Department of Real Estate and Construction Management
Real Estate Development and Financial Services
Real Estate Management
Thesis no. 26
Master of Science, 30 credits
What factors might explain the capital structure of
listed real estate firms in China?
Author:
Liufang Li
Supervisor:
Han-Suck Song
Stockholm 2010
Master of Science thesis
Title:
Authors
Department
Master Thesis number
Supervisor
Keywords
What factors might explain the capital structure of
listed real estate firms in China?
Liufang Li
Department of Real Estate and Construction
Management
No. 26
Han-Suck Song
Capital structure, Trade-off, Pecking order, Chinese
listed real estate firms, State-controlled enterprises,
Static panel data model, Dynamic panel data model
Abstract
This paper is an empirical study investigating the determinants of capital structure of
listed real estate firms in China in the period of 2003-2007. Using a panel data set,
both static and dynamic panel data models are estimated. The significant and positive
determinants are estimated to be the total asset, the state owned share percentage and
tangible asset ratio. While the significant and negative determinants are estimated to be
the growth rate of the income and return on assets. The lagged effects of explanatory
variables are found to explain the current debt ratio significantly according to the
estimated dynamic model.
The estimation results are compared with the literature study of capital structure
theories including Miler and Modigliani Theory, trade-off theory and pecking order
theory. An attempt has been made to study market-timing theory and institutional
factors influencing on capital structure decision. Most of the estimation from the
models is consistent with the trade-off theory and pecking-order theory. However,
these two theories cannot fully provide convincing explanations for the capital
choices of the Chinese real estate listed firms. Instead, some of the results are
explained by the practical situation gained from series of interviews with Chinese
developers, which suggesting that the institutional differences and financial
constraints in the capital markets especially for real estate firms in China are also the
factors influencing firms‘ capital structure decisions.
I
Acknowledgement
There are a lot of people who gave me support during my thesis writing. First of all I
own my gratitude to my supervisor Han-Suck Song who guided me and gave me
valuable advice in the whole process. My gratitude also gives to Jiang Liang who
assisted me with the section of empirical study in this thesis.
In addition, I am thankful to ShuoWang and Yue Xiao who helped me to get
substantial accounting and market data of Chinese listed real estate firms from China
Stock Market and Accounting Research Database (CSMAR). Also, I would like to
thank Zehao Xie, Miaorong He, Zhanhong Lu etc. from Chinese listed real estate
firms providing me with their experiences and information about the topic of capital
structure.
I am very grateful to all the lecturers in the master programme of Real Estate
Management at KTH who imparted me knowledge that is useful for this thesis as well
as my future career.
Last but not least, my appreciation goes to my family and friends for their constant
love and support.
Stockholm, 18th May 2010
Liufang Li
II
Table of Content
Master of Science thesis.................................................................................................. I
Abstract ........................................................................................................................... I
Acknowledgement ......................................................................................................... II
Table of Content ........................................................................................................... III
Chapter One - Introduction ............................................................................................ 1
1.1 Background ...................................................................................................... 1
1.2 Problem statement ............................................................................................ 2
1.3 Aims and research questions ............................................................................ 2
1.4 Methodology .................................................................................................... 3
1.5 Limitation ......................................................................................................... 3
1.6 Disposition ....................................................................................................... 3
Chapter Two - Theoretical Framework on Capital Structure ......................................... 5
2.1 Theory of capital structure ............................................................................... 5
2.1.1 The Miller and Modigliani theory......................................................... 5
2.1.2 The trade-off theory .............................................................................. 6
2.1.3 The pecking order theory ...................................................................... 7
2.1.4 The market timing theory...................................................................... 8
2.2 Literature review on capital structure determinants......................................... 9
2.2.1 Empirical findings from Chinese perspective ..................................... 10
2.2.2 Real estate related empirical findings ................................................. 12
2.2.3 Determinants of capital structure ........................................................ 13
Chapter Three - Descriptive Statistics ......................................................................... 18
3.1 Sample set ...................................................................................................... 18
3.2 Development of listed real estate firms ......................................................... 19
3.3 Chinese listed real estate firms have quite low leverage level. ...................... 20
3.4 High short-term vs. low long-term debt ratios ............................................... 21
3.5 Most of the listed real estate firms are state-controlled ................................. 22
Chapter Four - Empirical Analysis on Determinants of Capital Structure .................. 24
4.1 Variables ......................................................................................................... 24
4.2 Static panel data models................................................................................. 25
4.3 Dynamic panel data model............................................................................. 27
4.4 Regression results and interpretation ............................................................. 29
4.4.1 Static panel model ............................................................................... 29
4.4.2 Dynamic panel model ......................................................................... 31
4.5 Summary of findings...................................................................................... 36
Chapter Five – Conclusion........................................................................................... 39
5.1 Conclusion ..................................................................................................... 39
Reference ..................................................................................................................... 41
III
Chapter One - Introduction
1.1 Background
Since the late 1970s, China has experienced a transitional process from a planned to a
market economic system. At the beginning of formation of capital market, only
state-controlled enterprises were listed in the exchanges, which refer to the enterprise
that used to be a state-owned business but later became a listed company, in which the
state still keeps controlling shares. They were usually granted generous loans from
state-owned banks to improve their financial condition. Furthermore, imperfect legal
system and underdeveloped financial system hampered the growth of the Chinese
capital market. In order to rectify and standardize the order of the market economy, a
series of economic reforms has been launched. The economy has been gradually
transferred from dominating state owned enterprises to a mixed economy, while the
state still plays a significant dual role as a regulatory agency and owner of firms. Over
the years, Chinese firms have achieved remarkable success in expanding growth. This
biggest developing country grabs attention from all over the world. Due to the distinct
features in China‘s financial markets, it is worthy to examine whether the debt
financing behavior in Chinese public listed firms can be explained by the theories
derived from the western settings.
Schwartz & Aronson (1967) find that the capital structure choices differ among
industries. A majority of the empirical studies has proved the strong industry effect.
Focusing on a specific type of industry can yield this effect. In particular, real estate
has unique characteristics, which is famous as a source of collateral for mast amounts
of debt. Also, they are comparatively safe or defensive stocks, and equity performance
is likely to be closely tied to the underlying assets which are frequently valued and
held at market value on the Balance Sheet (Bond & Scott, 2006). Owusu-Ansah (2009)
provides evidence from Sweden that real estate firms borrow more than those in the IT
and the health care industries. Morri & Cristanziani (2009) analyze the determinants of
1
capital structure of real estate companies in European countries, showing the result is
consistent with pecking order theory and trade off model. Singh (2002) review the
evolution of innovative debt and equity structures in U.S. real estate finance, such as
securitization, Commercial Mortgage Backed Securities (CMBS), Collateralized
Mortgage Obligations (CMO) and Real Estate Investment Trusts (REITs), which
provide a lot of options except for traditional debt and equity capital sources.
Compared with developed countries, China's real estate finance is less developed
and immature due to a short history of real estate market started from 1978. Although
in recent years, the real estate finance has been diversified; problems of undeveloped
corporate markets, imperfect legal system for REITs and strong policy control on this
sector still exist. Thus, it is necessary to analyze the determinants of capital structure in
Chinese real estate industry in the condition of the unique features, combined with
institutional factor and real estate industry.
1.2 Problem statement
Although a lot of theoretical and empirical studies have been done in the area of
capital structure, most of them focus on the firms in developed countries. Few studies
have been undertaken in the developing counties, where the capital markets are in a
nascent phase. Further, the real estate has unique features that made them interesting
to study.
Wang (2003) and Lin (2006) in our school made attempts to study the
Chinese real estate financing behavior in the way of descriptive statistic analysis,
questionnaire and interview investigation, which provides deep insights. As discussed
above, the unique features of institutional factor and real estate industry brings
necessity to further analysis the capital structure determinants in China.
1.3 Aims and research questions
The central purpose of this research is to find out the main determinants of capital
structure in Chinese real estate listed companies, and analyze whether the trade off
theory and pecking order theory can be applied to them. Besides, this study makes an
2
attempt to find out whether state-owned shares have impact on the capital structure
for real estate listed firms.
1.4 Methodology
The study is mainly based on quantitative methods, and series of interviews are
conducted to explain the quantitative results. Descriptive statistic analysis is
undertaken to fully understand the feature of capital structure for Chinese listed real
estate firms in the period of 1999-2007. An empirical analysis is made to test the
capital structure determinants for Chinese real estate listed companies. This empirical
study uses a data set of both market and accounting data in time period of 2003-2007
for real estate companies listed in Shanghai and Shenzhen stock exchanges. The source
of this data set is China Stock Market and Accounting Research Database (CSMAR)
developed and maintained by Shenzhen GTA Information Technology Co. in China.
1.5 Limitation
Only listed companies are researched in this study due to the availability of data,
although the unlisted real estate makes up the large proportion of China‘s real estate
industry. This limitation, to some extent, also renders the problem of biased result
since most of listed real estate firms are large. Besides, because of the different
accounting system, this study only examines the real estate companies listed in the
exchanges of mainland with exception of the Hong Kong exchange where a lot of
mainland‘s private-owned real estate companies are prone to go public due to less
regulation and better transparency. Again, due to the data limitation of the share
ownership and fixed assets, a panel data regression is conducted in the period of
2003-2007 to examine determinant of capital structure.
1.6 Disposition
This study is divided into four chapters. Chapter one is an introduction of country
3
factors and industry factors for capital structure, and to describe purpose, method and
limitation of this study. Chapter two generates a framework of main capital structure
theories, identifies the potential determinants of capital structure, and made
hypothesizes for empirical study. Chapter three is descriptive statistic analysis in
terms of leverage ratios, short-term debt ratios, long term debt ratios and state
ownership. Chapter four is an empirical study by making panel data regressions to
find out the main determinants of capital structure. Chapter five presents the results of
the quantitative study and analyzes the findings.
4
Chapter Two - Theoretical Framework on
Capital Structure
2.1 Theory of capital structure
A firm‘s basic capital resource comes from the stream of cash flow produced by its
assets. Once there is a gap between internal equity and the cash that the firm need, the
firm must finance this gap by issuing equity, debt or hybrid securities. The term
capital structure refers to the mix of different types of securities (long-term debt,
common stock, preferred stock, convertible debt, etc) issued by a company to finance
its assets (see e.g. Song, 2005). There is always a question: How do firms choose their
capital structure? A lot of theories have been developed to answer this puzzling
question. This chapter reviews the primary theories of capital structure choices
containing the Modigliani and Miller theory, trade off theory, pecking order theory
and market timing theory in order to establish a theoretical framework for the
consequent empirical study.
2.1.1 The Miller and Modigliani theory
Modigliani and Miller (1958) made a breakthrough development on establishing the
first important theory of the capital structure. When financial managers are trying to
find the particular combination that maximizes the market value of the firm,
Modigliani and Miller‘s (MM‘s) famous proposition 1 states that no combination is
better than any other in a perfect market. The firm‘s value is determined by its real
assets, not by the securities it issues. It implies the financing choices do not affect the
firm‘s investment, borrowing, and operating policies. It also implies the choices of
long-term versus short-term debt should have no effect on the overall value of the
firm (Brealey, Myers, & Allen, 2008).
Furthermore, the MM‘s proposition 2 states that the capital structure does affect
the expected rate of return on the common stock. According to the weighted-average
5
cost of capital (WACC) developed by MM, return on equity increases in proportion to
the debt-equity ratio, but any increase in expected return is exactly offset by an
increase in risk and therefore leaving stockholders no better or worse off.
MM‘s two propositions are all based on the assumption of a perfect capital
market where there is no transaction cost related with capital raising or going into
bankruptcy, where there is no taxes and where there is disclosure of all available
information (Huang & Song, 2006). Although debt policy rarely matters in perfect
markets, it is not a practical guideline to employ in a real capital market with frictions
and imperfection. MM‘s theory cannot explain the fact that debt ratios vary regularly
from industry to industry. For instance, almost all real estate development companies
rely heavily on debt while high-tech growth companies rarely use much debt. No
matter what, MM theory is considered as a starting point of capital structure theory.
2.1.2 The trade-off theory
Evolved from MM theory, Miller (1977) finds it does exists an optimal capital structure
when taking consideration of corporate and personal taxes, bankruptcy costs and
agency costs. The trade-off theory successfully explains industry differences in capital
structure. Firm‘s debt-equity decision is a trade-off between interest tax shields and the
costs of financial distress. The trade-off theory of capital structure recognizes that target
debt ratios may vary from firm to firm. Companies with safe, tangible assets and plenty
of taxable income to shield ought to have high target ratios, while companies with risky,
intangible assets ought to rely primarily on equity financing. ―The theoretical optimum
is reached when the present value of tax savings due to further borrowing is just offset
by increase in the present value of costs of distress.‖ (Brealey, Myers, & Allen, 2008)
According to the trade-off theory, different firms should set their own target ratios that
maximize firms‘ value. This is so called static trade-off theory.
6
Figure 2.1.2 The Static Trade-off Theory of Capital Structure
Source: (Miller, 1977)
Furthermore, the costs of adjusting capital structure constrain the adjustment speed
toward the companies‘ target debt ratio. Thus the differences of actual debt ratios are
observable although their target debt ratios are the same. This is labeled as the dynamic
trade-off theory.
Although trade off theory can explain many industry differences in capital
structure, it cannot explain the fact that some of the most profitable companies borrow
the least. Under the trade-off theory, high profits imply higher market value and more
taxable income to shield thus should have strong incentives to borrow. It predicts the
reverse of how companies actually behave. Now the pecking order theory is presented
below in light of this problem.
2.1.3 The pecking order theory
Pecking order theory is a completely different theory of financing. First suggested by
Myers and Majluf (1984), it starts with the assumption of asymmetric information,
indicating that managers know more about their companies‘ prospects, risks, and values
than do outside investors. Companies try to time issues when shares are fairly priced or
overpriced. Investors understand it and the stock price usually falls when a stock issue
7
is announced. Thus when companies need external financing they prefer debt to
underpriced external equity. This leads to a pecking order, in which investment is
financed first with internal funds, reinvested earnings primarily; then by new issues of
debt; and finally with new issues of equity. (Brealey, Myers, & Allen, 2008, p.517) This
theory has no well-defined optimal target debt ratio because the current leverage of a
firm reflects its cumulative requirements of external financing (Morri & Cristanziani,
2009). Pecking order theory assumes the attraction of interest tax shields is the second
order. Debt is better than equity when the problem of asymmetric information is
considered as the most important issue.
2.1.4 The market timing theory
Market timing theory is developed from behavioral corporate finance. In the inefficient
or segmented capital markets, the investors are sometimes not as rational and stable as
the financial managers. Then the managers can take advantage by issuing shares when
the stock price is high and switch to debt when the price is low. In other words, equity
market timing refers to the practice of issuing shares at high prices and repurchasing at
low prices (Baker & Wurgler, 2002). ―Market timing can explain why companies tend
to issue shares after run-ups in stock prices and also why aggregate stock issues are
concentrated in bull markets and fall sharply in bear markets.‖ (Brealey, Myers, &
Allen, 2008)
Market timing theory has been examined recently. Baker and Wurgler (2002) find
that market timing has large, persistent effects on capital structure. They documents the
leverage of firms is negatively associated with their historical market valuation, as
measured by the market-to-book ratio. It implies the temporary fluctuation in market
valuation have long run impact on capital structure. Huang and Ritter (2004) study the
external financing decisions of publicly traded U.S. firms, identifying that a large
proportion of their financing deficit is financed by the external equity when the
expected equity risk premium is low.
The empirical studies of market timing theory in China are not consistent. Liu et
8
al. (2006) find that the market timing has a long run effect on capital structure and the
time length is five years. Li (2006) find that the market timing does have impact on
the capital structure but it is not persistent. Hu et al. (2008) suggest that market timing
has little influence on the capital structure. In this thesis, due to the limitation of data
the effect of market timing cannot be tested in quantitative study.
2.2 Literature review on capital structure determinants
This section develops a framework to justify which factors affect the capital structure of
listed firms by reviewing the empirical studies internationally, in China and in the area
of real estate respectively, then discusses the determinants that will be measured in the
coming study.
The theoretical and empirical studies in this field are extensive. Harris & Raviv
(1991) summarizes a number of empirical studies from US firms, suggesting that
―leverage increases with fixed assets, non-debt tax shields, investment opportunities
and firm size, and decreases with volatility, advertising expenditure, and the probability
of bankruptcy, profitability and uniqueness of the product‖. (p. 334)
Recently, academics have started to concern about the situation of capital
structure in developing countries. They generally show the factors that traditionally
explain the cross-section of leverage in the US are similarly correlated in other
countries, but also identify the consistent difference across countries, which indicate
the impact of different institutional features. Rajan and Zingales (1995) looked at the
difference in leverage and its determinants in the G7 countries: US, Japan, Germany,
France, Italy, UK and Canada. They find that aggregate leverage and determinants are
both roughly identical across these countries. Booth et al. (2001) focused on 10
developing countries, showing that capital structure is affected by the same variables as
in developed countries. However, a persistent impact of institutional features on capital
structure is prominent, which is still not fully understood. Furthermore, Fan, Titman,
and Twite (2008) examines the influence of institutions on the capital structure in
cross-section of firms in 39 developed and developing countries. They find different
9
legal system; taxes and the characteristics of the financial institutions explain the
cross-country variation in leverage.
Although a lot of empirical studies have been done to test the determinants of
capital structure, it is found that other determinants are used based on different
understanding. It implies that arguments still exists on this issue.
2.2.1 Empirical findings from Chinese perspective
China, the biggest developing country in the world, with the significant institutional
differences and financial constraints in the banking sector (Chen, 2004), arouses
researchers‘ interest to study its determinants of capital structure. Chen (2004)
develops a preliminary study to explore the determinants of capital structure in China
using firm-level panel data of 77 Chinese public-listed companies over the period
1994–2000, identifying special features of the China‘s corporatization and its
institutional environment which influence the firms‘ leverage decision. He found that
neither the trade-off model nor the Pecking order hypothesis derived from the
developing countries provides convincing explanations for the capital choices of the
Chinese firms. The capital choice decision of Chinese firms seems to follow a ‗‗new
Pecking order‘‘—retained profit, equity, and long-term debt. And it was pointed out
that further work is required to develop new hypotheses for the capital choice decisions
of Chinese firms and to design new variables to reflect the institutional influence.
Huang and Song (2006) use a database containing the market and accounting data
in period of 1994 -2003 from more than 1200 Chinese-listed companies to document
their capital structure characteristics. While they report state ownership or institutional
environment has no significant impact on capital structure. The reason is stated that
state ownership enterprises (SOEs) follow the basic rules of market economy, even if
the state does not give up its controlling right. And it implies competition helps in the
reform of SOEs. The empirical study of this article indicates that as in the developed
countries, leverage in Chinese firms increases with firm size and fixed assets, and
decreases with profitability, non-debt tax shields, growth opportunity, managerial
10
shareholdings and correlates with industries. However, Chen (2004) and Huang &
Song (2006) both agree on Chinese firms prefer short-term finance and tend to have
much lower long-term debt, which is different from western setting.
In recent years, Qian, Tian, & Wirjanto develope a series of study on capital
structure and corporate governance in China. They (Qian, Tian, & Wirjanto, 2007)
attempt to validate the work Chen (2004) and Huang & Song (2006) have done by
applying a larger panel-data set in over a more recent time period (1999-2004). And
unobserved cross-sectional and time effects as well as industry effects are taken into
consideration. Their empirical results show that firm size, tangibility and ownership
structure are positively associated with firm‘s leverage ratio, while profitability,
non-debt tax shields, growth and volatility are negatively related to firm‘s leverage
ratio. Furthermore, Qian, Tian, & Wirjanto (2009) study the dynamic capital structure
model for 650 Chinese publicly listed companies in the period of 1999-2004. They
formulate a dynamic panel-data model to test whether and to what extent the reforms
have succeeded in providing incentives for Chinese PLCs to achieve the target levels
of their capital structures. One of their main findings is that Chinese firms adjust
toward an equilibrium level of debt ratio in a given year at a very slow rate, indicating
the existence of financial costs prevent companies constantly rebalance leverage ratio.
Bharbra, Liu, & Tirtiroglu (2008) examine the capital structure decisions of listed
firms in China between 1992 and 2001. They emphasize the unique features in China
with high information asymmetry, phenomenal growth, highly concentrated
ownership, and a lack of external market for corporate control. Their empirical study
shows the leverage is positively related to firm size and tangibility, and negatively
related to profitability and growth options.
Li, Yue & Zhao (2009) highlight the important role of ownership structure and
institutional development when studying capital structure in emerging market like
China. They employ a data set in debt financing of Chinese non-public manufacturing
firms derived from National Bureau of Statistics (NBS) in the period of 2000-2004.
The empirical study shows that the expansionary power of ownership structure and
11
institution (6%) in leverage decisions is closed to the power of firm characteristics
(8%).
2.2.2 Real estate related empirical findings
Real estate is a capital-intensive sector. The capital demand of real estate industry is
very high. It requires real estate firms resort to external fund to finance tremendous
cost in the process of purchasing land and construction. Also, real estate firms have a
great deal of collateral to support high levels of debt. According to the trade-off theory,
this means the costs of financial distress are likely to be lower than average,
indicating real estate‘s optimal capital structure tends to be higher than other
industries. Owusu-Ansah (2009) provides evidence from Sweden, identifying real
estate firms borrow more than those in the IT and the health care industries.
From the existing literature, debt financing behavior in real estate sectors varies
from different countries because of different institution environment and development
phase. Morri & Cristanziani (2009) analyze the determinants of capital structure of
real estate companies in European countries, showing the result is consistent with
pecking order theory and trade off model. Singh (2002) review the evolution of
innovative debt and equity structures in U.S, such as securitization, Commercial
Mortgage Backed Securities (CMBS), Collateralized Mortgage Obligations (CMO)
and Real Estate Investment Trusts (REITs), developed in the period of national
recession combined to shut off funding for hotel projects in the early 1990s.
Ooi (2000) examines the ownership structure of 83 property listed companies in
UK and document nearly one fourth of the common shares issued by the companies
are held by the managers. He highlights the problem of managerial opportunism in the
corporate governance because of separating ownership from management. Bond &
Scott (2006) examines the pecking order theory and trade-off theory by testing on a
sample of 18 UK listed real estate, showing that real estate financing can be explained
by broader capital structure framework in which information asymmetries drive firm
financing behavior.
12
Current studies more focus on examining the capital structure decision for Real
Estate Investment (REITs). Feng, Ghosh and Sirmans (2007) find that the pecking
order is mostly relevant in explaining the capital structure U.S. REITs where the costs
of asymmetric information exceed the costs of financial distress. Morri & Cristanziani
(2009) examines both REITs and non-REITs companies in European countries.
Because REITs are exempted from the payment of taxes if most of the taxable income
is paid out to shareholders as dividend, this finding confirms the importance of the
tax-exempt status in affecting capital structure choices. They also find that non-REIT
companies are significantly more leveraged than REITs. However, in China REITs are
still in the early phase of real estate finance facing regulatory constraints.
Few literatures can be found in empirical study of capital structure choice in
Chinese real estate listed firms. Dai (2004) tests 35 real estate listed companies in
China in the period of 2000 to 2002, showing that variables of firm characteristics are
almost not related to capital structure with the exception of the corporate velocity
which has significant negative relation to short-term debt ratio. Also, tax shield effect
is positively related to the capital structure to some extent, while the samples are not
extensive enough to explain capital structure decision. Li, Luo, & Ao (2005) examine
46 real estate listed companies in 2003 and find most of the real estate companies in
China have state-owned stock with high liability. Their empirical study suggests that
the profitability, size and the property guarantee value are negatively related to the
total leverage. Growth and shareholders‘ equity are positively related to the total
leverage of the real estate listed companies．However, the choices of determinants did
not be fully explained in their study.
2.2.3 Determinants of capital structure
Based on the literature review, the following variables are selected and will be
measured in the consequent empirical study.
Size
Theoretically, the relationship between size and leverage is unclear. The Trade-off
13
theory states that large firms tend to be more diversified and less prone to bankruptcy.
They usually have lower transaction costs associated with long-term debt issuance, and
are able to issue debt at a cheaper rate than small firms. (Titman & Wessels, 1988) On
the other hand, pecking order theory suggests the negative relationship between size
and leverage. Informational asymmetries problem is expected to be lower for large
firms, thus large firms should be more capable of issuing informational sensitive
securities such as equity (Kester, 1986).
Empirical study such as Booth et al. (2001), Marsh (1982), Rajan and Zingales
(1995), and Wald (1999), find that leverage is positively correlated with firm size.
Marsh‘s (1982) survey of literature concluded that large firms more often choose
long-term debt while small firms choose short-term debt. Furthermore firm size has
different measures. Since most existing literatures show the size effect on leverage is
nonlinear, natural logarithm of assets or sales are used to measure this variable. In this
study, it is defined as the logarithm of assets.
Profitability
Pecking order theory indicates a negative relationship between profitability and debt.
Profitable firms prefer internal funds rather than external due to asymmetric
information or transaction costs. Furthermore, Jensen (1986) predicts a negative
relationship if the market for corporate control is ineffective. In addition, Fan, Titman,
and Twite (2008) identify that the relationship between profitability and leverage tends
to be stronger in countries with weaker shareholder protection. In this study it is
hypothesized that profitability is negatively related to the leverage. The year return of
asset (ROA) is used as measure profitability.
Growth opportunities
Theoretical studies generally suggest an ambiguous relationship between growth
opportunities and leverage. The trade-off theory predicts that firms with more
investment opportunities will be characterized by a lower amount of debt. Although the
empirical result in China confirms the same relationship, Chen (2004) identifies the
trade-off model does not apply to the Chinese firms due to low technology level of
Chinese firms. According to the pecking order theory, higher growth opportunities
14
imply higher capital demand and a greater preference for external financing through
preferred source of debt. This suggests a positive relationship between growth and
leverage. However, due to agency costs, firms investing in assets that may generate
higher growth opportunities in the future face difficulties in borrowing against such
assets (Myer, 1977). This suggests a negative relationship. Myers (1977) also pointed
out that this agency problem is relieved if the firms issues short-term rather than
long-term debt. Therefore, we may expect that short-term debt is positively related to
growth.
There are different proxies for growth opportunities. Rajan and Zingales (1995)
use Tobin Q defined as market-to-book ratio of total asset. Tobin Q is a measure for
future growth opportunities. Due to the availability of the data, sales growth rate is
used to examine the relationship with growth and leverage.
Tangibility
A positive relationship exists because tangible assets are easy to collateralize for debt,
especially for long-term loans. According to trade-off theory, tangible assets serve as
good collateral to support debt. Also, cost of financial distress is lower with high
tangibility. Thus firm with significant tangible assets such as real estate would be
expected to employ more debt than average. Chen (2004) confirms a positive
relationship between tangibility and leverage in China. It shows that asset tangibility
is an important criterion in banks‘ credit policy, and this is particularly true for
long-term loans. So it is assumed that the long-term loans of real estate companies are
higher than average level. This study follows Rajan and Zingales (1995) by defining
tangibility as the ratio of fixed assets over total assets. In this study, net value of fixed
assets and construction-in-progress are added up as value of fixed assets.
Liquidity
According to the trade-off theory, firms‘ decision of debt-equity ratio is a trade-off
between interest tax shields and the costs of financial distress. The financial distress
of the firm is measured as liquidity ratio. It expresses a company‘s ability to repay
short-term credits from banks. In Chinese listed real estate firms, short-term debt
dominants the majority of the total debt. Higher liquidity ratio indicates the lower
15
probability of financial distress. Here the liquidity ratio is predicted to be positive
related with the debt ratio.
Tax shield
According to MM theory, interest tax shield generate incentives for firms to raise
leverage. Interest on debt is a tax-deductible expense which creates a tax shield. A
positive relationship between tax shield effects and leverage is expected in this study.
Ownership structure
Agency theory suggests two types of conflicts in ownership structure: conflict
between share holders and managers, and conflicts between shareholders and debt
holders. It is expected to have a correlation between ownership structure and leverage.
However, there are some distinct features of ownership structure in China‘s stock
market. Shares are categorized as A shares, which are specified for domestic investors,
and B shares, which are specified for foreign investors. Further, A-shares are
classified as ―tradable shares‖ held mainly by public investors, and ―non-tradable
shares‖ consisting of legal-person shares and state shares owned by either the central
government or the local government. Since the formation of stock markets aimed to
contribute to the growth of state-owned enterprises, the non-tradable shares issue was
designed as a way to manage state assets through capital markets. According to
statistics of CSRC, as of the end of 2004, 64% of the total 714.9 billion shares issued
by Chinese listed companies were non-tradable in nature, of which state-owned shares
accounted for 74%. The existence of non-tradable shares has seriously constrained the
internationalization and further development of China‘s capital markets, such as
problem of dual-pricing of shares of the same listed companies. Thus the non-tradable
share reform was initiated in April 2005, aiming to eliminate the difference between
tradable and non-tradable shares. By the end of 2007, 98% of total listed companies
had either started or finished the process of the reform. However, the amount of
floating shares is still very low, accounting for less than 30% of the entire share
outstanding. It indicates that after 20 years of economic reforms, state in capital
market still plays a dual role as a majority shareholder as well as the owner of major
banks.
16
Trade-off theory indicates that ownership structure affects companies´ capital
structure. The literatures on state ownership shows that state-owned shares strengthen
the firm‘s access to debt, and lending decisions of state-owned banks are politically
motivated. (Khwaja & Milan, 2005; Dinç, 2005) Empirically, Gordon & Li (2003),
and Allen et al. (2005) indicates that Chinese SOEs receive a large share of credit
issued by state-owned banks. Thus in this study share-owned share is hypothesized to
be positively related to leverage ratio.
Table 2.2.3 Summary of the implications of capital structure theories and hypothesis
as well as measurement in this study
Expected sign by
theories
+ (trade off)
- (pecking order)
+ (trade off)
- (pecking order)
Hypothesis of
this study
-
return on asset
+
logarithm of total assets
- (trade off)
+
sales growth rate
tangibility
liquidity
+ (pecking order)
+ (trade off)
+(trade off)
+
+
fixed asset/total assets
currency ratio
Tax-shield
+(trade off)
+
logarithm of year debt interest
state-owned
share
ambiguous
+
state-owned shares/total shares
Determinants
profitability
size
growth
opportunities
17
Measurement
Chapter Three - Descriptive Statistics
3.1 Sample set
This quantitative study uses an unbalanced panel data set of both market and
accounting data in time period of 1991-2007 for real estate companies listed in
Shanghai and Shenzhen stock markets. The source of this data set is China Stock
Market and Accounting Research Database (CSMAR) developed and maintained by
Shenzhen GTA Information Technology Co. in China. Among the sample set, those
firms with missing observations, abnormal leverages (more than 100%) are excluded
from the sample.
Table 3.1.1 Mean and Median Leverage ratios, short-term debt ratios and long term
debt ratios for sample listed firms during 1991-2007
Year
N
Total liability/Total assets
Short-term debt/Capital
Long-term debt/Capital
Mean
Median
Mean
Median
Mean
Median
1991
1
66.5%
66.5%
65.9%
65.9%
0.6%
0.6%
1992
7
63.0%
59.4%
55.8%
58.4%
7.4%
0.0%
1993
27
42.6%
46.6%
39.6%
36.6%
5.2%
1.8%
1994
35
45.7%
48.4%
43.4%
45.2%
4.4%
2.1%
1995
36
50.4%
53.1%
46.9%
45.9%
5.8%
3.5%
1996
48
44.5%
46.9%
41.0%
43.4%
5.2%
1.8%
1997
52
44.2%
47.4%
40.2%
40.8%
5.2%
1.2%
1998
51
46.9%
47.9%
43.7%
44.7%
4.3%
2.4%
1999
56
48.2%
48.8%
46.1%
45.9%
3.3%
1.8%
2000
60
47.4%
47.8%
44.2%
42.2%
4.6%
2.2%
2001
65
49.7%
47.6%
45.2%
42.4%
5.1%
2.3%
2002
65
49.9%
51.2%
43.8%
1.9%
12.5%
4.3%
2003
57
53.9%
54.9%
47.0%
47.4%
7.8%
4.7%
2004
59
55.8%
59.4%
48.9%
48.8%
8.4%
5.2%
2005
59
57.2%
58.5%
50.3%
48.3%
8.5%
7.9%
2006
62
57.9%
61.6%
46.3%
46.7%
12.9%
10.3%
2007
70
55.7%
59.4%
42.5%
43.4%
13.7%
13.9%
Average
48
51.7%
53.3%
46.5%
44.0%
6.8%
3.9%
Note: Capital = total debt + book value of equity
18
Figure 3.1.2 Average leverage ratios, short-term ratios and long term ratios
70.0%
60.0%
50.0%
40.0%
Leverage (Mean)
30.0%
Short-term debt ratio
Long-term debt ratio
20.0%
10.0%
0.0%
1991 1993 1995 1997 1999 2001 2003 2005 2007
3.2 Development of listed real estate firms
China reopened its stock markets in 1991, nearly 40 years after they were closed in
1949. The two stock exchanges are located in Shanghai and Shenzhen. In 1993,
intuitional framework and supervisory framework were established with the formation
of the China Securities Regulatory Commission (CSRC) which supervises and
consolidate the capital market nationwide. According to the Industry Classification
Standard of Chinese Listed Companies issued by CSRC, real estate sector includes
Real Estate Development and Operation, Real Estate Management, and Real Estate
Intermediate Service. In the sample set, there was only one real estate firms listed in
Shenzhen stock market in 1991, and till the end of 2007, 70 real estate firms were
listed in Shanghai and Shenzhen exchanges by IPO and back-door listing.
At the early phase of the Shenzhen and Shanghai exchanges established, a lot of
real estate firms were listed in the stock markets with encouragement policies. Till the
end of 1993, the number of real estate listed firms accounted for 10% of the total
number of listed firms. Since the latter half of 1994, state had launched a series of
measures in order to restrain the overheating of real estate. In 1995, CSRC refused all
the rationed shares‘ application raising funds for the luxurious apartments, villas,
resorts, offices and the hotels of four-star standard and above. In terms of IPO, CSRC
19
did not accept and heard any case of financial and real estate enterprises until 2001.
During this period, some real estate firms resorted to back-door listing due to a strong
capital demand. Even after 2001, only a few real estate companies could be listed
which were mostly state-controlled enterprises, and many real estate firms had to
choose overseas listing or back-door listing.
3.3 Chinese listed real estate firms have quite low leverage
level.
In this study the leverage is measured as total liabilities over total assets, which is the
broadest definition of leverage. The yearly mean leverage accounts for 51.7% and the
corresponding median amounts to 53.3%. Both figures are lower than the leverage
level of total listed firms (56%) over this period, while they are much lower than the
mean leverage of total real estate developers in China (80%).1 It indicates that listed
real estate firms have a strong desire of equity finance.
The reasons for this equity finance preference are investigated by interviews.
Most of listed real estate firms resort to external finance especially equity finance
rather than accumulation of internal funds. First, equity finance is less costly than debt
finance due to a relatively high PE (price/earnings) ratio with an average level above
20. High PE ratio leads to high share issue price thus the listed firms have substantial
gains from stock markets. Also, the low rates of dividend distribution of listed real
estate firms further reduce the cost of equity finance. Second, according to the
statistics from People‘s Bank of China, retail investors occupied more than half
proportion of the total market capitalization in 2007, while the institutional investors
only accounted for 30%. This unbalance contributes a high volatility in the stock
market. It is because institutional investors are able to lower the risks due to
diversification and long-term investment horizon, while retail investors tend to act as
herd behavior or more sentiment-driven. Third, agency problem exists in
state-controlled enterprises where managers and shareholders are detached, and
1
Data arranged from China Statistical Bureau 1991-2007
20
managers tend to issue more equity for financing. Besides, as Chen (2004) states due
to series of corporate governance problems and the lack of enforcement of company
laws, individual shareholders do not have adequate investment protection. Share
capital has become somewhat a ‗‗free‘‘ source of finance.
3.4 High short-term vs. low long-term debt ratios
The unbalanced between the short-term and long-term debt ratio shows that real estate
listed firms prefer to use short-term loans rather than long-term debt, which is
consistent with the empirical study of capital structure for overall China‘s listed firms.
As the figure 3.1 shows, long-term debt ratio has an upward trend during 1999-2007,
although it is still much lower than short-term debt ratio. The main reason is that bond
markets are growing rapidly in China, but it is still very difficult for real estate
companies to get long-term financing from relatively undeveloped corporate bond
markets.
As an alternative way to raise capital other than by issuing equities or directly
borrow money from banks, corporate bonds can provide long-term capital without
diluting shareholder‘ interest, and there is no strict limitation imposed on the usage of
the funds compared to bank loans. However, China‘s bond market is still very small
compared to mature economies. In the early 1990s when China just opened the market,
the overheating of the economy and excessive corporate bond issuance resulted in a
high default rate and even disturbed the issuance of government bonds. Thus the
government strengthened the regulation of bond markets by limited quota and strict
administrative controls. The bond markets have developed very slowly until recent
years, when The Pilot Rules on the Issuance of Corporate Bonds was promulgated in
May 2007.
Currently in China, there are four types of corporate bonds: enterprise bonds,
convertible bonds, short-term corporate financing bills, and listed company bonds.
Enterprise bonds are the earliest type of corporate bonds in China and they are usually
allowed to be issued by the large-scale state-owned enterprises while listed company
21
bonds were just introduced to the exchanges in 2007, issued by listed companies and
traded in the exchanges. Thus most Chinese real estate listed firms tend to have
short-term debt financing.
3.5 Most of the listed real estate firms are state-controlled
Due to the availability of data, the type of listed real estate firms is analyzed in the
period of 2003-2007. The firms with state-owned shares dominate the majority of
total listed real estate firms. The statistics are consistent with the empirical study by
Li, Luo, & Ao (2005) that the state-controlled listed real estate firms lead the whole
industry and use the main part of available loans in banks thus the development of
real estate to some extent depend on the state-controlled companies‘ success．
Table3.1.4 Type of firms
Type of firms
2003
2004
2005
2006
2007
Firms with state-owned shares
40
42
41
43
38
Firms with private owned
17
17
18
18
25
Total number
57
59
59
61
63
In addition, during 2003-2007 the number of private owned firms was increasing and
the number of state-controlled firms was decreasing due to the non-tradable share
reform. This reform is called forth by a distinct feature of Chinese stock markets. In
China, equity structure of most listed companies has been segmented. A-shares are
classified as ―tradable shares‖ held mainly by public investors, and ―non-tradable
shares‖ largely owned by central or local governments. Since the formation of stock
markets aimed to contribute to the growth of state-owned enterprises, the non-tradable
shares issue was designed as a way to manage state assets through capital markets.
According to statistics of CSRC, as of the end of 2004, 64% of the total 714.9 billion
shares issued by Chinese listed companies were non-tradable in nature, among which
state-owned shares accounted for 74%. However, the existence of non-tradable shares
has seriously constrained the internationalization and further development of China‘s
capital markets, such as problem of dual-pricing of shares of the same listed
companies. Thus the non-tradable share reform was initiated in April 2005, aiming to
22
eliminate the difference between tradable and non-tradable shares. However, the
amount of tradable shares is still low; the state shares remain a significant role of the
listed real estate firms.
23
Chapter Four - Empirical Analysis on
Determinants of Capital Structure
This study relies on quantitative methods as the main methodology. An empirical
analysis is made to test the capital structure determinants for Chinese real estate listed
companies. Due to the data limitation of state-owned share percentage and fixed
assets ratio, the analysis is conducted by using a panel data linear regression model
where the dependent variable is leverage ratio during the time period of 2003-2007. In
this chapter, static and dynamic panel data models are introduced, and the results from
these two models are tested and interpreted respectively.
4.1 Variables
According to the research objectives and the literature research this study has made in
the previous chapters, the measurements of variables are chosen and presented in the
following table.
Table 4.1.1 Measurement of variables
Variables
Dependent variables
Leverage
Independent variables
Firm size
Profitability
Growth opportunities
Tangibility
Liquidity
Debt interest
State-owned shares
Measurement
total liabilities/total assets
logarithm of total assets
return on asset
gross income growth rate
fixed asset/total assets
currency ratio
logarithm of year debt interest
state-owned shares/total shares
The regression model is constructed as follow expression:
𝐿𝐸𝑉 = 𝑓(𝑠𝑖𝑧𝑒, 𝐹𝑡𝑇, 𝑅𝑂𝐴, 𝐺𝑟𝑜𝑤𝑡ℎ𝑅, 𝐶𝑅, 𝑇𝑎𝑥𝑆, 𝑂𝑤𝑛𝑒𝑟𝑠ℎ𝑖𝑝)
(3.1)
The dependent variable LEV means the debt ratio, the size is the total assets, the FtT is
the ratio of the fixed assets and total assets, the GrowthR is the growth of gross income
and total assets, the variable CR is the liquidity ratio and the TaxS is tax shield. The
24
percentage of the government share in the total equity share is used to indicate the
ownership of the real estate company.
Table 4.1.2 Summary of the variables
Variable
year
leverage
growth
tangibility
state-owned
shares
currency ratio
ROA
size
lndebt interest
Mean
Std. Dev.
Min
Max
2005.047
1.418
2003
2007
N
299
between
0.44
2005
2007
n
63
within
1.396
2003.047
2007.047
T-bar
4.746
0.175
0
1.011
N
299
between
0.146
0.164
0.907
n
63
within
0.095
0.037
0.890
T-bar
4.746
24.094
-0.955
400.677
N
299
between
10.371
-0.266
80.39
n
63
within
21.652
-78.286
322.806
T-bar
4.746
overall
overall
overall
overall
0.561
2.519
0.206
0.149
0.995
N
299
between
0.179
0.264
0.99
n
63
within
0.106
0.336
1.326
T-bar
4.746
0.237
0
0.797
N
299
between
0.221
0
0.697
n
63
within
0.089
-0.103
0.802
T-bar
4.746
1.331
0.181
14.579
N
298
between
0.953
0.389
5.927
n
63
within
0.914
-1.848
11.59
T-bar
4.73
0.055
-0.34
0.146
N
299
between
0.039
-0.115
0.091
n
63
within
0.039
-0.199
0.212
T-bar
4.746
7.61E+09
1.90E+08
1.00E+11
N
299
between
6.34E+09
3.55E+08
3.93E+10
n
63
within
4.88E+09
-2.46E+10
6.49E+10
T-bar
4.746
overall
overall
overall
overall
overall
0.747
Observations
0.242
1.943
0.026
4.16E+09
1.563
11.16
21.412
N
293
between
17.484
1.433
13.19
19.959
n
63
within
0.829
13.038
19.75
T-bar
4.651
As the table above shows, there are 63 real estate companies are being researched in our
models. The time panel for the research is from 2003 to 2007.All the variables vary
both within and between individual companies.
4.2 Static panel data models
Panel data models are classified according to their assumptions regarding the source of
25
randomness and correlations between model components. The initial naive model for
analyzing panel data is the pooled ordinary least squares regression (OLS). The
mathematical formulation of this model is:
yit = α + xit′ β + εit
(4.2.1)
Where in our case yit denotes the capital structure of real estate firm i in year t, xit is
the vector of independent variables, β is a coefficient vector of independent variables
which is constant over years and individuals, α is the constant intercept and εit is the
error term. If the IID property holds for εit then the OLS model is the best fitness
linear model.
However, the results of the estimation above would be biased if the individual
effect exists and each company (i) has a distinctive regression pattern. In this case a
model that is capable of capturing individual effects is preferable. The basic individual
effect model is the dummy variables model as follows:
yit =
N
i=1 βi
× Di +
7
j=1 xitj
× βj + εit
(4.2.2)
Di is a dummy variable and βi is the coefficient for the respective dummy variable.
The dummy variable captures constant coefficient of independent variables as well as
the estimation of individual effect by variant intercept of regression line by βi × Di .
However, the dummy variables model implies a significant lost of degrees of freedom if
the number of individuals is large. In this case fixed effect model or random effect
model are more appropriate. The general individual effect model is specified as follow:
yit = xit βk + zi δ + ui + εit
(4.2.3)
βk is a coefficient for the k independent variable which does not vary between different
individuals and times. Zi is time-invariant variable(such as ownership in our case) ,and
δ is the respective coefficient. In addition, ui is a time-invariant error term which
accounts for the individual effects that are not captured by the independent variables.
The independent and idiosyncratic error term is specified as eit .
When ui is correlated with xit , expression 3.3 specifies the fixed effect model (FE),
otherwise it is the random effect model (RE). In the case that all the assumptions of RE
26
model are valid then it is the ultimate model. But if the ui is correlated with xit , the
random effect model will give misleading results and the estimation results of
individual-invariant variables from random effects model would be inconsistent with
the fixed effected model. In order to determine which model is more appropriate for a
given database, several statistic tests were developed to test the assumption regarding
the correlation between the individual effect and the explanatory variables (namely:
Breuch-Pagan, Hausman and Hausman-Taylor).
The problem of autocorrelation which indicates that the variation within each
country is correlated between different years should also be taken care of if it exists.
This can be done by introducing the Cluster-robust standard errors into the models.
4.3 Dynamic panel data model
Dynamic panel data models enable us to capture the impact of the debt ratio and the
other independent variables in previous years on current debt ratio. So the dynamic
relationship includes lagged dependent and independent variables:
yit = δyi,t−1 + x′it β1 + x′i t−n β2 + vit + μi
(n≥ 1)
(4.3.1)
Where in our case, yit−1 is the capital structure of company i in last year, δ is a scalar,
x′it is the vector of independent variables in year t and β1 is its coefficient vector, x′it−nis
the vector of independent variables in previous years, and 𝛽 2 is its coefficient vector.
The error term includes a fixed effect component,μi , and an idiosyncratic component,
vit . It should be noted that the inclusion of lagged dependent variables implies the
inclusion of the previous error term as well.
The reasons for this dynamic relation to occur are specified as follow: First, the
impact of the independent variables on the capital structure of the firm is quite lagged;
the debt ratio of the firm can‘t response to the current change in company situation, so
the independent variables in the earlier periods is expected to impact the debt ratio.
Applying the ordinary least squares (OLS) method will cause the dynamic panel
biased - attributing a share of the company fixed effect to the lagged dependent variable.
27
While the least square dummy variables (LSDV) model controls for the FE, it does not
overcome the problem of endogenous relationship between the lagged dependent
variable and the error term in the dynamic model. However, the two models result in
estimators that serve as lower bound (LSDV) and upper bound (OLS) for the true
estimated value (closer to the upper bound) according to the rule of thumb.
The first difference model (FD) produce consistent parameter estimates once
instrumental vectors (IV) of lags of the dependent variable are introduced, as follows:
∆yit = α∆yi,t−1 + ∆x′it β1 + ∆x′i t−n β2 + ∆vit
(4.3.2)
Where the lagged independent variable ∆yi,t−1 has been instrumented.However, while
the difference generalized method of moments (difference GMM, also known as
Arellano-Bond estimator) solves the endogeneity problem of the fixed effect; it
introduced other endogeneity problems with lagged dependent variables. The
orthogonal deviation method overcomes this problem by subtracting the average of all
future observations from the current one. This method requires that the idiosyncratic
component of the error term will not be serially correlated.
Moreover, we will apply optimal GMM estimation by using longer lags of the
dependent variable as additional instruments. The system GMM estimator is regarded
as the optimal estimator if the required assumptions are fulfilled as indicated by the
appropriate statistical tests.
In order to apply these models, it is fundamental to classify our regressions to
three classes: endogenous; predetermined or weakly exogenous; and strictly exogenous.
For endogenous variables, the GMM estimator should be used to instrument it. In our
case, the capital structure of the firm in the last year can be determined to be
endogenous because it is linear correlated with the error term because of the structure of
the fixed effect model. The endogenourity of the other explaining variables should be
test by computing the correlation and some specific test in the models.
28
4.4 Regression results and interpretation
4.4.1 Static panel model
First, the static panel model using the leverage ratio as dependent variables is regressed,
and the result is showed as following table.
Table 4.4.1 Static panel data models
OLS model(Robust)
Coeffec
ient
Fixed
Random
model(Robust)
model(Robust)
t-value
VIF
Coeffecient
Z
Coeffecient
Z
lnsize
0.0264
1.05
3.36
0.1206
2.61
0.0563
2
lnDebt interest
0.0232
1.4
3.43
0.0098
0.43
0.0203
0.98
roa
-1.0982
-3.83
1.22
-1.0678
-3.12
-1.012
currency ratio
-0.212
-17.62
1.23
-0.1372
-5.96
-0.1621
-0.1664
-2.74
1.08
0.1884
1.18
-0.0701
tangible ratio
0.7768
10.11
1.29
0.6513
3.56
0.7001
growth
-0.0001
-0.12
1.04
-0.0001
-0.73
-0.0001
_cons
-1.7013
-4.85
-3.6364
-4
-2.3644
No. of Obs
292
state-owned
shares
Group No.
Mean VIF
292
292
63
63
-3.5
3
-7.9
-0.9
4
5.49
-0.4
6
-5.8
6
1.81
F value for B-P
test before
17.94
robust
R-squared
0.5893
F( 7, 284)
21.58
8.34
F for
non-constant
35.61
test
Chi2 value for
98.71
hausman test
The static relationship between the leverage ratio and the determinants is showed as
29
above. There are 292 observations are employed by the OLS model, fixed effect model
and random effect model.
For the first model, the statistics of variance inflation factor (VIF) are all below
10 for all the independent variables, indicating that the multicollinearity is not a
problem. The F value for B-P test is 17.94, significant high to reject the null hypothesis
that the squared error term of the OLS model is constant among the variation of the
independent variables. The problem of heteroscadasticy is bothering the estimation, so
the robust standard error procedure is introduced into the first model. In the robust OLS
model, around 59 percent of the variation of the leverage ratio is capture by OLS model.
The statistics of the F-test value is 21.58, strongly reject the null hypothesis that the
joint explaining power of the independent variables is insignificant.
Significant explanatory variables are estimated as ROA (-3.83), currency ratio
(-17.62), the percentage of state-owned shares (-2.74) and tangible ratio (10.11). In the
OLS model, most of the estimators are consistent with our expectation. For example,
the companies with larger total asset tend to have higher debt ratio: one percentage
increase in total asset will bring about 2.64 percent increases in leverage ratio. However,
the marginal effect of state own share percentage is negative, conflicts with the research
by (Li, Yue, & Zhao, 2009). The reason for it may be the fixed effect of the individual
company. The statistics of F test with hypothesis that the fixed effect among all the
companies remain constant is 35.61, the fixed effect exist. To cope with potential fixed
effect, the fixed effect model is estimated in the second model.
The statistics for F value in the fixed effect model is 8.34, so the hypothesis that
the joint explaining power of the independent variables is insignificant and is rejected.
The variation of leverage ratio can be explained within the individual company. In
order to capture the variation between the individuals, the random effects model is
estimated. However, the Chi2 statistics for the Hausman test is high(98.71) to reject the
null hypothesis that the difference of coefficient between the fixed effect model and
random effect model is not significant, the fixed effect error term is correlated with the
independent variables, so the estimation of random effect model should not be trusted.
Finally, the fixed model is tested to be the most appropriate estimation in static
30
models. According to the robust fixed effect estimation, the marginal effect of total
asset on the leverage ratio is estimated to be significant (2.61) and positive (0.1206).
The result complies with the trade off theory: the firms with larger size have higher
reputation and are often easier to get the debt with low interest.
The marginal effect of tax shield (measured by the interest on debt) is estimated
to be insignificant (0.043) and positive (0.0098), the result is consistent with trade-off
theory, which is company would raise the debt to avoid the tax because the interest of
debt is tax deductable.
The pecking order theory is supported by the estimation that one percentage
increase in return on asset would bring about 1.07 percent decreases in the leverage
ratio ,and one percent increase in growth of the income would bring down the debt ratio
by 0.01 percent. According to the estimation of the fixed model, one present increase in
state own share of the company would induce 0.19 percent increase in leverage ratio.
This estimation is complied with the research by (Li, Yue, & Zhao, 2009), the argument
would be the state own companies in China are easier to get access to cheaper debt
because of the support of the government.
The trade-off theory is supported by the estimation that one percentage increase
in tangible asset ratio would increase the leverage ratio by 0.65 percent. The argument
for it would be the companies with more tangible asset are safer to borrow the money
from the banks.
4.4.2 Dynamic panel model
To measure the lagged impacted from the independent variables and dependent variable,
the dynamic panel data models are estimated. These dynamic panel data models
includes OLS dynamic model, fixed effect dynamic model, difference GMM model
(one step and two step) and system GMM model(one step and two step). The estimation
results are showed as the following table.
Table.4.4.2 Dynamic panel data model
OLS
Fixed
(Robust)
effect
1 DGMM
31
1 S GMM
2 S GMM
L.lnlev
Sd Err for
L.lnlev
growth
coeffic
t-value
coeffic
t-value
coeffic
t-value
coeffic
t-value
coeffic
t-value
0.737
8.63
0.078
0.49
0.481
1.77
0.622
4.84
0.612
8.18
-0.085
-0.161
-0.271
-0.129
-0.075
-0.008
-1.3
0.001
0.09
-0.003
-0.68
-0.012
-1.27
-0.006
-1.27
L.growth
0
-0.83
0
0.04
0
-0.62
0
-1.07
0
-2.31
L2.growth
0
0.38
0
0.04
0
0.05
0
0.28
0
-0.1
Tangible ratio
0.285
1.86
0.596
3.13
0.28
0.91
0.477
1.73
0.417
2.56
L.tangible ratio
0.168
0.57
0.517
1.98
0.426
1.18
0.149
0.4
0.257
1.42
-0.483
-1.84
-0.182
-0.94
-0.406
-1.54
-0.448
-1.5
-0.391
-2.72
stateowned~e
0.032
0.16
-0.159
-0.63
-0.165
-0.59
0.353
1
0.47
3.47
L.stateown~e
0.091
0.43
0.179
1.02
0.004
0.02
-0.145
-0.42
-0.288
-2.23
L2.stateow~e
-0.183
-1.81
-0.104
-1.07
-0.103
-0.81
-0.231
-2.21
-0.205
-4.6
currenratio
-0.119
-3.89
-0.177
-4.36
-0.137
-2.56
-0.126
-3.28
-0.105
-4.66
L.currenra~o
0.067
2.35
-0.091
-2.24
-0.043
-0.52
0.037
1.18
0.016
0.71
L2.currenr~o
0.01
0.38
-0.021
-0.97
0.009
0.3
0
0.01
0.014
0.8
roa
-0.988
-4.06
-0.847
-3.19
-0.505
-1.48
-0.405
-0.64
-0.427
-0.94
L.roa
-0.123
-0.54
-0.668
-2.55
-0.108
-0.26
-0.348
-1.1
-0.234
-1.17
L2.roa
0.115
0.51
-0.245
-0.95
0.088
0.23
0.167
0.98
0.265
1.95
lnsize
0.192
3.42
0.124
2.5
0.147
2.56
0.155
1.71
0.158
4.35
L.lnsize
-0.065
-0.73
-0.13
-1.24
-0.249
-2.09
0.027
0.18
-0.082
-1.4
L2.lnsize
-0.095
-1.44
-0.159
-1.66
-0.119
-1.15
-0.098
-0.98
-0.015
-0.26
lnDinterest
0.004
0.22
0.008
0.42
-0.017
-0.52
0.003
0.11
0.007
0.59
L.lnDinter~t
-0.013
-0.43
-0.004
-0.12
0.004
0.12
-0.051
-1.03
-0.061
-2.7
L2.lnDinte~t
-0.013
-0.84
0.006
0.35
0.021
0.9
-0.025
-1.39
-0.013
-1.31
L2.tangible
ratio
yr1
0
0
yr2
0
0
yr3
0
-0.023
-0.66
0.015
0.3
0.052
1.25
0.056
2.41
yr4
0.027
1.14
0.015
0.46
0.038
0.91
0.086
2.35
0.077
3.79
yr5
-0.06
-1.96
0
_cons
-0.332
-0.75
2.695
-0.762
-0.74
-0.491
-1.1
169
Number of obs
169
Number of
groups
1.27
169
111
169
169
58
57
58
58
67
67
67
6.2
70.9
7737.6
0.0099
0.002
0.004
0.018
0
0
Number of
instruments
F test(F)
22.8
18.3
R-squared
0.829
0.667
F for B-P test
7.26
corr(u_i, Xb)
P for
AB-AR(1) test
-0.338
P for Sargan
32
test
P for Hansen
0.478
test
0.912
0.912
0.941
0.941
0.665
0.665
P for Hansen
test excluding
group
P for
Difference
-null H =
exogenous
In the OLS model, 169 observations are used. The F value for Breusch-Pagan test is
7.26 before robust, indicating that the problem of heteroskedasticy exists. The standard
error robust procedure is needed. After the robust procedure is performed, around 83%
of the variation in dependent variables can be explained by the explanatory variables.
The F value is high (22.8) to reject the null hypothesis that the independent variables
are jointly insignificant.
Although the estimation from the OLS model cannot be trusted because of the
problem of endogenerity and fixed effect in the error term of the model, the coefficient
of the lagged independent variable ―L.lnlev‖ in OLS model can be used as the up limit
of the rang that the ―true‖ value locates in. The reason for it is that the lagged
independent variable in OLS model is inflated because the predictive power from the
fixed effect is attribute to it. The coefficient for lagged leverage ratio is 0.737 and
significant (8.63 t value) according to the estimation in OLS model.
In the fixed effect model, 169 observations are separated into 58 groups. About
66.7% of the variation is captured in the fixed effect model. The F value is also high
(18.3) to reject the null hypothesis that the independent variables are jointly
insignificant. Although the fixed effect has been considered in the fixed effect model,
the problem of endogenerity still exists between the lagged independent variable and
the error term, the coefficient of the lagged independent variable can be used as the
lower limit of the rang of the ―true‖ value because some part of the explanatory power
of lagged independent variable is sliced off and given to the error term which is
correlated with it. In the fixed effect model, the coefficient decline from 0.737 in OLS
model to 0.078, and becomes insignificant (0.49) in explaining the dependent variable.
33
To cope with the fixed effect and endogenerity together, the difference model
using Generalized Method of Moments (GMM) is introduced into the estimation. In the
one step difference GMM model, 111 observations are used and separated into 57
groups to eliminate the fixed effect. There are 67 instrument variables in all to
instrument the lagged independent variable (only the lagged independent variable is
considered as endogenous). The F value is high again (6.2) to reject the null hypothesis
that the independent variables are jointly insignificant, and the P(probability) value for
A-B AR(1) test is low(0.0099) to reject the null hypothesis that the autocorrelation
exists in first lag. The P value for Sargan test is low (0.018) and for Hansen test is high
(0.478), indicating that the over identification is not a problem.
In the one step difference GMM model, the coefficient of the lagged independent
variable is 0.481, within the range of 0.737 to 0.078, but the explaining power of it is
not significant (1.77). The reason for it may be the autocorrelation doesn‘t exist, and the
leverage ratio may be closed to a random walk, and the past leverage ratio convey little
information about future changes. In this case, the system GMM model should be tried
to see if it can give a more satisfactory result.
Different from difference GMM, the system GMM model instrumented level term
endogenous variables with difference term variables. All of the independent variables
are assumed as endogenous and instrumented to get rid of the problem of fixed effect
and endogenerity. The one step system GMM and two step system GMM are using the
same number of observations (169), groups (58) and instrument variables (67). Both of
them have the same P value for Sargen test (0) and Hansen test (0.912), the problem of
over identification doesn‘t bother them. None of them has problem of autocorrelation
(P value for AB-AR (1) test are 0.002 and 0.004 respectively). Both of them have high
F values (71 and 7738) to reject the null hypothesis that the independent variables are
jointly insignificant. The endogenerity is also not a problem for the instrument
variables (P values for Hansen test and difference are 0.94 and 0.67 respectively).
In the five estimated models, the two-step system GMM model performs best in
measuring the marginal effect of the lagged independent variable, and it is chosen to be
interpreted.
34
The coefficient of lagged independent variable in this model is 6.12, within the
range from 0.078 to 0.737, and the standard deviation is the lowest of the five models
(0.075).
The growth rate in 2006 (-2.31 for t-value) performs better than that in 2007
(-1.27 for t-value) to explain the leverage ratio. One percentage increase in the income
growth in 2006 can induce the leverage rate decrease by 0.04%, the effect complies
with trade-off theory.
The tangible assets ratios in 2007 and in 2005 have opposite impact on the
leverage ratio in 2007 although both of them are significant (2.56 and -2.72 for t-value
respectively). According to the estimation, one percentage tangible assets ratio increase
in 2007 would render the leverage ratio increase by 41.7%, (consistence with trade-off
theory) while one percentage tangible assets ratio increase in 2005 would render the
leverage ratio decrease by 39.1% .
The marginal effects from percentage of state-owned share in 2007 (3.74 for
t-value), 2006 (-2.23 for t value) and 2005 (-4.6) are all significant for leverage ratio in
2007. One percentage state own share increase in 2007 can bring about one percent
increase in debt ratio, while the marginal impacts in 2006 and 2007 are -0.42% and
-2.21% respectively.
The impacts from total asset return and tax shield measured by interest for debt
are both lagged. According to the estimation, one percentage increase in return of the
2005 brings about 0.265% increases in debt ratio. While one percentage increases in
2006 interest for debt would bring down the leverage ratio by 0.06%.
The marginal impacts from financial distress measured by the currency ratio and
the size of total assets are the only two variables that have not lagged effect. One
percentage increase in total size would induce the debt ratio to increase by 0.16 %,
while the marginal effect for currency ratio is 0.1%.This conclusion complies with
trade-off theory.
35
4.5 Summary of findings
In static models, the fixed effect model is estimated as the most appropriate model to
capture the static or long term equilibrium relationship between the leverage ratio and
its determinants. In dynamic models, the lagged effect of the determinants can be
estimated including the lagged leverage ratio. The two step system GMM model is
estimated to be performing best. The significant and positive independent variables are
estimated to be the leverage ratio in 2006, tangible assets ratio in 2007, state owned
share percentage in 2007, return of assets in 2005, the currency ratio and total capital
size in 2007. While the significant and negative determinants are estimated to be
growth rate in 2006, tangible assets ratios in 2005, state owned share percentage in
2006 and 2007, and the interest for debt in 2006.
In conclusion, the significant and positive determinants are estimated to be the
total assets, the state-owned share and tangible asset ratio. While the significant and
negative determinants are estimated to be the growth rate of the income and return on
assets. The estimated results are consistent with existing empirical studies of Chinese
listed firms (See Huang & Song, 2006; Qian, Tian & Wirjanto, 2007; Bharbra, Liu, &
Tirtiroglu 2008) Moreover, the influence of the determinants on capital structure in
Chinese real estate listed firms works a similar way as in other countries. Comparison
of the signs is shown in the following table.
Table 4.5 Summary of results
Determinants
Expected sign by theories
Hypothesis
profitability
+
+
+
-
tangibility
currency ratio
tax shield
+ (trade off)
- (pecking order)
+ (trade off)
- (pecking order)
- (trade off)
+ (pecking order)
+ (trade off)
+(trade off)
+ (trade off)
Results of this study
-
+
+
+
state-owned shares
Ambiguous
+
+
+
size
growth opportunities
+
Negative relationship between leverage and profitability in Chinese real estate listed
firms seems to support the pecking order theory, which proposed that profitable firms
36
prefer internal funds rather than external due to asymmetric information and
transaction costs. However, since Chinese real estate listed firms heavily rely on
external fund, especially high levels of equity finance. There may be other reasons for
this negative relationship rather than pecking order theory. Three reasons are
investigated in this study: 1) the corporate bond markets in China is undeveloped; 2)
due to the separation of tradable and untradeable shares in Chinese stock markets,
equity issuance has relatively low impact on dilution of ownership; 3) agency problem
exists in state-controlled enterprises since the shareholders and mangers are detached
thus managers tend to issue equity rather than bonds. As Chen (2004) suggests,
Chinese listed firms appear a new pecking order – retained profit, then equity finance,
and lastly debt.
The relationship between size and leverage is positive, which is consistent with
trade-off theory suggesting that large firms tend to be more diversified and less
exposed to the bankruptcy cost thus they are able to have a high debt capacity.
Another reason of this positive relationship is investigated that Chinese real estate
firms have a strong desire of expanding. Large real estate developers are able to buy
more lands and have more underway projects. According to the constraint condition
of issuing loans for real estate developers in China, loans only can be issued for the
projects that possess four certificates including Certificate for the Use of State-owned
Land, Planning Permit on Land for Construction Use, Planning Permit on
Construction Works, and Working Permit on Construction Works. Even though the
banks permit to issue a large amount of loans for real estate firms, the loans are not
allowed to be granted if there is no underway project. Thus more underway projects
render more debt for real estate developers.
The negative relationship between growth opportunity and leverage is conflicted
with the hypothesis of this study. The result seems to support the pecking order theory
while the effect is statistically insignificant in the fixed model. It is likely because the
proxy for growth opportunities use the gross income growth rate, which measures past
growth experience. A more appropriate measure for growth opportunities should be
Tobin Q, which is measured as market-to-book ratio of total assets.
37
It is not surprised to see a positive relationship between leverage and tangibility
for real estate listed firms. The result is in accordance with trade-off theory suggesting
that tangible assets serve as good collaterals to support debt. Tangibility is also an
important criterion in banks‘ credit policy.
In this study, state-owned shares are measured as one of the determinants of
capital structure, and the result proves a significant positive relationship between
percentage of state-owned shares and leverage. It indicates that firms with larger
proportion of state-owned shares enhance the access to debt. It is also proved that
significant institutional difference with dual roles of government (a shareholder of
firms as well as a regulatory agency) in China is an important factor on firms‘ capital
structure, which at least is as important as the firms‘ factors such as profitability, size
etc.
38
Chapter Five – Conclusion
5.1 Conclusion
This paper examines the feature of capital structure for Chinese listed real estate firms
on the two Chinese stock exchanges in the period of 1999-2007, and founds that as
like other Chinese listed firms, real estate firms have lower leverage level than
developed countries and tend to rely on external finance especially equity finance;
they have high short-term debt finance and low amounts of long-term debt, and the
shares in listed real estate firms are still largely controlled by the state.
An empirical study is done to investigate the determinants of capital structure of
listed real estate companies in China. The fixed effect model is tested to be the most
appropriate estimation in static models and two step systems GMM model is estimated
to perform best in dynamic models. The significant and positive determinants are
estimated to be the total asset, the state-owned share percentage and tangible asset ratio.
While the significant and negative determinants are estimated to be the growth rate of
the income and return on assets. The lagged effects of explanatory variables are found
to be explaining the current debt ratio significantly according to the estimated dynamic
model.
The estimation results are compared with the literature study of capital structure
theories including Miler and Modigliani Theory, trade-off theory and pecking order
theory. An attempt has been made to study institutional factors influencing on capital
structure decision. Most of the estimation from the model is consistent with the
trade-off theory and pecking-order theory. However, these two theories cannot fully
provide convincing explanations for the capital choices of the Chinese real estate
listed firms. Instead, the some of the results are explained by the practical situation
gained from series of interviews with Chinese developers, which suggesting that the
institutional differences and financial constraints in the capital markets especially for
real estate firms in China are also the factors influencing firms‘ capital structure
decisions.
39
The significant institute differences and financial constraints in China are
discussed in this paper such as China is still in a transitional process from a planned to
a market economic system and the states plays an important dual role as a regulatory
agency and the owner of firms in real estate sectors. In the stock market, a deficient
share ownership system results in a preference of external equity finance. The factors
like a high degree of market segmentation in tradable and non-tradable shares, a small
proportion of institutional investors, and a lack of investment protection for individual
investors all render the share capital become somewhat a ―free‖ source of finance. In
banking sector, banks tend to lend money to the state-owned enterprises and large
private companies that have government support, while pay less attention to
medium-sized enterprises. This can be explained by the past practice of state-directed
lending, relationship-based credit decisions, and the general lack of confidence in
counterpart credit outside the realm of the public-sector entities (Hansakul, Dyck&
Kern, 2009). Besides, the corporate bond market in China is undeveloped and still
strictly controlled by the state; agency problem exists in state-controlled enterprises
that the shareholders and mangers are detached. Considering these distinct features of
Chinese financial market, it is not difficult to understand the capital structure of
Chinese listed real estate firms cannot be fully explained by the trade-off and pecking
order theory developed in western setting.
Lastly, some related issues are worthy to be further studied. This empirical study
neglects the potential different determinants with respect to short-term and long-term
debt ratio of real estate listed firms. In addition, most of the estimation from the
models is consistent with trade-off theory and pecking order theory, except some
lagged marginal impact in dynamic model, more work can be done to find out the
reasons, and the error correction model can be constructed to capture the adjustment
speed of the debt ratio. Last but not least, due to the data collection challenge, only
listed real estate firms are chosen to study the determinants of capital structure thus
the result is not convincing enough for the whole industry. It is essential to further
study this issue in the area of China‘s real estate industry.
40
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