MSc in Finance & International Business - AU...

MSc in Finance & International Business

Authors:

Anna Kaja Chudzinska AC70317

Stefan Lukas van der Bijl SV70600

Academic Advisor:

Jan Bartholdy, PhD

Capital Structure Determination of Small and Medium Sized Enterprises

in Eastern and Western Europe

-- An investigation based on the target adjustment model --

Aarhus School of Business

September 2007

Capital Structure Determination of Small and Medium Sized Enterprises in Eastern and Western Europe

MSc Thesis in Finance & International Business A.K. Chudzinska & S.L.van der Bijl

Abstract

An obligatory note to the reader:

We hereby declare that this MSc Thesis has been produced with the full input of both

authors. From the initial stages of finding a research subject to the analysis and

writing of the research findings, we have cooperated on every single part. No division

in workload has been made, since the full research has been conducted in the presence

of both of us.

Sincerely,

Anna Kaja Chudzinska

Stefan Lukas van der Bijl

This research paper investigates the differences in capital structures and their

determinants between Eastern and Western European Small and Medium sized

Enterprises. The Tradeoff Theory is the underlying theoretical framework which is

applied, therefore the research is based on a detailed analysis of differences in

institutional factors that fit to different aspects of the Tradeoff Theory: credit availability,

corporate taxes, bankruptcy costs and agency costs. The model tested is the target

adjustment model. Data from firms of six countries is used to study the differences in

capital structures between Eastern and Western European firms. The findings support the

hypotheses that differences exist between the two regions, and confirm that firms do have

different financing patterns. Eastern European firms have considerably lower amounts of

debt in their capital structures. These lower leverage ratios in Eastern Europe are found to

be the result of lower corporate taxes and higher bankruptcy costs. This indicates that the

role of shielding taxes is stronger in Western Europe. Besides, although bankruptcy costs

are found to be crucial in capital structure determination in both regions, they are higher

in Eastern Europe, and have a more negative influence on leverage ratios. The research

also confirms that agency costs do not have an important effect on capital structures in

Small and Medium sized Enterprises. The Tradeoff Theory is proven to explain capital

structure determination well on Small and Medium Sized Enterprises in both Eastern and

Western Europe. Besides, the research shows the relevance of improvements in the

financial systems and institutional factors of Eastern European countries.


MSc Thesis in Finance & International Business A.K. Chudzinska & S.L.van der Bijl

Table of Contents

Introduction ________________________________________________________________ 1

Chapter 1 Literature Review___________________________________________________ 4

1.1 Capital Structure Theories _______________________________________________________ 4

Tradeoff Theory _____________________________________________________________ 6

Pecking Order Theory_______________________________________________________ 10

1.2 Capital Structure internationally _________________________________________________ 12

1.3 Eastern and Western Europe ____________________________________________________ 17

1.4 Small and Medium Sized Enterprises in Europe_____________________________________ 21

Small and Medium sized Enterprises (SMEs) versus Large Enterprises (LEs)_____________ 22

SMEs in Eastern and Western Europe ___________________________________________ 24

Empirical findings on SME capital structure in Europe______________________________ 26

Chapter 2 Research Question & Hypotheses_____________________________________ 29

Chapter 3 Methodology______________________________________________________ 34

3.1 Model________________________________________________________________________ 34

3.2 Variables _____________________________________________________________________ 36

3.3 Data _________________________________________________________________________ 46

3.4 Two Stage Least Squares Regression Method_______________________________________ 52

Chapter 4 Regression Results of Eastern Europe vs. Western Europe ________________ 54

4.1 Regression Results of the Target Adjustment Model _________________________________ 54

4.2 Difference in Leverage between Eastern Europe and Western Europe __________________ 59

4.3 Differences in Proxies for Taxes between Eastern Europe and Western Europe __________ 61

4.4 Differences in Proxies for Bankruptcy and Agency Costs between Eastern Europe and

Western Europe _______________________________________________________________ 66

4.5 Conclusions of the Results in Eastern Europe and Western Europe_____________________ 80

4.6 Robustness Check _____________________________________________________________ 85

Conclusions _______________________________________________________________ 89

Limitations of the research__________________________________________________________ 94

Recommendations for further research _______________________________________________ 96

Bibliography_______________________________________________________________ 97

Appendices – Table of Contents ______________________________________________ 103


MSc Thesis in Finance & International Business A.K. Chudzinska & S.L.van der Bijl 1

Introduction

The determination of capital structures has been an ongoing area of research in the

field of corporate finance for more than fifty years. Indeed, Capital structure theory

forms the basic underlying fundamental on which financial theories and financial

management are founded. The underlying question of such research is how and why

companies come to the debt-equity ratios in their capital structures. For a long time it

has been believed that an optimal debt-equity choice exists for any firm, and that this

optimal capital structure is a tradeoff between the advantages of debt financing and

the disadvantages of bankruptcy risks. From a firm’s perspective, debt is ofen a

cheaper source of finance than equity because of tax advantages to be gained. Debt is

preferred over equity, especially where the firm does not face financial distress.

Therefore, a tradeoff exists between the benefits of debt financing and the risks of

debt financing. Since Modigliani and Miller (1958), many different insights into firm

financing have been examined. Modigliani and Miller (1958) stated their famous

irrelevance theory, where under perfect conditions, the choice of debt or equity is

irrelevant. Other researchers, such as Myers and Majluf (1984) saw information

asymmetry, which is a result of agency costs, as the underlying theory of how a firm

comes to its debt-equity distribution. Many other theories have been proposed and

tested, but the Tradeoff Theory, including agency costs as part of the tradeoff, is still

often applied and discussed in literature. It seems that no perfect theory exists, and

many theories explain only a part of the story. Perhaps one cannot hope to expect only

one theory to hold true for every firm’s capital structure determination (Myers, 2000).

Initially, the different financial theories of capital structure have been principally

developed and tested on large public firms from the Unites States. Evidence of capital

structure research studies is still limited in Europe, and other continents outside of

North America. One of the intentions of this research paper is to broaden the insights

of capital structure determination in Europe by studying the Tradeoff Theory. Europe,

in contrast to the United States, does not consist of one large economy, but rather of

many small, country-specific, economies which are often interlinked with each other.

Moreover, Europe contains both developed and developing economies, which in this

research paper, are broadly categorized as Western Europe and Eastern Europe,

respectively, due to their historic economical similarities. The majority of previous



research papers that study capital structures of firms from different countries,

concentrated on developed economies, e.g. Rajan and Zingales (1995). In developed

economies, market conditions are rather similar. However, there are reasons to believe

that capital structure theory and its determinants work differently in developing

economies. Another intention of this research paper is to investigate to what extent the

state of economic development contributes to differences in capital structure. What is

more, all current or previously developed theories have been aimed and tested almost

solely on public firms, even though the majority of firms in every country are

considerd to be Small and Medium sized Enterprises (SMEs). Consequently, this

research will focus on private SMEs in order to test whether capital structure theory

has application to smaller and private firms.

The Research Question of this study is:

To what extent do capital structures and their determinants differ between Small

and Medium sized Enterprises from Eastern and Western Europe, in light of the

Tradeoff Theory?

Since most accepted research findings on capital structures have been based on data

from public firms in the United States, it is essential for European financial

management to further expand its knowledge in this field, based on European firms.

The purpose of this study is to find an answer, which might help to explain whether

firms in developing countries in Eastern Europe have different financing patterns from

firms in the developed countries of Western Europe. This study will also help to shed

light on the question whether the Tradeoff Theory is a valid theory for testing capital

structures of Small and Medium sized Enterprises across different countries in two

distinct geographic regions. Moreover, this study will help to identify aspects which

Eastern European countries (and maybe some Western European countries) need to

improve in order to create a strong financial climate.

As it is outside the scope of this study to focus on the whole European continent, this

research will be based on a comparitive study of three countries from Western

Europe, Belgium, Ireland and the Netherlands, and three countries from Eastern

Europe, Hungary, Poland and Ukraine. The choice of the countries was based on a



detailed analysis of some institutional factors that are crucial for a country’s financial

climate, and fit into the different aspects of the Tradeoff Theory, i.e., credit

availability, corporate taxes, bankruptcy costs and agency costs.

The research itself was conducted with the use of the target adjustment model which

was tested with multiple Two Stage Least Squares regressions. The input for this

model was based on constructed variables from data from private Small and Medium

sized Enterprises from the six previously identified countries. The most important

variable in light of the Tradeoff Theory is the Leverage ratio. Thus, four possible

measures of Leverage were computed and studied. Other constructed variables, which

were included in the target adjustment model, are three measures for the tax effect,

KINK, STANDARDIZED KINK and EFFECTIVE TAX RATE, and several

measures for the bankruptcy costs and agency costs effects, TANGIBILITY, SIZE,

Z-SCORE, OPERATING RISK, PROFITABILITY, GROWTH and LAGGED

LEVERAGE.

The remainder of this research paper is organized as follows:

Chapter One serves as a literature review. Here, some theoretical frameworks on

capital structure are introduced, with a focus on the Tradeoff Theory. Also the

literature about international capital structure, Europe and Small and Medium sized

Enterprises is presented.

Chapter Two introduces the reader to the main Research Question which is linked to

the literature presented in Chapter One. Further, also based on the literature, the

hypotheses on the research question and on the relationship between leverage ratios

and the determinants of leverage are presented.

Chapter Three introduces the reader to the research methodology. Namely, this

chapter describes the model, variables, data and regression method used in this study.

Chapter Four presents the analysis of the results. Based on this analysis, the

hypotheses are controlled for. A robustness check is introduced, which serves to

control the the research approach applied in the study.

Chapter Five concludes this research paper and provides the implications of the

research findings. Finally, a presentation is given of limitations in this study and

suggestions for further future research on this important topic.



Chapter 1

Literature Review

The study of capital structure determination has been an important area of research

within the field of finance. Many theories have been developed, which try to explain

how a firm’s capital structure develops over time, that is, for which reasons some

firms are heavily debt-financed while other firms rely more on owners’ equity in their

capital structure. The first section of this chapter introduces literature that has been

written on this matter, and concentrates on the two most famous explanatory theories

of capital structure: the Tradeoff Theory and the Pecking Order Theory.

Multiple scholars compared capital structures of firms throughout different countries

and regions. Some major similarities were found, as well as differences among such

countries / regions. The second section of this chapter describes factors found in

existing literature, which have been related to country and regional differences in the

matter of capital structure determination.

The third section concentrates on country differences and regional differences, based

on literature and public data. It presents a study on differences between Eastern

Europe and Western Europe, in respect to institutional factors. Based on literature,

such differences have an impact on capital structure of firms in both regions. Hence,

the implications of these differences on leverage are discussed.

In the fourth section of this chapter, it will be explained to what extent such

institutional factors play a role on Small and Medium sized Enterprises (SMEs).

Based on literature, private SMEs are compared to large public companies and the

differences between Eastern and Western European SMEs are discussed. Last, a short

discussion of SMEs’ capital structures is presented.

1.1 Capital Structure Theories

The research on capital structure attempts to explain how and in which proportions

companies use debt and equity to finance their investments. The traditional capital

structure theory assumes that there is an optimal capital structure and that companies

can increase total value by the proper use of leverage. In short, debt is cheaper than



equity, from the costs of capital perspective. The use of debt also brings tax

advantage; therefore, companies are able to increase their required rate of return.

The study of capital structure determination is a relatively young discipline that

started less than fifty years ago. At present, there is no one universal capital structure

theory that is applicable in most companies around the world. Nevertheless, there are

several useful conditional theories (Myers, 2001), which are discussed below.

Modigliani and Miller (1958) introduced a concept of capital structure which, in

contrast to the traditional capital structure theory, argued that financing does not

matter in a perfect market. According to Modigliani and Miller (1958) the markets are

perfectly competitive when the information is costless and available to all market

agents, when there are no transaction costs or taxes in the issuance or trading of

securities and when the securities are infinitely divisible. The authors also assume that

all market agents are price takers and investors always act rationally. What is more,

Modigliani and Miller (1958) assume that average expected returns stay equal over

future periods, which means that expected earnings will always be the same as they

are today. Therefore, the authors state that financial leverage is irrelevant. Taking into

account all introduced assumptions and assuming that a firm’s total cash flows to its

debt and equity holders are not affected by financing, then the total market value of

the firm is not affected by financing. The total value of the firm is equal to the sum of

the market values of the items on the right-hand side of the balance sheet (i.e. debt

and equity). To illustrate this theory, one can imagine two firms that differ only in

respect to their capital structure. Firms can borrow at the risk free rate of return which,

by the above assumptions, implies that the investors can also borrow at a risk free rate

of return. The investors can employ two alternative strategies to obtain the same

return structure: hold a certain percentage of the leveraged firms stock or hold a

certain percentage of unleveraged firms stock and at the same time borrow on

personal account. Since the return stream for both companies is the same in both

strategies, the value of these two strategies is the same by a law of one price, thus the

net investment has to be the same. Therefore, the value of the leveraged firm is equal

to the value of the unleveraged firm. The driving force in this reasoning is the

assumption that both firms and investors can borrow and lend at the same rate of

interest and that the two cash flows are the same. In a perfect market, there is no



optimal capital structure. As Myers (2001) put it: In a perfect-market supermarket, the

value of a pizza does not depend on how it is sliced. This theory is widely accepted;

however, it is not really applicable. In the world as we know it, there is no perfect

market, there are taxes and information is not evenly distributed.

In a world with corporate taxes, leverage opportunities become valuable (Modigliani

and Miller, 1963). Since taxes have to be paid only on corporate income and interest

expenses are tax-deductible, an extra dollar in debt will decrease the tax payments. If

there were no offsetting costs, companies would attempt to shield as much taxable

income as possible and no company would pay taxes. However, costs of financial

distress exist and might influence capital structure decisions.

Tradeoff Theory

The Static Tradeoff Theory is a single period model in which a company is viewed as

setting a target debt-to-value ratio and then gradually moving towards it. According to

Myers (2001) firms will borrow up to the point where the marginal value of tax

shields on additional debt is just offset by the increase in the present value of possible

costs of financial distress.

In this view, costs of capital for equity are higher than the costs of capital for debt,

due to two factors. First, since debt lenders are assured of their income payments, they

will generally receive lower returns for their investment than equity investors.

Secondly, interest on debt is deductible from taxes when a company has high levels of

debt it can avoid paying corporate taxes due to tax deductibility from the interest

payments. This works as an extra benefit for organizations to finance their operations

and investments with debt. However, the higher the levels of debt, the higher the

probability of financial distress, hence bankruptcy costs increase. A corporation finds

itself in a situation of financial distress, when it is unable to pay its obligations or

when it is illiquid or insolvent (a corporation is insolvent when its liabilities exceed its

assets). Therefore, in order to avoid financial distress, it is crucial to find the optimum

between tax shields and bankruptcy risks. The static Tradeoff Theory explains this

optimum tradeoff between the benefits and the risks of debt financing and the

influence on the market value of the firm. This is depicted in the graph below.



Figure 1. The Static Tradeoff Theory of capital structure

Source: Myers (1984).

A slight deviation of the static Tradeoff Theory is a dynamic Tradeoff Theory, which

recognizes the role of time and concentrates on aspects that are usually ignored in the

single period model (Frank and Goyal, 2005). In a dynamic model, the financing

decisions depend on the future financing margins that the firm anticipates. Some firms

are planning to raise funds (equity or debt) while others plan to pay out funds. Fisher

et. al. (1989) and Leland (1994) introduced dynamic models in which the firms allow

their leverage ratio to fluctuate over time. This reflects accumulated profits and losses,

and leverage does not adjust towards the target ratio as long as the adjustment costs

exceed losses of suboptimal capital structure.

Nevertheless, the static as well as the dynamic forms of the Tradeoff Theory try to

explain the existence of an optimum in leverage whether it is constant or dynamic

through time.

According to Myers (1984) firms cannot immediately offset the random events that

bumped them away from their optimum. Therefore, a difference among firms of

actual debt ratios should exist across firms with the same target ratio. Adjustment

costs, especially large ones, might possibly explain the wide variation in actual debt

ratios among firms, since firms would be forced into long excursions away from their

target ratios. Besides, the size of adjustment costs might influence the speed with



which firms adjust to their targets. In most tradeoff literature, adjustment costs are

rarely mentioned. However, adjustment costs might have a profound impact on firms’

capital structures. The importance of bankruptcy costs is very crucial because it can

erode the firm value even if formal default is avoided. Bankruptcy costs are the costs

directly incurred when the perceived probability that the firm will default on financing

is greater then zero. According to Cassar and Holmes (2003) a subset of bankruptcy

costs are liquidation costs, which represent the loss of value as a result of liquidating

the net assets of the firm. The liquidation costs reduce the proceeds to the lender and

the firm can default on finance payments. Therefore, the firms can incur higher

finance costs due to the potential of liquidation costs. Firms can incur these costs,

even if non-lending stakeholders believe that the business is perceived to be close to

bankruptcy (e.g. customers not willing to buy products due to the risk of not receiving

the promised guarantee). Consequently, firms that have high distress costs should

decrease debt financing to lower these costs.

Miller (1977) sheds new light on the relation between taxes, bankruptcy costs and

leverage. He found that bankruptcy costs do exist, but that they are lower than

commonly assumed. For large public firms, bankruptcy costs are only between 1.7

and 5.3 percent of the value of the firm. For small firms, however, bankruptcy costs

are higher, equaling 20 percent. Miller (1977) points out that the costs for large public

firms are extremely small in relation to the tax savings that companies are able to

receive. Besides, the author also questioned that if the optimal capital structure was

only a matter of a tradeoff between tax advantages and bankruptcy costs, why then

does capital structures show so little change over time. Miller (1977) noticed that in

spite of rising tax rates, leverage ratios were found to remain almost the same. Minor

variations in the ratios were rather caused by cyclical movements of the economy.

Thus, the author concluded that “the tax advantages of debt financing must be

substantially less then the conventional wisdom suggests” (Miller, 1977 pp. 266).

Clearly, the tradeoff between tax advantages and bankruptcy costs is not the only

matter in obtaining the optimal capital structure.

Similarly, Jensen and Meckling (1976) found that leverage ratios were low in spite of

large tax advantages enjoyed by debt. They came to an alternative explanation in

which they add agency costs to the tradeoff function in order to explain these low



observed leverage ratios. The authors argue that a perfect market as described by

Modigliani and Miller (1958) is not realistic, and therefore besides taxes and

bankruptcy costs, agency costs should be taken into account. Agency costs are the

costs that arise as a result of two types of conflicts (Harris and Raviv, 1990). The first

conflict is between shareholders and managers and the second conflict arises between

shareholders and creditors. The existing literature concerning these conflicts differs in

respect to the way in which the conflicts arise.

According to Stulz (1990) a conflict develops between managers and investors when

managers tend to invest all available funds even if paying out cash would be in the

interest of investors. This is the so-called problem of overinvestment. As pointed out

by Jensen (1986) and Stulz (1990), in order to overcome this problem the company

can issue debt and the required interest payments will reduce free cash flows. This

reduces the conflict between management and shareholders and contributes to the

benefit of debt financing. However, debt serves also as an agency cost. Stulz’s model

indicated this cost is the possibility of using too much cash flows on required debt

payments and at the same time giving up profitable investments, hence the problem of

underinvestment. Harris and Raviv (1990) stated that managers always want to

continue investing in current projects, even though liquidation would be preferred by

the investors. In this view, outstanding debt reduces the conflict between investors

and management by shifting the controlling power to debt holders. Debt holders have

the option to pursue liquidation if the cash flow from the project is lower than

expected. Harris and Raviv (1990) pointed out the costs of debt from the creditors’

perspective, the costs of information gathering. They predicted that firms with high

tangible assets and low information costs, i.e., firms with higher liquidation value, can

issue more debt, and still, have higher market values in comparison to the firms with

lower liquidation costs.

The second type of conflict, which might occur between shareholders and creditors,

arises because the debt contract gives an incentive to the shareholder to invest in risky

projects (Harris and Raviv, 1990). If the project happens to be successful and yields

high returns that are higher than face value of the debt, the shareholders will receive

most of the gain. In the event when the project fails, only debt holders will bear the

consequences, and as a result the value of debt will decrease. Because of asset



substitution problems, i.e. a situation when a firm invests in assets that are riskier than

those that the debt holders expected, debt holders need to restrict and monitor the

firm’s behaviour. The lenders can only observe the firm’s default history and it is

possible for firms to build up a reputation of having only safe projects. However, the

investors can never be sure in what kind of project the firm invests. Therefore, costly

monitoring devices are incorporated into debt contracts in order to protect debt

holders from possible asset substitution problems.

Under the Tradeoff Theory, companies set an optimal target debt-to-value ratio. This

target is set on the level when firms borrow up to the amount when the value of tax

shields is offset by bankruptcy costs and agency costs. As argued by Myers (1984)

adjustment costs are an important factor in setting this optimal target ratio.

Nevertheless, according to Marsh (1982) and Shyam-Sunder and Myers (1999) the

target ratio is unobservable and, therefore, difficult to test. Possible proxies used for

testing the target debt level are the average historic debt level or a moving average of

debt over time (Jalilvand and Harris, 1984). To better capture changes in target debt

levels, Taggart (1977) and Jalilvand and Harris (1984) estimated target adjustment

models. They did not test the optimal target debt level itself, but indirectly tested the

adjustment towards a target level. In their research an adjustment coefficient was

tested and if this coefficient was higher than zero, the Tradeoff Theory held.

Pecking Order Theory

A second theory on capital structure determination which has received much attention

in the last twenty years is the Pecking Order Theory. This theory was introduced by

Myers and Majluf (1984) and Myers (1984) and was developed from an agency cost

perspective. The underlying assumption is asymmetric information, under which

management has information about the firm that investors do not have. Management

is expected to act merely in the benefit of existing shareholders. Asymmetric

information means that only management knows the true value of the firm and its

growth opportunities, while the market (possible new shareholders) can only infer

these values by observing management’s actions. Accordingly, the financing actions

of management are used by the market as a signal of the firm’s true value.

Management will only issue equity when it knows that investments will benefit



existing shareholders more than new shareholders. This happens if the net present

value of the investment is more than the over- or undervaluation of shares in the

market. If management issues equity, it signals to investors that their funds should be

worth less. Hence, new shareholders are only willing to invest when they are

compensated for this risk. This increases the costs of attracting funds for the firm.

Thus, the adverse selection costs make equity issues more expensive for the firm.

Because of these added costs, equity might not be issued and management might pass

on profitable net present value (NPV) projects. This is not the case when internally

generated funds are used, since internal funds do not bear these costs. External

investors, therefore, effectively force firms to follow a pecking order, in which

internal funds are always preferred over external funds. Besides, under this pecking

order, the use of debt is preferred over equity. From the investor’s perspective, debt

contracts are less risky than equity contracts, since debt covenants secure the face

value of debt on the firm’s assets (Frank and Goyal, 2005). Hence, the future value of

debt is less volatile than the future value of equity, after managements’ inside

information is revealed. Asymmetric information costs are lower for debt than for

equity. In essence, risk-free debt would work similarly to internal funds. Therefore,

according to the Pecking Order Theory, firms will always work down the pecking

order starting from internal funds to several types of debt. Thus internal sources are

followed by riskier debt, such as convertible securities, preferred stocks, and finally

equity as a last resort when all debt capacity is exhausted.

Clearly, under the Pecking Order Theory, an optimal capital structure does not exist.

Rather, the capital structure is nothing more than an explanation of the firms’ past

requirement for external financing over time. This theory predicts that more profitable

firms borrow less because they have more internal financing available. Less profitable

firms require external financing, and consequently accumulate more or heavier debt.



1.2 Capital Structure internationally

The focus of this section is to identify major country specific factors that are found to

be related to capital structures of firms. Most researchers limited their investigations

to public American firms. Later on, some focus was put on capital structure of firms

from Western European countries. In general, most authors found similarities in

capital structures and their determinants across borders. Rajan and Zingales (1995)

investigated determinants of capital structure in the G7 countries. The authors found

that leverage ratios were fairly similar in these countries, even though institutional

settings were very different. The same conclusion was drawn by Antoniou, Guney and

Paudyal (2002) who focused on differences in optimal capital structure decisions in

companies from France, Germany and the United Kingdom. Bancel and Mittoo

(2002) studied sixteen different European countries and found that European

managers used similar factors in financing decisions as United States managers.

Similarly, Booth et al. (2001) studied ten developing economies and found the same

factors to be related to leverage ratios in developing economies, as in developed

economies. These different sources of literature indicate that capital structure

determination seems to have some general similarities across countries. This, while

capital structures themselves are quite different among countries. Hence, one can

conclude that country differences do exist. “Knowing a firm’s nationality explains as

much of the firm’s capital structure as knowing the size of the independent variables

that determine capital structure under different theories” (Booth et.al., 2001, pp.119).

The focus in this section concentrates specifically on identifying how certain country

specific differences can play a role on capital structure. The focus lies in differences

in the legal systems, financial systems, taxes, bankruptcy laws and corporate

governance. Even though most literature concentrates on Western developed

economies, these institutional factors are expected to be even more important in

emerging economies. They determine the status of the capital market to a great extent,

and hence, the availability of finance (Demirguc-Kunt and Maksimovic, 1998). This

in turn, is important for firms financing policies, and thus, capital structures. The

theoretical analysis in this section serves as the basis for comparison of differences

between Western and Eastern European countries, which will be presented in the next

section.



Legal and Financial System

LaPorta et al. (1998) stated that the legal system is a fundamentally important

corporate governance mechanism. Besides, bankruptcy procedures are only effective

under a proper legal system (Demirguc-Kunt and Maksimovic, 1998). In particular,

La Porta et al. argue that the extent to which a country’s laws protect investor rights

and the extent to which those laws are enforced are the most basic determinants of the

ways in which corporate finance and corporate governance evolve in that country.

LaPorta et al. (1997) and Gonzalez (2002) stated that better legal protection leads to

investors demanding lower rates of return and companies using more external finance.

Two aspects of the legal system are discussed in the following paragraphs. The first

aspect is bankruptcy law and the second one is the legal investor protection. The

effective enforcement of certain legal systems is highly correlated to effective

bankruptcy proceedings and corporate governance. That is, the law is a necessary

factor for investors to enforce their legal rights when dealing on issues with

management.

Bancel and Mittoo (2002) found that differences in legal systems were more relevant

to factors related to debt than to factors related to equity, thus confirming the fact that

external financing in different countries is influenced to a high extend by its legal

environment. Hence, different legal systems lead to various financial systems. Rajan

and Zingales (1995) and Peltoniemi (2004) observed two major types of financial

systems. They classified countries based on the size, or power of the banking sector

versus the market sector, hence the term “bank oriented” (e.g. Germany, France, Italy

and Japan) and “market oriented” (e.g. the United States, the United Kingdom and

Canada). This classification can explain the way in which the firms capitalize their

investments. Banking (market) oriented economies are associated with a small (high)

proportion of quoted companies, a high (low) concentration of ownership, and long-

term (short-term) relations between banks and industry (Mayer 1994). In a market-

oriented system, the financial securities market is well developed with a high level of

competition, and institutional investors play an important role. In addition, direct

financing dominates, and the monitoring of firms is organized by the central exchange

(e.g. London Stock Exchange). In a bank-oriented system, bank lending dominates

and monitoring is arranged by banks through client-specific relationships. Banks are



the most important sources of external credit for firms and, simultaneously, the main

channels for financial intermediaries in financing different investment projects

(Mayer, 1994). However, the difference between these two types of systems is rather

the choice between private and public financing and not the amount of leverage

(Rajan and Zingales, 1995). It can be further observed that market-oriented countries

typically have a common law tradition, while bank oriented countries have civil law

codes.

Taxes

Another institutional difference is the tax code of the country under consideration. As

explained, taxes are a major determinant of leverage under the Tradeoff Theory, and,

therefore, fiscal deductibility of debt and tax levies on dividends can have a serious

influence on a firm’s leverage ratio. For instance, tax advantages are stronger in

Germany than in the United States (Rajan and Zingales, 1995). Moreover, corporate

taxes are used as a mechanism by countries for attracting investors (Kennedy, 2007).

Bankruptcy Law

Bankruptcy law has the following effects: strict enforcement of creditor rights

enhances credibility; it commits creditors to punishing management if the firm gets

into financial distress and it reduces costly negotiating between claimholders. Strong

bankruptcy laws therefore reduce bankruptcy costs for investors. Rajan and Zingales

(1995) found that the G7 countries differ in their bankruptcy procedures; bankruptcy

law in the United States is more management friendly while in Germany the law is

more creditor friendly.

Corporate Governance

Corporate governance essentially deals with the problem of agency costs identified

earlier. It is a broad phenomenon and goes beyond legal issues. The meaning of

corporate governance has been stated in different ways. Corporate governance

involves relations and controls among the stcockholders, boards of directors and the

firm’s senior management. It is most commonly found in corporate charters, firm’s

by-laws, governmental regulations and in legal decisions. The primary objective of

such corporate governance is to ensure that the interests of senior management are

aligned with the interests of the firm’s shareholders.



Where ownership and management are dispersed agency costs arise and corporate

governance is the tool of reducing these agency costs. However, in the previous

section it was identified that agency costs do not only exist between owners and

management, but also between creditors and management. According to Shleifer and

Vishny (1997), corporate governance should be seen as the ways in which suppliers of

finance to corporations assure themselves of getting a return on their investment. They

stated that corporate governance deals with three issues: the separation of ownership

and control (which causes agency costs), management discretion and incentive

contracts.

The first two issues of corporate governance are especially important in the light of

this research. In general terms, the investors and the managers sign a contract that

specifies what the manager does with the funds, and how the returns are divided

between him and the investors. However, since not all possible scenarios can be

foreseen in the contract, the manager and the investor have to allocate residual control

rights, i.e. the rights to make decisions in circumstances not fully foreseen by the

contract. Since investors are not as familiar with the business as the managers, they

are mostly not as well informed to decide what to do. As a consequence, the managers

end up with most of the residual control rights. Corporate governance deals with the

cases in which managers use their control rights, but are not in line with the investors’

wishes (Shleifer and Vishny, 1997). The second issue handles the question how

managers can allocate the investors’ funds. They can expropriate them in several

ways. Extreme forms of expropriation are: the construction of pyramid structures, in

which funds are transferred to separate units that are outside the legal reach of

investors, or by using transfer pricing techniques at below market prices to subsidiary

firms that are not owned by the investors. Bancel and Mittoo (2002) found that agency

costs of debt were usually higher in countries with a lower quality legal system.

Taxonomy of Corporate Governance Systems and Legal Systems in Western countries

Based on Rajan and Zingales (1995), Shleifer and Vishny (1997), LaPorta et al (1997,

1998, 1999) and Weimer and Pape (1999) a taxonomy is constructed of different legal

and corporate governance systems which are observed in Western European countries.

An overview of this taxonomy is depicted in Appendix I. According to Shleifer and

Vishny (1997) the corporate governance systems observed in the US and Germany

belong to the most effective systems in the world.



The general characteristics of the systems are as follows:

- The Anglo-Saxon system is a market oriented system. It has a common law

tradition and is shareholder oriented, and thus shareholder protection is strong.

Public companies have a one tier board system. There is an active external

market for corporate control, e.g. the role of credit rating agencies is

important. The relationship between stakeholders and management is

generally short term. Besides, there is low ownership concentration.

- The Germanic system has large oligarchic groups of stakeholders, of which

the most important are industrial banks and employees (unions). Public firms

generally have a two tier board system with an executive and a supervisory

board. There is no external market for corporate control, and stakeholders and

investors have to solve the corporate governance issues on their own and in

different ways then under the Anglo-Saxon system. There is a moderate to

high ownership concentration. Relationships between investors and

management are generally long term.

- Under the Latin system family control is relatively important. In general,

public firms have a one tier board system. The major stakeholders are often:

families, financial institutions and the government. There is no external market

for corporate control. Under this system, there is high ownership

concentration. Relationships between investors and management are generally

long term.



1.3 Eastern and Western Europe

In the previous section, a review of literature was presented that described factors of

international differences in capital structure. It can be observed that most studies on

such differences limit themselves to firms from the United States and Western

Europe. This, since data is more available on these countries. Even though differences

are observed between the United States and Western Europe, these countries have

many things in common, most importantly, they all have well-developed economies.

For several reasons, which will be mentioned, this section will investigate differences

between Eastern and Western Europe that might have an impact on capital structures

of firms from both regions. It will be identified which countries fit into Eastern and

Western Europe. Subsequently, a summary of the differences between institutional

factors of Eastern and Western Europe will be presented, as well as the implications

of these differences. For a detailed analysis of these differences, based on numerical

country scores, one is referred to Appendix II.

There are several reasons for comparing Eastern and Western European countries in

regards to capital structure. First of all, literature on capital structures in developing

economies of Eastern Europe is relatively scarce and mostly country specific.

Therefore, a comparative cross-country analysis between transition economies of

Eastern Europe and the developed economies of Western Europe might shed new

light on capital structure determination in Europe. Secondly, by comparing different

countries, the findings might indicate whether the Tradeoff Theory is a valid theory of

capital structure across Europe. Thirdly, it might become visible to what extent the

development of the economy is related to financing decisions of firms. Information on

the relationship between the development of an economy and financing conditions for

firms might help in deciding whether financial theories, such as the Tradeoff Theory,

are indeed as transportable across borders, as initially thought.

What Constitutes Eastern Europe and Western Europe?

It should be noted that no clear definition is found in literature on which countries

belong to Eastern Europe and which to Western Europe. In this research, a typical

Western European view on Eastern Europe is applied, in which Eastern Europe is

defined as the former Warsaw Pact region (Svejnar, 2002). This is the region of



countries that were under Soviet control since the Second World War and had

communist governments with planned economies, roughly until 1990. This definition

also contains numerous countries that geographically and historically might be called

Central Europe. Countries that fall under the applied definition of Eastern Europe, but

may be labeled Central Europe under different definitions, include: Poland, Hungary,

Czech Republic, Slovak Republic and Slovenia. With the exception of Slovenia, these

countries are also labeled as the Visegrád group. The historical view on Central

Europe also includes Germany, Switzerland and Austria. This view is based on a

combination of historical, cultural, geographic and religious factors. According to

Rupnik (2000) even Belgium and the Netherlands make up part of Central Europe.

Starting with the Second World War and Cold War, most of Central Europe became

Eastern Europe in the perception of many Western Europeans. The generalized

perception of Eastern Europe is still widely applied and might have even become

stronger with the original formation of the European Union, in which only Western

European countries participated. Today, with the accession of new member states

from Eastern Europe to the European Union, the concept of Central Europe as a

separate numerator of a block of countries becomes more widely accepted again

(Rupnik 2000).

Because the focus in this research mainly lies on the development of a country’s

economy and capital market, the definition of Eastern Europe as the ex-Warsaw Pact

countries (Soviet Union, Albania, Bulgaria, Czechoslovakia, East Germany, Hungary,

Poland, and Romania) is believed to be useful. Since all Warsaw Pact countries have

experienced a period of transition beginning in the early 1990s, they are more or less

in a similar state of development and provide an excellent study group for the research

of capital structure theories. Western European countries are considered to be the

original European Union member states (Austria, Belgium, Denmark, Finland,

France, Germany, Greece, Ireland, Italy, Luxembourg, the Netherlands, Portugal,

Spain, Sweden and United Kingdom) plus Switzerland and Norway.

Differences between Eastern Europe and Western Europe on Institutional factors

In Appendix II a thorough comparison is made on the previously identified

institutional factors. These comparisons are based on numerical scores from the

World Bank and various other (acknowledged) resources. First, several countries are



taken from the identified ‘Eastern Europe’ and ‘Western Europe’ and based on

country scores, Eastern European and Western European scores are calculated in

order to make inferences about differences between Eastern Europe and Western

Europe. The conclusions and implications of this comparison are presented here.

Access to Credit

Access to credit is weaker for firms in Eastern Europe than for firms in Western

Europe. The availability of commercial credits is lower. This is due to several factors.

In Eastern Europe, legal systems are weaker than in Western Europe, and creditors

therefore demand larger monitoring power over firms. Besides, in Eastern Europe,

banks have less credit information available about firms. This is because companies in

transition economies usually have short credit histories. Such short (and often less

qualitative) credit histories make banks and other creditors more reluctant to extend

credit. This leads to banks in Eastern Europe giving smaller loans and demanding

higher compensation, i.e., higher interest rates. Moreover, higher interest rates lead to

lower demand for loans.

Lower access to credit in Eastern Europe might lead to different capital structures than

in Western Europe, since availability of credits is lower and thus, on average, leverage

ratios might be lower too.

Corporate Taxes

Both Eastern and Western Europe have been following a similar trend of decreasing

corporate tax rates during years 2000-2005. However, Eastern Europe indicates much

lower average tax rates than Western Europe. This is due to the fact that emerging

economies in the Eastern European region literally compete against eachother (and

against Western European countries) with their tax rates, in order to attract foreign

investors. Besides, the heavy tax cuts should be seen as an incentive for firms to

invest, in order to offset the higher risks in these emerging economies, and the higher

costs of short-term bank borrowing at high interest rates.

It can be expected that the role of taxes in setting capital structures, is declining in

both the Western and Eastern European regions. Since the corporate tax rates are

lower than in previous years, the relative amounts of tax shields are also lower and

thus, in light of the Tradeoff Theory, it might be expected that in both regions

leverage ratios are lower today than they were a decade ago. Besides, the importance



of taxes in determining leverage ratios seems to be declining. Since corporate tax rates

are much lower in Eastern Europe, compared to Western Europe, the same reasoning

would indicate lower leverage ratios in Eastern Europe.

Bankruptcy Costs

It is observed that bankruptcy costs are higher in Eastern Europe than in Western

Europe. Creditors lending capital to Eastern European firms need to wait longer for

repayment in case of bankruptcy. Besides, they will have to pay more to legal

practitioners during a bankruptcy case, and afterwards they will find that a smaller

portion of their initial investment can be recovered. Implications from these findings

on capital structure determination might be that creditors will be more reluctant to

extend credit, due to higher risks and higher bankruptcy costs. Besides, they will ask

for higher interest rates in order to offset these risks and costs. Thus, higher

bankruptcy costs and higher credit risk may lead to lower leverage ratios in Eastern

Europe.


The differences observed in corporate governance scores between Western and

Eastern Europe are very diverse and country specific. Therefore, it is hard to

generalize between Eastern and Western Europe. It is observed that most of the

Eastern European law systems are still underdeveloped in comparison to Western

standards, indicating weak shareholder protection. A result from weaker law systems

is that investors have strong concentrations of ownership in firms. This is in line with

finding of LaPorta et al (1999), who found that in economies with weak shareholder

protection, relatively few firms are widely held. However, weak law systems do not

necessarily mean bad corporate governance, as long as investors are concentrated.

When connecting corporate governance to capital structure there are some

implications on leverage. In light of the Tradeoff Theory, countries with low

corporate governance, and hence, high agency costs, are expected to have lower

leverage, since creditors are less inclined to lend to companies from these countries.

The general assumption is that this is another reason to expect lower leverage ratios in

Eastern European countries.



1.4 Small and Medium Sized Enterprises in Europe

Most of the country differences that can be observed in literature are based on

findings of public or big firms, since data for these firms are more widely available.

An in-depth analysis on Small and Medium sized Enterprises (SMEs) and the way in

which such firms are different from large public firms is presented here. Even though

the country scores from the previous section are based on large firms, the inferences

based on these scores are also expected to apply to smaller and non-listed firms. That

is, these scores indicate differences in institutional settings of the countries’

economies and these influence all firms within those economies.

According to the European Commission (2003) the vast majority of firms in Europe

are Small and Medium sized Enterprises. In 2003 there were more than 19 million

enterprises in Europe of which only 40,000 were large firms, accounting for only 0,2

percent of all enterprises. This indicates that indeed, the majority of firms in Europe

(99,8 percent) are SMEs. These companies accounted for a significant amount of the

European job market (67 percent of European employment) and economic activities

(65 percent of European companies’ turnover).

Although the above figures indicate higher importance of SMEs than large enterprises

in the European economy, there is a limited number of studies conducted on capital

structure of SMEs. As Zingales (2000, p.1623, 1629) noted: “although the existing

theories have delivered very important and useful insights, … the emphasis on large

companies has led us to ignore (or study less than necessary) the rest of the universe:

the young and small firms, who do not have access to public markets”. Ang (1991)

supported this by pointing out that the theory of finance had not been developed with

the small businesses in mind.

The existing literature thus claims that financing patterns and capital structures are

different for Small and Medium sized Enterprises and big public firms. Therefore, it

is important to analyze the (possible) causes behind these differences.



Small and Medium sized Enterprises (SMEs) versus Large Enterprises (LEs)

In the following subsection the differences in terms of financing and corporate

governance between SMEs and LEs are investigated. When discussing SMEs versus

LEs, the focus does not only lie on size, but also on the ownership structure. Large

enterprises are more likely then SMEs to have access to public markets, and LEs are

more often publicly owned than SMEs. In this research the focus will lie on privately

held SMEs. Therefore, one should keep in mind that the ownership structure, hence

limited access to public markets, plays a bigger role than its size in private SMEs’

capital structures.

There are different external financing patterns evidenced between SMEs and LEs

(Claessens, Tzioumis, 2006) in developed economies; however, leverage ratios have

been found to be more or less similar (Berger and Udell, 2003).

Large public firms have access to public equity markets and if necessary can finance

their investments from new equity issues. Besides, they can raise funds by the use of

bond issues, commercial paper, syndicated loans or loans from several banks

(Bartholdy and Mateus, 2006). SMEs are mainly family owned or have a limited

amount of private owners. The equity part of their capital structure typically comes

from two resources: retained earnings (profits kept within the company), and owners’

private funds. Both of these resources are often limited for financing the firms’

investments. Debt financing is often critical, especially for the SME growth firms.

This type of financing is limited to bank loans and trade credits. Therefore, banks are

expected to be more important in financing for SMEs than for LEs (Claessens and

Tzioumis 2006, Mayer 1990).

The ownership structures are different between SMEs and LEs. Consequently,

corporate governance issues differ as well. Agency costs were stated to arise from two

types of conflict: conflicts between owners and management and owners and

creditors. The principal-agent problems are larger in bigger firms, which have

professional management and diffuse shareholder ownership. Most small and medium

sized firms are owner-managed (often family controlled companies), or have higher

concentrations of controlling shareholders. Agency costs between the managers and

equity holders are small or may even be irrelevant. Agency costs between owners and

creditors are also different between SMEs and LEs. Small firms do not have to report



any data while public companies are required to present annual information to the

market (Hutchinson, Hall, Michaelas, 1998). Accordingly, information availability is

far more voluminous for LEs than for SMEs. Public firms encounter smaller agency

problems when taking debt. Because the information about the firm is widely

available, there are various types of traded debt that the company can obtain. These

forms of traded debt are not available for smaller firms, and hence, SMEs can only

rely on banks for their external finance needs. The information asymmetry between

creditors and management is bigger in small business finance where the availability of

information is remarkably low, than in large public firm finance (Foglia, Laviola &

Reedtz, 1998).

Agency costs for creditors are much higher when investing in SMEs than investing in

large firms. Because of the lack of information on SMEs, banks need to solve the

asymmetric information problems of adverse selection and moral hazard themselves

(Sharpe, 1990, Petersen and Rajan, 1995). Thus, relationship banking becomes a very

important solution here (Harhoff & Körting, 1998, Berger & Udell, 1998). This is

defined as the specific bank-client relation between firms and financial institutions, in

which strong long term bank-firm relationships are able to provide extensive benefits

to bank and firm together (Peltoniemi, 2004). This implies that financial markets in

the banking sector are able to function more efficiently when the relationship is closer

and more long-term.

Wriston (1986) pointed out that financial institutions are particularly good in

processing and gathering information of their clients, in order to communicate this

with their own informational operations. Therefore, financial institutions actually

generate information where information is not publicly available. Bartholdy and

Mateus (2006) confirmed this and added that this information creation function is

enforced when the financial institution is the sole banking relation of the firm.

Since an SME has to make most of its payments through a bank, the banks are able to

collect appropriate cash flow information about the company. By obtaining this

information the bank is better able to analyze the creditworthiness of the firm than the

market can. Hence the bank can partly solve information asymmetry in a cheaper way

than financial markets and credit rating agencies (Bartholdy and Mateus, 2006).



Banks will thus use the private information from their payment function to

continuously monitor the firm, even after credit has been granted. A crucial part of the

banks’ monitoring and enforcing of its control rights is used by typically lending

short-term to small firms and continuously re-negotiating the debt contract (Sharpe,

1990, Petersen and Rajan, 1995). That is, the bank assures that agency costs remain

low by lending short term and keeping the option to revise or cancel the debt contract

every time the contract is due. This is a credible threat for the firm’s management and

therefore reduces moral hazard (Bartholdy and Mateus, 2006). If the firm’s

management decides to use the banks funds in ways that do not align with the bank’s

contract, the bank can decide not to renew the loan for the next period. This increases

the SMEs’ dependence on the bank even more. Collateral remains important in long-

term lending of banks to SMEs, in order to assure that the long-term loans will be

repaid (Hutchinson, Hall, Michaelas, 1998). However, the stronger and longer the

relationship between the bank and the SME and the wider the scope of this

relationship, the lower the collateral requirements. The length of the banking

relationship also determines to a great extent the premiums paid by the firm to the

bank. The longer the relationship, the lower the premiums on loans (Peltoniemi,

2004).

Previous research primarily focused on the differences mainly based on large, and

publiclly held Western European firms. However, the causes for differences between

large public firms and SMEs, as described above, are likely to be broader in

developing countries. Therefore, the differences between SMEs in Western and

Eastern Europe need to be analyzed.

SMEs in Eastern and Western Europe

Improving the access to finance is critical in fostering competition, innovation and

growth in all European countries. Nevertheless, the access to sufficient funds has been

problematic for many SMEs in both Western and Eastern Europe. The main reason

for this is the fact that many financial providers consider financing SMEs as a high-

risk activity due to high transaction costs and relatively low returns on investment

(EU Commission Report 2005).



Table 1 – Indicators of dependence of SMEs on banks

Use of Banks for

financing purposes

Long term

Loans

Short term

Loans

Overdrafts

Western European SMEs 79% 45% 45% 50%

Eastern European SMEs 66% 14% 36% 37%

Source: EU Commission Reports: SME Access to Finance (2005), SME Access to Finance in the New Member States (2006)

As shown in Table 1, banks are by far most used by European SMEs for financing

operations; 79 and 66 percent of SMEs from Western and Eastern European countries,

respectively. These findings were confirmed by Mayer (1988) and Berger and Udell

(1998, 2003). They found that (short term) bank loans constituted the most important

part of external small business finance in all markets.

Financing from banks to SMEs in general comes in two forms: loans and overdrafts

(the instant extension of credit from a lending institution). Loans are generally divided

between long term loans and short term loans. There is a difference between the two

regions in terms of the type of loan usually taken by the companies. In Western

European countries over 45 percent of the firms use long term loans (lasting over

three years) to finance the investments. In respect to Eastern European countries, only

14 percent of SMEs make use of this kind of loan. Both regions indicate intensive use

of short term loans. However, Western European SMEs also make more intensive use

of short term loans than the Eastern European SMEs. In Western Europe, 45 percent

of SMEs use short term loans and 50 percent use overdrafts, while in Eastern Europe,

36 percent of SMEs use short term loans and 37 percent use overdrafts. Even though

overdrafts seem to be more popularly used in Western European SMEs, they

constitute a higher relative amount of bank financing in Eastern European SMEs.

The difference in the access to bank financing in these two regions is a clear

confirmation of the existence of country specific and region specific differences. The

observed differences are evidence of credit markets being still underdeveloped in

Eastern European countries as compared to Western Europe. This, in turn, indicates

that these countries are still in the process of transformation (Johnson, McMilland and

Woodruff, 2002). Lower availability of credit in Eastern Europe is due to the fact that



SMEs are perceived as more risky than Western European companies. SMEs in

Eastern Europe are relatively younger and faster growing; it may be difficult or

impossible for the bank to rely on the history of the company or to predict the future

of the firm.

Another constraint faced by Eastern European SMEs in access to financing is poor

property rights. These have been found to be much weaker in Eastern European

countries compared to Western European countries. According to Johnson,

McMilland and Woodruff (2002), property rights in these countries are extremely

important because the managers will not invest if they are not sure they can keep “the

fruits of their investments”. Effective enforcement of property rights is another

indicator of the strength of a legal system. Property rights are necessary in order to

create a healthy investment environment, where entrepreneurs and business managers

can be assured of keeping the assets and future profits from their assets. Property

rights are positively correlated to the use of external financing. It has been found that

property rights in Eastern Europe are the strongest in Poland, while property rights in

Russia and other former Soviet Union countries including Ukraine tend to be the

weakest (McMilland and Woodruff, 2002). It is very common in these countries to

pay bribes and extra-illegal fees in order to be able to run a business. The availability

of loans surely matters for the existence and growth of SMEs. However, loans will

only be made available when property rights are perceived to be secure. When

property rights are insecure, it is immaterial whether external finance is available or

not, since the firms, entrepreneurs and financial institutions will be reluctant to invest.

Empirical findings on SME capital structure in Europe

As was previously stated, literature on capital structure determination in European

SMEs is relatively scarce. It seems that it is becoming a more popular field of

research, since most literature dates from the last decade. A common approach which

is used in testing SME capital structures, is to collect data within a specific country,

and to test existing theories on different samples, often based on research data and

findings conducted previously on large firm samples from the United States. Such an

approach makes it possible to infer whether differences exist in findings between

large and small firms, and whether the general theories are applicable on SME capital



structure. Some researchers focus on SMEs in transition economies, while others

focus on Western economies. Research that studies SME capital structure in

developing and developed economies in an integrated way has not been found, and

from the authors’ research and investigations, does not seem to exist.

Klapper, Sarria-Allende and Zaidi (2006) investigated the competing theories of

capital structure on a sample of SMEs in Poland. They studied the effect of four

different factors on leverage: the firms’ size, profitability, tangibility and growth

opportunities. The findings on the relations between these factors and leverage were

ambiguous and partly supported the Tradeoff Theory as well as the Pecking Order

Theory. In a similar fashion, Michaelas, Chittenden and Poutziouris (1999) tested

theories of capital structure on SMEs from the U.K. They studied the influence of the

same four factors, plus proxies for marginal tax rates, non-debt tax shields and

operating risk on leverage ratios. Some results found evidence for a Tradeoff Theory,

while other found a Pecking Order Theory to hold. Also Voulgaris, Asteriou and

Agiomirgianakis (2004) tested similar proxies for determinants of capital structures of

SMEs in the Greek manufacturing sector and compared this to large public firms.

They detected some similarities and some differences between SMEs and large firms.

Most interestingly, the authors found that SMEs were more liquid and less capital

intensive and made higher use of short term debt (and hence lower use of large term

debt) than large firms. The sources of debt are also different for SMEs: They rely

more on accounts receivables, suppliers’ credit and inventory. Sogorb-Mira (2005)

found inconclusive results for either the Tradeoff Theory or the Pecking Order Theory

to hold, on a sample of Spanish SMEs.

Hall, Hutchinson and Michaelas (2004) investigated country specific factors on the

financial behavior of SMEs. Their research was performed on eight Western

European countries: Belgium, Germany, Spain, Ireland, Italy, the Netherlands,

Portugal, and the U.K. The authors reported variability with respect to short-term and

long-term debt across the countries and also variations in determinants of capital

structure. As in the other researches, some factors clearly pointed towards a Tradeoff

Theory of capital structure, while others indicate a Pecking Order Theory to hold. The

findings were clearly different between these Western European countries, but an

important conclusion from the authors was that the variability in leverage ratios

among these countries results from country specific differences, and not from



differences in the studied determinants of capital structure. That is, the role these

determinants play is similar across countries, and differences in capital structure are

rather due to other country specific reasons. The tested factors were profitability,

growth, tangibility (asset structure), size and age.

All the above researchers found strong evidence for bankruptcy costs to exist and

mixed evidence for the effects of tax and agency costs on the leverage ratios of small

firms.



Chapter 2

Research Question & Hypotheses

This chapter introduces the reader to the research question and hypotheses of this

research paper. These are based on literature on capital structure determination and

Small and Medium sized Enterprises and on the differences between Western Europe

and Eastern Europe identified before.

Chapter One described the theoretical approach behind capital structure determination

from the Tradeoff Theory perspective. It showed the role of debt within a firm’s

capital structure as a function of benefits from possible tax shields and costs from the

risk of bankruptcy, the dispersion of ownership, and control of financiers. The

marginal benefits and marginal costs of adding debt into a capital structure causes an

optimum to exist at which point the net gains are highest, and hence maximum

shareholder value is created. This theory has received much attention, both positive

and negative, in the last decades. Another theory was introduced as well, the Pecking

Order Theory, which received at least as much attention as the Tradeoff Theory.

However, decades later, scholars still do not agree on one universal theory, and not

much reason is left to expect such agreement. Many changes have been made to the

original theories, and some seem to work better than others.

Some famous researchers, such as Rajan and Zingales (1995) proved that capital

structures are similar across countries, and that factors affecting capital structure are

rather similar as well. Even though no universal theory for capital structure exists, it

appears that capital structure has similar underlying factors, in many different parts of

the world.

However, this might not be completely true. Two aspects are not taken into account.

The first aspect is that most researchers concentrated on developed economies, where

conditions are rather similar, even though markets and banks have solved the

inefficiencies of a non-perfect market differently. There are reasons to believe that

capital structures theory and its determination might work differently in developing,

or transition economies. It has been observed, that capital structure of firms seems to

be related to country-specific factors. Therefore, Chapter One described country



differences between the developed economies of Western Europe and the transition

economies of Eastern Europe, based on factors that are important under the study of

the Tradeoff Theory. Clear differences are observed between individual countries and

especially between both regions. Differences were identified in corporate tax rates, the

state of development of capital markets, access to credit for firms, bankruptcy laws

and other legal issues, investor protection and corporate governance. There is strong

reason to believe that these country- or regional-specific factors, which originate from

the state of development of the economy, the law system, enforcement of law and

capital markets, have an impact on the capital structure of firms.

The second aspect which most researchers do not take into account when studying

capital structure determination, is how the size of a firm, and especially its ownership

structure influences the way in which firms make decisions on capital structures. Size

is often used as a variable explaining capital structure, but not as a variable classifying

the sample on which to test capital structure decisions. Although the Tradeoff Theory

was developed as a general theory and was meant to be applicable to all firms, many

research findings have gained credibility that leads to different implications on SME

capital structure. The same is true for the Pecking Order Theory, which also has not

been developed with the small firm in mind. The great focus of most authors in

finance (as described in the previous chapter) on large public firms is rather strange,

since Small and Medium sized Enterprises have been found to be far more important

in the European economy. There is reason to believe that small and private firms have

a different capital structure than large public firms, and hence, decision making on

capital structure will also be different for these firms.

Since it has been argued that country specific factors determine a firm’s capital

structure to a large extend, and since literature on SME financing is rather scarce, it

will be especially interesting when both aspects are combined, and examined. This

research paper will study capital structure determination of Small and Medium sized

Enterprises in developing Eastern European countries, compared to well developed

Western European countries. The research is conducted from a tradeoff perspective

and focuses on the differences between the two regions. That is, the focus is to

identify differences between Eastern and Western European SME capital structures,

with emphasis on the relationship between capital structure and the three Tradeoff

Theory determinants: taxes, bankruptcy costs and agency costs.



The central research question is:

“To what extent do capital structures and their determinants differ between Small

and Medium sized Enterprises from Eastern and Western Europe, in light of the

Tradeoff Theory?”

Answers to this question will yield valuable insights into financing patterns, hence

capital structure decision making, under less optimal economic conditions, such as

those of Eastern Europe. It might also answer whether the Tradeoff Theory is a good

underlying theoretical framework for testing capital structures of SMEs, and for

testing capital structures across borders.

Although numerous research papers have made an effort to test the validity of the

Tradeoff Theory, it is not the main objective of this study to prove whether the

Tradeoff Theory holds. However, it is important to identify whether the Tradeoff

Theory works as an underlying explanatory framework, since the analysis will be

based on this.

The capital structures are tested from a Tradeoff Theory perspective, and not from

another theoretical framework background, such as the Pecking Order Theory. This is

because the Tradeoff Theory is believed to be more applicable for small firms, while

the Pecking Order Theory is not. The Pecking Order Theory results from information

asymmetry between management and shareholders. As described, this asymmetry of

information is not expected to be strongly evident in private and small firms.

Moreover, the Tradeoff Theory is relatively easy to test. The effects of taxes,

bankruptcy costs and agency costs, which are expected to lead to an optimum, should

play the same role on leverage for all firms. Only the strength of each effect will differ

per firm and per situation. A target adjustment model is applied in order to indirectly

test the adjustment towards an optimum, based on these three effects. The Pecking

Order Theory is much more difficult to test on small private firms, since equity issues

do not take place, and no structured approach has been developed for testing pecking

order decisions. Yet, without equity, a pecking order might still exist, based on a

priority distribution of firms’ available internal funds and needs for external debt.

Since only a few forms of debt are available, it is even harder to test whether a clear

formal pecking order exists. Therefore, a more valid reasoning for the Tradeoff



Theory to work on the tested samples of firms, combined with more reliable data for

testing, leads to the Tradeoff Theory being the underlying theoretical framework of

this research.

From the findings of Chapter One and the expected differences in the relationship

between the development of financial markets and access to finance, the following

hypotheses are formalized for the research question:

The main hypothesis is:

- Leverage ratios are expected to be lower in Eastern Europe than in Western

Europe.

The hypotheses on the relationship between the determinants of leverage and leverage

itself are:

- Taxes are expected to have a positive influence on leverage in both Eastern

and in Western Europe, but the influence of taxes is expected to be lower in

Eastern Europe than in Western Europe.

- Bankruptcy costs are expected to be negatively related to leverage in Eastern

as well as in Western Europe, but the influence of bankruptcy costs is

expected to be higher in Eastern Europe than in Western Europe.

- Agency costs are expected to be negatively related to leverage in Eastern

Europe and in Western Europe, but are expected to play a much smaller role

than bankruptcy costs on SME capital structure.

It is impossible to focus on the complete Western and Eastern European regions as the

scope of such type of study is well beyond the purpose of this research paper.

Consequently, this research will focus on three countries from each region. Countries

were chosen for which proper data was available and which were expected to differ

from each other on the before mentioned factors: taxes, bankruptcy costs and agency

costs.

From Eastern Europe: Poland, Hungary and Ukraine were chosen, because of

differences indicated from the tables presented in Appendix II. Ukraine scored

considerably low on credit access for firms. Taxes were lowest in Hungary, and

bankruptcy and agency costs were highest in the Ukraine.



From Western Europe: Ireland, the Netherlands and Belgium were chosen. All three

countries employ a different legal framework, according to the taxonomy stated in

Chapter One. Ireland has highest credit information, promoting easier access to credit

for firms. Tax rates are considerably lower in Ireland, indicating a lower benefit of tax

shielding, while in the Netherlands and Belgium taxes are fairly equal. Bankruptcy

costs are more or less similar in all three countries. Agency costs are lowest in Ireland

and highest in the Netherlands.

Since both samples consist of countries that differ among each other in respect to

taxes, bankruptcy costs and agency costs, it will be less likely that both samples might

bias results because of country similarities. Hence, results will be more likely to

indicate whether differences between the two regions are truly valid for both regions

and not just outcomes of a biased country selection.



Chapter 3

Methodology

This chapter presents an in-depth analysis of the model which is applied. Since

existing literature has described several ways of testing the Tradeoff Theory, the

implications of this model are analyzed. The model needs variables as input. These

variables will be identified, and described in greater detail in the second section of this

chapter. Implications and expected relations between the variables and SME capital

structures will be described. The third section presents the data that is used in order to

calculate the variables for the model. It demonstrates how limitations on data are

applied, and how the data has been collected. The last section discusses the Two Stage

Least Squares regression analysis that is applied.

3.1 Methodology: Model

The model used in this research is based on the Tradeoff Theory, in the form of a

(partial) target adjustment model. The target adjustment model has been derived from

Taggart (1977) and Jalilvand and Harris (1984).

Shyam-Sunder and Myers (1994) have written the target adjustment model in the

following equation:

( ) ittiitit DDD εγ +−=∆ −1,

* [1]

itD∆ represents the difference in the level of debt for firm i between year t-1 and year

t. The Tradeoff Theory suggests firms to keep a target debt ratio, represented by

*

itD and 1, −tiD represents the firm’s previous year leverage (lagged leverage). The

speed at which firm i adjusts its debt level towards the target, is represented by γ . In a

perfect world γ would be equal to one, since there would be no adjustment costs.

However, in a world where adjustment is costly, γ is expected to be below one but

above zero, indicating positive adjustment costs. One example of such costs is

transaction costs (Frank and Goyal, 2005). Therefore, firms will adjust their debt

ratios gradually towards their target in times where they are ‘bumped away’ from their

target due to random events (Shyam-Sunder and Myers, 1994). The last factor,



random events, is represented in the model by itε . One such random event might be a

change in the firm’s external financing needs (Jalilvand and Harris, 1984). A need for

external finance itself might be due to changes in the structure of assets, or changes in

profits over time.

*

itD itself is unobservable, unspecified and changes over time. It also differs per

company, per industry and with many other factors; therefore it is hard to estimate a

value for *

itD . Because of the nature of the target adjustment model, a firm’s debt

level is expected to be mean-reverting, where *

itD is the average leverage over time.

A common approach is to find a proxy for the target debt level based on historic data.

Jalilvand and Harris (1984) calculated the historical average debt ratio for all

observed periods as well as a three-year moving average debt ratio.

The approach used here follows that of Bartholdy and Mateus (2006). This differs

from the above approach in that it does not try to estimate the target leverage ratio, but

assumes the target debt level to be the outcome at the crossing of the marginal cost

function and marginal benefit function of debt. This approach does not need to

estimate the target leverage ratio itself, but uses the factors that contribute to the

marginal costs and marginal benefits of debt as variables.

These factors, or variables, have been thoroughly explained in the literature chapter,

and are comprised of a TAX variable, BANKRUPTCY COSTS variables, and

AGENCY COSTS variables. This relationship is depicted in the following equation:

itititit COSTSAGENCYCOSTSBANKRUPTCYTAXD 321

* βββα +++= [2]

Here, once again, *

itD represents the target leverage ratio for firm i in year t, α is the

intercept term in the linear relation, itTAX represents the tax variable,

itCOSTSBANKRUPTCY represent the various variables indicating the bankruptcy

risk for firm i in year t, and tiCOSTSAGENCY , represent the variables indicating the

agency costs for firm i in year t. 1β , 2β and 3β are the coefficients that estimate the

influence of the various variables on the target debt level.



When substituting equation [2] into equation [1], the following more extensive target

adjustment model is created:

)( ittiitititit DCOSTSAGENCYCOSTSBANKRUPTCYTAXD εβββαγ +−+++=∆ −1,321 [3]

Slightly adjusting model [3] gives a linear relationship which can be statistically

tested:

)( ittiititit DCOSTSAGENCYCOSTSBANKRUPTCYTAXD εγβγβγβγαγ +−++++= −1,321 1 [4]

In this model, itD is the leverage ratio of firm i in year t and is called LEVERAGE. It

is a result of the adjustment from the previous year’s leverage ratio towards the target

leverage ratio and TAX , COSTSBANKRUPTCY and COSTSAGENCY variables.

Model [4] will be tested by using regression analysis. An in-depth description of

which regression method is applied can be found in section 3.4. In interpreting the

results of this regression analysis, one needs to be careful that in this linear

relationship, ,1β 2β and 3β are estimated in conjunction with γ . In order to find the

true values of 1β , 2β and ,3β the estimations from the model need to be divided by

the value of γ . This, in turn, can be found by taking the estimate in front of lagged

leverage, )( γ−1 , and subtracting it from 1.

Note that the primary purpose of this research is not to test the speed of adjustment,

hence the adjustment coefficient γ , but rather the complete model [4], that is, to test

the relationships of TAX , COSTSBANKRUPTCY and COSTSAGENCY on

LEVERAGE.

3.2 Methodology: Variables

All the variables used in the study are based on book values, which is in line with the

argument by Myers (1984) that book values are proxies for the value of assets in

place. Besides, it is very hard, if not impossible, to derive market values of private

firms, which comprise the focus group of this study.



Proxies for LEVERAGE

On the left side of the regression equation, Model [4], one can find the dependent

variable, LEVERAGE, which represents the capital structure of firm i in year t. The

capital structure is presented as a leverage ratio (liabilities to assets). Four different

measures of stock leverage are applied, which are tested in separate regressions. The

focus lies on stock leverage, since the analysis is on financing patterns of firms in the

past. It has been argued that if the focus would be on other matters, such as transfer of

control, a flow measure of leverage would be more suitable (Rajan and Zingales,

1995).

The first leverage ratio used is Total Leverage. This variable is calculated as:

AssetsTotal

sLiabilitieTotalLeverageTotal = [5]

This is the broadest measure of stock leverage available and can be viewed as what is

left for owners in case of liquidation (Rajan and Zingales, 1995). It might work as an

indicator of bankruptcy risk for the firms’ management, and therefore plays a role in

the firms’ leverage target setting. However, as Rajan and Zingales (1995) pointed out,

it might overstate the amount of leverage, since Total Liabilities also include items

like Accounts Payable, which may be used for transaction purposes rather than for

financing. Therefore, it is better to measure leverage by the ratio of Debt to Total

Assets. However, because of the lack of data on debt items (such as bank loans), short

term as well as long term, general ‘liabilities’ have been chosen instead. Other critics

on the measure of Total Leverage argue that for some determinants of capital

structure, Total Liabilities mask some opposite effects for long term and short term

liabilities. This means that certain effects of determinants are theorized to be negative

for short term debt while positive for long term debt, or vice versa (Van der Wijst and

Thurik, 1993; Chittenden et.al., 1996).

The second and third measures of leverage are Short Term Leverage and Long Term

Leverage. These measures have been used by a great number of authors, including

Jalilvand and Harris (1984), Frank and Goyal (2005), Shyam-Sunder and Myers

(1999), Hall et al. (2000).



Short Term Leverage is calculated as:

AssetsTotal

sLiabilitieTermShortLeverageTermShort = [6]

Short Term Liabilities are defined as the portion of a company’s Total Liabilities

repayable within one year, and include both Accounts Payable and Short Term Loans.

Long Term Leverage is calculated as:

AssetsTotal

sLiabilitieTermLongLeverageTermLong = [7]

Long Term Liabilities are those liabilities that are due for repayment beyond one year,

and include long term bank loans. As stated, these two measures of leverage might

indicate separate effects that determinants of capital structure have on short and long

term liabilities. The fourth measure of leverage that is applied is called Adjusted Total

Leverage, calculated as:

AssetsTotal

PayableAccountssLiabilitieTotalLeverageTotalAdjusted

−= [8]

This measure of leverage controls partly for working capital requirements by

subtracting Accounts Payable from Total Liabilities. This makes the measure a more

reliable indicator of the financing history of the firm.

On the right side of the regression equation, Model [4], one can find the independent

variables that are used to test for the tax effect, the bankruptcy effect, and the agency

cost effect on leverage. For these three effects, different proxies are used which are

combined within one model.

Proxies for TAX

Different tax variables are used in this research, since no clear-cut tax variable has

been indicated by literature, and, therefore, several proxies were created in order to

find which variable worked best in explaining the tax effect on leverage. Different tax

proxies might capture different aspects of the tax effect.



A first tax proxy is the KINK, derived from Graham (2000). This measure quantifies

how aggressively or conservatively a firm uses debt in order to shield its taxes. KINK

is calculated as:

Expenses Interest

EBITKINK = [9]

EBIT stands for Earnings Before Interest and Taxes. If KINK has a value higher than

one, it means the firm could increase its tax benefits by increasing its debt ratio, and

therefore its interest expenses. If KINK has a value lower than one, it means that EBT

(Earnings Before Taxes, but after Interest) is negative and therefore the benefits of

increasing the debt ratio are smaller than the statutory tax rate (Bartholdy and Mateus,

2006).

Figure 2 Marginal Tax Benefit and KINK

Source: Bartholdy and Mateus (2006)

Figure 2 shows the marginal tax benefit curve, which follows a horizontal function,

followed by a kink, after which the function is downward sloping. When EBT is

higher than zero, the function is horizontal, since any dollar increase in debt will lead

to the maximum dollar amount in taxes shielded (which ratio should be equal to the

statutory tax rate). That is, EBIT is higher than the interest expenses, and therefore an

aggressive debt user would increase its debt level to make better use of potential tax



shields. At the right hand side of the kink in the graph, the function is downward

sloping since every dollar increase in debt will lead to lower marginal benefits, but

still potential tax shields. This downward slope continues until the function becomes

negative, and EBT is lower than zero. Here, the debt user does not benefit anymore

from added debt in its capital structure. Therefore, a real aggressive debt user will

have KINK values lower than one, and above zero. A conservative debt user will have

KINK values higher than one. The values of KINK range between zero and eight,

where zero indicates the most aggressive debt users and eight indicates the most

conservative debt users (Bartholdy and Mateus, 2006). In light of the Tradeoff Theory

firms are expected to shield taxes by taking on debt. Consequently, a negative relation

is expected between KINK and leverage.

A second proxy for taxes is also derived from Graham (2000), and is known as the

STANDARDIZED KINK. In comparison to KINK, this measure helps to better

explain the aggressiveness and conservatism of debt use in firms. Firms with similar

KINK values might not necessarily be equally conservative, or aggressive in shielding

taxes. Firms’ earnings volatility should also be taken into account. The calculation of

STANDARDIZED KINK is as follows:

( )

( )EBIT

KINKExpenses InterestKINKEDSTANDARDIZ

σ

⋅= [10]

STANDARDIZED KINK is based on the measure of KINK, which is multiplied by

the amount of Interest Expenses. This outcome is divided by the standard deviation of

the firm’s Earnings Before Interest and Taxes (EBIT). It determines the length of the

flat part of the marginal tax benefit curve, per unit of earnings volatility (Graham,

2000). The shorter this flat part, the more likely a firm is to end up on the kink (KINK

= 1.0) or on the downward sloping part of the marginal tax benefit curve (KINK <

1.0). It is a measure of risk of becoming overly aggressive without intending to do so,

based on the volatility of earnings. A firm that fully benefits from its tax shields might

not do so if its taxable income decreases to a great extent in the following period of

time. A high STANDARDIZED KINK indicates smaller risk for the firm of

exceeding the KINK, and therefore a higher likelihood of keeping maximum marginal

tax savings from adding debt. In contrast to the KINK variable, the



STANDARDIZED KINK does not proxy the tax shield effect itself, but it proxies the

influence of risk on the decisions of tax shielding. A firm with a high value of KINK

would generally be thought of as a conservative debt user. However, if this firm has a

low STANDARDIZED KINK, it indicates that because of its earnings volatility the

firm might easily move to the downward sloping part of the marginal tax benefit

function. In this case, the advantage of adding debt into its capital structure would be

smaller than in case where a firm has a high STANDARDIZED KINK, since every

dollar added in debt leads to a lower dollar amount of taxes shielded. Thus,

STANDARDIZED KINK is expected to be positively related to leverage.

The third proxy for taxes is more general and should capture the overall tax effect. It

has been referred to as the EFFECTIVE TAX RATE, calculated as:

EBT

ExpensesTaxRATETAXEFFECTIVE = [11]

Here, Tax Expenses is the amount of taxes paid by firm i in year t, and EBT stands for

Earnings Before Taxes, but after interest. This proxy was used by Kim and Sorensen

(1986) and Sogorb-Mira (2005). Under the Tradeoff Theory, tax rates are expected to

be positively related to leverage, since the higher the tax rate, the more opportunities

to shield taxes by taking on debt.

Proxies for BANKRUPTCY COSTS and AGENCY COSTS

The measures used for testing the effect of bankruptcy costs and agency costs on

leverage have been taken from the most common literature and research on capital

structure. Bradley et al. (1984), Long and Malitz (1985), Harris and Raviv (1991),

Frank and Goyal (2003) and Rajan and Zingales (1995) all did extensive studies on

the factors best explaining capital structure. Most of the measures used here, have

been found to be highly correlated with leverage in many different contexts. Measures

such as TANGIBILITY of assets, SIZE, Altman’s Z-SCORE, OPERATING RISK,

PROFITABILITY and GROWTH have proven to be important to the study. Note that

these factors have not only been found to be influential on leverage under the

Tradeoff Theory, but also under different theoretical settings.



The first variable, TANGIBILITY, is measured by:

AssetsTotal

AssetsFixedTangibleYTANGIBILIT = [12]

This measure gives an indication of the amount of assets that can be collateralized. It

is found to be positively related to leverage, since from the perspective of the creditor

(bank), lending to a firm with a high ratio of collateral assets lowers the risk of default

and increases the value of the assets in the case of bankruptcy (Booth et al, 2001).

Besides diminishing the costs of bankruptcy from the creditor’s perspective, tangible

assets also diminish agency costs. TANGIBILITY makes it difficult for shareholders

to substitute high risk assets for low risk assets (Frank and Goyal, 2005). For these

reasons, a positive relation between tangible assets and leverage is expected.

However, the findings of Antoniou et al. (2002) and Mayer (1994) suggested that

TANGIBILITY of assets was more important in bank borrowing countries. Bartholdy

and Mateus (2006) stated that a high percentage of fixed assets might also imply

higher operating leverage, which increased the probability of bankruptcy suggesting a

negative relationship between fixed assets and debt.

SIZE has been found to be positively related to leverage. Size is calculated as:

)ln( AssetsTotalSIZE = [13]

Here, )ln( AssetsTotal stands for the natural logarithm of Total Assets. Large firms

are expected to be more diversified and are perceived to carry lower risk of default

(Harris and Raviv, 1991). According to Rajan and Zingales (1995) this relation should

be weaker in countries where bankruptcy costs are low. Besides, large firms are

typically more mature firms. These firms have a reputation in debt markets, and

consequently, face lower agency costs of debt (Frank and Goyal, 2005).

The Z-SCORE has been derived from Altman (1968). It is a proxy created to estimate

the probability of financial distress. This proxy is made up of several firm-specific

ratios which are multiplied by a weighted factor, and are observable by the market.

The original Z-SCORE included a factor based on the market value of the firm. This

measure is only useful for publicly traded companies, and Altman adjusted the



original Z-SCORE with two new scores that work well for privately owned firms.

One score is made specifically for private manufacturing firms, and the other one can

be applied to general private firms (Altman, 1977). In this study, the general Z-

SCORE for private firms will be applied.

This Z-SCORE is calculated as:

=− SCOREZ

+++AssetsTotal

EBIT

AssetsTotal

Earningstained

AssetsTotal

CapitalWorking72.6

Re26.356.6

sLiabilitieTotal

EquityTotal05.1 [14]

A high Z-SCORE indicates high probability of financial distress. It therefore is

expected to be negatively related to leverage.

A proxy used for OPERATING RISK is calculated as:

)(EBTRISKOPERATING σ= [15]

OPERATING RISK is the standard deviation of Earnings Before Tax (EBT). It is an

indication of the volatility of earnings, and therefore, the likelihood of financial

distress. Nguyen and Ramachandram (2006), Heshmati (2001) and Huang and Song

(2001) all found significant and negative results between this variable and different

measures of capital structure. All these studies were conducted on Small and Medium

sized Enterprises, and this measure is, therefore, expected to be negatively related to

leverage.

PROFITABILITY is a proxy both for bankruptcy costs and agency costs. Profitable

firms generate more cash, face lower probabilities of default, and therefore,

bankruptcy costs are smaller. Besides, interest tax shields are more valuable for

profitable firms (Frank and Goyal, 2005). Jensen (1986) showed that debt disciplines

managers especially in profitable firms. In highly profitable firms where cash

generation is bigger than investments, managers might be more inclined to act on their

own behalf than on behalf of the firm’s shareholders. Here, debt functions as a strong

discipline on management by mitigating the free cash flow problem (Jensen, 1986).

Therefore, in general, PROFITABILITY and leverage are expected to be positively



related under the Tradeoff Theory. Frank and Goyal (2005) stated that

PROFITABILITY can also be seen as a proxy for growth opportunities. Myers (1977)

argued that highly levered companies are more likely to pass up profitable investment

opportunities. From this argument, it can be inferred that future growth opportunities

are negatively related to leverage. If PROFITABILITY indeed is a sound proxy for

growth opportunities, it would be expected to be negatively related to leverage. Under

the theoretical framework of the pecking order, PROFITABILITY is expected to be

negative with leverage. Myers (1984) and Myers and Majluf (1984) showed that

management of firms prefer internal over external finance because of asymmetric

information, and therefore the need to rely on debt is much smaller for profitable

firms than it is for firms with lower internal cash generation.

Peltoniemi (2004) and Bartholdy and Mateus (2006) stated that for firms that are

primarily bank financed, the costs of asymmetric information are rather small. This is

because banks solve the asymmetric information problems by continuously

monitoring and controlling the firms. Since the samples used in this study are also

expected to be primarily bank financed, it is expected that information asymmetry

does not play a major role and a positive relationship between PROFITABILITY and

leverage is expected.

There are several possible measures of PROFITABILITY. Titman and Wessels

(1988) used a natural logarithm of Profit Before Tax divided by Revenues. In many

studies, PROFITABILITY is measured as the ratio of EBIT over Total Assets, e.g. see

Harris and Raviv (1991) or Rajan and Zingales (1995). Here, the proxy of

PROFITABILITY is slightly different, measured by:

AssetsTotal

EBTITYPROFITABIL = [16]

Here, EBT stands for Earnings Before Taxes. This measure of PROFITABILITY is

derived from Michaelas, Chittenden and Poutziouris (1999). The use of EBT over

Total Assets has been prefered over the measure of EBIT over Total Assets, since

better data can be obtained.



GROWTH is calculated as:

1,

1,

−

−−=

ti

tiit

AssetsTotal

AssetsTotalAssetsTotalGROWTH [17]

GROWTH is the percentage change in Total Assets from one year to the next year.

This is in line with other studies (Titman and Wessels, 1988; Chittenden et al., 1996;

Bartholdy and Mateus, 2006). High growth is generally considered by banks to

indicate a healthy development of a firm’s business. Banks will more easily lend to

high growth firms than to low growth firms. This suggests that a positive relationship

exists between GROWTH and leverage. As stated, GROWTH might also proxy future

growth opportunities, which are expected to be negatively related to leverage. This is

because growth opportunities indicate agency costs between management and

creditors. Increased debt may lead to underinvestment and therefore low future

growth.

The last variable in the target adjustment model is LAGGED LEVERAGE. This

measure was described before in models [1] and [4]. It is depicted as:

1, −= tiDLEVERAGELAGGED [18]

LAGGED LEVERAGE is nothing else than the variable LEVERAGE in period t-1.

This variable is included, since in order to measure the adjustment towards an

optimum over time, one needs to know the current leverage ratio as well as the

historical leverage ratio. Then, it can be determined to which extend a firm adjusted

its leverage ratio in one period. LAGGED LEVERAGE is calculated in the same four

ways as the dependent variable LEVERAGE.

In this research, another type of variable is used, a DUMMY variable, also known as

an indicator variable. Since the focus of this research is to compare capital structures

of firms from different groups (different regions and countries) with each other,

dummy variables are included in the model in order to capture the effects of such

differences. These are in fact, the main focus of the research. Dummy variables take

the value of 0 or 1, depending upon which group they represent, and which group is



the base group. Shift dummies and slope dummies are applied. A shift dummy is a

mere 0 or 1, used in the model in order to adjust the constant term of the regression

for the group that is studied. Slope dummies are dummies combined with the other

independent variables described above, e.g. TANGIBILITY multiplied by 0 or 1,

depending which country or region is studied in comparison to other countries or the

other region. Slope dummies are used in the regression model in order to find how

different the independent variables are between the different regions and countries.

G - 1 dummies are applied (the number of countries or regions minus one), in order to

avoid the dummy variable trap. The dummy variable trap takes place if none of the

countries or regions is excluded from the model, i.e. if no base group has been taken

into account. Then, perfect collinearity would be introduced. Dummy variables are

also used in order to control the model for year effects and industry effects. In this

case, the dummies represent years and industries.

3.3 Methodology: Data

This section presents the data that is used in order to calculate the variables for the

model, which have been presented in the previous section. It will be described how

the data was collected and which adjustments were made to the data. An overview is

presented of the available data after each adjustment.

In order to test the target adjustment model on SMEs from Eastern and Western

Europe, company data for computing the variables is needed. The Bureau Van Dijk’s

AMADEUS database was used for extracting firm-level information on privately

owned SMEs from the six countries that have been identified in the previous section:

Ireland, the Netherlands, Belgium, Poland, Hungary and Ukraine. The AMADEUS

database contains detailed accounting and financial information for each firm,

including balance sheets, profit and loss statements along with other specific firm-

data such as, the number of employees, industry codes, legal form, year of

incorporation, etc.

The companies chosen for this research were determined by the official European

Union definition of SMEs (European Commission 2005). The category of micro,



small and medium sized enterprises is made up of the companies which employ less

then 250 people, have an annual turnover not higher than 50 million Euros and / or

their annual balance sheet (total assets) are not higher than 43 million Euros. As this

research concentrates on SMEs, the micro firms are excluded. Therefore, the first

selection criterion was adjusted and the companies with more than 10 employees and

less than 250 employees were depicted (European Commission 2005).

Until 1996, AMADEUS included only large and publicly listed companies. Since

1998, the database coverage of SMEs has increased significantly. Therefore, the data

obtained for the studied sample includes the years 1998-2005.

The company search in this research included all those with represented

characteristics of SMEs. The sample consists of unlisted (private) firms only and

companies operating in financial industries were excluded in the research. All other

industries were included; however, for ease of calculation these are grouped into the

three following industry groups: Manufacturing, Service and Other (see Appendix III

for more complete details). In order to better detect country specific trends, the sample

represents only firms that have a maximum of 49 percent of foreign ownership. Only

SMEs were included that operated in any time frame within years 1998-2005. The

following table represents the number of SMEs in each country in this period.

Table 2 – Number of observed SMEs in the period 1998-2005

Ireland Netherlands Belgium Poland Hungary Ukraine

2.501 4.369 5.517 2.969 1.774 3.887

Pooled cross sections are used in this research, which were obtained by sampling at

different points in time. This technique increases the sample size and enables one to

see how the relationship has changed over time. For example, Ireland is represented

by 2501 SMEs in this period. This does not mean, however, that every SME is

existent in every year of this period. E.g. one company could exist only in the years

1999-2001; therefore, this company gives values only during three years. It will thus

be clear that the pooled cross sections are not panel data.

The following table represents the number of firm/year observations for every country

in the tested years 1998-2005. AMADEUS does not provide data for Ukraine for the

year 1998; therefore, this country’s sample includes company data for years 1999 -

2005 only.



Table 3 – Number of firm/year observations per country of analysis, in the period 1998 - 2005


20.008 34.952 44.136 23.752 14.192 27.209

The following accounting data is used to calculate the variables for the target

adjustment model.

The data is derived from balance sheets and profit and loss statements:

- Total Debt

- Total Assets

- Tangible Fixed Assets

- Earnings Before Interest and Taxes

- Interest Expenses

- Tax Expenses

- Earnings Before Taxes

- Working Capital

- Retained Earnings

- Total Equity

- Total Liabilities

- Total Current Liabilities (Short Term Liabilities)

- Current Liabilities: Creditors (Accounts Payable)

- Total Non-Current Liabilities (Long Term Liabilities)

Adjustments needed to be made for calculating the KINK and STANDARDIZED

KINK variables. From equation [5] above, it is shown that a firms’ interest expenses

are needed to calculate a firms’ KINK. As there are not many “Interest Expenses”

observations available in the AMADEUS database, calculations for the KINK

variable had to be generalized. If “Interest Expenses” for a firm does not exist (the

item is blank), it is assumed that it is equal to zero. With zero interest expensess,

KINK cannot be calculated, hence in these observations KINK is calculated as EBIT

divided by 1 (indicating very low interests). However, KINK values are limited in the

range from 0.0 to 8.0. A ceiling of 8.0 and a bottom of 0.0 are constructed so that all

values lower than zero become zero, and all values higher then eight become eight.



Similar, equation [6] shows the way STANDARDIZED KINK is constructed. Here,

bottom values of zero are constructed which apply on all values lower then zero.

Therefore, all negative values become equal to zero.

The last variable in the target adjustment model is LAGGED LEVERAGE. By

computing this variable, one (the first) year of observations for every country is lost.

Therefore, the research will be based on the years 1999-2000 for Ireland, the

Netherlands, Belgium, Poland and Hungary. Thus, Ukraine’s sample will include

years 2000-2005. As will be explained in the next section, constructing the LAGGED

LEVERAGE and Instrumental Variables (leverage lagged two periods), resulted in

the loss of two years of observations. Therefore, the final sample consists of years

2000-2005 for Ireland, the Netherlands, Belgium, Poland and Hungary. The data for

Ukraine includes years 2001-2005. The following table represents the number of

observations in the studied period.

Table 4 – Number of firm/year observations per country of analysis, in the period 2000 - 2005


15.006 26.214 33.102 17.814 10.644 19.435

As it was mentioned before, not every company exists in every year that this study

covers and consequently the data consists of some gaps. A Macro program in Visual

Basic, executed in the spreadsheet program Excel, is used to remove the firms with no

data available for the specific years of the study. Especially in Ireland, the

Netherlands and Poland the data was incomplete and the biggest number of firm/year

observations was removed.

Table 5 – Number of observations per country of analysis after removing incomplete data


1.798 6.532 30.486 7.305 6.820 15.835

After removing the incomplete data, the most extreme half percent of observations on

either side of the distribution were also removed. This served to remove outliers and

the most skewed and misreported data (Frank and Goyal, 2003).

The following table represents final firm/year observations in years 2000-2005.



Table 6 – Final number of firm/year observations used per country and per year of analysis

Year Ireland Netherlands Belgium Poland Hungary Ukraine Total

2005 339 1.439 5.466 1.978 1.209 3.443 13.874

2004 339 1.694 5.476 1.899 1.206 3.576 14.250

2003 355 1.399 5.264 1.464 1.122 3.628 13.232

2002 330 1.067 4.936 868 1.015 2.965 11.199

2001 237 456 4.600 544 1.028 2.066 8.931

2000 120 417 4.442 462 1.172 - 6.608

Total 1.780 6.467 30.184 7.233 6.752 15.678 68.094

After computing all the variables, descriptive statistics were calculated for the

Western and Eastern European samples. The descriptive statistics of the variables for

both regions are presented in Table 7. The descriptive statistics for the individual

countries are presented in Appendix IV. The descriptive statistics for Eastern and

Western Europe are discussed shortly.

Table 7 - Descriptive Statistics for Western and Eastern Europe

Observations Minimum Maximum Mean

Std.

Deviation

Total Leverage 38431 0,0128 3,1678 0,6853 0,2504

Short Term Leverage 38431 0,0009 2,9584 0,5385 0,2384

Long Term Leverage 38431 0,0000 1,7837 0,1468 0,1704

Adjusted Total Leverage 38431 0,0010 2,9527 0,4528 0,2470

KINK 38431 0,0000 8,0000 1,8381 2,1057

STANDARDIZED KINK 38431 0,0000 48,9939 1,6910 2,2752

EFFECTIVE TAX RATE 38431 -19,1736 22,4570 0,2657 0,8998

TANGIBILITY 38431 0,0000 0,9998 0,2417 0,2052

SIZE 38431 2,8904 13,1760 8,4015 1,0498

Z-SCORE 38431 -27,7410 83,2823 3,8174 3,2394

OPERATING RISK 38431 0,5774 14251,0936 372,4230 590,7144

PROFITABILITY 38431 -1,8270 1,2292 0,0551 0,1206

GROWTH 38431 -0,9989 21,8980 0,0765 0,3521

Western European Sample - Descriptive Statistics

Observations Minimum Maximum Mean Std. Deviation

Total Leverage 29 663 -0,5779 4,4148 0,4897 0,3397

Short Term Leverage 29 663 0,0000 4,4148 0,4203 0,3177

Long Term Leverage 29 663 -0,5463 2,3325 0,0694 0,1410

Adjusted Total Leverage 29 663 -0,3173 2,4972 0,3357 0,2803

KINK 29 663 0,0000 8,0000 4,1430 3,6473

STANDARDIZED KINK 29 663 0,0000 31,6635 0,8898 2,6349

EFFECTIVE TAX RATE 29 663 -17,0000 20,8846 1,3710 1,9826

TANGIBILITY 29 663 0,0000 2,6534 0,4673 0,2670

SIZE 29 663 1,1598 12,4474 7,0631 1,3190

Z-SCORE 29 663 -46,7200 298,6443 7,6553 15,7995

OPERATING RISK 29 663 0,0108 583 951,5484 268,5508 6 595,4900

PROFITABILITY 29 663 -1,8887 3,6075 0,0327 0,1566

GROWTH 29 663 -0,9977 8,9866 0,1212 0,4955

Eastern European Sample - Descriptive Statistics



In respect to the four types of LEVERAGE it was observed that the mean was lower

and the standard deviation was higher in Eastern than in the Western European

sample. It can be observed that in spite of removing 0.5 percent of outliers on both

sides of the distribution, both samples still included misreported data. In the Eastern

European sample three types of leverage indicate negative minimum values and in

both samples all maximum values were higher then one. Total Leverage was on

average 68 percent of Total Assets in Western Europe and 49 percent in Eastern

Europe. Short term liabilities made up the biggest extent of Total Leverage in both

regions. In Western Europe, short term liabilities accounted for an average of 79

percent of Total Leverage. In Eastern Europe, short term liabilities accounted for an

average of 86 percent of Total Leverage. Similarly, long term liabilities averaged 21

percent of Total Leverage in Western Europe and only 14 percent of Total Leverage

in Eastern Europe. It seems that short term financing is indeed more important in

SMEs, in Eastern as well as Western Europe. This observation is perfectly in line with

the European Commission findings in Table 1.

A comparison of Total Leverage with Adjusted Total Leverage, indicates that in both

samples Accounts Payable contributed a big portion to leverage. In the Western as

well as in the Eastern European samples, approximately 34 percent of total liabilities

consisted of Accounts Payable on average.

KINK is on average much lower in Western European SMEs: 1.84 compared to 4.14

for Eastern European SMEs. The standard deviation of KINK is also higher in Eastern

Europe. Hence, STANDARDIZED KINK is higher in Western Europe then in Eastern

Europe.

For the third indicator of TAX, the EFFECTIVE TAX RATE, it seems that data is not

reliable. For both regions, the outliers are very extreme, even after removing the 0.5

percent, the misreported data does not seem to be deleted well. This biases the

standard deviation for the EFFECTIVE TAX RATE in both regions and also the mean

in the Eastern European sample.

TANGIBILITY is on average almost 50 higher in Eastern Europe than in Western

Europe and amounts to 47 percent and 24 percent respectively. Descriptive statistics

indicate that Eastern European SMEs were on average smaller then Western European

SMEs. Altman’s Z-SCORE confirms that, on average, Eastern European SMEs have



much higher probability of financial distress than Western European SMEs. When

looking at the mean and standard deviation of OPERATING RISK, one can notice

extreme values in both samples. It has been observed that in the first years of sample

examination, data histories are scarce, which biased the calculations of OPERATING

RISK, by making it more extreme. This data problem is much bigger in the Eastern

European sample than in the Western European sample.

PROFITABILITY indicates that Western European SMEs were slightly more

profitable than Eastern European SMEs. GROWTH, however, is much higher in

Eastern Europe than in Western Europe and equals to 12.12 percent and 7.65 percent

respectively.

3.4 Methodology: Two Stage Least Squares Regression Method

As described, Model [4] will be tested by regression analyses. Normal multiple

Ordinary Least Squares (OLS) regression would be the preferred regression method.

This is a statistical tool that calculates the combined explanatory power of the

multiple independent variables on the dependent variable. However, when applying

OLS regressions, some problems might be included in the regression calculation,

which would severely bias the regression output.

The variable LAGGED LEVERAGE may cause such problems. The tested model

intends to explain Leverage in period t. There might be more variables than tested

which have explanatory power on Leverage as well. Such “omitted variables” are

likely to be correlated to LAGGED LEVERAGE, since this variable is in fact the

dependent variable “Leverage” but from the period before (t-1). Therefore, it is

expected that the residuals (ε ) of the tested model are correlated with the independent

variable (LAGGED LEVERAGE).

This would indicate that LAGGED LEVERAGE is endogenous. If OLS regressions

are estimated on the model that includes an endogenous variable, the results would be

biased and inconsistent for all independent variables. Several Hausman tests confirm

that LAGGED LEVERAGE is indeed endogenous (see Appendix V). Therefore, an

Instrumental Variable (IV) needs to be calculated. This variable does not appear in the

equation, is uncorrelated with the residuals (ε ), and is (partially) correlated with the



endogenous variable – LAGGED LEVERAGE (Introductory Econometrics, A

Modern Approach, 2006). This will remove the bias and inconsistency of the model.

A proper instrument for LAGGED LEVERAGE is Leverage lagged two periods (t-2).

In order to adjust the independent variable LAGGED LEVERAGE by its instrument,

Two Stage Least Squares (2SLS) regression will be estimated in order to test the

target adjustment model.

The first stage in 2SLS regression estimates “proper” values for LAGGED

LEVERAGE, by regressing all independent variables and the instrument on

LAGGED LEVERAGE, and taking the estimated value of this regression. This

removes the correlation between LAGGED LEVERAGE and the residual terms. The

second stage in 2SLS estimates the original regression model, but replaces the original

values of LAGGED LEVERAGE by the estimated values for LAGGED LEVERAGE

of the first stage.



Chapter 4

Regression Results of Eastern Europe vs. Western Europe

4.1 Regression Results of the Target Adjustment Model

In this section, an analysis of the results is presented, as examined in Table 8. The

analysis concentrates on the differences that are observed between the Western and

Eastern European SME capital structures, and the possible determinants of these

differences. Table 8 presents the results of the regressions with Eastern Europe as

base group. Since the approach of calculating the differences between Eastern and

Western Europe is estimated by shift and slope dummy variables, the presented

dummies refer to the Western European sample. Therefore, Table 8 shows the

significance of the individual coefficients for the Eastern European sample only. From

this table no inferences can be made on the significance of the coefficients for the

Western European sample. Consequently, the table with the Western European

sample as a base group can be found in Appendix VII. In the following sections both

tables are used for interpretation of the findings about differences between SMEs

from Eastern and Western Europe.

Regression Assumptions: Autocorrelation, heteroskedasticity & multicollinearity

The regressions were controlled for serial (auto)correlation, heteroskedasticity and

multicollinearity. Serial correlation was tested by calculating Durbin-Watson

statistics, which approach the value 2.0, indicating no first order correlation of the

errors to exist over time. Since the data was sampled as a pooled cross section rather

then panel data, autocorrelation should not be an issue. Even though multiple

firm/year observations are applied, the data is not ordered over time but over different

companies. If the statistical program indicates autocorrelation in the data, this would

rather be a coincidental relationship between firms than a relationship within data of

one firm over time. Heteroskedasticity was tested by LM tests. The presence of

heteroskedasticity indicates that error terms are not constant, that is, the distribution of

the variances of the residuals is dependant on one or more of the independent

variables. This might influence the significance of the coefficients. In order to adjust

for heteroskedasticity, the standard errors were replaced by White Heteroskedasticity-

Robust Standard Errors. Following the approach of Mansfield and Helms (1982),



tolerance levels were calculated to test for multicollinearity. All tolerance levels are

above the critical value of 0.1, thus no multicollinearity is present, indicating that the

independent variables are not highly correlated with each other. The tolerance level is

calculated as 1 – R-squared.

Explanatory Notes to Table 8:

White-heteroskedasticity adjusted 2SLS regressions, conducted in E-VIEWS, for years 2000 to 2005 on the complete

sample of all observations from Eastern Europe and Western Europe. In order to compare both regions, Eastern

Europe is the base group. Twelve tests on the following four different dependent variables were done: Total

Leverage, Short Term Leverage, Long Term Leverage and Adjusted Total Leverage.

Total Leverage is calculated as: Total Liabilities / Total Assets, Short Term Leverage is calculated as Current

Liabilities / Total Assets, Long Term Leverage is calculated as Non-Current Liabilities / Total Assets and Adjusted

Total Leverage is calculated as (Total Liabilities – Payables) / Total Assets.

Independent variables are: KINK, STANDARDIZED KINK, EFFECTIVE TAX RATE, TANGIBILITY, SIZE, Z-

SCORE, OPERATING RISK, PROFITABILITY, GROWTH, LAGGED LEVERAGE, and shift DUMMY and slope

DUMMY variables for Western Europe and shift dummy variables for Industries.

KINK is calculated as EBIT in year t / Interest Expenses in year t. STANDARDIZED KINK is computed as (KINK

in year t x Interest Expenses in year t) / Standard deviation of KINK over all years. The EFFECTIVE TAX RATE is

calculated as the observed Tax Expenses in year t / Earnings Before Taxes in year t. Note that only one tax variable is

used at a time.

SIZE is proxied by the natural logarithm of Total Assets in year t. Z-SCORE is Altman’s Z-SCORE for General use,

which is calculated for every year t as:

sLiabilitieTotal

EquityTotal

AssetsTotal

EBIT

AssetsTotalAssetsTotal

CapitalWorking Earnings Retained05,172,626,356,6 +++ .

OPERATING RISK is calculated as the standard deviation of Earnings Before Taxes over all years observed until

year t. PROFITABILITY is calculated as Earnings Before Taxes / Total Assets. GROWTH is calculated as the

percentage change of Total Assets in year t-1 to year t. LAGGED LEVERAGE is calculated as the respective

leverage ratio (the dependent variable of the model), in year t-1. Note that the correlation of LAGGED LEVERAGE

with the residuals of the model is removed by including an Instrumental Variable; leverage lagged two periods (t-2).

The dummy variables are qualitative variables with the value 1 if the observation belongs to the group it represents.

Therefore, the shift dummy Western Europe has a value of 1 for a firm from a company in one of the following

countries: Ireland, the Netherlands or Belgium. It has the value 0 if the observation is from a company in any of the

other countries. Similarly, the slope dummy variables are computed by multiplying the shift dummy by the respective

independent variables.

The Target Adjustment Coefficient is derived from model [4] and is computed as: 1 – the coefficient of LAGGED

LEVERAGE.

R² indicates the coefficient of determination, or the explanatory power of the model as a whole. DW statistics show

whether the regression results are affected by autocorrelation. A DW statistic close to 2.0 indicates no autocorrelation.

The number of variables with a Tolerance level smaller than 0,1 indicates whether multicollinearity is apparent in the

regression model. F statistics and their significance show whether a linear relationship between the dependent

variable and any of the independent variables exists, depending what group of independent variables is included in

the F-test.



Table 8 – White-heteroskedasticity adjusted 2SLS results of the Target Adjustment Model, comparing Eastern Europe vs. Western Europe. Eastern Europe serves as the base group.

Constant 0,1764 0,1760 0,1778 0,1791 0,1811 0,1833 -0,1757 -0,1699 -0,1650 -0,0697 -0,0547 -0,0495

[25,4781]*** [25,9937]*** [26,0388]*** [26,1512]*** [26,8709]*** [26,946]*** [-27,4435]*** [-27,0463]*** [-26,2311]*** [-8,1195]*** [-6,4892]*** [-5,8228]***

Dummy Service Ind. -0,0033 -0,0039 -0,0032 -0,0016 -0,0023 -0,0018 0,0026 0,0037 0,0041 0,0060 0,0067 0,0075

[-2,8067]*** [-3,4009]*** [-2,8153]*** [-1,1441] [-1,7124]* [-1,2975] [1,8155]* [2,6434]*** [2,9092]*** [3,5509]*** [3,9834]*** [4,4473]***

Dummy Manufacturing Ind. -0,0012 -0,0007 -0,0013 0,0024 0,0026 0,0021 -0,0098 -0,0078 -0,0085 -0,0086 -0,0068 -0,0078

[-0,9403] [-0,5462] [-0,9744] [1,6161] [1,7185]* [1,3859] [-6,3604]*** [-5,0965]*** [-5,5505]*** [-4,6061]*** [-3,6645]*** [-4,2261]***

Dummy Western Europe -0,0268 -0,0173 -0,0271 -0,0267 -0,0166 -0,0245 0,2201 0,1880 0,1799 0,2110 0,1767 0,1645

[-2,7985]*** [-1,8712]* [-2,9479]*** [-2,7303]*** [-1,7283]* [-2,5691]** [24,3914]*** [21,0196]*** [20,2342]*** [18,2371]*** [15,6246]*** [14,6143]***

KINK 0,0004 -- -- 0,0010 -- -- 0,0030 -- -- 0,0067 -- --

[0,5969] -- -- [1,7449]* -- -- [9,000]*** -- -- [10,8390]*** -- --

Dummy West KINK -0,0003 -- -- -0,0004 -- -- -0,0061 -- -- -0,0093 -- --

[-0,3791] -- -- [-0,5794] -- -- [-15,1531]*** -- -- [-13,1750]*** -- --

STANDARDIZED KINK -- 0,0035 -- -- 0,0034 -- -- 0,0052 -- -- 0,0078 --

-- [4,7947]*** -- -- [5,0222]*** -- -- [9,0733]*** -- -- [9,0777]*** --

Dummy West STAND. KINK -- 0,0016 -- -- 0,0004 -- -- -0,0030 -- -- -0,0033 --

-- [1,9324]* -- -- [0,4975] -- -- [-4,5453]*** -- -- [-3,4035]*** --

EFFECTIVE TAX RATE -- -- 0,0002 -- -- 0,0001 -- -- -0,0004 -- -- 0,0004

-- -- [0,1832] -- -- [0,0644] -- -- [-0,3582] -- -- [0,2148]

Dummy West EFF. TAX RATE -- -- 0,0007 -- -- 0,0009 -- -- -0,0009 -- -- -0,0002

-- -- [0,5812] -- -- [0,8029] -- -- [-0,7573] -- -- [-0,1315]

TANGIBILITY -0,1063 -0,1051 -0,1070 -0,1209 -0,1207 -0,1228 0,0778 0,0761 0,0728 0,0035 -0,0013 -0,0061

[-24,3561]*** [-23,1416]*** [-22,8834]*** [-26,8164]*** [-26,0386]*** [-25,8843]*** [20,3288]*** [19,9903]*** [19,3209]*** [0,6481] [-0,2341] [-1,0995]

Dummy West TANGIBILITY 0,0511 0,0503 0,0518 0,0105 0,0103 0,0120 0,1897 0,1930 0,1968 0,0317 0,0375 0,0420

[10,4484]*** [10,0087]*** [10,016]*** [2,1223]** [2,0443]** [2,3339]** [31,1741]*** [31,9173]*** [32,7152]*** [4,7066]*** [5,5578]*** [6,1982]***

SIZE -0,0035 -0,0039 -0,0034 -0,0050 -0,0053 -0,0048 0,0200 0,0197 0,0203 0,0142 0,0141 0,0150

[-4,502]*** [-5,0235]*** [-4,4182]*** [-6,4792]*** [-6,8776]*** [-6,3004]*** [26,1963]*** [25,7197]*** [26,6610]*** [13,4011]*** [13,2162]*** [14,2375]***

Dummy West SIZE 0,0048 0,0033 0,0046 0,0042 0,0027 0,0036 -0,0149 -0,0134 -0,0131 -0,0148 -0,0147 -0,0141

[4,2092]*** [2,9133]*** [4,1789]*** [3,599]*** [2,3144]** [3,1621]*** [-13,8030]*** [-12,2400]*** [-12,1605]*** [-10,7332]*** [-10,5293]*** [-10,2662]***

Z-SCORE -0,0016 -0,0016 -0,0016 -0,0013 -0,0013 -0,0013 -0,0004 -0,0004 -0,0004 -0,0005 -0,0004 -0,0005

[-16,9479]*** [-16,8574]*** [-16,9656]*** [-16,8302]*** [-16,738]*** [-16,8514]*** [-11,4535]*** [-11,0264]*** [-11,7109]*** [-11,6279]*** [-10,6667]*** [-11,3253]***

Dummy West Z-SCORE -0,0094 -0,0096 -0,0094 -0,0081 -0,0082 -0,0081 -0,0080 -0,0083 -0,0082 -0,0042 -0,0045 -0,0043

[-18,3043]*** [-18,4425]*** [-18,3735]*** [-18,9883]*** [-19,1558]*** [-19,1321]*** [-20,3974]*** [-20,6303]*** [-20,6985]*** [-13,1640]*** [-14,2334]*** [-13,7905]***

OPERATING RISK 0,0000 0,0000 0,0000 0,0000 0,0000 0,0000 0,0000 0,0000 0,0000 0,0000 0,0000 0,0000

[1,5809] [1,8811]* [1,5971] [1,6192] [1,9716]** [1,658]* [-2,6077]*** [-0,4446] [-2,3561]** [2,8404]*** [3,9614]*** [3,2000]***

Dummy West OPERATING RISK 0,0000 0,0000 0,0000 0,0000 0,0000 0,0000 0,0000 0,0000 0,0000 0,0000 0,0000 0,0000

[2,6448]*** [4,3138]*** [2,6831]*** [1,3979] [2,5729]*** [1,4503] [4,7341]*** [5,2682]*** [4,5977]*** [7,5263]*** [8,5354]*** [7,4211]***

PROFITABILITY -0,4529 -0,4648 -0,4481 -0,4135 -0,4167 -0,4002 0,0049 0,0181 0,0429 -0,2754 -0,2264 -0,1884

[-16,1421]*** [-19,5972]*** [-21,7877]*** [-16,1576]*** [-19,3173]*** [-21,3084]*** [0,4954] [2,0091]** [5,2310]*** [-11,5569]*** [-11,5288]*** [-11,0812]***

Dummy West PROFITABILITY 0,1836 0,1592 0,1801 0,2142 0,1989 0,2105 -0,0263 -0,1046 -0,1124 0,0534 -0,0694 -0,0739

[5,7799]*** [5,7981]*** [7,5041]*** [7,244]*** [7,7705]*** [9,409]*** [-1,9693]** [-8,2241]*** [-9,8155]*** [1,8805]* [-2,8778]*** [-3,5360]***

GROWTH 0,0442 0,0439 0,0443 0,0210 0,0211 0,0215 0,0171 0,0177 0,0183 0,0290 0,0307 0,0316

[11,8027]*** [11,6673]*** [11,7135]*** [5,0359]*** [5,0296]*** [5,1102]*** [5,8751]*** [6,1064]*** [6,3106]*** [6,1577]*** [6,4684]*** [6,6418]***

Dummy West GROWTH -0,0030 -0,0033 -0,0032 0,0065 0,0064 0,0064 -0,0108 -0,0130 -0,0134 -0,0111 -0,0143 -0,0148

[-0,2431] [-0,2636] [-0,2549] [0,5792] [0,5712] [0,5622] [-2,7944]*** [-3,4372]*** [-3,5414]*** [-1,2536] [-1,6421] [-1,6903]*

LAGGED LEVERAGE 0,8544 0,8540 0,8542 0,8502 0,8499 0,8497 0,2716 0,2712 0,2690 0,7501 0,7536 0,7532

[177,5463]*** [177,4293]*** [177,2598]*** [170,3187]*** [170,2211]*** [169,941]*** [46,8197]*** [47,1110]*** [46,7278]*** [135,2920]*** [137,9784]*** [137,3938]***

Target Adjustment coefficient 0,1456 0,1460 0,1458 0,1498 0,1501 0,1503 0,7284 0,7288 0,7310 0,2499 0,2464 0,2468

R-squared 0,8697 0,8703 0,8697 0,8207 0,8212 0,8207 0,3800 0,3791 0,3774 0,6325 0,6313 0,6296

Adjusted R-squared 0,8696 0,8703 0,8696 0,8207 0,8212 0,8207 0,3799 0,3789 0,3773 0,6324 0,6312 0,6295

DW Statistic 1,9717 1,9719 1,9717 1,9813 1,9815 1,9813 1,8632 1,8578 1,8560 1,8717 1,8609 1,8572

Tolerance 0,1303 0,1297 0,1303 0,1793 0,1788 0,1793 0,6200 0,6209 0,6226 0,3675 0,3687 0,3704

F-test West slope 78,9064*** 79,2508*** 75,4836*** 77,8963*** 75,3206*** 71,653*** 524,2658*** 463,1743*** 421,8537*** 112,3924*** 72,0594*** 57,8975***

F-test West shift + slope 69,0696*** 69,5312*** 66,507*** 73,7434*** 72,842*** 70,5181*** 1007,263*** 1001,141*** 986,2272*** 159,7741*** 145,8708*** 140,8751***

F-test Industry Effect 4,8874*** 8,8173*** 4,8375*** 7,4127*** 11,2119*** 6,8845*** 59,9305*** 51,2724*** 61,7237*** 53,5882*** 45,8903*** 59,2959***

TOTAL LEVERAGE SHORT TERM LEVERAGE LONG TERM LEVERAGE ADJUSTED TOTAL LEVERAGE

Total Liabilities / Total Assets Current Liabilities / Total Assets Non-Current Liabilities / Total Assets (Total Liabilities - Accounts Payable) / Total Assets

*, ** , *** = significant on the 0.1, 0.05 and 0.01 level of significance, respectively



The constant terms are highly significant in every regression, meaning that it takes

away variance which is not explained by the independent variables that are included.

From Table 8 and Appendix VII, it can be observed that the R-squared and adjusted

R-squared of all the regressions are very high, especially for the regressions with

Total Leverage and Short Term Leverage as dependent variables. R-squared is the

coefficient of determination, indicating the strength of the model. It is a calculation of

the ratio of the explained variation (called SSE, or the explained Sum of Squares) over

the total variation (called SST, or Total Sum of Squares). This indicates how good the

model predicts the observations. In this research study, the R-squared is the

coefficient that tests for the strength of the target adjustment model. An R-squared of

1 would mean that there is no variation that is not explained by the model. Therefore,

the observed R-squares of approximately 0.87 for the model on Total Leverage and

0.821 for the model on Short Term Leverage seem to indicate that the models explain

almost all of the variation in the data. The observed R-squares of approximately 0.35

for the model on Long Term Leverage and 0.53 for the model on Adjusted Total

Leverage are somewhat weaker, but are still considerably strong. These findings seem

to indicate that the target adjustment model explains capital structure determination of

SMEs in Eastern Europe and Western Europe very well. This seems to be a strong

input for validity of the model: since the model works well, the Tradeoff Theory

seems to hold, and therefore seems to be a valid underlying theoretical framework for

the research study.

Regretfully, one must be very careful when interpreting these relatively high R-

squares. Literature proves that R-squared is not a very useful indicator of the

goodness-of-fit in a Two-Stage Least Squares (2SLS) regression with one or more

instrumental variables. Since the 2SLS regression adjusts the variance of the

endogenous variable so that it does not correlate to the residuals of the model, the R-

squared as the ratio of SSE to SST is not useful, once the SSE has been adjusted.

Therefore, in 2SLS regressions, the R-squared is often left out of consideration, since

it is not a reliable estimator of the strength of the model (Woolridge, 2006). The next

reason for taking R-squares as an unreliable indicator is that in all regressions, the R-

squares is mainly so strong because of LAGGED LEVERAGE. This is shown by its

extreme high t-statistics. If this variable is not included in the regression, the R-

squares drop significantly. Thus, R-squared is not a reliable measure of the strength in



2SLS models, but rather an indication of the relationship between leverage and

LAGGED LEVERAGE.

Due to the fact that 2SLS regression methods have been applied, it is not possible to

test directly for the strength and applicability of the target adjustment model, hence,

the Tradeoff Theory. Literature suggests another way of indirectly testing whether the

target adjustment model holds. It can be observed that in all models, the target

adjustment coefficients are higher than zero, indicating that firms adjust toward target

debt ratios (Taggart, 1977; Jalilvand and Harris, 1984). The target adjustment

coefficients range from 15 percent in the models of Total Leverage and Short Term

Leverage, and 25 percent in the models og Adjusted Total Leverage, to as high as 73

percent in the models of Long Term Leverage. This indicates that SMEs from the two

regions seem to adjust much faster towards their long term target debt ratios.

The target adjustment coefficients imply that the Tradeoff Theory works. Another

way of testing whether the Tradeoff Theory is indeed a solid model for explaining

capital structure determination, is by analyzing the individual relations among

determinant variables and leverage. An in-depth analysis of these relations will be

presented in the following sections. The findings are concluded in the fifth section, by

controlling for the four hypothesis.



4.2 Difference in Leverage between Eastern Europe and Western Europe

As it was indicated with the descriptive statistics in the data section of this paper, the

averages of the four proxies of leverage are lower in Eastern SMEs than in Western

SMEs. However, these differences are only an indication and do not show the real

significance of these differences.

In order to test the significant difference on the four proxies for leverage between the

Eastern and Western European SMEs, one should look at the constant term and the

slope of the regression model for both regions. The constant terms presented are the

constants for the model of Eastern Europe. These, in relation to the shift dummies for

Western Europe, indicate the constant terms for Western Europe. The shift dummies

indicate the correction to the constant for Western European SMEs, and, therefore,

these give an overview of the difference of constants between the Eastern and

Western European samples studied.

It can be observed that for the model of Total Leverage, the constant is higher in

Eastern than in Western Europe, since the shift dummy for Western Europe is

negative and significant. The same results are observed for the model of Short Term

Leverage. The shift dummies for Western Europe on the models of Long Term

Leverage and Adjusted Total Leverage are positive and significant. These findings

appear to indicate that if all variables have the value of zero, Total Leverage and Short

Term Leverage are significantly higher in Eastern Europe, and Long Term Leverage

and Adjusted Total Leverage are significantly higher in Western Europe. These

differences explain only a part of the differences between leverage in Eastern and

Western Europe. The condition that all other variables have a value of zero is very

unlikely, and, therefore, one should also look at the differences in the slopes of the

models.

Since the slopes exist of all independent variables together, partial F-tests need to be

conducted in order to calculate the cumulative difference of all slope dummy

variables of Western Europe compared to the full model, i.e., Western and Eastern

Europe together. The partial F-test statistics are shown on the bottom of Table 8.

These are measures of the significant difference between the original model and a part

of this model, consisting of multiple variables.



The partial F-statistics are calculated as:

)]1(/[

/)()]1(,[

+−

−=+−

knSSE

rSSESSEF

U

UR

knr [19]

Here, the significance of the F-statistic is calculated on a F-distribution with r and

)1( +− kn degrees of freedom. r is the number of variables dropped (the restricted

part) from the unrestricted model, in this case, all slope dummies for the West. n is

the total number of observations in the model. k is the total number of independent

variables in the unrestricted (full) model. RSSE is the Sum of Squares for Error of the

Restricted model (the unrestricted model minus the r variables). USSE is the Sum of

Squares for Error of the Unrestricted (full) model.

When looking at the F-test statistics for slope dummies, one can see that, indeed, the

linear slopes of the regressions for Western Europe are significantly different from the

slopes of Eastern Europe, for all models (all four leverages). Similarly, F-tests were

computed to help explain the differences of the slope dummies and shift dummy

combined. It is found that the four leverages in Western and Eastern Europe are

highly different from each other indeed. Especially the Long Term leverage is highly

significantly different between the two sample regions.

One limitation of these F-tests is that it cannot be observed from the F-statistic which

region, Eastern or Western Europe, has a higher or lower leverage than the other. The

F-statistics are always positive, and, therefore, the same, whether Eastern Europe is

taken as base group or Western Europe is taken as base group, (see Table 8 and

Appendix VII).

In order to control for the F-tests and to see how the groups are different from each

other the distributions of data of the four proxies of leverage were compared to each

other. This was done by comparing the mean of the distributions of the Eastern

European leverage ratios with the total distribution of the Western European leverage

ratios, and vice versa. This was repeated for each proxy of leverage. T-tests were

calculated to estimate the probability that the leverage proxies of the West fit in the

distribution of leverage proxies of the East. Based on a 99 percent confidence interval,

each data distribution of the four leverages in the East were compared to the mean of

each data distribution of the four leverages in the West. In Appendix VIII the



outcomes of these T-tests are presented. The positive and significant difference

between all four proxies of leverage for the Western European sample compared to

the Eastern European sample, is confirmed. This indicates that, indeed, the findings of

the F-tests, as described above, are supported.

4.3 Differences in Proxies for Taxes between Eastern Europe and Western

Europe

Three tax proxies are used: KINK, STANDARDIZED KINK and EFFECTIVE TAX

RATE. In order to avoid multicollinearity effects, the three tax variables are used one

at a time. It can be observed that the estimated coefficients and significance levels of

other independent variables do not change much when different tax variable are used.

Also R-squares remain very similar.

Expectations for the Tax Effect

As stated in the hypotheses, it is expected that tax payments should be positively

related to leverage, in both Eastern Europe as well as Western Europe. However, this

relation is expected to be stronger in Western Europe. This is because in Eastern

Europe lower tax rates are observed, which would incline Eastern European managers

less to take on credit, compared to Western European managers. Lower amounts of

tax need to be shielded in Eastern Europe; hence, lower debt levels might be expected.

Besides, lower access to credits and stronger monitoring power by banks might result

in less willingness from banks to lend capital to Eastern European SMEs. Since

reliability on one bank is very high for an Eastern European firm, there are not many

different sources of credit for this firm. Thus, such firms might not have the luxury to

take on debt and optimize their capital structure.

This will mean that for both Eastern and Western Europe, the relation between the tax

variables and leverage are expected to be positive, but this relationship is likely to be

stronger in Western Europe as compared to Eastern Europe. The expected relationship

for the different proxies of leverage follows below.

The tax effect will only work on interest bearing liabilities such as loans, since these

are the liabilities that can shield taxes. The first and second proxy for leverage, Total



Leverage and Short Term Leverage, contain Accounts Payable, which do not bear

interest. As stated, Total Leverage in both Eastern and in Western Europe consist of

approximately one third of accounts payable. Especially for Short Term Leverage,

accounts payable make up a substantial amount: 43 percent in Western Europe and 37

percent in Eastern Europe, respectively. Besides, it is likely that total liabilities and

short term liabilities contain other non-interest bearing liabilities, such as accruals,

salaries payable and taxes payable etc. Obviously a large extent of Total Leverage and

Short Term Leverage is based on non-interest bearing liabilities.

Long Term Leverage is more likely to contain more interest bearing liabilities since

larger loans are expected to have timeframes of longer than one year. Mortgages are

just one example of such long term loan liability. The last proxy for leverage,

Adjusted Total Leverage, has been adjusted for accounts payable, and hence, contains

less non-interest bearing liabilities. Accordingly, it can be expected that the tax effect,

the relationship between the proxies for taxes and the proxies for leverage, will be

weaker for the proxies Total Leverage and Short Term Leverage, and stronger for the

proxies: Long Term Leverage and Adjusted Total Leverage.

Findings for KINK:

The first proxy for tax, KINK, is expected to be negatively related to leverage, since

the aggressive debt users shield more taxes by taking on extra debt and KINK

approaches 1.0, or for more aggressive debt users even lower than 1.0. Very

conservative users of debt rather take on less debt, even though there are marginal tax

benefits to be gained from taking on additional debt. Their KINK rates will be higher

than 1.0, and may go as high as 8.0. Hence, a firm with a low KINK is expected to be

aggressively shielding taxes by taking on a high degree of credit. Similarly, a firm

with a high KINK is considered to be a conservative tax shielder by having a

relatively lower leverage ratio.

Since the tax effect is expected to be stronger in Western Europe than in Eastern

Europe, a more negative and significantly different relationship between KINK and

leverage (especially Long Term Leverage and Adjusted Total Leverage) is expected

for Western European SMEs when compared to Eastern European SMEs. In Eastern

Europe, either a weak or no relation between KINK and leverage is expected to be

found.



Further evidence from Table 8 and Appendix VII indicates that, indeed, the KINK

variable does not have much, if any, significance on the first two proxies of leverage,

i.e., Total Leverage and Short Term Leverage. For these two proxies of leverage, the

difference between Western and Eastern Europe seems to indicate a somewhat more

negative relationship, but the slope dummies that calculated this difference are far

from significant.

For Long Term Leverage, and to a higher extend Adjusted Total Leverage, one can

see that KINK is highly significant in Eastern Europe, regardless of the low amounts

of long term debt used in this region. However, the relationship is positive. In Western

Europe, one can see the opposite effect between KINK and Long Term Leverage and

Adjusted Total Leverage. In Western Europe, KINK is also highly significant, but

negative. The dummies indicate that the relationship between KINK and these two

proxies of leverage is significantly different between Eastern and Western Europe.

The outcomes for the Western SMEs are partly as expected. However, for the Eastern

SMEs the relationship is contrary to all expectations, and might seem awkward. This

outcome indicates that in Eastern Europe conservative debt users take on more debt

than more aggressive debt users. This finding is illogical and clearly does not make

sense. However, the relationship is highly significant, which means that statistically

this relationship is beyond all doubt. Apparently, it means that the variable KINK

captures effects of variance that it was not intended to test for. A possible explanation

might be found in Rajan and Zingales (1995). They tested several proxies of leverage,

among others the ‘flow’ measure of leverage which they called the interest coverage

ratio. The proxy was calculated as Earnings Before Interest and Taxes, divided by

Interest Expenses. This is the exact same calculation that was used to calculate KINK.

If the interest coverage ratio is indeed a good proxy for leverage, it might seem

obvious that one proxy of leverage is positively and significantly related to another

proxy of leverage, since they both capture part of the same variance.

Apparently KINK works better as a proxy for taxes in Western Europe, although this

relationship is not very strong. KINK and Short Term Leverage are not related,

because of the high amount of non-interest bearing liabilities in this measure of

leverage. The difference between Western and Eastern Europe is partly as expected,



since Eastern European SMEs not always seem to have the luxury to shield taxes,

simply due to a lack of available long term credits.

If indeed KINK better explains Rajan and Zingales’ (1995) ‘Interest Coverage Ratio’,

than the question might be posed: why does it not do so for Western European SMEs?

Findings for STANDARDIZED KINK:

The STANDARDIZED KINK is the second proxy for the tax effect and, as stated,

also predicts a firm’s aggressiveness or conservativeness in using interest payments as

a tax shield. A positive relation between STANDARDIZED KINK and leverage is

expected, for Western SMEs as well as Eastern SMEs. This, because a firm with low

earnings volatility can more easily be an aggressive debt user, without bearing the

downside risks of having a high leverage ratio and higher interest payments in a

consequent period. Similar to KINK, it is expected that STANDARDIZED KINK will

be more positively related to Long Term Leverage and Adjusted Total Leverage.

STANDARDIZED KINK is not expected to have any effect on non-interest bearing

liabilities, which are more apparent in the first two proxies for leverage. Since KINK

was expected to be lower in Western Europe than in Eastern Europe,

STANDARDIZED KINK is expected to be higher in Western Europe than in Eastern

Europe. That is, it is expected that the risk of volatility of earnings will be smaller in

Western Europe than in Eastern Europe, translating into a higher significant

relationship between STANDARDIZED KINK and leverage.

In Table 8 and Appendix VII one can observe that the STANDARDIZED KINK is

indeed positively related and significant at the highest level of significance with all

four measures of leverage. The coefficients of STANDARDIZED KINK are indeed

higher for Long Term Leverage and Adjusted Total Leverage in both regions. This

finding is exactly as expected. The coefficients of STANDARDIZED KINK on Total

Leverage and Short Term Leverage in Western Europe are not significantly different

from those of Eastern Europe. There is a difference on the 0.1 level of significance

between Eastern and Western Europe in the relationship of STANDARDIZED KINK

and Total Leverage, but this level is not high enough to prove or provide any real

significance. These findings contradict two expectations: STANDARDIZED KINK is



not weakly related to Total Leverage and Short Term Leverage and the relationship is

not stronger in Western Europe than in Eastern Europe.

What is more surprising, however, is that the relationship between STANDARDIZED

KINK and Long Term Leverage and Adjusted Total Leverage is significantly lower

for Western Europe compared to Eastern Europe. The relationship is positive and

significant in both regions, as expected, but less positive in the West than in the East.

This is contrary to the expectations.

It seems that the STANDARDIZED KINK proves a stronger indicator for the tax

effect in Eastern European SMEs. This means that the role that volatility in earnings

plays on a firm’s debt policy is larger for Eastern European SMEs than for Western

European SMEs. Thus, because Eastern European SMEs have more volatility in their

earnings, the STANDARDIZED KINK is more strongly related to leverage decisions.

Findings for the EFFECTIVE TAX RATE

The EFFECTIVE TAX RATE is the third and last proxy for the tax effect used here.

It is expected to be positively related to leverage. As in the case of KINK and

STANDARDIZED KINK, it is expected to be more positively and significantly

related to Long Term Leverage and Adjusted Total Leverage. Besides, the effect is

expected to be stronger for Western European SMEs than for Eastern European

SMEs.

From Table 8 and Appendix VII it is observed that the EFFECTIVE TAX RATE does

not properly capture the variance explained by taxes, if any exists. It never shows

significance for the Eastern European sample, and it hardly shows significance for the

Western European sample either. Only for Long Term Leverage there is some small

significance (on the 0,05 level), but not enough to base any strong conclusions on.

There is no significant difference between Eastern and Western Europe. For some

proxies of leverage the signs are positive and for some they are negative, but since no

real significance in these relationships exist, it can be concluded that the EFFECTIVE

TAX RATE is not a good indicator of the tax effect.



4.4 Differences in proxies of Bankruptcy and Agency Costs between Eastern

Europe and Western Europe

TANGIBILITY

Expectations for TANGIBILITY

TANGIBILITY might proxy for different effects on leverage. From the Tradeoff

Theory, TANGIBILITY is expected to be positively related to leverage. As described,

firms with higher collateral value have lower potential bankruptcy costs: it would

seem to indicate that for such firms it is optimal to have a higher share of debt in their

capital structure.

Long term liabilities are more likely to be related to fixed assets, since fixed assets

give high relative collateral values to creditors. Also fixed assets normally are in use

for longer periods of time, and therefore act as better insurance for creditors. Property,

plants, equipment, etc. are long term investments and therefore, need long term

financing to match the investments. For these reasons, TANGIBILITY is expected to

be strongest and most positively related to Long Term Leverage. Also for Adjusted

Total Leverage the relationship is expected to be stronger and more positive than for

Total Leverage, since short- term Accounts Payable have been removed. Therefore,

the relation is expected to be weakest and least positive for Short Term Leverage.

The relationship between TANGIBILITY and leverage is expected to be different

between Eastern Europe and Western Europe. It was argued that for emerging

economies, secondary markets for tangible assets may not be deep enough to provide

good value for collateral (Cornelli, 1996). Besides, bankruptcy and liquidation

proceedings might be too slow for creating good value for collateral. Therefore, the

relationship between TANGIBILITY and leverage is expected to be less positive or

weaker in Eastern Europe than in Western Europe.

Findings for TANGIBILITY

From Table 8 and Appendix VII one may conclude that for Total Leverage and Short

Term Leverage a negative and highly significant relationship exists with

TANGIBILITY, in both Eastern as well as Western Europe. For Long Term

Leverage, a positive and significant relationship exists with TANGIBILITY in both



Eastern and Western Europe. For Adjusted Total Leverage, a significant and positive

relationship exists with TANGIBILITY only in Western Europe. In Eastern Europe,

this relation is not significant.

The findings for Total Leverage and Short Term Leverage are not according to the

expectations, since a weak and positive relationship was expected. The results indicate

a strong and negative relationship to exist. For Long Term Leverage and Adjusted

Total Leverage, the findings are more in line with the expectations, since the

relationship is positive and significant, especially for the Western sample.

The observed differences between Eastern and Western Europe can be summarized as

follows: even though Total Leverage and Short Term Leverage are negatively related

to TANGIBILITY, this relationship is less negative in Western Europe than in Eastern

Europe. Yet this difference is highly significant, especially for Total Leverage. For

Long Term Leverage and Adjusted Total Leverage, one can see that TANGIBILITY

is more positive and significantly different in Western Europe. That is,

TANGIBILITY in the Western European sample is always more positive or less

negative in relation to all proxies of leverage, when compared to the Eastern European

sample. This finding is exactly as expected.

Discussion of (unexpected) findings for TANGIBILITY

It can be observed that the relationship between TANGIBILITY and Long Term

Leverage is very positive and significant for SMEs in both regions. For Western

European SMEs, long term liabilities make up a greater part of Total Leverage than in

Eastern European SMEs. Therefore, it is logical that TANGIBILITY has a more

positive (or less negative) relationship with Total Leverage and Adjusted Total

Leverage. Besides, bankruptcy costs might be lower in Western Europe and therefore,

banks might be willing to lend more funds to Western European SMEs, leading to a

more positive relationship between TANGIBILITY and leverage.

The strong negative relationship between TANGIBILITY and Short Term Leverage is

rather similar in both regions. However, the difference in this relationship between

Eastern and Western Europe is weaker than for the other proxies of leverage. The

strong negative results are clearly not as expected, but have been explained by several

authors. Sogorb-Mira (2005) explained that this relation means that current liabilities



are used to finance non-fixed assets. Rajan and Zingales (1995) explained that a

possible negative relation might exist because TANGIBILITY can be a proxy for

operating leverage as well. Higher TANGIBILITY might indicate higher operating

leverage (the ratio of fixed to variable costs), which would indicate higher bankruptcy

costs. In this case, TANGIBILITY might be a negative as well as a positive proxy for

bankruptcy costs. This would not explain why TANGIBILITY is found to be

negatively related to Total Leverage and Short Term Leverage and positively related

to Long Term Leverage and Adjusted Total Leverage.

One explanation is that the variable TANGIBILITY catches multiple effects at the

same time. On Total Leverage and Short Term Leverage it works better as a positive

proxy for bankruptcy costs, while on Long Term Leverage and Adjusted Total

Leverage it works better as a negative proxy for bankruptcy costs. However, Booth et

al. (2001) came up with a more convincing argument. Similar to the findings of this

study, they found that for small firms, especially in developing countries, tangible

assets were negatively related to short term debt and positively related to long term

debt. They stated that it was often observed that the more tangible the asset mix, the

higher the long term debt ratio, but the smaller the short term debt ratio. This indicates

that the substitution of long-term for short-term debt is often less than one. That is, as

the tangibility of a firm’s assets increases, by say one percent, although the long-term

debt ratio also increases, the short-term debt ratio falls, and therefore, the total debt

ratio falls as well. This substitution effect of Long Term and Short Term Leverage

might very well be apparent in the two samples presented here, more so in the Eastern

European sample than in the Western European sample. Since it was observed from

the descriptive statistics (see Appendix IV) that Total Leverage consists mostly of

Short Term Leverage in both samples (but more so in Eastern Europe than in Western

Europe), this substitution effect also takes place on Total Leverage.

As previously explained, TANGIBILITY might also act as a (negative) proxy for

agency costs. This is not supported by the findings, since positive as well as negative

signs are identified. Consequently, TANGIBILITY must act as a positive cost

indicator of some kind. Hence, the identified positive relation with Long Term

Leverage and Adjusted Total Leverage can not be explained from the firm’s

perspective. Only from the banks’ perspective an explanation can be found; banks

would simply prefer giving long term loans to safer, more tangible, firms.



TANGIBILITY seems to better capture bankruptcy costs than agency costs, indicating

that, as expected, agency costs between shareholders and managers do not seem to

matter much within SMEs.

The observed differences between the Eastern and the Western European samples in

this relation is quite constant, meaning that the relation is always either more positive

or less negative in Western Europe, compared to Eastern Europe. This indicates that

Cornelli’s (1996) argument might hold. In Western Europe, secondary markets for

collateral are deeper and, therefore, give better value for money. This leads to lower

bankruptcy costs in Western Europe and banks are more willing to lend to Western

European SMEs.

Hall, Hutchinson and Michaelas (2004) argued that in Eastern European countries, the

importance of collateral seemed to be more important in raising long-term debt; this

argument does not seem to be supported since the relations are both very significant

and positive. However, in the Western European sample they are more significant,

thus stronger, and more positive.

SIZE

Expectations for SIZE

Similar to TANGIBILITY, SIZE might be a proxy for several effects as well. From

the Tradeoff Theory perspective, SIZE is a negative proxy for bankruptcy costs. That

is, the bigger the size of the firm, the lower its probability of financial distress and the

lower its expected bankruptcy costs. Since the Tradeoff Theory expects a negative

relationship between leverage and bankruptcy costs, SIZE and leverage are expected

to be positively related.

Besides being a proxy for bankruptcy costs, SIZE might also be a negative proxy for

agency costs. Larger firms have, in general, more diluted ownership. The higher the

separation between ownership and management, the higher the agency costs.

Therefore, the owner(s) might be inclined to increase debt as a control mechanism, so

that management has fewer opportunities for exploiting free cash flow to its own

benefit. In this manner of reasoning, the larger SIZE, the higher the expected leverage,

and the expectation of a positive relation. Since SIZE is expected to be positively



related to leverage in two ways, it is hard, if not impossible, to distinguish between

both types of relationship from the findings. Note that the second proxy is less likely

to hold, since the analysis is on SME’s, for which most of the firms have relatively

low agency costs between owners and managers.

SIZE is expected not to have a similar impact on all four proxies of leverage. Similar

to TANGIBILITY, SIZE is expected to have the strongest positive impact on Long

Term Leverage. This is, since long term loans are more risky investments from the

banks’ perspective, and safer, bigger firms are preferred. Similarly, from the agency

cost perspective, long term liabilities are stronger control devices than short term

liabilities and therefore, will be most directly related to SIZE. Short Term Leverage is

expected to have a weaker and less positive relationship with SIZE, since SIZE is

expected to matter less for generating short term (lower risk) loans and payables. The

findings on the relationships between SIZE and Total Leverage and Adjusted Total

Leverage are expected to fall in between these two.

The expected differences for Eastern Europe and Western Europe are as follows.

Since the focus lies on SMEs, which are by definition relatively small, SIZE plays a

major role in attracting funds, in both regions. However, since the availability of

credits is expected to be smaller in East, SIZE is expected to be a more important

determinant of attracting credits from banks in this region. As indicated, a positive

relationship between SIZE and LEVERAGE is expected. However, since bankruptcy

and agency costs are expected to be lower in Western Europe, the coefficients of the

relationships on all four proxies of leverage are expected to be more positive for the

Western European sample (indicating lower bankruptcy and agency costs) than for the

Eastern European sample.



Findings for SIZE

Table 8 and Appendix VII indicate that for Eastern European SMEs a negative and

significant relation exists between SIZE, Total Leverage and Short Term Leverage.

However, for Western European SMEs no significant relation among SIZE and these

two proxies of leverage is found.

For Long Term Leverage, a positive and highly significant relation exists for both

regions. For the Adjusted Total Leverage, only in Eastern Europe does a positive and

significant relation exist, while in Western Europe no relation is apparent with

Adjusted Total Leverage.

For the Western European sample, these findings are more or less in accordance with

expectations: the strongest and most significant relationship exists for Long Term

Leverage, while for the other proxies of leverage no clear relationship is observable,

since there is no significance in these findings.

For the Eastern European sample, only the positive and significant relationships

between SIZE and Long Term Leverage and the Adjusted Total Leverage are as

expected. However, the negative and significant relationships among SIZE, Total

Leverage and Short Term Leverage, are not as expected.

Another expectation that is supported by these findings is that the relationship

between SIZE and all leverages is stronger for the Eastern European sample. The t-

statistics and thus significance levels are always higher for the Eastern European

sample than those of the Western European sample. The difference in the relation of

SIZE on leverage between Eastern and Western Europe is highly significant on all

proxies of leverage. However, for Total Leverage and Short Term Leverage, the

relations are significantly more positive in Western Europe, while for Long Term

Leverage and Adjusted Total Leverage, the relationships are significantly more

negative in Western Europe. Yet, since most signs of coefficients in the Western

sample are not significant, the strong differences in relationships between Eastern and

Western Europe might be misleading.

Discussion of (unexpected) findings for SIZE

It has been demonstrated that SIZE matters more for Eastern European SMEs in order

to get credits. The relationships found are much more significant in Eastern Europe

than in Western Europe. However, for Short Term Leverage, the relationship with

SIZE proved negative. Since Total Leverage exists for the majority (around 90



percent) of Short Term Leverage, it is obvious that this same effect takes place on

Total Leverage as well. This negative relation is clearly not explained by the

bankruptcy cost and agency cost theory.

It can be further observed that for the Adjusted Total Leverage, the signs are positive

and significant, as expected. The only difference between the Adjusted Total

Leverage and Total Leverage is the Accounts Payable that have been removed from

Total Leverage. Since the signs of the relationship between both proxies of leverage

changes, it is certain that Accounts Payable have a profound influence on the

relationship. Indeed, one can observe that in both regions, accounts payables make up

a large part of Short Term Leverage and similarly Total Leverage. SIZE would

therefore be negatively related to Accounts Payable. This would indicate that small

firms rely more on trade credits (Accounts Payable) in their capital structure, and less

on other sources of credit. The bigger the size of the firm, the lower its reliance on

trade credits, and the higher its reliance on other (preferably longer term) sources of

credits, such as loans. Therefore, if a firm grows in size, it might want to lower its

accounts payables by substituting other types of debt. In that case, a negative

relationship would exist between SIZE and accounts payables, which is translated into

a negative relationship with Short Term Leverage and even into Total Leverage, due

to the heavy reliance on short term liabilities (and thus payables) of the Eastern SMEs.

This effect is present only in the Eastern European sample, and this unexpected effect

might further result in a lower availability of credit to small firms in Eastern Europe.

Since insufficient credits are available, small firms in this region might need to rely on

trade credits to a higher extent. Because of higher bankruptcy costs in Eastern Europe,

banks might be less willing to lend to firms with high amounts of trade credits. In this

case, Eastern European SMEs might seek to reduce their trade credits, relative to other

sources of debt, whenever possible, e.g. after growing in size.

In sum, the research results point toward SIZE being a good proxy for bankruptcy

costs (or agency costs) on long term debt in both regions, but in Western SMEs, SIZE

hardly plays a role on the other proxies for leverage. The differences in the

relationship of SIZE and leverage between Eastern and Western Europe are found to

be inconclusive.



Z-SCORE

Expectations for Z-SCORE

In contrast to the variables described above, Altman’s general Z-SCORE is simply a

proxy for one effect, the bankruptcy effect. As previously noted the Z-SCORE is an

indicator of financial distress. The higher the Z-SCORE, the higher the chance of

bankruptcy, and the higher bankruptcy costs. Therefore, from the tradeoff perspective,

a negative relation between Z-SCORE and leverage is expected. Once again, this

relation is expected to be negative and strongest with Long Term Leverage since long

term debts are more risky investments from the banks’ perspective and require low

bankruptcy costs. The relationship is expected to be least negative for short-term

liabilities. Inasmuch as Total Leverage and Short Term Leverage contain a large

quantity of Accounts Payable, which are not expected to be related to the Z-SCORE,

the relationship for these proxies is expected to be weaker.

Also since bankruptcy costs in Western Europe are expected to be smaller than those

for Eastern Europe, the relationship is expected to be more negative in Western

Europe than in Eastern Europe.

Findings for Z-SCORE

The Z-SCORE is negatively and significantly related to all four proxies of leverage, in

Eastern Europe as well as in Western Europe. This is exactly as expected. The

relationship is very strong for all measures of leverage, indicating that the expectation

of the Z-SCORE being weaker when related to Short Term Leverage is rejected. Both

in Eastern and in Western Europe, the Z-SCORE’s are even stronger as related to

Short Term Leverage and Total Leverage. This finding is contrary to expectations.

The differences between Eastern and Western Europe in the relation of the Z-SCORE

and leverage are unanimous. In Western Europe, the relation is always more negative

than in Eastern Europe, and this difference is always highly significant.

Discussion of (unexpected) findings for Z-SCORE

The findings for the relation of the Z-SCORE and leverage are to a great extent in line

with expectations, in both Eastern and Western Europe. This demonstrates that the Z-

SCORE is a good proxy for bankruptcy costs for SMEs in both regions of this study.

The results are most negative with Total Leverage and Short Term Leverage in both



regions, indicating that the Z-SCORE proxies bankruptcy costs for Short Term

Leverage very well. The significant difference between Eastern and Western Europe,

indicating a more negative relationship between the Z-SCORE and all leverages in

Western Europe, clearly proves that bankruptcy costs are lower in Western Europe,

which makes creditors more willing to lend to SMEs in this region.

The strong results do not only indicate that bankruptcy costs matter for banks that

lend long term, but are an important determinant for banks in lending short term as

well. This conclusion is supported by the finding that the differences for the Z-

SCORE are also significant and negative for Short Term Leverage in the Western

European sample. Bankruptcy costs are clearly lower in Western Europe, as indicated

by the Z-SCORE.

OPERATING RISK

Expectations for OPERATING RISK

As stated prior, OPERATING RISK is a proxy for bankruptcy costs only. The higher

the OPERATING RISK of a firm, the higher the probability of distress. A firm with

high bankruptcy costs is most likely to receive lower or no credits from a bank, thus a

negative relation is expected between OPERATING RISK and leverage.

Similar to the other variables above, the relation is expected to be most negative and

strongest with Long Term Leverage, since banks are expected to be most reluctant to

lend long term to firms. Similarly, the relation is expected to be less negative for Short

Term Leverage.

The expected difference between Eastern and Western Europe is that in Western

Europe the relations are expected to be more negative than in Eastern Europe, since

bankruptcy costs in general are expected to be smaller, which might cause banks to be

less reluctant in financing risky firms in Western Europe.

Findings for OPERATING RISK

It can be observed that the coefficients of OPERATING RISK are all extremely low,

approaching zero, while being significant in relation to some of the proxies of

leverage. This might be due to the extreme values of OPERATING RISK, as depicted

in the descriptive statistics of Table 7. Contrary to all expectations, OPERATING



RISK is found to be positively related to all proxies of leverage in the Western

European sample. In Eastern Europe, only one negative and significant relationship is

found between OPERATING RISK and Long Term Leverage and one positive

significant relation with Adjusted Total Leverage. Most of the results are

contradictory to expectations. OPERATING RISK is in most cases positively related

to leverage, instead of negatively. This is especially so for the Western European

sample. Instead of finding a more negative relation in Western Europe, a significantly

more positive relation is found, compared to Eastern Europe.

Discussion of (unexpected) findings for OPERATING RISK

Even though most findings show positive relations between OPERATING RISK and

leverage, a negative relation is found with Long Term Leverage in Eastern Europe.

This finding is according to expectations, and indicates that indeed in Eastern Europe

bankruptcy costs are highest. It also shows that bankruptcy costs matter most on Long

Term Leverage. Besides, when looking at the differences between Eastern and

Western Europe, one can see that in Eastern Europe the relations are always

significantly less positive, indicating a stronger bankruptcy cost effect to exist, and

hence, higher bankruptcy costs.

The most striking finding is the positive relations of OPERATING RISK with all

proxies of leverage in Western Europe, and with some proxies of leverage in Eastern

Europe. These relations do not seem to indicate a bankruptcy cost effect and have not

been described in literature. A possible explanation is that volatile firms have a bigger

need for credits, as operational buffers, meaning that a firm might keep extra credits

in order to absorb shocks in its cash flows. If the explanation is indeed due to such

buffers, it seems that in Western Europe these are not or hardly based on short term

debt, nor on Accounts Payable. The most significantly positive relation is with

Adjusted Total Leverage, in both regions, which might indicate that a greater

preference exists for stable sources of credit.

It can be questioned how volatile firms are able to attract such credits, since banks

will not easily extend credits to a firm facing high financial distress. Because of more

positive relations between OPERATING RISK and leverage in Western Europe, it

seems that Western European firms have more ease in collecting such credits, thus

indicating that overall bankruptcy costs are lower in Western Europe.



PROFITABILITY

Expectations for PROFITABILITY

PROFITABILITY can be a proxy for both bankruptcy and agency costs. Highly

profitable firms generate higher cash flows than less profitable firms. Probability of

default is expected to be lower in profitable firms, and thus bankruptcy costs are

lower. Banks are more willing to invest in profitable firms, and thus according to the

bankruptcy cost theory, the expected relationship between PROFITABILITY and

LEVERAGE is positive. As stated, according to Jensen (1986), PROFITABILITY

also serves as a proxy for agency costs between shareholders and managers.

Generations of high cash flows in the firm may encourage managers to invest in

negative Net Present Value projects or to “build empires”. In this case, higher levels

of debt serve as a control device and reduce the possibility of management abuse of

shareholders funds. This argument also suggests a positive relation between

PROFITABILITY and LEVERAGE. Another argument from the agency cost theory

is that PROFITABILITY is considered to be a proxy for information asymmetry.

Here, the firms prefer internal financing over external financing, as is explained by the

Pecking Order Theory. More profitable firms are expected to have lower amounts of

debt than less profitable firms, and thus a negative relation between

PROFITABILITY and leverage is expected.

Again, the relationship of PROFITABILITY to LEVERAGE is expected to be

strongest with Long Term Leverage for all proxies. From the bankruptcy cost point of

view, long term debt is the most risky investment for banks. From the agency cost

point of view, long term debt serves as the strongest control device over management,

and because of its duration, long term debt is a more risky commitment for the firm.

As already noted, agency costs in SMEs are expected to be weak, or not apparent.

Thus, the findings are expected to be in line with the bankruptcy cost theory and only

a positive relation of PROFITABILITY with leverage is expected. PROFITABILITY

is expected to be more positively related to leverage in Western Europe because

bankruptcy costs in general are expected to be lower in Western Europe than in

Eastern Europe.



Findings for PROFITABILITY

Contrary to the expectations, in the Western European sample the results indicate a

strongly negative relationship between PROFITABILITY and all types of

LEVERAGE. The same results are found in Eastern Europe, although only Long

Term Leverage is positively related to PROFITABILITY. The other three proxies of

LEVERAGE are negative and significant.

The relationship between PROFITABILITY, Total Leverage, and Short Term

Leverage is significantly more negative in Eastern Europe than in Western Europe.

However, the relationship between Long Term Leverage and Adjusted Total Leverage

proves significantly more positive in the Eastern European sample than in the Western

European sample.

Discussion of (unexpected) findings for PROFITABILITY

The findings in the Western European sample clearly contradict the expectations

based on the bankruptcy and agency costs, in light of the Tradeoff Theory. The

negative relation of PROFITABILITY with LEVERAGE are in line with the Pecking

Order theory, in which more profitable firms in Western Europe prefer internal

financing over external financing. A true Pecking Order, as described by Myers and

Majluf (1984) and Myers (1984) is very unlikely to exist in privately held SMEs,

since private firms cannot easily issue equity as a form of external capital. An

‘adjusted’ Pecking Order might, however, exist, in which the choice is merely

between internal funds and credit (Nguyen & Ramachandram, 2006). A negative

relationship between PROFITABILITY and LEVERAGE might thus indicate that a

firm prefers internal funds over external funds, since these are cheaper. Such ‘Pecking

Order’ findings are strongest in Eastern Europe for Total Leverage and Short Term

leverage. According to Cornelli (1996) the loan conditions in emerging markets are

not attractive and require high interest payments, especially for short term loans.

Thus, in Eastern Europe, cheap internal funds combined with expensive loan

conditions, often result in profitable firms becoming less likely to take external loans,

since they can afford to finance the investments from internal sources. Consequently,

only less profitable firms will use external financing (with high interest rates) as these

firms do not have the “luxury” of choosing between internal and external financing. It

seems that in Eastern Europe, PROFITABILITY also works as a proxy for bankruptcy

costs, since on Long Term Leverage the relation is positive. Apparently, banks in



Eastern Europe are only willing to lend long term loans to highly profitable firms, in

order to reduce their risks. This effect also seems to take place in Western Europe, but

to a smaller extent than in Eastern Europe. The relationship between

PROFITABILITY and Long Term Leverage is less negative than for the other three

proxies of LEVERAGE, indicating that the bankruptcy cost effect plays some role.

However, in Western Europe, the negative effect of internal financing is still stronger.

The findings clearly illustrate that bankruptcy costs play a bigger role in Eastern

Europe than in Western Europe.

GROWTH

Expectations for GROWTH

GROWTH is a proxy for bankruptcy costs as well as agency costs.

When looking at GROWTH as a proxy for bankruptcy costs, a positive relation with

all four proxies of LEVERAGE is expected. High growth firms are an indication of

safer investments to the banks, and, therefore, results in lower bankruptcy costs.

GROWTH might also indicate growth opportunities, which is considered a proxy for

agency costs between shareholders and management. Growth opportunities as a proxy

for agency costs has a relationship with leverage that is expected to be negative. Yet,

growth opportunities need to be financed by extra amounts of debt. Since more debt

requires higher amounts of interest payments, management may pass on profitable

investments, and use most of the firm’s cash flow for financing debt.

According to the agency costs theory, GROWTH might also be a good proxy for

information asymmetry between shareholders and creditors. Fast growing firms have

a bigger potential of risk shifting, thus a negative relation between LEVERAGE and

GROWTH is expected. From the bankruptcy cost theory perspective, GROWTH is

expected to be most strongly related to Long Term Leverage, and least related to

Short Term Leverage, for the reasons discussed above. Long Term Leverage is a more

risky investment for banks and, therefore, stronger growth is needed to offset this risk.

From the agency cost perspective, GROWTH might be strongest when related to

Short Term Leverage as well as Long Term leverage, depending on which agency

cost reasoning is applied. On average, interest costs are higher on short term debt



which would increase the probability of underinvestment. In this case, the strongest

negative relationship will be found between GROWTH and Short Term Leverage. On

the other hand, risk shifting might be possible with long term debt, which would

assume a stronger negative relationship between GROWTH and Long Term

Leverage.

There are different expectations for Eastern and Western European SMEs regarding

the relationship of GROWTH and LEVERAGE. As bankruptcy costs are expected to

be lower in Western Europe, when GROWTH works as a proxy for bankruptcy costs,

the relationship is expected to be more positive in Western Europe than in Eastern

Europe. In regards to agency costs, a less negative relationship is expected to be

observed in Western European SMEs. In general, agency costs are expected to be

lower in Western Europe. However, as agency costs and information asymmetries

between management and shareholders are less likely to exist in SMEs it is expected

that GROWTH as a proxy for agency costs will not be found or will not be

significant. Therefore, a relationship between GROWTH and LEVERAGE is

expected to be in line with the expectations from the reasoning of the bankruptcy cost

theory.

Findings for GROWTH

Looking at Table 8 and Appendix VII, one can see that the relationship of GROWTH

with all proxies of leverage is positive, in both Western and Eastern Europe. This

observation is consistent with the expectations based on the bankruptcy theory. In

Eastern Europe, the relations are significant to the highest extent in all four proxies of

LEVERAGE. In Western Europe, the relations also indicate high significance in Total

Leverage and Short Term Leverage, but lower significance in Long Term Leverage

and Adjusted Total Leverage. These weaker results in Western Europe may be due to

lower bankruptcy costs in this region and better access for SMEs to long term credits,

as compared to SMEs from Eastern Europe. SMEs in Western Europe do not

necessarily have to indicate high growth rates in order to receive long term credit. In

Eastern Europe, however, high significance between GROWTH and Long Term

Leverage and Adjusted Total Leverage confirms that growth firms are better able to

obtain external financing, and thus that banks are risk avoiding. This is a indication of

higher structural bankruptcy costs in Eastern Europe.



Discussion of (unexpected) findings for GROWTH

The research expectations on Eastern and Western Europe suggest that a more

positive relationship between GROWTH and LEVERAGE is to be expected in

Western Europe because of lower expected bankruptcy costs. The results, however,

indicate just the opposite. Reasons for this are as follows: the importance of firm’s

growth rates for banks in Eastern Europe is higher than in Western Europe and thus

banks value growth rates more in Eastern Europe. Banks in Eastern Europe view

lending to growing firms as a safer investment: bankruptcy costs are expected to be

lower in Western Europe, and banks have a lower need for good growth figures as an

insurance of loan repayment. Besides, Serria-Allende and Zaidi (2006) concluded that

high growth firms need more external financing, especially long term credits in order

to keep on growing. The strong relationship between GROWTH and LEVERAGE

comes from the bankruptcy cost theory reasoning. Agency costs between management

and shareholders, and information asymmetry between shareholders and banks, do not

appear to exist in European SMEs. This is according to the expectations.

4.5 Conclusions of the Results in Eastern Europe and Western Europe

LEVERAGE IN EASTERN AND WESTERN EUROPEAN SMEs

Descriptive statistics in the data section gave an indication that all four proxies of

leverage are lower in Eastern than in Western European SMEs. In order to check

whether the differences between the proxies of leverage are indeed statistically

significant, F-tests were conducted. These indicated that indeed, all leverage ratios in

Western Europe are significantly different from the leverage ratios in Eastern Europe.

T-tests were calculated to test whether these differences occur in the predicted

direction. These clearly show that the four different leverage variables in Eastern

Europe are lower then in Western Europe.

Consequently, Hypothesis 1 is confirmed, since all four proxies of Leverage are

higher in Western European SMEs than in Eastern European SMEs.



INFLUENCE OF TAX ON LEVERAGE:

The research findings on the different proxies for the tax effect are not completely

conclusive. The findings on KINK indicate that in Western European SMEs, KINK

has a negative influence on Long Term Leverage and Adjusted Total Leverage. On

Total Leverage and Short Term Leverage, tax does not seem to have much influence

in this region, although it was expected that some, minor, relation would exist. In the

Eastern European SMEs, KINK does not seem to explain LEVERAGE in the

expected directions, and therefore, does not seem to work well. These findings do not

clearly demonstrate that KINK is a good proxy for tax, but they do show that KINK

works better as a proxy for tax in Western Europe than in Eastern Europe. Tax

shielding is more apparent in Western Europe than in Eastern Europe, especially with

long term credits and non-working capital items. This in turn, provides some evidence

that indeed in Western Europe, where corporate tax rates are higher, shielding taxes

with credit is a more profound activity.

The findings on STANDARDIZED KINK indicate that the risk of shielding taxes

plays a significant role on capital structures in both regions and on all proxies of

LEVERAGE. This relationship is similar in the two regions on Total Leverage and

Short Term Leverage, but different for Long Term Leverage and Adjusted Total

Leverage. The risks of tax shielding play a smaller role on longer term debt financing

in Western Europe, as compared to Eastern Europe. It can be concluded that, all else

equal, such tax shielding risks are just lower in Western Europe. Lower risk of tax

shielding explains the stronger activity of tax shielding in Western Europe, as

observed before. Namely, the relation between KINK and Long Term Leverage and

Adjusted Total Leverage indicates that tax shielding is only apparent in Western

Europe. Lower risk of tax shielding might give extra proof for tax shielding only to be

apparent in Western Europe.

The variable EFFECTIVE TAX RATE does not seem to play any role in SME’s

capital structures in neither the Eastern nor the Western European sample. Since

KINK and STANDARDIZED KINK do seem to indicate a relationship with leverage,

it is most likely that the construction of the variable EFFECTIVE TAX RATE is

weak. That is, this variable does not capture any variance explained by the tax effect.



From these conclusions, Hypothesis 2 is partly confirmed. Taxes have a positive

influence on (Long Term) Leverage in Western Europe, and the influence of taxes

is higher for Western European SMEs than for Eastern European SMEs. The tax

effect does play a clear role in Western Europe (but only on Long Term Leverage),

but not in Eastern Europe.

INFLUENCE OF BANKRUPTCY COSTS ON LEVERAGE:

The two variables that solely proxy for bankruptcy costs, the Z-SCORE and

OPERATING RISK, indicate bankruptcy costs to be an important determinant of

LEVERAGE in both Eastern European SMEs and Western European SMEs.

Particularly the Z-SCORE demonstrates a very strong and negative relationship with

all proxies of LEVERAGE, indicating that bankruptcy costs play a strong role on

SMEs capital structures in both regions. The findings on OPERATING RISK are

harder to interpret, since the signs are contradictory to expectations on most proxies of

LEVERAGE in both samples. However, as expected, OPERATING RISK is

positively and significantly related to Long Term Leverage in Eastern Europe. This is

not the case in Western Europe and thus indicates that bankruptcy costs play a bigger

role on Long Term Leverage in the Eastern European SMEs.

Other variables that can be proxy either for bankruptcy costs or agency costs prove to

be better proxies for bankruptcy costs in both samples. This is especially the case in

relation to Long Term Leverage, and to a lesser extent to Adjusted Total Leverage

(which consists mostly of Long Term Leverage).

TANGIBILITY is negatively related to Total Leverage and Short Term Leverage;

however, it shows positive relations with Long Term Leverage and Adjusted Total

Leverage in the Eastern European sample as well as in the Western European sample.

This relation, however, is stronger in Western Europe. Similarly for SIZE, even

though in Eastern Europe SIZE is negatively related to Total Leverage and Short

Term Leverage, SIZE indicates positive relationships with Long Term Leverage and

Adjusted Total Leverage. The same is true, but to a lesser extent in Western Europe.

PROFITABILITY is in both regions negatively related to all proxies of leverage,

except for Long Term Leverage in Eastern Europe. This, again, indicates higher

bankruptcy costs in Eastern Europe. Finally, GROWTH is found to be a much



stronger proxy for bankruptcy costs than for agency costs, where it is always

positively related to all proxies of leverage, but strongest in the Eastern European

sample.

All above findings on bankruptcy costs clearly point out that bankruptcy costs matter

on capital structure determination of SMEs in both Eastern and Western Europe.

Bankruptcy costs, therefore, have a negative influence on leverage. Most variables

studied point towards bankruptcy costs being a more important issue in Eastern

European SMEs, further indicating that bankruptcy costs are considered to be

generally higher in Eastern Europe. Consequently, banks might be more reluctant to

lend to SMEs in this region. As expected, such reluctance holds particularly true in

the case of long term debt financing, as found in the study results.

From these conclusions, Hypothesis 3 is confirmed: bankruptcy costs are negatively

related to leverage, especially Long Term Leverage, in Western and Eastern

European SMEs. This relation is stronger in Eastern Europe where bankruptcy

costs are more negative.

INFLUENCE OF AGENCY COSTS ON LEVERAGE:

As described in the above section, most variables that might either proxy for agency

costs or bankruptcy costs seem to work better as bankruptcy cost indicators, since the

results have the expected bankruptcy proxy signs. For example, GROWTH works

better as a bankruptcy proxy for both Western and Eastern European SMEs. However,

some variables show relationships with proxies of leverage in the directions expected

from the agency costs theory. In all cases, other factors might explain these relations.

The positive relationship between TANGIBILITY and Long Term Leverage and

Adjusted Total Leverage in both Eastern and Western Europe, could be indicative of

agency costs working as a determinant of SME capital structure. This is unlikely

however, since these positive relations fit better with the bankruptcy cost theory, and

are supported by the stronger findings for other bankruptcy proxies on Long Term

Leverage and Adjusted Total Leverage.

The variable that clearly shows findings that are strongly in line with the agency cost

theory, is PROFITABILITY. The strong negative relationship between



PROFITABILITY and most proxies of leverage is a possible confirmation of an

agency cost effect, from the information asymmetry perspective. However, as stated,

other external forces might be underlying this effect, making it appear that an

‘adjusted’ pecking order of financing exists. The negative relations between

PROFITABILITY and leverage are strongest in Eastern Europe, which might indicate

that the availability of credit is lower, and conditions of debt contracts are worse.

Instead of having a formalized ‘pecking order’, SMEs in Western Europe and

especially those in Eastern Europe might not have the luxury of choosing between

different sources of finance, and, therefore, they might need to primarily rely on

internally generated cash. This would not contradict the Tradeoff Theory, but would

only limit it to a certain extent. Since SMEs have mostly combined management and

ownership, severe agency costs are unlikely to be existent.

Except for PROFITABILITY, no variable shows any evidence of agency costs to be

apparent. This, in combination with the likelihood that the negative relation for

profitability is caused by other, institutional factors for SMEs, makes it appear that

agency costs are not strong in both samples, exactly as was expected.

From these conclusions, Hypothesis 4 is partly confirmed: no clear, undisputed,

effects for agency costs are found in either of the regions. Consequently, agency

costs seem to be non-existent for Small and Medium sized Enterprises.

Since the four hypotheses have been confirmed, it can be stated that the Tradeoff

Theory works well as an explanatory theory of capital structure on SMEs in Eastern

Europe as well as Western Europe. It was identified above that the target adjustment

coefficients already indicated this theory to be valid. The individual relations between

the determinants of leverage, and leverage itself, are in the directions as predicted by

the Tradeoff Theory. This strengthens the observations from the target adjustment

coefficients, and implies that the target adjustment model, hence the Tradeoff Theory,

is a good underlying theoretical model for testing the differences between Eastern and

Western European SMEs. This, in turn, adds strength to the conclusions as stated

above.



4.6 Robustness Check

In Appendix IX an extensive Robustness Check is presented which serves to control

the approach that has been applied in this Chapter. Its purpose is to check for the

robustness of the results and conclusions of this study. In the previous section the

hypotheses have been controlled for. This was done by analyzing the differences

between Eastern and Western Europe based on leverage ratios and the relationships

between different proxies of leverage and independent variables of the model. These

findings have been linked to the expectations that were formalized. By comparing the

findings (relationships between variables) with the expectations, the hypotheses were

controlled for.

In a similar way country differences can be studied. This will yield numerous

interesting results to this research. Namely, it will be possible to check to what extent

SMEs from different countries are similar or different from each other, based on the

relationships between proxies for leverage, tax, bankruptcy costs and agency costs.

Since the focus of this research is to compare Eastern European SMEs with Western

European SMEs, such country comparisons are not a necessity to answer the Research

Question and to control the Hypotheses, as is done in the previous section. A very

important benefit of including this extra research is to see whether the methodology

which was applied to test the hypotheses, is valid. Namely, in a similar fashion

country differences can be detected, analyzed, and compared to expected country

specific factors. Such a country study therefore serves as a robustness check to the

research of Eastern European SMEs and Western European SMEs. A country specific

comparison will help to prove whether the resources that were used, the expectations /

hypotheses which were formalized, and the comparisons which were done, are valid.

The conclusions from the previous section, based on the expected regional differences

and regression results, are only valid if the results are indeed caused by such regional

differences, and not by possible other factors that have not been taken into account in

this study. It is not the purpose here to identify such possible other factors, but merely

to test whether the existence of possible other factors can be excluded.

The Robustness Check of Appendix IX is based on the exact same methodology as

was applied in the previous sections. SMEs from the three countries from the Western



European sample (Ireland, the Netherlands and Belgium) are studied in comparison to

each other. Similarly, SMEs from the Eastern European sample (Poland, Hungary and

Ukraine) are studied and compared to each other. Data from SMEs in these six

countries is available, since the exact same data was used before, in the study of

Eastern Europe versus Western Europe.

All details of these country analyses are described in Appendix IX, including the

output of the regression analyses. The approach of testing is similar as was done for

the regional tests. First, expectations on findings for each country under study were

constructed, based on the country scores that are presented in Appendix II. Second,

the expectations regarding country differences were made, also based on these

country scores. Third, these expectations were compared to the actual 2SLS

regression results. The regression output is more extensive than for the study of

Eastern Europe and Western Europe, and therefore besides Appendix IX, further

output is presented in Appendix X and Appendix XI. This, since country comparisons

are again made by using dummy variables for countries. For this reason, basegroups

need to be changed, and since the analysis is on six countries instead of only two

regions, there is more output to be presented.

The last stage in the Robustness Check is to dicuss the findings, after they are linked

to expectations.

In the following part of this section, a short summary of the findings and the

implications of the Robustness Check are described.

In both the Western and Eastern European countries, the findings for proxies of tax

are, to a large extent, in line with expectations. None of the proxies for tax seem to

play a significant role in the Irish and Hungarian SMEs; the tax rates have been low in

these countries and it was expected that tax will not have an influence on leverage. In

the Netherlands and Belgium, where corporate tax rates are higher, KINK has an

influence on Long Term Leverage and Adjusted Total Leverage. STANDARDIZED

KINK was found to be the best proxy for tax and is highly significant on all four types

of leverage in the Netherlands and Belgium. Also, according to expectations, country

dummies did not detect significant differences between tax effects on leverage

between Belgium and the Netherlands but these differences exist when comparing

these two countries to Ireland. In Eastern European countries, the tax effect was found



to be strong in Poland and Ukraine, especially while using the STANDARDIZED

KINK variable. The differences detected by country dummies among Eastern

European countries are not conclusive – this may be caused by factors that are not

included in country scores, for example weaker capital markets in Ukraine.

The analysis of the variables that proxy bankruptcy costs revealed that these costs are

a very important determinant on capital structure in every country studied in this

research. The findings were strong, especially for Long Term Leverage, which might

indicate that, indeed, bankruptcy costs are most important in lending and borrowing

for long periods of time. In Eastern European countries the findings on Z-SCORE,

GROWTH, TANGIBILITY and SIZE confirmed that bankruptcy costs are highest in

Ukraine and lowest in Hungary, especially on Long Term Leverage. In Western

European countries, Z-SCORE was negative and significant in every country and

dummy variables did not detect any difference among the countries.

Agency costs in SMEs from Eastern and Western European countries do not have any

strong impact on capital structure. This is in line with the expectations, since SMEs

often do not have dispersed ownership and are often owner-managed. Besides, banks

seem to solve agency costs themselves, by keeping close relations with SMEs and by

lending predominantly short term. The negative signs in GROWTH indicate that

agency costs do exist in Dutch SMEs, but only to a small extent. This is in line with

the country scores on corporate governance. The findings for Ireland and Belgium

indicate that agency costs are lower or nearly non-existent in leverage determination.

This is in line with the country scores as well. However, the country scores on

corporate governance are more applicable to large public firms; thus, findings are not

conclusive enough to state that agency costs exist within Dutch SMEs. In Eastern

European countries, none of the variables fully point towards expected signs for

agency costs.

From the above differences in corporate taxes, bankruptcy costs, agency costs and

access to credit, expectations were formed on differences in leverage ratios. These

expectations were compared to the F-tests and T-test statistics on observed differences

in leverage ratios. In Western Europe, it turned out that leverage ratios in Ireland were

considerably lower than in Belgium and the Netherlands, due to the much lower



corporate tax rates, and hence lower marginal benefits to shield taxes. In Eastern

Europe, it was found that leverage ratios were highest in Hungary, followed by Poland

and lowest in Ukraine. This was because of low availability of credits in Ukraine and

better access to credits in Hungary.

It turned out that the tested differences in leverage ratios between the countries, are

perfectly in line with the expected, and tested differences in taxes, bankruptcy costs,

and access to credit. Agency costs did, indeed not play any significant role.

Since all the expectations were confirmed, the robustness check proved that the

approach that has been used in this research detects the main characteristics of SMEs’

leverage determination and country differences. This shows that country scores are

indeed a good basis for this research. It is therefore found that the methodology which

is applied, is valid for the analysis of all countries. Hence, it can be concluded that this

approach is also valid for testing differences and similarities between the two regions:

Eastern Europe and Western Europe, since the expectations are based on the country

scores. This Robustness Check thus presents more evidence that indeed the observed

differences between SMEs from Eastern Europe and Western Europe are due to

institutional differences in respect to credit availability, corporate tax rates and

bankruptcy costs. Agency costs are not found to have a clear impact on SME capital

structure in Eastern Europe as well as Western Europe.



Conclusions

In this research paper, capital structures of Small and Medium sized Enterprises from

Eastern Europe and Western Europe were analyzed, based on leverage ratios and

determinants of capital structures that were found in literature. The methodology

applied was a comparative study, in which the emphasis relied on differences found

between Eastern Europe and Western Europe. These differences were computed by

applying multiple Two Stage Least Squares regression analyses including dummy

variables for both regions.

The research study followed a target adjustment model, based on the Tradeoff Theory

that analyzed the impact of corporate taxes, bankruptcy costs and agency costs on

changes in leverage. The Tradeoff Theory argues that firms will optimize their

leverage ratios based on a tradeoff between tax benefits from debt, and bankruptcy

costs and agency costs of debt.

The researchers specifically chose to analyze Eastern Europe and Western Europe,

since literature on intra-European comparisons is scarce, and country data studied

clearly identified differences between these two regions in regard to taxes, bankruptcy

costs and agency costs. The division between Eastern Europe and Western Europe

was not based specifically on the geographical positions of countries, but primarily to

identify differences in the state of development of economies or financial systems.

Eastern European countries, under the taxonomy that was applied, have a different

economic background than Western European countries, which was expected to

impact firms’ capital structures differently. Corporate tax rates were found to be

considerably lower in Eastern Europe, compared to Western Europe. Bankruptcy

laws, bankruptcy procedures and corporate governance systems were found to be

weaker in Eastern Europe than in Western Europe. From the Tradeoff Theory, the

hypothesis was made that firms’ capital structures have lower amounts of debt in

Eastern Europe. The reasoning was as follows: On one side of the tradeoff, lower tax

rates lead to lower marginal benefits of debt, inclining Eastern European firms less to

take on debt. On the other side of the tradeoff, a weaker bankruptcy system makes

creditors more reluctant in giving loans (and thus increasing bankruptcy costs), which

reduces the amount of debt in Eastern European capital structures. Weaker corporate



governance systems in Eastern Europe were expected to increase agency costs

between management and owners and between management and creditors. This would

further reduce the amount of debt in firms capital structures. Moreover, it was

observed that credit availability was lower in Eastern Europe, reducing the access to

credit for firms and thus their leverage ratios.

Most literature has analyzed capital structures of large (public) firms. This study

distinguished itself from that, by solely focusing on private Small and Medium sized

Enterprises (SMEs). Since SMEs make up the biggest share of a country’s economy,

it is surprising that literature on SME capital structure is rather scarce. This is

especially so, since for capital structure decision making, the size of a firm and its

ownership structure clearly make a difference. Small and private firms are mostly

owner-managed, which leaves fewer reasons to expect agency costs between owners

and management to play a role on capital structure determination. Agency costs

between creditors and management were also not expected to play a role on SME

capital structures, since it can be argued that banks solve this information asymmetry

in their own way. SMEs do not have the size, nor access to issue any public debt, and,

therefore, need to rely on bank financing. Thus SMEs are often characterized as

having very strong ties with banks. Data on SME performance is scarce, thus by

having a close relationship with firms, banks can gather their own data on borrowing

firms. It was identified that in the capital structures of SMEs, debt is mostly short

term. This is more evident in Eastern Europe rather than in Western Europe.

Two reasons for such differences were found in literature. Firms from Eastern Europe

have shorter data histories and are more risky investments. From the banks

perspective, long term loans are more risky than short term loans, and banks prefer to

finance Eastern European firms with short term debt. Besides, short term financing

provides a tool to continuously renegotiate the debt contract forcing the firm to act on

the banks behalf. Another hypothesis on the relationships between SME leverage and

its determinants was that agency costs have no influence on leverage ratios, or a

smaller influence on leverage ratios than bankruptcy costs, for both Eastern and

Western Europe. Hence, bankruptcy costs were expected to have a relatively more

important influence on leverage ratios.



Besides testing for the differences between Eastern and Western European firms, the

paper’s research also tested the effectiveness of the Tradeoff Theory in explaining

capital structure. Since it was impossible to test the strength of the target adjustment

model directly, the research showed that the tax effect, bankruptcy cost effect and

agency cost effect were related with leverage in the way that aligns with the Tradeoff

Theory.

The research was conducted by comparing several variables which proxied for the tax

effect, bankruptcy cost effect and agency cost effect on four different proxies of

leverage. The relationship among the proxies for the three effects on leverage and the

proxies for leverage itself, were computed by several multiple Two Stage Least

Squares regression analyses. The comparisons of these relations between Eastern

Europe and Western Europe were done by including dummy variables in the

regression analyses, which either represented Eastern Europe or Western Europe.

The data that was used to compute the variables and proxies, was limited to six

countries. For Western Europe the data was gathered from SMEs in Ireland, the

Netherlands and Belgium. For Eastern Europe this data came from SMEs in Poland,

Hungary and Ukraine.

The findings of this research clearly showed differences to exist between leverage

ratios from Eastern European SMEs and Western European SMEs. Because of

institutional differences such as corporate taxes, availability of credits, bankruptcy

laws and procedures, and corporate governance, leverage ratios were found to be

considerably lower in Eastern Europe as compared to Western Europe, for all four

proxies of leverage. The first hypothesis was supported, because of institutional

differences, firms have different financing patterns in both regions. The role of

corporate taxes on leverage was found to be considerably stronger in Western

European SMEs, indicating that the importance of shielding taxes with debt is lower

in Eastern Europe. This is the result of lower corporate tax rates which cause a much

smaller positive effect of adding debt to firm’s capital structures. The fact that

availability of credit is weaker, also limits firms from shielding taxes by debt. The

second hypothesis was accepted.



It was difficult to distinguish between bankruptcy cost effects and agency cost effects

on leverage. Indeed, several variables could proxy for both effects simultaneously.

From those variables that were found to only proxy for bankruptcy effects, it became

clear that indeed bankruptcy costs play a profound role on SME capital structures, in

both Eastern Europe as well as Western Europe. However, this bankruptcy effect was

found to be more important in Eastern Europe. Higher bankruptcy costs in Eastern

Europe caused a more negative effect on leverage for firms that tried to attract credits,

and banks were more reluctant to lend to firms, especially when firms were seeking

long term loans. These findings clearly lead to the third hypothesis to be accepted.

The regression analysis employed by the researchers and the data results proved that

indeed agency costs did not play an important effect on leverage ratios of SMEs in

both regions. The fourth hypothesis was thus accepted as well.

By means of a robustness check, the approach and reasoning of this research paper

were controlled for. In a similar approach as with the central research, the robustness

check investigated the differences and similarities between SME capital structures on

a country specific level, instead of a regional level. Based on the country-specific

scores and literature, expectations were formed on country-specific SME leverage

ratios and on the relationships between the variables. It was identified that also for

the individual countries, the regression results were in line with the expectations. This

observation created extra robustness to the research findings of Eastern and Western

Europe, since it diminished the probability that the variation of results was due to

unexplained factors.

One of the implications of this research paper is that, to a certain extent, the

generalization of Eastern Europe and Western Europe works rather well in explaining

regional capital structure differences. Even though Eastern Europe contains many

different countries with different backgrounds, cultures etc., it seems that the

economic history of the last two decades creates a comparable playing field for firms

in which to operate. It might be questioned however, how long this differentiation

might last, since in Eastern Europe, in the last decade, the different financial and

economic markets have grown rapidly and have become stronger. It is expected that

economic and financial improvements will continue, and the weak institutional factors



that were observed in the past years will improve, leading to more diversified and

stronger economies.

From this research it was argued that such improvements would directly lead to

changes in firms capital structures. It will be interesting to keep track on these

relationships over the next decade.

Another important implication from this research is that it clearly shows that a

country’s institutional settings, such as bankruptcy laws and availability of credit, are

directly translated into firms’ capital structures. In line with Booth et.al. (2001), it

proves that knowing a firm’s nationality, or regional origin, might explain a big extent

of its capital structure. The type, and availability of financial resources of the country,

or region, have a direct impact on firms’ investments and operations.

The last implication of this research paper is that the Tradeoff Theory works as an

explanatory theory behind debt-equity choices. Corporate taxes have a positive impact

on leverage ratios while bankruptcy costs have a negative impact on leverage ratios.

Capital structures thus seem to be the result of a tradeoff between these two effects.

Agency costs do not seem to play an observable role on SME capital structures, since

ownership and management are hardly dispersed, and banks solve the information

asymmetry themselves. This research showed that the Tradoff Theory is applicable

for smaller and private companies that do not have access to public markets. Besides,

the Tradeoff Theory works well across countries with a different stage of economic

development. This is an important input into the continuous Tradeoff Theory

discussion, namely it proves that the Tradeoff Theory is an applicable and useful

theory in explaining capital structure determination.

Another important implication from this research is that it clearly shows that a

country’s institutional settings, such as bankruptcy laws and availability of credit, are

directly translated into firms’ capital structures. The type, and availability of financial

resources have a direct impact on firms’ investments and operations.



Limitations of the research

This research paper focused on SMEs and on the differences between Eastern Europe

and Western Europe. It might be questioned whether such a division can really be

made. In the last fifteen years, many countries have been going through political,

social and economic transitions, and, therefore, the consideration of Eastern Europe as

one block of countries with similar economies might not be valid. Besides, only six

countries where selected from both Western Europe and Eastern Europe, which

represented both regions. It is probable that the research findings would have been

different if different, or more, countries would have represented both regions. It can

be noted that one of the underlying reasons of this study was to prove the differention

between Eastern and Western Europe. For that purpose, these six countries were

selected, because they differed from each other in respect to institutional factors. If

selected countries would have been more in line with each other in respect to taxes,

legal systems, bankruptcy proceedings and credit availability, the study could have

resulted in very different findings for this research.

The expectations and hyphotheses of this research paper were based and solely

focused on country scores that are continuously updated by scientists that cooperate

with the International Bank for Reconstruction and Development, which is part of the

World Bank. This resource was considered to be valid for the research, but resulted in

rather unilateral expectations. More objective expectations would have been created,

if multiple resources that compare institutional factors were available. Within the

timeframe and objectives of this research paper, more resources unfortunately were

not found.

Due to endogeneity in the original model, the target adjustment model had to be

computed by 2SLS regressions, instead of OLS regressions. Due to endogeneity in the

regression model, an Instrumental Variable had to be introduced, which caused the R-

squares to be unreliable in interpreting the strength of the model. This introduced

another limitation to the study.



The target adjustment model consists of three parts: taxes, bankruptcy costs and

agency costs. It is possible that slightly different results would have been obtained if

different variables would have been chosen as proxies for taxes, bankruptcy and

agency costs. Especially one of the tax variables included in this research study, the

EFFECTIVE TAX RATE, proved to be a weak explanator of the tax effect.

Another important point of limitation in this research was the availability of useful

data. Many firms and firm/year observations had to be deleted from the final data

samples, since certain data items were missing or incomplete. As can be observed in

the collected data for the sample countries in Chapter Three, and especially for

Ireland, the Netherlands, and Poland, the majority of original firm/year observations

had to be deleted. Regretfully, this made the research findings weaker than if data

availability would have been better, or consistent from country to country. Certain

variables had to be adjusted, because of data weaknesses, in order to make them

useful for this research. For the variables KINK and STANDARDIZED KINK,

bottom and ceiling values were created in order for the variation not to get too

extreme. From the descriptive statistics in Table 7, it can be seen that even after

adjusting for the most extreme 0,5 percent, still many extreme observations are found.

This is most observable for the variables OPERATING RISK and EFFECTIVE TAX

RATE. It seems that also for the four dependent variables still quite extreme values

have been used. These extreme outliers are especially apparent in Eastern Europe. A

possible solution could have been to delete more than the suggested 0,5 percent of the

outliers on both sides of the distributions, but this would have biased the results by

selecting only expected data values.

The data weaknesses limited the validity of the conclusions on these variables and on

the relationship between these variables and the measures of leverage.



Recommendations for further research

Central to this research paper is the state of development of a country’s economy, and

hence its financial system. This was shown to have a direct impact on firms capital

structure decisions. Since the state of development of Eastern European countries, and

Western European countries, does not stand still, it will be very interesting to see how

changes in countries legal systems, credit markets, tax policies etc. will impact the

development of the firms’ capital structures in these countries. Growth figures from

Eastern European economies indicate the rapid pace in which these economies are

changing. The more an economy grows, the more likely it is to get stronger and

improve in its financial climate. The conclusions of this research might be very

different in the future. It is, therefore, that researchers should continue to further study

the relationships between economy-specific factors and firms capital structures. Such

studies are important not only because they can yield interesting results, but more so

because they might indicate to policy makers how and in which way economic growth

might lead to improvements for national companies, and help to attract additional

foreign direct investments into the countries.

In contrast to most existing literature in the field of corporate finance, this study has

specifically focused on Small and Medium sized Enterprises. The share of the

economic activity in Europe that is provided by SMEs is far bigger than that of large

public firms. However, we still do not know much about SME financing which

suggests further research is needed. This research paper showed that the Tradeoff

Theory is a very useful and applicable theoretical framework to study and compare

SMEs from different regions and countries. Yet, it also identified many aspects of

SME leverage ratios that can not be typically explained by the Tradeoff Theory and

the target adjustment model. The focus of this research has been to test relationships

between SME leverage ratios and expected determinants of those leverage ratios, ex

post. Perhaps as economic growth hastens in Europe, information technology and

knowledge exchange will make financial data for new studies more readily available

to our understanding of capital markets, tradeoffs, debt leverage, etc. Indeed, such

new technology and data availability might yield more insights into SME financing

patterns and specific SME financing decisions. This is yet another recommendation

for future research.



Bibliography

Altman, E., 1968: “Financial Ratios, Discriminant Analysis and the Prediction of Corporate

Bankruptcy,” The Journal of Finance, Vol. 23, No. 4, pp. 589-609

Altman, E., R. Haldeman and P. Narayanan, 1977: “Zeta Analysis: A New Model to Identify

Bankruptcy Risk of Corporations,” Journal of Banking & Finance, Vol. 1

Ang, J.S., 1991: “Small Business Uniqueness & The Theory of Financial Management,”

Journal of Small Businesses Finance, Vol. 1, No.1, pp. 1-13

Antouniou, A., Y. Guney and K. Paudyal, 2002: “Determinants of Corporate Capital

Structure: Evidence from European Countries,” Working Paper, EFMA 2002 London

Meetings, London

Bancel, F. and U.R. Mittoo, 2002: “The Determinants of Capital Structure Choice: A Survey

of European firms,” Working Paper,

http://papers.ssrn.com/sol3/papers.cfm?abstract_id=299172

Bartholdy, J. and C. Mateus, 2006: “Debt and Taxes: Evidence from Bank-Financed Unlisted

Firms,” Working Paper F-2006-02, Aarhus School of Business, Department of Business

Studies

Berger, A.N. and G.F. Udell, 1998: “The Economics of Small Business Finance: The Roles of

Private Equity and Debt Markets in the Financial growth cycle,” Journal of Banking and

Finance, Vol. 22, pp. 613-673.

Berger, A.N. and G.F. Udell, 2003: “Small Business and Debt Finance,” Handbook of

Entrepreneurship, eds. Acs Z.J. & Audretsch D.B., Kluwer Academic Publishing.

Berglof, E., 2006: “Good moves for banks in Eastern Europe: The annual Transition report

from the European Bank for Reconstruction and Development finds big improvements in

the former Eastern Bloc,” BusinessWeek, 15 November 2006,

http://www.businessweek.com/globalbiz/content/nov2006/gb20061115_735945.htm

Booth, L., V. Aivazian, A. Demirguc-Kunt and L. Maksimovic, 2001: “Capital Structure in

Developing Countries,” The Journal of Finance, Vol. 56, No. 1, pp. 87-130

Bradley, M., G.A. Jarrel and H. Kim, 1984: “On the Existence of an Optimal Capital

Structure: Theory and Evidence,” The Journal of Finance, Vol. 39, No. 3, pp. 857-878.

Cassar and Holmes, 2003: “Capital structure and financing of SMEs: Australian Evidence,”

Accounting and Finance, Vol. 43, pp. 123-147.

Chittenden, F., G. Hall and P. Hutchinson, 1996: “Small Firm Growth, Access to Capital

Markets and Financial Structure: a Review of Issues and an Empirical Investigation,”

Small Business Economics, Vol.8, pp. 59- 67.

Claessens, S. and K. Tzioumis, 2006: “Ownership and financing structures,” OECD Report

Corporate Governance of Non Listed Companies in Emerging Markets, Chapter 2, pp.

39-64, OECD Publishing

Cornelli, F., R. Portes and M.E. Scaffer, 1996: “The Capital Structure of Firms in Central and

Eastern Europe,” Centre for Economic Policy Research, Discussion Paper No. 1392.



DeAngelo, H. and R.W Masulis, 1980: “Optimal Capital Structure under Corporate and

Personal Taxation,” Journal of Financial Economics, No. 8, pp. 3-29

Demirguc-Kunt, A., and V. Maksimovic, 1998: “Law, Finance and Firm Growth,” The

Journal of Finance, Vol. 53, No. 6, pp. 2107-2137

Djankov, S., O. Hart, C. McLiesh and A. Shleifer, 2006: “Efficiency in Bankruptcy, ongoing

research,” World Bank

Djankov, S., R. La Porta, F. Lopez-de-Silanes and A. Shleifer, 2006: “The Law and

Economics of Self-Dealing,” Journal of Financial Economics, forthcoming

Djankov, S., C. McLiesh and A. Shleifer, 2006: “Private Credit in 129 Countries,” Working

Paper, World Bank and Harvard University

Dzierzanowski, M and P. Tamowicz, 2004: “Ownership and Control of Polish Corporations,”

Corporate Ownership and Control, Vol. 1, No. 3, pp. 20-30

Erdilek, A., 2007: “Tax Competition in Europe,” Sunday’s Zaman, 30 April 2007,

http://www.todayszaman.com/tz-web/yazarDetay.do?haberno=109825

European Commission, 2003: “SMEs in Europe 2003,” Observatory of European SMEs,

No.7, European Commission Enterprise Publications.

European Commission, 2005: “The new SME definition: user guide and model declaration”,

Enterprise and Industry Publication, Publications Office, JURBIB SEP/2782,

Luxembourg

Fischer, E., R. Heinkel and J. Zechner, 1989: “Dynamic capital structure choice: theory and

tests,” Journal of Finance, Vol. 44, pp. 19–40.

Foglia, A., S. Laviola and P.M. Reedtz, 1998: “Multiple banking relationships and the

fragility of corporate borrowers,” Journal of Banking and Finance, Vol. 22, pp. 1441-

1456.

Frank, M.Z. and V.K. Goyal, 2003: “Testing the Pecking Order theory of Capital Structure,”

Journal of Financial Economics, Vol. 67, pp. 217-248.

Frank, M.Z. and V.K. Goyal, 2005: “Tradeoff and Pecking Order Theories of Debt,” Working

Paper, Center for Corporate Governance, Tuck School of Business at Dartsmouth

Freixas, X. and J.C. Rochet, 1997: “Microeconomics of Banking”, The MIT Press, and

unpublished notes for the second edition.

Gottlieb, M., 1999: “Accountancy in Poland’s Market Economy,” CPA Journal,

http://www.nysscpa.org/cpajournal/1999/0799/features/F34799.HTM

Graham, J.R., 2000: “How Big are the Benefits of Debt?,” The Journal of Finance, Vol.55,

No.5, pp.1901-1941

Grossman, S. and O. Hart, 1982: “Corporate financial structure and managerial incentive”,

The Economics of Information and Uncertainty, University of Chicago Press.



Hall, G.C., P.J. Hutchinson and N. Michaelas, 2000: “Industry Effects on the Determinants of

Unquoted SME’s Capital Structure,” International Journal of the Economics of Business,

Vol. 7, No. 3, pp.297-312

Hall, G.C., P.J. Hutchinson and N. Michaelas, 2004: “Determinants of the Capital Structure of

European SMEs,” Journal of Business Finance & Accounting, Vol. 31, No.5&6.

Hall, T.W., 2006: “National Variation in Financing Patterns,” OECD Report Corporate

Governance of Non Listed Companies in Emerging Markets, Chapter 3, pp. 65-90, OECD

Publishing

Harhoff, D.and T. Körting, 1998: “Lending relationships in Germany – Empirical evidence

from survey data,” Journal of Banking and Finance, Vol. 22, pp. 1317-1353

Harris, M. and A. Raviv, 1990: “Capital Structure and the information role of debt,” Journal

of Finance, Vol.45, pp. 321-249.

Harris, M. and A. Raviv, 1991: “The Theory of Capital Structure,” The Journal of Finance,

Vol. 46, No. 1, pp. 297-355.

Heshmati, A., 2001: “On the Growth of Micro and Small Firms: Evidence from Sweden,”

Small Business Economics, Vol. 17, No.3, pp. 213-228

Huang, S.G.H. and F.M. Song, 2001: “The Determinants of Capital Structure: Evidence from

China,” Working Paper, School of Economics and Finance and Center for China

Financial Research, The University of Hong Kong Press for SSRN.

Hutchinson, P., G. Hall and N. Michaelas, 1998: “The Determinants of Capital Structure in

Micro, Small and Medium – Sized Enterprises,” pp. 1-10,

http://www.sbaer.uca.edu/research/icsb/1998/pdf/44.pdf

Jalilvand, A. and R.S. Harris, 1984: “Corporate Behavior in Adjusting to Capital Structure

and Dividend Targets: An Econometric Study,” Journal of Finance, Vol. 39, No.1,

pp.127-145

Jensen, M., 1986: “Agency costs of free cash flow, corporate finance and takeovers,”

American Economic Review, Vol. 76, pp. 323-339.

Jensen, M. and H. Meckling, 1976: “Theory of the Firm: Managerial Behaviour, Agency

Costs and Ownership Structure,” Journal of Financial Economics, Vol. 3, pp. 305-360

Johnson, S., J. McMilland and C.M. Woodruff, 2002: “Property Rights and Finance,”

Working Paper, No. 231, Stanford Law School, John M. Olin Program in Law and

Economics

Kennedy, S., 2007: “Tax-Cut War Widens in Europe,” International Herald Tribune, 28 May

2007, Bloomberg News

Heidrick and Struggles, 2006: “Corporate governance in Europe: What’s the Outlook?”

Heidrick and Struggles 2006 study, Heidrick and Struggles, Amsterdam

Kim, W.S. and E.H. Sorensen, 1986: “Evidence on the Impact of the Agency Costs of Debt

on corporate Debt Policy,” Journal of Financial and Quantitative Analysis, Vol. 21, No.

2, pp. 131–144.



Klapper, L.F., V. Sarria-Allende and R. Zaidi, 2006: “A Firm-Level Analysis of Small and

Medium Enterprise Financing in Poland,” World Bank Policy Research, Working Paper

3984

La Porta, R., F. Lopez-de-Silanes and A. Shleifer, 1999: “Corporate Ownership around the

World,” The Journal of Finance, Vol. 54, No. 2, pp. 471-517

La Porta, R., F. Lopez-de-Silanes, A. Shleifer, and R.W. Vishny, 1997: “Legal Determinants

of External Finance,” Journal of Finance, Vol. 52, pp. 1131-1150.

La Porta, R., F. Lopez-de-Silanes, A. Shleifer, and R.W. Vishny, 1998: “Law and Finance,”

Journal of Political Economy, Vol. 106, pp. 1113-1155.

Leland, H., 1994: “Corporate debt value, bond covenants, and optimal capital structure,”

Journal of Finance, Vol. 49, pp. 1213–1252

Long, M.S. and E.B. Malitz, 1985: “Investment patterns and financial leverage,” In:

Freidman, B. (Ed.), Corporate Capital Structures in the United States. University of

Chicago Press, Chicago, IL.

Mansfield, E.R. and B.P. Helms, 1982: “Detecting multicollinearity,” The American

Statistician, Vol. 36, No. 3, pp. 158-160

Marsh, P., 1982: “The Choice Between Equity and Debt: An Empirical Study,” Journal of

Finance, Vol. 37, pp. 121-144.

Mayer, C., 1984: “The assessment: Money and banking: theory and evidence,” Oxford Review

of Economic Policy, Vol. 10, pp. 1-33

Mayer, C., 1988: “New issues in corporate finance,” European Economic Review, Vol. 32,

pp. 1167- 1189.

Mayer, C., 1990: “Financial Systems, Corporate Finance, and Economic Development,” In:

Hubbard G. (ed) Asymmetric Information, Corporate Finance, and Investment. The

University of Chicago Press, Chicago 1990.

Michaelas, N., F. Chittenden and P. Poutziouris, 1999: “Financial Policy and Capital

Structure Choice in U.K. SMEs: Empirical Evidence from Company Panel Data,” Small

Business Economics, Vol. 12, pp. 113-130

Miller, M.H., 1977: “Debt and Taxes,” The Journal of Finance, Vol. 32, No. 2

Modigliani, F. and M.H. Miller, 1958: “The Cost of Capital, Corporation Finance and the

Theory of Investment,” The American Economic Review, Vol. 48, pp. 261-297

Modigliani, F. and M.H. Miller, 1963: “Corporate Income Taxes and the Cost of Capital: A

Correction,” The American Economic Review, Vol. 55, Issue 3, pp. 524-536

Myers, S.C., 1977: “Determinants of Corporate Borrowing,” Journal of Financial Economics,

Vol. 5, pp. 147-176

Myers, S.C., 1984: “The Capital Structure Puzzle,” Journal of Finance, pp.575-592

Myers, S.C., 2001: “Capital Structure,” Journal of Economic Perspectives, Vol. 15, No. 2, pp.

81-102



Myers, S.C. and N. Majluf, 1984: “Corporate Financial and Investment Decisions when firms

have Information that Investors do not have,” Journal of Financial Economics, Vol.13,

No.2, pp.187-221

Nguyen, T.D.K. and N. Ramachandram, 2006: “Capital Structure in Small and Medium-sized

Enterprises – The Case of Vietnam,” ASEAN Economic Bulletin, Vol. 23, No. 2, pp. 192-

211

Peltoniemi, J., 2004: “The Value of Relationship Banking – Empirical Evidence on Small

Business Financing in Finnish Credit Markets,” Working Paper, University of Oulu,

Faculty of Economics and Business Administration, Dep. of Accounting and Finance

Petersen, M. and R. Rajan, 1994: “The benefits of lending relationships: Evidence from small

business data,” The Journal of Finance, Vol. 49, pp. 3-37

Petersen, M. and R. Rajan, 1995: “The effect of credit market competition on lending

relationships,” Quarterly Journal of Economics, Vol. 110, pp 407-433

Rajan, R.J. and L. Zingales, 1995: “What Do We Know About Capital Structure? Some

Evidence from International Data,” Journal of Finance, Vol. 50, pp. 1421-1460

Rupnik, J., 2000: “In Search of Central Europe: Ten Years Later,” In: Gardner, Hall, with

Schaeffer, Elinore & Kobtzeff, Oleg, (ed.), Central and South-central Europe in

Transition, Westport, Connecticut: Praeger (translated form French by Oleg Kobtzeff)

Sharpe, S., 1990: “Asymmetric information, bank lending, and implicit contracts: a stylized

model of customer relationship,” The Journal of Finance, Vol. 45, pp. 1069-1089

Shleifer, A. and R.W. Vishny, 1997: “A Survey of Corporate Governance,” The Journal of

Finance, Vol. 52, No. 2, pp. 737-783

Shyam-Sunder, L. and S.C. Myers, 1999: “Testing Static Trade Off Against Pecking Order

Models of Capital Structure,” Journal of Financial Economics, Vol. 51, pp. 219-244

Sogorb-Mira, F., 2005: “How SME Uniqueness Affects Capital Structure: Evidence From A

1994–1998 Spanish Data Panel,” Small Business Economics, Vol. 25, pp. 447–457

Stulz, R,. 1990: “Managerial Discretion and optimum financing policies,” Journal of

Financial Economics, Vol. 26, pp. 3-27

Svejnar, J., 2002: “Transition Economies: Performance and Challenges,” The Journal of

Economic Perspectives, Vol. 16, No.1, pp.3-28

Taggart jr., R.A., 1977: “A Model of Corporate Financing Decisions,” The Journal of

Finance, Vol. 32, No. 5, pp. 1467-1484

Titman, S. and R. Wessels, 1988: “The determinants of capital structure choice,” The Journal

of Finance, Vol. 43, pp. 1-21

Utrero Gonzalez, N., 2002: “Legal Environment, Capital Structure and Firm Growth:

International Evidence from Industry Data,” Working paper, Universidad Carlos III, Dep.

of Business Administration, Madrid, Spain

Van der Wijst, N., 1990: “Modeling inter-firm comparisons in small businesses,” Omega

International journal of Management Science, Vol. 18, No. 2, pp. 123-129



Van der Wijst, N. and R. Thurik, 1993: “Determinants of Small Firm Debt Ratios: an

Analysis of Retail Panel Data,” Small Business Economics, pp. 55- 65

Voulgaris, F., D. Asteriou and G. Agiomirgianakis, 2004: “Size and Determinants of Capital

Structure in the Greek Manufacturing Sector,” International Review of Applied

Economics, Vol. 18, No. 2, pp. 247-263

Warsaw Voice Online, 2003: “Legal Changes: New Bankruptcy Law,” Warsaw Voice Online,

16 January 2003, http://www.warsawvoice.pl/view/938/

Weimer, J. and J.C. Pape, 1999: “A Taxonomy of Systems of Corporate Governance,”

Corporate Governance, Vol. 7, No. 2, pp. 152-166

Woolridge, J.M., 2006: Introductory Econometrics, A Modern Approach, Thomson South –

Western, third edition

Wriston, W.B., 1997: “Maintaining Comparative Advantage in an Information Society,”

Proceedings, Federal Reserve Bank, Chicago, pp. 9-18

Zingales, L., 2000: “In Search of New Foundations,” The Journal of Finance, Vol. 55, No. 4,

pp. 1623-1653



Appendices – Table of Contents

Appendix I Generalized overview of corporate governance systems in Western

Europe __________________________________________________________ i

Appendix II Comparison between Eastern and Western Europe on institutional

factors __________________________________________________________ ii Development of capital markets in Eastern Europe __________________________________ ii Access to Credit _____________________________________________________________ iv Corporate Taxes ____________________________________________________________ vii Bankruptcy _________________________________________________________________ ix Corporate Governance _______________________________________________________ xii

Appendix III Overview of Industries ___________________________________________ xv

Appendix IV Descriptive Statistics for the individual countries ______________________xvi IV.I) Descriptive Statistics for Ireland, the Netherlands and Belgium___________________ xvi IV.II) Descriptive Statistics for Poland, Hungary and Ukraine _______________________ xvii

Appendix V Hausman tests_________________________________________________ xviii

Appendix VI Correlation Matrices____________________________________________ xix

Appendix VII 2SLS Regression Results for Eastern Europe versus Western Europe_____ xx

Appendix VIII T-test results for comparing the four proxies of Leverage_____________ xxii VIII.I)T-test Results of the four proxies of Leverage between the Western European

sample and the Eastern European sample _______________________________________ xxii VIII.II) T-test Results of the four proxies of Leverage among Ireland, the Netherlands

and Belgium ______________________________________________________________ xxiv VIII.III) T-test Results of the four proxies of Leverage among Poland, Hungary and

Ukraine __________________________________________________________________ xxvi

Appendix IX Robustness Check – Within the Western and Eastern European samples xxviii IX.I Within the Western European sample: Ireland, the Netherlands and Belgium ________ xxx IX.II Within the Eastern European Sample: Poland, Hungary and Ukraine _______________ xl

Appendix X Regression results for Ireland, the Netherlands and Belgium ____________ xlix

Appendix XI Regression results for Poland, Hungary and Ukraine __________________ liii


MSc Thesis in Finance & International Business A.K. Chudzinska & S.L.van der Bijl i

Appendix I

Generalized overview of corporate governance systems in Western Europe

System Countries

example

Authors Stakeholder orientation

Ownership Type of Law Role of Legal system in cases of

bankruptcy

External Market for corporate

control

Shareholder /

Creditor Protection

Relation between

stakeholders and Mgmt

Anglo-Saxon

UK, Ireland

Shleifer and Vishny (1997) Rajan and Zingales (1995) LaPorta et al (1997, 1998, 1999) Weimer and Pape (1999

Market

/ Shareholders

Dispersed

Common

law

Highly

Important

Yes

Shareholder

Short term

Germanic

Germany,

Netherlands, Switzerland,

Sweden, Austria,

Denmark, Norway and

Finland

Shleifer, Vishny (1997) Rajan and Zingales (1995) LaPorta et al (1997, 1998, 1999) Weimer and Pape (1999

Bank

/ Employees

(Unions)

Moderately to

highly concentrated

(German) civil law

Less important

No

Creditor

Long term

Latin

France, Italy, Spain, and

Belgium

Weimer and Pape (1999)

Family / bank /

Government

Highly

Concentrated

(French) civil

law

Not important

No

Creditor

Long term


MSc Thesis in Finance & International Business A.K. Chudzinska & S.L.van der Bijl ii

Appendix II

Comparison between Eastern and Western Europe on institutional factors

The observed differences between Eastern and Western Europe are broadly

categorized and supported by country scores from several resources. In order to find

general differences between Western and Eastern Europe, the country scores were

developed into two samples: one based on several Western European countries, and

the other based on several Eastern European countries. This section will conclude

with a short review of determinants of capital structure mostly found in existing

literature.

The development of capital markets in Eastern Europe started only after the fall of

communism, while in Western Europe well developed capital markets were far longer

in place. The financial circumstances under which firms operate are, therefore,

different in both geographic regions. Capital markets provide financing sources, and

as will be discussed later, the access to credit is important for most Small and Medium

sized Enterprises. Credit is the sole form of external financing for most small firms,

and consequently, it has a big impact on firms abilities to optimize investments and

capital structure. Before discussing the concrete differences between Western and

Eastern Europe in respect to credit access, corporate taxes, bankruptcy laws and

corporate governance laws, one must first discuss and understand the development of

capital markets in Eastern Europe.

Development of capital markets in Eastern Europe

A clear taxonomy that generalizes different capital markets and legal systems, as

presented for Western Europe in Appendix I, does not exist for Eastern Europe. Many

Eastern European countries that were under transition from centrally planned

economies introduced reforms to implement a Western style market economy

(Svejnar, 2002). Until the late 1990’s, many of these reforms proved to be

unsuccessful, because they lacked supportive legal structures. The importance of the

legal system was underestimated. Even though most Eastern European countries have

law systems based on the German and French jurisdiction, i.e. civil law codes, the law

systems were often old-fashioned and the enforcement of laws was weak. Gradually


MSc Thesis in Finance & International Business A.K. Chudzinska & S.L.van der Bijl iii

most countries started modernizing their law system in which property rights and

shareholder protection were crucial (Svejnar, 2002). The most successful countries in

implementing effective legal systems were Estonia, Latvia, Lithuania, Poland,

Hungary and Slovenia (Svejnar, 2002). Such legal reforms and protection were crucial

to the future development of financial markets.

In the absence of an effective financial market, the major supply of finance came from

banks. To offset the sole reliance on bank financing, in the early 1990s Eastern

European governments introduced plans to implement Anglo Saxon style capital

markets, in which ownership of public firms is more widely dispersed. These plans

proved to be disappointing because ownership ratios of public firms were actually

concentrated, as a result of high agency costs. Large investors concentrated their

block holdings in order to have a stronger influence on a company’s decision making,

to decrease abuse of funds. This resulted in minority shareholders getting smaller

influence, and hence, their agency costs rose.

Another development in the 1990’s was that the large former state-owned banks of

many Eastern European countries accumulated non-performing loans because of slow

economic improvements. In order to prevent a banking crisis, the governments of

Poland, Hungary and the Czech Republic privatized virtually all major banks and sold

them off to large Western banks in the late 1990’s. Since then, the role of the banking

sector in the Eastern European economies has grown significantly, especially with the

expertise and size of the new Western banking players. The first Eastern European

countries that joined the European Union (Estonia, Czech Republic, Hungary, Latvia,

Lithuania, Poland, Slovakia, and Slovenia) have made the largest progress in

reforming their banking systems. Banks in most of the region now enjoy better legal

protection, courts are better at enforcing laws, and banking supervision and regulation

have become more effective (Svejnar, 2002).

Bank lending is the major source of financing in Eastern Europe and contributes

largely to the high economic growth rates in this region. Even though banks dominate,

the financial sector is also broadening because of growing stock markets. This is due

to improvements in disclosure rules, private equity and pension funds promoting long

term saving (Berglof, 2006).


MSc Thesis in Finance & International Business A.K. Chudzinska & S.L.van der Bijl iv

Below, differences are analyzed between Western and Eastern European economies

on credit access, corporate tax rates, bankruptcy laws and proceedings and issues

related to corporate governance. Findings for the more economically developed

Western Europe are based on nine countries: Austria, Belgium, Denmark, France,

Germany, Ireland, Italy, the Netherlands and the United Kingdom. Findings for

Eastern Europe are based on seven countries: Czech Republic, Hungary, Poland,

Romania, Russia, Slovak Republic and Ukraine. Even though clear differences can be

observed when studying Eastern and Western Europe, the countries that determine the

Western and Eastern European samples differ with each other as well.

Access to Credit

A firm’s access to finance involves several institutional issues that firms face when

trying to attract investors. The focus here is on creditors, and the index used for the

analysis is called the “Access to Credit” index, developed by Djankov, McLiesh and

Shleifer (2006). This index explores three indicators of credit access for firms. The

first indicator deals with legal rights of creditors and borrowers. This measure shows

the degree of effectiveness of bankruptcy laws in facilitating lending by creditors, on

a scale of 0 to 10, where 10 indicates highest effectiveness. The second indicator is a

credit information index. This index measures accessibility and quality of credit

information available through credit registries. The third indicator shows the

availability of current credit information of individuals and firms from private and

public credit registries and bureaus. This indicator is expressed as a percentage of

registered adults with credit history (Djankov, McLiesh and Shleifer 2006).

Credit registries such as the UK Credit Registry, are institutions that collect and share

information on credit histories of firms and individuals. The information that is

provided helps creditors to assess risks and allocate credit most efficiently. Access to

credit is better in countries where credit registries have higher coverage and better

quality of information. In such countries, firms need not rely on personal relations

with lending institutions when trying to obtain credit. Countries with stronger legal

rights and more credit information about firms are associated with deeper credit

markets and lower default rates (Djankov, McLiesh and Shleifer, 2006).


MSc Thesis in Finance & International Business A.K. Chudzinska & S.L.van der Bijl v

In Table II.1 and Figure II.1 below, different legal rights, credit information and

coverage scores for Eastern and Western European countries under study are shown.

In Western Europe, it appears that the United Kingdom (UK), Ireland and Germany

score very high on all three indicators, meaning that creditors have more and better

information in these markets as such, and about companies specifically. Companies

from these countries are likely to have easier access to credit. This in turn might

translate into more favorable interest rates. Countries in Western Europe that score

lower on the legal rights index are Italy, followed by Austria, France and Belgium. In

terms of the availability and quality on credit information, France and Denmark score

considerably low.

In Eastern Europe, it appears that Czech Republic scores best on all three indicators

followed by Slovak Republic and Poland. Even though Ukraine scores very high on

the legal rights index, it has very weak quality and quantity of credit information

available. Overall, Russia scores worst on all three indicators in this region.

Table II.1 – Access to Credit in Western and Eastern European countries

Legal Rights

Index

Credit

Information

Index

Coverage (%

adults)

WESTERN EUROPE

Austria 5 6 41,1

Denmark 8 4 11,5

The Netherlands 7 5 68,9

Belgium 5 4 56,2

Germany 8 6 94,4

Ireland 8 5 100

Italy 3 5 74,8

France 5 4 12,3

United Kingdom 10 6 86,1

EASTERN EUROPE

Poland 4 4 38,1

Ukraine 8 0 0

Czech Republic 6 5 54,5

Hungary 6 5 5,9

Romania 4 5 8,1

Slovakia 9 3 46,3

Russia 3 0 0 Source: The International Bank for Reconstruction and Development / World Bank: www.doingbusiness.org


MSc Thesis in Finance & International Business A.K. Chudzinska & S.L.van der Bijl vi

Figure II.1 – Average Access-to-Credit scores in Western and Eastern Europe

Access to Credit

1

10

100

Legal Rights Index Credit Information Index Coverage (% adults)

Western Europe

Eastern Europe

Source: The International Bank for Reconstruction and Development / World Bank: www.doingbusiness.org

When comparing Western Europe with Eastern Europe, the following trends are

observable. First, Eastern and Western European countries score similar on legal

rights, on average. Even though legal systems in the Eastern transition economies are

often weaker compared to the Western standards, it may seem surprising that the legal

rights index appears to be similar for both blocks. There might be a logical reason for

this. Under weak legal systems, creditors are often found to have large monitoring

power. This larger power makes the role of courts less necessary. Creditors

monitoring power comes from a variety of control rights. They receive these control

rights when firms default or violate debt covenants and because they typically lend

short term. Therefore borrowers have to come back at regular, short intervals for more

funds (Shleifer and Vishny, 1997). The second observed trend is that Eastern

European countries score worse on credit information and coverage indexes,

compared to Western Europe. Indeed, the quality and availability of credit

information needs to be improved in the Eastern European market in order to decrease

the gap between Western and Eastern Europe.

Implications from these findings are as follows: in Eastern Europe banks have less

credit information available about firms. This is because companies in transition

economies usually have shorter credit histories due to the fact that keeping track of

credit started later than the actual transition itself. Also accounting standards changed

which lead firms having to rebuild their data history from scratch. For example, only

in 1995 Poland introduced an accountancy act that requires minimum record-keeping

(Gottlieb, 1999). These shorter and less qualitative credit histories make banks and


MSc Thesis in Finance & International Business A.K. Chudzinska & S.L.van der Bijl vii

other creditors more reluctant to extend credit while making lending decisions to

Eastern European firms. This leads to banks giving smaller loans and demanding

higher compensation, i.e., higher interest rates. Moreover, higher interest rates lead to

lower demand for loans. This in turn gives a reason to expect leverage ratios to be

lower in this region, when compared to Western Europe. A last implication of low

public credit information availability is that banks have to build up credit registries of

firms themselves. They will build up stronger personal relations with firms in order to

stay up-to-date with company records. Because of the competitive sensitivity of such

information, banks are not likely to share this information, and therefore will remain a

major stakeholder in the firms financial decision making. A strong hands-on approach

to borrowing is expected to be the end result.

Corporate Taxes

A wave of international corporate tax competition has been taking place around the

whole continent. The European countries have been decreasing corporate tax rates in

order to attract and keep investments. The recent tax rivalry has been referred to as a

“race to the bottom” or “predatory practices” (Erdilek, 2007). It started especially

after Ireland and new European Union member states from Eastern Europe succeeded

in attracting investment and irking their biggest competitors with tax rates below

twenty percent, which are among the world’s lowest (Kennedy, 2007). The larger

Western European countries were forced to follow this trend in order not to loose

investments, especially from multinational companies and to be able to compete with

other European countries. In 1993 the EU had an average corporate tax rate of 38

percent, while in 2006 the EU average had dropped to 25,8 percent (Erdilek, 2007).


MSc Thesis in Finance & International Business A.K. Chudzinska & S.L.van der Bijl viii

Table II.2 – Corporate Tax Rates in Western and Eastern European countries in % - over time

YEAR 2000 2001 2002 2003 2004 2005

WESTERN EUROPE

Austria 34,00 34,00 34,00 34,00 34,00 25,00

Denmark 32,00 30,00 30,00 30,00 30,00 28,00

The Netherlands 35,00 35,00 34,50 34,50 34,50 31,50

Belgium 40,17 40,17 40,17 33,99 33,99 33,99

Germany 51,60 38,36 38,36 39,58 38,29 38,31

Ireland 24,00 20,00 16,00 12,50 12,50 12,50

Italy 41,25 40,25 40,25 38,25 37,25 37,25

France 36,66 35,33 34,33 34,33 34,33 33,83

United Kingdom 30,00 30,00 30,00 30,00 30,00 30,00

EASTERN EUROPE

Poland 30,00 28,00 28,00 27,00 19,00 19,00

Ukraine 30,00 30,00 30,00 30,00 25,00 25,00

Czech Republic 31,00 31,00 31,00 31,00 28,00 26,00

Hungary 18,00 18,00 18,00 18,00 16,00 16,00

Romania 25,00 25,00 25,00 25,00 25,00 16,00

Slovakia N/A 29,00 25,00 25,00 19,00 19,00

Russia N/A 35,00 24,00 24,00 24,00 24,00 Source: KPMG's Corporate Tax Rate Survey, an international analysis of corporate tax rates from 1993 to 2006, KPMG Audit – Tax and Advisory, 2006

Table II.2 is based on the yearly corporate tax rates reports of KPMG Audit – Tax and

Advisory (2006), and indicates that Austria, Germany and Ireland had the highest

drop in corporate tax rates in the period 2000 - 2005 in Western Europe. This amounts

to nine, thirteen, and twelve percent, respectively. Similarly, Poland, Slovakia and

Russia decreased their rates in the same period with eleven, ten and eleven percent

respectively.

Figure II.2 – Average Corporate Tax Rates in Western and Eastern Europe - over time

Source: KPMG’s Corporate Tax Survey, and international analysis of corporate tax

rates from 1993 to 2006, KPMG Audit - Tax and Advisory, 2006

Average Corporate Tax Rates in Europe - %

15,00

20,00

25,00

30,00

35,00

40,00

2000 2001 2002 2003 2004 2005

Year

Ra

te

Western

Europe

Eastern

Europe


MSc Thesis in Finance & International Business A.K. Chudzinska & S.L.van der Bijl ix

From Figure II.2 one can see that both regions have been following a similar trend of

decreasing corporate tax rates during years 2000-2005. This trend is expected to

continue into the future (Erdilek, 2007). Nevertheless, research for Eastern Europe

indicates much lower average tax rates than Western Europe. This is due to the fact

that emerging economies in the Eastern region are in a bigger need of attracting

foreign investors and are offering very competitive tax rates. Besides, the heavy tax

cuts should be seen as an incentive for firms to invest, in order to offset the higher

risks in these emerging economies, and the higher costs of short-term bank borrowing

at high interest rates.

Looking at the European corporate tax trends, it can be expected that the role of taxes

in setting capital structures, is declining in both the Western and Eastern regions.

Since the percentage of payable taxes is lower than in previous years, the relative

amount of tax shields is also lower. Thus, in the light of the Tradeoff Theory, present

day leverage ratios are expected to be lower in both regions than a decade ago.

Besides, the importance of taxes in determining leverage ratios seems to be declining.

Looking at the relative tax rates between Eastern and Western Europe, the same

reasoning would indicate lower leverage ratios in Eastern Europe. What is more, since

in both regions the role of taxes is decreasing, the role of bankruptcy and agency costs

is expected to be higher in setting a target leverage ratio, under the Tradeoff Theory.

Bankruptcy

The methodology followed here is based on Djankov, Hart, McLiesh and Shleifer

(2006). In the table below three indicators of a country’s bankruptcy enforcement are

depicted. The first indicator shows the number of months required to complete a

typical bankruptcy case. The more efficient the bankruptcy proceedings, the shorter

the time it takes for an investor to receive his dues. The second indicator illustrates the

costs of the bankruptcy proceedings as a percentage of the estate’s value. These costs

include costs of courts, fees for insolvency practitioners, independent assessors,

lawyers and accountants. The third indicator measures the efficiency of foreclosure of

bankruptcy procedures. It estimates the percentage of the value that can be recovered

by creditors, tax authorities and employees from an insolvent firm. It should be

mentioned that the second and third indicator presented below are related: the higher


MSc Thesis in Finance & International Business A.K. Chudzinska & S.L.van der Bijl x

the costs of bankruptcy proceedings, the lower the recovery rate, since the bankruptcy

costs are incurred from the estate’s value (Djankov, Hart, McLiesh and Shleifer,

2006).

Table II.3 – Bankruptcy Scores for Western and Eastern European countries

Cost

% of estate

WESTERN EUROPE

Austria 13,2 18 73,7

Denmark 36 4 70,5

The Netherlands 20,4 1 86,3

Belgium 10,8 3,5 86,4

Germany 14,4 8 53,1

Ireland 4,8 9 87,9

Italy 14,4 22 39,7

France 22,8 9 48

United Kingdom 12 6 85,2

EASTERN EUROPE

Poland 36 22 27,9

Ukraine 34,8 42 8,7

Czech Republic 110,4 14,5 18,5

Hungary 24 14,5 39,7

Romania 55,2 9 19,9

Slovakia 48 18 48,1

Russia 45,6 9 28,7

Months Recovery Rate

Source: The International Bank for Reconstruction and Development / World Bank: www.doingbusiness.org based on Djankov, Hart, McLiesh and Shleifer (2006)

Figure II.3 – Average Bankruptcy Scores for Western and Eastern Europe

Average Bankruptcy Scores

0

10

20

30

40

50

60

70

80

Months Cost = % of estate Recovery Rate

Western Europe

Eastern Europe

Source: The International Bank for Reconstruction and Development / World Bank: www.doingbusiness.org based on Djankov, Hart, McLiesh and Shleifer (2006)


MSc Thesis in Finance & International Business A.K. Chudzinska & S.L.van der Bijl xi

From Table II.3 and Figure II.3 it can be observed that the duration of bankruptcy

proceedings takes over three times longer in the countries of Eastern Europe as

compared to those of Western Europe- 50 months to 18 months respectively.

Bankruptcy costs are on average twice as high in the East compared to the West- 19

percent to 9 percent. Partly because of these two weaknesses, the recovery rates of

only 28 percent are much lower in the Eastern region as well. Moreover, while

looking at the costs and recovery rates together, one can notice that in Eastern Europe

more value of the firm is lost from bankruptcy then in Western Europe. Ireland and

Belgium have the shortest bankruptcy durations in the Western sample, taking less

than one year. The Czech Republic has bankruptcy proceedings that can take up to

nine years.

The Netherlands and Belgium have the lowest costs, amounting to 1 and 3,5 percent,

respectively. These costs in Ukraine amount to 42 percent. Recovery rates are very

high in Western Europe, amounting to 70 percent on avarege. Ireland, Belgium and

the Netherlands perform especially very well: more than 85 percent of the firm’s

value is recovered after bankruptcy. In Eastern Europe, recovery rates are much

lower, with Ukraine showing the worst recovery rate of 8,7 percent. Such low

recovery rates tend to create higher financial risk for all investor groups.

The outdated bankruptcy laws in Eastern Europe are a possible explanation for the

observed differences. Nevertheless, the countries are improving their law systems by

introducing new Bankruptcy and Insolvency Acts, e.g. Poland and Estonia introduced

new Acts in 2003 and Czech Republic is replacing its bankruptcy law in 2007. The

previous bankruptcy laws in Czech Republic dated from before 1950, while in Poland

they dated from 1934 (Warsaw Voice Online, 2003).

The scores on the three bankruptcy indicators clearly suggest that bankruptcy costs

are higher in Eastern Europe than in Western Europe. Creditors lending capital to

Eastern European firms need to wait longer for repayment in case of bankruptcy.

Besides, they will have to pay more to the legal practitioners and afterwards will find

that they recover a smaller portion of their initial investment. When applying these

findings to capital structure theory, specifically the Tradeoff Theory, there are some

implications. Creditors in East will be more reluctant to give credits in the first place,

due to bigger risks and higher bankruptcy costs. Besides, they will ask for higher


MSc Thesis in Finance & International Business A.K. Chudzinska & S.L.van der Bijl xii

interest rates in order to offset these risks and costs. Thus, higher bankruptcy costs

and higher credit risk may lead to lower leverage ratios in Eastern Europe.


There are significant differences across countries in the degree of investor protection

(Demirguc-Kunt, 2002). The Investor Protection index, developed by Djankov, La

Porta, Lopez-de-Silanes and Shleifer (2006), measures such country differences. The

Investor Protection Index measures the strength of investor protection against

directors’ misuse of corporate assets for personal gain. This index is composed of

three dimensions and mainly focuses on legal issues. The first is a disclosure index

which indicates the transparency of transactions. The second indicates the extent to

which a director can be held liable or brought to court for mismanagement of the

investors’ funds. The third dimension indicates the extent to which the legal system

supports shareholders in case of disputes with management. The corporate governance

index ranges from 0 to 10, with higher values indicating better corporate governance

in the country.

Looking at the data in Table II.4 below one can notice that the average investor

protection index in Eastern Europe is slightly lower then in Western Europe, equal to

almost 5 and 6 points, respectively. Ireland and the U.K. represent the highest scores

from the two samples. This could be expected, as these countries have a common law

system in place in which high emphasis is put on investor protection. Surprisingly,

Austria and Ukraine indicate a similar, low score of 3,7. In Eastern Europe Poland

and Romania indicate the highest corporate governance equal to 6 points.


MSc Thesis in Finance & International Business A.K. Chudzinska & S.L.van der Bijl xiii

Table II.4 – Investor Protection Index for Western and Eastern European countries

Disclosure

Index

Director

Liability

Index

Shareholder

Suits Index

Investor

Protection

Index

WESTERN EUROPE

Austria 2 5 4 3,7

Denmark 7 5 7 6,3

Netherlands 4 4 6 4,7

Belgium 8 6 7 7

Germany 5 5 5 5

Ireland 10 6 9 8,3

Italy 7 2 6 5

France 10 1 5 5,3

United Kingdom 10 7 7 8

EASTERN EUROPE

Poland 7 2 9 6

Ukraine 1 3 7 3,7

Czech Republic 2 5 8 5

Hungary 2 4 7 4,3

Romania 9 5 4 6

Slovakia 2 4 7 4,3

Russia 7 2 7 5,3 Source: The International Bank for Reconstruction and Development / World Bank: www.doingbusiness.org developed by Djankov, La Porta, Lopez-de-Silanes and Shleifer (2006)

Figure II.4 – Average Investor Protection Index for Western and Eastern Europe

Source: The International Bank for Reconstruction and Development / World Bank: www.doingbusiness.org developed by Djankov, La Porta, Lopez-de-Silanes and Shleifer (2006)

It is worth noticing that the corporate governance scores presented above differ from

other available sources. For example, the report of Heidrick and Struggles (2006)

indicated that the Netherlands had the second strongest corporate governance scores

in Western Europe during the years 2003-2006. Italy and Germany, on the other hand,

scored relatively lower then the scores presented above. The differences in these

scores are most likely due to the assumptions that need to be made in calculating the

scores. Regretfully, the Heidrick and Struggles (2006) report did not include country

Investor Protection Index

4,4 4,6

4,8 5

5,2 5,4 5,6

5,8 6

Western Europe Eastern Europe


MSc Thesis in Finance & International Business A.K. Chudzinska & S.L.van der Bijl xiv

scores for Eastern Europe. Since the focus here lies on comparing Eastern with

Western Europe, the Heidrick and Struggles (2006) scores are not presented.

The differences observed in corporate governance scores between Western and

Eastern Europe are very diverse and country specific. Therefore, it is hard to

generalize between West and East. The variation between East and West mainly

comes from the first two indicators, the disclosure index and the director liability

index. They indicate that most of the Eastern European law systems are still

underdeveloped in comparison to Western standards, indicating weak shareholder

protection. A possible reason for the shareholder suits index not to under perform the

Eastern sample is a strong investor concentration in firms. LaPorta et al (1999) found

that in economies with weak shareholder protection, relatively few firms are widely

held. Dzierzanowski and Tamowicz (2004) found that in Poland and other Eastern

European transition economies, voting control in listed corporations is remarkably

concentrated. Shleifer and Vishny (1997, pp. 753) stated “in cases where legal

protection does not give enough control rights to small investors, investors can get

more effective control rights by being large or concentrated.” By being concentrated,

investors have much higher influence on management and solve disputes before they

occur. A legal system is not needed in such cases. In effect, concentrated ownership

provides the needed legal protection to investors. So, even though the investor

protection index only shows small deviations between Eastern and Western countries,

the problems concerning weak law systems in Eastern Europe might be bigger than

they seem to be.

On a country-specific basis one can infer that agency costs seem to be lowest for

Ireland, the UK and Belgium, due to strong and effective corporate governance.

Similarly in Eastern Europe, Ukraine, Slovak Republic and Hungary are likely to have

higher agency costs than the other countries presented, due to weak corporate

governance. One would expect higher agency costs in Eastern Europe, due to lower

transparency and less effective legal systems. When connecting corporate governance

to capital structure there are some implications on leverage. In light of the Tradeoff

Theory, countries with low corporate governance, and hence, high agency costs are

expected to have lower leverage, since creditors are less inclined to lend to companies

from these countries. The general assumption is that this is another reason to expect

lower leverage ratios in Eastern European countries.


MSc Thesis in Finance & International Business A.K. Chudzinska & S.L.van der Bijl xv

Appendix III

Overview of Industries

The industry classification used is the European Union’s NACE Rev.1.1. This classification is identical to the UK SIC (2003) industry

classification. At the left, the industry codes are shown. At the right it is displayed which generalization was applied to reduce the number

of industries used in testing for an industry effect. Firm data was collected from firms that are active in the following industries.

NACE Rev.1.1 Code Industry Name Industry classification used

- 1000 – 1450 Mining and Quarrying

- 1500 – 3720 Manufacturing Manufacturing

- 4000 – 4100 Electricity, Gas and Water

- 4500 – 4550 Construction

- 5000 – 5274 Wholesale and Retail

- 5500 – 5552 Hotels and Restaurants

- 6000 – 6420 Transport, Storage, Communication

- 7100 – 7499 Renting, Computer Business, R&D, Other Business

- 7500 – 7530 Public Administration and Defence Services

- 8000 – 8042 Education

- 8500 – 8532 Health and Social Work

- 9000 – 9305 Other Community, Social and Personal Service Activities

- 9500 – 9700 Activities and Households

- 6500 – 6720 Financial Intermediation Excluded from this research

- 7000 – 7032 Real Estate

- 0100 – 0202 Agriculture, hunting and forestring

- 0500 – 0502 Fishing Other

- 9900 – 9999 Extra-territorial Organizations and Bodies


MSc Thesis in Finance & International Business A.K. Chudzinska & S.L.van der Bijl xvi

Appendix IV

Descriptive Statistics for the individual countries

IV.I) Descriptive Statistics for Ireland, the Netherlands and Belgium


Total Leverage 1780 0,0247 1,6188 0,5434 0,2449




KINK 1780 0,0000 8,0000 5,3648 3,3062STANDARDIZED KINK 1780 0,0000 48,9939 2,7063 4,3471EFFECTIVE TAX RATE 1780 -3,6650 5,2895 0,1383 0,3715

TANGIBILITY 1780 0,0000 0,9873 0,2787 0,2261

SIZE 1780 3,7543 11,1133 8,7499 0,8823

Z-SCORE 1780 -6,3526 43,6558 5,2563 3,9034

OPERATING RISK 1780 1,0182 6799,1429 467,5815 596,2499

PROFITABILITY 1780 -0,7806 0,7226 0,0775 0,1084

GROWTH 1780 -0,6737 2,7290 0,1152 0,2778


Total Leverage 6467 0,0128 3,1678 0,7134 0,2810





TANGIBILITY 6467 0,0000 0,9998 0,2200 0,2188

SIZE 6467 2,8904 13,1760 8,9152 1,1538

Z-SCORE 6467 -27,7410 83,2823 5,1881 3,7571

OPERATING RISK 6467 0,5774 14251,0936 782,4664 1037,8399

PROFITABILITY 6467 -1,8270 1,2292 0,0743 0,1632

GROWTH 6467 -0,9989 21,8980 0,0757 0,5511


Total Leverage 30184 0,0325 3,0930 0,6877 0,2409





TANGIBILITY 30184 0,0000 0,9921 0,2441 0,2004

SIZE 30184 4,2905 11,4871 8,2709 0,9957

Z-SCORE 30184 -23,6479 35,0859 3,4388 2,9616

OPERATING RISK 30184 0,6283 5078,5513 278,9585 383,7862

PROFITABILITY 30184 -0,9288 0,8068 0,0496 0,1095

GROWTH 30184 -0,7960 7,0611 0,0744 0,2968

Ireland - Descriptive Statistics

The Netherlands - Descriptive Statistics

Belgium - Descriptive Statistics


MSc Thesis in Finance & International Business A.K. Chudzinska & S.L.van der Bijl xvii

Appendix IV Continued

Descriptive Statistics for the individual countries

IV.II) Descriptive Statistics for Poland, Hungary and Ukraine


Total Leverage 7.233 0,0218 2,0093 0,5354 0,2736

Short Term Leverage 7.233 0,0061 1,9413 0,4579 0,2583

Long Term Leverage 7.233 0,0000 1,3110 0,0775 0,1302

Adjusted Total Leverage 7.233 0,0010 1,7680 0,3009 0,2112

KINK 7.233 0,0000 8,0000 5,0104 3,2685

STANDARDIZED KINK 7.233 0,0000 18,0179 1,9532 1,9907

EFFECTIVE TAX RATE 7.233 -3,5333 4,0952 0,1881 0,2981

TANGIBILITY 7.233 0,0000 0,9952 0,3879 0,2593

SIZE 7.233 3,3376 10,6201 7,5273 1,1503

Z-SCORE 7.233 -10,9024 46,3644 5,0509 4,7594

OPERATING RISK 7.233 0,0362 3.741,1706 181,8478 272,7106

PROFITABILITY 7.233 -0,7374 1,2117 0,0785 0,1468

GROWTH 7.233 -0,8274 8,9866 0,1422 0,4003


Total Leverage 6.752 -0,5779 1,5177 0,5854 0,2222


Long Term Leverage 6.752 0,0000 1,0247 0,0877 0,1266

Adjusted Total Leverage 6.752 0,0157 1,3790 0,5525 0,2276

KINK 6.752 0,0000 8,0000 6,2505 3,0189



TANGIBILITY 6.752 0,0000 2,6534 0,3688 0,2190

SIZE 6.752 1,3480 11,4983 7,5504 1,1781

Z-SCORE 6.752 -8,6828 54,9938 5,9136 2,9749

OPERATING RISK 6.752 0,3016 583951,5484 683,3542 13797,7144

PROFITABILITY 6.752 -1,4941 3,6075 0,0821 0,1416

GROWTH 6.752 -0,9977 8,7573 0,2725 0,5744


Total Leverage 15.678 0,0035 4,4148 0,4274 0,3922


Long Term Leverage 15.678 -0,5463 2,3325 0,0579 0,1503

Adjusted Total Leverage 15.678 -0,3173 2,4972 0,2584 0,2810

KINK 15.678 0,0000 8,0000 2,8353 3,5138



TANGIBILITY 15.678 0,0000 1,0000 0,5463 0,2643

SIZE 15.678 1,1598 12,4474 6,6392 1,3047

Z-SCORE 15.678 -46,7200 298,6443 9,6069 21,2086

OPERATING RISK 15.678 0,0108 9659,1298 129,9087 439,0184

PROFITABILITY 15.678 -1,8887 1,6214 -0,0096 0,1550

GROWTH 15.678 -0,9925 6,9987 0,0464 0,4826

Poland - Descriptive Statistics

Hungary - Descriptive Statistics

Ukraine - Descriptive Statistics


MSc Thesis in Finance & International Business A.K. Chudzinska & S.L.van der Bijl xviii

Appendix V

Hausman tests

Lagged Total Leverage

Lagged Short Term Leverage

Lagged Long Term Leverage

Lagged Adjusted Total Leverage

Eastern Europe vs. Western Europe -0,0455 [-10,9148]***

-0,0962 [-11,5136]***

-0,0204 [-1,9836]**

-0,1501 [-15,8942]***

Within the Western European sample, testing differences between Ireland, the Netherlands and Belgium

-0,042 [-6,5987]***

-0,0857 [-6,9391]***

-0,038 [-3,4392]***

-0,0759 [-4,8274]***

Within the Eastern European sample, testing the differences between Poland, Hungary and Ukraine

-0,038 [-3,1812]***

-0,1022 [-9,4427]***

-0,04 [-2,6699]***

-0,1361 [-12,9317]***


Hausman tests were conducted in order to test for endogeneity of the LAGGED LEVERAGE variable. The LAGGED LEVERAGE variables represent the leverage of period t-1, which is included in the target adjustment model. All measures of lagged leverage are found to be endogenous, indicating that the Ordinary Least Squares regression method is not the proper method of testing the regression model. Hence, an instrumental variable (IV) needs to be calculated which replaces lagged leverage in the model. This model then can be tested by a 2-Stage Least Squares regression method. The Hausman test scores are calculated as follows:

• First, normal OLS regressions are run, where the suspect endogenous variable is used as the dependent variable and all independent variables (except the suspect endogenous variable) are included, including the Instrumental variable for the suspected endogenous variable. In this case, the endogenous variable is leverage lagged by one period, and the instrumental variable is leverage lagged by two periods.

• Second, the residuals of the OLS regression are used as an independent variable for the original OLS regression.

• Third, the original OLS regression is run, with the normal dependent variable on the left, and all independent variables (including the instrumental variable) plus the residuals of the first regression on the right.

• Fourth, the coefficients of the residuals are presented in the table below. In case these are significant, which they are, the suspected variable is indeed endogenous.


MSc Thesis in Finance & International Business A.K. Chudzinska & S.L.van der Bijl xix

Appendix VI

Correlation Matrices

Correlation Matrix for the Western European Sample

Total

Leverage

Short Term

Leverage

Long Term

Leverage

Adjusted Total

Leverage KINK

STANDARDIZED

KINK

EFFECTIVE

TAX RATE TANGIBILITY SIZE Z-SCORE

OPERATING

RISK PROFITABILITY GROWTH

Total Leverage 1,0000

Short Term Leverage 0,7580 1,0000

Long Term Leverage 0,4090 -0,2850 1,0000

Adjusted Total Leverage 0,7440 0,3960 0,5400 1,0000

KINK -0,1920 -0,1180 -0,1170 -0,1330 1,0000

STANDARDIZED KINK -0,1790 -0,1210 -0,0940 -0,1320 0,2550 1,0000

EFFECTIVE TAX RATE -0,0310 -0,0033 -0,0410 -0,0430 0,0230 0,0770 1,0000

TANGIBILITY 0,0450 -0,3140 0,5050 0,2150 -0,0850 -0,0770 -0,0410 1,0000

SIZE -0,0690 -0,1080 0,0490 0,0120 0,0810 0,0890 0,0067 0,0009 1,0000

Z-SCORE -0,6880 -0,4680 -0,3570 -0,3560 0,2410 0,2920 0,0440 -0,2600 0,0920 1,0000

OPERATING RISK -0,0260 -0,0410 0,0200 0,0690 0,1040 -0,0830 -0,0290 -0,0490 0,4900 0,0700 1,0000

PROFITABILITY -0,3610 -0,2290 -0,2100 -0,2600 0,3870 0,5180 0,0680 -0,1350 0,0140 0,4790 0,0630 1,0000

GROWTH 0,0440 0,0490 -0,0035 0,0003 0,0350 0,0670 0,0230 -0,0250 0,0500 -0,0280 -0,0240 0,0800 1,0000

Correlation Matrix for the Eastern European Sample

Total Leverage

Short Term

Leverage

Long Term

Leverage

Adjusted Total

Leverage KINK

STANDARDIZED

KINK

EFFECTIVE

TAX RATE TANGIBILITY SIZE Z-SCORE

OPERATING

RISK PROFITABILITY GROWTH

Total Leverage 1,0000

Short Term Leverage 0,9090 1,0000

Long Term Leverage 0,3580 -0,0600 1,0000

Adjusted Total Leverage 0,7620 0,6070 0,4710 1,0000

KINK -0,0440 -0,0380 -0,0200 0,0360 1,0000

STANDARDIZED KINK 0,0103 0,0031 0,0180 0,0490 0,2650 1,0000

EFFECTIVE TAX RATE -0,0320 -0,0260 -0,0180 -0,0270 0,0280 0,0890 1,0000

TANGIBILITY -0,4280 -0,4780 0,0480 -0,2790 -0,1240 -0,2400 -0,0230 1,0000

SIZE 0,0026 -0,0360 0,0870 0,0430 0,1500 0,1130 -0,0094 -0,0280 1,0000

Z-SCORE -0,4300 -0,3930 -0,1500 -0,3030 0,0300 0,0010 0,0120 0,1790 0,0200 1,0000

OPERATING RISK 0,0037 0,0055 -0,0034 0,0110 0,0270 -0,0066 -0,0007 -0,0270 0,0380 -0,0033 1,0000

PROFITABILITY -0,1840 -0,1730 -0,0540 -0,1050 0,3380 0,4960 0,0780 -0,2100 0,0720 0,1010 0,0160 1,0000

GROWTH 0,1250 0,0990 0,0690 0,1380 0,0900 0,1630 0,0130 -0,1690 0,1230 -0,0260 -0,0041 0,2210 1,0000


MSc Thesis in Finance & International Business A.K. Chudzinska & S.L.van der Bijl xx

Appendix VII

White-heteroskedasticity adjusted 2SLS results of the Target Adjustment Model, comparing Eastern Europe vs. Western Europe. Western Europe serves as the base group.

Constant 0,1496 0,1587 0,1507 0,1524 0,1645 0,1587 0,0444 0,0182 0,0149 0,1414 0,1219 0,1150

[19,8700]*** [21,5072]*** [20,7982]*** [19,1748]*** [20,656]*** [20,3151]*** [6,9459]*** [2,8694]*** [2,3812]** [17,6432]*** [15,8501]*** [15,2541]***

Dummy Service Ind. -0,0033 -0,0039 -0,0032 -0,0016 -0,0023 -0,0018 0,0026 0,0037 0,0041 0,0060 0,0067 0,0075

[-2,8067]*** [-3,4009]*** [-2,8153]*** [-1,1441] [-1,7124]* [-1,2975] [1,8155]* [2,6434]*** [2,9092]*** [3,5509]*** [3,9834]*** [4,4473]***

Dummy Manufacturing Ind. -0,0012 -0,0007 -0,0013 0,0024 0,0026 0,0021 -0,0098 -0,0078 -0,0085 -0,0086 -0,0068 -0,0078

[-0,9403] [-0,5462] [-0,9744] [1,6161] [1,7185]* [1,3859] [-6,3604]*** [-5,0965]*** [-5,5505]*** [-4,6061]*** [-3,6645]*** [-4,2261]***

Dummy Eastern Europe 0,0268 0,0173 0,0271 0,0267 0,0166 0,0245 -0,2201 -0,1880 -0,1799 -0,2110 -0,1767 -0,1645

[2,7985]*** [1,8712]* [2,9479]*** [2,7303]*** [1,7283]* [2,5691]** [-24,3914]*** [-21,0196]*** [-20,2342]*** [-18,2371]*** [-15,6246]*** [-14,6143]***

KINK 0,0001 -- -- 0,0006 -- -- -0,0032 -- -- -0,0026 -- --

[0,3972] -- -- [2,1505]** -- -- [-13,3249]*** -- -- [-7,9632]*** -- --

Dummy East KINK 0,0003 -- -- 0,0004 -- -- 0,0061 -- -- 0,0093 -- --

[0,3791] -- -- [0,5794] -- -- [15,1531]*** -- -- [13,1750]*** -- --

Standardized KINK -- 0,0051 -- -- 0,0038 -- -- 0,0022 -- -- 0,0045 --

-- [12,3114]*** -- -- [9,2044]*** -- -- [6,5964]*** -- -- [9,3445]*** --

Dummy East Stand. KINK -- -0,0016 -- -- -0,0004 -- -- 0,0030 -- -- 0,0033 --

-- [-1,9324]* -- -- [-0,4975] -- -- [4,5453]*** -- -- [3,4035]*** --

Effective Tax Rate -- -- 0,0009 -- -- 0,0010 -- -- -0,0013 -- -- 0,0001

-- -- [1,7312]* -- -- [1,6805]* -- -- [-2,1871]** -- -- [0,1908]

Dummy East Eff. Tax Rate -- -- -0,0007 -- -- -0,0009 -- -- 0,0009 -- -- 0,0002

-- -- [-0,5812] -- -- [-0,8029] -- -- [0,7573] -- -- [0,1315]

Tangibility -0,0552 -0,0547 -0,0552 -0,1104 -0,1104 -0,1108 0,2675 0,2691 0,2696 0,0353 0,0362 0,0359

[-17,8583]*** [-17,6758]*** [-17,638]*** [-25,8779]*** [-25,7673]*** [-25,7317] [57,6389]*** [57,9322]*** [57,8877]*** [9,6131]*** [9,9175]*** [9,7759]***

Dummy East Tangibility -0,0511 -0,0503 -0,0518 -0,0105 -0,0103 -0,0120 -0,1897 -0,1930 -0,1968 -0,0317 -0,0375 -0,0420

[-10,4484]*** [-10,0087]*** [-10,016]*** [-2,1223]** [-2,0443]** [-2,3339]** [-31,1741]*** [-31,9173]*** [-32,7152]*** [-4,7066]*** [-5,5578]*** [-6,1982]***

Size 0,0013 -0,0006 0,0012 -0,0008 -0,0026 -0,0012 0,0052 0,0063 0,0072 -0,0007 -0,0007 0,0010

[1,5742] [-0,7232] [1,5281] [-0,9496] [-2,9816]*** [-1,4832] [6,8997]*** [8,155]*** [9,5733]*** [-0,7652] [-0,7462] [1,1183]

Dummy East Size -0,0048 -0,0033 -0,0046 -0,0042 -0,0027 -0,0036 0,0149 0,0134 0,0131 0,0148 0,0147 0,0141

[-4,2092]*** [-2,9133]*** [-4,1789]*** [-3,599]*** [-2,3144]** [-3,1621]*** [13,8030]*** [12,2400]*** [12,1605]*** [10,7332]*** [10,5293]*** [10,2662]***

Z-Score -0,0111 -0,0112 -0,0111 -0,0095 -0,0096 -0,0095 -0,0083 -0,0087 -0,0086 -0,0047 -0,0050 -0,0048

[-20,9301]*** [-21,0224]*** [-21,0076]*** [-21,8525]*** [-21,9404]*** [-21,9791]*** [-21,518]*** [-21,6708]*** [-21,7506]*** [-14,695]*** [-15,5975]*** [-15,2342]***

Dummy East Z-Score 0,0094 0,0096 0,0094 0,0081 0,0082 0,0081 0,0080 0,0083 0,0082 0,0042 0,0045 0,0043

[18,3043]*** [18,4425]*** [18,3735]*** [18,9883]*** [19,1558]*** [19,1321]*** [20,3974]*** [20,6303]*** [20,6985]*** [13,1640]*** [14,2334]*** [13,7905]***

Operating Risk 0,0000 0,0000 0,0000 0,0000 0,0000 0,0000 0,0000 0,0000 0,0000 0,0000 0,0000 0,0000

[2,7308]*** [4,3989]*** [2,7541]*** [1,4607] [2,6616]*** [1,5183] [4,6994]*** [5,2626]*** [4,569]*** [7,6316]*** [8,6869]*** [7,5263]***

Dummy East Operating Risk 0,0000 0,0000 0,0000 0,0000 0,0000 0,0000 0,0000 0,0000 0,0000 0,0000 0,0000 0,0000

[-2,6448]*** [-4,3138]*** [-2,6831]*** [-1,3979] [-2,5729]*** [-1,4503] [-4,7341]*** [-5,2682]*** [-4,5977]*** [-7,5263]*** [-8,5354]*** [-7,4211]***

Profitability -0,2693 -0,3056 -0,2680 -0,1993 -0,2178 -0,1898 -0,0214 -0,0865 -0,0695 -0,2220 -0,2958 -0,2623

[-18,0931]*** [-22,2502]*** [-21,7439]*** [-13,4879]*** [-15,9157]*** [-15,7028]*** [-2,3749]** [-9,6529]*** [-8,7289]*** [-14,4886]*** [-21,1838*** [-21,5836]***

Dummy East Profitability -0,1836 -0,1592 -0,1801 -0,2142 -0,1989 -0,2105 0,0263 0,1046 0,1124 -0,0534 0,0694 0,0739

[-5,7799]*** [-5,7981]*** [-7,5041]*** [-7,244]*** [-7,7705]*** [-9,409]*** [1,9693]** [8,2241]*** [9,8155]*** [-1,8805]* [2,8778]*** [3,5360]***

Growth 0,0411 0,0407 0,0411 0,0276 0,0275 0,0278 0,0063 0,0047 0,0049 0,0179 0,0164 0,0168

[3,4624]*** [3,4600]*** [3,4727]*** [2,6328]*** [2,641]*** [2,656]*** [2,461]** [1,9075]* [1,9886]** [2,3979]** [2,2629]** [2,296]**

Dummy East Growth 0,003 0,0033 0,0032 -0,0065 -0,0064 -0,0064 0,0108 0,0130 0,0134 0,0111 0,0143 0,0148

[0,2431] [0,2636] [0,2549] [-0,5792] [-0,5712] [-0,5622] [2,7944]*** [3,4372]*** [3,5414]*** [1,2536] [1,6421] [1,6903]*

Lagged Leverage 0,8544 0,8540 0,8542 0,8502 0,8499 0,8497 0,2716 0,2712 0,2690 0,7501 0,7536 0,7532

[177,5463]*** [177,4293]*** [177,2598]*** [170,3187]*** [170,2211]*** [169,941]*** [46,8197]*** [47,1110]*** [46,7278]*** [135,2920]*** [137,9784]*** [137,3938]***

Target Adjustment coefficient 0,1456 0,1460 0,1458 0,1498 0,1501 0,1503 0,7284 0,7288 0,7310 0,2499 0,2464 0,2468

R-squared 0,8697 0,8703 0,8697 0,8207 0,8212 0,8207 0,3800 0,3791 0,3774 0,6325 0,6313 0,6296

Adjusted R-squared 0,8696 0,8703 0,8696 0,8207 0,8212 0,8207 0,3799 0,3789 0,3773 0,6324 0,6312 0,6295

DW Statistic 1,9717 1,9719 1,9717 1,9813 1,9815 1,9813 1,8632 1,8578 1,8560 1,8717 1,8609 1,8572

Tolerance 0,1303 0,1297 0,1303 0,1793 0,1788 0,1793 0,6200 0,6209 0,6226 0,3675 0,3687 0,3704

F-test West slope 78,9064*** 79,2508*** 75,4836*** 77,8963*** 75,3206*** 71,653*** 524,2658*** 463,1743*** 421,8537*** 112,3924*** 72,0594*** 57,8975***

F-test West shift + slope 69,0696*** 69,5312*** 66,507*** 73,7434*** 72,842*** 70,5181*** 1007,263*** 1001,141*** 986,2272*** 159,7741*** 145,8708*** 140,8751***

F-test Industry Effect 4,8874*** 8,8173*** 4,8375*** 7,4127*** 11,2119*** 6,8845*** 59,9305*** 51,2724*** 61,7237*** 53,5882*** 45,8903*** 59,2959***

Total Leverage Short Term Leverage Long Term Leverage Adjusted Total Leverage

Total Liabilities / Total Assets Current Liabilities / Total Assets Non-Current Liabilities / Total Assets (Total Liabilities - Payables) / Total Assets



MSc Thesis in Finance & International Business A.K. Chudzinska & S.L.van der Bijl xxi

Appendix VII Continued

Explanatory Notes to the table in Appendix VII:

White-heteroskedasticity adjusted 2SLS regressions, conducted in E-VIEWS, for years 2000 to 2005 on the complete sample of all observations from Eastern Europe and

Western Europe. In order to compare both regions, Western Europe is the base group. Twelve tests on the following four different dependent variables have been done: Total


Total Leverage is calculated as: Total Liabilities / Total Assets, Short Term Leverage is calculated as Current Liabilities / Total Assets, Long Term Leverage is calculated as

Non-Current Liabilities / Total Assets and Adjusted Total Leverage is calculated as (Total Liabilities – Payables) / Total Assets.

Independent variables are: KINK, STANDARDIZED KINK, EFFECTIVE TAX RATE, TANGIBILITY, SIZE, Z-SCORE, OPERATING RISK, PROFITABILITY, GROWTH,

LAGGED LEVERAGE, and shift DUMMY and slope DUMMY variables for Eastern Europe and shift dummy variables for Industries.

KINK is calculated as EBIT in year t / Interest Expenses in year t. STANDARDIZED KINK is computed as (KINK in year t x Interest Expenses in year t) / Standard deviation of

KINK over all years. The EFFECTIVE TAX RATE is calculated as the observed Tax Expenses in year t / Earnings Before Taxes in year t. Note that only one tax variable is used

at a time.

SIZE is proxied by the natural logarithm of Total Assets in year t. Z-SCORE is Altman’s Z-SCORE for General use, which is calculated for every year t as:

sLiabilitieTotal

EquityTotal

AssetsTotal

EBIT



OPERATING RISK is calculated as the standard deviation of Earnings Before Taxes over all years observed until year t. PROFITABILITY is calculated as Earnings Before

Taxes / Total Assets. GROWTH is calculated as the percentage change of Total Assets in year t-1 to year t. LAGGED LEVERAGE is calculated as the respective leverage ratio

(the dependent variable of the model), in year t-1. Note that the correlation of LAGGED LEVERAGE with the residuals of the model is removed by including an Instrumental

Variable; leverage lagged two periods (t-2).

The dummy variables are qualitative variables with the value 1 if the observation belongs to the group it represents. Therefore, the shift dummy Eastern Europe has a value of 1

for a firm from a company in one of the following countries: Poland, Hungary or Ukraine. It has the value 0 if the observation is from a company in any of the other countries.

Similarly, the slope dummy variables are computed by multiplying the shift dummy by the respective independent variables.

The Target Adjustment Coefficient is derived from model [4] and is computed as: 1 – the coefficient of LAGGED LEVERAGE.

R² indicates the coefficient of determination, or the explanatory power of the model as a whole. DW statistics show whether the regression results are affected by autocorrelation.

A DW statistic close to 2.0 indicates no autocorrelation. The number of variables with a Tolerance level smaller than 0,1 indicates whether multicollinearity is apparent in the

regression model. F statistics and their significance show whether a linear relationship between the dependent variable and any of the independent variables exists, depending

what group of independent variables is included in the F-test.


MSc Thesis in Finance & International Business A.K. Chudzinska & S.L.van der Bijl xxii

Appendix VIII

T-test results for comparing the four proxies of Leverage.

VIII.I) T-test Results of the four proxies of Leverage between the Western European

sample and the Eastern European sample

By calculating a T-test of one sample’s average (mean) leverage ratio over another

sample’s leverage data distribution, the T-test scores indicate whether the two samples

are statistically different from each other in respect to their leverage ratios. In case the

T-test scores are significant, the leverage ratios in the two samples are statistically

different from eachother. The higher the significance, the more certain one can be

about the statistical difference. The signs (positive or negative) in front of the T-test

scores indicate in which direction the leverage ratios of both samples are different.

Table VIII.1 T-test results for comparing the four proxies of Leverage between the

Western European sample and the Eastern European sample

Total Leverage

data distributions Western Europe mean Eastern Europe mean

data Western Europe --- [153,1327]***

data Eastern Europe [-99,1787]*** ---

Short Term Leverage




Long Term Leverage




Adjusted Total Leverage





Note that the T-tests are repeated, in both directions. This means that e.g. the data

distribution of leverage from Western Europe is tested on the average leverage ratio of

Eastern Europe, as well as the data distribution of the Eastern Europe is tested on the


MSc Thesis in Finance & International Business A.K. Chudzinska & S.L.van der Bijl xxiii

average leverage ratio of Western Europe. This is done in order to test for validity of

the results. The T-test scores are different in both directions, since the size of the

tested data samples differ. However, from this validity test it can be seen that if the

difference is positive in one direction, then, the difference must be negative in the

other direction.


MSc Thesis in Finance & International Business A.K. Chudzinska & S.L.van der Bijl xxiv

Appendix VIII Continued (1)


VIII.II) T-test Results of the four proxies of Leverage among Ireland, the Netherlands

and Belgium.








Table VIII.2 T-test results of comparing the four proxies of Leverage between the

three countries: Ireland, the Netherlands and Belgium.

Total Leverage

data distributions Ireland mean Netherlands mean Belgium mean

data Ireland --- [-21,5914]*** [-24,8615]***

data Netherlands [48,6615]*** --- [2,16441]**

data Belgium [104,0485]*** [-2,5646]*** --

Short Term Leverage


data Ireland --- [-29,2974]*** [-18,1099]***

data Netherlands [34,2625]*** --- [1,2162]

data Belgium [76,4419]*** [-1,6953]* --

Long Term Leverage


data Ireland --- [-14,7431]*** [-12,9293]***

data Netherlands [21,4439]*** --- [1,3594]

data Belgium [45,7651]*** [-1,9244]** --



data Ireland --- [-32,7709]*** [-6,6527]***

data Netherlands [50,6381]*** --- [40,3581]***

data Belgium [28,3227]*** [-111,2462]*** --



MSc Thesis in Finance & International Business A.K. Chudzinska & S.L.van der Bijl xxv


distribution of leverage from Ireland is tested on the average leverage ratio of the

Netherlands, as well as the data distribution of the Netherlands is tested on the

average leverage ratio of Ireland. This is done in order to test for validity of the

results. The T-test scores are different in both directions, since the size of the tested

data samples differ. However, from this validity test it can be seen that if the

difference is positive in one direction, then, the difference must be negative in the

other direction.


MSc Thesis in Finance & International Business A.K. Chudzinska & S.L.van der Bijl xxvi

Appendix VIII Continued (2)


VIII.III) T-test Results of the four proxies of Leverage among Poland, Hungary and

Ukraine.








Table VIII.3 T-test results for comparing the four proxies of Leverage between the

three countries: Poland, Hungary and Ukraine

Total Leverage

data distributions Poland mean Hungary mean Ukraine mean

data Poland --- [-15,5392]*** [33,5796]***

data Hungary [18,5036]*** --- [58,4557]***

data Ukraine [-34,5012]*** [-50,4739]*** --

Short Term Leverage


data Poland --- [-13,2049]*** [29,1005]***

data Hungary [14,5877]*** --- [46,7536]***

data Ukraine [-30,4317]*** [-44,2246]*** --

Long Term Leverage


data Poland --- [-6,6935]*** [12,7719]***

data Hungary [6,6263]*** --- [19,3148]***

data Ukraine [-16,3413]*** [-24,8072]*** --



data Poland --- [-101,3011]*** [17,1153]***

data Hungary [90,8382]*** --- [106,1844]***

data Ukraine [-18,9374]*** [-131,0397]*** -- *, ** , *** = significant on the 0.1, 0.05 and 0.01 level of significance, respectively


distribution of leverage from Poland is tested on the average leverage ratio of


MSc Thesis in Finance & International Business A.K. Chudzinska & S.L.van der Bijl xxvii

Hungary, as well as the data distribution of Hungary is tested on the average leverage

ratio of Poland. This is done in order to test for validity of the results. The T-test

scores are different in both directions, since the size of the tested data samples differ.

However, from this validity test it can be seen that if the difference is positive in one

direction, then, the difference must be negative in the other direction.


MSc Thesis in Finance & International Business A.K. Chudzinska & S.L.van der Bijl xxviii

Appendix IX

Robustness Check – Within the Western and Eastern European samples

In chapter Four of this research paper the differences between capital structures of

Western and Eastern European SMEs were analyzed. Each sample consisted of three

countries; each country was partly selected based on differences in credit scores,

corporate tax rates, bankruptcy and corporate governance scores, as identified in

Appendix II. The differences in these country indicators were shown to determine the

differences in capital structure of SMEs in the two tested samples.

The differences in capital structure between the two inter-country samples do not

necessarily indicate that country-specific results are similar within each sample. Since

the selected countries in each sample differ with each other based on the country

scores presented in Appendix II, it is expected that their capital structure and its

determinants are also different. In this Appendix, a review on country specific

differences within both samples is presented, similar to the prior analysis examined

between the Eastern and Western European samples in Chapter Four. Regression

analyses were conducted with country-specific dummy variables to identify the

country differences. This section will serve as a Robustness Check to the previous

analysis, in order to test the validity of the concluded relationships between regional

institutional differences and SME capital structures. Findings of differences between

the countries’ leverage ratios and independent variables can be related to the country

scores of Appendix II in a similar fashion as was performed in Chapter Four.

It was stated that a causal relationship exists between the institutional differences of

Eastern and Western Europe and SME leverage ratios and the relationship between

tax variables, bankruptcy cost variables and agency cost variables with SME leverage

ratios.

If this relationship is truly valid, similar conclusions should be found when comparing

institutional country differences with country specific SME capital structures.

Given that the expectations on country differences will hold, the evidence on a causal

relationship between the differences in country indicators and differences in capital

structure seem to be valid. The similarities found among the studied countries may

confirm that the generalization of Eastern and Western Europe holds. This analysis


MSc Thesis in Finance & International Business A.K. Chudzinska & S.L.van der Bijl xxix

will provide more insight into the strength of the observed and discussed differences

as well as the similarities in capital structure in the Eastern and Western European

SMEs.

The regression results are presented in separate tables, one for each country used as a

base group. Hence, six different tables can be found. Table IX.1 presents the results of

the Western European sample, with Ireland as basegroup. Table IX.2 presents the

results of the Eastern European sample, with Poland as basegroup. The other four

tables are presented in Appendix X and Appendix XI. The regression analyses provide

insight into country specific variance on capital structure based on the effects of taxes,

bankruptcy costs and agency costs. The country effects were tested on expectations

based on the country scores and indicators from Chapter One.

The independent variable EFFECTIVE TAX RATE appeared not to be significant for

Eastern and Western Europe. The country specific regressions also did not show any

significance on this variable. Therefore, the country analysis in this Appendix will not

include this variable and thus only two tax variables, KINK, and STANDARDIZED

KINK are included.

Simlar to the previous regression analyses, the regressions in this Appendix were

adjusted for industry effects. However, in addition, the regressions were also adjusted

for year effects. Since the focus here lies on country differences, there might be

macro-economic variables which are not included in the regression model, but which

might explain an extensive part of the variance in the data. Many macro-economic

effects take place on a country specific level, such as the interest rates, employment

figures, gross domestic product and the state of the economic cycle. By adjusting for

a year effect, as well as an industry effect, and by including shift dummies, such

unexplained variance is greatly removed.

Similar as in the central research, R-squares of the presented models for the individual

country comparisons, as presented in Table IX.1, Table IX.2, and Appendix X and XI,

cannot be interpreted without keeping in mind that they are biased. However, when

comparing the R-squares for the models within Western Europe and within Eastern

Europe, it still becomes visible that the target adjustment model is overall strong,

except for the tests on Long Term Leverage within Eastern Europe. This clearly


MSc Thesis in Finance & International Business A.K. Chudzinska & S.L.van der Bijl xxx

shows what was concluded before: small firms in Eastern Europe have great

difficulties in attracting long term debt, due to higher bankruptcy costs and greater

control from banks, who typically lend short term.

These R-squares cannot be used in controlling for the effectiveness of the target

adjustment model itself, nor the Tradeoff Theory. Target adjustment coefficients,

however, prove that all firms, in Western Europe as well as Eastern Europe, adjust

their leverage ratios towards some ‘target’. This works as indirect evidence for the

Tradeoff Theory to hold. The individual relationships between determinants and

leverage ratios, as will be presented in the following sections, show that the directions

and significance of the relations, once again prove that the Tradeoff Theory is a valid

underlying theoretical framework for this study.

An interesting observation from these target adjustment coefficients of the Western

European and Eastern European samples is that firms in Western Europe clearly

adjust their leverage ratios much faster towards their targets, especially on long term

debt. Once again, this proves the availability and access to (long term) credits to be

much stronger in Western Europe.

IX.I Within the Western European sample: Ireland, the Netherlands and

Belgium

Leverage

From the country scores in Appendix II it can be seen that corporate tax rates were

fairly similar between The Netherlands and Belgium, while considerably lower in

Ireland. This leads to the expectation that leverage ratios are lower in Irish SMEs, as

compared to Dutch and Belgian SMEs. Bankruptcy costs in the three countries are

low, and very similar, in spite of clear differences in law systems. These observed

similarities are therefore expected not to create any significant difference in leverage

ratios of SMEs in the three countries. Agency costs are somewhat different, but as

argued before, are not expected to play an important role on SME capital structures.

These three different aspects are discussed below in much greater detail. It is expected

that capital structures in the three Western European countries are mostly determined

by the tax effects, and to smaller extent by the bankrupty effect. Since the tax effect is


MSc Thesis in Finance & International Business A.K. Chudzinska & S.L.van der Bijl xxxi

expected to be the most dominant effect, Irish SMEs are expected to have lowest

leverage ratios, while Dutch and Belgian SMEs have higher, but rather similar

leverage ratios.

From the descriptive statistics (in Appendix IV.I) it seems that, indeed, leverage ratios

in the Netherlands and Belgium are very similar and only a few percentage points

higher in the Netherlands, as compared to Belgium. Leverage ratios for Ireland seem

to be substantially lower than in the other two countries. The F-tests in Table IX.1 and

Appendix X demonstrate that indeed the full models, (F-tests for shift dummies plus

slope dummies), are different from each other, thus leverage ratios are statistically

different, between the three countries. The F-tests do not detect the directions in

which the models are different. Therefore, T-tests were conducted between one

country’s data set on leverage and another country’s average (mean) leverage (see

Appendix VIII.II). The T-tests show how leverage ratios in two countries are

significantly different from each other; the tests were conducted in both directions to

control for validity. It can be further observed that on all proxies of leverage, Irish

SMEs have significantly lower leverage in their capital structure. The T-test statistics

indicate that no differences exist between Short Term Leverage and Long Term

Leverage between the Dutch and Belgian SMEs. On Total Leverage, only a small

significant difference is found. This proves that indeed the leverage ratios of the

Netherlands and Belgium are similar. When controlling by conducting the T-test the

other way around, that is, by comparing the mean leverage ratio from the Netherlands

to the data set from Belgium, it appears that the differences between the Netherlands

and Belgium are slightly bigger. However, these findings are rather a confirmation

that the data sample of the Belgian SMEs is bigger than the Dutch sample. If the

Belgian leverage ratios would be increased by less than one percent, the significant

differences between two countries are completely removed.

Thus, leverage ratios are hardly different between the Netherlands and Belgium and

they are significantly lower in Ireland. This confirms the expectations, but does not

yet prove the theoretical causes of these differences.

The next sub-sections of this appendix will therefore investigate whether this

difference in capital structure originates in the expected manner, commencing with a

discussion of taxes.


MSc Thesis in Finance & International Business A.K. Chudzinska & S.L.van der Bijl xxxii

Explanatory Notes to Table IX.1:

White-heteroskedasticity adjusted 2SLS regressions, conducted in E-VIEWS, for years 2000 to 2005 on the country-

samples from Western Europe: Ireland, the Netherlands and Belgium. In order to compare the three countries, Ireland

is the base group. Eight tests on the following four different dependent variables have been done: Total Leverage,

Short Term Leverage, Long Term Leverage and Adjusted Total Leverage.




Independent variables are: KINK, STANDARDIZED KINK, TANGIBILITY, SIZE, Z-SCORE, OPERATING

RISK, PROFITABILITY, GROWTH, LAGGED LEVERAGE and SHIFT DUMMY and SLOPE DUMMY variables

representing either the Netherlands or Belgium and SHIFT DUMMY variables for Industries and Years.

KINK is computed as EBIT in year t / Interest Expenses in year t. STANDARDIZED KINK is computed as (KINK in

year t x Interest Expenses in year t) / Standard deviation of KINK over all years. Note that only one tax variable is

used at a time. SIZE is proxied by the natural logarithm of Total Assets in year t. Z-SCORE is Altman’s Z-SCORE

for General use,which is calculated for every year t as:

sLiabilitieTotal

EquityTotal

AssetsTotal

EBIT



OPERATING RISK is computed as the standard deviation of Earnings Before Taxes over all years observed until



leverage ratio (the dependent variable of the model), in year t-1. Note that correlation of LAGGED LEVERAGE with

the residuals of the model is removed by including an Instrumental Variable; leverage lagged two periods (t-2).

The DUMMY variables are qualitative variables with the value 1 if the observation belongs to the group it represents.

Therefore, the shift dummy for the Netherlands has a value of 1 for a firm from the Netherlands. It has the value 0 if

the observation is from a company in any of the other countries.


LEVERAGE.


whether the regression results are affected by autocorrelation. A DW statistic close to 2.0 indicates no autocorrelation.

The number of variables with a Tolerance level smaller than 0,1 indicates whether multicollinearity is apparent in the

regression model. F statistics and their significance show whether a linear relationship between the dependent

variable and any of the independent variables exists, depending what group of independent variables is included in

the F-test.


MSc Thesis in Finance & International Business A.K. Chudzinska & S.L.van der Bijl xxxiii

Table IX.1 – White-heteroskedasticity adjusted 2SLS results of the Target Adjustment Model, comparing the three countries of the Western European sample: Ireland, the Netherlands and Belgium. Ireland serves as the base group.

Constant 0,1439 0,1446 0,1710 0,1688 -0,0413 -0,0422 -0,0152 -0,0153

[3,9106]*** [3,8109]*** [5,4165]*** [5,3398]*** [-1,1143] [-1,0967] [-0,3033] [-0,2981]

Shift Dummy 2001 -0,0026 -0,0019 -0,0081 -0,0076 0,0058 0,0060 0,0044 0,0048

[-1,7057]* [-1,2415] [-4,4379]*** [-4,1731]*** [3,9279]*** [4,0564]*** [2,0787]** [2,2792]**

Shift Dummy 2002 -0,0060 -0,0045 -0,0090 -0,0080 0,0031 0,0035 -0,0015 -0,0004

[-3,745]*** [-2,8311]*** [-4,9626]*** [-4,4031]*** [2,0626]** [2,349]** [-0,6804] [-0,1752]

Shift Dummy 2003 -0,0063 -0,0046 -0,0110 -0,0099 0,0050 0,0055 0,0000 0,0012

[-4,0167]*** [-2,9693]*** [-6,1444]*** [-5,4986]*** [3,4159]*** [3,7339]*** [-0,0009] [0,5759]

Shift Dummy 2004 -0,0016 0,0004 -0,0044 -0,0031 0,0031 0,0037 0,0006 0,0021

[-1,0406] [0,2804] [-2,4899]** [-1,7054]* [2,2072]** [2,6215]*** [0,2791] [1,0139]

Shift Dummy 2005 -0,0025 -0,0002 -0,0063 -0,0046 0,0041 0,0048 -0,0006 0,0011

[-1,6671]* [-0,1446] [-3,505]*** [-2,5744]** [2,7811]*** [3,2357]*** [-0,3108] [0,5349]

Shift Dummy Service Industry -0,0015 -0,0024 -0,0037 -0,0043 0,0018 0,0016 0,0004 -0,0002

[-1,4276] [-2,2526]** [-2,9829]*** [-3,5118]*** [1,8472]* [1,5557] [0,327] [-0,1399]

Shift Dummy Other Industries -0,0011 -0,0017 -0,0040 -0,0044 0,0027 0,0025 -0,0010 -0,0014

[-0,9037] [-1,3848] [-2,6204]*** [-2,91]*** [2,3285]** [2,176]** [-0,5899] [-0,7742]

Shift Dummy NL 0,1492 0,1545 0,0852 0,0890 0,0611 0,0625 0,1429 0,1458

[3,5563]*** [3,5717]*** [2,3061]** [2,3877]** [1,5583] [1,5368] [2,5467]** [2,5446]**

Shift Dummy BE 0,0656 0,0768 0,0405 0,0491 0,0266 0,0299 0,0765 0,0827

[1,7369]* [1,9684]** [1,2825] [1,5506] [0,7131] [0,7697] [1,5005] [1,5856]

KINK -0,0028 - -0,002 - -0,0005 -- -0,003 -

[-1,9169]* - [-2,3241]** - [-0,6283] -- [-2,1395]** -

Dummy KINK NL 0,0023 - 0,0037 - -0,0012 -- 0,0034 -

[1,7858]* - [1,7924]* - [-1,2623] -- [1,9288]* -

Dummy KINK BE 0,0025 - 0,0023 - 0,0000 -- 0,0017 -

[1,8427]** - [1,9519]** - [0,0311] -- [1,1586] -

STANDARDIZED KINK - 0,0011 - 0,0007 -- 0,0004 - 0,002

- [1,7503]* - [1,5895] -- [1,0093] - [1,8098]*

Dummy STANDARDIZED KINK NL - 0,003 - 0,0023 -- 0,0009 - 0,0022

- [3,2625]*** - [2,1278]** -- [2,2429]** - [1,4644]

Dummy STANDARDIZED KINK BE - 0,0056 - 0,004 -- 0,0015 - 0,0029

- [9,0016]*** - [6,4489]*** -- [2,6728]*** - [2,3065]**

TANGIBILITY -0,0585 -0,0591 -0,0798 -0,0803 0,0239 0,0244 -0,0125 -0,0119

[-3,8044]*** [-3,818]*** [-7,4862]*** [-7,5254]*** [1,5813] [1,6058] [-0,6367] [-0,6003]

Dummy TANGIBILITY NL -0,0173 -0,0228 -0,0649 -0,0687 0,0468 0,0450 0,0007 -0,0058

[-1,0019] [-1,2985] [-4,7579]*** [-4,9543]*** [2,1563]** [1,7365]* [0,0344] [-0,2674]

Dummy TANGIBILITY BE 0,0000 0,0017 -0,0515 -0,0505 0,0503 0,0509 0,0385 0,0394

[0,0011] [0,112] [-4,723]*** [-4,6326]*** [2,9517]*** [2,9656]*** [1,8977]* [1,9344]*

SIZE 0,0054 0,0051 -0,0020 -0,0020 0,0077 0,0076 0,0118 0,0112

[1,0589] [0,9793] [-0,5376] [-0,5383] [1,5119] [1,4339] [1,8231]* [1,6744]*

Dummy SIZE NL -0,0102 -0,0107 -0,0023 -0,0027 -0,0077 -0,0079 -0,0139 -0,0138

[-1,8616]* [-1,885]* [-0,5376] [-0,6235] [-1,4688] [-1,4291] [-1,9854]** [-1,9169]*

Dummy SIZE BE -0,0048 -0,0066 -0,0007 -0,0021 -0,0045 -0,0050 -0,0119 -0,0124

[-0,9458] [-1,2377] [-0,1848] [-0,5327] [-0,8826] [-0,9325] [-1,8126]* [-1,8487]*

Z-SCORE -0,0104 -0,0104 -0,0079 -0,0077 -0,0020 -0,0019 -0,0042 -0,0039

[-11,7844]*** [-12,3064]*** [-11,7043]*** [-11,697]*** [-4,024]*** [-4,0416]*** [-5,2431]*** [-5,1014]***

Dummy Z-SCORE NL -0,0024 -0,0027 -0,0020 -0,0022 -0,0005 -0,0006 0,0004 0,0000

[-1,4251] [-1,5956] [-1,5168] [-1,6837]* [-0,7444] [-0,9042] [0,3379] [0,0148]

Dummy Z-SCORE BE -0,0054 -0,0057 -0,0048 -0,0050 -0,0003 -0,0004 0,0015 0,0012

[-1,9684]** [-1,8343]** [-2,2196]** [-2,2113]** [-0,556] [-0,7232] [1,8099]* [1,5415]

OPERATING RISK 0,0000 0,0000 0,0000 0,0000 0,0000 0,0000 0,0000 0,0000

[-0,896] [-0,6416] [0,0445] [0,2984] [-0,8846] [-0,7744] [-0,8929] [-0,5135]

Dummy OPERATING RISK NL 0,0000 0,0000 0,0000 0,0000 0,0000 0,0000 0,0000 0,0000

[0,9055] [0,811] [-0,4706] [-0,5481] [1,1679] [1,1119] [1,0704] [0,8462]

Dummy OPERATING RISK BE 0,0000 0,0000 0,0000 0,0000 0,0000 0,0000 0,0000 0,0000

[0,7434] [1,0231] [-0,1847] [0,1659] [0,8636] [0,9111] [1,5034] [1,4726]

PROFITABILITY -0,3678 -0,3575 -0,2425 -0,2345 -0,1292 -0,1270 -0,4044 -0,3987

[-8,0051]*** [-7,9388]*** [-6,3473]*** [-6,2569]*** [-3,2025]*** [-3,3005]*** [-8,0185]*** [-7,8856]***

Dummy PROFITABILITY NL 0,0398 0,0269 -0,0192 -0,0290 0,0625 0,0593 0,0439 0,0396

[0,7400] [0,4954] [-0,4053] [-0,6013] [1,4919] [1,4727] [0,763] [0,6692]

Dummy PROFITABILITY BE 0,1265 0,0791 0,0836 0,0498 0,0420 0,0293 0,1667 0,1409

[2,6490]*** [1,6637]* [2,0794]** [1,2359] [1,0219] [0,7427] [3,1947]*** [2,6577]***

GROWTH 0,0823 0,0815 0,0607 0,0603 0,0216 0,0212 0,0895 0,0883

[4,8879]*** [4,8825]*** [3,6841]*** [3,6721]*** [2,0767]** [2,0465]** [4,1327]*** [4,1327]***

Dummy GROWTH NL -0,0901 -0,0894 -0,0670 -0,0667 -0,0235 -0,0232 -0,1038 -0,1028

[-3,9729]*** [-3,9751]*** [-2,9854]*** [-2,9833]*** [-2,1663]** [-2,144]** [-4,3106]*** [-4,3216]***

Dummy GROWTH BE -0,0113 -0,0107 -0,0121 -0,0117 0,0017 0,0021 -0,0495 -0,0482

[-0,6467] [-0,6186] [-0,6986] [-0,6775] [0,1556] [0,1855] [-2,2313]** [-2,2019]**

LAGGED LEVERAGE 0,7959 0,7915 0,8097 0,8086 0,0954 0,0969 0,5266 0,5293

[120,3166]*** [119,9259]*** [137,7572]*** [138,1799]*** [93,6261]*** [93,2164]*** [156,9714]*** [156,5888]***

Target adjustment Coefficient 0,2041 0,2085 0,1903 0,1914 0,9046 0,9031 0,4734 0,4707

Rsquare 0,8708 0,8712 0,8165 0,8168 0,7690 0,7691 0,7680 0,7680

Adjusted Rsquare 0,8707 0,8711 0,8164 0,8166 0,7688 0,7689 0,7678 0,7678

DW statistic 1,9939 1,9940 2,0000 2,0005 1,9973 1,9968 1,9858 1,9851

Tolerance 0,1292 0,1288 0,1835 0,1832 0,2310 0,2309 0,2320 0,2320

F-test year effect 4,7939*** 3,9883*** [9,2137]*** [8,1245]*** [3,7911]*** [4,243]*** [1,9048]* [1,5779]

F-test industry effect 1,0306 2,5562* [5,1964]*** [6,9174]*** [2,9111]* [2,4415]* [0,4158] [0,3351]

F test country shift 10,5528*** 9,4575*** [3,1527]** [3,0354]** [3,5284]** [3,1491]** [4,6758]*** [4,3806]**

F test country slope 7,793*** 13,1589*** [7,5622]*** [10,8279]*** [3,7885]*** [4,0084]*** [5,7774]*** [6,6614]***

F test country shift+slope 34,0425*** 36,9951*** [20,8873]*** [22,5514]*** [5,6738]*** [5,7532]*** [9,2141]*** [9,673]***

Total LeverageIRELAND as BASEGROUP Adjusted Total LeverageLong Term LeverageShort Term Leverage



MSc Thesis in Finance & International Business A.K. Chudzinska & S.L.van der Bijl xxxiv

Taxes

In Appendix II it was demonstrated that corporate tax rates were much lower in

Ireland over the last several years. Indeed, Irish tax rates went down from 24 to 12.5

percent in the period 2000 – 2005. In the same period the Dutch and Belgian tax rates

decreased from approximately 35 to 31.5 percent and 40 to 34 percent, respectively.

As noted previously,

Ireland is a trendsetter in its policy of lower corporate tax rates, forcing most other

European countries to follow its lead. Lower corporate tax rates in Ireland in

comparison to those of the Netherlands and Belgium, are expected to create a

difference in the effect of taxes on capital structure between the SMEs of the three

countries. The tax effect is expected to be smallest in Ireland. The differences between

the Dutch and Belgian tax rates are minute and became even smaller in the last years.

Therefore, it is expected that no clear differences exist on the tax effect of capital

structure between the Dutch and Belgian SMEs.

These expectations are confirmed. When looking at Table IX.1 and the two tables of

Appendix X, it can be observed that the KINK and STANDARDIZED KINK

variables are not or hardly significantly related to all four proxies of leverage in

Ireland. This indicates that in Ireland, taxes do not seem to matter much on capital

structure decisions. In the Netherlands and Belgium, however, the KINK variable

seems to matter, since it is negatively and significantly related to Long Term

Leverage and Adjusted Total Leverage. The same as in the whole Western European

sample taken together, KINK does not have much influence on Short Term Leverage

and on Total Leverage. STANDARDIZED KINK is a stronger indicator of the tax

effect, since it is highly significant on all four proxies of leverage in the Dutch and

Belgian samples. These findings are significantly different from the findings on

Ireland. The tax effect thus proved stronger in the Dutch and Belgian SMEs, than it is

in the Irish SMEs. The tax effect between the Dutch and Belgian SMEs does not seem

to be different, as was anticipated.

Bankruptcy costs

The bankruptcy scores presented in Appendix II noted that some differences exist in

bankruptcy procedures and costs among the three countries analyzed here. Durations

of a typical bankruptcy case varied from 5 months in Ireland to 20 months in the


MSc Thesis in Finance & International Business A.K. Chudzinska & S.L.van der Bijl xxxv

Netherlands, and 11 months in Belgium. Underlying these different procedural

durations are a multitude of reasons, among them the different legal systems,

bankruptcy law and law enforcement, of the three countries. A study of the legal and

bankruptcy procedures indicates that in Ireland, with its Anglo-Saxon common law

background, bankruptcy procedures are typically fastest. In the Netherlands, with a

Germanic civil law system, the procedures were found to be the slowest. In Belgium,

with a French civil law background, the speed of settling bankruptcy cases fall

somewhere in between these two extremes. The speed of settling such cases does not

say much about effectiveness of bankruptcy laws, however. It can be further noted

that the costs of bankruptcy cases are lowest in the Netherlands, followed by Belgium,

and remain highest in Ireland. Recovery rates for creditors are very similar for all

three countries, differing by only 1,5 percent. These observations indicate that

although a typical bankruptcy case in the Netherlands takes much longer than in

Ireland, the total costs are lower. In the end, the recovery rates are high, and very

similar, for all three countries. Despite the underlying legal differences among these

three countries, it seems that bankruptcy costs are relatively low in all three, and that

no clear differences in bankruptcy costs exist. From these observations, it can be

anticipated that the variables that proxy for bankruptcy costs would play a similar role

in all three countries, and the differences in the relationships with the bankruptcy

proxies, and leverage would be low, or not apparent at all.

As described previously, two variables proxy solely for bankruptcy costs: the Z-

SCORE and OPERATING RISK variables. These variables, indeed, do not prove any

major differences to exist in the relationships with leverage among the three countries.

The Z-SCORE is highly significant, and negatively related to leverage in all cases.

Except for some minor significant country dummies between Belgium and Ireland, no

real differences among the countries are observed. This proves that bankruptcy costs

matter in the capital structure for SMEs in all three Western European countries, and

that these costs are very similar. On the other hand, OPERATING RISK is found to

have no relationship with leverage, except for one measure of leverage found in

Belgium. There are also no significant differences between the three countries in

terms of OPERATING RISK. It can be argued further that OPERATING RISK does

not seem to be a good proxy for bankruptcy costs on a country specific level.

Strangely, OPERATING RISK was found to be significant and positive for the whole


MSc Thesis in Finance & International Business A.K. Chudzinska & S.L.van der Bijl xxxvi

Western European sample tested. This effect only seems evident when the countries

were tested together, but not when they were tested separately.

In Chapter Four, most other variables proved to proxy bankruptcy costs better than

agency costs. TANGIBILITY particularly seems to proxy bankruptcy costs best for

Long Term Leverage and Adjusted Total Leverage in the countries, especially in

Belgium and the Netherlands, where the effect of TANGIBILITY on leverage is

positive and significant. The results show that the effect in Ireland is lower than for

the other two countries, or non-existent. On Short Term Leverage, the relationships

are negative and might, as argued in Chapter Four, proxy operating leverage, and thus

indirectly bankruptcy costs. On Total Leverage, no differences among the countries

are observed in this negative relation with TANGIBILITY, but on Short Term

Leverage, the relation is clearly less negative for Ireland, indicating lower bankruptcy

costs. However, as was stated, a negative relationship between TANGIBILITY and

leverage might stand for substitution of short term for long term debt. That is, asset

TANGIBILITY might lead to more long term and less short term debt in the capital

structure. This effect was found to be weaker in Ireland.

Similar to TANGIBILITY, the results on SIZE showed no clear relationship with the

first two measures of leverage. This was also found in the Western European sample

as a whole, where SIZE proxied bankruptcy costs only for Long Term Leverage. In

the three country results, the effect of SIZE on Long Term Leverage seems to be

existent only in the Belgian sample, although no significant differences among the

three countries is observed. The effects of SIZE as a proxy of bankruptcy costs on

leverage are thus similar among all the three countries.

The findings for PROFITABILITY on the three countries indicate the same trend as

was found for the whole Western sample. For all measures of leverage, the

coefficients were negative, although for Long Term Leverage, this effect is smallest

and weakest. This indicates that PROFITABILITY as a proxy for bankruptcy costs

does play some role on Long Term Leverage, but not enough to offset possible agency

cost effects. The findings are similar for all three countries, although in Ireland the

effect is most negative, while in Belgium it is least negative. Belgium seems to have a

significant less negative relation between PROFITABILITY and leverage than the


MSc Thesis in Finance & International Business A.K. Chudzinska & S.L.van der Bijl xxxvii

other two countries. This means that the pecking order is weaker in Belgium, or that

bankruptcy costs play a more important role in Belgium than in the other two

countries.

GROWTH, as a last possible proxy for bankruptcy costs, was found to be positively

related to all measures of leverage in the Western European sample. However, the

results among the three countries are different. GROWTH is positive and significant

in Ireland and Belgium, but negative and not significantly related to leverage in the

Netherlands. The relationships of GROWTH and leverage are therefore, significantly

lower in the Netherlands than in the other two countries. Except for the last measure

of leverage, no differences between Ireland and Belgium were observed. If GROWTH

is a proxy for bankruptcy costs, than this would indicate bankruptcy costs to be lower

in the Netherlands than in Ireland and Belgium.

From the above analysis one may conclude that within the Western European sample,

bankruptcy costs play an important role on capital structure. The Z-SCORE and

OPERATING RISK variables showed no clear differences to exist in this relationship

among Ireland, the Netherlands and Belgium. The other variables seemed to proxy

bankruptcy costs as well, especially on Long Term Leverage. This might indicate that,

indeed, bankruptcy costs are most important in borrowing and lending long term.

Some differences among the countries in respect to the other variables were found.

However, these differences are inconclusive. They were only present on certain

measures of leverage and might proxy for other effects at the same time. Since the Z-

SCORE and OPERATING RISK variables showed high similarities between

bankruptcy costs, it can be concluded that bankruptcy costs play a very similar role on

leverage in the Irish, Dutch and Belgian SMEs, as was expected.

Agency Costs

In Appendix II, country scores were presented on corporate governance. Corporate

governance deals with the dispersion of ownership and management, and, therefore,

serves as an indicator of agency costs. Since country scores represent public firms,

they might not be valid indicators for privately held companies. This is particularly

true for the studied SMEs where it was argued previously that corporate governance

and agency costs were not necessarily present. This is because the dispersion of


MSc Thesis in Finance & International Business A.K. Chudzinska & S.L.van der Bijl xxxviii

ownership and control is often very small or non-existent, and agency costs between

creditors and management are solved by banks themselves, by keeping very close

relations with the SMEs and typically lending short term. To some extent, agency

costs might play a role, particularly for the largest firms within the sample of SMEs

tested.

The corporate governance scores from Appendix II should, therefore, be seen as a

general institutional country specific score. All the underlying reasons that created the

scores indicate something about the role of agency costs in the countries, e.g. legal

investor protection, and effectiveness of the law. From these scores it became clear

that corporate governance is strongest in Ireland, which, as discussed previously, has a

common law tradition in which investors are well protected. The Netherlands scored

worst on corporate governance, while Belgium also scored very well. Worth noticing

is that, from these country scores, the Netherlands score weakest, while, when looking

at the Heidrick and Struggles (2006) international comparison of corporate

governance, the Netherlands score very well on this matter. The main expectation on

agency costs is that it does not play a clear role on leverage, in none of the three

countries, since the focus lies on SMEs where agency costs are not expected to be of

importance. However, if agency costs do play a role on SMEs’ capital structure within

the sampled countries, this relationship would be expected to be highest in the

Netherlands. Similarly, it would be expected to be lowest in Ireland. For Belgium, the

expected sign of the relationship would be found between those of Ireland and the

Netherlands.

The findings from the regressions for the whole Western European sample tested

indicate no proof for agency costs to exist. Checking, whether this also holds true for

the country specific regressions, requires a closer look to be taken at those variables

that might proxy for agency costs as well as bankruptcy costs.

TANGIBILITY is found to be negatively related to Total Leverage and Short Term

Leverage. As previously argued, this could be a proxy for operating leverage as well

as an indication of debt substitution. A positive relationship is found on Long Term

Leverage and Adjusted Total Leverage. This relation might proxy agency costs, but as

described, is is more likely to indicate bankruptcy costs.


MSc Thesis in Finance & International Business A.K. Chudzinska & S.L.van der Bijl xxxix

SIZE might also proxy agency costs, but the signs are similar as in the proxy for

bankruptcy costs. Therefore, it is impossible to distinguish between bankruptcy costs

and agency costs, if a positive relationship between SIZE and leverage is found. The

findings for SIZE indicate that in all three countries SIZE does not work very well as

a proxy for agency costs.

As stated, PROFITABILITY is negatively related to all measures of leverage in all

three countries. This indicates a pecking order based on the underlying assumption of

information asymmetry, or simply bad credit conditions (low access to credit), and,

therefore, a preference for internal funding. Since access to credits was expected to be

good in all three Western countries, the negative relation might come from agency

costs. However, the relationship between PROFITABILITY and leverage is most

negative in Ireland, but not that different between the Netherlands and Belgium. This

means that either no differences in agency costs exist among the Irish, Dutch and

Belgian SMEs, or that PROFITABILITY does not measure the agency cost effect at

all.

GROWTH as a proxy for agency costs is negatively related to leverage. The findings

here are that for the Netherlands, GROWTH is significantly more negatively related

to leverage when compared to the other two countries. However, the relation between

GROWTH and leverage itself is never significant and therefore it is impossible to say

whether agency costs play a role on leverage in the Dutch sample, or not.

From the above findings, it can be concluded that agency costs in SMEs do not have

any strong impact on capital structure, possibly only to a minor extent. This is in line

with the expectation that in agency costs do not play a role in SME capital structure.

As stated, the country scores indicated weaker investor protection in the Netherlands,

but this could not be identified in the relations between variables. This might be

because the country scores on corporate governance are more applicable to large

publicly traded firms, or because the country scores do not work well for the

Netherlands. As stated, other sources indicated strong corporate governance in the

Netherlands. Indeed, agency costs are unlikely to be present in most of the sampled

SMEs.


MSc Thesis in Finance & International Business A.K. Chudzinska & S.L.van der Bijl xl

IX.II Within the Eastern European Sample: Poland, Hungary and Ukraine

Leverage

For the three Eastern European countries, the institutional factors: taxes, bankruptcy

costs and agency costs are discussed in greater detail below. As discussed before, in

Eastern Europe, the overall availability of credits is expected to play an important role

on firms’ capital structures. This is more so than in the three Western European

countries. It was argued that in Eastern Europe, getting credit is rather based on

external factors than on internal policies, hence Eastern European SMEs often do not

have the luxury position to shield taxes. This makes it difficult to make a formal

expectation on differences in leverage. Although Ukraine has the highest corporate

tax rates, it also has highest bankruptcy costs and weakest access to credit. Similary,

Hungary has lowest corporate tax rates, but also has lowest bankruptcy costs,

relatively low agency costs and better credit information. These factors would lead to

easier access to credits for firms. Since the tax effect was expected to be of minor

importance to capital structures than the other factors, it leads to the expectations that

eventhough Hungarian SMEs face lowest tax rates, they will have highest leverage

ratios. Ukrainian SMEs, with highest tax rates, are expected to have lowest leverage

ratios. Leverage ratios of Polish SMEs are expected to be lower than those of

Hungary, and higher than in Ukraine.

The descriptive statistics from the Eastern European sample (Appendix IV.II) indicate

that all four ratios of leverage are lower in Ukraine than in Poland and Hungary. In

Poland, Total Leverage and Short Term Leverage are slightly lower, while Long Term

Leverage is only one percent lower when compared to Hungary. Adjusted Total

Leverage, however, is much lower in Poland than in Hungary. This is due to the fact

that the amount of payables was much higher in Poland during the studied years. The

F-tests of shift dummies plus slope dummies (in Table IX.2 and Appendix XI)

confirm that the models for the three countries are indeed different from each other,

for every tested type of leverage. The T-tests shown in Appendix VIII.III clearly

confirm that leverage ratios are highest in Hungary and lowest in Ukraine. This is true

for all four proxies of leverage. Leverage ratios for Poland fall in between these two,

but are closer to the Hungarian ratios. These findings are according to the


MSc Thesis in Finance & International Business A.K. Chudzinska & S.L.van der Bijl xli

expectations. The analysis below will point out whether these differences, indeed,

follow from the the weak availability of credits or from the three determinants.

Explanatory Notes to Table IX.2:

White-heteroskedasticity adjusted 2SLS regressions, conducted in E-VIEWS, for years 2000 to 2005 on the

country-samples from Eastern Europe: Poland, Hungary and Ukraine. In order to compare the three countries,

Poland is the base group. Eight tests on the following four different dependent variables have been done: Total



Liabilities / Total Assets, Long Term Leverage is calculated as Non-Current Liabilities / Total Assets and

Adjusted Total Leverage is calculated as (Total Liabilities – Payables) / Total Assets.


RISK, PROFITABILITY, GROWTH, LAGGED LEVERAGE and SHIFT DUMMY and SLOPE DUMMY

variables representing either Hungary or Ukraine and SHIFT DUMMY variables for Industries and Years.

KINK is computed as EBIT in year t / Interest Expenses in year t. STANDARDIZED KINK is computed as

(KINK in year t x Interest Expenses in year t) / Standard deviation of KINK over all years. Note that only one

tax variable is used at a time. SIZE is proxied by the natural logarithm of Total Assets in year t. Z-SCORE is

Altman’s Z-SCORE for General use,which is calculated for every year t as:

sLiabilitieTotal

EquityTotal

AssetsTotal

EBIT



OPERATING RISK is computed as the standard deviation of Earnings Before Taxes over all years observed

until year t. PROFITABILITY is calculated as Earnings Before Taxes / Total Assets. GROWTH is calculated as

the percentage change of Total Assets in year t-1 to year t. LAGGED LEVERAGE is calculated as the respective

leverage ratio (the dependent variable of the model), in year t-1. Note that correlation of LAGGED LEVERAGE

with the residuals of the model is removed by including an Instrumental Variable; leverage lagged two periods

(t-2).

The DUMMY variables are qualitative variables with the value 1 if the observation belongs to the group it

represents. Therefore, the shift dummy for Hungary has a value of 1 for a firm from Hungary. It has the value 0

if the observation is from a company in any of the other countries.

The Target Adjustment Coefficient is derived from model [4] and is computed as: 1 – the coefficient of

LAGGED LEVERAGE.

R² indicates the coefficient of determination, or the explanatory power of the model as a whole. DW statistics

show whether the regression results are affected by autocorrelation. A DW statistic close to 2.0 indicates no

autocorrelation. The number of variables with a Tolerance level smaller than 0,1 indicates whether

multicollinearity is apparent in the regression model. F statistics and their significance show whether a linear

relationship between the dependent variable and any of the independent variables exists, depending what group

of independent variables is included in the F-test.


MSc Thesis in Finance & International Business A.K. Chudzinska & S.L.van der Bijl xlii

Table IX.2 – White-heteroskedasticity adjusted 2SLS results of the Target Adjustment Model, comparing the three countries of the Eastern European sample: Poland, Hungary and Ukraine. Poland serves as the base group.

Constant 0,1128 0,1150 0,1140 0,1160 0,0755 0,0643 0,2231 0,2098

[9,1959]*** [9,7160]*** [8,8605]*** [9,1200]*** [6,979]*** [5,9682]*** [15,7489]*** [15,1049]***

Shift Dummy 2001 -0,0177 -0,0183 -0,0154 -0,0150 -0,0110 -0,0108 0,0005 0,0038

[-4,8222]*** [-5,2355]*** [-3,5699]*** [-3,4757]*** [-2,9234]*** [-2,8754]*** [0,1056] [0,8502]

Shift Dummy 2002 -0,0017 -0,0014 0,0008 0,0020 -0,0076 -0,0063 0,0064 0,0112

[-0,4758] [-0,3943] [0,2000] [0,4779] [-2,0531]** [-1,6896]* [1,4155] [2,5387]**

Shift Dummy 2003 0,0130 0,0137 0,0104 0,0119 -0,0011 0,0001 0,0158 0,0209

[3,4874]*** [4,036]*** [2,4883]*** [2,9361]*** [-0,2876] [0,0357] [3,4549]*** [4,8825]***

Shift Dummy 2004 -0,0004 0,0006 0,0031 0,0049 -0,0055 -0,0039 0,0078 0,0131

[-0,1107] [0,1952] [0,754] [1,2117] [-1,5247] [-1,0885] [1,7086]* [3,0851]***

Shift Dummy 2005 0,0028 0,0040 0,0032 0,0053 -0,0057 -0,0045 0,0183 0,0236

[0,7372] [1,1505] [0,7653] [1,2863] [-1,5609] [-1,2279] [3,9200]*** [5,4445]***

Shift Dummy Service Industry -0,0064 -0,0069 -0,0086 -0,0090 0,0102 0,0098 0,0121 0,0118

[-4,0982]*** [-4,4429]*** [-5,266]*** [-5,5113]*** [5,9923]*** [5,781]*** [5,2615]*** [5,0965]***

Shift Dummy Other Industries 0,0428 0,0404 0,0367 0,0347 0,0027 0,0010 -0,0235 -0,0269

[2,5046]** [2,372]** [2,0215]** [1,9153]* [0,2299] [0,0809] [-0,9952] [-1,1385]

Shift Dummy HU 0,0601 0,0556 0,0691 0,0695 -0,0235 -0,0124 0,2378 0,2821

[3,6679]*** [3,7075]*** [3,7445]*** [3,9937]*** [-1,4753] [-0,7908] [10,9661]*** [13,8841]***

Shift Dummy UKR 0,0765 0,0815 0,0778 0,0845 -0,1272 -0,1226 -0,1349 -0,1326

[5,6194]*** [5,9845]*** [5,652]*** [6,0891]*** [-9,2349]*** [-9,0343]*** [-7,8873]*** [-7,8125]***

KINK -0,0005 -- 0,0001 -- -0,0045 -- -0,0036 --

[-0,9574] -- [0,125] -- [-8,6485]*** -- [-6,2333]*** --

Dummy KINK HU -0,0002 -- 0,0004 -- 0,0044 -- 0,0108 --

[-0,1554] -- [0,3239] -- [5,5774]*** -- [6,7798]*** --

Dummy KINK UKR 0,0031 -- 0,0029 -- 0,0040 -- 0,004 --

[3,6434]*** -- [3,515]*** -- [5,8436]*** -- [4,0532]*** --

STANDARDIZED KINK -- 0,0046 -- 0,0037 -- 0,0015 -- 0,0039

-- [7,0462]*** -- [5,7042*** -- [2,2665]** -- [4,9015]***

Dummy STANDARDIZED KINK HU -- -0,0031 -- -0,0009 -- -0,0012 -- -0,0047

-- [-1,5538] -- [-0,4596] -- [-1,0987] -- [-2,1041]**

Dummy STANDARDIZED KINK UKR -- -0,0004 -- 0 -- 0,0004 -- 0,0006

-- [-0,4172] -- [-0,0473] -- [0,4004] -- [0,4548]

TANGIBILITY -0,0593 -0,0567 -0,0852 -0,0837 0,1308 0,1351 0,0421 0,0469

[-10,761]*** [-10,339]*** [-12,8634]*** [-12,7008]*** [20,6188]*** [21,0522]*** [6,4025]*** [7,1049]***

Dummy TANGIBILITY HU -0,0316 -0,0349 -0,0455 -0,0475 0,0360 0,0313 -0,2185 -0,2208

[-3,6896]*** [-4,0587]*** [-4,5776]*** [-4,7855]*** [3,5358]*** [3,0724]*** [-17,0677]*** [-17,4273]***

Dummy TANGIBILITY UKR -0,0757 -0,0795 -0,0473 -0,0508 -0,1064 -0,1083 -0,0549 -0,0563

[-9,5471]*** [-9,9276]*** [-5,8661]*** [-6,2547]*** [-11,1096]*** [-11,2525]*** [-4,5493]*** [-4,6509]***

SIZE 0,0027 0,0008 0,0027 0,0013 -0,0016 -0,0031 -0,0083 -0,0103

[1,997]** [0,6237] [1,9226]* [0,9402] [-1,1703] [-2,1618]** [-4,9789]*** [-6,0405]***

Dummy SIZE HU -0,0014 0,0001 -0,0035 -0,0028 0,0032 0,0045 -0,0004 0,0010

[-0,7956] [0,0532] [-1,6864]* [-1,3221] [1,6667]* [2,3008]** [-0,1683] [0,4267]

Dummy SIZE UKR -0,0079 -0,0065 -0,0104 -0,0095 0,0122 0,0137 0,0056 0,0076

[-4,4144]*** [-3,5865]*** [-5,6729]*** [-5,1215]*** [6,8595]*** [7,5823]*** [2,3211]** [3,1284]***

Z-SCORE -0,0061 -0,0061 -0,0053 -0,0053 -0,0073 -0,0076 -0,0078 -0,0080

[-13,8659]*** [-13,7065]*** [-13,0782]*** [-12,9267]*** [-20,5452]*** [-20,6287]*** [-20,361]*** [-20,3664]***

Dummy Z-SCORE HU -0,0050 -0,0052 -0,0040 -0,0041 -0,0013 -0,0010 -0,0133 -0,0116

[-4,6994]*** [-4,7265]*** [-4,016]*** [-4,0224]*** [-1,3467] [-1,0176] [-8,492]*** [-7,952]***

Dummy Z-SCORE UKR 0,0048 0,0048 0,0043 0,0042 0,0067 0,0071 0,0069 0,0071

[11,274]*** [11,1488]*** [10,7082]*** [10,581]*** [18,8876]*** [18,9973]*** [18,2348]*** [18,3601]***

OPERATING RISK 0,0000 0,0000 0,0000 0,0000 0,0000 0,0000 0,0000 0,0000

[-1,8156]* [-0,4979] [-2,3039]** [-1,1341] [3,5358]*** [4,2194]*** [4,3449]*** [5,372]***

Dummy OPERATING RISK HU 0,0000 0,0000 0,0000 0,0000 0,0000 0,0000 0,0000 0,0000

[1,8344]* [0,5091] [2,3203]** [1,1467 [-3,5358]*** [-4,2194]*** [-4,3449]*** [-5,3846]***

Dummy OPERATING RISK UKR 0,0000 0,0000 0,0000 0,0000 0,0000 0,0000 0,0000 0,0000

[2,9433]*** [1,9504]* [3,1953]*** [2,3737]** [-2,1789]** [-2,7827]*** [-1,1028] [-1,9000]*

PROFITABILITY -0,2854 -0,3195 -0,2355 -0,2578 0,0999 0,0379 -0,1070 -0,1751

[-13,4016]*** [-16,0019]*** [-11,5804]*** [-13,3003]*** [7,6512]*** [2,9487]*** [-5,9782]*** [-9,9425]***

Dummy PROFITABILITY HU 0,1059 0,1300 0,0785 0,0934 -0,1110 -0,0499 -0,1072 0,0153

[1,5322] [1,8731]* [1,3314] [1,583] [-4,8768]*** [-2,1707]** [-1,3442] [0,2199]

Dummy PROFITABILITY UKR -0,2863 -0,2357 -0,3041 -0,2586 -0,1375 -0,0889 -0,2206 -0,1664

[-7,867]*** [-7,516]*** [-8,782]*** [-8,5912]*** [-7,2805]*** [-5,1125]*** [-6,4188]*** [-5,5101]***

GROWTH 0,0659 0,0651 0,0360 0,0356 0,0145 0,0121 0,0293 0,0273

[8,7813]*** [8,8112]*** [3,6604]*** [3,6403]*** [3,1841]*** [2,6917]*** [4,2262]*** [3,9539]***

Dummy GROWTH HU -0,0046 -0,0040 -0,0061 -0,0056 0,0084 0,0109 0,0262 0,0307

[-0,4931] [-0,4304] [-0,4821] [-0,4444] [1,3627] [1,7884]* [2,5909]*** [3,0130]***

Dummy GROWTH UKR -0,0421 -0,0412 -0,0271 -0,0265 -0,0025 -0,0004 -0,0228 -0,0214

[-4,3436]*** [-4,2941]*** [-2,3761]** [-2,3326]** [-0,4035] [-0,0669] [-2,4027]** [-2,2595]**

LAGGED LEVERAGE 0,8629 0,8620 0,8586 0,8575 0,8106 0,8091 0,8677 0,8652

[115,4669]*** [116,5184]*** [105,8657]*** [106,382]*** [13,2483]*** [13,4351]*** [50,7493]*** [51,4528]***


Rsquare 0,8585 0,8587 0,8218 0,8218 0,1170 0,1157 0,5678 0,5670


DW statistic 1,9861 1,9854 1,9880 1,9874 1,9648 1,9653 1,9904 1,9890

Tolerance 0,1415 0,1413 0,1782 0,1782 0,8830 0,8843 0,4322 0,4330

F-test year effect 25,5975*** 28,3051*** 16,1707*** 18,2693*** 3,48597*** 3,7505*** 7,0309*** 10,7321***

F-test industry effect 11,8711*** 13,0612*** 16,2664*** 17,4016*** 17,9485*** 16,7099*** 14,5045*** 13,8322***

F test country shift 16,7364*** 18,5881*** 17,2435*** 19,8227*** 48,9548*** 50,1880*** 155,7039*** 219,2634***

F test country slope 24,2911*** 24,5501*** 23,3613*** 23,7851*** 57,262*** 54,5390*** 53,5487*** 50,9385***

F test country shift+slope 25,3812*** 26,9669*** 21,8468*** 22,8381*** 82,7883*** 77,9992*** 192,7385*** 189,3353***

Total LeveragePOLAND as BASEGROUP Adjusted Total LeverageLong Term LeverageShort Term Leverage



MSc Thesis in Finance & International Business A.K. Chudzinska & S.L.van der Bijl xliii

Taxes

From Appendix II it can be seen that the corporate tax rates in Hungary have been

much lower than in Poland and Ukraine, with a decline from 18 to 16 percent

throughout the years 2000 - 2005. For the first four years of this study period, the tax

rates of Poland and Ukraine were similarly high, around 30 percent; however, in the

latter years, corporate tax rates declined in both countries. The Polish tax rate

decreased to 19 percent, approaching the rate of Hungary. Nonetheless, the tax rates

of Ukraine remained higher at 25 percent. The data sample covers all six years.

Therefore, over the entire period of this study, the tax rates in Hungary were

considerably lower than those found in both in Poland and Ukraine, while the

difference in tax rate between Poland and Ukraine remained smaller. Consequently,

the tax effect on leverage is expected to be stronger in Poland and Ukraine, when

compared to Hungary.

In Table IX.2 and the two tables of Appendix XI, it can be seen that only in Ukraine,

KINK is related to both Total Leverage and Short Term Leverage. However, the signs

are positive, and therefore, KINK does not capture the tax effect. As explained

previously, KINK is calculated similarly as Rajan and Zingales’ (1995) ‘interest

coverage ratio,’ which in essence, is another proxy for leverage. The weak effects of

KINK on Total Leverage and Short Term Leverage in the three countries are not

surprising, since it was found that KINK did not have much influence on these proxies

of leverage in Eastern Europe. The high amounts of non-interest bearing liabilities in

these measures of leverage seem to be the reason for these weak effects. It was found

that KINK did have a significant and negative relationship with Long Term Leverage

and Adjusted Total Leverage in Eastern Europe. When looking at the country specific

regressions, this negative effect seems to be present only in Poland. That is, KINK

proxies the tax effect in Poland on Long Term Leverage and Total Adjusted Leverage,

but does not do so in the other two countries under study.

STANDARDIZED KINK was found to be a stronger indicator of the tax effect on

Eastern Europe. Indeed, STANDARDIZED KINK seems to be more in line with the

expectations for the three countries. The findings on Poland and Ukraine indicate a

positive and significant relationship between STANDARDIZED KINK and all four

proxies of leverage. In Hungary no significant relationships are found. This indicates


MSc Thesis in Finance & International Business A.K. Chudzinska & S.L.van der Bijl xliv

a stronger tax effect in Poland and Ukraine, however, the dummy variables indicate

no clear differences between these countries and Hungary. Only on Adjusted Total

Leverage, the findings in Poland and Ukraine are significantly different from the

findings in Hungary. No difference in this relation between Poland and Ukraine is

found. These findings confirm that higher tax rates in both Poland and Ukraine have

had a significant influence on the capital structure of SMEs. Nevertheless, the tax

effect seems to be the strongest in Poland, and not in Ukraine. This might be due to

weaker capital markets in Ukraine, as indicated by the weaker access to credit in

Ukraine. Indeed, SMEs in Ukraine might not be in the ‘luxury’ position of being able

to shield their taxes.

Bankruptcy costs

The bankruptcy scores in Appendix II indicate that clear differences exist in the

enforcement of bankruptcy proceedings in Hungary, Poland and Ukraine. Moreover,

Ukraine has the least favorable bankruptcy settings: the costs of bankruptcy

proceeding are 42 percent, and recovery rates to creditors are only 9 percent of the

total value of the estate. The bankruptcy costs in Poland and Hungary are lower, at 22

and 14,5 percent, respectively. Similarly, recovery rates are higher in both countries,

amounting to 28 percent in Poland and 40 percent in Hungary. These scores indicate

that bankruptcy laws are the strongest in Hungary.

It was established that the Z-SCORE was a good indicator for bankruptcy costs in the

Eastern European sample. Identically to findings in the Eastern European sample, the

Z-SCORE in the three Eastern European countries indicates a negative and significant

relationship with all types of leverage. However, the country dummies indicate that all

the countries significantly differ from each other on this matter. Because the

bankruptcy costs are expected to be the highest in Ukraine, the relation between the Z-

SCORE and leverage is expected to be the strongest and least negative. Similarly, for

Hungary, the Z-SCORE is expected to have a more negative, but weaker relation with

leverage. That is, as Hungary indicated smaller bankruptcy costs, creditors are

expected to lend more to Hungarian firms.

In Table IX.2 and Appendix XI, it can be observed that the results of the Z-SCORE

are exactly according to expectations. The Z-SCORE is the most significantly

negative with the leverages in Hungary, and least negative in Ukraine. The


MSc Thesis in Finance & International Business A.K. Chudzinska & S.L.van der Bijl xlv

relationship between the Z-SCORE and leverage in Poland fall exactly in between

those of Hungary and the Ukraine.

Confirming the expectations and findings from Eastern European sample, Z-SCORE

is found to be a good proxy for bankruptcy costs in Ukraine, Hungary and Poland.

The findings on OPERATING RISK in the three countries are surprisingly not in line

with the findings on the total Eastern European sample, where OPERATING RISK is

negatively and significantly related to Long Term Leverage. In none of the countries

this finding occurs. For Total Leverage and Short Term Leverage, only in Poland

some very weak evidence is found for a negative relationship with OPERATING

RISK. Just as in the Western European sample, OPERATING RISK does not appear

to be a strong proxy for bankruptcy costs.

TANGIBILITY was found to be a bankruptcy cost proxy only for Long Term

Leverage in the total Eastern European sample. This is confirmed by the results in the

three countries studied. The findings indicate that TANGIBILITY is significantly

most positively related to Long Term Leverage in Hungary and least positively related

in Ukraine. For the other three proxies of leverage, TANGIBILITY is negatively

related, thereby possibly proxying bankruptcy costs in the form of operating leverage,

or not proxying bankruptcy costs when indicating asset substitution. It was argued that

asset substitution is common in small firms from developing economies, since by

adding tangible assets, the firm can take on extra long term liabilities, only when

repaying (often more) short term liabilities (Booth et.al, 2001). The findings on Long

Term Leverage are in line with the expectations based on bankruptcy scores, since

they indicate that bankruptcy costs are the lowest in Hungary and highest in Ukraine.

The positive influence of TANGIBILITY can also be found on Total Adjusted

Leverage in Poland, but not in the other two countries. This is probably due to the

high amount of payables in Polish SMEs’ capital structures, making the Total

Adjusted Leverage more similar to Long Term leverage.

SIZE was found to be a good proxy for bankruptcy costs on Long Term Leverage and

Adjusted Total Leverage in the total Eastern European sample. This effect seems to

have resulted solely from the Ukrainian sample, since in the other two countries the

relationship is either negative or not significant. The findings thus indicate that

bankruptcy costs play the most important role in Ukraine. Surprisingly, in none of the


MSc Thesis in Finance & International Business A.K. Chudzinska & S.L.van der Bijl xlvi

three countries a positive relationship is found between SIZE and Total Adjusted

Leverage, while this was the case for the three countries when examined together.

PROFITABILITY in the total Eastern European sample was found to be negative to

three of the four measures of leverage. It was found to be positively related only to

Long Term Leverage. In the country-specific findings, a similar trend is observed, in

which PROFITABILITY is almost always negatively related to leverage. It appears

that, just as in the Eastern European sample, two effects are taking place. A

bankruptcy effect, which makes PROFITABILITY positively related to leverage and

another effect that gives a negative relation. Only in Poland, a positive relation is

found with Long Term Leverage. Similarly for Hungary and Ukraine,

PROFITABILITY is least negative in its relation to Long Term Leverage. This

indicates that bankruptcy costs are indeed most important in long term credit

decisions. The bankruptcy cost effect on Long Term Leverage in Poland appears to be

strong enough to offset the negative effects that are present in other measures of

leverage. Since PROFITABILITY in Poland scores more negative on Total Leverage

and Short Term Leverage, it seems that bankruptcy costs are higher in Poland then in

Hungary. It was expected that bankruptcy costs would be highest in Ukraine;

however, this can not be confirmed from the findings of PROFITABILITY on Long

Term Leverage. The reason for this is probably not due to lower bankruptcy costs in

Ukraine, but due to stronger preference for internal funds. A stronger pecking order is

thus apparent, which is not because of agency costs, but because of a scarcity of

credits. Internal funds are preferred over debt to such an extent that bankruptcy effects

are completely overshadowed. This situation perfectly illustrates the low availability

of credit and bad credit conditions affecting the SMEs in Ukraine.

The last variable, GROWTH, was found to be significant and positive on all proxies

of leverage for the Eastern European sample. Similarly, GROWTH is found to be

positively related to all four types of leverage in all three countries. However, for

Ukraine, this relationship is significantly less positive than in the other two countries.

Even with the same percentage of growth as the other two countries, a Ukrainian firm

attracts less credit than Hungarian or Polish firms. This suggests that bankruptcy costs

tend to be higher in Ukraine, and thus, have a negative impact on credit lenders. The

effect of GROWTH is similar between Poland and Hungary, except for Adjusted


MSc Thesis in Finance & International Business A.K. Chudzinska & S.L.van der Bijl xlvii

Total Leverage, for which GROWTH is lower in Poland than in Hungary. This might

indicate slightly higher bankruptcy costs in Poland when compared to Hungary. At the

same time, it may also demonstrate that higher firm growth in Poland tends to attract

more payables.

From the findings noted, one may conclude that within the Eastern European sample,

bankruptcy costs are a very important determinant on capital structure, especially for

Long Term Leverage. These findings are in line with the expectations based on the

bankruptcy scores for the three countries. The findings on Z-SCORE and GROWTH

confirm that bankruptcy costs are highest in Ukraine and lowest in Hungary, for all

four proxies of leverage. Similarly, the results on TANGIBILITY and SIZE confirm

higher bankruptcy costs in Ukraine, on Long Term Leverage. PROFITABILITY

seems to indicate that the highest bankruptcy costs are found in Poland; however, the

findings on PROFITABILITY imply that a pecking order exists in all three countries,

indicating that access to credit is limited and loan conditions are unfavourable. This

pecking order is found to be strongest in Ukraine, followed by Poland and Hungary.

Agency Costs

The Investor Protection Index in Appendix II indicates that investor protection is

strongest in Poland, followed by Hungary and Ukraine. This translates into lower

agency costs in Poland, and higher agency costs in Ukraine. However, as stated

before, agency costs are not expected to play a profound role in capital structures in

SMEs.

Similar to the Western European countries, TANGIBILITY is found to be negatively

related to Total Leverage, Short Term Leverage and Adjusted Total Leverage for all

three Eastern European countries. As previously described, for Long Term Leverage,

this relationship is positive for all countries, and it is also positive for Poland in

Adjusted Total Leverage. The negative findings do not indicate agency costs, but the

positive signs might do so. However, these positive findings on Long Term Leverage

are more indicative of existing bankruptcy costs.

As was the situation in the Western European countries, positive relationships

between SIZE and Long Term Leverage and Adjusted Total Leverage are found.


MSc Thesis in Finance & International Business A.K. Chudzinska & S.L.van der Bijl xlviii

However, no distinction between agency costs and bankruptcy costs can be made

from these findings. Agency costs are less likely to be present than bankruptcy costs

in these SMEs.

The findings for PROFITABILITY imply the existence of a pecking order in all three

countries. It was noted that this observed pecking order might not be due to adverse

selection in Eastern European SMEs, but rather due to other country specific factors.

Unattractive loan conditions, especially on short term loans, and limited access to

credit, were recognized in the Eastern European region. Therefore, firms might prefer

internal funding over borrowing or acquiring external debt. Access to credit is

particularly weak in Ukraine. This result is in line with the regional findings,

especially since PROFITABILITY in Ukraine is significantly most negative with all

proxies of leverages, when compared to the other two sampled countries.

The findings on GROWTH clearly indicate that GROWTH does not proxy agency

costs, since no negative relationships with leverage are found in any of the three

countries.

In conclusion, agency costs do not seem to play a significant role in Eastern European

SMEs’ capital structures. None of the variables plainly point toward the expected

signs for agency costs. Agency costs between owners and management are, therefore,

either non-existent, or are solved internally, e.g. by ownership concentration. Agency

costs between creditors and management are solved by banks, in lending short term

and making firms reliant on the bank, thereby acting on the banks’ behalve.


MSc Thesis in Finance & International Business A.K. Chudzinska & S.L.van der Bijl xlix

Appendix X

Regression results for Ireland, the Netherlands and Belgium

Table X.1 White-heteroskedasticity adjusted 2SLS results of the Target Adjustment

Model, comparing the three countries of the Western European sample: Ireland, the Netherlands and Belgium. The Netherlands serve as the base group.

Constant 0,2931 0,2991 0,2561 0,2578 0,0198 0,0203 0,1276 0,1305[14,1213]*** [14,0912]*** [11,7721]*** [11,5959]*** [1,567] [1,5862] [5,1588]*** [5,1959]***

Shift Dummy 2001 -0,0026 -0,0019 -0,0081 -0,0076 0,0058 0,0060 0,0044 0,0048

[-1,7057]* [-1,2415] [-4,4379]*** [-4,1731]*** [3,9279]*** [4,0564]*** [2,0787]** [2,2792]**

Shift Dummy 2002 -0,0060 -0,0045 -0,0090 -0,0080 0,0031 0,0035 -0,0015 -0,0004

[-3,7450]*** [-2,8311]*** [-4,9626]*** [-4,4031]*** [2,0626]** [2,349]** [-0,6804] [-0,1752]

Shift Dummy 2003 -0,0063 -0,0046 -0,0110 -0,0099 0,0050 0,0055 0,0000 0,0012

[-4,0167]*** [-2,9693]*** [-6,1444]*** [-5,4986]*** [3,4159]*** [3,7339]*** [-0,0009] [0,5759]

Shift Dummy 2004 -0,0016 0,0004 -0,0044 -0,0031 0,0031 0,0037 0,0006 0,0021

[-1,0406] [0,2804] [-2,4899]** [-1,7054]* [2,2072]** [2,6215]*** [0,2791] [1,0139]

Shift Dummy 2005 -0,0025 -0,0002 -0,0063 -0,0046 0,0041 0,0048 -0,0006 0,0011

[-1,6671]* [-0,1446] [-3,505]*** [-2,5744]** [2,7811]*** [3,2357]*** [-0,3108] [0,5349]


[-1,4276] [-2,2526]** [-2,9829]*** [-3,5118]*** [1,8472]* [1,5557] [0,327] [-0,1399]


[-0,9037] [-1,3848] [-2,6204]*** [-2,91]*** [2,3285]** [2,176]** [-0,5899] [-0,7742]

Shift Dummy IR -0,1492 -0,1545 -0,0852 -0,0890 -0,0611 -0,0625 -0,1429 -0,1458

[-3,5563]*** [-3,5717]*** [-2,3061]** [-2,3877]** [-1,5583] [-1,5368] [-2,5467]** [-2,5446]**

Shift Dummy BE -0,0836 -0,0777 -0,0447 -0,0399 -0,0345 -0,0326 -0,0663 -0,0631

[-4,1726]*** [-3,7847]*** [-2,0551]** [-1,7858]* [-2,5258] [-2,3529]** [-2,6018]*** [-2,4321]**

KINK -0,0004 -- 0,0017 -- -0,0018 -- -0,0003 --

[-0,4769] -- [1,6751]* -- [-3,6411]*** -- [-3,3285]*** --

Dummy KINK IR -0,0023 -- -0,0037 -- 0,0012 -- -0,0027 --

[-1,7858]* -- [-1,7924]* -- [1,2623] -- [-1,9288]* --

Dummy KINK BE 0,0001 -- -0,0014 -- 0,0013 -- -0,0011 --

[0,1392] -- [-1,336] -- [2,4302]** -- [-1,5902] --


-- [4,9189]*** -- [2,9898]*** -- [2,3239]** -- [4,0813]***

Dummy STANDARDIZED KINK IR -- -0,003 -- -0,0023 -- -0,0009 -- -0,0022

-- [-3,2625]*** -- [-2,1278]** -- [-2,2429]** -- [-1,4644]

Dummy STANDARDIZED KINK BE -- 0,0026 -- 0,0018 -- 0,0006 -- 0,0006

-- [2,6518]*** -- [1,6386] -- [0,8858] -- [0,5315]

TANGIBILITY -0,0758 -0,0818 -0,1447 -0,1489 0,0708 0,0694 0,0118 0,0177

[-8,6679]*** [-8,9904]*** [-14,965]*** [-14,8965]*** [3,7437]*** [3,5334]*** [1,3159] [1,9337]*

Dummy TANGIBILITY IR 0,0173 0,0228 0,0649 0,0687 -0,0468 -0,0450 0,0007 -0,0058

[1,0019] [1,2985] [4,7579]*** [4,9543]*** [-2,1563]** [-1,7365]* [0,0344] [-0,2674]

Dummy TANGIBILITY BE 0,0173 0,0245 0,0133 0,0181 0,0035 0,0059 0,0142 0,0099

[1,9198]* [1,7209]* [1,3964] [1,8373]* [0,4337] [0,7243] [3,8882]*** [4,5729]***

SIZE -0,0048 -0,0056 -0,0043 -0,0048 0,0000 -0,0002 -0,0021 -0,0026

[-2,4004]** [-2,1675]** [-1,9518]* [-2,0616]** [-0,0333] [-0,1627] [-0,8006] [-0,9876]

Dummy SIZE IR 0,0102 0,0107 0,0023 0,0027 0,0077 0,0079 0,0139 0,0138

[1,8616]* [1,8850]* [0,5376] [0,6235] [1,4688] [1,4291] [1,9854]** [1,9169]*

Dummy SIZE BE 0,0054 0,0041 0,0016 0,0007 0,0033 0,0029 0,0020 0,0013

[2,4719]** [1,8282]* [0,6825] [0,2764] [2,0583]** [1,7759]* [0,734] [0,4663]

Z-SCORE -0,0129 -0,0131 -0,0099 -0,0099 -0,0025 -0,0026 -0,0038 -0,0039

[-8,0488]*** [-8,0122]*** [-8,3215]*** [-8,2714]*** [-4,8004]*** [-4,8086]*** [-4,9299]*** [-5,0219]***

Dummy Z-SCORE IR 0,0024 0,0027 0,0020 0,0022 0,0005 0,0006 -0,0004 0,0000

[1,4251] [1,5956] [1,5168] [1,6837]* [0,7444] [0,9042] [-0,3379] [-0,0148]

Dummy Z-SCORE BE -0,0029 -0,0030 -0,0028 -0,0028 0,0002 0,0003 0,0011 0,0012

[-1,9453]* [-1,9089]* [-2,3954]** [-2,3258]** [0,4159] [0,4656] [1,372] [1,4736]

OPERATING RISK 0,0000 0,0000 0,0000 0,0000 0,0000 0,0000 0,0000 0,0000

[0,1476] [1,0044] [-1,3794] [-0,7868] [1,9541]* [2,199]** [1,2752] [2,0511]**

Dummy OPERATING RISK IR 0,0000 0,0000 0,0000 0,0000 0,0000 0,0000 0,0000 0,0000

[-0,9055] [-0,811] [0,4706] [0,5481] [-1,1679] [-1,1119] [-1,0704] [-0,8462]

Dummy OPERATING RISK BE 0,0000 0,0000 0,0000 0,0000 0,0000 0,0000 0,0000 0,0000

[-0,4935] [0,7311] [0,5613] [1,3303] [-1,3322] [-0,8307] [1,6475]* [2,2183]**

PROFITABILITY -0,3280 -0,3306 -0,2617 -0,2635 -0,0667 -0,0676 -0,3605 -0,3591

[-11,57]*** [-10,8457]*** [-9,3809]*** [-8,6627]*** [-5,9841]*** [-5,6463]*** [-13,0516]*** [-11,7397]***

Dummy PROFITABILITY IR -0,0398 -0,0269 0,0192 0,0290 -0,0625 -0,0593 -0,0439 -0,0396

[-0,74] [-0,4954] [0,4053] [0,6013] [-1,4919] [-1,4727] [-0,763] [-0,6692]

Dummy PROFITABILITY BE 0,0866 0,0522 0,1028 0,0789 -0,0205 -0,0300 0,1228 0,1014

[2,7886]*** [1,5324] [3,3464]*** [2,3229]** [-1,5066] [-1,9994]** [3,9919]*** [2,9364]***

GROWTH -0,0078 -0,0079 -0,0063 -0,0064 -0,0019 -0,0020 -0,0143 -0,0145

[-0,5113] [-0,5241] [-0,4101] [-0,4199] [-0,6189] [-0,6429] [-1,3532] [-1,3831]

Dummy GROWTH IR 0,0901 0,0894 0,0670 0,0667 0,0235 0,0232 0,1038 0,1028

[3,9729]*** [3,9751]*** [2,9854]*** [2,9833]*** [2,1663]** [2,144]** [4,3106]*** [4,3216]***

Dummy GROWTH BE 0,0788 0,0787 0,0549 0,0550 0,0252 0,0253 0,0543 0,0546[4,9622]*** [4,9880]*** [3,4038]*** [3,4224]*** [4,7795]*** [4,788]*** [4,6824]*** [4,727]***

LAGGED LEVERAGE 0,7959 0,7915 0,8097 0,8086 0,0954 0,0969 0,5266 0,5293

[120,3166]*** [119,9259]*** [137,7572]*** [138,1799]*** [93,6261]*** [93,2164]*** [156,9714]*** [156,5888]***


Rsquare 0,8708 0,8712 0,8165 0,8168 0,7690 0,7691 0,7680 0,7680


DW statistic 1,9939 1,9940 2,0000 2,0005 1,9973 1,9968 1,9858 1,9851

Tolerance 0,1292 0,1288 0,1835 0,1832 0,2310 0,2309 0,2320 0,2320


F-test industry effect 1,0306 2,5562* [5,1964]*** [6,9174]*** [2,9111]* [2,4415]* [0,4158] [0,3351]




Total LeverageThe NETHERLANDS as BASEGROUP Adjusted Total LeverageLong Term LeverageShort Term Leverage



MSc Thesis in Finance & International Business A.K. Chudzinska & S.L.van der Bijl l

Appendix X Continued (1)

Explanatory Notes to Table X.1: White-heteroskedasticity adjusted 2SLS regressions, conducted in E-VIEWS, for years 2000 to 2005 on the

country-samples from Western Europe: Ireland, the Netherlands and Belgium. In order to compare the three

countries, the Netherlands is the base group. Eight tests on the following four different dependent variables have

been done: Total Leverage, Short Term Leverage, Long Term Leverage and Adjusted Total Leverage.






variables representing either Ireland or Belgium and SHIFT DUMMY variables for Industries and Years.

KINK is computed as EBIT in year t / Interest Expenses in year t. STANDARDIZED KINK is computed as (KINK

in year t x Interest Expenses in year t) / Standard deviation of KINK over all years. Note that only one tax variable

is used at a time. SIZE is proxied by the natural logarithm of Total Assets in year t. Z-SCORE is Altman’s Z-

SCORE for General use,which is calculated for every year t as:

sLiabilitieTotal

EquityTotal

AssetsTotal

EBIT








(t-2). The DUMMY variables are qualitative variables with the value 1 if the observation belongs to the group it

represents. Therefore, the shift dummy for Ireland has a value of 1 for a firm from Ireland. It has the value 0 if the

observation is from a company in any of the other countries.


LEVERAGE.


whether the regression results are affected by autocorrelation. A DW statistic close to 2.0 indicates no



relationship between the dependent variable and any of the independent variables exists, depending what group of

independent variables is included in the F-test.


MSc Thesis in Finance & International Business A.K. Chudzinska & S.L.van der Bijl li


Table X.2 White-heteroskedasticity adjusted 2SLS results of the Target Adjustment

Model, comparing the three countries of the Western European sample: Ireland, the Netherlands and Belgium. Belgium serves as the base group.

Constant 0,2095 0,2214 0,2114 0,2179 -0,0146 -0,0123 0,0613 0,0674

[24,3575]*** [24,7802]*** [24,7374]*** [24,8714]*** [-2,6551]*** [-2,174]** [7,456]*** [7,9225]***

Shift Dummy 2001 -0,0026 -0,0019 -0,0081 -0,0076 0,0058 0,0060 0,0044 0,0048

[-1,7057]* [-1,2415] [-4,4379]*** [-4,1731]*** [3,9279]*** [4,0564]*** [2,0787]** [2,2792]**

Shift Dummy 2002 -0,0060 -0,0045 -0,0090 -0,0080 0,0031 0,0035 -0,0015 -0,0004

[-3,745]*** [-2,8311]*** [-4,9626]*** [-4,4031]*** [2,0626]** [2,349]** [-0,6804] [-0,1752]

Shift Dummy 2003 -0,0063 -0,0046 -0,0110 -0,0099 0,0050 0,0055 0,0000 0,0012

[-4,0167]*** [-2,9693]*** [6,1444]*** [-5,4986]*** [3,4159]*** [3,7339]*** [-0,0009] [0,5759]

Shift Dummy 2004 -0,0016 0,0004 -0,0044 -0,0031 0,0031 0,0037 0,0006 0,0021

[-1,0406] [0,2804] [-2,4899]** [-1,7054]* [2,2072]** [2,6215]*** [0,2791] [1,0139]

Shift Dummy 2005 -0,0025 -0,0002 -0,0063 -0,0046 0,0041 0,0048 -0,0006 0,0011

[-1,6671]* [-0,1446] [-3,505]*** [-2,5744]** [2,7811]*** [3,2357]*** [-0,3108] [0,5349]


[-1,4276] [-2,2526]** [-2,9829]*** [-3,5118]*** [1,8472]* [1,5557] [0,327] [-0,1399]


[-0,9037] [-1,3848] [-2,6204]*** [-2,91]*** [2,3285]** [2,176]** [-0,5899] [-0,7742]

Shift Dummy IR -0,0656 -0,0768 -0,0405 -0,0491 -0,0266 -0,0299 -0,0765 -0,0827

[-1,7369]* [-1,9684]** [-1,2825] [-1,5506] [-0,7131] [-0,7697] [-1,5005] [-1,5856]

Shift Dummy NL 0,0836 0,0777 0,0447 0,0399 0,0345 0,0326 0,0663 0,0631

[4,1726]*** [3,7847]*** [2,0551]** [1,7858]* [2,5258]** [2,3529]** [2,6018]*** [2,4321]**

KINK -0,0003 -- 0,0003 -- -0,0005 -- -0,0014 --

[-1,2177] -- [2,1284]* -- [-2,6146]*** -- [-4,5623]*** --

Dummy KINK IR -0,0025 -- -0,0023 -- 0,0000 -- -0,0017 --

[-1,8427]* -- [-1,9519]* -- [-0,0311] -- [-1,1586] --

Dummy KINK NL -0,0203 -- 0,0014 -- -0,0013 -- 0,0011 --

[-0,1392] -- [1,336] -- [-2,4302]** -- [1,5902] --


-- [13,7449]*** -- [10,0707]*** -- [5,5735]*** -- [9,0129]***

Dummy STANDARDIZED KINK IR -- -0,0056 -- -0,004 -- -0,0015 -- -0,0029

-- [-9,0016]*** -- [-6,4489]*** -- [-2,6728]*** -- [-2,3065]**

Dummy STANDARDIZED KINK NL -- -0,0026 -- -0,0018 -- -0,0006 -- -0,0006

-- [-2,6518]*** -- [-1,6386] -- [-0,8858] -- [-0,5315]

TANGIBILITY -0,0585 -0,0573 -0,1314 -0,1308 0,0743 0,0753 0,0260 0,0276

[-8,6993]*** [-8,5221]*** [-27,1596]*** [-27,1246]*** [4,4754]*** [4,6612]*** [6,5985]*** [7,0275]***

Dummy TANGIBILITY IR 0,0000 -0,0017 0,0515 0,0505 -0,0503 -0,0509 -0,0134 -0,0157

[-0,0011] [-0,112] [4,723]*** [4,6326]*** [-2,9517]*** [-2,9656]*** [-1,8977]* [-1,9344]*

Dummy TANGIBILITY NL -0,0173 -0,0245 -0,0133 -0,0181 -0,0035 -0,0059 -0,0142 -0,0099

[-1,9198]* [-1,7209]* [-1,3964] [-1,8373]* [-0,4337] [-0,7243] [-3,8882]*** [-4,5729]***

SIZE 0,0005 -0,0014 -0,0027 -0,0041 0,0032 0,0027 -0,0001 -0,0013

[0,6851] [-1,7337]* [-3,2769]*** [-4,5628]*** [4,9246]*** [3,8757]*** [-0,0573] [-1,3323]

Dummy SIZE IR 0,0048 0,0066 0,0007 0,0021 0,0045 0,0050 0,0119 0,0124

[0,9458] [1,2377] [0,1848] [0,5327] [0,8826] [0,9325] [1,8126]* [1,8487]*

Dummy SIZE NL -0,0054 -0,0041 -0,0016 -0,0007 -0,0033 -0,0029 -0,0020 -0,0013

[-2,4719]** [-1,8282]* [-0,6825] [-0,2764] [-2,0583]** [-1,7759]* [-0,734] [-0,4663]

Z-SCORE -0,0158 -0,0161 -0,0127 -0,0127 -0,0023 -0,0023 -0,0027 -0,0027

[-14,1993]*** [-14,351]*** [-15,2578]*** [-15,2452]*** [-9,6653]*** [-9,6904]*** [-7,6218]*** [-7,6734]***

Dummy Z-SCORE IR 0,0054 0,0057 0,0048 0,0050 0,0003 0,0004 -0,0015 -0,0012

[1,9684]** [1,8343]** [2,2196]** [2,2113]** [0,556] [0,7232] [-1,8099]* [-1,5415]

Dummy Z-SCORE NL 0,0029 0,0030 0,0028 0,0028 -0,0002 -0,0003 -0,0011 -0,0012

[1,9453]* [1,9089]* [2,3954]** [2,3258]** [-0,4159] [-0,4656] [-1,372] [-1,4736]

OPERATING RISK 0,0000 0,0000 0,0000 0,0000 0,0000 0,0000 0,0000 0,0000

[-0,4923] [1,5559] [-0,3727] [1,071] [-0,0665] [0,8283] [2,7887]*** [4,0282]***

Dummy OPERATING RISK IR 0,0000 0,0000 0,0000 0,0000 0,0000 0,0000 0,0000 0,0000

[-0,7434] [-1,0231] [0,1847] [-0,1659] [-0,8636] [-0,9111] [-1,5034] [-1,4726]

Dummy OPERATING RISK NL 0,0000 0,0000 0,0000 0,0000 0,0000 0,0000 0,0000 0,0000

[0,4935] [-0,7311] [-0,5613] [-1,3303] [1,3322] [0,8307] [-1,6475]* [-2,2183]**

PROFITABILITY -0,2413 -0,2784 -0,1589 -0,1847 -0,0872 -0,0976 -0,2377 -0,2578

[-18,0666]*** [-17,9486]*** [-12,3069]*** [-12,2447]*** [-11,1816]*** [-10,7917]*** [-17,6115]*** [-16,1193]***

Dummy PROFITABILITY IR -0,1265 -0,0791 -0,0836 -0,0498 -0,0420 -0,0293 -0,1667 -0,1409

[-2,6490]*** [-1,6637]* [-2,0794]** [-1,2359] [-1,0219] [-0,7427] [-3,1947]*** [-2,6577]***

Dummy PROFITABILITY NL -0,0866 -0,0522 -0,1028 -0,0789 0,0205 0,0300 -0,1228 -0,1014

[-2,7886]*** [-1,5324] [-3,3464]*** [-2,3229]** [1,5066] [1,9994]** [-3,9919]*** [-2,9364]***

GROWTH 0,0710 0,0708 0,0486 0,0486 0,0233 0,0233 0,0400 0,0401

[15,3851]*** [15,3163]*** [9,098]*** [9,1088]*** [5,407]*** [5,4082]*** [8,2906]*** [8,2951]***

Dummy GROWTH IR 0,0113 0,0107 0,0121 0,0117 -0,0017 -0,0021 0,0495 0,0482

[0,6467] [0,6186] [0,6986] [0,6775] [-0,1556] [-0,1855] [2,2313]** [2,2019]**

Dummy GROWTH NL -0,0788 -0,0787 -0,0549 -0,0550 -0,0252 -0,0253 -0,0543 -0,0546

[-4,9622]*** [-4,988]*** [-3,4038]*** [-3,4224]*** [-4,7795]*** [-4,788]*** [-4,6824]*** [-4,727]***

LAGGED LEVERAGE 0,7959 0,7915 0,8097 0,8086 0,0954 0,0969 0,5266 0,5293

[120,3166]*** [119,9259]*** [137,7572]*** [138,1799]*** [93,6261]*** [93,2164]*** [156,9714]*** [156,5888]***


Rsquare 0,8708 0,8712 0,8165 0,8168 0,7690 0,7691 0,7680 0,7680


DW statistic 1,9939 1,9940 2,0000 2,0005 1,9973 1,9968 1,9858 1,9851

Tolerance 0,1292 0,1288 0,1835 0,1832 0,2310 0,2309 0,2320 0,2320


F-test industry effect 9,5118*** 2,5562* [5,1964]*** [6,9174]*** [2,9111]* [2,4415]* [0,4158] [0,3351]




Total LeverageBELGIUM as BASEGROUP Adjusted Total LeverageLong Term LeverageShort Term Leverage



MSc Thesis in Finance & International Business A.K. Chudzinska & S.L.van der Bijl lii


Explanatory Notes to Table X.2: White-heteroskedasticity adjusted 2SLS regressions, conducted in E-VIEWS, for years 2000 to 2005 on the

country-samples from Western Europe: Ireland, the Netherlands and Belgium. In order to compare the three

countries, Belgium is the base group. Eight tests on the following four different dependent variables have been

done: Total Leverage, Short Term Leverage, Long Term Leverage and Adjusted Total Leverage.






variables representing either Ireland or the Netherlands and SHIFT DUMMY variables for Industries and Years.


(KINK in year t x Interest Expenses in year t) / Standard deviation of KINK over all years. Note that only one tax

variable is used at a time. SIZE is proxied by the natural logarithm of Total Assets in year t. Z-SCORE is


sLiabilitieTotal

EquityTotal

AssetsTotal

EBIT









represents. Therefore, the shift dummy for Ireland has a value of 1 for a firm from Ireland. It has the value 0 if the



LEVERAGE.








MSc Thesis in Finance & International Business A.K. Chudzinska & S.L.van der Bijl liii

Appendix XI

Regression results for Poland, Hungary and Ukraine Table XI.1 White-heteroskedasticity adjusted 2SLS results of the Target Adjustment

Model, comparing the three countries of the Eastern European sample: Poland, Hungary and Ukraine. Hungary serves as the base group.

Constant 0,1729 0,1706 0,1831 0,1855 0,0520 0,0518 0,4609 0,4919

[11,9794]*** [12,291]*** [11,1355]*** [11,7553]*** [4,384]*** [4,4351]*** [24,0106]*** [26,6947]***

Shift Dummy 2001 -0,0177 -0,0183 -0,0154 -0,0150 -0,0110 -0,0108 0,0005 0,0038

[-4,8219]*** [-5,2355]*** [-3,5699]*** [-3,4757]*** [-2,9234]*** [-2,8754]*** [0,1056] [0,8502]

Shift Dummy 2002 -0,0017 -0,0014 0,0008 0,0020 -0,0076 -0,0063 0,0064 0,0112

[-0,4758] [-0,3943] [0,2000] [0,4779] [-2,0531]** [-1,6896]* [1,4155] [2,5387]**

Shift Dummy 2003 0,0130 0,0137 0,0104 0,0119 -0,0011 0,0001 0,0158 0,0209

[3,4874]*** [4,0360]*** [2,4883]** [2,9361]*** [-0,2876] [0,0357] [3,4549]*** [4,8825]***

Shift Dummy 2004 -0,0004 0,0006 0,0031 0,0049 -0,0055 -0,0039 0,0078 0,0131

[-0,1107] [0,1952] [0,754] [1,2117] [-1,5247] [-1,0885] [1,7086]* [3,0851]***

Shift Dummy 2005 0,0028 0,0040 0,0032 0,0053 -0,0057 -0,0045 0,0183 0,0236

[0,7372] [1,1505] [0,7653] [1,2863] [-1,5609] [-1,2279] [3,9200]*** [5,4445]***


[-4,0982]*** [-4,4429]*** [-5,266]*** [-5,5113]*** [5,9923]*** [5,781]*** [5,2615]*** [5,0965]***


[2,5046]** [2,3720]** [2,0215]** [1,9153]* [0,2299] [0,0809] [-0,9952] [-1,1385]

Shift Dummy PL -0,0601 -0,0556 -0,0691 -0,0695 0,0235 0,0124 -0,2378 -0,2821

[-3,6679]*** [-3,7075]*** [-3,7445]*** [-3,9937]*** [1,4753] [0,7908] [-10,9661]*** [-13,8841]***

Shift Dummy UKR 0,0164 0,0259 0,0087 0,0150 -0,1037 -0,1102 -0,3727 -0,4146

[1,0148] [1,7131]* [0,4855] [0,8740] [-6,8913]*** [-7,4805]*** [-17,5903]*** [-20,933]***

KINK -0,0007 -- 0,0005 -- 0,0000 -- 0,0072 --

[-0,5851] -- [0,4149] -- [-0,0331] -- [4,8889]*** --

Dummy KINK PL 0,0002 -- -0,0004 -- -0,0044 -- -0,0108 --

[0,1554] -- [-0,3239] -- [-5,5774]*** -- [-6,7798]*** --

Dummy KINK UKR 0,0033 -- 0,0025 -- -0,0004 -- -0,0068 --

[2,3957]** -- [1,9394]* -- [-0,5773] -- [-4,075]*** --

STANDARDIZED KINK -- 0,0014 -- 0,0028 -- 0,0003 -- -0,0007

-- [0,7453] -- [1,5857] -- [0,3412] -- [-0,3565]

Dummy STANDARDIZED KINK PL -- 0,0031 -- 0,0009 -- 0,0012 -- 0,0047

-- [1,5538] -- [0,4596] -- [1,0987] -- [2,1041]**

Dummy STANDARDIZED KINK UKR -- 0,0027 -- 0,0008 -- 0,0016 -- 0,0053

-- [1,3136] -- [0,4258] -- [1,4116] -- [2,2799]**

TANGIBILITY -0,0910 -0,0916 -0,1307 -0,1312 0,1668 0,1664 -0,1764 -0,1740

[-11,8800]*** [-11,985]*** [-13,7209]*** [-13,8257]*** [20,7647]*** [20,7827]*** [-16,6518]*** [-16,7383]***

Dummy TANGIBILITY PL 0,0316 0,0349 0,0455 0,0475 -0,0360 -0,0313 0,2185 0,2208

[3,6896]*** [4,0587]*** [4,5776]*** [4,7855]*** [-3,5358]*** [-3,0724]*** [17,0677]*** [17,4273]***

Dummy TANGIBILITY UKR -0,0440 -0,0446 -0,0018 -0,0033 -0,1424 -0,1396 0,1635 0,1646

[-4,8201]*** [-4,8566]*** [-0,1795] [-0,3222] [-12,9177]*** [-12,6882]*** [12,5604]*** [12,7263]***

SIZE 0,0012 0,0009 -0,0009 -0,0015 0,0016 0,0014 -0,0086 -0,0093

[1,0176] [0,7743] [-0,5671] [-0,9449] [1,2108] [1,0779] [-5,766]*** [-6,0803]***

Dummy SIZE PL 0,0014 -0,0001 0,0035 0,0028 -0,0032 -0,0045 0,0004 -0,0010

[0,7956] [-0,0532] [1,6864]* [1,3221] [-1,6667]* [-2,3008]** [0,1683] [-0,4267]

Dummy SIZE UKR -0,0065 -0,0066 -0,0068 -0,0067 0,0090 0,0092 0,0059 0,0066

[-3,7641]*** [-3,8136]*** [-3,4362]*** [-3,3500]*** [5,2158]*** [5,3422]*** [2,5574]** [2,8309]***

Z-SCORE -0,0111 -0,0113 -0,0094 -0,0094 -0,0086 -0,0086 -0,0211 -0,0196

[-10,2813]*** [-10,0793]*** [-9,3878]*** [-9,2102]*** [-9,4444]*** [-9,667]*** [-13,3552]*** [-13,3356]***

Dummy Z-SCORE PL 0,0050 0,0052 0,0040 0,0041 0,0013 0,0010 0,0133 0,0116

[4,6994]*** [4,7265]*** [4,016]*** [4,0224]*** [1,3467] [1,0176] [8,492]*** [7,952]***

Dummy Z-SCORE UKR 0,0098 0,0100 0,0083 0,0084 0,0080 0,0080 0,0202 0,0187

[9,1715]*** [9,0126]*** [8,381]*** [8,2461]*** [8,8416]*** [9,0529]*** [12,8587]*** [12,7985]***

OPERATING RISK 0,0000 0,0000 0,0000 0,0000 0,0000 0,0000 0,0000 0,0000

[0,9536] [1,0024] [0,7649] [0,9935] [-0,6335] [-0,5524] [-0,657] [-0,5067]

Dummy OPERATING RISK PL 0,0000 0,0000 0,0000 0,0000 0,0000 0,0000 0,0000 0,0000

[-1,8300]* [-0,5091] [-2,3203]** [-1,1467] [3,5358]*** [4,2194]*** [4,3449]*** [5,3846]***

Dummy OPERATING RISK UKR 0,0000 0,0000 0,0000 0,0000 0,0000 0,0000 0,0000 0,0000

[2,6000]*** [2,8352]*** [2,2293]** [2,437]** [1,578] [1,6543]* [2,6978]*** [2,8]***

PROFITABILITY -0,1795 -0,1895 -0,1570 -0,1644 -0,0110 -0,0121 -0,2142 -0,1598

[-2,7465]*** [-2,8594]*** [-2,8532]*** [-2,9564]*** [-0,5944] [-0,6334] [-2,7699]*** [-2,3783]**

Dummy PROFITABILITY PL -0,1059 -0,1300 -0,0785 -0,0934 0,1110 0,0499 0,1072 -0,0153

[-1,5322] [-1,8731]* [-1,3314] [-1,5830] [4,8768]*** [2,1707]** [1,3442] [-0,2199]

Dummy PROFITABILITY UKR -0,3922 -0,3657 -0,3826 -0,3520 -0,0265 -0,0390 -0,1133 -0,1817

[-5,4708]*** [-5,1849]*** [-6,2019]*** [-5,8537]*** [-1,1516] [-1,741]* [-1,371] [-2,5402]**

GROWTH 0,0613 0,0611 0,0299 0,0300 0,0229 0,0230 0,0555 0,0580

[10,9167]*** [10,7149]*** [3,728]*** [3,7485]*** [5,4823]*** [5,5247]*** [7,5106]*** [7,6837]***

Dummy GROWTH PL 0,0046 0,0040 0,0061 0,0056 -0,0084 -0,0109 -0,0262 -0,0307

[0,4931] [0,4304] [0,4821] [0,4444] [-1,3627] [-1,7884]* [-2,5909]*** [-3,013]***

Dummy GROWTH UKR -0,0375 -0,0372 -0,0210 -0,0209 -0,0109 -0,0113 -0,0489 -0,0521

[-4,3528]*** [-4,2927]*** [-2,066]** [-2,058]** [-1,7815]* [-1,8504]* [-4,8985]*** [-5,145]***

LAGGED LEVERAGE 0,8629 0,8620 0,8586 0,8575 0,8106 0,8091 0,8677 0,8652

[115,4669]*** [116,5184]*** [105,8657]*** [106,382]*** [13,2483]*** [13,4351]*** [50,7493]*** [51,4528]***


Rsquare 0,8585 0,8587 0,8217 0,8218 0,1170 0,1157 0,5678 0,5670


DW statistic 1,9861 1,9854 1,9880 1,9874 1,9648 1,9653 1,9904 1,9890

Tolerance 0,1415 0,1413 0,1783 0,1782 0,8830 0,8843 0,4322 0,4330






Total LeverageHUNGARY as BASEGROUP Adjusted Total LeverageLong Term LeverageShort Term Leverage



MSc Thesis in Finance & International Business A.K. Chudzinska & S.L.van der Bijl liv

Appendix XI Continued (1)

Explanatory Notes to Table XI.1:



Hungary is the base group. Eight tests on the following four different dependent variables have been done: Total







variables representing either Poland or Ukraine and SHIFT DUMMY variables for Industries and Years.


(KINK in year t x Interest Expenses in year t) / Standard deviation of KINK over all years. Note that only one

tax variable is used at a time. SIZE is proxied by the natural logarithm of Total Assets in year t. Z-SCORE is


sLiabilitieTotal

EquityTotal

AssetsTotal

EBIT



OPERATING RISK is computed as the standard deviation of Earnings Before Taxes over all years observed

until year t. PROFITABILITY is calculated as Earnings Before Taxes / Total Assets. GROWTH is calculated as

the percentage change of Total Assets in year t-1 to year t. LAGGED LEVERAGE is calculated as the respective




represents. Therefore, the shift dummy for Poland has a value of 1 for a firm from Poland. It has the value 0 if

the observation is from a company in any of the other countries.

The Target Adjustment Coefficient is derived from model [4] and is computed as: 1 – the coefficient of

LAGGED LEVERAGE.





relationship between the dependent variable and any of the independent variables exists, depending what group

of independent variables is included in the F-test.


MSc Thesis in Finance & International Business A.K. Chudzinska & S.L.van der Bijl lv


Table XI.2 White-heteroskedasticity adjusted 2SLS results of the Target Adjustment

Model, comparing the three countries of the Eastern European sample: Poland, Hungary and Ukraine. Ukraine serves as the base group.

Constant 0,1893 0,1965 0,1918 0,2005 -0,0518 -0,0584 0,0882 0,0772[17,7818]*** [18,8724]*** [18,1658]*** [19,2059]*** [-5,3572]*** [-6,1435]*** [6,2694]*** [5,6800]***

Shift Dummy 2001 -0,0177 -0,0183 -0,0154 -0,0150 -0,0110 -0,0108 0,0005 0,0038

[-4,8219]*** [-5,2355]*** [-3,5699]*** [-3,4757]*** [-2,9234]*** [-2,8754]*** [0,1056] [0,8502]

Shift Dummy 2002 -0,0017 -0,0014 0,0008 0,0020 -0,0076 -0,0063 0,0064 0,0112

[-0,4758] [-0,3943] [0,2000] [0,4779] [-2,0531]** [-1,6896]* [1,4155] [2,5387]**

Shift Dummy 2003 0,0130 0,0137 0,0104 0,0119 -0,0011 0,0001 0,0158 0,0209

[3,4874]*** [4,036]*** [2,4883]** [2,9361]*** [-0,2876] [0,0357] [3,4549]*** [4,8825]***

Shift Dummy 2004 -0,0004 0,0006 0,0031 0,0049 -0,0055 -0,0039 0,0078 0,0131

[-0,1107] [0,1952] [0,7540] [1,2117] [-1,5247] [-1,0885] [1,7086]* [3,0851]***

Shift Dummy 2005 0,0028 0,0040 0,0032 0,0053 -0,0057 -0,0045 0,0183 0,0236

[0,7372] [1,1505] [0,7653] [1,2863] [-1,5609] [-1,2279] [3,9200]*** [5,4445]***


[-4,0982]*** [-4,4429]*** [-5,266]*** [-5,5113]*** [5,9923]*** [5,781]*** [5,2615]*** [5,0965]***


[2,5046]** [2,3720]** [2,0215]** [1,9153]* [0,2299] [0,0809] [-0,9952] [-1,1385]

Shift Dummy PL -0,0765 -0,0815 -0,0778 -0,0845 0,1272 0,1226 0,1349 0,1326

[-5,6194]*** [-5,9845]*** [-5,652]*** [-6,0891]*** [9,2349]*** [9,0343]*** [7,8873]*** [7,8125]***

Shift Dummy HU -0,0164 -0,0259 -0,0087 -0,0150 0,1037 0,1102 0,3727 0,4146

[-1,0148] [-1,7131]* [-0,4855] [-0,874] [6,8913]*** [7,4805]*** [17,5903]*** [20,933]***

KINK 0,0026 -- 0,003 -- -0,0005 -- 0,0003 --

[3,7762]*** -- [4,5128]*** -- [-1,0156] -- [0,409] --

Dummy KINK PL -0,0031 -- -0,0029 -- -0,004 -- -0,004 --

[-3,6434]*** -- [-3,515]*** -- [-5,8436]*** -- [-4,0532]*** --

Dummy KINK HU -0,0033 -- -0,0025 -- 0,0004 -- 0,0068 --

[-2,3957]** -- [-1,9394]* -- [0,5773] -- [4,075]*** --


-- [5,4013]*** -- [4,9809]*** -- [2,6732]*** -- [4,5105]***

Dummy STANDARDIZED KINK PL -- 0,0004 -- 0,0000 -- -0,0004 -- -0,0006

-- [0,4172] -- [0,0473] -- [-0,4004] -- [-0,4548]

Dummy STANDARDIZED KINK HU -- -0,0027 -- -0,0008 -- -0,0016 -- -0,0053

-- [-1,3136] -- [-0,4258] -- [-1,4116] -- [-2,2799]**

TANGIBILITY -0,1350 -0,1362 -0,1325 -0,1345 0,0245 0,0268 -0,0129 -0,0094

[-18,8328]*** [-18,9509]*** [-18,9341]*** [-19,1659]*** [3,5471]*** [3,8923]*** [-1,3344] [-0,9732]

Dummy TANGIBILITY PL 0,0757 0,0795 0,0473 0,0508 0,1064 0,1083 0,0549 0,0563

[9,5471]*** [9,9276]*** [5,8661]*** [6,2547]*** [11,1096]*** [11,2525]*** [4,5493]*** [4,6509]***

Dummy TANGIBILITY HU 0,0440 0,0446 0,0018 0,0033 0,1424 0,1396 -0,1635 -0,1646

[4,8201]*** [4,8566]*** [0,1795] [0,3222] [12,9177]*** [12,6882]*** [-12,5604]*** [-12,7263]***

SIZE -0,0053 -0,0057 -0,0077 -0,0082 0,0105 0,0106 -0,0027 -0,0027

[-4,3388]*** [-4,6109]*** [-6,3798]*** [-6,6762]*** [9,4444]*** [9,4909]*** [-1,5321] [-1,5141]

Dummy SIZE PL 0,0079 0,0065 0,0104 0,0095 -0,0122 -0,0137 -0,0056 -0,0076

[4,4144]*** [3,5865]*** [5,6729]*** [5,1215]*** [-6,8595]*** [-7,5823]*** [-2,3211]** [-3,1284]***

Dummy SIZE HU 0,0065 0,0066 0,0068 0,0067 -0,0090 -0,0092 -0,0059 -0,0066

[3,7641]*** [3,8136]*** [3,4362]*** [3,35]*** [-5,2158]*** [-5,3422]*** [-2,5574]** [-2,8309]***

Z-SCORE -0,0013 -0,0013 -0,0011 -0,0010 -0,0005 -0,0005 -0,0009 -0,0009

[-15,3032]*** [-15,2324]*** [-14,9011]*** [-14,8085]*** [-12,0131]*** [-11,9214]*** [-14,9196]*** [-14,7665]***

Dummy Z-SCORE PL -0,0048 -0,0048 -0,0043 -0,0042 -0,0067 -0,0071 -0,0069 -0,0071

[-11,274]*** [-11,1488]*** [-10,7082]*** [-10,581]*** [-18,8876]*** [-18,9973]*** [-18,2348]*** [-18,3601]***

Dummy Z-SCORE HU -0,0098 -0,0100 -0,0083 -0,0084 -0,0080 -0,0080 -0,0202 -0,0187

[-9,1715]*** [-9,0126]*** [-8,381]*** [-8,2461]*** [-8,8416]*** [-9,0529]*** [-12,8587]*** [-12,7985]***

OPERATING RISK 0,0000 0,0000 0,0000 0,0000 0,0000 0,0000 0,0000 0,0000

[2,6222]*** [2,8571]*** [2,2505]** [2,458]** [1,5736] [1,6499]* [2,6848]*** [2,8000]***

Dummy OPERATING RISK PL 0,0000 0,0000 0,0000 0,0000 0,0000 0,0000 0,0000 0,0000

[-2,9433]*** [-1,9504]* [-3,1953]*** [-2,3737]** [2,1789]** [2,7827]*** [1,1028] [1,9000]*

Dummy OPERATING RISK HU 0,0000 0,0000 0,0000 0,0000 0,0000 0,0000 0,0000 0,0000

[-2,6000]*** [-2,8352]*** [-2,2293]** [-2,437]** [-1,578] [-1,6543]* [-2,6978]*** [-2,8000]***

PROFITABILITY -0,5717 -0,5552 -0,5396 -0,5164 -0,0376 -0,0510 -0,3275 -0,3415

[-19,1732]*** [-22,8113]*** [-19,1398]*** [-22,3308]*** [-2,7575]*** [-4,3605]*** [-11,1111]*** [-13,9109]***

Dummy PROFITABILITY PL 0,2863 0,2357 0,3041 0,2586 0,1375 0,0889 0,2206 0,1664

[7,867]*** [7,516]*** [8,782]*** [8,5912]*** [7,2805]*** [5,1125]*** [6,4188]*** [5,5101]***

Dummy PROFITABILITY HU 0,3922 0,3657 0,3826 0,3520 0,0265 0,0390 0,1133 0,1817

[5,4708]*** [5,1849]*** [6,2019]*** [5,8537]*** [1,1516] [1,741]* [1,371] [2,5402]**

GROWTH 0,0238 0,0240 0,0089 0,0092 0,0120 0,0117 0,0065 0,0059

[3,6476]*** [3,6697]*** [1,4343] [1,4727] [2,6833]*** [2,6046]*** [0,9626] [0,8700]

Dummy GROWTH PL 0,0421 0,0412 0,0271 0,0265 0,0025 0,0004 0,0228 0,0214

[4,3436]*** [4,2941]*** [2,3761]** [2,3326]** [0,4035] [0,0669] [2,4027]** [2,2595]**

Dummy GROWTH HU 0,0375 0,0372 0,0210 0,0209 0,0109 0,0113 0,0489 0,0521[4,3528]*** [4,2927]*** [2,066]** [2,058]** [1,7815]* [1,8504]* [4,8985]*** [5,1450]***

LAGGED LEVERAGE 0,8629 0,8620 0,8586 0,8575 0,8106 0,8091 0,8677 0,8652

[115,4669]*** [116,5184]*** [105,8657]*** [106,382]*** [13,2483]*** [13,4351]*** [50,7493]*** [51,4528]***


Rsquare 0,8585 0,8587 0,8218 0,8218 0,1170 0,1157 0,5678 0,5670


DW statistic 1,9861 1,9854 1,9880 1,9874 1,9648 1,9653 1,9904 1,9890

Tolerance 0,1415 0,1413 0,1782 0,1782 0,8830 0,8843 0,4322 0,4330






Total LeverageUKRAINE as BASEGROUP Adjusted Total LeverageLong Term LeverageShort Term Leverage



MSc Thesis in Finance & International Business A.K. Chudzinska & S.L.van der Bijl lvi


Explanatory Notes to Table XI.2:



Ukraine is the base group. Eight tests on the following four different dependent variables have been done: Total







variables representing either Poland or Hungary and SHIFT DUMMY variables for Industries and Years.

KINK is computed as EBIT in year t / Interest Expenses in year t. STANDARDIZED KINK is computed as (KINK

in year t x Interest Expenses in year t) / Standard deviation of KINK over all years. Note that only one tax variable

is used at a time. SIZE is proxied by the natural logarithm of Total Assets in year t. Z-SCORE is Altman’s Z-

SCORE for General use,which is calculated for every year t as:

sLiabilitieTotal

EquityTotal

AssetsTotal

EBIT









represents. Therefore, the shift dummy for Poland has a value of 1 for a firm from Poland. It has the value 0 if the



LEVERAGE.


whether the regression results are affected by autocorrelation. A DW statistic close to 2.0 indicates no





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