Offsetting Behavior and Compensation Reform∗
N. K. Chidambaran
Graduate School of Business
Fordham University
New York, NY 10023
(646) 828-7830
chidambaran@fordham.edu
Nagpurnanand R. Prabhala
Robert H. Smith School of Business
University of Maryland
College Park, MD 20742
(301) 405 2165
nprabhal@rhsmith.umd.edu
July 2009
JEL Classification: G30, G34, G38, K22, L51, M48
∗
We thank Doron Avramov, Effi Benmelech (the WFA discussant), Ren-Raw Chen, Jeffrey Coles,
Doug Emory, Ben Esty, Jon Ingersoll, Dirk Jenter, Kose John, Mudit Kapoor, Michael Long, Luann
Lynch, Dilip Madan, Vojislav Maksimovic, Robert Marquez, Stewart Mayhew, Oded Palmon, Charu
Raheja, seminar participants at CUNY Baruch, Darden School, FDIC, Fordham University, Indian
School of Business, University of Minnesota, Vanderbilt University, and the 2008 Western Finance
Association meetings for their comments.
Offsetting Behavior and Compensation Reform
ABSTRACT
Calls for regulation and reform of compensation have intensified following the
2008 financial crisis, as flawed compensation is implicated as a cause of the
crisis. Our study illustrates a key difficulty in implementing reform through
regulation: offsetting behavior, which can entirely undo regulatory intent and
even impose additional costs on shareholders. We show these effects in the context of compensation contracts, using as a laboratory the punitive disclosure
requirements imposed in 1998 to deter repricing. Firms demonstrate strong
offsetting behavior by squeezing out compensation through a substitute, which
is paradoxically costlier for shareholders. The excess costs are best explained
by a wedge between employee and firm incentive valuations and we characterize its nature. We also find offsetting behavior in broader samples of all firms
with underwater options after 1998. We discuss the implications for the design of compensation design and reforms likely to be most effective in curbing
compensation excesses.
Curbing executive compensation is like moving Jell-O.
When it’s limited in one area, it squirts out in another.
– Kayla Gillan, Ex-general counsel, CALPERS1
1
Introduction
The 2008 financial crisis has led to unprecedented levels of government intervention in
financial markets and calls for a new framework for regulating markets. A key target
for regulatory reform, one that has drawn extensive attention in the popular press,
is executive compensation contracts. Flawed compensation practices are held to be
one cause of excessive risk-taking that led to the 2008 financial crisis. Thus, compensation restrictions form an important component of the 2008 Emergency Economic
Stabilization Act.2 The discussion over reforming compensation practices is familiar
to researchers in executive compensation. The 1990s have witnessed a sharp growth
in executive compensation (see, e.g., Murphy (1999), Bebchuk and Fried (2004), or
Bebchuk and Grinstein (2005)), fueled largely by options and equity-linked incentive
compensation, Has this compensation growth been appropriate or excessive? How
could compensation be reined in or reformed? Firm answers have proven elusive and
the literature continues to debate the issues.
Several proposals to reform compensation involve imposing restrictions or penalties on particular elements of compensation packages. An early instance is a provision
in the 1993 Omnibus Budget Reconciliation Act that eliminates the tax deductibility
of base salaries over $1 million. Other restrictions include eliminating options, using
industry-indexed option payoffs, using restricted stock grants in lieu of options, or
1
Kayla Gillan, former general counsel of the California Public Employees Retirement System
(CALPERS) the largest pension fund in the U.S., and member of the Public Companies Accounting
Oversight Board, quoted in The Wall Street Journal, October 13, 2006
2
For instance, the CEOs and the top 100 executives of institutions receiving Troubled Asset
Relief Program (TARP) funding are subject to compensation restrictions as per the interim final
rules issued by the Financial Stability Board on June 10, 2009. Restricting compensation contracts
is also a target for future regulatory reforms aimed at preventing financial crises. See Section V.H of
the June 2009 document “Financial Regulatory Reform: A New Foundation” issued by the Treasury.
1
more recently, curbing certain types of compensation such as golden parachutes or
outright caps on bonuses or salaries through government regulations. The likely economic consequences of such efforts are of major interest to both academics and policy
makers and this is our central focus. We show that attempts to reform compensation
by restricting or penalizing specific pieces are particularly difficult. As suggested by
the introductory quote from the CALPERS general counsel, these efforts often lead
to simply shifting the compensation vehicle. Such shifts could actually end up imposing additional costs in unexpected ways as we show here. These effects need to
be incorporated into the efforts to reform compensation.
One difficulty with enforcing reform is pointed out by Benmelech and Moskowitz
(2009), who study 19th century usury laws. They point out that regulations are often
written for private rather than public interest. As a result, “... regulation designed
to serve the politically and financially weak has the unintended consequence of exacerbating their plight.” We illustrate that similar distortionary effects arise due to a
different economic force: offsetting behavior of the regulated in response to restrictions. As Peltzman (1975) points out, offsetting behavior could attenuate, entirely
reverse, or even produce harmful effects that undermine the intent of regulatory restrictions. We empirically illustrate offsetting behavior and its consequences in the
context of compensation contracts.
We find offsetting behavior of a particularly strong flavor: when faced with compensation restrictions, a sample of firms not only offset the restrictions through a
contracting mutation, but also end up incurring excess costs as a result. Using the
taxonomy of Peltzman (1975), this is an unusual instance of “more than complete”
offset. The fact that shareholders might incur extra costs may seem puzzling because substituting one form of compensation for another could perhaps be done in
a cost-neutral manner. However, as we show, contracting alternatives need not be
cost neutral mutations even if designed to deliver similar benefits. In fact, we show
that excess costs are exactly what one should expect when there is wedge between
how firms and employees value incentive contracts. We characterize the nature of this
2
wedge implied by our sample data and provide supplementary evidence on its nature
in an independent sample.
The empirical setting in our paper is a regulation-induced shift away from repricing
after 1998. To motivate the details of our analysis, consider a firm that grants an
employee stock option at date t. Consider a future date t + ∆t, where ∆t is of the
order of a few months, well before the typical option term of 10 years. The firm’s stock
price could increase over the interval (t, t + ∆t), in which case the option becomes
in-the-money. On the other hand, the stock price could drop, in which case the option
grant is “underwater” at t + ∆t. In principle, the firm could do nothing and let the
options be, on grounds that employees who benefit from options’ upside should also
suffer its downside consequences. In practice, however, firms make attempts to revive
deep out-of-the-money underwater options. Historically, one approach was to reprice
options, or lower their strikes. Vociferous opposition to repricing precipitated punitive
disclosure requirements that required repricers to expense options as a charge to
income. Not surprisingly, the new rules essentially extinguished repricing. As Figure
1 shows, repricing fell out of favor after 1998. The demise of repricing is especially
stark given the post-Internet bubble stock market crash after 2000. Ordinarily, such a
crash would increase repricing. Yet, Figure 1 shows that repricing is virtually extinct
after 1998.
While option repricing drops after 1998, option grants do not. Firms continue to
make option grants. These options can go underwater and the question is what firms
do given that the bar is raised on repricing. One possibility is that firms could do
nothing to make up for underwater options. Alternatively, they could work around
repricing restrictions, by squeezing out compensation through other means. In particular, one alternative used by firms was to make refresher’ grants. In this approach,
firms give employees some quantity of new at-the-money options to compensate for
the old options going underwater, leaving the old options as is. Figure 2 illustrates
out of cycle refresher grants for EXECUCOMP firms. Refresher grants show a clearly
3
visible spike right around the demise of repricing, indicating that firms seemed to use
refreshers to substitute for repricing (see, e.g., Murphy (1999) or Murphy (2003)).3
Our analysis of what firms do in response to the restrictions on repricing is in two
parts. First we examine in detail the costs when firms offset repricing restrictions
through out-of-cycle refresher grants. Next we examine the overall compensation in
all firms that have underwater options. A critical first step in our analysis of offsetting
behavior through refresher grants is to identify a reliable sample of firms that make
out of cycle refresher grants to compensate for options going underwater. A difficulty
is that compensation disclosures do not explicitly mark refresher grants. We adopt the
empirical strategy of Murphy (2003), who observes that multiple option grants within
the same year, or “out-of-cycle” grants, could mark refresher grants. We start with
a sample of multiple grant firms identified from the EXECUCOMP database. We
carefully refine the initial sample to preclude idiosyncratic causal factors unrelated
to underwater options through quantitative screens described later and ultimately
by reading proxies and 10-K’s for any inconsistencies or errors. Our final sample
comprises 135 cases in which firms use out-of-cycle grants to refresh portfolios of
underwater options.
The second step in our analysis is to identify the portfolio of outstanding underwater stock options. This is not straightforward given the nature of disclosures
available in proxies and 10-K statements, which do not clearly identify option portfolios of executives at the end of each year. We build the time t options portfolio of
each executive by sequentially updating information on t − 1 portfolios with the flow
of new grants, exercises, and cancellations reported in EXECUCOMP. We determine
the option moneyness and maturity of outstanding options as of the date on which a
refresher grant is made.
We compare the costs of refresher grants with the costs of a hypothetical counterfactual repricing of all the outstanding out-of-the money options. We use the
3
Footnote 26 in Murphy (1999) is especially relevant. Murphy (2003) discusses survey evidence
from compensation consultants on refresher grant usage and presents data on practices of new
economy firms.
4
Black-Scholes model to compute the cost of refresher grants and that of the hypothetical repricing. On a firm-wide basis, the median (mean) Black-Scholes cost of
the out-of-cycle refresher grants equal $8.1 mm ($49.8 mm). Had the companies in
our sample repriced the outstanding out-of-the-money options, the median (mean)
costs of repricing would be $2.2 mm ($17.7 mm). The median (mean) excess cost of
making refresher grants in lieu of repricing is positive and equals $4.2 mm ($32 mm).
This example illustrates offsetting behavior and its costs. We have an example of a
“Peltzman Effect” with more than complete offset, where the costs of the offsetting
behavior (refresher grants) exceed the costs of repricing being eliminated by firms.
The additional costs of refresher grants vis-a-vis repricing are economically intriguing in their own right. Why might it be economically rational for firms to incur
these costs? We consider and rule out several explanations for the excess refresher
costs. One explanation is that the excess costs reflect secular trends in the labor
markets after 1998 that led to increases in compensation for firms making refresher
grants. We test for this hypothesis using compensation regression specifications used
in the literature and show cannot explain the cost differentials. Another possibility
is that there are other compensation adjustments we do not pick up. This possibility
is rejected by our sampling strategy, the prior literature on repricing, and the compensation data around refresher grant dates. Yet another explanation is that perhaps
the portfolio of underwater and at-the-money option grants created by a refresher
grants strategy is a more “optimal” contact relative to repricing. This is implausible.
All received evidence we are aware of suggests that firms consider underwater options
a nuisance with poor motivational effects, which calls for such options to be extinguished rather instead of being kept alive, as in a refresher grant strategy. Above all,
the hypotheses has a hard time explaining the time series pattern in the evolution of
refresher grants, specifically, why refresher grants spike right around the 1998 regulations concerning repricing. Finally, we show that the features peculiar to employee
stock options such as early exercise cannot explain the cost differential.
5
In our view, a more convincing explanation for the excess costs is the existence of
a wedge between the ways employees and firms value equity incentives. Specifically,
if employees’ valuation of options declines faster than the firms’ valuation when stock
prices decline, strategies that keep underwater options alive (such as refresher grants)
will be costlier than strategies that extinguish underwater options (such as repricing).
Such a wedge arises, for instance, in valuation models where employees are risk averse
and under-diversified (Hall and Murphy (2000), Ingersoll (2006), Meulbroek (2001)).
Alternatively, the wedge could be a by-product of behavioral models of excessive
extrapolation where sentiments cause employee valuations to swing from optimism to
pessimism as stock prices decline (Bergman and Jenter (forthcoming)). Our results
are consistent with the existence of such an employee-firm valuation wedge.
The second part of our analysis broadens our analysis from just out of cycle
refresher granting firms to incorporate all firms facing significant underwater options.
The motivation for this exercise is that our first analysis covers only a subsample
of firms facing underwater options: those who respond with out of cycle refresher
grants. In this sample, offsetting behavior results in excess costs. To assess the
overall impact of the 1998 shift away from repricing, we need to examine other firms
who faced underwater options but who did not use out of cycle refreshers. Some
of these firms may have elected to forgo all repricing substitutes. Others could have
bundled substitute compensation within the normal cycle, masking its visibility to the
empirical researcher. If the broader effect of the 1998 shift is to cut back compensation
at firms whose options go underwater after 1998, the testable implication is that the
total compensation at these firms should decline relative to predicted compensation.
We implement this test by identifying underwater firms after 1998 and testing whether
their compensation is abnormally low relative to expected compensation. We find
little evidence of a post-1998 slowdown in compensation at these firms, suggesting
that the impact of offsetting behavior is detectable even in the aggregate universe of
all “underwater” firms and not just the subset who made refresher grants.
6
The rest of the paper proceeds as follows. Section 2 reviews the related literature.
Section 3 develops the refresher grant sample. Section 4 presents evidence on their
excess costs of refresher grants. Section 5 presents evidence from the broad universe of
all firms facing underwater options. Section 6 ties the excess costs of refresher grants
to the wedge between how firms and employees value incentive options. Section 7
concludes. The Appendix presents additional independent evidence from Microsoft’s
2003 tender offer for underwater stock options.
2
Related Literature
The theme of offsetting behavior has its origins in regulatory economics. The seminal study by Peltzman (1975) examines automobile seat belt regulations. Peltzman
argues that while mandating seat belts increases driver safety, this theoretical engineering benefit is diluted by offsetting behavior. Seat-belted drivers may drive faster
or in more reckless ways, lessening the benefit of seatbelts for themselves. A more
subtle effect is that more risky driving increases the hazards for pedestrians and other
less protected road users. Peltzman (2005) reviews other work on offsetting behavior,
including responses to the Endangered Species Act (Lueck and Michael (2003)) and,
in labor economics, responses to the Americans with Disabilities Act (Acemoglu and
Angrist (2001)). Offsetting behavior is also a staple of the tax policy literature on
the Laffer curve, where supply side economists argue that tax revenues may actually
increase after tax cuts because employees are incentivized to work harder and thus
generate more taxable income. Such effects are the focus of the “new tax responsiveness” literature that analyzes taxable income (rather than labor supply) responses to
tax changes (Feldstein (1995), Goolsbee (2002), Goolsbee (2000)).
Interesting offsetting behavior is also documented by Klick and Stratmann (2003),
who study workplace safety inspections. They report that more safety inspections
counterintuitively lead to higher death rates, suggesting that increased the benefits
of more safety measures are offset by riskier behavior on the part of workers. In a
7
different context, Milkovic, Nganje, and Onyango (2009) report that food poisoning
cases increase after new, more stringent food safety policies are enacted and implemented in the food processing sector.4 Other examples of offsetting behavior include
Galston (2007), who argues that campaign finance reforms were offset by politicians
who form well funded finance vehicles and following the 2006 reassertion of Buckley
v. Valeo, offset by increases in millionaire politicians and entrenched incumbents.
While offsetting behavior in the above cases has negative effects, it could also cause
unexpected positive effects. Yun (2002) is a case in point. Yun points out that oints
out that regulatory effects to improve fuel standards through Corporate Average Fuel
Economy (CAFE) effectively lower the weights of cars, which should have a detrimental effect on vehicle safety. Yet, the effect of CAFE is to reduce death rates by 6%,
which Yun attributes to less risky driving by drivers in lighter cars, who recognize
the greater vulnerability of their cars to accidents.
Other studies, while not couched in the language of Peltzman’s offsetting behavior
hypothesis, give similar insights. Multi-task agency models studied by Holmstrom
and Milgrom (1991) and Holmstrom and Milgrom (1994). These studies point out
that mandating measurable standardized testing for children could result in offsetting
behavior. More time could be spent on education outcomes that are measurable,
which could compromise unmeasured dimensions of learning such as creativity. Perry
and Zenner (2000) discuss offsets of the 162(m) salary cap. Restrictions on taxdeductibility of salaries are offset by firms by increasing incentive grants through
means such as options, in the spirit of the introductory quote by the CALPERS
counsel. In a Coasian setting, Hermalin and Weisbach (2007) make a similar point
about imposing governance reforms through external fiat.
Like the examples cited above, we too focus on potentially well-meaning reforms
or regulations and the difficulties created by offsetting behavioral responses. The
empirical setting, the alteration in the structure of equity incentive contracts, is new.
4
This is essentially the observation that people prefer their hamburgers rare and are more likely
to eat them rare when meat safety standards are perceived to be more strict.
8
Our setting also has desirable empirical features. We can observe individual-level
data on offsetting behavior, estimate its costs with standard and well accepted valuation methods from finance, and we can sign the costs net of offsetting behavior.
The specific estimates of offsetting behavior costs are also interesting: in our refresher
grant sample, the costs of refreshers exceed those of the repricing contracts they substitute for. Prior work in economics reports effects of varying degrees of significance,
ranging fom mixed to weak evidence on taxes (Feldstein (1995), Goolsbee (2002)) to
strong impact of offsetting behavior in the hiring of the disabled following the 1986
Americans with Disabilities Act ((Acemoglu and Angrist (2001)).
Our evidence is also relevant to the compensation literature. A prominent stream
of compensation research that has garnered much publicity is the apparent compensation excesses since the 1990s. Empirical evidence of compensation growth and
its causal roots in options is provided by Murphy (2002), who shows that over the
1990s, median CEO pay for S&P 500 industrial firms increases from $2.3 million
to $6.5 million, driven by stock options, which increase from 25% of pay to 51%
of pay. Explanations for the growth vary from the benign to the malevolent. Options growth could reflect belated recognition by shareholders that incentives were
historically deficient, as famously pointed out by Jensen and Murphy (1990). Compensation increases could also reflect managerial expropriation of shareholder wealth,
termed as “pay without performance” (Bebchuk and Fried (2004)), although this
view is not without its skeptics (Murphy and Zabojnik (2004)). Alternatively, Murphy (2002) and Jensen, Murphy, and Wruck (2004) suggest that compensation growth
simply reflects a consistent underestimation of option costs by boards and compensation committees. While the literature continues to debate the source and extent of
compensation excesses, a widespread perception that compensation growth has been
excessive has resulted in efforts to contain compensation costs by institutions. Our
work has implications for these efforts. We point out the types of reform measures
that are less likely to be successful and the approaches that could be more fruitful in
containing compensation growth.
9
There is now considerable work on option repricing (e.g., Brenner, Sundaram,
and Yermack (2000), Carter and Lynch (2001), Chance, Kumar, and Todd (2000)
and Chidambaran and Prabhala (2003), Chen (2004)). Repricing has two interpretations. One school of thought holds that it is expropriation of shareholder wealth by
managers. A countervailing view is that repricing reinvigorates option incentives or
helps employee morale or retention. Our study is not about why firms reprice and
our results have implications for both schools of thought. If repricing is viewed as a
beneficial mechanism, our results say that shutting it down pushes firms to substitutes that may end up being costlier to shareholders compared to repricing. If, on
the other hand, repricing is viewed as expropriation, our results say that stopping one
form of expropriation – repricing – simply pushes managers to search for another, and
the latter could turn out to be costlier. The broader point, of course, is that merely
constraining one form of compensation flows is not enough. Rather, it is necessary to
examine the totality of all flows and consider constraining the entire package if the
aim is to contain compensation. As we discuss later, a similar viewpoint applies even
with alternative objective functions such as the disclosure of pay packages.
Our evidence also adds to the theoretical work by Hall and Murphy (2000), Meulbroek (2001), and Ingersoll (2006), who model the difference between the cost of equity
incentives to firms and its value to the receiving employee. These models emphasize
a wedge due to the risk-aversion and under-diversification while Bergman and Jenter
(2005) suggest behavioral origins of the wedge, perhaps due to excessive extrapolation
by employees (Benartzi (2001)). More recently, Chidambaran (2008) shows that even
in executive stock option repricings, non-executive employees receive 72% of repriced
options. Thus, the primary targets of repricing flows are non-executive employees,
making their employee valuation functions a relevant framework for understanding
refresher grants vis-a-vis repricing. To this literature, we introduce some of the first
empirical evidence on the employee-firm valuation wedge. Our refresher grant evidence tests the necessary implication of such models, viz., that the wedge exists and
10
it widens as options go out of the money. In addition, the Appendix to this paper
provides separate evidence of the wedge from the Microsoft (2003) options exchange.
Finally, our findings are relevant to a broader debate on governance failures and
reforms in the wake of scandals such as Enron and WorldCom. One question in this
literature is how governance changes should be implemented. In particular, a question
is whether mandating change through a one-size-fits-all fiat is effective. Romano
(2005) expresses skepticism about the effectiveness of such an approach. There is
also mounting evidence that governance is an endogenously determined choice of firms
(e.g. Demsetz and Lehn (1985), Himmelberg, Hubbard, and Palia (1999), Hermalin
and Weisbach (2007)). If so, mandating reforms by fiat could yield rather diluted
results relative to those in a world where governance environments are exogenously
determined. Our evidence is consistent with this view.
3
Out-of-Cyle Refresher Grant Sample
In principle, firms whose options go underwater could make refresher option grants
either by adding them to the normal annual options grant or by making a supplementary “out of cycle” grant on a different date. Refresher grants made as part of
the annual grant are empirically difficult to work with because the refresher grant
is co-mingled with the normal annual grant and the two are reported as a package.
Disentangling the portion that represents a refresher grant requires a model of the
expected annual option grant, for which there is no standard specification. Indeed,
it is likely that option granting practices are perhaps non-stationary over the 1990s
(Bebchuk and Grinstein (2005)). With all these difficulties, it is hard to extract estimates of underwater option offsets that are embedded within annual in-cycle grants.
While we do consider the aggregated effects in later tests, the most interesting estimates come from out of cycle refresher grants because these are relatively precise
individual-level estimates of the costs of offsetting behavior. Moreover, the costs are
11
of independent interest to the compensation literature as they represent evidence of
a wedge between employee and firm valuation of equity incentves.
Working with out of cycle refresher grants offers other empirical advantages. One
is that the grant dates for out of cycle grants are different from the date of the
normal annual grant, so a set of candidate transactions are easier to identify in EXECUCOMP. Because the date of these grants is known, it is quite straightforward to
infer the magnitude and sign of the returns prior to the out of cycle grants. Thus,
we can readily rule out refresher grants that clearly do not respond to stock price
declines. Finally, out of cycle grants tend to be standalone. In particular, there are
no other accompanying compensation adjustments on the grant date, a fact that we
can (and do) verify in the data.
3.1
Identifying Firms That Make Refresher Grants
To focus our attention on the changes around the 1998 regime change and to avoid the
effects related to the 2002 Sarbanes-Oxley Act, we analyze samples of firms making
refresher grants prior to fiscal 2002. Our initial screen yields a candidate pool of 151
firms that make refresher grants. From this initial set, we read proxies and 10-K’s and
screen out cases with idiosyncratic causal factors for a second grant such as merger or
restructuring, reloads, or formula-based accelerated grants. For instance, Campbell
Soup appears to make multiple grants in fiscal 2002 but two of the grants reported
in ExecuComp appear to relate to a prior year. In fiscal 1998, Nova Corporation
appears to make multiple grants, but these grants are caused by a takeover of a firm
(PMT) that became a fully owned subsidiary. Timken Corporation makes multiple
grants in 1998, but the later grant was given in lieu of a cash bonus awarded for
superior performance.5 We describe the process used to construct our sample below.
To identify out of cycle grants, we begin our sample with the 2003 release of Standard & Poor’s ExecuComp database. Data is presented on a grant-level observation
5
A full list of the firms excluded is available from the authors.
12
basis. In other words, if an executive receives an option or stock grant in a given
year, each individual grant is treated as an observation. If an executive receives no
stock or option grants in a given year (but some form of compensation), then for that
year there will only be one observation for that executive. The initial data set has
152,159 observations. However, not all the data is relevant and/or reliable for the
purposes of understanding an executive’s outstanding options portfolio. We therefore
apply a series of filters to screen for errors and implement a procedure similar to Hall
and Knox (2004) to determine an executive’s portfolio of outstanding options and the
degree of moneyness of the portfolio. Table 1 gives a summary explanation of the
data and the result of using our filters to screen for errors in the options grant data.
3.2
Identifying Underwater Option Related Grants
ExecuComp reports the number of options, exercise price, and expiration date for
an individual grant. Unfortunately, the date on which the option is granted is not
reported. We use the following algorithm to determine the option grant date. We
assume that the option is granted on the month and day listed for the expiration
date. The year is determined by the grant fiscal year and the fiscal year end month
of the firm. For example, for an option granted in fiscal year 1999 with an expiration
date of June 30, 2006, the grant date is recorded as June 30, 1999. Additionally, the
number of an executive’s in-the-money and out-of-the-money options and number of
exercised options are available on an annual basis. We observe instances of an executive receiving separately listed grants that have the same strike price and expiration
date. We assume that these are not cases of multiple grants and aggregate these
grants into one grant. This reduces the number of observations by 4,989 to 147,170.
We next adjust the terms of the outstanding options for stock splits subsequent
to the grant date. The executive’s outstanding options portfolio on any given date
consists of options that were perhaps granted some years previously and there could
be intervening stock splits that alter the terms of the outstanding options similar
to the adjustments made for regular exchange traded options. The number of stock
13
options is increased by the split factor and the strike price is decreased by the split
factor. Using data on stock distributions from CRSP, we calculate a cumulative
adjustment factor for all instances of stock splits between the original grant date and
the current grant date. For some firms, this split-adjustment factor is missing, which
reduces our sample by 78 observations. An additional related complication is that
data reported in ExecuComp are split-adjusted for all splits between the grant date
and fiscal year-end. Since we need to construct portfolios at each option grant date,
we separately require split-adjustment factors for splits between the grant date and
the end of the fiscal year in order to reverse-adjust the strike prices and number of
options to determine the terms on the grant date. We eliminate all option grants
with strike prices less than $1.00. These grants appear to be “founder’s” grants that
are awarded to the founders/entrepreneurs of the firm and are not part of the regular
option incentive plans. This reduces our sample by 1,325 observations.
Some firms allow their executives to replenish options that they exercise. The
reload feature triggers automatic grants of options and does not represent extra grants
awarded by firms to compensate for underwater options, as discussed in Dybvig and
Loewenstein (2003). Multiple option grants may simply reflect the exercise of reload
options. For example, in one instance two executives receive 38 reload option grants
in one year. We therefore exclude reload options from our sample. The compound
option feature in reloads and variations in how they are exercised makes it infeasible to construct the portfolios outstanding for each executives without additional
(unverifiable) assumptions. Accordingly, we eliminate all current and future years of
observations at the firm level. This reduces our sample by 11,053 observations.
We also account for instances of a firm granting and listing option grants in stock
of a subsidiary and for instances when the firm retains an executive’s old options in
a target company that was acquired. For example, both Brian Roberts and Julian
Brodsky of Comcast Corporation received grants with strike prices of $1,216 and
$1,374 in 2001. In the same year, they also received option grants with strike prices
of $36.97. The footnotes in Comcast’s proxy statement disclose that the $1,216 and
14
$1,374 grants were for stock in QVC, a majority-owned subsidiary of Comcast. The
variation in strike prices might produce false impressions that the option grants are
related to underwater options. In all such instances where the stock underlying the
option is not the firm’s traded stock, the strike price will significantly differ from
the firm’s stock price. We filter out these cases by dropping any observations where
the strike price does not fall within a range that is determined by the minimum
and maximum stock price over a period of one month surrounding the grant date.
Split-adjusted stock prices are calculated from CRSP.
Requiring that the strike price fall between a lower and upper bound also filters
out errors that arise from erroneous estimation of option grant dates. EXECUCOMP
reports option expiries. The standard method for estimating grant dates in the literature is to assume that the option maturity is an integer (whole) number of years,
so the grant date can be readily inferred from the option expiry date. It is plausible
however, that the options are granted with a non-integer number of years, e.g., nine
and a half years, to maturity. In such cases, the stock price on the estimated grant
date will not be the true price on the date of the grant. Since options are often issued
at-the-money, requiring that the strike price be bounded by stock prices in the month
surrounding the estimated grant date helps to screen out grants where our estimate
of the grant date is incorrect. This bounded stock price filter reduces the data set by
31,585 observations.
Finally, we eliminate all option grants where the strike price is not equal to the
market price on the grant date. Some firms use “tiered” option grants, whereby
executives are given different grants on the same day with different exercise prices.
These instances of multiple grants do not represent grants in response to options
going underwater and we eliminate these. This step is also necessary because such
errors will have dynamic feed-forward impacts on the future construction of portfolios
of options. The requirement that strike price be equal to market price reduces the
sample by 2,955 observations.
15
We then narrow the focus to refresher grants that address underwater options by
restricting our attention to a subsample with price drops. We extract a subsample in
which firms make a regular annual grant, then experience price declines that push the
first grant out-of-the-money, and who then make a second grant within the same fiscal
year. To ensure that options being refreshed are sufficiently underwater, we require
that price drops between the first and second grants be at least -20%. We also require
that at least two executives receive the second refresher grant. This eliminates one-off
transactions idiosyncratic to particular employees.
The initial filters result in 100,175 executive-year observations of separate and
distinct option grants. We screen these for out of cycle refresher grants related to
underwater options, our final sample consists of 135 refresher grants that include 590
executive-years. Table 2 and Figure 2 illustrate the time trend in the sample. Our
algorithm appears to work well at identifying plausible substitutes for repricing. The
out of cycle refresher grants identified in our study are relatively muted before 1998
but rise sharply after 1998 after the demise of repricing.
4
The Costs of Offsetting Behavior: Refresher Grants
Estimating the costs of the offsetting behavior raises two empirical issues. One is to
estimate the costs of the new refresher grants. A second issue is to identify the portfolio of underwater options being refreshed and its characteristics such as moneyness
and maturity. Sections 4.1 and 4.2 discuss our approach towards dealing with these
questions. Sections 4.3 presents evidence on the costs of offsetting behavior.
4.1
Refresher Grant Costs
Estimating the costs of refresher grants requires information on the terms of options
involved, the risk-free rate, and volatility. Option terms such as the old and new
strikes and maturity, as well as the number of options granted, come from grant
16
tables reported in ExecuComp. The interest rate is the 10-year risk-free treasury
rate (Release H.15 from the Federal Reserve) as of the date of the option grant. We
estimate volatility as the historical standard deviation of daily returns estimated over
120 days prior to the beginning of the fiscal year of the refresher grant. We choose
a period prior to the fiscal year to avoid a look-ahead bias in volatility. Because we
require that the refresher grant is preceded by a 20% drop in prices between grant date
and the prior grant date, using the data after the first grant date would effectively
pick periods of high volatility. The total cost of the refresher grant for each executive
is the Black-Scholes value per option times the number of refresher options granted.
To estimate the costs that would be incurred had firm repriced instead, we must
infer what underwater options portfolio is refreshed by the new grant. The most
obvious candidate is the prior grant in the same fiscal year, year [0]. By construction,
this grant in the same year is out-of-the-money by at least 20% as of the refresher
grant date. However, other underwater options granted in prior years could also be
outstanding and lose moneyness as a result of the 20% price drop. We make the
assumption that an out of the cycle grant refreshes not just the same year grants
but also all prior grants outstanding that are underwater. This is a conservative
assumption that maximizes the number of underwater options being refreshed and
thus sets a lower bound on the costs of refresher grants relative to repricing. We next
discuss the identification of the underwater options portfolio from EXECUCOMP.
4.2
Identifying Underwater Option Portfolio Being Refreshed
To estimate the underwater options portfolio being refreshed, we need to infer the
characteristics of the options portfolio held by executives as of the date of the out of
cycle refresher grant. To this end, we build data on the strike prices and maturity
of option portfolios held by executives on a case-by-case basis using the history of
grants data, on the lines of Hall and Knox (2004).
17
4.2.1
The First Series of Option Grants
We begin with the first year in which ExecuComp reports data for an executive.
Since a firm’s disclosure of option grants to executives as reported by ExecuComp
does not start from the first date on which a firm granted options, it is possible that
an executive has outstanding options at the beginning of the the first year in which
the executive enters the database. Following Hall and Knox (2004), an executive’s
initial portfolio is equal to the the number of options outstanding at the end of the
fiscal year minus option grants in the current year plus any options exercised by the
executive. The result is equal to the number of initial options the executive held at
the beginning of the year. That is:
InitialOptions = Outstanding options at f irst year f iscal year − end
− Options grants in f irst year
+ Options exercised in f irst year
(1)
As in Hall and Knox, we treat the initial options oustanding as one block with seven
years to maturity. If the executive receives grants in the first year, we set the exercise
price on the initial options to be equal to the market price a year before the first
grant date. If the executive does not receive an option grant in the first year - i.e.
the executive only has data reported for outstanding options and option exercises we assume an exercise price equal to the stock price midway through the prior fiscal
year. If there is no CRSP price, we use the stock price at the end of the fiscal year.
4.2.2
Accounting For Repricing
Some options reported in EXECUCOMP could be repriced during a year. EXECUCOMP identifies the executives repriced but does not report the number of options
repriced nor the alterations in the option terms due to repricing. Additionally, firms
vary in how they report repricings, as noted by Chidambaran and Prabhala (2003).
We use the following algorithm to update the options portfolios for repricings.
18
Repricing is frequently accounted as a cancelation of old options outstanding and
regrant of new options. Many firms include the number of repriced options as part of
the option grants reported for the fiscal year. These firms will also correspondingly
increase the number of canceled options, but this data is not available in EXECUCOMP. Using grants alone to compute the number of outstanding options without
adjusting for canceled options will overestimate the total number of outstanding options in these cases. We use this fact to estimate the number of repriced options. For
each fiscal year, we first calculate the expected number of outstanding options at of
the end of the fiscal year. The difference between the number of expected options
outstanding at the end of the fiscal year and the actual number reported is set equal
to the number of options repriced for the repriced executives. The expected number of fiscal year-end outstanding options is set equal to the options outstanding at
the beginning of the fiscal year plus any options granted in the year and minus any
options exercised. That is:
Expected[Options
Outstanding at
F iscal Y ear End]
=
Options outstanding at f iscal year beginning
(2)
+ Options grants in f iscal year
− Options exercised inf iscal year
For the repriced executive, Number of Options Repriced = Expected [Options Outstanding at Fiscal Year-end] - Reported Number of Options Outstanding at Fiscal
Year-end. Because the current fiscal year grants already reflect the new terms of the
option terms, what remains is for us to cancel the old options that have been replaced.
Starting from the option grants that have the shortest maturity and are farthest outof-the-money, we reduce the number of outstanding options by an amount equal to
the number of repriced options.
For firms that do not include the repriced options in reporting option grants for
the year, we use an alternate methodology to update the strike price and maturity
of the outstanding options to reflect repricing. In these cases, we reset the exercise
price and maturity on all outstanding options that are out-of-the-money. We reset
19
the strike prices to be equal to the stock price on the date of the first option grant in
the fiscal year and reset the option maturities to 10 years.
The estimated expected number of outstanding options also allows us to run a
data integrity check. Since there is a lag between the end of the fiscal year and the
reporting date, firms sometimes include options transactions from outside the fiscal
year period in reporting the options outstanding for the year. Following Hall and
Knox (2004) we treat these as errors in reporting and not as instances of unreliable
data. If this number of expected options outstanding at the end of the fiscal year is
greater than the number reported, then we treat the differences as an error in the
reported number of exercised options and augment the reported number to determine
the actual number of options exercised in the fiscal year. If this number of expected
options outstanding at the end of the fiscal year is less than the reported number
of outstanding options, we treat the differences as an error in the number of option
grants reported and we augment the number of option grants in the executive’s first
option grant in the fiscal year.
4.2.3
Accounting for Options Exercise
Once we have established initial conditions, accounted for stock splits and repricing,
and checked for data consistency in the grants and options data, we have complete
details on all option grants that an executive has received up to a particular grant
date and through the end of the fiscal year. For each fiscal year, we next have
to adjust the options outstanding for option exercises. We assume that when an
executive exercises options, she first exercises the shortest maturity in-the-money
options, progressively stepping down the list of option grants till all the exercised
options are accounted for. Between option grants with the same maturity, we assume
that she exercises those that are deepest in-the-money. This process is repeated each
year going forward through the final year for which we have data on the executive.
At the end of the exercise, we have a complete picture detailing which of the option
grants are outstanding at any point in time within the data period. Collating the
20
data gives us the portfolio of outstanding options and the strike price and maturity
of each outstanding option.
Underwater portfolios being refreshed consist of the prior grant in the same fiscal
year [0] , grants in year [-1], and very rarely prior options grants. We scale up the
individual executive level estimates in the refresher grant year to firm-wide estimates
using data on total options granted by the firm. These are either extracted from
10-K statements reporting the stock and flow of options or based on the g/p statistic
derived from proxy statements as outlined in Section 3.
4.3
Costs of Refresher Grants Compared to Repricing
Table 3 reports estimates of the cost of refresher grants and the costs of a hypothetical
repricing had the refresher granting firm chosen to reprice. Column 1 reports the data
on a per executive basis, Column 2 adds costs for all executives in a firm and presents
the mean and median on a per firm basis for all executives. Finally, column 3 reports
data on a firm-wide basis for executives and non-executive employees. We present
data on both medians (upper row) and means (lower row) given the skewness of
compensation data.
The first row of Table 3 indicates that our refresher grant sample comprises 590
executives at 135 firms. Thus, about 4 listed executives per firm receive refresher
grants. Our screens appear to do a reasonable job in identifying economically meaningful refresher grant transactions that respond to firm-wide price shocks. Row 2
presents the total costs of refresher grants. The median (mean) Black-Scholes costs
of the refresher grants for all executives is $1.5 million ($3.7 million) and for all
employees is $8.1 million ($49.7 million).
We next turn to estimating the costs of a hypothetical repricing in which the
firms granting refresher grants simply reset the strike price of the outstanding out-ofthe-money options. The median (mean) Black-Scholes value of underwater options
equals $10.2 million ($99.3 mm). If firms had repriced these options, the new options
21
would have median (mean) Black-Scholes value of $12.7 million ($117.0 million). The
median (mean) repricing costs equal $2.3 million ($ 17.7 million).
Finally, we estimate the difference between the costs of refresher grants and the
costs of the hypothetical repricing. As shown in the last row in Table 3, firms incur
excess costs when using refresher grants in lieu of repricing. The median (mean) excess
costs of refresher grants over repricing amount to $0.93 ($2.45) million for executive
officers, and $4.2 ($32.0) million at the level of the firm including executives and nonexecutive employees. The skewness in the estimates, which is reflected in the spread
between medians and means, is quite typical of compensation data. In our sample, it
appears to mainly reflect the presence of some heavy users of options, particularly in
the technology sector. The cost difference suggests that firms bear deadweight costs
when offsetting the restrictions on repricing by using refresher grants.
5
Offsetting Behavior in All Underwater Firms
The previous section focuses on firms that choose to make refresher grants after
significant price declines make options go underwater, the condition that triggers
repricing. However, there could also be firms who face underwater options but choose
not to make refresher grants. We consider the universe of all underwater firms next,
to understand offsetting behavior in the aggregate rather than just for firms who
make out of cycle refresher grants.
Some firms that faced underwater options after 1998 may have responded by
electing to forgo all repricing substitutes. Other underwater firms may have conveyed
substitute compensation in ways not visible to the empirical researcher. For instance,
substitute grants could be given as in-cycle grants bundled within the normal annual
compensation package and result in a partial to total offset depending on the extent
of the compensation substitution. Offsetting behavior (or the lack of it) can be detected by examining the total compensation at underwater firms relative to predicted
22
compensation levels at such firms. We describe these tests and the empirical results
next.
5.1
Total Compensation
If there is no offsetting response among “underwater” firms, the testable proposition
is that the compensation for “underwater” firms should decline measurably after 1998
relative to predicted values based on pre-1998 data.
We implement this test by subtracting from the top executive compensation, EXECUCOMP variable TDC1 for underwater firms the level of expected compensation
and examine if the difference declines significantly after 1998. For this test, the sample of underwater firms is constructed by sampling EXECUCOMP firms for whom a
significant number of options went underwater in a fiscal year: we require that the
portfolio of executive options are at least 25% out of the money.6 We test the null
hypothesis that the abnormal compensation adjustment in these “underwater” firms
equals zero against the alternative that it is negative.
Table 4 presents the raw sample statistics, the median and mean total compensation TDC1 by year for firms with substantial underwater options (Panel A) and
for the refresher grants sample (Panels B and C). As seen in the table, the annual
compensation increases – even at firms with substantial underwater options, whose
stock has performed poorly. This reflects secular trends in the overall EXECUCOMP
sample (e.g., Bebchuk and Grinstein (2005), perhaps in response to labor market
pressures that increase CEO pay. Panels B and C deal with firms that make out of
cycle refresher grants. Excluding refresher grants (Panel B), compensation in these
firm-years is not different from the Panel A data for all underwater firms. However,
Panel C shows that when we include the refresher grants (Panel C), compensation is
substantially higher than for all firms with underwater options.
6
For robustness, we have also considered other cutoffs, e.g. the portfolio of executive options are
at least 15% and 20% out of the money. We get similar results.
23
5.2
Abnormal Total Compensation
We analyze abnormal compensation changes after 1998 using a difference in difference
approach. To estimate abnormal compensation, we specify a model for expected
compensation using variables suggested in prior research. The baseline estimates are
based on the specification suggested in recent work by Bebchuk and Grinstein (2005)
but other specifications such as the one in Himmelberg, Hubbard, and Palia (1999)
gives similar results. We estimate the model using pre-1998 data to keep the data
free of look-ahead bias. We then compute excess compensation for underwater firms
as the difference of actual over model-predicted compensation, and test whether this
declines significantly after 1998 for underwater firms.
The dependent variable in our primary specification is the industry-adjusted logarithm of total CEO compensation (LTDC1), which is the difference between the
logarithm of total compensation the CEO receives in a fiscal year (TDC1) and the
average of the logarithm of total CEO compensation for all firms in the Fama-French
48-industry sector. We prefer this specification of the LHS variable because it filters
out dynamic labor market induced variation at the level of the industry-year. We
can add time-invariant firm or industry effects plus year effects, or use these alone,
effectively specifying more macro controls. Using these as substitutes or in addition
has little effect, suggesting that using industry-adjusted compensation each year does
a good job of picking up trends. In the specification analyzed here, we regress year t
LTDC1 on known firm characteristics. These include firm size as measured by sales,
operating performance measured as the logarithm of (1 + the return on assets), stock
market performance as measured by the log of (1 + the stock return) for prior fiscal
year and the prior two fiscal years. These are the primary explanatory variables included in most studies. For robustness we also consider other variables that are less
frequently included in compensation studies such as volatility, sales growth, R&D
expenditures, leverage, and the number of employees. These have little effect on the
results.
24
Table 5 reports estimates of a baseline regression of CEO compensation on explanatory variables for 1994-1998. Industry-adjusted compensation is greater for large
firms, growing firms, and firms with good stock return performance. The year fixed effects do not matter, suggesting that industry-adjusting compensation does a good job
filtering out secular trends.7 We then assess whether compensation slows abnormally
after 1998. An empirical question is how long the post-1998 period should cover.
There are the usual tradeoffs. Longer windows well after 1998 bring in more data.
However, as the window lengthens, there are other confounding effects. One year
after 1998 is the minimum while three years seems to be a reasonable upper bound,
given the Internet bubble crash, the Sarbanes Oxley Act, and the promulgation of
Fin 44 standards after 2001. We report the broader encompassing results.
We use the coefficients estimates in Table 5 to generate the expected out of sample
compensation for each firm-year. The excess compensation is the actual (industryadjusted) compensation minus the prediction based on coefficients estimated from
the 1994-1998 data. We regress the excess compensation on the underwater dummy
variable. If the 1998 repricing regulations were successful, the coefficient for “underwater” should be significant and negative. Table 6 reports the results. The coefficient
for UNDERWATER is insignificant. Following the strategy of Bebchuk and Grinstein
(2005), Table 7 reports results similar to those in Table 5 but for all top 5 executives
of the firm. The results are similar: the coefficient for UNDERWATER are insignificant. There is little evidence that the 1998 repricing restrictions slowed compensation
growth in underwater firms. We also experimented with raw compensation levels and
year fixed effects rather than the industry-adjusted compensation, our preferred specification. The results are again similar.
It is possible that the regressions in Tables 5 and 6 lack power. However, observe
that the regression in Table 5 includes the variable REFRESHER, which is a 0/1
7
At first glance, the insignificance of year dummies may be surprising. However, when we run
tests using unadjusted CEO TDC1, the year dummies do have the familiar positive sign and are
significant. Even in these models, the results are similar: underwater firms do not have unusually
negative compensation growth after 1998.
25
dummy variable for whether a firm makes an out of cycle refresher grant. If our
hypothesis that refresher grants entail excess costs is correct, REFRESHER should be
significant even after adjusting for other determinants of compensation. This is indeed
what we find in the regression data. In terms of raw numbers, the mean and median
CEO compensation for underwater firms in general are $2.9 million and $1.7 million,
respectively. These are significantly lower than the numbers for refresher grant firms,
which are $5.9 million and $3.8 million, respectively. Taking out REFRESHER from
the Table 5/6 specifications makes little difference to the results. Compensation
growth does not slow at underwater firms after 1998.
5.3
Summary
Broadly speaking, we have two major results. One, we find no evidence that total
compensation slowed at underwater firms after the 1998 demise of repricing. The
result suggests that firms offset the restrictions on repricing due to punitive disclosure
requirements by shifting from repricing to other forms of compensation as a tool to
respond to underwater options. Two, in the sample of firms making out of cycle
refresher grants, we find that there is more than complete offset: refresher grants are
reliably more expensive than the repricing.
The excess costs of refresher grants represent a curious result that demand economic explanation. If the shift from repricing to a contracting substitute were a
neutral mutation, the net costs of moving to the contractual substitute for repricing
should be zero. We next consider why firms making refresher grants might rationally incur these excess compensation costs. We consider several explanations but in
the end lean towards an explanation based on a wedge between how employees and
firms value equity based incentives. We begin by characterizing the employee-firm
wedge and show how it predicts excess compensation costs. We consider the other
explanations in subsequent sections and discuss why they seem to be implausible.
26
6
Explaining The Excess Costs of Refresher Grants
In this section, we examine why firms might rationally incur excess costs when they
switch from repricing to refresher grants. Section 6.1 shows how differences between
employee and firm valuation of incentive contracts can explain the costs. Section 6.2
considers consistency of the wedge with received quantitative subjective valuation
models. Section 6.3 analyzes other hypotheses to explain the excess costs. While
some explanations are possible in a purely hypothetical sense, they are theoretically
or empirically unsupported or they do not explain key features of the data.
6.1
Wedge Between Employee and Firm Valuation Functions:
Theoretical Analysis
This section shows that refresher grants entail deadweight excess costs when employees and firms value options differently and the wedge between the two valuation
functions widens when options go out-of-the-money.
Let V (S, X) denote firms’ costs of granting options of strike X when stock price
is S and let U (S, X) be its subjective value to employees. For economy of notation,
we suppress the other determinants of option cost to firms or value to employees.
Consider a firm that reprices an option that has strike X1 to strike X2 < X1 ). If we
assume options are granted at-the-money, X2 = S, the stock price on the repricing
date. Let the cost of repricing to firm be ∆VRP , so that
∆VRP = V (S, S) − V (S, X1 )
(3)
The value of the repricing as perceived by the employee is based on the employee’s
valuation function, which is
∆URP = U (S, S) − U (S, X1 )
27
(4)
In designing a refresher grant in lieu of repricing, we need to specify the sense in
which a refresher grant is “equivalent” to repricing. We make the simple assumption
that the refresher grant is designed to convey the same subjective value as a repricing
would convey to an employee. This assumption simplifies exposition but it is worth
emphasizing that it is not critical. We could, for instance, equate the incentive deltas
instead of both alternatives. We could also consider refresher grants that are designed
to convey the equivalent of repricing plus more compensation or incentives. In that
case, we simply interpret our analysis as applying to the portion of the refresher grant
intended to substitute for repricing. These other assumptions complicate the notation
but deliver the same substantial results.
Thus, we consider a refresher grant that is designed to convey the same value as
that conveyed in a repricing, viz. ∆URP . In the baseline case, suppose that employees
and firms have similar valuation functions so that U ≡ V . In this case, the number
of options that the firm grants via a refresher grant, nU ≡V solves
∆URP = ∆VRP = V (S, S) − V (S, X1 ) = nU ≡V V (S, S)
(5)
or
nU ≡V =
V (S, S) − V (S, X1 )
V (S, S)
(6)
Now consider the case when the employee and valuation functions differ, so that
U 6= V . In this case, the firm making a refresher grant must grant a number of
options nU 6=V that solves
∆URP = ∆URP = U (S, S) − U (S, X1 ) = nU 6=V U (S, S)
(7)
which gives
nU 6=V =
U (S, S) − U (S, X1 )
U (S, S)
28
(8)
From Eqs. (5) and (8), it follows that
nU 6=V
U (S, S) − U (S, X1 )
V (S, S) − V (S, X1 )
≥1⇔
≥
nU ≡V
U (S, S)
V (S, S)
(9)
More compactly, we can rewrite this as the simple elasticity condition
nU 6=V
≥ 1 ⇔ ηU,V > 1
nU ≡V
(10)
The intuition underlying Eqs. (9) and (10) is straightforward. The denominator in
both equations is nU ≡V , or the number of options that the firm must give if it uses a
refresher grant in lieu of repricing and the firm and employee valuations are identical.
The numerator in either case is the number of options that the firm must give if the
valuation functions are not identical. Eq. (9) says that firms must give more options
than they would with identical employee and firm valuation functions if employee
valuations fall faster than firm valuations as options go out-of-the-money. Eq. (10)
restates this result in familiar elasticity terms.
In dollar terms, the excess cost of making refresher grants in lieu of repricing
equals the number of options granted in the refresher minus the number that would
have been granted had the valuation functions been identical, multiplied by the cost
per option to the firm. Equivalently,
Cexcess = (nU 6=V − nU ≡V )V (S, S) = nU ≡V V (S, S)(ηU,V − 1)
(11)
Thus, excess costs are positive, i.e., refresher grants are more costly, when the elasticity of employee valuation with respect to firms’ valuations ηU,V exceeds one. Equivalently, excess costs arise when employees valuations fall faster than firms’ valuations
when options go out-of-the-money. If the two valuation functions are similar, ηU,V ≡ 1
and excess costs equal zero. In this scenario, refresher grants cost the same as repricings. In the unlikely scenario in which firms’ valuation functions fall faster, ηU,V < 1
and excess costs would be negative, i.e., refresher grants would be cheaper.
29
Eqs (10) and (11) suggest a straightforward interpretation for why refresher grants
have proved to be empirically costlier than repricings: employee valuation functions
taper off much faster than firms’ own valuations of options when options go underwater. Thus, any contractual arrangement that leaves old underwater options
outstanding – such as refresher grants – will be more costly than strategies – such as
repricing – that take out the underwater options.
6.2
Consistency With Subjective Valuation Models
In this section, we discuss how a widening wedge between employee and firm valuation
functions when options go underwater is consistent with subjective valuation models
(Hall and Murphy (2000), Meulbroek (2001), and Ingersoll (2006)).
A numerical example illustrates the basic point. Consider an option on a nondividend paying stock initially granted at a strike price of $100. We assume that
the remaining maturity of the option is 8 years, which corresponds to the stage at
which a typical repricing occurs. Assume that the total volatility of the stock is 40%
and a risk-free rate of 5%. It is straightforward to compute the Black-Scholes cost
of the option for different strike prices. To compute the subjective value, we could
use one of several models. We use the Ingersoll (2006) model here because of the
computational ease with which it implements the valuation of both European and
American employee stock options. Other models also lead to similar conclusions.
In Ingersoll’s framework, the subjective discount depends on the employee’s relative risk aversion, the extent to which the employee’s wealth (human capital plus any
explicit shareholdings) is tied with the option granting firm, and the idiosyncratic risk
of the stock. Let the relative risk aversion coefficient be 4, let 25% of the wealth be
tied to the option issuer’s stock, and let the idiosyncratic risk be 90% of the total risk
of the stock.8 With these parameters, we can show that the subjective value declines
8
Firms in our sample tend to have high idiosyncratic volatility, with the median residual variance
in market model regressions of the order of 90%. This is not surprising given that repricers are
drawn from industries with high idiosyncratic volatility (Chidambaran and Prabhala (2003)).
30
by 69% for a 40% out-of-the-money option, while the Black-Scholes value declines by
36%. Thus, employees value options at greater discounts to the Black-Scholes value
as options go out of the money. The estimates in Table 3 are consistent with employees having a relative risk aversion (RRA) of 4 (RRA = 1- γ) and having substantial
amount of wealth tied to the firm.
It is worth pointing out that our evidence is also consistent with a class of behavioral models of valuation. For instance, employee valuations could be initially optimistic about firm prospects when they receive options (Bergman and Jenter (forthcoming)). However, the employees could turn pessimistic when stock prices drop
and options go underwater. Such behavior would be consistent with the excessive
extrapolation hypothesis of Benartzi (2001), where employees overweight recent data
as predictors of the future. Such fluctuations in sentiment resulting from excessive
extrapolation could drive time variation in the employee valuation functions in the
manner specified by Eq. (11). Our results are also consistent with such behavioral
origins of the subjective discount.9
6.3
6.3.1
Alternative Explanations for Excess Costs
The Black-Scholes Model Must Be Modified
The Black-Scholes model is widely used to measure option costs for firms. For instance, the EXECUCOMP database routinely reports Black-Scholes estimates. The
model is well understood, relatively simple to implement, and involves few assumptions other than the stock return volatility, so it is used by the overwhelming majority
of papers in the compensation literature.
There are two major issues with using the Black-Scholes model for valuing employee stock options: vesting and early exercise of options. The vesting issue is that
employees have rights to exercise an option only if they are still employed at the
vesting date. If they leave the firm before the option vests, the option is forfeited.
9
We thank (without implicating) Dirk Jenter for suggesting this explanation.
31
Incorporating vesting has little effect on the finding that refresher grants are costlier,
and the intuition is not hard to see. Vesting restarts characterize both repricings
(following Section 2) and refresher grants (new grants come with fresh vesting schedules), so incorporating these should not impact the rank ordering between the two
alternatives. Refresher grants continue to be more costly than repricings.
10
The early exercise issue is less straightforward but yields interesting insights in its
own right. One ad-hoc method of accounting for early exercise is to use an effective
maturity of (say) 5 years as option maturity instead of the actual maturity at grant.
This ad-hoc adjustment clearly has no impact the finding that refresher grants are
costlier because similar adjustments are made for both repricing and refresher grants.
These results are likely to be conservative, given that the out-of-the-money options
left outstanding under the refresher grant alternative are likely to have longer effective
maturities given the lower likelihood of early exercise for these options, which must
be incorporated to properly account for early exercise.
The early exercise adjusted costs of refresher grants over repricing costs equals
CRG,EE − CRP,EE = (CRG,BS − CRP,BS )κ(S, S) + V (S, S)[κ(S, X1 ) − κ(S, S)] (12)
In Eq. (12), κ(S, X) denotes the adjustment required for early exercise for an option
with strike X when the stock price is S. The LHS of Eq. (12) denotes the earlyexercise adjusted excess cost of refresher grants over repricing. The first term in the
RHS is proportional to the unadjusted (Black-Scholes) excess costs, while the second term represents the difference in early exercise adjustment for out-of-the-money
options relative to that for at-the-money options. Thus, adjusted costs of refresher
grants over repricing will always be positive if out-of-the-money options require less
adjustment for early exercise (i.e., κ is higher) than at-the-money options.11 This
10
Estimating a vesting-adjusted Black-Scholes model is not difficult but requires additional (unverifiable) assumptions about turnover and its functional dependence on stock returns (e.g., Cuny
and Jorion (1995)). Incorporating these would make little qualitative difference to our analysis.
11
This is a sufficient condition. The necessary condition is weaker: refresher grants will still be
more costly even if the early exercise adjustment for out-of-the-money options is greater (i.e., κ
32
condition is, in fact, a necessary implication of all received valuation models based
on early exercise.
Formal methods to handle early exercise include structural models based on utility
maximization or reduced form models of exercise (e.g., Carpenter (1998), Heath,
Huddart, and Lang (1999)). The intuition is best illustrated by following the approach
of Ingersoll (2006), who obtains the early exercise boundary based on employees’
optimal exercise strategy, and computes firms’ costs based on the derived optimal
exercise strategy for employees. This approach also leaves refresher grants as the
costlier alternative. The key driver of the result is that out-of-the-money options
are less likely to be exercised. Thus, the downward adjustment from Black-Scholes
for early exercise is lesser in the refresher grant strategy, in which out of the money
options remain outstanding. Under repricing, which cancels all the old underwater
options, the downward adjustment in costs is greater because early exercise is more
likely. In fact, we can even construct interesting examples where repricing represents
a value gain to firms instead of being a cost. This happens because the gains from
suboptimal early exercise of at-the-money ESOs more than offset the losses from
lowering the strike price. In short, the misspecification of the Black-Scholes formula
does not explain why refresher grants are empirically more costly than repricing.
6.3.2
Other Compensation Adjustments by Repricers or Refreshers
One empirical issue is that there could be other elements of the compensation package
accompanying refresher grants. Perhaps firms making refresher grants make adjustments to compensation that offset the incremental costs of refresher grants. Compensation adjustments associated with refresher grants would matter if we considered
in-cycle refresher grants, which, by definition are accompanied by a host of other
compensation transactions. However, our analysis focuses on out-of-cycle grants,
is lower), provided it is not excessively so. Equivalently, the only circumstance in which refresher
grants will not be costlier is when the adjustment in valuation for early exercise is far more than
that needed for at-the-money options, a condition that is not likely to arise.
33
which are standalone transactions. Empirically, there are no other adjustments in
compensation in our sample.
Alternatively, perhaps firms that reprice provide additional compensation to their
employees not provided by firms making refresher grants. Our results on excess
costs of refresher grants could be rationalized if repricing firms were making unusual
upward adjustments to compensation packages. The evidence in Chidambaran and
Prabhala (2003) suggests otherwise. They examine compensation adjustments for
a sample of repricers similar to ours (our repricing sample has 197 out of the 213
firms in their study). They find that repricers make adjustments that are lower than
those of comparable peers. Thus, the existence of other compensation adjustments in
repricers or refresher granting firms does not explain why refresher grants cost more.
6.3.3
Refresher Grants Made For Other Purposes
Our analysis thus far is based on the premise that refresher grants are chosen by firms
to deal with underwater options. Anecdotal evidence and prior literature on refresher
grants supports the use of refresher grants for dealing with underwater options (e.g.,
Chen (2004), Hall and Knox (2004), Murphy (2003)). However, it is possible at least
theoretically that the refresher grants are different, in the sense that they serve a
different purpose compared to repricing.
Purely hypothetically, the fact that refresher grants serve “some other” unspecified
purpose is certainly a possibility. However, this explanation does not explain the key
time series pattern picked up in our sample. Refresher grants increase right around the
1998 demise in repricing, which suggests that our sampling strategy does a good job
of picking up refresher grants that substitute for repricing. A second point is related
to our sampling strategy. We did find cases where refresher grants are not related
to underwater options. However, as as explained in Section 3, we filter out such
transactions by conditioning on options going underwater. We also read individual
firm disclosure statements to screen out other idiosyncratic factors. Our sample firms
have out-of-the-money options that are underwater by an average of about 40%. It is
34
in this specific sample that refresher grants are costlier than repricings. So while there
could be refreshers made for purposes other than dealing with underwater options,
these are filtered out of our sample.
6.3.4
Refresher Grants Convey Superior Benefits
We also consider an alternate explanation that firms recognize that refresher grants
are costlier but nevertheless give them because they expect to reap superior benefits
not available via standard repricing. This argument as stated has no empirical content
without specifying what these benefits are. Indeed, by this logic, any action by firms,
however costly, could be justified by appealing to purely hypothetical unspecified
benefits conveyed by the costlier alternative. In any event, the unspecified benefits
story is also implausible for two other reasons.
One is related to the types of portfolios that employees inherit under refresher
grant regime, which is a mix of out-of-the money and at-the-money options. Repricing replaces this package with only at-the-money options. For refresher grants to
have greater “benefits,” firms must find it beneficial to make employees hold out-ofthe-money options. We are unaware of any theoretical model that claims optimality
of such a package. In any case, in practice, firms strive to avoid underwater options. From a behavioral perspective, having some underwater options is not viewed
as being a useful motivational tool, especially because they create inequity between
underwater option holders, new employees with only at-the-money options, and of
course, executives who hold stock (Chidambaran and Prabhala (2003)). Virtually every proxy statement, annual report, or article in the press suggests that underwater
options are a nuisance to firms. We have seen little that extols the virtues of underwater options. In fact, the 2008 financial crisis created a new problem of underwater
options, and brought with it fresh calls for firms to find ways to extinguishing these.12
12
Press reports include “Technology Options Sink,” (The Wall Street Journal, November 10, 2008),
“Firms Jump to Salvage ‘Underwater’ Stock Options,” The Wall Street Journal, December 21, 2008.
35
To suggest that firms have found it useful to incorporate out-of-the-money options
flies in the face of a mass of evidence.
In fact, the pressure to deal with underwater options and the dilution costs associated with refresher grants led to an interesting development in 2001. FASB’s Fin
44 actually re-introduced a version of repricing called “6 and 1” repricing. In “6 &
1” repricing, options can be cancelled and regranted at the new strike price without
recognizing an expense against income, provided that the new option grant is at least
6 months and 1 day after the cancellation of the old underwater options. “6 & 1”
repricing has its own problems related to the six-month waiting period that makes it
an imperfect substitute for repricing. We thoroughly analyzed a sample of 6 and 1
repricings assembled by Institutional Shareholder Services and find that it has been
neither particularly popular nor effective. The details are available from the authors
upon request.
A second issue with superior benefits hypothesis is that it fails to explain the most
important empirical feature of the data: specifically, the rise in refresher grants after
December 1998. If refresher grants were indeed superior contracts, they should have
been equally widely used prior to 1998. Yet, as we show in Figure 2, this contracting
form came to life primarily after 1998. Firms clearly show differential preference for
repricing in a regime when both options were available. Thus, the superior benefits
hypothesis needs to explain not only the source of such superior benefits but also the
time series variation in such benefits, specifically why these benefits were discovered
after 1998 coincident with the demise of repricing.
6.3.5
Goal Is Improved Disclosure, Not Stopping Compensation Flows
Another version of the superior benefits hypothesis is that the anti-repricing push
by institutions may actually have the (benign) goal of improving disclosure rather
than stopping repricing. Under this view, the problem with the pre-1998 regime was
insufficient disclosure of repricing. Prior to 1998, repricing was only reported in a
36
10-year historical table as part of the proxy statements. By forcing firms to expense
repricing, compensation for underwater options becomes more visible.
Suppose that the desired outcome is indeed better and transparent disclosure of
compensation. Did requiring expensing achieve this goal? One could in fact argue that
post-1998, underwater option related compensation actually became more obscure.
Refresher grants are aggregated and disclosed together with regular option grants.
In some cases, information on refresher grants can be gleaned because they are outof-cycle. But in-cycle refresher grants or other forms of compensation in-cycle are
buried within the overall compensation package granted annually with no particular
requirement to disclose the portions related to underwater options. There is not even
a ten-year table that was formerly disclosed with repricing.
In short, even if the goal was to improve transparency, it did not materialize. In
fact, the result is arguably more opqueness due to offsetting behavior than undoes
regulatory intent. This is, of course, the now-familiar Peltzman effect with more than
complete offset, now applied to compensation transparency.
6.4
Summary
Empirically, we find that the switch from repricing to refresher grants entails extra
costs that might appear puzzling at first sight. These costs can in fact be explained
by a wedge between employee and firm valuation of equity incentives that widens
as options go out of the money. Several alternate explanations do not stand up to
scrutiny. They are either empirically unsupported or do not explain key features of
the data.
Two other pieces of evidence support the view that employee-firm valuation wedges
explain the excess costs of refresher grants. In a sample of 197 executive stock option repricings, Chidambaran (2008) finds that 72% of repriced options flow to nonexecutive employees. The subjective-objective valuation wedge is likely to be more
relevant for these rank-and-file employees since they are less wealthy, more risk averse,
37
and have less ability to diversify their wealth portfolios. Finally, supplementary evidence from an independent sample, an underwater options exchange program executed by Microsoft Corporation in 2003, also suggests a divergence between employee
and firm valuation of stock options. These data are presented in the Appendix.
7
Conclusion
The 2008 financial crisis has led to historically unprecedented government intervention
in the financial markets and in its wake, several calls for rethinking regulations and
developing new regulatory frameworks. As a result, the implications and economic
consequences of regulations are of interest today to both academics and policy makers.
A key focus of regulatory activities is the reform of compensation contracts. Echoing
strains of a decade-old debate in the compensation literature, much popular anger has
been directed at inappropriate compensation packages not only at failed institutions
such as AIG but more broadly in the corporate sector. In addition, these generous
compensation contracts are also seen as creating incentives to take on excessive risk,
which has been held as a key cause of the 2008 financial crisis. Designing effective
regulations to curb compensation excesses and understanding the consequences of
alternative regulatory approaches is a key concern for regulators.
Benmelech and Moskowitz (2009) point out one difficulty in designing effective
regulations. They focus on an ex-ante issue, the incentives of interested parties to
frame regulations for private rather than public benefit. As a consequence, they argue
that regulation may be ineffectively designed and may in fact worsen the blight of
the entities that it seeks to protect. Our study illustrates a complementary ex-post
force that can produce similar distortions: offsetting behavior. As Peltzman (1975)
points out, offsetting behavior can attenuate, undo, or more than completely offset the
intent of regulatory restrictions. In the specific context of compensation contracts,
there have been several efforts to rein in compensation by restricting or imposing
costs on specific types of compensation, such as caps or tax deductibility of salary
38
or bonuses, restricting options, or golden parachutes. Piecemeal restrictions of this
sort lead to compensation squeezing out through other avenues. The net effect, as we
empirically show, could be to actually increase costs for shareholders.
Our results suggest that regulations that merely target pieces of the compensation
package are less likely to be effective. Such curbs or restrictions on compensation
end up creating asymmetries across different pieces of the package and are merely
a recipe for inducing offsetting behavior. To be effective, regulations need to target
the totality of compensation symmetrically. The compensation rules for TARP firms,
which target total pay of the CEO and the top 20 to top 100 employees, appear to
be a step in the right direction. Likewise, the move to expense options makes its
treatment for reported earnings symmetric with bonuses and salaries. This is also
a step in the right direction, to prevent firms from shifting to contracts with less
punitive expensing requirements even when these alternatives are costlier from the
shareholder’s viewpoint.
Our results also suggest that regulations should adopt similar symmetric approach
towards compensation transparency. For instance, efforts to make repricing more
transparent to shareholders by requiring expensing does not necessarily result in increased transparency. Firms can offset the regulation by simply shifting to more
opaque contracts with even more minimal transparency. The superior regulatory
approach is to be more comprehensive and increase the transparency of all forms of
compensation. The current move to aggregate and disclose all compensation expenses
into “one” number is a step in this direction. However, given the variations in models, model validity, and inputs, it is not clear whether the move to report a aggregate
measure is enough. Firms could offset transparency requirements by choosing convenient inputs, models, or compensation structures and hide behind the opacity of
an aggregate measure. To avoid the consequences of offsetting behavior, regulators
should perhaps require standardized and full disclosure of compensation terms at the
level of individual transactions
39
Our analysis also contributes towards the literature on how firms and individuals
value incentive compensation contracts. Our evidence supports the view that there
is a wedge between how employees and firms value incentive contracts. The wedge
appears to have the property that it widens as stock prices decline and options go
out-of-the-money. This is some of the first evidence employees receiving options and
firms granting options attach different valuations to the same contracts. Our evidence
is particularly interesting because it is based on actual transactions of compensation
flows between firms and employees, and is thus based on the revealed preferences of
the contracting parties. The changes in the employee-firm wedge set up interesting
dynamic issues in renegotiation of incentive contracts. For instance, a widening wedge
could introduce another layer of time inconsistency that could lead to renegotiation
that undermines the incentive effects of options. These effects add complexity to what
have been traditionally viewed as simple incentive contracts and perhaps provide
another rationale for using even simpler incentive contracts such as restricted stock.
Such a trend is quite apparent in recent compensation data.
40
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43
Appendix
Microsoft’s Underwater Options Exchange
The evidence in our paper suggests that refresher grant excess costs are best explained
by a contracting environment in which employees and firms value incentive options
differently. We present other evidence of such valuation differentials outside the setting of refresher grants because there is little empirical evidence on the differentials
or how they vary with moneyness. More importantly, from a positive economics
viewpoint, it is desirable to develop evidence that firms and employees contract as if
such differentials exist. We develop some data on these lines using information on an
options exchange program where employees traded underwater options for cash.
A1. Transaction Background
In the wake of the crash that followed the Internet bubble in 2001, options granted by
Microsoft Corporation went underwater. Microsoft could have repriced its options,
but repricing was unattractive on account of the 1998 FASB rule change on expensing
repriced options. Microsoft instead opted to buy out the underwater options held by
its employees. With this intent, the firm made a tender offer for underwater ESOs
through J P Morgan. The transaction was initiated in June 2003 and consummated
in December 2003. Over 300 million options were surrendered by over 18,000 employees. The public disclosures made by the firm permit us to draw inferences about the
valuation of underwater options by Microsoft’s employees. Evidence from this transaction illustrates the existence and the economic significance of subjective valuation
of options.
A total of 621.4 million options held by 36,539 employees were eligible for surrender. Of these, 344.6 million options held by 18,503 employees were tendered to J P
Morgan, who paid $382 million for these options. $219 million was paid to employees
before December 2003, with the rest to be held by Microsoft and paid with interest
over the next two years contingent on the employee continuing to be employed by
Microsoft. The 10-Q for the quarter ended December 2003, which was filed on February 6, 2004, also reveals that the surrendered options had strike prices ranging from
$33.03 to $59.56. Against this, Microsoft’s closing price on the date of consummation of the deal, December 8, 2003, was $ 26.24. These data can be used to price
the surrendered employee stock options using the Black-Scholes approach, while the
price paid to the employee indicates the subjective valuation of the option to the
surrendering employee.
44
A2. Subjective Values in Exchange Transaction
Our broad approach is to compare the Black-Scholes value of the options surrendered
in the exchange offer with the prices that employees received for their surrendered
options. For this purpose, we need data on the terms of options surrendered such
as strike prices and maturities, as well as information on the price paid for the surrendered options. Firm-wide aggregate data are made available through company
disclosures, and these provide insight into the subjective valuation of options by employees. We read several filings made by Microsoft including Form SC-TO-I, a tender
offer statement on October 15, 2003 and subsequent amendments to this statement,
a prospectus under rule 424 (b)(2) dated October 24, 2003 and related supplements,
the 10-Q’s around the transaction dates, and contracts that Microsoft signed with J
P Morgan related to this transaction and available through SEC’s Edgar database.
Unfortunately, a detailed schedule of the strike price and maturity of each surrendered option is not disclosed. We can deduce some information based on aggregates
disclosed in annual compensation statements. For the strike price, Microsoft discloses
that the weighted average strike of surrendered options is equal to $38.70. For maturity, we turn to the stock-and-flow of options section in the 10-K’s or 10-Q’s, which
report the balance of options and their weighted average maturity of options in each
strike price bucket. The bucket of options with strike prices from $33.03 to $59.56
options (which were surrendered) had average maturities remaining of 4.7 to 4.9 years
as of December 2003. Finally, we can obtain volatility from historical return data for
Microsoft, or turn to implied volatilities of traded options. Implied volatilities of
exchange traded options range from 26% to 50% as of the Friday close on December
5, 2003. They vary based on the maturity and strike price. The longest maturity
exchange traded options on Microsoft were two year options with strikes from $20 to
$30 and these had implied volatilities of 26% to 30% . The historical volatility was
higher at 56%. Thus, we have a range of volatility estimates for computing the value
of options surrendered in the Microsoft exchange program.
With an average strike of $38.72 for surrendered options, spot price of $26.24,
a riskless rate of 2.3%, and volatility of 26%, and maturity of 4.7 years, the BlackScholes value of the average surrendered option equals $3.31. Employees, however,
received an average of $1.10 per option, which is equal to $382 million paid by J P
Morgan divided by the 344.6 million options surrendered. These baseline estimates
suggest that employees who surrendered their options place a significant 67% discount,
relative to the Black-Scholes value, on the out-of-the-money options. Our estimate of
the subjective discount can vary based on what we assume about the maturity of the
surrendered options. If we assumed the maturity is eight years, the objective value
would increase, so the subjective discount would widen to 80%. On the other hand, if
45
we assume a three year maturity, the objective value would decrease and the discount
would reduce to 42%. In all these cases, the discount is significant.
To interpret the valuation wedge, it may also be helpful to fit theoretical models
of subjective valuation and infer model parameters implied by the prices. The model
of Ingersoll (2006) is convenient for this purpose. Assuming the percentage wealth of
employees tied to Microsoft is 25%, the $1.10 subjective value of surrendered options
implies a relative risk aversion of 4.5 if idiosyncratic volatility were 95% of total
volatility. If the idiosyncratic risk were 90% or 85% of total volatility, the relative
risk aversion would be 5 and 5.6, respectively. Fixing the option’s value, an increase
in idiosyncratic volatility is needed to offset a lower risk aversion and vice versa.
An e-mail communication from Microsoft CEO Steve Ballmer on July 8, 2003
provides additional details about the pricing of the options as a function of the strike
price. Options with a strike of $33 would fetch a price of about $2, options with $42
strike would fetch $0.60 and options with $45 strike would fetch $0.25. The BlackScholes option prices for these three strike prices equal $4.48, $2.58, and $2.15. The
subjective discounts are 55%, 77%, and 88% for the three options. The subjective
discounting of options is substantial and increases as the options go underwater.
46
Fig. 1. Number of firms in EXECUCOMP with at least one repriced executive in a
fiscal year (i.e. PREPRICE = “TRUE”). Fiscal year tequals calendar year t if the
year end is between June and December and calendar year t-1 otherwise. Thus, the
1999 data includes repricing for firms with fiscal years beginning between June 1998
and May 1999, which spans a sub-period before the December 1998 FASB change in
repricing accounting.
47
Fig. 2. Number of firms in EXECUCOMP which gave two grants in a fiscal year
with at least two executives receiving options in each grant. Fiscal year t equals
calendar year t if the year end is between June and December and calendar year t-1
otherwise. Thus, the 1998 data includes firms with fiscal years beginning between
January 1999 and May 1999, a sub-period after the December 1998 FASB change in
repricing accounting.
48
Table 1: Option Grant Data
Table 1 reports the steps used in reading and sorting data on executive option grants as
disclosed in the 2003 edition of EXECUCOMP. Data is for options granted to executives
over the 10 year period from 1992 and 2003.
Observations
Dropped
Total executive years
Observations
Retained
152,159
Combine option grants that have same expiration
and strike price but reported as separate grants
4,989
147,170
78
147,092
Delete observations for years in which an executive
receives reload options and for all subsequent years
11,053
136,039
Drop observations when the exercise price is not
within ± 20% of stock price on grant date
31,585
103,129
Drop observations when exercise price 6= market price
2,954
100,175
Missing CRSP data
49
Table 2: The usage of out-of-cycle refresher grants
Table 2 reports the distribution by year of firms in the ExecuComp database using out-ofcycle refresher grants. Firms in this sample make an annual options grant in a fiscal year
and a second grant within the same fiscal year at an exercise price that is at least 20%
below that of the first grant. At least two executives should have received both grants for
inclusion in the sample. We exclude cases in which proxies and 10-K’s indicate that option
grants relate to reloads, grants in subsidiaries, mergers, and other idiosyncratic causes.
Fiscal year
# Firms
1992
1
1993
3
1994
7
1995
2
1996
2
1997
2
1998
24
1999
22
2000
26
2001
22
2002
24
1993 - 2002
135
50
Table 3: Deadweight costs of refresher grants
Table 8 reports the median and mean costs of making out of cycle refresher grants and
the costs if the firms had repriced instead for a sample of 135 firms in the EXECUCOMP
database that made out of cycle refresher grants. Column 2 reports the median (mean) costs
for each executive officer who receives a refresher grant. Column 3 sums the costs across all
executives for each firm and reports the median (mean) cost per firm in our sample. Column
4 reports the median (mean) costs for all employees receiving a refresher grants using data
on total option grants using information provided in the proxy statements. Executive level
grant data for each executive come from the EXECUCOMP database.
Per
executive
All
executives
(per firm)
All
employees
(per firm)
Total observations
590
135
135
Black-Scholes cost
of refresher grant (A)
327,338
(839,054)
1,534,138
(3,666,979)
8,086,731
(49,784,53)
Black-Scholes value
of underwater options
448,780
(1,543,723)
1,948,361
(6,746,643)
10,209,110
(99,277,850)
Black-Scholes value of
underwater options, repriced
534,787
(1,821,013)
2,559,311
(7,958,500)
12,754,290
(117,025,600)
% Change in
Black-Scholes value
25.38%
(113.73%)
25.71%
(136.79%)
25.18%
(136.79%)
Black-Scholes cost
of repricing (B)
86,229
(277,289)
427,709
(1,211,857)
2,278,727
(17,747,720)
Deadweight costs
(A) - (B)
193,821
(561,765)
930,998
(2,455,121)
4,213,566
(32,036,810)
51
Table 4: CEO Compensation by Year
Table 4 reports the median (mean) total compensation (EXECUCOMP Data Item TDC1)
(in $000) for the CEO by year. Panel A presents the compensation for companies in which
the portfolio of options held by the CEO is at least 25% out of the money. Panel B presents
data on CEO compensation for companies that use refresher grants, with CEO adjusted
for any refresher grant received by the CEO. Panel C presents the data on total CEO
compensation, including any refresher grants the CEO receives, for firms that use refresher
grants.
Panel A
CEO Option Portfolio
Out-of-the-Money >25%
Year
N
Median Mean
1993 169
$1498 $2179
1994 151
$1470 $1944
1995 311
$1191 $2046
1996 237
$1370 $2382
1997 254
$1599 $3196
1998 193
$1641 $3170
1999 382
$1883 $3810
2000 465
$2087 $4126
2001 405
$2041 $4498
2002 395
$2272 $4440
Total 2962 $1728 $3456
Year
N
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
Total
3
7
2
2
2
23
21
26
21
24
131
Panel B
Excl. Refresher Grant
Median Mean
$1226
$2170
$ 888
$ 977
$1959
$1959
$2437
$2437
$1492
$1492
$1623
$2327
$2054
$3782
$1871
$4865
$2417
$7909
$2490
$4249
$1892
$4218
52
Incl. Refresher Grant
Median Mean
$2544
$2610
$1298
$1458
$2621
$2621
$3083
$3083
$1863
$1863
$2035
$2965
$2769
$4554
$2779
$7168
$4960
$1479
$3867
$5779
$2955
$6356
Table 5: Multivariate Regression Analysis of Total CEO Compensation,
Table 5 reports regression results for CEO compensation. Regression include time dummies
and firm-level fixed effects. Data is over the period from 1993-1998. LogSales is the logarithm of Sales in the prior fiscal year. LogROA is the logarithm of (1+ ROA) where ROA
is defined as the ratio of Net Income over Total Assets in the prior fiscal year. LogRetM
is the logarithm of (1 + Rett−1 ) and LogRet2M is the logarithm of (1 + Rett−2 ), where
Rett−1 and Rett−2 are the return in the fiscal year t − 1 and t − 2 respectively. Vol is the
volatility of monthly returns over the prior 60 months. SalesGr is the ratio of the current
fiscal year sales to the sales in the prior fiscal year. RND is the level of R&D expenses
scaled by Sales. Lev is the ratio of Debt to Total Assets. LogEMP is the logarithm of the
Number of Employees in the firm. FEFEMP is the Free Cash Flow scaled by the Number of
Employees. REFRESHER is a dummy variable that takes a value of 1 if an observation is in
our out of cycle refresher grant sample . The variables DUM94-DUM98 are year dummies
for years 1994-1998 respectively. The superscripts ∗ ∗ and ∗ ∗ ∗ refer to significance at the
5% and the 1% respectively.
Variable
Intercept
LogSales
LogROA
LogRetM
LogRet2M
Vol
SalesGr
RND
LEV
LogEMP
FCFEMP
REFRESHER
DUM95
DUM96
DUM97
DUM98
Coefficient
p-Value
-1.999
0.248
0.328
0.058
-0.054
0.401
0.291
0.001
0.344
0.038
0.000
0.241
0.060
0.005
-0.009
-0.002
0.002
0.010
0.207
0.208
0.171
0.153
0.001
0.863
0.072
0.660
0.327
0.170
0.088
0.888
0.843
0.955
53
Table 6: Excess Compensation
Table 6 reports regressions results for excess compensation for the period from 1999-2002.
Excess Compensation is calculated as the difference between the actual compensation less
the predicted value using the model and coefficients specified in Table 5, i.e. using coefficients from the Pre 1998 period to estimate excess returns in the Post 1998 period.
UNDERWATER is a dummy for firms in which the CEO’s option portfolio is at least 25%
out-of-the money. DUM00, DUM01, and DUM02 are year dummies for 2000, 2001, and
2002 respectively.
Variable
Coefficient
p-Value
Intercept
0.046
0.304
UNDERWATER
-0.026
0.572
DUM00
-0.084
0.191
DUM01
-0.131
0.036
DUM02
-0.137
0.022
54