Asian Journal of Finance & Accounting

ISSN 1946-052X
2018, Vol. 10, No. 2

The Determinants of Capital Structure Decisions:

Evidence from Chinese Listed Companies

Sunitha Vijayakumaran(Corresponding author)

Department of Commerce
Faculty of Management Studies and Commerce
University of Jaffna, Sri Lanka

Ratnam Vijayakumaran
Department of Financial Management
Faculty of Management Studies and Commerce
University of Jaffna, Sri Lanka

Received: April 27, 2018 Accepted: Nov. 13, 2018 Published: December 1, 2018
doi:10.5296/ajfa.v10i2.13065 URL: https://doi.org/10.5296/ajfa.v10i2.13065

Abstract
In this paper, we examine the determinants of leverage in the context of China using a sample
of 1844 Chinese non-financial firms over the period 2003 to 2010. This study shows that the
average leverage ratio of Chinese listed firms is similar to those observed in other developing
countries. The study also finds thatsize, tangibility, volatility and firm age are positively and
significantly associated with leverage. Firm’s profitability has statistically significant
negative impact on leverage. Furthermore, we find that firm size, profitability, tangibility,
volatility and firm age are the robust determinants of leverage of Chinese listed firms.
Keywords: Capital Structure, Leverage, firm size, profitability, tangibility, growth
opportunities, volatility, China.
JEL Classification F30; G10; G32

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1. Introduction
Corporate capital structure decisions are not only important for firms to maximize their value
but also for the growth and stability of firms and the corporate economy as a whole (Jensen
and Meckling, 1976; Benito, 2003). Firm financing patterns have long been the object of
study in the corporate finance literature (Haris & Raviv, 1991). The main issue of debate
revolve primarily around the optimal capital structure that maximizes the value of the firm
(e.g., Modigliani and Miller, 1958 and 1963; Miller, 1977). Capital structure means the mix
of different sources of financing such as equity and debt (Panday, 2006). Capital structure
choices of corporations have traditionally been analysed in the Modigliani-Miller (1958)
framework, expanded to incorporate taxes, bankruptcy costs, agency cost, and asymmetric
information issues (such as signalling, adverse selection). The trade-off theory, pecking order
theory (Myers and Majluf, 1984; Myers, 1984), and agency theory (Jensen and Meckling,
1976) are main theories that explain the use of leverage in firms’ capital structure.
A large number of studies, for example Titman and Wessels (1988), Wald (1999), have
empirically examined determinants of capital structure in the context of developed economies.
Most early papers examine the case of US companies (see Haris and Raviv(1991) and
Graham, & Leary, (2011) for reviews), whilst Rajan and Zingales (1995) test the theoretical
and empirical lessons learnt from the US studies for the G7 countries. These authors find a
similar behaviour of leverage across countries, thus refuting the idea that firms in
bank-oriented countries are more leveraged than those in market-oriented countries. (Note1)
Rajan and Zingales (1995) also find that the determinants of capital structure that have been
reported for the USA (size, growth opportunities, profitability, and tangible assets) are
important in other countries as well. They show that a good understanding of the relevant
institutional context (bankruptcy law, fiscal treatment, ownership concentration, and
accounting standards) is required when identifying the fundamental determinants of capital
structure (Note that capital structure, leverage and debt capital or debt financing are used
interchangeable throughout the paper). Recently, there has been a growing body of literature
on capital structure decisions from developing countries, for example Wiwattanakantang
(1999), Booth et al. (2001), Deesomsak et al. (2004), Kim et al. (2006), and Črnigoj and
Mramor (2009).
In the context of China, a handful of empirical studies examine capital structure decisions
(for example, Chen, 2004; Huang and Song, 2006; Zou and Xiao, 2006; Qian et al., 2009;
Moosa et al., 2011). A common feature of all these studies based on Chinese listed companies
is that they use data before 2005. Therefore, these studies do not consider changes occurred
after the major split-share reform initiated by Chinese Securities Regulatory Committee
(CSRC hereafter) and Chinese government in 2005. The aim of the 2005 split-share
structure reform is to convert non-tradable shares into tradable shares in order to facilitate the
liquidity in the secondary market. Before implementing the reform, the non-tradable
shareholders of a firm have to negotiate with tradable shareholders to ensure that they get a
suitable compensation package before trading occurs. (Note 2) This study, therefore, provides
first empirical evidence using a longer period of latest Chinese listed company data on the
effects of ownership reform on the corporate financing decisions.
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The reminder of the paper is organized as follows. Section 2 reviews the theories and prior
studies related to capital structure decisions and develops testable hypotheses. Section 3
presents regression model and discusses our estimation methodologies. Section 4 describes
data and presents descriptive statistics. In Section 5, we discuss our empirical results, before
concluding in Section 6.
2. Review of the literature and hypothesis development
This section presents the main capital structure theories: the static trade-off theory, the
pecking order theory and the agency theory. The aim of reviewing the capital structure
theories is to develop a theoretical framework for predicting the effects of the determinants of
capital structure. In addition, we review prior evidence and develop hypotheses.
2.1 Relevant capital structure theories
For a long time, the issue of capital structure choices and the resulting effect on the value of
the firm has been a controversial and much disputed area. The main issue of debate revolve
primarily around the optimal capital structure that maximizes the value of the firm
(Modigliani and Miller, 1958 and 1963; Miller, 1977). Capital structure means the mix of
different sources of financing such as equity and debt (Pandy, 2008). In fact, the debate on the
modern theory of capital structure began with the seminal paper of Modigliani and Miller
(1958) which shows that subject to some conditions, the impact of debt financing on the
value of the firm is irrelevant. They contend in their first proposition that the market value of
any firm is independent to its capital structure, and is given by capitalizing its expected return
at the rate appropriate to the risk class (i.e. the levels of risk of the firm) (Modigliani and
Miller 1958). This first proposition has been criticized and the main argument is that it is
theoretically very sound but is based on the assumptions of perfect capital market, no taxes
(personal or corporate), no distress costs and equal access to information which are not valid
in reality.
Following the work of Modigliani and Miller (1958), the literature on capital structure has
been expanded through many theoretical and empirical contributions. Scholars have placed
much emphasis on releasing the assumptions made by Modigliani and Miller (1958), in
particular by taking into account corporate taxes (Modigliani and Miller, 1963), bankruptcy
costs (Stiglitz,1972; Titman,1984), agency costs (Jensen and Meckling, 1976), information
asymmetries (Myers and Majluf, 1984; Myers, 1984), and imperfect capital markets (Baker
and Jeffrey, 2002). The alternative theories that currently dominate the empirical studies
include the trade-off theory, the pecking order theory and, the agency theory.
The trade-off theory is a result of releasing assumptions of ‘no corporate taxes’ and ‘no
financial distress costs’ (i.e. bankruptcy costs). For example, Modigliani and Miller (1963)
relaxed their assumptions by incorporating the effect of taxes on the cost of capital. Tax
shields occur when firms use debt financing in their capital structure, as firms have to pay
interest on debt which is generally tax deductible. Thus, interest payments act as a tax shield
and allow the firm to increase its value. However, when considering the financial distress
costs that arise from maintaining high levels of debt (e.g., bankruptcy costs) (Stiglitz, 1972),

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the value of the firm is determined by its net benefits (i.e. tax shield benefits minus costs).
Therefore, the trade-off theory posits that firms maximise their value when the benefits that
stem from debt (e.g. the tax shield and reduced costs of informational asymmetry attached to
debt compared to outside equity) outweigh or equal the marginal cost of debt (e.g. bankruptcy
costs, and agency costs) (Modigliani and Miller, 1963; Stiglitz, 1972; Jensen and
Meckling, 1976; Myers, 1977; Titman, 1984).
The pecking order theory, which was developed by Myers and Majluf (1984) and Myers
(1984) is linked to information asymmetries existing between insiders of the firm and
outsiders (i.e. the capital market). The theory suggests that managers adapt their financing
policies to minimise the cost of information asymmetries. Managers therefore prefer internal
financing to external financing, and risky debt to equity since debt capital suffers less from
information asymmetries than equity. That is debt is the first source of external finance on the
pecking order and equity is issued only as a last resort, when the debt capacity is fully
utilised.
The above two theories (i.e. the static trade-off and pecking order theories) are based on the
assumption that the interest of the managers of a corporation with dispersed ownership is
always aligned with that of shareholders. In contrast, the agency theory assumes that
self-interested managers always pursue their own objectives at the expenses of shareholders
(Jensen and Meckling, 1976).Jensen and Meckling (1976) formally model the agency costs of
equity and debt capital in a modern corporation. They identify two types of agency conflicts
namely, conflicts between managers and shareholders, and conflicts between shareholders
and bond holders, and the related agency costs in a firm. As argued in Jensen and Meckling
(1976) and Jensen (1986), debt capital not only brings in additional monitoring from
outsiders (i.e. creditors) but also make managers commit to pay out cash, (i.e., it reduces the
amount available to managers to overinvest). Therefore, using debt capital in the capital
structure helps to reduce agency costs arising from conflicts between managers and
shareholders (see S.Vijayakumaran (2016) and R. Vijayakumaran (2017) for a discussion on
the governance role of debt). The use of debt capital in the capital structure leads to conflicts
between debt-holders and equity holders because debt contracts give equity holders an
incentive to invest sub-optimally (Jensen and Meckling, 1976). That is, managers (who act on
behalf of shareholders) have incentive to shift funds from low-risk investment projects to
high-risk ones in order to earn higher profit in the short term period (also known as assets
substitution effect). Myers (1977) identifies another agency cost of debt. He argues that when
firms are likely to go bankrupt in the near future, equity holders have no incentive to
contribute new capital, even to invest in value-increasing projects because they bear the entire
cost of the investment, while the returns may be captured mainly by debt-holders (i.e. the
debt overhang problem). Therefore, the firm may undesirably forego positive NPV projects
due to the conflicts of interest between equity holders and debt holders. This is well
documented as “under invest problem” in the agency literature. Considering the benefits and
costs of debt financing from an agency perspective, the agency theory provides
predictions for the potential effects of the firm characterises on the use of debt capital in the
firm.

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2.2 The determinants of Capital structure and hypotheses

Following Rajan and Zingales (1995), Titman and Wessels (1988), and Frank and Goyal
(2009), the study uses seven important firm characteristics The expected relationship between
the determinant variables and leverage are primarily guided by relevant theories as well as
previous empirical studies.
2.2.1 Firm size
As discussed in Rajan and Zingales (1995), the theoretical prediction for the effect of size on
leverage is ambiguous. It is argued that larger firms tend to be more diversified and have
more tangible assets, stable cash flows and better reputations. The trade-off theory therefore
postulates that compared to smaller ones, ceteris paribus, larger firms are expected to have a
higher debt capacity due to a lower risk of bankruptcy (bankruptcy cost).
In contrast, the pecking-order theory suggests that, bigger firms are more likely to use less
debt due to lower asymmetric information problems between insiders and outside investors
(i.e. larger firms provide more information to lenders than smaller firms, so the cost of
issuing new equity is lower than the debt issuing cost).
Previous empirical studies from developed countries (Titman and Wessels, 1988; Berger et al.,
1997; Brailsfore et al., 2002; Florackis and Ozken, 2009), from developing economics
(Wiwattanakantang, 1999; Booth et al., 2001; Pandey, 2001; Deesomsak et al., 2004), as well
as in the context of Chinese firms (Chen and Strange, 2005; Zou and Xiao 2006; Haung and
Song, 2006; Qian et al., 2009) all uniformly find that leverage is positively related to firm
size. We therefore expect a positive relationship between firm size and leverage. Therefore,
our hypothesis is that
H1: There is a positive significant relationship between firm size and leverage.
2.2.2 Profitability
According to the static trade-off theory, the more profitable the firms, the greater the use of
leverage, ceteris paribus, due to an increase in the tax shield benefits and, to a lower financial
distress and agency costs of debt. Thus, this theory predicts that profitability is positively
related to leverage. In contrast, there is an opposite prediction based on the pecking-order
theory that the most profitable firms tend to borrow less. In other, the pecking-order theory
suggests that firms first finance their investment using internal resources (i.e. retained profit),
and then move to debt and new equity financing as a last resort.
Previous empirical findings on financing behaviour of firms in developed economies (Titman
and Wessels, 1988; Chiarella et al., 1992; Allem, 1993; Wald, 1999; Rajan and Singales, 1995;
Berger et al., 1997; Brailsfore et al., 2002; Fama and French, 2002; Frank and Goyal, 2003;
Florackis and Ozken, 2009), and in emerging economies (Wiwattanakantang, 1999; Booth et
al., 2001; Pandey, 2001; Deesomsak et al., 2004) find a negative relationship between
leverage ratios and profitability. In the context of Chinese enterprises, several authors (Chen,
2004; Chen and Strange, 2005; Zou and Xiao 2006; Haung and Song, 2006; Qian et al., 2009)
also report a similar relationship. In this study, the measure of earnings before interest and
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taxes scaled by total assets is employed to measure profitability (profit) of firms. We test the
following hypothesis:
H2: There is a negative significant relationship between profitability and leverage.
2.2.3 Tangibility
According to the pecking-order theory, firms with more fixed assets can easily access secured
debt since tangible assets are used as collateral for debt. The static trade-off theory postulates
that the larger the fixed assets of the firm (fixed assets are collateralised for debt and thus
they reduce the risk of lenders), the lower the bankruptcy and financial distress costs. In line
with the explanation of both theories, a positive relationship between tangibility and leverage
is expected.
The findings of prior empirical research on leverage based on developed countries (Rajan and
Singales, 1995; Berger et al., 1997; Wald, 1999; Brailsfore et al., 2002; Florackis and Ozken,
2009), and developing countries (Wiwattanakantang, 1999; Deesomsak et al., 2004) as well
as China (Chen, 2004; Chen and Strange, 2005; Zou and Xiao 2006; Haung and Song, 2006;
Qian et al., 2009), confirm this theoretical prediction. We thus expect a positive relationship
between tangibility and leverage. In this study, tangibility (tang) is measured by net fixed
assets normalized by total assets of the firm. We test the following hypothesis:
H3: There is a positive significant relationship between tangibility and leverage.
2.2.4 Growth opportunities
According to the static trade-off theory, firms with high growth opportunities (which are a
form of intangible assets) in the future are likely to be high risk, and this leads to a greater
financial distress costs (Frank and Goyal, 2009). Therefore, an inverse relationship between
growth opportunities and leverage is postulated. The pecking-order theory, in contrast,
predicts a positive relationship between these variables since high-growth opportunity firms
are likely to face more information asymmetry problems between insiders and outsiders (i.e.
company managers know more about their future investment opportunities than outside
investors). So these firms use more debt than equity in the financing hierarchy, since debt
capital suffers less from information asymmetries.
Most empirical work on capital structure decisions show an opposite relationship between
growth opportunities and leverage. Empirical studies from developed countries (Titman and
Wessels, 1988; Rajan and Singales, 1995; Fama and French, 2002; Brailsfore et al., 2002;
Frank and Goyal, 2009; Florackis and Ozken, 2009), from developing countries
(Wiwattanakantang, 1999; Deesomsak et al., 2004), and from China (Zou and Xiao 2006;
Haung and Song, 2006) obtain an inverse relationship between leverage and growth
opportunities. Therefore, we also expect to find a negative relationship between the two
variables in Chinese listed companies. Following Rajan and Zingales (1995) and Booth et al.
(2001), we use the ratio of the sum of the market value of equity and the book value of debt
to the book value of total assets to measure growth opportunities (growth) in our study. We
test the following hypothesis:

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H4: There is a negative significant relationship between growth opportunities and leverage.
2.2.5 Non-debt tax shields
Non-debt tax shields (nontaxshd) represent tax credits for investments and depreciation.
Non-debt tax shields reduce a firm’s tax payments and thus reduces the need for debt
financing as a means to obtain tax advantages (Dammon and Senbet, 1988). That is, non-debt
tax shields are substitutes for the tax benefits of debt financing. Therefore, the trade-off
theory predicts a negative relationship between non-debt tax shields and leverage.
Previous empirical studies from developed countries (Wald, 1999; Chaplinsky and Niehaus,
1993), from developing countries (Wiwattanakantang, 1999; Deesomsak et al., 2004), as well
as in the context of Chinese firms (Huang and Song, 2006; Qian et al., 2009), find an inverse
relationship between non-debt tax shields and leverage. Therefore, we expect a similar
relationship between non-debt tax shields and leverage. In this study, we use non-debt tax
shields (notaxshd) measured by depreciation scaled by the total assets as an inverse proxy for
tax shield advantage. We test the following hypothesis:
H5: There is a significant negative relationship between non-debt tax shields and leverage
2.2.6 Volatility
According to the trade-off theory, a firm with higher earnings volatility has a higher
probability of financial distress, since the volatility of earnings is the chief factor in
determining firms’ ability to meet debt obligations, such as interest charges. Therefore, an
inverse relationship between volatility and leverage is postulated.
The findings of prior empirical studies on leverage based on developed countries (Titman and
Wessels, 1988), and developing countries (Booth et al., 2001) as well as Chinese firms
(Huang and Song, 2006) provide an inverse relationship between the volatility of earnings
and leverage. We thus also expect to find a negative relationship between volatility and
leverage. Following Johnson (2003), in this study, we define volatility (vol) as the standard
deviation of the first differences of earnings before taxes and depreciation over the four years
preceding the sample year, divided by average total assets for that period. We test the
following hypothesis:
H6: There is a negative significant relationship between volatility and leverage.
2.2.7 Firm age
Both the static-trade off and pecking-order theories are silent as regards the relationship
between the firm age and leverage. However, based on the agency framework, some authors
(e.g. Du et al., 2010) suggest that the older firms are less likely to face asymmetric
information problems and should have much easier access debt financing compared to
younger ones, ceteris paribus. Also, older firms are less likely to invest in risky projects, since
they are established over many years and well reputed in the market (Diamond, 1991).
Moreover, Tian and Estrin (2007) also mention that firm with long history can easily establish
their reputation in the debt market, resulting older firms are more likely to have a higher

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leverage ratio than younger ones.

Previous empirical studies on Chinese firms (for example, Chen and Strange, 2005) find that
firm age is positively related to leverage. In line with the above explanation and previous
findings, we expect a positive relationship between firm age and leverage. In this study, firm
age (firmage) is measured by the natural logarithm of years since the establishment of the
firm. We test the following hypothesis:
H7: There is a positive significant relationship between fim age and leverage.
3. Model specification and estimation methodology
In this section, we first present our model specifications: static baseline model and dynamic
model. Following the model specification, the control variables that are used in the study are
described lengthily with reference to relevant theories and prior empirical studies. Finally,
estimation methodologies are discussed.
3.1 Model specification
levit= β0 + β1sizeit + β2profitit + β3tangit + β4growthit + β5nontaxshdit +β6volit +
β7firmageit + vi + vt + vj+ vk+ eit. (1)
where i indexes firm, t years. The term vi, vt,, vj, and vk represent time-invariant firm specific
fixed effects, time-specific effects, industry effects, and effects of regional differences,
respectively; eit is a random/ idiosyncratic error term.
3.2 Control variables
In addition to the considered determinants of capital structure, we include year, industry and
regional dummies as controls to control for year-specific, industry-specific and geographic
effects.
Lemmon et al. (2008) provide strong evidence that firm-specific unobservable characteristics
(vi) of the firm have a significant impact on firms’ capital structure decisions. They vary
across firms but are assumed to remain constant for each firm through time. They include
variables such as the quality of management, managers’ attitudes towards risk, and market
reputation, etc. On the other hand, time-specific effects (vt), which we control for by
including time dummies, vary through time but are the same for all the firms at a given point
in time. vt captures macroeconomic factors such as interest rates, inflation and business cycle
effects, which are outside the control of firms. Finally, vj, effects of regional differences.
On the left hand side of the Equation (1), our dependent variable is the leverage ratio (the
ratio of the book value of total debt to the book value of the firm’s total assets) of firm i in
year t. On the right hand side a set of explanatory variables in addition to a set of control
variables. The list of variables used in the paper, their definition and expected sign are
summarized in Table1 1.

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Table 1. Definition of variables

Variables Name Measures Expected

sign

Capital structure variable

Leverage lev : Total debt / total assets

Determinants of leverage

Size size : Natural logarithm of total real assets +

Profitability profit : ROA =Return on assets = Earnings before interest, -

taxes and depreciation / total assets

Tangibility tang : Net fixed assets/ Total assets +

Growth growth : Ratio of the sum of the market value of equity and -
opportunities the book value of debt to the book value of total
assets. Non-tradable share price is used to
calculate as the market value of the tradable
equity.

Non-debt tax shield notaxshd : Depreciation / total assets -

Volatility vol : Standard deviation of the first differences of -

earnings before taxes and depreciation over the
four years preceding the sample period, divided
by average total assets for that period.

Firm age firmage : Log of the number of years since the +

establishment of the firm

Year dummies vt : Year dummies for the years 2003 to 2010

Industry dummies vj : CSMAR B classification: 5 industries : Utilities,

Properties, Conglomerates, Industry, Commerce
(except financial industries)

Regional dummies vk : Dummies indicating whether the firm is located in

the Coastal, Western, or Central region of China
Notes: ‘+’ means that leverage increases with the variables, ‘-’ means that leverage decreases with the
variables.

3.3 Estimation methodology

In this study panel data estimation methodologies are used to examine the relationship
between leverage and its determinants. Panel data analysis presents several advantages: it
increases the degree of freedom owing to large number observation, reduces the possibility of
collinearity among the explanatory variables, and results in more efficient estimates. We first
use a pooled OLS (Ordinary Least Square) model. However, a pooled OLS does not take into
account the potential unobserved firm heterogeneity, which may affect both the leverage and

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its determinants. Therefore, we use fixed effects method which allows us to control for
unobservable heterogeneity and, therefore, eliminates the risk of obtaining biased estimates
for our variables.
4. Data and descriptive statistics
In this section, we describe the dataset and sample that is used in our study, and explain how
the data is processed. This section also provides a discussion on summary statistics and
correlation analysis of our variables.
4.1 Data and sample selection
Our sample includes all the publicly held firms that have been listed on the Shanghai and
Shenzhen stock exchanges over the period of 2003 to 2010. Data are collected from the China
Stock Market and Accounting Research (CSMAR) database and Sino-fin. We first delete
firms in the financial industry, since their capital structure is subject to many regulations. We
then remove the potential outliers (i.e., extreme observations) by deleting observations below
the 1st and above the 99th percentile of all our regression variables, expect dummy variables.
Our final sample has 1844 Chinese firms and covers an unbalanced panel of 9624 firm-year
observations.
4.2. Summary statistics
Table 2 presents descriptive statistics for the dependent variables, explanatory used in our
regression analysis. Included are mean, median, standard deviations, minimum and maximum
for the variables used in our study.
Table 2. Summary statistics of leverage, corporate governance, and control variables of
Chinese listed firms over the period of 2003 to 2010

Variables Obs. Mean Median Std. Dev. Min Max

Dependent variable
lev 9624 0.503 0.505 0.214 0.058 3.061
Determinates of capital structure
size (billion RMB) 9624 1.463 0.732 2.471 0.067 26.136
profit 9624 0.074 0.074 0.063 -0.254 0.268
tang 9624 0.460 0.457 0.165 0.061 0.845
growth 9624 2.082 1.659 1.237 0.807 8.504
notaxshd 9624 0.025 0.022 0.015 0.000 0.081
vol 9624 0.037 0.020 0.049 0.000 0.368
firmage 9624 10.543 10.000 4.132 1.000 26.000
Note: Definitions for all variables are provided in Table.1.

The minimum and maximum book values of leverage (lev) ratios for the sample firms range
from 5.8 % to 306.1 % with an average of about 50% (median 50 %). This suggests that on
average, half of total assets are financed by debt capital. Furthermore, this figure implies that
our sample firms in China have similar mean leverage compared with the findings of the
previous studies in developed economies. For example, in their sample of firms from G-7
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countries Rajan and Zingales (1995) report that mean leverage ratios (in book value) of
52 % and 54 %, respectively for the United States and the UK. Moreover, the mean
leverage ratio of Chinese listed firms is also similar to the ratios observed in developing
countries (which average 51 % according to Booth et al., 2001).
With regard to the determinants of leverage, the mean (median) of firm size (size) proxied by
total assets of the firm is 1.463 billion RMB (732 billion RMB).(Note 3) Moreover, according
to the sample, Chinese firms have an average profitability (profit) of 7.4 %, an average
tangibility (tang) of 46 %, and average growth opportunity (growth) of 20.82 %.
Average (median) non- debt tax shield (notaxshd) for the sample firms is 2.5% (2.2%) while
average (median) volatility (vol) for the sample firms is 3.7 % (2%) . The average level of
firm age (firmage) is 10.5 (10.00). Using data 972 Chinese PLCs in 2003, Chen and Strange
(2005) find a mean of 6.60 for firm age. The mean values of these variables are comparable
to those reported in studies on capital structure decision in the context of China, such as
Huang and Song (2006) among others.
5. Empirical results
5.1 Correlation analysis
Table 3 reports the Pearson correlation coefficients between variables used in our regression
analysis. Firstly, firm size (size) shows a positive and statistically significant correlation with
total leverage (lev). Similarly, tangibility (tang) exhibits a positive association with total
leverage. As expected, profitability (profit) and growth opportunities (growth)show a
negative correlation with total leverage.The non-debt tax shield (nontaxshd) is positively and
significantly related to leverage. Finally, firm age (firmage) is positively and significantly
related to leverage.
Furthermore, Table 3 suggests that given that the observed correlation coefficients between
independent variables are relatively low, multicollinearity should not be a serious problem in
our study.
Table 3. Pearson correlation matrices
lev size profit tang growth nontaxshd vol firmage
lev 1.00
size 0.20* 1.00
profit -0.33* 0.19* 1.00
tang 0.20* 0.16* 0.09* 1.00
growth -0.22* -0.30* 0.21* -0.21* 1.00
nontaxshd 0.29* -0.12* -0.21* -0.01 0.07* 1.00
vol -0.02 0.06* 0.23* 0.50* -0.08* 0.06* 1.00
firmage 0.25* 0.15* -0.10* 0.08* -0.03* 0.29* 0.02* 1.00
Notes: * denotes significance at the 5% level. See Table1 for definitions of all variables.

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5.2 Multivariate analysis

This section discusses the empirical results. We first report estimates from model obtained
using the pooled OLS and then firm fixed effects regressions which enable us to directly
compare our results with previous studies based on developed countries as well as in the
context of Chinese firms.
We can see that in Column 1 of Table4, firm size (size) attracts a positive and statistically
significant coefficient. This is consistent with the previous empirical findings (e.g., Berger et
al., 1997; Booth et al., 2001) and with the trade-off and agency theories, meaning that larger
firms are more diversified compared to small ones and consequently, they are associated with
a lower risk of bankruptcy cost and better borrowing capacity relative to smaller firms.
In line with the pecking-order theory, the relationship between profitability (profit) and
leverage is negative and precisely determined in all columns. This provides additional
support to findings of previous empirical studies in the context of Chinese listed companies.
For example Chen (2004) proposes ‘a new pecking-order’, whereby firms use first retained
profits, then equity financing, and debt capital as a last resort since bond markets in China
are not very much developed. He finds that firms with more profitable projects tend to use
less external financing since these firms have better access to internal financing than firms
with lower profits.

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Table 4. The traditional determinants of capital structure

Variables Predicted sign OLS Fixed effects
(1) (2)
size + 0.049*** 0.050***
(0.004) (0.008)
profit - -0.974*** -0.606***
(0.066) (0.044)
tang + 0.270*** 0.175***
(0.026) (0.023)
growth - -0.012*** -0.002
(0.005) (0.003)
nontaxshd - -1.109*** -0.212
(0.300) (0.352)
vol - 1.042*** 0.566***
(0.140) (0.110)
firmage + 0.053*** 0.072***
(0.009) (0.019)
Constant -0.744*** -0.815***
(0.097) (0.172)
Industry dummies yes Yes
Regional dummies yes yes
Year dummies yes yes
Firm fixed effects yes
Observations 9624 9624
2
R 0.298 0.192
adj. R2 0.296 0.191
F value 82.761 46.929
P values 0.000 0.000
Notes: Cluster robust standard errors are reported in columns 1 and 2 (clustered on firms). ***, **, and * denote
significance levels of 1%, 5%and 10%, respectively. See Table 1 for definitions of all variables.

Tangibility (tang) is positively related to leverage in columns 1 and 2, suggesting that the
larger the proportion of fixed to total assets the firm has, the lower the bankruptcy costs. This
finding is in line with the trade-off theory and the findings of previous empirical studies (e.g.,
Rajan and Zinghales, 1995; Zou and Xiao, 2006). Furthermore, as can be seen in the OLS
regression in columns 1, growth opportunities (growth) exhibit a negative significant
coefficient in line with the static trade-off theory. This can also be explained following Myers
(1977) who argues that high growth firms tend to use less leverage in order to reduce
underinvestment problem. This result is also consistent with the findings of the previous
studies in the context of China (Zou and Xiao 2006; Haung and Song, 2006; Moosa et al.,
2011), from developed countries (Frank and Goyal, 2003; Florackis and Ozken, 2009), and
from developing countries (see e.g., Wiwattanakantang, 1999; Deesomsak et al., 2004).
However, results from the fixed effects model in column 2 indicate that growth opportunities

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have no significant impact on leverage ratios.

Column 1 of Table 4 show that the non-debt tax shield (nontaxshd) is negatively and
significantly related to leverage, confirming non-debt tax shields are substitutes for the tax
benefits of debt financing (DeAngelo and Masulis, 1980). This result is consistent with the
findings of the previous empirical studies (Deesomsak et al., 2004; Wiwattanakantang, 1999;
Qian et al., 2009). However, as shown in column (2), the coefficient on non-debt tax shields
is insignificant in fixed effects regressions.
As can be seen in columns (1) and (2) of Table 4, the estimated coefficient on volatility (vol)
is significantly positive. These results are consistent with the findings of the previous studies
for Chinese listed firms (Qian et al., 2009).
Finally, the results from both OLS and fixed effects regressions in columns (1) and (2)
indicate that firm age (firmage) is positively and significantly related to leverage, suggesting
that older firms have much easier to access debt financing since they face less asymmetric
information problems. This is consistent with the previous empirical finding in the context of
Chinese listed firms (Chen and Strang, 2005).
So far, our analysis indicates that the average leverage ratio of Chinese listed firms is similar
to those observed in other developing countries and the level of leverage is well explained by
the firm specific determinants such as firm size, profitability, tangibility, volatility and firm
age.
6. Conclusions
Capital structure theories have been subjected to extensive empirical testing in the context of
developed economies, particularly the United States. In this paper, we study the determinants
of leverage in the context of China. We use a sample of 1844 Chinese non-financial firms
over the period 2003 to 2010 for our empirical analysis. This empirical study uses data after
the 2005 spilt -share reform. Firstly, our study finds that that the average leverage ratio of
Chinese listed firms is similar to those observed in other developing countries. The study also
finds that size, tangibility, volatility and firm age are positively and significantly associated
with leverage. Furthermore, firm’s profitability has statistically significant negative impact on
leverage. Furthermore, we find that we find that firm size, profitability, tangibility, volatility
and firm age are the robust determinants of leverage of Chinese listed firms.
Notes
Note1. Market-oriented countries include the UK and the USA. Bank-oriented countries
include Japan, France and Germany.
Note 2. The compensation package/ plan should be approved by 2/3 of the total voting
shareholders and the voting tradable shareholders.
Note 3. It should be noted that although firm size is measured as the logarithm of total real
assets in the regression analysis, the figure reported in the descriptive statistics in Table 3.1is
not in logarithms as actual value is easier to interpret.

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References
Antoniou A., Guney Y., & Paudyal K. (2008). The determinants of capital structure: Capital
market-oriented versus bank-oriented institutions. Journal of Finance and Quantitative
Analysis, 43(1), 59-92. https://doi.org/10.1017/S0022109000002751
Arellano, M., & Bond, S. (1991). Some tests of specification for panel data: Monte Carlo
evidence and an application to employment equations. The Review of Economic Studies,
58(2), 277-297. https://doi.org/10.2307/2297968
Berger, P.G., Ofek, E., & Yermack, D.L. (1997) Managerial entrenchment and capital
structure decisions. Journal of Finance, 52, 1411-1438.
https://doi.org/10.1111/j.1540-6261.1997.tb01115.x
Baker, M., & Jeffrey, W. (2002) Market timing and capital structure. Journal of Finance, 57,
1-32. https://doi.org/10.1111/1540-6261.00414
Bhabra, H. S., Liu, T., & Tirtiroglu, D. (2008). Capital structure choice in a nascent market:
Evidence from listed firms in China. Financial Management, 37(2), 341-364.
https://doi.org/10.1111/j.1755-053X.2008.00015.x
Benito, A. (2003). The capital structure decisions of firms: is there a pecking order? Bank of
Spain Working Paper No. 0310. Retrieved from
https://ideas.repec.org/p/b-de/wpaper/0310.html
Booth, L., Aivazian, V., Demirguc-Kunt, A., & Maksimovic, V. (2001). Capital structure in
developing countries. Journal of Finance, 56(1), 87-130.
https://doi.org/10.1111/0022-1082.00320
Bradley, M., Jarrell, G. A., & and Kim, E. H. (1984). On the existence of an optimal capital
structure: Theory and evidence. The Journal of Finance, 39(3), 857-878.
https://doi.org/10.1111/j.1540-6261.1984.tb03680.x
Brandt, L., & Li, H. (2003). Bank discrimination in transition economies: Ideology,
information, or incentives? Journal of Comparative Economics, 31(3), 387-413.
https://doi.org/10.1016/S0147-5967(03)00080-5
Chen, J. J. (2004). Determinants of capital structure of Chinese-listed companies. Journal of
Business Research, 57(12), 1341-1351. https://doi.org/10.1016/S0148-2963(03)00070-5
Chen, J., Jiang, C., & Lin, Y. (2014). What determine firms' capital structure in China?
Managerial Finance, 40(10), 1024-1039. https://doi.org/10.1108/MF-06-2013-0163
Chen, J., & Strange, R. (2005). The determinants of capital structure: Evidence from Chinese
listed companies. Economic Change and Restructuring, 38(1), 11-35.
https://doi.org/10.1007/s10644-005-4521-7
Choi, J., & Richardson, M. (2016).The volatility of a firm's assets and the leverage effect.
Journal of Financial Economics, 121(2), 254-277.
https://doi.org/10.1016/j.jfineco.2016.05.009
77 ajfa.macrothink.org
 Asian Journal of Finance & Accounting
ISSN 1946-052X
2018, Vol. 10, No. 2

DeAngelo, H., & Masulis, R. W. (1980).Optimal capital structure under corporate and
personal taxation. Journal of Financial Economics, 8(1), 3-29.
https://doi.org/10.1016/0304-405X(80)90019-7
Deesomsak, R., Paudyal, K., & Pescetto, G. (2004). The determinants of capital structure:
evidence from the Asia Pacific region. Journal of Multinational Financial Management,
14(4), 387-405. https://doi.org/10.1016/j.mulfin.2004.03.001
DeJong, A., Nguyen, T. T., & Kabir, R. (2008). Capital structure around the world: The roles
of firm- and country-specific determinants. Journal of Banking and Finance, 32(9),
1954-1969. https://doi.org/10.1016/j.jbankfin.2007.12.034
De Miguel, A., & Pindado, J. (2001). Determinants of capital structure: new evidence from
Spanish panel data. Journal of Corporate Finance, 7(1), 77-99.
https://doi.org/10.1016/S0929-1199(00)00020-1
Dickinson, G. (2001). Enterprise risk management: Its origins and conceptual foundation. The
Geneva Papers on Risk and Insurance. Issues and Practice, 26(3), 360-366.
https://doi.org/10.1111/1468-0440.00121
Dixon, R., Guariglia, A., & Vijayakumaran, R. (2015). Managerial ownership, corporate
governance and firms' exporting decisions: Evidence from Chinese listed companies. The
European Journal of Finance, 1-39.
Faccio, M. (2006).Politically connected firms. The American Economic Review, 96(1),
369-386. https://doi.org/10.1257/000282806776157704
Fischer, E. O., Heinkel, R., & Zechner, J. (1989). Dynamic capital structure choice: Theory
and tests. The Journal of Finance, 44(1), 19-40.
https://doi.org/10.1111/j.1540-6261.1989.tb02402.x
Flannery, M., &Rangan, K. (2006).Partial adjustment toward target capital structures. Journal
of Financial Economics, 79(3), 469-506. https://doi.org/10.1016/j.jfineco.2005.03.004
Frank, M. Z., & Goyal, V. K. (2009). Capital structure decisions: Which factors are reliably
important? Financial Management, 38(1), 1-37.
https://doi.org/10.1111/j.1755-053X.2009.01026.x
Diamond, D. W. (1991). Debt maturity structure and liquidity risk. The Quarterly Journal of
Economics, 709-737. https://doi.org/10.2307/2937924
Dixon, R., Guariglia, A., & Vijayakumaran, R. (2015). Managerial ownership, corporate
governance and firms' exporting decisions: evidence from Chinese listed companies. The
European Journal of Finance, 1-39.
Du, J., Guariglia, A., & Newman, A. (2015). Do Social Capital Building Strategies Influence
the Financing Behavior of Chinese Private Small and Medium Sized Enterprises?.
Entrepreneurship Theory and Practice, 39(3), 601-631. https://doi.org/10.1111/etap.12051
Graham, J. R., & Leary, M. T. (2011). A Review of Empirical Capital Structure Research and
78 ajfa.macrothink.org
 Asian Journal of Finance & Accounting
ISSN 1946-052X
2018, Vol. 10, No. 2

Directions for the Future. (April 7, 2011). Annual Review of Financial Economics, 3, 2011.
https://doi.org/10.2139/ssrn.1729388
Grossman, S. J., & Hart, O. D. (1982). Corporate financial structure and managerial
incentives. In The economics of information and uncertainty (pp. 107-140).University of
Chicago Press.
Guarigli, A., & Vijayakumaran, S. (2013). Capital structure decisions and corporate
governance: Evidence from the Chinese listed companies. Proceedings of 4th Asia-Pacific
Business Research Conference, Singapore. Retrieved from
https://wbiworldconpro.com/uploads/singapore-conference-013/finance/1380694634_310-Su
nitha.pdf
Harris, M., & Raviv, A. (1991).The theory of capital structure. The Journal of Finance, 46(1),
297-355. https://doi.org/10.1111/j.1540-6261.1991.tb03753.x
Hart, O & Moore, J. (1995). Debt and seniority: an analysis of hard claims in constraining
management. American Economic Review, 85, 567-585.
Hsiao, C. (1985). Benefits and limitations of panel data. Econometric Reviews, 4, 127-74.
https://doi.org/10.1080/07474938508800078
Huang, G., & Song, F. M. (2006). The determinants of capital structure: Evidence from China.
China Economic Review, 17(1), 14-36. https://doi.org/10.1016/j.chieco.2005.02.007
Jensen, M. C. (1986). Agency costs of free cash flow, corporate finance, and takeovers. The
American Economic Review, 76(2), 323-329.
Jensen, M., & Meckling, M. (1976). Theory of the firm: Managerial behavior, agency costs
and ownership structure. Journal of Financial Economics, 3(4), 305-360.
https://doi.org/10.1016/0304-405X(76)90026-X
Johnson, S. (2003). Debt maturity and the effects of growth opportunities and liquidity risk
on leverage, Review of Financial Studies, 16, 209-236. https://doi.org/10.1093/rfs/16.1.0209
Lemmon, M. L., Roberts, M. R., & Zender, J. F. (2008). Back to the beginning: persistence
and the cross‐section of corporate capital structure. The Journal of Finance, 63(4), 1575-1608.
https://doi.org/10.1111/j.1540-6261.2008.01369.x
Kim, H., Heshmati, A., & Aoun, D. (2006). Dynamics of capital structure: The case of
Korean listed manufacturing companies. Asian Economic Journal, 20(3), 275-302.
https://doi.org/10.1111/j.1467-8381.2006.00236.x
Miller, M. (1977). Debt and taxes.Journal of Finance, 32(2), 261-275.
https://doi.org/10.1111/j.1540-6261.1977.tb03267.x
Modigliani, F., & Miller, M. (1963). Corporate income taxes and the cost of capital: A
correction. American Economic Review, 53(3), 433-443.
Modigliani, F., & Miller, M. H. (1958). The cost of capital, corporation finance and the theory

79 ajfa.macrothink.org
 Asian Journal of Finance & Accounting
ISSN 1946-052X
2018, Vol. 10, No. 2

of investment. American Economic Review, 48(3), 261-297.

Moosa, I., Li, L., & Naughton, T. (2011). Robust and fragile firm-specific determinants of the
capital structure of Chinese firms. Applied financial economics, 21(18), 1331-1343.
https://doi.org/10.1080/09603107.2011.570714
Myers, S. C. (1977). Determinants of corporate borrowing.Journal of Financial Economics,
5(2), 147-175. https://doi.org/10.1016/0304-405X(77)90015-0
Myers, S. C. (1984). The capital structure puzzle.The Journal of Finance, 39(3), 574-592.
https://doi.org/10.1111/j.1540-6261.1984.tb03646.x
Myers, S. C., &Majluf, N. S. (1984). Corporate financing and investment decisions when
firms have information that investors do not have. Journal of Financial Economics, 13(2),
187-221. https://doi.org/10.1016/0304-405X(84)90023-0
Ozkan, A. (2001). Determinants of capital structure and adjustment to long run target:
evidence from UK company panel data. Journal of Business Finance & Accounting, 28(1-2),
175-198. https://doi.org/10.1111/1468-5957.00370
Pandy, I. M. (2008). Financial Management, Vikas Pub. House Pvt. Ltd., Noida, 9th ed.
Reprint, 520.
Qian, Y., Tian, Y., &Wirjanto, T. S. (2009). Do Chinese publicly listed companies adjust their
capital structure toward a target level? China Economic Review, 20(4), 662-676.
https://doi.org/10.1016/j.chieco.2009.06.001
Rajan, R. G., &Zingales, L. (1995). What do we know about capital structure? Some evidence
from international data. The Journal of Finance, 50(5), 1421-1460.
https://doi.org/10.1111/j.1540-6261.1995.tb05184.x
Stiglitz, J. E. (1972). Some aspects of the pure theory of corporate finance: bankruptcies and
take-overs. The Bell Journal of Economics and Management Science, 458-482.
https://doi.org/10.2307/3003033
Stulz, R. (1990). Managerial discretion and optimal financing policies. Journal of financial
Economics, 26(1), 3-27. https://doi.org/10.1016/0304-405X(90)90011-N
Tian, L., & Estrin, S. (2007). Debt financing, soft budget constraints, and government
ownership: evidence from China. Economics of Transition, 15(3), 461-481.
https://doi.org/10.1111/j.1468-0351.2007.00292.x
Titman, S., & Grinblatt, M. (1998). Financial market and corporate strategy. US: McGraw-
Hill.
Titman, S. (1984). The effect of capital structure on a firm's liquidation decision. Journal of
financial economics, 13(1), 137-151. https://doi.org/10.1016/0304-405X(84)90035-7
Titman, S., & Wessels, R. (1988). The determinants of capital structure choice. The Journal
of Finance, 43(1), 1-19. https://doi.org/10.1111/j.1540-6261.1988.tb02585.x

80 ajfa.macrothink.org
 Asian Journal of Finance & Accounting
ISSN 1946-052X
2018, Vol. 10, No. 2

Vijayakumaran, R. (2014). Corporate governance and corporate finance: evidence from

Chinese listed companies. Unpublished doctoral dissertation. Durham University, Durham.
Retrieved from http://etheses.dur.ac.uk/10965/
Vijayakumaran, R. (2015). Capital structure decisions, agency conflict and corporate
performance: evidence from Sri Lankan listed manufacturing companies. International
Journal of Accounting and Business Finance, 1(1), 1-14. Retrieved from
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2666511
Vijayakumaran, R., (2017). Capital structure decisions and corporate performance: evidence
from Chinese listed industrial firms. International Journal of Accounting and Financial
Reporting, 7(2). https://doi.org/10.5296/ijafr.v7i2
Vijayakumaran, R., &Vijayakumaran, S. (2011). Determinants of capital structure in Sri
Lanka: evidence from panel data. Proceedings of the international conference, of the
international conference of Sri Ram Institute of Management Studies, India, 295-305.
Vijayakumaran, R., & Vijayakumaran, S. (2017). Institutional reforms and development of
corporate governance and banking system in China. Asian Journal of Finance & Accounting,
9(2), 352-368. https://doi.org/10.5296/ajfa.v9i2.12382
Vijayakumaran, S. (2016).Leverage and debt maturity of Chinese listed firms: determinants
and effects on corporate performance, Doctoral dissertation, Durham University. Retrieved
from http://etheses.dur.ac.uk/11897/
Wald, J. K. (1999). How firm characteristics affect capital structure: An international
comparison. Journal of Financial Research, 22(2), 161-187.
https://doi.org/10.1111/j.1475-6803.1999.tb00721.x
Wiwattanakantang, Y. (1999). An empirical study on the determinants of the capital structure
of Thai firms. Pacific-Basin Finance Journal, 7(3-4), 371-403.
https://doi.org/10.1016/S0927-538X(99)00007-4
Zhang, T. (2008). The problems and causes of capital structure within Chinese listed firms.
Journal of Yunnan University, 3(1), 13-16.
Zou, H., & Xiao, J. Z. (2006).The financing behaviour of listed Chinese firms. The British
Accounting Review, 38(3), 239-258. https://doi.org/10.1016/j.bar.2006.04.008

81 ajfa.macrothink.org