Stock Options-based Chief Executive Officer Compensation and
Firm Performance in the United States
Abstract
This thesis examines the relationship between stock options-based compensation and firm performance in the United States from 2010 to 2019. This relationship is studied in order to analyze whether the provided incentives of stock options-based compensation are in
alignment with firm performance. Based on an extensive review of the literature and prior empirical research, two quantitative models are established to study the pay-for-performance relationship. The first model, the pay-performance elasticity, measures the relative changes of firm performance and CEO payment. In addition, the second model, the pay-performance sensitivity, measures the absolute changes. Both models are common proxies to measure incentives. There is hypothesized that stock-options based compensation has a positive effect on firm performance. The hypothesis is supported in both models, suggesting that higher stock options pay leads to higher firm performance. Therefore, there is concluded that stock options-based CEO compensation provides CEOs with incentives in line with firm
performance.
Tessa Seijmonsbergen 11898755
Business Administration Finance
Name supervisor: dr. E. Zhivotova Date: June 29, 2020
Statement of Originality
This document is written by Tessa Seijmonsbergen who declares to
take full responsibility for the contents of this document.
I declare that the text and the work presented in this document are original and that no sources other than
those mentioned in the text and its references have been used in creating it.
The Faculty of Economics and Business is responsible solely for the supervision of completion of the
Table of contents
1. Introduction 4
2. Literature review 6
2.1 Theoretical background 6
2.2 Positive pay-for-performance relationship 7
2.3 Negative pay-for-performance relationship 8
2.4 Hypothesis formulation 10
3. Research methodology 11
3.1 Data description 11
3.2 Research method 11
3.3 Dealing with outliers 12
3.4 Design of the models 13
4. Results 16 4.1 Sample characteristics 16 4.2 Regression results 18 4.3 Robustness checks 23 5. Conclusion 24 6. References 26 7. Appendices 29
7.1 Appendix A Description of the variables 29
7.2 Appendix B Descriptive statistics 30
7.3 Appendix C Regression results in more detail 31
7.3 Appendix D Testing assumptions 35
1. Introduction
Since the 1980s, stock options payment to Chief Executive officers was implemented and augmented at a growing speed in the United States (Tzioumis, 2008). It became already then, a much-debated and sensitive subject with conflicting opinions. Academic researchers aimed to examine executive payment, and in particular more recent types of compensation, such as stock options. Whether this high level of compensation is justified for the work that CEOs deliver is a recurring question, and is examined to evaluate different structures of executive compensation.
Again, after the financial crisis in 2008 executive compensation became a prominent research topic. As a response to the financial crisis, President Obama imposed new laws with respect to executive payment for bailout recipients. New regulations included the limitation of the executive payment to 500,000 U.S. dollars and the obligation for businesses to publish their executive compensation goals. According to multiple studies, CEO compensation provided inappropriate incentives that led to excessive risk-taking, which was one of the causes of the financial crisis (Bhagat & Bolton, 2014; Bebchuk, Cohen, & Spamann, 2010). After the financial crisis, the structure of executive compensation changed (Vemala, L. Nguyen, D. Nguyen, & Kommasani, 2014). Specifically, the level of equity-based
compensation increased further, and cash-based compensation diminished. Accordingly, there may be a reason for the increment in the level of equity-based compensation. After the
financial crisis, the purpose of executive compensation was to provide CEOs with appropriate incentives and to decline excessive risk-taking. Equity-based compensation can be seen as an instrument to fulfill this purpose. Therefore, it is expected that equity-based compensation is more performance-based and provides incentives in alignment with the firm.
The main question to be asked in this thesis is whether the current construction of stock options payment provides CEOs with the appropriate incentives, rewarding CEOs in alignment with firm performance. This thesis focuses on stock options as a component of executive compensation. Much research is already completed to explore the relationship between the different components of compensation and the incentives they provide. However, research is ambiguous about the nature of the provided incentives to CEOs.
In practice, maximizing firm performance is essential for shareholders. The conflict of interest between CEOs and shareholders, also known as the agency problem, still exists in modern businesses (J. Campbell, T. Campbell, Sirmon, Bierman, & Tuggle, 2012).
Research yields inconclusive results on whether stock option-based compensation is a result of agency problems and reinforces agency conflicts, or whether stock options payment diminishes agency problems. Therefore, it is still necessary to examine stock options-based compensation and the practical implementation of stock options.
In this thesis, the pay-for-performance relationship is studied to further explore stock options and the incentives they provide. Accordingly, the pay-for-performance relationship is measured through two models: the pay-performance elasticity and the pay-performance sensitivity. The dataset consists of a total sample of 3,334 companies in the United States between 2010 and 2019. The data is retrieved from 2010 onwards, because from then on, reforms initiated by the financial crisis, are researched and implemented. In addition, up-to-date research on stock options pay is necessary due to the lack of studies using data since 2010. The United States is selected as the country of interest because stock options pay, the variable of interest in this study, is most frequently used in the United States. Moreover, CEOs in the United States receive a higher level of compensation, but additionally receive a larger part of their payment in stock options (Murphy, 1999).
In short, the purpose of this thesis is to investigate the incentives provided by CEO pay in stock options. This thesis is of importance due to the puzzle in literature and a shortage of studies done after the financial crisis of 2008.
To examine the provided incentives by stock options payment through the pay-for-performance relationship, the following research question is stated:
What is the relationship between executive compensation in stock options and firm performance in the United States?
The remainder of the thesis consists of four sections. The first part provides an outline of the existing literature. The second section specifies the nature of the used data, describes which research method is used, and illustrates the design of the model. After that, empirical analyses are performed, and the results are demonstrated. The last section states the key conclusions of the research and provides an answer to the research question.
2. Literature review
The first section of the literature review gives a broad overview of the theoretical
background. Thereafter, research is discussed that proved a positive option-based pay-for-performance relationship, claiming that stock options provide desirable incentives.
Subsequently, researchers who found the opposite are addressed. Furthermore, the added value of this thesis is explained. Finally, the hypothesis is formulated.
2.1 Theoretical background
Researchers have already examined the level of CEO compensation and the relationship between executive compensation and firm performance for a long time. The interest in the level of payment shifted progressively to the structure of executive payment and the relationship between certain forms of executive payment and firm performance (Hall & Liebman, 1998; Core, Holthausen, & Larcker, 1999). The research of Mehran (1995), confirmed this by indicating that a certain structure of payment stimulates CEOs, not the level. The adoption of incentive-based compensation was highlighted, and stock options payment was recognized as one of the components of executive compensation providing these incentives.
Especially after the financial crisis of 2008, executive compensation became again the target of media attention. There was much criticism on the payout structure of businesses, which may have been the reason for the excessive CEO compensation and the excessive risk-taking, as already mentioned in the introduction. As a result, many executive compensation reform proposals emerged during the aftermath of the crisis. An example is the Dodd-Frank Act, launched in 2010. The say-on-pay rules were part of the act and introduced the right for shareholders to vote on the level of executive compensation. According to Balsam, Boone, Liu, and Yin (2016) the say-on-pay practice decreased executive compensation and made executive compensation more performance-based. Research by Vemala et al. (2014) supported the common opinion on the excessive payment of CEOs before and during the financial crisis. Furthermore, as mentioned in the introduction, the researchers claimed that the financial crisis had an impact on the structure of executive compensation. Specifically, equity-based compensation raised in usage and cash compensation diminished after the financial crisis. The two preceding studies of Balsam et al. (2016) and Vemala et al. (2014) signify that after the financial crisis the composition of executive compensation changed and thereby the purpose of executive compensation.
In addition, Jensen and Murphy (2010) stressed the importance of the structure of executive compensation for the performance of businesses. In the research, they described fundamental factors to provide the best incentives to CEOs. First of all, the CEO must have a high
percentage of ownership into the firm. Besides that, executive compensation must be constructed in a certain way, allowing to compensate executives for performance. Furthermore, they concluded with emphasizing the significance of certain structures of payment that create incentives by linking pay to firm performance.
Benmelech, Kandel, and Veronesi (2010) indicated that stock-based compensation creates higher incentives compared to cash-based compensation. Nevertheless, these
incentives lead to satisfying and dissatisfying behavior of the CEO. On the one hand, equity-based compensation aligns the interests of the managers with the interests of the shareholders, and motivates the CEO to perform well in favor of the firm. On the other hand, when there are poor investment opportunities the CEO tends to hide this and continues to invest in bad investments. The researchers concluded that a composition of stock-based compensation together with cash-based compensation is most optimal for the firm. Besides that, they claimed that each company differs and the approach to executive compensation should therefore not be identical across all firms. This study summarizes the opinions of the proponents and opponents of stock options, which will be discussed more in detail.
2.2 Positive pay-for- performance relationship
Hall and Liebman (1998) examined the pay-performance elasticity in the United States from 1980 to 1994. They observed that the elasticity of CEO payment increased substantially over time. Share ownership of the CEO and stock options-based compensation were the explaining factors of this increasing performance elasticity. Furthermore, they studied the pay-performance elasticity of compensation in shares, stock options, base salary and bonus. The results indicated that the pay-performance elasticity of stock and stock options was thirty times higher than for the base salary and bonus. The researchers therefore emphasized the significance of equity-based compensation. In the study, two explanations were given for the extreme increment in the use of stock options payment. First, stock options pay was claimed to fulfill the aim of the board of directors who wanted to align CEO payment and firm
performance after criticism of excessive executive compensation. The second potential reason the researchers suggested was that stock options pay is less apparent and less measurable than cash compensation. Therefore, stock options pay is a useful instrument for the board of
According to Murphy (1999), the pay-for-performance relationship is stronger in the United States compared to other countries. This higher pay-to-performance sensitivity is entirely enforced by stock options and the ownership of the CEO into the business. Moreover, Murphy indicated that the degree of the pay-for-performance relationship depends on the size of the firm and the industry. He examined the pay-for-performance relationship in percentages (elasticity) and pay-for-performance relationship in dollars (sensitivity). The sample consisted of S&P 500 Index firms. In order to measure the pay-performance elasticity, different
components of executive compensation are used as the dependent variable and the
shareholder value as the independent variable. The dependent and independent variables are both expressed in a natural logarithm. In the pay-for-performance sensitivity model, the dependent variable and independent variables are similar, but without the logarithm.
Mehran (1995) indicated that the effort of CEOs is determined by the structure of pay. Furthermore, the results of the study approve the usage of incentive-based compensation. The empirical results suggested a positive relationship between firm performance and stock options-based compensation plus equity holdings of the CEO. An Ordinary Least Squares regression is performed with return on assets and Tobin’s Q as the dependent variables. The sample consisted of 153 manufacturing companies from the United States. The independent variables include, among other variables, stock options-based compensation, and the stock holdings of the CEOs. Examples of control variables used are business risk, firm size, and the leverage ratio.
2.3 Negative pay-for-performance relationship
Besides research that proved a significant positive relationship between stock options pay and firm performance, the following studies concluded differently.
Harris (2009) outlined many complaints in his paper about executive compensation. He claimed that different components of executive compensation, which were intended to provide the correct incentives for CEOs, did not resolve the agency problem at all. On the contrary, Harris claimed that CEOs who receive incentive-based compensation are more likely to cheat with a malicious intent to receive more compensation. In short, the study suggested that incentive payments do not align the interests of the CEO and the firm itself, but they misalign these interests. Furthermore, there is argued that as a consequence of the
misbehavior of managers, incentive payments lead to lower firm performance.
Harris concluded with the recommendation to other researchers to pay more attention to the effectiveness and justness of executive payment.
Burns and Kedia (2006) studied the relationship between different forms of CEO pay and the tendency to falsify financial statements. The researchers examined businesses in the S&P small-cap, mid-cap, and large-cap indexes that revised their financial statements between 1995 and 2001. On the one hand, they discovered that managers who received executive compensation in stock options are most likely to misstate. They suggested that convexity is the reason for this. Moreover, convexity diminishes the exposure to the downside risk of disclosure of falsifying financial information for the reason that managers are not harmed in distressing times. On the other hand, the alternative types of CEO pay such as base salary did not influence the tendency to misreport at all.
According to Bebchuk and Fried (2003), linking pay to performance is important to optimize firm value. They indicated that non-equity-based CEO payments do not tie executive compensation to firm performance. On the one hand, previous literature suggests that stock options are a solution to relate pay to performance. On the other hand, Bebchuk and Fried argued in their study that stock options pay reward CEOs for performance that is beyond the control of the CEO. Moreover, they mentioned solutions to decrease the link between CEO payment and the increment of stock prices that are beyond the control of the CEO. For
example, the study suggested to connect the exercise price of stock options to one ratio related to the market. Furthermore, they stated that the structure of CEO compensation, including stock options, not only reduces the agency problem, but this structure is additionally a result of the agency problem. According to the managerial power approach, CEOs have substantial influence over their pay and are able to design the structure of payment as they desire most. Concluding, Bebchuk and Fried emphasized the importance of a well-constructed structure of payment that creates correct incentives for CEOs to optimize firm value.
After the implementation of the new laws and reforms in response to the financial crisis, the structure of executive compensation should be reinvestigated. Most studies that proved a positive relationship between stock options and firm performance are dated back to the late 90s. Many studies who argued that stock options are inefficiently and lead to
misbehavior are published at the beginning of 2000. In addition, research is inconclusive about the relationship between stock options pay and firm performance. As illustrated above, multiple studies suggested a positive pay-for-performance relationship and concluded that stock options payment provides CEOs with appropriate incentives.
On the contrary, other studies proved a negative pay-for-performance relationship, implying that stock options pay creates incentives in misalignment with firm performance.
This thesis will contribute to the existing literature by researching the relationship between stock options and firm performance in more recent years. Therefore, the relationship between stock options CEO payment and firm performance is examined from 2010 to 2019.
2.4 Hypothesis formulation
First of all, a well-designed package of executive compensation is essential to provide
incentives (Jensen & Murphy, 2010; Bebchuk & Fried, 2003). The examined prior literature is inconclusive about the relationship between stock options-based compensation and firm performance. The studies of Hall and Liebman (1998), Mehran (1995), and Murphy (1999) indicated that stock options provide satisfying incentives to CEOs that consequently improves firm performance. On the contrary, Harris (2006), Burns and Kedia (2006), and Bebchuk and Fried (2003) suggested that stock options lead to ‘bad’ incentives, rewarding misbehavior of CEOs, which eventually contributes to the diminishing of firm performance.
The following hypotheses are formulated, by taking the previous literature into consideration:
Null hypothesis (H0): stock options-based CEO compensation does not have an impact on firm performance.
Alternative hypothesis (H1): stock options-based CEO compensation has a positive impact on firm performance.
+
Figure 1. Conceptual framework of the expected relationship between the stock options pay and firm performance.
Stock options pay CEO Independent variable
Firm performance Dependent variable
3. Research methodology
This section describes the data in detail, elaborates on the research method, and demonstrates the design of the model.
3.1 Data description
The data of this research is secondary data and obtained from Wharton Research Data Services (WRDS). Moreover, the data concerning executive compensation is retrieved from the ExecuComp database. The ExecuComp database covers more than 80 compensation items on circa 12,500 executives. The database consulted for the financial data is Compustat,
specifically the Compustat North-America database. The Compustat North-America database contains data on 24,000 public corporations. The executive and financial related data are combined into one dataset via a one-to-one on key variables merge, with CUSIP code and year as the key variables. The combination of the variables CUSIP code and year results in exclusive observations for each CEO and year. As a result, the dataset contains observations ranging over time across CEOs. This type of data is labeled as panel data. The time period of this data is ranging from the fiscal year 2010 up to and including 2019. The ExecuComp dataset contains data of the five highest executives of the studied firm. For the sake of this study, CEOs are preserved in this dataset and the other executives are excluded from the dataset. In addition, CEOs that manage more than one firm in a year are excluded from the dataset. Furthermore, firms with more than one CEO in a year are eliminated from the dataset. Subsequently, the sample consists of 3,334 unique CEOs in the period between 2010 to 2019.
3.2 Research method
The main purpose of this research is to consider whether the incentives provided to CEOs are in alignment with firm performance when using stock options-based compensation. In
previous research, the pay-performance elasticity and pay-performance sensitivity are used frequently to examine the provided incentives (Mehran, 1995; Hall & Liebman, 1998; Jensen & Murphy, 1990; Baker & Hall, 2004; Edmans, 2009; Conyon & Murphy, 2000; McKnight & Tomkins, 1999). In this research, the performance elasticity (PPE) and
pay-performance sensitivity (PPS) are both used. The pay-pay-performance elasticity analyses relative changes in the pay and performance variables and the pay-performance sensitivity the
First of all, the pay-performance elasticity is examined by performing a regression with the variables firm performance and stock options payment denoted in a logarithm. Accordingly, the results are interpreted as the percentage change in firm performance when stock options-based compensation increases with 1%. Besides that, the pay-performance sensitivity is examined by performing a PPS Ordinary Least Squares regression. The dependent variable in the model is the performance indicator of the firm and stock options-based compensation is the independent variable. In this model, the results are interpreted as the effect of a one-unit change in stock options-based compensation on firm performance. The data in the dataset is labeled as panel data, and this is taken into consideration. Therefore, time fixed effects and industry fixed effects are implemented to capture the selection bias in the estimation of causal effects. Time fixed effects control for factors that change over time and are constant across entities. In addition, industry fixed effects are included to control for the differences across industries. Furthermore, clustered standard errors on the CEO-level will be implemented to adjust standard errors and to allow for correlation over time within the same CEO.
3.3 Dealing with outliers
The winsorization method is utilized often to reduce the influence of extreme values in the dataset. Financial variables have frequently a skewed distribution and winsorization can be applied to solve for this problem (Favre-Martinoz, Haziza, & Beaumont, 2015). In this study, the variables are winsorized at a 2% level. This implies that the values above the 99th
percentile are set at the 99th percentile and values below the 1st percentile are set at the 1st percentile. As a result, extreme values are only transformed but not removed from the sample.
3.4 Design of the models
In order to test the hypothesis, the two following models are constructed.
(1) The pay-performance elasticity: 𝑙𝑛𝐹𝑖𝑟𝑚𝑝𝑒𝑟𝑓𝑜𝑟𝑚𝑎𝑛𝑐𝑒
= 𝛽!+ 𝛽"𝐼𝑛𝑜𝑝𝑡𝑖𝑜𝑛𝑠 + 𝛽#𝑇𝑒𝑛𝑢𝑟𝑒 + 𝛽$𝐴𝑔𝑒 + 𝛽%𝐶𝑎𝑝𝑖𝑡𝑎𝑙𝑒𝑥𝑝𝑒𝑛𝑑𝑖𝑡𝑢𝑟𝑒𝑠 + 𝛽&𝐿𝑒𝑣𝑒𝑟𝑎𝑔𝑒 + 𝛽& 𝐿𝑖𝑞𝑢𝑖𝑑𝑖𝑡𝑦 + 𝛽( 𝐺𝑟𝑜𝑤𝑡ℎ𝑜𝑝𝑝𝑜𝑟𝑡𝑢𝑛𝑖𝑡𝑖𝑒𝑠
+ 𝛽)%𝐶𝐸𝑂𝑜𝑤𝑛𝑠 + 𝛽*𝑆𝑎𝑙𝑒𝑠 + 𝛽"! 𝑀𝑎𝑙𝑒 + 𝛿++ 𝜆,+ 𝑢,+
(2) The pay-performance sensitivity: 𝐹𝑖𝑟𝑚𝑝𝑒𝑟𝑓𝑜𝑟𝑚𝑎𝑛𝑐𝑒
= 𝛽!+ 𝛽"𝑂𝑝𝑡𝑖𝑜𝑛𝑠 + 𝛽#𝑇𝑒𝑛𝑢𝑟𝑒 + 𝛽$𝐴𝑔𝑒 + 𝛽%𝐶𝑎𝑝𝑖𝑡𝑎𝑙𝑒𝑥𝑝𝑒𝑛𝑑𝑖𝑡𝑢𝑟𝑒𝑠 + 𝛽&𝐿𝑒𝑣𝑒𝑟𝑎𝑔𝑒 + 𝛽& 𝐿𝑖𝑞𝑢𝑖𝑑𝑖𝑡𝑦 + 𝛽( 𝐺𝑟𝑜𝑤𝑡ℎ𝑜𝑝𝑝𝑜𝑟𝑡𝑢𝑛𝑖𝑡𝑖𝑒𝑠
+ 𝛽)%𝐶𝐸𝑂𝑜𝑤𝑛𝑠 + 𝛽*𝑆𝑎𝑙𝑒𝑠 + 𝛽"! 𝑀𝑎𝑙𝑒 + 𝛿++ 𝜆,+ 𝑢,+ Where:
Firm performance is the performance indicator estimated by the market value of the firm and the pretax income. As a result, the performance indicator is composed of a market-value measure and an accounting measure. The market value of the company is measured by multiplying the current market price of the shares by the common shares outstanding. Market values are forward-looking and therefore an important measure of firm performance. Pretax income is the net income of the business before taxes are charged. In a meta-analysis on executive compensation, firm performance, and size, performed by Tosi, Werner, Katz, and Gomez-Mejia (2000), the most commonly used performance measures are examined. The absolute financial performance level factor consisting of the variables net income, net income previous years, pretax income, and pretax income previous year is a powerful performance measure with a Cronbach’s alpha of 0.97. Therefore, the variable with the most explaining power in the meta-analysis, pretax income, is included in the performance measure.
Stock options pay is the main independent variable. The granted stock options are valued at the grant date via the fair value measurement method.
The fair value of the stock options is the price at which the stock can be purchased at the grant date in the competitive market. In the first regression model, stock options pay is expressed in a logarithm.
CEO tenure (Tenure) is measured by the number of years the CEO is in charge of the firm as the CEO. Cremers and Palia (2011) proved in their research the positive relationship between CEO tenure and CEO pay, and subsequently the positive relationship between CEO tenure and the pay-performance sensitivity. Besides that, Farmer, Archbold and Meyer (2013), among others, included tenure as a control variable to examine the pay-for-performance relationship. The variable tenure is generated manually because it was not available in the database.
Age of the CEO (Age) is measured in years. This variable is added as a control variable because the age of the CEO is positively related to firm performance (Chaudhuri, Kumbhakar, & Sundaram, 2016).
Riskiness of the firm (Capitalexpenditures) is measured by the total amount of capital expenditures. Chen and Lee (2010) suggested that the riskiness of a business affects the payout of stock options. Furthermore, Lippert and Porter (1997) and Farmer et al. (2013) used the riskiness of the firm as a control variable in their study of the pay-performance sensitivity. Level of leverage (Leverage) is measured by dividing the book value of total debt by the book value of total equity. Many studies included leverage ratio as a control variable due to the high expected influence of leverage on firm performance (Mehran 1995; Elayan, Lau, & Meyer, 2003).
Liquidity constraints (Liquidity) is determined by the current ratio, which is computed by dividing the current assets by the current liabilities of a firm. Bryan, Hwang, and Lilien (2005) included in their research liquidity constraints as a control variable due to the expectation that liquidity constraints influence the level of equity-based compensation. Growth opportunities (Growthopportunties) is measured by the market-to-book ratio. Firms with substantial growth opportunities have a higher probability to perform better. Farmer et al. (2013), among others, included the market-to-book ratio as a measure of growth
opportunities to control for the pay-for-performance relationship.
% of ownership of CEO in the firm (%CEOowns) indicates the percentage of ownership of the CEO into the firm. This variable is added to control for the effect of the CEO’s ownership into the firm on the pay-for-performance relationship. According to Mehran (1995), CEOs with more ownership in the business receive lower equity-based CEO pay.
In previous analyses, the variable firm size is widely applied as a control variable (Mehran, 1995; Lippert & Porter, 1997; Bryan et al., 2005). Furthermore, according to Chaudhuri et al. (2016), firm size is positively linked to firm performance. In addition, many studies indicated that firm size influences the level of CEO pay significantly and is therefore included as a control variable (Tosi et al., 2000; Elayan et al., 2003).
Gender of CEO (Male) indicates the gender of the CEO. A dummy variable is constructed, which indicates 1 when the CEO is male and 0 when the CEO is a female. According to Khan and Vieito (2013), the gender of the CEO influences the performance of the company.
Year (𝜹𝒕) indicates the fiscal year. The variable year is included as a time fixed effect to eliminate bias from omitted variables and not omitted variables that differ over time but are fixed over entities.
Industry classification (𝝀𝒊) specifies the type of industry the business is working in. The sample consists of 9 different industries and the industries are classified according to their SIC-code. Industry is included as a fixed effect. Industry fixed effects control for differences in performance, regulations and risk characteristics across different industries. Different studies that examined the pay-for-performance relationship included the type of industry as a control variable (Elayan et al., 2003; Gregg, Jewell, & Tonks, 2005).
CEO ID number is the identification number of the CEO. This ID number is a unique identifier for each CEO in the dataset. A CEO is usually observed over more years because most CEOs manage the business for longer than one year. As a result, there is correlation over time. To control for correlation, clustering is done at the CEO-level. This implies that
observations within the same cluster, business managed by the same CEO, are allowed to be correlated over time. On the contrary, the observations should be uncorrelated among the different CEOs. Cluster adjusted standard errors deal with the within-cluster correlation and heteroskedasticity.
4. Results
In this section, the results of the empirical regression analyses of the discussed models are demonstrated and examined.
4.1 Sample characteristics
Table I presents the descriptive statistics of the main and control variables included in the two regression models. The dataset is unbalanced due to the merge of the Compustat and
ExecuComp databases. The sample consists of 3,334 CEOs. In the first model, a total of 4,960 observations are employed. In the second model, the total number of observations is 11,325. In the dataset, 95% of the studied CEOs are male. Therefore, it is valuable that gender is included as a control variable to control for the differences in gender. Moreover, the average age of the CEO is 56 years and the average tenure is 4 years. The observations are evenly spread from 2010 up to and including 2019. Most of the firms are located in the
manufacturing industry, services industry, and transportation industry. The total performance, which is composed of the variable pretax income and market value of equity, is on average 5,430,671,000 U.S. dollars. Furthermore, the average stock options-based payment to CEOs is 878,552 U.S. dollars.
Table I. Descriptive Statistics
This table provides the number of observations, means, minimum and maximum values and the standard deviations of the independent, dependent and control variables. The variables are further defined in Appendix A. The sample is based on 3,334 CEOs from 2010 to 2019. The variables related to the CEO and the compensation are retrieved from the ExecuComp database. The financial variables are retrieved from the Compustat database. Totalperformance and options pay are in panel A denoted in natural logarithms and in panel B without a logarithm. Furthermore, panel A reports the control variables after the log transformation and Panel B and C before the log transformation. The variables in panel B are expressed in 1000 dollars. In panel C the mean of the variables age and tenure is denoted in years. Lastly, the mean of the variable % CEO owns in the firm is denoted in percentages and the variable male is a ratio in panel C.
Table I. (continued)
Variable Observations Mean Standard deviation
Minimum Maximum
Panel A: variables after log transformation
Total
performance 14531 7.175 1.737 -3.1 11.345 Options pay 6518 7.182 1.019 -1.165 9.135
Panel B: variables before log transformation (denoted in $1000)
Total performance 14772 5,430,671 13,245,160 -38,293 92,467,480 Options pay 15247 878.552 1,654.401 0 9,269.777 Sales 15211 6560.048 15,162,590 20,137 107,000,000 Capital expenditure 15201 413,286 1,046,905 25 7,199,000 Growth opportunities 14768 3,205 5,678 -23,199 33,679 Liquidity 14976 2,349 1,749 385 10,854 Leverage 14973 2,449,202 5,326,665 0 32,909140
Panel C: variables before log transformation
Male 15252 .954 .209 0 1 Tenure 15252 4.119 2.359 1 10 % CEO owns in firm 14514 1.725 4.275 0 27.035 Age 15243 56.381 6.959 40 76 Tenure 15252 1.19 .715 0 2.303 Age 15243 4.024 .124 3.689 4.331 Capital expenditure 15201 4.178 2.163 -3.689 8.882 Leverage 13079 6.033 2.869 -12.944 10.402 Liquidity 14796 .644 .637 -.955 2.359 Growth opportunities 14089 .967 .854 -3.679 7.341 % CEO owns in the firm 14199 -1.009 1.811 -6.908 4.605 Sales 15211 7.499 1.635 3.003 11.582
4.2 Regression results
Before performing the regression analysis, the assumptions are checked. In appendix D, the tests and the corresponding solutions for endogeneity, autocorrelation, homoskedasticity and multicollinearity are demonstrated. After that, two regressions models were performed. The first model, the pay-performance elasticity, is estimated by employing the log-log model, meaning that the dependent and independent variables are both expressed in a natural
logarithmic form. Table II demonstrates three different regressions using the log-log model to observe differences in results including and excluding control variables and fixed effects. The first regression only considers the two main variables, the options payment and firm
performance. As a result, the coefficient of the independent variable is 1.02 and significant at a 1% level, indicating a statistically significant positive relationship between the independent and dependent variables for a 1% significance level. However, the effect of options pay is overestimated since it contains the effect of the omitted control variables. Additionally, the second model includes the control variables. The control variables are first winsorized at a 2% level and then transformed into a natural logarithm. The log transformation is applied to reduce the skewed distribution of the variables. The control variable male is not transformed into a logarithmic form, because it is a dummy variable. The coefficient of options-based compensation declines but is still highly significant in column (2) and (3). The third
regression incorporates time and industry fixed effects. The time fixed effects are proposed to capture the effect of time-series trends. Industry fixed effects are incorporated to control for the different characteristics of the multiple industries CEOs work in. The regression results including the separate coefficients of the time fixed effects and industry fixed effects are presented in appendix C.
The adjusted R-squared, demonstrated at the bottom of table II, indicates how much of the variance of the dependent variable is explained by the model. In the third regression, the adjusted R-squared is the highest. It implies that 84.6% of the variance of the dependent variable is explained by the model. As table II indicates, the coefficient of the variable options pay is 0.257 and is significant at 1% level. This implies that when options pay increases with 1%, the firm performance increases by 0.257%, while holding the other variables constant. The results of this analysis suggest that executive payment in options is a significant predictor of total firm performance (t=12.52). The control variables should be interpreted in the same manner as explained above for the main independent variable stock options pay.
Table II. Empirical results of the regression of the pay-performance elasticity.
The table reports the regression results of the first model. The dependent variable is the natural logarithm of total performance and consists of the performance measures pretax income and the market firm value. The independent variable is the natural logarithm of options-based compensation. All variables are precisely described in Appendix A. Furthermore, the sample includes 3,334 CEOs in the period 2010 to 2019 and consists of 4,960 observations. The first column only includes the two main independent and dependent variables. In column (2), the control variables are incorporated. All the control variables, except for the dummy variable male, are denoted in a natural logarithm. Last, in column (3), time fixed effects and industry fixed effects are included. Time fixed effects are based on the fiscal year and industry fixed effects on the SIC-code. The standard errors are clustered at the CEO-level. The t-statistics, given in the parentheses, are based on robust standard errors. The statistical significance of the results is denoted by *= 5%, ** = 10% and *** = 1%.
(1) (2) (3)
Totalperformance Totalperformance Totalperformance Options pay 1.020*** 0.277*** 0.257*** (31.42) (13.21) (12.52) Tenure 0.0679*** 0.0569*** (4.69) (4.25) Age 0.551*** 0.433** (3.41) (2.74) Capital expenditure 0.268*** 0.234*** (15.76) (11.64) Leverage 0.0200** 0.00253 (2.59) (0.35)
Table II. (continued)
(1) (2) (3)
Totalperformance Totalperformance Totalperformance
Liquidity 0.328*** 0.341*** (9.75) (9.66) Growth opportunities 0.506*** 0.502*** (17.84) (17.32) % CEO owns in firm -0.104*** -0.0897**
(-8.63) (-7.94) Sales 0.465*** 0.561*** (20.73) (24.23) Male -0.0855 0.0948 (0.96) (1.16) Constant 0.105 -2.723*** -2.929*** (0.45) (-4.15) (-5.31)
Time fixed effects NO NO YES
Industry fixed effects NO NO YES
N 6331 4960 4960
R2 0.373 0.833 0.846
Table III demonstrates the regression results of the second model, the pay-performance sensitivity. Total performance and options-based payment, are contrary to the first model, not expressed in logarithms. The pay-performance sensitivity is measured by performing a simple linear regression. The three columns are similar to the regressions in the first model. First, total performance is regressed exclusively on options pay. In column (2), the control variables are included. The control variables are transformed in the same manner as in the first model. Finally, in the last column time and industry fixed effects are incorporated. In column (1), options pay is significant at 1% significance level. Furthermore, in column (2) and (3) stock options pay is a significant predictor of firm performance at a 5% significance level. The R-squared is extremely small in the first column. Moreover, the R-R-squared is 0.065, which indicates that 6.5% of the variance in firm performance is explained by, the dependent variable of interest, the model. The value of the adjusted R-squared is the most substantial in the third regression, indicating that the model explains 40.6% of the variance in firm
performance.
Column (3) reports regression results of the complete model, including the control variables, time fixed effects, and industry fixed effects. Correspondingly, the beta coefficient of options-based pay is 0.514, which suggests a positive relationship between stock options pay and total performance. The beta coefficient is statistically significant at a 5% significance level, meaning that there exists sufficient evidence to assume that options pay affects firm performance at the population level. Total performance is expressed in million dollars and options pay in thousand dollars. For a one-unit change in the independent variable the dependent variable will increase with the beta coefficient. Therefore, the coefficient of 0.514 indicates that when options payment increases with $1, then total performance increases with $514, while holding the other variables constant. The control variables are expressed in a natural logarithm while the dependent variable is not. Therefore, the coefficients of the controls should be interpreted in a different way. For a 1% change in the control variable, the dependent variable increases by (𝛽/100) units of the dependent variable.
Table III. Empirical results of the regression of the pay-performance sensitivity.
The table illustrates the regression results of the second model. The dependent variable is the total performance and is a combined measure of pretax income and market value of equity of the firm. The independent variable is the natural logarithm of options-based compensation. All the variables are specified in more detail in Appendix A. The sample consists of 3,334 CEOs and 11,325 observations between 2010 to 2019. Column (1) only includes the main independent and dependent variables. In column (2), the control variables are incorporated. The control variables, with the exception of the dummy variable male, are expressed in a natural logarithm. Lastly, in column (3), time fixed effects and industry fixed effects are included. The fiscal year captures the time fixed effects and the SIC-code the industry fixed effects. Standard errors are clustered at the CEO-level. The t-statistics, in parentheses, are based on robust standard errors. The statistical significance of the results is indicated by *, ** and *** for a 5%, 10% and 1% significance level, respectively.
(1) (2) (3)
Totalperformance Totalperformance Totalperformance Options pay 2.048*** 0.555* 0.514* (9.38) (2.48) (2.31) Tenure 263.9 230.7 (1.83) (1.68) Age 1469.1 1496.0 (0.56) (0.60) Capital expenditure 1316.6*** 1082.3*** (6.43) (4.42) Leverage -207.4** -320.5*** (-2.62) (-3.90) Liquidity 2007.2*** 2268.7*** (3.91) (3.94)
Table III. (continued)
4.3 Robustness checks
Last, there are some robustness checks performed to observe whether the conclusions change when the assumptions change. First of all, as illustrated in appendix E, the performance measures are tested separately. In this study, the firm performance indicator consists of two measures, the market value of equity and pretax income. There is observed whether the empirical results differ when the separate firm performance measures are utilized instead of a firm performance ratio. The coefficients do not change the direction or the significance of the coefficients.
Furthermore, there is tested whether the income after taxes, net income, as the dependent variable changes the results. Net income is denoted in a natural logarithm. As a result, the coefficient is still significant and positive, and thus a significant predictor of stock options-based compensation.
(1) (2) (3)
Totalperformance Totalperformance Totalperformance Growth opportunities 2645.0*** 2426.6***
(8.66) (7.94) % CEO owns in firm -725.1** -663.5**
(-2.88) (-2.77) Sales 3872.7*** 4511.8*** (11.61) (12.04) Male -518.2 -383.0 (-0.39) (-0.30) Constant 3616.7*** -39433.2*** -47338.5*** (13.91) (-4.17) (-5.31)
Time fixed effects NO NO YES
Industry fixed effects NO NO YES
N 14770 11325 11325
R2 0.065 0.392 0.407
5. Conclusion
The first part of this section reflects on the research. Thereafter, limitations and suggestions for future research are given. Last, the implications are discussed, and the research question is answered.
This thesis aimed to research the effect of stock options-based executive compensation and firm performance, and to determine whether the provided incentives are in alignment with firm performance. In previous research, the pay-for-performance relationship is examined through the pay-performance elasticity and pay-performance sensitivity. According to the first quantitative model, it can be concluded that stock options payment is positively related to firm performance. The results indicated that stock options provide CEOs with the correct incentives and therefore improve the performance of the firm. In addition to the first model, the second quantitative model proved a significant positive relationship between firm performance and stock options-based compensation. This is consistent with the previous findings of the researchers Hall and Liebman (1998), Murphy (1999), and Mehran (1995).
This study contains some limitations that may have influenced the results. First of all, the results are potentially biased due to reversed causality. This implies that not only stock option-based compensation influences firm performance, but firm performance might also influence stock options payment. Therefore, the use of instrumental variables is preferred. However, the appropriate variables highly correlated with the independent variable and not with the dependent variable were difficult to find, and thus not included. Nonetheless, the results should be interpreted with caution. Future research should search for instrumental variables to better perform this panel data analysis. Secondly, although firms are managed by a team of executives, this study is based on the Chief Executive Officer. However, the CEO does not solely have the power to influence firm performance and has to cooperate with a team of executives. In future work, studying the entire executive team might be essential. Moreover, the only component of executive payment that is researched in this study is stock options pay. Therefore, the comparison to other forms of executive payment is neglected. Lastly, this thesis is based solely on financial firm performance. Besides that, firm
performance can also be measured by more qualitative metrics such as customer satisfaction and sustainability. Future research should consider more qualitative performance metrics when studying the pay-for-performance relationship.
This thesis is of practical relevance for firms because it studies whether stock options-based compensation, a commonly applied form of executive compensation, leads to higher firm performance. As already mentioned in the research of Benmelech (2008) every business is unique and requires a firm-specific approach. Therefore, the businesses should also
investigate which executive compensation package is most suitable to the organizational structure of the firm.
This study provides a good initiation of further research on the stock options-based payment to performance relationship using more recent data. Most studies used data before the financial crisis, thus more up-to-date research is of importance. The most powerful researches studying the pay-for-performance relationship are published in the late 90s.
However, the pay-for-performance relationship, after significant changes in regulations on the payout structure and changes in the business environment, is not supported by powerful studies.
Overall, this research provides a clear inference on the options-based payment to firm performance relationship. The empirical results suggest stock option-based compensation leads to higher firm performance. Therefore, there is concluded that stock options pay
provides CEO with incentives in alignment with the performance of the business. Eventually, it is important to persistently research the effect of certain forms of CEO compensation on firm performance due to the rapidly changing business environment and the changing purpose of executive compensation.
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7.1 Appendix A. Description of the variables
Table I. The name, type, estimate and unit of the included variables.
First, the table reports the specific variable name retrieved from the databases. Column (2), specifies whether the variable is a dependent, independent or control variable. In column (3) the estimates of the variables are indicated. Last, there is stated in which units the variables are expressed.
(1)
Variable name
(2)
Main type of variable
(3) Estimate
(4) Units
Market value Mkvalt Part of the dependent
variable
Total market value Millions
Pretax income Pi Part of the dependent
variable
Pretax income Millions
Firm performance Totalperformance Main dependent variable
Total market value + Pre-tax income
Millions
Stock options pay Option_awards_fv Main independent variable
Grant date fair value of options granted
Thousands
% of ownership of CEO in the firm
Shrown_excl_opts_pct Control variable Percentage of total shares owned, excluding stock
options
Percentage
Size of the firm Sales Control variable Sales Millions
Growth opportunities Growth opportunities Control variable Total market value/ (book value per share
* common shares outstanding)
Millions
Level of leverage Leverage Control variable (long-term debt + debt in current liabilities) /
(common shares outstanding * book
value per share)
Millions
7.2 Appendix B. Descriptive statistics
Table II. Tabulation of the variable male.
The table reports the frequency and percentages for the dummy variable male. The value 0 indicates a female and the value 1 a male.
Freq. Percent Cum.
0 700 4.59 4.59
1 14552 95.41 100.00
Table III. Tabulation of variable year.
Data Year - Fiscal Freq. Percent Cum.
2010 1730 11.34 11.34 2011 1699 11.14 22.48 2012 1674 10.98 33.46 2013 1648 10.81 44.26 2014 1621 10.63 54.89 2015 1558 10.22 65.11 2016 1500 9.83 74.94 2017 1450 9.51 84.45 2018 1381 9.05 93.50 2019 991 6.50 100.00 (1) Variable name (2)
Main type of variable
(3)
Estimate
(4)
Units
Liquidity constraints Liquidity Control variable (Total current assets / total current liabilities)
Millions
Age of CEO Age Control variable Executive’s age in
years
Years
CEO tenure Tenure Control variable Tenure in years Years
Gender of CEO Male Control variable Dummy male = 1;
Female= 0
-
Executive ID number Execid Standard errors
clustered on ID number
Identification number CEO
-
Fiscal year _Iyear_ Time fixed effects Fiscal year -
Industry _Iind_group_ Industry fixed effects Standard Industrial Classification Code
7.3 Appendix C. Regression results in more detail
Table IIII. Regressions results of the pay-performance elasticity.
The table reports all the separate coefficients of the fixed time effects and the fixed industry effects. Totalperformance is the dependent variable and options is the independent variable. The independent and dependent variables are both expressed in a natural logarithm. The t-statistics (in parentheses) are based on robust standard errors. In the three columns, * denotes significance at a 5% level, ** denotes significance at a 10% level and *** denotes significance at a 1% level.
(1) (2) (3)
Totalperformance Totalperformance Totalperformance Options pay 1.020*** 0.277*** 0.257*** (31.42) (13.31) (12.52) Tenure 0.0679*** 0.0569*** (4.69) (4.25) Age 0.551*** 0.433** (3.41) (2.74) Capital expenditures 0.268*** 0.234*** (15.76) (11.64) Leverage 0.0200** 0.00253 (2.59) (0.35) Liquidity 0.328*** 0.341*** (9.66) (9.75) Growth opportunities 0.506*** 0.502*** (17.84) (17.32) % CEO owns in firm -0.104*** -0.0897***
(-8.63) (-7.94) Sales 0.465*** 0.561*** (20.73) (24.23) Male 0.0855 0.0948 (0.96) (1.16) _Iyear_2011 -0.116*** (-4.68) _Iyear_2012 -0.0525 (-1.76) _Iyear_2013 0.0388 (1.27)
Table IIII. (continued)
(1) (2) (3) Totalperformance Totalperformance Totalperformance
_Iyear_2014 0.00408 (0.12) _Iyear_2015 -0.0508 (-1.37) _Iyear_2016 0.0826* (2.09) _Iyear_2017 0.0330 (0.77) _Iyear_2018 0.0466 (0.99) _Iyear_2019 0.167** (3.28) _Iind_group_2 0.915*** (5.14) _Iind_group_3 0.360** (3.12) _Iind_group_4 0.434** (2.90) _Iind_group_5 0.228 (0.70) _Iind_group_6 -0.108 (-0.86) _Iind_group_7 0.474*** (3.86) _Iind_group_8 0.531*** (4.09) _Iind_group_9 -0.0670 (-0.49) Constant 0.105 -2.723*** -2.929*** (0.45) (-4.15) (-4.48) N 6331 4960 4960 R2 0.373 0.833 0.846 Adjusted-R2 0.372 0.833 0.846
Table V. Regression results of the pay-performance sensitivity.
The table presents the regressions results of the pay-performance sensitivity. The dependent variable is the total firm performance and the independent variables is stock options pay. In column (3) the separate fixed effects are included. The t-statistics are reported in parentheses and based on robust standard errors. * denotes significance at a 5% level, ** denotes significance at a 10% level and *** denotes significance at a 1% level.
(1) (2) (3)
Totalperformance Totalperformance Totalperformance Options pay 2.048*** 0.555* 0.514* (9.38) (2.48) (2.31) Tenure 263.9 230.7 (1.83) (1.68) Age 1469.1 1496.0 (0.56) (0.60) Capital expenditures 1316.6*** 1082.3*** (6.43) (4.42) Leverage -207.4** -320.5*** (-2.62) (-3.90) Liquidity 2007.2*** 2268.7*** (3.91) (3.94) Growth opportunities 2645.0*** 2426.6*** (8.66) (7.94) % CEO owns in firm -725.1** -663.5**
(-2.88) (-2.77) Sales 3872.7*** 4511.8*** (11.61) (12.04) Male -518.2 -383.0 (-0.39) (-0.30) _Iyear_2011 -371.0* (-2.16) _Iyear_2012 -394.4 (-1.61) _Iyear_2013 -48.12 (-0.18)
Table V. (continued)
(1) (2) (3)
Totalperformance Totalperformance Totalperformance
_Iyear_2014 -30.33 (-0.09) _Iyear_2015 285.5 (0.82) _Iyear_2016 618.6 (1.64) _Iyear_2017 1179.7** (2.85) _Iyear_2018 1004.0* (2.28) _Iyear_2019 1548.1** (3.02) _Iind_group_2 7227.1*** (5.31) _Iind_group_3 4362.2*** (4.32) _Iind_group_4 4760.4*** (3.83) _Iind_group_5 17287.9** (3.19) _Iind_group_6 1888.2 (1.43) _Iind_group_7 6270.1*** (5.22) _Iind_group_8 4656.0*** (3.32) _Iind_group_9 -1376.7 (-1.22) Constant 3616.7*** -39433.2*** -47338.5*** (13.91) (-4.17) (-5.31) N 14770 11325 11325 R2 0.065 0.392 0.407 Adjusted-R2 0.065 0.391 0.406
7.4 Appendix D. Testing assumptions
Wooldridge test for autocorrelation in panel data H0: no first-order autocorrelation
F (1, 717) = 48.142 Prob > F = 0.0000
The null hypothesis is not rejected, which implies autocorrelation. Therefore, standard errors are clustered on the identification number of the CEO, to take correlation over time into account in the standard errors.
Modified Wald test for heteroskedasticity
Implemented heteroskedasticity-robust errors, for the reason that they are valid when the standard errors are either heteroskedastic or homoscedastic. Thus, whether or not the data is heteroskedastic, the robust function deals with it.
Hausman test for endogeneity test lnoptions_res (1) lnoptions_res = 0 F(1, 1632) = 1.9e+18 Prob > F = 0.0000 test option_awards_fv_res (1) option_awards_fv_res = 0 F (1,1632) = 0.00 Prob > F = 0.9520
The null hypothesis of the first model is rejected, which means that the independent variable is endogenous. As a result, instruments are necessary. However, as mentioned as one of the limitations, variables that are correlated with stock options payment and uncorrelated with firm performance, were not identified. Therefore, cautious interpretation of the results is essential. In the second model, the null hypothesis is not rejected, which indicates that the independent variable is exogenous.
Pearson correlation matrix
Multicollinearity occurs when two independent variables are highly correlated with each other. A correlation coefficient of above 0.7 signifies multicollinearity.
Table VI. Correlation matrix of the independent variable and control variables. Variables (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (1) Options pay 1.000 (2) (In)options 0.847 1.000 (3) Tenure 0.026 0.035 1.000 (4) Age 0.029 0.014 0.004 1.000 (5) Capital expenditure 0.370 0.450 0.058 0.091 1.000 (6) Leverage 0.336 0.421 0.049 0.050 0.683 1.000 (7) Liquidity -0.107 -0.119 -0.018 -0.005 -0.390 -0.323 1.000 (8) Growth opportunities 0.193 0.221 0.058 -0.025 0.020 0.092 -0.114 1.000 (9) % CEO owns in firm -0.202 -0.265 0.037 0.223 -0.367 -0.355 0.106 -0.104 1.000 (10) Sales 0.450 0.528 0.055 0.105 0.823 0.686 -0.353 0.084 -0.403 1.000 (11) Male 0.033 0.050 -0.000 0.058 0.010 0.033 0.038 -0.033 0.019 -0.001 1.000 (12) year 0.074 0.111 0.150 0.077 0.079 0.183 -0.081 0.165 -0.063 0.103 -0.011 1.000
7.5 Appendix E. Robustness check
Table VII. Regression results of the robustness check.
In this table, the dependent variables are the single performance indicators, market value of equity, pretax income and net income. The single performance indicators are tested instead of total performance measure containing a mixed ratio of two variables. In column (1), (2) and (3) the dependent variables are expressed in a natural logarithm. The independent variable is the stock options-based pay. * denotes significance at a 5% level, ** denotes significance at a 10% level and *** denotes significance at a 1% level.
(1) (2) (3) (In) market value firm (In) pretax income (In) net income (In) options pay 0.262*** 0.152*** 0.184***
(12.97) (6.14) (7.81) Tenure 0.0565*** 0.0668*** 0.0580** (4.41) (3.94) (3.14) Age 0.420** 0.693*** 0.690*** (2.76) (3.79) (3.89) Capital expenditures 0.233*** 0.208*** 0.196*** (11.90) (8.65) (7.43) Leverage 0.00222 -0.00119 0.0110 (0.32) (-0.14) (1.26) Liquidity 0.333*** 0.316*** 0.271*** (9.79) (7.59) (6.76) Growth opportunities 0.498 *** 0.348*** 0.301*** (18.01) (12.09) (10.90) % CEO owns in firm -0.0948*** -0.0747*** -0.0780***
(-8.38) (-5.54) (-5.79) Sales 0.539*** 0.754*** 0.714***
(23.65) (25.89) (23.78)
Table VII. (continued)
(1) (2) (3) (In)market value firm (In)pretax income (In) net income
Male 0.0982 0.0224 0.0353 (1.19) (0.28) (0.42) Constant -2.065*** (-3.30) -6.259*** (-8.36) -6.665*** (-9.06) N 4992 4338 4295 R2 0.856 0.795 0.781 Adjusted-R2 0.855 0.794 0.779
