An-Najah National University 

Faculty of Graduate Studies 
 

 

 
 
 

 

THE MODERATING EFFECT OF 

CORPORATE GOVERNANCE ON THE 

RELATIONSHIP BETWEEN EARNINGS 

MANAGEMENT AND CAPITAL STRUCTURE 
 

 

 

 
 

 

By 

Shams Al-Doha Mohammad Abu Alhassan 
 
 

 

 

Supervisors 

Prof. Dr. Abdul Naser Nour 

Dr. Sameh Atout 
 

 
 

 

This Thesis is Submitted in Partial Fulfillment of the Requirements for the Degree 

of Master in Accounting, Faculty of Graduate Studies, An-Najah National 

University, Nablus, Palestine. 

2023 



ii 

 

 

 

 

 

 

 

 

 

 

 
 

 
 

 

THE MODERATING EFFECT OF CORPORATE 

GOVERNANCE ON THE RELATIONSHIP BETWEEN 

EARNINGS MANAGEMENT AND CAPITAL STRUCTURE 
 

 
By 

Shams Al-Doha Mohammad Abu Alhassan 

 

This Thesis was defended successfully on 25/05/2023 and approved by: 

 

 



iii 

Dedication 

 ...   إلى روح والدي الذي طالما تمنى أن يراني في هذا اليوم لكن إرادة هللا فوق كل شيء

 ...  إلى أمي أنيسة الروح وبهجة القلب 

 ... إلى إخوتي ضلعي الثابت الذي ال يميل  

 ... وكل من أحب 

 أهدي هذا االنجاز 

  



iv 

Acknowledgment 

 

" 
َ
ك
َ
كَر ِنْعَمت

ْ
ش
َ
 أ
ْ
ن
َ
ي أ ِ
ْوِزْعن 

َ
 - سورة النمل  – "  َربِّ أ

 

هلل رب العالمين الرازق الكريم على ما انعمه علّي برفعي درجة علمية ثانية.. والصالة و السالم    بعد الحمد

 اشرف الخلق والمرسلين سيدنا محمد صل هللا عليه وسلم.. على 

  اتقدم بجزيل الشكر والعرفان لهذا الصرح العلمي العريق جامعتي جامعة النجاح الوطنية، مشرفي األول أ. 

مشرفي الثاني الدكتور سامح العطعوط على دعمعها المستمر في مسيرة إعداد هذه    عبد الناصر نور،  .د

 االطروحة.  

كما اتقدم بالشكر الجزيل لجميع القامات العلمية الداعمة اللذين لم يبخلوا بعلمهم ووقتهم خالل رحلة الماجستير  

د. معز أبو عليا، د. إسالم عبد الجواد، د. غسان دعاس. الشكر موصول للقامة العلمية صاحب الخبرة  

 االحصائي. والعطاء د. كامل جبرين الذي ساهم في إتمام وإثراء قسم التحليل  

 كل االمتنان للصديقة والباحثة المتميزة أ. ضحى ربايعة على نصائحها المستمرة. 

 أحسبها عند هللا ِعلمًا نافعًا.. 

 

  



v 

Declaration 

I, the undersigned, declare that I submitted the thesis entitled: 

THE MODERATING EFFECT OF CORPORATE 

GOVERNANCE ON THE RELATIONSHIP BETWEEN 

EARNINGS MANAGEMENT AND CAPITAL STRUCTURE  

 

I declare that the work provided in this thesis, unless otherwise referenced, is the 

researcher’s own work, and has not been submitted elsewhere for any other degree or 

qualification. 

 

  Student's Name:  

 
Signature: 

  Date: 

 

  

Shams Al-Doha M. Abu Alhassan 
 

25/05/2023 



vi 

Table of Contents 
 

Dedication .............................................................................................................................. iii 

Acknowledgment ....................................................................................................................iv 

Declaration .............................................................................................................................. v 

Table of Contents ...................................................................................................................vi 

List of Tables ....................................................................................................................... viii 

List of Figures .........................................................................................................................ix 

List of Appendices ................................................................................................................... x 

Abstract ………………………………………………………………...……………….…. xi 

Chapter One: Introduction and Theoretical Background ................................................. 1 

1.1 Introduction ....................................................................................................................... 1 

1.2 Research Problem .............................................................................................................. 2 

1.3 Research Importance ......................................................................................................... 3 

1.4 Research Objectives .......................................................................................................... 4 

1.5 Research Questions ........................................................................................................... 5 

Chapter Two: Literature Review and Hypothesis Development ...................................... 6 

2.1 Introduction ....................................................................................................................... 6 

2.2 Definition of the Concepts ................................................................................................. 6 

2.2.1 Earnings Management .................................................................................................... 6 

2.2.2 Capital Structure ............................................................................................................. 7 

2.2.3 Corporate Governance .................................................................................................. 10 

2.2.3.1 Corporate Governance in Palestine ........................................................................... 10 

2.2.3.2 Corporate Governance in Jordan ............................................................................... 12 

2.3 Literature Review and Hypothesis Development ............................................................ 12 

2.3.1 Earnings Management and Capital Structure ............................................................... 12 

2.3.2 The Size of the Board of Directors ............................................................................... 13 

2.3.3 Board independence ..................................................................................................... 14 

2.3.4 Gender Diversity .......................................................................................................... 15 

2.3.5 CEO Duality ................................................................................................................. 16 

2.3.6 Institutional Ownership ................................................................................................ 18 

Chapter Three: Research Methodology ............................................................................ 21 

3.1 Introduction ..................................................................................................................... 21 

3.2 Data Collection ................................................................................................................ 21 

3.2.1 Palestine Exchange ....................................................................................................... 22 

3.2.2 Amman Stock Exchange .............................................................................................. 22 



vii 

3.3 Research Sample ............................................................................................................. 23 

3.4 Research Model ............................................................................................................... 24 

3.5 Variable Measurement ..................................................................................................... 24 

3.5.1 Dependent Variable ...................................................................................................... 24 

3.5.2 Independent Variables .................................................................................................. 24 

3.5.3 Control variables .......................................................................................................... 26 

3.6 R/RStudio Package .......................................................................................................... 26 

Chapter Four: Results and Discussion .............................................................................. 28 

4.1 Correlation Analysis ........................................................................................................ 28 

4.1.1 Overall Correlation Analysis ........................................................................................ 28 

4.1.2 Overall Correlation Analysis for Palestinian Manufacturing Firms ............................. 31 

4.1.3 Overall Correlation Analysis for Jordanian Manufacturing Firms ............................... 33 

4.2 Results of Descriptive Statistics ...................................................................................... 35 

4.2.1 Overall Results of Descriptive Statistics ...................................................................... 35 

4.2.2 Results of Descriptive Statistics for Palestine .............................................................. 38 

4.2.3 Results of Descriptive Statistics for Jordan .................................................................. 40 

4.3 Regression Results ........................................................................................................... 42 

4.3.1 Overall Earnings Management and Capital Structure .................................................. 42 

4.3.2 The Overall Role of Corporate Governance on the Relationship Between Earnings 

Management and Capital Structure Decisions ............................................................. 43 

4.3.4 Earnings Management and Capital Structure in Palestine ............................................ 45 

4.3.6 The Role of Corporate Governance in the Relationship Between Earnings Management 

and Capital Structure in Palestine ................................................................................ 46 

4.3.8 Earnings Management and Capital Structure in Jordan ............................................... 48 

4.3.9 The Role of Corporate Governance in the Relationship Between Earnings Management 

and Capital Structure in Jordan .................................................................................... 49 

4.4 Statical Learning Results ................................................................................................. 50 

Chapter Five: Conclusion and Recommendations ........................................................... 55 

5.1 Overview ......................................................................................................................... 55 

5.2 Conclusion ....................................................................................................................... 55 

5.3 Recommendations ........................................................................................................... 56 

5.4 Future Research ............................................................................................................... 58 

List of Abbreviations ............................................................................................................. 59 

References ............................................................................................................................. 60 

Appendices ............................................................................................................................ 73 

 ب  ......................................................................................................................................المخلص 



viii 

 

List of Tables  

[ 

Table (1): Summary of hypotheses ................................................................................. 19 

Table (2): Summary of previous literature ..................................................................... 20 

Table (3): Variable labels and data sources .................................................................... 21 

Table (4): Distribution of the research sample ............................................................... 23 

Table (5): Measurement of research variables ............................................................... 27 

Table (6): Correlation matrix for overall manufacturing firm’s sample ........................ 30 

Table (7): Correlation matrix for Palestinian manufacturing firm’s sample .................. 32 

Table (8): Correlation matrix for Jordanian manufacturing firm’s sample .................... 34 

Table (9): Descriptive Statistics for Palestinian and Jordanian manufacturing firms listed 

in PEX and ASE (Overall) .................................................................................... 37 

Table (10): Descriptive Statistics for Palestinian manufacturing firms listed PEX ....... 40 

Table (11): Descriptive Statistics for Jordanian manufacturing firms listed in ASE ..... 73 

Table (12): Answering hypothesis (H1) in Palestinian and Jordanian manufacturing firms 

listed in PEX and ASE .......................................................................................... 74 

Table (13): Answering hypothesis (H2a-e) in Palestinian and Jordanian manufacturing 

firms listed in PEX and ASE (Overall) ................................................................. 75 

Table (14): Answering hypothesis (H1) in Palestinian manufacturing firms listed in PEX

 ............................................................................................................................... 77 

Table (15): Answering hypothesis (H2a-e) in Palestinian manufacturing firms listed in 

PEX ....................................................................................................................... 78 

Table (16): Answering hypothesis (H1) in Jordanian manufacturing firms listed in ASE

 ............................................................................................................................... 80 

Table (17): Answering hypothesis (H2a-e) in Jordanian manufacturing firms listed in 

ASE ....................................................................................................................... 81 

Table (18): Experimental Results with five CV (averaged over 100 runs) for the CS data 

set and all independent variables ........................................................................... 84 

Table (19): Overall sample results summary .................................................................. 85 

Table (20): Palestinian manufacturing firm’s results summary ..................................... 85 

Table (21): Jordanian manufacturing firms results summery ........................................ 85 

Table (22): Test of study’s hypothesis ............................................................................ 86 

Table (23): Results of descriptive analysis for overall sample 2013-2020 ..................... 87 

Table (24): Results of descriptive analysis for PEX 2013-2020 .................................... 89 

Table (25): Results of descriptive analysis for ASE 2013-2020 .................................... 91 

Table (26): Study’s sample ............................................................................................. 91 



ix 

 

List of Figures  

 

Figure (1): The interactive effect of earnings management and corporate governance on 

capital structure ..................................................................................................... 26 

Figure (2): Trend of earnings management for Palestinian manufacturing firms from 

2013–2020 ............................................................................................................. 46 

Figure (3): Trend of earnings management for Jordanian manufacturing firms from 2013 

to 2020 ................................................................................................................... 48 

Figure (4): Prediction model of capital structure component in Palestinian manufacturing 

firms ....................................................................................................................... 52 

Figure (5): Prediction model of capital structure component in Jordanian manufacturing 

firms ....................................................................................................................... 52 

Figure (6): Prediction model of capital structure component in Palestinian and Jordanian 

manufacturing firms .............................................................................................. 53 

 

 

 

  



x 

List of Appendices  

 

Appendix (A): Tables ..................................................................................................... 73 

 

 

  



xi 

THE MODERATING EFFECT OF CORPORATE 

GOVERNANCE ON THE RELATIONSHIP BETWEEN 

EARNINGS MANAGEMENT AND CAPITAL STRUCTURE 

By 

Shams Al-Doha Mohammad Abu Alhassan 

Supervisors 

Prof. Abdul Naser Nour 

Dr. Sameh Atout 

Abstract 

This study seeks to investigate the moderating role of corporate governance (CG) in the 

relationship between earnings management (EM) and debt level in capital structure of 

the firm (DCAPS). The annual reports of 38 firms from the manufacturing sector were 

analyzed for the period 2013–2020. By employing RStudio, the researcher included both 

fixed and random effect regressions in the analysis. 

The overall results showed that EM improved debt ratio. Regarding the moderating role 

of CG, non-executive directors and female board members increased the high leverage 

implications of EM.  

Palestinian manufacturing firms debt levels were significantly reduced by EM. The 

moderating role of CG states that larger boards and female directors increase the high-

leverage impact of EM, while CEO duality reduces it. However, EM had an insignificant 

impact on debt in Jordanian manufacturing firms. The presence of female board members 

increased the firm's reliance on debt financing as a result of EM practices, while 

institutional investors mitigated the effect of EM on debt financing, decreasing debt 

reliance. 

This study anticipated to contribute to capital structure literature by explaining the link 

between CG and EM and CAPS. Helping decision makers and investors assess the 

efficacy of existing CG reforms for strengthening CAPS management, managing EM 

practices, monitoring procedures, and creating the optimal capital structure. This study 

recommends that the board of directors and management should evaluate its tolerance 

while making decisions, especially regarding its financial structure. Capital-structure 

policy should be considered when making financial decisions. Second, managers should 

carefully design an optimal capital structure and protect their organization from risk. 



xii 

Since lenders depend on CG procedures, enterprises may need to maintain a specific 

level of CG practices to get more debt financing. This method improves monitoring, 

helping firms attract superior resources.  

Study limitations are as follows: Few CG features that may directly affect financing 

decisions are not included in this study. The Palestinian sample employed in this study 

is three times smaller than the Jordanian sample, which may have affected the 

conclusions. Due to small sample size and time period, the findings cannot be applied to 

non-manufacturing samples. 

Keywords: Capital structure, corporate governance, earnings management, Palestine 

exchange, Amman stock exchange 



1 

Chapter One 

Introduction and Theoretical Background 

1.1 Introduction 

The external auditors of one of the big four auditing firms, KPMG, have recently faced 

renewed public criticism for their lack of due diligence and quality (Bodoni et al., 2021) 

bringing back to memory the downfall of high profile firms such as Enron and 

WorldCom. It is thought that the failure of these organizations was caused by a lack of 

quality and trustworthy financial information essential for their efficient operation. 

( Idialu, 2008) Bassically, the scandel surrounding these organizations may be viewed as 

the result of their financial statements being doctored or manipulated. (Shen et. al, 2015) 

clarified that by managers actions of using innovative methods, known as earnings 

management (EM), to report and characterize assets, liabilities and incomes in a format 

that deceives stakeholders about the true performance of the company while remaining 

within the parameters of the financial reporting framework. Therefore, by practicing EM 

managers fails to reflect the actual performance of the firm leading to compromise 

financial reports’ ability functioning as a guide for making the best business decisions 

(Amidu et al, 2019). 

Capital structure, as a critical area impacting the overall operating level of corporates, has 

been a focus in the field of finance and accounting literature (Feng et al., 2020). Capital 

structure, i.e., financing decision, is concerned with the optimal mix of debt and equity. 

Firms with optimal debt-equity ratios can improve their performance and reduce the cost 

of capital and the likelihood of financial distress (An et al., 2016). A firm’s capital 

structure specifically relies on profitability, liquidity, cash flow and earnings volatility 

(Ramli et al, 2019), which shows that the financial information reported by a firm 

influences its capital structure decision. Consequently, financial statements must convey 

the underlying economic performance as a change, or manipulation in financial reports 

would make capital structure selections less appropriate (Okyere et al., 2021). 

Due to the unfavorable implications EM has on capital structure decisions, efforts have 

been focused on diminishing the prevalence of EM among organizations. Some studies 

contend that monitoring mechanisms could monitor opportunistic management behavior. 

Consequently, such techniques would control the practice of EM and improve financial 



2 

information’s quality. However, corporate governance (CG) provides a firm with control 

tools and mechanisms to help create a system of profit sharing, achieving efficiencies for 

the firm and a balanced wealth for the stakeholders (Haryetti, 2015). On the same hand, 

researchers and practitioners seem to agree that excellent CG mechanisms have a key 

impact on the overall performance of the firm, particularly in achieving massive 

expansion in the business sector, meeting the firm’s objectives and legal compliances and 

protecting the shareholders’ rights. As a result, CG seems to attract more capital (Feng et 

al., 2020; Sheikh & Wang, 2012). 

Although EM has substantial implications for corporate decisions, little is known about 

its impact on firms' financing choices. In addition, there is a scarcity of research on 

developing economies, where the practice is thought to be more prevalent. (He et al, 2017) 

As a result, this study responds to past studies' calls for more research on corporate 

finance decisions and EM. Also, following to steps of (Okyere et al., 2021). Additionally, 

because CG, a significant mitigating element in the practice of EM, has been ignored in 

earlier researches, the effect of CG in the link between EM and corporate finance 

decision-making is still unclear. Therefore, this study aims to assess the impact of EM on 

capital strcture. It will also examine the interactive effect of the CG and EM relationship 

on the capital structure of a sample of 38 manufacturing firms listed on the Palestine 

Exchange (PEX) and Amman Stock Exchange (ASE) during the period 2013-2020. 

1.2 Research Problem 

Since 1980s, EM has become a researchable issue as a result of the failure of many 

corporations because due to accounting scandals. Additionally, EM becomes a greater 

priority for the majority of firms' managers who want to improve their capital structure 

(Tahir et al., 2011). For decades, numerous empirical and theoretical research in 

accounting and finance literature have focused on capital structure and its determinants. 

A substantial number of studies has been conducted to investigate the elements that 

determine or are connected to capital structure. However, the relationship between CAPS 

and EM did not get enough attention of accounting academics. Proven by An et al. (2016) 

that EM, as a significant indicator for information quality supplied by insiders, is 

shockingly disregarded in the current literatures.  



3 

On the other hand, the goal of CG is to maximize the value of shareholders through 

organizational management which has always been linked to agency issues. (Uwuigbe, 

2014) (Gerged & Elheddad, 2020) Corporate governance specifically seeks to prevent 

opportunistic conduct by minimizing agency issues that may include both insiders and 

outsiders such as debt holders and shareholders. Additionally, it minimizes the effects of 

asymmetric information issues and promotes the development of a specific skill needed 

in strategic choices such as creation of optimal capital structure (Yu & Wang, 2018). 

Despite a wealth of studies supporting the factors that influence capital structure (Abor 

& Biekpe, 2007) ( Neves et. al, 2020). current research contends that the choice of capital 

structure remains a puzzle to accounting researchers and financiers. This puzzle can be 

clarified by the existing of different theories of capital structure and several models of 

quantitative regression. So, in this study we investigate the impact of EM on capital 

structure, as well as the interactive effect of CG features (i.e., the size of the board of 

directors, board independence, gender diversity, CEO duality and institutional ownership) 

and EM on capital structure in a sample of 38 manufacturing firms listed on the PEX and 

the ASE from 2013 to 2020.  

1.3 Research Importance 

This study seeks to examine the moderating role of CG in the relationship between EM 

and capital structure through the statical regression model examined using R. In addition, 

the study aims to establish the determinants of the CAPS of a firm by applying additional 

statical tests. Empirical results on EM and CG contribute to the financing decisions of a 

firm, thereby influencing the value of the firm and the total wealth of its shareholders.  

This study provides significant and various contributions. First, it demonstrates the 

importance of CG features and EM in determining the capital structure of a sample of 

Palestinian and Jordanian manufacturing firms. Second, it clarifies the interactive effect 

of EM with CG features on the DCAPS of the sample. Third, the study supports the 

application of the R/RStudio package program in the accounting industry, where Rstudio 

is one of the most potent statistical programming languages. Drasah (2022) contends that 

according to 2020 statistics, Rstudio is the best statistical software. Fourth, to categorize 

the significant variables, artificial intelligence-based statistical techniques were employed 

in the study. However, it must be noted that such techniques are regarded as innovators 



4 

in contemporary and academic research. Fifth, the study connects management and 

behavioral theories, such as agency theory, pecking order theory, trade-off theory, free 

cash flow (FCF) theory and signaling theory. Sixth, this study also provides theory and 

practice contributions. The existing capital structure literature has struggled to present a 

compelling and unambiguous view of the critical function of CG in creating a firm’s 

CAPS. Furthermore, finance literature depicts contradictory opinions on the relationship 

between CG and capital structure. This study broadens the existing debate by delving into 

the relationship between CG and another vital factor, i.e., EM, to endorse and support its 

effect on a firm’s financing decisions.  Finally, to the best of our knowledge, there is no 

concrete proof of CG and EM application on capital structure either in Palestine or Jordan. 

Hence, this paper is the first to simultaneously assess the relationship between these three 

variables in Arab countries.  

1.4 Research Objectives 

This study highlights the impact of EM on debt level in capital structure. The study also 

aims to assess the interactive effect of EM and CG features represented by the size of the 

‘board of directors, board independence, gender diversity, CEO duality and institutional 

ownership, on DCAPS in a sample of 38 manufacturing firms listed on the PEX and the 

ASE through the period of 2013–2020.  

This study aims to achieve the following objectives: 

O1: test the impact of EM practices on DCAPS in Palestinian and Jordanian 

manufacturing firms. 

O2: examine the interactive impact of EM and CG features on DCAPS in Palestinian and 

Jordanian manufacturing firms. 

O2a: investigate the impact of the size of the board of directors (BSIZE) on the 

relationship between EM and DCAPS in Palestinian and Jordanian manufacturing firms. 

O2b: test the impact of board independence (IND) the relationship between EM and 

DCAPS in Palestinian and Jordanian manufacturing firms. 

O2c: examine the impact of gender diversity (GEN) on the relationship between EM and 

DCAPS in Palestinian and Jordanian manufacturing firms. 



5 

O2d: test the impact of CEO duality (CEOD) on the relationship between EM and DCAPS 

in Palestinian and Jordanian manufacturing firms. 

O2e: determine the impact of institutional ownership (INSOWNER) on the relationship 

between EM and DCAPS in Palestinian and Jordanian manufacturing firms. 

1.5 Research Questions  

The following questions illustrate the research problem surrounding the moderating role 

of CG features on the relationship between EM and DCAPS: 

Q1: What is the impact of EM on DCAPS in Palestinian and Jordanian manufacturing 

firms? 

Q2: What is the interactive impact of CG features and EM on DCAPS in Palestinian and 

Jordanian manufacturing firms? 

Q2a: What is the impact of the size of the board (BSIZE) on the relationship between EM 

and DCAPS in Palestinian and Jordanian manufacturing firms? 

Q2b: What is the impact of board independence (BIND) on the relationship between EM 

and DCAPS in Palestinian and Jordanian manufacturing firms? 

Q2c: What is the impact of gender diversity (GEN) on the relationship between EM and 

DCAPS in Palestinian and Jordanian manufacturing firms? 

Q2d: What is the impact of CEO duality on the relationship between EM and DCAPS in 

Palestinian and Jordanian manufacturing firms? 

Q2e: What is the impact of institutional ownership (INSOWNER) on the relationship 

between EM and DCAPS in Palestinian and Jordanian manufacturing firms? 

The remainder of the thesis is organized as follows. The second chapter presents the 

literature review, theoretical framework and hypotheses. The third chapter illustrates the 

methodology of the research, including data sources, the research sample, the 

measurement of various variables and the research model. Chapter four depicts the 

empirical results of the tested hypotheses, along with the discussion. Finally, the last 

chapter provides an overview, the conclusion and limitations of the study and 

recommendations for future studies.   



6 

Chapter Two 

Literature Review and Hypothesis Development 

2.1 Introduction 

This chapter provides a thorough examination of the existing literature on the relationship 

between the study variables and the related theories. The clarification of the concepts of 

CG, EM and DCAPS and the related theories has been provided, followed by the 

formulation of the study hypotheses. 

2.2 Definition of the Concepts 

In this section, the basic concepts of the study, such as EM, CG and capital structure, 

based on the previous literature are described in accordance with the related organizations 

and theories.  

2.2.1 Earnings Management 

One of the first definitions of EM was given by Schipper (1989), who mentioned EM is 

a deliberate attempt to influence the external financial reporting process with the goal of 

gaining a personal advantage. Healy and Wahlen (1999) mentioned that EM occurs when 

managers use discretion in financial reporting and transaction structuring to change 

financial reports to deceive some stakeholders about their firm’s true economic 

performance or influence the results of the contracts based on reported accounting 

numbers (Höglund, 2012).  

EM can be both opportunistic and beneficial (Jiraporn et al., 2008). However, when 

managers of a firm utilize EM opportunistically for their self-interest rather than the 

benefit of stockholders, the result is detrimental to the firm. Meanwhile, when managers 

exercise discretion over earnings within GAAP to protect shareholders’ interests, it is 

regarded as ethical and advantageous. Additionally, EM is morally right and helpful in 

informing the public and stockholders about private information (Nia et al., 2015). EM 

can be defined as a manipulation of earnings using the discretion granted by corporate 

laws and accounting standards and/or restructuring activities, which predict that firm 

value is not negatively impacted. Generally, the previous definition split EM into two 

types. First is “Paper EM”, which depends on manipulating earnings using discretion 

granted by corporate laws and accounting standards that rely on accounting estimates, 



7 

policies and accruals. According to Commerford et al. (2016), it can also be known as 

“accounting EM”. Accounting EM helps in comprehending accounting accruals and their 

impact on a firm’s performance measurement (Schipper, 1989). The second type of EM 

is “Real EM”, which refers to the manipulation of earnings by employing restructuring 

activities to positively impact a firm, such as by expanding product lines, or show a 

neutral impact on expected firm value, such as by accelerating the time of sales. Real EM 

considers the management of earnings through strategic timing of operating, financing 

and investing decisions. In other words, a powerful instrument for real EM practice is the 

timing of revenue-generating activities and reporting (Abu Alia et al., 2020). 

Many reasons encourage firms to engage in EM. They allegedly use it to smooth earnings, 

restrain debt covenants, increase share prices, boost management wealth, bargain with 

labor unions, execute management buyouts and implement bonus plans. Moreover, 

compensation plans and the value of executives’ stock and stock options are the main 

incentives that EM provides (Burilovich & Kattelus, 1997; Oberholzer-Gee & Wulf, 

2012). 

2.2.2 Capital Structure 

Capital structure can be defined as the source of financing employed by a firm to finance 

its assets, growth opportunities and daily operations (Martin & Baker, 2011). There are 

many measures for capital structure which represented by “leverage”. Leverage is 

measured using four distinct methods by (Rajan & Zingales, 1995). The ratio of total debt 

to total assets is the original and most general definition of leverage. Second, the ratio of 

short/long term debt to total assets. Third, total debt to net assets. Finally, total debt to 

capital, where capital represents total debt in addition to equity. This ratio focuses on 

“capital employed”, therefore it most accurately depicts the impact of prior financial 

choices. Additionally, it most immediately links to the agency issues with debt. 

Corporate finance still lacks a unified capital structure theory, even 50 years after 

Modigliani and Miller (1958). Although the present theories are useful as analytical tools 

for analyzing the empirical results, none of them is able to fully account for the decision 

to choose a capital structure. Each theory can explain certain stylized facts but not others. 

According to the literatures, the most reliable indicators for describing corporate leverage 

are the following: profitability, size of firm, tangibility, median industry leverage, 



8 

expected inflation rate, and market-to-book ratio. These are the "core leverage factors" 

that determine capital structure decisions, according to ( Frank & Goyal, 2009). 

To clarify, according to the capital structure theory, the value of a firm increases if the 

capital structure includes a high amount of debt due to the tax benefits afforded by the 

debt (Modigliani & Miller, 1958, 1963). The authors believe that if the neutrality theory 

is contested, two competing theories must be considered while choosing external 

financing: the pecking order theory and the trade-off theory. The first theory implies that 

due to information asymmetry, firms are required to finance in a hierarchical order. Myers 

and Majluf (1984) clarified the theory by the existence of leadership behavior. A 

decreasing financial hierarchy is used when managers consider the investors’ interests, 

which begins with using retained earnings, debt and new equity issuance, respectively. 

On the other hand, when managers act in their own interest, the hierarchy is altered; 

hence, it will first address retained earnings, followed by the issue of new equity and 

finally using debt to avoid its disciplinary role.  

The tradeoff theory suggests that the trade-off between benefits and costs of debt 

determines a firm’s capital structure (Bradley et al., 1984). Tax deductibility is the main 

benefit of using debt. Modigliani and Miller (1963) demonstrated that to take advantage 

of tax savings on debt, firms must employ debt. However, excessive use of debt increases 

the agency cost (Jensen and Meckling, 1976) and the cost of bankruptcy (Kraus and 

Litzenberger, 1973). From this perspective, it is worth noting that this theory allows for 

the detection of an optimal debt level from which the firm obtains the most tax benefits 

(Okyere et al., 2021). 

However, when a firm chooses internal funding (FCF) as the main source of capital, it 

does not regard the agency problem according to the pecking order theory. Accordingly, 

Jensen (1986) defined FCF as surplus cash that remains after all projects have been 

funded with a positive net present value (NPV). The existence of surplus cash in a firm 

leads to a conflict between shareholders and management, reflecting that with an excess 

of FCF, top management may engage in wasteful and inefficient investments. In other 

words, in case of excess FCF, managers are more likely to invest in new initiatives, even 

if the NPV is negative (Park and Jangb, 2013). Therefore, the FCF theory assumes that 



9 

the creation of debt represents legal duties that must be paid by management, preventing 

executive managers from overinvesting and utilizing the financial resources of a firm. 

Michaelas et al. (1999) suggest that the choice of capital structure of a firm is considered 

a signal that shareholders send to investors. The signaling theory stresses that firms should 

be careful about the signals they send to the market. This implies that any significant 

changes in the capital structure of a firm will be interpreted by outsiders as a signal of the 

firm’s prospective performance. Shareholders may interpret the announcement of debt 

financing as a good indication. A debt issuance indicates that the firm’s financial 

prospects are so promising that management does not want additional shareholders to 

share its possible profits (Koch and Shenoy, 1999). 

Jensen and Meckling (1976) define an agency relationship as an agreement in which one 

or more individuals (the principal(s)) hire another individual (the agent) to execute some 

job on their behalf, which includes delegating some decision-making authority to the 

agent. This theory assumes that managers with self-interest attitudes always seek to fulfill 

their goals at the expense of their firms’ shareholders. The agency cost of outside equity 

occurs due to the conflicts between managers and shareholders since managers in large 

firms with diffuse ownership do not have total residual claims and cannot profit fully 

from their value-maximizing actions. As a result, they work less hard to manage the 

resources of the firms and are more inclined to transfer those resources for their own 

personal gain. In other words, such managers pursue their activities in a way that does not 

maximize shareholder wealth, which leads to them consuming more perks and investing 

in unrelated businesses to expand their empires, such as luxury offices, private jets and 

compensation. Furthermore, choosing suboptimal CAPS, such as less debt, in capital 

structure is also another perk enjoyed by these managers.  

In accordance with the agency theory, debt financing can be utilized as a helpful 

governance tool to lessen the conflict of interests between managers and shareholders. In 

particular, debt can be used as a substitute mechanism to minimize the effect of FCF 

agency costs available to managers by requiring them to disgorge it to investors (Jensen, 

1986).  

 



10 

2.2.3 Corporate Governance 

CG was first proposed by the Cadbury committee. The committee believed CG to be a 

system for directing, controlling and managing firms by determining the role of the board 

of directors and shareholders. The board of directors oversees the management of their 

firms, whereas the shareholders’ responsibility in governance is to select the directors and 

auditors and ensure that an effective governance framework is in place (Cadbury Report, 

1992). 

A governance system can also be defined in a broader sense as a set of complex 

constraints that determine granted profit by the firm in the course of relationships with 

stakeholders and shape the ex-post bargaining over them (Zingales, 1998). This definition 

encompasses rules for both determination of value addition by firms and their distribution 

among the stakeholders (Claessens & Yurtoglu, 2013). According to the Organization of 

Economic Cooperation (OECD; 2012), CG can be defined as a group of relationships 

between a firm’s managers, board of directors, stakeholders and shareholders. CG also 

encompasses the mechanism that determines a firm’s goals and provides methods for 

meeting those goals. 

CG is a type of governance with pre-determined connections among the numerous 

stakeholders in a firm and how they influence its direction and performance. Effective 

CG plays a major role in economic growth opportunities due to its ability to reduce 

customer risks, draw venture capital and increase business efficiency (Spanos, 2005). In 

addition, Claessens and Yurtoglu (2013) believe that better corporate frameworks assist 

enterprises by lowering the cost of capital, increasing access to funding, improving 

performance and treating all stakeholders fairly. On the other hand, besides causing 

hazardous financing and subpar performance, poor CG also contributes to 

macroeconomic crises. 

2.2.3.1 Corporate Governance in Palestine 

In Palestine, the CG topic first emerged in 2005 (Abdelkarim & Zuriqi, 2020). Following 

direct coordination between the Palestinian capital market authority, PEX, the Monetary 

Authority and the International Financial Corporation (IFC), the rules of CG were 

established. A national committee was formed for CG in Palestine, which was composed 

of regulators, academic agencies and economic legal representatives responsible for the 



11 

formulation of the code of rules of CG. These rules were required to be in compliance 

with the legislations and circumstances existing in Palestine while also considering the 

steadfast principles in the field of regional and global CG.  

In November 2009, the code of CG was issued in Palestine and applied to public 

shareholders firms, which was defined as “a system of guidelines and practices that the 

board of directors, executive management, shareholders, and other interested parties 

coordinate in order to manage and supervise the company’s operations, including its 

social and environmental responsibilities” (Code of CG in Palestine, 2009). This code 

was imported from Jordanian corporate law number 12 for the year 1964. 

CG rules mainly aim to boost a firm’s capability for competition and improve the 

performance of the board of directors. Furthermore, these rules reaffirm the value of a 

firm and raise confidence among all involved parties. In addition, the regulations enhance 

the environment for investing, reactivating, and growing the financial market. 

A number of accomplishments regarding CG were achieved in 2021, most of which were 

geared toward improving sectoral governance at the developmental stage. Additionally, 

an agreement with IFC sought to strengthen CG application through developing the Code 

of Environmental, Social and Corporate Governance and continuing the integrated 

program of CG in Palestinian universities. More importantly, by the end of 2021, a 

Palestinian corporate law number 42 was issued and implemented on all Palestinian firms, 

including publicly listed firms in PEX. This law stipulated that the management of a 

public shareholding firm shall be undertaken by a board of directors of not less than five 

and not more than thirteen members. The board of directors had to ensure the presence of 

representatives of both genders (i.e., at least one-third of the members should be women) 

and executive and non-executive members, provided that at least one of them is 

independent. Moreover, another requirement was the separation of duties between the 

manager of the firm and the chairman. A manager’s responsibility is to supervise and 

manage the firm in cooperation with its board of directors. Further, a general manager is 

not allowed to simultaneously work at more than one public shareholding firm. On the 

other hand, executive directors are responsible for managing the daily business of a firm, 

practicing the financial management of the firm; taking into account the adequacy of 

capital and the availability of the necessary liquidity; ensuring the effective application 



12 

of the firm’s CG rules and principles, risk management procedures and internal auditing; 

and making sure that the non-executive members of the firm’s board of directors obtain 

all information related to the firm’s management in a timely manner. 

2.2.3.2 Corporate Governance in Jordan  

Following the global interest in CG concepts, interest in CG and its applications 

flourished in Jordan as well. Globally, the OECD’s introduction of CG regulations in 

1999 was the first step toward CG implementation in Jordan. Locally, the Bank’s Code 

of Conduct was released in 2002, followed by a CG guide issued by Jordanian Central 

Bank in 2004. To comply with international requirements for attracting foreign 

investment and reducing corruption, Jordan Securities Commission (JSC) released 

a guide for public shareholding firms in 2008, which was to be implemented and enforced 

in 2009 (Altawalbeh, 2020). The Board of the Securities Commission approved the 

CG guidelines for shareholding listed firms, which were issued in 2017 and are based 

on Securities Law No. 18. These guidelines apply to all shareholding firms listed on 

the financial market (JSC, 2017) to enhance the levels of the Jordanian economy and 

expand the national capital market by attracting foreign investments (Shahwan & 

Mohammad, 2016). 

2.3 Literature Review and Hypothesis Development 

2.3.1 Earnings Management and Capital Structure 

Many intensive managers must engage in EM due to the cost of capital. Nikoomaram et 

al. (2016) exposed a positive significant relationship between debt ratio and discretionary 

accruals. Moreover, An et al. (2016) studied the impact of EM on capital structure. Their 

results depicted that EM has a positive and significant relationship with the debt ratio. 

This result is consistent with the disciplining role of debt to minimize the agency cost of 

FCF when combined with the assumption that a firm’s EM reflects the agency conflicts 

of information asymmetry between managers and investors. 

On the other hand, Jelinek (2007) revealed a negative association between leverage and 

opportunistic behavior, implying that an increase in leverage leads to a decrease in 

managers’ opportunistic behavior and EM. Additionally, Tahir et al. (2011) examined 

various factors related to EM impacting the capital structure of Pakistani non-financial 



13 

firms for a period of five years. The results revealed that EM represented by return on 

assets (ROA) had a negative impact on the gearing ratio. Hence, based on the 

aforementioned argument, the researcher assumes the following: 

H1: A positive significant association exists between EM and DCAPS of the firm. 

22.3.  The Size of the Board of Directors 

The responsibility of managing a firm’s activity and making strategic decisions regarding 

the financial mix is dependent on the firm’s board of directors. The impact of the size of 

the board of directors on the capital structure of a firm has been well examined in prior 

studies (Abobakr & Elgiziry, 2015; Alabdullah et al., 2018; Butt & Hasan, 2009; Feng et 

al., 2020; Grabinska et al., 2021; Sheikh & Wang, 2012; Wen et al., 2002; Yusuf & 

Sulung, 2019). However, the empirical results for this relationship are mixed.  

Wen et al. (2002) proved a positive relationship between the size of the board of directors 

and capital structure. The authors suggested that a greater size of the board will follow a 

higher level of gearing policy. The authors also mentioned that when the number of 

directors increases, the limitation to arriving at a clear decision also increases, which is 

reflected in the efficiency of CG mechanisms, causing higher levels of financial leverage. 

Furthermore, Sheikh and Wang (2012) revealed a significant positive relationship 

between the size of the board and the debt ratio. This result was supported by the theory 

of resource dependency, implying that when the size of the board is greater, the ability of 

the board to raise funds and improve the firm’s value also rises. Feng et al. (2020) showed 

that the size of the board has a positive influence on capital structure of the firm. 

On the other hand, the result of Butt and Hasan (2009) demonstrated a negative 

relationship between the size of the board and capital structure, suggesting that a large 

number of directors can pressurize the managers to have a low level of gearing policy and 

improve their firm’s performance. Moreover, Alabdullah et al. (2018) investigated the 

impact of two key measurements of board features on the growth and capital structure of 

emerging market Jordanian non-financial firms using data from a sample of 100 firms 

made available by this sector. The results showed a negative relationship between the size 

of the board and the debt ratio. Moreover, Abobakr and Elgiziry (2015) proved a negative 

and significant impact of the board size on capital structure. According to the findings of 



14 

Yusuf and Sulung (2019), the top managers experienced a positive impact on the capital 

structure’s book value. Therefore, as their years of experience increase, their firm’s total 

and long-term debt will also increase. However, the board size was found to have a 

negative significant impact on the capital structure. Finally, Grabinska et al. (2021) 

concluded that larger supervisory bodies with more in-depth and advanced financial 

knowledge were significantly negatively associated with debt ratio due to reduced 

information asymmetry, which makes it easier to acquire equity capital at a lower cost. 

Hence, based on the previous argument, the researcher assumes the following hypothesis: 

H2a: size of the board of directors has a significantly positive role in moderating the 

relationship between EM and DCAPS of the firm.  

2.3.3 Board independence 

Non-executive directors are an essential component of modern CG. A few studies have 

examined the relationship between the presence of non-executive directors and capital 

structure, although the pieces of evidence vary. Wen et al. (2002) proved that the 

existence of non-executive directors depicted a significant negative relationship with 

gearing levels. A probable explanation of this finding may be that non-executive directors 

supervise managers more effectively, forcing managers to seek lower gearing levels to 

achieve superior results. Moreover, Uwuigbe (2014) revealed a negative and significant 

relationship between board independence and debt-to-equity ratio. 

Agyei and Owusu (2014) proved that non-executive directors have a substantial impact 

on leverage. This may be due to the fact that in large firms, non-executives are typical 

representatives of financial institutions, so they acquire the required funds easily. 

Furthermore, Alves et al. (2015) mentioned that the higher the proportion of independent 

directors (non-executives), the higher the reliance on external financing sources (debt) 

rather than internal sources (retained earnings). Moreover, Tarus and Ayabei (2016) 

concluded that board independence had a significant impact on CAPS. Director 

independence, in particular, was found to be positively related to leverage. In addition, 

Ehikioya et al. (2021) revealed that board independence has a positive significant 

relationship with debt ratio; this can be supported by the agency theory, which asserts the 

ability of outside directors to exert pressure on and influence managers to increase debt 



15 

financing to increase the firm value. Following the viewpoint of the agency theory, the 

researcher proposes the second hypothesis, as follows: 

H2b: non-executive directors have a significantly positive role in moderating the 

relationship between EM and DCAPS of the firm. 

2.3.4 Gender Diversity 

Women on boards of directors tend to be more independent as they operate independently 

of the network. According to OECD (2012), a greater representation of female directors 

may introduce heterogeneity in values, beliefs and attitudes, broadening the range of 

perspectives in the process of decision-making. (Carter et al., 2003) Women on boards 

devote more time to observe the executive directors as they are more committed to 

attending board meetings and keeping better records than male directors (Adams & 

Ferreira, 2009). Many researchers have explored the impact of gender diversity on firm 

performance (Brahma et al., 2021; Marinova et al., 2016). Other researchers have 

investigated the effect of women’s presence on the value of a firm, including Isidro and 

Sobral (2015), who discussed whether a firm experiences economic benefits from having 

more women on its board of directors following the introduction of legally binding quotas 

for women on corporate boards in European firms by the European Commission. The 

results revealed that more female representation on corporate boards of major European 

firms indirectly increased firm value. A portion of the indirect effect is due to greater 

adherence to ethical standards, which accounting-based financial performance does not 

consider. However, Datta et al. (2021) studied the effect of board gender diversity on the 

financing decision, specifically the relationship between executives’ gender on debt 

maturity over a sample from 1992 to 2014. The results indicate that after adjusting for 

other proven predictors of debt maturity structure, female CEOs are more consistent with 

shareholders’ interests because they choose a higher proportion of short-maturity debt. 

The economic relevance of this finding is that enterprises with female executives had a 

3.64% and a 4.53% increase in the share of debt due within three and five years, 

respectively. 

Zaid et al. (2020) conducted a unique study that sheds light on another perspective of the 

effect of gender diversity on financing decisions. They empirically investigated the 

relationship between board characteristics and financing decisions of 34 non-financial 



16 

listed firms in Palestine from 2013 to 2018, as well as the way the degree of gender 

diversity influenced and moderated their prior relationship. The findings demonstrated 

that under conditions of high levels of gender diversity, the effects of board size and board 

independence were more favorable, whereas the impact of CEO duality on the firm’s 

leverage changed from adverse to favorable. In other words, gender diversity limited the 

impact of board structure on a firm’s financial choices. 

According to the researcher’s knowledge, few studies have explored the direct impact of 

gender diversity on financing decisions. One of them is Ahmed and Atif’s (2021) study, 

which explores the existence of women in board rooms in the context of using debt 

financing for a sample of 326 firms listed on ASE through a period from 2009 to 2014. 

The results presented unique findings by claiming that the percentage of women on 

corporate boards was positively associated with firms’ debt financing, increasing 

sensitivity to many types of risks, including default risk. However, Elmoursy (2020) 

mentioned that women executives on the board of directors were negatively and 

significantly associated with debt financing.  

Additionally, previous studies have argued that moderating risky firm decisions is related 

to the existence of women on a firm’s board of directors because they tend to strengthen 

the monitoring function by considering risk averseness, which leads to decreased reliance 

on debt in the firm’s capital structure (Adams & Ferreira, 2009). However, Abobakr and 

Elgiziry (2015), along with Heng Teh and Azrbaijani (2012), proved that female existence 

on the board of directors showed an insignificant impact on CAPS. As a result, the 

researcher developed the following hypothesis: 

H2c: female board members have a significantly positive role in moderating the 

relationship between EM and DCAPS of the firm. 

2.3.5 CEO Duality 

Board structure typology’s nature (CEO duality) has a connection with a firm’s financing 

decisions, according to a stream of earlier empirical studies. In this situation, the chairman 

is in charge of leading the firm and establishing its strategic goals, whereas the CEO is 

responsible for managing the operations of the firm. CEO duality occurs when a firm’s 

CEO also serves as the board chairman (Peng et al., 2007). According to the stewardship 

theory, if the CEO also serves as chairman, power and authority will be concentrated in 



17 

the hands of one person. Consequently, the benefits of the traditional unity of direction 

and control will be enhanced as a result of the firm leadership being more understandable 

for subordinate managers and board members by reducing information asymmetry. 

The impact of a CEO’s dual roles on a firm’s capital structure is a topic of ongoing 

discussion. Zaid et al. (2019) stated that the CEO and chairman combination increases 

the risk of authority abuse, thereby leading to distorted managerial decisions. Thus, giving 

the same person both tasks might weaken the control process and pose a negative effect 

on a firm’s performance, which can impact its reputation of the “ability of debt-paying” 

in the eyes of creditors and lending institutions. In other words, due to the high perception 

of the risks associated with CEO duality, professional lenders will not invest in such 

firms. On the other hand, given that CEOs are highly skilled and knowledgeable 

individuals, CEO duality may increase the firm’s value. Keeping this in mind, the 

researcher can contend that when a firm faces a CEO duality situation, it is more likely 

to use an ideal level of debt in its capital structure (Mande et al., 2012). Thus, while 

duality is probably beneficial for some firms, separation will be advantageous for others. 

However, there is conflicting empirical data regarding the direction of the relationship 

between CEO duality and financing decisions. Agyei and Owusu (2014) and Butt and 

Hasan (2009) showed that CEO duality has an insignificant negative effect on financing 

decisions. Furthermore, Kyereboah‐Coleman and Biekpe (2006) revealed that CEO 

duality has a significant negative relationship with total leverage, mentioning that when 

a CEO also serves chairman duties, it increases the agency cost and negatively impacts 

the willingness of creditors to lend to these firms. In addition, Alves et al. (2015) 

suggested that when a firm is facing the situation of CEO duality, it has less external 

equity and a higher proportion of retained earnings. Tarus and Ayabei (2016) mentioned 

that CEO duality has a negative relationship with leverage. On the other hand, 

Dimitropoulos (2014) and Sewpersadh (2020) proved a positive correlation between CEO 

duality and leverage, implying that CEO duality may lead to a higher level of leverage. 

Based on the previous argument, the researcher assumes the following hypothesis: 

H2d: CEO duality situation have a significantly positive role in moderating the 

relationship between EM and DCAPS of the firm. 

  



18 

2.3.6 Institutional Ownership 

The active monitoring hypothesis states that the presence of institutional investors can 

reduce the managerial moral hazard issue in a firm by closely controlling and monitoring 

the performance of the firm (Jensen, 1986). The presence of institutional shareholding in 

a firm allows it to raise long-term financing at a lower cost. First, these institutional 

investors serve as a source of long-term debt. Second, they act as an effective control 

mechanism for the firm’s strategic decisions by reducing managerial opportunism and 

agency costs, which leads to an increase in the confidence of investors and lenders. In 

other words, high institutional ownership guarantees that managers will implement 

corporate strategies in the best interests of the shareholders (Barclay & Warner, 1993). 

Moreover, institutional investors are perceived to be more at risk than small shareholders 

as they hold larger ownership stakes, which also encourages them to keep a close eye on 

the managers.  

Using a representative sample of all UK firms from 1998 to 2012, Sun et al. (2016) 

investigated whether and how agency conflicts in ownership structure influence firm 

leverage ratios and external financing decisions. The results proved that firms with larger 

institutional ownership are more likely to have a higher proportion of debt in their capital 

structure. Since financial institutions make up most institutional investors in the UK, easy 

access to capital also contributes to a reduction in the cost of debt. Another explanation 

has been proposed by Tufano (1996), who states that to diffuse their risk, most 

institutional shareholders invest in a range of businesses. Hence, they might only be 

concerned with a firm’s immediate performance. To adopt investment strategies that 

satisfy the needs of institutional investors, firms may raise capital through debt financing. 

Furthermore, in the context of a developing market economy, Kumar (2015) revealed that 

firms with a higher proportion of foreign ownership or a smaller proportion of 

institutional ownership have lower debt levels. Additionally, Butt and Hasan (2009) 

suggested a positive relationship between institutional ownership and capital structure. 

Tayachi et al. (2021) mentioned that financing choices and dividend policy for sample 

firms are positively impacted by institutional ownership, causing investors to decide to 

invest more in institutional ownership that lowers the agency cost rather than in firms 

with a higher percentage of managerial ownership. However, Liao et al. (2015) proved 



19 

that the adjustments of the capital structure toward a target of shareholders are encouraged 

by institutional ownership rather than the desired level of managers. 

On the contrary, Puspita and Suherman (2018) illustrated that institutional ownership has 

a significantly negative impact on the debt-to-equity ratio, as well as a negative but 

insignificant impact on the debt-to-assets ratio. This implies that the higher is the 

proportion of institutional ownership, the more closely the management’s performance is 

monitored. As a result, management becomes more cautious in allocating its investment 

activities to use its debt policy and reduce the firm’s debt. This idea can be supported by 

the substitution theory, which asserts that institutional ownership replaces debt financing. 

Moreover, the complementary theory states that institutional ownership can serve as a 

complement device to debt. Based on the previous argument, the researcher assumes the 

following hypothesis: 

H2e: institutional ownership has a significantly positive role in moderating the 

relationship between EM and DCAPS of the firm. 

Table (1)  

Summary of hypotheses 

Hypothesis 
Content 

H1 
A positive significant association exists between earnings management 

and DCAPS of the firm. 

H2a 
 size of the board of directors has a significantly positive role in 

moderating the relationship between EM and DCAPS of the firm. 

H2b 
non-executive directors have a significantly positive role in moderating 

the relationship between EM and DCAPS of the firm. 

H2c 
female board members have a significantly positive role in moderating 

the relationship between EM and DCAPS of the firm. 

H2d 
CEO duality situation have a significantly positive role in moderating the 

relationship between EM and DCAPS of the firm. 

H2e 
institutional ownership has a significantly positive role in moderating the 

relationship between EM and debt level in DCAPS of the firm. 

 

  



20 

Table (2)  

Summary of previous literature 

 

 

  

 Significant Insignificant 

 Positive Negative Positive Negative 

Earnings 

management 

An et al. (2016), 

Nikoomaram et al. 

(2016), Tian et al. 

(2018) 

Jelinek (2007),  

Tahir et al. 

(2011), Talebniya 

and Ravanshad 

(2011) 

Al-Mohareb 

and 

Alkhalaileh 

(2019) 

 

Board size 

Agyei and Owusu 

(2014), Anderson et al. 

(2003),  

Feng et al. (2020), 

Mulwa and Ndede 

(2021), Sheikh and 

Wang (2012), Wen et 

al. (2002) 

Abobakr and 

Elgiziry (2015), 

Alabdullah et al. 

(2018), Butt and 

Hasan (2009), 

Grabinska et al. 

(2021), Yusuf and 

Sulung (2019) 

  

Board 

independence 

Abor and Biekpe 

(2007), Agyei and 

Owusu (2014), Alves 

et al. (2015), Ehikioya 

et al. (2021), Tarus and 

Ayabei (2016)  

UWUIGBE 

(2014), Wen et al. 

(2002)  

Al-Saidi 

(2020), El-

Habashy 

(2018), Feng 

et al. (2020), 

Jaradat (2015)  

Abobakr and 

Elgiziry 

(2015) 

Board 

diversity 

Ahmed and Atif 

(2021), Datta et al. 

(2021) 

Elmoursy (2020)  

Abobakr and 

Elgiziry 

(2015) 

CEO duality 

Dimitropoulos (2014), 

El-Habashy (2018), 

Sewpersadh (2020) 

Alves et al. 

(2015), 

Kyereboah‐

Coleman and 

Biekpe (2006), 

Tarus and Ayabei 

(2016) 

Abobakr and 

Elgiziry 

(2015), I and 

Azrbaijani 

(2012) 

Agyei and 

Owusu 

(2014), Butt 

and Hasan 

(2009)  

Institutional 

ownership 

Butt and Hasan 

(2009), Kumar (2015), 

Sun et al. (2016), 

Tayachi et al. (2021) 

Puspita and 

Suherman (2018) 
  



21 

Chapter Three 

Research Methodology 

3.1 Introduction 

This study aims to examine the impact of EM on debt level in CAPS and the moderating 

role of CG on the relationship between EM and debt level in CAPS in a sample of 

Palestinian manufacturing firms and Jordanian manufacturing firms listed in the PEX and 

the ASE. In this section, the researcher presents the data sources, research sample, 

measurement of variables, research models and research techniques used to conduct this 

study.  

3.2 Data Collection 

Utilizing secondary data associated with the study variables is mandatory to meet the 

study objectives. The data required to measure CG features (i.e., the size of the board of 

directors, board independence, gender diversity, CEO duality and institutional 

ownership), as well as EM and debt level in CAPS of the firm, were primarily collected 

from the annual reports of industrial sector firms listed on the PEX and the ASE websites 

between 2013 and 2020. During this period, Palestinian-listed firms began to shift their 

focus and adhere to CG principles. In total, 304 observations were made for this study, 

including the data for 38 firms in a period of eight years. The labels of the variables, along 

with the sources of data collection, are displayed in Table 3. 

Table (3)  

Variable labels and data sources 

Variables Label Source of data 

Debt level in Capital structure  DCAPS Annual report 

Size of the board of directors BSIZE Annual report 

Board independence BIND Annual report 

Gender diversity GEN Annual report 

CEO duality CEOD Annual report 

Institutional ownership INSOWNER Annual report 

Earnings management EM Annual report 

Firm size FSIZE Annual report 

Firm age FAGE Annual report 

Dividend payout ratio DIV Annual report 



22 

3.2.1 Palestine Exchange 

PEX was established in early 1995 as a private shareholder firm in Nablus. PEX aims to 

encourage investment in the Palestinian environment by investing in securities and 

directing savings for the benefit of the national economy. In 2010, PEX underwent 

privatization and became the second publicly traded Arab stock exchange. Forty-eight 

firms with a combined valuation of $2.8 billion were listed on the PEX as of July 2012. 

Moreover, Jordanian dinars are used by half of the listed firms, whereas US dollars are 

used by the remainder (PEX, 2022). Currently, the firms listed on the PEX represent five 

industries: insurance, investment, manufacturing, services and banking and financial 

services (PEX, 2022). The goal of PEX is to provide a trading environment that helps and 

safeguards investors. Furthermore, it also aims to create enduring connections with 

institutions of finance, both domestically and abroad; present investment opportunities 

that draw funding from abroad and the Palestinian diaspora; and boost public 

understanding of financial markets and products by increasing the depth of the market 

and providing a wider array of financial products and services. However, in this study, 

the sample relies only on the manufacturing sector. 

3.2.2 Amman Stock Exchange 

ASE was founded at the beginning of 1999 as an independent non-profit organization 

before being permitted to operate in Jordan as a regulated market for trading securities. 

In 2017, ASE was registered as an entirely government-owned public shareholding 

corporation under the name of ASE corporation. The seven directors of the ASE 

corporation are chosen by the general assembly, and the CEO is responsible for running 

the firm on a daily basis. ASE seeks to practice, manage, run and develop all Jordanian 

and international market activities. In addition, it has the goal of establishing an 

environment conducive to the interaction of the forces of supply and demand in the 

trading of securities in accordance with transparent, righteous, and ethical trading 

practices. ASE also aims to increase people’s comprehension of investing in the financial 

markets, along with promoting its own services. The main firm categories included in 

ASE are financial institutions (banks, insurance, diversified financial services and real 

state); services (such as health, educational, transport, tourism and technology); and 

manufacturing (pharmaceutical and chemical, food, mining and extraction, and 

engineering and electrical) (ASE, 2022). 



23 

3.3 Research Sample 

The necessary information used in this study was collected manually. The ASE and PEX 

databases were used as the primary sources of these data. The study’s sample was based 

on the following criteria:  

1. The firm was listed in the stock market before 2013.  

2. The firm does not have any underwriting in the stock market to raise its capital after 

2012.  

3. The firm does not have a merger with any firm after 2012.  

Extraction and mining manufacturing firms were excluded from this study as these firms 

do not exist in the Palestinian market. 

Financial listed firms were not included in the development of the study sample as they 

have unique accounting system characteristics and organizational and conceptual 

differences from other firms. It is critical to clarify that approximately 70% (25 firms) of 

the research sample are Jordanian firms, whereas only 30% (13 firms) are Palestinian 

firms, implying that the sample is disproportionate. This disproportionality might 

influence the result of the study. The reason behind choosing this  combination is to 

expand the sample of the study1. 

Table 4 displays the data distribution by market and type of industry. All firms in our 

sample are mentioned in Appendix 1. 

Table (4)  

Distribution of the research sample 

The market Type Number of the firms 

Palestinian firms listed in PEX Manufacturing (industrial) 13 

Jordanian firms listed in ASE Manufacturing (industrial) 25 

 

  

 
1 The combination of research sample was suggested by the supervisor Prof. Abdul Nasser Nour. 



24 

3.4 Research Model  

Regression modeling is used to test the two models on which the study is based. First, the 

effects of EM practices on debt level in CAPS were examined to meet the study 

objectives. Both the fixed effect model and random effect model were evaluated using 

the Hausman test for each regression. The interaction term between EM and CG features 

is incorporated into the model to analyze the interactive effect of these two factors on the 

choice of capital structure: 

DCAPS= β0 + β1(EM)it + β2 (CG)it +β3(FSIZE) it + β4(FAGE) it +β5 (DIV) it 

β6(EMit*CGit) + εt ……….(1) 

3.5 Variable Measurement 

3.5.1 Dependent Variable 

Capital structure is the dependent variable in this study. In this regard, CAPS (or 

financing decision) is defined as the optimal mix between debt and equity, used by firms 

to finance their daily operations and growth opportunities. To illustrate this, there are two 

proxies for CAPS. First, in line with Okyere et al. (2021), the debt-to-equity ratio is 

employed, which is measured by dividing total debt by total equity (DCAPS1). Second, 

the leverage ratio is used, following Zaid et al. (2020), which is computed as the total 

debt of a firm divided by its total assets (DCAPS2). 

3.5.2 Independent Variables 

EM is the independent variable, various methods for identifying and evaluating EM 

among organizations have been presented in the literature. Particularly, it has been argued 

that financial firms use discretionary loan loss provisions to manage their earnings. 

However, in the case of non-financial firm discretionary revenues, discretionary accruals 

are recommended. Since manufacturing firms (non-financial firms) are being investigated 

in this study, the method of discretionary accruals is used following to previous literature.  

However, there are stages of developing discretionary accruals models. Starting with The 

Jones (1991), cross sectional Jones model (DeFond and Jiambalvo, 1994) and modified 

Jones model (Dechow et al., 1995). Then Kasznik (1999) and Kothari et al. (2005) further 

applied some modification to the previous model. 



25 

In this research, the first model is the Modified Jones model, which is the most powerful 

model to detect EM (Dechow et al., 1995). 

TAit / Ait-1 = α1 (1/Ait-1) + α2 ((Δ REV–t - Δ RECit)/ Ait-1) + α3 (PPE it/ Ait-1) + εit    ….(2) 

The second model is performance-matched discretionary accruals, as suggested by 

Kothari et al. (2005). They modified the previous Jones model to account for 

performance variations and added the ROAs as an additional regressor. The described 

model is as follows: 

TAit/Ait-1 = α1(1/Ait-1) + α2((Δ REV–t - Δ RECit)/Ait-1) + α3(PPE it/Ait-1) + α4ROA+εit …(3)     

Where: 

• TA: total accruals of the firm (net income before extraordinary items of the firm 

minus net operating cash flow) 

• A-1: total assets of the firm (past year) 

• Δ REV: change in revenues  

• ΔREC: change in account receivables 

• PPE: the total of property plant and equipment  

• ROA: ratio of return on assets (net income divided by total assets) 

• (i): the firm  

• (t): the time (years) 

• εit: the residual, indicating the discretionary portion of total accruals and represents 

the EM of the firm. 

  



26 

Figure (1) 

The interactive effect of earnings management and corporate governance on capital 

structure 

 

3.5.3 Control variables 

Previous studies assessed a variety of other variables that influence CAPS in firms, 

namely the size of the firm, firm age, profitability, tangibility, liquidity, dividend policy, 

growth rate and ROA. Moreover, some studies have also examined macroeconomic 

variables such as GDP and inflation rate. In this study, the focus was on firm-specific 

variables that have proven to exert a significant impact on CAPS in most studies. Hence, 

the size and age of the firm, as well as the dividend payout ratio, are examined as control 

variables. The following table clarifies the way in which each variable is measured.  

3.6 R/RStudio Package 

To solve problems and analyze natural or social phenomena, a variety of statistical and 

computing skills are used in statistical computing. Analyses of computational statistics 

are performed using well-known programs, such as Excel, SPSS, MATLAB, S, Minitab 

R and Python. These programs seek to develop an algorithm to apply computerized 

statistical methods. However, R/RStudio is a differently developed implantation of S. It 

can be defined as a package with a collection of clearly defined parts, data and functional 

codes that enable users to begin with a specific set of inputs (Hufnagel et al., 2020). One 

of R’s advantages is the ease of creating well-designed plots, including mathematical 

symbols and formulae. In 1996, the first version of R programming language was 

developed by Ross Ilhaca and Robert Gentleman, statisticians from the University of 

Corporate governance features: 

1. Size of the board 

2. Board independence 

3. Gender diversity 

4. CEO duality 

5. Institutional ownership 

 

Earnings 

management 

Capital  

structure decision 

 



27 

Auckland in New Zealand. The first letter of the creators’ names serves as the language’s 

moniker. 

Table (5)  

Measurement of research variables 

Variables Measurement Previous Literature 

Dependent Variable 

DCAPS 
1. Debt-to-equity ratio 

2. Total debt to total assets 

1. Al-Saidi (2020), Butt and Hasan 

(2009), Hussainey and Aljifri (2012)   

2. Itopa et al. (2019), Zaid et al. (2020) 

Independent Variables 

EM 

1. Performance-matched 

discretionary accruals 

(Kothari, 2005) 

2. Modified Jones model 

(Dechow et al., 1995) 

1. Okyere et al. (2021) 

2. Abdelkarim and Zuriqi (2020), Abu 

Alia et al. (2020)  

 

Moderator variables 

Size of the board 

of directors 

Natural logarithm of the 

number of boards of directors 

Feng et al. (2020), Sheikh and Wang 

(2012) 

Board 

independence 

The number of non-executive 

directors divided by the number 

of total board members 

Agyei and Owusu (2014), Alves et al. 

(2015), Tarus and Ayabei (2016) 

 

Gender diversity 

Percentage of female members 

divided by the number of total 

board members  

Abdeljawad and Masri (2020), Ahmed 

and Atif (2021), Datta et al. (2021)  

CEO duality 

The dummy variable equals 1 if 

the CEO is the same person as 

the chairman, 0 otherwise 

Kyereboah‐Coleman and Biekpe 

(2006), Sewpersadh (2020), Tarus and 

Ayabei (2016)  

Institutional 

ownership 

Percentage of outstanding 

shares owned by institutional 

investors to total firms 

Kumar (2015), Liao et al. (2015), 

Puspita and Suherman (2018) 

Control Variables 

Firm size 
Natural logarithm of the total 

assets of the firm 

Butt and Hasan (2009), Hasanuddin et 

al. (2021), Oktaviani (2020),  

Firm age Number of years in operation Kieschnick and Moussawi (2018) 

Dividend payout 

ratio 

Dividing dividends paid to 

shareholders on net income 
Hussainey and Aljifri (2012) 

 

 

  



28 

Chapter Four 

Results and Discussion 

In this section, we have presented various statistical analyses for this study. The overall 

statistics were presented by country, and group descriptive analysis by year was also 

calculated. The group comparisons for quantitative and qualitative variables were 

conducted through the Chi-squared test (denoted by a), ANOVA-test (denoted by b) or 

Kruskal-Wallis non-parametric test (denoted by c), based on the statistical distribution of 

variables. The panel linear regression model for fix and the random effect was performed 

on the data to detect the association between capital struct and other features, as well as 

to answer the seven statistical hypotheses. Moreover, the Hausman test was employed to 

select the appropriate model, either the fixed effect model or the random effect model, to 

investigate the relationship between EM and debt level in CAPS in Palestine and Jordan. 

The results revealed that all Hausman P-values were higher than 5%, implying that the 

random model was more appropriate, except in the case of association between CG and 

CAPS in Palestine where the Hausman P-value of less than 5%, implying that a fixed 

model should be used. Moreover, the variance inflation factor was used to detect the 

multicollinearity between independent variables in the regression model. We did not 

encounter problems with multicollinearity between the independent variables. 

Additionally, statistical learning was employed also to test the data accuracy in predicting 

the capital structure given the other features.  R version 4.1.1 was utilized to analyze the 

collected data. Based on the extensive works of literature discussed in Chapter (II), the 

findings are explained in detail. 

The researcher also conducted several checks to examine the robustness of the results. 

The findings of the study are robust to the various measurements of EM as well as 

different debt level in CAPS measures, CG features, and samples. 

4.1 Correlation Analysis 

4.1.1 Overall Correlation Analysis  

Table 6 displays the findings of the correlation matrix between variables of the study and 

the overall sample (i.e., Palestinian and Jordanian manufacturing firms). As presented in 

the table, debt level in CAPS1 was negatively and significantly related to BSIZE with 



29 

coefficient of (-.011), and positively and significantly related to INSOWNER with 

coefficient of (0.29). The other features of CG depicted an insignificant correlation with 

debt level in CAPS1. EM was positively and insignificantly related to debt level in 

CAPS1 in both Kothari and Modified Jones models. The other proxy for DCAPS, debt 

level in CAPS2, demonstrated only a significant and positive relation with INSWONER 

with coefficient of (0.3). Debt level in CAPS2 was associated insignificantly with the 

other features of CG and EM in both measurements of Kothari and Modified Jones. It is 

evident from the table that the explanatory variable with the highest correlation with 

coefficient of (-0.4) was between CEOD and BIND. Debt level in CAPS was also linked 

significantly to several firm-specific factors, including FSIZE and DIV. To clarify, debt 

level in CAPS1 and debt level in CAPS2 are positively and significantly related to FSIZE 

with coefficient of (0.18). Moreover, debt level in CAPS1 and debt level in CAPS2 are 

negatively and significantly related to DIV with coefficient of (-0.16); (-0.2), 

respectively. On the other hand, FAGE was found to relate insignificantly with debt level 

in CAPS. 

  



30 

Table (6)  

Correlation matrix for overall manufacturing firm’s sample 

 BSIZE BIND GEN CEOD INSOWNER DCAPS1 DCAPS2 FSIZE FAGE DIV EM_kothari EM_Jones 

 BSIZE 1(NA)            

BIND 0.14(0.012) 1(NA)           

GEN -0.07(0.219) 0.05(0.345) 1(NA)          

CEOD 0.07(0.216) -0.4(<0.001) 0.02(0.752) 1(NA)         

INSOWNER -0.1(0.069) 0.29(<0.001) -0.2(0.001) -0.34(<0.001) 1(NA)        

DCAPS1 -0.11(0.047) 0.06(0.304) -0.05(0.37) -0.03(0.651) 0.29(<0.001) 1(NA)       

DCAPS2 -0.05(0.382) 0.1(0.074) 0.01(0.901) -0.02(0.792) 0.3(<0.001) 0.87(<0.001) 1(NA)      

FSIZE 0.22(<0.001) -0.16(0.006) -0.04(0.45) 0.06(0.265) 0.12(0.041) 0.18(0.002) 0.18(0.002) 1(NA)     

FAGE 0.01(0.829) -0.02(0.735) 0.19(0.001) 0.13(0.021) -0.12(0.033) -0.02(0.773) -0.1(0.076) -0.02(0.76) 1(NA)    

DIV 0.08(0.169) 0.06(0.291) 0.02(0.714) -0.01(0.86) -0.06(0.339) -0.16(0.006) -0.2(<0.001) -0.05(0.415) 0.06(0.329) 1(NA)   

EM_kothar -0.05(0.396) 0.04(0.473) 0.09(0.11) 0.11(0.054) -0.15(0.011) 0.06(0.301) 0.12(0.039) 0(0.95) -0.01(0.845) -0.01(0.883) 1(NA)  

EM_Jones -0.02(0.699) 0.08(0.143) 0.16(0.004) 0.08(0.183) -0.16(0.006) 0.05(0.377) 0.11(0.049) 0.03(0.599) 0.04(0.533) -0.01(0.914) 0.96(<0.001) 1(NA) 



31 

4.1.2 Overall Correlation Analysis for Palestinian Manufacturing Firms 

The correlation matrix for Palestinian manufacturing firms is presented in Table 7. Debt 

level in CAPS1 is positively and significantly associated with BSIZE (coefficient = 0.2), 

CEOD (coefficient= 0.23), and INSOWNER (coefficient= 0.22). The same result for 

debt level in CAPS2 was found, with coefficient of equal to 0.25 for BSIZE, 0.26 for 

CEOD, and 0.2 for INSOWNER, respectively. This implies that a bigger size of the board 

leads to a higher percentage of institutional investors, and the existence of CEO duality 

causes a higher level of debt financing. The other CG features, BIND and GEN, were 

found to be insignificantly related to both ratios of debt level in CAPS. In accordance 

with the second independent variable, debt level in CAPS1 is related negatively and 

significantly to both measurements of EM (Kothari and Modified Jones) with coefficient 

equal to -0.21 and -0.5, respectively. On the other hand, debt level in CAPS2 is 

insignificantly and negatively associated with both measurements of EM. Firm-specific 

variables, i.e., FSIZE, FAGE and DIV, were found to have an insignificant and negative 

relationship with both CAPS.  

  



32 

Table (7)  

Correlation matrix for Palestinian manufacturing firm’s sample 

 BSIZE BIND GEN CEOD INSOWNER DCAPS1 DCAPS2 FSIZE FAGE DIV EM_kothari EM_Jones 

BSIZE 1(NA)            

BIND 0.06(0.528) 1(NA)           

GEN -0.23(0.019) 0.13(0.205) 1(NA)          

CEOD 0.31(0.002) -0.56(<0.001) -0.19(0.055) 1(NA)         

INSOWNER 0(0.966) 0.21(0.031) -0.27(0.006) -0.48(<0.001) 1(NA)        

DCAPS1 0.2(0.042) 0.07(0.496) 0.05(0.61) 0.23(0.02) 0.22(0.027) 1(NA)       

DCAPS2 0.25(0.01) 0.09(0.375) 0.13(0.187) 0.26(0.008) 0.2(0.043) 0.96(<0.001) 1(NA)      

FSIZE 0.65(<0.001) -0.35(<0.001) -0.07(0.494) 0.48(<0.001) -0.15(0.132) -0.06(0.523) 0(0.995) 1(NA)     

FAGE -0.15(0.128) -0.19(0.054) 0.29(0.003) 0.14(0.153) -0.43(<0.001) -0.16(0.105) -0.18(0.066) 0.21(0.033) 1(NA)    

DIV 0.04(0.71) 0.08(0.41) -0.05(0.638) -0.03(0.753) -0.13(0.204) -0.16(0.114) -0.17(0.082) -0.06(0.567) -0.02(0.819) 1(NA)   

EM_kothar -0.05(0.65) 0.23(0.019) -0.02(0.866) -0.04(0.689) -0.14(0.167) -0.21(0.033) -0.18(0.068) -0.12(0.226) 0.03(0.745) 0.1(0.326) 1(NA)  

EM_Jones -0.08(0.445) 0.35(<0.001) 0.07(0.511) -0.12(0.226) -0.1(0.307) -0.19(0.05) -0.16(0.109) -0.16(0.101) 0.12(0.211) 0.08(0.392) 0.96(<0.001) 1(NA) 



33 

4.1.3 Overall Correlation Analysis for Jordanian Manufacturing Firms 

The correlation coefficient matrix for all the factors is displayed in Table 8 for Jordanian 

manufacturing firms. DCAPS1 is significantly and negatively related to BSIZE with 

coefficient of (-0.18) and significantly and positively related to INSOWNER with 

coefficient of (0.34). The other features of CG, BIND, GEN and CEOD are found to be 

insignificantly related to DCAPS1. DCAPS2 is significantly and negatively associated 

with BSIZE (-0.16) and CEOD (-0.17) and positively and significantly associated with 

INSOWNER with coefficient of (0.35). Moreover, DCAPS1 is positively and 

significantly related to EM in both models, i.e., Kothari (coefficient = 0.13) and Modified 

Jones (coefficient = 0.15). DCAPS2 is also found to have a significantly positive 

relationship with both measurements of EM, i.e.,  = (0.25) and (0.23) , respectively. With 

respect to the control variables, DCAPS1 and DCAPS2 are positively and significantly 

related to FSIZE with coefficient of (0.28) and negatively and significantly related to DIV 

with coefficient of (-0.34) and with coefficient of (-0.47). Meanwhile, FAGE did not show 

any relationship to CAPS. 

  



34 

Table (8)  

Correlation matrix for Jordanian manufacturing firm’s sample 

 BSIZE BIND GEN CEOD INSOWNER DCAPS1 DCAPS2 FSIZE FAGE DIV EM_kothari EM_Jones 

BSIZE 1(NA)            

BIND 0.18(0.01) 1(NA)           

GEN 0(0.97) -0.07(0.32) 1(NA)          

CEOD -0.11(0.122) -0.31(<0.001) 0.21(0.003) 1(NA)         

INSOWNER -0.16(0.024) 0.34(<0.001) -0.2(0.004) -0.25(<0.001) 1(NA)        

DCAPS1 -0.18(0.012) 0.07(0.351) -0.07(0.349) -0.09(0.195) 0.34(<0.001) 1(NA)       

DCAPS2 -0.16(0.027) 0.12(0.105) -0.07(0.346) -0.17(0.017) 0.35(<0.001) 0.86(<0.001) 1(NA)      

FSIZE -0.01(0.917) -0.01(0.87) 0.16(0.027) -0.3(<0.001) 0.33(<0.001) 0.28(<0.001) 0.28(<0.001) 1(NA)     

FAGE 0.04(0.541) 0.06(0.415) -0.16(0.024) 0.03(0.677) 0.03(0.687) 0.06(0.372) -0.05(0.523) -0.09(0.185) 1(NA)    

DIV 0.2(0.005) 0.03(0.633) -0.01(0.911) -0.13(0.068) 0.07(0.299) -0.34(<0.001) -0.47(<0.001) 0.07(0.345) 0.09(0.224) 1(NA)   

EM_kothar -0.08(0.278) -0.05(0.466) 0.08(0.284) 0.14(0.042) -0.16(0.02) 0.15(0.036) 0.25(<0.001) 0.11(0.132) -0.12(0.085) -0.37(<0.001) 1(NA)  

EM_Jones -0.03(0.677) -0.04(0.619) 0.19(0.008) 0.13(0.067) -0.2(0.005) 0.13(0.065) 0.23(0.001) 0.18(0.011) -0.08(0.237) -0.33(<0.001) 0.96(<0.001) 1(NA) 



35 

4.2 Results of Descriptive Statistics 

This section contains analyses of the descriptive statistics pertaining to each study 

variable. Table 9 displays the results of the descriptive statistics for the total sample of 

the study, including the sample of the selected manufacturing firms listed on ASE and 

PEX as a group. Table 10 presents a descriptive analysis of PEX, whereas Table 11 (See 

Appendix A) shows a descriptive analysis of ASE firms. The maximum and minimum 

values, as well as mean, median and standard deviation, are presented with inputs. 

4.2.1 Overall Results of Descriptive Statistics  

Table 9 displays the descriptive statistics for all variables related to the sample used in 

this study, including dependent variable (DCAPS), independent variable (EM), moderator 

variables (CG) and control variables. The sample included 38 manufacturing firms from 

Palestine and Jordan listed in PEX and ASE respectively during 2013–2020 (See 

Appendix 1). 

The decision of the capital structure variable was measured with the help of two ratios. 

First, the debt-to-equity ratio had a mean of approximately 68%. This indicates that the 

research sample owned 68% of the firm to its creditors. The maximum value was 7.5, 

whereas the minimum was zero. The standard deviation was 78%. The second ratio was 

the debt ratio with a mean of 33%, which implies that the firms financed approximately 

33% of their total assets with debt, meaning that firms financed the greater portion of 

their assets with other financing options, such as equity. The maximum value of the debt 

ratio was 88% whereas the minimum was zero. EM was also measured by two models: 

the Kothari model, depicting performance-matched discretionary accruals, and the 

Modified Jones model, which reported an average of zero. This result was expected since 

EM is the residual from the regression equation. The maximum and minimum values for 

the Kothari model were 43% and -66%, respectively, while the maximum and minimum 

values for the Modified Jones model were 1.46 and -1.65, respectively. This suggests that 

the firms were engaging in both upward and downward EM. 

For CG features, BSIZE was first assessed, which was measured by the logarithm of 

board size, which had a mean of 0.89. The maximum board size was 1.23, i.e., the 

logarithm of seventeen board members, and the minimum board size was 0.6, i.e., the 

logarithm of five board members. Second, BIND was examined, which was measured by 



36 

the percentage of non-executive directors to total board members. BIND depicted a mean 

of 92%, indicating that the board size consisted of more than half of independent 

directors. Third, Gen was measured by the percentage of women on the board of directors 

to total board members. Gen had a mean of 0.03, suggesting that on average, 3% of the 

board of directors were women. The maximum and minimum values were 0.04 and 0, 

respectively. Fourth, CEOD was a dummy variable, wherein “yes” referred to the 

existence of a CEO duality situation in the firm and “no” otherwise. Approximately 15% 

of the “yes” responses depicted a CEO duality situation, whereas 85% of “no” responses 

meant no CEO duality situation. This implied that CEO duality is not a regular 

phenomenon and a good level of compliance with CG rules. Finally, INSOWNER was 

measured by dividing institution ownership shares on the firm by total outstanding shares. 

The mean for this variable was 43%, which meant that almost the half of research sample 

shareholders were institutions. The maximum and minimum values were 97% and zero, 

respectively.  

For the control variables, FSIZE was measured by the logarithm of the total assets of the 

firm. The mean score for the size of the firm was 7.34, which is the logarithm of 22 

million total assets. The standard deviation was 0.45, with a minimum and maximum 

value of 5.91 and 8.27, respectively. The mean score of FAGE indicates that the sample 

of firms has been in operation for over 30 years. The maximum age was 68 years, whereas 

the minimum was five years. The dividend payout ratio was measured by dividing the 

cash dividend by net income. The mean of DIV was 0.49, implying that the firms 

approximately distributed half of their net income as cash dividends. The maximum ratio 

was 16.49, and the minimum was -2.17.   

  



37 

Table (9)  

Descriptive Statistics for Palestinian and Jordanian manufacturing firms listed in PEX 

and ASE (Overall) 

Variable n (%) 

Debt level in Capital Structure   

DCAPS1 

n (Missing) 304 (0) 

Mean ± Std-Dev 0.68 ± 0.78 

Median (Q1-Q3) 0.43 (0.22-0.83) 

Min, Max 0, 7.5 

DCAPS2 

n (Missing) 304 (0) 

Mean ± Std-Dev 0.33 ± 0.19 

Median (Q1-Q3) 0.3 (0.18-0.45) 

Min, Max 0, 0.88 

Earning Management (EM)  

EM_kothari 

n (Missing) 304 (0) 

Mean ± Std-Dev 0 ± 0.29 

Median (Q1-Q3) 0.03 (-0.03-0.1) 

Min, Max -0.66, 0.43 

EM_Jones 

n (Missing) 304 (0) 

Mean ± Std-Dev 0 ± 0.3 

Median (Q1-Q3) 0.02 (-0.04-0.09) 

Min, Max -1.65, 1.46 

BSIZE 

n (Missing) 304 (1) 

Mean ± Std-Dev 0.89 ± 0.12 

Median (Q1-Q3) 0.85 (0.85-0.95) 

Min, Max 0.6, 1.23 

BIND 

n (Missing) 304 (1) 

Mean ± Std-Dev 0.92 ± 0.1 

Median (Q1-Q3) 0.93 (0.86-1) 

Min, Max 0, 1 

GEN 

n (Missing) 304 (1) 

Mean ± Std-Dev 0.03 ± 0.08 

Median (Q1-Q3) 0 (0-0) 

Min, Max 0, 0.4 

CEOD 
No 258 (84.87%) 

Yes 45 (14.8%) 

INSOWNER 

n (Missing)  304 (1) 

Mean ± Std-Dev  0.43 ± 0.31 

Median (Q1-Q3)  0.4 (0.16-0.7) 

Min, Max  0, 0.97 

FSIZE 

n (Missing) 304 (0) 

Mean ± Std-Dev 7.34 ± 0.45 

Median (Q1-Q3) 7.35 (7.05-7.72) 

Min, Max 5.91, 8.27 

FAGE 

n (Missing) 304 (0) 

Mean ± Std-Dev 32.78 ± 17.32 

Median (Q1-Q3) 30 (18-47) 

Min, Max 5, 68 

DIV 

n (Missing) 304 (0) 

Mean ± Std-Dev 0.49 ± 1.32 

Median (Q1-Q3) 0.21 (0-0.68) 

Min, Max -2.17, 16.49  
Min, Max 0, 97867135 

 



38 

4.2.2 Results of Descriptive Statistics for Palestine 

Descriptive statical analysis for the years 2013 to 2020, related to all research variables 

including the dependent variable (DCAPS), independent variable (EM), moderator (CG) 

and control variables, for manufacturing firms listed on PEX, are shown in Table 10. 

Regarding the DCAPS variable, we noticed that the mean of the debt-to-equity ratio was 

0.57 with a median of 0.4. The maximum value was 2.44, whereas the minimum value 

was 0. The total debt to total assets ratio had a mean of 0.31, meaning that less than half 

of the total assets in Palestinian manufacturing firms were financed by debt. The standard 

deviation was 0.17, with a median of 0.29. The maximum and minimum values were 0.71 

and 0, respectively.  

With respect to the independent variables, EM was assessed first, which was measured 

by two models. First, EM under the Kothari model had a mean of 0.08, with a standard 

deviation of 0.24 and a median of 0.07. The minimum and maximum values of EM using 

the Kothari model were -0.84 and 1.43, respectively. Second, EM under the Modified 

Jones model had a mean of 0.08 and a median of 0.06. The maximum value was 0.46, 

and the minimum was -0.82. This implies that Palestinian manufacturing firms engage in 

upward and downward EM practices. 

For CG features, the board size (BSIZE) had a mean and median of 0.9, with a standard 

deviation of 0.11. The minimum value was 0.6, which is the logarithm value of five 

members, and the maximum value was 1.18, which is the logarithm of fifteen members. 

This implies that all Palestinian manufacturing firms had board members ranging from 5 

to 15. This finding indicates that most Palestinian manufacturing firms complied with CG 

rules, which state that the board of directors must have at least 5 and no more than 11 

members (Code of CG in Palestine, 2009). Second, board independence (INDE) scored a 

mean of 0.92 with a standard deviation of 0.13. The maximum value was 1, with the 

minimum value being 0. Consequently, one of the most important provisions of the CG 

code, i.e., the requirement of independent directors on the board, was met. Third, gender 

diversity (GEN) scored a mean of 0.07, meaning that women composed 7% of the 

Palestinian manufacturing firm boardrooms, despite a recent surge in calls for more 

women to serve on corporate boards of Palestinian firms. The maximum and minimum 

values were 0.4 and 0, respectively. Fourth, the result of the CEO duality (CEOD) feature 



39 

proved that around 75% of Palestinian manufacturing firms separate the role between the 

chairman and chief executive officer, indicating the compliance of Palestinian 

manufacturing firms with the CG code of conduct. Finally, institutional ownership 

(INSOWNER) had a mean of 0.44 with a standard deviation of 0.34. The maximum value 

was 0.94, whereas the minimum value was 0, meaning that almost half of the Palestinian 

manufacturing firm owners are institutional investors. 

Regarding the control group, the size of Palestinian manufacturing firms (FSIZE) scored 

a mean of 7.25, standard deviation of 0.53, maximum value of 8 and minimum value of 

5.91. The average age of the Palestinian manufacturing firms was 40.04 years. The oldest 

manufacturing firm in the Palestinian market has been in operation for 68 years, whereas 

the latest launched firm has been in the market for 16 years. The dividend payout ratio 

scored a mean of 0.79, with a standard deviation of 2.14. This indicates a fluctuat