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UNIVERSITY OF CALIFORNIA
Los Angeles
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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© Copyright by
Guohua Yang
2003
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The Dissertation of Guohua Yang is Approved.
Ekaterini K1 idou
Fred Weston
/A ........................................
Harold Demsetz, Committee Chair /Y
University of California, Los Angeles
2003
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For
My parents: Qi Yang and Fengqin Chong
&
Wei Liu
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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
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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.
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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
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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
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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.
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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
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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.
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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).
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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.
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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.,
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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.
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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.
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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
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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
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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
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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
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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
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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).
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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
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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
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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,
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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.
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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.
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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
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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.
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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
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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
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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$\t — 0) — 2 * w\ — K — 0.
3.3 Equilibrium Contract under Incom plete Information
If the investor is not completely passive, however, the equilibrium result will be
different from what Noe and Rebello (1996) and Hart and Moore(1994) demonstrate.
I suggest that the investor can underpay the incumbent in the initial stage of the
employment contract and overpay him at the ending stage of the contract. Such a
compensation arrangement can prevent the incumbent’s opportunistic behavior and
in equilibrium the incumbent only earns a market equilibrium return on his human
capital.
Formally, realizing the possibility of being held up by the incumbent, the investor
agrees to pay the incumbent only wnl — { 1 — a) * w\, (0 < a < 1 ) at time 1, and
commits to pay him wn 3 — a * + W3 at time 3. The incumbent can only earn
W3 at time 3 from the labor market if he withdraws his human capital from the
project. Therefore the incumbent’s threat to withdraw becomes incredible, since
the incumbent will lose a * w\ at time 3 If he indeed withdraws. In total the
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incumbent will be paid market equilibrium wage rate Wi + w3.
Assumption 9. a = (^3/ — wi)/w\.
AssumptionlO. \{-Krh - wh) > (w3i - wt) > A (irrh - wh).
Assum ptionll. A i — A iq > ~ wh)-
Assumption9 implies that the incumbent entrepreneur will lose w%i — wi if he
is replaced by the rival entrepreneur. Note that w$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.
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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,
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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
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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
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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
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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
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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,
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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.
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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
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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.
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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
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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
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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.
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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.
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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.
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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,
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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.
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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
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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
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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.
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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
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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
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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
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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
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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
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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.
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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
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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-
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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.
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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.
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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}
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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.
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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:
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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.
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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________________________________________________
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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
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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
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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
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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
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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
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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
VariableDepent Variable: Lev a
OLS Fixed Effects Random Effects
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
VariableDepent Variable: Lev a
OLS Fixed Effects Random Effects
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
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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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