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Job Security and CEO Compensation

Preprint · August 2020

DOI: 10.13140/RG.2.2.21725.64487

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James S. Ang

[email protected]

College of Business, Florida State University

Wei Chen

[email protected]

School of Management, Fuzhou Institute of Technology

August 2020

1

1


Abstract

We analyze a sample of 2,914 hiring contracts and show how job security plays a

role in pay negotiation. We find a significant impact of job security on CEO pay using

both firm and industry-level measures across US firms. In general, CEO candidates and

the board members would trade pay for job security. We find this pay for job security

also follows a hierarchy order; when a firm is unable to compensate job risk with a

higher pay, the CEO will seek for alternative ways of compensation, including a

severance agreement, a shorter grant vesting period, and an incentive plan metric that

is easier to achieve. Moreover, job security has no value among risk-takers, and this

value increases with CEO’s risk aversion. By examining the impact of job security

under different market condition, we find that this pay concession disappears when the

CEO labor market is more favorable to the CEOs.

Keywords：Job Security, CEO Compensation, Risk Preference

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1. Introduction

Job security is important to both firms and the executives. Commitment to job

security is an essential ingredient to motivate firm innovation (Manso, 2011).

Conversely, a less secured job could motivate managers to engage in accounting fraud,

such as earning smoothing (Fundenberg and Tirole, 1995; Defond and Park, 1997), to

protect their positions. Since job insecurity implies uncertainty about future wealth, the

consequences could extend beyond the executive’s loss of current employment,

including reduced future career options (Brickley et al., 1999) and the loss of equity-

based compensation (Dahiya and Yermack, 2008).

In this paper, we examine the role of job security in initial pay negotiation between

the new CEOs and their boards. More specifically, we examine whether job security

has value to the CEO, which should be manifested in negotiating total pay package at

hiring. If so, how much, in what way (compensation type), in what circumstance (firm’s

situations) and to whom (type of managers).

Prior literature studying the relation between job security and worker’s income

spans mostly in the labor economic literature. Campbell et al. (2007) study a sample of

British adults and find that high fears of unemployment are found to be associated with

lower levels of wage growth for men and no significant link with wage growth for

women. Bertola (1990) finds that wages tend to be lower in high job security countries.

Böckerman et al. (2011) find that high excessive worker turnover is positively related

to individual wages. Firms can be reluctant to compensate job security risk. Lazear

(1990) studies the European data and finds that severance pay requirements can even

reduce employment. All studies suggest the strong evidence that there is a tradeoff

between pay and job security. Although a substantial empirical literature explores the

relationship between executive compensation and risk-taking (Coles et al., 2006;

Kempf et al., 2009; Low, 2009), the study of how job security is linked with CEO

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compensation has merely been discussed before.

The reasons that such area has received relatively little attention are in part because

theoretical predictions concerning the effects of the threat are ambiguous, and empirical

measurement of its effect is complicated by the endogeneity of CEO power on own pay,

which is related to the firm’s governance. Sitting CEOs gain power over the board, and

could cause the board to initiate changes in firm’s investment and financing policies to

increase the CEOs’ job security; this is related to the problem of entrenchment due to

poor governance. Studies have shown that firm governance weakens as incumbent

CEO’s tenure increases since the board learns about the CEO’s ability and the CEO

gains bargaining power and negotiates for greater autonomy (Hermalin and Weisbach,

1998; Hermalin, 2005). Therefore, these circumstances complicate attempts to study

the value of job security to the CEOs. To avoid these complications, the prefer

laboratory setting to study the value of job security should involve one in which job

security is exogenous, i.e., outside the control of the CEO, and pay is negotiated in arm

length. This setting occurs when a new CEO engages in pay negotiation at hiring. The

degree of job security in these firms exists before pay negotiation and is thus exogenous,

i.e., whether the firms have a penchant to dismiss CEOs, or whether the industry or firm

specific risk of failure is greater.

Peters and Wagners (2014) were the first ones to discuss such topic in the field of

corporate finance, and empirically showed the link between CEO dismissal risk and

CEO compensation. It is important to differentiate our paper to that of Peters and

Wagners. We focus on CEO pay negotiations at hiring, such that CEOs can only use ex-

ante events to estimate their job risk. On the other hand, Peters and Wagners study a

sample that involves mostly sitting CEOs, and use ex-post events (eg. forced turnovers)

to estimates one’s dismissal risk. In our paper, we provide extensive measures for job

security based on both industry-level and firm-level information prior to the hiring

event to simulate how CEO candidates evaluate their job risks. Overall, our design is

much closer to a real negotiation process such that one can only attempt to use ex-ante

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events to forecast future possibilities. Our paper also provides a more comprehensive

analysis for CEO compensation including the magnitude of pay, structure of pay,

severance agreement, rate of disbursement, and the difficulty of obtaining such payment.

Moreover, we provide empirical evidence how CEO risk preference plays a role in

between. Using the CEO’s type of career advancement, employment status and

expertise, we are able to categorize CEOs into different risk preference groups, and

study how risk preference modulates the value of job security. On top it, we enrich the

research by adding CEO job market condition and examine its impact on the value of

job security.

We study 2,914 CEO’s hiring contracts (including 1,349 severance agreements,

and 1,143 grant vesting schedules) from Execucomp and Incentive Lab during 2000 to

2013. First, we find that job security has a price. CEOs and the board members would

trade pay for job security. Second, we find that such compensation of risk follows a

hierarchy order. When firms are unable to grant higher pay, CEOs would seek for

alternatives to compensate their job risk; these include a severance agreement, a shorter

grant vesting period, and an incentive plan metric that is easier achieve. Third, we find

this pay concession disappears among risk-takers (i.e. ambitious CEOs or turnaround

experts in financially distressed firms), and this price increases with CEO’s risk

aversion (i.e. external hires, outside industry hires and previously unemployed CEOs).

Lastly, by examining the impact of job security under different labor market condition,

we find that this premium diminishes under well CEO labor market condition.

Our research compliments that of Peters and Wagners (2014), and is the first

empirical study to examine the impact of CEO’s risk preference and CEO market

condition on the value of job security. This paper also contributes to the existing

literature on determinants of CEO pay (Baber et al., 1996; Baber et al., 1999; Cadman

et al., 2010; Cadman et al., 2013) and severance contract (Almazan and Suarez, 2003;

Rau and Xu, 2013), and provides a new perspective on determinants of CEO pay

packages and the optimal design of managerial incentive contracts at US firms.

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The paper continues as follows. In Section 2, we develop our hypotheses in further

details. Section 3 describes our methodology and data. In section 4, we provide

empirical analysis on relationship between job security and CEO compensation, and

also on how CEO risk preference and labor market condition play a role in between.

Section 4 concludes.

2. Literature Background and Hypothesis Development

CEO faces very real risk of dismissal in a firm. If an organization experiences an

extreme change in performance, observers tends to attribute such change to the

organization leaders (Meindl et al., 1985). When the performance is improved, CEOs

are praised with generous rewards (Wade et al., 2006). However, when firms fail, CEOs

are often the ones to blame and are likely to be held accountable via dismissal (Zemba

et al., 2006). CEOs could be at risk as soon as the start of their tenure. Studies have

pointed out that CEO turnovers are more likely to occur in the first few years of their

tenure (Sebora, 1996; Allgood and Farrell, 2000). Therefore, CEOs are expected to take

into consideration the risk of job loss at hiring and to negotiate terms that can

compensate this risk.

As mentioned earlier, both boards and CEOs value the importance of job security.

Better job security can stimulate firm innovation (Manso, 2011), and firms with less

job security would lead CEOs to take counterproductive actions to hold on to their jobs,

e.g., earning smoothing (Fundenberg and Tirole, 1995; Defond and Park, 1997). From

the CEO’s stand point, dismissal from current employment might lead to reduced future

career options (Brickley et al., 1999) and loss of equity-based compensation (Dahiya

and Yermack, 2008). The value of job security may, therefore, be priced in the

managerial labor market. When job security is absent, CEOs may demand greater pay

to compensate for the increasing risk of dismissal. On the other hand, the board of

directors would try to negotiate a lesser pay package with these CEOs. The result of the

pay negotiation is the prediction that there is a tradeoff between job security and pay.

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H1: Job security has a price; CEO candidates and the board members would

trade pay for job security; Or, more secured jobs pay less.

However, in certain cases when the firms do not have sufficient financial resources

to grant greater total pay (i.e. financially distressed firms), CEOs would seek for a more

foreseeable, stable, and shorter term pay structure as alternatives; that is higher

percentage in cash pay, higher likelihood of signing a severance pay, shorter vesting

period, and an incentive plan that is easier to achieve.

H1 Corollary: In case when firms are financially stressed, CEO candidates

would demand a more secured pay structure to compensation for low job security.

Matching a person to the job based on ability and characteristics such as risk

taking/ aversion is crucial for the success of many organizations and is often a device

to retain employees. A substantial empirical literatures argue and find evidence

consistent with the matching hypothesis, whereby boards of directors seek to match the

“right” executives to their firm’s unique characteristics (Finkelstein and Hambrick,

1997; Gibbons and Murphy, 1992). The implication is that good matches are more

productive than bad matches. These executives may also self-select into their desired

firms. In terms of their compensation package, Lazear (1998) and Abdel-khalik (2003)

find that CEOs self-select into compensation schemes that are consistent with their risk

preferences. It is important to note that the extent the CEO is concerned with job

security may also lead to self-selection into job matches. Firms with a need for either a

stable management team or commit to a business strategy may choose to hire a CEO

who values job security. On the other hand, Risk-averse (risk-seeking) CEOs may self-

select to serve in a company that provides high (low) job security.

Consistent with risk averse employees’ preference for job security, previous

literatures find a positive relationship between incentives pay and job risk (Demsetz

and Lehn, 1985; Core and Guay, 1999; Oyer, 2004). They show that employees require

to be compensated for employment in riskier jobs. Less secured jobs could truncate

lifetime incomes if ousted CEO faced diminished prospects for a similar CEO job, or a

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top managerial position. Dismissals often leave these CEOs the stigma of a “damaged

goods”. Ward et al. (1995) study the subsequent career outcomes for CEOs who are

fired during 1988 to 1992 and find that 39 of the 60 former CEOs have not resumed an

executive career after being dismissed.

More secured CEO jobs provide longer duration and more certain lifetime incomes,

which are things that Risk-averse prefer. Everything else equals, they would demand

greater pay in case they should be offered a high job risk position. Less risk-averse

CEOs, on the other hand, are more willing to take these higher risk jobs and thus, more

likely to be hired into such jobs as they demand less premium to compensate for the

risk of job loss.

H2: Price of job security increases with CEO’s risk aversion. Conversely,

less risk averse CEOs demand lower job security related pay.

In a more extreme case, when the CEOs are risk seeking, job security should lose

its value.

H2 Corollary: Job security has no value among the risk-seeking CEOs.

CEO candidates’ availability of job alternatives could dramatically influence

CEO’s concern for job security in a particular position. More and better job alternatives

reduce their perceived dependence on the boards (Kelley et al., 1967; Bacharach and

Lawler, 1980; Mannix et al., 1989; Pinkley et al., 1994). When the availability of

alternatives is limited, the difficulty of finding another position in case of dismissal is

greater. To a CEO job candidate, the availability of job alternative depends both on the

number of available CEO job vacancies and the number of similar CEO candidates

looking for similar jobs, we examine the effect of supply and demand in the CEO job

market. If number of job openings is limited relative to the number of qualified

executives for the CEO jobs, i.e., job market is unfavorable to CEO job seekers, hiring

firms can afford to offer less generous terms and job candidates are more eager to accept.

Accordingly, boards with more secured jobs are more likely to demand a discount in

pay in exchange for job security. Similarly, CEOs in this market condition would not

8

be able to demand a higher premium to compensate for riskier jobs.

H3: Price of job security increases (decreases) when the labor market

condition is favorable (unfavorable) to CEO job seekers.

3. Methodology and Data

Our sample consists of 2914 CEO hiring events in the 2000-2013 period drawn

from the Execucomp1; 2895 of which include the dollar amount of total annual pay, and

1349 of which include a severance agreement2. We solely look at new hires because

sitting CEO could gain power over the board during their tenure (Hermalin and

Weishbach, 2003), and use their control of information and board appointment to

entrench themselves, resulting in having greater influence on their own pay and job

security. To concentrate on pay of new hires, we can avoid this endogenous influence

on pay. Moreover, we use Incentive Lab database to incorporate 1143 detailed incentive

plans at hiring, including the metrics and vesting schedules of the incentive grants. The

accounting data are from the Compustat database.

The empirical analyses are carried out in three stages. The first stage verifies the

validity of our choice of job security variables. Since we focus on only hiring contracts,

the CEO candidates can only use past information of firm to evaluate the security of

their potential jobs. Therefore, the proxy for job security should be available from the

firm or industry before the arrival of the new CEO. Moreover, for a job security proxy

to be valid, it should be highly correlated with the new hired CEO’s tenure and the

chance of dismissal. The second stage explores how job security plays a role in pay

negotiation. More precisely, we investigate whether the new hires can, in turn, use job

security to negotiate higher pay with the boards. In the cases when the firms do not have

sufficient financial resources to provide greater pay, CEOs could seek for an alternative

pay structure to compensate the lower job security; that is, in terms of more cash pay,

1 To avoid complications, we exclude firms with two CEOs at the same time.

2 Execucomp database includes severance information starting from 2006.

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more likelihood of signing a severance agreement, shorter vesting period and an

incentive plan that is easier to achieve. In the third stage, we incorporate CEO risk

preference and CEO labor market condition into the study, and discuss how they would

modulate the value of job security.

3.1 Job Security Measures

We categorize our measures into firm-level and industry-level. The first firm-level

measure we use is the average job tenure before the appointment of the new CEO. A

long job tenure would be a strong indicator of high job security. We argue that this

measure captures the overall firm-level risk of job security, and should be highly

correlated with the future tenure of the incoming CEO. The job tenure information is

gathered from Execucomp. We calculate the average job tenure of a firm by summing

up the total years of CEO tenures and then divide by the number of CEOs.

𝐹𝑖𝑟𝑚_𝐽𝑆1,𝑖,𝑗 = ∑𝑇𝑒𝑛𝑢𝑟𝑒𝑙

𝑁𝑖,𝑗

𝑁𝑙=1 (1)

Where Firm_JS1,i,j is the level of job security measure by the average past CEO

tenure of Firm j in year i; Tenurel is CEO l’s tenure; and N is firm’s total number of past

CEOs.

One might argue that job tenure can be time specific since information about

tenure are less during the earlier years in the database. This problem is alleviated

because our sample focus on later years alone, which starts at 2000 whereas the starting

year for Execucomp is 1993. The fact that newly listed firms have less tenure

information can also indicate greater uncertainty. This set up is also in line with our job

security definition.

The second firm-level variable is based on outgoing CEO’s reason of departure. If

the outgoing CEO leaves the firm at a retiring age, the chance of him successfully

ending his CEO career would be high, and thus would pass on a positive signal of a

secured job to the next CEO. We set Firm_JS2,i,j equal to 1 when the outgoing CEO left

10

the firm at an age of 60 or greater, and 0 otherwise.

The third firm-level variable we use is an indicator for financially distressed firms.

Studies have shown that the likelihood of dismissal is much greater among the

distressed firms (Gilson, 1989; Engel et al., 2003; Desai et al. 2006). These firms are

identified based on Altman’s (1968) Z-score. Altman estimates the financial health of a

firm using an overall index Z, where

𝑍 = 0.012𝑋1 + 0.014𝑋2 + 0.033𝑋3 + 0.006𝑋4 + 0.999𝑋5 (2)

X1 is the working capital to total assets ratio, X2 is the retained earnings to total

assets ratio, X3 is the earnings before interest and tax to total assets, X4 is the market

value of equity to total liabilities ratio, and X5 is the sales to total assets ratio. For firms

achieving an Altman’s Z-score less than 2.7 are categorized as financially distressed

firms, and set Firm_JS3,i,j equal to 1.

For industry-level job security, we construct measures based on industry market

performance, cash flows and credit ratings. Studies have shown that firm’s stock market

performance are highly correlated with the likelihood of CEO dismissal (Kaplan and

Minton, 2012; Jenter and Kannan, 2015). We construct our first industry-level measure

accordingly by capturing the stock market risk. which is defined as the ratio of industry

annual stock return average to their standard deviation

𝐼𝑛𝑑_𝐽𝑆 1,𝑖,𝑘 =∑

𝑠𝑡𝑜𝑐𝑘 𝑟𝑒𝑡𝑢𝑟𝑛𝑠,𝑖,𝑘

𝑀𝑖,𝑘

𝑀𝑠=1

𝜎(𝑠𝑡𝑜𝑐𝑘_𝑟𝑒𝑡𝑢𝑟𝑛𝑘)⁄ (3)

Where Ind_JS1,i,j is the industry-level job security measure by stock market risk in

year i and in industry k; stock_return is firm’s annual stock return; M is total number of

firms in the industry. The measure is negatively correlated with the industry risk of

stock market and therefore positively correlated industry’s job security.

The second measure we follow Nguyen (2007)’s methodology, and use the ratio

of the industry’s average cash flows to their standard deviation to capture cash flow risk.

11

𝐼𝑛𝑑_𝐽𝑆 2,𝑖,𝑘 =∑

𝑁𝐶𝐹𝑠,𝑖,𝑘

𝑀𝑖,𝑘

𝑀𝑠=1

𝜎(𝑁𝐶𝐹𝑘)⁄ (4)

Where NCF is firm’s net operating cash flow. The measure is negatively correlated

with the industry risk of cash flow and therefore positively correlated industry’s job

security.

For our third measure, we use S&P long-term issuer credit ratings from Compustat.

Compustat maps the official rating categories into integer values from 2 (AAA) to 23

(CC). We further scale these numbers such that a unit increase in our variable

corresponds to the difference between an AAA and a BBB rating (the scaling factor is

1/9). Finally, we compute the industry-level job security by computing the average

industry credit ratings. The higher Ind_JS3,i,j is the risker industry, and thus lower

industry job security.

𝐼𝑛𝑑_𝐽𝑆3,𝑖,𝑘 = ∑𝐶𝑟𝑒𝑑𝑖𝑡 𝑟𝑎𝑡𝑖𝑛𝑔𝑠,𝑖,𝑘

𝑀𝑖,𝑘

𝑀𝑠=1 (5)

3.2 Compensation Measures

We use the total annual compensation (tdc1 scaled to the $ millions) in Execucomp

to measure the total pay described on the hiring contract. This variable consists of the

following components: base salary, annual bonus, stock awards, option awards, long

term incentive plan, other benefits and other components.

We use the ratio of annual cash pay to total annual compensation to measure cash

payout. Cash pay is calculated as the sum of total annual cash awards plus base salary.

In addition to these pay components, we also study the payout when the executive

leaves employment at the firm. This is called the severance package which is instituted

to help protect the newly unemployed. In general, there are two types of severance

agreement. The first type is the severance agreement upon involuntary termination

which describes the payment for the named executive officer who are involuntarily

terminated but not for cause, e.g., reason other than deliberate and serious disloyal or

dishonest conduct. The second type of severance protects the CEOs in case of change-

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in-control. This agreement describes the payments that are triggered by the occurrence

of an acquisition. Severance is a measurement for CEO’s protection against dismissal.

Therefore, we would expect CEOs to be more likely to demand for a severance

agreement when the firm is unable to grant a higher total pay for job risk. In Execucomp,

severance data are available for the years after and including 2006. We define a dummy

variable, severance, equal to 1 for CEOs who sign severance agreements (i.e. the dollar

amount of severance data is non-zero and non-missing).

Firms often break their incentive awards into several vesting periods with amount

and schedule based on either year of employment or firm performance. These

mechanisms are used to motivate CEO for better performance. However, when the CEO

is dismissed, it would also lead to the forfeiture of these unvested compensation.

Therefore, a shorter vesting period would provide a greater expected payoff (or greater

certainty equivalent) to the employees since it consists of payments that are either

guaranteed or foreseeable in the near future. We take advantage of detailed grant vesting

schedules provided by the Inventive Lab database, and calculated the weighted average

vesting period using a modified method of Gopalan et al. (2014).

𝐴𝑣𝑒𝑟𝑎𝑔𝑒_𝑣𝑒𝑠𝑡𝑖𝑛𝑔_𝑝𝑒𝑟𝑖𝑜𝑑

= ∑ 𝐶𝑎𝑠ℎ𝑖 × 𝑡𝑖

𝑛𝑐𝑖=1 + ∑ 𝑅𝑒𝑠𝑡𝑟𝑖𝑐𝑡𝑒𝑑_𝑠𝑡𝑜𝑐𝑘𝑗 × 𝑡𝑗

𝑛𝑠𝑗=1 + ∑ 𝑂𝑝𝑡𝑖𝑜𝑛𝑘 × 𝑡𝑘

𝑛𝑜𝑘=1

∑ 𝐶𝑎𝑠ℎ𝑖𝑛𝑐𝑖=1 + ∑ 𝑅𝑒𝑠𝑡𝑟𝑖𝑐𝑡𝑒𝑑_𝑠𝑡𝑜𝑐𝑘𝑗

𝑛𝑠𝑗=1 + ∑ 𝑂𝑝𝑡𝑖𝑜𝑛𝑘

𝑛𝑜𝑘=1

(6)

Where the subscript i denotes a cash grant, the subscript j denotes a restricted stock

grant, and subscript k denotes an option grant. Cash is the dollar value of cash grant i

with corresponding vesting period ti. Restricted_stocki is the dollar value of restricted

stock grant i with corresponding vesting period ti in months. Optionk is the Black-

Scholes value of option grant k with the corresponding vesting period tk in months. In

our equation we do not include salary and bonus as the one in Gopalan et al. (2014)

because we solely study the vesting schedule of the grants.

Lastly, we measure the ease of incentive plan by considering metrics of the plan.

13

In general, incentive plans are paid out based on either time or performance. A time-

based incentive plan is designed to encourage CEOs to work longer for the firm,

whereas a performance-based incentive plan is designed to motivate CEOs to achieve

certain goal performance. When the high job risk is present, an early dismissal would

certainly lead to the forfeiture of unvested time-based compensation. However, under

performance-based incentive plan, there is still a chance for CEO to receive the

compensation (in the worst case scenario, CEO can manipulate accounting

performance). Therefore, time-based incentive plan should be avoided. We calculate

time-based portion of the incentive grants by dividing total amount of time-based

incentive plan granted by total amount of incentive plan granted.

3.3 Risk Preference Measures

Special circumstances may cause the relationship between job security and CEO

pay to deviate from the standard prediction above. In the third stage, we point out these

special circumstance. We use information of new hires’ previous employment, career

advancement and financial data from Execucomp and Compustat to construct our risk

preference measures.

When CEOs possess certain expertise that the firms require, they would have less

desire for job security for two reasons. First, the firm would value the expertise of the

CEO and should, therefore, be more lenient on the CEO which in turn decreases the

likelihood of early dismissal. Second, this type of CEOs will likely to be more self-

assured that their specialties would cause an impact on the firm, and thus they are more

confident of the possible outcomes; this over-confidence which leads to the neglect of

job security. A good example would be a turnaround expert in a financially distressed

firm. When a firm desperately needs to change its financial situation, its board will

likely to hire a turnaround expert (Ellis,2012). The boards will be more lenient on the

CEO who the possesses such turnaround specialty by providing enough time for him to

improve the firm. Meanwhile, due to the previous successful experience, these CEO are

14

confident of the possible impact he could bring, and thus would depreciate the value of

job security.

The turnaround experts in our sample are defined as the executives who has

participated in turning a financially distressed firm into a healthy firm. In other words,

these executives have increased a firm’s Z-score from lower than 2.7 to greater than 3.

The dummy variable, D(Expert), equals to 1 if such CEO is a turnaround expert, and 0

otherwise.

We identify ambitious CEO candidates as our second measure of risk preference.

These are defined as the ones who are promoted or hired from a rank lower than the

third position (we rank executive by their total compensation). This type of candidates

has less concerns for job security than the need to excel (or be famous). At the same

time, because the increase in pay is much higher than their original compensation at a

lower position, which would further diminish their value for job security.

For the third measure, we identify those CEOs who are extremely risk-averse.

Campbell et al. (2007) find that workers’ fears of unemployment are increased by their

previous unemployment experience. Since having successive dismissals on their

resume could adversely reduce their prospect to finding the next job, we expect that

CEO candidates who were unemployed for a considerable period of time, before taking

on a job, would place greater value on job security. We calculate the gap in years

between the current and previous employment from Execucomp, and set the

D(Unemployed) variable to 1 if the CEO was out of job for more than 2 consecutive

years.

Lastly, we create two specific risk preference variables for external hires.

Information asymmetry plays a big part in CEO compensation negotiation.

Stakeholders who control information can release only enough to manipulate the

outcome that they desire (Pfeffer, 1981). In general, CEO candidates can be sourced

from inside or outside the firm. Internal candidates have more firm-specific human

capital than external candidates (Harris et al, 1997), and have the access to the first-

15

hand information about the firm, thus they have the upper hand in evaluating firm’s

current situation comparing to the external candidates. Therefore, it would cause the

external hires to value the publicly available firm-level information even more.

Similarly, CEOs who are hired outside of the industry would value publicly available

industry-level information more than their counterparts.

Following Parrino (1997), we define an external hire to be an executive who has

been appeared for the first time with the firm. All others are classified as external hires.

The dummy variable D(External) equals to 1 if such new CEO is an external hire and

0 otherwise. We also use Execucomp to verify CEO’s previous employment. If the CEO

was hired from outside industry, we set D(Outside_Ind) to 1 and 0 otherwise.

As shown in Table 1 We separate our CEOs into three categories based on their

risk preference, risk-seeking, risk-neutral/unknown and risk-averse.

<INSERT TABLE 1 HERE>

3.4 CEO Labor Market Condition Measures

For CEO labor market condition, we calculate the demand/supply ratio of the

entire CEO labor market and the market within the same industry during each year. The

demand is calculated as total number of CEO turnovers (new CEO openings) plus total

number of incumbent CEOs who are older than 60 in a given year. The supply is

calculated as the total number of dismissed CEOs who are 55 or under in the previous

year plus the total number of executive who are 55 or under in number 2 and 3 position

in a given year.


Table 2 summarizes our key variables (please also see Appendix A for our variable

definitions). On average, the total annual pay on hiring year is about $5.28 million. Of

which, 37.3% consists of cash pay. Since the availability of severance data on

Execucomp from 2006, 83.3% of the new hires sign a severance agreement. Using the

Incentive Lab sample, we find that the weighted average grant vesting period is around

16

38.51 months. For all the CEOs in our sample, 32.9% are ambitious, 4.5% were

unemployed before the appointment, 78% are internally promoted CEOs, and 13.4%

are turnaround experts. The average age at hiring is 52.64. One other thing to notice is

that 44.7% (Firm_JS3) of the firms are financially distressed. Along with the close-to-

zero average ROA and negative stock performance in our sample, our results are

consistent with prior literature that a CEO turnover occurs when firm performance is

poor (Meindl et al., 1985; Zemba et al., 2006).

4. Empirical Analysis

4.1 Verification of Job Security Measures

In this section, we verify the validity of the two types of job security measures we

discussed in the previous section.

<INSERT Figure 1a and 1b HERE>

Figure 1a and 1b depict the relationship between our choice of job security

measures and future CEO tenures. We separate our sample into high and low job

security groups based on the median of each continuous job security variable. For

dummy variable Firm_JS3, we categorize as high job security when it equals to 0, and

low job security if it equals to 1. We observe difference between the two groups. The

new hires in highly secured jobs have longer tenure than the ones in lowly secured jobs,

and the results are significant at 5% level at least across all six measures.

<INSERT FIGURE 2a and 2b HERE>

A valid job security measure should also capture CEO’s early dismissal risk.

Figure 2a and 2b depicts the relationship between our choice of job security measures

and the chance of dismissal during the first 3 years of tenure. The chance of dismissal

is calculated as the total number of dismissed CEOs during the first 3 years of tenure

divided by the total number of new CEOs at year 0. Again, we find that CEOs are at

greater risk of dismissal in lowly secured jobs than its counter-group across all six

17

measures. Thus, these results verify our choice of job secure measures.

4.2 Job Security and CEO Compensation

In this section, we examine the relationship between CEO compensation and the

two main types of job security measures discussed in the previous section: industry-

level measure and firm-level measure. Due the large amount of missing corporate

governance data, we consider regression specifications in which control variables are

added sequentially. The baseline specification includes only firm characteristics and

CEO characteristics, and the second specification includes corporate governance

controls is reported in our robustness test section.

In Table 3, we investigate the impact of industry-level job security in column 1 to

3. First, our results indicate that industry-level job security is highly correlated with

CEO total pay. Consistent with Hypothesis 1, CEOs are compensated for higher

industry job risk; or more secured job pay less. We calibrate the economic significance

of these findings. On average, each unit increase in the inverse coefficient of variation

(or one-unit decrease in risk) in industry-average stock return and cash flow would lead

would lead to a $984 thousand and $914 thousand decrease in total pay respectively for

newly hired CEOs. Each unit increase in industry-average credit rating (or from an

AAA to a BBB, or an BBB to CCC, etc…), new hires’ total pay would increase by $5

million.

In the latter half of Table 3, we investigate the impact of firm-level job security. In

column 3 and 4, our results indicate that firm-level job security is highly correlated with

CEO total pay. Again, consistent with Hypothesis 1, CEOs are compensated for higher

firm-level job risk. On average, each year decrease in average past CEO tenure would

lead to a $24 thousand increase in total pay for newly hired CEOs. New hires’ total pay

would decrease by $405 thousand if their predecessors have successfully retired.

In Column 6, we find that firm-level job security measured by financially

condition produces a contrary result; more secured jobs pay more. This special case

18

requires further care, and we will discuss it in the next section.

4.3 Job security and CEO Compensation in Firms with Financial Constraints.

The above analyses cover the general cases. However, in a special case when the

firms do not have sufficient financial resources to grant greater total pay, we would

expect CEOs to seek for alternatives to compensate low job security.


In Panel A of Table 4, we conduct a univariate analysis for total pay scaled by firm

size based on different firm-level job security measures. Indeed, we find that new hires

in financially distressed firms receive a lower total pay. This shows distressed firms are

unable to grant CEOs with the same pay level as the normal firms. Therefore, CEOs in

this type of firms would seek alternative ways to compensate the job risk. And this is

exactly what we find in the latter Panels of Table 5. In Panel B, we find that CEOs in

financially distressed firms demand 2.395% higher in cash pay than those in normal

firms. We also include alternative job security measures and show that there is no

significant difference in the general case. We make use of the Incentive Lab database,

and compare the vesting periods and the metrics of incentive plans between high and

low job security groups in Panel C. Again, we confirm that CEOs in distressed firm

demand a shorter vesting period. In Panel D we find that CEOs in distressed firm tend

to choose a metric that is easier to achieve. Using alternative measures, we do not find

a similar result. All these results indicate that compensation for job risk follows a

hierarchy; CEO will ask for alternative ways of compensation when the firm cannot

grant a higher pay.


In Table 5, we conduct a more rigorous analysis using regression, and again

confirm our findings in the univariate analysis. In terms of economic significance, when

the firm cannot grant a higher pay, CEOs on average demand 3.1% higher in cash pay,

3.4% increase in the likelihood of signing a severance agreement, and 1.765 months

19

shortened average vesting period in less secured jobs. In column 4, we do not find a

significant impact on the choice of metrics of incentive plans. We suspect that is due to

the differences in CEO risk preference, which will be discussed in more details in the

next section.

4.3 Job Security under Different Risk Preferences

We would expect the above findings to hold under normal conditions. However,

special circumstances may cause the payment scheme to deviate from the standard

prediction above. In this section we study the impact of job security under different

CEO risk preference. Table 6, we separate our sample based on how CEOs were

promoted to current position. We classify those ambitious CEO who were promoted

from a lower rank to be ambitious (or risk-seeking). Corroborate with our prediction in

Hypothesis 2, job security has a value only among the non-ambitious CEO and has no

impact on the CEO’s total pay among ambitious ones. From table 5 in the previous

section, we suspect that CEO’s risk preference causes the insignificant correlation

between job security and the choice of incentive plan metrics. The findings in column

5 and 6 of Table 6 confirm this suspicion. We find CEOs, in financially distressed firms,

choose a metric that is easier to achieve to compensation the job risk only if they are

non-ambitious. Again, these findings support Hypothesis 2 such that job security has

no value among the risk seeking CEOs.


Next, we examine whether external hires would value job security differently from

the internal hires. We first study the firm-level measures in Column 1 and 2. The

interaction term of external hire indicator and job security measures demonstrate that

external hires value the firm-level security more than the internal hires. On average,

external hires require a $97 thousand higher premium for each year decrease in average

past CEO tenure than their counterparts. At the industry-level, we compare the

difference between CEOs who are hired from outside industry and within industry in

20

Column 3 and 4. The signs of interaction terms of outside-industry hire indicator and

job security measures are same as we expected, and are significant at 5% level in

Column 4 and 5. These findings confirm that CEOs hired from outside industry value

industry-level job security more than those who are hired within. In terms of economic

significance, CEOs hired from outside-industry would demand a $6.39 million higher

premium for each unit decrease in the inverse coefficient of variation (or one-unit

increase in risk) in industry-average cash flow than their counterparts; for each unit

increase in industry-average credit rating (or from an AAA to a BBB, or an BBB to

CCC, etc…), outside-industry hires would demand a $14.40 million higher premium

than inside industry hires ($1.66 million higher premium from an AAA to a AA or AA

to A, etc...).


We expect that CEO candidate who were unemployed for a long period of time,

before taking on the job, should especially value job security. Consistent with our

prediction, the interaction term of job security and unemployment indicator is strongly

correlated with total pay in Column 1 of Table 8. On average, CEOs who were

unemployed previously require a $197 thousand higher premium for each year decrease

in average past CEO tenure than their counterparts. We also develop a case specifically

for financial distressed firms in column 3. The interaction term of turnaround expert

indicator and job security measure is negatively correlated with the likelihood of

signing a severance agreement. As an economic interpretation, turnaround experts in

financially distressed firms are 10.6% less likely to sign a severance agreement as a

compensation for job risk than their counterparts.

Overall, the regression results corroborate the prediction stated in Hypothesis 2.

We find that job security has no value among CEOs who are risk-seeking (i.e. ambitious

or turnaround experts in financially distressed firms). However, when CEOs are not

risk-seeking, job security will have an impact on their compensation, and this impact

will increase as CEOs become risk-adverse (i.e. external hires, outside-industry hires,

21

or previous unemployed CEOs)

4.4 Job Security under Different Labor Market conditions

We now investigate the link between job security and CEO pay under different

labor market conditions.


Hypothesis 3 predicts that the price of job security increases with CEO’s risk

aversion; conversely, less risk averse CEOs demand lower job security related pay. To

test this prediction, we separate our sample into two groups by industry labor market

conditions. We categorize industry-years with above average demand/supply ratio as

well market condition, and, vice versa, categorize industry-years with below average

demand/supply ratio as poor labor market condition.

As shown in Table 9, job security under well labor market condition has no

significant impact on CEO pay. This is because it is relatively easier for CEO candidate

to find jobs under such market condition, and therefore the need of job security

diminishes. On the other hand, job security has significant impact on CEO pay under

poor market condition across all three job security measures. Overall, the regression

results corroborate the prediction stated in Hypothesis 3. On average, under poor labor

market condition, CEOs are compensated with $39 thousand for each year decrease in

average CEO tenure; $479 thousand for CEOs with a predecessor who was not

successfully retired; and 2.4% increase in the likelihood of demanding the easier

incentive metric when the firm is unable to provide a greater pay.


We also examine the impacts of industry-level job security measure under different

labor market condition categorized by the median of year average demand/supply ratio.

We find that job security has a significant impact across all industry-level measures

under poor labor market condition. Although some of the results also show that job

security has a value even under well market condition, one of the measures, Ind_JS1,

22

becomes statistical insignificant. This is an evidence that job security’s impact

somewhat decreases under well market condition.

4.5 Robustness Test

We do not include corporate governance controls in our main analysis since it

would dramatically decrease our sample size. However, the correlation between

corporate governance and CEO compensation has been proven by many scholars (Core

and Larcker, 1999; Dicks, 2012). In Appendix B, we add corporate governance index

(G-index), board independence (percentage of independent directors) and board size

into our main models. At the same time, instead of measuring the total annual pay by

the millions, we take the total pay reported on Execucomp (tdc1) by the natural log.

Overall, the results remain in line with our main analysis.

We have proved that when firms are unable to grant the CEOs with greater pay,

CEOs would seek for higher cash pay portion to compensate their job risks. However,

this pay alternative might simply be the compensation scheme at financially distressed

firms instead of firm’s concession for job risk. For example, the high cash pay ratio can

be merely caused by low valuation of firm stocks.

We give two explanations for this concern. First, low valuation of firm stock

should not change CEO’s cash pay ratio. CEOs are able to use prior firm performance

to evaluate the firm and demand a favorable pay structure accordingly. When firm’s

stock price is low, CEO can, in return, demand more shares to compensate for such low

valuation, and thus the cash pay ratio should remain the same. Second, if job security

truly has a value in financially distressed firms, this value should increase as the job

risk increases. We conduct a regression analysis by adding the interaction term of job

security measure and financially distressed indicator (Firm_JS3). We construct a

dummy variable equal to 1 when the predecessor of the CEO did not successfully retire,

and thus indicating a risky job position. In Appendix C, the results show that the

interaction term is signicantly positive, and therefore confirm that a more conservative

23

pay structure in distressed firms is indeed caused by the decrease in job security.

5. Conclusion

We examine how job security has a price during CEO pay negotiation at hiring.

We find significant impact of job security using different measures across firms. In

general, CEO candidates and the board members would trade pay for job security; that

is in terms of higher total pay. We further find this compensation for risk follow a

hierarchy order; when firms are unable to provide a higher pay, the CEOs will seek for

alternative ways to compensate their job risk, including a severance agreement, a

shorter grant vesting period, and an incentive plan metric that is easier to achieve.

Moreover, job security has no value among risk-takers (i.e. ambitious CEOs or

turnaround experts in financially distressed firms), and this price increases with CEO’s

risk aversion (i.e. external hires, outside industry hires and previously unemployed

CEOs). By examining the impact of job security under different market condition, we

find that this premium disappears under favorable CEO labor market. Overall, our

results are in line with the findings in labor economic literatures that workers are

compensated in low job security positions. Our research compliments that of Peters and

Wagners (2014), and is the first empirical study to examine the impact of CEO’s risk

preference and CEO market condition on the value of job security. We hope to add a

new perspective to the determinants of CEO compensation and on the optimal design

of managerial incentive contracts.

24

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28

Table 1: CEO Risk Preference Groups

CEO Risk Preference Groups

Risk-seeking Risk-neutral/Unknown Risk-averse

Ambitious CEO Non-ambitious

Turnaround expert Non-turnaround expert

Employed Unemployed

Internally hired Externally hired

Hired from within industry Hired from outside industry

29

Table 2: Summary Statistics

The sample consists of all CEO who are hired or promoted during 2000-2013 in Execucomp

firms. The data for severance agreement are available from 2006-2013 only. The information

on vesting period are taken from Incentive Labs. All variables except dummies are winsorized

at the 1st and 99th percentiles.

Obs Mean Std. Dev. Min Max

Compensation

Total pay ($million) 2895 5.283 6.562 0.135 38.247

Cash/Total pay 2891 0.373 0.279 0.000 1.000

Severance 1620 0.833 0.373 0.000 1.000

Average vesting period 1116 38.513 14.098 0.170 88.730

Time based grant ratio 1143 0.726 0.321 0.000 1.000

Job security

Firm_JS1 2895 8.709 6.306 1.000 36.000

Firm_JS2 2854 0.415 0.493 0.000 1.000

Firm_JS3 2380 0.441 0.497 0.000 1.000

Ind_JS1 2528 0.095 0.583 -2.222 2.440

Ind_JS2 2815 0.185 0.346 -0.065 2.163

Ind_JS3 1581 1.399 0.194 0.937 1.993

CEO Characteristics

Ambitious 2914 0.329 0.470 0.000 1.000

Unemployed 2536 0.045 0.206 0.000 1.000

Outsider 2914 0.220 0.414 0.000 1.000

Insider 2914 0.780 0.414 0.000 1.000

Expert 2914 0.134 0.341 0.000 1.000

CEO age 2891 52.644 7.008 30.000 80.000

Outside Industry 2536 0.040 0.197 0.000 1.000

Market condition

Demand/supply 2914 0.363 0.481 0.000 1.000

Industry demand/supply 2880 0.339 0.473 0.000 1.000

Firm Characteristics

lnSize 2832 7.681 1.868 3.308 12.532

Sales growth 2818 0.049 0.239 -0.587 1.243

ROA 2829 0.001 0.162 -0.859 0.305

Leverage 2817 0.237 0.210 0.000 0.991

M/B 2769 2.524 3.237 -9.614 20.332

R&D 2822 0.042 0.090 0.000 0.558

Stock volatility 2661 0.031 0.018 0.009 0.102

Industry-adj stock return 2542 -0.101 0.442 -1.231 1.445

Firm age 2612 24.947 19.26 0.038 88.06

30

Figure 1a: Average Tenure of Newly Hired CEOs Based on Firm-level Job

Security Measures

The sample consists of all CEO who are hired or promoted during 2000-2013 in an Execucomp

firm. We separate the level of job security into two groups based on the median of each firm-

level job security measure.

Figure 1b: Average Tenure of Newly Hired CEOs Based on Industry-level Job

Security Measures


firm. We separate the level of job security into two groups based on the median of each industry-

level job security measure.

3.969 4.0054.217

4.644 4.7854.531

0.000

1.000

2.000

3.000

4.000

5.000

6.000

Firm JS₁ Firm JS₂ Firm JS₃

CE

O T

enu

re

Job Security Measures

Low Job Security High Job Security

4.306 4.302

4.210

4.573

4.375

4.642

3.900

4.000

4.100

4.200

4.300

4.400

4.500

4.600

4.700

Ind JS₁ Ind JS₂ Ind JS₃

CE

O T

enu

re



31

Figure 2a: Rate of Dismissal within the First 3 Years Based on Firm-level Job

Security Measures


firm. We separate the level of job security into two groups based on the median of each firm-

level job security measure. The chance of dismissal is calculated as number dismissed CEOs

during the first 3 years of tenure divided by the total number of CEOs.

Figure 2b – Rate of Dismissal within the First 3 Years Based on Industry-level



firm. We separate the level of job security into two groups based on the median of each industry-

level job security measure. The chance of dismissal is calculated as number dismissed CEOs

during the first 3 years of tenure divided by the total number of CEOs.

41.53% 41.54%38.49%

28.00%25.53%

32.99%

0.00%

5.00%

10.00%

15.00%

20.00%

25.00%

30.00%

35.00%

40.00%

45.00%

Firm JS₁ Firm JS₂ Firm JS₃

Ch

an

ce o

f D

ism

issa

l



32

35.78% 36.61% 36.18%

30.15%

33.29%

29.32%

0.00%

5.00%

10.00%

15.00%

20.00%

25.00%

30.00%

35.00%

40.00%

Ind JS₁ Ind JS₂ Ind JS₃

Ch

an

ce o

f D

ism

issa

l



33

Table 3: Regression Analysis for Job Security and CEO Pay

Column 1 to 3 report the OLS regression of industry-level job security measure on CEO total

pay. Ind_JS1 and Ind_JS2 are the inverse coefficient of variation in industry stock returns and

cash flow, which both are positively correlated with industry job security. Ind_JS3 is the industry

average credit rating (inversely correlated with industry job security). Column 3 to 6 report the

OLS regression of firm-level job security measure on the CEO total pay. Both Firm_JS1 and

Firm_JS2 are positively correlated with firm job security; Firm_JS1 is the average tenure of past

CEOs and Firm_JS2 is an indicator whether the predecessor successfully retired. Firm_JS3 is

an indicators equal to 1 if the firm is financially distressed. All other independent variables are

defined in Appendix A. Robust standard errors are reported in parentheses. The symbol ***

indicates that the p-value is less than 0.01, ** that it is less than 0.05, and * that it is less than

0.1.

Dependent Variable: Industry-level

Firm-level

Total pay (1) (2) (3)

(4) (5) (6)

Ind_JS1 -0.984**

(-2.31)

Ind_JS2 -0.914***

(-3.36)

Ind_JS3 5.461***

(5.43)

Firm_JS1

-0.024*

(-1.66)

Firm_JS2

-0.405**

(-2.43)

Firm_JS3

-0.725***

(-2.92)

Size 1.977*** 2.003*** 2.495***

2.251*** 2.271*** 2.272***

(21.68) (20.80) (16.28)

(22.20) (16.15) (20.55)

Sales growth 1.221* 1.066* 1.484

1.218** 1.249** 0.863

(1.94) (1.67) (1.41) (2.00) (2.03) (1.36)

ROA -1.642* -0.885 -2.057

-1.625 -1.576 -1.970*

(-1.69) (-0.88) (-0.76)

(-1.58) (-1.21) (-1.91)

Leverage -1.786*** -1.708*** -2.308**

-1.792*** -1.642** -1.804***

(-3.06) (-2.88) (-2.22)

(-3.12) (-2.12) (-2.98)

MB 0.225*** 0.223*** 0.229***

0.192*** 0.193*** 0.170***

(5.19) (5.11) (3.74) (4.66) (3.68) (4.40)

RD 9.111*** 9.092*** 14.702***

7.005*** 6.235*** 5.880***

(6.63) (6.63) (3.95)

(4.63) (3.58) (3.64)

Stock return

20.470** -3.221

17.753* 15.225 29.099***

volatility

(2.09) (-0.19)

(1.87) (1.40) (2.88)

Industry-adj. 0.423 0.537* 0.912*

0.679** 0.724*** 0.679**

stock return (1.55) (1.93) (1.87) (2.43) (3.37) (2.52)

34

D(Insider) -2.899*** -2.886*** -3.630***

-2.869*** -2.792*** -3.032***

(-8.58) (-8.52) (-6.73)

(-8.66) (-9.90) (-8.83)

CEO age -0.052*** -0.050*** -0.071*** -0.048*** -0.041*** -0.047***

(-3.49) (-3.41) (-2.68)

(-3.25) (-3.37) (-3.29)

Firm age -0.003 0.000 -0.004

0.000 0.000 0.004

(-0.49) (0.05) (-0.57)

(0.03) (0.04) (0.63)

Constant -4.243*** -5.502*** -14.521***

-7.430*** -8.352*** -8.270***

(-3.87) (-4.48) (-5.71)

(-5.21) (-7.17) (-4.41)

Industry FE 2,441 2,441 1,336 2,441 2,409 2,117

Year FE NO NO NO

YES YES YES

Observations YES YES YES

YES YES YES

Adj. R-squared 0.319 0.321 0.288

0.357 0.363 0.394

35

Table 4: Univariate Analysis for Financially Distressed Firms

We separate our sample into high and low job security firms based on the median of each firm-

level job security measure. The total annual pay, cash pay portion, average vesting period and

time-based portion of the grant are compared between two groups.

Panel A: Annual Pay (scaled by size)

Low Job Security High Job Security Difference T-value

Obs. Mean Obs. Mean

Firm_JS1 1349 0.433% 1337 0.300% 0.133% 5.5676

Firm_JS2 2371 0.369% 442 0.336% 0.034% 1.0642

Firm_JS3 909 0.368% 1125 0.440% -0.072% -2.7112

Panel B: Cash pay/Annual pay


Obs. Mean Obs. Mean

Firm_JS1 1384 37.211% 1379 37.400% -0.189% -0.178

Firm_JS2 1663 36.940% 1185 37.500% -0.560% -0.530

Firm_JS3 1051 37.984% 1331 35.588% 2.395% 2.101

Panel C: Vesting period


Obs. Mean Obs. Mean

Firm_JS1 457 36.724 479 39.888 -3.164 -3.501

Firm_JS2 313 38.809 267 39.380 -0.572 -0.502

Firm_JS3 408 36.681 525 39.470 -2.789 -3.095

Panel D: Time based grant/Total incentive grant


Obs. Mean Obs. Mean

Firm_JS1 473 71.825% 485 70.851% 0.974% 0.470

Firm_JS2 320 71.764% 273 67.850% 3.914% 1.488

Firm_JS3 408 69.484% 525 75.605% -6.121% -2.941

36

Table 5: Regression Analysis for Financially Distressed Firm and CEO Pay

Column 1 reports the result of OLS regression. The dependent variable is CEO cash pay ratio.

In Column 2, the dependent variable is an indicator that is one if CEO signs a severance

agreement and zero otherwise. Logit regression model is used and the marginal effects of the

coefficients are reported. Column 3 and 4 report the results of Tobit regressions; the dependent

variable are average vesting period and time based portion of the total grant respectively. The

independent variables are defined in Appendix A. Robust standard errors are reported in

parentheses. The symbol *** indicates that the p-value is less than 0.01, ** that it is less than

0.05, and * that it is less than 0.1.

(1) (2) (3) (4)

Dependent variable: Cash/Total pay D(Severance) Vesting period Time based grant/Total grant

Firm_JS3 0.031** 0.034* -1.765** 0.035

(2.38) (1.69) (-3.16) (1.57)

Size -0.057*** 0.012 0.780 -0.027***

(-13.63) (1.39) (1.68) (-3.11)

Sales growth 0.036 -0.019 1.185 -0.056

(1.13) (-0.43) (1.15) (-1.16)

ROA -0.040 0.074 14.070*** -0.042

(-0.77) (1.05) (21.23) (-0.49)

Leverage -0.051 0.029 -0.551 -0.026

(-1.40) (0.54) (-0.22) (-0.24)

MB -0.007*** 0.004 -0.046 -0.001

(-4.47) (1.16) (-0.78) (-0.53)

RD -0.437*** -0.115 4.170 0.186

(-5.40) (-0.99) (0.82) (1.40)

Stock return 0.313 -2.524*** 25.857 0.399

volatility (0.58) (-3.42) (0.75) (0.39)

Industry-adj. -0.046*** -0.009 1.338 0.043**

stock return (-3.19) (-0.39) (1.51) (2.33)

D(Insider) 0.153*** 0.009 -3.194* -0.035

(11.45) (0.46) (-2.76) (-1.27)

CEO age 0.004*** -0.004*** 0.001 -0.001

(4.85) (-2.83) (0.03) (-1.43)

Firm age 0.001*** -0.001 -0.261*** 0.001

(2.99) (-1.10) (-5.88) (0.63)

Constant 0.534*** 50.143*** 1.118***

(4.56) (12.87) (8.64)

Industry FE YES YES YES YES

Year FE YES YES YES YES

Observations 2,115 1,103 729 744

Adj. R-squared 0.140

Pseudo R-squared 0.608 0.188 0.667

37

Table 6: Regression Analysis for CEOs with different Risk Preference

Column 1 to 4 report the OLS regression of firm-level job security on CEO total pay among ambitious and non-ambitious CEOs respectively. Ambitious CEOs

are defined as the ones who were promoted or hired from a rank lower than the 3rd position. Column 5 and 6 report the Tobit regression of job security on time

based grant ratio (left censored at 0 and right censored at 1). The independent variables are defined in Appendix A. Robust standard errors are reported in

parentheses. The symbol *** indicates that the p-value is less than 0.01, ** that it is less than 0.05, and * that it is less than 0.1.

Dependent Variable Total pay Time based grant/Total grant

Ambitious Non-ambitious Ambitious Non-ambitious

Ambitious Non-ambitious

(1) (2) (3) (4) (5) (6)

Firm_JS1 -0.007 -0.028*

(-0.22) (-2.06)

Firm_JS2 -0.534 -0.469**

(-1.14) (-2.43)

Firm_JS3 0.009 0.073***

(0.17) (2.65)

Size 2.418*** 2.243*** 2.443*** 2.234***

-0.053*** -0.014

(11.53) (13.68) (11.57) (15.98)

(-2.65) (-1.37)

Sales growth 0.439 2.053** 0.481 2.329***

-0.150 0.016

(0.38) (2.98) (0.42) (2.79)

(-1.46) (0.19)

ROA -1.223 -1.789 -1.403 -1.818 -0.083 -0.145

(-0.67) (-1.56) (-0.72) (-1.19)

(-0.41) (-1.14)

Leverage -2.403** -1.436*** -2.714** -1.629*

-0.048 -0.052

(-2.09) (-3.64) (-2.28) (-1.89)

(-0.30) (-0.43)

MB 0.210** 0.166** 0.218** 0.166***

0.002 -0.001

(2.22) (2.86) (2.24) (3.29)

(0.38) (-0.23)

RD 7.083** 6.130*** 6.699* 6.183*** 0.376 -0.001

38

(2.01) (5.20) (1.84) (4.21)

(1.55) (-0.01)

Stock return volatility 25.471 18.339 21.431 17.034

1.073 0.887

(1.34) (1.31) (1.10) (1.35) (0.42) (0.75)

Industry-adj. stock return -0.013 1.049** -0.023 1.027***

0.027 0.073*

(-0.02) (2.78) (-0.04) (3.63)

(0.57) (1.78)

D(Insider) -3.000*** -3.378*** -2.968*** -3.323***

0.004 -0.019

(-6.13) (-5.16) (-5.98) (-6.37)

(0.09) (-0.39)

CEO age -0.110*** -0.002 -0.109*** -0.003

0.000 -0.001

(-4.07) (-0.12) (-3.93) (-0.18) (0.14) (-0.82)

Firm age 0.006 -0.002 0.007 -0.003

0.000 -0.002*

(0.36) (-0.20) (0.39) (-0.33)

(0.16) (-1.84)

Constant -4.401 -10.477*** -4.449 -9.904***

1.480*** 1.263***

(-1.17) (-5.24) (-1.14) (-6.56)

(4.88) (575.28)


YES YES

Year FE YES YES YES YES YES YES

Observations 745 1,696 734 1,675 430 314

Adj. R-squared 0.324 0.385 0.326 0.376

Pseudo R-squared 0.431 0.826

39

Table 7: Regression Analysis for Unemployed CEOs

The results of OLS regressions are reported in this table. The dependent variable in Table 7 is

CEO total pay. D(External) is an indicator that equals to 1 if the CEO was hired externally.

D(Outside industry) is an indicator equal to 1 if the CEO was hired from outside industry. The




Dependent variable: Firm-level

Industry-level

Total pay (1) (2)

(3) (4) (5)

D(External)*Firm_JS1 -0.097**

(-2.13)

D(External)*Firm_JS2 -0.336

(-0.51)

D(Outside industry)*Ind_JS1 -1.698

(-1.20)

D(Outside industry)*Ind_JS2 -6.398**

(-2.01)

D(Outside industry)*Ind_JS3 14.401**

(2.66)

Firm_JS1 0.002

(0.10)

Firm_JS2 -0.449**

(-2.01)

Ind_JS1 1.600

(0.70)

Ind_JS2 0.042

(0.02)

Ind_JS3 3.317

(0.57)

D(External) 3.719*** 2.936***

(8.01) (6.94)

D(Outside Industry)

-0.895 -0.354 -23.037**

(-1.00) (-0.32) (-2.98)

Size 2.128*** 2.253***

2.387*** 2.361*** 2.850***

(30.48) (22.09)

(6.36) (6.61) (3.14)

Sales growth 1.193** 1.325** 1.103 1.475 -1.228

(2.41) (2.16)

(0.37) (0.51) (-0.39)

ROA -1.417 -1.616

1.157 -0.762 0.994

(-1.59) (-1.53)

(0.23) (-0.15) (0.10)

Leverage -1.655*** -1.869***

-3.762 -3.570 -4.366

(-2.89) (-3.18)

(-1.27) (-1.23) (-0.95)

MB 0.217*** 0.194*** 0.494*** 0.534*** 0.405**

40

(6.44) (4.64)

(2.95) (3.33) (2.73)

RD 4.925*** 6.788***

12.008 10.736 5.122

(3.25) (4.41) (1.39) (1.21) (0.48)

Stock return volatility 21.204** 15.758

-47.573 -130.018

(2.50) (1.62)

(-1.11) (-1.37)

Industry-adj. stock return 0.608** 0.687**

-0.831 -1.488 -0.720

(2.32) (2.43)

(-0.47) (-0.82) (-0.22)

CEO age -0.050*** -0.046***

-0.382*** -0.384*** -0.458***

(-3.28) (-3.04) (-4.62) (-4.82) (-3.84)

Firm age -0.003 0.000

0.036 0.038 0.042

(-0.46) (0.02)

(1.12) (1.20) (1.26)

Constant -8.843*** -10.375***

9.528* 11.744** 10.358

(-7.96) (-7.14)

(1.81) (2.16) (0.81)

Observations 2,441 2,409

189 189 112

Industry FE YES YES

NO NO NO

Year FE YES YES YES YES YES

Adj. R-squared 0.339 0.356

0.294 0.308 0.260

41

Table 8: Regression Analysis for Turnover Experts

Column 1 and 2 reports the results of OLS regression. The dependent variables in Column 1

and 2 are CEO total pay. D(Unemployed) is an indicator that equals to 1 if the CEO was

previously unemployed. The dependent variable in Column 3 is an indicator whether the CEO

signs a severance agreement. Logit regression model is used in this column and the marginal

effects of the coefficients are reported. The independent variables are defined in Appendix A.

Robust standard errors are reported in parentheses. The symbol *** indicates that the p-value

is less than 0.01, ** that it is less than 0.05, and * that it is less than 0.1.

Dependent variable Total pay D(Severance)

(1) (2) (3)

D(Unemployed)* Firm_JS1 -0.197***

(-2.60)

D(Unemployed)* Firm_JS2 1.538

(0.91)

D(Expert)*Firm_JS3 -0.102**

(-2.11)

D(Expert)*Firm_JS3 0.094***

(2.76)

D(Unemployed) -2.930*** -4.862***

(-2.60) (-5.53)

Firm_JS1 -0.021

(-1.40)

Firm_JS2 -0.424*

(-1.90)

Firm_JS3 0.046**

(2.17)

Size 2.169*** 2.175***

0.010

(20.84) (20.42)

(1.20)

Sales growth 1.272** 1.302** -0.017

(2.01) (2.01)

(-0.42)

ROA -1.224 -1.211

0.066

(-1.12) (-1.07)

(0.95)

Leverage -1.803*** -1.722***

0.026

(-2.94) (-2.72)

(0.47)

MB 0.183*** 0.183*** 0.003

(4.19) (4.24)

(1.14)

RD 5.843*** 5.296***

-0.126

(4.24) (3.65)

(-1.08)

Stock return volatility 20.576** 18.027*

-2.556***

(2.13) (1.83)

(-3.52)

42

Industry-adj. stock return 0.683** 0.712**

-0.012

(2.37) (2.45)

(-0.51)

D(Insider) -5.450*** -5.391*** 0.005

(-7.02) (-6.88)

(0.23)

CEO age -0.033** -0.027*

-0.004***

(-2.22) (-1.74)

(-2.87)

Firm age 0.001 0.002

-0.001

(0.15) (0.24)

(-1.19)

Constant -5.609*** -7.426***

(-3.49) (-4.30)

Industry FE YES YES

YES

Year FE YES YES

YES


1,103


Adj. Pseudo R-squared

0.197

43

Table 9: Regression Analysis for Firm-level Job Security under Different Labor Market Conditions

Column 1 to 4 report the OLS regression of CEO total pay across the three firm-level job security measures under different CEO labor market condition. We

use Tobit regression for Time based grant/Total grant in Column 5 and 6 (left censored at 0 and right censored at 1). We separate our sample into two labor

market condition groups based on the median of the CEO industry-year demand/supply ratio. The independent variables are defined in Appendix A. Robust

standard errors are reported in parentheses. The symbol *** indicates that the p-value is less than 0.01, ** that it is less than 0.05, and * that it is less than 0.1.

Dependent variable Total pay

Time based grant/Total grant

(1) (2) (3) (4)

(5) (6)

Market condition Poor Well Poor Well

Poor Well

Firm_JS1 -0.039*** 0.026

(-3.08) (0.99)

Firm_JS2 -0.479** -0.422

(-2.13) (-1.27)

Firm_JS3 0.024* 0.026

(1.70) (1.14)

Size 2.340*** 2.160*** 2.340*** 2.165***

-0.052*** -0.061***

(9.87) (12.48) (12.90) (12.51)

(-10.95) (-9.12)

Sales growth 1.722*** -0.302 1.881** -0.308

0.002 0.093*

(3.19) (-0.31) (2.45) (-0.31) (0.08) (1.85)

ROA -1.741 0.040 -1.616 -0.182

-0.035 -0.038

(-1.48) (0.02) (-0.97) (-0.10)

(-0.86) (-0.38)

Leverage -1.995*** 0.219 -2.124** 0.100

-0.050 -0.082

(-3.29) (0.24) (-2.37) (0.11)

(-0.99) (-1.42)

MB 0.175** 0.168** 0.197*** 0.165**

-0.008*** -0.003

(2.30) (2.25) (2.86) (2.19) (-3.73) (-1.34)

RD 5.563*** 8.911*** 6.341*** 8.712***

-0.389*** -0.525***

44

(4.58) (2.78) (4.34) (2.69)

(-6.54) (-3.55)

Stock return volatility 34.811** -17.196 37.408*** -19.534

0.111 1.963**

(2.37) (-1.16) (3.01) (-1.34) (0.17) (2.06)

Industry-adj. stock return 0.818 0.427 0.779*** 0.480

-0.040** -0.060**

(1.64) (1.08) (3.12) (1.21)

(-2.02) (-2.42)

D(Insider) -2.856*** -2.803*** -2.828*** -2.733***

0.157*** 0.150***

(-7.20) (-5.15) (-7.25) (-4.94)

(8.44) (6.08)

CEO age -0.033* -0.052** -0.030* -0.055**

0.004*** 0.005***

(-2.00) (-2.47) (-1.86) (-2.58) (5.38) (3.29)

Firm age 0.002 -0.001 0.001 0.000

0.001*** 0.001

(0.18) (-0.05) (0.16) (0.03)

(3.55) (1.49)

Constant -10.036*** -6.858*** -9.378*** -5.470***

0.496*** 0.551*

(-3.62) (-3.60) (-6.24) (-2.87)

(6.08) (1.91)


YES YES


Observations 1,589 820 1,564 813 1,394 690

Adj. R-squared 0.353 0.408 0.353 0.406

Pseudo R-squared

1.150 2.134

45

Table 10: Regression Analysis for Industry-level Job Security under Different

Labor Market Conditions

Table 10 reports the OLS regression of CEO total pay across the three industry-level job

security measures under different CEO labor market condition. We separate our sample into

two labor market condition groups based on the median of the CEO demand/supply ratio. The




Dependent variable Total Pay

(1) (2) (3) (4) (5) (6)

Market condition Poor Well Poor Well Poor Well

Ind_JS1 -1.023** -1.219

(-2.25) (-1.33)

Ind_JS2 -0.584* -1.450***

(-1.84) (-2.98)

Ind_JS3 6.366*** 3.603**

(5.02) (2.17)

Size 1.881*** 2.348*** 1.880*** 2.313*** 2.431*** 2.693***

(17.02) (12.87) (16.83) (12.87) (13.44) (9.57)

Sales growth 1.559* 0.970 1.398 0.846 2.034 1.434

(1.80) (1.05) (1.60) (0.93) (1.42) (0.99)

ROA -0.941 -0.557 -1.033 -0.759 -3.349 1.981

(-0.71) (-0.39) (-0.77) (-0.54) (-0.91) (0.62)

Leverage -1.663** -2.580** -1.647** -2.147* -2.606* -1.705

(-2.49) (-2.30) (-2.43) (-1.89) (-1.96) (-1.12)

MB 0.207*** 0.250** 0.208*** 0.235** 0.187*** 0.275**

(4.75) (2.47) (4.81) (2.31) (2.79) (2.06)

RD 9.409*** 7.032** 9.510*** 7.231*** 19.215*** 5.615

(6.12) (2.53) (6.13) (2.66) (3.84) (0.97)

Stock return 7.355 62.507*** 8.530 56.665*** -24.489 73.406**

volatility (0.67) (3.26) (0.75) (3.01) (-1.29) (2.21)

Industry-adj. -0.055 1.176** -0.010 1.279*** 0.247 1.868**

stock return (-0.17) (2.41) (-0.03) (2.63) (0.43) (2.33)

D(Insider) -2.547*** -3.734*** -2.540*** -3.726*** -3.678*** -3.780***

(-6.22) (-6.25) (-6.19) (-6.21) (-5.22) (-4.38)

CEO age -0.061*** -0.038 -0.061*** -0.033 -0.087** -0.052

(-3.27) (-1.55) (-3.31) (-1.37) (-2.52) (-1.23)

Firm age 0.010 -0.021* 0.009 -0.016 0.002 -0.013

(1.18) (-1.72) (1.13) (-1.31) (0.25) (-0.95)

Constant -3.816*** -8.615*** -3.651** -8.595*** -13.409*** -17.008***

(-2.67) (-4.20) (-2.56) (-4.26) (-4.18) (-4.07)

Industry FE YES YES YES YES YES YES

46


Observations 1,548 893 1,548 893 835 501

Adj. R-squared 0.321 0.332 0.319 0.335 0.279 0.310

47

Appendix A: Variable Description

Variable Name Description

Compensation

Total pay ($million) Total annual compensation including salary, bonus and the grant date

fair values of stock and option grants, long-term incentive payouts, and

other compensation (tdc1 in Execucomp)

Total pay (scaled by size) Total annual pay scaled by firm size

Cash/Total pay Total cash components (salary + annual cash bonus) as a percentage of

the total annual pay

Severance Equals to 1 if the CEO signs a severance agreement

Avg. vesting period The weighted average vesting period of grants

Time based/Total grant Time based incentive grants over total grant

Job security

Firm_JS1 Firm average CEO tenure before the new appointment

Firm_JS2 Equals to 1 if the outgoing CEO left the firm at a retiring age, and 0

otherwise

Firm_JS3 Equals to 1 for financially distressed firms, and 0 otherwise

Ind_JS1 The inverse coefficient of variation in industry stock returns

Ind_JS2 The inverse coefficient of variation in industry cash flow

Ind_JS3 The industry average credit rating

CEO Characteristics

Ambitious Equals to 1 if the CEO was from a rank lower than #3

D(Unemployeed) Equals to 1 if the CEO was unemployed for 2 or more years

D(Insider) Equals to 1 if the CEO was an internal hire

D(External) Equals to 1 if the CEO was an external hire

D(Outside industry) Equals to 1 if the CEO was an outside industry hire

D(Expert) Equals to 1 if the CEO is a workout expert

D(Unsuccessful outgoing CEO) Equals to 1 if the predecessor was not successfully retired

Market condition

Demand/supply The total CEO job demand divide by the total CEO supplies in a given

year

Industry demand/supply The total CEO job demand divide by the total CEO supplies within the

industry in a given year

Controls

lnSize Natural logarithm of total asset

Sales growth Logarithm of the ratio between sales and lagges sales

ROA Operating income divided by total assets

Leverage The sum of long-term debt and debt in current liabilities divided by total

assets

M/B Market value of total assets over book value of total assets

R&D Research and development expense divided by total assets

Stock volatility The standard deviation of daily stock returns in each year

48

Industry-adj. stock return Annual stock return minus its industry mean.

G-index Gompers, Ishii, and Metrick (2003) governance Index

Board independence Percentage of independent directors on board

Board size Number of board members

49

Appendix B: Robustness Tests for the General Case

Column 1 to 5 report the results of OLS regression. The dependent variable is the neutral log

of the total pay reported in the Execucomp database. Robust standard errors are reported in

parentheses. We add corporate governance index (G-index), board independence (percentage

of independent directors) and board size into our main models as control variables. The symbol

*** indicates that the p-value is less than 0.01, ** that it is less than 0.05, and * that it is less

than 0.1.

Dependent Variable: Industry-level Firm-level

Total pay (1) (2) (3) (4) (5)

Ind_JS1 -0.114**

(-2.98)

Ind_JS2 -0.184***

(-3.55)

Ind_JS3 1.031***

(6.75)

Firm_JS1 -0.002

(-0.68)

Firm_JS2 -0.090**

(-2.45)

Size 0.394*** 0.397*** 0.400***

0.445*** 0.447***

(9.50) (23.34) (16.54)

(21.44) (20.10)

Sales growth 0.066 0.050 0.024

0.042 0.080

(0.43) (0.45) (0.17)

(0.30) (0.73)

ROA 0.614 0.800*** 0.509 0.645 0.705**

(1.23) (3.08) (1.27)

(1.75) (2.26)

Leverage 0.080 0.131 -0.308*

0.020 -0.001

(0.32) (0.95) (-1.67)

(0.11) (-0.01)

MB 0.038** 0.038*** 0.034***

0.032*** 0.033***

(2.91) (5.21) (3.46)

(3.40) (3.66)

RD 2.327*** 2.250*** 2.888*** 1.816*** 1.739***

(7.84) (7.04) (5.30)

(6.34) (6.03)

Stock return volatility

5.780*** 2.063

5.745** 5.415

(2.75) (0.77)

(2.27) (1.73)

Industry-adj. stock return 0.168* 0.187*** 0.161**

0.213*** 0.222***

(2.16) (3.43) (2.11)

(3.18) (9.93)

D(Insider) -0.524*** -0.515*** -0.512*** -0.497*** -0.484***

(-6.25) (-8.31) (-6.41)

(-6.97) (-8.66)

CEO age -0.017*** -0.017*** -0.018***

-0.016*** -0.016***

(-11.29) (-4.71) (-3.51)

(-8.64) (-7.60)

Firm age -0.001 -0.000 -0.001

-0.002** -0.002

(-0.72) (-0.19) (-0.62)

(-2.44) (-1.54)

G-index 0.017 0.016 0.017 0.020** 0.022*

50

(1.93) (1.28) (1.07)

(2.20) (2.09)

Board independence -0.694 -0.597 -0.114

-0.417 -0.453

(-1.16) (-1.45) (-0.19) (-0.83) (-0.96)

Board size -0.018 -0.015 -0.011

-0.005 -0.007

(-0.96) (-1.38) (-0.82)

(-0.46) (-0.56)

Constant 6.303*** 5.977*** 4.808***

5.696*** 5.683***

(50.52) (22.73) (12.43)

(30.34) (22.84)

Observations 1,638 1,638 972

1,638 1,617

Industry FE NO NO NO YES YES

Year FE YES YES YES

YES YES

Adj. R-squared 0.415 0.422 0.365

0.462 0.465

51

Appendix C: Robustness Tests for Financially Distressed Firms

The following table report the results of Tobit regression (left censored at 0 and right censored

at 1). The dependent variable is cash pay ratio. D(Unsuccessful outgoing CEO) is an indicator

that equal to one if the outgoing CEO was not successfully retired. In Column 2, we add

corporate governance index (G-index), board independence (percentage of independent

directors) and board size into our main models as control variables. The symbol *** indicates

that the p-value is less than 0.01, ** that it is less than 0.05, and * that it is less than 0.1.

Dependent Variable: (1) (2)

Cash/Total pay

Firm_JS3*D(Unsuccessful outgoing CEO) 0.039*** 0.046**

(4.46) (2.48)

Firm_JS3 0.007 0.020

(0.94) (1.28)

D(Unsuccessful outgoing CEO) -0.022*** -0.019**

(-3.60) (-2.07)

Size -0.056*** -0.058***

(-14.83) (-14.36)

Sales growth 0.034 0.062

(1.17) (1.32)

ROA -0.050 -0.043

(-0.89) (-0.66)

Leverage -0.050*** -0.052

(-3.22) (-1.10)

MB -0.007*** -0.007***

(-5.49) (-3.79)

RD -0.431*** -0.381***

(-15.52) (-3.43)

Stock return volatility 0.409 -0.123

(0.56) (-0.13)

Industry-adj. stock return -0.046*** -0.054***

(-3.48) (-4.55)

D(Insider) 0.152*** 0.144***

(12.34) (7.78)

CEO age 0.004*** 0.005***

(4.62) (5.18)

Firm age 0.001*** 0.001***

(5.42) (5.30)

G-index

-0.006

(-1.40)

Board independence

0.256*

(1.71)

Board size

-0.000

52

(-0.05)

Constant 0.549*** 0.609***

(7.61) (7.76)


Industry FE YES YES

Year FE YES YES


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