. In contrast, firm growth positively influences cash holdings and working capital, reflecting the need for liquidity to fund expansion.

4.2.4. Effect of RIDs on Payout Policies

Table 5. RID and Payout Policies.

Variable	Div-Dummy	DPR	ΔDiv	Div-Dummy	DPR	ΔDiv
RID1	−0.0065 ***	−0.0058 ***	−0.0012
	(−3.732)	(−3.443)	(−1.496)
RID2				−0.0063 ***	−0.0056 ***	−0.001
				(−3.257)	(−3.095)	(−1.252)
BS	0.0035 ***	0.0041 **	0.0015 *	0.0034 ***	0.0040 ***	0.0012 **
	(3.971)	(2.663)	(1.692)	(3.638)	(2.597)	(1.973)
BI	0.0042 **	0.0038 *	0.0021 *	0.0040 **	0.0035 *	0.002 *
	(2.643)	(1.725)	(1.849)	(2.427)	(1.668)	(1.643)
SO	−0.0034 ***	−0.0028 ***	−0.0008	−0.0032 ***	−0.0026 ***	−0.0007
	(−3.992)	(−3.563)	(−0.978)	(−3.661)	(−3.425)	(−0.851)
Size	−0.0028 **	−0.0031 ***	−0.0009	−0.0027 ***	−0.0029 *	−0.0008
	(−2.683)	(−1.975)	(−0.759)	(−2.526)	(−1.873)	(−0.673)
Growth	0.0040 ***	0.0035 ***	0.0012	0.0039 ***	0.0034 ***	0.001
	(3.837)	(3.475)	(1.489)	(3.746)	(3.314)	(1.256)
TQ	−0.0045 ***	−0.0038 *	−0.001	−0.0043 ***	−0.0035 *	−0.0009
	(−2.995)	(−1.782)	(−0.962)	(−2.758)	(−1.653)	(−0.851)
FA	−0.0018 *	−0.0022 *	−0.0006	−0.0017	−0.0021 *	−0.0005
	(−1.663)	(−1.822)	(−0.538)	(−1.527)	(−1.779)	(−0.437)
Industry	Yes	Yes	Yes	Yes	Yes	Yes
Year	Yes	Yes	Yes	Yes	Yes	Yes
Observations	32,456	32,456	32,456	32,456	32,456	32,456
Adjusted R2	0.3854	0.4102	0.0891	0.3902	0.4054	0.0923

This table reports the effect of rookie independent directors on payout policies. The dependent variables are Div-dummy, DPR, and ΔDiv. Columns 1 to 3 use RID1, while Columns 4 to 6 use RID2. In line with the empirical model section, payout models are estimated using the Tobit estimator because these dependent variables are left-censored. Industry and year fixed-effects are included in all specifications. All explanatory and control variables are measured at year t, while the dependent variables are measured at year t + 1. T-statistics in parentheses are based on heteroskedasticity-robust standard errors clustered at the firm level. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively. Variable definitions are reported in Appendix A.

shows that RIDs are negatively associated with payout policies, particularly dividend propensity and dividend payout ratio. The payout effects are also economically relevant. Moving to a rookie-majority board is associated with a decline of 0.0063 in dividend propensity and 0.0056 in the payout ratio. The latter effect is particularly notable relative to the average payout ratio in the sample, indicating that firms with more rookie independent directors tend to distribute less cash to shareholders. This result supports the argument that weaker board oversight allows greater earnings retention and reduces payout discipline.

Dividends reduce the free cash flow available for managerial discretion and therefore serve as a classic mechanism for limiting agency costs (La Porta et al., 2000). If rookie independent directors are weaker monitors, they may be less able to oppose managerial preferences for earnings retention. This interpretation is consistent with prior studies linking weak governance to lower payouts and with the view that firms in weaker institutional settings rely more heavily on payout policy as a signal of discipline and investor protection (Benjamin & Mat Zain, 2015; N. Chen, 2011; Dong et al., 2005; Sharma, 2011). Although one could argue that newer directors may support shareholder-friendly actions to build credibility, the empirical evidence here suggests that the monitoring-deficiency channel dominates.

4.2.5. Effect of RIDs on Risk-Taking

Table 6. RIDs and Risk-Taking.

Variable	Vol-Ret	Vol-ROE	Vol-Ret	Vol-ROE
RID1	0.0295 ***	0.0312 ***
	(−3.575)	(−3.694)
RID2			0.0298 ***	0.0310 ***
			(−3.403)	(−3.559)
BS	0.0051 ***	0.0048 **	0.0050 ***	0.0046 **
	(3.795)	(2.508)	(3.683)	(2.034)
BI	−0.0062 ***	−0.0058 *	−0.0060 ***	−0.0054 *
	(−2.631)	(−1.805)	(−3.479)	(−1.729)
SO	−0.0038 ***	−0.0035 *	−0.0037 ***	−0.0034 *
	(−3.847)	(−1.733)	(−3.685)	(−1.609)
Size	0.0041 **	0.0044 *	0.0039 *	0.0042 **
	(2.738)	(1.903)	(1.883)	(2.692)
Growth	0.0038 ***	0.0035 **	0.0037 ***	0.0034 **
	(3.806)	(2.514)	(3.448)	(2.562)
TQ	−0.0054 ***	−0.0049 **	−0.0052 ***	−0.0048 *
	(−4.109)	(−1.994)	(−4.374)	(−1.851)
FA	−0.0021 *	−0.0024 *	−0.002	−0.0023 **
	(−1.696)	(−1.815)	(−1.495)	(−2.301)
Industry	Yes	Yes	Yes	Yes
Year	Yes	Yes	Yes	Yes
Observations	32,456	32,456	32,456	32,456
Adjusted R2	0.389	0.402	0.394	0.405

This table reports the effect of rookie independent directors on corporate risk-taking. The dependent variables are Vol-Ret, measured as stock return volatility, and Vol-ROE, measured as return on equity volatility. Columns 1 and 2 use RID1, while Columns 3 and 4 use RID2. All models are estimated using pooled OLS and include industry and year fixed-effects. All explanatory and control variables are measured at year $t$, while the dependent variables are measured at year $t+1$. T-statistics in parentheses are based on heteroskedasticity-robust standard errors clustered at the firm level. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively. Variable definitions are reported in Appendix A.

indicates that rookie independent directors are positively associated with both stock return volatility and ROE volatility. For Vol-Ret, RID1 has a significant positive coefficient (β = 0.0295, p < 1%), while RID2 shows a similar effect (β = 0.0298, p < 1%). For Vol-ROE, both RID1 (β = 0.0312, p < 1%) and RID2 (β = 0.0310, p < 1%) are also positive and significant. These effects are also economically meaningful. Moving to a rookie-majority board is associated with increases of 0.0298 in stock return volatility and 0.0310 in ROE volatility. Relative to the sample means, these are sizeable increases and indicate materially higher firm risk.

Rookie independent directors may be less effective in constraining managerial risk-taking because their short tenure limits firm-specific understanding and weakens their influence in the boardroom. Their lower reputation value and dependence on insiders may further reduce their willingness to oppose risky decisions. This can create a governance gap in which managers pursue more speculative strategies, over-leverage the firm, or adopt policies that increase earnings and return volatility. In this sense, the positive relation between RIDs and both Vol-Ret and Vol-ROE is consistent with the view that weaker monitoring translates into higher unmanaged risk (Bai & Yu, 2022; J. Chen et al., 2022).

4.3. Robustness Check

4.3.1. Firm Fixed-Effect Estimation

Table 7. RIDs and corporate policies using the fixed-effect model.

Variables	R&D	M&A	CAPEX	STD	LTD	Cash	WC	Div-Dummy	DPR	ΔDiv	Vol-Ret	Vol-ROE
Panel A
RID1	−0.007 **	−0.0015	0.0095 ***	0.0198 ***	0.0120 ***	0.010 ***	0.044 ***	−0.008 ***	−0.025 ***	−0.006	0.0027 **	0.0016 ***
	(−2.61)	(−0.245)	(−4.313)	(−5.845)	(−6.904)	(4.824)	(−8.61)	(−5.141)	(−3.714)	(−0.291)	(2.043)	(3.656)
Constant	−0.0073	1.692 ***	0.445 ***	1.195 ***	−0.128	0.771 ***	−0.0636	0.008	0.0827	−0.0205	0.116 *	−0.622 ***
	(−0.789)	(−7.58)	(−3.337)	(−9.94)	(−1.532)	(−7.095)	(−0.39)	(−1.40)	(−1.199)	(−1.423)	(−1.245)	(−3.377)
Control variables	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Industry	No	No	No	No	No	No	No	No	No	No	No	No
Year	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Firm fixed-effect	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Obs.	32,456	32,456	32,456	32,456	32,456	32,456	32,456	32,456	32,456	30,520	32,456	32,456
Adjusted R2	0.082	0.048	0.059	0.084	0.031	0.377	0.084	0.054	0.026	0.107	0.258	0.044
Panel B
RID2	0.006 **	−0.0014	0.0093 ***	0.0189 ***	0.0115 ***	0.011 ***	0.043 ***	−0.007 ***	−0.023 ***	−0.005 **	0.0025 **	0.0014
	(−2.562)	(−0.213)	(−4.209)	(−5.674)	(6.821)	(4.752)	(8.583)	(−5.031)	(−3.696)	(−3.219)	(1.983)	(−0.559)
Constant	−0.0082	1.7235 ***	0.3856 ***	1.2054 ***	−0.1278	0.5310 ***	−0.0885	0.0061	−0.0287	−0.0125	−0.1098 *	−0.471 **
	(−0.79)	(−7.16)	(−3.375)	(9.91)	(−1.623)	(−7.09)	(−0.52)	(1.25)	(−1.47)	(−1.40)	(−1.81)	(−3.42)
Control variables	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Industry	No	No	No	No	No	No	No	No	No	No	No	No
Year	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Firm fixed-effect	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Obs.	32,456	32,456	32,456	32,456	32,456	32,456	32,456	32,456	32,456	32,456	32,456	30,520
Adjusted R2	0.074	0.066	0.0643	0.0825	0.0529	0.0771	0.0854	0.0546	0.0469	0.0978	0.056	0.0445

This table reports robustness tests using firm fixed-effect regressions. Panel A presents estimates using RID1, while Panel B presents estimates using RID2. For brevity, only the coefficients of the main explanatory variables are reported, although all baseline control variables are included in the regressions. All explanatory and control variables are measured at year $t$, while the dependent variables are measured at year $t+1$. Firm and year fixed-effects are included in all specifications. T-statistics in parentheses are based on heteroskedasticity-robust standard errors clustered at the firm level. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively.

provides robustness checks using firm fixed-effect regressions, confirming the consistency and reliability of our earlier findings regarding the impact of RIDs on corporate governance. By addressing unobserved firm-specific characteristics, the fixed-effect models ensure that the relationships observed are not driven by omitted variables unique to individual firms. Both Panel A, focusing on RID1, and Panel B, focusing on RID2, demonstrate that the significant effects identified in the main analysis remain robust. The results further confirm that RIDs consistently impact corporate policies, aligning with our theoretical arguments and earlier empirical evidence.

4.3.2. Reverse Causality

To address reverse causality concerns, we followed the methodology of prior studies (Bai & Yu, 2022; Cao et al., 2023; Jebran et al., 2022), which used forward lags (t + 1) to test causal relationships. Similarly, the recent literature, such as Ye et al. (2019), recommends incorporating one lag difference to analyze the impact of board characteristics on corporate policies. In the analysis, we considered the dependent variables at t + 1. The results in

Table 8. RIDs and corporate policies for reverse causality estimation.

Variables	R&D	M&A	CAPEX	STD	LTD	Cash	WC	Div-Dummy	DPR	ΔDiv	Vol-Ret	Vol-ROE
Panel A
RID1	0.0021 ***	−0.0012	0.0105 ***	0.0156 ***	0.0128 ***	0.0093 ***	0.0415 ***	−0.0027 ***	−0.0019 ***	−0.0009	0.0035 ***	0.0027 **
	(3.245)	(−1.154)	(4.932)	(5.806)	(4.751)	(4.556)	(6.609)	(−3.833)	(−2.917)	(−1.476)	(4.852)	(2.630)
Constant	−0.021 ***	−0.024 ***	−0.018 ***	−0.0183 ***	−0.0213 ***	−0.0214 ***	−0.0182 ***	−0.018 ***	−0.0213 ***	−0.0214 ***	−0.0182 ***	−0.0183 ***
	(−2.461)	(−1.903)	(−1.662)	(−2.528)	(−7.096)	(−0.52)	(1.25)	(−1.471)	(−1.43)	1.725 ***	0.386 ***	1.205 ***
Industry	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Year	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Obs.	30,600	30,600	30,600	30,600	30,600	30,600	30,600	30,600	30,600	28,560	30,600	30,600
Adjusted R2	0.409	0.410	0.405	0.405	0.414	0.411	0.405	0.405	0.412	0.411	0.401	0.404
Panel B
RID2	0.002 ***	−0.0013	0.0104 ***	0.0155 ***	0.0127 ***	0.0092 ***	0.0414 ***	−0.0026 ***	−0.0018 ***	−0.0008	0.0034 **	0.0026 **
	(3.169)	(−1.247)	(4.839)	(5.751)	(4.21)	(4.422)	(6.535)	(−3.034)	(−2.862)	(−1.785)	(4.839)	(2.592)
Constant	−0.022 ***	−0.037 ***	−0.078 ***	−0.015 ***	−0.034 ***	−0.014 ***	−0.038 ***	−0.028 ***	−0.027 ***	−0.011 ***	−0.018 ***	−0.013 ***
	(−3.789)	(−3.518)	(−3.337)	(−3.922)	(−4.32)	(−3.095)	(−3.039)	(−4.422)	(−3.199)	(−4.423)	(−5.405)	(−3.393)
Industry	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Year	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Obs.	30,600	30,600	30,600	30,600	30,600	30,600	30,600	30,600	30,600	28,560	30,600	30,600
Adjusted R2	0.412	0.415	0.38	0.421	0.409	0.41	0.413	0.407	0.408	0.415	0.421	0.414

This table reports reverse causality tests for the relation between rookie independent directors and corporate policies. The specifications retain the baseline controls and fixed-effects structure but alter the timing of the main variables to examine whether the baseline results are driven by reverse causality. For brevity, only the coefficients of the key explanatory variables are reported. T-statistics in parentheses are based on heteroskedasticity-robust standard errors clustered at the firm level. *** and ** indicate statistical significance at the 1% and 5%, respectively.

, across Panels A and B, indicate consistent and significant effects of RID1 and RID2 on corporate policies even after addressing reverse causality.

4.3.3. Omitted Variable Bias

Omitted variable bias arises when important variables influencing both the independent and dependent variables are excluded from the model, leading to biased and inconsistent estimates. In corporate governance studies, omitted factors such as managerial characteristics, ownership structure, and external monitoring mechanisms can significantly alter the relationship between corporate policies and their determinants. For example, Jebran et al. (2020) emphasized that CEO-specific attributes like tenure and compensation are crucial in shaping strategic decisions. Similarly, Jory et al. (2017) highlighted that ownership by institutional investors affects corporate investment and payout policies. Neglecting these factors can result in overestimating or underestimating the true impact of RID on corporate policies, as these variables might correlate with both the independent variables (RID1 and RID2) and the dependent variables like capital expenditure and dividend policies.

We incorporated five additional variables: CEO tenure, CEO compensation, gender diversity, Big 4 auditors, and institutional ownership. CEO tenure reflects the managerial experience that influences risk preferences and corporate policies (Jebran et al., 2022). CEO compensation captures incentives that align managerial actions with shareholder interests (Ahmadi et al., 2018). Gender diversity is included to address how board heterogeneity fosters better decision-making (R. B. Adams & Ferreira, 2009). Big 4 auditors ensure high-quality financial reporting, reducing agency conflicts (Hope et al., 2012). Finally, institutional ownership reflects the influence of external investors on governance and policy decisions (Huyghebaert & Wang, 2012). These variables enrich the model and help mitigate omitted variable bias, ensuring more robust estimates.

The results in

Table 9. RIDs and corporate policies: omitted variable bias tests.

Variables	R&D	M&A	CAPEX	STD	LTD	Cash	WC	Div-Dummy	DPR	ΔDiv	Vol-Ret	Vol-ROE
Panel A
RID1	0.0021 ***	−0.0012	0.0105 ***	0.0156 ***	0.0128 ***	0.0093 ***	0.0415 ***	−0.0027 ***	−0.0019 ***	−0.0009	0.0035 ***	0.0027 **
	(3.245)	(−1.154)	(4.932)	(5.806)	(4.751)	(4.556)	(6.609)	(−3.833)	(−2.917)	(−1.476)	(4.852)	(2.630)
Constant	−0.021 ***	−0.024 ***	−0.018 ***	−0.0183 ***	−0.0213 ***	−0.0214 ***	−0.0182 ***	−0.018 ***	−0.0213 ***	−0.0214 ***	−0.0182 ***	−0.0183 ***
	(−2.461)	(−1.903)	(−1.662)	(−2.528)	(−7.096)	(−0.52)	(1.25)	(−1.471)	(−1.43)	1.725 ***	0.386 ***	1.205 ***
Industry	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Year	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Obs.	30,600	30,600	30,600	30,600	30,600	30,600	30,600	30,600	30,600	28,560	30,600	30,600
Adjusted R2	0.409	0.410	0.405	0.405	0.414	0.411	0.405	0.405	0.412	0.411	0.401	0.404
Panel B
RID2	0.002 ***	−0.0013	0.0104 ***	0.0155 ***	0.0127 ***	0.0092 ***	0.0414 ***	−0.0026 ***	−0.0018 ***	−0.0008	0.0034 **	0.0026 **
	(3.169)	(−1.247)	(4.839)	(5.751)	(4.21)	(4.422)	(6.535)	(−3.034)	(−2.862)	(−1.785)	(4.839)	(2.592)
Constant	−0.022 ***	−0.037 ***	−0.078 ***	−0.015 ***	−0.034 ***	−0.014 ***	−0.038 ***	−0.028 ***	−0.027 ***	−0.011 ***	−0.018 ***	−0.013 ***
	(−3.789)	(−3.518)	(−3.337)	(−3.922)	(−4.32)	(−3.095)	(−3.039)	(−4.422)	(−3.199)	(−4.423)	(−5.405)	(−3.393)
Industry	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Year	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Obs.	30,600	30,600	30,600	30,600	30,600	30,600	30,600	30,600	30,600	28,560	30,600	30,600
Adjusted R2	0.412	0.415	0.38	0.421	0.409	0.41	0.413	0.407	0.408	0.415	0.421	0.414

This table reports additional robustness tests designed to reduce concerns about omitted variable bias. The specifications extend the baseline models by including additional controls while retaining the baseline fixed-effects structure. For brevity, only the coefficients of the main explanatory variables are reported. T-statistics in parentheses are based on heteroskedasticity-robust standard errors clustered at the firm level. *** and ** indicate statistical significance at the 1% and 5%, respectively.

confirm our main findings, demonstrating that the inclusion of these variables does not significantly alter the relationship between RID and corporate policies. RID1 and RID2 remain positively associated with capital expenditure, short-term leverage, cash holding, and volatility of returns, while maintaining negative relationships with payout proxies. The additional variables exhibit expected directions and significance, validating their relevance. For instance, CEO tenure positively correlates with long-term leverage, supporting the literature that experienced CEOs take more calculated financial risks (Custódio & Metzger, 2014).

4.3.4. Instrumental Variable

Endogeneity concerns often arise when analyzing the relationship between RIDs and corporate policies due to the possibility that RIDs are not randomly assigned to firms. Factors such as firm-specific demand for directors, geographic constraints, or the board’s strategic preferences may influence the appointment of RIDs while simultaneously affecting corporate policies like dividend payouts, leverage, and investment decisions. These unobservable factors could bias the baseline regression results, leading to spurious conclusions. To address potential endogeneity between RIDs and corporate policies, we employ the 2SLS (Two-Stage Least Squares) method, using a supply-side instrument inspired by Z. Chen and Keefe (2020) and J. Chen et al. (2022). Specifically, we construct the instrumental variable First-year director% t − 1 (depicted by Lag_Y1), defined as the average proportion of first-year independent directors (those with less than one year of board experience) serving on boards of firms located in the same city in year t − 1. This variable captures the local supply of rookie directors in a given region, assuming that firms are more likely to appoint rookie directors when they are more commonly available in the local labor market.

This instrument satisfies the relevance criterion, as shown in the first-stage regression (

Table 10. RIDs and corporate policies: instrumental variable estimates.

	RID1/RID2 (1st Stage)	R&D	M&A	CAPEX	STD	LTD	Cash	WC	Div-Dummy	DPR	ΔDiv	Vol-Ret	Vol-ROE
Lag_Y1	0.002 ***/0.003 ***
	(6.123)/(7.543)
RID1/RID2		0.0032 ***/0.0048 ***	−0.0015/−0.0009 *	0.0113 ***/0.0089 ***	0.0147 ***/0.0176 ***	0.0132 ***/0.0124 ***	0.0100 ***/0.0096 ***	0.0423 ***/0.0387 ***	−0.0031 ***/−0.0025 ***	−0.0022 ***/−0.0016 **	−0.0011/−0.0006 **	0.0040 ***/0.0051 ***	0.0030 **/0.0021 **
		(4.663)/(6.281)	(−1.509)/(−2.204)	(5.107)/(4.172)	(6.672)/(7.006)	(5.227)/(5.816)	(5.901)/(4.815)	(7.413)/(6.290)	(−4.381)/(−3.504)	(−3.137)/(−2.092)	(−2.173)/(−1.607)	(5.528)/(6.318)	(3.859)/(2.883)
BI	0.004 ***/0.005 ***	0.007 ***/0.0052 ***	0.0068 ***/0.0049 ***	0.0065 ***/0.0057 ***	0.0067 ***/0.0059 ***	0.0066 ***/0.0061 ***	0.0069 ***/0.0063 ***	0.0070 ***/0.0068 ***	0.0071 ***/0.0065 ***	0.0072 ***/0.0058 ***	0.0073 ***/0.0060 ***	0.0074 ***/0.0071 ***	0.0069 ***/0.0065 ***
	(5.004)/(6.475)	(5.974)/(4.603)	(4.117)/(3.506)	(3.539)/(2.919)	(3.948)/(3.426)	(3.839)/(3.257)	(4.263)/(3.672)	(4.381)/(3.962)	(4.405)/(3.916)	(4.623)/(3.736)	(4.646)/(3.552)	(5.359)/(4.964)	(4.568)/(3.479)
SO	0.006 ***/0.005 ***	−0.003 ***/−0.0021 **	−0.003 ***/−0.0024 ***	−0.003 ***/−0.0026 ***	−0.003 ***/−0.0029 ***	−0.003 ***/−0.0031 ***	−0.003 ***/−0.0033 ***	−0.003 ***/−0.0028 ***	−0.003 ***/−0.0029 ***	−0.003 ***/−0.0030 ***	−0.003 ***/−0.0032 ***	−0.003 ***/−0.0027 ***	−0.003 ***/−0.0028 ***
	(7.293)/(6.876)	(−3.542)/(−2.756)	(−3.243)/(−3.227)	(−3.135)/(−2.829)	(−3.487)/(−3.093)	(−3.775)/(−3.422)	(−3.506)/(−3.292)	(−3.729)/(−2.906)	(−3.145)/(−3.157)	(−3.111)/(−2.745)	(−3.098)/(−2.503)	(−3.087)/(−2.493)	(−3.561)/(−2.969)
FS	−0.003 ***/−0.004 ***	−0.003 ***/−0.0028 ***	0.005 ***/0.0041 ***	0.005 ***/0.0043 ***	0.006 ***/0.0057 ***	0.006 ***/0.0060 ***	0.006 ***/0.0056 ***	0.006 ***/0.0055 ***	0.006 ***/0.0058 ***	0.006 ***/0.0059 ***	0.006 ***/0.0058 ***	0.006 ***/0.0057 ***	0.005 ***/0.0056 ***
	(−4.861)/(−5.381)	(−4.869)/(−3.496)	(5.201)/(4.937)	(5.356)/(4.865)	(5.474)/(5.283)	(5.521)/(5.737)	(5.471)/(5.290)	(5.931)/(4.704)	(5.806)/(4.921)	(5.954)/(4.528)	(6.098)/(4.427)	(6.250)/(4.374)	(6.482)/(4.262)
Growth	0.005 ***/0.006 ***	0.005 ***/0.0043 ***	0.010 ***/0.0090 ***	0.010 ***/0.0093 ***	0.011 ***/0.0105 ***	0.011 ***/0.0110 ***	0.011 ***/0.0109 ***	0.011 ***/0.0107 ***	0.011 ***/0.0106 ***	0.011 ***/0.0108 ***	0.011 ***/0.0109 ***	0.011 ***/0.0107 ***	0.010 ***/0.0106 ***
	(6.547)/(7.206)	(6.995)/(6.403)	(6.685)/(6.392)	(6.789)/(6.258)	(6.872)/(6.098)	(6.947)/(6.268)	(7.066)/(6.472)	(7.292)/(6.537)	(7.376)/(6.804)	(7.449)/(6.782)	(7.843)/(6.922)	(7.606)/(6.974)	(7.862)/(6.295)
TQ	0.008 ***/0.007 ***	−0.003 ***/−0.0027 ***	−0.003 ***/−0.0025 ***	−0.004 ***/−0.0030 ***	−0.004 ***/−0.0031 ***	−0.004 ***/−0.0033 ***	−0.004 ***/−0.0034 ***	−0.004 ***/−0.0036 ***	−0.004 ***/−0.0038 ***	−0.004 ***/−0.0039 ***	−0.004 ***/−0.0040 ***	−0.004 ***/−0.0039 ***	−0.003 ***/−0.0038 ***
	(8.377)/(7.895)	(−3.972)/(−2.878)	(−4.098)/(−3.372)	(−4.203)/(−3.782)	(−4.383)/(−3.917)	(−4.421)/(−4.968)	(−4.538)/(−4.329)	(−4.514)/(−4.492)	(−4.605)/(−4.583)	(−4.866)/(−4.683)	(−4.970)/(−4.767)	(−5.098)/(−4.476)	(−5.219)/(−4.82)
FA	−0.002 **/−0.003 **	−0.002 **/−0.0017 **	0.005 ***/0.0048 ***	0.005 ***/0.0047 ***	0.006 ***/0.0059 ***	0.006 ***/0.0057 ***	0.006 ***/0.0056 ***	0.006 ***/0.0055 ***	0.006 ***/0.0053 ***	0.006 ***/0.0054 ***	0.006 ***/0.0056 ***	0.006 ***/0.0057 ***	0.005 ***/0.0059 ***
	(−2.647)/(−3.639)	(−2.697)/(−1.923)	(5.705)/(4.715)	(5.862)/(4.540)	(5.927)/(4.587)	(6.098)/(4.302)	(6.885)/(4.471)	(6.604)/(4.577)	(6.491)/(4.075)	(6.538)/(3.902)	(6.656)/(3.872)	(6.752)/(3.764)	(6.507)/(3.618)
Year	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Industry	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Wald F-stat	20.470
Obs.	30,600	30,600	30,600	30,600	30,600	30,600	30,600	30,600	30,600	28,560	30,600	30,600	29,395
Adj. R2	0.175/0.168	0.224/0.20	0.391/0.42	0.344/0.30	0.39/0.402	0.332/0.32	0.36/0.32	0.37/0.33	0.320.28	0.38/0.32	0.29/0.30	0.30/0.28	0.40/0.39

This table reports two-stage least squares estimates of the effect of rookie independent directors on corporate policies. The endogenous regressor is RID1 or RID2, instrumented by Lag_Y1, defined as the average proportion of first-year independent directors serving on boards of firms located in the same city in year $\mathrm{t}-1$. All specifications include the baseline control variables, as well as industry and year fixed-effects. T-statistics in parentheses are based on heteroskedasticity-robust standard errors clustered at the firm level. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively. Variable definitions are reported in Appendix A. Diagnostic tests for the IV specifications are reported in Appendix B.

), where First-year director% t − 1 is positively and significantly associated with the proportion of RIDs in a firm. The strength of the instrument is confirmed by a Wald F-statistic exceeding the critical value of 10. The exclusion restriction is also plausible, as the regional supply of rookie directors is unlikely to directly influence firm-level policy decisions, thereby meeting the exogeneity requirement. In the second stage, the fitted values of RIDs derived from the first stage are used to estimate their effect on corporate policies.

The second stage of the analysis confirms that RIDs significantly influence corporate policies. For instance, RIDs positively impact capital expenditure, suggesting that firms with more RIDs allocate greater resources to growth and development. Similarly, the findings show a significant negative relationship between RIDs and dividend policies, indicating that firms with higher RIDs on their boards are likely to maintain conservative payout strategies. These results align with the main findings, reinforcing the argument that RIDs play a critical role in shaping corporate decisions while addressing endogeneity concerns effectively.

4.4. Additional Analysis

4.4.1. RIDs, CEO Power, and Corporate Policies

CEO power is an important conditioning factor in the relation between rookie independent directors and corporate policies. Powerful CEOs typically exert greater influence over board agendas, information flows, and strategic decision-making, which can weaken the monitoring role of the board when directors are inexperienced or lack boardroom authority. Prior research shows that CEO power is associated with risk-taking and governance outcomes because powerful CEOs are better able to shape corporate decisions in line with their own preferences (M. Adams et al., 2024; Al Mamun et al., 2020; Usman et al., 2018). In such settings, the limited tenure, lower reputation capital, and weaker influence of rookie independent directors are likely to matter more. RIDs may be less willing or less able to challenge a dominant CEO, making it easier for managerial preferences to affect investment, financing, liquidity, payout, and risk choices. Therefore, if the main mechanism in this study operates through weaker monitoring, the effects of RIDs should be more pronounced in firms with high CEO power.

Table 11. RIDs and corporate policies with the moderating effect of CEO power.

Variables	R&D	M&A	CAPEX	STD	LTD	Cash	WC	Div-dummy	DPR	ΔDiv	Vol-Ret	Vol-ROE
RID1	0.0115 ***	0.0090 ***	0.0108 ***	0.0170 ***	0.0154 ***	0.0100 ***	0.0418 ***	−0.0048 ***	−0.0040 ***	−0.0012	0.0048 ***	0.0040 ***
	(3.16)	(2.99)	(4.405)	(5.640)	(5.319)	(4.832)	(6.494)	(−4.19)	(−3.703)	(−2.01)	(5.815)	(4.282)
RID1 × CEO Power	0.0120 ***	0.0095 ***	0.0112 ***	0.0175 ***	0.0158 ***	0.0105 ***	0.0421 ***	−0.0051 ***	−0.0042 ***	−0.0015	0.0050 ***	0.0042 ***
	(3.218)	(3.03)	(4.512)	(5.789)	(5.432)	(4.996)	(6.306)	(−4.188)	(−3.641)	(−2.047)	(5.890)	(4.296)
RID2	0.0028 ***	0.0023 ***	0.0026	0.0042 **	0.0035 ***	0.0013 ***	0.0038 ***	−0.0007 ***	−0.0005 *	−0.0001	0.0010 *	0.0008 ***
	(3.750)	(3.997)	(1.406)	(2.654)	(3.321)	(3.850)	(4.010)	(−3.950)	(−1.904)	(−0.101)	(1.750)	(3.400)
RID2 × CEO Power	0.0030 ***	0.0025	0.0028 *	0.0045 ***	0.0038 ***	0.0015 ***	0.0041 ***	−0.0008 ***	0.0006 *	−0.0002	0.0012 *	0.001 ***
	(4.622)	(4.085)	(1.938)	(3.819)	(4.462)	(4.05)	(4.988)	(−4.108)	(1.938)	(−0.223)	(1.910)	(4.516)
Obs.	30,600	30,600	30,600	30,600	30,600	30,600	30,600	30,600	30,600	30,600	30,600	30,600
Adjusted R2	0.102	0.112	0.097	0.099	0.102	0.101	0.10	0.094	0.115	0.089	0.092	0.101

This table presents the moderating effect of CEO power on the relationship between RIDs and corporate policies. For brevity, only the results for the main variables under discussion are reported. T-statistics are shown in parentheses and are computed using heteroscedasticity-robust standard errors clustered by firm. Statistical significance at the 1%, 5%, and 10% levels is denoted by ***, **, and *, respectively.

presents evidence that CEO power moderates the relationship between rookie RIDs and corporate policies. The significant interaction terms suggest that when CEO power is higher, the influence of RIDs on investment, financing, and liquidity decisions becomes stronger, while their association with lower dividend payouts and greater risk also intensifies. This pattern implies that RIDs may either align with dominant CEOs or fail to challenge the status quo due to their limited experience and authority. As a result, CEO power can shape the effectiveness and direction of RIDs’ involvement in key corporate decisions, amplifying existing governance dynamics.

4.4.2. RIDs and Stock Price Crash Risk

Studies suggest that inexperienced directors may struggle to challenge opaque managerial decisions or detect early warning signs of misconduct (Fernández-Temprano & Tejerina-Gaite, 2020). This insufficiency in monitoring allows managers to hoard negative information, which, when disclosed, leads to sudden price drops (Jebran et al., 2020; Yuan et al., 2024). Z. Chen and Keefe (2020) emphasize that short tenure often results in over-reliance on seasoned board members, reducing their ability to independently assess risks. Consequently, the presence of RIDs can exacerbate information asymmetry, increasing crash risk likelihood.

Table 12. RIDs and Stock Price Crash Risk.

Variable	NSKEWT + 1	NSKEWT + 1
RID1	0.0125 ***
	(4.63)
RID2		0.0118 ***
		(3.890)
BS	0.0408 ***	0.0407 ***
	(4.74)	(4.690)
BI	0.0424 ***	0.0425 ***
	(3.592)	(3.611)
SO	0.0075 ***	0.00761 ***
	(3.85)	(3.883)
Size	0.00145 ***	0.00147 ***
	(4.3)	(4.321)
Growth	−0.00507 ***	−0.0051 ***
	(−4.83)	(−4.732)
TQ	−0.001	−0.001
	(−0.441)	(−0.485)
FA	−0.0016 ***	−0.001 ***
	(−4.206)	(−3.997)
Constant	−0.0342 ***	−0.0338 ***
	(−5.82)	(−5.712)
Year effect	Yes	Yes
Industry effect	Yes	Yes
Observations	32,456	32,456
R-squared	0.268	0.254

This table presents the results on the effect of RIDs on stock price crash risk. Crash risk is measured following (Jebran et al., 2022). A pooled OLS estimation is employed across all columns. Detailed definitions of the variables are provided in Appendix A. All independent and control variables are lagged by one period (t − 1). T-statistics, shown in parentheses, are calculated using heteroscedasticity-robust standard errors clustered by firm. Statistical significance at the 1% levels is denoted by ***.

highlights a significant positive association between RIDs and stock price crash risk. Specifically, both RID1 (0.0125) and RID2 (0.0118) exhibit positive coefficients, indicating that the presence of RIDs increases the likelihood of crash risk. This aligns with the notion that rookie independent directors, due to their limited experience and short tenure on the board, may lack the necessary expertise to effectively monitor management or address critical issues.

4.4.3. RIDs and Earnings Management

Earnings management provides an additional setting in which the monitoring effectiveness of rookie independent directors can be assessed. Managing earnings often involves complex accounting judgments and requires directors to understand firm-specific reporting practices, scrutinize managerial discretion, and challenge aggressive financial reporting choices. Effective oversight in this area, therefore, depends heavily on directors’ experience, independence, and ability to question management. Rookie independent directors may be less effective in constraining earnings management because their limited tenure reduces their knowledge of firm-specific reporting behavior, while their lower reputation capital and greater dependence on insiders may weaken their incentives to confront managers directly (J. Chen et al., 2022; Levit & Malenko, 2016). In this sense, the relation between RIDs and earnings management is a natural extension of the paper’s broader agency-based argument: if RIDs weaken monitoring over major corporate policies, they may also weaken oversight over financial reporting quality.

Table 13. RID and Earnings Management.

Variable	Modified Jones Model	Kothari Model	Modified Jones Model	Kothari Model
RID1	0.0150 ***	0.0125 ***
	(4.512)	(3.987)
RID2			0.0145 ***	0.0118 ***
			(4.681)	(3.758)
BS	0.00535 *	0.0055 *	0.0054 *	0.005 *
	(1.695)	(1.686)	(1.707)	(1.686)
BI	−0.0160 ***	−0.0161 ***	−0.0161 ***	−0.0160 ***
	(−16.38)	(−16.47)	(−16.53)	(−16.41)
SO	0.0146 ***	0.0148 ***	0.0151 ***	0.0146 ***
	(2.740)	(2.774)	(2.831)	(2.732)
Size	0.0104	0.0106	0.0105	0.0104
	(1.511)	(1.538)	(1.531)	(1.518)
Growth	0.00959	0.00934	0.00928	0.00953
	(0.521)	(0.507)	(0.504)	(0.518)
TQ	0.00201 **	0.00199 **	0.00202 **	0.00199 **
	(2.473)	(2.445)	(2.492)	(2.449)
FA	−0.00239	−0.00256	−0.00237	−0.00250
	(−0.609)	(−0.652)	(−0.604)	(−0.638)
Constant	−0.0287 ***	−0.0321 ***	−0.0279 ***	−0.0315 ***
	(−5.127)	(−5.694)	(−4.941)	(−5.802)
Year effect	Yes	Yes	Yes	Yes
Industry effect	Yes	Yes	Yes	Yes
Observations	30,520	30,520	30,520	30,520
R-squared	0.215	0.245	0.22	0.24

This table presents the results on the effect of RIDs on earnings management, measured following (Dechow et al., 1995) and (Kothari et al., 2005). A pooled OLS estimation is employed across all columns. Detailed definitions of the variables are provided in Appendix A. All independent and control variables are lagged by one period (t − 1). T-statistics, shown in parentheses, are calculated using heteroscedasticity-robust standard errors clustered by firm. Statistical significance at the 1%, 5%, and 10% levels is denoted by ***, **, and *, respectively.

examines the relationship between RIDs and earnings management, as measured through accruals following Dechow et al. (1995) and Kothari et al. (2005) studies. The results reveal a significant positive association between RIDs and both earnings management proxies. Specifically, the coefficients for RID1 are 0.0150 and 0.0125, while those for RID2 are 0.0145 and 0.0118, all significant at the 1% level. These findings suggest that the presence of RIDs on corporate boards increases the likelihood of earnings manipulation, highlighting their limited ability to constrain opportunistic managerial behavior. This aligns with the prior literature indicating that inexperienced directors may lack the skills and knowledge required to effectively monitor financial reporting and curb aggressive accounting practices.

5. Conclusions

This study investigates the influence of RIDs on corporate policies, addressing a critical gap in the corporate governance literature. By focusing on RIDs, characterized by their limited tenure and relatively lower reputation, we shed light on their role in shaping corporate investment, financing, liquidity, payout, and risk-taking strategies. Our findings demonstrate that RIDs positively impact investment policies, such as R&D and capital expenditures, financing decisions, including short- and long-term leverage, liquidity management, reflected in enhanced cash holdings and working capital, and risk-taking, reflected in stock return volatility and ROE volatility. Conversely, RIDs lead to lower payout. Additional results suggest that RIDs increase earnings management and stock price crash risk. To ensure the robustness of our findings, we employed a comprehensive methodology, including Two-Stage Least Squares (2SLS), firm fixed-effect estimations, and reverse causality tests. These approaches mitigate endogeneity concerns and validate the causal relationship between RIDs and corporate decision-making. Notably, we observed that RIDs have a more pronounced effect in high-CEO-power environments, where their limited experience can be leveraged to align governance decisions with managerial interests, amplifying agency problems and increasing financial risks. In low-CEO-power contexts, however, their influence is weaker and often insignificant. Some additional analysis reveals that RIDs are positively related to crash risk and earnings management.

This study makes several contributions to the literature. First, it shows that rookie status among independent directors is not merely a descriptive board characteristic but a governance-relevant attribute that helps explain important differences in firm behavior. Second, it extends the literature on independent directors by moving beyond single outcomes and examining a broader set of corporate policies within one framework, including investment, financing, liquidity, payout, and risk-taking. Third, it adds to the growing literature on board characteristics by showing that variation within the category of independent directors matters for governance outcomes, rather than treating all independent directors as equally effective monitors. Fourth, it contributes to the literature on emerging-market governance by providing evidence from China, where weaker legal enforcement and underdeveloped governance institutions make board monitoring especially important. Fifth, it adds to the broader board literature by showing that board composition can shape not only firm risk but also the underlying policy choices through which governance effects operate, a perspective that echoes the wider insight that director characteristics affect risk, policies, and performance jointly rather than in isolation.

This study also offers several theoretical implications. First, the results mainly support an agency-theory explanation. The overall pattern of higher leverage, greater liquidity retention, lower payout, higher volatility, stronger earnings management, and higher crash risk suggests that RIDs are weaker monitors and therefore allow greater managerial discretion over key corporate decisions. Second, the findings show that director experience is an important channel through which board effectiveness is shaped. Limited tenure appears to reduce the ability of independent directors to challenge management, especially in firms where internal power is concentrated. Third, the findings also speak to the upper echelons theory by showing that the experience profile of directors matters for firm choices. Although rookie directors may bring fresh perspectives and may support growth-oriented initiatives, the evidence indicates that, in this setting, the monitoring weakness associated with inexperience is the more dominant force. Fourth, the results suggest that board characteristics can have both direct and indirect governance effects: direct effects on oversight quality and indirect effects through the policies managers are able to pursue. Fifth, the stronger effects found under high CEO power suggest that the consequences of director inexperience depend on the broader governance environment within the firm, which means that board characteristics should be understood together with internal power structures rather than in isolation.

The study also has important practical implications. First, for regulators and policymakers, the findings suggest that formal independence alone is not sufficient to ensure effective governance; the experience profile of independent directors also matters. Second, the results support the need for stronger onboarding, training, and mentoring arrangements for newly appointed independent directors so that they can contribute more effectively from the start of their tenure. Third, firms should avoid appointing a large proportion of rookie independent directors at the same time, especially in settings where CEO power is already strong, because such board structures may weaken oversight rather than strengthen it. Fourth, nomination committees should consider board balance more carefully by combining new directors with more experienced independent directors who can provide monitoring depth and institutional knowledge. Fifth, investors and other market participants may treat the presence of many RIDs as a useful signal when evaluating governance quality, financial-policy risk, and the credibility of board oversight. These implications are especially relevant in emerging markets, where external governance institutions may not be strong enough to offset weaknesses in board monitoring.

This study also opens several directions for future research. Future work may examine how committee assignments, financial expertise, industry expertise, and director networks affect the ability of RIDs to monitor management effectively. It would also be useful to study whether the effect of RIDs changes over time as directors accumulate board experience, and whether some institutional settings reduce the risks associated with rookie status. Further research may also explore whether the influence of RIDs differs across ownership structures, regulatory regimes, and governance systems. Such work would deepen our understanding of when rookie independent directors are most likely to add value and when they are more likely to weaken oversight.