Non-Executive Employee Ownership and Corporate Risk-Taking
Francesco Bova
Joseph L. Rotman School of Management, University of Toronto [email protected]
Kalin Kolev
Yale School of Management [email protected]
Jake Thomas
Yale School of Management [email protected]
Frank Zhang
Yale School of Management [email protected]
July 29, 2013
Abstract. Prior research documents a negative link between risk and executive holding of stock (positive link observed for options). We find a similar negative relation for non-executive holding of stock. Our finding is consistent with the view that non-executives not only face significant incentives to reduce risk when they hold stock, but they are also able to influence decisions that affect firm risk. While endogeneity cannot be fully ruled out, the results of a battery of tests suggest that it plays a limited role. A second robust result is that the documented relation becomes more negative as option-based executive compensation increases. Overall, corporate risk is related to the incentives created by stock and options held by both executives and non-executives, as well as interactions among those incentives.
JEL classification: G30.
Keywords: Employee ownership, employee compensation, executive compensation, risk-taking
We thank Mingming Qiu, Ilona Babenko, and Rik Sen for sharing data, and Joseph Blasi, Robert Bushman, Brian Cadman, Mary Ellen Carter, John Core, Richard Frankel, Wayne Guay, Rachel Hayes, Gilles Hilary, Doug Kruse, DJ Nanda, Darius Palia, Dan Taylor, Robert Verrechia, Terry Warfield, and seminar participants at the 2012 AAA Annual Meeting, University of Alberta Accounting Research Conference, Colorado Summer Accounting Research Conference, Concordia University, London Business School, the Louis O. Kelso Fellowship mid-year meeting, IAFEP Conference, INSEAD, University of Maastricht, University of Miami, the University of Toronto, and University of Wisconsin for their valuable feedback. Bova and Thomas are grateful for funding from the Louis O. Kelso Faculty Fellowship for research in employee ownership and Yale School of Management, respectively.
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1. Introduction
An extensive literature considers how different forms of compensation create incentives
for managers to alter the distribution of stock returns. A subset of that research focuses on
incentives to alter the variance of stock returns by actions that either increase volatility (e.g.,
invest in risky projects or increase leverage) or decrease volatility (e.g., hedge exposure to
operating risk). Prior research (e.g., Stulz 1984; Smith and Stulz 1985; Guay 1999) predicts a
negative relation between stockholding and stock volatility. The empirical evidence (e.g., May
1995) confirms that prediction. These findings are based mainly on compensation paid to senior
executives, however. We investigate here whether the negative relation between stockholding
and stock volatility observed in prior research extends to non-executive employees.
Our motivation to do so is two-fold. First, of the eight subgroups of research investigating
equity-based compensation—represented by the interaction among how holdings of
stocks/options create incentives for executives/non-executives to affect the first/second moment
of returns—the incentives created by stock held by non-executives to affect the second moment
of returns is the subgroup receiving the least attention. Second, the arguments for a negative
relation between risk and stockholding are weaker for non-executives, relative to executives,
because of the lower likelihood that two necessary conditions hold: a) the fraction of wealth—
including human capital (future compensation)—that is correlated with employer stock price has
to be large enough to result in significant incentives to reduce risk, and b) employees have the
ability to alter risk, either by eliminating risky alternatives from consideration or by taking
actions subsequently to reduce volatility. We discuss in the following section reasons why these
conditions may or may not hold for non-executives. Even though our research hypothesis is
stated as if the conditions hold, we are agnostic and “let the data speak.”
2
Returning to the theory underlying empirical investigations of the relation between
efforts to alter stock volatility and managerial holding of stocks, prior research (e.g., Stulz 1984;
Smith and Stulz 1985; Guay 1999) suggests that the relation will, in most cases, be negative.
While higher stock volatility increases the value of compensation that has a convex relation with
stock price, such convexity becomes relevant for stockholding only when firms are close to
financial distress. Even if convexity is present, higher stock volatility creates greater disutility for
managers holding stock, relative to non-employee equity holders. Managers are both a) more risk
averse, and b) less able to diversify, and thus bear both systematic and idiosyncratic risk. Further,
higher stock volatility creates additional manager disutility if human capital (future
compensation) is also tied to the firm’s fortunes. We believe that the same arguments apply to
non-executive employees.
For our dependent variable we use two measures that reflect the extent to which
employees influence corporate decisions that affect stock volatility. Our first risk measure is the
standard deviation of daily stock returns over the 12 months following the disclosure of non-
executive stockholding. As stock volatility is affected by factors other than those controlled by
employees, it represents a noisy measure of intent to affect risk.1 To provide an alternative and
potentially less noisy measure of such intent, we consider a second risk proxy based on
accounting data: the standard deviation of seasonally-differenced quarterly accounting return on
assets over the next 20 quarters. Finally, we also consider other indirect measures of risk-taking,
such as the level of R&D expenditures.
Our main independent variable, employee stockholding, is derived from Form 5500 data
filed with the Department of Labor for defined contribution plans invested in employer stock.
1 One alternative is to replace observed stock volatility with volatility implied by put and call option prices. We
find similar results for a subsample with available option data. Section 5 contains robustness analyses that investigate the extent to which our results are sensitive to alternative proxies for our variables.
3
These data cover retirement plans, for which employees are eligible to receive benefits at or after
retirement—such as employee stock ownership plans and 401(k) plans, but exclude non-
retirement plans—such as restricted stock and stock purchase plans (see Frye 2004 for a
discussion of this taxonomy). Thus, while our measure does not encompass all employer stock
held by non-executive employees, it captures a significant portion of the holdings that are
involuntary (voluntary holdings, which can be sold at any time, should create lower incentives on
a sustained basis to reduce risk).
The research design in more recent studies (e.g., Guay 1999; Coles et al. 2006) uses the
vega of executive wealth (options plus stock plus human capital) as the explanatory variable,
while controlling for its delta, where delta (vega) represents the sensitivity of managerial wealth
to changes in share price (return variance). As the available data do not allow estimation of delta
and vega for non-executive wealth, we use the research design from earlier studies where the
explanatory variable is the level of employee shareholding.
We consider a number of control variables to incorporate other factors that are likely to
affect our risk measures. These variables include market capitalization, book-to-market ratio,
leverage, presence of tax loss carryforwards, and effective tax rates. To control for the incentives
of senior executives to also influence risk, we include shares held by those executives (again, as
a percent of total shares outstanding) and option awards (as a percent of total compensation).
While our main results do not include controls for non-executive optionholdings as these data are
not available on Compustat before 2004, we include those controls for the subperiod with
available data to confirm that our inferences are not sensitive to this omission.
Given that we are unable to control for all relevant effects and the variables we use are
likely measured with error, we consider possible ways in which the omission of controls and
measurement error might bias negatively our estimated coefficient on non-executive
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stockholding. We conduct extensive sensitivity analyses and conclude that any bias that is
created works against our predictions.2
We find two robust relations. First, employee stockholding is strongly negatively related
to risk measures that reflect efforts to increase stock volatility. Second, this relation becomes
more negative as the level of executive optionholding increases. Taken together, our results
suggest that corporate risk is affected by executive and non-executive holdings of stock and
options, as well as interactions among these holdings.3
Moving from association to inferred causality, our first result is consistent with the view
that higher stockholding for non-executives increases significantly their incentives to reduce
stock volatility, and that these employees have the ability to take the necessary risk-reducing
actions. This latter inference is new to compensation research in accounting and finance.
However, work in labor economics and organizational behavior suggests that non-executives are
directly and indirectly able to influence corporate decisions, and that this influence increases
with employee ownership. In essence, employee ownership is most effective when combined
with increased employee participation, which results in increased cooperation, delegation, and
responsibility sharing (e.g., Kamil, Pendleton, and Poutsma 2005; Foss 2003; Pendleton,
McDonald, and Robinson 1995; Blasi et al. 2010). And if higher executive optionholding creates
incentives for executives to increase risk, our second result is consistent with the view that the
incentives for non-executives to reduce stock volatility are heightened in cases where executives
have greater incentives to increase stock volatility.
2 For example, we should consider executive holdings, not annual grants, of options and should deflate stock and
option holdings by total wealth. Absent data on senior executive wealth, we considered different combinations of stock and option holdings and grants (see robustness analyses in Section 5) and different scaling variables. The reported results reflect the two measures with the most significant coefficients on the control variables, to reduce any potential negative bias associated with the coefficient on our measure of non-executive stockholding.
3 We assume that senior executives within the same firm face similar incentives to affect risk. Some prior research, however, suggests that there are differences (e.g., between CEOs and CFOs in Chava and Purnanandam 2010).
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We are keenly aware of the concerns expressed in prior research about endogeneity
affecting the associations we document, as well as any inferences made about causality. We
focus on three common sources of endogeneity (e.g., Roberts and Whited 2012): simultaneity,
correlated omitted variables, and measurement error. There are few exogenous variables in our
analysis, and many variables are determined jointly.4 Alternative explanations include reverse
causality and omitted correlated variables, and our coefficient estimates might be biased because
the above-mentioned measurement errors associated with regressors are related to
a) measurement error associated with other regressors, or b) the regression error.
We consider different ways to address these concerns, both when we seek to establish
association and when we provide evidence about causal relationships. First, we use an
instrumental variables approach, where we model employee ownership in the first-stage
regression. Second, to control for potential omitted variables, we include lagged values of the
dependent variables as an additional explanatory variable. Surviving this difficult hurdle
increases substantially the robustness of our findings, as it addresses many alternative
explanations, including omitted correlated variables and reverse causality. Finally, we examine
cross-sectional variation in the relation between non-executive stockholding and risk along
dimensions that might help us eliminate alternative explanations for our results. We observe
similar results for several relatively independent approaches which increases the reliability of our
inferences.
We believe we are the first to examine the link between non-executive employees and
corporate risk-taking. Our main contribution is to document the two robust patterns we observe,
and to alert researchers that incentives created by equity-based compensation for non-executive
4 To the extent that actual compensation deviates from optimal compensation, for idiosyncratic reasons, the
explanatory variables considered in our analyses are more likely to be exogenously determined. These idiosyncratic reasons include unexpected changes in the factors that determine optimal compensation (e.g., Core and Guay, 1999) and managers that override weak boards.
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employees might also play an important role in corporate decisions. Consideration of non-
executive employees may be relevant even for studies focused on senior executives if, as our
results suggest, there appear to be interaction effects between the two sets of incentives. We hope
our results will spur future work and develop a better understanding of causal relationships.
The paper proceeds as follows. Section 2 discusses prior research and develops our
hypotheses. Section 3 discusses the data and provides descriptive statistics. Section 4 presents
the main results and Section 5 contains robustness checks. Section 6 concludes.
2. Literature review and hypothesis development
Smith and Stulz (1985) and Stulz (1984) identify two opposing incentives in the
framework they present to assess the impact of stock and option holdings on a manager’s desire
to affect risk. The two incentives relate to the two steps that link managerial utility to stock
volatility. The first step connects managerial utility to volatility in managerial wealth, and the
second step connects managerial wealth to stock volatility. The first incentive, which causes
managers to reduce risk, relates to the first step above and is due to managerial risk aversion.
Because risk-averse managers exhibit a concave relation between utility and wealth, volatility in
the portion of wealth that is positively related to stock price results in a risk premium, where the
certainty equivalent amount of wealth is discounted relative to mean or expected values of
wealth. The portion of managerial wealth that is positively related to stock price includes
stockholding, optionholding, and any human capital that is employer-specific.5
The second incentive, which causes managers to increase risk, arises when the second
step relation (between stock volatility and the portion of stock price-sensitive wealth) is convex.
Such convexity in the second step reverses first-step concavity created by risk aversion, and
5 Incentives to reduce risk are also created by a different component of managerial wealth referred to as “inside
debt” (e.g., Anderson and Core 2012 and Cassell et al. 2012), which includes unsecured deferred compensation. Given the absence of data on such claims for non-executives, we ignore incentives related to such claims.
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could indeed overcome that concavity if it is sufficiently strong. Convexity in the second step is
created by option-like features of equity-based wealth. Specifically, options that are close to or
below the exercise price and stocks that are close to financial distress (when asset values
approach levels of debt) exhibit convexity in the second step. In contrast, options that are deep-
in-the-money and stocks that are not close to financial distress exhibit little convexity.
Whether stockholding and optionholding create net incentives to increase or decrease risk
depends on the relative magnitudes of the two incentives above. Given that the incentives to
increase risk are generally weak for managerial stockholding, except when the firm is in
financial distress, the incentives to reduce risk are expected to dominate. For managerial
optionholding, however, the incentive to increase risk becomes more relevant, and the net effect
varies from case to case depending on the relative strengths of the two opposing incentives.6
Note that total stock volatility, both idiosyncratic and systematic, is relevant here. To be
sure, managers might reduce the impact of both types of risk on their holdings, by judicious asset
allocation when investing their remaining wealth or by purchasing collars that eliminate both
upside and downside risk. However, the maintained assumption in the literature is that a
substantial portion of the underlying exposure of managerial wealth to stock price volatility
remains unhedged at the personal level.7
Prior evidence supports the predicted negative relation between managerial stockholding
and incentives to take on more risk. For example, Tufano (1996) investigates a sample of
publicly-traded gold mines and finds that executives compensated with stock are more likely to
6 Lambert et al. (1991), Carpenter (2000), and Lewellen (2006) illustrate why higher optionholding might increase
manager’ incentives to reduce stock price volatility, especially if the probability of having options finish in-the-money is sufficiently high.
7 Empirical evidence provides support for this assumption. As an example, Hirshleifer et al. (2012) document that firms with overconfident CEOs have higher return volatility and these CEOs are likely to continue holding options past the vesting period.
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hedge gold price risk. Similarly, the findings in May (1995) suggest that executives with very
large stock holdings undertake risk-reducing acquisitions to better diversify assets.
The evidence on options-based compensation is mixed. While the early evidence suggests
that, in general, options create incentives to increase risk, more recent evidence suggests that
options do not create net incentives to increase risk. Examples of the early evidence include
Rajgopal and Shevlin (2002), which shows that the sensitivity of a CEO’s option-based pay to
stock return volatility for a sample of oil and gas producers is positively linked to the variability
of future cash flows from exploration activities. Examples of the more recent evidence include
Hayes et al. (2012), which shows that the substantial reduction in option grants after 2006, when
the expensing of options becomes mandatory under SFAS 123R, is not associated with efforts by
managers to decrease risk.
Rather than considering separately the effects of stockholding and optionholding, more
recent research combines stock and optionholding and estimates the sensitivity of managerial
wealth to stock volatility. Guay (1999) derives a measure of the convexity of CEO compensation
by measuring the convexity contributed by a stock option or share of common stock as the
change in the security’s value for a one percent change in the annualized standard deviation of
stock returns. Guay (1999) finds that this measure of convexity, referred to as vega by
subsequent research, is more meaningful for firms with growth options, reflected by higher
investments in R&D and capital expenditures. Coles et al. (2006) and Low (2009) use the Guay
(1999) measure and show that a higher sensitivity leads to riskier policy choices, more
investment in R&D, and increased total, systematic, and idiosyncratic risk. Similarly, Armstrong
and Vashishtha (2012) also document a positive relationship between vega and a firm’s
systematic risk.
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Are the results observed for executive stockholding also likely to be observed for non-
executive stockholding? As discussed in Section 1, even though stockholding should in theory
create incentives for all employees to reduce risk, there are a priori reasons to believe that the
same negative relation might not be observed for non-executive employees. Levels of
stockholding might be low relative to non-executive employee wealth, resulting in low
incentives to reduce risk. Even if such incentives are strong, the rights to investment, financing,
and operational decisions may be retained by senior executives, leaving non-executives with
limited ability to reduce risk. Although we do not have strong prior beliefs about the actual
incentives of employees to reduce risk and their ability to do so, we present arguments below for
why a negative relation between non-executive stockholding and risk might be observed.
Regarding the first question—whether non-executive stockholding is large enough to
create meaningful incentives to reduce risk—we note that about two-thirds of our sample firms
are associated with zero employee stockholding, based on our Form 5500 data on retirement
plans. For the remaining firms with positive employee stockholding, we provide below two
estimates for levels of stockholding for the typical employee. We divide value of stock held in
retirement plans (from Form 5500 reports) by total number of employees (from Compustat) and
obtain a mean (median) stockholding per employee of $10,214 ($3,245). An alternative estimate
is obtained by dividing plan assets by plan participants for ESOP plans which results in a mean
(median) of $82,000 ($32,000). The former estimate is likely to be a lower bound because many
employees are not included in retirement plans, whereas the latter estimate is likely to be an
upper bound because it includes retirees.
We are unable to find reliable savings data for typical employees in our sample against
which to compare those estimates of employee stockholding. The general evidence (e.g.,
Browning and Lusardi 1996; Lusardi, Schneider, and Tufano 2011) suggests that median savings
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levels are low.8 While savings levels increase with income, they are negative for the first two
quintiles of income and become high only for the highest quintile of earners. If so, typical
savings levels (especially wealth held as financial assets) for employees in our sample firms
during our sample period are also probably quite low. We assume that total savings are of the
same order of magnitude as annual pay. Moving from total savings to the portion held as
employer stock, estimates in Blasi et al. (2010) for a sample of firms with positive employee
stockholding suggest that the average value of employer stock held is 65 percent of annual pay.
If so, the typical holding of stock may indeed be large enough, as a proportion of total savings, to
make employees sensitive to risk.9
Turning to the second question—whether non-executive employees are able to alter firm
risk—we conclude, for reasons described below, that there is sufficient basis to believe that
corporate decisions reflect the preferences of non-executives. We note that firm risk can be
altered at different stages and in different ways. For example, risk can be reduced not only by
selecting less risky projects, but also by filtering out risky projects when considering potential
investments or by hedging risk after projects have been selected. And risk is affected by
operational and financial decisions, not just investment decisions. While the main basis for our
conclusion is that executives share with non-executives many of the decision rights the Board
8 For example, Lusardi, Schneider, and Tufano (2011) examine households’ financial fragility by looking at their
capacity to come up with $2,000 in 30 days. Using data from the 2009 TNS Global Economic Crisis survey, they document widespread financial weakness in the United States: Approximately one quarter of Americans report that they would not be able to come up with $2,000 in the allotted time. The results suggest that, for the average American, even a $10,000 investment in company stock represents a large portion of their overall wealth.
9 Arguments could also be made for why non-executives are likely to be more sensitive to risk, relative to executives. For example, the human capital of non-executive employees might be tied more closely to firm performance, and non-executives might diversify less than executives because they are less sophisticated investors and may not have as much access to financial advisors.
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assigns to them, there is also reason to believe that executives will at times keep non-executives’
preferences in mind when making decisions. 10
Unlike the compensation and governance literatures in accounting and finance, which
have conducted limited investigation of non-executives, the labor economics and organizational
behavior literatures have explored different aspects of the interaction between executives and
non-executives. Many reasons (e.g., economic, psychological, and sociological) to share decision
rights with non-executives have been considered and evidence supporting those reasons has been
documented. A common theme in this literature is that organizational performance is enhanced
with some level of decentralization, participation, and cooperation.
This literature (e.g., Rosen et al. 2005 and Blinder 1990) has also concluded that
delegation of decision rights should be higher when employees hold stock. Importantly, this
expectation is borne out empirically, as firms often adopt employee ownership plans and increase
employee decision rights jointly. These joint initiatives allow employees to become the residual
beneficiaries of their own decisions (Foss 2003) and lead to greater employee participation at the
task-related level (see Kalmi et al. 2005 and Pendleton et al. 1995). Similar inferences are found
in Blasi et al. (2010), a study that assesses data from the 2002 and 2006 General Social Surveys
and the 2001 and 2006 NBER Company Surveys. The study concludes that employees
compensated with company stock are more likely to: a) be involved in making decisions on the
job and setting department goals; b) operate with minimal supervision; c) have an increased say
10 Acharya et al. (2011) present a model of internal governance in firms which suggests that senior executives are
sensitive to differences between their own interests and those of their subordinates, but accommodate their subordinates’ interests in exchange for more cooperation and effort. Another example is the judge/advisor model (e.g., Sniezek and Buckley 1995) where judges (executives) use information from advisors (non-executives) to make better decisions, recognizing that advisors may have their own incentives regarding the information provided.
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about what happens on the job; and d) make decisions cooperatively.11 Moreover, further
evidence suggests that increased stockholding and optionholding results not only in higher levels
of cooperation, but also in more stringent monitoring by non-executive employees (e.g., Fitzroy
and Kraft 1986 and Hochberg and Lindsey 2010).
Finally, to assess whether there is anecdotal support for the notion that non-executives
can impact corporate risk-taking, we interviewed several executives of publicly-traded
companies where non-executives have substantial stockholdings. We highlight the comments
from one executive in particular—Steven Fisher, Senior VP and Treasurer of SAIC (NYSE:
SAI). As a private company, SAIC was formerly majority-owned by its employees. After SAIC
went public, employees divested some of their holdings leading SAIC to no longer be majority-
owned by its employees. As such, Mr. Fisher has observed variation in employee ownership over
time and is able to comment on the effect of that variation on firm outcomes. To summarize his
responses, he feels that an equity stake for employees can impact the firm’s decision-making by:
a) increasing the breadth of participation in the firm’s decision-making; b) increasing employees’
commitment to seeing decisions through, and c) increasing decentralization of authority with
respect to decision-making. Each of these outcomes plays a role in the firm taking on less risky
decisions and on the firm realizing less volatile returns.12
Overall, we believe that it is possible that increased stockholding by non-executive
employees creates incentives to reduce stock volatility, and that those incentives result in actions
11 The General Social Survey assesses a national area probability sample of non-institutionalized adults. The survey
was conducted by the National Opinion Research Center of the University of Chicago in 2002 and 2006. The NBER Company survey assesses data from employee surveys across 14 companies (and 323 worksites) in 2001 and 2006.
12 The transcript of the interview is available from the authors upon request.
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aimed at lowering stock return volatility. 13 Thus, we state our research hypothesis, in its
alternative form, as follows.
H1: Cross-sectionally, higher levels of non-executive employee stock ownership are related to lower stock volatility.
3. Sample and descriptive statistics
3.1 Sample
We obtain data on employee stock ownership following the methodology of Bova et al.
(2013). Specifically, the U.S. Department of Labor Form 5500 filings were searched for defined
contribution plans that allow direct investment in employer stock. As described in Bova et al. (p.
14), we include “employee stock ownership plans (ESOPs), 401(k) plans that allow an
investment in employer stock as an option, deferred profit sharing plans invested in employer
stock, and employer stock bonus plans.” When there are discontinuities in the data series for a
firm, we impute the missing observation for year t as the average of values obtained for years t-1
and t+1. To merge these data with Compustat, stockholdings are aggregated across plan sponsors
with the same Employee Identification Number (EIN).
In concept, employee stockholdings should be measured as a fraction of employee
wealth. In the absence of data on employee wealth, we scale employee stockholding (EMPSTK)
by the number of shares outstanding (details of all variables are provided in the Appendix).
While the deflator we use could cause considerable measurement error, we believe this error
would bias the estimated coefficient on EMPSTK toward zero if other variables are measured 13 A related issue pertains to the reasons why non-executive employees tolerate such high exposure to the
performance of their employer. One potential explanation is overconfidence: Employees are excessively optimistic about the prospects of the company and believe their actions can influence the firm performance. Focusing on CEOs, Hirshleifer et al. (2012) provide a discussion of the theoretical and behavioral literature underlying this relation. Even if employees are overconfident, however, they are not likely to perceive the company stock as riskless and have clear incentives to reduce risk. In addition, the employees who hold stock likely believe they can influence firm risk. On another note, we do not anticipate Boards to be concerned about employees’ risk-reducing actions. We take as given that the main reason to grant stock to employees is to induce an increase in the first moment of the return distribution. For example, Beatty (1995) documents value increases from ESOP adoption arising from different sources, such as tax savings and improved labor productivity.
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correctly in the regression (Roberts and Whited 2012). To verify that the choice of deflator does
not drive our results, we explore other proxies of EMPSTK (Section 5). In particular, to avoid
scaling EMPSTK by outstanding shares, we consider the dollar value of stock holdings per
employee. The thrust of our results remains unaffected.
The following is a brief description of the types of stock ownership included in and
excluded from EMPSTK. Bova et al. (2013) describe the fractions of different types of plans, as
well as the fraction of total stock value held by those plan categories. For example, fewer firms
have ESOP plans than non-ESOP plans (29.7 percent versus 83.4 percent), but ESOP plans hold
a larger fraction of stock (71.4 percent of combined employee holdings versus 28.6 percent for
non-ESOP plans).14
While EMPSTK includes employee stock held in various retirement plans, it excludes
stock held in non-retirement plans, such as restricted stock and employee stock purchase plans.
For some firms (e.g., Apple), the fraction of total employee stockholding excluded from
EMPSTK is substantial. We note that the exclusion of some employee stockholdings from
EMPSTK does not bias our findings in favor of H1. If the excluded holdings are unrelated to
holdings included in EMPSTK, the associated measurement error biases the coefficient on
EMPSTK toward zero. Even if the excluded and included stockholdings are positively related,
the magnitude of the negative coefficient on EMPSTK is biased upward but the magnitude of the
associated t-statistic should not be biased upward.
We provide below a brief discussion of stockholdings excluded from EMPSTK. Omitting
stock held voluntarily, as in employee stock purchase plans (ESPPs), is less of a concern because
14 When we partition EMPSTK into variables capturing equity held in ESOP plans and equity held in non-ESOP
plans, our inferences remain unchanged across each partition.
15
employees could elect to sell those shares at any time.15 Employee holding of restricted stock, on
the other hand, should create incentives similar to plans included in EMPSTK that require
employees to hold stock. To investigate potential bias in the estimate of EMPSTK, we examine
the correlation between EMPSTK and grants (not holdings) of restricted stock for a 2005 sample
obtained from Mingming Qiu at University of Utah. That correlation is 0.058, which we view as
low. Additional empirical analysis suggests that the coefficients on EMPSTK are virtually
unchanged when we include grants of restricted stock as an additional variable in the regressions
for year 2005. Overall, while EMPSTK likely measures non-executive stockholding with error,
we believe those errors bias the coefficient on EMPSTK toward zero, rather than toward a larger
negative value.
We examine our sample to determine industry clustering based on levels of EMPSTK, but
detect few obvious patterns. For example, most technology firms have low levels of EMPSTK
(recall that restricted stock is not included in EMPSTK). But for the most part, we note that
industries have both firms with zero and with high levels of EMPSTK. In particular, firms in the
top 10 percent of EMPSTK belong to a broad range of industries. Examples of firms in the top
decile include DuPont, Guidant, Exxon, Abbott Labs, GE, AT&T, Kroger, Southwest Airlines,
and Proctor & Gamble.
Even though our focus is on non-executive stockholdings, their optionholdings are also
likely to affect incentives to alter risk and should be controlled for. However, we are unable to
obtain option data for the earlier part of our sample period, as Compustat only provides data for
option variables beginning in 2004. Therefore while our main results exclude non-executive
15 Nevertheless, in untabulated analyses, we include stockholding held in ESPPs and find that our results are robust
to this modification. We thank Ilona Babenko and Rick Sen for allowing us to use the ESPP data hand collected for Babenko and Sen (2011). We do not include ESPP data in our main EMPSTK variable for the tabulated analysis, as fair market values for the stock held in ESPPs are voluntarily disclosed by firms. As a result there are comparatively few firm-years with data in the ESPP sample.
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optionholdings, we confirm that those results remain relatively unchanged when we include
estimates for option grants (EMPOPT), for the subperiod beginning in 2004.
Because prior research documents a significant relationship between executive
compensation and risk, we also control for executive holdings of options and stocks. We obtain
the necessary data from Execucomp and Thomson Reuters, respectively. We scale shares held by
executives (EXESTK) and options granted to executives (EXEOPT) by total shares outstanding
and total executive compensation, respectively.
We considered two additional measures of EXEOPT based on options held by executives,
rather than annual grants: a) number of options held, scaled by total shares outstanding; and
b) fair value of options held, scaled by market value of equity. While option holdings should be
more relevant here than option grants, we find that the coefficient on EXEOPT is closer to zero
for these alternative measures of EXEOPT. To reduce the likelihood that the magnitude of the
coefficient on EMPSTK is overstated, because the impact of EXEOPT is suppressed when we use
these alternative measures as controls, we conduct the analysis using the grant-based measure of
EXEOPT. Overall, even though we use a simpler specification and our estimates of EXESTK and
EXEOPT are measured with error, we do not see a reason why our approach would bias
downward (i.e., make more negative) the estimated coefficient on EMPSTK.
After matching the Form 5500 data with Compustat, our sample contains 60,235
observations for 9,677 individual firms for the period 1999-2009. As we also require data on the
structure of executive compensation, we next merge the data set with Thomson Reuters Insider
Filing data to gather information on executive stockholding, EXESTK. This step decreases the
sample size to 18,417 observations for 5,371 individual companies. Our sample size decreases
further to 8,702 firm-years when we obtain information on executive options, EXEOPT, from
Execucomp, a database that generally covers only current and past members of the S&P 1500.
17
We obtain additional financial and market data from Compustat Annual and CRSP, respectively.
As we use all available observations for each of our tests, sample size varies across tests
depending on the specific variables included. To mitigate the potential disproportionate influence
of outliers, we Winsorize all continuous variables, other than the three market-based variables
(MV, SD_RET, and RET), at 1 and 99 percent of each year’s cross-sectional distributions.
3.2 Risk-taking proxies
In concept, the dependent variable should be the portion of expected stock volatility that
reflects employee efforts to reduce risk. The first empirical proxy we use is observed future stock
volatility. We measure stock volatility as the standard deviation of daily stock returns (SD_RET)
over the 12-month period starting from the fifth month after fiscal year-end.16 As prior research
calculates stock volatility in different ways (e.g., Chen et al. 2006, Core and Guay 1999), we
conduct robustness tests to confirm that our results are not sensitive to the specific stock
volatility measure used. For example, we obtain similar results when we repeat the analyses
using idiosyncratic volatility, measured as the standard deviation of market-model residuals over
the same window, or implied stock volatility based on put and call option prices, as of six
months after the year-end (see Section 5).
As stock volatility is affected by factors other than employees’ intent to influence stock
market volatility, such as unexpected revisions in discount rates and forecasts of future cash
flows, we consider an alternative empirical proxy: the volatility of accounting rates of return
(e.g., Beaver et al. 1970). The risk measure we construct (SD_∆ROA) is the standard deviation of
seasonally differenced quarterly accounting return on assets over the subsequent five years,
16 The five month lag allows the market to learn about a firm’s financial information. We obtain similar results
when using a four- or six-month lag.
18
where return on assets is income before extraordinary items, scaled by average total assets for the
quarter.
The literature also uses R&D and capital expenditure (CAPEX) as proxies for risk-taking.
Whereas the incentive to increase risk is expected to be positively related to R&D levels,
arguments have been made for both positive and negative links with CAPEX. The evidence
confirms the positive relation expected for R&D, but both positive (e.g., Guay 1999; Bargeron,
Lehn, and Zutter 2010; Cohen, Dey, and Lys 2013) and negative (e.g., Coles, Daniel, and
Naveen 2006; Hayes, Lemmon, and Qiu 2012) relations have been observed for CAPEX. As a
practical matter, we find that the negative relation between non-executive stock ownership and
our two primary risk measures—volatility of stock returns and ROA changes—are also observed
for R&D and CAPEX.
3.3 Descriptive statistics
As reported in Table 1, Panel A, the average (median) company in our sample has a
market capitalization of $4.2 billion ($484 million). Requiring data on executive compensation
skews our sample toward larger companies. Employees hold, on average, 0.8 percent of the
employer’s outstanding stock in retirement plans. As described earlier, this mean value is
depressed by the two-thirds of the firm-years in our sample with zero EMPSTK.17 Company
stock held by the top four executives is, on average, higher (3.5 percent of shares outstanding)
than that for employees. Stock option grants account, on average, for 31.4 percent of total
executive compensation. As with employee stockholding, there is considerable variation across
the sample in both executive stockholding and option grants.
17 For firms with positive EMPSTK, employees and executives, on average, hold 2.7 and 2.3 percent of shares
outstanding, respectively.
19
Turning to the two primary risk measures, the mean (median) values for standard
deviation of stock returns is 3.9 (3.1) percent, and standard deviation of seasonally-differenced
quarterly ROA is 5.1 (2.1) percent. The remaining columns in Panel A, which describe the
distributions for these two variables, suggest considerable cross-sectional variation.
We report in Table 1, Panel B, the Pearson and Spearman correlations between pairs of
the main variables of interest. Even though these correlations reflect the effects of other
correlated variables, they provide preliminary evidence on the relation between compensation
and corporate risk-taking. Consistent with the conjecture that corporate risk-taking decreases in
non-executive stock ownership, the Pearson and Spearman correlations between EMPSTK and
the two risk measures are significantly negative. Consistent with the results of some prior
research (e.g., Rajgopal and Shevlin 2002), the proportion of executive compensation
attributable to option grants, EXEOPT, is positively related to both risk-taking measures.
However, the proportion of shares outstanding held by executives, EXESTK, is also positively
correlated with SD_∆ROA and SD_RET, which is at odds with the negative relation predicted by
theory and documented in prior evidence.
4. Results
We present our main results in this Section, and in Section 5 explore a variety of analyses
designed to test alternative explanations for these results.
4.1 Employee ownership and corporate risk-taking
To investigate whether non-executive employee stock ownership creates incentives to
reduce risk, we test for the conjectured negative relation predicted by H1 between future risk-
taking measures (CRTt+1) and our measure of non-executive stockholding for year t (EMPSTK),
using the following regression model:
20
effectsYearFixedExedEffectsIndustryFiRETNOLCF
BMMVLogEXEOPTEXESTKEMPSTKCRTt
876
5432101 )(
(1)
CRT represents the two primary measures of corporate risk taking—SD_∆ROA and SD_RET—as
well as the two additional measures we consider—R&D and CAPEX.18 We estimate the models
using ordinary least squares and allow the errors to cluster by firm (Petersen 2009).
Our main control variables are executive stock ownership (EXESTK) and option-based
compensation (EXEOPT). We include six additional variables identified in extant research as
important controls in analyzing the relationship between risk-taking and compensation (Tufano
1996; Core and Guay 1999; Chen et al. 2006; Coles et al. 2006). Specifically, we include the log
of market value of equity (MV) at the end of year t, to control for potential economies of scale
and the cost of external financing.19 To account for growth and investment opportunities, we
include the book-to-market ratio (BM) for equity at the end of year t. We also consider leverage
(LEV) and free cash flows (CF) as measures of financial distress and capital availability,
respectively.20 We define LEV as the three-year average of short-term and long-term debt, and
CF as cash flow from operations minus cash flow from investing and cash dividends (e.g., Core
and Guay 2001). Both variables are scaled by total assets and three-year averages are computed
over years t-2 through t. To control for the impact of the employer’s tax rate on compensation,
we include an indicator variable set to one if the company has a positive net operating loss carry-
18 We consider both R&D and CAPEX in our primary regression model (Tables 2 and 7), but exclude them from
subsequent analyses. We confirm, however, that the results hold for both variables in subsequent analyses (Tables 3-5).
19 We recognize that the market value of equity may not fully control for firm size. In untabulated analysis, we consider sales (Log(S)) and the number of employees (Log(EMP)) as additional control variables. In addition, to address potential non-linear effects related to size, we include the square of Log(MV), Log(S), and Log(EMP) in the regression. The coefficients on EMPSTK remain significantly negative after including the additional size and non-linearity controls.
20 We considered alternative proxies for financial distress and capital availability. For example, to capture cash flow availability, we use interest burden, defined as the three-year average of interest expense scaled by operating income before depreciation. The main results remain qualitatively similar.
21
forward (NOL) in year t, zero otherwise. Last, we control for the company’s stock return (RET)
over year t.
To control for industry-specific and macro-economic factors, we include industry and
year fixed effects, where the industries are defined in accordance with the Fama and French
(1997) industry classification. The industry fixed effects act as additional controls, as the
association between non-executive employee ownership and corporate risk-taking may vary
across industries, and certain industries require higher levels of non-transferrable, firm-specific
human capital.21
Table 2 presents coefficient estimates for model (1). Consistent with Hypothesis H1,
EMPSTK is significantly negatively associated with all four future risk measures. These
relationships are economically significant as well. Based on the Model (1) results reported in
column 1 of Table 2, the coefficient estimates imply that one standard deviation increase in
EMPSTK in the current period is associated with a (0.077*0.023) / 0.051 = 3.47 percent decrease
in SD_∆ROA during the next five years, expressed as a percent of mean levels of SD_∆ROA.
Similar calculations based on the results reported in column 2 imply that one standard deviation
increase in EMPSTK decreases SD_RET, the volatility in the company’s stock return in year t1,
by (0.027*0.023) / 0.039 = 1.59 percent of the mean levels of SD_RET.
Turning to the control variables, the coefficients on stock option grants for executives
(EXEOPT) are all significantly positive, suggesting that increased option grants for executives
are associated with higher firm risk, ceteris paribus. The evidence regarding executive
stockholding is mixed, however. The coefficient on EXESTK is negative and significant for
21 This conjecture would be supported if the negative relation between CRTt+1 and EMPSTK became stronger when
industry fixed effects are dropped from equation (1). In untabulated analyses, we find that the coefficients on EMPSTK are indeed more negative without industry fixed effects. We also find that manufacturing and technology firms tend to have more negative coefficients on EMPSTK than agriculture, financial, and service firms.
22
SD_∆ROA, marginally significant for SD_RET and insignificant for the remaining two risk
measures.
We confirm that the negative relation between EMPSTK and our two primary risk
measures is monotonic and not driven by a few observations with extreme values of EMPSTK.
Specifically, in untabulated analyses we compare the mean levels of the two risk measures for
low, medium, and high terciles, based on the distribution of EMPSTK each year, and note that
the differences in risk between low and medium subgroups equal those between medium and
high.22 We also repeat the analysis reported in columns 1 and 2 of Table 2 after replacing
EMPSTK with indicator variables that allow us to capture the mean risk for the three terciles,
after controlling for year and firm fixed effects. Again, the results suggest that mean levels of
risk increase monotonically in EMPSTK.
4.2 The interaction between non-executive and executive incentives
Our analysis so far assumes that stockholding for non-executive employees and stock and
optionholding for executives have independent effects on risk. We consider next the possibility
that the relation between risk and EMPSTK varies with the level of EXESTK and EXEOPT. It is
possible, for example, that the relation between risk and EMPSTK is weaker for higher levels of
EXESTK, if executives have already decreased levels of risk sufficiently to leave little
opportunity for non-executive employees to reduce risk further as their stockholding increases.
The model described below adds these two interactions to equation (1):
effectsYearFixedExedEffectsIndustryFiRETNOL
CFBMMVLogEXEOPTEMPSTK
EXESTKEMPSTKEXEOPTEXESTKEMPSTKCRTt
109
8765
432101
)(*
*
(2)
22 We create terciles as follows: We include all firm-years with a value for EMPSTK equal to zero into the first
tercile. We then divide all firm-years with strictly positive values for EMPSTK equally in to the second and third tercile.
23
We report the regression results in Table 3. Turning to Panel A, the main finding is the
significantly negative coefficient on 5 , the EMPSTK*EXEOPT interaction, for both risk
measures. That is, the relation between non-executive employee stockholding and risk becomes
increasingly negative as the level of options granted to senior executives increases. The estimate
of -0.309 for 5 in column 1 implies that one standard deviation increase in EXEOPT is
associated with an increase in the reduction of risk associated with employee stockholding of
0.0803 (= -0.309*0.260). That reduction in risk is more negative than the 0.077 reported in
column 1 of Table 2, which represents the average reduction in SD_∆ROA associated with
increases in employee stockholding across all levels of EXEOPT. Similar calculations for
SD_RET from column 2 suggest that the corresponding economic magnitude is 0.030 (=
0.116*0.260). This value is more negative than the 0.027 reported in column 2 of Table 2,
which represents the average reduction in SD_RET associated with increases in employee
stockholding across all levels of EXEOPT.
The coefficient on the EMPSTK*EXESTK interaction, 4, is also negative, but generally
insignificant. Apparently, the negative relation between EMPSTK and risk becomes more
negative, not less negative, as the level of executive stockholding increases. Notably, while
allowing for interaction effects with executive compensation causes the coefficient on EMPSTK
to decline substantially (from Table 2 to Table 3), the coefficients on both EXESTK and
EXEOPT remain relatively unchanged. That is, even though increases in the level of executive
optionholding increases the negative relation between employee stockholding and risk, changes
in levels of employee stockholding have no effect on the impact of executive optionholding and
stockholding on risk.
24
To provide a more intuitive interpretation of the interaction between non-executive
stockholding and executive optionholding, we also report the results of estimating the relation
between risk and EMPSTK for low, medium, and high levels of EXEOPT (Table 3, Panel B). The
two indicator variables, MEDEXEOPT and HIGHEXEOPT are set to 1 for the middle and highest
terciles of EXEOPT, respectively, based on the distribution of EXEOPT each year. The negative
coefficients on the interaction between EMPSTK and MEDEXEOPT (EMPSTK and HIGHEXEOPT)
describe the extent to which the coefficient on EMPSTK becomes more negative as the level of
EXEOPT moves from the bottom tercile to the middle (highest) tercile. Turning to the regression
results, the relation between risk and EMPSTK becomes more negative and the associated
statistical significance increase for the low/high EXEOPT comparison, relative to the
low/medium comparison, for both measures of risk-taking. These results confirm the robustness
of the interaction effect documented in Panel A.
The negative relation between non-executive stock ownership and risk documented in
Tables 2 and 3 is consistent with the prediction of H1: Higher levels of non-executive employee
ownership of stock increase the incentives for those employees to take actions that reduce stock
volatility. The results of Table 3 are consistent with the view that those risk-reducing incentives
increase further when optionholdings of executives are higher. The latter result is also consistent
with the notion that non-executive stockholdings lead to reduced risk-taking mostly in those
settings where executives are incentivized to take on more risk.
5. Additional analysis and robustness checks
We acknowledge that the models estimated so far rely on a number of assumptions that
may not hold. We assume, for example, that the levels of non-executive stockholding are
exogenously determined. And, we do not consider reverse causality, where the level of stock
25
volatility is exogenous and determines levels of stock granted to non-executive employees.
Similarly, we cannot rule out the possibility that omitted variables determine both the level of
non-executive stockholding and stock volatility. For example, firms with more loyal employees
and longer tenure could be associated with higher employee stock ownership and more stable
performance. We consider below a variety of analyses that are designed to shed light on the
validity of these alternative explanations.23 After presenting our results, we review the extent to
which the different alternative explanations are supported or rejected.
5.1 The instrumental variables approach
Rather than assume that EMPSTK is determined exogenously, we search for instrumental
variables that are related to EMPSTK in the first stage of a Two Stage Least Square (2SLS)
regression, and then use the predicted value of EMPSTK in the second stage, where we estimate
the model of risk measures described by equation (1). A critical factor for the successful
implementation of the estimator is the identification of instrumental variables correlated with
EMPSTK, but not correlated with the error term in the second-stage model (Greene 2003).
Theory suggests that firms grant employee stocks due to tax and employee retention
considerations. In particular, we consider the effective tax rate for the company-year
(CASHETR), as tax considerations have been shown to be important drivers for the adoption of
employee stock ownership plans (Beatty 1994, 1995). Following Dyreng et al. (2008), we
calculate CASHETR as the five-year cash outlay for tax, scaled by pre-tax income excluding
special items.
23 In this section, we consider multiple approaches to address the potential endogeneity of EMPSTK, which is our
main variable of interest. While EXESTK and EXEOPT are also likely to be endogenous, these variables enter our analyses only as controls; hence, we do not attempt to model them. To the extent that the same underlying factor determines EMPSTK, EXESTK, and EXEOPT, including EXESTK and EXEOPT as controls in the regression helps to address the potential endogeneity of EMPSTK.
26
To incorporate employee retention, we consider two region-specific factors: the firm’s
“local beta” (LOCBETA) and enforceability of non-competition agreements (NCOMPENF). The
use of LOCBETA is motivated by arguments that a company’s stock price is correlated with the
employees’ outside opportunities. Thus, as equity compensation serves as an employee-retention
tool, the propensity to use an employee stock ownership plan to compensate employees might be
related to the comovement of the employer’s share price with that of competing employers in the
area (Kedia and Rajgopal 2009, Oyer 2004, Oyer and Schaefer 2005, and Pirinsky and Wang
2006). NCOMPENF reflects the idea that non-competition agreements limit the employees’ job
mobility, effectively serving as a retention device. Following Kedia and Rajgopal (2009), we
measure NCOMPENF as the non-competition enforceability index in Garmaise (2011).
Hopefully, these tax and employee retention consideration proxies do not directly affect
corporate risk taking other than through the employee channel. 24
The first stage of the 2SLS model takes the form:
eNCOMPENFLOCBETACASHETREMPSTK 3210 (3)
Using estimated coefficients from equation (3), we calculate P_EMPSTK, which is the predicted
value of EMPSTK, and substitute it in equation (1) to get the following second stage model:
effectsYearFixedExedEffectsIndustryFiRETNOLCF
BMMVLogEXEOPTEXESTKEMPSTKPCRTt
876
5432101 )(_
(4)
We report results for the first and second stages of the 2SLS analysis in Panels A and B,
respectively, of Table 4. In Panel A, all three instruments are significantly related to EMPSTK.
Specifically, firms with higher levels of stockholding for their non-executive employees are
associated with higher effective tax rates and higher co-movement of stock prices with those of
24 CASHETR may not be a good instrumental variable if it directly affects corporate risk taking via channels other
than employee stock ownership. In untabulated analysis, we exclude CASHETR from the first stage regression and find qualitatively similar results. F-value is 32.74, which is larger than the benchmark of 11.59, suggesting that LOCBETA and NCOMPENF do not impose a weak-instrument problem (Larcker and Rusticus 2010).
27
local competitors, and are domiciled in states where non-competition agreements are less likely
to be enforced. The F-value from the first stage regression is 63.68, which is higher than the
benchmark of 12.83 with three instrumental variables, suggesting that the model does not suffer
from a weak-instrument problem (Larcker and Rusticus 2010). Results for the second stage,
reported in Panel B, are qualitatively similar to the OLS results reported in Table 2: consistent
with H1, the level of non-executive stock ownership is significantly negatively associated with
both risk measures.25
5.2. Controlling for lagged dependent variables
Another technique used to address endogeneity issues, especially omitted correlated
variables and reverse causality, is to include the lagged value of the dependent variable as an
additional control. The underlying logic of this methodology is that, to the extent that omitted
correlated variables and reverse causality are relatively stable, their effects can be captured by
the lagged values of the dependent variable. One aspect of this technique is that the lagged
dependent variable might suppress the contribution of the included regressors, particularly
EMPSTK, if those regressors are also relatively stable over time. This would bias against finding
support for H1. Overall, observing significant coefficients on EMPSTK in the presence of
controls for lagged values of the dependent variable, despite the bias toward zero for the
estimated coefficient on EMPSTK, is an important result. It increases considerably the likelihood
that our main result arises due to the factors underlying H1 rather than the many alternative
explanations we consider, especially those based on omitted correlated variables or reverse
causality.
In Table 5 we present the regression for equation (1) after including the lagged value of
CRT as an additional control. Lagged SD_ROA is computed over the prior five years, t-4 to t,
25 P_EMPSTK has a mean of 0.012 and standard deviation of 0.005.
28
whereas lagged SD_RET is computed for year t. As expected, the coefficients on the lagged
dependent variable are positive and have large t-statistics. The deviation of those coefficient
estimates from one is attributable to the underlying mean reversion in the risk measures. More
relevant to H1, the coefficient on EMPSTK remains significantly negative in both specifications.
Thus, even though including lagged risk measures, which reflect the effects of lagged values of
both included and excluded regressors, reduces the estimated coefficients on EMPSTK (relative
to those reported in columns 1 and 2 of Table 2), the remaining effect remains statistically and
economically significant as conjectured. Also, we continue to observe significant positive
coefficients on EXEOPT across both risk measures.
5.3 Cross-sectional variation in the impact of non-executive employee ownership
Another approach we consider to address endogeneity concerns is investigating the cross-
sectional variation in the estimated coefficient on EMPSTK, 1 from equation (1) in settings
where 1 should vary predictably. In Table 3 we already document that 1 becomes more
negative with increases in the level of option-based compensation paid to executives. We
consider next the extent to which 1 varies with the fraction of independent directors, a proxy
used in prior research for the quality of governance (e.g., Klein 2002; Rosenstein and Wyatt
1990). Specifically, we conjecture that strong boards, with more independent directors, provide
an alternative mechanism to protect non-executive stakeholders.26 We also conjecture (and
confirm below for our sample) that compensation for such executives is likely to be tilted more
toward options, relative to stock-based compensation. As a result, the incentives for non-
executive employees to reduce risk as their stockholding increases (captured by the coefficient
1) should be even greater for firms with weak boards.
26 It is also possible that employees seek greater risk reduction when senior executives ignore weak boards, because
the behavior of senior executives is less predictable.
29
We partition our sample at the median level of independent directors each year, and
assume that firms above (below) the median, labeled as high (low) corporate governance
partitions, are associated with strong (weak) corporate governance. We first check whether firms
with strong governance have lower mean and median levels of option-based compensation for
senior executives. Consistent with our conjecture, the mean value of EXEOPT for the strong
(weak) governance partition is 34.01 (36.35) percent of total compensation for senior executives,
and the corresponding median value is 31.43 (34.34) percent. The results on the cross-sectional
variation in the coefficient on EMPSTK (Table 6) are consistent with our predictions: the
estimate of 1 is substantially more negative for firms with fewer independent directors, relative
to that for the high governance partition, for both risk measures.
5.4. Robustness analysis
We consider a number of analyses to determine whether our results are robust to
alternative ways of measuring our dependent and independent variables, as well as alternative
regression specifications.27 The results of some of those analyses are summarized below.
Our main independent variable, EMPSTK is measured as employee stockholding, scaled
by the number of shares outstanding. As an alternative specification motivated by the analysis in
Bova et al. (2013), we consider the logarithm of one plus the dollar value of employer stock held
per employee. Untabulated results show that our findings continue to hold with this alternative
specification. For example, focusing on the SD_ROA implementation of equation (1), the
27 As another robustness check, we examine if the EMPSTK effect changes post the Sarbanes-Oxley (SOX)
legislation. If firms generally lower their risk-taking due to SOX, then there is lower incentive for employees to reduce risk, suggesting the relationship between EMPSTK and corporate risk-taking should be diminished in the post-SOX period. We find partial support for this argument. In another robustness check, we control for institutional ownership and find that our inference remain unchanged. Compared with the results in Table 2, the coefficients on EMPSTK are slightly stronger in the SD_∆ROA and SD_RET regressions and largely unchanged in the R&D and CAPEX regressions after controlling for institutional ownership.
30
coefficient on EMPSTK is 0.700 (t= 6.26), which is comparable to the estimate of 0.077 (t=
3.97) reported in column 1 of Table 2, based on the original measure of EMPSTK.28
For EXEOPT, we also consider total options held by senior executives, scaled by
outstanding shares, and value of total options held, scaled by market value of equity. Whereas
theory calls for optionholdings, rather than options granted in any year, our results suggest that
the alternative variables based on optionholdings are associated with greater measurement error.
We find that the magnitude and significance of the coefficients on EXEOPT in Table 2 and Table
3 decline when we use the alternative measures of EXEOPT. We interpret this decline as
suggesting that the greater measurement error associated with these alternative measures biases
the otherwise strong positive relation between risk and EXEOPT towards zero.29 Consistent with
our view that increased error weakens our ability to capture the effect of executive
optionholdings, we observe a stronger effect for non-executive employee stockholdings,
reflected in higher magnitude and significance of the coefficients on EMPSTK. It is possible that
the greater measurement error we observe for alternative measures of EXEOPT is due to a
mismatched scaling variable. Theory calls for optionholdings to be scaled by total executive
wealth, which is not easy to estimate. Therefore, our robustness analyses should not be
interpreted as suggesting that option grants are preferable to optionholdings. Our results suggest
instead that, for the scaling variables available to us, option grants appear to measure the
underlying variable of interest with less error.
We also consider alternative measures for our dependent variable that are based on
expected, rather than observed, stock volatility. In particular, we estimate the average implied
28 Whereas the magnitudes of the coefficients are not comparable, as the variables are defined differently,
comparison of the t-statistics is meaningful. 29 It is possible that option grants, which are typically at-the-money, better reflect convexity in the relation between
employee wealth and stock price volatility, relative to option holdings, which may include substantial amounts of options that are deep-in-the-money.
31
volatility from put and call options on contracts with the same strike price. We use data as of six
months after the fiscal year-end and limit our sample to contracts with a six-month expiration
and the smallest amount of in-the- and out-of-the-moneyness. When replicating the analyses in
Table 2 and Table 3, we find estimated coefficients similar to those observed for SD_RET.
In addition to considering the impact of measurement error associated with our variables,
we also conduct other analyses designed to confirm the robustness of our two main results. For
example, we separately examine the subsample of observations with positive employee
ownership to address the concern that our main results are unduly influenced by the large
fraction of our sample with zero employee ownership. Table 7 contains the results of estimating
equation (1) on this subsample of 5,995 firm-years with positive EMPSTK. Similar to our Table
2 results, we find that the coefficients on EMPSTK are significantly negative across all four
measures of risk-taking.
To investigate the robustness of our findings to the assumption that non-executive
employees have the ability to influence stock volatility, we consider two cases, one where we
expect evidence of non-executive employees taking actions to alter risk in response to exogenous
shocks, and the other where we do not expect evidence of risk reduction because we do not
believe non-executive employees have the decision rights for those actions. Observing
significant results in the first case, but not in the second, would support the notion that our
assumption is valid.
The first case we consider is based on shocks to housing prices. Assuming that non-
executive employees are risk averse on average, the intuition underlying H1 predicts that a
decrease in housing prices causes non-executive employees to seek to reduce risk at a higher rate
because of an increase in the fraction of total wealth that is positively correlated with stock
prices. (The opposite effects are expected when housing prices increase.) One reason for the
32
increase in the fraction of stock-price-sensitive employee wealth is that a decline in home values
increases the relative fraction of total wealth held in equity-based compensation. Another reason
is our conjecture that employees become less mobile after housing price declines, which then
increases the positive correlation between human capital and the employer’s stock price, thereby
increasing the sensitivity of total employee wealth to stock price volatility.
To study the impact of housing-price changes, we replace EMPSTK in equation (1) with
the changes in residential housing price index (∆HPI). We measure the changes in housing prices
by computing the annual percentage change in the residential house price index for the state
where the firm is headquartered. To capture the impact of changes in housing prices, the
dependent variable should reflect changes in the incentives to reduce risk, and models of risk
changes should include changes in all the independent variables in equation (1). Since some of
our variables are measured over extended periods that will overlap when we take first
differences, we include the lagged value of risk measures as an additional regressor, rather than
transforming equation (1) to first differences. Specifically, we estimate the following model:
1 0 1 2 3 4 5 6
7 8 9
( )t tCRT CRT HPI EXESTK EXEOPT Log MV BM
CF NOL RET IndustryFixedEffects YearFixedEffects e
(5)
We report the regression results in Table 8. Turning to columns 1 and 3, the coefficient
on ∆HPI is positive but insignificant for both risk measures, providing only weak support for H1.
We next investigate whether the relation between changes in risk-taking and housing price
changes varies over time depending on the level of housing prices. Specifically, we repeat the
analysis allowing for a different relationship during the housing boom period: from 2004 to
2007. Our motivation for this test is to incorporate the possibility that employee risk aversion is
33
lower during the housing boom, as evidenced by lower down-payments and riskier mortgages.30
If the conjectured relationship holds, we expect the coefficient on housing price changes to imply
a smaller impact on the employee incentives to reduce risk during the boom period. To test this
relationship, we modify equation (5) by including an interactive variable (=HPI*BOOM),
where BOOM is an indicator variable that is set to 1 for years 2004 through 2007. The results in
columns 2 and 4 suggest that the coefficients on ∆HPI are close to zero during the 2004-2007
housing boom period (indicated by the sum of the coefficients on HPI and HPI*BOOM), but
significantly positive during the remaining years of the sample period. We view these results as
supportive of H1.
We turn next to the case where we examine corporate decisions that are unlikely to be
affected by non-executive employees. Specifically, we examine corporate acquisitions, a
decision for which we assume decision rights rest mainly with senior executives.31 We also
assume that one motivation for acquisitions is to reduce stock volatility by diversifying some
unsystematic risk (e.g., May, 1995).32 If so, we expect to find no relation between the likelihood
of acquisitions and employee stockholding (EMPSTK).
We estimate a logistic model that predicts the likelihood of firms making “large”
acquisitions, i.e., acquisitions that increase sales in the following year by at least 5 percent. The
independent variables we consider are EMPSTK, EXESTK, EXEOPT, Log (MV), BM, RET, cash
30 The desire to avoid risk could decline for a number of reasons during this period, as housing prices increased
steadily. For example, employees might underestimate subjective probabilities of downside scenarios. It is also possible that corporate risk was deemed less important, as the relative fraction of employee wealth related to housing (employer stock price) increased (declined).
31 Our intuition is supported by our conversations with Steven Fisher, Senior V-P and Treasurer of SAIC. He suggests that a firm’s decision to acquire a target should arise independent of the level of non-executive stockholding, but that the type and ultimate success of the acquisition should be affected by the level of non-executive stockholding.
32 May (1995) and our paper have different research designs and thus are not directly comparable. May (1995) takes acquisition as given and then examines whether executive personal risk affects diversification or consolidation of the company’s business. In contrast, we examine whether employee and executive risk preference affect the company’s acquisition decision regardless of the choice to consolidate or not.
34
holdings, LEV, CF, sales growth, and profitability (ROA). The results are consistent with our
conjectures, as the coefficient on EMPSTK is insignificant across specifications.
To summarize, the analyses described in Section 5, which represent our best efforts to
investigate the three sources of endogeneity described in Roberts and Whited (2011)—omitted
variables, simultaneity, and measurement error—do not reveal any reasons to invalidate our
results relating to H1. Although such concerns cannot be eliminated fully, we believe that the
portfolio of results we present reduces the likelihood that our results are spurious, as any validity
threats should have surfaced somewhere in the large number of supporting analyses we conduct.
Simultaneity, and reverse causality in particular, are important concerns for us. We
believe, however, that finding significant results when we include lagged values of the dependent
variable as an additional regressor allays much of those concerns. If stock volatility determined
levels of employee stockholding, why would employee stockholding continue to explain future
stock volatility even after controlling for contemporaneous stock volatility? Also, it seems
unlikely that reverse causality from stock volatility to employee stockholding would explain the
results for volatility of accounting rates of return, the other dependent variable we consider.
Similarly, we believe that our Two Stage Least Squares implementation offers independent
supporting evidence that simultaneity is less likely to be the driving force here.
6. Conclusion
In this study we investigate the relation between stock held by non-executive employees
and corporate risk-taking. Prior research finds this relation to be negative for senior executives,
which is consistent with risk-averse executives attempting to reduce the volatility of the portion
of their wealth that is related to stock prices. We investigate whether a similar negative relation
is observed for non-executive employees, based on the assumptions that these employees also
35
have a) incentives to decrease risk when stockholding increases, as well as b) the ability to take
actions that reduce corporate risk.
Consistent with that hypothesis, we find that the greater the amount of company stock
owned by non-executive employees, the lower the firm’s subsequent risk-taking. Probing further,
we find that this relationship is more pronounced when senior executives are compensated more
with option-based pay (which appears to increase the incentives for managers to take on risk).
Finally, we find that these two results survive a battery of careful attempts to control for potential
endogeneity.
The collective evidence suggests that the risk preferences of non-executive employees
has an impact on subsequent corporate risk-taking. As with any paper in the literature,
endogeneity cannot be completely ruled out. Thus, inferences from our evidence about the
validity of our explanation should be viewed as a platform for future analysis. Holding aside
endogeneity concerns, we view our main contribution in that we document two robust
associations that are new to the literature. First, we find a strong negative relation between non-
executive stockholding and corporate risk-taking. Second, we find a consistent interaction
between that negative relation and the level of executive optionholding. This latter interaction
provides some of the first evidence that non-executive stockholding may mitigate risk-taking in
those environments where executives are incentivized to take on greater risk.
36
Appendix: Variable Definitions
Variable Description*
EMPSTK Employee stock ownership as a percentage of shares outstanding in year t. Data are obtained from Form 5500 filings for defined contribution (retirement) plans invested in employer stock. Includes employee stock ownership plans, 401(k) plans, deferred profit sharing plans, and employer stock bonus plans. Excludes non-retirement plans, such as restricted stock plans and employee purchase plans.
EMPOPT The fair value of options outstanding, estimated using the Black-Scholes model and Compustat data, minus fair values of options held by executives (from EXECUCOMP), scaled by the market value of equity.
EXESTK Stock ownership of senior executives, as a percentage of the shares outstanding in year t, where stock ownership includes both the direct and indirect shares held by the top four managers (Chairman of Board, Chief Executive Officer, Chief Financial Officer, and President). Data obtained from Thomson Reuters Insider Filing.
EXEOPT The value of option awards as a percentage of total compensation (TDC1) from the EXECUCOMP annual compensation table in year t, where both option awards and total compensation are summed across all executives covered by EXECUCOMP. Most firms report the top five executives, although EXECUCOMP collects data for up to nine executives for some firm-years.
SD_∆ROA The standard deviation of seasonally differenced quarterly return on assets over the next five years (t+1 to t+5), where return on assets is measured as income before extraordinary items (IBQ) scaled by average total assets ((ATQt + ATQt-1)/2).
SD_RET The standard deviation of daily stock returns for the 12-month period starting from the fifth month after fiscal year-end.
R&D Annual research and development expense (XRD) scaled by sales (SALE) in year t+1.
CAPEX Annual capital expenditure (CAPX) scaled by net property, plant, and equipment in year t+1.
MV The market value of equity (CSHO*PRCC_F) at the end of year t.
BM The book-to-market ratio (CEQ/( CSHO*PRCC_F)) at the end of year t.
LEV Leverage, measured as the three-year average of short-term and long-term debts, scaled by total assets ((DLCt + DLTTt) / ATt) from year t-2 through t.
NOL Net operating loss, measured as an indicator variable equal to one if the firm has positive net operating loss carry-forwards (TLCF) in year t.
37
CF Cash flow, measured as the three-year average of cash flow from operations minus cash flow from investing and cash dividends, scaled by total assets ((OANCFt – IVNCFt – DVt) / ATt from year t-2 through t.
RET Return on the firm’s stock in fiscal year t.
CASHETR The long-run cash effective tax rate, computed as the sum of income tax paid (TXPDt), divided by the sum of a firm’s pre-tax income (PIt), less special items (SPIt) over the previous five years.
LOCBETA The local beta LOC is estimated using the following time-series regression over 1999-2007 for each firm:
∝
where refers to the monthly return of stock i in month t; is the monthly return of other firms headquartered in the same Metropolitan Statistical Area (MSA) as firm i; is the monthly return of the market portfolio; and is the monthly industry return (based on 48 Fama-French industries) corresponding to stock i. All returns are in excess of the 30-day T-bill rates.
NCOMPENF Non-competition enforceability index compiled by Garmaise (2011).
* Annual COMPUSTAT data items are provided in parentheses.
38
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Table 1 Descriptive statistics
Panel A: Univariate statistics
Variable N Mean Stdev Min Q1 Median Q3 Max
SD_∆ROA 17,983 0.051 0.079 0.000 0.009 0.021 0.059 1.039
SD_RET 18,189 0.039 0.029 0.000 0.021 0.031 0.047 1.217
EMPSTK 18,417 0.008 0.023 0.000 0.000 0.000 0.004 0.183
EXESTK 18,417 0.035 0.082 0.000 0.001 0.004 0.022 0.571
EXEOPT 8,702 0.314 0.260 0.000 0.084 0.277 0.500 0.974
MV 18,417 4,190 20,054 0 112 484 1,794 508,329
BM 18,320 0.553 0.624 -3.144 0.230 0.417 0.696 6.098
LEV 18,319 0.214 0.208 0.000 0.024 0.173 0.338 1.062
CF 18,314 0.111 0.249 -1.265 0.049 0.142 0.239 0.627
NOL 18,417 0.401 0.490 0 0 0 1 1
RET 18,216 0.256 1.144 -0.994 -0.246 0.065 0.429 28.10
Panel B: Pearson (Spearman) correlations are above (below) the diagonal
SD_∆ROA SD_RET EMPSTK EXESTK EXEOPT Log(MV) BM
SD_∆ROA 1 0.432 -0.121 0.023 0.121 -0.270 -0.043
SD_RET 0.541 1 -0.123 0.103 0.118 -0.389 0.104
EMPSTK -0.244 -0.251 1 -0.062 -0.103 0.101 0.037
EXESTK 0.084 0.185 -0.101 1 -0.009 -0.238 0.041
EXEOPT 0.085 0.075 -0.126 -0.107 1 0.190 -0.182
Log(MV) -0.350 -0.461 0.240 -0.369 0.178 1 -0.346
BM -0.097 -0.010 0.072 0.067 -0.255 -0.332 1
We define all variables in the Appendix. The sample includes 18,417 firm-year observations with non-missing EMPSTK and EXESTK from 1999 to 2009. Each year, all variables except for MV, SD_RET, NOL, and RET are Winsorized at 1 and 99 percent. In Panel B, all correlations are significant at the 5 percent level, except for the Pearson correlation between EXESTK and EXEOPT, and the Spearman correlation between BM and SD_RET.
43
Table 2 The relationship between employee stock ownership and firm risk-taking
Dependent variable: Future firm risk-taking measures
SD_∆ROA SD_RET R&D CAPEX
1 2 3 4
EMPSTK -0.077 (-3.97)
-0.027 (-4.06)
-0.278 (-4.81)
-0.276 (-3.98)
EXESTK -0.025 (-2.18)
0.008 (1.85)
-0.040 (-0.81)
0.057 (0.91)
EXEOPT 0.023 (7.94)
0.010 (12.60)
0.090 (7.38)
0.059 (6.62)
Log(MV) -0.005 (-8.37)
-0.003 (-17.63)
-0.005 (-2.60)
-0.013 (-7.72)
BM 0.005 (2.52)
0.001 (1.73)
-0.022 (-3.02)
-0.054 (-9.94)
LEV 0.001 (0.18)
0.002 (1.54)
-0.041 (-1.66)
-0.128 (-7.31)
CF -0.059 (-6.00)
-0.009 (-5.16)
-0.253 (-6.27)
0.098 (4.54)
NOL 0.002 (1.43)
0.001 (1.83)
0.011 (1.67)
0.001 (0.26)
RET 0.003 (3.57)
0.002 (6.15)
-0.002 (-0.89)
0.029 (6.69)
Industry FE YES YES YES YES
Time FE YES YES YES YES
R2 0.432 0.850 0.562 0.806
We define all variables in the Appendix. All continuous variables except for MV, SD_RET, NOL, and RET are Winsorized at 1 percent and 99 percent each year. We estimate the regressions using OLS and allow the errors to cluster by firm. All regressions include industry and year fixed effects (FE), where industries are defined using the Fama-French (1997) 48-industry classification. We report t-statistics in parentheses below the estimated coefficients. The sample includes 8,702 firm-year observations with non-missing variables from 1999 to 2009.
44
Table 3 Incorporating the interaction between executive stock and optionholding and the relation
between employee stockholding and risk-taking
Panel A: Linear specification for the interactions with executive stockholding (EXESTK) and optionholding (EXEOPT)
Dependent variable: Future firm risk-taking measures
SD_∆ROA SD_RET
1 2
EMPSTK 0.017 (0.60)
0.007 (0.68)
EXESTK -0.019 (-1.57)
0.009 (2.00)
EXEOPT 0.026 (8.15)
0.012 (12.88)
EMPSTK×EXESTK -1.173 (-1.77)
-0.280 (-1.12)
EMPSTK×EXEOPT -0.309 (-3.54)
-0.116 (-4.53)
Log(MV) -0.005 (-8.28)
-0.003 (-17.74)
BM 0.005 (2.58)
0.002 (1.79)
LEV 0.001 (0.28)
0.002 (1.67)
CF -0.059 (-6.02)
-0.009 (-5.23)
NOL 0.002 (1.39)
0.001 (1.77)
RET 0.003 (3.53)
0.002 (6.16)
Industry FE YES YES
Time FE YES YES
R2 0.433 0.850
45
Panel B: Discrete variable specification for the interaction with executive optionholding (EXEOPT).
Dependent variable: Future firm risk-taking measures
SD_∆ROA SD_RET
1 2
EMPSTK -0.005 (-0.18)
-0.015 (-1.74)
EMPSTK×MEDEXEOPT -0.092 (-3.31)
-0.008 (-0.80)
EMPSTK× HIGHEXEOPT -0.160 (-3.19)
-0.035 (-2.48)
EXESTK -0.019 (-1.50)
0.010 (2.01)
EXEOPT 0.026 (8.40)
0.011 (12.38)
EMPSTK×EXESTK -1.008 (-1.43)
-0.187 (-0.75)
Log(MV) -0.005 (-8.55)
-0.003 (-17.73)
BM 0.005 (2.26)
0.001 (1.76)
LEV 0.002 (0.35)
0.002 (1.59)
CF -0.069 (-6.33)
-0.011 (-5.35)
NOL 0.002 (1.31)
0.001 (1.73)
RET 0.003 (3.21)
0.002 (5.78)
Industry FE YES YES
Time FE YES YES
R2 0.445 0.851
We define all variables in the Appendix. All continuous variables except for MV, SD_RET, NOL, and RET are Winsorized at 1 percent and 99 percent each year. In Panel B, each year, we sort firms into three equally-sized groups based on EXEOPT. MEDEXEOPT is an indicator variable set to 1 for the middle terciles of EXEOPT and 0 otherwise. HIGHEXEOPT is an indicator variable set to 1 for the highest EXEOPT tercile and 0 otherwise. We estimate the regressions using OLS and cluster the errors by firm. All regressions include industry and year fixed effects (FE), where industries are defined using the Fama-French (1997) 48-industry classification. We report t-statistics in parentheses below the estimated coefficients. The sample includes 8,702 firm-year observations with non-missing variables from 1999 to 2009.
46
Table 4 Using instrumental variables and a two-stage least squares methodology
Panel A: 1st-stage regressions of employee stock ownership on instrumental variables
Intercept CASHETR LOCBETA NCOMPENF Adj. R2
EMPSTK 0.009 (11.09)
0.024 (9.85)
0.002 (6.78)
-0.001 (-7.42) 0.022
Panel B: 2nd-stage regressions of firm risk-taking on predicted employee stock ownership and controls
Dependent variable: Future firm risk-taking measures
SD_∆ROA SD_RET
1 2
P_EMPSTK -1.761 (-6.84)
-0.588 (-8.17)
EXESTK -0.024 (-1.97)
0.004 (1.01)
EXEOPT 0.014 (4.74)
0.008 (8.76)
Log(MV) -0.004 (-6.05)
-0.003 (-16.45)
BM 0.00 (2.21)
0.004 (3.45)
LEV 0.002 (0.31)
0.004 (2.12)
CF -0.054 (-5.95)
-0.009 (-4.50)
NOL 0.000 (0.16)
-0.000 (-0.41)
RET 0.005 (2.90)
0.002 (6.07)
Industry FE YES YES Time FE YES YES
Adj. R2 0.463 0.862
P_EMPSTK is the predicted value obtained from a first-stage regression of EMPSTK on CASHETR, LOCBETA, and NCOMPENF. We define all other variables in the Appendix. All continuous variables except for MV, SD_RET, NOL, and RET are Winsorized at 1 percent and 99 percent each year. We estimate the regressions using OLS and allow the errors to cluster by firm. All regressions include industry and year fixed effects (FE), where industries are defined using the Fama-French (1997) 48-industry classification. We report t-statistics in parentheses below the estimated coefficients. The sample includes 5,211 firm-year observations with non-missing variables from 1999 to 2009.
47
Table 5 Including controls for lagged values of the dependent variable
Dependent variable: Future firm risk-taking measures
SD_∆ROA SD_RET
1 2
Lag(Dep. Var) 0.252
(10.30) 0.539
(22.70)
EMPSTK -0.047 (-2.71)
-0.011 (-2.66)
EXESTK -0.019 (-1.91)
0.004 (1.52)
EXEOPT 0.019 (6.72)
0.004 (6.13)
Log(MV) -0.003 (-6.44)
-0.001 (-9.58)
BM 0.007 (3.21)
0.001 (0.74)
LEV 0.003 (0.65)
0.002 (1.97)
CF -0.050 (-4.40)
-0.005 (-3.24)
NOL 0.001 (0.80)
0.000 (0.23)
RET 0.002 (2.10)
0.001 (1.12)
Industry FE YES YES
Time FE YES YES
R2 0.477 0.880
We define all variables in the Appendix. Lagged dependent variables are measured as follows. Lagged SD_∆ROA is the standard deviation of seasonally differenced quarterly return on assets over the prior five years [t-4, t]. Lagged SD_RET is the standard deviation of daily returns in fiscal year t. All continuous variables except for MV, SD_RET, NOL, and RET are Winsorized at 1 percent and 99 percent each year. We estimate the regressions using OLS and allow the errors to cluster by firm. All regressions include industry and year fixed effects (FE), where industries are defined using the Fama-French (1997) 48-industry classification. We report t-statistics in parentheses below the estimated coefficients. The sample includes 8,637 firm-year observations with non-missing variables from 1999 to 2009.
48
Table 6 Variation in the relation between employee stock ownership and firm risk-taking across
strong and weak corporate governance
Dependent variable: Future firm risk-taking measures
SD_∆ROA SD_RET
Partitions based on the fraction of independent board directors
Low High Low High
1 2 3 4
EMPSTK -0.064 (-2.50)
-0.041 (-1.89)
-0.022 (-3.18)
-0.007 (-0.71)
EXESTK 0.002 (0.14)
0.003 (0.06)
0.009 (1.91)
0.020 (1.45)
EXEOPT 0.015 (4.58)
0.027 (5.28)
0.008 (7.98)
0.012 (7.96)
Log(MV) -0.002 (-2.41)
-0.005 (-4.88)
-0.002 (-9.92)
-0.003 (-6.71)
BM 0.010 (2.43)
0.006 (1.43)
0.001 (1.06)
0.001 (0.26)
LEV 0.009 (1.35)
-0.001 (-0.20)
0.000 (0.15)
-0.000 (-0.14)
CF -0.010 (-1.36)
-0.054 (-4.58)
0.003 (1.14)
-0.012 (-3.69)
NOL 0.002 (0.87)
0.001 (0.27)
0.002 (2.79)
0.001 (1.22)
RET 0.004 (1.78)
0.008 (2.64)
0.003 (8.34)
0.002 (2.03)
Industry FE YES YES YES YES
Time FE YES YES YES YES
R2 0.467 0.470 0.899 0.841
Ratio of EMPSTK (High/Low) 0.64 0.32
Corporate governance is measured as the percentage of independent directors on the board. High and Low refer to subsamples with below- and above-median corporate governance, respectively. We define all other variables in the Appendix. All continuous variables except for MV, SD_RET, NOL, and RET are Winsorized at 1 percent and 99 percent each year. We estimate the regressions using OLS and allow the errors to cluster by firm. All regressions include industry and year fixed effects (FE), where industries are defined using the Fama-French (1997) 48-industry classification. We report t-statistics in parentheses below the estimated coefficients. The sample includes 5,135 firm-year observations with non-missing variables from 1999 to 2009.
49
Table 7 Relationship between employee stock ownership and firm risk-taking using the subsample
of positive EMPSTK
Dependent variable: Future firm risk-taking measures
SD_∆ROA SD_RET R&D CAPEX
1 2 3 4
EMPSTK -0.040 (-1.97)
-0.015 (-2.42)
-0.114 (-2.39)
-0.207 (-3.32)
EXESTK -0.052 (-3.86)
0.006 (0.95)
-0.010 (-0.11)
0.111 (1.02)
EXEOPT 0.014 (4.12)
0.008 (6.79)
0.084 (3.49)
0.046 (3.88)
Log(MV) -0.005 (-6.78)
-0.003 (-11.45)
-0.003 (-1.16)
-0.007 (-3.75)
BM 0.001 (0.24)
0.002 (1.30)
-0.024 (-2.28)
-0.029 (-4.36)
LEV 0.008 (1.27)
0.002 (0.92)
-0.022 (-0.74)
-0.101 (-4.48)
CF -0.038 (-3.14)
-0.010 (-3.83)
-0.262 (-3.15)
0.117 (3.69)
NOL -0.001 (-0.50)
0.000 (0.71)
-0.005 (-0.48)
-0.003 (-0.46)
RET 0.001 (1.29)
0.001 (2.77)
-0.001 (-0.40)
0.019 (3.57)
Industry FE YES YES YES YES
Time FE YES YES YES YES
R2 0.442 0.851 0.522 0.824
This table is based on the subsample of 4,204 firm-year observations with positive EMPSTK and non-missing dependent and other control variables. We define all variables in the Appendix. All continuous variables except for MV, SD_RET, NOL, and RET are Winsorized at 1 percent and 99 percent each year. We estimate the regressions using OLS and allow the errors to cluster by firm. All regressions include industry and year fixed effects (FE), where industries are defined using the Fama-French (1997) 48-industry classification. We report t-statistics in parentheses below the estimated coefficients.
50
Table 8 Relation between housing price changes and firm risk-taking
Dependent variable: Future firm risk-taking measures
SD_∆ROA SD_RET
1 2 3 4
Lag(Dep. Var) 0.162 (6.41)
0.161 (6.41)
0.439
(18.33) 0.437
(18.33)
∆HPI 0.016 (1.59)
0.041 (2.43)
0.004 (1.21)
0.012 (2.21)
BOOM*∆HPI -0.043 (-2.09)
-0.016 (-2.49)
EXESTK -0.012 (-0.91)
-0.012 (-0.95)
0.007 (1.61)
0.006 (1.59)
EXEOPT 0.017 (6.12)
0.017 (6.09)
0.005 (6.60)
0.005 (6.59)
Log(MV) -0.004 (-7.12)
-0.004 (-7.13)
-0.002 (-9.51)
-0.002 (-9.63)
BM 0.006 (2.64)
0.006 (2.64)
0.001 (1.23)
0.001 (1.22)
LEV 0.003 (0.55)
0.003 (0.60)
0.002 (1.87)
0.002 (1.93)
CF -0.055 (-6.33)
-0.055 (-6.31)
-0.007 (-3.92)
-0.007 (-3.89)
NOL 0.002 (1.43)
0.002 (1.43)
0.000 (0.59)
0.000 (0.60)
RET 0.002 (2.47)
0.002 (2.45)
0.000 (0.68)
0.000 (0.68)
Industry FE YES YES YES YES Time FE YES YES YES YES R2 0.480 0.481 0.865 0.865
Lagged dependent variables are measured as follows. Lagged SD_∆ROA is the standard deviation of seasonally differenced quarterly return on assets over the prior five years [t-4, t]. Lagged SD_RET is the standard deviation of daily returns in fiscal year t. ∆HPI is the annual percentage change in house price index for the state in which the firm is headquartered (http://www.fhfa.gov/Default.aspx?Page=87). BOOM is an indicator variable set to 1 for the observations during the housing booming period (2004-2007) and 0 otherwise. We define all other variables in the Appendix. All continuous variables except for MV, SD_RET, NOL, and RET are Winsorized at 1 percent and 99 percent each year. We estimate the regressions using OLS and allow the errors to cluster by firm. All regressions include industry and year fixed effects (FE), where industries are defined using the Fama-French (1997) 48-industry classification. We report t-statistics in parentheses below the estimated coefficients. The sample includes 8,702 firm-year observations with non-missing variables from 1999 to 2009.