Download Capital Structure of SMEs: Does Firm Size Matter?

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
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, JohnPaul 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 sizedbased 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
5.
6.
7.
4.1.
Data ........................................................................................................................ 35
4.2.
Model specification and testing procedures ........................................................... 37
4.3.
Dependent and explanatory variables .................................................................... 44
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
Conclusion ..................................................................................................................... 65
6.1.
Concluding remarks ............................................................................................... 65
6.2.
Limitations of the thesis and suggestions for further research .............................. 67
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 longterm 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 realworld 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 tradeoff models emphasize the importance of time in capital structure decisions. The static tradeoff 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 tradeoff, 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-tobook 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 tradeoff 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.
Page | 13
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 twostep 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.
Page | 14
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 firstorder 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 | 15
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 | 16
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 | 17
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 firmspecific 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 | 18
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 | 19
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 mediumsized 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 | 20
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 | 21
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 medium5
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.
Page | 22
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.
Page | 23
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 | 24
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 | 25
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
Page | 26
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.
Eurostat, http://epp.eurostat.ec.europa.eu/tgm/table.do?tab=table&init=1&plugin=1&language=en&pcode=
tsieb020.
8
Page | 27
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
Estonia
Latvia
Lithuania
NMS
EU-15
GDP per
capita
(PPP), US $
18,519
14,460
17,185
20,052
37,421
GDP
growth,
%
0.3
-0.1
1.4
2.2
0.7
Inflation
rate, %
4.9
6.8
5.2
4.2
2.1
Statutory
tax rate,
%
21
15
15
18
27
Total tax
rate, %
of profit
49.6
38.5
38.7
41.1
46.4
Domestic
credit, %
of GDP
89.4
86.7
57.1
85.1
150.2
Market
capitalization,
% of GDP
22.3
10.4
22.2
29.5
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 | 28
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 | 29
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 | 30
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)
Estonia
Latvia
Lithuania
NMS
EU-15
Legal
rights
index
6.0
9.0
5.0
7.4
6.3
Credit
Enforcing contracts Recovery
Time
Cost (% rate, cents
information
(days)
of claim)
of $
index
5.0
425
26.3
37.5
5.0
309
23.1
29.0
6.0
275
23.6
49.4
4.3
592
22.3
38.9
4.6
511
19.5
69.5
Investor
protection
index
5.7
5.7
5.0
5.5
5.6
Corruption
perceptions
index
6.5
4.3
5.0
5.0
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 | 31
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 | 32
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 | 33
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 mediumsized 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 twopart fractional regression model (FRM) was used. More detailed information is presented in
the next chapter.
Page | 34
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 | 35
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)
114,051 33,674 33,388 33,050
Estonia
142,387 50,670 50,211 44,034
Latvia
Lithuania 123,216 82,701 82,228 53,158
379,654 167,045 165,827 130,242
Total
(5)
(6)
(7)
(8)
(9)
33,049 30,866 3,866 2,447 2,423
44,027 43,309 1,948 1,421 1,407
53,152 51,794 6,662
857
849
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 | 36
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
< 10
≤ € 2 million
and/or
≤ € 2 million
Micro firms
< 50
≤ € 10 million
and/or
≤ € 10 million
Small firms
< 250
≤ € 50 million
and/or
≤ € 43 million
Medium-sized firms
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
1,527
666
230
Estonia
379
594
434
Latvia
72
346
431
Lithuania
Total
1,978
1,606
1,095
Total
2,423
1,407
849
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 | 37
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:
0, if y 0
y 1, if y 0,1,
12
1
yi is the leverage ratio of firm i in the sample. To see how yi is defined, see section 4.3.
Page | 38
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:
Pry 1|x Pry 0,1|x Fx 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:
Ey |x , y 0,1 Gx 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 Gx 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 Ey |x can be
broken down as:
Page | 39
Ey |x Ey |x , y 0 · Pry 0|x Ey |x , y 0,1 · Pry 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:
Ey |x Ey |x , y 0,1 · Pry 0,1|x Gx β · Fx α.
4
Two components of the model, Fx α and Gx β, are estimated separately. The
coefficients of variables, α and β, do not necessarily have to be the same. Therefore, a twopart 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
Ey |x , y 0,1 and Pry 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:
Ey |x Gx β · Fx α #
e!" $
#
e!" '
#
e!" $('
·
.
#
#
#
#
%1 e!" $ & %1 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
#
∂Pry 1|x e!" $
α+
.
#
∂x+
1 e!" $ ,
6
Page | 40
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:
#
∂Ey |x , y 0,1
e!" '
β+
.
#
∂x+
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:
∂Gx β
∂Ey |x ∂Fx α
· Gx β · Fx α ∂x+
∂x+
∂x+
α+
#
e!" $
#
e!" '
#
e!" '
#
e!" $
·
β+
·
.
#
#
#
#
1 e!" $ , 1 e!" ' 1 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 | 41
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:
Pry 1|x Fx 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:
Ey |x , y 0,1 Gx 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, loglikelihood. 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 | 42
LR statistic 2L;< = 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
B G%x βA&, g@ g%x βA& and
by Papke & Wooldridge (1996), firstly, u@ y = G%x βA&, G
B 1 = G
B are defined, i.e., the residuals u@ , the predicted leverage ratios G
B,
uD u@ /FG
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
B 1 = G
B . The weighted gradient with respect to γ
with respect to β is G' m
I g@ x /FG
B 1 = G
B , where Gγ m
is Gγ m
I Gγ m
J /F G
J g@ · d · x . Then, it is necessary to
regress Gγ m
I on G' m
I , 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 | 43
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,
,
sPQR SPQT
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 sPQR SPQT U
VTR
WR
VT
WT .
T
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 longterm 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 | 44
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 | 45
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, SariaAllende & 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 | 46
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 tradeoff 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 | 47
(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 | 48
Table 6. Dependent and explanatory variables
AbbreVariable
Definition
viation
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
(Total assets in 2009 – Total assets in 2008) / Total assets
Growth
GROWTH
in 2008
Growth opportunities GOP
Intangible fixed assets / Total assets
Profitability
PROFIT
EBIT / Total assets
Number of years from the date of incorporation until end
Age
AGE
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 | 49
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 | 50
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
Micro firms
Small firms
Medium firms
no.
%
no.
%
no.
%
949
62.1
248
37.2
53
23.0
Estonia
187
49.3
153
25.8
108
24.9
Latvia
35
48.6
110
31.8
83
19.3
Lithuania
1,171
59.2
511
31.8
244
22.3
Total
Total
no.
1,250
448
228
1,926
%
51.6
31.8
26.9
41.2
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.
Page | 51
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 mediumsized 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
Mean
0.027
0.086
Median
0.000
0.000
ETR
St. dev.
0.613
0.683
Mean
0.270
0.348
Median
0.144
0.287
TANG
St. dev.
0.297
0.286
Mean
11.775
14.161
Median
11.865
14.127
SIZE
St. dev.
1.617
1.140
Mean
0.420
0.035
Median
-0.042
-0.096
GROWTH
St. dev.
2.090
1.031
Mean
0.012
0.011
Median
0.000
0.000
GOP
St. dev.
0.072
0.058
Mean
0.023
0.023
Median
0.022
0.029
PROFIT
St. dev.
0.463
0.206
Mean
0.028
0.030
Median
0.005
0.014
LIQ
St. dev.
0.234
0.197
Mean
8.269
12.159
Median
6.728
12.214
AGE
St. dev.
6.093
7.179
Medium firms
0.124
0.161
0.281
0.401
0.389
0.255
15.489
15.422
0.963
-0.042
-0.095
0.550
0.010
0.000
0.051
0.040
0.033
0.153
0.025
0.017
0.164
14.320
14.797
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 | 52
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 | 53
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
Only firms with non-zero
Entire sample
leverage ratios
Small firms
Medium firms
Small firms
Medium firms
-0.080***
-0.074***
0.068***
0.124***
Micro firms
(0.010)
(0.111)
(0.014)
(0.014)
0.006
0.056***
Small firms
(0.012)
(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 | 54
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
Micro
Small
Medium
-0.074
-0.042
0.288
ETR
(0.088)
(0.080)
(0.274)
2.877***
3.895*** 3.243***
TANG
(0.197)
(0.313)
(0.427)
0.374***
0.295***
0.164*
SIZE
(0.041)
(0.064)
(0.087)
-0.003
-0.125**
-0.005
GROWTH
(0.239)
(0.054)
(0.165)
1.506**
1.887*
7.629***
GOP
(2.320)
(0.667)
(0.974)
-0.339**
-0.229
-1.153**
PROFIT
(0.148)
(0.310)
(0.588)
-0.058
-0.673**
-0.526
LIQ
(0.217)
(0.298)
(0.484)
-0.015
-0.021**
0.011
AGE
(0.010)
(0.010)
(0.013)
-5.507*** -4.808*** -2.391*
CONSTANT
(0.532)
(0.928)
(1.382)
Industry
Yes
Yes
Yes
dummies
Country
Yes
Yes
Yes
dummies
No. of obs.
Pseudo R2
Heteroskedasticity test
RESET test
Micro
-0.130
(0.085)
0.668***
(0.157)
-0.155***
(0.039)
0.080***
(0.025)
-0.089
(0.541)
-0.344*
(0.197)
-0.531**
(0.258)
-0.034***
(0.011)
1.528***
(0.536)
Part II: FRM
Small
0.070
(0.053)
1.191***
(0.169)
-0.051
(0.043)
0.069
(0.043)
1.076
(0.659)
-0.986***
(0.262)
-0.701***
(0.235)
-0.033***
(0.010)
-0.115
(0.649)
Medium
-0.192
(0.118)
1.362***
(0.214)
0.064
(0.047)
0.241**
(0.120)
3.422***
(0.609)
-0.994***
(0.367)
-0.394
(0.293)
-0.021***
(0.007)
-2.266***
(0.733)
Yes
Yes
Yes
Yes
Yes
Yes
1,978
0.153
1,606
0.166
1,095
0.124
807
0.163
1,095
0.214
851
0.212
0.207
0.303
0.134
-
-
-
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 | 55
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 longterm 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 | 56
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 | 57
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 longterm 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 | 58
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
-0.014 -0.031 -0.019 -0.007 0.015 0.008 0.043 -0.040 -0.018
ETR
0.560 0.158 0.317 0.680 0.262 0.408 0.485 0.284 0.366
TANG
0.073 -0.037 0.018 0.052 -0.011 0.010 0.024 0.013 0.018
SIZE
GROWTH -0.001 0.019 0.007 -0.022 0.015 0.003 -0.001 0.050 0.039
0.293 -0.021 0.124 0.329 0.237 0.272 1.141 0.713 0.896
GOP
-0.066 -0.081 -0.062 -0.040 -0.217 -0.161 -0.172 -0.207 -0.212
PROFIT
-0.011 -0.126 -0.056 -0.118 -0.154 -0.145 -0.079 -0.082 -0.087
LIQ
-0.003 -0.008 -0.005 -0.004 -0.007 -0.006 0.002 -0.004 -0.003
AGE
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 | 59
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 | 60
Table 14 reports the LR and LM test statistics, defined in equations (11) and (12), and pvalues, 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
ETR
TANG
SIZE
GROWTH
GOP
PROFIT
LIQ
AGE
Micro vs.
small
0.21
(0.650)
15.08***
(0.000)
4.47**
(0.034)
2.23
(0.135)
0.07
(0.796)
0.15
(0.697)
3.46*
(0.063)
3.00*
(0.083)
Micro vs.
medium
0.54
(0.461)
0.40
(0.529)
6.51**
(0.011)
0.69
(0.406)
3.89**
(0.049)
7.72***
(0.006)
1.03
(0.311)
2.71*
(0.100)
Small vs.
medium
0.75
(0.385)
1.41
(0.235)
4.83**
(0.028)
0.10
(0.752)
3.41*
(0.065)
5.78**
(0.016)
0.26
(0.610)
1.39
(0.238)
Micro vs.
small
4.08**
(0.043)
3.30*
(0.069)
0.21
(0.644)
0.54
(0.462)
1.16
(0.282)
2.38
(0.122)
0.62
(0.431)
0.19
(0.664)
Micro vs.
medium
0.30
(0.582)
2.79*
(0.095)
4.32**
(0.038)
1.83
(0.176)
7.68***
(0.006)
0.51
(0.476)
0.02
(0.886)
0.38
(0.536)
Small vs.
medium
3.77*
(0.052)
1.93
(0.165)
4.00**
(0.046)
3.27*
(0.070)
3.85**
(0.050)
0.01
(0.911)
0.85
(0.357)
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 | 61
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
Medium-sized
Medium-sized
Small firms
Small firms
firms
firms
21.56***
17.86**
11.50
17.33**
Micro firms
(0.006)
(0.022)
(0.175)
(0.027)
19.57**
16.45**
Small firms
(0.012)
(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). Pvalues 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 | 62
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 | 63
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 | 64
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 sizebased 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 twopart 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 | 65
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 | 66
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 | 67
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 | 68
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 | 69
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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 | 77
Appendix 1. Taxes, macroeconomic and financial sector development variables in EU-27 countries
GDP per capita
Average GDP
Average
Statutory
Total tax rate
2010 (PPP), US
growth rate
inflation rate
corporate tax
2010, % of
$
2006-2010, %
2006-2010, %
rate 2010, %
profit
Bulgaria
12,851
2.8
6.5
10
29.0
Cyprus
28,256
2.4
2.3
10
23.2
Czech Republic
24,869
2.7
2.6
19
48.8
Estonia
18,519
0.3
4.9
21
49.6
Hungary
18,738
-0.1
5.3
19
53.3
Latvia
14,460
-0.1
6.8
15
38.5
Lithuania
17,185
1.4
5.2
15
38.7
Malta
24,792
2.4
2.4
35
n/a
Poland
18,936
4.7
3.0
19
42.3
Romania
11,860
2.6
6.2
16
44.9
Slovakia
22,129
4.8
2.3
19
48.7
Slovenia
28,030
1.9
3.0
20
35.4
Average of NMS
20,052
2.2
4.2
18
41.1
Austria
39,634
1.5
1.8
25
55.5
Belgium
36,100
1.2
2.2
33.99
57.0
Denmark
36,450
0.2
2.1
25
29.2
Finland
34,585
1.1
2.0
26
44.6
France
34,077
0.7
1.7
33.33
65.8
Germany
36,033
1.2
1.7
29.41
48.2
Greece
28,434
0.8
3.3
24
47.2
Ireland
38,550
-0.2
1.1
12.5
26.5
Italy
29,392
-0.3
2.0
31.4
68.6
Luxembourg
81,383
2.6
2.5
28.59
21.1
Netherlands
40,765
1.4
1.5
25.5
40.5
Portugal
23,223
0.5
1.7
25
43.3
Spain
29,742
0.9
2.5
30
56.5
Sweden
38,031
1.5
2.1
26.3
54.6
UK
34,920
0.4
2.7
28
37.3
Average EU-15
37,421
0.7
2.1
27
46.4
Sources: Economy Watch, Eurostat, KPMG (2010), Doing Business and World Development Indicators.
Country
Average domestic
credit 2005-2009,
% of GDP
54.1
240.7
53.1
89.4
73.0
86.7
57.1
140.1
49.4
168.1
50.8
80.1
85.1
132.3
111.5
201.7
86.5
119.6
129.9
108.4
192.3
124.6
166.7
194.6
168.1
195.4
129.6
192.5
150.2
Average total market
capitalization 20052009, % of GDP
26.4
62.3
31.5
22.3
27.0
10.4
22.2
56.1
34.4
20.6
6.8
33.7
29.5
38.7
69.8
68.3
96.1
84.5
46.5
53.3
43.5
37.7
192.6
88.8
42.8
93.0
108.7
125.1
79.3
Page | 78
Appendix 2. Institutional factors in EU-27 countries (all values are from year 2010)
Credit
information index
Bulgaria
8
6
Cyprus
9
0
Czech Republic
6
5
Estonia
6
5
Hungary
7
5
Latvia
9
5
Lithuania
5
6
Malta
n/a
n/a
Poland
9
4
Romania
8
5
Slovakia
9
4
Slovenia
5
2
Average of NMS
7.4
4.3
Austria
7
6
Belgium
7
4
Denmark
9
4
Finland
7
5
France
7
4
Germany
7
6
Greece
3
5
Ireland
8
5
Italy
3
5
Luxembourg
7
0
Netherlands
6
5
Portugal
3
5
Spain
6
5
Sweden
5
4
UK
9
6
Average EU-15
6.3
4.6
Sources: Doing Business and Transparency International.
Country
Legal rights index
Enforcing contacts
Time (days)
Cost (% of claim)
564
23.8
735
16.4
611
33.0
425
26.3
395
15.0
309
23.1
275
23.6
n/a
n/a
830
12.0
512
28.9
565
30.0
1290
12.7
592
22.3
397
18.0
505
16.6
380
23.3
375
13.3
331
17.4
394
14.4
819
14.4
515
26.9
1210
29.9
321
9.7
514
24.4
547
13.0
515
17.2
508
31.2
399
23.4
515
19.5
Investor protection
index
6.0
5.0
5.0
5.7
4.3
5.7
5.0
n/a
6.0
6.0
4.7
6.7
5.5
4.0
7.0
6.3
5.7
5.3
5.0
3.3
8.3
5.7
4.3
4.7
6.0
5.0
5.7
8.0
5.6
Corruption
perceptions index
3.6
6.3
4.6
6.5
4.7
4.3
5.0
5.6
5.3
3.7
4.3
6.4
5.0
7.9
7.1
9.3
9.2
6.8
7.9
3.5
8.0
3.9
8.5
8.8
6.0
6.1
9.2
7.6
7.3
Page | 79
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: varu 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
fx J
α
FFA 1 = FA x ,
fx J
α · x J
α
FFA 1 = FA z ,
14
where FA is the fitted probability, x J
α is the fitted index and f(⋅) is the derivative of the
cumulative logistic function. FA , x J
α and f(x J
α 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 | 80
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:
Pry 1|x F%x α ϕ> x J
α, ϕ, x J
α] &,
15
where F(⋅) is the logistic function and x J
α is the fitted index. If the null hypothesis cannot
be rejected, x J
α, and x J
α] 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"
;
B" >S^
B" F^
on
_`"
B" >S^
B" F^
x ,
_`"
B" >S^
B" F^
x J
α, ,
_`"
B" >S^
B" F^
x J
α]
16
α, FA Fx J
α and f̀ fx J
α. u@ , FA and f̀ are obtained after
where u@ y = Fx J
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:
Ey |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 | 81
Obtaining heteroskedasticity-robust LM statistic requires some calculation. Firstly, we
B G%x βA&, g@ g%x βA& and uD u@ /FG
B 1 = G
B . Secondly,
define u@ y = G%x βA&, G
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' m
I B 1 = G
B . The weighted gradient with respect to θ is Gb m
g@ x /FG
I Gb m
J /
B 1 = G
B , where Gb m
FG
J cg@ · x βA, , g@ · x βA] d. Then, we have to regress Gb m
I
on G' m
I , 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 | 82
Appendix 5. Overview of the SMEs capital structure studies, dependent and explanatory variables used and results
Study
Sample
Time Dependent
country (-ies) period
variable
Michaelas,
Chittenden &
Poutziouris (1998)
UK
19861995
Hall, Hutchinson
Long-term
debt / Total
assets
1995
Long-term
debt / Total
assets
15 Eastern
European and
Central Asian
countries
1999
Long-term
debt / book
value of
equity
Cassar & Holmes
(2003)
Australia
19951998
Long-term
liabilities /
Total assets
Hall, Hutchinson &
Belgium, Italy,
1995
Long-term
& Michaelas
UK
(2000)
Klapper, Sarria-
Allende & Sulla
(2002)
Explanatory variables and definitions
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
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
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
Size = log10 (total assets)
Tangibility = Non-current assets / total assets
Profitability = ROA
Risk = Coefficient of variation in profitability
Growth = Growth in sales
Profitability = EBT / Sales
Results
-**
+***
-***
+***
+***
-***
+***
+*
+***
+***
+
+
+***
-***
-**
+***
-*
+***
+***
-**
+***
+***
-***
+
-
Page | 83
debt / Total
assets
Michaelas (2004)
Germany,
Spain, Ireland,
Netherlands,
Portugal, UK
Sogorb-Mira (2005)
Spain
19941998
Long-term
debt / Total
assets
Poland
19982002
Long-term
debt / Total
assets
Netherlands
20022005
Long-term
debt / Total
assets
Klapper, Sarria-
Allende & Zaidi
(2006)
Degryse, Goeij &
Kappert (2009)
Growth = Percentage change in sales over previous 3 years
Tangibility = Fixed assets / Total assets
Size = Total assets
Age = 1995 – year of incorporation
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
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)
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 | 84
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
ETR
TANG
SIZE
GROWTH
GOP
PROFIT
LIQ
AGE
CONSTANT
No. of obs.
Pseudo R2
Entire sample
-0.074
(0.088)
2.877***
(0.197)
0.374***
(0.041)
-0.003
(0.239)
1.506**
(0.667)
-0.339**
(0.148)
-0.058
(0.217)
-0.015
(0.010)
-5.507***
(0.532)
1,978
0.153
Estonia
-0.042
(0.082)
2.780***
(0.214)
0.412***
(0.047)
-0.019
(0.029)
1.360**
(0.664)
-0.180
(0.131)
-0.242
(0.264)
-0.021*
(0.012)
-5.827***
(0.605)
1,527
0.161
Latvia
-0.126
(0.192)
3.222***
(0.569)
0.287***
(0.092)
0.039
(0.043)
9.838
(12.911)
-1.359**
(0.591)
0.201
(0.469)
0.021
(0.024)
-4.659***
(1.234)
Lithuania
0.861
(0.641)
8.610**
(3.524)
0.118
(0.294)
-0.649**
(0.319)
10.140
(22.002)
-0.050
(1.829)
2.482
(1.812)
-0.078
(0.078)
-2.324
(3.911)
379
0.139
72
0.336
Small firms
ETR
TANG
Entire sample
-0.042
(0.080)
3.895***
(0.313)
Estonia
-0.050
(0.160)
4.036***
(0.432)
Latvia
-0.119
(0.145)
4.068***
(0.655)
Lithuania
0.058
(0.148)
3.549***
(0.744)
Page | 85
SIZE
GROWTH
GOP
PROFIT
LIQ
AGE
CONSTANT
No. of obs.
Pseudo R2
0.295***
(0.064)
-0.125**
(0.054)
1.887*
(0.974)
-0.229
(0.310)
-0.673**
(0.298)
-0.021**
(0.010)
-4.808***
(0.928)
1,606
0.166
0.536***
(0.103)
-0.156**
(0.074)
2.350*
(1.255)
0.169
(0.373)
-1.345***
(0.476)
-0.020*
(0.011)
-8.156***
(1.482)
666
0.230
0.079
(0.102)
-0.159
(0.112)
4.433
(5.552)
-0.839
(0.636)
-0.258
(0.538)
-0.042*
(0.023)
-1.325
(1.485)
0.159
(0.154)
-0.014
(0.133)
2.086
(4.505)
-0.815
(1.047)
-0.067
(0.647)
-0.011
(0.030)
-2.466
(2.296)
594
0.135
346
0.118
Medium-sized firms
ETR
TANG
SIZE
GROWTH
GOP
PROFIT
LIQ
AGE
CONSTANT
No. of obs.
Pseudo R2
Entire sample
0.288
(0.274)
3.243***
(0.427)
0.164*
(0.087)
-0.005
(0.165)
7.629***
(2.320)
-1.153**
(0.588)
-0.526
(0.484)
0.011
(0.013)
-2.391*
(1.382)
1,095
0.124
Estonia
0.144
(0.559)
2.497***
(0.836)
-0.159
(0.196)
0.367
(0.562)
13.380*
(7.154)
-1.272
(1.398)
0.405
(1.089)
0.000
(0.015)
4.087
(3.159)
230
0.188
Latvia
0.336
(0.324)
3.548***
(0.679)
0.226*
(0.135)
-0.559**
(0.242)
2.744
(1.876)
-1.492*
(0.778)
-1.164
(0.846)
-0.007
(0.030)
-3.638*
(2.207)
434
0.164
Lithuania
0.117
(0.649)
4.307***
(0.787)
0.413**
(0.177)
-0.163
(0.165)
5.424**
(2.621)
0.370
(1.298)
-0.624
(0.820)
0.064**
(0.032)
-7.579***
(2.859)
431
0.154
Page | 86
Part II: FRM
Micro firms
ETR
TANG
SIZE
GROWTH
GOP
PROFIT
LIQ
AGE
CONSTANT
No. of obs.
Pseudo R2
Entire sample
-0.130
(0.085)
0.668***
(0.157)
-0.155***
(0.039)
0.080***
(0.025)
-0.089
(0.541)
-0.344*
(0.197)
-0.531**
(0.258)
-0.034***
(0.011)
1.528***
(0.536)
807
0.163
Estonia
-0.169***
(0.066)
0.540***
(0.178)
-0.169***
(0.039)
0.047
(0.032)
-0.202
(0.539)
-0.249
(0.221)
-0.671*
(0.364)
-0.031**
(0.013)
1.727***
(0.532)
578
0.136
Latvia
-0.058
(0.099)
1.314***
(0.423)
-0.082
(0.115)
0.115***
(0.029)
6.865
(7.199)
-0.408
(0.444)
-0.373
(0.407)
-0.049***
(0.018)
1.199
(1.666)
192
0.181
Lithuania
0.427
(0.549)
3.464***
(1.297)
-0.006
(0.257)
0.911***
(0.327)
10.202
(11.330)
-3.071**
(1.250)
0.715
(1.538)
-0.009
(0.059)
-1.385
(3.073)
37
0.586
Small firms
ETR
TANG
SIZE
GROWTH
GOP
PROFIT
LIQ
AGE
Entire sample
0.070
(0.053)
1.191***
(0.169)
-0.051
(0.043)
0.069
(0.043)
1.076
(0.659)
-0.986***
(0.262)
-0.701***
(0.235)
-0.033***
(0.010)
Estonia
0.106
(0.064)
1.566***
(0.276)
0.039
(0.072)
0.114*
(0.068)
1.126
(0.780)
-0.953***
(0.360)
-1.005**
(0.432)
-0.028**
(0.014)
Latvia
0.142*
(0.081)
0.609**
(0.268)
-0.070
(0.062)
-0.082
(0.136)
-0.538
(1.879)
-1.051**
(0.430)
-0.307
(0.340)
-0.044***
(0.013)
Lithuania
-0.007
(0.054)
2.365***
(0.380)
-0.112
(0.105)
0.056
(0.046)
7.021***
(1.388)
-1.103
(0.804)
-1.102**
(0.521)
-0.034*
(0.019)
Page | 87
CONSTANT
No. of obs.
Pseudo R2
-0.115
(0.649)
-1.515
(1.082)
1.088
(0.936)
0.428
(1.566)
1,095
0.214
418
0.209
441
0.102
236
0.415
Medium-sized firms
ETR
TANG
SIZE
GROWTH
GOP
PROFIT
LIQ
AGE
CONSTANT
No. of obs.
Pseudo R2
Entire sample
-0.192
(0.118)
1.362***
(0.214)
0.064
(0.047)
0.241**
(0.120)
3.422***
(0.609)
-0.994***
(0.367)
-0.394
(0.293)
-0.021***
(0.007)
-2.266***
(0.733)
851
0.212
Estonia
0.050
(0.302)
1.942***
(0.426)
0.172*
(0.101)
0.172
(0.328)
5.373***
(1.089)
-0.749
(0.545)
-0.979
(0.689)
-0.014*
(0.008)
-4.310***
(1.543)
177
0.352
Latvia
-0.260*
(0.158)
0.739**
(0.320)
-0.034
(0.068)
0.379***
(0.142)
2.495***
(0.720)
-0.737
(0.546)
-0.056
(0.430)
-0.052***
(0.015)
0.484
(1.111)
326
0.173
Lithuania
0.147
(0.362)
1.999***
(0.391)
0.106
(0.079)
0.118
(0.179)
1.750
(1.275)
-1.903***
(0.635)
-0.520
(0.483)
-0.035*
(0.018)
-2.768**
(1.284)
348
0.284
Page | 88