Determinants of capital structure: an empirical study of firms in Iran

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International Journal of Law and Management
Determinants of capital structure: an empirical study of firms in Iran
Mohammad Alipour Mir Farhad Seddigh Mohammadi Hojjatollah Derakhshan
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Determinants of capital
structure: an empirical study
of firms in Iran
Mohammad Alipour
Determinants
of capital
structure
53
Department of Accounting and Management, Islamic Azad University,
Khalkhal, Iran
Mir Farhad Seddigh Mohammadi
Department of Accounting and Economic, Islamic Azad University,
Khalkhal branch, Iran, and
Hojjatollah Derakhshan
Department of Accounting and Management, Islamic Azad University,
Khalkhal branch, Iran
Abstract
Purpose – This paper aims to investigate the determinants of capital structure of non-financial firms
in Iran.
Design/methodology/approach – This paper reviews different conditional theories of capital
structure to formulate testable propositions concerning the determinants of capital structure of Iranian
companies. Pooled ordinary least squares and panel econometric techniques such as fixed effects and
random effects are used to investigate the most significant factors that affect the capital structure choice
of manufacturing firms listed on Tehran Stock Exchange Iran during 2003-2007.
Findings – The results of the study suggest that variables such as firm’s size, financial flexibility, asset
structure, profitability, liquidity, growth, risk and state ownership affect all measures of capital structure of
Iranian corporations. Short-term debt is found to represent an important financing source for corporations in
Iran. The results of the present research are consistent with some capital structure theories.
Research limitations/implications – In general, the results provide evidence that the five theories
discussed influence emerging markets. Due to the existence of a negative relationship between profitability
and capital structure, investors must consider capital structure before making investment decisions.
Practical implications – This study has laid some groundwork to explore the determinants of
capital structure of Iranian firms upon which a more detailed evaluation could be based. Furthermore,
the empirical findings will help corporate managers in making optimal capital structure decisions.
Originality/value – To the authors’ knowledge, this is the first study that explores the determinants of
capital structure of manufacturing firms in Iran by using the most recent data. Moreover, this paper provides
a theoretical model to explain the mechanism of how the ownership structure impacts debt financing.
Keywords Ownership structure, Capital structure, Iran, Emerging markets, financing,
Tehran stock exchange
Paper type Research paper
The authors would like to thank Professor Chris Gale (Bradford University School of
Management, UK) and one anonymous reviewer for their helpful comments on earlier versions of
the manuscript.
International Journal of Law and
Management
Vol. 57 No. 1, 2015
pp. 53-83
© Emerald Group Publishing Limited
1754-243X
DOI 10.1108/IJLMA-01-2013-0004
IJLMA
57,1
54
1. Introduction
Generally, capital structure emphasizes on a combination of debt and equity to finance
a firm. The various financing decisions are vital for the financial welfare of the firm.
A false decision about the capital structure may lead to financial distress and eventually
to bankruptcy. The management of a firm sets its capital structure in a way that the
firm’s value is maximized. Many studies have attempted to discover the optimal capital
structure. Despite the huge body of research on capital structure, no specific method is
developed for managers to determine optimal capital structure (Sheikh and Wang,
2011). Myers and Majluf (1984) show how managers do not attempt to maintain a
particular capital structure. Instead, corporate financing choices are driven by the costs
of adverse selection that arise because of information asymmetry between
better-informed managers and less-informed investors (Hovakimian et al., 2004). An
appropriate capital structure is a critical decision for any business organization (Simerly
and Li, 2000).
The reason why researchers try to find the optimal capital structure is that it
decreases the cost of capital and increases firm market value (Viviani, 2008). A review of
the literature reveals that many studies have been carried out on the capital structure of
industrialized and developed countries (Hovakimian et al., 2004; Myers, 1977; Rajan and
Zingales, 1995) and developing countries (Booth et al., 2001; Huang and Song, 2006).
However, the results of these studies have not led to a consensus regarding the
importance of determining capital structure. This may be because firms use short-term
and long-term debts due to their ownership structure and the context of developing and
developed countries. Thus, the purpose of the present research is to study the
determinants of capital structure in the firms listed in Tehran Stock Exchange (TSE),
and the following reasons justify the necessity of carrying out such a research in Iran.
There is no consensus among researchers regarding the factors influencing capital
structure decisions, and the results obtained for other studies are inapplicable for Iran.
Moreover, the research findings will help managers in making optimal capital structure
decisions. It is important to determine whether factors that affect the capital structure of
developed countries (e.g. effective tax rate, sales growth, risk, asset structure, firm size,
liquidity, financial flexibility, profitability, etc.) can also affect the capital structure of
Iranian firms and whether the effect of these factors (if any) is different.
There is a lack of empirical research on the financing behavior of Iranian firms with
respect to the abovementioned internal factors, while the importance of capital structure
in maximizing the value of these firms is evident. What distinguishes Iran from other
countries is the presence of the state in the ownership structure of firms as well as other
external factors such as trade and economic sanctions against Iran. Approximately 70
per cent of the Iranian economy is still controlled by the government (Mirshekary and
Saudagaran, 2005). Therefore, it is necessary to study the effects of these factors on the
capital structure of Iranian firms and to determine the capital structure of these firms
with respect to the said characteristics. Iran is a developing, emerging country with a
weak capital market, and debt rather than equity is the dominant source of financing
(Mashayekhi and Mashayekh, 2008).
Thus, a gap in the literature and inconclusive empirical findings are a few reasons
that have evoked the need for this empirical investigation.
This article contributes to the literature in four ways. First, it provides major
evidence about the capital structure puzzle in Iran. Second, it uses different statistical
methods to investigate the determinants of capital structure. Third, it provides the first
evidence about the target capital structure issue in Iran. Finally, in this paper, we fill the
gap by using samples of Iran’s listed firms from 2003 to 2007 to examine how the
ownership structure affects the capital structure. Using pooled and panel data
regression, we concluded that variables such as firm’s size, financial flexibility, asset
structure, profitability, liquidity, growth, risk and state ownership affect the capital
structure of Iranian corporations. We conclude that, in terms of the determinants of
capital structure, Iranian corporations are very similar to firms in other countries. The
empirical results imply that the Modigliani and Miller (1963), trade-off, pecking order,
agency and market timing theories of capital structure partially explain the leverage
decisions made by Iranian corporations. The paper is organized as follows: In Section 2,
we will offer explanations regarding the context of Iran and its capital structure. Section 3
presents the theoretical background. Section 4 deals with the determinants and
driving factors of the capital structure of Iranian firms. Section 5 presents the research
methodology. Section 6 presents the results of the empirical analysis. Section 7 discusses
the findings of the study. Finally, Section 8 summarizes the findings of the research and
also concludes the discussion.
2. Economic and financial markets in Iran
Iran, a country of more than 75 million people, is situated in West Asia, in a region
commonly referred to as the Middle East. Iran is a country with rich resources of oil, gas
and other natural reserves. However, it is considered a developing country. The idea of
having a well-organized stock market and accelerating the process of industrialization
dates back to the 1930s in Iran when the National Bank of Iran undertook a study on the
subject. A report completed in 1936 worked out the details for the formation of a stock
market and laid the foundations for the plan. The outbreak of World War II and
subsequent economic and political events delayed the establishment of the stock
exchange until 1967, when the Stock Exchange Act was passed. The Tehran Stock
Exchange (TSE) opened in April 1968. Initially, only government bonds and certain
state-backed certificates were traded on the market. It currently includes 450 listed
companies, and it is one of the oldest and biggest capital markets in the Middle East
region. There is a good relationship between banks and companies because most of the
banks are governmental and those that are not are severely under the control and
regulations applied by the government. The government or semi-governmental units
are themselves one of the big stockholders at TSE. It can be said that bank loan is a main
resource for the managers in Iran and is the best way of financial support. In Iran, there
is no organized system for corporations to issue bonds for public investors. At the same
time, institutional changes like the transfer of shares of public companies and large
monopolies to employees and the private sector led to the expansion of stock market
activity (Mashayekhi and Mashayekh, 2008). While there have been some changes in the
accounting institutions, the structure of the Iranian capital market has not changed in
any significant way. According to Pourjalali and Meek (1995), lack of knowledge about
long-term government plans, high inflation rates, foreign sanctions and general
unavailability of essential goods resulted in great uncertainty of Iranians about their
future, causing uncertainty avoidance in the form of decreased savings in cash or
current assets. The capital market in Iran is very new and somewhat inefficient. The
institutional environment for Iranian firms and this country has several salient features:
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• State-owned companies and the state now own more than half of the publicly held
stocks on TSE.
• The economy of Iran is under severe economic and trade sanctions.
These unique characteristics distinguish Iranian firms from those in other countries and
signify the necessity of studying the effect of these and other factors on the capital
structure of Iranian firms.
3. Theoretical discussion and empirical determinants
In this empirical paper, we will only make a brief review of the different theories of
capital structure. The objective is to better explain the choice of the various empirical
determinants of capital structure and their links to the different theories. Then, we will
describe in more depth each empirical determinant that will be used in the empirical
study.
3.1 Capital structure theories
Indeed, firms’ financing decisions may be influenced by many factors; each capital
structure theory that we will mention has its own specific assumptions and they cannot
fully explain financing decisions. The literature on capital structure is very
comprehensive, and many theories have been proposed in the literature on finance
regarding capital structure[1], but it was Modigliani and Miller (1958) who formed the
basis of the first capital structure theorem. Modigliani and Miller (1958) argued that the
capital structure of a firm is irrelevant to the firm’s value, assuming perfect markets and
zero transaction costs. Moreover, they believed that a firm’s leverage has no effect on its
market value. This theory involved the following assumptions: capital market is
efficient and individuals external and internal to the organization have information
symmetry; there are no transaction costs or bankruptcy costs, and choosing between
debt and equity financing is irrelevant. Their theory sees capital structure as the result
of mainly financial, tax and growth factors (Modigliani and Miller, 1958). Modigliani
and Miller (1963) reviewed their previous theory, included tax advantages in the theory
as a determinant of capital structure and concluded that firms use debt financing to
make use of tax advantages, and to maximize firms’ market value, more debt has to be
used in the capital structure. Moreover, Miller (1977) identified three tax rates that
influenced total market value of a firm and concluded that firms’ market value depends
on the relative levels of each of these tax rates. Thus, capital structure has no effect on
firms’ market value, even by considering taxes.
Since Modigliani and Miller, there have been many empirical and theoretic research
studies on capital structure. Jensen and Meckling (1976) founded the trade-off theory
(TOT). Based on this theory, there is an optimal capital structure and the capital
structure of a firm can be determined by creating balance between tax effects, agency
costs, bankruptcy costs and so on. It asserts that the optimal capital structure of a firm
can be determined by agency costs. To reduce agency costs, the optimal structure of
ownership and debt must be determined (Jensen and Meckling, 1976). Jensen and
Meckling (1976) were the first to examine capital structure from the agency cost
perspective. TOT states that profit, size and growth are positively related to capital
structure because they are all proxies for high debt-related tax benefits and/or low
debt-related bankruptcy costs. On the other hand, the works of Jensen and Meckling
(1976) and Myers (1977) provide evidence on positive relationships between firms’ debt
ratios and their collateralized assets. Ross (1977) and other researchers proposed
signaling theory based on the problem of information asymmetry between managers
and investors. This theory asserts that debt financing signals investors about firms’
cash flow, as managers sometimes use changes in the capital structure to communicate
the risks and profitability of firms to external users. Myers (1984) founded pecking order
theory (POT), which is based on the hypothesized existence of information asymmetry
between shareholders, managers and creditors when either debt or equity is used. POT
rejects the existence of an optimal capital structure and argues that firms normally
follow a pecking order in corporate finance; that is, preferring internal funding instead of
external funding and preferring debt funding instead of equity funding. Jensen’s (1986)
free cash flow theory (FCFT) discusses that, firms with substantial free cash flow face
conflicts of interest between stockholders and managers. When a firm finances with
debt, the firm is obligated to make periodic interest payments. This reduces the cash
balance held by the firm, thus reducing the incentive to misuse the firm’s cash (Stretcher
and Johnson, 2011). Debt can also reduce agency costs by reducing free cash flow and
forcing the management to operate more efficiently in order to service the debt and
reduce the threat of bankruptcy.
Baker and Wurgler (2002) have suggested a new theory of capital structure: the
“market timing theory of capital structure”. This theory states that the current capital
structure is the cumulative outcome of past attempts to time the equity market.
According to this theory, share price fluctuations should have an effect on a firm’s
capital structure and there is no optimal capital structure. Moreover, this theory
indicates that firms issue debt and equity securities only when their market value is in
a good condition, and when their market value is lower, they repurchase their shares
(Baker and Wurgler, 2002). None of these theories per se determines firms’ capital
structure, as each of these theories emphasizes on its own subject matter; for instance,
POT deals with information asymmetry, TOT is on tax and FCFT emphasizes on
agency costs[2] (Myers, 2001).
4. Determinants of capital structure
Based on data availability, the following determinants of capital structure are analyzed
in this paper: effective tax rate (ETAX), size, liquidity [current ratio (CR) and working
capital ratio (WCR)], financial flexibility (FLEX), share price performance (SPP), assets
structure [tangibility (ASST)], growth opportunities [sale growth (SAGR) and expected
growth (EXPGR)], risk [volatility (VOLAT) and variability coefficient of profit (VCP)],
profitability (ROA), assets utilization ratio [agency cost (AUR)] and ownership structure
[state ownership percentage (SOP)]. The general form of the model can be specified as
follows:
DR ⫽ f (ETAX, SIZE, CR, WCR, FLEX, SPP, ASST, SAGR, EXPGR, VOLAT,
ROA, VCP, AUR, SOP)
4.1 Effective tax rate
Tax rate has a predicted positive impact on debt. A company facing a high effective
corporate tax rate has a need for, or will benefit from, taking up more debt to maximize
the tax deduction of the debt interest. Modigliani and Miller (1963) concluded that firms
would prefer debt to other financing resources due to the tax deductibility of interest
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payments. The gains from borrowing increase with the rate of tax (Antoniou et al., 2008).
Therefore, a positive relationship is expected between effective tax rate and debt.
Moreover, based on TOT, income tax is positively associated with debt (DeAngelo and
Masulis, 1980). Graham (1996) and Zimmerman (1983) found that there is a significant
positive relationship between effective tax rate of firms and long-term debt ratio and
that tax affects financing decisions. Yet, Antoniou et al. (2008) concluded that there is a
negative relationship between effective tax rate and debt ratios, arguing that the effect
of this rate on capital structure depends on tax regulations of each country. Moreover,
Karadeniz et al. (2009) and Sogorb-Mira and How (2005) too affirmed the negative
relationship between effective tax rate and debt ratios. Huang and Song (2006)
concluded that there is no relationship between effective tax rate and the amount of debt
in capital structure. We use effective tax rate that is calculated as the ratio of paid taxes
to earnings before taxes. The paid taxes are calculated by subtracting earnings after
taxes from earnings before taxes. Values for this variable are between zero and one.
Therefore, our first hypothesis will be:
H1. There is a positive relationship between effective tax rate and debt ratios.
4.2 Firm size
A firm’s size has a predicted positive impact on debt level. A large-sized company is less
likely to become bankrupt, and therefore attracts more debt. According to the TOT, debt
ratios should have a positive relationship with firm size, as larger firms tend to be more
diversified and to have lower variance of earnings, enabling them to tolerate high debt
ratios (Castanias, 1983; Titman and Wessels, 1988). In addition, POT explains that a
large-sized company gives rise to greater information asymmetry and, therefore,
attracts less debt, or large firms have more access to equity funding than small firms
(Marsh, 1982). The existence of a negative relationship between firm size and capital
structure may be due to the fact that larger firms have the ability of financing through
share issuance rather than debt financing; thus, larger firms use less debt in their capital
structure (Deloof and Overfelt, 2008). Many researchers have concluded that firm size
has a positive relationship with the capital structure of the firm (Al-Fayoumi and
Abuzayed, 2009;Yu and Aquino, 2009;Du and Dai, 2005; Eriotis et al., 2007; Huang and
Song, 2006; Ezeoha, 2011;Bae, 2009;Hovakimian et al., 2004; Agrawal and Nagarajan,
1990). Further, Karadeniz et al. (2009) concluded that firm size has no significant
relationship with debt ratios and capital structure. In addition, the results of Rajagopal
(2011) revealed that larger firms have less debt. Suto (2003) and Driffield et al. (2007)
have used natural logarithm of total assets to compute firm size. Accordingly, the
following hypothesis is specified:
H2. There is a negative relationship between firm size and debt ratios.
4.3 Liquidity
Liquidity ratios may have a mixed impact on capital structure decisions. According to
the TOT, firms should ensure sufficient liquidity through receiving debt in order to meet
their commitments and considering this theory, there has to be a positive relationship
between liquidity and debt ratios/capital structure. In contrast, based on POT, agency
theory (AT) and FCFT, there must be a negative relationship between liquidity and
capital structure; for according to these theories, firms that have enough liquidity have
less requirement for external financing and borrowing. A further argument for this
negative relationship is provided by Myers and Rajan (1998), who argue that when
agency costs of liquidity are high, outside creditors limit the amount of debt financing
available to the company. Eldomiaty and Azim (2008) found that current ratio (as a
measure for assessing liquidity) has a significant negative relationship with debt ratios
at each risk level. They also showed that cash ratio has a negative relationship with debt
ratios at lower-risk levels, and these relationships confirm POT. Deesomsak et al. (2004)
concluded that there is a negative relationship between liquidity and debt ratios and
reported that firms with high liquidity prefer internal financing to external financing
and, as a result, have less debt; this is consistent with POT. Moreover, other researchers
have proven the existence of a negative relationship between liquidity ratios and debt
ratios (Eriotis et al., 2007; Sheikh and Wang, 2011). In this paper, we use current ratio and
working capital ratio to assess the liquidity of firms, where current ratio is calculated by
dividing current assets by current debts, and working capital ratio is calculated by
dividing working capital (difference of current assets and current liabilities) by total
assets. Considering the above explanations and AT and FCFT, we can formulate the
following hypothesis:
H3. There is a negative relationship between liquidity ratios and debt ratios.
4.4 Financial flexibility
Marsh (1982) argues that expected retention rate (financial flexibility) affects target debt
ratio. Myers and Majluf (1984) concluded that firms with more profitability have less
inclination for external financing. Also according to the POT, managers prefer internal
financing to external financing. Financial weakness and strength of firms and the
subsequent use or disuse of debt financing depend on the financial flexibility of the
firms. According to Beattie et al. (2006), firms with more financial flexibility have less
debt, as these firms obviate the need for external financing by increasing their
flexibility. Moreover, other researchers concluded that financial flexibility is a key
determinant of optimal capital structure, and this issue is consistent with TOT (Graham
and Harvey, 2001; Brounen et al., 2005). Eldomiaty (2007) did not observe any
relationship between financial flexibility of firms and capital structure. In the present
paper, financial flexibility of firms has been calculated by dividing retained earnings by
total assets, and the following hypothesis has been formulated for the relationship
between financial flexibility and capital structure:
H4. There is a negative relationship between financial flexibility and debt
ratios.
4.5 Share price performance
Market timing theory (MTT) states that the current capital structure is the cumulative
outcome of past attempts to time the equity market. According to this theory, share price
fluctuations should have an effect on a firm’s capital structure and there is no optimal
capital structure (Luigi and Sorin, 2009). Moreover, this theory indicates that firms issue
debt and equity securities only when their market value is in a good condition and when
their market value is lower, they repurchase their shares (Baker and Wurgler,
2002).Thus, share price performance is a measure that is taken into account by
managers in capital structure decisions. Based on MTT, there is a significant negative
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relationship between firms’ market value and leverage (Deesomsak et al., 2004). Usually
when share price of firms increases, they issue shares and rarely receive debt (Antoniou
et al., 2008). Hovakimian et al. (2004) concluded that debt ratios and leverage are
negatively associated with share price performance. In addition, some other researchers
reported the existence of a negative relationship between share price and debt ratios
(Deesomsak et al., 2004;Antoniou et al., 2008). In this paper, share price performance has
been calculated as follows: share price of each year has been deducted from the price of
the previous year, and the result has been divided by the share price of the previous year.
Considering the corresponding theory to this issue, we can formulate the following
hypothesis:
H5. There is a negative relationship between share price performance and debt
ratios.
4.6 Asset structure
Fixed assets are usually those purchased through debt and are regarded as backing for
creditors at the time of firm liquidation. We can also say that a part of firm debt capacity
is encompassed with intangible assets, which are referred to as asset structure (Schwarz
and Aronson, 1967). According to agency cost theory, the shareholders of a leveraged
firm have an incentive to invest sub-optimally (Titman and Wessels, 1988). Based on
TOT, a firm’s tangibility has a predicted positive impact on debt level. A company with
more tangible assets would need to have more collateral assets to service debt in the
event of bankruptcy and, therefore, would have a greater ability to attract more debt.
Tangible assets could also have a negative impact on financial leverage by augmenting
risk through the increase of operating leverage (Hutchinson and Hunter, 1995).Chiang
et al. (2010) concluded that there is a positive relationship between asset structure and
long-term debt ratio. Moreover, many researchers have shown that there is a positive
relationship between asset structure and debt ratios (Al-Najjar and Taylor, 2008; Teker
et al., 2009; Deloof and Overfelt, 2008; Mitton, 2007; Heshmati, 2001; Viviani, 2008;
Antoniou et al., 2008; Frank and Goyal, 2002). In addition, the results of research on
small and medium enterprises indicate a significant positive relationship between asset
structure and long-term debt ratio (Sogorb-Mira and How, 2005). Further, some studies
have arrived at dichotomous results. Amidu (2007) and Abor and Biekpe (2009)
concluded that asset structure is negatively associated with short-term debt ratio, but
positively associated with long-term debt ratio. Also Booth et al. (2001) concluded that
there is a negative relationship between asset structure ratio and total debts ratio
(leverage), but asset structure is positively associated with long-term debt ratio.
Moreover, the results of some other studies suggested, contrary to what is expected
according to TOT, that there is negative relationship between asset structure and debt
ratios (Sheikh and Wang, 2011; Vicente-Lorente, 2001). Finally, other researchers such
as Al-Fayoumi and Abuzayed (2009) have found no relationship between asset
structures and debt ratios. In the present study, asset structure has been calculated as
fixed assets divided by total assets, and considering the AT and TOT, the following
hypothesis is specified:
H6. There is a positive relationship between asset structure and debt ratios.
4.7 Growth opportunities
Applying pecking order arguments, growing firms place a greater demand on the
internally generated funds of the firm. Consequentially, firms with relatively high
growth will tend to issue securities less subject to information asymmetries, i.e.
short-term debt. This should lead to firms with relatively higher growth having more
leverage (Viviani, 2008). Because, companies with fast growth need to borrow more and
are able to borrow more. The AT stipulates that firm’s financing is a vehicle by which
investors and managers can solve the problem of free cash flow. Based on this theory,
firms with more growth opportunities have more debt. Rajan and Zingales (1995) came
to the conclusion that when firms with more growth opportunities need financing, they
can do it by increasing equity and using less debt in their future financing decisions.
Therefore, firms with high-growth opportunity may not issue debt in the first place, and
leverage is expected to be negatively related with growth opportunities. Moreover,
based on the TOT, there is a negative relationship between growth opportunities and
debt ratios (Lasfer, 1999). Cassar and Holmes (2003) showed a significant positive
relationship between a firm’s growth opportunities and debt ratios and concluded that
firms with more growth opportunities move faster toward optimal capital structure.
Moreover, the results of some other research studies revealed that there is a significant
positive relationship between growth opportunities and debt ratios (Amidu, 2007;
Heshmati, 2001). Karadeniz et al. (2009) and Eriotis et al. (2007) concluded that there is no
significant relationship between growth opportunities and debt ratios. Berens and Cuny
(1995) argued that growth implies significant equity financing and low leverage.
Finally, Ooi (1999) and Huang and Song (2006) concluded that the relationship between
growth opportunities and debt ratios is negative. Deesomsak et al. (2004) pointed out
that except in Australia, there is a negative relationship between growth opportunities
and leverage. In this study, two measures have been used to calculate growth
opportunities. The first measure is sales growth, which is calculated by subtracting
current year sales from that of the previous year and dividing the result by previous year
sales. The second measure is expected assets growth, which is calculated by subtracting
current year assets from that of the previous year and dividing the result by previous
year assets. Based on capital structure theories, we can formulate the following
hypothesis:
H7. There is a negative relationship between growth opportunities and debt ratios.
4.8 Risk
Risk plays a very important role in capital structure (Baranoff et al., 2007). The theory of
finance (TOT) suggests that risky firms or firms that have high possibility to default
should not be highly levered (Wiwattanakantang, 1999; Titman and Wessels, 1988).
Thus, according to the TOT, risk is negatively associated with debt. A negative
relationship between operating risk and leverage is also expected from a POT
perspective. A company with high risk or great volatility in earnings is more likely to go
bankrupt, and therefore has low credit-worthiness for debt. Jordan et al. (1998)
concluded that there is a positive relationship between risk and market value of debts
when market has a higher growth. This positive relationship may be because
bankruptcy risk increases with the firm’s debt. In addition, Omran and Pointon (2009)
concluded that firms with more risk have more long-term debt. Ezeoha (2011) showed
that business risk has no significant relationship with debt ratios. Results of other
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research studies as well confirm the absence of a relationship between risk and debt
ratios (Viviani, 2008; Cassar and Holmes, 2003; Su, 2010). Further, many studies have
shown that the relationship between risk and capital structure is negative, i.e. a firm’s
debt decreases with increased risk (Eldomiaty, 2007; Sheikh and Wang, 2011; Low and
Chen, 2004; Abor and Biekpe, 2009; Al-Najjar and Taylor, 2008; Chung, 1993; Heshmati,
2001). The existence of a negative relationship between risk and capital structure may
be due to the fact that firms with more risk tend to avoid using external financing and
instead rely on internal financing to prevent bankruptcy. Two measures have been used
in this paper to assess firms’ risk. The first measure is variability of profit, which is
calculated by subtracting current year profit from that of the previous year and dividing
the result by previous year profit. The second measure is standard deviation of
profitability (ROA) or firm’s business risk. Volatility or business risk is a proxy for the
probability of financial distress:
H8. There is a negative relationship between risk and debt ratios.
4.9 Profitability
Based on the TOT, firms with greater profitability should have more leverage and debt
ratios, as firms that have great profitability have less bankruptcy risk and creditors
have much tendency for funding these firms. The result of the research of Leland and
Pyle (1977) showed that the amount of leverage of a firm due to information asymmetry
has a significant positive relationship with profitability. Several other researchers such
as Chiang et al. (2010), Reinhard and Li (2010), Jordan et al. (1998) and Margaritis and
Psillaki (2007) concluded that there is a positive relationship between profitability and
debt ratios. Yet, based on POT, firms with more profitability have less debt ratios; in
fact, firms with more profitability do not need external financing and often use internal
financing, and for that reason, they have less debt in their capital structure. This
suggests that highly profitable companies will tend to finance investments with
retained earnings rather than using debt. Lemmon and Zender (2010) too concluded that
firms need external funds for financing, that receiving debt has priority over equity
issuance in financing decisions and that this theory better specifies the financing
behavior of firms.
According to the findings of Fama and French (2002), firms with more profitability
have less debt, and short-term cash flows of firms are expended on paying and settling
debts. Many research studies confirm this matter (Rajan and Zingales, 1995; Graham,
2000; Lasfer, 1999; Ezeoha, 2008; Sogorb-Mira and How, 2005; Huang and Song, 2006;
Al-Najjar and Taylor, 2008; Karadeniz et al., 2009; Lemmon and Zender, 2010;
Al-Fayoumi and Abuzayed, 2009; Yu and Aquino, 2009; Deloof and Overfelt, 2008; Brav,
2009; Kim et al., 2006; Gaud et al., 2005; Hall et al., 2004; Abor and Biekpe, 2009;
Heshmati, 2001; Ezeoha, 2011; Eldomiaty, 2007; Amidu, 2007; Sheikh and Wang, 2011;
Viviani, 2008; Strebulaev, 2007). Abor (2005) showed that profitability is positively
associated with short-term debt ratio and negative associated with long-term debt ratio.
Chittenden et al. (1996) concluded that there is no significant relationship between
long-term debt ratio and profitability, while profitability in small firms has a negative
relationship with short-term debt ratio and total debt ratio. Al-Sakran (2001) concluded
that profitability has no significant relationship with debt ratio in industrial sector
firms; yet in large firms, there exists a negative relationship. In addition, the results
show that there is no relationship between capital structure and profitability
(Hovakimian et al., 2004; El-Sayed Ebaid, 2009). In this study, profitability is proxied by
return on assets (defined as earnings before interest and tax divided by total assets).The
following hypothesis can be formulated:
Determinants
of capital
structure
H9. There is a negative relationship between profitability and debt ratios.
4.10 Asset utilization ratio
Using debt in capital structure creates agency costs (Sheikh and Wang, 2011).Utilizing
assets and their calculated ratio signify the strategic importance of agency costs. Based
on FCFT, the higher this ratio, the more will be the efficiency of managers in adopting
and utilizing assets (Eldomiaty and Azim, 2008). Based on agency costs, this ratio is
expected to be higher and it emphasizes on reduction of costs and efficiency of
operations (Jermias, 2008). Thus, this ratio is expected to have a negative relationship
with debt ratio, as with the increase of this ratio, managers’ efficiency in utilization of
assets increases and it leads to more cash flow in the firm; hence, there is no need for
external financing. In the present study, this ratio is calculated by dividing sales by total
assets. Considering the above explanations, we can formulate the following hypothesis:
H10. There is a negative relationship between assets utilization and debt ratios.
4.11 Ownership structure
Ownership structure is likely to have significant effects on capital structure choices of
firms, as it can affect agency incentives (Booth et al., 2001). Therefore, evidence on the
difference (if any) in capital structure choices between companies with State
shareholdings could contribute additional insights. AT (Jensen and Meckling, 1976;
Jensen, 1986) suggests that the optimal structure of leverage and ownership may be used
to minimize total agency costs, and the creation of a capital structure can influence the
governance structure of a firm (Jensen, 1986). In terms of the role of ownership structures
in corporate decisions, Shleifer and Vishny (1994) suggest that direct state ownership is
often associated with the pursuit of political objectives at the expense of other
stakeholders in the firm. Consistent with their view, Dewenter and Malatesta (2001)
show that state-owned firms are more highly leveraged, and privately owned firms have
better performance than state-owned enterprises. Li et al. (2009) showed that state
ownership is positively associated with leverage and firms’ access to long-term debt.
Huang and song (2006) found that ownership structure also affects capital structure.
Firms with higher state shareholding and lower institutional shareholding tend to have
lower total liabilities ratio and lower total debt ratio. According to Su (2010), there is
strong evidence that government-controlled firms use less debt financing and that state
ownership weakens the positive relationship between unrelated diversification and
leverage. Brav (2009) found that private firms that rely almost exclusively on debt
financing have higher leverage ratios:
H11. There is a positive relationship between state ownership and capital structure.
5. Method
5.1 Data
This study investigates the capital structure debate in emerging markets using Iranian
non-financial companies. Firms that have reported their annual accounts without
significant gaps for this period are selected. The data set for this analysis is
63
IJLMA
57,1
64
hand-collected from the Iranian Companies Guide from 2003 to 2007. Our sample
ultimately includes 327 firms with data ending in 2003, 357 ending in 2004, 410 ending
in 2005, 412 in 2006 and 412 in 2007. In aggregate, we have 1918 observations. All firms
with any missing observations for any variable during the sample period have been
dropped. Firms which operate in the financial sector are not included in this analysis, as
their balance sheets have a different structure from those of the non-financial firms.
Finally, 1,562 firm-year observations were investigated within a five-year period. Our
data are an unbalanced panel due to missing observations. The choice of the sample
period, from 2003 to 2007, is guided by the availability of data and the objective of
maintaining the same time frame to allow for comparability.
5.2 Model
While most of the existing studies have used the ordinary least squares (OLS)
regressions, we utilize recent development in the econometrics of panel data (fixed
effects and random effects) to estimate the parameters in our capital structure model.
Panel data models are powerful research instruments, which take into account the
effects of cross-sectional data. This in turn may help us estimate the appropriate
empirical model. We use general models for panel data that make it possible to produce
an empirical estimate of the relationship between leverage (dependent) and firm-specific
(independent) variables. We formulate behavioral differences between the various
cross-section elements as follows:
Yit ⫽ ␤0 ⫹ ␤ X it ⫹ ␧ it
Yit ⫽ ␤0i ⫹ ␤ X it ⫹ ␮ it
Yit ⫽ ␤0 ⫹ ␤ X it ⫹ ␧ it ⫹ ␮ it
(pooled model)
(1)
(fixed effects model)
(2)
(random effects model)
(3)
where Yit is the leverage measure of firm i in year t; ␤0 is common y-intercept; Xit is a
column vector of firm-specific variables for firm i in year t, which represents the
explanatory variables as outlined in Section 4 above and which are reported in Table I;
␧it is the stochastic error term of firm i at time t; ␤0i is the y-intercept of firm I; and ␮it is
the error term of firm i at time t. The panel data suggest the use of fixed or random effects
that are able to control for unobserved firm and/or year effects (Green, 2003). To
distinguish the preferable set of results statistically, the results of the Lagrange
multiplier and Hausman tests are presented. If the Lagrange multiplier test gives a
significant result, then the panel results are preferred over the pooled results.
If the Hausman test gives a significant result, then the fixed effect[3] results are
statistically preferred to the random effects results. Another advantage of using the
panel data set is that, because of the several data points, degrees of freedom
are increased and collinearity among the explanatory variables is reduced; thus, the
efficiency of economic estimates is improved. As pointed out by Hsiao (1986), simple
least squares estimation of pooled cross-section and time-series data may be
seriously biased. Then we report the results for the regression model using pooled
and panel models.
Variables
Dependent variables
Short-term debt ratio
Description
Adapted from
STD: short-term debt divided by total
assets
Hall et al. (2004), Li et al. (2009),
Viviani (2008), Ezeoha (2008), SogorbMira and How (2005), Eldomiaty and
Azim (2008), Titman and Wessels’
(1988), Amidu (2007)
Eldomiaty and Azim (2008), Ezeoha
(2008), Sogorb-Mira and How (2005),
Hall et al. (2004), Amidu (2007)
Amidu (2007), Viviani (2008), Su (2010)
Long-term debt ratio
LTD: long-term debt divided by total
assets
Total debt ratio
TD: total debt divided by total assets
Independent variables
Effective tax rate
Firm size
ETAX: tax divided by earnings before
taxes
SIZE: natural logarithm of total assets
Determinants
of capital
structure
65
Karadeniz et al. (2009)
Su (2010), Abor and Biekpe (2009),
Sheikh and Wang (2011)
Liquidity (current ratio) CR: current assets divided by current
Eldomiaty and Azim (2008), Eldomiaty
and (working capital*
debt
(2007), Sheikh and Wang (2011),
ratio) (*working
WCR: working capital divided by total Graham (2000)
capital ⫽ current assets ⫺ assets
current liabilities)
Financial flexibility
FLEX: retained earnings divided by
Marsh (1982), Eldomiaty (2007), Beattie
total assets
et al. (2006)
Share price performance SPP: difference between share prices at Hovakimian et al. (2004), Deesomsak
times [t] and [t-1] to share price at time et al. (2004), Antoniou et al. (2008)
[t-1]
Assets structure
ASST: fix assets divided by total
Su (2010), Abor and Biekpe (2009),
assets
Karadeniz et al. (2009), Sheikh and
Wang (2011), Abor and Biekpe (2009),
Titman and Wessels (1988)
Growth opportunities
SAGR: difference between sale at times Abor and Biekpe (2009), Karadeniz
(sale growth) and
[t] and [t-1] to sale at time [t-1]
et al. (2009), Eriotis et al. (2007), Ooi
(expected growth)
EXPGR: Difference between total
(1999), Deesomsak et al. (2004)
assets at times [t] and [t ⫺ 1] to total
assets at time [t ⫺ 1]
Risk (volatility) and
VOLAT: standard deviation of (ROA) Abor and Biekpe (2009), Sheikh and
(variability coefficient of VCP: difference between profit at times Wang (2011), Cassar and Holmes
profit)
[t] and [t ⫺ 1] to profit at time [t ⫺ 1]
(2003), Ezeoha (2011)
Profitability
ROA: earnings before interest and tax Leland and Pyle (1977), Su (2010), Abor
divided by total assets
and Biekpe (2009), Ezeoha (2008),
Sheikh and Wang (2011)
Assets utilization ratio
AUR: sales divided by total assets
Eldomiaty and Azim (2008), Jermias
(2008)
State ownership
SOP: the percentage of state ownership Su (2010), Li et al. (2009)
Control variable
Economic and trade
sanctions
SANC: takes a value of 1 for the years Authors’
with trade and economic sanctions and
a value of 0 for the years without
sanctions
Table I.
Definitions of the
variables
IJLMA
57,1
66
5.3 Measures
5.3.1 Dependent variables. According to Hall et al. (2004) and Ezeoha (2008), three
dependent variables have been used to assess capital structure and specify its
determinants, i.e. short-term debt ratio (STD), long-term debt ratio (LTD) and total debt
ratio (TD), and the way of calculating them is summarized in Table I.
5.3.2 Independent and control variables. There are several independent variables in
this paper. Effective tax rate, firm size, liquidity, financial flexibility, share price
performance, asset structure, growth opportunities, risk, profitability, asset utilization
ratio and state ownership. Further, economic and trade sanctions, is chosen to control
the effect of sanctions on capital structure. The way of calculating them is briefly
presented in Table I.
6. Results
6.1 Descriptive statistics and correlation analysis
In Table II, descriptive statistics of dependent and independent variables are presented.
Based on this table, we can say that mean short-term debt ratio (STD) is 61.2 per cent and
its median is 61.8 per cent. Mean long-term debt ratio (LTD) is 11.2 per cent and its
median is 6.75 per cent, and mean total debts ratio (TD) is 72.5 per cent and its median
is 72.03 per cent, indicating that the most important financing source of Iranian firms is
debts, and in particular short-term debts. As can be seen in Table II, total debt ratio (TD)
in the Iran is much higher than in G7 and European countries (Rajan and Zingales, 1995;
Aggarwal and Kyaw, 2009) or China (Huang and Song, 2002).
Variables
N
Mean
Median
Minimum
Maximum
SD
JB-statistics
CR
ETAX
EXPGR
FLEX
VCP
ROA
SAGR
SIZE
SPP
AUR
ASST
VOLAT
WCR
SOP
SANC
STD
LTD
TD
1,562
1,562
1,562
1,562
1,562
1,562
1,562
1,562
1,562
1,562
1,562
1,562
1,562
1,562
1,562
1,562
1,562
1,562
1.110168
0.079332
0.24131
0.030352
0.494933
0.136405
0.89350
12.28623
⫺0.024869
0.854261
0.226252
0.653922
0.041954
0.6745
1
0.612690
0.112528
0.725218
0.992053
0.035533
0.138093
0.007946
0.005658
0.122094
0.105588
12.17373
⫺0.114191
0.776511
0.1899932
0.222744
0.051807
0.7112
1
0.618892
0.067524
0.720350
0.035251
0.00000
⫺0.868696
⫺4.379051
⫺22.8081
⫺1.31512
⫺10.9211
8.157370
⫺1.000000
⫺0.078283
0.00000
0.008688
⫺1.119102
0.0000
1
0.036357
0.000000
0.037387
14.67100
0.432790
27.53754
11.64394
65.2894
3.48884
12.2579
17.85143
7.153959
17.82582
0.858921
5.202854
0.921339
0.34454
0
1.652613
1.488901
2.449417
0.872016
0.088780
0.801252
0.519758
4.101549
0.20567
1.14064
1.380038
0.737367
0.801859
0.167908
1.194054
1.197702
0.906535
1
0.217202
0.154102
0.262132
405,164
172.6084
1.71E ⫹ 08
4,891,160
25,147,207
1.57E ⫹ 08
1.59E ⫹ 08
109.7384
28,205.24
64,363,113
219.4924
3,844.239
376.1475
1,048.249
NA
485.1017
28,766.80
2,248.250
Notes: CR: current ratio; ETAX: effective tax rate; EXPGR: expected growth; FLEX: financial
flexibility; VCP: variability of profit for firm; ROA: return on assets; SAGR: sale growth; SIZE: firm size;
SPP: share price performance; AUR: asset utilization ratio; ASST: asset structure; VOLAT: volatility;
Table II.
Descriptive statistics WCR: working capital ratio; SOP: state ownership percentage; SANC: economic and trade sanctions;
of variables
STD: short-term debt ratio; LTD: long-term debt ratio; TD: total debt ratio
Maximum total debt ratio (TD) in the research sample is 2.44 per cent, indicating that
this ratio in Iranian firms is lower than that reported by Tse and Jia (2007) and Brailsford
et al. (2002) reported in UK and Australia (176.36 and 380.35 per cent, respectively). It
may be due to the fact that accessing long-term debt sources like loans and bonds is not
possible for firms or they may rarely use long-term sources in their financing decisions
due to the risks associated with them. Moreover, mean current ratio is 1.11 and mean
working capital ratio is 4.1 per cent.
Mean income tax rate during the five-year period is 8 per cent, indicating that Iranian
firms have paid only 8 per cent of tax on average, while income tax rate in Iran is 25 per
cent. The findings revealed that the Iranians’ capital structure choice is a result of low
corporate income tax rate (8 per cent for listed companies). The expected asset growth of
these firms is 24.1 per cent, signifying a considerable growth in the assets of these firms;
further, their sales growth is also considerable, as the mean sales growth of these firms
is 8.9 per cent. These firms have a poor financial flexibility whose mean is 3.1 per cent.
The first risk assessment measure of these firms, that is variability of profit, equals 49.4
per cent on average. The second measure, which is standard deviation of return on
assets, is 65.3 per cent. Mean return on assets (ROA) and asset utilization ratio are 13.6
and 85.4 per cent, respectively, signifying that on average, firms have made 13.6 per cent
profit from each Riyal of asset utilized and that their sales is 85.4 times greater than their
assets. Moreover, according to the table, we can say that, on average, 67 per cent of
Iranian firm’s ownership is public and the rest is private. To assess the distributional
properties of the data, Table II reports a statistical summary of the variables.
Descriptive statistics of variables show that all the variables’ means are greater than
their medians (except for WCR), indicating that the distribution is skewed to the right. A
formal test for normality of the series can also be done by using the Jarque–Bera (JB) test,
which combines the skewness and kurtosis results. The results of the JB test accept the
null hypothesis of normality for all series at the 5 per cent level. Moreover, to determine
the absence of multicollinearity problems, the Pearson’s correlation coefficients between
explanatory variables were tested. It has been suggested that multicollinearity shall be
considered as a serious problem only if the correlation coefficient between explanatory
variables is more than 0.8 (Kennedy, 1985) or more than 0.9 (Tabachnick and Fidell,
1996). As shown in Table III, the correlation coefficients between explanatory variables
are not high. As a result, we can ignore any multicollinearity problems.
6.2 Regression results
In this section, the empirical analysis of the capital structure drivers is presented. The
results of regression analysis (pooled and panel) are provided in Tables IV-VI.
According to Tables IV and VI, the Lagrange multiplier test is statistically significant,
suggesting the suitability of panel models over the pooled model. The Hausman test is
statistically significant, indicating that the fixed-effects model is “more preferable” than
the random-effects specifications. Moreover, Table V shows that the pooled models are
more favorable than the panel models, the Lagrange multipliers are 1.452102 and
statistically insignificant at 5 per cent.
The coefficient for effective tax rate (ETAX) suggests only a positive relation with
short-term debt ratio (STD) and leverage (TD), but the total debt model lacks statistical
significance, which appears to support the H1 suggested by Modigliani and Miller’
(1963) theory and TOT. Therefore, an increase in effective tax rate can affect short-term
Determinants
of capital
structure
67
1
0.625** (0.000)
0.039 (0.120)
0.079** (0.001)
⫺0.274** (0.000)
0.019 (0.441)
⫺0.012 (0.635)
0.015 (0.545)
0.162** (0.000)
0.038 (0.124)
0.004 (0.868)
0.049 (0.051)
⫺0.400** (0.000)
⫺0.089** (0.000)
⫺0.383** (0.000)
1
0.175** (0.000)
0.042 (0.096)
⫺0.417** (0.000)
0.034 (0.171)
⫺0.015 (0.535)
0.025 (0.308)
0.121** (0.000)
0.044 (0.077)
0.008 (0.752)
0.013 (0.613)
⫺0.526** (0.000)
⫺0.086** (0.001)
⫺0.569** (0.000)
1
0.007 (0.765)
⫺0.048 (0.054)
⫺0.012 (0.645)
0.000 (0.991)
0.002 (0.937)
0.010 (0.683)
0.020 (0.423)
⫺0.004 (0.858)
0.006 (0.800)
⫺0.202** (0.000)
⫺0.047 (0.061)
⫺0.195** (0.000)
1
⫺0.015 (0.557)
0.008 (0.744)
⫺0.022 (0.384)
0.014 (0.563)
0.067** (0.007)
0.006 (0.820)
⫺0.016 (0.522)
0.054* (0.030)
⫺0.069** (0.006)
⫺0.062** (0.013)
⫺0.094** (0.000)
1
⫺0.018 (0.474)
0.053* (0.035)
⫺0.022 (0.376)
⫺0.093** (0.000)
0.022 (0.387)
0.045 (0.071)
⫺0.012 (0.627)
⫺0.073** (0.003)
0.183** (0.000)
0.047 (0.060)
ASST
1
0.023 (0.809)
0.023 (0.355)
⫺0.004 (0.878)
⫺0.058* (0.020)
0.416** (0.000)
0.008 (0.749)
⫺0.012 (0.617)
⫺0.020 (0.423)
⫺0.022 (0.376)
1
0.046 (0.992)
⫺0.010 (0.688)
0.158 (0.991)
0.572** (0.000)
0.010 (0.702)
⫺0.028 (0.263)
0.022 (0.380)
⫺0.010 (0.681)
EXPGR
Notes: * Correlation is significant at the 0.05 level (2-tailed); ** correlation is significant at the 0.01 level (2-tailed); CR: current ratio, ETAX: effective tax rate; EXPGR: expected growth;
FLEX: financial flexibility; VCP: variability of profit for firm; ROA: return on assets; SAGR: sale growth; SIZE: firm size; SPP: share price performance; AUR: asset utilization ratio; ASST:
asset structure; VOLAT: volatility; WCR: working capital ratio; SOP: state ownership percentage; STD: short-term debt ratio; LTD: long-term debt ratio; TD: total debt ratio
(continued)
1
⫺0.020 (0.412)
⫺0.034 (0.170)
0.038 (0.131)
⫺0.011 (0.670)
⫺0.130** (0.000)
⫺0.058* (0.021)
0.032 (0.205)
0.049** (0.048)
⫺0.090** (0.000)
⫺0.090** (0.000)
⫺0.035 (0.159)
0.008 (0.737)
⫺0.150** (0.000)
⫺0.124** (0.000)
⫺0.197** (0.000)
SPP
1
⫺0.016 (0.517)
⫺0.064* (0.010)
0.057* (0.021)
0.071** (0.005)
0.046 (0.064)
0.048 (0.053)
⫺0.017 (0.503)
⫺0.012 (0.638)
⫺0.010 (0.703)
⫺0.238** (0.000)
0.005 (0.828)
⫺0.019 (0.452)
⫺0.135** (0.000)
020 (0.416)
⫺0.065** (0.009)
⫺0.021 (0.393)
FLEX
Table III.
Correlation matrix
ETAX
SIZE
CR
WCR
FLEX
SPP
ASST
SAGR
EXPGR
VCP
VOLAT
ROA
AUR
SOP
STD
LTD
TD
WCR
SAGR
CR
ETAX
SIZE
68
Variable
IJLMA
57,1
VCP
1
⫺0.008 (0.737)
0.001 (0.969)
0.009 (0.727)
0.029 (0.252)
⫺0.031 (0.216)
⫺0.032 (0.203)
⫺0.044 (0.076)
Variable
ETAX
SIZE
CR
WCR
FLEX
SPP
ASST
SAGR
EXPGR
VCP
VOLAT
ROA
AUR
SOP
STD
LTD
TD
1
0.112** (0.000)
0.015 (0.549)
0.010 (0.700)
⫺0.085** (0.001)
⫺0.022 (0.369)
⫺0.084** (0.001)
VOLAT
1
0.223** (0.000)
0.009 (0.714)
⫺0.080** (0.001)
⫺0.032 (0.203)
⫺0.085** (0.001)
ROA
1
0.016 (0.527)
⫺0.035 (0.157)
0.008 (0.746)
⫺0.025 (0.326)
AUR
1
⫺0.031 (0.209)
0.043 (0.087)
⫺0.001 (0.972)
SOP
1
⫺0.033 (0.186)
0.609** (0.000)
STD
TD
1
LTD
1
0.561** (0.000)
Determinants
of capital
structure
69
Table III.
Table IV.
Regression analysis
(0.0000)*
(0.0000)*
(0.0000)*
(0.1132)
(0.0000)*
(0.0000)*
(0.0000)*
(0.0000)*
(0.0000)*
(0.0000)*
(0.1563)
(0.0002)*
(0.0000)*
(0.0000)*
(0.0036)*
Yes
0.61
0.61
3051.507 (0.0000)*
1.46
15.78159 (0.0000)*
68.668625 (0.0000)*
1.3549
0.2184
⫺0.0339
⫺0.0019
⫺0.7242
⫺0.0314
⫺0.0129
⫺0.5761
⫺0.0019
⫺0.0048
⫺4.15E⫺05
⫺0.0027
⫺0.0563
0.0144
⫺0.3551
Yes
(0.0001)*
(0.0000)*
(0.0000)*
(0.0004)*
(0.0000)*
(0.0001)*
(0.0067)*
(0.0000)*
(0.0000)*
(0.0000)*
(0.9487)
(0.4945)
(0.0000)*
(0.0000)*
(0.5938)
Yes
0.68
0.66
34.98449 (0.00000)*
1.76
–
–
0.9380
0.1755
⫺0.0256
0.0193
⫺0.7468
⫺0.0262
⫺0.0124
⫺0.6175
⫺0.0014
⫺0.0036
7.35E-06
⫺0.0084
⫺0.0435
0.0109
0.2558
Yes
(0.0000)*
(0.0000)*
(0.0000)*
(0.0457)*
(0.0000)*
(0.0000)*
(0.0048)*
(0.0000)*
(0.0000)*
(0.0000)*
(0.9329)
(0.2869)
(0.0000)*
(0.0000)*
(0.9835)
Yes
0.61
0.64
180.8301 (0.00000)*
1.62
–
–
1.1065
0.1931
⫺0.0290
0.0102
⫺0.7371
⫺0.0280
⫺0.0126
⫺0.5989
⫺0.0016
⫺0.0041
⫺9.54E-06
⫺0.0047
⫺0.0487
0.0121
0.0098
Yes
Notes: * Regression significant at 5% level of significance; ETAX: effective tax rate; SIZE: firm size; CR: current ratio; WCR: working capital ratio; FLEX:
financial flexibility; SPP: share price performance; ASST: asset structure; SAGR: sale growth; EXPGR: expected growth; VCP: variability of profit for firm;
VOLAT: volatility; ROA: return on assets; AUR: asset utilization ratio; SOP: state ownership percentage; SANC: economic and trade sanctions
C
ETAX
SIZE
CR
WCR
FLEX
SPP
ASST
SAGR
EXPGR
VCP
VOLAT
ROA
AUR
SOP
SANC
R-square
Adjusted R-square
F-statistic (p-value)
Durbin–Watson
Lagrange multiplier test
Hausman test df.15 ␹2 (p-value)
Dependent variable: short-term debt ratio (STD)
Fixed effects
Random effects
Coefficient
Probability
Coefficient
Probability
70
Independent variable
Pooled model
Coefficient
Probability
IJLMA
57,1
(0.0005)*
(0.0000)*
(0.0000)*
(0.0000)*
(0.0000)*
(0.0000)*
(0.0000)*
(0.0000)*
(0.0000)*
(0.0000)*
(0.0001)*
(0.0025)*
(0.0000)*
(0.0000)*
(0.0000)*
Yes
0.06
0.06
130.5805 (0.00000)*
1.85
1.452102 (12.17)
22.371342 (0.0713)
⫺0.2372
⫺0.1289
⫺0.0113
⫺0.0113
0.0260
⫺0.0093
⫺0.0110
0.1534
⫺0.0005
⫺0.0012
⫺0.0001
⫺0.0024
⫺0.0171
0.0038
0.9411
Yes
0.4040
(0.0039)*
(0.0001)*
(0.0423)*
(0.2377)
(0.2100)
(0.0325)*
(0.0000)*
(0.0864)
(0.1351)
(0.3470)
(0.4646)
(0.0471)*
(0.1216)
(0.0902)
Yes
0.06
0.05
7.613603 (0.00000)*
1.85
–
–
⫺0.2372
⫺0.1289
⫺0.0113
⫺0.0113
0.0260
⫺0.0093
⫺0.0110
0.1538
⫺0.0005
⫺0.0012
⫺0.0001
⫺0.0024
⫺0.0171
0.0038
0.9410
Yes
(0.3965)
(0.0041)*
(0.0001)*
(0.0473)*
(0.2494)
(0.2075)
(0.0348)*
(0.0000)*
(0.0839)
(0.1320)
(0.3454)
(0.4691)
(0.0455)*
(0.1186)
(0.0867)
Yes
0.06
0.05
7.586347 (0.00000)*
1.85
–
–
⫺0.2404
⫺0.1279
⫺0.0114
⫺0.0111
0.0253
⫺0.0093
⫺0.0108
0.1535
⫺0.0005
⫺0.0012
⫺0.0001
⫺0.0024
⫺0.0172
0.0038
0.9492
Yes
Dependent variable: long-term debt ratio (LTD)
Fixed effects
Random effects
Coefficient
Probability
Coefficient
Probability
C
ETAX
SIZE
CR
WCR
FLEX
SPP
ASST
SAGR
EXPGR
VCP
VOLAT
ROA
AUR
SOP
SANC
R-square
Adjusted R-square
Durbin–Watson
Pooled model
Coefficient
Probability
Determinants
of capital
structure
71
Table V.
Regression analysis
Table VI.
Regression analysis
(0.0000)*
(0.0000)*
(0.0000)*
(0.0000)*
(0.0000)*
(0.0000)*
(0.0000)*
(0.0000)*
(0.0000)*
(0.0000)*
(0.0001)*
(0.0000)*
(0.0000)*
(0.0000)*
(0.0000)*
Yes
0.46
0.46
1,684.234 (0.00000)*
1.75
4.2302 (0.0000)*
64.246119 (0.00000)*
1.1177
0.0895
⫺0.0452
⫺0.0133
⫺0.6982
⫺0.0407
⫺0.0239
⫺0.4227
⫺0.0025
⫺0.0060
⫺0.0001
⫺0.0051
⫺0.0734
0.0182
0.5860
Yes
(0.0023)*
(0.1186)
(0.0000)*
(0.0646)
(0.0000)*
(0.0000)*
(0.0003)*
(0.0000)*
(0.0000)*
(0.0000)*
(0.3308)
(0.2261)
(0.0000)*
(0.0000)*
0.4122
Yes
0.46
0.45
98.20066 (0.000000)*
1.75
–
–
1.1177
0.0895
⫺0.0452
⫺0.0133
⫺0.6982
⫺0.0407
⫺0.0239
⫺0.4227
⫺0.0025
⫺0.0060
⫺0.0001
⫺0.0051
⫺0.0734
0.0182
0.5860
Yes
(0.0069)*
(0.1259)
(0.0000)*
(0.4814)
(0.0000)*
(0.0000)*
(0.0007)*
(0.0000)*
(0.0000)*
(0.0000)*
(0.3650)
(0.2182)
(0.0000)*
(0.0000)*
(0.2287)
Yes
0.45
0.45
95.00958 (0.000000)*
1.80
–
–
0.9667
0.0866
⫺0.0439
⫺0.0051
⫺0.7108
⫺0.0393
⫺0.0223
⫺0.4321
⫺0.0023
⫺0.0057
⫺0.0001
⫺0.0061
⫺0.0705
0.0174
0.8402
Yes
Random effects
Coefficient
Probability
C
ETAX
SIZE
CR
WCR
FLEX
SPP
ASST
SAGR
EXPGR
VCP
VOLAT
ROA
AUR
SOP
SANC
R-square
Adjusted R-square
Durbin–Watson
Dependent variable: total debt ratio (TD)
Fixed effects
Coefficient
Probability
72
Pooled model
Coefficient
Probability
IJLMA
57,1
debt positively. In fact, a company facing a high effective corporate tax rate has a need
for, or will benefit from, taking up more debt to maximize the tax deduction of the debt
interest. In this case, companies with a higher short-term debt level would pay more tax.
Contrary to the trade-off model, the regression results show a negative relationship
between corporate tax and long-term debt ratio (LTD). It can be argued that this is due
to reverse causality, i.e. firms with higher long-term debt pay low effective tax.
The results of the regression analysis show that there is a negative relationship between
firm size and all measures of capital structure (STD, LTD and TD); thus, H2 is accepted.
The results indicate that smaller companies may not have much choice but to rely on
bank loans. However, this finding is consistent with the POT. It is for this reason that
information asymmetry is a less severe issue in big firms.
The figures in Tables IV, V and VI show mixed results regarding the effect of
liquidity and capital structure. The results from liquidity variables (current ratio) seem
to be positively related to short-term debt ratio, but negatively related to long-term debt
ratio. Moreover, other measures for assessing the liquidity of firms, that is working
capital ratio (WCR), have a significantly negative relationship with two measures of
capital structure (short-term and total debt ratio), indicating that liquid firms prefer
internal resources for financial needs, and this is consistent with the POT. Thus, H3 is
accepted. The reason for negative relationship of liquidity in Iran is that firms tend to
use their liquid assets to finance their investment in preference to raising external debt.
The results indicate that there is a negative relationship between firms’ financial
flexibility (FLEX) and all measures of capital structure (STD, LTD and TD). This
provides support for H4. Considering abovementioned tables, there is a significant
negative relationship between share price performance (SPP) and all measures of capital
structure (STD, LTD and TD). Thus, H5 is accepted, which is consistent with MTT. The
reason for negative relationship of share price performance in Iran is that they tend to
prefer equity to debt when share prices are rising. The results show that there is a
significant negative relationship between asset structure (ASST) and short-term (STD)
and total debt (TD) ratios. This is not consistent with the TOT, but with the AT. We may
attribute this finding to insufficient long-term capital sources in Iran. Thus, excessive
use of short-term debt becomes obligatory. This excessive usage of short-term debt may
explain the negative relationship between assets structure and leverage. Therefore, H6
is rejected. The results also show a positive relationship between asset structure and
long-term debt. The results suggest that Iranian firms with a higher proportion of fix
assets are financed by long-term debt capital. Further, there is a negative relationship
between sales growth (SAGR) and assets growth ratio (EXPGR) as a measure for
assessing growth opportunities and all measures of capital structure (STD, LTD and
TD), which is statistically significant and is consistent with the TOT. The sign for
the growth variable in terms of the relationship with capital structure is consistent
with our H7.
The result for two measures of risk assessment is mixed. The risk variables [e.g.
volatility (VOLAT) and variability coefficient of profit (VCP)] indicate a negative
relationship with long-term debt ratio but a positive relationship with short-term debt
ratio. However, the results of the short-term debt model appear not to be statistically
significant, which support H8. Consistent with the pecking order arguments, in all
regressions, the coefficients for profitability (ROA) are negative and significant,
signifying that firms with more profitability use internal sources in their financing
Determinants
of capital
structure
73
IJLMA
57,1
74
decisions. The negative coefficient on profitability implies evidence for the POT, where
more profitable corporations tend to use lesser debt when financing their activity. Thus,
H9 is accepted. The existence of a significant negative relationship between debt and
profitability depends on information asymmetry between managers and investors; that
is, the amount of debt depends on the amount of information asymmetry, and in this
theory, presence of debt in firms’ capital structures depends on past profitability and
investment opportunities.
Finally, there is a positive relationship between asset utilization ratio (AUR) and all
measures of capital structure (STD, LTD and TD), so H10 is rejected. Moreover,
ownership appears to play an important role in firms’ capital structure decisions. State
ownership is significantly and positively associated with long-term debt ratio (LTD),
consistent with our H11. According to Tables IV, V and VI, we also test the first-order
serial correlation and multicollinearity in our data. Serial correlation was analyzed by
examining the Durbin–Watson statistics. The D-W statistics of the residuals report
1.46-1.85 for all regression equations. D-W statistic ranges in value from 0 to 3, with an
ideal value of 2, indicating that errors are not correlated and in all models, the level of
F-statistic is significant, suggesting the validity of the regression estimation. Moreover,
the explanatory power of a model is measured by its adjusted R2. The adjusted R2 for the
fixed-effects model is uniformly higher than for the simple pooling model, indicating the
existence of omitted variables. The adjusted R2 of Panel 4 and pooled OLS is 0.61 with an
F-statistic of 30.51 (p ⬍ 0.05). Compared with fixed-effect regression, the fixed-effect
model improves the adjusted R2 to 0.66 with an F-statistic of 34.98 (p ⬍ 0.05). The
adjusted R2 of Panel 5 and pooled OLS is 0.06 with an F-statistic of 130.58 (p ⬍ 0.05). For
the total debt ratio (TD) in Table VI, the adjusted R2 for the simple pooling model is 46
per cent. Nevertheless, when fixed-effect regressions are used, the regressions have a
higher adjusted R2. The adjusted R2 indicates that the explanatory variables explain 66,
6, and 46 per cent of the variation in the short-term debt (STD), long-term debt (LTD) and
total debt (TD) ratios, respectively.
7. Discussion
The study shows very interesting results in terms of the capital structure of Iranian
firms. Short-term debt constitutes a relatively high proportion of total debt of Iranian
firms. This represents 61 per cent of total financing. It can be said that bank loan
(short-term debt) is a main resource for the managers in Iran and is the best way of
financial support. In Iran, there is no organized system for corporations to issue bonds
for public investors. This is because corporations would have a preference for
short-term debt financing over long-term debt financing, as the cost of long-term
financing is greater for the firm. Our finding on effective tax rate and short-term debt
confirms the results of Modigliani and Miller (1963), Graham (1996) and Zimmerman
(1983). However, our results are inconsistent with those obtained by most prior studies
(Antoniou et al., 2008; Karadeniz et al., 2009). Consistent to theorizing, the results of this
study show that size is also negatively related to all measures of capital structure. The
negative coefficient is, nevertheless, consistent with Marsh (1982) and Titman and
Wessels’ (1988) contention that accessibility to the equity market and economies of scale
with respect to issue costs influence the firm’s debt-equity choice. The effect of liquidity
(WCR) on two leverage measures (short-term and total debt) is found to be negative and
significant. Also considering the obtained results, current ratio (CR) has a negative
relationship with long-term debt ratio (LTD), signifying that firms with sufficient liquid
assets will be able to finance future investments with less reliance on external (debt)
sources. This result is consistent with the results of Myers and Rajan (1998), Eldomiaty
and Azim (2008), Deesomsak et al. (2004), Eriotis et al. (2007) and Sheikh and Wang
(2011). On the other hand, current ratio (CR) is positively related to short-term financing.
The study finds strong evidence that financial flexibility may play a role in
determining firms’ capital structure. This finding is consistent with the results of prior
studies (Beattie et al., 2006). Furthermore, US and European evidence suggests that
financial flexibility is considered a more important influence on a firm’s financing
decisions (Graham and Harvey, 2001). The relation between capital structure and share
price performance of firms in the TSE is also negative, in line with the findings of Baker
and Wurgler (2002) and Hovakimian et al. (2004), who found a negative association
between share price performance and leverage. However, the negative (positive)
association found between asset structure and short-term (long-term) debt ratio implies
that corporations in Iran try to finance their fixed assets with long-term, and their
current assets with short-term debt, thus supporting the asset maturity matching
principle in corporations. The result is in line with the findings of Amidu (2007),
Sogorb-Mira and How (2005) and Abor and Biekpe (2009). In terms of firm growth, we
can explain that a company of fast growth is normally expected to have potentially
sufficient internal funds, and therefore, this company prefers using internal funds over
external funds. This is also consistent with the findings of Thies and Klock (1992),
Flannery and Rangan (2006) and Hovakimian (2006). The results also show a negative
relationship between risk and long-term debt. It is consistent with the arguments that
high-risk firms normally use less debt (Omran and Pointon, 2009; Jordan et al., 1998).
The overall negative relation between profitability (ROA) and capital structure suggests
that internal financing through retained earnings plays an important financing role.
These results are consistent with strong empirical evidence from previous studies
(Titman and Wessels, 1988; Hutchinson and Hunter, 1995; Ghosh and Cai, 1999; Booth
et al., 2001; Cassar and Holmes, 2003; Gaud et al., 2005). The relationship between state
ownership and debt levels implies that higher state ownership increases the level of
long-term debt financing; thus, our result shows that state ownership enhances firms’
access to long-term debt. This result is inconsistent with the findings of Zou and Xiao
(2006) and Huang and Song (2006), and consistent with the findings of Li et al. (2009).
8. Conclusion
This study examined the determinants of capital structure of corporations in the context
of emerging markets, where the issue has been under-researched. This study
specifically focused on industrial firms in Iran. Of course, there may be many factors
interfering with identification of capital structure, but in this research, several variables
have been studied and pooled and panel data analysis has been used to analyze the
models. This paper examines the debt financing as a proxy for corporate capital
structure. It is necessary to outline the basic factors that affect debt-financing decision.
First, the results show that short-term debt constitutes a relatively high proportion of
total debt of Iranian firms. Firm’s capital structure involves a tradeoff between the
common types of financing: equity and debt financing. The results of this study show
that firms use both types of debt interchangeably. Firms use more debt relative to equity
for financing their operations, which does not conform to the principles of financing.
Determinants
of capital
structure
75
IJLMA
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76
Managers of Iranian firms must pay attention to this issue, for they may face problems
in their long-term investments due to utilization and quick settlement of short-term
debts. They will face liquidity deficit and this can affect the performance of these firms.
Second, the negative relationships between all the measures of capital structure and size
suggest that size of the firms is very important in influencing firms’ access to debt
finance. Third, the significantly negative relationship between financial flexibility
(FLEX), share price performance (SPP), sales growth (SAGR), expected growth of assets
(EXPGR) and return on assets (ROA) and capital structure (STD, LTD and TD) denotes
the fact that these variables play an important role in corporations’ access to debt
finance. The results clearly support that firms which have more flexibility, growth,
share price performance and profitability demand less debt. While, the results for
effective tax rate (ETAX), liquidity (CR), firm risk (VCP and VOLAT), asset structure
(ASST) and state ownership (SOP) are mixed. Fourth, our results generally confirm the
fact that corporations in Iran try to finance their fixed assets with long-term debts, and
their current assets with short-term debts. Fifth, we found a puzzling positive relation
between assets utilization ratio and the leverage. Sixth, as for the effects of liquidity, the
results of the regression analysis show that the estimates of the liquidity are negative
and statistically significant. These negative relationships indicate that firms with a
higher liquidity ratio will borrow less (Deesomsak et al., 2004). Seventh, the relationship
between all measures of capital structure and growth opportunities is negative. This
finding suggests that firms with higher future growth opportunities should use more
equity financing. Finally, the results showed that state ownership has a significant
positive relationship with long-term debt ratios.
We conclude that, in terms of the determinants of capital structure, Iranian
corporations are very similar to firms in other countries. Booth et al. (2001) have
concluded that corporate financing decisions in developing markets are influenced
relatively by many determinants of capital structure that have evolved in developed
markets. The empirical results imply that the Modigliani and Miller (1963), trade-off,
pecking order, agency and market timing theories of capital structure partially explain
the leverage decisions made by Iranian corporations. In general, the major difference of
the capital structure of Iranian firms is that these corporations exhibit a higher leverage
than firms in developed countries and firms in the majority of developing countries
when measured by book value of total liabilities ratio (Rajan and Zingales, 1995; Harris
and Raviv, 1991; Booth et al., 2001; Huang and Song, 2002). This suggests that these
firms significantly use debt in their capital structure. This goes along with the results
reached by Booth et al. (2001) that a great deal of similarity exists between developed
and developing countries with regard to the determinants of capital structure, although
Iran was not included in the sample of developing countries examined by Booth et al.
(2001). The contribution of the paper is that the reported results converge relatively and
considerably to the results of other related studies in developing markets, which is
considered an element of external validity. In addition, this study contributes to the
literature in a way that it shows that capital structure theories that are applied to Middle
Eastern companies also apply to Iranian companies. The findings of the study certainly
provide a framework for understanding the capital structure and financing of Iranian
firms, and have significant theoretical and practical implications.
Due to the existence of a negative relationship between firm size, financial flexibility,
share price performance, growth and capital structure, firms’ management has to take
into account these factors when making borrowing decisions. There is a negative
relationship between share price performance (SPP) and profitability and all measures
of capital structure (debt ratios), thus managers must consider this issue when
financing, for increased debts in these firms is followed by decreased profitability and
SPP. Instead, they must try to use such resources as equity financing. We suggest the
managers of these firms to consider the capital structure of firms in order to reduce
business risk, for there is a negative relationship between business risk and debt ratios
of firms. However, it is likely that investors do appear to consider capital structure when
purchasing firms due to the existence of a negative relationship between, growth,
profitability and capital structure. Therefore, it is suggested that the following
directions for research on Iranian capital structure be considered in the future:
• Some capital structure scholars have examined the implications of industry
classification for the study of capital structure. This may be particularly important
in considerations of the impact of capital structure.
• There is a need for further research that will examine the determinants of capital
structure in sample firms over a longer period, and over a number of economic
cycles, if we are to better understand capital structure policies in these firms.
• Also, the role of corporate governance in determining borrowing needs further
research to develop further some of the insights delivered by this study.
• Further research is needed to investigate the effect of institutional ownership,
ownership concentration and capital structure. Finally, further research can
provide more explanations by considering cost of capital that determines
corporate capital structure.
Notes
1. Normally, capital structure is defined as the ratio of debt to equity (DE) and the ratio of debt
to total asset (DTA). Debt includes short-term debt and long-term debt. Total debt which
includes both short-term and long-term debt is part of total liabilities. We consider multiple
definitions for leverage – partially to illustrate the dominant types of debt in Iran. The
broadest definition of leverage is total liabilities to total assets. It is the most common
definition in previous research (Frank and Goyal, 2002). However, as there are important
differences between long-term and short-term debt, there exists a need to examine long-term
debt ratio and short-term debt ratio separately.
2. Agency costs arise from two agency relations – the relation between owners and debt holders
and the relation between owners and managers. Jensen (1986) shows that agency costs
increase with free cash flow. According to Drobetz (2005), three forms of agency problems
have received particular attraction: risk shifting (or asset substitution), the underinvestment
problem and the FCFT.
3. Given that the “between” estimator averages variable observations, random-effect estimator
can reduce the bias caused by the measurement error by averaging the measurement errors.
On the other hand, the random-effect estimator can be biased if the explanatory variables are
correlated with the residuals, while the fixed estimator is always unbiased (because it allows
for dummies for different intercepts). Further, the fixed-effect estimator can be more robust to
selection bias problems compared to the random-effect estimator.
Determinants
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structure
77
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78
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About the authors
Mohammad Alipour is a member of the academic staff of the Accounting and Finance
Department, Islamic Azad University, Khalkhal Branch (Young Researchers and Elite Club,
Khalkhal Branch, Islamic Azad University, Khalkhal, Iran). He has published articles in
International Journal of Commerce and Management, World Applied Science Journal,
Management Research Review, Measuring Business Excellence, Strategic Change and has
published an article in the Accountants Journal in Iran. He is also interested in such subjects as
corporate governance, intellectual capital and capital structure. Mohammad Alipour can be
contacted at: [email protected]
Mir Farhad Seddigh Mohammadi is a Member of the Faculty of Economics at Islamic Azad
University (Khalkhal Branch) and is a PhD candidate in economics at Islamic Azad University
(Arak Branch, Iran). He has authored two books (in Persian). His research interests include
financial economics and econometrics.
Hojjatollah Derakhshan is a Lecturer in the Department of Accounting and Management,
Islamic Azad University, Khalkhal Branch, Khalkhal, Iran. He is a Doctoral Candidate in
management at the Department of Management, Islamic Azad University, Tehran Center Branch,
Tehran, Iran.
For instructions on how to order reprints of this article, please visit our website:
www.emeraldgrouppublishing.com/licensing/reprints.htm
Or contact us for further details: [email protected]
Determinants
of capital
structure
83

Determinants of capital structure: an empirical study of firms in Iran

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