# Investment efficiency under financial constraints: The role of CEO power

Muhammad Ansar Majeed, Irfan Ullah, Tanveer Ahsan, Bakr Al-Gamrh

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Muhammad Ansar Majeed, Irfan Ullah, Tanveer Ahsan, Bakr Al-Gamrh. Investment efficiency under financial constraints: The role of CEO power. Research in International Business and Finance, 2025, 79, 10.1016/j.ribaf.2025.103059. hal-05224745

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# Investment efficiency under financial constraints: The role of CEO power

Muhammad Ansar Majeed<sup>a</sup>, Irfan Ullah<sup>b</sup>, Tanveer Ahsan<sup>c, *,</sup>, Bakr Al-Gamrh<sup>c</sup>

## ARTICLE INFO

JEL Classifications:

G30

G31

Keywords:

Financial constraints

Investment efficiency

CEO power

External governance

CEO gender

## ABSTRACT

Financial limitations can influence how effectively corporations invest, with influential CEOs playing a key role in making investment choices. This study investigates the link between CEO power and investment effectiveness in the context of financial limitations, using a dataset of Chinese firms listed from 2005 to 2022. We observe a negative association between CEO power and investment efficiency. Nonetheless, powerful CEOs facing financial pressures can notably enhance investment efficiency through adept strategic choices. Our findings remain robust across a range of alternative measures of investment efficiency and financial constraints, as well as various econometric specifications. Further examination shows that the investment inefficiencies associated with powerful CEOs mainly arise from overinvestment, while external governance factors, such as analyst coverage and media attention, as well as CEO gender, significantly impact the relationship between CEO power and investment efficiency. This research contributes to the existing body of knowledge on CEO power by integrating agency theory and strategic choice frameworks to clarify the relationship between CEO power and investment efficiency in financially constrained situations.

## 1. Introduction

Chief Executive Officers (CEOs) serve as principal strategists, making their power a critical determinant of corporate outcomes (Ampofo and Barkhi, 2024; Brahma and Economou, 2023; Jiraporn et al., 2014; Shahab et al., 2022). A crucial dimension of a CEO's personality is the power that they hold in the organization. Power is a relative concept (Emerson, 1964), that enables executives to shape outcomes and direct organizational behavior. From an organizational perspective, power may grant CEOs the ability to deal with complex situations and make swift decisions in times of uncertainty, or it may lead to rent-seeking and agency issues (Fast et al., 2012; Greve and Mitsuhashi, 2007; Jaroenjitrkam et al., 2020; Lewellyn and Muller-Kahle, 2012). Keeping in view the different impact of CEO power on corporate behavior, this study explores the impact of CEO power on investment efficiency, aiming to discern whether such power encourages or undermines optimal capital allocation. Recognizing that the influence of CEO power may not be uniform across all contexts, we further study how this association is moderated by financial constraints, CEO gender, and external governance mechanisms. By doing so, we seek to identify the boundary conditions under which CEO power either contributes to or detracts from

E-mail addresses: muhammadansar.majeed@kedgebs.com (M.A. Majeed), irfanbuneri524@hotmail.com (I. Ullah), tanveer.ahsan@rennes-sb.com (T. Ahsan), bakr.ali@rennes-sb.com (B. Al-Gamrh).

<footnote><sup>a</sup> Kedge Business School, Marseille, France</footnote>

<footnote><sup>b</sup> Jiangxi Normal University, Nanchang, China</footnote>

<footnote><sup>c</sup> Rennes School of Business, Rennes, France</footnote>

<footnote>* Corresponding author.</footnote>
efficient investment behavior.

The drivers of corporate investment decisions have long been a central theme in corporate finance literature. In a perfect world, for optimal investment decisions, firms should pursue all the investments with a net present value (NPV) greater than zero and ignore others with an NPV less than zero. However, the real world is not perfect; information asymmetry, resulting in adverse selection and agency problems, leads to sub-optimal investment outcomes (Biddle et al., 2009; Gomariz and Ballesta, 2014; Mirza et al., 2020). A substantial body of extant literature documents the downside of CEO power. Prior research suggests that CEO power is associated with over-confidence, unwarranted risk-taking, and agency problems (Fast et al., 2012; Greve and Mitsuhashi, 2007; Jaroenjitrkam et al., 2020; Lewellyn and Muller-Kahle, 2012) which leads to value destruction, rigging compensation contracts in their favor, lower-quality financial reporting, bond ratings, and tax aggressiveness (Bebchuk et al., 2011; Lisic et al., 2016; Liu and Jiraporn, 2010; Morse et al., 2011; Shahab et al., 2022). Building on these theoretical foundations, we posit that unconstrained CEO power may lead to sub-optimal investment decisions, ultimately undermining investment efficiency.

On the contrary, literature also notes some potential advantages of CEO power. Strategic choice perspective suggests that powerful CEOs, often endowed with greater autonomy and authority, are better positioned to respond swiftly and decisively to external challenges, enabling agile and informed strategic decision-making (Boyd, 1995; Finkelstein and D'aveni, 1994). Thus, powerful CEOs are better able to choose projects of strategic importance for the organizations, resulting in efficient investment decisions. Jiraporn et al. (2014) note that powerful CEOs are entrenched and well shielded from removal threats, reducing the need for masking actual firm performance, thereby decreasing incentives for an opaque information environment. Furthermore, powerful CEOs have a reputation that facilitates access to the capital market. Reputational and capital market benefits of transparency further encourage powerful CEOs to strive for lower information asymmetry (Cao et al., 2015; La Porta et al., 2000). Ultimately, lower information asymmetry coupled with quick and timely decision-making may result in greater investment efficiency. Hence, powerful CEOs can make efficient investment decisions. Jiraporn et al. (2014) argue that entrenched CEOs, secure in their roles, have less incentive to cloak firm performance, fostering a transparent information environment. Additionally, the reputational capital associated with powerful CEOs can enhance access to external financing, further enhancing incentives for transparency and reducing information asymmetry (Cao et al., 2015; La Porta et al., 2000). These contrasting perspectives of CEO power, with its capacity to either impede or enhance investment efficiency, serve as the main motivation for this study. This context exhibits both challenges and unique opportunities, particularly for financially constrained firms. When facing limited resources, powerful CEOs' autocratic decision-making authority enables rapid strategic choice, which may either optimize resource allocation or exacerbate inefficiencies (Brahma and Economou, 2023).

Using various proxies of investment efficiency and CEO power, we find that CEO power is negatively associated with investment efficiency of the Chinese firms listed from 2005 to 2022. Our results demonstrate that powerful CEOs make sub-optimal investment decisions and lower investment efficiency. However, powerful CEOs tend to make relatively efficient investment decisions when facing financial hardships. Our results remain robust to alternative econometric specifications and alternative proxies of investment efficiency, financial constraints, and CEO power. We deal with endogeneity problems using lagged values of independent variables, two-stage least squares (2SLS), two-step system GMM, and two-step Heckman regression. Our additional results suggest that powerful CEOs are associated with overinvestment as they are more likely to take unwarranted risks, leading to overinvestment. Our findings also reveal that powerful female CEOs make efficient investment decisions as compared to their male counterparts. We also document that the external governance mechanisms (analyst following and media coverage) improve the investment decisions of powerful CEOs and result in greater investment efficiency.

This study makes several significant contributions to existing literature. Firstly, we provide robust empirical evidence on the influence of CEO power on corporate investment efficiency. While prior studies have explored various determinants of investment efficiency, they have largely overlooked how the power dynamics within the top executive positions can shape investment decisions. By addressing this gap, we contribute to the growing body of literature that examines CEO characteristics and their implications for corporate outcomes. Secondly, we extend theoretical understanding by integrating both agency theory and the strategic choice perspective to explain how CEO power may either enhance or hinder investment efficiency. The strategic choice view is particularly instrumental in explicating the impact of CEO power on investment efficiency under financial constraints. Our findings reveal that CEO power interacts significantly with financial frictions, highlighting the critical role of managerial discretion in capital allocation decisions within resource-constrained settings. This extends the strategic choice perspective to the settings characterized by resource scarcity, a contribution with broad relevance, as financial constraints are a universal challenge for firms across diverse institutional contexts globally. Our findings incorporate the strategic choice view to exhibit that the consequences of CEO power are vastly context dependent. CEO power in financially constrained environments can bring agile and informed decision making and boost investment efficiency, challenging the conventional view that CEO power primarily worsens agency problems.

Thirdly, our study also highlights the moderating role of external governance mechanisms, such as analyst following and media coverage. These findings enrich governance literature by explaining how market-based oversight can mitigate potential inefficiencies stemming from CEO power. This provides broader implications for understanding how external governance mechanisms impact the nexus of CEO power and investment efficiency across different institutional environments. Additionally, we note that CEO gender plays a significant role in the nexus of CEO power and investment efficiency, further enriching the discussion on diversity and corporate governance. Lastly, although our empirical investigation is based on the Chinese context, the theoretical insights and mechanisms we examine possess wider applicability. Various emerging and developed economies display variations in CEO power concentrations, financial constraints, and the efficacy of external governance. Therefore, the fundamental connections between CEO power, investment efficiency, and governance mechanisms can be theoretically extrapolated to contexts that share similar structural traits.
The remainder of the study proceeds as follows. 2 discusses the preceding literature and develops hypotheses. 3 explains the methodology and sample. 4 presents empirical findings. 5 provides robustness checks. 6 presents additional tests, and 7 concludes the paper.

## 2. Institutional background and hypotheses development

### 2.1. *Institutional background*

Like other emerging economies, China is an interesting setting with a weak legal, institutional, and corporate information environment (Piotroski and Wong, 2012). Political, cultural, and business settings motivate the opaque information environment (Majeed et al., 2024; Mirza et al., 2024), leading to higher information opacity. Another pertinent aspect that influences corporate decisions is the corporate ownership structure in China. State-owned enterprises (SOEs) make up a large part of the listed firms in China (Wu et al., 2016) with the primary purpose of achieving socio-economic goals (Faccio, 2006). These firms also differ from private firms in terms of risk, access to finance, and financial reporting practices (Feng et al., 2015; Tan et al., 2014) resulting in fewer financial constraints (Haider et al., 2018). Banks in China are the principal providers of finance to the corporate sector, limiting managerial opportunism and adverse selection problems. Furthermore, most of the financing for industrial and commercial purposes is provided by the four largest banks, which are also state-owned. Therefore, in addition to the state-ownership, the government can influence organizational decisions through the credit policies of the banks (Berger et al., 2009). Moreover, the Chinese capital market is in a formative stage and continues to evolve within a dynamic institutional framework (Jiang and Kim, 2020). Unlike developed markets, a key attribute of this market is the predominance of retail investors with informational inefficiencies (Chen et al., 2022). While institutional investors are present, their pronounced transient behavior disincentivizes dynamical participation in active monitoring and corporate governance (Jiang and Kim, 2024). This investor composition further limits the effectiveness of external governance mechanisms (Jiang and Kim, 2020). Such a unique institutional context influences the allocation of resources and presents a fascinating background for studying the consequences of CEO power under financial constraints.

### 2.2. *CEO power and investment efficiency*

Investment decisions of the firms, in frictionless settings, are likely to be efficient (Modigliani and Miller, 1958) however, the firms depart from optimal investment because of information asymmetry and agency problems (Myers, 1977) resulting in inefficiency. Inefficient investment is categorized as under-investment (less than optimal level) and over-investment (higher than optimal level). Prior literature suggests that moral hazard, as well as adverse selection, are the basis of inefficient investment decisions (Chen et al., 2017; Mirza et al., 2020). Managerial opportunism due to empire-building and maximization of personal benefits leads to moral hazard (agency problem), resulting in inefficient investment decisions (Aggarwal and Samwick, 2006; Jensen, 1986). Hence, moral hazard (agency problem) may lead to inefficient investment decisions. Furthermore, agency problems result in a higher cost of equity (Pantzalis and Park, 2014), which reduces the availability of funds for future projects, leading to future underinvestment.

The adverse selection framework suggests that managers with superior information sell securities at higher prices to generate excess funds and pursue all the investment avenues they like, leading to overinvestment. Conversely, external suppliers of capital facing information asymmetry may become skeptical and increase the firm's cost of financing. This universal effect would influence all the firms, whether they have an adverse selection problem or not. In such situations, managers may not be willing to sell undervalued assets to finance attractive investment projects (Chen et al., 2017). Therefore, investors' capital rationing results in overall under-investment (Biddle et al., 2009; Myers and Majluf, 1984). The adverse selection is not limited to equity financing but extends to debt financing as well (Easley and O'hara, 2004). A higher cost of capital resulting from adverse selection would lead to underinvestment, making investment inefficient.

Power is defined as “the ability to get things done the way one wants them to be done” (Salancik and Pfeffer, 1977). Power is defined as being in control of others' behavior and actions because of the ability to reward others (Emerson, 1964). Power grants the ability to produce desired results. Power is a relative concept, which means it can be described only if we recognize that a person is powerful relative to others (Greve and Mitsuhashi, 2007). Preceding studies suggest that power leads to overconfidence in CEOs. Such CEOs may overestimate their own abilities and are less likely to accept expert suggestions from top management (Fast et al., 2012; Greve and Mitsuhashi, 2007), leading to a higher likelihood of costly mistakes. Such costly mistakes may result in a loss of firm value. Moreover, greater CEO power leads to “excessive and unmanaged risk-taking” that other CEOs generally avoid (Lewellyn and Muller-Kahle, 2012). Power leads to drastic decisions in a corporate environment, which usually has detrimental outcomes (Brahma and Economou, 2023). Hence, excessive risk-taking by CEOs may result in adverse selection leading to overinvestment. Powerful CEOs also have a higher tendency to invest in risky trophy ventures. This inclination may further enhance the likelihood of accepting projects with negative NPV (over-investment). However, such investment decisions would result in capital rationing by the investors and a shortage in internal funds, leading to the rejection of projects with positive NPV (under-investment) (Gomariz and Ballesta, 2014; Haixia, 2017; Lewellyn and Muller-Kahle, 2012).

From the *agency theory* perspective, power lets CEOs be entrenched, which may harm the firm's value. Bertrand and Mullainathan (2003) note that entrenched CEOs in weak governance settings (such as China) are more likely to extract rent. They also argue that CEOs are not rewarded for hard work but for luck. Prior literature suggests that CEO power leads to poor firm performance, lower firm value, lower credit ratings, greater cost of debt capital, and higher pay-performance sensitivity (Amzaleg et al., 2014; Bebchuk et al., 2011; Liu and Jiraporn, 2010). Further, if monitored poorly, powerful CEOs tend to have a higher tendency to pursue risky ventures
(Pathan, 2009). The rigging of compensation contracts also becomes more likely when CEOs have greater power (Morse et al., 2011). Powerful CEOs tend to hold negative information, which increases the likelihood of a stock price crash risk (Shahab et al., 2020). These arguments suggest that greater rent extraction by the powerful CEOs would increase agency costs, which may lead to suboptimal investment decisions. Further, information asymmetry leads to adverse selection and moral hazards. Financial reporting transparency reduces information asymmetry, facilitates effective monitoring of managerial activities, and lowers agency problems and adverse selection, limiting sub-optimal investment decisions (Biddle et al., 2009). However, CEO power leads to an opaque information environment and greater information asymmetry (Liu and Jiraporn, 2010) ultimately leading to adverse selection and sub-optimal investment decisions.

On the other hand, power is very important for organizational leaders, as the chief planner, it enables them to change and direct the corporate strategies and resources to achieve specific goals (Berger et al., 2009; Pfeffer and Salancik, 1978). The *strategic choice view* discussed how leaders in organizations influence the choices to achieve goals, keeping in mind the confines of the context (Child, 1972). The powerful CEOs have been known to have a positive influence on value creation. As they can accelerate decision-making and respond to complications or unexpected changes in external environments in a timely fashion (Boyd, 1995; Finkelstein and D'aveni, 1994). The *transparency hypothesis* (Jiraporn et al., 2014) also suggests that greater CEO power results in greater transparency (lower information asymmetry). Powerful CEOs are not only well shielded from removal threats but also face fewer career concerns. As powerful CEOs face limited negative consequences for poor performance, they do not need to mask their actual performance (Armstrong et al., 2012). Therefore, powerful CEOs are less likely to hide actual firm performance, which may curb information asymmetry and ultimately may reduce adverse selection and agency problems, leading to higher investment efficiency. Another motivation for a transparent information environment comes from the capital market. Managerial reputation generates perception among investors and plays a vital role in influencing the cost of capital (Cao et al., 2015; La Porta et al., 2000). Therefore, powerful CEOs may choose greater and higher quality information disclosure, resulting in higher transparency. Consequently, concerns for personal reputation and capital markets provide incentives to powerful CEOs for higher transparency (Cao et al., 2015). Higher informational transparency reduces adverse selection and improves governance, resulting in higher investment efficiency. Building on these arguments and the empirical evidence, we develop our first hypothesis as under:

**H1.** : CEO power is positively (negatively) associated with investment efficiency.

## 2.3. CEO power, financial constraint, and investment efficiency

Powerful CEOs have a greater risk appetite, which increases the likelihood of overinvestment. Overinvestment is a major problem in Chinese institutional settings where the majority of the firms' investment is greater than the optimal level (Mirza et al., 2020). Since financial constraints reduce the funds at the disposal of the managers, it decreases the ability to invest in all the projects. Hence, financial constraints may lead to the selection of the most attractive (lucrative) projects, only resulting in lower adverse selection and ultimately superior investment efficiency (Hovakimian, 2011). Therefore, financial constraints may result in positive externality, i.e., greater investment efficiency. Limited funds restrict the powerful CEOs from investing optimally by selecting the most rewarding projects, thereby reducing the overinvestment problem. Accordingly, powerful CEOs would invest where the marginal value is higher relative to other investment avenues, leading to efficient investment decisions. The *strategic choice view* also reinforces this argument by suggesting that CEOs with greater power have greater discretion in the decision-making process of the organizational hierarchy. Therefore, the powerful CEOs accelerate the decisions leading to apt responses, particularly in the wake of the crisis (Boyd, 1995; Finkelstein and D'aveni, 1994). We argue that quick response in crises enhances the ability of powerful CEOs to exploit investment opportunities that yield maximum returns. Therefore, powerful CEOs are more likely to make the best utilization of resources, especially in crises (Heaton, 2002; Lin et al., 2005). Based on these arguments, we conjecture that powerful CEOs increase investment efficiency when facing financial constraints:

**H2.** : Powerful CEOs enhance investment efficiency if firms are financially constrained.

# 3. Methodology

## 3.1. Sample and data

We collect data on Chinese A-share listed firms for the period from 2005 to 2022. This selection is based on two key factors. First, several essential variables needed for our study, especially those used to construct the CEO power proxy, were consistently unavailable before 2005. Additionally, for the limited pre-2005 data that does exist, our initial review of earlier data revealed a substantial proportion of missing observations, which could have introduced bias and compromised the reliability of our findings. By commencing our analysis in 2005, we ensure both data completeness and consistency, thus strengthening the reliability of our empirical results. Consistent with earlier research, financial firms (e.g., banks, investment companies, insurance companies, etc.) are not part of this study. These firms are quite different from non-financial firms since they operate in a different regulatory environment (Feng et al., 2022; Mirza et al., 2020). We exclude firm-year observations with transaction status of ST (special treatment), \*ST (suspension from trading) firms, and missing values, and finalize a dataset of 30,646 firm-year observations spanning from 2005 to 2022.
## 3.2. Measurement of CEO power

Following Finkelstein (1992) and Han et al. (2016), our proxy of CEO power is an index built on three dimensions of power. These three dimensions include structural, ownership, and expert dimensions. We exclude the prestige dimension following Tang et al. (2011) because it is the “least proximal measure” of power. Operational ambiguity and missing data increase the concerns regarding the measurement of the prestige dimension and concerns regarding the validity of the CEO power measure as well. Therefore, following Han et al. (2016), we use three dimensions of power. The organizational hierarchical structure includes three components, i.e., CEO pay slice, CEO duality, and board independence. CEO pay slice (CPS) is defined as total CEO compensation divided by the overall compensation of the top five executives (Bebchuk et al., 2011). The concentration of titles is also used to measure power (Morse et al., 2011). Therefore, we use CEO duality, which is a binary variable taking the value of 1 if the CEO also holds the title of chairman of the board of directors and 0 otherwise. Board structure also affects CEO power as independent directors actually perform better in monitoring CEOs (Hermalin and Weisbach, 1988). A greater number of independent directors is more likely to curb the autocratic decisions of the CEOs. So, a binary variable of board power is formed, which takes the value of 1 if the ratio of independent directors is lower than the industry median and 0 otherwise. Another aspect is based on ownership structure. Founder CEOs take the value of 1, and non-founder CEOs take the value of 0. Similarly, CEOs’ greater stock ownership enhances their ability to impose their decisions on the board (Finkelstein, 1992). If CEOs’ stock ownership is greater than the industry median, it is given a value of 1 and 0 otherwise. Expert power is measured by the length of time for which the CEO has been running the business. When a CEO holds the position for a longer period, it increases their influence and reduces the monitoring capability of the board (Hermalin and Weisbach, 1988; Huybrechts et al., 2013). So, a dummy variable of CEO expert power takes the value of 1 if the CEO holds the position for a period higher than the industry median and 0 otherwise. The power variable is the sum of all the binary variables mentioned above. The range between 0 and 6 denotes the extent of CEO power. The greater the value of the power variable, the greater the power that the CEO holds in the firm.

## 3.3. Measurement of investment efficiency

Consistent with prior research (Biddle et al., 2009; Richardson, 2006), we employ two distinct proxies to measure investment efficiency in our baseline regression analysis. The first proxy (IE_R) is derived from the predicted investment levels following Richardson (2006). In line with established methodological practices, we estimate this model for each industry-year combination to account for industry-specific investment dynamics and time-varying macroeconomic conditions. The residuals obtained from these estimations represent deviations from the predicted (optimal) investment levels. To construct our investment efficiency measure, we take the absolute value of these residuals—capturing the extent of both under- and overinvestment—and multiply them by -1. Consequently, higher values of IE_R indicate greater investment efficiency. This methodological approach aligns with several recent studies (Biggerstaff et al., 2025; Lin et al., 2025; Liu and Li, 2025; Zhang et al., 2024).

$$IE_{R,t} = \rho_0 + \rho_1 TQ_{i,t-1} + \rho_2 Lev_{i,t-1} + \rho_3 Cash_{i,t-1} + \rho_4 Age_{i,t-1} + \rho_5 Size_{i,t-1} + \rho_6 Ret_{i,t-1} + \rho_7 Invest_{i,t-1} \\ + \sum Industry + \sum year + \epsilon_{i,t} \quad (1)$$

where $IE_{R,t}$ represents the ratio of net capital investment over the total assets of firm i at time t. $TQ_{i,t-1}$ is the Tobin's Q (ratio of the sum of the market value of shares and liabilities to the book value of assets) ratio of firm i at time t-1. $Lev_{i,t-1}$ is the ratio of total liabilities to total assets of firm i at time t-1. $Cash_{i,t-1}$ is calculated as the sum of cash and cash equivalents divided by the average total assets of firm i at time t-1. $Age_{i,t-1}$ represents the age of the firm which is estimated by taking the natural log of listing age of firm i at time t-1. $Size_{i,t-1}$ is the log of the total assets of firm i at time t-1. $Ret_{i,t-1}$ is the annual stock return of firm i at time t-1 and $Invest_{i,t-1}$ is a one-year lagged value of the dependent variables. $\sum Industry$ and $\sum year$ represents industry and year dummies. $\epsilon_{i,t}$ represents standard errors.

Following Biddle et al. (2009), we measure our second proxy of investment efficiency (IE_B) as under:

$$IE_{B,t} = \emptyset_0 + \emptyset_1 SalesGrowth_{i,t-1} + \emptyset_{i,t} \quad (2)$$

where $IE_{B,t}$ represents the ratio of net capital investment over the total assets of firm i at time t. $SalesGrowth_{i,t-1}$ is the annual growth in sales of firm i at time t-1. Our investment efficiency measure is calculated by multiplying the negative one to the absolute value of the residual of Eq. 2. A greater value of $IE_B$ represents higher investment efficiency. Positive (negative) residuals represent over (under)-investment as in the previous measure of investment efficiency.

## 3.4. Measurement of financial constraints

We employ the HP model (Hadlock and Pierce, 2010) to measure financial constraints. The HP index captures various aspects of financial constraints. Hadlock and Pierce (2010) suggest that this measure of financial constraints avoids various issues like simul-taneity, sample selection, and measurement error. Higher index scores denote unconstrained firms and vice versa. The HP model is defined as under:

$$HP_{i,t} = -0.737 * Size_{i,t} + 0.043 * Size^2_{i,t} - 0.040 * Age_{i,t} \quad (3)$$
where $HP_{i,t}$ represents the HP index of firm i at time t. $Size_{i,t}$ is the log of the total assets of firm i at time t. $Age_{i,t}$ represents the age of the firm which is estimated by taking the natural log of listing age of firm i at time t.

### 3.5. Empirical methodology

We employ the following econometric model to study the impact of CEO power on investment efficiency:

$$IE_{i,t} = \beta_0 + \beta_1 CEOP_{i,t} + \beta_{2-12} Control_{i,t} + \sum Industry + \sum year + \epsilon_{i,t} \quad (4)$$

where $IE_{i,t}$ represents one of the two proxies of investment efficiency (IE_R, IR_B) measured as Eqs. (1) and (2). $CEOP_{i,t}$ represents an index-based measure of CEO power as explained in 3.2. $Control_{i,t}$ represents 11 firm-level control variables (i.e., leverage ($Lev_{i,t}$), firm age ($Age_{i,t}$), firm size ($Size_{i,t}$), market to book ratio ($MB_{i,t}$), ownership type ($SOE_{i,t}$), tangibility ($Tang_{i,t}$), financial reporting quality ($FRQ_{i,t}$), big 4 auditors ($Big4_{i,t}$), ownership concentration ($Top1_{i,t}$), slack ($Slack_{i,t}$), and cash flow volatility ($OCF_{i,t}$)) explained in Appendix A. $\sum Industry$ and $\sum year$ represents industry and year dummies. $\epsilon_{i,t}$ represents standard errors.

Next, to investigate the impact of CEO power on investment efficiency under financial constraints, we modify Eq. (4) as under:

$$IE_{i,t} = \beta_0 + \beta_1 CEOP_{i,t} + \beta_2 FC_{i,t} + \beta_3 CEOP_{i,t} * FC_{i,t} + \beta_{2-12} Control_{i,t} + \sum Industry + \sum year + \epsilon_{i,t} \quad (5)$$

where $FC_{i,t}$ represents financial constraints measured as the HP model using Eq. (3), and other variables are as explained in Eq. (4).

### 3.6. Descriptive statistics and correlation matrix

Descriptive statistics are reported in Table 1. The results show that the mean (standard deviation) of the absolute value of investment efficiency, i.e., IE_R and IE_B, are -0.049 (0.051) and -0.048 (0.063), respectively. These values reflect notable variability in investment efficiency across the sample firms. The mean value of the CEOP (2.821) with a median of 3.000 suggests that almost half of the sample firms have CEOs with average power structures.

Table 2 presents the Pearson correlation matrix. The variable CEO power (CEOP) exhibits a statistically significant negative correlation with both proxies of investment efficiency (IE_R and IE_B), indicating that higher CEO power is associated with reduced investment efficiency. Additionally, the moderate magnitude of the correlation coefficients suggests a low probability of multicollinearity among the explanatory variables.

## 4. Results and discussion

Panel A of Table 3 presents the results of OLS and fixed-effects regressions investigating the direct impact of CEO power on investment efficiency (H1). We observe a significant negative association of CEOP with IE_R and IE_B for both OLS and fixed-effects regression results. Since higher values of our measures of investment efficiency suggest higher efficiency, a negative association suggests that powerful CEOs are associated with lower investment efficiency. These findings indicate that entrenched CEOs are more likely to make inefficient investment decisions. The plausible reasons can be that firms with powerful CEOs tend to take reckless and excessive risks, which increases the likelihood of optimal investment decisions (Lewellyn and Muller-Kahle, 2012). These findings support our argument, suggesting that powerful CEOs enhance information opacity, increasing moral hazard as well as adverse selection, leading to inefficient investment decisions. The empirical findings also support the notion that power leads to drastic decisions

<table><caption>**Table 1**

Descriptive statistics.</caption><thead><tr><th>Variables</th><th>Mean</th><th>SD</th><th>25 %</th><th>Median</th><th>75 %</th><th>N</th></tr></thead><tbody><tr><td>IE_R</td><td>-0.049</td><td>0.051</td><td>-0.063</td><td>-0.034</td><td>-0.015</td><td>30,646</td></tr><tr><td>IE_B</td><td>-0.048</td><td>0.063</td><td>-0.059</td><td>-0.023</td><td>-0.011</td><td>30,646</td></tr><tr><td>CEOP</td><td>2.821</td><td>1.067</td><td>2.000</td><td>3.000</td><td>4.000</td><td>30,646</td></tr><tr><td>FC</td><td>1.564</td><td>2.824</td><td>0.122</td><td>1.750</td><td>3.157</td><td>30,646</td></tr><tr><td>Lev</td><td>0.301</td><td>1.952</td><td>1.041</td><td>0.448</td><td>0.644</td><td>30,646</td></tr><tr><td>Age</td><td>2.676</td><td>0.423</td><td>2.484</td><td>2.772</td><td>2.995</td><td>30,646</td></tr><tr><td>Size</td><td>22.116</td><td>1.283</td><td>21.207</td><td>21.973</td><td>22.870</td><td>30,646</td></tr><tr><td>MB</td><td>2.084</td><td>1.987</td><td>1.873</td><td>2.876</td><td>2.990</td><td>30,646</td></tr><tr><td>SOEs</td><td>0.419</td><td>0.493</td><td>0.000</td><td>0.000</td><td>1.000</td><td>30,646</td></tr><tr><td>Tang</td><td>0.225</td><td>0.171</td><td>0.091</td><td>0.189</td><td>0.325</td><td>30,646</td></tr><tr><td>FRQ</td><td>-0.100</td><td>0.312</td><td>-0.111</td><td>-0.056</td><td>-0.024</td><td>30,646</td></tr><tr><td>Big4</td><td>0.059</td><td>0.236</td><td>0.000</td><td>0.000</td><td>0.000</td><td>30,646</td></tr><tr><td>Top1</td><td>0.350</td><td>0.158</td><td>0.640</td><td>0.348</td><td>0.479</td><td>30,646</td></tr><tr><td>Slack</td><td>0.182</td><td>0.133</td><td>0.088</td><td>0.144</td><td>0.236</td><td>30,646</td></tr><tr><td>OCF</td><td>0.058</td><td>0.300</td><td>0.026</td><td>0.041</td><td>0.065</td><td>30,646</td></tr></tbody></table>

Note: This table reports the summary statistics of variables used in the study for the period from 2005 to 2022. All the variables are defined in Appendix A
<table><caption>**Table 2**
Correlation matrix.</caption><thead><tr><th></th><th>IE_R</th><th>IE_B</th><th>CEOP</th><th>FC</th><th>Lev</th><th>Age</th><th>Size</th><th>MB</th><th>SOEs</th><th>Tang</th><th>FRQ</th><th>Big4</th><th>Top1</th><th>Slack</th><th>OCF</th></tr></thead><tbody><tr><td>IE_R</td><td>1</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>IE_B</td><td>0.825<sup>***</sup></td><td>1</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>CEOP</td><td>-0.055<sup>***</sup></td><td>-0.049<sup>***</sup></td><td>1</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>FC</td><td>0.039</td><td>0.033<sup>***</sup></td><td>-0.143<sup>***</sup></td><td>1</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Lev</td><td>0.069<sup>***</sup></td><td>-0.034</td><td>-0.083<sup>***</sup></td><td>0.348<sup>***</sup></td><td>1</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Age</td><td>0.367<sup>***</sup></td><td>0.147<sup>***</sup></td><td>-0.087<sup>***</sup></td><td>0.187<sup>***</sup></td><td>0.165<sup>***</sup></td><td>1</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Size</td><td>-0.001</td><td>-0.043<sup>***</sup></td><td>-0.156<sup>***</sup></td><td>0.095<sup>***</sup></td><td>0.364<sup>***</sup></td><td>0.169<sup>***</sup></td><td>1</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>MB</td><td>-0.059<sup>***</sup></td><td>-0.019<sup>**</sup></td><td>0.157<sup>***</sup></td><td>-0.459<sup>***</sup></td><td>-0.495<sup>***</sup></td><td>-0.067<sup>***</sup></td><td>-0.467<sup>***</sup></td><td>1</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>SOEs</td><td>0.063<sup>***</sup></td><td>0.031<sup>***</sup></td><td>-0.075<sup>***</sup></td><td>0.012<sup>***</sup></td><td>0.188<sup>***</sup></td><td>0.182<sup>***</sup></td><td>0.205<sup>***</sup></td><td>-0.146<sup>***</sup></td><td>1</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Tang</td><td>-0.062<sup>***</sup></td><td>-0.121<sup>***</sup></td><td>-0.034<sup>***</sup></td><td>0.039<sup>***</sup></td><td>0.145<sup>***</sup></td><td>-0.061<sup>***</sup></td><td>0.049<sup>***</sup></td><td>-0.143<sup>***</sup></td><td>0.121<sup>***</sup></td><td>1</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>FRQ</td><td>-0.133<sup>***</sup></td><td>-0.164<sup>***</sup></td><td>-0.151<sup>***</sup></td><td>0.016<sup>***</sup></td><td>0.076<sup>***</sup></td><td>-0.066<sup>*</sup></td><td>0.056<sup>**</sup></td><td>-0.125<sup>**</sup></td><td>-0.012<sup>***</sup></td><td>-0.046<sup>**</sup></td><td>1</td><td></td><td></td><td></td><td></td></tr><tr><td>Big4</td><td>-0.045<sup>***</sup></td><td>-0.056<sup>**</sup></td><td>-0.043<sup>***</sup></td><td>0.376<sup>***</sup></td><td>0.076<sup>***</sup></td><td>0.076</td><td>0.365<sup>***</sup></td><td>-0.145<sup>***</sup></td><td>0.012<sup>***</sup></td><td>0.067<sup>***</sup></td><td>-0.056<sup>**</sup></td><td>1</td><td></td><td></td><td></td></tr><tr><td>Top1</td><td>-0.056<sup>***</sup></td><td>-0.034<sup>***</sup></td><td>-0.065<sup>***</sup></td><td>0.267<sup>***</sup></td><td>0.067<sup>***</sup></td><td>-0.155<sup>***</sup></td><td>0.254<sup>***</sup></td><td>-0.165<sup>***</sup></td><td>0.032<sup>***</sup></td><td>0.087<sup>***</sup></td><td>0.026<sup>**</sup></td><td>0.125<sup>***</sup></td><td>1</td><td></td><td></td></tr><tr><td>Slack</td><td>-0.167<sup>***</sup></td><td>0.060<sup>***</sup></td><td>0.056<sup>**</sup></td><td>-0.165<sup>***</sup></td><td>-0.334<sup>***</sup></td><td>-0.078<sup>***</sup></td><td>-0.165<sup>***</sup></td><td>0.255<sup>***</sup></td><td>-0.063<sup>***</sup></td><td>-0.442<sup>***</sup></td><td>0.251<sup>***</sup></td><td>-0.065<sup>***</sup></td><td>-0.025<sup>**</sup></td><td>1</td><td></td></tr><tr><td>OCF</td><td>0.023<sup>**</sup></td><td>0.023<sup>***</sup></td><td>0.014<sup>*</sup></td><td>-0.142<sup>***</sup></td><td>0.145<sup>***</sup></td><td>0.066<sup>***</sup></td><td>-0.097<sup>***</sup></td><td>0.074<sup>***</sup></td><td>0.041<sup>***</sup></td><td>-0.241<sup>***</sup></td><td>0.014<sup>**</sup></td><td>-0.065<sup>***</sup></td><td>0.045<sup>***</sup></td><td>0.035<sup>***</sup></td><td>1</td></tr></tbody></table>

Note: This table presents pairwise correlations. ***, **, and * denote significance level at 1 %, 5 %, and 10 %. All the variables are defined in Appendix A.
<table><caption>**Table 3**
CEO power, financial constraints, and investment efficiency.</caption><thead><tr><th rowspan="3"></th><th colspan="4">Panel A</th><th colspan="4">Panel B</th></tr><tr><th colspan="2">IE_R</th><th colspan="2">IE_B</th><th colspan="2">IE_R</th><th colspan="2">IE_B</th></tr><tr><th>OLS</th><th>FE</th><th>OLS</th><th>FE</th><th>OLS</th><th>FE</th><th>OLS</th><th>FE</th></tr></thead><tbody><tr><td>CEOP</td><td>-0.0020**<br>(-2.5704)</td><td>-0.0019**<br>(-2.38)</td><td>-0.0025***<br>(-4.32)</td><td>-0.0029***<br>(-2.76)</td><td>-0.0020**<br>(-2.57)</td><td>-0.0019**<br>(-2.41)</td><td>-0.0024**<br>(-2.32)</td><td>-0.0105**<br>(-2.48)</td></tr><tr><td>FC</td><td></td><td></td><td></td><td></td><td>0.0077**<br>(2.35)</td><td>0.0068**<br>(2.02)</td><td>0.0131***<br>(3.38)</td><td>0.0203***<br>(4.64)</td></tr><tr><td>CEOP*FC</td><td></td><td></td><td></td><td></td><td>0.0094***<br>(2.80)</td><td>0.0100***<br>(2.77)</td><td>0.0167***<br>(4.25)</td><td>0.0247***<br>(5.53)</td></tr><tr><td>Lev</td><td>0.0001***<br>(3.35)</td><td>0.0003***<br>(7.79)</td><td>0.0005***<br>(8.84)</td><td>0.0009*<br>(1.72)</td><td>0.0093***<br>(2.77)</td><td>0.0154***<br>(4.15)</td><td>0.0182***<br>(4.49)</td><td>0.0299***<br>(6.46)</td></tr><tr><td>Age</td><td>-0.0024***<br>(-16.05)</td><td>-0.0026***<br>(14.39)</td><td>-0.0009***<br>(-4.37)</td><td>-0.0035***<br>(-14.91)</td><td>-0.0024***<br>(-15.97)</td><td>-0.0001**<br>(-2.06)</td><td>-0.0009***<br>(-4.38)</td><td>-0.0015***<br>(-5.58)</td></tr><tr><td>Size</td><td>-0.0042***<br>(-7.41)</td><td>-0.0051***<br>(-5.83)</td><td>-0.0036***<br>(-4.83)</td><td>-0.0059***<br>(-4.74)</td><td>-0.0043<br>(-1.11)</td><td>-0.0065*<br>(-1.86)</td><td>-0.0057<br>(-1.10)</td><td>-0.0111*<br>(-1.70)</td></tr><tr><td>MB</td><td>-0.0153<br>(-1.04)</td><td>-0.0110<br>(-1.47)</td><td>-0.0011***<br>(-4.77)</td><td>-0.0007***<br>(-5.45)</td><td>-0.0151<br>(-0.87)</td><td>0.0009<br>(0.06)</td><td>0.0015*<br>(1.65)</td><td>0.0011<br>(1.19)</td></tr><tr><td>SOEs</td><td>0.0055***<br>(4.17)</td><td>0.0013***<br>(8.84)</td><td>0.0096***<br>(5.52)</td><td>-0.0009<br>(-0.42)</td><td>-0.0054***<br>(-4.17)</td><td>0.0013<br>(0.85)</td><td>0.0096***<br>(5.53)</td><td>0.0017<br>(0.83)</td></tr><tr><td>Tang</td><td>-0.0302***<br>(-7.19)</td><td>-0.0349***<br>(-5.27)</td><td>-0.0645***<br>(-10.56)</td><td>-0.0357***<br>(-4.17)</td><td>-0.0302***<br>(-7.18)</td><td>-0.0350***<br>(-5.28)</td><td>-0.0645***<br>(-10.55)</td><td>-0.0399***<br>(-4.42)</td></tr><tr><td>FRQ</td><td>-0.0141***<br>(-4.82)</td><td>0.0121***<br>(4.45)</td><td>0.0260***<br>(4.67)</td><td>0.0229***<br>(5.16)</td><td>0.0141***<br>(4.82)</td><td>0.0121***<br>(4.46)</td><td>0.0260***<br>(4.67)</td><td>0.0224***<br>(5.10)</td></tr><tr><td>Big4</td><td>0.0015<br>(0.63)</td><td>0.0018<br>(0.47)</td><td>0.0056<br>(1.63)</td><td>0.0040<br>(0.76)</td><td>0.0014<br>(0.60)</td><td>-0.0020<br>(-0.53)</td><td>0.0055<br>(1.57)</td><td>0.0057<br>(1.09)</td></tr><tr><td>Top1</td><td>-0.0002<br>(-0.76)</td><td>-0.0003***<br>(-1.94)</td><td>0.0001***<br>(3.28)</td><td>-0.0005***<br>(-4.96)</td><td>-0.0000<br>(-0.51)</td><td>-0.0003***<br>(-3.92)</td><td>0.0000<br>(0.27)</td><td>-0.0005***<br>(-4.52)</td></tr><tr><td>Slack</td><td>-0.0833***<br>(-15.53)</td><td>-0.074***<br>(-11.86)</td><td>-0.0025***<br>(-5.39)</td><td>-0.0091***<br>(-7.25)</td><td>-0.0833***<br>(-15.53)</td><td>-0.0740***<br>(-11.87)</td><td>-0.0024<br>(-0.38)</td><td>-0.0045<br>(-0.61)</td></tr><tr><td>OCF</td><td>-0.0008<br>(-0.65)</td><td>-0.0008<br>(-1.06)</td><td>-0.0022<br>(-0.98)</td><td>-0.0013<br>(-0.68)</td><td>-0.0008<br>(-0.62)</td><td>0.0006<br>(0.48)</td><td>-0.0020<br>(-0.81)</td><td>-0.0009<br>(-0.47)</td></tr><tr><td>Year</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Industry</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td></tr><tr><td>Firm</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td></tr><tr><td>Intercept</td><td>0.0209*<br>(1.74)</td><td>0.0205<br>(1.12)</td><td>0.0180<br>(1.15)</td><td>0.0402<br>(1.52)</td><td>0.0212<br>(1.47)</td><td>0.0127<br>(0.60)</td><td>0.0224<br>(1.19)</td><td>0.0969***<br>(3.29)</td></tr><tr><td>R<sup>2</sup></td><td>0.146</td><td>0.111</td><td>0.079</td><td>0.014</td><td>0.146</td><td>0.111</td><td>0.079</td><td>0.098</td></tr><tr><td>N</td><td>30,646</td><td>30,646</td><td>30,646</td><td>30,646</td><td>30,646</td><td>30,646</td><td>30,646</td><td>30,646</td></tr></tbody></table>

Note. This table presents OLS regression and firm-fixed effect regression results. Panel A shows OLS regression and firm fixed effects results for the effect of CEO power on investment efficiency. Panel B shows OLS and firm fixed effect regression results for the effect of financial constraints on the relationship between CEO power and investment efficiency. The standard errors are clustered at the firm level. The t-statistics are in parentheses. ***, **, and * denote significance level at 1 %, 5 %, and 10 %. All the variables are defined in Appendix A.

in a corporate environment, which usually has detrimental outcomes (Liu and Jiraporn, 2010).

Panel B of Table 3 presents the results of OLS and fixed-effects regressions investigating the impact of CEO power on investment efficiency under financial constraints (H2). We observe significant positive associations of the interaction term CEOP*FC with IE_R as well as IR_B for both OLS and fixed-effects regression results. These results suggest that powerful CEOs facing financial constraints make more efficient investment decisions as compared to those with fewer financial constraints. Financial constraints lead to the selection of the most lucrative projects, which leads to positive externality, i.e., lower adverse selection and ultimately superior investment efficiency (Hovakimian, 2011). The results also support the strategic choice view, which argues that the ability of powerful CEOs to exploit investment opportunities quickly with maximum returns in a financially constrained environment leads to higher investment efficiency (Boyd, 1995; Finkelstein and D'aveni, 1994).

# 5. Robustness

## 5.1. Alternative measures

Following Han et al. (2016), we use an alternative proxy of CEO power by re-developing our index-based measure of CEO power explained in 3.2. We introduce an additional variable (insider CEO) in the composite measure of CEO power discussed in 3.2. Panel A of Table 4 presents the results of OLS and fixed-effects regressions using this alternative proxy of CEO power. We observe significant negative associations of CEOP and significant positive associations of the interaction term (CEOP*FC) with IE_R, as well as IR_B for both OLS and fixed-effects regression results, consistent with the main findings.

Next, we use two alternative proxies for financial constraints. First, we employ the KZ Index developed by Kaplan and Zingales
<table><caption>**Table 4**

Robustness - using alternative proxies.</caption><thead><tr><th colspan="10">Panel A: Using an alternative proxy for CEO power</th></tr><tr><th colspan="4">CEO power and investment efficiency</th><th colspan="4">CEO power, financial constraints, and investment efficiency</th></tr><tr><th></th><th>IE<sub>R</sub><br>OLS</th><th>FE</th><th>IE<sub>B</sub><br>OLS</th><th>FE</th><th>IE<sub>R</sub><br>OLS</th><th>FE</th><th>IE<sub>B</sub><br>OLS</th><th>FE</th></tr></thead><tbody><tr><td>CEOP</td><td>-0.0006**<br>(-2.94)</td><td>-0.0106**<br>(-2.10)</td><td>-0.0008**<br>(-2.31)</td><td>-0.0013**<br>(-2.28)</td><td>-0.0006**<br>(-2.17)</td><td>-0.0012***<br>(-2.76)</td><td>-0.0008**<br>(-2.31)</td><td>-0.0016**<br>(-2.07)</td></tr><tr><td>FC</td><td></td><td></td><td></td><td></td><td>0.0085***<br>(2.59)</td><td>0.0148***<br>(4.01)</td><td>0.0172***<br>(4.39)</td><td>0.0249***<br>(5.57)</td></tr><tr><td>CEOP*FC</td><td></td><td></td><td></td><td></td><td>0.0092***<br>(2.76)</td><td>0.0160***<br>(4.31)</td><td>0.0178***<br>(4.46)</td><td>0.0273***<br>(6.00)</td></tr><tr><td>Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Year</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Firm</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td></tr><tr><td>Industry</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td></tr><tr><td>Intercept</td><td>0.0189<br>(1.58)</td><td>0.0368*<br>(1.71)</td><td>0.0153<br>(0.98)</td><td>0.0312<br>(1.20)</td><td>0.0223<br>(1.56)</td><td>0.0163<br>(0.78)</td><td>0.0155<br>(0.99)</td><td>0.1023***<br>(3.46)</td></tr><tr><td>R<sup>2</sup></td><td>0.146</td><td>0.114</td><td>0.079</td><td>0.013</td><td>0.1463</td><td>0.0198</td><td>0.0789</td><td>0.1101</td></tr><tr><td>N</td><td>30,646</td><td>30,646</td><td>30,646</td><td>30,646</td><td>30,646</td><td>30,646</td><td>30,646</td><td>30,646</td></tr><tr><th colspan="10">Panel B: Using alternative proxies for financial constraints</th></tr><tr><th colspan="4">Using the KZ index</th><th colspan="4">Using the WW index</th></tr><tr><th></th><th>IE<sub>R</sub><br>OLS</th><th>FE</th><th>IE<sub>B</sub><br>OLS</th><th>FE</th><th>IE<sub>R</sub><br>OLS</th><th>FE</th><th>IE<sub>B</sub><br>OLS</th><th>FE</th></tr><tr><td>CEOP</td><td>-0.0019***<br>(-4.63)</td><td>-0.0022***<br>(-5.10)</td><td>-0.0023***<br>(-4.48)</td><td>-0.0026***<br>(-4.36)</td><td>-0.0006**<br>(-2.17)</td><td>-0.0012***<br>(-2.76)</td><td>-0.0009**<br>(-2.26)</td><td>-0.0016**<br>(-2.07)</td></tr><tr><td>FC</td><td>0.0031***<br>(5.53)</td><td>0.0026***<br>(4.56)</td><td>0.0039***<br>(3.99)</td><td>0.0037***<br>(4.01)</td><td>0.0085***<br>(2.59)</td><td>0.0148***<br>(4.01)</td><td>0.0172***<br>(4.39)</td><td>0.0249***<br>(5.57)</td></tr><tr><td>CEOP*FC</td><td>0.0117***<br>(2.60)</td><td>0.0098**<br>(2.25)</td><td>-0.0156**<br>(-2.48)</td><td>0.0098*<br>(1.66)</td><td>0.0092***<br>(2.76)</td><td>0.0160***<br>(4.31)</td><td>0.0178***<br>(4.46)</td><td>0.0273***<br>(6.00)</td></tr><tr><td>Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Year</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Industry</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td></tr><tr><td>Firm</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td></tr><tr><td>Intercept</td><td>0.3573***<br>(5.6868)</td><td>0.2874***<br>(4.3265)</td><td>0.4343***<br>(4.0004)</td><td>0.4698***<br>(4.2936)</td><td>0.0223<br>(1.5672)</td><td>0.0163<br>(0.7852)</td><td>0.0239<br>(1.2719)</td><td>0.1023***<br>(3.46)</td></tr><tr><td>R<sup>2</sup></td><td>0.156</td><td>0.166</td><td>0.090</td><td>0.089</td><td>0.1463</td><td>1458</td><td>0.0790</td><td>0.080</td></tr><tr><td>N</td><td>30,646</td><td>30,646</td><td>30,646</td><td>30,646</td><td>30,646</td><td>30,646</td><td>30,646</td><td>30,646</td></tr><tr><th colspan="10">Panel C: Using alternative proxies for investment efficiency</th></tr><tr><th colspan="4">CEO power and investment efficiency</th><th colspan="4">CEO power, financial constraints, and investment efficiency</th></tr><tr><th></th><th>Chen (2011)</th><th>Chen (2013)</th><th></th><th></th><th>Chen (2011)</th><th>Chen (2013)</th><th></th><th></th></tr><tr><td>CEOP</td><td>-0.0029***<br>(-2.78)</td><td>-0.0021**<br>(-2.02)</td><td>-0.0026**<br>(-2.28)</td><td>-0.0039***<br>(-3.77)</td><td>-0.0029***<br>(-2.79)</td><td>-0.0021**<br>(-2.04)</td><td>-0.0026**<br>(-2.27)</td><td>-0.0025**<br>(-2.42)</td></tr><tr><td>FC</td><td></td><td></td><td></td><td></td><td>0.0116***<br>(3.03)</td><td>0.0178***<br>(4.07)</td><td>0.0111***<br>(2.59)</td><td>0.0122*<br>(1.86)</td></tr><tr><td>CEOP*FC</td><td></td><td></td><td></td><td></td><td>0.0154***<br>(0.29)</td><td>0.0223***<br>(1.74)</td><td>0.0193***<br>(4.44)</td><td>0.0171**<br>(1.74)</td></tr><tr><td>Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Industry</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td></tr><tr><td>Firm</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td></tr><tr><td>Year</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Intercept</td><td>0.0012<br>(0.08)</td><td>0.0488*<br>(1.95)</td><td>0.0744***<br>(4.39)</td><td>0.0477*<br>(1.93)</td><td>0.0015<br>(0.08)</td><td>0.0438<br>(1.62)</td><td>0.0786***<br>(3.96)</td><td>0.0929***<br>(3.24)</td></tr><tr><td>R<sup>2</sup></td><td>0.074</td><td>0.081</td><td>0.090</td><td>0.098</td><td>0.071</td><td>0.087</td><td>0.084</td><td>0.095</td></tr><tr><td>N</td><td>30,646</td><td>30,646</td><td>30,646</td><td>30,646</td><td>30,646</td><td>30,646</td><td>30,646</td><td>30,646</td></tr></tbody></table>

Note. This table presents regression effect regression results using alternative measures of CEO Power, financial constraints, and investment efficiency. The standard errors are clustered at the firm level. The t-statistics are in parentheses. ***, **, and * denote significance level at 1 %, 5 %, and 10 %. All the variables are defined in Appendix A.

(1997) as an alternative measure of financial constraints (The higher the values of the KZ index, represent more financially constrained firms). Second, in addition to the KZ index, we use the WW index proposed by Whited and Wu (2006) to measure financial constraints. We follow Guariglia and Yang (2016) and Ding et al. (2022) to measure the WW index. We calculate the index in the following way:

$$WW_{i,t} = -0.091 \times \frac{CF_{i,t}}{BV_{i,t-1}} - 0.062 \times DIVP_{i,t} + 0.021 \times \frac{LDT_{i,t}}{Asset_{i,t-1}} - 0.044 \times Size_{i,t} - 0.035 \times SG_{i,t} + 0.102 \times ISG_{i,t} \quad (6)$$

Where CF is cash flow, BV represents book value scaled by total assets, DIVP is a dummy variable equal to 1 if the dividend is positive,
LDT is long-term debt, Asset denotes total assets, Size is the logarithm of total assets, SG is sales growth, and ISG means industrial sales growth. A firm with a higher value of the WW index can be considered to be more financially constrained. Panel B of Table 4 presents the results of OLS and fixed-effects regressions using alternative proxies of financial constraints. The results remain consistent.

Further, we employ alternative measures of investment efficiency to enhance the reliability of the findings. Our first alternative measure is estimated following Chen et al. (2011) using the following equation:

$$ \text{Invest}_{i,t} = \theta_0 + \theta_1 \text{NEG}_{i,t-1} + \theta_2 \text{SG}_{i,t-1} + \theta_3 \text{NEG} * \text{SG}_{i,t-1} + \varepsilon_{i,t} \quad (7) $$

where Invest$_{i,t}$ represents the net capital investment divided by lagged total assets. NEG$_{i,t-1}$ has a value of 1 if revenue growth is negative and 0 for positive revenue growth. SG$_{i,t-1}$ is the annual revenue growth.

Our second alternative measure of investment efficiency is suggested by Chen et al. (2013) as explained in the following model:

$$ \text{Invest}_{i,t} = \rho_0 + \rho_1 Q_{i,t-1} + \rho_2 \text{SaleGrowth}_{i,t-1} + \varepsilon_{i,t+1} \quad (8) $$

where Invest$_{i,t}$ represents the ratio of net capital investment over lagged total assets of firm i at time t. SalesGrowth$_{i,t-1}$ is the annual growth in sales of firm i at time t-1. Q$_{i,t-1}$ is Tobin's Q ratio of firm i at time t-1.

Panel C of Table 4 presents the results of OLS and fixed-effects regressions using alternative proxies of investment efficiency. We observe significant negative associations of CEOP and significant positive associations of the interaction term (CEOP*FC) with IE_R as well as IR_B for both OLS and fixed-effects regression results, consistent with the main findings.

## 5.2. Endogeneity checks

The fixed effects regression can help mitigate time-invariant heterogeneity, however, the results may still suffer from endogeneity due to reverse causality and selection bias (Mirza et al., 2024). We apply various techniques to deal with endogeneity problems. First, we use the lag of independent variables to address endogeneity issues. Using lagged independent variables can reduce endogeneity (Feng et al., 2022) because CEO power may have a lagged effect on corporate investment efficiency. Panel A of Table 5 presents the regression results. The results remain consistent with the findings reported in Table 3. Second, we employ the system GMM model as it provides valid instruments to address the unobserved heterogeneity (Hungra et al., 2024; Mirza et al., 2024; Shahab et al., 2022). Panel B of Table 5 reports the results for the two-step system GMM estimator. The relationship between CEO power and investment efficiency continues to hold, which is consistent with our prior results. In addition, the diagnostics tests indicate that our models are well-fitted. The AR(2) stats show the absence of second-order serial correlations in the residuals. The Hansen (J-Statistics) over-identification test confirms that the instruments used in the model are valid.

We also use a two-stage least squares (2SLS) estimation to address endogeneity (Hossain et al., 2023). We use the industry average of CEO power (Ind_CEOP) as an instrumental variable (IV). In the first stage (while using Ind_CEOP for CEO power), we find highly significant coefficients showing the suitability of the IV used. The results of the second stage are reported in panel C of Table 5, showing that CEO power is negatively associated with investment efficiency. We also find that financial constraint positively moderates the nexus between CEO power and investment efficiency. These results are also consistent with our previous results, confirming that endogeneity concerns do not drive our results.

Fourth, to mitigate self-selection concerns, following Mirza et al. (2024), we re-estimate our regression using a Heckman two-stage selection model. For the Heckman procedure, we need a suitable application for exclusion restrictions, i.e., including no less than one such variable in the first stage that is not incorporated in the second stage but is not associated with investment decisions. For Heckman's two-stage model in our case, the first stage is Probit regression. We construct a dummy variable representing a high-power CEO, which is equal to 1 if CEO power is more than the industry mean of the CEO power variable and 0 otherwise. We calculate the inverse Mills ratio (IMR) based on regression estimates. Then we use IMR in our model (second stage) to control potential selection bias. Panel D of Table 5 shows the second stage of the Heckman two-stage selection model. The coefficients are statistically significant, suggesting that our main findings are robust.

# 6. Additional analysis

## 6.1. CEO power over/under-investment

Following prior studies (Gomariz and Ballesta, 2014; Mirza et al., 2020), we divide our sample into two investment inefficiency scenarios, i.e., underinvestment and overinvestment. We use absolute values of over(under)investment, so higher values suggest higher over(under)investment. Powerful CEOs may have a higher tendency to overinvest as they are associated with excessive risk-taking and investing in trophy projects (Lewellyn and Muller-Kahle, 2012). Further, powerful CEOs exaggerate their abilities, pay less attention to the suggestions of the board members (Fast et al., 2012; Greve and Mitsuhashi, 2007) and have a higher inclination for reckless investment decisions, leading to acceptance of investment projects with negative NPV. Furthermore, entrenched CEOs have a greater likelihood of overinvestment for rent extraction purposes. Our results, presented in Table 6, suggest that CEOP is significantly and positively associated with overinvestment but has no significant association with underinvestment. These findings support our reasoning that powerful CEOs are more prone to making reckless investment decisions, leading to overinvestment.
<table><caption>**Table 5**

Robustness - using alternative regression techniques.</caption><thead><tr><th colspan="5">Panel A: Using lagged independent variable</th></tr><tr><th></th><th colspan="2">(1)</th><th colspan="2">(2)</th></tr><tr><th></th><th>IE_R</th><th>IE_B</th><th>IE_R</th><th>IE_B</th></tr></thead><tbody><tr><td>CEOP</td><td>-0.0022***<br>(-3.9677)</td><td>-0.0034***<br>(-4.1966)</td><td>-0.0023***<br>(-4.0075)</td><td>-0.0034***<br>(-4.2168)</td></tr><tr><td>FC</td><td></td><td></td><td>0.0052***<br>(2.8463)</td><td>0.0058***<br>(2.8309)</td></tr><tr><td>CEOP*FC</td><td></td><td></td><td>0.0258***<br>(10.1700)</td><td>0.0490***<br>(8.9180)</td></tr><tr><td>Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Year</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Industry</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Intercept</td><td>0.0366**<br>(2.5572)</td><td>0.0461***<br>(2.7800)</td><td>0.0174<br>(0.9128)</td><td>0.0387**<br>(2.0015)</td></tr><tr><td>R<sup>2</sup></td><td>0.155</td><td>0.090</td><td>0.165</td><td>0.096</td></tr><tr><td>N</td><td>27,748</td><td>27,748</td><td>27,748</td><td>27,748</td></tr><tr><th colspan="5">Panel B: GMM</th></tr><tr><th></th><th colspan="2">(1)</th><th colspan="2">(2)</th></tr><tr><th></th><th>IE_R</th><th>IE_B</th><th>IE_R</th><th>IE_B</th></tr><tr><td>CEOP</td><td>-0.0130***<br>(-3.84)</td><td>-0.0767***<br>(-3.41)</td><td>-0.0035**<br>(-2.44)</td><td>-0.0058**<br>(-2.35)</td></tr><tr><td>FC</td><td></td><td></td><td>0.0014***<br>(2.97)</td><td>0.0025***<br>(2.62)</td></tr><tr><td>CEOP*FC</td><td></td><td></td><td>0.0038***<br>(10.04)</td><td>0.0024**<br>(2.45)</td></tr><tr><td>Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Year</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Industry</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Intercept</td><td>-0.1487***<br>(-3.74)</td><td>-0.0464<br>(-1.16)</td><td>-0.1024<br>(-1.43)</td><td>0.1620<br>(0.92)</td></tr><tr><td>AR (1)</td><td>-8.12***</td><td>-7.67***</td><td>-13.24***</td><td>-10.67***</td></tr><tr><td>AR (2)</td><td>-1.13</td><td>0.69</td><td>-1.77</td><td>0.78</td></tr><tr><td>J-Statistics</td><td>230.40</td><td>234.90</td><td>132.67</td><td>123.76</td></tr><tr><td>N</td><td>27,748</td><td>27,748</td><td>27,748</td><td>27,748</td></tr><tr><th colspan="5">Panel C: 2SLS</th></tr><tr><th></th><th colspan="2">(1)</th><th colspan="2">(2)</th></tr><tr><th></th><th>IE_R</th><th>IE_B</th><th>IE_R</th><th>IE_B</th></tr><tr><td>CEOP</td><td>-0.0136***<br>(-3.04)</td><td>-0.0199***<br>(-3.00)</td><td>-0.0070**<br>(-2.08)</td><td>-0.0102*<br>(-1.76)</td></tr><tr><td>FC</td><td></td><td></td><td>0.0001***<br>(3.09)</td><td>0.0078*<br>(1.85)</td></tr><tr><td>CEOP*FC</td><td></td><td></td><td>0.0027***<br>(5.46)</td><td>0.0034***<br>(3.75)</td></tr><tr><td>Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Year</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Industry</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Intercept</td><td>0.0562***<br>(3.26)</td><td>0.0690***<br>(2.7148)</td><td>0.3313***<br>(5.8089)</td><td>0.4087***<br>(3.9696)</td></tr><tr><td>R<sup>2</sup></td><td>0.1636</td><td>0.1998</td><td>0.1644</td><td>0.1013</td></tr><tr><td>N</td><td>30,646</td><td>30,646</td><td>30,646</td><td>30,646</td></tr><tr><td>Cragg- Donald</td><td>97.312</td><td>97.312</td><td>95.174</td><td>95.174</td></tr><tr><th colspan="5">Panel D: Heckman Two-step regression</th></tr><tr><th></th><th colspan="2">(1)</th><th colspan="2">(2)</th></tr><tr><th></th><th>IE_R</th><th>IE_B</th><th>IE_R</th><th>IE_B</th></tr><tr><td>CEOP</td><td>0.0133***<br>(3.89)</td><td>0.0239***<br>(5.47)</td><td>0.0158***<br>(4.31)</td><td>0.0268***<br>(5.64)</td></tr><tr><td>FC</td><td></td><td></td><td>0.0122***<br>(3.67)</td><td>0.0221***<br>(5.47)</td></tr><tr><td>CEOP*FC</td><td></td><td></td><td>0.0121***<br>(3.64)</td><td>0.0219***<br>(5.42)</td></tr><tr><td>Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Year</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Industry</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Intercept</td><td>0.0055<br>(1.4972)</td><td>0.0109**<br>(2.3732)</td><td>0.0060<br>(1.5917)</td><td>0.0115**<br>(2.4226)</td></tr><tr><td>IMR</td><td>-0.4593***<br>(-11.43)</td><td>-0.0898***<br>(-12.34)</td><td>-0.0167*<br>(-1.81)</td><td>-0.0900***<br>(-12.34)</td></tr><tr><td>N</td><td>30,646</td><td>30,646</td><td>30,646</td><td>30,646</td></tr></tbody></table>
**Note.** This table presents regression results using alternative regression techniques. The standard errors are clustered at the firm level. The t-statistics are in parentheses. ***, **, and * denote significance level at 1 %, 5 %, and 10 %. All the variables are defined in Appendix A.

<table><caption>**Table 6**
CEO power, under-investment, and over-investment.</caption><thead><tr><th rowspan="2"></th><th colspan="2">Panel A: Underinvestment</th><th colspan="2">Panel B: Overinvestment</th></tr><tr><th>UI_R</th><th>UI_B</th><th>OI_R</th><th>OI_B</th></tr></thead><tbody><tr><td>CEOP</td><td>-0.0007<br>(-0.74)</td><td>-0.0005<br>(-1.30)</td><td>-0.0026**<br>(-2.45)</td><td>-0.0030***<br>(-2.97)</td></tr><tr><td>Lev</td><td>0.0007*<br>(1.87)</td><td>0.0002<br>(1.38)</td><td>0.0099***<br>(2.70)</td><td>0.0074*<br>(1.74)</td></tr><tr><td>Age</td><td>0.0000<br>(0.10)</td><td>-0.0003***<br>(-5.04)</td><td>-0.0021***<br>(-10.12)</td><td>0.0012***<br>(6.60)</td></tr><tr><td>Size</td><td>-0.0012*<br>(-1.79)</td><td>0.0017***<br>(6.46)</td><td>0.0037***<br>(4.49)</td><td>-0.0033***<br>(-4.38)</td></tr><tr><td>MB</td><td>-0.3355*<br>(-1.71)</td><td>-0.0000<br>(-0.54)</td><td>0.0126<br>(0.72)</td><td>0.0013***<br>(5.12)</td></tr><tr><td>SOEs</td><td>0.0028*<br>(1.94)</td><td>-0.0003<br>(-0.50)</td><td>-0.0080***<br>(-4.75)</td><td>0.0094***<br>(5.61)</td></tr><tr><td>Tang</td><td>-0.0180***<br>(-3.90)</td><td>0.0030<br>(1.62)</td><td>0.0457***<br>(8.04)</td><td>-0.0750***<br>(-14.00)</td></tr><tr><td>FRQ</td><td>0.0010<br>(0.91)</td><td>0.0021***<br>(4.14)</td><td>-0.0175***<br>(-5.26)</td><td>0.0361***<br>(5.46)</td></tr><tr><td>Big4</td><td>0.0022<br>(0.73)</td><td>-0.0003<br>(-0.25)</td><td>-0.0039<br>(-1.35)</td><td>0.0021<br>(0.63)</td></tr><tr><td>Top1</td><td>-0.0001<br>(-1.23)</td><td>-0.0000**<br>(-2.12)</td><td>-0.0000<br>(-0.06)</td><td>0.0001<br>(0.95)</td></tr><tr><td>Slack</td><td>-0.0093<br>(-1.31)</td><td>0.0027<br>(1.27)</td><td>0.0703***<br>(10.55)</td><td>-0.0008<br>(-0.13)</td></tr><tr><td>OCF</td><td>-0.0012<br>(-0.62)</td><td>-0.0014<br>(-1.27)</td><td>0.0224***<br>(2.94)</td><td>-0.0017<br>(-0.76)</td></tr><tr><td>Industry</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Year</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Intercept</td><td>0.0125<br>(0.81)</td><td>-0.0482***<br>(-8.89)</td><td>-0.0102<br>(-0.59)</td><td>0.0079<br>(0.50)</td></tr><tr><td>R<sup>2</sup></td><td>0.0279</td><td>0.0517</td><td>0.0970</td><td>0.1130</td></tr><tr><td>N</td><td>7516</td><td>10,799</td><td>23,130</td><td>19,867</td></tr></tbody></table>

**Note.** This table presents OLS regression results with robust standard errors clustered at the firm level for the impact of CEOs' power on under/over-investment. Panel A reports the results for the effect of CEO power on underinvestment using Richardson (2006) and Biddle et al. (2009). Panel B reports the results for the effect of CEO power on overinvestment using Richardson (2006) and Biddle et al. (2009). The t-statistics are in parentheses. ***, **, and * denote significance level at 1 %, 5 %, and 10 %. All the variables are defined in Appendix A.

## 6.2. CEO power, analyst following, and investment efficiency

Financial analysts play a vital role in disseminating financial information. These analysts increase the visibility of the firms (Bowen et al., 2008) and decrease information asymmetry and managerial opportunism (Healy and Palepu, 2001). Analysts are equipped with industry knowledge, follow the firms regularly, and interact with the managers to obtain information, making them ideal for monitoring managerial activities (Chen et al., 2017). The monitoring role of the analysts (Irani and Oesch, 2013) improves corporate governance, enhances audit quality, decreases information asymmetry, and curtails managerial opportunism (Chen et al., 2017; Jiang and Kim, 2020; Majeed et al., 2024). We argue that the monitoring role played by the analyst would curb the inefficient investment decisions by powerful CEOs. For that purpose, we divide our sample into low and higher analyst following groups based on the industry median analyst following. The results reported in Panel A of Table 7 suggest that CEO power is positively associated with investment efficiency for firms with higher analyst following. However, our results for firms with low analyst following exhibit a positive but insignificant association of CEO power with investment efficiency. These results suggest that the monitoring role played by analysts improves corporate governance, which ultimately reverses the destructive impact of CEO power on investment efficiency.

## 6.3. CEO power, media coverage, and investment efficiency

Higher media coverage of a firm increases its visibility. Consequently, greater media coverage increases the information available to the stakeholders, decreases the information asymmetry (Bushee et al., 2010; Liao et al., 2021), and acts as an external governance tool. The reputational and litigation risk induced by greater media coverage disciplines the management and improves corporate governance (Burke et al., 2019; Dyck et al., 2008). Earlier studies have documented the positive effect of media coverage on investment efficiency (Gao et al., 2021). We study the influence of the governance effect of media coverage on the relationship between CEO power and investment efficiency. To explore the impact of media coverage, we divide the sample into firms with higher (lower) media
<table><caption>**Table 7**
CEO power, analysts' following, media coverage, and investment efficiency.</caption><thead><tr><th rowspan="3"></th><th colspan="4">Panel A: Analysts' Following</th><th colspan="4">Panel B: Media Coverage</th></tr><tr><th colspan="2">High Analysts' Following</th><th colspan="2">Low Analysts' Following</th><th colspan="2">High Media Coverage</th><th colspan="2">Low Media Coverage</th></tr><tr><th>IE_R</th><th>IE_B</th><th>IE_R</th><th>IE_B</th><th>IE_R</th><th>IE_B</th><th>IE_R</th><th>IE_B</th></tr></thead><tbody><tr><td>CEOP</td><td>0.0029***<br>(8.97)</td><td>0.0028***<br>(6.58)</td><td>-0.0123<br>(-0.74)</td><td>-0.0231<br>(-1.05)</td><td>0.0058***<br>(3.09)</td><td>0.0221**<br>(2.01)</td><td>0.0016<br>(1.09)</td><td>0.0218<br>(1.14)</td></tr><tr><td>Lev</td><td>0.0140*<br>(1.68)</td><td>0.0309**<br>(2.03)</td><td>-0.0049<br>(-1.01)</td><td>0.0042<br>(0.87)</td><td>-0.0509**<br>(-2.04)</td><td>-0.0276<br>(-1.62)</td><td>-0.0026<br>(-0.43)</td><td>0.0061<br>(1.00)</td></tr><tr><td>Age</td><td>0.0025*<br>(1.69)</td><td>0.0628*<br>(1.69)</td><td>0.0024***<br>(14.22)</td><td>0.0024***<br>(14.19)</td><td>0.0013***<br>(3.46)</td><td>0.0012***<br>(2.96)</td><td>0.0013***<br>(7.16)</td><td>0.0007***<br>(3.71)</td></tr><tr><td>Size</td><td>-0.0015<br>(-0.85)</td><td>0.0216*<br>(1.95)</td><td>-0.0040***<br>(-4.16)</td><td>0.0231<br>(1.15)</td><td>0.0018<br>(0.85)</td><td>-0.0381***<br>(-3.04)</td><td>-0.0022**<br>(-1.93)</td><td>0.0359<br>(1.25)</td></tr><tr><td>MB</td><td>-3.4295**<br>(-2.79)</td><td>-2.3929*<br>(-1.69)</td><td>-0.0079*<br>(-1.92)</td><td>-0.1238<br>(-1.29)</td><td>-4.4863***<br>(-3.77)</td><td>-3.1863***<br>(-2.73)</td><td>-0.0225<br>(-0.32)</td><td>-0.2479*<br>(-1.77)</td></tr><tr><td>SOEs</td><td>0.0072*<br>(1.99)</td><td>0.0125**<br>(2.64)</td><td>0.0092***<br>(5.17)</td><td>0.0091***<br>(5.15)</td><td>0.0102**<br>(2.22)</td><td>0.0121**<br>(2.40)</td><td>0.0128***<br>(5.67)</td><td>0.0174***<br>(7.70)</td></tr><tr><td>Tang</td><td>-0.0507***<br>(-4.89)</td><td>-0.0421**<br>(-2.46)</td><td>-0.0183***<br>(-3.27)</td><td>-0.0178***<br>(-3.17)</td><td>-0.1174***<br>(-7.61)</td><td>-0.111***<br>(-5.95)</td><td>-0.0640<br>(-9.49)</td><td>-0.0527***<br>(-7.26)</td></tr><tr><td>FRQ</td><td>-0.0035*<br>(-1.85)</td><td>-0.0147*<br>(-1.69)</td><td>-0.0070*<br>(-1.73)</td><td>-0.0060*<br>(-1.69)</td><td>-0.0003*<br>(-1.69)</td><td>-0.0629**<br>(-2.49)</td><td>-0.2261**<br>(-2.02)</td><td>-0.2281**<br>(-2.25)</td></tr><tr><td>Big4</td><td>-0.0028<br>(-0.49)</td><td>-0.0013<br>(-0.21)</td><td>-0.0094*<br>(-1.69)</td><td>-0.0091<br>(-1.65)</td><td>0.0042<br>(0.57)</td><td>0.0020<br>(0.26)</td><td>-0.0139**<br>(-2.18)</td><td>-0.0081<br>(-1.02)</td></tr><tr><td>Top1</td><td>-0.0001<br>(-0.69)</td><td>-0.0001<br>(-1.47)</td><td>-0.0000<br>(-0.31)</td><td>-0.0009<br>(-0.16)</td><td>-0.0000<br>(-0.36)</td><td>-0.0001<br>(-1.26)</td><td>-0.0001<br>(-0.16)</td><td>0.0001<br>(0.81)</td></tr><tr><td>Slack</td><td>-0.1157***<br>(-8.15)</td><td>-0.1089***<br>(-8.05)</td><td>-0.0881***<br>(-12.47)</td><td>-0.0881***<br>(12.46)</td><td>-0.0173<br>(1.14)</td><td>-0.0279*<br>(-1.76)</td><td>0.0009<br>(0.11)</td><td>0.0270***<br>(3.22)</td></tr><tr><td>OCF</td><td>0.0346<br>(0.70)</td><td>0.0095<br>(0.17)</td><td>0.0001<br>(0.20)</td><td>0.0001<br>(0.19)</td><td>-0.0165<br>(-0.29)</td><td>-0.0269<br>(-0.41)</td><td>-0.0004<br>(-0.24)</td><td>0.0003<br>(0.98)</td></tr><tr><td>Year</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Industry</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Intercept</td><td>-0.0103<br>(-0.24)</td><td>0.0016<br>(0.02)</td><td>0.0144<br>(0.73)</td><td>0.0372<br>(0.68)</td><td>-0.0759<br>(-1.58)</td><td>-0.0293<br>(-0.29)</td><td>-0.0116<br>(-0.48)</td><td>0.0095<br>(0.12)</td></tr><tr><td>N</td><td>19,920</td><td>19,613</td><td>10,726</td><td>11,033</td><td>15,017</td><td>15,629</td><td>15,629</td><td>15,017</td></tr><tr><td>R<sup>2</sup></td><td>0.209</td><td>0.300</td><td>0.141</td><td>0.251</td><td>0.140</td><td>0.041</td><td>0.141</td><td>0.169</td></tr></tbody></table>

Note. This table presents OLS regression results for the impact of analyst following and media coverage on the relationship between CEO power and investment efficiency. Panel A reports the results for the effect of analysts following on the relationship between CEO power and investment efficiency. Panel B reports the effects of media coverage on the relationship between CEO power and investment efficiency. The t-statistics are in parentheses. ***, **, and * denote significance level at 1 %, 5 %, and 10 %. All the variables are defined in Appendix A.

coverage using the industry median media coverage. As reported in panel B of Table 7, our results suggest that the media plays a governance role and improves investment decisions that are in line with prior studies (Gao et al., 2021). Our findings suggest that greater media coverage improves investment decisions, and powerful CEOs are associated with greater investment efficiency when media coverage is high.

## 6.4. CEO power, CEOs' gender, and investment efficiency

The gender of the top leadership plays a critical role in shaping corporate behavior, performance, and agency problems. For example, female CEOs, as compared to their male counterparts, are more likely to improve firm performance (Khan and Vieito, 2013). Female CEOs are associated with lower earnings volatility and risk-taking (Faccio et al., 2016). As a result of the conservative behavior, female CEOs are associated with greater accounting conservatism and higher corporate cash holdings (Francis et al., 2015). Female CEOs are also more likely to choose less risky financing decisions (Faccio et al., 2016), improve financial reporting quality, and reduce information asymmetry (Francis et al., 2015; Srinidhi et al., 2011). Prior studies have also documented that the effect of CEO gender on information asymmetry, agency problems, and differences in risk-taking behavior of the male and female CEOs is translated into investment decisions of the firms and investment efficiency (Faccio et al., 2016; Mirza et al., 2020). Since the gender of CEOs plays an important role in the investment decisions of the firms, therefore, we argue that powerful female CEOs may affect the corporate investment decisions differently.

To investigate the impact of male and female CEOs on investment efficiency, we divide our sample into two subsamples, i.e., male CEOs and female CEOs, and re-estimate our baseline regression for each subsample. Table 8 presents the results for the effect of CEO gender on the relationship between CEO power and investment efficiency. We document a significant positive relationship between CEOP and investment efficiency when the CEO is female. However, we document an insignificant effect of CEO power on corporate investment for male CEOs. The significant impact of powerful female CEOs can be attributed to the argument that female CEOs are associated with lower agency costs, resulting in higher investment efficiency (Ullah et al., 2020).
<table><caption>**Table 8**
CEO power, CEO's gender, and investment efficiency.</caption><thead><tr><th rowspan="3"></th><th colspan="4">Panel: (A) Female CEOs</th><th colspan="4">Panel: (B) Male CEOs</th></tr><tr><th>OLS</th><th>OLS</th><th>FE</th><th>FE</th><th>OLS</th><th>OLS</th><th>FE</th><th>FE</th></tr><tr><th>IE_R</th><th>IE_B</th><th>IE_R</th><th>IE_B</th><th>IE_R</th><th>IE_B</th><th>IE_R</th><th>IE_B</th></tr></thead><tbody><tr><td>CEOP</td><td>0.0023***<br>(4.36)</td><td>0.0044***<br>(6.88)</td><td>0.0018**<br>(2.54)</td><td>0.0021***<br>(2.60)</td><td>-0.0016<br>(-0.68)</td><td>0.0009<br>(0.28)</td><td>0.0003<br>(0.05)</td><td>-0.0008<br>(-0.14)</td></tr><tr><td>Lev</td><td>0.0198<br>(1.62)</td><td>0.0135<br>(0.86)</td><td>-0.0317<br>(-1.31)</td><td>-0.0560*<br>(-1.87)</td><td>0.0007***<br>(3.55)</td><td>0.0010**<br>(2.52)</td><td>0.0008<br>(1.36)</td><td>0.0005<br>(0.71)</td></tr><tr><td>Age</td><td>0.0024***<br>(6.94)</td><td>0.0012**<br>(2.47)</td><td>-0.0038<br>(-0.19)</td><td>0.0038<br>(0.19)</td><td>0.0027***<br>(25.01)</td><td>0.0010***<br>(7.37)</td><td>0.0080***<br>(2.96)</td><td>0.0094***<br>(3.27)</td></tr><tr><td>Size</td><td>-0.0041**<br>(-2.03)</td><td>-0.0018<br>(-0.69)</td><td>-0.0111*<br>(-1.67)</td><td>-0.0212***<br>(-2.72)</td><td>-0.0041***<br>(-9.28)</td><td>-0.0035***<br>(-6.18)</td><td>-0.0095***<br>(-8.13)</td><td>-0.0169***<br>(-12.66)</td></tr><tr><td>MB</td><td>-1.3252<br>(-1.10)</td><td>-1.9010<br>(-1.38)</td><td>-2.8152*<br>(-1.68)</td><td>-1.4973<br>(-0.80)</td><td>-0.1382**<br>(-2.13)</td><td>-0.1230<br>(-1.16)</td><td>-0.2091**<br>(-2.48)</td><td>-0.1652**<br>(-2.21)</td></tr><tr><td>SOEs</td><td>0.0043<br>(1.01)</td><td>0.0056<br>(0.95)</td><td>-0.0044<br>(-0.35)</td><td>-0.0173<br>(-1.11)</td><td>0.0061***<br>(6.11)</td><td>0.0108***<br>(8.51)</td><td>0.0015<br>(0.83)</td><td>0.0007<br>(0.33)</td></tr><tr><td>Tang</td><td>-0.0638***<br>(-4.37)</td><td>-0.1023***<br>(-4.97)</td><td>0.1968***<br>(4.89)</td><td>0.1697***<br>(3.33)</td><td>-0.0315***<br>(-10.32)</td><td>-0.0639***<br>(-16.37)</td><td>0.1031***<br>(16.38)</td><td>0.1264***<br>(17.64)</td></tr><tr><td>FRQ</td><td>0.0036<br>(0.19)</td><td>0.0351<br>(1.37)</td><td>0.0155<br>(0.63)</td><td>0.0413**<br>(2.00)</td><td>-0.0099***<br>(-2.80)</td><td>-0.0116***<br>(-2.78)</td><td>-0.0104***<br>(-3.23)</td><td>-0.0115***<br>(-3.15)</td></tr><tr><td>Big4</td><td>-0.0319**<br>(-2.37)</td><td>-0.0327**<br>(-2.16)</td><td>-0.1265***<br>(-4.60)</td><td>-0.1113***<br>(-3.35)</td><td>0.0016<br>(0.89)</td><td>0.0053**<br>(2.20)</td><td>-0.0033<br>(-0.76)</td><td>-0.0046<br>(-0.90)</td></tr><tr><td>Top1</td><td>-0.0002<br>(-1.24)</td><td>-0.0003*<br>(-1.68)</td><td>-0.0020***<br>(-4.17)</td><td>-0.0021***<br>(-4.04)</td><td>-0.0000<br>(-0.41)</td><td>0.0000<br>(0.55)</td><td>-0.0006***<br>(-7.49)</td><td>-0.0007***<br>(-7.66)</td></tr><tr><td>Slack</td><td>-0.0732***<br>(-4.50)</td><td>0.0157<br>(0.67)</td><td>-0.0302<br>(-0.98)</td><td>-0.0024<br>(-0.06)</td><td>-0.0955***<br>(-22.51)</td><td>0.0028<br>(0.56)</td><td>-0.0501***<br>(-7.76)</td><td>0.0250***<br>(3.60)</td></tr><tr><td>OCF5</td><td>-0.0604<br>(-1.28)</td><td>-0.0141<br>(-0.20)</td><td>-0.1245<br>(-1.33)</td><td>-0.1014<br>(-0.96)</td><td>-0.0011<br>(-0.95)</td><td>-0.0011<br>(-0.79)</td><td>0.0015<br>(1.04)</td><td>0.0005<br>(0.29)</td></tr><tr><td>Year</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Industry</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Intercept</td><td>0.0371<br>(0.89)</td><td>0.0256<br>(0.47)</td><td>0.2681<br>(1.26)</td><td>0.4272*<br>(1.80)</td><td>0.0477***<br>(3.40)</td><td>-0.0372<br>(-0.78)</td><td>0.0978**<br>(2.04)</td><td>0.1452***<br>(2.65)</td></tr><tr><td>R<sup>2</sup></td><td>0.2180</td><td>0.1277</td><td>0.2637</td><td>0.2241</td><td>0.1495</td><td>0.0703</td><td>0.1443</td><td>0.1073</td></tr><tr><td>N</td><td>2758</td><td>2758</td><td>2758</td><td>2758</td><td>27,880</td><td>27,880</td><td>27,880</td><td>27,880</td></tr></tbody></table>

Note. This table presents OLS regression results for the impact of CEOs gender on the relationship between CEO power and investment efficiency. Panel A reports the results for Female CEOs and Panel B reports the results of Male CEOs. The t-statistics are in parentheses. ***, **, and * denote significance level at 1%, 5%, and 10%. All the variables are defined in Appendix A.

## 7. Conclusion

Power grants the ability to control the behavior and actions of others to produce desired results (Emerson, 1964). The consequences of CEO power concerning corporate decisions have been recognized in earlier literature (Greve and Mitsuhashi, 2007; Lewellyn and Muller-Kahle, 2012; Shahab et al., 2022). CEO power leads to unnecessary risk-taking, agency problems, and adverse selection (Bebchuk et al., 2011; Lewellyn and Muller-Kahle, 2012). This study examines whether CEO power is associated with investment efficiency. Our findings exhibit that CEO power is negatively associated with investment efficiency. Our results support the arguments that greater CEO power leads to adverse selection and moral hazard, leading to inefficient investment decisions. Our findings further suggest that the negative effect of CEO power weakens when firms face financial constraints. Our additional results also indicate that CEO power is positively associated with overinvestment. These findings support the argument that CEO power results in over-confidence and excessive risk-taking, leading to overinvestment. Our findings further suggest that the effect of CEO power on investment efficiency becomes positive when firms have a higher analyst following and greater media coverage. These results show that external governance mechanisms prevent powerful CEOs from making inefficient investment decisions.

Overall, our findings imply that CEO power plays a vital role in shaping investment efficiency. Financial constraints decrease the negative effect of CEO power and bring out the benefits associated with concentrated power. Moreover, CEOs with greater power are more prone to excessive risk-taking by overinvesting. However, the external governance mechanism influences the investment decisions of powerful CEOs. These findings offer policy implications. The regulators and policymakers should formulate policies to strengthen the regulatory framework to mitigate the negative effects of powerful CEOs. Furthermore, stakeholders, particularly investors, may introduce governance mechanisms that can facilitate the efficient utilization of scarce firm resources. While our analysis focuses on the Chinese market, offering a particular institutional landscape branded by ownership concentration, extensive government interventions, and relatively weaker investor protections, which may influence the applicability of our findings. In more developed markets such as the U.S. and Europe, where corporate governance mechanisms are generally robust and market discipline is more evident, the relationship between CEO power and investment efficiency may vary. Nonetheless, fundamental economic mechanisms such as the impact of financial constraints, agency conflicts, and information asymmetry on investment efficiency are likely to hold across different institutional contexts. Future investigations could explore our hypotheses in alternative settings to determine
boundary conditions or broaden this research to cross-country settings to analyze how institutional frameworks influence the observed dynamics.

## CRediT authorship contribution statement

**Irfan Ullah:** Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision, Software, Resources, Project administration, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. **Muhammad Ansar Majeed:** Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision, Software, Resources, Project administration, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. **Bakr Al-Gamrh:** Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision, Software, Resources, Project administration, Methodology, Investigation, Formal analysis, Conceptualization. **Tanveer Ahsan:** Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision, Software, Resources, Project administration, Methodology, Investigation, Formal analysis, Conceptualization.

## Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

## Declaration of Competing Interest

There are no conflicts of interest to declare.

## Acknowledgments

None.

## Appendix A. : Variable Definition

<table><thead><tr><td>Variable</td><td>Definition</td></tr></thead><tbody><tr><td>Investment Efficiency (IE_R)</td><td>Following Richardson (2006), the non-negative values of residual obtained from Equation [1], multiplied by -1</td></tr><tr><td>Investment efficiency (IE_B)</td><td>Following Biddle et al., (2009) model, the absolute value of residual term from Equation [2], multiplied by -1.</td></tr><tr><td>CEO Power (CEOP)</td><td>The sum of 6 binary measures CEO power including Duality, Pay Slice, Ownership, Independence, Founder, Tenure. Thus, its value ranges from 0 to 6 (Han et al., 2016)</td></tr><tr><td>Financial Constraint (FC)</td><td>FC/SA1 = -0.737Assets + 0.043Assets<sup>2</sup> + 0.040Firm's age (Hadlock and Pierce, 2010)</td></tr><tr><td>Leverage (Lev)</td><td>It is the ratio of long-term liabilities scald by total assets</td></tr><tr><td>Firm's Age (Age)</td><td>It is the log of the age of the firm since inception</td></tr><tr><td>Firm's Size (Size)</td><td>It is the log of the total assets</td></tr><tr><td>Market to Book ratio (MB)</td><td>It is the ratio of the market value of asset to the book value of the assets.</td></tr><tr><td>Stat owned firms (SOEs)</td><td>Dummy variable, that equals one if the firm is owned by the state, and zero otherwise</td></tr><tr><td>Tangibility (Tang)</td><td>It is measured as plant, property and equipment divided by total assets</td></tr><tr><td>Financial Reporting Quality (FRQ)</td><td>FRQ is the absolute value of residuals of the Kothari et al. (2005) model, multiplied by -1.</td></tr><tr><td>Big 4 auditors (Big4)</td><td>Dummy variable, that equals one if the firm is audited by big 4 auditors, and zero otherwise</td></tr><tr><td>Control (Top1)</td><td>Shareholders holding more than 50 % of shares</td></tr><tr><td>Slack (Slack)</td><td>It is measured as cash divided by total assets</td></tr><tr><td>Cash flow volatility (OCF)</td><td>Standard deviation of cash flow from operations divided by average total assets.</td></tr><tr><td>Under-investment (UI_R)</td><td>Exhibits underinvestment calculated from the investment efficiency model following Richardson (2006).</td></tr><tr><td>Under-investment (UI_B)</td><td>Exhibits underinvestment calculated from the investment efficiency model following Biddle et al., (2009).</td></tr><tr><td>Under-investment (OI_R)</td><td>Exhibits overinvestment calculated from the investment efficiency model following Richardson (2006).</td></tr><tr><td>Under-investment (OI_B)</td><td>Exhibits overinvestment calculated from the investment efficiency model following Biddle et al., (2009).</td></tr><tr><td>Media coverage (MC)</td><td>MC is measured using the number of media reports regarding the firms. Media reports have been extracted from the topmost influential newspapers in China, including China Security News, Securities Daily, Securities Times, China Business, Economic Observer, Shanghai Security News, and 21st Century Economic Report. The data relating to MC has been obtained from the Chinese Research Data Services (CNRDS) database. We use the industry median to divide the sample into two subsamples—the firms with greater media coverage and firms with lower media coverage.</td></tr><tr><td>Analysts Following (AF)</td><td>Dummy variable, equals one if the firms have greater analysts following, and zero otherwise.</td></tr><tr><td>Industry</td><td>Industry fixed effects</td></tr><tr><td>Year</td><td>Year fixed effects</td></tr></tbody></table>

## Data availability

Data will be made available on request.
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