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Human Capital, Incomplete Information, and

Capital Structure: Theory and Evidence

A dissertation submitted in partial satisfaction of the

requirements for the degree Doctor of Philosophy

in Economics

by

Guohua Yang

2003

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UMI Number: 3121231

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© Copyright by

Guohua Yang

2003


The Dissertation of Guohua Yang is Approved.

Ekaterini K1 idou

Fred Weston

/A ........................................

Harold Demsetz, Committee Chair /Y

University of California, Los Angeles

2003

ii

R eproduced with perm ission o f the copyright owner. Further reproduction prohibited without perm ission.

For

My parents: Qi Yang and Fengqin Chong

&

Wei Liu

iii


Table of Contents

1. Introduction 1

2. Literature Review 8

2.1 Tax-based Theories 8

2.2 Pecking-Order Theory 12

2.3 Non-tax Theories 13

2.4 A Recent Empirical Challenge 15

3. The Formal Model 17

3.1 Settings and Assumptions 17

3.2 Optimal Contract under Incomplete Information 23

3.3 Equilibrium Contract under Incomplete Information 26

3.4 Further Analysis of the Equilibrium Contract 29

3.4.1 A Second Best Policy 29

3..4.2 Implications for Financing Policy and Capital Structure 30

4. Further Discussion 32

4.1 Debt v.s. Equity 32

4.2 The Separation of Ownership with Control 34

5. Empirical Test 38

5.1 The Empirical Model 38

5.2 Data and Variable Specification 39

5.3 Univariate Test 44

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5.4 Regression Analysis 46

5.5 Robust Analysis 4 8

5.6 Further Discussion of the Proxy of Human Capital Intensity 50

6. Conclusion 54

Appendix 1 57

Appendix 2 58

Appendix 3 60

Figures and Tables

Figure 1 62

Table 1A 63

Table IB 64

Table 2 65

Table 3A 66

■Table 3B 67

Table 4A 68

Table 4B 69

References 70

v


ACKNOWLEDGMENTS

I am greatly indebted to Professor Harold Demsetz, the chair of my

dissertation committee, and I thank Professors Daniel Ackerberg, Hongbin Cai,

Ekaterini Kyriazidou, Fred Weston, Jean-Laurent Rosenthal, and all the

participants of I. O. seminars for their helpful comments.

vi


VITA

August, 2, 1972 Bom, Jilin Province, China

1995 B.A., EconomicsPeking University,Beijing, China

1998 M.A., EconomicsPeking University,Beijing, China

1998-99 University F ellowship,University of California, Los Angeles Los Angeles, California

1999-00 Teaching Assistant,Department of Economics University of California, Los Angeles Los Angeles, California

2001 M.A., Economics,University of California, Los Angeles Los Angeles, California

2001 Ph.D. Candidate in Economics,University of California, Los Angeles Los Angeles, California

Presentation

Yang, G. (July, 2003). Human Capital, Incomplete Information, and Capital Structure: Theory and Evidence. Paper presented at the annual meeting of Western Economic Association International Denver, Colorado

vii


ABSTRACT OF THE DISSERTATION

Human Capital, Incomplete Information, and

Capital Structure: Theory and Evidence

by

Guohua Yang

Doctor of Philosophy in Economics

University of California, Los Angeles, 2003

Professor Harold Demsetz, Chair

A puzzling issue in the field of capital structure is that the observed debt-

equity ratios of many firms are much lower than finance theory would predict.

In this paper, we explain this phenomenon in terms of specificity of human

capital and contracting incompleteness. In a costly information world, a

contract cannot be complete, and therefore managers and investors have not

only the incentive but also the ability to engage in opportunistic behaviors. We

viii


demonstrate that under these conditions the pattern of observed debt-equity

ratios is consistent with the need to resolve the "two-sided" holdup problem

embedded in dealings between management and equity owners. The seeming

inefficiency of financing practices results from a second-best policy for

protecting the interests of investors when information is incomplete. We Apply

our theory to explain the capital structure choice by human capital intensive

firms. We find strong supporting evidence that there is a negative relationship

between the importances of debt in a firm's capital structure and the

importance of human capital to its operations.

ix


1 Introduction

Modigliani and Miller (1958) demonstrate that the capital structure of a firm is

irrelevant in determining the market value of the firm if capital markets are perfect

and taxes are neutral between debt and equity. This striking conclusion, however,

is surely not consistent with the financing practices in the real world, and many

theories have been developed to explain why capital structure matters to a firm’s

stakeholders. These theories have greatly improved our understanding of the deter

minants of capital structure, yet there are still some important empirical findings

that existing theories cannot explain satisfactorily. For example, the debt-equity ra

tios of many firms, especially those that are profitable, are significantly lower than

the efficient level suggested by capital structure theory if debt is treated preferen

tially by the tax authorities.

In the U.S. and many other countries, interest payments to creditors are shielded

from corporate income tax while dividends paid to shareholders are not. There

fore, in these countries, it would seem that a firm can increase its market value

by raising capital through sale of debt. Accordingly, economists have developed

a trade-off capital structure theory which states that firms identify their optimal

debt-equity ratios by weighing the tax benefits of debt against the costs of debt,

e.g., the potential bankruptcy costs. However, Miller (1977) points out that the

observed debt-equity ratios of most firms are significantly lower than the trade-off

1


theory predicts. Moreover, firm profitability is significantly negatively correlated

with the debt-equity ratio across firms and time periods(e.g., Tit man and Wessels

(1988), Rajan and Zingales (1995), and Graham (2000)). High-profit firms have

more taxable income to shield, a lower probability of bankruptcy and lower borrow

ing costs than low-profit firms. According to the trade-off theory high-profit firms

should have higher debt-equity ratios than low-profit firms. Myers(1984) called the

disparity between trade-off theory predictions and actual data a capital structure

puzzle. Existing explanations of this puzzle have theoretical shortcomings and lack

convincing empirical support. 1

In this paper, I propose a new explanation for this seeming paradox by taking

into account the specificity of human capital and the potential for opportunistic

behavior. The theory is based on two basic assumptions. The first is that it is

costly for the owner of a firm to replace managers and key employees who possess

special skills and knowledge with regard to the tasks they conduct. The second is

that information is imperfect, and therefore economic agents planning to cooper

ate cannot write a complete contract that takes all the future contingencies into

account. Contract incompleteness leaves room for contracting parties to engage in

opportunistic behavior.

Firms produce goods through a combination of the physical capital provided

1 We will discuss this briefly in the literature review contained in part two.

2


by investors and the human capital provided by firm managers and employees who

here are identified as insiders. As they gain work experience, insiders may develop

special skills that are valuable to the firm. Such skills give them bargaining power

that allows them to exact more compensations from the firm’s owners because it is

too costly to have written a wage contract that fully anticipates this. In an imperfect

information world, it is impossible to write a complete contract that would prespecify

every future contingency and thus prevent investors from being held up by insiders2.

Realizing this, investors have reduced incentives to invest, even if the investment

projects proposed by insiders are profitable. This is because the insiders cannot

make a credible commitment to pay investors all the investment returns specified

by the original contract (for a detailed analysis, see Hart and Moore(1994)).

This holdup problem can be solved by an under-and-over compensation contract,

under which investors underpay insiders in the initial phase of their employment and

overpay them in the final phase. Under-and-over payments are calculated according

to the equilibrium wage rate in the labor market. This compensation arrangement,

however, creates an opportunity for investors to hold up insiders. If unpredicted new

profitable investment opportunities emerge before insiders have received the entire

overpayment portion of their compensation, investors can threaten to liquidate the

firm and transfer the revenue to new investment opportunities, thus denying insiders

2For further analysis of this “holdup” problem, see Noe and Rebello (1996), Hart and Moore(1994), Parsons(1986) and Grout(1984).

3


the overpayment they are due. This is feasible between present insiders do not

obviously have a type of human capital that guarantees superior performance in the

new opportunities.

When information is incomplete, it is impossible to arrive at a complete con

tract that would eliminate investors’ opportunistic behavior. In the absence of such

protection, insiders have an incentive to acquire influence over the reinvestment of

their firm’s residual earnings, thus to forestall investors’ opportunistic behavior.3 By

investing in projects targeted at their own unique skills, insiders can increase the

liquidation costs bom by investors. To assure the fundings for these special projects,

insiders are inclined to retain earnings higher than the level efficient to investors,

which leads to a debt-equity ratio lower than the level optimal to investors. The

difference between an insider-efficient earning payout ratio and an investor-efficient

payout ratio is determined by trading off the agency cost of investing in insider-

specific assets with the cost of replacing the insiders with special human capital.

Given that the profitability of a firm is an indicator of the quality of its human cap

ital, insiders at profitable firms can retain a higher level of earnings than those at

less profitable firms. Thus, other things being equal, profitable firms will generally

have a lower debt to equity ratio than less profitable firms..

In an incomplete information world, it is a second-best policy for investors to

3We will discuss in detail why insiders have significant power of deciding the allocation of residual earnings in part four.

4


adopt an under-and-over compensation mechanism and thus allow for a certain de

gree of agency costs associated with insider-specific investment. Investors endow

insiders with managerial power, including the right of deciding the allocation of

residual earnings, because insiders possess special human capital. The separation of

ownership and control allows both investors and insiders to obtain the best possi

ble return from their investments in physical and human capital respectively. The

possibility of being held up by insiders makes it necessary for investors to adopt

an under-and-over compensation arrangement. Such an arrangement will result in

agency cost when insiders leverage their power of determining the allocation of resid

ual earnings to secure their future overpayment. If insiders could not protect their

investment return, they would lack incentive to make firm-specific investments in

human capital. Observed debt-equity ratios of firms may not be optimal if judged

solely from the perspective of investors, but they are efficient if the necessity for pro

tecting insiders is taken into account of. Maintaining such ratios can help preserve

the incentive for insiders to invest in firm-specific human capital. Assuming the firm

has a relatively effective governance mechanism, the benefits investors obtain from

the under-and-over compensation arrangement will be greater than the agency costs

associated with it.

With a few exceptions (e.g., Myers (2000)), most theories addressing the rela

tionship between management and investors focus on managerial opportunism, i.e.,

5


managers pursuing their interests at the expense of investors. My analysis shows

that investors also have an incentive to engage in opportunistic behavior if they

have to overpay managers to compensate them for their previous contributions. To

prevent investors’ opportunistic behavior, insiders have a motivation not to invest

in the projects that can enhance insiders’ bargaining power but may not be in the

best interest of investors.4 Shleifer and Vishny(1989) suggests that managers can

become entrenched in a firm by making manager-specific investments that make it

costly for investors to replace them. My theory extends their ideas to incomplete

and asymmetric information situations.

My theory adopts similar frameworks as financial contracting literature devel

oped during the last ten years or so (e.g., Aghion and Bolton (1992), Dewatripont

and Tirole (1994), Hart (1995), Hart and Moore (1998), etc.). This literature re

gards firms’ financial structures as mechanisms that help to ameliorate problems

of incomplete contracts between investors and entrepreneurs. The literature em

phasizes that there are always certain circumstances which are uncontractible when

entrepreneurs seek financing from investors. In these circumstances agents may

behave opportunistically at the cost of their partners’ benefits. A firm’s financial

4Myers (2000) holds a similar idea and suggests that going public can help preserve incentives for insiders to make firm-specific investment in human capital. The reason is that the ownership of a firm will become diffused after the firm goes public, and this can increase the intervention costs for the equity holders of the firm to replace the insiders.. What Myers(2000) suggests is that insiders can take advantage of the collective action problem facing investors to protect their interests.

6


structure, however, can allocate residual control rights in a way that limits the ten

dency of its agents to behave inefficiently ex post. A theoretical shortcoming of

this literature is the identification of control with ownership (for recent survey, see

Zingales (2000)). My analysis separates these two entities. Although insiders do not

own residual earnings, they can take advantage of their control power of residual

earnings to limit the opportunistic behavior of equity holders.

In Part Two of this paper I briefly discuss existing theories. Part Three is the

formal model. Part Four extends the discussion. I conduct empirical test in Part

Five, and state conclusions in Part Six.

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2 Literature Review

The modern corporate finance theory starts from Franco Modigliani and Merton

Miller’s classical work in 1958. In this later so-called Modigliani and Miller(MM)

theorem, these two scholars demonstrate that the market value of a firm is unaffected

by how the firm is financed if there are no taxes, incentive or information problems

in capital market. Since then academic research has focused on whether the intro

duction of market imperfections into the Modigliani and Miller (MM) framework

makes financing decisions relevant.

2.1 Tax-based Theories

The first important factor to be considered is the tax benefits of debt. Modiliani and

Miller (1963) modify their irrelevancy theory by introducing the effect of corporate

tax. In the U.S. and many other countries dividends are not tax deductible but

interest payments are. This implies that if investors choose to finance projects with

debt they can save all the taxes which they need to pay if instead investors finance

projects with equity. Therefore if judged solely from the taxes point of view, a firm’s

overall value can be increased if the firm increases the debt to equity ratio in its

capital structure. The proposition of Modiliani and Miller(1963) actually implies

that firms should be financed entirely with debt.


The implication of Modiliani and Miller(1963), however, is too extreme and is

definitely not the case in the real world. Researchers believe that offsetting costs

must exist to discourage 100% debt financing. In Modiliani and Miller(1963) firm

bankruptcy is assumed to be cost free. Researchers believe that this assumption is

unrealistic and therefore the first cost proposed in the literature is the cost of bank

ruptcy, or more generally, costs of financial distress. (Kraus and Lizenberger(1973),

and Scott(1976)). According to these researchers the optimal debt level is achieved

through balancing the tax benefits of debt against the bankruptcy costs associated

with debt usage. This is the basic of classic trade-off theory in capital structure.

Until late 1970’s this is the dominant theory in capital structure.

On the other hand, empirical evidence show that the bankruptcy cost alone can

not explain corporate financing practice in the real world. Warner (1977) shows that

direct costs of bankruptcy for large railroads average no more than 5.3% ex post.

Weiss(1990) found that direct bankruptcy costs average about 3.1% of the total

value of debt and equity for large firms. For small firms, these costs may be fairly

large, perhaps 20-25 percent of a firm’s value. (Ang, Chua, and McConnell(1982)

and Altman(1984)). More recently, Andrade and Kaplan(1998) show that the ex

post costs of financial distress brought about by financing choice amount to 20% of

firm value for a group of industrial firms. Miller(1977) points out that firms choose

optimal debt policy by considering ex ante costs of distress. This indicates that

9


the costs mentioned above need to be multiplied by the conditional probability of

distress to measure ex ante costs. The ex ante costs of financial distress, however,

appears to be too small to balance the apparently large tax benefits of debt. Miller

explained this point with a comparison of Horse to Rabbit: if the tax benefit of debt

financing is as big as a horse, the ex ante bankruptcy cost is then only as big as a

rabbit, and they can not cancel out each other.

Miller (1977) proposes the difference in personal taxes on debt interest and equity

incomes(dividend and capital gain) to offset the tax benefits of debt. He shows that

although there is a tax advantage of debt financing at the firm level, investors in

person need to pay more taxes on their interest incomes than on equity incomes, and

under certain conditions, these two effects will cancel each other out. This implies

there does not exist an optimal balance between debt and equity financing for an

individual firm. Miller’s new point implies that overall taxes can not change the

original conclusion in Modiliani and Miller (1958).

Since Miller’s work academic researchers have suggested various non-bankruptcy

leverage-related costs such as agency costs of debt, loss of non-debt tax shields,

recapitalization costs, to revise the classic trade-off theory( e.g., Bradley, al (1984),

Brenman and Schwartz(1984), DeAngelo and Masulis(1980), and Fischer, Heinkel,

Zechner(1989)).Overall, these research suggests that although there is a measurable

personal tax disadvantage of debt, it does not appear large enough to offset the

10


corporate tax benefits of debt. When the personal tax penalty of debt is combined

with significant leverage-related costs, such as bankruptcy costs, loss of non-debt tax

shields, agency costs of debt, it is sufficient to offset the corporate tax advantage of

debt at the margin and leads to interior optimal debt-equity ratios.

The analysis of trade-off theory shows that taxes play an important role in

determining the debt-equity mix of U.S. corporations. Firms that are generating

substantial taxable earnings before interest and taxes(EBIT) should use a substan

tial amount of debt financing to take advantage of the tax benefit of debt. On the

other hand, firms with substantial amount of other tax shields, such as depreciation

deductions and R&D expenses, are likely to have lower EBIT relative to their values

and would thus choose lower debt-equity ratios.

In reality, however, we do not observe a positive cross-sectional relation between

EBIT and debt ratios. Indeed, those firms that generate the largest amount of tax

able earnings tend to have the lowest debt ratios( ex., Titman and Wessels(1988)).

This may be the most embarrassing fact which can not be reconciled with the trade

off theory. On the other hand, empirical research shows that other leverage-related

costs do not appear to be economically significant enough to influence the costs of

corporate borrowing. Parrio and Weibach(1999) use simulations to conclude that

the agency costs of debt are too small to offset the tax benefits of debt, and none

of the other non-bankruptcy costs have been demonstrated to be large enough to

11


offset the apparent tax benefits.

2.2 Pecking-Order Theory

Myers(1984) and Myers and Majluf (1984) suggest a approach different from trade

off theory to explain the obvious difference between corporate financing practice

and academic theories. Their explanation starts from Gordon Donaldson’s find

ings in 1961. Donaldson(1961) observes that firms prefer internal funds to exter

nal funds(e.g., through issuing debt or outside equity) to finance their investment

projects. If external funds are needed, firms issue the safest security first. That is,

they start with bank loans and corporate debt, then possibly hybrid securities such

as convertible bonds, then perhaps equity as a last resort. Donaldson called this

the pecking order of financial choices. Myers and Majluf(1984) give a theoretical

explanation for this by the information asymmetry that exists between corporate

insiders and outside investors.

Myers and Majluf(1984) claims that financing through external markets, includ

ing both equity and debt financing, is more expensive than internal financing. They

do not assume that the transaction costs of outside financing is precludingly high.

Empirical evidence shows that the transaction costs cannot make up the huge tax

benefits of debt financing. Their approach relies on information asymmetry to jus

tify the higher cost of external financing. The theory assumes that firm managers

12


have better information than the market about the project in need of financing,

and that firm managers act on behalf of existing shareholders. Because the market

believes that firm managers will turn to the capital market for financing only when

it is beneficial to the existing shareholders, the market will undervalue any risky

securities issued by the firms. Realizing this, firm managers will choose to finance

their investment projects, first with internal funds, then with riskless debt, followed

by risky debt and lastly with equity, because the uncertainty of the project has

more influence on the value of equity than on debt. This pecking order theory of

financing argues that it may be efficient for profitable firms to retain great amount

of earnings for future financial slack. Hart (2001), however, points out that the

adverse selection problem in this case is essentially a managerial incentive problem

and that it can be actually resolved by paying managers based on the firm’s total

market value. Hart argues that it is puzzling to use capital structure instead of

an incentive scheme to solve a moral hazard problem. Empirical findings are also

controversial in supporting pecking order theory (e.g., see Fama and French (2002)).

2.3 N on-tax Theories

In addition to trade-off and pecking order theories, in existing literature there are

also various nontax capital structure theories based on agency costs, asymmetric

information, product/input market interactions, and corporate control considera

tions (a good survey of these theories is Harris and Raviv(1991)). Jensen (1986) is

13


a representative work of these theories and it is based on agency conflicts between

managers and shareholders. It argues that firm managers suffer more loss than

shareholders if the firm goes bankrupt, because managers gain private benefits from

operating the firm. To prevent losing power during financial distress, managers may

prefer a debt level lower than is optimal from the perspective of shareholders.

This agency theory, however, can not explain why equity holders are so passive

in deciding firms’ capital structure. If an inefficient capital structure is solely due

to manager entrenchment, equity holders can just specify a bottom line for earning

payout and debt-equity ratios in the corporate chapter. However we rarely see

investors do so. Another shortcoming of this agency theory is that it is almost

impossible to quantify private benefits. Therefore it can never be known if these

benefits are great enough to trade off the reward managers can obtain if they choose

debt levels optimal to firms’ market value. This agency theory is also inconsistent

with fact in as much as it is profitable firms that seem to have great short fall of debt

in their capital structures. The theory cannot explain why managers in profitable

firms are more likely to fail to optimize firm value than their counterparts in less

profitable firms. Moreover, empirical research does not find significant evidence to

support the agency problem argument, since debt usage and management ownership

are only weakly correlated, if at all. (Graham (2000) finds the correlation not

significantly different from zero).

14


Most of other non-tax driven theories predict a positive relation between prof

itability and firm’s debt usage and None of these theories can explain why we per

sistently observe that profitable firms tend to have much lower debt to equity ratios

than less profitable firms.

2.4 A R ecent Empirical Challenge

Although the opinion is unanimous in academia that there is a tax advantage debt

at firm level, there is a great divergence among researchers as to its size. Significant

progress has been made recently in empirical research.(e.g., Graham(1996)and Gra-

ham(2000)) dealing with this issue. Through an innovative method Graham(2000)

measures the magnitude of tax benefits. It finds that the tax benefits of debt forgone

by the U.S. firms are indeed in huge amount. The average tax benefits forgone by the

U.S. firms are around 10% of the market value of these firms and debt conservatism

is persistent.

Graham(2QQ0) also tests the explanation power of various existing theories and

finds that none of them can explain satisfactorily the current corporate financing

practices. He shows that large, liquid, profitable firms with low expected distress

costs, such as Boeing, Coca-Cola, Compaq, Eastman Kodak, Exxon, GE, Hewlett-

Packard, Mcdonalds, Merck, Microsoft, 3M, Phillip Morris, Procter and Gamble,

Westvaco, Intel, AT&T, American Airlines, Chevron, Ford Motor Company, General

15


Motors, IBM, Lockheed Martin, Pepsi, etc tend to use debt conservatively. Some

of these firms may experience a brief unprofitable periods, but once they return to

a very profitable state, their leverage declines to (or below) its predifficulty level.

These findings show that trade-off theories alone can not explain why firms choose

their capital structures as what we observed in practice.

Further investigation in Graham(2000) finds that, in general, firms using debt

conservatively pay dividends, have positive owners’ equity. Graham(2000) does not

find significant evidence of management entrenchment. These findings contradict

directly pecking-order theory and agency-conflict based theories.

Graham and Harvey(2001) conducts a comprehensive survey of 392 firms that

describes the current practice of corporate finance. Their results show that firms care

most about financial flexibility (the question in the survey is: we restrict debt so we

have enough internal funds available to pursue new projects when they come along)

and credit rating (as assigned by rating agencies) when they make debt policies. The

tax advantage of interest deductibility is ranked 4th, moderately important. The

factor ranking 3rd is the volatility of firm earnings and cash flows. The transactions

costs and fees for issuing debt is ranked 5th. Only 21.35% of the firms believe the

potential cost of bankruptcy or financial distress is an important or very important

factor when they choose the appropriate amount of debt for their firms.

These empirical findings demonstrate that researchers must develop a new theory

16


to explain c o r p o r a te financing p ra ctice .

3 The Formal M odel

3.1 Setting and Assum ptions

I consider a model with four time periods and three agents: an investor, and two

entrepreneurs. To simplify my analysis I assume that interest rate is zero. All

the agents are risk-neutral, expected-value maximizers. The investor owns only

physical capital, and the entrepreneurs possess only human capital. The investor

initially chooses and only chooses to cooperate with one of the entrepreneurs who

becomes the incumbent. Subsequently, the investor has the choice to stay with the

incumbent or switch to the other entrepreneur who I call the rival.

At time 0 the investor and the incumbent negotiate a financing contract for an

investment project. The earnings generated by the project are verifiable. This makes

equity financing feasible to the project.5 The investor and the incumbent agree on an

equity contract, which endows the ownership of the project to the investor, and the

control rights of the project to the incumbent.6 The investor will pay the incumbent

5 Some economists argue that outside equity financing can still be feasible even if the cash flow generated by a firm cannot be verified (Fluck (1998), Myers (2000)). In their models, however, the payment paths of outside equity are more like that of debt than that of equity, and equity investors can receive their returns on their investment only at a break-even level.

6 The Investor and the incumbent can also choose another type of financing instrument - debt. Financing the project with debt will make the incumbent the owner of the project. Debt financing,

17


a market wage and receive all the residual earnings created by the project as her

investment return. The cost needed to set up the project at time 0 is K . K is

provided by the investor since the incumbent has no personal wealth and the only

production asset he can provide to the market is his human capital. I assume that

both the investor and the incumbent can withdraw their capital from the project.7I

also assume that information is symmetric, that is, all the information is publicly

known to all the agents.

The incumbent’s human capital, indexed by i, can be either high quality or low

quality, that is, i € {L, H }. I assume that neither the investor nor the entrepreneurs

know the value of i until the end of the project. The prior information about i is

that proh(i = L\ t = 0) = prob(i = H\ t = 0) = | . At time 1, earnings of Yiare

realized. The distribution function of Yi, / (Yi), conditional on i, satisfies the

following conditions: E{Y\\ i = L) = ttu, E{Y\\ i — H) — ix\h, and Hu < 7Ti*.. The

agents’ beliefs about i will be updated at later time periods by applying Bayes rule.

however, may not be feasible due to asset substitution problem (Miller (1977)) and managerial opportunism. We will discuss this point further in part four. Also in that part we will discuss why the investor delegates the control rights to the incumbent.

7The Investor and the incumbent can restrict each other from withdrawing their capital in the contract. In an uncertain world, however, neither the investor nor the incumbent can know for sure if the current project is in their best interests. Therefore neither of them has an incentive to self-restrict in exchange for restricting the other.

18


Figure 1

t=0 t= l t=2 t=3

Project is set Earnings of Yi Value of Earnings of Y3

up at cost K are realized r is revealed are realized

An additional project be set up at either time 1 or 2

The investor can hire the rival to replace the incumbent at either time 1 or time

28. The rival’s human capital, indexed by r, can also either be low quality or high

quality, that’s, r £ {L, H }, and f(Yi\ r ) is the same as / (Yi|i). At time 0 and 1 it

is publicly known that prob(r = L) = prob(r = H) = At time 2, the value of r

will be revealed to all the agents.

At time 3, final earnings of Y3 are realized. The distribution function of I3, /

(Y3),depends on the value of i (or r), E(Y$\ i — L) — ix3;, E(Y3\ i = H) — 7r3/l,

E(Y3 1 r = L) = 7Tri, and E(Y3\ r = H) = 7trh.

8 In our model, we assume that investor always has an alternative option to invest her physical capital. Therefore, here the rival represents not only an replacement manager the investor can choose to replace the incumbent but also an alternative investment opportunity for the physical assets of the existing project.

19


Assumption 1. H31 > 7trl, tt3fe > Trrh and nrh > 7r3/.

7t3/ > 7rr; and 773 ̂ > 7rrh imply that the incumbent can develop special skills from

his work experience at time 1 and the value of the project will depend partially on

such skills. This makes it costly to replace the incumbent after the project is set up.

TTrh > 7T3( means that it is beneficial to replace an incompetent entrepreneur, even

if he has developed special skills in previous time periods.

An additional project can be set up along with the existing project either at time

1 or at time 2. The cost to set up the project is I. The expected value added by

the additional project at time 3 is A tti i£ i — L, and A7Th if i = H. Note 0 < A tti <

I < Ai(,.

Assumption 2. E(Y3\ r = L ,/) —£?(3/3 1 r = L) = 0, and E{Y^\ r = H, I) — E{Y^\

r = H) = 0.

I assume that the additional project can help entrench the incumbent in the firm,

that is, after I is invested in the existing project, it will be more costly to replace

the incumbent.

The capital I can also be invested in a separate project proposed by the rival,

and I assume that the corresponding profit functions are /?(/ | r = L) = A7T; and

P { I \ r = H ) ~ A7Tft.9

9The intuition supporting assumption 2 is that insiders of a firm can invest the residual earnings

20


I assume that the labor market is efficient. The market wage rate for the entre

preneurs is wi if i(or r) = L, and Wh if i(or r) — H, where w\ < Wh■ The market

wage rate that the entrepreneurs can earn will change according to the market belief

about the quality of their human capital. Both entrepreneurs can earn w\ at time 1,

and at time 3 the incumbent will be paid w$. w\ and w% are defined below.10 Since

the value of r is revealed at time 2, the replacement manager will earn either wi or

Wh at time 3.

D efin it ion1. w\ = ^*wi + ~*Whf and w$ = wi*prob( i — L | Y\)+Wh*prob{i = H

\Yx).

Assumption^. E(Y\ + Y$\t — 0) — 2 * wi — K = 0, and (7iq/j + 7^ ) — 2 * W h > K .

I assume that the expected NPV of the project at time 0 is zero, and the project

is expected to earn profit if and only if i — H.

of the firm into the assets that are specific to their human capital. Insiders may also invest in the projects that need a long time period to realize the returns. These activities may not be value- maximizing, but they can increase the cost for investors to liquidate the firm and therefore can help entrench insiders in the firm. Shleifer and Vishy (1989) show that managers bind shareholders to themselves by using shareholders’ money to make manager-specific investments. Here we take a similar approach. In our model, however, we do not prespecify such investment projects as value-decreasing ones. We will elaborate on this point later.

10In our model we do not consider manager shirking problem. Note the performance of incumbent at time 1 has an influence on the wage rate he can receive in the labor market. Such a compensation mechanism will motivate the incumbent to fulfill his duty.

21


Lemma 1. (tiu + 7t3j) — 2 * Wi < K.

This can be easily derived from assumption 3, and it shows that it is inefficient

to build up the project if i = L.

Assumption 4. 7rr; = wi and 7xTh > w .̂

Lemma 2. 7v3i > wi, and 773 ̂ > w .̂

This lemma can be easily derived based on assumptions 1 and 4. In combination

with assumption 4 it means that it is profitable to replace the incumbent with the

rival if and only if i — L and at the same time r = H.

Assumption 5. \ * Aith + \A'Ki = I.

I assume that the expected NPV of the additional project is also zero at time 0.

In my model, once the capital K and I are invested, they can not be used for

consumption. This implies that once the investment projects are set up, the investor

only has one choice: whether to continue cooperating with the incumbent or turn

to the rival.

Assumption 6 . 7t3h - u>h = (^3 i - ™i) + (nrh - wh).

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In this model — wi to represent the value of the special human capital devel

oped by the high quality entrepreneur.

To further specify the properties of the additional project I make the following

assumptions.

Assumption 7. | ( 7Tih + nil) — w\ > I.

Assumption 8 . I > \{^ rh — Wh).

Assumption? implies that if the project is set up at time 0 can earn an above

normal return then the income earned at time 1 will be able to cover the construction

cost of the additional project. Assumption8 defines the lower bound of the scale of

the additional project. According to assumption 2 this is also the lower bound of

the agency cost when the incumbent entrenches himself in the role of controlling the

project.

3.2 O ptim al Contract under Incom plete Information.

First let us clarify an important condition for the incumbent manager to continue

the project.

Lemma 3. 15(F3|!i) — w$ > 0.

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Proof :

E(Y3\Y 1) — 7T3l* prob( i - L\ Yx) + irih *prob(i = H | Yi).

from lemma 2 we know k%i > wi, and nzh > Wh. By applying the definition of

ic3, we can easily obtain the result.

Q.E.D.

Therefore, if r = L, it the investor will let him continue the project regardless

of his performance at time 1, since the investor has no better options. The special

skills developed by the incumbent can at least make up part of the investor’s invest

ment returns. The theorem below states the optimal contract under the incomplete

information condition.

Theorem 1 . After the project is set up at time 0, the investor has the right to

decide when to replace the incumbent entrepreneur and when to set up the additional

project. The compensation for the incumbent is w\ at time 1, and is w3 at time 3

if the investor continues hiring the incumbent.

Proof: See Appendix 1.

According to this contract, the project will be set up at time 0. The investor

will receive max{Y\ — u>i,0} at time 1. At time 2, if r = L, the incumbent will

continue the project, and if Aiti* prob( i — L\ Yi) + ATXh *prob(i = H \ Y\) > I, the

24


additional capital will be invested into the existing project otherwise the investor

will refuse to finance the additional project. In this situation the investor will

receive max{Y 3 — w 3 , 0}. If r = H, the incumbent will be replaced if and only if

E(Y3 \Y\) — u>3 < 7irh — Wh. If the rival controls the project the investor will receive

max{Y 3 — wh, 0} at time 3. The additional project will be set up with the rival

no matter who controls the existing project. I define the optimal contract as the

first-best policy.

This optimal contract, however, is not an equilibrium result. Note when r = L,

E(Y3 \Yi) — ws > 7Tri — wi. In this case the incumbent can hold up the investor and

extract the rent E(Y3 \Yi) — w3 from the investor. Even when r = H, if E(Y 3 \Yi) —

w 3 > TXrh—Wh, the incumbent can still hold up the investor. The intuition supporting

this argument is that after working for the project for a certain time period, the

incumbent entrepreneur may have developed special skills that partially determines

the prospects of the project. The incumbent can take advantage of his special skills

to hold up the investor, e.g., the incumbent can threaten to withdraw his human

capital from the project and extract a rent from the investor.

Noe and Rebello (1996) acknowledge this holdup problem and argue that the

incumbent can capture a share of the rents generated by his special skills if he

cannot be forced to work for the firm. Hart and Moore(1994) show that some

positive NPV projects may not be able to be financed or may be liquidated at a

25


suboptimal time due to this holdup problem. My analysis has a similar result (Note

the project has a zero NPV at time 0). If the investor realizes that the entrepreneur

has opportunities to capture a return more than W3 at time 3 (E(w3\t = 0) = w 1),

she will have no incentive to invest at time 0 since she will suffer loss due to the

holdup potential. This is because E{Y\ + Y$\t — 0) — 2 * w\ — K — 0.

3.3 Equilibrium Contract under Incom plete Information

If the investor is not completely passive, however, the equilibrium result will be

different from what Noe and Rebello (1996) and Hart and Moore(1994) demonstrate.

I suggest that the investor can underpay the incumbent in the initial stage of the

employment contract and overpay him at the ending stage of the contract. Such a

compensation arrangement can prevent the incumbent’s opportunistic behavior and

in equilibrium the incumbent only earns a market equilibrium return on his human

capital.

Formally, realizing the possibility of being held up by the incumbent, the investor

agrees to pay the incumbent only wnl — { 1 — a) * w\, (0 < a < 1 ) at time 1, and

commits to pay him wn 3 — a * + W3 at time 3. The incumbent can only earn

W3 at time 3 from the labor market if he withdraws his human capital from the

project. Therefore the incumbent’s threat to withdraw becomes incredible, since

the incumbent will lose a * w\ at time 3 If he indeed withdraws. In total the

26


incumbent will be paid market equilibrium wage rate Wi + w3.

Assumption 9. a = (^3/ — wi)/w\.

AssumptionlO. \{-Krh - wh) > (w3i - wt) > A (irrh - wh).

Assum ptionll. A i — A iq > ~ wh)-

Assumption9 implies that the incumbent entrepreneur will lose w%i — wi if he

is replaced by the rival entrepreneur. Note that w$i — wi measures the value of

firm-specific human capital. Assumption 10 gives the boundaries of the value of

firm-specific human capital. Assumption 11 gives the lower bound of the value of

the additional project.

This compensation arrangement, however, has a time-inconsistency problem.

The investor’s commitment at time 0 is not creditable because she has the right

to withdraw her assets from the project at any time. If at time 2 E(Y3 \Yi) —

(w3 + a * u>i) < 7rrh — wh, the investor will always choose to liquidate the project

instead of fulfilling her commitment. This is an instance of the holdup problem: the

entrepreneur can accept the under-and-over compensation arrangement and have

his human capital specific to the project, but his incentive is reduced by the risk

that the investor might choose to liquidate the project and not to pay him the

overpayment part of his compensation.

27


To motivate the incumbent entrepreneur to participate in the project, there must

be a mechanism to ensure that the investor will fulfill his commitment. Shleifer and

Vishny(1989) suggest that managers can entrench themselves in a firm by making

manager-specific investments that makes it costly for investors to replace them.

Similarly, I assume that in the contract made at time 0 the investor allows the

incumbent manager to set up the additional project at time 1 if and only if prob(i =

H\Y1)*A nh+prob{i = L\Y1)*Att1 > I. Since E(Y3\ i = H, I ) - E(Y3\ i = H) = A nh

but E(Y3\ r — H, I) — E{Y3\ r = H) — 0, authorizing the incumbent entrepreneur

this power can secure the entrepreneur’s stake in the project and preserve both

contracting parties’ incentive to participate in the project. The following theorem

states the equilibrium contract under the incomplete information condition.

Theorem 2 . At time 0 the investor agrees to pay the entrepreneur (1 — \a )w \ at

time 1 and commits to pay him (au)\ + w3) at time 3. The investor has the power

to replace the entrepreneur at any of the subsequent times. If at time 1 prob(i = H

| Tj) > prob(i — H\t — 0) = ~, the incumbent will be given the power to set up the

additional project at time 1 .

Proof: See Appendix 2.

Under the equilibrium contract, if Yi > |(ttu + nth), the incumbent will set up

the additional contract at time 1 and continue operating the project till the end. If

Yi < | ( 7Tii + nth), only at time 2 can the additional project be set up. In this case,

28


if r = H at time 2, the incumbent will be replaced by the rival and the additional

project will be set up with the rival. If, however, r = L, the incumbent will continue

operating the project but the additional project will not be set up.

3.4 Further Analysis o f the Equilibrium Contract

3.4.1 A Second Best Policy

In this part I demonstrate that the equilibrium contract is a second best policy for

the investor.

The other approach to protect the incumbent entrepreneur from being held up by

the investor is a compensation arrangement. Instead of authorizing the incumbent

entrepreneur the power to set up the additional project at timel, the investor will pay

the incumbent more than (miq + w3) at time 3 so that the entrepreneur’s expected

income at time 3 is still (awi + W3 ) even if there is a possibility that the incumbent

may be replaced. I claim that such a compensation arrangement will bring less

profits to the investor than the equilibrium contract I proposed in theorem 2.

Claim 1 A compensation contract necessary to protect the incumbent entrepreneur’ stake in the project is an inefficient choice.

Proof: See Appendix3.

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The expected payoff of the additional project under the equilibrium contract

is less than that under the optimal contract, since under the optimal contract the

investor has the option to replace the incumbent even if prob(i — H\Y{) >

Therefore the equilibrium contract is only a second best policy.

3.4.2 Im plications for Financing Policy and Capital Structure

In this part I show that it is in the investor’s interest to allow the incumbent to

finance the additional project with internal funds. I have already shown that only

the incumbent manager with an above normal performance at time 1 can set up

the additional project. Because of these two reasons the profitable firms will retain

relatively more earnings than less profitable firms.

The incumbent entrepreneur has two ways to finance the additional project. One

way is to use the earnings realized at time 1 , and the other is to use the outside

capital market, by issuing either debt or outside equity. Modiliani and Miller(1958)

demonstrate that no matter which financing method a firm choose, investors will

gain the same investment return if the capital market is perfect. Modiliani and

Miller(1958), however, does not consider the consequences of agent conflict. My

model shows that managers of a firm have incentive to invest in manager-specific

projects, which are not optimal in the investors’ interests. This agent problem will

make the investors of a firm prefer equity to debt financing. In the following analysis

30


I assume that in the capital market creditors are risk-averse. Creditors care about

not only the expected return of their lending but also the probability for them to

receive the returns. Risk-neutral investors (equity owners), however, care only about

expected returns of their investment.

If the outside investors in the capital market are risk-neutral, it does not matter

to the incumbent investor of the existing project whether the earnings realized at

time 1 are distributed to her or invested in the additional project. If the incumbent

entrepreneur finances the additional project with outside funds the cost of financ

ing will be |(A 7Tfe — I). On the other hand, if the incumbent manager finances

the additional project with outside funds, the earnings realized at time 1 can be

distributed to the incumbent investor. She can invest these earnings in the project

set up by the outside entrepreneur (r = H) at time2. The expected return will be

|(A 7Th — I), which will trade off exactly the financing cost the incumbent investor

has to undertake.

If the outside investors in the capital market are risk-averse, the cost of outside

financing will be more than |(A 7Xh — I). The outside investors have two investment

options at time 1. They can either choose to finance the additional project proposed

by the incumbent manager of the existing project, or choose to wait until time 2 for

the potential investment opportunity. Define rj and as the investment returns of

the two options respectively.. For the first option, ry = m in{|A 7T/j, Ya} — I, and

31


for the second option r2 = 0 or AiTh — I. Since E(Y3 | prob(i = H\Yi) > |) > §A7th,

then E(ri | prob(i — H\Yi) > |) = E(r2\t = 1) = |(A7rft — I). The lower bounds of

ri and r2, however, are different, ri may be less than 0, but r2 > 0. The incumbent

manager must pay the outside investors more than — I) to compensate the

risk they undertake for their financing the additional project. Therefore, if outside

investors are risk-averse, and the incumbent investor of the existing project is risk

neutral, then it will be in the interest of the incumbent investor’s interest to finance

the additional project with internal funds.

4 F u r th e r Discussion

In this part I further discuss three topics: why the initial project is financed with

equity, what are other reasons for insiders to have the power in determining the

usage of residual earnings, and how the separation of ownership and control changes

the interactions between equity holders and insiders.

4.1 D ebt v.s. Equity

My model assumes that initially the investment project proposed by the entrepre

neur is financed with equity. The investor is the owner of the firm, in the sense

that she has the property rights to all the nonhuman assets (tangible and intangible

assets) created by the project. The project can also be financed with debt, and in

32


this case the entrepreneur will be the owner of the project. Debt financing, however,

may not be feasible in many cases. Consider the following example.

Investors may have an incentive to invest in a risky project proposed by an en

trepreneur who has no personal wealth to undertake this project. A fixed payment

financing contract may not be acceptable to the investors due to the asset substi

tution problem (Miller (1977)). Suppose that the entrepreneur has two ways to

conduct the project, where one is riskier than the other. The investors can only

observe the final result, and they cannot tell with certainty by which way the re

sult is realized. Under such conditions, the entrepreneur has incentive to adopt the

riskier approach. If the project turns out the entrepreneur will gain all the residual

income. If the project fails, the entrepreneur will lose nothing but his reputational

capital. The asset substitution problem, certainly does not only exist in this initial

investment case I analyze. Miller (1977) argue that it is an important reason why

the interest of debt holders often conflict that of equity holders.

One way to resolve the asset substitution problem is to allow the investor to share

the unlimited upside of the project with the entrepreneur when the project succeeds.

A profit-sharing contract, however, cannot guarantee that the entrepreneur will

behave in the interests of both parties. The entrepreneur may act opportunistically

after the investors make their investment in the project, e.g., the entrepreneur can

choose to invest in the projects benefiting only himself and leave nothing on the

33


account co-owned by the two parties. In reality, it is difficult to predict every future

contingency, therefore it is impossible to write ex ante a contract to specify what

the parties should do in all contingencies.

If investors have property rights to the project assets, they can decide how to

use the project assets in situations that are not prespecified in the initial contract.

Therefore endowing investors the property rights to the project assets can help

prevent the entrepreneur from engaging in opportunistic behaviors (Grossman and

Hart (1986), and Hart (1995)).11

4.2 The Separation o f Ownership w ith Control

Corporate law has developed a business judgment rule that conveys considerable

operational control to insiders. 12 This arrangement is efficient because insiders pos

sess special skills and knowledge. Insiders have their human capital specialized in

the firm’s production. Compared with investors who only invest in physical capital,

insiders possess more information about the firm’s operation. Therefore insiders

can make better decisions about operations than investors. If investors control the

11 Several papers have addressed managerial opportunism when managers seek outside financing for projects they initiate (Hart and Moore (1994), Noe and Rebello (1996). For a recent survey, see Shleifer and Vishny (1997)). Hart and Moore (1994) show that in the case where an entrepreneur can hold up investors, debt financing is not feasible for some positive NPV projects. Shleifer and Vishny (2000) emphasize that outside financiers should be protected against expropriation of insiders through the residual right of control.

12Professor Demsetz argues that this applies mainly to corporations, not proprietorship, and it reflects the difficulty in allowing a multiplicity of shareholders make operational decisions,

34


allocation of residual earnings, they need to evaluate each of the insiders’ financing

requirements. Investors must gather sufficient information for every evaluation and

this can be very costly. To save the information costs, investors endow insiders with

the right to determine the allocation of residual earnings under the supervision of

the representatives of shareholders. 13

Standard agency theory emphasizes that diffuseness of ownership structure gives

insiders the power to allocate residual earnings at their will to a great degree. If

we look at the relation between investors and insiders only from the perspective of

agency costs, it is puzzling that insiders are entitled to use residual earnings. If

shareholders are worried that insiders may use residual earnings for their personal

benefits rather than investors’ interests, they could demand ex ante in the equity

contract an immediate payout of residual earnings once they are realized. Under such

a contract, insiders would be restricted in their ability to take advantage of investors,

although to some degree insiders can "hide" part of the earnings by converting them

into operation costs. In reality, however, we do not see investors and insiders making

such an agreement. From my point of view, this is because insiders specialize their

13In reality, shareholders delegate their control rights to the board of directors, which serves a monitoring role in constraining insiders’ behaviors. For example, management must consult with and report to the board of directors when determining dividend and payout policies. Empirical research, however, finds that the board of directors plays such a limited role in constraining management that many researchers would regard it as a part of management rather than the representative of shareholders. For most corporations, the board has a significant percentage of insider members whose major investment in the firm is composed of human capital rather than physical capital. In our point of view, this is because shareholders believe that directors with specialized human capital can do more in promoting shareholders’ interests than the directors who have only a financial stake in the firm.

35


human capital in the firm operation, and therefore they can make better decisions

on reinvesting residual earnings into firm production. It is efficient for shareholders

to delegate the control rights of residual earnings to insiders only if the agency costs

are less than the information costs saved.

Different from standard agency theory, I emphasize that the reason for insiders

not to allocate residual earnings in a way optimal for shareholders is that insiders

need to protect themselves from being held up by investors. Investors may engage

in opportunistic behavior when new investment opportunities arise in outside mar

kets If insiders anticipate the emergence of these new threats, they may invest the

residual residual earnings of the firm into the assets that are specific to their human

capital. Investors can use the residual earnings to support a bigger marketing plan

of the products of the existing firm, to improve workers’ skills, or to increase R&D

expenditures. The intangible and human assets produced by these investments are

specific to the insiders’ human capital and a long time period is needed to realize

the whole return from these investments. If the shareholders of the firm choose to

liquidate the firm, they may lose great returns from their investment in these assets.

Profit of the existing firm may be improved, but the shareholders of the existing

firm may gain more if the residual earnings were invested in outside opportunities.

If shareholders controlled the residual earnings, they could choose to wait for

new in form at,ion to become available before they reinvested their money into the

36


firm. If we look at the relation between investors and insiders as a dynamic game,

investors will gain first-mover advantage if they control the allocation of residual

earnings. Investors could keep their money in hand and refuse to invest in the firm

even if insiders threaten to leave, since the threat would not be credible. This is

because insiders expect to receive the overpayment from the firm in the future.

If, however, insiders control the allocation of residual earnings, they will gain

first-mover advantage. Insiders can first allocate residual earnings according to their

interests and then leave the investors in a position to decide whether to intervene.

In principle, insiders must have the permission from the board of directors on their

payout or investment decisions, but in most cases insiders have overwhelming power

in determining the decisions of the board.4 Outside directors may only hold minority

positions in the board. They may be insufficiently well informed to evaluate the

investment. Even they can determine that a particular investment has made the

firm more depend on the insiders it may still be difficult to conclude whether the

investment is value-maximizing. A great deal of research shows that for many firms,

the board of directors is controlled by managers. Under these conditions insiders can

always implement their plans to a certain degree before the diffused shareholders

4The agency problem cannot explain why the board of directors fails in monitoring management. Shareholders can choose to pay their representatives to monitor management, and large shareholders may have sufficient incentive to do so. The reason why this arrangement fails is that investors are not as competent as insiders in operating the firm so they give up a lot of power to the insiders. Another reason why large shareholders in the board might cooperate with insiders is that the large shareholders can take advantage of insider information. Incentive contract for the board of directors cannot take account of this.

37


determine that these plans are not in their best interests and can act to stop them.

The only possible intervention action which investors could choose at that point

would be to replace insiders. Under this condition investors need to trade off the

agency costs associated with investing in insiders-specific assets with the replacement

costs of the insiders who possess high quality human capital. It might not be in the

interest of investors to replace the insiders before sufficient information about the

new investment opportunity is available.

5 Empirical Test

In this section I test the main prediction of my theory, that’s, for a firm the more

important is human capital, other conditions equal, the less debt the firm will choose

in its capital structure. If my prediction is true, we should observe significantly

negative relationship between leverage and human capital intensity, after controlling

for other factors that might affect capital structure choices.

5.1 T he Empirical M odel

I construct the following linear model to test the prediction of my theory.

l = a + 0 1 * H I + P2 * X + e\ (5.1)

Where I is the firm’s leverage ratio, and H I is an indicator of human capital

intensity, defined as a firm’s per capita labor costs. In the model X is a vector of

38


other explanatory variables, including a firm’s size, profitability, tobin’s Q, tangibil

ity of assets, and operation risk, e is the error term that represents other unknown

factors. The preceding discussion leads us to the following hypothesis regarding the

relationship between H I and I:

Hq : Pi < 0, and H\ : /3X > 0.

If we find /31 is negative at a significant level, we can conclude that on average

human capital intensive firms tend to have lower debt to equity ratios in their capital

structure than the firms with less intensive human capital.

5.2 D ata and Variable Specification

To estimate the model, I use firm level data from the annual Compustat database

during the period 1989-1998. I only consider those firms that consistently disclose

their labor costs in their financial reports. My sample contains 207 firms. Since the

generally accept accounting principles do not require the labor costs to be identified

separately, my sample are those firms that voluntarily disclose their labor costs.

They account for about 11% of firms in Compustat.

Compustat only contains public-traded firms’ financial and accounting informa

tion, therefore my sample does not include private firms and startup firms. Ballester,

Livnat and Sinha(2001) find that larger firms, firms in regulated industries and firms

39


in labor-intensive industries are more likely to report labor-related costs voluntar

ily. This data bias problem, however, will not affect the effectiveness of my testing.

My research question is why many profitable firms are not optimalizing their cap

ital structures. This puzzling issue is more often an observation made about large

public-traded firms. Compared with small and private firms, large public firms have

less costly access to outside capital markets. If the prediction of my theory is right,

we should be able to find that firm-specific human capital is a significant factor in

affecting capital structure choices for large public firms.

In my analysis I use firms’ per capita labor costs as a proxy for human capital

intensity. This is because in a relatively efficient labor market labor cost per em

ployee is proportional to the marginal contribution of employees to firm production,

and overall it can reflect the relative importance of employees’ human capital to a

firm’s production14

For firms that have consistently disclosed their labor costs during 1978 and 1997,

figure 1 in the appendix shows the trends of labor related expenses. As we can see

from figure 1 , average labor expenses have increased steadily and significantly during

this period. It is about $560 million in 1978, and $1600 million in 1997. The annual

growth rate is about 5.7%. During the same time period the average sales per

14In the existing literature, another proxy for human capital intensity is per capita labor costs weighted by the ratio of labor costs to capital expenditures. I will give a detailed discussion of the proxies of human capital in part 5.6.

40


employee increased from $86,000 in 1978 to 282,000 in 1997, and the annual growth

rate is about 6.5%. A further investigation of the data shows that average labor

expense per dollar of sales declined consistently from the late 1980’s to 1997. An

implication of this information seems to be that for those firms in my sample data

employee productivity has increased significantly since the late 1980’s. If this is true

it tells us that human capital plays an important role in the development of these

firms. 15

To measure a firm’s leverage ratio I use the ratio of book value of total debt to

book value of total assets. Other measures of leverage are also considered in my

analysis, they are book value of total debt divided by market value of total equity,

and long-term debt divided by book value of total assets and market value of total

equity respectively.

Previous literature has identified several factors that affect capital structure. In

my analysis I choose five factors according to Harris and Raviv(1991) and Raj an

and Zingales(1995): tangibility of assets, growth opportunity, firm size, profitabil

ity and operation risk. There are two reasons for us to focus on these variables.

First, these five factors have been shown most correlated with leverage in previous

studies (e.g., Bradley, Jarrel and Kim(1984), Titman and Wessels(1988), Raj an and

15The improvement in non-human capital may play a critical role in improving the productivity of my sample firms. In the following analysis I use the ratio of tangible assets to total assets to control the effect of non-human assets.

41


Zingales(1995), and Graham(2000)). Second, those factors that have influence on

capital structure but are not taken into account in my analysis have little correlation

with human capital, if judged solely from the economics point of view.16

Firms with valuable asset collateral face relatively less borrowing costs than the

firms without valuable assets to use a pledge in borrowing contract. In my analysis

I use tangibility of assets as the proxy of asset collateral. The rationale underlying

this factor is that tangible assets are easy to collateralize and thus they reduce the

credit risk that debt holders have to undertake. The tangibility of assets is measured

by the ratio of fixed assets to total assets.

Debt can be costly to firms with excellent growth opportunities. Myers(1977)

argues that shareholders sometimes forgo positive NPV investments if project ben

efits accrue to a firm’s existing bondholders. The severity of this problem increases

with the proportion of firm value comprised of growth options, implying that growth

firms should use less debt. In existing literature Tobin’s Q has been widely used

to measure firms’ growth opportunity. In my analysis the proxy for Tobin’s Q is

market-to-book ratio(the sum of preferred stock, the market value of common eq

uity, long-term debt, and net short-term liabilities, all divided by total assets). This

ratio has also been widely utilized in empirical literature.

16For example, some economists find that nondebt tax shields have a significant correlation with leverage( e.g., Bradley, Jarrel and Kim(1984)). This factor, however, is relatively independent with human capital intensity, therefore it should not affect the effectiveness of our test even of we omit this factor in our regression model.

42


Large firms often face lower informational costs when borrowing. Large firms

may also have low ex ante costs of financial distress, perhaps because they are more

diversified or because their size better allows them to “weather the storm”. Most

of empirical research finds a positive relationship between leverage level and firm

size. I measure firm size with log value of sales, total assets and employee numbers

respectively.

The significantly reverse relationship between profitability and leverage has been

found in almost every single empirical work on capital structure. In my work I use

three indicators of profitability. They are return on assets(incomes before extra

ordinary terms on total assets), earnings per share, and after tax returns on net

operation assets.

Firms facing less operation risk should have less credit risk and can often borrow

on relatively favorable terms. This implies that firms with low operation risk should

borrow more than the firms facing high operation risk. Empirical evidences, however,

are mixed in supporting this argument. The volatility of incomes is an excellent

indicator in revealing the operation risk a firm faces. I use the ratio of standard

deviation of past incomes to average incomes to measure incomes’ volatility.

Table 1A and IB present the summary statistics of both dependent and explana

tory variables. Each of the variables exhibits reasonable variations across the sample.

An interesting result is that the mean value of lev_m and lev_ldm are very high,

43


especially lev_m In my analysis lev__m represents the ratio of total debt to market

value of total equity, and lev_ldm represents the ratio of long-term debt to market

value of total assets. The mean value of ev_m is 1.05. This result is actually due to

the outlier characteristics of a certain sample firms. After examining carefully the

sample data I find that most of sample firms have debt to equity(in market value)

ratios much lower than 1 0 0 %, but for some sample firms the ratios are extremely

high. For example, the maximum value of the ratio is 126.87. When taking simple

average of the ratios over all the sample firms the mean value is driven up by those

firms with extremely high debt to equity ratios. In my theory, profitability is an

indicator of the quality of insiders’ human capital. In my empirical test I use labor

costs per employee as the proxy of human capital intensity. Table 2B shows that

the proxies for profitability and the proxy for human capital intensity are positively

correlated.

5.3 Univariate Test

In this part I conduct a univariate comparison of firm’s leverage by quartiles of

human capital intensity. I am interested to know if human capital intensive firms,

such as the firms in the fourth quartile, differ in leverage level from those firms that

are not human capital intensive, such as the firms in the first quartile. I conduct

a t-test on the hypothesis that the mean leverage levels differ between firms in the

fourth quartile and firms in the first quartile.

44


Table 3 displays the mean and median values of leverage ratios in each quartile

of human capital intensity. The first row is the first quartile firms that have the

lowest human capital intensity, the fourth row is the fourth quartile firms that have

the highest human capital intensity, and the second and third quartile firms have the

medium human capital intensity. The last row of the table shows the t-statistics for

testing the difference of mean leverage ratios between the first and fourth quartile.

The results of comparison show that the firms in the first quartile of human capi

tal intensity differ significantly from the firms in the fourth quartile in their leverage

levels. Most of the t-statistics corresponding to the leverage ratio comparison are

above 1 0 % significance level, except for the ratio of total debt to market value of

total equity. The mean leverage ratio of the first quartile firms is significantly lower

than that of the fourth quartile firms at each of the four leverage measurements.

The median leverage ratio of the first quartile firms are also significantly lower than

that of the fourth quartile firms for one half of the leverage measurements( the ratios

of total debt and long-term debt to total assets). An interesting comparison here is

that the mean ratio of total debt to market value of equity for the first quartile firms

is lower than that for the fourth quartile firms, but the mean ratio of long-term debt

to market value of equity for the first quartile firms is significantly higher than that

for the fourth quartile firms. A potential reason for this may be that the insiders of

high market value firms prefer a debt structure biased toward short-term debts in

45


order not to jeopardize their long-term interests in the firm.

It turns out that the average leverage ratios do not always decrease monotonically

across quartiles. For example, the mean value of debt to asset ratio for the second

quartile firms is 0.2425, but for the third quartile firms it is 0.2540. In addition to

this, the median leverage values for the first quartile firms are higher than that of

the fourth quartile firms when leverage level is calculated as the ratio of total debt

to market value of total equity. Therefore just comparing leverage levels between

the first quartile and the fourth quartile firms may not be sufficient to describe the

true relationship between leverage level and human capital intensity.

5.4 Regression A nalysis

To further investigate the relationship between leverage and human capital I apply

three regression methods to estimate model 5.1: They are OLS regression, fixed

effects and random effects panel data approach. In the fixed effects approach I

assume a firm-specific constant term that varies across firms but is constant across

years. In the random effects approach I assume that part of the firm-specific effects

is unobservable. Economically it makes more sense to choose either fixed effects

or random effects approach than to choose the OLS model. This is because any

econometric analysis can not cover all the factors that have significant influence on

firms’ capital structure choices and many of those unknown factors may be very

46


firm-specific. Since my model is a time-series cross-sectional data, it should be more

appropriate to assume an unique firm-specific term for each individual firm.17 This

implies that statistically fixed effects or random effects should fit better my data.

Table 3A present my estimation results.

In this part I use the ratio of total debt to total assets as the proxy of leverage

level, use annual income divided by total debt as the proxy for profitability, and

use log value of sales as the proxy for firm size. Other proxies are considered in the

following robustness check.

All the three approaches confirm the findings of previous research: Leverage

is significantly negatively related with growth opportunity and profitability, and

significantly positively related with firm size and tangibility of assets. All the coeffi

cient estimates on these variables are above 1.6% significance level. The coefficient

estimate for risk has a weakly positive sign in all the three models.

The most important result I find from this part of analysis is that my estimation

results support the prediction of my theory: leverage ratio has a significantly reverse

relationship with human capital intensity. For both fixed effects and random effects

model the coefficient estimates for human capital intensity has a negative sign and

17Fixed effects model implies that individual firms have intrinsically different leverage levels. I believe that this is appropriate because there has not been developed a capital structure theory that can explain completely firms’ capital structure choices. My dissertation develops capital structure theory by introducing an important factor - firm-specific human capital which has been ignored by the previous literature.

47


the significance level is above 1%. The OLS method confirms a weakly negative

relationship between leverage level and human capital intensity.

In the following robust analysis I choose fixed effects model. This is because

all the coefficient estimates in the fixed effects model are significant and the fixed

effects model has the highest R2 (0.8334). among all the three models. The R2 of

the random effects model is 0.1627, and the R2 of the OLS model is 0.1182. By

comparing the estimation results of fixed effects model with OLS model we can

see fixed effects model is better in describing the relationship between leverage and

human capital intensity.

5 .5 Robust Analysis

To investigate the robustness of my results with respect to the chosen proxies for

model variables I apply fixed effects estimation algorithm several times using dif

ferent proxies for profitability, firm size and leverage ratio Table 3B shows the es

timation results. The upper part of the table presents the results using different

measurement of firm size and profitability, and the bottom part of the table shows

the results using different proxies of leverage ratio.

My estimation results in this part is consistent with the results in the first step

regression analysis. I find that the negative relationship between leverage ratio and

human capital intensity is consistently significant for all the different measurements

48


of profitability and firm size, when leverage ratio is measured by total debt divided

by total assets. When using the other measurements of leverage ratio and using the

same explanatory variables as in the first step regression analysis, the relationship

between human capital intensity and leverage level is still negative, but not as

significant as using the ratio of total debt to total asset as the dependent variable.

For the model using the ratio of long-term debt to market value of total equity as

the proxy of leverage level, the relationship is insignificant, but it is still a negative

sign.

The relationship between leverage ratio and tangibility of assets is consistently

positive across all the models using different measurements of leverage, profitability

and firm size, and the relationship is consistently significant across all the models.

The positive relationship between leverage and firm size is also consistently signifi

cant across all the models. Overall, the relationship between firm size and leverage

ratio remain significantly positive when different measurements of leverage ratio,

profitability and firm size are chosen for estimation. Most of the models confirm the

positive relationship between profitability and leverage. My analysis does not find

a significantly negative relationship between risk and firms’ leverage ratios.

Generally speaking, my empirical analysis supports the prediction of my the

ory: firm-specific human capital is an important factor in determining firms’ capital

structure, and the more important the human capital is, the less debt the firm will

49


choose to finance its investment, other conditions equal.

5.6 Further D iscussion o f th e Proxy o f H um an Capital Intensity

In the existing literature there is no consensus with regard to choice of proxy of

human capital intensity. I use per capita labor costs in my analysis. The ratio

nale underlying this proxy is that in a relatively efficient labor market per capita

labor cost is proportional to employees’ marginal contribution to firm production,

and in general it can show how important employees’ human capital is for a firm’s

production.

There are two potential problems in applying labor costs per employee as the

proxy of human capital intensity. The first problem is the relationship that underlies

per capita labor costs and firm-specific human capital. My theory demonstrates that

specificity of human capital is an important determinant of a firm’s capital structure.

This implies that any proxy of human capital intensity used in my empirical test

should be able to indicate the value of firm-specific human capital. Although per

capita labor costs is an increasing function of firm-specific human capital, it can

also be driven up by the increase in the value of general human capital. In order to

single out the relationship between per capita labor costs and firm-specific human

capital there must be a way to control the effect of general human capital. This,

however, is a complex issue in empirical research when using firm level data. I will

50


separate the effect of general human capital from that of firm-specific human capital

in my future research. However, there is no reason to believe that labor costs per

employee will not correlate with the value of firm-specific human capital.

The second problem is due to the location of firm-specific human capital in

employee structure. A firm with a large number of employees does not necessarily

have less valuable firm-specific human capital, compared with a firm with a relatively

smaller number of employees but higher per capita labor costs. The insiders of a

firm may still prefer a conservative debt policy even if only a small percent of total

employees own highly valuable firm-specific human capital. What matters to a firm’s

capital structure is the total value of human capital, not necessarily the per capital

value. Using per capita labor costs may miscalculate the intensity of firm-specific

human capital of a firm because of the delusion effect of employee numbers. It

is difficult, however, in both economic and statistical terms to obtain an accurate

estimation of the total value of firm-specific human capital for an individual firm.

There is also a potential advantage of using labor costs per employee in my test.

Existing research with regard to the interest conflicts between investors and firm

insiders focuses on the agency problems between firm owners and top managers.

My theory, however, does not limit the agency problems to top management. My

theory emphasizes the importance of firm-specific human capital in determining

firms’ financing policies. Valuable firm-specific human capital is located not only

51


in top management but also in lower levels of firms’ organization hierarchy. If the

distribution of firm-specific human capital is biased toward middle and low levels of

organization hierarchy in most of the industrial companies, which I believe is true,

labor costs per employee will be a better indicator of the value of firm-specific human

capital than the compensation of top managers. My sample data comes from the

industry segment of Compustat and it will be better to use per capita labor costs

in my test.

In addition to labor costs per employee several other indicators of human capital

intensity have been proposed by researchers. For example, instead of using per

capita labor costs, Qian(2001) uses the product of per capita labor costs and the

ratio of labor costs to capital expenditures as an indicator of human capital intensity.

The reason for constructing the indicator in this way is that human capital intensity

should be measured relative to the intensity of non-human assets. I also include this

proxy in my analysis. Table 4A and 4B presents my regression results.

Contrary to Qian(2001) my analysis does not find that the proxy proposed by

Qian(2001) is significantly related to any of the leverage ratio variables. In all the

three regression methods (OLS, fixed effects, and random effects), the coefficient on

that proxy is consistently near zero regardless of the choice of leverage ratio variables

and control variables. The coefficient is also consistently insignificant in all the tests.

A potential explanation for these puzzling results is that the new proxy does not

52


measure accurately the importance of labor relative to capital. On the contrary,

it miscalculates the interaction between human capital and non-human assets. My

point here is, it is wrong to regard non-human assets as a complete substitute of

human capital. The interaction of non-human assets and human capital is a complex

issue. These two factors can be complementary in some cases. If a huge amount

of non-human assets is involved in a firm’s production, this may imply that the

employees of the firm need to acquire a significant amount of specific skills during

the production process. It does not necessarily imply that the employees’ firm-

specific human capital plays an unimportant role in the production even if the labor

costs are relatively small compared with the capital expenditures of the firm. I

believe that the function of per capita capital expenditures is mixed in determining

the relative importance of human capital and further research is needed in revealing

the effect of non-human assets.

53


6 Conclusion

A persistent capital structure puzzle is that many profitable firms adopt conserv

ative financing policies and seemingly give up apparently big tax benefits of debt.

In my dissertation, I propose a new explanation to this puzzle based on the speci

ficity of human capital and information incompleteness. My theory demonstrates

that in an incomplete information world where specific human capital is valuable

to production, the pattern of observed debt-equity ratios is consistent with a com

pensation arrangement designed to protect investors from being held up by insiders

who possess unique skills.

An implicit assumption of the existing capital structure theories is that labor

markets are perfectly competitive. It assumes that firm investors can always find

the type of managers and employees which they need and the searching process is

costless. I believe that in reality this is not so. Human capital suppliers have a

monopoly power over their human assets. This monopoly power comes from two

sources: the incompleteness of information and the uniqueness of human skills.

There also is value in preserving the integrity of a firm’s organization. This means

that the process of replacing incumbent managers or key employees with outside

ones is not cost free. With these conditions as background, I analyze the relationship

among investors, firm, and employees. My theory assumes that employees are more

attached to the firm where they work than investors who only invest physical assets

54


in the firm. The attachment of employees to the firm results from the contract

by which investors, through a time-based compensation plan, limit the monopoly

power which the employees have over their human capital. The employment contract

between investors and insiders have a two-fold effect, it protects investors from being

held up by insiders, and it also promotes insiders to seek a safe capital structure

to protect them from being held up by investors. The loss of tax benefits of debt

can be regarded as an agency cost of the employment contract, but the employment

contract saves investors the cost that may incur as a consequence of being held up

by insiders. The employment contract makes it more difficult for insiders to hold

up investors, but at the same time it secures insiders’ investment in firm-specific

human capital.

To test my theory I conduct an empirical analysis over a cross-sectional time

series data from Compustat. The regression results are consistent with the predic

tion of my theory, and in some parts significantly support the theory. My analysis

shows that the indicator of human capital intensity has a consistent significantly

negative relationship with leverage ratios. Previous literature finds that firm size,

tangibility of assets, growth opportunity, and profitability are the most important

factors in determining firms’ capital structure. My analysis confirms the relation

ships that previous research have found between leverage ratio and these factors.

I find that leverage ratios are significantly positively related to firm size and tan-

55


gibility of assets, and significantly negatively related to growth opportunity and

profitability. Existing empirical evidences are mixed in identifying the relationship

between leverage level and operation risk. In my analysis I find that leverage ratios

and operation risk are weakly related, and the interaction term of labor costs and

capital expenditures is not related to leverage ratios. Most important, my study

shows an inverse relationship between leverage and human capital intensity.

56


Appendix 1

Proof of Theorem 1.

It is optimal for the investor to pay the entrepreneur only market equilibrium

wage, and have the right of replacing the incumbent manager and setting up the

additional project.

Note if the entrepreneur participates in the project, he becomes the incumbent

entrepreneur and his expected income is E (w i + w3 \t = 0) = 2w\. If he does not, he

becomes the rival entrepreneur and his expected income is w\ + Eq{w \̂ the value of

r realized at time 2)=w\ + \* w i + \ * w h = 2wx. Therefore the entrepreneur always

has incentive to participate in the project at time 0 regardless the probability he is

replaced at subsequent times.

The N P V of the project E (Y i+ Y 3\t = 0) - 2*uq - K — 0, therefore the investor

always has incentive to finance the project at time 0 .

Q.E.D.

57


Appendix 2.

Proof of Theorem 2.

Under the contract specified in theorem 2, the incumbent entrepreneur has the

power to set up the additional project if prob(i — H \ Yi) > i After the additional

project is set up at time 1 , the expected return by continuing hiring the incumbent

is:

ERi=prob(i = H | Yx)*(-Kih + ̂ n h)+ prob(i = L | Pi) * (nzi -f Ani) - (w3 + auq).

Note ERj > \ - wh) + $ix3/ - wt) - a w t + I (A2.1)

By applying Assumption 9, a — / w\ to A2.1, we have ERj > | (ttzh — wh)

—1(^31 — wi) + I. Based on assumption 8 we have ERj > (TTrh — w^).

If replacing the incumbent with the high quality rival, the expected investment

return is ElUh = (iTrh — Wh)- Therefore ERj is larger than E R ^, and the investor

has no incentive to replace the incumbent even if at time 2 r equals to H.

The expected payoff for the entrepreneur is:

EW=(1 — |a)u>i + prob (prob(i — H\Y$ > | ) * (wz + awi)-\-

prob {jyrob{i = H\Yi) < | ) * {porb(r = L) * (wz + aw \) + porb(r — H) * Wz}

58


EW(t — 0)=(1 — §a)w1 + |(w 1 + mu1) + | { |(w i + aw i) + |w i} = 2wx, therefore

the entrepreneur has an incentive to take part in the project.

The expected payoff for the investor is:

E R i= | * (nxh + nu) - (1 - \ot)wi +prob (prob(i = H\YX) > | ) {prob(i = H\YX) *

(n3h+ A n h)+prob(i = L\Yx)*(n3i+ A n i)- (w 3+ a w x)-I }+ p r o b (prob(i = H\YX) < | )

{prob(r = L) * \prob(i = H\YX) * n3h + prob(i = L|Yi) * nsi - (w3 + aw x)] +

prob(r = H )* [(nrh - wh) + (A nh - I ) ] } - K.

ERi(t = 0) = \ * (nxh + nxi) - (1 - \a )w x + \ J™(prob(i = H\YX) * {n3h +

A nh) + prob(i = L|TX) * (7r3/ + A n t) - (w3 + a^ i) - I)dYx + \ { \ J^1 (prob(i =

+pro6(i = L\Yx)n3i - (w3 + aw x))dYx + § [(7Tr/l - w?,) + (A ^ - /)]} - i f

= |(7rm + ttu) - (1 - \a)wx + |( |(7 r3/l + Att^ - wh) + \(n 3l + A nt -

wi) - aw x - /) + \{{\{nzh - w h) + \{n 3l - w i ) - a w i ) + \ \{nrh - wh) + (A7rft - I ) \ - K

= - u>h) + \{A n h - Att;) - \{n 3i - w;)

According to assumption 5, 6 and assumption 10 ERi(f = 0) > ^ A ^ — I),

therefore the investor has incentive to set up the project and allow the entrepreneur

to set up the additional project at time 1 if prob(i — .HjYi) >

Q.E.D.

59


Appendix 3.

Proof of Claim 1.

Let is first consider what will happen to the incumbent entrepreneur if r = H

at time 2. If continue hiring the incumbent at time 3, the expected payoff for the

investor will be:

ERj = prob(i = H\Yi)(nzh - wh) + (prob(i = L\Yi)(tt3i - wt) +

max{0 , prob(i — H\Yx)AiTh + prob(i — L\Yi )A tti — 1}

ERj(t = 0) = l(irsh - wh) + | ( 7r3/ - wt).

If hiring the rival entrepreneur,

ERrH = (TTrh - Wh) + (A7Tfe - I).

According to Assumption 10 and 11, ERr# >ERj(t = 0), therefore, based on

the information available at time 0 , the incumbent entrepreneur believes he will

be replaced if r = H at time 2. To preserve the incentive of the entrepreneur to

participate in the project, the overpayment wc at time 3 must satisfies the following

condition:

(1 — a)w i + prob (r = H ) w 3 + prob (r = L) (w ̂+ wc) > 2wx.

This implies wc > 2aw\. Suppose the investor choose to pay the incumbent

rc3 + 2awx if he continues hiring the incumbent at time 3. The expected payoff for

the investor under this compensation arrangement is:

60


ERc—̂ (Ttih+iTu)- ( l -a )w i+ p r o b (r = H) {prob(ERrH > ERi\r = H)({'Krh-

wh) + (A7T/J - /)) + prob(ERrH < Ei?j|r = H )E R i} + prob(i — L){prob{i =

# |Yi)7r3h +prob(i = L\Yi )tvzi

+ max {0, prob(i = if|Y i)A 7Tft + prob(i = L\Yi)A tti — 1} — (w3 + 2awi)} — K

E R c ( t = 0) = I * (7Txfe + TTU) - (1 - a ) w ! + |((7Trh - Wft) + (A7rft - / ) ) H- |[ |( v r 3/l +

7r3i) - (1 + 2 a)wi] - i f

= |(7 T rfl - Wh) + ^ A ^ - I ) - (7T31 - 10;)

According to Assumption 6 and Assumption 10,

ERc(£ = 0)—ER/(t = 0) < 0.

Q.E.D.

61


Figure 1

Labor Expenses aid Ratios

0.11300

0.105250

200 0.1

0.095

0.09100

0.085

0.0888 89 90 91 92 93 94 95 96 97

- ■ Average Labor (in $ 10 Millions) — — Average Sales/employee (in $000,3

— Avenge labor/sales________________________________________________

62


Table 1A

Summary Statistics

HI is the indicator of human capital intensity, calculated as firm's per capita labor costs. HI2 is HI weighted by the ratio of labor costs to capital expenditures. Lev_a is the total debt divided by total assets, and levjm is the total debt divided by the sum of market value of common equity. Levjda is the long-term debt divided by total assets, and levjdm is the long-term debt divided by the market value of common equity. Tangible is net property, plant and equipment divided by total assets. Roa is the ratio of income before extraordinary items to total assets. Roal is the earnings per share, and roa2((aftertax return on net operating assets) is operating income after depreciation and amortization divided by net operation assets. Tobinq is market-to-book ratio, sizel is the log value of sales, size2 is the log value of total assets, and size3 is the log value of employee numbers. Risk the standard deviation of income divided by the mean income. HI is measured with thousands dollars. All the other variables, except, roal (earnings per share), risk and size variables, are the value in percentage.

Variable N Mean Std Dev Minimum Maximum

HI 2000 49.1246 33.7099 0.0375 377.5129HI2 2000 620.7193 2416.0000 0.0001 88204.0000lev_a 1999 0.2569 0.1833 0.0000 1.1512lev_m 1999 1.0527 3.9148 0.0000 126.8726levjda 2000 0.1779 0.1451 0.0000 0.8697levjdm 2000 0.5249 2.3493 0.0000 91.9205tangible 2000 0.4090 0.2764 0.0012 0.9423roa2 1668 0.2192 2.5020 -60.7500 74.1677roal 2000 1.7265 4.5560 -57.5200 144.1500roa 2000 0.0355 0.0860 -1.4454 0.7499tobinq 2000 0.8299 0.8914 0.0025 11.3849sizel 2000 6.8340 2.3139 -5.8091 12.0361size2 2000 7.0661 2.3654 -0.8301 12.6685size3 2000 1.7796 2.1088 -6.2146 6.6530risk 1982 2.1241 17.5814 0.0000 675.0015

63


Table IB

Correlation Matrix

This table displays Pearson correlations between the variables in my model. The significance levels of the correlation are below the correlation coefficients. Refer to Table 2A for variable definition.

HI HE2 Sevjt tev_nt ““ le v jd a , lev.ldm tangible n>&2 roal m s tobinq sse2 si» 3 n *

HI I.CGQO

HtZ 0.3439

<0.0001

1.0000

fev_a 41.0319

0.1474

-0.0423

0.0384

I.OCOO

lev_m 0.1403

<.0001

0.0478

0.0327

0.3713

<0001

1.0000

lev_Ma •o.i a n

<0001

-0.1320

<0.0001

0.7431

<0001

0.0821

0.0002

1,0000

lev_ldns -0.0298

0.1741

-0.0114

0.6117

0.2317

<0001

0.8394

<.0001

0.2274

<0001

1.0000

tangible •0.3294

<0001

-0,261!

<0.000!

9.1736

<000!

-0.0552

0.0/31

0-42W

<0001

0.0753

0,0096

1.0080

ro&2 0.0218

0.3680

0.0312

Q.2Q2S

0,0002

0.9932

•0.0031

C.8977

i i -0.0033

0.8908

•0.0462

0.0565

1.0000

roal 0.0824

0.0002

-0.0190

0.3948

41.0159

0.4702

-0.0473

0.0316

-0.0205

0.3506

-0.0818

0.0002

-0.0045

0-9382

0.0107

0.6586

1.0000

roa 0-0446

0.G42J

-0.0333

0.0865

-0.26S2

<000!

•0.1346

<0001

-0.1326

<000!

-0.1125

<0001

-0.1017

<000!

0.0720

0,0030

0.1837

<000!

1.0000

tobinq •Q.0804

0.0002

-9,0373

0.0097

-0.3196

<0001

-0.1921

<0001

•0.2306

<.0001

-0.1425

<0001

-0.0926

<.0001

0.0494

0.0393

0.0159

0.4689

0.2784

<0301

1.0000

sue! 0.0379

0.0844

-0.1569

<0.000!

0.1091

<0001

0.0452

0.0400

0.1399

<0001

0.0247

0.2609

0.1611

<0001

0.0028

0.9069

0.1718

<0001

0.1742

<0001

•0.1142

<0001

1.0000

sbse2 0.1513

<.0001

-O.M80

<0.0001

0.190S

<0001

0.1934

<0001

0.1436

<0001

O.Q313

0.1541

0.1261

<000!

-0.0051

0.9315

0.1812

<0001

0.1212

<000!

-0.1645

<.0001

0.9502

<0001

1.0000

sbe3 -0,1647

<0001

0.1905

<0.9001

0.1321

<0001

0.0345

0.1171

0.1862

<0001

0.0407

0.0643

0.2009

<.0001

-0.0019

0.9362

0.1323

<0001

0.1033

<.000!

-0.1086

<0001

0.9326

<000{

0.8709

<0001

1.0000

rsk -0.0189

0.3927

0.OO79

07258

09326

01396

0.0041

0.3539

0.0277

0.2098

0.0067

0.7614

•0.0341

0.1223

•0.0037

0.8780

-0.0406

0.0653

•0.0394

Q.Q066

-0.0208

Q.3471

-0.0250

0.2572

•0.03Q7

0.1643

-0.0245

0.2682

1.0000

64


Table 2

Univariate Test

This table displays the mean and median values o f leverage ratios in each quartile o f human capital intensity. In the last row, the differences o f the mean value between the first and fourth quartile is calculated and the t-statistic is shown in the parentheses. HI is human capital intensity as defined before, first quartile includes the firms with the lowest human capital intensity, the fourth quartile represents the firms with highest human capital intensity, and the second and third quartile are those firms with medium intensities. Lev a - Lev ldm are those leverage ratios defined before.

HILev_a

mean median

Lev

mean median

Levjda

mean median

Lev_

mean

Jdm

median

1 rstQuartile 0.2916 0.2639 1.1424 0.2582 0.2065 0.1774 0.7000 0.17782ndQuartile 0.2425 0.2439 0.8175 0.2813 0.1803 0.1595 0.5346 0.18903rd Quartile 0.2540 0.2533 0.7161 0.3330 0.1873 0.1741 0.4849 0.22814th Quartile 0.2395 0.2144 1.5347 0.3347 0.1374 0.0900 0.3798 0.1928

T-Test 4,25(<0.0001) -1.23(0.2201) 7.63(0.0001) 1.69(0.0923)

Lev a Lev m Lev Ida Lev IdmHI2

mean median mean median mean median mean median

1rstQuartile 0.2911 0.2858 0.6722 0.3323 0.2277 0.2255 0.4874 0.25262ndQuartile 0.2763 0.2879 1.0102 0.3591 0.2118 0.2102 0.6916 0.27473rd Quartile 0.2367 0.2220 0.6163 0.2075 0.1761 0.1555 0.4149 0.15274th Quartile 0.2320 0.1619 1.9065 0.2474 0.1075 0.0533 0.5239 0.1121

T-Test 4.54(0.0001) -5.75(0.0001) 13.00(0.0001) -0.43(0.6706)

65


Table 3A

Regression Results

Table 3 A presents the regression results o f three regression models: OLS, Fixed effects panel data model and random effects panel data model. Parameter estimates(with t-statistics in parentheses) and R-Squares are listed for each model.

VariableDepent Variable: Lev a

OLS Fixed Effects Random Effects

Intercept 0.2290 (15.00) 0.2167 (7.92)HI -0.00003 (-0.26) -0.0006 (-4.93) -0.0005 (-4.44)

sizel 0.0080 (4.69) 0.0113 (2.42) 0.0089 (2.68)tangible 0.0760 (5.19) 0.1351 (4.52) 0.1054 (4.39)

roa -0.4207 (-8.86) -0.2890 (-9.5) -0.3004 (-10.03)tobinq -0.0505 (-11.12) -0.0294 (-6.74) -0.0326 (-7.86)

risk 0.0002 (1.09) 0.0002 (2.01) 0.0002 (1.95)R-Square 0.1627 0.8334 0.1182

66


Table 3B

Robust Tests

Table 3B shows the regression results of the models with different proxies for the dependent variables and also explanatory variables, such profitability and size. Each model is treated as a fixed effects panel data model.

Dependent Variable: Lev a

Models:Variable 1 2 3 4

Intercept

HI -0.0006 (-4.85) -0.0089 (-4.35) -0.0007 (-5.47) -0.0005 (-4.11)sizel 0.0073 (1.55) 0.0140 (2.54)size2 0.0226 (4.85)size3 0.0081 (1.53)tangible 0.1748 (5.76) 0.1700 (5.30) 0.1544 (5.14) 0.1251 (4.23)roa -0.2825 (-9.32)roal -0.0003 (-0.67)roa2 0.0011 (1.23) -0.2767 (-9.17)tobinq -0.0325 (-7.29) -0.0349 (-7.51) -0.0301 (-6.94) -0.0292 (-6.67)

risk 0.0003 (2.53) 0.0003 (2.40) 0.0002 (2.12) 0.0002 (1.99)R-Square 0.8251 0.7884 0.8348 0.8348

Dependent Variables:

Lev a Lev m Lev Ida Lev 1dm

InterceptHI -0.0006 (-4.93) -0.0062 (-1.21) -0.0002 (-1.86) -0.0014 (-0.39)sizel 0.0113 (2.42) -0.1605 (-0.86) 0.0066 (1.48) 0.0382 (0.30)tangible 0.1351 (4.52) 4.1094 (3.43) 0.0801 (2.79) 3.2078 (3.93)roa -0.2890 (-9.5) -3.0087 (-2.46) -0.0399 (-1.37) -1.4797 (-1.78)tobinq -0.0294 (-6.74) -0.3127 (-1.78) -0.0252 (-6.01) -0.2271 (-1.91)risk 0.0002 (2.01) -0.0017 (-0.40) 0.0002 (1.69) -0.0006 (-0.21)R-Square 0.8334 0.4157 0.7544 0.2485


Table 4A

Regression Results(HI2)

Table 4A presents the regression results with HI2 as the proxy of human capital intensity. Part I shows the results when only HI2 is used in the models, and Part II shows the results when both HI and HI2 are included in the regression models.

Part I



Intercept 0.2401 (16.44) 0.2008 (7.22)HI2 0.0000 (-0.90) 0.0000 (-0.44) 0.0000 (-0.42)sizel 0.0060 (3.41) 0.0076 (1.64) 0.0064 (1.92)tangible 0.0815 (5.61) 0.1502 (4.94) 0.1242 (5.16)roa -0.4065 (-8.50) -0.2861 (-9.30) -0.2967 (-9..79)tobinq -0.0504 (-11.02) -0.0295 (-6.62) -0.0324 (-7.65)risk 0.0002 (1.07) 0.0002 (1.89) 0.0002 (1.86)R-Square 0.1591 0.8299 0.1081

Part II



Intercept 0.2330 (14.71) 0.2155 (7.52)HI 0.0002 (1.16) -0.0007 (4.81) -0.0005 (-4.19)HI2 0.0000 (-1.21) 0.0000 (0.55) 0.0000 0.46)

sizel 0.0057 (3.18) 0.0122 (2.59) 0.0088 (2.60)tangible 0.0862 (5.72) 0.1343 (4.42) 0.1080 (4.41)

roa -0.4066 (-8.50) -0.2856 (-9.34) -0.2960 (-9.83)tobinq -0.0499 (-10.86) -0.0291 (-6.56) -0.0320 (-7.60)

risk 0.0002 (1.10) 0.0002 (2.05) 0.0002 (1.98)R-Square 0.1597 0.8321 0.1157


Table 4B

Robust Tests(HI2)

Table 3B shows the regression results of the models when HI2 is used as the proxy for human capital intensity along with different proxies for the dependent variables and also explanatory variables. Each model is treated as a fixed effects panel data model.

Dependent Variable: Lev a

Models:Variable 1 2 3 4

InterceptHI2 0.0000 (-0.50) 0.0000 (-1.99) 0.0000 (-0.37) 0.0000 (-0.29)sizel 0.0037 (0.79) 0.0106 (1.92)size2 0.0176 (3.82)size3 0.0124 (2.36)tangible 0.1901 (6.15) 0.1959 (6.12) 0.1688 (5.51) 0.1406 (4.71)roa -0.2776 (-9.09) -0.2814 (-9.19)roal -0.0003 (0.68)roa2 0.0011 (1.22)tobinq -0.0324 (-7.71) -0.0349 (-7.40) -0.0302 (-6.81) -0.0298 (-6.70)risk 0.0003 (2.40) 0.0003 (2.37) 0.0002 (1.97) 0.0002 (1.95)R-Square 0.8217 0.7842 0.8311 0.8302

Dependent Variables:

Lev a Lev m Lev Ida Lev 1dmInterceptHI 0.0000 (-0.44) 0.0000 (0.08) 0.0000 (0.08) 0.0000 (0.12)sizel 0.0076 (1.64) -0.1918 (-1.03) 0.0059 (1.31) 0.0326 (0.26)tangible 0.1502 (4.94) 4.4581 (3.63) 0.0815 (2.79) 3.3375 (.3.99)roa -0.2861 (-9.30) -2.9466 (-2.37) -0.0412 (-1.39) -1.4661 (-1.73)tobinq -0.0295 (-6.62) -0.3209 (-1.78) -0.0250 (-5.85) -0.2314 (-1.89)risk 0.0002 (1.89) -0.0019 (-0.43) 0.0002 (1.62) -0.0007 (-0.22)R-Square 0.8299 0.4148 0.7519 0.2477

69


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