Capital Structure in European SMEs

Transcription

1 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

2 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

3 Table of Contents 1. Introduction Outline of the paper Literature review Theories of capital structure Miller and Modigliani s irrelevance proposition The static Trade-off theory Pecking order theory SME financing gap Capital structure in East vs. West Market power Research question and hypotheses Methodology Fixed effects vs. random effects model Dummy variables Data collection Firm specific data Country specific data The final dataset Proxies Dependent variables Independent variables... 41

4 7. Analysis Descriptive statistics Regression output Interpretation of results Hypothesis 1 Leverage in Eastern and Western Europe Hypothesis 2 Corporate governance Hypothesis 3 Legal environment Hypothesis 4 Financial development Hypothesis 5 Bank concentration Hypothesis 6 Bank profitability Hypothesis 7 Eastern and Western Europe respond differently Conclusion Critical assessment and suggestions for further research Bibliography Appendices Table of contents

5 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 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: 1

6 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 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. 2

7 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) 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): 3

8 The market value of any firm is independent of its capital structure and is given by capitalizing its expected return at the rate ρ k 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. V L = V U 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. 4

9 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. 5

10 Figure 1 Source: (Myers 1984) 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 6

11 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 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 7

12 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 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 8

13 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 Montanus 1 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

14 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 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. 10

15 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 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 11

16 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 12

17 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) 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 13

18 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 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 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, 14

19 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 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 15

20 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 equityfinancing. 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 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 16

21 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 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. 17

22 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 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 18

23 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 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 19

24 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. 20

25 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 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 21

26 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 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). 22

27 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 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 23

28 companies exist, and expects a negative relation between concentration and credit availability 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 24

29 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. 25

30 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 26

31 Bulgaria where corruption is a much bigger issue than in Finland 2. 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 One of the advantages of using panel data is the possibility to control for firm specific heterogeneity (Wooldridge 2006) 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

32 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 Eviews 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 28

33 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 29

34 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) 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:, 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 30

35 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 betacoefficients 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

36 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 32

37 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 nonavailability 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. 33

38 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 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 Source: 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 34

39 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 nonincorporated 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: 35

40 Table 2 Elimination criteria Operating revenue time t Operating revenue time t-1 Total assets time t Total assets time t-1 Return on assets Year of incorporation Tangible fixed assets Other fixed assets Depreciation Long-term debt Loans 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 Age Tangible Fixed Assets + Other Fixed Assets Long-term Debt + Loans 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. 36

41 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 Contract Enforcement Source 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 GDP Growth Bank Concentration Net Interest Margin Global Market Information Database World Development Indicators International Monetary Fund 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 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 37

42 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 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). 38

43 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 ,504 2,968 61, ,086 5,185 73, ,814 5,119 86, ,662 7,154 79, ,642 9,170 87, ,251 8, ,734 Total 451,959 38, , 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 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 39

44 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: 2. SHORTBANKDEBT: 3. NARROWLEVERAGE: 4. SHORTTERMDEBT 4 : 5. CURRENTLIABILITIES: 6. BROADLEVERAGE: 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. 40

45 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 transactionpurposes 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 tradecredits. 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 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 41

46 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 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 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 debtfinancing. 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 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 42

47 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 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 shortterm leverage measures. Table 4 in appendix 7 gives an overview of different studies identifying positive as well as negative relationships between growth and leverage. 43

48 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 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 44

49 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 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. 45

50 Country specific variables Here follows a description of the country specific proxies 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 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 taxshield is increased, because part of the principal repayment is then tax-deductible (Myers 2001). Therefore a positive relationship between inflation and leverage is expected 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 46

51 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 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 47

52 (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 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 48

53 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 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. 49

54 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 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). 50

55 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 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 business 5 : 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

56 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 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 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 on agency costs, covenants can be 52

57 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 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 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 53

58 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 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 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. 54

59 Table 7 -Descriptive Statistics of the different leverage-measures SHORTTERMDEBT Mean Max Min. Std. Dev. Obs. West ,887 East ,643 All ,530 SHORTBANKDEBT Mean Max Min. Std. Dev. Obs. West ,959 East ,778 All ,737 NARROWLEVERAGE Mean Max Min. Std. Dev. Obs. West ,959 East ,778 All ,737 LONGBANKDEBT Mean Max Min. Std. Dev. Obs. West ,959 East ,079 All ,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 meanvalue for the Eastern sample with 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 55

60 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. 56

61 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 ,947 East ,079 All ,026 GROWTHASSETS Mean Median Max Min. Std. Dev. Obs. West ,959 East ,079 All ,038 LNTURNOVER Mean Median Max Min. Std. Dev. Obs. West ,958 East ,079 All ,037 NARROWTANGIBILITY Mean Median Max Min. Std. Dev. Obs. West ,959 East ,078 All ,037 NDTSHIELD Mean Median Max Min. Std. Dev. Obs. West E ,959 East ,304 All ,263 ROA Mean Median Max Min. Std. Dev. Obs. West ,959 East ,079 All ,038 57

62 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. 58

63 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 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 59

64 having the highest GDP growth as well as inflation rate. When looking at the stockmarket 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 contractenforcement. 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 60

65 (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 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 to 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 61

66 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. 62

67 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 -* -*** -** -* netinterstmargin +*** +*** +*** +*** recoveryrate +*** +*** +*** +*** roa -*** -*** -*** -*** * = significant at the 10 % level ** = significant at the 5 % level ***= significant at the 1 % level 63

68 Table 10 Dummy interactions shortermdebt shorttermbankdebt narrowleverage longbankdebt age -*** -*** -*** -*** bankconcentration contractentforcement -*** -*** -*** + corruption -*** -*** -*** - creditinf + - -** -** disclosure -*** -*** -*** -*** gdpgrowth -*** - +*** +*** growthassets +*** +*** - -** inflation +*** +*** + -*** investorprotection +*** +*** +*** +*** legalrights + - -* -*** lnturnover -*** -*** -*** -*** marketcaptogdp +*** +*** +*** +*** narrowtangibility -*** + -*** -*** ndtshield +** +** +*** +*** netinterstmargin +*** +*** +*** +*** 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 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 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. 64

69 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 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 65

70 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. 66

71 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 67

72 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 68

73 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 longterm 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 tradecredit. 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 69

74 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 70

75 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 shortterm 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 , 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 71

76 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 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 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 72

77 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 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 73

78 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 curve 6. 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. 74

79 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 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 75

80 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 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 76

81 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 1 st 2007, banks which operate internationally are required to hold capital equal to 8 percent of their riskweighted 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 % 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 bankequity 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 1 st 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 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 77

82 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 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. 78

83 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 79

84 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. 80

85 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. 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 shortterm debt serves as a buffer and will absorb here and now changes in profitability. 81

86 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 shortterm 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 82

87 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 maturitymatching. So when companies make capital investments, they will try to acquire longterm 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 83

88 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 shortterm 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 tradecredit 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 84

89 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 85

90 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 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 debtfinancing (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. 86

91 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 87

92 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 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 88

93 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 cashflow 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 shortterm 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 89

94 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 , 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 90

95 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 91

96 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 92

97 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 93

98 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. 94

99 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 creditratings 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. 95

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108 12. Appendices Table of contents Appendix 1: Risk Shifting Appendix 2: Institutional factors in Eastern and Western Europe Appendix 3: Country Risk Appendix 4: Regression equation Appendix 5: The special case of collecting data in 2001 and 2002 I II V VI VII Appendix 6: Regression output for Current Liabilities and Broadleverage VIII Appendix 7: Empirical Evidence on firm specific variables Appendix 8: Per country descriptive statistics of the leverage measures X XIV Appendix 9: Per country descriptive statistics of firm specific variables XV Appendix 10: Mean values of country specific factors per country Appendix 11: Regression output Appendix 12: Pooled regression Appendix 13: Regression for the Eastern European sample only Appendix 14: Hausmann test Appendix 15: F-test for joint significance of the fixed effects Appendix 16: Equality test of leverage Appendix 17: Example on Net interest margin XVI XVIII XXII XXIII XXV XXVI XXVII XXVIII 104

109 Appendix 1 - Risk shifting Overall project Shareholder payoff Debt holder payoff Low volatility E(V) = 100 E(V) = 50 E(V) = High volatility E(V) = 100 E(V) = 62.5 E(V) = 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. I

110 Appendix 2 Institutional factors in Eastern and Western Europe Corporate governance is in many Eastern European countries a fairly new issue. Before the fall of 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 markets have evolved, and suddenly the issue of good corporate governance has become important in order to attract external capital (Mcgee, Preobragenskaya 2004). The need for good corporate governance in the Eastern countries is maybe 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, (Bobirca, Miclaus 2007) finds that Corporate Governance in Eastern Europe is on average lagging behind, as a natural consequence of the fact that the systems are still young. In order to verify that, data on different country specific measures from a sample of Eastern and Western countries has been collected and presented below in order to give a general overview. 8,00 6,00 4,00 2,00 0,00 MARKETCAPTOGDP CREDITINF INV. PROTECTION LEGALRIGHTS CORRUPTION CONTRACTENF Western Europe Eastern Europe Western Europe: Austria, Belgium, Switzerland, Germany. Spain, Finland, France, Great Britain, 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, Average values from 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. II

111 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 III

112 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. IV

113 Appendix 3 - Country Risk The country-risk measures have been obtained from which is a project of the World-check. World-check is one of the leading providers of risk intelligence in the world. V

114 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 VI

115 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 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 M. Revenue 1.5 M M. Employees 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 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 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. VII

116 Appendix 6 Regression output for Current Liabilities and Broadleverage BROADLEVERAGE CURRENTLIABILITIES Variable Coefficient t-statistic p-value Coefficient t-statistic p-value C AGE AGE*DUMMY BANKCONCENTRATION BANKCONCENTRATION*DUMMY CONTRACTENF E CONTRACTENF*DUMMY -9.46E CORRUPTION CORRUPTION*DUMMY CREDITINF CREDITINF*DUMMY DISCLOSURE DISCLOSURE*DUMMY GDPGROWTH GDPGROWTH*DUMMY GROWTHASSETS GROWTHASSETS*DUMMY INFLATION INFLATION*DUMMY INVESTORPROTECTION INVESTORPROTECTION*DUMMY LEGALRIGHTS LEGALRIGHTS*DUMMY LNTURNOVER LNTURNOVER*DUMMY MARKETCAPTOGDP MARKETCAPTOGDP*DUMMY NARROWTANGIBILITY NARROWTANGIBILITY*DUMMY NDTSHIELD NDTSHIELD*DUMMY NETINTERESTMARGIN NETINTERESTMARGIN*DUMMY RECOVERYRATE -1.49E RECOVERYRATE*DUMMY ROA ROA*DUMMY E VIII

117 Effects Specification Cross-section fixed effect Effects Specification Cross-section fixed effect R-squared R-squared Adjusted R-squared Adjusted R-squared S.E. of regression S.E. of regression Sum squared resid Sum squared resid Log likelihood Log likelihood Durbin-Watson stat Durbin-Watson stat Mean dependent var Mean dependent var S.D. dependent var S.D. dependent var Akaike info criterion Akaike info criterion Schwarz criterion Schwarz criterion F-statistic F-statistic Prob(F-statistic) 0 Prob(F-statistic) 0 IX

118 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) X

119 Table 2: The Influence of Size on Firm Leverage Source: (Prasad, Green & Murinde 2001) XI

120 Table 3: The Influence of Profitability on Firm Leverage Source: (Prasad, Green & Murinde 2001) XII

121 Table 4: The Influence of Growth on Firm Leverage Source: (Prasad, Green & Murinde 2001) Table 5: The Influence of Non-debt Tax-shields on Firm Leverage Source: (Prasad, Green & Murinde 2001) XIII

122 Appendix 8 - Per country descriptive statistics of the leverage measures Western Europe SHORTTERMDEBT SHORTBANKDEBT NARROWLEVERAGE LONGBANKDEBT Austria Belgium Switzerland Germany Spain Finland France Great Britain Greece Italia Netherlands Portugal Sweden Eastern Europe SHORTTERMDEBT SHORTBANKDEBT NARROWLEVERAGE LONGBANKDEBT Bulgaria Czech Republic Estonia Croatia Hungary Lithuania Latvia Poland Romania Serbia Slovakia The presented values are the mean-values per country averaged over time. XIV

123 Appendix 9 - Per country descriptive statistics of firm specific variables Western Europe ROA age growth assets lnturnover non debt tax-shield Austria Belgium Switzerland Germany Spain Finland France Great Britain Greece Italia Netherlands Portugal Sweden Eastern Europe ROA age growth assets lnturnover non debt tax-shield Bulgaria Czech Republic Estonia Croatia Hungary Lithuania Latvia Poland Romania Serbia Slovakia The presented values are the mean-values per country averaged over time. XV

124 Appendix 10 Mean values of country specific factors per country Western Europe GDPGROWTH INFLATION MARKETCAPTOGDP BANKCONCENTRATION NETINTERESTMARGIN RECOVERYRATE Austria Belgium Switzerland Germany Spain Finland France Great Britain Greece Italia Netherlands Portugal Sweden Eastern Europe GDPGROWTH INFLATION MARKETCAPTOGDP BANKCONCENTRATION NETINTERESTMARGIN RECOVERYRATE Bulgaria Czech Republic Estonia Croatia Hungary Lithuania Latvia Poland Romania Serbia Slovakia XVI

125 Western Europe CONTRACTENF CORRUPTION CREDITINF DISCLOSURE INVESTORPROTECTION LEGALRIGHTS Austria Belgium Switzerland Germany Spain Finland France Great Britain Greece Italia Netherlands Portugal Sweden Eastern Europe CONTRACTENF CORRUPTION CREDITINF DISCLOSURE INVESTORPROTECTION LEGALRIGHTS Bulgaria Czech Republic Estonia Croatia Hungary Lithuania Latvia Poland Romania Serbia Slovakia The presented values are the mean-values per country averaged over time. XVII

126 Appendix 11 Regression output West is serving as base group while the DUMMY indicates the Eastern sample SHORTTERMDEBT SHORTBANKDEBT Variable Coefficient t-statistic p-value Coefficient t-statistic p-value C AGE AGE*DUMMY BANKCONCENTRATION BANKCONCENTRATION*DUMMY CONTRACTENF 8.52E E CONTRACTENF*DUMMY CORRUPTION CORRUPTION*DUMMY CREDITINF CREDITINF*DUMMY DISCLOSURE DISCLOSURE*DUMMY GDPGROWTH E GDPGROWTH*DUMMY GROWTHASSETS GROWTHASSETS*DUMMY INFLATION INFLATION*DUMMY INVESTORPROTECTION INVESTORPROTECTION*DUMMY LEGALRIGHTS LEGALRIGHTS*DUMMY LNTURNOVER LNTURNOVER*DUMMY MARKETCAPTOGDP MARKETCAPTOGDP*DUMMY NARROWTANGIBILITY NARROWTANGIBILITY*DUMMY NDTSHIELD NDTSHIELD*DUMMY NETINTERESTMARGIN NETINTERESTMARGIN*DUMMY RECOVERYRATE RECOVERYRATE*DUMMY ROA ROA*DUMMY XVIII

127 SHORTTERMDEBT Effects Specification Cross-section fixed effect SHORTBANKDEBT Effects Specification Cross-section fixed effect R-squared R-squared Adjusted R-squared Adjusted R-squared S.E. of regression S.E. of regression Sum squared resid Sum squared resid Log likelihood Log likelihood Durbin-Watson stat Durbin-Watson stat Mean dependent var Mean dependent var S.D. dependent var S.D. dependent var Akaike info criterion Akaike info criterion Schwarz criterion Schwarz criterion F-statistic F-statistic Prob(F-statistic) Prob(F-statistic) XIX

128 NARROWLEVERAGE LONGBANKDEBT Variable Coefficient t-statistic p-value Coefficient t-statistic p-value C AGE AGE*DUMMY BANKCONCENTRATION BANKCONCENTRATION*DUMMY CONTRACTENF CONTRACTENF*DUMMY E CORRUPTION CORRUPTION*DUMMY CREDITINF CREDITINF*DUMMY DISCLOSURE DISCLOSURE*DUMMY GDPGROWTH GDPGROWTH*DUMMY GROWTHASSETS GROWTHASSETS*DUMMY INFLATION INFLATION*DUMMY INVESTORPROTECTION INVESTORPROTECTION*DUMMY LEGALRIGHTS LEGALRIGHTS*DUMMY LNTURNOVER LNTURNOVER*DUMMY MARKETCAPTOGDP MARKETCAPTOGDP*DUMMY NARROWTANGIBILITY NARROWTANGIBILITY*DUMMY NDTSHIELD NDTSHIELD*DUMMY NETINTERESTMARGIN NETINTERESTMARGIN*DUMMY RECOVERYRATE RECOVERYRATE*DUMMY ROA ROA*DUMMY XX

129 NARROWLEVERAGE Effects Specification Cross-section fixed effect LONGBANKDEBT Effects Specification Cross-section fixed effect R-squared R-squared Adjusted R-squared Adjusted R-squared S.E. of regression S.E. of regression Sum squared residuals Sum squared residuals Log likelihood Log likelihood Durbin-Watson stat Durbin-Watson stat Mean dependent var Mean dependent var S.D. dependent var S.D. dependent var Akaike info criterion Akaike info criterion Schwarz criterion Schwarz criterion F-statistic F-statistic Prob(F-statistic) Prob(F-statistic) XXI

130 Appendix 12 - Pooled Regression SHORTTERMDEBT SHORTBANKDEBT NARROWLEVERAGE LONGBANKDEBT Variable Coefficient t-statistic p-value Coefficient t-statistic p-value Coefficient t-statistic p-value Coefficient t-statistic p-value C AGE BANKCONCENTRATION CONTRACTENF -7.96E E E CORRUPTION CREDITINF DISCLOSURE GDPGROWTH GROWTHASSETS INFLATION INVESTORPROTECTION LEGALRIGHTS LNTURNOVER MARKETCAPTOGDP NARROWTANGIBILITY NDTSHIELD NETINTERESTMARGIN RECOVERYRATE ROA 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 R-squared R-squared R-squared Adjusted R-squared Adjusted R-squared Adjusted R-squared Adjusted R-squared S.E. of regression S.E. of regression S.E. of regression S.E. of regression Sum squared resid Sum squared resid Sum squared resid Sum squared resid Log likelihood Log likelihood Log likelihood Log likelihood Durbin-Watson stat Durbin-Watson stat Durbin-Watson stat Durbin-Watson stat Mean dependent var Mean dependent var Mean dependent var Mean dependent var S.D. dependent var S.D. dependent var S.D. dependent var S.D. dependent var Akaike info criterion Akaike info criterion Akaike info criterion Akaike info criterion Schwarz criterion Schwarz criterion Schwarz criterion Schwarz criterion F-statistic F-statistic F-statistic F-statistic Prob(F-statistic) Prob(F-statistic) Prob(F-statistic) Prob(F-statistic) XXII

131 Appendix 13 - Regression for the Eastern European sample only SHORTTERMDEBT SHORTBANKDEBT Variable Coefficient t-statistic p-value Coefficient t-statistic p-value C AGE BANKCONCENTRATION CONTRACTENF CORRUPTION -4.17E CREDITINF DISCLOSURE GDPGROWTH GROWTHASSETS INFLATION INVESTORPROTECTION LEGALRIGHTS LNTURNOVER MARKETCAPTOGDP NARROWTANGIBILITY NDTSHIELD NETINTERESTMARGIN RECOVERYRATE ROA Effects Specification Cross-section fixed effect Effects Specification Cross-section fixed effect R-squared R-squared Adjusted R-squared Adjusted R-squared S.E. of regression S.E. of regression Sum squared resid Sum squared resid Log likelihood Log likelihood Durbin-Watson stat Durbin-Watson stat Mean dependent var Mean dependent var S.D. dependent var S.D. dependent var Akaike info criterion Akaike info criterion Schwarz criterion Schwarz criterion F-statistic F-statistic Prob(F-statistic) Prob(F-statistic) XXIII

132 NARROWLEVERAGE LONGBANKDEBT Variable Coefficient t-statistic p-value Coefficient t-statistic p-value C AGE BANKCONCENTRATION CONTRACTENF E CORRUPTION CREDITINF DISCLOSURE GDPGROWTH GROWTHASSETS INFLATION INVESTORPROTECTION LEGALRIGHTS LNTURNOVER MARKETCAPTOGDP NARROWTANGIBILITY NDTSHIELD NETINTERESTMARGIN RECOVERYRATE ROA Effects Specification Cross-section fixed effect Effects Specification Cross-section fixed effect R-squared R-squared Adjusted R-squared Adjusted R-squared S.E. of regression S.E. of regression Sum squared resid Sum squared resid Log likelihood Log likelihood Durbin-Watson stat Durbin-Watson stat Mean dependent var Mean dependent var S.D. dependent var S.D. dependent var Akaike info criterion Akaike info criterion Schwarz criterion Schwarz criterion F-statistic F-statistic Prob(F-statistic) Prob(F-statistic) XXIV

133 Appendix 14 Hausman test Correlated Random Effects - Hausman Test SHORTTERMDEBT Test Summary Chi-Sq. Statistic Chi-Sq. d.f. p-value Cross-section random SHORTBANKDEBT Test Summary Chi-Sq. Statistic Chi-Sq. d.f. p-value Cross-section random NARROWLEVERAGE Test Summary Chi-Sq. Statistic Chi-Sq. d.f. p-value Cross-section random LONGBANKDEBT Test Summary Chi-Sq. Statistic Chi-Sq. d.f. p-value Cross-section random 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. XXV

134 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 Cross-section Chi-square SHORTBANKDEBT Effects Test Statistic d.f. p-value Cross-section F Cross-section Chi-square NARROWLEVERAGE Effects Test Statistic d.f. p-value Cross-section F Cross-section Chi-square LONGBANKDEBT Effects Test Statistic d.f. p-value Cross-section F Cross-section Chi-square XXVI

135 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 t-test Anova F-statistic ( ) Anova F-statistic ( ) 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 Between Within Within Total Total Category Statistics Category Statistics Std. Err. Std. Err. Count Mean Std. Dev. of Mean Count Mean Std. Dev. of Mean West West East East All All 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 t-test Anova F-statistic ( ) Anova F-statistic ( ) 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 Between Within Within Total Total Category Statistics Category Statistics Std. Err. Std. Err. Count Mean Std. Dev. of Mean Count Mean Std. Dev. of Mean West West East East All All XXVII

136 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. 0,35 0,3 0,25 leverage 0,2 0,15 0,1 0,05 West East Lineær (West) Lineær (East) Net interest margin XXVIII