msc. finance the determinants of capital structure: a

Master Thesis

MSc. Finance

The Determinants of Capital Structure: A Comparative

Study of Public and Private Firms

Submitted by: Z. Afzal

ANR: 465562

Supervisor: dr. M. Da Rin

Chair Person: dr. F. Feriozzi

Date: 21 September, 2012

ii

Abstract

This paper investigates the determinants of capital structure of a large sample of quoted and

unquoted firms incorporated in the United Kingdom, The Netherlands and Germany. It explores

whether the traditional determinants of leverage of quoted firms, namely firm size, profitability,

tangibility, growth and earnings volatility also hold across unquoted firms. Further, I attempt to

analyse the differences in capital structure and financing behaviour of quoted and unquoted

firms. My findings suggest that private firms have significantly higher leverage than public

firms. Also, my results support the traditional determinants of leverage. In the case of public

firms, leverage is positively correlated to size, tangibility and volatility and negatively correlated

with profitability. In the case of private firms, leverage appears to be positively correlated with

growth, tangibility and volatility and negatively correlated with size and profitability, ceteris

paribus. Hence, there seems to be a difference in the financing behaviour of public and private

firms with regards to size and growth opportunities. However, a critical finding of my study is

the importance of institutional factors in the determination of capital structure of firms. Firms in

the Netherlands are less levered whereas those in Germany are more highly levered as compared

to the firms in the United Kingdom.

iii

Table of Contents

I. Introduction…………………………………………………………………………..1

II. Theoretical Framework……………………………………………………………….4

A. The Pecking Order Theory………………………………………………………..4

B. The Trade-off Theory……………………………………………………………..5

C. The Agency Theory……………………………………………………………….5

D. The Signalling Theory…………………………………………………………….6

E. Determinants of Leverage………………………………………………………...7

E.1. Overview……………………………………………………………………..7

E.2. Size………………………………………………………………………….11

E.3. Asset Tangibility………………………………………………………...….12

E.4. Growth……………………………………………………………………...12

E.5. Profitability…………………………………………………………………13

E.6. Earnings Volatility………………………………………………………….14

F. Differences in Capital Structure of Public and Private Firms…………………….14

F.1. Access to Capital Market……………………………………………………15

F.2. Information Asymmetry………………………………………………..........15

F.3. Ownership Concentration…………………………………………………...15

G. Institutional Factors……………………………………………………………...16

III. Hypothesis Development…………………………………………………………...18

iv

IV. Data…………………………………………………………………………………..19

A. Sources...…………………………………………………………………………19

B. Sample……………………………………………………………………………20

C. Research Methodology…………………………………………………………...22

D. Variable Measurement……………….…………………………………………...23

E. Descriptive Statistics...…………………………………………………………...26

V. Empirical Results…………………………………………………………………….30

A. Multivariate Analysis…………………………………………………………….30

B. Cross-Country Analysis………………………………………………………….34

C. Analysis of Variance……………………………………………………………..36

D. Institutional Differences………………………………………………………….37

E. Robustness Tests…………………………………………………………………39

F. Limitations……………………………………………………………………….44

VI. Conclusion…………………………………………………………………………...44

References…………………………………………………………………………………...46

Appendix…………………………………………………………………………………….50

Determinants of Capital Structure:

A Comparative Study of Public and Private Firms

1

I. Introduction

The influential paper of Modigliani and Miller on capital structure irrelevancy has been

followed by extensive theoretical research to determine the “optimal capital structure”.

Modigliani and Miller show that firm value is independent of the capital structure under the

strict assumptions of perfect and frictionless capital markets (1958). However, in reality

market frictions exist. Latter literature has been devoted to studying these market

imperfections in order to determine optimal capital structure. Four main theories have been

proposed since then which attempt to explain the amount of leverage to be undertaken

through a cost-benefit analysis of leverage. These theories try to explain the choices behind

corporate financing sources in light of potential costs associated with each source vis-à-vis

agency, information asymmetry, transaction and bankruptcy costs.

There have been extensive empirical studies investigating the theories of capital structure.

These studies have identified certain key determinants of leverage such as collateral value of

assets, firm size, growth opportunities and profitability amongst many (Titman and Wessels,

1988; Harris and Raviv, 1991; Rajan and Zingales, 1995). However, these studies have

almost exclusively focused on public firms due to data availability. Consequently, this leaves

a gap in the literature focusing on the financing behaviour of private firms. It is assumed that

the general theories of capital structure are applicable across the private sector as well.

However, this may not be the case as public and private firms are inherently faced with

different costs of financing. This may lead to different financing choices. Public firms have

access to capital markets whereas this access is limited for private firms. As a result, private

firms face relatively higher costs of both debt and equity (Brav, 2009).

Other than these traditional firm-specific characteristics, certain institutional factors may

determine the capital structure of a firm. Differences in bankruptcy and tax laws, lender-

borrower relationship, ownership concentration and financial orientation may also effect

leverage (Rajan and Zingales, 1995; Antoniou et al., 2008). To give an example, Germany

and the United Kingdom have strict creditor rights in place as compared to the Netherlands

(La Porta et al., 1998). This might lead us to observe relatively higher leverage in the UK and

Germany as suppliers would be more willing to lend if their rights are well-protected. Similar

differences persist across countries depending on their legal traditions which might dictate

leverage.



2

Fundamental questions thus arise as to whether the predictions offered by the theories of

capital structure are also applicable to private firms. If not, then what drives the capital

structure of private firms and how does that differ from its public counterpart. Are the

stylized factors determining private firm leverage different from those of public firms? Also,

are factors other than firm-specific characteristics such as institutional setup important

determinants of leverage? The objective of my study is to answer these questions.

I attempt to identify the characteristics that determine the leverage of unquoted firms and to

analyse the differences between the capital structure of public and private firms. I further

extend my investigation to provide a cross country analysis of the differences in leverage. I

try to explain these differences in light of institutional factors facing each country. This paper

thus explores two key areas in the field of corporate finance. It first examines the

determinants of capital structure of private firms based on a large sample of private firms in

the United Kingdom, the Netherlands and Germany for the period 2003-2011. Second, it

incorporates the effects of the legal economy in which the firm operates on leverage by

providing a cross-country comparison.

I limit myself to the study of five firm-specific factors, namely, asset tangibility, profitability,

growth, size and earnings volatility. Four of these factors (asset tangibility, profitability,

growth and size) have been identified as being consistent determinants of leverage, namely,

asset tangibility, profitability, growth, size and earnings volatility (Bradley et al., 1984; Rajan

and Zingales, 1995; Frank and Goyal, 20071). However, these have been found to be

consistent factors for public firms only. Some initial studies on private sector by Deloof and

Verschueren (1998) and Schoubben and Hulle (2004) find a significant relationship between

earnings volatility and leverage. Hence, I include this variable in my analysis as well.

My choice of the three countries is motivated by two reasons. First, these countries are the

financial hubs of Europe. Hence, access to capital markets can be considered homogenous

across the countries and does not bias my analysis. Second, these countries are characterized

by different legal traditions. The UK is defined by the English legal origin, Germany by the

Germanic legal origin and the Netherlands by the French legal origin. Therefore, these

countries provide me with an ideal data set to study the relationship between institutional

factors and leverage.

1 Frank and Goyal (2007) also find industry mean leverage to be a reliable determinant of capital structure.

However, I exclude this from my analysis as I introduce industry fixed effects in my analysis to control for

inherent variation across industries.



3

There are certain limitations to the existing literature on capital structure. Due to data

limitations, the study on private firms has largely been neglected. Results derived from the

study of public firms are generalized to the private firms. Since studies have focused

primarily on firm-specific characteristics only, this leaves a gap between the relationship of

capital structure with institutional factors which might be very important. Also, a significant

portion of current literature is based on cross-sectional data. My study thus contributes to the

existing literature in three ways. First, it extends the existing empirical analysis of firm-

specific determinants of leverage to the private sector. Second, it incorporates an analysis of

institutional factors in the study of capital structure. Third, the application of panel data

reduces the collinearity among explanatory variables and gives more efficient coefficients

(Hsiao, 1985).

My findings suggest that private firms have significantly higher leverage than public firms.

Also, my results support the traditional determinants of leverage. In the case of public firms,

leverage is positively correlated to size, tangibility and volatility and negatively correlated

with profitability. The relationship with growth is statistically insignificant. In the case of

private firms, leverage appears to be positively correlated with growth, tangibility and

volatility and negatively correlated with size and profitability, ceteris paribus. Hence, there

seems to be a difference in the financing behaviour of public and private firms with regards to

size and growth opportunities.

However, a critical finding of my study is the importance of institutional factors in the

determination of capital structure of firms. Firms in the Netherlands are less levered whereas

those in Germany are more highly levered as compared to the firms in the United Kingdom.

This can be explained in terms of creditor rights protection. As the legal origin of the

Netherlands allows for poor creditor protection, in terms of bankruptcy laws, reorganization

and automatic stays, Dutch firms have lower leverage. This may be explained by possibly a

higher cost of debt in the Netherlands to incorporate the higher risk that lenders face.

The following paper is organized as follows: In Section II, I review the existing literature on

the subject; in Section III, I develop my hypothesis in light of the empirical evidence; in

Section IV, I discuss the sample and my research methodology; in Section V, I comment on

the empirical findings of the study followed by the limitations; and in Section VI, I conclude

and draw reference to the future scope of the research.



4

II. Theoretical Framework

Four main theories have been proposed which provide some insight into the financing

behavior of firms. These theories hypothesize the amount of leverage to be undertaken

through a cost-benefit analysis of leverage. The benefits of debt as a source of capital

primarily include the tax-advantage of debt as interest expense is tax deductible. On the other

side, potential costs of debt are the agency costs, bankruptcy costs and the loss of non-debt

tax shields (Brealy and Myers, 2002). Theoretically, the optimal capital structure then

involves a careful balancing between these costs and benefits. A brief overview of the

theories put forth is as follows:

A. The Pecking Order Theory

According to this theory, the firms follow a financing hierarchy due to information costs

(Myers and Majluf, 1984). Firms primarily face two potential costs when they approach the

external markets to raise capital, information asymmetry costs and transaction costs. These

additional costs make external capital more expensive and naturally lead firms to use internal

over external funds.

Information asymmetry arises due to the separation of ownership and management. Managers

have more information about the value of the firm and would attempt to issue equity when its

market value is higher (Myers and Majluf, 1984). Due to this information asymmetry

between outside investors and managers, equity may be under-priced to account for this

managerial incentive. This may make equity an expensive source of financing and lead firms

to under-invest. Retained earnings are unaffected by such problems. Also, as debt requires

fixed payments of interest, it is less sensitive to information asymmetries.

Similarly, transaction costs can dictate a firm’s sources of financing. Baskin (1989) has found

that costs for borrowing can be as low as 1% of the amount raised whereas the costs for

issuing equity are anywhere between 4% and 15% of the total amount. This evidence

suggests that debt would be a preferred source of external financing compared to equity.

Taking these costs into consideration, firms will prefer internal financing to external

financing, and debt to equity in the event of external financing (Donaldoson, 1961). This

theory suggests that there is no optimal capital structure. In fact the capital structure is a



5

function of the firm’s needs to tap the external markets when internal funds are insufficient to

meet investment opportunities.

B. The Trade-off Theory

This theory is an off-shoot of the Modigliani and Miller model. Interest expense is tax

deductible. Hence, a larger interest expense will result in lower taxable profits and

consequently lower taxes. By increasing the amount of debt on their balance sheets, firms can

derive tax benefit through the interest tax shield. However, increasing debt can also increase

financial distress. With very high levels of debt, firms may be unable to meet their debt

obligations increasing the probability of default. There is thus a trade-off between the costs

and benefits of debt.

Firms are faced with a diminishing marginal benefit of debt and an increasing marginal cost

of debt. In an attempt to maximize value, firms would then borrow up to a point where the

marginal tax benefit is offset by the marginal costs of bankruptcy (Myers, 1984).

C. The Agency Theory

Agency costs stem from the separation of ownership and management which inherently leads

to a conflict of interest between the managers and the shareholders. A classical case of the

agency problem has been put forth by Jensen (1986) also known as the free cash flow

problem. He argues that the managers of a firm having excess free cash flows may over-

invest and engage in value destroying activities such as empire building. Firms can thereby

increase leverage to discipline the managers. Increased leverage commits management to pay

out the excess free cash flows in interest payments and invest in profitable ventures to service

the debt. In such a case, leverage maybe desirable even when internal funds are available. It

serves as a control mechanism to discipline managers and limits the expropriation of private

benefits (Jensen, 1986; Dewatripont and Tirole, 1994; Lewis and Sappington, 1995).

Another implication of the agency theory is the potential conflict of interest between the

bondholders and the shareholders (Jensen and Meckling, 1976). Debt-holders have a priority

on claims over equity-holders. Equity-holders can either engage in riskier projects or under-

invest to minimize the flow of benefits to debt-holders. Myers (1977) notes that the problem

of under-investment is particularly stronger for growth companies as it will cause them to

pass on valuable investment opportunities. Such firms are better off under equity financing.



6

However, Grossman and Hart (1988) suggest that the problem of under-investment can be

overcome by the use of short term debt. Short term debt can help align the interests of the

shareholders and the management.

D. The Signaling Theory

This theory attempts to address the problem of under-investment caused by information

asymmetries through the choice of capital structure. Ross (1977) develops a model to show

that information can be transferred and firm value can be signalled to the outside investors by

considering various financing alternatives. He argues that that higher leverage signals higher

quality earnings and future cash flows to investors. By increasing debt levels, firms are in

effect implicitly stating that they would be able to meet the additional debt obligation

(increased interest expense) vis-a-vis higher future profitability and cash flows. Hence, firms

may commit to higher debt levels to signal their future expectations to the market.

However, the question arises that “how do firms choose their capital structure?” (Myers,

1984). Certain firm-specific characteristics that determine the capital structure of firms have

come to light. These test the theories developed over the years that focus on agency costs,

information asymmetry and tax benefits particularly. Titman and Wessels (1988) identify the

following characteristics that can impact the financing behaviour of firms: asset structure,

non-debt tax shields, growth opportunities, uniqueness, industry classification, size, earnings

volatility and profitability.

The hypothesized relationships between these firm-specific characteristics and leverage are

based on groundings in theory. In part E, I first briefly discuss the theories surrounding the

determinants proposed by Titman and Wessels (1988), followed by a detailed theoretical

framework of my variables of interest which are firm size, asset tangibility, profitability,

growth and earnings volatility. Four of these factors, namely firm size, asset tangibility,

profitability and growth have been identified by Rajan and Zingales (1995) and Frank and

Goyal (2007) as being the most reliable determinants2. However, these have been found to be

consistent factors for public firms only. Some initial studies on private sector by Deloof and

Verschueren (1998) and Schoubben and Hulle (2004) find a significant relationship between

earnings volatility and leverage. Some other factors such as equity risk premium and share

2 Frank and Goyal (2007) also find industry mean leverage to be a reliable determinant of capital structure. However, I exclude this from my analysis as I introduce industry fixed effects in my analysis to control for inherent variation across industries.



7

price performance have also been used as determinants of capital structure. However, these

can only be studied for public firms. I, therefore, limit my study to the aforementioned five

firm-specific characteristics. In part F, I draw references to the fundamental differences

between public and private firms and how these differences may lead to differences in

funding behaviour. In part G, I discuss certain institutional factors that may contribute to the

differences in leverage across countries.

E. Determinants of Leverage

E.1. Overview

Myers and Majluf (1984) propose a positive relation between the collateral value of assets

and leverage. They argue that firms may be better-off selling secured debt as means to reduce

information asymmetries. According to DeAnglo and Masulis (1980), firms having large

non-debt tax shields will have a reduced incentive to benefit from the tax advantage of debt.

Consequently they would undertake less leverage. Firms having high amounts of debt are

likely to forego profitable investment opportunities (Myers, 1977). Therefore, firms

expecting high future growth will be motivated to issue equity to finance their projects.

Similarly, firms offering unique products face higher costs of bankruptcy in the event of

liquidation (Titman, 1988). This is due to the specialized skills and needs of the employees

and customers respectively that cannot be duplicated easily. Hence, such firms would be

expected to have lower leverage. Building onto this hypothesis, Titman (1988) argues that

manufacturing firms should have lower leverage compared to specialized industry firms.

Likewise, larger firms are able to undertake higher leverage as they tend to be more

diversified and hence, face lower bankruptcy risks (Ang, Chua and McConnell, 1982;

Warner, 1977). Firms with more volatile earnings also have less incentive to have high debt

levels due to limited tax advantage of debt (Deloof and Verschueren, 1998). Last, in line with

the Pecking Order theory, the literature suggests that profitable firms will utilize internal

funds and thus have lower leverage (Myers, 1984; Donaldson, 1961).

In their survey of European firms, Bancel and Mittoo (2011) find that that financial

flexibility, credit ratings and tax advantages of debt are the most important factors shaping

the debt policy of firms. Moreover, the level of interest rates and share price are considered in

timing the debt and equity issues. An enormous amount of literature has been devoted to



8

empirically test the determinants of capital structure. The results suggest significant

departures from theory in practice.

Titman and Wessels (1988) find that uniqueness and profitability of firms are negatively

related to the debt levels. However, they find no evidence between the relationship of

leverage and firm’s expected growth, non-debt tax shields, collateral value of assets and

earnings volatility. On the other hand, Harris and Raviv (1991) perform a survey and find that

leverage is positively correlated to firm size, asset tangibility, non-debt tax shields and

investment opportunities but negatively related to bankruptcy risk and uniqueness. Rajan and

Zingales (1995) report that leverage is positively correlated with size and asset tangibility but

negatively correlated with profitability and growth. A summary of some of the earlier

empirical studies conducted on public and private firms are provided in the following tables.



9

Table 1

Relationship between Leverage and its Determinants in prior Empirical Studies on Public Firms

Study Sample

Period Country Relationship between Leverage and Firm-Characteristics

Size Tangibility Growth Profitability Earnings

volatility

Effective

Tax Rate

Dividend

Payout

Non-debt Tax

Shields

Share Price

Performance

Term

Structure of

Interest

Equity

Premium

Titman and

Wessels (1988)

1974-

1982 USA + NS NS - NS X X NS X X X

Rajan and

Zingales (1995)

1987-

1991 G-7 + + - - X X X X X X X

Frank and Goyal

(2001)

1971-

1993 USA + + - - - X X X X X X

Antoniou, Guney

and Paudyal

(2008)

1987-

2000 G-5 + + - - NS - NS - + - +

X: Not Applicable

NS: Not siginificant

All other hypothesized relationships are significant



10

Table 2

Relationship between Leverage and its Determinants in prior Empirical Studies on Public and Private Firms

Study Sample Period Country Relationship between Leverage and Firm-Characteristics

Size Tangibility Growth Profitability Earnings volatility

Shuetrim, Lowe and Morling

(1993) 1973-1991 Australia + + + - X

Deloof and Verschueren (1998) 1992-1994 Belgium + + + - -

Schoubben and Hulle (2004) 1992-2002 Belgium + + + - -

Brav (2009) 1993-2002 UK + + + - X

X: Not Applicable

NS: Not siginificant

All other hypothesized relationships are significant



11

The above tables give an insight into the capital structure determinants of public and private

firms. Empirical research suggests that firm size and tangibility are overall positively

correlated with leverage. Profitability appears to be negatively correlated with leverage across

public and private firms. However, leverage tends to differ amongst public and private firms

with respect to growth opportunities. While public firms have a negative relation with

leverage, private firms appear to have a negative relation. This could be due to the inability of

private firms to generate sufficient internal funds to fund their operations relative to the public

counterparts. They may therefore, tap the debt markets to raise funds.

Below, the theories behind the observed relationships between leverage and firm-specific

characteristics are provided.

E.2. Size

Ang et al. (1982) suggest that there is an inverse relation between bankruptcy costs as a

portion of firm value and firm value itself. They state that “direct bankruptcy costs appear to

constitute a larger proportion of firm value as that value decreases”. Also, it appears that

larger firms face lower bankruptcy costs as they tend to be more diversified (Titman and

Wessels, 1988). Hence, in line with the trade-off theory, larger firms may be more levered as

they have lower costs of financial distress. Rajan and Zingales (1995) report leverage to be

positively correlated to firm size for all G-7 countries except Germany which shows a

negative relationship. Deloof and Verschueren (1998) also conclude size to be positively

reported to leverage but this relationship does not hold when considering short-term debt only.

However, some other studies have also shown that due to higher information asymmetries, the

cost of issuing equity for small firms is relatively high (Smith, 1977). Rajan and Zingales

(1995) suggest that information asymmetry between the inside managers and external capital

markets is less in larger firms. Thus the cost of equity should correspondingly be lower for

larger firms and hence a preferred medium of financing. Issuance costs maybe another

consideration when deciding between different sources of external capital. These costs may be

a major deterrent for small firms to tap equity markets (Schoubben and Hulle, 2004). They

may therefore issue debt to reduce these issuance costs. The latter two theories predict a

negative relationship between size and leverage.



12

E.3. Asset Tangibility

The type of assets owned by a firm may motivate the financing behaviour of firms. Myers and

Majluf (1984) propose a positive relation between the collateral value of assets and leverage.

They argue that firms may be better-off selling secured debt as means to reduce information

asymmetries. It may be more costly for firms to sell a security about which outside investors

have little information. Likewise, Scott (1977) has proposed that firms may increase the value

of their equity by issuing secured debt. Extending this argument, Galai and Masulis (1976)

suggest that if debt is collateralized, borrowers are constrained to use the funds for a specific

project only. Since no such restriction can be enforced in the case of unsecured debt, lenders

may negotiate more costly terms of debt financing. This may lead firms to issue equity rather

than debt. Rajan and Zingales (1995) suggest that the collateral value of assets should serve to

reduce the agency costs of debt and equity such as risk shifting. Lenders would thus be more

willing to provide credit to firms having high asset tangibility.

On the contrary, Grossman and Hart (1982) propose leverage to be negatively correlated with

asset tangibility in line with the agency theory. Higher levels of debt can be undertaken to

align the interests of the managers and the shareholders. Higher leverage would induce higher

bankruptcy costs and thus limit the expropriation of private benefits by managers. Grossman

and Hart (1982) argue that agency costs maybe higher for firms having lower collateralizable

assets as it is more difficult to monitor the capital outlay of such firms. It may thus be the case

that firms with low collateral value of assets may be more levered in an attempt to discipline

managers. Previous empirical studies, such as Rajan and Zingales (1995) and Harris and

Raviv (1991), have generally reported a positive relationship between asset tangibility and

leverage.

E.4. Growth

The relationship between growth and leverage is ambiguous. Financing firm operations

through debt commits the firms to service the debt. On one hand, growth firms may avoid

taking debt as it may lead them to pass on profitable investment opportunities due to debt

servicing (Myers, 1977). Titman and Wessels (1988) note that “growth opportunities are

capital assets that add value to a firm but cannot be collateralized and do not generate current

taxable income”. Hence, this suggests a negative relationship between debt and growth

opportunities consistent with the aforementioned theories. On the other hand, growth firms



13

may be in need of capital, beyond internal financing, to fund their investments. Hence, they

may be more likely to tap the debt market rather than equity markets as hypothesized by

Myers and Majluf’s Pecking Order theory (1984). Deloof and Verschueren (1998) find a

positive relationship between leverage and growth.

Galai and Masulis (1976) and Jensen and Meckling (1976) have modelled that shareholders of

levered firms have an incentive to invest sub-optimally to divert wealth from bondholders.

They find that this agency problem is more pronounced for growth firms that have

significantly large investment opportunities. In order to avoid the sub-optimal investment,

firms in growing industries would prefer to use equity financing over debt financing. Myers

(1984) suggests that this agency problem can be mitigated through the issue of short term debt

rather than long term debt and Green (1984) suggests the use of convertible debt.

Rajan and Zingales (1995) find a negative relation between growth and leverage. However,

they suggest that this negative relationship could also be due to firms timing their equity issue

when their stock prices are high. This temporarily causes leverage to be lower. Alternatively,

Fama and French (1992) argue that high leverage induces high costs of financial distress. The

market tends to discount the shares of firms in financial distress at a higher rate thus leading

to the above stated negative relation.

E.5. Profitability

The relationship between profitability and leverage is the most critical means of testing the

Pecking Order theory proposed by Myers and Majluf (1984). According to this theory, firms

follow a financing hierarchy. In an environment characterized by information asymmetry, it is

costly to issue a security about which outside investors have little information. Thus, internal

financing is the cheapest means of funding projects. As debt-holders have a higher claim on

firm assets as compared to equity-holders and they receive regular streams of interest

payments, debt suffers less from information asymmetry as compared to equity. Hence in

deciding the sources of financing, firms will prefer internal funds to debt and debt to equity.

The Pecking Order theory suggests that profitable firms will have lower leverage as they will

primarily meet their financing needs through retained earnings. Also, cash flow rich firms

may suffer from the agency problems of free cash flows as proposed by Jensen (1986).

Managers may expropriate private benefits creating a conflict of interest between the

managers and the shareholders. Leverage may thereby be increased to discipline the managers



14

and limit their consumption or perquisites. This predicts a negative relationship between

leverage and profitability.

However, it may be the case that lenders may be more willing to lend to profitable firms. If

so, more profitable firms would have greater access to debt markets. Moreover, profitable

firms would more likely be able to benefit from greater tax advantages of debt. This might

induce them to be more levered as dictated by the Trade-off theory. Also, Schoubben and

Hulle (2004) suggest that profitable firms may be less inclined to take debt in an attempt to

reinstate their profitability as a signal of high quality. Titman and Wessels (1988) and Rajan

and Zingales (1995) report a negative relationship between leverage and profitability.

E.6. Earnings Volatility

Volatility of earnings is a proxy for firm risk. The riskier the firm, the higher are the costs of

financial distress and greater is the probability of default. The Trade-off theory predicts that

riskier firms would then be less levered due to high bankruptcy costs. Also, firms having

volatile earnings may not be able to fully benefit from the tax advantage of debt. Similarly,

Titman and Wessels (1988) state optimal debt level to be a decreasing function of earnings

volatility. Deloof and Verschueren (1998) report a negative relationship between earnings

volatility and leverage.

Schoubben and Hulle (2004) argue that as risk of a firm increases, the cost of debt increases

simultaneously. Creditors incorporate the cost of bankruptcy in their debt contracts to protect

themselves. This causes the cost of debt to increase. Hence, in line with the Pecking order

theory and the general higher cost of debt, risky firms should rely on internal funds rather than

debt. This suggests a negative relation between leverage and a firm’s earnings volatility.

However, they argue that the impact of asymmetric information is heightened in riskier firms.

There is thus a need for “quality signalling and discipline” (Schoubben and Hulle, 2004). As

per the capital structure theories, this would imply a positive relationship between the two.

F. Differences in Capital Structure of Public and Private Firms

Public and private firms differ primarily in their listing status, ownership concentration and

information asymmetry. Public firms are listed on a stock exchange, usually have a diversified

shareholder base and are legally required to disseminate detailed information to their

shareholders through quarterly and annual reports. On the other hand, private firms are



15

unlisted, usually have concentrated ownership and are not required to make detailed financial

disclosures.

F.1. Access to Capital Markets

Public firms can issue capital to the general public whereas private firms cannot approach the

general public for capital as they are not listed on an exchange. Consequently, public firms

have a greater access to capital markets than their private counterparts. Brav (2009)

categorizes the predictions offered by the theories of capital structure into two effects, namely

the level effect and the sensitivity effect. According to the level effect, he argues that the level

of debt ratios of private firms is higher than the public firm debt ratios due to high costs of

equity of private firms relative to the cost of debt. As per the sensitivity effect, he states that

due to the overall higher costs of tapping the capital markets, private firms are likely to

exhibit passive financial behaviour. His study finds strong support for these effects. A similar

study done by Faulkender and Petersen (2006) on public firms shows that firms which have

greater access to debt markets (as proxied by a credit rating) have a greater ability to borrow

and hence, have higher leverage.

F.2. Information Asymmetry

Similarly, the degree of information asymmetry can also influence the financing behaviour of

firms. Private firms are more opaque than public firms as they are not required to make

detailed disclosures about their financial position. Outside investors only have limited

information available to assess the financial health of private companies. As equity has a

secondary claim on the firm’s cash flows as compared to debt, it is more sensitive to

information asymmetry (Myers and Majluf, 1984). Investors would, thus, require a higher

return to compensate from this additional risk of opacity. Noe (1988) notes that due to the

lack of transparency, the cost of equity is much higher for private firms than public firms.

Also, the cost of equity relative to debt is higher for private firms (Brav, 2009). Hence, private

firms would prefer debt to equity financing.

F.3. Ownership Concentration

As aforementioned, private firms have high ownership concentration. On the contrary, public

firms have a diversified shareholder base. Thus, control is highly valuable for equity share



16

blockholders of unquoted firms. This has two important implications for the cost of equity of

private firms relative to public equity.

First, as Stulz (1990) points out that a majority shareholder would be unwilling to resort to

equity financing if it would potentially result in a dilution of his shareholding and thus, his

control. Hence, the cost of giving away this control (cost of equity) is higher for private firms

(Amihud et al., 1990). The existing majority shareholders would require a greater

compensation to transfer their control. Also, the new shareholders would not only be paying

for a residual claim on the firms cash flows but will also pay for the value of control. As,

public firms have diluted ownership, the value of control is almost non-existent.

Second, as minority shareholders of private firms do not have the same protection and

disclosure as under public firms, equity of private firms is riskier for a minority shareholder

(Brav, 2009). He would thus require a higher return to compensate him for this additional risk

factor in order to purchase it.

There is a third dimension added to this argument by Morrellec (2004). He argues that due to

a more prominent separation between the management and the ownership in public firms, the

managers of a public firm may use equity as a means to dilute the ownership of any single

large shareholder. This implies that equity becomes a preferred means of financing for public

firms as compared to private firms.

G. Institutional Factors

Differences in leverage across countries can be attributable to differences in institutional

factors. Countries are broadly characterized as belonging to two distinct legal traditions

namely the common law regime and the civil (code) law regime. The English common law is

the most dominant legal origin of the common law regimes. The civil law regime has various

legal origins which include the Germanic code, the French code and the Scandinavian code.

The legal environment of each country is determined by first and foremost the prevalent legal

tradition and second by the legal origin to which they belong. Investor protection is then a

function of these two factors. My sample countries are ideal for studying these institutional

differences. The UK belongs to the English common law regime whereas Germany and the

Netherlands are characterized by the Germanic code law and the French code law

respectively.



17

Each of these three legal origins has distinct laws which provide for the protection of

investors. Such laws and the degree of enforcement of these laws might dictate the financing

behaviour of firms. La Porta et al. (1997, 1998) make an attempt to extensively investigate the

impact of legal origins on certain firm specific characteristics such as ownership

concentration. They provide an index measuring the degree of investor protection (creditors

and shareholders) based on different enacted laws such as bankruptcy laws, shareholder

activism laws and so on3. The index calculated by them rates creditor and shareholder

protection on a scale of 0 to 4 and 0 to 6 respectively, 4 and 6 being the most highly protected

environment.

The UK is reported to have a creditor rights rating of 4, whereas it is 3 and 2 for Germany and

the Netherlands respectively. Due to stronger creditor protection in the UK, one might expect

UK firms to have higher leverage as creditors will be willing to lend on favourable terms.

Similarly, shareholder rights, as proxied by the anti-director rights, are highest for the UK (5)

and lowest for Germany (1) whereas the Netherlands is midway at a rating of two. Also, La

Porta et al. (1998) report a highly efficient judicial system in all three countries implying a

high degree of enforcement. These measures seem to suggest that investors overall are most

highly protected in the UK. Low shareholder protection and relatively high creditor protection

in the Germany might make debt a preferred medium of financing for firms as cost of equity

might be very high.

Similarly, poor outside shareholder protection in Germany and the Netherlands suggests why

firms in these countries are closely held whereas UK firms have dispersed ownership. Also,

the relatively high ownership concentration in Germany may very well suggest that firms in

Germany on average have higher leverage as shareholders may not be willing to dilute their

control by issuing equity.

Some prior studies on capital structure have identified cross-country differences in leverage.

Rajan and Zingales (1995) find that countries with similar capital markets, such as the UK and

the United States, have very different leverage. Hence, other institutional factors such as tax

laws and bankruptcy laws are important considerations. Antoniou et al. (2008) find that

leverage across countries differ significantly. They further conclude that ownership

3 For a greater explanation of the index creation, please refer to La Porta, Rafael; Florencio Lopez-de-Silanes;

Andrei Shleifer; and Robert W. Vishny, 1998, Law and Finance, Journal of Political Economy 106, 1113–1155.



18

concentration and leverage are positively related. This is in line with the agency theory

whereby closely held firms would prefer debt over equity to avoid dilution of their control.

Creditor rights and leverage are also found to be positively related as higher protection of

creditors reduces the cost of debt and makes debt a favourable financing medium. However,

they also report a positive relation between debt and shareholder protection. They argue that

higher shareholder protection reduces information asymmetry and thereby increases debt

capacity. Overall, it can be seen that significant differences in leverage are seen to persist

across countries.

III. Hypothesis Development

In line with the theoretical framework and prior empirical investigations, I develop my

hypotheses.

Consistent with the trade-off theory, I expect size and leverage to be positively correlated.

Private firms by nature suffer from high information asymmetries. Hence, large private firms

should have the ability to have more debt due to lower bankruptcy costs and greater access to

capital markets than small private ones.

H1a: Larger firms are likely to have higher leverage, ceteris paribus.

Firms may decrease their cost of debt by issuing secured debt. Alternatively, lenders may also

be more willing to provide credit to firms that have a high collateral value of assets. Hence,

firms having higher asset tangibility should have more access to debt markets.

H1b: Firms with higher asset tangibility are likely to have higher leverage, ceteris paribus.

Theory supports a negative relation between firm leverage and growth opportunities as higher

costs of external capital may cause firms to pass up profitable investments. However, majority

of the prior empirical studies on public firms have shown a positive relationship between

these variables. This can be due to limited internal funds to finance the growth opportunities

and hence a reliance on debt rather than equity in line with the Pecking Order theory. Hence, I

expect to find a positive correlation between leverage and growth.

H1c: Firms with higher growth are likely to have higher leverage, ceteris paribus.



19

In line with the Pecking Order theory and prior empirical findings, I expect private firms

having sufficient retained earnings to be less levered. I expect the more profitable firms to

utilize internal funds before tapping the external market.

H1d: Firms having high profitability are more likely to have lower leverage, all else equal.

Higher earnings volatility may reduce the tax benefit incentive of private firms. Also, riskier

private firms may be more reluctant to finance using debt due to higher costs of bankruptcy.

H1e: Firms having higher earnings volatility more likely to have lower leverage, ceteris

paribus.

Private firms have limited access to capital markets relative to public firms. Hence, it is likely

that they tap the external markets less frequently. In the event of raising external capital, the

cost of issuing equity for private firms is much higher relative to, both, debt and public equity.

Hence, when faced with financing decisions, private firms are more likely to resort to debt

compared to public firms, leading to higher leverage ratios.

H2: Private firms are likely to have relatively higher leverage than public firms, ceteris

paribus.

IV. Data

A. Sources

I use the Amadeus database, managed by the Bureau van Dijk (BvD) to collect financial

statement information (balance sheet, income statement and cash flow statement items) of

private and public companies in the UK, Germany and the Netherlands for the period 2003-

2011. BvD only covers consolidated statements for up to a maximum of ten years for any

firm. Data for an active firm therefore is available till 2002 at the latest, whereas it may go

beyond for an inactive firm. Therefore, I restrict my study to the period 2003-2011 to avoid

any selection bias. As aforementioned, my choice of the three countries is motivated by their

developed financial markets and their different legal traditions. The UK is defined by the

English legal origin, Germany by the Germanic legal origin and the Netherlands by the

French legal origin.



20

Amadeus is a comprehensive database containing financial information for over 19 million

public and private companies across forty-one countries in Europe. It focuses specifically on

private firms and hence, is an ideal source for my research. In fact, the strength of the

database lies in its extensive coverage of private firms which otherwise offer very little

information to the general public. Moreover, it represents data in a standard format which is

easy to compare and understand. Bureau van Dijk partners with various information

providers to collect, process and deliver authentic and updated information products. In

addition to this, Amadeus particularly combines news and information from various sources

such as Financial Times, Reuters and original annual/interim reports of companies.

In addition to the financial data, I require information regarding the listing status of the firms

under study. Though this is provided by the Amadeus database, the variable reported only

represents the status of the firm as of the latest fiscal year. Since my study spans a period of

ten years, there is a likelihood that some firms in the sample were taken private while some

had an initial public offering (IPO). Hence, I make use of the Zephyr database, also managed

by the BvD, to complement the data extracted from Amadeus. The Zephyr database, like

Amadeus, contains extensive coverage deal information related to Mergers and Acquisitions,

Initial Public Offerings, Private Equity and Venture capital deals. Moreover, as both these

databases are managed by BvD, I can easily merge the datasets using the unique BvD

identification code for each company. I identify all take private and IPO deals within the three

countries and merge it with my original data set.

B. Sample

My sample includes all the incorporated entities in the UK, Germany and the Netherlands for

the period 2003-2011. Very small firms often have missing data as they are not required to

furbish an income statement. Hence, these firms are automatically excluded from the analysis.

I follow certain criteria to restrict my sample to obtain robust findings.

First, I screen firms on the basis of the type of accounts they prepare and report. Rajan and

Zingales (1995) duly notify that firms with unconsolidated balance sheets tend to understate

leverage as compared to comparable firms that provide consolidated statements. The reasons

for this are two-fold.



21

(i) Firms having unconsolidated balance sheets report an affiliate’s net assets as long

term investments resulting in a higher asset base.

(ii) These firms may report lower debt on the balance sheet via inter-firm credit

transactions conducted with an affiliate.

In line with these arguments put forth by Rajan and Zingales (1995), I only include firms

reporting consolidated financial statements to avoid inconsistencies in the analysis.

Second, I exclude firms belonging to certain industries as identified by the first two digits of

their Nace-Rev 2 Code. Brav (2009) identifies certain industries whose capital structure is

regulated and have an entirely different scope and nature of operations. These firms include

the financial sector firms (Nace-Rev 2: 64-66), public sector firms (Nace-Rev 2: 84) and

public utilities (Nace-Rev 2: 35-39).

Third, I follow the approach used by Brav (2009) in his analysis on private firms. According

to this approach, I include only the following types of firms: Private/Public Limited Liability,

Public Not Quoted, Public Quoted, Public Quoted OFEX (Off-Exchange) and Public AIM

(Alternative Investment Management) as the theories of capital structure are based on limited

liability companies. My analysis excludes Guarantee, Limited Liability Partnership, Public

Investment Trust and Unlimited firms.

Last, I identify firms that went public via an IPO or were taken private during the study

period. For a firm that underwent an IPO, I define the status of the firm as private pre-IPO and

public thereafter. Similarly, with the take private deals, I earmark the firm as being public

before the deal year and private thereafter.

After screening my dataset on these criteria, my sample consists of a total of 14,863 firms of

which 5,598 are public and 9,265 are private, 17 IPO deals and 10 take-private deals as shown

in Table 3 below.

Table 3

Sample Statistics

Germany Netherlands United Kingdom All

Public firms 54 112 877 5,598

Private firms 802 4,667 8,351 9,265

All firms 856 4,779 9,228 14,863



22

C. Research Methodology

I follow the methodology used by Brav (2009) in his empirical study on the funding behavior

of public and private firms in the UK. Following this methodology, I employ a pooled panel

ordinary least squares (OLS) regression model to study the correlation between the different

hypothesized determinants of capital structure namely, firm size, asset tangibility,

profitability, growth and earnings volatility with leverage.

Panel data has the advantage over other forms of data in that it can yield robust results for a

relatively short time series across several different observations in a cross-section. Hsiao

(1985) states that a panel data offers a large set of data points which reduces the collinearity

among explanatory variables and produces efficient coefficients. I interact my independent

variables with the status of the firm whereby “Public” takes the value of 1 if the firm is quoted

and 0 otherwise and “Private” takes the value of 1 of the firm is unquoted and 0 otherwise.

However, I include year, country and industry fixed effects in my model. Leary and Roberts

(2005) model initial leverage in their analysis and conclude that firms tend to revert to a mean

long run optimal leverage in the long run. Hence, I control for this by using year fixed effects.

Similarly, as my study spans across three countries, I introduce the country fixed effects to

cater to the institutional differences between the countries. Rajan and Zingales (2005) states

the importance of considering institutional factors especially in terms of bankruptcy laws and

market orientation. Germany and the Netherlands are bank-oriented whereas UK is a market-

oriented economy. Antoniou et al. (2008) argue that firm’s leverage ratio is crucially

influenced by the role of capital markets, investor protection, tax systems and corporate

governance practices. Hence, these differences need to be controlled for. Likewise, leverage

can vary across industries whereby manufacturing firms tend to be more highly levered

(Titman and Wessels, 1988). Frank and Goyal (2007) find leverage to be significantly related

to industry classification. Hence, I introduce industry fixed effects. The equation to be

estimated is as follows:

Leverage(i,t) = β0 + β(i)`X(i,t-1) + Country Fixed Effect + Industry Fixed Effect +

Year Fixed Effect + εi,t

where,

X(i,t) is a matrix of independent variables interacted with the firm status to provide a pooled

data. In order to avoid any endogeneity problems, I lag my independent variables one year.



23

D. Variable Measurement

Before defining my variables, I first make an adjustment to balance sheet data to make

consistent comparisons as is done by Rajan and Zingales (1995). They argue that German

liabilities are largely composed of dubious provisions which are in reality equity components.

I, therefore, reclassify provisions as shareholders equity across all countries.

My dependant variable is leverage. Theory provides several measures of leverage. Such

measures include total liabilities to total assets, total debt (short-term debt plus long-term

debt) to total assets, total debt to total capital. From the perspective of solvency, yet another

measure of leverage can also be the interest coverage ratio. However, all these measures have

their own shortcomings as elaborated by Rajan and Zingales (1995) in their study of capital

structure. The ratio total liabilities to total assets provides a measure of the shareholders

residual interest. However, this ratio includes items such as trade credits and pension

liabilities which do not represent financing activities and hence leverage may be overstated.

Also, this ratio is not a good indicator of default risk. This leads to the second measure of

leverage defined as the ratio of total debt to total assets. This measure, however, does not take

account for the fact that certain assets and non-debt liabilities may act contra to each other. An

example is of accounts payable and accounts receivable which may jointly be affected by

industry considerations. One might then revert to a measure of total debt to net assets whereby

net assets are given by total assets less accounts payable and other liabilities. However, this

ratio may also be affected by non-financing factors such as pension assets. The ratio debt to

capital seems appropriate as it reflects the proportion of firm capital financed through debt.

However, there is a limitation with this measure as well. A firm might be faced with negative

equity due to write-offs pertaining to currency depreciation, impairments or leveraged

buyouts. Since these are non- firm specific characteristics, this measure might not be suitable

for analysis of this study. Similarly, the interest coverage ratio is a flow measure and very

sensitive to fluctuations in net income. The following table gives an overview of these

different measures of leverage for each country as well as for the entire sample.



24

Table 4

Different measures of Leverage for the entire sample of Public and Private Firms and the subsample of firms in each country. The Difference statistic reports the difference in

the respective leverage measure between quoted and unquoted firms. *, **, *** denote significance at the 10%, 5% and 1% level respectively.

Germany Netherlands United Kingdom All

Public Private Difference Public Private Difference Public Private Difference Public Private Difference

Debt to Total Assets

Mean

0.275 0.303 -0.028*** 0.148 0.055 0.094*** 0.203 0.329 -0.126*** 0.196 0.223 -0.027***

Median

0.206 0.280 0.093 0.000 0.163 0.258 0.158 0.116

N 60 3332 839 25601 5215 38389 6114 67322

Debt to Net Assets

Mean

0.357 0.390 -0.033*** 0.188 0.073 0.115*** 0.250 0.401 -0.151*** 0.242 0.275 -0.033***

Median

0.266 0.382 0.130 0.000 0.204 0.335 0.198 0.156

N 60 3308 839 25600 5209 38154 6108 67062

Nonequity Liabilities to Total Assets

Mean

0.533 0.543 -0.009*** 0.524 0.607 -0.083** 0.520 0.664 -0.144*** 0.521 0.637 -0.116***

Median

0.529 0.553 0.523 0.624 0.501 0.662 0.504 0.639

N 61 3645 710 23278 5157 38581 5928 65504

Debt to Total Capital

Mean

0.441 0.476 -0.034*** 0.239 0.106 0.133** 0.338 0.516 -0.178*** 0.325 0.359 -0.034***

Median

0.276 0.477 0.197 0.000 0.262 0.439 0.256 0.232

N 60 3332 837 25313 5215 38378 6112 67023

Interest Coverage

Mean

5.070 8.961 -3.891*** 7.757 11.244 -3.487*** 7.225 7.185 0.040*** 7.262 7.816 -0.554***

Median

1.163 3.806 4.208 4.503 3.391 1.884 3.466 2.311

N 50 3417 564 5697 4616 37144 5230 46258



25

The table provides some initial evidence that private firms are more highly levered as

compared to public firms. The leverage ratios across all different measures of leverage are

significantly higher for private firms. Looking at the entire sample statistics, private firms

appear to have 22.3% debt to total assets and 35.9% debt to total capital. The same ratio for

public firms is 19.6% and 32.1% respectively. To the contrary, the interest coverage ratio is

higher for public firms which may suggest debt levels, vis-a-vis interest payments, are lower

for public firms. This could be a consequence of a lower absolute cost of debt for public firms

that could lead to lower interest payments relative to private firms.

These findings also hold across the subsample of different countries with the exception of the

Netherlands. Private firms in the UK and Germany have higher leverage ratios as compared to

public firms. However, this is reversed for firms in the Netherlands whereby public firms

report higher leverage ratios than private firms. However, the Netherlands seems to be an

exception. Public firms in the Netherlands appear to be more highly levered than private

firms. Across the countries, German and British firms have relatively higher leverage as

compared to the Dutch firms which have the least leverage across all measures for both public

and private firms. The relatively higher leverage ratios for German and British firms can be

attributed to their Germanic and English legal origins, respectively, which offer strong

creditor protection. The Netherlands is characterized by the French legal origin which has the

weakest creditor protection and this might be the reason for relatively low debt measures.

Amongst the firms in the UK and Germany, private British firms appear to have higher

leverage than likewise private German firms (32.9% vs. 30.3% debt to total assets ratio and

51.6% vs. 47.6% debt to total capital ratio respectively). Again looking at creditor protection

metrics provided by La Porta et al. (1998), UK emerges as the strongest creditor protected

state (See Appendix, Table B). Hence, creditors may be willing to lend at more favourable

terms to private firms in the UK, resulting in higher proportion of debt as compared to

Germany.

Given the pros and cons of each measure proposed by Rajan and Zingales (1995)4, I employ

the ratio total debt to total assets as a measure of leverage because of two reasons. First, it

appears to capture the essence of my analysis to explain the proportion of debt taken by a firm

by certain firm-specific factors. Second, it is one of the most widely employed measures of

4 Please refer to the paper for an in-depth discussion on each measure of leverage (Rajan and Zingales, 1995).



26

leverage in empirical literature for example in Antoniou et al. (2008), Brav (2009), Schoubben

and Hulle (2004). Also, it is important to note that since my sample consists of private firms, I

can only make use of book leverage and not market leverage in my analysis. Previous studies

such as those undertaken by Rajan and Zingales (1995), Leary and Roberts (2005) suggest

that this is not a major limitation as results are more or less robust across both measures. To

make consistent comparisons, I use book leverage for public firms as well.

I follow the empirical literature to define my independent variables. Firm size is given by the

natural logarithm of turnover, tangibility by the natural logarithm of tangible assets,

profitability as the ratio of operating income to total assets (return on assets), growth as the

growth in sales and earnings volatility as the three-year standard deviation in return on assets.

The measure of earnings volatility causes me to lose the first two years of my data. Also, as I

lag my variables by one year, this results in an analysis period of 2005-2011. I winsorize all

my variables at the 0.5% level at each tail to eliminate the problem of outliers. Moreover,

since leverage cannot be negative, I further eliminate observations reporting a negative

leverage due to data errors.

E. Descriptive Statistics

Table 5 and Table 6 provide the summary statistics for the entire sample and the subsample

pertaining to each country respectively. The tables report the statistics for public and private

firms separately along with their means and medians.

Again, as can be seen in Table 5, private firms have higher leverage as compared to public

firms. Public firms have an average of 22.3% debt as a proportion of total assets whereas the

same mean ratio is only 19.6% of all public firms. Similarly, the average ratio of debt to total

capital for private firms is 35.9% and for public firms it stands at 32.5%. Also, an interesting

thing to note is that short-term debt constitutes 48.4% of total debt for private firms but only

41.0% for public firms. This provides additional support to two prior findings. First, it

supports the hypothesis suggested by Grossman and Hart (1988) that firms may use short term

debt alleviate the problems of information asymmetry. Second, it may be so that private firms

use short-term debt to provide liquidity to their creditors as suggested by Brav (2009). Public

firms can provide liquidity to their creditors through the issue of new shares. This option is

unavailable to private firms and hence the use of short-term debt as an alternative. Public

firms also have higher cash holdings as a proportion of total assets, 15.8% versus 12.7% for



27

private firms. This seems strange as one might as well expect private firms to have a higher

cash base for transactions and speculative purposes. Also, as public firms are more likely to

easily access capital markets, one might expect them to hold lower cash balances to optimize

working capital. However, this does not appear to be the case in my sample of firms.

Also, public firms are larger than private firms both in terms of total assets and sales.

However, the mean tangibility of private firms is higher than that of public firms suggesting

that public firms on average have higher intangible assets. Mean growth of public firms is

higher than private firms. Higher growth prospects may in fact be one of the reasons that

firms may decide to go public to tap the equity markets. Firms having growth opportunities

may be unable to generate sufficient internal funds to finance these opportunities. Thus,

public equity may then be a viable alternative to fund growth. Private firms appear to be more

profitable on average as compared to public firms. The mean return on assets for private firms

is 4.6% whereas it is -2.5% for public firms. This might in turn also explain the high volatility

in earnings of public firms as opposed to private firms.

Looking at the subsample of each country in Table 6, the same results hold broadly. Across all

countries, public firms are larger both in terms of sales and total assets, have higher growth

prospects, higher volatility in earnings and are less profitable compared to private firms. For

firms in the UK and Germany, private firms have higher leverage than public firms. Similarly,

short-term debt constitutes a large portion of total debt for private firms, 50.0% and 33.0% for

firms in the UK and Germany respectively. However, results pertaining to debt ratios are not

consistent across Dutch firms. Dutch private firms have lower leverage than public firms.

Moreover, this debt is almost equally distributed between long-term and short-term debt. As

aforementioned, one of the reasons why private firms might have higher levels of debt is that

the cost of debt may be lower than the cost of equity, thereby making debt a more attractive

financing choice. Given the poor creditor protection provided by the Dutch legal system, this

may not hold. In order to safeguard their interests, lenders may then only be willing to lend at

a high cost to incorporate this risk. It could also be that lenders may be less willing to provide

credit to private firms which by nature have high information asymmetries. Hence, this could

be the reason why Dutch private firms have lower levels of debt compared to public firms.

Also, private firms in the Netherlands and Germany have higher cash holdings as a percentage

of total assets which is in line with the principles of working capital management which

predict that smaller firms should have higher cash holdings ceteris paribus.



28

Table 5

Summary Statistics for the entire sample of public and private firms

Summary statistics for the sample of public and private firms. The Difference statistic reports the difference in the respective leverage measure between quoted and unquoted

firms. All ratios are given in basis points. *, **, *** denote significance at the 10%, 5% and 1% level respectively. Mean Median SD Min Max N


Public

0.196 0.158 0.198 0.00 1.164 6,114

Private

0.223 0.116 0.275 0.00 1.184 67,322

Difference -0.027***


Public

0.325 0.256 0.387 0.00 2.591 6,112

Private

0.359 0.232 0.442 0.00 2.962 67,023

Difference

-0.034***

Short-term Debt to Total Debt

Public

0.410 0.301 0.354 0.00 1.000 5,242

Private

0.484 0.441 0.365 0.00 3.814 45,629


Cash to Total Assets

Public

0.158 0.090 0.354 0.00 1.000 6,818

Private

0.127 0.060 0.365 0.00 3.814 74,506

Difference 0.031**

Total Assets (€ ‘000)

Public

1,636,756 73,198 8,454,098 10.00 221,000,000 6,976

Private

193,331 22,495 1,418,232 0.00 67,600,000 77,125

Difference 1,443,425***

Turnover (€ ‘000)

Public

1,305,118 74,046 5,115,899 1,418,415 72,000,000 6,810

Private

214,551 37,026 1,817,878 200,227 140,000,000 64,933

Difference

1,090,567*** Size

Public

11.137 11.229 2.589 4.595 15.462 6,788

Private

10.427 10.522 1.793 4.595 15.462 64,874

Difference 0.710*** Growth

Public

0.208 0.059 0.768 -0.820 4.325 5,805

Private

0.111 0.032 0.585 -0.629 4.778 52,328

Difference 0.096***

Profitability

Public

-0.025 0.050 0.252 -0.930 0.450 6,973

Private

0.046 0.051 0.157 -0.930 0.432 75,494

Difference

-0.071***

Tangibility

Public

0.199 0.114 0.222 0.00 0.954 6,932

Private

0.281 0.211 0.256 0.00 0.922 75,386


Volatility

Public

0.083 0.034 0.127 0.001 0.603 5,001

Private

0.049 0.027 0.078 0.001 0.603 49,178

Difference 0.034***



29

Table 6

Summary statistics of public and private firms for each Country

Summary statistics for public and private firms in each country. The Difference statistic reports the difference in the respective leverage measure between quoted and

unquoted firms. All ratios are given in basis points. *, **, *** denote significance at the 10%, 5% and 1% level respectively. Germany Netherlands United Kingdom

Mean Median N Mean Median N Mean Median N


Public 0.275 0.206 60 0.148 0.093 839 0.203 0.163 5,215

Private 0.303 0.280 3,332 0.055 0.00 25,601 0.329 0.258 38,389

Difference -0.028*** 0.094***

-0.126***


Public 0.441 0.276 60 0.239 0.197 837 0.338 0.262 5,215

Private 0.476 0.477 3,332 0.106 0.00 25,313 0.516 0.439 38,378


0.133***

-0.178***

Short-term Debt to Total Debt

Public 0.237 0.137 53 0.490 0.425 573 0.402 0.289 4,616

Private 0.330 0.249 3,201 0.481 0.429 6,233 0.499 0.467 36,195

Difference -0.094*** 0.009***

-0.097***

Cash to Total Assets

Public 0.099 0.076 61 0.114 0.070 836 0.165 0.094 5,921

Private 0.103 0.059 3,638 0.116 0.050 26,436 0.136 0.066 44,391

Difference -0.004*** -0.002

0.029***

Total Assets (€ ‘000)

Public 11,700,000 102,618 61 3,030,745 297,044 851 1,339,981 62,509 6,064

Private 302,949 82,599 3,647 192,594 20,548 27,318 185,106 21,391 46,160

Difference 11,397,052*** 2,838,151***

1,154,875***

Turnover (€ ‘000)

Public 6,991,723 120,475 61 2,874,516 377,179 840 1,023,314 60,875 5,909

Private 385,634 133,962 3,562 367,107 63,536 16,626 144,247 25,276 44,745

Difference 6,606,089***

2,507,409***

879,068***

Size

Public 11.898 11.699 61 12.495 12.840 840 10.935 11.029 5,887

Private 11.221 11.805 3,562 11.141 11.058 16,647 10.044 10.141 44,665

Difference 0.677*** 1.353***

0.892***

Growth

Public 0.155 0.048 50 0.142 0.056 725 0.218 0.060 5,030

Private 0.065 0.041 2,748 0.108 0.048 13,099 0.116 0.024 36,481

Difference 0.089*** 0.035***

0.102***

Profitability

Public -0.077 0.032 61 0.045 0.065 851 -0.034 0.047 6,061

Private 0.088 0.082 3,575 0.078 0.068 26,114 0.024 0.040 45,805


-0.033 ***

-0.058*** Tangibility

Public 0.259 0.228 61 0.245 0.174 850 0.192 0.106 6,021

Private 0.317 0.296 3,641 0.290 0.231 26,994 0.273 0.186 44,751

Difference -0.058*** -0.045***

-0.081***

Volatility

Public 0.112 0.026 40 0.049 0.023 626 0.088 0.037 4,335

Private 0.032 0.022 2,001 0.045 0.029 17,218 0.053 0.026 29,959

Difference 0.080*** 0.004***

0.035***



30

A univariate analysis can only at best give an initial glimpse of the data characteristics. One

cannot derive any conclusion from the results of Table 5 and 6. For, example, it can be seen

that private firms have higher leverage than public firms. However, this difference could also

be driven by other factors such as size or even profitability. As can also be seen that private

firms on average have higher profitability than public firms. Hence, it could very well be that

higher leverage may be driven by the higher profitability of private firms and not firm status.

Hence, I run a multivariate regression to conclusively test my hypotheses.

V. Empirical Results

A. Multivariate Analysis

Table 7 shows the results of a pooled panel ordinary least squares regression for the entire

sample of public and private firms. Column A reports the results without the country fixed

effects whereas Column B includes country fixed effects. As can be seen, the status of the

firm (as proxied by the dummy variable “Public”) is, both economically and statistically,

significant across both specifications. Public firms have approximately 31.5% lower leverage

than private firms without the country fixed effects and 25.6% lower leverage with the

inclusion of country fixed effects, ceteris paribus.

An interesting observation is the discrepancy between the effect of size of private and public

firms on leverage. A 1% increase in firm size of public firms causes leverage to increase by

0.011%. Hence, leverage is increasing in firm size for public firms as predicted by earlier

empirical studies as well such as Rajan and Zingales (1995) and Frank and Goyal (2003). This

finding lends support to the ability of firms to borrow more as they become larger and more

diversified. Also, as firms become more diversified, the cost of bankruptcy decreases

enhancing their ability to borrow more. In terms of economic significance, a one standard

deviation increase in size of public firms would increase leverage by 14.5% on average

around the mean leverage.

However, my findings suggest a negative relation between firm size and leverage of private

firms. So as to say, as firm size increases by 1%, leverage decreases by 0.011% for the private

firm sample. Similarly, in terms of economic significance, a one standard deviation increase

in firm size would decrease leverage by 8.7% around the mean. This negative result has been

reported earlier by Rajan and Zingales (1995) for their subsample of German firms. This



31

supports two prior thoughts. First, it reinforces the hypothesis proposed by Titman and

Wessels (1988) and Schoubben and Hulle (2004) which states that larger firms have lower

equity issuance costs. Therefore, equity may be a preferred medium of financing for larger

firms. Second is the theory proposed by Smith (1977) which states that information

asymmetry and agency costs are a decreasing function of size. Hence, the cost of issuing

equity for small firms is relatively high and they may then turn to debt. These latter two

theories seem to hold for the sample of private firms. However, with the introduction of

country dummies, size of private firms becomes positively correlated with leverage. A 1%

increase in size of private firms would increase leverage by 0.002%. Hence, size becomes

positively correlated with leverage with the introduction of country fixed effects. The size

variable, though statistically significant, appears to be economically insignificant around the

mean values of leverage (19% and 22% for public and private firms respectively).

Also, growth of public firms is negatively correlated with leverage but is statistically

insignificant (not significantly different from zero) as has also been found by Titman and

Wessels (1988) in their study on capital structure. However, the growth of private firms is

positively correlated with leverage and statistically significant. This relation is in line with the

findings of Deloof and Vershueren (2004) and Brav (2009). A 1% increase in growth

prospects increases leverage of private firms by 1.7%. This shows that private firms need to

tap the external debt markets to fund their growth opportunities as internal funds may be

insufficient. Alternatively, a one standard deviation increase in growth would increase

leverage of private firms by 4.46% around the mean leverage.

Across the public and private firm sample respectively, profitability is strongly negatively

correlated with leverage whereas Asset tangibility and volatility are positively correlated with

leverage. The findings of the correlation of leverage with profitability and tangibility are in

line with the results of prior studies such as those by Rajan and Zingales (1995), Schoubben

and Hulle (2004) and Brav (2009). The positive correlation between leverage and volatility is

supported by an earlier study by Bennet and Donnelly (1993) but contradicts studies by

Schoubben and Hulle (2004) and Deloof and Vershueren (2004).

Looking at these variables separately for the subsample of public and private firms, I find that

a 1% increase in ROA (profitability measure) decreases leverage by 8.8% for public firms and

39.0% for private firms. This supports the Pecking Order theory whereby as firms become

more profitable, they utilize internal funds before approaching the external markets. Also,



32

profitable firms may use their profits to repay debt further supporting the Pecking Order

Theory. It is worth noting, the leverage of private firms is more sensitive to profitability

compared to public firms. This can be explained in terms of the cost of capital for private

firms. As profitability increases, private firms will reduce their leverage more than public

firms as the absolute cost of tapping the external market is higher for private firms. Hence,

private firms are more likely to strongly rely on internal funds, all other things staying the

same.

Similarly, with a 1% increase in tangibility, leverage increases by 0.171% and 0.111 %

respectively for public and private firms. Similarly, a one standard deviation increase in

tangibility will increase leverage by approximately 19% and 13% around the mean leverage,

for public and private firms respectively. Thus, as the ability of firms to issue secured debt

increases (in terms of asset collateral), their debt levels increase. However, for the same

increase in asset tangibility, public firms seem to borrow more as compared to private firms.

Last, a 1% increase in earnings volatility causes leverage of public firms to increase by 24.3%

whereas it is only 6.2% in case of private firms. The positive relation can be explained by the

reasoning of Bennett and Donnelly (1993). They argue that as the risk of a firm increases, the

agency costs pertaining to asymmetric information increase as well. Increasing debt levels

would be a way to signal quality earnings. In terms of economic significance, a one standard

deviation increase in earnings volatility would increase leverage by 15.7% and 2.1% around

the mean leverage for public and private firms respectively.

The traditional determinants of leverage appear to be both statistically and economically

significant across both the private and public firm subsample (with the exception of growth of

public firms). It can be seen that leverage of private firms is less sensitive to asset tangibility

and volatility. This is again attributable to the differential cost of external capital for public

and private firms. As it is more costly for private firms to raise external capital, they will be

more reluctant to increase their leverage with an increase in debt capacity. Moreover, I

employ a difference of means test to test whether the mean of variable X for private firms

equals the mean of variable X for public firms whereby variable X is each of the independent

variables. The p-values of the test are reported in Panel B of Table 7. The results show that

each of the variables for public firms are significantly different from their private

counterparts.



33

Table 7

Determinants of Leverage

Panel A shows the results from a pooled panel OLS regression. The t-statistics of the regression are reported in

the brackets and are corrected for heteroskedasticity. The dependent variable is given by the ratio of debt to total

assets.I interact each of my independent variables with the status of the firm. Public and Private are dummy

variables with Public taking the value of 1 (0) if the firm is quoted (unquoted) and Private taking the value 1 (0)

if the firm is unquoted (quoted). Column 1 reports the results without the country fixed effects. Column 2 reports

the regression results with the inclusion of country dummies. The definitions of the independent variables are

provided in the Appendix. Panel B reports the p-values of the test of differences whereby I test whether Private

X = Public X, X being each of the independent variables. *, **, *** denote significance at the 10%, 5% and 1%

level respectively.

Panel A

Leverage Leverage

Public -0.315*** -0.256***

(-15.01) (-12.48)

Public Size 0.011*** 0.016***

(7.62) (11.41)

Private Size -0.011*** 0.002**

(-12.02) (2.14)

Public Growth -0.002 -0.001

(-0.34) (-0.13)

Private Growth 0.017*** 0.018***

(4.30) (4.97)

Public Profitability -0.088*** -0.099***

(-3.63) (-4.23)

Private Profitability -0.390*** -0.333***

(-25.02) (-22.5)

Public Tangibility 0.171*** 0.176***

(11.43) (11.79)

Private Tangibility 0.111*** 0.114***

(16.79) (18.72)

Public Volatility 0.243*** 0.235***

(5.2) (5.2)

Private Volatility 0.062* 0.166***

(1.74) (5.01)

Germany

0.023***

(3.93)

Netherlands

-0.204***

(-83.41)

Constant 0.325 0.241

23.32 17.9

Country Fixed Effects No Yes

Time Fixed Effects Yes Yes

Industry Fixed Effects Yes Yes

Adjusted R-squared 0.109 0.215 Number of Observations 40601 40601

Panel B

Size 0.000*** 0.000***

Growth 0.000*** 0.000***

Profitability 0.000*** 0.000***

Tangibility 0.000*** 0.000***

Volatility 0.000*** 0.000***

Similar results hold in Column B with the specification of country fixed effects added to the

original regression equation. However, an interesting finding is the coefficients for the

country dummies in Column B. These coefficients are highly significant. They show that

leverage of firms in the Netherlands is approximately 20% lower than the leverage of firms in

the UK. Similarly, German firms on have around 2.3% higher leverage than firms in the UK.

This finding suggests that country-specific factors other than the traditional hypothesized

variables (size, growth, profitability, tangibility and volatility) are also important determinants



34

of leverage. Institutional laws such as shareholder and creditor protection rights may explain

why leverage in one country is higher or lower. In order to understand these differences, I

repeat the multivariate regression above for each of the sample countries separately. The

results are reported in Table 8.

B. Cross-Country Analysis

Looking at the country subsample, the coefficient of the public status loses its significance for

the German firms suggesting that leverage of private firms is not statistically different from

the leverage of public firms. Moreover, it is positive for the Dutch firms, which suggests that

the leverage of public firms is 8.8% higher than the leverage of private firms. As discussed in

the summary statistics, one reason for this could be the poor creditor protection rights in the

Dutch legal system which deters lenders to easily lend to private firms as private firms

inherently suffer from lack of transparency and information asymmetries. However, public

firms in the UK have 23.3% lower leverage than private firms which is consistent with the

results of Table 7.

The German firms do not report any significant relationship between size and leverage for

both public and private firms. For Netherlands, public firms do not report a significant

relationship whereas private firms report a significant positive relationship. UK firms, both

public and private respectively, report a significant positive relationship suggesting that

leverage is increasing in firm size. Amongst the private firms, the leverage of Dutch firms is

more sensitive to size compared to UK firms. For the same increase in size, Dutch private

firms tend to increase their leverage more as compared to private firms in the UK.

Only public firms in Germany report a significantly negative relationship with leverage> It

shows that public firms in Germany faced with growth opportunities do not want to be

committed to servicing higher debt as it may cause them to pass up profitable investment

opportunities. Hence, they will prefer lower leverage. Similar results hold for private firms in

the Netherlands which show a negative correlation with leverage. Consistent with the findings

of Table 7, growth of private firms in Germany and UK is significantly positively correlated

to leverage. Hence, one sees opposite effects of growth of private firms on leverage in the

Netherlands and Germany and UK. Lenders may only be willing to lend credit to private

Dutch firms at a very high rate to protect themselves. This may deter private firms to opt for



35

debt to finance their growth prospects as it may be difficult for them to simultaneously service

the debt at high cost and invest.

Again, profitability is negatively correlated with leverage for both the public and private firms

across the three countries, though it is insignificant for public German firms. This lends

further support to the Pecking Order Theory. Also for all countries, leverage is more sensitive

to the profitability of private firms as compared to the public firms. Amongst the subsample of

private firms, leverage of German firms is most sensitive to profitability. For a 1% increase in

profitability of private firms, leverage of German firms decreases by 59.1% whereas it only

decreases by 19.3% and 41.3% respectively for the Dutch and British firms.

Also, tangibility is positively correlated with leverage for both public and private firms across

the three countries. However, the effect of tangibility on leverage for German public firms is

not statistically different from zero. For firms in the Netherlands and UK, leverage of public

firms is more sensitive to tangibility than the leverage of private firms. As tangibility

increases, German private firms increase leverage by most compared to other countries.

With regards to volatility, German public firms and British public and private firms show a

significantly positive correlation with leverage supporting the results of Table 7. However,

private firms in the Netherlands are characterized by a negative relationship between leverage

and volatility. As risk increases, probability of default increases and bankruptcy costs increase

making debt less desirable.

Panel B of Table 8 again reports the p-values of the test of differences of mean. As can be

seen, all variables for public firms are statistically different from their private counterparts for

each of the three countries.



36

Table 8


Panel A shows the results from a pooled panel OLS regression for the subsample of firms in each country. The t-

statistics of the regression are reported in the brackets and are corrected for heteroskedasticity. The dependent

variable is given by the ratio of debt to total assets. I interact each of my independent variables with the status of

the firm. Public and Private are dummy variables with Public taking the value of 1 (0) if the firm is quoted

(unquoted) and Private taking the value 1 (0) if the firm is unquoted (quoted). Column 1 reports the results

without the country fixed effects. Column 2 reports the regression results with the inclusion of country dummies.

The definitions of the independent variables are provided in the Appendix. Panel B reports the p-values of the

test of differences whereby I test whether Private X = Public X, X being each of the independent variables. *, **,

*** denote significance at the 10%, 5% and 1% level respectively.

Panel A

Leverage

Germany Netherlands United Kingdom

Public

0.118

0.088**

-0.233***

(0.45)

(1.96)

(-8.22)

Public Size

-0.012

0.002

0.014***

(-0.58)

(0.72)

(6.37) Private Size

-0.003

0.007***

0.003***

(-0.74)

(6.69)

(2.75)

Public Growth

-0.133***

0.016

-0.001

(-2.35)

(1.46)

(-0.12)

Private Growth

0.085***

-0.007**

0.021***

(3.89)

(-1.96)

(5.88) Public Profitability -0.054

-0.123***

-0.091***

(-0.26)

(-2.66)

(-3.41)

Private Profitability -0.591***

-0.193***

-0.412***

(-10.87)

(-7.07)

(-29.93)

Public Tangibility

0.214

0.190***

0.152***

(1.36)

(6.61)

(7.32)

Private Tangibility 0.385***

0.072***

0.117***

(14.77)

(10.37)

(17.13)

Public Volatility

0.037***

-0.047

0.23***

(2.71)

(-0.5)

(4.55)

Private Volatility

-0.087

-0.148***

0.227***

(-0.67)

(-5.21)

(8.44) Constant

0.28

-0.059

0.193

(4.51)

(-3.64)

(11.57) Time Fixed Effects Yes

Yes

Yes

Industry Fixed Effects Yes

Yes

Yes

Adjusted R-squared 0.234

0.142

0.138

Number of Observations 1920

10738

27943

Panel B

Size

0.000***

0.000***

0.000***

Growth

0.000***

0.000***

0.000*** Profitability

0.000***

0.000***

0.000***

Tangibility

0.000***

0.000***

0.000***

Volatility

0.000***

0.000***

0.000***

C. Analysis of Variance

Having looked at cross-country differences between the traditional determinants of leverage, I

perform an Analysis of Variance (ANOVA). This breaks down the variation in leverage due

to each of the independent variables. It can be seen in column (a) that the status of the firm

explains 3.8% of the variation in leverage in our model. Similarly, size explains 6.4%, growth

1.3%, profitability 45.7%, tangibility 42.5% and volatility 0.2% variation in leverage. The

adjusted R-squared from this specification is only 6.3%. However, the adjusted R-squared



37

increases to 18.1%with the inclusion of country fixed effects (as shown in column (d)) and

21.0% with the inclusion of country, year and industry fixed effects (shown in column (h)).

Also to note in column (h) is that country dummies explain 62.5% variation in our modeled

leverage and industry fixed effects explain 16.5% of the variation in leverage. This shows the

importance of both institutional factors and industry specifications as determinants of

leverage.

Table 9

Analysis of Variance

Variance Decomposition

(a) (b) (c) (d) (e) (f) (g) (h)

Public Status 0.038 0.015 0.038 0.049

0.033

Size 0.064 0.020 0.062 0.003

0.006

Growth 0.013 0.007 0.010 0.005

0.003

Profitability 0.457 0.296 0.454 0.097

0.093

Tangibility 0.425 0.145 0.422 0.131

0.062

Volatility 0.002 0.002 0.002 0.006

0.007

Industry

0.514

1.000

0.169

Year

0.012

1.000

0.002

Country

0.709

1.000 0.625

Adjusted R-Squared 0.063 0.099 0.0643 0.181 0.082 0.0002 0.210 0.210

No. of Observations 40601 40601 40601 40601 73436 73436 73436 40601

D. Institutional Differences

Having looked at the cross country differences in leverage and established the importance of

institutional factors in the determination of leverage, I directly explore the relationship

between institutional factors and leverage. For this purpose, I make use of four additional

factors in the determination of leverage, namely rule of law, ownership concentration,

shareholders rights (proxied by anti-director rights) and creditors rights as discussed by La

Porta et al. (1998). These factors capture the essence of cross country differences in terms of

different institutions. Table 10 reports the results from an OLS regression. It can be seen that

the traditional determinants of leverage retain their significance and relationship with leverage

across the sample of public and private firms. It is pertinent to mention that the rule of law

and creditor rights protection imply two different legal measures. For example, while in the

Netherlands, creditor rights are weak, rule of law is the highest. This only shows that while

the rights of the creditors are not protected very highly by law but the Dutch code law is

highly enforced.



38

The variables of interest in this table are the institutional factors. Creditor rights and

ownership concentration are positively correlated with leverage whereas anti-director rights

(also shareholder rights) and rule of law are negatively correlated with leverage across both

public and private firms. All these variables are statistically highly significant. An increase of

1 unit scale of creditor rights increases leverage by 8.3% in terms of public firms and 17.1%

in terms of private firms. The greater the rights of the creditors are protected, the more will

the creditors be willing to lend at a lower rate. In cases where the rights of creditors are not

secured by law, creditors incorporate the additional risk in their borrowing rate leading to

higher cost of debt. Hence, we see high debt levels for firms in Germany and the UK

compared to Netherlands, consistent with the degree of protection offered to creditors in these

countries respectively.

Similarly, the ownership concentration is negatively correlated with leverage. The reasoning

behind this may be taken from the discussion on public and private firms as well. The higher

the ownership concentration, the more reluctant are the current shareholders to issue equity as

it would result in a dilution of control. A 1% increase in ownership concentration will result in

a 13.4% and 22.5% increase in leverage across public and private firms respectively. German

firms have the highest ownership concentration measure. Consistent with this, German public

firms also have the highest leverage measure as discussed in Table 6. However, UK private

firms appear to have the highest leverage amongst the sample even though they have the

lowest mean percentage ownership concentration.

Also, the rule of law is negatively correlated with leverage. It should be noted that the rule of

law incorporates the enforcement of different laws, including bankruptcy laws. Hence, it may

be so that in order to avoid the stringent bankruptcy laws, firms may be reluctant to have high

debt so as to avoid bankruptcy. This appears to be true for the Netherlands as it has the

highest ranking of the rule of law. Similarly, shareholder rights are negatively correlated with

leverage. This makes intuitive sense. The more the rights of the shareholders are protected,

the lower will be the risk faced by shareholders of the firm and the lower will be the relative

cost of equity. In lieu of a relatively lower cost of equity, equity may be preferred over debt.



39

Table 10

Institutional Factors in the Determination of Leverage

The table shows the results from an OLS regression. The t-statistics of the regression are reported in the brackets

and are corrected for heteroskedasticity. The dependent variable is given by the ratio of debt to total assets. I

include four additional independent variables as a proxy for institutional factors. The definitions of the

independent variables, including the institutional factors, are provided in the Appendix. *, **, *** denote

significance at the 10%, 5% and 1% level respectively.

Public Firms Private Firms

Leverage Leverage

Size 0.012*** 0.012*** 0.003*** 0.003***

(8.00) (8.00) (3.08) (3.08)

Growth -0.002 -0.002 0.018*** 0.018***

(-0.39) (-0.39) (4.99) (4.99)

Profitability -0.089*** -0.089*** -0.33*** -0.33***

(-3.75) (-3.75) (-22.31) (-22.31)

Tangibility 0.149*** 0.149*** 0.115*** 0.115***

(8.48) (8.48) (18.68) (18.68)

Volatility 0.263*** 0.263*** 0.166*** 0.166***

(5.65) (5.65) (5.01) (5.01)

Creditor Rights 0.083***

0.17***

(4.03)

(56.76)

Anti-Director Rights -0.049***

-0.063***

(-2.45)

(-21.98)

Rule of Law

-0.172***

-0.302***

(-3.26)

(-40.69)

Ownership Concentration

0.134***

0.225***

(4.64)

(32.66)

Adjusted R-squared 0.19 0.19 0.222 0.222

Number of Observations 4379 4379 36222 36222

E. Robustness Tests

In order to check the robustness of the results, I employ three additional regressions. First, I

replace my independent variable. I use debt-to-capital as my measure of leverage. Capital is

defined as the sum of total debt and total shareholder’s funds. The results of the pooled panel

OLS regression are reported in Table 11. The results are robust against the debt to capital

ratio. Across the entire sample, with and without country dummies, leverage is positively

correlated to size of public firms, tangibility and volatility of both public and private firms and

is negatively correlated to the status of the firm, size and growth of private firms and

profitability of public and private firms. These results reinforce the findings of the regression

in Table 7. However, the coefficient of the growth of public firms is negatively correlated

with leverage and is significant at the 10% level. This is consistent with the findings of earlier

studies such as Rajan and Zingales and Frank and Goyal (2001). Important to note, the status

of the firm is highly significant across the entire sample. Public firms have approximately

55.3% lower debt as a percentage of total capital compared to private firms (as reported in the

first column without country dummies).



40

Across the subsample of individual countries, a few variables lose their significance against

the new measure of leverage but the results are consistent at large.

Table 11


The table shows the results from a pooled panel OLS regression for the entire sample and subsample of firms in

each country. The t-statistics of the regression are reported in the brackets and are corrected for

heteroskedasticity. The dependent variable is given by the ratio of debt to total capital. I interact each of my

independent variables with the status of the firm. Public and Private are dummy variables with Public taking the

value of 1 (0) if the firm is quoted (unquoted) and Private taking the value 1 (0) if the firm is unquoted (quoted).

Column 1 reports the results without the country fixed effects. Column 2 reports the regression results with the

inclusion of country dummies for the entire sample. The definitions of the independent variables are provided in

the Appendix. *, **, *** denote significance at the 10%, 5% and 1% level respectively.

All Germany Netherlands United Kingdom

Public -0.553*** -0.463***

-0.158

0.017

-0.418***

(-13.14) (-11.2)

(-0.28)

(0.26)

(-9.05)

Public Size 0.030*** 0.038***

0.007

0.013***

0.035***

(10.27) (13.23)

(0.19)

(3.07)

(10.96)

Private Size -0.005*** 0.014***

0.006

0.013***

0.018***

(-3.35) (9.4)

(0.92)

(6.75)

(9.05)

Public Growth -0.018** -0.016*

-0.181

0.012

-0.017*

(-1.99) (-1.79)

(-1.39)

(0.52)

(-1.78)

Private Growth 0.017*** 0.018***

0.132***

-0.01

0.017***

(2.68) (3.19)

(2.96)

(-0.77)

(2.32)


-0.399

-0.28***

-0.222***

(-3.88) (-4.26)

(-0.58)

(-3.97)

(-3.47)


-0.932***

-0.32***

-0.781***

(-23.19) (-21.19)

(-8.15)

(-10.3)

(-18.96)


0.373

0.211***

0.15***

(7.91) (8.17)

(1.31)

(4.5)

(5.88)


0.374***

0.086***

0.005

(2.45) (3.08)

(8.49)

(5.96)

(0.43)


1.805

-0.09

0.728***

(6.16) (6.3)

(1.53)

(-0.6)

(5.98)

Private Volatility 0.616*** 0.772***

0.07

-0.19***

0.986***

(8.29) (10.79)

(0.29)

(-3.54)

(11.21)

Germany

0.025***

(2.610)

Netherlands

-0.307***

(-70.69)


0.341

-0.08

0.232

(18.35) (13.32)

(3.7)

(-2.68)

(7.6)


-

-

-


Yes

Yes

Yes


Yes

Yes

Yes

Adjusted R-squared 0.094 0.178 0.193

0.099

0.121

Number of Observations 40424 40424 1920

10569

27935



41

Second, I replace the measures of three of my independent variables, namely growth, size and

profitability. I define growth as a two year growth in earnings before interest, tax and

depreciation (EBITDA). A growth in earnings gives an insight into the future growth of the

firm and its current investment opportunities. Similarly, I replace my profitability measure

with the net income margin. For size, I employ the natural logarithm of total assets. Again, the

results are robust against these new measures of growth, size and profitability as well. Across

the entire sample of firms, growth of public firms still remains insignificant whereas the

growth of private firms is negatively correlated with leverage. Similarly, profitability is

negatively correlated with leverage for both public and private firms across the entire sample.

Also, as reported initially, the leverage of private firms is more sensitive to profitability as

compared to public firms.

Results are robust against the cross country regressions as well. However, the growth variable

loses its significance for the subsample of UK and Netherlands firms. It appears that firms in

each of the countries increase or decrease leverage in response to their growth in sales rather

than earnings.



42

Table 12


The table shows the results from a pooled panel OLS regression for the subsample of firms in each country. The

t-statistics of the regression are reported in the brackets and are corrected for heteroskedasticity. The dependent

variable is given by the ratio of debt to total assets. I interact each of my independent variables with the status of

the firm. Public and Private are dummy variables with Public taking the value of 1 (0) if the firm is quoted

(unquoted) and Private taking the value 1 (0) if the firm is unquoted (quoted). Column 1 reports the results

without the country fixed effects. Column 2 reports the regression results with the inclusion of country dummies.

The definitions of the independent variables are provided in the Appendix. *, **, *** denote significance at the

10%, 5% and 1% level respectively.


Public -0.129*** -0.107***

-0.073

0.01

-0.041*

(-5.73) (-4.78)

(-0.31)

(0.19)

(-1.68)

Public Size 0.02*** 0.023***

0.014

0.009***

0.021***

(12.94) (15.11)

(0.96)

(2.64)

(12.33)

Private Size -0.015*** 0.022***

0.007

0.009***

0.027***

(-13.91) (20.56)

(1.5)

(5.37)

(20.46)

Public Growth -0.001 -0.001

-0.128*

-0.018

0.002

(-0.12) (-0.12)

(-1.64)

(-0.84)

(0.19)

Private Growth -0.012* -0.007**

0.027

-0.012

0.009

(-1.77) (-1.98)

(1.25)

(-1.6)

(1.53)

Public Profitability -0.014*** -0.011**

-0.152*

-0.023

-0.016***

(-3.01) (-2.27)

(-1.68)

(-1.22)

(-3.50)


-0.396***

-0.022

-0.017***

(-3.93) (-3.44)

(-6.81)

(-1.07)

(-2.83)


0.089

0.216***

0.139***

(10.94) (10.39)

(0.44)

(5.25)

(8.74)


0.444***

0.127***

0.136***

(21.41) (21.15)

(14.65)

(10.48)

(16.68)


2.014***

0.107

0.448***

(7.61) (7.49)

(3.55)

(0.43)

(7.56)


-0.105

-0.141***

0.593***

(8.11) (9.67)

(-0.59)

(-3.34)

(10.7)

Germany

-0.012*

(-1.87)

Netherlands

-0.175***

(-57.92)

Constant 0.019 -0.021

0.109

-0.1

-0.09

(1.24) (-1.45)

(1.71)

(-4.47)

-4.88)


-

-

-


Yes

Yes

Yes


Yes

Yes

Yes

Adjusted R-squared 0.095 0.163

0.20

0.14

0.132

Number of Observations 30684 30684

1789

5953

22942



43

Third, I adjust my dependent variable in line with one of the adjustments proposed by Rajan

and Zingales (1995). I exclude intangible assets from my analysis. This eliminates any impact

of goodwill arising from merger and acquisition activity which have little to do with the

capital structure of the firm. Hence, I subtract intangible assets from total assets and equity

correspondingly. The results of pooled panel regression with these changes are reported in

Table 13. The results are robust against this change as well. The coefficients across the entire

sample retain their stated relationship and their significance as well. Cross country analysis is

also consistent with earlier findings.

Table 13


The table shows the results from a pooled panel OLS regression for the subsample of firms in each country. The

t-statistics of the regression are reported in the brackets and are corrected for heteroskedasticity. The dependent

variable is given by the ratio of debt to total assets. The impact of intangible assets has been excluded from both

equity and total assets. I interact each of my independent variables with the status of the firm. Public and Private

are dummy variables with Public taking the value of 1 (0) if the firm is quoted (unquoted) and Private taking the

value 1 (0) if the firm is unquoted (quoted). Column 1 reports the results without the country fixed effects.

Column 2 reports the regression results with the inclusion of country dummies. The definitions of the

independent variables are provided in the Appendix. *, **, *** denote significance at the 10%, 5% and 1% level

respectively.


Public -0.389*** -0.306***

0.701

0.063

-0.29***

(-9.09) (-7.26)

(1.34)

(1.05)

(-5.99)

Public Size 0.018*** 0.026***

-0.03

0.008*

0.024***

(5.94) (8.51)

(-0.9)

(1.81)

(6.86)

Private Size -0.013*** 0.005***

0.011

0.008***

0.006***

(-8.8) (3.18)

(1.46)

(6.15)

(2.99)

Public Growth 0.013 0.014

-0.063

0.063

0.012

(1.07) (1.17)

(-0.58)

(1.27)

(0.96)

Private Growth 0.012** 0.013***

0.101***

-0.008**

0.012*

(2.23) (2.65)

(2.37)

(-2.25)

(1.77)


-0.500

-0.056

-0.117***

(-2.36) (-2.82)

(-1.34)

(-1.18)

(-2.53)


-0.555***

-0.03

-0.435***

(-13.48) (-12.12)

(-3.66)

(-1.49)

(-11.87)

Public Tangibility 0.046** 0.038*

-0.059

0.119***

0.103***

(1.95) (1.63)

(-0.27)

(2.89)

(3.87)


0.265***

0.053***

0.056***

(3.25) (2.82)

(5.19)

(5.48)

(4.79)


0.092

0.094

0.285***

(3.11) (3.00)

(0.11)

(0.57)

(2.56)


-0.115

-0.163***

0.551***

(4.13) (6.14)

(-0.51)

(-5.43)

(6.44)

Germany

0.021**

(2.18)

Netherlands

-0.269***

(-69.28)


0.185

-0.062

0.262

(19.01) (13.69)

(1.65)

(-3.08)

(8.74)


-

-

- Time Fixed Effects Yes Yes

Yes

Yes

Yes


Yes

Yes

Yes Adjusted R-squared 0.075 0.145

0.113

0.110

0.092

Number of Observations 40601 40601

1920

10738

27943



44

F. Limitations

The limitations of my research primarily pertain to the study of private firms. As there is only

limited information available on private firms, I cannot extend my analysis to other variables

such as dividend payouts, share price performance and market equity premium. The latter two

variables are particularly unavailable for private firms. Moreover, I can only employ the

measure of book leverage in my analysis as market value of equity for private firms cannot be

obtained. Though earlier studies like Rajan and Zingales (1995) and Fama and French (2002)

report consistent results across both book and market leverage, it would have been interesting

to include the analysis on market leverage in my study as well.

Another limitation of my study is the small sample size of German firms which might induce

selection bias. Though my initial sample of German firms is large, I eliminate firms that do

not report consolidated data and hence, am left with only a small sample size for Germany.

However, I attempt to eliminate this bias by first introducing country fixed effects in my

analysis and second, by providing a cross-country analysis.

VI. Conclusion

The influential paper of Modigliani and Miller on capital structure irrelevancy has initiated

intense debate in corporate finance as to what determines the optimal capital structure of a

firm. Some theories such as the Pecking Order Theory have been proposed which attempt to

explain the capital structure of firms. These theories draw reference to the potential costs and

benefits regarding agency costs, transaction and bankruptcy costs and information asymmetry

in determining the financing behavior of firms.

This paper investigates the determinants of capital structure of a firm with respect to five

firm-specific characteristics, namely asset tangibility, profitability, growth, size and earnings

volatility. The sample data includes both public and private firms from Germany, the

Netherlands and the United Kingdom from the period 2003 to 2011. I set out to explore

whether the general theories of capital structure linked to public firms are applicable across

the private sector as well. I attempt to identify the characteristics that determine the leverage

of unquoted firms and to analyse the differences between the capital structure of public and

private firms. I further extend my investigation to provide a cross country analysis of the

differences in leverage. I try to explain these differences in light of institutional factors facing

each country.



45

My findings, from a pooled panel Ordinary Least Squares regression, suggest that private

firms have significantly higher leverage than public firms. Also, my results support the

traditional determinants of leverage. In the case of public firms, leverage is positively

correlated to size, tangibility and volatility and negatively correlated with profitability. The

relationship with growth is statistically insignificant. In the case of private firms, leverage

appears to be positively correlated with growth, tangibility and volatility and negatively

correlated with size and profitability, ceteris paribus. Hence, there seems to be a difference in

the financing behaviour of public and private firms with regards to size and growth

opportunities. Also, the leverage of private firms appears to be more sensitive to profitability

and less sensitive to the other factors compared to public firms.

However, a critical finding of my study is the importance of institutional factors in the

determination of capital structure of firms. My choice of the three countries provides an ideal

setup to study the relationship between leverage and institutional factors as these countries are

characterized by different legal traditions. The UK is defined by the English legal origin,

Germany by the Germanic legal origin and the Netherlands by the French legal origin.

Firms in the Netherlands are less levered whereas those in Germany are more highly levered

as compared to the firms in the United Kingdom. This can be explained in terms of creditor

rights protection. As the legal origin of the Netherlands allows for poor creditor protection, in

terms of bankruptcy laws, reorganization and automatic stays, Dutch firms have lower

leverage. This may be explained by possibly a higher cost of debt in the Netherlands to

incorporate the higher risk that lenders face. Also, firms in Germany have a high ownership

concentration. This suggests that these firms prefer to issue debt rather than equity in order to

avoid dilution of control of existing shareholders.

This paper fills two important gaps in existing literature. First, it incorporates the analysis of

capital structure determinants for unquoted firms which have largely been neglected due to

data unavailability. Second, it gives an insight into the importance of institutional factors in

the determination of capital structure. Future research now needs to be directed into the

examination of the relationship between institutional factors and leverage.



46

References

Amihud, Y., Mendelson, H., and Wood, R., 1990, Liquidity and the 1987 stock market crash,

Journal of Portfolio Management 16, 65–69.

Ang, J., Chua, J., and J. McConnell, 1982, The Administrative Costs of Corporate

Bankruptcy: A Note, Journal of Finance 37, 219-26.

Antoniou, A.; Y. Guney; and K. Paudyal, 2006, The Determinants of Debt Maturity Structure:

Evidence from France, Germany and the UK, European Financial Management 12, 161–194.

Antoniou, A.; Y. Guney; and K. Paudyal, 2008, The Determinants of Capital Structure:

Capital Market-Oriented versus Bank-Oriented Institutions, Journal of Financial and

Quantitative Analysis 43, 1, 59-92.

Bancel, Franck, and Usha R.Mittoo, The Determinants of Capital Structure Choice: A Survey

of European Firms, AFA 2003 Washington, DC Meetings; EFMA 2002 London Meetings.

Baskin, J. B., 1989, An Empirical Investigation of the Pecking Order Hypothesis, Financial

Management 18, 26-35.

Bennett, M. and R. Donnelley, 1993, The Determinants of Capital Structure: Some UK

Evidence, British Accounting Review 25, 43-59.

Bradley, M.; G. A. Jarrell; and E. H. Kim, 1984, On the Existence of an Optimal Capital

Structure: Theory and Evidence, Journal of Finance 39, 857–877.

Brav, Omer, 2009, Access to Capital, Capital Structure and the Funding of the Firm, Journal

of Finance 64, 1, 263-308.

Brealey, R., and S. Myers, 1984, Principles of Corporate Finance, New York: McGraw-Hill.

Chaplinsky, S., 1983, The Economic Determinants of Leverage: Theories and Evidence,

Unpublished Ph.D. Dissertation, University of Chicago.

Deloof, M. and I. Verschueren, 1998, De determinanten van de kapitaalstructuur van

Belgische ondernemingen, Tijdschrift voor Economie en Management 42, 2, 165-188.

DeAngelo, H., and R. W. Masulis, 1980, Optimal Capital Structure under Corporate and

Personal Taxation, Journal of Financial Economics l8, 3–29.

Dewatripont, Mathias, and Jean Tirole, 1994, A theory of debt and equity: diversity of

securities and manager-shareholder congruence, Quarterly Journal of Economics 1027-1054.

Donaldson, G., 1961, Corporate Debt Capacity: A Study of Corporate Debt Policy and the

Determination of Corporate Debt Capacity, Boston: Division of Research, Harvard School of

Business Administration.

Fama, Eugene F., and Kenneth R. French, 1992, The Cross-Section of Expected Stock

Returns, Journal of Finance 47, 427-465.



47

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

predictions about dividends and debt, Review of Financial Studies 15, 1-33.

Faulkender, Michael, and Mitchell A. Petersen, 2006, Does the source of capital affect capital

structure?, Review of Financial Studies 19, 45-79.

Frank, Murray Z., and Vidhan K. Goyal, 2003, Testing the pecking order theory of capital

structure, Journal of Financial Economics 67, 217-248.

Frank, Murray Z., And Vidhan K. Goyal, 2007, Capital Structure Decisions: Which Factors

are Reliably Important?, Financial Management 38, 1, 1-37.

Galai, D., and R. Masulis, 1976, The Option Pricing Model and the Risk Factor of Stock,

Journal of Financial Economics 3, 53-81.

Green, R., 1984, Investment incentives, debt, and warrants’, Journal of Financial

Economics13, 115-36.

Grossman, S., and 0. Hart, 1982, Corporate Financial Structure and Managerial Incentives, In

J. McCall (ed.), The Economics of Information and Uncertainty (Chicago: University of

Chicago Press).

Grossman, S., and O. Hart, 1988, One Share/One Vote and the Market for Corporate Control,

Journal of Financial Economics 20, 175-202.

Harris, Milton, and Artur Raviv, 1990, Capital Structure and the Informational Role of Debt,

Journal of Finance 45, 321–349.

Harris, Milton, and Artur Raviv, 1991, The Theory of Capital Structure, Journal of Finance

46, 297–355.

Hart, Oliver, 1993, Theories of optimal capital structure: a managerial discretion perspective,

in Margaret M. Blair, ed.: The deal decade (Brookings Institution, Washington).

Hovakimian, Armen, Gayane Hovakimian, and Hassan Tehranian, 2004, Determinants of

target capital structure: The case of dual debt and equity issues, Journal of Financial

Economics 71, 517-540.

Hovakimian, A.; T. Opler; and S. Titman, 2001, The Debt-Equity Choice, Journal of

Financial and Quantitative Analysis 36, 1–24.

Hsiao, C, 1985, Benefits and Limitations of Panel Data, Econometric Reviews 4, 121–174.

Jensen, M., 1986, Agency Costs of Free Cash Flows, Corporate Finance and Takeovers,

American Economic Review 76, 323–339.

Jensen, M. C., and W. H. Meckling, 1976, Theory of the Firm: Managerial Behavior, Agency

Costs and Ownership Structure, Journal of Financial Economics 3, 305–360.

Korajczyk, Robert A., and Amnon Levy, 2003, Capital structure choice: Macroeconomic

conditions and financial constraints, Journal of Financial Economics 68, 75-109.



48

La Porta, Rafael; Florencio Lopez-de-Silanes; Andrei Shleifer; and Robert W. Vishny, 1997,

Legal Determinants of External Finance, Journal of Finance 52, 1131–1150.

La Porta, Rafael; Florencio Lopez-de-Silanes; Andrei Shleifer; and Robert W. Vishny, 1998,

Law and Finance, Journal of Political Economy 106, 1113–1155.

Leary, Mark T., and Michael R. Roberts, 2005, Do firms rebalance their capital structure?

Journal of Finance 60, 2575-2619.

Leary, Mark T., and Michael R. Roberts, 2006, The pecking order, debt capacity, and

information asymmetry, Working paper, Cornell University.

Lewis, Tracy R., and David E. M. Sappington, 1995, Optimal capital structure in agency

relationships, RAND Journal of Economics 26, 343-361.

Marsh, P., 1982, The Choice between Equity and Debt: An Empirical Study, Journal of

Finance 37, 121-44.

Miller, M. H., 1977, Debt and Taxes, Journal of Finance 32, 261–274.

Modigliani, Franco, and Merton H. Miller, 1958, The cost of capital, corporation finance and

the theory of investment, American Economic Review 48, 261-297.

Morellec, E., 2004, Can managerial discretion explain observed leverage ratios? Review of

Financial Studies 17, 257-294.

Myers, Stewart C., 1977, Determinants of Corporate Borrowing, Journal of Financial

Economics 5,147–175.

Myers, Stewart C., 1984, The Capital Structure Puzzle, Journal of Finance 39, 575–592.

Myers, Stewart C., and Nicholas S. Majluf, 1984, Corporate financing and investment

decisions when firms have information that investors do not have, Journal of Financial

Economics 13, 187-221.

Noe, Thomas H., 1988, Capital structure and signaling game equilibria, Review of Financial

Studies 1, 331-355.

Rajan, Raghuram G., and Luigi Zingales, 1995, What do we know about capital structure?

Some evidence from international data, Journal of Finance 50, 1421-1460.

Ross, S.A., 1977, The Determination of Financial Structure: the Incentive-Signalling

Approach, The Bell Journal of Economics 8, 23-40.

Schoubben, F., and C. Van Hulle, 2004, The Determinants of Leverage; Differencess between

Quoted and Non Quoted Firms, Tijdschrift voor Economie en Management 69, 4, 589-620.

Scott, James H. Jr., 1977, Bankruptcy, Secured Debt, and Optimal Capital Structure, Journal

of Finance 32, 1, 1-19.

Shuetrim, G., Lowe, P., and Steve Morling, 1993, The Determinants of Corporate Leverage:

A Panel Data Analysis, RBA Research Discussion Papers 9313, (Reserve Bank of Australia).

http://ideas.repec.org/p/rba/rbardp/rdp9313.html

http://ideas.repec.org/p/rba/rbardp/rdp9313.html

http://ideas.repec.org/s/rba/rbardp.html



49

Smith, Clifford W., 1977, Alternative methods for raising capital: Rights versus underwritten

offering, Journal of Financial Economics 5, 273-307.

Stulz, René M., 1990, Managerial discretion and optimal financing policies, Journal of

Financial Economics 26, 3-27.

Titman, S., and R. Wessels, 1988, The Determinants of Capital Structure Choice, Journal of

Finance 43, 1–19.

Warner, J. B., 1977, Bankruptcy Costs: Some Evidence, Journal of Finance 32, 2, 337-347.



50

Appendix

Figure A

Ratio of Debt to Total Assets over the years

The following figures show the development of the debt to total assets ratio for private and public firms across

the entire sample as well as individually across countries. Generally, it can be observed that leverage of private

firms has been consistently higher than the leverage of public firms over the years, with the exception of German

firms for the years 2007-09. One explanation might be the outbreak of the financial crisis which might have lead

German public firms to raise debt rather than equity.

A.1. Entire Sample

A.2. Germany

0.00%

5.00%

10.00%

15.00%

20.00%

25.00%

% D

ebt

to T

A

Average Debt to Total Assets for the Entire Sample

Public Firms

Private Firms

0.00%

5.00%

10.00%

15.00%

20.00%

25.00%

30.00%

35.00%

40.00%

45.00%

% D

ebt

to T

A

Average Debt to Total Assets for the German Firms

Public Firms

Private Firms



51

A.3. Netherlands

A.4. United Kingdom

0.00%

5.00%

10.00%

15.00%

20.00%

25.00% %

Deb

t to

TA

Average Debt to Total Assets for the Dutch Firms

Public Firms

Private Firms

0.00%

5.00%

10.00%

15.00%

20.00%

25.00%

30.00%

35.00%

40.00%

% D

ebt

to T

A

Average Debt to Total Assets for the UK Firms

Public Firms

Private Firms



52

Table A

Correlation of Variables

The table shows the correlation between the dependent and independent variables. As can be seen, the two

proxies for leverage, debt to total assets and debt to total capital are highly positively correlated. The correlation

matrix shows that leverage is positively correlated to growth, tangibility and volatility whereas negatively

correlated to size and profitability across the entire sample.

Correlation Matrix

Debt to

Assets

Debt to

Capital Size Growth Profitability Tangibility Volatility

Debt to Assets 1

Debt to Capital 0.8093 1

Size -0.1211 -0.0641 1

Growth 0.0123 0.0016 0.0232 1

Profitability -0.1955 -0.2492 0.246 0.0515 1

Tangibility 0.160 0.013 -0.0564 -0.0461 0.0056 1

Volatility 0.0975 0.2106 -0.2078 0.0756 -0.4208 -0.173 1

Table B

Institutional Factors as reported by La Porta et al. (1998).

The legal tradition and the legal origin pertain to the laws governing the respective countries. Creditor rights is

an index rating on a scale from 0-4 (4 being the most highly protected creditors) depending on the rights of the

creditors on different metrics. Anti-director rights is also an index rating and is a proxy for shareholder rights.

The scale ranges from 0-6 (6 being the most strongly protected shareholder rights). Ownership concentration is

the average percentage of common shares owned by the three largest shareholders in the ten largest non-financial

firms. Rule of law is the degree of enforcement of the law on a scale of 0 to 10 (10 being the most highly

enforced). For a more detailed discussion of these variables, please refer to La Porta et al. (1998).

Germany Netherlands United Kingdom

Legal Tradition Code Code Common

Legal Origin Germanic French English

Creditor Rights 3 2 4

Anti-Director Rights 1 2 5

Ownership Concentration (%) 0.48 0.39 0.19

Rule of Law 9.23 10.00 8.57

Table C

Definition of Variables

Total Debt Long-term debt + Short-term debt

Total Capital Total debt + Shareholders funds

Debt to Total Assets Total debt / Total Assets

Debt to Total Capital Total debt / Total Capital

Firm Size Natural Log (Turnover)

Growth (Turnover t / Turnover t-1 ) – 1



53

Profitability (Return on Assets) Operating Income / Total Assets

Tangibility Natural Log (Tangible Assets)

Earnings Volatility Three year standard deviation in Profitability

msc. finance the determinants of capital structure: a

Documents