Impact of CorporateGovernance on leverage

and firm performanceA study of firms listed on the Official Market inMauritius

A dissertation submitted to University Of Mauritius in partial fulfilment of the

requirements for the degree of BSc (Hons.) Finance (Minor: Law), Faculty of Law

and Management.

Author: Hensley RAMOOGUR

April 2013

ii

ContentsList of tables..............................................................................................................................iv

List of figures............................................................................................................................iv

Acknowledgements....................................................................................................................v

Project declaration form....................................................................................................vi

Abstract ....................................................................................................................................vii

List of Abbreviations ............................................................................................................. viii

Chapter 1: Introduction ..............................................................................................................1

1.1 Purpose of study...............................................................................................................2

1.2 Objectives of the study.....................................................................................................2

1.3 Potential Contributions of the study ................................................................................3

1.4 Delimitations of the study................................................................................................4

Chapter 2: Literature Review.....................................................................................................5

2.1 Introduction of Corporate Governance in Mauritius .......................................................5

2.2 Theoretical Background...................................................................................................6

The Agency Theory ...........................................................................................................7

The Stakeholder Theory.....................................................................................................7

The Stewardship Theory....................................................................................................8

2.3 Empirical background......................................................................................................9

2.3.1 Corporate governance and Capital structure.............................................................9

2.3.2 Corporate Governance and Performance of Firms .................................................10

2.4 Corporate governance Mechanisms...............................................................................12

Ownership Structure ........................................................................................................12

Ownership concentration .................................................................................................13

Board Size........................................................................................................................13

Board Composition ..........................................................................................................14

CEO duality .....................................................................................................................14

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Audit Committee Independency ......................................................................................14

Disclosure of Stakeholders’ interests; Corporate Social Responsibility (CSR) ..............15

Chapter 3; Research Methodology...........................................................................................16

3.1 Sample/ Research Design ..............................................................................................16

3.2 Independent and Controlling Variables .........................................................................17

3.3 Sources of data...............................................................................................................18

3.4 Proxies used ...................................................................................................................18

3.5 Model Specification .......................................................................................................19

The Multiple OLS Regression .........................................................................................20

Fixed versus Random Effects ..........................................................................................20

F-test for Fixed Effects ....................................................................................................21

Breusch-Pagan LM Test for Random Effects ..................................................................21

Hausman Test for Comparing Fixed and Random Effects ..............................................21

Chapter 4; Data Analysis and Results......................................................................................22

4.1 Descriptive statistics ......................................................................................................22

4.2 Corporate Governance and capital structure..................................................................25

4.2.1 Correlation ..............................................................................................................25

4.2.2 Regression results and discussion...........................................................................27

4.3 Corporate Governance and Firm Performance ..............................................................34

4.3.1 Correlation ..............................................................................................................34

4.3.2 Regression results and discussion...........................................................................35

ROA as dependent ...........................................................................................................35

Tobin’s Q as dependent variable......................................................................................37

Altman Z Score as dependent variable ............................................................................39

Chapter 5 Conclusion and Recommendation...........................................................................40

List of References ....................................................................................................................42

Appendices...............................................................................................................................48

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List of tables

Table 1: Number of firms in the sample _________________________________________16

Table 2 List of independent variables ___________________________________________17

Table 3: Dependent variables_________________________________________________18

Table 4: Descriptive statistics _________________________________________________22

Table 5: Correlation capital structure and corporate governance _____________________26

Table 6: Regression results, capital structure and corporate governance _______________29

Table 7 Multiple regression result: leverage and corporate governance ________________30

Table 8 Correlation: Corporate governance and performance________________________34

Table 9 Regression results: ROA and governance (Fixed Effect model) _________________35

Table 10 Regression result for Tobin's Q and governance ( OLS) ______________________37

Table 11 Regression result; Altman Z Score and corporate governance ( OLS) ___________39

List of figures

Figure 1 Hausman test for leverage and corporate governance .......................................................................... 54

Figure 2 Fixed effects (within) estimation for leverage and corporate governance, including F-test................. 55

Figure 3 OLS estimation for leverage and corporate governance ........................................................................ 55

Figure 4 LSDV estimation for leverage and corporate governance ...................................................................... 56

Figure 5 Random effects estimation for leverage and corporate governance ..................................................... 57

Figure 6 LSDV estimation for roa and corporate governance............................................................................... 58

Figure 7 regression, anova and VIF results for Tobin's Q and corporate governance .......................................... 59

Figure 8 regression, anova and VIF results for Altman Z Score and corporate governance ................................. 60

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Acknowledgements

I gained a lot of experience and learnt a lot about the corporate world and its

principles when working my research. While browsing through annual report, I became

familiar to standard presentation and guidance of company’s financial statements. Also, I

understood the basics of corporate governance conduct and its importance which deter

fraudulent act. Completing this dissertation was a formidable task of intimidating length and

exacting expectations. Praise the lord on whom I always have faith.

I am desirous for expressing my gratitude to my supervisor, Mr Mudhoo Dourgeswar

who has provided scholastic guidance, utmost cooperation, clues, reviews, feedbacks as well as

critical comments and suggestions to improve my work. His valuable guidance was vital for

the completion of my study.

I am indebted to my parents and family who always encourage and influence me in all

aspects of my life. I am also grateful to my best friends Girish, Keshav, Nivenda, Rajneesh,

Urnesh and Sreshta who are always my best support. I am also thankful to Kunal and Vithi

who always had the words to cheer me up, for providing me all the necessary support and

encouragement. Finally, I wish to express my feeling of gratitude to my fellow classmates.

I have received much useful advice throughout the writing of my dissertation, but all

the faults that remain are obstinately my own. Lastly, I would like to dedicate this

dissertation especially to my grandpa and late grandma for both of them have showered their

love in my upbringing.

(Akshay Ramoogur)

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Project declaration form

vii

Abstract

If companies are governed properly and the interests of all stakeholders are taken care

of, a healthy corporate culture could be built. There exist very few research on this field in

Mauritius but yet is a concern. At the heart is the agency theory which according to Jensen, if

agency costs are reduced, the firm performs better and increases firm value. The theory

specifically emphasises on board independence and CEO duality. Furthermore, various

theories about corporate governance were developed but its effect on firm performance is not

quite measurable. The purpose of the present study is twofold. First we have to produce

quantitative information about the present corporate governance system in Mauritius and

critically analyse it. Second, we have to investigate whether there is any relationship between

features of corporate governance and performance of listed firms in the Official Market of

The Stock Exchange of Mauritius, and as such whether the agency problems is minimised in

Mauritius. A sample of 39 firms were analysed for the period 2007-2011. The ‘Ownership

Structure’, ‘Ownership Concentration’, ‘Board Independence’, ‘Board Size’, ‘Independent

Audit Committee’, ‘CEO duality’ and ‘Corporate Social Responsibility’ were considered as

core principles of corporate governance. Debt ratio was used to measure leverage and the

latter proved to have significant relationship with corporate governance. Performance was

measured by Return on Asset, Tobin’s Q and Altman Z-score. Various statistical models,

including correlation, OLS multiple regression, fixed and random effect model were used

coupled with appropriate tests. While most studies used a bivariate analysis, the study

employed a multivariate analysis. Some findings were consistent while some have opposite

views. The study answers some of past study questions like: what impact has corporate

governance created? (Implementation and Impact of Corporate Governance in Mauritius by

Mahadeo, J D and Soobaroyen, T ). Results indicate that the direction and the extent of impact of

governance are dependent on the performance measure being examined. Specifically, the

findings show that board equity, board size and size of the company affects performance.

Keywords: corporate governance, capital structure, firm performance, agency cost, Mauritius

viii

List of Abbreviations

Bank of Mauritius (BoM)

Central Depository, Clearing and Settlement System (CDS)

Companies Act (Co. Act)

Financial Services Commission (FSC)

Global Business Companies (GBC)

Institute of Directors (IOD)

International Accounting Standards (IAS)

International Accounting Standards (IFRS)

Mauritius Institute of Directors (MID)

National Committee on Corporate governance (NCCG)

Net Present Value (NPV)

Report on Code of Corporate Governance (RCCG)

Organization for Economic Co-operation and Development (OECD)

Stock Exchange of Mauritius Ltd (SEM)

Chapter 1: Introduction

Corporate governance has a buzzword in the corporate world. It is the most happening

area where several bodies across several countries are trying to improve the standards of

governance in corporate world. The other aspect which is required to be looked into is

whether standard of governance affect capital structure. As the research is on the corporate

governance related topic, before delving further on the subject, it is important to dwell upon

the concept of corporate governance. According to James Wolfensohn former World Bank

Group President, corporate governance is about promoting corporate fairness, transparency

and accountability (Financial Times, 1999). Effective governance practices decreases ‘control

rights’ of managers which are conferred by the shareholders and creditors. This increases the

likelihood of managers investing in positive net present value projects (Shleifer and Vishny,

1997).

These are foremost to protect the interests of shareholders. Governance is a requisite

for survival and a gauge of how predictable the system for doing business in any country is.

While public attention towards the importance of corporate governance gained momentum

only after the unearthing of major scandals such as Enron(2001)1, AOL(2002), Tyco

International (2002), World Com(2002), and Satyam Computer Services(2009) and more

recently Lehman Brothers (2010) where Ernst & Young ( the Audit firm) fail to disclose

Repo 105 transactions to investors. These scandals have stressed the need and usefulness for

proper analysis of financial statements of companies using different tools so as to detect and

avoid these collapses and to help investors in their investment decisions. It would be wrong to

assume that the concept of corporate governance is something new; the need for strong

corporate governance arose at about the same time as the ownership and management of

corporate entities were separated and the application of agency theories set in as will be

discussed later in details in literature chapter.

1 The ENRON Scandal is considered to be one of the most notorious within American history. ENRONscandals; deregulation, misrepresentation, fraudulent energy crisis, embezzlement. Due to the actions of theENRON executives, the ENRON Company went bankrupt.

2

1.1 Purpose of study

Following these corporate failures and scandals, the last two decades witnessed an

increasing intensity of researches on corporate governance. Firms with weaker standards tend

to face more agency problems which weaken their foundation for better performance.

Furthermore, it helps management in decisions like board compositions, mergers and

acquisitions. There are many researches which targeted the effect of CEO duality, Board

Size, Board Balance, Ownership, and Board Dedication on Firm’s Performance and capital

structure, but few on in Mauritius. Capital structure decisions are some of the core decisions

of today’s businesses. The inclusion of debt in the capital structure may affect the overall

performance and market value of the company. This research will provide the policy makers

with insight to the type of corporate governance which may ensure an optimal capital

structure.

This study tries to analyze the different variables of corporate governance to find out

their impacts on financial performance and find whether there is relationship between

governance and performance.

1.2 Objectives of the study

The goal of this research is twofold. First, it gathers, for the first time, quantitative

measures of the corporate governance and performance ratios of companies in Mauritius. A

wide array of official and private sources was used for this research. Second, we test the

predictions of recent theories based on theoretical framework and hypotheses. There are

many researches which targeted the effect of CEO duality, Board Size, Board Balance,

Ownership, and Audit on Firm’s Performance on developed and emerging countries.

(Note that the analysis will break into two parts: corporate governance and capital structure,

and, corporate governance and firm performance explaining ‘/’ in the hypotheses)

H1: There is a significant relationship between ownership structure and firms' capital

structure / firm performance

H2: There is a significant relationship between ownership concentration and firms' capital

structure / firm performance

3

H3: There is a significant relationship between board size and firms' capital structure / firm

performance

H4: There is a significant relationship between board independence and firms' capital

structure / firm performance

H5: There is a significant relationship between independent audit committee and firms'

capital structure / firm performance

H6: There is a significant relationship between CEO duality and firms' capital structure / firm

performance

H7: There is a significant relationship between social responsibility and firms' capital

structure / firm performance

1.3 Potential Contributions of the study

This study contributes to the existing literature through many sites which may be

elaborated, as follows:

1- A practical based, practitioner’s suggested, and corporate governance variable for a

developing sector is the core achievement of this study.

2- Inclusion of a separate factor of corporate governance in capital structure is the other

major contribution of this study.

3- There are few researches on corporate governance and firm performance in Mauritius, and

I intend to provide scope for the study with corporate variables whilst others followed the

scorecard approach.

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1.4 Delimitations of the study

Despite of some efforts, there are several limitations of this study; they can be

mentioned as under:

1. The study is conducted mainly by depending upon the secondary sources of information

2. The corporate governance study is calculated by calculating a specific variable for each

corporate governance element which has a scope for further research.

3. Limitations of Financial Statement Analysis

Financial statement analysis is widely used but it has two major drawbacks. Firstly it

involves the comparability of financial data between companies and secondly the need to

look beyond ratios.

4. The study was delimited to the period of 2007 to 2011.

5. The inclusion of all sectors in the same sample did make data interpretation difficult in

Chapter 4.

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Chapter 2: Literature Review2.1 Introduction of Corporate Governance in Mauritius

Among African companies, corporate governance is often regarded as a weak link to

performance. Mauritius has adopted a more or less a free market economy as economic

model. Private investment is the heart of the system. Government provide incentives and

infrastructure through various forms and sets the rules. Basic finance theory differentiates

between those having an excess of money and those in need of money. Savings can be both at

individual and at institutional level. A system of corporate governance is vital to keep the

balance between the interests of the investors (individuals and institutions), the entrepreneur

(and his family), the management and other stakeholders, as does the sustainability of the

business. Governance was an issue in Mauritius as elsewhere for many years. However, it

was in 2001 that Mr Sushil Kushiram, the then Minister of Finance, Economic Development

and Financial Services, and his government decided, it was time to create a legal and

institutional framework to enhance corporate governance and as part of the modernization of

the Mauritian Economy

From 2001, began a number of reforms, laws were revised and new laws were enacted

and also institutions were set up. The Committee of Corporate Governance came to birth and

the Minister asked Mr Tim Taylor to chair it; the Listing Rules of the SEM were reviewed; a

new Companies Act was passed and International Accounting Standards (IFRS) were

introduced. Mauritius has taken the lead in lending institutional support to corporate

governance. The National Committee on Corporate Governance (NCCG) was created under

the aegis of the Financial Reporting Act2, and in turn this spawned the creation of the

Mauritius Institute of Directors. The process continued and the Securities Act, the Insurance

Act and the Insolvency Act were passed. In 2001, the Financial Services Commission3 was

set up, The NCCG, with the help of Prof. Mervyn King, published the Code of Corporate

Governance 4in 2003, and also issued “Guidance Notes for State-Owned Enterprises” in

2006. In 2009, the “Statutory Bodies (Accounts and Audit) Act” was amended to make it

2 THE FINANCIAL REPORTING ACT 2004: S63-71 :PART V - THE NATIONAL COMMITTEE ONCORPORATE GOVERNANCE3 FSC regulates and licenses financial and non-banking activities in Mauritius4 The Report comprises eight sections dealing with - (i)Compliance and Enforcement, (ii) Boards andDirectors, (iii) Board Committees, iv) Role and Function of the Company Secretary, (v) RiskManagement, Internal Control and Internal Audit, (vi) Auditing and Accounting, (vii) IntegratedSustainability Reporting, and (viii) Communication and Disclosure.

6

compulsory for statutory bodies to include in their annual reports a corporate governance

report in accordance with the National Code of Corporate Governance, this being effective as

from 2011.

Mauritius is often referred to for its economic success story. Many economists and

other researchers have marvelled at the spectacular transformation of the country. Ali Zafar,

macroeconomist at the World Bank pointed out in a recent report5 that this was due to a

combination of political stability, strong institutional framework, low level of corruption, and

favourable regulatory environment. Not surprisingly these fundamentals are reflected in

governance indices like the Mo Ibrahim Index, which for three consecutive years ranked

Mauritius as first in Africa. It is difficult to say whether the Mauritian miracle can be

attributed solely to good governance, but when contrasted with many other countries,

including the highly developed ones, it is quite tempting to believe that this may well be the

case. The financial crisis has been attributed, not only to a failure of the state level to manage

and control systemic economic risks through timely policies and regulations but also poor

corporate governance.

.

2.2 Theoretical Background

There is no such accepted theoretical base for corporate governance, (Carver, 2000;

Tricker, 2000; Parum, 2005; Larcker,; Harris and Raviv, 2008). Citing Pettigrew (1992),

Tricker (2000) and Parum (2005) argued that corporate governance research lacks coherence;

either is theoretically, empirically or methodologically since the modern organisation is

complex. Per se, a number of alternative frameworks for corporate governance came to light

from different disciplines like economics, management and even psychology and sociology.

There are three well known corporate theories/frameworks namely the Agency Theory (from

1930’s onwards), the Stakeholder Theory (from 1970’s onwards) and the Stewardship

Theory (from the 1990’s onwards). Using various terminologies, these frameworks view

corporate governance from different perspectives. However, these frameworks often overlap

theoretically and do share significant commonalities (Solomon and Solomon, 2004)6.

5 Mauritius: An Economic Success Story, January 20116 For example, agency theory and transaction-cost economics share key assumptions and approaches inconceptualizing boards (Stiles and Taylor, 2002), and it is often difficult to clearly distinguish between theconcept of stewardship and trusteeship (Learmount, 2002).

7

The Agency Theory

Emanating from the classical thesis on The Modern Corporation and Private Property by

Berle and Means (1932), this model was developed by Jensen and Meckling (1976) and

further by Fama and Jensen (1983).It is perhaps starting point and the most known model.

The theory surges to explain the agency problem and the costs associated with it. The

discussion about the need for improving the governance of the firms is a response to many

cases of expropriation of shareholders’ wealth by the top executives, but also by the majority

shareholders at the expense of the minority shareholders. This phenomenon describes quite

well the agency problem, when the agents take decisions maximising their own interests

rather maximising shareholder’s value (the same apply to the appropriation by the majority

shareholders of the private benefits of control). Solutions to agency problems involve

establishing a ‘nexus’ of optimal contracts (explicit as well as implicit) between the owners

and management of the company. The key issues towards addressing opportunistic behaviour

from managers within the agency theory are board independence and CEO duality. It is argued

that this reduces conflict of interest and ensures a board’s independence in monitoring and

passing fair and unbiased judgement on management. CEO duality reduces the concentration of

power in one individual and thus greatly reduces undue influence of particular management.

The Stakeholder Theory

By expanding the spectrum of interested parties, the stakeholder theory stipulates that, a

corporate entity invariably seeks to provide a balance between the interests of its diverse

stakeholders in order to ensure that each interest constituency receives some degree of

satisfaction (Abrams, 1951). The stakeholder theory therefore encloses creditors, customers,

employees; banks, governments, and society are regarded as relevant stakeholders. Related to the

above discussion, John and Senbet (1998) emphasize the role of non-market mechanisms such as

the size of the board, committee structure as important to firm performance.

Stakeholder theory has infiltrated the academic dialogue in management and a wide

array of disciplines. Much attention has been paid to some basic themes that are now familiar

in the literature – 1.that firms have stakeholders and should proactively pay attention to them

(i.e., Freeman, 1984), 2.that stakeholder theory exists in tension (at least) with shareholder

theory (i.e., Friedman, 1970), 3.that stakeholder theory provides a vehicle for connecting

ethics and strategy (i.e., Phillips, 2003), and 4.that firms that diligently seek to serve the

interests of a broad group of stakeholders will create more value over time (i.e., Campbell,

8

1997; Freeman, 1984; Freeman, Harrison & Wicks, 2009). However, there are so many

different interpretations of basic stakeholder ideas that theory development has been difficult

(Scherer & Patzer, 2011). An extension of the theory called an enlightened stakeholder theory

was proposed. However, problems relating to empirical testing of the extension have limited its

relevance (Sanda et al., 2005).

The Stewardship Theory

Relative to agency theory, stewardship theory has received limited attention as a

theoretical model for explaining the relationship between firm managers and firm owners.

Human beings are seen as self-interested, opportunistic, utility maximisers whose primary

focus is economic benefit (Jensen and Meckling 1976). According to the stewardship theory, a

manager’s objective is primarily to maximize the firm’s performance because a manager’s need

of achievement and success are satisfied when the firm is performing well. A tension between

principal and agent occurs as both parties cannot maximize their economic utility in the

principal-agent relationship. Stewardship theory addresses the underlying agency theory

assumption that there is a tension between the risk propensity of principals and their agents

whereby agents focus their actions upon mitigating their personal risk at the expense of

principals. Stewardship theory assumes that managers behave as trustworthy stewards of the

organization and focus on the collective good of the constituents in the firm regardless of the

manager’s self-interests (Davis et al. 1997, Donaldson and Davis 1991).

The stewardship theory considers the following summary as essential for ensuring

effective corporate governance in any entity: 1. the involvement of non-executive directors

(NEDs) is viewed as critical to enhance the effectiveness of the board’s activities, 2. the positions

of CEO and board chair should be concentrated in the same individual. The reason is that it

affords the CEO the opportunity to carry through decision quickly without the hindrance of undue

bureaucracy. 3. Small board sizes should be encouraged to promote effective communication and

decision-making even though it fails to find an optimal ‘small’ board size?

Nevertheless, none of the above theories or frameworks offers a clear picture of the

exact direction of the causality between governance and capital structure nor firm

performance. Governance theories suggest that strong shareholder rights can mitigate agency

problems and, as a consequence, increase firm value. However, shareholders rights can be

restricted by the managers. Therefore, no causal inferences can be drawn from the theory

9

since it is not clear that there is a causal relationship and its direction. Due to this lacuna in

the theoretical framework, many researchers have been showing empirically that governance

drives performance. However, they point out the limitations of their results warning that they

may not be robust to some unobservable firms’ characteristics. In the sequence, an empirical

review on the field of corporate governance is provided, giving special attention to the

relationship between governance and performance.

2.3 Empirical background

2.3.1 Corporate governance and Capital structure

This study has been conducted with the hypothesis that a relationship may exist

between corporate governance and capital structure. The selection of capital structure is a

topic which has been under discussion for a long period of time. Modigliani and Miller

(1958) propounded a theory of capital structure, known as MM theory, which states that there

is no optimal capital structure because each structure is based on different assumptions like

perfect a market, no taxes, etc. After their research, a lot of researchers in the world tried to

find out different determinants of capital structure. (Kim & Berger (2008) and Toy et al.

(1974) found growth, profitability, and international risk as the determinants of capital

structure. After this study, firm size, industry class, business risk, and operating leverage

were tested by Ferri and Jonnes (1979) as the possible determinants of capital structure.

Titman and Wessel (1988) found profitability having negative relationship with capital

structure. They also found that small firms rely on short term financings. Laporta at al. (1998)

worked to find out why firms have different financing behavior in different countries and

found that different legal protection in different countries explains the firms’ financing

behaviors. In a country where legal protection is weak, the chances of agency conflict

increases. In this situation, leverage can play a role to alleviate the agency cost between

managers and shareholders (Grossman and Hart, 1982).

Various research studies have also tried to find out the effect of ownership structure in

determination of optimal capital structure. Slutz (1988) developed a model for firms’ targets,

capital structure, and ownership structure. Extensive research is found on different corporate

governance characteristics with capital structure decisions (Wen, Rwegasira and Biderbeek

(2002). Jiraporn and Liu (2008) conducted a study to find the relationship between a

10

staggered board and capital structure. They found that the companies which have a staggered

board are less leveraged than the other boards. Berger et al (1997) conducted a study to find

the relationship between board size and capital structure decision and found that there is a

negative relationship between board size and leverage and also found a positive relationship

between the presences of outside directors on boards with debt in the capital structure. Lipton

& Lorsch (1992) argued that there is a significant relationship between board size and capital

structure. Jensen (1986) found that big boards have larger debt in their capital structure..

Managerial equity proportion has also been studied by various researchers and both positive

and negative evidence has been found with capital structure. Agrawal and Mandelker (1987)

and Amihud et al. (1990) found a positive relationship between these two variables, while

Friend and Hasbrouk (1998) found a negative relationship between these two variables.

2.3.2 Corporate Governance and Performance of Firms

A recent study was carried by Lamport M J, Latona M N, Seetanah B and Sannassee

R V on the impact of corporate governance on firm performance in Mauritius. The study used

Taffler Z-score as proxy for performance and calculated a corporate governance score. They

found no significant relationship between corporate governance and firm performance.

However this alone cannot conclude the hypothesis. No more such study are found for

Mauritius. A number of studies exist though about the implementation of corporate

governance. Thus, the study will also attempt to answer this hypothesis.

Empirical studies from various countries are abundant. Some researchers had looked

for a direct evidence of a link between corporate governance and corporate performance

while other researchers have tried to study the correlation between the corporate governance

and firm’s performance. Much of the previous literature has shown a positive relationship

(Brickley et al, 1994; Brickley and James, 1987; Byrd and Hickman, 1992; Chung et al,

2003; Hossain et al, 2000; Lee et al, 1992; Rosenstein and Wyatt, 1990; Weisbach, 1988)

between governance and firm performance assuming that governance is an independent

regressor, i.e. it is exogenously determined, in a firm performance regression. This would

suggest that firms are not in equilibrium, and improvements in governance would lead to

improvements in firm performance. On the other hand, other studies have reported negative

relationship between corporate governance and firm performance (Bathala and Rao, 1995;

Hutchinson, 2002) or have not found any relationship (Park and Shin, 2003; Prevost et al.

2002; Singh and Davidson, 2003; Young, 2003). Demsetz and Lehn (1985).

11

Several explanations have been given to account for these apparent inconsistencies.

Some have argued that the problem lies in the use of either publicly available data or survey

data as these sources are generally restricted in scope. It has also been pointed out that the

nature of performance measures (i.e. restrictive use of accounting based measures such as

return on assets (ROA), return on equity (ROE), return on capital employed (ROCE) or

restrictive use of market based measures (such as market value of equities) could also

contribute to this inconsistency (Gani and Jermias, 2006). Furthermore, it has been argued

that the “theoretical and empirical literature in corporate governance considers the

relationship between corporate performance and ownership or structure of boards of directors

mostly using only two of these variables at a time” (Krivogorsky, 2006). For instance,

Hermalin and Weisbach (1991) and McAvoy et al. (1983) studied the correlation between

board composition and performance, whiles Hermalin and Weisbach (1991), Himmelberg et

al. (1999), and Demsetz and Villalonga (2001) studied the relationship between managerial

ownership and firm performance.

To address some of the aforementioned problems, it is recommended that a look at

corporate governance and its correlation with firm performance should take a multivariate

approach. The present study adds to the literature by employing both market based and

accounting based performance measures such as return on assets and Tobin’s Q and test the

relationship between them and selected governance variables. In addition to board

characteristics, we also include board activity intensity as well as audit committee practices

and characteristics, social responsibility as an extended arm of governance. We combine

survey and publicly available governance data to broaden the scope of governance variables.

12

2.4 Corporate governance Mechanisms

Recent studies have used different kind of approaches to measure corporate

governance. Fundamentally, each research possesses its own way of evaluating corporate

governance, some constructed indices, and others calculated corporate governance absolute

variables. For example Klapper and Love (2004) evaluate the differences in the governance

practices of 14 companies in emerging markets through the use of a corporate governance

index developed by the Credit Lyonnais Securities Asia (CLSA), an investment bank.

Gompers Ishii and Metrick (2003) used different provisions to construct an index.

The present study will attempt to use rather a variable for each corporate governance

item. Each item/variable is assessed as used in previous researches in this field of study. This

is further elaborated below and in chapter 3(see table xxx). In this study, the researcher’s

corporate governance variables are Ownership structure, Ownership concentration, Board

size, Board independence, Audit Committee independency, CEO duality, and finally Social

responsibility. The following literature provides a summary.

Ownership Structure

Ownership structure has been under extensive discussion for a long time. Several

authors have given reasons for the difference in this ownership structure. Sun and Tong

(2003) indicated that different kind of ownership exists: legal ownership, employee

ownership, board ownership institutional ownership, and public ownership. Structure was

measured by board ownership as conducted by Eric Sevrin (2001). Jensen and Meckling

(1976), Fama and Jensen (1983) and Shleifer and Vishny (1986), among others, have

suggested that the structure of equity ownership has an important effect on managerial

incentives and firm value. Kim and Sorenson (1986), and Agrawal and Mandelker (1987) for

American firms; Friedman et al. (2003) for Asian firms; Boubaker (2007) for French firms;

and Holmen et al. (2004) for Swedish firms all find evidence of a positive relationship

between debt and control.

13

Ownership concentration

Ownership concentration will be identified as major 10 shareholders of a company or

holding equivalent of 5% or more of the outstanding shares. Shlifer and Vishney (1997)

analysed how ownership concentration is one of the important determinants of corporate

governance. Several views of ownership concentration are found in the literature. Some say it

is good, and Johnson et.al (2000) evaluated ownership concentration as a source of

tunnelling; large shareholders become the managers and cause serious agency problems for

minority shareholders. Laporta et al. (1999, 2002) regarded ownership concentration as one

of the big agency problems in the countries where legal protection is weak. Most prior

evidence shows that firms with high ownership concentration have higher leverage levels

(Grossman and Hart, 1986; Anderson et al., 2003). Controlling shareholders prefer debt to

equity financing, since they tend to maintain their level of voting control for a given level of

equity. Again mixed results were found as discussed above.

Board Size

Limiting board size to a particular level is generally believed to improve the

performance of a firm because the benefits by larger boards of increased monitoring are

outweighed by the poorer communication and decision making of larger groups. Empirical

studies on board size seem to provide the same conclusion: a fairly clear negative relationship

appears to exist between board size and firm value (Rouf , 2011). Lipston and Lorsh (1992)

and Jensen (1993) also indicate that the larger board is less effective. Berger et al.(1997)

found that larger board of directors result in low leverage levels. Dalton and Dalton(2005)

found superior performance resulted from larger boards while Hermalin and Wiesbach (2003)

and Bhagat and Black (1999) proposed an opposite view.

14

Board Composition

A board is more independent if it has more non-executive directors (NEDs). As to

how this relates to firm performance, empirical results have been inconclusive. In one

breadth, it is asserted that executive (inside) directors are more familiar with a firm’s

activities and, therefore, are in a better position to monitor top management. On the other

hand, it is contended that NEDs may act as “professional referees” to ensure that competition

among insiders stimulates actions consistent with shareholder value maximization (Fama,

1980). Some studies find better performances for firms with boards of directors dominated by

outsiders (Jensen 1986, Berger et al 1997 and Abor 1997), while Weir and Laing (2001) and

Pinteris (2002) find no such relationship in terms of accounting profit or firm value. Also,

Forsberg (1989) find no relationship between the proportion of outside directors and various

performance measures. In the same vein, Hermalin and Weisbach (1991) and Bhagat and

Black (2002) find no correlation between the degree of board independence and four

measures of firm performance.

CEO duality

Several studies have examined the separation of CEO and chairman of the board,

positing that agency problems are higher when the same person occupies the two positions.

Using a sample of 452 firms in the annual Forbes Magazine rankings of the 500 largest USA

public firms between 1984 and 1991, Yermack (1996) shows that firms are more valuable

when the CEO and the chairman of the board positions are occupied by different persons.

Sanda et al (2003) found a positive relationship between separate CEO and chairman

positions and firm’s performance while Abor (2007) concluded that there is a positive

correlation between CEO duality7 and capital structure and Rechner and Dalton (1991) found

that firms with CEO duality performed better while.

Audit Committee Independency

The audit committee also plays an important role in the improvement of firm value by

implementing corporate governance principles. The principles of corporate governance

suggest that the audit committee should work independently and perform their duties with

professional care. The audit committee monitors mechanisms that improve quality of

7 CEO duality; when the positions of Chairman and CEO are held by the same person.

15

information flows between shareholders and managers (Rouf, 2011, p.240), which in turn,

help to minimize agency problems. Most empirical works like Ho 2005 have revealed

positive findings whilst some, like Brown and Caylor (2005), have concluded that the

significance of the relationship lies between audit quality and dividend yield and not with

operating performance. Klein (2002) reports a negative correlation between earnings

management and audit committee independence. Anderson, Mansi and Reeb (2004) find that

entirely independent audit committees have lower debt financing costs.

Disclosure of Stakeholders’ interests; Corporate Social Responsibility

(CSR)

Corporate social responsibility is the commitment of business to contribute to

sustainable economic, development, working with employees, their families, the local

community and society at large to improve their quality of life. Therefore, ethical deeds

would send the correct signal to the different stakeholders and impact on performance. For

example, Ho (2005) illustrated in his survey that firms perform better than without theses

fundamentals In a study by Hackston and Milne (1999), it was seen that New Zealand

companies make most social disclosures on human resources, with environment and

community themes also receiving significant attention.

.

In summary, the empirical studies reveal mixed views about the relationship between

governance and performances. Unfortunately, generalizability of such findings may not

extend across national boundaries due to different regulatory and economic environments,

cultural differences, the size of capital markets and the effectiveness of governance

mechanisms.

16

Chapter 3; Research Methodology

This chapter contains a description of the methodology of the study which covers

Population, Sample, List of variables, Data collection, model specification and the proxies

that were used. Panel data methodology was adopted because it combined time series and

cross sectional data. The method of analysis is multiple regression, fixed and random effects

model.

3.1 Sample/ Research Design

The data used for this study were derived from the audited financial statements of the

firms listed on the official market for the year 2007-2011. The sample of the firms were

selected using the combination of non- probability sampling technique (firms with the

required information; were initially selected) and stratified random technique (firms were

then selected based on their sectorial classification) while some sectors were excluded8. A

total of 39 firms (see Appendix A) were finally used as sample as shown in the table below.

SECTOR Number of firms

Banks & Insurance and other finance 7

Commerce 5

Industry 8

Investments 12

Leisure & Hotels 4

Sugar 2

Transport 1

Total: 39

Table 1: Number of firms in the sample

8 The following sectors were excluded from the sample because of their complicated regulations; debt, foreign,

global and specialised firms, specialised debt securities , global business companies.

17

3.2 Independent and Controlling Variables

To increase the confidence of the results, there are set of controls variables which are

included in the regressions. The study expects firm size may have a negative effect if size is

correlated with the exhaustion of growth opportunities, but may contrarily have a positive

impact whenever size is correlated with more diversification, greater economies of scale and

scope, more professionalized management, and less severe financial constraints. Sales growth

is a proxy for the product demand faced by the firm and its productivity.

Therefore to account for these, additional independent variables were added other

than corporate governance elements, to the model as shown in the table below.

Independent Variables Abbreviations Description

Ownership structure OS Shares held by board of directors/ Total no. of

shares outstanding, following Eric Sevrin

(2001),

Ownership concentration OC Shares owned by top10 shareholders/ Total no. of

shares following Lin Chen et. al (2008)

Board Size BS Ln of total No. of Board members

Board Independence BI NED/ Total No. of Directors

in Board) being in line with Kee et al (2003), Lin

Chen (2008)

Audit Committee

Independence

ACI Non-Executive directors in Audit committee/

Total No. of Directors in Audit Committee)

following Forker’s (1992)

CEO Duality CEOD Whether CEO and Chairman is the same person.

Sales Growth SG Current sales minus previous years sale/

previous years sale following signalling theory

Return on Equity ROE Net Profit/ Shares Holders equity.

Size of the firm Size Ln of total Assets following Scott and Martin

(1975)

Table 2 List of independent variables

18

3.3 Sources of data

To be able undertake our research, we must collect the maximum data available, that

is qualitative and quantitative data. We cannot concentrate on only one sort of data. First of

all I derived a list of all companies since the population of listed companies is small; most of

the firms were selected apart from the debt and GBC sector because of their specific

regulations. Also, eliminate all utility and affiliates of foreign firms.

Data on required variables is collected through primary and secondary sources. Data

were collected through self-administrated survey, mail survey, interviews and annual reports

(2007-2011). The Stock Exchange of Mauritius website and individual companies’ website

were extremely useful.

3.4 Proxies used

Variable Abbr. Description

Capital structure measure

Leverage ( Market value) LevMV Total debt/(Total debt+ MV of shareholder’s

equity)

Firm Performance measure

Return on asset ROA Profit after tax/ total asset

Tobin’s Q Q Total market value of firm/ total asset value

Where Total market value of firm= equity market value +

book value of debt

Since market value of debt is not publish.

Altman Z-score AltZ See Appendix B

Table 3: Dependent variables

19

Leverage will be used as a proxy for capital structure. ROA will be used as an

indicator for profitability and efficiency. Tobin’s Q selection as a ratio to measure firm value,

as employed by previous studies mentioned in literature part. Altman Z Score was used as a

proxy for financial health of the company. It is an improvement of the debt ratio in the sense

that it takes into account the current liquidity position held by the company. This score

calculated by five ratios:

1. Liquidity on total assets

2. Sales to total assets

3. Equity to debt

4. Working capital to total assets

5. Retained earnings to total assets

Altman Z Score is the summation of these ratios multiplied by a predetermined

weight factor. Score above 2.99 are considered to be financially sound, while those scoring

below 1.81 are in fiscal danger, maybe even heading toward bankruptcy. Therefore, scores

within the range 1.81-2.99 indicate potential trouble (See Appendix B for more details).

3.5 Model Specification

Given well-organized panel data, panel data models are definitely attractive and

appealing since they provide ways of dealing with heterogeneity and examine fixed and/or

random effects in the longitudinal data. Our model is a micro panel (N is large and T is less

or equal to 5 years). However, panel data modelling is not as easy as it sounds. We should not

rush in just choosing fixed effect model or random effect model. Carelessness could lead to

wrong interpretation and inappropriate modelling.

In our empirical panel data, we are concerned about choosing between three alternative

regressions. This choice is between Ordinary Least Squares method, fixed effects (or within,

or least squares dummy variables) estimation and random effects (or feasible generalized

least squares) estimation. A series of tests are carried to test for fixed and random effects in

20

the model and appropriate model will then be fitted. The F-test for fixed effects, Breusch-

Pagan LM Test for Random Effects and Hausman Test for Comparing Fixed and Random

Effects.

The Multiple OLS Regression

A multiple OLS regression is concerned with the relationship between a dependent

variable and a series of independent variables. The multiple regression allows the analyst to

control for the multiple factors that simultaneously affect a dependent variable. The following

represents the relationships in our model= + + + + + + + + ++ eit ,

where Y is the dependent variable and the assumptions of linearity, reliability of

measurement, homoscedasticity, and normality and no autocorrelation (Gauss-Markov

Theorem).

Fixed versus Random Effects

The distinction lies in how the parameter estimate of the dummy variable is treated. It

is a part of the intercept in a fixed effect model while an error component in a random effect

model. Slopes remain the same across group or time period in either fixed or random effect

model. The functional forms of one-way fixed and random effect models are

Fixed effect model: y ( ui ) X it' vit

Random effect model: y X it ui vit ,

where i u is a fixed or random effect specific to individual (group) or time period that

is not included in the regression.

21

F-test for Fixed Effects

In a regression of Y X it , the null hypothesis is that all dummy

parameters except for one for the dropped are all zero, :

H0: … .

The alternative hypothesis is that at least one dummy parameter is not zero. This

hypothesis is tested by an F test, which is based on loss of goodness-of-fit. This test contrasts

LSDV (robust model) with the pooled OLS (efficient model) and examines the extent that the

goodness-of-fit measures (SSE or R2) changed. If the null hypothesis is rejected (at least one

group/time specific intercept ui is not zero), you may conclude that there is a significant fixed

effect or significant increase in goodness-of-fit in the fixed effect model; therefore, the fixed

effect model is better than the pooled OLS.

Breusch-Pagan LM Test for Random Effects

Breusch and Pagan’s (1980) Lagrange multiplier (LM) test examines if individual (or

time) specific variance components are zero,

H0:

The LM statistic follows the chi-squared distribution with one degree of freedom. If the

null hypothesis is rejected, you can conclude that there is a significant random effect in the panel

data, and that the random effect model is able to deal with heterogeneity better than does the

pooled OLS.

Hausman Test for Comparing Fixed and Random Effects

How do we know which effect (fixed effect or random effect) is more relevant and

significant in the panel data? The Hausman specification test compares fixed and random effect

models under the null hypothesis that individual effects are uncorrelated with any regressor in the

model. Hausman test examines if “the random effects estimate is insignificantly different from

the unbiased fixed effect estimate” (Kennedy, 2008). If the null hypothesis of no correlation is

rejected, you may conclude that individual effects ui are significantly correlated with at least one

independent variable in the model and thus the fixed effect model is preferred.

22

Chapter 4; Data Analysis and Results

Note: Raw data used in this study can be found in Appendix C.

4.1 Descriptive statistics

Variable Mean Std.

Dev.

Min Max Percentiles Skewness Kurtosis

50% 75%

os 0.09 0.14 0 0.69 0.2444 0.1167 2.5561 9.5855

oc 0.61 0.221 0.12 0.9 0.6979 0.7855 6.8495 47.9520

bs* 10.381 0.190 7.029 15.029 11.0000 12.0000 -0.3006 2.4542

bi 0.78 0.11 0.29 0.93 0.8182 0.8462 -2.0272 9.1234

aci 0.94 0.11 0.67 1 1 1 -1.6247 3.9341

ceod 1 0 0 1 1 1 -5.5110 31.3643

csr 14.51 1.87 9.25 18.56 14.2393 15.9909 -0.1949 2.7429

sg 0.2 1.09 -1 10.56 0.1029 0.1915 -1.1589 9.0235

roe 0.15 0.23 -0.11 1.93 0.0892 0.1789 5.5249 37.5252

size 22.52 1.79 16.29 26.24 22.7659 23.4938 -0.7716 4.5833

levmv 0.24 0.24 0 0.92 0.1763 0.3425 1.3031 4.1188

roa 0.076 0.085 -0.137 0.53 0.0618 0.0896 2.7724 14.1578

Tobin’s Q 0.923 1.016 0.059 7.113 0.6589 0.9891 4.4896 25.8754

altz 3.824 5.928 0.116 33.113 1.9089 3.6978 3.4560 15.5165

*bs was returned to exponential form, i.e. absolute values

Source: STATA output

Table 4: Descriptive statistics

Table 4 shows number of observations, mean, median, standard deviation, maximum,

and minimum as well the skewness which will give us an idea of how the distribution behave.

The mean shows the average figure of the variable for the data set. The standard deviation

(sd) is an indication of how the data deviates around the mean. It is a measure of dispersion

23

(variability). The higher the figure, the higher it deviates/scatters around the mean value and

is an indication of margin of errors. The skewness measures the degree and direction of

asymmetry, whether the mean is less or greater than the median is the indication. If mean is

less than the median, then the distribution is said to be negatively skewed and in the other

hand, if mean is greater than median, the distribution is said to be positively skewed. A

normal distribution has a skweness of 0. Kurtosis indicates how peak or pointed the

distribution is. Bell curve (mesocurtic , are considered to be perfect and has a kurtosis of 3 ).

Heavy tailed distribution, with many outliers, will have a kurtosis greater than 3 (Flat -

platykurtic). Light tailed distributions, less outliers will have kurtosis less than 3 (Sharp –

leptokurtic). The figure 3 serves as a benchmark.

We would focus more on the governance variables and from reference to the table

above, board directors on average own only 9% which is significantly good and in line with

agency theory, as principals have minority interest aligning maximising shareholders’ wealth.

However, this figure does not reflect the whole sample. Some directors do not even have

interest in company, or are relatively low. Whilst, some were originally private companies

before listing, obviously have board members holding significant interest in the company. For

example directors of Gamma Civic Ltd owned more than 50% of the outstanding shares. The

sd is 0.14 meaning that values varies 14% around the mean and margin of errors is low. As

it can be seen in the percentile range, the median (24.4%) is greater than the mean (9%), the

distribution is concentrated to the right and the kurtosis of 9.5 shows that the distribution is

heavily tailed and thus assumes a flat distribution.

Ownership concentration is very high. The mean is 61%, i.e. top 10 shareholders

holds on average 61% of the company’s outstanding shares. Margin of error is also low. In

some companies, concentration is even 90%. 50% of companies in the sample have

ownership concentration of 69.79 or more. The skewness is significantly positive and

therefore the data is significantly concentrated to the right and is heavily tailed. Board size

averages to 10 members. Lipton and Lorsch (1992) suggest an optimal board size between seven

and nine directors 75% of companies have 12 or more directors in the board. The distribution

is however close to normal (skewness of -0.3 and kurtosis < 3). Board independence also is

an important variable. The figures show that board are majorly consisted of non-executives

(and/ or independent directors). 75% of companies consist of mainly non-executive s (and/or

independent directors). This is in line with the RCCG which specifies at least two two

independent and two executive directors in the board. Audit committees have high

24

independency level. This is a major requirement of the Corporate Governance Code of

Mauritius. Companies are therefore tending to adopt the code practices. This is the same for

CEO duality where every company separated the powers of the CEO and the Chairperson and

are occupied by two different persons. Again, this is in line with the code and while

collecting data, I can further conclude that most of all CEO are non-executives. The CSR

variable cannot be described since (i) it is in exponential form and (ii) we are more concerned

on its impact than CSR itself since some contributes massively and even have foundations

while some contribute a relatively low amount either they are subsidiaries of the foundation

or otherwise.

Firms on average have a debt ratio of 24% which is relatively low. Firms are low

geared. Variability is also low; low sd, 75% have debt/equity of at least 34.25%, skewness is

positive and low and also kurtosis is 4.12. However, some firms are highly geared (92% as

maximum). Return on asset averages to 7.6%. sd is less than 10%. While some companies

reported negative return. Tobin’s Q mean is 92.3% which means 92.3% of total assets

contribute to the total market value of firm. This means that total assets are wisely employed.

Again, the distribution is heavily tailed to the right.

Altman Z-score on average is 3.824 (> 3 indicating high bankruptcy). This data alone

is not reliable since the sd is 598.28%! This is due to the presence of the inclusion of different

industries in the same sample. Every company need not to have same ratios which are

weighted in the formula. Some companies, especially those in banking sector had negative

working capital/ total assets ratio since liabilities are greater than assets. Deposits from

customers are liabilities! The distribution is heavily tailed to the right and is therefore

affected by outliers.

25

4.2 Corporate Governance and capital structure

A very laborious literature is found on the debate about the link between capital

structure and corporate governance which is at heart of agency theory. All the corporate

variables and controlling variables were analysed on dependent variable, which is debt to

total of debt and market equity. The following are the variables used in this section:

Dependent variable (Y): levmv

Independent variables (X): os, oc, bs , bi, aci, ceod, csr, sg, roe, size

4.2.1 Correlation

The Person correlation was performed to measure the degree of linear relationship

between the variables. It shows how close two variables are assuming that it is linear and is a

measure of how tightly cluster data are about the correlation line. Ranging from -1 to +1,

negative coefficient indicates a negative relationship. The table below present the correlations

coefficients and its significance in parentheses.

26

levmv os oc bs bi aci ceod csr sg roe size

os -0.041 1

(0.683)

oc -0.137 -0.044 1

(0.175) (0.667)

bs 0.156 -0.475* 0.146 1

(0.121) (0) (0.148)

bi 0.168** -0.267** 0.075 0.338* 1

(0.096) (0.007) (0.456) (0.001)

aci -0.138 -0.018 0.068 0.107 0.348* 1

(0.171) (0.856) (0.503) (0.291) (0)

ceod -0.089 -0.446* 0.028 0.1762** 0.023 0.044 1

(0.377) (0) (0.784) (0.08) (0.818) (0.665)

csr -0.061 -0.075 0.157 0.4164* 0.046 0.009 -0.059 1

(0.55) (0.462) (0.118) (0) (0.653) (0.931) (0.559)

sg 0.126 0.041 0.128 -0.159 -0.230** -0.073 -0.021 0.057 1

(0.211) (0.685) (0.204) (0.115) (0.022) (0.47) (0.832) (0.573)

roe -0.025 -0.035 0.4109* 0.137 0.036 0.104 0.037 0.141 0.022 1

(0.807) (0.732) (0) (0.173) (0.719) (0.305) (0.717) (0.161) (0.827)

size 0.276* -0.180** -0.358* 0.235** 0.001 -0.016 0.048 0.438* -0.015 -0.191** 1

(0.006) (0.073) (0) (0.019) (0.995) (0.875) (0.638) (0) (0.884) (0.057)

Table 5: Correlation capital structure and corporate governance

27

From table 5, using the Pearson correlation, levmv is positively correlated to board

independence and is significant at the 0.01significance level and firm size also is positive and

significant at the 0.05 level. Board shareholding, ownership concentration, and audit

committee are negatively correlated. This means that, for instance, when board shareholding

increases, decisions about debt tend to be less favourable and prefer equity finance. This is a

rational behaviour as they fear any risk of bankruptcy being heavily debt. They tend to avoid

debt finance. This is consistent with Jensen (1986), Berger et al. (1997) and Abor (1997).

Board size is positively correlated to leverage. Thus This is consistent with Wen et

al.(2002) and Abor (2007). Berger et al. (1997) on the other hand, found that large board size

results in low leverage levels. Furthermore, CEOs also seem to avoid leverage but the value

is not significant.

A thorough study of the relationship between dependent variables reveals that they are

not, at most perfectly correlated. Correlation coefficients ranges from –0.5 to +0.5 .Testing

for correlation is important as this may lead to high coefficient standard errors and low t-

statistics making it harder to reject the null hypothesis. However, as we shall consider in the

next section while doing the OLS estimation (after being tested), multicollinearity presence

did not violate the estimations. OLS estimates are still unbiased and BLUE (Best Linear

Unbiased Estimators)

4.2.2 Regression results and discussion

Before beginning with analysis of the study, we have first to ascertain which

estimation model is appropriate and fits the data the best. The model with best with better

goodness-of-fit measures (like R 2 ,test for heterosdasticity and test for autocorrelation),

parameter estimates with their standard errors, and test results will be selected. This will then

be coupled with Hausman test for fixed versus random effects, F-tests to test for any fixed

effects, Breusch and Pagan Lagrangian Multiplier test for any random effects.

28

Now we start our analysis. I first use Hausman test. The Prob>chi2 = 0.1296, and

thus the test provides decisive evidence that the H0 is wrong at the 0.05 level. We therefore

reject the null hypothesis at the 0.1 level and thus the random model is preferred. In

connection, we run the BPLM test for random effects. The chibar2(01) = 46.10 and

Prob > chibar2 = 0.0000, we reject Ho at the 0.05 level and thus the random effects is again

favoured against the OLS. However, we also run the F-test to validate the non-presence of

fixed effects. The xtreg command renders Prob > F = 0.0000 which is less than the 0.05. We

therefore reject the null hypothesis at the 0.05 level as there is sufficient evidence; significant

increase in goodness of fit in the fixed effect. We can still not conclude on which estimation

suits the data best. We shall now compute the three estimation models and observed the

goodness of fit.

The table below summarises estimation results from OLS, fixed effects (LSDV) and

random effects (re theta and GLS). It appears that the random effect model does not suit the

estimation. The goodness of fit values is insignificant and also no significant coefficients are

produced. The fixed effect model renders R2 of 77.16% but produces no sufficient

information about the link between the variables. The OLS seems appropriate. Table 6 shows

the statistics when different estimation model applied.

Variable OLS Fixed effects Random effects

os 0.23455 0.14386 0.04819

(0.1381) (0.1955) (0.1536)

[1.699] [0.7358] [0.3138]

oc -0.00093 0.00019 0.00078

(0.00111) (0.00224) (0.00086)

[-0.8424] [0.08605] [0.9049]

bs 0.3355 0.26105 0.27873

(0.1746) (0.1558) (0.308)

[1.922] [1.676] [0.9051]

bi .57292** 0.30406 0.21723

(0.1763) (0.1966) (0.1317)

[3.249] [1.547] [1.649]

aci -0.49383 -.37182* -0.30666

(0.2559) (0.1612) (0.2329)

[-1.93] [-2.306] [-1.316]

ceod -.14908* -0.13009 -0.11539

29

(0.06941) (0.1044) (0.07418)

[-2.148] [-1.246] [-1.555]

csr -.0483*** -.02466* -0.01933

(0.01416) (0.0119) (0.01287)

[-3.411] [-2.072] [-1.502]

sg .20684** 0.09055 0.06775

(0.06155) (0.06678) (0.04732)

[3.361] [1.356] [1.432]

Roe .10648* 0.03331 0.00821

(0.04614) (0.07959) (0.06782)

[2.308] [0.4185] [0.1211]

Size .05518*** 0.03168 0.00621

(0.01287) (0.01643) (0.01217)

[4.286] [1.928] [0.5097]

_cons -.976* -0.52045 -0.31626

(0.4408) (0.5048) (0.8541)

[-2.214] [-1.031] [-0.3703]

F-test

(model)

2.98 6.46 13.02

p-value 0.0028* 0* 0.2226

DF 99 94 94

R2 0.2506 0.7716 0.3268

SSE

(SRMSE)

4.11087 1.25266661 0.13882289

Root /mse 0.21492 0.13882

σu 0.170409

θ 0.6577

Effect test 6.18* 5.74

N 100 100 100

Coefficient/ standard error/t-statistic

* p<0.05; ** p<0.01; *** p<0.001

Significant coefficients in bold

STATA Output

Table 6: Regression results, capital structure and corporate governance

30

[ R2 = 0.2936; Adj R2=0.1664, SSE= 4.11, F=2.98* , mse= 0.21492]Regression equation: levmv= 0.235 OS – 0.001 OC+0.336 BS+0.573 BI-0.494 ACI-0.149 CEOD-0.048 CSR+0.207 SG+0.107 ROE+0.55 SIZE- 0.976Special Wald Test: F( 10, 89) = 3.70, Prob > F = 0.0004

Unstandardised coefficients standardisedcoefficients

Collinearity statistics

levmv Coef. Robust Std.Err.

Beta t P>t [95% Conf.Interval]

VIF tolerance

os 0.2345 0.1381 0.1441 1.7 0.093 -0.1403 0.6094 1.69 0.5911

oc -0.0009 0.0011 -0.033 -0.84 0.402 -0.0072 0.0053 1.54 0.6511

bs 0.3355 0.1746 0.2663 1.92 0.058 0.0381 0.6329 1.78 0.5623

bi 0.5729** 0.1763 0.2697 3.25 0.002 0.1300 1.0159 1.39 0.7206

aci -0.4938 0.2559 -0.2342 -1.93 0.057 -0.8970 -0.0907 1.17 0.8571

ceod -0.1491* 0.0694 -0.1086 -2.15 0.034 -0.4252 0.1271 1.29 0.7748

csr -0.0483*** 0.0142 -0.3844 -3.41 0.001 -0.0774 -0.0192 1.71 0.5858

sg 0.2068** 0.0615 0.235 3.36 0.001 0.0423 0.3714 1.12 0.8966

roe 0.1065* 0.0461 0.105 2.31 0.023 -0.0940 0.3069 1.25 0.8018

size 0.0552*** 0.0129 0.4204 4.29 0 0.0247 0.0856 1.72 0.5831

_cons -0.9760* 0.4408 . -2.21 0.029 -1.8797 -0.0723

Mean VIF=1.46

* p<0.05; ** p<0.01; *** p<0.001 level of significance

Table 7 Multiple regression result: leverage and corporate governance

31

IM test for Ho: homoscedasticity; Chi(2)=87.20, p=0.0534,

Likelihood-ratio test : sig= 0.03

Durbin-Watson d-statistic( 11, 100) = 1.359768 9

The Special Wald test was first performed as to test whether at least one of the

governance variables is significant at the 0.05 level. The p-value is 0.0004 which is lower

than the 5% level of alpha. Hence, we can reject the null and at least some independent

variables are significant. This reinforced significant values if only looking at the F-test. The

high p-value of the IM test for heteroskedasticity shows that heteroskedasticity was a

problem at the 5% level and the robustness function was used to capture any presence since

the no-one knows the true value of p. A test for autocorrelation was also performed,

represented by the Durbin-Watson test. The d-statistic is 1.36, and stands in the zone of

indecision for possible autocorrelation. As a rule of thumb, if a VIF is in excess of 9 or a

tolerance (1/VIF) is .05 or less, there might be a problem of multicollinearity (Lazaridis and

Tryfonidis, 2006). We therefore assume that the OLS assumptions are not violated. The

likelihood–ratio test is significant at the 0.05 level, thus there is an evidence of association

between leverage and the corporate variables.

From the table 8, the R-square is 29.36%. This means that 29.36% of the variance in

leverage is explained by the independent governance variables. The higher the figure, the

better the variance is explained. The model being significant but R2 small, it implies that

observed values are widely scatter around the regression line. The F-statistic of the OLS

estimation is 2.98 and therefore significant at the 0.05 level. This means that the model

statistically reliable and is not spurious. The RMSE is the square root of the variance of the

residuals. RMSE is an absolute measure of fit. From Table 7, 21.5% of the observed data

points are close to the model’s predicted value. The beta value of is a measure of how

strongly each independent variable influences leverage. The beta is measured in units of

standard deviation. For example, a beta value of 0.1441 for ownership structure indicates that

a change of one standard deviation in the leverage will result in a change of 0.1441 standard

deviations in the ownership structure variable. Thus, the higher the beta value the greater the

impact of the independent variables on leverage. Having analysed goodness of fit, we are

now going to test our previous hypotheses introduced in chapter 1.

9 From Durbin Watson significance table, dU=1.314, dL=1.79

32

H1: There is a significant relationship between ownership structure and firms' capital

structure

Board ownership is positively related to leverage. The standard deviation is 0.1381, i.e.

observed values differ by 13.8% around the mean value. However, no significant relationship

was found. Agrawal and Mandelker(1987) also found a positive relationship.

H2: There is a significant relationship between ownership concentration and firms' capital

structure

Ownership concentration is negatively related to leverage. No significant relationship was

found.

H3: There is a significant relationship between board size and firms' capital structure

Even board size is positively related to leverage. Despite not being significant, this result is

consistent with Wen et al.(2002) and Abor(2007). Also big boards have larger debt in their

capital as argued by Jensen (1986). Larger board size translates into strong pressure from the

corporate board to make managers pursue lower leverage or debt ratio rather than have larger

boards.

H4: There is a significant relationship between board independence and firms' capital

structure.

There is a positive and significant relationship (at the 0.01 level) between board

independence and leverage. This means that executive board members prefer low leverage

and therefore use internal finance first. Since they have interest on the company, they will not

want to take huge risks in leverages. This is refuting Wen et al. (2002) where a significant

negative relationship was found but is consistent with Berger et al. (1997). The coefficient

0.573 means that for every one % increase in board independence, i.e. a non-executive

joining in, leverage is expected to increase by 0.573 %, assuming ceteris paribus.

33

H5: There is a significant relationship between independent audit committee and firms'

capital structure

A negative relationship exists between audit and leverage. Besides, it is not significant.

H6: There is a significant relationship between CEO duality and firms' capital structure

CEO duality has a negative and significant relationship at the 0.05 level. This means that so

long as CEO and Chairman Position are occupied by the different persons, leverage will

decrease. This might reveal that CEO and Chairman decisions on leverages do not tally.

H7: There is a significant relationship between social responsibility and firms' capital

structure

As the companies contribute more towards social responsibility, leverages tend to decrease.

The study shows a negative relationship and this is statistically significant at the 0.001

significance level. This might possibly means that as company contributes more to CSR,

sales revenue increases and building internal finance in the form of retained earnings. This is

further backed by a positive relationship between sales growth and leverage. This might

explain the pecking order theory.

The study also reveals that profitability (roe) is also significantly related to leverage (at the

0.05 level) and this is consistent with pecking order hypothesis. For every one % increase in

profitability, leverage is expected to increase by 0.106%. In addition, the study reports that

other determinants that do not proxy for control rights are consistent with previous findings.

Firms that are larger have more tangible assets and are also more leveraged.

In this vein, the submission of this study is that the issue of capital structure is more of

an empirical issue than theoretical proposition since it is different from countries to countries,

perhaps depending on the level of development. However, the limitation of this study is that

we cannot conclude if this assertion also holds across different sectors in the same country or

economy.

34

4.3 Corporate Governance and Firm Performance

4.3.1 Correlation

ROA Q AltZ

os 0.008 -0.025 -0.002

(0.938) (0.803) (0.987)

oc 0.5796* 0.4551* 0.5589*

(0.000) (0.000) (0.000)

bs 0.098 0.2295* 0.130

(0.334) (0.022) (0.208)

bi -0.034 0.138 0.120

(0.735) (0.170) (0.244)

aci 0.166 0.054 0.121

(0.099) (0.597) (0.240)

ceod 0.027 0.089 0.058

(0.787) (0.377) (0.573)

csr -0.028 0.054 -0.024

(0.786) (0.591) (0.817)

sg -0.086 -0.020 -0.039

(0.394) (0.845) (0.710)

roe 0.4617* 0.3874*

(0.000) (0.000)

size -0.470* -0.4523* -0.6171*

(0.000) (0.000) (0.000)

Correlation coefficient and significance value given in ( ).

* p<0.05; level of significance

Table 8 Correlation: Corporate governance and performance

In the table, variables which are statistically significant at respective significance level are

shown in bold. Board ownership, large block shareholders, board size, corporate social

responsibility and sales growth are negatively correlated. Only board independence, ceod,

return on equity and size is positively related. Inclusion of outside directors in the board

lowers return on asset but however is positively related to npm, Tobin’s Q and altman Z

Score. This is consistent with Brown and Caylor (2006) who found that firms with more

35

independent directors performed well while Agrawal and Knoeber (1996) found an opposite

view. As from the table, it is more likely to be positive related, to three dependent variables.

CEO duality is positively correlated to all performance proxies. This is consistent Sanda et al.

(2003) who found a positive relationship between separate CEO and Chairman positions and

firm performance. CSR seems to be relatively uncorrelated. Size is negatively related to most

proxies. As expected, an increase in assets does not necessarily increase performance.

Increase in size may be attributed to other reasons like manufacturing process. However, it is

statistically significant at the 0.01 level.

4.3.2 Regression results and discussion

The Fixed effect model (Least Square Dummy Variable) was used for return on asset

while Tobin’s Q and Altman Z-score used OLS multiple regression model coupled with

appropriate tests. Regression results can be found in Appendix E.

ROA as dependent

roa Coef. Std. Err. t P>t

os 0.024 0.096 0.25 0.801

oc 0.002* 0.001 2.55 0.013

bs 0.118 0.077 1.53 0.13

bi -0.115 0.078 -1.47 0.147

aci 0.001 0.060 0.02 0.987

ceod -0.020 0.039 -0.51 0.611

csr -0.005 0.005 -1.09 0.279

sg -0.018 0.025 -0.73 0.468

size 0.018* 0.008 2.25 0.028

_cons -0.224 0.262 -0.85 0.396

F test (model) 5.53, p-value 0.0000, Obs.=100, R2 =0.7337, SSE 0.1913

Breusch-Pagan / Cook-Weisberg test for heteroskedasticity: chi2(1) = 68.27,Prob > chi2 = 0.0000

Durbin-Watson d-statistic( 10, 100) = 1.79

* p<0.05; ** p<0.01; *** p<0.001 level of significance

Table 9 Regression results: ROA and governance (Fixed Effect model)

36

BP test shows that variance are constant, i.e heteroskedasticity was not a problem (p-

value< 0.05 at the 5%level). The d statistics is 1.7910 and therefore in in the vicinity of 2,

indicating that autocorrelation was not at least present. This reinforces the p-value of the

fixed effect model and therefore will produce unbiased estimates. The core assumptions of

the panel data model were not violated.

The fixed effect model for return on asset shows a negative relationship between

board equity, board independence, CEO duality CSR and sales growth. Ownership

concentration, board size, audit and size have positive relationship. The p-value for the model

is 0.0000 which therefore reveals that the model fits the observed data at the 0.05 level. R2 is

high at 0.7337. 73.37% variation in roa is explained by independent variables. This low p-

value and high R2 means that the observed value are closely scattered around the regression

line. The sum of squares, i.e. residuals is very low. This further explained the goodness fit of

the model. The F-test for fixed effect test is 2.50 (p-value 0.0018) and therefore we reject the

null hypothesis that means of dummy variables are zero as the 0.05 level and hence use fixed

effects as there are significant statistical evidence. The regression output revealed that

ownership concentration is positively and statistically significant (at the 0.001) level with roa.

As more large shareholders invest massively, the firm successfully grow and profitability

increases. Large shareholders are often institutions and other companies. This adds more

pressure to the board to perform well.

10 d < 2 for positive autocorrelation of the residuals, d >2 for negative autocorrelation and d~2 for zerocorrelation

37

Tobin’s Q as dependent variable

Unstandardisedcoefficients

standardisedcoefficients

Collinearitystatistics

Q Coef. RobustStd. Err.

Beta t P>t VIF tolerance

os 0.692* 0.304 0.098 2.27 0.025 0.025 0.591

oc 0.016 0.035 0.133 0.47 0.642 0.642 0.651

bs 1.328* 0.659 0.244 2.02 0.047 0.047 0.562

bi 0.643 0.465 0.07 1.38 0.17 0.17 0.721

aci -0.416 0.482 -0.046 -0.86 0.39 0.39 0.857

ceod 0.611** 0.222 0.103 2.75 0.007 0.007 0.775

csr 0.052 0.031 0.096 1.68 0.096 0.096 0.586

sg -0.02 0.177 -0.005 -0.11 0.91 0.91 0.897

roe 1.217 1.123 0.278 1.08 0.281 0.281 0.802

size -0.249* 0.110 -0.439 -2.27 0.026 0.026 0.583

_cons 1.688 1.911 . 0.88 0.38 0.38

F test (model)=7.67, p-value= 0.0000, Obs.=100, R2= 0.4628, Adj R2= 0.4024, SSE= 54.866Mean, VIF=1.46White's test: chi2=111.58 p-value= 0.0007Likelihood-ratio test : LR chi2(1) = 7.91, Prob > chi2 = 0.0049Durbin-Watson d-statistic( 11, 100) = 1.586145

Table 10 Regression result for Tobin's Q and governance ( OLS)

OLS was deemed to be appropriate for Tobin’s Q dependent variable. Goodness of fit

measures is significmant. The likelihood ratio test shows the evidence of linear association

between Q and governance variables. Ownership structure is positively and significantly

related to firm’s value. Jensen and Meckling (1976) showed that when managerial ownership

falls, the agency costs increase, since anagers can benefit from the consumption of non-

pecuniary benefits. Managers here are however motivated by incentives as they will benefit

from a larger proportion of the benefits associated with their effort. This ultimately increases

firm value. Director’s remuneration and share schemes should be considered. Also, the study

38

revealed that board size is positively related to firm value and is statistically significant at

0.05 level. The standard error is more than 50%. But accordingly, it means that as more one

more director is appointed in the board, firm value will increase by 1.3%. I deduced that the

board skills/ qualifications and experiences are therefore important. The addition brings on

expertise and the performance and firm value increases.

Furthermore, there exist a positive and significant relationship between CEO duality

and firm value. The positive relation between the proportion of outside directors and the

likelihood that an outside director is appointed as CEO and such an appointment benefits

shareholders. This is consistent with Rouf (2011) findings.

Size on the other hand is negatively related to firm value. The coefficient is

statistically significant at the 0.001 level and has good parameter estimates sd. An increase in

size does not increase firm value. This can simply be demonstrated in the formula of Tobin’s

Q, where total asset is in the denominator. Thus increase in size, measured by the natural

logarithm of total asset.

39

Altman Z Score as dependent variable

Unstandardisedcoefficients

standardisedcoefficients

Collinearitystatistics

levmv Coef. Std.Err.

Beta t P>t VIF tolerance

os 1.169 4.672 0.029 0.25 0.803 1.67 0.599oc 0.217* 0.098 0.309 2.2 0.03 1.41 0.709bs 3.752 3.177 0.117 1.18 0.241 1.78 0.561bi 0.638 3.896 0.012 0.16 0.87 1.4 0.715aci 3.624 4.005 0.069 0.9 0.368 1.15 0.868

ceod 2.545 2.678 0.075 0.95 0.345 1.31 0.763csr 0.413 0.235 0.128 1.76 0.083 1.62 0.619sg -

2.309**1.962 -0.092 -1.18 0.243 1.16 0.862

size -1.936 0.642 -0.59 -3.02 0.003 1.66 0.603_cons 26.074* 10.502 2.48 0.015 Mean

VIF=1.46

* p<0.05; ** p<0.01; *** p<0.00188.52 57 0.0047

Likelihood-ratio test : LR chi2(1) = 3.22, Prob > chi2 = 0.0729Durbin-Watson d-statistic( 10, 96) = 1.47058111

Table 11 Regression result; Altman Z Score and corporate governance ( OLS)

Lastly, ownership concentration showed a positive and significant (at the 0.05 level)

relationship with Z score. This means that as more major shareholders are present, the risk of

bankruptcy increases. Block shareholders do often put pressure on management via restrictive

convenience and the board need to deal with this kind of problem. This why, as per most

company’s constitution, any shareholder having more than an appropriate shareholding, for

example more than 5%, call for a board approval. There is the risk of bankruptcy or taken

over through merger and acquisition.

11 D-statistics within dL=1.34 and dU=1.790 from DW table. Therefore critical value lies within zone ofindecision(see Gujarati )

40

Chapter 5 Conclusion and Recommendation

The study completely fulfils its twofold major purpose: to evaluate corporate

governance system providing qualitative responses and to provide quantitative results about

the relationship between corporate governance and firm performance and capital structure.

No doubt that several empirical exists in the field (and is still on-going), and is a

contribution for the Mauritian case. The purpose was to find the impact of corporate

governance and financial leverage and performance and that do firm really follow the

Corporate Governance Code in Mauritius. As such, all companies do comply with the

Corporate Governance as stipulated in S1.1 of the CCG. This is further backed by the S74 of

the Finance Act 2009. Boards are dominated by NED and independent directors which is line

with S2.2.2 of the CCG. Besides, as per S2 Ch .3.1, all firms have separate person fulfilling

the role of the CEO and Chairman. A separate audit committee is also respected among firms.

Firms also disclose ethical, social and environmental issues. So overall, the implementation

of the code is well present among the firms in Mauritius. However, perhaps if I had to

recommend, I will suggest that the corporate governance is not yet fully developed in

Mauritius as it has been in our regional partners. The Code adopted in Mauritius rooted from

King Report on Corporate Governance in South Africa 2002. However, South Africa already

revised and updated their corporate governance system in 2009: King III. Mauritius has not

evolved its corporate governance system and may lag behind. I believe there is a weak

determination in NCCG since its been years it has been heard to be reviewed. Risk

management, Integrated Reporting and Sustainability should be given more attention

following the recent collapse of big international firms. Stakeholder relationship should be

enlightened, perhaps a new chapter can be considered as King III. There is a global move

from the single to the triple bottom line, which embraces the economic, environmental and

social aspects of a company’s activities.

41

On the second part, a mixed of analytical and statistical estimation was used to evaluate

the relationship. The study reveals the following result:

i. Board equity, sales growth; return on equity and size of firm have a positive and

significant relationship with leverage.

ii. CEO duality and CSR have a negative and significant relationship with leverage

iii. Ownership concentration, board size and audit committee do not have a significant

relationship; in contrary to some empirical studies in the past.

iv. Ownership concentration has a positive and significant relationship with profitability

(ROA) and Altman Z-score(bankruptcy)

v. There is a positive and significant relationship between board equity, board size, CEO

and firm value measured by Tobin’s Q.

Such findings lend some support to the findings of Black (2001), Gompers et al.(2003),

Klapper and Love (2004) amongst others and did contradicts some findings also. Findings are

not so conclusive in the end since corporate governance itself is self-evolving in Mauritius.

Ever since its introduction in 2004, some people believe that these principles have been

promoting the image of Mauritius and reflected in governance indices like the Mo Ibrahim

Index.

For enhanced performance of corporate entities, I recommend a clear separation of the

positions of CEO and board chair and also to maintain relatively independent audit

committees. Regarding future possible line of research, efforts should be put on institutional

ownership. The variable was lastly dropped since data for the specific variable few years after

the introduction of the code was rarely available. Finally, we associate corporate governance

with firm performance, but our results do not necessarily imply causality. Our caveat

regarding absence of causality is consistent with other studies (e.g., Larcker et al. 2004) that

recognize the impossibility of solving the endogeneity issue, especially given the very limited

temporal data. Far more temporal data are needed before one can attempt to infer causality

data, perhaps by using Granger causality.

42

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48

AppendicesAppendix A: List of firm used in the sample.

The sample includes firms listed only in the Official Market of the SEM as at end of year

2011. Below is the list and their symbols as quoted on SEM.

BANKS & INSURANCE AND OTHERFINANCEThe Mauritius Commercial BankLtd

MCB

State Bank of Mauritius Ltd SBMMauritius Union Assurance Co.Ltd

MUA

Mauritian Eagle Insurance Co. Ltd MEI12

Swan Insurance Co. Ltd SWAN

COMMERCERogers & Co. Ltd ROGERSCompagnie des MagasinsPopulaires Ltée

CMPL

Harel Mallac Ltd HMLInnodis Ltd HWFIreland Blyth Ltd IBL12

INDUSTRYGamma Civic Ltd GCLPhoenix Beverages Ltd MBLMauritius Chemical & FertilizerIndustry Ltd

MCFI

United Basalt Products Ltd UBP

INVESTMENTSCaudan Development Ltd CAUDENL Commercial Limited GIDCThe Mauritius DevelopmentInvestment Trust Co. Ltd

MDIT

National Investment Trust Ltd NITLPromotion and Development Ltd PADP. O. L. I. C. Y Ltd POL

12 IBL acquired MEI in 2012

Terra Mauricia Ltd TERA13

United Docks Ltd UTDL

LEISURE & HOTELSAutomatic Systems Ltd ASLNew Mauritius Hotels Ltd NMHLLux Island Resort Ltd14 NRLSun Resorts Ltd SUN

SUGAROmnicane Ltd MTMDENL Land Ltd15 SAVA.N

TRANSPORTAir Mauritius Ltd AIRM

13Amalgamation of Harel Freres Ltd and itsSubsidiaries (HFL Group) and rebranding. Terrabecame listed on 01/Jan/201214Previously known as Naiade Resorts Ltd (Re-branding in 2011)15 Savannah Ltd became ENL on 30 Non 2010 andonly ENL Ordinary shares were included incalculations

49

Appendix B

Altman's Z-Score formula weighs five ratios then adds them together to come up with

a bankruptcy prediction estimate for a company. This estimate of bankruptcy for publicly-

held companies depends on the resulting score of Altman's Z-Score formula, which the score

is divided into four categories:

Less than 1.8: Bankruptcy risk is high

Between 1.8 and 2.7: Bankruptcy risk is fair

Between 2.7 and 3.0: Bankruptcy risk is possible, but not likely in the near-future

Higher than 3.0: Bankruptcy risk is low

The original Z-score formula was as follows:

Z = 0.012T1 + 0.014T2 + 0.033T3 + 0.006T4 + 0.009T5.

T1 = Working Capital / Total Assets.

Measures liquid assets in relation to the size of the company.

T2 = Retained Earnings / Total Assets.

Measures profitability that reflects the company's age and earning power.

T3 = Earnings Before Interest and Taxes / Total Assets.

Measures operating efficiency apart from tax and leveraging factors. It

recognizes operating earnings as being important to long-term viability.

T4 = Market Value of Equity / Book Value of Total Liabilities.

Adds market dimension that can show up security price fluctuation as a

possible red flag.

T5 = Sales/ Total Assets.

Standard measure for total asset turnover (varies greatly from industry to

industry).

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Appendix C: Raw data used

company year os oc bs bi aci ceod csr sg roe size levmv npm roa q altzASL 2007 0.050 0.596 2.708 0.867 1 1 16.067 0.219 0.426 17.846 0.022 0.014 0.234 2.422 19.582ASL 2008 0.062 59.600 2.485 0.833 1 1 16.519 0.484 0.650 18.165 0.026 0.016 0.312 1.573 19.893ASL 2009 0.042 59.600 2.565 0.846 1 1 16.734 0.167 0.977 18.020 0.007 0.020 0.530 6.753 32.913ASL 2010 0.033 0.596 2.565 0.846 1 1 13.314 -0.145 0.709 17.911 0.007 0.017 0.459 7.113 33.113ASL 2011 0.024 0.596 2.485 0.833 1 1 13.028 -0.107 0.308 18.537 0.005 0.012 0.153 3.385 14.412

CAUD 2007 0.001 0.653 2.485 0.833 1 1 13.122 0.133 0.029 21.692 0.149 0.265 0.034 0.983 4.023CAUD 2009 0.001 0.654 2.398 0.818 1 1 14.102 0.263 0.036 22.108 0.214 0.259 0.034 0.553 2.507CAUD 2010 0.001 0.675 2.398 0.818 1 1 14.101 0.081 0.018 22.112 0.187 0.117 0.015 0.630 2.592CAUD 2011 0.001 0.675 2.398 0.818 1 1 14.110 0.096 0.019 22.126 0.212 0.114 0.033 0.517 2.400CMPLL 2011 0.123 0.834 2.079 0.875 1 1 11.970 0.087 0.016 18.954 0.074 0.005 0.014 0.296 2.559GIDC 2008 0.179 0.711 1.946 0.286 1 1 10.597 0.142 0.403 21.356 0.067 0.330 0.266 0.415 3.279GIDC 2009 0.177 0.713 1.946 0.714 1 1 12.206 0.049 0.027 21.288 0.112 0.023 0.040 0.376 3.098GIDC 2010 0.177 0.732 1.946 0.714 1 1 13.073 0.016 0.082 21.384 0.087 0.067 0.068 0.458 2.733GIDC 2011 0.182 0.754 1.946 0.714 1 1 12.359 0.956 0.085 21.653 0.228 0.034 0.069 0.484 2.348SAVA 2008 0.186 0.782 2.197 0.667 0.667 1 12.506 -0.045 -0.038 21.937 0.221 -0.253 0.021 0.490 1.080SAVA 2009 0.187 0.789 2.197 0.667 1 1 13.514 0.197 0.006 22.331 0.140 0.061 0.005 0.410 1.865SAVA 2010 0.181 0.737 2.197 0.667 0.667 1 13.775 0.337 -0.005 16.293 0.110 -0.107 -0.137 0.910 1.953SAVA 2011 0.004 0.661 2.197 0.667 0.667 1 12.914 0.509 0.143 23.494 0.091 2.610 0.121 0.658 4.263GCL 2007 0.618 0.391 2.079 0.750 1 0 15.365 0.128 0.088 21.424 0.353 0.035 0.066 0.343 1.698GCL 2008 0.648 0.425 2.079 0.750 1 0 15.320 0.222 0.142 21.725 0.358 0.049 0.072 0.325 1.404GCL 2010 0.686 0.635 2.079 0.500 0.8 1 15.932 0.354 0.116 22.246 0.231 0.041 0.037 1.107 1.491GCL 2011 0.584 0.761 2.079 0.500 0.8 1 15.180 0.448 0.254 22.521 0.153 0.128 0.099 1.066 2.541HML 2007 0.059 0.896 2.303 0.800 1 1 13.773 0.131 0.090 21.451 0.121 0.047 0.086 0.412 3.425HML 2008 0.083 0.902 2.303 0.300 0.667 1 13.785 0.230 0.183 21.652 0.101 0.089 0.125 0.338 3.342HML 2010 0.110 0.858 2.303 0.700 0.75 1 15.009 0.003 0.087 21.889 0.057 0.051 0.082 0.832 4.448HML 2011 0.083 0.856 2.398 0.818 1 1 15.343 0.118 0.087 22.010 0.042 0.056 0.071 0.647 3.991

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company year os oc bs bi aci ceod csr sg roe size levmv npm roa q altzHWF 2007 0.000 0.700 2.079 0.750 1 1 10.094 0.186 0.025 21.393 0.181 0.009 0.024 0.614 2.392HWF 2008 0.000 0.728 2.079 0.750 1 1 9.250 0.150 0.110 21.433 0.355 0.040 0.085 0.946 3.407HWF 2010 0.000 0.478 2.079 0.875 0.667 1 14.509 0.019 0.010 21.575 0.339 0.005 0.077 0.944 3.433HWF 2011 0.002 0.766 2.079 0.875 1 1 14.947 0.335 0.125 21.762 0.086 0.059 0.097 0.607 3.259IBL 2007 0.011 0.753 2.398 0.818 1 1 15.895 0.166 0.137 23.297 0.317 0.039 0.080 0.562 1.640IBL 2008 0.010 0.835 2.398 0.818 1 1 15.425 0.104 0.086 23.332 0.308 0.023 0.064 0.417 1.524IBL 2010 0.006 0.728 2.398 0.818 1 1 16.050 0.011 0.119 23.421 0.222 0.035 0.069 0.519 1.639IBL 2011 0.007 0.319 2.398 0.818 1 1 16.617 0.216 0.139 23.421 0.172 0.036 0.066 0.502 1.809NRL 2007 0.076 0.710 2.303 0.800 0.75 1 14.636 0.167 0.238 22.496 0.154 0.234 0.140 2.056 3.125NRL 2008 0.097 0.658 2.303 0.800 0.75 0 14.713 0.024 0.076 22.952 0.372 0.112 0.051 0.563 2.633NRL 2009 0.100 0.627 2.303 0.800 1 1 13.621 -0.032 -0.113 22.899 0.382 -0.154 0.015 0.740 0.925NRL 2011 0.067 0.637 2.398 0.818 1 1 13.450 0.745 0.002 23.059 0.494 0.002 0.036 0.780 0.876MCFI 2009 0.060 0.824 2.639 0.929 1 1 13.934 -0.156 0.106 20.495 0.017 0.124 0.121 0.720 8.400MCFI 2010 0.060 0.847 2.303 0.900 1 1 13.907 0.043 0.111 20.612 0.013 0.138 0.107 0.955 10.221MCB 2007 0.025 0.225 2.708 0.800 1 1 16.811 0.164 0.183 25.425 0.897 0.683 0.028 3.171 0.143MCB 2008 0.012 0.213 2.485 0.833 1 1 17.055 0.519 0.226 25.613 0.376 0.887 0.034 0.565 0.352MCB 2009 0.064 0.189 2.485 0.833 1 1 17.217 0.209 0.213 25.737 0.389 0.787 0.033 0.429 0.548MCB 2010 0.024 0.180 2.485 0.750 1 1 17.655 -0.099 0.168 25.815 0.049 0.366 0.025 0.274 0.458MCB 2011 0.030 0.171 2.485 0.750 1 1 17.527 0.064 0.190 25.875 0.292 0.455 0.031 0.388 0.116MEI 2007 0.000 0.750 2.303 0.800 1 1 11.184 0.236 0.091 21.668 0.618 0.037 0.075 0.688 1.126MEI 2008 0.000 0.750 2.303 0.900 1 1 11.198 -0.059 0.125 21.698 0.647 0.053 0.078 0.642 1.099MEI 2009 0.000 0.750 2.485 0.833 1 1 13.026 -0.013 0.084 21.762 0.628 0.033 0.057 0.691 1.088MEI 2010 0.000 0.750 2.303 0.800 1 1 13.816 0.043 0.075 21.857 0.576 0.052 0.030 0.658 0.940MEI 2011 0.000 0.750 2.565 0.769 1 1 13.816 -0.126 0.061 21.839 0.684 0.051 0.052 0.615 0.767

MUA 2007 0.332 0.352 2.197 0.778 1 1 13.080 0.211 0.065 22.385 0.624 0.262 0.002 0.870 0.647MUA 2008 0.030 0.428 1.946 0.714 0.75 1 13.838 0.197 0.139 22.192 0.582 0.150 0.035 1.065 1.101MUA 2010 0.183 0.449 2.079 0.875 1 1 13.998 0.666 0.334 22.796 0.631 0.198 0.049 1.192 1.096MUA 2011 0.191 0.247 2.197 0.889 1 1 13.635 0.163 0.332 22.818 0.644 0.263 0.038 1.105 0.973

52

company year os oc bs bi aci ceod csr sg roe size levmv npm roa q altzNMHL 2007 0.139 0.478 2.398 0.727 1 1 16.706 0.317 0.215 23.494 0.160 0.255 0.163 2.016 4.438NMHL 2008 0.076 0.508 2.197 0.667 1 1 16.811 0.047 0.187 23.676 0.161 0.228 0.126 0.885 2.387NMHL 2009 0.076 0.490 2.303 0.700 1 1 16.811 -0.086 0.109 23.808 0.155 0.159 0.076 1.247 2.455NMHL 2010 0.075 0.149 2.303 0.600 0.75 1 16.524 -0.077 0.060 23.941 0.225 0.097 0.048 1.025 1.762NMHL 2011 0.076 0.464 2.303 0.600 0.75 1 16.300 0.115 0.062 24.099 0.313 0.099 0.044 0.819 1.508MTMD 2007 0.001 0.822 2.565 0.769 1 1 12.365 -0.016 0.033 22.654 0.009 0.431 0.040 0.591 7.827MTMD 2008 0.000 0.836 2.485 0.750 0.8 1 14.993 -0.012 0.043 23.406 0.330 0.616 0.027 0.630 0.900MTMD 2009 0.001 0.838 2.485 0.750 1 1 11.695 0.013 0.040 23.441 0.296 0.081 0.070 0.658 1.194MTMD 2010 0.001 0.804 2.485 0.833 1 1 16.596 0.090 0.040 23.416 0.345 0.072 0.070 0.718 1.265MTMD 2011 0.001 0.843 2.565 0.846 0.667 1 16.215 0.125 0.071 23.451 0.293 0.125 0.076 0.630 1.339

POL 2007 0.174 0.124 2.079 0.875 1 1 10.915 -0.135 0.047 21.341 0.000 0.851 0.047 0.771POL 2008 0.214 0.128 2.197 0.778 1 1 11.608 -0.997 0.232 20.979 0.000 0.975 0.232 0.923POL 2009 0.228 0.138 2.197 0.778 1 1 13.332 0.083 0.246 20.992 0.000 0.959 0.245 1.210POL 2010 0.219 0.139 2.079 0.875 1 1 13.470 -0.631 0.082 21.034 0.000 0.903 0.081 1.179POL 2011 0.400 0.696 2.079 0.875 1 1 13.129 -0.134 0.076 20.985 0.000 0.884 0.073 1.159 24.998MBL 2008 0.020 0.620 2.485 0.750 1 1 14.403 0.069 0.115 21.724 0.083 0.077 0.109 0.659 4.565MBL 2011 0.000 0.649 2.485 0.750 1 1 18.564 0.102 0.075 21.882 0.055 0.044 0.090 1.118 5.237PAD 2007 0.012 0.516 2.303 0.700 0.667 1 13.494 0.120 0.077 22.781 0.025 0.729 0.071 0.544 10.110PAD 2008 0.012 0.519 2.197 0.778 1 1 13.154 0.336 0.099 22.960 0.063 0.869 0.122 0.516 6.857PAD 2009 0.014 0.565 2.197 0.778 1 1 13.423 0.250 0.033 22.963 0.072 0.568 0.036 0.511 5.724PAD 2010 0.017 0.523 2.197 0.778 1 1 15.238 0.039 -0.005 22.980 0.074 -0.073 0.008 0.514 4.875

ROGERS 2007 0.153 0.675 2.485 0.917 1 1 13.265 0.018 0.076 23.685 0.417 0.089 0.060 1.096 1.533ROGERS 2008 0.226 0.686 2.485 0.917 1 1 13.459 -0.887 0.190 23.739 0.213 0.903 0.127 0.665 1.857ROGERS 2009 0.153 0.706 2.398 0.909 1 1 13.305 -0.032 0.070 23.839 0.241 0.075 0.057 0.570 1.726ROGERS 2010 0.001 0.704 2.485 0.917 1 1 17.306 -0.890 0.065 23.936 0.254 0.652 0.046 0.525 1.253ROGERS 2011 0.023 0.732 2.485 0.917 1 1 17.456 0.094 0.045 26.239 0.251 0.050 0.004 0.059 0.971

SBM 2007 0.000 0.618 2.398 0.818 0.667 1 14.369 -0.147 0.158 24.821 0.063 0.482 0.029 0.241 0.475SBM 2008 0.000 0.656 2.398 0.636 1 1 16.524 0.313 0.193 24.936 0.033 0.515 0.035 0.385 0.903

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company year os oc bs bi aci ceod csr sg roe size levmv npm roa q altzSBM 2009 0.000 0.705 2.398 0.818 1 1 16.598 -0.030 0.156 25.096 0.051 0.509 0.030 0.332 0.550SBM 2010 0.022 0.769 2.398 0.818 1 1 17.217 0.039 0.127 25.103 0.052 0.746 0.027 0.370 0.506SBM 2011 0.002 0.765 2.398 0.818 1 1 17.670 0.096 0.126 25.285 0.008 0.479 0.026 0.270 0.756SUN 2007 0.047 0.887 2.398 0.909 1 1 15.151 0.263 0.246 22.751 0.184 0.330 0.085 2.047 3.963SUN 2008 0.012 0.885 2.398 0.818 1 1 15.519 0.001 1.930 22.957 0.383 0.216 0.107 0.778 1.348SUN 2010 0.050 0.139 2.485 0.833 1 1 17.453 0.015 0.051 23.119 0.340 0.007 0.038 0.983 4.125SUN 2011 0.509 0.121 2.398 0.818 1 1 14.863 0.147 0.050 23.172 0.320 0.064 0.046 0.874 1.313

SWAN 2007 0.000 0.798 2.639 0.857 0.8 1 13.757 0.125 0.087 23.687 0.921 0.027 0.004 0.995 0.280SWAN 2008 0.000 0.817 2.565 0.846 0.8 1 13.140 0.061 0.166 23.579 0.924 0.051 0.009 0.979 0.333SWAN 2009 0.000 0.823 2.565 0.846 0.8 1 14.711 0.026 0.189 23.733 0.907 0.067 0.011 1.002 0.329SWAN 2010 0.002 0.825 2.639 0.857 1 1 15.253 0.133 0.182 23.853 0.012 0.089 0.010 0.096 0.317SWAN 2011 0.001 0.823 2.398 0.818 0.8 1 15.405 0.185 0.176 23.852 0.862 0.086 0.010 1.039 0.364TERA 2007 0.010 0.806 2.708 0.733 1 1 13.592 0.065 0.076 22.994 0.196 0.160 0.101 0.480 2.346TERA 2008 0.021 0.790 2.565 0.692 1 1 14.914 0.116 0.064 23.022 0.171 0.126 0.090 0.375 2.349TERA 2010 0.024 0.142 2.398 0.636 1 1 15.870 0.122 0.022 23.391 0.051 0.069 0.044 0.669 5.426TERA 2011 0.025 0.726 2.398 0.727 1 1 15.895 0.159 0.049 23.397 0.031 0.138 0.066 0.637 7.280UBP 2009 0.009 0.527 2.303 0.900 1 1 13.653 0.184 0.086 21.815 0.130 0.073 0.090 0.761 3.369UBP 2011 0.008 0.524 2.303 0.900 1 1 13.862 0.162 0.129 22.011 0.097 0.098 0.102 0.998 3.263

54

Appendix D

In this section, all the statistical results are displayed. Output is from STATA SE 12.

Figure 1 Hausman test for leverage and corporate governance

55

Figure 2 Fixed effects (within) estimation for leverage and corporate governance, including F-test

Figure 3 OLS estimation for leverage and corporate governance

56

Figure 4 LSDV estimation for leverage and corporate governance

57

Figure 5 Random effects estimation for leverage and corporate governance

58

Figure 6 LSDV estimation for roa and corporate governance

59

Figure 7 regression, anova and VIF results for Tobin's Q and corporate governance

60

Figure 8 regression, anova and VIF results for Altman Z Score and corporate governance

61

Appendix E: Dissertation Progress log