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Corporate Social performance, CEO Overconfidence and

CEO Duality

Master’s thesis

Name: Longliang Li

Student number: S2508915

Study program: MSc Finance

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Abstract

This paper examines the impacts of CEO overconfidence on corporate social performance (CSP), and the interaction effects of CEO duality on the relationship between CEO overconfidence and CSP. The research uses CEOs’ unexercised but issued option holdings to measure CEO overconfidence for a large sample of US-listed firms. I find CEO overconfidence is negative related to CSP, especially for the management commitment and effectiveness towards human rights. The higher level of CEO overconfidence, the firm tends to have worse social performance and hence destroy the social reputation and public image. Further analysis finds that CEO duality does not exacerbate the effects of CEO duality on CSP. The last finding is CEO overconfidence affect not only CSP but also has a negative relationship with firm profitability, which is consistent with prior literature and provides more empirical evidence.

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3 1. Introduction

In this paper, I propose and test the impacts of CEO overconfidence on corporate social performance (CSP), and whether CEO duality has the interaction effects between CEO overconfidence and CSP. Specifically, as one of the most common biases that lead executives to make irrational decisions, CEO overconfidence can be defined as the tendency of CEOs to overestimate the accuracy of the predictions, abilities, and prospects of future gains. Previous literature argues overconfidence is a significant and robust personal trait across some career fields, especially for executives (Alicke & Govorun, 2005; Moore & Healy, 2008). Moreover, even though most of the previous research focuses on the effect of CEO overconfidence on corporate financial performance, it is also essential to analyze whether there is a correlation between CEO overconfidence and corporate social performance.

Corporate social performance has become an important topic for an increasing number of scholars in recent decades. Wartick and Cochran (1985) published a paper based on Carroll’s three-dimensional conceptual model (1979) and define CSP as involving “the underlying interaction among the principles of social responsibility, the process of social responsiveness, and the policies developed to address social issues.” Subsequently, CSP is concerned by numerous theoretical and empirical research (Hocevar & Bhambri, 1989; Wood, 1991; Clarkson, 1995) and some research argues CSP relates to the outcomes of action taken by firms in corporate social responsibility (De Bakker et al., 2005), and the impacts of corporate financial performance and corporate governance on CSP (McWilliams & Siegel, 2000; Wood, 2010). Besides, CSP can also improve financial performance, and the improvement drives differentiation between firms and industries (Hull & Rothenberg, 2008).

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underestimate the information and underreact. Thus, overconfidence CEOs systematically overestimate the probability of good outcomes and underestimate the probability of bad outcomes resulting from their actions (Heaton, 2002). For instance, overconfident CEOs misperceive negative net present value projects as value creating, which can lead to stock price crashes (Kim et al., 2016).

Most of the research concerns the impact of CEO overconfidence on corporate investment or financial performance. However, CEO overconfidence influences not only the corporate financial performance but also sustainable corporate development. Jo & Harjoto (2011) find the internal corporate social performance, for instance, employees’ diversity and products quality, can enhance the value of a firm more than external activities such as environmental concerns. McCarthy et al. (2017) argue CEO confidence is negatively related to the level of corporate social responsibility, and this effect is stronger in the institutional aspects than technical aspects of corporate social responsibility. Based on their findings, further analysis is needed, specifically, as a self-attribution bias of personal trait, what are the impacts of CEO overconfidence on corporate social performance, such as the management commitment and effectiveness towards maintaining the firms’ reputation; creating value-added products and services; and protecting human rights.

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Following the research of McCarthy et al. (2017), and combine the findings of prior literature, I defined the research topic is “The impact of CEO overconfidence on corporate social performance and whether CEO duality can further exacerbate CEO overconfidence to reduce CSP.” Specifically, this paper uses 815 US-listed firms for the period 2006 – 2017. As one of the most open markets in the world, the US has a market with relatively large consumption, and the market demand is increasingly diversified. Thus, CEO overconfidence in US-listed firms may have more significant impacts on sustainable corporate development. Also, analyzing US firms will provide some empirical evidence for future research on other countries or regions. The CEO overconfidence is measured based on the method of Kim et al. (2016), and CSP data is collected from the ESG database. I use a dummy variable to represent CEO duality to measure whether CEO duality has interaction effects of the relationship between CEO overconfidence and CSP. Also, I conduct the measurement to rule out other personal traits and financial performance that may influence social performance, including gender, age, tenure, return on assets, firm size, and firm leverage.

Using a large sample of US-listed firms, the main finding in this paper is CEO overconfidence has a negative impact on future CSP. More detailed, a CEO with higher overconfident level leads to the lower management commitment and effectiveness towards human rights, but I find CEO overconfidence cannot explain workforce loyalty and productivity, community, and product responsibility. Moreover, the results from total CSP scores indicate CEO overconfidence has a significant negative relationship with the overall CSP. The second finding is CEO duality does not have the interaction effects on the relationship between CEO overconfidence and CSP, so individuals are both CEOs and Chairmen cannot exacerbate the impacts of overconfidence on CSP. Moreover, consistent with the prior findings, I report CEO overconfidence has significant adverse effects on firm profitability, and this finding provides empirical evidence for the previous literature.

The paper is organized as follows: Section 2 reviews the previous relevant literature and develops the hypotheses. Section 3 describes the data, main variables, and the methodology. Section 4 presents the empirical analysis and results. Section 5 provides robustness checks. Section 6 concludes the paper.

2. Literature Review

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introduces the concept of “dynamic process” (Wartick & Cochran, 1985), and the consequences of CSP can reflect social and policy impacts (Wood, 1991). Until now, these models still have a significant contribution to follow-up research on CSP. The broad area of CSP research is well developed, Greenning & Turban (2000) find a firm with better CSP activities can attract more talented and quality workforce, thereby results to competitive advantage in the future. Meanwhile, a morally managed firm also provides a signal of product differentiation and good quality, and citizens have the preferences for products associated with CSP and personal dedications to social causes (Baron, 2009). Consequently, CSP can establish the right image for stakeholders, that influences the development and relationship to the firm (Jones & Murrell, 2001).

A large amount of research focuses on the relationship between CSP and corporate financial performance. However, the views of the research are mixed. Some literature argues CSP has a positive connection with financial performance (Waddock & Graves, 1997; Ruf et al., 2001; Orlitzky et al., 2003). Simpson & Kohers (2002) provides evidence to support a positive relationship by analyzing 385 banks. In addition, Schuler & Cording (2006) construct a behavioral model linking CSP and financial performance, and they find the information intensity for CSP will affect customers’ purchase intention, hence influence the firm’s financial performance. Nevertheless, some opposite opinion finds CSP has no correlation with corporate financial performance, and CSP has not developed into a viable theoretical or operational construct, because measuring CSP is depend on the operational setting, it is difficult to produce worthwhile comparisons across studies or generalizing beyond the boundaries of a specific study (Griffin & Mahon, 1997; Rowley & Berman, 2000). Besides, Mahoney & Roberts (2007) perform empirical research on a large sample of publicly held Canadian firms and find no significant relationship with financial performance and CSP. Instead, firms’ international activities and product quality have significant impacts on CSP.

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Consistent with the link between CEO overconfidence and corporate financial performance, some research also finds CEO overconfidence has an impact on CSP or corporate social responsibility. Social performance can conduct as a hedging instrument by reducing the negative effects of damaging events on firm value or corporate reputation (Brammer & Pavelin, 2006; Oliver et al., 2014). However, overconfidence harms the hedging feature and leads the negative influence to social responsibility (McCarthy et al., 2017), and overconfident CEOs are less responsive to corrective feedback in improving management forecast accuracy (Chen et al., 2015). Moreover, overconfidence CEOs overestimate the prominent and compelling information when making decisions, and tend to gather the information that supports their own beliefs and overestimate the probability of success. Thus, overconfident CEOs tend to make more value-destroying investments and harm social reputation (Goel & Thakor, 2008). Based on their perspectives, I expect the level of CEO overconfidence hurts CSP, and my first hypothesis is:

Hypothesis 1. CEO overconfidence has a negative impact on CSP.

Whether the firm should make a chairman acts as the CEO concurrently (CEO duality) has always been the subject of discussion in theoretical and empirical research. According to the agency theory, the board of directors is a supervisory mechanism set up to protect the interests of shareholders. An independent board of directors can effectively supervise the CEO’s decision and ensure that these decisions are approved by shareholders. Jensen (1993) argue that when a CEO also serves as the decision maker of the board of directors, it is easy for the board’s monitoring function to lose its efficiency, as it may enhance its welfare through the process of executing the decision-making process and increase the agency costs of the company. Therefore, the agency theorists believe CEO duality will reduce the performance of the firm (Young et al., 2000; Bliss, 2011).

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Hypothesis 2. For the CEOs with duality, the effects of CEO overconfidence on CSP are stronger than the CEOs without duality.

CEO overconfidence does not only affect CSP but also has significant impacts on firm financial performance. A large number of empirical studies have confirmed that people are “limited rationality.” They believe that overconfidence is a common psychological feature of human beings and is more evident among managers and executives (Malmendier & Tate, 2005). The irrational feature has an impact on corporate investment behavior, ultimately affecting financial performance (Fairchild, 2007; Kim et al., 2016). The characteristics of cognitive bias in overconfident CEO can easily lead to more irrational activities, for instance, Real Estate Investment Trusts (REITs) with overconfident CEOs tend to invest more, but the shares of these firms perform relatively worse, and CEOs have lower property investment performance (Eichholtz & Yönder, 2015). Ho et al. (2016) analyze bank industry from the period 2007 to 2009, and they find overconfident CEOs tend to weaken lending standards and enhance leverage before the financial crisis. During the crisis, these overconfident CEOs take on greater risk and higher failure probability, and they have worse stock performance than non-overconfident CEOs. Consistent with Ho et al. (2016), Kim et al. (2016) show that overconfident CEOs have more future stock price crash risk.

Recent literature also links CEO overconfidence to other activities which may influence firm financial performance. Ferris et al. (2013) find CEO overconfidence is important in explaining the frequencies of acquisitions and more value-destroying mergers. Also, when individuals experience short-term success, attribution bias leads them to be overconfident to forecast future returns (Hilary & Hsu, 2011). Additionally, CEOs are more overconfidence than non-professionals, and overconfident CEOs are easy to make biased decisions, which lead to financial distress and more likely to bankrupt (Fairchild, 2007). I expect to provide the new empirical evidence that CEO overconfidence reduces firm profitability, and my third hypothesis is:

Hypothesis 3. CEO overconfidence has a negative impact on firm profitability.

3. Data and Research Method

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term debts. Execucompt and Compustat are available via WRDS. Due to the data coverage of these databases, the sample covers 815 firms with year period from 2006 to 2017.

3.1 Measuring CSP score

ESG database separates various factors into three pillars that correlated with corporate social responsibility: environmental, social, and governance. Environmental pillar measures a company’s influence on living and non-living natural systems, covering air, water, and land, as well as complete ecosystems. It reflects a firm's management commitment and effectiveness towards avoiding environmental risks and capitalize on environmental opportunities. Social pillar demonstrates a firm’s capacity to generate trust and loyalty with its employees, consumers, and society, through its use of best management practices. It is a reflection of the firm’s reputation and the health of its license to operate. Governance pillar analyses a company's systems and processes, which ensure that its board of directors and employers act in the best interests of its shareholders. It reflects the company's use of best management practices to control its rights and responsibilities through the creation of incentives, as well as checks and balances (Diebecker & Sommer, 2017). Each pillar covers several categories with scores that measure the performance based on firm-reported information. I focus on how CSP is affected by CEO overconfidence, so only the social pillar is used in this study.

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10 3.2 Measuring Overconfidence

Overconfidence is measured by CEO faith related to stock option-based indicators, which is constructed by Malmendier and Tate (2005). Earlier research finds that CEOs are highly exposed to the specific risks of their firms due to the value of their human capital is closely related to firm performance. Thus, based on the high level of CEO under-diversification, CEOs should exercise their firm stock options early to avoid risks (Hall and Murphy 2002). However, Malmendier and Tate (2005) find some CEOs in their sample are unwilling their stock options early because overconfidence drives these CEOs to overestimate their firms’ future performance and underestimate the risk of future stock prices decrease. Furthermore, they argue that CEOs are overconfidence if they fail to exercise their options more than 67% in the money. Because of the data limitation, it is hard to find out the detailed data of Malmendier and Tate (2005), and there is no accurate data which can reflect overconfidence directly, so I use an alternative approach to measure overconfidence as per Kim et al. (2016).

I combine the data on CEO option holdings from Execucomp and year-end stock price from Compustat. Specifically, Execucomp provides both the amount of unexercised but issued options and the value of these options. I use the following way to construct the overconfidence variable: First, the value per option is calculated by using the value of the CEO’s unexercised but issued options divided by the amount of these options. Then, I figure the overconfidence level as the value per option divided by the year-end stock price. This method is also approved by other empirical research (McCarthy et al., 2017). The formulas to calculate CEO overconfidence in the following:

𝑉𝑎𝑙𝑢𝑒 𝑝𝑒𝑟 𝑜𝑝𝑡𝑖𝑜𝑛 = 𝑣𝑎𝑙𝑢𝑒 𝑜𝑓 𝑢𝑛𝑒𝑥𝑒𝑟𝑐𝑖𝑠𝑒𝑑 𝑏𝑢𝑡 𝑖𝑠𝑠𝑢𝑒𝑑 𝑜𝑝𝑡𝑖𝑜𝑛𝑠

𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑢𝑛𝑒𝑥𝑒𝑟𝑐𝑖𝑠𝑒𝑑 𝑏𝑢𝑡 𝑖𝑠𝑠𝑢𝑒𝑑 𝑜𝑝𝑡𝑖𝑜𝑛𝑠

𝑂𝑣𝑒𝑟𝑐𝑜𝑛𝑓𝑖𝑑𝑒𝑛𝑐𝑒 = 𝑉𝑎𝑙𝑢𝑒 𝑝𝑒𝑟 𝑜𝑝𝑡𝑖𝑜𝑛

𝑦𝑒𝑎𝑟 𝑒𝑛𝑑 𝑠𝑡𝑜𝑐𝑘 𝑝𝑟𝑖𝑐𝑒

3.3 Measuring CEO duality

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11 3.4 Measuring control variables

Following previous literature, I use several control variables for CEO personal trait and firm performance that may affect a firm’s decision to engage in CSP. The personal trait control variables include gender, age and tenure, and firm characteristic control variables include return on assets (ROA), firm size, and firm leverage.

Prior research finds the gender of CEO can influence the level of CSP, Manner (2010) finds that strong or exemplary CSP is positively related to the CEO being female. Marquis & Lee (2013) analyze Fortune 500 firms and notice when the CEO is female, and then the firm will do more corporate philanthropic activities, which will enhance their social performance. To construct a variable to reflect the gender of CEO, I use a dummy variable equals 1 if the CEO is male, and 0 if the CEO is female. Following Fabrizi et al. (2014), I control CEO age because they find a positive relationship between CEO age and corporate social responsibility. CEO tenure reflects the number of years the CEO in this position. Geddes & Vindo (1997) find CEO tenure is determined by the cost of firing the CEO, as the cost of firing the CEO increases, tenure also increases. Moreover, Simsek (2007) argue CEO tenure should be included to determine firm performance.

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Table 1. Variable description

Variables Measurement

Dependent variables

WFi,t The workforce category score in a given year t for firm i HRi,t The human right category score in a given year t for firm i COMi,t The community category score in a given year t for firm i

PRi,t The product responsibility category score in a given year t for firm i TOTALi,t The total corporate social performance score in a given year t for firm i Independent variables

OCi,t The level of overconfidence calculated as per McCarthy et al. (2017) for firm i in year t Control variables

DUALITYi,t A dummy variable equals 1 if the CEO combined the role of CEO and Chairman and 0 otherwise for firm i in year t

GENDERi,t A dummy variable equals 1 if the CEO is male and 0 otherwise in a given year t for firm i AGEi,t The age of CEO in a given year t for firm i

TENUREi,t The number of years the CEO in this position in a given year t for firm i

ROAi,t Net income divided by book value of total assets at the beginning of given year t for firm i SIZEi,t Natural log of the book value of total assets at the begging of a given year t for firm i LEVi,t Long-term debts divided by book value of total assets at the begging of a year t for firm i

4. Empirical Research and Results

Table 2 shows the summary statistics for each variable. There are 8150 firm-year observations available, due to some missing value and outliers appear within the sample, then I delete 3725 observations with missing value and outliers. The outliers appear due to the databases provide the exact numbers, but these numbers cannot be explained by logic and common sense, such as extreme large overconfidence level. Most of CEO overconfidence levels have a range from 0 to 1, but there are several outliers that are greater than 20. Hence, I exclude the outliers to reduce the influence of those outliers on results, and the final sample covers 4425 firm-year observations.

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Furthermore, within 4425 firm-year observations, 73% of CEOs are both chairmen, and 97% of CEOs in the sample are male. In average, CEOs have an age of 55.52 years and the tenure of 4.71 years. For firm financial control variables, ROA has an average value of 0.06 and the smallest value of -0.85. The negative value means the firms have a net loss during the year. The smallest firm size is 1.48, and the largest firm size is 6.29. At last, the average firm leverage is 0.22 shows these firms have 22% debt to total assets on average.

Table 3 indicates the correlation matrix between variables. It is clear to see the overall CSP score (TOTAL) is highly correlated with four categories, respectively (0.925, 0.667, 0.666, and 0.700). CEO overconfidence (OC) has a negative relationship with these CSP scores, and this consistent with my expectation that overconfidence is negative related to CSP. CEO duality (DUALITY) has a positive relationship with CSP scores and CEO overconfidence. In addition, CEO gender (GENDER) shows a negative correlation with the workforce (-0.027), product responsibility (-0.006), and the overall CSP (-0.014), but it has a positive relationship with human rights (0.006) and community (0.015). However, these correlation coefficients are relatively small. CEO age (AGE) is positively related to CSP score, follows Fabrizi et al. (2014), I expect the elder CEOs tend to have the better social performance, but it has a tiny link with

Table 2. Summary statistics

N Mean Std. Dev. Min Median Max

Dependent Variables WRi,t 4425 48.95 28.14 0.15 48.13 99.78 HRi,t 4425 51.14 24.45 2.22 39.42 99.67 COMi,t 4425 70.87 20.82 0.19 74.84 99.79 PRi,t 4425 55.05 26.10 0.44 55.45 99.71 TOTALi,t 4425 55.38 20.52 6.24 54.72 98.58 Independent Variable OCi,t-1 4425 0.58 0.14 0.09 0.56 0.97 Control Variables

DUALITYi,t-1 (1-Yes;

0-No)

4425 0.73 0.44 0 1 1

GENDERi,t-1 (1-Male;

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CEO overconfidence (0.001). Also, CEO tenure (TENURE) has a positive correlation with CSP scores but a negative correlation with CEO overconfidence. Moreover, for financial control variables, ROA, firm size (SIZE), and firm leverage (LEV) are all have a positive correlation with CSP scores, which is consistent with the findings of Waddock & Graves (1997) and Orlitzky (2001). On the other hand, ROA and firm size are positively related to CEO overconfidence (-0.132 and -0.140), but firm leverage has a negative correlation with CEO overconfidence (0.039).

Table 3. Correlation Matrix

WF HR COM PR TOTAL OC DUALI TY GENDE R AGE TENUR E ROA SIZE LE V WF 1.000 HR 0.521 1.000 COM 0.471 0.353 1.000 PR 0.480 0.451 0.371 1.000 TOTAL 0.925 0.667 0.666 0.700 1.000 OC -0.059 -0.071 -0.037 -0.041 -0.066 1.000 DUALITY 0.086 0.048 0.073 0.057 0.092 0.016 1.000 GENDER -0.027 0.006 0.015 -0.006 -0.014 -0.008 -0.033 1.000 AGE 0.105 0.081 0.105 0.101 0.127 0.001 0.219 0.030 1.000 TENURE -0.024 -0.008 -0.046 -0.053 -0.040 0.049 0.040 0.050 0.250 1.000 ROA 0.062 0.095 0.030 0.057 0.074 -0.132 0.033 -0.026 -0.022 0.034 1.000 SIZE 0.191 0.084 0.176 0.155 0.210 -0.140 0.186 0.010 0.149 -0.101 -0.077 1.000 LEV 0.041 0.082 0.031 0.059 0.060 0.039 0.042 -0.042 0.010 -0.055 -0.142 -0.118 1.000

Table 4. Variance inflation factors (VIF)

OC DUALITY GENDER AGE TENURE ROA SIZE LEV

VIF 1.05 1.09 1.01 1.15 1.10 1.06 1.13 1.05

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multicollinearity. Table 4 shows the VIF for the independent variable and control variables, and all values are well below 10. Hence multicollinearity is not a problem in the data.

I expect the CSP in the current year is influenced by CEO personal characteristics and corporate financial performance in the past year (McCarthy et al., 2017; Kim et al., 2016), so I use independent variables and control variables in the prior year to measure the CSP in the current year. To analyze CSP, I use each category score and a total score as my dependent variable, respectively. Thus, there are five fixed effects models to test the first hypothesis, while CSP represents WF, HR, COM, PR, and TOTAL separately. The model is shown as follow:

𝐶𝑆𝑃𝑖,𝑡 = 𝛼1+ 𝛼2𝑂𝐶𝑖,𝑡−1+ 𝛼3𝐺𝐸𝑁𝐷𝐸𝑅𝑖,𝑡−1+ 𝛼4𝐴𝐺𝐸𝑖,𝑡−1+ 𝛼5𝑇𝐸𝑁𝑈𝑅𝐸𝑖,𝑡−1

+ 𝛼6𝑅𝑂𝐴𝑖,𝑡−1+ 𝛼7𝑆𝐼𝑍𝐸𝑖,𝑡−1+ 𝛼8𝐿𝐸𝑉𝑖,𝑡−1+ 𝜀𝑖,𝑡

Then I run a Hausman test to measure whether to choose a fixed effects model or random effects model (Table 4). For panel data, taking account of either fixed effects or random effects can have significant implications in explaining the dependent variable. In the fixed effects model, there has an unobservable effect that does not change over time, and if this part of the effect is related to the independent variables, it is a fixed effect. Fixed effects model includes both individual fixed effects and time fixed effects. The time fixed effects model is a model with different intercepts for different sections (time points). If it is known that the intercepts of the model are significantly different for different sections, but for different time series (individuals) intercepts are the same, then a time fixed effects model should be established. Specifically, in my panel data, some variables change through time but do not vary across individuals, such as firm and gender. Thus, I specify the fixed effects as time fixed effects. The formula of time fixed effects can be written as follow:

𝑦𝑖𝑡 = 𝛾𝑡+ ∑ 𝛽𝑘𝑥𝑘𝑖𝑡

𝐾

𝑘=2

+ 𝑢𝑖𝑡

Where x represents independent variables, y is the dependent variable, β is the coefficients of independent variables, and γ shows the intercept in the different time points.

Table 5 presents the results of the Hausman test. The null hypothesis is time fixed effects are not related to the independent variables, which means random effects model is better than the time fixed effects model. The alternative hypothesis is in contrast, that time fixed effects model is better fitting. The results indicate the Chi-square for each model is significant at 1% level. Thus, the null hypothesis can be rejected, then the time fixed effects model is better than the random effects model.

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Table 5. Hausman test

H0: time fixed effects are not related to the independent variables

Model 1.1 (WFi,t) Model 1.2 (HRi,t) Model 1.3 (COMi,t) Model 1.4 (PRi,t) Model 1.5 (TOTALi,t) Chi2 134.06*** 25.13*** 64.99*** 52.08*** 162.83***

*, **, *** indicates the results are significant at 10%, 5%, 1% respectively.

*, **, *** indicates the results are significant at 10%, 5%, 1% respectively. Standard errors clustered on firm are given in brackets.

Table 6. Relationship between CEO overconfidence and CSP

Variables (1) Model 1.1 (WFi,t) (2) Model 1.2 (HRi,t) (3) Model 1.3 (COMi,t) (4) Model 1.4 (PRi,t) (5) Model 1.5 (TOTALi,t) Independent variable OCi,t-1 -1.215 (3.334) -5.981** (3.002) 2.162 (2.514) -0.012 (3.166) -0.821** (2.377) Control Variables DUALITYi,t-1 4.207*** (1.050) 1.167** (0.946) 1.776** (0.792) 2.211** (0.998) 2.880*** (0.749) GENDERi,t-1 -0.537 (2.442) 2.475 (2.200) 2.465 (1.843) 1.411 (2.320) 0.906 (1.742) AGEi,t-1 0.105 (0.082) 0.206*** (0.074) 0.121* (0.062) 0.266*** (0.078) 0.153*** (0.059) TENUREi,t-1 -0.056 (0.106) -0.082 (0.095) -0.187** (0.080) -0.363*** (0.100) -0.149** (0.075) ROAi,t-1 30.441*** (5.430) 31.262*** (4.890) 14.738*** (4.095) 24.187*** (5.157) 25.775*** (3.871) SIZEi,t-1 7.694*** (0.753) 2.941*** (0.678) 5.787*** (0.568) 6.502*** (0.715) 6.427*** (0.537) LEVi,t-1 15.279*** (2.734) 18.505*** (2.462) 7.733*** (2.062) 16.578*** (2.596) 14.245*** (1.949) Constant 11.189* 23.232*** 36.301*** 10.759* 18.293***

Year fixed effects Yes Yes Yes Yes Yes

Observations 4425 4425 4425 4425 4425

Adjusted R2 0.143 0.065 0.116 0.101 0.176

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Table 6 indicates the main results of model 1. The coefficient for each independent variables and control variables are presented, and the values in brackets are standard errors clustered on year. Column 1 measures the relationship between CEO overconfidence (OC) and workforce score (WF). The coefficient of overconfidence is -1.215, which is insignificant at the 10% level, so CEO overconfidence has insignificant effects on increasing workforce loyalty and productivity. For control variables, the coefficients are insignificant for gender, age, and CEO tenure, and the irrelevant relationships are in contrast to the findings of prior research (Marquis & Lee, 2013; Fabrizi et al., 2014). However, the CEO duality (DUALITY) has a significant positive effect on workforce score (4.207), this positive relationship is consistent with Petrenko et al. (2016) that CEOs with duality are more willing to fulfill social responsibility and maintain an excellent image to stockholders. Also, model 1.1 provides a strong relationship between workforce score and financial performance variables (ROA, SIZE, and LEV). The adjusted R-square for model 1.1 is 0.143, and the model has time fixed effects.

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and product responsibility. These two models have time fixed effects, and the adjusted R-squares are 0.116 and 0.101, which are lower than model 1.1 but higher than model 1.2. Finally, column 5 represents the relationship between CEO overconfidence and the overall CSP score (TOTAL). The overall CSP score is the weighted average value of the four categories (WF, HR, COM, and PR), it is more accurate to predict a firm’s social performance and reflects the overall behavior to maintain the reputation and social responsibility. The former four columns only measure every part of CSP, even though the results indicate that only the human rights category is affected by CEO overconfidence, the overall score gives a whole perspective of CSP. In column 5, a finding is CEO overconfidence is significant negative related to the overall CSP score at 5% level (-0.821), which means when a CEO with higher overconfidence level, his/her firm tends to have lower CSP. Besides, comparing with former four columns, model 1.5 also shows the significant effects of CEO characteristics and firm financial performance on CSP, but CEO gender is consistent with the previous four models that it has the insignificant effects on CSP. In addition, for the control variables which reflect CEO characteristics, duality, and age have significant positive impacts on the overall CSP score, and tenure can affect CSP negatively (-0.149). The coefficients of financial performance variables are the same as the former four models that all of them have significant positive effects on CSP. Moreover, model 1.5 has the time fixed effects, and the adjusted R-square is relatively higher than the former models (0.176).

In short, when measuring CSP by using each category, only human rights are negatively affected by CEO overconfidence, and CEO overconfidence has an insignificant relationship with the workforce, community, and products responsibility. On the other hand, the results show a strong negative correlation with overall CSP measure, which supports my first hypothesis that CEO overconfidence has a negative impact on CSP. Moreover, I find CEO gender has no relationship with CSP, and this finding is in contrast to Marquis & Lee (2013). One reason is there are only 3% of CEOs are female in my sample (Table 2), so too few female CEOs may lead to the bias of my results. CEO duality shows a strong positive relationship with CSP in model 1, and it is constructive for my further research, as well as the financial performance variables (ROA, SIZE, and LEV) have significant positive relationship with CSP, and consistent with the prior literature (Waddock & Graves, 1997; Orlitzky, 2001).

For the second hypothesis, I apply an interaction term (or called moderator) “DUALITY× OC” to predict whether CEO duality intensifies or moderates the effects of CEO overconfidence on CSP. The following regression equation represents the extended model briefly:

𝑌 = 𝛼0+ 𝛼1𝑋 + 𝛼2𝑍 + 𝛼3𝑋𝑍 + 𝜀

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Z is the interaction term. Then two subsamples based on CEO duality need to be measured. For the first subgroup, where CEO duality = 0, the interaction term equals 0 and has the multicollinearity, thus it should be deleted, and the fitting model is:

𝑌 = 𝛼0+ 𝛼1𝑋 + 𝜀

For the second subgroup, where CEO duality = 1, the fitting model is: 𝑌 = (𝛼0+ 𝛼2) + (𝛼1+ 𝛼3)𝑋 + 𝜀

For the extended model, if the coefficient of interaction term Z is statistically significant, then the subsamples have different coefficients for independent variable X, and if the coefficient of X reaches the statistically significant level, then the subgroups have different intercepts. The coefficient of X and/or Z are statistically significant means two subgroups are needed. However, if the coefficient of X and Z are both insignificant, then the two subgroups used the same model, and I can conclude variable Z has no interaction effects between X and Y. The extended model with the interaction term is:

𝐶𝑆𝑃𝑖,𝑡 = 𝛼1+ 𝛼2𝑂𝐶𝑖,𝑡−1+ 𝛼3(𝐷𝑈𝐴𝐿𝐼𝑇𝑌𝑖,𝑡−1× 𝑂𝐶𝑖,𝑡−1) + 𝛼4 𝐷𝑈𝐴𝐿𝐼𝑇𝑌𝑖,𝑡−1 + 𝛼5𝐺𝐸𝑁𝐷𝐸𝑅𝑖,𝑡−1+ 𝛼6𝐴𝐺𝐸𝑖,𝑡−1+ 𝛼7𝑇𝐸𝑁𝑈𝑅𝐸𝑖,𝑡−1+ 𝛼8𝑅𝑂𝐴𝑖,𝑡−1 + 𝛼9𝑆𝐼𝑍𝐸𝑖,𝑡−1+ 𝛼10𝐿𝐸𝑉𝑖,𝑡−1+ 𝜀𝑖,𝑡

Table 7 indicates the results of the extended model. Due to the interaction term is measured by both CEO duality and CEO overconfidence, multicollinearity needs to be concerned during the analysis. Then I implement the measure of VIF, and the results show VIF of DUALITY× OC is 1.33, also I find the variables with maximum VIF is CEO duality (1.34). The values of VIF are smaller than 10, so there is no multicollinearity in model 2. In column 1, when the interaction term DUALITY× OC is included in the model, it has a coefficient of -0.393, which means CEO duality has an insignificant effect on the relationship between CEO overconfidence and the workforce. Same as model 1.1 in Table 6, CEO overconfidence has an insignificant effect on the workforce (-0.393). Moreover, the results indicate the coefficient of CEO overconfidence only has a significant impact on human rights score (column 2), but it has the weaker explanatory power to human rights, which is significant at 10% level compared with a 5% level in Table 6. The interaction term only has a significant positive relationship with product responsibility in column 4 (1.711). For the overall CSP score (column 5), the interaction term does not affect CSP, and the coefficient of CEO overconfidence is statistically insignificant as well (-0.933). Furthermore, the CEO characteristics control variables for these columns remain the same sign and significance level within the extended model, and financial performance control variables still have the strong positive correlations with the independent variable.

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*, **, *** indicates the results are significant at 10%, 5%, 1% respectively. Standard errors clustered on firm are given in brackets.

In general, although the results show the interaction term has a significant effect on the product responsibility, and CEO overconfidence has a significant correlation with product responsibility

Table 7. Relationship between CEO overconfidence and CSP with interaction term DUALITY × OC

Variables (1) Model 2.1 (WFi,t) (2) Model 2.2 (HRi,t) (3) Model 2.3 (COMi,t) (4) Model 2.4 (PRi,t) (5) Model 2.5 (TOTALi,t) Independent variable OCi,t-1 -0.393 (3.441) -6.006* (3.099) 1.606 (2.595) -1.806 (3.266) -0.933 (2.454) Indicator variable

DUALITYi,t-1 ×OCi,t-1 -0.784

(0.813) 0.024 (0.732) 0.531 (0.613) 1.711** (0.772) 0.107 (0.580) Control Variables DUALITYi,t-1 4.761*** (1.156) 1.153 (1.041) 1.461* (0.872) 1.197 (1.097) 2.817*** (0.824) GENDERi,t-1 -0.511 (2.444) 2.475 (2.201) 2.447 (1.843) 1.354 (2.319) 0.902 (1.742) AGEi,t-1 0.104 (0.083) 0.206*** (0.074) 0.122* (0.062) 0.269*** (0.078) 0.153*** (0.059) TENUREi,t-1 -0.060 (0.106) -0.082 (0.095) -0.185** (0.080) -0.355*** (0.100) -0.149** (0.075) ROAi,t-1 30.674*** (5.436) 31.255*** (4.895) 14.580*** (4.100) 23.677*** (5.159) 25.743*** (3.876) SIZEi,t-1 7.695*** (0.753) 2.941*** (0.678) 5.786*** (0.568) 6.500*** (0.715) 6.427*** (0.537) LEVi,t-1 15.191*** (2.735) 18.508*** (2.463) 7.793*** (2.063) 16.771*** (2.596) 14.257*** (1.950) Constant 10.760* (6.186) 23.245*** (5.571) 36.591*** (4.666) 11.697** (5.871) 18.351*** (4.411)

Year fixed effects Yes Yes Yes Yes Yes

Observations 4425 4425 4425 4425 4425

Adjusted R2 0.143 0.065 0.116 0.102 0.176

F-statistic 33.24*** 13.95*** 26.10*** 22.66*** 42.64***

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in the extended model, it cannot confirm that CEO duality has an interaction effect between CEO overconfidence and CSP. Since the coefficients of the interaction term are insignificant with other independent variables, especially for the overall CSP score, so the further tests for the two subsamples are needed to approve the finding. I divide the whole sample into two groups: one group with CEO duality and the other one without CEO duality, and then I analyze these two subsamples by using the first model and compare the differences. Also, due to the subsamples are separated based on CEO duality, then the variable DUALITY is excluded in the model.

As shown in Table 8, there are 1188 firm-year observations in the group “CEO duality = 0” and 3237 firm-year observations in another group “CEO duality = 1”. Due to the sample size changed, I re-estimate the VIF and find both subsamples have no multicollinearity (the maximum VIF for the group “CEO duality = 0” is 1.14 and for another group “CEO duality = 1” is 1.13). The results represent clearly that for both of the subsamples, the coefficients of CEO overconfidence have insignificant effects on CSP, except model 1.2 in column 7 expresses a significant negative relationship with human rights at 10% level. Comparing with the results of Table 6, the coefficients of total CSP score become insignificant in both subsamples. Accordingly, even though Chikh & Filbien (2011) argue CEO duality enhances CEO overconfidence, I find CEO duality does not have interaction effects on the relationship between CEO overconfidence and CSP. Thus, the second hypothesis should be rejected.

Furthermore, the CEO characteristics variables have some significant coefficients in both subsamples, the interesting thing is, in the subsample “CEO duality = 0”, ROA has a statistically insignificant relationship with CSP, but the firm size and firm leverage have the strong correlation with CSP. In addition, in the subsample “CEO duality = 1”, all of these three financial performance variables have significant effects on CSP. It may provide a proposition for future research.

Finally, according to the findings of Fairchild (2007) and Kim et al. (2016), I will test and verify whether CEO overconfidence has a negative effect on firm profitability. Since the same research is analyzed in many prior studies of literature, the purpose is providing the new empirical evidence to approve their findings. I use ROA and firm leverage as the measures to reflect firm profitability, because ROA is an indicator of how much net profit per unit of assets is created, and firm leverage demonstrates a firm’s financial risk and structure on equity fund income. Same as my previous models, I use several CEO characteristics indicators as my control variables, and the model is:

𝑓𝑖𝑟𝑚 𝑝𝑟𝑜𝑓𝑖𝑡𝑎𝑏𝑖𝑙𝑖𝑡𝑦𝑖,𝑡

= 𝛼1+ 𝛼2𝑂𝐶𝑖,𝑡+ 𝛼3𝐷𝑈𝐴𝐿𝐼𝑇𝑌𝑖,𝑡+ 𝛼4𝐺𝐸𝑁𝐷𝐸𝑅𝑖,𝑡+ 𝛼5𝐴𝐺𝐸𝑖,𝑡

+ 𝛼6𝑇𝐸𝑁𝑈𝑅𝐸𝑖,𝑡 + 𝜀𝑖,𝑡

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Table 8. Relationship between CSP and CEO overconfidence of two sub-samples (DUALITY=0 and DUALITY=1)

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23 Table 8 (continue) Constant -4.599 (15.924) -22.375* (13.247) 34.330*** (11.204) -18.390 (13.847) -0.799 (10.894) 18.273** (7.707) 33.395*** (7.071) 36.093*** (5.808) 19.882*** (7.539) 24.526*** (5.524)

Year fixed effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

Observations 1188 1188 1188 1188 1188 3237 3237 3237 3237 3237

Adjusted R2 0.169 0.165 0.146 0.130 0.216 0.159 0.078 0.127 0.103 0.192

F-statistic 6.91*** 6.71*** 5.83*** 5.09*** 9.36*** 28.46*** 12.85*** 22.03*** *** 35.75***

Maximum VIF 1.14 1.14 1.14 1.14 1.14 1.13 1.13 1.13 1.13 1.13

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Table 9 shows the relationship between financial profitability and CEO overconfidence. Model 3.1 indicates CEO overconfidence has a significant negative correlation with ROA (-0.077). Follows Eichholtz & Yönder (2015), the higher overconfidence level a CEO has, he/she tends to have more value-destroying investments and thus have worse returns. Moreover, CEO duality and tenure have significant impacts on ROA, which provide a similar perspective on CSP. In column 2, CEO overconfidence has a significant positive effect on firm leverage (0.058), which means a CEO with higher overconfidence is willing to hold more debt, thus faces higher risks and greater probability to failure. The finding is consistent with Ho et al. (2016). Furthermore, CEO duality does not affect firm leverage, but the results show gender has a significant negative relationship with firm leverage. In short, I find CEO overconfidence has significant effects on firm profitability, the higher CEO overconfidence, the worse firm profitability.

Table 9. Relationship between financial profitability and CEO overconfidence

Variables (1) Model 3.1 (ROA) (2) Model 3.2 (LEV) Independent variable OCi,t -0.077*** (0.010) 0.058*** (0.020) Control variables DUALITYi,t 0.010*** (0.003) 0.006 (0.006) GENDERi,t -0.011 (0.008) -0.047*** (0.015) AGEi,t -0.000 (0.000) 0.000 (0.001) TENUREi,t -0.001** (0.000) -0.003*** (0.001) Constant 0.119*** (0.017) 0.219*** (0.034)

Year fixed effects Yes Yes

Observations 4425 4425

Adjusted R2 0.023 0.020

F-statistic 6.16*** 5.11***

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25 5. Robustness Check

The alternative proxy of CEO overconfidence is conducted to measure the impacts on CSP. Hireshleifer et al. (2012) provide an alternative way to reflects CEO overconfidence by measuring the moneyness of a CEO holds for the year-end stock options. The process of this alternative proxy is:

𝐴𝑣𝑒𝑟𝑎𝑔𝑒 𝑟𝑒𝑎𝑙𝑖𝑧𝑎𝑏𝑙𝑒 𝑣𝑎𝑙𝑢𝑒 = 𝑣𝑎𝑙𝑢𝑒 𝑜𝑓 𝑢𝑛𝑒𝑥𝑒𝑟𝑐𝑖𝑠𝑒𝑑 𝑏𝑢𝑡 𝑖𝑠𝑠𝑢𝑒𝑑 𝑜𝑝𝑡𝑖𝑜𝑛𝑠

𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑢𝑛𝑒𝑥𝑒𝑟𝑐𝑖𝑠𝑒𝑑 𝑏𝑢𝑡 𝑖𝑠𝑠𝑢𝑒𝑑 𝑜𝑝𝑡𝑖𝑜𝑛𝑠 𝐴𝑣𝑒𝑟𝑎𝑔𝑒 𝑠𝑡𝑟𝑖𝑘𝑒 𝑝𝑟𝑖𝑐𝑒 = 𝑌𝑒𝑎𝑟 𝑒𝑛𝑑 𝑠𝑡𝑜𝑐𝑘 𝑝𝑟𝑖𝑐𝑒 − 𝐴𝑣𝑒𝑟𝑎𝑔𝑒 𝑟𝑒𝑎𝑙𝑖𝑧𝑎𝑏𝑙𝑒 𝑣𝑎𝑙𝑢𝑒

𝑀𝑜𝑛𝑒𝑦𝑛𝑒𝑠𝑠 = 𝑌𝑒𝑎𝑟 𝑒𝑛𝑑 𝑠𝑡𝑜𝑐𝑘 𝑝𝑟𝑖𝑐𝑒

𝐴𝑣𝑒𝑟𝑎𝑔𝑒 𝑠𝑡𝑟𝑖𝑘𝑒 𝑝𝑟𝑖𝑐𝑒− 1

The first step of this alternative proxy is same as the measurement of CEO overconfidence, the average realizable value, also called Value per option (section 3.2), is calculated by using the value of unexercised but issued options divided by the number of these options a CEO holds. Then the average strike price equals year-end stock price minus average realizable value. Finally, the moneyness equals year-end stock price divided by average strike price and then minus 1. Similar to the research of CEO overconfidence, a CEO has higher moneyness leads to the higher CEO overconfidence level.

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Table 10. Relationship between CEO overconfidence (Moneyness) and CSP

Variables (1) Model 1.5a (2) Model 1.5b Independent variable OCi,t-1 -0.821** (2.377) Moneynessi,t-1 -0.175** (0.243) Control variables DUALITYi,t-1 2.880*** (0.749) 2.884*** (0.748) GENDERi,t-1 0.906 (1.742) 0.907 (1.742) AGEi,t-1 0.153*** (0.059) 0.153*** (0.059) TENUREi,t-1 -0.149** (0.075) -0.149** (0.075) ROAi,t-1 25.775*** (3.871) 25.601*** (3.866) SIZEi,t-1 6.427*** (0.537) 6.413*** (0.533) LEVi,t-1 14.245*** (1.949) 14.268*** (1.949) Constant 18.293*** 18.123***

Year fixed effects Yes Yes

Observations 4425 4425

Adjusted R2 0.176 0.176

F-statistic 45.16*** 45.19***

*, **, *** indicates the results are significant at 10%, 5%, 1% respectively. Standard errors clustered on firm are given in brackets.

6. Conclusion

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may lead a firm to the worse financial and social performance. By using a large sample of CEOs in US-listed firms for the period 2006 - 2017, I measure CEO overconfidence by the unexercised but issued option holdings, and I find CEO overconfidence has the significant negative effects on future CSP.

Firstly, this research analyzes CSP by four categories: workforce, human rights, community, and product responsibility, and uses the weighted average of these four categories to reflects the overall CSP, which is more convincing due to the comprehensive coverage. I find CEO overconfidence leads to the lower management commitment and effectiveness towards human rights, but there is no significant impact on the other three categories. However, if I combine the consideration of these categories, it is reported that CEO overconfidence can affect the overall CSP negatively. Hence, I conclude overconfident CEOs tend to have more value-destroying activities and then harm the overall CSP in the future, but it is not very convincing for the single category.

Secondly, different corporate governance mechanisms may also lead to different impacts on CEO overconfidence and even CSP, and the simplest example is CEO duality. Follows agency theory, CEO duality enhances the conflicts between agents and principles, CEOs with duality tend to have less board control and monitoring, then stimulate their overconfidence and have worse firm financial and social performance. However, I find CEO duality does not exacerbate the effects on the relationship between CEO overconfidence and CSP. In other words, for the firms with CEO duality, there has no evidence to approve that CEO overconfidence has worse effects on CSP than the firms without CEO duality.

The last finding is CEO overconfidence has significant negative effects on firm profitability as well, and this finding is consistent with the previous literature. Specifically, the higher level of overconfidence a CEO has, his/her firm tends to have lower ROA and higher firm leverage, which means the firm has less profitable performance and a higher probability of failure. This finding provides more evidence to confirm the studies in prior literature.

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