The Relation between Corporate Governance and CEOs’ Equity Grants
Lawrence D. Brown
Georgia State University
Email: ldb@gsu.edu
Yen-Jung Lee
National Taiwan University
Email: ylee@ ntu.edu.tw
September 2009
Abstract: We investigate whether the firm’s corporate governance affects the value of equity grants
for its CEO. Consistent with the managerial power view, we find that more poorly governed firms
grant higher values of stock options and restricted stock to their CEOs after controlling for the
economic determinants of these grants. We show that the negative relation between governance
strength and equity grants cannot be attributed to omitted economic factors, the substitution effect
between governance strength and equity incentives, or the divergence between the subjective value
and the fair value of equity grants. As further evidence consistent with the managerial power view,
we show that firms with poorer governance in the pre-Enron era cut back more on the use of
employee stock options (ESOs) for their CEOs in the post-Enron era, a period when CEO
compensation received intensified public scrutiny and when the transparency and accounting cost of
ESOs increased.
Key words: corporate governance, executive compensation, stock options, SFAS 123R
JEL Classifications: G30, G34, J31, J33
We thank Ashiq Ali, Eli Bartov, Dan Collins, Christi Gleason, Paul Healy, Tom Lys, Maria Nondorf, Shiva Rajgopal,
Doug Skinner, Abbie Smith, Franco Wong, and workshop participants at the 2007 American Accounting Association
Annual Meetings, 2007 Harvard University IMO conference, 2007 University of Okalahoma Accounting Research
Conference, Georgia State University, National Taiwan University, University of Iowa, and the University of Texas
at Dallas for helpful comments.
The Relation between Corporate Governance and CEOs’ Equity Grants
ABSTRACT
We investigate whether the firm’s corporate governance affects the value of equity grants for its CEO.
Consistent with the managerial power view, we find that more poorly governed firms grant higher
values of stock options and restricted stock to their CEOs after controlling for the economic
determinants of these grants. We show that the negative relation between governance strength and
equity grants cannot be attributed to omitted economic factors, the substitution effect between
governance strength and equity incentives, or the divergence between the subjective value and the
fair value of equity grants. As further evidence consistent with the managerial power view, we show
that firms with poorer governance in the pre-Enron era cut back more on the use of employee stock
options (ESOs) for their CEOs in the post-Enron era, a period when CEO compensation received
intensified public scrutiny and when the transparency and accounting cost of ESOs increased.
Key words: corporate governance, executive compensation, stock options, SFAS 123R
JEL Classifications: G30, G34, J31, J33
1. Introduction
We investigate the effect of a firm’s corporate governance on its CEO’s equity grants, defined as
employee stock options (ESOs) and restricted stock. Corporate governance is a set of mechanisms
that helps mitigate agency problems between managers and shareholders. Theoretically, equity grants
can be considered an aspect of corporate governance because it ties managers’ personal wealth to
their firms’ stock price performance (Shleifer and Vishny 1997; Core et al. 2003), reducing the
possibility that managers take suboptimal actions to harm shareholder value. However, a number of
practitioners, institutional investors, shareholder activists, and academic researchers contend that
instead of being an effective governance mechanism, the option granting practice is a manifestation
of poor corporate governance (e.g., Biggs 2002; Gillan 2005; Guay et al. 2003; Coombes 2005).1
While there is evidence that poor governance is associated with excessive cash and total
compensation (Core et al. 1999), empirical evidence on the relation between corporate governance
and employee equity grants is limited.2 Unlike cash, stock options and restricted stock are provided
to employees for reasons besides just conveying compensation.3 Both survey and archival evidence
suggests that retaining existing employees and attracting new employees are the most important
reasons for implementing equity grant programs (Ittner et al. 2003 and Oyer and Schaefer 2005).
1
Although the use of restricted stock also increased during our sample period, the average value of restricted stock
accounted for a much smaller percentage of total compensation as defined by Execucomp, increasing from less than
4 percent in early 1990s to 14 percent in 2006.
2
It might be inappropriate to infer the association between corporate governance and equity grants from Core et al.
(1999) because Core et al.’s sample period ranges between 1982 and 1984, a period when cash compensation
constitutes the majority of CEOs’ compensation packages (see footnote 8 for more discussion), suggesting that Core
et al.’s (1999) total compensation results could be driven by the CEO’s cash compensation. In addition, inconsistent
with Core et al. (1999), Anderson and Bizjak (2003) find that CEOs who sit on compensation committees receive
less total compensation.
3
Other reasons for providing equity grants include providing incentives, attracting and retaining high quality
employees, conserving cash, avoiding accounting expense, and joint tax savings between the manager and the firm.
1
Moreover, researchers demonstrate that stock options and restricted stock are inefficient forms of
compensation when compared to cash, raising the question about the relative importance of equity
grants’ compensation versus their attracting, retaining and other economic roles. 4 Thus prior
research’s cash compensation results cannot be readily generalized to equity grants. We fill this gap
in the literature by examining how the CEO’s equity grants relates to the firm’s governance structure
and exploring alternative explanations for the observed relation.
The extant executive compensation literature offers two distinct views on the relation between
corporate governance and the CEO’s equity awards: efficient contracting and managerial power. The
efficient contracting view contends that boards grant optimal equity incentives that maximize firm
value (e.g., Himmelberg et al. 1999; and Core and Guay 1999). According to the efficient contracting
view, there is no systematic relation between corporate governance and the value of equity grants for
top executives after controlling for the economic determinants of equity grants. The managerial
power view contends that CEOs exert substantial influence over corporate boards and structure
compensation contracts to benefit themselves rather than outside shareholders (e.g., Shleifer and
Vishny 1997; Murphy 1999; Bebchuk and Fried 2003). As such, weaker governance gives CEOs
relatively more power vis-à-vis the board, allowing them to receive more compensation than justified
economically. The managerial power view predicts a negative association between governance
4
Lambert et al. (1991), Meulbroek (2001), Hall and Murphy (2002), and Ingersoll (2006) show that ESOs and
restricted stock are worth less to employees than their economic cost to the issuing company because employees are
risk averse and poorly diversified. For example, Hall and Murphy (2002) demonstrate that under reasonable
assumptions about risk aversion and diversification, employees value ESOs granted at the money at about 50% of
their economic cost to the firm. Employees attach a smaller subjective valuation discount to restricted stock than to
ESOs. Given that equity grants are inefficient compensation vehicles, conveying compensation might not be the
most important reason for providing stock options and restricted stock to employees.
2
strength and the value of the CEO’s equity grants.
To determine which view is more descriptive of reality, we utilize 8,084 observations
representing 1,719 firms from 1998 to 2006 with necessary compensation data from ExecuComp and
governance data from RiskMetrics to examine the relation between firms’ corporate governance and
the grant day fair values of new equity grants for their CEOs. Because governance mechanisms do
not operate independently to resolve agency problems, we consider jointly the structuring of internal
and external governance. We use Core et al.’s (1999) 12 board and ownership structure variables to
measure internal monitoring and Gompers et al.’s (2003) G-index to capture external monitoring. We
use two methods, an equally weighted sum of the standardized governance variables and a factor
analysis on all governance measures, to collapse the 13 governance variables into one parsimonious
measure of overall governance strength.
Consistent with the managerial power view, we find a negative relation between governance
strength and the value of equity grants. This negative relation, however, is consistent with two
alternative explanations: (1) omitted economic factors affecting equilibrium equity compensation
(hereafter, omitted economic factors explanation); and (2) substitution effects between governance
mechanisms and equity incentives (hereafter, substitution explanation). To test for validity of the
omitted economic factors explanation, we examine how the portion of the CEO’s equity grants
explained by governance variables affects future firm performance. Under the managerial power
view, governance-motivated equity grants reflect unresolved agency conflicts, which should not have
a favorable effect on future firm performance. In contrast, the omitted economic factors explanation
3
suggests that the portion of equity grants explained by governance variables proxies for the demand
for a higher level of equilibrium equity compensation, which should not impair future firm
performance. Regressing accounting earnings and abnormal stock returns over the three subsequent
years on governance- and economic-explained equity grant values, we find that governanceexplained (economic-explained) equity grant value is negatively (positively) associated with future
accounting and stock return performance, inconsistent with the omitted economic factors explanation.
Given that equity grants are used to align managers’ and shareholders’ interests, firms are likely
to substitute equity incentives for active monitoring in cases where direct monitoring by outside
directors is less cost-beneficial. To explore the substitution explanation, we sort all observations into
22=4 portfolios based on overall governance strength and abnormal equity grants, which we define
as the component of equity grants not explained by economic determinants of these grants.5 If the
substitution explanation is valid, firms with weak governance and high abnormal equity grants
should outperform firms with weak governance and low abnormal equity grants. We examine future
accounting and stock return performance for each of the 4 portfolios and do not find evidence
consistent with the substitution explanation.
One obstacle hindering the interpretation of our results is that both corporate governance and
equity grants are endogenously determined by such factors as monitoring costs, firm performance,
and growth opportunities. Comparing compensation decisions between firms with good and bad
governance may capture the effects of differences in the environments wherein firms operate rather
than the effects of their governance. However, finding a good instrument for governance that is
5
See section 4.2 for further discussion.
4
exogenous in the equity grant decision is extremely difficult so we provide two other analyses to
mitigate this endogeneity concern.
Our first approach follows Bebchuk et al. (2008) and Dittmar and Mahrt-Smith (2007) wherein
we repeat our analysis retaining firms from the second half of our sample period (2003-2006) and
replace the governance variables by their beginning-of-the-sample-period values (i.e., 1998 values).
Our rationale is that since governance structure changes slowly over time, the initial value of
governance structure is less affected by future business and operating environments, making it more
exogenous to future compensation decisions than is contemporaneous governance structure. 6
Consistent with our primary analysis, the results reveal that past corporate governance has a
significantly negative effect on future abnormal equity grants.
As a second way to address endogeneity concerns and to add validity to our managerial power
interpretation, we examine changes in CEOs’ ESO grant values in the post-Enron era, a period
characterized by an increased public attention to executive compensation, various regulatory actions
such as the Sarbanes-Oxley Act of 2002 and listing requirements at the stock exchanges, and
issuance of Statement of Financial Accounting Standards 123R (SFAS 123R) - Share-Based Payment
(FASB 2004) that requires that ESOs be expensed on income statements.
Prior to SFAS 123R, firms were not required to record any ESO expense as long as the exercise
price of their ESO was not below the grant day market price of the underlying stock. Not surprisingly,
virtually all ESOs are granted “at-the-money”- that is, their exercise price is set to the grant-date
6
If the governance structure does not change at all, the initial governance structure is as endogenous as the
contemporaneous governance structure. All of our sample firms experience some changes in at least one of the
thirteen corporate governance variables during the 1998-2006 periods.
5
market price (Murphy 1999). ESOs’ zero accounting cost provides firms with opportunities to extract
excessive compensation via ESOs. Under the managerial power view, CEOs of more poorlygoverned firms are more likely to take advantage of the lack of ESO expensing and award
themselves more ESOs than could be justified by economic factors.
The mandatory expensing requirement along with the increased public scrutiny of executive
compensation increased the outrage and accounting costs of ESOs, forcing firms that used “too
many” ESOs in the pre-Enron period to scale back their ESO use in the post-Enron era.7 We show
that firms with weaker corporate governance in the pre-Enron period cut back more on the use of
ESOs post-Enron after controlling for changes in governance structures and other economic
determinants of equity grants during the period. This evidence is consistent with CEOs exercising
power over poor governance structure to grant themselves excessive ESOs in the pre-Enron period,
providing further evidence on the managerial power view.
We contribute to the literature along several important dimensions. First, we add to the literature
examining the relation between corporate governance and agency problems. While prior studies
show that managers exercise power to their own benefits (e.g., expropriating funds; empire building;
consuming perquisites), direct evidence relating corporate governance to equity grants is limited.
Using proprietary data for 205 firms from 1982-1984, Core et al. (1999) find that poorly-governed
SFAS 123R increased both the accounting cost and the “outrage” costs of ESOs. Bebchuk and Fried (2003) argue
that under the managerial power view, the “outrage” costs limit managers’ ability to extract rent. If an executive’s
compensation is perceived by outsiders as being excessive, outside investors might become angry and upset. This
outrage can be costly to directors and executives by causing embarrassment and reputational harm. The opaque ESO
accounting prior to SFAS 123R allowed managers to camouflage their favorable compensation packages to avoid
outrage by the public and shareholders. Once SFAS 123R removed the favorable accounting treatment, ESO
accounting could no longer be used to camouflage excessive ESO grants, forcing boards to cut back on their ESO
use that was not cost-beneficial.
7
6
firms award their CEOs with more cash and total compensation. We complement and extend Core et
al. (1999) by focusing on the equity component of the CEO compensation and by using a broader
sample and data from a more recent period when ESOs become a major component of executive
compensation.8
Second, we extend the literature examining substitution effects among governance mechanisms.
Sundaramurthy et al. (1997) find that equity market investors react less negatively to antitakeover
provisions adopted by firms whose CEOs do not chair their boards than to anti-takeover provisions
adopted by firms whose CEOs do chair their boards, consistent with the market perceiving stronger
board monitoring substitutes for weak monitoring from the takeover market. Gillan et al. (2006) find
firms with more independent boards have a higher G-Index, suggesting that the market for corporate
control plays a less important monitoring role when the firm has a more powerful board. We extend
this literature by showing that firms do not use incentive compensation to substitute for weak
governance. In fact, our evidence is inconsistent with incentive compensation serving as an effective
governance mechanism.
Third, we contribute to the literature examining CEOs’ opportunistic behavior with respect to
stock option grants. Yermack (1997), Aboody and Kasznik (2000), and Lie (2005) show that
managers time corporate disclosures or information flows around ESO grants to increase the value of
their stock option grants. Choudhary et al. (2009) reported that some firms accelerated the vesting of
8
According to Hall and Liebman (1998), only 30% of Forbes 500 CEOs receive new option grants in 1980, with the
value of ESO grants accounting for 19% of the total direct compensation, where direct compensation is defined as
salary, bonus, and the value of annual stock option grants. By 2000, 85% of the S&P 500 companies received new
stock option grants, with the value of ESO grants accounting for about 60% of total direct compensation
(Execucomp data).
7
outstanding ESOs to reduce accounting expenses that would need to be reported under SFAS 123R.
Our findings suggest weak corporate governance allowed CEOs to take advantage of opaque ESO
accounting to enrich themselves via ESOs. The rest of our paper proceeds as follows. Section 2
presents our research design. Section 3 describes our data and reports the descriptive statistics. We
show results of our multivariate analyses in section 4 and of additional analyses in section 5. Section
6 concludes.
2. Research Design
We estimate the following Tobit model on a pooled cross-sectional and time-series basis to
assess the relation between governance strength and equity grants for the CEO:9
EQUITYi ,t   0   1GOVSCORE i ,t 1 (GOVFACTORI ,T 1 )   2 INCENRESIDi ,t 1   3 SALES i ,t 1
  4 BM i ,t 1   5 NOLi ,t 1   6 SHORTFALL i ,t 1   7 DIV _ CONSTRAIN i ,t 1  8 RETi ,t 1   9 RETi ,t
  10 INVOPPi ,t 1   11 ROA i ,t 1   12 STDROA i ,t 1   13 STDRET i ,t 1   14 NONEQCOMP i ,t
47
2005
d 1
k 1998
  Bd IndustryDu mmiesd 
 D YearDummies
k
y
  i ,t
(1)
where EQUITY is the logarithm of (1 + new equity grants to the CEO for the year). New equity
grants to the CEO is calculated as the sum of the grant day fair value of new ESOs as computed by
Execucomp plus the grant day fair value of restricted stock.10
GOVSCORE and GOVFACTOR are measures of governance strength. Corporate governance
mechanisms used to mitigate agency conflicts between shareholders and managers can be either
internal (i.e., the board of directors, insider ownership, and blockholders) or external (e.g., the market
9
A Tobit model is used because about 17% of our sample firm-years do not make any equity grants to their CEOs,
resulting in a cluster of zero values for EQUITY.
10
Execucomp calculates the grant day fair value of ESOs using the Black-Scholes (1973) option-pricing model as
modified by Merton (1973) to account for dividend payments.
8
for corporate control). We use takeover vulnerability, measured as a transformation of Gompers et
al.’s (2003) G-Index, to proxy for external governance strength, and use board monitoring and
ownership structure, measured following Core et al. (1999), to proxy for internal governance strength.
G-Index is based on 24 anti-takeover provisions from RiskMetrics and is determined by adding one
point for each anti-takeover provision the firm has in place and zero otherwise. A higher G-index
imposes higher costs on takeover activities. We transform G-Index to takeover vulnerability using a
linear transformation, denoted VULNERABILITY=24G-Index so that a higher value of
VULNERABILITY indicates stronger external governance.11
Core et al. (1999) used the following 12 variables to measure internal governance strength: (1)
CEOCHAIR, an indicator variable equal to one if the board chair is the firm’s CEO and zero
otherwise; (2) BOARDSIZE, total number of board directors; (3) INSIDEDIR, percent of the board
who are managers, retired managers, or relatives of current managers; (4) HIREDBYCEO, number
of outside directors appointed by the CEO, scaled by board size; (5) GRAYDIR, number of outside
directors who are former employees or whose employers have a financial relationship with the
company, scaled by board size; (6) INTERLOCKDIR, number of outside directors who are
interlocked (a director is interlocked if an inside officer of the firm serves on the board of an outside
director’s company), scaled by board size; (7) OLDDIR, number of outside directors over age 69,
scaled by the number of outside directors; and (8) BUSYDIR, number of outside directors who serve
on three or more other boards (six or more for retired outside directors), scaled by the number of
11
Anti-takeover provisions make it harder to replace incumbent management, giving managers more power over
shareholders. Thus, G-Index proxies for the extent to which managers are protected against takeovers (or the degree
of management entrenchment).
9
outside directors. (9) CEOHOLDING, percent of the firm’s outstanding shares owned by the CEO;
(10) NONCEOOWN5, indicator variable set equal to one if at least one inside board member other
than the CEO owns 5% or more of the firm’s outstanding shares, and zero otherwise; (11)
OUTDIROWN, percent of the firm’s outstanding shares owned by outside directors divided by the
number of outside directors; and (12) BLOCKHOLDER, indicator variable equal to one if the firm
has an external blockholder who owns at least 5% of the its outstanding shares, and zero otherwise.
Because different governance mechanisms interact to achieve overall monitoring effectiveness,
we use an equally weighted sum of the standardized internal and external governance variables,
denoted GOVSCORE, to capture overall governance strength. Consistent with Core et al. (1999), we
expect that higher values of internal governance variables (1) to (8) provide more power to the CEO
in its relationship with the board, and thus imply a weaker governance structure. Higher values of
internal governance variables (9) to (12) indicate stronger non-CEO insider or outside shareholder
monitoring, and therefore suggest a stronger governance structure. Variables (1) to (8) are multiplied
by negative one before entering into the calculation of GOVSCORE. A higher value of GOVSCORE
indicates stronger corporate governance.
One limitation of GOVSCORE is the need to form expectations about the direction that each
governance variable affects governance strength. As another way to measure overall governance
strength, we perform a factor analysis on VULNERABILITY and the 12 internal governance
variables.12 The factor analysis does not rely on a priori expectations about the relation between each
12
We also report results using the 13 governance measures in lieu of GOVFACTOR in Panel B of Table 4. If the
optimal contracting view is descriptive of reality, there should be no association between abnormal equity grants and
governance strength. However, if the managerial power view is descriptive of reality, CEOCHAIR, BOARDSIZE,
10
governance variable and overall governance strength.
The factor analysis is conducted on a pooled cross-sectional and time-series basis. Two factors
result from the factor analysis. The first factor explains 16% of total variation in the 13 governance
variables, with loadings for OUTDIROWN, CEOHOLDING, INSIDEDIR, NONCEOOWN5,
VULNERABILITY, and BOARDSIZE of 0.87, 0.70, 0.56, 0.48, 0.35, and -0.33, respectively. The
second factor explains 6% of the total variation and loads on CEOCHAIR and HIREDBYCEO, with
loadings of -0.45 and -0.36, respectively. These two factors appear to capture intensity of board
monitoring and the inverse of CEO power respectively. We add the two standardized factor scores
together to create a governance strength factor, denoted GOVFACTOR.13 A larger GOVFACTOR
indicates stronger corporate governance.14
To isolate the abnormal portion of total equity grants, we control for the economic determinants
of equity use in executive compensation packages in equation (1). Core and Guay (1999) show that
firms grant fewer equity incentives to their CEOs when their CEOs’ pre-existing equity incentives
exceed the optimal incentive level. Accordingly, we include the deviation of the CEO’s pre-existing
equity incentives from the optimal incentive level, denoted INCENRESID, at the beginning of the
INSIDEDIR, HIREDBYCEO, GRAYDIR, INTERLOCKDIR, OLDDIR, and BUSYDIR (CEOHOLDING,
NONCEOOWN5, OUTDIROWN, and BLOCKHOLDER) should be positively (negatively) associated with equity
grants because they proxy for the CEO’s influence on the board (non-CEO owners’ monitoring), with higher (lower)
values indicating greater potential for rent extraction.
13
We obtain qualitatively similar results if we constrain the factor analysis to obtain only one factor. The resulting
factor explains 17% of the total variation and loads on OUTDIROWN,CEOHOLDING,INSIDEDIR,
NONCEOOWN5,VULNERABILITY, and BOARDSIZE with loadings of 0.87, 0.63, 0.61, 0.54, 0.35, and -0.32,
respectively.
14
GOVSCORE and GOVFACTOR are intended to capture the strength of monitoring or governance. Stronger
governance might not always be optimal. For example, boards dominated by independent directors are considered
stronger boards. However, a board comprising exclusively outsiders might make poorer decisions than a board with
some insiders because outside directors do not possess as much firm-specific knowledge and expertise as do inside
directors.
11
year in equation (1). Following Core and Guay (1999), INCENRESID is the residual from a
regression of the CEO’s equity incentives derived from stock option and restricted stock holdings on
firm size, monitoring difficulty, growth opportunities, CEO tenure, and the free cash flow problem as
described in the Appendix. We expect a negative coefficient on INCENRESID.
We control for the logarithm of total sales, denoted SALES, because Smith and Watts (1992)
suggest that the level of CEO compensation is positively associated with firm sales. Core and Guay
(1999) show that firms use more equity incentives to mitigate agency problems when monitoring
employee efforts is costly. Following Core and Guay (1999), we presume shareholders face greater
monitoring costs for firms with greater growth opportunities (Smith and Watts 1992) as proxied by a
lower book to market ratio, denoted BM. BM is measured as the book value of assets divided by the
market value of assets, and is used to proxy for growth opportunities, where market value of assets is
the sum of book value of debt and market value of common equity. We expect a positive coefficient
on SALE and a negative coefficient on BM.
Prior studies contend that firms bear tax costs when substituting ESOs for other forms of
compensation because there is no tax deduction for incentive ESOs and the tax deduction for
nonqualified ESOs is deferred until options are exercised. Therefore, substituting ESOs for other
forms of compensation is costlier for firms with higher marginal tax rates. We use NOL to proxy for
firms’ marginal tax rates. NOL is an indicator variable set equal to one if the firm has net operating
loss carry-forwards in any of the previous three years, and zero otherwise. A positive NOL implies a
lower effective tax rate so we expect the coefficient on NOL to be positive. Because granting ESOs
12
requires no cash outlays, firms facing liquidity problems are more likely to use options in lieu of cash
compensation to conserve cash. Following Core and Guay (1999), we include SHORTFALL,
calculated as the three-year average of cash flow used in investing activities plus common and
preferred dividends minus cash flow from operations, all deflated by total assets. The coefficient on
SHORTFALL is expected to be positive.
Dechow et al. (1996) and Core and Guay (1999) suggest ESO use is positively related to the
extent to which lack of retained earnings constrains a firm’s ability to pay dividends and repurchase
shares. We follow Dechow et al. (1996) and Core and Guay (1999) and measure the dividend
constraint using an indicator variable, DIV_CONSTRAIN, set equal to one if [(retained earnings +
cash dividends + stock repurchases)/the prior year’s cash dividends and stock repurchases] is less
than 2.0 in any of the previous three years, and zero otherwise. DIV_CONSTRAIN is also set equal
to one if the denominator is zero for all three years. We expect the coefficient on DIV_CONSTRAIN
to be positive. Core and Guay (1999) argue that the level of executive compensation is an increasing
function of the firm’s stock price performance. We include the prior year and the current year stock
returns (LAGRET and RET respectively) to control for the link between stock performance and CEO
compensation and expect the coefficients on lagged RET and RET to be positive.
In addition to the incentive, tax, and liquidity considerations, stock options and restricted stock
can be used to convey compensation. We control for the factors that have been shown by Core et al.
(1999) to be associated with total compensation, including the firm’s investment opportunities
(INVOPP), defined as the firm’s year-end book-to-market ratio averaged over the five-year period
13
ended the current year multiplied by negative one. ROA is income before extraordinary items after
ESO expense divided by lagged total assets.15 STDROA is the standard deviation of ROA for the
five-year period ending the current year. STDRET is the standard deviation of RET for the five-year
period ending the current year.
We include NONEQCOMP, measured as the logarithm of the CEO’s total compensation minus
the grant day fair value of stock options and restricted stock. On the one hand, more talented
managers could receive greater total and equity grants, implying a positive association between
NONEQCOMP and EQUITY. On the other hand, if different forms of compensation substitute for
each other, granting non-equity compensation could reduce the need for granting stock options and
restricted stock, implying a negative association between EQUITY and NONEQCOMP. Hence, we
make no prediction about the sign of NONEQCOMP. Industry dummies are based on the 48 Fama
and French (1997) industry classifications and year dummies are created for the years included in our
sample period. We use Petersen’s (2009) double cluster procedure to allow inter-correlations of
residuals across firms and across time. The expected signs of all variables are presented in Table 4.
3. Sample and descriptive statistics
Table 1 presents our sample selection procedures. Our initial sample begins with 14,056 firmyears between 1998 and 2006 that are covered by ExecuComp under the ExecuComp 1992 reporting
format. We obtain compensation data from ExecuComp, corporate governance and G-Index data
15
Virtually all firms accounted for ESOs using the intrinsic value method and did not include any ESO expense in
their operating expenses prior to mid-2002. SFAS 123 requires firms using the intrinsic value method to disclose
unrecognized ESO expense in a financial statement footnote. This disclosed (pro forma) ESO expense is recorded in
Compustat data XINTOPT.
14
from RiskMetrics, stock price data from CRSP, financial data from Compustat, and institutional
ownership data from Thomson Financial. Our sample period starts from 1998 because the director
ownership data, which is necessary for constructing ownership structure variables, became available
on RiskMetrics in 1998. The sample period ends in 2006 because ExecuComp changed its reporting
format in the 2006 fiscal year and the new reporting format discontinues the reporting of several data
items necessary to estimate equation (1).16 17
After excluding firms without necessary data, we are left with 8,084 firm-years from 1998-2006.
Table 2 reports descriptive statistics for selected variables of interest. Panel A provides an overview
of the CEO’s total compensation and compensation components. On average, our sample firm-years
granted 3.5 (1.6) million dollars of equity (cash) compensation to their CEOs, with a median of 1.3
(1.1) million. The mean grant-day fair value of stock options is 2.8 million, about four times as large
as the grant day fair value of restricted stock. The average (median) CEO receives 5.6 (2.9) million
dollars of total compensation. Equity (cash) compensation accounts for 44% (48%) of total
compensation for our average sample firm. About 17.29% of our sample firm-years do not grant any
stock options or restricted stock. For firm-years that do grant equity-based compensation, the mean
equity-based (cash) compensation granted to the CEO is about 53% (39%) of the CEO’s total
compensation.
This reporting format change is a response to the SEC’s new executive compensation disclosure requirements,
which took effect for proxy statements for fiscal years ending on or after December 15, 2006 and filed on or after
December 15, 2006. The definition of total compensation and several compensation components on Execucomp are
not directly comparable before and after the reporting format change.
17
For example, firms no longer disclose the grant day fair value of restricted stock for the top five executives on
their proxy statements but instead report the compensation expense related to restricted stock for the top five
executives under SFAS 123R. Our initial (final) sample includes 285 (173) companies still reporting under the 1992
old format for 2006 fiscal year. Excluding these firms yield qualitatively similar results to those tabulated.
16
15
Panel B of Table 2 reports the descriptive statistics on corporate governance variables. About
68% of the CEOs in our sample firm-years serve as chairmen of their firms’ boards. The average
board consists of 9.33 directors of which 26% are insiders, 37% are hired by the CEO, 7% are gray,
and 1% is interlocked. On average, 12% of outside directors are over age 69 and 12% are classified
as busy directors. The average CEO owns 3% of the firm’s outstanding equity, with a median of 1%.
The mean ownership per outside director is 2% of the firm’s outstanding shares. About 86% of the
sample firms have at least one external shareholder and 12% have at least one internal board member
other than the CEO that owns at least 5% of the firm’s outstanding shares. The mean (median) firm
has 9.24 (9) anti-takeover provisions.
Panel C of Table 2 describes variables used in the regression analyses. To mitigate the undue
influence of outliers, we winsorize all continuous regression variables at the 1% and 99% levels. The
median logarithm of equity grants is 7.14, about the same size as the median logarithm of non-equity
compensation. The distribution of incentive residuals is skewed slightly to the left, with a mean
(median) value of 0.11 (0.08). Although the mean of the incentive residuals from estimating equation
(2) should be zero by construction, the mean incentive residual reported in Panel C of Table 2 is not.
This is because we estimate the portfolio incentive equation (i.e., equation (2)) using all ExecuComp
observations with necessary data and we lose some observations in Table 2 because of our sample
selection procedures described in Table 1.
Table 3 reports Pearson and Spearman correlations for selected regression variables.
Consistent with the managerial power view, Pearson correlations show that EQUITY are negatively
16
correlated with GOVSCORE and GOVFACTOR, indicating that better-governed firms provide their
CEOs with smaller values of stock options and restricted stock. Consistent with firms using new
equity grants to manage optimal equity incentives, EQUITY is negatively correlated with lagged
INCENRESID. Larger firms, firms with more growth opportunities, firms with net operating loss
carry-forwards, and firms with more volatile operating income grant more options and restricted
stock to their CEOs. Inconsistent with the notion that stock-based compensation substitutes for other
forms of compensation, EQUITY is positively correlated with NONEQCOMP, suggesting that CEOs
with more equity awards also receive higher non-equity compensation.
4. Multivariate Analyses
4.1 The association between governance strength and ESO grant values
Panel A of Table 4 presents results from estimating equation (1). Columns 2 and 3 present the
expected signs under the efficient contracting and managerial power views respectively. Models (1)
and (2) report results when governance strength is measured by GOVSCORE and GOVFACTOR
respectively. Consistent with the managerial power view, the coefficients on GOVSCORE and
GOVFACTOR are significantly negative, suggesting that firms with stronger governance grant less
abnormal equity grants. The coefficient on the incentive residual is significantly negative, suggesting
that companies grant less equity-based compensation when the CEO’s existing stock and option
holdings provide incentives exceeding economically-justified levels. Most control variables have
their expected signs and are significant. More specifically, equity grants are significantly larger for
bigger firms, firms which are more cash-and-dividend-constrained, and firms with better growth
17
opportunities and lower marginal tax rates. Equity grants are also positively associated with stock
price performance.18
To assess the economic significance of governance strength on equity-based compensation,
we calculate the marginal effect of all continuous (indicator) independent variables as the percentage
change in the dollar amount of equity-based compensation for a change in the independent variable
from the 25th percentile to the 75th percentile of the variable in the sample (from 0 to 1), holding other
independent variables at their mean values. The marginal effect of GOVSCORE (GOVFACTOR) is 0.398 (-0.544), indicating that the CEO receives 39.8% (54.4%) less equity awards when
GOVSCORE (GOVFACTOR) improves from the 25th percentile to the 75th percentile. GOVSCORE
and GOVFACTOR are among the most economically significant variables in the model. The only
two more influential determinants of equity grants are firm size and non-equity based compensation,
with marginal effects of around 70% and 50%, respectively.
For completeness, we replace GOVSCORE and GOVFACTOR with the 13 governance
variables used to construct GOVSCORE and GOVFACTOR. We report these results in Panel B of
Table 4. Model (1) presents results for equation (1) when only internal governance mechanisms are
included. Consistent with expectations, we find firms with the following governance characteristics
provide their CEOs with less abnormal grants: (1) fewer outside busy directors; (2) CEO owns a
higher percentage of the company’s outstanding shares; (3) at least one non-CEO insider owns 5% of
the firm’s outstanding shares, and (4) higher percentage ownership per outside director. Consistent
18
Caution should be used when interpreting this result as evidence that companies reward higher stock price
performance with more equity grants because stock price directly affects the values of restricted stock and ESOs.
The association between stock return and equity grants could be mechanical.
18
with Core et al. (1999), we find that CEOs of firms with a higher percentage of inside directors
receive less abnormal equity grants. Inconsistent with our prediction that older directors are less
effective monitors, we find firms with more outside directors that are over age 69 grant less abnormal
equity grants.
4.2 Alternative Explanations
We interpret our results in Section 4.1 as weaker corporate governance enabling managers to
exercise influence over boards and extract rents in the form of excess equity grants. The negative
association between governance variables and the amount of equity grants is also consistent with two
other explanations. First, governance variables could proxy for some aspects of economic
determinants of equity grants not modeled or fully captured in equation (1), such as the CEO’s job
complexity or the CEO’s quality. Second, they could reflect substitution between governance
structure and incentive compensation. We next perform tests to determine the descriptive validity of
these two plausible explanations.
4.2.1 Omitted Economic Factors
To test for the omitted economic factors explanation, we examine how the component of equity
grants arising from governance factors relates to future performance. Bartov and Mohanram (2004)
show that excessive ESOs provide perverse incentives for managers to inflate short-term earnings
and stock prices to increase cash payouts of ESO exercises. Bebchuk and Fried (2003) argue that the
largest cost arising from managers' ability to influence the boards’ compensation decisions is firm
value loss due to suboptimal incentives rather than the rents captured by managers. If the association
19
between equity grants and governance strength reflects managerial power, we expect a zero or
negative
association
between
governance-explained
equity
grants
and
subsequent
firm
performance.19 In contrast, if governance structure captures omitted economic factors that affect the
equilibrium level of equity grants, we expect a favorable effect of governance-predicted equity grants
on subsequent firm performance.
We decompose equity grants into its three components – the portion of equity grants that is
explained by (1) governance structure (EQUITY_GOV); (2) economic factors (EQUITY_ECO); and
(3) neither economic nor governance factors (EQUITY_RESID), and we estimate the following
equation:
FUTABROAi ,t  k ( FUTABRETi ,t  k )   0   1 EQCOMP _ GOVi ,t   2 EQCOMP _ ECOi ,t
  3 EQCOMP _ RESID i ,t   4 ABROA i ,t ( ABRET i ,t )   5 SIZE i ,t   6 INVOPPi ,t   7 STDROA i ,t   8 STDRET i ,t
  9 LEVERAGEi ,t  IndustryDu mmies  YearDummie s   i ,t
(2)
where FUTABROA is industry-adjusted return on assets, calculated as income before extraordinary
items after ESO expense summed over the subsequent three years scaled by total assets at the end of
the current year, denoted FUTROA, minus the industry median FUTROA, where the industry
assignments are based on the 48 Fama-French industry classifications. 20 FUTABRET is future
19
A zero association between governance-explained equity grants and future operating performance is consistent
with the managerial power view because this part of compensation does not translate into future performance and
therefore represents excessive compensation. One could argue that a zero association is also consistent with the
optimal contracting view because if boards and managers contract optimally, higher (lower) incentive compensation
will be used in firms where agency problems are more (less) severe. Therefore, equity grants are not expected to be
associated with firm performance under the optimal contracting view. Not all researchers subscribe to the extreme
view of optimal contracting. For example, Milgrom and Roberts (1992) contend that boards learn to make good
decisions and firms adapt by experimentation and imitation. At any point in time, a cross-sectional sample consists
of firms that vary with respect to their proximity to optimal adoption of a given practice. Core et al. (2003) argue
that firms choose optimal managerial equity incentives when they first contract but transaction costs prohibit
continuous recontracting, causing equity incentives to gradually deviate from their optimal levels over time.
20
We obtain inferentially similar results if we use pre-ESO expense income to construct FUTROA.
20
abnormal stock return performance, measured as the intercept coefficient, or alpha, from the firmspecific Carhart’s (1997) four factor model estimated over the three-year period following the end of
the current year as follows:
Ri ,t    1 RMRF t   2 SMBt   3 HML t   4 Momentumt   i ,t
(3)
where R is firm return minus risk-free rate, RMRF is the value-weighted market return minus the
risk-free rate, and the terms SMB (small minus big), HML (high minus low), and Momentum are the
monthly returns on zero-investment factor-mimicking portfolios capturing size, book-to-market, and
momentum effects, respectively.
EQUITY_GOV and EQUITY_ECO are the predicted components of equity grants arising
from governance and economic factors, respectively. 21 Specifically, we compute predicted equity
grants due to governance factors for each firm-year, after controlling for the economic determinants
of ESO grants as follows:
EQUITY _ GOVi ,t   1GOVSCORE i ,t 1 (or GOVFACTORi ,t 1 )
(4)
where 1 is the estimated coefficient on GOVSCORE or GOVFACTOR reported in model (1) or
model (2) in Panel A of Table 4.
We compute the predicted component of equity grants that is related to economic
determinants for each firm-year, after controlling for the corporate governance factors as follows:
21
Core et al. (2006) find that firms with weaker shareholder rights as proxied by a higher G-Index exhibit poorer
one-year ahead operating performance, suggesting that governance strength should be included in equation (2) as a
determinant of future operating performance. However, given our EQUITY_GOV is a linear transformation of
GOVSCORE or GOVFACTOR, including these two governance strength variables along with EQUITY_GOV in
equation (3) creates the perfect linearity problem.
21
EQUITY _ ECOi ,t   2 INCENRESIDi ,t 1   3 SALES i ,t 1   4 BM i ,t 1   5 NOLi ,t 1
  6 SHORTFALL i ,t 1   7 DIV _ CONSTRAIN i ,t 1  8 RETi ,t 1   9 RETi ,t   10 INVOPPi ,t 1
47
  11 ROA i ,t 1   12 STDROA i ,t 1   13 STDRET i ,t 1   14 NONEQCOMP i ,t   Bd IndustryDu mmiesd
d 1
(5)
where j is the estimated coefficient on each economic determinant j reported in either model (1) or
model (2) of Panel A in Table 4. The residual equity grant value explained by neither economic nor
governance factors, denoted EQUITY_RESID, is calculated as EQUITY minus the sum of
EQUITY_GOV
and
EQUITY_ECO.
Given
that
EQUITY_GOV,
EQUITY_ECO,
and
EQUITY_RESID are predicted values based on equation (1), the standard errors from the regular
OLS for equation (2) are invalid. We use the bootstrap procedure described by Cameron et al. (2007)
to calculate bootstrapped clustered standard errors in both the firm and time dimension.
Barber and Lyon (1996) argue that it is important to control for past performance when testing
the association between corporate events and future firm performance because earnings are serially
correlated and corporate events (granting ESOs in our case) may be correlated with recent firm
performance. Therefore, we include current operating and abnormal stock return performance –
ABROA or ABRET – in equation (2), where ABROA is ROA minus the median ROA for all firms in
the same Fama-French 48 industry group and ABRET is the intercept coefficient from the firmspecific Carhart’s (1997) four factor model estimated over the 12-month period ending the current
year end. We control for firm size (SIZE), investment opportunities (INVOPP), earnings volatility
(STDROA), stock return volatility (STDRET) and firm leverage (LEVERAGE) in the operating
performance equation, where LEVERAGE is total debt divided by total assets and SIZE, INVOPP,
22
STDROA, and STDRET are as defined in Section 2.
Panel A (Panel B) of Table 5 presents the results for equation (2) when governance strength is
measured using GOVSCORE (GOVFACTOR). Models 1 and 2 (3 and 4) report the results when
future performance is measured by FUTABROA (FUTABRET). The coefficient on EQUITY_GOV
(EQUITY_ECO) is significantly negative (positive) across both panels, consistent with the
managerial power view but inconsistent with the omitted economic factor explanation.
4.2.2 Substitution
The negative association between governance structure and abnormal equity grants is also
consistent with the substitution explanation, which suggests a tradeoff between governance
monitoring and incentive pay. Gillan et al. (2006) show that board structures and anti-takeover
provisions substitute for each other. The authors also contend that boards of directors monitor less in
riskier/noisier environments because it is more difficult for outsiders to determine appropriateness of
managers’ actions. In cases when active monitoring by shareholders is costly or ineffective, firms
may substitute equity incentives such as ESOs for active monitoring. If the substitution explanation is
valid, firms with weak governance structure and more equity incentives should outperform firms
with weak governance structure and less equity incentives. Similarly, firms with low equity
incentives and stronger governance structure should outperform firms with low equity incentives and
weaker governance structure.
To test for the substitution explanation, we examine how overall governance strength and
incentive compensation interact to affect future firm performance. We individually classify all firms
23
into 22=4 portfolios based on governance strength and excessive equity grants. 22 We define
excessive (or abnorm) equity grants, denoted EXCESS_EQUITY, as the portion of equity grants not
explained by economic determinants and industry dummies specified in equation (1).23 Specifically,
EXCESS_EQUITY is the residual from the following regression:
EQUITYi ,t   0   1 INCENRESIDi ,t 1   2 SALES i ,t 1   3 BM i ,t 1   4 NOLi ,t 1   5 SHORTFALL i ,t 1
  6 DIV _ CONSTRAIN i ,t 1 7 RETi ,t 1   8 RETi ,t   9 INVOPPi ,t 1   10 ROA i ,t 1   11 STDROA i ,t 1
47
  12 STDRET i ,t 1   13 NONEQCOMP i ,t    d IndustryDu mmiesd   i ,t
d 1
(6)
All variables are as defined previously. Panel A of Table 6 presents the mean and median
FUTABROA for each of the 4 portfolios. Holding overall governance strength constant at the weak
level, firms with the high EXCESS_EQUITY report a mean (median) FUTABROA of 0.1243
(0.0570), which is significantly lower than 0.1828 (0.0834) reported by firms with the lowest
EXCESS_EQUITY, suggesting that firms granting large EXCESS_EQUITY does not outperform
firms granting small EXCESS_EQUITY, inconsistent with the substitution explanation. Keeping the
overall excessive compensation at the low level, the mean FUTABROA for firms with strong
corporate governance is significantly higher than that for firms with weak corporate governance.
However, strong corporate governance is associated with a higher future operating performance
regardless of the level of EXCESS_EQUITY. Similar results can be obtain from Panel B of Table 6
where future performance is measured by abnormal stock return relative to the Carhart’s (1997) four
22
For brevity, we only tabulate the results when governance strength is measured by GOVSCORE in Table 5. We
obtain qualitatively similar results if governance strength is measured by GOVFACTOR. Measuring future income
and cash flows over the subsequent one year rather than three years does not alter our conclusion.
23
Sorting all firms first on GOVERNANCE and then on EXCESS_EQUITY yields inferentially similar results.
24
factor model. Taken together, our results suggest that the substitution effect is not a valid explanation
for the negative association between governance strength and ESO grant values. 24
5. Additional Analyses
5.1 Endogeneity
The negative association between corporate governance and abnormal equity grants could be
driven by unmodeled or imperfectly measured factors that affect both governance structure and
equity grants. Ideally, we would adopt an instrumental variable approach to help mitigate
endogeneity effects. But the choice of appropriate instruments, while never easy, is very challenging
in our context. Prior studies (Fama and Jensen 1983; Demsetz and Lehn 1985; Coles et al. 2008)
show a firm’s governance structure is affected by its growth potential, operational complexity, recent
performance, and uncertainty in its business and information environments. Virtually all of these
factors have been shown to influence a firm’s decision to use equity grants (e.g., Core et al. 1999;
Ittner et al. 2003), making it extremely difficult to find instruments that identify governance strength
in the equity grants equation. To mitigate endogeneity concerns, we perform two additional tests to
supplement our primary analyses in Section 4.
First, following Bebchuk, Cohen, and Ferrell (2008) and Dittmar and Mahrt-Smith (2007),
we repeat the analysis in Panel A of Table 4 retaining only observations from the second half of our
sample period (2003-2006) and replacing GOVSCORE and GOVFACTOR by their 1998 (beginning
24
We obtain similar results to those reported in Table 6 if we measure future operating performance using
cumulative operating cash flows over the next 3 years and future stock return performance using size-adjusted buy
and hold returns over the subsequent 3 years.
25
of our sample period) values, denoted GOVSCORE98 and GOVFACTOR98 respectively.25 The results
confirm our previous findings: the coefficients on GOVSCORE98 and GOVFACTOR98 remain
significantly negative, suggesting that governance structures in earlier years affect the compensation
decisions in later years. The control variables behave similarly to those reported in Panel A of Table 4.
As a second way to address endogeneity concerns, we examine ESO changes post versus pre
Enron. Following Enron and other major U.S. corporate scandals, public attitude turned against ESOs
because excessive option grants were blamed for creating perverse incentives for managers to
commit accounting frauds. The post-Enron era was characterized by increased media attention on
executive compensation, institutional shareholder activism, and various corporate reforms such as
Sarbanes-Oxley Act of 2002 and new listing requirements at stock exchanges. In addition, the FASB
passed SFAS 123R on December 16, 2004, requiring firms to recognize a compensation expense
equal to the grant-day fair value of ESOs on their income statements.
Prior to SFAS 123R, GAAP allowed firms to avoid any compensation expense if they granted
ESOs with an exercise price not lower than the grant-date market price of the underlying stock
(known as the intrinsic value method) as long as they disclosed information about the grant date fair
value of ESO grants in the financial statement footnotes.26 ESOs bore zero accounting cost as long as
they were not granted in the money. Bodie et al. (2003) argued that failure to recognize ESO expense
provided an “accounting subsidy to stock options”, encouraging firms to use them to excess because
virtually all other forms of compensation required expensing. Making compensation decisions based
25
We use initial governance values because governance structure changes slowly over time and the initial
governance structure is more exogenous to future equity grant amounts than contemporaneous governance structure.
26
The intrinsic value of an ESO is equal to the market price of the underlying stock minus the option exercise price.
26
on ESOs’ zero accounting cost rather than their true economic costs is costly to shareholders because
it results in compensation arrangements that provide worse incentives than those provided by arm's
length contracts.
The managerial power view predicts that firms with poorer corporate governance are more
likely to exercise power over weak corporate governance and extract excessive compensation in the
form of ESOs. The intensified public scrutiny and the mandatory ESO expensing requirement in the
post-Enron period increased the visibility of ESOs and drew more attention to the real cost of stock
options, making it harder for firms to justify excessive executive pay (Guay et al. 2003). If poor
corporate governance facilitated the excessive ESO use, one would expect to observe a greater
decrease in ESO use in the post-Enron period for firms with poorer corporate governance. To test this
prediction, we estimated the following OLS regression:
DIFFOPTION i   0   1GOVSCORE 9801i (GOVFACTOR9801i )   2 DIFFRSTK i
  3 DIFFGOVSCORE i (or DIFFGOVFACTOR i )   4 DIFFINCENRESIDi   5 DIFFSALE i
  6 DIFFBM i   7 DIFFNOLi   8 DIFFSHORFA LLi   9 DIFFDIVCON STRAIN i   10 DIFFLAGRETi
  11 DIFFRETi  12DIFFINVOPPi   13 DIFFROA i   14 DIFFSTDROA i   15 DIFFSTDRET i
  16 DIFFNONEQCOMPi  IndustryDu mmies   i
(7)
The dependent variable, DIFFOPTION, is the average OPTION over the post-Enron period
(2002-2006) minus the average OPTION over the pre-Enron period (1998-2001), where OPTION is
the logarithm of (1+grant day fair value of ESOs for the CEO).27 A smaller DIFFOPTION indicates a
greater decrease in ESO grant values in the post-Enron period. GOVSCORE9801 and
GOVFACTOR9801 are the mean GOVSCORE and GOVFACTOR over the pre-Enron period (199827
Our results in this section are robust to the exclusion of firms that voluntarily expense stock options prior to 2002.
27
2001), respectively. We include the change in the grant day fair value of restricted stock from the preto the post-Enron period, denoted DIFFRSTK, to control for the potential substitution between ESOs
and restricted stock. DIFFRSTK is calculated as the average RSTK over the post-Enron period
(2002-2006) minus the average RSTK over the pre-Enron period (1998-2001), where RSTK is the
logarithm of (1+ grant day fair value of restricted stock for the CEO). DIFF_Y is average Y over
2002-2006 minus average Y over 1998-2001, where Y is one of the 14 independent variables in
equation (1).
Table 7 shows the coefficients on GOVSCORE9801 and GOVFACTOR9801 are significantly
positive, suggesting that firms with weaker (stronger) corporate governance cut back more (less) on
ESO use after controlling for changes in determinants of ESOs including changes in governance
structure from the pre- to the post-Enron period. 28 In sum, our evidence is consistent with the
prediction of the managerial power view that more poorly governed firms took greater advantage of
ESO accounting prior to SFAS 123R to award their CEOs with more ESOs than are justifiable
economically. 29
5.2 Subjective Valuation of Equity Grants
We now explore how the subjective value of equity grants affects our results. We define
subjective value as valuation the executive places on ESOs and restricted stock she receives. Because
28
The coefficients on DIFFGOVSCORE and DIFFGOVFACTOR are not significantly different from zero probably
due to the lack of variation in governance structure.
29
To ensure that the negative association between the change in ESO grant values and pre-Enron governance
structure is not mechanical or documented by chance, we perform two pseudo changes analyses. Specifically, we
rerun equation (6) for the pre- and post-Enron periods separately. For the pre- (post-) Enron regression, we define
the pre-period as 1998-1999 (2002-2003), and the post-period as 2000-2001 (2004-2006). All changes variables are
calculated as the average values over 2000-2001 (2004-2006) minus the average values over 1998-1999 (20022003). We do not find a significant coefficient on the pre-period governance strength in either of the two pseudo
changes regressions.
28
employees are risk-averse and under-diversified, Lambert et al. (1991); Meulbroek (2001), Hall and
Murphy (2002), and Ingersoll (2006) among others show that the lack of diversification makes the
subjective value of incentive compensation such as stock options and restricted stock less than their
fair market value. Anecdotal evidence suggests that employees value cash compensation much more
highly than ESOs and restricted stock awards. For example, Hall and Murphy (2002, pgs.15-16)
report that firms conducting explicit exchanges of cash for stock-based compensation typically gave
participating executives a “risk premium” for accepting stock-based pay.30
If firms compensate their CEOs for the subjective valuation discount that employees apply to
stock-based compensation by granting higher nominal values of equity grants, the coefficient
estimate on corporate governance will be unreliable when the divergence between the fair value and
the subjective value of equity grants is correlated with governance strength.31 To examine how the
subjective valuation of the equity grants (i.e., subjective value discount) affects our results, we adopt
the Ingersoll (2006) model to estimate the subjective value of equity grants.32 Ingersoll (2006) shows
that the subjective value of options can be obtained via a downward adjustment to the Black-Scholes
formula. The downward adjustment is a function of employee risk aversion, the idiosyncratic
30
For example, EK Co’s exchange program provides employees with the choice to have cash compensation paid in
shares of restricted stock valued at 130% of the foregone cash or in stock options valued at 250% of the foregone
cash (Hall and Murphy 2002, pg. 15).
31
For example, when direct monitoring by the board is costly or difficult, as proxied by the weaker board structure,
the board might use more equity incentives to motivate the manager. A risk-averse and poorly diversified manager
will require higher levels of compensation since they are imposed a higher level of risk on their personal wealth. In
this case, the amount of nominal value used to compensate for the subjective value discount is correlated with
governance strength.
32
Core and Guay (2003) analytically demonstrated that if managers are allowed to sell stock to “undo” the risk
imposed by equity grants (e.g., when options are granted as compensation), options or restricted stock do not
necessarily impose additional risk on the employee. In this case, a market-based pricing model such as the BlackScholes model provides a reasonable measure of values and incentives provided by ESOs and stock awards. In
contrast, if equity grants are given primarily for incentive purposes, the subjective measures better capture the
valuation and incentive effects of options and stock from the perspective of the executive.
29
volatility of the stock, and the proportion of the executive wealth tied to the company’s stock. In
Ingersoll (2006), the subjective value of an ESO (VIngersoll) to a risk adverse employee is given by the
following equation:
VIngersoll  Se  qˆT N (h1 )  Ke  rT N (h2 )
h1 
ln(
(8)
S
)  (rˆ  qˆ  0.5 *  2 )(T )
K
 T
h2  h1   T
qˆ  q  (1   ) (1   ) 2
rˆ  r  (1   ) 2 2
where S is the price of the underlying stock on the grant day, q is the dividend yield on the stock, T is
the expected life of the option, K is the exercise price of the employee stock option, r is the risk-free
interest rate, N(.) is the cumulative normal distribution function,  2 is the annualized total variance
of the stock, σ is the expected annual stock price volatility,  (  1) >0 is the relative risk aversion
parameter,  2 is the residual firm specific variance of the stock, and  is the fraction of his own
wealth an executive is constrained to hold in his firm’s stock beyond that represented in the market
portfolio. Without loss of generality, we set the expected life equal to the maturity of the option grant.
We obtain the risk-free rate, dividend yield, and total variance of the stock from Execucomp. The
residual firm specific variance is the annualized variance of the residual from a firm-specific rolling
36-month market model regression.
The subjective value of a restricted stock (SIngersoll) is given by the following equation:
30
S Ingersoll  Se  qˆT
(9)
where S is the market price of restricted stock. We compute VIngersoll (SIngersoll) for each individual
option (stock) grant. The subjective value of the CEO’s equity grants, denoted SUBJECTIVE, is
calculated as the sum of VIngersoll and SIngersoll for all option and stock grants that the CEO received in
a particular year.
Following Hall and Murphy (2002), we use relative risk aversion parameters of 2.0 and 3.0
which are at the low end of the reasonable range of estimates in the literature. We assume that 50%
of the CEO’s wealth is constrained to hold the company’s stock beyond that represented in the
market portfolio. We create SUB_FV, defined as SUBJECTIVE divided by the grant day fair value
of the option and stock awards, to measure the subjective valuation discount. The higher the
SUB_FV, the closer the CEO’s subjective valuation is to the fair value of his/her equity grants (and
the lower the subjective valuation discount). We set SUB_FV equal to one if the firm did not grant
any equity grants during the year. We re-estimate equation (1) including SUB_FV and expect the
coefficient SUB_FV to be negative because the larger the subjective valuation discount (i.e., a
smaller SUB_FV), the greater the nominal value of equity grants that the company has to provide in
order to make up for the divergence between the subjective and objective values of the equity grants.
Table 8 reports results for a risk aversion parameter equal to 2.0.33 In our sample, the mean
subjective value of new ESO (restricted stock) grants is about 52% (90%) of their fair value. The
mean subjective value of a dollar of total equity grants is about 52 cents (not tabulated). Consistent
33
We obtain very similar results as those reported in Table 8 if we change the relative risk aversion parameter to 3.0
or if we change  to 25% or 75%.
31
with our Table 4 results, the coefficients on GOVSCORE and GOVFACTOR are significantly
negative. As expected, the coefficient on SUB_FV is significantly negative, suggesting companies
pay greater equity grants when CEOs place a greater discount on their equity awards. In sum, our
results indicate the negative relation between governance strength and abnormal equity grants cannot
be attributed to the subjective value discount employees attach to stock-based compensation.
6. Concluding Remarks
We examine whether and why corporate governance affects the CEO’s equity grant values.
Although it has often been argued that equity incentives are a useful component of a firm’s
governance structure, empirical evidence as to whether it actually aligns managers’ and shareholders’
interests is lacking. We consider the two predominant views on the relation between governance and
equity grants: efficient contracting and managerial power. The efficient contracting view maintains
that boards always optimize their equity-granting decisions. Under this view, there is no systematic
relation between equity grants and governance strength. In contrast, the managerial power view
maintains that managers in firms of weak corporate governance exercise power over the boards’
compensation decisions to extract excessive compensation. Under this view, there should be a
negative relation between governance strength and abnormal equity grants.
Our evidence, covering 8,048 firm-year observations from 1,719 firms over the years 19982006, suggests that weaker corporate governance as proxied by lower takeover vulnerability and
weaker board and shareholder monitoring is associated with greater abnormal equity grants for the
CEO after controlling for the economic determinants of equity grants. We measure takeover
32
vulnerability (external governance) based on Gompers et al.’s (2003) G-Index and the intensity of
board and shareholder monitoring (internal governance) using the 12 governance variables of Core et
al. (1999). Although our principal finding of a negative association between ESOs and corporate
governance is consistent with the managerial power view, it is also consistent with two alternative
explanations: (1) corporate governance variables proxy for unmodeled (omitted) economic factors
affecting ESO use; or (2) incentive compensation substitutes for governance mechanisms to mitigate
agency problems. We perform additional tests to determine the validity of these alternative
explanations for our findings.
To test for the omitted economic factors explanation, we decompose equity grants into three
components – the portion of equity grants explained by (1) governance strength; (2) economic factors;
(3) neither governance strength nor economic factors. Inconsistent with the omitted economic factors
explanation but consistent with the managerial power view, the predicted component of ESO grant
value attributable to governance factors is inversely related to future operating performance.
To examine the validity of the substitution explanation, we sort firms into 22=4 portfolios
based on overall governance strength and the value of abnormal equity grants, and compared future
operating and stock return performance across the four portfolios. In contrast to the prediction under
the substitution explanation, we find that firms with larger excessive ESO grant values do not
outperform firms with smaller excessive ESO grant values for portfolios with the weakest corporate
governance.
We further validate our managerial power interpretation of the relation between governance
33
strength and equity grants by showing that poorly-governed firms cut back more on ESO grant values
in the post-Enron era, a period characterized by greater investors’ scrutiny of CEO compensation,
major regulatory actions including the enactment of Sarbanes-Oxley Act of 2002, and adoption of a
new accounting rule requiring firms to report the fair value of ESOs on their income statements as an
expense. This finding is consistent with CEOs in poorly governed firms being more likely to abuse
the favorable ESO accounting treatment to deliver stealth compensation in the form of ESOs in the
pre-Enron period. The increase in public attention on executive compensation along with the
mandatory ESO expensing requirement increased the transparency and accounting cost of ESOs,
made it harder for poorly governed firms to justify excessive ESO packages in the post-Enron period.
Consistent with the managerial power view, poorly-governed firms granted the most excessive
compensation and cut back the most on ESO use in the post-Enron era.
We extend prior research examining the association between CEO compensation and corporate
governance. Although Core et al. (1999) showed that poor governance contributes to excessive cash
and total compensation, empirical evidence on how governance relates to equity grants is limited. It
is inappropriate to infer the association between corporate governance and equity grants from
Core et al. (1999) because cash compensation constitutes the majority of CEO compensation
during Core et al.’s (1999) sample period (1982-1984), suggesting that Core et al.’s total
compensation results could be driven by cash compensation. We contribute to the literature by
explicitly examining alternative explanations for the negative association between governance
strength and abnormal equity grants. We show that incentive compensation neither substitutes for
34
weak governance nor is it an effective governance mechanism. We add to research that examines
CEOs’ opportunistic behavior regarding stock option grants (Yermack, 1997; Aboody and Kasznik,
2000; Lie, 2005; Choudhary et al. 2006) by showing that weak corporate governance along with
opaque ESO accounting prior to SFAS 123R enabled CEOs to abuse ESO grants for their own
benefits.
35
Appendix: Estimation of the Deviation of the CEO’s Pre-existing Equity Incentives from the
Optimal Equity Incentives
This appendix describes the estimation of the deviation of the CEO’s pre-existing equity
incentives from the optimal equity incentives. Core and Guay (1999) show that firms use new option
and restricted stock grants to manage the optimal level of equity incentives. In particular, they find
that annual grants of new stock options and restricted stock to CEOs are negatively associated with
CEOs’ incentive residual prior to the equity grants, where the incentive residual is the portion of preexisting equity incentives that is in excess of the optimal incentive level. Following Core and Guay
(1999), we model the optimal level of the CEOs' portfolio holdings of equity incentives as a function
of firm size, monitoring difficulty, growth opportunities, CEO tenure, and the free cash flow
problem.34 The incentive residual, denoted INCENRESID, is the residual from the following
regression model.
EQPORT i ,t   0   1 MVEi ,t   2 IDRISK i ,t   3 BM i ,t   4TENUREi ,t   5 FCFi ,t  IndustryDu mmies
 YearDummie s   i ,t
(10)
where EQPORT is the CEO's total portfolio incentives from options and restricted stock at the end of
the year. The incentives provided by the CEO’s holding of restricted stock are estimated by
multiplying the year-end stock value by 1%. We use the procedures outlined in Core and Guay (1999)
to estimate the incentives provided by the CEO’s option holding. Core and Guay (1999) break the
total option holding into those options granted in the current year and those options received from
previous years. The incentives provided by newly granted options are estimated as the sensitivity of
the Black-Scholes value of the new option grant to a 1% change in the year-end stock price. All the
inputs required to calculate the Black-Scholes value – year-end stock price, option exercise price,
34
Jensen (1986) argues that the combination of low growth opportunities and high free cash flow creates agency
problems that can be mitigated with higher levels of equity incentives.
36
time-to-maturity, stock return volatility, dividend yield, and the risk-free rate are available on
Execucomp. The incentives provided by options granted in previous years are measured using
weighted average exercise price and weighted average time-to-maturity as described in Core and
Guay (1999).
MVE is logarithm of the market value of equity and is a proxy for firm size. IDRISK is
logarithm of the idiosyncratic risk, measured as the standard deviation of the residual from a rolling
36-month market model regression and is used to measure the monitoring difficulty. BM is book
value of assets divided by the market value of assets and is used to proxy for growth opportunities,
where market value of assets is the sum of the book value of debt and the market value of common
equity. TENURE is CEO tenure, measured as the number of years between the year when the CEO
first took office and the current year-end. FCF is the degree of the free cash flow problem, measured
as the three-year average of (operating cash flow - common and preferred dividends) / total assets if
the firm has a book-to-market assets ratio greater than one (a proxy for low growth opportunities),
and zero otherwise.
37
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41
TABLE 1
Sample Selection
# of firm-years
All firm-years reporting under the Execucomp 1992 reporting format between 1998
and 2006 with non-missing CEO compensation data
Less: firm-years with missing information about the grant day fair value of stock
options or restricted stock
Less: firm-years without sufficient data on Compustat necessary to estimate the
equity grants model
Less: firm-years without sufficient data on CRSP necessary to estimate the equity
grant model
Less: firm-years without sufficient data on RiskMetrics to construct governance
variables
Sample firm-years
42
14,056
(107)
(3,451)
(94)
(2,320)
8,084
TABLE 2
Descriptive Statistics on Selected Variables
(N=8,084)
Penal A: CEO Compensation (Thousands of Dollars)
Mean
Std Dev
Median
Q1
Q3
(ESOt+RESTRICTEDt)
3,544.81
13,158.6
1,254.97
271.209
3,451.94
ESOt
2,824.36
1,0551.5
915.450
80.7060
2,773.63
RESTRICTEDt
720.449
7,630.89
0.0000
0.0000
203.000
CASHt
1,579.86
1,773.59
1,112.51
669.787
1,906.55
TOTCOMPt
5,649.69
13,906.9
2,916.24
1,373.48
6,003.77
(ESOt+RESTRICTEDt)/TOTCOMPt
(for all sample firm-years)
0.4419
0.2896
0.4721
0.2080
0.6741
CASHt/TOTCOMPt
(for all sample firm-years)
0.4752
0.2737
0.4246
0.2623
0.6546
(ESOt+RESTRICTEDt)/TOTCOMPt
(for 6,686 firm-years that grant equity comp)
0.5332
0.2284
0.5437
0.3642
0.7108
CASHt/TOTCOMPt
(for 6,686 firm-years that grant equity comp)
0.3940
0.2064
0.3734
0.2384
0.5330
This panel describes components of the CEO compensation. The sample covers 8,084 firm-years from 1998 to 2006
in which equity grants were provided in 6,686 firm-years. ESO is the grant day Black-Scholes value for stock option
awards as computed by Execucomp. RESTRICTED is the grant day fair value of restricted stock awards.
TOTCOMP is the total compensation, defined as the sum of base salary, bonus, grant-date Black-Scholes value of
stock option awards, grant-date fair value of restricted stock awards, payouts under long-term incentive plans, other
annual compensation, and all other compensation. CASH is cash compensation, calculated as the sum of salary and
bonus.
Penal B: Governance Variables
Mean
Std Dev
Median
Q1
Q3
CEOCHAIR t-1
0.6813
0.4660
1.0000
0.0000
1.0000
BOARDSIZE t-1
9.3340
2.4881
9.0000
7.0000
11.0000
INSIDEDIR t-1
0.2576
0.1313
0.2308
0.1429
0.3333
HIREDBYCEO t-1
0.3695
0.2492
0.3636
0.1667
0.5714
GRAYDIR t-1
0.0730
0.1021
0.0000
0.0000
0.1250
INTERLOCKDIR t-1
0.0100
0.0276
0.0000
0.0000
0.0000
OLDDIR t-1
0.1187
0.1541
0.0000
0.0000
0.2000
BUSYDIR t-1
0.1202
0.1510
0.0000
0.0000
0.2000
43
CEOHOLDING t-1
0.0335
0.0623
0.0116
0.0045
0.0292
NONCEOOWN5 t-1
0.1241
0.3297
0.0000
0.0000
0.0000
OUTDIROWNt-1
0.0163
0.0276
0.0054
0.0017
0.0171
BLOCKHOLDER t-1
0.8607
0.3463
1.0000
1.0000
1.0000
VULNERABILITY
14.7636
2.5781
15.0000
17.0000
13.0000
GOVSCORE t-1
-0.1971
3.9499
-0.1903
-2.6891
2.1828
GOVFACTORt-1
-0.0097
1.2986
-0.3691
-0.8786
0.3944
This panel provides descriptive statistics of governance variables. The sample covers 8,084 firm-years from 1998 to
2006. CEOCHAIR: indicator variable set equal to one if the CEO is also chairman of the board, and zero otherwise.
BOARDSIZE: number of directors on the board. INSIDEDIR: percent of the board who are managers, retired
managers, or relatives of current managers. HIREDBYCEO: number of outside directors on the board appointed by
the CEO as a percent of the board size. GRAYDIR: number of outside directors whose employers have a financial
relationship with the company or who are former employees of the company, as a percent of board size.
INTERLOCKDIR: number of outside directors who are interlocked (a director is interlocked if an inside officer of
the firm serves on the board of an outside director’s company), as a percent of board size. OLDDIR: percent of
outside directors who are over age 69. BUSYDIR: percent of outside directors who serve on three or more other
boards (six or more for retired outside directors). CEOHOLDING: percent of outstanding shares owned by the CEO.
NONCEOOWN5: indicator variable set equal to one if the firm has an internal board member other than the CEO
who owns at least 5% of the firm’s outstanding shares, and zero otherwise. OUTDIROWN: total percentage of
outstanding shares owned by outside directors divided by the number of outside directors. BLOCKHOLDER:
indicator variable set equal to one if the firm has an external blockholder who owns at least 5% of the outstanding
shares, and zero otherwise. GOVSCORE: an equally weighted sum of the standardized internal and external
governance variables. Since higher values of the first eight internal governance variables indicate a board structure
that provides more power to the CEO in its relationship with the board, these governance variables are multiplied by
negative one before entering into the calculation of GOVSCORE. GOVFACTOR is the sum of the two standardized
factor scores resulting naturally from a factor analysis on all thirteen internal and external governance variables.
Penal C: Selected Regression Variables
Mean
Std Dev
Median
Q1
Q3
EQUITYt
6.1513
3.0823
7.1357
5.6066
8.1470
NONEQCOMPt
7.1848
1.0250
7.1644
6.6184
7.7655
INCENRESIDt-1
0.1081
0.9773
0.0773
-0.4889
0.6707
SALE t-1
7.3360
1.5027
7.2308
6.3163
8.3392
BM t-1
0.6422
0.2721
0.6505
0.4321
0.8476
NOL t-1
0.3289
0.4699
0.0000
0.0000
1.0000
44
SHORTFALL t-1
0.0099
0.0806
0.0027
-0.0339
0.0434
DIV_CONSTRAIN t-1
0.3564
0.4790
0.0000
0.0000
1.0000
RET t
0.1367
0.4968
0.0775
-0.1594
0.3312
RET t-1
0.1678
0.5204
0.1046
-0.1452
0.3663
-0.6217
0.2455
-0.6280
-0.4320
-0.8159
ROA t-1
0.0387
0.1592
0.0450
0.0123
0.0873
STDROA t-1
0.0566
0.0848
0.0308
0.0150
0.0640
STDRET t-1
0.1277
0.0584
0.1128
0.0860
0.1561
NONEQCOMP t-1
7.1848
1.0250
7.1644
6.6184
7.7655
FUTABROAt
0.1358
0.3230
0.1405
0.0334
0.2822
FUTABRETt
0.0028
0.0291
0.0027
-0.0092
0.0145
INVOPP t-1
This panel provides descriptive statistics on selected regression variables. The sample covers 8,084 firm-years from
1998 to 2006. To mitigate undue influence from outliers, all continuous variables are winsorized at the 1% and 99%
levels. EQUITY is the logarithm of (1+new equity grants to the CEO for the year). New equity grants to the CEO is
calculated as the sum of the grant day fair value of new ESOs for the CEO as computed by Execucomp plus the
grant day fair value of restricted stock. INCENRESID is the residual from the following regression model.
EQPORT i ,t   0   1 MVEi ,t   2 IDRISK i ,t   3 BM i ,t   4TENUREi ,t   5 FCFi ,t  IndustryDu mmies
 YearDummie s   i ,t
(10)
where EQPORT is the CEO's total portfolio incentives from stock and option holdings at the end of the year. The
incentives provided by the CEO’s restricted stock holdings are estimated by multiplying year-end stock value by 1%.
We use the procedures outlined in Core and Guay (1999) to estimate the incentives provided by the CEO’s option
holding. Core and Guay (1999) break the total option holding into those options granted in the current year and
those options received from the previous years. Incentives from newly granted options are estimated as the
sensitivity of the Black-Scholes value of the new option grant to a 1% change in the year-end stock price. All the
inputs required to calculate the Black-Scholes value - stock price, exercise price, time-to-maturity, stock return
volatility, dividend yield, and the risk-free rate - are available on Execucomp. Incentives from options granted in
previous years are measured using the weighted average exercise price and weighted average time-to-maturity as
described in Core and Guay (1999). MVE is logarithm of the market value of equity. IDRISK is logarithm of the
idiosyncratic risk measured as the standard deviation of the residual from a 36-month market model regression. BM
is book value of assets divided by the market value of assets, where market value of assets is the sum of book value
of debt and market value of common equity. TENURE is CEO tenure, measured as the number of years between the
year that the CEO first took office and the current year-end. FCF is the degree of the free cash flow problem
measured as the three-year average (operating cash flow - common and preferred dividends) / total assets if the firm
has a book-to-market ratio greater than one (a proxy for low growth opportunities), and zero otherwise. SALE is the
logarithm of sales. BM is the book to market ratio. NOL is an indicator variable set equal to one if the firm has net
operating loss carry-forwards in any of the previous three years, and zero otherwise. SHORTFALL is cash flow
45
shortfall, measured as the three-year average of cash flow used in investing activities plus common and preferred
dividends minus cash flow from operations, all deflated by total assets. DIV_CONSTRAIN is an indicator variable
set equal to one if [(retained earnings + cash dividends + stock repurchases)/the prior year’s cash dividends and
stock repurchases] is less than 2.0 in any of the previous three years, and zero otherwise. DIV_CONSTRAIN is also
set equal to one if the denominator is zero for all three years. RET is the current stock return. INVOPP is the firm’s
investment opportunities, calculated as negative one multiplied by the firm’s year-end book-to-market ratio averaged
over the five-year period ending the current year, where the market-to-book ratio is defined as the market value of
equity plus the book value of debt divided by total assets. ROA is income before extraordinary items after ESO
expense divided by lagged total assets. STDROA is the standard deviation of ROA for the five-year period ending
the current year. STDRET is the standard deviation of RET for the five-year period ending the current year.
FUTABROA is industry-adjusted operating performance, calculated as income before extraordinary items after ESO
expense summed over the subsequent three years scaled by total assets at the end of the current year, denoted
FUTROA, minus the industry median FUTROA, where the industry assignments are based on the 48 Fama-French
industry classifications. FUTABRET is the intercept coefficient, or alpha, from the firm-specific Carhart’s (1997)
four factor model estimated over the three-year period following the end of the current year:
Ri ,t    1 RMRF t   2 SMBt   3 HML t   4 Momentumt   i ,t
(3)
where R is firm return minus risk-free rate, RMRF is the value-weighted market return minus the risk-free rate, and
the terms SMB (small minus big), HML (high minus low), and Momentum are the monthly returns on zeroinvestment factor-mimicking portfolios capturing size, book-to-market, and momentum effects, respectively.
46
TABLE 3
Correlation Matrix on Selected Regression Variables
EQUITYt
GOVSCOREt-1
GOVFACTORt-1
INCENRESIDt--1
SALEt--1
BM t--1
NOLt-1
SHORT_
FALLt-1
DIVCON_
STRAINt-1
RETt
INVOPPt-1
ROAt-1
STDROAt-1
STDRETt-1
NONEQCOMPt-1
INCEN_
RESID t--1
EQUITYt
GOVSCOREt-1
GOVFACTORt-1
1.000
-0.240
-0.273
-0.031
0.385
-0.210
0.051
-0.051
-0.215
1.000
0.537
0.099
-0.430
-0.073
0.066
-0.019
-0.258
0.576
1.000
0.098
-0.378
-0.093
-0.006
0.024
-0.080
0.118
0.145
1.000
-0.003
0.075
-0.012
0.021
-0.006
0.146
0.116
0.017
SALE t--1
BM t--1
NOL t--1
SHORT_
FALL t--1
DIVCON_
STRAINt--1
RETt
INVOPPt-1
0.255
-0.416
-0.286
0.006
1.000
0.105
-0.042
-0.127
-0.138
-0.054
-0.069
0.062
0.093
1.000
-0.026
0.235
0.040
0.065
-0.032
-0.008
-0.049
-0.023
1.000
0.011
-0.026
0.001
0.030
0.033
-0.198
0.148
0.033
1.000
-0.015
0.141
0.080
0.012
-0.270
-0.072
0.131
0.211
0.047
0.031
0.013
0.017
-0.032
0.099
0.021
-0.045
0.117
0.088
0.099
-0.012
-0.151
-0.863
0.038
-0.056
0.028
-0.039
0.011
0.007
0.155
-0.194
-0.110
-0.393
0.011
0.175
0.081
-0.025
-0.328
-0.206
0.137
0.222
-0.035
0.309
0.157
-0.021
-0.444
-0.119
0.216
0.219
0.280
-0.326
-0.273
-0.035
0.543
0.005
-0.026
-0.158
-0.260 -0.073
0.131
0.184
1.000
0.031
0.108
-0.156
0.323
0.415
-0.169
ROA t--1 STDROA t--1
STDRET t--1 NONEQCOMP t--1
0.007
-0.016
0.028 0.030 0.108 0.006 -0.102
0.016
0.052
-0.034 1.000
-0.033 -0.019
0.051
-0.028 -0.162 -0.867 0.039 -0.129
1.000 0.110
0.186
0.099
0.121
0.108 -0.061
0.303
0.235
-0.018
0.008 0.081 -0.528 -0.134 -0.351
0.082
0.051
-0.178 0.020
0.427 1.000
-0.373
-0.270
-0.002
1.000
0.251
0.173
-0.035 -0.399 -0.271 0.193 0.020
0.341 -0.051
0.382 -0.131
0.566
-0.021
-0.005 -0.421 -0.104 0.230 0.081
1.000
0.340
0.248
0.399 -0.001
0.205 -0.218
0.623
-0.004 0.648 -0.006 -0.027 -0.159
0.429
-0.365
-0.364
-0.191 0.200
-0.091 0.130
-0.276
-0.310
All variables are as defined in Table 2.
Pearson (Spearman) correlation coefficients are in the upper (lower) triangle. Bold, italic, and underlined numbers indicate significance at the 0.01, 0.05, and 0.10 levels or
better, respectively.
47
0.115
-0.090
0.127
-0.214
-0.310
1.000
TABLE 4
Tobit Analysis of the Relation between Equity Grants and Corporate Governance
EQUITYi ,t   0   1GOVSCORE i ,t 1 (GOVFACTORI ,T 1 )   2 INCENRESIDi ,t 1   3 SALES i ,t 1
  4 BM i ,t 1   5 NOLi ,t 1   6 SHORTFALL i ,t 1   7 DIV _ CONSTRAIN i ,t 1  8 RETi ,t 1   9 RETi ,t
  10 INVOPPi ,t 1   11 ROA i ,t 1   12 STDROA i ,t 1   13 STDRET i ,t 1   14 NONEQCOMP i ,t
47
2005
d 1
k 1998
  Bd IndustryDu mmiesd 
 D YearDummies
k
y
  i ,t
(1)
Panel A: The relation between equity grant values and corporate governance
Variable
Expected
Sign
Efficient
contracting
?
Expected
Sign
Managerial
Power
?
GOVSCOREt-1
0
-
GOVFACTORt-1
0
-
INCENRESIDt-1
-
-
SALEt-1
+
+
BMt-1
-
-
NOLt-1
+
+
SHORTFALLt-1
+
+
DIV_CONSTRAINt-1
+
+
RETt-1
+
+
RETt
+
+
INVOPP t-1
+
+
ROA t-1
+
+
STDROA t-1
?
?
STDRETt-1
?
?
NONEQCOMPt
?
?
Intercept
Model (1)
Coefficient
(Standard Error)
-0.016
(0.985)
-0.094 ***
(0.012)
-0.242
(0.054)
0.416
(0.044)
-1.110
(0.136)
0.164
(0.076)
0.934
(0.458)
0.127
(0.076)
0.027
(0.104)
0.320
(0.031)
0.733
(0.211)
0.020
(0.113)
1.421
(0.405)
1.912
(1.340)
0.573
(0.079)
48
Model (2)
Marginal
Effect
-0.251
***
0.727
***
-0.390
**
0.133
**
0.062
*
0.104
0.011
***
0.134
***
-0.251
0.002
0.059
0.116
***
Marginal
Effect
-0.398
***
***
Coefficient
(Standard Error)
0.860
(0.799)
0.537
-0.499
(0.038)
-0.184
(0.052)
0.403
(0.041)
-1.130
(0.143)
0.087
(0.072)
1.171
(0.446)
0.124
(0.070)
0.044
(0.104)
0.349
(0.030)
0.808
(0.201)
0.094
(0.102)
1.168
(0.400)
1.806
(1.780)
0.528
(0.075)
***
-0.544
***
-0.191
***
0.703
***
-0.405
0.072
***
0.078
*
0.095
0.018
***
0.143
***
-0.268
0.006
***
0.049
0.106
***
0.496
Industry dummies
Year Dummies
N
Pseudo R2
Yes
Yes
8,084
0.0334
Yes
Yes
8,084
0.0375
*,**,*** significant at the 0.1, 0.05 and 0.01 levels (two-tailed).
This panel provides Tobit results from estimating equation (1). Standard errors are calculated based on the Petersen’s (2009)
double cluster procedure to allow inter-correlations of residuals across firms or across time. All variables are as defined in
Table 2. Coefficients on the industry and year dummies are suppressed for expositional convenience. Industry dummies are
created based on the Fama-French 48 industry classifications and the year dummies are created for the years included in our
sample period.
49
TABLE 4
Tobit Analysis of the Relation between Equity Grants and Corporate Governance
EQUITYi ,t   0   1GOVi ,t 1   2 INCENRESIDi ,t 1   3 SALES i ,t 1   4 BM i ,t 1
  5 NOLi ,t 1   6 SHORTFALL i ,t 1   7 DIV _ CONSTRAIN i ,t 1  8 RETi ,t 1   9 RETi ,t   10 INVOPPi ,t 1
47
  11 ROA i ,t 1   12 STDROA i ,t 1   13 RETVOL i ,t 1   14 NONEQCOMP i ,t   B d IndustryDu mmies d
(1)’
d 1

2005
 D YearDummies
k 1998
k
y
  i ,t
Panel B: The relation between equity grant values and corporate governance with GOVINDEX replaced by five
internal governance variables
Variable
Expected
Sign
Efficient
contracting
?
Expected
Sign
Managerial
Power
?
CEOCHAIRt-1
0
+
BOARDSIZEt-1
0
+
INSIDEDIRt-1
0
+
HIREDBYCEOt-1
0
+
GRAYDIRt-1
0
+
INTERLOCKDIRt-1
0
+
OLDDIRt-1
0
+
BUSYDIRt-1
0
+
CEOHOLDINGt-1
0
-
NONCEOOWN5t-1
0
-
OUTDIROWNt-1
0
-
BLOCKHOLDERt-1
0
-
VULNERABILITYt-1
0
-
INCENRESIDt-1
-
-
SALEt-1
+
+
BMt-1
-
-
Control Variables
Intercept
Model (1)
Model (2)
Model (3)
Coefficient
(Standard Error)
1.079
(1.517)
0.062
(0.073)
0.009
(0.018)
-1.441 ***
(0.355)
0.035
(0.110)
-0.187
(0.521)
0.239
(1.635)
-0.834 ***
(0.247)
0.832 ***
(0.234)
-11.532 ***
(1.373)
-0.402 **
(0.154)
-9.109 ***
(3.936)
0.117
(0.086)
Coefficient
(Standard Error)
-1.325
(1.152)
Coefficient
(Standard Error)
2.467 ***
(1.212)
0.062
(0.075)
0.011
(0.018)
-1.471 ***
(0.357)
0.011
(0.111)
-0.263
(0.524)
0.216
(1.618)
-0.834 ***
(0.246)
0.851 ***
(0.236)
-11.756 ***
(1.406)
-0.405 ***
(0.158)
-9.268 **
(3.965)
0.112
(0.087)
0.002
(0.016)
0.198 ***
(0.047)
0.336 ***
(0.042)
-1.175 ***
(0.125)
0.186 ***
(0.046)
0.353 ***
(0.042)
-1.280 ***
(0.127)
50
-0.082
(0.018)
-0.282
(0.056)
0.485
(0.043)
-1.093
(0.138)
***
***
***
***
NOLt-1
+
+
SHORTFALLt-1
+
+
DIV_CONSTRAINt-1
+
+
RETt-1
+
+
RETt
+
+
INVOPP t-1
+
+
ROA t-1
+
+
STDROA t-1
?
?
STDRETt-1
?
?
NONEQCOMPt
?
?
0.047
(0.065)
0.515
(0.415)
0.132
(0.062)
0.169
(0.101)
0.408
(0.024)
0.576
(0.176)
0.118
(0.039)
1.048
(0.284)
2.763
(0.760)
0.439
(0.061)
Yes
Yes
8,084
0.0446
Industry dummies
Year Dummies
N
Pseudo R2
**
***
***
***
***
***
***
0.140
(0.078)
1.243
(0.467)
0.145
(0.079)
0.020
(0.105)
0.315
(0.031)
0.838
(0.222)
-0.017
(0.117)
1.460
(0.424)
1.801
(1.390)
0.604
(0.081)
Yes
Yes
8,084
0.0324
*
***
*
***
***
***
***
0.073
(0.065)
0.934
(0.407)
0.136
(0.064)
0.169
(0.101)
0.389
(0.025)
0.692
(0.176)
0.249
(0.098)
0.993
(0.297)
2.597
(0.782)
0.457
(0.063)
Yes
Yes
8,084
0.0452
**
**
***
***
**
***
***
***
*,**,*** significant at the 0.1, 0.05 and 0.01 levels (two-tailed).
This panel provides Tobit results from estimating equation (1)’. Standard errors are calculated based on the Petersen’s
(2009) double cluster procedure to allow inter-correlations of residuals across firms and across time. Coefficients on the
industry and year dummies are suppressed for expositional convenience. GOV is one of the following thirteen internal or
external governance mechanisms. CEOCHAIR: indicator variable set equal to one if the CEO is also chairman of the board,
and zero otherwise. BOARDSIZE: number of directors on the board. INSIDEDIR: percent of the board who are managers,
retired managers, or relatives of current managers. HIREDBYCEO: number of outside directors on the board appointed by
the CEO as a percent of the board size. GRAYDIR: number of outside directors whose employers have a financial
relationship with the company or who are former employees of the company, as a percent of board size. INTERLOCKDIR:
number of outside directors who are interlocked (a director is interlocked if an inside officer of the firm serves on the board
of an outside director’s company), as a percent of board size. OLDDIR: percent of outside directors who are over age 69.
BUSYDIR: percent of outside directors who serve on three or more other boards (six or more for retired outside directors).
CEOHOLDING: percent of outstanding shares owned by the CEO. NONCEOOWN5: indicator variable set equal to one if
the firm has an internal board member other than the CEO who owns at least 5% of the firm’s outstanding shares, and zero
otherwise. OUTSIDEROWN: total percentage of outstanding shares owned by outside directors divided by the number of
outside directors. BLOCKHOLDER: indicator variable set equal to one if the firm has an external blockholder who owns at
least 5% of the outstanding shares, and zero otherwise. VULNERABILITY is equal to 24 minus G-Index. All other
variables are as defined in Table 2.
51
TABLE 5
The Relation between Future Performance and Components of Equity Grants
FUTABROAi ,t  k ( FUTABRETi ,t  k )   0   1 EQUITY _ GOVi ,t   2 EQUITY _ ECOi ,t   3 EQUITY _ RESID i ,t
  4 ABROA i ,t ( ABRET i ,t )   5 SIZE i ,t   6 INVOPPi ,t   7 STDROA i ,t   8 STDRET i ,t   9 LEVERAGEi ,t
 IndustryDu mmies  YearDummie s   i ,t
(2)
Panel A: OLS regression of future performance on components of equity grants when governance strength is
measured by GOVSCORE.
Variable
Dependent Variable
Intercept (0)
EQUITY_GOVt
EQUITY_ECOt
EQUITY_RESIDt
ABROAt
Coefficient
(Standard Error)
Model (1)
FUTABROA
0.050
(0.241)
-0.041 ***
(0.012)
0.091 ***
(0.014)
-0.001
(0.001)
1.153 ***
(0.065)
ABRETt
SIZEt
INVOPPt
STDROAt
STDRETt
LEVERAGEt
Industry dummies
Year Dummies
N
Adj. R2
-0.035 ***
(0.005)
0.211 ***
(0.027)
0.046
(0.081)
-0.627 ***
(0.141)
-0.011
(0.027)
Yes
Yes
7,535
0.6266
Coefficient
(Standard Error)
Model (2)
FUTABRET
0.000
(0.004)
-0.004 ***
(0.001)
0.003 ***
(0.001)
0.000
(0.000)
0.006
(0.015)
-0.001 ***
(0.000)
0.001
(0.002)
0.001
(0.007)
-0.011
(0.012)
-0.003
(0.003)
Yes
Yes
7,522
0.0514
*,**,*** significant at the 0.1, 0.05 and 0.01 levels (two-tailed).
This panel presents OLS results from estimating equation (2). Standard errors are calculated based on the Cameron’s (2008)
bootstrapped clustered standard errors in both a firm and time dimension. Coefficients on the industry and year dummies
are suppressed for expositional convenience. FUTABROA is industry-adjusted operating performance, calculated as
income before extraordinary items after ESO expense summed over the subsequent three years scaled by total assets at the
end of the current year, denoted FUTROA, minus the industry median FUTROA, where the industry assignments are based
on the 48 Fama-French industry classifications. FUTABRET is the intercept coefficient, or alpha, from the firm-specific
Carhart’s (1997) four factor model estimated over the three-year period following the end of the current year as follows:
Ri ,t    1 RMRF t   2 SMBt   3 HML t   4 Momentumt   i ,t
(3)
where R is firm return minus risk-free rate, RMRF is the value-weighted market return minus the risk-free rate, and the
52
terms SMB (small minus big), HML (high minus low), and Momentum are the monthly returns on zero-investment factormimicking portfolios capturing size, book-to-market, and momentum effects, respectively. EQUITY_GOV and
EQUITY_ECO are the predicted components of equity grants arising from governance and economic factors, respectively.
EQUITY_GOV is computed as follows:
EQUITY _ GOVi ,t  1GOVSCOREi ,t 1
(4)
where 1 is the estimated coefficient on GOVSCORE reported in model (1) of Panel A in Table 4.
EQUITY _ ECOi ,t   2 INCEN _ RESIDi ,t 1  3 SALESi ,t 1   4 BM i ,t 1  5 NOLi ,t 1   6 SHORTFALLi ,t 1   7 DIV _ CONSTRAIN i ,t 1
47
 8 RETi ,t 1  9 RETi ,t  10 INVOPPi ,t 1  11ROAi ,t 1  12STDROAi ,t 1  13STDRETi ,t 1  14 NONEQCOMP i ,t   Bd IndustryDu mmiesd
d 1
(5)
where j is the estimated coefficient on each economic determinant j reported in model (1) of Panel A in Table 4. The
residual equity grant value explained by neither economic nor governance factors, denoted EQUITY_RESID, is calculated
as EQUITY minus the sum of EQUITY_GOV and EQUITY_ECO. Size is the logarithm of total assets. Industry dummies
are based on the 48 Fama-French industry classifications. All other variables are as defined in Table 2.
53
TABLE 5
The Relation between Future Performance and Components of Equity Grants
FUTABROAi ,t  k ( FUTABRETi ,t  k )   0   1 EQUITY _ GOVi ,t   2 EQUITY _ ECOi ,t
  3 EQUITY _ RESID i ,t   4 ABROA i ,t ( ABRET i ,t )   5 SIZE i ,t   6 INVOPPi ,t   7 STDROA i ,t   8 STDRET i ,t
  9 LEVERAGEi ,t  IndustryDu mmies  YearDummie s   i ,t
(2)
Panel B: OLS regression of future performance on components of equity grants when governance strength is
measured by GOVFACTOR.
Variable
Dependent Variable
Intercept (0)
EQUITY_GOVt
EQUITY_ECOt
EQUITY_RESIDt
ABROAt
Coefficient
(Standard Error)
Model (1)
FUTABROA
0.047 ***
(0.239)
-0.021 ***
(0.005)
0.098 ***
(0.015)
-0.001
(0.001)
1.156 ***
(0.062)
Coefficient
(Standard Error)
Model (2)
FUTABRET
0.000
(0.005)
-0.002 ***
(0.001)
0.0013 ***
(0.0007)
0.0001
(0.0002)
ABRETt
SIZEt
INVOPPt
STDROAt
STDRETt
LEVERAGEt
Industry dummies
Year Dummies
N
Adj. R2
0.007
(0.016)
-0.001 ***
(0.000)
0.000
(0.002)
0.001
(0.008)
-0.012
(0.012)
-0.003
(0.003 )
Yes
Yes
7,522
0.051
-0.038 ***
(0.005)
0.203 ***
(0.028)
0.047
(0.085)
-0.619 ***
(0.135)
-0.008
(0.027)
Yes
Yes
7,535
0.6265
*,**,*** significant at the 0.1, 0.05 and 0.01 levels (two-tailed). Standard errors are calculated based on Cameron’s (2008)
bootstrapped clustered standard errors in both a firm and time dimension. All variables are as defined in Table 2.
Coefficients on the industry and year dummies are suppressed for expositional convenience. FUTABROA is industryadjusted operating performance, calculated as income before extraordinary items after ESO expense summed over the
subsequent three years scaled by total assets at the end of the current year, denoted FUTROA minus the industry median
FUTROA, where the industry assignments are based on the 48 Fama-French industry classifications.; FUTABRET is the
intercept coefficient, or alpha, from the firm-specific Carhart’s (1997) four factor model estimated over the three-year
period following the end of the current year:
Ri ,t    1 RMRF t   2 SMBt   3 HML t   4 Momentumt   i ,t
(3)
where R is firm return minus risk-free rate, RMRF is the value-weighted market return minus the risk-free rate, and the
54
terms SMB (small minus big), HML (high minus low), and Momentum are the monthly returns on zero-investment
factor-mimicking portfolios capturing size, book-to-market, and momentum effects, respectively. EQUITY_GOV and
EQUITY_ECO are the predicted components of equity grants arising from governance and economic factors, respectively.
EQUITY_GOV is computed as follows:
EQUITY _ GOVi ,t  1 GOVFACTORi ,t 1
(4)
where 1 is the estimated coefficient on GOVFACTOR reported in model (2) in Panel A of Table 4.
EQUITY _ ECOi ,t   2 INCEN _ RESIDi ,t 1  3 SALESi ,t 1   4 BM i ,t 1  5 NOLi ,t 1   6 SHORTFALLi ,t 1   7 DIV _ CONSTRAIN i ,t 1
47
 8 RETi ,t 1  9 RETi ,t  10 INVOPPi ,t 1  11ROAi ,t 1  12 STDROAi ,t 1  13RETVOLi ,t 1  14 NONEQCOMP i ,t   Bd IndustryDu mmiesd
d 1
(5)
where j is the estimated coefficient on each economic determinant j reported in model (2) of Panel A in Table 4. The
residual equity grant value explained by neither economic nor governance factors, denoted EQUITY_RESID, is calculated
as EQUITY minus the sum of EQUITY_GOV and EQUITY_ECO. Size is logarithm of total assets. Industry dummies are
based on the 48 Fama-French industry classifications. All other variables are as defined in Table 2.
55
TABLE 6
Test of the Substitution Explanation
Panel A: Mean (median) future operating performance measured as income before extraordinary items after
ESO expense cumulated over the three-year period subsequent to the grants of stock options and restricted
stock (FUTABROA)
Mean
[Median]
FUTABROA
GOVSCORE
EXCESS_EQUITY
t-test
Wilcoxon
Low
High
Weak
0.1828
[0.0834]
0.1243
[0.0570]
Strong
0.1933
[0.1343]
0.1532
[0.1025]
p-value for
Strong-Weak
0.0417
[<0.0001]
0.0043
[<0.0001]
p-value for
High-Low
<0.0001
[<0.0001]
<0.0001
[<0.0001]
We sort all firm-years independently into 22 portfolios based on GOVSCORE and EXCESS_EQUITY. We report the
mean and median industry-adjusted future accounting earnings (FUTABROA) measured as income before extraordinary
items after ESO expense summed over the subsequent one year (three years) scaled by total assets at the end of the current
year, denoted FUTROA, minus industry median FUTROA, where the industry assignments are based on the 48 FamaFrench industry classifications. EXCESS_EQUITY is the residual from equation (6). All variables are as defined in Table
2.
EQUITYi ,t   0  1INCENRESIDi ,t 1   2 SALESi ,t 1  3 BM i ,t 1   4 NOLi ,t 1  5 SHORTFALL i ,t 1   6 DIV _ CONSTRAIN i ,t 1
  7 RETi ,t 1  8 RETi ,t  9 INVOPPi ,t 1  10 ROAi ,t 1  11STDROAi ,t 1  12 STDRETi ,t 1  13 NONEQCOMP i ,t
48
 d IndustryDu mmiesd   i ,t
d 1
(6)
56
TABLE 6
Test of the Substitution Explanation
Panel B: Mean (median) abnormal return over the three-year period subsequent to the grants of stock options
and restricted stock (FUTABRET)
Mean
[Median]
FUTABRET
GOVSCORE
EXCESS_EQUITY
t-test
Wilcoxon
Low
High
Weak
0.0024
[0.0031]
0.0016
[0.0025]
p-value for
High-Low
<0.0001
[0.0059]
Strong
0.0040
[0.0041]
0.0036
[0.0039]
0.1300
[0.3012]
p-value for
Strong-Weak
0.0571
[0.040]
0.0053
[<0.0001]
We sort all firm-years independently into 44 portfolios based on GOVSCORE and EXCESS_EQUITY. We report the
mean and median industry-adjusted abnormal return performance (FUTABRET), measured as operating cash flows
summed over the subsequent three years scaled by total assets at the end of the current year, denoted FUTROA, minus
industry median FUTROA, where the industry assignments are based on the 48 Fama-French industry classifications.
EXCESS_EQUITY is the residual from equation (6). All variables are as defined in Table 2.
EQUITYi ,t   0  1INCENRESIDi ,t 1   2 SALESi ,t 1  3 BM i ,t 1   4 NOLi ,t 1  5 SHORTFALL i ,t 1   6 DIV _ CONSTRAIN i ,t 1
  7 RETi ,t 1  8 RETi ,t  9 INVOPPi ,t 1  10 ROAi ,t 1  11STDROAi ,t 1  12 STDRETi ,t 1  13 NONEQCOMP i ,t
48
 d IndustryDu mmiesd   i ,t
d 1
(6)
57
TABLE 7
Changes in ESO Grant Values Pre- versus Post-Enron
DIFFOPTION i   0   1GOVSCORE 9801i (GOVFACTOR9801i )   2 DIFFRSTK i
  3 DIFFGOVSCORE i (or DIFFGOVFACTOR i )   4 DIFFINCENRESIDi   5 DIFFSALE i
  6 DIFFBM i   7 DIFFNOLi   8 DIFFSHORFA LLi   9 DIFFDIVCON STRAIN i   10 DIFFLAGRETi
  11 DIFFRETi  12DIFFINVOPPi   13 DIFFROA i   14 DIFFSTDROA i   15 DIFFSTDRET i
  16 DIFFNONEQCOMPi  IndustryDu mmies   i
(7)
Variable
Coefficient
(Standard Error)
Model (1)
1.941 ***
(0.372)
0.073 ***
(0.026)
Control Variables
Intercept
GOVSCORE9801
GOVFACTOR9801
DIFFRSTK
0.211 ***
(0.079)
-0.014
(0.040)
-0.011
(0.040)
0.035
(0.041)
DIFFGOVSCORE
DIFFGOVFACTOR
DIFFINCENRESID
-0.085
(0.128)
0.143
(0.228)
0.230
(0.929)
0.183
(0.255)
-0.027
(1.392)
-0.178
(0.261)
0.540
(0.328)
0.702
(0.288)
3.732
(0.980)
-1.463
(1.348)
0.480
(1.569)
-5.280
(3.046)
0.384
(0.148)
Yes
DIFFSALE
DIFFBM
DIFFNOL
DIFFSHORTFALL
DIFFDIVCONSTRAIN
DIFFLAGRET
DIFFRET
DIFFINVOPP
DIFFROA
DIFFSTDROA
DIFFSTDRET
DIFFNONEQCOMP
Industry dummies
58
Model (2)
1.961 **
(0.376 )
*
**
***
*
***
0.091
(0.128)
-0.052
(0.129)
0.136
(0.225)
0.256
(0.915)
0.218
(0.259)
-0.026
(1.387)
-0.232
(0.259)
0.537
(0.321)
0.711
(0.285)
3.691
(0.963)
-1.335
(1.345)
0.311
(1.584)
-4.811
(3.073)
0.377
(0.146)
Yes
*
**
***
***
N
Adj. R2
1007
0.0331
1007
0.0334
*,**,*** significant at the 0.1, 0.05 and 0.01 levels (two-tailed).
This panel presents OLS results from estimating equation (7). t-statistics are Huber-White sandwich robust standard errors.
Coefficients on the industry and year dummies are suppressed for expositional convenience. DIFFOPTION is the average
OPTION over the post-Enron period (2002-2006) minus the average OPTION over the pre-Enron period (1998-2001),
where OPTION is defined as the logarithm of (1+ grant day fair value of ESOs for the CEO). GOVSCORE9801 and
GOVFACTOR9801 are the mean GOVSCORE and GOVFACTOR over the pre-Enron period (1998-2001), respectively.
DIFFRSTK is the average RSTK over the post-Enron period (2002-2006) minus the average RSTK over the pre-Enron
period (1998-2001), where RSTK is the logarithm of (1+ grant day fair value of restricted stock for the CEO). We include
the change in the grant day fair value of restricted stock from the pre- to the post-Enron period, denoted DIFFRSTK, to
control for the potential substitution between ESOs and restricted stock. DIFF_Y is average Y over 2002-2006 minus
average Y over 1998-2001, where Y is one of the 14 independent variables in equation (1).
59
TABLE 8
The Effect of the Divergence between the Subjective Value and the Fair Value of Equity Awards
on the Association between Equity Grants and Corporate Governance
Variable
Coefficient
(Standard Error)
Model (1)
8.988 ***
(0.700)
-0.047 ***
(0.007)
Intercept
GOVFSCORE t-1
GOVFACTOR t-1
SUB_FVt-1
-10.508
(0.145)
-0.001
(0.028)
0.536
(0.026)
-1.900
(0.466)
0.053
(0.049)
-0.101
(0.276)
-0.231
(0.051)
0.016
(0.060)
0.311
(0.009)
0.035
(0.172)
-0.014
(0.037)
0.154
(0.237)
-9.942
(0.641)
0.435
(0.041)
Yes
8084
0.2328
INCENRESID t-1
SALEt-1
BMt-1
NOLt-1
SHORTFALLt-1
DIV_CONSTRAINt-1
RETt-1
RETt
INVOPPt-1
ROAt-1
STDROAt-1
STDRETt-1
NONEQCOMPt
Industry dummies
N
Pseudo R2
***
***
***
***
***
***
***
Coefficient
(Standard Error)
Model (2)
9.200 ***
(0.765)
-0.224
(0.022)
-10.429
(0.142)
0.022
(0.028)
0.534
(0.025)
-1.889
(0.451)
0.020
(0.048)
0.019
(0.269)
-0.237
(0.049)
0.085
(0.017)
0.322
(0.009)
0.017
(0.170)
0.014
(0.035)
0.056
(0.245)
-9.914
(0.636)
0.417
(0.038)
Yes
8084
0.2345
***
***
***
***
***
***
***
***
***
*,**,*** significant at the 0.1, 0.05 and 0.01 levels (two-tailed). Standard errors are calculated based on the Petersen’s
(2009) double cluster procedure to allow inter-correlations of residuals across firms or across time. All variables are as
defined in Table 2. Coefficients on the industry and year dummies are suppressed for expositional convenience. SUB_FV
is the subjective value of equity grants over its fair value. We use the Ingersoll (2006) model to compute the subjective
value. In Ingersoll (2006), the subjective value of an ESO to a risk adverse employee is given by the following equation
60
VIngersoll  Se  qˆT N (h1 )  Ke rT N (h2 )
(8)
Where ,
s
ln( )  (rˆ  qˆ  0.5 *  2 )(T )
k
h1 
 T
h2  h1   T
qˆ  q  (1   ) (1   ) 2
rˆ  r  (1   ) 2 2
where S is the price of the underlying stock on the grant day, q is the dividend yield on the stock, T is the expected life of
the option, K is the exercise price of the employee stock option, r is the risk-free interest rate, N(.) is the cumulative
normal distribution function, is the annualized total variance of the stock, σ is the expected annual stock price volatility,
 (  1) >0 is the relative risk aversion parameter,  2 is the residual firm specific variance of the stock, and  is the
fraction of his own wealth an executive is constrained to hold in his firm’s stock beyond that represented in the market
portfolio. Without loss of generality, we set the expected life equal to the contractual life of the option. We obtain the riskfree rate, dividend yield, and total variance of the stock from ExecuComp. The residual firm specific variance is the
annualized variance of the residual from a firm-specific rolling 36-month market model regression. The subjective value of
a restricted stock is given by the following equation.
S Ingersoll  Se  qˆT
(9)
Where S is the market price of restricted stock. We compute VIngersoll (SIngersoll) for each individual option (stock) grant. The
subjective value of the CEO’s equity grants, denoted SUBJECTIVE, is calculated as the sum of VIngersoll and SIngersoll for all
option (stock) grants that the CEO received in a particular year. In Table 8, we set the relative risk aversion parameter
equal to 2.0 and assume that  is equal to 50%. SUB_FV is defined as SUBJECTIVE divided by the grant day fair value
of the option and restricted stock awards. The grant day fair values of options and restricted stock are obtained from
Execucomp. Our results are inferentially similar if we set  equal to 25% or 75% or use a relative risk aversion parameter
of 3.0.
61