This document provides a literature review on capital structure choice of private firms. It discusses the major theories of capital structure including Modigliani & Miller's propositions, the pecking order theory, and the trade-off theory. Previous empirical studies have found private firms are more leveraged than public firms and rely more on short-term debt due to information asymmetry and limited access to capital markets. Measuring leverage in private firms poses challenges as market values are unavailable. The literature review informs the research design and hypotheses testing of the factors that influence capital structure choices between private and public firms.

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Capital Structure Choice of
Private Firms in Sweden
Patrick Thomenius
Stockholm Business School
Master’s Degree Thesis 30 HE credits
Subject: Finance
Program: Master's Programme in Banking and Finance 120 HE credits
Autumn/Spring semester 2016
Supervisor: Sabur Mullah

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Table of Contents
Abstract............................................................................................................................................. 3
1. Introduction ........................................................................................................................ 4
2. Literature review ................................................................................................................ 6
2.1 The theory of corporate capital structure..................................................................... 6
2.2 Literature survey............................................................................................................... 7
2.3 Conclusion....................................................................................................................... 11
2.4 Theoretical framework .................................................................................................. 12
3. Research design............................................................................................................... 13
3.1 Problematizing and research question......................................................................... 13
3.2 Data.................................................................................................................................. 15
3.3 Summary statistics.......................................................................................................... 16
3.4 Scholarly perspective...................................................................................................... 16
3.5 Method............................................................................................................................. 18
3.6 Leverage........................................................................................................................... 20
3.7 Model specification........................................................................................................ 20
3.8 Multivariate linear regression model............................................................................ 22
3.9 Controlling for OLS assumptions................................................................................ 23
3.10 Source critical consideration ........................................................................................ 26
4. Analysis and findings ..................................................................................................... 27
4.1 Descriptive statistics ...................................................................................................... 27
4.2 Findings ........................................................................................................................... 30
4.3 Leverage........................................................................................................................... 30
4.4 Independent variables.................................................................................................... 32
5. Discussion ......................................................................................................................... 34
6. Conclusion......................................................................................................................... 36
7. Limitations of research .................................................................................................. 37
References...................................................................................................................................... 39
Appendix......................................................................................................................................... 43

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Abstract
This study examines the capital structure of private and public firms in Sweden. The result
suggests that private firms are more leveraged than public firms. Three proxies for leverage
are used and the difference in leverage is found to be significant for all three measures. To
help explain the differences in leverage, four firm-specific characteristics are studied using a
fixed effects model. In previous studies, the characteristics where all found to be
determinants to capital structure and this study finds that, all except one, have causal effect
on leverage. The findings suggest that the difference in leverage can to some extent be
explained by profitability, tangibility and size but not by growth opportunities. Leverage for
private firms is found to be more sensitive to changes in asset tangibility, whilst leverage
for public firms is more sensitive to changes in profitability. These differences are
explained by the trade-off and pecking order theory and the theories also give some insight
to the difference in capital structure of private and public firms. The findings support the
hypothesis that firms faced with greater information asymmetry have higher leverage.
Key word: Capital Structure, Private firms
JEL classification: G32

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1. Introduction
Theories on capital structure aim to explain the variations in leverage ratios between
different firms, in different risk classes and in different industries. A number of theories
have emerged in this field focusing on determinants for capital structure and the optimal
capital structure in a firm, if such a structure exists (Titman & Wessels, 1988). Research has
mainly focused on capital structure in public firms and a vast number of determinants to
capital structure have been studied. Research in the field suggest that firms have different
capital structures due to macroeconomic conditions, industry- and firm specific
characteristics (McCumber, 2014). A number of characteristics and market variables have
been tested against different leverage ratios to determine inference. What ratio to use as a
proxy for capital structure together with which determinants influence the capital structure
have been debated in empirical research. Although the majority of empirical studies have
been made on public firms, mainly due to data availability, public firms constitute only a
fraction of registered firms worldwide. They have been found to use different capital
sources and different levels of leverage to that of private firms (Brav, 2009). Private firms
have different attributes and are more restricted to capital markets, as they are more
opaque and are associated with asymmetric information (Huynh, Paligorova, and Petrunia,
2012). Only three studies have been found that look at capital structure of private firms
and only one of which have been published in a peer reviewed journal. Two of these
studies by Brav (2009) and Huynh et al. (2012) also compares the results against public
firms. Both of the studies found that higher leverage in private firms is mainly associated
with higher levels of short-term debt. The results from Brav (2009) showed on average a
50% higher leverage ratio for private firms than public; the results are based on a large data
set of private and public firms in the United Kingdom. Huynh et al. (2012) base their

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research on a data set of private and public firms in Canada, and found higher leverage for
private firms. Both studies also found that private firms have higher sensitivity in capital
structure in regards to sales or earnings volatility. The third study by McCumber (2014)
looks into private firms in the U.S and considers debt heterogeneity and debt specialization.
The study found that there is considerable heterogeneity in the private firms’ debt, even
though they are more opaque than public firms.
This study will carry on the work of previous studies and look at characteristics for
private and public firms and discuss differences in a theoretical context. This will be done
by means of studying a large sample of private and public firms in Sweden. First, it tests a
similar approach to previous studies and then proceed to test a new measure of capital
structure in private firms. Previous studies have not been thorough enough in discussing
the problem associated with measuring capital structure in private firms. McCumber (2014)
stated that commonly used measures for leverage in public firms are questionable when
used for private firms. He mentioned that equity concentration and illiquidity as issues for
measurement in private firms and that other approaches to this problem are needed in this
field (McCumber, 2014). This study discusses other measures and the problem with
measures used in previous work.
This study aims to explore capital structure in private firms in Sweden. A data set
of private and public firms in Sweden is used and the results will hopefully encourage more
studies to be made on private firms’ capital structure. The study is conducted using a
quantitative research design and a research question is formulated and tested through four
hypotheses. These hypotheses are tested using a multivariate regression analysis that helps
explain the differences in leverage. Four variables are used in the regression as proxies for
profitability, tangibility, firm size and growth opportunities. The expected result is that

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private firms are more leveraged than public firms and the variables tested will help to
explain the choice of capital structure taken by private firms.
The rest of the paper proceeds as follows: Section 2, a literature review is
conducted looking at general theories and specific empirical studies on private firms’ capital
structure. Section 3, the problem of capital structure is discussed along with data selection
and methods for testing the data. Section 4 presents the empirical findings and discusses
the results in relation to previous work. Section 5 discusses this study in a broader
perspective in relation to general theories within the field. Section 6 presents concluding
remarks and encourages future studies, and section 7 discusses the limitation to the study.
2. Literature review
2.1 The theory of corporate capital structure
Few studies have looked in to the capital structure of private firms because of data
limitation (Brav, 2009). The studies that exist show that there are significant differences in
the capital structure between private and public firms (Huynh et al. (2012). Private firms
are faced with market imperfections which influence financing possibilities. According to
Huynh et al. (2012) asymmetric information as well as limited capital market access
constitutes a major constraint for financing availability. The purpose of this study is to
examine private firms in Sweden and to study differences in the capital structure compared
to that of public firms listed on Swedish markets. Any research on the capital structure of
private firms is faced with a problem of identifying the appropriate measure or ratio for
leverage (McCumber, 2014). This is because debt and equity are not valued at market price,
therefore only book value can be measured. Also problematic is that debt and equity are

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specialized and illiquid (McCumber, 2014). This study will therefore look into different
measures used in previous studies along with adopting new measures of leverage.
This study aims to explore differences in capital structure between private and
public firms in Sweden and also give new insight to what measures can be used to test
leverage ratios in private firms.
Chosen literature is divided into two parts: central theories concerning market
assumptions and empirical research on capital structure in private and public firms. The
first influential theory assumed frictionless markets and more modern theories have
assumed some market imperfections. A vast number of empirical studies have looked at
different determinants of leverage in public firms but only a few have studied private firms,
because of data limitation.
2.2 Literature survey
The field of corporate capital structure is theoretically and empirically covered to a large
extent. Starting in 1958, Modigliani and Miller introduced two propositions regarding the
implication of capital structure on firm value, that laid the foundation to what would
become a well-studied area within corporate finance (Modigliani & Miller, 1958).
Modigliani and Miller’s (1958) first proposition also known as the irrelevance propositions,
means that the value of the firm is independent of its capital structure in a frictionless
market. This implies that a firm consisting solely of equity should have the same value as
the same firm consisting of debt and equity. The second proposition, states that a firm’s
cost of capital remains the same at all levels of financial leverage, regardless the
combination of debt and equity (Modigliani & Miller, 1958). This means that there exists
no optimal level of debt to equity for any firm or industry, and the value of the firm is
independent of the capital structure when there are no transaction costs and free market

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access (Modigliani & Miller, 1958; Miller, 1988). Testing the assumptions underlying the
propositions, Stiglitz (1969) could show that the results may still hold in the context of
limitations to individual borrowings and the possibility of bankruptcy. Critics such as
Robichek and Myers (1966) concluded that “the additional assumptions necessary to prove
Proposition I do not in fact hold in the world assumed by MM, and, therefore, that the
conclusions embodied in Proposition I are compromised” (p.2). Theoretical and empirical
research have tested the market conditions of a frictionless market and which market
conditions would violate the propositions. Different market imperfections may give
different conclusions.
The pecking order theory was according to Myers (1984) first described by Gordon
Donaldson in 1961 and extended by Myers in 1984. The theory suggests that financing
through internal sources or debt will be preferred over issuing new equity due to
information asymmetry, a market condition violating the Modigliani and Miller
propositions (Myers, 1984). The theory also suggests that the decision to issue new equity
will convey negative information to the market and the price of the firm will drop (Myers,
1984). Myers and Majluf (1984) suggested that firms will prefer to avoid this problem by
using riskless debt. Krasker (1986) recognized this problem and could show that the more
new equity issued, the worse the negative effect. Rational investors will therefore try to
infer insider information from the capital structure of the firm in a market where
transaction costs exist, according to Flannery (1986). When using the assumption that debt
is not risk-free and carries risk, the results are trivial according to Narayanan (1988). Firms
that are less transparent to outsiders would prefer debt over equity financing as it is less
sensitive to information asymmetry according to the pecking order (Myers, 1984). This is
shown empirically by Brav (2009) who found that private firms rely most solely on debt
financing, and they were higher leveraged and tended to avoid external capital markets.

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Titman and Wessels (1988) found that smaller firms tend to use significantly more short-
term debt than larger firms and Huynh et al. (2012) found that private firms in Canada are
higher levered than public firms and use more short-term debt.
Many theories and empirical studies have been made on the behavior of firms in
regard to external financing considering the implication from the pecking order theory.
These theories regard something that Modigliani and Miller did not assume: the situation of
asymmetric information, the signaling value of capital structure and restructuring capital,
and bankruptcy which could all have some explanatory value for empirical results (Stiglitz,
2002). Firms cannot finance themselves fully by debt, because the bankruptcy costs in
reality would prohibit them from doing so according to Robichek and Mayers (1966).
From the implication of cost of debt and the tax benefit of debt, the trade-off
theory emerged in 1966 with Robichek and Mayers (1966) who proposed that there exists a
trade-off between the benefits of leverage regarding a tax rebate and the cost of leverage,
direct and indirect relating to bankruptcy. According to Robichek and Mayers the optimum
point of leverage is at a state where the marginal cost of debt equals the marginal benefits
of increasing debt (Robichek & Mayers, 1966, p.20). Carrying on the results Kraus and
Litzenberger (1973) formed a model for capital structure including tax shield advantages of
debt and the bankruptcy cost of debt, introducing a model for the trade-off theory. The
trade-off theory predicts that the firm will continue to increase its leverage until the
marginal cost of its equity is equal to the marginal cost of its debt. Therefore, at the optimal
debt ratio, the decision to raise capital, debt or equity, in the external capital markets
becomes costlier for private firms and hence they have a stronger preference for internal
financing (Brav, 2009). In addition, McCumber (2014) found that there is a negative
association between firm opacity and leverage and Brav (2009) found that raising new
equity bears a higher cost for private firms than for public. Brav (2009) also pointed out

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that the difference between private and public firms “raise the question of what exactly are
the market frictions that violate the M&M theorem and lead private equity to be more
costly than public equity.” (p.3). It is debatable whether bankruptcy cost of debt or tax
benefit of debt influence the choice for capital of an individual firm. Robichek and Myers
(1966) showed that regardless of if corporate income is taxed or not, this is irrelevant for
optimal capital structure.
The pecking order theory and the trade-off theory have been developed under
assumptions of imperfect markets. In an imperfect market Cotei and Farhat (2009) found
that the pecking order and trade-off theory are not mutually exclusive. They found that
pecking order factors are determinants under the trade-off theory assumptions and that
trade-off theory factors are determinants under the pecking order assumptions (Cotei &
Farhat, 2009). In 1977 Miller argued that under some conditions the value of the firm is
still independent of its capital structure even though interest payments are fully deductible
and firms are subject to bankruptcy cost (Miller, 1977). He stated that “there would be no
optimum debt ratio for any individual firm” (p.9) therefore challenging the view of the
trade-off theory (Miller, 1977). In the paper by Bradley, Jarrell, and Kim (1984) they
discussed the existence of an optimal capital structure and highlighted the question of
whether the cost of debt and higher average cost of capital is a large enough factor to
influence the choice of capital of a firm, and that this may be regarded as an empirical
problem rather than a theoretical one (Bradley et al., 1984).
In the empirical studies of capital structure, leverage has been measured in different
ways. McCumber (2014) found in his study that for public companies, market value is often
used for debt and equity. For private firms, because of the lack of market values, book
value of debt and equity is measured (McCumber, 2014). Both studies by Huynh et al.
(2012) and McCumber (2014) used three different measures of leverage. The importance of

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this is illustrated by McCumber (2014) who found different correlations depending on the
measure used, suggesting that a firm can have a high or low leverage ratio depending on the
proxy for leverage. In the research on capital structure, a vast amount of firm-, industry-,
and market attributes have been empirically tested on private and public companies to
determine the influence on leverage and capital structure (Titman & Wessels, 1988, p.1).
Titman and Wessels (1988) highlighted in their article that that there are several attributes
different theories of capital structure suggest may affect the firm’s capital structure. They
are: asset structure, non-debt tax shield, growth, uniqueness, industry classification, size,
earnings volatility, and profitability (p.2). Furthermore, Huynh et al. (2012) found that the
firm specific factors that influence leverage in private firms are profitability, size, tangibility,
and sales growth. They also found that sales volatility as an industry factor has a positive
correlation with leverage and stated that the pecking order theory and imperfect market
conditions can explain the leverage choice of private firms (Huynh et al., 2012). This study
will look in to the measures used in previous studies for firm leverage and determinants to
leverage.
2.3 Conclusion
Only a handful of empirical studies have been made on private firms’ capital structure
because of data limitation. The differences between private and public firms are
considerable and therefore it is important to recognize the private firm’s capital choice in
order to understand capital structure in a broader sense. Private firms operate in an
imperfect market characterized by information asymmetry and face costs of selling debt in
the market. Theories such as pecking order and trade-off theory use assumptions for
private firms that better match empirical results. Studies on capital structure also suggest
that other measures for leverage than those used for public firms should be used for

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private firms. This study discusses its findings in relation to previous work on private firm’s
capital structure.
2.4 Theoretical framework
To better understand the underlying drivers for differences in leverage between private and
public firms, a framework is created to help analyze the results of this study. Firstly,
according to the pecking order private and public firms prefer internal sources of funding,
such as retained earnings, since it bears the lowest cost. For private firms this is more
significant since the alternative cost of raising either debt or equity is assumed to be higher
(Brav, 2009). Secondly, private and public firms prefer raising debt before new equity,
according to the pecking order theory. Raising new equity bear a higher cost, mainly due to
higher transaction cost and the negative signaling value of raising equity. If raising new
equity do in fact bear higher costs, the trade-off theory suggests that debt will be used until
the marginal cost of issuing new debt equals the marginal cost of raising new equity (Kraus
& Litzenberger, 1973). Between private and public firms, the theory suggests that since
private firms are more opaque the effect of information asymmetry, signaling value and
higher transaction cost will be higher, implying a greater cost for private firms to rase new
equity (Brav, 2009). Therefore, the general hypothesis for private firms is that it will have
higher leverage and also be more sensitive to attributes in relation to transaction costs and
information asymmetry (Brav, 2009). If private firms bear higher cost of information
asymmetry, they will prefer to borrow short-term since they assume to be able to borrow at
lower cost in the future (Huynh et al., 2012).
Both the pecking order and trade-off theory suggest firm attributes to affect the
capital structure differently. Some of these attributes will be discussed in detail later on in
this paper. This study aims to find that private firms have higher leverage ratios and that

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this holds using different measures for leverage. The results will be discussed in a context
of this framework.
3. Research design
3.1 Problematizing and research question
In order to clarify the contribution of this study a discussion regarding previous research
will follow. Almost all previous studies have been conducted on listed firms, despite that
they make up a very small portion of the total number of firms; in Sweden less than 0.3%
of all firms are listed (Statistiska centralbyrån, 2016). A study conducted by Rajan and
Zingales (1995) found that for 8,000 traded firms in the G-7 economies, they have
approximately the same levels of leverage in all countries except for the UK and Germany.
According to previous studies, private firms face different conditions such as larger
information asymmetry, less access to capital markets, ownership concentration, and more
specialist capital structure (Brav, 2009). Previous studies have found some differences
between private and public firms that may now be considered when studying capital
structure. Although some empirical studies on private firms during the last years, it is still
just a handful. The result of this study will give more insight to private firms’ capital
structure choice.
This study will aim to answer the question “are private firms in Sweden more
leveraged than public firms?”. To answer this question four hypotheses are developed.
These are described in the next section.
Measuring leverage in private and public firms differ because of data limitation.
Public firms have market value of equity and often debt, that is also liquid. Due to market
valuation of equity the value of the firm is changing as the stock price changes and the

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firms’ capital structure adjust accordingly, which may not always reflect intentional changes
by managers (Givoly, Havn, Ofer & Sarig, 1986). For private firms, market value of debt
and equity are seldom available, hence book value is used. Book value for private firms are
subject to accounting standards, that is historic figures that often represents the initial
value. Equity and debt may therefore change in different manors. This may not pose a
problem. Results from Bowman (1980) show that little difference is made to the valuation
if using book or market value of debt. In this study, debt will be measured as book value
taken from the annual report of each firm.
Looking into the results of Brav (2009) and Huynh et al. (2012), both studies found
that private firms have higher leverage measured as a ratio of total debt and short-term
debt to assets, and that private firms’ capital structure is more sensitive to volatility in
earnings. This study therefore starts by testing hypotheses drawn from previous results. To
be able to compare results similar measure of leverage must be used since the measurement
error can be large. Both previous studies use a common measure and that is total debt to
total assets. Therefore, this study uses the same measure for the following three
hypotheses:
H1: Private firms have higher leverage than public firms
H2: Private firms have higher short-term leverage than public firms
H3: Private firms’ capital structure is more sensitive to profitability than public firms
The aim with the measure for leverage is to get a proxy for a firm’s capital
structure. This is often considered as the ratio of debt to equity which constitutes the major
part. For private firms the level of debt to equity might not be feasible as a measure.
McCumber (2014) found that a firm’s leverage tends to be dynamic and stated in his article
that “Like individuals and households it is possible for private firms to have negative equity
and still meet all debt obligations and thus remain a going concern outside a state of

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bankruptcy or default” (p.1). Private firms may therefore still be able to operate in a
situation of negative equity as long as it can maintain its interest expense, and few have
looked at the interest cover as the level of leverage for private firms (McCumber, 2014).
Although, both the article by Axelson, Jenkinson, Stromberg and Weisbach (2013), and
that by McCumber (2014) measured leverage as the ratio of debt to EBITDA, which is
close to an interest cover ratio. Debt to EBITDA did also have the highest correlation
between leverage ratios that was used in McCumbers’s study (McCumber, 2014). Interest
cover ratio may be a good measure since private firms can operate with different equity
levels and might be inclined to do so because of equity being so illiquid. Rajan and Zingales
(1995) also argued for the use of interest coverage ratio as a proxy for leverage. Although, a
problem with this measure is that it is very sensitive to income fluctuations (Rajan &
Zingales, 1995). This study will use interest bearing debt to EBITDA as a proxy for
leverage to test a fourth hypothesis:
H4: Private firms have a higher interest coverage ratio and can be said to be more
leveraged
3.2 Data
This study collects its data from the “Retriever Business” database. This database contains
detailed information from financial reports from all Swedish firms. Firms in Sweden must
admit financial reports at the latest 6 months after end-of-year. In Sweden, authorities
distinguish between private and public firms. Private firms are either Swedish limited (AB)
or sole proprietorships and partnerships. In this study only limited firms (AB) are included.
Private firms are not allowed to sell or market its shares to more than 200 investors
(Notisum, 2016). Public firms, listed and non-listed, may issue shares to third party and
also be traded on a stock exchange, although public firms that are not listed share similar

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market conditions to that of private firms. This study considers private firms as non-traded
and public as traded firms. The following Swedish stock markets are considered for
sampling public firms: Aktietorget, Bequoted, First North, OMX- Large Cap, -Mid Cap, -
Small Cap, NMG Equity, and NGM OTC.
Private and public firms are retrieved with the following attributes: The firm is
registered prior to January 1st 2004, has a turnover at end-of-year 2015 of 50 to 500 million
Swedish Krona and is registered as a limited firm (AB). 11,337 private and 103 public firms
are found (Retriever, 2016). For the regressions, years that do not have any sales on the
year-end-report are excluded from the sample as well as years with zero assets of a specific
firm. This excluded 19,383 private- and 20 public firm-year observations.
3.3 Summary statistics
The total population consists of 412,216 Swedish limited firms (AB), 395 of these are
traded (Retriever, 2016). The sample is drawn from the total population and consists of
11,440 firms, 11,337 private and 103 publicly traded. The sample period for all companies
are 2007 – 2015 and the length of the sample period is 9 years. The data includes 485,475
end-of-year observations for private firms and 5,024 end-of year observations for public
firms.
3.4 Scholarly perspective
This thesis is built on a deductive method, where hypotheses are developed based on
theory and empirical research. The epistemological issue concerns if the natural sciences
can be regarded as acceptable knowledge and the imitation of natural science in empirical
research or if subject matters and firms’ behavior can be explained by social science
(Bryman & Bell, 2011). This discussion considers two views, that of positivism and that of

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realism. Positivism considers that the scientific conceptualization of reality, directly reflects
the reality, whereas realism argues that scientific conceptualization is simply a way of
knowing the reality (Bryman & Bell, 2011). This study considers the epistemology of
positivism, where the explanation of behavior implies that research can be made on the
collection of data upon which hypotheses can be tested. On this study’s ontological
consideration, the position of objectivism is taken. Firms are influenced by external factors
that are beyond the reach of the firm in contrast to the position taken by constructionists.
Constructionism suggests that organizations and culture are not pre-given (Bryman & Bell,
2011). Considering the views taken, a research question and four hypotheses are developed.
The questions asked in this study represent an understanding that firms can be viewed
externally and no consideration is taken to individuals. Furthermore, this study aims to
describe choices taken by firms but not to make any judgment on what firms ought to do.
The choices taken can be explained by social construction that influence them rather than
voices of individuals within. This view has met some criticism for the lack of social
explanation and testing measures that are assumed rather than those of reality (Bryman &
Bell, 2011, p.167-168).
The methodology of this study is characterized by the use of a deductive approach,
it incorporates norms of positivism and views social reality as external and objective, and is
constructed as a quantitative research strategy. The chosen quantitative research strategy is
a commonly used approach in business studies but fails to incorporate individual behaviors,
subjective matters and will miss deviations in corporate choice caused by individual
behaviors (Bryman & Bell, 2011). It may be too strict in its view on corporations and also
adhere to an old paradigm that may be under change.
The research design of this thesis is that of a longitudinal study. The reason for
using a longitudinal approach is that higher inference can be drawn. A cross-sectional

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design studies multiple variables over just one time-period. This limits the inference that
can be drawn from the results because the results cannot establish a direction of casual
relationship (Bryman & Bell, 2011). Longitudinal design with panel data regression can
draw higher level of inference form the results due to the year variable (Bryman & Bell,
2011).
The level of the study is on organizations and no regards to individuals are
considered. This ensures that no misinterpretations are made since data is collected on
organizational level objectively and inferences are drawn regarding organizations as an
entity or object.
3.5 Method
This study tests the research question by performing a multivariate linear regression
analysis with panel data. Leverage ratio is used as a dependent variable and profitability,
firm size, tangibility, and growth opportunities are used as independent variables. This is
similar to the studies by Rajan and Zingales (1995), Brav (2009), and Huynh et al. (2012).
The choice of regressors is difficult and depend on economic theory, empirical research
and logical reasoning. Not including some variables could increase the bias in the error
term and including more variables would increase the model predictability, R2
, but does not
have to mean increased statistical significance (Stock & Watson, 2012). This study does not
focus on achieving a high model fit, instead variables that are thought to help explain the
hypotheses are included. The data set is analyzed over a nine-year interval and the variables
are not lagged, as opposed to in the study by Brav (2009). This study reasons that between
the firms’ year-end reports, changes to capital structure due to changes in the variables used
are considered to be made within the same year. The regression is performed in the
following tests: pooled OLS, fixed effects, and random effects. To measure consistency of

19. 19
the different regression methods, a Lagrange Multiplier test, F-test, and a panel Hausman
test is computed. The assumptions underlying the ordinary least square model are discussed
in section 3.9 and controlled for. The regression coefficients are estimated using a single
sample, this creates some sampling uncertainty since the OLS estimator have a joint
sampling distribution (Stock & Watson, 2012).
The use of a linear regression model assumes a linear relation between the
variables, although the relation is not straightforward to depict when multiple regressors
are used. This relation can in fact be nonlinear and this requires a nonlinear regression
model. There are also multiple types of nonlinear relationship and numbers of nonlinear
models to adopt. A general approach to test for nonlinearity between variables is to
perform one or more nonlinear regression models and determine if the model fit increases;
the models can also be tested using a t-statistics (Stock & Watson, 2012). For this study
computing a nonlinear regression model would be cumbersome, therefore a scatterplot
matrix of the relations is analyzed to determine relationship characteristics. Large outliers
are removed in the scatterplot to make the plots clearer and easier to interpret. From Table
1 in the appendix, the relation is determined to be linear and little sign of nonlinearity is
detected. All coefficients in this study are described as having a linear effect on leverage
(Stock & Watson, 2012).
The four hypotheses developed in this study all use the same independent variables
but different measures for the dependent variable. A regression is performed on each
dependent variable. The mean value of each sample distribution is tested using a two-sided
t-test, against the null hypothesis of no difference in mean value. The null hypothesis is
rejected at a 5% significance level.

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3.6 Leverage
The aim of this study is to test for differences in capital structure between private and
public firms. Therefore, leverage as a proxy for capital structure is used as the dependent
variable. To test the different hypotheses, the following proxies for leverage are used. For
hypothesis one, leverage is measured as a ratio of total debt to total assets and will be
denoted DTA onwards. For hypothesis two, the ratio of short-term debt to total assets is
used and will be denoted SDTA onwards. For hypothesis three, the ratio DTA will be used
and for hypothesis four the ratio used is interest bearing debt to EBITDA and will be
denoted IDTE onwards.
3.7 Model specification
The regressors used in this study are used as proxies for the following: profitability, firm
size, tangibility, and growth opportunities.
3.7.1 Profitability
The trade-off theory suggests that a more profitable firm will be less concerned with
bankruptcy and therefore it will take more advantage of the tax shield of debt (Frank &
Goyal, 2009). The pecking order suggests that a more profitable firm has access to more
internal funds for its financing need and will thus have lower need for leverage (Titman &
Wessels, 1988). Both the studies by Brav (2009) and Huynh et al. (2012) found a negative
relationship between profitability and leverage.

21. 21
3.7.2 Firm size
In the article by Rajan and Zingales (1995) the authors suggest that firm size may be a
proxy for the inverse probability of default. According to the trade-off theory, a larger firm
have less concern with bankruptcy and will have lower cost of debt financing, and can
therefore use more debt until marginal cost of more debt is the same as equity. The
pecking order theory suggest that a large firm has grown due to earnings and therefore
have more access to retained earnings which it will prefer to use. Brav (2009) and Huynh et
al. (2012) found a positive relationship to firm size and leverage.
3.7.3 Tangibility
According to the study by Huynh et al. (2012) higher tangibility suggests higher liquidation
value, which will lower the bankruptcy costs. According to Rajan and Zingales (1995)
higher tangibility reduces the agency cost of debt, due to the fact that fixed assets are easier
to collateralize. Both the papers by Huynh et al. (2012) and Brav (2009) state that the trade-
off theory predicts increased borrowing capacity and firms will tend to borrow more for
optimal capital structure. They found a positive relationship between tangibility and
leverage (Brav, 2009; Huynh et al., 2012).
3.7.4 Growth opportunities
Growth opportunities may imply that the firm is in greater need of capital to maintain its
expansion. Some of the capital may come from retained earnings from previous years, as
predicted by the pecking order. Moreover, higher growth rate may decrease the signaling
value and thus lower the cost of raising equity. High growth might also decrease the

22. 22
information asymmetry problem by showing outsiders of its potential, leading to lower cost
of debt. Brav (2009) found leverage to be positively correlated with sales growth, but also
that firms with high growth are more likely to raise equity, and he suggests that firms with
high growth rate tend to go public. Huynh et al. (2012) found an opposite relation for
private and public firms.
3.8 Multivariate linear regression model
The regression model is performed in three ways: pooled OLS, random effects and fixed
effects. The performance of each model is tested and the result from the model with the
highest consistency is presented in section four.
3.8.1 Lagrange Multiplier test
This test is used to measure consistency between the random effect versus pooled OLS.
Each test has a p value smaller than 0,01 which indicates more support for the random
effects model in comparison to the pooled OLS.
3.8.2 F-test
This test is used to measures consistency between the fixed effect versus pooled OLS.
Each test has a p value smaller than 0,01 which indicates more support for the fixed effects
model in comparison to the random effects model.
3.8.3 Hausman test
When using panel data, the Hausman test is needed to test for consistency in the model.
This test is used to test for support for the fixed effects versus random effects model. For
all six regressions, the Hausman test indicates that the random model is inconsistent with a

23. 23
p value less than 0.01. Therefore, the fixed effects model is used to give consistent
estimates. If there exists correlations between the fixed effects and the variables, it is
important to control for these otherwise a problem of omitted variable bias can occur
(Stock & Watson, 2012).
3.8.4 The model
This study uses a fixed effect regression which controls for some omitted variables in panel
data that vary across entities and time (Stock & Watson, 2012). These fixed effects are
controlled for by the model and no dummy variable is created externally. The following
model is used to estimate the coefficients of the regressors:
represents the error term. β is the coefficient for the independent variable. η represents
the firm specific intercept or firm fixed effects and λ represent time fixed effects. X
represents the three leverage ratios used, total debt to assets (DTA), short-term debt to
assets (SDTA) and interest bearing debt to EBITDA (IDTE).
3.9 Controlling for OLS assumptions
Conducting a multiple linear regression, it is important to consider the assumptions needed
for the minimization problem of residuals using Ordinary Least Square (OLS) estimates
with panel data. First, OLS assumes that the error term has a mean of zero unconditional
on the value of each variable, or the mean of the error term is uncorrelated with the
variables. This also implies no omitted variable bias (Stock & Watson, 2012). The variance
of the error term may still be depended on the variable over time. Whether the variance of
the error term is constant or not given the variable, i.e. either homoscedastic or
heteroscedastic, may have some practical implications. The assumption for the OLS

24. 24
estimator place no restriction on variance of the error term. According to Stock and
Watson (2012) in economic studies it is also wise to treat errors as heteroscedastic, unless
there is compelling reason. In the sample used, there is no compelling reason to treat the
error term as homoscedastic, since difference in firm size is large and firms are not
separated by industry characteristics. For the practical implication this study controls for
this and uses heteroscedastic-robust errors. When the sample size is large the use of
heteroscedastic-robust errors in an OLS regression does not violate the first assumption
due to the multivariate central limit theorem, and the OLS estimator is still the best linear
conditionally unbiased estimator (Stock & Watson, 2012). Using heteroscedastic-robust
errors the F-test for testing the model is invalid and the test of the regression results are
made with Wald statistic.
The second assumptions consider the variables to be distributed identically to and
independently of the other variables. This assumption, in contrast to the assumption made
for OLS when not using panel data or time series data, does not consider each variable to
be uncorrelated with itself over time. Like heteroscedasticity, autocorrelation within the
variable over time does not bias the fixed effect estimator but it affects the variance and
therefore has implication for how to compute standard errors. Standard errors may still be
autocorrelated and according to Stock and Watson, 2012, “as long as some omitted factors
are autocorrelated, then will be autocorrelated” (p.406). With autocorrelated standard
errors, heteroscedastic-robust errors are no longer valid since the assumption for them are
no autocorrelation. To make the standard errors valid, this study uses heteroscedasticity-
and autocorrelated-consistent (HAC) standard errors, a form of clustered standard errors
(Stock & Watson, 2012). This is similar to Huynh et al. (2012) and McCumber (2014) who
also used heteroscedastic-robust errors, and Brav (2009) who used heteroscedastic- and
autocorrelated-robust errors.

25. 25
The third assumption is that large outliers are unlikely. Large outliers can make
OLS regression results misleading. Mathematically this can be described as variables having
finite kurtosis (Stock & Watson, 2012). In statistical analysis outliers can cause bias to the
mean values calculated in the regression. In the data samples used, some larger outliers are
found that affect the mean of each sample. These outliers can be dealt with accordingly by
either, omitting, winsorizing or keeping. To deal with outliers, they must first be
understood. Do they arise from sampling errors, data entry errors or can they be extreme
values but still be valid? Therefore, when not fully understanding the outliers it may be
dangerous to omit them. But keeping the outliers will bias the results. This study finds all
outliers to be extreme values not caused by sampling errors. Large outliers are few in
regards to the sample size. The outliers are therefore kept.
The forth assumption considers the relation between the regressors. If the
regressors are a perfect linear function of one of the other, it is said to exist perfectly
multicollinearity between them and the OLS estimator will be impossible to compute
(Stock & Watson, 2012). For the OLS to be a good estimate the assumption of non-perfect
multicollinearity must hold. Imperfect multicollinearity exists when the variables are highly
correlated although it is still possible to compute the OLS, but this also causes a problem
to the estimation (Stock & Watson, 2012). Multicollinearity can arise by choosing variables
that are related and the solution can be to modify the regressors. Imperfect
multicollinearity may be present in the variables chosen and omitting them causes bias, but
the implication of multicollinearity is lower precision in estimating the effects of the
regressors (Stock & Watson, 2012). Studying the covariance matrices, no large correlation is
found between variables except for firm size and profitability in the public sample.

26. 26
If the four assumptions hold, the distribution of the estimates are normal in large
samples and the standard errors can be used (Stock & Watson, 2012). All data analysis will
be performed in R.
3.10 Source critical consideration
The main empirical studies referred to are those that study capital structure in private firms
in other data sets. They include Brav (2009), Huynh et al. (2012) and McCumber (2014).
The empirical foundation of this study is therefore somewhat limited to the three articles.
The main critical consideration of these empirical studies is that only one has been
published in a peer reviewed journal, that by Brav (2009). The choice to include
unpublished studies comes from the limited number of existing studies on private firms,
but causes a threat to the validity of this study. They may be of lower methodical quality
and for the most case a peer-review is absent. A survey conducted by Cook (1993) on the
inclusion of unpublished data and opinions of authors if unpublished data should be
included could show that 30.7% included unpublished data and that 77.7% believed that
unpublished data should be included. For editors 46.9% felt that unpublished data should
be included. Cook (1993) concluded that “While inclusion of unpublished data in scientific
overviews remains controversial, most investigators directly involved in meta-analysis
believe that unpublished data should not be systematically excluded” (p.5). Trespidi, Barbui
and Cipriani (2011) highlighted the problem of published data bias. According to the
authors studies with statistical significance have a higher probability of being published.
Unpublished data may also include information of importance to the study (Trespidi et al.
(2001). The unpublished studies are included in this report but are critically thought of
throughout. The data used by McCumber (2014) is not fully explained and there is no
mention of how negative EBITDA values were addressed in the measures. Errors are

27. 27
heteroskedastic-robust but no explanation to test of model consistency is mentioned or if
any fixed effects are controlled for. Bravs (2009) use of regression model is fully described
and throughout this study more confidence is given to Brav’s results.
4. Analysis and findings
To test the hypotheses developed to answer the question if private firms are more
leveraged than public, the mean of leverage is tested. The null hypothesis is for no
difference in mean values and this is tested with a two sided t-test with non-similar
variances. To help explain the differences between the mean values a multivariate analysis
is conducted using four variables. These variables have been found to be key determinants
of leverage in private firms in previous studies. The regression is a firm and time fixed
effect model and is specified as:
Three measures for leverage are used: the ratio of total debt to total assets (DTA), short-
term debt to total assets (SDTA) and interest bearing debt to EBITDA (IDTE). None of
the variables’ outliers have been tampered with. To test for significance in the coefficients
of the regressors a t-statistics and corresponding p value is used. The multivariate models
will be tested for explanatory power; the Wald test is used to test the null hypothesis of R2
equals zero.
4.1 Descriptive statistics
Table 2 in the Appendix, breaks down the number of firm years used in each regression
and t-test. The numbers deviate due to the exclusion of years without a complete data set.
The number of public firm years is approximately 1% of the total number for each

28. 28
regression. This is somewhat more than in the total population, where the number of
public firms is less than 0.1%. The number of firm years is about 8.5 for each firm for the
first two regressions and for the third regression it is about 4.5 and 5.7 for private and
public firms respectively. The t-test of difference in size, shown in Table 3, indicate that
public firms are just slightly bigger, in average around 2%.
Table 4, describes the statistics for each leverage ratio. The mean of the ratio of
total debt to assets is 0.59 for private firms and 0.46 for public firms. The span between the
smallest and largest leverage ratio is much bigger for private firms in all three ratios, hence
outliers are also larger in private firms which may bias the means. The mean of the ratio of
short-term debt to assets is 0.45 for private firms and 0.37 for public firms. The short-term
debt constitutes 76% of the leverage ratio of debt to assets in private firms and 81% in
public firms. For the leverage ratio measured as interest bearing debt to EBITDA, private
firms have a mean of 3,49 and public -1,36, looking at the medians instead private firms
have a median of 1,46 and public 0,26. All three leverage ratios show higher leverage for
private firms, 30% higher measuring total debt to assets, 21,5% higher measuring short-
term debt to assets and 5,6 times higher using the median in the ratio of interest bearing
debt to EBITDA. The difference in mean for the three measures are all significant at
0.01%.
Table 5, describes the mean and standard deviation of each regression variable.
Profitability, tangibility and sales growth is generally higher in private firms. Firm size is in
general higher in public firms, although the difference in firm size is small. The similarity in
firm size is no surprise since the sample selection included sales as a parameter.
In Table 6, the coefficients for each independent variable are presented along with
the standard error and p value of the t-statistics for the coefficient. The p values are
significant for almost all coefficients in regressions using DTA and SDTA. In regression

29. 29
using IDTE almost none of the coefficients are significant. Profitability is negatively
correlated with DTA and SDTA for both samples. Firm size is in general positively
correlated with DTA and SDTA in both samples. Tangibility have a positive correlation
with DTA but a negative correlation with SDTA for both samples. Sales growth is
positively correlated with DTA and SDTA for both samples, although the correlation is
very small. The highest correlation of sales growth is found with DTA in public firms were
one-unit increase in sales growth increases DTA with 0,00004 units. Table 6 displays the
result from the Wald tests on the level of significance for R2
, for the models using DTA
and SDTA the result show that these models have some explanatory value and are
significance at the 0.1% level. The test for significance of R2
to the IDTE model show that
it is not different from zero and the model used for this regression therefore cannot be said
to have explanatory value.
Table 6. Coefficients, standard error and p value of t-statistic for each regression
Variables Private std, Error p-value Public std, Error p-value
DTA
Profitability -0,173 0,052 7,63*** -0,207 0,066 1,67**
Firmsize 0,053 0,005 2,2*** -0,006 0,037 0,865
Tangibility 0,126 0,008 2,2*** 0,054 0,108 0,616
Salesgrowth 0,000003 0,000001 5,0*** 0,00004 0,00002 0,041*
2,22*** 8,10***
SDTA
Profitability -0,050 0,013 1,25*** -0,164 0,028 4,77***
Firmsize 0,063 0,004 2,2*** 0,033 0,015 2,72**
Tangibility -0,369 0,007 2,2*** -0,277 0,039 3,88***
Salesgrowth 0,000002 0,000001 3,47** 1,01E-07 0,00003 0,998
2,22*** 2,22***
IDTE
Profitability -0,121 1,061 0,909 2,974 7,024 0,672
Firmsize -1,592 2,428 0,512 -2,266 3,377 0,503
Tangibility 9,787 3,180 2,09** 20,806 8,355 1,31*
Salesgrowth -0,00017 0,001 0,858 0,002 0,005 0,732
0,04 0,16
Significance code *** 0,001, ** 0,01, * 0,05. Profitability is measured as EBIDTA divided by total assets.
Firm size is measured as the natural logarithm of sales. Tangibility is measured as tangible assets to
total assets. Sales growth is measured as turnover + turnover previous year divided to turnover previous
year.
² ²
² ²
² ²

30. 30
4.2 Findings
The findings are analyzed in the context of the theoretical framework developed earlier.
Two main theories are discussed to help explain the results from this study. The trade-off
theory, which suggests that firms chose capital structure to reach an optimal level of debt
and equity. This optimal level is reached when the marginal cost of new debt equals the
marginal cost of new equity. Firm characteristics that affect the marginal cost of debt and
equity will therefore be determinants to the leverage ratio. The pecking order theory, which
suggests that firms have a preference order of capital: internal funds, debt and lastly equity.
This may be because of the cost incurred by the information asymmetry that private firms
experience. The cost of information asymmetry is almost zero for internal funds, somewhat
mitigated for debt with bank monitoring, but is prevalent for issuing new equity (Titman &
Wessels, 1988). Cost of debt and equity may also exist because of the transaction costs
suffered with these financing options. These costs are assumed to be higher for private
than public firms and the effects by the determinants will hence be higher for private firms.
Firm characteristics that affect the need and cost of debt and equity will therefore be a
determinant to the leverage ratio. The two theories can help explain the measured effects
of firm characteristics on leverage.
4.3 Leverage
The differences in leverage between private and public firms are significant in all three
regressions. For DTA and SDTA, the t-test results shown in Table 7, rejects the null that
leverage in private and public firms are the same at the 1% level. This evidence is
consistent with Brav (2009) and Huynh et al. (2012). For IDTE, the t-test results reject the
null at a 5% level. The results also show that depending on leverage measure used, private

31. 31
firms can have higher or lower leverage compared to public firms. Testing hypothesis one,
private firms have a 30% higher leverage ratio. Testing hypothesis two, private firms have a
21.5% higher leverage ratio compared to public. Results for hypothesis one and two are
consistent with results from Brav (2009) and Huynh et al. (2012). Testing hypothesis four,
the mean of IDTE are harder to interpret. Public firms have a negative leverage ratio,
indicating that earnings are in general negative over the sample period. Public firms can
therefore be considered more leverage regarding the possibility to service its dept. Looking
at the median instead private firms have a median of 1,46 and public 0,26, indicating a
higher leverage for private firms. The big difference between mean and median arises from
large outliers that bias the result in mean values. The test result on IDTE should therefore
be analyzed looking at medians. The findings suggest that private firms are in general more
leveraged when considering the ratio of total debt to assets, short-term debt to assets and
the interest cover ratio.
Table 7. Test of the mean value between samples in each regression
Private Public p-value
DTA
mean 0,59 0,46 2,2***
N 97095 893
SDTA
mean 0,45 0,37 2,2***
N 97074 891
IDTE
mean 3,49 -1,37 0,006**
N 51127 588
Significance code *** 0,001, ** 0,01, * 0,05,
To put the findings in a theoretical context, the results are in line with what is
assumed by theory. Leverage ratio is higher in private firms due to market implications and
firm specific attributes. The reason behind the difference in leverage is not tested and can
only be speculated upon with the help of theories. The implication of the results is that

32. 32
private firms do face market conditions that violate the assumption of Modigliani and
Miller causing capital structure to differ between groups. More insights to help understand
the results may be found in the effects of some of the determinants to capital structure.
4.4 Independent variables
4.4.1 Profitability
Both the pecking order theory and the trade-off theory suggest that higher profitability
have a negative correlation to leverage. Higher profitability would suggest more retained
earnings for a firm, which would lower its need for more debt. It would also suggest to
lower the risk for lenders which lowers the marginal cost of debt for the firm, whilst the
equity cost is not lowered since the earnings demanded by equity holders will be increased.
The results are in line with the theories, profitability is negatively correlated with leverage
measured as debt over assets and short-term debt over assets. The negative correlation is
also significant. This is in line with the results of Brav (2009) and Huynh et al. (2012). The
results for leverage measured as interest cover ratio is not significant. Furthermore, the
results for leverage as the ratio of total debt and short-term debt over assets show higher
correlation to profitability in the public sample. According to Rajan and Zingales (1995)
larger public firms tend to issue less equity, which could explain some of the difference.
The public sample is slightly larger than the private sample in all three regressions. Public
firms are also less opaque than private, so the cost of debt is lowered by less asymmetric
information, which suggests that public firms have lower marginal cost of debt and hence a
higher correlation. The result rejects the third hypothesis that private firms’ capital
structure is more sensitive to profitability than public firms’.

33. 33
4.4.2 Firm size
A larger firm may imply lower risk of default and therefore have lower marginal cost of
debt. Titman and Wessels (1988) suggested in their paper that firm size is negatively
correlated with cost of issuing debt and equity, which would indicate a positive correlation
with leverage. Both the trade-off theory and the pecking order theory suggest a small
positive correlation. The result shows a small positive effect in the regression on DTA and
SDTA. Since the size is narrowed in the sampling, a small effect is no surprise. The results
are in line with Brav (2009) and Huynh et al. (2012) who also found a small positive
correlation. Furthermore, the correlation is higher in private firms similar to the results
found by Huynh et al. (2012).
4.4.3 Tangibility
According to Rajan and Zingales (1995) tangible assets in firms may function as collateral
to debtors and lower the bankruptcy cost. This would imply that the marginal cost of debt
decreases for firms with higher levels of tangible assets. The trade-off theory would predict
leverage to increase for both private and public firms as marginal cost of debt is lower.
Results from the regressions show that tangibility is positively correlated with total debt
over assets and negatively correlated with leverage measured as short-term debt to assets.
The positive correlation with total debt is in line with results from Brav (2009) and Huynh
et al. (2012). Although the negative correlation with short-term debt is not in line with
previous results. Both the positive and negative correlation is larger for private firms,
indicating that private firm’s’ capital structure is more sensitive to the level of tangibility.
4.4.4 Growth opportunities
Growing firms are generally in need of more capital for its expansion, some of this capital
may come from retained earnings from previous years. Theories give different

34. 34
explanations, since growth can have multiple effects. High growth rate may decrease the
signaling value and thus lower the cost of raising equity and also decreases the information
asymmetry problem by showing outsiders of its potential, lowering the cost of debt. The
pecking order theory suggests that since private firms have higher costs of external
financing, they would use more leverage in a situation of higher growth. Public firms have
easier access to external financing and therefore bear lower cost already and a situation of
higher growth opportunities will therefore not affect leverage as much as in private firms.
The result from the regressions show that the coefficient for growth opportunities is very
small. For both samples, the coefficient is very close to zero and therefore cannot help
explain leverage in either private nor public firms. Brav (2009) found leverage to be
positively correlated with sales growth, but also that firms with higher growth are more
likely to raise equity, and suggest that firms with high growth rate tend to go public. Huynh
et al. (2012) found an opposite relation for private and public firms.
5. Discussion
This study examines capital structure in private and public firms in Sweden using a sample
of 11,337 private and 103 public firms. The capital structure of private firms is of interest
and the study aims to answer the question whether private firms are more leveraged than
public firms. By testing four hypotheses the results suggest that private firms are more
leveraged than public firms in Sweden. All three hypothesis tests show that the difference
in mean of leverage is significant. Two sampling parameters are used, registration prior to
January 1st
2004, and firm size. The parameter for size select firms based on sales at the
year-end-report from 2015 between 50 million to 500 million SEK. This study finds that
private firms have a 30% higher leverage ratio compared to public firms measuring the

35. 35
ratio of total debt to total assets. Private firms have a 21.5% higher leverage when leverage
is measured as short-term debt to total assets. This study also finds a higher leverage for
private firms when using interest cover ratio as a proxy, where private firms have a median
of 1,46 and public of 0,26. The effect of the determinants are tested in three regression
models, summarized in Table 7. Models for DTA and SDTA are found to have
explanatory value, whilst the model for IDTE is not found to have explanatory value. The
results from regression on IDTE will therefore not be presented. The four determinants
that are used to help explain differences in leverage are: profitability, firm size, tangibility
and growth opportunity. Leverage have the highest correlation with tangibility and
profitability, and the lowest correlation with firm size and sales growth. Profitability is
negatively correlated with leverage in both regression models and tangibility is positively
correlated with DTA and negatively correlated with SDTA in both the private and public
sample.
The overall findings suggest that private firms are in general more leveraged when
considering the ratio of total debt to assets and short-term debt to assets. The results are
generally in line with previous studies on capital structure in private firms by Brav (2009)
and Huynh et al. (2012). It is also found that private firms are more sensitive to the level of
tangibility which has a positive correlation on leverage. Private firms are sensitive to
changes in earnings but less than the public sample.
Theory suggests that firm attributes that are related to the cost of either debt or
equity will affect leverage. The trade-off theory predicts attributes that changes the optimal
level of capital structure, such as firm size and tangibility to be more sensitive to leverage.
The results suggest that private firms are more sensitive to both size and tangibility and are
thus more sensitive to traditional trade-off determinants. The pecking order theory predicts
attributes that changes the level of retained earnings and the need for external financing to

36. 36
affect the level of leverage. Attributes such as profitability is predicted to create more
retained earnings and lower the cost of debt. The results suggest that public firms are more
sensitive to pecking order attributes. The results from this study gives insights to some
differences between private and public firms and relate the findings to theories that can
explain some of the differences. Under the prediction by the trade-off theory firms adjust
leverage to a capital ratio target and private firms are faster when adjusting to this target.
Under the prediction by the pecking order theory, firms prefer internal financing before
external financing and debt is preferred over equity. The results suggest that public firms
adhere more to a ranking preference of financing. Higher information asymmetry in private
firms result in a choice between lower cost of external financing rather than a preferred
order. A preferred order may not be possible for private firms, because of limited
availability of financial choice.
The significance of the findings can be discussed critically considering the difficulty
to measure accurately the attributes that theory describes. The results from the regressions
have a low consistency and are also to some extent contradicting. This demonstrates the
problematic task of measurement and relating the findings to theory. Interest cover ratio is
predicted by previous studies to have explanatory power but fails to give a correct measure
in this study. This study still gives some insight into the differences that exist in capital
structure and the choice for new capital between private and public firms.
6. Conclusion
The intention of this study is to explore the differences in capital structure of private and
public firms. The study aims to find difference in leverage between the two groups and if
these differences can be explained with relevant theory. The research question asks

37. 37
whether private firms are more leveraged than public firms. This is examined by testing the
difference in mean value of leverage. Leverage is calculated in three ways to capture any
measurement errors. The explanatory variables are tested using a regression analysis with a
fixed effects model. The multivariate analysis suggests that public firms are more sensitive
to firm specific attributes predicted by the pecking order theory and private firms are more
sensitive to firm specific attributes predicted by the trade-off theory. The study did not
include attributes other than firm specific, such as industry specific attributes. Although,
there might be more insight to be found from the study of firms between industries.
Furthermore, to explore what conditions affect capital structure, proxies for transaction
cost and information asymmetry may be of help in explain these conditions. For further
studies, caution should be taken to the measurement error that can exist when creating
proxies for theoretical attributes. Emphasis should be put at creating consistent measures
for leverage and its determinants.
7. Limitations of research
To empirically test the theories of capital structure may be problematic, because of data
limitation and measurement error. Theories may suggest that certain characteristics should
effect the choice of capital structure but those characteristics might be hard to observe in
real life. The characteristics may also be abstract and hard to measure empirically (Titman
& Wessels, 1988). According to Titman and Wessels (1988) in empirical research the
general approach to estimate theoretical characteristics has been to estimate regression
equations with proxies for the characteristics. They state a number of problems with this
approach. There may be no unique measure of the characteristics, instead there might be
multiple measures. Secondly, characteristics are often correlated and it may be hard to find

38. 38
those that are not related. Thirdly, the independent variables that are used in the regression
are not a perfect measure of the theoretical characteristics they aim to measure. According
to Titman and Wessels (1988) this may cause a “errors-in-variable” problem. Lastly, the
errors of the dependent variable may be correlated with the errors term of the independent
variables (Titman & Wessels, 1988). This study limits its focus to three measures of
leverage, one of which fails to be a good estimator and has no explanatory value. There
exist more measures for leverage that could be valid to include. The risk of using too few
measures for leverage is that the true measure predicted by theory is missed. This can be
explained by Berk (2010) who states that the problems with regression models that try to
find casual effect have been discussed in a large number of literature and “it is very difficult
to find empirical research demonstrably based on nearly right models” (p.2). Furthermore,
the narrow scope of this study limits the inference that can be drawn from the results. Few
variables have been included in the regression leaving deterministic variables out. This
could not only create bias from omitted variables but also the study could miss some
valuable insight that could be made from these variables. Furthermore, this study uses only
a small sample to represent the whole population, this could lead to false conclusion
regarding the population. At last the use of only one published study for the main empirical
concept poses a limitation to the study since it narrows the guidance and scope.

39. 39
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Appendix
Table 1
Covariance matrix of private firms

44. 44
Table 2
Number of firms for each regression in both samples
N
Private
DTA 97095
SDTA 97074
IDTE 51127
Public
DTA 893
SDTA 891
IDTE 588
Number of firms
Table 3
Firm size of each sample measured as log(sales)
Firm size Private Public p-value
DTA
mean 4,95 5,04 1,63***
N 97095 893
SDTA
mean 4,95 5,04 1,38***
N 97074 891
IDTE
mean 4,96 5,08 2,24***
N 51127 588
Significance code *** 0,001, ** 0,01, * 0,05,

45. 45
Table 4
Mean of each leverage ratio
DTA SDTA IDTE
Mean Mean Mean
Private
Min 0,00003 0,00001 -25831
1st Qu 0,42833 0,26182 0,34
Median 0,61001 0,43134 1,46
Mean 0,59381 0,44866 3,49
3rd Qu 0,77248 0,62400 4,03
Max 24,88 2,83 31161
Mean Mean Mean
Public
Min 0,00216 0,002165 -266,46
1st Qu 0,26613 0,190303 -0,62
Median 0,43866 0,334817 0,26
Mean 0,45603 0,369178 -1,368
3rd Qu 0,61876 0,493683 1,77
Max 1,40975 1,3955 238,16
Table 5
Mean and standard deviation of variables for each regression
Mean Std. Mean Std. Mean Std.
Private
Leverage 0,59 0,25 0,45 0,24 3,49 254
Profitability 0,14 0,23 0,14 0,23 0,12 1,06
Firm size 4,95 0,43 4,95 0,43 4,96 0,40
Tangibility 0,23 0,27 0,23 0,27 0,31 0,29
Sales growth 13,2 985 13,2 985 16,9 1220
Mean Std. Mean Std. Mean Std.
Public
Leverage 0,46 0,24 0,37 0,23 -1,37 33,1
Profitability 0,04 0,24 0,04 0,24 0,02 0,20
Firm size 5,04 0,54 5,04 0,54 5,08 0,54
Tangibility 0,11 0,20 0,10 0,20 0,14 0,23
Sales growth 8,03 180 8,05 179 11,59 222
Leverage 1 Leverage 2 Leverage 3

46. 46
Table 6
Coefficients, standard error and p value of t-statistic for each regression
Variables Private std, Error p-value Public std, Error p-value
DTA
Profitability -0,173 0,052 7,63*** -0,207 0,066 1,67**
Firmsize 0,053 0,005 2,2*** -0,006 0,037 0,865
Tangibility 0,126 0,008 2,2*** 0,054 0,108 0,616
Salesgrowth 0,000003 0,000001 5,0*** 0,00004 0,00002 0,041*
2,22*** 8,10***
SDTA
Profitability -0,050 0,013 1,25*** -0,164 0,028 4,77***
Firmsize 0,063 0,004 2,2*** 0,033 0,015 2,72**
Tangibility -0,369 0,007 2,2*** -0,277 0,039 3,88***
Salesgrowth 0,000002 0,000001 3,47** 0,000 0,00003 0,998
2,22*** 2,22***
IDTE
Profitability -0,121 1,061 0,909 2,974 7,024 0,672
Firmsize -1,592 2,428 0,512 -2,266 3,377 0,503
Tangibility 9,787 3,180 2,09** 20,806 8,355 1,31*
Salesgrowth -0,00017 0,001 0,858 0,002 0,005 0,732
0,04 0,16
Significance code *** 0,001, ** 0,01, * 0,05. Profitability is measured as EBIDTA divided by total assets.
Firm size is measured as the natural logarithm of sales. Tangibility is measured as tangible assets to
total assets. Sales growth is measured as turnover + turnover previous year divided to turnover previous
year.
² ²
² ²
² ²
Table 7
Test of the mean value between samples in each regression
Private Public p-value
DTA
mean 0,59 0,46 2,2***
N 97095 893
SDTA
mean 0,45 0,37 2,2***
N 97074 891
IDTE
mean 3,49 -1,37 0,006**
N 51127 588
Significance code *** 0,001, ** 0,01, * 0,05,

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