ESSAYS ON CORPORATE GOVERNANCE - t U

ESSAYS ON CORPORATE GOVERNANCE

BY

MS. JUTAMAS WONGKANTARAKORN

A DISSERTATION SUBMITTED IN PARTIAL FULFILLMENT

OF THE REQUIREMENTS FOR THE DEGREE OF

DOCTOR OF PHILOSOPHY (BUSINESS ADMINSTRATION)

FACALTY OF COMMERCE AND ACCOUNTANCY

THAMMASAT UNIVERSITY

ACADEMIC YEAR 2017

COPYRIGHT OF THAMMASAT UNIVERSITY

Ref. code: 25605502310021IBC

ESSAYS ON CORPORATE GOVERNANCE

BY

MS. JUTAMAS WONGKANTARAKORN

A DISSERTATION SUBMITTED IN PARTIAL FULFILLMENT

OF THE REQUIREMENTS FOR THE DEGREE OF

DOCTOR OF PHILOSOPHY (BUSINESS ADMINSTRATION)

FACULTY OF COMMERCE AND ACCOUNTANCY

THAMMASAT UNIVERSITY

ACADEMIC YEAR 2017

COPYRIGHT OF THAMMASAT UNIVERSITY

Ref. code: 25605502310021IBC

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Dissertation Title ESSAYS ON CORPORATE GOVERNANCE

Author Ms. Jutamas Wongkantarakorn

Degree Doctor of Philosophy (Business Administration)

Faculty Faculty of Commerce and Accountancy

University Thammasat University

Dissertation Advisor Assistant Professor Chaiyuth Padungsaksawasdi, Ph.D.

Academic Years 2017

ABSTRACT

These essays investigate the role of corporate governance from international-

evidence to firm-level. The first essay provides international corporate governance

spillover among country groups, namely G7, BRICS, and PIGS in six dimensions of

corporate governance of the Worldwide Governance Indicators (WGI) developed by

the World Bank. Empirical evidence shows that there exists corporate governance

spillover across these groups. The second essay examines international agency cost in

state-owned enterprise (SOE) that determines the effect of ownership structure of SOE

on firm performance. Empirically, SOE has positive impact on firm performance,

suggesting that SOE firm perform better than non-SOE firm. The third essay explores

the relationship between corporate governance and stock liquidity in Thailand using a

treatment effects. Consistent with prior literature, corporate governance has positive

effect on stock liquidity and its impact is more pronounced for listed firms with good

corporate governance. Firms with good corporate governance are viewed to have lower

information asymmetry that investors are more confident to trade more stocks, leading

to higher stock liquidity.

Keywords: corporate governance, index, state-owned enterprise, liquidity

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ACKNOWLEDGEMENTS

First and foremost, I would like to express my sincere gratitude to my

advisor, Assistant Professor Dr. Chaiyuth Padungsaksawasdi for tremendous and

continuous support of my Ph.D. study. I highly appreciate all his contributions of time,

motivation, advice, and idea. His guidance helps me in all the time of research and

writing this dissertation.

Besides my advisor, I would like to thank the rest of my dissertation

committees, and Associate Professor Dr. Seksak Jumreornvong and Professor Dr.

Pornchai Chunhachinda for insightful comments and encouragement. My special

appreciation goes to Associate Professor Dr. Tatre Jantarakolica and Dr. Thanomsak

Suwannoi for their brilliant suggestions that incent me to widen my dissertation from

various perspectives.

My time at Thammasat University was made enjoyable due to many friends.

Special mention goes to Massaporn Cheuathonghua and Phasin Wanidwaranan for time

spent throughout my study and their kind assistance.

Last but not least, I would like to thank my family for encouragement and

support that motivate me to complete my Ph.D. study.

Ms. Jutamas Wongkantarakorn

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TABLE OF CONTENTS

Page

ABSTRACT (1)

ACKNOWLEDGEMENTS (2)

LIST OF TABLES (6)

LIST OF FIGURES (8)

LIST OF ABBREVIATIONS (9)

CHAPTER 1 INTRODUCTION 1

1.1 Motivation 1

1.2 Background 3

1.2.1 Corporate governance 3

1.2.2 Corporate governance index 4

1.2.3 History of State-Owned Enterprise (SOE) 5

1.2.4 Liquidity and corporate governance 5

1.3 Objective and contribution 5

1.4 Structure of dissertation 6

CHAPTER 2 INTERNATIONAL CORPORATE GOVERNANCE

SPILLOVER; EVIDENCE FROM PANEL DATA

8

2.1 Introduction 8

2.2 Literature Review 9

2.2.1 Governance indicators as a proxy for corporate governance

practices

9

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Page

2.2.2 Corporate governance spillovers 10

2.2.3 Hypothesis development 12

2.3 Data description and research methodology 12

2.3.1 Data 12

2.3.2 Methodology 13

2.4 Results and discussion 18

2.4.1 Data and descriptive statistics 18

2.4.2 Testing results of international corporate governance spillover 22

2.4.3 Testing results of relationship between financial integration

and corporate governance spillover 23

2.4.4 Testing results of panel cointegration 23

2.4.5 Testing results of panel VARs 30

2.5 Conclusion 35

CHAPTER 3 AGENCY COST IN STATE-OWNED ENTERPRISES:

INTERNATIONAL EVIDENCE 36

3.1 Introduction 36

3.2 Theoretical framework 38

3.2.1 Theory of SOE 38

3.3 Literature review 39

3.3.1 SOE and performance 39

3.3.2 SOE by Industry 39


3.4 Data description and research methodology 41

3.4.1 Data 41

3.4.2 Research methodology 41

3.4.2.1 Difference-in-differences (DiD) 41

3.4.2.2 Matching firm method 43

3.5 Empirical results 44

3.6 Conclusion 60

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Page

CHAPTER 4 CORPORATE GOVERNANCE AND LIQUIDITY 61

4.1 Introduction 61

4.2 Literature review 62

4.2.1 Trading under information asymmetry and adverse selection 62

4.2.2 Corporate governance and liquidity 63


4.3 Data and data description 65

4.3.1 Data 65

4.3.1.1 Corporate governance of the firms 66

4.3.1.2 Liquidity measure 66

4.3.1.3 Control variables 66

4.3.2 Methodology 66

4.3.2.1 Panel Random-effects Tobit Model 66

4.3.2.2 Panel Random-effects Tobit Model 68

4.3.2.3 Panel Fixed-effects Quantile Regression Model 68

4.3.2.4 Robustness Check 68

4.4 Empirical results 69

4.4.1 Descriptive statistics 69

4.4.2 Estimated results of econometric models 71

4.4.3 Robustness tests 74

4.5 Discussion & Conclusion 78

REFERENCES 80

APPENDIX

APPENDIX A: WGI DATA SOURCES 93

BIOGRAPHY 94

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LIST OF TABLES

Tables Page

2.1 Countries by group of G7, PIGS, and BRICS 13

2.2 Descriptive statistics 19

2.3 Panel Unitroot test 22

2.4 Panel-data cointegration, KAO test of BRICS country 24

2.5 Panel-data cointegration, Pedoni test of BRICS country 25

2.6 Panel-data cointegration, Pedroni test of BRICS country 25

2.7 Panel-data cointegration, Westerlund test of BRICS country 26

2.8 Panel-data cointegration, KAO test of PIGS country 26

2.9 Panel-data cointegration, Pedroni test of PIGS country 27

2.10 Panel-data cointegration, KAO test of G7 country 28

2.11 Panel-data cointegration, Pedroni test of G7 country 28

2.12 Panel-data cointegration, Westerlund test of G7 country 29

2.13 Multivariate Panel vector autoregressive models (XTVAR) 32

3.1 Number of firms in each country that have SOE 45

3.2 Number of observations by industry 46

3.3 Descriptive statistics of variables 46

3.4 Correlation of variables 47

3.5 Panel-data regression of all sample 49

3.6 Panel regression random effects of China 50

3.7 Panel regression random effects by industry 52

3.8 Panel-data regression by group of law system 57

3.9 Effect of SOE on performance of firm by using the treatment effects and

propensity-score matching method 59

4.1 Descriptive statistics of stock return and liquidity measures. 70

4.2 Estimated Results of Random-effects Linear Model, Random-effects Tobit

Model, and Fixed-effects Quantile Regression Model using Annual Data 72

4.3 Estimated Results of Random-effects Linear Model, Random-effects Tobit

Model, and Fixed-effects Quantile Regression Model using Monthly Data 73

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Tables Page

4.4 Frequency of Firm-year Categorized by IOD’s Corporate Governance

Index (GovIndex) and Level of Illiquidity (ILLIQ_Level) 74

4.5 Frequency of Firm-month Categorized by IOD’s Corporate Governance

Index (GovIndex) and Level of Illiquidity (ILLIQ_Level) 75

4.6 Estimated Results of Random-effects Ordered Probit Model using

Annually Data and Monthly Data 76

4.7 Descriptive Statistical Indices of Change of Amihud’s Illiquidity (ILLIQ)

After Change in IOD’s Corporate Governance Index (GovIndex) during

2007-2011 and 2012-2017 77

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LIST OF FIGURES

Figures Page

2.1 Average of six dimensions of corporate governance index of 16

countries, 1998-2014

22

4.1 Histogram of Amihud’s Illiquidity (ILLIQit) 67

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LIST OF ABBREVIATIONS

Symbols/Abbreviations Terms

SOE State-owned enterprise

CG Corporate governance

CSS Country share SOE share

OECD The Organization for Economic and

Cooperation Development

WGI The World Governance indicators

ISS Institutional Shareholder Services

ESG The Environmental, Social, and Governance

of corporate

G7 Group of Seven

BRICS Brazil, Russia, India, China, and South Africa

PIGS Portugal, Italy, Greece, and Spain

M&A Mergers and acquisitions

UCM The unobserved components model

XTVAR The multivariate panel vector autoregressive

models

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CHAPTER 1

INTRODUCTION

1.1 Motivation

Corporate governance has been an important issue in modern finance for

more than 20 years (Cheffins, 2012). It was blamed as one of the major causes that

ignited Asian financial crisis in 1997. During that period, corporate governance did not

only cause financial crisis in the Asian countries, but it also affected financial condition

in the United States and European countries on both macro-level and micro-level. At

the macro-level, the changes of country’s corporate governance are in rules and

regulations that impact social and financial environments. On the other hand, corporate

governance also influences investors and managers decision. Additionally, publics are

not only interested in country-level governance indicators, but they also consider firm-

level corporate governance measures such as Dow Jones Sustainability Index (DJSI).

Therefore, companies, especially the listed ones, are encouraged to adopt good

governance practices which mainly raise business profit by reducing various types of

cost, such as agency, monitoring, and bonding costs.

Since corporate governance is the system of guideline to control and direct

an organization, the framework is related to rules and procedures regarding company’s

decisions. Most importantly, the mechanism is designed for the balance of

stakeholders’ interest. According to the agency theory, there are two principal-agent

problems. First, the conflict between manager and shareholders is the circumstance

that manager does not perform for the best interest of shareholders. Second, the conflict

between majority and minority shareholders is largely driven by the one-share one-vote

principle (Jensen & Meckling, 1976; La Porta, Lopez-De-Silanes, & Shleifer, 1999; La

Porta, Lopez-de-Silanes, Shleifer, & Vishny, 2000). With an imbalance of interest of

stakeholders, this is an evidence of agency problem. As integrity and reliability of the

firm are in doubt, bad governance is an important source of business difficulty which

easily spreads to entire industry, country, and global economy. The development of

corporate governance is normally stimulated by financial crises. Stakeholders, who

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suffer from the cost of bad governance, try to manage their future. As the loss is much

higher for developed countries, they are the leader of corporate governance

improvement. Furthermore, they have better resources than developing economies.

Developed countries also motivate others for an adoption of their framework by

providing incentive and punishment. Besides, the corporate governance spillover,

which is a transfer of corporate governance practices between countries, is also fueled

by market integration, the advancement of information technology, merger, and

acquisitions. Stulz (1999) suggests that good governance reduces cost through

globalization. Moreover, everyone is better-off from the acceptance of corporate

governance (Bris, Brisley, & Cabolis, 2008). For these reasons, corporate governance

spillover is an important issue. However, prior studies primarily investigate the firm-

level spillover of corporate governance, especially through merger deals (Martynova &

Renneboog, 2008), and cross-border trading (Abdallah & Goergen, 2008). In terms of

country-level, Marshall, Nguyen, Nguyen, & Visaltanachoti (2016) examine the

relationship between country governance and international equity returns prediction.

On the contrary, this study focuses on country-level corporate governance spillover by

using country corporate governance index with panel regression.

State-owned enterprise (SOE) is an economic entity. It is a company which

is significantly controlled by government through an ownership. SOE is established by

government in order to conduct commercial activities on behalf of state. SOE is

important for world economy, especially in developing countries, because it accounts

for approximately 10 percent of the world’s GDP (Peng, Bruton, Stan, & Huang, 2016).

For example, China has the Country SOE Share (CSS) index more than 90 percent. The

Country SOE Share (CSS) index represents the share of state ownership which accounts

for all sales and market value of each country. The change in ownership structure and

corporate behavior can be driven by corporate governance framework, especially in

SOE (Grosman, Okhmatovskiy, & Wright, 2016). This ownership structure has some

benefits and drawbacks because government is expected to act for the best interest of

public which is in this case, the company’s stakeholders. Unfortunately, the

effectiveness of federal institution is still in doubt. As a result, corporate governance of

SOE is unclear. The effect of this ownership structure on corporate governance should

be investigated. Previous studies regarding corporate governance of SOE are limited.

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Research in SOE mainly focuses on Chinese firms which mostly studies the relationship

between privatization companies and their performances. Moreover, they also

emphasize on the association in both firm and industry levels. This study fulfills the

gap by exploring the firm-level corporate governance of SOE in global context.

Lastly, the micro-level issue of corporate governance, which is stock

liquidity, is examined. Liquidity is crucial for listed company because it affects market

value of firm (Amihud & Mendelson, 2008). Since illiquidity is caused by adverse

selection, the information asymmetry links to stock liquidity and pricing. As corporate

governance reduces asymmetric information, it influences the stock liquidity (Glosten

& Milgrom, 1985). Prior studies in asset pricing have some arguments about an impact

of liquidity to the firm. They consider various kinds of control variables such as market

condition and rule and regulation which affect liquidity and the relationship between

liquidity and corporate governance. Literature regarding the association between

corporate governance and liquidity generally employs specific characteristics as a

proxy of corporate governance which cannot represent every perspective of firm’s

governance. Besides, most of them focus on developed markets. This study aims to

explore the relationship between corporate governance and liquidity in firm-level.

1.2 Background

1.2.1 Corporate governance

The history of corporate governance occurred since corporation was

formed in the 16th century and officially stated in the Federal Registrar in 1976 in US

(Cheffins, 2011). The issue that is emphasized is about management accountability.

There is no consensus of definition of corporate governance. There are two dimensions

of the definitions which are corporate activity, and finance and law environment

dimensions (Claessens & Yurtoglu, 2013). According to the difference in economic and

financial system between U.S. (the earliest country that uses the term corporate

governance) and the rest of the world, corporate governance in each country is different

(Morck & Steier, 2005). However, the term reached the world interest by the 1997

Asian financial crisis. Until 1976, Jensen & Meckling (1976) reveal that agency cost

is the important issue of corporate governance.

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In capitalism, both developed and developing countries are affected by

problems of corporate governance. Economy, market structure, politics, laws, culture,

and financial system make corporate governance of each country distinctive. Therefore,

mutual understanding among countries is very important. In 1999, Asian roundtable on

corporate governance was organized by the Organization for Economic and

Cooperation Development (OECD) (OECD, 2014) to establish comprehensive

guideline for all countries. However, the role model country of corporate governance

also faces the scandals, for example Enron and WorldCom (Cheffins, 2012; Ke,

Huddart, & Petroni, 2003).

1.2.2 Corporate governance index

Corporate governance cannot be quantified directly and therefore

researchers have to measure corporate governance via proxy (Black, Gledson De

Carvalho, Khanna, Kim, & Yurtoglu, 2017). Prior studies use two types of proxies of

corporate governance. The first type is a secondary data proxy which is derived by

institutions or data providers that construct corporate governance according to their

objectives of study. Data consist of corporate governance and relate to indices such as

world governance index (WGI) by World Bank, and Institutional Shareholder Services

(ISS), the Environmental, Social, and Governance of corporate (ESG) by Thomson

Reuters Corporate Responsibility Ratings. The second type is to construct corporate

governance index. Researchers primarily construct index from check-list of each

dimension of corporate governance or from survey data (K. H. Chung, Elder, & Kim,

2010; K. H. Chung, Kim, Park, & Sung, 2012; Lei, Lin, & Wei, 2013; Prommin,

Jumreornvong, & Jiraporn, 2014; Tang & Wang, 2011). Recently, study on corporate

governance construction is G index (Gompers, Ishii, & Metrick, 2003) which construct

index by using equally weight of 24 check-lists in firm level. The benefit of corporate

governance index is to reduce information asymmetry by signaling investors about the

corporate governance status of country or company. However, the limitation of

corporate governance index is a comparison across countries due to different

measurements and availability of data.

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1.2.3 History of State-Owned Enterprise (SOE)

SOE is an important type of ownership structure. Definition of SOE

varies according to law of each country and area of study. In general, SOE is usually

under government ownership and control, as PwC (2015) stated “enterprises where the

state has significant control through full, majority, or significant minority ownership”.

SOE has been used as government tools for social and economic objectives since

ancient Greece and ancient China. However, SOE has problems in management,

control, and allocation that affect efficiency in competitive economy. Problem of

inefficiency stems from performance of the firms that leads to privatization. According

to property rights theory, private ownership structure is better than SOE in terms of

performance (Boardman & Vining, 1989). This study defines SOE with more than 50%

shareholder of government ownership.

1.2.4 Liquidity and corporate governance

Stock liquidity is defined as “the ease with which it is traded”

(Brunnermeier & Pedersen, 2009). Liquidity of stock affects value of the firm (Amihud

& Mendelson, 2008). There are many studies about stock liquidity. Moreover, there are

issues of corporate governance relationship to stock liquidity. Trading against adverse

selection from information asymmetry affects stock liquidity (Glosten & Milgrom,

1985). Illiquidity implies to trading cost in study on asset pricing which affects expected

returns. The information asymmetry between manager and shareholders leads to

implicit transaction cost like illiquidity. The well-known liquidity measure is Illiquidity

ratio (Amihud, 2002).

1.3 Objective and contribution

The objective of this study is to investigate corporate governance in both

macro and micro levels which separate into three parts. The first part is to examine

international corporate governance spillover. This study focuses on corporate

governance transmission among countries by using panel data which is corporate

governance data in country level overtime, and also uses corporate governance index

which is measured by six dimensions of corporate governance.

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The second part is to investigate corporate governance and ownership

structure focusing on listed SOE of separate industries. This part uses constructed

corporate governance index of listed SOE around the world by using panel data in firm

level with multi-level which consists of country, industry, and firm level, and employs

matching firm and difference-in-differences methods to compare effect of corporate

governance in ownership structure to stock returns and performances.

The third part is to determine the relationship between corporate

governance and stock liquidity in Thailand. This part uses the same constructed

corporate governance index method to determine effect of corporate governance on

stock liquidity in multi-level effect in firm, industry, and corporate governance levels.

The contributions of this study are as follows. The first one is

comprehensive corporate governance spillover which covers a number of countries

around the world. This study highlights the dimension of corporate governance that has

the most impact on corporate governance transmission. Second, this study is the first

one that investigates corporate governance in all industry of listed companies. The last

contribution is to examine the relationship between corporate governance and liquidity

in microstructure level under emerging market condition.

1.4 Structure of dissertation

Chapter 2 examines corporate governance spillover among a group of

countries based on economic relevance (G7 and BRICS) and regional union (PIGS).

This chapter employs the Worldwide Governance Indicators (WGI) developed by the

World Bank to test corporate governance spillovers in six dimensions among the group

of these countries.

Chapter 3 investigates corporate governance and ownership structure

focusing on state-owned enterprise (SOE). This chapter uses difference-in-difference

(DiD) to determine effects of SOE on the independent variable and matching firm

methods to determine effects of SOE on non-experimental causal study.

Chapter 4 examines the relationship between corporate governance and

stock liquidity of listed companies in Thailand by using a panel analysis. This analysis

employs Random-effects Tobit Model and Fixed-effects Quantile Regression Model to

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assess corporate governance index of Thai Institute of Director (Thai IOD) and

illiquidity measure of Amihud (2002).

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CHAPTER 2

INTERNATIONAL CORPORATE GOVERNANCE SPILLOVER;

EVIDENCE FROM PANEL DATA

2.1 Introduction

Corporate governance has been used to rationalize the Asian financial crisis

in 1997, subprime crisis of mortgage-backed securities in the United States in 2008 and

European sovereign debt crisis in 2012 (See Aebi, Sabato, & Schmid (2012) and Van

Essen, Engelen, & Carney (2013) for a discussion of the role of good corporate

governance in the crisis). When the crisis occurs, a negative market disturbance usually

spreads from one country to the others. The process of co-movements in the financial

asset prices (e.g., exchange rats and stock prices) and international capital flows is

referred in the literature as financial contagion (See Bekaert, Ehrmansn, Fratzscher, &

Mehl (2014) and Popov & Van Horen (2015) for a literature of the contagion effect

during the crisis). When a country attempts to integrate its financial system into the

international financial markets and institutions through globalization, financial

contagion can be a potential risk.

However, there is a hero in every villain. A good corporate governance

paradigm is considered an effective means to minimize, if not avert, a possibility of a

crisis and eventually financial contagion (See Beltratti & Stulz (2012) and Van Essen

et al. (2013) for a discussion of the importance of good corporate governance to avert

the crisis). This paper aims to examine whether the good governance practices can

extend across the economically-relevant countries (e.g., G7 and BRICS) or regions

(e.g., PIGS). Studies of the spillovers of corporate governance practices can be at the

firm and industry level (e.g., Bris, Brisley, & Cabolis (2008), Cheng (2011), and

Holmstrom (1982)) or at the cross-countries level (e.g., Bris et al. (2008), Martynova

& Renneboog (2008), Bhagat, Malhotra, & Zhu (2011), and Martynova & Renneboog,

(2011)). This study is interested to understand if the governance practices can extend

across countries that are economically cohesive (e.g., G7 and BRICS) or regionally

unified (e.g., PIGS).

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Using the Worldwide Governance Indicators (WGI) developed by the

World Bank, the results show that there are governance spillovers among the G7,

BRICS and PIGS countries. The panel unit root tests suggest stationary panel time

series properties of the WGIs among the G7, BRICS and PIGS countries. The panel

cointegration tests confirm the spillover effect among the examining countries.

However, the panel VARs tests partially supports the governance spillover hypothesis.

Specifically, the results from the VARs suggest that the US has a governance-influence

over the PIGS and BRICS countries but has no impact on the G7 countries.

The paper is organized as follows. The next section reviews the literature

of corporate governance spillovers. Section 3 describes the data and methodology.

Section 4 discusses the empirical results and Section 5 contains summary and

conclusion.

2.2 Literature review

This section first explains a proxy for corporate governance practices used in this

study. It then discusses a literature on corporate governance spillovers and proposes a

hypothesis for the unit root, cointegration, and panel VAR tests.

2.2.1 Governance indicators as a proxy for corporate governance practices

Kaufmann, Kraay, & Zoido-Lobatón (1999b) provide evidence of a

positive relationship between governance and economic development outcomes in 150

countries. They use the Worldwide Governance Indicators (WGI) which covers over

200 countries and territories and measures six dimensions of governance: (1) Voice and

Accountability, (2) Political Stability and Absence of Violence/Terrorism, (3)

Government Effectiveness, (4) Regulatory Quality, (5) Rule of Law, and (6) Control of

Corruption. The data start in 1996 and reflect the perceptions of interested parties –

residents of a country, entrepreneurs, foreign investors, and civil society at large

regarding the quality of governance in a country. The aggregate indicators are based on

several underlying variables, taken from a wide variety of existing data sources (See

Kaufmann, Kraay, & Zoido (1999a, 1999b) for the governance dimensions measured

and aggregation methodology).

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This paper follows Kaufmann, Kraay, & Zoido (1999a, 1999b) and

Kaufmann, Kraay and Mastruzzi (2009) and use the WGI as a proxy for governance

practices in a country. The WGI captures the perceptions of 6 dimensions of

governance. First, Voice and Accountability captures the degree to which a country's

citizens are able to participate in selecting their government, as well as freedom of

expression, freedom of association, and a free media. Second, Political Stability and

Absence of Violence captures the likelihood that the government will be destabilized or

overthrown by unconstitutional or violent means (e.g., politically-motivated violence

and terrorism). Third, Government Effectiveness measures the quality of public and

civil services, degree of independence from political pressures, quality of policy

formulation and implementation, and credibility of the government's commitment to

such policies. Fourth, Regulatory Quality measures the ability of the government to

formulate and implement sound policies and regulations promoting private sector

development. Fifth, Rule of Law measures the extent to which agents have confidence

in and abide by the rules of society, and the quality of contract enforcement, property

rights, the police, and the courts, as well as the likelihood of crime and violence. Lastly,

Control of Corruption indicates the perceptions of the extent to which public power is

exercised for private gain.

The WGI is not without a limitation. Critics have focused on problems

of bias or lack of comparability of these indicators. For example, Thomas (2010)

focuses on whether the indicators have a construction validity and whether they

measure what they are supposed to measure. He argue that the WGI is based on personal

and untested notions of governance and that the WGI claim to measure governance; as

yet no evidence has been offered that this is true. Langbein and Knack (2010) also argue

that the WGI is tautological and is not measuring what claims to measure. However,

this study believes that the WGI is a reasonable proxy for the country’s governance

practices as mentioned in (Kaufmann, Kraay, & Mastruzzi, 2010).

2.2.2 Corporate governance spillovers

Corporate governance cross-firm spillovers can be explained by the

model of career concerns (Cheng, 2011; Holmstrom, 1982; Meyer & Vickers, 1997).

According to the career concerns model suggested by Cheng (2011), managers of the

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two firms compare each other’s performance and decide how much to inflate earnings

based on their performance differential. The competing managers, who are motivated

by their own career concerns, are more likely to inflate their own earnings to boost up

the stock prices1.

Based on the micro foundation of spillovers, the corporate governance

practices can extend across the borders due to, for instance, the economic integration,

cross-border mergers and acquisitions and financial market integration. For the past

decades, free trade agreements among countries in the same region and across regions

have been widely established and implemented (e.g., the European Union (EU),

ASEAN Economic Community (AEC) and The North American Free Trade Agreement

(NAFTA), resulting in an increased level of economic integration among the groups of

countries under the agreements have rapidly increased2. In addition, firms in the

countries under the free trade agreement have been integrated through the mergers and

acquisitions (M&A). Corporate governance of the firms can then be spillover between

the two merged firms3. With the higher level of international trade and economic

integration among countries, the cross-country M&As have enforced the notion of the

corporate governance spillovers. That is, corporate governance spillovers can spread

between the two countries through the spillover of the two mergers firms.

Financial market integration among countries have also largely

increased through the world financial market during the past decades. The financial

integration allows cross-border capital flows and cross-border listings in foreign stock

1 The inflating earnings minimize bad perceptions on their performance (Holmstrom, 1982; Meyer &

Vickers, 1997; Stein, 1989). Graham, Harvey, & Rajgopal (2005) found evidence from survey study

supporting that career concerns and external labor market reputations are the first concern for managers.

Gibbons & Murphy (1990) and Jenter & Kanaan (2015) revealed the significant relationship between

relative performance evaluation in stock price performance and observably poor labor market outcomes such as being fired. 2 Petri, Plummer, & Zhai (2012) found that ASEAN market, especially ASEAN-5, had been integrated

and converged in term of economic growth, both productivity and unemployment. Trade among ASEAN

has increased from about 18% in 1985 to more than 30% in 2015. 3 Mostly, the acquirer firm with high level of corporate governance transfers its governance practice to

the target firm with lower level of corporate governance. Bris et al. (2008) revealed that the spillover of

corporate governance had been implemented via transfer of accounting standards and shareholder

protection improve Tobin's Q. Based on the law hypothesis, positive spillover states that spillover of

corporate governance caused by M&A spreads from high level bidder firm to improve the low level

corporate governance of target firm (Goergen & Renne, Lim, Brooks, & Hinich, 2008; Martynova &

Renneboog, 2008)

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markets. For example, Liao & Ferris (2015) examined the intra-industry spillover from

the cross-listing firms by explaining that foreign cross-listing firm must comply with

SEC and exchange regulation indicating higher level of corporate governance. All in

all, there is strong evidence that the level of governance practices can extend across the

borders from one country to the other.

2.2.3 Hypothesis development

This paper hypothesizes that the cross-country corporate governance

spillover – caused by the economic integration, financial market integration, cross-

border M&As, and cross-listing of the foreign companies – exists. This study focuses

on three major economic group countries, i.e., the G7, BRICS and PIGS countries and

tests whether there exist corporate governance spillovers among these groups of

countries.

Hypothesis: There exists corporate governance spillover among countries.

This study focuses on three major economic group countries, including

G7, BRICS, and PIGS countries.

2.3 Data description and research methodology

2.3.1 Data

2.3.1.1 Governance indices

While other studies focus on corporate governance at the firm

level based on several aspects, such as, the sustainability index, individual governance

index, Environmental, Social, and Governance of corporate (ESG) by Thomson Reuters

Corporate Responsibility Rating, this study focuses on the country level using the

Worldwide Governance Indicators (WGI) of the World Bank.4 Kaufmann, Kraay, &

Mastruzzi (2011) employed the unobserved components model (UCM) to create WGI.5

They grouped 31 sources of survey data into six dimensions of governance. The initial

data consists of annual data of 215 countries with six dimensions of governance: Voice

4 For the data sources of WGI, see Appendix A. 5 For methodology that constructed WGI, see Kaufmann et al. (2011)

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and Accountability, Political Stability and Absence of Violence/Terrorism, Government

Effectiveness, Regulatory Quality, Rule of Law, and Control of Corruption.

The WGI data are based on the survey of participant perceptions

of governance. The WGI is constructed from many sources of expert survey, for

example, the Global Competitiveness survey, BERI survey, and Economic Freedom

Index Poll. The sample excludes missing data and countries that do not have the stock

exchanges. The aggregate governance indicators are available from 1996-2014. The

remaining sample is 182 countries and the period of study is 1996-2014. To provide an

economic justification on the governance spillovers of the economic/financial

integration, this study uses 3 groups of sample countries including the G7, BRICS and

PIGS countries as follows.

Table 2.1 Countries by group of G7, PIGS, and BRICS

G7 PIGS BRICS

Canada Portugal Brazil

France Italy Russia

Germany Ireland India

Italy Greece China

Japan Spain South Africa

United Kingdom

United States

2.3.1.2 Stock market indices

The annual stock market index returns are calculated from

daily closing stock index price. The stock market indices are collected from Thomson

Reuters Eikon.

2.3.2 Methodology

Methods of the study in this study consist of (i) testing methods of

international corporate governance spillover; and (ii) testing method of the relationship

corporate governance spillover.

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2.3.2.1 Testing methods of international corporate governance

spillover

This study employs a panel unit root test. This study

hypothesizes that a cross-country corporate governance spillover exists among

countries with the same economic groups. If so, the panel time series using the annual

WGI of these groups (G7, BRICS and PIGS) should be stationary. These tests assume

that cross-sectional (countries) WGIs in the panel are all independent or there is no

relationship among the cross-sectional (countries) WGIs. The tests include the LLC

test (Levin, Lin, & Chu (2002).

The LLC test computes the test statistic by averaging single

time-series Augmented Dickey Fuller (ADF) t-tests of all cross-sectional units

(countries) assuming homogenous across cross-sectional units (countries). The null

hypothesis is that each individual country CG time series contains a unit root against

the alternative that each country CG time series is stationary. The testing model is

, 1

1

ip

it i i t iL it L im imt it

L

CG CG CG d − −

=

= + + + (1)

where: i represents countries which 1,2,3, ,i k= , mtd is vector of deterministic

variables, 1,2,3m = , 1 { }td empty set= , 2 {1}td = , 3 {1, }td t= , and mi is vector of

coefficients.

The test procedures can be divided into three steps. The first

step begins by performing separate augmented Dickey-Fuller (ADF) (equation (1)) for

each i cross-sectional country currency where lag order ip can be varied across i. For

given time T, optimal lag ip can be determined. The two regression models are then

estimated (i) using itCG as dependent variable and it LCG − (for all L=1,…, pi) and

imtd as independent variables to obtain residual ite and (ii) using 1itCG − as dependent

variable and it LCG − (for all L=1,…, pi) and imtd as independent variables to get

residual 1itv − . Then, standardized values of the two residuals should be computed as

ˆ ît it ie e = and , 1

ˆ î t it iv v − = , where ˆ

i is standard error from each ADF test.

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The second step is to compute the ratio of long-run to short-run

standard deviations. The long-run variance can be computed as:

2 2

, 1

2 1 2

1 1ˆ 2

1 1i

T K T

CG it it i tKLt L t L

CG w CG CGT T

−

= = = +

= + − −

(2)

where: K is optimal truncated lag and ( )( )1 1KL

w L K= − + . Then, ratio of long- run

standard deviation to innovation standard deviation is computed as ˆ ˆ î ii Rs = and

average standard deviation can also be computed as 1

1ˆ

N

i

i

S sN =

= which N=k countries.

The last step is to compute the panel unit root test statistics by

estimating pooled regression based on NT observations of

, 1it i t ite v −= + (3)

where: 1T T p= − − and 1

N

i

i

p p N=

= .

Then, the panel unit root t-test for 0 : 0H = can be computed:

ˆ

ˆ

ˆt

= (4)

where: 2

, 1 , 1

1 2 1 2

ˆ

i i

N T N T

i t it i t

i t p i t p

v e v − −

= = + = = +

= and

1 2

2

ˆ , 1

1 2

ˆ î

i

TN

i t

i t p

v −

= = +

=

and ( )22

, 1

1 2

1ˆˆ

i

N T

it i t

i t p

e vNT

−

= = +

= −

Finally, to obtain asymptotic property, the adjusted t-statistic

can be computed:

2 *

ˆ*

*

ˆ ˆ ˆ(0,1)

N mT

mT

t NTSt N

−−= (5)

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where: *

mT and

*

mT are the mean and standard deviation adjustments obtained from

LLC computations. As a result, the *t is asymptotically distribution as (0,1)N .

Note that the LLC test has also been claimed that its limitations

are caused by cross-sectional independent assumption and test only no unit-root of all

cross-sectional units.

2.3.2.2 Panel-data cointegration tests

This study also employs a panel cointegration test and the

multivariate panel vector autoregressive models (XTVAR). Based on the economic and

financial market integration, this study hypothesizes that a cross-countries corporate

governance spillover exists among these groups of countries. If so, cointegrating

relationship between the domestic governance and foreign (US in this study)

governance should be found. Thus, a test in this section include a panel cointegration

test using the panel annual WGIs against the US’s WGI.

To test the governance spillover from the foreign (US) country

to other countries, a panel cointegration test between the panel of WGIs of other

countries and WGI of the US is applied to test the existence of long-run relationship

between the two series. The Pedroni (2004) test (based on Engle-Granger) is employed

to test the long-run cointegrated relationship between the two WGI panel series by

assuming the asymptotic and finite sample properties of the panel data. Consider the

long-run relationship.

To test the governance spillover from the foreign (US) country

to other countries, a panel cointegration test between the panel of WGIs of other

countries and WGI of the US is applied to test the existence of long-run relationship

between the two series. The Pedroni (2004) test (based on Engle-Granger) is employed

to test the long-run cointegrated relationship between the two WGI panel series by

assuming the asymptotic and finite sample properties of the panel data. Consider the

long-run relationship.

it i i USt itCG CG = + + (6)

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where: i and i are cointegrating equation parameters, which may or may not be

homogeneous across i.

Based on Pedroni (1996), the between-dimension, group-mean

panel Fully Modified Ordinary Least Squares (FMOLS) can be estimated as

1

1

ˆ î

N

GFM FMOLS

i

N −

=

= (7)

where: îFMOLS is the conventional time series FMOLS estimator (Phillips & Hansen,

1990) for country i. Then, t-statistic for the between-dimension estimator can be

computed as

1 2

ˆ ˆ

1GFM FMOLSi

N

i

t N t

−

=

= (8)

where: ˆFMOLSi

t

is t-statistic of FMOLS estimator îFMOLS .

In order to reconfirm the results, this study also employs panel

VARs to determine interdependence and dynamic relationship between economic and

financial market integration and CG index of among the countries within the same

group.

Currently, there are two methods of estimation for panel VARs,

including, Least Squares Dummy Variable (LSDV) procedure known as XTVAR

(Cagala & Glogowsky, 2014), and panel Generalized Method of Moments (PGMM)

known as PVAR (Abrigo & Love, 2016).

PGMM requires asymptotic properties and suitable for large

size sample with more cross-sectional units (i) and long length period of time (t) (Bun

& Kiviet, 2006) while XTVAR employs least squares dummy variable technique, which

is suitable for panel data with less number of cross-sectional units. Since this study

emphasized on G7, BRICS, and PIGS, which consists of only 5-7 countries (cross-

sectional units), XTVAR is applied since it is more appropriate for finite data than

PGMM. Shank & Vianna (2016) uses XTVAR to investigate investors’ behavior in ETFs

that their data are similar to this study. Their data have the fixed number of ETFs and

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time tends to be increasing. Therefore, this study uses XTVAR, the dynamic spillover

is analyzed by Panel Vector Autoregressions based on Cagala & Glogowsky (2014).

The panel VAR model can be stated as:

, , 1 , 211 11 12 12

, , 1 , 221 21 22 22

1 1 , , 1 ,1

2 2 , , 2 ,2

...

i t i t i t

j t j t j t

p p i t p i t i ti

p p j t p j t i ti

CG CG CG

CG CG CG

CG Dumf

CG Dumf

− −

− −

−

−

= +

+ + + +

, (9)

where ,i tCG is corporate governance index of country i at period t. The panel index i

represents k countries of the three group countries. The optimal lag length p is obtained

from LLC unitroot test. fi is unobserved fixed effect of the panel model, which

represents the effects of each country.

2.4 Results and discussion

2.4.1 Data and descriptive statistics

Table 2.2 shows descriptive statistics of all six aspects (including,

Voice & Accountability, Political Stability, Government Effectiveness, Regulatory

Quality, Rule of Law, and Control of Corruption) of CG index of G7, BRICS, and PIGS

countries. Panel A illustrates Voice and Accountability aspect, Panel B reveals Political

Stability and Absence of Violence/Terrorism aspects, Panel C shows Government

Effectiveness aspects, Panel D states Regulatory Quality aspect, Panel E illustrates Rule

of Law aspects, and Panel F shows Control of Corruption aspect. Based on group

evaluation, mean values of each indicates the same direction that mean of CG index of

G7 and PIGS are mostly at the same level for all six aspects while mean of CG index

of all aspects of BRICS are deviated from each other. G7 countries have average CG

index higher than those of the two groups countries. BRICS countries which are

differences in terms of location, economic background, and culture show mean

differences among CG index of all aspects. And figure 2.1 shows graph comparision of

average of corporate goverance index of all six aspects.

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Table 2.2 Descriptive statistics

Panel A Voice and Accountability

Country Mean Median SD Min Max

Brazil 1.4924 1.4511 0.0910 1.3757 1.6752

Canada 1.2411 1.2203 0.1076 1.0885 1.4745

China 1.3757 1.3619 0.0556 1.2911 1.4728

France 1.0128 1.0235 0.0766 0.8913 1.1555

Germany 1.0045 1.0222 0.0636 0.8888 1.0999

Greece 1.3356 1.3144 0.0897 1.1976 1.6109

India 1.2571 1.2287 0.1729 0.9967 1.5407

Ireland 1.3818 1.3721 0.0939 1.2247 1.6146

Italy 0.9053 0.9463 0.1625 0.5649 1.1355

Japan 1.1616 1.1457 0.1191 0.9657 1.3275

Portugal 0.3869 0.4212 0.1328 0.0924 0.5297

Russia -0.7420 -0.8581 0.2350 -1.0423 -0.2984

South Africa 0.3913 0.4076 0.0544 0.2574 0.4502

Spain -1.5359 -1.5743 0.1262 -1.6816 -1.2854

United Kingdom 0.6516 0.6384 0.1015 0.5516 0.8740

United States 1.1988 1.1275 0.1237 1.0503 1.3659

Panel B Political Stability and Absence of Violence/Terrorism


Brazil 1.0179 1.0300 0.1136 0.7906 1.1762

Canada 0.5439 0.5523 0.1820 0.1751 0.8502

China 0.9235 0.9279 0.2023 0.5454 1.3246

France 0.5766 0.5009 0.2407 0.2750 1.1261

Germany 1.0063 0.9902 0.0977 0.8365 1.1895

Greece 0.4832 0.4480 0.2738 0.0947 0.9834

India 0.9756 0.9382 0.2460 0.7019 1.3586

Ireland 1.1662 1.1518 0.1943 0.8772 1.4961

Italy 0.2924 0.4565 0.3665 -0.2240 0.7943

Japan -0.0064 0.0100 0.2697 -0.4656 0.4193

Portugal -0.1142 -0.1840 0.2108 -0.3779 0.2860

Russia -1.0146 -0.9320 0.2344 -1.4622 -0.7360

South Africa -1.1617 -1.1647 0.1589 -1.5269 -0.9124

Spain -0.4635 -0.4787 0.1260 -0.6572 -0.1665

United Kingdom -0.1332 -0.0944 0.2025 -0.5774 0.1986

United States 0.4653 0.5244 0.3362 -0.1960 1.0132

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Table 2.2 Continued

Panel C Government Effectiveness


Brazil 1.8553 1.8339 0.0962 1.7523 2.0131

Canada 1.5451 1.5355 0.1426 1.3293 1.8147

China 1.6367 1.5809 0.1508 1.4013 1.9312

France 0.5430 0.4489 0.2136 0.2136 0.8687

Germany 1.3762 1.4376 0.2231 0.9566 1.8190

Greece 1.7025 1.6902 0.1552 1.4708 1.9165

India 1.0683 1.0710 0.1006 0.8826 1.2273

Ireland 1.5643 1.5717 0.1278 1.3363 1.7772

Italy 0.6221 0.6312 0.1543 0.3075 0.8344

Japan 1.3037 1.1501 0.3626 0.8907 1.8988

Portugal -0.0725 -0.0975 0.1072 -0.2299 0.1797

Russia -0.4349 -0.4114 0.1604 -0.7660 -0.0785

South Africa -0.0772 -0.0829 0.0838 -0.2044 0.1110

Spain 0.0269 0.0032 0.1405 -0.2483 0.3393

United Kingdom 0.5395 0.5107 0.1547 0.3252 0.8765

United States 1.6198 1.6028 0.1194 1.4575 1.8426

Panel D Regulatory Quality


Brazil 1.6183 1.6222 0.1014 1.4258 1.8309

Canada 1.1330 1.1843 0.1497 0.8075 1.3109

China 1.5127 1.5262 0.1042 1.2184 1.6951

France 0.8714 0.9053 0.1259 0.6614 1.0925

Germany 1.0081 1.1023 0.2381 0.4841 1.2597

Greece 1.7718 1.7608 0.1263 1.5931 2.0229

India 1.0206 1.0741 0.2162 0.6337 1.2896

Ireland 1.6887 1.6536 0.1241 1.5351 1.9169

Italy 0.7408 0.8082 0.1911 0.3450 0.9980

Japan 1.1601 1.1903 0.1603 0.7772 1.3535

Portugal 0.1472 0.0957 0.1614 -0.0726 0.4120

Russia -0.3331 -0.3587 0.1126 -0.5640 -0.1130

South Africa -0.3560 -0.3680 0.0926 -0.4807 -0.1584

Spain -0.2448 -0.2351 0.1020 -0.5306 -0.1294

United Kingdom 0.4840 0.4060 0.1585 0.2672 0.7784

United States 1.5070 1.5596 0.1460 1.2524 1.7394

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Table 2.2 Continued

Panel E Rule of Law


Brazil 1.7430 1.7415 0.0691 1.6328 1.8925

Canada 1.4141 1.4302 0.0718 1.1967 1.5115

China 1.6547 1.6272 0.0757 1.5654 1.8522

France 0.5225 0.4279 0.1998 0.3370 0.9822

Germany 1.3129 1.3188 0.1006 1.1359 1.5987

Greece 1.6835 1.6690 0.0789 1.5468 1.8870

India 1.1176 1.0885 0.1158 0.9529 1.2941

Ireland 1.6484 1.6931 0.1109 1.4666 1.8010

Italy 0.7000 0.7450 0.1906 0.3448 0.9762

Japan 1.1638 1.1456 0.1266 0.9370 1.3918

Portugal -0.2680 -0.3069 0.1577 -0.4924 -0.0037

Russia -0.8709 -0.8687 0.1058 -1.1265 -0.7114

South Africa 0.0652 0.0625 0.1404 -0.1119 0.2896

Spain -0.4202 -0.4328 0.0691 -0.5473 -0.3219

United Kingdom 0.0951 0.0910 0.0584 -0.0126 0.2372

United States 1.5539 1.5628 0.0581 1.4307 1.6298

Panel F Control of Corruption


Brazil 2.0161 1.9946 0.1345 1.8178 2.2386

Canada 1.3713 1.3678 0.0783 1.2391 1.5221

China 1.8447 1.8072 0.1312 1.6976 2.1648

France 0.2802 0.3326 0.2500 -0.1100 0.7208

Germany 1.3138 1.2712 0.2570 0.8569 1.7303

Greece 1.8507 1.7571 0.2412 1.5605 2.2409

India 1.0885 1.0479 0.1710 0.8846 1.5229

Ireland 1.5885 1.5783 0.1491 1.2967 1.7933

Italy 0.2293 0.2946 0.3718 -0.2546 1.0572

Japan 1.1081 1.0799 0.2292 0.5260 1.3734

Portugal -0.0541 -0.0463 0.1243 -0.3783 0.1457

Russia -0.9339 -0.9432 0.1164 -1.0876 -0.7105

South Africa -0.4393 -0.4303 0.0891 -0.5728 -0.2830

Spain -0.4692 -0.5259 0.1424 -0.6537 -0.2405

United Kingdom 0.2803 0.2778 0.2945 -0.1653 0.7609

United States 1.4853 1.3935 0.2336 1.2597 2.0114

Table 2.2 shows descriptive statistics of six dimensions of corporate

governance index of 16 countries of G7, BRICS, and PIGS from 1998 to 2014.

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Figure 2.1 Average of six dimensions of corporate governance index of 16 countries,

1998-2014

2.4.2 Testing results of international corporate governance spillover

Panel unit root tests of CG index of the countries are performed by

grouping countries into three groups, G7, BRICS, and PIGS.

Table 2.3 Panel Unitroot test

Group Variables Adjusted t delta star

G7 Voice & Accountability -2.6109 ***

G7 Political Stability -8.3776 ***

G7 Government Effectiveness -1.6778 **

G7 Regulatory Quality -0.1972

G7 Rule of Law -1.6103 *

G7 Control of Corruption -2.6279 ***

PIGS Voice & Accountability -0.4565

PIGS Political Stability -2.5185 ***

PIGS Government Effectiveness -1.3146 *

PIGS Regulatory Quality 1.8646

PIGS Rule of Law -1.5527 *

PIGS Control of Corruption -0.5782

BRICS Voice & Accountability -6.0127 ***

BRICS Political Stability -3.2020 ***

BRICS Government Effectiveness -0.8396

BRICS Regulatory Quality -1.5006 *

BRICS Rule of Law -2.2306 **

BRICS Control of Corruption -1.5306 *

-10

-5

0

5

10

15

Average of six dimensions of corporate governance

index of 16 countries, 1998-2014

Voice and Accountability Political Stability Government Effectiveness

Regulatory Quality Rule of Law Control of Corruption

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Table 2.3 presents the panel unitroot test (LLC) of governance indexes

of six dimensions in the G7, BRICS and PIGS countries. The Adjusted t delta star

provides tests for no unit-root of all cross-sectional units. The The Adjusted t delta star

equation is

2 *

ˆ*

*

ˆ ˆ ˆ(0,1)

N mT

mT

t NTSt N

−−= . The ***, **, and * indicate statistical

significance at the 1%, 5%, and 10% level, respectively.

Table 2.3 shows panel unit root tests of all six aspects (including,

Voice & Accountability, Political Stability, Government Effectiveness, Regulatory

Quality, Rule of Law, and Control of Corruption) of the CG index of G7, BRICS, and

PIGS countries. The results reveal that panel unit root tests of four aspects including

Voice & Accountability, Political Stability, Government Effectiveness, and Control of

Corruption, of CG index of G7 countries group are stationary, thus, the four aspects

have corporate governance spillover among G7 countries, except Rule of Law and

Regulatory Quality, which are nonstationary, indicating no CG spillover of these two

aspects. The test results of CG index of BRICS countries show stationary properties of

only three aspects including Voice & Accountability, Political Stability, and Rule of

Law indicating the CG spillover on these three aspects are still valid while the other

two aspects are not. However, the tests of stationarity of CG index of PIGS show less

significantly level of the panel unit roots. Only one aspect, Political Stability, shows

significant result indicating stationary of this aspect. Political Stability aspect of CG

index has spread and spillover thought out the peer firm.

2.4.3 Testing results of relationship between financial integration and

corporate governance spillover

The tests are divided into two testing methods, including, panel

cointegration tests and panel VARs.

2.4.4 Testing results of panel cointegration

Test results of panel cointegration are divided into three panel

cointegration testing methods, including Kao test, Pedroni test, and Westerlund test.

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Table 2.4 Panel-data cointegration, KAO test of BRICS country

Corporate Governance

dimensions

Modified

Dickey-Fuller t Dickey-Fuller t

Augmented

Dickey-Fuller t

Unadjusted

modified

Dickey-Fuller t

Unadjusted

Dickey-Fuller t

Voice and Accountability 0.1305 -2.5980 *** 0.4072 -1.7262 ** -3.8482 ***

Political Stability -4.5599 *** -4.5358 *** -3.4739 *** -4.5599 *** -4.5358 ***

Government Effectiveness -1.6547 ** -1.6166 * 0.2405 -2.4159 *** -1.9263 **

Regulatory Quality -2.3058 ** -3.2272 *** -2.5552 *** -2.9382 *** -3.4308 ***

Rule of Law -2.1242 ** -2.1902 ** 0.3534 -1.9952 ** -2.1454 **

Control of Corruption -2.5200 *** -2.0565 ** 0.1159 -2.5200 *** -2.0565 **

Table 2.4 presents panel-data cointegration test null hypothesis of non-

stationarity of corporate governance index of six dimensions in BRICS country,

including Brazil, Russia, India, China, and South Africa. The KAO test provides

Modified Dickey-Fuller t, Dickey-Fuller t, Augmented Dickey-Fuller t, Unadjusted

modified Dickey-Fuller t, Unadjusted Dickey-Fuller t, which are tests for stationary of

ite in cointegration equation it it i it i itCG UScg z e = + + . ***, **, and * indicate

statistical significance at the 1%, 5%, and 10% level, respectively.

Table 2.4 shows Panel-data cointegration using KAO testing method

of CG index of BRICS country. Table 2.5 illustrates Panel-data cointegration using

Pedroni testing method of CG index of BRICS country. Table 2.6 reveals Panel-data

cointegration using Westerlund testing method of CG index of BRICS country. The test

results of KAO testing method and Pedroni testing method confirm panel cointegrating

equations of all aspects of CG index of BRICS countries and those of US country exist.

However, Westerlund testing method detect only two aspects, including, Voice and

Accountability aspect and Government Effectiveness aspect.

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Table 2.5 Panel-data cointegration, Pedoni test of BRICS country


dimensions

Modified variance

ratio

Modified Phillips-Perron t

Phillips-Perron t Augmented

Dickey-Fuller t

Voice and Accountability -5.9377 *** -2.1261 ** 10.5771 *** 15.5371 ***

Political Stability 1.6516 ** -1.1040 -2.9380 *** -2.8384 ***

Government Effectiveness 0.7887 -1.7058 ** -2.4352 *** -2.1608 **

Regulatory Quality 0.1054 -1.0454 -2.6633 *** -2.4490 ***

Rule of Law -0.3240 -1.2109 -2.4549 *** -3.6267 ***

Control of Corruption -0.5046 -0.8209 -1.7587 ** -1.6814 **

Table 2.5 presents the panel-data cointegration test of the null

hypothesis of non-stationarity of governance indexes of six dimensions in the G7,

BRICS and PIGS countries. The Pedroni test provides the Phillips-Perron t-test, which

are tests for a stationary of itein the cointegration equation it it i it i itCG UScg z e = + +

.

The ***, **, and * indicate statistical significance at the 1%, 5%, and 10% level,

respectively.

Table 2.6 Panel-data cointegration, Pedroni test of BRICS country


dimensions

Modified

variance ratio

Modified

Phillips-Perron t

Phillips-

Perron t

Augmented

Dickey-Fuller t

Voice and Accountability -5.9377 *** -2.1261 ** 10.5771 *** 15.5371 ***


Government Effectiveness 0.7887 -1.7058 ** -2.4352 *** -2.1608 **


Rule of Law -0.3240 -1.2109 -2.4549 *** -3.6267 ***

Control of Corruption -0.5046 -0.8209 -1.7587 ** -1.6814 **



including Brazil, Russia, India, China, and South Africa. The Pedroni test provides

Modified variance ratio, Modified Phillips-Perron t, Phillips-Perron t, Augmented

Dickey-Fuller t, which are tests for stationary of ite in cointegration equation

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it it i it i itCG UScg z e = + + . ***, **, and * indicate statistical significance at the 1%,

5%, and 10% level, respectively.

Table 2.7 Panel-data cointegration, Westerlund test of BRICS country

Corporate Governance dimensions Variance ratio

Voice and Accountability -1.8642 **

Political Stability -0.2314

Government Effectiveness -1.7373 **

Regulatory Quality -1.4862 *

Rule of Law -0.5463

Control of Corruption -0.9509



including Brazil, Russia, India, China, and South Africa. The Westerlund test provides,

which is test for stationary of ite in cointegration equation it it i it i itCG UScg z e = + + .

***, **, and * indicate statistical significance at the 1%, 5%, and 10% level, respectively.

Table 2.8 Panel-data cointegration, KAO test of PIGS country


dimensions

Modified

Dickey-

Fuller t

Dickey-

Fuller t

Augmented

Dickey-

Fuller t

Unadjusted

modified

Dickey-

Fuller t

Unadjusted

Dickey-

Fuller t

Voice and Accountability 0.3836 0.2959 -0.4320 -0.8053 -0.5828

Political Stability -0.7471 -1.7377 ** -2.3834 *** -0.7471 -1.7377 **

Government Effectiveness -1.5885 * -2.1292 ** -1.4239 * -2.6676 *** -2.5654 ***

Regulatory Quality -3.6059 *** -3.1889 *** -2.4040 *** -4.0619 *** -3.3042 ***

Rule of Law -0.7368 -0.9813 0.4972 -0.7368 -0.9813

Control of Corruption -1.1606 -1.2113 0.6837 -1.1606 -1.2113


stationarity of corporate governance index of six dimensions in PIGS country, including

Portugal, Italy, Greece, and Spain. The KAO test provides Modified Dickey-Fuller t,

Dickey-Fuller t, Augmented Dickey-Fuller t, Unadjusted modified Dickey-Fuller t,

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Unadjusted Dickey-Fuller t, which are tests for stationary of ite in cointegration

equation it it i it i itCG UScg z e = + + . ***, **, and * indicate statistical significance at

the 1%, 5%, and 10% level, respectively.

Table 2.9 Panel-data cointegration, Pedroni test of PIGS country


dimensions

Modified

variance ratio

Modified Phillips-

Perron t

Phillips-

Perron t

Augmented Dickey-

Fuller t

Voice and Accountability -0.9702 -0.2139 -0.7228 -0.6850

Political Stability -1.1141 1.1298 -0.2138 -0.8856

Government Effectiveness -0.9735 -1.2089 -2.5823 *** -2.8050 ***

Regulatory Quality -0.7684 -2.0953 ** -3.0954 *** -3.8420 ***

Rule of Law -0.6857 -0.2819 -1.7074 ** -3.5510 ***

Control of Corruption -1.0484 -0.8957 -2.6119 *** -1.8599 **


stationarity of corporate governance index of six dimensions in PIGS country, including

Portugal, Italy, Greece, and Spain. The Pedroni test provides Modified variance ratio,

Modified Phillips- Perron t, Phillips- Perron t, Augmented Dickey- Fuller t, which are

tests for stationary of ite in cointegration equation it it i it i itCG UScg z e = + + . ***, **,

and * indicate statistical significance at the 1%, 5%, and 10% level, respectively.

Table 2.7 shows Panel-data cointegration using KAO testing method

of CG index of PIGS country. Table 2.8 illustrates Panel-data cointegration using

Pedroni testing method of CG index of PIGS country. Table 2.9 reveals Panel-data

cointegration using Westerlund testing method of CG index of PIGS country.The test

results of KAO testing method, Pedroni testing method, and Westerlund testing method

confirm panel cointegrating equations of three aspects, including, Political Stability,

Government Effectiveness, and Regulatory Quality of CG index of PIGS countries and

those of US country exist.

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Table 2.10 Panel-data cointegration, KAO test of G7 country

Corporate

Governance

dimensions

Modified

Dickey-

Fuller t

Dickey-

Fuller t

Augmented

Dickey-

Fuller t

Unadjusted

modified

Dickey-

Fuller t

Unadjusted

Dickey-

Fuller t

Voice and Accountability -2.7407 *** -3.0543 *** -2.8143 *** -3.6090 *** -3.3333 ***

Political Stability -5.3351 *** -5.1080 *** -6.2044 *** -5.3351 *** -5.1080 ***

Government Effectiveness -0.4666 -0.4601 -1.6159 * -0.8206 -0.6727

Regulatory Quality -1.1564 -1.7153 ** -3.5965 *** -2.6872 *** -2.4284 ***

Rule of Law -1.2679 -1.0664 -0.9111 -1.6617 ** -1.2542

Control of Corruption -0.2069 -0.6686 -0.4395 -0.2069 -0.6686

Table 2.10 presents panel-data cointegration test null hypothesis of

non-stationarity of corporate governance index of six dimensions in G7 country,

including Canada, France, Germany, Italy, Japan, the United Kingdom and the United

States. The KAO test provides Modified Dickey-Fuller t, Dickey-Fuller t, Augmented

Dickey-Fuller t, Unadjusted modified Dickey-Fuller t, Unadjusted Dickey-Fuller t,

which are tests for stationary of ite in cointegration equation it it i it i itCG UScg z e = + +

. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% level,

respectively.

Table 2.11 Panel-data cointegration, Pedroni test of G7 country


dimensions

Modified

variance ratio

Modified Phillips-

Perron t

Phillips-

Perron t

Augmented Dickey-

Fuller t

Voice and Accountability 0.2893 -1.2914 * -2.5963 *** -2.4112 ***


Government Effectiveness -3.7686 *** -4.8416 *** 15.8355 *** 14.8547 ***


Rule of Law 0.7689 -1.0146 -1.9406 ** -3.0982 ***

Control of Corruption -1.0823 -1.1351 -2.9233 *** -3.1471 ***




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States. The Pedroni test provides Modified variance ratio, Modified Phillips-Perron t,

Phillips-Perron t, Augmented Dickey-Fuller t, which are tests for stationary of ite in

cointegration equation it it i it i itCG UScg z e = + + . ***, **, and * indicate statistical

significance at the 1%, 5%, and 10% level, respectively.

Table 2.12 Panel-data cointegration, Westerlund test of G7 country

Corporate Governance dimensions Variance ratio

Voice and Accountability -1.8680 **

Political Stability 1.1168

Government Effectiveness -1.4844 *

Regulatory Quality -1.7975 **

Rule of Law -1.2858 *

Control of Corruption -0.4763




States. The Westerlund test provides, which is test for stationary of ite in cointegration

equation it it i it i itCG UScg z e = + + . ***, **, and * indicate statistical significance at

the 1%, 5%, and 10% level, respectively.

Table 2.10 shows Panel-data cointegration using KAO testing

method of CG index of G7 country. Table 2.11 illustrates Panel-data cointegration

using Pedroni test of CG index of G7 country. Table 2.12 shows Panel-data

cointegration using Westerlund test of CG index of G7 country. The test results of KAO

testing method, Pedroni testing method, and Westerlund testing method confirm panel

cointegrating equations of three aspects, including, Political Stability, Government

Effectiveness, and Regulatory Quality of CG index of G7 countries and those of US

country exist.

The test results of Pedroni testing method confirm panel cointegrating

equations of all aspects of CG index of G7 countries and those of US country exist.

However, KAO testing method and Westerlund testing method detect only two aspects,

including, Voice and Accountability aspect and Regulatory Quality aspect.

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For regulatory quality in BRICS and PIGS, government effectiveness,

and rule of law are highly driven by individual countries characteristics. As a result,

they are insignificant and unlikely to spillover. Regulatory quality, which are

nonstationary, indicating there is no corporate governance spillover of two aspects. The

test results of corporate governance index of BRICS countries show stationary

properties of only three aspects including Voice & Accountability, Political Stability,

and Rule of Law indicating the CG spillover on these three aspects are still valid while

the other two aspects are not.

The test results of Pedroni testing method confirm panel cointegrating

equations of all aspects of CG index of G7, PIGS, and BRICS countries and those of

US country exist. G7 and BRICS are well cointegrated in all dimension, according to

the same level of economy, developed country and emerging market.

However, in PIGS country, Voice and Accountability and Political

Stability and Absence of Violence/Terrorism dimension are not cointegrating within

the country group. According to the different within country group such as economics

level, the group consists of developed and developing countries. According to panel

unitroot test and panel cointegration test in table 2.3 to table 2.12, the results of mean

reversion testing show that corporate governance index of group of country G7, PIGS,

and BRICS are stationary, except regulatory quality, government effectiveness, and

rule of law of G7 and PIGS in panel cointegration using Pedoni test. Which mean that

the three significant dimensions of corporate governance can be spillover within group

of country. The implication of this study is corporate governance practices can be

improved by voice of accountability, control of corruption, and political stability. There

exists spillover in term of Political Stability, the group of counties that share the similar

economy and political conditions. According to Panel cointegration and unitroot test,

the results partially confirm that there exists corporate governance spillover.

2.4.5 Testing results of panel VARs

In order to confirm the results of CG spillover among G7, BRICS, and

PIGS countries, this study employs Panel VARs in order to determine the interdepence

and dynamic relationship of CG index among countries within the same group and CG

index of foreign (US) countries. Table 2.13 shows the estimated results of Multivariate

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Panel vector autoregressive models (XTVAR) between CG index among countries

within G7, BRICS, and PIGS group and CG index of foreign (US) countries.

For G7 country group, corporate government dimension of Voice and

Accountability, Political Stability, and Absence of Violence/Terrorism dimension of

US statistically significant positive effect to those corporate government of G7. The

spillover support by that US is part a of G7 country which normally utilize the same

economic policy and join in many military operations, US has leading country in G7

both economy and military policy, and US is a country who set standard and policy of

corporate governance, financial standard, and military movement. While Control of

Corruption of G7 has positive effect US., the effect of developed country group will

spillover via accounting standard, and Voice and Accountability of G7have negative

effect to US. The negative effect can be explained by the Career Concerns Model. The

bad governance in the peer group will induced a bad effect to the high governance.

Panel B: PIGS, Voice and Accountability, Government Effectiveness,

Regulatory Quality, and Control of Corruption of US has positive effect to PIGS.

However, The European debt crisis of PIGS spillover to other countries including US.

PIGS has positive effect of and Absence of Violence/Terrorism, Government

Effectiveness, Regulatory Quality, and Control of Corruption to US. While Political

Stability, and Rule of Law has negative effect to US. The negative effect can be

explained by the Career Concerns Model. The bad governance in the peer group will

induced a bad effect to the high governance.

Panel C: BRICS, Political Stability and Absence of Violence/Terrorism,

Regulatory Quality, Control of Corruption of US has positive effect to PIGS. While

BRICS has positive effect of Control of Corruption to US.

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Table 2.13 Multivariate Panel vector autoregressive models (XTVAR)

Panel A: G7

Voice and

Accountability Political Stability

Government

Effectiveness

G7 US G7 US G7 US

L1_G7 0.428*** -0.174** 0.260** -0.224 0.768*** 0.01

L1_US 0.121* 0.864*** 0.161*** 0.623*** -0.06 0.540***

N 84 84 84 84 84 84

R-Squared 0.31 0.780 0.319 0.342 0.617 0.399


G7 US G7 US G7 US

L1_G7 0.506*** -0.06 0.701*** -0.017 0.8096*** 0.2584**

L1_US -0.057 0.859*** -0.053 0.400*** 0.017 0.711***

N 84 84 84 84 84 84

R-Squared 0.382 0.663 0.428 0.145 0.735 0.591

Panel A presents the multivariate panel vector autoregressive models

results of corporate governance spillovers between the G7 countries and the US. There

are six dimensions of corporate governance spillovers. There are the coefficients of lag

term ( ) of equation , 1 ,

1

p

it i t it ij i t j it

j

CG CG CG u − −

=

= + + + in the table 2.13. There are two

equations of the spillover tests in XTVAR. The first equation has the WGIs of the G7

countries as a dependent variable and a lagged dependent variable and the US’s WGI

as the independent variables. The second equation uses the US’s WGI as a dependent

variable and the same set of independent variables. The ***, **, and * indicate

statistical significance at the 1%, 5%, and 10% level, respectively.

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Table 2.13 Continued

Panel B: PIGS


PIGS US PIGS US PIGS US

PIGS

L.PIGS 0.6839*** 0.0071 0.7509*** -0.2830** 0.6170*** 0.1648***

L.US 0.1774* 0.8418*** 0.0584 0.5660*** 0.4715*** 0.4472***

N 70 70 70 70 70 70

R-Squared 0.6225 0.7642 0.6726 0.3881 0.6856 0.4567


PIGS US PIGS US PIGS US

PIGS

L.PIGS 0.5559*** 0.1860** 0.8564*** -0.1122** 0.7922*** 0.2453**

L.US 0.5196*** 0.7503*** 0.0059 0.3199** 0.1314** 0.6495***

N 70 70 70 70 70 70

R-Squared 0.6851 0.6843 0.699 0.2124 0.8052 0.5963

Panel B presents the multivariate panel vector autoregressive models

results of corporate governance spillovers between the PIGS countries and the US.

There are six dimensions of corporate governance spillovers. There are the coefficients

of lag term ( ) of equation , 1 ,

1

p


j

CG CG CG u − −

=

= + + + in the table. There are

two equations of the spillover tests in XTVAR. The first equation has the WGIs of the

PIGS countries as a dependent variable and a lagged dependent variable and the US’s

WGI as the independent variables. The second equation uses the US’s WGI as a

dependent variable and the same set of independent variables. The ***, **, and *

indicate statistical significance at the 1%, 5%, and 10% level, respectively.

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Table 2.13 Continued

Panel C: BRICS


BRICS US BRICS US BRICS US

L.BRICS 0.7263*** 0.0158 0.3024*** -0.0487 0.6456*** -0.0183

L.US 0.0567 0.8398*** 0.1089* 0.5547*** 0.0601 0.6015***

N 70 70 70 70 70 70

R-Squared 0.6723 0.7644 0.2301 0.332 0.3799 0.384


BRICS US BRICS US BRICS US

L.BRICS 0.5411*** 0.0512 0.6503*** 0.0591 0.6510*** 0.3429**

L.US 0.1858** 0.8562*** 0.1948 0.3728*** 0.1326** 0.6722***

N 70 70 70 70 70 70

R-Squared 0.4776 0.6612 0.4702 0.1583 0.5801 0.5932

Panel C presents the multivariate panel vector autoregressive models

results of corporate governance spillovers between the BRICS countries and the US.

There are six dimensions of corporate governance spillovers. There are the coefficients

of lag term ( ) of equation , 1 ,

1

p


j

CG CG CG u − −

=

= + + + in the table 2.13. There

are two equations of the spillover tests in XTVAR. The first equation has the WGIs of

the BRICS countries as a dependent variable and a lagged dependent variable and the

US’s WGI as the independent variables. The second equation uses the US’s WGI as a

dependent variable and the same set of independent variables. The ***, **, and *

indicate statistical significance at the 1%, 5%, and 10% level, respectively.

The estimated results of Panel VARs between CG index of PIGS

countries and CG index of US and the estimated results of Panel VARs between CG

index of BRICS countries and CG index of US indicated the dependency of CG index

of PIGS and BRICS countries on the aspects of Government Effectiveness, Regulatory

Quality, and Control of Corruption, on the CG index of the US. The results of Panel

VARs between CG index of US and those of G7 should insignificant interdependent

and dynamic relationship between the two variables. These results can be implied that

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US has dominated over PIGS and BRICS, but less impact on G7 countries. As a result,

corporate governance of US has spread to PIGS and BRICS countries on the aspects of

Government Effectiveness, Regulatory Quality, and Control of Corruption. According

to both Panel cointegration test and Panel VARs, the results partially confirm that

financial integration leads to corporate governance spillover.

2.5 Conclusion

This study intends to reveal cross-country corporate governance spillover.

The worldwide corporate governance indicators (WGI) is a proxy for corporate

governance practices spillover. This There are six dimensions of governance: Voice and

Accountability, Political Stability and Absence of Violence/Terrorism, Government

Effectiveness, Regulatory Quality, Rule of Law, and Control of Corruption. According

to fundamental economic and financial integration, cross-border M&A, and cross-

listing among countries, a positive and negative effect of corporate governance

spillover cause an increasing and decreeing in corporate governance (Martynova &

Renneboog, 2008). There are empirical evidences in prior study positive spillover by

law hypothesis and the bootstrapping hypothesis (Lim, Brooks, & Hinich, 2008;

Martynova & Renneboog, 2008). The study employs panel data technique in determine

corporate governance spillover among countries within economic group. Based on

panel unit root tests, the testing results show stationary panel time series of CG index

among US paring with G7, BRICS, and PIGS, thus, the hypothesis that “there exist

corporate governance spillover among economic countries,” is confirmed. However,

the results of panel cointegration tests and panel VARs partially (not all aspects)

confirms the hypothesis that “there exist corporate governance spillover among

economic countries.

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CHAPTER 3

AGENCY COST IN STATE-OWNED ENTERPRISES :

INTERNATIONAL EVIDENCE

3.1 Introduction

The state-owned enterprise (SOE) has the important role in society and

economy. SOE is used as political tools or political ideology. The objectives of SOE

are political control, society and culture, public goods, government financing, etc.

However, the performance and efficiency of SOE have the arguments in empirical

evidence. Corporate governance in SOE is doubtful. Researches in SOE are mainly in

industry level, region, group of countries such as transitions economy, especially in

China. The issues mostly study on the relationship between privatization firms and

performance.

The state-owned enterprise is one type of ownership structure of the firm.

The definitions of SOE depend on law of the country and objective of study. Consistent

with Megginson & Netter (2001), this study follows the World Bank (1995) that defines

SOE as "government-owned or government-controlled economic entities that generate

the bulk of their revenues from selling goods and services. Furthermore, OECD defines

SOE as “enterprises where the state has significant control through full, majority, or

significant minority ownership” (PwC, 2015). Moreover, Robinett (2006) indicates that

SOE is parastatals, public enterprises, or public sector enterprises. SOE occur because

of social and economic factors.

If there are Pareto optimum and no externality, government activity is not

necessary. When there exist public goods or natural monopoly, there are free riders

which cause inefficiency for private sector to produce public goods. Therefore,

government intervention or state-ownership will solve public goods problem (Pindyck

& Rubinfeld, 2005).

SOE exists in both western and eastern economies. In ancient Greece 200-

300 B.C., for example, the deforestation by government for fuel purposes such as coal

and petroleum is present (Hughes & Thirgood, 1982). In ancient China 221 to 206 B.C.,

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the absolute monarchy, Qin dynasty government provides public goods which are the

national defense by building the Great Wall of China (Lattimore, 1937). Under the

political system in ancient era, the feudalism is the reason of doing government activity.

SOE is important for economy as well as political and social benefits that government

has to produce public goods to solve market failure (Megginson & Netter, 2001). Until

the Industrial Revolution (Jensen, 1993), there is an increase in competitive market.

Then, the concept of competitive economy without social or political goals influences

SOE to become inefficient allocations. The performance of SOE is less than private

firm under property rights theory of the firm (Boardman & Vining, 1989). Importance

of SOE is measured by Country SOE Share, combining total assets (Kowalski, Buge,

Sztajerowska, & Egeland, 2013).

Literature about SOE is close to privatization studies. SOE has unique

board structure, ownership structure, board composition. Moreover, SOE is subject to

different rules and regulations. Mostly, studies on SOE are about assessment of

efficiency of SOE or performance, stock pricing, performance, the impact of

privatization (change in ownership structure) and compare to private ownership. The

summary of empirical studies in group of countries, industry, on comparing firms

before and after privatization are in Megginson, (2005a); Megginson & Netter (2001).

Literature of SOE on firm performance in natural monopoly industry such as utilities,

energy and regulated market in banks, airline (Belloc, 2014). SOE in transition

economy is also important for developing countries such as China (Zhong, 2015) and

India (SOM, 2013).

Prior literature uses the elements of corporate governance as a corporate

governance proxy, but this study uses constructed corporate governance index. The

objectives of this study are to test whether SOE structure affects corporate governance

index, SOE structure affects returns, and SOE structure affects firm performance. The

main contribution is that this study is the first one in corporate governance centric in

SOE.

This study is organized as follows; section 2 theoretical framework about

SOE, section 3 literature review of SOE and corporate governance. Section 4 describes

the data and methodology. Section 5 provides empirical results, and section 6 contains

summary and conclusion.

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3.2 Theoretical framework

3.2.1 Theory of SOE

The difference of SOE and private firms is ownership structure. Peng,

Bruton, Stan, & Huang (2016) summarize the theories that explain SOE which consists

of property right theory, transaction cost theory of the firm, agency theory, and

resource-based theory. These theories support and against the existence of SOE.

Property right theory involves the right of owner in term of the claim of firm’s income,

control and transfer or sale of property. Transaction cost theory supports SOE according

to economy of scales under good governance condition. Agency cost occurs when

principal and agent have conflicts of interests and this problem is important in SOE.

Ownership structure is also important in previous studies on property rights and

transaction cost theory (Williamson, 1985).

The property right theory is based on the theory of production and

exchange. The traditional analysis of decision making by manager regarding to

maximize shareholder profit must change according to a new utility function of

manager or decision maker agent. Moreover, the more efficient resources allocation are

in the private-ownership (Furubotn & Pejovich, 1972). Literature on property right

theory is to assess the efficiency of resources allocation by measuring cost efficiency.

The studies compare production efficiency in public and private firms in many

industries. The study in water utility industry in US uses Cobb-Douglas production

function to estimate marginal productivity of firm and modifies into cost function to

compare operating of private and public firms (Crain & Zardkoohi, 1978). Meanwhile,

in line with the property theory, public firm performs less efficiently than private firm

(Frech III, 1976). Transaction cost and agency theories focus on unit analysis of

individuals while property right theory mainly focuses on institution of utilization and

social welfare. The comparison of these theories are in an example of oil field utilization

that is comprehensive in contracts and analysis of ownership (J. Kim & Mahoney,

2005).

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3.3.1 SOE and performance

Literature of privatization until 2000 focuses on privatization effect to

performance of firm. The studies investigate in industry across countries such as utility,

airline, telecommunication, financial, especially in comparing pre and post

privatization. There are SOE studies in developed country such as OECD counties, US,

UK, Mexico, and developing countries such as China and South America. These studies

mostly focus on region or industry because of data limitation and comparable reason.

The objective of SOE also concerns about social and political issues. Therefore, the

comparison among firms has many concerns about social benefit more than economic

benefit. For example, banking industry, La Porta, Lopez-de-Silanes, Shleifer, &

Vishny (2000) study performance of SOE banks in 92 countries. The board structure of

SOE affects SOE performance in Lithuania (Jurkonis & Petrusauskaitė, 2014; Jurkonis

& Aničas, 2015). Moreover, SOE structure affects growth of the firms (Khuong, 2015).

According to, Megginson & Netter (2001), survey on privatization literature suggests

that recent studies mostly are in developed country, OECD, and some in transitory

economy, but little in ASEAN country.

The ownership structure is used as corporate governance proxy. The

ownership structures are categorized into block holders’ activity (Becht, 1999; Cueto

& Switzer, 2013; Edmans, Fang, & Zur, 2013; Sakawa, Ubukata, & Watanabel, 2014),

institutional investor (Cheung, Chung, & Fung, 2015), foreign (Jackson, 2013; Sakawa

et al., 2014), and family firm (Fu, Lu-Andrews, & Yu-Thompson, 2015; Jackson, 2013)

while others studies use transparency as a corporate governance (W. P. Chen, Chung,

Lee, & Liao, 2007; Li, Chen, & French, 2012).

3.3.2 SOE by industry

Prior literature focuses on country level or across country in the same

industry. The main industries of SOE are bank, utility, and airline have empirical

evidence as follows.

Banking sector is not likely to be a natural monopoly, but it is an

oligopoly or nearly competitive market in some countries. The purposes of government

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ownership in banks are to facilitate country development and special purpose for special

unit of economy. Country development and economy stability are usually the main

objectives of SOE. Therefore, performance of banks has to measure not only as private

banks, but also measure other output such as country development and growth of

economy.

Empirical evidence about bank in country level shows that the more

government ownership leads to lower country development in 92 countries (La Porta,

Lopez de Silanes, & Shleifer, 2002) and finds the same effect in the former Soviet

Union and in Russia (Berkowitz, Hoekstra, & Schoors, 2014).

The survey of empirical literature examining bank privatization before

and after privatized finds that privatized banks outperform after privatization

(Megginson, 2005). Mohsni & Otchere (2014) find a decrease in risk taking behavior

after privatization and the U-shaped relationship between private ownership and risk

taking. Stated-owned bank has lower performance than domestic and foreign banks

(Berger, Clarke, Cull, Klapper, & Udell, 2005).

Literature that focuses on the relationship of corporate governance of

SOE in banking industry to many measures of outputs of bank. Governance in country

level affects performance of banks and privatized banks outperform normal banks,

especially in developing countries and good national governance (Ho, Lin, & Tsai,

2016). Other important factors for banks are risk taking behavior, country governance

effect and risk taking of privatized banks. There is the U-shaped relationship between

risk taking and ownership concentration (Williams, 2014). Other issues in SOE are

ownership structure effect, effective tax rate (Zhang, M, Zhang, & Yi, 2016), and

corporate financial fraud (G. Chen, Firth, & Rui, 2006).


According to property rights theory, ownership power is how

economic agent has rights to control firm (Peng et al., 2016). The studies of ownership

structure show that agency cost is cost of separation of ownership and control (Fama &

Jensen, 1983; Jensen & Meckling, 1976) and this problem also occurs in SOE.

However, SOE is special case of separation of ownership and control. SOE firms are

object to different market conditions, monopoly market, and board composition. SOE

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has both positive and negative effects to performance of firms. The negative effect of

SOE is caused by inefficiency in assets allocation (Boardman & Vining, 1989). There

is positive effect of SOE to performance, political, connections and economy of scale.

Therefore, ownership structure affects behavior of manager, especially on SOE

(Grosman et al., 2016). Hypothesis is defined as follows;

H1: Performance of SOE firm is lower than that of private firm.

3.4 Data description and research methodology

3.4.1 Data

This study uses listed SOE which have more than 25% of government

ownership classified by Orbis Bureau van Dijk ownership. Financial data are retrieved

from Thomson Reuters Eikon, DataStream and Orbis Bureau van Dijk. The criterion is

an ultimate owner of SOE obtained from Orbis Bureau van Dijk ownership data with a

total number of 134,463 companies (both listed and non-listed companies) between

2000 and 2017. The listed companies consist of 39 countries. The performance

efficiency proxies are ROA, ROE, TobinQ, and stock index return (Boardman &

Vining, 1989; Megginson, Nash, & Randenborgh, 1994). Control variables in this study

include stock market capitalization, long-term debt, short-term debt, cash holdings,

profit, book-to-market (Lins, Servaes, & Tamayo, 2017) and price inverse.

3.4.2 Research methodology

3.4.2.1 Difference-in-differences (DiD)

This study uses difference-in-difference (DiD) method

(Jiraporn, Jumreornvong, Jiraporn, & Singh, 2015) to determine effects of SOE on

independent variables. The difference-in-difference effect is determined by coefficient

of dummy variables in regression analysis. Following, Boardman & Vining (1989),

Megginson et al. (1994), and Lins, Servaes, & Tamayo (2017) this study employs

regression analysis for SOE performance. Moreover, this study includes financial crisis

dummy variable to capture performance of SOE in crisis period. The full equation as

follows;

, , , , , ,i t i t i t SOE it D i t i t SOE i t XY SOE D SOE D X = + + + + (10)

Ref. code: 25605502310021IBC

42

where

, 1 , 2 ,it i t i t i tD Crisis Law Law = (11)

and

, 1 , 2 , 3 , 4 , 5 , 6 , 7 , , , ,i t i t i t i t i t i t i t i t i t i t i tX X X X X X X X Ind Country Region = (12)

Yi,t refers to matrix of firm performances that are returns, ROA,

ROE, return, and TobinQ. i represents an individual firm and t is monthly and annually

data. SOE refers to dummy variable of state-owned enterprise of firms that its value

equals to 1 if firm has government ownership greater than 25 percent, and 0 otherwise.

Di,t is matrix of dummy variables of interest which consists of crisis, civil law, and .

common law. Crisis refers to time dummy variable that its value equals to 1 if year

equals to 2008 - 2009, and 0 otherwise. Law dummy variables consist of civil law,

common law, and mixed law system of country, this study take mixed law system as a

based dummy variable according to the least number of country and to focusing on civil

and common law. Moreover, this study investigates the difference-in-difference (DiD)

method which employ interaction terms. , ,i t i tSOE D , is the interaction terms between

SOE dummy variables and crisis and law system. These variables are classified as the

treatment effects.

For control variables, 1 , 2 , 3 , 4 , 5 , 6 , 7 ,i t i t i t i t i t i t i tX X X X X X X is

matrix of control variables that effect liquidity and represent firm financial health (Lins

et al., 2017; Prommin et al., 2014; Prommin, Jumreornvong, Jiraporn, & Tong, 2016),

the control variables as follow. MarketCap is the logarithm of stock market

capitalization of firms. LongDebt and ShortDebt are long-term debt and short-term debt

divided by total assets. CashHoldings is cash holdings divided by total assets. Profit is

operating income divided by total assets. Book2Market is book value of equity divided

by market value of equity. 1/P is one divided by price of stock. This study also includes

dummy variables for control. IndustryDummy, CountryDummy, RegionDummy, and

YearDummy refer to the dummy variables of industry, country, region, and year,

respectively.

Ref. code: 25605502310021IBC

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3.4.2.2 Matching firm method

According to unique characteristics of SOE in each industry

and country, there are limitations of data, SOE consist of very unique characteristics of

the firm. The estimations of effects of SOE have to estimate treatment effect (SOE

firms) and control effect (private firms). Matching method is frequency used in labor

economics and clinical experiment studies (Heinrich, Maffioli, & Vázquez, 2010). This

study uses matching methods to determine effects of SOE in nonexperimental causal

study. This study considers many dimensions of observable characteristics. There are

many matching methods that this study uses propensity score-matching method because

it is appropriate to estimate treatment effect in nonexperimental study (Dehejia &

Wahba, 2002). This study follows propensity score matching in Ho, Lin, & Tsai (2016).

The matching methods compare groups of the firms by matching the set of

characteristics of the firm. For example, treatment (SOE firm) and control (private firm)

have the similar set of characteristics which are considered random sampling (Ho et al.,

2016). The treatment effect is the difference between two groups.

The propensity score-matching reduce the dimension of

characteristics of firm into one probability to reduce bias in matching method

(Rosenbaum & Rubin, 1983). The score helps in matching the same firm to compare

the effect of treatment. The probability score as follows;

( ) ( )1SOEP D F X= = , (13)

where SOE refers state-owned enterprises dummy variable that its value equals to 1 if

firm has government ownership greater than 2007, and 0 otherwise. The ( )F X is

the cumulative probability density function of logistic or probit distribution of X set of

characteristics of firm. The set of characteristics which important in this study are

government ownership, stock market capitalization, profit, region, law system, and

industry. The outcome of treatment in this study are firm performance which consists

of ROA, ROE, TobinQ, and return. The sample are both government and non-

government ownership. The model selects non-government ownership firm that have

the same score as government firm to compare effect of government ownership to firm

performance.

Ref. code: 25605502310021IBC

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The perfectly matched firm to compare performance of SOE

and non-SOE cannot be performed in real world. Therefore, theoretical comparing the

effect of SOE is possible by assuming non-SOE characteristics that likely to be SOE.

ATT is the average effect of treatment on nonSOE that match characteristics with SOE.

The probability of set of characteristics are used in matching firm. Then the estimates

of mean difference of firm’s performance between government ownership and

nongovernment ownership firms calculate as follow;

ATT = E[YSOE - Y NonSOE] (14)

where Y(1) is performance of SOE firm, Y(0) is performance of non-SOE firm.

3.5 Empirical results

Table 3.1 shows descriptive statistics of number of firms in each country

that have SOE. There are 32 countries in this study during 2000-2017. The annually

data consists of 18 years of total 95,976 observations. The highest number of SOE is

China, which accounts more than 50 percent of all SOE, and account for about 1/3 of

number of listed firms in their country. The listed SOE are mostly in developing

country, for example, India, Bermuda, and Malaysia. The detail of firm by industry are

in table 3.2. There are eighth industries in this study; agriculture, mining, construction,

manufacturing, transportation, wholesale trade, retail trade, and services. This study

excludes finance industry. Manufacturing industry is the highest number of firms in this

sample.

Table 3.3 show descriptive statistics of variables of all sample, non-SOE,

and SOE samples in panel A, B, and C respectively. Panel C shows mean different of

variables in this study between non-SOE and SOE. The results form panel C indicate

that ROA, ROE, TOBINQ, return, and stock market capitalization of non-SOE are

statistically significant less than SOE. Table 3.4 shows correlation relationship of all

variables in this study.

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Table 3.1 Number of firms in each country that have SOE

No. Country Non-SOE SOE Total

1 CHINA 11,913 5,700 17,613

2 INDIA 4,503 494 4,997

3 BERMUDA 4,180 418 4,598

4 MALAYSIA 4,769 342 5,111

5 HONG KONG 1,273 342 1,615

6 BRAZIL 1,672 285 1,957

7 SINGAPORE 2,280 209 2,489

8 GERMANY 4,655 171 4,826

9 ITALY 1,634 133 1,767

10 NEW ZEALAND 475 133 608

11 THAILAND 3,230 114 3,344

12 INDONESIA 1,881 114 1,995

13 RUSSIAN FEDERATION 152 114 266

14 KOREA, REPUBLIC OF 8,626 95 8,721

15 FRANCE 5,073 95 5,168

16 SWITZERLAND 1,691 95 1,786

17 PAKISTAN 1,026 95 1,121

18 SOUTH AFRICA 1,805 76 1,881

19 GREECE 1,444 76 1,520

20 NORWAY 684 76 760

21 AUSTRIA 418 76 494

22 EGYPT 95 76 171

23 BELGIUM 931 57 988

24 ARGENTINA 684 57 741

25 CAYMAN ISLANDS 684 57 741

26 POLAND 627 57 684

27 HUNGARY 95 57 152

28 JAPAN 39,596 38 39,634

29 SWEDEN 1,729 38 1,767

30 CHILE 1,330 38 1,368

31 FINLAND 1,216 38 1,254

32 CZECH REPUBLIC 38 38 76

33 UNITED KINGDOM 6,631 19 6,650

34 AUSTRALIA 4,180 19 4,199

35 TURKEY 1,596 19 1,615

36 NETHERLANDS 1,140 19 1,159

37 IRELAND 551 19 570

38 VENEZUELA 19 19 38

39 MONACO 0 19 19 Total 124,526 9,937 134,463

Ref. code: 25605502310021IBC

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Table 3.2 Number of observations by industry

Industry Freq. Percent

Agriculture 1,672 1.24

Mining 4,788 3.56

Construction 6,346 4.72

Manufacturing 78,926 58.7

Transportation 13,433 9.99

Wholesale Trade 7,600 5.65

Retail Trade 7,505 5.58

Services 14,193 10.56

Total 134,463 100

Table 3.3 Descriptive statistics of variables

Panel A: All sample

Variable N Mean S.D. Min Max

ROA 131,905 0.0131 3.6393 -1319.9000 8.2564

ROE 120,870 4.9307 31.6785 -3043.6800 456.1500

TOBINQ 130,661 1.5217 3.8539 -4.5683 1090.7330

RETURN 125,629 0.1702 0.6254 -1.0000 29.6484

MARKET CAP 133,428 5.2691 1.9657 -4.6052 12.2822

LONG-TERM DEBT 131,217 0.2962 13.9615 0.0000 1226.1130

SHORT-TERM DEBT 130,296 0.3865 20.6741 0.0000 1825.8590

CASH HOLDDINGS 131,854 0.2125 2.8238 0.0000 433.8000

PROFIT 131,786 0.0433 7.1199 -2561.6000 62.8929

BOOK TO MARKET 132,257 1.0405 1.5807 -100.0000 100.0000

PRICE INVERSE 133,274 3.4023 17.0933 0.00003 1428.5710

Panel B: Non-SOE

Variable Obs Mean Std. Dev. Min Max

ROA 122,317 0.0115 3.7791 -1319.9000 8.2564

ROE 112,113 4.8094 32.0371 -3043.6800 456.1500

TOBINQ 121,434 1.5065 3.9805 -4.5683 1090.7330

RETURN 116,357 0.1651 0.6134 -1.0000 29.6484

MARKET CAP 123,621 5.1832 1.9556 -4.6052 12.2822

LONG-TERM DEBT 121,643 0.3094 14.4997 0.0000 1226.1130

SHORT-TERM DEBT 120,767 0.4042 21.4737 0.0000 1825.8590

CASH HOLDDINGS 122,271 0.2162 2.9320 0.0000 433.8000

PROFIT 122,215 0.0430 7.3933 -2561.6000 62.8929

BOOK TO MARKET 122,580 1.0650 1.6151 -100.0000 100.0000

PRICE INVERSE 123,470 3.4480 17.5888 0.0000 1428.5710

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Table 3.3 Continued

Panel C: SOE

Variable N Mean S.D. Min Max

ROA 9,588 0.0341 0.0977 -2.4552 0.6659

ROE 8,757 6.4845 26.6170 -1212.7700 271.8200

TOBINQ 9,227 1.7223 1.3284 -2.7358 21.6776

RETURN 9,272 0.2340 0.7575 -1.0000 6.5244

MARKET CAP 9,807 6.3511 1.7607 -2.8134 11.8653

LONG-TERM DEBT 9,574 0.1286 0.5256 0.0000 49.4175

SHORT-TERM DEBT 9,529 0.1622 0.4881 0.0000 45.0472

CASH HOLDDINGS 9,583 0.1645 0.1762 0.0000 2.4132

PROFIT 9,571 0.0465 0.0926 -0.8883 1.2758

BOOK TO MARKET 9,677 0.7298 1.0017 -8.3333 33.3333

PRICE INVERSE 9,804 2.8269 8.6837 0.0002 166.6667

Panel D: Mean different of variables between Non-SOE and SOE

Variables Mean for Non-SOE Mean for SOE Diff. (NonSOE-SOE)

ROA 0.0115 0.0341 -0.0226 **

ROE 4.8094 6.4845 -1.6751 ***

TOBINQ 1.5065 1.7223 -0.2158 ***

RETURN 0.1651 0.2340 -0.0689 ***

MARKET CAP 5.1832 6.3511 -1.1678 ***

LONG-TERM DEBT 0.3094 0.1286 0.1808

SHORT-TERM DEBT 0.4042 0.1622 0.2420

CASH HOLDDINGS 0.2162 0.1645 0.0518

PROFIT 0.0430 0.0465 -0.0035

BOOK TO MARKET 1.0650 0.7298 0.3352

PRICE INVERSE 3.4480 2.8269 0.6212

Table 3.4 Correlation of variables

ROA ROE TobinQ Return Market

Cap

Long-

term

debt

Short-

term

debt

Cash

holdings Profit

Book-

to-

market

1/P

ROA 1

ROE 0.57 1

TobinQ -0.06 0.00 1

Return 0.11 0.14 0.12 1

Market Cap 0.19 0.22 0.12 0.12 1

Long-term debt 0.01 0.00 0.00 0.01 -0.01 1

Short-term debt 0.00 0.00 0.00 0.00 -0.01 0.97 1

Cash holdings 0.03 0.00 0.01 0.01 -0.02 0.90 0.89 1

Profit 0.13 0.09 0.00 0.02 0.03 0.82 0.85 0.81 1

Book-to-market -0.06 -0.05 -0.19 -0.13 -0.39 0.00 0.00 0.00 -0.02 1

1/P -0.10 -0.08 -0.01 -0.04 -0.19 0.00 0.00 0.00 -0.02 0.12 1

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This study focuses on state-own enterprises as a proxy of agency cost in

corporate governance. Therefore, the methodology of treatment effects is employed to

test effects of SOE to performance of firm. The results of panel regression of firm

performance and government ownership firm are in table 3.5 The treatment effects in

this model are interaction term of government ownership (SOE) and crisis, and

interaction term of government ownership (SOE) and law system.

The estimated results of panel-data regression of all sample are in table 3.5.

The result shows that there are significantly and negatively of SOE dummy variable on

ROE and return. State-owned enterprise significantly less performs than private firms.

This confirms with hypothesis that government ownership firm is less perform than

private firm, and support property right theory. However, dummy variable of

SOExT2008-9, SOE during crisis period 2008 to 2009, are positively significant to

TobinQ and return. According to the results, SOE is better than private firm in crisis

period. This support the prior literature that SOE can access more funding form

government, and nature of SOE that not sensitive to change in financial crisis. For law

system in SOE, SOExCivil and SOExCommon, state-owned firm with civil law system

are positively significant to ROE and return. While state-owned firm with common law

system positive significantly to ROA, which inconclusive with prior literature that civil

law system is better than common law system in term of minority protection and

process (La Porta et al., 2000).

Ref. code: 25605502310021IBC

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Table 3.5 Panel-data regression of all sample

ROA ROE TobinQ Return

SOE -0.0233 -6.2546** -0.1963 -0.0710***

T2008-9 -0.0070*** -1.0770*** -0.2098*** -0.2357***

SOExT2008-9 0.0075 1.5813 0.2368*** 0.1782***

Civil -0.0231*** -3.2342*** 0.1772*** -0.0565***

Common -0.0284*** -0.1129 0.4360*** 0.0278***

SOExCivil 0.0205 4.6138* 0.3507 0.0970***

SOExCommon 0.0349* 2.6231 -0.2935 0.0442

Asia & Pacific -0.0127** 0.2926 -0.3371*** -0.0796***

Europe -0.0035 0.9009 -0.3072*** -0.1154***

Market Cap 0.0204*** 5.1339*** 0.1450*** 0.0331***

Long-term debt -0.0002 0.1192*** -0.0050* 0.0006

Short-term debt -0.0029*** -0.2320*** 0.0077*** -0.0016***

Cash holdings 0.0084*** -0.0033 0.0134* 0.0016

Profit 0.0748*** 5.6615*** -0.1993*** 0.0401***

Book-to-market 0.0042*** 2.0389*** -0.1097*** -0.0312***

1/P -0.0003*** -0.0349*** -0.0018*** -0.0015***

Agriculture 0.0000 0.0000 0.0000 0.0000

Mining -0.0869*** -10.6648*** -0.1378 -0.0148

Construction -0.0025 -0.5204 -0.7675*** -0.0275

Manufacturing 0.0074 0.8256 -0.4150** -0.0188

Transportation -0.0122 -2.2029 -0.6165*** -0.0836***

Wholesale Trade 0.0105 1.7468 -0.5711*** -0.0277

Retail Trade 0.0108 3.0982 -0.4150** -0.0674***

Services -0.0093 -1.0435 -0.0999 -0.0355*

Constant -0.0624*** -22.9592*** 1.4102*** 0.1954***

N 128,412 118,144 126,859 122,352

No. group 7,077 7,077 7,077 7,075

Degree of freedom 23 23 23 23

RMSE 0.1598 27.2152 1.996 0.6103

R2_overall group 0.0613 0.0616 0.0314 0.0403

Chi-Square 8,753.64 5,467.34 2,065.29 5,140.94

p 0 0 0 0

* p < 0.05, ** p < 0.01, *** p < 0.001

Table 3.5 shows panel-data regression of all sample, SOE is dummy variable

of state-owned enterprise of firms that its value equals to 1 if firm has government

Ref. code: 25605502310021IBC

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ownership greater than 25 percent, and 0 otherwise. T2008-9 is time dummy variable

that its value equals to 1 if year equals to 2008 - 2009, and 0 otherwise. SOExT2008-9

is an interaction term between SOE and time dummy of crisis. Civil and Common are

civil and common law systems. SOExCivil and SOExCommon are intraction terms

between SOE and law system. Asia & Pacific and Europe are region dummy variables.

Market Cap is the logarithm of stock market capitalization of firms. Long-term debt

and Short-term debt are long-term debt and short-term debt divided by total assets. Cash

holdings is cash holdings divided by total assets. Profit is operating income divided by

total assets. Book-to-market is book value of equity divided by market value of equity.

1/P is one divided by price of stock. This study also includes dummy variables for

control. * p < 0.05, ** p < 0.01, *** p < 0.001.

Table 3.6 shows significant result of SOE effect on performance in China.

There exists negative significant effect of SOE on performance on TobinQ. However,

dummy variable of SOExT2008-9, SOE during crisis period 2008 to 2009, are

positively significant to TobinQ. These results is confirmed with all sample data that at

normal period, SOE less performs than private firm. However, in crisis period, SOE

performs better than private firm.

Table 3.6 Panel regression random effects of China

ROA ROE TobinQ Return

SOE -0.0008 -0.0766 -0.2637*** -0.0122

T2008-9 -0.0005 0.0606 -0.1813*** -0.1689***

SOExT2008-9 0.0018 0.3975 0.0397 0.1144**

Market Cap 0.0012** 0.6282*** 0.1960*** 0.1029***

Long-term debt -0.0280*** -3.0749** -1.9093*** -0.0635

Short-term debt -0.0454*** -11.8781*** -1.4929*** 0.0922*

Cash holdings 0.0555*** -2.7715*** 1.1431*** 0.1081**

Profit 0.7303*** 167.6518*** -0.0752 0.6988***

Book-to-market 0.0078*** 5.5577*** -2.0701*** -0.2473***

1/P -0.0009*** -0.5961*** 0.0027 -0.0305***

Constant -0.0074* -4.1533*** 2.4118*** -0.2636***

N 16509 15193 15168 16228

No. group 927 927 927 927


RMSE 0.0435 12.9327 1.2783 0.7827

R2_overall group 0.6054 0.4215 0.2926 0.0708

Chi-Square 24129.8796 10852.883 4480.7687 1235.3358

p 0 0 0 0

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Table 3.6 shows panel-data regression of China. SOE is dummy variable of

state-owned enterprise of firms that its value equals to 1 if firm has government




civil and common law systems. Market Cap is the logarithm of stock market

capitalization of firms. Long-term debt and Short-term debt are long-term debt and

short-term debt divided by total assets. Cash holdings is cash holdings divided by total

assets. Profit is operating income divided by total assets. Book-to-market is book value

of equity divided by market value of equity. 1/P is one divided by price of stock. This

study also includes dummy variables for control. * p < 0.05, ** p < 0.01, *** p < 0.001

Subsample panel-data regression by industry are in table 3.7, there

subsample of eighth industries. The result in Panel B: Mining industry shows that there

are significantly and positively of SOExT2008-9 on return. The result in Panel D:

Manufacturing industry, shows that there are significantly and negatively of SOE

dummy variable on ROA and ROE, except, negatively on TobinQ. The result in Panel

E: Transportation & Communications industry shows that there are significantly and

positively of SOE dummy variable on return. The result in Panel F: Wholesale Trade

industry shows that there are significantly and negatively of SOE dummy variable on

ROE. The result in Panel H: Services industry shows that there are significantly and

positively of SOExT2008-9 on return. While the results in Panel A: Agriculture, Panel

C: Construction, and Panel G: Retail Trade are not significantly SOE dummy variable

on performance.

Ref. code: 25605502310021IBC

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Table 3.7 Panel regression random effects by industry

Panel A: Agriculture ROA ROE TobinQ Return

SOE 0.0297 -3.9599 -1.7488 -0.0844

T2008-9 -0.0137 3.2180 0.3693 -0.1988*

SOExT2008-9 -0.0033 -1.9950 1.3257 0.2486

Civil 0.0081 5.8998 -0.0360 -0.0405

Common -0.0115 14.5679 -0.7853 0.0004

Asia & Pacific 0.0011 -6.4378 -0.7030 -0.0895

Europe -0.0041 -21.3384* 0.3020 0.0284

Market Cap -0.0076** 3.7846* 0.4276** 0.0444**

Long-term debt 0.0339*** -8.1416 -1.2774* 0.0397

Short-term debt -0.0757*** -16.5967 -2.4335 0.2308

Cash holdings 0.0608*** 0.7478 -0.4670*** 0.0019

Profit 1.5408*** 65.3700*** -40.6720*** -0.0150

Book-to-market -0.0012* 0.5139 -0.0001 -0.0047

1/P -0.0003* -0.1251 -0.0050 -0.0018

Constant -0.0078 -12.3205 3.0463** 0.0504

N 1574 1431 1540 1499

No. group 88 88 88 88


RMSE 0.0936 82.9248 6.8874 0.9646

R2_overall group 0.9723 0.0293 0.3336 0.0149

Chi-Square 73670.1728 42.0803 891.7833 22.4319

p 0 0.0001 0 0.0702

Panel B: Mining ROA ROE TobinQ Return

SOE -0.0151 -0.6878 -0.4888 -0.0394

T2008-9 -0.0223 -2.7887 -0.0861 -0.2437***

SOExT2008-9 0.0260 5.5131 0.2326 0.2661*

Civil 0.0401 1.9171 0.7459* 0.0467

Common -0.0739* -7.0429* 0.0655 0.0470

Asia & Pacific -0.0110 -0.6650 0.0366 0.0335

Europe -0.0001 -2.0428 -0.0415 -0.0862

Market Cap 0.0185*** 4.1545*** 0.0681* 0.0254***

Long-term debt -0.1952*** -16.4323*** -0.6253 -0.0217

Short-term debt -0.4779*** -16.4070* -1.5541*** -0.2387*

Cash holdings -0.2047*** 36.7685*** -1.2394*** 0.3627***

Profit 0.4773*** 91.6216*** -4.8959*** 0.1884***

Book-to-market 0.0108* 4.3101*** -0.4910*** -0.0844***

1/P -0.0008*** -0.0616** -0.0011 -0.0017***

Constant -0.0201 -31.4560*** 2.2497*** 0.1102

N 4403 4059 4348 4250

No. group 252 252 252 252


RMSE 0.3406 37.8613 3.0125 0.8435

R2_overall group 0.4222 0.4259 0.2995 0.065

Chi-Square 2405.4467 2261.8522 1642.6311 294.5831

p 0 0 0 0

Ref. code: 25605502310021IBC

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Table 3.7 Continued

Panel C: Construction ROA ROE TobinQ Return

SOE 0.0113 2.6300 0.1157 -0.0110

T2008-9 -0.0043 -0.1374 -0.0738* -0.2495***

SOExT2008-9 0.0061 1.5464 0.0480 0.1911

Civil -0.0066 0.2920 -0.0594 -0.0345

Common 0.0096 3.8652* -0.0732 0.0502

Asia & Pacific -0.0249*** -4.8348* -0.0686 -0.1567***

Europe -0.0164* -0.9472 0.0876 -0.1621***

Market Cap 0.0002 2.2802*** 0.1051*** 0.0376***

Long-term debt 0.0222* -8.4168** -0.5931*** 0.1015

Short-term debt -0.0336** -32.8546*** 0.3570** 0.1065

Cash holdings 0.0516*** 26.0092*** 1.0798*** 0.3878***

Profit 1.0657*** 188.3224*** -3.0757*** 1.1672***

Book-to-market 0.0034*** 2.4311*** -0.1056*** -0.0093*

1/P -0.0012*** 0.1042** 0.0181*** -0.0018**

Constant -0.0035 -15.1661*** 0.6928*** 0.0433

N 6138 5620 6090 5815

No. group 334 334 334 334


RMSE 0.0962 20.7305 0.8097 0.5959

R2_overall group 0.4286 0.2969 0.2045 0.07

Chi-Square 4286.3613 2146.1525 1408.4146 436.7634

p 0 0 0 0

Panel D: Manufacturing ROA ROE TobinQ Return

SOE -0.0157*** -3.7193*** 0.2152** 0.0054

T2008-9 -0.0101*** -2.0433*** -0.1933*** -0.2357***

SOExT2008-9 0.0079* 1.9125* 0.2785*** 0.1925***

Civil -0.0218*** -3.6855*** 0.2268*** -0.0732***

Common -0.0068* 1.2187* 0.4172*** 0.0241**

Asia & Pacific 0.0027 2.2030** -0.3144*** -0.0685***

Europe 0.0089* 3.2293*** -0.3068*** -0.1092***

Market Cap 0.0182*** 4.3578*** 0.1716*** 0.0331***

Long-term debt 0.0005*** 0.0525* -0.0025 0.0015*

Short-term debt -0.0007*** -0.0945*** -0.0004 -0.0012**

Cash holdings -0.0009 -0.0252 0.0141 -0.0072*

Profit 0.0172*** 2.4217*** 0.0206 0.0397***

Book-to-market 0.0045*** 1.4458*** -0.1116*** -0.0465***

1/P 0.0000 0.0188** -0.0049*** -0.0010***

Constant -0.0575*** -18.9838*** 0.7946*** 0.1951***

N 75550 69429 74486 71922

No. group 4154 4154 4154 4152


RMSE 0.0832 18.9973 1.5967 0.6041

R2_overall group 0.0619 0.0659 0.0492 0.0438

Chi-Square 3966.7952 3622.0142 1873.8353 3294.3971

p 0 0 0 0

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Table 3.7 Continued

Panel E: Transportation

& Communications ROA ROE TobinQ Return

SOE 0.0063 -0.5590 -0.1341 0.0309*

T2008-9 -0.0013 -0.3556 -0.1841*** -0.2440***

SOExT2008-9 0.0030 -0.2894 0.0520 0.0813

Civil -0.0048 0.7208 -0.2103 -0.0160

Common -0.0064 3.5632 0.0530 0.0292

Asia & Pacific -0.0022 4.0701* -0.1030 -0.1091***

Europe -0.0108* 2.5223 -0.0964 -0.1301***

Market Cap 0.0060*** 3.2539*** 0.0959*** 0.0102***

Long-term debt -0.0021 1.5220** -0.0917*** -0.0113

Short-term debt -0.0983*** -26.9705*** 0.3356* 0.0334

Cash holdings -0.0632*** -8.1287*** 0.0879** -0.0463***

Profit 0.3934*** 54.2034*** -0.3094** 0.2749***

Book-to-market 0.0021 1.1841** -0.2267*** -0.0404***

1/P -0.0010*** -0.0508 0.0012 -0.0032***

Constant 0.0009 -17.9328*** 1.3514*** 0.2545***

N 12826 11809 12697 12276

No. group 707 707 707 707


RMSE 0.1285 36.6506 1.5114 0.5716

R2_overall group 0.1837 0.0689 0.0404 0.0395

Chi-Square 2346.1841 676.9807 339.7406 504.7295

p 0 0 0 0

Panel F: Wholesale Trade ROA ROE TobinQ Return

SOE -0.0174 -12.4644*** 0.1635 0.0102

T2008-9 -0.0085** -2.1246* -0.1328* -0.2067***

SOExT2008-9 0.0091 11.8616 -0.0208 0.1820

Civil -0.0180*** 1.0020 -0.0361 -0.0905***

Common -0.0145* 5.8445** 0.4952* 0.0149

Asia & Pacific -0.0011 2.6995 -0.0378 -0.0137

Europe 0.0091 1.9591 0.2790 -0.0145

Market Cap 0.0124*** 4.7794*** 0.0808*** 0.0368***

Long-term debt -0.0239*** -7.6613*** -0.0049 -0.0400

Short-term debt 0.0017 -0.1902 0.0166 -0.0084

Cash holdings 0.0148*** 4.2656*** 0.0224 0.0220**

Profit 0.0747*** 21.4162*** 0.0569 0.0799**

Book-to-market 0.0035*** 3.7320*** -0.1733*** -0.0394***

1/P -0.0001 0.0370 -0.0091** -0.0027***

Constant -0.0293** -28.4884*** 1.0509*** 0.1191**

N 7301 6769 7234 6960

No. group 400 400 400 400


RMSE 0.0769 26.7813 1.4297 0.5218

R2_overall group 0.0936 0.0705 0.0687 0.0523

Chi-Square 527.5153 457.0076 173.8674 383.3871

p 0 0 0 0

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Table 3.7 Continued

Panel G: Retail Trade ROA ROE TobinQ Return

SOE 0.0076 2.4476 0.1083 0.0021

T2008-9 -0.0007 0.5873 -0.1494 -0.1655***

SOExT2008-9 -0.0143 -1.3738 0.1865 0.1513

Civil -0.0256*** -6.2216* -0.2099 -0.0419

Common -0.0174*** -0.7056 0.3400 0.0260

Asia & Pacific -0.0074 -0.1569 -0.3231 -0.1027***

Europe -0.0050 1.6012 -0.1778 -0.1490***

Market Cap 0.0028*** 1.9502*** 0.0506 0.0280***

Long-term debt -0.0316*** 6.7808** 1.1128*** 0.0164

Short-term debt -0.0687*** -14.2988*** 1.4854*** 0.1709**

Cash holdings 0.0027 5.7279** 1.3994*** 0.2382***

Profit 0.7258*** 164.7810*** 3.3705*** 0.5147***

Book-to-market 0.0032*** 2.2102*** -0.1720*** -0.0249***

1/P 0.0000 -0.0372 -0.0047 -0.0020**

Constant 0.0129 -10.2232** 1.0909*** 0.0667

N 7262 6768 7195 6916

No. group 395 395 395 395


RMSE 0.0523 15.7406 2.3225 0.5205

R2_overall group 0.5452 0.3663 0.0523 0.0511

Chi-Square 6146.5848 2699.0364 184.6681 371.4004

p 0 0 0 0

Panel H: Services ROA ROE TobinQ Return

SOE 0.0232 2.5544 -0.3606 0.0038

T2008-9 -0.0110 1.4311 -0.3913*** -0.2798***

SOExT2008-9 0.0053 1.5750 0.3029 0.3353***

Civil -0.0040 -1.6967 0.6805* -0.0412*

Common -0.0300* -4.2478 0.8267** -0.0042

Asia & Pacific 0.0066 3.1605 -1.6057*** -0.1142***

Europe 0.0072 2.0779 -1.5142*** -0.1328***

Market Cap 0.0118*** 5.6224*** 0.3517*** 0.0422***

Long-term debt 0.0133*** 1.4679*** -0.0514*** -0.0027

Short-term debt -0.1268*** -38.5138*** -0.2340*** -0.0774***

Cash holdings -0.0450*** 5.8907*** 0.2955*** 0.0632***

Profit 0.7463*** 72.5378*** -1.1362*** -0.0005

Book-to-market 0.0014 5.1060*** -0.0551*** -0.0143***

1/P 0.0005** -0.1931*** -0.0028 -0.0022***

Constant -0.0590** -32.0055*** 1.0350** 0.1270***

N 13358 12259 13269 12714

No. group 747 747 747 747


RMSE 0.2134 37.2275 2.0695 0.6128

R2_overall group 0.5153 0.2398 0.0459 0.0514

Chi-Square 12097.3244 2709.9422 622.0588 688.7519

p 0 0 0 0

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Table 3.7 shows panel-data regression by industry. SOE is dummy variable

of state-owned enterprise of firms that its value equals to 1 if firm has government




civil and common law systems. Asia & Pacific and Europe are region dummy variables.

Market Cap is the logarithm of stock market capitalization of firms. Long-term debt

and Short-term debt are long-term debt and short-term debt divided by total assets. Cash

holdings is cash holdings divided by total assets. Profit is operating income divided by

total assets. Book-to-market is book value of equity divided by market value of equity.

1/P is one divided by price of stock. This study also includes dummy variables for

control. * p < 0.05, ** p < 0.01, *** p < 0.001

The estimated results of panel-data regression by group of law system,

civil, common, and mixed law systems, are in table 3.8. Panel A: ROA and Panel B:

ROE, the result shows that there are significantly and negatively of SOE dummy

variable on ROA, but positively significant of SOExT2008-9 in civil law system

sample.

Panel C: TobinQ, the result shows that there are significantly and

negatively of SOE dummy variable on TobinQ in common law system sample.

Moreover, there is a significantly and positively of SOExT2008-9 on TobinQ in civil

law system sample.

Panel D: Return, the result shows that there are significantly and positively

of SOE dummy variable on return, and SOExT2008-9 in civil law system sample.

While, there are significantly and negatively of SOE dummy variable on return, but

positively significant of SOExT2008-9 in mixed law system sample.

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Table 3.8 Panel-data regression by group of law system

Panel A: ROA Civil Common Mixed

SOE -0.0059** -0.0161 -0.0058

T2008-9 -0.0072*** -0.0135* 0.0011

SOExT2008-9 0.0083** 0.0211 -0.0134

Market Cap 0.0132*** 0.0309*** 0.0117***

Long-term debt -0.0122*** 0.0049*** -0.1900***

Short-term debt -0.0306*** -0.0055*** -0.1176***

Cash holdings 0.0162*** -0.0245*** 0.0410***

Profit 0.0669*** 0.1780*** 0.1853***

Book-to-market 0.0014*** 0.0180*** 0.0042***

1/P 0.0001 -0.0005*** -0.0005***

Constant -0.0460*** -0.1645*** -0.0052

N 83271 27665 17476

No. group 4573 1543 961

Degree of freedom 10 10 10

RMSE 0.0709 0.3272 0.0861

R2_overall group 0.1025 0.1579 0.4142

Chi-Square 7091.2428 4878.6313 12041.9006

p 0 0 0

Panel B: ROE Civil Common Mixed

SOE -1.6686** -7.0870*** -3.4809

T2008-9 -1.7931*** -0.2842 1.0445

SOExT2008-9 2.2417** -0.3988 0.7699

Market Cap 3.5880*** 6.8123*** 3.4149***

Long-term debt -1.3374*** 0.1786*** -25.1891***

Short-term debt -8.7993*** -0.2543*** -27.0415***

Cash holding 3.4214*** -0.5742* 0.1596

Profit 17.9745*** 7.5181*** 54.5686***

Book-to-market 0.6500*** 4.0731*** 2.6643***

1/P 0.0479*** -0.0590*** -0.0587

Constant -15.4909*** -33.7630*** -11.7393***

N 77080 25207 15857

No. group 4573 1543 961


RMSE 18.2696 38.3446 40.816

R2_overall group 0.0939 0.1171 0.072

Chi-Square 6315.1928 2124.968 1070.5215

p 0 0 0

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Table 3.8 Continued

Panel C: TobinQ Civil Common Mixed

SOE 0.1372 -0.4897*** -0.1740

T2008-9 -0.2054*** -0.2531*** -0.1199*

SOExT2008-9 0.2795*** 0.0922 0.1517

Market Cap 0.1456*** 0.1541*** 0.0942***

Long-term debt -0.0208 -0.0077*** 1.2225***

Short-term debt 0.1683*** 0.0064*** 0.0637

Cash holdings 0.0256 0.0413*** -0.0154

Profit -0.3644*** -0.1955*** -2.5107***

Book-to-market -0.0789*** -0.2245*** -0.1795***

1/P -0.0035* -0.0006 -0.0045

Constant 0.8071*** 1.2559*** 1.0366***

N 81868 27547 17444

No. group 4573 1543 961


RMSE 1.9789 2.0198 2.0163

R2_overall group 0.0183 0.0526 0.0369

Chi-Square 872.0866 941.7966 761.3795

p 0 0 0

Panel D: Return Civil Common Mixed

SOE 0.0382*** -0.0088 -0.0806***

T2008-9 -0.1957*** -0.3191*** -0.2837***

SOExT2008-9 0.1440*** 0.2296*** 0.2360***

Market Cap 0.0320*** 0.0212*** 0.0265***

Long-term debt -0.0336*** 0.0006 -0.1515***

Short-term debt -0.0739*** -0.0008 0.0106

Cash holdings 0.0420*** 0.0007 0.0013

Profit 0.2789*** 0.0167* 0.2671***

Book-to-market -0.0220*** -0.0520*** -0.0538***

1/P -0.0012*** -0.0013*** -0.0025***

Constant 0.0090 0.2084*** 0.1715***

N 79392 26493 16467

No. group 4573 1543 959


RMSE 0.5694 0.7247 0.5949

R2_overall group 0.0366 0.0411 0.0567

Chi-Square 3018.1641 1134.9412 989.8379

p 0 0 0

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Table 3.8 shows panel-data regression by group of law system, civil, common,

and mixed law systems. SOE is dummy variable of state-owned enterprise of firms that its

value equals to 1 if firm has government ownership greater than 25 percent, and 0 otherwise.

T2008-9 is time dummy variable that its value equals to 1 if year equals to 2008 - 2009, and 0

otherwise. SOExT2008-9 is an interaction term between SOE and time dummy of crisis.

Market Cap is the logarithm of stock market capitalization of firms. Long-term debt and Short-

term debt are long-term debt and short-term debt divided by total assets. Cash holdings is cash

holdings divided by total assets. Profit is operating income divided by total assets. Book-to-

market is book value of equity divided by market value of equity. 1/P is one divided by price

of stock. This study also includes dummy variables for control. * p < 0.05, ** p < 0.01, *** p <

0.001

For the propensity score-matching, Table 3.9 shows that effect of SOE on

performance of firm by using the treatment effects and propensity-score matching method. The

set of control variables for matching are stock market capitalization, operating profit, region,

industry, and law system. ATT is average treatment effects of treatment on treated (SOE firm)

on performance. The ATE are significantly positively on performance variables except TobinQ.

Table 3.9 Effect of SOE on performance of firm by using the treatment effects and

propensity-score matching method

Variable Sample Treated Controls Difference S.E. t-stat

ROA Unmatched 0.0355 0.0253 0.0101 0.0018 5.6

ATT 0.0355 0.0241 0.0114 0.0024 4.68

ROE Unmatched 6.4260 4.8899 1.5360 0.3594 4.27

ATT 6.4260 6.3073 0.1186 0.4171 0.28

TobinQ Unmatched 1.6946 1.4541 0.2405 0.0439 5.48

ATT 1.6946 1.5696 0.1250 0.0261 4.8

Return Unmatched 0.2408 0.1641 0.0767 0.0071 10.82

ATT 0.2408 0.1911 0.0497 0.0114 4.35

SOE is dummy variable. SOE eqauls to 1 when firm is state-own enterprise.

Average Treatment Effects on the Treated (ATT) of SOE on performance of firms by using

psmatch2 model. The set of control variables for matching are stock market capitalization,

operating profit, region, industry, and law system. Number of observations equals to 96,338.

Number of SOE observation equals to 3,438.

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3.6 Conclusion

This study focuses on state-own enterprises as a proxy of agency cost in

governance in the world context. The study uses DiD and treatment effect technique

with panel data and propensity score matching to determine effect of ownership

structure of SOE to firm performance. The treatment effects in this model are

interaction term of government ownership (SOE) and crisis, and interaction term of

government ownership (SOE) and law system. Based on DiD test, the result shows that

there are significantly and negatively SOE dummy variable on firm performance.

However, interaction term SOE during crisis period 2008 to 2009, are positively

significant to firm performance. The results support property right theory, theory of

firm, and hypothesis of this study that state-owned enterprise significantly less performs

than private firms.

Another treatment effect of SOE is law system in SOE, civil and common

law system are positively significant to ROE and return. While state-owned firm with

common law system positive significantly to ROA, which inconclusive with prior

literature that civil law system is better than common law system in term of minority

protection and process (La Porta et al., 2000). The results about law system results is

confirmed with by subsample panel-data regression by law system, that negatively

significant in SOE dummy variable but positively significant in interaction term.

In addition to treatment effect by panel-data regression, propensity score

matching is employed to investigate effect of SOE on firm performance. For the

propensity score-matching, shows that effect of SOE on performance of firm by using

the treatment effects and propensity-score matching method. After control matching for

all aspect of charateristics of firm like firm financial health, stock market capitalization,

operating profit, region, industry, law system, and governament ownership, the results

show that the ATE are significantly positively on performance variables except

TobinQ. However, in treatment effect with propensity score matching, SOE has

positive effect on firm performance. Therefore, the hypothesis that SOE firm less

perform than non-SOE firm is contrary to the results.

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CHAPTER 4

CORPORATE GOVERNANCE AND LIQUIDITY

4.1 Introduction

According to the separation of ownership and control (Fama & Jensen,

1983) and information asymmetry, stock liquidity becomes important because it affects

value of the firm (Amihud & Mendelson, 2008). Though researchers categorize

meaning of liquidity into different terms based on areas of study such as micro and

macro levels of liquidity, its concept is not clear (Benson, Faff, & Smith, 2015). In asset

pricing, a number of previous studies document the positive relationship between

liquidity and stock return. Specifically, low information asymmetry improves liquidity

in the stock market because informed and uninformed traders are likely to know the

same set of information. Thus, high liquidity is associated with high stock return.

Moreover, corporate governance affects stock liquidity by the flow of information

asymmetry which causes adverse selection and hence affects liquidity (Glosten & Milgrom,

1985). Prior literature examines corporate governance and stock liquidity and demonstrates that

high corporate governance is associated with high liquidity (Chung et al., 2010&2012; and

Prommin et al., 2014). In particular, high corporate governance suggests high public

information that leads to low information asymmetry. Meanwhile, Lei et al. (2013) study the

relationship among stock liquidity, corporate governance, family firm, and state-owned

enterprise in China and find the consistent evidence with recent literature. However, those

studies typically use specific characteristics of corporate governance that represent all

perspectives of corporate governance rather than a general corporate governance index.

This study concentrates on a panel analysis for corporate governance and

stock liquidity in Thailand. The main objective is to assess whether corporate governance

has an impact on stock liquidity of listed firms that employs data on corporate governance

index of Thai Institute of Director (Thai IOD) and illiquidity measure of Amihud (2002).

This analysis employs Random-effects Tobit Model and Fixed-effects Quantile

Regression Model that consider the positively skew distribution of stock liquidity

measured by Amihud’s illiquidity. The sample period ranges from 2006 to 2017.

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The main finding demonstrates the consistent evidence with recent

literature that firms with high corporate governance have high stock liquidity. The

positive impact of corporate governance on stock liquidity is more pronounced for firms

with good corporate governance scores (3-star, 4-star and 5-star). This empirical

evidence exhibits that listed firms with better corporate governance score have lower

information asymmetry that leads to an increase in their stock liquidity because

investors have more confidence in stocks of these firms and subsequently trade more

on their stocks.

This study is organized as follows. In the next section, literature review of

stock liquidity measure and the constructed corporate governance index. Section 3

describes the data and methodology. Section 4 provides empirical results, and section

5 contains summary and conclusion.


4.2.1 Trading under information asymmetry and adverse selection

Information asymmetry is a friction in security markets. Adverse

selection theory describes an influence of asymmetric information on market liquidity

as follows. Since a lack of information transparency weakens trading’s decisions,

informed traders normally get more benefits which is on cost to counterparties. In order

to protect their interest, uninformed traders and market-makers widen bid-ask spread to

insurance the risk of missing information (Copeland & Galai, 1983; Glosten &

Milgrom, 1985). Moreover, Kavajecz (1999) studies trading activities around

information events. With an intension to manage their exposure to information opacity,

market-makers also lower bid and ask quoted sizes. Therefore, information asymmetry

deteriorates both market width and depth which are the key measures of market

liquidity.

Prior studies suggest a strong link between information asymmetry and

corporate governance practice. Leuz & Verrecchia (2000) focus on a change of

financial reporting standard in German companies. Under better disclosure

environment, firms’ information asymmetry is reduced. Diamond (1985) & Verrecchia

(2001) specify that voluntary disclosure enhances firms’ public information which

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declines information cost and asymmetric information. Whereas, quality of voluntary

disclosure is influenced by ownership structure and board composition (Eng & Mak,

2003). Ajinkya, Bhojraj, & Sengupta (2005) and Karamanou & Vafeas (2005) examine

an effect of board structure, institutional ownership, and audit committee on quantity

and quality of earnings forecast. They indicate that information fairness can be

improved by corporate governance policy. Also, Kanagaretnam, Lobo, & Whalen

(2007) investigate quarterly announcement periods. Their results indicate that board

independence, board activity, and corporate insiders’ stock holdings lessen bid-ask

spreads which is used as a proxy of information asymmetry. Besides, (Cormier,

Ledoux, Magnan, & Aerts, 2010) and Cai, Liu, Qian, & Yu (2015) show that corporate

governance practices, such as monitoring activities and voluntary disclosures, decrease

information asymmetry. While, Elbadry, Gounopoulos, & Skinner (2015) add that

managerial monitoring is driven by level of board independence, board activeness,

executive compensation and debt financing.

On the other hand, good governance can also escalate information

asymmetry when costs of disclosure are high (Bamber & Cheon, 1998; Verrecchia,

1983). Furthermore, sophisticated investors have better information processing ability

than unsophisticated investors. The announcement, that provides new information to

both types of investors, intensifies information gap (Coller & Yohn, 1997; O. Kim &

Verrecchia, 1994; Lee, Mucklow, & Ready, 1993). Still, (Amiram, Owens, &

Rozenbaum, 2016) study analyst forecast announcements. They propose that

information from analyst forecast does not provide additional information to

sophisticated traders. But it is valuable for ingenuous ones. Consequently, analyst

forecast announcements decrease information asymmetry.

4.2.2 Corporate governance and liquidity

Considering many dimensions of governance, plenty of governance

measures are constructed by institutions and researchers(Jackson, 2013; Lei et al., 2013;

Prommin et al., 2014; Tang & Wang, 2011), for example, the Environmental, Social,

and Governance of corporate (ESG) by Thomson Reuters Corporate Responsibility

Ratings, Transparency index, Corruption index, World Governance Index (WGI) by

World Bank, and International Shareholder Services (ISS). Researchers mostly

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constructed governance indices by employing equally weighed technique, however

other methodologies also existed (Jackson, 2013).

There are many literatures regarding an association between corporate

governance, information asymmetry, and liquidity. Diamond & Verrecchia (1991),

Welker (1995), and Healy, Hutton, & Palepu (1999) do not only demonstrate that

information disclosure diminish asymmetric information. But they also conclude that

information disclosure encourages more investors which increase securities’ liquidity.

Some researchers conclude that regulatory environment, which is external corporate

governance factor, enhance market liquidity (Bacidore & Sofianos, 2002; Brockman &

Chung, 2003; H. Chung, 2006).

Chung, Elder, & Kim (2010) is the pioneer paper that emphasis on the

impact of internal corporate governance attributes on stock liquidity. By using US data

during 2001 to 2004, they construct their own index based on 24 governance standards.

Panel regression’s results reveal that stock liquidity is significantly improved by

corporate governance policy. By employing limited corporate governance

characteristics and liquidity proxies with Malaysian listed companies in 2007, Foo &

Zain (2010) support Chung, Elder, & Kim (2010). The same conclusions are found in

155 French stocks during 2008 and 2009 (Karmani & Ajina, 2012). By using survey

data from 25 international markets during 2003 to 2010, Chung, Kim, Park, & Sung

(2012) indicate positive impact of shareholder protection right on stock liquidity. Li et

al. (2012) examine the association in Russian companies. They document that liquidity

enhances corporate governance. Tang & Wang (2011) and Lei, Lin, & Wei (2013) study

Chinese market over the period from 1999 to 2004 and 2006 to 2008. They are not only

backing Chung, Elder, & Kim (2010), but the latter also propose the effect of different

types of agency conflicts on the relationship between corporate governance and stock

liquidity. Edmans et al. (2013) employ Amihud’s liquidity measure with panel

regression. The positive relationship between liquidity and the likelihood of

blockholder formation is documented. While, Cueto & Switzer (2013) analyze the

association by using Brazil and Chile intraday data. As high concentration of ownership

structure is a proxy of weak minority shareholder protection, dominant shareholders do

not decrease market liquidity. Because the dominant owners have to maintain a low-

cost exit strategy. Jackson (2013) shows conflicting findings. 71 Caribbean firms with

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concentrated ownership are linked to lower liquidity during 2005 to 2011. By

constructing four-dimensions corporate governance index, (Prommin et al., 2014) and

(Prommin et al., 2016) denote the similar results as Chung, Elder, & Kim (2010). The

positive relationship between governance quality and liquidity in found within firm-

level. However, they investigate the relationship by employing only 100 largest stocks

in Thailand for a small period between 2006 and 2009. On the other hand, Fu et al.

(2015) suggest that family firms have more liquidity than the others due to effective

monitoring activities and lower agency problem. As prior studies are mostly affected

by limited sample and inadequate liquidity proxies, Ali, Liu, & Su (2017) examine the

influence of corporate governance quality (CGQ) index on various types of liquidity

measure by using 1,207 Australian listed firms for the period of 2001 to 2013. Their

results show a positive relationship between corporate governance and stock liquidity.


This study considers information asymmetry as corporate governance

proxy that affect stock liquidity. Trading under information asymmetry leads to an

adverse selection problem of investor behavior, uninform traders , dealer (Glosten &

Milgrom, 1985).

This study focusses on Thailand which is an emerging market and

transition in corporate governance since Asian financial crisis in 1997. This study

categorizes firm by level of corporate governance ranking into groups to investigate

effect of corporate governance to stock liquidity.

H3: High corporate governance firm has more stock liquidity than low

corporate governance firm.

4.3 Data and data description

4.3.1 Data

This study uses listed company in the stock exchange of Thailand. Data

are retrieved from Thomson Reuters Eikon. The data consists of 395 companies, during

2006 – 2017. Total observation is 4,740 firm-year.

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4.3.1.1 Corporate governance of the firms

This study uses corporate governance index by Thai Institute

of Director (Thai IOD). Data collected from Thai IOD website. This data is publicly

available and generally used by financial institution, for example mutual fund that has

objective of investment on good corporate governance.

4.3.1.2 Liquidity measure

This study uses liquidity measure follow (Prommin et al.,

2014), which is includes Illiquidity ratio (Amihud, 2002). Illiquidity ratio Amihud's

(2002) the average ratio of the daily absolute stock return to trading volume on one-

time period.

1

1/

iyD

iy iyd iydtiy

ILLIQ R VOLDD =

= (15)

where Riyd represents the return on stock i on day d of year y, VOLDiyd is the respective

daily volumet, and Diy is the number of days when data are available for stock i in year

y (Amihud, 2002).

4.3.1.3 Control variables

This study uses firm characteristic as control variables, total

assets, stock price, return of stock, stock return volatility, firm age, institutional

ownership, industry, country, and year dummy follow Chung et al. (2010).

4.3.2 Methodology

4.3.2.1 Panel Random-effects Tobit Model

Based on panel data, this study employs Panel Random-effects

linear regression model to analyze the impact of level of good corporate governance of

the firm on level of illiquidity of that particular firm (Roberts & Whited, 2011).

Following Chung et al., (2010); Lei et al., (2013), the Panel Random-effects regression

model can be stated as follows.

it it itILLIQ X u= + (16)

and it i itu = + , 1,2, ,i N= , 1,2, ,t T=

where ILLIQ is illiquidity measure that is Amihud’s Illiquidity ratio (Amihud, 2002).

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Xit is NTx8 matrix of independent and control variables

1it it it it it it it

it

X GovIndex Return Volatility FirmSize Age TradingVolumeprice

=

(17)

GovIndexit is corporate governance index of Thai Institute of

Directors of stock i in year t. Priceit is price of stock i in year t. Returnit is return of

stock i in year t. Volatilityit is stock return volatility of stock i in year t. FirmSizeit

represents firm size which is measured by total assets of stock i in year t. Ageit is firm

age of stock i in year t. TradingVolumeit is trading volume of stock i in year t. i is

cross-sectional Random-effects of stock i. it is stochastic random error term of stock i

in year t.

However, since the dependent variable, ILLIQit (Illiquidity

ratio), has positively skew distribution with very high extreme values, random-effects

upper bound Tobit model is also applied in order to avoid biased result which is caused

by the high extreme values. Figure illustrates distribution of Amihud’s illiquidity

(ILLIQit) of the data in this study, which show extremely positively skew distribution.

Figure 4.1 Histogram of Amihud’s Illiquidity (ILLIQit)

0

.00

2.0

04

.00

6

Den

sity

0 1000 2000 3000 4000 5000Amihud

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4.3.2.2 Panel Random-effects Tobit Model

The random-effects upper bound Tobit model can be stated as:

it it it

it

it

X u if ILLIQILLIQ

if ILLIQ

+ =

(18)

and it i itu = +

where represents the upper limit censored point.

This Random-effects upper bound Tobit model is estimated by

Maximum Likelihood estimation using Guass-Hermite Quadrature method.

4.3.2.3 Panel Fixed-effects Quantile Regression Model

Alternative model, panel fixed-effects quantile regression

model, is also applied.

( )it i it itQ ILLIQ X = + + (19)

where ( )itQ ILLIQ represents quantile of ILLIQ of stock i at year t. i is cross-

sectional fixed-effects.

This panel fixed-effects quantile regression model is estimated

by using Markov Chain Monte Carlo (MCMC) methods.

4.3.2.4 Robustness Check

In order to ensure the results of the study, nonparametric test,

Goodman & Kruskal Gamma, is employed. The test attempt to test the rank correlation

between the two ordinal variables. Since IOD’s Corporate Governance Index is

measured as ordinal level measure variable, thus, rank order correlation should also be

measured to reveal the relationship with liquidity level. Hence, to perform the tests,

ILLIQ is transformed from ratio level measure to be ordinal measure based on its

quartile, as ILLIQ_Level, which has value range from 1 to 4. Then, Goodman & Kruskal

Gamma of the rank correlation between GovIndex and ILLIQ_Level can be computed.

Goodman & Kruskal Gamma can be determined by the following formula:

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c d

c d

N NGamma

N N

+=

− (20)

where Gamma is rank correlation, value ranges between -1 to 1. Nc is the total number

of concordant pairs. Nd is the total number of discordant pairs.

Additionally, multivariate analysis of the ordinal level measure

of Amihud’s illiquidity, ILLIQ_Level, using Random-effects Ordered Probit model is

also estimated. The model can be stated as:

it it itI X u= + (21)

and it i itu = +

1

1 2 1

1 2 3 1 2

1 2 3

Pr( _ 1) ( )

Pr( _ 2) ( ) ( )

Pr( _ 3) ( ) ( )

Pr( _ 4) 1 ( )

it it

it it it

it it it

it it

ILLIQ Level I

ILLIQ Level I I

ILLIQ Level I I

ILLIQ Level I

= = +

= = + + − +

= = + + + − + +

= = − + + +

(22)

where Iit is unobservable latent variable of ordered probit model. (.) is cumulative

normal probability distribution function. j is threshold value at level j. j = 1, 2, 3.

This Random-effects Ordered Probit model is estimated by

Maximum Likelihood estimation using Guass-Hermite Quadrature method.

4.4 Empirical results

This study collects stock data from Thomson Reuters Eikon, and corporate

governance index from Thai Institute of Directors during 2006-2017. The analysis

includes both annual and monthly data for the result verification. The data are from

total of 364 listed companies in the stock exchange of Thailand (SET).

4.4.1 Descriptive statistics

To determine whether there exists the differences between annual and

monthly data, descriptive statistics of the two data are separately shown in Panel A and

Panel B of table 4.1, respectively. illustrates descriptive statistics of Amihud’s

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illiquidity, all data and categorized by corporate governance index of Thai Institute of

Directors (IOD), and the factors determining liquidity, including price inverse, stock

return volatility, firm age, size, turnover by volume.

Table 4.1 Descriptive statistics of stock return and liquidity measures.

Panel A: Annual Data

Variable Obs Mean Median Std. Dev. Min Max

Illiq 2,977 112.7085 3.5022 302.1050 0.0000 4783.2490

CG No-star 811 116.8941 4.1077 280.1586 0.0000 2396.8790

CG 3-star 902 155.8630 6.1194 333.8065 0.0000 4359.7980

CG 4-star 901 92.2577 3.0384 319.4313 0.0002 4783.2490

CG 5-star 363 46.8856 0.2432 183.3089 0.0001 2084.5550

1/Price 2,977 0.4894 0.1980 1.2567 0.0013 33.3333

Return Volatility 2,977 0.0553 0.0287 0.3947 0.0013 12.4128

Firm age 2,977 23.4068 23.8028 8.2900 10.8389 42.9972

Ln(Firm Size) 2,977 15.6822 15.3448 1.6965 11.2037 21.8458

Ln(Turnover by Volume) 2,977 10.8437 10.8766 4.4799 0.5596 23.5943

Panel B: Monthly Data

Variable Obs Mean Median Std. Dev. Min Max

Illiq 53,479 94.6613 0.5273 356.2681 0.0000 9881.1620

CG No-star 18,752 135.9254 0.7901 438.7916 0.0000 9881.1620

CG 3-star 15,189 104.4503 1.2660 361.5682 0.0000 7774.7830

CG 4-star 14,131 59.6603 0.3453 274.0753 0.0000 5905.4650

CG 5-star 5,407 15.5283 0.1035 92.2504 0.0000 2175.2220

1/Price 53,479 0.7366 0.1905 3.8962 0.0003 100.0000

Return Volatility 53,479 0.0956 0.0510 0.2335 0.0000 15.0625

Firm age 53,479 17.1174 17.2868 8.6402 0.0411 42.6585

Ln(Firm Size) 53,479 11.0066 11.3318 2.8721 1.2321 24.0579

Ln(Turnover by Volume) 53,479 8.9640 9.6499 3.4891 -2.3026 19.3114

Table 4.1 shows descriptive statistics of variables in this study during

2006-2017. Illiq is Amihud illiquidity ratio, all data and categorized by corporate

governance index of Thai Institute of Directors (IOD). 1/Price is an inverse of stock

price data. Return Volatility is the standard deviation of stock return. Firm age is age

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of firm from IPO date. Ln(Firm Size) is measured by logarithm of total assets.

Ln(Turnover by Volume) is measured by logarithm of turnover by volume.

For both annual and monthly data, mean and median of Amihud’s

illiquidity are differences among different groups of corporate governance. High level

of corporate governance groups, 4-star and 5-star firms, have lower mean and median

of Amihud’s illiquidity, implying that they have higher liquidity compare to those with

lower level of corporate governance. Additionally, with the hugh differences between

mean and median of Amihud’s illiquidity, it indicates that this variable, Amihud’s

illiquidity, has positively skew distribution with very high positive extreme value (the

maximum value is very high). This positively skew distribution suggests that the

econometric model of this dependent variable should be either Panel Random-effects

Tobit Model or Panel Fixed-effects Quantile Regression Model.

Descriptive statistics of the control variables, including price inverse,

stock return volatility, firm age, firm size, and turnover by volume, show relatively

symetric distribution since their mean and median are less different with relatively

moderate standard deviation.

4.4.2 Estimated results of econometric models

The estimated results of Random-effects Linear Model, Random-

effects Tobit Model, and Fixed-effects Quantile Regression Model using annual data

and monthly data are illustrated in and table 4.2, respectively. Based on the estimated

results using annual data in table 4.2, the estimated results of Random-effects Linear

Model reveal positive significant impact of 3-star CG score on Amihud’s illiquidity,

which is opposite direction from that suggested by the theory, and insignificant negative

impacts of 4-star and 5-star. These unfavorable results might be caused by the positively

skew distribution of the dependent variable, Amihud’s illiquidity. To cope with this

problem, Random-effects Tobit Model and Fixed-effects Quantile Regression are

estimated. According to the results of Random-effects Tobit Model, 3-star CG score

indicates insignificant positive coefficient, implied no impact, while 4-star and 5-star

reports significant negative impacts on illiquidity. Negative impacts of corporate

governance are also confirmed by the estimated results of Fixed-effects Quantile

Regression Model. All corporate governance variables, 3-star, 4-star, and 5-star reveal

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negative impacts on Amihud’s illiquidity. These results imply that good corporate

governance can lead to higher market liquidity of the listed company. This conclusion

is also confirmed by the estimated results of all three models using monthly data. As

shown in table 4.3, the estimated results of all three models illustrate negative impacts

of corporate governance index, 3-star, 4-star, and 5-star, on Amihud’s illiquidity.

Table 4.2 Estimated Results of Random-effects Linear Model, Random-effects Tobit

Model, and Fixed-effects Quantile Regression Model using Annual Data

RE-Linear RE-Tobit FE-QReg

cg3 24.8099 * 0.2229 -1.0934

cg4 -7.0201 -0.4572 ** -14.0985 ***

cg5 -0.3996 -1.3231 *** -4.9257 ***

priceinverse 17.5850 *** 0.4501 *** 20.9306 ***

volatility 49.9615 *** 0.9726 *** 29.7871 ***

age -0.7653 -0.0728 *** -0.4736 ***

lnta 2.1368 0.0753 * 1.4423 ***

lntv -30.4129 *** -1.1105 *** -11.5906 ***

Constant 410.1833 *** 18.1968 ***

sigma_u 1.7872 ***

sigma_e 3.3717 ***

N 2977 2977 2977

No Group 364 364 364

Chi-square 777.19 *** 2733.61 ***

Overall R2 0.2075

* p < 0.05, ** p < 0.01, *** p < 0.001

RE-Linear is Random-effects Linear Model. RE-Tobit is Random-

effects Tobit Model. FE-QReg is Fixed-effects Quantile Regression Model. cg3 is

dummy variable of IOD corporate governance index, value equals to 1 for 3-star and 0

otherwise. cg4 is dummy variable of IOD corporate governance index, value equals to

1 for 4-star and 0 otherwise. cg5 is dummy variable of IOD corporate governance index,

value equals to 1 for 5-star and 0 otherwise. priceinverse is an inverse of stock price

data. volatility is the standard deviation of stock return. age is age of firm from IPO

date. lnta represents size of the firm measured by logarithm of total assets. lntv

represents turnover of the stock measured by logarithm of turnover by volume.

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Based on the estimated results of both annual data and monthly data,

all control variables do have significant impacts on Amihud’s illiquidity as suggested

by the conceptual framework. Inverse of stock price (priceinverse), volatility of stock

return (volatility), and size of the firm (lnta) have significant positive influence on

Amihud’s illiquidity. This implies that when price of stock increase or decrease,

liquidity of that stock will increase and decrease as well. Increase in volatility of stock

return will result in reducing liquidity of that stock. Smaller size firms, in term of total

asset, tend to have higher level of liquidity than those with bigger size.

Age of the firm (age) and turnover of the stock (lntv) all have

significant impacts on Amihud’s illiquidity. The results are according to what are

expected by the conceptual framework. The companies that have been listed for longer

period of time have more liquidity than those with shorter period of listing time. Higher

turnover of the stock leads to lower liquidity of that stock.

Table 4.3 Estimated Results of Random-effects Linear Model, Random-effects Tobit

Model, and Fixed-effects Quantile Regression Model using Monthly Data

RE-Linear RE-Tobit FE-QReg

cg3 -4.0607 ** -0.0453 -2.7144 ***

cg4 -2.2887 -0.2215 *** -3.8422 ***

cg5 -2.7418 -0.1668 ** -4.2833 ***

priceinverse 0.7860 *** 0.0180 *** 0.5381 ***

volatility 149.0707 *** 1.5877 *** 370.1578 ***

age -0.2912 -0.0500 *** -0.3468 ***

lnta 2.8783 *** 0.0410 *** 0.8160 ***

lntv -19.7908 *** -1.0099 *** -5.7240 ***

Constant 201.7362 *** 13.2204 ***

sigma_u 2.1887 ***

sigma_e 2.5556 ***

N 53479 53479 53479

No Group 364 364 364

Chi-square 2527.76 *** 16356.04 ***

Overall R2 0.1965

* p < 0.05, ** p < 0.01, *** p < 0.001

RE-Linear is Random-effects Linear Model. RE-Tobit is Random-

effects Tobit Model. FE-QReg is Fixed-effects Quantile Regression Model. cg3 is

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dummy variable of IOD corporate governance index, value equals to 1 for 3-star and 0

otherwise. cg4 is dummy variable of IOD corporate governance index, value equals to

1 for 4-star and 0 otherwise. cg5 is dummy variable of IOD corporate governance index,

value equals to 1 for 5-star and 0 otherwise. priceinverse is an inverse of stock price

data. volatility is the standard deviation of stock return. age is age of firm from IPO

date. lnta represents size of the firm measured by logarithm of total assets. lntv

represents turnover of the stock measured by logarithm of turnover by volume.

4.4.3 Robustness tests

In order to confirm the finding of this study, robustness tests are also

performed. The nonparametric testing results of Gamma rank correlation between

IOD’s Corporate Governance Index (GovIndex) and Level of Illiquidity (ILLIQ_Level)

using annual data and monthly data indicate significant negative rank correlation

between these two ordinal-measured variables. Table 4.6 and table 4.7 illustrate

frequency of observations categorized by IOD’s Corporate Governance Index

(GovIndex) and Level of Illiquidity (ILLIQ_Level) and Gamma rank correlation using

annual data and monthly data, respectively. Gamma values of -0.1964 and -0.2032

imply that firms with higher level of IOD corporate governance score can be expected

to have lower level of Amihud’s illiquidity (or higher liquidity).

Table 4.4 Frequency of Firm-year Categorized by IOD’s Corporate Governance Index

(GovIndex) and Level of Illiquidity (ILLIQ_Level).

ILLIQ_Level

GovIndex 1 2 3 4 Total

No Star 158 220 213 220 811 19.5% 27.1% 26.3% 27.1% 100%

3-Star 160 217 224 301 902 17.7% 24.1% 24.8% 33.4% 100%

4-Star 230 241 247 183 901 25.5% 26.8% 27.4% 20.3% 100%

5-Star 185 73 61 44 363 51.0% 20.1% 16.8% 12.1% 100%

Total 733 751 745 748 2,977 24.7% 25.2% 25.0% 25.1% 100%

Gamma = -0.1964***

Note: *** indicates significant at 0.01.

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Table 4.5 Frequency of Firm-month Categorized by IOD’s Corporate Governance

Index (GovIndex) and Level of Illiquidity (ILLIQ_Level).

ILLIQ_Level

GovIndex 1 2 3 4 Total

No Star 3,421 4,655 4,525 6,151 18,752 18.2% 24.8% 24.1% 32.8% 100%

3-Star 2,091 3,670 4,595 4,833 15,189 13.8% 24.2% 30.3% 31.8% 100%

4-Star 3,240 4,182 3,895 2,814 14,131 22.9% 29.6% 27.6% 19.9% 100%

5-Star 2,109 1,681 1,201 416 5,407 39.0% 31.1% 22.2% 7.7% 100%

Total 10,861 14,188 14,216 14,214 53,479 20.3% 26.5% 26.6% 26.6% 100%

Gamma = -0.2032***

Note: *** indicates significant at 0.01.

To reconfirm the robustness test of the rank correlation, Random-

effects Ordered Probit models are estimated using annual data and monthly data.

Table 4.8 reveals the estimated results of Random-effects Ordered

Probit Model using annual data and monthly data. The significant negative estimated

results of coefficients of corporate governance dummy variables (cg3, cg4, and cg5)

confirm that higher level of corporate governance score lead to lower level (rank) of

Amihud’s illiquidity.

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Table 4.6 Estimated Results of Random-effects Ordered Probit Model using Annually

Data and Monthly Data.

ILLIQ_Level Annual Data Monthly Data

cg3 0.1079 -0.0499 ***

cg4 -0.0423 ** -0.0533 **

cg5 -0.4179 *** -0.1383 ***

priceinverse 0.1218 *** 0.0521 ***

volatility 0.4347 *** 0.5012 **

age -0.0263 *** -0.0394 ***

lnta -0.1300 *** -0.0405 ***

lntv -0.3884 *** -0.6028 ***

1 -8.1586 *** -7.7730 ***

2 -6.6801 *** -5.9117 ***

3 -5.1983 *** -3.9149 ***

sigma_u 0.5391 *** 0.8298 ***

N 2977 53479

No Group 364 364

Log-likelihood -2584.96 -34739.76

Overall Chi-square Test 1587.47 *** 19072.99 ***

Chi-square-Bar 237.34 *** 8295.53 ***

* p < 0.05, ** p < 0.01, *** p < 0.001

cg3 is dummy variable of IOD corporate governance index, value

equals to 1 for 3-star and 0 otherwise. cg4 is dummy variable of IOD corporate

governance index, value equals to 1 for 4-star and 0 otherwise. cg5 is dummy variable

of IOD corporate governance index, value equals to 1 for 5-star and 0 otherwise.

priceinverse is an inverse of stock price data. volatility is the standard deviation of stock

return. age is age of firm from IPO date. lnta represents size of the firm measured by

logarithm of total assets. lntv represents turnover of the stock measured by logarithm

of turnover by volume.

According to the robustness tests results in table 4.4, table 4.5, and

table 4.6, these ordinal level measure of illiquidity analyses also help confirm impacts

of good corporate governance on liquidity of the stock. In addition, table 4.7 shows

average change of Amihud’s illiquidity (ILLIQ) caused by one level change in IOD’s

corporate governance index (GovIndex) during two sub-periods (2007-2011 and 2012-

2017).

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Table 4.7 Descriptive Statistical Indices of Change of Amihud’s Illiquidity (ILLIQ)

After Change in IOD’s Corporate Governance Index (GovIndex) during 2007-2011 and

2012-2017

Period 2007-2011 2012-2017

CG-Change 0 → 3 3 → 4 4 → 5 0 → 3 3 → 4 4 → 5

Firm-year (# obs.) 74 70 32 96 119 71

Mean 102.663 3.180 0.930 -20.514 -35.417 -4.455

Median 67.553 -1.981 -1.563 -0.008 -0.001 -0.001

Std. Dev. 446.413 459.553 373.120 75.107 131.842 22.906

Minimum -972.357 -1723.934 -569.291 -508.400 -702.241 -146.127

Maximum 1187.658 1390.975 1833.472 12.596 13.193 5.623

0 → 3 represents the case that the firm IOD corporate governance score

increases one level from no-star to 3-star. 3 → 4 represents the case that the firm IOD

corporate governance score increases one level from 3-star to 4-star. 4 → 5 represents

the case that the firm IOD corporate governance score increases one level from 4-star

to 5-star.

During the first sub-period 2007-2011, the cases of one level change

in IOD corporate governance score, including no-star to 3-star, 3-star to 4-star, and 4-

star to 5-star, have positive mean (increase) in Amihud’s illiquidity (102.663, 3.180,

and 0.930) with very high standard deviation (446.413, 459.553, and 373.120). Since

the median of the changes are a lot less than the mean, it implies that the magnitudes of

the changes in Amihud’s illiquidity caused by one level change in IOD corporate

governance score are positively skew distributed with high positive extreme value

(maximum).

Based on the second sub-period 2012-2017, all cases of one level

change in IOD corporate governance score, including no-star to 3-star, 3-star to 4-star,

and 4-star to 5-star, have negative mean (decrease) in Amihud’s illiquidity (-20.514, -

35.417, and -4.455) with moderately level of standard deviation (75.107, 131.842, and

22.906). The median of the changes are more than the mean, which indicates negatively

skew distribution of the changes in Amihud’s illiquidity with low negative extreme

value (minimum).

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The opposite direction of the impacts of improving corporate

governance during the first sub-period 2007-2011 and the second sub-period 2012-2017

indicate that before 2012, impact of improving corporate governance score does not

help increasing liquidity of the stock but instead reducing liquidity. After 2012,

improving one level of corporate governance score helps increasing liquidity of the

stock. Therefore, these findings also help confirm the relationship between good

corporate governance and liquidity.

4.5 Discussion & Conclusion

This study has provided evidences of relationship between good corporate

governance and stock market liquidity. Based on information asymmetry and adverse

selection concept, listed companies can provide investors their better operating

performance information by sending signal through good corporate governance

practice (Chung, et al., 2010; Prommin et al., 2014; and Prommin, et al., 2016). Better

corporate governance score of the stocks help reducing level of information asymmetry.

Then, investors have more confident in these stocks and trade more on these stocks,

thus, trading volumes of these stocks increase as well as stocks market liquidity. Unlike

other studies, this study takes into account of the positively skew distribution of stock

market liquidity measured by Amihud’s illiquidity by employing Random-effects Tobit

Model, and Fixed-effects Quantile Regression Model. Then, the estimated results of

this study reveal the significant impacts of corporate governance measured by IOD

corporate governance index (no-star, 3-star, 4-star, or 5-star) on stock liquidity

measured by Amihud’s illiquidity, which is in line with previous studies (Chung, et al.,

2010; Prommin et al., 2014; and Prommin, et al., 2016). Thus, hypothesis of the study

is confirmed. Similar to Chung, et al. (2010), the results suggest that listed companies

can alleviate information-based trading and improve stock market liquidity by

improving their corporate governance score, which can help lower information

asymmetry problem.

By changing scale of measurement of liquidity as ordinal level, robustness

test using nonparametric rank correlation and Random-effects Ordered Probit model

also reveal the ordinal-level relationship between IOD corporate governance score and

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level of liquidity. The findings of this study are remarkably robust to alternative

statistical tests and different scale of measurement of liquidity. Additionally, corporate

governance score improvement lead to higher level of liquidity. Based on sub-period

analysis, Thai investors seems not to value much on IOD corporate governance index

during 2007-2011 since the result indicate positive average change in Amihud’s

illiquidity. After 2011, Thai IOD reconstructed its corporate governance index, mutual

fund managers then took into account of corporate governance score of the stocks to

help forming their portfolio. As a result, corporate governance score improvement

during 2012-2017 reveals negative average change in Amihud’s illiquidity, which

imply increasing liquidity. Furthermore, similar to previous studies (Foo & Zain, 2010;

Chung, et al., 2010; Karmani & Ajina, 2012; Chung, et al., 2012; Lei, et al., 2013), the

estimated results of all regression models show significant effects of all control

variables on stock liquidity.

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APPENDIX

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APPENDIX A

WGI DATA SOURCES

1 ADB African Development Bank Country Policy and Institutional Assessments

2 IRP African Electoral Index

3 AFR Afrobarometer

4 ASD Asian Development Bank Country Policy and Institutional Assessments

5 BPS Business Enterprise Environment Survey

6 BTI Bertelsmann Transformation Index

7 HUM Cingranelli Richards Human Rights Database

8 EBR European Bank for Reconstruction and Development Transition Report

9 EIU Economist Intelligence Unit

10 FRH Freedom House

11 CCR Freedom House -- Countries at the Crossroads

12 GCB Global Corruption Barometer Survey

13 GCS Global Competitiveness Report

14 WMO Global Insight Business Condition and Risk Indicators

15 GII Global Integrity Index

16 GWP Gallup World Poll

17 HER Heritage Foundation Index of Economic Freedom

18 IFD IFAD Rural Sector Performance Assessments

19 IJT iJET Country Security Risk Ratings

20 WCY Institute for Management & Development World Competitiveness Yearbook

21 IPD Institutional Profiles Database

22 MSI International Research & Exchanges Board Media Sustainability Index

23 OBI International Budget Project Open Budget Index

24 LBO Latinobarometro

25 PRC Political Economic Risk Consultancy

26 PRS Political Risk Services International Country Risk Guide

27 PTS Political Terror Scale

28 RSF Reporters Without Borders Press Freedom Index

29 TPR US State Department Trafficking in People report

30 VAB Vanderbilt University's AmericasBarometer

31 VDM Varieties of Democracy Project

32 PIA World Bank Country Policy and Institutional Assessments

33 WJP World Justice Project Rule of Law Index

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BIOGRAPHY

Name Ms. Jutamas Wongkantarakorn

Date of Birth November 3, 1984

Educational Attainment 2007: Bachelor of Economics,

Thammasat University

2009: Master of Science (Finance),

Thammasat University

Work Position Lecturer

Rajamangala University of Technology

Rattanakosin

Scholarship Year 2012: Ph.D., Thammasat University

Scholarship

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