Aarhus University

Business and Social Sciences

Capital Structure of SMEs: Does Firm Size Matter?

Empirical investigation of the Baltic countries

Master thesis

Author: Egle Krasauskaite

MSc in Finance & International Business

Advisor: Stefan Hirth

Associate Professor, PhD

Department of Economics and Business

October, 2011

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Acknowledgements This master thesis finalise my two-year Master of Science in Finance and International

Business at Aarhus University, Business and Social Sciences.

My utmost gratitude goes to my academic advisor, Stefan Hirth, for his helpful advice and

constructive comments during the thesis writing process.

Very special thanks go to my friend, Dominyka Sakalauskaite, and my coursemate, John-

Paul Pearson. Their time spent for proofreading my thesis and providing constructive

criticism is highly appreciated.

I am also thankful for my family, who supported and encouraged me not only during the

thesis writing, but also during the entire period of my studies.

I also would like to thank my wonderful flatmate, Indre Radzeviciute, for creating working

atmosphere, supporting and enduring me during the periods without inspiration.

Egle Krasauskaite

October, 2011

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Abstract Since the seminal papers by Modigliani & Miller (1958, 1963) the analysis of the capital

structure decisions has been an important area of research within the field of finance. In

accordance, the purpose of this thesis is to investigate the leverage decisions of micro,

small and medium-sized enterprises (SMEs) in the Baltic countries, namely the

determinants of long-term debt financing of these enterprises. Instead of viewing SMEs as a

homogenous group, in this paper, it is distinguished among micro, small and medium-sized

enterprises and examined whether the factors that affect capital structure are the same for

companies belonging to these different size-based groups. In addition, given that substantial

proportions of SMEs in the Baltic countries have zero long-term debt, it is analysed

whether determinants of the probability that a firm is using long-term debt financing are the

same as determinants of the proportion of this type of financing in capital structure. The

results suggest that firm size has a conflicting influence on leverage. Micro firms, on

average, are less levered than small or medium-sized firms. However, when only firms with

positive long-term debt amounts are considered, the relationship between firm size and the

leverage ratio reverses: micro firms, on average, are more indebted than small firms, and

small firms, on average, have higher leverage ratios than medium-sized enterprises. In

addition, if it is distinguished between the decision to obtain long-term debt financing and

the decision on the relative amount of this source of financing, the results of the empirical

analysis suggest that the determinants of these two decisions are not the same. Finally,

although the results imply that all three size-based groups of SMEs in the Baltic countries

behave in accordance with the pecking order theory regarding their capital structure, there

are significant differences in the determinants of leverage among these groups. Therefore,

in the studies of capital structure of SMEs, it might be useful to consider the three sized-

based groups of SMEs separately.

Keywords: capital structure, leverage, pecking order theory, trade-off theory, agency

theory, long-term debt financing, SME, Baltic countries.

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Table of Contents

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

2. Literature Review ............................................................................................................ 5

2.1. Theories of capital structure..................................................................................... 5

2.1.1. The Modigliani – Miller irrelevance proposition ............................................. 5

2.1.2. The trade-off theory .......................................................................................... 7

2.1.3. The pecking order theory .................................................................................. 9

2.1.4. The agency theory .......................................................................................... 10

2.2. Empirical tests of the theories of capital structure ................................................. 12

2.3. Empirical findings on capital structure of SMEs ................................................... 16

2.4. Differences in financing patterns of SMEs and large enterprises .......................... 20

2.5. Firm size and debt financing .................................................................................. 24

2.6. Macroeconomic and institutional environment in the Baltic countries ................. 26

3. Research Question and Hypotheses .............................................................................. 32

4. Data and Methodology .................................................................................................. 35

4.1. Data ........................................................................................................................ 35

4.2. Model specification and testing procedures ........................................................... 37

4.3. Dependent and explanatory variables .................................................................... 44

5. Empirical analysis ......................................................................................................... 51

5.1. Sample statistics and descriptive statistics of variables ......................................... 51

5.2. Results of regressions and tests.............................................................................. 54

5.3. Robustness check ................................................................................................... 63

6. Conclusion ..................................................................................................................... 65

6.1. Concluding remarks ............................................................................................... 65

6.2. Limitations of the thesis and suggestions for further research .............................. 67

7. References ..................................................................................................................... 70

Appendices ........................................................................................................................... 77

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List of Tables Table 1. Taxes, macroeconomic and financial sector development variables of the Baltic states,

NMS and EU-15 ......................................................................................................................... 28

Table 2. Institutional factors in the Baltic countries, NMS and EU-15 (year 2010) .................. 31

Table 3. Number of firms by country in the sample.................................................................... 36

Table 4. Criteria to distinguish between micro, small and medium-sized firms set by the EC .. 37

Table 5. Distribution of the sample by firm size and country .................................................... 37

Table 6. Dependent and explanatory variables .......................................................................... 49

Table 7. Division of firms in the sample according to NACE Rev. 2 core code ......................... 49

Table 8. Firms with zero leverage ratios in the sample ............................................................. 51

Table 9. Descriptive statistics for the explanatory variables ..................................................... 52

Table 10. Summary statistics of the leverage ratios ................................................................... 53

Table 11. Pair-wise comparison of mean leverage ratios for subgroups of SMEs .................... 54

Table 12. Results of regressions of the two-part FRM ............................................................... 55

Table 13. Average partial effects of the explanatory variables .................................................. 59

Table 14. LR and LM test statistics and p-values for the null hypotheses of the equality of the

coefficients of each explanatory variable ................................................................................... 61

Table 15. LR and LM test statistics and p-values for the null hypothesis of the equality of all the

coefficients .................................................................................................................................. 62

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1. Introduction

Modigliani and Miller’s (1958, 1963) capital structure irrelevance propositions have

motivated debates among the financial economists regarding the optimal capital structure of

a firm. In the perfect Modigliani and Miller’s world, capital structure is irrelevant for the

value of a firm. Despite the fact that a number of subsequent leverage relevance theories

have tried to incorporate market imperfections, the empirical research implies that these

theories are still not accurate enough to explain the broad patterns of firms’ financing

decisions.

The literature on capital structure is extensive; however, the majority of the papers have

focused on the financing choices of large publicly listed firms. It was recognized by

policymakers and researchers that SMEs play a vital role in the economies around the

world (European Commission 2010). In the European Union, in 2008, the vast majority

(99.8%) of enterprises were SMEs, which accounted for more than two thirds (67.4%) of

total employment (European Commission 2010). Thus, acknowledging the importance of

SMEs, the number of empirical studies on SMEs capital structure decisions has increased.

Today there is a number of studies focusing on SMEs debt policy decisions in Western

European countries (for example, Michaelas, Chittenden & Poutziouris 1998; Hall,

Hutchinson & Michaelas 2000; Sogorb-Mira 2005; Degryse, Goeij & Kappert 2009).

Despite the extensive literature on capital structure, the empirical analysis of SMEs capital

structure in Eastern European countries, including the Baltic countries, is relatively scarce.

In many studies, this region has been neglected due to the lack of available and reliable data

(Bartholdy & Mateus 2008). Trying to fill the gap, the purpose of the thesis is to analyse if

the factors identified in the capital structure literature and found to have an influence on the

financing decisions help to explain leverage of Estonian, Latvian and Lithuanian SMEs. On

the one hand, it is relevant because, as found by Hall, Hutchinson & Michaelas (2004),

differences in the effects of the determinants of capital structure do exist across countries.

These findings suggest that not only firm-specific, but also country-specific factors, such as

macroeconomic, institutional and legal environment, have an impact on capital structure.

Although the conformance of the legal and institutional systems in the new member states

of the European Union (EU) has been a prerequisite for the accession to the EU, differences

in the macroeconomic environment and capital markets development are still evident

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between the new member countries and Western European countries. On the other hand, to

the best of my knowledge, there are no capital structure studies focusing solely on the case

of SMEs in the Baltic countries. There are more differences between this thesis and the

former SMEs capital structure studies.

Firstly, the sampling of SMEs was performed following the unified definition of SMEs set

by the European Commission in 2003. According to this definition, a company is

considered as an SME if it employs fewer than 250 employees and which has an annual

turnover not exceeding 50 million euros, and/or an annual balance sheet not exceeding 43

million euros (European Commission 2003). In contrast, many other studies have defined

SME quite differently. For instance, Hall, Hutchinson & Michaelas (2004) define an SME

as an enterprise with less than 200 employees; Degryse, Goeij & Kappert (2009) analyse

companies with sales below 20 million euros; Mac an Bhaird & Lucey (2010) use only a

criterion regarding the number of employees (less than 250). In addition, some studies

exclude micro firms and partially small firms from their analysis1. Given the fact that micro

firms comprise 91.8% of all enterprises in the EU (European Commission 2010), this group

of firms is important per se, deserves attention and is included in the analysis in this work.

Secondly, the majority of the previous empirical studies on SMEs capital structure treats all

SMEs as a unique, homogenous group and does not distinguish among micro, small and

medium-sized firms2. However, companies from these size-based groups can be very

different, making firm size a critical factor for capital structure decisions. Treating all

SMEs as a uniform group, most of the previous studies ignore the possibility that there

might be disparities in the effects of different capital structure factors between size-based

groups of SMEs. For instance, is asset structure more important for smaller firms due to the

higher risk associated with them? Therefore, the thesis analyses whether there are

differences, at least in magnitude, in the determinants of capital structure among the

subgroups of micro, small and medium-sized companies.

Lastly, different econometric methodology is applied in this thesis. The majority of the

previous work employs linear models to investigate the determinants of capital structure

1 For example, Bartholdy & Mateus (2008) exclude all firms with less than 25 employees; Mac an Bhaird &

Lucey (2010) exclude all firms with less than 20 employees; Joeveer (2005) includes a firm in a sample if the

number of employees is greater than 10. 2 Two exceptions are the papers by Ramalho & Vidigal da Silva (2009) and Daskalakis & Thanou (2010).

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decisions of SMEs. Since a leverage ratio is observed only in a closed interval [0;1] and

substantial proportions of SMEs follow a zero-debt policy, linear models lead to biased

results. However, the specific nature of the leverage ratio so far has received little attention

in the empirical literature on capital structure. When the data used in this thesis was

analysed for the first time, it was noticed that large proportions of SMEs in the Baltic

countries do not have long-term debt. Therefore, differently than most of the prior research,

it is not assumed that the influences on a company’s decision to obtain debt financing are

the same as those that affect its decision on the relative amount of debt financing obtained.

Hence, this thesis also investigates if the determinants of the above mentioned two

decisions are the same. In the empirical analysis, a two-part fractional regression model

(FRM), developed by Ramalho & Vidigal da Silva (2009) is used, which allows modeling

each decision separately.

The results of the empirical analysis show that the larger the SME, the more likely it is that

it uses long-term debt financing. As the proportion of micro firms that report zero long-

term debt is larger than the proportions of small or medium-sized firms without long-term

debt financing, micro firms, on average, are less levered than the other subgroups of SMEs.

However, when the comparison is limited to only firms with non-zero long-term debt, the

relation between firm’s size and leverage becomes reverse: micro firms, on average, are

more levered than small firms and small firms are more indebted than medium-sized firms.

In addition, in some cases it is found that the influence of some explanatory variables,

namely tangibility, profitability, growth opportunities and size, on the capital structure

decisions differs between the size-based groups of SMEs in the Baltic countries. The

empirical results also suggest that the determinants of the decision to obtain long-term debt

are not the same as determinants of the proportion of long-term debt in capital structure.

The remainder of this thesis is structured as follows. Chapter 2 reviews some capital

structure theories, empirical tests of them, findings regarding capital structure of SMEs,

differences in the capital structure decisions of large enterprises and SMEs, and then

provides some background information about the Baltic countries. Chapter 3 formulates the

research question and hypotheses. Chapter 4 describes the data set used in this thesis,

explains the econometric methodology applied and defines the dependent and explanatory

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variables. Chapter 5 presents the results of the empirical analysis. Finally, chapter 6

concludes and discusses the limitations of this thesis and suggestions for further research.

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2. Literature Review

The analysis of capital structure, which attempts to explain how companies choose a mix of

securities and financing sources to finance their investments, has been an important area of

research within a field of finance. Various imperfections, such as taxes, bankruptcy costs,

agency conflicts, issues of asymmetric information and adverse selection, have been

pointed out as explanations for the use of debt financing and synthesized into the trade-off

and pecking order theories of capital structure. The extensive empirical evidence and tests

of these theories can be found in the capital structure literature. As noted by Frank & Goyal

(2008), to understand the evidence, it is important to recognize the differences of the

financing behaviour between small private firms and large enterprises.

2.1. Theories of capital structure

During last fifty years several different theories, trying to explain the determination of

capital structure decisions, were developed, but as Myers (2001, p. 81) points, “there is no

universal theory of the debt-equity choice and no reason to expect one”. However, he

mentions that there are several conditional useful theories. As Frank & Goyal (2008)

describe, both the pecking order theory and the trade-off theory can be considered as

‘point-of-view’ theories, which are not explicit models, but provide some guidelines for the

development of models and tests.

2.1.1. The Modigliani – Miller irrelevance proposition

A modern theory of business finance begins by the Modigliani & Miller (1958) capital

structure irrelevance proposition. Before their work was published, there was no theory of

capital structure that was generally accepted.

The Modigliani & Miller (1958) analysis is based on the assumption that a probability

distribution of the firm’s cash flows does not depend on the capital structure decision it

makes and that all investors share the same expectations regarding the cash flows. They

also assume that there is a perfect capital market, where investors, who act rationally and

are well informed, are free to buy and sell securities and can borrow funds at the same

terms as companies do. Under assumptions that there are no transaction costs and corporate

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taxes, Modigliani & Miller (1958) prove that the leverage of a firm has no effect on a

market value of a firm. When the firm chooses its debt-equity mix to finance its assets, all

that it does is determine a division of cash flows between debt holders and equity holders.

Explicitly Modigliani & Miller (1958, p. 268) state this as Proposition I: “The market value

of any firm is independent of its capital structure and is given by capitalizing its expected

return at the rate ρk appropriate to its class”. The underlying logic of this proposition, as

Myers (2001) puts it, is that, in a perfect-market supermarket, the value of a pizza does not

depend upon how it is sliced.

According to Frank & Goyal (2008), there are two fundamentally different types of the

capital structure irrelevance proposition. The classic foundation of the Modigliani-Miller

hypothesis is an arbitrage process, which enables investors to pursue homemade leverage

by switching their investments from an unlevered firm to a levered firm or vice versa. By

borrowing on a personal account at a risk-free rate and buying shares of the unlevered firm

investors can create homemade leverage. The other way around, investors can undo

undesirable leverage by buying fewer stocks of the levered firm and lending at a risk-free

rate. As investors have this opportunity, they are not willing to pay a premium for levered

firms over unlevered firms. Hence, the values of two companies, identical in all aspects

except their capital structures, should be equal. The second type of capital structure

irrelevance is related to multiple equilibria (Frank & Goyal 2008). Miller (1977) considers

both personal and corporate taxes, which determine the equilibrium level of aggregate

corporate debt and, hence, an equilibrium debt-equity ratio for a whole corporate sector.

However, Miller’s (1977) model does not specify how aggregate quantities are split up

among individual firms. Although tax considerations establish an economy-wide leverage

ratio, there are multiple equilibria in which debt is issued by different firms (Frank & Goyal

2008). Miller (1977) concludes that it would be still true that the value of any firm, in

equilibrium, would be independent of its capital structure.

Modigliani-Miller’s theorem, although being intuitive, has been criticized widely for its

limitations. Again referring to the pizza example, Myers (2001) questions credibility of the

Modigliani-Miller theory and argues that the value of the pizza actually depends on how it

is sliced because consumers are willing to pay more for the slices than for the equivalent

whole. A proposition that financing does not matter holds in synthetic Modigliani and

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Miller’s world with strict simplifications, but it seems an unlikely description of how real-

world companies are financed. The irrelevance proposition triggered a wave of research

where scholars showed that the Modigliani-Miller theorem does not hold under a variety of

imperfections. Researchers took into consideration various elements, such as taxes,

bankruptcy costs, transaction costs, agency conflicts, or problems of asymmetric

information. As the extensive list of costs and imperfections is available, alternative

theories have been developed, which differ in terms of how they interpret these costs and

imperfections or which ones they emphasize.

In a subsequent paper, Modigliani & Miller (1963) relax one of their assumptions and

recognize the importance of corporate taxes. Because interest expenses are tax deductible,

they introduce an interest tax shield in their model. Due to the interest tax shield, the value

of the levered firm increases or the cost of capital decreases. Every extra dollar of debt

lowers tax payments. If debt is assumed to be risk-free and there are no offsetting costs

associated with leverage, firms will try to shield as much taxable income as possible. Yet,

in the real world there are no companies using exclusively debt financing. Hence, other

factors, such as bankruptcy costs or agency costs, which increase in the present value of

costs as the proportion of debt increases, were considered and led to the trade-off theory of

capital structure.

2.1.2. The trade-off theory

A family of related theories is described under the term of the trade-off theory. The idea,

which is general in all of these theories, is that a manager running a company assesses

benefits and costs of alternative leverage plans. However, trade-off theories might differ in

the way they recognize a role of time in capital structure decisions. This leads to two

different types of the trade-off theory, namely the static trade-off theory and the dynamic

trade-off theory.

Static trade-off theory In order to avoid an extreme prediction of the Modigliani and Miller’s model with

corporate taxes considered that firms should use only debt financing, offsetting costs

associated with a use of debt are essential. Researchers proposed that a possible element

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could be bankruptcy. Kraus & Litzenberger (1973) provide a classical statement that

optimization of the firm’s financial structure involves a trade-off between a tax advantage

of debt and bankruptcy penalties. When referring to bankruptcy penalties, they mean direct

bankruptcy costs. Miller (1977) argues that these costs do indeed exist, but they seem

disproportionately small relative to tax savings they are supposedly balancing. Hence,

Miller (1977) questions the validity of the statement that the optimal capital structure is

simply a matter of balancing tax advantages against bankruptcy costs by stating that

observed capital structures have shown too much stability over time. Thus, not only direct

costs of bankruptcy, but also indirect costs of bankruptcy should be considered in the static

trade-off models. Myers (1984) extends the definition of offsetting costs and defines them

as costs of financial distress, which include not only legal and administrative costs of

bankruptcy, but also subtler agency, moral hazard, monitoring and contracting costs which

can erode the firm’s value even if there is no formal default. According to Myers (1984), a

firm is viewed as one that sets a target debt-to-value ratio and gradually moves towards it3.

The trade-off theory suggests that the firm will use debt up to the point where the marginal

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

of potential costs of financial distress (Myers 2001). The firm substitutes debt for equity or

equity for debt until the point where the market value of the firm is maximized.

Dynamic trade-off theory The main difference between the static and dynamic trade-off models is that dynamic trade-

off models emphasize the importance of time in capital structure decisions. The static trade-

off model provides the solution of the optimal capital structure for one period and, hence,

suggests that firms should have the optimal capital structure in all periods. However, it is

unlikely that companies plan their decisions regarding capital structure just one period

ahead. In the dynamic trade-off models, what is the optimal capital structure choice in the

current period depends on what is expected to be the optimal capital structure in the next

period and so on. Some firms may plan to pay out funds in the next period, while others

3 Frank and Goyal (2008) break Myers’ (1984) definition into two parts: static trade-off theory and target

adjustment behaviour. They define the firm as following the static trade-off theory if its leverage is

determined by a single period trade-off between the tax benefits of debt and the deadweight costs of

bankruptcy, while target adjustment behaviour is if the firm has a target level of leverage and if deviations

from the target are gradually removed over time.

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may plan to raise funds either in the form of equity or debt. Thus, the dynamic trade-off

models incorporate roles of expectations and adjustment costs.

The early dynamic trade-off models consider the tax savings and bankruptcy costs trade-

off, but do not incorporate transaction costs (for example, Kane, Marcus & McDonald

1984; Brennan & Schwartz 1984). Firms receive annual adverse shocks to asset values, but,

as a recapitalization is costless, they react immediately and maintain high levels of debt to

take advantage of the tax shields.

Later Fischer, Heinkel & Zechner (1989) develop a model of a dynamic capital structure

choice with recapitalization costs. Their model allows avoiding the unrealistic rapid

rebalancing prediction of the early dynamic models. The model also implies that there is no

optimal leverage ratio, but rather a range over which a firm allows its debt ratio to vary

(Fischer, Heinkel & Zechner 1989). Hence, they assert that even small recapitalization

costs are responsible for the observations of wide swings in the firms’ leverage ratios. As a

constant rebalancing is costly, a company does not take any action regarding its capital

structure as long as leverage does not reach an upper or lower bound. If leverage reaches a

bound, a firm undertakes a discrete rebalancing.

2.1.3. The pecking order theory

Myers (1984) and Myers & Majluf (1984) propose an alternative explanation of why firms

choose certain capital structure, known as the pecking order theory. The pecking order

theory is a preference order theory, which describes how firms choose to obtain new

financing for their future activities and growth. The key underlying assumption of the

pecking order model is asymmetric information between managers of a firm and external

investors. The asymmetric information means that management, which is assumed to act in

the interest of existing shareholders, knows the true value of the existing assets and growth

opportunities, while external investors are able only to guess these values. Hence,

management’s actions regarding financing are perceived as a signal about the true value of

the firm. A decision to issue stock is perceived as a negative signal by prospective investors

because they infer that management is willing to sell equity because the firm is overvalued.

New shareholders are willing to invest only if the shares are sold at a marked-down price,

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which increases the costs of attracting additional funds for the firm. As adverse selection

costs make the new issuance of stock more expensive, management might decide not to

issue new equity and not to undertake positive NPV projects. If the firm needs external

financing and if the issue of debt is not possible, management considers issuing

undervalued stock only if the NPV of the new investment exceeds the costs incurred due to

undervaluation. Internal funds are always preferred over the external financing because

such financing always allows avoiding problems of asymmetric information.

Moreover, in the pecking order, a use of debt is preferred over a use of equity. Debt holders

of the firm face less risk than shareholders because debt has a senior claim on the assets and

earnings of the firm. The volatility of the future value of debt is lower than the volatility of

the future value of equity, i.e., costs of asymmetric information of debt are lower than of

equity. Hence, if internal sources are not available or sufficient and external financing is

necessary, firms generally prefer to issue debt first, which is the safest security, and then

hybrid securities such as convertible bonds or preferred equity. Equity is the last resort of

external financing when debt capacity is exhausted.

In contrast to the trade-off theory, in the pecking order theory, there is no optimal capital

structure. Changes in the firm’s debt ratio reflect only needs for external financing, not an

objective to reach optimal capital structure. The pecking order theory explains a negative

relationship between profitability and leverage: more profitable firms borrow less not

because their target debt ratio is low, but because more profitable firms have more internal

financing available (Myers 2001). External financing is necessary for less profitable firms

and, hence, they accumulate debt. As stated by Myers & Majluf (1984), the pecking order

can be interpreted as managerial capitalism – managers’ effort to avoid the discipline of

capital markets and to cut the ties that bind managers’ to shareholders’ interests.

2.1.4. The agency theory Both the trade-off theory and the pecking order theory assume that the interests of firm’s

management and its stockholders are perfectly aligned. However, theoretically and

practically perfect alignment is impossible. Jensen & Meckling (1976) argue that there are

unavoidable agency costs in corporate finance, which arise due to two types of conflicts: a

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conflict between firm’s management and its shareholders and a conflict between

shareholders and debt holders. In case of SMEs, managers often are also shareholders of a

firm. Therefore, an issue of a conflict of interest between management and shareholders is

not of much concern for SMEs. However, the agency conflict between equity holders and

debt holders may be an acute problem for SMEs.

A potential benefit of debt is a restriction of managerial discretion, which is related to the

free cash flow hypothesis developed by Jensen (1986). Free cash flow is a cash flow which

exceeds the funds required to finance all positive NPV projects available to the firm. Then,

as Jensen (1986, p. 323) states, the issue is “how to motivate managers to disgorge cash

rather than investing it at below the cost of capital or wasting it on organization

inefficiencies”. When management has a large amount of cash available, it tends to spend it

on increasing the size of the firm by using, for example, negative NPV projects, or on

consumption of perks. A possible solution for this problem might be debt creation. Issuance

of more debt and thereby increasing interest and principal payments reduce available free

cash flows and, hence, reduce agency costs. Debt issuance effectively commits managers to

pay out future cash flows. If the firm fails to make interest and principal payments, debt

holders have a right to take the firm into a bankruptcy procedure. This threat acts as a

motivating force to increase the efficiency of the firm. The problem of the free cash flow is

more severe in companies which generate large cash flows, but have low growth

opportunities. Hence, the control function of debt is more critical in such organizations.

Another potential problem that can trigger agency costs is a problem of risk shifting

identified by Jensen & Meckling (1976). If management acts in the interest of shareholders

(these two parties might be the same people in case of SMEs) and there is a possibility of

default, managers may try to take actions to transfer value from the debt holders to

shareholders. As only cash flows in non-bankrupt states matter, the firm might tend to

undertake projects that are too risky and generate large payoffs in good states. If a project is

successful and generates return higher than the face value of debt, equity investors will

receive most of the gain. If the project fails, the debt holders will bear the consequences. To

mitigate asset substitution problems, costly monitoring devices are included in debt

contracts to protect debt investors.

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Moreover, Myers (1977) emphasizes the underinvestment or debt overhang problem, which

means that a firm can pass up some positive NPV projects. Not investing in such projects is

to the detriment of debt holders because they are better off if the value of the firm increases.

Under normal circumstances, the firm invests up to the point where the added present value

of the project is equal to the required investment. However, a portion of this additional

value goes to the existing debt holders of the firm, who are better protected. The benefit

from investment for existing debt holders increases with the increasing risk of default.

Thus, the increase of the market value of debt can be considered as a tax on new

investment. If the tax is substantial, managers may try to reduce the size of the firm and pay

out cash to shareholders.

Myers (2001) also suggests that, if a company is already in a situation where creditors

could force bankruptcy or reorganization, managers can ‘play for time’ by withholding

problems. Such actions increase the effective maturity and the risk of debt. Again, debt

holders suffer, while shareholders gain.

The agency theory can be viewed as overlapping with both the trade-off theory and the

pecking order theory. The trade-off theory can also include the agency costs as a part of

costs of financial distress. Conflicts of interest between managers and shareholders and

between equity and debt holders may be equally relevant in the explanation why firms do

not fully utilize tax advantages of debt. Myers (2003) argues that some versions of the

agency theory infer a financing hierarchy as in the pecking order theory. For example,

agency costs of equity might result in the pecking order.

Having theoretical frameworks of capital structure theories constructed, the research has

developed specific models and tested empirically capital structure theories. The tests of

capital structure theories analyse if debt ratios vary across firms as predicted by the theory

(Frank & Goyal 2008).

2.2. Empirical tests of the theories of capital structure

Both the trade-off theory and the pecking order theory have been tested extensively,

particularly on samples of large listed firms. The analysis so far has revealed that capital

structure decisions are too complex to be explained by using either theory. Taken

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separately, the theories are also not able to clarify some important facts of the firms’

behavior regarding capital structure decisions.

The trade-off theory has been tested using cross-sectional observations. Researchers have

investigated the determinants of firms’ actual debt ratios using various proxies for taxes and

costs of financial distress. Such method of analysis allows making conclusions whether

predictions of the trade-off theory are supported by data. For example, proxies such as tax

loss carry-forwards, business risk, measured as volatility of the firm value or earnings,

intangible assets, measured as annual advertising and R&D expenses, should be negatively

related to debt levels. These proxies in the early studies by Bradley, Jarell & Kim (1984)

and Titman & Wessels (1988) have worked quite well in the cross-sectional tests.

Moreover, the trade-off theory predicts that the larger the firm, the more debt it should have

because larger firms are assumed to be more diversified and the risk of default is lower for

them. If the firm goes into distress, tangible assets lose more value than intangible assets;

therefore, firms with more tangible assets should borrow more than the companies with

mainly intangible assets. Growth firms (having high market-to-book ratios) lose more if

they go into distress; thus, there should be an inverse relationship between the market-to-

book ratio and debt ratios. Most of these predictions are confirmed; for example, Rajan &

Zingales (1995) and Frank & Goyal (2009) find support for these predictions of the trade-

off theory.

However, empirical evidence regarding the effects of taxes is mixed. Under the trade-off

theory, firms with higher tax rates should have higher debt ratios because higher taxes

allow firms to shield more taxable income. Companies with substantial non-debt tax

shields, such as depreciation, should tend to borrow less. If the tax rates increase over time,

debt ratios should also increase. The studies by Bradley, Jarell & Kim (1984) and Titman &

Wessels (1988) find a positive relationship between leverage and non-debt tax shields,

which contradicts the prediction of the trade-off theory. Graham (1996) concludes that a tax

status clearly affects corporate debt policy because a positive relationship between tax

status and incremental debt policy is found. However, Graham (1996) also stresses that the

explanatory power of taxes for a debt policy is relatively low. Wright (2004) finds that

leverage has been quite stable over more than one hundred years despite the fact that there

have been large differences in the tax rates over the same period.

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As Myers (2001) points, the trade-off theory is in immediate trouble on the tax front. He

states that there are too many established, highly profitable firms that have low debt levels.

The trade-off theory fails on the prediction that more profitable firms should tend to borrow

more. Research consistently has found the opposite relationship (for example, Titman &

Wessels 1988; Fama & French 2002; Frank & Goyal 2009). Rajan & Zingales (1995) also

find a negative relationship between profitability and leverage ratios in the samples of firms

from the USA, Canada and Japan, while insignificant relationship for firms from the UK,

France, Germany and Italy.

The trade-off theory has also been tested using a target adjustment model. In this model, a

firm has a target debt ratio, which is dependent on a value of interest tax shields and costs

of financial distress. If there are costs of adjustment, the firm adjusts gradually to the target.

The main issue in testing the target adjustment model is that the target debt ratio is directly

unobservable. The early studies measure firms’ target debt ratios as a long-term average of

the actual debt ratios (for instance, Jalilvand & Harris 1984; Auerbach 1985). These studies

find rapid speeds of adjustment. However, the approach of the target debt ratio, which is

kept constant, might raise doubts about the validity of these results. It is unlikely that the

target does not change over time, as characteristics of a company, which affect leverage, do

change.

More recent studies use a different approach of the target debt level. They employ a two-

step procedure in which, first, the target is estimated, and then a fitted value is substituted

into the equation of adjustment (for example, Fama & French 2002; Leary & Roberts

2005). Studies agree on the fact that debt ratios are mean reverting, but there is a

disagreement about how rapid the adjustment is. Fama & French (2002) estimate that the

speed of adjustment for firms which pay dividends is between 7% and 10%, and between

15% and 18% for dividend non-payers. According to Fama & French (2002), results

suggest that a speed of adjustment is too slow to be assumed as a first-order determinant of

capital structure decisions. Contrary, Leary & Roberts (2005) find that firms do indeed

rebalance their capital structures and respond to the issues of equity and equity price shocks

by changing leverage over the next two to four years.

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Empirical tests of the pecking order theory have also been conducted extensively. In the

assessment of the pecking order theory, changes in debt levels play a key role. According to

the theory, the firm’s financing deficit, which is equal to internally generated cash flow less

cash spent on capital investments and dividends, should be covered with debt issuance.

Shyam-Sunder & Myers (1999) test both the trade-off and the pecking order theories and

find support for both theories. However, they stress the importance of the statistical power

of tests and conclude that the test of the pecking order theory has statistical power relative

to the alternative of the trade-off theory. In their simulation, the target adjustment model is

not rejected when it is false, while the pecking order model does not suffer from this

problem. This result is interpreted as implying that “the pecking order is an excellent first-

order descriptor of corporate financing behaviour” (Shyam-Sunder & Myers 1999, p. 242).

Shyam-Sunder and Myers’s (1999) approach has received much attention in the subsequent

research. Chirinko & Singha (2000) raise concerns about Shyam-Sunder and Myers’s

(1999) results and argue that financing deficit regressions that they employ are not able to

distinguish between the competing hypotheses. Chirinko & Singha (2000) give some

examples where the pecking order model generates false inferences about probable patterns

of external financing. Frank & Goyal (2003) have some doubts about the validity of

Shyam-Sunder and Myers’s (1999) results because of the size of the sample (157 mature,

public firms), which might have a bias towards large companies having conservative debt

levels. Hence, Frank & Goyal (2003) test applicability of the pecking order theory on a

much broader sample of public US firms for the period of 1971-1998. Frank & Goyal

(2003) find that net equity issues follow the financing deficit more closely than do net debt

issues. This result does not match the prediction of the pecking order theory. They also

show that taking into consideration firm size is critical: the strongest support for the

pecking order predictions is found among the largest quartile of the firms, while, for the

smallest quartile, the pecking order is rejected. The evidence that firms follow the pecking

order is also weak in the analysis of the data from 1990s. Therefore, Frank & Goyal (2003)

conclude that the pecking order theory does not explain the broad patterns in the data.

To conclude, both the trade-off and the pecking order theories have success and failure in

explaining broad patterns of observed capital structures. As Frank & Goyal (2008) point

out, when the trade-off and pecking order theories are formulated as specific models, which

Page | 16

require simplifying assumptions, it is quite easy to reject them, but not all rejections are

equally significant. Although the model is rejected, it still might provide a valuable way to

think about the data. Proxies are used in the tests of models; therefore, when an unexpected

result is found regarding a proxy, it is not clear if the issue is a poorly specified proxy or the

theory itself. Graham & Leary (2011) also state that possible explanations of the

shortcomings of the models might be that either the list of the relevant market frictions is

incomplete, even though the general frameworks of the models are appropriate, or that

correct frictions have been identified, but the implications of these frictions for financial

policies are incomplete without additional considerations.

2.3. Empirical findings on capital structure of SMEs

It is commonly agreed among the researchers that the traditional capital structure theories

have not been developed having SMEs in mind, but rather are based on large, listed

companies. SME sectors constitute major parts of all economies in terms of both their

number among the total number of enterprises and their contribution to employment4.

However, compared to the academic research on capital structure of large companies,

studies on capital structure of SMEs have been of a ‘neglected’ and ‘much ignored’ area of

research (Mac an Bhaird & Lucey 2010). Acknowledging the importance of SMEs,

empirical analysis in the past two decades has also turned to SMEs and their capital

structure decisions.

Some researchers argue that capital structure decisions of SMEs can be explained by most

known theories of capital structure (Michaelas, Chittenden & Poutziouris 1998; Cassar &

Holmes 2003; Sogorb-Mira 2005). A common method employed in the studies of financing

decisions of SMEs is to test whether the major capital structure theories are ‘portable’ to

the SME sector. A method usually adopted in the previous studies is to test the hypotheses

based on the theories of capital structure employing static multivariate regression models

on the cross-sectional data of a single country (Hall, Hutchinson & Michaelas 2000; Cassar

& Holmes 2003), on the panel data of a single country (Michaelas, Chittenden &

4 According to the European Commission (2010), based on the estimates for 2008, SMEs accounted for 99.8

% of the total number of enterprises and provided 67.4 % of total employment in the EU-27 countries in the

non-financial business economy.

Page | 17

Poutziouris 1998; Sogorb-Mira 2005; Degryse, Goeij & Kappert 2009) or on the panel data

of several countries (Hall, Hutchinson & Michaelas 2004; Joeveer 2005; Psillaki &

Daskalakis 2009). Such approach allows researchers to investigate whether differences in

financing patterns between large enterprises and SMEs exist and whether capital structure

theories are also applicable for SMEs capital structure. Studies mentioned above analyse

the relationship between firm characteristic variables and the means of financing, using

various debt ratios as dependent variables. Moreover, as Hall, Hutchinson & Michaelas

(2000, p. 300) note, there is a consistency in the regressors commonly selected: “From

consideration of the previous studies of the determinants of the capital structure of small

enterprises it becomes clear that profitability, growth, asset structure, size and age and

possibly industry are, prima facie, likely to be related to capital structure”.

Among the determinants of capital structure, taxation might be considered as the most

debated. This is also true in the analysis of capital structure of SMEs. According to the

trade-off theory, firms obtain debt financing to gain the benefits of the tax shields due to

deductible interest expenses. However, a firm, which already has other sources of the tax

shields, such as depreciation, might be willing to use less debt financing. Researchers have

used two proxies to examine the effects of taxation for SMEs, namely the effective tax rate

and the amount of non-debt tax shields. Use of these two proxy variables has resulted in

conflicting evidence. On the one hand, studies by Michaelas, Chittenden & Poutziouris

(1998) of the UK SMEs, Sogorb-Mira (2005) on the Spanish data and Degryse, Goeij &

Kappert (2009) for the Dutch SMEs find that the regression coefficients of the effective tax

rates are not statistically significant and in some cases turn out to be negative, contrary to

the expected positive relationship with leverage. On the other hand, the results of these

studies regarding the effects of non-debt tax shields provide some evidence that tax

considerations may have influence on the capital structure decisions, as the non-debt tax

shields are found to be negatively related to debt.

The empirical evidence that tax considerations are important for SMEs remains ambiguous

and as Michaelas, Chittenden & Poutziouris (1998, p. 120) conclude: “It is, nevertheless,

hard to say that a firm’s tax status has predictable, material effects on its debt policy”. As

Pettit & Singer (1985) argue, a potential explanation could be that SMEs are expected to be

less profitable compared to large firms and, hence, might have less need for tax shields.

Page | 18

Moreover, some small firms have lower marginal tax rates compared to large companies,

which also reduce the benefits of the tax shields. In addition, small firms face a greater risk

of financial distress, which implies that smaller companies use less debt than the larger

ones. Empirical investigation of the trade-off theory in the SME sector provides little

support for it.

Contrary to the trade-off theory, many studies find support for the pecking order theory in

the SME sector. Hall, Hutchinson & Michaelas (2000) study the determinants of capital

structure on the sample of the UK SMEs. They conclude that the results of the study are

consistent with the pecking order theory as profitability is negatively related to short-term

debt and age is negatively related to both long-term debt and short-term debt. In addition,

the results suggest that agency problems, particularly asymmetric information, have an

influence on firms’ capital structures.

Watson & Wilson (2002) empirically test the pecking order model implications on the

sample of the UK SMEs. As the pecking order predicts, Watson & Wilson (2002) find that,

when additional financing is necessary, SMEs prefer to use retained earnings over debt and

that debt is preferred over an issue of new shares to outsiders. The pattern of coefficients in

the regressions Watson and Wilson (2002) use is found to be consistent with the pecking

order model predictions, particularly in closely-held firms, where issue of information

asymmetry and commonality of interests between managers and shareholders are most

evident.

Sogorb-Mira (2005) also finds support for the pecking order theory and concludes that the

predictions of the pecking order theory seem to explain debt policy of Spanish SMEs quite

well. The results also suggest that Spanish SMEs follow the maturity matching principle, as

they attempt to finance fixed assets with long-term debt and current assets with short-term

debt.

Degryse, Goeij & Kappert (2009) analyse the effect of the firm and industry characteristics

on the capital structure decisions of Dutch small firms. Their results on the impact of firm-

specific variables, such as size, asset structure, profitability and growth, are generally in

line with the pecking order theory. Degryse, Goeij & Kappert (2009) find that, as SMEs

prefer internal funds over external funds, they use profits to reduce the debt levels.

Page | 19

However, if a firm is growing, it increases its leverage, as the internal funds are exhausted

and not sufficient to cover the financing needs. Profitability has an effect on the short-term

debt, whereas asset growth only affects long-term debt. Degryse, Goeij & Kappert (2009)

conclude that, after internal funds, long-term debt is next in the financing hierarchy of

SMEs.

The above mentioned studies consider SMEs as a homogenous group. Although a category

of SMEs contains firms that can be very diverse, there is little empirical evidence whether

companies belonging to different size groups of SMEs behave differently regarding their

capital structure decisions. A couple of noteworthy exceptions are studies by Ramalho &

Vidigal da Silva (2009) and Daskalakis & Thanou (2010). Ramalho & Vidigal da Silva

(2009) test if the determinants of capital structure are different for micro, small, medium

and large companies. On the sample of Portuguese firms, they test if the factors, such as

collateral, profitability, firm’s age, growth, size and liquidity, are relevant for the capital

structure decisions of the four size-based groups of firms and if the influence of these

factors is similar in those groups. Their results suggest that there are some differences

among micro, small, medium and large companies regarding the determinants of long-term

debt financing. Although the direction of relationships (positive or negative) between the

determinants and leverage is found to be the same among all groups of firms, there are

significant differences in the magnitudes of the coefficients in some cases. Differences in

the values of coefficients are significant when comparing micro to medium or large firms

and small to large firms.

Daskalakis & Thanou (2010) use a different approach to test whether the magnitude of

coefficients of the regressors is different among micro, small and medium-sized firms in the

sample of Greek SMEs. Although the subsamples of SMEs are constructed in the same

manner as in Ramalho & Vidigal da Silva (2009), instead of the cross-sectional data,

Daskalakis and Thanou (2010) use the panel data and a different model to test their

hypothesis. Daskalakis & Thanou (2010) find that the average leverage ratios of micro,

small and medium firms are quite identical, although medium-sized companies have, on

average, lower debt ratios. To find out if there are any differences in the relative

contribution of the determinants of capital structure among the groups, they apply F test,

which turns out to be insignificant. This implies that there do not seem to be any disparities

Page | 20

in the magnitude of the coefficients regarding their contribution to the debt ratios. Hence,

Daskalakis & Thanou (2010) conclude that for the subgroups of micro, small and medium-

sized firms the relationship between debt and firm-specific variables is similar and that

capital structure is determined in the same manner across all subgroups of the SME

category.

In general, from the previous studies it is complicated to conclude that the influence of the

determinants of capital structure among the subgroups of SMEs is analogous, not only

because the empirical evidence is relatively scarce, but also because the empirical studies

so far have produced conflicting results. Moreover, the differences in the methodologies

applied compound a comparison of the studies. While Ramalho & Vidigal da Silva (2009)

use the cross-sectional data, Daskalakis & Thanou (2010) employ the panel data. Moreover,

the method Daskalakis & Thanou (2010) use to estimate the coefficients does not correct

for a serial correlation problem, which might invalidate the results of the hypotheses

testing.

To conclude, there is a consensus that the determinants of capital structure, typically

relevant for large firms, appear to be relevant for SMEs, as well. The majority of the studies

conclude that SMEs seem to follow the pecking order in their capital structure decisions.

The research finds evidence that bankruptcy costs, agency costs and problems of

asymmetric information have an impact on capital structure of SMEs, while the evidence

that tax considerations are important for SMEs remains limited. However, as Frank &

Goyal (2008) note, differences appear when the financing behaviour of small and large

firms is examined.

2.4. Differences in financing patterns of SMEs and large enterprises

Academic research has documented that there are differences in financing patterns between

SMEs and large firms and analysed possible causes of these differences. Cressy & Olofsson

(1997) note that smaller businesses are heavily reliant on retained earnings to finance their

investment flows and obtain most of additional finance from banks, while other resources,

especially equity, are less important. Brighi & Torluccio (2007) use data from an Italian

SMEs survey and find that on average self-financing, as a major form of finance, is the

Page | 21

preferred choice of the youngest firms. They also find that a preference for self-financing is

related to the firm’s size: the smaller the firm, the more common self-financing of

investments. Although these findings seem consistent with the predictions of the pecking

order theory, there might be alternative explanations why smaller firms prefer internal

resources over debt and debt over outside equity, related to both the supply-side and

demand-side effects.

As Watson & Wilson (2002) note, the pecking order theory does not account for the fact

that capital structure choices are themselves typically constrained by information

asymmetry and other market imperfections, which might have influence on the availability

and costs of different types of financing means. One of the reasons why SMEs may

experience difficulties in sourcing finance for investment is the informational opacity,

which is assumed to be negatively related to the firm’s size (Berger & Udell 1998). Public

information about SMEs is less voluminous because, in general, SMEs do not enter into

contracts which details are available to the general public or covered in the press.

Moreover, they do not issue traded securities; hence, there are no objective foundations for

the valuation of such firms. In addition, financial statements of many of the smallest firms

might be not audited. Therefore, providers of external financing might have no reliable

information to distinguish between good and bad risks. Consequently, SMEs may face

difficulties in overcoming information asymmetry or have higher costs to resolve it with

debt providers. In general, SMEs are not able to obtain financing in public debt markets and

have to rely on financial intermediaries, such as commercial banks, which might be

reluctant to provide all the funding they need or might offer it at rates higher than for large

firms. These effects might discourage SMEs from using external financing. Due to

exacerbated agency problems between debt holders and managers and asymmetric

information problems, SMEs might be forced to rely solely on internal sources of finance,

resulting from the institutional failure of providing them the necessary amount of finance.

In addition, the research suggests the firm’s size does matter in access to finance: the

smaller the firm, the greater difficulties it tends to face in obtaining financing. Beck et al.

(2006) analyse data of a survey, which was conducted in eighty developing and developed

countries, to identify obstacles to firm performance and growth. Beck et al. (2006) find that

small firms report significantly higher financing obstacles than medium firms, and both

Page | 22

groups of firms report higher financing obstacles than large firms. The study by Beck et al.

(2006) reports that the probability that a small firm rates financing as a major obstacle is

38.7%, while it is 37.7% and 28.5% for a medium and large firm, respectively. A survey,

organized by the European Commission and conducted in late 2006 in twenty seven

countries of the EU, has investigated the perceptions of SMEs on business constraints

among other issues (European Commission 2007). The survey reveals that the limited

access to finance is not the primary concern of most SMEs, but 21.1% of surveyed

companies report it as a constraint. Moreover, it is also found that there are differences in

the views regarding access to finance as a business constraint among the categories of

companies according to their size. 20.3% of micro firms encounter limited access to

finance, whereas the percentages for small, medium and large enterprises were 19.6, 17.6

and 15.5, respectively (European Commission 2007). Hence, it seems that the smaller the

enterprise, the more likely it is to experience difficulties in obtaining financing. It is also

worth mentioning that, like many other constraints, limited access to the necessary finance

is a more serious problem for companies in the twelve new member states of the EU than

for firms in fifteen old member states5.

It is also likely that SMEs are more vulnerable to credit crunches during economic

downturns or financial crises than larger enterprises. The European Central Bank (ECB)

and the European Commission twice a year conduct a survey of SMEs to analyse their

financing conditions in the euro area. The surveys from 2009 provide evidence that the

financial and economic crisis had an adverse effect on the availability of external financing

for SMEs (ECB 2009, 2010). The surveys reveal that access to finance was the second most

serious problem, reported by 17% of SMEs in the first half of 2009 and by 19% in the

second half of 20096 (ECB 2009, 2010). Although around three out of four applications for

the bank loans were successful either wholly or in part, the results suggest that the bigger

and older the firm applying for a bank loan is, the more likely it is that the loan is granted.

In the survey of the first half of 2009, around half of micro firms report that they received

the full amount of loans they applied for, while this is the case for around 70% of medium-

5 On average, 25.2% of the enterprises in the twelve new member states report that they encounter constraints

or difficulties in access to finance, while the percentage for fifteen old member countries is 20.3. 6 The most pressing problem SMEs in the euro area were facing was finding customers, reported by 27% of

SMEs in the first half of 2009 and 28% in the second half of 2009.

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sized and large companies (ECB 2009). Similarly, the number of rejected applications is

significantly higher for the smallest firms than for larger companies. Hence, it seems that

the smaller the firm, the more severely it might be affected by the deteriorating economic

conditions.

Given the constraints on the supply side of debt financing, an option for SMEs would be to

resort to external equity financing, for example, private investors and business angels (Mac

an Bhaird & Lucey 2010). Owners of SMEs, particularly of those which have high growth

possibilities, might be willing to concede some control in a firm and attract venture capital

funding. Nevertheless, formal venture capital by institutional investors has been so far a

viable option only for a very small minority of SMEs, the ones with high growth and

feasible exit possibilities for outside investors (European Commission 2010). Moreover, the

supply of venture capital is insufficient, and the costs of this form of finance for SMEs at

the start-up stage are high.

As the above discussed gaps in the supply side of financing for small firms were

recognized, alternative capital structure theories were developed specifically designed for

small firms. One example of these theories is the financial bootstrapping theory, which

seeks to explain how, facing the limitations of the supply of finance, small firms develop

alternative resources of financing without borrowing money from a bank or raising equity

financing. ‘Bootstrapping’ is characterized by a heavy reliance on loans from friends or

family, credit cards, home equity loans, leases or supplier credit as the alternative sources

of funding (Van Auken & Neeley 1996). Other related approach is the financing life-cycle

theory, which argues that financing alternatives that are available to firms change through

the life of the business (Vos & Forlong 1996). As the size and age of a firm are linked,

small firms might be considered as having a financial growth cycle, in which available

financing options change as the company grows and problems of informational opaqueness

become less severe. In the beginning, younger and smaller firms have to rely on initial

insider finance and, if they remain to exist and grow, the use of other sources of finance,

such as trade credit or bank loans, becomes available. Eventually, firms gain access to

public debt and equity markets.

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The financial bootstrapping theory and financial life-cycle theory focus on the supply side

of financing. However, alternative explanations, which stress the importance of the demand

side and the influence of the entrepreneur on financing decisions, were also developed.

Even if the supply-side constraints were absent, the demand-side effects might be able to

explain why smaller firms are less willing to use debt financing and rely on internal equity

or, if external financing is required, why they prefer debt over outside equity. One

explanation why smaller companies may not need or be willing to use debt financing is the

‘contentment hypothesis’ (Bell & Vos 2009). Many small firms are established as family

businesses, which may not pursue growth strategies, and the ‘contentment hypothesis’

argues that SMEs attach a greater utility value on connections and relationships than

financial wealth. Moreover, if SMEs have unconstrained choice between external debt and

internal resources, they will choose not to use debt financing because of a desire to retain

control and independence (Bell & Vos 2009). It is also likely that SMEs might be managed

by the owners whose expert skills are not in the field of finance. Due to constrained

knowledge and management skills, they may not understand the benefits and costs of debt

and other funding options. Consequently, the owners of SMEs may show a strong

preference for the funding options, which have minimal or no intrusion into their

companies, i.e., retained earnings and personal savings. If external financing is necessary,

they prefer debt financing over an introduction of new equity investors, which implies an

ultimate intrusion into their businesses. Hence, the smaller the firm, the higher might be the

probability that it is not using external financing deliberately.

To conclude, both supply-side constraints, which have an impact on the availability of

SMEs financing options, and demand-side effects related to preferences and knowledge of

the owners of SMEs might be possible explanations of the differences in the financing

patterns of SMEs and large enterprises.

2.5. Firm size and debt financing

There are several theoretical reasons why firm size is related to capital structure, including

economies of scale in lowering information asymmetry, scale in transaction costs and

market access. Smaller firms are more informationally opaque than larger firms and,

consequently, the costs to resolve information asymmetry with lenders are higher for small

Page | 25

firms than for large enterprises. Financing decisions might also be affected by the

transaction costs associated with a specific type of financing. As Titman and Wessels

(1988) point out, transaction costs are a function of scale. Hence, relatively high transaction

costs may effectively make some financing options unavailable for smaller firms. For

example, public debt issuance is generally not an alternative to obtain external financing for

smaller firms, as scale is required for such debt issuance. These theoretical reasons suggest

that smaller firms should have lower debt levels.

In general, the empirical evidence finds a positive relationship between firm size and

leverage, measured as the proportion of total debt or long-term debt to total assets (for

example, Michaelas, Chittenden & Poutziouris 1998; Hall, Hutchinson & Michaelas 2000;

Sogorb-Mira 2005). Recently, several studies have documented that a substantial

proportion of companies follow a zero-debt policy. Strebulaev & Yang (2006) find that

over the period 1962-2003, on average, 9% of large public non-financial US firms have

leverage ratios of zero. Moreover, they also report that more than a quarter of firms with

zero leverage ratios refrain from obtaining debt financing for at least five consecutive years

and that zero-leverage firms are smaller than other firms in the same industries. Strebulaev

& Yang (2006) argue that this zero leverage behaviour is a persistent phenomenon which is

neither an outlier nor an aberration, and that traditional capital structure theories lack the

ability to provide a potential explanation for it. In the context of SMEs, Ramalho & Vidigal

da Silva (2009) document that the proportion of zero-leverage firms in their sample of

Portuguese SMEs is even higher than a proportion of zero-leverage large public firms

found by Strebulaev & Yang (2006). Particularly, among the micro firms, 88.7% of them

do not have long-term debt, while the percentages for small and medium firms are 76.8 and

51.2, respectively. However, the majority of the previous empirical studies on capital

structure of SMEs usually do not report the proportions of firms with zero-leverage in their

samples.

Findings that substantial proportions of firms follow a zero-leverage policy can invalidate

the prediction of the traditional capital structure theories that leverage should be positively

related to firm size. Strebulaev & Yang (2006) and Faulkender & Petersen (2006) find that

it is more likely that larger firms have some debt, but conditional on having some debt,

larger firms have lower leverage ratios. Faulkender & Petersen (2006) document that the

Page | 26

smallest quartile of firms which report positive debt have, on average, a leverage ratio

higher by 3 percentage points than the leverage ratio of the largest quartile of firms.

Moreover, Strebulaev & Kurshev (2006) argue that the results of a positive relationship

between leverage and firm size may be contaminated by the presence of zero-leverage

firms, which are also smallest in terms of size. They find that, controlling for unlevered

firms, the relationship between firm size and leverage becomes slightly but significantly

negative. Strebulaev & Kurshev (2006) provide a theoretical clarification for the opposite

effects of firm size on leverage. Due to fixed costs of external financing, smaller firms

choose to refinance less frequently than larger firms because they are more affected by

these fixed costs in relative terms. Hence, small firms choose to operate at a higher leverage

level at a refinancing moment to compensate for less frequent rebalancing. This argument

explains why smaller firms, if they have some debt, are more levered than larger firms. In

addition, as the time period between restructurings is longer for small firms, on average,

they have lower leverage ratios.

Ramalho & Vidigal da Silva (2009) confirm the empirical evidence based on large firms

and find that, conditional on having debt, firm size is negatively related to the proportion of

long-term debt in capital structure of Portuguese SMEs. They divide the sample into micro,

small, medium and large firms and find that the relationship between leverage and firm size

is statistically significant negative for small and medium non-zero leverage firms.

2.6. Macroeconomic and institutional environment in the Baltic countries

The previous empirical studies, which analyse leverage and its determinants in Eastern

Europe, reveal several aspects how financing patterns in this region differ from the patterns

observed in the Western European countries. Firstly, a number of papers find that firms in

Central and Eastern Europe (CEE) are less levered compared to their Western European

counterparts (for example, Klapper, Sarria-Allende & Sulla 2002; Haas & Peeters 2004;

Nivorozhkin 2005; Joeveer 2006; Peev & Yurtoglu 2008). Secondly, the empirical

evidence reveals that capital structures of firms in the EU accession countries tend to

converge and gradually approach the leverage levels observed in the old EU countries

(Nivorozhkin 2005). Nevertheless, differences observed between capital structures in the

Western and Eastern European companies indicate that country-specific macroeconomic

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and institutional factors might have an impact on the financing decisions of firms. Despite

the process of convergence, differences between capital structures in Western and Eastern

Europe are still evident. Acknowledging the importance of macroeconomic and

institutional factors for the capital structure decisions, it was decided to compare the Baltic

states with the new member states of the EU (NMS), which joined it in 2004 or 2007, and

with the fifteen old member states (EU-15).

After the fall of the Berlin Wall and the collapse of the Soviet Union, the Baltic countries,

as well as the CEE countries, began a process of transition. Socialistic institutions

disappeared, and new well-functioning legal and financial systems had to be established in

the process. Absent, but essential financial markets and banking systems, which were

almost entirely state-owned, had created a hostile environment for new entrepreneurs,

where it was complicated to attract external financing and, thus, firms relied on internal

funds (Haas & Peeters 2004). In contrast to the SMEs in the Western European countries or

US, many SMEs in Eastern Europe were established primarily due to the privatization of

state-owned enterprises or as new entities after the move to a market economy.

Table 1 reports some macroeconomic variables and measures of external capital markets

development in the Baltic countries and the average values of these variables of the NMS

and EU-157. As of 2010, GDP per capita in the Baltic states amounted to around 40-50% of

the EU-15 average and fell behind the average GDP per capita of the NMS. In years 2006

and 2007, all three Baltic countries were among the fastest growing economies in the

region of NMS. GDP growth rates of these countries were approximately three times higher

than the average growth rate in the EU-15. However, the financial and economic crisis hit

the Baltic states more severely than the whole EU. Latvia and Estonia were the first

countries with a steep decline of GDP already in 2008 (-4.2% and -5.1%, respectively)8,

while the rest of the NMS still showed positive growth rates and the average growth rate of

the EU-15 region was recorded as close to zero. In addition, the recession in the Baltic

countries was the deepest among all EU countries: GDP declined by 13.9%, 18% and

7 Detailed values for all twelve new member states and the old member states can be found in Appendix 1.

8 Eurostat, http://epp.eurostat.ec.europa.eu/tgm/table.do?tab=table&init=1&plugin=1&language=en&pcode=

tsieb020.

Page | 28

14.7% in Estonia, Latvia and Lithuania, respectively in 20099. These might be the reasons

why GDP per capita in 2010 was lower in all three Baltic states compared to the average

GDP per capita of the NMS and why the average GDP growth over the period 2006-2010

was negligible or close to zero.

Table 1 also reports statutory corporate tax rates as of 2010 obtained from the KPMG

Corporate and Indirect Tax Survey 2010, and the total tax rate, reported by Doing Business

2011. The total tax rate differs from the statutory corporate tax rate as it includes not only

profit or corporate income tax, but also other taxes borne by the enterprises, such as social

contributions or labor taxes, property taxes or turnover taxes (Doing Business 2011). In

general, the statutory tax rates in the NMS were significantly lower than in the EU-15. This

might be explained by the fact that governments in the NMS have been striving to provide

investment incentives for foreign investors. However, when the burden of other taxes is

also considered, the divergence between the Baltic states, NMS and EU-15 is of a lesser

extent. Indeed, the total tax rate in Estonia turns out to be not only higher than the average

total tax rate in the NMS, but also higher than in the EU-15.

Table 1. Taxes, macroeconomic and financial sector development variables of the Baltic

states, NMS and EU-15

GDP per capita

(PPP), US $

GDP growth,

%

Inflation rate, %

Statutory tax rate,

%

Total tax rate, % of profit

Domestic credit, % of GDP

Market capitalization,

% of GDP Estonia 18,519 0.3 4.9 21 49.6 89.4 22.3

Latvia 14,460 -0.1 6.8 15 38.5 86.7 10.4

Lithuania 17,185 1.4 5.2 15 38.7 57.1 22.2

NMS 20,052 2.2 4.2 18 41.1 85.1 29.5

EU-15 37,421 0.7 2.1 27 46.4 150.2 79.3 Note: Table 1 reports GDP per capita in purchasing power parity (PPP) as of 2010. GDP growth and annual

inflation rate are the average values over the period 2006-2010. Statutory corporate tax rate is reported as of

the 1st January, 2010, while total tax rate is reported for year 2009. Domestic credit and market capitalization

are the average values over the period 2005-2009.

Sources: Economy Watch, Eurostat, KPMG (2010), Doing Business and World Development Indicators.

In addition, Table 1 presents information about the size of the banking sector and the stock

market. As an indicator of the size of the banking sector, the average value of domestic

credit provided by the banking sector, which includes all credit to various sectors of the

9 Eurostat, http://epp.eurostat.ec.europa.eu/tgm/table.do?tab=table&init=1&plugin=1&language=en&pcode=

tsieb020.

Page | 29

economy, over the period 2005-2009 was chosen. The average total market capitalization of

listed firms as a percentage of GDP over the same period indicates the development of the

stock market. On average, size of the banking sector in the NMS was three times larger

than the size of the stock market. In the EU-15 region, a similar financial system is

observed. However, when these two regions are compared, the differences are obvious: the

NMS region has close to two times less domestic credit provided by the banking sector and

close to three times lower stock market capitalization. Although in the pre-crisis period

credit markets in the Baltic states, as well as in the majority of the NMS, were booming, the

same trends were observed in the EU-15 and, hence, the gap between these two regions has

not reduced. Despite the efforts of policy-makers to develop local stock exchanges, they

remained underdeveloped in the NMS region. Stock markets in the Baltic states were even

less developed than in the NMS, especially in Latvia.

To compare the Baltic countries with the NMS and old EU member countries regarding the

institutional factors, which are presented in Table 2, various indicators were collected from

Doing Business and Transparency International initiatives10

. The strength of legal rights

index, which ranges between zero and ten, measures the effectiveness of collateral and

bankruptcy laws in protection of the rights of borrowers and lenders, where higher scores

indicate higher effectiveness (Doing Business 2011). The second indicator of the strength

of credit information index measures the accessibility of credit information either from the

public credit registries or private credit bureaus (Doing Business 2011). This index is on a

scale from zero to six, with higher values indicating more credit information available. In

terms of the legal rights index, differences between the Baltic countries are noticeable, as

the legal rights index of Latvia is significantly higher than of Lithuania or Estonia. Quite

surprisingly, the average legal rights index of the NMS is higher than the average score of

the EU-15 countries and credit information indices are quite similar for these two regions.

The lower legal rights index of the EU-15 region might be explained by the fact that low

scores of Southern Europe countries, such as Greece, Italy and Portugal, has a negative

impact on the average score of the EU-15 region. Hence, it seems that the laws and credit

information registries, which promote the development of credit markets and improve

10

Detailed values for all EU-27 countries can be found in Appendix 2.

Page | 30

access to financing for firms, are already well-developed both in the Baltic countries and

NMS.

The contract enforcement and recovery rate can be considered as summary measures of the

efficiency of the legal system. The first variable of contract enforcement is the time

measured in calendar days it takes for commercial dispute resolution, while the second

reflects costs, such as court costs, enforcement costs and attorney fees, incurred if a lawsuit

is filed (Doing Business 2011). The recovery rate measure reflects the quality and

effectiveness of the bankruptcy laws as it records the value recouped by creditors in case of

reorganization, liquidation or debt enforcement (Doing Business 2011). All three Baltic

countries score better at the time required to resolve dispute compared to the average of the

NMS or EU-15. This suggests that the Baltic countries have been the most successful

countries in the implementation of the effective legal systems among the NMS. After the

restoration of independence, such legal reforms were crucial for the development of

financial markets in these countries. However, the costs of dispute resolution are slightly

higher than the average costs in the NMS or EU-15. Further, obvious differences between

the EU countries are found when they are compared on the basis of the recovery rates. In

the NMS region, as well as Baltic countries, creditors recoup less than in the EU-15 region.

The only exception is Lithuania, being the closest to the EU-15 standards and even

exceeding the values of the recovery rates in some of the EU-15 countries, for example,

France or Greece.

An investor protection index reflects differences between countries regarding corporate

governance. This index, which ranges between zero and ten, measures the strength of

investor protection against directors’ misuse of corporate assets for personal gains. In

general, the average investor protection indices of the NMS and EU-15 look quite similar.

The Baltic states are not an exception, and already show high compliance levels with the

core principles of corporate governance.

The corruption perceptions index ranges on a scale from zero to ten and measures the

perceived level of corruption in the public sector. In general, the EU-15 countries have

higher values on the corruption perceptions index than the NMS. In the Baltic countries,

with the exception of Estonia, where the level of corruption is the lowest amongst all NMS,

Page | 31

corruption still remains quite a severe problem and is viewed as a key obstacle in the

business environment (EBRD 2010).

Table 2. Institutional factors in the Baltic countries, NMS and EU-15 (year 2010)

Legal

rights

index

Credit

information

index

Enforcing contracts Recovery rate, cents of $

Investor

protection

index

Corruption perceptions index

Time (days)

Cost (% of claim)

Estonia 6.0 5.0 425 26.3 37.5 5.7 6.5

Latvia 9.0 5.0 309 23.1 29.0 5.7 4.3

Lithuania 5.0 6.0 275 23.6 49.4 5.0 5.0

NMS 7.4 4.3 592 22.3 38.9 5.5 5.0

EU-15 6.3 4.6 511 19.5 69.5 5.6 7.3 Sources: Doing Business and Transparency International.

The analysis indicates that in terms of the institutional environment and legal system,

differences between the Baltic countries, as well as new member countries of the EU, and

old member states of the EU are negligible. This might be explained by the fact that legal

and institutional reforms were prerequisites for the accession to the EU. However,

differences in the economic and capital markets development between the new member

countries and Western European countries are evident.

Despite the differences in the financing patterns between small firms and large enterprises,

empirical evidence regarding the applicability of the capital structure theories for SMEs

suggests that firm-specific factors that have an influence on the financing decisions of large

firms are also important determinants of capital structure of SMEs. However, this evidence

is based on the samples of firms from the US or Western European countries. Given

different economic environment of the Baltic countries, the next section formulates the

research question and hypotheses.

Page | 32

3. Research Question and Hypotheses

The capital structure literature has identified various firm-specific factors that have an

impact on the leverage decisions of firms. Some researchers, such as Rajan & Zingales

(1995), prove that capital structures are similar across countries and that factors affecting

the leverage decisions are quite common. However, the majority of the research

investigates capital structure in the developed economies of the US or Western European

countries, where conditions are quite similar, although the inefficiencies of non-perfect

markets are solved differently by markets or banks. The research on the capital structure

determinants of firms in the Baltic countries is relatively scarce compared to the work,

which analyses capital structure decisions in Western Europe. Although the firms of the

Baltic states, are included in the samples of studies analysing capital structure in the entire

CEE region, to the best of my knowledge, there is no empirical work which solely studies

capital structure of SMEs in the Baltic countries.

Countries in Eastern Europe, including the Baltic states, are quite different compared to the

Western European countries regarding the macroeconomic development, the state of capital

markets development and firms’ access to credit. Moreover, most of the SMEs in this

region were established more recently compared to the Western European counterparts.

Therefore, SMEs from the Baltic countries, where market economies and modern capital

markets emerged only during recent decades, are a good sample to study the capital

structure determination. Hence, the research question of this thesis is formulated as follows:

Do firm-specific factors identified in the capital structure literature help to explain

leverage decisions of the SMEs in the Baltic countries?

Both the trade-off and the pecking order theories assume that firms are not financially

constrained and can obtain unlimited external financing at an acceptable price. However, as

noted in the literature, in practice SMEs may suffer financing gaps and have limited access

to finance, especially in the new member countries of the EU. The obstacles in acquiring

external financing, including debt financing, seem to be inversely related to firm size. In

addition, due to constrained knowledge or preferences of owners, smaller firms might not

use debt financing deliberately. If larger proportions of smaller firms are not only able or

choose not to obtain debt financing, it would be observed that the larger proportion of, for

Page | 33

example, micro firms do not have long-term debt financing at all than the proportion of

small firms with zero long-term debt. Hence, these expectations lead to the following

hypothesis.

Hypothesis 1. The smaller the firm is, the lower leverage it has, i.e., micro firms, on

average, are less levered than small firms and small firms, on average, are less levered

than medium-sized firms.

Recent findings of Strebulaev & Yang (2006) and Ramalho & Vidigal da Silva (2009)

suggest that the leverage ratio might be negatively related to firm size if firms with zero

leverage ratios are excluded from consideration. These findings contradict the propositions

of the trade-off theory and the pecking order theory. Therefore, the following hypothesis is

formulated.

Hypothesis 2. Conditionally on having some debt in their capital structure, micro firms, on

average, are more indebted than small firms and small firms, on average, are more

indebted than medium-sized firms.

These two hypotheses are related to two potentially different decisions of leverage: the first

considers the decision to obtain debt financing, while the second considers only firms with

positive debt levels and their decision on the relative amount of debt financing in capital

structure. The previous discussion and two hypotheses focus on the conflicting influence of

firm size on these two leverage decisions. Other determinants of leverage, previously

identified in the capital structure literature, might have the same effect (positive or

negative) on leverage. Nevertheless, even though one variable might have the same effect

on the decision to use debt financing and the proportion of debt among the sources of

financing, it might be possible that the determinants of these two decisions do not coincide.

If this is the case, this would imply that the decision to obtain debt financing and the

decision of how much debt to obtain are taken separately. Hence, the following hypothesis

is derived.

Hypothesis 3. The determinants of the decision to obtain debt financing are different from

the determinants of the proportion of debt in capital structure in companies which do

obtain debt financing.

Page | 34

The majority of the previous research on capital structure of SMEs, with a few exceptions,

considers SMEs as a homogenous group, neglecting the variety which might exist among

the firms belonging to different size-based groups of SMEs. Moreover, firm size is

considered as one of the explanatory variables of leverage, but rarely used as an indicator to

divide samples into the subgroups, i.e., to distinguish among micro, small and medium-

sized enterprises. As Cassar & Holmes (2003, p. 139) argue, “The same influences that may

cause differences between SMEs and larger listed firms, may also affect relationships

within the SME group, due to wide variation of sizes present”. Therefore, in this thesis it is

tested which firm-specific variables are significant for the capital structure decisions of the

separate size-based groups of SMEs and if the influence of these variables is similar among

the subgroups of SMEs. The following hypothesis is formulated.

Hypothesis 4. The determinants of leverage differ, at least in the magnitude, among micro,

small and medium-sized firms.

To test the presented hypotheses, data were collected from the Orbis database and a two-

part fractional regression model (FRM) was used. More detailed information is presented in

the next chapter.

Page | 35

4. Data and Methodology

4.1. Data

The data used in this thesis were collected in May, 2011 from the Orbis database provided

by Bureau van Dijk. The Orbis database provides comprehensive information about over 80

million private and public companies worldwide, including 40 million European

companies. The financial data of the companies are presented in a standardized and

comparable format.

Table 3 illustrates the number of firms by country after the search steps and other criteria

were applied for sampling. As the focus of this thesis is on the leverage decisions of firms

in the Baltic countries, the first column presents the total number of companies in each

country that the Orbis database provides information about without any further restrictions.

Further, only companies belonging to the category of SMEs were extracted, and their

number is presented in the second column. The definition of SMEs set by the European

Commission (recommendation 2003/361/EC) was used. Recommendation 2003/361/EC

defines that the category of SMEs consists of enterprises which employ less than 250

employees and have an annual turnover not exceeding 50 million euros and/or an annual

balance sheet not exceeding 43 million euros. These criteria were applied to the year 2009

and reduced the total sample by more than a half.

The third search step (column 3) was to exclude banks, financial and insurance companies

due to their specific nature of business and the format of financial statements. In the next

step (column 4), only limited firms or limited liability firms were included. All other legal

forms, such as cooperatives, limited partnerships or state institutions, were excluded. The

fifth search step (column 5) excluded listed firms, but it did not affect the size of the sample

considerably as there were only few enterprises belonging to the category of SMEs and

listed on the stock exchanges.

In order to identify country-specific trends better, enterprises in which foreign shareholders

own a direct or total participation greater than 51% were excluded from the sample (column

6). The last search step allowed excluding companies with little or no recent available

financial information or only with consolidated financial statements (column 7). Enterprises

with limited information were excluded because the information provided would not allow

Page | 36

constructing the variables used in the analysis. Firms with only consolidated accounts were

excluded because consolidated accounts, even if a firm is located in the Baltic countries,

might reflect business in several countries due to the existence of subsidiaries.

After the application of these steps to extract the list of enterprises, the accounting

information from the balance sheets and P&L statements (for example, information about

total assets, tangible fixed assets, shareholders’ funds, non-current liabilities, long-term

debt, operating turnover, earnings before interest and taxes, etc.) of year 2009 and other

information (for example, date of incorporation, number of employees, etc.) were extracted

and exported to Excel. Even though several criteria were applied in the Orbis database, this

sample still included firms with incomplete information to construct the variables for the

analysis. Hence, enterprises with, for example, their industry membership, date of

incorporation, total assets, operating turnover, shareholders’ funds, non-current liabilities or

long-term debt not given, were also excluded (column 8). Besides missing information,

firms, which were not operational and had sales of zero in year 2009, were also excluded

from the sample. Furthermore, in this step companies with negative values of equity were

discarded.

After checking the descriptive statistics of the explanatory and dependent variables, it was

chosen to eliminate outliers with the most extreme values of growth of total assets11

(column 9). The extreme growth rates stem from the data of companies founded one year

prior to the year of the analysis (year 2009). Therefore, 0.5% of the observations in each

side of the distribution were eliminated. The final data set consisted of 4,679 firms.

Table 3. Number of firms by country in the sample

(1) (2) (3) (4) (5) (6) (7) (8) (9) Estonia 114,051 33,674 33,388 33,050 33,049 30,866 3,866 2,447 2,423 Latvia 142,387 50,670 50,211 44,034 44,027 43,309 1,948 1,421 1,407 Lithuania 123,216 82,701 82,228 53,158 53,152 51,794 6,662 857 849 Total 379,654 167,045 165,827 130,242 130,228 125,969 12,476 4,725 4,679

Source: Own calculations.

To investigate if the influence of capital structure determinants is similar across the

subgroups of SMEs, the sample was partitioned into three subsamples of micro, small and 11

Growth of total assets was calculated as the difference between total assets in year 2009 and total assets in

year 2008 and divided by total assets in year 2008.

Page | 37

medium-sized enterprises following the definitions of the European Commission

(recommendation 2003/361/EC) of these firms, which is summarized in Table 4.

Table 4. Criteria to distinguish between micro, small and medium-sized firms set by the EC

Category Headcount Annual turnover Annual balance sheet Micro firms < 10 ≤ € 2 million and/or ≤ € 2 million

Small firms < 50 ≤ € 10 million and/or ≤ € 10 million

Medium-sized firms < 250 ≤ € 50 million and/or ≤ € 43 million Source: European Commission, recommendation 2003/361/EC.

Table 5 presents the breakdown of the sample by size of the firm and country.

Table 5. Distribution of the sample by firm size and country

Micro firms Small firms Medium-sized firms Total Estonia 1,527 666 230 2,423 Latvia 379 594 434 1,407 Lithuania 72 346 431 849 Total 1,978 1,606 1,095 4,679 Source: Own calculations.

Estonian SMEs account for a half of the sample, whereas Latvian and Lithuanian SMEs

constitute approximately 30% and 20% of the sample, respectively. The largest subsample

is of the micro firms (1978 firms), while the subsamples of small and medium-sized firms

comprise approximately one third and one fourth of the total sample.

4.2. Model specification and testing procedures

The majority of the empirical studies on the capital structure decisions, which focuses on

testing the trade-off theory or the pecking order theory, employs one-part models to explain

the leverage decisions of firms. The limitation of one-part models is that they do not

distinguish between a decision to use debt financing and a decision regarding a proportion

of debt in capital structure. These studies assume that the influence of a specific

explanatory variable on a decision to use some type of financing is the same as the

influence on how much of this type of financing to use. Hence, linear regression models,

which are estimated by least squares-based methods, are used to explain observed leverage

ratios of firms. However, as noted by Ramalho & Vidigal da Silva (2009), leverage ratios

Page | 38

have two statistical properties, which invalidate the application of linear regression models.

Firstly, leverage ratios by definition are bounded between zero and one and, secondly, there

are many firms, which do not use debt financing. As the effect of any independent variable

cannot be constant throughout the entire range, the assumption of linearity is unlikely to

hold. In addition, linear models cannot guarantee that the predicted values of leverage ratios

are in the interval of one unit.

Given the existence of many firms with zero leverage ratios in the samples, some

researchers (for example, Rajan & Zingales 1995) use a tobit model, censored at zero, to

explain observed leverage ratios. Although a tobit model assumes nonlinear relationship

between leverage ratios and explanatory variables, it still has some drawbacks. Firstly,

although it has a lower bound at zero, it still does not have an upper bound. Secondly, a

tobit model has strict assumptions, which might be easily violated, with regards to the error

term, which has to be homoskedastic and have a normal distribution.

Due to the complications in using a linear regression model or tobit model to explain the

leverage ratios, Ramalho and Vidigal da Silva (2009) develops a two-part fractional

regression model (FRM) to explain the leverage decisions of firms. Given the fact that zero

leverage ratios occur with large frequency, Ramalho and Vidigal da Silva (2009) assume

that the factors explaining the decision to use debt financing are not the same as those

explaining the proportion of debt in capital structure. Hence, the two parts of the model

reflect these two decisions separately. Following the methodology of Ramalho and Vidigal

da Silva (2009), in this thesis the analysis of the leverage decisions of SMEs in the Baltic

countries is based on the application of a similar two-part model.

The first part of the two-part FRM is a standard binary choice model, which governs the

probability that a firm uses debt financing (i.e., the probability of observing a positive

outcome). The dependent variable y�� is a binary variable, which obtains a value of zero if

the leverage ratio of firm i is equal to zero and a value of one if the leverage ratio of firm i

falls in the interval (0;1]. Hence, it is defined in the following way12

:

y�� � � 0, if y� � 01, if y� � 0,1�, 1��

12

yi is the leverage ratio of firm i in the sample. To see how yi is defined, see section 4.3.

Page | 39

where i = 1, 2, ..., N and N is the sample size. In a binary choice model, interest lies

primarily in the response probability. Hence, the first part of the two-part FRM is defined

as:

Pr y�� � �1|x�� � Pr �y� � 0,1�|x�� � F x��α�, 2�

where xi is a vector of observations on explanatory variables for ith

dependent variable, α is

a vector of coefficients to be estimated and F(⋅) is a known nonlinear function taking on

values strictly between zero and one to ensure that estimated response probabilities are

between zero and one. Possible specifications for F(⋅) can be a cumulative normal

distribution function or cumulative logistic distribution function. The resulting probit or

logit model can be estimated by the maximum likelihood estimation using the entire sample

of firms.

The second part of the two-part FRM governs the magnitude of non-zero leverage ratios.

This part is known as a fractional regression model because the dependent variable is the

proportion of a firm’s total capitalization accounted for by debt capital. Wooldridge (2002)

suggests that, when a dependent variable is restricted to the interval (0,1], a possible choice

to model the expected leverage ratios is to adopt similar nonlinear functions as for function

F(⋅). Hence, the second part of the model is specified in the following way:

E �y�|x�, y� � 0,1�� � G x��β�, 3�

where yi is a fractional variable of interest (the leverage ratio), β is a vector of coefficients,

xi is a vector of observations on explanatory variables and G(⋅) is a function ensuring that

predicted values of leverage ratios are in the interval from zero to one. Wooldridge (2002)

notes that G x��β� might be estimated by the quasi-maximum likelihood method. In this part,

only data with firms having positive leverage ratios are used to estimate the model. The

mechanics to obtain estimated coefficients are identical to the binary choice model case

(Wooldridge 2002).

From the two parts of the model, it follows that the expected leverage ratio E �y�|x�� can be

broken down as:

Page | 40

E �y�|x�� � E �y�|x�, y� � 0� · Pr �y� � 0|x�� � E �y�|x�, y� � 0,1� · Pr �y� � 0,1�|x��.

The first part on the right side of the above expression is identically zero. Therefore, the

two-part FRM can be written as:

E �y�|x�� � E �y�|x�, y� � 0,1�� · Pr �y� � 0,1�|x�� � G x��β� · F x��α�. 4�

Two components of the model, F x��α� and G x��β�, are estimated separately. The

coefficients of variables, α and β, do not necessarily have to be the same. Therefore, a two-

part FRM allows the independent variables to have differing influence on the firm’s choice

to use debt financing and the firm’s decision on the proportion of debt financing in its

capital structure.

A crucial requirement to estimate the coefficients α and β consistently is that both

E �y�|x�, y� � 0,1�� and Pr y�� � �1|x�� are correctly specified, i.e., that the functions F(⋅)

and G(⋅) are chosen correctly. Ramalho, Ramalho & Murteira (2011) test the logistic

specification against other alternatives of nonlinear functions for F(⋅) and G(⋅) and provide

evidence that the logistic specification does not cause a problem of misspecification. As in

Ramalho and Vidigal da Silva (2009), in this thesis logistic specification for both functions

F(⋅) and G(⋅) is assumed. Therefore, from equation (4) it follows that:

E �y�|x�� � G x��β� · F x��α� � e!"#$%1 � e!"#$& ·

e!"#'%1 � e!"#'& �

e!"# $('�%1 � e!"#$& 1 � e!"#'�. 5�

In contrast to the linear model, the magnitudes of each estimated coefficient αj and βj

cannot be interpreted directly as partial effects of a change by one unit in the explanatory

variable xij. Instead, partial derivatives of functions F(⋅) and G(⋅) have to be calculated.

Therefore, the partial effect of a change of the explanatory variable xij on the probability

that a firm uses debt financing is calculated as

∂Pr y�� � �1|x��∂x�+ � α+

e!"#$ 1 � e!"#$�, . 6�

Page | 41

Similarly, if a firm is using debt financing, the partial effect of a change of the explanatory

variable xij on the proportion of debt financing is calculated as:

∂E �y�|x�, y� � 0,1��∂x�+ � β+

e!"#' 1 � e!"#'�, . 7�

In addition, from equations (6) and (7) it is also possible to calculate the effect of a change

of xij on the proportion of debt financing in capital structure for all firms:

∂E �y�|x��∂x�+ � ∂F x��α�

∂x�+ · G x��β� � ∂G x��β�∂x�+ · F x��α� �

� α+e!"#$

1 � e!"#$�, ·e!"#'

1 � e!"#'� � β+e!"#'

1 � e!"#'�, ·e!"#$

1 � e!"#$� . 8�

Two specification problems might arise in the estimation of the first part of the model.

Since the first part is a binary response model, the first issue might be a general functional

form misspecification and the second issue might be heteroskedasticity in the error term. In

contrast to the linear model, calculation of the robust standard errors is not a solution for

heteroskedasticity in the binary response model to obtain robust test statistics. If the

variance of the error term depends on explanatory variables, the response probability no

longer has the form of the logistic function. Instead, it depends on the form of the variance

and requires more general estimation (Wooldridge 2003). For the second part of the model,

only the issue of functional form misspecification might be relevant. There is no need to

test for heteroskedasticity in this case because fractional regression models with a finite

number of boundary observations are always heteroskedastic and the estimation method

adopted, the quasi-maximum likelihood method, takes that into account (Ramalho &

Vidigal da Silva 2009). How the tests for heteroskedasticity and functional form

misspecification were performed is explained in Appendix 3 and Appendix 4.

To test the hypothesis whether the effect of a certain explanatory variable differs between

two subgroups of SMEs, data on the dependent variable and explanatory variables from the

two subsamples of SMEs have to be pooled. This pooling results in three new sets of data

(micro and small firms, micro and medium firms, small and medium firms). In addition, a

new dummy variable d, which takes on the value of one for one subgroup of SMEs (e.g.,

Page | 42

micro firms) and the value of zero for another subgroup (e.g., small firms) has to be

included in the regression model. Having these three new sets of data, in addition to the

models, defined in equations (2) and (3), augmented models have to be estimated. For the

first part of the model, a binary choice model, the augmented model is defined in the

following way:

Pr y�� � �1|x�� � F x��α � d� · x���δ�, 9�

where xi is a vector of observations on explanatory variables and δ is a coefficient

associated with the interaction terms d� · x��. As it is tested if the influence of a single

explanatory variable differs between the two subgroups, it is necessary to estimate k

augmented regression models, where k is the number of explanatory variables.

For the second part of the model, FRM, the augmented model is defined in the following

way:

E �y�|x�, y� � 0,1�� � G x��β� d� · x���γ�. 10�

In the first part of the model, the null hypothesis H0: δ = 0 is tested against the alternative

H1: δ ≠ 0, while in the second part, the null hypothesis H0: γ = 0 is tested against the

alternative H1: γ ≠ 0. If the null hypotheses cannot be rejected, there are no significant

differences in the effects of a certain explanatory variable between two subgroups of SMEs.

As the first part of the model is estimated by maximum likelihood, the likelihood ratio (LR)

test is used to test the null hypothesis H0: δ = 0. The LR test is based on the difference

between the log-likelihood functions for the unrestricted and restricted models (Wooldridge

2003). The unrestricted model is the one defined in equation (9), while the restricted model

is the one defined in equation (2). Because maximum-likelihood estimation maximizes the

log-likelihood function, dropping variable results in a smaller, or at least not larger, log-

likelihood. To be able to conclude that dropped variable is important, it is necessary to

determine if the fall in the log-likelihood is large enough. This can be determined by

comparing the LR statistic with critical values. The LR statistic is twice the difference in

the log-likelihoods:

Page | 43

LR statistic � 2 L;< = L<�, 11�

where Lur is the log-likelihood value of the unrestricted model and Lr is the log-likelihood

value of the restricted model (Wooldridge 2003). The LR statistic is approximately

distributed as χ>,. If the computed LR statistic exceeds the critical value, the null hypothesis

is rejected. Therefore, it can be concluded that the effect of a particular explanatory variable

on the probability that a firm is using debt financing differs between two subgroups of

SMEs.

Since the second part of the two-part FRM is estimated by quasi-maximum likelihood,

testing the null hypothesis H0: γ = 0 has to be performed following the lines of the robust

RESET test outlined in Papke and Wooldridge (1996). Testing the null hypothesis is based

on the computation of the heteroskedasticity-robust Lagrange multiplier (LM) test. For the

second part of the model, the unrestricted model is the one defined in equation (10), while

the restricted model is the one defined in equation (3). Following the procedure, described

by Papke & Wooldridge (1996), firstly, u@ � � y� = G%x��βA&, GB� � G%x��βA&, g@ � � g%x��βA& and

uD � � u@ �/FGB� 1 = GB�� are defined, i.e., the residuals u@ �, the predicted leverage ratios GB�, partial derivatives g@ � and the weighted residuals uD � are obtained after the estimation of the

model defined in equation (3). Secondly, the weighted gradients of the function defined on

the right side of equation (10) with respect to γ and β are necessary. The weighted gradient

with respect to β is G'mI � � g@ �x��/FGB� 1 = GB��. The weighted gradient with respect to γ

is GγmI � � GγmJ �/FGB� 1 = GB��, where GγmJ � � g@ � · d� · x���. Then, it is necessary to

regress GγmI � on G'mI �, save the residuals rD� and obtain vector uD �rD�. Finally, the auxiliary

regression of 1 on uD �rD� without an intercept has to be run. The LM statistic is calculated as

LM statistic � N = SSR, 12�

where SSR is the sum of squared residuals from this final regression and N is the number of

observations. The LM statistic is distributed approximately as χ>,. If the computed LM

statistic exceeds the critical value, the null hypothesis can be rejected. Therefore, it can be

concluded that the effect of a particular explanatory variable on the proportion of debt

financing in capital structure differs between two subgroups of SMEs.

Page | 44

In order to test whether average leverage ratios are different between two subgroups of

SMEs, Welch’s (1947) t test is applied. T tests are applied for each pair of the three

possible pairs of size-based groups of SMEs. In addition, the average leverage ratio for

each subgroup is calculated using the leverage ratios of all firms in the subgroup and using

the leverage ratios of firms only with non-zero leverage ratios in the subgroup. In total, this

results in the calculation of six t statistics. The null hypothesis H0: yN> � yN, is tested against

the alternative H1: yN> O yN,. The t statistic can be calculated as follows:

t � yN> = yN,sPQRSPQT

, 13�

where yN> is the average leverage ratio of one subgroup of SMEs (e.g., micro firms), yN, is

the average leverage ratio of another subgroup of SMEs (e.g., small firms) and sPQRSPQT �UVRT

WR �VTTWT.

s>, and s,, are the variances of leverage ratios of the two subgroups, n1 and n2 are the

numbers of firms in each subgroup. Test statistic approximately follows t-distribution. If

the calculated t statistic exceeds the critical value, the null hypothesis that the average

leverage ratios of two subgroups are equal can be rejected.

4.3. Dependent and explanatory variables

Most of the factors that appear in the model described in section 4.2 are not directly

observable attributes. Therefore, explanatory variables that work as proxies for these

attributes are constructed. This section explains how, following the common definitions

found in the capital structure literature, the dependent variable, leverage ratio, and the

explanatory variables are constructed.

Dependent variable Similarly as in Joeveer (2005) and Ramalho & Vidigal da Silva (2009), the ratio of long-

term debt to long-term capital assets is used in this thesis as a measure of financial

leverage. Long-term debt is defined as long-term financial debt, for example, to credit

Page | 45

institutions, due for repayment beyond one year. Long-term capital assets are defined as the

sum of long-term debt and shareholders’ funds. Book values of long-term debt and

shareholders’ funds are used to construct the leverage ratios as only unlisted firms are in the

sample, and market values of long-term debt and shareholders’ funds are not available.

Only long-term debt is considered because capital structure theories mainly focus on the

decision that companies make between long-term debt and equity to finance their activities.

All other possible financing sources, for example, short-term debt or trade credit, are not

considered because one of the goals of this thesis is to investigate to what extent capital

structure theories can be applied to the three size-based groups of SMEs in the Baltic

countries.

Explanatory variables Effective tax rate is defined as taxes paid over earnings before taxes (EBT) (Sogorb-Mira

2005; Degryse, Goeij & Kappert 2009). According to the trade-off theory, firms prefer debt

financing due to interest tax shields (Modigliani & Miller 1963). The higher the effective

tax rate, the larger incentives companies have to benefit from interest tax shields.

Therefore, effective tax rate should be positively related to the leverage ratio. However,

previous studies on capital structure of SMEs find the opposite relationship (Michaelas,

Chittenden & Poutziouris 1998; Sogorb-Mira 2005; Degryse, Goeij & Kappert 2009)13

.

Tangibility is calculated as tangible fixed assets divided by total assets (Sogorb-Mira 2005;

Degryse, Goeij & Kappert 2009). A company with a higher proportion of total assets

composed of tangible fixed assets has a higher capacity to raise debt because tangible fixed

assets can be pledged as collateral for loans. Moreover, in case of liquidation, tangible fixed

assets keep their value (Myers 1977). If a firm has large tangible assets and poor cash

flows, shareholders may prefer to liquidate current operations. Management may be willing

to continue firm’s current operations; therefore, obtaining debt can serve as a mechanism to

increase a default probability and give a right for debt holders to force liquidation (Harris &

Raviv 1990). Due to asymmetric information, lenders can determine the value of tangible

assets than the value of intangible assets easier. Hence, companies with higher proportions

13

An overview of the previous studies on capital structure of SMEs and their results is in Appendix 5.

Page | 46

of tangible assets have better opportunities to obtain debt financing (Myers & Majluf 1984).

Therefore, the trade-off theory, agency theory and pecking order theory predict that

tangibility should be positively related to debt. Previous empirical studies consistently find

significant positive relationship between tangibility and long-term debt (Klapper, Saria-

Allende & Sulla 2002; Hall, Hutchinson & Michaelas 2004; Sogorb-Mira 2005).

Size is defined as a natural logarithm of sales (Klapper, Sarria-Allende & Sulla 2002;

Klapper, Sarria-Allende & Zaidi 2006). Larger firms tend to be more diversified; hence, for

larger firms the probability of default is relatively lower and they incur lower costs of

financial distress. In addition, size of a firm is assumed to be negatively related to

information opacity. As information asymmetry is less severe problem for larger firms, it is

easier for them to obtain debt financing (Myers 1984). Hence, both the trade-off theory and

the pecking order theory predict a positive relationship between firm size and leverage. In

most of the previous studies of SMEs capital structure, size is found to be positively related

to leverage (Hall, Hutchinson & Michaelas 2000; Klapper, Saria-Allende & Sulla 2002,

Cassar & Holmes 2003). However, these studies do not distinguish between the firm’s

decision to obtain debt financing and the firm’s decision regarding the proportion of debt in

its capital structure. Ramalho and Vidigal da Silva (2009) separate these two decisions in

their analysis. While size has a positive influence on the probability that a firm resorts to

debt financing for all three size-based groups of SMEs, the relationship between size and

the proportion of debt financing turns out to be negative for small and medium-sized firms.

Growth is measured as a change in total assets from year 2008 to year 2009 divided by total

assets in year 2008 (Michaelas, Chittenden & Poutziouris 1998; Degryse, Goeij & Kappert

2009). Costs of financial distress are higher for firms with higher growth rates. Therefore,

these firms may be not willing to take on large amounts of debt to avoid an increase in their

bankruptcy probability (Myers 1977). Growth in total assets also represents investment

opportunities a firm has undertaken. Hence, a firm with more investment opportunities

undertaken has less need for using debt as a disciplining mechanism of management to

control free cash flows (Jensen 1986). Therefore, according to the trade-off theory and

agency theory, growth should be negatively related to debt. According to the pecking order

theory, companies with higher growth rates are more likely to exhaust internally generated

funds, suggesting a positive relationship between leverage and growth (Shyam-Sunder &

Page | 47

Myers 1999). Previous empirical studies, which apply a similar definition of growth, find

positive relationship between leverage and growth (Michaelas, Chittenden & Poutziouris

1998; Degryse, Goeij & Kappert 2009).

Growth opportunities are defined as a ratio of intangible fixed assets to total assets

(Michaelas, Chittenden & Poutziouris 1998; Sogorb-Mira 2005; Degryse, Goeij & Kappert

2009). As growth opportunities represent an intangible asset, costs of financial distress are

higher for firms with higher growth opportunities. Hence, similarly as for growth, the trade-

off theory predicts a negative relationship between growth opportunities and a debt level.

The pecking order theory is ambiguous regarding the prediction related to growth

opportunities. On the one hand, firms, which have potential to grow, are more likely to be

short of internal funds to finance all investment opportunities. If the internal funds are not

sufficient, these firms have to obtain external financing, suggesting a positive relationship

between growth opportunities and leverage On the other hand, it is complicated for

outsiders to determine the value of growth opportunities, suggesting that issues of

information asymmetry are more severe for the firms with higher growth opportunities.

Therefore, it is expected that growth opportunities should be negatively related to leverage.

Michaelas, Chittenden & Poutziouris (1998), Sogorb-Mira (2005) and Degryse, Goeij &

Kappert (2009) find a strong positive relationship between long-term debt and growth

opportunities.

Profitability is measured as a ratio of earnings before interest and taxes (EBIT) to total

assets (Cassar & Holmes 2003; Sogorb-Mira 2005; Degryse, Goeij & Kappert 2009). In the

trade-off framework, higher profitability increases the creditworthiness of a firm because

the probability of failing to pay interest payments is lower. In addition, more profitable

firms have an incentive to use debt financing to benefit from interest tax shields. Following

the agency theory arguments, the higher the profitability of a firm, the higher level of debt

should be used to discipline the behaviour of management (Jensen & Meckling 1976).

Hence, the trade-off theory and agency theory postulate a positive relationship between

debt and profitability. In contrast, the pecking order theory predicts the opposite

relationship because higher profitability reduces the need to raise debt due to greater

availability of internally generated funds (Myers 1984). Empirical evidence from previous

studies examining SMEs capital structure is consistent with the pecking order arguments

Page | 48

(Michaelas, Chittenden & Poutziouris 1998; Cassar & Holmes 2003; Sogorb-Mira 2005,

Klapper, Saria-Allende & Zaidi 2006).

Age is defined as a number of years between the date of incorporation of a firm and the end

of year 2009 (Michaelas, Chittenden & Poutziouris 1998; Hall, Hutchinson & Michaelas

2000; Hall, Hutchinson & Michaelas 2004). Firm age can be considered as a proxy for its

creditworthiness because older firms may have established relationships with lenders. The

longer the firm’s credit history of repaying debt, the lower borrowing costs are as lenders

are able to observe that a firm does not undertake asset substitution projects. Thus, the

trade-off theory predicts that age has a positive impact on a debt level. In contrast,

according to the pecking order arguments, older firms have time to retain funds and the

necessity to borrow is lower for them, suggesting that age should be negatively related to

debt. Although Hall, Hutchinson & Michaelas (2004) find that age has a positive impact on

a debt level, the effect is negligible. In addition, the results of previous studies are generally

in line with the pecking order predictions as age is found to be negatively related to debt

(Michaelas, Chittenden & Poutziouris 1998; Hall, Hutchinson & Michaelas 2000; Klapper,

Saria-Allende & Sulla 2002).

As a proxy for liquidity, net debtors, which are calculated as a difference between debtors

and creditors and scaled by total assets, are used (Michaelas, Chittenden & Poutziouris

1998; Degryse, Goeij & Kappert 2009). Illiquid firms have restrictions in obtaining debt

because bankruptcy costs for them are high. Therefore, according to the trade-off theory,

liquidity should have a positive effect on the debt level. As SMEs put less pressure on

collecting payments from customers, they might choose to finance late payments by trade

credit (Degryse, Goeij & Kappert 2009). Companies may prefer trade credit as it represents

less intrusion in the business than debt financing. Suppliers may have superior information

about their customers’ liquidity compared to banks and may be willing to grant trade credit.

Following the pecking order arguments, it can, therefore, be expected that liquidity is

negatively related to a debt level. Michaelas, Chittenden & Poutziouris (1998) and Degryse,

Goeij & Kappert (2009) report positive coefficients of net debtors on long-term debt.

Table 6 summarizes the descriptions of dependent and explanatory variables.

Page | 49

Table 6. Dependent and explanatory variables

Variable Abbre-viation

Definition

Leverage ratio y Long-term debt / (Long-term debt + Shareholders’ funds)

Effective tax rate ETR Taxation / EBT

Size SIZE ln (Sales)

Tangibility TANG Tangible fixed assets / Total assets

Growth GROWTH (Total assets in 2009 – Total assets in 2008) / Total assets

in 2008

Growth opportunities GOP Intangible fixed assets / Total assets

Profitability PROFIT EBIT / Total assets

Age AGE Number of years from the date of incorporation until end

of 2009

Liquidity LIQ (Debtors – creditors) / Total assets Note: In the definition section, the titles of Orbis items are used. All variables are constructed using data from

financial statements of year 2009.

Evidence from previous studies on capital structure of SMEs is inconclusive whether

industry membership has a significant effect on SMEs capital structure. In order to

investigate industry effects on capital structure, studies include industry dummies in the

regressions. While Michaelas, Chittenten & Poutziouris (1998) and Degryse, Goeij &

Kappert (2009) report statistically significant coefficients of industry dummies, Cassar &

Holmes (2003) find them to be statistically insignificant. Nevertheless, in this work, in all

the regressions run, industry dummies are included to make sure that the findings are not

affected by firms’ industry membership. First, firms in the sample are divided into nine

groups according to the first two digits of their NACE Rev. 2 core code14

. Division of firms

into these groups is presented in Table 7.

Table 7. Division of firms in the sample according to NACE Rev. 2 core code

First two digits of NACE Rev. 2 code Group name 01 – 09 Primary sector

10 – 33 Manufacturing

35 – 39 Utilities

41 – 43 Construction

45 – 47 Wholesale and retail trade

49 – 53 Transport

55 – 56 Hotels and restaurants

58 – 63; 68 – 75; 77 – 82; 90 – 96 Other services

85 – 88 Education and health

14

Detailed structure of NACE Rev. 2 statistical classification is available at: http://circa.europa.eu/irc/dsis/

nacecpacon/info/data/en/NACE%20Rev%202%20structure%20+%20explanatory%20notes%20-%20EN.pdf.

Page | 50

Secondly, eight dummy variables are created, taking on a value of one if a firm belongs to a

particular group and a value of zero, otherwise15

.

Empirical evidence whether the determinants of capital structure of SMEs are firm- or

country-specific is also mixed. Conflicting results are reported by Hall, Hutchinson &

Michaelas (2004) and Psillaki & Daskalakis (2009). Nonetheless, as the sample of SMEs is

pooled from the three Baltic countries, country dummy variables are created and included

in the regressions. Two country dummy variables are created, taking on either a value of

one or a value of zero. Estonian firms are chosen as a base group, for which a country

dummy variable is not created.

After all variables were constructed and calculated for the sample firms in Excel, the data

were imported in Stata 9, where all regressions were run and tests performed. The results of

the empirical analysis are presented in the next section.

15

The number of dummy variables is eight to avoid the dummy variable trap and multicollinearity.

Manufacturing group is chosen as a base group, for which dummy variable is not created.

Page | 51

5. Empirical analysis

5.1. Sample statistics and descriptive statistics of variables

Having all variables constructed and before running the regressions, it was checked how

many firms in the sample have null leverage ratios. Table 8 shows that, on average, 41.2%

of SMEs in the sample do not have long-term debt in their capital structure. These results

are consistent with the previous studies, which also report that substantial proportions of

firms follow a zero-debt policy (for example, Strebulaev & Kurshev 2006; Ramalho &

Vidigal da Silva 2009). The differences between the Baltic countries are also evident.

While only 26.9% of Lithuanian SMEs do not have long-term debt, more than a half of

Estonian SMEs do not use long-term debt financing. The fact that there is a large

proportion of firms in the sample with null leverage ratios gives a clear indication that

simple OLS regressions would not be appropriate to investigate the determinants of

leverage decisions.

Table 8. Firms with zero leverage ratios in the sample

Note: Table 8 shows the numbers of firms which do not have long-term debt in each subgroup of SMEs and

each country. Percentages are calculated as the number of companies which do not have long-term debt

divided by the total number of companies in each subgroup or each country in the sample.

From Table 8 it is also evident that there is a size effect on the probability that a firm is

using long-term debt financing, as the larger the firm, the more likely it is that a firm

obtains long-term debt financing. The percentages of companies that do not use long-term

debt financing are, on average, 59.2%, 31.8% and 22.3% for the subgroups of micro, small

and medium companies, respectively. Differences among the proportions of firms that do

not have long-term debt are most obvious if subgroups of Estonian enterprises are

compared, while they are least apparent for Latvian companies, especially between small

and medium-sized Latvian companies.

Micro firms Small firms Medium firms Total no. % no. % no. % no. %

Estonia 949 62.1 248 37.2 53 23.0 1,250 51.6

Latvia 187 49.3 153 25.8 108 24.9 448 31.8

Lithuania 35 48.6 110 31.8 83 19.3 228 26.9

Total 1,171 59.2 511 31.8 244 22.3 1,926 41.2

Page | 52

Descriptive statistics of the explanatory variables are reported in Table 9. The average

values of the effective tax rate show that, on average, the smaller the firm, the lower tax

burden it has as effective tax rates are 2.7%, 8.6% and 12.4% for micro, small and medium-

sized firms, respectively. However, the median values of the effective tax rate for micro

and small companies are zero, indicating that in year 2009 more than a half of these

companies were tax-exempted. Tangible fixed assets comprise 27%, 34.8% and 40.1% of

total assets for micro, small and medium-sized companies.

Table 9. Descriptive statistics for the explanatory variables

Variable Micro firms Small firms Medium firms

ETR

Mean 0.027 0.086 0.124

Median 0.000 0.000 0.161

St. dev. 0.613 0.683 0.281

TANG

Mean 0.270 0.348 0.401

Median 0.144 0.287 0.389

St. dev. 0.297 0.286 0.255

SIZE

Mean 11.775 14.161 15.489

Median 11.865 14.127 15.422

St. dev. 1.617 1.140 0.963

GROWTH

Mean 0.420 0.035 -0.042

Median -0.042 -0.096 -0.095

St. dev. 2.090 1.031 0.550

GOP

Mean 0.012 0.011 0.010

Median 0.000 0.000 0.000

St. dev. 0.072 0.058 0.051

PROFIT

Mean 0.023 0.023 0.040

Median 0.022 0.029 0.033

St. dev. 0.463 0.206 0.153

LIQ

Mean 0.028 0.030 0.025

Median 0.005 0.014 0.017

St. dev. 0.234 0.197 0.164

AGE

Mean 8.269 12.159 14.320

Median 6.728 12.214 14.797

St. dev. 6.093 7.179 6.766 Source: Own calculations.

Considering sales, it is obvious that there are large differences between the subgroups of

SMEs. On average, sales for micro firms amount only close to 130 thousand euros, while

they exceed 1.4 million and 5.3 million euros for small and medium firms. The average

Page | 53

values of growth rates indicate that smaller firms are faster growing. Nevertheless, the

extreme values of growth significantly affect the mean values of growth rates of micro and

small firms. If the median values are compared, the value of total assets declined for more

than a half of firms in all subgroups of SMEs in 2009. Intangible fixed assets, on average,

constitute close to 1% of total assets for all three groups of SMEs. However, the median

values are zero, indicating that more than a half of all SMEs do not have intangible fixed

assets. Despite the fact that year 2009 was the crisis time, all SMEs report positive values

of EBIT, with larger firms being more profitable. On average, net debtors amount to 2.8%,

3% and 2.5% of total assets for groups of micro, small and medium-sized enterprises,

respectively. From Table 9 it is also clear that size and age of a firm are linked. In general,

SMEs in three Baltic countries were established after the restoration of independence of

these countries and, hence, are much younger than their Western European counterparts.

Table 10 reports the summary statistics of the leverage ratios for each category of SMEs. If

the average and median leverage ratios are compared, the size effect on the leverage ratio is

evident from panels A and B of Table 10. Micro firms, on average, have significantly lower

leverage ratios than small or medium-sized firms, while the difference between the average

leverage ratios of small and medium-sized enterprises is negligible (panel A). However,

when the comparison is limited only to companies that have long-term debt financing, the

trend is opposite: the larger the firm, the lower leverage ratio it has (panel B). Hence, the

results from these two panels of Table 10 are contradictory: once smaller companies decide

and manage to obtain long-term debt financing, they use this type of financing in larger

proportions than larger companies.

Table 10. Summary statistics of the leverage ratios

Panel A: Leverage ratios of the whole sample Micro firms Small firms Medium firms

Mean 0.190 0.270 0.264

Median 0.000 0.126 0.161

St. dev. 0.302 0.310 0.281

Panel B: Leverage ratios of the firms with non-zero leverage ratios Micro firms Small firms Medium firms

Mean 0.464 0.396 0.340

Median 0.460 0.344 0.291

St. dev. 0.310 0.302 0.275 Source: Own calculations.

Page | 54

5.2. Results of regressions and tests

To test the hypothesis 1 and hypothesis 2, difference-in-mean tests for the three pairs of

subgroups of SMEs were performed. The results of these tests are reported in Table 11.

Table 11. Pair-wise comparison of mean leverage ratios for subgroups of SMEs

Entire sample

Only firms with non-zero leverage ratios

Small firms Medium firms Small firms Medium firms

Micro firms -0.080***

(0.010)

-0.074***

(0.111)

0.068***

(0.014)

0.124***

(0.014)

Small firms 0.006

(0.012)

0.056***

(0.013) Note: The first number in cells of the table 11 indicates the difference of the mean leverage ratios between

two subgroups of SMEs compared. Standard deviations are in parentheses. *** indicates statistical

significance at 1% level.

When the comparison of the mean leverage ratios is based on the entire sample of firms,

micro firms have lower leverage ratios than small firms or medium-sized firms and these

differences are statistically significant at 1% level. Interestingly, small firms have higher

leverage ratios than medium-sized firms, but the difference in the mean leverage ratios is

negligible and statistically insignificant. When the differences in the mean leverage ratios

are calculated excluding SMEs with zero long-term debt, micro firms are more levered than

small firms and small firms are more levered than medium-sized firms. In this case,

differences in the mean leverage ratios are statistically significant at 1% level for all three

pairs of subgroups of SMEs. Therefore, size of firm affects the probability that a firm

manages to obtain and is using long-term debt financing and the decision regarding the

proportion of long-term debt in capital structure in an inverse way. As the differences in the

mean leverage ratios between the categories of SMEs are found with expected signs and are

statistically significant in five out of six cases, the results of these tests provide support for

hypotheses 1 and 2.

The empirical results obtained from the estimation of the two parts of the model, described

in section 4.2, are reported in Table 1216

. First, considering the empirical adequacy of the

model, it fits the data relatively well. Obtained pseudo R2 values are quite low, but are

16

Stata commands written to obtain the estimated coefficients and perform heteroskedasticity and RESET

tests can be found in Appendices 8-10.

Page | 55

common in cross-sectional studies. The results of the heteroskedasticity test for the binary

part of the model indicate that the problem of heteroskedasticity is not present. Hence, there

is no need to change the estimation method for the binary part of the model. RESET tests

give no indication that functional forms of both parts of the model are incorrectly specified.

Therefore, alternative functional forms are not considered.

Table 12. Results of regressions of the two-part FRM

Part I: binary model Part II: FRM Micro Small Medium Micro Small Medium

ETR -0.074

(0.088)

-0.042

(0.080)

0.288

(0.274)

-0.130

(0.085)

0.070

(0.053)

-0.192

(0.118)

TANG 2.877***

(0.197)

3.895***

(0.313)

3.243***

(0.427)

0.668***

(0.157)

1.191***

(0.169)

1.362***

(0.214)

SIZE 0.374***

(0.041)

0.295***

(0.064)

0.164*

(0.087)

-0.155***

(0.039)

-0.051

(0.043)

0.064

(0.047)

GROWTH -0.003

(0.239)

-0.125**

(0.054)

-0.005

(0.165)

0.080***

(0.025)

0.069

(0.043)

0.241**

(0.120)

GOP 1.506**

(0.667)

1.887*

(0.974)

7.629***

(2.320)

-0.089

(0.541)

1.076

(0.659)

3.422***

(0.609)

PROFIT -0.339**

(0.148)

-0.229

(0.310)

-1.153**

(0.588)

-0.344*

(0.197)

-0.986***

(0.262)

-0.994***

(0.367)

LIQ -0.058

(0.217)

-0.673**

(0.298)

-0.526

(0.484)

-0.531**

(0.258)

-0.701***

(0.235)

-0.394

(0.293)

AGE -0.015

(0.010)

-0.021**

(0.010)

0.011

(0.013)

-0.034***

(0.011)

-0.033***

(0.010)

-0.021***

(0.007)

CONSTANT -5.507***

(0.532)

-4.808***

(0.928)

-2.391*

(1.382)

1.528***

(0.536)

-0.115

(0.649)

-2.266***

(0.733)

Industry dummies

Yes Yes Yes Yes Yes Yes

Country dummies

Yes Yes Yes Yes Yes Yes

No. of obs. 1,978 1,606 1,095 807 1,095 851

Pseudo R2 0.153 0.166 0.124 0.163 0.214 0.212

Heteroskeda-sticity test

0.207 0.303 0.134 - - -

RESET test 0.224 0.724 0.968 0.165 0.121 0.238 Note: In the regressions of part I, the dependent variable is a binary variable, taking on a value of one if a

firm has a non-zero value of a leverage ratio and a value of zero, otherwise. In the regressions of part II, the

dependent variable is a leverage ratio, as defined in section 4.3. In this part, coefficients are estimated only

on the sample of firms with non-zero leverage ratios. Below the coefficients, robust standard errors are

reported in parentheses. *, **, *** indicate statistical significance at 10%, 5% and 1% level, respectively.

Industry and country dummies are included in all regressions. P-values are reported for heteroskedasticity

tests and RESET tests.

Page | 56

The estimated coefficients of the part I, the binary model, reveal that tangibility, size and

growth opportunities are the most robust determinants of the decision of obtaining long-

term debt financing or not. Obtained coefficients are consistently statistically significant for

all three groups of SMEs. In all cases, the larger the proportion of a firm’s total assets that

is composed of tangible fixed assets or intangible fixed assets, the higher the probability

that a firm is using long-term debt financing. The coefficients of the tangibility variable

may indicate that for all SMEs their ability to pledge collateral is one of the most important

factors of success in obtaining long-term debt financing. The fact that the coefficients of the

tangibility variable are consistently statistically significant is in contrast to the results of

previous studies of firms’ capital structure in Eastern European countries (for example,

Haas & Peeters 2004; Joeveer 2006). These studies find that tangibility is not a significant

determinant of capital structure and provide an explanation for that. As collateral laws were

weak and credit information registries were poor, collateral was not considered as an

effective guarantee against bankruptcy and recovery of debt for lenders in these countries.

As the lending environment improved during the transition process in Eastern Europe,

including the Baltic countries, the result of this thesis regarding the effect of tangibility is

not surprising. The positive effect of growth opportunities on the probability of obtaining

long-term debt imply that firms having investment opportunities are more likely to have a

shortage of internal funds to finance these opportunities. Despite the fact that investment

opportunities might be difficult to assess for the outsiders, issues of information asymmetry

are usually solved.

A negative relationship between the probability of using long-term debt financing and

growth variable is found. It is in line with the trade-off theory and agency theory and in

opposition to the pecking order theory. This relationship provides weak evidence for

Myers’ (1977) underinvestment hypothesis because the coefficient is significant only for

small firms. A negative relationship found in this work between the firm’s growth and the

probability of using long-term debt contradicts the results of the previous studies on SMEs

capital structure (for example, Michaelas, Chittenden & Poutziouris 1998; Degryse, Goeij

& Kappert 2009).

In addition, a negative effect of profitability on the probability of obtaining long-term debt

is found, which is statistically significant for micro and medium-sized companies. In

Page | 57

accordance with the pecking order theory and in contrast to the trade-off and agency

theories, SMEs seem to prefer internally generated funds to external resources to finance

their activities. The negative coefficients of the liquidity variable also provide support for

the pecking order theory. However, the coefficient of liquidity is found to be statistically

significant only for small firms.

Considering the age and effective tax rate variables, the results of regressions of the binary

part of the model are quite ambiguous. In contrast to the predictions of the trade-off theory,

effective tax rate is found to be negatively related to the probability of using long-term debt

for micro and small firms. Nevertheless, in no case a significant relationship is found

between this variable and the probability of obtaining long-term debt. With respect to age,

the results are also quite uncertain. Although age has statistically significant negative effect

on the resort to long-term debt financing for small firms, the coefficient changes its sign for

medium-sized firms.

From the results of regressions for the second part of the model, which are based only on

the sample of firms with non-zero leverage ratios, tangibility, profitability and age are the

most robust determinants of the relative amount of long-term debt in capital structure. The

effects of tangibility, profitability and age variables are statistically significant for all three

subgroups of SMEs. Similarly to the results in the first part of the model, the positive effect

of tangibility imply that greater ability to pledge collateral may alleviate the agency costs of

debt and that SMEs, which can offer collateral, are able more easily obtain long-term debt

financing. As predicted by the pecking order theory, more profitable SMEs prefer to use

internal sources of finance to external ones due to information asymmetry between firms

and lenders, which causes costs of external financing to be higher. Age turns out to have a

statistically significant negative effect on the proportion of long-term debt for all groups of

SMEs. A possible explanation for the latter effect may be the accumulation of retained

earnings by companies which are successful and survive for a longer time. Hence, the older

the firm, the less need it has to obtain long-term debt financing.

Comparing the results obtained for both parts of the model, the effect of the size variable in

the second part of the model changes from being positive to negative for micro and small

firms, although it is statistically significant only for micro firms. These results are

Page | 58

consistent with the findings by Ramalho & Vidigal da Silva (2009) and may be explained

by the presence of transaction costs in obtaining long-term debt financing. Due to these

transaction costs smaller firms choose to operate at higher levels of leverage at the time

when they obtain long-term debt financing. In addition, contrary to the results of the binary

choice model, the signs of the coefficients of the growth variable change from negative to

positive for all three groups of SMEs. Firm’s growth puts a strain on its retained earnings

and induces it to resort to debt financing. This result could also be interpreted as a supply

side phenomenon, where companies with higher growth rates have better access to long-

term debt financing.

In contrast to the first part of the model, the coefficients of growth opportunities provide

ambiguous results in the second part of the model. The negative sign of the coefficient of

growth opportunities for micro firms would support Myers’ (1977) underinvestment

hypothesis. However, the effect is statistically insignificant. As in the first part of the

model, the coefficient of growth opportunities remains to be positive for small and medium

firms (although statistically significant only for the latter), suggesting that firms with higher

proportions of intangible assets are able to obtain long-term debt to finance their future

growth.

Consistently with the results for the binary choice model, liquidity has a negative impact on

the relative amount of long-term debt used by SMEs. Companies with lower net debtors

have higher proportions of long-term debt, ceteris paribus. As in the first part of the model,

no evidence is found that SMEs have incentives to increase leverage because of corporate

taxes, as the effective tax variable is statistically insignificant for all three groups of SMEs.

A comparison of the results from the first and second part of the model imply that the

determinants of the probability that a firm uses long-term debt financing are not the same as

those of the proportion of long-term debt in capital structure of SMEs. Firm size and

growth have opposite effects on each decision. Moreover, while growth opportunities are

significant for all groups of SMEs in the first part of the model, they show statistical

significance only for medium-sized firms in the regressions of the second part of the model.

Similarly, age is a significant determinant of the probability that a firm resorts to long-term

debt financing only for small firms, while it has a negative effect on the relative amount of

Page | 59

long-term debt in capital structure for all size-based groups of SMEs. Differences between

the two parts of the model in terms of the signs of the estimated coefficients and their

statistical significance support hypothesis 3.

Based solely on the estimated coefficients in the two parts of the model, it is not possible to

identify a partial effect of a change by one unit of each variable. In addition, growth and

size variables have effects with opposite signs in the two parts of the model. Therefore, it is

not possible to know which effect, positive or negative, dominates and what the total effect

of each variable is. It is unclear, for example, if larger micro firms, on average, use more or

less long-term debt financing. To find out, Table 13 reports the estimated partial effects of a

change in each explanatory variable. These partial effects are calculated as the averages of

the partial effects, evaluated for each company in the sample17

. In Table 13, three different

partial effects are reported: the partial effect on the probability that a firm uses long-term

debt financing, defined in equation (6); the partial effect on the proportion of long-term

debt, based on the firms which already use long-term debt financing and defined in

equation (7); and the effect on the proportion of long-term debt for all firms, defined in

equation (8). The last above mentioned effect gives the average joint effect of a change by

one unit in each explanatory variable on the proportion of long-term debt in capital

structure for all firms.

Table 13. Average partial effects of the explanatory variables

Micro firms Small firms Medium-sized firms

∆∆∆∆Pr ∆∆∆∆E1 ∆∆∆∆E ∆∆∆∆Pr ∆∆∆∆E1 ∆∆∆∆E ∆∆∆∆Pr ∆∆∆∆E1 ∆∆∆∆E ETR -0.014 -0.031 -0.019 -0.007 0.015 0.008 0.043 -0.040 -0.018

TANG 0.560 0.158 0.317 0.680 0.262 0.408 0.485 0.284 0.366

SIZE 0.073 -0.037 0.018 0.052 -0.011 0.010 0.024 0.013 0.018

GROWTH -0.001 0.019 0.007 -0.022 0.015 0.003 -0.001 0.050 0.039

GOP 0.293 -0.021 0.124 0.329 0.237 0.272 1.141 0.713 0.896

PROFIT -0.066 -0.081 -0.062 -0.040 -0.217 -0.161 -0.172 -0.207 -0.212

LIQ -0.011 -0.126 -0.056 -0.118 -0.154 -0.145 -0.079 -0.082 -0.087

AGE -0.003 -0.008 -0.005 -0.004 -0.007 -0.006 0.002 -0.004 -0.003 Note: ∆Pr is the partial effect of a change of each explanatory variable on the probability of using long-term

debt financing, ∆E1 is the partial effect on the proportion of long-term debt in capital structure of firms that

already use long-term debt financing, and ∆E is the effect on the proportion of long-term debt financing used

by all firms. Each partial effect is calculated as the average sample effect.

17

Stata commands written to estimate the partial effects are provided in Appendix 11.

Page | 60

The overall partial effects reported in Table 13 (column labelled ∆E) indicate that

tangibility, size, growth and growth opportunities have a positive effect on the relative

amount of long-term debt financing, profitability and liquidity have a negative influence on

it, and the effect of age is close to zero for all size-based groups of SMEs. It could be

expected that the importance of collateral should be greater for micro firms than for small

or medium-sized firms, as micro firms, in general, are more recently established businesses

without close connections to lenders. However, the magnitude of the partial effects of

tangibility does not provide support that the ability to pledge collateral is more important

for micro firms.

The positive total effect of the size variable is consistent with the previous empirical studies

on capital structure which, differently than in this thesis, use only one-part models to

investigate the determinants of capital structure. Although the total partial effect of growth

is negligible for micro and small firms, its magnitude increases for medium-sized firms.

Similarly, the magnitude of the effect of growth opportunities increases with the size of a

firm. These patterns of the total partial effects suggest that past growth and future growth

opportunities become more important in obtaining long-term debt financing as a firm

grows. The larger the firm gets, the more critical it becomes to show that a firm has had a

healthy growth and has viable investment opportunities. The signs of the overall partial

effects found for profitability and liquidity indicate that all three size-based groups of

SMEs seem to follow the pecking order. Contradicting the predictions of the trade-off

theory, the overall partial effect of the effective tax rate is found to be negative for micro

and medium-sized firms. A possible explanation for this inconsistency might be that higher

taxes stem from higher profits, which in turn reduce the need for debt financing (Degryse,

Goeij & Kappert 2009).

The results reported in Table 12 also suggest that the determinants either of the probability

of using long-term debt financing or the proportion of long-term debt financing in capital

structure differ among micro, small and medium-sized companies. For instance, in the

second part of the model, size is statistically significant determinant only for micro firms,

whereas growth opportunities are important only for medium-sized firms. Moreover, even

when the signs of the coefficients, estimated for each subgroup of SMEs, are the same,

there might be significant differences in the magnitude of them.

Page | 61

Table 14 reports the LR and LM test statistics, defined in equations (11) and (12), and p-

values, estimated for both parts of the model, for the null hypothesis that there are no

significant differences between the coefficients of a particular explanatory variable in each

pair of subgroups of SMEs18

.

Table 14. LR and LM test statistics and p-values for the null hypotheses of the equality of

the coefficients of each explanatory variable

Part I: binary model Part II: FRM

Micro vs. small

Micro vs. medium

Small vs. medium

Micro vs. small

Micro vs. medium

Small vs. medium

ETR 0.21

(0.650)

0.54

(0.461)

0.75

(0.385)

4.08**

(0.043)

0.30

(0.582)

3.77*

(0.052)

TANG 15.08***

(0.000)

0.40

(0.529)

1.41

(0.235)

3.30*

(0.069)

2.79*

(0.095)

1.93

(0.165)

SIZE 4.47**

(0.034)

6.51**

(0.011)

4.83**

(0.028)

0.21

(0.644)

4.32**

(0.038)

4.00**

(0.046)

GROWTH 2.23

(0.135)

0.69

(0.406)

0.10

(0.752)

0.54

(0.462)

1.83

(0.176)

3.27*

(0.070)

GOP 0.07

(0.796)

3.89**

(0.049)

3.41*

(0.065)

1.16

(0.282)

7.68***

(0.006)

3.85**

(0.050)

PROFIT 0.15

(0.697)

7.72***

(0.006)

5.78**

(0.016)

2.38

(0.122)

0.51

(0.476)

0.01

(0.911)

LIQ 3.46*

(0.063)

1.03

(0.311)

0.26

(0.610)

0.62

(0.431)

0.02

(0.886)

0.85

(0.357)

AGE 3.00*

(0.083)

2.71*

(0.100)

1.39

(0.238)

0.19

(0.664)

0.38

(0.536)

1.13

(0.287) Note: Table 14 reports LR/LM statistics and p-values to each pair of size-based groups of SMEs for the H0 of

the equality of the coefficients of each explanatory variable. For the first part of the model, binary choice

model, LR statistics are reported, while for the second part of the model, fractional regression model, LM

statistics are shown. LR statistics are defined in equation (11), LM statistics in equation (12). P-values are

reported in parentheses. *, ** and *** denote statistical significance at 10%, 5% and 1% level, respectively.

As can be seen from Table 14, substantial differences between the coefficients of

explanatory variables occur in some cases. Considering the LR/LM tests results for both

parts of the model, they might indicate that micro firms behave similarly as small firms

regarding their decisions of long-term debt financing, while medium-sized firms are a more

distinctive group. Significant differences in the coefficients of growth opportunities and

profitability variables are found when micro firms are compared with medium-sized firms

and small firms are compared with medium-sized firms, but not in the case when micro

18

Stata commands written to obtain test statistics for both parts of the model can be found in Appendix 12.

Page | 62

firms are compared with small firms. The coefficients of age and tangibility significantly

differ when micro firms are compared to small or medium-sized firms. A possible

explanation could be that, as micro firms, in general, are younger and more informationally

opaque than larger companies, lenders consider micro firms’ age as a measure of their

reputation. A longer history of operations might help to alleviate problems of information

asymmetry and improve access to credit market for micro firms. In addition, as micro firms

might lack track records of repaying debt and might not have relationships established with

lenders, their ability to pledge collateral is more important to obtain long-term debt

financing.

Table 15 reports the LR and LM test statistics and p-values obtained to test the null

hypothesis that there are no significant differences between all the coefficients of

explanatory variables in each pair of subgroups of SMEs. Taking into consideration both

parts of the model, statistically significant differences are found in five out of six cases. The

only one pair where the hypothesis of the equality of all the coefficients cannot be rejected

is the pair of micro and small firms in the second part of the model. Therefore, these results

reinforce the findings that micro firms might behave similarly to small firms regarding the

determination of capital structure, while medium firms are a more divergent group.

Table 15. LR and LM test statistics and p-values for the null hypothesis of the equality of all

the coefficients

Part I: Binary model Part II: FRM

Small firms Medium-sized

firms Small firms

Medium-sized firms

Micro firms 21.56***

(0.006)

17.86**

(0.022)

11.50

(0.175)

17.33**

(0.027)

Small firms 19.57**

(0.012)

16.45**

(0.036) Note: Table 15 reports LR/LM statistics and p-values to each pair of subgroups of SMEs for the H0 of no

significant differences between all the coefficients of explanatory variables. For the first part of the model,

binary choice model, LR statistics are reported, while for the second part of the model, fractional regression

model, LM statistics are shown. LR statistics are defined in equation (11), LM statistics in equation (12). P-

values are reported in parentheses. *, ** and *** denote statistical significance at 10%, 5% and 1% level,

respectively.

As the test statistics, reported in Table 15, are significant in most cases, there does seem to

be differences in the magnitudes of regressor coefficients. Companies belonging to

Page | 63

different size-based groups of SMEs do not seem to behave similarly regarding the decision

to obtain long-term debt financing or the decision on the proportion of long-term debt

financing in capital structure. The results indicate that there is significant diversity within

the category of SMEs and support hypothesis 4.

5.3. Robustness check As the subsamples of micro, small and medium-sized companies were constructed by

pooling the data from three Baltic countries together, the regressions for both parts of the

model were run again by each country separately19

. The results from these regressions are

then compared with the results of the entire sample, which are reported in Table 12. It is a

necessary procedure to check whether the results are not driven by one particular country

because the numbers of observations for each country are different.

Certainly, some differences in the statistical significance of the coefficients, obtained from

running the regressions on the entire sample, and obtained from running the regressions on

each country separately, are found. For example, while profitability has a negative and

significant effect on the probability that a micro firm is using long-term debt financing in

the entire sample, the effect of it is not statistically significant for Estonian and Lithuanian

micro firms. Nevertheless, there are no cases found that the sign of the estimated coefficient

of a particular explanatory variable for a particular country is with the opposite sign than

the sign of the coefficient obtained on the entire sample. If the coefficient appears with the

opposite sign, there are no cases that the variable is statistically significant.

Regarding the explanatory variables, tangibility remains to be the most robust determinant

of leverage decisions, as for all subgroups of SMEs in all three countries it is found to be

positively related and statistically significant in both parts of the model. For all groups of

firms and all three countries, size is positively related to the probability of using long-term

debt financing. However, in the second part of the model, despite the fact that the

coefficient of the size variable in most cases is negative, it is statistically insignificant. In

the second part of the model, age has a significant negative impact on the proportion of

long-term debt in capital structure for all size-based groups of firms. This might imply that

19

The results of running regressions by country are reported in Appendix 6.

Page | 64

firms accumulate retained earnings and, hence, have a lower demand for external sources of

financing. In addition, a negative relationship between profitability and either the

probability of using long-term debt financing or the proportion of long-term debt is verified

for all size-based groups of SMEs and all three Baltic countries, indicating pecking order

behaviour. Overall, there are no qualitative differences found between the results of

regressions on the whole sample and for each country, and results seems to be robust.

Page | 65

6. Conclusion

6.1. Concluding remarks

Capital structure has attracted intense debate and attention in the field of finance over the

past five decades. Despite the extensive empirical analysis of the leverage decisions of

large public companies, the empirical investigation of capital structure of SMEs has started

relatively recently. In addition, the analysis of financing decisions of SMEs in Eastern

Europe, including the Baltic countries, is still scarce. Thus, this thesis studies the leverage

decisions of SMEs in the Baltic countries, namely the determinants of long-term debt

financing of micro, small and medium-sized companies.

Instead of viewing SMEs as a homogenous group, the thesis distinguishes among the size-

based groups of SMEs and investigates whether the determinants of capital structure are the

same for micro, small and medium-sized enterprises. In addition, given the fact that

substantial proportions of SMEs follow a zero long-term debt policy, this thesis studies

whether there are differences between the factors that have an impact on the probability of

obtaining long-term debt financing and the factors that have an influence on the proportion

of long-term debt financing in capital structure.

This thesis applies a two-part fractional regression model instead of a one-part model,

which might lead to biased results. In the two-part fractional regression model, the

determinants of the probability of using long-term debt and the determinants of the

proportion of long-term debt are not considered to be the same. The first part of the two-

part fractional regression model allows determining the effects of explanatory variables on

the probability that a firm is using long-term debt financing, while the second part helps to

determine the effects of independent variables on the proportion of long-term debt

financing for firms that already use it. The regressor coefficients in the first part of the

model are estimated using a binary choice model, while in the latter a fractional regression

model is used, which takes into consideration the fact that leverage ratios are of a bounded

nature.

The results suggest that it is more likely that larger firms have long-term debt in their

capital structure as the proportions of micro firms in the Baltic countries, which do not have

long-term debt, are higher than the proportions of small or medium-sized companies with

Page | 66

zero long-term debt. Hence, the average leverage ratio of micro firms is significantly lower

than the average leverage ratios of small or medium-sized firms. When the average

leverage ratios of small and medium-sized firms are compared, no significant differences

are found. Nevertheless, when the comparison of the average leverage ratios is based only

on the firms with non-zero leverage ratios, micro firms appear to be significantly more

levered than small firms and small firms are more indebted than medium-sized firms. This

result is in line with the recent findings by Strebulaev & Kurshev (2006) and Ramalho &

Vidigal da Silva (2009), who find a negative relationship between firm size and leverage

ratio, if only firms with positive amounts of long-term debt are considered.

The results of the empirical analysis support the hypothesis that there are significant

differences in terms of a direction, significance or magnitude of some regression

coefficients of the capital structure determinants between the sized-based groups of SMEs.

When the null hypothesis of no significant differences in the effects of all explanatory

variables is tested, the null hypothesis is rejected in most cases, except in the case when the

coefficients of explanatory variables, estimated for the subgroups of micro and small

companies, are compared. When the null hypothesis of the equality of regressor coefficients

is tested for each explanatory variable separately, significant differences are also found in

some cases, particularly for the variables of growth opportunities, tangibility, size and

profitability. The pattern of significant differences in the direction, significance or at least

magnitude of the regressor coefficients might indicate that micro firms behave similarly to

small firms regarding the capital structure decisions, while medium-sized companies are a

more divergent group. However, for all three size-based groups of SMEs findings

consistently suggest that profitability, liquidity and age are negatively related to leverage,

implying that the pecking order theory might be more appropriate than the trade-off or

agency theories to describe the capital structure decisions of SMEs in the Baltic countries.

The empirical analysis also suggests that some explanatory variables, namely size and past

growth, have opposite effects on the dependent variable in the two parts of the model.

Moreover, some variables, namely growth opportunities and age, mainly show statistical

significance only in one part of the model. Therefore, some support is found that the

determinants of the two financial leverage decisions (i.e., the decision to obtain long-term

Page | 67

debt and the decision on the relative amount of long-term debt financing in capital

structure) are not the same.

The empirical analysis of this thesis finds support for the hypotheses brought forward.

However, it also raises some questions and is subject to limitations.

6.2. Limitations of the thesis and suggestions for further research

As any other academic paper, this thesis has shortcomings, is subject to criticism and poses

some questions. The first limitation is the choice of the sample period. At the time of the

data collection, the data in the Orbis database for year 2010 were available for few SMEs in

the Baltic countries. Therefore, year 2009 was chosen as the sampling period. This

significantly increased the number of observations. However, in year 2009 financial

markets were remarkably affected by the financial crisis, which also had significant macro

effects on entire economies. The supply of external capital was radically restricted, and

commercial banks immediately adopted extremely conservative lending practices. Given

the fact that substantial proportions of SMEs in the sample, used in this thesis, have zero

long-term debt, it is difficult to interpret whether this is caused by the supply-side or

demand-side effects. On the one hand, SMEs might choose not to obtain long-term debt

financing deliberately. The negative coefficients obtained for profitability variable for all

groups of SMEs point to the issue of information asymmetry, which leads to higher

external financing premiums and pecking order behaviour. On the other hand, such a

situation may be supply driven, where negative coefficients reflect not pecking order

behaviour, but a bank credit crunch and the related effect of credit rationing. In this case,

SMEs are forced to rely on internal sources of finance. Therefore, it might be beneficial to

analyse the financing patterns of SMEs in the Baltic countries in different time periods, for

example, in the pre-crisis period, when circumstances in the credit markets and, in general,

in the economies were different.

In addition, instead of the cross-sectional data, the panel data could be used in the analysis,

which would allow analyzing time-specific effects on the financing decisions of SMEs.

Despite the fact that much more advanced models and methods than employed in this work

would have to be used on the panel data, such an approach would allow incorporating

Page | 68

country-specific variables in the analysis. An investigation of the impact of

macroeconomic, institutional and legal factors on the financing decisions of SMEs might be

a potential and important area for further research.

The empirical analysis of the thesis suggests that the determinants of the capital structure

decisions are different among micro, small and medium-sized enterprises. Although the

literature on capital structure identifies how financing patterns of SMEs are different from

the patterns of large enterprises and what the potential explanations for these differences

are, there is no detailed analysis of what causes different financing patterns among the

micro, small and medium-sized firms. As the results of this work suggest, the category of

SMEs cannot be considered as uniform. Therefore, an in-depth analysis of the reasons of

differences in financing decisions between the size-based groups of SMEs could be a

potential area for research.

Other limitations arise due to the definitions of explanatory and dependent variables used in

this thesis. Although the variables for the model were constructed following common

definitions of them found in the capital structure literature, many factors that appear in the

model are not directly observable attributes and proxies have to be used. It is difficult to

expect that we could find a perfect proxy; therefore, as any other empirical capital structure

study, the results of this thesis have to be interpreted with caution. In addition, all data used

in the thesis rely on the accessibility and accurateness of the data in the Orbis database.

Despite that the coverage of the Orbis database has increased for the firms in Eastern

Europe in the last years, some information is still not available. Consequently, this

limitation does not allow constructing additional explanatory variables or alternative

definitions of them, which could be used to test the robustness of the results.

One more limitation is caused by the definition of SMEs and how sized-based subgroups

were created. The thesis adopts the definition of SMEs and the respective subgroups, set by

the European Commission. Yet, one could still argue to what extent this definition is

objective.

In summary, despite the fact that the topic of capital structure has been extensively

analysed for over fifty years, we still lack the theory, which could explain the broad

observed financing patterns of firms, and most probably we cannot expect such one to be

Page | 69

developed. In addition, the capital structure decisions of SMEs, especially on the samples

of Eastern European countries, are relatively under-researched. A deeper knowledge of the

financing decisions of the enterprises in these countries might be useful for policymakers

given their highly significant role and that SMEs quite obviously are the engines of the

economy.

Page | 70

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Page | 77

Appendices

1. Taxes, macroeconomic and financial sector development variables in EU-27 countries

2. Institutional factors in EU-27 countries (all values are from year 2010)

3. Heteroskedasticity test

4. Functional form testing

5. Overview of the SMEs capital structure studies, dependent and explanatory variables

used and results

6. Results of robustness check

7. Excel file with firms in the sample (CD-Rom)

8. Stata commands to estimate two parts of the model (CD-Rom)

9. Stata commands for heteroskedasticity test (CD-Rom)

10. Stata commands for RESET tests (CD-Rom)

11. Stata commands to obtain partial effects (CD-Rom)

12. Stata commands to test the equality of the coefficients of each explanatory variable

relative to each pair of subgroups of SMEs (CD-Rom)

13. Folder with research articles (CD-Rom)

Page | 78

Appendix 1. Taxes, macroeconomic and financial sector development variables in EU-27 countries

Country GDP per capita 2010 (PPP), US

$

Average GDP growth rate

2006-2010, %

Average inflation rate 2006-2010, %

Statutory corporate tax rate 2010, %

Total tax rate 2010, % of

profit

Average domestic credit 2005-2009,

% of GDP

Average total market capitalization 2005-

2009, % of GDP Bulgaria 12,851 2.8 6.5 10 29.0 54.1 26.4

Cyprus 28,256 2.4 2.3 10 23.2 240.7 62.3

Czech Republic 24,869 2.7 2.6 19 48.8 53.1 31.5

Estonia 18,519 0.3 4.9 21 49.6 89.4 22.3

Hungary 18,738 -0.1 5.3 19 53.3 73.0 27.0

Latvia 14,460 -0.1 6.8 15 38.5 86.7 10.4

Lithuania 17,185 1.4 5.2 15 38.7 57.1 22.2

Malta 24,792 2.4 2.4 35 n/a 140.1 56.1

Poland 18,936 4.7 3.0 19 42.3 49.4 34.4

Romania 11,860 2.6 6.2 16 44.9 168.1 20.6

Slovakia 22,129 4.8 2.3 19 48.7 50.8 6.8

Slovenia 28,030 1.9 3.0 20 35.4 80.1 33.7

Average of NMS 20,052 2.2 4.2 18 41.1 85.1 29.5 Austria 39,634 1.5 1.8 25 55.5 132.3 38.7

Belgium 36,100 1.2 2.2 33.99 57.0 111.5 69.8

Denmark 36,450 0.2 2.1 25 29.2 201.7 68.3

Finland 34,585 1.1 2.0 26 44.6 86.5 96.1

France 34,077 0.7 1.7 33.33 65.8 119.6 84.5

Germany 36,033 1.2 1.7 29.41 48.2 129.9 46.5

Greece 28,434 0.8 3.3 24 47.2 108.4 53.3

Ireland 38,550 -0.2 1.1 12.5 26.5 192.3 43.5

Italy 29,392 -0.3 2.0 31.4 68.6 124.6 37.7

Luxembourg 81,383 2.6 2.5 28.59 21.1 166.7 192.6

Netherlands 40,765 1.4 1.5 25.5 40.5 194.6 88.8

Portugal 23,223 0.5 1.7 25 43.3 168.1 42.8

Spain 29,742 0.9 2.5 30 56.5 195.4 93.0

Sweden 38,031 1.5 2.1 26.3 54.6 129.6 108.7

UK 34,920 0.4 2.7 28 37.3 192.5 125.1

Average EU-15 37,421 0.7 2.1 27 46.4 150.2 79.3 Sources: Economy Watch, Eurostat, KPMG (2010), Doing Business and World Development Indicators.

Page | 79

Appendix 2. Institutional factors in EU-27 countries (all values are from year 2010)

Country Legal rights index Credit

information index Enforcing contacts Investor protection

index Corruption

perceptions index Time (days) Cost (% of claim) Bulgaria 8 6 564 23.8 6.0 3.6

Cyprus 9 0 735 16.4 5.0 6.3

Czech Republic 6 5 611 33.0 5.0 4.6

Estonia 6 5 425 26.3 5.7 6.5

Hungary 7 5 395 15.0 4.3 4.7

Latvia 9 5 309 23.1 5.7 4.3

Lithuania 5 6 275 23.6 5.0 5.0

Malta n/a n/a n/a n/a n/a 5.6

Poland 9 4 830 12.0 6.0 5.3

Romania 8 5 512 28.9 6.0 3.7

Slovakia 9 4 565 30.0 4.7 4.3

Slovenia 5 2 1290 12.7 6.7 6.4

Average of NMS 7.4 4.3 592 22.3 5.5 5.0 Austria 7 6 397 18.0 4.0 7.9

Belgium 7 4 505 16.6 7.0 7.1

Denmark 9 4 380 23.3 6.3 9.3

Finland 7 5 375 13.3 5.7 9.2

France 7 4 331 17.4 5.3 6.8

Germany 7 6 394 14.4 5.0 7.9

Greece 3 5 819 14.4 3.3 3.5

Ireland 8 5 515 26.9 8.3 8.0

Italy 3 5 1210 29.9 5.7 3.9

Luxembourg 7 0 321 9.7 4.3 8.5

Netherlands 6 5 514 24.4 4.7 8.8

Portugal 3 5 547 13.0 6.0 6.0

Spain 6 5 515 17.2 5.0 6.1

Sweden 5 4 508 31.2 5.7 9.2

UK 9 6 399 23.4 8.0 7.6

Average EU-15 6.3 4.6 515 19.5 5.6 7.3 Sources: Doing Business and Transparency International.

Page | 80

Appendix 3. Heteroskedasticity test Heteroskedasticity test was performed following the procedure described by Davidson &

MacKinnon (1984) and was based on the adoption of a Lagrange multiplier (LM) test. The

null hypothesis of homoskedasticity is tested against the alternative of heteroskedasticity of

the form H1: var u�� � exp 2z��γ�, where γ is a vector of unknown parameters and zi is a

vector of observations on explanatory variables due to which heteroskedasticity is

suspected to arise20

. The test statistic can be obtained as the explained sum of squares from

the regression

y�� = FA�FFA� 1 = FA��

on f x��αJ�FFA� 1 = FA��

x��, f x��αJ� · x��αJ�FFA� 1 = FA��

z��, 14�

where FA� is the fitted probability, x��αJ is the fitted index and f(⋅) is the derivative of the

cumulative logistic function. FA�, x��αJ and f(x��αJ� are obtained after the estimation of the

model defined in equation (2). The test statistic is asymptotically distributed as χ2 with

degrees of freedom equal to the number of explanatory variables in z (in this case eight as

the number of explanatory variables in z was eight). If the value of the test statistic exceeds

a critical value of χ2, we can reject the null hypothesis of homoskedasticity.

20

When testing for heteroskedasticity, zi included all explanatory variables except industry and country

dummies. For a detailed description of all explanatory variables, see section 4.3.

Page | 81

Appendix 4. Functional form testing To test for the functional form misspecification for both parts of the model, RESET-type

test, described by Papke & Wooldridge (1996), was used. For the first part of the two-part

FRM, testing the hypothesis that the specification of the model, defined in equation (2), is

correct is equivalent to testing for H0: ϕ1 = 0, ϕ2 = 0 in the augmented model:

Pr y�� � �1|x�� � F%x��α � ϕ> x��αJ�, � ϕ, x��αJ�]&, 15�

where F(⋅) is the logistic function and x��αJ is the fitted index. If the null hypothesis cannot

be rejected, x��αJ�, and x��αJ�] are not relevant, and F(x��α) is an appropriate specification

for the first part of the two-part FRM, used in this thesis. To test the null hypothesis, the

LM test is used. It can be computed as the explained sum of squares of the auxiliary

regression:

;J"

FB̂" >SB̂"�on _̀"

FB̂" >SB̂"� x��, _̀"FB̂" >SB̂"� x�

�αJ�,, _̀"FB̂" >SB̂"� x�

�αJ�] 16�

where u@ � � y� = F x��αJ�, FA� � F x��αJ� and f̀� � f x��αJ�. u@ �, FA� and f̀� are obtained after

estimating the model without quadratic and cubic terms. The LM statistic obtained from the

regression (16) is distributed approximately as χ,,.

As the second part of the two-part FRM is estimated by quasi-maximum likelihood, in

order to test for the functional form misspecification, it is necessary to compute the

heteroskedasticity-robust LM statistic. The model, alternative to the one defined in equation

(3), is specified in the following way:

E �y�|x�� � G%x��β� θ> x��βA�, � θ, x��βA�]&, 17�

where G(⋅) is the logistic function and x��βA is the fitted index. The null hypothesis is H0: θ1 =

0, θ2 = 0. If the null hypothesis cannot be rejected, x��βA�, and x��βA�] are not relevant, and

G(x��β) is an appropriate specification for the second part of the two-part FRM, used in this

thesis.

Page | 82

Obtaining heteroskedasticity-robust LM statistic requires some calculation. Firstly, we

define u@ � � y� = G%x��βA&, GB� � G%x��βA&, g@ � � g%x��βA& and uD � � u@ �/FGB� 1 = GB��. Secondly,

the weighted gradients of the function defined in on the right hand side of equation (17)

with respect to θ and β are necessary. The weighted gradient with respect to β is G'mI � �� g@ �x��/FGB� 1 = GB��. The weighted gradient with respect to θ is GbmI � � GbmJ �/FGB� 1 = GB��, where GbmJ � � cg@ � · x��βA�,, g@ � · x��βA�]d. Then, we have to regress GbmI � on G'mI �, save the residuals rD� � rD�>, rD�,� and obtain vector uD �rD� � uD �rD�>, uD �rD�,�. The LM

statistic can be calculated as LM = N – SSR, where N is the sample size and SSR is the sum

of squared residuals from the auxiliary regression of unity on uD �rD�. The LM statistic is

approximately distributed as χ,,.

Page | 83

Appendix 5. Overview of the SMEs capital structure studies, dependent and explanatory variables used and results

Study Sample

country (-ies) Time

period Dependent

variable Explanatory variables and definitions Results

Michaelas,

Chittenden &

Poutziouris (1998)

UK 1986-

1995

Long-term

debt / Total

assets

Effective tax rate = Tax liability / EBT -

Non-debt tax shields = Depreciation / Total assets -**

Size = Total assets +***

Profitability = EBT / Total assets -***

Past growth = Percentage change of total assets over previous 3

years

+***

Growth opportunities = Intangible assets / Total assets +***

Age = Age of firm at the time since date of incorporation -***

Tangibility = Fixed assets / Total assets +***

Operating risk = Coefficient of variation in profitability over 4

years period +*

Liquidity = (Debtors – creditors) / total assets +***

Hall, Hutchinson

& Michaelas

(2000)

UK 1995

Long-term

debt / Total

assets

Size = Total assets +***

Profitability = EBT / Sales +

Growth = Percentage change of sales over previous 3 years +

Tangibility = Fixed assets / Total assets +***

Age = 1995 – year of incorporation -***

Klapper, Sarria-

Allende & Sulla (2002)

15 Eastern

European and

Central Asian

countries

1999

Long-term

debt / book

value of

equity

Non-debt tax shields = Depreciation / Total assets -**

Size = ln (Sales) +***

Profitability = ROE -*

Growth = 1 year growth rate of sales +***

Tangibility = Fixed assets / Total assets +***

Age = Number of years since incorporation -**

Cassar & Holmes

(2003) Australia

1995-

1998

Long-term

liabilities /

Total assets

Size = log10 (total assets) +***

Tangibility = Non-current assets / total assets +***

Profitability = ROA -***

Risk = Coefficient of variation in profitability -

Growth = Growth in sales +

Hall, Hutchinson & Belgium, Italy, 1995 Long-term Profitability = EBT / Sales -

Page | 84

Michaelas (2004) Germany,

Spain, Ireland,

Netherlands,

Portugal, UK

debt / Total

assets

Growth = Percentage change in sales over previous 3 years +

Tangibility = Fixed assets / Total assets +***

Size = Total assets +***

Age = 1995 – year of incorporation +

Sogorb-Mira (2005) Spain 1994-

1998

Long-term

debt / Total

assets

Effective tax rate = Taxes paid / EBT -***

Non-debt tax shields = Depreciation / Total assets -***

Size = ln (Total assets) +***

Profitability = EBIT / Total assets -***

Growth opportunities = Intangible assets / Total assets +***

Tangibility = Tangible assets / Total assets +***

Klapper, Sarria-

Allende & Zaidi (2006)

Poland 1998-

2002

Long-term

debt / Total

assets

Non-debt tax shields = Depreciation / Total assets -**

Size = ln (Sales) -*

Profitability = ROA -**

Growth = Percentage change of sales from previous year +

Tangibility = Fixed assets / Total assets +***

Age = ln (age of firm at the time since date of incorporation) -***

Degryse, Goeij &

Kappert (2009) Netherlands

2002-

2005

Long-term

debt / Total

assets

Effective tax rate = Taxes paid / EBT -***

Non-debt tax shields = Depreciation / Total assets -***

Size = ln (Total assets) +***

Profitability = ROA -

Tangibility = Tangible assets / Total assets +***

Past growth = Change of total assets from previous year +**

Growth opportunities = Intangible assets / Total assets +**

Liquidity = (Debtors – creditors) / Total assets +* Note: In the results section, only the signs of coefficients are shown. *, ** and *** indicate statistical significance at 10%, 5 % and 1%, respectively.

Page | 85

Appendix 6. Results of robustness check For the robustness check, similar regressions, which results are reported in Table 12, were

run based on the samples of each country separately. Tables below report the results of

these regressions for each size-based group of firms and for both parts of the model. In each

table, the estimated coefficients of variables are reported together with robust standard

errors given in parentheses. *, ** and *** denote statistical significance at 10%, 5% and

1%, respectively.

Part I: Binary choice model

Micro firms

Entire sample Estonia Latvia Lithuania

ETR -0.074

(0.088)

-0.042

(0.082)

-0.126

(0.192)

0.861

(0.641)

TANG 2.877***

(0.197)

2.780***

(0.214)

3.222***

(0.569)

8.610**

(3.524)

SIZE 0.374***

(0.041)

0.412***

(0.047)

0.287***

(0.092)

0.118

(0.294)

GROWTH -0.003

(0.239)

-0.019

(0.029)

0.039

(0.043)

-0.649**

(0.319)

GOP 1.506**

(0.667)

1.360**

(0.664)

9.838

(12.911)

10.140

(22.002)

PROFIT -0.339**

(0.148)

-0.180

(0.131)

-1.359**

(0.591)

-0.050

(1.829)

LIQ -0.058

(0.217)

-0.242

(0.264)

0.201

(0.469)

2.482

(1.812)

AGE -0.015

(0.010)

-0.021*

(0.012)

0.021

(0.024)

-0.078

(0.078)

CONSTANT -5.507***

(0.532)

-5.827***

(0.605)

-4.659***

(1.234)

-2.324

(3.911)

No. of obs. 1,978 1,527 379 72

Pseudo R2 0.153 0.161 0.139 0.336

Small firms

Entire sample Estonia Latvia Lithuania

ETR -0.042

(0.080)

-0.050

(0.160)

-0.119

(0.145)

0.058

(0.148)

TANG 3.895***

(0.313)

4.036***

(0.432)

4.068***

(0.655)

3.549***

(0.744)

Page | 86

SIZE 0.295***

(0.064)

0.536***

(0.103)

0.079

(0.102)

0.159

(0.154)

GROWTH -0.125**

(0.054)

-0.156**

(0.074)

-0.159

(0.112)

-0.014

(0.133)

GOP 1.887*

(0.974)

2.350*

(1.255)

4.433

(5.552)

2.086

(4.505)

PROFIT -0.229

(0.310)

0.169

(0.373)

-0.839

(0.636)

-0.815

(1.047)

LIQ -0.673**

(0.298)

-1.345***

(0.476)

-0.258

(0.538)

-0.067

(0.647)

AGE -0.021**

(0.010)

-0.020*

(0.011)

-0.042*

(0.023)

-0.011

(0.030)

CONSTANT -4.808***

(0.928)

-8.156***

(1.482)

-1.325

(1.485)

-2.466

(2.296)

No. of obs. 1,606 666 594 346

Pseudo R2 0.166 0.230 0.135 0.118

Medium-sized firms

Entire sample Estonia Latvia Lithuania

ETR 0.288

(0.274)

0.144

(0.559)

0.336

(0.324)

0.117

(0.649)

TANG 3.243***

(0.427)

2.497***

(0.836)

3.548***

(0.679)

4.307***

(0.787)

SIZE 0.164*

(0.087)

-0.159

(0.196)

0.226*

(0.135)

0.413**

(0.177)

GROWTH -0.005

(0.165)

0.367

(0.562)

-0.559**

(0.242)

-0.163

(0.165)

GOP 7.629***

(2.320)

13.380*

(7.154)

2.744

(1.876)

5.424**

(2.621)

PROFIT -1.153**

(0.588)

-1.272

(1.398)

-1.492*

(0.778)

0.370

(1.298)

LIQ -0.526

(0.484)

0.405

(1.089)

-1.164

(0.846)

-0.624

(0.820)

AGE 0.011

(0.013)

0.000

(0.015)

-0.007

(0.030)

0.064**

(0.032)

CONSTANT -2.391*

(1.382)

4.087

(3.159)

-3.638*

(2.207)

-7.579***

(2.859)

No. of obs. 1,095 230 434 431

Pseudo R2 0.124 0.188 0.164 0.154

Page | 87

Part II: FRM

Micro firms

Entire sample Estonia Latvia Lithuania

ETR -0.130

(0.085)

-0.169***

(0.066)

-0.058

(0.099)

0.427

(0.549)

TANG 0.668***

(0.157)

0.540***

(0.178)

1.314***

(0.423)

3.464***

(1.297)

SIZE -0.155***

(0.039)

-0.169***

(0.039)

-0.082

(0.115)

-0.006

(0.257)

GROWTH 0.080***

(0.025)

0.047

(0.032)

0.115***

(0.029)

0.911***

(0.327)

GOP -0.089

(0.541)

-0.202

(0.539)

6.865

(7.199)

10.202

(11.330)

PROFIT -0.344*

(0.197)

-0.249

(0.221)

-0.408

(0.444)

-3.071**

(1.250)

LIQ -0.531**

(0.258)

-0.671*

(0.364)

-0.373

(0.407)

0.715

(1.538)

AGE -0.034***

(0.011)

-0.031**

(0.013)

-0.049***

(0.018)

-0.009

(0.059)

CONSTANT 1.528***

(0.536)

1.727***

(0.532)

1.199

(1.666)

-1.385

(3.073)

No. of obs. 807 578 192 37

Pseudo R2 0.163 0.136 0.181 0.586

Small firms

Entire sample Estonia Latvia Lithuania

ETR 0.070

(0.053)

0.106

(0.064)

0.142*

(0.081)

-0.007

(0.054)

TANG 1.191***

(0.169)

1.566***

(0.276)

0.609**

(0.268)

2.365***

(0.380)

SIZE -0.051

(0.043)

0.039

(0.072)

-0.070

(0.062)

-0.112

(0.105)

GROWTH 0.069

(0.043)

0.114*

(0.068)

-0.082

(0.136)

0.056

(0.046)

GOP 1.076

(0.659)

1.126

(0.780)

-0.538

(1.879)

7.021***

(1.388)

PROFIT -0.986***

(0.262)

-0.953***

(0.360)

-1.051**

(0.430)

-1.103

(0.804)

LIQ -0.701***

(0.235)

-1.005**

(0.432)

-0.307

(0.340)

-1.102**

(0.521)

AGE -0.033***

(0.010)

-0.028**

(0.014)

-0.044***

(0.013)

-0.034*

(0.019)

Page | 88

CONSTANT -0.115

(0.649)

-1.515

(1.082)

1.088

(0.936)

0.428

(1.566)

No. of obs. 1,095 418 441 236

Pseudo R2 0.214 0.209 0.102 0.415

Medium-sized firms

Entire sample Estonia Latvia Lithuania

ETR -0.192

(0.118)

0.050

(0.302)

-0.260*

(0.158)

0.147

(0.362)

TANG 1.362***

(0.214)

1.942***

(0.426)

0.739**

(0.320)

1.999***

(0.391)

SIZE 0.064

(0.047)

0.172*

(0.101)

-0.034

(0.068)

0.106

(0.079)

GROWTH 0.241**

(0.120)

0.172

(0.328)

0.379***

(0.142)

0.118

(0.179)

GOP 3.422***

(0.609)

5.373***

(1.089)

2.495***

(0.720)

1.750

(1.275)

PROFIT -0.994***

(0.367)

-0.749

(0.545)

-0.737

(0.546)

-1.903***

(0.635)

LIQ -0.394

(0.293)

-0.979

(0.689)

-0.056

(0.430)

-0.520

(0.483)

AGE -0.021***

(0.007)

-0.014*

(0.008)

-0.052***

(0.015)

-0.035*

(0.018)

CONSTANT -2.266***

(0.733)

-4.310***

(1.543)

0.484

(1.111)

-2.768**

(1.284)

No. of obs. 851 177 326 348

Pseudo R2 0.212 0.352 0.173 0.284