Corporate Leverage Adjustment:

How Much Do Managers Really Matter?

Murray Z. Frank and Vidhan K. Goyal

May 12, 2006

ABSTRACT

Managers have a significant impact on corporate leverage. The CFO has a

somewhat stronger effect than does the CEO. Managerial fixed effects largely wipe

out the impact of firm fixed effects. But readily observable managerial attributes

and contracts do not account for much of the variation in leverage. The commonly

reported negative effect of profits on leverage is observed when a leverage ratio is

studied. But theory suggests running separate equations for debt and equity. When

this is done, the effect of profits on net equity issuing is negative, and the effect of

profits on net debt issuing is positive.

JEL classification: G32

Keywords: Capital structure targets, executive compensation, leverage adjust-

ment.

Murray Z. Frank is the Piper Jaffray Professor of Finance, Carlson School of Management, Universityof Minnesota, Minneapolis, MN 55455. E-mail: Murray.Frank@csom.umn.edu.

Vidhan K. Goyal is with the Department of Finance, Hong Kong University of Science and Technol-ogy, Clear Water Bay, Kowloon, Hong Kong. Phone: +852 2358-7678, Fax: +852 2358-1749, E-mail:goyal@ust.hk.

Acknowledgments: We would like to thank Raj Aggrawal and Paul Povel for helpful comments.Murray Z. Frank thanks Piper Jaffray for financial support. Vidhan K. Goyal thanks the Research GrantsCouncil of Hong Kong for financial support. We alone are responsible for any errors. c© 2006 by MurrayZ. Frank and Vidhan K. Goyal. All rights reserved.

I. Introduction

When one CEO (or CFO) is replaced by another, the costs and benefits of corporate

debt such as tax savings or deadweight costs of bankruptcy do not change. Thus one

might imagine that any effect on corporate leverage ought to be very minor. However,

if the agency cost of debt and equity have significant manager-specific components then

nontrivial changes might be observed. Similarly, if some managers are more subject to

behavioral biases than are other managers, then again top management changes might

have important effects on corporate leverage.

How important are such managerial considerations relative to the more traditional

factors? That is the question addressed in this paper. Both CEOs and CFOs are studied.

The analysis is first carried out within a target adjustment framework, and then in the

context of a model in which the firm has target debt and equity levels.

We find that:

• Manager fixed effects are empirically relevant and their impact is not minor. Differ-

ent managers do choose significantly different leverage policies. Firm fixed effects

are frequently included in empirical models of leverage. However, in the presence of

either a CEO-effect or a CFO-effect, the firm-effect becomes minor and it is com-

monly not statistically significant. The CFO-effect is generally stronger than the

CEO-effect.

• While manager fixed effects are important, when we try to tie them back to eas-

ily observable characteristics these characteristics do not account for much of the

variation. Several managerial characteristics are correlated with leverage. Debt is

higher for CEOs with more experience at other companies, and if the CEO had a

finance employment background. Firms with a CFO that has been with the firm

longer tends to have higher leverage. Debt is lower if the CEO founded the firm,

1

if the CEO is female, and if CEO has an MBA or a technical education. Debt is

also lower at firms where the CFO has had a career path in finance, where the CFO

has an MBA, and those firms at which the CFO has more work experience at other

firms. Older CEOs adjust leverage more slowly while those with an MBA adjust

faster.

• Firms issue less debt around CEO and CFO turnover events and leverage adjustment

speeds decline when there is a turnover among CEOs or CFOs. Leverage adjustment

speeds are faster when CEOs and CFOs have a compensation scheme that is highly

sensitive to performance. The effect of ownership on adjustment speeds are weak.

• Within the target adjustment framework we find the conventional result that profits

are negatively associated with the change in the corporate leverage ratio. However,

this conventional result is a mix of two distinct effects. We find that a firm with

more profit tends to reduce the net equity issues (fewer issues and more repurchases).

More interestingly on average a firm with more profits increases the net debt issues.

The impact of profits on equity seems to be stronger than the impact of profits on

debt.

The evidence suggests that in order to better understand corporate leverage policies,

it would be helpful to pay more attention to the managers who are actually in charge

of those policies. Our evidence suggests that an improved understanding of the differing

roles of the Board, the CEO and the CFO might be very useful as well. Finally, it seems

important to distinguish the effects of factors on debt and on equity in separate equations

rather than using leverage ratios.

Our attention to managerial attributes was motivated by the burgeoning literature

on behavioral finance. The fact that person-specific factors matter in financial deci-

sions has been a major theme in that literature. Various scholars (Chevalier and Ellison,

2

1999; Bertrand and Schoar, 2003; Malmendier and Tate, 2005; Beber and Fabbri, 2006)

have documented the importance of individual manger attributes. For instance when

top manager-specific fixed effects are added to a model of firm productivity (Lieberman

et al., 1990; Bertrand and Schoar, 2003) these fixed effect are statistically significant.

When managerial characteristics such as education (Chevalier and Ellison, 1999; Beber

and Fabbri, 2006) or career path (Güner et al., 2005) are included, these too seem to play

a role. Accordingly the idea that CEO attributes might be important for the speed of

leverage adjustment would be natural from the perspective of behavioral finance.

As already noted, we find that founding CEOs, female CEOs, CEOs who have an

MBA, and CEOs who have a technical education, use less debt. CEOs who have worked

at more firms, have had a career path in finance, and are older use more debt. CFOs who

have worked at more companies, have had a career path in finance, and have an MBA

use less debt. CFOs who have been at the firm longer use more debt. These managerial

characteristics are statistically significant at conventional levels. However, we find that

these characteristics do not provide a large improvement in the fraction of the variation

in the data that is explained.

Our attention to managerial contracts is due to the large literature on agency theory

and executive compensation. The idea that managerial contracts might play a role in

leverage decisions is not novel. It has shown up in theories such as Brander and Poitevin

(1992) or John and John (1993). It has also shown up in various empirical studies. In a

study of large firms between 1984 and 1991 Berger et al. (1997) report lower leverage at

firms with: a CEO that has had a long tenure, low CEO pay-to-performance sensitivity,

larger board, and fewer outside directors. Coles et al. (2006) also examine the effect of

design of compensation contracts on leverage ratio. They report that firms with higher

pay-performance sensitivity have lower leverage. Faulkender et al. (2005) report that

firms with higher executive compensation have lower leverage.

3

Our evidence suggests that managerial contracts play a relatively minor role in the

corporate leverage decisions. This might happen if the contracts are truly unimportant.

But it might also be the case that in some crucial manner our tests are unable to capture

the role that the contracts are playing. What is more we show that from a theoretical

perspective the sign of such effects is ambiguous without detained information about

certain parameters.

The conventional wisdom seems to be that entrenched managers prefer less debt. So

managerial attributes such as age, being the founder, and the level of stock ownership

by the manager should be associated with less debt. In contrast Litov (2005) reports

that more entrenched managers use more debt to fund financing deficits and they also

maintain higher leverage ratios overall. Thus the literature is not settled.

The idea that entrenchment matters seems appealing. However, the simple entrench-

ment story is inadequate to explain our evidence. In particular we find that CEOs who

are founders use less debt, but older CEOs have more debt. High CEO stock ownership

and high CEO pay-for-performance are associated with less debt. But this result does not

carry over to CFO compensation. If anything it seems that higher CFO stock ownership

is associated with greater leverage.1

The framework that we present also has implications for more traditional studies of

capital structure. It is conventional to study the impact of various factors on the firm’s

debt-to-value ratio (eg. Fama and French (2002), Graham and Tucker (2005), Frank and

Goyal (2005)). The ratio is not a natural outcome of a simple model of optimal debt and

equity choice. Accordingly the coefficients that are estimated in a debt-to-value regression

do not have easy theoretical interpretation.

1It is not hard to imagine stories in which differences emerge been the compensation of CEOs andCFOs. But at this stage any such stories would be rather speculative. Developing a solid account of thisdifference is well away from the purpose of the current paper, and so we do not explore the issue anyfurther.

4

We find that this matters for the impact of profits. In debt-to-value regressions profits

is normally found to have a negative sign. This is commonly (Fama and French (2002))

described as a major flaw of the trade-off theory of leverage. However there is an important

compositional issue that is masked in a debt-to-value regression. We find that when a firm

has more profits it has a negative effect on net equity issues. The firm tends repurchase

outstanding equity and not issue new equity. When a firm has more profits there is a

positive effect on net debt issues. These predictions are broadly consistent with the trade-

off theory. What is not adequately explained by the usual trade-off theory is why, in many

specifications, the impact of profits on net equity issues is stronger than the impact on

net debt issues.

In a very interesting paper Lemmon et al. (2006) make the point that firm fixed effects

are extremely important. The fixed effects account for more of the variation in leverage

than almost anything else. Accordingly they argue that leverage is extremely persistent.

We find that firm fixed effects do not survive the inclusion of managerial fixed effects.

II. An Organizing Framework

Before presenting our suggested framework for analysis we consider the traditional

target adjustment model. This model has frequently been estimated.2 Much discussion in

the literature revolves around the coefficients in this model. Hence it forms an important

benchmark.

2The fact that leverage adjusts towards a target has been documented in many papers going backat least to (Jalilvand and Harris, 1984). Some papers report very slow adjustment (Shyam-Sunderand Myers, 1999; Fama and French, 2002; Welch, 2004; Huang and Ritter, 2005), while others reportmuch faster adjustment (Alti, 2006; Flannery and Rangan, 2006). Intermediate positions have also beenreported (Frank and Goyal, 2004; Leary and Roberts, 2005)

5

A. The Target Adjustment Approach

We start with a target adjustment model along the lines of the useful study by Flan-

nery and Rangan (2006). The target adjustment model of corporate leverage is just an

autoregressive model – an AR(1). Let TDMi,t denote the market leverage ratio for firm

i at time t. Then we have,

TDMi,t = αi + βiTDMi,t−1 + εi,t, (1)

where αi and βi are parameters to be estimated and εi,t ∼ N(0, σ2i ) iid. Let ∆xt = xt−xt−1

for any variable xt. We rewrite equation 1 as

∆TDMi,t = αi + (βi − 1)TDMi,t−1 + εi,t. (2)

Let TFi,t denote the traditional leverage factors from Frank and Goyal (2005) and Graham

and Tucker (2005), let CGi,t denote the executive compensation factors at firm i on date

t derived using the method of Core and Guay (2002), and let EA denote the vector

executive attributes. All of these factors might affect either α or β. Hence we can write

the possibilities as,

αi = γa0 + γa1TFi,t + γa2CGi,t + γa3EAi,t (3)

βi = γb0 + γb1TFi,t + γb2CGi,t + γb3EAi,t.

Substitute 3 into 2 and then rearrange as follows,

∆TDMi,t = γa0 + γa1TFi,t + γa2CGi,t + γa3EAi,t + (γb0 − 1)TDMi,t−1 + (4)

γb1TFi,tTDMi,t−1 + γb2CGi,tTDMi,t−1 + γb3EAi,tTDMi,t−1 + εi,t

6

Within this setting we can ask whether all of the factors can be safely ignored.

Hypothesis 1 The idea that executive contracts do not matter is: γa2 = γb2 = 0. The

idea that managerial attributes do not matter is: γa3 = γb3 = 0.

How important are managers altogether? This question can be answered by comparing

the fit of 3 to the same equations in which we impose γa2 = γb2 = γa3 = γb3 = 0.

B. A Stylized Model

A drawback to the target adjustment model is that it is a statistical model rather

than a financial model. Accordingly the estimated coefficients do not always have a clear

financial interpretation. This motivated us to consider an alternative approach that,

as much as possible, keeps the simplicity of the target adjustment model, but provides

an easier interpretation of the coefficients. The example provides simple conditions that

motivate running a pair of linear regressions, one explaining debt and the other explaining

equity. A key idea is that the estimated coefficients in these two equations ought to have

signs that are related in a predictable manner.

We consider a firm run by a manager who chooses both debt and equity. The manager

is motivated by an incentive contract that is (implicitly) set by the Board of Directors. The

compensation is composed of a constant wage as well as a pay-for-performance component.

The manager faces agency costs of debt and of equity. The firm has an optimal target

debt and equity. Being away from the targets is costly to the firm.

The Firm. The firm’s revenue is given by p(I − 12I2) where p is the output price

(assumed to be greater than 1) and I is the level of investment. Investment is I =

D + E + R−B where D is debt issued, E is equity issued , R is retained earnings, and B

is the payment due on the bonds outstanding. Outside investors are risk-neutral and the

7

opportunity cost of funds is β. The target debt level for the firm (D∗) is exogenous, as

is the target equity level (E∗). It is assumed that the firm would like to be close to the

target debt and equity levels, and that it bears a cost that is a function of the distance

from the target. For analytic simplicity the cost function is assumed to be quadratic.

There is a random noise term ε with a variance of σ2.

If there were no agency problem then the firm’s problem would simply be

maxD,E

p(I − 12I2)− β(D + E)− 1

2(D −D∗)2 − 1

2(E − E∗)2 + ε (5)

s.t. I = D + E + R−B

Here it is assumed that debt and equity are symmetric. There is little theoretical basis

for this. More generally we could replace 12(D−D∗)2 with kD

2(D−D∗)2, and 1

2(E −E∗)2

with kE2

(E − E∗)2 where kD and kE become parameters that need to be estimated. The

model that we are actually analyzing would amount to an assumption that kD = kE = 1.

For simplicity we stick with the symmetric formulation of the example.

The Manager. Next suppose that the firm is actually being run by a manager who

earns a wage. So W = w+απ, where w is a fixed base salary, α is the pay-for-performance

component, and π are the corporate profits.3 The manager has a coefficient of risk aversion

denoted r. The manager has a per unit agency cost of debt denoted as δ and a per unit

agency cost of equity denoted by γ.

When studying differences among real firms and real managers, presumably the agency

costs will differ from manager to manager. However these costs are not directly observable.

3This linear function functional form is often attributed to Holmstrom and Milgrom (1987). Aggrawaland Samwick have used the Homstrom and Milgrom approach in a number of interesting papers such asAggarwal and Samwick (2002)

8

The manager chooses debt (D) and equity (E) to maximize the certainty equivalent

of income (CE). This is given by

CE = w + α[ p(I − 12I2)− β(D + E)− 1

2(D −D∗)2 − 1

2(E − E∗)2]− δD − γE − r

2α2σ2

The first order conditions for the manager are

∂CE

∂D= 0 ,

∂CE

∂E= 0.

For now it is simply assumed that the second order conditions are satisfied and that

the parameters are in the range with an interior solution. To simplify notation define

A0 = (1− ( p1+p)2), A1 = (

p1+p

), and A2 = (1

1+p).

Solving the first order conditions we get

D = A0A1(1− A1)(1 + B −R) + A0A2(D∗ − A1E∗ + (γA1 − δ)1

α+ (A1 − 1)β) (6)

E = A0A1(1− A1)(1 + B −R) + A0A2(E∗ − A1D∗ + (δA1 − γ)1

α+ (A1 − 1)β).

These conditions directly suggest an approach to estimating. The implied empirical

specification is given by

D = d0 + d1B + d2R + d3β + d4D∗ + d5E

∗ + d61

α+ εd (7)

E = e0 + e1B + e2R + e3β + e4D∗ + e5E

∗ + e61

α+ εe.

If the model is correct what do we expect to find when we estimate 7? The predictions

can be described in the following manner:

Hypothesis 2 Greater existing debt is associated with increased debt (d1 > 0) and equity

issues (e1 > 0). Greater retained earnings are associated with reduced debt issues (d2 < 0)

9

and reduced equity issues (e2 < 0). Greater opportunity cost of funds is associated with

reduced debt (d3 < 0) and equity issues (e3 < 0). Greater debt target is associated with

greater debt issues (d4 > 0) and reduced equity issues (e4 < 0). Greater equity target is

associated with reduced debt (d5 < 0) and greater equity issues (e5 > 0). The signs of d6

and e6 depend on (γA1− δ) and (δA1− γ). Greater agency cost of debt is associated with

reduced debt (∂D∂δ

< 0) and increased equity (∂E∂δ

> 0). Greater agency cost of equity is

associated with increased debt (∂D∂γ

> 0) but reduced equity issues (∂E∂γ

< 0).

We do not directly observe the targets for debt and for equity. Instead we use con-

ventional proxy variables. The use of proxies matters. Suppose that we have a proxy

factor X that is related to target debt and target equity in a particularly simple manner,

D∗ = a0 + a1X, and E∗ = b0 + b1X. Substitute these into 6 and then look at the co-

effiecients on X. In the expression for D the coefficient on X will be A0A2(a1−A1b1). In

the expression for E the coefficient will be A0A2(b1 − A1a1).

When we have the actual debt and equity targets their coefficients will have the op-

posite signs. But this does not carryover cleanly to the proxies. In general we might still

expect to observe the opposite signs. For example suppose that a1 = 0.5, b1 = 1, and

p = 5, then a1 − A1b1 = 0.5− 51+51 = −0.333, and b1 − A1a1 = 1−5

1+50.5 = 0.583. But

can we be sure that the signs will be opposite? Unfortunately not. We know that A0,

A1 and A2 are all strictly between zero and one. Suppose that a1 and b1 are very close

together. In that case it is possible to have the same sign on X in both equations. For

example, again let p = 5, but this time set a1 = b1 = 1, then a1 − A1b1 = 1 − 51+51 =16

and b1 − A1a1 = 16 .

The coefficients d6 and e6 can have either sign when considered individually. But in

a manner similar to the coefficients on any target proxies, these coeffeicints cannot be

assumed to take the opposite signs. If δ = γ then d6 and e6 will take on the same sign.

10

The framework tells us to analyze 7. However, in the literature it is conventional to

combine debt and equity into a debt-to-value leverage ratio DD+E

. Suppose that the model

is correct. Further suppose that we decide to follow the literature in studying the effect of

various factors on the debt-to-value ratio. What will we find? The DD+E

ratio is a complex

expression which can be viewed as composed from 6.4 In the debt-to-value ratio all of the

exogenous parameters appear in both the numerator and in the denominator along with

the parameters.

As a result of the complexity, some predictions wind up appearing more complex than

the underlying theory would suggest. For instance

∂( DD+E

)

∂B=

(−A1 − 1) (δ − γ − αD∗ + αE∗) αA1A2(γA2 − 2αA1 + δA2 − 2BαA1 + 2RαA1 + 2αβA2 − αA2D∗ − αA2E∗)2 (A1 − 1)

.

This can be reduced to the sign of (δ − γ − αD∗ + αE∗). If this term is positive (negative)

then the expression is positive (negative) overall.

Presumably the reason for running a regression using B to explain DD+E

is that we

want to understand how B affects D. The denominator is intended to be an innocuous

normalization. But it will not be innocuous if the component parts are themselves objects

of choice. For example, in the model, the effect of B on D is given by A0A1(1−A1) which

is positive. But it does not match the key part of that determines the sign in the above

expression. This is because of a mismatch between the theory and the testing specification.

4For simplicity we are treating D + E as the full value of assets. More generally one would need torecognize that debt is not all short lived, and that some of the equity is prior equity, not just the newlyissued equity. Taking these things into account seem likely to lead to yet more complex expressions.Exactly how they would factor in will depend on various detailed assumptions.

11

The problem is not unique to our stylized model. It is likely to reemerge when the

theory depends on a first order condition for debt as well as a first order condition for

equity.5

C. Adjusting Debt and Equity in Good Times and in Bad Times

An interesting aspect of the framework is that all of the comparative statics depend

on crucially on p. By definition high p is good for the firm, while low p is bad for the firm.

Accordingly it is also possible to ask, what happens to the predictions when p changes?

To ask this question empirically we need a method of distinguishing good times from

bad times. What does the model predict will change when times are better for the firm?

When the partial derivatives have the same sign as the original comparative static, then

we say that in good times the firm reacts more strongly. When the partial derivative has

the opposite sign we say that in good times the firm reaction more weakly (closer to zero).

Hypothesis 3 In good times the firm’s debt issue decisions and the firm’s equity issuing

decisions will react less strongly to: the opportunity cost of funds (β), the debt target (D∗),

the equity target (E∗), the agency cost of debt (δ), and the agency cost of equity (γ).

These predictions are derived by differentiating the comparative statics with respect to

p. The intuition that underlies these predictions is that when times are good the revenue

effects are relatively more important. When times are not so good the cost impacts loom

larger.

These results can be tested by examining whether firms adjust more rapidly in good

times (high p) than in bad (low p). To directly test this hypothesis we can sort the

5Presumably it would be possible to write down a model in which it is directly assumed that DD+E isthe object of choice. We have not explored this since it strikes us as an unnatural way to represent thefirm’s problem.

12

industries according the median firm’s market-to-book ratio in that industry for that year.

Then take the top 1/3 and and the bottom 1/3. Then we can compar the magnitudes

of the observed adjustments. The hypothesis predicts which cases we should find greater

equity adjustment and which cases should have lesser equity adjustment.

[Note: This test has not yet been carried out.]

III. Data Description

A. Financial statement and stock returns data

We begin with non-financial firm-year observations in CRSP-Compustat files between

1993-2004. The data are annual and are converted into constant 2000 dollars using the

GDP deflator. We exclude financial firms, firms involved in major mergers (Compustat

footnote code AB), and firms with missing book value of assets. Appendix A provides

definitions of key variables with relevant Compustat data item numbers. The variables

are winsorized at the 0.50% level in both tails to replace outliers and the most extremely

misrecorded data.

Appendix A also provides descriptive statistics for the leverage ratio and the six lever-

age factors for non-financial Compustat firms during the 1993-2004 period.6 A comparison

between this sample and the longer 1950-2003 sample reported in Frank and Goyal (2005)

shows that firms during the 1993-2004 period are relatively less levered, have lower in-

dustry median leverage, higher market-to-book ratio, lower collateral, lower profitability

and a smaller number pay dividends.

6Frank and Goyal (2005) have found these leverage factors are reliable determinants of leverage ratios(ie. debt-to-value).

13

B. Executive compensation data

The next step is to merge financial statement data with executive compensation data

from the Execucomp database. The compensation data are publicly available for the

five highest-paid officers in each company and are reported by Execucomp for firms in

S&P500, S&P MidCap400, and S&P SmallCap 600 for the period from 1993 to 2004. Two

samples are constructed. The first sample is of chief executive officers (CEOs), identified

as individuals that held the title of CEO for the longest time during the year. Table

I shows that Execucomp-Compustat merged database has 16,719 firm-year observations

consisting of 2,246 firms and 3,992 CEOs during the 1993-2004 period.

The second sample is of top finance officers in the merged Execucomp-Compustat

database. Among the five highest-paid executives reported in a firm’s proxy statements

(and in the Execucomp database), the officers with responsibility for the finance function

most often have the designation of ‘CFOs’ (or ‘Chief Finance Officers’ or ‘Chief Financial

Officers’). When a CFO is not listed in this list, we include individuals that are designated

as ‘VP-Finance’. When both CFO and VP-Finance are not in the list of top executives, we

include individuals with the ‘Treasurer’ title as finance officer. As a last step, we include

‘Controller’ if all other previously indicated finance titles are not in the top executive list.7

Table I shows that 12,783 firm-year observations include an individual with respon-

sibility for the finance function in the list of five highest paid executives. These consist

of 2,138 firms and 4,000 finance officers during the 1993-2004 period. Panel C, which

reports the breakdown of the finance officer by titles, shows that finance executives most

commonly have the CFO title (about 90%) followed by VP-Finance (about 7%). The

7Only ‘Treasurers’ and ‘Controllers’ without the additional CFO designation are included in this laststep.

14

remaining titles consist of Treasurers and Controllers, which are only included when CFO

or VP-Finance was missing in the list.8

Table II, Panel A reports descriptive statistics for the leverage ratio and leverage

factors for the matched Execucomp-Compustat CEO sample. As Execucomp firms belong

to S&P1500, it is not surprising that they are large compared to a typical Compustat

firm during the same period. More of the Execucomp firms pay dividends, they are more

profitable, have higher collateral, and have a lower market-to-book ratio compared to a

broader Compustat sample. However, in terms of leverage and industry median leverage

they are similar to typical Compustat firms.

Table II, Panel B provides summary statistics on total direct compensation, wealth

gains on option and stock portfolios, and pay-performance sensitivities for the CEOs in

the sample. Panel C provides similar information for the Finance Officers.

Managerial incentives are measured as the change in CEO/CFO’s firm-specific wealth

for every one thousand dollar increase in shareholder wealth. The higher the pay-

performance sensitivity, the greater are the managerial incentives to increase firm value.

Core and Guay (2002) and Jensen and Murphy (1990) argue that incentives from stock

and options grants and holdings are much greater than those provided by direct compen-

sation in the form of salary and bonus.

The pay-performance sensitivity is therefore measured using both stockholdings and

option portfolios. The stock pay-performance sensitivity is the change in the value of

managerial stockholdings for every $1000 increase in shareholder value.9 The option pay-

performance sensitivity is the change in the value of option portfolio for every $1000

increase in shareholder value. Appendix B describes the procedure adopted to value new

8The finance officer sample is smaller than the CEO sample because not every firm has a finance officeramong the five highest paid officers disclosed in proxy statements (and in Execucomp).

9Several prior studies use the fraction of shares owned by executives as a measure of incentives (Dem-setz and Lehn, 1985; Jensen and Murphy, 1990; Yermack, 1995).

15

option grants and option portfolios of unexercised exercisable and unexercised unexercis-

able options. Information on data sources for the six inputs to the Black-Scholes model

used in valuing the options is also provided in the appendix.10

As Panel B shows, the median CEO received annual flow compensation of over $2

million consisting of $525 thousand in salary, $300 thousand in bonus, and about $658

thousand in option grants valued using Black-Scholes method. The rest is other com-

pensation and long-term payouts. The median wealth gains from both stock and option

holdings are zero. But the averages are quite large suggesting a skewed distribution. The

median pay-performance sensitivity for CEOs is $9.6 for every $1000 increase in share-

holder wealth. CEOs own a median of 0.35% shares in the firm and only about 1.3% are

females.

Panel C provides compensation and pay-performance sensitivity for finance officers

(CFOs). A comparison of Panels B and C shows that CFOs receive less than half as much

as CEOs in annual flow compensation. The median finance officer received $863 thousand

in annual flow compensation consisting of $255 thousand in salary, $115 thousand in

bonus, and $274 thousand in new option grants. The total pay-performance sensitivity

of CFOs is a median of $1.5 for every $1000 increase in shareholder wealth. This is

much lower than $9.6 for the CEOs. These differences are consistent with Aggarwal and

Samwick (2002) who argue that the level of responsibility determines the pay-performance

sensitivity. CFOs own a median of 0.04% shares in the firm and about 5% are females.

10The option portfolio is valued using the methodology described in Core and Guay (1998) who showthat the method yields estimates of equity incentives that are unbiased and 99% correlated with valuesthat would be obtained if the parameters of CEO’s option portfolio were known. The option portfoliosensitivities and stockholding sensitivities are combined to estimate total pay to performance sensitivities.

16

C. Managerial Characteristics

In addition, we build a database with personal information on CEOs’ and CFO’s of

S&P 500 firms from various editions of “Who’s Who in Finance and Industry” executives

bios on company web-sites, biographical information contained in news releases, and pro-

files posted on Internet. The key data items in this database are the year in which the

executive was born, tenure with the firm and in the current position, number of companies

previously worked for, employment histories and educational backgrounds.

As before, we construct two separate databases – one for the CEOs and the other

for the finance officers (which we label as CFOs because 95% of the finance officers are

designated CFOs).

The CEO characteristics sample has 3,285 firm-years with information on 733 exec-

utives at 400 firms. This is an unbalanced panel spanning 1993-2004, The CFO charac-

teristics sample has 1,994 firm-years with information on 593 CFOs at 358 firms. Again,

this is an unbalanced panel spanning 12 years from 1993-2004.

The executive age is estimated from information about the year in which the person

was born. Similarly, tenure with the firm and tenure as CEO/CFO is estimated from the

year in which the executive joined the firm and the year in which the executive assumed

his/her current position. Founder dummy is assigned a value of one if the executive

was a founder or co-founder of the firm; it is otherwise zero. In addition, executives are

classified based on their employment histories and educational backgrounds. They have a

finance education if they have an undergraduate or graduate degree in business, finance,

accounting or economics. They have a technical education if they hold undergraduate or

graduate degree in natural sciences or engineering. We also code executives that have an

MBA degree (all executives with MBA are also classified as having a finance education).

Executives are also classified based on their employment background. They have a finance

17

career if they previously worked in the finance function with their current employer or in

their previous employment or worked for a finance, auditing or accounting firm. They have

a technical career if they worked as an engineer or as a technically oriented professional.

Table III presents summary statistics for the CEO characteristics. Since this sample

is restricted to firms in the S&P 500, the CEOs with personal characteristics data have

higher pay, much larger wealth gains from stock and option holdings but significantly

lower pay-performance sensitivity. They also own less stock compared to Execucomp

CEO described in Table II. The median CEO in this sample is about 56 years old, has

worked with the firm for about 19 years, five of which are in the CEO position. The

median CEO is unlikely to be a founder and has worked at one other company prior

to joining this firm. About 11 percent of CEOs founded the firm they are currently

managing. About 22 percent were born in the depression era of 1930s. About 22 percent

have a financial career and 16 percent have a technical career suggesting that majority

of the CEOs have a general management background. About 43 percent of CEOs have a

business education. If they have postgraduate degree, almost 55 percent of the time it is

an MBA. More CEOs (about 48 percent) have a technical education.

Table IV presents summary statistics for the CFO characteristics. Compared to CEOs,

the CFOs are younger; the median CFO is about 51 years old. They have a shorter tenure

with the firm and also shorter tenure as a CFO. Almost none of them founded the firm.

Compared to CEOs, they have worked at more companies prior to becoming CFO. About

9 percent of them were born in the depression years. A large majority of them (about 88

percent) have had a finance career. Almost 71 percent have finance education. Among

CFOs with postgraduate degree, 85 percent have an MBA. About 35 percent have a

technical education.

18

IV. Correlations

Table V, Panel A presents correlations between leverage factors, CEO ownership,

gender and pay-performance sensitivity. Frank and Goyal (2005) provide an extensive

discussion of correlations between leverage and leverage factors. The correlations we

obtain are similar and therefore are not discussed here. Other correlations show that

pay-performance sensitivity is positively correlated with the market-to-book ratio and

profitability but negatively correlated with firm size, dividend dummy, collateral and

leverage. CEO ownership is negatively correlated with leverage, dividend paying dummy,

and firm size but positively correlated with the market-to-book ratio and profitability.

Ownership is also highly correlated with pay-performance sensitivity which is unsurprising

since pay-performance sensitivity depends to a large extent on executive’s stockholding.

Table V, Panel B reports correlations between leverage, managerial characteristics,

pay-performance sensitivity and ownership for our sample of managerial characteristics of

non-financial S&P 500 firms. These correlations show that leverage is positively correlated

with CEO’s tenure with the firm, age and work experience. Leverage is higher if CEOs

have a finance career. Leverage is negatively correlated with the a dummy that take

a value of one if CEO is the founder. While the correlation between finance education

and leverage is insignificant there is a large negative relation between technical education

and leverage. CEOs with MBAs also have less leverage. Consistent with Panel A, pay-

performance sensitivity and ownership are both negatively related to leverage.

CEOs with finance education have a shorter tenure with their firm. Executives with

technical education or technical career have a longer working experience at other firms.

MBAs are more likely to have a finance career and a finance education. They are less likely

to have a technical education of a technical career. Similarly executives with a technical

career are less likely to have an MBA or a finance education and are more likely to have

technical education. Older executives have a longer tenure. Older executives are less

19

likely to have an MBA or finance education, Founder CEOs own a lot more stock in the

firm and consequently they have higher pay-performance sensitivity. For executives with

finance career and/or finance education, pay performance sensitivity is lower. Executives

with finance career, MBA or finance education own less stock in their own firm. Female

CEOs have a shorter tenure with the firm; they have worked at more companies previously

and they are younger. Founder CEOs more often have technical education and technical

career but less often finance education (or an MBA degree) or a finance career.

Table V, Panel C reports similar correlations for CFOs of non-financial S&P500 firms.

Leverage ratios are uncorrelated with most CFO characteristics except CFO tenure to

which it exhibits a positive correlation and to CFO finance career dummy with which

it has a negative correlation. CFOs with MBA degree have a shorter tenure and they

are more likely to have had a finance career. CFOs with financial education or finance

career are more likely to have worked at more companies previously and have shorter

tenure with their current firm. CFO pay-to-performance sensitivity is unrelated to the

CFO characteristics.

V. Partial Adjustment Estimates with Manager

Effects

A. Firm- and manager-fixed effects

Flannery and Rangan (2006) show that including firm fixed effects significantly im-

proves leverage adjustment speeds. The idea is that in a pooled regression it is inevitable

that the target is measured with error. The firm fixed effect will improve on the estimated

firm target and thus improve the estimate of the speed of adjustment. Lemmon et al.

(2006) further emphasize the importance of firm fixed effects and argue that they account

20

for much of the cross-sectional variation in leverage. How important are manager-effects?

Which managers are important in determining leverage adjustment speeds? Table VI

addresses some of these questions. Columns (1) and (2) report estimates from pooled

OLS and provide a benchmark for adjustment speeds in the absence of either firm- or

manager-fixe effects. The adjustment speeds are similar to those reported in Fama and

French (2002) and Flannery and Rangan (2006) and are about 9% in a model without

leverage factors and about 15% in the model with factors.

Consistent with both Flannery and Rangan (2006) and Lemmon et al. (2006) adding

firm fixed effects substantially increases the adjustment speeds to 43% without factors

and 46% with factors.11

However, when we go a step further to include either a CEO fixed effect, or a CFO

fixed effect, model fit improves quite substantially. Adding CEO fixed effects increases

adjustment speeds to 54% without factors and 57% with factors. This is over 25% (54%

versus 43%) increase in relative terms. In regressions not listed in the Table, we tried

adding both firm effects and manager effects. However, the firm effects have little ex-

planatory power in the presence of the manager fixed effects. It seems that the firm fixed

effects are serving as a noisy proxy for the manager effects. Columns (7) to (9) examine

partial adjustment regressions with firm and manager fixed effects for the CFO sample.

It shows that CFO effects are slightly more important than CEO effects.

The coefficient on leverage factors are consistent with those reported in Frank and

Goyal (2005). Higher industry median leverage increases debt results in higher debt

issuances. Higher market-to-book ratio lowers debt issuances. Collateral positively affects

11In some untabulated regressions we follow Lemmon et al. (2006) by including the initial leverage asa factor in a leverage regression and in a leverage change regression. The lagged leverage and the initialleverage have a correlation of about 0.23. We find that initial leverage is significant in leverage levelregression, but it is not significant in leverage change regressions. This supports the claim by Lemmonet al. (2006) that initial leverage is capturing some of the fixed effects.

21

debt issuances. Profits have a negative effect. Dividends positively affect debt issuances

and larger firms issue more debt.

Also note that including CEO-fixed effects in place of firm-fixed effects does not lead

to any deterioration of estimated coefficients on leverage factors. In fact, the estimated

coefficients increase in magnitudes and significance levels. CFO fixed effects produces

similar results except the sign on profits becomes insignificant in the presence of CFO-

effects.

B. Leverage adjustment speeds around managerial turnover

The importance of the manager effects must be driven by changes in managers since

for any given manager it is a constant. Accordingly, in Table VII, we add a turnover

dummy variable along with an interaction term between the turnover dummy and the

lagged leverage. The turnover dummy takes a value of one in the year of managerial

turnover and the year after. The change in leverage declines in the year of the turnover.

The interaction between turnover and lagged leverage suggests that the effect of a

turnover is to bias things towards zero. In other words managerial change is associated

with a slow down in leverage adjustment. The turnover events slow down adjustment

speeds by about 3 to 4%. CFO turnover events results in a larger slowdown in adjustment

speeds compared to CEO turnover.

C. Incentives, Ownership and Adjustment Speeds

Results in previous sections suggest that manager fixed effects are important. Are their

observable manager characteristics which are associated with slower or faster adjustment

speeds. In this section, we analyze the effects of incentives and ownership on leverage

22

adjustment speeds. The next section examines other manager characteristics such as age,

educational background and employment history on adjustment speeds.

In Columns (1) and (2) of Table VIII, we interact pay-performance sensitivity with

lagged leverage. The coefficient on the interaction term is negative and statistically signif-

icant at the one percent level of significance. This suggests that higher pay-performance

sensitivity increases adjustment speeds significantly.

In Columns (3) to (5), we interact the ownership structure with lagged leverage to

examine the effect of ownership on adjustment speeds. Ownership is highly non-linear with

a majority of executives owning small amounts of equity but a few who own substantial

amounts. There is a large literature that argues that ownership has non-linear effects

on firm performance with ownership providing incentives at low levels and entrenching

managers at higher levels. We therefore construct three different interaction terms. The

first interaction term multiplies ownership with lagged leverage for managers who own

less that 1% of the equity in the firm. This interaction is zero for other managers. The

second interaction term captures managerial ownership in the range of 1% to 5% while

the last interaction term captures managerial ownership greater than 5%.

Overall, ownership does not have a big effect on adjustment speeds. For CEOs, there

is some evidence that at low ownership levels, an increase in ownership increases adjust-

ment speeds. However, the reverse is true at high ownership levels where an increase in

ownership lowers adjustment speeds.

Table IX reproduces similar estimates for CFOs. Again, PPS has significantly increase

adjustment speeds. Ownership has no impact on adjustment speeds for CFOs. CFOs do

not own much stock in their firms and their stock ownership does not materially affect

adjustment speeds.

23

D. Manager Characteristics and Leverage Adjustment

Certain managerial characteristics are associated with conservatism. It has been ar-

gued in earlier literature that female managers, older managers, and managers with finance

background might behave differently. Could one associate certain managerial character-

istics with faster or slower leverage adjustment speeds? This section presents results on

interactions between observable executive characteristics and adjustment speeds. We an-

alyze the effects of age, educational background and employment history on adjustment

speeds.

Table X presents partial adjustment regressions with interactions between CEO char-

acteristics and lagged leverage. The results show that CEOs with an MBA have slightly

higher speeds of adjustment. An MBA degree adds about 9% to adjustment speed. Older

CEOs adjust less. Employment background has no apparent effects.

VI. Estimates for Debt and for Equity

Table XI reports the results from estimating Equation 7. Three sets of regression

estimates are presented. All of the regression estimates control for year-fixed effects.

According to the model the target debt and equity levels are important, but we do not

observe them. Thus a simple pooled regression is likely to be incomplete. In the model

the targets are assumed to be firm-specific. Accordingly the model suggests the use of

firm-specific fixed effects. However, an important theme for our paper is the importance

of managers. It is therefore natural to also examine is whether a manager fixed effect

might play an important role.

Accordingly we present results for all three approaches. Columns (1) and (2) present

results from pooled OLS regressions with error terms corrected for heteroscedasticity and

24

firm-level clustering. Columns (3) and (4) present results from estimated after controlling

for firm-fixed effects. The last two columns (5) and (6) control for manager fixed effects.

With some exceptions, the results across these estimations are reasonably consistent.

According to the model past debt should be positively associated with current net

debt and net equity issues. That is found with one exception. The exception is when

manager fixed effects are included we get a negative sign on the prediction of past debt

for current debt issues.

Retained earnings are supposed to be associated with lower net debt and net equity

issues. That is again observed but with one exception. In this case the exception is in

the specification with firm fixed effects. Now the effect of past retained earnings on net

debt issues is positive.

As proxies for the debt and equity targets we use the factors from Frank and Goyal

(2005). The key idea in this case is that if these are properly proxying for the targets,

then each proxy should have a statistically significant coefficient.12

Expected inflation has a positive and significant effect on net debt, but an insignificant

impact on net equity issues. Median industry leverage has a negative impact on net debt,

but a positive impact on net equity issues. In the firm fixed effect and the manager fixed

effect versions the market to book ratio has a positive effect on net equity issues. It has

an insignificant impact on net debt issues. Collateral has surprisingly weak effects in all

specifications.

Profits have a consistently significant and negative impact on net equity issues. This

is found in all specifications. Profits have a positive impact on net debt issues in all

specifications. The effect is significant in the pooled regression and in the firm fixed

effects regression. It is not quite significant in the manager fixed effects specification.

12It is worth observing that the usual factors are derived from a literature that focuses on levels inratio form. Accordingly they may be slightly misspecified when use to explain changes. In subsequentdrafts we will give this issue further attention.

25

The effect of dividends is generally statistically insignificant in these specifications.

The effect of firm size is fairly symmetric. In all specifications the log total assets has

a negative effect on both net debt issues and on net equity issues. The negative sign on

firm size is not hard to describe. It says that larger firms have lower net debt issues and

lower net equity issues. In untabulated regressions we find that this effect seems to stem

from the repurchasing activity rather than from the issuing activity. It seems that larger

firms are more likely to engage in repurchases that are otherwise similar smaller firms.

Recall that the coefficient on the inverse pay for performance can take on either sign

depending on parameters. Accordingly it is not too surprising that we do not find con-

sistently significant results for this factor. Since the model suggested ambiguity under

the assumption of an agency conflict, the lack of statistical significance on inverse pay for

performance should not be interpreted to mean that agency does not matter.

VII. Conclusion

Managers really do matter for corporate leverage. CFOs matter somewhat more than

CEOs. CEO effects and CFO effects both matter more than do firm fixed effects.

For interpretative purposes it would be nice to provide clear empirical links between

various managerial characteristics and the manager fixed effect. This is hard to do. As

described in the introduction we find that some characteristics have a statistically signifi-

cant impact. However they do not generally account for all that much of the variation in

leverage or leverage adjustment. In other words, we find that managers matter. But we

also find that it is not easy to predict a manager’s approach to leverage based on readily

observable managerial traits.

A model was presented and used to motivate a basic approach to estimation. The

model suggests running two linear regressions one for debt and another for equity. Com-

26

bining the debt choice and the equity choice into a single equation debt-to-value ratio is

problematic. When running regressions to explain that ratio the coefficients become hard

to relate to an underlying theory.

It is well known that in a debt-to-value regression profit has a negative coefficient. We

look separately at the effect of profits on net debt and net equity. A firm that has more

profits tends to cut back on net equity issues. They are less likely to issue equity and more

likely to repurchase equity. A firm with more profits tends to increase net debt issues.

In contrast to frequent claims in the literature, the effect of profits on corporate net debt

issues and net equity issues does not seem so hard to understand from the perspective of

the trade-off theory.

27

References

Aggarwal, Rajesh K., and Andrew A. Samwick, 2002, Performance incentives within firms:

The effect of managerial responsibility, working Paper, Dartmouth College.

Alti, Aydogan, 2006, How persistent is the impact of market timing on capital structure?,

Journal of Finance, forthcoming .

Beber, Alessandro, and Daniela Fabbri, 2006, Who times the foreign exchange market?

corporate speculation and ceo characteristics Working Paper, HEC, University of Lau-

sanne and FAME.

Berger, Philip G., Eli Ofek, and David L. Yermack, 1997, Managerial entrenchment and

capital structure decisions, Journal of Finance 52, 1411–1438.

Bertrand, Marianne, and Antoinette Schoar, 2003, Managing with style: The effect of

managers on firm policies, Quarterly Journal of Economics 118, 1169–1208.

Brander, James A., and Michel Poitevin, 1992, Managerial compensation and the agency

costs of debt finance, Managerial and Decision Economics 13, 55–64.

Chevalier, Judith, and Glenn Ellison, 1999, Career concerns of mutural fund managers,

Quarterly Journal of Economics 114, 389–432.

Coles, Jeff, Naveen Daniel, and Lalitha Naveen, 2006, Managerial incentives and risk-

taking, Journal of Financial Economics 79, 431–468.

Core, John, and Wayne Guay, 2002, Estimating the value of employee stock option port-

folios and their sensitivities to price and volatility, Journal of Accounting Research 40,

613–630.

Demsetz, Harold, and Kenneth M. Lehn, 1985, The structure of corporate ownership:

causes and consequences, Journal of Political Economy 93, 1155–1177.

28

Fama, Eugene, and Kenneth R. French, 2002, Testing trade-off and pecking order predic-

tions about dividends and debt, Review of Financial Studies 15, 1–33.

Faulkender, Michael, Todd Milbourn, and Anjan Thakor, 2005, Does corporate perfor-

mance determine capital structure and dividend policy Working paper, Washington

University, St. Louis.

Flannery, Mark, and Kasturi Rangan, 2006, Partial adjustment towards target capital

structures, Journal of Financial Economics 79, 469–506.

Frank, Murray Z., and Vidhan K. Goyal, 2004, The effect of market conditions on capital

structure adjustment, Finance Research Letters 1, 47–55.

Frank, Murray Z., and Vidhan K. Goyal, 2005, Capital structure decisions: Which factors

are reliably important?, working Paper, University of Minnesota and HKUST.

Graham, John R., and Alan L. Tucker, 2005, Tax shelters and corporate debt policy,

Journal of Financial Economics forthcoming.

Güner, A. Burak, Ulrike Malmendier, and Geoffrey Alan Tate, 2005, The impact of boards

with financial expertise on corporate policies, working Paper, Stanford University and

Wharton School.

Huang, Rongbing, and Jay Ritter, 2005, Testing the market timing theory of capital

structure, working Paper, University of Florida.

Jalilvand, Abolhassan, and Robert S. Harris, 1984, Corporate behavior in adjusting to

capital structure and dividend targets, Journal of Finance 39, 127–145.

Jensen, Michael, and Kevin J. Murphy, 1990, Performance pay and top management

incentives, Journal of Political Economy 98, 225–264.

29

John, Kose, and Teresa A. John, 1993, Top-management compensation and capital struc-

ture, Journal of Finance 48, 949–974.

Leary, Mark T., and Michael R. Roberts, 2005, Do firms rebalance their capital struc-

tures?, Journal of Finance 60, 2575–2619.

Lemmon, Michael L., Michael R. Roberts, and Jaime F. Zender, 2006, Back to the begin-

ning: Persistence and the cross-section of corporate capital structure Working paper,

http://ssrn.com/abstract=881899.

Lieberman, Marvin, Lawrence Lau, and Mark Williams, 1990, Firm-level productivity

and management influence: A comparison of U.S. and japanese automobile producers,

Management Science 36, 1193–1215.

Litov, Lubomir P., 2005, Corporate governance and financing policy: New evidence Work-

ing paper, Washington University, St. Louis.

Malmendier, Ulrike, and Geoffrey Tate, 2005, Superstar ceos Working Paper, Stanford

University and University of Pennsylvania.

Shyam-Sunder, Lakshmi, and Stewart C. Myers, 1999, Testing static tradeoff against

pecking order models of capital structure, Journal of Financial Economics 51, 219–

244.

Welch, I., 2004, Capital structure and stock returns, Journal of Political Economy 112,

106–131.

Yermack, David, 1995, Do corporations award CEO stock options effectively?, Journal of

Financial Economics 39, 237–269.

30

Appendix A. Variable definitions

Total debt/market value of assets (TDM): the ratio of total debt (item 34, debt in current

liabilities + item 9, long-term debt) to MVA, market value of assets. MVA is obtained

as the sum of market value of equity (item 199, price-close item 54, shares outstanding)

+ item 34, debt in current liabilities + item 9, long-term debt + item 10, preferred-

liquidation value, - item 35, deferred taxes and investment tax credit.

Median industry leverage (IndustLev): the median of total debt to market value of assets

by SIC code and by year. Industry is defined at the four-digit SIC code level.

Market to Book ratio (Mktbk): the ratio of market value of assets (MVA) to Compustat

item 6, assets. MVA is obtained as the sum of the market value of equity (item 199,

price-close item 54, shares outstanding) + item 34, debt in current liabilities + item

9, long-term debt + item 10, preferred- liquidation value, - item 35, deferred taxes and

investment tax credit.

Collateral (Colltrl): the ratio of (Compustat item 3, inventory + item 8, net PPE) to item

6, assets.

Profitability (Profit): the ratio of Compustat item 13, operating income before depreciation,

to item 6, assets.

Dividend Paying Dummy (Dividend): dummy variable that takes a value of one if item

21, common dividends, is positive and it is otherwise zero.

Log of Assets (Assets): the natural log of Compustat item 6, assets, where assets are deflated

by the GDP deflator.

31

Appendix Table 1: Descriptive Statistics Compustat Sample

This table provides descriptive statistics on leverage ratios and leverage factors for non-financial Compu-stat firms from 1993-2004. Variable definitions are provided above.

Variable Variable Definition N Mean Median 25th %ile 75th %ile

TDM Total debt to market assets 68,402 0.231 0.146 0.013 0.382

IndustLev Industry Median Leverage 70,694 0.194 0.185 0.067 0.296(MB

)Market-to-Book ratio 68,402 1.918 1.192 0.793 2.080

Colltrl Collateral to total assets ratio 69,863 0.419 0.413 0.195 0.621

Profit Profitability ratio 70,083 0.015 0.099 -0.004 0.161

Dividend Dividend paying dummy 70,694 0.277 0.000 0.000 1.000

Assets Assets in Millions of Constant $ 70,694 2,164.6 138.3 33.3 706.4

32

Appendix B. Estimating Option Values

Valuing newly granted options

Modified Black-Scholes option valuation method is used to value newly granted options. The

inputs requires are obtained as follows:

• Option exercise price per share: Exercise price per share for newly granted options from

ExecuComp (EXPRIC).

• Option maturity for newly granted options: Options are assumed to be granted on July

1st of the particular year. Option maturity is the time-span in years between the option

expiration date (EXDATE) and option grant date, rounded to the nearest whole year.

• Risk-free rate: The risk-free rates corresponding to various option maturities are read-off

the Treasury yield curve, constructed monthly from the historical interest rate provided

by the Federal Reserve Statistical Release.13 Yield on one-year bond is used for options

maturing in one year; yield on two-year bonds is used for options maturing in two years;

three year bond yield for options maturing in three years; five year bond yield for options

maturing between four and five years; seven year bond yield for options maturing between

six and eight years; 10 year bond yield for options maturing beyond nine years.

• Stock price per share at fiscal-year end : Fiscal-end closing stock price is from CRSP.

• Stock price volatility : Stock return volatility over a 60-month period obtained from Exe-

cuComp (BS VOLAT).

• Dividend Yield : Average dividend yield over a three-year period obtained from Execu-

Comp (BS YIELD).

Valuing unexercised options

We follow Core and Guay (2002) approximation method to value unexercised options held by

executives. The inputs are obtained as follows:

13The data can be downloaded from http://132.200.33.130/releases/h15/data.htm.

33

• Exercise price for unexercised options: To estimate the average exercise price for unexer-

cised exercisable options, follow the two-step process in Core and Guay. First, compute

the ratio of the realizable value of in-the-money exercisable options (INMONEX) and the

number of unexercised exercisable options (UEXNUMEX). In the second step subtract

this ratio from the fiscal year-end stock price. The resulting number is an estimate of the

average exercise price for unexercised exercisable options held by executives. Similarly, an

estimate of average exercise price of unexercised unexercisable options can be obtained by

subtracting the ratio of (in-the-money unexercisable options (INMONUN) to the number

of unexercised unexercisable options (UEXNUMUN)) from the fiscal year-end stock price.

• Option maturity for unexercised exercisable options: The maturity of unexercised exercis-

able options is assumed to be four years less than the average maturity of the new grants.

In case no grants are made this year, it is set at 6 years. The maturity of unexercisable

options is set at one less than the average maturity of the new grants. In case no grants

are made this year, it is set at 9 years.

• Stock price, risk-free rate, dividend yield and volatility : These are obtained as above from

CRSP and ExecuComp database.

Total flow compensation comprises of salary, bonus, other annual compensation, total

value of restricted stock granted, total value of stock options granted (using Black Scholes method

described above), long-term incentive payout and all other total. It is estimated by adding Black-

Scholes value of newly granted options to the difference in two ExecuComp variables (TDC1 -

BLK VALU).

Wealth change from stock holdings: The change in stock related wealth is estimated by

multiplying the value of CEO/CFO stock holdings at time t-1 with stock returns (in %) during

the year.

Wealth change from option holdings: The change in option related wealth is estimated

as the value of newly granted options plus the change in the value of unexercised options (both

exercisable and unexercisable).

34

Pay-performance sensitivity: The pay-for-performance sensitivity is the change in exec-

utive’s firm specific wealth (from both options and stocks) for every one-thousand dollar increase

in shareholder wealth (measured by multiplying the beginning of year market value with the

annual stock returns including distributions). The compounded annual returns are obtained by

cumulating CRSP monthly stock returns with dividends for each firm during its fiscal year.

35

Appendix C. Defining Managerial Attributes

Gender: dummy variables that takes a value of one if the CEO/CFO is a female and equal to

zero if the CEO/CFO is a male (ExecuComp variable PGENDER and biographical data

from Who’s Who in Finance and Industry (various editions) and Internet).

Age: age in years obtained by subtracting the year in which the executive was born from the

year in the data. The year of birth is from Who’s Who in Finance and Industry (various

editions), Executive Biographies and profiles on the Internet.

Executive founder: dummy variable that takes a value of 1 if the executive is a founder/co-

founder of the firm (Who’s Who in Finance and Industry (various editions), Executive

Biographies and profiles on the Internet).

CEO/CFO stock ownership: equals the percent of shares owned by the CEO or the CFO

(mainly SHROWNPC in ExecuComp. If this variable is missing in the database, it is

replaced by the ratio of shares owned by executives and shares outstanding.

Tenure as CEO/CFO: number of years the executive has been in the CEO or the CFO po-

sition (ExecuComp database for CEOs and Who’s Who in Finance and Industry (various

editions), Executive Biographies and profiles on the Internet for CFOs).

Tenure at the firm: number of years the executive has been working at the firm (ExecuComp

database for CEOs and Who’s Who in Finance and Industry (various editions), Executive

Biographies and profiles on the Internet for CFOs)

Number of children: number of children reported in the executive biography in Who’s Who

in Finance and Industry (various editions). Where applicable and reported, children from

multiple marriages are aggregated.

Number of companies previously worked for: number of companies the executive has

worked for prior to joining this company (Who’s Who in Finance and Industry (various

editions), Executive Biographies and profiles on the Internet).

36

Bachelor Art: dummy variable equal to one if the CEO/CFO has a Bachelor degree in

Art and equal to zero otherwise (Who’s Who in Finance and Industry (various editions),

Executive Biographies and profiles on the Internet).

Bachelor Science: dummy variable equal to one if the CEO/CFO has a Bachelor degree

in Science and equal to zero otherwise (Who’s Who in Finance and Industry (various

editions), Executive Biographies and profiles on the Internet).

Bachelor Bus.Adm. (BBA): dummy variable equal to one if the CEO/CFO has a Bachelor

degree in Business Administration and equal to zero otherwise (Who’s Who in Finance

and Industry (various editions), Executive Biographies and profiles on the Internet).

Master Art: dummy variable equal to one if the CEO/CFO has a Master degree in Art and

equal to zero otherwise (Who’s Who in Finance and Industry (various editions), Executive

Biographies and profiles on the Internet).

Master Science: dummy variable equal to one if the CEO/CFO has a Master degree in Science

and equal to zero otherwise (Who’s Who in Finance and Industry (various editions),

Executive Biographies and profiles on the Internet).

Master Bus.Adm. (MBA): dummy variable equal to one if the CEO/CFO has an MBA

degree and equal to zero otherwise (Who’s Who in Finance and Industry (various editions),

Executive Biographies and profiles on the Internet).

Finance education: dummy variable that takes a value of one if the CEO/CFO has a Bachelor

degree in Business Administration; or Bachelor in Accounting or in Economics; or a Master

degree in Finance and Accounting or an MBA and equal to zero otherwise (Who’s Who

in Finance and Industry (various editions), Executive Biographies and profiles on the

Internet).

Technical education: dummy variable that takes a value of one if the CEO/CFO has a

Bachelor degree in Science; or a Bachelor degree in Engineering; or a Master degree in

37

Science or in Engineering and no MBA degree and zero otherwise (Who’s Who in Finance

and Industry (various editions), Executive Biographies and profiles on the Internet).

Finance career: dummy variable equal to one if the CEO/CFO has previously worked in a

financial institution, or if previously worked as a CFO, Treasurer, Accountant, or finance

related professions (Who’s Who in Finance and Industry (various editions), Executive

Biographies and profiles on the Internet).

Technical career: dummy variable equal to one if the CEO/CFO previously worked as an

engineer or other technically oriented professions (Who’s Who in Finance and Industry

(various editions), Executive Biographies and profiles on the Internet).

38

Table ICEOs and Finance Officers

This table provides the composition of individuals in our sample by their reported titles in Execucomp.Individuals are designated as CEOs if they held the CEO title for the longest time during the year. Forexecutives with finance function responsibility, individuals are classified as ”Chief Financial Officers” ifthey held the title of ‘CFO’, or ‘Chief Financial Officer’, or ‘Chief Finance Officer’ in the ExecuCompdatabase. Individuals are classified as ”VP-Finance” if they held the title of ‘VP-Finance’ or ‘VicePresident-Finance’. Individuals with ‘Controller’ or ‘Treasurer’ responsibilities are those that held thesetitles and were not CFOs at the same time.

Panel A: CEOs in Execucomp-Compustat Sample

Number of CEO-Year observations 16,719Number of firms 2,246Number of individuals 3,992Time period 1993-2004

Panel B: Finance Officers in Execucomp-Compustat Sample

Number of Finance Officer-Year observations 12,783Number of firms 2,138Number of individuals 4,000Time period 1993-2004

Panel C: Distribution of Finance Officer Titles

Executive Title N Percent of sample

Chief Financial Officer 11,544 90.3%VP-Finance 854 6.7%Treasurer 314 2.5%Controller 71 0.6%

Total 12,783 100.0%

39

Table IIDescriptive Statistics - Matched Execucomp sample

Panel A provides descriptive statistics on leverage ratio and leverage factors for nonfinancial Compu-stat firms with data in Execucomp. Panel B provides descriptive statistics for CEO compensation,pay-performance sensitivities, ownership and gender of CEOs in this sample. Panel C provides similarinformation for CFO and other finance executives. Variable definitions are provided in Appendices Aand B.

Panel A: Financial variables

TDM Total debt to market assets 16,599 0.232 0.172 0.042 0.365IndustLev Industry Median Leverage 16,719 0.212 0.222 0.087 0.313(

MB

)Market-to-Book ratio 16,599 1.823 1.252 0.855 2.051

Colltrl Collateral to total assets ratio 16,537 0.458 0.456 0.274 0.641Profit Profitability ratio 16,648 0.133 0.137 0.093 0.190Dividend Dividend paying dummy 16,719 0.537 1.000 0.000 1.000Assets Assets in Millions of Constant $ 16,719 4,894.3 1,057.3 398.3 3,280.4

Panel B: CEO Compensation and Pay-Performance Sensitivity

Variable Variable Definition N Mean Median 25th %ile 75th %ile

Salary Salary ($’000) 16,719 589.1 525.0 366.0 750.0Bonus Bonus ($’000) 16,719 574.5 300.0 50.7 688.9BSOptionV al Value of option grants ($’000) 16,719 3,870.9 658.5 0.0 2,962.2ExecF lowComp Total flow compensation 16,619 5,786.3 2,168.5 965.6 5,424.3WlthChgOwn Wealth gain from stock ownership 16,719 10,841.2 0.0 -68.0 994.5WlthChgOpt Wealth gain from option holdings 16,719 2,096.4 0.0 0.0 921.7TotalPPS Total Pay-Performance Sensitivity (PPS) 14,391 34.8 9.6 1.1 37.3

(per $1,000 change in shareholder wealth)

CEO Ownership and gender

ExecOwn CEO Ownership (%) 16,454 2.95 0.35 0.09 1.85Gender Gender Dummy (1 if female) 16,719 0.013 0.000 0.000 0.000

40

Panel C: Finance Officers (FO) Compensation and Pay-Performance Sensitivity

Variable Variable Definition N Mean Median 25th %ile 75th %ile

Salary Salary ($’000) 12,783 284.5 255.5 194.0 344.2Bonus Bonus ($’000) 12,783 190.0 115.0 36.8 242.0BSOptionV al Value of option grants ($’000) 12,783 1,484.7 274.1 27.2 1020.9ExecF lowComp Total flow compensation 12,709 2,171.4 862.7 445.9 1,891.8WlthChgOwn Wealth gain from stock ownership 12,783 122.3 0.0 0.0 55.0WlthChgOpt Wealth gain: Option holdings 12,783 492.7 0.0 0.0 183.4TotalPPS Total Pay-Performance Sensitivity (PPS) 9,571 3.7 1.5 0.07 5.4

(per $1,000 change in shareholder wealth)

Finance officers ownership and gender

ExecOwn Finance officers ownership (%) 12,308 0.22 0.04 0.01 0.12Gender Finance officer Gender (1 if female) 12,783 0.052 0.000 0.000 0.000

41

Table IIICEO Personal Characteristics

Summary statistics on compensation, ownership and personal characteristics of 733 CEOs at 400 nonfi-nancial S&P 500 firms.

Variable Variable Definition N Mean Median 25th %ile 75th %ile

Compensation and Pay-Performance Sensitivity

Salary Salary ($’000) 3,285 837.7 815.0 614.6 1000.0Bonus Bonus ($’000) 3,285 1,094.0 787.8 375.0 1,375.9BSOptionV al Value of option grants ($’000) 3,285 9,445.3 3,886.3 1,384.6 10,294.0ExecF lowComp Total flow compensation 3,280 12,885.1 7,000.0 3,543.5 15,147.5WlthChgOwn Wealth gain from stock ownership 3,285 25,408.0 201.8 -8.5 2,880.7WlthChgOpt Wealth gain from option holdings 3,285 7,281.3 126.8 0.0 6,550.9TotalPPS Total Pay-Performance Sensitivity (PPS) 3,087 17.7 4.7 0.9 16.7

(per $1,000 change in shareholder wealth)

Ownership and gender

ExecOwn CEO Ownership (%) 3,259 1.19 0.14 0.05 0.44Gender Gender Dummy (1 if female) 3,285 0.01 0.00 0.00 0.00

Personal Characteristics

Age Age (years) 3,131 55.82 56.00 52.00 60.00NumCompW Num. companies prev. worked 1,876 1.66 1.00 0.00 3.00TenureF irm Years with firm 2,529 19.62 19.00 10.00 29.00TenurePos Years as CEO 3,224 7.17 5.00 3.00 10.00Founder Founder dummy 2,084 0.11 0.00 0.00 0.00FinEduc Finance Education Dummy 2,475 0.43 0.00 0.00 1.00MBA MBA Dummy 1,711 0.55 1.00 0.00 1.00TechEduc Technical Education Dummy 2,475 0.48 0.00 0.00 1.00FinCareer Financial Career 2,006 0.217 0.00 0.00 0.00TechCareer Technical Career 1,974 0.16 0.00 0.00 0.00

42

Table IVCFO Personal Characteristics

Summary statistics on compensation, ownership, gender and personal characteristics of 593 CFOs at 358nonfinancial S&P 500 firms.

Variable Variable Definition N Mean Median 25th %ile 75th %ile

Compensation and Pay-Performance Sensitivity

Salary Salary ($’000) 1,994 402.0 380.0 300.0 478.2Bonus Bonus ($’000) 1,994 367.1 278.5 143.2 475.0BSOptionV al Value of option grants ($’000) 1,994 3,596.7 1,200.3 444.3 3,057.9ExecF lowComp Total flow compensation 1,990 4,786.6 2,385.1 1,266.8 4,671.0WlthChgOwn Wealth gain from stock ownership 1,994 327.7 0.0 0.0 258.3WlthChgOpt Wealth gain from option holdings 1,994 1,768.4 0.0 0.0 1,439.5TotalPPS Total Pay-Performance Sensitivity (PPS) 1,576 2.1 1.1 0.2 3.1

(per $1,000 change in shareholder wealth)

Ownership and gender

ExecOwn Ownership (%) 1,960 0.10 0.02 0.01 0.05Gender Female Dummy 1,994 0.07 0.00 0.00 0.00

Personal Characteristics

Age Age (years) 1,531 50.43 51.00 46.00 55.00NumCompW Number of Companies Previously Worked 781 1.95 2.00 1.00 3.00TenureF irm Years with firm 1,341 12.63 10.00 4.00 20.00TenurePos Years as CFO 1,300 4.53 4.00 2.00 7.00Founder Founder dummy 1,187 0.01 0.00 0.00 0.00FinEduc Finance Education Dummy 1,311 0.71 1.00 0.00 1.00MBA MBA Dummy 902 0.85 1.00 1.00 1.00TechEduc Technical Education Dummy 1,311 0.35 0.00 0.00 1.000FinCareer Financial Career 1,169 0.88 1.00 1.00 1.00TechCareer Technical Career 1,102 0.04 0.00 0.00 0.00

43

Tab

leV

:C

orr

ela

tions

Pan

elA

repo

rts

corr

elat

ions

betw

een

leve

rage

rati

o,le

vera

gefa

ctor

s,pa

y-pe

rfor

man

cese

nsit

ivity,

owne

rshi

pan

dge

nder

.Pan

elB

repo

rts

corr

elat

ion

betw

een

leve

rage

and

man

ager

char

acte

rist

ics

for

CE

Os

ofno

nfina

ncia

lS&

P50

0fir

ms

wit

hbi

ogra

phic

alin

form

atio

n.Pan

elC

prov

ides

sim

ilar

corr

elat

ion

for

CFO

s.

Pan

elA

:M

atch

edC

ompust

at-E

xec

uco

mp

Sam

ple

N=

16,5

94

TD

MIndust

Lev

( M B)C

ollt

rlP

rofit

Div

iden

dA

sset

sto

tpps

fem

dum

Ow

nP

ct

TD

M1.

000

Indust

Lev

0.57

71.

000

(0.0

0)

( M B)-0

.446

-0.3

391.

000

(0.0

0)(0

.00)

Col

ltrl

0.33

60.

471

-0.2

831.

000

(0.0

0)(0

.00)

(0.0

0)

Pro

fit

-0.1

760.

051

0.10

20.

129

1.00

0(0

.00)

(0.0

0)(0

.00)

(0.0

0)

Div

iden

d0.

151

0.30

4-0

.179

0.26

40.

177

1.00

0(0

.00)

(0.0

0)(0

.00)

(0.0

0)(0

.00)

Ass

ets

0.34

40.

326

-0.2

020.

158

0.14

00.

385

1.00

0(0

.00)

(0.0

0)(0

.00)

(0.0

0)(0

.00)

(0.0

0)

totp

ps

-0.1

25-0

.083

0.10

4-0

.041

0.04

1-0

.122

-0.2

051.

000

(0.0

0)(0

.00)

(0.0

0)(0

.00)

(0.0

0)(0

.00)

(0.0

0)

fem

dum

-0.0

46-0

.054

0.02

3-0

.059

-0.0

06-0

.014

-0.0

460.

027

1.00

0(0

.00)

(0.0

0)(0

.00)

(0.0

0)(0

.43)

(0.0

7)(0

.00)

(0.0

0)

ownpct

-0.0

92-0

.039

0.07

2-0

.007

0.05

3-0

.079

-0.1

930.

801

0.01

01.

000

(0.0

0)(0

.00)

(0.0

0)(0

.37)

(0.0

0)(0

.00)

(0.0

0)(0

.00)

(0.1

8)

44

Pan

elB

:Lev

erag

ean

dM

anag

erC

har

acte

rist

ics

-C

EO

s,N

=3,

258

TD

MT

enure

Fir

mN

um

Com

pW

Fou

nder

Fin

Carr

Tec

hC

arr

MB

AD

um

Fin

educ

Tec

hE

duc

AgeE

xec

TD

M1.

000

Ten

ure

Fir

m0.

071

1.00

0(0

.00)

Num

Com

pW

0.07

0-0

.556

1.00

0(0

.00)

(0.0

0)

Fou

nder

-0.1

450.

078

0.02

41.

000

(0.0

0)(0

.00)

(0.3

6)

Fin

Carr

er0.

093

-0.0

970.

135

-0.0

351.

000

(0.0

0)(0

.00)

(0.0

0)(0

.17)

Tec

hC

are

er-0

.019

-0.0

520.

163

0.14

2-0

.189

1.00

0(0

.41)

(0.0

3)(0

.00)

(0.0

0)(0

.00)

MB

AD

um

my

-0.0

76-0

.064

-0.1

26-0

.226

0.27

3-0

.223

1.00

0(0

.00)

(0.0

2)(0

.00)

(0.0

0)(0

.00)

(0.0

0)

Fin

Educ

0.01

8-0

.107

-0.0

26-0

.190

0.27

1-0

.147

0.96

01.

000

(0.3

7)(0

.00)

(0.2

9)(0

.00)

(0.0

0)(0

.00)

(0.0

0)

Tec

hE

duc

-0.0

730.

011

-0.0

23-0

.039

-0.1

350.

263

-0.1

37-0

.149

1.00

0(0

.00)

(0.6

2)(0

.36)

(0.1

2)(0

.00)

(0.0

0)(0

.00)

(0.0

0)

AgeE

xec

0.11

70.

424

0.04

00.

041

0.01

2-0

.012

-0.2

61-0

.167

-0.0

041.

000

(0.0

0)(0

.00)

(0.0

9)(0

.07)

(0.6

0)(0

.61)

(0.0

0)(0

.00)

(0.8

6)

Tot

PP

S-0

.132

0.03

6-0

.046

0.33

2-0

.098

80.

013

-0.0

37-0

.075

-0.0

07-0

.040

(0.0

0)(0

.08)

(0.0

5)(0

.00)

(0.0

0)(0

.59)

(0.1

4)(0

.00)

(0.7

4)(0

.03)

Ow

nP

ct-0

.121

0.08

8-0

.094

0.30

8-0

.098

-0.0

39-0

.086

-0.1

13-0

.025

-0.0

63(0

.00)

(0.0

0)(0

.00)

(0.0

0)(0

.00)

(0.0

9)(0

.00)

(0.0

0)(0

.22)

(0.0

0)

Fem

Dum

-0.0

44-0

.068

0.08

90.

018

0.03

80.

051

0.00

0-0

.019

-0.0

45-0

.099

(0.0

1)(0

.00)

(0.0

0)(0

.41)

(0.0

9)(0

.02)

(0.9

8)(0

.34)

(0.0

2)(0

.02)

45

Pan

elC

:Lev

erag

ean

dM

anag

erC

har

acte

rist

ics

-C

FO

s,N

=1,

983

TD

MT

enure

Fir

mN

um

Com

pW

Fou

nder

Fin

Carr

Tec

hC

arr

MB

AD

um

Fin

educ

Tec

hE

duc

AgeE

xec

TD

M1.

000

Ten

ure

Fir

m0.

093

1.00

0(0

.00)

Num

Com

pW

-0.2

00-0

.472

1.00

0(0

.00)

(0.0

0)

Fou

nder

-0.0

410.

2113

N/A

1.00

0(0

.16)

(0.0

0)(.

)

Fin

Carr

er-0

.152

-0.2

960.

396

0.04

01.

000

(0.0

0)(0

.00)

(0.0

0)(0

.25)

Tec

hC

are

er0.

010

0.05

5-0

.103

-0.0

20-0

.295

1.00

0(0

.74)

(0.1

0)(0

.00)

(0.5

8)(0

.00)

MB

AD

um

my

-0.0

57-0

.115

0.10

00.

045

0.12

9-0

.311

1.00

0(0

.09)

(0.0

0)(0

.03)

(0.3

1)(0

.00)

(0.0

0)

Fin

Educ

-0.0

260.

070

0.14

10.

064

0.07

7-0

.200

0.93

51.

000

(0.3

5)(0

.03)

(0.0

0)(0

.08)

(0.0

2)(0

.00)

(0.0

0)

Tec

hE

duc

0.01

90.

013

0.02

70.

114

0.11

70.

009

-0.0

75-0

.168

1.00

0(0

.50)

(0.6

8)(0

.48)

(0.0

0)(0

.00)

(0.7

8)(0

.03)

(0.0

0)

AgeE

xec

0.00

40.

441

0.02

70.

167

-0.1

300.

038

-0.0

340.

028

0.00

81.

000

(0.8

9)(0

.00)

(0.5

3)(0

.00)

(0.0

0)(0

.28)

(0.3

9)(0

.40)

(0.8

2)

Tot

PP

S0.

016

-0.0

190.

020

0.06

90.

005

0.03

80.

028

0.02

6-0

.047

-0.0

17(0

.52)

(0.5

3)(0

.62)

(0.0

3)(0

.88)

(0.2

7)(0

.46)

(0.4

1)(0

.14)

(0.5

5)

Ow