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Empirical analysis of factors driving stock options grants and firms volatility
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Empirical analysis of factors driving stock options grants and firms volatility
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Content
EMPIRICAL ANALYSIS OF FACTORS
DRIVING STOCK OPTIONS GRANTS AND FIRMS’ VOLATILITY
by
Ladan Masoudie
A Dissertation Presented to the
FACULTY OF THE GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(ECONOMICS)
May 2008
Copyright 2008 Ladan Masoudie
ii
Dedication
This work is dedicated to my parents for being my first and greatest teachers and to my
husband because of his unconditional and perpetual love and support.
iii
Acknowledgments
I wish to express my sincere thanks to Professor Fernando Zapatero for being the best
advisor I could have ever had during the years of my education. I appreciate what I have
learned from him, not only the broad knowledge, sharp scientific instinct and creative ideas,
but also the optimistic and friendly attitude.
During the past few years in USC, I had the privilege to get to know people who had
influence in my scientific and personal life. I am particularly thankful to the members of my
thesis committee, Prof. John Ham, Prof. Kevin Murphy and Prof. Sandino Tatiana, for their
active participations and the insightful feedbacks. Among the friends, I would like to
especially thank Pouyan Mashayekh, Michele Milano, Engin Volkan, Arash Massoudieh and
Arshan Arshi.
I would like to thank my mom; her love and support have kept me strong even during the
most difficult days. I am also grateful to my dad for his unreserved support in my life.
Finally, thanks to my husband Arash, for giving me encouragement, and enriching me with
his brilliant ideas, for being full of love and support and making my life so colorful and for
always having bigger dreams for me.
iv
Table of Contents
Dedication ii
Acknowledgements iii
List of Tables vi
List of Figures viii
Abstract ix
Chapter 1: Empirical Analysis of Gender Differences in 1
Risk Behavior of Top-Level Executives in
Compensation and Decision Making
1-1 Introduction 1
1-1-1 Previous Literature 2
1-2 Research Design 4
1-2-1 Firm’s Risk Level 4
1-2-2 Stock Options Compensation 6
1-2-3 Option Exercising Behavior 8
1-2-4 Stock Ownership 8
1-3 Data 9
1-4 Econometric Analysis 12
1-4-1 Firm’s Risk Level 12
1-4-1-1 Monthly Standard Deviation 12
of Logarithm of Stock Prices
1-4-1-2 Firms’ Idiosyncratic Risk 13
1-4-1-3 Leverage 13
1-4-2 Stock Options Compensation 15
1-4-3 Option Exercising Behavior 17
1-4-4 Stock Ownership 19
1-5 Results 20
1-5-1 Firm’s Risk Level 20
1-5-2 Stock Options Compensation 22
1-5-3 Option Exercising Behavior 36
1-5-4 Stock Ownership 36
1-6 Conclusions 41
v
Chapter 2: Empirical Analysis of the Association between 42
Screening Executives and Stock Option
Compensation
2-1 Introduction 42
1-1-1 Previous Literature 43
2-2 Research Design 47
2-3 Data 48
2-4 Econometric Analysis 50
2-5 Results 54
2-6 Conclusions 60
Bibliography 61
Appendix 65
vi
List of Tables
Table 1-1: Random-Effects Models of Monthly Standard 23
Deviation of Logarithm of Stock Price with Firm
Level Data
Table 1-2: Fixed-Effects Models of Monthly Standard 24
Deviation of Logarithm of Stock Price with Firm Level Data
Table 1-3: Test of Endogeneity of Female Dummy Variable for 25
Monthly Standard Deviation of Logarithm of Stock Price
Table 1-4: Random-Effects Models of Firm’s Idiosyncratic 26
Risk with Firm Level Data
Table 1-5: Fixed-Effects Models of Firm’s Idiosyncratic Risk 27
with Firm Level Data
Table 1-6: Test of Endogeneity of Female Dummy Variable 28
for Firm’s Idiosyncratic Risk with Firm Level Data
Table 1-7: Random-Effects Models of Firm’s Leverage with 29
Firm Level Data
Table 1-8: Fixed-Effects Models of Firm’s Leverage with 30
Firm Level Data
Table 1-9: Test of Endogeneity of Female Dummy Variable 31
for Firm’s Leverage
Table 1-10: Random-Effects Models of Stock Option Compensation 32
to Total Compensation with Executive Level Data
Table 1-11: Random-Effects Models of Stock Option Compensation to 33
Total Compensation for Sub Sample where variable AGE is
available with Executive Level Data
Table 1-12: Fixed-Effects Models of Stock Option Compensation to 34
Total Compensation with Executive Level Data
Table 1-13: Fixed-Effects Models of Stock Option Compensation to 35
Total Compensation for Sub Sample where variable AGE is
available with Executive Level Data
vii
Table 1-14: Random-Effects Models of Ratio of Value of Exercisable 37
In-The-Money Unexercised Options to the value of Total
Options held at the year end with Executive Level Data
Table 1-15: Fixed-Effects Models of Ratio of Value of Exercisable 38
In-The-Money Unexercised Options to the value of Total
Options held at the year end with Executive Level Data
Table 1-16: Random-Effects Models of Value of Stock Holding 39
with Executive Level Data
Table 1-17: Fixed-Effects Models of Value of Stock Holding 40
with Executive Level Data
Table 2-1: Random-Effects Models of 4 Year Accumulated Stock 56
Option Compensation to Total Compensation with
Executive Level Data
Table 2-2: Fixed-Effects Models of 4 Year Accumulated Stock 57
Option Compensation to Total Compensation with
Executive Level Data
Table 2-3: Random-Effects Models of 4 Year Average of Stock 58
Option Compensation to Total Compensation with
Executive Level Data
Table 2-4: Fixed-Effects Models of 4 Year Average of Stock Option 59
Compensation to Total Compensation with Executive
Level Data
viii
List of Figures
Figure 1-1: Number of Female Executives for all Years 10
Figure 1-2: Percentage of Firms with at least one Female 11
Executive among Top Five Executives
Figure 1-3: Percentage of Firm-Years with at least one Female 11
Executive among Top Five Executives
Figure 2-1: Grant-Date Value of Option Grant to all Employees 49
Figure 2-2: Average Stock Option Compensation to Total Employees 50
ix
Abstract
Top executives behavior and their decision making are important factors affecting the
performance of the firms and business market. First part of this dissertation is an
empirical analysis of gender differences in risk taking behavior, compensation structure,
options exercising behavior and firm’s stock holding of U.S. public firms’ top executives.
This study used the data from U.S. public firms over the period 1992 to 2004. The results
suggest that firms with at least one female executive among the top five executives have
lower risk levels. Based on the results from exogeneity tests, it is suggested that the
presence of a female executive among top executives of a firm decreases the firm’s risk
level. Compared to their male counterparts, top female executives possess higher share of
stock options in their compensation, and exercise their stock options more frequently.
The firms’ share ownership of top five executives is not significantly affected by gender.
The second part is an empirical analysis of the association between screening top
executives and stock option payment. Data from U.S. public firms during 1992 to 2005
was used. Several theoretical studies have shown that stock options can be used to screen
out low type executives. Two variables were introduced to capture the intensity of
screening problem. The results support the hypothesis that stock options are partly given
to screen out low type executives.
1
Chapter 1
Empirical Analysis of Gender Differences in Risk Behavior of
Top-Level Executives in Compensation and Decision Making
1-1- Introduction
In the last two decades the percentage of women in full- and part-time employment
rose by almost 10 percent. As employment among higher educated women has increased,
the number of females in executive positions also has increased. Currently, there are
more women in top executive ranks in major corporations than there were in 1990s. In
2004 the number of top level female executives was six times higher than that of 1992.
With more women in top executive ranks, financial decision-making in the business
world is also expected to change. According to the existing literature on women’s risk
aversion, this can be attributed to women being more risk averse than men.
5,11,14,25,36
Moreover, from theoretical standpoints,
9,17,18,23,35,39
even the composition of output based
compensation, such as stocks and stock-option holdings, is expected to be different across
gender lines.
This paper will present an empirical study to test female executives’ behavior towards
risk, and will compare their attitude to their male counterparts. The study will be based
on compensation structures and decision making of women versus men. Using a dataset
which contains unexplored information on women in top executive positions, this study
contributes to the previous literature by studying the following issues:
1) Is there a relationship between a firm’s stock price volatility and the existence of
female executives in the five highest-paid positions of a firm?
2) Does gender affect the level of risk in the executives’ compensation structure?
2
3) Does exercising of options differ based on executives’ gender?
4) Does gender affect share ownership?
The results of this study indicate that firms with at least one woman in the top five
executive spots display a lower risk level in the stock market as well as lower leverage
(debt-to-asset ratio). These results also suggest that female executives’ stock-option
shares in total compensation are higher than that of male executives. Moreover, the
results indicate that women in executive positions exercise their stock options more
frequently than men in comparable positions. These results support the hypotheses that
women are more risk averse than men. Finally, contrary to previous results, I find that the
value of executives’ share ownership in firms is not affected by gender.
1-1-1- Previous Literature
This study is related to a strand of literature on the difference in risk aversion across
gender lines. Within this strand of literature, this study is the first to analyze the
difference in risk aversion across women and men in top executive positions.
Nevertheless, there are a number of studies that have shown differences between
women’s and men’s risk aversion behavior in financial decision making, and I will be
presenting those papers in this section.
In a random sample of US households based on the 1989 Survey of Consumer
Finances, Jinakoplos and Bernasek(1998) find that single women are relatively more
risk-averse in financial decision-making than single men. According to their results, as
single women’s wealth increases, the increase in the proportion of wealth they hold in
risky assets is lower than that of single men. Using survey data from University of
3
Michigan Health and Retirement Study, Halek and Eisenhauer(2001) also show that
women are significantly more risk averse than men both in pure and speculative types of
risk.
*
Riley and Chow (1992) find that in asset allocation decisions, women are more
risk averse than men. Bajtelsmit and Vanderhei (1996) using a plan-level data on 20,000
mid-level management employees of a single US firm in 1993, show significant
differences in investment of pension assets across genders.
On the other hand, Schubert et al. (2000), in an experimental study, demonstrated that
women are not different from men in their risk aversion, while they are significantly
more ambiguity-averse than men. Another study that confirmed this hypothesis was
performed by Daruvala (2006). He found no significant difference in pure risk-aversion
among men and women using a different experimental approach.
Among the four papers presented above, the initial three have similarities in choice of
datasets. They each use a random sample of individuals. On the other hand, the last paper
uses a specific group of people who hold management level positions in only one firm in
the US. My paper is based on data from a large number of public firms in the United
States. The dataset contains information on the highest-paid executives at these firms. It
is notable that this group of employees significantly affects their respective firms’
investment and risk management strategy. Thus, the dataset allows me to address issues
which are of interest to a firm’s board of directors when making decisions about the
firm’s executives and their compensation structure.
*
They use term life insurance to calculate pure or downside risk aversion for head of the household. To
study speculative risk, respondents are questioned regarding their willingness to accept a new job with a 50
percent chance of doubling their current household income and a 50 percent chance of reducing it by one
third.
4
The major benefit of this research is the study of the impact of gender on stock option
compensations, firm’s volatility, option exercising behavior and share ownership.
1-2- Research Design
1-2-1- Firm’s risk level
Risk aversion of corporate executives can affect the volatility of a firm’s value. It can
be shown that, taking everything else as a given, as the executive’s risk aversion
increases, the amount of risk he/she is willing to take decreases. If female executives are
more risk averse than male executives, then we can expect lower risk levels for firms
with top level female executives.
In this research, I analyze the relationship between a firm’s risk level and the
existence of at least one female executive among top level executives, with respect to
stock price volatility and idiosyncratic volatility. I also analyze the relationship between a
firm’s leverage and female executives since the firm’s risk level is associated with its
debt level. According to CAPM and Modigliani and Miller theory, borrowing while
maintaining a fixed amount of equity increases the risk to the investor. Thus, the
covariance of the asset’s rate of return with the market portfolio’s rate of return would
increase with the debt-equity ratio. Using CRSP and Compustat data for the period of
1948-1967, Hamada (1971) confirmed the positive effect of leverage on stock price
volatility. In a theoretical framework, Bensoussan, Crouhy and Galay (1994) show that
stock price volatility is positively affected by debt value. Therefore, managers can affect
stock price volatility when deciding on the capital structure of their firm.
5
Other factors that serve as explanatory variables are average share of stock option
compensation to total compensation, firm size and cash flow shortfall. These variables
are chosen in reference to the following studies:
Carpenter (2000) has shown that the risk behavior of a manager is related to his or her
stock-option compensation. According to these results, a risk-averse manager will not
necessarily prefer to increase asset volatility. Rather she/he dynamically adjusts the
volatility in response to changes in the asset value. As the asset value grows, the manager
moderates the risk. When a stock price is high enough, the manager’s risk taking is equal
to when he/she was holding the original stocks. However, options that are deep out of the
money seem to provide for excessive risk taking.
Rajgopal and Shevlin (2002), using a sample of oil and gas producers, also showed
the influence of executive stock options on the risk-taking behavior of CEOs. Their
results demonstrate that as the sensitivity of stock option value to stock return volatility
increases, managers tend to choose riskier projects. This is consistent with the hypothesis
that stock option payments would mitigate an executive’s high risk aversion.
Ross (2004) answers the question of when an option-like incentive schedule leads to
increased risk taking. He shows that for the usually assumed preferences, “put options”
make individuals less risk averse while “call options” do not. He claims that the intuition
that giving options to agents makes them less risk averse is false.
Considering that exogenous market conditions can also affect a firm’s volatility at
each point in time, I have also used time and industry dummies. For instance, Voth
(2002) shows that political risk can account for a large part of stock price volatility.
6
1-2-2- Stock Options Compensation
The effect of gender on the share of stock options in compensation of executives is
now analyzed. In this respect, the ratio of stock option compensation to total
compensation is used as a proxy for the level of risk of the compensation package.
The value of the stock option for a risk-averse undiversified employee is lower than
the cost of granting it for an employer. This is due to the fact that employees demand a
risk-premium for bearing the risk
40
. As risk aversion increases, the demanded risk-
premium will also increase. Therefore, it is less efficient for a diversified employer to
compensate its executives by stock options. Hence, more risk-averse executives are
expected to receive fewer stock option payments. Given the hypothesis that women are
more risk averse, it is then expected that the female executives’ compensation packages
have fewer stock option shares than those of male executives. Thus, gender differences
are expected to change stock option compensations in firms.
Other factors that are used as explanatory variables are cost of monitoring, growth
opportunities, expected stock prices, cash constraints, share ownership and industry
dummy variables. These variables are chosen in reference to the following studies:
Yermack (1995) studies several agency and financial contracting theories to examine
if they are supported by the empirical data on stock option payments. His findings
suggest that: 1) Companies in highly regulated industries are less likely to use stock
options as a source of managerial incentive, 2) The firms tend to use stock option
compensation more commonly when accounting earnings are too noisy to be monitored
and 3) A firm uses stock options more commonly when dealing with internal liquidity
problems.
7
Gilson and Vetsuypens (1995) show that when firms are in financial distress, the
sensitivity of the CEO’s wealth to stock price performance increases. Anderson, Banker
and Ravindran (2000) investigate whether the greater use of stock options in executive
compensations in the IT industry can be explained on the basis of general economic
relationships that apply to firms in all other industries. Accordingly, they show that, after
controlling for differences in economic factors across industries, the executives of firms
in the IT industry receive a greater portion of their compensations in the form of stock
options than those in other fields.
Rosenberg (2003) empirically investigates whether firms use stock options to mitigate
the principal-agent problem for Finnish firms. He introduces proxies to measure the
intensity of the agency problem for each firm, including monitoring costs, growth
opportunities, ownership structure and risk. His results indicate that agency theory can
explain the use of stock options quite well. In a similar vein, using empirical data from
Finland, Pasternak (2002) examines the factors driving stock option grants and finds
support for the explanatory power of several agency-theory-based variables such as
liquidity as a measure of the role of stock option grants to facilitate payments, and CEO
ownership as a measure of agency costs.
In addition to the variables mentioned above, I have also included year dummies.
This is mainly because in recent years we have observed a decline in the amount of stock
options grants
9
.
8
1-2-3- Option Exercising Behavior
Economic models, Hall and Murphy (2002), Lambert, Larcker, and Verrecchia (1991)
and Huddart (1994), predict that a more risk-averse executive would exercise his/her
stock options sooner than the less risk-averse, since for the former the uncertain profit in
the future is worth less than to the latter. Again assuming that top female executives are
more risk-averse than their male counterparts, gender differences are expected to affect
the option exercising behavior of executives.
Other factors that are used as explanatory variables are wealth, stock price volatility,
expected future stock prices, shares of stock options in total compensation and firm size.
These variables are chosen in reference to the following studies:
Theoretically, for a given risk aversion, employees would be more likely to exercise
their stock options early: 1) if a greater proportion of their wealth was tied to stock prices,
2) if the stock price volatility was high, 3) if future stock prices were anticipated to fall.
4) Moreover, an employee with less wealth would be more inclined to exercise his/her
stock option earlier to meet his/her liquidity constraints.
Core and Guay (2001) study non-executive exercising patterns and use logarithm of
sale to control for a firm’s size. Heath, Huddart, and Lang (1999) find that short-term and
long-term price trends affect these exercising behaviors.
1-2-4- Stock Ownership
The amount of voluntary firm share ownership that executives hold is important in
understanding their investment behavior. As an executive’s risk aversion increases,
his/her desire to diversify his/her investment would increase. Therefore less voluntary
9
share ownership is predicted for a higher level of risk aversion. Moreover, since risk
aversion in top female executives is expected to be higher than in top male executives,
gender differences are expected to affect the amount of voluntary share ownership.
For similar reasons, other factors that are used as explanatory variables are wealth,
stock price volatility, expected future stock prices, share of stock option in total
compensation and firm size.
Monitoring and agency costs are greater in large firms; thus top executives are
expected to hold higher share ownership to ensure that they are maximizing the
shareholders’ value. In addition, large firms are likely to employ more skilled managers,
who are consequently wealthier, suggesting a higher level of managerial ownership
(Himmelberg, Hubbard and Palia 1999).
1-3- Data
Three sources of data are being exploited, namely, the Compustat North America
dataset, historical daily stock prices data from CRSP
†
and Who’s Who online database.
The Compustat North America dataset contains panel data on firms’ executive
compensation and firms’ financial information from 1992 to 2005. The executive
compensation database contains over 2500 companies, both active and inactive. The
universe of firms covers the S&P 1500, plus companies that were once part of the 1500,
plus companies removed from the index that are still trading, and some client requests.
Data collection on the S&P 1500 began in 1994. However, there is data back to 1992 but
it does not include the entire S&P 1500. It is mostly for the S&P 500. All of the firms’
executives are ranked based on their total compensation in a year. Accordingly, for each
†
Center for Research in Security Prices
10
year, data on the five highest-paid executives of each firm have been collected. The
CRSP data is used for the daily stock prices of each firm to obtain the firm’s annual
idiosyncratic risk and annual standard deviation of stock price logarithm. This
information is ultimately used to proxy for the firms’ risk levels. The Who’s Who online
database is used to collect data on executives’ ages.
‡
A total of 27,889 executives’ data were analyzed. For 2,664 firms, the dataset
consisted of 4,592 female executive-years versus 110,706 male executive-years serving
throughout the period the dataset covers. In the aforementioned numbers, an executive is
counted once for every year served. According to statistics obtained from the dataset, the
number of women working in these firms has increased over time. In 2004 the percentage
of women in the top five executive positions of a firm increased from one percent in1992
to six percent. The number of firms with more than one female in top executive positions
is 560 for all years (Figure 1-1). Figure 1-2 demonstrates the percentage of female
executives for each year. The percentage of firms with at least one female executive for
all years across industries is shown in Figure 1-3.
Figure 1-1: Number of Female Executives for all Years
3401
493
60
6 1
0
500
1000
1500
2000
2500
3000
3500
# Firm-Year
12 3 45
# Female Executive
‡
Compustat North America dataset does not provide a complete set of executive age information.
11
Figure 1-2: Percentage of Firms with at least one Female Executive among Top Five
Executives
0
5
10
15
20
25
30
%
1992 1994 1996 1998 2000 2002 2004
Figure 1-3: Percentage of Firm-Years with at least one Female Executive among
Top Five Executives
Agriculture
Mining
Construction
Manufacturing
Transportation
Wholesale
Retail
Finance
Services
Public Administration
0
5
10
15
20
25
30
%
12
1-4- Econometric analysis
1-4-1- Firm’s Risk Level
To study the correlation between a firm volatility and female executives, three
variables are analyzed: monthly standard deviation of log stock prices over one year, the
firm idiosyncratic risk and the firm leverage. The relationships are tested with both fixed
and random effect models.
1-4-1-1- Monthly Standard Deviation of Logarithm of Stock Prices
In this section, I test for the relationship between the monthly Standard Deviation of
log stock prices, SD_LOGP, and a female dummy variable, DFEM. This variable takes
on value 1 if a firm at one year has at least one female executive in its top five positions.
Thus, the model is characterized as
it i it it it
x DFEM LOGP SD ε ν β γ + + + = _ (3)
where x is a 1×k matrix for k explanatory variables and β is k×1 vector of
coefficients. The explanatory variables are the following:
Year Dummy Variables: To focus on the firm’s specific volatility I entered dummy
variables for years to control for exogenous shocks to the markets that have affected its
volatility.
Industry Dummy Variables: Based on their specific characteristics, some industries
may be more volatile than others. To control for this type of industry-specific volatility, I
used dummy variables for each industry sector, based on their Standard Industrial
Classification.
13
Firm’s Size: Larger firms are usually less volatile than smaller ones. Therefore, to
control for firm size, I used the logarithm of the firms’ annual net sales.
Average of Stock Option Compensation to Total Compensation: The amount of stock
options compensation of top level executives can affect their decisions to take on
different types of projects, and thus the firm’s volatility.
Cash Flow Shortfall: When companies are cash constrained, they will take less risk.
Therefore, cash flow shortfall is used to control for binding liquidity constraints. The
variable is defined as the ratio between the sum of common and preferred dividends, cash
flow used in investing activities net of cash flow from operations and total assets.
10
1-4-1-2- Firms’ Idiosyncratic Risk
A Firm Idiosyncratic Risk, FS_Risk, is estimated from the CAPM model using CRSP
historical stock prices. In this section I test for the relationship between a firm
idiosyncratic risk and the female dummy variable. The model is characterized as
it i it it it
x DFEM Risk FS ε ν β γ + + + = _ (3)
The explanatory variables, x, are the variables used in section 4.1.2.
1-4-1-3- Leverage
In this section, I test for the affect of female executives on the leverage of their firms,
LEV. This variable is chosen to be the ratio of total debt to total assets (Rajan and
Zingales 1995) for each firm-year. The model is characterized as
it i it it it
x DFEM LEV ε ν β γ + + + = (3)
14
Based on the literature on the determinant of a firm’s capital structure, I use the
following explanatory variables:
Year Dummy Variables: Year dummies are expected to capture the effect of
exogenous shocks to credit markets that would affect the capital structure of firms.
Industry Dummy Variables: Industry dummies are used to capture the differences in
capital structure of firms across industries. Titman and Wessels (1988)
43
show that
industries that have higher liquidation costs are financed with relatively less debt.
Firm’s Size: Larger firms are more diversified and less likely to go bankrupt.
Therefore, large firms are more indebted than small firms (Titman and Wessels 1988,
Harris and Raviv 1991).
Profitability: Past profits of firms have been used for a firm’s capital financing needs,
decreasing their debt-to-asset ratio (Titman and Wessels 1988, Drobetz W. and Fix R.
2003 and Harris and Raviv 1991). Operating income over sales and operating income
over total assets are separately used as proxies for a firm’s profitability.
Growth: Agency problems are more intense in growing industries where managers
can choose from a larger number of projects. Taking into account that higher levels of
debt mitigate agency costs, there could be a positive relationship between growth
opportunities and leverage. However, considering that growth opportunities are in fact
intangible assets of a firm which cannot be collateralized and do not produce taxable
income, there may be a negative relationship between growth opportunities and leverage.
Growth opportunities are calculated by capital expenditure over total assets, R&D
expenditures and by the percentage of change in total assets. (Titman and Wessels 1988,
Harris and Raviv 1991)
15
Collateral Value of Assets: Firms with collateral assets use more debt for their
financing at a lower cost. To measure the level of collateral assets, the ratio of intangible
to tangible assets can be used. (Titman and Wessels 1988, Harris and Raviv 1991)
Non-Debt Tax Shields: Firms with large non-debt tax shields use less debt financing.
The ratio of investment tax credits over total assets, depreciation over total assets and a
direct estimate of non-debt tax shields, NDT, are used to derive non-debt tax shields.
(Titman and Wessels 1988, Harris and Raviv 1991) Non-debt tax shields is defined as
NDT=OI-i-T/0.48
where OI is operating income, i represents interest payments and T represents federal
income tax payments. Note that 0.48 is the corporate tax rate during the sample period.
1-4-2- Stock Option Compensation
In order to test the relationship between gender differences in top executive positions
and the stock option compensation, I used Compustat Execucomp. The model is
characterized as:
it i it it it
x DG SO ε ν β γ + + + = (1)
where SO is the ratio of stock option compensation to total compensation for each
executive and DG is a dummy variable for gender which will take on value 1 if the
executive is a woman. The other explanatory variables are the followings:
Rank of Executive: Executives are ranked based on their total compensation.
Moreover, with respect to their effects on the firm’s value, high ranking executives hold
more stock options than low ranking executives. I assume the executives who earn more
compensation have more influence on the value of the firm and therefore are paid more.
16
Year Dummy Variables: Year dummies are used to capture the changes in time trend
in the prevalence of stock option payments to the employee.
Firms Size: As the firm size increases, monitoring employees becomes more
difficult
30
. Therefore more stock option based payments are expected in larger firms
38
.
Industry Dummy Variables: Industries are expected to have different tendencies in
stock option payment offers. For instance, in industries where monitoring is higher, stock
option payments are used more frequently.
Stock Ownership: Stock ownership by executives would decrease the agency problem
as it ties the executive’s wealth to the firm’s market performance. Thus, fewer stock
options are expected as stock ownership increases.
Age: According to incentive theory, stock option payments may differ based on top
executives’ ages. However, the relationship between these variables is ambiguous. Below
I discuss how age is related to stock option payments. For example, Gibbons and Murphy
(1992) argue that executives who are close to retirement would have fewer career
concerns, which in turn may cause an incentive problem. In this respect, more stock
option payments should be expected in compensation packages for executives who are
close to retirement
26
. Additionally, executives who are nearing retirement would have
less incentive for long-term investments in a firm. This may create a moral hazard
problem. Stock option payments with long-term vesting periods would overcome this
moral hazard problem.
Lazear (1995) states that, as opposed to executives who are older, less information is
available about young executives. This problem of incomplete information on young
17
executives may create adverse selection problems for the firms. In this respect, more
stock option payments can be expected to overcome the adverse selection problem.
Risk aversion is expected to vary across ages. Given that older people are more risk-
averse
14
, it also has been suggested that fewer stock option payments should be made to
older executives. On the other hand, if on average older executives are wealthier, they
would have a lower absolute risk aversion rate. Thus, they may be given more stock
option payments. In this context, if we do not control for wealth, then age may be
positively related to stock option payments.
Cash Flow Shortfall: When companies are cash constrained they will substitute cash
with stock options.
Growth Opportunities: As growth opportunities for a firm increases, it becomes more
difficult for the board of directors to evaluate the performance of the firm’s manager,
who has private information about the value of different projects and investments. So as
growth opportunities increase, more stock-based compensation should be realized. Book-
to-Market ratio is used as a proxy for growth opportunities.
44
Stock Price Growth: If stock prices are believed to be rising then managers will be
more willing to accept stock options as their payment. Stock price growth can be used as
a proxy for manager’s expectations of the future stock price trend.
1-4-3- Option Exercising Behavior
The ratio of value of in-the-money unexercised exercisable stock options to the value
of total options that the executive holds at the end year, UNEXER, is used to analyze
executives’ exercising. If they keep an exercisable stock option they give up a certain
18
income now for risky income in the future. This can reveal their risk taking behavior.
The model is characterized as:
it i it it it
x DG UNEXER ε ν β γ + + + = (4)
Explanatory variables are as follow:
Rank of Executive: Higher paid executives may be less risk-averse. They are also
wealthier and so they may have lower absolute risk aversion.
Year Dummy Variables: There may be some time specific characteristics which affect
a manager’s willingness to exercise stock options. Year dummies can control for this
trend.
Firm’s Size: Larger firms are usually less volatile than smaller ones. This may affect a
manager’s decision to exercise his/her vested stock option.
Industry Dummy variables: Each industry may have specific characteristics that make
its employers more or less prone to exercise stock options. Also the characteristics and
beliefs of the employees from one industry to another may differ. Industry dummies
control for industry specific characteristics.
Firm’s Volatility: High firm volatility may increase the willingness of risk averse
executives to exercise vested stock options.
Total Compensation: As the executive acquires more wealth the absolute measure of
risk aversion decreases, so he/she becomes more willing to risk not exercising his/her
stock options.
Stock Price Growth: If stock prices are believed to be rising then managers will be
more willing not to exercise vested stock options early. Stock price growth can be used as
a proxy of a manager’s expectations for trends in the future stock prices.
19
Growth Opportunities: As the growth opportunities for the firm increases the
executives become more optimistic about the future of the firm, and so are less willing to
exercise stock options early. Book to Market Ratio can be used as a proxy for growth
opportunities.
Age: Older people have higher relative risk aversion
14
thus they exercise their stock
options more frequently. On the other hand, older people tend to have more wealth so
they have a lower absolute risk aversion and also they do not need to exercise their stock
options early to satisfy their financial needs.
Share ownership: More share ownership of the same stock makes the total assets
owned by the executive more risky and so may induce him/her to decrease the risk by
exercising his/her stock options early.
1-4-4- Stock Ownership
The data on the share ownership of executives is available for a subset of Compustat
executive compensation dataset. The amount of voluntarily stock ownership of
executives can reveal their investment decisions to some extent. This is true in the case of
unrestricted stocks. I have calculated the value of voluntary stock holding SHROWN.
Here is the equation that is estimated:
it i it it it
x DG SHROWN ε ν β γ + + + = (5)
Other variables believed to affect stock ownership include:
Rank of Executive: Higher paid executives hold more stock than lower paid
executives since they are wealthier.
20
Year Dummy Variables: There may be some time specific characteristics which affect
a manager’s willingness to own stock. Year dummies can control for this trend.
Firm’s Size: Higher share ownership is predicted for larger firms (Himmelberg,
Hubbard and Palia 1999).
Industry Dummy variables: Each industry may have a specific characteristic that
makes its employers more or less prone to hold stocks of the firm for which they are
working. Industry dummies control for industry specific characteristics.
Firm Volatility: High firm volatility may decrease the willingness of a risk-averse
executive to hold stocks in the company.
Total Compensation: As the executive becomes wealthier the amount of stock
holding will increase.
Stock Price Growth: If stock prices are believed to be rising then managers will be
more willing to increase their stock ownership. Stock price growth can be used as a proxy
of a manager’s expectations of the trends in future stock prices.
Growth Opportunities: As growth opportunities for the firm increase, the executives
become more optimistic about the future of the firm, and so are more willing to own
stock in the firm. Book to Market Ratio can be used as a proxy for growth opportunities.
1-5- Results
1-5-1- Firm’s Risk Level
The results for both fixed-effects and random-effects models show that the female
dummy variable, i.e. presence of at least one female in the top five executive positions,
21
and all three proxies of firm’s risk level are negatively related (Tables 1-1 through 1-9).
The three proxies for a firm’s risk level are the monthly standard deviation of logarithm
of stock prices, the firm’s idiosyncratic risk and the firm’s leverage. The results suggest
that women are more risk averse.
Although the results strongly suggest the negative relation between the two variables,
the direction of causality is undetermined. Thus, the following questions needs to be
answered: is it the presence of female executives which lowers a firm’s risk or does the
firm’s less risky behavior attract female executives? In order to determine the direction
of causality, I have used the Hausman endogeneity test.
Under the endogeneity assumption, using a two-stage estimation method, the
following model is estimated:
it i it it it
x DFEM LOGP SD ε ν β γ + + + =_
where (6)
it i it it it it
Part FEM DFEM LOGP SD DFEM ε ν δ θ α + + + + =
−
_ _
1
Note that estimated coefficients of (6) are consistent under both exogeneity and
endogeneity assumptions. In the above model, the instrumental variable are taken as the
lagged female dummy variable and female labor force participation rate based on state
and year.
On the other hand, under the exogeneity assumption, the following model is
estimated:
it i it it it
x PCFEM LOGP SD ε ν β γ + + + = _ (7)
Note that the estimated coefficients in (7) are consistent and efficient only under
exogeneity assumption.
22
Tables 1-3, 1-6 and 1-9 demonstrate the results of the Hausman tests for each
dependent variable. Accordingly, it is safe to assume that the female dummy variable is
exogenous. Thus, it can be concluded that having a female in one of the top five
executive positions is the cause for a decrease in a firm’s volatility.
1-5-2- Stock Options Compensation
Table 10-13 reports the model results for stock option compensations. Accordingly,
for both fixed-effect and random-effect models the gender differences positively affect
the share of stock options in total compensation. Huasman test suggests that random-
effect models are both consistent and efficient. However, with the inclusion of age as an
exogenous variable I observe that in both models the effect of gender differences on share
of stock options in total compensation declines. This is mainly because female top
executives in the sample are on average younger than male executives and younger
executives are offered more stock options as compensation.
Meanwhile, if women are believed to be more risk averse they may be paid with more
options to induce risk taking behavior in them. This argument may explain why women
are given more stock based compensation than men.
23
Table 1-1: Random-Effects Models of Monthly Standard Deviation of Logarithm
of Stock Price with Firm Level Data
Dependent Variable: Monthly Standard Deviation of Logarithm of Stock Price
(1) (2) (3) (4) (5)
# obs.
18234 18234 18234 16681 15933
Female Dummy
-0.008***
(0.003)
-0.008***
(0.003)
-0.008***
(0.003)
-0.007***
(0.003)
-0.008***
(0.003)
Year Dummies
Yes Yes Yes Yes Yes
Log Sale
-0.015***
(0.001)
-0.012***
(0.001)
-0.013***
(0.001)
-0.013***
(0.001)
-0.009***
(0.001)
Average of SO/Total
0.041***
(0.005)
0.035***
(0.005)
Cash Flow Shortfall
0.071***
(0.006)
Industry Dummies
New Economy
0.090***
(0.005)
0.087***
(0.005)
0.074***
(0.005)
Agriculture
-0.055
(0.045)
Mining
-0.074**
(0.033)
Construction
-0.043
(0.036)
Manufacturing
-0.054*
(0.032)
Transportation
-0.085***
(0.033)
Whole Sale
-0.052*
(0.034)
Retail Sale
-0.051*
(0.033)
Finance
-0.094***
(0.033)
Services
-0.022
(0.033)
Constant
0.257***
(0.007)
0.227***
(0.007)
0.307***
(0.033)
0.231***
(0.008)
0.210***
(0.009)
* 1.64>t>1.28
** 2.32>t>1.64
*** t>2.32
The numbers in the parentheses are standard deviations.
24
Table 1-2: Fixed-Effects Models of Monthly Standard Deviation of Logarithm of
Stock Price with Firm Level Data
Dependent Variable: Monthly Standard Deviation of
Logarithm of Stock Price
(1) (2) (3)
#obs.
18234 16681 15933
Female Dummy
-0.008***
(0.003)
-0.007**
(0.003)
-0.007**
(0.003)
Year dummies
Yes Yes Yes
Log Sale
0.0004
(0.002)
0.0001
(0.002)
0.004*
(0.002)
Average of
SO/Total
0.016***
(0.005)
0.016***
(0.005)
Cash Flow
Shortfall
0.039
(0.016)
Constant
0.161***
(0.013)
0.164***
(0.015)
0.140***
(0.016)
* 1.64>t>1.28
** 2.32>t>1.64
*** t>2.32
The numbers in the parentheses are standard deviations.
25
Table 1-3: Test of Endogeneity of Female Dummy Variable for Monthly Standard
Deviation of Logarithm of Stock Price
Dependent Variable: Monthly Standard Deviation of Logarithm of Stock Prices
Instruments: Female Dummy lagged Value , Female Labor Force Participation Rate by Year and State
Two Stage
Instrumental
OLS
Two Stage
Instrumental
OLS
Two Stage
Instrumental
OLS
# obs.
14105 14105 14105 14105 13025 13025
Female
Dummy
-0.006**
(0.004)
-0.007***
(0.003)
-0.006**
(0.004)
-0.007***
(0.003)
-0.011***
(0.003)
-0.009***
(0.003)
Year
Dummies
Yes Yes Yes Yes Yes Yes
Log Sale
-0.017***
(-0.001)
-0.017***
(-0.001)
-0.014***
(-0.001)
-0.014***
(-0.001)
-0.011***
(-0.001)
-0.011***
(-0.001)
Average of
SO/Total
0.058***
(0.005)
0.058***
(0.005)
Cash Flow
Shortfall
0.093***
(0.005)
0.093***
(0.005)
New
Economy
0.090***
(0.003)
0.090***
(0.003)
0.064***
(0.003)
0.064***
(0.003)
Constant
0.255***
(0.006)
0.255***
(0.006)
0.224***
(0.006)
0.224***
(0.006)
0.190***
(0.007)
0.190***
(0.007)
chi2(13) =0.25
Prob>chi2=1.00
chi2(14) =0.12
Prob>chi2=1.00
chi2(15)=0.50
Prob>chi2=1.00
* 1.64>t>1.28
** 2.32>t>1.64
*** t>2.32
The numbers in the parentheses are standard deviations.
26
Table 1-4: Random-Effects Models of Firm’s Idiosyncratic Risk with Firm Level
Data
Dependent Variable: Firm’s Idiosyncratic Risk
(1) (2) (3) (4) (5)
# obs.
17647 17647 17647 16250 15488
Female Dummy
-0.002**
(0.001)
-0.002**
(0.001)
-0.002***
(0.001)
-0.002***
(0.001)
-0.002***
(0.001)
Year Dummies
Yes Yes Yes Yes Yes
Log Sale
-0.005***
(0.0003)
-0.004***
(0.0003)
-0.005***
(0.0003)
-0.005***
(0.0003)
-0.004***
(0.0003)
Average of SO/Total
0.008***
(0.001)
0.007***
(0.001)
Cash Flow Shortfall
0.024***
(0.002)
Industry Dummies
New Economy
0.025***
(0.001)
0.023***
(0.001)
0.019***
(0.001)
Agriculture
-0.014
(0.013)
Mining
-0.012
(0.010)
Construction
-0.008
(0.011)
Manufacturing
-0.006
(0.010)
Transportation
-0.010
(0.010)
Whole Sale
-0.006
(0.010)
Retail Sale
-0.008
(0.010)
Finance
-0.017**
(0.010)
Services
0.003
(0.010)
Constant
0.050***
(0.002)
0.042***
(0.002)
0.055***
(0.010)
0.043***
(0.002)
0.036***
(0.002)
* 1.64>t>1.28
** 2.32>t>1.64
*** t>2.32
The numbers in the parentheses are standard deviations.
27
Table 1-5: Fixed-Effects Models of Firm’s Idiosyncratic Risk with Firm Level Data
Dependent Variable: Firm’s Idiosyncratic Risk
(1) (2) (3)
#obs.
17647 16250 15488
Female Dummy
-0.002***
(0.001)
-0.002***
(0.001)
-0.002***
(0.001)
Year dummies
Yes Yes Yes
Log Sale
-0.001***
(0.001)
-0.002***
(0.001)
-0.002***
(0.001)
Average of SO/Total
0.003**
(0.001)
0.003**
(0.001)
Cash Flow Sorthfall
Constant
0.026***
(0.004)
0.030***
(0.004)
0.030***
(0.004)
* 1.64>t>1.28
** 2.32>t>1.64
*** t>2.32
The numbers in the parentheses are standard deviations.
28
Table 1-6: Test of Endogeneity of Female Dummy Variable for Firm’s Idiosyncratic
Risk with Firm Level Data
Dependent Variable: Firm’s Idiosyncratic Risk
Instruments: Female Dummy lagged Value , Female Labor Force Participation Rate by Year and State
Two Stage
Instrumental
OLS
Two Stage
Instrumental
OLS
Two Stage
Instrumental
OLS
# obs.
13827 13827 13827 13827 12792 12792
Female
Dummy
-0.002**
(0.001)
-0.002***
(0.001)
-0.002**
(0.001)
-0.002***
(0.001)
-0.003***
(0.001)
-0.003***
(0.001)
Year
Dummies
Yes Yes Yes Yes Yes Yes
Log Sale
-0.006***
(-0.0002)
-0.006***
(-0.0002)
-0.005***
(-0.0002)
-0.005***
(-0.0002)
-0.004***
(-0.0002)
-0.004***
(-0.0002)
Average of
SO/Total
0.014***
(0.001)
0.015***
(0.001)
Cash Flow
Shortfall
0.030***
(0.001)
0.029***
(0.001)
New Economy
0.022***
(0.001)
0.022***
(0.001)
0.014***
(0.001)
0.014***
(0.001)
Constant
0.049***
(0.002)
0.049***
(0.002)
0.041***
(0.002)
0.041***
(0.002)
0.031***
(0.002)
0.031***
(0.002)
chi2(13) =0.7
Prob>chi2=1.00
chi2(16) =0.43
Prob>chi2=1.00
chi2(15)=0.19
Prob>chi2=1.00
* 1.64>t>1.28
** 2.32>t>1.64
*** t>2.32
The numbers in the parentheses are standard deviations.
29
Table 1-7: Random-Effects Models of Firm’s Leverage with Firm Level Data
Dependent Variable: Total Debt to Total Asset
(1) (2) (3) (4) (5) (6)
# obs.
22710 18322 9645 10563 10379 10379
Female Dummy -0.002**
(0.001)
-0.002*
(0.002)
-0.006**
(0.003)
-0.005*
(0.003)
-0.004*
(0.003)
-0.004*
(0.003)
Year Dummies
Yes Yes Yes Yes Yes Yes
Log Sale -0.001**
(0.0003)
-0.002***
(0.0004)
-0.004***
(0.001)
-0.001
(0.001)
-0.001
(0.001)
-0.0005
(0.001)
Intangibles/Total
Asset
0.016***
(0.004)
0.028***
(0.010)
0.027***
(0.009)
0.024***
(0.009)
0.025***
(0.009)
NDT
0.000003***
(0.000001)
0.000001
(0.000001)
Depreciation to
Total Assets
-0.049**
(0.021)
-0.068***
(0.023)
-0.072***
(0.023)
Capital
Expenditure/Assets
-0.003
(0.025)
R&D/Sales
-0.0002
(0.0001)
-0.0004***
(0.0001)
-0.0004***
(0.0001)
-0.0004***
(0.0001)
Operating
Income to
Total Assets
-0.059***
(0.007)
-0.063***
(0.007)
-0.064***
(0.007)
Book to
Market Ratio
0.00002
(0.00003)
0.00002
(0.00003)
Industry Dummies
New Economy
-0.009**
(0.004)
Agriculture
-0.012
(0.041)
Mining
-0.003
(0.033)
Construction
0.003
(0.043)
Manufacturing
-0.002
(0.028)
Transportation
0.007
(0.032)
Whole Sale
-0.011
(0.030)
Retail Sale
-0.002
(0.029)
Finance
0.001
(0.032)
Services
-0.005
(0.028)
Constant 0.020***
(0.003)
0.027***
(0.004)
0.034***
(0.008)
0.027***
(0.007)
0.030***
(0.007)
0.028
(0.029)
* 1.64>t>1.28
** 2.32>t>1.64
*** t>2.32
The numbers in the parentheses are standard deviations.
30
Table 1-8: Fixed-Effects Models of Firm’s Leverage with Firm Level Data
Dependent Variable: Total Debt to Total Asset
(1) (2) (3) (4) (5) (6)
#obs.
22710 19700 18322 9645 10563 10379
Female Dummy
-0.004**
(0.002)
-0.005**
(0.002)
-0.005**
(0.002)
-0.008**
(0.004)
-0.006**
(0.004)
-0.006**
(0.004)
Year dummies
Yes Yes Yes Yes Yes Yes
Log Sale
-0.004***
(0.001)
-0.005***
(0.001)
-0.006***
(0.001)
-0.010***
(0.002)
-0.005***
(0.002)
-0.005**
(0.002)
Intangibles/Total Asset
0.018***
(0.007)
0.017**
(0.008)
0.039***
(0.013)
0.033**
(0.012)
0.029***
(0.012)
NDT
-0.000001
(0.000001)
-0.000002
(0.000002)
Depreciation/Total Assets
-0.101***
(0.025)
-0.137***
(0.027)
Capital
Expenditure/Assets
-0.010
(0.030)
R&D/Sales
-0.0003**
(0.0001)
-0.0004***
(0.0001)
-0.0004***
(0.0001)
Operating Income/Total
Assets
-0.056***
(0.008)
-0.064***
(0.009)
Book /Market Ratio
0.00001
(0.00003)
Constant
0.044***
(0.008)
0.051***
(0.009)
0.073***
(0.015)
0.055***
(0.013)
0.052***
(0.014)
* 1.64>t>1.28
** 2.32>t>1.64
*** t>2.32
The numbers in the parentheses are standard deviations.
31
Table 1-9: Test of Endogeneity of Female Dummy Variable for Firm’s Leverage
Dependent Variable: Total Debt to Total Asset
Instruments: Female Dummy lagged Value , Female Labor Force Participation Rate by Year and State
Two Stage
instrumental
OLS
Two Stage
instrumental
OLS
Two Stage
instrumental
OLS
# obs. 15495 15495 15495 15495 7397 7397
Female Dummy
-0.001
(0.002)
-0.002
(0.002)
-0.001
(0.002)
-0.002
(0.002)
0.001
(0.004)
-0.001
(0.004)
Year Dummies Yes Yes Yes Yes Yes Yes
Log Sale
-0.0002
(0.0004)
-0.0002
(0.0004)
-0.0004
(0.0004)
-0.0004
(0.0004)
0.001
(0.001)
0.0005
(0.001)
Intangibles/Total
Asset
0.034***
(0.009)
0.034***
(0.009)
Depreciation/Total
Assets
-0.106***
(0.027)
-0.106***
(0.027)
R&D/Sales
-0.001***
(0.0002)
-0.001***
(0.0002)
Operating
Income/Total Assets
-0.118***
(0.008)
-0.118***
(0.008)
Book/Market Ratio
0.00002
(0.00003)
0.00002
(0.00003)
New Economy
-0.006***
(0.002)
-0.006***
(0.002)
-0.008***
(0.003)
-0.008***
(0.003)
Constant
0.017***
(0.003)
0.017***
(0.003)
0.020***
(0.003)
0.020***
(0.003)
0.029***
(0.006)
0.029***
(0.006)
Hausman Test
chi2(13) =0.28
Prob>chi2=1.00
chi2(14) =0.29
Prob>chi2=1.00
chi2(18)=0.51
Prob>chi2=1.00
* 1.64>t>1.28
** 2.32>t>1.64
*** t>2.32
The numbers in the parentheses are standard deviations.
32
Table 1-10: Random-Effects Models of Stock Option Compensation to Total
Compensation with Executive Level Data
Dependent Variable: Stock Option Compensation to Total Compensation
(1) (2) (3) (4) (5) (6)
# obs. 109476 99664 99105 99105 93916 86034
Gender 0.0337***
(0.0059)
0.032***
(0.006)
0.031***
(0.006)
0.032***
(0.006)
0.036***
(0.006)
0.034***
(0.006)
Rank 1 0.1736***
(0.0028)
0.171***
(0.003)
0.168***
(0.003)
0.170***
(0.003)
0.175***
(0.003)
0.165***
(0.003)
Rank 2 0.1056***
(0.0025)
0.104***
(0.003)
0.103***
(0.003)
0.104***
(0.003)
0.108***
(0.003)
0.100***
(0.003)
Rank 3 0.0674***
(0.0024)
0.067***
(0.002)
0.066***
(0.002)
0.067***
(0.002)
0.069***
(0.003)
0.064***
(0.003)
Rank 4 0.0377***
(0.0023)
0.038***
(0.002)
0.038***
(0.002)
0.038***
(0.002)
0.039***
(0.002)
0.035***
(0.003)
Year Dummies Yes Yes Yes Yes Yes Yes
Log Sale 0.0023***
(0.0007)
0.002***
(0.001)
0.007***
(0.001)
0.008***
(0.001)
0.011***
(0.001)
0.012***
(0.001)
Share Ownership
-0.00000001***
(0.000000002)
Stock Price
Growth
0.000003
(0.00002)
3 year SH return
0.0004***
(0.00002)
0.0004***
(0.00002)
0.0004***
(0.00002)
0.0005***
(0.00003)
Book/Market
Ratio
-0.0001***
(0.00002)
-0.0001***
(0.00002)
-0.0002***
(0.00002)
-0.0001***
(0.00002)
Cash Flow
Shortfall
0.106***
(0.005)
0.117***
(0.005)
Industry
Dummies
New Economy
0.188***
(0.004)
0.168***
(0.004)
0.171***
(0.004)
Agriculture
0.024
(0.030)
Mining
0.085***
(0.022)
Construction
-0.026
(0.024)
Manufacturing
0.089***
(0.021)
Transportation
-0.020
(0.021)
Whole Sale
0.032*
(0.022)
Retail Sale
0.069***
(0.021)
Finance
0.015
(0.021)
Services
0.166***
(0.021)
Constant 0.128***
(0.006)
0.123***
(0.006)
0.064***
(0.006)
0.014
(0.022)
0.073***
(0.006)
0.046***
(0.007)
33
Table 1-11: Random-Effects Models of Stock Option Compensation to
Total Compensation for Sub Sample where variable AGE is
available with Executive Level Data
Dependent Variable: Stock Option Compensation to Total Compensation
(1) (2) (3) (4) (5)
# obs.
40764 40323 35714 33248 37984
Gender
0.019**
(0.011)
0.019**
(0.011)
0.019**
(0.011)
0.019**
(0.012)
0.015*
(0.011)
Rank 1
0.183***
(0.005)
0.185***
(0.005)
0.184***
(0.005)
0.185***
(0.005)
0.181***
(0.005)
Rank 2
0.107***
(0.005)
0.109***
(0.005)
0.108***
(0.005)
0.111***
(0.005)
0.107***
(0.005)
Rank 3
0.065***
(0.005)
0.067***
(0.005)
0.067***
(0.005)
0.067***
(0.005)
0.065***
(0.005)
Rank 4
0.039***
(0.005)
0.040***
(0.005)
0.037***
(0.005)
0.038***
(0.005)
0.040***
(0.005)
Year Dummies
Yes Yes Yes Yes Yes
Age
-0.006***
(0.0003)
-0.006***
(0.0003)
-0.006***
(0.0003)
-0.006***
(0.0003)
-0.006***
(0.0003)
Log Sale
0.016***
(0.001)
0.015***
(0.001)
0.017***
(0.001)
0.016***
(0.001)
0.015***
(0.001)
Book to Market Ratio
-0.0002***
(0.00004)
-0.0002***
(0.00004)
-0.0002***
(0.00004)
-0.0002***
(0.00004)
Cash Flow Shortfall
0.082***
(0.009)
0.089***
(0.009)
Share Ownership
-0.000000004***
(0.000000002)
-0.000000005***
(0.000000002)
3 year SH return
0.0003***
(0.00004)
Stock Growth
0.000007***
(0.00003)
New Economy
0.159***
(0.007)
0.158***
(0.007)
0.146***
(0.007)
0.152***
(0.007)
0.166***
(0.007)
Constant
0.286***
(0.015)
0.300***
(0.015)
0.321***
(0.016)
0.339***
(0.016)
0.291***
(0.015)
* 1.64>t>1.28
** 2.32>t>1.64
*** t>2.32
The numbers in the parentheses are standard deviations.
34
Table 1-12: Fixed-Effects Models of Stock Option Compensation to Total
Compensation with Executive Level Data
Dependent Variable: Stock Option Compensation to Total Compensation
(1) (2) (3) (4) (5) (6)
# obs.
109476 99664 99105 98910 93916 86034
Gender
0.018***
(0.004)
0.016***
(0.004)
0.015***
(0.004)
0.019***
(0.004)
0.019***
(0.004)
0.018***
(0.004)
Rank 1
0.126***
(0.002)
0.124***
(0.002)
0.124***
(0.002)
0.130***
(0.002)
0.130***
(0.002)
0.122***
(0.122)
Rank 2
0.086***
(0.002)
0.084***
(0.002)
0.084***
(0.002)
0.089***
(0.002)
0.089***
(0.002)
0.082***
(0.082)
Rank 3
0.060***
(0.002)
0.059***
(0.002)
0.059***
(0.002)
0.062***
(0.002)
0.062***
(0.002)
0.058***
(0.058)
Rank 4
0.037***
(0.002)
0.037***
(0.002)
0.037***
(0.002)
0.038***
(0.002)
0.038***
(0.002)
0.035***
(0.035)
Year Dummies
Yes Yes Yes Yes Yes Yes
Log Sale
0.047***
(0.002)
0.036***
(0.002)
0.035***
(0.002)
0.036***
(0.002)
0.038***
(0.002)
Share Ownership
-0.00000001***
(-0.00000001)
Stock Price Growth
-0.00001
(0.00002)
-0.00001
(0.00002)
3 year SH return
0.0003***
(0.00002)
0.0003***
(0.00002)
0.0003***
(0.0003)
Book/Market Ratio
-0.0001***
(0.00002)
-0.0001***
(0.00002)
-0.0001***
(0.00002)
-0.0001***
(-0.0001)
Cash Flow Shortfall
0.036***
(0.007)
0.041***
(0.041)
Constant
-0.155***
(0.010)
-0.099***
(0.012)
-0.096***
(0.012)
-0.066***
(0.012)
-0.079***
(0.013)
-0.084***
(-0.084)
Hausman Test
Results
0.129 0.128 0.128 0.144 0.128
* 1.64>t>1.28
** 2.32>t>1.64
*** t>2.32
The numbers in the parentheses are standard deviations.
35
Table 1-13: Fixed-Effects Models of Stock Option Compensation to
Total Compensation for Sub Sample where variable AGE is
available with Executive Level Data
Dependent Variable: Stock Option Compensation to Total Compensation
(1) (2) (3) (4) (5)
# obs.
40764 40323 35714 33248 37984
Gender 0.009*
(0.007)
0.010*
(0.007)
0.009*
(0.007)
0.009*
(0.007)
0.009*
(0.007)
Rank 1 0.149***
(0.004)
0.150***
(0.004)
0.149***
(0.004)
0.151***
(0.004)
0.146***
(0.004)
Rank 2 0.092***
(0.004)
0.093***
(0.004)
0.093***
(0.004)
0.096***
(0.004)
0.091***
(0.004)
Rank 3 0.062***
(0.004)
0.063***
(0.004)
0.063***
(0.004)
0.063***
(0.004)
0.061***
(0.004)
Rank 4 0.039***
(0.004)
0.039***
(0.004)
0.038***
(0.004)
0.038***
(0.005)
0.040***
(0.004)
Year Dummies
Yes Yes Yes Yes Yes
Age -0.004***
(0.0002)
-0.004***
(0.0002)
-0.004***
(0.0002)
-0.004***
(0.0002)
-0.004***
(0.0002)
Log Sale 0.042***
(0.003)
0.038***
(0.003)
0.035***
(0.003)
0.032***
(0.004)
0.035***
(0.003)
Book to Market Ratio
-0.0002***
(0.00004)
-0.0001***
(0.00004)
-0.0001***
(0.00004)
-0.0001***
(0.00004)
Cash Flow Shortfall
0.025***
(0.011)
0.025**
(0.012)
Share Ownership
-0.000000005***
(0.000000001)
-0.000000004***
(0.000000001)
3 year SH return
0.0002***
(0.00004)
Stock Growth
0.000001
(0.00003)
New Economy
Constant 0.048***
(0.021)
0.078***
(0.021)
0.123***
(0.024)
0.157***
(0.026)
0.089***
(0.023)
* 1.64>t>1.28
** 2.32>t>1.64
*** t>2.32
The numbers in the parentheses are standard deviations.
36
1-5-3- Options Exercising Behavior
Table 14 and 15 reports the results for the options exercising behavior. The results for
both fixed-effect and random-effect models suggest that female top executives exercise
their options more frequently. Huasman test suggests that random-effect models are both
consistent and efficient. Similar to the previous model results, the age factor slightly
weakens the aforementioned relationship. This is attributed to the differences in total
wealth across ages. Older people are comparatively wealthier, thus less liquidity
constrained and therefore, they exercise their options less frequently. Moreover, in the
sample women are on average younger than men. These two factors explain the
weakening of the relationship between gender and option exercising behavior with age.
However, the coefficient of female dummy in this model is still significantly negative
where this suggests that (i) men in the sample are wealthier than women even when age is
controlled for and/or (ii) women in the sample are more risk averse and exercise their
options more frequently.
1-5-4- Stock Ownership
Table 16 and 17 report the results for the random-effect and fixed-effect models on
voluntary stock ownership. Huasman test suggests that random-effect models are not
consistent, and fixed-effect models should be used. According to the results, gender
differences do not play any role in explaining the value of executives’ stock holding.
37
Table 1-14: Random-Effects Models of Ratio of Value of Exercisable In-The-Money
Unexercised Options to the value of Total Options held at the year end with
Executive Level Data
Dependent Variable: Ratio of Value of Exercisable In-The-Money Unexercised options to the value of
total Options held at year end
(1) (2) (3) (4) (5) (6)
# obs. 88098 81229 76709 33934 29322 33706
Gender
-0.056***
(0.009)
-0.050***
(0.009)
-0.049***
(0.009)
-0.029***
(0.016)
-0.034**
(0.017)
-0.030**
(0.016)
Rank 1
-0.012***
(0.004)
0.018***
(0.004)
0.014***
(0.004)
-0.038***
(0.007)
-0.031***
(0.007)
-0.010*
(0.007)
Rank 2
-0.011***
(0.004)
0.006**
(0.004)
0.003
(0.004)
-0.018***
(0.006)
-0.014***
(0.007)
-0.002
()
Rank 3
-0.010***
(0.003)
0.001
(0.003)
-0.001
(0.004)
-0.016***
(0.006)
-0.015**
(0.007)
-0.005
(0.006)
Rank 4
-0.005*
(0.003)
0.002
(0.003)
0.001
(0.003)
-0.007
(0.006)
-0.004
(0.007)
-0.001
(0.006)
Year Dummies
Yes Yes Yes Yes Yes Yes
Log Sale
0.021***
(0.001)
0.015***
(0.001)
0.010***
(0.001)
0.005***
(0.002)
0.012***
(0.002)
Total
Compensation
-0.0000004***
(0.0000002)
BS volatility
-0.125***
(0.008)
-0.121***
(0.011)
Stock Price
Growth
-0.0001***
(0.00003)
-0.00001
(0.00003)
-0.00003
(0.00004)
Stock Option
Compensation
to Total
-0.136***
(0.004)
-0.118***
(0.005)
-0.128***
(0.007)
Age
0.011***
(0.0004)
0.009***
(0.0004)
0.009***
(0.0004)
New Economy
-0.070***
(0.005)
-0.044***
(0.006)
-0.029***
(0.010)
-0.034***
(0.010)
Constant
0.285***
(0.009)
0.403***
(0.009)
0.481***
(0.010)
-0.058***
(0.020)
0.066***
(0.025)
-0.031***
(0.022)
* 1.64>t>1.28
** 2.32>t>1.64
*** t>2.32
The numbers in the parentheses are standard deviations.
38
Table 1-15: Fixed-Effects Models of Ratio of Value of Exercisable In-The-Money
Unexercised Options to the value of Total Options held at the year end with
Executive Level Data
Dependent Variable: Ratio of Value of Exercisable In-The-Money Unexercised options to the value of total
Options held at year end
(1) (2) (3) (4) (5) (6)
# obs. 88098 81229 76709 33934 29322 33706
Gender
-0.027***
(0.005)
-0.025***
(0.005)
-0.026***
(0.006)
-0.026***
(0.010)
-0.029***
(0.011)
-0.026***
(0.010)
Rank 1
0.046***
(0.003)
0.071***
(0.003)
0.069***
(0.003)
0.015***
(0.006)
0.020***
(0.006)
0.042***
(0.006)
Rank 2
0.010***
(0.003)
0.026***
(0.003)
0.023***
(0.003)
0.011***
(0.006)
0.012***
(0.006)
0.027***
(0.006)
Rank 3
-0.001
(0.003)
0.009***
(0.003)
0.008***
(0.003)
0.001***
(0.006)
0.003***
(0.006)
0.012***
(0.006)
Rank 4
-0.002
(0.003)
0.005*
(0.003)
0.005*
(0.003)
0.003***
(0.006)
0.005***
(0.006)
0.008***
(0.006)
Year Dummies
Yes Yes Yes Yes Yes Yes
Log Sale
0.053***
(0.003)
0.058***
(0.003)
0.058***
(0.003)
0.057***
(0.005)
0.061***
(0.004)
Total Compensation
0.0000002
(0.0000002)
BS volatility
-0.112***
(0.010)
-0.136***
(0.015)
Stock Price Growth
-0.0001**
(0.00003)
0.00002
(0.00003)
0.00001***
(0.00004)
Stock Option
Compensation to
Total
-0.174***
(0.005)
-0.165***
(0.005)
-0.160***
(0.007)
Age
0.006***
(0.0003)
0.006***
(0.0003)
0.005***
(0.0003)
Constant
0.157***
(0.017)
0.193***
(0.018)
0.234***
(0.020)
0.182***
(0.015)
-0.104***
(0.037)
-0.155***
(0.032)
Hausnan Test Results
0.210 0.156 0.132 0.0018 0.004 0.003
* 1.64>t>1.28
** 2.32>t>1.64
*** t>2.32
The numbers in the parentheses are standard deviations.
39
Table 1-16: Random-Effects Models of Value of Stock Holding
with Executive Level Data
Dependent Variable: Value of Stock Holding (Excl. Restricted)
(1) (3) (4) (5) (6) (7)
# obs.
96195 96161 96161 88998 95492 33750
Gender
-14503*
(9890)
-14187*
(9914)
-12160
(9890)
-12330
(10517)
-11995
(9958)
-30742
(35009)
Rank 1
9023*
(5941)
15246***
(5999)
16430***
(6001)
19260***
(6511)
16559***
(6049)
1228**
(16636)
Rank 2
10844**
(5672)
15043***
(5705)
15720***
(5705)
17390***
(6239)
16017***
(5757)
27130
(16426)
Rank 3
-306
(5585)
2556
(5604)
2966
(5604)
2703
(6144)
3080
(5657)
15595.6
(16639)
Rank 4
-480
(5561)
1224
(5572)
1513
(5572)
2196
(6115)
1710
(5627)
3877
(16525)
Year Dummies
Yes Yes Yes Yes Yes Yes
Log Sale
11813***
(1217)
13468***
(1262)
13474***
(1238)
15501***
(1341)
13633***
(1246)
31317***
(3944)
Total Compensation
3.4***
(0.3)
3.6***
(0.3)
3.6***
(0.3)
3.6***
(0.3)
3.6***
(0.3)
5.2***
(0.6)
BS volatility
Share of Stock Option
in Compensation
-49703***
(6896)
-55729***
(6945)
-63444***
(7649)
-56808***
(7021)
-88489***
(17951)
Stock Price Growth
-4.3
(59.3)
-4.9
(102)
Book to Market Ratio
-41.1
(50.7)
-75.5
(144)
Age
345
(314)
Industry Dummies
New Economy
51368***
(5840)
58166***
(6283)
51640***
(5875)
131458***
(20738)
Agriculture
15904
(50300)
Mining
8668
(36215)
Construction
-5745
(39520)
Manufacturing
8991
(34897)
Transportation
-426
(35227)
Whole Sale
-6973
(36452)
Retail Sale
11499
(35408)
Finance
22497
(35151)
Services
55936*
(35193)
Constant
-82368***
(12150)
-99811***
(36943)
-90819***
(12385)
-102136***
(12457)
-91643***
(12473)
-263067***
(37706)
40
Table 1-17: Fixed-Effects Models of Value of Stock Holding
with Executive Level Data
Dependent Variable: Value of Stock Holding (Excl. Restricted)
(1) (2) (3) (4) (5) (6)
# obs.
96195 88125 96161 88998 95492 33750
Gender
-10715
(10087)
-9981*
(6314)
-9022
(10087)
-9002
(10730)
-8998
(10161)
-16447
(31571)
Rank 1
14590***
(5930)
22063***
(3731)
22751***
(6014)
23744***
(6436)
22522***
(6054)
34594**
(16853)
Rank 2
6158
(5852)
2436
(3642)
11977**
(5896)
12295**
(6304)
12105**
(5935)
44311***
(17197)
Rank 3
-11722**
(5854)
-8262***
(3625)
-7626*
(5877)
-7789
(6274)
-7601*
(5916)
-13302
(17854)
Rank 4
-384
(5911)
-2410
(3645)
2134
(5920)
2803
(6302)
2286
(5960)
11725
(18194)
Year Dummies
Yes Yes Yes Yes Yes Yes
Log Sale
24216***
(4530)
15940***
(2945)
26608***
(4543)
30569***
(5155)
26655***
(4597)
72893***
(13548)
Total
Compensation
2.3***
(0.3)
3.6***
(0.2)
2.6***
(0.3)
2.6***
(0.3)
2.7***
(0.3)
4.6***
(0.7)
BS volatility
-11870
(9805)
Share of Stock
Option in
Compensation
-69914***
(5675)
-71624***
(8795)
-73235***
(9554)
-71897***
(8875)
-100001***
(22608)
Stock Price
Growth
2.1
(63)
-2.4
(118)
Book to Market
Ratio
-16.4
(58.0)
-23
(174)
Age
399*
(305)
Constant
-145642***
(30413)
-77223***
(20676)
-149385***
(30440)
-182254***
(34402)
-149718***
(30766)
-502451***
(94532)
Hausman Test
Results
72837 2322 49985 51998 44246 59437
* 1.64>t>1.28
** 2.32>t>1.64
*** t>2.32
The numbers in the parentheses are standard deviations.
41
1-6- Conclusion
Empirical analysis of firm’s volatility conclude that firms with at least one female
among their five highest-paid executives have lower risk levels than firms without any
female executive among the top five. Endogeneity tests on the existence of female
executives suggest that female executives negatively affect a firm’s riskiness. This result
supports the hypothesis that female executives are more risk averse than their male
counterparts.
Women in executive positions are prone to receive more of their compensation in the
form of stock option compensation. Although this effect considerably decreases when age
is included in the models, it does not disappear. This suggests that employers have a
tendency to make female’s compensation more option based. This might encourage
female executives’ risk taking behavior. Another explanation would be that female
executives may have more difficulty in proving their qualifications, so they signal their
qualifications by accepting more stock option as their compensation.
The estimation results imply that the female executives exercise their stock option
holdings earlier than their male counterparts. Again this suggests that the female
executives are more risk averse than their male counterparts. Finally, gender effect is
found to be insignificant when the executives’ value of share ownership is analyzed.
Chapter 2
Empirical Analysis of the Association between Screening Executives
and Stock-Option Compensation
2-1- Introduction
All firms aim to hire the most qualified employees while some degree of information
asymmetry about the abilities of the employee is commonly unavoidable. The employers
compare several observable indicators of quality such as education, work experience and
achievements to assess the true value of the employee. When it comes to top-level
executives, distinguishing high-quality executives from the low-quality ones is vital for
the firms as the impact of top executive on the firm’s performance is much higher than a
regular employee. On the other hand, predicting the executive performance upon his/her
observable characteristics is a difficult task since the past qualifications does not
necessarily correlate with the future performance. One potential solution to this problem
is to use stock-options as part of executive’s compensation package. Previous studies
have shown that stock-option payments can act as a screening tool to pass over the low-
quality executives
2,4,8
.
Granting stock-options as compensation has been widely used in the past decade and
has turn into an important part of the compensation packages. There are a large number
of empirical and theoretical studies that investigated the motives for extensive usage of
stock-options as a part of a compensation package and its consequences. The reason for
this vast interest seems to be the augmented trend of stock-option grants in compensation.
As an example, the average value of the stock-option grants in S&P 500 firms
showed a continuous increase from 1992 to 2000 where it reached its peak (Hall and
42
Murphy, 2003). Although this trend became decreasing after 2000 as reported by Hall
and Murphy in 2003, the averaged value of the stock-option grants to all employees in
2002 increased seven-fold with respect to 1992.
The present study empirically investigates the association between stock-option
compensation and screening problem at top level executives. By exploring the data
related to top executives of U.S. firms, this study tries to find out if firms use stock-
option compensation as a screening mechanism for their top executives. Previous
theoretical studies suggest that stock-option compensation can be used to mitigate
screening problems in recruiting top executives no study has empirically addressed this
question thoroughly.
The results of this study shows that top level executives during their early years of
serving get higher portion of their compensation in stock-option compared to later years.
This is when we control for other relevant factors and support the hypothesis that one
reason for stock-option compensation can be to screen top level executives.
2-1-1- Previous Literature
There are a number of theoretical and empirical studies on the relationship between
incentives and stock-option grants
1, 3, 8, 7, 21, 32, 35, 37, 39, 41, 42
. However, there are only very
few empirical studies tested if stock-option payments are used to screen low quality
executives. Among those
1,41
none has performed a complete quantitative analysis to relate
firm’s screening problem to stock-options payment; St-Onge et al, (2001) interviewed 18
high level executives, and suggested that stock-option plans are used to align
management incentives, attract and retain key personals and facilitate high-level
payments to executives. Anderson et al, (2000) used “industry turnover” and executive’s
43
age as a proxy for executives’ demands, i.e. a measure of the intensity of screening
problem, only in “Information Technology” industry. They defined “industry turnover
(IndTO
i
)” in industry i as:
∑
=
=
T
t
i
i
i
NF
NTFE
IndTO
1
where NTFE is the number of employees in top five positions of firms in the industry
i and NF is the number of firms in industry i. The dependent variable in their model is
“stock-option share” in the compensation package. Their result suggests that “industry
turnover” does not significantly affect stock-option share. However executive’s age is
found to be is inversely related to share of stock-option in compensation.
On the other hand, there are several theoretical studies suggesting that stock-option
payments in compensation packages can be used to attract high quality executives.
Among these studies, Balmaceda (2004) introduced a sorting theory to justify the use of
pay-for-performance. He showed that in fairly stable environments, firms offer both
straight salary jobs and pay-for-performance contracts to their employees; while high-
ability workers will self-select into pay-for-performance contracts and low-ability ones
will choose straight salary jobs. Alternatively in riskier environments, pay-for-
performance contract is the only option to survive in equilibrium for firms. His model
predicts low pay-for-performance sensitivity and positive relationship between pay-for-
performance and uncertainty.
Arya and Mittendort (2005) suggest that the stock-option payment is a mean to ensure
a manager’s pay is in accordance with his worth. Their model show how options can tie
44
manager’s talent to his payment and defer him from overstating his ability such that only
a gifted manager accept the offer to work for the firm.
Cadenillas, Cvitanic and Zapatero (2005) studied the problem of screening, in case of
a risk-neutral principal who wants to hire a risk-averse executive of unknown type in a
dynamic setting. In their model low-quality executive may have incentive to pretend
being a high-quality. They show that a possible role for options is to discourage low-type
executives -even if low-type executives are less expensive- to apply for the job.
Prendergast (1999) showed that when a firm shifts from a fixed-payment scheme to
piece-rates scheme, the workers would have incentive to work better, and the average
quality of the workers improves by the fact that quality workers taking the place of weak
ones.
Other possible reasons for use of stock-options compensation are (A) solving moral-
hazard problem; (B) tax and accounting considerations and (C) liquidity constraint.
Jensen and Murphy (1990) examined the relationship between the incentive based
payments for CEOs and firm’s performance. They considered bonus pay, stockholding,
options and probability of dismissals as incentive based payments. On that basis, they
found that pay-to-performance sensitivity is low. They estimated that the ratio of CEOs’
wealth to the shareholders’ wealth is very low; i.e. 0.35%. Therefore, low pay-to-
performance sensitivity does not solve the moral hazard problem and might be due to
public disapproval for high rewards to top executives. On the other hand, Hubrich (1994)
concludes that even low profit-shares provide enough incentive and increase the value of
firm considering the assumptions about risk aversion, CEO effort. In a more recent study,
Murphy (1999) showed that pay-to-performance sensitivity has increased mostly due to
45
increase in the use of stock-options in compensation however there still exists a large gap
between managers and shareholders’ interests.
Tax and accounting issues are other reasons behind extensive use of stock-options in
compensation packages
1, 15, 42
. Hall and Murphy (2003) studied the extensive use of
stock-options for low level employees. They suggest that too many options are granted
because the perceived cost of options is substantially lower than their economic cost due
to accounting and cash flow considerations. They conclude that changes in corporate
governance and tax laws, managerial influence over their own pay package and the bull
market in equities of 1990s have influenced the increasing use of options.
Financial constraints and the fact that stock-option grants can be substituted for cash
payments is considered another reason for implementing stock-option plans
9, 31, 39, 42
.
Core and Guay (2000) empirically examined the determinant of the broad-based stock-
option grants. They found that firms use more stock-option compensation when financial
constraints exist and more capital is required. As a confirmation Pasternak (2002)
examines the factors driving stock-options grants and find support for explanatory power
of several agency-theory-based variables as well as liquidity constraints.
Some researchers have claimed that managers can influence their payments and it is
easier for them to increase their stock-option part of compensation packages which was
rewarding especially in the bull market of 90s
4, 31
. For example Benz, Kucher and Stutzer
(2001) show that stock-option grants are substantially lower when control by board of
directors and shareholders is higher and competition on the product market of the firm is
stronger.
46
2-2- Research Design
Screening is more important when there is information asymmetry between the board
of directors and executives. In other words if the principal is confident and
knowledgeable about the abilities of the agents there is no need to use additional
instruments to differentiate between high quality and bad quality agents. As the
knowledge of the principal about the qualities of the agent decreases, the gain from a
screening mechanism increases. As the period that an employee is working in a specific
position for a firm increases more information will be revealed about his/her type to the
employer and therefore the benefit of screening him/her through a mechanism decreases.
Therefore if stock-option is used to screen low quality executives, more stock-option
payments is expected for newly hired executives taken all other relevant variables as
given. In this research I try to relate stock-options payment to the period of time that the
executive holds his position.
I assume that the vesting period of stock-option grants is 4 years. Therefore executive
at time t holds stock-options granted to him during the previous four years. To test this
relationship two indices are constructed. First for each executive the time he/she serves is
divided to four year periods and the average of stock-options compensation to total
compensation for each of these periods is calculated. This number is used as dependent
variable and is called SOtoTotal4Ave in the models. Key explanatory variable is the index
for four year periods. We would expect that the coefficient for this variable to be negative
and significant.
The second index is constructed as follow:
1.
j1
1 1
/Total
j j
SO SOtoTotal =
47
2. ) Total /(Total ) (
j2 j1 2 1 2
+ + =
j j j
SO SO SOtoTotal
3. ) Total Total /(Total ) (
j3 j2 j1 3 2 1 3
+ + + + =
j j j j
SO SO SO SOtoTotal
4.
3 ) Total
Total /(Total ) SO (
1) - j(t
2) - j(t 3) - j(t jt ) 1 ( ) 2 ( ) 3 (
> + +
+ + + + =
− − −
t if Total
SO SO SO SOtoTotal
jt
t j t j t j jt
Where j is the executive index and t is the number of years that executive j has hold
the position until that fiscal year. is the value of total compensation for executive j
at year t that he holds the position. We assume that vesting period for stock-option grants
is four years. is the dependent variable. For each executive each year that
he/she serves is numbered 1 through T (let T be the last year he/she serves). This is called
the year index variable and it is the key explanatory variable in this model. Based on
screening theory, taken into account all other relevant variables, a negative relationship
between the dependent variable and the year index variable is expected. In other words as
the number of years that the executive holds a position increases fewer stock-option is
expected in his/her compensation given all other relevant factors.
jt
Total
jt
SOtoTotal
3. Data
Two sources of data are being exploited, namely, the Compustat North America
dataset, and Who’s Who online database.
The Compustat North America dataset contains panel data on firms’ executive
compensation and firms’ financial information from 1992 to 2005. The executive
compensation database contains over 2500 companies, both active and inactive. The
universe of firms covers the S&P 1500, plus companies that were once part of the 1500,
plus companies removed from the index that are still trading, and some client requests.
Data collection on the S&P 1500 began in 1994. However, there is data back to 1992 but
48
it does not include the entire S&P 1500. It is mostly for the S&P 500. All of the firms’
executives are ranked based on their total compensation in a year. Accordingly, for each
year, data on the five highest-paid executives of each firm have been collected. The
Who’s Who online database is used to collect data on executives’ ages.
1
A total of 29,567 executives’ data were analyzed. For 2,754 firms, the dataset
consisted of 124,581 executive-years serving throughout the period the dataset covers. In
the aforementioned numbers, an executive is counted once for every year served.
Average number of years that an executive holds his position among the top five
executives is 4.21. Average stock-option grant to all employees for S&P 1500 firms
between 1992 and 2005 is shown in Figure 2-1. Option grant gets its peak value in 2000
and then it has a decreasing trend. Figure 2-2 shows the average percentage of stock-
option compensation to total compensation for top five highest paid executives from 1992
to 2005. Again the trend is increasing until 2001 and then it is decreasing.
Figure 2-1: Grant-Date Value of Option Grant to all Employees
1
Compustat North America dataset does not provide a complete set of executive age information.
49
Figure 2-2: Average Stock Option Compensation to Total Employees
4. Econometric Analysis
In order to test weather stock-option compensation is used for screening top
executives, I used Compustat Execucomp. The models are characterized as:
it i it it it
x YearIdex Ave SOtoTotal ε ν β γ + + + = 4 (1)
it i it it it
x YearIdex SOtoTotal ε ν β γ + + + = (2)
where is the index explained in section 2 and is a the number
of years that executive holds executive position in a firm. The other explanatory variables
are the followings:
it
SOtoTotal
it
YearIdex
Rank of Executive: Executives are ranked based on their total compensation.
Moreover, with respect to their effects on the firm’s value, high ranking executives hold
more stock-options than low ranking executives. I assume the executives who earn more
compensation have more influence on the value of the firm and therefore are paid more.
50
Year Dummy Variables: Year dummies are used to capture the changes in time trend
in the prevalence of stock-option payments to the employee.
Firms Size: As the firm size increases, monitoring employees becomes more
difficult
23
. Therefore more stock-option based payments are expected in larger firms
12
.
Logarithm of the sale is chosen as an index of the firm’s size.
Industry Dummy Variables: Industries are expected to have different tendencies in
stock-option payment offers. For instance, in industries where monitoring is higher,
stock-option payments are used more frequently.
Stock Ownership: Stock ownership by executives would decrease the agency problem
as it ties the executive’s wealth to the firm’s market performance. Thus, fewer stock-
options are expected as stock ownership increases.
Age: According to incentive theory, stock-option payments may differ based on top
executives’ ages. However, the relationship between these variables is ambiguous. Below
I discuss how age is related to stock-option payments. For example, Gibbons and Murphy
(1992) argue that executives who are close to retirement would have fewer career
concerns, which in turn may cause an incentive problem. In this respect, more stock-
option payments should be expected in compensation packages for executives who are
close to retirement
37
. Additionally, executives who are nearing retirement would have
less incentive for long-term investments in a firm. This may create a moral hazard
problem. Stock-option payments with long-term vesting periods would overcome this
moral hazard problem.
Lazear (1995) states that, as opposed to executives who are older, less information is
available about young executives. Therefore the problem of screening low type
51
executives is more acute when executives are younger. In this respect, more stock-option
payments can be expected for younger employees to overcome screening problem.
Risk aversion is expected to vary across ages. Given that older people are more risk-
averse
14
, it also has been suggested that fewer stock-option payments should be made to
older executives. On the other hand, if on average older executives are wealthier; they
would have a lower absolute risk aversion rate. Thus, they may be given more stock-
option payments. In this context, if we do not control for wealth, then age may be
positively related to stock-option payments.
Firm’s Volatility: Risky projects increase the cost of debt. As leverage increases, the
stock-option portion of payment is expected to decrease to discourage managers from
taking risky projects. Conversely Jensen (1986) argues that debt serves as a control
mechanism which reduces agency costs.
Cash Flow Shortfall: When companies are cash constrained they will substitute cash
with stock-options.
Growth Opportunities: As growth opportunities for a firm increases, it becomes more
difficult for the board of directors to evaluate the performance of the firm’s manager,
who has private information about the value of different projects and investments. So as
growth opportunities increase, more stock-based compensation should be realized.
Lazear (1995) uses a model to answer the questions regarding the demand for risky
workers. He shows that risky workers are preferred to the safe ones due to their potential
upside value since the firm can retain the high quality worker and fire the low quality
worker. He concludes that new firms in growing industries prefer younger riskier workers
more than firms in declining industries. Although hiring risky workers increase the option
52
value, information is always valuable and firms are willing to pay to get more
information to eliminate the cost of hiring low quality workers. Form this we anticipate
that new firms in growing industries would like to hire riskier worker and will choose
their workers form a more volatile distribution compared to the old firms in declining
industries. Therefore these firms are in need of more information to truncate the low type
workers from the distribution of job candidates. Firms in growing industries use more
stock-options as a screening mechanism for high level employees and to discourage low
type workers to apply compared to old firms in declining industries. Book-to-Market ratio
is used as a proxy for growth opportunities
37
. R&D expenses scaled by assets have also
been mentioned in the literature as indicators of the growth.
Stock Price Growth: If stock prices are believed to be rising then managers will be
more willing to accept stock-options as their payment. Stock price growth can be used as
a proxy for manager’s expectations of the future stock price trend.
New Economy Firms: There is less information on the new jobs or industries work
force and so screening is more difficult in new economy firms. As the job grew old or
become common there develop more signals to show the quality of each worker (which
university graduates are good, which race is more compatible with the job or industry
etc.). In these industries signaling through the conventional method may turns out to be
inefficient as well. Stock-options of new firms are more risky than the stock-options from
old established firms. Therefore we may expect less stock-option payment when the firm
is new since managers will value stock-option less since they are risk averse and there is
a trade off between incentive and insurance. However based on screening theory there
53
should be more stock-option paid when the industry is new or the firm is new since there
are less information about the types of workers.
The same relationship is suggested by the theory of incentive since in these firms less
is known about different project and their prospect by the board of directors and the
asymmetry of information between board of directors and executives can be large.
5. Results
The results of the regressions in both fixed- and random-effect models for two
dependent variables introduced in section 2 are provided in Table 1-4. Accordingly, the
year-index was found to negatively affect the dependent variables for all models. In other
words, as the number of years in the position increases for an executive, the ratio of
his/her average share of stock-option grants to total compensation decreases. Huasman
test suggests that random-effect models are not consistent and results obtained from
fixed-effect models should be considered.
Our empirical analysis suggests a significant inverse relationship between book-to-
market ratio and the dependent variables. As mentioned earlier in section 4, the book-to-
market ratio can be used to control growth firms while it has an inverse relationship with
the growth opportunities in a firm. Screening problem is more intense in firms with high
growth opportunities. Therefore, the present empirical result corroborates the screening
theory.
Age is also shown to be inversely related to the ratio of stock-option payment to total
compensation, as the screening theory predicts. More information is available on the
54
abilities of older people rather than the younger ones, and therefore less effort is needed
to screen them through their payment mechanism.
There is strong direct relationship between new economy dummy variable and the
stock-option payment. This was also predicted by screening theory since there is less
information available about the people working in new economy firms compared to other
firms.
Explanatory variables irrelevant to screen theory
Rank of Executive: As predicted, higher rank executives have higher portion of their
compensation in stock-options.
Firms Size: Logarithm of sale has a positive significant effect on the share of stock-
options in compensation as predicted.
Stock Ownership: This variable is found to be insignificant.
Cash Flow Shortfall: The coefficient of cash-flow shortfall is significant and positive,
supporting the theory that liquidity constraint would positively affect stock-option
payments.
Stock Price Growth: The effect of this variable on stock-option payment is
ambiguous. This variable is found to be significant in random-effect models while it is
found to be insignificant in the fixed-effect models. Since random-effect models are not
consistent based on Hausman test it can be concluded that stock price growth does not
have a significant effect on stock-option compensation.
55
Table 2-1: Random-Effects Models of 4 Year Accumulated Stock Option
Compensation to Total Compensation with Executive Level Data
Dependent Variable: 4 Year Accumulated Stock-Options Compensation to Total Compensation
(1) (2) (3) (4) (5) (6)
# obs.
106750 98692 98692 94928 77453 31969
Year Index -0.002***
(0.0004)
-0.002***
(0.0004)
-0.002***
(0.0004)
-0.002***
(0.0004)
-0.004***
(0.0004)
-0.002***
(0.001)
Rank 1 0.081***
(0.002)
0.080***
(0.002)
0.081***
(0.002)
0.083***
(0.002)
0.085***
(0.002)
0.083***
(0.004)
Rank 2 0.049***
(0.002)
0.049***
(0.002)
0.049***
(0.002)
0.051***
(0.002)
0.052***
(0.002)
0.053***
(0.003)
Rank 3 0.033***
(0.002)
0.033***
(0.002)
0.033***
(0.002)
0.034***
(0.002)
0.033***
(0.002)
0.035***
(0.003)
Rank 4 0.019***
(0.002)
0.019***
(0.001)
0.019***
(0.001)
0.019***
(0.001)
0.019***
(0.002)
0.021***
(0.003)
Year Dummies
Yes Yes Yes Yes Yes Yes
Log Sale 0.010***
(0.001)
0.009***
(0.001)
0.010***
(0.001)
0.006***
(0.001)
0.006***
(0.001)
0.010***
(0.001)
Share Ownership
0.00000005
(0.00000005)
Stock Price
Growth
-0.00003**
(0.00002)
-0.00001
(0.00002)
-0.00002
(0.00002)
3 year SH return
-0.0002***
(0.00001)
-0.0002***
(0.00001)
Book/Market Ratio
-0.0001***
(0.00001)
-0.0001***
(0.00001)
-0.00008***
(0.00001)
-0.00007***
(0.00001)
-0.00008***
(0.00003)
Cash Flow
Shortfall
0.016***
(0.001)
0.017***
(0.002)
Age
-0.006***
(0.0004)
Industry Dummies
New
Economy
0.218***
(0.004)
0.218***
(0.004)
0.198***
(0.004)
Agriculture
0.063**
(0.037)
Mining
0.075***
(0.027)
Construction
-0.047*
(0.029)
Manufacturing
0.082***
(0.026)
Transportation
-0.040*
(0.026)
Whole Sale
0.018
(0.027)
Retail Sale
0.078***
(0.026)
Finance
-0.007
(0.026)
Services
0.183***
(0.026)
Constant 0.122***
(0.005)
0.102***
(0.006)
0.060**
(0.026)
0.157***
(0.005)
0.177***
(0.006)
0.433***
(0.019)
56
Table 2-2: Fixed-Effects Models of 4 Year Accumulated Stock Option
Compensation to Total Compensation with Executive Level Data
Dependent Variable: 4 Year Average of Stock-Option Compensation to Total Compensation
(1) (2) (3) (4) (5) (6)
# obs.
106850 98692 94928 79808 77453 31969
Year Index
-0.003***
(0.001)
-0.004***
(0.001)
-0.004***
(0.001)
-0.005***
(0.001)
-0.003***
(0.001)
-0.010***
(0.003)
Rank 1
0.070***
(0.002)
0.069***
(0.002)
0.070***
(0.002)
0.072***
(0.002)
0.070***
(0.002)
0.071***
(0.004)
Rank 2
0.040***
(0.002)
0.040***
(0.002)
0.042***
(0.002)
0.043***
(0.002)
0.040***
(0.002)
0.044***
(0.003)
Rank 3
0.027***
(0.002)
0.027***
(0.002)
0.028***
(0.002)
0.028***
(0.002)
0.025***
(0.002)
0.029***
(0.003)
Rank 4
0.015***
(0.002)
0.015***
(0.002)
0.015***
(0.002)
0.016***
(0.002)
0.014***
(0.002)
0.018***
(0.003)
Year
Dummies
Yes Yes Yes Yes Yes Yes
Log Sale
0.052***
(0.001)
0.038***
(0.001)
0.033***
(0.001)
0.038***
(0.002)
0.036***
(0.002)
0.038***
(0.002)
Share
Ownership
0.0000001
(0.0000001)
Stock Price
Growth
-0.00002
(0.00002)
-0.00001
(0.00002)
-0.000001
(0.00002)
-0.00001
(0.00002)
3 year SH
return
-0.0002***
(0.00002)
Book to
Market Ratio
-0.0001***
(0.00001)
-0.00006***
(0.00001)
-0.00005***
(0.00001)
-0.00005***
(0.00001)
-0.00006**
(0.00003)
Cash Flow
Shortfall
0.010***
(0.001)
0.010***
(0.001)
0.009***
(0.002)
Age
-0.008***
(0.002)
Constant
-0.145***
(0.009)
-0.053***
(0.010)
0.020**
(0.009)
0.020**
(0.010)
0.032***
(0.010)
0.390***
(0.120)
57
Table 2-3: Random-Effects Models of 4 Year Average of Stock Option
Compensation to Total Compensation with Executive Level Data
Dependent Variable: 4 Year Average of Stock-Option Compensation to Total Compensation
(1) (2) (3) (4) (5) (6)
# obs. 118466 107624 107624 104833 84219 34564
4 Year Periods
-0.022***
(0.001)
-0.022***
(0.001)
-0.022***
(0.001)
-0.021***
(0.001)
-0.027***
(0.001)
-0.023***
(0.002)
Rank 1
0.047***
(0.002)
0.049***
(0.002)
0.049***
(0.002)
0.051***
(0.002)
0.052***
(0.002)
0.055***
(0.003)
Rank 2
0.026***
(0.001)
0.027***
(0.001)
0.027***
(0.001)
0.029***
(0.001)
0.028***
(0.002)
0.027***
(0.003)
Rank 3
0.015***
(0.001)
0.016***
(0.001)
0.016***
(0.001)
0.017***
(0.001)
0.016***
(0.001)
0.014***
(0.003)
Rank 4
0.007***
(0.001)
0.008***
(0.001)
0.008***
(0.001)
0.008***
(0.001)
0.007***
(0.001)
0.007***
(0.003)
Year Dummies Yes Yes Yes Yes Yes Yes
Log Sale
0.005***
(0.001)
0.009***
(0.001)
0.009***
(0.001)
0.008***
(0.001)
0.008***
(0.001)
0.013***
(0.001)
Share Ownership
-1.59E-07***
(4.00E-08)
Stock Price Growth
-0.00004***
(0.00001)
-0.00003*
(0.00001)
-0.00004***
(0.00001)
3 year SH return
0.00006***
(0.00001)
0.00006***
(0.00001)
Book/Market Ratio
-0.00005***
(0.00001)
-0.00005***
(0.00001)
-0.00006***
(0.00001)
-0.00005***
(0.00001)
-0.00005**
(0.00002)
Cash Flow Shortfall
0.010***
(0.001)
0.009***
(0.001)
Age
-0.004***
(0.0003)
Industry Dummies
New Economy
0.187***
(0.003)
0.184***
(0.003)
0.171***
(0.003)
0.141***
(0.007)
Agriculture
0.014
(0.032)
Mining
0.079***
(0.024)
Construction
-0.023
(0.026)
Manufacturing
0.093***
(0.023)
Transportation
-0.019
(0.023)
Whole Sale
0.033*
(0.024)
Retail Sale
0.077***
(0.023)
Finance
0.017
(0.023)
Services
0.167***
(0.023)
Constant
0.219***
(0.004)
0.160***
(0.005)
0.110***
(0.023)
0.173***
(0.004)
0.189***
(0.005)
0.321***
(0.015)
58
Table 2-4: Fixed-Effects Models of 4 Year Average of Stock Option Compensation
to Total Compensation with Executive Level Data
Dependent Variable: 4 Year Average of Stock-Option Compensation to Total Compensation
(1) (3) (4) (5) (6) (7)
# obs. 118466 107624 104833 88287 84219
4 Year
Periods
-0.017***
(0.001)
-0.019***
(0.001)
-0.019***
(0.001)
-0.023***
(0.001)
-0.023***
(0.001)
-0.029***
(0.002)
Rank 1
0.039***
(0.002)
0.040***
(0.002)
0.041***
(0.002)
0.041***
(0.002)
0.040***
(0.002)
0.043***
(0.003)
Rank 2
0.019***
(0.001)
0.020***
(0.001)
0.021***
(0.001)
0.020***
(0.002)
0.019***
(0.002)
0.018***
(0.003)
Rank 3
0.010***
(0.001)
0.011***
(0.001)
0.011***
(0.001)
0.010***
(0.001)
0.009***
(0.002)
0.007***
(0.003)
Rank 4
0.004***
(0.001)
0.004***
(0.001)
0.004***
(0.001)
0.004***
(0.001)
0.003***
(0.001)
0.003
(0.003)
Year
Dummies
Yes Yes Yes Yes Yes Yes
Log Sale
0.020***
(0.001)
0.020***
(0.001)
0.020***
(0.001)
0.023***
(0.001)
0.023***
(0.001)
0.025***
(0.002)
Share
Ownership
-1.51E-07***
(4.07E-08)
Stock Price
Growth
-0.00001
(0.00001)
-0.00002
(0.00002)
-0.00001
(0.00001)
-0.00001
(0.00002)
3 year SH
return
0.00002**
(0.00001)
Book/Market
Ratio
-0.00003***
(0.00001)
-0.00004***
(0.00001)
-0.00003***
(0.00001)
-0.00003***
(0.00001)
-0.00002
(0.00002)
Cash Flow
Shortfall
0.004***
(0.001)
0.004***
(0.001)
0.004***
(0.001)
Age
0.003***
(0.0006)
Constant
0.123***
(0.006)
0.117***
(0.007)
0.125***
(0.007)
0.131***
(0.008)
0.129***
(0.008)
0.056**
(0.029)
* 1.64>t>1.28
** 2.32>t>1.64
*** t>2.32
The numbers in the parentheses are standard deviations.
59
60
6. Conclusions
In this research the amount of stock-option payments during the time a top executive
is serving in a firm has been analyzed. More specifically, the effect of executive’s tenure
in a position on stock-option payments was examined. The results show that as the
number of years that an executive works for a company in a specific position increases
the ratio of stock-option payment to total compensation decreases. This is in accordance
with the hypothesis that employers grant stock-options to top executives in part to screen
among executives.
Based on screening theory, stock-option payment in compensation is affected by age
of the executive, growth opportunities of the firm and firm being in new economy
industries.
The prediction of screening theory on the negative relation between age and stock-
option payment is shown by the results and support what screening theory suggests.
However this negative relationship can be explained by other theories as well.
Significant negative relation between book-to-market ratio and our dependent
variables also support the prediction of screening theory that growth industries will use
more stock-options in their compensation package. This relationship is also predicted by
the theory of incentive.
The strong positive relationship between new economy dummy variable and stock-
option payment is predicted by the screening theory. The same prediction comes from
incentive theory.
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65
Appendix
New Economy: Firms in the following categories are defined as New Economy Firms (Murphy 2002):
SIC Code Definition
3571 Electronic Computers
3572 Computer Storage Devices
3577 Computer Peripheral Equipment
3661 Telephone and Telegraph Apparatus
3674 Semiconductors and Related Devices
4812 Radiotelephone Communications
4813 Telephone Communications, Except Radiotelephone
5045 Computers and Computer Peripheral Equipment and Software
5046 Commercial Equipment
5961 Catalog and Mail-Order Houses
7371 Computer Programming Services
7372 Prepackaged Software
7273 Computer Integrated Systems Design
Abstract (if available)
Abstract
Top executives behavior and their decision making are important factors affecting the performance of the firms and business market. First part of this dissertation is an empirical analysis of gender differences in risk taking behavior, compensation structure, options exercising behavior and firm' s stock holding of U.S. public firms' top executives. This study used the data from U.S. public firms over the period 1992 to 2004. The results suggest that firms with at least one female executive among the top five executives have lower risk levels. Based on the results from exogeneity tests, it is suggested that the presence of a female executive among top executives of a firm decreases the firm 's risk level. Compared to their male counterparts, top female executives possess higher share of stock options in their compensation, and exercise their stock options more frequently. The firms' share ownership of top five executives is not significantly affected by gender. The second part is an empirical analysis of the association between screening top executives and stock option payment. Data from U.S. public firms during 1992 to 2005 was used. Several theoretical studies have shown that stock options can be used to screen out low type executives. Two variables were introduced to capture the intensity of screening problem. The results support the hypothesis that stock options are partly given to screen out low type executives.
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Creator
Masoudie, Ladan
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Core Title
Empirical analysis of factors driving stock options grants and firms volatility
School
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Degree
Doctor of Philosophy
Degree Program
Economics
Publication Date
04/24/2008
Defense Date
03/12/2008
Publisher
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contract theory,corporate finance,executive compensation,labor economics,OAI-PMH Harvest,risk aversion
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Language
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committee chair
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), Murphy, Kevin J. (
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), Tatiana, Sandino (
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