Ownership Structure, Capital Structure and Firm

Performance:

A Study of Vietnamese Listed Firms

Thi Phuong Vy LE

A thesis submitted in partial fulfilment of the requirements for the degree of

Doctor of Business Administration

University of Western Sydney

2015

ii

Statement of Authentication

The work in the thesis has been prepared by me to partially fulfil the requirements of the

Doctor of Business Administration at the University of Western Sydney.

I hereby declare that the work is a result of my own research, except where

acknowledgement is made. It is an original work and I have not submitted this material,

either in whole or in part, for a degree at this or any other institution.

Signed:

Date: Feb 2015

iii

Acknowledgements

This thesis would not be finished without the support, assistance and encouragement of

many people. I would like to express my deepest gratitude and appreciation to my

principal supervisor, Dr Kathy Tannous, for her invaluable mentorship throughout my

dissertation stage. I also would like to thank my co-supervisor, Dr Kyung Hwan Yoon,

who has provided valuable comments and advice for my research. The completion of this

thesis is because of their kind assistance and contribution.

I would like to thank Assoc. Prof. Yi-Chen Lan, Prof. Clive Smallman, Assoc. Prof.

Anneke Fitzgerald, Prof. Nguyen Dinh Tho and Dr Tran Ha Minh Quan for their guidance

and support in my DBA journey. I would like to acknowledge all the DBA lecturers, Prof.

Terry Sloan, Prof. George Lafferty and Prof. Margaret Vickers, for their devotion to

teaching the DBA courses. My thanks also go to the staff in the School of Business,

Amanda Reed, Judy Foster, Bec Campisi, Barbara Pinning, and Elite editing company; I

will always appreciate their support.

Above all, I sincerely thank my family and friends, especially my parents, Le Trong Hoa

and Le Thi Van, for their encouragement. My deepest gratitude to my beloved husband,

Trinh Minh Viet, for his support and for looking after our baby while I was busy studying.

I love you all.

iv

Preface

Research papers derived from this thesis have been published in a refereed journal and

presented at international conferences.

Journal

Thi Phuong Vy Le 2013, ‘Foreign ownership, capital structure and firm performance:

Empirical evidence from Vietnamese listed firms’, IUP Journal of Corporate

Governance, vol. 12, no. 2, pp. 40–58 (co-author: Duc Nam Phung).

Conferences

Thi Phuong Vy Le 2014, ‘The impact of ownership structure on capital structure decision:

A study of Vietnamese listed firms’, 27th Australian Finance and Banking Conference,

Sydney, Australia, December (co-author: Kathy Tannous).

Thi Phuong Vy Le 2014, ‘Leverage and investment: A view of prominent role of state

ownership’, Vietnam International Conference in Finance, Hanoi, Vietnam, June (co-

authors: Thi Phuong Thao Hoang, Duc Nam Phung).

Thi Phuong Vy Le 2013, ‘Capital structure and firm performance: A study of Vietnamese

listed firms’, World Business and Social Science Research Conference, Bangkok,

Thailand, October (co-author: Kathy Tannous).

v

Abstract

Although several studies have focused on diverse aspects of ownership and capital

structure, some limitations still exist. First, most previous studies have concentrated on

the influence of ownership structure on firm performance, but there is a lack of research

about its effect on financing decisions. Concretely, while the relationship between

ownership structure and capital structure has been established theoretically, only a few

empirical studies have examined this linkage, especially in developing countries. In

addition, the studies have often concentrated on managerial ownership, insider ownership,

institutional ownership, or ownership concentration. There is limited empirical evidence

on the influence of foreign and state ownership on capital structure, although these kinds

of ownership have recently become common and important in developing economies

because of a sharp increase in foreign investment and the equitisation process. Second,

several theories and studies have examined capital structure; however, there is no single

theory that can fully interpret the effect of capital structure on firm performance.

Specifically, empirical evidence shows different and contradictory results and indicates

that this relationship depends significantly on the specific circumstances. Therefore, the

purpose of this research was to investigate the effect of ownership on capital structure and,

subsequently, the influence of capital structure on firm performance in Vietnam, which is

considered a typical developing country.

To achieve this objective, the research used unbalanced panel data from all non-financial

listed firms in Vietnam during the period 2007–2012 and employed pooled ordinary least

squares, random effects and fixed effects regression methods, and the dynamic panel

generalised method of moments for analysing data. Although various approaches were

applied, all results were consistent.

Specifically, the study found that whereas foreign ownership has a negative effect on

leverage, state ownership has a positive influence. Managerial ownership has a positive

relation, but the effect of large ownership on debt level is not conclusive. Additionally,

the results revealed that foreign ownership affects inside ownership influence on financing

vi

decisions. In particular, foreign ownership decreases the positive effect of managerial

ownership on debt level. The research results also indicate that all ratios of long-term debt,

short-term debt and total debt in both book and market value are significantly and

negatively related to return on asset, return on equity and Tobin Q. A non-linear

relationship between leverage and firm performance only appears when performance is

measured by return on equity and capital structure measured by total debt and short-term

debt.

These findings imply that firms with different ownership type may not be equal with

respect to access to capital sources. State-owned firms could have substantial advantages

through access to the debt market because they receive preferential treatment from state-

owned banks. Additionally, this research provides evidence of an active monitoring role

of foreign investors. Foreign owners with experience, knowledge and incentives can help

firms to reduce agency costs of equity through actively monitoring the management. This

research also supports the argument that one of the greatest concerns of managers is to

retain or increase their control because it provides them with discretion in making

decisions or accessing their private benefits. Moreover, the research findings indicate that

in developing countries, an increase in debt can decrease firm performance because of

high interest rates, exhausted cash flow or inefficient monitoring of debt.

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Contents

Statement of Authentication .................................................................................... ii

Acknowledgements ................................................................................................... iii

Preface ....................................................................................................................... iv

Abstract ...................................................................................................................... v

Contents ................................................................................................................... vii

List of Figures ............................................................................................................ x

List of Tables ............................................................................................................ xi

List of Abbreviations ............................................................................................. xiii

Chapter 1: Introduction ........................................................................................... 1 1.1 Research motivations and purpose .................................................................... 1 1.2 Research questions ............................................................................................ 3 1.3 Overview of research context: Vietnam ............................................................ 3

1.3.1 Vietnamese economy ................................................................................. 3 1.3.2 Vietnamese financial market ...................................................................... 4 1.3.3 Vietnamese listed firms .............................................................................. 5

1.4 Research contribution ........................................................................................ 5 1.5 Structure of research ......................................................................................... 7

Chapter 2: Overview of Research Context ............................................................. 9 2.1 Introduction ....................................................................................................... 9

2.2 Vietnamese economy ........................................................................................ 9

2.2.1 Vietnamese economy overall ..................................................................... 9

2.2.2 Equitisation process ................................................................................. 13

2.3 Vietnamese financial market ........................................................................... 14

2.3.1 Banking sector .......................................................................................... 15

2.3.1.1 Number and size of banks ................................................................. 15

2.3.1.2 Market share ...................................................................................... 16

2.3.1.3 Interest rate ........................................................................................ 16

2.3.1.4 Credit distribution ............................................................................. 17

2.3.2 Bond market ............................................................................................. 18

2.3.2.1 Size of the bond market .................................................................... 18

2.3.2.2 Local currency bond market.............................................................. 19

2.3.2.3 Foreign currency bond market .......................................................... 20

2.3.2.4 Government bonds ............................................................................ 21

2.3.2.5 Corporate bonds ................................................................................ 21

2.3.3 Stock market ............................................................................................ 22

2.3.3.1 History ............................................................................................... 22

2.3.3.2 Regulation framework ....................................................................... 23

2.3.3.3 Market capitalisation ......................................................................... 23

2.3.3.4 Number of listed firms ...................................................................... 24

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2.3.3.5 Some indicators of market ................................................................ 25

2.3.3.7 Foreign investment in Vietnamese stock market .............................. 26

2.4 Conclusion....................................................................................................... 27

Chapter 3: Literature Review ................................................................................ 29 3.1 Introduction ..................................................................................................... 29 3.2 Ownership structure and capital structure ....................................................... 30

3.2.1 Theory perspective ................................................................................... 31 3.2.2 Empirical evidence ................................................................................... 33

3.2.2.1 Managerial ownership ....................................................................... 33 3.2.2.2 State ownership ................................................................................. 36 3.2.2.3 Foreign ownership ............................................................................. 38 3.2.2.4 Large ownership ................................................................................ 40

3.3 Capital structure and firm performance .......................................................... 42 3.3.1 Theoretical perspective ............................................................................ 42 3.3.2 Empirical evidence ................................................................................... 45

3.4 Capital structure: Previous studies in Vietnam ............................................... 47 3.5 Conclusion....................................................................................................... 49

Chapter 4: Methodology ......................................................................................... 51 4.1 Introduction ..................................................................................................... 51 4.2 Hypothesis development ................................................................................. 52

4.2.1 Ownership structure and capital structure ................................................ 52 4.2.1.1 State ownership ................................................................................. 52

4.2.1.2 Foreign ownership ............................................................................. 53 4.2.1.3 Managerial ownership ....................................................................... 54 4.2.1.4 Large ownership ................................................................................ 55 4.2.1.5 Moderating effect of ownership structure ......................................... 56 4.2.1.6 Non-linear relationship between ownership structure and capital

structure ............................................................................................ 57 4.2.2 Capital structure and firm performance ................................................... 58

4.3 Data ................................................................................................................. 59 4.3.1 Sample ...................................................................................................... 59

4.3.2 Data collection ......................................................................................... 60 4.4 The variables ................................................................................................... 63

4.4.1 Measure of firm performance .................................................................. 63 4.4.2 Measure of capital structure ..................................................................... 63 4.4.3 Measure of ownership structure ............................................................... 64

4.4.3.1 Managerial ownership ....................................................................... 64

4.4.3.2 State ownership, foreign ownership and large ownership ................ 64

4.4.4 Measure of control variables .................................................................... 65

4.5 Data analysis ................................................................................................... 66

4.6 Empirical model .............................................................................................. 69

4.6.1 The capital structure model ...................................................................... 69

4.6.2 The firm performance model ................................................................... 75

4.7 Conclusion....................................................................................................... 78

Chapter 5: Findings ................................................................................................ 80

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5.1 Introduction ..................................................................................................... 80

5.2 Descriptive statistics of data ............................................................................ 80

5.3 Capital structure model: The effect of ownership structure on capital structure ........................................................................................................... 86

5.3.1 Correlation analysis .................................................................................. 86

5.3.2 Pooled OLS regression ............................................................................. 88

5.3.3 Random and fixed effect regression ......................................................... 90

5.3.4 GMM estimator and dynamic capital structure model ............................. 95

5.3.5 Results ...................................................................................................... 98

5.3.5.1 State ownership ................................................................................. 98

5.3.5.2 Foreign ownership ............................................................................. 98

5.3.5.3 Managerial ownership ....................................................................... 99

5.3.5.4 Large ownership ................................................................................ 99

5.3.5.5 Control variables ............................................................................... 99

5.3.6 Robustness check ................................................................................... 101

5.3.7 Moderating effect of the relationship between managerial ownership and capital structure ...................................................................................... 106

5.3.8 Non-linear relationship between ownership structure and capital structure ................................................................................................. 108

5.4 Firm performance model: The effect of capital structure on firm performance ................................................................................................... 111

5.4.1 Correlation analysis ................................................................................ 111

5.4.2 Pooled OLS regression ........................................................................... 111

5.4.3 Random and fixed effect regression ....................................................... 115

5.4.4 GMM estimator ...................................................................................... 120

5.4.5 Results .................................................................................................... 122

5.3.5.1 Capital structure .............................................................................. 122

5.3.5.2 Control variables ............................................................................. 122

5.4.6 Robustness check ................................................................................... 123

5.4.7 Non-linear relationship between capital structure and firm performance ........................................................................................... 129

5.5 Conclusion..................................................................................................... 132

Chapter 6: Conclusion and Discussion ................................................................ 133 6.1 Conclusion..................................................................................................... 133 6.2 Discussion ..................................................................................................... 135

6.2.1 The effect of ownership structure on capital structure ........................... 135 6.2.1.1 State ownership ............................................................................... 135 6.2.1.2 Foreign ownership ........................................................................... 135 6.2.1.3 Managerial ownership ..................................................................... 136 6.2.1.4 Large ownership .............................................................................. 137

6.2.2 The effect of capital structure and firm performance............................. 137 6.3 Policy implications ........................................................................................ 139 6.4 Limitations .................................................................................................... 140

References .............................................................................................................. 142

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List of Figures

Figure 2.1: GDP growth (%) ..................................................................................... 10

Figure 2.2: Number of equitised SOEs ..................................................................... 14

Figure 2.3: Deposit market share .............................................................................. 16

Figure 2.4: Credit market share ................................................................................. 16

Figure 2.5: Interest rate (%) ...................................................................................... 17

Figure 2.6: Bonds outstanding in major markets (% of GDP) in 2013 ..................... 19

Figure 2.7: Composition of LCY bond market (USD billion) .................................. 20

Figure 2.8: Composition of FCY bond market (USD billion) .................................. 21

Figure 2.9: Market capitalisation of Vietnamese stock market (% GDP)................. 24

Figure 2.10: Number of listed firms in HOSE and HNX .......................................... 25

Figure 2.11: Net inflow portfolio equity of foreign investors (USD million) .......... 26

xi

List of Tables

Table 2.1: Main Vietnamese economic indicators .................................................... 12

Table 2.2: Financial market in Vietnam (% of GDP) ............................................... 15

Table 2.3: Proportion of registered Vietnamese firms by ownership (%) ................ 18

Table 2.4: Credit to economy (% of total) ................................................................ 18

Table 2.5: Some indicators of Vietnam stock market in 2012 .................................. 26

Table 2.6: Trading volume of foreign investors........................................................ 27

Table 4.1 Number of observations separated by year, industry and ownership

structure .................................................................................................... 62

Table 4.2 Variables used in the measure of firm performance, capital structure and

ownership structure .................................................................................. 65

Table 4.3: Control variables used in this study ......................................................... 65

Table 4.4: Tests and models used in this study ......................................................... 67

Table 5.1: Descriptive Statistics of capital structure (CS), firm performance (FP) and

ownership structure (OS)—Full sample................................................... 82

Table 5.2: Mean of capital structure (CS), firm performance (FP) and ownership

structure (OS) - Separated by industry and year ...................................... 85

Table 5.3: Correlation coefficients between measures of ownership structure and

capital structure ........................................................................................ 87

Table 5.4: The effect of ownership on capital structure—Pooled OLS regression .. 89

Table 5.5: The effect of ownership on capital structure—Random effect

regression ................................................................................................. 91

Table 5.6: The effect of ownership on capital structure—Fixed effect regression ... 92

Table 5.7: The effect of ownership on capital structure—Fixed effect regression

with robust standard error ........................................................................ 94

Table 5.8: The effect of ownership structure on capital structure – System two-step

GMM estimators with robust standard error ............................................ 97

Table 5.9: The effect of ownership structure measured by dummy variables on

capital structure ...................................................................................... 102

xii

Table 5.10: The effect of ownership on capital structure measured by market

value ....................................................................................................... 103

Table 5.11: The effect of ownership on capital structure- Fixed industry .............. 104

Table 5.12: The effect of ownership on capital structure- Fixed year .................... 105

Table 5.13: The effect of outsider ownership on the relationship between inside

ownership and capital structure—Random and fixed effect models ..... 107

Table 5.14: Non-linear relationship between ownership structure and capital

structure .................................................................................................. 109

Table 5.15: Correlation coefficients between measures of capital structure and firm

performance............................................................................................ 112

Table 5.16: The effect of capital structure on firm performance—Pooled OLS

regression ............................................................................................... 114

Table 5.17: The effect of capital structure on firm performance—RE and FE

regressions .............................................................................................. 116

Table 5.18: The effect of capital structure on firm performance—Fixed effect

estimator with robust standard error ...................................................... 119

Table 5.19: The effect of capital structure on firm performance—System two-step

GMM estimator with robust standard error ........................................... 121

Table 5.20: The effect of capital structure on firm performance—Fixed industry . 125

Table 5.21: The effect of capital structure on firm performance—Fixed year ....... 126

Table 5.22: The effect of capital structure measured by long-term debt ratios on firm

performance............................................................................................ 127

Table 5.23: The effect of capital structure measured by short-term debt ratios on

firm performance .................................................................................... 128

Table 5.24: Non-linear relationship between capital structure and firm

performance............................................................................................ 130

Table 6.1: Summary of results ................................................................................ 134

xiii

List of Abbreviations

ASEAN Association of Southeast Asian Nations

ATM Automated teller machine

BIDV Bank for Investment and Development of Vietnam

CEO Chief executive officer

CF Cash flow

CS Capital structure

DIV Dividend

FBB Foreign bank branch

FCY Foreign currency

FDI Foreign direct investment

FE Fixed effect

FO Foreign ownership

FP Firm performance

GDP Gross domestic product

GMM Generalised method of moments

GRO Growth

HNX Hanoi Stock Exchange

HOSE HoChiMinh Stock Exchange

ICB Industry Classification Benchmark

IMF International Monetary Fund

INV Investment

IV Instrument variable

JSCB Joint-stock commercial bank

JVB Joint-venture bank

LCY Local currency

LIQ Liquidity

LLEV Long-term leverage

LM Lagrange Multiplier

LO Large ownership

xiv

MLLEV Market long-term leverage

MM Modigliani–Miller

MO Managerial ownership

MSLEV Market short-term leverage

MTLEV Market total leverage

NPL Non-performing loan

OLS Ordinary least squares

OS Ownership structure

P/B Price-to-book

P/E Price-earnings

PRO Profitability

RE Random effect

RISK Risk

ROA Return on assets

ROE Return on equity

S&P Standard & Poor’s

SBV State Bank of Vietnam

SIZE Size

SLEV Short-term leverage

SME small to medium-sized enterprise

SO State ownership

SOCB State-owned commercial bank

SOE State-owned enterprise

STC Securities Trading Centre

TAN Tangibility

TAX Tax

TLEV Total leverage

VBARD Vietnam Bank for Agriculture and Rural Development

VCB Vietcombank

VIF Variance inflation factor

WTO World Trade Organization

1

Chapter 1: Introduction

This chapter presents an overall introduction to the thesis, including research motivations,

research questions and contributions.

1.1 Research motivations and purpose

The limitation of empirical evidence relating to the influence of ownership structure on

capital structure as well as the influence of capital structure on firm performance in

developing countries, especially in the context of Vietnamese listed firms, was the

motivation for this research.

Research to date has focused on diversified aspects of ownership structure. However, most

studies have concentrated on the influence of ownership structure on firm performance,

and there is limited research that explains the relationship between ownership structure

and financing decisions (Bokpin & Arko 2009; Brailsford, Oliver & Pua 2002; Friend &

Lang 1988; Li, Yue & Zhao 2009; Margaritis & Psillaki 2010; Ruan, Tian & Ma 2011).

Concretely, studies that link ownership structure with capital structure only attempt to

identify the determinants of capital structure. Jiraporn and Liu (2008), Nigel and

Sarmistha (2007), Brailsford, Oliver and Pua (2002) and Margaritis and Psillaki (2010)

have argued that more research should be required and that in-depth investigation of this

relationship could provide important insights into capital structure decision, especially in

developing economies.

The literature examining the effect of ownership structure on capital structure often

focuses on managerial, insider and institution ownership, or ownership concentration

(Berger, Ofek & Yermack 1997; Chaganti & Damanpour 1991; Chen et al. 2005; Cho

1998; Chu 2011). A limited number of empirical studies (Gurunlu & Gursoy 2010; Huang,

Lin & Huang 2011; Zou & Xiao 2006) have examined the influence of foreign and state

ownership on capital structure. Meanwhile, state ownership is common in transition

2

countries because of the privatisation process whereby many state-owned enterprises

(SOEs) have been converted to private companies. Foreign ownership has recently begun

to play an important role in developing economies because of a sharp increase in foreign

investment (World Bank 2011). These limitations indicate the importance of further

detailed investigations into this issue. Therefore, one of the main purposes of this research

is to obtain a better understanding of the role of ownership structure in influencing

financing decisions. Specifically, this study investigates the effect of managerial

ownership, ownership concentration as well as foreign ownership and state ownership on

capital structure decisions.

Several theories and studies have examined capital structure; however, there is no single

theory that can fully interpret the effect of capital structure on firm performance. Empirical

evidence shows different and contradictory results on this relationship and indicates that

it depends significantly on the specific circumstances. Additionally, most previous studies

relating to capital structure (Booth et al. 2001; Frank & Goyal 2009; Huang & Song 2006;

Pandey 2001; Titman & Wessels 1988) have investigated the determinants of capital

structure decisions. Tian and Zeitun (2007) and Joshua (2007) argued that there is a lack

of empirical evidence on the effect of capital structure on firm performance, especially in

emerging markets. The above issues motivate new studies on the relationship between

capital structure and firm performance.

Vietnam has an economy typical of a developing country. Although many studies have

been conducted in other countries, few in-depth investigations have been conducted there.

In addition, there are still some limitations in the previous research relating to ownership

and capital structure in Vietnam. First, the purpose of most prior research was to examine

factors affecting capital structure. Few studies aimed to investigate intensively the

relationship between capital structure and firm performance in Vietnam. Second, research

that has linked ownership structure with capital structure only attempted to identify

determinants of capital structure, and did not deeply explore the effect of ownership

structure on leverage. Furthermore, previous studies only considered state ownership; few

studies have investigated the influence of other types of ownership structures, such as

3

foreign ownership, managerial ownership or large ownership on capital structure.

Moreover, with respect to state ownership, previous research employed dummy variables

while the exact measurement of state ownership as the percentage of shares held by the

state was not conducted. All of these limitations highlight the need for further research

conducted in Vietnam.

1.2 Research questions

This research extends the current literature and provides further evidence by attempting

to answer the following questions:

1. Does ownership structure (including managerial, large, foreign and state

ownership) influence capital structure in Vietnamese listed firms?

2. Does capital structure influence firm performance in Vietnamese listed firms?

1.3 Overview of research context: Vietnam

1.3.1 Vietnamese economy

Vietnam followed a central planned model of socialism based on SOEs, agriculture and

heavy industry until 1986. At that time, the Vietnamese government launched an

economic reform programme called Doi Moi, or economic renovation, that transferred the

central planned economy to a market economy. The Vietnamese economy has undergone

many significant changes, achieving high and stable growth rates. In particular, the

country has experienced high growth rates of gross domestic product (GDP) in recent

years and is one of the countries that have a high economic growth rate in Asia

(International Monetary Fund [IMF] 2010). It is also one of the highest recipients of

foreign direct investment (FDI), which averaged over 7% of its GDP during 2005–2013

(World Bank 2014). Vietnam’s economy is forecasted to continue to develop given its

strong export performance within the Association of Southeast Asian Nations (ASEAN)

countries (Viet Capital Securities 2011) and high profitability expectations (Grant

Thornton 2011). The equitisation programme of Vietnam has had a number of

4

achievements since its official beginning in 1992; however, its progress is still modest and

state ownership remains the popular ownership structure in Vietnam (World Bank 2011).

1.3.2 Vietnamese financial market

In recent years, the Vietnamese banking sector has diversified by type, size and ownership.

It is still concentrated in four state-owned commercial banks (SOCBs) that occupy nearly

50% of the total loan and deposit market. A noticeable point is that nearly one-third of the

bank loans of the total market is distributed to SOEs while SOEs account for only around

1% of total registered firms. In addition, SOEs normally have a higher debt ratio than non-

state and foreign firms, their returns are lower and they have a high proportion of non-

performing loans (NPLs) (VPBank Securities 2014). Given this, it can be hypothesised

that ownership structure may affect the ability of a Vietnamese firm to access bank loans.

Similarly, the Vietnam bond market is still dominated by entities that to some extent have

a relationship with the government. The majority of bonds, in both number and size, are

issued or guaranteed by the government, and the big holders are commercial banks, the

four largest of which belong to the government. The dominance of SOEs and large

corporations in the corporate bond market also prevents small to medium-sized enterprises

(SMEs) from accessing this debt financing option. Only few big joint-stock companies

are able to raise local currency funds by issuing bonds.

The formal equity market has been developing gradually since 2000, becoming an

important financing channel for corporations. An overview of the Vietnam stock market

reveals that foreign investors could be considered as leaders of most market movements.

In addition, foreign ownership in listed firms is increasing significantly, and foreign

investors appear to be playing an important role in the listed firms’ performance.

5

1.3.3 Vietnamese listed firms

The number of listed firms has been increasing noticeably since 2000. Specifically, on the

first trading day, only two stocks with a total market capitalisation of USD 27.95 million

were listed. In the next five years, the growth by number of listed companies was rather

slow. However, the situation changed quickly in the period 2006–2013. The number of

listed companies increased sharply from 193 in 2006 to 696 in 2013 (HoChiMinh Stock

Exchange [HOSE] 2013).

An examination of the ownership structure of listed firms reveals that, although the

proportion of shares owned by foreign investors in Vietnam is limited to 49% by law

(Robinson 2012), this ownership is an essential part of the ownership structure in listed

firms in Vietnam. In terms of state ownership, through the privatisation programme, the

average of state ownership dramatically decreased; however, state ownership still

accounts for a significant proportion of listed Vietnamese firms. Another point is that the

percentage of foreign and state ownership varies considerably from industry to industry,

and from firm to firm. Foreign ownership is significantly higher in health care, oil, gas

and technology, which also have high performance in both accounting and market value.

Hence, questions can be raised regarding whether relationships exist among ownership

structure, capital structure and firm performance.

1.4 Research contribution

This research contributes to theory and methodology, as well as to practice.

First, previous research has focused mainly on the relationship between ownership and

firm performance; this study extends the literature by investigating whether ownership

structure can explain variation in capital structure. More specifically, this is one of the

first studies to identify the relationship between different aspects of ownership structure

(managerial, state, foreign and large ownership) and capital structure. Therefore, it

6

contributes to theoretical knowledge by providing a full picture of the influence of

ownership structure.

Second, this study investigated the relationship between capital structure and firm

performance, which has not been studied previously in relation to Vietnamese listed firms.

As empirical evidence of a typical country, this study contributes to the theoretical

perspective by providing an insight into the relationship between capital structure and firm

performance in an emerging market. In addition, it provides evidence for testing the

validity of financial theories in explaining the relationship between capital structure and

firm performance in a developing country.

Third, the study contributes to research through its collation and usage of different

measures for capital structure, firm performance and ownership structure. Capital

structure was measured by total debt, long-term debt and short-term debt to total assets,

using both book and market value. In terms of firm performance, Tobin’s Q was used to

capture the firm’s market performance, while return on assets (ROA) and return on equity

(ROE) were employed for presenting accounting performance. In addition, whereas

previous research on Vietnam measured state ownership by dummy variables, this study

used the percentage of shares held by the state as a proxy for state ownership. Furthermore,

this study collected annual data for non-financial Vietnamese listed firms on both the

HoChiMinh Stock Exchange (HOSE) and the Hanoi Stock Exchange (HNX) from 2007

to 2012, thereby creating an updated and detailed database of capital structure and

ownership of Vietnamese firms.

Moreover, the data analysis of this study was conducted using various methods to solve

the problems arising when running regression analysis and conducting financial empirical

tests, such as unobserved heterogeneity, heteroskedasticity and endogeneity. More

importantly, different from previous research using a Vietnamese dataset, this thesis is

one of the first studies to use the generalised method of moments (GMM) model to test

the relationship among ownership structure, capital structure and firm performance.

7

Finally, this research will provide understanding on the influence of capital structure

decisions on firm performance in Vietnamese listed firms. This may contribute to

Vietnamese firms’ decisions on capital structure and direct policymakers in their review

of corporate governance.

1.5 Structure of research

This thesis consists of six chapters. The main content of each chapter is as follows.

Chapter 1: Introduction

This chapter presents an overview of the thesis including the research context, motivation,

questions and significance.

Chapter 2: Overview of research context

This chapter presents an overview of Vietnam’s economy as a typical developing country

as well as detailed discussion on Vietnam’s financial market and the ability of Vietnamese

firms to access available sources of finance.

Chapter 3: Literature review

This chapter reviews the existing literature on ownership structure and capital structure.

It begins by providing an overview of the influence of ownership structure on capital

structure, and then focuses on the relationship between capital structure and firm

performance. This is followed by a summary of previous studies relating to capital

structure in Vietnam. Finally, the chapter identifies some gaps in the literature that

motivated this research.

Chapter 4: Methodology

This chapter begins by developing hypotheses that reply to arguments introduced in the

previous studies and within the specific context of Vietnamese firms. Later, the chapter

details the data collection process and quantitative empirical models used to explore the

8

cause-and-effect relationship between ownership structure and capital structure as well as

between capital structure and firm performance.

Chapter 5: Findings

This chapter presents the empirical results of the research. It begins by demonstrating the

influence of ownership structure, including managerial, state, foreign and large ownership,

on debt level using a capital structure model. This is followed by a presentation of the

influence of debt level on firm performance using a firm performance model.

Chapter 6: Conclusion and discussion

This chapter presents the main findings of the study, compares them with the findings of

previous studies, and provides a discussion of the results. It concludes by identifying the

implications for policymakers and Vietnamese listed firms as well as indicating the

limitations of the study.

9

Chapter 2: Overview of Research Context

2.1 Introduction

Until the mid-1980s, Vietnam followed a central planned model of socialism based on

SOEs, agriculture and heavy industry. At that time, Vietnam’s economy was suffering

from hyperinflation and a fiscal crisis. Therefore, in 1986, the Vietnamese government

began an economic reform programme, known as Doi Moi, to transfer the central planned

economy to a market economy with the aim of achieving economic development and

stability. In response, Vietnam’s economy underwent many significant changes,

exhibiting high and stable growth rates, and attracting world attention (World Bank 2011,

2014). This chapter provides a discussion of Vietnam’s economy as a typical developing

country, followed by a presentation of the characteristics of the Vietnamese financial

market in order to detail the sources of funding for Vietnamese firms.

2.2 Vietnamese economy

2.2.1 Vietnamese economy overall

In 1986, Vietnam launched a renewal programme for politics and economics. Since then,

the economy of Vietnam has exhibited impressive performance and affirmed the efforts

to modernise the economy. Specifically, one of its greatest achievements was the

successful accession to the World Trade Organization (WTO) in 2007, which has led to

Vietnam being more competitive and increasing its export-driven industries. The country

has been commended for its achievements in macroeconomic stability, controlling

inflation and strengthening external accounts (World Bank 2014).

Specifically, the GDP grow rate has remained at a high level for the past 25 years. During

the period from 2001 to 2013, the average Vietnam GDP growth rate was around 6.3%

(see Figure 2.1). This rate is high in comparison to regional countries and the world

10

average. Vietnam is among the highest GDP growth rate economies in the world

(Deutsche Bank Research 2007; World Bank 2011) and has been one of the fastest-

growing countries in Asia in recent years (Business Monitor International [BMI] 2014a).

Moreover, the various sectors’ contributions to the output of the economy have changed

over the past two decades. The agriculture sector’s share of GDP has declined, while the

services and industry sectors’ shares have increased significantly. This reflects the sector

restructuring movement of the economy.

Source: www.data.worldbank.org

Figure 2.1: GDP growth (%)

An important factor in the economy’s growth has been the FDI, which has increased

gradually from 1996 to 2013, averaging 7% of the GDP during this period (World Bank

2011). In particular, up to USD 2,315 million in FDI flowed into Vietnam in 2009, despite

the stiff competition from other regional countries such as China and Thailand. This was

regarded by economists as a positive result during the global financial crisis and Vietnam

is still one of the highest recipients of FDI in the world (World Bank 2011). Over two-

thirds of this capital flow was investment in the manufacturing industry, resulting in the

creation of higher employment and stimulus of the economy of Vietnam in the years

following.

-2

0

2

4

6

8

10

2006 2007 2008 2009 2010 2011 2012 2013 2014F

World Average Emerging Market and Developing Economies Vietnam

11

Legally there are two forms for foreign investment in Vietnam: “direct” investment or

“indirect” investment. According to Vietnamese Investment Law, a foreign direct

investment includes: founding wholly foreign-owned enterprises, establishing joint

ventures, contributing fund to enterprises to join in management, or investing a contract

such as Build Transfer-Operate, Business Cooperation Contract, Build-Operate-Transfer

or Build-Transfer contract. While, a foreign indirect investment includes: buying of

shares, bonds and other valuable papers; investing through securities investment funds,

and investing through intermediary financial institutions. So far, most foreign direct

investors establish wholly foreign-owned enterprises or joint ventures to perform a project

in Vietnam. The Vietnamese Law stipulates that a foreign direct investor have to obtain

an investment certificate for a project. In respect of capital structure, generally, there is no

requirement; however, direct investors must have enough fund to set up companies and

achieve the goals that list in their investment certificate. The investment capital stated in

the investment certificate includes the charter capital that is the amount of equity

contributed by investors in a certain period and loans or other assets. An important thing

is that foreign-invested enterprises cannot use debt in excess of its stated loan capital. In

addition, foreign investors established a limited liability company must pay fully the

registered capital within 36 months from the date of issuance of the investment certificate.

They also cannot reduce their capital contribution unless their company operates more

than two years and ensures to be able to pay all its debt and other obligations (Mayer

Brown 2014).

Vietnam’s export sector has been an integral part of the country’s economic development.

The total export value of Vietnamese-made products has been growing rapidly,

particularly following the US lifting of its embargo against Vietnam and the country

joining ASEAN, both of which occurred in 1995. The export sector was further supported

by the US-Vietnam Bilateral Trade Agreement signed in 2000 and continually boosted

when Vietnam became a WTO member in 2007. The export value of goods increased

rapidly from USD 39,826 million in 2006 to USD 132,135 million in 2013 (see Table 2.1).

The main destinations for exports were to America, followed by Europe, ASEAN

countries, Japan and China.

12

Table 2.1: Main Vietnamese economic indicators

2006 2007 2008 2009 2010 2011 2012 2013

GDP (million USD)

66371 77414 99130 106014 115931 135539 155820 171392

GDP growth (annual %)

7.0 7.1 5.7 5.4 6.4 6.2 5.2 5.4

GDP per capita (current USD)

797 919 1165 1232 1334 1543 1755 1910

Inflation rate (annual %)

7.4 8.3 23.1 7.1 8.9 18.7 9.1 6.6

Export, fob (million USD)

39826 48561 62685 57096 72237 96906 114529 132135

Import, fob (million USD)

42602 58999 75468 64703 77373 97356 105815 123405

Trade balance –2776 –10438 –12783 –7607 –5136 –450 8714 8730

FDI (million USD)

12004 21347 71726 23107 19886 15618 16348 22352

Sources: www.adb.org; data.worldbank.org/country/vietnam

Import volume into Vietnam has grown dramatically. Before 2008, import values were

greater than export values; therefore, Vietnam faced enormous trade deficits. However,

thanks to the stable export growth experienced in recent years, Vietnam’s trade deficit is

decreasing gradually. In the past two years, the export value exceeded the import value,

creating a surplus in trade account (see Table 2.1).

From 1996 to 2013, the inflation rate of Vietnam was unstable, and from 1996 until 2007,

it was low. After this period, the inflation increased sharply. However, thanks to

experience in controlling inflation in the past as well as sufficient food supplies, and soft

domestic demand, inflation has declined and stabilised in recent years, averaging 6.6% in

2013 (World Bank 2013).

Therefore, overall, Vietnam has made an effort to modernise the economy by enhancing

the GDP growth rate, controlling the inflation rate, attracting FDI and restructuring the

sectors of the economy to integrate more and more into the world economy. In addition,

by participating intensively in regional manufacturing supply chains, Vietnam has

13

strongly contributed to the development of the global and regional economy (World Bank

2014).

2.2.2 Equitisation process

Equitisation is the process of transforming an SOE into a joint-stock company. As a part

of the Doi Moi process, the government has equitised or privatised a number of SOEs.

The Vietnamese government defines SOE as an enterprise that is 100% state owned,

whereas the Vietnamese General Statistics Office uses a broader definition that includes

any enterprise in which the government has a controlling stake, that is, 51% or more

(Vietnamese Congress 1995). Based on Decision No. 14 (Socialist Republic of Vietnam

2011), the State retained 100% ownership in public utilities, power transmission, oil and

gas, aviation and railways, and 50% ownership in energy, mining, telecommunications,

infrastructure, cement and steel production, sanitation and water supply, and banking and

insurance. Hence, the equitisation of SOEs in Vietnam can only be considered partial

equitisation.

The privatisation of SOEs has generally been slower than expected, according to a report

of the World Bank (2013). The initial aim was for the equitisation process to be completed

in 2010, but this target was not achieved (Vietnam Ministry of Foreign Affairs 2010).

Through equitisation, divestment, mergers, acquisitions and liquidation, the speed of

equitisation rapidly increased in the 2000s, peaking in 2004 with 804 privatised SOEs (see

Figure 2.2). However, this has slowed in recent periods. Since 1992, a total of 4,032 SOEs

have been equitised and 3,135 SOEs remained in 2013 (World Bank 2013). In addition,

the process of equitisation has concentrated mostly on small and medium-sized SOEs, and

only a small percentage of equitised assets has been actually held by non-state

shareholders (World Bank 2014).

14

Source: World Bank (2013)

Figure 2.2: Number of equitised SOEs

Therefore, overall, the equitisation programme of Vietnam has achieved a certain success;

however, this progress is still considered modest. According to the World Bank (2011,

2014), after 28 years of Doi Moi, state ownership remains a popular ownership structure

in the Vietnamese economy.

2.3 Vietnamese financial market

Vietnam has had a number of significant achievements in economic development since

the reform programme was initiated; however, the financial system is underdeveloped,

and reform of the financial sector has lagged behind that of other sectors (International

Finance Corporation [IFC] 2007; Leung 2009). Vietnam’s financial market is dominated

by the banking sector; bank loans represented 103% of GDP in 2013. Stock and bond

markets are gradually developing; nevertheless, their capitalisations are quite small, at

31% and 19% of GDP in 2013, respectively. Regarding insurance and pension funds, in

many countries these funds provide long-term finance to firms; however, in Vietnam these

sources are underdeveloped, and they occupy only about 1.5% and 4% of GDP

respectively (see Table 2.2). Therefore, this section focuses only on a brief summary of

the banking sector, bond market and stock market to highlight some noticeable features

of the financial markets and consider the ability of these sources for Vietnamese firms.

127

461506

621

856 813

359

116 117

13 43

0

100

200

300

400

500

600

700

800

900

15

Table 2.2: Financial market in Vietnam (% of GDP)

Year 2005 (%)

2006 (%)

2007 (%)

2008 (%)

2009 (%)

2010 (%)

2011 (%)

2012 (%)

2013 (%)

Deposits 67 78 99 92 101 113 105 99 89

Bank loans 70 75 96 95 112 126 124 102 103

Stock market (total capitalisation)

1.11 22 43 15 38 37 24 26 31

Outstanding bonds (LCY bond)

5.0 8.3 13.8 15.6 13.3 15.4 14 16.1 16.8

Outstanding bonds (FCY bond)

3.4 2.9 2.7 2.2 2.3 2.9 2.4 1.8 2.0

Insurance premiums (life and non-life)

1.6 1.5 1.4 1.4 1.5 1.6 1.5 1.4 1.5

Pension fund 4.0 3.7 N/A N/A N/A N/A N/A N/A N/A

Sources: Calculated from www.ssc.gov.vn; http://asianbondsonline.adb.org/vietnam/data.php;

(BMI 2014a, 2014b; Leung, 2009)

2.3.1 Banking sector

2.3.1.1 Number and size of banks

Before 1990, the banking system of Vietnam consisted of the State Bank of Vietnam

(SBV) as the central bank and four SOCBs: the Vietnam Bank for Agriculture and Rural

Development (VBARD), Bank for Investment and Development of Vietnam (BIDV),

Vietcombank (VCB) and VietinBank. Since the economic reform, the Vietnam banking

sector has quickly diversified and developed in terms of type, size and ownership. By the

end of 2013, there were five SOCBs, 34 joint-stock commercial banks (JSCBs), 32 foreign

bank branches (FBBs) and five joint-venture banks (JVBs). In addition, the operating

network of Vietnam’s banking sector has grown significantly. The number of branches,

transaction offices and automated teller machines (ATMs) has increased dramatically

(BMI 2014a).

Small and medium-sized banks whose charter capitals are VND 3,000–5,000 billion,

equivalent to USD 150–250 million, dominate the banking system, accounting for

16

approximately 75% of all banks. Four SOCBs have the largest chartered capital (VND

64,037 billion or around USD 3.2 billion). However, when compared with other countries,

their total capital is roughly equivalent to that of one medium-sized bank in the region.

Hence, the capital sizes of Vietnamese banks are still very small in comparison with

regional banks (Vietcombank Securities 2011).

2.3.1.2 Market share

SOCBs continue to dominate deposit and credit markets. Although SOCBs’ credit market

share has significantly decreased recently, it is still much higher than that of the others.

Up to 50% of the total loans were granted by SOCBs in 2013. Similarly, SOCBs’ deposit

market share has declined considerably over the years; however, it still accounts for nearly

50% of the entire market. JSCBs, which have diversified shareholder composition and

operate more actively, are steadily taking market share from SOCBs. Market share by

JSCBs has grown dramatically in recent years, reaching 38% of total credit and 41.2% of

deposits in 2013 (see Figure 2.3 and Figure 2.4).

Source: VPBank Securities (2014); Vietcombank Securities (2011); Vietnam Chamber of

Commerce and Industry Enterprise Development Foundation [VCCI] (2013)

Figure 2.3: Deposit market share Figure 2.4: Credit market share

2.3.1.3 Interest rate

The interest rates, both deposit and lending rates have fluctuated since 2008. In the period

from 2009 to 2011, shortage of liquidity in the market and high inflation of the economy

0.00%

20.00%

40.00%

60.00%

80.00%

100.00%

SOCBs JSCBs JVBs & FBs

0.00%

20.00%

40.00%

60.00%

80.00%

100.00%

SOCBs JSCBs JVBs & FBs

17

were two main reasons that the interest rates increased sharply. The deposit rate nearly

doubled from 7.9% in 2009 to 14% in 2011. Meanwhile, the lending rate increased from

10.1% to 17% in the respective years. In 2012 and 2013, as a result of government policy

and a more stable economy, deposit and lending interest rates have stabilised and are more

in line with pre–global financial crisis rates. However, they are still much higher than

those of other countries (see Figure 2.5).

Sources: VPBank Securities (2014); Vietnam business annual report (VCCI 2013)

Figure 2.5: Interest rate (%)

2.3.1.4 Credit distribution

Vietnamese firms’ external sources of funds are predominantly loans, mainly from bank

financial intermediaries (IFC 2007). Meanwhile, the debt capital market has been

dominated by banks, mainly SOCBs. This has led to a serious imbalance in allocation of

funds to the various economic sectors, and the private sector’s access to funds is weak

(Vuong & Tran 2010).

A noticeable point is that although SOCBs dominate the deposit and credit market, their

traditional customers are mainly SOEs, which have higher NPLs than other types of

enterprises. According to VPBank Securities (2014), SOCBs have a much higher NPL

ratio than other banks and 70% of total NPLs belonged to SOEs in 2012.

9.7%11.0% 11.2% 11.2%

15.8%

10.1%

13.1%

17.0%

13.5%11.5%

6.2% 7.1% 7.6% 7.5%

12.7%

7.9%

11.2%14.0%

10.5%8.5%

0.0%

5.0%

10.0%

15.0%

20.0%

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

Lending rate Deposit rate

18

Specifically, in the period from 2005 to 2012, the proportion of SOEs accounted for only

1% of total Vietnamese registered enterprises, but they occupied around 20% of total

credit outstanding. Conversely, the number of non-SOEs has been growing rapidly,

occupying more than 95% of the total number of Vietnamese firms, but receiving only

70% to 80% of total credit (see Table 2.3 and Table 2.4). Furthermore, according to a

report of the World Bank (2011), SOEs had the highest debt-to-asset ratio among SOEs,

non-state and foreign groups.

Table 2.3: Proportion of registered Vietnamese firms by ownership (%)

Year 2006 (%)

2007 (%)

2008 (%)

2009 (%)

2010 (%)

2011 (%)

2012 (%)

SOEs 2.71 2.24 1.60 1.43 1.15 1 0.9

Non-SOEs 94.09 94.57 95.57 95.76 96.35 96.30 96.6

FDI enterprises 3.20 3.18 2.74 2.81 2.50 2.70 2.5

Source: Vietnam business annual report (VCCI 2011, 2012, 2013)

Table 2.4: Credit to economy (% of total)

Year 2006 (%)

2007 (%)

2008 (%)

2009 (%)

2010 (%)

2011 (%)

2012 (%)

2013 (%)

Claims on SOEs 31 31 31 29 19 17 17 16.5

Claims on others 69 69 69 71 81 83 83 82.5

Total credit to economy 100 100 100 100 100 100 100 100

Source: IMF country report (IMF 2014)

2.3.2 Bond market

2.3.2.1 Size of the bond market

Vietnam’s bond market has improved significantly since the launch of the economic

reform programme. However, the size of the bond market and the ratio of bonds

outstanding to GDP is still much smaller than in other countries in the region. In 2013, in

terms of size, the amount of total bonds outstanding was only USD 29 billion, compared

with USD 101 billion in the Philippines, USD 312 billion in Malaysia and USD 1,641

billion in Korea. The ratio of bonds outstanding per GDP also stood at the lowest level of

19

only 16.9%, though this figure is somewhat similar to that of Indonesia but much lower

than in other Asian countries such as Thailand, Singapore and Korea (see Figure 2.6).

Source: Asian Bond Monitor report (ADB, 2014)

Figure 2.6: Bonds outstanding in major markets (% of GDP) in 2013

2.3.2.2 Local currency bond market

Despite the small size of Vietnam’s bond market, the growth rate of local currency (LCY)

bonds has been strong for over a decade. From USD 0.09 billion in 2000, this number

increased spectacularly to USD 10 billion in 2007 and reached approximately USD 29

billion in 2013, showing a growth rate of over 300% in six years.

The LCY bond market is dominated by the government. Until 2006, the outstanding

amount of corporate bonds was approximately zero. This number only slightly increased

with the booming of the Vietnam stock market in 2007 and was USD 0.68 billion by the

end of 2013. Meanwhile, the volume of government bonds was six times larger, from USD

5 billion in 2006 to USD 28 billion in 2013, accounting for up to 96% of the total

outstanding local bonds (see Figure 2.7).

47.40%

14.40%

135.20%

105.70%

38.80%

85.00%72.60%

16.90%

0.00%

20.00%

40.00%

60.00%

80.00%

100.00%

120.00%

140.00%

160.00%

China Indonesia Korea Malaysia Philippines Singapore Thailand Vietnam

20

Source: www.asianbondsonline.adb.org/vietnam/data.php

Figure 2.7: Composition of LCY bond market (USD billion)

2.3.2.3 Foreign currency bond market

Vietnam has been most modest in its issuance of foreign currency (FCY) bonds; USD

3.53 billion was issued in 2013, triple the amount of 2005. This accounts for 2% of GDP,

which is considered a very modest rate.

Government bonds similarly dominate this market; 60% of foreign bonds outstanding

belonged to the government in 2013. Specifically, while the Vietnamese government

successfully issued a total of USD 2.06 billion outstanding in 2003, the number of

corporate bonds was unnoticeable at only USD 0.02 billion in that same period (see Figure

2.8). The main challenge for Vietnamese corporations in issuing FCY bonds is that very

few firms are rated by international credit rating agencies such as Standard & Poor’s

(S&P), Moody’s or Fitch Ratings. For the firms that are rated by these agencies, their

credit rating is also at a very low level, which makes it challenging to attract foreign

investors.

2.645.02

9.6512.82

10.9513.71

15.36

24.0428.01

0 0.01 0.35 0.68 1.47 2.3 2.03 1.07 0.68

0

5

10

15

20

25

30

2005 2006 2007 2008 2009 2010 2011 2012 2013

Government Bond Corporate Bond

21

Source: www.asianbondsonline.adb.org/vietnam/data.php

Figure 2.8: Composition of FCY bond market (USD billion)

2.3.2.4 Government bonds

Vietnam government bonds can be issued by the Treasury, the Vietnam Central Bank, the

Vietnam Development Bank or SOEs. These are guaranteed by the Vietnamese

government so they are all considered government bonds. Among these issuers, bonds

issued by the Central Bank are unremarkable and the balance was USD 2 billion in 2013.

The proportion of outstanding bonds for the two main issuers, the Treasury and the group

of the Vietnam Development Bank and SOEs, is almost equal. While the Vietnam

Treasury held USD 16 billion, the Development Bank and SOEs owned USD 10 billion

of outstanding bonds in 2013 (Asian Development Bank [ADB] 2014). In terms of

government bond holders, commercial banks are the traditional customers of government

bonds. They are also the most active trading entities in the government bond market,

followed by securities and finance companies.

2.3.2.5 Corporate bonds

The size of the corporate bond market in Vietnam has increased; however, its value is still

very small: only USD 0.7 billion at the end of 2013 (ADB 2014). In addition, the corporate

bond is not a popular financial vehicle in Vietnam’s business community; only 15 firms

1.14 1.14 1.14 1.14

1.77

2.42 2.312.09 2.06

0 0 0 0 0 0 00.25

1.45

0 0 0 0 0.07 0.07 0.150.39

0.020

0.5

1

1.5

2

2.5

3

2005 2006 2007 2008 2009 2010 2011 2012 2013

Government Banks and Financial Institutions Corporates

22

were active in the corporate bond market in 2013 .This figure represents a small portion

of the increasingly populated corporate sector, recorded as up to 350,000 enterprises

(Vietnam Chamber of Commerce and Industry Enterprise Development Foundation

[VCCI] 2013).

The participants in this market are mostly large, well-known firms with close and

trustworthy relationships with market dealers and institutional investors. These firms are

predominantly SOEs that have a higher rate of bond issuance than that of the private

sector. The dominance of SOEs and large corporations may crowd out SMEs from issuing

bonds (Vuong & Tran 2010).

By classifying and analysing corporate bond values by industry, Vuong and Tran (2010)

concluded that Vietnam corporate bonds are distributed to 20 main industries. The

industries that have a high bond value and account for over 10% of the market are the

banking sector (34%), electricity (15%), ship building (13%) and real estate (16%). An

interesting point is that these industries share the properties of being a monopoly, having

large financing needs, being closely linked to state ownership and being classified into

strategic fields of development by the government.

2.3.3 Stock market

2.3.3.1 History

The Vietnamese stock market, known formally as the Securities Trading Centre (STC),

was established in Ho Chi Minh City on 28 July 2000. In July 2007, the STC was officially

converted to a stock exchange, the HOSE. In March 2005, the HNX was also opened.

While the HOSE is considered the official stock exchange, mainly trading equities and

bonds of listed companies, the HNX is considered the trading floor for government bonds.

In addition, companies trading on the HNX are mostly those that do not fulfil the

requirements for being listed on the HOSE (Robinson 2012).

23

2.3.3.2 Regulation framework

2.3.3.2.1 Listing requirements

To be listed on the HOSE, a company must meet all of the listing requirements prior to

obtaining a listing licence. Specifically, according to Decree 14/2007/NĐ-CP of 2007, a

company has to fulfil the following conditions:

“ (i) Must have a minimum book value of VND 80 billion (approximately USD 3.8 million) in paid-up charter capital; (ii) The applicant company must have been profitable for the last two consecutive years and there must not be any accumulated losses up to the year of listing; (iii) any overdue debt must be earmarked for payment; (iv) At least 20% of the voting shares of the applicant company must be held by at least 100 shareholders; (v) A shareholder who is a board member, inspection committee member, general director, deputy general director and chief accountant must agree to not sell any of its shares for the first 6 months after listing and 50% of its shares for the following 6 months ” ( Robinson 2012, p. 5)

2.3.3.2.2 Percentage of foreign investor participation in stock market

Foreign investors are allowed to buy shares in Vietnamese securities or investment fund

management companies, or contribute capital to establish new joint-venture securities or

investment fund management companies with Vietnamese partners.

According to Vietnamese law, Decree 55/2009/NĐ-CP of 2009, foreign organisations and

individuals selling and purchasing securities on the Vietnamese securities market may

hold a maximum of 49% of the total shares in listed companies, and a maximum of only

30% in banks.

2.3.3.3 Market capitalisation

In terms of market capitalisation, the market volume increased from VND 444,000 million

(USD 27.95 million) at the first trading session to VND 6,337,478 million (USD 398.96

million) by the end of 2005. Although the volume enlarged nearly 14 times in only five

years, the market was still rather thin since market capitalisation on GDP accounted for

24

only 1.11% in 2005. The year 2007 witnessed the highest capitalisation market ratio,

accounting for 43% of GDP, because of the dramatic increase in listed companies and

high share prices. This was greatly affected by the global financial crisis, with market

capitalisation as a percentage of GDP dropping to 15% in 2008 before recovering to 31%

by the end of 2013 (see Figure 2.9).

Source: Calculated from State Securities Commission of Vietnam (www.ssc.gov.vn)

Figure 2.9: Market capitalisation of Vietnamese stock market (% GDP)

2.3.3.4 Number of listed firms

The growth in the number of listed firms was slow in early 2000. By the end of 2005, only

41 joint-stock companies had been given permission to list on both the HOSE and the

HNX. The low number of firms listed during this period may have been caused by slow

development of the stock market. However, the situation changed quickly in the period

2006–2013. The number of listed companies on the stock market increased sharply to 696

in 2013 (see Figure 2.10). This trend was the result of a booming Vietnamese stock market

in 2007 and business development in general.

0.24% 0.33% 0.47% 0.39% 0.55% 1.11%

22%

43%

15%

38% 37%

24% 26%31%

0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

25

Source: Calculated from State Securities Commission of Vietnam (www.ssc.gov.vn)

Figure 2.10: Number of listed firms in HOSE and HNX

2.3.3.5 Some indicators of market

Market capitalisation of Vietnam’s two markets is smaller than that of other markets in

the region. The market size is modest because only a small proportion of Vietnamese firms

are listed on the stock market. However, a more detailed examination of the numbers

reveals that the Vietnamese stock market is really a potential investment channel, in

comparison with other countries in ASEAN. While the price-earnings (P/E) ratio of other

markets was greater than 16, the P/E ratio of the HOSE and HNX were only 13.23 and

15.81 respectively. Similarly, the price-to-book (P/B) ratio of the Vietnam stock market

was the lowest: only 1.00 for the HNX and 1.89 for the HOSE (see Table 2.5). Conversely,

the average dividend yield in Vietnam was much higher than the other markets (Rong Viet

Securities 2011). It appears that investors may view Vietnamese markets as having a better

return (Bao Viet Securities 2011; Phu Hung Securities 2011).

5 11 20 22 26 32106

140175 203

277 303 308 301

5 11 20 22 26 41

193252

343

466

645698 704 696

0

100

200

300

400

500

600

700

800

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

HOSE HNX Total

26

Table 2.5: Some indicators of Vietnam stock market in 2012

Indicators VN

Index HNX Index

FTSE Bursa

Malaysia

Jakarta Composite

Index

PSEi Philippine SE Index

Stock EXCH of

Thai Index

P/E 13.23 15.81 16.86 20.8 21.02 16.63

P/B 1.89 1.00 2.28 2.47 2.73 2.08

Capitalisation (billion USD)

29.1 5.3 268.2 384.3 121.3 315.6

Capitalisation/ GDP

26% 5% 156.2% 45.3% 105.6% 104.7%

Source: Rong Viet Securities (2014)

2.3.3.7 Foreign investment in Vietnamese stock market

During the global financial crisis of 2008, the net foreign inflow into the Vietnam equity

market decreased sharply before recovering in recent years. The net foreign portfolio

inflow was higher than those of Indonesia, Thailand and Philippines, reaching USD 1.3

billion in 2013 (see Figure 2.11). The Vietnam stock market is becoming more and more

of an attractive investment channel for foreign investors, and the market expectation is for

continued growth in foreign portfolio investment inflows in the future. This can be

explained by the fact that Vietnam is an emerging market with a high growth rate and that

the Vietnamese firms are undervalued and considered cheap compared with their regional

peers in Asia (Rong Viet Securities 2011, 2014).

Source: http://data.worldbank.org/indicator

Figure 2.11: Net inflow portfolio equity of foreign investors (USD million)

-10,000

-5,000

0

5,000

10,000

2007 2008 2009 2010 2011 2012 2013

Vietnam Thailand Philippines Indonesia

27

Among the over one million investors in the Vietnam stock market, foreign investors

account for a modest number, around 1% to 2% of total investors. However, foreign

investors play an important role through their trading activities. Their volume of market

trading occupied 15% of the HOSE market in the period from 2011 to 2013 (HOSE 2012,

2013) (see Table 2.6).

Table 2.6: Trading volume of foreign investors

Year Foreign Trading

volume Total Trading

volume % Total market

2010 1767527356 11643346488 15.18

2011 1296896130 8281562409 15.66

2012 2357729515 13980559995 16.86

2013 2366155883 16078051147 14.72

Source: Calculated from State Securities Commission of Vietnam (www.ssc.gov.vn)

2.4 Conclusion

As a result of economic and political reform, Vietnam has begun to grow strongly and

occupy an important position in the South-East Asian market. Vietnam has had high

growth rates in its GDP in recent years and is one of the developing Asian countries that

has a high economic growth rate (IMF 2010). It is also one of the highest recipients of

FDI, averaging over 7% of its GDP between 2005 and 2013 (World Bank 2011, 2014).

The country’s economy has good prospects given its strong export performance within the

ASEAN countries (Viet Capital Securities 2011) and the profitability expectations of its

top firms (Grant Thornton 2011). With respect to the equitisation process, it resulted in a

number of privatised firms, at a somewhat modest level. State ownership continues to be

a popular ownership structure in Vietnam (World Bank 2011).

Regarding the financial market, although the Vietnamese banking sector has diversified

in terms of type, size and ownership in recent years, the industry is still concentrated in

four SOCBs, which occupied nearly 50% of the total loan and deposit market in 2013

(VPBank Securities 2014; VCCI 2013). A noticeable point is that nearly one-third of bank

28

loans is distributed to SOEs, whereas SOEs account for only around 1% of registered

firms. In addition, SOEs normally have a higher debt ratio than non-state and foreign

firms, while their returns are lower and 70% of NPLs of the bank sector were to SOEs in

2012 (VPBank Securities 2014). Therefore, this suggests that a firm’s ownership structure

may affect its ability to access bank loans.

Similarly, Vietnam’s bond market is still dominated by entities that to some extent have a

relationship with the state or national government. A majority in both number and size of

bonds are issued or guaranteed by the government. Only some large joint-stock companies

are able to raise funds by issuing bonds. The dominance of SOEs and large corporations

in the corporate bond market may affect SMEs’ ability to access this debt financing option.

Vietnamese stock markets have been developing gradually and becoming an important

financing channel for corporations. The markets are becoming more and more of an

attractive investment channel for foreign investors and are expected to gain stronger

foreign portfolio investment inflows in the future. In addition, it is observed that foreign

ownership in listed firms is increasing significantly, and increasingly playing an important

role in listed firms’ performance.

29

Chapter 3: Literature Review

3.1 Introduction

The aim of this chapter is to review the literature on ownership structure and capital

structure, in particular, the influence of ownership structure on leverage and of leverage

on firm performance.

Agency theory (Jensen 1986; Jensen & Meckling 1976) is widely used to explain the

relationship between ownership structure and capital structure. It states that ownership

structure can affect agency costs and subsequently influence capital structure decisions.

Jensen and Meckling (1976) claimed there are two types of agency costs. The first is the

agency cost of equity caused by the conflict between shareholders and managers, and the

second is the agency cost of debt caused by the conflict between debt holders and equity

holders. Managers are widely responsible for their decisions; nevertheless, they do not

gain fully from their profit activities. Therefore, managers may choose a firm’s capital

structure to pursue their own interests instead of those of shareholders or debt holders.

This theory also suggests that outside ownership through a monitoring mechanism could

mitigate the conflict between managers and shareholders. It implies that different types of

ownership could have different effects on the capital structure decision of a firm.

In terms of the relation between capital structure and firm performance, Modigliani–Miller

(MM) theory (Modigliani & Miller 1958) posits that the firm value is not influenced by

its capital structure. However, this theory is based on restrictive assumptions of a perfect

capital market that does not exist in the real world. To account for an imperfect market,

the three main theories that have been suggested as alternatives to MM theory are trade-

off theory, pecking order theory and agency theory. Trade-off theory (Kraus &

Litzenberger 1973; Myers 1984) claims that a firm will trade off costs and benefits of debt

to maximise firm value. The benefit of debt primarily comes from the tax shield of

decreasing income through paying interest (Miller & Modigliani 1963). The cost of debt

30

is derived from direct and indirect bankruptcy costs through the increase in financial risk

(Kim 1978; Kraus & Litzenberger 1973). The pecking order theory (Myers & Majluf

1984; Ross 1977) states that financing follows hierarchy: internal financing is used first,

then debt is issued, and equity is issued when no more debt can be approached. Agency

cost theory, developed by Jensen and Meckling (1976), Jensen (1986) and Hart and Moore

(1994), contends that there are target conflicts target among managers, shareholders and

debt holders, and an optimal capital structure to maximise firm value is one that helps to

minimise total agency costs.

This chapter is organised as follows. Section 3.2 provides an overview of the influence of

ownership structure on capital structure. Section 3.3 focuses on the relation between

capital structure and firm performance. Section 3.4 summarises previous studies relating

to capital structure in Vietnam. The final section details gaps still existing in the literature.

3.2 Ownership structure and capital structure

Ownership structure has a number of definitions. Jensen and Meckling (1976) classified

ownership structure in terms of capital contributions that comprise of inside investors

(managers) and outside investors (debt holder and equity holder).

Chaganti and Damanpour (1991) claimed there are two ways of classifying ownership.

The first distinguishes between those who directly affect firm decisions and activities—a

situation that is called ‘involvement’—and those who do not, which is called

‘detachment’. The second way distinguishes firms that have stocks concentrated with

some shareholders, which is called ‘concentration’, and firms whose stocks are dispersed

to many shareholders, called ‘dispersion’. The crossing of these two classifications creates

four kinds of ownership: concentrated–involved, concentrated–detached, dispersed–

involved and dispersed–detached. A point worth noting is that shareholders whose

ownership are more involved and concentrated have a strong influence on firm

performance.

31

Abel Ebel and Okafor (2010) categorised ownership structure as the percentage of shares

held by managers (managerial ownership), institutions (institutional ownership),

government (state ownership), foreign investors (foreign ownership), family (family

ownership) and so on. Fazlzadeh (2011) stated that ownership structure has two

implications: identity of owners and ownership concentration, that is, the distribution of

shares owned by controlling shareholders.

This research focuses on managerial ownership, state ownership, foreign ownership and

large ownership, given the scope and data limitations. Managerial ownership in this study

is defined as the percentage of shares held by managers and directors. Large ownership is

used to denote the percentage of shares held by large investors that hold at least 5% of

total shares. State ownership and foreign ownership are measured by the percentage of

shares held by the state and foreigners respectively.

3.2.1 Theory perspective

To interpret the relationship between ownership structure and capital structure, most

scholars employ the agency theory (Jensen 1986; Jensen & Meckling 1976), which states

that ownership structure can affect agency costs and influence capital structure decisions.

According to Jensen and Meckling (1976), there are two types of conflicts for firms: those

between shareholders and managers and those between debt holders and equity holders.

While managers are entirely responsible for firms’ decisions, they do not personally gain

fully from their firms’ profit activities. Therefore, managers may manage their firms to

pursue their own interests instead of those of the shareholders. This may involve them

relocating firm assets for their interests, using firm resources for personal benefits or

building their own empire. However, if the percentage of equity held by managers is

raised, the loss from this conflict is mitigated. Meanwhile, an increase in debt level can

help to increase the fraction of the manager’s share. Moreover, as indicated by Jensen

(1986), debt reduces the free cash available to managers to pursue their personal interests.

As a result, mitigating the conflict between managers and equity holders is a benefit of

32

debt. However, debt will enhance the conflict between debt holders and equity holders.

Shareholders in firms with a high debt level have more motivation to invest in risky

projects. The reason is that they are aware that if the project is successful, they capture

most of the gain; conversely, if the investment fails, debt holders suffer the majority of

the loss. In contrast, when a firm nearly goes bankrupt, equity holders have no motivation

to invest in high-quality projects. This could be explained by the fact that, while they bear

the cost of investment, debt holders may capture most of the project’s profit.

The agency theory was extended by Grossman and Hart (1982), who studied the influence

of a bankruptcy threat on management activities. Their research also rests on the idea of

moral hazards in the principal–agent relation. The goals of managers may differ from

those of shareholders. Managers will prefer their own income or perquisites, while

shareholders’ interest is purely profit or market value maximisation. To deal with this

problem, Grossman and Hart (1982) suggested that besides salary incentive schemes and

takeover bids, bankruptcy probability could be an important factor in encouraging

managers to act in line with shareholders’ interests. When bankruptcy occurs, managers

lose all their income position. Therefore, debt puts pressure on managers to make better

investment decisions because, at a high debt level, firms face a higher probability of

bankruptcy. In this situation, managers will not decide to issue debt. However, Grossman

and Hart also indicated that the benefit of debt to managers included managers wanting to

increase the firm’s market value because their salary and reputation depend on it; the threat

of a takeover bid can decrease and new capital can be raised more easily. Through issuing

debt, managers can convince the market that they will act in line with the shareholders’

interest. Consequently, the market will put high valuation on the firm.

Stulz (1990) focused on agency costs of managerial discretion that could result in

overinvestment and underinvestment costs. The view is that when cash flow is high,

managers tend to overinvest even if projects are not profitable because they benefit from

increased investment. In other words, overinvestment costs increase. Conversely, when

cash flow is low, underinvestment costs arise since managers lack funds to invest in

profitable projects. Stulz (1990) noted that debt reduces available cash, then prevents

33

managers from investing inefficient investments to pursue their own interests. Therefore,

debt can reduce overinvestment costs. However, debt will increase underinvestment costs

when cash flow is too low. An equity issue that increases funds under the manager’s

control could mitigate underinvestment costs, but increase overinvestment costs when

cash flow is high.

Zwiebel (1996) replies on agency cost to build a capital structure model that considers

mainly managerial interests and incentives rather than those of shareholders. The model

claims that while debt is undesirable for managers as it prevents them from expanding

their empire because of the risk of bankruptcy, managers may voluntarily choose debt to

commit to not undertaking inefficient projects, thereby preventing a threat of takeover. In

other words, when managers are free to decide the capital structure, debt could serve as a

voluntary self-constraint to ensure good investments that align with shareholders’ interests

are pursued.

3.2.2 Empirical evidence

3.2.2.1 Managerial ownership

Capital structure decision is affected not only by firm characteristics or contextual factors,

but also by managers’ views, goals and desires, which are influenced by managerial

ownership structure (Brailsford, Oliver & Pua 2002). The rationale for explaining the

positive influence of managerial ownership on capital structure relates to the issue of

control (Ghaddar 2003; Kim & Sorensen 1986). A major concern for managers is retaining

or increasing their control because it provides them with discretion in making decisions

and access to private benefits. Meanwhile, debt is a means to avoid share dilution. Harris

and Raviv (1988) affirmed that an increase in debt helps managers to reinforce their

control and resist takeovers. Managers sometimes use excess debt as a transitory device

to signal a covenant to sell assets, thereby preventing takeover attempts from outside

investors. In addition, with high debt, managers have more cash to pursue suboptimum

investments for their own interests.

34

In some of the first empirical evidence, Kim and Sorensen (1986) found that firms with

higher insider ownership have greater debt levels than those with lower insider ownership.

Their methodology was to use both analysis of variance and regression techniques to

compare long-term debt to total capitalisation of firms that have high inside ownership

with those that have low inside ownership. Insider firms may have motivation to issue

debt to maintain control and avoid the agency cost of external equity. Furthermore, firms

with heavy insider ownership could have lower agency cost of debt since debt holders

may prefer to lend to these firms.

Agrawal and Gershon (1987), Mehran (1992), Berger, Ofek and Yermack (1997) and

Jiraporn and Liu (2008) conducted studies in the United States that also indicated a

positive and statistically significant association between book value leverage, market

value leverage and chief executive officer (CEO) stock ownership. They interpreted this

result based on the conjecture of Stulz (1990) that managers increase debt level to

consolidate their control. This finding supports the argument that with high managerial

ownership, managers have incentives to use more debt to enhance the value of the

company, thus increasing the value of their own wealth. The same findings were obtained

by Wiwattanakantang (1999) with a sample of Thai firms, and Bokpin and Arko (2009)

with firms on the Ghana Stock Exchange: debt level is positively related with the extent

of managerial shareholdings, measured by shares held by CEOs or directors.

Managerial ownership has also been found to be negatively related to leverage for the

following reasons. First, the agency theory (Jensen & Meckling 1976) argues that a high

managerial ownership can help a firm to mitigate agency costs between managers and

shareholders since the interests of managers and shareholders are aligned. In this case,

using debt as a device to mitigate owner–manager conflict is not needed. Second, firms

with high managerial ownership have low agency costs of equity but have high agency

costs of debt because manager interests are more closely aligned with those of

shareholders than with those of debt holders. Consequently, creditors may require a high

interest rate or have more requirements for firms with high managerial ownership to

35

compensate for agency costs of debt. Begley and Feltham (1999) indicate that an increase

in managerial ownership increases the number of covenants in debt contracts because debt

holders worry about whether managers’ decisions are based on the shareholders’ benefits

rather than their interests. Thus, higher managerial ownership may lead to lower debt level

to avoid an increase in financial costs or reduce bankruptcy risks.

The other explanation for negative relations is that managers who have non-diversifiable

human capital have motivation to reduce their employment risk by decreasing the debt

ratio to ensure the viability of the firm because debt may increase the probability of

bankruptcy of a firm (Brailsford, Oliver & Pua 2002; Friend & Lang 1988; Jensen, Solberg

& Zorn 1992). Jensen, Solberg and Zorn (1992) utilised the three-stage least squares

method for controlling endogenous issues to investigate the relationship among dividend,

debt policy and inside ownership. They pointed out that inside ownership leads to less

debt. The negative relationship is caused by the insiders’ motivation to reduce bankruptcy

risk due to low diversified human capital and high agency cost of debt. Ellili (2011), using

panel data of 1,500 American companies during the period 2001–2004, showed that firms

with high managerial ownership, measured by the percentage of shares held by the chief

executive, avoid debt to preserve their position in the firm and to escape performance

pressure from monitors of debt holders.

An alternative view is that a non-linear relationship exists between debt ratio and

managerial ownership; in other words, managerial ownership has both negative and

positive associations with leverage. Brailsford, Oliver and Pua (2002), using a sample of

500 firms listed on the Australian Stock Exchange from 1989 to 1995, showed a non-

linear inverted U-shaped relation between managerial ownership and capital structure.

They observed that at the low level of managerial ownership, managerial ownership,

measured by the percentage of shares held by executive and non-executive directors,

positively affects debt ratio, but at a high level (over 49%), managerial ownership is

related negatively to debt ratio.

36

Ruan, Tian and Ma (2011) employed a cubic function to test the relation between

managerial ownership and leverage in 197 Chinese listed firms over the period 2002 to

2007. They pointed out there is a non-linear N-shape relation between managerial

ownership and leverage. More specifically, there is a negative relationship between

managerial ownership, measured by the percentage of equity owned by inside holders,

and debt ratio when this ownership is lower (18%) and higher (46%). Within a range from

18% to 46%, the debt ratio is positively associated with managerial ownership. They

argued that at low level, an increase in managerial ownership can reduce the conflict

between managers and shareholders; therefore, using less debt can maximise

shareholders’ wealth and firm value by avoiding financial distress. However, when

managerial ownership increases beyond a certain level, managers may increase debt to

have more cash flow to achieve their own interests or prevent share dilution to protect

their control of the firm. Nevertheless, when managerial ownership reaches a relatively

high level, the interests of managers and shareholders are completely aligned, and the firm

will use less debt to reduce bankruptcy risks.

In short, the relationship between managerial ownership and capital structure is complex,

and empirical evidence is not consistent about the direction of this relation.

3.2.2.2 State ownership

There has been a growing interest in research on the linkage between ownership structure

and capital structure, but there continues to be limited evidence on the association between

capital structure and state ownership for transition economies that have undertaken an

equitisation process. Zou and Xiao (2006), conducting a study on listed Chinese firms,

argued that state ownership is positively related to debt ratio for three reasons. First, firms

with high state ownership may have better access to the debt market because they have

less chance of bankruptcy because of the guarantee of the state. Second, representatives

of state ownership might prefer to have a high level of debt to avoid share dilution or to

preserve their control. Third, agency problems between owners and managers tend to be

severe in firms with a high level of state ownership because there is segregation between

37

voting and cash flow rights. While the ultimate owners of state-owned shares are the

citizens, the voting rights belong to government departments or bureaucrats whose salaries

are normally not directly linked to the performance of the firms that they monitor and

control. As a result, bureaucrats are not motivated to manage firm operations efficiently.

Therefore, to reduce considerable agency costs of equity, state-controlled firms tend to

use high debt as a monitoring channel.

A study conducted by Li, Yue and Zhao (2009) on non-publicly traded Chinese firms

indicated a positive relationship between state ownership, measured by the fraction of

shares held by state, and all measures of leverage, including short-term, long-term and

total debt to total assets. They found that the high degree of state ownership can enhance

a firm’s access to debt because of soft budget constraints and bailouts from the

government.

Pöyry and Maury (2010), investigating a sample of 95 Russian listed firms from 2000 to

2004, also found that firms with high state ownership have a significantly higher debt level

than others. This result implies that firms with other types of ownership do not have equal

access to capital sources. Specifically, state-owned firms may have substantial advantages

with respect to access to the debt market because of the preferential treatment they receive

from state-owned banks. For example, while other private firms must rely on capital

sources with high costs, state-owned firms can obtain debt financing at a low cost. As a

result, they can use more debt than other corporations in general.

Similarly, Huang, Lin and Huang (2011), using panel data of Chinese listed firms between

2002 to 2005, found that state ownership has a positive effect on debt ratio. They explained

that the agency cost of equity can rise in firms with high state ownership because of the

possibility of takeover by controlling managers. Therefore, to mitigate the agency cost of

equity, firms will issue more debt.

Studies specifically on Vietnam by Okuda and Nhung (2010) and Nguyen, Diaz-Rainey

and Gregoriou (2012) considered the effect of state ownership on capital structure in an

38

attempt to identify determinants of capital structure decisions in Vietnamese listed firms.

However, their studies had different results when investigating the link between state

ownership and capital structure. Whereas Okuda and Nhung (2010) indicated that state

ownership does not affect debt ratio, Nguyen, Diaz-Rainey and Gregoriou (2012) found a

positive relationship between state ownership and debt ratio. In addition, both studies used

dummy variables—value 1 if state ownership and 0 if non-state ownership—whereas the

most common measurement used in other studies is the percentage of shares held by the

state (Gurunlu & Gursoy 2010; Huang, Lin & Huang 2011; Li, Yue & Zhao 2009; Zou &

Xiao 2006). These limitations highlight the necessity for further detailed research on this

issue for Vietnam.

3.2.2.3 Foreign ownership

In emerging markets, because of booming foreign investment inflow, the influence of

foreign investors on firms’ activities is increasing (Vo 2011); however, little research has

been conducted using a detailed dataset to examine the link between foreign ownership

and the capital structure decisions of a firm. Most studies in this area posit that there is a

negative relationship between foreign ownership and debt level, except for the studies of

Zou and Xiao (2006) and Gurunlu and Gursoy (2010). These studies hypothesised a

positive effect of foreign ownership on capital structure; nevertheless, contrary to their

predictions, their study findings indicate a negative association between foreign

ownership and capital structure. More specifically, Zou and Xiao (2006) believed that in

emerging markets foreign investors normally face more severe information asymmetry

than other investors and that foreign investors often have a diversified portfolio in which

the percentage of shareholding in each firm is generally low. Therefore, the power of

foreign investors to monitor management is not sufficient, forcing firms to use more debt

as a further management monitoring mechanism. Nonetheless, in their investigation of the

financing behaviour of Chinese public listed companies from 1997 to 2000, Zou and Xiao

(2006) did not find a significant influence of foreign ownership on capital structure.

Gurunlu and Gursoy (2010) contended that foreign investors normally suffer more types

of risks, including country risk, currency risk and business risk, than domestic investors.

39

As a result, foreign investors are motived to minimise their risk by influencing the

operations of firms in which they have invested through bringing not only their capital but

also their technology and ability to access new capital markets. Therefore, it was expected

that firms with high foreign ownership could use more debt because foreign investors help

them to access more and cheaper debt from new creditors. However, when testing the

effect of foreign ownership on capital structure using multivariate regression analysis with

a dataset of 143 non-financial firms listed on the Istanbul Stock Exchange from 2007 to

2008, Gurunlu and Gursoy (2010) found that foreign ownership (percentage of shares

owned by foreign shareholders) is significantly negatively related to long-term leverage.

They explained this result by a reason that the need for external financing like debt of

firms with high foreign ownership decreases due to the equity contribution from foreign

investors.

Li, Yue and Zhao (2009), conducting research in non-publicly traded Chinese firms, found

that foreign ownership, measured as the fraction of ownership by foreign investors, is

negatively related to all measures of leverage, including total debt, short-term debt and

long-term debt divided by total assets. This result was explained by two factors. First,

firms with high foreign ownership have more diversified financing channels to access

capital than others because of their reputations and relationships. Second, in China,

foreign-owned firms normally have lower corporate tax rates than others; therefore, they

tend to use less debt because of the low of tax-shield saving. Huang, Lin and Huang

(2011), investigating Chinese listed firms in the period from 2002 to 2005, reached the

same outcome as Li, Yue and Zhao (2009); nevertheless, they explained that foreign

owners, which are mainly institutional investors, have considerable experience in

monitoring managers. Specifically, institutional or foreign investors have better access to

information and better knowledge for interpreting this information on firm performance

(Al-Najjar & Taylor 2008). As a result, foreign ownership helps to control the

overinvestment problem of managers or reduces the agency cost between managers and

shareholders. Therefore, foreign ownership and leverage may serve as substitutes in

controlling managerial self-interest (Moon 2001). An alternative view that leads to a

similar conclusion is that there is a negative signal in firms with a high debt level because

40

these firms could face financial difficulties in the future (Tong & Ning 2004). Therefore,

foreign investors prefer firms with a low debt ratio. Consequently, it is widely agreed that

firms with high foreign ownership tend to use less debt.

3.2.2.4 Large ownership

A number of previous studies suggested that large ownership influenced capital structure

decisions by controlling the conflicts that occurred between managers and shareholders.

However, the empirical evidence of this relation has not been consistent and clear as yet.

McConnell and Servaes (1995) and Zeckhauser and Pound (1990) stated that block

holders can enhance the quality of corporate governance and increase manager efficiency

by monitoring managers, which may determine the debt level. Specifically, in firms with

dispersed ownership and control, small shareholders have less motivation to monitor

managers, and less influence, because they hold a small proportion of shares and the loss

caused by manager discretion is shared among many investors. In addition, the cost of

having information and monitoring may outweigh its benefits. Conversely, in firms with

concentrated ownership, large shareholders have both motivation and power to monitor

managers to protect their investments (McConnell & Servaes 1995; Zeckhauser & Pound

1990). Supporting this idea, Shleifer and Vishny (1986) and Wiwattanakantang (1999)

argued that block holders play an active role in firm performance. Large investors can take

control of the firm, replace managers or cut managers’ benefits if the firm’s performance

is poor. As a consequence, large ownership could serve as an alternative for debt financing

to boost the monitoring of managers.

Zeckhauser and Pound (1990) explained the negative relation between debt level and large

ownership using the signalling view. They argued that the presence of larger shareholders

can work as an effective signal to the market that managers will act in the best interests of

the shareholders, thereby reducing the conflicts between managers and shareholders.

Therefore, firms with high large ownership do not need to issue additional debt while still

conveying a signal of the good performance prospects to the market and other investors.

41

In contrast, Friend and Lang (1988) and Fosberg (2004) found that firms with high large

ownership have a higher debt level than those with low large ownership. These authors

claimed that firms with high large shareholder ownership can have greater monitoring of

managers, thereby forcing managers to issue more debt than their desire, which is assumed

to be lower than the optimal debt ratio to avoid bankruptcy possibility. Brailsford, Oliver

and Pua (2002), studying 500 listed firms in Australia from 1989 to 1995, provided

supportive evidence of the positive relation between block ownership and leverage. This

outcome was explained by the active monitoring role, which advocates that large

shareholders have a strong motivation to monitor managers, thereby reducing managerial

opportunism and agency conflicts.

Another argument to support the positive relation between debt level and large ownership

was introduced in Zeckhauser and Pound (1990). These authors stated that large

shareholders can mitigate the agency cost from debt financing. Through monitoring

managers, large shareholders can ensure that managers will invest in the efficient projects

expected by debt holders. Therefore, firms with large investors therefore can access debt

with lower costs than firms without such investors. Consequently, the debt ratio is

expected to be higher when firms have higher large ownership.

Al-Fayoumi and Abuzayed (2009) used both static and dynamic models to investigate the

effect of ownership structure on leverage for Jordanian firms in the period 2001 to 2005.

This study provided inconclusive evidence the relationship between capital structure and

individual block holders’ ownership. Block holder ownership was determined to be

positively and significantly related to firm leverage measured by total book debt ratio,

implying that block holders can improve corporate governance by supervising managers.

However, this outcome was not confirmed under the total market debt ratio. Large

ownership was still positively but insignificantly associated with leverage.

Pound (1988) found that large investors can have positive and negative effects.

Institutional investors can boost the monitoring of managers more than individual

42

investors. However, a negative effect can occur if they collude with managers against

other shareholders’ interests. In such cases, if large shareholders conspire with managers

to pursue their interests instead of those of dispersed shareholders, the association between

financial leverage and large ownership will be consistent with the relationship between

leverage and manager ownership.

3.3 Capital structure and firm performance

One of the core issues discussed in the financial literature is the influence of debt level on

firm performance. However, there is considerable debate, in relation to both theoretical

knowledge and empirical evidence, on the linkage between capital structure and firm

value. Arguments have centred on the nature of the relation between debt level and firm

value and whether an optimal capital structure for maximising firm value exists.

3.3.1 Theoretical perspective

Capital structure refers to the combination of equity and debt used by a company to finance

its assets (Brounen, De Jong & Koedijk 2006). Debt refers to funds sourced from the

market or from financial institutions. Equity can include common stock, preferred stock

or retained earnings. Capital structure theories focus on explaining the mix of financing

sources used by firms, determinants of capital structure and the link between capital

structure and firm value. Major theories underpinning this issue are MM, trade-off,

pecking order, agency cost and market timing theory.

MM theory, created by Modigliani and Miller (1958), is regarded as the foundational

theory on this issue. The theory points out that a firm’s value is not influenced by its

capital structure. In particular, firm value will be determined by its own assets, not by the

proportion of debt or equity issued; that is, any mixture of debt and equity does not affect

firm value. However, MM theory is based on the following critical assumptions of a

perfect capital market: no bankruptcy, taxes or transaction costs exist; perfect information

is available to all investors; value maximisation is a common purpose among managers;

43

investors can lend and borrow at the same interest rate and they have homogeneous

expectations about the firm’s profits; and firms operating with similar conditions have the

same risk level.

MM theory uses an arbitrage argument in which if a firm using debt has a higher value,

investors will sell this firm stock and then purchase stocks of a no debt company. Because

there is no transaction cost, investors using this arbitrage process earn profit without risk.

This route will continue until the stock prices of the two companies (with and without

debt) are equal. The process occurs very quickly in a perfect market; as a result, MM

theory concludes that the firm value does not depend on its leverage. However, in an

imperfect market where these above-mentioned assumptions do not exist, the result will

be very different, implying that capital structure does affect firm value.

Employing a different approach, the trade-off theory (Kraus & Litzenberger 1973; Myers

1984) claims that a firm will trade off the costs and benefits of debt associated with tax

savings and financial distress to create an optimal capital structure for maximising firm

value. The gain of debt primarily comes from the tax shield (Miller & Modigliani 1963),

which implies that a firm can reduce tax liability by decreasing income through paying

interest. The costs of debt mainly derive from direct and indirect bankruptcy costs by

increasing the financial risk (Kim 1978; Kraus & Litzenberger 1973). In short, this theory

asserts that the value of a firm with debt is equal to that of a firm without debt plus tax

shield after deducting financial distress costs.

The pecking order theory, conceived by Myers and Majluf (1984), states that financing

follows hierarchy. Firms prefer internal to external financing and debt to equity; that is,

internal financing is used first, then debt is issued and when no more debt can be

approached, equity is issued. The pecking order was traditionally clarified by transaction

costs, issuing costs and asymmetric information. Retained earnings involve fewer

transaction costs, issuing costs than other sources. Issuing debts acquire lower information

costs than that of equity. Furthermore, this theory argues that managers often have more

information about their own firm than external investors do. External investors require a

44

higher return of equity because it has higher risk compared with debt. Thus, retained

earnings are better than outside funds and debt is better for firms than equity if the firm

needs external funds. In this theory, an optimal debt ratio to maximise firm value is not

mentioned. Changes of debt ratio derive from raising external financing demand when

internal funds are fully used.

Developed by Jensen and Meckling (1976), Jensen (1986) and Hart and Moore (1994),

agency cost theory contends that target conflicts exist among managers, shareholders and

debt holders. An optimal capital structure to maximise firm value is one that helps to

minimise total agency costs. Specifically, Jensen and Meckling (1976) claimed there are

two kinds of agency costs. The agency cost of equity is caused by the conflict between

shareholders and managers, and the agency cost of debt is caused by the conflict of debt

holders and equity holders. The conflict between managers and shareholders implies that

managers try to achieve their personal aims instead of maximising the firm’s value and

shareholders’ returns. For example, with an excess free cash flow, managers have

opportunities to invest in non-profitable projects for personal goals. Jensen (1986) argued

that with high debt, managers are under pressure to invest in profitable projects to create

cash flow to pay interest. Therefore, through reducing agency cost relating to managers

and shareholders, debt can have a positive effect on firm value. Whereas debt is an

efficient means to reduce shareholder–manager conflict, it increases shareholder–debt

holder conflict (Myers 1977). This conflict arises because debt can lead shareholders to

invest suboptimally (Harris & Raviv 1991). In addition, Myers stated that when debt is

high, debt holders will require higher interest rates to compensate for the higher risk of

liquidation or underinvestment. Therefore, in this respect, debt does have a negative effect

on a firm’s value.

Market timing theory states that the choice of debt or equity issuance depends on the

history of the firm’s market value (Baker & Wurgler 2002; Kayhan & Titman 2007; Myers

1984). In other words, this theory maintains that capital structure decisions are influenced

by the market conditions of share prices or managers base on stock market to decide

financing options. Indeed, managers will issue stocks after an increase in stock prices or

45

if their stocks are overvalued to take advantage of the situation and tend to use debt

following a decline of stock prices. There is no notion of optimal capital structure to

maximise firm value in this theory.

3.3.2 Empirical evidence

As mentioned, most of these theories agree that in an imperfect market in the real world,

debt can influence firm value or firm performance in several ways. However, the

relationship between capital structure and firm performance has been a debated subject,

and the empirical evidence leads to differing conclusions on this association.

Regarding the empirical evidence, most studies indicate that there is a positive relationship

between leverage and firm performance. Ross (1977) stated that a firm with worse

prospects will issue less debt than one with better prospects since the debt issuance may

lead to a higher probability of bankruptcy. Thus, the firm value can rise with debt level

because an increase in debt level enhances the market perception of the firm’s situation.

Berger and Bonaccorsi di Patti (2006), using data on the US banking industry, pointed out

that a higher debt ratio is associated with higher firm performance as represented by profit

efficiency. In particular, an increase of 1% in debt ratio leads to a 6% increase in profit

efficiency. Even when the leverage is very high, there is still a significant positive linkage

between leverage and firm performance. They argued that using more debt can reduce the

agency cost of equity or encourage managers to act more in the shareholders’ interests,

when then boosts the firm’s value. Abor (2005), using correlations and regression analyses

to examine the relationship between capital structure and profitability in firms listed on

the Ghana Stock Exchange in the five years from 1998 to 2002, claimed that there is a

significant positive effect of debt measured by short-term debt to total assets and total debt

to total assets on return on equity. Using the same method as Abor (2005), Gill, Biger and

Mathur (2011) demonstrated that a significant positive association exists between capital

structure measured by total debt to total assets, short-term debt to total assets and long-

term debt to total assets and firm performance.

46

However, some studies, especially those conducted in emerging or transition economies

have shown a negative association between capital structure and firm performance. With

an unbalanced panel of 167 Jordanian companies during 1989 to 2003 and a random effect

(RE) model for unbalanced data, Tian and Zeitun (2007) revealed that debt has a negative

influence on firm performance, in both the accounting and the market performance. In the

study, the market performance of the firms was measured by their market value to book

value, price per share to the earnings per share, market value of equity to the book value

of equity, while ROE, ROA, and earnings before interest and tax plus depreciation to total

assets were employed as measures representing accounting performance. Their

explanation based on an argument of Harris and Raviv (1991) is that underestimating

bankruptcy costs of liquidation or reorganisation may lead firms to have more debt than

they should; therefore, high debt ratio will decrease firm performance. Joshua (2007),

using a panel regression model with a sample of SMEs in Ghana and South Africa, found

that the long-term and total debt level is negatively associated with firm performance

measured by Tobin’s Q. This result implies that to reduce conflicts of interest between

managers and shareholders, firms may actually use a higher debt ratio than an appropriate

level; consequently, a high debt ratio produces a low performance. Majumdar and

Chhibber (1999), using a regression model to investigate over 1,000 Indian firms, also

found that there is a negative relationship between capital structure (measured by debt to

equity) and firm profitability (measured by the percentage of profit to sales). However,

their interpretation was based on Indian market conditions, where the role of debt as a

monitoring channel to improve firm performance is not considerable. Thus, large cash

flow from debt can lead managers to undertake discretionary behaviour or negatively

affect firm performance.

In addition, some studies have found a non-linear relationship between capital structure

and firm performance; that is, capital structure has both positive and negative effects on

firm performance. Stulz (1990) developed a model in which debt can have both effects on

firm performance. In particular, the positive effect of debt is that debt payment obligates

managers to pay out cash flow and hence reduces overinvestment. The negative effect of

debt is that debt payments may exhaust cash flow or reduce available funds for profitable

47

investment, thus exacerbating the underinvestment problem. Lin and Chang (2009)

explored the relation between debt ratio and firm performance by employing an advanced

panel threshold regression model to test whether there is a threshold debt ratio.

Specifically, they used a sample of 196 Taiwanese listed firms over 13 years (1993–2005)

and measured firm performance by Tobin’s Q. They claimed that there are two threshold

effects between debt ratio and firm performance. When debt ratio is less than 9.86%, an

increase of 1% in the debt ratio leads to an increase of 0.0546% in Tobin’s Q (a proxy of

firm value). When debt ratio is between 9.86% and 33.33%, Tobin’s Q increases 0.0057%

with an increase of 1% in the debt ratio. When debt ratio is higher than 33.33%, there is

no relation between debt ratio and firm value. Margaritis and Psillaki (2010) investigated

the linkage between capital structure and firm performance in French manufacturing

firms. They employed a quadratic functional form that included debt ratio and the square

of debt ratio to allow the relationship between capital structure and profit efficiency to be

non-monotonic and reverse signs when debt ratio is high. They found a positive

relationship between leverage and firm performance measured by X-inefficiency;

however, this relationship switches from positive to negative at a high leverage in some

industries.

3.4 Capital structure: Previous studies in Vietnam

Although several studies relating to capital structure have been conducted, there have been

only a limited number of studies in this field in Vietnam. To date, there have only been

few published studies considering capital structure decisions in Vietnamese firms.

However, most of these studies only attempted to identify determinants of capital

structure, and did not investigate in depth the effect of ownership structure on leverage.

In addition, the linkage between leverage and firm performance has not been sufficiently

explored, although most Vietnamese firms are aware of the strong effect of debt on their

business activities.

Nguyen and Ramachandran (2006) utilised data from 558 Vietnamese SMEs, firms that

have fewer than 300 employees or have registered capital less than VND 10 billion

48

(equivalent to nearly USD 500,000), in the period 1998 to 2001, to investigate

determinants of capital structure. The data comprised interviews with financial officers

and financial statements. Using the ordinary least squares (OLS) method, they found that

while firm leverage is positively related to growth, risk and firm size, it is negatively

associated with tangibility with all measures of capital structure. They further determined

that there is no relationship between profitability on capital structure and that state-owned

firms, which were measured by dummy variables, have higher debt ratio than privately

owned firms. This could be due to favourable treatment given to state-owned firms in

accessing the debt market. In addition, they further observed that firms with a strong

relationship with banks or networks will obtain large amounts of bank loans.

Biger, Nguyen and Hoang (2007), using data from a survey conducted by the Vietnamese

Statistics Bureau to examine elements affecting capital structure decisions in Vietnamese

firms. The research sample consisted of 3,778 mainly unlisted firms from 2002 to 2003.

In line with the findings of Nguyen and Ramachandran (2006), Biger’s research results

revealed that while financial leverage of Vietnamese firms is associated positively with

firm size and growth, it is negatively related to fixed assets, profitability, and non-debt tax

shield. Firms’ leverage was found to be influenced by the industry characteristics;

however, there is no difference in debt level between state-owned firms and other firms.

Using panel data from Vietnamese listed firms from 2006 to 2008, Okuda and Nhung

(2010) investigated factors determining capital structure decisions. Besides employing

OLS regression, this study utilised fixed effect (FE) and RE models to analyse the data.

They concluded that trade-off theory and agency theory may explain the capital structure

decisions in Vietnamese listed firms. They further observed that debt ratios have a positive

correlation with tax and size, whereas they have a negative relationship with profitability.

By using dummy variables as a proxy of state-owned firms, their found that state-owned

firms have less bankruptcy risk and lower motivation to issue debts for tax saving than

other firms.

49

In another work, Nguyen, Diaz-Rainey and Gregoriou (2012) explored the determinants

of capital structure in Vietnamese listed firms by using panel data from 116 non-financial

firms on the Vietnam stock market for the period 2007–2010. Using the GMM system

estimator (panel GMM), the study found that profitability and liquidity are negatively

related to debt ratios, while growth is positively associated to leverage. The study was

unclear on the effect of size and tangibility on leverage because it was positively related

to long-term debt ratio but negatively associated with short-term leverage. They further

determined that state ownership has a positive relationship with debt ratios, reflecting that

state-owned firms have preferential treatments when accessing capital markets.

3.5 Conclusion

Several theories and studies have focused on diverse aspects of capital structure, but there

is no single theory that can fully interpret the effect of capital structure on firm

performance. Empirical evidence shows different and contradictory results on this relation

and indicates that it depends significantly on the specific circumstances. In addition, most

previous studies relating to capital structure investigated determinants of capital structure

decisions (e.g. Booth et al. 2001; Frank & Goyal 2009; Huang & Song 2006; Pandey 2001;

Titman & Wessels 1988). Tian and Zeitun (2007) and Joshua (2007) argued that there is

a lack of empirical evidence about the effect of capital structure on firm performance,

especially in emerging economies. These above issues motivate new studies on the

relationship between capital structure and firm performance.

The relationship between ownership structure and capital structure has been established

theoretically, but there are limited empirical studies examining this linkage (Bokpin &

Arko 2009; Brailsford, Oliver & Pua 2002; Friend & Lang 1988; Margaritis & Psillaki

2010; Ruan, Tian & Ma 2011). Additionally, studies that link ownership structure with

capital structure only attempt to identify determinants of capital structure. Jiraporn and

Liu (2008), Nigel and Sarmistha (2007), Brailsford, Oliver and Pua (2002) and Margaritis

and Psillaki (2010) have argued that investigating this relationship in depth could provide

important insights into the decisions on capital structure. Therefore, one of the main

50

purposes of this research is to create a better understanding that ownership structure could

play an important role in influencing financing decisions.

With respect to Vietnam, there are still some limitations in the previous research on capital

structure decisions. First, the previous research focused on factors affecting capital

structure but did not investigate intensively the relationship between capital structure and

firm value. In addition, research that links ownership structure with capital structure only

attempted to identify determinants of capital structure and did not explore in depth the

effect of ownership structure on leverage. In addition, the previous research only

considered state ownership, and few studies have investigated the effect of other types of

ownership structure, such as foreign ownership and managerial ownership, on capital

structure. Moreover, relating to state ownership, all the studies used dummy variables

(value 1 if state ownership and 0 if non-state ownership) although the exact measurement

of state ownership is the percentage of shares hold by state. Two recent studies (Nguyen,

Diaz-Rainey & Gregoriou 2012; Okuda & Nhung 2010) had different results in terms of

the linkage between state ownership and capital structure. Research by Nguyen and

Ramachandran (2006) and Biger, Nguyen and Hoang (2007) are now dated, given the

many changes that have since occurred in Vietnam. All of these limitations highlight the

need for further research to be conducted in Vietnam.

51

Chapter 4: Methodology

4.1 Introduction

A number of studies have focused on diverse aspects of ownership and capital structure,

as detailed in Chapter 3; however, some gaps still exist. Previous studies concentrated on

the influence of ownership structure on firm performance, but there is a lack of research

on its effect on financing decisions. Concretely, the relationship between ownership

structure and capital structure has been established theoretically, only a few empirical

studies have examined this linkage, especially in developing countries (Bokpin & Arko

2009; Brailsford, Oliver & Pua 2002; Friend & Lang 1988; Margaritis & Psillaki 2010;

Ruan, Tian & Ma 2011). Second, theories and empirical evidence regarding the linkage

of capital structure and firm performance have provided mixed and contradictory results

and indicated that this relationship depends significantly on specific circumstances.

Therefore, the aim of this research is to investigate the effect of ownership on capital

structure and subsequently the effect of capital structure on firm performance in Vietnam.

To achieve these objectives, this chapter begins by presenting the hypotheses developed

in response to arguments introduced in the previous studies and within the specific context

of Vietnam. The research employed predominantly quantitative methodology to explore

the cause-and-effect relationship between ownership structure and capital structure as well

as between capital structure and firm performance.

The chapter’s structure is arranged as follows. Section 4.2 presents the developed

hypotheses. Section 4.3 presents the sample size used and the process of data collection.

Section 4.4 demonstrates the variable measurements, describes the empirical models and

discusses different methods for analysis. The final section presents the conclusion.

52

4.2 Hypothesis development

4.2.1 Ownership structure and capital structure

The relationship between ownership and capital structures was hypothesised by following

the agency theory of Jensen and Meckling (1976) and Jensen (1986). Within the context

of this research, four aspects of ownership structure were employed, including state,

foreign, managerial and large ownership. The decision was based on Vietnam’s context

as a transition and emerging economy with distinct ownership features and data

limitations. Specifically, the first salient point is that state ownership is common among

Vietnamese listed firms. This is due to the equitisation process whereby many SOEs have

been converted to private companies, and efficient companies have been listed on the

stock market. Therefore, the Vietnamese stock market has many listed companies that

were formed by the privatisation of SOEs that continue to have a large percentage of

shares owned by the state (Okuda & Nhung 2010). Second, as an emerging market with

high economic growth and a stable political system, Vietnam has attracted a large amount

of foreign investment (World Bank 2014).Therefore, it is widely agreed that foreign

ownership has a strong influence on the Vietnamese stock market as well as listed firms

(Vo 2011). Finally, managerial ownership and ownership concentration proxies by large

ownership have become a prominent topic in most Vietnamese listed firms as they adopt

Western corporate governance.

4.2.1.1 State ownership

Most previous research (Huang, Lin & Huang 2011; Li, Yue & Zhao 2009; Nguyen, Diaz-

Rainey & Gregoriou 2012; Zou & Xiao 2006) has posited that state ownership is positively

related to debt level and the leverage capacity of firms. The following reasons are given

for this positive relationship. First, a high level of state ownership can increase a firm’s

capacity to access the debt market because of the guarantee provided by the state (Huang,

Lin & Huang 2011; Li, Yue & Zhao 2009; Zou & Xiao 2006). Second, state-owned firms

have incentives to increase debt to preserve their control or avoid share dilution. Finally,

53

firms with a high degree of state ownership normally have high agency costs of equity

due to the segregation between voting and cash flow; therefore, firms tend to raise more

debt as a monitoring channel to reduce the agency costs.

State-controlled firms in Vietnam have historically had a close relationship with state-

owned banks, which dominate the country’s bank industry (IMF 2010; Vietcombank

Securities 2011). Vietnamese firms with significant state ownership are expected to have

greater ability to access bank debt, based more on the relations and less on performance

and availability of collateral (Okuda & Nhung 2010). Additionally, with the guarantee of

the government, state-owned firms have preferential treatment in accessing the debt

market, for example, obtaining debt financing at a low cost. As a result, they could access

more debt than other corporations in general. This study therefore hypothesised that:

H1: There is a positive relationship between state ownership and leverage in Vietnamese

listed firms.

4.2.1.2 Foreign ownership

Foreign investors investing in Vietnam stock markets are mainly institutional investors

(Vo 2011) who have the experience and motivation to influence managers to protect their

investment (Brailsford, Oliver & Pua 2002; Friend & Lang 1988). Institutional

shareholders invest in firms on behalf of other individual investors or their customers, and

their responsibility is to ensure a high return on these investments. In addition, it is

believed that institutional or foreign investors have better access to information as well as

better knowledge for using this information to interpret firm performance (Agrawal &

Mandelker 1992). They have the experience and strong incentive to supervise managers’

activities to protect their portfolios. The influence of institutional investors can be ‘latent’

control to restrain management’s decisions rather than ‘active’ power that is exerted

directly on firm decisions. Moreover, they can affect firm performance by targeting

specific issues and controlling managers’ decisions through power voting as a member on

the firm’s board (Chaganti & Damanpour 1991).

54

Through monitoring of management, institutions or foreign investors can lead managers

to invest in efficient projects, which reduces agency costs and the conflict problem

between managers and shareholders. Consequently, foreign ownership can be negatively

associated with debt level because it can substitute for debt in decreasing agency costs of

equity (Huang, Lin & Huang 2011; Moon 2001). Another explanation for the negative

relationship between foreign ownership and debt level is that firms with high foreign

ownership normally have better performance and reputation; therefore, they can access

diversified financing sources instead of issuing debt (Li, Yue & Zhao 2009). Furthermore,

the demand for external financing, including the debt of firms with high foreign

ownership, may decline given the equity contributions from foreign investors (Gurunlu &

Gursoy 2010). This study therefore hypothesised that:

H2: There is a negative relationship between foreign ownership and leverage in

Vietnamese listed firms.

4.2.1.3 Managerial ownership

The relationship between managerial ownership and debt level is not consistent and can

be both positive and negative because of agency problems, control issues, employment

risks or the managers’ view. The positive effect of managerial ownership on leverage is

based on control issues, with debt being a means to help managers to avoid share dilution,

reinforce their control or resist takeovers (Ghaddar 2003; Kim & Sorensen 1986). In

addition, with high debt level, managers have more funds to pursue suboptimal

investments for their own interests (Harris & Raviv 1988). In addition, managers will be

given incentives to use more debt to boost the value of the company and increase the

wealth of managers (Mehran 1992; Stulz 1990). However, managerial ownership on

capital structure may have negative effects. Firms with high managerial ownership have

low agency costs of equity; therefore, it is not essential to use debt as a monitoring device

to mitigate shareholder–manager conflict (Jensen & Meckling 1976). Furthermore,

because of non-diversifiable human capital, managers tend to avoid debt to reduce

55

bankruptcy risks or preserve their status (Brailsford, Oliver & Pua 2002; Friend & Lang

1988; Jensen, Solberg & Zorn 1992).

In Vietnam, although the economic reform programme began in the early 1990s, the

financial system continues to be regarded as underdeveloped (IFC 2007; Leung 2009).

Therefore, it is widely suggested that the benefit of debt as a monitoring manager to reduce

the agency costs of equity may be not substantial. From the firm’s point of view, managers

are aware of the inefficient monitoring of debt; therefore, an increase of debt can help

them to acquire more cash to undertake discretionary investment for their personal

interests as well as to retain or increase their control. This study therefore hypothesised

that:

H3: There is a positive relationship between managerial ownership and leverage in

Vietnamese listed firms.

4.2.1.4 Large ownership

The relation between large ownership and leverage is still mixed to some extent. However,

most researchers (McConnell & Servaes 1995; Shleifer & Vishny 1986; Zeckhauser &

Pound 1990) firmly maintain that large stockholders have motivation and influence for

monitoring management given the size of their investment, which affects capital structure

decisions. The presence of large investors may warrant that managers cannot adjust the

debt level for their own interest, which is assumed to be lower than the optimal debt ratio

to avoid the possibility of bankruptcy. Therefore, the debt ratio can be expected to be

higher when firms have higher large ownership. In addition, through actively monitoring

managers, large shareholders can ensure that managers will invest in efficient projects as

expected by debt holders, thereby reducing the agency cost of debt. Specifically,

Chidambaran and John (2000) argued that large investors play a key role in transmitting

information on firm activities to other shareholders. They can obtain internal information

from management and transfer it to other shareholders and debt holders, which decreases

the conflict among investors. Therefore, firms with large investors can access debt with

56

lower cost than firms without such investors. Consequently, the debt ratio is expected to

be higher in firms with higher large ownership. This study therefore hypothesised that:

H4: There is a positive relationship between large ownership and leverage in Vietnamese

listed firms.

4.2.1.5 Moderating effect of ownership structure

Outside ownership such as state, foreign or large ownership may influence the relationship

between inside ownership and capital structure. It is widely argued that high stocks held

by outside investors will decrease the power of insider owners in controlling the firm’s

strategy. Mehran (1992) argued that major shareholders who are not hired by managers

and thus not under the control of management can monitor managers effectively and

indirectly influence the financial decisions of the firm. Shleifer and Vishny (1986)

suggested that outside shareholders can influence the relationship between managers and

shareholders because they have motivation and the authority to monitor managers and

reduce the conflict between managers and shareholders. Specifically, to achieve the best

return on their investment, outside investors with latent powers can influence management

to ensure that managers pursue the maximisation of firm value rather than their personal

interests, thus affecting the managers’ decisions on financing choice. This research

therefore hypothesised that:

H5a: Foreign ownership decreases the influence of managerial ownership on leverage.

H5b: State ownership decreases the influence of managerial ownership on leverage.

H5c: Large ownership decreases the influence of managerial ownership on leverage.

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4.2.1.6 Non-linear relationship between ownership structure and capital structure

Researchers (Brailsford, Oliver & Pua 2002; Céspedes, González & Molina 2010; Ruan,

Tian & Ma 2011) have debated the existence of a non-linear relationship between

ownership structure and capital structure. Brailsford, Oliver and Pua (2002) argued that

there is an inverse U-shaped relationship between managerial ownership and debt ratio.

When the level of managerial ownership is low, managers have incentives to use debt to

preserve their control in the firm or acquire more cash flow to pursue their own interests.

However, at a high level of managerial ownership, when the interests between managers

and shareholders are aligned, managers try to reduce their non-diversifiable employment

risk by decreasing debt ratio to avoid bankruptcy risks. De La Bruslerie and Latrous

(2012) supported an inverted U-shaped relationship between controlling shareholder

ownership and leverage, positive at the beginning but switching to negative at a high point.

They relied on the non-dilution entrenchment and risk deduction effects to explain the

result. In particular, at a low level, controlling shareholder ownership is positively related

to debt ratio because debt allows major shareholders to control more resources without

share dilution. However, when controlling shareholder ownership increases, their interests

will align with those of other shareholders. Thus, controlling shareholder ownership is

negatively associated with debt ratio because major shareholders prefer to use less debt to

avoid financial distress or bankruptcy risk. This study therefore hypothesised that:

H6a: There is a non-linear relationship between managerial ownership and leverage in

Vietnam’s listed firms.

H6b: There is a non-linear relationship between state ownership and leverage in

Vietnam’s listed firms.

H6c: There is a non-linear relationship between foreign ownership and leverage in

Vietnam’s listed firms.

58

H6d: There is a non-linear relationship between large ownership and leverage in

Vietnam’s listed firms.

4.2.2 Capital structure and firm performance

Empirical evidence regarding the linkage of capital structure and firm performance

provides mixed and contradictory results, and little research has been conducted on

emerging or transition economies. Furthermore, while most theories relating to capital

structure and empirical evidence conducted in developed countries posit a positive

relationship between capital structure and firm performance, some studies investigating

this relationship in emerging markets have found a negative relationship between capital

structure and firm performance. Specifically, the studies of Berger and Bonaccorsi di Patti

(2006), Gill, Biger and Mathur (2011) and Margaritis and Psillaki (2010) conducted in the

United States and France found that a higher debt ratio is associated with higher firm

performance because use of more debt reduces agency costs of equity or encourages

managers to act more in the shareholders’ interests. However, Tian and Zeitun (2007),

Joshua (2007) and Majumdar and Chhibber (1999), studying in Jordan, Ghana, South

Africa and India, found a negative effect of leverage on firm performance. They argued

that underestimating bankruptcy costs of liquidation may lead firms to have more debt

than they should; therefore, a high debt ratio will decrease firm performance. Additionally,

the role of debt as a monitoring channel to improve firm performance is not considerable

in emerging markets. Thus, large cash flow from debt can lead managers to undertake

discretionary behaviour or negatively affect firm performance. Using Vietnam as a typical

emerging market, it was therefore hypothesised that:

H7: There is a negative relationship between leverage and firm performance in

Vietnamese listed firms.

A number of recent studies have found a non-linear relationship between capital structure

and firm performance, that is, capital structure can have both positive and negative effects

on firm performance. Specifically, at a low level, debt can increase firm performance

59

through the tax shield, reducing agency costs of equity or informing a better prospect.

However, when leverage is sufficiently high, an increase of debt ratio can decrease firm

performance because the benefits of debt are overcome by the costs of debt, including

financial distress and agency costs of debt (Jensen 1986; Kraus & Litzenberger 1973;

Myers 1984; Myers & Majluf 1984). Therefore, this research also allowed for the presence

both effects of debt level, including positive and negative influences on firm performance,

using a quadratic function, as used by Berger and Bonaccorsi di Patti (2006) and

Margaritis and Psillaki (2010). The study hypothesised that:

H8: There is a non-linear inverted U-shaped relationship between capital structure and

firm value in Vietnam listed firms (leverage is associated positively with firm value;

however, at a high leverage, the relationship switches from positive to negative).

4.3 Data

4.3.1 Sample

The sample for this research is non-financial firms that were listed on the Vietnam stock

market over the six-year period from 2007 to 2012. The starting date for the data was

chosen because of major difficulties encountered in collecting sufficient data in the period

prior to 2007 as well as the implementation of the country’s Law on Securities in 2007.

The industry classifications used in this study, which are based on the Industry

Classification Benchmark (ICB), are oil and gas, basic materials, industrials, consumer

goods, health care, consumer services, telecommunications, utilities, financial services,

construction and real estate, and technology. Firms in the financial industry classification,

including banks, insurance and financial services, were excluded because their financial

statements differ substantially from those of other firms (Basil & Khaled 2011; Pandey

2001). In addition, firms that had violated the information disclosure regulations or were

under special monitoring of the Vietnamese State Securities Commission were excluded

for data quality assurance.

60

4.3.2 Data collection

A panel of secondary annual data of Vietnamese listed firms’ financial figures and stock

prices from 2007 to 2012 were utilised in this research. The raw data were obtained from

the Tai Viet Corporation (Vietstock), a nationally recognised company providing

information services on Vietnam’s financial and securities market. These data include

details of all Vietnamese listed firms’ annual reports, stock prices, stock volumes and

ownership structures extracted from explanation reports on financial statements.

The data were set up in a panel form to utilise the advantages of estimation with increased

numbers of observations or degrees of freedom, thereby improving the efficiency of

estimators. In addition, analysis of the panel data provides control of unobserved time-

invariant heterogeneity such as culture factors or the differences across companies;

enables testing of the dynamics of individual behaviours that cannot be estimated in cross-

sectional data. Finally, instrument variables are easier to obtain with panel data to treat

endogeneity, which is a common issue in research—specifically, exogenous variables in

previous times employed as instruments for endogenous variables in the current period

(Arellano & Bond 1991). Therefore, panel data provide an abundance of instruments.

From the raw data, all variables, including dependent, independent and control variables,

used in the capital structure and firm performance models were manually calculated by

the researcher. In the next step, the data cleaning process was begun by dropping variables

and observations with large missing main data or containing extreme data. For example,

observations with a debt ratio above 100% or below 0% were removed. The observations

of firms that were subject to mergers or acquisition were also eliminated to minimise the

bias of regression results. The process of broad collection and data cleaning resulted in

4,015 firm-year observations, dropping to a final unbalanced panel dataset of 2,797 firm-

year observations spanning from 2007 to 2012. The data include some missing firm-year

observations because of some firms listing or delisting during this time period or data

unavailability.

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The classifications of the data by year, industry and ownership structure are detailed in

Table 4.1. This table shows that foreign ownership is popular in Vietnamese listed firms,

with 2,353 per 2,797 firm-year observations having this ownership. Similarly, managerial

and large ownership are common in the sample. State ownership is also relatively

significant, with more than half of the observations having state ownership.

62

Table 4.1 Number of observations separated by year, industry and ownership structure

Year Number of

observations

Industry Number of

observations

Number of

observations

with foreign

ownership > 0

Numbers of

observations

with state

ownership > 0

Numbers of

observations

with

managerial

ownership > 0

Number of

observations

with large

ownership > 0

2007 226 Basic materials 297 281 159 238 251

2008 303 Consumer goods 421 397 219 319 386

2009 406 Consumer services 249 213 206 191 243

2010 586 Construction and real estate 244 227 91 187 219

2011 636 Health care 67 63 50 56 62

2012 640 Industrials 1272 1127 888 907 1136

Oil & Gas 20 20 20 11 20

Technology 108 100 41 67 987

Telecommunications 9 6 2 9 9

Utilities 110 105 88 95 109

Total 2797 Total 2797 2353 1757 2080 2522

63

4.4 The variables

4.4.1 Measure of firm performance

Three measures for firm performance were used: ROA; ROE, which is based on studies

of Jiraporn and Liu (2008) and Tian and Zeitun (2007); and Tobin’s Q, which is based on

studies of Nigel and Sarmistha (2007) and King and Santor (2008). While Tobin’s Q was

used to capture the firm’s market performance, ROA and ROE were employed for

presenting accounting performance. The Tobin’s Q indicator was calculated as the firm’s

market value to book value. The firm’s market value contains the market value of debt

and market value of equity. The market value of debt can be considered the book value,

while the current market capitalisation of equity was used as the market value of equity.

ROA was calculated by dividing earnings after interest and tax into total assets and ROE

was calculated by dividing earnings after interest and tax into total equity.

4.4.2 Measure of capital structure

Capital structure refers to a company’s funding source for its assets and the mix of equity

and debt (Brounen, De Jong & Koedijk 2006). Capital structure can be measured in

different ways, including long-term debt to total assets, short-term debt to total assets, and

total debts to total assets (Céspedes, González & Molina 2010; Chakraborty 2010; Kayo

& Kimura 2011; Pandey 2001). In addition, each debt ratio may be determined using the

book value and/or the market value (Frank & Goyal 2009). This research used the ratios

of long-term debt, short-term debt and total debts to book value and market value of total

assets to measure capital structure. Specifically, the ratios of total debt to book value of

total assets were employed primarily, and the other ratios were used in the robustness test.

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4.4.3 Measure of ownership structure

Ownership structure is defined by the distribution of equity with regard to capital and

votes as well as the identity of the equity owners. Ownership structure examined in the

study includes managerial ownership, state ownership, foreign ownership and large

ownership.

4.4.3.1 Managerial ownership

Managerial ownership has been defined as the ownership of the members of the board of

directors and their immediate families (Morck, Shleifer & Vishny 1988), as the fraction

of equity held by dominant managerial insiders (Berger, Ofek & Yermack 1997; Friend

& Lang 1988) or the part of the capital held by the CEO (Ellili 2011). In this research,

managerial ownership is defined as the percentage of ordinary shares held by all directors

(Brailsford, Oliver & Pua 2002; Ruan, Tian & Ma 2011).

4.4.3.2 State ownership, foreign ownership and large ownership

The percentage of shares held by the state, the percentage of shares held by foreign

investors and the percentage of shares held by large investors who hold at least 5% of

shares were used in this research to represent state ownership, foreign ownership and large

ownership respectively, consistently with most studies (Gurunlu & Gursoy 2010; Huang,

Lin & Huang 2011; Li, Yue & Zhao 2009; Zou & Xiao, 2006). Table 4.2 provides a

summary of the variables that were used in this study together with the measures for each

variable.

65

Table 4.2 Variables used in the measure of firm performance, capital structure and

ownership structure

Variables Measure Tobin’s Q (Market share price * Number of outstanding shares + Book

value of debt) / Book value of total assets ROA Earnings after interest and tax / Book value of total assets ROE Earnings after interest and tax / Book value of equity Capital structure Debt/(Debt + Equity) Managerial ownership Ordinary shares held by all directors / Shares outstanding State ownership Ordinary shares held by state / Shares outstanding Foreign ownership Ordinary shares held by foreign investors / Shares outstanding Large ownership Ordinary shares held by large investors who hold at least 5% of

total shares / Shares outstanding

4.4.4 Measure of control variables

This study used a number of control variables, based on studies by Brailsford, Oliver and

Pua (2002), Fazlzadeh (2011), Kayo and Kimura (2011), Gurcharan (2010), Chakraborty

(2010), Huang and Song (2006) and Pandey (2001). Table 4.3 provides details of the

control variables used in this study together with their measures.

Table 4.3: Control variables used in this study

Variables Measure Size (SIZE) Logarithm of total assets Growth (GRO) The percentage change in sales over the year Tangibility (TAN) The ratio between fixed assets and total assets

Tax (TAX) The effective tax rate, which is calculated by dividing total taxes by the pre-tax income

Risk (RISK) The standard deviation of the ratio of operating income before interest, taxes, and depreciation to total assets—this research used the previous three years when calculating standard deviation

Investment (INV) The ratio of capital expenditure to total assets

Cash flow (CF) The ratio of earnings after taxes plus annual depreciation to total assets

Profitability (PRO) The ratio of earnings before interest and taxes to total sales Liquidity (LIQ) The ratio of cash and cash equivalent to total assets Dividend (DIV) Dividend per share over share’s market price

66

4.5 Data analysis

This study employed Stata v12 software in analysing the data. To explore the data and to

assist in identification of potential data errors, descriptive statistics were utilised to

summarise and describe the firms’ variables by industry and in total. This stage was used

to explore the data and identify any potential data errors.

Second, correlation analysis for variables was used to discover the links between

ownership structure and capital structure and between capital structure and firm

performance. This step, together with the variance inflation factor (VIF) test, was also

used to check for the existence of multicollinearity among the variables.

The next step, multiple regression analysis on the panel data, was undertaken to

investigate the degree and direction of the variables’ relationships, after controlling for

firm characteristics. In general, pooled OLS, FE and RE estimation methods are common

techniques for estimation of panel data. Specifically, the linear model can be presented as

follows:

��� = � + ��,� ∗ � + ���

where i is firm and t is time and

���: the dependent variable of firm i in year t

��,�: K x 1 vector of explanatory variables

�: K x 1 vector of constants

���: error term

If unobserved heterogeneity is missing altogether and the ��� is independent to ���, the

estimators of OLS are unbiased and consistent. If the unobserved individual effects (firm

specific effects) appear, which is common in non-experimental research (Baltagi 2005),

the FE or RE estimators are better than the OLS method. In this case, it is assumed that

��� equals �� plus ���, with �� individual error component at firm level and ���

67

idiosyncratic error, that which is independent with both ��� and ��. The model therefore

becomes:

��� = � + ��,� ∗ � + �� + ���

If �� is correlated to ���, meaning that ��� is correlated to ���, the FE model would give

consistent estimators whereas OLS estimators would be inconsistent. If �� is not correlated

to ���, OLS estimators would be consistent but inefficient because ��� is heteroskadistic

and serial autocorrelated. To increase the efficiency, the RE model is then suggested.

To determine which model is better, an F-test for the FE model, the Breusch-Pagan

Lagrange Multiplier (LM) test for RE and the Hausman test for both fixed and random

models were conducted. Based on the results of these tests, the suitable models for this

research were chosen as detailed in Table 4.4.

Table 4.4: Tests and models used in this study

F-test Breusch-Pagan test Hausman test The model is chosen Ho is not rejected (not FE model)

Ho is not rejected (not RE model)

Pool OLS

Ho is not rejected (not FE model)

Ho is rejected (RE model)

Random effect model

Ho is rejected (FE model)

Ho is not rejected (not RE model)

Fixed effect model

Ho is rejected (FE model)

Ho is rejected (RE model)

Ho is rejected (FE model)

Fixed effect model

Ho is rejected (FE model)

Ho is rejected (RE model)

Ho is not rejected (RE model)

Random effect model

Moreover, to increase the efficiency of the model, testing for groupwise heteroskedasticity

through the Wald test and autocorrelation by the Wooldridge test were conducted. If

heteroskedasticity and autocorrelation exist in the model, robust standard errors will be

calculated to enhance the efficiency of estimators.

Although a model-adjusted standard error can deal with heteroskedasticity problems and

autocorrelation, Wintoki, Linck and Netter (2012) have claimed that bias relating

endogeneity still exists. This is because the FE and RE models mainly control for

68

unobserved heterogeneity. They do not account for the endogeneity problem, which is

caused by the measurement errors, time-invariant endogenous variables and reverse

causality that often take place in the field of finance research. Therefore, using FE or RE

models could still be biased, especially in short panel data (Cameron & Trivedi 2005). To

deal with this issue, some previous studies have suggested using instrument variable

estimators (IV estimators) or dynamic panel GMM. However, the problem when applying

IV estimators is the difficulty in finding variables that can serve as valid instruments

because with weak instruments, the IV estimators are likely to be biased. In other words,

IV estimates with invalid instruments could offer no improvement over OLS estimators.

Therefore, this research applied the dynamic panel GMM explored by Arellano and Bond

(1991) to deal with the endogeneity issue.

One of the advantages of the GMM model over the instrument estimator method is that it

is much easier to have instrument variables as exogenous variables in other time periods

or lag of variables can be used as instruments for endogenous variables in the current time

period. Therefore, GMM provides an abundance of instrument variables, which makes it

easier to achieve the conditions of valid instruments and overidentification of estimators.

In addition, the Arellano and Bond estimator is suitable for short panel data that have

small T and large N, meaning few time periods and many individuals. In a large T panel,

the specific characteristics of firms that appear in the error term will decrease with time

and the correlation of lagged variables with the error term is insignificant (Roodman

2006). In this case, other methods can be better than the Arellano and Bond estimator.

This research used short panel data with large companies and only six years, so the GMM

method introduced by Arellano and Bond (1991) was employed and believed to be

appropriate.

An issue of the original Arellano and Bond (1991) estimator, which is called difference

GMM, is that lagged variables can be weak instruments if the variables in regressions are

close to a random walk, because lagged levels transfer little information about changes in

the future. In addition, the difference GMM has a weakness when there are many gaps in

unbalanced panels (Roodman 2006). Therefore, Arellano and Bover (1995) and Blundell

69

and Bond (2000) developed the system GMM, in which the original equation is added to

the system to increase instruments, thereby increasing the efficiency of the estimators. In

this estimator, lagged differences are employed as instrument variables for equations in

levels and lagged levels are used as instruments for equations in first differences.

Arellano and Bond (1991) suggested two key tests to check for the validity of the GMM

model. The first test is the Sargan test or Hansen test of overidentification. GMM requires

that the overidentifying restrictions be valid. The second test is the Arellano-Bond test for

autocorrelation errors. The residuals in first differences AR (1) are expected to correlate,

but there should be no serial correlation in second differences AR (2). The condition for

the second-order serial correlation test is that any historical value of dependent variables

beyond the certain lags, which control for the dynamic aspects of an empirical

relationship, is a valid instrument because it will be exogenous to the current shocks of

dependent variables (Wintoki, Linck & Netter 2012).

Although GMM estimators are now becoming increasingly popular, one disadvantage of

the difference and system GMM is that they are quite complicated and so easily create

invalid estimates (Roodman 2006). Therefore, this research reported all results of OLS,

RE, FE and GMM to compare and ensure the reliability of the findings.

Finally, for checking the robustness of the results, this research ran regressions in which

industry and year dummy variables were included to capture industry- or year-specific

FE. In addition, alternative measurements of dependent or independent variables were

applied to retest the results.

The estimated regression models utilised to test the hypotheses are detailed in the next

section.

4.6 Empirical model

4.6.1 The capital structure model

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To test the relationship between ownership and capital structure, the following models

follows were used:

���,� = � + �����,� + ���,� + ��,� (1)

���,� = � + �����,� + ���,� + ��,� (2)

���,� = � + �����,� + ���,� + ��,� (3)

���,� = � + �����,� + ���,� + ��,� (4)

���,� = � + �����,� + �����,� + �����,� + �����,� + ���,� + ��,� (5)

where CSi,t is capital structure of firm i at time t; SOi,t is a state ownership of firm i at time

t; FOi,t is a foreign ownership of firm i at time t; MOi,t is a managerial ownership of firm i

at time t; LOi,t is a larger ownership of firm i at time t; and Xi,t is a vector of control

variables.

Hypothesis 1 (H1), hypothesis 3 (H3) and hypothesis 4 (H4) proposed that the effects of

state, managerial and large ownership on capital structure would be positive, while

hypothesis 2 (H2) proposed that there would be a negative relationship between foreign

ownership and capital structure. Hence, based on these hypotheses, a positive sign on ��

in models 1, 3 and 4, and a negative sign on �� in model 2 were expected.

All state, foreign, large and managerial ownership were included in model 4 to retest the

effect of these variables on capital structure. Responses on the hypotheses ��>0, �� <

0, �� > 0 and ��>0 were expected in model 5.

In addition to using a static model, as above, this research applied the dynamic capital

model developed by Tsyplakov and Titman (2005) to test the effect of ownership structure

on capital structure. An advantage of this model is that it allows for the relationship

between ownership and capital structure to be dynamic in nature, similarly to the GMM

estimator for investigating the effect of ownership structure on capital structure, thereby

71

increasing the efficiency of the results. In addition, the dynamic capital model takes into

account the fact that the actual and target capital structures may differ.

The dynamic model is presented as:

���,� − ���,��� = � (���,�∗ − ���,��� )

with 0 < � < 1

where ���,�∗ is the target capital structure estimated from the following equation:

���,�∗ = � + �����,� + �����,� + �����,� + �����,� + ���,� + ��,�

Therefore, the dynamic capital structure model becomes:

���,� = �� + (1 − �)���,��� + ������,� + ������,� + ������,� + ������,� +

����,� + ��,� (6)

where � is the adjustment speech, representing the magnitude of adjustment from actual

to target capital structure. The � is between 0 and 1. When � = 0, then ���,� = ���,��� ,

meaning that there is no adjustment to the target capital structure; conversely, when � =

1 then ���,� = ���,�∗ , which means that adjustment appears without friction.

To test the moderating effect of outside ownership on the relationship between insider

ownership and capital structure, the following models were used:

���,� = � + �����,� + �����,� ∗ ��� + ���,� + ��,� (7)

���,� = � + �����,� + �����,� ∗ ��� + ���,� + ��,� (8)

���,� = � + �����,� + �����,� ∗ ��� + ���,� + ��,� (9)

where Dso dummy variable has a value of 1 if state ownership is higher than the mean of

state ownership and zero otherwise; Dfo dummy variable is 1 if foreign ownership is

higher than the mean of foreign ownership and zero otherwise; Dlo dummy variable is 1

if larger ownership is higher than the mean of larger ownership and zero otherwise. It was

expected that ��and �� in models 7, 8 and 9 would be significant.

To test whether there is a non-linear relationship between ownership structure and capital

structure, this research employed the following models:

72

���,� = � + �����,� + �����,�� + ���,� + ��,� (10)

���,� = � + �����,� + �����,�� + ���,� + ��,� (11)

���,� = � + �����,� + �����,�� + ���,� + ��,� (12)

���,� = � + �����,� + �����,�� + ���,� + ��,� (13)

These quadratic equations include ownership variables and square of ownership structure,

to accept the relationship between ownership structure and capital structure as non-

monotonic and reverse signs when ownership structure is high.

Control variables in models

The control variables used and the expected relationship between them and the firms’

leverage are detailed below.

Size

The theoretical forecast and previous research on the effect of size on capital structure

provide mixed results. The trade-off theory (Myers 1984) shows that larger firms can have

high debt due to lower non-payment risk and Booth et al. (2001) and Huang and Song

(2006) found positive links between size and leverage. In contrast, the pecking order

theory (Myers & Majluf, 1984) predicts an inverse relation between leverage and size.

Bevan and Danbolt (2002) and Deesomsak, Paudyal and Pescetto (2004) pointed out that

firm size negatively relates to short-term debt and positively relates to long-term debt.

They argued that it is easier for large firms, that are well known, to raise equity than small

ones.

Tangibility

Theories and empirical evidence show mixed results for the relationship between

tangibility and debt ratio. Both trade-off and agency cost theory predict that tangibility

will have a positive relationship with debt level because tangible assets can reduce

financial distress costs (Myers 1984) and decrease risks that debt holders suffer from

73

agency problems (Jensen & Meckling 1976). However, the pecking order theory gives an

opposite forecast because firms that have more tangible assets bear low information

asymmetry, so an issuance of equity is less costly (Myers & Majluf 1984). Empirical

studies by Wiwattanakantang (1999) found a positive relationship between tangibility and

leverage whereas Booth et al. (2001) and Huang and Song (2006) found a negative

relationship.

Tax

Theoretically, there is a positive relationship of tax on capital structure with tax rate

positively affecting tax saving. A firm with high tax rates is expected to use more debt to

obtain a higher tax shield (Modigliani & Miller 1958). Studies conducted by

Wiwattanakantang (1999), Goodacre, Beattie and Thomson (2004), Huang and Song

(2006) and Gurcharan (2010) proved the strong effect of tax on firm leverage.

Business risk

Theories and prior studies give mixed results on the relationship between firm risk and

debt levels. The trade-off theory suggests that a firm with high business risk keeps low

levels of debt in its capital structure. The explanation is that firms facing high risk with

unstable cash flows will have higher financial distress when increasing debt. In addition,

the more unstable cash flow decreases the probability of tax shields from using debt

(Myers 1984). Conversely, the pecking order theory will predict that riskier firms have

higher leverage because firms suffer more asymmetric information. On the other side,

Frank and Goyal (2009) found no significant relationship between risk and leverage.

Investment

Dudley (2012) examined the relationship between capital structure and investment

decisions using a sample from the period 1972 to 2010. The author found a positive

74

relationship between total leverage and investment decisions because investment will

bring the opportunity for firms to adjust leverage at low marginal cost.

Liquidity

The relationship between liquidity and capital structure is mixed. Sibilkov (2009) found

that firms with higher liquidity assets will increase the leverage and debt of company

because if such firms are not able to pay current liabilities, they can cover this obligation

with liquidity assets. From this view, there is a positive relationship between liquidity and

capital structure. However, from another view, Lipson and Mortal (2009) found a negative

relationship between liquidity and leverage because the more liquidity firms have the more

inclined they are to use internal resources for finance.

Cash flow

The literature on the effect of available cash flow on capital structure is complex. Huang

and Song (2006) found a positive relationship between free cash flow and capital structure

because the higher the amount of free cash flow a firm has, the stronger the behaviour of

managers influences their firms. In contrast, Brailsford, Oliver and Pua (2002) found a

negative effect of free cash flow on capital structure. The authors explained that firms with

large amounts of free cash flows will require low amounts of debt.

Growth

Agency cost theory (Jensen & Meckling 1976) posits that high-growth-rate firms may

invest suboptimally; therefore, agency costs of debt may increase. Therefore, this theory

concludes that the growth rate has a negative relation with debt. In contrast, pecking order

theory (Myers & Majluf 1984) implies that managers prefer debt to equity if they need

external funds. Hence, under the pecking order theory, a high-growth firm that needs more

75

external sources to finance projects tends to have high debt. Regarding empirical evidence,

while Huang and Song (2006) reported a significant positive relation between growth and

leverage, Gurcharan (2010) found an inverse relationship between growth and debt level.

Profitability

The trade-off theory predicts that profitable firms can have high debt because they have

lower costs of financial distress and higher tax savings. Agency costs theory (Jensen 1986)

suggests that profitable firms often face serious free cash flow problems, so high debt is

more valuable in reducing agency cost. However, most empirical studies (Booth et al.

2001; Frank & Goyal 2009; Huang & Song 2006) support the pecking order theory, which

argues that profitable firms will use less debt because of an excess of internal sources.

4.6.2 The firm performance model

To test the relationship between capital structure and firm performance, this research used

the following model:

���,� = � + �����,� + � ��,� + ��,� (14)

where FPi,t is firm performance of firm i at time t and measured by Tobin Q, ROA and

ROE; CSi,t is a capital structure of firm i at time t and measured by the ratios of long-term

debt, short-term debt and total debts to book value and market value of total assets; Zi,t is

a vector of control variables.

According to hypothesis 7 (H7), leverage would have a negative effect on firm

performance, hence, a negative sign on �� in model 14 was expected.

To capture the industry- and year-specific FE, this research employed the following

models:

���,� = � + �����,� + � ��,� + �. �������� + ��,�

���,� = � + �����,� + � ��,� + �. ���� + ��,�

76

In addition, testing the linear relationship between capital structure and firm performance,

this research used the quadratic function underpinned by the studies of Berger and

Bonaccorsi di Patti (2006) and Margaritis and Psillaki (2010) to allow a non-linear

relation.

���,� = � + �����,� + �����,�� + � ��,� + ��,� (15)

Hypothesis 8 (H8) proposed that there would be an inverse U-shaped relationship between

capital structure and firm value. In particular, leverage is associated positively with firm

value; however, at a high leverage, the relationship switches from positive to negative.

This quadratic function allows that the relationship between capital structure and firm

performance may not be monotonic, that is, it may switch from positive to negative at

higher debt ratio. The adequate condition for the inverse U-shaped relationship between

capital structure and firm value is that ��> 0 and ��< 0.

Control variables in the models

Based on studies of King and Santor (2008), Tian and Zeitun (2007), Margaritis and

Psillaki (2010), some other main factors affecting firm performance are detailed as

follows:

Growth

Most previous studies posit that growth is positively correlated to firm performance

because firms with a high growth rate are able to create more profit and value from

investment opportunities. King and Santor (2008) found a positive association between

growth rate measured by sales growth and firm performance measured by Tobin’s Q. Tian

and Zeitun (2007), Gleason, Mathur and Mathur (2000) and Jiraporn and Liu (2008) found

that a firm’s growth opportunity has a positive and significant effect on firm performance

measured by ROA. Margaritis and Psillaki (2010) found a positive relationship between

sale growth and firm efficiency.

Investment

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Prior studies agree that firm performance is related to investment opportunities. Cho

(1998) argued that firms with many investment opportunities may have high firm

performance as a result of their investing. Hoshi, Kashyap and Scharfstein (1991),

studying in Japanese firms, and Kaplan and Zingales (1995) conducting research in the

United States, provided evidence that the effect of investment on Tobin’s Q is significant

and positive.

Liquidity

Cho (1998) argued that liquidity was one of the signals of firm performance and prospects.

Firms with high liquidity are expected to have good performances and more investment

opportunities. In addition, firms with a high level of cash can support their new projects,

pay dividends or mitigate financial distress problems. Therefore, liquidity is predicted to

relate positively to firm performance.

Profitability

Most prior studies have indicated a positive effect of profitability on firm performance.

They have argued that firms with high profitability are generally better performers and

more efficient, and thus are expected to have high firm performance (Margaritis & Psillaki

2010).

Risk

Tian and Zeitun (2007) pointed out that there is a significant negative relationship between

risk and firm value because a higher risk implies higher financial distress cost, thereby

reducing firm performance. Bloom and Milkovich (1998) observed that high risk level

may be related to poor firm performance because the greater variability in a firm’s

outcomes increases the probability of corporate ruin. In addition, they indicated that high

78

business risk makes firms more difficult to formulate a strategy or future actions, thus

negatively affecting firm performance.

Cash flow

Jensen (1986) stated that with high cash flow, firms may invest in inefficient projects;

hence, it will harm the firm’s performance. In a study by Chung, Firth and Kim (2005),

cash flow raised the agency problems between the insiders and outsiders. Managers of

firms with large cash flow have opportunities to increase the scope of their authority. As

a result, it leads to underinvestment and reduces the wealth of shareholders. However,

Gregory (2005) and Chang, Chen and Huang (2007) documented a positive association

between cash flow and firm performance. These authors explained that high cash flow

enables firms to undertake positive investment without raising external funds at high cost.

Dividend

In a perfectly competitive market, dividend policy is irrelevant to firm performance

(Miller & Modigliani 1963). However, in an imperfect market, dividend policy is seen a

relevant to firm performance. Dhanani (2005) provided evidence on the positive

relationship between dividends and firm performance. The author argued that dividends

can resolve agency problems or information asymmetry between managers and

shareholders, thereby enhancing the firm performance. Amidu (2007) argued that

dividends are the best and most reliable signal of firm prospects. A high dividend payout

signals that the firm is confident of its strong earnings in the future.

4.7 Conclusion

This chapter, following a review of the literature and specific features of Vietnam,

developed hypotheses on the linkages between ownership structure and capital structure

79

and capital structure and firm performance for Vietnamese listed firms. This research

investigated managerial ownership and ownership concentration as previous studies. In

addition, it focused on state ownership and foreign ownership, which are considered

typical features in emerging and transition markets.

The chapter also provided a summary of the data, empirical models and research methods

used to examine the different relationships. Specifically, the data for this research are

panel data on non-financial firms that were listed on the Vietnam stock markets, including

the HOSE and HNX, in the period from 2007 to 2012. Capital structure and firm

performance models were used to explore the causal relationship among ownership

structure, capital structure and firm performance. The various methods used to analyse the

panel data included RE and FE models for controlling the unobserved heterogeneity and

GMM for capturing the endogenous problem. The findings from the data analysis are

presented in Chapter 5.

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Chapter 5: Findings

5.1 Introduction

Findings from the research are presented in this chapter. It begins by demonstrating the

effect of ownership structure, including managerial, state, foreign and large ownership, on

debt level through the capital structure model. Next, the influence of debt level on firm

performance through the firm performance model is presented. A noticeable point is that

different methods, including pooled OLS, RE, FE and GMM, were utilised to deal with

econometric issues of unobserved heterogeneity or the endogenous problem, thereby

increasing the robustness of the results.

This chapter is organised as follows. Section 5.2 provides a descriptive summary of data

in terms of the full sample and industry classifications. Sections 5.3 and 5.4 present the

results on the effect of ownership structure on capital structure and the influence of capital

structure on firm performance, respectively. The final section presents the conclusions.

5.2 Descriptive statistics of data

Summary statistics of all variables as proxies of capital structure, firm performance,

ownership structure and control variables are shown in Table 5.1. The average of total

book leverage (TLEV) and total market leverage (MTLEV) overall account for 51.92%

and 53.52% during the period from 2007 to 2012 and widely disperses, from 0.26% to

97.79% and from 0.26% to 98.12%, respectively. These ratios reveal that Vietnamese

firms overleveraged compared with those in other countries. Specifically, they are higher

than firms in developed countries: 22% observed by De La Bruslerie and Latrous (2012)

for French companies during the period 1998–2009; 33.4% reported by Lin et al. (2011)

for 22 Western European and East Asian countries from 1996 to 2008; and only relatively

the same 47% reported by Zou and Xiao (2006) for China listed firms. This is possibly

due to, as mentioned in the context analysis, the domination of the banking sector and the

81

early stage of development of the country’s stock market and financial market. In other

words, Vietnamese listed firms are mainly financed by the banking sector rather than the

stock market or other sources. In addition, the mean ratio of short-term debt (short-term

leverage [SLEV]) is 41.09% and much higher than long-term debt ratio (long-term

leverage [LLEV]), which is only 10.83 %. Similarity, while market long-term debt

[MLLEV] is only 11.02%, market short-term debt [MSLEV] accounts up to 42.59% in

total capital structure. This indicates that Vietnamese listed firms are heavily dependent

on short-term debt rather than long-term one, which could lead to a substantial effect on

firm’s performance as short-term debt drives firms to the risks of refinancing and liquidity.

It also needs to be noted that all debt ratios based on market value (MTLEV, MLLEV and

MSLEV) are slightly higher than those measured by the book value (TLEV, LLEV and

SLEV). This could be a result of the depreciation of the Vietnamese stock market during

this period.

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Table 5.1: Descriptive Statistics of capital structure (CS), firm performance (FP)

and ownership structure (OS)—Full sample

Variables Observation Mean Std Dev Min Max Capital structure TLEV 2797 0.5192 0.2212 0.0026 0.9779 LLEV 2797 0.1083 0.1466 0.0000 0.8007 SLEV 2797 0.4109 0.2057 0.0026 0.9779 MTLEV 2797 0.5352 0.2594 0.0026 0.9812 MLLEV 2797 0.1102 0.1536 0.0000 0.8561 MSLEV 2797 0.4250 0.2378 0.0026 0.9793 Firm performance ROA 2797 0.0632 0.0843 –0.8302 0.5875 ROE 2797 0.1030 0.4714 –15.282 0.9541 Tobin Q 2797 1.1518 0.7949 0.2610 20.933 Ownership structure SO 2762 0.2260 0.2373 0.0000 0.8295 FO 2761 0.0746 0.1189 0.0000 0.4900 MO 2206 0.1659 0.1662 0.0000 0.9672 LO 2522 0.4770 0.1851 0.0000 0.9672 Control variables SIZE 2797 26.697 1.4068 23.220 31.653 PRO 2791 0.0726 0.5617 –22.384 2.2978 INV 2788 0.0703 0.4452 –13.923 9.2205 TAN 2797 0.2031 0.1960 0.0000 0.9764 LIQ 2797 0.0934 0.1060 0.0001 0.9436 TAX 2792 0.1572 0.7194 –25.636 5.7606 CF 2794 0.0915 0.0988 –0.8101 1.6677 GRO 2778 0.2454 0.9128 –1.0000 23.139 DIV 2781 0.0716 0.0755 0.0000 1.2692 RISK 2649 0.0404 0.0425 0.0000 0.4910

Note: TLEV: the ratio of total debt to book value of total assets; LLEV: the ratio of long-term debt to book value of total assets; SLEV: the ratio of short-term debt to book value of total assets; MTLEV: the ratio of total debt to market value of total assets; MLLEV: the ratio of long-term debt to market value of total assets; MSLEV: the ratio of short-term debt to market value of total assets; ROA: the ratio of earnings after interest and tax to book value of total assets; ROE: the ratio of earning after interest and tax to book value of total equity; Tobin Q: the ratio of the firm’s market value to firm’s book value; MO: the percentage of ordinary shares held by all directors; SO: the percentage of shares held by the state; FO: the percentage of shares held by foreign investors; LO: the percentage of shares held by large investors; SIZE: firm size; GRO: firm growth; TAN: tangibility; tax; risk; INV: investment; CF: cash flow; PRO: profitability; DIV: dividends

83

Firm performance indicators are presented by ROA, ROE and Tobin Q. The average of

returns on equity and assets for the full sample as a whole are 6.3% and 10.3%,

respectively. The average values of Tobin Q represented market performance is 1.15,

which is lower than that observed for Singapore firms (2.03), Malaysian firms (1.77) (Mak

& Kusnadi 2005) and Chinese firms (1.41) (Ruan, Tian & Ma 2011). Figures also show

that the value of Tobin’s Q of listed firms varies from 0.26 to 20.93. The two alternative

measures of firm performance, ROA and ROE, suggest a large spread in their value. The

ROA of listed firms ranges from –0.83 to 0.59, and the ROE of those firms ranges from –

15.28 to 0.95. This implies that there was a significant gap in firm performance among

Vietnamese listed firms during this period.

Large ownership, state ownership and managerial ownership account for a significant

proportion—approximately 47.7%, 22.6% and 16.66% respectively—in the ownership

structure of listed Vietnamese firms, whereas foreign ownership is only about 7.46%. A

prominent point is that the maximum of foreign ownership is much lower than other

ownerships—only 49% compared with 82% of state ownership, 79% of managerial

ownership and 96% of larger ownership. This is due to the proportion of shares that can

be owned by foreign investors in Vietnam being limited to 49% by law.

Table 5.2 reports the mean of all measurements of capital structure, firm performance and

ownership structure separated by sectors and years. The largest ratio of total debt to total

book assets is in the industrial, and oil and gas sectors (57.24% and 57.92% respectively),

while consumer services and telecommunications have the lowest total book leverage

(38.27% and 39.41% respectively). These data demonstrate that although the debt ratios

tend to fluctuate over time, an upward trend in leverage over time also exists. For instance,

total book debt ratio increased from 48.14% in 2007 to 52.39% in 2012, while total market

debt ratio rose from 25.06% in 2007 to 62.59% in 2012.

The average of firm performance indicators exhibit the highest value in oil, gas and health

care industries, followed by consumer goods and real estate sectors. As illustration, the

ROE of the oil and gas sector is 19.68%, nearly double the mean value of the whole sample

84

at 10.3%. It needs to be noted that ROA, ROE and Tobin Q drop gradually over the six-

year period from 8.99%, 18.75% and 2.67 in 2007 to 3.64%, 0.16% and 0.89 in 2012,

respectively, reflecting the long-lasting economic recession in Vietnam during this period.

Through the privatisation programme, the average of state ownership dramatically

decreased, from 31.48% in 2007 to 13.46% in 2012, while the mean of foreign, managerial

and large ownership as a whole did not change significantly across the years. The

government holds a large proportion of oil and gas, and utilities (44.67% and 38.12%

respectively), which are considered the strategic industries of the country. Foreign

ownership is significantly high in health care, oil and gas, and technology, which also

have high performance in both accounting and market value.

85

Table 5.2: Mean of capital structure (CS), firm performance (FP) and ownership structure (OS) - Separated by industry and

year

Variables Capital structure Firm performance Ownership structure

TLEV LLEV SLEV MTLEV MLLEV MSLEV ROA ROE Tobin Q SO FO MO LO

Separated by industry

Basic materials 0.5114 0.0967 0.4147 0.5100 0.0955 0.4146 0.0830 0.1427 1.4979 0.2244 0.0748 0.2324 0.4820

Consumer goods 0.4868 0.0654 0.4213 0.4794 0.0615 0.4179 0.0777 0.1159 1.2937 0.1601 0.1083 0.1728 0.4913

Consumer services 0.3827 0.0631 0.3195 0.4080 0.0678 0.3402 0.0827 0.1320 1.2878 0.2535 0.0468 0.1771 0.4627

Real estate 0.5316 0.1882 0.3433 0.5467 0.1890 0.3576 0.0490 0.1159 1.2981 0.0997 0.1023 0.1903 0.4700

Health care 0.4022 0.0494 0.3528 0.3702 0.0515 0.3186 0.0917 0.1503 1.4698 0.1817 0.1753 0.0885 0.4479

Industrials 0.5724 0.1155 0.4569 0.6000 0.1201 0.4798 0.0514 0.0760 1.1418 0.2632 0.0516 0.1467 0.4637

Oil and gas 0.5792 0.1795 0.3997 0.4971 0.1379 0.3592 0.0747 0.1968 1.8470 0.4467 0.2038 0.0541 0.6172

Technology 0.4502 0.0445 0.4056 0.4955 0.0514 0.4441 0.0419 0.0736 1.2299 0.1146 0.1237 0.1349 0.4857

Telecommunications 0.3941 0.0710 0.3231 0.5418 0.0859 0.4558 0.0131 0.0252 0.9521 0.1015 0.0581 0.1480 0.3846

Utilities 0.4683 0.2330 0.2352 0.4764 0.2337 0.2427 0.0836 0.1547 1.1973 0.3812 0.0776 0.1757 0.5909

Separated by year

2007 0.4814 0.0942 0.3871 0.2506 0.0503 0.2003 0.0899 0.1857 2.6678 0.3148 0.1054 0.1569 0.4753

2008 0.5086 0.1098 0.3988 0.5340 0.1135 0.4205 0.0674 0.1149 1.3625 0.2806 0.0891 0.1765 0.4639

2009 0.5261 0.1145 0.4116 0.4456 0.0964 0.3491 0.0854 0.1807 1.2924 0.2649 0.0737 0.1785 0.4669

2010 0.5160 0.1147 0.4013 0.4888 0.1095 0.3793 0.0757 0.1404 1.2869 0.2304 0.0653 0.1829 0.4712

2011 0.5314 0.1109 0.4206 0.6456 0.1316 0.5140 0.0530 0.0860 0.9717 0.2262 0.0648 0.1559 0.4825

2012 0.5239 0.1002 0.4237 0.6259 0.1179 0.5080 0.0364 0.0016 0.8987 0.1363 0.0755 0.1543 0.4902

Note: TLEV: the ratio of total debt to book value of total assets; LLEV: the ratio of long-term debt to book value of total assets; SLEV: the ratio of short-term debt to book value of total assets; MTLEV: the ratio of total debt to market value of total assets; MLLEV: the ratio of long-term debt to market value of total assets; MSLEV: the ratio of short-term debt to market value of total assets; ROA: the ratio of earnings after interest and tax to book value of total assets; ROE: the ratio of earnings after interest and tax to book value of total equity; Tobin Q: the ratio of the firm’s market value to firm’s book value; MO: the percentage of ordinary shares held by all directors; SO: the percentage of shares held by the state; FO: the percentage of shares held by foreign investors; LO: the percentage of shares held by large investors

86

5.3 Capital structure model: The effect of ownership structure on capital

structure

5.3.1 Correlation analysis

Correlation analysis was used to determine the links between ownership structure and

capital structure. The pairwise correlation matrix presented in Table 5.3 shows the

correlation between all variables considered in regression. Overall, most correlation

coefficients among variables are quite low. The highest coefficients (0.50 and 0.47)

present respectively the relation between profitability (PRO) and investment (INV) and

the relationship between risk (RISK) and size (SIZE). The other coefficients are quite

close to 10%. In addition, to assess the existence of multicollinearity among the variables,

this study conducted the VIF test for panel data through the collin command. The result

of the VIF test shows that all figures are less than 10%, reflecting that the multicollinearity

problem is not serious.

Analysis of Table 5.3 also indicates that capital structure measured by total debt to total

assets is significantly and positively related to state ownership and large ownership,

whereas it is negatively associated with foreign ownership at the 5% significant level.

Specifically, the correlation coefficients for the linkage between capital structure and state

ownership, foreign ownership, managerial ownership and large ownership are 0.0097, –

0.2149, 0.0331 and 0.0449, correspondingly. Foreign ownership is noted to have the

highest coefficient among these figures, reflecting the strongest relation of ownership and

capital structures.

87

Table 5.3: Correlation coefficients between measures of ownership structure and capital structure

CS SO FO LO MO SIZE GRO INV TAN LIQ TAX PRO RISK CF

CS 1.00

SO 0.0970* 1.00

FO –0.2149* –0.1350* 1.00

LO 0.0449* 0.3466* 0.0706* 1.00

MO 0.0331 0.0976* –0.0418 0.2967* 1.00

SIZE 0.3180* –0.0268 0.3550* 0.1613* 0.0551* 1.00

GRO –0.0011 –0.0420* –0.0069 –0.0273 0.0387 0.0652* 1.00

INV 0.0479* 0.0349 0.0046 0.0284 0.0112 0.0891* 0.0910* 1.00

TAN 0.0180 0.1692* –0.0160 0.1233* 0.0690* 0.0125 –0.0209 0.2376* 1.00

LIQ –0.3231* 0.0831* 0.0802* 0.0447* –0.0673* –0.1142* 0.0070 –0.0326 –0.1483* 1.00

TAX 0.0101 0.0247 –0.0176 0.0162 0.0061 –0.0088 0.0213 0.0094 0.0051 0.0140 1.00

PRO –0.0210 0.0548* 0.0357 0.0229 0.0461* 0.0466* 0.0750* 0.5063* 0.0802* 0.0550* 0.0124 1.00

RISK –0.1904* –0.1256* 0.0366 –0.0649* –0.0227 –0.0961* 0.0148 –0.0597* –0.0125 –0.0403 –0.0620* -0.0620* 1.00

CF –0.1677* 0.1349* 0.0949* 0.0855* 0.0616* –0.0440* 0.0877* 0.1568* 0.2351* 0.1595* 0.0157 0.1662* -0.0169 1.00

Note: CS: capital structure measured by the ratio of total debt to book value of total assets; MO: the percentage of ordinary shares held by all

directors; SO: the percentage of shares held by the state; FO: the percentage of shares held by foreign investors; LO: the percentage of shares

held by large investors; SIZE: firm size; GRO: firm growth; TAN: tangibility; TAX: tax rate; LIQ: liquidity; risk; INV: investment; CF: cash

flow; PRO: profitability.

88

5.3.2 Pooled OLS regression

Pooled OLS regression was employed to analyse the link between ownership structure

and debt level while controlling for other determinants of capital structure, namely, size,

growth rate, investment, tangibility, liquidity, tax, profitability, risk and cash flow (see

Table 5.4). Models 1 to 4 show the regression results of the separate effects of state,

foreign, managerial and large ownership on capital structure, in that order. The last column

reports the results of combining all the ownership structure variables. The standard error

values are indicated in parentheses below each coefficient.

The OLS regression results in all models from 1 to 4 show that while state and foreign

ownership have significant effect on capital structure, managerial and large ownership do

not affect the leverage. Specifically, the coefficient of state ownership is positive (0.11)

and significant at the 1% level. Conversely, the coefficient of foreign ownership is

negative (–0.58) at the 1% level of significance, implying that an increase of 1% in foreign

ownership will lead to 0.58% decrease in total debt ratio.

Meanwhile, model 5, which includes all ownership variables, reveals the same outcomes.

A notable point is that the R2 of the models is moderate, from 23% to 35%, which denotes

that the models considerably explain capital structure decisions in general. Moreover,

overall F-tests with p-values under 0.05 also indicate a good fit of the models.

The consistency and efficiency of coefficients in analysing panel data by pooled OLS are

widely questioned in analysing panel data because the pooled OLS model does not

consider unobserved effects or individual effect problems common in non-experimental

research (Baltagi 2005). Hence, to deal with the unobserved heterogeneity, RE and FE

methods were employed.

89

Table 5.4: The effect of ownership on capital structure—Pooled OLS regression

���,� = � + �����,� + ���,� + ��,� This table reports the results of examining the relationships between four kinds of ownership structure (OS)

and capital structure (CS) measured by the ratio of total debt to total assets, which was estimated by pool OLS regression. Statistics are based on annual data for the years 2007–2012. Models 1–4 examined the

separate effects respectively of state ownership (SO), foreign ownership (FO), managerial ownership (MO) and large ownership (LO); model 5 examined the total effects of the four kinds of ownership structure on capital structure. There are nine control variables: firm size (SIZE), growth (GRO), investment (INV),

tangibility (TAN), liquidity (LIQ), tax (TAX), profitability (PRO), risk (RISK) and cash flow (CF).

Model 1 Model 2 Model 3 Model 4 Model 5 SO 0.1150*** 0.0785*** (0.016) (0.018) FO –0.580*** –0.566*** (0.023) (0.026) MO 0.0207 –0.0135 (0.025) (0.025) LO 0.0193 –0.0359 (0.021) (0.024) SIZE 0.0419*** 0.0606*** 0.0457*** 0.0424*** 0.0646*** (0.002) (0.002) (0.003) (0.002) (0.003) GRO –0.0006 –0.0048 –0.0029 –0.0039 –0.0053 (0.004) (0.003) (0.004) (0.004) (0.004) INV 0.0245** 0.0150 0.0220* 0.0212* 0.0143 (0.010) (0.010) (0.012) (0.010) (0.011) TAN –0.0196 –0.0058 –0.0111 0.0072 –0.0225 (0.021) (0.019) (0.023) (0.021) (0.022) LIQ –0.570*** –0.481*** –0.529*** –0.557*** –0.4810*** (0.037) (0.036) (0.041) (0.038) (0.040) TAX 0.0036 0.0031 0.0042 0.0038 0.0025 (0.005) (0.004) (0.005) (0.005) (0.004) PRO –0.0164* –0.0117 –0.0221 –0.0129 –0.0188 (0.008) (0.008) (0.015) (0.009) (0.014) RISK –0.722*** –0.682*** –0.902*** –0.786*** –0.831*** (0.090) (0.085) (0.101) (0.095) (0.101) CF –0.337*** –0.219*** –0.344*** –0.350*** –0.260*** (0.044) (0.042) (0.049) (0.045) (0.047) Constant –0.513*** –0.963*** –0.592*** –0.507*** –1.060*** (0.074) (0.076) (0.085) (0.078) (0.087) Observations 2604 2604 2076 2375 1940 Adj R-squared 0.2373 0.3074 0.2468 0.2428 0.3507 F-test that all β = 0 Pro > F

81.98 0.0000

116.52 0.0000

69.00 0.0000

77.11 0.0000

81.55 0.0000

Standard error in parentheses; * significant at the 10% level; ** significant at the 5% level; *** significant at the 1% level.

90

5.3.3 Random and fixed effect regression

Table 5.5 and Table 5.6 show the regression results of the RE and FE models. The models

present the same signs of all coefficients of ownership variables but vary slightly in

significance levels. To determine which model is better, this research conducted an F-test

for the FE model, the Breusch-Pagan LM test for the RE model and the Hausman test for

choosing the FE model versus the RE model. Specifically, the F-test statistic shows that

the FE model is better than pooled OLS. The p-value of both the Breusch and the Pagan

tests are 0.0000, which suggests that the RE model is more suitable than pooled OLS for

all regressions. Meanwhile, the Hausman test statistic (Prob > Chi2 = 0.0000) indicates

that the RE method may give bias and inconsistent estimators. Conversely, the FE model

remains an unbiased and consistent estimator. Therefore, in this research the FE model is

better than pooled OLS and RE estimations to indicate the effect of state, foreign,

managerial and large ownership on capital structure.

The results of the FE estimators are reported in Table 5.6. Specifically, the coefficients of

state and managerial ownership in models 1, 3 and 5 are positive and statistically

significant. In contrast, models 2 and 5 demonstrate that foreign ownership is negatively

related to total leverage at the 1% level of significance. These results are in line with the

expected hypotheses of these ownership factors. Regarding large ownership, there is

inconclusive evidence of its effect on capital structure. Model 4 shows a positive effect of

larger ownership on total leverage at 5% significant level, while model 5 that includes all

ownership variables indicates insignificant effect of larger ownership on capital structure.

91

Table 5.5: The effect of ownership on capital structure—Random effect regression

���,� = � + �����,� + ���,� + ��,� This table reports the results of examining the relationships between four kinds of ownership structure (OS) and capital structure (CS) measured by the ratio of total debt to book value of total assets, which was estimated by random effect (RE) regression. Statistics are based on annual data for the years 2007–2012. Models 1–4 examined the separate effects respectively of state ownership (SO), foreign ownership (FO), managerial ownership (MO) and large ownership (LO); model 5 examined the total effects of the four kinds of ownership structure on capital structure. There are nine control variables: firm size (SIZE), growth (GRO), investment (INV), tangibility (TAN), liquidity (LIQ), tax (TAX), profitability (PRO), risk (RISK) and cash flow (CF).

Model 1 Model 2 Model 3 Model 4 Model 5 SO 0.0479*** 0.0374** (0.014) (0.014) FO –0.324*** –0.366*** (0.030) (0.033) MO 0.0478** 0.0353* (0.018) (0.019) LO 0.0344** 0.0216 (0.017) (0.019) SIZE 0.0659*** 0.0721*** 0.0665*** 0.0626*** 0.0779*** (0.004) (0.003) (0.004) (0.004) (0.004) GRO –0.00016 –0.0009 –0.0007 –0.0008 –0.0014 (0.001) (0.001) (0.002) (0.002) (0.002) INV 0.0147*** 0.0142*** 0.0152** 0.0122** 0.0141** (0.005) (0.005) (0.006) (0.005) (0.005) TAN 0.0233 0.0222 0.0277 0.0224 0.0265 (0.020) (0.020) (0.022) (0.020) (0.022) LIQ –0.158*** –0.153*** –0.187*** –0.154*** –0.174*** (0.024) (0.023) (0.028) (0.024) (0.028) TAX –0.0009 –0.0006 –0.0007 –0.0004 –0.00004 (0.002) (0.002) (0.002) (0.002) (0.002) PRO –0.0070* –0.00609 –0.00593 –0.00239 –0.00221 (0.004) (0.004) (0.008) (0.004) (0.008) RISK –0.0343 –0.0165 –0.198*** –0.0560 –0.182*** (0.059) (0.058) (0.070) (0.061) (0.070) CF –0.194*** –0.187*** –0.198*** –0.201*** –0.198*** (0.025) (0.024) (0.028) (0.026) (0.029) Constant –1.215*** –1.350*** –1.222*** –1.131*** –1.518*** (0.108) (0.106) (0.115) (0.109) (0.117) Observations 2604 2604 2076 2375 1940 R-square (within) 0.1832 0.2470 19.43 0.1687 0.2723 Wald (chi2) Prob > Chi2

431.18 0.0000

552.23 0.0000

402.60 0.0000

400.49 0.0000

536.25 0.0000

Breusch and Pagan test Prob > Chibar2

2939.03 0.0000

2714.64 0.0000

1906.71 0.0000

2473.33 0.0000

1545.44 0.0000

Standard error in parentheses; * significant at the 10% level; ** significant at the 5% level;*** significant at the 1% level.

92

Table 5.6: The effect of ownership on capital structure—Fixed effect regression

���,� = � + �����,� + ���,� + ��,� This table reports the results of examining the relationships between four kinds of ownership structure (OS) and capital structure (CS), which were estimated by fixed effect (FE) regression. Statistics are based on annual data for the years 2007–2012. Models 1–4 examined the separate effects respectively of state ownership (SO), foreign ownership (FO), managerial ownership (MO) and large ownership (LO); model (5) examined the total effects of the four kinds of ownership structure on capital structure.

Model 1 Model 2 Model 3 Model 4 Model 5 SO 0.0375** 0.0325** (0.015) (0.015) FO –0.240*** –0.285*** (0.032) (0.037) MO 0.0590*** 0.0529*** (0.019) (0.020) LO 0.0436** 0.0351 (0.018) (0.021) SIZE 0.0854*** 0.0881*** 0.0951*** 0.0843*** 0.107*** (0.005) (0.005) (0.006) (0.006) (0.006) GRO 0.0007 0.0009 0.0006 0.0003 0.0002 (0.001) (0.001) (0.002) (0.002) (0.002) INV 0.0121** 0.0121** 0.0111* 0.00908* 0.0113* (0.005) (0.004) (0.006) (0.005) (0.005) TANG 0.0390* 0.0358 0.0545** 0.0381 0.0429 (0.023) (0.022) (0.026) (0.023) (0.026) LIQ –0.104*** –0.104*** –0.123*** –0.0960*** –0.111*** (0.024) (0.024) (0.029) (0.025) (0.029) TAX –0.0015 –0.0013 –0.0014 –0.001 –0.0003 (0.002) (0.002) (0.002) (0.002) (0.002) PROF –0.0053 –0.0049 –0.0039 –0.0004 0.0007 (0.004) (0.004) (0.008) (0.004) (0.008) RISK 0.0977 0.112* –0.0187 0.0793 –0.0184 (0.060) (0.059) (0.072) (0.063) (0.073) CF –0.158*** –0.157*** –0.149*** –0.162*** –0.160*** (0.025) (0.024) (0.028) (0.027) (0.030) Constant –1.763*** –1.808*** –2.028*** –1.741*** –2.346*** (0.159) (0.155) (0.184) (0.163) (0.187) Observations 2604 2604 2076 2375 1940 R-square (within) 0.1495 0.1928 0.1524 0.1357 0.2088 Overall F-test Pro > F

30.97 0.0000

36.40 0.0000

28.23 0.0000

29.29 0.0000

27.04 0.0000

F-test that all u_i = 0 Pro > F

23.26 0.0000

21.43 0.0000

19.52 0.0000

22.16 0.0000

18.01 0.0000

Hausman test Chi2 Prob > Chi2

409.38 0.0000

335.65 0.0000

143.42 0.0000

180.40 0.0000

164.31 0.0000

Wald test for heteroskedasticity Chi2 Prob > Chi2

1.9e + 36 0.0000

4.2e + 35 0.0000

3.2e + 36 0.0000

3.6e + 34 0.0000

1.2e + 35 0.0000

Wooldridge test for autocorrelation

337.70 0.0000

333.14 0.0000

188.80 0.0000

306.29 0.0000

162.70 0.0000

Standard error in parentheses: * significant at the 10% level; ** 5% level; *** 1% level.

93

In order to increase the efficiency of the FE model, testing for groupwise

heteroskedasticity and autocorrelation in panel data was conducted. The result of the Wald

test shows that heteroskedasticity exists in the FE model. Similarly, the Wooldridge test

revealed the presence of autocorrelation in all five models. Therefore, to cope with these

issues, the FE model with robust standard errors method was applied.

Table 5.7 shows the effect of state, foreign, managerial and large ownership on total

leverage of the Vietnamese firms over six years following FE model cluster standard

errors. The results of all ownership variables observed in this table are of the predicted

signs and statistically significant except for large ownership. To be specific, the

coefficients of state (0.0375) and managerial ownership (0.059) are positive and

statistically significant at the 5% level, in contrast to the coefficients of foreign ownership

(–0.24), negative at the 1% significance level. Large ownership is positively but

insignificantly related to capital structure. A considerable point is that the highest

coefficient among ownership structure variables belongs to foreign ownership, implying

that there is a stronger effect of foreign investors on financial decisions of Vietnamese

listed firms.

94

Table 5.7: The effect of ownership on capital structure—Fixed effect regression

with robust standard error

���,� = � + �����,� + ���,� + ��,� This table reports the results of examining the relationships between four kinds of ownership structure (OS) and capital structure (CS), which were estimated by fixed effect (FE) regression adjusted standard error. Statistics are based on annual data for the years 2007–2012. Models 1–4 examined the separate effects respectively of state ownership (SO), foreign ownership (FO), managerial ownership (MO) and large ownership (LO); model 5 examined the total effects of the four kinds of ownership structure on capital structure. There are nine control variables: firm size (SIZE), growth (GRO), investment (INV), tangibility (TAN), liquidity (LIQ), tax (TAX), profitability (PRO), risk (RISK) and cash flow (CF).

Model 1 Model 2 Model 3 Model 4 Model 5 SO 0.0375** 0.0325** (0.016) (0.015) FO –0.240*** –0.285*** (0.044) (0.055) MO 0.0590** 0.0529** (0.022) (0.026) LO 0.0436 0.0351 (0.027) (0.028) SIZE 0.0854*** 0.0881*** 0.0951*** 0.0843*** 0.107*** (0.011) (0.011) (0.011) (0.010) (0.011) GRO 0.0007 0.0009 0.0006 0.0003 0.0002 (0.002) (0.002) (0.003) (0.002) (0.002) INV 0.0121* 0.0121** 0.0111* 0.00908* 0.0113* (0.006) (0.006) (0.006) (0.005) (0.005) TAN 0.0390 0.0358 0.0545 0.0381 0.0429 (0.033) (0.033) (0.035) (0.034) (0.033) LIQ –0.104*** –0.104*** –0.123*** –0.0960*** –0.111*** (0.031) (0.030) (0.040) (0.033) (0.040) TAX –0.0015 –0.0013 –0.0014 –0.001 –0.0003 (0.001) (0.001) (0.001) (0.001) (0.002) PRO –0.0053 –0.0049 –0.0039 –0.0003 0.0007 (0.005) (0.005) (0.013) (0.004) (0.014) RISK 0.0977 0.112 –0.0187 0.0793 –0.0184 (0.120) (0.123) (0.125) (0.127) (0.130) CF –0.158*** –0.157*** –0.149*** –0.162*** –0.160*** (0.036) (0.036) (0.040) (0.038) (0.044) Constant –1.763*** –1.808*** –2.028*** –1.741*** –2.346*** (0.314) (0.306) (0.318) (0.028) (0.324) Observations 2604 2604 2076 2375 1940 R-square (within)

0.1495 0.1928 0.1641 0.1358 0.2036

Overall F-test Pro > F

11.15 0.0000

14.08 0.0000

13.79 0.0000

13.86 0.0000

12.84 0.0000

Standard error in parentheses: * significant at the 10% level; ** significant at the 5% level; *** significant at the 1% level.

95

5.3.4 GMM estimator and dynamic capital structure model

Although FE model with robust standard error was applied to deal with unobserved

heteroskedasticity problems, Wintoki, Linck and Netter (2012) claimed that bias relating

endogeneity still exists when testing the relationship ownership structure and leverage.

Specifically, in much of the previous research, ownership structure was assumed to be an

exogenous characteristic that causes changes in capital structure. However, this view has

recently been challenged with the belief that ownership structure is also partially driven

by firm features, including debt level. In investigating 511 corporations in the United

States, Demsetz and Lehn (1985) provided persuasive evidence that the variation of

corporate ownership can be explained by many firm factors, such as size, instability of

profit or even research and development expenditures.

When ownership structure is endogenous, the results of some previous studies using only

pooled OLS, RE or FE models to estimate the effect of ownership on capital structure can

be misleading. To deal with this issue, Cameron and Trivedi (2005) suggested the use of

IV estimators when obtaining strong instrument variables or the dynamic panel GMM of

Arellano and Bond (1991).

Because it is difficult to find variables that can serve as strong instruments for all the types

of ownership—state, foreign, managerial and large—this study continued to apply the

GMM method. Another point is that empirical corporate finance research has recently

focused on the dynamic capital model developed by Tsyplakov and Titman (2005) to test

the effect of ownership structure on capital structure to take account into the fact that

actual and targe capital structure may differ and that the relationship between ownership

and capital structure is dynamic in nature.

Therefore, this research applied the dynamic panel GMM of Arellano and Bond (1991) to

deal with endogenous problems as well as to allow for the dynamic relationship between

ownership structure and capital structure. An issue of the original Arellano and Bond

(1991) method, which is called difference GMM, is that lagged variables can be weak

96

instruments if the variables in regressions are close to a random walk. Hence, Arellano

and Bover (1995) developed a system GMM in which the original equation is added to

the system to increase the instruments, thereby increasing the efficiency of the estimators.

Table 5.8 outcomes of two-step system GMM employing the xtabond2 command, which

introduced by Roodman (2006). The standard errors of the results of two-step system

GMM are robust to increase the efficiency. From this table, state and managerial

ownership are found to have a statistically positive significant relation with the total

leverage of firms at the 5% level or 10% level. Foreign ownership is reconfirmed as

negatively relating to leverage at the 1% significant level, while larger ownership is

insignificantly associated with capital structure. These estimation results are consistent

with the panel FE model as well as FE model cluster standard errors. Moreover, the

coefficient of total leverage lag one is positive and significant. It indicates that total

leverage in the current time is affected by the previous decision about capital structure.

This also implies that an annual rate of adjustment in the debt ratio is about 79.7%.

One point to be emphasised is that Table 5.8 also reports the results of the Sargan and

Hansen test of overidentifying restrictions and the Arellano-Bond test for autocorrelation

error. The Sargan and Hansen tests yield all p-values above 0.10, which means that a null

hypothesis could not be rejected. Hence, overidentification restrictions are valid. The

AR(1) tests indicate that the residuals in first differences are correlated as expectation,

while the AR(2) tests give p-values above 0.10, which means that a null hypothesis of no

second-order serial correlation could not be rejected. Therefore, all results of the system

GMM model are valid.

97

Table 5.8: The effect of ownership structure on capital structure – System two-step

GMM estimators with robust standard error

���,� = �� + (1 − �)���,��� + ������,� + ����,� + ��,� (6) This table reports the results of examining the relationships between four kinds of ownership structure (OS) and capital structure (CS), which is estimated by system two-step GMM with adjusted standard error. Statistics based on annual data for the year 2007 – 2012. Models 1– 4 examined the separate effects of respectively state ownership (SO), foreign ownership (FO), managerial ownership (MO) and large ownership (LO), model (5) examined the total effects of the four kinds of ownership structure on capital structure. There are nine control variables, including firm size (SIZE), growth (GRO), investment (INV), tangibility (TAN), liquidity (LIQ), tax (TAX), profitability (PRO), risk (RISK) and cash flow (CF).

Model 1 Model 2 Model 3 Model 4 Model 5 CSt-1 0.925*** 0.889*** 0.906*** 0.922*** 0.797*** (0.053) (0.057) (0.0476 (0.037) (0.055) SO 0.0538** 0.0456** (0.023) (0.021) FO -0.159*** -0.229*** (0.054) (0.060) MO 0.0932*** 0.0361* (0.034) (0.020) LO -0.0185 -0.0147 (0.035) (0.039) SIZE 0.0125* 0.0219*** 0.0081*** 0.0056* 0.0235*** (0.006) (0.006) (0.002) (0.003) (0.008) GRO -0.0013 0.0027 -0.0028 -0.0010 -0.0093 (0.016) (0.014) (0.007) (0.010) (0.011) INV 0.0098 -0.0023 0.0358*** 0.0197 0.0234 (0.031) (0.018) (0.012) (0.018) (0.017) TAN -0.0594 -0.0503 -0.0433** -0.0220 -0.0440 (0.043) (0.035) (0.017) (0.028) (0.034) LIQ -0.140 -0.122 -0.203** -0.126* -0.165** (0.128) (0.123) (0.083) (0.076) (0.080) TAX -0.0287 0.0018 -0.0103 -0.0119 0.0047 (0.027) (0.016) (0.023) (0.016) (0.008) PRO -0.0037 0.0038 0.0108 -0.0078 0.0128 (0.022) (0.015) (0.013) (0.018) (0.013) RISK 0.367** 0.344** -0.0586 0.0605 -0.127 (0.165) (0.153) (0.086) (0.109) (0.137) CF -0.0685 0.0431 -0.169*** -0.119 -0.169** (0.104) (0.095) (0.045) (0.074) (0.072) Constant -0.283 -0.507*** -0.135** -0.0697 -0.470** (0.175) (0.170) (0.058) (0.118) (0.194) Observations 2102 2101 1646 1893 1553 Wald Chi2 Prob > Chi2

1183.93 0.000

1171.64 0.000

1873.99 0.000

1992.72 0.000

1484.73 0.000

AR(1) AR(2)

0.000 0.897

0.000 0.825

0.000 0.425

0.000 0.637

0.000 0.485

Sargan test 0.693 0.562 0.166 0.551 0.152 Hansen test 0.110 0.155 0.158 0.120 0.450 Standard error in parentheses: * significant at the 10% level; ** significant at the 5% level; *** significant at the 1% level.

98

5.3.5 Results

5.3.5.1 State ownership

The findings of OLS, RE, FE, FE robust and GMM model all support hypothesis 1, that

government ownership measured by the percentage of shares held by the state has a

positive effect on capital structure measured by total book debt on total assets at the 5%

significant level. Specifically, one percentage increase in state ownership is expected to

add debt ratio by around 0.05%, holding all other variables constant. This result is

consistent with most previous studies including Zou and Xiao (2006), Li, Yue and Zhao

(2009), and Huang, Lin and Huang (2011). The positive influence of state ownership on

leverage is explained by the fact that a high degree of state ownership can enhance firms’

access to the debt market because of soft budget constraints and bailouts from the

government. In other words, firms with high state ownership can increase leverage

because they are considered to have much less bankruptcy risk because of the guarantee

of the state. In addition, representatives of state ownership may prefer a high level of debt

to avoid share dilution or to preserve their control.

5.3.5.2 Foreign ownership

Under all regressions, the estimated coefficients of foreign ownership are negative and

significant at the 1% level. This result supports hypothesis 2, that foreign ownership

discourages an increase in debt ratio, in line with the studies of Li, Yue and Zhao (2009)

and Huang, Lin and Huang (2011). This can be explained as follows. First, firms with

high foreign ownership will have more diversified financing channels to access capital

than others because of their reputation and relationship. In addition, the need for external

financing in firms with high foreign ownership can decrease because of the equity

contribution from foreign investors. Second, foreign owners that are mainly institutional

investors are better at monitoring managers. As a result, foreign ownership helps to control

the overinvestment problem of managers or reduce the agency cost between managers and

99

shareholders. Therefore, foreign ownership and leverage may serve similar roles in

controlling managerial self-interest.

5.3.5.3 Managerial ownership

All regression results show a positive and statistically significant relationship between

firm leverage and managerial ownership. This result is consistent with the proposed

hypothesis. The rationale for explaining this finding is the control issue (Ghaddar 2003;

Kim & Sorensen 1986). In particular, one of the main concerns of every manager is to

retain or increase their control because it provides them with discretion in making

decisions or accessing their private benefits. Meanwhile, debt is a means to restrain share

dilution. Harris and Raviv (1988) affirmed that an increase in debt helps managers to

reinforce their control and resist takeovers. In addition, with high debt, managers have

more cash to pursue suboptimum investments for their own interests.

5.3.5.4 Large ownership

The study found inconclusive evidence on the effects of large ownership on capital

structure. Both RE and FE regressions provide a positive relationship between large

ownership and capital structure at the 5% significant level. However, this result is not

confirmed under FE with the robust option and GMM methods, which, as argued before,

are more precise. While the FE robust method indicates that large ownership has a positive

but not significant effect on debt ratio, the coefficient of large ownership on two-step

system GMM is negative and not significant. This implies that there is no obvious

evidence of the active monitoring role of large ownership in Vietnamese listed firms.

5.3.5.5 Control variables

Other variables’ effects on leverage were examined. Some control variables, including

size, investment, liquidity, risk and cash flow, have statistically significant coefficients,

whereas tax, profitability, tangibility and growth have no effect on debt ratio.

100

Specifically, all models, including the pooled OLS, RE model, FE model, FE model

clustered standard errors and GMM model, gave the same result for size. Size of firm has

a statistically positive effect, significant at the 1% level, which is consistent with the

studies of Bradley, Jarrell and Kim (1984), Booth et al. (2001), Huang and Song (2006)

and Al-Fayoumi and Abuzayed (2009). This could be explained by the trade-off theory,

which states that large sized firms, which are normally well known and have lower non-

payment risk, are able to increase their debt more than small firms. Similarly, the

coefficients of investment expense are positively significant, implying that investment is

positively correlated to leverage, consistent with the study of Dudley (2012). This could

imply that investment will bring the opportunity for firms to adjust leverage at low capital

cost.

Conversely, liquidity and cash flow variables are statistically significantly negative for all

regressions, which is in line with the studies of Lipson and Mortal (2009) and Brailsford,

Oliver and Pua (2002). This effect is explained by the fact that firms with high cash flow

will require a low amount of debt because of their large available internal funds. Business

risk in most models also has a negative effect on debt level; however, it is only significant

in OLS, RE and difference GMM regression. The explanation could lie in the trade-off

theory, which suggests that firms with high risk will result in high financial distress when

increasing debt.

An interesting point is that in most models, tax ratio (TAX), tangibility (TAN), growth

rate (GRO) and profitability (PRO), which were some of the main factors used to test the

reliability of trade-off or pecking order theories in most previous studies, are not

statistically related to capital structure decision in Vietnamese firms. In particular, the

coefficients of these variables are not significant in most regressions. This suggests that

the pecking order theory and trade-off theory are only partially supported in developing

markets such as Vietnam in explaining corporate financing decisions.

101

5.3.6 Robustness check

For checking the robustness of the models, this research used alternative measurements

for key independent and dependent variables, including ownership structure and capital

structure. Although different measurements were utilised, most results are consistent with

original regressions. The study first used dummy variables to measure state, foreign,

managerial and large ownership instead of the percentage share held by state, foreign,

managerial and large investors. To be specific, Dso, Dfo, Dmo and Dlo are dummy

variables given a value of 1 if state, foreign, managerial and large ownership are higher

than their mean respectively and zero otherwise. The fixed effect model with adjusted

standard error was employed with the alternative measurements. Table 5.9 reports the

regression results, in which there is no important change on major variables. The

coefficients of state ownership and managerial ownership are positive and significant at

the 5% level; in contrast, the coefficient of foreign ownership is negative and significant

at 1%.

The research then uses the ratio of total debt to the market value of total assets instead of

the ratio of total debt to book value of total assets as proxy for capital structure. Comparing

the results with those of using total book debt ratio, the findings shown in Table 5.10

indicate the same signs of the effect of state, foreign and managerial ownership on capital

structure. The only difference is that the coefficient of large ownership is now negative,

which is consistent with outcomes of system GMM but inconsistent with those of other

regressions, implying once again inconclusive evidence on the effects of large ownership

on capital structure and an unclear active monitoring role of large investors in Vietnamese

listed firms. Finally, the research controls industry- and year-specific fixed effects by

using dummy variables. The table 15.11 and 15.12 reports the results of these texts.

Overall, the outcomes remain similar with original regressions. In details, the coefficients

of state ownership and managerial ownership are positive and significant. The coefficient

of foreign ownership is negative and significant at 1% level, while the effect of large

ownership on leverage is insignificant, in line with previous outcomes of this study.

102

Table 5.9: The effect of ownership structure measured by dummy variables on

capital structure

���,� = � + �����,� + ���,� + ��,� This table reports the results of examining the relationships between four kinds of ownership structure (OS) and capital structure (CS), which is estimated by fixed effect (FE) regression adjusted standard error. Statistics are based on annual data for the years 2007–2012. Dso, Dfo, Dmo and Dlo are dummy variables that are given a value of 1 if state, foreign, managerial and large ownership are higher than their mean respectively and zero otherwise. Models 1–4 examined the separate effects respectively of state, foreign, managerial and large ownership; model 5 examined the total effects of the four kinds of ownership structure on capital structure.

Model 1 Model 2 Model 3 Model 4 Model 5

Dso 0.0166** 0.0153** (0.007) (0.007)

Dfo –0.0282*** –0.0285***

(0.007) (0.007) Dmo 0.0102** 0.0111**

(0.004) (0.004) Dlo –0.00518 –0.00691 (0.005) (0.005)

SIZE 0.0845*** 0.0838*** 0.0837*** 0.0824*** 0.0875*** (0.005) (0.005) (0.005) (0.005) (0.005)

GROW 0.0007 0.0010 0.0006 0.0007 0.0009 (0.001) (0.001) (0.001) (0.001) (0.001) INV 0.0121** 0.0124** 0.0123** 0.0121** 0.0128**

(0.005) (0.005) (0.005) (0.005) (0.005) TANG 0.0387* 0.0393* 0.0394* 0.0397* 0.0386*

(0.023) (0.023) (0.023) (0.023) (0.023) LIQ –0.104*** –0.102*** –0.106*** –0.107*** –0.100*** (0.024) (0.024) (0.024) (0.024) (0.024)

TAX –0.0015 –0.0014 –0.0013 –0.0015 –0.0012 (0.002) (0.002) (0.007) (0.002) (0.002)

PROF –0.0053 –0.0053 –0.0053 –0.0050 –0.0056 (0.004) (0.004) (0.004) (0.004) (0.004) RISK 0.100* 0.112* 0.0945 0.0997* 0.112*

(0.060) (0.060) (0.060) (0.060) (0.060) CF –0.158*** –0.156*** –0.155*** –0.155*** –0.161*** (0.025) (0.025) (0.025) (0.025) (0.025)

Constant –1.737*** –1.703*** –1.715*** –1.671*** –1.810*** (0.158) (0.156) (0.157) (0.156) (0.159)

Observations 2636 2636 2636 2636 2636

R-square (within) 0.1464 0.1629 0.1431 0.1421 0.1663

Overall F test Pro>F

119.00 0.0000

31.99 0.0000

30.80 0.0000

30.27 0.0000

25.21 0.0000

Standard error in parentheses: * significant at the 10% level; ** significant at the 5% level; *** significant at the 1% level.

103

Table 5.10: The effect of ownership on capital structure measured by market value

���,� = � + �1���,� + ���,� + ��,�

This table reports the results of examining the relationships between four kinds of ownership structure (OS) and capital structure (CS) measured by the ratio of total debt to the market value of total assets, which is estimated by fixed effect regression. Statistics are based on annual data for the years 2007–2012. Models 1–4 examined the separate effects respectively of state ownership (SO), foreign ownership (FO), managerial ownership (MO) and large ownership (LO); model (5) examined the total effects of the four kinds of ownership structure on capital structure. There are nine control variables: firm size (SIZE), growth (GRO), investment (INV), tangibility (TAN), liquidity (LIQ), tax (TAX), profitability (PRO), risk (RISK) and cash flow (CF).

Model 1 Model 2 Model 3 Model 4 Model 5 SO 0.0350* 0.0305* (0.018) (0.018) FO –0.293*** –0.309*** (0.036) (0.042) MO 0.00465 0.0159 (0.022) (0.023) LO –0.0546*** –0.0558** (0.021) (0.024) SIZE 0.109*** 0.119*** 0.115*** 0.122*** 0.134*** (0.008) (0.008) (0.010) (0.009) (0.010) GROW –0.005** –0.005** –0.005** –0.006*** –0.005** (0.002) (0.002) (0.002) (0.002) (0.002) INV 0.0013 0.0007 –0.0006 –0.00009 –0.0004 (0.005) (0.005) (0.006) (0.005) (0.006) TAN 0.0743*** 0.0716*** 0.0747** 0.0645** 0.0723** (0.025) (0.025) (0.030) (0.026) (0.029) LIQ –0.105*** –0.103*** –0.0997*** –0.0985*** –0.0770** (0.027) (0.027) (0.033) (0.028) (0.033) TAX 0.00364 0.0040 0.0029 0.0045 0.0039 (0.002) (0.002) (0.002) (0.002) (0.002) PRO –0.0031 –0.0023 –0.0063 0.0010 –0.0022 (0.004) (0.004) (0.009) (0.004) (0.010) RISK –0.0926 –0.0640 –0.131 –0.0867 –0.109 (0.067) (0.066) (0.081) (0.070) (0.083) CF –0.195*** –0.195*** –0.185*** –0.214*** –0.203*** (0.028) (0.027) (0.032) (0.030) (0.034) Constant –2.556*** –2.767*** –2.704*** –2.850*** –3.152*** (0.229) (0.227) (0.272) (0.246) (0.278) Observations 2604 2604 2076 2375 1940 Fixed year Yes Yes Yes Yes Yes R-square (within)

0.5898 0.6025 0.5923 0.5997 0.6175

Overall F test Pro > F

187.37 0.0000

197.57 0.0000

139.76 0.0000

171.48 0.0000

119.00 0.0000

Standard error in parentheses: * significant at the 10% level; ** significant at the 5% level; *** significant at the 1% level.

104

Table 5.11: The effect of ownership on capital structure- Fixed industry

���,� = � + �1���,� + ���,� + �. �������� + �

�,�

This table reports the results of examining the relationships between four kinds of ownership structure (OS) and capital structure (CS) measured by the ratio of total debt to the market value of total assets, which includes industry dummy variables to capture industry-specific fixed effects. Statistics are based on annual data for the years 2007–2012. Models 1–4 examined the separate effects respectively of state ownership (SO), foreign ownership (FO), managerial ownership (MO) and large ownership (LO); model (5) examined the total effects of the four kinds of ownership structure on capital structure. There are nine control variables: firm size (SIZE), growth (GRO), investment (INV), tangibility (TAN), liquidity (LIQ), tax (TAX), profitability (PRO), risk (RISK) and cash flow (CF).

Model 1 Model 2 Model 3 Model 4 Model 5 SO 0.0579*** 0.0303* (0.015) (0.017) FO –0.4536*** –0.4323*** (0.031) (0.036) MO 0.0244* 0.0159 (0.014) (0.023) LO 0.0122 –0.025 (0.020) (0.023) SIZE 0.0417*** 0.0571*** 0.0471*** 0.0449*** 0.0618*** (0.002) (0.002) (0.003) (0.002) (0.003) GROW –0.0002 –0.002 –0.0010 –0.0026 –0.0037 (0.004) (0.003) (0.004) (0.003) (0.004) INV 0.0122 0.0064 –0.123 –0.0106 –0.0083 (0.009) (0.009) (0.011) (0.009) (0.011) TAN 0.1279*** 0.1280*** 0.1373*** 0.1324*** 0.1187*** (0.025) (0.024) (0.028) (0.025) (0.027) LIQ –0.4016*** –0.3571*** –0.3699*** –0.3842*** –0.3498** (0.036) (0.035) (0.039) (0.036) (0.039) TAX 0.0024 0.0020 0.0032 0.0025 0.0019 (0.004) (0.004) (0.004) (0.004) (0.004) PRO –0.0044 –0.0024 –0.0075 0.0009 –0.0024 (0.008) (0.007) (0.013) (0.008) (0.013) RISK –0.5325*** –0.4759*** –0.6354*** –0.5410*** –0.6294 (0.085) (0.081) (0.096) (0.089) (0.097) CF –0.2064*** –0.1479*** –0.2120*** –0.2299*** –0.1741*** (0.042) (0.040) (0.046) (0.042) (0.045) Constant –0.5180*** –0.9176*** –0.6366*** –0.6025*** –1.008*** (0.089) (0.090) (0.096) (0.090) (0.100) Observations 2604 2604 2076 2375 1940 Fixed industry

Yes Yes Yes Yes Yes

Adj R-squared

0.3870 0.4408 0.4175 0.4139 0.4715

Overall F test Pro > F

38.87 0.0000

40.25 0.0000

29.03 0.0000

32.17 0.0000

31.75 0.0000

Standard error in parentheses: * significant at the 10% level; ** significant at the 5% level; *** significant at the 1% level.

105

Table 5.12: The effect of ownership on capital structure- Fixed year

���,� = � + �1���,� + ���,� + �. ���� + �

�,�

This table reports the results of examining the relationships between four kinds of ownership structure (OS) and capital structure (CS) measured by the ratio of total debt to the market value of total assets, which includes year dummy variables to capture year-specific fixed effects. Statistics are based on annual data for the years 2007–2012. Models 1–4 examined the separate effects respectively of state ownership (SO), foreign ownership (FO), managerial ownership (MO) and large ownership (LO); model (5) examined the total effects of the four kinds of ownership structure on capital structure. There are nine control variables: firm size (SIZE), growth (GRO), investment (INV), tangibility (TAN), liquidity (LIQ), tax (TAX), profitability (PRO), risk (RISK) and cash flow (CF).

Model 1 Model 2 Model 3 Model 4 Model 5 SO 0.1222*** 0.0848*** (0.016) (0.019) FO –0.5790*** –0.5650*** (0.033) (0.037) MO 0.0201 0.0115 (0.025) (0.025) LO 0.0201 –0.0399 (0.021) (0.025) SIZE 0.0409*** 0.0605*** 0.0449*** 0.0420*** 0.0644*** (0.002) (0.002) (0.003) (0.002) (0.003) GROW 0.0009 –0.0047 –0.0026 –0.0038 –0.0048 (0.004) (0.004) (0.004) (0.004) (0.004) INV 0.0251** 0.0145 0.0220 0.0209 –0.0146 (0.010) (0.010) (0.012) (0.010) (0.012) TAN -0.0217 -0.0062 -0.0110 0.0067 -0.0240 (0.021) (0.019) (0.023) (0.021) (0.022) LIQ –0.5782*** –0.4850*** –0.5355*** –0.5621*** –0.4869*** (0.037) (0.036) (0.041) (0.038) (0.040) TAX 0.0031 0.0030 0.0042 0.0038 0.0023 (0.005) (0.004) (0.005) (0.005) (0.004) PRO –0.0161* –0.0118 –0.0218 -0.0132 –0.0170 (0.008) (0.008) (0.015) (0.009) (0.014) RISK –0.7317*** –0.6852*** –0.9030*** –0.7951*** –0.8261*** (0.090) (0.086) (0.101) (0.095) (0.101) CF –0.3290*** –0.2260*** –0.3395*** –0.3530*** –0.2526*** (0.044) (0.042) (0.049) (0.045) (0.047) Constant –0.5326*** –0.9656*** –0.5916*** –0.5212*** –1.043*** (0.075) (0.076) (0.086) (0.078) (0.088) Observations 2604 2604 2076 2375 1940 Fixed year Yes Yes Yes Yes Yes Adj R-squared

0.2455 0.3115 0.2529 0.2492 0.3561

Overall F test Pro > F

56.13 0.0000

78.05 0.0000

46.49 0.0000

52.20 0.0000

59.02 0.0000

Standard error in parentheses: * significant at the 10% level; ** significant at the 5% level; *** significant at the 1% level.

106

5.3.7 Moderating effect of the relationship between managerial ownership and

capital structure

Besides the direct effect of various ownership structures on capital structure, it is widely

argued that a high proportion of shares held by outside investors will decrease the power

of inside owners in controlling firms’ strategy (Mehran 1992; Shleifer & Vishny 1986).

This research was extended by examining the effect of outside ownership, including state,

foreign and large ownership, on the relationship between inside ownership and leverage.

Table 5.13 details the model findings on the effect of state ownership, foreign ownership

and larger ownership on the relationship between managerial ownership and leverage. As

detailed in the previous section, the OLS model cannot capture the unobserved

heterogeneity, so the findings report the results of the FE and RE model. The Hausman

test rejects that differences in coefficients between RE and FE are not systematic, with the

significance at 1%; thus, the FE model was chosen.

The estimated results reveal that only foreign ownership affects managerial ownership

influence on capital structure. Specifically, the coefficient of dummy foreign ownership

is negative and significant at 1%, which implies that foreign ownership decreases the

positive effect of managerial ownership on capital structure. The coefficient of dummy

state ownership is significant but managerial ownership is insignificant. Similarly, the

coefficient of dummy larger ownership is statistically insignificant. These findings imply

that the role of state ownership and larger ownership in influencing management action in

Vietnamese firms is inconsiderable. Meanwhile, foreign investors who invest in

Vietnamese stock markets are mainly institutional investors (Vo 2011), so they have

experience in monitoring managers to protect their capital. Specifically, foreign

shareholders can improve the governance system of firms through monitoring the

management, thereby preventing managers from increasing the debt level above the

optimal capital structure to reinforce their control and pursue their own interests.

107

Table 5.13: The effect of outsider ownership on the relationship between inside

ownership and capital structure—Random and fixed effect models

���,� = � + �����,� + �����,� ∗ ��� + ���,� + ��,� (7); ���,� = � + �����,� + �����,� ∗ ��� + ���,� + ��,�(8)

���,� = � + �����,� + �����,� ∗ ��� + ���,� + ��,� (9)

This table reports the results of examining separately the effects of state ownership, foreign ownership and large ownership on the relationships between managerial ownership (MO) and capital structure (CS), which were estimated by random effect and fixed effect estimators. Dso, Dfo and Dlo are dummy variables that are given a value of 1 if state ownership, foreign ownership and large ownership are higher than their mean respectively and zero otherwise.

Model 7 Model 8 Model 9 RE FE RE FE RE FE MO 0.0019 0.0187 0.0848*** 0.0856*** 0.0421 0.0467 (0.024) (0.026) (0.020) (0.021) (0.031) (0.032) MO*Dso 0.0716*** 0.0607** (0.025) (0.026) MO*Dfo –0.128*** –0.0941*** (0.026) (0.027) MO*Dlo 0.00675 0.0149 (0.029) (0.030) SIZE 0.0676*** 0.0969*** 0.0678*** 0.0956*** 0.0664*** 0.0951*** (0.004) (0.006) (0.004) (0.006) (0.004) (0.006) GROW –0.0007 0.0005 –0.0003 0.0009 –0.0002 0.0006 (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) INV 0.0155** 0.0112* 0.0158*** 0.0117* 0.0152** 0.0112* (0.006) (0.006) (0.006) (0.006) (0.006) (0.006) TANG 0.0256 0.0542** 0.0270 0.0534** 0.0276 0.0544** (0.022) (0.026) (0.022) (0.026) (0.022) (0.026) LIQ –0.186*** –0.120*** –0.183*** –0.119*** –0.187*** –0.122*** (0.028) (0.029) (0.028) (0.029) (0.028) (0.029) TAX –0.0008 –0.0014 –0.0007 –0.0013 –0.0008 –0.0014 (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) PROF –0.00577 –0.00382 –0.00460 –0.00304 –0.00596 –0.00407 (0.008) (0.008) (0.008) (0.008) (0.008) (0.008) RISK –0.201*** –0.0209 –0.190*** –0.0143 –0.198*** –0.0197 (0.070) (0.072) (0.069) (0.072) (0.070) (0.072) CF –0.203*** –0.152*** –0.197*** –0.150*** –0.198*** –0.149*** (0.028) (0.028) (0.028) (0.028) (0.028) (0.028) Constant –1.251*** –2.072*** –1.258*** –2.041*** –1.221*** –2.028*** (0.115) (0.185) (0.114) (0.183) (0.115) (0.184) Observations 2076 2076 2076 2076 2076 2076 R-square 0.1994 0.1553 0.2082 0.1610 0.1942 0.1528 Overall F-test Prob > F

26.22 0.0000

26.90 0.0000

25.67 0.000

Wald test Prob > Chi2

412.2 0.0000

430.03 0.0000

402.44 0.000

Hausman test Prob > Chi2

162.36 0.000

147.6 0.0000

145.44 0.000

Standard error in parentheses: * significant at the 10% level; ** significant at the 5% level; *** significant at the 1% level.

108

5.3.8 Non-linear relationship between ownership structure and capital structure

It has recently been hypothesised that a non-linear relationship exists between ownership

structure and capital structure (Brailsford, Oliver & Pua 2002; Céspedes, González &

Molina 2010; Ruan, Tian & Ma 2011). Thus, this study also tests whether there is a non-

linear relationship between state, foreign, managerial and larger ownership and leverage.

The results of these tests are presented in Table 5.14. Regression models including

ownership and square of ownership variable were used to allow a non-linear relationship

between ownership structure and capital structure. The value of the Hausman test suggests

that FE estimators are better than those of RE in all regressions.

The results show that there is no apparent evidence of a non-linear relationship between

ownership structure and debt level. To be specific, the coefficients of most ownership

variables are consistent with expected signs and significant, indicating a positive effect of

state and managerial ownership and a negative effect of foreign ownership on debt level.

However, the coefficients of square of state, foreign and large ownership in models 10, 11

and 13 are insignificant, implying that the reverse sign when ownership variables beyond

a certain level is not supported. The coefficient of square of managerial ownership in

model 12 is positive and significant, but the coefficient of managerial ownership in this

model is not significant; therefore, it cannot support the U-shaped relationship between

managerial ownership and debt level.

Table 5.14: Non-linear relationship between ownership structure and capital structure

���,� = � + �����,� + �����,�� + ���,� + ��,�

This table reports the results of examining the non-linear relationships between four kinds of ownership structure (OS) and capital structure (CS), which were estimated by random effect (RE) and fixed effect (FE) estimators. Statistics are based on annual data for the years 2007–2012. Models 10–13 examined the separate non-linear effects respectively of state ownership (SO), foreign ownership (FO), managerial ownership (MO) and large ownership (LO) on capital structure. There are nine control variables: firm size (SIZE), growth (GRO), investment (INV), tangibility (TAN), liquidity (LIQ), tax (TAX), profitability (PRO), risk (RISK) and cash flow (CF).

Model 10 Model 11 Model 12 Model 13 RE FE RE FE RE FE RE FE SO 0.0976* 0.0431 0.0376** 0.0327** 0.0370** 0.0324** 0.0364** 0.0322** (0.050) (0.053) (0.014) (0.015) (0.014) (0.015) (0.014) (0.015) SO2 –0.111 –0.0196 (0.088) (0.094) FO –0.368*** –0.285*** –0.562*** –0.411*** –0.366*** –0.284*** –0.366*** –0.285*** (0.033) (0.037) (0.086) (0.091) (0.033) (0.037) (0.033) (0.037) FO2 0.465** 0.295 (0.188) (0.196) MO 0.0366* 0.0531*** 0.0364* 0.0536*** –0.0598 –0.0843 0.0346* 0.0526*** (0.019) (0.020) (0.019) (0.020) (0.047) (0.060) (0.019) (0.020) MO2 0.195** 0.287*** (0.089) (0.096) LO 0.0242 0.0354 0.0205 0.0345 0.0205 0.0350 0.0878 0.0677 (0.020) (0.021) (0.019) (0.021) (0.019) (0.021) (0.067) (0.071) LO2 –0.0709 –0.0348 (0.069) (0.072) SIZE 0.0785*** 0.107*** 0.0791*** 0.108*** 0.0782*** 0.108*** 0.0780*** 0.107*** (0.004) (0.007) (0.004) (0.006) (0.004) (0.006) (0.004) (0.006) GRO –0.0014 0.00018 –0.0015 0.00011 –0.0014 0.00022 –0.0015 0.00013 (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) INV 0.0142** 0.0113* 0.0144** 0.0116** 0.0143** 0.0116** 0.0144** 0.0115** (0.005) (0.005) (0.005) (0.005) (0.005) (0.005) (0.005) (0.058)

Model 10 Model 11 Model 12 Model 13 RE FE RE FE RE FE RE FE TAN 0.0284 0.0433 0.0252 0.0421 0.0271 0.0430 0.0259 0.0424 (0.022) (0.026) (0.022) (0.026) (0.022) (0.026) (0.022) (0.026) LIQ –0.175*** –0.111*** –0.171*** –0.110*** –0.175*** –0.114*** –0.174*** –0.112*** (0.028) (0.029) (0.028) (0.029) (0.028) (0.029) (0.028) (0.029) TAX –0.00006 –0.0003 –0.00009 –0.00035 –0.00021 –0.00057 –0.000004 –0.00031 (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) PRO –0.0025 0.00069 –0.0020 0.00078 –0.00173 0.00142 –0.00207 0.00080 (0.008) (0.008) (0.008) (0.08) (0.008) (0.008) (0.008) (0.008) RISK –0.179** –0.0178 –0.169** –0.0116 –0.177** –0.0108 –0.182*** –0.0192 (0.070) (0.073) (0.070) (0.073) (0.070) (0.073) (0.070) (0.073) CF –0.201*** –0.160*** –0.199*** –0.161*** –0.198*** –0.159*** –0.199*** –0.160*** (0.029) (0.030) (0.029) (0.030) (0.029) (0.030) (0.029) (0.030) Constant –1.539*** –2.350*** –1.547*** –2.363*** –1.521*** –2.366*** –1.534*** –2.353*** (0.119) (0.188) (0.118) (0.188) (0.117) (0.187) (0.118) (0.188) Observations 1940 1940 1940 1940 1940 1940 1940 1940 R-square (within) 0.2755 0.2039 0.2769 0.2069 0.2701 0.2140 0.2732 0.2042 F-test Prob>F

25.09 0.0000

25.29 0.0000

25.88 0.000

25.11 0.0000

Wald test Prob > Chi2

538.66 0.0000

543.89 0.0000

541.53 0.0000

537.25 0.0000

F-test (all u_i = 0)

17.82 0.000

18.00 0.000

18.13 0.000

18.00 0.000

Hausman test Prob > Chi2

189.71 0.0000

152.21 0.0000

177.55 0.0000

156.99 0.0000

Standard error in parentheses: * significant at the 10% level; ** significant at the 5% level; *** significant at the 1% level.

111

5.4 Firm performance model: The effect of capital structure on firm

performance

5.4.1 Correlation analysis

The correlation coefficients between variables used in the regression models are presented

in Table 5.15. It can be observed that the correlation of debt ratios used as proxies of

capital structure are high. In particular, the correlation coefficient between market total

debt ratio (MTLEV) and book total debt ratio (TLEV) is 0.82. Therefore, instead of

combining both debt ratios in only one regression, this research separately examined the

effect of each type of debt ratio on firm performance to minimise the multicollinearity

problem. Other correlation coefficients are quite small (below 0.5), implying that other

variables are suitable in the regression models.

Debt ratios, including market total debt ratio (MTLEV) and book total debt ratio (TLEV),

were found to be negatively related to ROA, ROE and Tobin Q because all coefficients of

pairwise correlation among these variables are negative and significant at the 5% level.

Specifically, the correlation coefficients presenting the link of TLEV with ROA, ROE and

Tobin Q are –0.4305, –0.1294 and –0.1679, while the figures of MTLEV are –0.5730, –

0.1805 and –0.4398, respectively.

5.4.2 Pooled OLS regression

First, the OLS regression (pooled OLS) was performed. Table 5.16 presents the pooled

OLS results for firm performance equation using ROA, ROE and Tobin’s Q as dependent

variables, respectively. While ROA and ROE were used to capture accounting

performance, Tobin Q was employed to capture market performance.

11

2

Table 5.15: Correlation coefficients between measures of capital structure and firm performance

ROA ROE Tobin Q TLEV MLEV GRO INV CF RISK LIQ DIV

ROA 1.00

ROE 0.4885* 1.00

Tobin Q 0.3450* 0.0912* 1.00

TLEV –0.4305* –0.1294* –0.1679* 1.00

MTLEV –0.5730* –0.1805* –0.4398* 0.8264* 1.00

GRO 0.1422* 0.0963* 0.1085* –0.0011 –0.0964* 1.00

INV 0.0371 0.0366 0.0358 0.0479* 0.0101 0.0910* 1.00

CF 0.6092* 0.2920* 0.2098* –0.1677* –0.2799* 0.0877* 0.1568* 1.00

RISK –0.0184 –0.1997* 0.1189* –0.1940* –0.2017* 0.0148 –0.0597* –0.0169 1.00

LIQ 0.3998* 0.1317* 0.1430* –0.3231* –0.3597* 0.0070 –0.0326 0.1595* 0.0443* 1.00

DIV 0.2584* 0.1516* –0.1616* 0.0096 0.1076* 0.0151 –0.0124 0.2091* –0.1366* 0.1284* 1.00

Note: ROA: the ratio of earnings after interest and tax to book value of total assets; ROE: the ratio of earnings after interest and tax to book value of total equity;

Tobin Q: the ratio of the firm’s market value to firm’s book value; TLEV: the ratio of total debt to book value of total assets; MTLEV: the ratio of total debt to market

value of total assets; GRO: growth rate measured by the percentage change in sales over the year; TAX: the effective tax rate, which is calculated by dividing total

taxes by the pre-tax income; Risk; INV: investment; CF: cash flow; LIQ: liquidity ratio; DIV: dividend yield.

113

As shown in Table 5.16, capital structure is negatively associated with firm performance

because the coefficients estimators for the debt ratios are significantly negative at the 1%

level. Specifically, the coefficient of book total debt ratio and market total debt ratio in

columns 1 and 2 are –0.105 and –0.132, which denotes that an increase of 1% in total debt

ratio will lead to a decrease of approximately 0.1% in ROA, holding all other variables

constant. However, the coefficients of debt ratios in columns 3 and 4 are around –0.2,

suggesting that when total leverage rises 1%, the ROE will fall about 0.2%, all else held

equal. Remarkably, the coefficient of market total debt ratio in Tobin Q regression is –

1.101, which is much higher than other debt ratio coefficients in other regressions (from

–0.1 to –0.2), implying that the effect of debt ratios on Tobin Q is much stronger than on

ROA and ROE.

Another significant point is that overall F-tests with all p-values below 1% report good

fitness of the models. In addition, most adjusted R-squared values are moderate, from

0.1327 to 0.6484. Especially in ROA regressions, the values of adjusted R-squared are

around 0.60, reflecting that the models can explain 60% the change of ROA. However, as

discussed in the methodology chapter, regression using the OLS method cannot control

for unobserved individual effects, which commonly appear in most research using cross-

sectional data. Therefore, FE and RE modelling were conducted alongside pooled OLS

for unobserved individual effects.

114

Table 5.16: The effect of capital structure on firm performance—Pooled OLS

regression

���,� = � + �����,� + � ��,� + ��,�(14) This table reports the results of examining the relationships between capital structure (CS) and firm performance, which were estimated by pooled OLS estimators. Statistics are based on annual data for the

years 2007–2012. Columns 1 and 2 examined the effects respectively of total debt to book value of total assets (TLEV) and total debt to market value of total assets (MTLEV) on return on assets (ROA). Columns

3 and 4 examined the effects respectively of total debt to book value of total assets (TLEV) and total debt to market value of total assets (MTLEV) on return on equity (ROE). Columns 5 and 6 examined the effects respectively of total debt to book value of total assets (TLEV) and total debt to market value of total assets

(MTLEV) on Tobin Q. There are six control variables: firm growth (GRO), investment (INV), liquidity (LIQ), risk (RISK), dividend (DIV) and cash flow (CF).

Dependent variable: ROA

Dependent variable: ROE

Dependent variable: Tobin Q

(1) (2) (3) (4) (5) (6) TLEV –0.105*** –0.2047*** –0.254*** (0.005) (0.039) (0.075)

MTLEV –0.132*** –0.242*** –1.101*** (0.004) (0.036) (0.065) GRO 0.0082*** 0.0053*** 0.0362*** 0.0309*** 0.0783*** 0.0555*** (0.001) (0.001) (0.008) (0.008) (0.016) (0.015) INV –0.0061** –0.0057** –0.0148 –0.0144 –0.0108 0.0089 (0.002) (0.002) (0.018) (0.018) (0.035) (0.033) CF 0.490*** 0.424*** 1.2883*** 1.171*** 2.252*** 1.460*** (0.012) (0.011) (0.093) (0.096) (0.176) (0.173) RISK –0.120*** –0.157*** –2.2907*** –2.348*** 1.522*** 0.667* (0.025) (0.024) (0.196) (0.195) (0.371) (0.345) LIQ 0.170*** 0.130*** 0.2393*** 0.174** 0.774*** 0.0639 (0.010) (0.010) (0.082) (0.083) (0.156) (0.151) DIV 0.119*** 0.182*** 0.3566*** 0.469*** –2.260*** –1.627*** (0.014) (0.013) (0.110) (0.112) (0.209) (0.202) Constant 0.0519*** 0.0762*** 0.129*** 0.166*** 1.156*** 1.749*** (0.003) (0.003) (0.029) (0.029) (0.056) (0.053) Observations 2625 2625 2625 2625 2625 2625 Adj R–squared

0.5930 0.6484 0.1615 0.1695 0.1327 0.2137

F-test Prob > F

544.61 0.0000

692.19 0.0000

73.19 0.0000

76.29 0.0000

57.2 0.0000

101.6 0.0000

Standard error in parentheses: * significant at the 10% level; ** significant at the 5% level; *** significant at the 1% level

115

5.4.3 Random and fixed effect regression

Table 5.17 shows the outcomes of FE and RE models. Both models’ results provide that

the coefficients of leverage ratio are negative but differ slightly in the significance levels.

To select the appropriate model between FE and RE, the Hausman test was performed.

The results of chi-square statistics are all significant at the 1% level, favouring the FE

model over the RE model. The Wald test also shows that the FE model is better than

pooled OLS. Hence, the FE estimator was used to investigate the effect of leverage on

firm performance.

The results of the empirical model using the FE method in Table 5.17 confirm that the

relationship between capital structure and firm performance is negative. Columns 1 to 4

report the results of examining the relationship between capital structures on ROA,

columns 5 to 8 present the results of regression using ROE as a dependent variable and

columns 9 to 12 report the results of Tobin Q regression. In general, it reveals a negative

relationship between capital structure and firm performance because most estimated

coefficients of debt ratios measured by total book debts and total market debts are negative

and statistically significant at the 1% level except the coefficient of book leverage in Tobin

Q regression. On average, a 1% increase in total book debt will decrease 0.134% in ROA,

0.393% in ROE and 0.303 units in Tobin Q. Similarly, when market total debt increases

1%, ROA, ROE and Tobin Q will decline 0.137%, 0.281% and 2.06 units respectively,

holding all other variables constant.

Besides unobserved individual factors, other potential concerns are heteroskedasticity and

the autocorrelation phenomenon, which can lead to inefficiency of the model coefficients.

Therefore, the Wald test for heteroskedasticity and the Wooldridge test for autocorrelation

were conducted.

Table 5.17: The effect of capital structure on firm performance—RE and FE regressions

���,� = � + �����,� + � ��,� + �� + ��� This table reports the results of examining the relationships between capital structure (CS) and firm performance measured by return on assets (ROA), return on equity (ROE) and Tobin Q, which were estimated by random effect and fix effect estimators. Statistics are based on annual data for the years 2007–2012. There are six control variables: firm growth (GRO), investment (INV), liquidity (LIQ), risk (RISK), dividend (DIV) and cash flow (CF).

Dependent variable: ROA Dependent variable: ROE Dependent variable: Tobin Q (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) RE FE RE FE RE FE RE FE RE FE RE FE TLEV –0.115*** –0.134*** –0.309*** –0.393*** –0.254*** –0.303 (0.006) (0.012) (0.056) (0.101) (0.075) (0.208) MTLEV –0.138*** –0.137*** –0.293*** –0.281*** –1.119*** –2.066*** (0.004) (0.007) (0.044) (0.059) (0.066) (0.114) GRO 0.00831*** 0.00928*** 0.00487*** 0.00506*** 0.0274*** 0.0220** 0.0197** 0.0135 0.0783*** 0.102*** 0.0548*** 0.0358** (0.001) (0.001) (0.001) (0.001) (0.008) (0.009) (0.008) (0.009) (0.016) (0.019) (0.015) (0.018) INV –0.00413* –0.00129 –0.00423** –0.00211 –0.00269 0.00906 –0.00422 0.00586 –0.0108 –0.00160 0.00884 0.00780 (0.002) (0.002) (0.002) (0.002) (0.017) (0.018) (0.017) (0.018) (0.035) (0.038) (0.033) (0.035) CF 0.440*** 0.348*** 0.383*** 0.309*** 1.257*** 1.147*** 1.158*** 1.092*** 2.252*** 1.416*** 1.412*** 0.498** (0.012) (0.015) (0.012) (0.014) (0.102) (0.119) (0.104) (0.121) (0.176) (0.246) (0.174) (0.231) RISK –0.210*** –0.402*** –0.224*** –0.390*** –2.968*** –3.486*** –2.957*** –3.462*** 1.522*** –0.243 0.608* –0.0576 (0.027) (0.035) (0.025) (0.033) (0.229) (0.281) (0.228) (0.281) (0.371) (0.580) (0.357) (0.537) LIQ 0.156*** 0.118*** 0.121*** 0.0920*** 0.215** 0.184 0.165* 0.145 0.774*** 0.120 0.0372 –0.451** (0.011) (0.014) (0.010) (0.013) (0.094) (0.115) (0.095) (0.115) (0.156) (0.237) (0.152) (0.221) DIV 0.102*** 0.0624*** 0.171*** 0.139*** 0.222** 0.0978 0.368*** 0.257** –2.260*** –2.185*** –1.601*** –1.084*** (0.014) (0.015) (0.013) (0.015) (0.111) (0.124) (0.113) (0.128) (0.209) (0.256) (0.202) (0.244) Constant 0.0668*** 0.0992*** 0.0875*** 0.105*** 0.221*** 0.313*** 0.225*** 0.260*** 1.156*** 1.378*** 1.765*** 2.400*** (0.004) (0.007) (0.004) (0.004) (0.040) (0.059) (0.036) (0.040) (0.056) (0.122) (0.054) (0.078) Observations 2625 2625 2625 2625 2625 2625 2625 2625 2625 2625 2625 2625 R-square (within)

0.7074 0.6287 0.7570 0.7141 0.1519 0.1544 0.1617 0.1573 0.2263 0.1464 0.2662 0.2184

F-test (overall) Prob > F

189.50 0.0000

248.14 0.0000

51.09 0.0000

52.23 0.0000

20.62 0.0000

70.10 0.0000

Wald test Prob > Chi2

2823.23 0.0000

3704.48 0.0000

473.78 0.0000

489.43 0.0000

400.37 0.0000

696.73 0.0000

F-test that all u_i = 0 Prob > F

2.82 0.0000

2.64 0.0000

2.33 0.0000

2.31 0.0000

1.50 0.0000

1.76 0.0000

Dependent variable: ROA Dependent variable: ROE Dependent variable: Tobin Q (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) RE FE RE FE RE FE RE FE RE FE RE FE B &Pagan test Prob > Chi2

301.09 0.0030

282.59 0.0045

7.55 0.0030

6.8 0.0045

0.00 1.0000

247.41 0.0000

Hausman test Prob > Chi2

195.85 0.0000

150.69 0.0000

21.30 0.0033

18.05 0.0118

47.29 0.0000

134.61 0.0000

Wald test for heteroskedasticity Prob > Chi2

2.2e+36 0.0000

1.7e+35 0.0000

7.6e+37 0.0000

2.0e+35 0.0000

4.4e + 35 0.0000

2.0e + 35 0.0000

Wooldridge test

16.243 0.0000

7.273 0.0001

57.161 0.0000

48.199 0.0000

147.378 0.0000

118.474 0.0000

Standard error in parentheses: * significant at the 10% level; ** significant at the 5% level; *** significant at the 1% level.

118

The results of the Wooldridge test for autocorrelation are not deemed reliable when the

time period is short. In this study, the data spans six years; therefore, it is widely accepted

that the autocorrelation issue could be neglected in the short panel data. However, this

research still conducted the Wooldridge test for autocorrelation as a reference. The results

of the Wald test (all Prob > Chi2 = 0.000) report that the heteroskedasticity problem exists

in the models. Similarly, the Wooldridge test revealed a presence of autocorrelation in all

regressions. To control these issues, the study then adopted the FE model with adjusted

standard errors.

Table 5.18 reports the outcomes on the relationship between capital structure and firm

performance, which were estimated by the FE estimator with adjusted standard error.

Overall, all results remain similar with the FE model reconfirming the negative

relationship between leverage and firm performance in Vietnamese listed firms.

Specifically, most coefficients of capital structure variables are negative and statistically

significant at the 1% level except the coefficient of book debt ratio in Tobin Q regression,

which is still negative but insignificant. This means that an increase in the total debt ratios,

ceteris paribus, is associated with a decrease in firm performance. Results also

demonstrate that the effect of leverage ratio on Tobin Q is stronger than that of leverage

ratio on ROA and ROE because the coefficients of book debt and market debt ratios in

Tobin Q regression are much higher than those in ROA and ROE regression. A worthy

point is that the R-squared values in all regressions are quite good, from 0.1460 to 0.7147.

In particular, these figures in the ROA model are considerably high (around 0.70),

implying that the model could explain up to 70% of the change of ROA in Vietnamese

listed firms. In addition, the F-test results show that the fitness of models is fairly good.

119

Table 5.18: The effect of capital structure on firm performance—Fixed effect

estimator with robust standard error

���,� = � + �����,� + � ��,� + �� + ��� This table reports the results of examining the relationships between capital structure (CS) measured by total debt to book value of total assets (TLEV) and total debt to market value of total assets (MTLEV), and firm performance measured by ROA, ROE and Tobin Q, which were estimated by fixed effect estimator adjusted standard error. Statistics are based on annual data for the years 2007–2012. Columns 1 and 2 examined the effects respectively of TLEV and MTLEV on return on assets (ROA). Columns 3 and 4 examined the effects respectively of TLEV and MTLEV on return on equity (ROE). Columns 5 and 6 examined the effects respectively of TLEV and MTLEV on Tobin Q. There are six control variables: firm growth (GRO), investment (INV), liquidity (LIQ), risk (RISK), dividend (DIV) and cash flow (CF).

Dependent variable: ROA

Dependent variable: ROE

Dependent variable: Tobin Q

(1) (2) (3) (4) (5) (6) TLEV –0.134*** –0.393*** –0.303 (0.017) (0.133) (0.262) MTLEV –0.137*** –0.281*** –2.066*** (0.010) (0.062) (0.111) GRO 0.0093*** 0.005** 0.022* 0.0135 0.102*** 0.0358** (0.002) (0.002) (0.011) (0.009) (0.026) (0.016) INV –0.0013 –0.0021 0.009 0.006 –0.0016 0.0078 (0.005) (0.005) (0.018) (0.017) (0.024) (0.018) CF 0.348*** 0.309*** 1.147** 1.092** 1.416*** 0.498 (0.078) (0.074) (0.427) (0.457) (0.401) (0.316) RISK –0.402*** –0.390*** –3.486*** –3.462*** –0.243 –0.0576 (0.098) (0.101) (1.02) (1.04) (0.675) (0.621) LIQ 0.118*** 0.0920*** 0.184*** 0.145*** 0.120 –0.451 (0.017) (0.016) (0.055) (0.049) (0.557) (0.553) DIV 0.0624*** 0.139*** 0.0978 0.257** –2.185*** –1.084*** (0.016) (0.022) (0.078) (0.102) (0.469) (0.411) Constant 0.0992*** 0.105*** 0.313*** 0.260*** 1.378*** 2.400*** (0.014) (0.012) (0.094) (0.049) (0.148) (0.082) Observations 2625 2625 2625 2625 2625 2625 R-square 0.6287 0.7141 0.1401 0.1460 0.1467 0.2184 F-test Prob > F

33.62 0.0000

82.16 0.0000

14.19 0.0000

26.48 0.0000

8.36 0.0000

63.53 0.0000

Standard error in parentheses: * significant at the 10% level; ** significant at the 5% level; *** significant at the 1% level.

120

5.4.4 GMM estimator

Using the FE model with robust standard error can help to control unobserved effects as

well as heteroskedasticity; however, the endogenous issue, which leads to biased and

inconsistent estimators, may still exist. This is caused by the inability to ascertain if a

simultaneous reverse relation link exists between capital structure and firm performance

(i.e. firm performance also affects capital structure decisions). In addition, capital

structure can be considered simply an indicator of unobserved features that influence

performance. To strengthen the research outcomes, system two-step GMM with adjusted

standard error was applied to cope with the endogenous problem.

The outcomes of the system GMM are reported in Table 5.19. It once again confirms the

negative relationship between capital structure and firm performance. This negative

relation is mostly statistically significant at the 5% and 1% levels in most models, except

the coefficient of total debt ratios in the Tobin Q equation and the coefficients of market

total debt ratios in the ROE equation. These are negative but insignificant. The results also

reveal that the signs of most control variables, including growth rate, risk business, cash

flow and dividend yield, are consistent with OLS, RE and FE methods, but slightly

different in significance level.

The results of the specification tests of the models are reported in Table 5.19. While AR(1)

and AR(2) test the first- and second-order serial correlation, the Hansen J tests the

overidentifying restrictions. All p-values of AR(2) tests in the table are higher than 0.10,

which means that the null hypothesis of no second-order serial correlation cannot be

rejected. Similarly, the results of the Hansen J tests reveal that the null hypothesis that

instrument variables are valid or cannot be rejected.

121

Table 5.19: The effect of capital structure on firm performance—System two-step

GMM estimator with robust standard error

���,� = � + �����,� + � ��,� + �� + ��� This table reports the results of examining the relationships between capital structure (CS) measured by total debt to book value of total assets (TLEV) and total debt to market value of total assets (MTLEV), and firm performance measured by ROA, ROE and Tobin Q, which were estimated by the system GMM estimator. Statistics were based on annual data for the years 2007–2012. Columns 1 and 2 examined the effects respectively of TLEV and MTLEV on return on assets (ROA). Columns 3 and 4 examined the effects respectively of TLEV and MTLEV on return on equity (ROE). Columns 5 and 6 examined the effects respectively of TLEV and MTLEV on Tobin Q. There are six control variables: firm growth (GRO), investment (INV), liquidity (LIQ), risk (RISK), dividend (DIV) and cash flow (CF).

Dependent variable: ROA

Dependent variable: ROE

Dependent variable: Tobin Q

(1) (2) (3) (4) (5) (6) TLEV –0.0907*** –0.377** –0.0961 (0.026) (0.163) (0.205) MTLEV –0.0691*** –0.122 –0.305** (0.019) (0.097) (0.150) GRO 0.0058 0.0002 0.0424 0.0011 0.0607 0.0312 (0.008) (0.008) (0.049) (0.055) (0.051) (0.045) INV 0.050** 0.0448*** 0.317* 0.336** 0.139 0.158* (0.019) (0.017) (0.164) (0.161) (0.104) (0.083) CF 0.845*** 0.780*** 2.266*** 1.484*** 2.633*** 2.294*** (0.085) (0.102) (0.468) (0.446) (0.798) (0.747) RISK –0.011 0.0713 –1.324* –0.0966 –0.139 –0.223 (0.101) (0.106) (0.683) (0.519) (0.138) (0.490) LIQ 0.113*** 0.117*** –0.0643 0.226 0.0581 –0.433 (0.033) (0.032) (0.126) (0.166) (0.058) (0.579) DIV –0.174** –0.0715 –0.609 –0.186 –3.926*** –3.262*** (0.074) (0.068) (0.410) (0.344) (0.645) (0.664) L.ROA 0.0225 0.0111 (0.081) (0.078) L.ROE –0.219 –0.142 (0.143) (0.160) L.TOBIN Q 0.514*** 0.474*** (0.091) (0.075) Constant 0.0306 0.0194 0.201 0.0281 0.608*** 0.807*** (0.020) (0.017) (0.122) (0.32) (0.179) (0.139) Observations 1338 1338 1338 1338 1338 1338 Wald Chi2 423.28 514.09 61.52 94.37 296.18 408.33 Prob > Chi2 0.000 0.000 0.000 0.000 0.000 0.000 AR(1) 0.024 0.023 0.049 0.086 0.001 0.000 AR(2) 0.825 0.614 0.858 0.630 0.222 0.312 Hansen J test 0.134 0.116 0.261 0.384 0.412 0.562

122

5.4.5 Results

5.3.5.1 Capital structure

A negative relationship between capital structure and firm performance is supported by

all models. The consistency of the debt ratios sign under the different methods applied

illustrates the robustness of the findings. Remarkably, the effect magnitude of market total

debt ratio is considerably higher than those of book total market. These results are not

consistent with the studies of Berger and Bonaccorsi di Patti (2006), Gill, Biger and

Mathur (2011) and Margaritis and Psillaki (2010), but are in line with work by Majumdar

and Chhibber (1999), Tian and Zeitun (2007) and Joshua (2007). This could be explained

by Harris and Raviv (1991), who suggested that underestimating bankruptcy costs of

liquidation or reorganisation may lead firms to have more debt than the appropriate level;

therefore, a high debt ratio would decrease firm performance. In addition, large cash flow

from debt can lead managers to undertake discretionary behaviour or negatively affect

firm performance. Furthermore, Stulz (1990) argued that interest payments from issuing

debt may exhaust firm cash flow and reduce available funds for profitable investment,

which negatively affects firm performance. Further discussion in the next chapter sheds

more light on this argument.

5.3.5.2 Control variables

In reference to control variables, whereas growth rate, cash flow, investment, liquidity,

and risk have significant coefficients in most regressions, the others have not consistent

effects on firm performance.

The estimated coefficients of growth rate and investment are positive and statistically

significant, indicating that firms with higher growth opportunities can enhance their

performance measured by ROA, ROE and Tobin Q. The result is consistent with the

studies of Tian and Zeitun (2007), Gleason, Mathur and Mathur (2000), Jiraporn and Liu

(2008) and Margaritis and Psillaki (2010), which argued that firms with high growth rate

are able to create more profit and value from investment opportunities.

123

The coefficient estimates of cash flow are positive and statistically significant at the 1%

level in all models, suggesting that cash flow is an important factor affecting firm

performance. This result is in line with the studies of Gregory (2005), Chang, Chen and

Huang (2007). These authors explained that firms with high cash flow could undertake

positive investment without using external funds at high cost. The coefficients of the

liquidity factor are positively significant at the 1% level in the ROA equation, but

insignificant in ROE and Tobin Q equations. The explanation could lie in Cho (1998),

which argued that high liquidity enables firms to have more investments and mitigates

financial distress problems, thereby increasing firm performance.

Conversely, the risk of business is negatively related to firm performance as its

coefficients are significantly negative at 1% level in OLS, RE and FE regressions, but not

significant in all GMM estimators. This finding is consistent with most previous studies,

for example the studies of Tian and Zeitun (2007) and Bloom and Milkovich (1998). This

could explain that higher risk leads to higher financial distress costs or increases the

probability of corporate ruin, thereby reducing firm performance. In addition, high risk

makes firm more difficult to decide a future strategy, thus negatively affecting firm

performance.

5.4.6 Robustness check

The results of the robustness tests conducted are consistent with the main results of this

study and generally support the conclusion that there is a negative relationship between

capital structure and firm performance.

First, the research conducted estimations in which dummy variables were included to

capture industry- and year-specific FE. The results of these tests are presented in Table

5.20, which reports industry-specific FE, and Table 5.21, which captures year-specific FE.

A valuable point is that the coefficients of the industry and year dummies are not shown

to conserve space. The outcomes indicate that there is no important change on major

124

variables. To be specific, both total book debt ratio and total market debt ratio are

negatively associated with firm performance at the 1% level of significance. When there

is a 1% increase in total book debt ratio, the firm ROA, ROE and Tobin Q decrease

approximately 0.1%, 0.2% and 0.2 units respectively, holding all other variables constant.

The research then uses the alternative measure of capital structure to test the robustness

of the original empirical model. Long-term debt and short-term debt based on book and

market value were used as proxies for capital structure instead of total debt ratios. The FE

model with adjusted standard error was employed again with the alternative

measurements.

Table 5.22 illustrates the regression results in which the capital structure was measured

by long-term debt ratios, and Table 5.23 demonstrates the effect of short-term debt ratios

on firm performance. In line with the previous empirical results of this study, the

coefficients of leverage in ROE, ROA and Tobin Q in Table 5.22 are all negatively

significant at the 1% and 5% level when using the proxy of long-term debts to market

value of total assets. Likewise, the coefficient of book leverage in the ROA equation is

negative and significant at the 1% level, respectively. These figures in ROE and Tobin Q

equations are insignificant under the proxy of long-term debts to book value of total assets.

Similarly, the results shown in Table 5.23 have the same sign as the original models, which

use total debt ratios as proxies of capital structure. The coefficients of leverage are

significant and negative at 5% but the coefficients of ROA and Tobin Q are insignificant.

The results of other variables of growth rate, risk, liquidity or dividend yield appear to be

robust under different equations of capital structure proxies.

125

Table 5.20: The effect of capital structure on firm performance—Fixed industry

���,� = � + �����,� + � ��,� + �. �������� + ��,� This table reports the results of examining the relationships between capital structure (CS) measured by total debt to book value of total assets (TLEV) and total debt to market value of total assets (MTLEV), and firm performance measured by ROA, ROE and Tobin Q, which include industry dummy variables to capture industry-specific fixed effects. Statistics are based on annual data for the years 2007–2012. Columns 1 and 2 examined the effects respectively of TLEV and MTLEV on return on assets (ROA). Columns 3 and 4 examined the effects respectively of TLEV and MTLEV on return on equity (ROE). Columns 5 and 6 examined the effects respectively of TLEV and MTLEV on Tobin Q. There are six control variables: firm growth (GRO), investment (INV), liquidity (LIQ), risk (RISK), dividend (DIV) and cash flow (CF).

Dependent variable: ROA

Dependent variable: ROE

Dependent variable: Tobin Q

(1) (2) (3) (4) (5) (6) TLEV –0.107*** –0.210*** –0.250*** (0.005) (0.041) (0.007) MTLEV –0.133*** –0.244*** –1.130*** (0.004) (0.037) (0.067) GRO 0.0072*** 0.0044*** 0.0304*** 0.0253*** 0.0707*** 0.0473*** (0.001) (0.001) (0.008) (0.008) (0.016) (0.015) INV –0.0062** –0.0058** –0.0133 –0.0130 –0.0059 0.0116 (0.002) (0.002) (0.018) (0.018) (0.035) (0.033) CF 0.495*** 0.430*** 1.339*** 1.224*** 2.254*** 1.458*** (0.012) (0.012) (0.095) (0.098) (0.180) (0.177) RISK –0.139*** –0.175*** –2.424*** –2.476*** 1.365*** 0.464 (0.026) (0.026) (0.199) (0.198) (0.378) (0.359) LIQ 0.174*** 0.136*** 0.257*** 0.196** 0.779*** 0.0924 (0.010) (0.010) (0.083) (0.084) (0.158) (0.152) DIV 0.120*** 0.182*** 0.380*** 0.491*** –2.206*** –1.583*** (0.014) (0.014) (0.111) (0.112) (0.210) (0.203) Constant 0.0571*** 0.0812*** 0.148*** 0.183*** 1.282*** 1.903*** (0.005) (0.005) (0.039) (0.039) (0.075) (0.071) Fixed Industry Yes Yes Yes Yes Yes Yes Observations 2625 2625 2625 2625 2625 2625 Adj R-squared 0.5992 0.6536 0.1662 0.1719 0.1351 0.2168 F-test Prob > F

246.16 0.0000

310.42 0.0000

33.67 0.0000

34.95 0.0000

25.50 0.0000

44.90 0.0000

Standard error in parentheses: * significant at the 10% level; ** significant at the 5% level; *** significant at the 1% level

126

Table 5.21: The effect of capital structure on firm performance—Fixed year

���,� = � + �����,� + � ��,� + �. ���� + ��,� This table reports the results of examining the relationships between capital structure (CS) measured by total debt to book value of total assets (TLEV) and total debt to market value of total assets (MTLEV), and firm performance measured by ROA, ROE and Tobin Q, which include year dummy variables to capture year-specific fixed effects. Statistics are based on annual data for the years 2007–2012. Columns 1 and 2 examined the effects respectively of TLEV and MTLEV on return on assets (ROA). Columns 3 and 4 examined the effects respectively of TLEV and MTLEV on return on equity (ROE). Columns 5 and 6 examined the effects respectively of TLEV and MTLEV on Tobin Q. There are six control variables: firm growth (GRO), investment (INV), liquidity (LIQ), risk (RISK), dividend (DIV) and cash flow (CF).

Dependent variable: ROA

Dependent variable: ROE

Dependent variable: Tobin Q

(1) (2) (3) (4) (5) (6) TLEV –0.106*** –0.207*** –0.147** (0.005) (0.039) (0.066) MTLEV –0.136*** –0.220*** –0.565*** (0.004) (0.039) (0.065) GRO 0.00676*** 0.00535*** 0.0311*** 0.0288*** 0.0343** 0.0292** (0.001) (0.001) (0.008) (0.008) (0.014) (0.014) INV –0.00696*** –0.00587*** –0.0190 –0.0181 –0.0279 –0.0159 (0.002) (0.002) (0.018) (0.018) (0.030) (0.030) CF 0.469*** 0.421*** 1.209*** 1.146*** 1.730*** 1.423*** (0.012) (0.011) (0.094) (0.096) (0.156) (0.158) RISK –0.107*** –0.167*** –2.263*** –2.328*** 2.309*** 1.800*** (0.025) (0.024) (0.196) (0.198) (0.326) (0.325) LIQ 0.163*** 0.128*** 0.220*** 0.185** 0.726*** 0.400*** (0.010) (0.010) (0.082) (0.084) (0.137) (0.138) DIV 0.157*** 0.175*** 0.458*** 0.482*** –1.078*** –0.954*** (0.014) (0.014) (0.115) (0.115) (0.192) (0.190) Constant 0.0709*** 0.0648*** 0.174*** 0.143*** 2.476*** 2.622*** (0.005) (0.004) (0.042) (0.038) (0.070) (0.063) Fixed Year Yes Yes Yes Yes Yes Yes Observations 2625 2625 2625 2625 2625 2625 Adj R-squared 0.6101 0.6522 0.1721 0.1735 0.3367 0.3541 F-test Prob>F

340.65 0.0000

408.16 0.0000

45.26 0.0000

45.68 0.0000

118.48 0.0000

119.31 0.0000

Standard error in parentheses: * significant at the 10% level; ** significant at the 5% level; *** significant at the 1% level.

127

Table 5.22: The effect of capital structure measured by long-term debt ratios on

firm performance

���,� = � + �����,� + � ��,� + �� + ��� This table reports the results of examining the relationships between capital structure (CS) measured by long-term debt to book value of total assets (LLEV) and long-term debt to market value of total assets (MLLEV), and firm performance measured by ROA, ROE and Tobin Q, which were estimated by fixed effect estimator with adjusted standard error. Statistics are based on annual data for the years 2007–2012. Columns 1 and 2 examined the effects respectively of LLEV and MLLEV on return on assets (ROA). Columns 3 and 4 examined the effects respectively of LLEV and MLLEV on return on equity (ROE). Columns 5 and 6 examined the effects respectively of LLEV and MLLEV on Tobin Q. There are six control variables: firm growth (GRO), investment (INV), liquidity (LIQ), risk (RISK), dividend (DIV) and cash flow (CF).

Dependent variable : ROA

Dependent variable : ROE

Dependent variable: Tobin Q

(1) (2) (3) (4) (5) (6) LLEV –0.0614** –0.240 –0.349 (0.024) (0.214) (0.302) MLLEV –

0.0956*** –0.246** –1.787***

(0.019) (0.104) (0.244) GRO 0.0093*** 0.0085*** 0.0220* 0.0203* 0.102*** 0.0858*** (0.003) (0.002) (0.011) (0.011) (0.026) (0.024) INV –0.0026 –0.0027 0.0054 0.0047 –0.0034 –0.00032 (0.005) (0.005) (0.018) (0.017) (0.024) (0.022) CF 0.369*** 0.364*** 1.206*** 1.198*** 1.449*** 1.280*** (0.080) (0.080) (0.429) (0.435) (0.407) (0.385) RISK –0.406*** –0.408*** –3.500*** –3.501*** –0.262 –0.345 (0.105) (0.106) (1.058) (1.054) (0.670) (0.650) LIQ 0.130*** 0.124*** 0.219*** 0.206*** 0.142 0.00329 (0.018) (0.018) (0.054) (0.054) (0.563) (0.559) DIV 0.0640*** 0.0713*** 0.101 0.122 –2.188*** –2.080*** (0.064) (0.017) (0.082) (0.075) (0.466) (0.463) Constant 0.0339*** 0.0387*** 0.128** 0.130*** 1.255*** 1.444*** (0.033) (0.009) (0.050) (0.040) (0.074) (0.064) Observations 2625 2625 2625 2625 2625 2625 R-square (within) 0.5608 0.5605 0.1105 0.1111 0.1279 0.0707 F-test Prob > F

22.47 0.0000

27.67 0.0000

13.50 0.0000

14.05 0.0000

8.25 0.0000

13.8 0.0000

Standard error in parentheses: * significant at the 10% level; ** significant at the 5% level; *** significant at the 1% level.

128

Table 5.23: The effect of capital structure measured by short-term debt ratios on

firm performance

���,� = � + �����,� + � ��,� + �� + ��� This table reports the results of examining the relationships between capital structure (CS) measured by short-term debts to book value of total assets (SLEV) and short-term debts to market value of total assets (MSLEV), and firm performance measured by ROA, ROE and Tobin Q, which were estimated by fixed effect estimator with adjusted standard error. Statistics are based on annual data for the years 2007–2012. Columns 1 and 2 examined the effects respectively of SLEV and MSLEV on return on assets (ROA). Columns 3 and 4 examined the effects respectively of SLEV and MSLEV on return on equity (ROE). Columns 5 and 6 examined the effects respectively of SLEV and MSLEV on Tobin Q. There are six control variables: firm growth (GRO), investment (INV), liquidity (LIQ), risk (RISK), dividend (DIV) and cash flow (CF).

Dependent variable : ROA

Dependent variable: ROE Dependent variable: Tobin Q

(1) (2) (3) (4) (5) (6) SLEV –0.0905*** –0.237** –0.114 (0.014) (0.103) (0.220) MSLEV –0.133*** –0.258*** –1.910*** (0.012) (0.086) (0.105) GRO 0.0095*** 0.0064** 0.0228** 0.0167* 0.103*** 0.0588*** (0.002) (0.002) (0.011) (0.009) (0.026) (0.019) INV –0.0023 –0.0025 0.0056 0.0049 –0.0046 0.0012 (0.005) (0.005) (0.017) (0.017) (0.024) (0.020) CF 0.363*** 0.326*** 1.196*** 1.131** 1.461*** 0.781** (0.080) (0.076) (0.445) (0.464) (0.401) (0.332) RISK –0.398*** –0.383*** –3.474*** –3.449*** –0.237 0.0377 (0.101) (0.100) (1.033) (1.054) (0.671) (0.639) LIQ 0.125*** 0.104*** 0.208*** 0.173*** 0.144 –0.246 (0.018) (0.016) (0.052) (0.050) (0.553) (0.551) DIV 0.0667*** 0.129*** 0.110 0.231** –2.175*** –1.269*** (0.017) (0.022) (0.077) (0.112) (0.466) (0.413) Constant 0.0645*** 0.0855*** 0.198*** 0.213*** 1.260*** 2.062*** (0.011) (0.011) (0.060) (0.047) (0.094) (0.069) Observations 2625 2625 2625 2625 2625 2625 R-square (within) 0.5834 0.6097 0.1331 0.1385 0.1364 0.1762 F-test Prob > F

29.34 0.0000

68.30 0.0000

13.49 0.0000

25.11 0.0000

8.20 0.0000

59.43 0.0000

Standard error in parentheses * Significant at the 10% level.** Significant at the 5% level.*** Significant at the 1% level

129

5.4.7 Non-linear relationship between capital structure and firm performance

Tests were conducted to determine the existence of a non-linear relationship between

capital structure and firm performance. The quadratic function that is underpinned by the

work of Berger and Bonaccorsi di Patti (2006) and Margaritis and Psillaki (2010) was

used to allocate a non-linear relation. The result reported in Table 5.24 shows that a non-

linear relationship only appears when performance is measured by ROE and capital

structure measured by total and short-term debt. The coefficient of debt ratio is positively

significant and square of debt ratio significantly negative in the ROE equation, but these

coefficients are negative and insignificant in ROA and Tobin Q regressions. This indicates

that debt ratio is negatively related with ROA and Tobin Q even at a high level.

Meanwhile, at low levels, debt ratio is associated positively with ROE; however, at a high

level, the relationship switches from positive to negative. The results could be due to ROE

measured by ROA multiplied by financial leverage. At low levels, an increase of debt

ratio will increase ROE through rising financial leverage. However, an increase of debt

ratio also decreases ROA. Therefore, at a high level of debt ratio, when the decline of

ROA overwhelms the increase in financial leverage, ROE will reduce. It could be

concluded that capital structure generally negatively affects firm performance. In the

special case of ROE, the existence of a non-linear relationship between ROE and capital

structure is simply due to the usage of financial leverage.

13

0

Table 5.24: Non-linear relationship between capital structure and firm performance

���,� = � + �����,� + �����,�� + � ��,� + ��,�

This table reports the results of examining the non-linear relationships between capital structure (CS) and firm performance (FP), which were estimated by fixed

effect (FE) estimators with adjusted standard error. Statistics are based on annual data for the years 2007–2012.Columns 1, 2 and 3 examined the non-linear effects respectively of total debt ratio (TLEV), short-term debt ratio (SLEV) and long-term debt ratio (LLEV) on return on assets (ROA). Columns 4, 5 and 6 examined the non-linear effects respectively of total debt ratio (TLEV), short-term debt ratio (SLEV) and long-term debt ratio (LLEV) on return on equity (ROE). Columns

7, 8 and 9 examined the non-linear effects respectively of total debt ratio (TLEV), short-term debt ratio (SLEV) and long-term debt ratio (LLEV) on Tobin Q. There are six control variables: firm growth (GRO), investment (INV), liquidity (LIQ), risk (RISK), dividend (DIV) and cash flow (CF).

Dependent variable :ROA Dependent variable: ROE Dependent variable: Tobin Q (1) (2) (3) (4) (5) (6) (7) (8) (9) TLEV 0.00367 2.160*** –0.307 (0.047) (0.675) (0.739) TLEV2 –0.143*** –2.661*** 0.00433 (0.047) (0.818) (0.662) SLEV –0.0464 1.073* –1.349 (0.041) (0.607) (0.927) SLEV2 –0.0524 –1.558* 1.469 (0.050) (0.828) (0.952) LLEV –0.0797** –0.157 –0.262 (0.038) (0.228) (0.622) LLEV2 0.0394 –0.178 –0.188 (0.075) (0.514) (0.932) GRO 0.0093*** 0.0095*** 0.0093*** 0.0228** 0.0233** 0.0221* 0.102*** 0.102*** 0.102*** (0.003) (0.002) (0.003) (0.011) (0.011) (0.011) (0.026) (0.026) (0.026) INV –0.00127 –0.00238 –0.00264 0.00953 0.00534 0.00551 –0.00160 –0.00436 –0.00337 (0.005) (0.005) (0.005) (0.017) (0.017) (0.018) (0.024) (0.024) (0.024) CF 0.343*** 0.362*** 0.370*** 1.061*** 1.166*** 1.206*** 1.416*** 1.490*** 1.448*** (0.077) (0.080) (0.080) (0.402) (0.449) (0.428) (0.409) (0.414) (0.406) RISK –0.391*** –0.395*** –0.407*** –3.276*** –3.394*** –3.496*** –0.243 –0.312 –0.258 (0.098) (0.101) (0.105) (0.895) (1.030) (1.053) (0.697) (0.686) (0.671) LIQ 0.121*** 0.126*** 0.130*** 0.258*** 0.236*** 0.219*** 0.120 0.118 0.142 (0.017) (0.018) (0.018) (0.055) (0.054) (0.054) (0.549) (0.539) (0.563) DIV 0.0596*** 0.0660*** 0.0637*** 0.0451 0.0909 0.102 –2.185*** –2.157*** –2.187***

13

1

Dependent variable :ROA Dependent variable: ROE Dependent variable: Tobin Q (1) (2) (3) (4) (5) (6) (7) (8) (9) (0.016) (0.016) (0.017) (0.085) (0.077) (0.081) (0.465) (0.459) (0.464) Constant 0.0732*** 0.0574*** 0.0346*** –0.171* –0.0126 0.125*** 1.378*** 1.459*** 1.252*** (0.015) (0.011) (0.009) (0.087) (0.075) (0.048) (0.199) (0.173) (0.083) Observations 2625 2625 2625 2625 2625 2625 2625 2625 2625 R-square 0.6137 0.5827 0.5626 0.1879 0.1605 0.1493 0.1468 0.1268 0.1273 F-test Pro > F

30.38 0.0000

25.86 0.0000

20.35 0.0000

12.29 0.0000

12.04 0.0000

11.89 0.0000

7.48 0.0000

7.19 0.0000

7.3 0.0000

132

5.5 Conclusion

This chapter investigated the effect of ownership structure on leverage and leverage on

firm performance by using pooled OLS, RE, FE and GMM methods for all non-financial

listed firms in Vietnam during the period of 2007 to 2012. Although various approaches

were applied, all results from those models are consistent. Specifically, the study found

that whereas foreign ownership has a negative effect on leverage, state ownership has a

positive influence. Managerial ownership has a positive relationship with debt level,

whereas the effect of large ownership on debt level is not conclusive. In addition, the

results reveal that only foreign ownership affects inside ownership influence on financial

decisions. Specifically, foreign ownership decreases the positive effect of managerial

ownership on debt level.

The results also illustrate that debt levels, including short-term, long-term and total debt,

have a significant negative linear effect on firm performance measured by Tobin Q, ROA

and ROE. The non-linear relationship between leverage and firm performance only

appears when firm performance is measured by ROE. These findings do not support most

existing theories that imply that there is a positive relationship between capital structure

and firm performance, but are consistent with most of the empirical research about

emerging countries.

133

Chapter 6: Conclusion and Discussion

6.1 Conclusion

This study was motivated by the lack of empirical evidence relating to the effect of

ownership structure on financing decisions as well as the effect of capital structure on firm

performance in emerging countries, especially in the context of Vietnamese listed firms.

After using the pooled OLS model to test the relationship between corporate ownership

and capital structure, RE and FE models were chosen to deal with unobserved

heterogeneity. In addition, to control the heteroskedasticity phenomenon in the FE model,

FE model cluster error was applied. This study differs from previous research on Vietnam

because it is one of the first studies to use the GMM model to test the relationship between

ownership structure and capital structure and the relationship between capital structure

and firm performance to control the existence of the endogeneity issue. Although various

approaches were applied in this research, all results from those models are consistent and

in line with expectation. Table 6.1 restates the hypotheses raised in this study and lists

whether the results supported or did not support each hypothesis.

134

Table 6.1: Summary of results

Hypothesis Empirical result

H1: There is a positive relationship between state ownership and

leverage in Vietnamese listed firms. Strong support

H2: There is a negative relationship between foreign ownership and

leverage in Vietnamese listed firms. Strong support

H3: There is a positive relationship between managerial ownership and

leverage in Vietnamese listed firms. Strong support

H4: There is a positive relationship between large ownership and

leverage in Vietnamese listed firms. Partial support

H5a: Foreign ownership decreases the influence of managerial

ownership on leverage.

H5b: State ownership decreases the influence of managerial ownership

on leverage.

H5c: Large ownership decreases the influence of managerial ownership

on leverage.

Strong support

No support

No support

H6a: There is a non-linear relationship between managerial ownership

and leverage in Vietnam’s listed firms.

H6b: There is a non-linear relationship between state ownership and

leverage in Vietnam’s listed firms.

H6c: There is a non-linear relationship between foreign ownership and

leverage in Vietnam’s listed firms.

H6d: There is a non-linear relationship between large ownership and

leverage in Vietnam’s listed firms.

No support

No support

No support

No support

H7: There is a negative relationship between leverage and firm

performance in Vietnamese listed firms.

H8: There is a non-linear inverted U-shaped relationship between

capital structure and firm value in Vietnamese listed firms (leverage is

associated positively with firm value; however, at a high leverage, the

relationship switches from positive to negative).

Strong support

Partial support

135

6.2 Discussion

6.2.1 The effect of ownership structure on capital structure

6.2.1.1 State ownership

This research reveals that there is a significant positive relationship between state

ownership and capital structure in Vietnamese listed firms, which is consistent with prior

studies of Zou and Xiao (2006), Li, Yue and Zhao (2009) and Huang, Lin and Huang

(2011). This evidence once again confirms the argument that firms with high state

ownership can increase the accession ability to debt markets because of the guarantee

provided by the government (Huang, Lin & Huang 2011; Li, Yue and Zhao 2009; Zou &

Xiao 2006). To be specific, in Vietnam, most state-controlled firms are traditional

customers of or have close relationships with state-owned banks that dominate the

country’s banking industry (IMF 2010; Vietcombank Securities 2011). Therefore,

Vietnamese firms with significant state ownership have a high capacity to access to bank

loans regardless of their performance or quantity of collateral (Okuda & Nhung 2010;

World Bank 2011). Additionally, state-owned firms can have preferential treatment in

admission to the debt market, for example, obtaining debt financing with a lower cost

resulting in the use of more debt than other corporations in general. In addition, state-

owned firms can have motivation to increase debt level to preserve their control or avoid

share dilution.

6.2.1.2 Foreign ownership

There is a negative relationship between foreign ownership and capital structure. This

negative relation in Vietnamese markets can be explained by several factors. First, foreign

ownership firms usually have reputation and strong financial condition. Therefore, they

can access finance using various channels. In addition, the demand for issuing debt of

firms with high foreign ownership can be reduced because of the equity contributions from

foreign investors. Second, foreign investors investing in Vietnam stock markets are mostly

institutional investors (Vo 2011) that have the incentive and experience to monitor

136

managers to protect their investment (Brailsford, Oliver & Pua 2002; Friend & Lang

1988). Through monitoring of the management, foreign investors can help to reduce the

conflict problem between managers and shareholders. Consequently, foreign ownership

is negatively related to debt level because it can substitute for debt in decreasing agency

cost of equity (Huang, Lin & Huang 2011; Moon 2001).

The estimated results also reveal that foreign ownership decreases the effect of managerial

ownership on capital structure. This reconfirms that foreign shareholders can improve

firms’ governance system through monitoring management, thereby restricting managers

in deciding the debt level for their own interests. Thus, foreign ownership as a monitoring

mechanism reduces the effect of managerial ownership on financial decisions.

6.2.1.3 Managerial ownership

The research found a positive and statistically significant relationship between firm

leverage and managerial ownership. This result is consistent with the proposed hypothesis.

The rationale for this finding is the control issue (Ghaddar 2003; Kim & Sorensen 1986).

In particular, one of the main concerns of every manager is to retain or increase their

control because it provides them with discretion in making decisions or accessing their

private benefits. Meanwhile, debt is a means to restrain share dilution. Harris and Raviv

(1988) affirmed that an increase in debt helps managers to reinforce their control and resist

takeovers. In addition, with high debt, managers can have more funds to achieve their own

interests.

In Vietnam, as mentioned in Chapter 2, the financial system continues to be considered

underdeveloped and the role of debt as a monitoring mechanism may be not substantial.

From the firm view, managers may be aware of the inefficient monitoring of debt, so they

may increase debt to have more funds to achieve their own interests as well as retain their

control.

137

6.2.1.4 Large ownership

The research found unobvious evidence of the effect of large ownership on capital

structure. Both RE and FE regressions provided a positive significant relationship between

large ownership and capital structure. However, this result was not confirmed under FE

with standard error correction and GMM methods, which, as argued in the methodology

chapter, are more precise. This implies that there is no apparent evidence of a monitoring

role in large ownership among Vietnamese listed firms.

6.2.2 The effect of capital structure and firm performance

This study provided evidence of capital structure negatively affecting firm performance.

Specifically, when testing the linear relation between leverage and firm performance, the

finding indicates that all ratios of long-term debt, short-term debt and total debt in both

book and market value are significantly and negatively related to ROA, ROE and Tobin

Q. When allowing a non-linear relation between capital structure and firm performance,

the research shows that the non-linear relation only appears when performance is

measured by ROE and capital structure measured by total debt and short-term debt. This

outcome is consistent with the research of Tian and Zeitun (2007), Joshua (2007) and

Majumdar and Chhibber (1999) in the context of emerging markets; however, it is not in

accordance with most studies conducted in developed countries, which posit a positive

relationship between capital structure and firm performance.

The negative relationship found between capital structure and firm performance may be

explained by a few factors. As a transitional and emerging market, Vietnam may have

unique aspects when compared with other developed countries. First, although in the early

1990s Vietnam initiated an economic reform programme that transferred the central

planned economy to a market economy, the financial system is underdeveloped (IFC

2007; Leung 2009). The Vietnamese financial market is dominated by a banking sector in

which SOCBs still control deposit and credit markets. Majumdar and Chhibber (1999)

138

argued that state-owned financial institutions do not suffer from bad loan decisions made

by their own principals as privately owned companies do, because their owner, the

government, theoretically always has deep pockets. Therefore, the incentive for

monitoring their customers in state-owned financial institutions is not very significant.

Thus, in the Vietnam context, the benefit of debt as a monitoring manager to reduce the

equity agency cost may be not substantial. Furthermore, from the firms’ point of view,

managers are aware of the debt monitoring inefficiency, so an increase in debt can help

them to acquire more cash to undertake discretionary investments, which negatively

affects firm performance.

Second, interest rates in Vietnam, both deposit and lending rates, increased sharply and to

a much higher level than other countries in the period from 2007 to 2011. Specifically, the

deposit and lending rates nearly doubled in 2011 compared with 2009 (Mirae Asset

Securities 2011). Therefore, interest payments have become a burden for most Vietnamese

firms. Additionally, the research shows that tax is not a significant determinant of capital

structure decisions; in other words, Vietnamese firms have not taken advantage of tax

shields by issuing debt. Therefore, the benefits of debt from tax saving overcome by the

costs of debt including financial distress and liquidity issues. Furthermore, Stulz (1990)

argued that interest payments may exhaust firm cash flow and reduce available funds for

profitable investments, which negatively affects firm performance. With the high interest

rates, this may have occurred for Vietnamese firms.

In addition, as outlined in the data description, short-term debt accounts for most of the

capital structure of Vietnamese listed firms, which frequently occurs in developing

countries. To be specific, while the average long-term debt ratio is only 10.83%, the mean

ratio of short-term debt is 41.09%. Meanwhile, it is widely agreed that short-term debt

pushes firms to the risk of refinancing, thereby negatively affecting firm performance.

Furthermore, a low ratio of long-term debt implies a low level of long-term investment

and thus low profit in the future.

139

Finally, McConnell and Servaes (1995) and Stulz (1990) posited a positive relationship

between firm performance and leverage in low-growth firms; conversely, firm

performance is negatively associated to leverage for those with high growth. A reasonable

explanation is that a positive effect appears in firms with fewer growth opportunities

because an increase in debt prevents managers from investing in unprofitable projects or

reduces the overinvestment problem. Conversely, in high-growth-opportunity firms, there

is a negative effect of debt on firm performance because an increase in debt forces

managers to forego profitable projects or increase the underinvestment issue. In contrast,

high-growth firms are common in fast-growing countries (Ruan, Tian & Ma 2011) and

Vietnam is one of the world’s highest growth rate countries (Deutsche Bank Research

2007; World Bank 2011). Therefore, a negative relationship may exist between capital

structure and firm performance in Vietnamese firms.

6.3 Policy implications

The findings from this research suggest some recommendations for policymakers and

Vietnamese firms. The research shows that access to capital sources is not equal for all

types of firms. State-owned firms may have substantial advantages in access to the debt

market because of the preferential treatment from state-owned banks. Therefore, the

research is supportive of the current direction of the market and the financial reform that

has privatised the banking sector and is gradually decreasing the level of government in

state-owned firms. This process could ensure that the distributing of bank finance to all

kinds of firms relies purely on commercial principles.

In addition, the study gives evidence of an active monitoring role by foreign investors.

Foreign ownership with experience and knowledge could help firms to reduce agency cost

of equity through actively monitoring management. To assist in the development of good

corporate governance mechanisms for Vietnamese firms, it is recommended that

consideration be given to enabling foreign ownership in Vietnamese listed firms to

increase above the current rate of 49%.

140

The research findings also imply that the monitoring role of debt is not substantial and

conflict of interest exists between managers and other investors because of the existence

of information asymmetry and an underdeveloped financial system. Therefore, greater

information transparency and availability in the market is required and more regulations

should be considered.

The study observed that increasing debt can decrease firm performance because of high

interest rates, exhausted cash flow or inefficient monitoring of debt. Therefore, firms need

to deliberate when deciding to issue debt or access a bank loan in order to develop.

Additionally, the research provides evidence of the important influence of ownership

structure on funding policy; therefore, Vietnamese listed firms need to ponder fully this

factor when deciding their own capital structure.

6.4 Limitations

Although this research has answered the proposed questions and provided insight on the

effect of ownership structure on capital structure as well as the influence of capital

structure on firm performance in a typical emerging market, it still contains some

limitations.

First, the research sample period is relatively short. Observation spans only six years, from

2007 to 2012, which may influence the significance of testing. Additionally, the research

faced obstacles in collecting adequate data relating to ownership structure because, in

Vietnam, there is a culture of secrecy relating to asset information and laws are not strict

enough to force all companies to provide sufficient reports. This resulted in some missing

values in the data, which in turn may affect the results’ reliability. In addition, the research

results may be distorted by the influence of the global financial crisis that occurred in this

period. This study used a dummy variable presented to year to control the effects of the

global financial crisis. However, it may be argued that the effect of the global financial

crisis on the result should be explored in more detail.

141

Second, examining only one country may be a weak point of the research results’

application. Although Vietnam is an emerging and transition country, it is widely argued

that a study with a sample including many countries may give results that are more

persuasive. However, Vietnam is a typical case; other developing countries may have

similar features and findings to those obtained in this research. In addition, a study on a

single country enabled deep investigation that may not have been possible in multiple-

country research.

Finally, although several different methods, including pooled OLS, RE, FE and GMM

were applied in the research to capture normality issues such as heteroskedasticity,

unobserved effects and potential endogeneity problems, it is not certain that all

econometric issues were completely controlled, especially in terms of endogeneity. This

is because the FE and RE models mainly capture for unobserved heterogeneity. They do

not account for the endogeneity problem, which is caused by the measurement errors,

time-invariant endogenous variables and reverse causality that often take place in the field

of finance research. In addition, GMM estimators are now becoming increasingly popular

to control endogeneity issues, one disadvantage of the difference and system GMM is that

they are quite complicated and so easily create invalid estimates (Roodman 2006).

142

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