capital structure in european smes - pure

MSc. Finance & International Business Authors:

Niels Stoustrup Jensen (270404)

Fabian Thomas Uhl (280905)

Academic advisor:

Jan Bartholdy, PhD

Capital Structure in European SMEs

An analysis of firm- and country specific variables

in determining leverage

Aarhus School of Business

University of Aarhus

Aug. 2008

ABSTRACT

This study investigates how country specific factors related to macroeconomic development,

corporate governance, legal- and financial environment affect the capital structure of

European small and medium sized enterprises. Using regression analysis on a data panel

consisting of nearly 500,000 observations from a total of 24 countries, the study shows

significant relationships between proxies for different institutional factors and leverage. This

suggests that policy makers can affect the environment for SMEs to operate in, by improving

certain country characteristics. By distinguishing between Eastern and Western Europe using

a dummy variable, the study further shows that there are differences in the impact of firm as

well as country specific variables on leverage, depending on the region a company is

incorporated in.

More specifically, the study finds evidence that leverage is in general lower in Eastern

Europe. This does to some extend support the findings of the OECD, that there exist an SME

financing gap in transition countries which is argued to be due to a lack of institutional

development, affecting the credit availability of SMEs.

The expectation that corporate governance, legal- and financial environment are positively

related to leverage, is only partly supported since a few surprising results are obtained

regarding certain variables. Contrary, there is clear evidence that bank concentration is

negatively correlated to leverage. Further, more profitable banks enhance the level of debt in

the capital structure of SMEs.

The study thereby questions the ability of the traditional ground pillars of capital structure

theory, namely the static trade-off- and pecking order theory, to fully explain leverage, since

they are argued to not sufficiently take into account the supply side of the financing decision.

Obligatory note to the reader

We hereby declare that this paper has been produced in full cooperation by the two authors.

No division has been done in terms of workload, and each of us is therefore responsible for

the entire paper.

Best regards,

Fabian Thomas Uhl

Niels Stoustrup Jensen

Table of Contents

1. Introduction ............................................................................................................ 1

1.1. Outline of the paper ............................................................................................ 2

2. Literature review .................................................................................................... 3

2.1. Theories of capital structure ............................................................................... 3

2.2. Miller and Modigliani’s irrelevance proposition ................................................ 3

2.3. The static Trade-off theory ................................................................................. 5

2.4. Pecking order theory ......................................................................................... 11

2.5. SME financing gap ........................................................................................... 15

2.6. Capital structure in East vs. West ..................................................................... 17

2.7. Market power .................................................................................................... 21

3. Research question and hypotheses ..................................................................... 24

4. Methodology ......................................................................................................... 27

4.1. Fixed effects vs. random effects model ............................................................ 27

4.2. Dummy variables .............................................................................................. 30

5. Data collection ...................................................................................................... 34

5.1. Firm specific data .............................................................................................. 34

5.2. Country specific data ........................................................................................ 37

5.3. The final dataset ................................................................................................ 38

6. Proxies ................................................................................................................... 39

6.1. Dependent variables .......................................................................................... 39

6.2. Independent variables ....................................................................................... 41

7. Analysis ................................................................................................................. 54

7.1. Descriptive statistics ......................................................................................... 54

7.2. Regression output ............................................................................................. 61

8. Interpretation of results ....................................................................................... 64

8.1. Hypothesis 1 – Leverage in Eastern and Western Europe ................................ 64

8.2. Hypothesis 2 – Corporate governance .............................................................. 65

8.3. Hypothesis 3 – Legal environment ................................................................... 67

8.4. Hypothesis 4 – Financial development ............................................................. 72

8.5. Hypothesis 5 – Bank concentration .................................................................. 75

8.6. Hypothesis 6 – Bank profitability ..................................................................... 76

8.7. Hypothesis 7 – Eastern and Western Europe respond differently .................... 78

9. Conclusion ............................................................................................................. 90

10. Critical assessment and suggestions for further research ................................ 93

11. Bibliography ......................................................................................................... 96

12. Appendices – Table of contents ......................................................................... 104

MSc Finance & International Business

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1. Introduction

Ever since Miller and Modigliani in 1958 stated their famous irrelevance proposition,

capital structure theory has been of great interest to many scholars around the world.

Out of the wide body of research that has evolved over the last fifty years, the static

trade-off and pecking order theories are standing as the ground pillars of capital

structure theory. Lots of empirical work has so far been conducted in order to test the

two theories, and some has tried to favor one over the other. But today, there is still no

clear cut answer to what theory fits reality the best. Instead it seems like both theories

has its drawbacks and are only partly able to explain capital structure of companies.

The vast majority of the empirical work on capital structure has in the past been carried

out on large listed companies in the US due to data availability. This research paper will

instead focus on the capital structure of small and medium sized enterprises (SMEs) in

Europe. According to the OECD, SMEs in OECD countries stand for 60-70 percent of

net job creation and contribute innovation and general dynamism to the economy. This

gives an idea of the immense importance of SMEs in all economies. Acknowledging

this fact, policy makers around the world should be concerned about fostering a fruitful

environment for SMEs in order to promote growth. Different scholars have concluded

that institutional factors are able to explain parts of the deviation in capital structure in

cross country studies. This has lead to the primary motivation behind this research

paper, which is to identify some of these country specific factors, and to determine their

impact on capital structure. This rests on a belief that some SMEs, especially in

transition countries, find it hard to acquire appropriate external financing in order to

pursue their growth opportunities. This is supported by the OECD, who is specifically

talking about an “SME Financing GAP”, which is determined by a country’s

macroeconomic, legal, regulatory and financial development (OECD 2006).

This study is based on an analysis of the capital structure of companies from 24 Eastern

and Western European countries. The goal of the analysis is to identify significant

relations between firm specific as well as country specific factors, and leverage. The

study then goes one step further by testing whether there are differences between

Eastern and Western Europe in the use of leverage, and more importantly, whether the

impact of different variables on leverage is different between the two samples. This can

generally be summed up to the following central research question:


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Central research question:

“To what extend do country specific variables concerning macroeconomic

development, corporate governance, legal and financial environment help in explaining

leverage in small and medium sized enterprises in Eastern and Western Europe”

Besides, hopefully contributing valuable information to policy makers on which factors

affect SMEs access to finance, it can potentially add explanatory power in terms of

better predicting the capital structure of companies. It is suggested that the traditional

capital structure theories do not, to a sufficient extend, take into account country

specific factors that influence SMEs access to external financing, and therefore only

present an incomplete picture. Specifically, the supply side of external finance is not

sufficiently reflected in the traditional theories of capital structure. The authors of this

study suggest that especially for SMEs, that usually do not have access to international

capital markets, the potential lack in supply of external finance is an important

determinant of capital structure that cannot be ignored.

1.1. Outline of the paper

The paper will continue as follows. Section 2 will consist of a literature review going

over the two main theories of capital structure along with relevant empirical evidence.

The literature review will also cover previous work on differences in capital structure

between Eastern and Western Europe, along with a discussion of what institutional

factors can potentially be responsible for this. Section 3 will state the research question

and formulate the hypotheses that are going to be investigated in the paper. Section 4

will carry forward by describing the adopted methodology to test the hypotheses.

Section 5 will describe the data collection including the different data sources and

discuss the processing of the data in terms of eliminating outliers, observations with

missing data etc. The adopted methodology implies the use of different proxies, which

will be presented and discussed in section 6, together with expectations regarding their

individual impact on leverage. Section 7 will present the results from the statistical

analysis while these will be interpreted in section 8. The final conclusion of the paper

will be presented in section 9.


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2. Literature review

2.1. Theories of capital structure

Trying to understand how firms choose their capital structure has been of great interest

to scholars around the world for a very long time. Most effort has been done trying to

explain the proportion of debt relative to equity, instead of the exact combination of

different kinds of securities, such as long-term vs. short-term debt etc. During the last

50 years, several different theories have emerged. The next section will give a short

review of Miller and Modigliani’s theory of the irrelevance of the financing decision

which started the era on capital structure research. After that, a review of today’s most

popular theories on capital structure, namely the trade-off and pecking order theories

will be performed. According to (Frank, Goyal 2007), both of these theories are so

called “point of view” –theories. A “point of view” –theory is characterized by offering

a framework or guidelines, in which explicit models can be developed. It formulates

some basic underlying principles and ideas that should serve as guidelines, whereas e.g.

the well known Capital Asset Pricing Model (CAPM) is explicitly expressed in

mathematical terms. Therefore when testing the pecking order or trade-off theory, it is

necessary to formulate a specific model, which requires different assumptions to be

made (Frank, Goyal 2007).

2.2. Miller and Modigliani’s irrelevance proposition

The extensive number of research papers within the field of capital structure accelerated

after 1958, where the later Nobel Prize awarded, Merton Miller and his colleague

Franco Modigliani published their seminal paper on capital structure (Modigliani,

Merton H. Miller 1958). In this paper they presented what is nowadays often referred to

as M&M’s proposition I, also known as “The Irrelevance Proposition”, which is

considered to be the first real theory on capital structure, even though a similar idea was

presented by (Williams 1938) 20 years before. As a matter of fact, (Weston 1955)

argued that several teachers of business finance at that time actually doubted whether it

was possible at all to develop theories of capital structure. Therefore it most likely came

as a surprise when M&M in their paper stated that financing is irrelevant. Explicitly

M&M stated the following (Modigliani, Merton H. Miller 1958, p. 268):


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“The market value of any firm is independent of its capital structure and is given by

capitalizing its expected return at the rate ρk appropriate to its class.”

Mathematically, this is expressed by the simple equation below, similar to saying that

the value of a levered firm is equal to the value of an unlevered firm.

VL = VU

The underlying logic behind M&M’s proposition was that the value of a pizza does not

depend on how it is sliced (Myers 2001). Applying this intuitive point of view to a

company basically means that depending on the composition of assets on the left hand

side of the balance sheet, a company will receive a given expected stream of cash flows.

Finding the value of the company is then done by capitalizing these cash flows at the

appropriate discount rate, depending on the operating risk of the company. According to

the irrelevance theory, the amount of debt relative to equity only serves to determine the

successive split of cash flows between debt holders and equity holders, and does not

affect the aggregate value of the company. M&M proved that under their assumptions,

investors can create “home-made leverage”, by borrowing at the risk-free rate and

buying stocks in an unlevered company. The other way around, shareholders can also

undo unwanted leverage in a company by buying fewer stocks and lend money at the

risk-free rate. Because investors can easily create or undo leverage on there own, the

rationale is that they should not be willing to pay a premium for companies with a

specific capital structure, due to possible arbitrage. Hence the value of two differently

levered, but otherwise identical companies should be equal.

However, no matter how appealing this simple statement sounds, it only holds in the

synthetic world of M&M, where capital markets are perfect, i.e. no taxes, no business

disruption costs etc. But even though the theory today does not make much sense

because of the many strict and unrealistic assumptions, it brought about something else

very valuable, namely focus on capital structure theory. “The Irrelevance Proposition”

triggered a wave of research trying to develop evidence against M&M, i.e. that

financing actually matters. Referring to the pizza again, (Myers 2001) argues that the

value of a pizza actually depends on how it is sliced, since consumers gladly pay more

for the many slices, than for a whole pizza.


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2.3. The static Trade-off theory

Probably due to the many critics of the unrealistic assumptions behind their proposition

1, M&M added taxes to their, still hypothetical world, in a later paper (Modigliani,

Merton H. Miller 1963). By taking into account that interests were (and in most

countries to some extend still are) tax deductable and therefore decreased the amount of

taxes to be paid, their model introduced an interest tax shield. When debt is assumed to

be risk-free, and there is no “counterweight” in the form of increasing costs due to high

leverage, this resulted in an optimum capital structure consisting of 100% debt. This

theoretical optimum fits very badly with the empirical observations, and that was

probably one of the things that helped the trade-off theory to quickly become so

popular. The trade-off theory suggests namely that the optimal capital structure is based

on a trade-off between the value of the interest tax shield and the costs associated with

leverage. The optimal capital structure is at the point where the marginal increase in the

costs associated with additional leverage exactly offsets the marginal benefit of the

increase in the interest tax shield from additional leverage. Traditionally when referring

to “the costs associated with leverage”, one explicitly meant direct costs of bankruptcy

i.e. lawyers fees, administration expenses etc. However a study (Warner 1977) showed

that the direct costs of bankruptcy are negligible, and therefore do not alone rationalize

the observed moderate borrowing among most firms. In a study among others (Altman

1984), evidence was found, indicating that indirect costs are way more important than

direct costs, which is one of the reasons why we today refer to the costs associated with

high leverage as business disruption or financial distress costs. Examples of indirect

costs of bankruptcy that are incurred because of being highly levered, could be lost

business or lost investment opportunities (Copeland 2005). Below a graphical

presentation of the trade-off theory is shown. It can be seen that after some point (the

optimum), the marginal financial distress costs are bigger than the marginal benefits

from the interest tax shield, resulting in a decrease in the market value of the company.


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Figure 1

Source: (Myers 1984)

2.3.1. Agency costs and the trade-off theory

(Myers 2001) argues that agency costs are also a part of financial distress costs, and an

important contribution to the trade-off theory because it adds further counterweight to

the interest tax shield, helping to justify the moderate borrowing that is usually observed

in empirical findings. (Jensen, Meckling 1976) is probably one of the most well known

articles describing different principle-agent conflicts and the agency costs in connection

with these. Specifically relevant to the trade-off theory, the paper describes the agency

costs associated with debt.

One of the things described, is what the authors call “The Incentive Effects Associated

with Debt”. This concerns the fact that there is an asymmetric payoff scheme between

debt and equity holders. Equity holders have a residual claim on the company, whereas

debt holders (if we assume non-convertible debt and other forms of equity-like

securities) have a fixed promised payoff. If the manager acts 100% in the interest of

shareholders, he would have an incentive to transfer wealth from debt holders to equity

holders. This is possible through “risk shifting”, where after receiving debt financing

conditional on a certain project, the company undertakes a different project with higher

volatility. Even though the expected value of the project as a whole might be the same,

equity holders will increase their expected payoff because they have an upside chance,

while debt holders will see the value of their claim diminishing. The explanation for this


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is that debt holders in a “good scenario” still only get their promised pay off due to the

way a usual debt contract is constructed, while equity holders get the rest of the value.

In a “bad scenario” where the company becomes worthless, debt holders get nothing

exactly as equity holders. The incentive behind risk shifting can be illustrated very easy

by considering the two scenarios as the only possible outcomes. Since equity holders

never get anything in the bad scenario but get more in the good scenario depending on

volatility, there is an incentive to increase the risk, in order to get the highest expected

value from their perspective. For a numerical example of risk shifting see Appendix 1.

Debt holders are of cause aware of this incentive, and will therefore demand debt

covenants or monitoring devices in order to avoid such behavior of the stockholders

who have the ultimate control over the company. Since the costs associated with debt

covenants and especially monitoring devices are material, it will directly result in more

expensive borrowing in order to compensate for the additional costs. Therefore the

potential incentive among some borrowers to cheat the lenders, will effectively mean

that all borrowers (with good or bad intentions), will end up paying more for their loans

than if such an agency conflict did not exist between the two groups of capital

providers. Another consequence of debt covenants could be that managers are constraint

in their ability to distribute company profits to the stockholders, because the covenant

could restrict doing so if not certain economic key ratios are reached. Furthermore it

could be specified that additional borrowing is only possible under certain conditions or

that further borrowing is even prohibited. If covenants are violated, it could result in the

entire loan to fall due, and therefore the threat of financial distress could mean that the

company has to pass up profitable investment opportunities. This is not a direct

financial cost, but certainly a serious cost in terms of lost flexibility in that managers are

restricted to take certain actions because of the covenants. To sum up, agency costs

makes the use of debt less attractive because of financial as well as non-financial costs

associated with it.

2.3.2. Dynamic trade-off theory

One major drawback of the static trade-off model is that it is a “static” one-period

model. The model solves for the best possible capital structure given the factors

discussed above (interest tax shield, distress costs etc.) and implicitly assumes that all

companies should at all time be at the optimal capital structure (Frank, Goyal 2007).

However it is not realistic to expect companies to plan the financial structure only one


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period ahead. This fact resulted in several scholars turning away from the underlying

ideas of the trade-off theory (taxation and bankruptcy costs) and instead focusing on

other theories trying to explain capital structure (Frank, Goyal 2007). In the later years,

interest for a model based on the traditional trade-off ideas, but incorporating the fact

that capital structure planning is not a 1 period problem, has increased leading to the

formulation of a “dynamic trade-off theory”. By emphasizing for instance transaction

costs, several dynamic models have emerged in the literature, leading to somewhat

different conclusions. The underlying idea of all dynamic trade-off models is however

that the optimal capital structure in period t+1 depends on the optimal capital structure

in period t+2 which depend on t+3 and so on.

One interesting thing about dynamic trade-off models is that they essentially allow

companies to be at suboptimal levels of leverage. By introducing transactions costs it is

not efficient to make constant rebalancing of the capital structure, which will from time

to time drive companies away from their optimal capital structure. (Fischer, Heinkel &

Zechner 1989, p. 19) finds that: “even small recapitalizing costs leads to wide swings in

a firm’s debt ratio over time”. The authors thereby state that recapitalizing costs or

transaction costs are responsible for the observed deviations in capital structure for

companies that are essential similar. At the same time they argue that all else equal,

similar companies should have the exact same recapitalizing criteria’s. Interestingly

they find that smaller companies display larger swings in their capital structure which

could be interpreted as a sign of higher transaction costs for SMEs.

2.3.3. Empirical findings concerning the trade-off theory

A major weakness of the trade-off model is that it is very difficult to test. Nevertheless,

several studies have tried to test the theory in different ways, and some of them will be

highlighted in the following. One study (MacKie-Mason 1990) finds evidence that the

amount of tax loss carry-forwards is negatively correlated to the amount of new debt

issues. This is in line with the trade-off theory, since large tax loss carry-forwards

would make the interest tax shield created through the use of debt redundant if the

company does not earn enough taxable income to benefit from both.

(Bradley, Jarrell & Kim 1984) also interprets their study as supporting a theory of

optimal capital structure i.e. trade-off theory. The support is based on results showing

that volatility of earnings has an impact on leverage together with strong industry


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effects. The intuition according to the trade of theory is that when considering a given

level of costs associated with actual bankruptcy proceedings, the volatility of earnings

has an impact on the expected bankruptcy costs by simply affecting the probability of

default. Therefore companies in risky businesses where earnings are highly volatile will

incur higher levels of expected bankruptcy costs, and should therefore lever to a smaller

degree. The strong industry effects are interpreted as being in favor of an optimal capital

structure which is the trademark of the static trade-off theory. This is because factors

like the magnitude of financial distress costs, non-debt tax shield and the variability of

firm value, are expected to exhibit similarities within different industry classes. This

expectation does not seem very far fetched, since obvious determinants of bankruptcy

costs like e.g. the amount of tangible assets is highly industry specific.

Other studies have used a “target adjustment model” for testing whether companies over

time adjust towards an optimal capital structure. See for example (Auerbach 1985) or

(Jalilvand, Harris 1984). They find significant adjustment coefficients, and interpret it

as support for target adjustment behavior, hence also as support for the trade-off theory.

But before one gets too excited about these findings it should be noted that the statistical

power when testing the target adjustment model is essentially non-existing according to

(Shyam-Sunder, Myers 1999). In their paper, Shyam-Sunder and Myers test the

statistical power of the target adjustment model by applying it to a hypothetical dataset

generated by following the pecking order model. Interestingly they find the target

adjustment model to be accepted even though the observations in the simulated dataset

were created, strictly based on pecking order behavior. They also did the experiment the

other way around, by testing the pecking order theory based on a dataset generated

based on an alternative capital structure theory, and here the pecking order hypothesis

was correctly rejected. According to this evidence, one should be careful when

interpreting tests of the trade-off theory. (Myers 2001, p. 94) explains it in the following

way: “such results might support the theory if it were the only game in town…”. The

point here is that several ideas about capital structure exist, so one cannot test whether

one is correct over the others if you are not aware of the expectations about the other

theories. Otherwise one implicitly applies the logic of Erasmus Montanus1 when he tells

his mother that because a stone does not fly, and she does not fly, she must be a stone.

1 Erasmus Montanus is the name of a play written by Ludvig Holberg in 1723


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Naturally there are also studies that fail to find support of the trade-off theory or where

there is only partly evidence in favor of it because some puzzling results show up at the

same time. And finally some argues that there is no reason to believe that the static

trade-off theory has any explanatory power in terms of the amount of leverage that

companies take on. Critics point out that it is very hard for the trade-off theory to

explain why quite some profitable firms for years have been running at only moderate

levels of debt (Myers 2001). (Graham 2000) concludes that the average company in a

subsample consisting of half the companies in his survey could add a non-trivial

amount, equal to approximately 7.5 % to its value by increasing leverage. In the end,

one must conclude that the opinions about the validity of the trade-off theory are split.

2.3.4. SMEs and the trade-off theory

Traditionally, most research on capital structure including the trade-off theory has been

performed on samples consisting of large listed US firms, due to the better availability

of data. The scope of this paper is concerned with SMEs and therefore it is felt

necessary to elaborate on whether one can expect SMEs to behave similar to large listed

companies in terms of the trade-off theory. Some scholars argue that the actions taken

by managers of SMEs regarding financial decisions can be explained by the same

theories that are usually applied to large listed companies i.e. trade-off and pecking

order (Sogorb-Mira 2005). In the framework of the trade-off theory it is hard to argue

that SMEs would not face the same trade-off between interest tax shield and distress

costs. However it is possible that SMEs might put more emphasis on certain issues or

face problems that large listed companies do not face to the same degree. Here some of

these issues and the possible implications for the capital structure of SMEs will briefly

be discussed.

One possible reason that could explain why SMEs might not follow the trade-off theory

is simple lack of knowledge among managers. If the financing decision should be made

according to the trade-off theory, it is naturally a necessity that managers are aware of

the advantages of an interest tax shield. Within the SME segment, one could expect that

many companies are led by entrepreneurs with their expert skills lying within a field

different from finance, and therefore might not possess the knowledge or for some other

reason, be ignorant of the interest tax shield and therefore do not take advantage of it

(OECD 2006). If managers are not aware of the benefits of leverage, they might tend to

operate at lower debt levels all else equal.


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Another interesting factor is the potential financial constraint of SMEs. If some SMEs

are in fact financially constrained it would mean that independently of whether

managers are aware of the trade-off theory and recognize the advantage of debt, they

might not be able to lever up to their optimal capital structure. This is by the authors

suggested to be an important issue, since that would imply that it is for external reasons

that SMEs might not have sufficient debt according to the trade-off theory. From an

isolated perspective, it does not seem like a big issue if companies have less debt

compared to what is suggested by the theory. But one can imagine serious consequences

if the lack of debt financing results in the company having to pass up profitable

investment opportunities and thereby restricting growth. Lack of debt-financing does

not necessarily mean that no financing is available at all. It can also mean that the price

of the available finance is prohibitive. Section 2.5 will elaborate on this issue.

Finally, one reason why SMEs could have a different capital structure than their large

listed counterparts could be that their “experienced” bankruptcy costs are higher due to

a lot of them being family owned. Besides the expected financial distress costs and the

economic loss due to bankruptcy, a family owned company most likely also represents a

great amount of sentimental value to the owners. Therefore one can argue that this

dimension of distress costs will increase the expected costs of debt, and therefore lower

the optimal capital structure of family owned companies. A convenient thing about this

is that it explains within the framework of the trade-off theory, why the capital structure

of SMEs might deviate from the one of large listed companies.

2.4. Pecking order theory

In 1984, Myers proposed an alternative approach to capital structure theory by

introducing the pecking order theory. This theory states that firms prefer internal

financing to external financing, and if external financing has to be used, the cheapest

possible security is chosen first. Corporations will, when using external finance, first

use debt, then hybrid instruments, and as a last resort, issue equity. In this framework

there is no optimal debt ratio and companies do not try to maintain a target debt-ratio.

Instead, the debt-equity mix of a company is determined by their need for external

finance (Myers 1984).

The basic assumption underlying the pecking order theory is that managers act in the

interest of existing shareholders, and do have better information about the future


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prospects of the company than potential outside investors. A planned stock-issue is

perceived as a bad signal by prospective investors, because they assume that the goal of

the management is to maximize the value of the existing shareholders. The rational

supposition of outside investors is that a company issues shares because management

thinks that the shares of the company are overvalued. Hence, this perceived information

only makes an equity issue possible at a marked-down price. Therefore managers who

are in need of external funds to finance a positive NPV-project, assuming issuance of

debt is not possible, will only consider issuing undervalued shares, if the NPV of the

project is higher than the cost incurred through the undervaluation in the stock-issue.

This means that even when a company has significant growth opportunities, it will not

realize these growth opportunities by means of a stock issue, if the undervaluation due

to the signaling effect, exceeds the potential gains from the projects. Several studies

have confirmed the signaling effect, in that the announcement of a stock issue has a

subsequent negative impact on the stock-price. In a study of large listed companies by

(Asquith, Mullins 1986), the announcement of a stock issue caused an average fall in

the stock-price of about three percent. Furthermore it has been shown that the

magnitude of the price drop is related to how strong the information asymmetry

between inside management and outside investors is. Even if a company has good

prospects for the future, the perceived signaling effect has a negative impact on the

value of the firm in the short-run, i.e. the bad news of a stock issue outweigh the news

of good investment opportunities of the company.

When comparing debt to equity, debt has a senior claim on assets and earnings of the

company. This implies that creditors face less risk compared to equity holders. Only if

the risk of bankruptcy is high, the impact of the announcement of a debt issue will affect

the share-price. Taking this into consideration it can be assumed that only pessimistic

managers will make an equity issue if debt is available at a fair price. The key

predictions of the pecking order theory are therefore as follows:

1) Internal financing is preferred to external financing if available, since

asymmetric information is only relevant for external financing


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2) Changes in the net cash-flow of a listed company will usually be accompanied

by changes in external financing since dividends are in general rather “sticky”,

and cannot be changed in the short-run to finance capital expenditures

3) If external financing is necessary, i.e. the internally generated cash-flow is not

sufficient to cover capital expenditures, debt, which is the safest security, will be

issued first followed by hybrid instruments and then equity.

4) The need for external financing of a company is reflected in its debt ratio

The pecking order theory is therefore, contrary to the trade-off theory, able to explain

why profitable firms have less debt compared to less profitable companies. The reason

is not that they have a low target debt ratio, but that they to a higher degree are able to

generate sufficient internal funds to finance necessary investments (Myers 2001).

2.4.1. Empirical findings concerning the pecking order theory

Like the trade-off theory, contradicting evidence is also observed when turning to the

empirical literature concerning the pecking order theory. However it seems like the

majority of research papers are not able to find convincing overall support of the theory.

A paper that does find evidence for a pecking order is (Shyam-Sunder, Myers 1999). In

the paper, both the static trade-off model as well as the pecking order model is tested.

The paper finds that the pecking order model has more explanatory power than the

trade-off theory, and is a much better first-cut explanation of debt-equity choice. Partly

evidence in favor of the pecking order is found by (Frank, Goyal 2003). These authors

show that large firms show some aspects of pecking order behavior, but do not consider

the results robust enough. A common interpretation in favor of pecking order behavior

is when researchers find a negative correlation between profits and debt. This is also the

argument in (Fama, French 2002), (Titman, Wessels 1988), (Rajan, Zingales 1995) and

others, who find that more profitable firms have less debt.

In a later paper, Eugene Fama and Kenneth French however look critical at the pecking

order (Fama, French 2005). In this article the authors study when, and how often, firms

issue equity. They find that more than half of the companies in their sample violate the

pecking order predictions. This is interpreted from their results showing that between

54% and 72% of their sample depending on the period, makes net equity issues each


14

year. Far from all of these companies are under distress, so the pecking order is not able

to explain the behavior of these firms.

(Galpin 2004) argues that the fundamental assumption of the pecking order, that equity

is used as a last resort due to the high issue costs, is not valid. In his study he concludes

that the costs of debt issues often exceed the cost of issuing equity. Galpin shows that

issuance costs have evolved over time. In 1973 debt costs amounted to 50% of equity

costs, increasing to 140% in 2002. This might suggest that the pecking order was valid

at the time it was invented, but that times have changed and it might not hold anymore.

It has to be said that the study was performed on large listed companies and that the

cost-structure could very well be different for SMEs.

2.4.2. SMEs and the pecking order theory

The development of the pecking order theory is largely based on observations from

large listed companies in the US. The structure of SMEs as well as their access to

capital markets is very different to that of large listed companies. Therefore it is

interesting to see if it is possible to verify the validity of the theory for this kind of

companies, similar to what was done regarding the static trade-off theory. Furthermore,

when testing the static trade-off theory or the pecking order theory for SMEs, it is

important to question the reason why SMEs behave according to one theory or another,

since the reason can be very different compared to large listed companies. It turns out

that there are very compelling reasons why the pecking order theory should be able to

explain the behavior of SMEs regarding capital structure. One reason is that small firms

are often owned by only one shareholder who is at the same time the director of the

company. An issue of new equity would dilute the shareholding of the owner-manager,

and can therefore lead to a loss of control in the company. To avoid this, the natural

response would be to turn to debt instead of equity for financing (Lopez-Gracia,

Sogorb-Mira 2008).

Another argument against the use of equity by SMEs is that the cost of external equity

is even higher to them compared to large listed companies. An initial public offering is

not only expensive to organize, but also subject to under pricing which has been shown

to be particularly severe for small companies (Chittenden, Hall & Hutchinson 1996).

Another source of equity finance stems from private placements with private equity

companies or business angels. Apart from the potential loss of control in the company,


15

this source of finance also has significant transaction costs due to the complexity of the

contracts that have to be negotiated (Ou, Haynes 2006).

The size of a company also has an impact on the availability of debt-financing. This is

reflected in the fact that smaller companies rely more strongly on short-term financing

than larger companies, since financial constraints are mainly present when attempting to

acquire long-term finance. Therefore the pecking order for SMEs is expanded in the

sense that there is a propensity towards short-term financing over long-term financing

(Lopez-Gracia, Sogorb-Mira 2008). The circumstance that SMEs may be confronted

with constraints in acquiring debt-financing will be discussed in greater detail in the

next section, since it can have a potentially large effect on the capital-structure of SMEs.

2.5. SME financing gap

An issue that has a possible impact on the capital-structure of SMEs is the so-called

“SME Financing Gap”. In a survey performed by the “OECD SME Task Force”, most

OECD member countries agreed that a lack of appropriate financing does have a

negative impact on the growth of innovative SMEs. The “SME Financing Gap” is

commonly defined as the situation where a significant share of SMEs cannot fulfill the

financing needs which exceed their internal financing capacities, through banks, capital

markets or other suppliers of finance. There are different reasons why the financial

constraint of SMEs is larger than that of large companies. One reason is that the

problem of asymmetric information is more severe in SMEs (OECD 2006). This is

partly due to the fact that in many cases the company is very much tied to the

entrepreneur. This leads to a situation where the entrepreneur has considerably superior

information on the situation of the company. Related to this is also the problem that a

manager in an SME is more likely to have insufficient management skills compared to

the managers in large companies. Therefore potential investors have a more difficult

time to assess whether an SME manager is making bad management decisions which

could potentially threaten the well-being of the company. Morale hazard considerations

also play a significant role for the availability of credit to SMEs. The lending bank is

mainly interested in a firm’s capability to repay its loan, while the company might

prefer a high risk and high return strategy, which could lead to risk shifting (see section

2.3.1). Even though risk shifting is a potential problem with any kind of debt financing,

it is usually more severe when lending to SMEs because, as mentioned, the asymmetric


16

information present when dealing with SMEs is higher compared to large listed firms

(OECD 2006).

The empirical evidence whether an “SME Financing Gap” exists in reality is rather

mixed depending on region. Most empirical studies have problems with data

availability. Nevertheless there is a general tendency in the empirical studies performed

by the OECD. It has been shown that the financing gap is more severe in OECD

countries that are considered transition countries compared to developed OECD

countries, while it is most significant in non-OECD countries. The research regarding

SME financing has shown that there are different types of financing gaps. For instance

in some emerging countries, the financial system is very much geared towards large

firms, making it much more difficult for SMEs to obtain bank-credit. This leads to a

situation where the growth potential of SMEs is constrained, and the ability of SMEs to

be the innovators of the economy, which is a role they often play, is thereby limited.

Another issue which is particularly widespread in the bank-dominated countries in

central Europe is the rather decent access to debt-financing but a lack of equity-

financing.

It is argued that a crucial component for SME financing is a solid legal, institutional and

regulatory environment. In the case of debt-financing, it is important for lenders to get

reliable financial information about prospective borrowers. In this context, weak

accounting standards are argued to be a problem. A related issue that can complicate the

access to debt-financing is weak creditor-rights. Weak creditor-rights could be

expressed through for instance a weak bankruptcy code, where bankruptcy procedures

take a very long time and the access to collateral is difficult (OECD 2006).

Several studies have been performed, looking at financial development and access to

finance in Eastern Europe, and more specifically on the possibility of an “SME

Financing Gap” in that region. Some of the empirical evidence is presented here.

(Cornelli, Portes & Schaffer 1996), (Chaves et al. 2001) and (Egerer 1995) all find

indication that leverage in Eastern Europe is low and the access to external finance is

insufficient, either in terms of the associated cost or the availability. All studies attribute

this problem to some sort of institutional factors. A more detailed description on these

studies will be presented in section 2.6.1.

More evidence for a “SME finance gap” has been presented by (Bratkowski, Grosfeld &

Rostowski 1998). In this study the authors state that banks in transition countries are


17

more reluctant to provide debt-financing to SMEs than in developed countries. Another

explanation for the lower debt-levels in Eastern Europe compared to Western Europe

has been presented by (Jõeveer 2005). In her study of nine Eastern European countries,

she points out that domestic credit provided by the banking sector compared to GDP, is

around 40 percent in the observed region of Eastern Europe, and more than 100 percent

in Western Europe. This view is to some extend also supported by a survey that was

commissioned by the European Union, which was supposed to investigate the access to

finance of SMEs in Eastern and Western Europe. In this survey, less than two third of

the interviewed SMEs in Eastern Europe said that they had sufficient financing to see

their projects through (EOS Gallup Europe 2006). On the contrary more than three

quarters of the interviewed SMEs from Western Europe said that they had sufficient

financing opportunities for their projects. In this context it is also interesting to note that

59 percent of the interviewed companies in Eastern Europe believe that banks are not

willing to take on the risk associated with lending to SMEs.

It has to be mentioned that the availability of external-finance is of cause also highly

dependent on the type of SME. For instance, innovative SMEs that are for example

developing a new product and have at present, negative cash-flows and high uncertain

growth opportunities might not be able to acquire debt-financing independent of

location. The risk-premium associated with a loan for such a company could potentially

drive up the cost to prohibitive heights.

It has to be pointed out that for example the type of SMEs in the respective sample, as

mentioned above, also has to be considered when drawing conclusions about a potential

financing gap. Nevertheless, to sum up the empirical evidence, it is supported that

companies in Eastern Europe rely to a smaller degree on debt-financing and it has also

been put forward that this is due to short-comings in the institutional environment.

2.6. Capital structure in East vs. West

Since this study is dealing with capital structure in Western as well as Eastern Europe, it

is necessary to look at some previous work, concerning differences in capital structure

from a geographical point of view. Thereby the research questions and expectations

about the findings in this study can be formulated based on a review of other people’s

experience, and hopefully shed light on new unexplored issues.


18

As previously mentioned, most research on capital structure has been performed on

datasets consisting of large listed companies mainly located in the US. Even though an

increasing body of literature has lately focused on SMEs, the research has primarily

been based on US or Western European firms (Klapper, Sarria-Allende & Sulla 2002).

Some of the studies that highlight the general importance of country specific factors are

e.g. (Porta et al. 1998), (Booth et al. 2001), (Giannetti 2000), (Jõeveer 2005) and several

others. After reviewing these studies there is no doubt as to the importance of country

specific factors in general. An important statement in this connection is that of (Jõeveer

2006). She argues that country specific factors have a larger impact on the capital

structure of small unlisted companies, while firm specific factors explain a relatively

larger portion of the capital structure of listed and large unlisted companies. Her study

highlights the importance of country specific variables in the context of this study since

it exclusively deals with unlisted SMEs. This might suggests that when a company

reaches a certain size, the importance of country specific factors are reduced because the

company for instance has access to international capital markets. A brief discussion of

differences in institutional factors in Eastern and Western Europe can be found in

appendix 2.

2.6.1. Empirical findings

In the following, some empirical work specific to the capital structure of Eastern

European countries, and how it may deviate from their Western counterparts, will be

highlighted.

According to (Klapper, Sarria-Allende & Sulla 2002), Eastern Europe offers an

interesting study base, because of the unique state of financial development and market

characteristics, and therefore one can expect that SMEs incorporated in these countries

will exhibit a different financing behavior, compared to Western companies. The

expected difference in the environment for SMEs is also what makes it ideal in terms of

this study, where the variability is a necessity for making inference about the

relationship between leverage and the different country specific factors.

One of the later studies (Nivorozhkin 2005) looks at leverage in five countries

(Bulgaria, the Czech Republic, Poland, Romania and Estonia) from Eastern Europe. The

paper observes that on average, the companies in transition countries operate at lower

debt levels than comparable Western European firms. Trying to explain the leverage

ratio, a panel data regression is being performed with leverage as the dependent variable


19

and different country characteristics as the independent variable. Among other things,

they find that leverage is positively correlated with variables that proxy for financial

development like “domestic credit to private sector as a proportion of GDP”. The results

of the macroeconomic variables showed that inflation is negatively related, while

growth in GDP is positively related to leverage. In the study the authors distinguish

between advanced transition countries and less advanced transition countries. Their

results show that the more advanced countries are more similar to the Western countries

on some aspects while the less advanced countries are more different. This intuitively

makes good sense when taking the importance of the country specific factors into

consideration, since one would also expect these factors to be more like the West in the

advanced transition countries (e.g. Poland and Czech Republic).

Lower leverage in Eastern Europe (Poland and Hungary) is also found by (Cornelli,

Portes & Schaffer 1996). Here the authors conclude that the reason for the lower

leverage in East is a supply side phenomenon. By this it is meant that sufficient finance

is not available to the firms who are actually willing to take on more debt. The lack of

financial supply is interpreted as being a consequence of country specific factors like

underdeveloped financial markets and legal environment etc.

Similarly, a country specific study of Romanian firms, performed by (Chaves et al.

2001), shows that companies in this region suffer from insufficient finance. It further

suggests that the reason for the insufficient finance is high inflation and a weak legal

system, which makes it very difficult for firms to obtain long-term financing.

Finally a similar conclusion is drawn in another country specific study (Egerer 1995).

Here it is found that firms in the Czech Republic also have insufficient access to finance

due to country specific factors. The author argues that the financing difficulties arise

from weak creditor rights and collateral laws. So to some degree, similar factors as in

Romania are responsible for the observed financing gap.

2.6.2. How do country specific factors fit with traditional capital structure theory

The observed differences in institutional factors and their implications for capital

structure will here briefly be discussed, within the framework of the two main capital

structure theories, as described in section 2.3 and 2.4.

According to the trade-off theory, the benefit of a tax shield is affected by the statutory

tax rate, which is highly individual from country to country. A higher tax rate should all

else equal increase the potential gain from a tax shield, and will therefore make the use


20

of debt more attractive. Looking at the costs of debt i.e. distress costs, it is argued that

the relevance of country specific factors can only partly be explained in the trade-off

model. Tangible assets are usually, within the trade-off model said to increase leverage,

because it secures the claim of creditors in case of bankruptcy. The same rationale could

be employed in the case of good bankruptcy laws or a general high development of the

legal system. All else equal, this should be an advantage for creditors in terms of a

potential bankruptcy proceeding, and help the creditor to recover as much as possible of

his claim. Another thing is that in terms of bad law enforcement, debt covenants could

be hard to enforce, and therefore increase agency costs of debt. These examples

illustrate that it is to some degree possible to interpret the effects of some country

specific factors in terms of the trade-off theory.

In terms of the pecking order theory, it is more difficult to see the same relevance of

these country specific factors. The theory implies that transparency of the firms is

important since it has implications for the level of asymmetric information and thereby

the agency costs. (Jõeveer 2006) argues that the level of asymmetric information is

particularly high in companies in transition economies. This could e.g. stem from

weaker accounting standards, less presence of credit registries etc. These factors are

some of the better examples, of how country specific factors can influence the

opaqueness of companies. It is however difficult to think about how other country

specific variables like e.g. effectiveness of legal system, investor protection etc. fit

within the pecking order.

Even though it is possible to interpret some country specific factors in the context of the

traditional capital structure theories, it is argued that these models do not explicitly

incorporate these factors. In the empirical literature there is evidence suggesting that the

supply side of financing, i.e. the availability and price of external financing is affected

by country specific factors (Cornelli, Portes & Schaffer 1996). It is suggested that

traditional capital structure theories do, to a too large extend, assume perfect capital

markets in the sense that companies can frictionless acquire the financing they need.

The supply side of financing, which is affected by country specific factors, might

therefore deserve more attention than that it has gotten so far. The impact of country

specific factors on leverage can be seen as an indicator that the traditional theories of

capital structure are, through having to strong assumptions, incomplete in the real

world.


21

2.7. Market power

Besides the two major theories about the determinants of capital structure as discussed

above, a few scholars have made an effort to develop other theories about the influence

of some variables (firm specific as well as country specific) on leverage or the

availability of credit. Some of these theories are concerned with the banking

concentration in a country and the credit availability to SMEs, which is believed to

affect leverage. In this paragraph, different theories concerning the banking

concentration within an economy will be discussed. The theories have contradicting

expectations about the relationship between the concentration ratio and credit

availability. In the literature, empirical evidence for both theories exists, so it is difficult

to make priori inferences about the relationship between banking concentration and

leverage.

Generally throughout the ninety’s, the banking industry has been characterized by

lowering the barriers to trade (Lipczynski 2006 p. 11), changes in the legal environment

through the Second Banking Directive (Gual 1999), the common market and other

factors resulting in the competition among banks to intensify. A consequence of this has

been a consolidation in the industry which brought about an increase in the

concentration on most European banking markets (Lipczynski 2006). However large

differences can still be observed in the concentration ratios between countries, which

makes it possible to investigate whether banking concentration has a positive or

negative relation with leverage. But first a short review of two contradicting theories

will be presented.

2.7.1. Information based / efficient structure hypothesis

The information based- or efficient structure hypothesis, as it is also referred to as,

suggests that higher banking concentration eases companies’ access to finance. This can

be in the form of lower interest rates, or just by increased willingness among banks to

lend out money. According to (Corvoisier, Gropp 2002), this is brought about by an

expected higher efficiency of the overall sector in concentrated markets, which the

customers (in this case SMEs) are benefitting from. This explanation is rooted in the

“Chicago School” from Industrial Organization see e.g. (Lipczynski 2006). This very

liberal point of view argues that a high concentration ratio is the result of the most

efficient banks being able to grow faster than the less efficient ones and/or even take

over the inefficient banks and thereby driving them out of the market. Thereby


22

eventually only the streamlined and efficient banks will be left in the market. If

borrowers are in fact supposed to benefit from this, through a higher degree of credit

availability, it is of course necessary that the banks do not take advantage of their

market power, but instead pass through the efficiency gain to the borrowers. The point

of view of the “The Chicago School” on this issue is that it should not be a problem

since the banks will have a wish for sustaining their position in the market. So in

general, competition will ensure that the customers will continue to benefit from the

efficient sector. Regarding the threat from potential collusion, “The Chicago School”

argues that cartels are inherently unstable and thereby eliminate themselves. A main

implication of the Chicago point of view is that good credit availability is not the result

of the high concentration ratio itself. It is more a result of the efficient banks growing

big and the less efficient banks being driven out of the market, which as a side effect

increases the concentration ratio.

2.7.1.1. Relationship lending

Another possible and interesting explanation for a positive relation between banking

concentration and credit availability is suggested by (Berger, Udell 2005). This study

looks at the credit availability of SMEs based on an analysis of different lending

technologies. Here it is argued that SMEs, and in particularly those who are

informational opaque, rely very much on relationship lending rather than other lending

technologies like e.g. financial statement lending, asset-based lending or other

transaction based lending technologies. Relationship lending is characterized by the

lender making an assessment of soft or qualitative information of the borrower, whereas

transaction based lending is based on hard quantitative information. In relationship

lending, the soft information is mainly acquired by the loan officer responsible for the

loan approval. The information is gathered over a period of time through direct contact

with the company, but also by observing the general performance and an analysis of the

future prospects for the company (Berger, Udell 2005). This “due diligence” analysis

could consist of an assessment of the company’s environment i.e. customers, suppliers

and competitors. A high degree of concentration makes it more attractive for the bank to

invest in lending relationships. This is because these relationships are expensive, and a

high concentration results in the probability that the company will find other sources of

finance in the future to be smaller than in a less concentrated market (Berger, Udell

2005).


23

2.7.1.2. Empirical evidence

(Petersen, Rajan 1995) find evidence in favor of a positive relationship between banking

concentration and credit availability. They find that young firms in markets with high

banking concentration receive more institutional finance than similar companies in less

concentrated markets. Moreover it seems like young companies in concentrated markets

get credit at a more favorable price. However this favorable rate is reversed into an

unfavorable rate once the company gets older. The authors argue that a reason for this

could be that the banks in the concentrated areas are willing to lend at low rates up front

because they have some sort of assurance of being able to lend money to the same

company in the future but at a more profitable rate. This is in line with the explanation

previously referred to in (Berger, Udell 2005), that it is more attractive to invest in a

lending relationship in concentrated markets.

Other studies like e.g. (Dell'Ariccia, Bonaccorsi di Patti 2004) also find evidence

supporting a positive relationship between banking concentration and credit availability.

However as we will see soon, empirical evidence supporting the exact reversed

relationship between bank concentration and credit availability also exist. This kind of

relationship is predicted by the market power hypothesis also known as the structure

performance hypothesis.

2.7.2. Structure performance hypothesis

The structure performance hypothesis states that the relation between banking

concentration and access to finance should be negative. The reasoning behind the theory

can likewise be found in the industrial organization literature, but in the “Structure-

Conduct-Performance Paradigm”, see e.g. (Lipczynski 2006). According to this

paradigm, a market where the structure is characterized by a high degree of

concentration (e.g. oligopoly), will affect the conduct of the companies, in the sense that

they will tend to exploit their market power and extract abnormal profits from their

customers. In the case of banks, this could show up as e.g. charging higher interest rates

from the companies they lend to. Naturally, collusion is potentially possible even on a

market with low concentration and small dispersed banks, but the fact is, that it is much

easier to sustain a well functioning cartel, when the number of participants is rather low

(Lipczynski 2006). Opposing to “The Chicago School”, the Structure-Conduct-

Performance Paradigm thereby acknowledge the presence and sustainability of cartels

e.g. in the form of tacit collusion where no explicit agreement between two or more


24

companies exist, and expects a negative relation between concentration and credit

availability.

2.7.2.1. Empirical evidence

In a cross-country study, (Beck, Demirguc-Kunt & Maksimovic 2004) find that high

bank concentration creates difficulties for SMEs to obtain finance, but only in countries

with low levels of economic and institutional development. They find this effect to be

strongest for SMEs compared to large companies, which makes it even more relevant

for the study performed in this paper. Their study is however only considered to

partially support the structure-performance hypothesis, since the consequence of a high

bank concentration relies on a lack of financial development, and is thereby not a

“ceteris paribus” effect. One important thing to be learned from that paper is that when

trying to make inference about the effect of bank concentration, it is very important to

control for economic, institutional and regulatory factors.

Other evidence supporting the structure-performance hypothesis is found in a study

(Corvoisier, Gropp 2002) where the impact of bank concentration on different bank

products is analyzed. Specific to bank loans, the study suggests that a higher degree of

concentration leads to higher interest margins i.e. more expensive financing (Beck,

Demirguc-Kunt & Maksimovic 2004).

3. Research question and hypotheses

In the previous literature review, traditional capital structure theories were reviewed

together with some empirical evidence. It was also described how focus on country

specific factors in determining leverage has increased in the later years. The

fundamental expectation underlying this research paper is that the traditional capital

structure theories can only partly explain leverage, since they seem to ignore an

important factor in the system of mechanisms that determine leverage among

companies. The missing factor is argued to be the supply side of the financing equation,

which is only partly incorporated in traditional capital structure theory. It is expected

that country specific factors in terms of corporate governance systems and financial

infrastructure, influence the supply and price of finance, by affecting the overall risk of

lending to SMEs. The idea is that both the trade-off as well as the pecking order theory,

takes the point of view of the company, and assumes that companies are not financially


25

constraint in the sense that they can acquire unlimited external financing at a certain

acceptable price. In this study it is expected that the access to finance is influenced by

the legal environment, corporate governance etc. In countries where these factors are

less developed, it is expected that firms will experience difficulties in obtaining credit at

normal market rates. This expectation can be summarized by the following main

research question:

“To what extend do country specific variables concerning macroeconomic

development, corporate governance, legal and financial environment help in explaining

leverage in small and medium sized enterprises in Eastern and Western Europe”

If significant relationships between certain country specific variables and leverage can

be successfully identified, it will add valuable information to policy makers, maybe

especially in Eastern Europe. This rests on a belief that corporate governance, legal and

financial environment is generally less developed in this region. This could very well be

the reason behind the empirical evidence described in section 2.5, which seems to

suggest that companies in Eastern Europe are more financially constraint compared to

Western European companies. By being financially constraint, it is as previously

explained not meant that SMEs cannot get financing at all, but rather that it can only be

obtained at unfavorable rates to compensate for the higher risk that the environment

implies. This should all else equal lead to less use of debt in the capital structure of

Eastern European SMEs, which is the foundation of the first hypothesis of this paper.

Hypothesis 1) Leverage ratios are on average lower in Eastern Europe compared to

Western Europe.

The expectation that country specific variables have explanatory power for the level of

leverage can be summarized into the following hypotheses:

Hypothesis 2) The level of corporate governance in a country is positively correlated

to firm leverage.


26

Hypothesis 3) The strength of the legal environment is positively correlated to firm

leverage.

Hypothesis 4) Financial development is positively correlated to firm leverage.

Hypothesis 5) Bank concentration is negatively correlated to firm leverage.

Hypothesis 6) Bank profitability is positively correlated to firm leverage.

The above hypotheses are mainly based on the economic intuition of the authors, since

comparable research is scarce, probably due to the availability of reliable data. The

availability of data has however changed due to among others, the Doing Business

initiative of the World Bank, which will be one of the primary data sources in this

study. Using this new source of data and investigating several countries to create

variation in the country specific variables, it is expected to be possible to quantify the

relationship between certain variables and leverage. This is a new and fairly unexplored

area of capital structure research and it is expected to add new insights to the

importance of country specific factors. As previously mentioned, several studies have

acknowledged the fact that country specific factors seems to influence capital structure.

But due to the issue of data availability, they have only been able to infer that

differences in leverage across geographic regions are stemming from country specific

variables. This is of cause a first step. But without knowing which specific factors

influence capital structure, it is hard for policy makers to use the information in the

creation of reforms and development of the financial and legal environment.

This study goes one step further by expecting that there might be a difference in how

countries in Eastern and Western Europe respond to the different variables. This is

based on the general expectation that corporate governance, legal system and financial

development on average are different in Eastern Europe. This gives the authors reason

to believe that the marginal impact of changes in specific variables might be different

across the two regions. It is for instance argued to be a reasonable assumption that the

importance of say corruption is diminishing as it gets lower. By this it is meant that the

marginal benefit of lowering the corruption is expected to be higher in countries like


27

Bulgaria where corruption is a much bigger issue than in Finland2. This expectation is

behind the final hypothesis of this research paper.

Hypothesis 7) Companies in Eastern and Western Europe respond differently to

country specific as well as firm specific variables in either strength or

sign.

The seven presented hypotheses will form the main scope of this research paper.

However this does not mean that interesting findings during the analysis will not be

elaborated on. If feasible, further interesting findings will be analyzed and concluded

on, or otherwise suggested for further research.

4. Methodology

In order to test the above stated hypotheses, a panel data methodology will be used. The

data panel will consist of data collected over the time-period 2001 to 2006. One of the

advantages of using panel data is the possibility to control for firm specific

heterogeneity (Wooldridge 2006).

4.1. Fixed effects vs. random effects model

There are in general two different methods to estimate panel data models that

incorporate unobserved effects. There is the so-called fixed effect model and the

random effect model. A fixed effect model can be specified in the following way:

�� α� ε��

Within the fixed effect model, it is possible to control for cross-sectional as well as

time-specific unobserved effects. Here the variable α� captures the firm specific,

unobserved, time-constant effects that influence ��. This variable therefore picks up

effects that are not controlled for through the independent variables included in the

regression, such as industry effects. A model with these specifications is also called a

one-way fixed effect model. It is also theoretically possible to specify a two-way fixed

2 http://www.transparency.org/policy_research/surveys_indices/cpi/2007


28

effect model. Here the second fixed effect reflects time-specific effects, i.e. the variable

could pick up e.g. uncontrolled macroeconomic factors that influence firm leverage in a

specific year. This approach is not being used for the following reasons. Firstly, several

macroeconomic control variables are going to be included in the regression to control

for these effects. Secondly, there is a significant loss in terms of degrees of freedom,

which also explains why a two-way fixed effect model is rarely used in econometric

studies (Greene 2003).

A crucial characteristic of the fixed effect model is that it allows the unobserved effects

to be correlated with the included independent variables. On the contrary, the random

effect model does not allow such a correlation, i.e. a necessary condition for the random

effects model is the following:

�� , �� 0 .

From a pure economical perspective, this condition seems very unlikely because it can

be assumed that industry effects, which are captured within α�, are correlated with for

instance tangibility (Wooldridge 2006). Nevertheless it will be formally tested whether

a random-effect or a fixed effect model is appropriate for this analysis. In order to do

this, a random effect model will be specified and a Hausman-test will be performed.

This test identifies whether the random effects are uncorrelated with the independent

variables. The null hypothesis of the Hausman-test is that there is no misspecification of

the model, i.e. that the random effects are uncorrelated with the independent variables.

If the null hypothesis can be rejected, a fixed effect specification is appropriate

(Quantitative Micro Software 2004).

When employing a fixed effect model, it is necessary to test for the significance of the

estimated fixed effects. To do this, an F-test can be employed, implying that an

unrestricted model, including the fixed effects in question, has to be estimated along

with the appropriate restricted model. The regression as well as the additional analysis

is performed in Eviews3 5.1. When testing the significance of the effects in a one-way

fixed effect model, Eviews uses two different methods to perform the test. The joint

significance of the cross-section fixed effects is tested using the sum of square, i.e. an F-

test and the likelihood function (Chi-squared test) (Quantitative Micro Software 2005).

3 Eviews is an econometrics package provided by QMS: Quantitative Micro Software


29

The fixed effect model can be estimated in different ways, for instance by fixed effects

transformation or via a least square dummy variable model. Only the fixed effects

transformation will be described here, because this is the method which is used by

Eviews, as described in the accompanying user manual. The initial fixed effect model

can be represented by the following equation:

�� , � 1,2, … . , %.

This equation is then averaged over time for each i (cross-sections). This leads to:

�&� � ��'�� '�� '�� &�

If the second, over time averaged equation is subtracted from the first equation, one

ends up with the following equation:

�(�� (�� (�� … ��(�� ( ��, � 1,2, … . , %.

This, time-demeaned model, is called the fixed effects transformation or the within

transformation. In this equation, the unobserved effect �� has disappeared. It has to be

noted that it is not possible to include explanatory variables that are constant over time

for all i, because these will be swept away by the fixed effect transformation. It becomes

clear that the estimated one-way fixed effect model should not include an overall

intercept, because the time-demeaning eliminates the fixed effects which represent the

cross-section individual intercepts. Eviews nevertheless reports an overall intercept,

which according to (Wooldridge 2006), some econometric packages do, because they

see the fixed effect as a parameter to estimate. The reported intercept is then usually the

average of the �)� across all i. That means that the reported intercept is the average of the

individual fixed effects. It is however more meaningful to interpret the ��’s, as omitted

variables that are controlled for through the fixed effect transformation. This way of

viewing the ��’s is reinforced by the fact, that the manner in which the �� can be

estimated is generally weak. This is because when an additional cross-sectional

observation is added, another �� is included into the model. Better estimates of �� would

be possible with larger T (time periods), but the usual composition of panel datasets are


30

made up of a large cross-section and a rather small time-frame. The focus of the model

lies on the estimated beta coefficients that are estimated by means of the fixed effects

transformation (Wooldridge 2006).

Another issue that has to be addressed is heteroskedasticity. Heteroskedasticity means

that the variance of the error term is not constant. This does not have an impact on the

beta coefficients, i.e. they are still unbiased, but it does have an impact on the estimated

standard errors, and hence on the calculated t-statistics and p-values. In the presence of

heteroskedasticity, the ordinary-least square t-statistics do not have t-distributions. This

matter is not resolved by using a large sample-size. Therefore in order to correct for this

type of potential bias, heteroskedasticity robust standard errors (White standard errors)

will be computed. It has to be noted that there will not explicitly be tested for

heteroskedasticity, which is an increasingly common practice in applied work with large

sample-sizes. If the sample-size is large enough, the asymptotic properties of the White

standard errors are unambiguous (Greene 2003).

4.2. Dummy variables

The focus of this analysis is to test the impact of firm specific and especially country

specific variables on leverage, and whether this impact is different when comparing a

sample of Western European companies to a sample of Eastern European companies. In

order to test this, dummy variables will be incorporated into the regression in the form

of slope dummies, to distinguish between the two groups. The resulting model is as

follows:

�� *� + ,�--�./0� + �� *� + ,�--�./0� + ��

�� *� + ,�--�./0� + �� , � 1,2, … . , %.

Such a model allows the researcher to investigate whether specific variables influence

leverage in different ways in the two samples. However this model does not allow

making inference about which variables are statistically significant in the Eastern

European sample. For example the beta coefficient for a specific variable could be

positive in the Western sample with a statistically significant p-value, and the

corresponding coefficient of the dummy, representing the Eastern sample, could be

statistically significantly negative. When adding up the two coefficients, a slightly

positive coefficient for the Eastern sample turns up. Now it would be possible to say


31

that the Eastern and Western Sample behave differently regarding this variable, but it is

not possible to determine whether the coefficient is statistically significant for the

Eastern sample. The significant p-value associated with the dummy-coefficient for a

specific variable only states that the coefficient for the Eastern sample is different than

the one for the Western sample. To test whether the coefficient of a specific variable is

statistically significant for the Eastern sample, a separate regression has to be run only

on the Eastern sample. The resulting beta-coefficients of this regression will be equal to

adding up the coefficient determined for the Western sample and the corresponding

dummy coefficient, but the p-values will be different. The p-values of this regression

accordingly express if a specific variable helps to explain leverage in the Eastern

sample. To test the robustness of the estimation, a separate regression for the Western

sample will likewise be performed even though it should be redundant. This is done

with the expectation that there is no change compared to the regression with the dummy

variables. This regression will therefore not be reported.

It came to the attention of the authors that Eviews, when specifying a one-way fixed

effect model, reports under effect specifications; “cross-section fixed dummy

variables”. This would suggest that Eviews uses a method referred to as the “least

square dummy variable model”, when estimating the model. This contradicts the

Eviews user-guide, where it is stated that the above described fixed effects

transformation is used to estimate the model. One indicator that Eviews actually uses

the “least square dummy variable model” to estimate the fixed-effect model, is that the

R-squared that is obtained from the regressions is rather high. Under the “least square

dummy variable model”, the high R-squared is explained by the fact that there is a

dummy variable included for each cross-section which explains much of the variation in

the data. It is rather intuitive that most of the variation in the data can be explained when

a dummy variable for each cross-section is used. Therefore not that much emphasis

should be put on R-squared. More important is the economic interpretation of the beta-

coefficients of the variables as well as the corresponding p-values. For practical matters

it is not important which model is used by Eviews. Both models yield the same results

regarding beta coefficients as well as standard errors and hence t-statistics and p-values.

The fact that an unbalanced panel data is used does not cause any estimation problems

since this kind of data input is supported in Eviews 5.1.


32

A matter that has to be addressed is that the employed fixed effect model allows for

heterogeneity in the intercept, but assumes slope homogeneity. In a first step, the

authors run a regression where all countries are pooled into one sample. It is possible to

consistently estimate the mean of the parameter in the population, and it is at the same

time interesting to see which sign predominates when the coefficients are estimated.

Nevertheless the assumption of parameter stability, i.e. that the coefficients are the same

for each country in the sample is strong. Due to the diversity of the countries in the

sample regarding the firm specific variables as well as the country specific variables,

“sample homogeneity” is a rather strong assumption, even though it is often assumed in

empirical cross-country studies (Fforde 2004). Expecting complete parameter

heterogeneity, meaning that all countries behave differently in respect to capital

structure has major drawbacks in the sense that panel-data analysis is not feasible

anymore. Instead time-series estimation on a per-country basis has to be employed. But

while this is possible for the firm specific variables, data availability puts serious

constraints on the country specific variables. Inflation data or GDP growth can usually

be expected to change yearly, but factors like investor protection which are measured as

an index, do not change very often e.g. only when the country in question undertakes

institutional reforms which are reflected in the index. If indexes are used to proxy for

investor protections etc. then the way to test the impact of investor protection on

leverage in a regression environment, is with a cross-country panel, because there the

necessary variability in the variable is present. If the impact of institutional development

on leverage is supposed to be tested in a single country, then more refined proxies for

the institutional development has to be used, which are to the knowledge of the authors,

not available at the moment. It can be seen that there are very compelling arguments

supporting the use of cross-country panel data. Nevertheless the issue of parameter

stability has to be addressed and dealt with (Lin, Ng 2007).

The introduction of a dummy variable which distinguishes between Eastern and

Western Europe, do therefore not only provide interesting insights to whether there are

differences between these two groups regarding capital structure. It also helps to

potentially reduce the heterogeneity in the sample.

One way to reduce the heterogeneity in the sample is to construct groups based on a

priori economic information, such as for instance whether a country is member of the

OECD, or based on geographic criteria’s. The initial rational used in this study for


33

grouping into Eastern and Western Europe, is the geographic distinction and more

important, the historical political and economic divergence. Nevertheless this is not the

only reason for this way of grouping the data. Descriptive statistics of the two groups

have been analyzed and for many variables (but not all), a rather clear-cut pattern can be

identified, meaning that either most Eastern or most Western countries have higher

values for a specific variable. Furthermore, alternative means of sorting the data have

been considered e.g. by a measure of country-risk (see Appendix 3). In this measure of

country-risk, most of the Western European countries have a better ranking than the

Eastern European countries, and therefore this also support this way of grouping the

observations.

It has to be mentioned that in a regression framework with many different independent

variables, there are likely several ways how the sample can be partitioned. It can be

rather difficult to determine which way of partitioning is optimal from a statistical

viewpoint. There is argued to be support for the way the grouping is done in this study,

when looking at the distribution of the firm specific- and the majority of the country

specific variables.

Several statistical methods have been suggested, which formally propose the ideal

grouping of observations from a statistical perspective. One of these models takes into

consideration the different variables to calculate, so-called “pseudo thresholds”, in order

to sort the data (Lin, Ng 2007). Due to the complexity of the algorithm and the non-

availability of this kind of procedure in the used econometric software, this kind of

analysis has not been performed. It is the view of the authors, that the chosen grouping

is the most efficient one, taking into account the available data and the number of

countries in the sample. An additional division of the countries into more sub-groups,

with for example a separate group for the Mediterranean countries, is when looking at

descriptive statistics not necessarily appropriate. Furthermore, such a division would

reduce the variability of the country specific factors to an unacceptable degree.

The used regression equation, as it is presented in Eviews, is presented in Appendix 4.


34

5. Data collection

In the following paragraphs, a description of the data needs and how it was collected

will take place. First the firm-level data will be discussed and later the country specific

data will follow.

5.1. Firm specific data

The need for firm specific data, which includes detailed data on balance sheet and P&L

items, are fulfilled by the ORBIS database offered by Bureau van Dijk. Here it is

possible to find information about more than 40 million companies worldwide, of which

approximately 18 million companies are European. Even though companies comply

with different accounting standards across countries, the numbers found in ORBIS are

comparable since Bureau van Dijk has performed a harmonization of the financial

statements (Jõeveer 2005). Knowing this makes it reliable to make a cross country

investigation.

The scope of this paper is capital structure of SMEs, implying that only data on SMEs

has to be extracted from ORBIS. Since “SME” is a sometimes arbitrary definition,

reasonable criteria’s has to be setup in order to define what an SME is in this context. In

this study it has been chosen to rely on the SME definition made by the European

Commission. According to this, an SME has to fulfill the criteria’s seen in the table

below.

Table 1 – SME Criteria’s

Total Assets 2 M. € ≤ 43 M. €

Revenue 2 M. € ≤ 50 M. €

Employees 10 ≤ 250

Source:http://ec.europa.eu/enterprise/enterprise_policy/sme_definition/index_en.htm

In the search process it is possible to filter out companies that do not fulfill these

criteria’s, by setting up a filter in the ORBIS search interface. Since a data panel is

being used (see section 4), data covering a certain period of time has to be collected. It

is recognized that the status of a company can change within this period e.g. a company

is categorized as an SME in one year, but subsequently grows in size and may not be an

SME in the following year. On the contrary a company could also be an SME in one

year but go bankrupt the next, and therefore not be present in the sample anymore. A


35

simple way of dealing with this is obviously to define the filter so that a company

simultaneously has to fulfill the criteria’s for all years in the period. A drawback of this

approach is however that it would introduce survivorship bias in the data, in the sense

that only companies that were able not to go bankrupt in the period, will be in the

sample. At the same time very good companies that are growing out of the SME

segment in the end of the period would also be eliminated entirely from the sample by

moving into the “big enterprise” category. To overcome this problem, the filter was

applied to one year at a time, and all companies that are considered SMEs in that

specific year were exported. After applying this procedure to each year, the datasets

were merged into one large dataset consisting of all observations covering the entire

period. The procedure was slightly different in terms of the years 2001 and 2002,

compared to the rest of the years due to the search possibilities in the ORBIS interface.

This issue is discussed more in depth in appendix 5. This way of collecting the data will

result in an unbalanced dataset because not all cross-sectional units will have

observations in all time periods. But this issue can relatively easy be dealt with in the

statistical analysis, and therefore this trade-off is considered to be fair.

Besides fulfilling the SME criteria’s, it has also been chosen to filter out insurance

companies and financial intermediaries. The impact of explicitly eliminating these

companies is considered to be very low, since it appears as if all of them fell for the

SME criteria’s in the first place, in terms of primarily size. Moreover, listed companies

are filtered out since their capital structure is believed to be highly influenced by their

ability to make use of the capital market. In order to get a more homogenous sample, it

is therefore decided to only include privately held companies. As a further constraint it

was chosen only to include incorporated companies. The argument for excluding non-

incorporated firms is that it is desired to minimize the number of companies where

private collateral is pledged as security for loans to the business, since it is believed to

possibly bias the results of the analysis.

An implication of applying the SME filter is that all companies not publishing

information on total assets, revenues or employees, will automatically be filtered out.

But after this, it was still necessary to eliminate observations from the sample because

of missing data in one or more of the crucial variables. This elimination was performed

on the raw export from ORBIS in excel. Companies with missing data in one of the

following variables were eliminated:


36

Table 2 – Elimination criteria

Operating revenue time t Tangible fixed assets

Operating revenue time t-1 Other fixed assets

Total assets time t Depreciation

Total assets time t-1 Long-term debt

Return on assets Loans

Year of incorporation

Furthermore companies with negative values in one of the following variables or

combinations were also eliminated from the dataset because it is considered to be flaws:

Table 3 – Further elimination criteria

Total Assets Tangible Fixed Assets + Other Fixed Assets

Age Long-term Debt + Loans

5.1.1. Merging in SAS and creating new variables

From the raw data in terms of pure accounting numbers and information like e.g. “date

of incorporation”, new variables had to be computed to satisfy the needs of the proxies

that will be presented in section 6. This includes among others, variables like age,

tangibility, profitability and naturally the different leverage measures etc. Also a

dummy variable was created based on the geographic origin of the companies. The

dummy takes the value of 0 in the case of the Western companies and the value of 1 for

companies incorporated in Eastern Europe. Due to the size of the aggregated dataset,

and the limitations of Microsoft Excel 2003 in terms of handling more than

approximately 65,000 observations, this data manipulation was performed using SAS

Enterprise Guide. After having a look at the descriptive statistics, it was chosen to

eliminate outliers with extreme values in growth of assets. The very extreme growth

rates are believed to stem from companies founded in the year prior to the one where the

data is extracted. Thereby extremely high growth rates can show up, even if the growth

in nominal terms is not that great. In order to deal with this, 0.5% of the observations in

each side of the distribution were deleted.


37

5.2. Country specific data

After having collected, aggregated and manipulated the firm level data, the country

specific data was collected and merged with the firm specific data in order to get the

final data set, serving as input for the regression analysis. Below, the sources for the

different country specific data can be seen.

Table 4 – Data sources

Variable Source

Contract Enforcement Doing Business

Legal Rights Doing Business

Credit Information Doing Business

Disclosure Doing Business

Investor Protection Doing Business

Recovery Rate Doing Business

Market Capitalization to GDP Global Market Information Database

Inflation Global Market Information Database

GDP Growth World Development Indicators

Bank Concentration International Monetary Fund

Net Interest Margin International Monetary Fund

Nearly all variables are readily available from the source, without the need to further

manipulate them. Market Capitalization to GDP is the only variable that was computed

based on raw data available at Global Market Information Database, which is provided

by Euromonitor. Doing Business is a fairly new initiative taken by the World Bank, and

contains country specific index data on several issues related to opening or running a

business around the world. Unfortunately due to Doing Business being a young project,

it is not able to provide full coverage of all years present in this study for all variables

across all countries. It has therefore been necessary to make approximations regarding

some of the index values in the early years. Specifically, if not available, it has been

assumed that the index value has not changed, meaning that if for instance data is not

available from 2003, then it is explicitly assumed that the value is identical to the one

for 2004. This is expected to be a reasonable assumption, since there is not a lot of

variability over time within the individual countries. It is also consistent with the belief


38

of the authors that the variables available from Doing Business, is relatively rigid, and

does not change from year to year like e.g. inflation, GDP growth etc. Regarding GDP

Growth, Bank Concentration and Net Interest Margin, full detailed coverage for each

country is available from World Development Indicators and the International Monetary

Fund respectively.

5.3. The final dataset

By merging the country specific data with the firm specific data, the final dataset is

constructed. This will serve as input for the regression analysis described in section 4.

Since there is not data available for all companies during all years, the dataset is

characterized by being unbalanced. The final number of observations per country can be

seen in the table below:

Table 5 – Observations per country

Western sample Eastern sample

Austria 109 Bulgaria 1,576

Belgium 12,658 Czech Republic 16,257

Switzerland 321 Estonia 1,250

Germany 2972 Croatia 1,022

Spain 130,226 Hungary 1,546

Finland 8,968 Lithuania 228

France 29,388 Latvia 320

Great Britain 27,963 Poland 4,928

Greece 20,086 Romania 6,235

Italy 187,496 Serbia 2,024

Netherland 921 Slovakia 2,693

Portugal 5,665

Sweden 25,186

Total 451,959 Total 38,079

As expected, the number of observations is a lot higher in the Western sample. The

difference in population does not alone justify this difference. A suggested reason for

the relatively small number of observations in the Eastern sample is that only the formal

business sector is available at ORBIS. This means that only registered companies that

pay registration fees, taxes etc. is available. The size of the informal business sector

could be different across countries and could therefore account for the lack of

observations in the Eastern sample (Klapper, Sarria-Allende & Sulla 2002).


39

Looking at the number of observations per year as seen in the table below, it can be seen

that the observations are fairly equally distributed between the different years.

Table 6 – Observations per year

Year Western Sample Eastern Sample Total

2001 58,504 2,968 61,472

2002 68,086 5,185 73,271

2003 81,814 5,119 86,933

2004 72,662 7,154 79,816

2005 78,642 9,170 87,812

2006 92,251 8,483 100,734

Total 451,959 38,079 490,038

2001 and 2002 has the smallest number of observations, which is argued to be due to

the special circumstances under which the data was collected, see appendix 5. Detailed

descriptive statistics will follow in the analysis part.

6. Proxies

In the following paragraphs, the different proxies that will be employed in the

regression analysis will be explained. The explanation will cover a definition of what

the proxy measures, how it is constructed, what it is intended to proxy for and finally

the name it is represented by in the regression. Regarding the independent variables,

other empirical evidence employing a similar proxy will be highlighted when possible.

This is done in order to help forming expectations about the relationship, between the

proxy and the leverage measures.

6.1. Dependent variables

When constructing leverage measures, theories of capital structure usually refer to the

market-value of equity, rather than the book-value of equity. In this study, only the

book-value of equity is used when calculating the different leverage measures. The

main reason for this is that the sample consists of unlisted companies, and therefore the

market-value of equity is not readily available. This is not considered a major drawback

since it has been shown that leverage, based on market-value, is highly correlated with

leverage based on book values (Bowman 1980). Furthermore, book-values reflect the

relative amount of capital received from different external sources, and can therefore


40

appropriately reflect the financing mix a company uses (Baskin 1989). Finally, in

relation to the static trade-off theory, managers usually set their target debt-ratios based

on book-values (Thies Clifford F, Klock Mark S 1992).

In this paper, six different measures of leverage are considered as dependent variables.

These measures are as follows:

1. LONGTERMBANKDEBT: 12345�678 9:3; <69�

=2�:> ?@@6�@

2. SHORTBANKDEBT: 12:3@

=2�:> ?@@6�@

3. NARROWLEVERAGE: 12:3@A 12345�678 9:3; <69�

=2�:> ?@@6�@

4. SHORTTERMDEBT4:

12:3@A=7:<6 B76<��

=2�:> ?@@6�@

5. CURRENTLIABILITIES: CD7763� 1�:9�>��6@

=2�:> ?@@6�@

6. BROADLEVERAGE: =2�:> 1�:9�>��6@

=2�:> ?@@6�@

The last two measures, namely CURRENTLIABILITIES and BROADLEVERAGE are

only included in the study for completeness because they are used in several other

studies (see e.g. (Jõeveer 2005), (Michaelas, Chittenden & Poutziouris 1999)). These

measures will not be the main focus of the study, and the results will only be presented

in appendix 6. This is because there are several reasons why these measures could

potentially be biased. The general problem with the measure BROADLEVERAGE, is

that total liabilities also include short-term as well as long-term provisions, which can

constitute a large part of total liabilities due to for example pension-provisions.

Provisions are usually not used for financing, and should therefore not be included in

4 This measure reflects all means of short-term-financing that can be derived from a balance-sheet. The

authors are aware of the fact that leasing and also renting is another source of short-term financing.

However the unconsolidated balance-sheets used in this study are prepared in accordance with local

GAAP, and therefore it is not possible to doubtless identify the amount of leasing used by a company as a

mean of short-term financing.


41

the measure of leverage. The same reasoning goes for the measure

CURRENTLIABILITES. This is because short-term provisions are also included in this

measure, which are of cause potential liabilities but not a mean of financing. For

example, a provision which has been created for a pending court case does not have

anything to do with the way the company finances itself. Furthermore, both leverage

measures include trade-credits which could potentially be used more for transaction-

purposes than for financing purposes and should therefore not be included in the

measure of leverage (Rajan, Zingales 1995). It has nevertheless been suggested that

trade-credit is an important source of finance for SMEs, and especially in Eastern

Europe. To test that prediction, trade-credits are included in the measure

SHORTTERMDEBT (Jõeveer 2005),(OECD 2006) . To sum up, it is expected that the

measures BROADLEVERAGE and CURRENTLIABILITIES overestimate leverage,

and therefore the main focus will be on the first four measures of leverage, which in the

understanding of the authors, are more precise measures of leverage in the context of

this study.

A measure of long-term bank debt as well as short-term bank debt is included as

dependent variables. This is because it has been shown that these measures are

influenced in different ways by determinants of leverage. In order to detect the overall

impact of our different independent variables on bank debt, the measure

NARROWLEVERAGE is included. If a specific independent variable, such as for

example tangibility, is positively related to long-term bank debt but negatively related to

short-term bank debt, it is possible to determine what the overall effect on bank-debt is.

As stated earlier, trade-credits can be used as a source of financing. In order to evaluate

this, SHORTTERMDEBT is included as a measure of leverage which includes trade-

credits. By comparing the coefficients of SHORTTERMDEBT with

SHORTBANKDEBT, it is not only possible to determine if trade-credits are influenced

by certain independent variables, but also in which manner.

6.2. Independent variables

Here follows a description of the independent variables that will be used to explain the

different leverage measures in the regression. The choice of variables is to a large

extend based on other empirical evidence, but some are also based on the intuition and

curiosity of the authors. For each proxy, a discussion of the measure, as well as the


42

expected sign of the coefficient will take place. If not otherwise stated, the expectation

about the relationship between the proxy and all measures of leverage will be the same.

6.2.1. Firm specific variables

First a description of the firm specific variables will take place. These variables are

more or less the classical explanatory variables when looking at empirical work on

capital structure theory.

6.2.1.1. Size

The natural logarithm of turnover (LNTURNOVER) will be used to proxy for the size

of a company. One reason why size is included in the regression is that size is argued to

proxy for the inverse probability of default (Rajan, Zingales 1995). Small firms have a

higher risk of default compared to large firms. This stems from larger companies in

general being more diversified, and this should lead to a lower leverage ratio among

small firms. This line of reasoning is in line with the static trade-off theory, because the

benefits of more debt-financing are weighted against the potential costs of bankruptcy

which is affected by the probability of default (Rajan, Zingales 1995). Another

argument why size is related to leverage stems from the fact that larger firms are

expected to be more transparent than smaller firms, because the quality of financial

information available about the company is higher. This reduces the problem of

asymmetric information, and therefore larger firms should have easier access to debt-

financing. Regarding the proxy for size, the static trade-off theory as well as the pecking

order theory point in the same direction, namely that size is positively related to

leverage (Jõeveer 2006). The majority of empirical research also suggests this

relationship. Some of them are (Rajan, Zingales 1995), (Cornelli, Portes & Schaffer

1996), (Shenoy, Koch 1996) and (Friend, Lang 1988). For a more exhaustive list, see

table 2 in appendix 7. Based on this, the expectation for size is that it is positively

related to all leverage measures.

6.2.1.2. Profitability

The profitability measure employed in this study is return on assets (ROA), which is

provided by the ORBIS database, and is measured as profit before tax over total assets.

The theory about the effect of profitability on leverage is two-fold, depending on the

theory which is conferred with. The static trade-off theory predicts that profitable firms

will have higher debt-ratios than less profitable firms in order to shield their income


43

with a larger interest tax-shield. Contrary to this prediction, several studies have shown

that the most profitable firms actually borrow the least, which is in line with the pecking

order theory (Wald 1999). Explained in the framework of pecking order theory,

profitable firms generate more internal funds to finance new projects, and do therefore

not depend as much on raising funds via an issue of debt (or equity).

When turning to the wide body of empirical work, the evidence is mixed. This is

illustrated in table 3 in appendix 7. Therefore it does not give a clear cut picture of what

to expect by just turning to other studies. In this study it is believed that the pecking

order theory is more suitable than the static trade-off theory in explaining the impact of

profitability on leverage, thereby leading to an expected negative relation between

profitability and leverage. This should be seen in the light of this study dealing strictly

with SMEs, where a part of these are assumed to be family owned. These families are

expected to behave according to pecking order theory, in order not to give up too much

control. The expectation is therefore that profitability is negatively related to leverage.

6.2.1.3. Growth

Growth in assets (GROWTHASSETS) is used to proxy for the growth opportunities a

company has. Intuitively a company that has profitable growth opportunities will seek

to pursue these. Following the pecking order theory, first retained earnings will be used

to finance these projects. It is however expected that retained earnings are not sufficient

to entirely finance these projects, meaning that debt financing has to be sought. Here it

seems reasonable to expect SMEs to first use short-term debt in the form of short-term

loans or trade-credits to finance these projects before long-term loans are used. A reason

for this has been brought forward by (Myers 1977), since growth opportunities have the

potential to create moral hazard. Lenders will only recover the amount of their loans,

and will therefore not participate in the benefits of the growth opportunities beyond

their initial claim. These benefits will solely go to the SME, but the down-side risk of

bankruptcy is on the other hand shared between the lender and the SME. This risk will

be addressed by increasing the cost of long-term debt, which makes it less attractive for

the company (Hall, Hutchinson & Michaelas 2004). It is expected that growth is in

general positively related to leverage while being more positively related to the short-

term leverage measures. Table 4 in appendix 7 gives an overview of different studies

identifying positive as well as negative relationships between growth and leverage.


44

6.2.1.4. Tangibility

Tangibility is calculated based on the balance sheet data from the ORBIS database, and

is given by tangible fixed assets over total assets (NARROWTANGIBILITY). The

reason for using a tangibility measure as a variable to explain leverage, is that its

relevance is well documented in great many empirical studies see e.g. (Rajan, Zingales

1995). An overview of some of these studies is presented in table 1 in appendix 7.

Tangibility is suggested to proxy for the amount of collateral a company has to secure a

loan with. It is therefore believed that tangibility will be positively associated with the

amount of debt a company can carry. Some studies use a broader measure of tangibility,

like all tangible assets over total assets (Chen, Hammes 2004). But since usually only

tangible fixed assets, such as real-estate, machinery or land etc., can be used as

collateral for a loan, it is believed that the measure employed in this study is the better

one. Firms with a high level of tangible fixed assets will usually have a higher

liquidation value in the case of bankruptcy. It has also been stated that firms with a high

level of tangible assets are mature firms, and therefore less risky. It is nevertheless

unclear if this also applies to SMEs (Chen, Hammes 2004). When accounts payable is

used as a source of financing, the importance of collateral in the form of tangible fixed

assets should be reduced. Trade credit is given in the course of regular business

activities and is suggested to be independent of the asset structure of the company.

Therefore the results regarding leverage measures including trade credits could deviate

from the general expectation that tangibility is positively related to leverage.

6.2.1.5. Age

Age is measured as the number of years since the date of incorporation and is

represented in the regression as (AGE). This way of measuring the age is a very feasible

solution, but it is also exposed to a possible slight bias, since some companies can have

existed under another corporate form before. This means that the true age of the

companies in the sample is on average expected to be slightly higher than what is

actually measured. This bias is however considered to be sufficiently small to not affect

the robustness of the results.

Age is not one of the “typical” variables to include, when looking at most of the well

known empirical evidence about capital structure. (Pfaffermayr, Stöckl 2008) is one of

the studies that do incorporate age, and finds a negative relation between it and the use

of debt. Further, a U-shaped relation is observed in the study, in the sense that the


45

relationship changes to positive at an age of around 105 years. Another study (Bhaird,

Ciarán mac an, Lucey 2007) also finds a significant negative relation between age and

long-term debt, while it is insignificant for short-term debt. Finally, (Lucey, Bhaird,

Ciarán mac an 2006) find the same result regarding long-term debt and argues that it is

consistent with SMEs following a life cycle model of financing. Older firms have in

general had the opportunity to accumulate more retained earnings over the years than

younger firms and should therefore to a higher degree be able to finance projects with

internal funds. Therefore they do not have to seek external funds to do so. Based on this

empirical evidence, it is expected to see a tendency for older firms to rely more on

internal funds i.e. less debt financing. Yet it will be interesting to see how and if the

Eastern European sample deviates from the Western European sample, since many

companies in Eastern Europe have been founded only in the beginning of the 1990’s. In

the light of the suggested U-shape by (Pfaffermayr, Stöckl 2008), it could potentially

turn out that the younger firms in East will behave different. However the general

expectation is that age will be negatively related to leverage.

6.2.1.6. Non debt tax shield

Non debt tax shield is calculated as depreciation over total assets (NDTSHIELD).

Interest payments are not the only way to reduce tax payments. Depreciations for

instance, are also tax deductible, and therefore when determining the optimal capital

structure from a tax perspective, also the non debt tax shield has to be considered. When

the non debt tax shield is sufficiently high, e.g. depreciations are high, maybe due to

accelerated depreciation plans, the gain from using debt for tax saving purposes

diminishes. The advantage of being able to use a non debt tax shield instead of a debt

induced tax shield is that distress costs and adjustment costs can be circumvented. In

certain countries, the non debt tax shield can be of specific significance to SMEs,

because they receive special treatment under the local tax code. It is expected to see a

negative relationship between non debt tax shield and leverage, because if the non debt

tax shield is sufficiently high, the need for a debt tax shield is reduced (Lopez-Gracia,

Sogorb-Mira 2008). Table 5 in appendix 7 illustrates other studies incorporating Non

debt tax shield.


46

6.2.2. Country specific variables

Here follows a description of the country specific proxies.

6.2.2.1. GDP growth

GDP growth (GDPGROWTH) is used as a macroeconomic control variable to proxy for

the overall state of the economy in a country. If the GDP of a country is growing, it is a

signal that companies have better investment opportunities and are thereby expected to

create more profit and hence, more internal funds can be used for investments. Under

the static trade-off theory, more debt should be taken on to shield these profits from

taxation. On the contrary under the pecking order theory, these improved economic

conditions lead to a higher free cash-flow that can be used for investments, and thereby

the need for external financing in the form of debt diminishes. It is believed that the

pecking order theory is dominating for SMEs, and it is therefore believed that GDP

growth will lead to a situation where more projects can be financed with internal funds.

The variable will likely pick up other effects as well, however GDP growth is expected

to be negatively associated to leverage.

6.2.2.2. Inflation

Inflation is represented in the regression as (INFLATION). It is used to proxy for the

cost of capital in a country, since the prime interest rate is partly a consequence of

inflation in a country. If the prime interest rate in a country gets higher, the cost of

equity as well as the cost of debt for companies should increase. This might

nevertheless not be the case in real terms, due to the deterioration of the real value of the

principal at the time of repayment. Furthermore, when part of the interest paid on a loan

is actually compensation for deterioration of the principal, then also the value of the tax-

shield is increased, because part of the principal repayment is then tax-deductible

(Myers 2001). Therefore a positive relationship between inflation and leverage is

expected.

6.2.2.3. Market capitalization to GDP

Market capitalization to GDP (MARKETCAPTOGDP) is a proxy for the financial

development and depth of the capital market within a country. The ease of acquiring

external finance is to some degree influenced by the development of capital markets, i.e.

bond markets as well as stock markets. It has to be mentioned that initial public


47

offerings in the form of a stock market flotation do not play a big role in the financing

considerations of SMEs. One reason is for instance that the transaction costs associated

with an initial public offering are high, as well as the potential loss of control by the

initial shareholders. The proxy should therefore be understood more as an indicator of

the state of the financial development in a specific country. A high stock market

capitalization compared to GDP is only possible if a certain level of regulatory

requirements as well as institutional development within a country is fulfilled. High

regulatory requirements lead to a higher degree of security on the side of potential

lenders as well as equity investors. These considerations lead to the prediction that

market capitalization to GDP is positively related to leverage. It is expected that the

impact of market capitalization to GDP is higher on leverage measures not

incorporating trade credits, because the effect of regulatory environment on the

availability of trade-credits should be rather small.

6.2.2.4. Bank Concentration

The argument behind including bank concentration (BANKCONCENTRATION) as an

independent variable is to control for the competitive situation in the banking sector.

This is argued to influence the availability of credit, however there are different

expectations regarding the impact on leverage as described in section 2.7. Different

measures of market concentration are widely used in the literature, while the N-firm

concentration ratio, Herfindahl-Hirsch-, Hannah-Kay- and Lerner -index being the most

common. To a large extend, the choice of concentration measure in this study is

determined by the availability of data from a reliable source. The extensive dataset

covering 24 countries over a 6 year period narrows down the possibilities for getting

satisfactory data. However the choice fell upon a 3-firm concentration index provided

by the International Monetary Fund. This variable has a value between 0 and 1, and

measures the combined asset value of the three largest banks, relative to the entire bank

market in a country. Choosing among the different concentration measures can possibly

have an effect on the outcome of the relationship between the proxy and leverage.

(Santiago Carbó-Valverde, Francisco Rodriquez-Fernandez & Udell 2006) have tested

the relationship between competition on the banking market and leverage among SMEs

using different proxies for banking concentration. Very interestingly, they find

ambiguous results supporting both the information hypothesis (positive relationship

between leverage and concentration) and the structure-performance hypothesis


48

(negative relation between concentration and leverage), depending on the concentration

measure they use. For a thorough discussion of the two hypotheses, see section 2.7.

Specifically (Santiago Carbó-Valverde, Francisco Rodriquez-Fernandez & Udell 2006)

find evidence supporting the information hypothesis, when using a Herfindahl-Hirsch

indicator, which is calculated by summing the squares of the market shares of each

individual company. By doing that, the measure automatically puts more emphasis on

the big companies due to the squaring. The other measure that is used in the article is

the Lerner index, which is normally defined by the following mathematical expression:

L = (P-MC)/P

In the article it is slightly modified to fit the context and is defined as: ”price of total

assets – marginal costs of total assets)/price” (Santiago Carbó-Valverde, Francisco

Rodriquez-Fernandez & Udell 2006 p. 17) . Using this measure, the results are different

and support the structure-performance hypothesis. Naturally this ambiguity concerning

the use of the different measures raised a discussion about the appropriateness of each.

The authors eventually conclude that in the context of financial constraints, the Lerner

index is a better indicator.

The reason for highlighting this article even though, due to data availability, this paper

utilizes a third concentration measure, is to make the reader aware that interpretation of

the coefficient on banking concentration should be made with caution. Making

expectations about the relationship is therefore also made with some uncertainty. As

described in section 2.7, the empirical evidence is split, possibly due to the use of

different measures of concentration. It is however expected that the results will show a

negative relation between concentration in the banking sector and leverage (i.e. support

for structure-performance hypothesis) because, based on the economic intuition of the

authors, it seems more plausible that banks will take advantage of oligopolistic market

conditions and thereby making it more difficult for SMEs to get debt financing at

reasonable costs.

6.2.2.5. Net interest margin

The data needed for the variable is like banking concentration retrieved from the dataset

provided by the IMF. The variable is constructed by using data from Fitch’s BankScope

database, and is measured as: “accounting value of bank’s net interest revenue as a


49

share of its interest-bearing (total earnings) assets.” NETINTERESTMARGIN can be

regarded as a profitability measure of banking activities, and according to the literature

of industrial organization, it could therefore be related to the concentration measure.

According to the Structure Conduct Performance paradigm, as previously mentioned in

section 2.7.2, a positive relationship between concentration and profitability is expected

due to companies taking advantage of their strong position in the market e.g. cournot

behavior (Santiago Carbó-Valverde, Francisco Rodriquez-Fernandez & Udell 2006).

However, it should be noted that some studies specifically have rejected such behavior

in the banking industry, see e.g. (Roberts 1984) and (Berg, Kim 1994).

It is hard to find studies where the same measure is being employed in a similar context

as this paper. (Berger, Udell 2006) finds that bank profitability is negatively related to

their proxy for financial constraints, i.e. more profitable banks all else equal enhance

credit availability. Having no reason to expect something different, the expectation in

this study is similarly a positive relation between net interest margin of banks, and

leverage.

6.2.2.6. Recovery rate

RECOVERYRATE shows the average relative amount recouped by creditors through a

bankruptcy or insolvency proceeding in a country. It is thus a general measure of the

expected payoff to creditors in the case of bankruptcy, but does not say anything about

the probability of a company going into bankruptcy. The data is from Doing Business,

and takes the form of a number between 0 and 100, telling how many percent creditors

can expect to get back in the case of bankruptcy. Being a general measure of the

efficiency of the bankruptcy law, it captures different underlying determinants hereof.

The relationship between the recovery rate and leverage is all else equal expected to be

positive. No support for this belief has however been found in the empirical literature.

This is not because a reverse relationship has been proven, but rather because of lack of

studies employing recovery rate as an explanatory variable. If the variable turns out to

be significant, it is expected to have a positive coefficient associated with it. The

economic reasoning behind this statement is that the higher the expected payoff in the

case of bankruptcy, the better for the creditors which could be translated into less risk.

This should make it easier for SMEs to get financing and/or lower the price hereof.


50

6.2.2.7. Contract enforcement

Measured in days, the contract enforcement variable (CONTRACTENF) measures how

long it takes on average to solve a commercial dispute between two parties. Intuitively,

one would expect better contract enforcement to increase credit availability by

protecting creditors in the case of a dispute. Of course it is interrelated to the measures

of the legal system, meaning that contract enforcement does not in itself protect

creditors, if there is not at the same time authority in the law. Naturally it also goes the

other way around, meaning for instance that superior legal rights does not help creditors

if they are not at the same time enforced. In a recent study (Arellano, Bai & Zhang

2007), good contract enforcement is argued to act like a subsidy on the amount firms

can borrow, and as a tax in the case of bad contract enforcement. Further it is argued

that small firms are affected more by the degree of contract enforcement. Since the

variable measures the time it takes to solve a dispute, the expectation is that contract

enforcement is negatively related to leverage.

6.2.2.8. Corruption

CORRUPTION is based on an index (Corruption Perception Index: CPI), constructed

by Transparency International, which is an organization with the goal to fight corruption

around the world. As argued by e.g. (Hillman, Krausz 2005), corruption is often closely

related to financial stability. Figure 2 shows a clear trend towards increasing financial

strength as the CPI score increases (i.e. lower corruption).


51

Figure 2

Source: (Hillman, Krausz 2005)

This relation suggests that less corruption increases leverage. (Hillman, Krausz 2005)

argues that corruption reduces financial mediation, which makes sense because as a

potential creditor, you would most likely feel more comfortable operating in a less

corrupt environment. Therefore highly corrupt areas are expected to be characterized by

more expensive credit and/or less credit availability. Since the variable is constructed so

that a higher score equals less corruption, the expectation is that the variable is

positively correlated to leverage.

6.2.2.9. Credit Information

CREDITINF also takes the form of an index, and the data is provided by Doing

Business. The following description of the variable can be found at the website of doing

business5: “… measures the coverage, scope, quality and accessibility of credit

information available through public and private credit registries”. The data is

constructed by assigning the score of 1 to each of 6 different features that the public

5 www.doingbusiness.org


52

and/or private credit registry fulfills. This methodology was developed in the paper

(Djankov, McLiesh & Shleifer 2007), and is adopted by Doing Business with minor

changes. A potential weakness about the data is considered to arise from the way it is

constructed. Consider for instance two countries with the same score of say 3. The same

score of 3 could be assigned to the two countries even though they have two rather

different credit information systems. One country could fulfill the first 3 criteria’s,

whereas the second country might fulfill criteria 4-6. This means that the index only

makes sense as a sort of measure of strength of the credit information system in general.

It does not make sense to talk about the marginal effect of an increase in the index, since

this increase can come about by fulfilling one additional criteria out of many. However

when this is said, it is believed that this variable will add explanatory power to the

model by controlling for the development of the credit information system. The

economic reasoning behind the variable is that a more developed credit information

system can help creditors to better assess the risk of SMEs (or any other company) by

offering information on e.g. repayment history, unpaid debts etc. This is information

that at least to some extent helps to mitigate the problem of asymmetric information.

Increasing the level of information concerning the loan applicant, will help lowering the

overall risk of the loan, and should thereby lower the price and/or increase the

availability of credit to SMEs. Concerning the latter, (Jappelli, Pagano 2000) and

(Pagano, Jappelli 1993) has shown that credit bureaus have an effect on credit

availability. Based on the discussion above, the expectation is that credit information is

positively correlated to leverage.

6.2.2.10. Disclosure

DISCLOSURE is constructed to measure the ability of a majority shareholder to make

transactions that he personally will benefit from, on the cost of minority shareholders

and creditors in the company (See www.doingbusiness.org for further description on the

adopted methodology). The index is based on voting rights, requirements for disclosure

e.g. immediate announcement to the public about the personal conflict of interests or

disclosure in the annual report, and the demand for an external body e.g. an auditor, to

approve the transaction. It is therefore a corporate governance measure, which points

towards a positive relation with leverage. On the other hand, one would maybe expect

lenders to include covenants in the loan agreements and by doing so, governing their

own rights. But as also discussed in section 2.3.1 on agency costs, covenants can be


53

costly to implement and enforce, so it is doubtful how feasible these are when used on

rather small commercial loans to SMEs. A comparable empirical study, where a similar

measure of disclosure is incorporated as an explanatory variable, has not successfully

been identified. Therefore the expectations regarding this proxy is solely based on the

intuition of the authors, suggesting that better corporate governance should increase

leverage by helping to secure the claims of the creditors.

6.2.2.11. Investor protection

Like the disclosure index, this variable measures the strength of one dimension of the

corporate governance system. INVESTORPROTECTION is also constructed by Doing

Business, following the methodology of (Djankov et al. 2005). Among other things, the

index measures the degree to which directors can be held personal liable and the ease of

shareholder suits. It is argued that not only shareholders but also creditors will benefit

from a high level of investor protection when measured in this way. Therefore the

relation between this measure of investor protection and leverage is not expected to

behave as suggested by (Cheng, Shiu 2007). This study indicates that good investor

protection increases the supply of equity funds thus leading to less use of debt by

companies, which in a sense can be considered as a reverse pecking order. Instead it is

believed that better investor protection will be associated with better access to loan

financing for SMEs, since director liability and shareholder suits can help lower agency

costs in connection with bank financing, because managers or majority shareholders

cannot without consequence get away with expropriating wealth from the company. It

should also be kept in mind as previously explained, that SMEs are argued to prefer

debt over equity in order not to give up control and this does not fit well with the

findings by (Cheng, Shiu 2007). The expectation is therefore that investor protection is

positively related to leverage.

6.2.2.12. Legal rights

LEGALRIGHTS is again taken from the website of Doing Business. The data is

constructed in a similar way as Credit Information in the sense that it captures the

presence of 7 aspects of collateral law, and 3 aspects in the bankruptcy law of a country.

A score of 1 is then assigned to each of the 10 attributes, if they are defined in the law,

and the index is then aggregating the values. The expected relation between legal rights

and leverage is positive and should be quite intuitive. If legal rights are very low, one


54

would most likely expect creditors to hesitate before approving a loan, or at least

demand a very high risk premium. This expected relation is supported by (Hall,

Jörgensen 2006), who shows a positive correlation between, what they call creditor

rights and leverage in a sample of Central and Eastern European companies. Their

measure is argued to be comparable despite its different name, since it as described in

the paper, measures both legal remedy in the case of bankruptcy, and effectiveness of

collateral laws. Similar results are found in (Safavivan, Sharma 2007), who also find a

significantly positive relation between creditor rights and firms access to credit. As also

argued in the paragraph concerning contract enforcement, these authors conclude that

creditor rights and the enforcement of such rights are interrelated. Their results show

that the relation is much weaker in areas where the rights are not backed by efficient

court systems, which could potentially be the case in Eastern Europe. Summing up, the

expectation regarding legal rights is that it is positively related to leverage.

7. Analysis

In this section, the actual regression will be performed and the corresponding results

presented. The regression is as previously mentioned, performed in Eviews 5.1 which is

a standard econometrics package. First, different relevant descriptive statistics will be

discussed in order to give the reader a feel for the data. Later an overview of the

estimated coefficients along with the level of significance will be presented. The actual

regression output along with the estimation statistics can be found in appendix 11.

7.1. Descriptive statistics

Descriptive statistics have been calculated for the different variables, included in the

model. The structure is so that descriptive statistics of the different leverage-measures

will be presented first. After that follows a description of the firm specific variables, and

finally the country specific variables.

7.1.1. Measures of Leverage

Along with the primary regression analysis, Eviews offers different opportunities for

analyzing the dataset in the form of descriptive statistics. Table 7 below, describes the

mean and distribution of the different leverage measures employed in this study.


55

Table 7 -Descriptive Statistics of the different leverage-measures

SHORTTERMDEBT

Mean Max Min. Std. Dev. Obs.

West 0.308542 2.957429 0.000000 0.266927 451,887

East 0.266417 3.997787 0.000000 0.256485 37,643

All 0.305303 3.997787 0.000000 0.266375 489,530

SHORTBANKDEBT


West 0.110858 1.756930 0.000000 0.148832 451,959

East 0.065948 1.510541 0.000000 0.109352 37,778

All 0.107393 1.756930 0.000000 0.146657 489,737

NARROWLEVERAGE


West 0.207581 1.972945 0.000000 0.200207 451,959

East 0.131536 2.085988 0.000000 0.174599 37,778

All 0.201715 2.085988 0.000000 0.199384 489,737

LONGBANKDEBT


West 0.096723 1.972945 0.000000 0.145961 451,959

East 0.065334 1.445051 0.000000 0.135809 38,079

All 0.094284 1.972945 0.000000 0.145441 490,038

The table shows that all measures have one thing in common, namely that mean

leverage is higher for the Western sample, which is in line with other studies, see e.g.

(Nivorozhkin 2005) or (Jõeveer 2005). A potential explanation for this observation has

been presented in the section 2.5, where it has been suggested that availability of

external-finance is inferior in Eastern Europe compared to Western Europe. This does

not necessarily mean that external-financing is not available in sufficient quantities in

Eastern Europe, but can also mean that the cost of the available finance is very high.

The mean value of trade credits to total assets can be determined by deducting the

mean-value of SHORTTERMDEBT from the mean-value of SHORTBANKDEBT. The

mean value for the Western sample can thereby be computed as 0.198, while the mean-

value for the Eastern sample with 0.200 is very similar. This suggests that Eastern- and

Western European companies use comparable amounts of trade-credits.

The main difference between the two samples can be found in the amount of bank-debt

used for financing. Here it has to be mentioned that while on average, bank-debt is


56

higher in the Western sample compared to the Eastern sample, there is some variation

between the different countries in the two samples. For instance, French companies

have rather low levels of long-term debt in their capital structure compared to the other

countries in the Western sample. The likely explanation for this can be attributed to

industry effects, which are controlled for within the cross-section fixed effect in the

model. Approximately 60 percent of the selected French companies operate in the

service industry compared to only 50 percent in the rest of Western Europe. This is also

supported by the fact that French companies have low values of tangible fixed assets,

which is typical for companies in the service industry. This implies that the differences

in leverage within the two samples do not necessarily reject the expectation that there is

a general Western European and an Eastern European pattern regarding leverage, when

for instance industry effects are controlled for. The existence of a general pattern is

further supported when analyzing the firm specific descriptive statistics, as well as the

country specific descriptive statistics. Per country descriptive statistics of the different

leverage measures, are presented in appendix 8.


57

7.1.2. Firm specific variables

Here follows a description of the firm specific variables. Table 8 below is similar to

table 7, except it also reports the median.

Table 8 - Descriptive statistics of the different firm specific variables

AGE

Mean Median Max Min. Std. Dev. Obs.

West 20.81286 17.86301 348.0000 0.000000 14.72829 451,947

East 11.65810 10.84932 261.0000 1.002740 9.395925 38,079

All 20.10146 16.86849 348.0000 0.000000 14.59221 490,026

GROWTHASSETS


West 0.135311 0.065975 6.939042 -0.842820 0.364812 451,959

East 0.299226 0.155607 6.959731 -0.842963 0.582938 38,079

All 0.148048 0.070978 6.959731 -0.842963 0.388686 490,038

LNTURNOVER


West 8.881256 8.793522 11.31479 3.573272 0.739809 451,958

East 8.821058 8.752311 11.24271 2.010393 0.717542 38,079

All 8.876579 8.790210 11.31479 2.010393 0.738278 490,037

NARROWTANGIBILITY


West 0.234313 0.177354 1.392733 0.000000 0.207648 451,959

East 0.353513 0.332930 1.484537 0.000000 0.234261 38,078

All 0.243575 0.186735 1.484537 0.000000 0.212249 490,037

NDTSHIELD


West 0.040930 0.030168 2.266872 2.03E-07 0.039617 451,959

East 0.043223 0.032570 4.147329 -0.200882 0.050860 37,304

All 0.041105 0.030358 4.147329 -0.200882 0.040589 489,263

ROA


West 4.908862 3.400 99.858 -99.976 9.643392 451,959

East 8.749457 5.598 99.548 -99.210 1.353914 38,079

All 5.207300 3.512 99.858 -99.976 1.005334 490,038


58

It can be seen from the descriptive statistics for the variable age, that Eastern European

companies are on average younger than the ones in Western Europe. This result has

been confirmed by other studies (Klapper, Sarria-Allende & Sulla 2002). The average

age in the Eastern European sample is around 11.7 years, which suggests that most

companies have been founded in the transition period after the break-down of the Soviet

Union. Even when considering that some of these companies are likely to be spin-offs

of previously state-owned companies, it is apparent that many of these companies

operate under a fairly new corporate structure. For comparison, the average firm in the

Western sample is 20.8 years old.

When interpreting the descriptive statistics of the employed growth measure (growth in

assets), it is argued to be more meaningful to base the interpretation on the median

instead of the mean. This is because the median as a measure of central tendency in

contrast to the mean, is unaffected by extreme values in the data. Even though the data

was corrected for extreme outliers, there is still a skew in the distribution for the growth

measure. The main reason for that is that newly founded companies often do not have a

lot of assets in the year of founding. Therefore the growth in assets, when measured in

percent, can be extremely high in the following year. This result would be similar if

another growth measure such as growth in turnover is used. When considering this, it is

interesting to see that the median growth in assets is higher in Eastern Europe compared

to Western Europe, with growth rates of 15.6% and 6.6% respectively. This finding

could be related to the fact that companies in the East are on average younger than

companies in the West, and therefore more companies might be in an earlier stage of the

company life-cycle, i.e. more companies could be in the growth stage in Eastern

Europe. These results are related to GDP growth, which is basically higher in all

Eastern European countries compared to Western Europe. In a faster growing economy

it is natural to also expect companies to have higher individual growth rates.

The employed proxy for size is LNTURNOVER. It can be seen that the mean size is

rather similar in the two samples. This is at first glance surprising, because one could

for instance expect that the, on average younger firms in Eastern Europe, would be

smaller. The obtained result can to some degree be explained by how the data has been

selected, meaning that a company had to have a turnover between 2 M. and 43 M. to be

selected. Therefore if the distribution of firms regarding size is approximately the same

in East and West within this interval, the obtained results should show up.


59

On average, companies in Eastern Europe have more tangible fixed assets than

companies in Western Europe. The most obvious explanation for this is that the

industry-distribution in the two samples are different, meaning for instance that there

are more companies in the service-industry in the Western sample. Industry effects are

controlled for in this study by using a cross-section fixed effect model, and the issue is

therefore dealt with. The variable NDTSHIELD is slightly higher in the Eastern sample

with a mean value of 4.3% compared to 4.1% in the Western sample, which can be

explained by the, on average, higher level of tangible fixed assets which can be

depreciated. Nevertheless, it has to be kept in mind that the data used to calculate the

proxy stems from different local GAAPs, and therefore the rules regarding depreciations

could be different depending on the country.

The used profitability measure shows that companies in Eastern Europe are on average

more profitable with an average ROA of 8.75%, compared to an average of 4.91% in

the West. This result is also confirmed when looking at the individual country statistics

where almost all Eastern countries have a higher return on assets than their Western

counterparts. In the Western sample, Finland is the only country with a comparably high

ROA of 8.68%, while in the Eastern sample, Czech Republic has a rather low ROA of

only 1.49% on average. Initially, this result was not expected by the authors, but when

viewed in conjunction with the observed GDP growth as well as the Growth in assets, it

is a reasonable result. Per country descriptive statistics of the different firm specific

variables are presented in appendix 9.

7.1.3. Country specific variables

For the country specific variables, it is interesting to show the variables per country, to

give an impression of the dispersion of values, not only between the two samples, but

also within each. This is one of the caveats of this study, that by pooling Eastern and

Western European countries together into two groups, it is implicitly suggested that the

countries within the groups are homogenous. Of course there are very convincing

arguments suggesting important similarities between the Western European countries as

well as between the Eastern European countries, based on for instance the political and

economic development in the past. To acknowledge the differences between the

countries within the sample, the mean values of the country specific variables are

presented separately in appendix 10. When looking at the macro-economic control

variables, it can be observed that there is a trend towards Eastern European countries


60

having the highest GDP growth as well as inflation rate. When looking at the stock-

market capitalization to GDP, there is also a rather clear cut distinction between

Western and Eastern Europe, with most Western European countries having higher

values, indicating a higher development of the financial markets. When looking at bank

concentration, the distinction between East and West is not that clear cut, while on

average the bank concentration is higher in the West. On the other hand, when

analyzing the profitability of banks with the variable net interest margin, it can be seen

that there is a strong tendency for banks in Eastern Europe to be more profitable. When

looking at the variable recovery rate, which is an indicator of the quality and

effectiveness of the bankruptcy law, it can be observed that nearly all Western European

countries have higher values than any of the Eastern countries. The only exception is

Lithuania being at Western standards with a value of 50 percent. The variable contract

enforcement is more ambiguous, meaning that while the mean-value in the East is

slightly higher, there is some variation within the different groups. It can for example be

observed that in the Eastern sample, the Baltic countries, Estonia, Latvia and Lithuania

have low (good) contract enforcement values, while Mediterranean countries like Italy,

Greece, Portugal and to a lower extend also Spain, have high (bad) values of contract-

enforcement. This could suggest that cultural commonalities are playing a role in

explaining contract enforcement, and not only whether a country is located in Eastern or

Western Europe. To some extend, national culture, which might have an impact on the

tendency of a company to acquire external financing, is captured in the fixed effect.

Western European countries have in general higher values in the corruption index (less

corruption) than the Eastern European countries. Again, Italy and Greece depart to some

degree from the general tendency, meaning that Estonia actually has a better value in

the corruption index than Italy. This is also the case in terms of Greece, were Hungary,

Lithuania and Estonia have a better value. Taking into consideration the overall picture,

there is however a clear tendency towards higher values in the West compared to East.

When looking at the variables that proxy for credit information, disclosure, investor

protection and legal rights, only for credit-information the values are on average higher

in the West. For the other three variables there is no clear-cut pattern. Some variation is

present within the groups, for instance Great Britain has extraordinarily high values for

disclosure and investor-protection, which could be due to the fact that Great Britain is a

market oriented economy where the majority of the other countries are bank oriented


61

(Brounen, Jong & Koedijk 2005). While being interesting to see which sign the

coefficients for these variables have and whether they are statistically significant, the

variation within the groups could potentially have a negative impact on the robustness

of these variables in the model. Taking into account the scope of this paper, which is

among other things to show that country specific factors have an impact on leverage and

that this impact is potentially different between Eastern and Western Europe, this

limitation has to be accepted. Due to data availability on these country specific factors, a

more in-depth segmentation of the countries in more sub-groups is not feasible.

7.2. Regression output

In Table 9 the sign and significance of the coefficients from the regression are presented

separately for each sample. In table 10, the sign and significance of the dummy

interactions are illustrated, showing whether the Eastern sample behaves different from

the Western one. The regression output from Eviews is presented in appendix 11. For

reference, also a regression where Eastern and Western Europe are pooled into one

group is presented in appendix 12. The interpretation is based on the model where

Western Europe serves as the base group and the dummy-interactions represent how and

if the Eastern European sample deviates. Based on this, it is possible to see whether the

Eastern sample behaves different for a specific variable compared to the Western

sample. However, no inference about the significance of the coefficients in the Eastern

European sample can be made from this regression. Therefore a regression for the

Eastern European sample only, is presented in Appendix 13. This regression enables

one to determine the significance of the coefficients in the Eastern sample, and this is

illustrated in the lower part of table 9

The Hausman test presented in appendix 14, confirms that the fixed effects model is the

appropriate specification, while the F-test presented in appendix 15 shows that the fixed

effects are jointly significant.

The effect specifications of the four different regressions all show high R-squared

values, ranging from 0.826 to 0.860 for SHORTBANKDEBT and LONGBANKDEBT

respectively. As has been explained in section 4.1, the high R-squared are as expected

due to the inclusion of a dummy variable for each cross-section.

Serial correlation, meaning that the error terms are correlated over time, does not bias

the coefficient estimates, but can potentially bias the standard errors and therefore the


62

associated t-statistics. The presence of serial correlation can be tested using the Durbin-

Watson statistic. A value close to 2.0 indicates that there is no autocorrelation present.

A value considerably smaller would indicate positive autocorrelation, while a value

significantly bigger points toward negative autocorrelation. The actual values of the

Durbin-Watson statistic obtained for the four regressions range from 1.77 for

NARROWLEVERAGE to 1.90 for LONGBANKDEBT. This suggests that there is no

serial correlation.

The issue of potential non constant variance has been addressed by using “White’s

heteroskedasticity robust standard errors”. This issue should therefore not cause any

concern.


63

Table 9 – Regression results Western Sample

Shortermdebt shorttermbankdebt narrowleverage longbankdebt

age +*** +*** +*** +***

bankconcentration -*** -*** -*** -***

contractentforcement +*** -*** -*** -***

corruption - +** -*** -***

creditinf -*** -*** - +***

disclosure -*** + -*** -***

gdpgrowth -*** - -*** -***

growthassets -*** -*** +*** +***

inflation -*** -*** + +***

investorprotection +*** +*** +*** +*

legalrights -*** -*** +*** +***

lnturnover +*** +*** +*** +*

marketcaptogdp +*** +*** +*** +*

narrowtangibility -*** -*** +*** +***

ndtshield -*** -*** -*** -***

netinterstmargin +*** +*** +*** +***

recoveryrate -*** -*** -*** +***

roa -*** -*** -*** -***

Eastern sample

shortermdebt shorttermbankdebt narrowleverage longbankdebt

age - -*** -*** -**

bankconcentration -*** -** -*** -

contractentforcement -*** -*** -*** -***

corruption -*** -*** -*** -**

creditinf -*** -*** -*** -

disclosure -*** -*** -*** -***

gdpgrowth -*** - -*** -***

growthassets +*** + +*** +***

inflation +*** +*** +*** +***

investorprotection +*** +*** +*** +***

legalrights - - - -

lnturnover +* +*** + -**

marketcaptogdp +*** +*** +*** +**

narrowtangibility +*** -** +*** +***

ndtshield -* -*** -** -*


recoveryrate +*** +*** +*** +***

roa -*** -*** -*** -***

* = significant at the 10 % level

** = significant at the 5 % level

***= significant at the 1 % level


64

Table 10 – Dummy interactions shortermdebt shorttermbankdebt narrowleverage longbankdebt

age -*** -*** -*** -***

bankconcentration - - - +

contractentforcement -*** -*** -*** +

corruption -*** -*** -*** -

creditinf + - -** -**

disclosure -*** -*** -*** -***

gdpgrowth -*** - +*** +***

growthassets +*** +*** - -**

inflation +*** +*** + -***

investorprotection +*** +*** +*** +***

legalrights + - -* -***

lnturnover -*** -*** -*** -***

marketcaptogdp +*** +*** +*** +***

narrowtangibility -*** + -*** -***

ndtshield +** +** +*** +***


recoveryrate +*** +*** +*** +**

roa +*** +*** +*** +***

* = significant at the 10 % level

** = significant at the 5 % level

***= significant at the 1 % level

8. Interpretation of results

In the following paragraphs, each hypothesis stated in section 3 will be evaluated based

on the results from the statistical analysis.

8.1. Hypothesis 1 – Leverage in Eastern and Western Europe

It is concluded that there is support for hypothesis 1, namely that leverage is in

general lower in Eastern Europe compared to Western Europe.

The descriptive statistics in table 7 in section 7.1.1 gave an indicator of this, and the

formal statistical test shown in appendix 16, confirms the first hand impression by

showing that there is a significant difference in all four leverage measures between East

and West. This result is confirmed by other studies e.g. (Nivorozhkin 2005) and

(Jõeveer 2005), and is therefore not surprising.


65

One thing that was mentioned when describing the descriptive statistics and should be

kept in mind is that the analysis carried out, only shows the difference in average.

Within each group there is some variation, meaning that one cannot interpret the results

as all countries in Western Europe have higher debt ratios than any country in Eastern

Europe.

8.2. Hypothesis 2 – Corporate governance

The regression results only partly support hypothesis 2, that higher levels of corporate

governance are positively related to leverage.

Two measures of corporate governance have been employed in this study, namely

investor protection and disclosure. Investor protection generally shows the results that

were expected, namely a positive coefficient across all leverage measures in both East

and West. Except for LONGBANKDEBT in the Western sample, all coefficients are

significant at the 1%-level. LONGBANKDEBT in West is only significant at the 10%-

level, however with a p-value of 0.059, and is therefore considered to be significant

with fairly strong certainty. This is suggested to clearly indicate that better investor

protection helps SMEs to get credit, both long-term as well as short-term. This goes

well with the static trade-off theory in the sense that agency costs of debt are argued to

decline when investor protection is good. This all else equal increases the level of debt

in the optimal capital structure from the trade-off perspective. The availability of debt is

improved, because for instance the possibility of banks to hold directors personally

liable are improved when investor-protection is high and thereby reduces the

uncertainty of repayment of a loan.

Looking at the dummy interactions in the regressions indicate that the influence of

investor protection is a great deal stronger in the East for all measures of leverage.

Further it looks like trade credits are affected in a similar positive way, since the

coefficient SHORTTERMDEBT is larger than SHORTBANKDEBT in both East and

West.

The fact that East seems to respond more heavily to investor protection than their

Western counterparts can of course stem from several reasons. One reason could be that

banks in the East simply put more weight on investor protection. This could be


66

especially important in the East, where corruption is on average higher, see appendix

10. When the probability of corruption is higher, the ability to hold directors personally

liable could be an important tool to restrain managers, and avoid bad behavior. This is

purely a supply side explanation in the sense that it is the banks that are limiting the

credit availability in more corrupt areas if investor protection is not sufficiently good.

Therefore it can be concluded that improved investor-protection has a positive impact

on the availability of debt-financing in Eastern as well as Western Europe, while the

impact is even stronger in Eastern Europe.

The expected positive impact of better corporate governance on leverage is more

ambiguous when looking at disclosure. Quite surprisingly, this proxy has a statistically

significant negative impact on all leverage measures except SHORTBANKDEBT in the

West. The variable was chosen because it was expected to be a proxy for a certain

dimension of the corporate governance system. It results thereby suggest that better

corporate governance should be associated with less debt, which there is no obvious

explanation for. A possible explanation could be that this proxy to a higher degree

proxies for the protection of equity investors. This could mean that a high degree of

disclosure makes equity more available and relatively cheaper; hence debt is not used as

widely. This of cause, strongly contradicts the pecking order theory, and might seem a

bit far fetched, especially when considering that it has been shown that new equity

issues only play a minor role in the capital structure decisions of SMEs. Another and

more plausible reason for the peculiar results regarding the sign of the coefficients,

could be that this variable is not a sufficiently good measure of corporate governance,

and picks up other factors which have not been considered in this study.

To sum up, the argument that better corporate governance has a positive impact on

leverage can only partly be supported, while investor protection supports the hypothesis,

disclosure does not. One possible explanation for the somehow ambiguous results could

be that the employed proxies are not refined enough, and also that not every

improvement of corporate governance, actually leads to improved access to finance.


67

8.3. Hypothesis 3 – Legal environment

There is only partly support for hypothesis 3 that a strong legal environment is

positively related to leverage.

The reason for the results only partly supporting the hypothesis is that corruption shows

a negative relation with leverage, which is contrary to what was expected. Again the

reader should keep in mind that a high corruption score means a low degree of

corruption. Contract Enforcement shows the expected sign, and confirms that it is of

importance in determining leverage. Recovery rate also shows the expected sign for all

measures of leverage in the Eastern sample, but only for LONGBANKDEBT in the

Western sample. Finally Legal rights are insignificant in East, while showing negative

and positive signs for short and long-term measures of leverage respectively, in the

west.

Looking more thoroughly at the impact of the proxies on the different leverage

measures, one can see that in the Western sample, recovery rate is negatively related to

SHORTTERMDEBT, SHORTBANKDEBT and NARROWLEVERAGE, while it is

positively related to LONGBANKDEBT. The most negative coefficient is found for

SHORTTERMDEBT, followed by SHORTBANKDEBT. In the Eastern sample,

Recovery rate is positively related to all measures of leverage. All coefficients in the

Western and Eastern samples are significant at the one percent level. The dummy

variable is statistically significant at the one percent level of confidence for all leverage

measures besides LONGBANKDEBT, where it is only significant at the five percent

level of confidence. The coefficients in the Eastern sample are more positive than the

corresponding coefficients in the Western sample, for all measures of leverage.

Recovery rate is a measure for how much creditors can recover in the case of

bankruptcy of the debtor. Hence, it was expected that recovery rate would be positively

related to all measures of leverage, which however is not the case. In the Western

sample, recovery rate is only positively related to LONGBANKDEBT and negatively

related to the rest. The impact and importance of the recovery rate on the propensity of

banks to provide debt-financing, is argued to depend decisively on the probability of

default of the debtor. The probability that a company will default on a short-term loan is


68

much smaller than the probability that it will default on a long-term loan. This is due to

the fact that a bank is much more capable to assess the capability of a company to repay

a short-term loan, because the short-term prospects and cash-flows of a company are

much more certain than their long-term prospects and cash-flows. According to this, it

makes sense that the impact of the recovery rate is highest for long-term debt in the

West. The negative relationship between the recovery rate and the short-term debt

measure can be explained as follows: Assuming that recovery rate does not affect the

amount of short-term debt, but does affect the amount of long-term debt and thereby

also increases total assets, given that long-term debt is not taken on to retire short-term

debt, then the short-term debt ratio decreases. In this line of reasoning, the negative

impact on short-term debt actually stems from the impact of long-term debt on the

denominator (total assets) of the short-term leverage measures. The fact that recovery

rate in the East is positively related to all leverage measures, including the ones for

short-term debt, can be seen as a sign of the business environment being more unstable

and volatile in Eastern Europe, meaning that banks consider the possibility of default of

the borrower even when lending short-term. If this is true, it makes sense that a higher

recovery rate will convince more lenders to provide short-term debt or trade credits.

However it is still puzzling that recovery rate is most strongly related to

SHORTTERMDEBT. A possible explanation for this observation is that the proxy

picks up factors that have not been considered by the authors, and therefore further

research is suggested.

The variable contract enforcement is as expected negatively related to all four measures

of leverage in the Eastern sample. The same results are observed for all the leverage

measures but SHORTTERMDEBT in the Western sample. All coefficients are

significant at the one percent confidence level. When comparing the results of the

Eastern sample with the ones from the Western sample, it can be seen that contract

enforcement has an economically higher negative impact on leverage in the Eastern

sample. This is true for all measures of leverage besides LONGTERMBANKDEBT

where no statistically significant difference between the two samples can be identified.

When looking at the Western sample, it can be seen that contract enforcement has the

biggest negative impact on NARROWLEVERAGE, followed by LONGBANKDEBT

and SHORTBANKDEBT, while the smallest economical impact can be observed for


69

SHORTTERMDEBT. Looking from the supply-side of bank-credit, it makes sense that

long-term bank debt is more strongly affected by the ease of enforcing a contract than

short-term bank debt. As previously argued, it is easier for a bank to assess the

probability of repayment of a short-term loan. Therefore the importance of the ease to

enforce a claim by legal action increases for a long-term loan because it is more likely

that it becomes necessary to do so, compared to a short-term loan. The supply of long-

term loans should therefore be more negatively affected by long contract enforcement

periods. This line of reasoning is also supported by the fact that SHORTTERMDEBT is

not as strongly affected by long contract enforcement periods as SHORTBANKDEBT

is. The apparent reason here is that the supply of trade-credits is not that much affected

by the length of contract-enforcement, because the probability that it becomes necessary

to enforce the trade-credit by legal action is argued to be smaller, compared to both

short and long-term debt, due to the usually even shorter maturity of trade-credit. The

coefficient on SHORTTERMDEBT suggests that trade-credits are actually positively

related to the length of contract-enforcement. This indicates that companies make more

use of trade-credits when it becomes harder to acquire long and short-term financing.

When comparing the impact of contract enforcement on the different leverage-measure

in Eastern Europe, the picture becomes different. Economically speaking, contract

enforcement has the biggest negative impact on SHORTTERMDEBT, which indicates

that the access to trade-credit as well as short-term debt becomes relatively more

difficult when the time spent to enforce a contract increases. A potential reason for the

fact that contract enforcement is negatively related to trade-credits could be that

repayment behavior concerning trade credits is worse in Eastern Europe. For instance

(Tom 2005) states that the payment behavior of companies in Czech Republic and also

to a lower extend Poland, leaves a lot to be desired. If that is a general pattern, the

probability to have to enforce a claim by legal action is increased, and then it seems

intuitive that a longer processing time of these claims, worsens the access to trade-

credit. The core of this argument is that trade-credit in Eastern Europe is more risky

than it is in Western Europe and is therefore affected by contract enforcement in a

different manner. Contract enforcement also affects SHORTBANKDEBT more

economically significant in Eastern Europe than it does in Western Europe. A potential

explanation could be that default risk on short-term loans is all else equal higher in

Eastern Europe, which is reflected in a higher negative coefficient. To sum up the


70

results, it can be noted that in both regions, the ease to enforce a contract is related to

the availability of credit. The explanation for the negative impact of a bad contract

enforcement environment on leverage can only be explained when looking at the

supply-side of debt. Banks take into consideration the ease of recovering their loans

juridical when handing out loans. This seems to be even more important in Eastern

Europe compared to Western Europe.

The proxy for corruption is based on an index from transparency international, where a

higher index value represents less corruption in the country. Therefore a positive

coefficient would mean that all else equal, the less corrupt a country, is the more

leverage a firm in that country is expected to have. In the Western sample the proxy is

statistically insignificant for SHORTERMDEBT and positively related to

SHORTBANKDEBT but only at the five percent significance level. On the other hand

it is negatively related to NARROWLEVERGE and LONGTERMDEBT, both at the

one percent significance level. In the Eastern sample, all leverage measures are

negatively related to corruption at the one percent significance level. In addition the

coefficients of the Eastern sample are all more negative than the corresponding

coefficients in the Western sample. The predominantly negative sign, leads to the

interpretation that the less corrupt a country is the less debt the companies in that

country use. This seems to be counterintuitive at first sight. An explanation for the

result could be that debt is used as a disciplining tool. The corruption index employed

might proxy for the overall propensity of managers to behave unethical. A low value in

the corruption index is also a statement of a countries attitude towards good governance

and what business practices are commonly accepted. If the owner of a company can

expect that the managers of a company do not behave in the best interest of the

company, by for instance making excessive use of perks, it might be reasonable to take

on debt as a disciplining measure. The use of debt as a device to discipline managers

has been stated by Jensen in the so-called “free cash flow hypothesis”. There it is

pointed out that debt has the capability to reduce the agency costs associated with free

cash flows, and can serve to streamline the organization (Jensen 1986). One problem

with this line of reasoning is that many SMEs are managed by the owner, which should

make debt as a restraining measure unnecessary. The detected negative coefficient is


71

therefore to some extend puzzling, and the explanation given has to be taken with some

critical skepticism.

The last proxy for the legal environment is Legal Rights. In the Western sample,

LONGBANKDEBT as well as NARROWLEVERAGE shows the expected positive

relation, being significant at the 1%-level. The coefficients are also economically

significant, the values of the index taken into consideration. When turning to the short-

term leverage measures, the picture is different. Here the relationship is negative, also

significant at the 1%-level, however with less economically significant coefficients. In

the East, nothing shows up significant in any of the regressions. This basically suggests

three different relationships depending on leverage measure and region.

One explanation for legal rights to be without importance for leverage in the Eastern

sample could be that it is a bad proxy for the security of lenders in this region, when

employed as a stand-alone measure. As previously argued, this could be due to the

benefits of good legal rights being very much dependent on the enforcement of these

rights. This is what was also described in section 6.2.2.12, and found by (Safavivan,

Sharma 2007). It makes sense that strong legal rights are not worth much if not

enforced. It should be noted that the index is based on whether certain criteria’s are

defined in the law. It is the impression of the authors that the interpretation of laws and

court practice is very important in determining the expected outcome of say a financial

dispute. Therefore the formal definition of some specific rights, may not tell everything

about the actual rights in a country. The insignificant coefficients in the Eastern sample

could be a result of court practice being relatively more important in this region or

enforcement of the law being weaker.

It is challenging to try to explain the sign-change regarding the coefficients on short vs.

long-term debt in the Western sample. Intuitively it seems wrong that better legal rights

should mean that companies have higher difficulties in getting credit, as the coefficients

on SHORTBANKDEBT and SHORTTERMDEBT might suggest when looking at them

isolated. When looking more closely at the two regressions, it can be seen that

SHORTTERMDEBT has a less negative coefficient compared to SHORTBANKDEBT,

meaning that trade credits must pull in the opposite direction i.e. be positively related to

legal rights. Therefore it seems like it is only SHORTBANKDEBT that is negatively

related to legal rights. One interpretation for this is that SHORTBANKDEBT is used as


72

a last resort when it is difficult to obtain long-term financing, triggered by weak legal

rights. E.g. it is expected that a company will be more prone to use its overdraft facility

in situations where long-term financing is not possible. This is due to the usually higher

price of an overdraft facility compared to collateral based long-term loans. Since an

overdraft facility is considered a short-term loan, it is included in the

SHORTTERMBANK measure of leverage. If good legal rights ease the availability of

the relatively cheaper long-term financing, it is argued to lower the incentive for using

overdraft facilities and other short-term credit facilities which could be why the negative

coefficient comes up significant.

8.4. Hypothesis 4 – Financial development

Hypothesis 4 that financial development is positively related to firm leverage is to a

large extend supported by the regression results.

The measures used to proxy for financial development are Marketcap to GDP and credit

information. Of these two, Marketcap to GDP is argued to be the most important

measure of financial development in this study. Positive coefficients for Marketcap to

GDP in all regressions across both regions are in accordance with the expected

hypothesis, that a well developed financial market should increase the availability of

credit all else equal. The economical significance turns out to be greater in Eastern

Europe for all leverage measures, and it should also be noted that in the

LONGBANKDEBT regression in West, the statistical significance is somewhat low

with a p-value of 0.086.

An explanation for a positive sign has been presented by (Demirguc-Kunt, Maksimovic

1995), suggesting that a developed stock market helps to convey information from

better informed investors to the market. Thereby it helps to solve the asymmetric

information problem. This explanation does not fit into the context of this research,

since none of the companies in the sample are listed on a stock exchange. Therefore the

answer has to be found somewhere else. In a cross country study (Demirguc-Kunt,

Levine 1995) finds that the stock market development is highly correlated to the

development of the banking market. Assuming this is correct, it makes sense that a

more developed banking market eases the availability of credit for companies. This

could be brought about by developed banking markets being characterized by more


73

liquidity, better information flow, better legal environment etc. The general positive

sign is considered intuitive and is not very surprising. A more interesting question arises

from the fact that short-term financing in general, and the companies in the East, turns

out to be more affected. The fact that companies in the East are more affected could

stem from a possible diminishing return from stock market development. That fits with

the results presented in appendix 10 showing that the mean value of market cap to GDP

is lower in East. This is interpreted in the direction that the marginal benefit from an

increase in the financial market development is more beneficial in areas where it is less

developed. Changing relationships depending on the initial development of the stock

market was also found by (Demirguc-Kunt, Maksimovic 1995). In that study the change

is as dramatic as a sign-flip suggesting that in developed markets, further development

decrease the use of equity. The reason that the change is not as dramatic in this study, is

maybe because the companies here do not have the same opportunity for getting

financing through the stock market and thereby substitute this for debt.

Regarding Credit information, the result for LONGBANKDEBT in the Western sample

is statistically but only borderline economically significant, based on the coefficient

being 0.0035 and the highest possible index value being 6. The positive coefficient is as

expected based on the reasoning that a better developed credit information system

should all else equal help creditors in assessing the risk of potential lenders, and thereby

easing SMEs access to credit. By adding the dummy interaction coefficient to the

Western coefficient, it looks like the Eastern sample behaves in the opposite direction

i.e. a negative relation on LONGBANKDEBT. But this can not be confirmed when

turning to the regression performed strictly on the Eastern sample, since the relationship

is not significant (See appendix 13). This result is to some extend surprising, but might

be explained by Eastern European SMEs being additional informational opaque. This

could for instance have the implication that banks do not rely solely on information

from credit registries even if available, but consider it necessary to carry out a thorough

due diligence on the lender, before a loan will be approved. Also the fact that the

companies in East are on average younger, and hence have a shorter and for some

companies insufficient credit history, supports this line of reasoning.

Looking at the other leverage measures, there is a clear trend towards a negative relation

where only the coefficient for NARROWLEVERAGE in the Western sample is

insignificant. This is a result of the two offsetting effects from, the positive coefficient


74

on LONGBANKDEBT on the one side, and the negative effect from

SHORTBANKDEBT on the other side. This could suggest a substitution effect of

LONGBANKDEBT for SHORTBANKDEBT. The rationale behind such a substitution

effect based on credit information is nevertheless unclear. From the point of view of the

company, long-term debt is usually more expensive than short-term debt illustrated by

the usually upward sloping yield curve6. The obvious advantage of long-term debt is

that it exposes the company to less refinancing risk. Refinancing risk is however not

considered to be that big of an issue, since it is expected that larger investments are

financed with maturity matched debt. Moreover, larger investments are to a large extend

characterized by being tangible, which is already controlled for in the analysis. Such a

substitution effect meaning that companies take on long-term debt to pay of short-term

debt, stemming from an increase in creditor information, is therefore not expected to be

the case. The SHORTTERMDEBT and SHORTBANKDEBT in both East and West

shows negative coefficients, the former being a lot more economically significant,

suggesting that trade credits are considerably negative related to the development of the

credit information system. This contradicts the expectations and is generally found hard

to explain. It is difficult to think of anything brought about by the presence of credit

registries, that could reduce credit availability or SMEs demand for debt, except

potentially transaction costs. If SMEs in order to get a loan from a bank in the presence

of credit registries have to submit certain information, financial as well as non-financial,

it will add transaction costs to the overall price of the loan making it less attractive.

Here it is reasonable that the effect of these transaction costs is relatively higher on

short-term financing, because these loans are potentially smaller than long-term loans,

while the costs associated with transmitting financial statements to credit-registries is

the same. While this is seen from the demand-side, a possible supply side explanation

also exists. If banks in order to be competitive, perceive it necessary to purchase

information from credit registries if available, and pass on these additional costs to the

clients, it is also going to have an impact on the price of credit and/or the availability

hereof. Summing up, the outcome of the variable was only as expected in the case of

LONGBANKDEBT in the Western sample. For the other cases, possible explanations

have here been argued, but yet they should not be considered definite answers.

6 See e.g. http://www.bloomberg.com/markets/rates/


75

Summing up, the hypothesis that financial development is positively correlated to firm

leverage can be supported based on the results from the market-cap to GDP variable.

This measure is in the view of the authors also the most reliable measure for financial

development compared to credit information. The reason is that it is not based on an

index which only covers a narrow area of financial development, but on country specific

data that picks up a broader latitude of financial development. Also when looking at

credit information, the expected result is obtained for LONGBANKDEBT in the West.

It is expected that financial development should be most influential on long-term debt.

Nevertheless it has to be mentioned that the results for the effect of credit information

on the short-term measures of leverage are not clear-cut.

8.5. Hypothesis 5 – Bank concentration

Hypothesis 5 expecting that bank concentration is negatively related to leverage, is

accepted.

This is based on the regression results showing negative coefficients in both regions

across all leverage measures. Only the coefficient on LONGBANKDEBT in the Eastern

sample is insignificant, otherwise the rest is significant at the 1% level except

SHORTTERMDEBT in Eastern Europe, which is significant at the 5% level. These

results are as expected, and provide evidence in favor of the structure-performance

hypothesis, stating that banks are taking advantage of market power by demanding high

prices or by limiting credit availability.

At first sight it might seem surprising that the coefficient for LONGBANKDEBT in the

East is insignificant. The dummy interaction in the pooled regression is not significant,

meaning that it does not prove that the coefficient in the Eastern sample is significantly

different from the one in the Western sample. This would suggest that the coefficients

are fairly equal in the two samples, and it is therefore a good example of the importance

of testing the robustness of the results by running a separate regression on the Eastern

sample. The answer for why the coefficient can neither be tested significantly different

from 0 in the regression only on the Eastern sample, nor significantly different from the

negative coefficient in the Western sample, should be found in the large standard errors.

(Klapper, Sarria-Allende & Sulla 2002) argues that the current financial development

and market structure in Eastern Europe is of a unique nature. This could potentially help


76

explain why bank concentration is not significant for LONGBANKDEBT in the East.

The idea is that if the financial infrastructure is still under development, banks in this

region are maybe not to the same extend, able to exploit their market position as

expected by the market power hypothesis. Instead they could be focused on adapting to

the changes in the competitive environment, where foreign banks are expanding their

presence (Clarke et al. 2003). However when looking at the three other measures of

leverage, there is no significant evidence that the relationship is either stronger or

weaker in the Eastern sample i.e. the dummy interaction is insignificant. Bearing in

mind that different measures of bank concentration can lead to contradicting results, and

the difficulties in explaining the insignificant coefficient in the Eastern sample

regarding LONGBANKDEBT, the aggregate results is interpreted in the way of

supporting the structure-performance hypothesis.

8.6. Hypothesis 6 – Bank profitability

Hypothesis 6 that bank profitability is positively correlated to firm leverage is

supported by the regression results.

Net interest margin is as expected positively related to all measures of leverage in both

regions, being significant at the one percent confidence level. Looking at the dummy

representing the Eastern sample, it can be seen that it is always positive and also

significant at the one percent significance level, meaning that net interest margin has a

bigger positive impact on all leverage measures in the Eastern sample.

There are several possible reasons why banking profitability is positively related to

leverage and also to the availability of debt. One reason is that banks that are very

profitable are able to retain more earnings and can thereby improve their equity base.

The profitability and the equity base of a bank, directly influence the rating of the bank.

This rating in turns influences the cost of refinancing for the bank. A bank with a better

rating can refinance itself cheaper and thereby also offer loans at a cheaper rate. This

will increase the demand for bank-credit by SMEs. A related issue is that profitable

banks with a therefore good equity base, can lend out more money. National legislators

limit the amount a bank is allowed to lend to its customers based on the equity base of a

bank. This means that banks are allowed to lend out a multiple of their capital base as


77

determined by national regulators. According to the Basel I agreement, which set out

the rules for capital requirement in the EU and the US up to January 1st 2007, banks

which operate internationally are required to hold capital equal to 8 percent of their risk-

weighted assets. It should be noted that Basel I became internationally accepted in the

1990s and has from then on been the standard in roughly one hundred countries. The

risk weights under Basel I was assigned in the following way:

Table 11 – Risk weights under Basel I

Risk weight in % 0 20 50 100

Debtor-category State Bank Mortgages Companies/private customers

This means that a bank with a large equity base which has been acquired for example by

retained earnings can lend more and also take on more risk by lending relatively more to

companies. It has to be noted that one of the main criticisms of Basel I was that it did

not incorporate the creditworthiness of companies into the formula for calculating the

minimum capital requirements, which meant that the same amount of underlying bank-

equity is needed for lending to a company with a high rating as is for lending to a

company with a low rating. This is one of many reasons why new risk-adjusted capital

requirements were introduced within an agreement called Basel II, which became

effective in the EU through the EU-directive 2006/48/EG and 2006/49/EG on the

January 1st 2007 (European Parliament 2006). Under the Basel II regulations there are

now different risk-weights for loans to companies based on their credit rating and hence

the required underlying bank-equity differs depending on this credit rating. This makes

credit more expensive to companies with a low credit-rating in order for the bank to get

the same return on equity, which is in this sense a scarce resource. This study uses data

from the years 2001 to 2006. This means that at this time Basel II was not in effect yet.

This could be an explanation for why bank profitability has a higher impact on leverage

in Eastern Europe than it has in Western Europe, when the expectation is made that

companies in Eastern Europe are on average less creditworthy than firms in Western

Europe. This is due to the fact that there is an incentive for banks to lend to less

creditworthy companies under the regulations of Basel I, because the interest-rates that

can be charged are higher while the necessary underlying equity is the same as for loans

to a company with a better credit rating. So when taking into account that risk is related


78

to return, meaning that returns are higher when a bank takes on more risk, it makes

sense that net interest margin has a higher impact on the Eastern sample. An example

clarifying this proposition is presented in Appendix 17. This situation will of course

only prevail as long as taking more risk is profitable for banks. Under Basel II, lending

to companies with a bad credit rating will become more expensive for banks because

these loans have to be backed up with more equity then. Therefore the cost of capital of

lending to companies with a good credit rating is comparably lower. It can therefore be

expected that companies with a bad credit rating will face higher costs for acquiring

debt financing, because the higher costs incurred by the banks will be passed on to the

companies. This expectation would make it very interesting to analyze the impact of

Basel II on SME financing, especially in areas where many companies can expect a

poor credit rating. It would also be interesting to perform a study on the difference

between credit rating in Eastern and Western Europe and to investigate the impact of

bank profitability on leverage under Basel I and how this relationship might change

under Basel II.

8.7. Hypothesis 7 – Eastern and Western Europe respond differently

Hypothesis 7 that companies in the East and West respond differently to country

specific as well as firm specific variables either in strength or sign is supported.

One result of this study, apart from showing that country specific factors do have an

impact on leverage, is that this impact is different when comparing Eastern to Western

Europe for the country specific variables but also for the firm specific variables. This is

regarded as support for hypothesis 7.

When looking at the dummy variables distinguishing the Eastern from the Western

sample, it can be seen that majority is statistically significant. There is only one variable

where the dummy is insignificant for all four leverage measures. This variable is

banking-concentration, indicating that the impact of this variable is not statistically

different between Eastern and Western Europe. Other insignificant dummy variables

can for example be observed for the effect of credit information and legal rights on the

two short-term debt measures. Nevertheless the overall tendency for most variables is

that there are significant differences either in strength or sign.


79

Subsequently a discussion of the different variables that have not previously been

discussed will take place, where emphasis will be put on the possible reasons for

differences between the Eastern and the Western sample where applicable.

Firm specific variables

Size

In the Western sample, size behaves as expected and supports by the static trade-off

theory i.e. a positive relation with all leverage measures. This observation fits well with

the general perception that size proxies for the inverse probability of default. Further, as

argued by (Ang, Chua & McConnell 1982), bankruptcy costs are considered to

constitute a relatively larger part of firm value for small firms. In East the coefficient is

insignificant for NARROWLEVERAGE and significantly negative regarding

LONGBANKDEBT, while at the same time being positive for the short-term measures.

Looking at the Western sample first, comparing the regressions show a clear tendency

for the short-term measures of leverage being more affected than LONGBANKDEBT,

in the sense that the size of the coefficients are larger. As previously mentioned, size is

normally argued to proxy for the inverse probability of default. According to this, the

observed difference in impact makes good sense. Bankruptcy costs are not only

determined by the probability of a company going into bankruptcy, but also the costs

that will be incurred during a bankruptcy proceeding. Long-term debt is to a larger

degree expected to be secured by collateral, compared to short-term debt. The implied

cost of bankruptcy for creditors holding collateralized long-term claims is smaller, since

the value of the loan will be backed by an asset, which the creditor have a senior claim

on. Since the value of a collateralized loan should then be less volatile based on

fluctuations in the probability of default, it is argued to be the reason why long-term

debt ratios are not affected to the same degree as short-term debt ratios. It should be

noted that the relation in the LONGBANKDEBT regression is only significant at the

10%-level. But looking at the p-value (0.0555), it shows that it is just on the borderline

of being significant at 5%. The evidence is therefore considered fairly strong.

In all regressions the dummy interactions are significant, showing that the East is

behaving different either in terms of the size of the coefficient or more extremely even

with a sign-change. A sign-change is what is found in the LONGBANKDEBT

regression for the East. The negative coefficient in the East is economically significant


80

and statistically significant at the 5%-level. A reverse relationship between regions

regarding size has been seen before e.g. (Rajan, Zingales 1995). They generally find a

positively relation between size and leverage, but surprisingly shows a negative relation

in Germany. They are unfortunately not able to explain this behavior, but suggest

further research. The fact that companies in East seems to have less long-term bank debt

as they grow older, can be seen from both the demand side and the supply side. Seen

from the demand side, the less levered older firms could be a result of their own

preference, while the supply side point of view would suggest that the credit institutions

are responsible for the lack of debt financing. It is not clear why larger firms would

have a preference for smaller debt ratios, but the observed result can stem from larger

firms not being as dependent on debt financing. Larger companies may tend to be more

mature companies with a cash-generating product portfolio i.e. stable cash flows

without the same need to invest heavily in e.g. research and development. By having a

smaller financing need in general, the amount of retained earnings will therefore likely

constitute a relatively larger part of the total financing, thereby bringing down the debt

ratio. This stronger reliance on internal finance points in the direction of the pecking

order theory. However the authors believe that at least to some extend, the answer

should be found on the supply side which will be explained next.

Looking at it from the supply side, the underlying assumption is that companies from

the East have an inferior access to debt-financing compared to the companies in the

West. When companies cannot get sufficient external financing and therefore finance

their operations with retained earnings, it will most likely harm their growth. However

some very successful companies will manage to cope with the financial limitations and

grow larger without using external financing. This could also be a reason among others

why the Western sample is relatively bigger than the Eastern sample i.e. that in the East

lack of availability of debt hinders many companies to reach the threshold for selection

applied in this study. An indicator here fore has been presented by (Klapper, Sarria-

Allende & Sulla 2002) who has identified a very large number of very small companies

in for instance Romania. To some degree this proposition can also be supported by the

fact that on average, Eastern European companies in our sample are almost twice as

profitable as their Western European counterparts. This argument could bring about the

negative relationship between size and leverage. The negative relationship is therefore

suggested to not be brought about by companies paying of debt when they grow older.


81

This would also contradict the findings in the West and the vast majority of empirical

research. Instead the unique factors constituting the capital markets in Eastern Europe

potentially restrain the growth of certain companies, resulting in the majority of those

who grow big, to be less levered.

Profitability

As expected, profitability shows a significant negative relationship with all leverage

measures, which is supporting the pecking order theory where more profitable firms use

less debt, because they have more internal funds available. Since this study is only

dealing with SMEs, there is a chance that the manager is also the owner. Considering

this, the question arises about what behavioral pattern is responsible for this negative

relationship. Is the preference for internal funds against external, caused by asymmetric

information, or is it instead rooted in managers wish for retaining control. It is the belief

of the authors that the effect of the latter is definitely not negligible and could very well

count for the majority of the combined effect when talking about SMEs. Looking at the

mathematically construction of the leverage measures, it is clear that even if managers

do not take any actions at all, leverage will decrease as profitable companies are

retaining their earnings.

EF�FG�HF I� JFK -F�L�GF M%� �N OLLF L P

As illustrated above, when a company retains earnings it will increase the asset side of

the balance sheet e.g. by increasing the cash balance, marketable securities etc.

Assuming the company does not use the cash to pay off debt, the passive side of the

balance sheet will only be affected by increasing shareholders funds. In terms of the

leverage measures, the nominator (amount of debt) will stay unchanged, while the

denominator (total assets) will increase, resulting in the leverage measure to decline.

This illustrates that without the management taking any actions regarding debt, the

leverage can decline as a result of increased total assets.

When looking more closely at the regressions, it can be seen that the coefficients are

less economically significant in the LONGBANKDEBT regressions in both East and

West compared to the short-term measures. This suggests that profitability has less to

say in determining long-term debt levels. One explanation for this could be that short-

term debt serves as a buffer and will absorb here and now changes in profitability.


82

Long-term debt levels are expected to be more rigid relative to short-term, in the sense

that a firm is not expected to make extraordinary payments on long-term loans, unless it

is perfectly sure that it has enough liquidity for the near future. At the same time,

obtaining long-term financing is costly and will therefore only be taken after a thorough

financial planning process or in connection with a large investment. The point here is

that short-term debt will be used to accommodate the daily liquidity need through e.g.

an overdraft facility because it is more flexible. Cash inflows are likewise expected to

reduce the balance on the overdraft facility as they are incurred, and therefore bringing

down short-term debt. The profitability measure employed here is a somewhat short-

term measure since it goes back only one year. The explanation for the larger impact on

the short-term debt levels is suggested to stem from this type of debt being used as a

liquidity buffer, and is thereby highly dependent on the current profitability of the

company.

Comparing the coefficients between the two regions shows that they are generally less

negative in the East, meaning that these companies respond less to profitability. This

could indicate a finance gap, since profitable companies do not to the same extend as in

the west, substitute debt with internal funds.

Tangibility

In both samples Narrow tangibility is negatively related to SHORTTERMDEBT and

SHORTBANKDEBT, while being positively related to NARROWLEVERAGE and

LONGBANKDEBT. All coefficients are significant at the one percent level of

confidence. When looking at the dummy it can be seen that it is statistically significant

at the one percent level for all four measures of leverage. When comparing the size of

the coefficients of the Eastern Sample with the Western sample, it can be noticed that

the coefficient is higher in the East for SHORTBANKDEBT, while it is higher in the

West for the other three leverage-measures.

The positive impact of narrow tangibility on the long-term leverage measures is as

expected. The reason is that tangible fixed assets can serve as collateral, and therefore

reduce the agency costs of debt (Rajan, Zingales 1995). One reason for the negative

impact of narrow tangibility on the short-term leverage measures is that collateral does

not have the same significance to banks when lending short-term, because they can

more accurately estimate the probability of default. Nevertheless this is not the only


83

reason for the negative impact. To fully explain the impact of narrow tangibility on

short-term leverage, one has to look at the way companies finance their investments.

Narrow tangibility is determined as tangible fixed assets over total assets. Tangible

fixed assets contain for instance buildings or machinery. These kinds of capital

investments are expected to usually be financed long-term to achieve maturity-

matching. So when companies make capital investments, they will try to acquire long-

term financing and not short-term financing. Therefore it can be expected for capital

investments, that while total assets increase, short-term debt stays constant, and hence a

negative relationship will be observed (Heyman, Deloof & Ooghe 2003). The more

positive coefficients in the Western sample for the long-term debt measures can

possibly be attributed to the fact that the financial development and also the

enforceability of contracts is in general better in Western Europe. Therefore the relative

value of collateral is improved meaning that in case of default of the debtor, the bank

can easier take hold of the collateral.

Growth

The impact of growth in assets on the different leverage measures is different for the

Eastern and the Western sample. In the Western sample growth in assets is negatively

related to SHORTTERMDEBT and SHORTBANKDEBT, while the former has the

more negative coefficient, implying that not only short-term bank debt, but also trade

credits are negatively influenced by growth. LONGBANKDEBT and

NARROWLEVERAGE are positively related to growth assets. It is clear that the

coefficient on LONGBANKDEBT is bigger than that on NARROWLEVERAGE

because of the negative impact of growth in assets on short-term bank debt, which is

included in the narrow leverage measure. All coefficients for the Western sample are

significant at the one percent significance level. When looking at the dummy variable it

can be seen that in the Eastern sample the impact of growth in assets on the employed

leverage measures is different for all leverages measures, besides

NARROWLEVERAGE. Contrary to the relationship in the Western sample,

SHORTTERMDEBT is positively related to growth in assets in the Eastern sample and

significant at the one percent level. The coefficient for SHORTBANKDEBT has a

positive sign, but is statistically insignificant indicating that the effect observed for

SHORTTERMDEBT stems from trade-credits. The positive sign examined in the


84

regressions on NARROWLEVERAGE and LONGBANKDEBT is in line with the

results of the Western sample.

The obtained result for the Western sample is not in line with expectations based on

agency theory, because the sign of growth in assets in the regressions on the two short-

term leverage measures are negative. Myers suggested that the problem of asset

substitution can be resolved with the use of short-term debt to finance growth (Myers

1977), and hence a positive relationship between short-term debt and growth is

expected. To shed light on this effect, the regression result for tangibility, has to be

taken into consideration. As shown above, Tangibility is negatively related to short-term

leverage and positively related to NARROWLEVERAGE and long-term leverage. This

implies that additions to tangible fixed assets are all else equal, financed with long-term

debt. This suggests that companies try to match the maturity of debt to the maturity of

their assets. This result has previously been shown in a study on Belgian SMEs, also

using data from Bureau van Dijk. The same study also proposes that firms with a better

credit-score tend to borrow more long-term, while firms that demonstrate a poor credit

quality borrow short-term (Heyman, Deloof & Ooghe 2003). Maturity matching

provides an explanation for the negative relationship between growth in assets and

short-term debt, as well as for the positive relationship between growth in assets and

long-term debt, i.e. capital investments are financed long-term rather than short-term. If

long-term debt is used to finance capital investments necessary for growth, then total

assets will increase and the short-term debt ration will decrease. Myers’ theory

regarding asset substitution and the connected problem of moral hazard can therefore

not be supported for the Western sample. On the contrary in the Eastern sample, asset

growth is positively related to all measures of leverage but insignificant for

SHORTBANKDEBT. The strongest positive relation in the Eastern sample can be

observed for SHORTTERMDEBT, which would imply that growth is largely financed

with the help of trade-credits. Furthermore the coefficient for LONGBANKDEBT is

higher for the Western sample than it is for the Eastern sample. The proposition is

therefore that companies in the East rely on long-term debt supplemented by trade-

credit to finance growth. In the view of the authors, the reason why trade-credits are

also used to finance growth in Eastern Europe can be attributed to inferior access to

bank-debt compared to Western Europe. This insufficient access to external finance

compared to Western Europe is, as also discussed in section 2.5, supported by a survey


85

performed by the European Union as well as by the finding that debt-levels are on

average lower in Eastern Europe compared to Western Europe. It has been suggested

that the access to finance is related to the success of macroeconomic and institutional

reforms, as well as to the development of the capital market in the country (EOS Gallup

Europe 2006) and (Nivorozhkin 2005). The obtained results suggest that companies in

the East as well as in the West, finance growth in assets to some degree with long-term

debt in an effort to match the maturities of debt and assets. This goal is achieved to a

higher degree in the West than in the East, which is believed to be due to financial

constraints in Eastern Europe. No solid evidence supporting Myers’ theory of assets

substitution was found.

Age

Age was expected to be negatively correlated to leverage, based on the belief that older

companies are able to finance a larger part of their investments with funds generated

internally, which is in line with the pecking order theory.

Looking at the model output shows that the Western sample contradicts the expectations

by showing positive coefficients at the 1% level for all 4 leverage measures. The

dummy interaction is significant in all regressions and indicates that the companies in

the Eastern sample respond exactly the other way around i.e. with a negative relation

between age and leverage. This is confirmed when looking at the regressions performed

on only the Eastern sample, except in case of SHORTTERMDEBT, where the variable

is statistically insignificant. Even though it in the expectations was mentioned that the

Eastern sample could behave differently, it is surprising to generally see such a large

difference as a sign shift.

The positive coefficient for West can be interpreted in favor of the static trade-off

theory, while it is hard to explain from the point of view of pecking order theory. In the

framework of static trade-off theory, companies that can show a long credit history

could be associated with less uncertainty since the lender is able to observe how the

company has handled its debt from a historical perspective. Furthermore, older

companies are normally associated with more stable cash flows and are therefore less

risky, which all else equal is argued to ease their access to credit. According to pecking

order theory, older companies have had the opportunity to plow back more retained


86

earnings, and therefore should have more internal funds to finance their investments

with, which all else equal is expected to lead to lesser use of debt financing.

One argument why age could be positively related to leverage stems from research on

relationship-lending as also referred to in section 2.7.1.1. It has for instance been shown

that companies that have long-standing relationships with their bank get credit at more

favorable rates. This suggests that older companies that have grown relationships with

their banks for several years have better and also more attractive access to debt-

financing (Santiago Carbó-Valverde, Francisco Rodriquez-Fernandez & Udell 2006). In

a further study, (Booth et al. 2001) states that collateral requirements as well as interest

rates become smaller, the longer the relationship between the company and the bank has

lasted, which is further evidence, pointing towards a positive relation. Moral hazard

problems are also significantly reduced if a company uses the same bank for all their

financial transactions, because the bank can then monitor the companies’ capability to

repay its loans through cash-flow movements on the accounts the company has with the

bank. These arguments could lead to a situation where older firms have more debt than

younger firms, due to better relationships with their bank, which is suggested to result in

better access to debt. Furthermore the positive relationship also fits with the positive

relationship obtained for the size proxy, assuming that both variables are inverse proxies

of default risk, i.e. the availability of debt is higher because the probability of default is

lower.

Regarding the sign shift for the Eastern sample, (Klapper, Sarria-Allende & Sulla 2002)

find a similar negative relation on their sample of Eastern European companies. A

potential explanation for this can be the existence of a similar phenomenon as the one

previously explained in (Pfaffermayr, Stöckl 2008), namely the U-shaped relation

between age and leverage. As can be seen in the descriptive statistics, the companies in

the Eastern sample are in general younger than the companies in the West. This could

suggest a similar U-shape, meaning that in the early years, age is negatively associated

with leverage but the relationship then changes to be positive in the later years. While

this could be due to age only serving as an inverse proxy of default after a critical age

has been reached, it is not possible to say when this sign shift occurs.


87

Non debt tax-shield

The results regarding non debt tax-shield generally supports the expectations i.e. a

negative relation with leverage. However the significance levels are different in the two

samples. More precise, the coefficients in the SHORTTERMDEBT and

LONGBANKDEBT regressions are only significant at the 10%-level in the East while

being significant at the 1% level in the West. The negative coefficients across all

leverage measures in both regions provide evidence supporting the trade-off theory. It

means that companies with high tax shields stemming from depreciations are less likely

to utilize debt for tax purposes. Even though it is in line with the trade-off theory, it

does not mean that it contradicts the competing theory of pecking order. Imagine a

company with a given stream of cash flows. If the company for some reason gets an

increase in the non debt tax-shield, the earnings after tax, and thereby all else equal

retained earnings, will increase. This would reduce the need for external financing i.e.

debt, and should therefore result in a smaller debt ratio.

A reason for the LONGBANKDEBT to be less significant in East could stem from the

way the measure is constructed. In this study the non debt tax-shield only takes

depreciation into consideration. (DeAngelo, Masulis 1980) shows that besides

depreciations, factors that can substitute the role of debt for tax purposes can be

research and development costs, investment deductions etc. Without having researched

on the exact tax code of the different countries, it is not possible to argue whether the

ones of Eastern Europe in general gives larger tax advantages in terms of other things

but debt. This could potentially explain why the coefficients are less significant both

economically and statistically in the East. This question is however left for further

research.

Macroeconomic control variables

GDP growth

GDP growth has a negative coefficient for all measures of leverage in both regions. The

measure is not statistically significant for SHORTBANKDEBT in the East as well as in

the West, but for all other leverage measures the coefficients are significant at the one

percent confidence level. There is a significant difference in the behavior of the variable

between East and West for all four employed leverage-measures. For

SHORTBANKDEBT and SHORTTERMDEBT, the coefficient is more negative for the


88

Eastern sample while for the other two measures, the coefficient for the west is more

negative. The negative coefficient of this macroeconomic control variable is in

accordance with what was expected.

Inflation

In the Western sample, inflation is positively related to LONGBANKDEBT and is

statistical significant at the one percent level. On the contrary, the effect of inflation on

NARROWLEVERAGE is not statistically significant. This appears to be due to the

offsetting negative effect, inflation has on SHORTBANKDEBT which appears to

cancel out the positive effect on LONGBANKDEBT. Furthermore,

SHORTTERMDEBT has the highest negative coefficient. The dummy variables are all

significant at the one percent level besides the one in the regression on

NARROWLEVERAGE, where it is not statistically significant. In the Eastern sample,

inflation is positively related to all four measures of leverage which is in accordance

with the general expectation. The expectation that inflation is positively related to

leverage is on the one hand based on the fact that (a priori) the “real” cost of debt is

reduced in inflationary periods by deteriorating the real value of the principal. It has

previously been shown (for developed countries) that companies use more debt in

inflationary periods (Modigliani 1983). Furthermore, interest-payments are in fact only

partly true interest payments, while the other part is actually compensation for the loss

of real value of the principal. For tax-purposes, companies are nevertheless able to

deduct their entire interest expenses, including the part which is effectively repayment

of principal. It is interesting to see that the short-term leverage measures are more

positively related to inflation than the long-term leverage measures in the Eastern

sample. There are two possible explanations for this result. When looking at descriptive

statistics, it can be seen that the mean inflation in the East is more than two times bigger

than in the West, with the largest inflation being in Serbia in 2001 with 91.1 percent.

Here it has to be mentioned that in 2001, Serbia was just making the transition to

democracy, and recovering from a war. Nevertheless, very high values of inflation can

be observed for other countries in Eastern Europe as well. Romania for instance

experienced inflation rates of 34.5 percent in 2001, and 22.5 percent in 2002. This leads

to a problem which has been formulated by Mozes in the following way: “There is a

common misconception that in a high inflation environment, long-term investments can


89

be funded by long-term loans as long as a high nominal interest rate is charged”

(Mozes 1995, p. 1). From the perspective of the lender, long-term loans in high inflation

environments can yield an acceptable real compensation if the nominal interest rate is

sufficiently high, and the interest-rate risk is managed appropriately. The real cost of the

long-term loan might also be reasonable to the borrower, but it might entail a cash-flow

structure that is unsuitable for financing long-term investments. High nominal interest

rates, make the cash-outflows, stemming from a long-term loan, comparably high in the

early stage of the loan, and could therefore potentially be greater than the expected

cash-inflows from the project at that stage. Capital investments usually require a large

initial cash outlay which has to be recovered over a long period. The inappropriate cash-

flow structure of long-term debt in periods of high inflation might hustle companies

towards taking on short-term debt instead of long-term debt which is supported by the

result for Eastern Europe (Mozes 1995). It has also been shown that in countries with

high inflation the availability of long-term debt from banks, due to the interest-rate risk

encountered by these institutions, is reduced which is further evidence supporting the

observed results (Caprio, Demirgu-Kunt 1998). A reason for the reduced availability of

long-term debt can also be found in the fact that there is a relation between high, and

especially volatile inflation rates, and poor levels of financial development (Boyd,

Levine & Smith 2001). This study employing the variable market-cap to GDP, shows

that good financial development has a positive impact on long-term debt (Demirguc-

Kunt, Thorsten & Levine 2005). The interpretations of the obtained results are

somehow substantiated by the results for the Western sample. Here the impact of

inflation is positive for long-term debt compared to a slightly negative impact on short-

term debt, which indicates that the availability of long-term debt is not that constrained,

because the volatility of inflation rates is smaller and the correlated financial

development is better. Therefore companies are able to make use of the advantages of

inflation on the after-tax costs of debt.

Summing up, there is strong evidence supporting the hypothesis that companies in the

East and West respond differently to country specific variables as well as firm specific

variables either in strength or sign. There are, as discussed above, diverse reasons for

the different impacts of the variables in this study on the employed measures of

leverage, when comparing the Western with the Eastern sample. One interesting point


90

referring to the differences observed in the firm specific variables should be mentioned.

(Jong, Kabir & Nguyen 2007) showed in a study covering 42 countries, that country

specific factors apart from having a direct impact on leverage, also have an indirect

impact, by influencing firm specific variables. In this study, this phenomenon is for

instance suggested to be responsible for the different impact of tangibility on leverage.

This is however not explicitly tested for, by for instance interacting firm specific

variables with country specific variables. This could be an interesting topic which is left

for further research.

9. Conclusion

In this study, the impact of firm- as well as country specific variables on leverage has

been investigated. The country specific variables consist of macroeconomic control

variables and variables that proxy for corporate governance, legal as well as financial

environment. The study has been performed by investigating the capital structure of a

sample of nearly 160,000 small and medium sized enterprises in Europe, collected over

the period 2001-2006, which in total amounts to almost half a million observations. To

be able to identify possible differences in the impact of the different variables in

different regions, the sample was subdivided into Eastern and Western Europe with 11

and 13 countries respectively. The rationale behind this division was based on historical

reasons and validated through an assessment of the descriptive statistics. In order to

estimate the impact of the different variables on leverage, an OLS regression with a

cross-sectional fixed effect was performed.

The first finding of this study is that the companies in the Eastern sample on average

have lower leverage. This difference is significant across the four employed measures of

leverage. This relationship has also been observed by other researchers, such as (Jõeveer

2005) and (Nivorozhkin 2005). It is suggested that the difference is not a result of a

smaller propensity towards debt financing in Eastern Europe, but rather due to inferior

availability of credit in this region, influenced by differences in institutional factors.

In terms of the country specific variables, the expectations for corporate governance,

legal and financial environment were only partly fulfilled. While there is some

convincing evidence for all these factors being positively related to leverage, there is


91

some disturbing evidence regarding certain proxies. It is however beyond doubt that

these three aspects are important in determining leverage of SMEs.

Turning to the proxies for corporate governance, it can be seen that investor protection

shows the expected positive relationship, while disclosure shows a surprising negative

relationship with leverage. On the one hand, this unexpected result supports the

proposition that not every improvement of corporate governance has a positive impact

on debt financing. On the other hand it could show a potential weakness of the index

data, through picking up other effects besides the intended, and thereby not being

refined enough. Based on this, only partly evidence in favor of corporate governance

being positively related to leverage, was found.

There is evidence that the effectiveness of the legal environment is positively associated

with leverage. This is captured by the proxies, contract enforcement and recovery rate.

The legal rights proxy, through being insignificant in the Eastern sample, indicates that

the benefit of a good legal code is very much dependent on effective enforcement. This

is substantiated by the descriptive statistics showing that contract enforcement and

recovery rate are on average worse in Eastern Europe. This line of reasoning is also

suggested by (Safavivan, Sharma 2007). The last proxy concerning the legal

environment is corruption. It was initially expected that less corruption would be

associated with more leverage. The results do however not support this expectation.

Instead it can be seen, that more corruption all else equal leads to more debt among

SMEs. While a potential explanation can be found in the free cash flow hypothesis, this

result is still puzzling, especially when taking into consideration that a non-trivial

amount of SMEs is expected to be owner-managed. Therefore the disciplining function

of debt should be unnecessary.

When looking at the proxies for financial development, it can be seen that the very

broad proxy “Marketcap to GDP” shows the expected result, namely that a more

developed financial environment increases leverage among SMEs. This is interpreted

as an enhancement of credit availability when the overall development of the financial

market increases. Contrary to this intuitive result, the negative impact of credit

information, which is the other proxy for financial development employed in this study,

is challenging to interpret. The negative coefficient could stem from transaction costs

associated with SMEs having to supply credit registries with detailed credit information

when applying for debt financing. However, credit information is picking up a very


92

narrow dimension of the degree of financial development, and the overall conclusion is

therefore that financial development is positively associated with leverage of SMEs.

Further, evidence was found supporting that banking concentration is negatively related

to leverage. This is in line with the structure-performance hypothesis, that banks take

advantage of high market power by restraining credit availability and/or increasing the

price of credit.

Banking profitability was shown to have a positive effect on the amount of debt in the

capital structure of SMEs. This is argued to be due to cheaper refinancing possibilities

for profitable banks, and the fact that Basel 1, which was applicable at that time, allows

banks to lend out a certain multiple of their equity base. Profitable banks are assumed to

all else equal increase their equity base, and thereby being able to lend out more money,

which should also be to the benefit of SMEs. Under Basel 1, the credit rating of the

borrower did not affect the amount of equity the bank had to underlie a loan with. With

the implementation of the Basel 2 accords in the beginning of 2007, the amount of

equity a bank has to underlie a loan with is now based on the credit rating of the

borrower. This is suggested to likely harm the credit availability for SMEs, since they

are in general considered to be more risky than for instance large listed companies.

There is in general strong evidence that companies in the two regions respond

differently to country- as well as firm specific variables. This is shown by the majority

of the dummy interactions being significant. The different impact of firm specific

variables can for instance be attributed to country specific factors also having an

indirect impact, through interacting with the firm specific variables. As an example,

tangibility is more economically significant in the West, which could be due to e.g. the

better enforcement of contracts c.f. descriptive statistics. The different impact of country

specific variables can among other things be attributed to elasticity effects, stemming

from the current state of the institutional environment being different in the two regions.

Overall there can be no doubt to the importance of country specific factors in

determining leverage of SMEs. While the static trade-off- and pecking order theory are

useful in explaining the impact of certain firm specific variables on leverage, it has been

beyond the scope of this study to explicitly test which one better explains capital

structure of European SMEs. However it is argued that both models neglect the

importance of the supply side of the financing decision. It has been suggested that


93

SMEs, especially in transition economies, are not able to acquire all the financing they

need for pursuing their investment opportunities (OECD 2006). The observation in this

study, that SMEs in the Eastern sample has lower debt ratios, could very well indicate

such a financing gap. OECD has further proposed that the availability of debt financing

is dependent on the development of institutional factors within a country. This study

confirms that, by proving that specific factors related to corporate governance, legal and

financial development are positively correlated to leverage among SMEs.

This is a first attempt to identify relationships between specific institutional factors and

leverage. When further research has confirmed these results, this information can be

very important for policy makers initiating reforms pointed towards enhancing the

environment for SMEs. Especially the fact that the marginal impact of changes in

institutional factors can be different across regions, asks for a careful assessment before

implementation of reforms.

10. Critical assessment and suggestions for further research

The conclusions drawn from this study are based on a thorough analysis. However the

authors are aware of certain weaknesses. While the shortcomings are not considered to

invalidate the overall findings, they should be kept in mind, and are therefore presented

in the following.

Some of the country specific variables used to proxy for institutional factors are based

on index data. This index data is to some degree exposed to subjective judgment about

what factors the index should be based on. E.g. the composition of the determinants of

Investor protection might not reflect all issues of investor protection that could be

relevant to leverage. This means that the indices could potentially pick up unintended

effects. The authors acknowledge this caveat, but were not able to verify the robustness

of the collected indices, based on no data availability from other sources.

The majority of this index data has been gathered and constructed by Doing Business

(the World Bank) which is a fairly new initiative. The fact that these indices are only

available for a narrow timeframe, and that institutional factors do not change very often,

results in moderate intra-country variation. This means that it is not possible to conduct

the study on a “per country” basis. This is the reason why a cross country regression has

been employed. This raises the question of parameter stability within the sample. This


94

issue was addressed by subdividing the sample into Eastern and Western Europe. In the

eyes of the authors, this is the most feasible division of these countries, based on a priori

economic expectations and a critical assessment of the descriptive statistics. A formal

cluster analysis has not been performed due to the large number of explanatory

variables. Nevertheless it has to be acknowledged, that a different pooling could

potentially lead to different results.

Another issue is that the coverage of firm specific data from ORBIS is not equally

comprehensive across all countries, stemming from different reporting obligations etc.

This unequal distribution of the sample put some strains on the power of the analysis.

Nevertheless, when sample homogeneity is expected, the severity of this problem is

reduced.

An SME financing gap has been suggested in this paper. This belief rests on evidence

from e.g. the OECD and (Jõeveer 2005). The impact of the country specific factors on

leverage is interpreted as mainly affecting the supply side of financing, and therefore

supports the possibility of a financing gap. It is suggested that further research should be

done regarding the presence and severity of this financing gap. This could for instance

be done through estimating a disequilibrium model as suggested in (Santiago Carbó-

Valverde, Francisco Rodriquez-Fernandez & Udell 2006). Further more, it would be

interesting to shed light on the economic impact of this financing gap and the constraint

on growth.

It has been mentioned that country specific factors do not only have a direct impact on

leverage, but also an indirect effect, through influencing the impact of firm specific

variables. This expectation could be further investigated by including interaction terms

into a regression framework.

In order to surmount the problems associated with evaluating the impact of country

specific variables on leverage through a cross-country study, it would also be very

interesting to perform the analysis on a single country. A potential way to do this could

be, by means of a time-series analysis, where the impact of certain reforms in the

institutional environment over time is assessed. This could also address the issue that

the impact of institutional reforms on leverage is likely to be time-delayed. A potential

drawback of this approach is the possible non-availability of country specific data over

a sufficiently long time-frame especially when index data is used.


95

Another important field for further research could be the identification of good proxies

for country specific factors. Many indices are not available for a very long time, or

might lack the necessary refinement for use in an academic study. It would therefore be

very useful to create indices for a longer timeframe in order to make cross country

studies, as well as potentially single country time series analysis, more comprehensive.

A more specific area of further research could be the evaluation of the impact of the

Basel 2 accords on the availability of bank debt to SMEs in Eastern as well as Western

Europe. To the knowledge of the authors, no study has so far compared the credit-

ratings of Eastern European countries with Western European countries.

It would certainly also be interesting to compare the impact of country-specific

variables on leverage, between large listed companies and SMEs. In the view of the

authors it can be expected that the impact on SMEs is larger because they do not to the

same extend have access to international capital markets. Large listed companies can to

some degree, circumvent the effects of country-specific factors through this access

Lastly, it will be interesting to further investigate the impact of institutional reforms on

especially Eastern European SMEs. As mentioned by the World Bank, some countries

in Eastern Europe have introduced massive institutional reforms in the last couple of

years. While the pace of development is not the same across all Eastern European

countries it can be seen that for instance Estonia already have quiet strong institutional

systems. Therefore it needs to be carefully reassessed in the coming years, to what

extend a division of Europe into East and West in respect to capital structure and also

more generally, is still appropriate. In the future, a grouping into Northern and Southern

Europe is maybe more appropriate for a cross-country study like this. An indicator of

this could be that countries like Portugal, Greece and Italy are lacking behind the rest of

Western Europe when it comes to certain institutional factors. This is however only one

possible way of grouping which needs further investigation in the time to come.


96

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104

12. Appendices – Table of contents

Appendix 1: Risk Shifting I

Appendix 2: Institutional factors in Eastern and Western Europe II

Appendix 3: Country Risk V

Appendix 4: Regression equation VI

Appendix 5: The special case of collecting data in 2001 and 2002 VII

Appendix 6: Regression output for Current Liabilities and Broadleverage VIII

Appendix 7: Empirical Evidence on firm specific variables X

Appendix 8: Per country descriptive statistics of the leverage measures XIV

Appendix 9: Per country descriptive statistics of firm specific variables XV

Appendix 10: Mean values of country specific factors per country XVI

Appendix 11: Regression output XVIII

Appendix 12: Pooled regression XXII

Appendix 13: Regression for the Eastern European sample only XXIII

Appendix 14: Hausmann test XXV

Appendix 15: F-test for joint significance of the fixed effects XXVI

Appendix 16: Equality test of leverage XXVII

Appendix 17: Example on Net interest margin XXVIII


I

Appendix 1 - Risk shifting

Overall project Shareholder payoff Debt holder payoff

125

75

50

Low volatility E(V) = 100 E(V) = 50 E(V) = 50

75 25 50

175

125

50

High volatility E(V) = 100 E(V) = 62.5 E(V) = 37.5

25 0 25 Assumption: Initial project is financed with 50 in equity and 50 in debt. Probability of good/bad scenario is 50%

The above figure shows how shareholders can extract wealth from the debt holders by undertaking

a more volatile project, after receiving debt financing.

The first column named “Overall project”, shows the two different projects that the company can

invest in. Both projects have an expected value of 100, but the risk is different, illustrated by the

different cash flows in the good and bad states. Moving to the right, the next column shows

shareholders payoff in good and bad states, along with the expected value from their perspective.

Shareholders payoff in a specific state is computed by subtracting the debt (equal to 50) from the

cash flow of the overall project in the equivalent state. The last column shows the payoff to debt

holders. They have a promised payoff of 50, which they will exactly get in all states, except if the

company defaults on the debt i.e. the cash flow from the overall project is not sufficient.

If the company undertakes the low volatility project, then the value of the shareholders stake in the

company is 50, as seen by the expected value. Similarly, debt holders get 50, and thereby the

overall project value of 100 is equally split between the two.

Now if the company undertakes the high volatility project the expected value of the overall project

is still 100. Shareholders can however see the value their stake, increase to 62.5 while debt holders

will see the value of their claim diminishing.


Appendix 2 – Institutional factors in Eastern and

Corporate governance is in many Eastern European countries a fairly new

the Berlin Wall and the collapse of the Soviet Union, corporate governance was not an issue since

companies were owned by the state. After this, things have changed in the old East block.

Companies have been privatized, capital m

corporate governance has become important in order to attract external capital

Preobragenskaya 2004). The need for good corporate governance in the

even more important than in the West, because they do not have the long established financial

infrastructure to monitor corporations and take care of corporate governance issues, as argued by

(Bobirca, Miclaus 2007). In line with the general perception,

Corporate Governance in Eastern Europe is on average

the fact that the systems are still young.

In order to verify that, data on different country specific measures from a sample of

Western countries has been collected and presented below in order to give a general overview.

Credit-information, investor-protection, legal rights and corruption are index data, while market cap

to GDP is a ratio. For all these measure a high value is better. Contract enforcement is measured in

years, and a smaller value is therefore better

0,00

2,00

4,00

6,00

8,00

MARKETCAPTOGDP CREDITINF

Western Europe

Western Europe: Austria, Belgium, Switzerland, Ge

Greece, Italy, Netherlands, Portugal and Sweden.

Eastern Europe: Bulgaria, Czech Republic, Estonia, Croatia, Hungary, Lithuania, Latvia, Poland,

Romania, Serbia and Slovakia

Source: Doingbusiness.org and GMID,


II

Institutional factors in Eastern and Western Europe

Corporate governance is in many Eastern European countries a fairly new issue. Before the fall of



Companies have been privatized, capital markets have evolved, and suddenly the issue of good

corporate governance has become important in order to attract external capital

. The need for good corporate governance in the Eastern



. In line with the general perception, (Bobirca, Miclaus

ernance in Eastern Europe is on average lagging behind, as a natural consequence of

young.

In order to verify that, data on different country specific measures from a sample of

countries has been collected and presented below in order to give a general overview.

protection, legal rights and corruption are index data, while market cap


is therefore better.

INV. PROTECTION LEGALRIGHTS CORRUPTION CONTRACTENF

Western Europe Eastern Europe

Europe: Austria, Belgium, Switzerland, Germany. Spain, Finland, France, Great Britain,

Greece, Italy, Netherlands, Portugal and Sweden.


Source: Doingbusiness.org and GMID, Average values from 2001-2006

Europe

issue. Before the fall of



and suddenly the issue of good

corporate governance has become important in order to attract external capital (Mcgee,

Eastern countries is maybe



(Bobirca, Miclaus 2007) finds that

lagging behind, as a natural consequence of

In order to verify that, data on different country specific measures from a sample of Eastern and

countries has been collected and presented below in order to give a general overview.

protection, legal rights and corruption are index data, while market cap


CONTRACTENF

Spain, Finland, France, Great Britain,



III

The graph is intended to confirm the perception that Western European countries are generally

speaking on a higher level of development, but at the same time raise the question whether it in the

future will still be correct to talk about the East and West as two separate blocks of countries.

Looking at the graph gives a good grasp of the current situation and shows that the West is on

overall more developed. But it is also clear that at least parts of Eastern Europe are catching up on

certain aspects, like for instance investor protection and legal rights, as can be seen in the graph and

also from the appendix 3. One can for instance mention Estonia which is a good example of an

Eastern European country with consistently high GDP growth rates which can at least partly be

attributed to the good institutional reforms in the country. Regarding investor protection and legal

rights, it is however important to keep in mind that e.g. good legal rights has to be enforced in order

to be effective. Investor protection and legal rights in this graph only tells something about the

presence of certain rights in the law, but not the strength of the legal system in general.

Financial development proxied by market cap to GDP, shows the most clear cut difference between

East and West. Also when turning to corruption, which is argued to be a crucial factor, the East has

room for improvement. Credit Information which is another variable related to the development of

financial markets is also indicating that the West is more developed.

On certain aspects of corporate governance like investor protection and legal rights, it is however

not possible to make a clear-cut distinction. This is argued to be due to two things. First, some

countries in Western Europe e.g. Greece and Italy are lacking behind the rest of Western Europe

regarding a few factors, and are thereby dragging down the average score. Secondly, and this is

most likely the most important thing. Eastern Europe has in the later period being among the top

reformers in the world (World Bank). This has as a result helped several of the countries to improve

the environment for doing business, bringing it closer to Western standards.

It is argued that the EU used to, and still plays an important role in the development and

transformation of corporate governance systems in the East. Czech Republic serves as a good

example for this importance. In Czech Republic’s process toward accession into the European

Union, the EU was exporting a political agenda that set out to regulate markets and improve the

overall corporate governance in the country (Vliegenhart, Horn 2007). One of the initiatives was to

privatize the banking sector, which together with a more developed capital market, is argued to

attract foreign capital, which in turn demands for better corporate governance (Vliegenhart, Horn

2007). The acceptance of the new EU member states will likely put pressure on these to enhance

their corporate governance system as well as financial development. This will possibly lead to a


IV

further convergence between countries in the EU. Since some of the Mediterranean countries in

Western Europe are not performing as good as the rest of the Western countries, there is a danger

that they will lack behind some of the new economies in the future, if they do not improve.

Summing up, it might be valid to talk about East and West right now, but in the future the picture

could become more blurred.


V

Appendix 3 - Country Risk

The country-risk measures have been obtained from www.country-check.com, which is a project of

the World-check. World-check is one of the leading providers of risk intelligence in the world.


VI

Appendix 4 - Regression equation

The equation below the one estimated in Eviews:

LEVERAGE = C(1) + C(2)*AGE + C(3)*AGE*DUMMY + C(4)*BANKCONCENTRATION

+ C(5)*BANKCONCENTRATION*DUMMY + C(6)*CONTRACTENF +

C(7)*CONTRACTENF*DUMMY + C(8)*CORRUPTION + C(9)*CORRUPTION*DUMMY

+ C(10)*CREDITINF + C(11)*CREDITINF*DUMMY + C(12)*DISCLOSURE +

C(13)*DISCLOSURE*DUMMY + C(14)*GDPGROWTH + C(15)*GDPGROWTH*DUMMY

+ C(16)*GROWTHASSETS + C(17)*GROWTHASSETS*DUMMY + C(18)*INFLATION +

C(19)*INFLATION*DUMMY + C(20)*INVESTORPROTECTION +

C(21)*INVESTORPROTECTION*DUMMY + C(22)*LEGALRIGHTS +

C(23)*LEGALRIGHTS*DUMMY + C(24)*LNTURNOVER +

C(25)*LNTURNOVER*DUMMY + C(26)*MARKETCAPTOGDP +

C(27)*MARKETCAPTOGDP*DUMMY + C(28)*NARROWTANGIBILITY +

C(29)*NARROWTANGIBILITY*DUMMY + C(30)*NDTSHIELD +

C(31)*NDTSHIELD*DUMMY + C(32)*NETINTERESTMARGIN +

C(33)*NETINTERESTMARGIN*DUMMY + C(34)*RECOVERYRATE +

C(35)*RECOVERYRATE*DUMMY + C(36)*ROA + C(37)*ROA*DUMMY


VII

Appendix 5 – The special case of collecting data in 2001 and 2002

When it comes to collecting data for 2001 and 2002, it is not possible in the ORBIS search interface

to apply the SME filter for these years. The reason for this is that it unfortunately is not possible to

specify search criteria’s for data prior to 2003.

This issue was dealt with by applying the SME filter so as the companies fulfill it in year 2003, but

adding some slack in both ends of the intervals in order for allowing companies to have changed

their status since the year of interest. The used criteria’s can be seen in the table below, which for

total assets and revenue is the equivalent of adding/subtracting 25% in each end, and for employees

expanding the interval to all companies with up to 350 employees, compared to the SME filter used

for the other years.

Search criteria’s for 2001 and 2002

Total Assets 1.5 M. € ≤ 53.75 M. €

Revenue 1.5 M. € ≤ 62.5 M. €

Employees 1 ≤ 350

After this search, the data were sorted in Excel in order to filter out companies who did not fulfill

the correct SME criteria’s for 2001 and 2002.

This way of identifying SMEs in 2001 and 2002 is of course not optimal, but is considered to be a

fair way of coping with the problem of specifying criteria’s for the years prior to 2003. This is due

to the data for these years only being considered slightly biased in terms of survivorship. This is

argued to be the case because the time difference is only 1 and 2 years respectively, so the number

of companies being kept out of the sample in the 2 years because of bankruptcy in a later year

should be very small.


VIII

Appendix 6 – Regression output for Current Liabilities and Broadleverage

BROADLEVERAGE CURRENTLIABILITIES

Variable Coefficient t-Statistic p-value Coefficient t-Statistic p-value

C 0.544875 40.95203 0.0000 0.236587 16.26893 0.0000

AGE -0.008003 -50.54367 0.0000 -0.009673 -57.57299 0.0000

AGE*DUMMY 0.003879 3.851842 0.0001 0.007028 9.355788 0.0000

BANKCONCENTRATION -0.03165 -18.72651 0.0000 -0.0069 -3.533217 0.0004

BANKCONCENTRATION*DUMMY 0.089434 6.154233 0.0000 0.055726 3.538776 0.0004

CONTRACTENF -0.000152 -17.95902 0.0000 -5.72E-05 -5.834278 0.0000

CONTRACTENF*DUMMY -9.46E-05 -2.337623 0.0194 -0.000144 -3.556336 0.0004

CORRUPTION 0.004538 5.060028 0.0000 0.016454 16.47227 0.0000

CORRUPTION*DUMMY -0.042662 -8.987829 0.0000 -0.048052 -9.925184 0.0000

CREDITINF 0.010159 7.558801 0.0000 0.007783 5.072618 0.0000

CREDITINF*DUMMY -0.023315 -7.933174 0.0000 -0.021927 -7.263715 0.0000

DISCLOSURE -0.000755 -1.713366 0.0866 0.005127 10.92427 0.0000

DISCLOSURE*DUMMY -0.052069 -7.883944 0.0000 -0.06012 -7.887215 0.0000

GDPGROWTH -0.00176 -7.116468 0.0000 0.003283 11.83396 0.0000

GDPGROWTH*DUMMY 0.0008 1.087255 0.2769 -0.002986 -3.53918 0.0004

GROWTHASSETS 0.037079 52.61015 0.0000 0.028728 35.71632 0.0000

GROWTHASSETS*DUMMY -0.004935 -2.208572 0.0272 -0.00462 -1.82749 0.0676

INFLATION 0.003943 9.028658 0.0000 -0.002813 -6.348211 0.0000

INFLATION*DUMMY -0.003581 -7.824476 0.0000 0.003412 7.318107 0.0000

INVESTORPROTECTION -0.001058 -3.326248 0.0009 0.000197 0.462973 0.6434

INVESTORPROTECTION*DUMMY 0.042668 2.021863 0.0432 0.047619 2.08976 0.0366

LEGALRIGHTS 0.002043 2.406966 0.0161 0.00143 1.610502 0.1073

LEGALRIGHTS*DUMMY 0.01879 2.277877 0.0227 0.032305 3.706914 0.0002

LNTURNOVER 0.04546 45.02174 0.0000 0.054644 49.00926 0.0000

LNTURNOVER*DUMMY -0.011493 -3.060687 0.0022 -0.023507 -5.772433 0.0000

MARKETCAPTOGDP 0.000736 0.538262 0.5904 -0.001527 -1.107258 0.2682

MARKETCAPTOGDP*DUMMY -0.165107 -8.440041 0.0000 -0.14744 -8.305126 0.0000

NARROWTANGIBILITY -0.014598 -4.349368 0.0000 -0.25407 -68.12744 0.0000

NARROWTANGIBILITY*DUMMY -0.058704 -3.982305 0.0001 -0.036005 -2.308714 0.0210

NDTSHIELD -0.086894 -5.393873 0.0000 -0.002106 -0.125979 0.8997

NDTSHIELD*DUMMY 0.010277 0.231386 0.8170 -0.04793 -1.365354 0.1721

NETINTERESTMARGIN 0.308172 11.02949 0.0000 -0.002514 -0.080309 0.9360

NETINTERESTMARGIN*DUMMY 0.928474 4.674462 0.0000 1.272306 5.725902 0.0000

RECOVERYRATE -1.49E-06 -0.057388 0.9542 -0.000311 -11.06551 0.0000

RECOVERYRATE*DUMMY 0.001215 5.951133 0.0000 0.001181 5.739573 0.0000

ROA -0.004597 -89.56732 0.0000 -0.003193 -63.1101 0.0000

ROA*DUMMY 0.000203 1.317626 0.1876 -6.08E-05 -0.398009 0.6906


IX

Effects Specification Effects Specification

Cross-section fixed effect Cross-section fixed effect

R-squared 0.937211 R-squared 0.914057

Adjusted R-squared 0.907263 Adjusted R-squared 0.873081

S.E. of regression 0.07008 S.E. of regression 0.083655

Sum squared resid 1624.754 Sum squared resid 2315.904

Log likelihood 700768.8 Log likelihood 614334.5

Durbin-Watson stat 1.621983 Durbin-Watson stat 1.937125

Mean dependent var 0.692523 Mean dependent var 0.541041

S.D. dependent var 0.230126 S.D. dependent var 0.234816

Akaike info criterion -2.222483 Akaike info criterion -1.868399

Schwarz criterion 1.361888 Schwarz criterion 1.715069

F-statistic 31.29457 F-statistic 22.30748

Prob(F-statistic) 0 Prob(F-statistic) 0


X

Appendix 7 – Empirical evidence on firm specific variables

This appendix presents five tables from (Prasad, Green & Murinde 2001). The tables are intended to

give an overview of some of the empirical work that has been done on capital structure

incorporating the same firm specific variables as in this paper (except age). The tables show the

estimated relationship between the applied proxy and leverage. In order to obtain detailed

references, the reader must turn to the original source (Prasad, Green & Murinde 2001).

Table 1: The Influence of Tangibility on Firm Leverage

Source: (Prasad, Green & Murinde 2001)


XI

Table 2: The Influence of Size on Firm Leverage



XII

Table 3: The Influence of Profitability on Firm Leverage



XIII

Table 4: The Influence of Growth on Firm Leverage


Table 5: The Influence of Non-debt Tax-shields on Firm Leverage



XIV

Appendix 8 - Per country descriptive statistics of the leverage measures

Western Europe

SHORTTERMDEBT SHORTBANKDEBT NARROWLEVERAGE LONGBANKDEBT

Austria 0.209219 0.107058 0.351426 0.244367

Belgium 0.347052 0.090384 0.216822 0.126439

Switzerland 0.105516 0.03398 0.312323 0.278342

Germany 0.252077 0.110686 0.318816 0.20813

Spain 0.184611 0.065196 0.203473 0.138276

Finland 0.177956 0.056096 0.250623 0.194527

France 0.32945 0.075436 0.097644 0.022208

Great Britain 0.317848 0.127264 0.245074 0.11781

Greece 0.484531 0.226481 0.286143 0.059662

Italia 0.393845 0.147647 0.204875 0.057228

Netherlands 0.230834 0.095375 0.178067 0.082692

Portugal 0.352476 0.12348 0.279394 0.155914

Sweden 0.168945 0.03252 0.222837 0.190317

Eastern Europe


Bulgaria 0.373192 0.072366 0.261077 0.188711

Czech Republic 0.261357 0.063792 0.128589 0.064797

Estonia 0.301823 0.097549 0.225317 0.127768

Croatia 0.41631 0.098441 0.098441 0

Hungary 0.17887 0.06674 0.093691 0.025305

Lithuania 0.360198 0.109521 0.23167 0.121613

Latvia 0.364891 0.116725 0.331634 0.214909

Poland 0.375541 0.111745 0.195235 0.083851

Romania 0.075322 0.016232 0.028436 0.012205

Serbia 0.409212 0.072113 0.143344 0.071231

Slovakia 0.330135 0.06588 0.143201 0.077321

The presented values are the mean-values per country averaged over time.


XV

Appendix 9 - Per country descriptive statistics of firm specific variables

Western Europe

ROA age growth assets lnturnover non debt tax-shield

Austria 4.008138 24.56881 0.075239 9.542729 0.055047

Belgium 4.437309 25.49147 0.07615 9.249163 0.060933

Switzerland 1.492009 57.29377 0.008437 9.200415 0.085868

Germany 6.440368 32.50811 0.096234 9.505262 0.052522

Spain 5.562199 18.40663 0.18768 8.677875 0.036948

Finland 8.682205 23.55905 0.124814 8.993366 0.057249

France 6.034566 24.94775 0.114788 8.889978 0.041025

Great Britain 5.122258 23.96678 0.081189 9.300126 0.043672

Greece 5.354362 15.77751 0.148139 8.653191 0.03632

Italia 3.909415 21.09404 0.117192 8.930449 0.039882

Netherlands 6.25537 29.85384 0.029331 10.11215 0.057503

Portugal 2.784845 22.76193 0.13192 8.619583 0.048023

Sweden 6.254 20.89434 0.11783 8.981759 0.049774

Eastern Europe

ROA age growth assets lnturnover non debt tax-shield

Bulgaria 7.894293 11.62597 0.430672 8.706006 0.041202

Czech

Republic 7.167192 10.13237 0.203619 8.877043 0.04246

Estonia 13.33254 11.77357 0.345556 8.706374 0.043505

Croatia 10.25619 18.5846 0.26398 8.644041 0.053717

Hungary 6.80947 11.54895 0.225479 8.938311 0.04327

Lithuania 7.404724 8.521774 0.281597 8.464121 0

Latvia 6.012997 8.973545 0.188526 8.723039 0.051898

Poland 7.73307 16.93466 0.213599 9.039773 0.051913

Romania 12.32692 9.457831 0.627685 8.598792 0.037398

Serbia 13.66336 17.57267 0.244222 8.463155 0.032099

Slovakia 7.539241 9.845564 0.285871 9.028751 0.051345



XVI

Appendix 10 – Mean values of country specific factors per country

Western Europe

GDPGROWTH INFLATION MARKETCAPTOGDP BANKCONCENTRATION NETINTERESTMARGIN RECOVERYRATE

Austria 2.18 1.98 0.29 0.64 0.02 72.29

Belgium 1.76 2.05 1.01 0.85 0.02 78.25

Switzerland 1.44 0.91 2.37 0.87 0.02 46.60

Germany 1.67 1.71 0.43 0.69 0.03 53.63

Spain 3.28 3.26 0.81 0.76 0.03 77.39

Finland 2.97 1.23 1.11 0.98 0.02 88.37

France 1.55 1.88 0.80 0.58 0.03 46.68

Great Britain 2.43 2.58 1.34 0.57 0.02 85.68

Greece 4.36 3.36 0.57 0.85 0.03 43.91

Italia 0.86 2.37 0.43 0.49 0.03 54.46

Netherlands 1.08 2.12 1.06 0.60 0.01 87.53

Portugal 1.08 2.76 0.37 0.92 0.03 74.64

Sweden 2.78 1.37 1.00 0.96 0.03 77.23

Eastern Europe

GDPGROWTH INFLATION MARKETCAPTOGDP BANKCONCENTRATION NETINTERESTMARGIN RECOVERYRATE

Bulgaria 5.78 5.70 0.17 0.47 0.05 33.49

Czech Republic 4.42 2.12 0.26 0.66 0.02 17.41

Estonia 9.35 3.73 0.35 0.98 0.03 39.23

Croatia 4.71 2.60 0.31 0.58 0.04 28.83

Hungary 4.15 4.22 0.32 0.65 0.05 38.29

Lithuania 6.92 0.30 0.10 0.80 0.03 50.00

Latvia 7.08 2.58 0.09 0.55 0.03 35.86

Poland 3.78 2.28 0.26 0.57 0.04 36.26

Romania 6.23 12.40 0.19 0.69 0.06 13.56

Serbia 5.67 21.27 0.14 0.48 0.08 27.48

Slovakia 6.28 4.84 0.09 0.78 0.03 42.36


XVII

Western Europe

CONTRACTENF CORRUPTION CREDITINF DISCLOSURE INVESTORPROTECTION LEGALRIGHTS

Austria 397.00 8.34 6.00 3.00 4.00 5.00

Belgium 505.00 7.28 4.00 8.00 7.00 5.00

Switzerland 417.00 8.90 5.00 0.00 3.00 6.00

Germany 396.78 8.03 6.00 5.00 5.00 8.00

Spain 515.00 6.98 6.00 5.00 5.00 6.00

Finland 256.09 9.69 5.00 6.00 5.70 6.00

France 331.00 6.99 4.00 10.00 5.30 3.38

Great Britain 404.00 8.59 6.00 10.00 8.00 10.00

Greece 819.00 4.29 3.54 1.00 3.00 3.00

Italia 1351.74 5.11 5.79 7.00 5.38 3.00

Netherlands 514.00 8.78 5.00 4.00 4.70 7.00

Portugal 577.00 6.56 4.00 6.00 6.00 4.00

Sweden 508.00 9.20 4.00 2.84 4.60 6.00

Eastern Europe

CONTRACTENF CORRUPTION CREDITINF DISCLOSURE INVESTORPROTECTION LEGALRIGHTS

Bulgaria 564.00 4.00 3.40 10.00 6.00 6.00

Czech Republic 825.92 4.19 4.41 2.00 5.00 6.00

Estonia 425.00 6.09 5.00 8.00 6.00 4.00

Croatia 561.00 3.62 0.00 1.00 4.00 5.00

Hungary 335.00 5.06 5.00 2.00 4.30 5.80

Lithuania 210.00 4.80 3.00 5.00 5.00 4.00

Latvia 281.72 3.78 0.41 5.00 5.70 8.00

Poland 992.00 3.67 4.00 7.00 5.76 4.00

Romania 537.00 2.94 4.56 8.31 5.79 5.00

Serbia 865.29 2.57 2.07 7.00 5.30 3.54

Slovakia 582.55 4.27 3.00 3.00 4.70 9.00



XVIII

Appendix 11 – Regression output

West is serving as base group while the DUMMY indicates the Eastern sample

SHORTTERMDEBT SHORTBANKDEBT


C -0.356595 -16.43295 0.0000 -0.062349 -5.255416 0.0000

AGE 0.004059 19.52964 0.0000 0.002086 15.38304 0.0000

AGE*DUMMY -0.005042 -7.657114 0.0000 -0.00321 -8.889094 0.0000


BANKCONCENTRATION*DUMMY -0.012783 -0.688895 0.4909 -0.013104 -1.358885 0.1742

CONTRACTENF 8.52E-05 6.927934 0.0000 -4.21E-05 -4.547285 0.0000

CONTRACTENF*DUMMY -0.000856 -18.51851 0.0000 -0.000163 -6.498419 0.0000

CORRUPTION -0.001523 -1.255836 0.2092 0.001731 2.132711 0.0329


CREDITINF -0.048669 -27.0485 0.0000 -0.005756 -3.941373 0.0001

CREDITINF*DUMMY 0.00092 0.273872 0.7842 -0.000456 -0.218379 0.8271

DISCLOSURE -0.001587 -3.066102 0.0022 0.000219 0.677345 0.4982


GDPGROWTH -0.004104 -10.99335 0.0000 -6.06E-05 -0.256888 0.7973

GDPGROWTH*DUMMY -0.002449 -2.645894 0.0081 -0.00027 -0.539523 0.5895

GROWTHASSETS -0.003645 -3.322606 0.0009 -0.003066 -5.176581 0.0000

GROWTHASSETS*DUMMY 0.020725 7.071083 0.0000 0.00355 2.734081 0.0063

INFLATION -0.004901 -10.08576 0.0000 -0.002736 -8.245583 0.0000

INFLATION*DUMMY 0.007976 15.12896 0.0000 0.003048 8.86107 0.0000

INVESTORPROTECTION 0.004494 6.234145 0.0000 0.002431 5.874891 0.0000


LEGALRIGHTS -0.014044 -16.73296 0.0000 -0.006763 -11.13993 0.0000

LEGALRIGHTS*DUMMY 0.007554 0.251676 0.8013 -0.002513 -0.213548 0.8309

LNTURNOVER 0.109055 70.99107 0.0000 0.028499 31.65438 0.0000


MARKETCAPTOGDP 0.009552 7.108677 0.0000 0.004185 4.354435 0.0000

MARKETCAPTOGDP*DUMMY 0.196949 9.690301 0.0000 0.050042 3.856024 0.0001


NARROWTANGIBILITY*DUMMY -0.079878 -5.302238 0.0000 0.005356 0.640166 0.5221

NDTSHIELD -0.117767 -6.954373 0.0000 -0.071403 -6.479236 0.0000

NDTSHIELD*DUMMY 0.068053 2.244488 0.0248 0.037078 2.392496 0.0167

NETINTERESTMARGIN 0.335571 7.298433 0.0000 0.082511 3.087266 0.0020


RECOVERYRATE -0.000567 -17.13467 0.0000 -0.000338 -14.73483 0.0000


ROA -0.002998 -56.68835 0.0000 -0.00175 -51.23967 0.0000

ROA*DUMMY 0.000966 5.908602 0.0000 0.001008 13.77707 0.0000


XIX


Effects Specification

Cross-section fixed effect














Prob(F-statistic) 0.0000 Prob(F-statistic) 0.0000


XX

NARROWLEVERAGE LONGBANKDEBT


C -0.042669 -2.783944 0.0054 0.020985 1.981135 0.0476

AGE 0.003336 18.873 0.0000 0.001249 9.213776 0.0000

AGE*DUMMY -0.005387 -10.17655 0.0000 -0.00223 -5.413416 0.0000


BANKCONCENTRATION*DUMMY -0.005572 -0.395361 0.6926 0.008443 0.736986 0.4611

CONTRACTENF -0.000169 -16.66415 0.0000 -0.000127 -17.02023 0.0000

CONTRACTENF*DUMMY -0.000138 -3.467421 0.0005 2.81E-05 0.810638 0.4176

CORRUPTION -0.005409 -5.359362 0.0000 -0.00714 -9.495449 0.0000


CREDITINF -0.002241 -1.465408 0.1428 0.003515 3.085084 0.0020

CREDITINF*DUMMY -0.006649 -2.198125 0.0279 -0.006041 -2.313098 0.0207

DISCLOSURE -0.002451 -4.853816 0.0000 -0.002669 -5.829236 0.0000


GDPGROWTH -0.005257 -16.96321 0.0000 -0.005197 -21.65295 0.0000

GDPGROWTH*DUMMY 0.001838 2.702311 0.0069 0.002061 3.804945 0.0001

GROWTHASSETS 0.006998 8.77097 0.0000 0.010063 17.10187 0.0000

GROWTHASSETS*DUMMY -0.000102 -0.051533 0.9589 -0.003374 -2.21236 0.0269

INFLATION 0.000481 0.982924 0.3256 0.003217 7.677999 0.0000

INFLATION*DUMMY 0.000169 0.333994 0.7384 -0.00287 -6.684052 0.0000



LEGALRIGHTS 0.021144 23.50868 0.0000 0.027907 38.42343 0.0000

LEGALRIGHTS*DUMMY -0.028813 -1.857008 0.0633 -0.027471 -5.206423 0.0000

LNTURNOVER 0.030009 26.65847 0.0000 0.00151 1.91479 0.0555


MARKETCAPTOGDP 0.006191 4.468722 0.0000 0.002006 1.71672 0.0860

MARKETCAPTOGDP*DUMMY 0.084647 4.797579 0.0000 0.033659 2.604042 0.0092

NARROWTANGIBILITY 0.190621 47.58052 0.0000 0.213548 65.83541 0.0000

NARROWTANGIBILITY*DUMMY -0.105061 -7.586604 0.0000 -0.108292 -9.443655 0.0000

NDTSHIELD -0.202504 -13.65936 0.0000 -0.131101 -13.31248 0.0000

NDTSHIELD*DUMMY 0.126392 3.797191 0.0001 0.088405 3.472127 0.0005



RECOVERYRATE -0.000196 -6.817521 0.0000 0.000142 6.341044 0.0000


ROA -0.00285 -64.389 0.0000 -0.001101 -34.05773 0.0000

ROA*DUMMY 0.001491 13.44296 0.0000 0.000499 5.654928 0.0000


XXI









Sum squared residuals 2809.277 Sum squared residuals 1449.177










XXII

Appendix 12 - Pooled Regression


Variable Coefficient t-Statistic p-value Coefficient t-Statistic p-value Coefficient t-Statistic p-value Coefficient t-Statistic p-value

C -0.267837 -17.47212 0.0000 -0.042922 -4.706313 0.0000 -0.006444 -0.546039 0.5850 0.03657 4.205998 0.0000

AGE 0.003791 18.46086 0.0000 0.002021 15.58239 0.0000 0.003359 19.78636 0.0000 0.001333 10.82168 0.0000

BANKCONCENTRATION -0.038043 -14.07873 0.0000 -0.011355 -6.989628 0.0000 -0.040624 -20.22102 0.0000 -0.029221 -19.9388 0.0000

CONTRACTENF -7.96E-05 -10.0732 0.0000 -6.64E-05 -14.59186 0.0000 -0.00016 -27.1593 0.0000 -9.35E-05 -21.40323 0.0000

CORRUPTION -0.008834 -7.585581 0.0000 -0.000255 -0.338967 0.7346 -0.006369 -6.674962 0.0000 -0.006056 -8.427925 0.0000

CREDITINF -0.026136 -27.07714 0.0000 -0.003539 -6.456333 0.0000 -0.004432 -6.002973 0.0000 -0.00089 -1.476153 0.1399

DISCLOSURE 0.000163 0.325663 0.7447 0.000712 2.298372 0.0215 -0.001598 -3.261187 0.0011 -0.002307 -5.146983 0.0000

GDPGROWTH -0.006089 -18.80696 0.0000 -0.00095 -4.957478 0.0000 -0.005308 -20.9839 0.0000 -0.004371 -22.51376 0.0000

GROWTHASSETS -0.001159 -1.121752 0.2620 -0.002631 -4.879834 0.0000 0.007163 9.565985 0.0000 0.009821 17.71761 0.0000

INFLATION 0.004101 15.86754 0.0000 0.000666 7.190013 0.0000 0.001397 9.808018 0.0000 0.000736 7.988493 0.0000

INVESTORPROTECTION 0.005099 7.202461 0.0000 0.00265 6.577176 0.0000 0.002403 5.288869 0.0000 -0.000242 -0.822003 0.4111

LEGALRIGHTS -0.009808 -11.7808 0.0000 -0.0053 -8.872246 0.0000 0.0217 24.50562 0.0000 0.026924 37.89059 0.0000

LNTURNOVER 0.096611 65.28325 0.0000 0.025631 30.70856 0.0000 0.026502 24.95537 0.0000 0.000858 1.146517 0.2516

MARKETCAPTOGDP 0.009548 7.128536 0.0000 0.004274 4.515717 0.0000 0.005084 3.755125 0.0002 0.000792 0.697499 0.4855

NARROWTANGIBILITY -0.135734 -32.05841 0.0000 -0.023814 -9.053644 0.0000 0.178544 46.1117 0.0000 0.202498 64.54212 0.0000

NDTSHIELD -0.099711 -5.850368 0.0000 -0.060758 -6.211754 0.0000 -0.172472 -8.789897 0.0000 -0.111714 -8.033587 0.0000

NETINTERESTMARGIN 0.568868 12.53487 0.0000 0.151153 5.947818 0.0000 0.50164 14.83831 0.0000 0.349413 13.60278 0.0000

RECOVERYRATE -0.000384 -11.83042 0.0000 -0.000275 -12.4763 0.0000 -0.000143 -5.219139 0.0000 0.000134 6.464406 0.0000

ROA -0.002872 -56.9369 0.0000 -0.001638 -52.25711 0.0000 -0.002688 -64.77585 0.0000 -0.001049 -34.44586 0.0000

Effects Specification: Cross-section fixed effect Effects Specification: Cross-section fixed effect Effects Specification: Cross-section fixed effect Effects Specification: Cross-section fixed effect

R-squared 0.849541 R-squared 0.825376 R-squared 0.854877 R-squared 0.859495

Adjusted R-squared 0.777817 Adjusted R-squared 0.742128 Adjusted R-squared 0.785693 Adjusted R-squared 0.792506

S.E. of regression 0.125589 S.E. of regression 0.074508 S.E. of regression 0.092306 S.E. of regression 0.066234

Sum squared resid 5216.399 Sum squared resid 1836.734 Sum squared resid 2819.049 Sum squared resid 1452.288

Log likelihood 415471.2 Log likelihood 670750.4 Log likelihood 566091.8 Log likelihood 728713.9

Durbin-Watson stat 1.85158 Durbin-Watson stat 1.899028 Durbin-Watson stat 1.767964 Durbin-Watson stat 1.902392

Mean dependent var 0.3052 Mean dependent var 0.1074 Mean dependent var 0.2017 Mean dependent var 0.0942

S.D. dependent var 0.2664 S.D. dependent var 0.1467 S.D. dependent var 0.1994 S.D. dependent var 0.1454

Akaike info criterion -1.0558 Akaike info criterion -2.1000 Akaike info criterion -1.6716 Akaike info criterion -2.3354

Schwarz criterion 2.5271 Schwarz criterion 1.4832 Schwarz criterion 1.9116 Schwarz criterion 1.2482

F-statistic 11.8446 F-statistic 9.9147 F-statistic 12.3565 F-statistic 12.8304

Prob(F-statistic) 0.0000 Prob(F-statistic) 0.0000 Prob(F-statistic) 0.0000 Prob(F-statistic) 0.0000


XXIII

Appendix 13 - Regression for the Eastern European sample only



C -0.187981 -0.8292 0.4070 -0.047102 -0.446033 0.6556

AGE -0.000982 -1.4584 0.1447 -0.001124 -3.11453 0.0018


CONTRACTENF -0.000771 -16.0439 0.0000 -0.000205 -8.162366 0.0000

CORRUPTION -4.17E-02 -7.2268 0.0000 -0.009112 -2.689035 0.0072

CREDITINF -0.047749 -15.6094 0.0000 -0.006212 -3.855349 0.0001

DISCLOSURE -0.067493 -8.0355 0.0000 -0.026951 -6.30168 0.0000

GDPGROWTH -0.006553 -7.1760 0.0000 -0.000331 -0.694627 0.4873

GROWTHASSETS 0.01708 5.8283 0.0000 0.000485 0.388908 0.6973

INFLATION 0.003075 13.9492 0.0000 0.000313 3.189167 0.0014


LEGALRIGHTS -0.006489 -0.2006 0.8410 -0.009276 -0.732177 0.4641

LNTURNOVER 0.008914 1.8357 0.0664 0.006955 3.13392 0.0017

MARKETCAPTOGDP 0.206502 9.4439 0.0000 0.054227 3.886541 0.0001


NDTSHIELD -0.049714 -1.8333 0.0668 -0.034324 -2.921848 0.0035


RECOVERYRATE 0.002602 10.2065 0.0000 0.000914 5.658685 0.0000

ROA -0.002032 -12.1809 0.0000 -0.000741 -10.62379 0.0000
















XXIV



C -0.2363 -1.687134 0.0916 -0.170225 -1.953828 0.0507

AGE -0.0021 -3.813157 0.0001 -0.000981 -2.340411 0.0193


CONTRACTENF -0.0003 -7.401755 0.0000 -9.90E-05 -2.714454 0.0066

CORRUPTION -0.0184 -3.842165 0.0001 -0.008324 -2.184543 0.0289

CREDITINF -0.0089 -3.159365 0.0016 -0.002526 -0.997398 0.3186

DISCLOSURE -0.0443 -7.685186 0.0000 -0.016153 -3.431897 0.0006

GDPGROWTH -0.0034 -5.236034 0.0000 -0.003136 -5.99232 0.0000

GROWTHASSETS 0.0069 3.542583 0.0004 0.00669 4.412763 0.0000

INFLATION 0.0007 4.566536 0.0000 0.000347 3.443653 0.0006


LEGALRIGHTS -0.0077 -0.459266 0.6460 0.000436 0.077428 0.9383

LNTURNOVER 0.0007 0.211758 0.8323 -0.006401 -2.370185 0.0178

MARKETCAPTOGDP 0.0908 4.790367 0.0000 0.035665 2.571073 0.0101

NARROWTANGIBILITY 0.0856 5.986976 0.0000 0.105256 8.880715 0.0000

NDTSHIELD -0.0761 -2.369001 0.0178 -0.042697 -1.687531 0.0915


RECOVERYRATE 0.0014 6.360805 0.0000 0.000489 2.742587 0.0061

ROA -0.0014 -12.4005 0.0000 -0.000602 -6.809169 0.0000
















XXV

Appendix 14 – Hausman test

Correlated Random Effects - Hausman Test

SHORTTERMDEBT

Test Summary Chi-Sq. Statistic Chi-Sq. d.f. p-value

Cross-section random 10475.71654 36 0.0000

SHORTBANKDEBT



NARROWLEVERAGE



LONGBANKDEBT



In all four regressions the null-hypothesis, saying that there is no misspecification in the model, is

rejected based on the p-values being equal to zero. This implies that the correct model to use is a

fixed-effects model. This can seem obvious from an economic point of view, since a random effects

model does not allow the estimated effect to be correlated with the explanatory variables. The

estimated effect is among other things going to capture industry effects. These are commonly

known to be highly correlated with the amount of tangible assets i.e. the tangibility proxy, and

thereby violates the assumptions behind the random effects model.


XXVI

Appendix 15 – F-test for joint significance of the fixed effects

Test for the redundancy of cross-section fixed effects

H0 = fixed effects are redundant

Redundant Fixed Effects Tests

SHORTTERMDEBT

Effects Test Statistic d.f. p-value

Cross-section F 7.2058 -157639.331 0.0000

Cross-section Chi-square 727439.5245 157639 0.0000

SHORTBANKDEBT


Cross-section F 7.7462 -157712.331 0.0000


NARROWLEVERAGE


Cross-section F 9.8489 -157712.331 0.0000


LONGBANKDEBT


Cross-section F 9.1265 -157817.331 0.0000



XXVII

Appendix 16 - Equality test of leverage

Test for Equality of Means of SHORTTERMDEBT Test for Equality of Means of SHORTBANKDEBT

Method df Value P-value Method df Value P-value

t-test 489528 29.50507 0.0000 t-test 489735 57.36907 0.0000

Anova F-statistic (1.489528) 870.549 0.0000 Anova F-statistic (1.489735) 3291.211 0.0000

Analysis of Variance Analysis of Variance

Source of Variation df Sum of Sq. Mean Sq. Source of Variation df Sum of Sq. Mean Sq.

Between 1 61.66081 61.6608 Between 1 70.3156 70.3156

Within 489528 34673.17 0.0708 Within 489735 10463.02 0.0214

Total 489529 34734.83 0.0710 Total 489736 10533.34 0.0215

Category Statistics Category Statistics

Std. Err. Std. Err.

Count Mean Std. Dev. of Mean Count Mean Std. Dev. of Mean

West 451887 0.308542 0.266927 0.0004 West 451959 0.110858 0.148832 0.0002

East 37643 0.266417 0.256485 0.0013 East 37778 0.065948 0.109352 0.0006

All 489530 0.305303 0.266375 0.0004 All 489737 0.107393 0.146657 0.0002

Test for Equality of Means of NARROWLEVERAGE Test for Equality of Means of LONGBANKDEBT

Method df Value P-value Method df Value P-value

t-test 489735 71.58544 0.0000 t-test 490036 40.51292 0.0000

Anova F-statistic (1.48973) 5124.475 0.0000 Anova F-statistic (1.490036) 1641.297 0.0000

Analysis of Variance Analysis of Variance

Source of Variation df Sum of Sq. Mean Sq. Source of Variation df Sum of Sq. Mean Sq.

Between 1 201.6095 201.6095 Between 1 34.60243 34.6024

Within 489735 19267.38 0.0393 Within 490036 10331.12 0.0211

Total 489736 19468.99 0.0398 Total 490037 10365.72 0.0212

Category Statistics Category Statistics

Std. Err. Std. Err.

Count Mean Std. Dev. of Mean Count Mean Std. Dev. of Mean

West 451959 0.207581 0.200207 0.0003 West 451959 0.096723 0.145961 0.0002

East 37778 0.131536 0.174599 0.0009 East 38079 0.065334 0.135809 0.0007

All 489737 0.201715 0.199384 0.0003 All 490038 0.094284 0.145441 0.0002


XXVIII

Appendix 17 - Example on Net interest margin

In this example it is assumed that SMEs in different countries have the same average demand for

external financing. If 80 percent of all companies in the West, but only 40 percent of the companies

in the East, can satisfy their need for external financing at a certain net interest margin level and that

at a net interest margin this is increased to 85 and 75 percent respectively. Then the marginal impact

of an increase in net interest margin is higher on the East. This is illustrated in the figure below.

0

0,05

0,1

0,15

0,2

0,25

0,3

0,35

0 1 2 3 4 5 6 7

lev

era

ge

Net interest margin

West

East

Lineær (West)

Lineær (East)

capital structure in european smes - pure

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