DETERMINANTS OF LEVERAGE AND AGENCY PROBLEMS

DETERMINANTS OF LEVERAGE AND AGENCY PROBLEMS

by

Abe de Jong and Ronald van Dijk*

Fifth draft, December 2002

JEL classification: G32, G34

Keywords: Capital structure; The Netherlands;

Agency problems; Corporate governance

* Erasmus University Rotterdam and ING Investment Management, respectively. Correspondence to: Abe de Jong, Erasmus University Rotterdam, Department of Financial Management, P.O. Box 1738, 3000 DR Rotterdam, the Netherlands, e-mail [email protected], tel +31-10-4081022. The views expressed in this paper are ours and not necessarily those of ING. The authors are grateful to Arturo Bris, Hans Degryse, Uli Hege, Jim Mahar, Piet Moerland, Theo Nijman, Steven Ongena, Aswin van Oijen, Miguel Rosellón Cifuentes, Chris Veld, participants of seminars at McGill University, Groningen University and Tilburg University and participants of the 1998 European Financial Management Meetings (Lisbon, Portugal), the 1998 European Finance Association Meetings (Fontainebleau, France), the 1998 FinBel Meetings (Rotterdam, the Netherlands) and the 1999 Southern Finance Association Meetings (Key West, U.S.A.) for providing many helpful comments. The usual disclaimer applies.


Abstract

In this paper we empirically investigate the determinants of leverage and agency problems, and we examine the

relations between leverage and agency problems. We apply two approaches. First, we use publicly available data

and estimate a regression model in which we explain leverage and Tobin’s q, a proxy for agency problems.

Second, we use private data obtained through questionnaires. We estimate a regression model in which we

explain leverage and four agency problems, i.e. direct wealth transfer, asset substitution, underinvestment and

overinvestment. The two approaches are carried out on the same sample of Dutch firms. Irrespectively of the

type of data, our results confirm that the trade-off between tax advantages and bankruptcy costs determines

leverage. Free cash flow and corporate-governance characteristics appear to be determinants of overinvestment.

Despite findings that agency problems are present, we find no evidence for direct relations between leverage and

the agency problems. Our study shows that tests on public and private data are complementary and both

approaches contribute to our understanding of leverage and agency problems.

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1. Introduction

A vast and rapidly growing literature deals with potential relations between the debt-equity ratio and agency

problems. Three well-known predictions prevail. First, leverage aggravates agency conflicts between bondholders

and shareholders. Frequently cited examples are the direct wealth-transfer problem, the asset-substitution

problem and the underinvestment problem (see Smith and Warner (1979), Jensen and Meckling (1976) and

Myers (1977), respectively). Second, leverage mitigates agency problems that arise from managerial behavior that

conflicts with the interest of shareholders. A well-known example is the overinvestment problem (see e.g. Jensen

(1986)). Finally, the relative amount of debt raises the costs of agency problems with stakeholders like customers

and employees (see e.g. Titman (1984)). Leverage is merely one potential factor in agency problems as the

problems can be aggravated and mitigated by numerous factors. For example, the overinvestment problem is

related to leverage, free cash flow, growth opportunities and several corporate-governance characteristics, such

as bank relations and managerial option plans (Shleifer and Vishny (1997)). Previous empirical studies provide

tests of the relevance of agency problems in a capital-structure setting. Examples for US firms are Titman and

Wessels (1988), Smith and Watts (1992) and Lang, Ofek and Stulz (1996). Rajan and Zingales (1995) study the

determinants of capital structure for G-7 countries.

We study non-financial firms, listed on the Amsterdam Exchanges.1 Corporate-governance structures

in the Netherlands differ from those in the US setting, which constitutes the cradle of most corporate-governance

theories. A market for external corporate control is virtually absent, while in the US hostile takeovers prevail (see

Shleifer and Vishny (1997, p.756)). In the Dutch legal system firms are allowed to build up an array of technical

takeover defenses. The defenses reduce the disciplining of the market for corporate control and they limit the

influence of shareholders on managerial decisions. La Porta, Lopez-de-Silanes, Shleifer and Vishny (1998)

describe for 49 countries the level of shareholder-rights protection. On a scale from zero (no protection) to six

(high protection), Dutch firms receive a score of two. This is very low in comparison with the score of five for

firms in the US and UK. On the other hand, Dutch firms resemble aspects of firms in other European countries.

Germany and France score one and three, respectively. While external control is weak, the prerequisites for an

effective internal or arm-length control system are in place. The prevalent mechanisms are a two-tier supervisory

and management board, large shareholdings and long-term involvement of banks (see De Jong, DeJong, Mertens

and Wasley (2001)). These devices differ from the situation in the United States where a mixed-tier system is

commonly in place and large shareholdings are rather uncommon (see Shleifer and Vishny (1997, p.754)). We

expect that shareholder-bondholder agency problems are not significantly present , because of the limited

influence of shareholders on managers and the strong position of banks. On the other hand, in the Dutch

corporate-governance system we expect the overinvestment problem to be relevant in case internal control

mechanisms fail.

In this study we apply two approaches. Our first approach is similar to earlier studies for US samples

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in which publicly available data is used.2 We estimate reduced-form equations to explain leverage (see e.g. Titman

and Wessels (1988)) or Tobin’s q (see e.g. McConnell and Servaes (1995)). We also test a simultaneous model in

which the endogeneity of leverage and q is taken into account. In our second approach we use questionnaire data

to retrieve private information from managers. We obtain scores on one or more questions per variable from

financial managers and use confirmatory factor analysis to estimate the optimal weighting. We weight the

questions and obtain values per variable, which are used in regression models to estimate the relations between

variables. We measure variables such as leverage and the presence of four agency problems, i.e. direct wealth

transfer, asset substitution, underinvestment and overinvestment. It is important to notice that the method in this

paper differs substantially from most existing studies in our field that use questionnaires. In previous papers the

questions directly concern the relations between firm characteristics, while in the present paper the relations are

estimated from the information about the characteristics.3

Our three main findings are as follows. First, we show that the trade-off between tax advantages and

bankruptcy costs determines leverage. The positive effect of collateral value of assets is present in both analyses.

For the public data, size also has the expected positive effect, while in the private data the hypothesized impact of

industry risk and the marginal tax rate is found. Second, we find that overinvestment is highly relevant in Dutch

firms, but contrary to studies for US samples (e.g. Smith and Watts (1992) and McConnell and Servaes (1995)),

we do not find that leverage is used as a disciplining device. Free cash flow, which is required for overinvestment

has a negative effect on leverage (public data) and a positive effect on overinvestment (private data). We find a

positive influence of insider shareholdings and a negative impact of takeover defenses and block holdings on firm

performance, measured as q (public data) and reduced overinvestment (private data). The latter findings are a

result of the typical governance structure in the Netherlands. Our third result is that both analyses provide no

evidence for the relevance of three agency problems between shareholders and bondholders, i.e. direct wealth

transfer, asset substitution and underinvestment. This result contrasts with US studies, such as Smith and Watts

(1992) and McConnell and Servaes (1995). We conjecture that this finding is caused by the weak position of

shareholders and the strong position of banks.

The contribution of the paper is twofold. First, we document the relevance of agency problems outside

the often-studied American setting. Our results contradict previous findings for the US. This shows that the

institutional setting is relevant for agency problems. Second, we introduce a novel method for the empirical

testing of agency theories, i.e. regression analysis on questionnaire data. This approach has a distinct advantage.

While the outcome of capital structure choice is published in the firm’s annual report, some driving forces of

these choices are less visible. According to agency theory, divergent interests and information asymmetries

determine capital structure choice. The information differences are caused by superior information of insiders.

We study moral hazard problems, in which insiders may harm outsiders, after contracts are signed. Relevant

variables concern the likelihood of different kinds of moral hazard behavior. Questionnaire data allow us to

measure these variables, because they are obtained from the insiders themselves. Although respondents’ bias is a

disadvantage of private data, the subjectivity of the data is minimized through the design of the questionnaire and

factor analysis. Moreover, the respondents are not asked to judge the relevance of the theoretical relations but

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only about plain characteristics of their firms. Using questionnaire data should be seen as complementary to the

use of publicly available accounting and stock-market data. The trade-off is between the transparency and

objectivity of publicly available data and the rich data set obtainable from questionnaires. Our comparison of the

results for public and private data shows a strong resemblance, which endorses the reliability of both analyses.

The remainder of the paper is structured as follows. In Section 2 we describe capital-structure theories

and the resulting hypotheses. In Section 3 we describe our first empirical test, based on publicly available data. In

Section 4 our tests with private questionnaire data are discussed. Section 5 discusses the advantages and

disadvantages of public and private data. Finally, Section 6 concludes.

2. Capital-structure theories and hypotheses

In the path-breaking paper of Modigliani and Miller (1958) the debt-equity choice is irrelevant to the value of the

firm. This proposition is derived under stringent assumptions and successive research describes factors that

make capital-structure decisions relevant. See for a comprehensive overview of the literature, for example, Harris

and Raviv (1991). We briefly summarize the theories that we test in the present article. In addition, we discuss

empirical studies and present our hypotheses.

2.1. Taxes, bankruptcy costs and leverage

Under the assumption of a capital market in which corporate taxation is the single market imperfection,

Modigliani and Miller (1963) show that firms prefer debt financing if interest costs are tax-deductible. DeAngelo

and Masulis (1980) claim that the actual impact of the deductibility depends on the existence of a crowding-out

effect by non-debt tax shields. The empirical implications are that an increasing marginal tax rate positively

influences leverage and that non-debt tax shields, such as depreciation, tax-loss carry forwards and investment

tax credits, reduce leverage. MacKie-Mason (1990) approximates non-debt tax shields by tax-loss carry

forwards and investment tax credits. Titman and Wessels (1988) provide an indirect measure of non-debt tax

shields. Graham (1996) uses similar proxies for the US and adds a simulated marginal tax rate.

After Modigliani and Miller (1963) a line of research emerged in which direct and indirect bankruptcy

costs are introduced. An increase of debt induces an increased probability of distress and eventually liquidation.

Consequently, the relative amount of debt is expected to decrease with the failure probability and with the costs in

case of financial distress or bankruptcy. In empirical studies failure probability is frequently approximated by the

variance in earnings (e.g. Titman and Wessels (1988)) and by measures for size (e.g. Titman and Wessels

(1988)). Bankruptcy costs are estimated by the inverse of a proxy for the collateral value of assets (e.g. Titman

and Wessels (1988)).

2.2. Agency problems and leverage

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2.2.1. Shareholder-bondholder conflicts

In the shareholder-bondholder conflicts the (representatives of) shareholders make decisions transferring wealth

from bondholders to shareholders. However, the bondholders are aware of the situations in which this wealth

expropriation may occur. Therefore, they will demand a higher return on their bonds. Shareholders, foreseeing

the bondholders’ reaction, can mitigate the potential conflicts. Three conflicts can be distinguished: direct wealth

transfer, asset substitution and underinvestment. In the case of direct wealth-transfer conflicts, dividends are

increased or debt with higher priority is issued (Smith and Warner (1979)). In the case of asset substitution, the

firm substitutes current projects for projects which have higher risk (Jensen and Meckling (1976)). If the

bondholders are compensated given the risk of the current projects, wealth is transferred from bondholders to

shareholders. In Myers’ (1977) underinvestment problem, growth options will not be exercised because, due to

the overhang of debt, equity required to finance these growth opportunities will not be provided by shareholders.

The shareholder-bondholder conflicts can be mitigated by adjusting the properties of the debt contract. This can

take several forms. First, the debt contract can be adjusted by including covenants (Smith and Warner (1979)).

For example, a covenant can contain restrictions on the payment of dividends or the disposition of assets.

Second, debt can be secured by collateralization of tangible assets in the debt contract (Stulz and Johnson

(1985)). Third, convertible debt or debt with warrants can be issued (Jensen and Meckling (1976) and Green

(1984)). Fourth, the maturity of debt can be shortened (Myers (1977)).

The empirical studies related to the shareholder-bondholder conflicts mainly focus on the degree to

which a firm can secure its debt and the firm’s growth opportunities, both in relation to the relative amount of

debt. In Titman and Wessels (1988) the relative amount of fixed assets is used to approximate the relative amount

of secured debt, which is a potential mitigating factor of wealth distribution and asset substitution. Titman and

Wessels find no significant relation. In Titman and Wessels (1988), Smith and Watts (1992), McConnell and

Servaes (1995) and Lang, Ofek and Stulz (1996) variables are used to approximate growth opportunities, which

are hypothesized to aggravate underinvestment. Titman and Wessels (1988) do not find the expected negative

influence of proxies for growth opportunities on leverage, whereas Smith and Watts (1992) find the predicted

effect. McConnell and Servaes (1995) and Lang, Ofek and Stulz (1996) perform similar tests for a sub-sample of

high-growth firms. The former study notices a significantly negative relation between growth opportunities and

leverage, while the latter finds no relation. Barclay and Smith (1995) investigate the maturity structure of debt as

a mitigating factor of underinvestment. Consistent with expectations, the authors find a significantly negative

relation between growth opportunities (approximated by the market-to-book ratio and R&D expenditures) and

maturity of debt.

2.2.2. The shareholder-management conflicts

The conflicts between shareholders and management that stem from the separation of ownership and control are

introduced by Jensen and Meckling (1976). The authors argue that in case managers are not or only partial

owners of the firm, the costs of the consumption of perquisites are not or partially born by the managers. The

overinvestment problem of Jensen (1986) is a special case of their theory. According to the overinvestment

hypothesis, managers have incentives to cause their firm to grow beyond the optimal size and to accept projects

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with a negative value to the firm. Jensen argues that overinvestment is aggravated by more free cash flow and

less growth opportunities. The overinvestment problem can be mitigated by issuing debt and Jensen refers to its

non-discretionary nature as the disciplining role of debt. Alternative mechanisms to control overinvestment exist.

First, the managers’ income can be made dependent upon the performance of the firm. This can be accomplished

by means of managerial shareholdings, option plans or by compensation schemes. Second, internal and external

corporate-control mechanisms may mitigate overinvestment. The internal control mechanisms include monitoring

by the board, large shareholders or banks. An example of an external control mechanism is the market for

corporate control, which is characterized by hostile takeovers.

Several empirical studies examine the overinvestment problem by analyzing the relation between growth

opportunities and free cash flow on the one hand, and leverage on the other hand. Smith and Watts (1992) do not

differentiate between overinvestment and underinvestment and find the predicted negative relation between debt

and growth opportunities. McConnell and Servaes (1995) amend the test by examining the overinvestment

hypothesis for a sample of low-growth firms and include managerial shareholdings as a mitigating factor. They

conclude that the results confirm the overinvestment hypothesis. In the study of Lang, Ofek and Stulz (1996) the

overinvestment hypothesis is also tested for a sample of low-growth firms and a proxy for the availability of free

cash flow is included. In line with the overinvestment hypothesis, a significantly negative relation between debt

and proxies for growth opportunities is found. Mehran (1992) and Berger, Ofek and Yermack (1997) test the

influence of governance characteristics on leverage. Both studies find that alignment of interest, through

managerial shareholdings and option plans, induces leverage. As a result of monitoring, the presence of large

shareholders is found to increase leverage. Similarly, relations with banks induce leverage.

2.2.3. Conflicts with outside stakeholders

Titman (1984) and Maksimovic and Titman (1991) study the relation between capital-structure choice and

outside stakeholders. Titman (1984) argues that the liquidation of a firm may impose costs on the customers and

employees. As a result they demand risk premia on products and wages. These costs are transferred to the

shareholders. However, if the shareholders would commit to liquidate only when the gains of liquidation exceed

all costs, including those of customers and employees, this would decrease the cost of capital and increase the

value of equity. Titman (1984) shows that capital structure can be used to control these risk premiums and

argues that firms with higher liquidation costs to customers and employees will have less debt. Titman and

Wessels (1988) have investigated the relation between uniqueness and leverage. The expected negative relation

has been found. Maksimovic and Titman (1991) argue that firms with products with low liquidation costs to

customers are also subject to a similar interaction. Firms that require a reputation for producing high quality

products are expected to have less debt.

2.3. Summary of the hypotheses

In Figure 1 we summarize our discussion and present our hypotheses. The figure illustrates the relations between

leverage and four agency problems. In addition, it shows the determinants of leverage and the four agency

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problems.

[Please insert Figure 1 here]

Leverage and the four agency problems are in the center of Figure 1. Direct wealth transfer, asset substitution

and underinvestment are agency problems between shareholders and bondholders, while overinvestment is a

shareholder-management agency problem. The determinants of leverage are given on the left-hand side. These

determinants are based on tax-based theory (marginal tax rate and non-debt tax shields), bankruptcy-cost theory

(collateral value and business risk) and agency theories on conflicts with outside stakeholders (uniqueness and

importance of quality). The direction of the arrow describes the causality and the sign concerns the hypothesized

relation. For example, we hypothesize that the marginal corporate tax rate has a positive effect on leverage. Next

to the determinants on the left-hand side of Figure 1, overinvestment is also a determinant of leverage. It is

important to notice that the figure reports the direction of causality between leverage and the agency constructs

as suggested by theory. Specifically, theory suggests that firms with a before-financing overinvestment problem

will use relatively more debt as a disciplining device. Therefore, we expect a positive influence of the

overinvestment problem on leverage. In the shareholder-bondholder conflicts leverage is a determinant of agency

problems, because theory suggests that an increased presence of debt (bondholders) induces moral hazard

behavior by the shareholders. For example, due to the overhang of debt the underinvestment problem is

aggravated. On the right-hand side, the figure shows the determinants of four agency problems. The direct

wealth transfer, asset substitution and underinvestment problems are reduced by covenants, secured debt,

convertible debt and short-term debt. The overinvestment and the underinvestment problems are, respectively,

negatively and positively influenced by growth opportunities. Finally, free cash flow affects the overinvestment

problem positively and governance characteristics (inventive and control structure) affect the overinvestment

problem negatively.

Two remarks should be made about the modeling of the agency problems in our model. First, based on

theory, we assume specific causal relations between leverage and direct wealth transfer, asset substitution,

underinvestment and overinvestment. However, for example in the case of overinvestment, a purely before-

financing overinvestment problem is practically not measurable. Moreover, theory does not exclude the existence

of a reverse relation, even in the before-financing situation. A similar reasing holds for the other agency problems.

Therefore, in our robustness analyses we also test for alternative causal relations. A second remark is that we

choose to model four specifc agency problems explicitly.4 In addition, we include two product characteristics in

order to measure their impact on leverage, while we assume that these characteristics represent agency-based

theories.5

3. Analysis of public data

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In this section we test the hypotheses in Figure 1 with publicly available data of Dutch firms. We start with a

description of the data set and the definitions of our variables. Then we discuss the summary statistics and

provide regression results.

3.1. Data sources

The starting point of our analysis are all non-financial firms listed at the Amsterdam Exchanges per April 1, 1997.

In total 168 firms were listed. For these firms we collect data for the three-year period 1996-1998 from publicly

available sources. We require that at least two years of data are available over the three-year period, such that

1997 is always included and we are able to use averages over two or three years. Of the 168 firms, 154 (92%)

firms meet this requirement. The 14 firms that we cannot include were taken over, merged or delisted during

1997, or the annual reports were not available.

We use several public sources. We use a data set of the Dutch Central Bureau of Statistics that includes

annual report data. We use annual overviews of block holdings from Het Financieele Dagblad (the Dutch

financial daily). By Dutch company law, block holdings have to be announced if they pass the 5% level. From

Jaarboek Nederlandse Ondernemingen 1995/1996 to 1997/1998 we obtain the names of the firm’s officers. The

officers of Dutch banks are also included in this book, which allows us to find interlocking directorates, i.e.

board members of banks that are also board members of other firms. From the Gids bij de Prijscourant van de

Amsterdamse Effectenbeurs 1996 to 1998, which is issued by the Amsterdam Stock Exchange, we collect data

on takeover defenses. Data on the structured regime is obtained from the report of the Monitoring Committee

Corporate Governance (1998). Finally, in case data are missing from the sources mentioned above, we consult

the firms’ annual reports.

3.2. Definitions of variables and summary statistics

Leverage is measured as long-term debt over the book value of total assets.6 Convertible and short-term debt are

also measured in book values. We use Titman and Wessels’ (1988, p. 4) proxy for non-debt tax shields, defined

as operating income, minus interest expenses, minus tax payments over the corporate tax rate. We scale non-debt

tax shields by total assets. These non-debt tax shields may come from three sources, i.e. tax-loss carry forwards,

depreciation and investment tax credits. The latter source is not relevant in the Dutch tax regime. We approximate

tax-loss carry forwards with a dummy that has a value of one when the firm has past negative net profits that

have not been fully compensated by profits. Depreciation/total assets is the ratio of depreciation and total assets.

We have no proxy for the marginal tax rate. As a proxy for the collateral value of assets we use the ratio of fixed

tangible assets and total assets. We measure business risk as the standard deviation of changes in operating

income and sales over a five-year period. Several studies also include size as a measure of (reduced) business

risk. We measure size as the logarithm of total assets. We include two dummy variables for groups of industries.

The first dummy is for high-tech and capital intensive industries. Examples are manufacturing of electronics and

communication. These industries have a relatively high business risk due to the speed of innovation. Also the

uniqueness to customers and employees and the importance of quality seem relatively important. Titman and

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Wessels (1988) include a similar industry-dummy for SIC codes between 3400 and 4000, which are the firms

producing machines and other equipment. We also include a dummy for high-tech and capital non-intensive

industries, such as computer services. These industries also exhibit higher business risk, uniqueness to customers

and importance of quality. Moreover, these industries are relatively labor-intensive and the uniqueness to

employees is expected to be large.

In the overinvestment and underinvestment problems, growth opportunities is an explanatory variable.

We approximate growth opportunities by Tobin’s q, as in, for example, Smith and Watts (1992). Tobin’s q is the

book value of total assets minus the book value of equity plus the year-end market value of equity, divided by the

replacement value of total assets. The replacement value of total assets is approximated as described by Perfect

and Wiles’ second technique (1994, p. 329).7 Following McConnell and Servaes (1995), we also use q as a proxy

for agency costs. Because the expected present value of future agency costs has a negative impact on the market

value, we hypothesize that q is inversely related to agency costs. Another determinant of overinvestment is free

cash flow. We use the proxy introduced by Lehn and Poulsen (1989, p. 777), i.e. operating income minus taxes,

interest expenditures and dividends paid, divided by total assets. We also use cash and liquid assets, over total

assets in order to measure the accumulation of cash flows over time.

We have several proxies for the incentive and control structure of the firms. The incentive structure of

Dutch managers consists of performance-based compensation and ownership of shares and options. The dummy

performance-based income has a value of one if the correlation over the past five years between net profits and

managerial compensation is larger than 50%, and zero otherwise. The managerial compensation excludes the

stock and option holdings. The variable insider shareholdings is the fraction of shares held by managerial and

supervisory board members. The dummy managerial option plans has a value of one when the firm has an option

plan for managerial board members or a general option plan from which these board members are not explicitly

excluded, and zero otherwise. Dutch firms can shield themselves from disciplining by the market for corporate

control by adopting takeover defenses. We measure the number of takeover defenses as the number of measures

the firm has adopted from the list: structured regime, priority shares, preferred shares and certificates.8 In Dutch

firms, control may be exercised by banks. Firm-bank relations are measured by bank block holdings with

interlocking directorates. We measure the fraction of shares held by a bank of which a board member serves on

the board of the firm. Finally, large shareholders may control the firms’ managers. Block holdings is the fraction

of shares held by shareholders with a stake of at least 5%. In Table 1 we provide the definitions of our proxies

and the summary statistics for the variables.

[Please insert Table 1 here]

We find that the average long-term debt ratio over the period 1996-1998 is 12.8%. In total 18.4% of the firms

issued convertible debt and on average this type of debt is 2% of total debt. The ratio of short-term to total debt

is 43.5%. Average firm size is 1426 million euro. We find that 24% of the firms are in high-tech industries and

11% in high-tech and labor-intensive industries. On average, Tobin’s q is 1.85. Of all firms, 26.6% have a

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correlation larger than 50% between management compensation and profits. Besides, insider shareholdings are on

average 6.5%. Option plans for managers are used in 68.2% of the firms. The average number of takeover

defenses is 1.8, while a single defense is already sufficient to successfully prevent a hostile takeover. Bank

ownership combined with an interlock is on average 0.7%. In the Netherlands firms have relatively concentrated

ownership, as on average 45.6% is owned by block holders.

3.3. Regression results

We perform two analyses on the publicly available data. First, we regress firm characteristics on leverage, see

also Titman and Wessels (1988) and Rajan and Zingales (1995), among others. Second, similar to McConnell

and Servaes (1995) we regress firm characteristics on Tobin’s q, which serves as an approximation of agency

costs. The regression on leverage includes q as an explanatory variable, which might be non-exogenous, i.e.

leverage might affect q. An identical problem occurs in the regression on q, where leverage is one of the

explanatory variables. The presence of endogenous variables among the explanatory variables may lead to biased

and inconsistent OLS estimates. Therefore, we also use Two-Stage Least Squares (2SLS) regressions that

provide a correction for the potential endogeneity of leverage and Tobin’s q (see Verbeek (2000, pp. 122-139)).

As suggested by Haitovsky (1969), the variables we include in the 2SLS regressions are obtained by stepwise

elimination in OLS regressions. We also include the endogenous variables. The results of the analyses are

presented in Table 2.


The results for the determinants of the long-term debt ratio are in Panel A. First, in column (1) we estimate an

OLS regression that includes the variables on the left-hand side of Figure 1. We find that the coefficient for non-

debt tax shields is –1.02 and the t-value is –0.34. Although the sign of the coefficient confirms the hypothesis,

the result is not significant. Fixed tangible assets have the expected positive effect that is significant at the 1%

level. The proxies for business risk, i.e. the standard deviations of changes in operating income and sales, are

both insignificant. Size has a significantly (5% level) positive effect, which confirms the bankruptcy cost

hypothesis. Both our industry dummies for bankruptcy costs and agency problems with stakeholders result in

insignificant coefficients. The R2 of the regression is 0.18. In general, these results confirm the impact of

bankruptcy costs and are in line with earlier studies for US firms and our results are also similar to the results of

Rajan and Zingales (1995) for the G7-countries. They use proxies for collateral value and size, which are found

to be positively related to leverage. Although Titman and Wessels (1988) do not find significant results for the

relation between leverage and tangibility or volatility of operating income, the signs of the estimates generally

confirm the trade-off. In contrast with our findings, Titman and Wessels (1988) find a significantly negative

relation between uniqueness and leverage.9 We are not aware of other empirical studies in which the relation

between importance of quality and leverage is tested.

In order to incorporate agency problems between managers, shareholders and bondholders, we have to

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derive a reduced-form regression for leverage. Because overinvestment determines leverage, we can include the

determinants of overinvestment. We now assume that relations between leverage and determinants of

overinvestment are caused by the overinvestment problem. In column (2) we use variables that were not

excluded from model (1) after stepwise deletion and add variables for q, free cash flow and the incentive and

control structures. We find that q, the firm’s growth opportunities, have a positive effect, significant at the 10%

level. The two measures for the firm’s liquidity, free cash flow and liquid assets, both have a negative coefficient,

significant at the 1% level. This result is the opposite of a disciplining role for leverage, as found in the US by

Smith and Watts (1992), among others. Dutch firms with less growth opportunities and more free cash are

found to have lower leverage, while the disciplining is most valuable to these firms. Similar to Mehran (1992) and

Berger, Ofek and Yermack (1997) we also investigate the impact of governance variables on leverage. Mehran

(1992) and Berger, Ofek and Yermack (1997) find that both managerial shareholdings and option plans increase

leverage. We only find a significant effect for option plans. However, the effect is positive, while we

hypothesized a negative effect.10 The R2 of the regression has increased to 0.29.

In Panel B we investigate the determinants of Tobin’s q. We interpret Tobin’s q as an approximation of

firm value and in this interpretation q reflects the inverse of the total agency costs of the firm. Unfortunately, this

proxy incorporates the costs of all four agency problems, since ultimately agency costs are borne by the

shareholders. The R2 of the regression is 0.24. In model (3) we describe the influence of the determinants of

overinvestment on q. Free cash flow and liquid assets are expected to have a negative impact on q, because

managers may waste the cash. In contrast, we find significantly positive coefficients for both free cash flow and

liquid assets (at 1% level). Although the finding contradicts overinvestment theory, the result is obvious, because

q measures performance and cash (flow) is the result of past performance. The three forms of managerial

incentives have a positive impact on q, but only for managerial shareholdings the coefficient is significant (10%

level). The internal and external control structures, which are typical to the Dutch setting, all have a negative

coefficient. The number of takeover defenses has a negative impact that is significant at the 1% level. This

finding confirms the negative effects of the individual measure documented by De Jong, Dejong, Mertens and

Wasley (2001). The coefficient for bank relations is insignificant. Block holdings have a negative sign, with a t-

value of –1.51. Apparently, banks and large shareholders do not have a positive role in the disciplining of Dutch

managers. The finding for block holders has two potential reasons. First, block holders, which were expected to

monitor the management, are passive monitors and a threat to sell the shares may be implausible due to the

liquidity costs. Second, block holdings serve as a takeover barrier, because block holders may have strategic

interests and do not allow rival firms to take over the firm. This argument is supported by Kabir, Cantrijn and

Jeunink (1997), in which a negative relation between technical takeover barriers and block holdings is reported

for the Netherlands. In contrast with our findings, Mehran (1992) and Berger, Ofek and Yermack (1997) find

evidence on effective monitoring by large shareholders in the US. Apparently, the influence of large shareholders

in the Netherlands is essentially different. Firm size is included as a control variable and turns out to be

insignificant.

The relevance of agency problems between shareholder and bondholders is tested in model (4). We

11

include fixed tangible assets as a proxy for secured debt and assume that all assets that can be used to secure

debt are used for this purpose. We also include leverage, convertible debt and short-term debt and have no proxy

for covenants because this information is not publicly available. Only the coefficient for fixed assets is significant

(1% level), but contradicts the hypothesis. The R2 of the regression is 0.06, which is very low in comparison

with model (3).

So far, we have tested separate regression models for leverage and q. We also use Two-Stage Least

Squares regressions in which the joint estimation of the two regressions includes a correction for the potential

endogeneity of leverage and Tobin’s q. The results are presented in columns (5a) and (5b). The conclusions do

not change. An exception is block holdings in the equation for q, which becomes significant at the 5% level.

As a robustness check on our regressions we test two additional models. First, we investigate the non-

debt tax shields in model (1) in more detail. We remove the variable non-debt tax shield and include the dummy

tax-loss carry forwards and depreciation. The results (not reported) indicate that these variables also have no

significant influence on leverage. Second, we excluded the determinants of shareholder-bondholder problems in

the reduced-form equation for leverage, because the hypothesized causality runs from leverage to the agency

problem problems. As a robustness check, we allow for the inverse causality and include in model (1) the

variables dummy convertible debt and short-term debt (fixed tangible assets are already included). Our

conclusions do not change.11

4. Analysis of private data

In this section we further test the hypotheses in Figure 1 We use questionnaire data on the Dutch firms. We start

with a description of the data set. Next, we describe the empirical method. Finally, we discuss the summary

statistics and provide regression results.

4.1. Questionnaire data

The starting point for our analysis with private data are the 168 Dutch, non-financial firms that were listed at the

Amsterdam Exchanges as of April 1, 1997. This is the same set of firms we study in section 3 with public data.

In April 1997 we sent questionnaires to the firms and in May 1997 we mailed a follow-up. In total, 102

questionnaires were returned, which yields a response rate of 61%.12 This rate compares favorably with response

rates of other surveys. Pinegar and Wilbricht (1989) and Graham and Harvey (2001) have response rates of 35%

and 9%, respectively. A translation of the questionnaire is included as Appendix A. 13 For the 102 questionnaires

and the 37 questions we have used, the percentage of missing values is 1.07%.14

4.2. Methodology

Our empirical model consists of two parts. The first part is a measurement model that measures unobservable

characteristics (latent variables) by combining observable data (responses to the questions). The second part is a

12

standard regression model that describes the relations among the latent variables. Anderson and Gerbing (1988,

1992) and Lance, Cornwell and Mulaik (1988) advocate to apply a two-step approach to estimate the two

models. In each step, one of the two parts is estimated. In the first step, confirmatory factor analysis is used to

measure latent variables. Tests can be carried out to examine the quality of these approximations. In the second

step, regression equations are estimated to test the hypothesized relations between the latent variables.15

In the measurement model the questionnaire data provide the indicators that are used to measure the

latent variables using confirmatory factor analysis (see Bollen (1989) and Jöreskog and Sörbom (1989, 1993)).

The confirmatory factor analysis model is

x = ?? + d, (1)

where x is a vector of q indicators, ? is a vector of s latent variables, ? is a matrix of factor loadings that relate

the latent variables to the indicators and d is a vector of measurement errors. Hence, in the confimatory factor

analysis we are primarily interested in ?ij, i.e. the i,jth element of ? for i and j. The correlation matrix S for the

indicators is

S = ?F?’ + T d, (2)

in which F and Td are the covariance matrices of ? and d, respectively. The confirmatory factor analysis

estimates S, such that the fit between the sample correlation matrix S and S is maximized according to a distance

function which, in general, can be interpreted as a maximum-likelihood criterion. We use the estimate of the

sample correlation matrix S as suggested by Jöreskog and Sörbom (1993, p. 7). Consequently, the correlations

are polychoric (two ordinal variables), polyserial (one ordinal and one continuous variable), or Pearson product

moment correlations (two continuous variables). We apply confirmatory factor analysis on separate sub-models,

each consisting of a set of indicators measuring one or more variables. The choice of variables included in each

sub-model is based upon two considerations. First, according to the ‘two-indicator rule’, for a sub-model to be

identified, it is necessary to include two or more latent variables in case not more than two indicators are related

to a latent variable (see Bollen (1989, p. 244)). Second, latent variables have to be correlated, such that indicators

of variables may attribute to the measurement of other latent variables. In order to obtain an approximation of the

unobservable characteristics, we first estimate the factor scores between the indicators and latent variables.

Thereafter, these factor scores for each of the indicators are multiplied by the scores of the respondents on the

indicators, which gives the values for the latent variables.

We use questionnaire data that is designed to describe firm characteristics. The answers to the questions

measure firm characteristics. For each (latent) variable we ask our respondents one, two or three homogeneous

questions.16 A potential problem with questionnaire data is that questions may not be fully and equally understood.

We reduce this potential bias in three ways. First, all questions deal with homogeneous firm characteristics about

which we expect the respondent to have sufficient knowledge. Second, the questions are formulated, such that

we expect managers to be able to understand them. We avoid indistinct concepts and provide definitions if

necessary. Third, we use confirmatory factor analysis to reduce measurement error. An additional potential

problem with questionnaire data, that is especially relevant for agency problems, is that managers may give

socially-desirable answers. We have aimed to reduce this problem in three ways. First, all questionnaires were

13

returned anonymously. Second, we excluded sensitive topics. For example, we did not include perquisite

consumption as a management-shareholder problem, because we could not formulate a question that would not

induce a socially-desired response.Third, our questions on agency problems are formulated with some prudence.

This implies that a low score on these questions does not necessarily imply the presence of agency costs. For

example, the question on wealth transfer with dividends asks for the payment of extraordinary dividends on

shareholders’ request. In case of sufficient cash, an extraordinary payment may not harm bondholders. However,

the higher the score on this question, the more likely the existence of the agency problem is. A similar reasoning

holds for the other agency problems.

In order to evaluate the measurement models, we calculate the fit of each of the models and measures

for the validity and reliability of the latent variables (see Bollen (1989, Chapter 6)). The model fit is assessed by

the adjusted goodness-of-fit index and the root mean squared residuals (see Bollen (1989, Chapter 7)). The

adjusted goodness-of-fit index is an indication of the covariance between the indicators that is explained by the

estimated model, with the degrees of freedom taken into account. The maximum value is one and the minimum is

zero. The root mean squared residuals is a measure based on the average variance of the estimates of the

measurement errors. The value is between zero and one, with a better fit for values closer to zero. A validity

statistic indicates whether the indicator or latent variable measures what it is expected to measure. The validity of

indicator i for latent variable j is measured by the t-value of the estimate for ? ij. The validity of a latent variable j

is based on the total variance extracted and can be calculated as (S? ij)2/((S? ij)2+Sdij), where i runs over all

indicators of the latent variable j. The reliability tests show whether the results can be duplicated with similar data

and procedures. The reliability of an indicator is the R2, i.e. the variance in the indicator that is accounted for by

the latent variable. R2 is the squared correlation coefficient of a regression with the indicator as a dependent

variable and the construct as independent variable. To measure the reliability of a latent variable j from the factor

loadings, we calculate (S? ij2)/((S? ij

2)+Sdij), where i runs over all indicators of the latent variable j.

For the regression model, the hypotheses in Figure 1 suggest five explanatory variables, i.e. leverage

and the four agency problems. Similar to the regression analyses with public data we will first estimate the single-

equation OLS regressions. Then, after stepwise deletion of regressors, we will estimate a simultaneous model

with multiple equations using 2SLS.

4.3. Summary statistics and confirmatory factor analysis

In this section we provide summary statistics for the questionnaire data and we measure the latent variables from

this data. The results are presented in Table 3 and Appendix B.


In the first column in Table 3 the eight measurement models that we have estimated are mentioned. In the second

column the latent variables in each model are mentioned. The third column gives the indicators, i.e. the questions

in the questionnaire. The fourth column provides the average and the fifth column the standard deviations of the

14

scores on the individual questions. The first three columns show that two latent variables are measured by three

indicators (free cash flow and covenants), that five latent variables are measured by two indicators (non-debt tax

shields, firm-specific risk, growth opportunities, asset substitution, and underinvestment), and the remainder of

the latent variables determines one indicator. Each of the indicators is related to only one latent variable. Latent

variables that are heterogeneous are related to two or three indicators, each measuring a homogeneous aspect of

the variable. Most of the heterogeneous variables were found to determine the related indicators.17

For the two debt measures, we can compare the average values in the questionnaire data and publicly

available data. In the questionnaire data the average debt ratio is 15.3%, based on the mid-points of each the 20

intervals. Similarly, the average short-term debt ratio is 49.0%. In Table 1 we report for the public data a debt

ratio of 12.8% and a short-term debt ratio of 43.5%. These results indicate that the questionnaire data closely

resemble publicly available data. For other variables, a similar direct comparison between public data and firm

characteristics derived from the questionnaires is not possible because different scales are used. The individual

scores for the agency problems in model (8) are interesting. The highest average score is obtained for

overinvestment, 4.19 on a 1 to 7 scale. Overinvestment is measured by asking the respondents whether a project

is accepted if the managers consider it useful, even if it is expected to cause a decrease in firm value. This

question closely resembles the concept of overinvestment, but the managers may provide socially-desired

responses. We expect that this problem has also been of minor importance, because the average score is above

the middle of the scale, while socially-desired response would have yielded an average below the middle.

In Appendix B we provide the statistics of the confirmatory factor analysis. The adjusted goodness-of-fit

index and the root mean squared residuals assess the fit of the models. The adjusted goodness-of-fit index shows

that the fit of the models is good, as a large part of the covariance in the data is explained by the model (77% to

90%). Consequently, the root mean squared residuals is also good, because the variance of the residuals that is

not explained by the model, is small. The validity and reliability of the latent variables are also presented in

Appendix B. It can be concluded that all latent variables have an acceptable reliability. The validity is reasonable

given the fairly small number of indicators for the variables. The estimated coefficients of the relation between

the questions and latent variables are in the eighth column and have the expected sign in every case. Besides, all t-

values are significant at the 5% level and the reliability of the indicators (R2s) is acceptable. As a result of the

measurement model, estimates of the latent variables for each firm are obtained by multiplying the indicators with

their respective estimated scores. We conclude that the use of the measurement models on the questionnaire data

leads to relatively good estimates of the unobservable firm characteristics mentioned in Figure 1.

4.4. Regression results

In the second step of our analysis we examine the determinants of leverage, wealth transfer (dividends and

seniority), asset substitution, underinvestment and overinvestment. Estimates for these variables are obtained

from the measurement models that are described in Section 4.3. Initially, we estimate single-equation models.

We standardize each variable by dividing it by its standard deviation. This transformation aids the interpretation of

the coefficients, as all variables are denominated at the same scale. The regression results are in Table 4.

15


The determinants of leverage are examined in Panel A. In the left column the variables are given. In column (1)

the results of a regression with all potential determinants of leverage is shown. The results in Panel A show

strong confirmation of the non-agency trade-off model. First, the results suggest that the marginal tax rate has a

positive impact on leverage (at the 1% level). Moreover, the hypothesis that collateral value is positively related to

debt, is confirmed by the data (at the 5% level). This suggests an inverse relation between bankruptcy costs and

leverage. Also, the coefficient for industry-specific risk is significant at the 10% level, which suggests that an

increasing bankruptcy probability affects leverage negatively. In our model, no confirmation is found for the

hypotheses concerning non-debt tax shields, uniqueness, and importance of quality. We find an insignificant

coefficient for overinvestment, which indicates that Dutch managers do not increase leverage if they are more

likely to overinvest. Investigating the R2 shows that the explanatory power of the model is relatively high, with an

R2 of 0.27.

In Panel B the results for the determinants of the four agency problems are described. The first three

shareholder-bondholder problems, i.e. agency problems wealth transfer with dividends (model (2)), wealth

transfer with senior debt (model (3)) and asset substitution (model (4)), seem to be unrelated to the hypothesized

determinants. Most interestingly, in each of the equations, leverage is not significantly related to the agency

conflicts. Since it is difficult to argue that bondholder-shareholder problems are independent of the level of debt

while the mitigating factors seem to be unrelated, we conclude that these conflicts are absent. In line with this

conclusion, the R2s show that the explanatory power for these determinants is very low. We conjecture that the

absence of agency conflicts between shareholders and bondholders in the Netherlands has two causes. First,

shareholders need to persuade the management to expropriate wealth from bondholders. However, due to the

high level of entrenchment of the management and the relatively low managerial shareholdings, the management

has neither the incentives nor feels the pressure to expropriate wealth from bondholders. Second, banks are an

important provider of debt for Dutch firms. Firms benefit from a long-term relationship with the banks and banks

have multiple relationship with the firms, among others as financiers through both debt and equity, advisors, and

by board representation. For these reasons, firms are not likely to expropriate wealth from banks that act as

bondholders.

In the empirical literature much attention is paid to the underinvestment problem, and the emphasis is

on the role of growth opportunities. An interesting result in model (5) of Panel B is the measurement of the

overhang of debt in the underinvestment problem. Myers’ (1977) hypothesis predicts a positive sign, but the

parameter estimate for leverage is insignificant at all conventional significance levels. In addition, growth

opportunities are hypothesized to aggravate underinvestment while several properties of the debt contract may

mitigate underinvestment. In our results, only the hypothesis that increasing short-term debt mitigates

underinvestment is confirmed at the 5% level. This finding confirms the results of Barclay and Smith (1995). The

parameter estimates of leverage and growth opportunities are insignificant. These findings contrast with Smith

and Watts (1992) and McConnell and Servaes (1995). Similar to the other shareholder-bondholder conflicts, we

16

conjecture that the absence of underinvestment is caused by the strong positions of managers and banks in the

Netherlands.

The regression in model (6) of Panel B deals with the overinvestment problem. This agency problem

between managers and shareholders is expected to be aggravated by free cash flow. The results confirm

Jensen’s (1986) ideas that overinvestment is positively affected by free cash flow. A similar result is obtained by

Lang, Ofek and Stulz (1996). Our analysis provides interesting information about the mitigating factors. First, no

confirmation of the mitigating role of growth opportunities is found. In contrast, Smith and Watts (1992),

McConnell and Servaes (1995), and Lang, Ofek and Stulz (1996) confirm the mitigating role of growth

opportunities. Second, the results show strong confirmation of the expected negative impact of managerial

shareholdings. For performance-based income the result is also negative, but insignificant. It is noteworthy that

the coefficient for option plans is positive, but insignificant. Third, the less the firm is exposed to the market for

corporate control, the larger the overinvestment problem. This result was expected and is due to the effectiveness

of the technical takeover defenses that characterize the Dutch institutional setting. Finally, the presence of lasting

relations with house banks shows to have an insignificant impact on overinvestment. Interestingly, we find a

significantly positive relation between block holdings and overinvestment. This result indicates that block holders

induce overinvestment. The relevance of the model is reflected by the R2 of 0.22.

So far, we used single-equation regressions. In order to improve the efficiency our estimations, we

define a structural-equations model. Hence, this model includes two types of latent variables that are related to

leverage, i.e. agency problems and the constructs from the taxes/bankruptcy trade-off model that have significant

parameter estimates. The requirement for an agency problem to be included in the structural-equations model is

to have acceptable explanatory power from the exogenous variables. In our model we include underinvestment

and overinvestment, and the exogenous variables that were not excluded through stepwise deletion. An exception

is the inclusion of growth opportunities. This variable is included to enable a comparison of our model with

existing empirical studies. The results of the structural-equations model are presented in columns (7a), (7b) and

(7c) of Table 4.The structural model shows that our conclusions do not change.

We performed several robustness checks on the tests with private data. Two sets of adjustments to the

model are interesting to discuss. First, alternative assumptions with respect to the causality between leverage and

agency problems can be tested for. For the shareholder-bondholder agency conflicts, we have argued that

leverage determines these conflicts. In an alternative model we test for the reversed causality. Thus,

underinvestment is included as a determinant of leverage and leverage is removed from the equation for

underinvestment. The results (not reported) show that the coefficient of underinvestment becomes 0.08 with a t-

value of 0.97. Similarly, we assumed that overinvestment determines leverage. Keeping the remainder constant, a

reverse of the causality assumption yields a coefficient for overinvestment equal to 0.09 with a t-value of 0.89

(results not reported). The above-mentioned tests provide evidence that the absence of a relation between

leverage and the underinvestment and overinvestment agency problems is not sensitive to the assumptions with

respect to causality. Our second robustness test involves the overinvestment problem. In the test with public data

we estimated a reduced-form regression in which the determinants of overinvestment were related to leverage.

We also perform this test for private data. The results (not reported) indicate that leverage is not influenced by

17

the determinants of overinvestment, because growth opportunities, free cash flow and governance variables are

not significantly related to leverage.18 This supports our conclusion that overinvestment does not affect leverage

in our sample.

5. Discussion

In this study we apply two methods based on two different data sources for the same set of firms and the same

hypotheses. In this section we will briefly mention the conclusions both methods have in common and we will

discuss differences. Next, we will discuss the advantages and disadvantages of public and private data.

The static trade-off theory of tax benefits and bankruptcy costs is confirmed. In the public -data analysis

we find that fixed assets and size influence leverage, which confirms the relevance of collateral value and

business risk. In the private-data tests, collateral value and industry risk are significant variables. While these

results are similar, the private data includes a measure for the marginal tax, which turns out to be significant.

Non-debt tax shields are insignificant in both tests. Also, both tests find no evidence for the influence of agency

relations between the firm and stakeholders. All hypotheses on agency problems between bondholder and

shareholders are rejected in both analyses. The results in the public -data regressions for the overinvestment

problem are striking. We conclude that firms with a potential problem, i.e. with low growth opportunities and

high free cash flow, have lower leverage. This is inconsistent with the theory that overinvestment is controlled by

more leverage. Also in the private data we find no relation between overinvestment and leverage, while free cash

induces overinvestment. When we consider the influence of governance on overinvestment and firm value the

result are again comparable. Takeover defenses have a negative effect on q (public data) and the reduced threat

from the market for corporate control induce overinvestment (private data). Managerial incentives through

shareholdings increase q (public) and reduce overinvestment (private). Finally, block holders appear to be weak

monitors as block holdings decrease q (public) and increase overinvestment (private). The comparison of the

findings shows that the results of the two analyses are highly similar.

The use of public data is the standard approach in the empirical corporate finance literature. This

approach has provided much knowledge on financial policies of firms. For testing some theories, the public data

approach seems less succesfull.19 In the remainder of this section we will discuss the strengths and weaknesses

of public and private data. The major advantage of the use of public data is transparency. Variables can be based

on data that is relatively easily available to researchers. The data can be found in the widely available financial

statements, voluntary disclosures, stock market databases and reports by regulators and exchanges. Tests can be

replicated, in order to investigate the robustness of results in other samples and over time. Another advantage of

public data is that the coefficients that describe relations between variables allow economic interpretation, such as

the percentage change in leverage when an explanatory variable changes with a specific percentage. Of course,

not all firm-specific information that a researcher would like to have, is available in the public sources. For

example, in our study, we could not trace any public source for data on covenants in debt contracts. Therefore,

18

we could not test this hypothesis using public data. Thus, the disadvantage of public data is that only variables

can be used for which data is available.

An advantage of private data is that almost any information the decision-making manager has can be

retrieved through the questionnaire. This even includes private or inside information. By definition, public data

cannot reveal inside information. However, the use of private data has two caveats. First, the respondent may

intentionally provide incorrect answers in order to give socially desired responses. Second, the manager may

unintentionally provide incorrect answers because the questions are too difficult. Through the design of the

questionnaire and the use of confirmatory factor analysis we aim to reduce these potential biases (see Section

4.2).

Both the empirical results and the discussion of the advantages and disadvantages of public versus

private data show that the methods are complementary. Some results with public data cannot be found in public

data sources and vice versa. Also, both types of data have their limitations. However, when the methods are used

as complements the findings are richer and more reliable.

6. Conclusions

In this paper we investigate the relations between leverage and agency problems and the determinants of leverage

and agency problems. We do this for a data set of Dutch listed firms. The institutional setting in the Netherlands

is interesting, because firms can protect themselves with technical takeover defenses from the disciplinary forces

of the threat of hostile takeovers as an external control mechanism. Therefore, overinvestment is potentially

relevant and the Dutch setting offers a first-rate case to test the influence of this agency problem. It is an

empirical question to what extent this problem is alleviated by the disciplining role of leverage or by alternative

corporate-control mechanisms. Although the analysis concerns Dutch firms, the results hold interest for firms in

most countries outside the market-oriented American setting. For example, firms in European countries are also

characterized by a relative low disciplining force from hostile takeovers, higher concentration of ownership, and

an important role for banks.

In this paper, we find evidence that the level of leverage in the Netherlands is largely determined by

factors from the taxes/bankruptcy trade-off model. Agency problems are not significantly related to leverage.

This does neither imply that agency problems are absent, nor that the firm’s corporate finance policy and

corporate governance are irrelevant. We identified that this holds for the overinvestment problem, which is

caused by absence of the market for corporate-control and monitoring by block holders. Managerial

shareholdings are found to mitigate overinvestment and improve firm performance. We find no results that show

the relevance of agency problems between shareholders and bondholders. We conjecture that this finding is

driven by two typical aspects of the Dutch institutional setting, i.e. the role of banks and managerial entrenchment

due to anti-takeover barriers. The agency problems from relations with outside stakeholders are not confirmed,

because we find no relations between leverage and product uniqueness or the importance of quality.

19

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data, Journal of Finance 50, 1421-1460. Shleifer, A., and R.W. Vishny, 1997, A survey of corporate governance, Journal of Finance 52, 737-783. Skomp, S.E. and C.W.R. Ward, 1983, The capital structure policies of U.K. companies: A comparative study,

International Journal of Accounting: Education and Research 1983 (Fall), 55-64. Smith, C.W. and J.B. Warner, 1979, On financial contracting: An analysis of bond covenants, Journal of

Financial Economics 7, 117-161. Smith, C.W. and R.L. Watts, 1992, The investment opportunity set and corporate financing, dividend, and

compensation policies, Journal of Financial Economics 32, 263-292. Stulz, R.M. and H. Johnson, 1985, An analysis of secured debt, Journal of Financial Economics 14, 501-522. Titman, S., 1984, The effect of capital structure on a firm’s liquidation decision, Journal of Financial

Economics 13, 137-151. Titman, S. and R. Wessels, 1988, The determinants of capital structure choice, Journal of Finance 43, 1-19. Verbeek, M., 2000, A guide to modern econometrics, (John Wiley & Sons, New York) Zwiebel, J., 1996, Dynamic capital structure under managerial entrenchment, American Economic Review 86,

1197-1215.

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Figure 1: Hypotheses on determinants of leverage and agency problems This figure displays leverage, four agency problems and the determinants in the boxes. The arrows represent the direction of causality. The ‘+’ and ‘-’ signs imply, respectively, positive and negative hypothesized relationships.

Marginal tax rate

Non-debt tax shields

Collateral value

Business risk

Uniqueness

Importance of quality

Leverage

Overinvestment

Underinvestment

Asset substitution

Direct wealth transfer

Incentives and control structure

Free cash flow

Growth opportunities

Short-term debt

Convertible debt

Secured debt

Covenants

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Table 1: Summary statistics (public data) Variable Average Standard deviation Long-term debt/total assets 0.128 0.103 Convertible debt/total debt 0.020 0.061 Dummy convertible debt 0.184 - Short-term debt/total debt 0.435 0.150 Total assets (in million €) 1426 4394 Non-debt tax shields/total assets 0.012 0.028 Dummy tax-loss carry forwards 0.159 - Depreciation/total assets 0.053 0.034 Fixed tangible assets/total assets 0.341 0.224 St.dev. changes in operating income 0.035 0.036 St.dev. changes in sales 0.208 0.228 Dummy high-tech/capital intensive industry 0.129 - Dummy high-tech/capital non-intensive industry 0.110 - Tobin’s q 1.851 1.570 Free cash flow/total assets 0.020 0.066 Cash and liquid assets/total assets 0.099 0.135 Dummy performance-based income 0.266 - Insider shareholdings 0.065 0.161 Dummy managerial option plans 0.682 - Number of takeover defenses 1.854 1.001 Bank block holdings with interlocks 0.007 0.022 Block holdings 0.456 0.257 The table displays summary statistics of firm characteristics. First, we average each variable for each firm over the three-years period 1996-1998. Then, we calculate for each variable its average and standard deviation. In total we have 154 firms in our sample. Long-term debt/total assets is the ratio of the book values of long-term debt and total assets. Convertible debt/total debt is the ratio of the book values of convertible debt and total debt. The dummy convertible debt has a value of one when the amount of convertible debt is larger than zero, and zero otherwise. Short-term debt/total debt is the ratio of the book values of short-term debt and total debt. Total assets is the book value of total assets, in millions of Euro’s. Non-debt tax shields/total assets is operating income minus interest expenses minus tax payments over the corporate tax rate, divided by the book value of total assets. The dummy tax-loss carry forwards has a value of one when the firm has negative net profits in the past that have not been fully compensated by profits, and zero otherwise. Depreciation/total assets is the ratio of depreciation and the book value of total assets. Fixed tangible assets/total assets is the ratio of fixed tangible assets and the book value of total assets. St.dev. changes in operating income (sales) is the standard deviation of five yearly changes in operating income (sales) over past five years (t-5 until t). The dummy high-tech/capital intensive (non-intensive) industry has a value of one for firms in industries that are high-tech and capital intensive (non-intensive), and zero otherwise. Tobin’s q is the book value of total assets minus the book value of equity plus the year-end market value of equity, divided by the replacement value of total assets. The replacement value is approximated as described by Perfect and Wiles’ second technique (1994, p. 329). Free cash flow/total assets is operating income minus tax payments, interest expenses and dividend payments, divided by the book value of total assets. Cash and liquid assets/total assets is cash plus liquid assets over the book value of total assets. The dummy performance-based income has a value of one when the correlation over the past five years between net profits and managerial compensation is larger than 50%, and zero otherwise. Insider shareholdings is the fraction of shares held by managerial and supervisory board members. The dummy managerial option plans has a value of one when the firm has an option plan for managerial board members or a general option plan from which these board members are not excluded, and zero otherwise. Number of takeover defenses is the number of defenses the firm adopted from the list: structured regime, priority shares, preferred shares and certificates. Bank block holdings with interlocks is the fraction of shares held by a bank of which a managerial board member serves on the managerial board of the firm. Block holdings is the fraction of shares held by shareholders with a stake of at least 5%. Data sources are the Central Bureau of Statistics, Het Financieele Dagblad, Jaarboek Nederlandse Ondernemingen, Gids bij de Prijscourant van de Amsterdamse Effectenbeurs, the report of the Monitoring Committee Corporate Governance (1998), and the firms’ annual reports.

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Table 2: Determinants of leverage and q (public data)

Panel A: Determinants of leverage Long-term debt (1): OLS (2): OLS (5a): 2SLS Intercept -0.004 (-0.12) -0.003 (-0.07) 0.015 ( 0.52) Non-debt tax shields -0.102 (-0.34) Fixed tangible assets 0.161 ( 4.34)*** 0.131 ( 3.75)*** 0.129 ( 3.74)*** St.dev. changes in operating income 0.370 ( 1.55) St.dev. changes in sales -0.004 (-0.10) Log(total assets) 0.010 ( 2.36)** 0.007 ( 1.63) High-tech/capital int. industry 0.035 ( 1.48) High-tech/capital non-int. industry -0.027 (-1.02) Tobin’s q 0.009 ( 1.68)* 0.009 ( 1.79)* Free cash flow -0.332 (-2.79)*** -0.340 (-2.95)*** Cash and liquid assets -0.227 (-3.88)*** -0.214 (-3.76)*** Performance-based income 0.001 ( 0.04) Insider shareholdings 0.063 ( 1.27) Managerial option plans 0.058 ( 3.48)*** 0.056 ( 3.50)*** Number of takeover defenses 0.003 ( 0.39) Bank block holdings with interlocks 0.002 ( 0.62) Block holdings 0.005 ( 0.16) R2 0.18 0.29 0.30

Panel B: Determinants of Tobin’s q Tobin’s q (3): OLS (4): OLS (5b): 2SLS Intercept 1.998 ( 3.01)*** 1.638 ( 2.66)*** 2.125 ( 5.20)*** Log(total assets) 0.029 ( 0.43) 0.040 ( 0.57) Free cash flow 6.870 ( 3.89)*** 7.550 ( 4.49)*** Cash and liquid assets 2.495 ( 2.90)*** 3.187 ( 3.67)*** Performance-based income 0.073 ( 0.27) Insider shareholdings 1.360 ( 1.79)* 2.389 ( 2.79)*** Managerial option plans 0.338 ( 1.31) Number of takeover defenses -0.352 (-2.92)*** -0.347 (-3.04)*** Bank blocks with interlocks -0.057 (-1.10) Block holdings -0.788 (-1.51) -0.915 (-2.05)** Long-term debt 0.848 ( 0.60) 1.577 ( 1.38) Fixed tangible assets -1.839 (-2.92)*** Dummy convertible debt 0.369 ( 0.97) Short-term debt 0.947 ( 1.09) R2 0.24 0.06 0.28 Panel A reports regressions in which the explained variable is long-term debt/total assets. Panel B reports regressions in which the explained variable is Tobin’s q. All variables are defined in Table 1. The coefficients and the t-values (in parentheses) are reported. The symbol ‘***’ denotes that the estimate is significant at the 1% level (t>2.57). The symbol ‘**’ denotes 5% significance level (t>1.96) and ‘*’ denotes 10% significance level (t>1.65). Columns (1), (2), (3) and (4) contain Ordinary Least Squares regressions. Columns (5a) and (5b) contain one Two-Stage Least Squares regression model. R2 is the adjusted coefficient of determination. The number of observations is 154.

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Table 3: Summary statistics (private data) Model Variable Question Avg. St.dev. (1) Leverage leverage (long term debt/total assets) 3.66 2.64

Convertible debt convertible debt (and warrant bond loans) 0.16 0.37 Short-term debt short-term debt (short term debt/total debt) 10.38 5.61

(2) Marginal tax rate marginal tax rate 4.01 1.85 Non-debt tax shields non-debt tax shields from carry forwards 3.28 1.97

non-debt tax shields from depreciation 4.58 1.43 (3) Firm-specific risk volatility profits 3.45 1.52

volatility sales 3.26 1.15 Industry-specific risk industry risk 4.28 1.69 Uniqueness (customers) uniqueness towards customers 3.97 1.79 Uniqueness (employees) uniqueness towards employees 3.38 1.43 Importance of quality importance of quality 3.92 1.55

(4) Covenants covenants on dividends 2.20 1.49 covenants on contracts 3.43 1.92 covenants on investments 2.61 1.56

Secured debt secured debt 3.20 1.92 (5) Growth opportunities growth opportunities from R&D 4.19 1.79

growth opportunities from marketing 4.71 1.40 Collateral value collateral value 5.04 1.62

(6) Free cash flow free cash flow (projects) 5.56 1.28 free cash flow (liquid assets) 3.15 1.85 free cash flow (internal funds) 4.21 1.70

(7) Performance-based income impact performance on managerial income 2.99 1.69 Managerial shareholdings managerial shareholdings 2.51 1.81 Managerial option plans managerial option plans 2.80 1.82 Market corporate control (threat) market corporate control (threat) 3.26 1.66 Market corporate control (barriers) market corporate control (barriers) 3.26 2.03 Bank monitoring relationships with house banks 5.94 1.26 Block holdings block holdings 4.55 1.98 Asymmetric information asymmetric information 5.04 1.24

(8) Wealth transfers (dividends) direct wealth transfer with dividends 2.35 1.56 Wealth transfers (seniority) direct wealth transfer with seniority 3.68 1.48 Asset substitution asset substitution (problem) 3.19 1.39

asset substitution (distress) 2.20 1.37 Underinvestment underinvestment (problem) 2.67 1.64

underinvestment (distress) 3.94 1.62 Overinvestment overinvestment 4.19 1.52

The table reports the averages (Avg.) and standard deviations (St.dev.) of the 102 responses to the 37 questions. The questions are grouped per variable and in 8 factor-analysis models. The questions are included as Appendix A and the statistics of the confirmatory factor-analysis models are in Appendix B. All questions are measured on a 1-7 scale, except leverage (1-20), short-term debt (1-20), and convertible debt (0-1).

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Table 4: Determinants of leverage and agency problems (private data)

Panel A: Determinants of leverage Leverage (1): OLS (7a): 2SLS Intercept 0.09 ( 0.15) 0.05 ( 0.10) Marginal tax rate 0.40 ( 2.94)*** 0.41 ( 4.41)*** Non-debt tax shields 0.00 ( 0.02) Collateral value 0.19 ( 2.00)** 0.21 ( 2.19)** Firm-specific risk -0.02 (-0.23) Industry-specific risk -0.16 (-1.74)* -0.15 (-1.68)* Uniqueness (customers) -0.07 (-0.77) Uniqueness (employees) 0.09 ( 0.96) Importance of quality 0.04 ( 0.39) Overinvestment 0.05 ( 0.59) 0.71 ( 0.81) R2 0.27 0.32

Panel B: Determinants of agency problems Wealth transfer (dividends) (2): OLS Intercept 0.69 ( 1.71)* Leverage 0.03 ( 0.28) Covenants 0.17 ( 1.51) Secured debt 0.10 ( 0.84) Convertible debt -0.10 (-1.02) Short-term debt 0.14 ( 1.35) R2 0.04 Wealth transfer (seniority) (3): OLS Intercept 2.16 ( 5.20)*** Leverage -0.04 (-0.39) Covenants 0.04 ( 0.35) Secured debt 0.13 ( 1.13) Convertible debt 0.11 ( 1.10) Short-term debt 0.04 ( 0.36) R2 0.00 Asset substitution (4): OLS Intercept 1.96 ( 4.73)*** Leverage 0.01 ( 0.07) Covenants 0.15 ( 1.27) Secured debt 0.06 ( 0.53) Convertible debt -0.06 (-0.63) Short-term debt 0.08 ( 0.72) R2 0.00 Underinvestment (5): OLS (7b): 2SLS Intercept 2.00 ( 4.65)*** 2.05 ( 5.21)*** Leverage 0.01 ( 0.07) 0.01 ( 0.10) Covenants 0.17 ( 1.47) 0.15 ( 1.48) Secured debt -0.01 (-0.02) Convertible debt -0.01 (-0.02) Short-term debt -0.18 (-1.70)* -0.20 (-1.88)* Growth opportunities -0.08 (-0.70) -0.08 (-0.69) R2 0.03 0.08

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Table 4 (continued): Determinants of leverage and agency problems (private data) Overinvestment (6): OLS (7c): 2SLS Intercept 2.73 ( 3.78)*** 2.72 ( 5.44)** Growth opportunities 0.08 ( 0.84) 0.08 ( 0.87) Free cash flow 0.20 ( 2.08)** 0.20 ( 2.05)** Performance-based income -0.20 (-1.52) Managerial shareholdings -0.25 (-2.31)** -0.24 (-2.31)** Managerial option plans 0.16 ( 1.14) Market control (threat) -0.19 (-1.97)** -0.19 (-1.93)* Market control (barriers) -0.17 (-1.82)* -0.17 (-1.77)* Bank monitoring -0.02 (-0.22) Block holdings 0.23 ( 2.33)** 0.24 ( 2.44)** Asymmetric information 0.03 ( 0.32) R2 0.22 0.19 Panel A reports regressions in which the explained variable is leverage. Panel B reports regressions in which the explained variables are the agency problems wealth transfer (dividends), wealth transfer (seniority), asset substitution, underinvestment and overinvestment. All variables are defined in Table 3. The coefficients and the t-values (in parentheses) are reported. The symbol ‘***’ denotes that the estimate is significant at the 1% level (t>2.57). The symbol ‘**’ denotes 5% significance level (t>1.96) and ‘*’ denotes 10% significance level (t>1.65). Columns (1) to (6) contain Ordinary Least Squares regressions. Columns (7a), (7b) and (7c) contain one Two-Stage Least Squares regression model. R2 is the adjusted coefficient of determination. The number of observations is 102.

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Appendix A: The questionnaire Leverage: “The debt ratio is debt with a time to maturity of more than one year divided by total assets . What is in your

firm the debt ratio (measured in book values)?” (20 intervals, from 0-4 till 95-100) Convertible debt: “Are convertible bonds or bonds with warrants part of your financial structure (exclude arrangements

for employees)?” (2 options, yes/no) Short-term debt: “The short-term debt ratio is debt with a time to maturity of less than one year divided by total debt.

What is in your firm the short-term debt ratio (measured in book values)?” (20 intervals, from 0-4 till 95-100) Marginal tax rate: “Assume that the deductibility of interest payments is abolished. To what extent will the amount of

taxes that have to be paid increase?” (7 points scale, very little/very much) Non-debt tax shield from carry forwards: “Carry forwards reduce future tax payments. What is the size of this effect of

carry forwards for your firm?” (7 points scale, very small/very large) Non-debt tax shield from depreciation: “Depreciation reduces future tax payments. What is the size of this effect of

depreciation on tax payments for your firm?” (7 points scale, very small/very large) Volatility profits: “Are the profits of your firm stable over time?” (7 points scale, very unstable/very stable, inverted) Volatility sales: “What is the degree of predictability of the sales of your firm for the next three years?” (7 points scale,

very good/very bad, inverted) Industry risk: “My firm operates in markets where, because of innovations, products and services, age continuously,

and succeed each other rapidly” (7 points scale, fully disagree/fully agree) Uniqueness towards customers: “The products and services of my firm are easily replaceable by customers for products

and services of another firm.” (7 points scale, fully agree/fully disagree, inverted) Uniqueness towards employees: “My employees depend upon the continuity of my firm, because it is difficult for them

to find a suitable position in another firm.” (7 points scale, fully agree/fully disagree, inverted) Importance of quality: “For the products and services of my firm it holds that comp etition is primarily based on quality

and not on price.” (7 points scale, fully disagree/fully agree) Covenants on dividends: “Are restrictions with respect to dividend payments included in long-term debt contracts?” (7

points scale, never/always) Covenants on other contracts: “Are restrictions with respect to concluding other contracts included in long-term debt

contracts?” (7 points scale, never/always) Covenants on investment decisions: “Are restrictions with respect to investment decisions included in long-term debt

contracts?” (7 points scale, never/always) Secured debt: “Is security used in long term debt contracts?” (7 points scale, never/always) Growth opportunities from R&D: “My firm invests much in R&D.” (7 points scale, fully disagree/fully agree) Growth opportunities from marketing: “My firm invests much in marketing.” (7 points scale, fully disagree/fully agree) Collateral value: “Assume that your firm has not secured any of its assets yet. Which part of your assets is suitable for

securitization?” (7 points scale, very small/very large) Free cash flow (projects): “My firm has ample opportunity to carry out new, profitable projects.” (7 points scale, fully

disagree/fully agree) Free cash flow (liquid assets): “My firm has disposal over much liquidity for which there is no clear destination yet.” (7

points scale, fully disagree/fully agree) Free cash flow (internal funds): “The current and future internal funds are more than sufficient to finance all future

profitable projects.” (7 points scale, fully disagree/fully agree) Performance-based income: “The part of total income of the management (of which you are a member) that is determined

by the stock price of the firm, is large. (Take e.g. bonuses, shares, and options into consideration.)” (7 points scale, fully disagree/fully agree)

Managerial shareholdings: “Does the management of your firm own shares of the firm?” (7 points scale, very little/very much)

Managerial option plans: “Does the management of your firm own options or warrants of the firm?” (7 points scale, very little/very much)

Market for corporate control (threat): “If the management of my firm would function insufficiently, the firm would be taken over by another firm.” (7 points scale, fully disagree/fully agree)

Market for corporate control (barriers): “The current takeover barriers of my firm make a hostile takeover impossible.” (7 points scale, fully agree/fully disagree, inverted)

Bank relationship: “My firm has close and lasting relations with house bankers.” (7 points scale, fully disagree/fully agree)

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Appendix A (continued): The questionnaire Block holdings: “Shares of my firm are to a large extent owned by large shareholders.” (7 points scale, fully disagree/fully

agree) Asymmetric information: “The shareholders of my firm are fully informed about the course of events within the firm.” (7

points scale, fully disagree/fully agree) Direct wealth transfer with dividends: “The management of my firm can be successfully approached by the shareholders

to pay out extraordinary cash dividends.” (7 points scale, fully disagree/fully agree) Direct wealth transfer with debt seniority: “My firm will only conclude long-term loans with lower or equal seniority as

the current loans.” (7 points scale, fully agree/fully disagree, inverted) Asset substitution (problem): “I expect that our shareholders will insist on a more aggressive investment policy, in case

of structural low profits, in the hope of higher profits.” (7 points scale, fully disagree/fully agree) Asset substitution (distress): “Projects of which the uncertainty is unacceptable under normal circumstances, will be

undertaken in case of a threat of bankruptcy.” (7 points scale, fully disagree/fully agree) Overinvestment: “In my firm a project is accepted if it is useful to the management’s opinion, even it is expected that this

causes a reduction in the stock price.” (7 points scale, fully disagree/fully agree) Underinvestment (distress): “In worse days for the firm our shareholders will be extra reserved with respect to financing

new promising projects.” (7 points scale, fully disagree/fully agree) Underinvestment (problem): “In my firm it might occur that a potentially successful project is not started, because new

shares have to be issued to finance the project.” (7 points scale, fully disagree/fully agree) The sequence of the questions is adapted to the sequence in Table 3. The questions are translated from Dutch.

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Appendix B: Statistics of confirmatory factor analysis Model AGFI RMSR Variable Validity Reliability Question ?ij (t-value) R2 (2) n.a. n.a. non-debt tax shields 0.26 0.40 non-debt tax shields from carry forwards 0.38 (2.75) 0.14 non-debt tax shields from depreciation 0.61 (3.35) 0.37 (3) 0.77 0.05 firm-specific risk 0.58 0.73 volatility profits 0.83 (5.86) 0.68 volatility sales 0.69 (5.32) 0.69 (4) 0.89 0.12 covenants 0.54 0.78 covenants on dividends 0.73 (7.35) 0.53 covenants on contracts 0.76 (7.68) 0.57 covenants on investments 0.71 (7.20) 0.51 (5) n.a. n.a. growth opportunities 0.20 0.32 growth opportunities from R&D 0.33 (2.27) 0.11 growth opportunities from marketing 0.54 (2.67) 0.29 (6) n.a. n.a. free cash flow 0.42 0.68 free cash flow (projects) 0.49 (4.22) 0.24 free cash flow (liquid assets) 0.78 (5.75) 0.61 free cash flow (internal funds) 0.64 (5.11) 0.41 (8) 0.90 0.05 underinvestment 0.37 0.52 underinvestment (problem) 0.45 (3.55) 0.20 underinvestment (distress) 0.73 (4.50) 0.53 asset substitution 0.28 0.39 asset substitution (problem) 0.71 (2.81) 0.51 asset substitution (distress) 0.25 (1.96) 0.06 This table reports statistics on the relations between the responses to the questions and the latent variables. The column ‘Model’ gives the numbers of the confirmatory factor analysis models. For each model, the column ‘AGFI’ provides the adjusted goodness-of-fit indices and the column ‘RMSR’ gives the root mean squared residuals. ‘n.a.’ means not available. The column ‘Variable’ gives the names of the latent variables. For each variable, the columns ‘Validity’ and ‘Reliability’ provide the measures for validity and reliability, respectively. The column ‘Question’ lists brief descriptions of the questions. For each question, the columns ‘?ij’ and ‘(t-value)’ give estimates for the elements of ? and their t-values. The elements of ? represent the relation between the question and the variable. The column ‘R2’ represents the part of the variance in the question that is explained by the variable. The numbers of the models, the names of the variables and the questions correspond to those in Table 3.

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Notes 1 The Amsterdam Exchanges makes the Netherlands the country with the eighth largest stock-market capitalization in the world (as of March 31st 1997, The Economist, April 26th 1997). 2 We choose the compare our results with earlier studies for US samples. Another country that has been studied intensively is the UK. Skomp and Ward (1983) compare the capital structure policies of UK and US firms based on questionnaire data. Becht and Mayer (2001) provide an excellent overview of differences between governance structures in the US and UK settings. Another interesting empirical study for the UK is Lasfer (1995), in which leverage is explained from corporate taxation and agency perspective. These three studies highlight the many differences between the UK and US systems. The only paper we are aware of about Dutch firms is De Jong (2002). The author finds that firms with both low investment opportunities and high free cash flow have less debt. On the other hand, the debt ratio is negatively related to (excess) investment. These two results imply that in Dutch firms overinvestment is relevant because firms avoid the disciplining of leverage, while, in case leverage is present, it does discipline the management. 3 Examples of studies based on questionnaires are Pinegar and Wilbricht (1989) and Graham and Harvey (2001). For example, Pinegar and Wilbricht ask respondents to give the perceived importance of several capital-structure inputs, such as ‘voting control’ and ‘bankruptcy costs’. An exception is Ang and Jung (1993), who test the pecking-order hypothesis by comparing scores on questions for asymmetric information with the preferred marginal financing choices. Similar to our approach, they estimate relations from scores on questions. 4 Our selection of agency problems implies that we exclude other agency conflicts. Es pecially shareholder-manager conflicts may manifest themselves in other ways than overinvestment, such as excessive compensation or overdiversification. 5 In this paper we examine the static trade-off model, in which the firms’ optimal capital structures are modeled. Alternatively, dynamic theories model incremental capital structure decisions (see Myers (1984)). De Jong and Veld (2001) provide an empirical test of dynamic capital structure theories for Dutch listed firms and find that the entrenched managers tend to avoid the disciplining role of debt. An analysis of the abnormal returns of announcements of issues shows that shareholders recognize overinvestment problems in the managerial decisions. Their findings imply that overinvestment is relevant, but the problem is not disciplined by leverage. 6 In this study, leverage is defined in book values and not in market values for two reasons. First, Cools (1993) conducted interviews with 50 Dutch CFOs and all respondents indicated that the capital structure is measured in book values. Of the respondents, 74% cannot mention a reason why market values would be relevant and many consider the question ‘absurd’ or ‘difficult to understand’ (Cools, 1993, p. 270). A second reason is that a debt-ratio in market values induces spurious correlation with a market-to-book ratio, and thus Tobin’s q (Titman and Wessels (1988)). 7 In the annual reports Dutch firms either present replacement values or historical costs. If replacement values are presented no adjustment is made. In case of historical costs we adjust the value to approximate the replacement value for plant and equipment. We assume that in a base year the replacement value equals the historical costs. For each subsequent year we adjust this replacement value by adding new investments and corrections for the growth in capital good prices and subtracting depreciation. The base year is 1974 or the first year for which firm data is available. Growth in capital good prices is based upon the price index of investment goods, as provided by the Statistics Netherlands. The replacement value of the assets is the book value of assets plus the difference between the replacement value and historical value of plant and equipment. 8 The structured regime (structuurregime) delegates specific shareholders’ rights to the supervisory board. Under the structured regime, the members of this board are appointed by means of cooptation (current members elect new members). The members of the managerial board are appointed and dismissed by the supervisory board. The supervisory board also establishes the annual statement of accounts. Finally, the supervisory board has to approve important decisions of the managerial board. Priority shares are a small number of shares that carry superior voting rights, e.g. with regard to takeover attempts. Preferred shares are an arrangement that allows an issue of preferred shares without further approval of shareholders and for which only 25% of the nominal value has to be paid up. In case of a takeover attempt, the firm can place these shares with a befriended party and have the shares paid with debt. The dilution creates an effective takeover defense. In case of certificates the shareholders own receipts that only carry the cash flow rights, while the voting rights remain with a trust that owns the shares and issued the receipts. See also De Jong, Dejong, Mertens and Wasley (2001). 9 Titman and Wessels (1988) measure uniqueness as a weighted average of relative R&D expenditures, relative selling expenses, and the voluntary workers’ quit rate in the firm’s industry. Their factor analysis shows that the latter indicator receives the lowest weight. Hence, the construct of uniqueness shows a large resemblance with growth opportunities or intangible assets.

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10 A plausible explanation for this effect is provided by DeFusco, Zorn and Johnson (1991). The value of stock options increases with the risk of the shares. This risk borne by shareholders can be increased with more risky projects and more debt. Thus, managers may choose to increase the value of their option plans by increasing leverage. 11 The results (not reported) show that convertible debt has a significantly positive effect (at the 1% level). We conjecture that this result is caused by spurious correlation, because convertible debt is part of long-term debt. In contrast with theory, short-term debt has a negative coefficient (significant at the 5% level). The other variables have insignificant coefficients. 12 We started by contacting each firm by telephone to obtain the correct address and the name of the CFO. If the name of the CFO could not be found, the questionnaire was sent to the CEO. The positions mentioned on return forms generally indicate that the respondent is the person who is responsible for the firm’s financial management. The positions most frequently mentioned are CFO (43% of the positions mentioned), CEO (20%) and controller (17%). We ensured anonymity by using separate return envelopes for the questionnaire and the form for the respondents name, position, and address to obtain the results of the research. In order to maximize the response a cover letter informed the recipients about the research project and that responses would kept anonymously. Moreover, a research report was promised to those who filled in the separate form containing information on the respondent. In the follow-up we excluded the firms that had requested to be informed about the results. In the first round 81 questionnaires were returned and in the follow-up 21 questionnaires were returned. All surveys were sent back within three months from the first mailing. 13 The original questionnaire (in Dutch) is available upon request from the authors. 14 We dealt with missing values by mean imputation, as described by Little and Rubin (1987). 15 As an alternative to our two-step approach, a one-step approach can be used in which the measurement model and the regression model are estimated simultaneously (see Bollen (1989) and Jöreskog and Sörbom (1989, 1993)). This approach is often referred to as LISREL, a software package including confirmatory factor analysis and structural-equations modeling. However, LISREL can be applied for both the one-step and the two-step approach. The one-step approach has a number of drawbacks. First, the one-step approach requires a sufficient fit of the data with respect to the theoretical model. In case of insufficient fit, the model has to be adjusted towards the data. The goal of the procedure is then not to test a hypothesis, but to describe the data by a model (Cliff (1983)). Second, incorrect specifications influence all estimates in the one-step approach. In the two-step approach the measurement model can be evaluated before the testing model is estimated, and this latter model can be evaluated separately (Anderson and Gerbing (1988)). Third, simultaneous testing induces interpretational confounding, which means that a latent variable is given different empirical meaning than would have been given by an individual before estimating unknown variables (Anderson and Gerbing (1988)). 16 Homogeneous questions ask the respondent about a single characteristic. On the contrary, heterogeneous questions incorporate multiple characteristics and therefore scores cannot be attributed to single characteristics. Within our measurement model the responses to homogeneous questions are the indicators for a latent variable that can be either heterogeneous or homogeneous. 17 Table 3 presents the final model. In a previous model it was found that the latent variables risk, uniqueness, market for corporate-control, and wealth transfer each had to be separated into two latent variables, because the hypothesis that the indicators of these latent variables measure a single construct was rejected. This results in four pairs of new variables, i.e. firm-specific and industry-specific risk, uniqueness to customers and uniqueness to employees, corporate-control threat and corporate-control barriers, and wealth transfer with dividends and wealth transfer with debt seniority. 18 In addition to the reduced-form regression, the absence of a disciplining role of debt may imply that our specification of the overinvestment problem is inappropriate. We assumed, in line with Grossman and Hart (1982) and Jensen (1986), that debt serves as a bonding device, i.e. managers voluntarily choose to commit to refrain from overinvestment by engaging in debt. However, as is argued by Zwiebel (1996), managers may seek to avoid debt and will only engage in debt, if they are motivated through corporate-governance devices. This point of view on overinvestment implies that the governance characteristics, growth opportunities, and free cash flow are determinants of the agency problem as well as leverage. Our results do not suggest that governance reduce debt-avoidance. 19 Titman and Wessels (1988, p. 17) conclude their paper stating that “If stronger linkages between observable indicator variables and the relevant attributes can be developed, then the methods suggested in this paper can be used to test more precisely the extant theories of optimal capital structure.” Also Rajan and Zingales (1995, p. 1458) conclude “..it is necessary to strengthen the relationships between theoretical models and empirical specifications of these models. This, we believe, will be possible only with more detailed data which will enable us to identify more accurate proxies.”

DETERMINANTS OF LEVERAGE AND AGENCY PROBLEMS

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