Non-Executive Employee Ownership and Corporate Risk-Taking

Francesco Bova

Joseph L. Rotman School of Management, University of Toronto Francesco.Bova@rotman.utoronto.ca

Kalin Kolev

Yale School of Management kalin.kolev@yale.edu

Jake Thomas

Yale School of Management jake.thomas@yale.edu

Frank Zhang

Yale School of Management frank.zhang@yale.edu

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.”

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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.

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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.

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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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42

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.