A THEORETICALAND EMPIRICAL STUDYOF STOCK

A THEORETICAL AND EMPIRICAL STUDY OF STOCK

MARKET DEVELOPMENT, ECONOMIC REFORM AND

ECONOMIC GROWTH: A CASE STUDY OF ARAB COUNTRIES

By

RATEB MOH D AHMAD ABU-SHARIA

A Thesis Submitted in Fulfilment of the Requirements

For the Award of the Degree

Doctor of Philosophy (Economics and Finance)

SCHOOL OF ECONOMICS AND FINANCE

COLLEGE OF LAW AND BUSINESS

UNIVERSITY OF WESTERN SYDNEY

AUSTRALIA

AUGUST 2005

II

A THEORETICAL AND EMPIRICAL STUDY OF STOCK MARKET DEVELOPMENT,

ECONOMIC REFORM AND ECONOMIC GROWTH: A CASE STUDY OF ARAB COUNTRIES

Abstract

The relationship between stock market development and economic growth has been an

important issue of debate. A well functioning stock market can affect economic growth

through the channelling of more saving to investment and the improvement of capital

productivity with efficient allocation of resources. This contrasts with the view that

stock market development has little relevance, or is even unimportant, to real economic

activity. In this respect, the majority of the empirical studies are concerned with

advanced markets and developed emerging markets, and none exist for Arab markets.

The argument of this study is that economic growth is a function of stock market

development and economic reform indicators, with the main determinants of growth as

the control variables set. The study considered a comprehensive theoretical framework

that linked stock market development to economic growth. It presented a comparative

assessment on macroeconomic level and stock market development indicators for the

Arab countries with the East Asia-Pacific countries and the G-7 economies. The

empirical work applied sophisticated panel data econometric techniques, using the three

different econometric methods of OLS, 2SLS and GMM estimators, to the core sample

of our study, Arab countries. It then compared the estimation results with two different

groups, the East Asia-Pacific countries and the G-7 economies, over the period 1980-

2002.

The most important finding indicated that Arab stock markets have no significant effect

on economic growth due to the lack of transparency and illiquidity that limit the

effectiveness of these markets in the economy. In contrast, the results from the East

Asia-Pacific countries and the G-7 economies suggested that stock market development

has a significant effect on, and is positively correlated with, economic growth.

III

STATEMENT OF AUTHENTICATION

I, Rateb Abu-Sharia, declare that this thesis has not been submitted, either in whole or in

part, for a degree at this university or any other academic institution. I also certify that

the work presented in this thesis is, to the best of my knowledge and belief, my own

work and original except as acknowledged in the text.

Rateb Abu-Sharia

------------------------------

Signature of Candidate

IV

DEDICATION

TO MY PARENTS,

SISTERS AND BROTHERS…YOUR FAITH IN ME

TO SAMIRA,

RAZAN, OWEN AND GHENA…YOUR PATIENCE,

ENCOURAGING AND SUPPORTING ME

WITH MY LOVE…

V

ACKNOWLEDGMENTS

THANK YOU,

To the following people who assisted me and made the completion of this thesis

possible by their support, encouragement and professional consultation in different

ways:

First of all, I am indebted to Sheikh Yosuf Al-Shelash the Chairman of Eamar Albyader

Holding Company in Saudi Arabia, for awarding me the PhD scholarship with his

generous financial support during my study. My sincere gratitude also extends to my

friend Abdullatif Al-Shelash due to his willingness to help and continuing interest with

this study. Thank you for your friendship and encouragement given.

To Professor P.N. (Raja) Junankar, my principal supervisor, thank you so much for all

the valuable advice and encouragement that I received from you, and for your

professional support and entrusting me with this work. Thank you for your confidence

and generosity with your time during my PhD study, especially the last few months.

To my Co-supervisors, Dr Gary Tian and Dr Girijasankar Mallik, thank you for your

supervision and discussions, and for your interest and comments throughout this study.

I wish also to thank both the academic and administrative staff in the School of

Economics and Finance at the University of Western Sydney for necessary support and

help. Mark Reed, I appreciate and thank you for your time in proof reading.

VI

Many thanks and appreciation goes to Professor Thorsten Beck, Senior Economist at the

World Bank Group, and Professor Ross Levine, at the University of Minnesota, for

providing me with the necessary files that helped me in the empirical work of this study.

I thank Professor Mark Schaffer, Director of the Centre for Economic Reform and

Transformation at Heriot-Watt University, for his prompt advice on panel estimation and

STATA program. Many thanks and regards to the participants of the 2004 Multinational

Finance Society Annual Conference (Rutgers, The State University of New Jersey) for

helpful comments.

Thank you to Beshr Bakheet, Managing Director at Bakheet Financial Advisors, for

providing me with Arab stock markets reports and data. I would also express my

sincerest gratitude to Dr Husam Al-Malkawi who was my best friend and colleague

during my PhD study in Australia. My appreciation goes to Dr Sohail Magableh who

was supportive and sympathetic of my coming to Australia for this PhD study. Special

thanks go to my friends Ahmad Masadeh and Ala Al-Najjar for their friendly assistance

and encouragement in this study.

Thank you all, without your support, this study would have not been possible.

Rateb Abu-Sharia

VII

TABLE OF CONTENTS

Abstract………………………………………………………………………………….II

Statement ofAuthentication…………………………………………………………….III

Dedication……………………………………………………………………………....IV

Acknowledgment………………………………………………………………………..V

List of Tables and Figures………………………………………………………………XI

1. CHAPTER ONE: SCOPE AND FRAMEWORK OF THE STUDY....................................... 1

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

1.2 Research Problem and Questions......................................................................... 6

1.3 Why Is the Problem Worthy of Research?.......................................................... 8

1.4 Econometric Approach and the Data................................................................... 9

1.5 Thesis Outline .....................................................................................................11

2. CHAPTER TWO: LITERATURE REVIEW....................................................................14

2.1 Overview .............................................................................................................14

2.2 The Evidence on Finance and Economic Growth: Arab Countries.................16

2.3 Financial Development and Growth: Cross Country Studies ..........................20

2.3.1 Part One: On Economic Growth Led Finance.....................................21

2.3.2 Part Two: On Finance Led Economic Growth ....................................23

2.4 A Critical Evaluation of the Empirical Studies.................................................33

2.5 Microeconomic View: Industry and Firm Level...............................................36

2.5.1 Industry-Level Studies .............................................................................37

2.5.2 Firm-Level Studies ...................................................................................41

2.6 Finance and Growth: Evidence From Individual Country Analysis................45

2.7 Recent Evidence on Economic Reform and Economic Growth ......................48

2.8 Conclusion...........................................................................................................50

3. CHAPTER THREE: THEORETICAL CONSIDERATION OF STOCK MARKET

DEVELOPMENT, ECONOMIC REFORM AND ECONOMIC GROWTH.........................53

3.1 Introduction .........................................................................................................53

3.2 Theory of Economic Growth..............................................................................55

3.2.1 Neoclassical Growth Model.....................................................................55

VIII

3.2.2 Endogenous Economic Growth Model ...................................................57

3.3 Finance, Stock Market Development and Economic Growth..........................61

3.3.1 Channels to Economic Growth: A Theoretical Overview.....................61

3.3.1.1 On the Financial System.............................................................61

3.3.1.2 Stock Market As A Cause of Economic Growth ......................65

3.4 The Effect of Stock Market on Saving and Investment....................................67

3.4.1 Stock Market Development and Saving..................................................67

3.4.1.1 The Markowitz Mean Variance Formulation.........................68

3.4.1.2 The Potential Effects...................................................................76

3.4.2 Stock Market Development and Investment...........................................78

3.4.2.1 On the q Theory of Investment .................................................79

3.4.2.2 Stock Market Development and Liquidity Supply ...................83

3.5 Economic Reform and Economic Growth ........................................................86

3.6 Conclusion...........................................................................................................87

4. CHAPTER FOUR: AN OVERVIEW OF ARAB ECONOMIES AND STOCK MARKET

DEVELOPMENT............................................................................................................91

4.1 Introduction .........................................................................................................91

4.2 Major Trends and Outlook .................................................................................93

4.2.1 The Oil Boom ...........................................................................................93

4.2.2 Real GDP and Per Capita GDP Growth..................................................96

4.2.3 Education and Labour Market .................................................................99

4.2.4 The Investment and Saving Picture.......................................................100

4.2.5 Macroeconomic Stability and Political Instability ...............................101

4.3 Economic and Structural Reform.....................................................................102

4.4 Financial Development in Arab Countries......................................................105

4.4.1 Background.............................................................................................105

4.4.2 Financial Sector Indicators ....................................................................108

4.4.3 Arab Stock Markets.................................................................................113

4.4.3.1 General Preview.........................................................................113

4.4.3.2 Oil-Exporting Countries ............................................................119

4.4.3.3 Non-Oil Countries......................................................................133

4.5 Conclusion.........................................................................................................143

IX

5. CHAPTER FIVE: A COMPARISON OF ARAB STOCK MARKETS WITH EAST ASIA-

PACIFIC AND THE G-7 ECONOMIES ........................................................................146

5.1 Introduction .......................................................................................................146

5.2 Macroeconomic Development and Economic Reform ..................................147

5.2.1 The Main Economic Indicators .............................................................147

5.2.2 Economic Reform Indicators.................................................................150

5.3 Financial Market Development on International Level..................................152

5.3.1 The Banking Sector ................................................................................153

5.3.2 Stock Market Development ...................................................................156

5.3 Conclusion.........................................................................................................166

6. CHAPTER SIX: AN ECONOMETRIC ANALYSIS OF ARAB STOCK MARKETS ........168

6.1 Introduction .......................................................................................................168

6.2 Stock Market and Growth: Brief Theoretical Framework .............................171

6.3 Econometric Methodology...............................................................................175

6.3.1 The Model Specification........................................................................175

6.3.2 The Hypotheses and Variables ..............................................................189

6.3.3 The Sample and the Data .......................................................................197

6.4 Estimation Results ............................................................................................199

6.4.1 OLS and 2SLS Instrumental Variable Methods ...................................199

6.4.2 Dynamic Panel Model............................................................................203

6.5 Robustness Tests and Extensions.....................................................................206

6.5.1 Outlier Analysis ......................................................................................207

6.5.2 Alternative Measures of Stock Market and Economic Reform...........207

6.5.3 Individual Country Results ....................................................................212

6.6 Conclusion..........................................................................................................216

7. CHAPTER SEVEN: AN ECONOMETRIC ANALYSIS COMPARISON STUDY ..........219

7.1 Introduction .......................................................................................................219

7.2 Econometric Approach .....................................................................................221

7.3 The Data and Variables ....................................................................................223

7.4 Estimation Alternatives ....................................................................................224

7.4.1 The Case of Arab Countries...................................................................225

7.4.2 The Case of East Asia-Pacific ...............................................................225

X

7.4.3 The Case of G-7 Economies ..................................................................227

7.4.4 The Whole Sample Estimation ..............................................................229

7.5 Empirical Results ..............................................................................................231

7.6 Robustness Tests and Possible Extensions......................................................239

7.6.1 Outlier Analysis ......................................................................................240

7.6.2 Alternative Measures of Stock Market and Economic Reform...........241

7.6.3 Individual Country Results ....................................................................242

7.7 Conclusion.........................................................................................................244

8. CHAPTER EIGHT: CONCLUSION AND FUTURE RESEARCH ...................................247

8.1 Introduction .......................................................................................................247

8.2 Summary of the Study ......................................................................................248

8.3 Key Findings and Policy Implications.............................................................256

8.4 Study Limitations..............................................................................................261

8.5 Avenues for Future Research ...........................................................................262

9. APPENDICES..........................................................................................................265

9.1 Appendix A: Panel Data Approach .................................................................265

9.2 Appendix B: Time Series Approach................................................................282

10. REFERENCES ........................................................................................................290

XI

LIST OF TABLES AND FIGURES

List of Tables:

Table 2.1: Summary of Empirical Studies on Finance and Growth: Arab Countries 19

Table 2.2: Summary of Empirical Studies on Finance and Economic Growth 31

Table 4.1: Oil Revenues, Oil and Gas Exports Indicators 95

Table 4.2: Real GDP and Per Capita GDP Growth Rates: Oil Vs. Non-Oil Countries 97

Table 4.3: Public Spending on Education, Illiteracy Rate and Gross Enrolment Ratio 99

Table 4.4: Saving-Investment Balance of Arab Countries 100

Table 4.5: Arab World FDI Stocks and Flows (USD $million and Percentages) 101

Table 4.6: Annual Freedom Scores and Institutional Quality Index, 2004 103

Table 4.7: National Currency and Exchange Rate Regimes, 2003 104

Table 4.8: Middle East and North Africa: Financial Development Ranking 107

Table 4.9: Interest Rate Indicators (1990-2003) 109

Table 4.10: Monetary Indicators (1990-2003) 111

Table 4.11: Credit Indicators (1990-2003) 113

Table 4.12: Arab Stock Markets, Main Indicators (USD $million, 2003) 115

Table 4.13: Arab Stock Markets With its Official Term and Establishment Date 119

Table 4.14: Schedule of Commitments for Bahrain under the GATS rules 122

Table 4.15: Schedule of Commitments for Kuwait under the GATS rules 125

Table 4.16: Schedule of Commitments for Oman under the GATS rules 127

Table 4.17: Schedule of Commitments for Qatar under the GATS rules 129

Table 4.18: Schedule of Commitments for UAE under the GATS rules 132

Table 4.19: Schedule of Commitments for Egypt under the GATS rules 135

Table 4.20: Schedule of Commitments for Jordan under the GATS rules 138

Table 5.1: The Macroeconomic Indicators (USD $million) 148

Table 5.2: International Freedom Scores and Institutional Quality Index, 2004 152

Table 5.3: The Main Indicators of the Banking Sector 156

Table 5.4: International Stock Markets, (USD $millions, 2003) 159

Table 5.5: The Performance of Stock Markets in the Selected Three Groups in 2003 165

Table 6.1: Theoretical Discussion (Hypotheses) and Expected Sign of the Explanatory Variables 196

Table 6.2: Descriptive Statistics: Arab Countries, 1980-2002 197

Table 6.3: Correlation Matrix: Arab Countries, 1980-2002 198

Table 6.4: Growth and Stock Market Equation: OLS and 2SLS Estimates 200

Table 6.5: Dynamic Growth and Stock Market Equation: GMM Estimators 204

XII

Table 6.6: Growth and Stock Market Equation: OLS Estimation 209

Table 6.7: Dynamic Growth and Stock Market Equation: GMM Difference Estimator 210

Table 6.8: Dynamic Growth and Stock Market Equation: GMM System Estimator 211

Table 6.9: OLS Estimation-Time Series Approach: Arab Countries 215

Table 7.1: Descriptive Statistics: East Asia-Pacific, 1980-2002 226

Table 7.2: Correlation Matrix: East Asia-Pacific, 1980-2002 227

Table 7.3: Descriptive Statistics: G-7 Economies, 1980-2002 228

Table 7.4: Correlation Matrix: G-7 Economies, 1980-2002 229

Table 7.5: Descriptive Statistics: Full Sample, 1980-2002 230

Table 7.6: Correlation Matrix: Full Sample, 1980-2002 231

Table 7.7: Summary Results of the Main Variables in Different Set Methods 234

Table 7.8: Growth and Stock Market Equation-Full Sample: OLS Estimation 242

Table 7.9: OLS Estimation-Time Series Approach: International Comparison 244

Table 8.1: Summary Panel Estimation Results in this Study 260

Table 8.2: A Comparison With Different Empirical Studies 260

Appendix A: Panel Data Approach 265

Arab Countries: Tables (A.1-A.4)

East Asia-Pacific Countries: Tables (A.5-A.8)

The G-7 Economies: Tables (A.9-A.12)

The Full Sample: Tables (A.13-A.16)

Appendix B: Time Series Approach 282

Arab Countries: Tables (B.1-B.5)

East Asia-Pacific Countries: Tables (B.6-B.14)

The G-7 Economies: Tables (B.15-B.21)

XIII

List of Figures:

Figure 3.1: Financial Market and Flow of Funds Relationship 62

Figure 3.2: A Theoretical Approach to Finance and Growth 63

Figure 3.3: The Markowitz Portfolio Selection Model 74

Figure 4.1: Annual GDP Growth Rate and Average Oil Prices (1990-2002) 97

Figure 4.2: Real GDP and Per Capita GDP Growth Rate (1990-2002) 98

Figure 4.3: Arab Countries: Share of GDP (1990-2002) 98

Figure 4.4: Arab Stock Market Capitalization (1990-2003) 115

Figure 4.5: Average Arab Stock Market Capitalization as a Share of GDP (1990-2003) 117

Figure 4.6: Stock Market Capitalization, (International Review, 1990-2003) 117

Figure 4.7: Arab Stock Markets Performance in 2001, 2002 and 2003 118

Figure 5.1: Average GDP at Market Prices, 1990-2003 149

Figure 5.2: Total GDP at Market Prices (USD $million, 2003) and Percentages 150

Figure 5.3: The Main Macroeconomic Indicators (Percentages) 150

Figure 5.4: Global Indicators of Freedom House Rating and Institutional Quality Index, 2004 151

Figure 5.5: A Global Comparison of Financial Indicators, 2003 155

Figure 5.6: Stock Market Development Indicators (Percentage, 2003) 160

Figure 5.7: Total Market Capitalization, By Region, 1990-2003 161

Figure 5.8: Average Market Capitalization, 1990-2003 161

Figure 5.9: Total Value Traded, 1990-2003 162

Figure 5.10: Average Market Value Traded, 1990-2003 162

Figure 5.11: Average Turnover Ratio (%), 1990-2003 163

Figure 5.12: Total Number of Listed Domestic Companies, 1990-2003 164

Figure 5.13: Average Number of Listed Companies, 1990-2003 164

1

Chapter One

1. CHAPTER ONE: SCOPE AND FRAMEWORK OF THE STUDY

1.1 Introduction

Greater risk sharing through internationally integrated stock markets (portfolio diversification) can

actually reduce saving rate and slow economic growth in a standard model of endogenous growth.

More generally, financial integration or other financial development may affect growth by affecting

saving rate, by affecting the allocation of saving, or by affecting the marginal product of capital

(Devereux and Smith, 1994, p. 547).

…The theoretical explanation of this process is intuitively appealing. A stock market provides

greater opportunity for both risk spreading and risk pooling. Furthermore, a formal market for

securities greatly increases the amount of information available to investors about firms and their

proposed investment projects. Both result in a more efficient allocation of resources and should thus

raise the marginal product of capital (Harris, 1997, pp. 139-140).

Considering the conflicting theoretical perspectives on the importance of well-

functioning stock markets for economic growth, this study examines the effect of both

stock market development and economic reform on economic growth. It identifies and

discusses the main functions of the stock market within the economy, analyses trends

and quantitative measures of financial development in general and stock markets in

particular, including the market size as measured by market capitalization, market

activity by value traded, and turnover ratio for stock market liquidity.

2

This study tests empirically the importance of stock market development and economic

reform as determinants of economic growth for Arab countries1 and presents a

comparative study with the East Asia-Pacific2 and the G-7 economies3, using

sophisticated econometric techniques on panel data sets for these three groups and the

whole sample of 28 countries over the period 1980-2002. The countries in the study are

of particular interest since they have different growth rates, different fundamental

characteristics of financial structure, and stock market development levels (more-

developed, developed and less-developed).

On the other hand, the study looks at stock market development and assesses its

economic impact at an international level, not in terms of profitability to investors

(which is outside the scope of our study), but rather in terms of development relative to

the size of these economies and the financing requirements of capital expenditure in

these countries. A comparison of Arab stock markets with different financial systems of

East Asia-Pacific markets and G-7 markets has not been widely, if ever, applied to these

countries. Nevertheless, there exists very little empirical evidence on the importance of

stock market development to economic growth and almost none exists regarding the

Arab countries.

1There are 11 Arab countries that have stock markets involved in the study: Bahrain, Egypt, Jordan,

Kuwait, Lebanon, Morocco, Oman, Qatar, Saudi Arabia, Tunisia, and the United Arab Emirates.

2The East Asia-Pacific (10) countries that are included in the study are Australia, China, Hong Kong,

Indonesia, South Korea, Malaysia, New Zealand, Philippines, Singapore, and Thailand.

3The members of the Group of Seven (G-7) are Canada, France, Germany, Italy, Japan, the United

Kingdom and the United States, which together account for about two-thirds of the world's economicoutput.

3

This study encompasses a theoretical understanding of how the financial system and

stock market development could affect saving and investment, and hence the real

growth rate, after controlling the determinants of economic growth in view of the

neoclassical and endogenous growth models.

The literature work in our study is organized to review the contradictory views of

economic growth led-finance and finance led-economic growth with reference to

the empirical studies that use macroeconomic approach to cross-country and time series

analysis, and apply microeconomic data at the industry and firm-level.

The most important findings of our study are, firstly, that Arab stock markets have no

significant effect on economic growth due to the lack of transparency and illiquidity

problems that limit the effectiveness of these markets in the economy. In contrast, the

results of the East Asia-Pacific countries and the G-7 economies in all alternative

estimation methods suggest that stock market development have significant effect on,

and is positively correlated with, economic growth.

The last part of our empirical analysis tests the implications of stock market

development on economic growth in a more general way, by analysing the three groups

together as a pooled sample. The estimation results show that stock market development

is statistically significant, and positive in general, at the five percent significance level,

when using the turnover ratio as a measure of stock market liquidity. However, this

result is driven by the East Asia-Pacific and the G-7 economies.

4

Secondly, the empirical results of the impact of economic reform as measured by the

institutional quality index from (International Country Risk Guide ICRG ) are

different between the groups in the study with respect to the estimated methods.

Finally, the results of investment and inflation have shown an economic impact that is

consistent with the economic theory and highly significant on economic growth for all

countries as a whole sample and for each of the separate groups of the Arab countries,

the East Asia-Pacific and the G-7 economies. The other macroeconomic determinants

results reveal some variations in sign and significance that are explained by differences

in the choice of the estimation method and the sample group. Interestingly, the catch-up

effect of income convergence across countries has a negative sign, and is significant in

all estimation methods of Ordinary Least Squares (OLS), Two Stage Least Squares

(2SLS) and Generalized Method of Moments (GMM) estimators.

The major challenge of our study has been to draw together diverse aspects of analysis

across three different groups of countries. The study has evolved rapidly with the

dramatic growth and reallocative function of Arab stock markets as an alternative

mechanism to bank borrowing for the finance of the private sector and state projects in

the region s economy. It has taken into account the new economic environment and

structural reform in Arab countries during the period 1980-2002 and its impact on

economic growth.

Although stock markets have functioned formally and informally in the Arab countries

since the 1970s, until quite recently it was difficult to find reliable and consistent data

5

on the size and growth of these markets. The research has been undertaken, on average,

from the available data of the Arab stock markets. The lack of data limits our empirical

analysis of cointegration methods and Granger-Causality tests, and our ability to

examine the view that financial development follows economic growth. This view is

known as demand-following; a high economic growth may create demand for certain

financial instruments and arrangements and the financial markets are effectively

responding to these demands and changes.

What has changed in recent years is the growing international interest in emerging stock

markets, the moves in the Arab governments to open their stock markets to foreign

investors, and the general trend towards responsible government regulation of these

markets. With these changes has come a greater degree of accessibility to data. In

particular, global financial institutions, such as the International Financial Corporation

(IFC) has included most Arab stock markets and begun to assemble and publish key

indicators of market size and activity in the region.

The empirical work does not tackle the effect of financial development in general; it

does not look at variables such as money supply, bank assets and domestic credit

provided to the private sector and public sector. The work focuses solely on the effect of

stock market development and economic reform on economic growth, and presents a

global comparison study using the database of the key indicators of stock market

development and the most important determinants that could affect economic growth.

This set of explanatory variables is selected according to the insights provided by

economic theory and previous empirical literature.

6

1.2 Research Problem and Questions

Although there have been numerous studies analysing the role of stock market

development in economic growth, most of them have focused on developed economies,

and some on emerging economies excluding Arab countries. Research from developed

economies suggests that stock market development does help economic growth. This

study considers whether stock markets play a similar role in the Arab countries, given

that stock markets have an important role in investment and corporate finance.

The argument of this study is that economic growth is a function of stock market

development and economic reform indicators, with the main determinants of growth

being used as control variables. This study raises a number of critical questions for

economists and policy-makers in the Arab region, confirmed by the existing literature,

as follows:

Question One: Do stock market development and economic reform have an effect on

economic growth and, if so, how?

Question Two: How can Arab economies benefit from stock market development?

Question Three: Are stock markets important for economic growth?

In the existing literature there are two general hypotheses about the relationship between

stock market development and economic growth:

7

First Hypothesis: A well functioning stock market may affect economic activity in an

economy through growth of saving, efficient allocation of resources, and better

utilization of the existing resources4.

Second Hypothesis: This views the stock market as a Casino that has little positive

impact and perhaps even a negative impact on economic growth5.

This study answers these questions raised and examines the hypotheses by using

dynamic growth estimation techniques with cross-country and time series analysis on

annual observations of 28 countries and an unbalanced panel data set over the period

1980-2002. The data set presents fundamental differences of countries with different

numbers of observations according to data availability, with the aim of increasing the

understanding of the relationship between stock market development and economic

growth.

The existing literature and theory have neither a common concept nor a common

measure of stock market development. This study uses different measures of stock

market development including turnover ratio for liquidity, stock market size, and

activity as measured by market capitalization, and value traded, respectively. This study

adds a new variable, representing the interaction of investment and turnover ratio, as

one of the explanatory variables on the right hand side in the general model

4For more references on this hypotheses see, for example, King and Levine (1993), Atje and Jovanovic

(1993), Levine and Zervos (1996, 1998a), and Wachtel (2002).

5 See, for example, Devereux and Smith (1994).

8

specification. This variable was introduced because of the possible effect of stock

market development on economic growth through investment activity.

1.3 Why Is the Problem Worthy of Research?

Earlier research emphasizes the role of the banking sector and its impact on economic

growth. Most of the empirical evidence uses general measures of financial development

such as the ratio of liquid liability of financial intermediaries to GDP and domestic

credit to the private sector divided by GDP.

However, recent research has increasingly shifted its focus to the links between stock

market development and economic growth, due to the increasing role of capital markets

in investment and real economic activities. Since the late 1980s, most Arab countries

have acknowledged the importance of stock market development and the private sector

in the achievement of economic development. The interest and commitment these

countries exhibited in developing and reforming their economic structure was reflected

in the increase in the number of active stock markets from only four in the 1970s

(Egypt, Jordan, Kuwait and Lebanon) to 11 by the year 2002.

Arab stock market capitalization increased from USD $32.5 billion in 1990 to USD

$360.9 billion by the end of year 2003. Total value traded increased from USD $734

million to USD $230.4 billion over the period 1990-2003, and the total number of listed

companies almost doubled from 847 to 1,673 companies during the same period 1990-

2003. Indeed, the average turnover ratio for the 11 Arab stock markets reflects the

9

relatively small liquidity of these markets with 31.9 percent over the period 1990-2003

and 27.7 percent in 2003.

The overall aim of this study is to examine empirically the performance of Arab stock

markets and economic reform and assess its impact on economic growth, in comparison

to the other markets which belong to the two different groups of the East Asia-Pacific

countries and the G-7 economies. This will inform and update future policy decision-

makers about how Arab stock markets could be improved, and how these markets can

be compared with other emerging and developed stock markets as one of the key

financial instruments in the economy.

1.4 Econometric Approach and the Data

With reference to the general growth and stock market equation using an unbalanced

panel data set over the period 1980-2002, the study applied three different econometric

methods. Firstly, using the basic methods of Ordinary Least Squares (OLS), secondly,

the instrumental variables method of Two Stage Least Squares (2SLS), and thirdly, by

using the dynamic panel model of Generalized Method of Moments (GMM) as

proposed by Arellano and Bond (1991), Arellano and Bover (1995) and Blundell and

Bond (2000).

The application of the dynamic panel data methods has important advantages over

simple cross-section regressions and other estimation methods. The latter methods may

contain biased estimation caused by omitted variables and unobserved country-specific

10

effects. Moreover, it is a more efficient and more realistic model of estimation as it takes

into consideration variations both across time and individual countries, and also offers

to investigate heterogeneity in adjustment dynamics between different types of

countries.

In the empirical work, the study uses Real Per Capita GDP Growth Rate as a dependent

variable to measure economic growth. The explanatory variables have been grouped in

three different categories: (1) Stock Market Development Indicators, (2) Economic

Reform Indicators, (3) Growth Determinants as the Control Variables Set. The variables

of stock market development focus on market size (market capitalization, as a

proportion of GDP), market activity (value traded, as a proportion of GDP) and liquidity

as measured by turnover ratio. Given the importance of convergence issues, it includes

the logarithm of initial level of per capita GDP to allow for a catch-up effect.

The data on stock market development indicators has been collected from Standard and

Poor s emerging stock markets database, Arab Monetary Fund, and supplemented by

other data from the annual reports of the stock markets. All the macroeconomic

indicators are available at the Global Development Network Growth Database (Sewadeh

and Easterly, 2002), the World Bank World Tables (EconData, DX databases), and the

World Bank s national account data files Country Profile Table and World Economic

Outlook .

11

1.5 Thesis Outline

This study extends a comprehensive body of theoretical and empirical work on Arab and

global stock markets and their impact on economic growth in an endogenous growth

model. It provides theoretical considerations for stock market development effects on

saving and investment through the determination of risk-return ratios, which is a central

issue for corporate investment.

The study presents an extensive analysis on theory and descriptive economies before an

empirical examination. It is organized in Eight Chapters as follows: Chapter Two

discusses the literature review of empirical evidence on finance, stock market

development and economic growth for Arab countries, East Asia-Pacific and G-7

economies with reference to cross-country and time series analysis. The second part

covers the possible effects of stock market development through the empirical evidence

that uses microeconomic data on an industry and firm-level. In the final part of chapter

two, some empirical evidence is presented on the relationship between economic reform

and economic growth.

Chapter Three considers theoretically how the financial system and stock market

development could affect real economic growth in the neoclassical Solow-Swan model

(1956) and the endogenous growth model of Lucas (1988) and Romer (1986). It

establishes the theoretical link between finance, stock market development, saving,

investment and economic growth.

12

In Chapter Four, the study introduces an overview of Arab economies and provides an

historical perspective of Arab financial development in general and stock markets in

particular. Given the major determinants and characteristics of Arab economies, it

demonstrates the impact of oil on these economies. The analysis here distinguishes

between two main groups of Arab countries that are included in the study6: oil-exporting

countries (GCC)7

in descending order of population: Saudi Arabia, Oman, Qatar, the

United Arab Emirates, Kuwait and Bahrain; and non-oil countries in descending order

of population: Egypt, Morocco, Tunisia, Jordan and Lebanon.

Chapter Five presents a comparative analysis of Arab countries with the East Asia-

Pacific and the G-7 economies. It initially provides stylised facts of major

macroeconomic and economic reform indicators. It then introduces different measures

of the financial system and stock market development for the three groups. This chapter

is necessary to understand the financial data and its impact on the relationship between

stock market development and economic growth in the empirical work.

The empirical work is organized into two chapters: Chapter Six applies econometric

techniques for Arab stock markets and discusses the estimation methodology of

dynamic panel data models, as well as the hypotheses in the study. In this chapter, the

study provides an empirical assessment of the effect of Arab stock markets and

6 Due to the different structure of the Arab economies, and abundance of oil in some of them. On the otherhand, this study aims to include all the Arab countries, given that some countries have not yet createdstock markets (for example, Iraq, Libya, Syria, and Yemen), and other countries established stock marketsvery recently (Algeria and Sudan).

7The Gulf Cooperation Council (GCC) is an economic and political policy-coordinating forum for the six

member states (Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and the United Arab Emirates-UAE).

13

economic reform on economic growth with reference to the panel data set and time

series analysis at individual country level.

Chapter Seven introduces a comparison on a global level of Arab countries with East

Asia-Pacific and the G-7 economies by using an econometric methodology of dynamic

growth and stock market equation. This chapter applies three methods of estimation,

namely OLS, 2SLS and GMM, to the panel data sets of four groups: (1) Arab stock

markets, (2) East Asia-Pacific countries, (3) the G-7 economies, and then (4) the

empirical analysis on the whole sample of 28 countries. It also compares the estimation

results of the panel data set using a time series and individual country approach.

Finally, Chapter Eight summarizes the results and provides the policy implications as

well as suggestions for further research.

14

Chapter Two

2. CHAPTER TWO: LITERATURE REVIEW

2.1 Overview

The growing importance of stock markets around the world has recently opened a new

avenue of research into the relationship between financial development and economic

growth, which focuses on the effect of stock market development (Arestis, Demetriades

and Luintel, 2001, p. 16). The general idea that economic growth is related to financial

development can go back at least to Schumpeter8

(1912) and more recently to

McKinnon (1973) and Shaw (1973). Fischer (2003) emphasizes that the most important

and thorough early contribution on financial development and economic growth came

from Joseph Schumpeter who, in his book (The Theory of Economic Development),

said:

The banker… is not so much primarily a middleman in the commodity purchasing power as a

producer of this commodity… He stands between those who wish to form new combinations and

the possessors of productive means. He is essentially a phenomenon of development, though

only when no central authority directs the social process. He makes possible the carrying out of

new combinations, authorizes people, in the name of society as it were, to form them. He is the

ephor [overseer] of the exchange economy (Schumpeter, 1912, p. 74).

8Schumpeter argues that the services provided by financial intermediaries-mobilizing saving, evaluating

projects, managing risk, monitoring managers, and facilitating transactions-are essential for technologicalinnovation and economic development (King and Levine, 1993).

15

Theoretically, the link between financial intermediation and economic growth stems

from the insights of endogenous growth models, in which economic growth is self-

sustaining without exogenous technical progress and influenced by various initial

attributes of the economy. In this framework, financial intermediation is shown to not

only have level effects, but also growth effects (Agarwal, 2001).

More recently, the emphasis has increasingly shifted to stock market measures and the

effect of stock market development on economic growth. Caporal, Howells and Soliman

(2004) describe briefly that a well-developed stock market is supposed to increase

saving and efficiently allocate capital to productive investments, which leads to an

increase in the rate of economic growth. On the other hand, stock markets contribute to

the mobilization of domestic saving by enhancing the set of financial instruments

available to savers to diversify their portfolios and play a key role in allocating capital to

the corporate sector, which will have a real effect on the overall economic growth.

This chapter is organized as follows. The next section presents the empirical evidence

on finance and economic growth for the Arab countries. Section three discusses the

contradictory views of overall financial development and stock market development on

economic growth. In section four, we evaluate critically the existing literature. Section

five describes the empirical literature that used data on an industry and firm-level.

Section six presents some of the empirical studies based on time series and individual-

country analysis. Section seven, present the empirical evidence on economic reform and

economic growth. Finally, section eight contains our conclusion.

16

2.2 The Evidence on Finance and Economic Growth: Arab Countries

The empirical studies testing the relationship between financial development and

economic growth are concerned with advanced economies and developed emerging

markets. Nevertheless, few studies are concerned with Arab financial markets. In

addition, the link between Arab stock markets and economic growth remains a relatively

unexplored area of research for these countries. Table 2.1 summarizes the empirical

studies on financial development and economic growth in Arab countries.

Darrat (1999) focuses on the hypothesis of supply-leading which states that the

presence of efficient financial markets increases the supply of financial services in

advance of the demand for them in the real sector of the economy. Darrat investigates

empirically the relationship between financial deepening and economic growth for three

Middle-Eastern developing countries (Saudi Arabia, Turkey, and the United Arab

Emirates).

By using Granger-Causality tests within an error correction framework, the empirical

results suggested that the economic stimulus of more sophisticated and efficient

financial markets in Saudi Arabia and Turkey become noticeable only gradually as the

economies grow and mature in the long-run, and financial deepening may influence

only some, but not all, sectors of the economy.

Al-Tamimi, Al-Awad and Charif (2001) use cointegration tests, Granger-Causality, and

the impulse response function to investigate the causal relationship between financial

17

development and economic growth for eight Arab countries (Algeria 1970-1995,

Bahrain 1975-1998, Egypt 1952-1999, Jordan 1970-1998, Kuwait 1973-1998, Morocco

1958-1998, Saudi Arabia 1964-1998, and Syria 1963-1998).

The study of Al-Tamimi et al. (2001) examines causality tests from financial

development to real GDP and the reverse causality by using the vector autoregression

(VAR) model. The empirical results show that financial development and real GDP

growth are strongly linked in the long run. The relationship is weak in the short-run as

Granger-Causality tests and the impulse response functions illustrated.

Al-Awad and Harb (2003) consider the relationship between financial deepening and

economic growth for ten developing countries (Algeria, Egypt, Iran, Jordan, Kuwait,

Morocco, Saudi Arabia, Syria, Tunisia and Turkey) using new methods of panel

cointegration along with the popular time series methodologies (Johansen s

Cointegration, Granger-Causality, and the variance decompositions).

They provide empirical evidence that a positive relationship between financial

development and economic growth may exist at some levels as suggested by the panel

and Johansen s Cointegration tests. On the other hand, in the short run the link is very

weak as shown by Granger-Causality for panel data and the variance decompositions

tests for time series data, which is consistent with the argument of Lucas (1988) that the

financial sector has no important role in real economic growth, and economists

frequently exaggerate the role of financial factors in economic development.

18

Omran and Bolbol (2003) construct a growth equation that captures the interaction

between Foreign Direct Investment (FDI) and various indicators of financial

development in the context of Arab countries. They use averaged five years cross-

sectional data for the period 1975-1999. The estimation model is based on the growth

accounting framework of the Cobb-Douglas production function, as follows:

0 1 2i i i iy x z= + + + (2.1)

( * )Y TP FDI FD L K= (2.2)

In Equation (2.1), where y is per capita GDP growth rate in the Arab world, and x

represents financial development indicators of the banking sector and the stock market.

z is a vector of control variables that are usually used in the estimation (initial per capita

income, human capital, investment/GDP, inflation rate, government consumption/GDP,

openness of trade/GDP, and exchange rate), and is the error term.

In Equation (2.2), where Y represents per capita GDP, TP (total productivity), FDI

(foreign direct investment), FD (financial development), L (labour), K (capital), and ,

are coefficients of labour and capital respectively.

They find that FDI has a positive impact on economic growth, which depends on local

conditions and absorptive capacities, where financial development is one of the

important capacities. Moreover, it could be easier in the medium term to attract more

FDI if stock market development of these countries was healthier and supplemented by

an active economic policy.

19

On the micro-level, Omet and Mashharawe (2003) focus on the importance of the stock

market in general, and the operational efficiency of the Amman Securities Market

(ASM). The empirical results show large transaction costs may induce corporations to

cross-list their stocks in more liquid and developed markets and thereby hinder domestic

market development.

Table 2.1: Summary of Empirical Studies on Finance and Growth: Arab Countries

Author(s) Empiricalstudy

Sample size andperiod

EstimationMethod

Main Findings

Darrat, 1999 Are financialdeepening andeconomic growthcausality related?Another look atthe evidence

3 countries (SaudiArabia, Turkey, andUnited ArabEmirate), 1964-1993

Granger-Causalitytests within anerror-correctionmodel

For Turkey, expanding and refining thefinancial sector should prove beneficial toeconomic growth. For SaudiArabia andUAE, financial deepening is necessary as acausal factor in economic growth, but theevidence is not as strong.

Al-Tamimi,Al-Awad andCharif, 2001

Finance andgrowth: evidencefrom some Arabcountries

8 Arab countries, anunbalanced paneldata set.

Cointegrationtests, Granger-Causality, andimpulse-responsefunctions

Financial development and real GDPgrowth are strongly linked in the long-run.In contrast, in the short-run, the linkage isweak as Granger-Causality tests and theimpulse-response functions indicate thatcausality between real GDP and financialdevelopment exists only in some cases.

Al-Awad andHarb, 2003

Financialdeepening andeconomic growthin the Middle East

10 countries, 1969-2000

Johansen sCointegration,Granger-Causality, andVarianceDecompositions

In the long-run, financial development andeconomic growth may be related to someextent. However, in the short-run, theevidence of causality is very weak.

Omran andBolbol, 2003

FDI, financialdevelopment, andeconomic growth:evidence fromArab countries

All Arab countries(except, ComorosIsland, Djibouti,Iraq, Palestine, andSomalia), 1975-1999

Ordinary LeastSquares (OLS),and Causalitytests

Arab FDI has a positive impact oneconomic growth. Moreover, it could beeasier in the medium term to attract moreFDI if stock market development of thesecountries was healthier and supplementedby active economic policy.

Omet andMashharawe,2003

Themicrostructure ofthe Jordaniancapital market:electronic tradingand liquidity cost

Amman SecuritiesMarket (AMS), 10companies over theperiod (18 June2000-30 December2001)

OLS, SeeminglyUnrelatedRegression(SUR), and GMM

The results show that the transaction costsin the ASM are quite high. Also, thesecurity price, trading frequency and pricevolatility consistently affect the bid-askspread.

20

2.3 Financial Development and Growth: Cross Country Studies

As described briefly by Fischer (2003), with regard to the relationship between finance

and economic growth, there are two views on the importance of the financial system

during economic development. First, the financial sector does not matter very much, and

that any correlation between finance and economic growth is a result of growth leading

financial development. Robert Lucas (1988) sustains this point, as follows:

I will…be abstracting from all monetary matters, treating all exchange as through it involved

goods-for-goods. In general, I believe that the importance of financial matters is very badly over-

stressed in popular and even much more professional discussion and so am not inclined to be

apologetic for going to the other extreme (Lucas, 1988, p. 6)9.

The second view is that an efficient financial system is important to economic

development. Financial markets promote economic growth by funding entrepreneurs

and, in particular, by channelling capital to the entrepreneurs with high return projects.

In this section we present the empirical research that discusses a link between the

financial system and stock market development, and economic growth using cross-

country analysis. Table 2.2 presents a summary of the main recent empirical evidence on

finance and economic growth.

9As quoted by Fischer (2003); see, for more detail Lucas (1988), "On the Mechanics of Economic

Development", Journal of Monetary Economics, 22, pp. 3-42.

21

2.3.1 Part One: On Economic Growth Led Finance10

This section considers that high economic growth creates demand for certain financial

instruments and thereby financial markets are effectively responding to these demands

and changes (Choong et al., 2003). In this part, the literature supports the view that

financial development follows economic growth (demand-following).

Following this hypothesis, Demetriades and Hussein (1996) argue that financial

development responds to economic growth when real growth has taken place, so that the

expansion of financial institutions is only a result of the need of real economic activities.

The empirical results of Demetriades and Hussein are consistent with the view of

Robinson (1952) that the financial system does not spur economic growth; that is,

financial development simply responds to development in the real sector. Thus, many

influential economists give a very minor, if any, role to the financial system in economic

growth.

In another paper supporting this view, Ram (1999) considers the correlation coefficients

at a simple level for each country based on annual data of 95 countries over the period

1960-1989. He estimates the relationship between financial development as measured

by ratio of liquid liabilities/GDP, and economic growth, measured by the real growth of

per capita GDP, using multiple regressions for the full sample and some selected

countries. The main estimated model is given by:

10 According to this view of demand-following , as the real side of the economy expands, its demand forfinancial services increases, leading to the economic growth of these services (Al-Yousif, 2002, p. 132).

22

0 2 3t t t ty x z= + + + (2.3)

where y denotes annual rates of real GDP growth. x stands for liquid liabilities/GDP. The

vector z represents control variables of labour force, exports and gross domestic

investment/GDP. is an error term.

Ram (1999) concludes that the association between financial development and

economic growth shows considerable variation across countries. Therefore, the

empirical results reported in several recent studies do not encourage one to share the

view that financial development promotes economic growth.

The multiple regression estimates of individual-countries do not indicate a positive link

between financial development and economic growth, and the estimation of cross-

country averaged data is not a reliable basis for parametric inference. On the other hand,

the average individual-country correlation between financial development and economic

growth is in sharp contrast to the cross-country correlation between the same variables.

In addition to this, there is a view that finance in general, and stock markets in

particular, are unimportant. Devereux and Smith (1994) emphasize that greater risk

sharing through internationally integrated stock markets can actually reduce the saving

rate and slow down economic growth. Stern (1989) does not mention the role of the

financial system in economic growth. Mayer (1988) argues that even large stock

markets are unimportant sources of corporate finance. Stiglitz (1985) says that stock

23

market liquidity would not enhance incentives for acquiring information about firms or

exerting corporate governance.

2.3.2 Part Two: On Finance Led Economic Growth11

In contrast, this area of research argues that the existence of well-functioning financial

intermediaries channelling limited resources from surplus units to deficit units would

provide an efficient allocation of resources, thereby promoting economic growth

(Choong et al., 2003). In this part, there are two views regarding the role of the financial

system and stock market development. Firstly, financial intermediation has a positive

effect on economic growth; that is, the supply-leading view. Secondly, the view of bi-

directional causality between financial development and economic growth.

In an important paper, Levine (1991) constructs an endogenous economic growth

model, associated with the work of Romer (1986, 1990) and Lucas (1988) in which a

stock market emerges to allocate risk and explores how the stock market alters

investment incentives in ways that change steady state growth rates. He explained the

role of the stock market in economic growth, as follows:

Stock markets affect growth in two ways. The first involves firm efficiency and depends on the

externality of human capital production. Stock markets increase firm efficiency by eliminating

the premature withdrawal of capital from firms. This accelerates the growth rate of human capital

and per capita output. The second way stock markets can affect growth is to raise the fraction of

11 Al-Yousif (2002) explains the view of supply-leading that financial intermediation contributes toeconomic growth by increasing the size of saving and improving the efficiency of investment.

24

resources devoted to firms. This does not necessarily depend on externalities. By increasing the

liquidity of firm investment, reducing productivity risk, and improving firm efficiency, stock

markets encourage firm investment. This stimulates human capital production and growth .

(Levine, 1991, p. 1453)

King and Levine (1993) provide the starting point in the empirical research on the

relationship between finance and economic growth. They study four financial indicators

(the size of the financial intermediary sector relative to GDP, the importance of banks

relative to the central bank, the percentage of credit allocated to private firms out of total

credit, and the ratio of credit issued to private firms to GDP) and the growth indicators

(real per capita GDP growth, the rate of physical capital accumulation, and the ratio of

domestic investment to GDP).

The empirical results of this paper show that the indicators of financial development are

strongly and robustly correlated with economic growth indicators, using cross-country

analyses for 77 countries over the period 1960-1989.

Further evidence of the positive effect of stock market development in the economy

came from the study of Atje and Jovanovic (1993). They use cross-sectional data of 39

countries over the period 1980-1988. The empirical results provide strong evidence that

stock market development has a significant effect on subsequent economic growth.

The estimated cross-section model is given as:

0 1 2i i i iy x z= + + + (2.4)

25

where y is the growth rate of per capita GDP, x is the product of investment and value

traded of stock market activity. The variable z represents the investment-output ratio and

the growth rate of the labour force.

De Gregorio and Guidotti (1995) assess the effect of financial development as measured

by the banking sector indicator of the ratio of bank credit provided to the private sector

over GDP on economic growth. They explore this relationship for two different data

sets, a cross-country growth regression for 98 countries over the period 1960-1985, and

then on a panel data set for 12 Latin American countries from 1950 to 1985.

The empirical results find that there is a strong positive effect of financial development

on economic growth in the middle and low-income countries, and a weak relationship

for high-income countries. On the other hand, they suggest that the effect of financial

intermediation is due mainly to its impact on the efficiency of investment, rather than its

level.

In two empirical studies of stock market development and economic growth, Levine and

Zervos (1996, 1998a) attempt to test this relationship. In the first paper, Levine and

Zervos (1996) provide empirical evidence on the major theoretical debates regarding the

linkages between stock market development and long-run economic growth using cross-

sectional data on 41 countries from 1976 to 1993.

26

The results find that stock market liquidity is positively and significantly correlated with

economic growth, even after controlling for economic and political factors. They use a

model that includes an aggregate index of overall stock market development constructed

by Demirguc-Kunt and Levine (1996) as follows:

1 2i i i iy x z= + + (2.5)

where the dependent variable y is the real growth rate of per capita GDP. The variable x

represents the overall stock market development, and equals the average values of

market capitalization/GDP, value traded/GDP, turnover ratio, and stock market

integration. The vector z is a set of control variables, includes the logarithm of initial

real per capita GDP, the logarithm of the initial secondary school enrolment rate, the

number of revolutions and coups, the ratio of government consumption

expenditure/GDP, the inflation rate, and the black market exchange rate premium12. is

an error term.

Furthermore, Levine and Zervos (1998a) use cross-country regressions on 47 countries

over the period 1976-1993 in order to test the relationship between several indicators of

banking and stock market development and three different economic growth indicators

(per capita GDP growth, capital accumulation, and productivity). The empirical results

illustrate that stock market liquidity and banking development are positively and

significantly correlated with all economic growth indicators when entered together in

12Following the recent economic growth literature, Levine and Zervos (1996) include the logarithm of

initial level of real per capita GDP and the logarithm of the initial secondary school enrolment rate due tothe important link between long-run growth and the initial per capita levels of physical and human capital.

27

regressions. These results are consistent with the views that financial markets and

institutions provide important and different financial services for long-run economic

growth.

Filer, Hanousek and Campos (1999) find that there is a positive and significant causal

relationship between stock market development and economic growth, particularly for

the low income and less developed countries. They use Granger-Causality tests on an

unbalanced panel data set for 64 countries over the period 1985-1997. The two basic

estimated equations were the following:

1 2

0

1 1

k k

t i t i i t i t

i i

y y x− −= =

= + + +∑ ∑ (2.6)

3 4

0

1 1

k k

t i t i i t i t

i i

x y x− −= =

= + + +∑ ∑ (2.7)

where y denotes the growth rate of per capita GDP, x represents an indicator of stock

market development (market capitalization/GDP, turnover velocity, and the change in

the number of domestic shares listed), and the subscripts t and t i− represent the current

and lagged values. The error terms are and .

Agarwal (2001) looks at the impact of stock market development on economic growth

using a sample of 9 African countries over the period 1992-1997. He uses a simple

correlation test on stock market indicators (market capitalization/GDP, total value

traded/GDP, and turnover ratio), and macroeconomic variables (economic growth,

investment as a proportion of GDP, and primary school enrolment). The paper does not

28

conduct any regression analysis to establish this causality, mainly due to the lack of data

since there are not sufficient numbers of stock markets in African countries.

The correlation results find that the stock market capitalization/GDP and value

traded/GDP are correlated to investment. Due to the direct correlation between

investment and economic growth, stock market development is correlated with

investment and in turn with economic growth.

Bekaert, Harvey, and Lundblad (2001) explore the relationship between financial stock

market liberalization and economic growth as measured by real per capita GDP growth,

using cross-sectional and time-series data for 30 emerging markets during 1980-1997.

The empirical results show a positive and significant effect of financial liberalization

and economic growth: average real economic growth increases between 1-2% per

annum after a financial liberalization. On the other hand, the estimated relationship

between financial liberalization and economic growth after controlling for a

comprehensive set of macroeconomic and financial variables (such as banking and stock

market development) is generally unaffected and remains significant.

Leahy, Schich, Wehinger, Pelgrin and Thorgeirsson (2001) support the view that

financial development is important to boost economic growth through its relationship

with investment by using three different indicators of financial development (liquid

liabilities, private credit provided to the private sector, and stock market capitalization).

29

Leahy et al. (2001) apply an unbalanced panel data set for 19 OECD countries over the

period 1970-1997, and four different estimation techniques (Dynamic Fixed Effects,

Mean Group estimator, Pooled Mean Group estimator, and Static Fixed Effects

estimator)13. The empirical results appear to be strongest for stock market capitalization,

although the contribution of private credit issued by deposit money banks is significant.

The unrestricted model specification is as follows:

, 0 1 , 1 2 , 3 , 1 4 , 5 , 1

6 , 1 7 , 1 ,

i t i i t i i t i i t i i t i i t

i i t i i t i t

i i Y Y x x

r r

− − −

− −

= + + + + ++ +

(2.8)

where the dependent variable, i is the real gross investment. Y is the real GDP and x

represents financial development measures including liquid liabilities, stock market

capitalization, and private credit. Also r represents interest rate and is an error term.

The subscripts t and i represents time and country, respectively.

Caporale, Howells and Soliman (2003) study the causal linkages between stock market

development, investment and economic growth for four developing countries (Chile,

Korea, Malaysia, and Philippines), using cross-sectional analysis for quarterly data over

the period 1977Q1 to 1998Q4.

13Leahy et al. (2001) give some explanation of the above mentioned estimation methods. The Mean

Group estimator consists of estimating N separate regressions and calculating the coefficients asunweighted means of the estimated coefficient for the individual countries. The Dynamic Fixed Effects

estimator imposes equality on all slope coefficients and error variances, allowing only the intercepts todiffer across countries. The Pooled Mean Group estimator defined as an intermediate procedure betweenthese extreme approaches because it involves both pooling and averaging.

30

In their empirical analysis they measure the level of investment by the ratio of gross

fixed capital formation to nominal GDP, investment productivity proxied by the ratio of

real change of GDP to real level of total investment, and two measures of stock market

development (market capitalization to GDP, and total value traded to GDP).

The evidence suggests that stock market development affects long-run economic growth

through its impact on investment productivity; that is, the results are consistent with the

findings by Levine and Zervos (1996) that stock markets can give a big boost to

economic growth.

Calderon and Liu (2003) study the possible directions of causality between financial

development and economic growth using two measures of financial development (the

ratio of broad money to GDP, and the ratio of credits provided by financial

intermediaries to private sector), and economic growth measured by the real per capita

GDP growth rate. They apply a panel data set for 109 industrial and developing

countries over the period 1960-1994.

The empirical results provide that financial development generally leads to economic

growth in all countries, and bi-directional linkages between financial development and

economic growth coexist in all countries. Furthermore, they find that financial

deepening contributes more to the causal relationship in the developing countries than in

the industrial economies due to the potential opportunity for financial and economic

improvement in developing countries.

31

Table 2.2: Summary of Empirical Studies on Finance and Economic Growth

Author(s) Empirical study Sample size andperiod

EstimationMethod

Main Findings

King andLevine, 1993

Finance andgrowth:Schumpeter mightbe right

77 countries,1960-1989

Cross-countryanalysis

Financial development is strongly androbustly correlated with economicgrowth. Therefore, Schumpeter mighthave been right about the importance offinance to economic growth.

Atje andJovanovic,1993

Stock markets anddevelopment



There are substantial effects of stockmarket development on the level and/orthe growth rate of economic activity.

De Gregorioand Guidotti,1995

Financialdevelopment andeconomic growth

98 countries,1960-1985.Panel data for 12Latin Americacountries, 1950-1985

Cross-section andpanel dataanalysis

Financial development is positivelycorrelated with economic growth in alarge cross-country sample, but itsimpact changes across countries, and isnegative in a panel data for LatinAmerica.

Levine andZervos, 1998a

Stock markets,banks, andeconomic growth


Cross-countryregression andInstrumentalVariables method

Stock market liquidity and bankingdevelopment are positively andsignificantly correlated with economicgrowth.

Filer,Hanousek andCampos, 1999

Do stock marketspromote economicgrowth?

64 countries, anunbalanced paneldata set, 1985-1997

Granger-Causality tests

A positive causal relationship fromstock market development to economicgrowth for the middle and low-incomecountries, but not for high-incomecountries.

Garcia andLiu, 1999

Macroeconomicdeterminants ofstock marketdevelopment


Panel dataanalysis

Financial development-as measured bydomestic credit allocated to privatesector over GDP, value traded/GDP,real income, and saving rate-has apositive and significant effect on stockmarket capitalization.

Beck, Levineand Loayza,2000

Finance and thesources of growth


Cross-sectionanalysis, andGMM dynamicpanel estimator

A significant and positive causal impactof financial development on economicgrowth and productivity growth. Incontrast, the results are ambiguous forbanking development with privatesaving rate and physical capitalaccumulation.

Levine,Loayza andBeck, 2000

Financialintermediation andgrowth: causalityand causes


Cross-sectionanalysis, GMMdifference, andsystem estimators

Financial intermediaries exert astatistically and economically largeimpact on economic growth. Inaddition, countries with legal andaccounting reforms can boost financialdevelopment and thereby accelerateeconomic growth.

Rousseau andWachtel, 2000

Equity marketsand growth: cross-country evidenceon timing andoutcomes


Cross-sectionalregression, anddynamic panelvectorautoregression(VAR)

The stock market size and liquidity-asmeasured by market capitalization andvalue traded-are very plausiblechannels, and highlight the importanceof liquidity to stimulate marketdevelopment and economic growth.

32

Author(s) Empirical study Sample sizeand period

EstimationMethod

Main Findings

Agarwal,2001

Stock marketdevelopment andeconomic growth:preliminary evidencefrom African countries


Correlation test Stock market development-as measuredby market capitalization and valuetraded- is positively correlated andsignificant with economic growth.

Bekaert,Harvey andLundblad,2001

Emerging equity marketsand economicdevelopment


Cross-sectionaland time-seriesanalysis

The impact of financial liberalizationon economic growth is robust aftercontrolling for macroeconomic andfinancial variables (such as banking andstock market development).

Leahy et al.,2001

Contributions offinancial systems togrowth in OECDcountries


Cross-sectionalanalysis

Financial development is important toboost economic growth through itsrelationship with investment.

Arestis,Demetriadesand Luintel,2001

Financial developmentand economic growth:the role of stock markets

5 countries,quarterly data atdifferent times,1968Q2-1998Q1

Unit root testsand vectorautoregressionanalysis

Banking sector and stock marketdevelopment may be able to promotelong-run economic growth, but theeffect of banks is more important thanstock markets.

Al-Yousif,2002

Financial developmentand economic growth:another look at theevidence fromdeveloping countries


Unit root test,and Granger-Causality testsfor times seriesand panel dataset

There is a bi-directional causalitybetween two indicators of financialdevelopment (the ratio of currency tonarrow money stock, and the ratio ofbroad money stock to nominal GDP)and real per capita GDP.

Deidda andFattouh,2002

Non-linearity betweenfinance and growth

119 countries,1960-1989


The relationship between financialdevelopment and economic growth ishighly significant for the high-incomecountries, but not for the low-incomecountries.

Caporale,Howells andSoliman,2003

Endogenous growthmodels and stock marketdevelopment: evidencefrom four countries

4 countries,quarterly dataover the period1977Q1-1998Q4

Unit root, andvectorautoregression

Stock market development (marketcapitalization to GDP, and total valuetraded to GDP) affects long-runeconomic growth through its impact oninvestment productivity.

Minier, 2003 Are small stock marketsdifferent?


Regression treeanalysis

Financial development has a strong andpositive relationship with economicgrowth for countries with highlydeveloped financial sectors, and doesnot exist at lower levels of financialdevelopment.

Calderon andLiu, 2003

The direction ofcausality betweenfinancial developmentand economic growth

109 countries,1960-1994

Granger-Causality,(likelihoodratios)

Financial development stimulateseconomic growth, and simultaneously,economic growth propels financialdevelopment in the developingcountries more than in the industrialeconomies.

Beck andLevine, 2004

Stock markets, banks,and growth: panelevidence


Panel dataanalysis, andGMMestimators

Stock markets and banks are importantto economic growth, and always enterjointly significant in all GMMestimators.

33

2.4 A Critical Evaluation of the Empirical Studies

The literature provides extensive empirical evidence and indicates different views on the

existence of a relationship between financial development and economic growth, with

different aspects of this link at both theoretical and empirical levels. The majority of the

empirical studies were conducted based on macroeconomic level (cross-sectional, time

series and panel data) and microeconomic level (firm and industry data). These studies

mainly differ in data coverage in terms of the estimation methods, the choice of the

explanatory variables, and the sample of countries and time periods.

To evaluate the main empirical literature that consider whether economic growth leads

to financial development, or financial development leads to economic growth in the

previous section, we present the following models that describe this causal relationship:

Model one: ( , )y f x z= (2.9)

Model two: ( , )x g y z= (2.10)

where y is the growth rate of per capita GDP. The vector x equals the most common

measures of financial development and the stock market, including the ratio of liquid

liabilities/GDP, the ratio of broad money (M2)/GDP, domestic credit provided to private

sector/GDP, stock market capitalization/GDP, value traded/GDP, and turnover ratio for

stock market liquidity. On the other hand, z represents a set of control variables

encompassing the main determinants of economic growth (e.g. investment/GDP,

34

inflation rate, government consumption/GDP, trade openness/GDP, and employment

growth rate).

Following the first model (2.9), King and Levine (1993) and Atje and Jovanovic (1993)

examine the direct effect of financial development and the stock market on economic

growth using cross-sectional data with different samples of 77 and 39 countries and time

periods 1960-1989 and 1980-1988, respectively. They provide evidence that the banking

sector and stock market development are important and lead to economic growth.

In the second model (2.10), Al-Yousif (2002) applies Granger-Causality tests for time

series on a panel data set for 30 countries over the period 1970-1999. He finds that there

is a bi-directional causality between financial development and economic growth.

Arestis and Demetriades (1997) provide the important differences between cross-

country and time series analysis in the causality relationship between financial

development and economic growth for two individual countries (Germany and the

United States) over the period 1979Q1-1991Q4. They find that the causality linkage is

uni-directional from financial development to real GDP for Germany, while in the case

of the United States the evidence supports the reverse causality that real GDP affects

stock market development and the banking system.

In general, the methods used at the aggregate level are quite uniform. The main tools

applied are cross-country and time series regressions, in which financial variables of a

large set of countries together with a set of macroeconomic determinants are regressed

on economic growth. The dependent variable mainly consists of the real GDP or real per

35

capita GDP growth rate. Stock market variables often display indicators of stock market

capitalization, value traded and turnover ratio all expressed in relation to GDP. Control

variables are selected from the economic theory and a large body of literature on growth

determinants (Thiel, 2001).

Most of the empirical studies deal either with cross-sectional studies based on country

averages over time or on five year averaged panel data. The argument for using five-

year averaged data presented by Beck and Levine (2004) is to avoid high frequency

variation in variables due to the business cycle. Arai, Kinnwall and Thoursie (2004)

consider that the costs of aggregation may appear in terms of losing information (within

period variation), restricting the possibility of testing the stability of estimated

parameters over time, and limiting the possibilities of studying the dynamic structure of

the problem by using lagged variables.

In addition, there are no common measures of financial development either for the

banking system or stock market development performed in the previous studies; that is,

the first point facing the literature is how to measure stock market development. The

most commonly used measures of stock market size and activity are market

capitalization and value traded both divided by GDP, respectively. Recently, some of the

empirical studies include turnover ratio as the measure of stock market liquidity. In our

econometric analysis, we consider the merits and shortcomings of these measures.

In summary, this study differs in three key respects from the existing empirical

literature. Firstly, it provides a theoretical consideration Theoretical Framework of the

36

endogenous economic growth model and the effects of stock market development on

saving and investment. It discusses the concept of economic reform and its measures.

Secondly, the study presents a quantitative assessment on a macroeconomic level and

analyses stock market development indicators for Arab countries at individual country

level. Then, the study uses cross-country analysis for the other two groups of the East

Asia-Pacific and the G-7 economies.

The third difference between this study and the existing literature is that it performs

three different estimation methods on panel data sets, Ordinary Least Squares (OLS),

Instrumental Variables (Two Stage Least Squares, 2SLS) and the Generalized Method of

Moments (GMM) after controlling for the main determinants of economic growth. The

estimation results are compared with time series approach of an individual-country

analysis. These estimators allow us to control sources of bias in the OLS results,

eliminating the unobserved country effects and using lagged instruments to correct for

the potential simultaneity bias.

2.5 Microeconomic View: Industry and Firm Level

In the previous section, the literature review focused on the potential effects of financial

development and the stock market on economic growth that used cross-country analysis.

This section reviews the empirical studies that analyse the effects of financial

development along with the stock market mechanism with reference to microeconomic

data on an industry and firm-level. The data examined whether the corporate sector

37

(firms and industries), which relies more heavily on external financing, will grow faster

in economies with more developed financial markets.

2.5.1 Industry-Level Studies

Rajan and Zingales (1998) use industry-level data across a large number of countries to

clarify the channels through which finance may affect economic growth, as well as to

test the causality linkages between financial development and economic growth. In this

view, the financial system helps reduce the information and transaction costs associated

with the external financing of investment. They presume that if finance affects economic

growth, financial market development should benefit, disproportionately, industries that

are highly dependent on external finance. Thus, industries with a greater need for

external finance should grow relatively faster in well-developed financial markets than

in less-developed ones.

On the econometrics methodology, Rajan and Zingales (1998) estimate a model that

controls for country and industry fixed effects, and for an interaction between country

and industry characteristics, in order to avoid the problem of omitted explanatory

variables bias or model specification. They use data for U.S. firms as a benchmark for

the external finance demand of industries in other countries, under the assumption that

financial markets in the United States are among the most developed in the world so as

to provide each industry with its desired level of external finance. They apply annual

data on 36 industries across 41 countries over the period 1980-1990.

38

The general model of Rajan and Zingales (p. 563) is given by:

, 1 2

3

4

( ) ( )

( ' )

(

j kGrowth constant Country Indicators Industry Indicators

Industry j s share of manufacturing in country k in 1980

External Dependence of Ind

= + +

++

,) j k

ustry j Financial Development

of country k

∗+

(2.11)

where the dependent variable, Growthj,k is the average growth rate of value added in

industry j and country k over the period 1980-1990. The characteristics of each country

and industry are represented by indicators, one for each country and each industry. The

variable that varies with both industry and country, that is, industry j s share in country k

in 1980. The last explanatory variable indicates the interaction between industry j s

dependence on external financing and financial market development in country k, which

is the primary variable in this paper.

The empirical results provide three main findings (Rajan and Zingales, 1998, p. 584).

First, financial development as measured by the ratio of domestic credit plus stock

market capitalization to GDP, and the accounting standards in a country to reflect the

potential for obtaining finance, rather than the actual finance, has a substantial

supportive influence on the rate of economic growth. Second, financial market

constraints have an impact on investment and economic growth. Finally, the effect of

interaction between industry external finance dependence and the domestic degree of

financial development is positive and significant at the one percent level; that is, the

existence of a well-developed market in a certain country represents a source of

comparative advantage for that country in industries that are more dependent on external

finance.

39

In further evidence on the microeconomic level, Cetorelli and Gambera (2001) present a

similar analysis to Rajan and Zingales (1998) with the modification that they focus on

the importance of banking market structure, rather than size, for industrial sectors that

rely on external sources of finance, and thereby promote economic growth. In other

words, they test whether, by and large, industries grow more or less if they are in

countries with a more concentrated banking sector (p. 619).

In the econometric model, the dependent variable is the average growth rate of value

added for each industrial sector in each country over the period 1980-1990. The main

regressor is the concentration ratio of the banking sector for each country, calculated as

the sum of market shares (measured in total assets) of the three largest banks. The other

set of control variables represents the level of bank development (the ratio of domestic

credit to private sector over GDP), the logarithm of per capita GDP in 1980 (for the

convergence effect), stock market capitalization over GDP in 1980 (as an alternative

source of external finance), the index of accounting standards, and the level of human

capital accumulation as measured by the average years of schooling.

However, they apply three model specifications to test the hypothesis whether bank

concentration affects positively/negatively economic growth14. In the basic model, they

test the effect of bank concentration on industrial growth, regardless of specific industry

characteristics.

14Indeed, if bank concentration simply results in lower credit availability, then growth should be slower

in countries with a more concentrated banking market. On the other hand, if the market power associatedwith bank concentration generates positive effects by enhancing the formation of lending relationships,then growth should be faster in countries with a concentrated banking sector (Cetorelli and Gambera,2001, p. 619)

40

In the extended models, they examine the differential effect of bank concentration

across industries by adding two control variables for the interaction between the level of

external financial dependence in industry j and both of, firstly, bank concentration, and

secondly, the level of bank development in country k, respectively. Moreover, they use

the data set of Rajan and Zingales (1998) on both cross-industry and cross-country

characteristics, on 36 industries across 41 countries over the period 1980-1990.

The estimation results obtained with the basic and extended models show that financial

development as measured by the size of the banking sector has a positive and significant

effect on long-run economic growth. Of the primary variables of the study, bank

concentration has a negative effect on growth across industries.

Due to the interaction between the level of external financial dependence and the size of

stock market capitalization over GDP as a measure of the relative importance of

alternative sources of external finance, the estimated coefficient is positive but

insignificant.

Carlin and Mayer (2003) analyse the effect of the financial system structure and

industrial sector characteristics, on economic growth and investment for industries in

different countries. They apply cross-sectional regressions on 14 OECD countries and

27 industries over the period 1970-1995. They use the explanatory variables of the

interaction between a set of three country structural variables, (information disclosure,

bank concentration, and ownership concentration), with industry characteristics

41

variables15

, represented by the dependence of industries on equity markets, banks, and

investment by other stakeholders.

The paper results find that the structure of financial systems and industry characteristics

has a positive and significant effect on industrial growth in advanced economies,

particularly for industrial sectors that are dependent on equity finance. In contrast,

industries that are bank-finance dependent have a positive relationship with industrial

growth in the lower per capita GDP countries.

2.5.2 Firm-Level Studies

One obvious advantage of using firm-level data is that the structure of the financial

system can be considered exogenous with respect to the performance of individual

firms, especially if data on small and medium-sized firms are used (Giannetti et al.,

2002, p. 23).

Demirguc-Kunt and Maksimovic (1996, 1998) compare the financial policies of firms in

different countries by investigating whether the underdevelopment of legal and financial

systems prevents firms from investing in profitable growth opportunities. They use

15They use the measures of industry external finance dependence on equity market in the United States,

on bank loans in Japan, and on skills in Germany, due to the assumptions of (1) the US equity market isthe most highly developed and liberal financial market in the world, (2) Japan has the highest ratio ofbank credit/GDP in OECD sample in the study, and (3) Germany has a highest level of investment inskills and training (Carlin and Mayer, 2003, p. 204).

42

microeconomic firm-level data in 30 developed and developing countries over the

period 1981-199116.

In contrast to Rajan and Zingales (1998), they examine the specific characteristics of

externally financed firms in each country, and how the differences in legal and financial

systems that is associated with long-term financing could affect firm growth. The

hypothesis of the study as advanced by King and Levine (1993) and Levine and Zervos

(1998a) is that the degree to which financial markets and intermediaries are developed is

a determinant of economic growth.

The model framework for both tests is represented by two equations, Equation (2.12) for

firms growing above predicted rates and their characteristics in each country, and

Equation (2.13) using cross-country regression to estimate the excess growth of the

firms and external financing. The equations are given by (Demirguc-Kunt and

Maksimovic, 1998, pp. 2117-2130) as follows:

( , ) 1 , 2 ,

3 , 4 , 5 ,

6 , 1 7 , 1 ,

/ /

/ /

/ /

firm i t i t i t

i t i t i t

i t i t i t

Excess Growth NFA TA DIV TA

PROFIT TA GDP NS NFA

INV TA LTD TA− −

= + +

+ + ++ + +

(2.12)

where the dependent variable, Excess Growth for each firm, is the proportion of years in

which its growth rate in the sample period exceeds its predicted growth rate. For firm

characteristics in each country, they use the ratio of net fixed assets to total assets

16Demirguc-Kunt and Maksimovic (1998) use the firm-level data of financial statements for the largest

publicly traded firms in each country, using to the Global Vantage database for developed countries overthe period 1983-1991, and from IFC s Corporate Finance database for developing countries between1980-1988.

43

(NFA/TA), dividends over total assets (DIV/TA), the income before interest and taxes

over total assets (PROFIT), the total assets of the firm divided by the GDP of the

country (TA/GDP), the ratio of net sales to net fixed assets (NS/NFA), and the lags of

the ratios of total investment and long-term debt, each divided by total assets (INV/TA)

and (LTD/TA), respectively.

( , ) 1 , 2 , 3 ,

4 , 5 , 6 ,

7

/

/ &

.

country i t i t i t i t

i t i t i t

Excess Growth MCAP GDP TOR INFLATION

BANK GDP LAW ORDER GROWTH

GOV SUB

= + + +

+ + ++ , -1 8 , -1 9 ,

10 , 11 , ,

/ / /i t i t i t

i t i t i t

S GDP NFA TA GDP CAP

PERLTD PEREQ

+ +

+ + +

(2.13)

The dependent variable, Excess Growth for each country, is the proportion of firms in

each country that grow faster than their predicted growth rate. For a country s financial

system and legal variables, they apply two indicators of stock market development

(market capitalization over GDP, and turnover ratio), Inflation rate, and the domestic

assets of deposit banks over GDP (BANK/GDP). LAW & ORDER measures the

efficiency of the state in enforcing property rights within each country, while GROWTH

is the real per capita GDP growth rate. The ratio of government subsidies to GDP

measures the government intervention in the economy (GOV. SUB/GDP). Net fixed

assets over total assets are represented as (NFA/TA), while real GDP per capita is

represented as (GDP/CAP). Finally, PERLTD and PEREQ are the proportions of firms

investment financed by long-term debt and equity, respectively17.

17The values of the dependent variable (Excess Growth for each country), PERLTD and PEREQ are

averaged over the period 1986-1991, whereas all other variables are averaged over the 1980-1985 to theextent available.

44

The empirical results of equation (2.12) provide evidence that firms in countries with an

active stock market, as measured by turnover ratio, and better legal system are able to

obtain external long-term finance and equity, and thereby grow faster at rates exceeding

the predicted growth rate in each country.

On the other hand, the estimation of equation (2.13) finds that financial development

(measured by domestic assets of deposit banks over GDP) and stock market liquidity are

positively correlated with the excess growth of firms. Moreover, the size of the stock

market as measured by market capitalization over GDP is not as important in mobilizing

financing, as is the level of stock market liquidity.

On the signalling role of the stock market in investment, Samuel (2001) focuses on the

relative roles of managerial and market perceptions in capital expenditure decisions at

the firm level; that is, do managers base their investment decisions by their own

valuation (managerial perception) of fundamentals or market valuation (market

perception)? He uses a panel data set of 603 manufacturing firms in the United States

over the period 1972-1990.

In the empirical analysis, he applies q-theory of investment that provides a direct link

between the stock market and investment decisions at the firm level. The estimation

results present evidence that managerial valuation (as measured by cash flows, the level

of sales, sales growth rate, and dividends) is more important than market valuation (as

measured by the ratio of the firm s stock market value to its replacement cost of capital,

and share price) for capital expenditure decisions at the firm level. That is, the

45

manager s own perception of fundamentals facing a firm has a strong effect and a

primary importance to investment decisions at the firm level, while the stock market

signals and activity are only of secondary importance.

2.6 Finance and Growth: Evidence From Individual Country Analysis

While the last two sections provide an overview and evaluation on the macroeconomic

level of cross-country analysis and microeconomic level of cross-sectional industry and

firm data studies, it is important to review the previous studies that assessed the effect of

financial development and the stock market based on an individual-country analysis and

time series approach.

Arestis and Demetriades (1997) compare the empirical evidence of cross-country and

time-series analysis on the relationship between financial development and economic

growth. Using the cross-sectional database of King and Levine (1993), they find that the

contemporaneous correlation between financial development and economic growth is

much stronger than the correlation between lagged financial development and growth

(pp. 784-785). Then, they applied a time-series approach on quarterly data for two

countries (Germany and the United States) over the period 1979Q1-1991Q4.

In this approach of time-series analysis, they find important differences in the causality

relationship between financial development and economic growth for the individual two

countries in the study. They use the same three variables for each country, namely the

logarithms of real per capita GDP, stock market capitalization as a proportion of GDP,

46

and stock market volatility index. The fourth variable for Germany is the logarithm of

ratio of broad money stock (M2)/nominal GDP, and for the United States is the

logarithm of the ratio of domestic bank credit/nominal GDP.

The results of unit root and cointegration analysis for Germany point out that the

causality linkage is a uni-directional one from financial development to real GDP, while

in the case of the United States the evidence supports the reverse causality that real GDP

affects stock market development and the banking system. Therefore, Arestis and

Demetriades (1997, p. 790) suggest that a time-series analysis may yield deeper insights

into the relationship between financial development and real output than cross-country

regressions.

Hansson and Jonung (1997) study the long-run relationship between financial

development and economic growth in Sweden by using long time-series data over the

period 1834-1991. They use cointegration analysis techniques that include both non-

stationarity and common long-run trends among the variables in the study, and divide

the sample into three sub-samples (pre-1890, 1890-1939, and post-World War II) to

establish whether or not the cointegration relationship is stable over time.

The main findings of this paper are different with respect to the sample period and

control variables that are included in the analysis. In the simple model of economic

growth and financial development, there is a unique cointegration of the Swedish

financial system s impact on economic growth for all the sample periods.

47

After controlling for the effect of investment, they find that financial development does

not play an independent role separate from investment in generating economic growth

due to the hypothesis that the financial sector merely serves as a proxy for investment

when the relationship with economic growth is examined. However, the largest impact

of financial development on economic growth in Sweden appears particularly in the

period 1890-1939.

Mazur and Alexander (2001) examine the relationship between financial development

and economic growth in New Zealand, using time-series data over the period 1970-

1996. For economic growth, they employ two measures of real per capita GDP growth

rate and the level of real per capita GDP. The stock market size is measured by the ratio

of Capital Index/GDP (as a proxy of market capitalization) and the two measures of

liquidity are turnover ratio and value traded.

The paper presents two tests of Engle-Granger cointegration analysis and Ordinary

Least Squares (OLS) on the relationship of the banking sector and stock market

development to economic growth, as well as financial development and saving (as

measured by the ratio of gross private saving/GDP). The estimation results find that

banking sector development has a positive effect and cointegrating relationship with the

level of output, but no significant effect on output growth and saving. Conversely, stock

market development has no effect on the level of output but there is a cointegrating

relationship and ambiguous effect on output growth and saving.

48

In another paper of time-series evaluation, Arestis, Demetriades, and Luintel (2001)

examine the relationship between several measures of financial development (such as

the ratio of stock market capitalization to GDP, the ratio of domestic bank credit to

nominal GDP, and the stock market volatility) and economic growth measured by real

GDP. They apply time series methods (unit root tests and vector autoregression analysis)

on quarterly data for five developed economies (France 1974Q1 1998Q1, Germany

1973Q1-1997Q4, Japan 1974Q2-1998Q1, United Kingdom 1968Q2-1997Q4, and the

United States 1972Q2-1998Q1).

The empirical results find that both the banking sector and stock market development

may be able to promote long-run economic growth, but the effect of banks is more

important than stock markets. On the other hand, Arestis et al. (2001, p. 16) suggest that

the effects of stock market development on economic growth may have been

exaggerated by previous studies that utilize cross-country growth regressions.

2.7 Recent Evidence on Economic Reform and Economic Growth

This section reviews the recent empirical studies on economic reform as an important

prerequisite for economic growth. Economic reform is the second primary variable in

our study. The study will focus on the importance of stock market development with

respect to economic and structural environment in the economic performance of the

Arab region, and precede a comparative analysis with the rest of the world. Previous

studies in this regard have argued the main measures of economic reform in terms of its

effect on the allocation of resources, and then economic growth.

49

Gwartney, Lawson and Holcombe (1999) consider the effect of economic freedom,

political freedom and economic environment on economic growth, using a cross-

sectional data set of 82 countries over the period 1980-1995. The dependent variable of

economic growth is measured by real per capita GDP growth rate, and an independent

variable of Economic Freedom Network Index18 that measures the four broad areas of

money and inflation, structure of the economy, takings and discriminatory taxation, and

international trade. Political freedom across countries was measured using the Freedom

House country rating of political and civil liberties index19

. On the regression

estimation, they control for economic environment as represented by the two variables

of investment and human capital accumulation.

The empirical results find that the economic freedom index has a strong and robust

relationship with economic growth, even after controlling for investment and human

capital variables. On the other hand, economic freedom has a more important

explanatory effect than political freedom and civil liberties on economic growth.

In an interesting paper on Arab capital market development and institutions, Eltony and

Babiker (2004) analyse the effect of the macroeconomic environment, the level of

globalisation, and development of good institutions on the efficiency of Arab stock

18 The index of Economic Freedom Network (EFN) is based on the main elements of economic freedomand contains 17 components for more than 100 countries, each component having a rating in the range 0-10, where the higher ratings were indicative of institutions and/or policies more consistent with economicfreedom (Gwartney et al., 1999).

19It is an annual assessment of political and civil liberties, and representing the state freedom in all

countries. Each country is assigned a rating of 1-7, where 1 is the highest degree of freedom and 7 is thelowest degree of freedom.

50

markets. They use the price adjustment coefficient of the weekly stock returns for these

markets over the period 1994-2002.

Investigating the hypothesis that institutional environment development has a significant

impact on the operation and efficiency of stock market, they employ the International

Country Risk Guide (ICRG) as an index of institutions quality. The empirical results

find that small increases in the quality of institutions leads to large improvements in

stock market efficiency.

2.8 Conclusion

The objective of this chapter was to point out the contradictory views regarding the

effect of financial development and the stock market on economic growth with

reference to the empirical analysis approach of cross-sectional and time series data on

both a macroeconomic and microeconomic level. It also reviewed some of the empirical

studies on measures of economic reform and economic freedom and their impact on

economic growth.

In the case of Arab countries, the literature finds that the effect of financial development

is very weak even though it may exist at some levels in the economy. On the other hand,

most of these studies focused on the causal linkages between financial development and

economic growth using Granger-Causality and Cointegration tests on the banking sector

indicators and suggested further research on capital markets as a dynamic factor in

economic growth.

51

Additionally, this chapter considered a comprehensive work of literature that examined

the effects of finance and stock market development on economic growth. It showed

that there are three views on the importance of financial development and the stock

market with economic growth. The First view, demand-following, where high growth

may create demand for certain financial services and the financial markets effectively

respond to this demand. The Second view, supply-following, in which the financial

sector and intermediaries contribute to economic growth by increasing the size of saving

and improving the efficiency of investment. And the Third view, that stock market is an

unimportant source of corporate finance and does not enhance economic growth.

The empirical literature, using cross-country and time series analysis, finds that the

relationship between finance, stock market development and economic growth is

ambiguous; it may be positive, negative, or no effect. The positive effect is due to the

banks and stock markets channelling the financial resources from savers to investors, as

well as increasing the liquidity of firm investment, which would provide efficient

allocation of resources and thereby stimulates economic growth.

On the other hand, several previous studies do not support the proposed effect that

financial development promotes economic growth, due to the econometric differences in

model specification of cross-country and individual country analysis.

In summary, the literature review in this chapter indicating there is no clear evidence on

the relationship between stock market development and economic growth. This study, in

52

contrast, will provide further evidence on this relationship by introducing sophisticated

econometric methods on panel data and a comparative analysis of stock market

development at a global level with different rates of economic growth.

53

Chapter Three

3. CHAPTER THREE: THEORETICAL CONSIDERATION OF STOCK MARKET

DEVELOPMENT, ECONOMIC REFORM AND ECONOMIC GROWTH

3.1 Introduction

In line with the main theoretical and empirical literature on financial development, stock

market and economic growth, this chapter outlines a comprehensive work of theoretical

consideration and presents a general framework on the possible effects of stock market

development on saving and investment, and thereby economic growth, with reference to

the neoclassical and endogenous economic growth models.

However, the broadest division of the financial system is between either lenders or

borrowers or financial intermediaries (banks, insurance companies, and pension funds)

and markets (bonds and stock markets). A large part of an economy s saving is

intermediated towards productive investment through financial intermediaries and stock

markets, providing a set of choices with differing risk and return characteristics, and

helping investors find the financing they need. Since the rate of capital accumulation is

one of the fundamental determinants in long-run economic growth, an efficient financial

system is essential for an economy to boost the growth rate (Garcia and Liu, 1999).

In carrying out their functions, financial intermediaries reduce transaction costs for

savers and investors and help reduce problems of asymmetric information that are

54

inherent in the relationship between investors and entrepreneurs. The development of

sophisticated derivative instruments can improve the allocation of risk in the economy,

and increase the efficiency of the saving-investment process (Fischer, 2003).

As part of the financial system, stock market development plays an important role in

economic growth. In principle, a well-developed stock market is supposed to increase

saving and efficiently allocate financial capital to productive real investment in the

corporate sector, which leads to an increase in the rate of economic growth. Stock

markets also contribute to the mobilization of domestic saving by enhancing the set of

financial instruments available to savers to diversify their portfolios (Caporale, Howells

and Soliman, 2004).

This chapter is organized to explain theoretically, how the financial system and stock

market development could affect real economic growth. Section two discusses briefly

the determinants of economic growth in view of the neoclassical and endogenous

growth models. Section three establishes the theoretical link between finance, stock

market development and economic growth. Section four argues the effects of stock

market development on saving and investment. Section five considers the relationship

between economic reform and economic growth. A chapter summary follows.

55

3.2 Theory of Economic Growth20

Theoretical models of economic growth predict that higher saving and investment will

result in a higher level of per capita income and faster economic growth (Claus, Haugh,

Scobie and Tornquist, 2001). In considering the economic growth process, a question

that is raised is: can economic growth be sustained in the long-run? If so, what

determines the growth rate? (Grossman and Helpman, 1994). This section focuses on

the determinants of the growth rate of output over the long-run period. There are two

complementary approaches to explain these determinants, the standard neoclassical

growth theory, the Solow-Swan model (1956), and the endogenous economic growth

theory of Lucas (1988) and Romer (1986).

3.2.1 Neoclassical Growth Model21

In the neoclassical growth theory, we follow the explanation of Dornbusch and Fischer

(1994, pp. 269-72) to analyse the sources of economic growth by using the production

function, that output produced depends on the factor inputs of capital, K, and labour, L,

and the state of technology A, as in equation (3.1):

( , )Y AF K L= (3.1)

20Economic growth theories have emphasised three (related) determinants: (1) capital accumulation, (2)

human capital (including learning), and (3) research, development and innovation (Stern, 1991).

21For further details, see Robert Solow (1956, 1994). His model assumes the supply of goods and services

upon a production function with constant returns to scale and imperfect substitution between classicalfactors of production, capital and labour.

56

As the returns to scale are usually assumed constant in neoclassical theory, increasing all

inputs in the same proportion raises output in that same proportion. So, the change in

output due to technical progress and to changes in inputs can be written as:

/ [(1 ) / ] ( / ) /Y Y L L K K A A∆ = − ×∆ + × ∆ + ∆ (3.2)

Assuming perfect competition where inputs are paid their marginal products,

(1 ) and− are the marginal products of labour and capital, respectively. For

convenience, assume a given and constant rate of labour force growth, /L L n∆ = , and

also there is no technical progress, that is, / 0.A A∆ = Equation (3.2) becomes:

( )y f k= (3.3)

where y is economic growth and k is capital.

Given the assumptions of no technical progress and a fixed population growth rate, the

only variable element left in equation (3.3) is the growth rate of capital. Capital growth

is determined by saving, which in turn, depends on income22

.

Alternatively, endogenous growth models show that economic growth performance is

related to financial development, technology and income distribution (Caporale et al.,

22 For more an implication model on steady investment-driven growth, see Bertola (1993).

57

2003). There are two basic ways of endogenizing the steady-state growth rate23

. First,

the rate of technical progress can be endogenous, /A A∆ . Second, if there are constant

returns to factors of production that can be accumulated, then the steady-state growth

rate is determined by the growth of technological change24.

In addition to the relationship between output and investment, we need to think about

the channels through which stock market development may be linked to economic

growth. Following the work of Romer (1986, 1990) and Lucas (1988), the endogenous

growth model in the next section gives more detail on these channels.

3.2.2 Endogenous Economic Growth Model25

The recent motivation of interest in the link between financial development and

economic growth stems mainly from the insights and techniques of endogenous growth

models, which have shown that there can be self-sustaining economic growth without

exogenous technical progress and that growth can be related to preferences, technology,

income distribution and institutional arrangement (Pagano, 1993). This possibility has

also revived interest among theorists regarding the link between stock market

23The steady-state is a condition of the economy in which output and capital per worker do not change

over time. This is due to the rate of new capital production from invested saving exactly equalling the rateof existing capital depreciation (Swan, 1956).

24In the neoclassical growth models, steady state growth is independent of the saving rate (Blanchard,

2003).

25The early contributions in the endogenous growth theory were by Lucas (1988) and Romer (1986).

They explore the proposal that the steady state growth rate depends on the levels of both physical andhuman capital accumulation in the long-run.

58

development and economic growth, and has led to the emergence of several models that

highlight potential links (Gronski, 2001).

The endogenous growth model AK , which resembles those of Greenwood and

Jovanovic (1990)26, Pagano (1993)27 and Gronski (2001), will clarify how stock market

development may affect economic growth through saving and investment28

. Let us

assume we have a closed economy, where aggregate output, ( )tY is produced during

period t and is a linear function of the aggregate capital stock, ( )tK29:

( ) ( )t tY AK= (3.4)

K(t) is the aggregate capital stock including physical and human capital as in Lucas

(1988), and A is the social marginal productivity of capital.

26The Greenwood-Jovanovic (1990) model has an AK structure, with no diminishing returns to the

reproducible factor, and a permanent, exogenous improvement in financial structure which would cause apermanent increase in the rate of growth.

27Pagano (1993) also considers the simplest endogenous growth model, AK , and finds that the financial

intermediation (stock market) can affect economic growth by acting on the saving rate, on the fraction ofsaving channelled to investment, or on the social marginal productivity of investment.

28Saving and investment naturally play an important role in economic growth and development. Saving

determines the national capacity to invest and thus to produce, which in turn affects the potentialeconomic growth.

29In contrast to neoclassical models, where increased saving does not have a long-run impact on

economic growth, the AK model predicts that there will be a permanent change through capitaldeepening, since the production function in this model has constant returns to scale in capital (i.e. the

production function in the AK model sets, , equal to 1 in equation1

( ) ( ) ( )( )t t t

Y AK L −= and is given

by equation (3.4).

59

The growth rate ( ) ( 1)(( ) 1)t tY Y − − , is represented by y which at time ( 1)t + , is determined

only by the growth of capital input as:

( 1)

( 1)

( )

1t

t

t

Ky

K

++ = − (3.5)

Assume that the economy produces a single good that can be either invested or

consumed. If invested, it depreciates at the rate, per period. Gross investment, ( )tI is

given by:

( ) ( 1) ( )(1 )t t tI K K+= − − (3.6)

i.e., gross investment equals the difference between the capital stock at time ( 1)t + and

time ( )t , plus the depreciated capital stock at time ( )t .

In this closed economy without government, the financial market equilibrium assumes

the equality between gross saving, ( )tS and gross investment, ( )tI (i.e. gross investment

can be only financed by gross saving). Assuming further that a proportion of saving,

(1 )− is lost in the process of financial intermediation as a consequence of transaction

costs, such that in equilibrium only a fraction of saved resources ( )tS is channelled to

investment ( )tI as follows:

( ) ( )t tI S= (3.7)

60

The amount of saving absorbed by the financial system is then ( )(1 ) tS− and the higher

ϕ, the lesser the capital accumulation in the economy.

From equations (3.5) and (3.6), the growth rate of the economy at time ( 1)t + is ( 1) ,ty +

and can be expressed as the ratio of gross investment to capital minus depreciation

( 1) ( ) ( )( ( ) )t t ty I K+ = − . In a second step, capital can be substituted by the ratio of

output to productivity obtained from equation (3.4), as:

( )

( 1)

( )

t

t

t

Iy A

Y+ = − (3.8)

Using the capital market equilibrium equation (3.7) and denoting the gross saving rate

( )S Y by s, the steady-state growth rate can be expressed by the following equation:

y A s= − (3.9)

It appears then from this simple model that stock market development may affect the

economic growth process through the following:

• Firstly, through an increase of the saving rate (s), ( )S Y , (or also the investment

rate) by using economic policies affecting directly the determinants of private

saving behaviour.

61

• Secondly, through the channelling of more saving to investment by avoiding the

loss of funds during the intermediation process through a rise in the fraction ,

(i. e. an increase in a in equation (3.9) increases the growth rate, y).

• Finally, through the improvement of capital productivity (A) resources being

allocated more productively. Thus saving channelled through financial

intermediaries (stock market) is allocated more efficiently, and the higher capital

productivity results in higher economic growth.

3.3 Finance, Stock Market Development and Economic Growth

3.3.1 Channels to Economic Growth: A Theoretical Overview

3.3.1.1 On the Financial System

The theoretical relationship between financial development and economic growth has

remained an important issue of debate. The pioneering contributions of Goldsmith

(1969), McKinnon (1973) and Shaw (1973) all coincide in suggesting that there is a

strong positive correlation between the extent of financial development and economic

growth30

(De Gregorio and Guidotti, 1995).

A well-functioning financial system serves very important functions within the

economy. Greenwood and Smith (1997) and Viney (2003) emphasize that the financial

markets are the most prominent means of encouraging and allocating saving to

30Goldsmith (1969) also argues that the correlation reflected a two-way casual relationship, and that

financial markets enhance economic growth by raising the efficiency of investment. McKinnon (1973)and Shaw (1973) extend the earlier argument by noting that financial markets raise the growth rate ofsaving and investment.

62

competing users by providing financial instruments that possess a range of mixes of the

attributes of risk and return (i.e. channelling investment capital to its highest expected

returns).

On the other hand, financial markets provide liquidity and permit individuals to allocate

their current income to saving and/or spending, which alters the social composition of

saving in a way that is potentially favourable to enhancing capital accumulation. Finally,

financial markets foster specialization in entrepreneurship, entrepreneurial development,

and the adoption of new technologies.

The role of the financial markets is to bring together savers who buy financial

instruments and the users of funds who issue financial instruments. The flow of funds,

the relationship between savers and users of funds, and the place of the financial

markets in the flow, are illustrated by Viney (2003) in Figure 3.1.

Figure 3.1: Financial Market and Flow of Funds Relationship

Who supply funds Who receive funds

and and

receive financial instruments issue financial instruments

Source: Viney (2003, p. 7)

Suppliers of Funds:

• Surplus (Saving) Units

Demanders of Funds:

• Deficit Units

Lenders:

• Householders

• Companies

• Governments

• Rest of World

Borrowers:

• Householders

• Companies

• Governments

• Rest of World

FFiinnaanncciiaall MMaarrkkeettss

63

Alternatively, Levine (1997) describes the basic functions and channels through which

financial markets and intermediaries may be linked to economic growth31. Figure 3.2

presents these functions and suggests that a well-functioning financial system might

permit a higher level of saving and investment, and hence economic growth.

Figure 3.2: A Theoretical Approach to Finance and Growth

Source: Levine (1997, p. 691)

To understand how the financial system might influence economic growth in theory,

Khan (2000, pp. 6-7) summarizes these functions in more detail. Firstly, mobilizing

saving, that is financial markets and institutions pool the saving of diverse households

and make these funds available for lending. This activity reduces the transaction costs

associated with external finance for both firms and households. Secondly, allocating

31For more information on the relationship of financial development and economic growth, see

Greenwood and Smith (1997), Levine (1997), King and Levine (1993), Wurgler (2000), Pagano (1993)and Fischer et al. (1984).

Market Frictions- Information costs- Transaction costs

Financial marketsand intermediaries

Financial functions- Mobilize saving- Allocate resources- Exert corporate control- Facilitate risk management

- Ease trading of goods, services and contracts

Channels to growth- Capital accumulation- Technological innovation

Growth

64

saving by determining which investment opportunities are worthwhile and judging the

creditworthiness of borrowers at lower cost than the average small investor.

Thirdly, reducing risk by spreading investors saving across many different investment

opportunities. Fourthly, by creating liquidity in view of the fact that the financial system

allocates funds to both short-run and long-run investment. Fifthly, facilitating trade by

extending credit and guaranteeing payments. For example, letters of credit help firms

and the private sector order the inputs for investment and production. Finally,

monitoring managers and exerting corporate control. Banks monitor borrowers, and

equity markets allow shareholders to discipline managers by voting out poor

management.

Furthermore, the literature shows that differences in how well financial systems reduce

information and transaction costs will influence saving, investment decisions,

technological innovation, and long-run economic growth rate. Using the theory of the

endogenous growth model, Levine (1997) examined two channels through which

financial markets may affect economic growth: capital accumulation and technological

innovation (see Figure 3.2). Human capital reflects the educational level of the

workforce: as an individual becomes more specialized and better trained, his/her

productivity increases. Technological Innovations reflect scientific development, and are

evidenced by new production techniques and the creation of entirely new goods and

services.

65

3.3.1.2 Stock Market As A Cause of Economic Growth

A stock market serves as the primary market through which shares are initially issued in

order to obtain finance for the development and expansion of an investment. This

transaction raises new funding for a corporation and allows increased investment in

productive capital and economic growth (Viney, 2003).

However, in most stock market literature, the main channels to economic growth are

seen in the efficiency of capital allocation, encourage saving, and lead to more capital

formation32. On a microeconomic level, such channels can be discussed in terms of the

impact on corporate finance and corporate governance (Laurenceson, 2002).

Stock market development is supposed to encourage saving by providing households

with additional instruments which may better meet their risk preferences and liquidity

needs. Liquid equity markets make investment less risky and more attractive because

they allow savers to acquire asset equity and to sell it quickly and cheaply if they need

access to their portfolios. At the same time, companies enjoy permanent access to

capital raised through equity issues. However, by facilitating long-term investment and

making it more profitable, stock market liquidity improves the allocation of capital and

enhances prospects for long-run economic growth (Levine, 1996).

32Theory points out a rich array of channels (market size, liquidity, integration with world capital

markets, and volatility) through which the stock markets may be linked to economic growth (Garcia andLiu, 1999).

66

Moreover, Rousseau and Wachtel (2000, pp. 1936-37) describe the reasons why stock

market is an important financial institution even when equity issuance is a relatively

minor source of funds. Firstly, the stock market provides investors and entrepreneurs

with a potential exit mechanism33. Secondly, capital inflows in both foreign direct

investment and portfolio are potentially important sources of investment funds for

emerging market and transition economies34

. Thirdly, the provision of liquidity through

organized stock markets encourages both international and domestic investors to

transfer their surplus from short-run assets to the long-run capital market, where the

funds can provide access to permanent capital for firms to finance large projects that

enjoy substantive economies of scale. Finally, the existence of the stock market provides

important information that improves the efficiency of financial intermediation generally.

For traded companies, the stock market improves the flow of information from

management to owners and quickly produces a market evaluation of company

developments35.

Considering the importance of stock market development in economic growth an

important question is raised how the stock market development could lead to increased

aggregate saving and investment, or raise the productivity of investment. Therefore, the

33The option to exit through a liquid stock market mechanism makes venture capital investment more

attractive and might well increase entrepreneurial activity generally (Rousseau and Wachtel, 2000,p.1936).

34For literature on stock market and capital liberalization, see Henry (2000), Levine and Zervos (1998b),

and Singh (1997).

35A variety of finance and investment references discuss the important advantages of holding listed

companies in the stock market over other forms of business organization. See for example, Viney (2003),and Bodie et al. (2002).

67

next section considers the theoretical links of stock market development on saving and

investment, and then economic growth.

3.4 The Effect of Stock Market on Saving and Investment

3.4.1 Stock Market Development and Saving

Bonser-Neal and Dewenter (1999) consider examine how stock market development

could affect saving by considering three factors: (1) how it affects the return on saving,

(2) how it affects the riskiness of the saving, and (3) the response of individuals to these

changes in return and risk36. Theory suggests that stock market development should

raise the rate of return on saving for two reasons. Firstly, the ability to add stocks to a

portfolio will raise the expected rate of return. Secondly, if capital controls of

investment opportunities prevent individuals from holding their optimal portfolio, then a

liberalization and expansion of the stock market would allow individuals to channel

financial resources for optimal use through the purchase of shares. This more efficient

reallocation of resources should consequently lead to a higher rate of return on saving in

the economy.

Given the previous discussion of how stock market development affects economic

growth, we consider in the next section the impact of stock market development on

36They also show that there is evidence of a significant positive relationship between saving and stock

market size and liquidity for 16 emerging markets over the period 1982-1993.

68

saving through the determination of risk-return ratios, which is a central issue for

corporate investment.

3.4.1.1 The Markowitz Mean Variance Formulation37

This section follows closely the description of the Markowitz portfolio selection model

by Dobbins, Witt and Fielding (1994) and Bodie, Kane and Marcus (2002), as well as

the explanation of Gronski (2001), to analyse the influence of stock market development

on saving mobilization through the rate of return and the risk of saving.

Modern portfolio theory38

is concerned with the choice of efficient combinations of

assets, and its foundation lies in the work of Markowitz (1952, 1959). His model

represented the first substantial quantitative analysis of stock market development and

the benefits resulting from diversification of security saving and, hence, increases

saver s possibilities to maximize their expected rate of return for a given risk, or

minimize their risk for a given rate of return39. In general, diversification of assets

holdings permits a reduction in the variance of returns, even for the same level of

expected return.

37Harry Markowitz (1952) published a formal model of portfolio selection embodying diversification

principles, thereby paving the way for his 1990 Nobel Prize in economics.

38Modern Portfolio Theory (MPT): analysis and evaluation of rational portfolio choices based on risk-

return trade-offs and efficient diversification (Markowitz, 1952).

39This model is based on the assumptions of the Markowitz mean-variance approach, (i.e.) the selection

of portfolios based on the means and variances of their returns, the choice of the higher ( )pE r for a given

level of variance or the lower variance portfolio for a given ( )p

E r . For more detail, see Dobbins, Witt

and Fielding (1994), and Bodie et al. (2002).

69

The expected return of a portfolio comprising n securities is the weighted average of the

expected return of each security in the portfolio:

1

( ) ( )n

p i i

i

E r w E r=

= ∑ (3.10)

where ( )pE r is the expected return of the portfolio, iw is the weight of security i, (with

1, 2)i = in the portfolio, and ( )iE r is the expected return of security i. the simplest case

is the two asset portfolio, and here equation (3.10) reduces to:

1 1 1 2( ) ( ) (1 ) ( )pE r w E r w E r= + − (3.11)

The risk of a portfolio is measured by the variance of its return, and this is determined

by the variance of return of each security and also by the covariance of returns between

each pair of securities:

2 2 2 2 2

1 1 2 2 1 2 1 22 ( , )p

w w w w Cov r r= + + (3.12)

where 2

pis the variance of return of the portfolio. The variances of return for securities

1 and 2 are, 2 2

1 2and , respectively. 1 2 1,2( , )Cov r r = is the covariance of returns

between securities 1 and 2. The covariance can be computed from the correlation

coefficient of the two securities, 1,2 , as:

70

1,2 1,2 1 2= (3.13)

Substituting equation (3.13) into equation (3.12) gives:

2 2 2 2 2

1 1 2 2 1 2 1,2 1 22p

w w w w= + + (3.14)

It is important to note that the variance of return of a portfolio is determined by the

correlation coefficient 1,2( 1 1),− ≤ ≤ as well as the variance of return of each security.

In the two-asset portfolio situation, equation (3.14) reduces to40:

2 2 2 2 2

1 1 1 2 1 1 1,2 1 2(1 ) 2 (1 )p w w w w= + − + − (3.15)

since, 2 11w w= − .

That is, the risk of return can be reduced by portfolio diversification easily by assuming

that the variance of the expected returns of both securities is equal (it is supposed for

simplicity that 1 2= = ). If all investment takes place in security 1, then the risk of

the portfolio return is:

40Equation (3.15) reveals that portfolio risk is reduced if the covariance term ( 1,2 1,2 1 2= ) is

negative (it could erase risk completely if the returns were perfectly negatively correlated). However, it isimportant to recognize that even if the covariance term is positive, the portfolio risk still is less than theweighted average of the individual security risks, unless the correlation coefficient is equal to one

( 1,2 1= , perfect positive correlation).

71

2 2 2

11.p

= = (3.16)

Similarly, if all investment takes place in security 2, then:

2 2 2

21.p = = (3.17)

However, if the portfolio consists of both securities (that is, diversification takes place

and because of the assumption of similar variances), then:

2 2 2 2 2 2

1 1 1 1 1,2

2 2 2

1 1 1 1 1,2

(1 ) 2 (1 )

[ (1 ) 2 (1 ) ]

p w w w w

w w w w

= + − + −

= + − + −(3.18)

But it can be seen that:

2 2 2

1 1 1 1 1 1[ (1 )] 1 (1 ) 2 (1 )w w w w w w+ − = = + − + − (3.19)

Substituting equation (3.19) into equation (3.18) shows that:

2 2

p = , if 1,2 1.= (3.20)

If 1,2 1,< however, then:

2 2

p < (3.21)

72

The risk of the portfolio with perfect positive correlation ( 1,2 1)= is just the weighted

average of the component risks. In all other cases, the correlation coefficient is less than

1, making portfolio risk less than the weighted average of the component risks (i.e.

portfolios with less than perfectly correlated assets always offer better risk-return

opportunities than the individual component securities on their own).

To summarize, the expected return of any portfolio is simply the weighted average of

the asset expected returns. Potential benefits from diversification arise when correlation

is less than perfectly positive, (i.e. the lower the correlation, the greater the potential

benefit from diversification).

In practice, returns on securities tend to be highly positively correlated, as they are all

influenced by the same economic and political factors. Hence, only a certain part of total

risk may be eliminated by diversification (when security returns are uncorrelated, the

power of diversification to limit portfolio risk is unlimited), and this is known as

diversifiable or unsystematic risk. Diversification, therefore, permits the specific risk

relating to individual securities to be removed, but not the systematic (or market) risk41

.

In order to determine the minimum level to which portfolio risk can be held, Equation

(3.15) may be partially differentiated with respect to 1w , the derivative set equal to zero,

and solved for 1w . Hence the risk-minimizing value of 1w is given by:

41Systematic risk refers to risk factors common to the entire economy (macroeconomic factors). But the

risk that can be reduced by diversification is called also firm-specific risk or unsystematic risk.

73

2

2 1,2 1 2

1 2 2

1 2 1,2 1 22w

−=

+ −(3.22)

The minimum portfolio risk that is possible with this pair of securities may therefore be

calculated, along with the portfolio expected rate of return at this minimum risk level.

The Markowitz portfolio selection model presents the identification of the efficient set

of portfolios, or as it is often called, the efficient frontier of risky assets42. However,

the objective function43, f , incorporates the concept of trading off risk against return

and is given by:

2[ ( )] , 0p pf A E r A= − + ≤ ≤ ∞ (3.23)

On the other hand, Figure 3.3 shows the determination of an efficient portfolio set and

an appropriate complete portfolio by mixing the risk-free asset with the optimal risky

portfolio. All the portfolios that lie on the minimum-variance frontier from the global

minimum variance portfolio and upward provide the best risk-return combinations and,

thus, are candidates for the optimal portfolio. The part of the frontier that lies above the

global minimum-variance portfolio therefore, is called the efficient frontier of risk assets

42It means that the frontier set of risky portfolios that minimize the risk at each level of expected return.

43Where, A , is a coefficient of risk aversion. If 0,A = the portfolio with the lowest variance of return

will be selected. As A increases, the investor becomes more willing to accept risk in order to achieve a

higher expected return, and if ,A = ∞ the portfolio with the highest expected return will be optimal.

However, we can minimize this function mathematically (Equation (3.23)) to obtain a set of efficient

portfolios. The minimization is subject to two constraints: (1) 0iw ≥ , negative investment (selling short)

is not permitted, and (2)1

1,n

i

i

w=

=∑ the portfolio consists of n securities.

74

and the bottom half of the frontier is inefficient (Bodie et al., 2002, p. 225). The Capital

Allocation Line44 (CAL) that is supported by the optimal risky portfolio (P), is tangent

to the efficient frontier.

Figure 3.3: The Markowitz Portfolio Selection Model

Expected return (%) CAL

Indifference Curve45

Efficient frontier of risky assets

Optimal risky portfolio

Global minimum-variance portfolio

fr (%)

Optimal complete portfolio

Standard deviation (%)

Source: Bodie, Kane and Marcus (2002, p. 222)

Following the analysis of Gronski (2001, pp. 22-26), the use of a risk-return efficient

formulation explains how stock market development could increase the expected return

on saving for a given rate of risk or, vice versa, reduce the risk for a given expected rate

of return. In other words, stock market development can be interpreted as an increasing

function of the supply of shares, share trading, efficiency, and global integration. This

hypothesis includes the following considerations46

:

44Represents all feasible risk return combinations of a risky and risk free asset.

45The curve connects all portfolios with the same utility according to their means and standard deviations.

46 These parameters can contribute to lifting the efficient market line upwards (Gronski, 2001, p. 25).

75

• An increasing supply of shares, for example, increases the volume of attainable

portfolios.

• Increasing trading volume (liquidity) reduces share price volatility because

demand and supply can be matched faster and more smoothly (Grossman and

Miller, 1988), and thereby reduces the risk of return.

• Increased market efficiency occurs because of an increasing number of players

(e.g. brokers, banks)-and thus competition-on the stock market, and because of a

faster transaction of share deals. Both reduce the transaction costs of share

trading (such as commissions and opportunity costs of capital), and

consequently, increase the expected net return on shares (Bencivenga, Smith and

Starr, 1996).

• Increasing global integration of stock markets improves the risk diversification

possibilities47.

In assessing the Markowitz model (1952) in analysing the effects of stock market

development on saving based on risk-return ratios, the main objection to applying this

approach is the very large number of calculations that have to be performed. Sharpe

(1963) has greatly clarified the problem of estimating covariances and demonstrated that

the model is reasonably accurate. So, equipped with a database, computer algorithms

and methods of estimation, the modern portfolio theorist is able to trace out mean

variance frontiers for large universes of securities (Markowitz, 1991).

47Devereux and Smith (1994) examine an explicitly economic model of diversification and economic

growth, and illustrate how the risk reduction implied by diversification may promote economic growthrate.

76

Before discussing the effects of stock market development on investment, we conclude

the above analysis with a look at the potential effects of stock market development on

saving mobilization (Gronski, 2001, pp. 28-32).

3.4.1.2 The Potential Effects

Stock market development does have a positive, negative, or uncertain effect on saving.

• The Positive Incentive Effect

A positive effect of stock market development on saving may occur due to an increase in

the rate of return on saving that provides an incentive for individuals to postpone

consumption. A stock market, and the securities issued, simultaneously meet portfolio

preferences of savers (surplus units) and debt requirements of borrowers (deficit units),

thereby leading to a higher level of saving, S , and more funds being channelled into

real investment, I .

Theoretical models of financial market development and economic growth also suggest

that stock market development may reduce the riskiness of income while, at the same

time, increasing the rate of return. Levine (1991) considers that liberalization and

expansion of stock markets allow individuals to better diversify their risk, and then

stock market development could be associated with a decrease in the riskiness of saving.

77

• The Negative Wealth Effect

Stock market development may decrease saving because of two wealth effects. The first

refers to the degree of uncertainty that distinguishes the two sources of lifetime income.

Income from labour is much more uncertain than from tangible assets. The permanent

income hypothesis, therefore, states that the ratio of tangible assets to labour income is

an important variable for determining consumption and, hence, saving propensity

(Gapinsky, 1993). Continuing this statement it is possible to argue that stock market

development may further decrease the propensity to save because it increases the

tradability of assets, thereby reducing the transaction costs that occur for lenders.

Secondly, an increase in the rate of return on saving also increases wealth, which in turn

increases consumption and decreases saving.

• The Uncertain Effect

The theoretical effects of a change in risk on the saving rate are ambiguous and depend

critically on assumptions regarding preferences. Rothschild and Stiglitz (1971), in fact,

show that risk and saving are positively related only if the coefficient of relative risk

aversion48

is nonincreasing and greater than one, a condition consistent with a

precautionary motive for saving. Whether saving increases or decreases with a change in

48A risk-averse investor will consider risky portfolios only if they provide compensation for risk via a risk

premium, that is, implies that when facing choices with comparable returns, investors tend chose the less-risky alternative (Bodie et al., 2002).

78

risk, therefore, depends critically on the coefficient of relative risk aversion (Bonser-

Neal and Dewenter, 1999).

In summary, the impact of stock market development on saving is ambiguous.

Nevertheless, the actual, net impact has critical implications for economic growth.

Models by Bencivenga and Smith (1991), Jappelli and Pagano (1994), Devereux and

Smith (1994), and Obstfeld (1994) identify conditions in which the stock market could

cause saving to fall enough so that the overall economic growth rate falls.

3.4.2 Stock Market Development and Investment49

The first step for an economy to make capital available to investors is to create the

institutions that will mobilize domestic saving so as to accumulate capital within the

economy. Monetary institutions, including financial and stock markets, are crucial to

facilitate or impede channelling of domestic saving to domestic investment

opportunities. Therefore, they are crucial to determine if the country is a recipient of

cross border capital flows (Cavallo, 2002).

In the literature, Samuel (2001) describes briefly that there are two contrasting views

regarding the role of the stock market with respect to investment decisions in the

economy. One view argues that the stock market is essentially a sideshow. Bosworth et

al. (1975) show that if managers are concerned about the market value of the firm in the

49The stock market and investment behavior are intimately bound together since firms invest to earn

profits, and activity in the stock market represents an attempt by investors to evaluate the magnitudes ofthat stream of profits (See, for example, Bosworth et al. (1975) and Blanchard et al. (1993)).

79

long-run while undertaking investment decisions, they should ignore share price

changes in the short-run if they do not reflect the firm s longer-term prospects.

An opposing view is that stock market valuation matters for investment. Fischer and

Merton (1984) explain that if the objective of managers is to maximize the wealth of

existing shareholders, they should respond to market valuation even when this deviates

from the true value of the firm. This is because the role of the stock market is to value

the firm as well as provide finance.

3.4.2.1 On the q Theory of Investment50

In this section, the framework is a Securities Valuation Model51, similar to that of

Anderson and Subbaraman (1996). In neoclassical investment theory52

, the competitive

firm attempts to maximize the present value of its future income stream (Bosworth et

al., 1975). Managers have to make an assessment of the expected future profits that the

increment to the capital stock will generate, discounted at some appropriate rate, and

contrast it with the marginal cost of capital to determine whether the investment should

go ahead, as:

50For a more detailed discussion on this theory see, for example, Samuel (2001), Ogawa and Kitasaka

(1999), Barro (1990), Mullins and Wadhwani (1989), Yoshikawa (1980), and Bosworth et al. (1975).

51An alternative model to analysing the link between the stock market and investment, it places greater

emphasis upon the stock market as a determinant of investment demand (Bosworth et al., 1975).

52It provides that investment and share prices are closely linked, as share prices are forward looking

variables, which condense information regarding a firm s expected value. Movements in share prices andinvestment should, in theory, be correlated (Anderson and Subbaraman, 1996).

80

{ }1 It

t t t

t

IE P

K = − (3.24)

where I is investment, K is the capital stock, { }E is the discounted present value of the

revenue expected to be generated by the additional unit of capital, I

tP is the marginal

cost of capital and is the cost of adjusting the existing capital stock.

A similar framework can be used to calculate the value of the firm. The fundamental

value of the firm (Vt) is equal to the discounted present value of the cash flow stream

that the existing capital stock is expected to generate:

{ }t t t tV E K= (3.25)

Thus, the same types of expectation variables that determine the investment decision

also determine the value of the firm (the firm s share price). This relationship was

recognised by Tobin (1969) in the q theory of investment where q is defined as the

ratio of the market value of the firm to the replacement cost of its existing capital

stock53

:

t

t I

t t

Vq

P K= (3.26)

53A literature initiated by Tobin (1969) relates investment to q, which is the ratio of the market s

valuation of capital to the cost of acquiring new capital. Therefore, q theory can rationalize a positiverelationship between investment and current and lagged changes in stock market prices (Barro, 1990).

81

Combining equations (3.24) to (3.26) illustrates the basic relationship between

investment and Tobin s q, as:

1( 1) It

t t

t

Iq P

K= − (3.27)

Equation (3.27) illustrates that when the market value of capital exceeds its replacement

cost ( 1)q > , a firm is able to increase its value by investing. Further, as the dominant

source of variation in q comes from the numerator-the market value of the firm-the

equation also illustrates that investment is related to real share prices ( / )I

t tV P .

In essence, the fundamental value of the firm, F

tV can be expressed as its market value,

tV less its speculative value S

tV , as follows:

F S

t t tV V V= − (3.28)

Given the existence of speculative and fundamental elements to a firm s share price, the

q investment model (equation (3.27)) can be re-written as:

1 11F I S It

t t t t

t

Iq P q P

K = − + (3.29)

82

That is, equation (3.29) provides the theoretical framework of the relationship between

share prices and investment, as well as testing whether speculative share prices

movements distort investment decisions.

In conclusion, Tobin and Brainard (1977) illustrate that the neoclassical theory of

corporate investment is based on the assumption that management seeks to maximize

the present net worth of the company, the market value of the outstanding common

shares. An investment project should be undertaken if and only if it increased the value

of the shares.

The stock markets appraise the project, its expected contributions to the future earnings

of the company, and its risks. If the value of the project as appraised by investors

exceeds the cost, then the company shares will appreciate to the benefit of existing

stockholders. That is, the market will value the project more than the cash used to pay

for it. If new debt or equity securities are issued to raise the cash, the prospectus leads to

an increase of share prices (Yoshikawa, 1980).

The securities-valuation approach offers the advantage of not requiring the explicit

measurement of the effect of taxes, expected output, and expected prices needed in the

neoclassical version. This simplification results from the assumption that the market

correctly values the future earning capacity of the firm. An estimate of the discrepancy

between the actual and desired capital stock of the neoclassical model is available by

comparing the market value of the firm with the replacement cost of its current capital

stock (Bosworth et al., 1975).

83

3.4.2.2 Stock Market Development and Liquidity Supply54

In the previous sections we have characterized the effects of stock market development

on saving and investment. In this section, we consider stock market liquidity, Levine

(1991), Bencivenga, Smith and Starr (1996), and Garcia and Liu (1999) show that stock

market liquidity reduces the disincentives to investing in long-run projects because

investors can easily sell their stake in the project if they need their saving before the

project matures. Therefore, more liquid stock markets ease investment, thereby

improving the allocation of capital and enhancing prospects for long-run economic

growth (Levine and Zervos, 1998a).

A model of the stock market resembling those presented by Fulghieri and Rovelli (1998)

follows. Individuals in a new-born generation start every new round of production by

investing an amount 1I ≤ in the technological process. They assume that there are three

types of firms: new firms (f), which have started production, intermediate firms (i), and

mature firms ready to pay a liquidating dividend.

Three markets are open each period in this economy: (a) a market for physical goods,

with their unit price normalized to one; (b) a market for shares in newly formed firms,

54Liquidity to an economy may be supplied either directly through trade on capital markets, or indirectly

through financial intermediaries. In the first case, liquidity is created by allowing agents with differentcash flow needs to trade amongst themselves claims on productive assets. In the second case, specializedintermediaries interposed between producers and consumers hold in their portfolios claims on productiveassets and issue to individual investors secondary instruments which are better suited to match theirconsumption needs (Fulghieri and Rovelli, 1998).

84

with unit price, fP ; and (c) a market for shares in firms at the intermediate stage with

unit price, iP .

Given the ability to trade goods and shares in the above markets, agents now face a

richer set of opportunities. Letting k

jI represents the share of firm J, { , }J f i∈ owned

by an individual at stage K, { , , , }K n e s d∈ , individuals face at different periods of their

lives the following set of budget constraints (where beginning of period resources

appears on the right hand side of the equations, and consumption and end of period

investments appears on the left hand side):

Stage n: 1 ( 1)n n

i i f f fP I P I P I+ = + − (3.30)

Stage e: 1

n n

i i fC RI P I= + (3.31)

Stage s: s s n n

i i f f i i fP I P I RI P I+ = + (3.32)

Stage d: 2

s s

i i fC RI PI= + (3.33)

Consider a new-born individual. He/she may devote an amount of resources I to a new

round of production and/or invest in shares for new, n

fI and intermediate firms, n

iI .

Resources for investment are given by the unit endowment plus the sale of shares in the

new firm,f

P . Note that the last term on the right side of equation (3.30) is the capital

gain that new-born individuals make as they sell the new technology at the market price

for new shares, fP and invest the proceeds in production. This gain represents the net

85

present value from operating a new firm and it is proportional to the level, I of initial

investment.

In the following period, those individuals who turn out to be early consumers finance

consumption with the liquidating dividends of maturing firms, and with the sale of their

holdings of intermediate shares, leading to equation (3.31). Buyers in this market are

provided by new-born individuals from the generation that follows, and by survivors.

Note that the presence of a secondary market for shares allows early consumers to avoid

inefficient liquidation of physical capital. Survivors are active only in the markets for

shares to rebalance their portfolios. They reinvest their share of the liquidating dividends

of mature firms by buying additional shares of the intermediate firms and/or shares in

the new firms started by the new generation, giving equation (3.32). Finally, late

consumers consume their accumulated wealth, giving equation (3.33).

However, the theory is unclear about the growth effects of greater stock market

liquidity55

. Arestis, Demetriades and Luintel (2001), and Demirguc-Kunt and Levine

(1996) demonstrate that increased liquidity can also influence economic growth

negatively. Greater stock market liquidity by increasing the investment return may

reduce the saving rate, and dissatisfied investors find it easy to sell quickly. This can

lead to disincentives to exert corporate control, thus adversely affecting corporate

finance and slow down economic growth in the process.

55 For a more detailed discussion on theoretical disagreement see, for example, Levine and Zervos (1996),Devereux and Smith (1994), Obstfeld (1994), and Bencivenga and Smith (1991).

86

3.5 Economic Reform and Economic Growth

The new growth theory stresses the importance of an economic environment that is

consistent with the development and efficient use of resources (Gwartney et al. (1999),

and De Haan and Siermann (1998)). The measures of economic reform have been

developed by the Freedom House Country Rating, the Heritage Foundation, and the

Fraser Institute. Each of these organizations independently reaches the same conclusion,

as do several academic studies using different proxies for institutional conditions or the

policy regime (De Haan and Sturm, 2000).

Economic theory indicates that economic reform and freedom affects incentives,

productive effort, and the effectiveness of resource use. Economists and economic

historians have argued that the freedom to choose and supply resources, competition in

business, trade with others and secure property rights, are central ingredients for

economic progress (Sturm and De Haan, 2001).

Why does economic freedom matter to economic growth? Economic theory indicates

that private investment will tend to flow toward economic environments that are more

attractive for productive activities. Free economies will attract more investment, which

in turn promotes economic growth. In contrast, high taxes, excessive regulation, biased

enforcement of contracts, lack of legal resources, insecure property rights, and monetary

instability will deter both investment and growth (Gwartney and Lawson, 2004).

Freedom House has analysed democracy trends and found that, as a general principle,

87

economic growth is accelerated in an environment of political freedom (Freedom House

Rating, 2002).

The empirical analysis of our study will use two different measures of economic reform,

are Annual Freedom Scores and Institutional Quality Index. Firstly, Annual Freedom

Scores (developed by Freedom House Country Rating), which use 1 to represents the

Most Free countries with the best Political Rights and Civilian Liberties, while 7

represents the Least Free countries with the worst Political Rights and Civilian

Liberties. Secondly, the Institutional Quality Index (from International Country Risk

Guide, ICRG)56

which scored from zero to six (i.e. zero the worst and 6 for the best).

The relationship between the quality of institutions represented as an increase in the

score indicates an improvement in the institutional structure.

3.6 Conclusion

This chapter provided a comprehensive theoretical consideration of how the financial

system and stock market development could affect real economic growth. In finance

theory, there are four basic functions and channels in which the stock market may

influence economic growth: (1) the stock market provides investors and entrepreneurs

with a potential exit mechanism, (2) capital inflows in both foreign direct investment

and portfolio are potentially important sources of investment funds, (3) the provision of

56A measure of corruption within the political system that is a threat to foreign investment by distorting

the economic and financial environment, reducing the efficiency of government and business by enablingpeople to assume positions of power through patronage rather than ability, and introducing inherentinstability into the political process (International country Risk Guide, 2004).

88

liquidity through an organized stock market encourages both international and domestic

investors to transfer their surplus from short-run assets to the long-run capital market,

and finally (4) the existence of the stock market provides important information that

improves the efficiency of financial intermediation generally.

In contrast, economic theory of the endogenous growth model illustrates that stock

market development may affect economic growth through an increase in the saving rate,

the channelling of more saving to investment, and the improvement of capital

productivity with better resource allocation toward their most productive use. Thus

saving channelled through the stock market is allocated more efficiently, and the higher

capital productivity leads to higher economic growth.

In an important section, this chapter established the theoretical framework of the effect

of stock market on saving and investment. We considered the effect on saving

mobilization through the determination of return-risk ratios as presented in modern

portfolio theory (Markowitz, 1952), which is concerned with the choice of efficient

combinations of assets and the benefits resulting from diversification of security saving

and hence increases saver s possibilities to maximize their expected rate of return. In

practice, diversification permits the firm-specific risk relating to individual securities to

be removed, but not the systematic (or market) risk.

The potential effect of stock market development on saving is ambiguous and depends

critically on assumptions regarding risk-return ratio and saving. A positive effect may

occur due to an increase in the rate of return on saving that provides an incentive for

89

individuals to postpone consumption. In contrast, the stock market may decrease saving

because of a wealth effect where an increase in the rate of return on saving also

increases wealth, which in turn increases consumption and decreases saving.

We considered two contrasting views to explain the effect of stock market development

on investment. One view argued that managers should ignore share price changes in the

short-run if they are concerned about the market value of the firm in the long-run. The

opposite view argued that stock market valuation matters for investment, and managers

should respond to market valuation, even when this deviates from the true value of the

firm, to maximize the wealth of existing shareholders.

Looking at the q-theory of investment, we concluded that management seeks to

maximize the present net worth of the company and the market value of the outstanding

common shares. In addition, stock markets appraise the project with its expected

contributions to the future earnings of the company and its risks. That is, the stock

market will value the project more than the cash used to pay for it.

Finally, we discussed the importance of economic reform and freedom in economic

growth. Two measures of economic reform were introduced; Annual Freedom Scores

and Institutional Quality Index.

This chapter is of fundamental importance and presented a comprehensive theoretical

framework of how stock market development affects economic growth. The subsequent

90

chapters will assess the empirical relevance of the role of stock markets in explaining

economic growth.

91

Chapter Four

4. CHAPTER FOUR: AN OVERVIEW OF ARAB ECONOMIES AND STOCK

MARKET DEVELOPMENT

4.1 Introduction

This study primarily focuses on the Arab stock markets. It represents the first attempt, to

our knowledge, of studying, theoretically and empirically, the performance of these

markets and economic growth. This chapter introduces an overview of Arab economies

and provides an historical account of Arab financial development and stock markets.

Given the major determinants and characteristics of Arab economies, it is important to

demonstrate the role of the oil sector in these economies.

The twenty-two Arab speaking countries of the Middle East and North Africa

(MENA)57

are extremely large and diverse in terms of geography, resources, and

development levels. They covers a territory of 14.8 million square km, while 295

million Arabs live on a small portion of land that represents only 4.2 percent of the total

territory, equivalent to no more than a medium-sized country like Spain. The region is

the poorest in the world in terms of water and agricultural resources, but it remains the

richest in the world in terms of hydrocarbon reserves, includes both the oil-exporting

57The MENA region includes: Algeria, Bahrain, Comoros, Djibouti, Egypt, Iran, Iraq, Jordan, Kuwait,

Lebanon, Libya, Mauritania, Morocco, Oman, Qatar, Saudi Arabia, Somalia, Sudan, Syria, Tunisia,Turkey, United Arab Emirates, and Yemen (The World Bank, 2003).

92

countries58

and non-oil countries that are resource-scarce. It has over 45 percent of

global oil reserves and 30 percent of total world gas reserves. Nonetheless, total MENA

GDP at current prices reached only USD $717 billion in 2002, with an average per

capita of GDP $2,445 that varies greatly among MENA countries, from as low as USD

$353 in Mauritania to USD $30,000 in Qatar (Al-Hamad, 2003).

The MENA region s poor economic growth performance during the 1980s and 1990s

also contrasts sharply with the 1970s, when annual per capita GDP growth averaged 2.3

percent, exceeding that of other developing countries (excluding East Asia) by nearly

two-thirds of a percentage point (Hakura, 2004). The growth rates have been remarkably

volatile and linked to several characteristics. These are, mainly: heavy dependence on

oil, weak economic base, high population growth and unemployment rates, low rates of

return on investment in physical and human capital, low level of integration in the world

economy, under-development of market institutions, and the legacy of economic

policies and structures that had emphasized a leading role for the state (Makdisi, Fattah

and Limam, 2000).

Despite a large dispersion in per capita GDP, size, economic structure, and geo-political

circumstances between Arab countries, there are some common structural issues.

Starting in the late 1980s, many of the Arab economies committed to far-reaching

programs of economic reform, which were designed to restore macroeconomic balance

and help in the transition from state-led to private sector-led development. The effort

contributed to improved economic performance, with GDP growth increasing from an

58 MENA oil-exporting countries: Algeria, Bahrain, Iran, Iraq, Kuwait, Libya, Oman, Qatar, Saudi Arabia,and the United Arab Emirates.

93

average of 2.4 percent a year in 1981-1990 to 3.1 percent between 1991-2000 (The

World Bank, 2003).

The analysis here distinguishes between two main groups of Arab countries that are

included in the study, oil-exporting countries (Gulf Cooperation Council, GCC) and

non-oil countries. In this respect, most of the used measures of macroeconomic and

financial development are updated to 2003 (if data were available).

The remainder of this chapter is structured as follows. Section two surveys the main

determinants and trends ofArab economies, while section three presents the measures of

economic reform. In section four, we provide quantitative measures of the Arab banking

sector and stock markets. Section five concludes the chapter.

4.2 Major Trends and Outlook

4.2.1 The Oil Boom

The second half of the twentieth century witnessed a significant widening of the

differentials in per capita GDP between the Arab countries59

. For this reason, it has

become increasingly difficult to talk about trends for the region as a whole (Pamuk,

2002). The GCC countries are dependent on oil resources, while other countries lack oil

and inadequately utilize their other natural resources (Abbas, 1999).

59 For more information on economic development in the Arab countries, see Babiker (2003) andElbadawi (2002).

94

• Oil-Exporting Countries

Over the past three decades the GCC countries have witnessed an unprecedented

economic and social transformation. Oil proceeds have been used to modernize

infrastructure and improve social and economic indicators. The GCC countries share

many economic characteristics: oil contributes about to one-third of total GDP and to

three-quarters of annual government revenues and exports. Together, these countries

account for about 45 percent of the world oil reserves and 25 percent of crude oil

exports (Saudi Arabia is the largest world oil exporter), and possess at least 15 percent

of the global natural gas reserves (Qatar has become the fourth-largest exporter of

liquefied natural gas), (Fasano and Iqbal, 2003).

Alternatively, there are important differences among the GCC countries: per capita GDP

ranges from less than USD $8,000 in Oman to USD $30,000 in Qatar, the weight of the

manufacturing sector has been growing very rapidly in Saudi Arabia and the United

Arab Emirates, while the banking and insurance sectors are the most important in

Bahrain. In Qatar, natural gas is well on the road to bypassing oil as the key sector in the

economy, and in Oman developing natural gas and tourism has just begun to bear fruit

(Fasano and Iqbal, 2003).

Oil export revenues have contributed to the improvement of welfare and helped finance

investment in infrastructure and human capital in most Arab countries, as shown in

Table 4.1. In fact, many Arab oil-exporting countries such as Kuwait, Bahrain, Qatar

95

and the United Arab Emirates, are ranked by the Human Development Report among

the top 45 countries in the world in terms of the United Nation Human Development

Index (Makdisi et al., 2000).

GCC countries face a number of new and immediate challenges related to oil s

dominant role in their economies and the risk arising from the variability of prices. They

have to accelerate their economic reform to reduce their dependency on oil, including by

promoting investment and private sector growth, thereby creating jobs for their

population (Okogu, 2003).

Table 4.1: Oil Revenues, Oil and Gas Exports Indicators

Oil Revenue(% of Total Revenue)

Oil and Gas Exports(% of Total Exports)*

GCC Countries 1995-2000 2001 2002 1995-2000 2001 2002

Bahrain

KuwaitOmanQatarSaudi ArabiaUAE

59.1

65.375.067.572.852

68.6

68.280.370.980.658.8

69.9

66.476.772.078.063.3

63.9

92.376.974.581.446.2

70.9

92.680.285.581.748.4

69.8

92.477.284.281.645.7

Average 65.3 71.2 71.1 72.5 76.6 75.2

Notes: * During the period 1990-1997, non-oil exports account for 30% of total export proceeds inBahrain, 18% in Oman and Qatar, and only around 10% in the oil rich members of Kuwait, Saudi Arabiaand the United Arab Emirates.

Source: The World Bank, Country data files, 2003.

• Non-Oil Countries

The small population size and limited availability of natural resources have placed

serious limitations on industrialization strategies based on the domestic market. As a

result, the more successful countries in this group have chosen to keep their economies

relatively open and have pursued diverse specialties based on skills in education, trade,

finance, and industry. More generally, the emergence of oil-based economic growth in

96

the GCC countries since the 1970s attracted large numbers of workers from the Arab

countries of the region, with the remittances constituting an important source of income

in all these countries, both small and large, increasingly linked to the changes of the oil-

exporting countries (Pamuk, 2002).

4.2.2 Real GDP and Per Capita GDP Growth

The relatively better economic growth performance of Arab countries in the 1960s,

1970s and the first half of the 1980s is largely attributed to the high oil export prices.

This situation has been reversed in the second half of the 1980s and early 1990s

resulting in sharp declines in domestic investment, saving and economic growth

(Makdisi et al., 2000).

Table 4.2 shows the GDP and per capita GDP growth rate for GCC countries and non-

oil countries. All GCC countries improved as a result of increased oil prices and

revenues. Moreover, non-oil sectors performed well in 2003 boosted by restored

confidence and better economic prospects following the short-war in Iraq. Other factors

that contributed to the exceptional economic growth during 2003 are strong public

expenditure, high domestic liquidity and low interest rates, which enhanced domestic

consumption and investment (ESCWA, 2004).

Non-oil countries faced worsening conditions in 2002, with GDP growth falling to 2.2

percent, a decline of 2 percentage points from 2001. External factors leading to this

decline include the deterioration in export market growth for Egypt, Morocco, and

97

Tunisia, as well as a sharp decline in the tourism sector in North Africa and several

countries in the Levant (Jordan and Lebanon), following the events of September 11,

2001 (The World Bank, 2003).

Figure 4.1: Annual GDP Growth Rate and Average Oil Prices (1990-2002)

-40.0

-30.0

-20.0

-10.0

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002

Arab Countries GDP Growth Rate (Annual %) WTI Oil Prices (Annual %)

Source: (1) The World Bank Group, 2002.(2) Bakheet Financial Advisor, WTI (West Texas Intermediate) Oil Prices.

Table 4.2: Real GDP and Per Capita GDP Growth Rates: Oil Vs. Non-Oil Countries

Country Real GDP Growth (%) Real Per Capita GDP Growth (%)

Oil-Countries (GCC) 1993-2003 2002 2003 1993-2003 2002 2003BahrainKuwaitOmanQatarSaudi ArabiaUAE

Average (1)

2.61.37.78.22.09.0

5.1

5.2-0.42.33.00.12.2

2.1

5.69.91.46.17.27.0

6.2

0.2-2.75.05.2-0.75.2

2.0

1.9-3.2-0.20.8-3.1-0.2

-0.7

2.36.9N/A3.93.74.4

4.2

Non-Oil Countries

EgyptJordanLebanonMoroccoTunisiaAverage (2)

4.63.82.83.24.83.8

3.25.02.23.21.73.1

3.23.22.75.55.64.0

2.60.61.31.53.41.9

1.22.21.01.60.61.3

1.20.51.43.84.42.3

Source: - The World Bank, Country at Glance, 2005.- N/A for Not Available data.

98

Figure 4.2: Real GDP and Per Capita GDP Growth Rate (1990-2002)

-2

0

2

4

6

8

10

Bahrain Egypt Jordan Kuwait Lebanon Morocco Oman Qatar Saudi

Arabia

Tunisia UAE

Real GDP Growth Per Capita GDP Growth

Source: The World Bank Group, 2002.

Figure 4.3: Arab Countries: Share of GDP (1990-2002)

Bahrain

1.6% Egypt

17.6%

Lebanon

3.0%Morocco

8.4%Q atar

2.7%

Saudi Arabia

36.4%

Tuni sia

4.6%

United Arab Emirates

12.5%

O man

4.3%

Kuwait

7.1%

Jordan

1.8%


99

4.2.3 Education and Labour Market60

The weighted average share of public spending on education in Arab countries recorded

an annual rate of increase of 0.7 percent and reached 5.8 percent of GNP in the late

1990s (Billeh, 2002). The non-oil countries export labour and receive large inflows of

remittances as a source of foreign exchange earnings, while the GCC countries are

relatively dependent on imported labour. Table 4.3 shows the main indicators of literacy

rate and gross enrolment ratio for both males and females of primary and secondary

schools. On the other hand, the unemployment rate is calculated as an average over the

period 1995-2001.

Table 4.3: Public Spending on Education, Illiteracy Rate and Gross Enrolment Ratio

Country Spending onEducation (% of GNP)

Illiteracy Rate(%)

Gross EnrolmentRatio (%)*

UnemploymentRate (%)

GCC Countries 1990s 2000 2002/2003 1995-2001BahrainKuwaitOmanQatarSaudi ArabiaUAE

Average (1)

3.45.64.63.45.51.8

4.1

12.518.128.318.823.823.8

20.9

96.091.081.0100.067.086.0

86.8

3.11.017.22.3N/A1.8

5.1

Non-Oil CountriesEgyptJordanLebanonMorocco

Tunisia

Average (2)

5.66.32.35.6

6.8

5.3

44.510.214.051.2

29.0

29.8

91.093.091.077.0

92.0

88.8

7.413.78.413.71

15.92

11.8

Notes: *Gross Enrolment Ratio for Primary and Secondary (Male and Female).1

and2, Unemployment rate for 2000.

Source: - The World Bank, World Development Indicators, 2002.

- UNESCO institute for statistics, 2004.

60Ali Abdel Gadir (2002) evaluates the education system in the Arab countries and concluded that

education in the region is in need of a comprehensive long-term reform strategy that focuses onmonitoring indicators, increasing the participation of the private sector in the provision of educationalservices, and improved internal efficiency.

100

4.2.4 The Investment and Saving Picture

Table 4.4 summarizes the balance of saving-investment for oil-exporting countries and

non-oil countries in two periods: the high growth/high investment period (1974-1985)

and the low growth/low investment period (1986-1996). The dramatic improvement in

saving rate was associated with the two oil price increases of 1973/1974 and 1979/1980.

Table 4.4: Saving-Investment Balance of Arab Countries,

Country 1974-1985 1986-1996 1997 1998

GCC CountriesInvestment (% of GDP)Total Saving (% of GDP)- National saving- Foreign saving

26.726.744.9

-18.2

22.522.518.34.2

21.033.7

23.427.2

Non-Oil CountriesInvestment (% of GDP)Total Saving (% of GDP)- National saving- Foreign saving

26.226.219.56.6

20.220.217.52.7

21.815.9

23.115.2

Sources: - World Economic Outlook, IMF.- Bisat, El-Erian and Helbling, 1997.

On the other hand, Foreign Direct Investment (FDI) stocks and flows in the Arab

countries are concentrated in a few countries and sectors. More than 80 percent of FDI

stocks are concentrated in five countries: Saudi Arabia, Egypt, Tunisia, Bahrain, and

Morocco. Saudi Arabia and Egypt account for more than half of the total FDI stocks in

the region. Countries at the lower end of the range include Oman, UAE, Jordan, Qatar,

Lebanon, and Kuwait (Eid and Paua, 2003). Table 4.5 describes briefly the rank of Arab

countries according to FDI stocks and flows.

101

Table 4.5: Arab World FDI Stocks and Flows (USD $million and Percentages)

Country Inward FDI Stocks1 Inward FDI Flows2

GCC Countries 2003% ofGDP

% ofTotal3 2003

% of Gross FixedCapital Formation

% ofTotal4

BahrainKuwaitOman

QatarSaudi ArabiaUAE

Total (1)Average (1)

6,720535

2,735

3,24725,5763,560

42,3737,062

72.412.012.6

16.012.14.4

7.00.62.8

3.426.63.7

51767

138

400208480

1,810302

50.42.25.5

11.40.63.7

9.21.22.4

7.13.78.5

Non-Oil CountriesEgyptJordanLebanonMoroccoTunisiaTotal (2)Average (2)Total (1) + (2)

20,9832,7931,98111,60816,56753,93210,78696,305

26.228.311.026.066.0

21.82.92.1

12.117.2

100.0

237379358

2,279584

3,837767

5,647

2.019.212.122.29.6

4.26.76.3

40.410.3

100.0

Arab World Inflows 1985-1995 2000Arab WorldAll Developing CountriesThe WorldAs % of Flows to Developing CountriesAs % of Total World FDI Flows

2,18550,745

180,3004.31.2

4,570240,167

1,270,7641.90.4

Notes:1 FDI stocks for associates and subsidiary enterprises represent the value of the share of their

capital and reserves (including retained profits) attributable to the parent enterprises (this is equal to totalassets minus total liabilities), plus the net indebtedness of the associates or subsidiary to the parent firm.

On the other hand,2 FDI flows consist of the net sales of shares and loans (including non-cash

acquisitions made against equipment, manufacturing rights, etc.) to the parent company plus the parentfirm s share of the affiliate s reinvested earnings plus total net intra-company loans (short and long-term)provided by the parent company. In

3and

4, we calculate the percentage for each country divided by the

total of two groups (Total (1) + (2)).

Source: UNCTAD, World Investment Report, 2004.

4.2.5 Macroeconomic Stability and Political Instability

By the early 1990s, the Arab countries had begun macroeconomic stabilization

programs that focused on reducing budget deficits, by canceling and rescheduling large

parts of government debt, reducing pressure on central banks to finance deficits by

creating more money. As a result, inflation has fallen to acceptable levels throughout the

102

region. Most MENA countries fix their currency exchange rates to the US dollar as part

of their macroeconomic reform programs.

• Political Instability

Economic growth is affected by conflicts (war and social violence), which have been

widespread throughout the Arab world during the period that we are considering in our

study, 1980-200261

. This kind of instability has direct implications on the level of

income through destruction of productive capacity and does not help to create the

business environment necessary for any economy to prosper. These conflicts have

certainly deterred investment and slowed the process of economic growth and

development in the Arab region (Sala-i-Martin and Artadi, 2003).

4.3 Economic and Structural Reform

In recent years, many Arab governments have embarked on plans to liberalize their

economies, strengthen free market mechanisms, reduce barriers to trade, assist the

formation of stock markets, and increase employment levels. Arab countries also

adopted privatization programs, in Egypt, Kuwait, Jordan, Bahrain, and Tunisia that

have been relatively successful. By contrast, in Saudi Arabia, privatization has been

undertaken to prolong the life of existing political and economic regimes. In Qatar and

the United Arab Emirates, it is still in its early stages of development (Abbas, 1999).

61 For example, the First Gulf-War (1980-1988) and the Second Gulf-War (1990).

103

With reference to economic reform, Table 4.6 shows two indices of Annual Freedom

Scores and Institutional Quality Index for Arab countries.

Table 4.6: Annual Freedom Scores62 and Institutional Quality Index, 2004

Country Annual Freedom Ratings1 Institutional Quality Index2

GCC countries Political Rights Civil Liberties Freedom Status (Out of 6 points)BahrainKuwaitOmanQatarSaudi ArabiaUAEAverage (1)

546676

5.7

555576

5.5

PFPFNFNFNFNFNF

223222

2.2

Non-Oil Countries

EgyptJordanLebanonMoroccoTunisia

Average (2)

65656

5.6

54545

4.6

NFPFNFPFNF

PF

23132

2.2

Notes: -1Freedom Scores between 1 = the most free (perfect), and 7 = the least free rating (dismal).

- PF = Partly Free, NF = Not Free.- 2 Institutional Quality Index represents the maximum score of 6 points (i.e. 0 the worst and 6for the best), 2003.

Sources: Freedom House FH Country Ratings, 2005, and International Country Risk Guide ICRG ,2004.

• Reforms in The GCC Countries

The GCC countries face two main challenges. Firstly, to accelerate the growth rate of

non-oil sectors to generate adequate employment opportunities for the 70 percent of the

population under age 30. The second is reducing vulnerability to oil price fluctuations.

With regard to exchange rate policies, the GCC countries have agreed to establish a

monetary union by 2010 with a single currency pegged to the US dollar (see Table 4.7).

62The characters representing scores for each year are, from left to right: Political Rights, Civil Liberties,

and Freedom Status. Each of the first two is measured on a one-seven scale, with one representing thehighest degree of freedom and seven the lowest. F , PF , and NF respectively stand for Free ,Partly Free , and Not Free . Countries whose combined averages for political rights and for civil

liberties fall between 1.0 and 2.5 are designated Free , between 3.0 and 5.5 Partly Free , and between5.5 and 7.0 Not Free , (http://www.freedomhouse.org/ratings/).

http://www.freedomhouse.org/ratings/)

104

WTO rules will further open the economies of the GCC, which are already relatively

liberal, by discouraging laws stipulating that goods be imported through exclusive

commercial agents.

• Reforms in Non-Oil Countries

This group is relatively advanced in the broad direction of economic reform. On the

issue of exchange rates, Tunisia has a managed float, with a real exchange rate target.

Jordan and Morocco have pegged exchange rate policies, and Egypt recently shifted to a

floating exchange rate system as shown by Table 4.7 (Jbili et al., 2003). Most of these

countries have signed agreements with the European Union and WTO. In addition,

Jordan recently signed a free trade agreement with the United States.

Table 4.7: National Currency and Exchange Rate Regimes63, 2003

Country National Currency Exchange Regime1

GCC CountriesBahrainKuwaitOmanQatar

Saudi ArabiaUAE

Bahrain DinarKuwait DinarRiyal OmaniRiyal Qatari

Saudi Arabian RiyalUAE Dirham

Fixed peg arrangement against a single currency2

Fixed peg arrangement against a single currencyFixed peg arrangement against a single currencyFixed peg arrangement against a single currency

Fixed peg arrangement against a single currencyFixed peg arrangement against a single currency

Non-Oil Countries


Egyptian PoundJordan DinarLebanese PoundMoroccan DirhamTunisian Dinar

Managed float with no preannounced path for exchange rateFixed peg arrangement against a single currencyFixed peg arrangement against a single currencyFixed peg arrangement against a compositeCrawling peg

Note:1

Exchange rate regime classification by IFS (as ofApril 2003), and2Single Currency = US dollar.

Source: International Monetary Fund (IMF), Annual Report 2003.

63Exchange rate regimes in the region currently range from a hard peg to variants of float, but pegged

regimes are predominant (IMF, 2003).

105

4.4 Financial Development in Arab Countries64

4.4.1 Background

Creane, Goyal, Mobarak and Sab (2003) assess the financial sector in the Arab countries

and construct a detailed set of financial development indicators. They organize the data

according to six themes: (1) development of the monetary sector and monetary policy,

(2) banking sector size, structure, and efficiency (including the role of the government),

(3) quality of banking regulations and supervision, (4) development of the non-bank

financial sector, (5) financial openness, and (6) the institutional environment.

Analysis of the data suggests common strengths, trends, and weaknesses, and points to

future areas for economic reform. We consider the main findings of Creane et al. (2003)

as follows:

• Monetary Policy

For most MENA countries, interest rates are freely determined, indirect monetary policy

tools are employed, and government securities exist.

• Banking Sector

In general, the banking sector is dominated by the public sector, which is characterized

by government intervention in credit allocation, losses and liquidity problems, and wide

interest rate spreads.

64Arab financial markets are still smaller and less active than the developing countries. They also suffer

from concentrated ownership, a modest number of listings, and a fair number of closed companies(Bolbol, 2004).

106

• Regulation and Supervision

The countries of the GCC, Jordan, Lebanon, Morocco, and Tunisia, have strengthened

banking supervision and regulation, and established up-to-date procedures to collect

prudential information on a regular basis. They have taken steps to conform to

international Basel standards by increasing capital adequacy ratios and reducing non-

performing loans.

• Non-bank Financial Sector

This sector needs further development, includes stock markets, corporate bond markets,

insurance companies, pension and mutual funds. Where such markets exist, trading is

usually quite limited. The development of these markets is complicated by legal

limitations on ownership and the need for a clear and stable legislative framework.

• Financial Openness

MENA countries have gradually opened up their current and capital accounts. Nearly

half the countries have open financial sectors, although many maintain restrictions on

foreign ownership of assets and repatriation of earnings. Some countries continue to

maintain parallel exchange markets and/or multiple currency rates.

• Institutional Environment

In most of the MENA countries, the quality of institutions, including the judicial system,

bureaucracy, law and order, and property rights, is poor.

107

However, based on an index of qualitative and quantitative data (2000-2001), scoring 0

to 10, with 0 the worst and 10 representing the highest level of financial development,

Creane et al. (2003) find that the grouping of countries into high, medium, and low

financial development categories was robust to the different weighting schemes,

although the relative ranking of countries within each group changed slightly (see Table

4.8).

Table 4.8: Middle East and North Africa: Financial Development Ranking

Level of Financial Development

HighBahrainJordanKuwaitLebanonOmanQatarSaudi ArabiaUAE

MediumAlgeriaDjiboutiEgyptMauritaniaMoroccoPakistanTunisia

LowIranLibyaSudanSyriaYemen

Comparative Financial Development Indicators (Comprehensive index, scale 0-10, 2000-2001)ComprehensiveIndex

BankingSector

Non-BankFinancial

Sector

Regulationand

Supervision

MonetarySector and

Policy

FinancialOpenness

InstitutionalEnvironment

MENAAverage

5.4 5.3 4.8 6.5 5.4 6.1 4.7

Financial Development Levels (Average Scores)1

High 7.5 7.3 6.7 8.9 7.3 8.9 5.9

Medium 5.3 5.0 4.1 6.5 5.6 6.1 4.8Low 3.3 3.1 2.7 3.5 3.1 3.9 3.8

Note:1

within the overall scale of (0-10), intermediate scales are as follows: High-above 6, Medium (4-6),and Low-below 4.

Source: (Creane, Goyal, Mobarak and Sab, 2003).

On average, countries at higher levels of financial development outperformed countries

at lower levels in each of the six aspects of financial development (see Table 4.8). That

is, countries in the highest level of financial development received particularly high

marks for regulation and supervision and financial openness. Countries in the medium

level also scored fairly well in these two areas. Across the MENA region, countries fare

108

poorly on development of a strong institutional environment and the non-bank financial

sector.

4.4.2 Financial Sector Indicators

Following Eltony (2004) in providing quantitative measures of financial development,

this section considers three sets of indicators relating to interest rates, monetary and

credit aggregates of the financial sector.

(1) Interest Rates

The two indicators related to interest rate are the level of real interest rate, which

demonstrates the allocative efficiency of financial resource use, and the nominal interest

rate margin (i.e. the difference between deposit and lending rates) which point to the

level of financial sector competition.

Because of the region s low inflation rate, average real interest rates have generally been

positive for all Arab countries in our study but it is higher for non-oil countries, the

average values of 8.7 percent and 6.0 percent against the average rates prevailing in the

GCC countries, varies from 5.3 to 2.0 percent over the periods (1990-2000) and 2003

(see Table 4.9). In addition, the average nominal interest rate margin ranges from 3.0 to

4.5 percent in GCC countries compared to 5.0 to 6.3 percent for non-oil countries over

the periods (1990-2000) and 2003. Nominal interest rate margins are higher in Lebanon

109

and Morocco than those prevailing in other countries, indicating an increase in

competition within the financial market, as shown in Table 4.9.

Table 4.9: Interest Rate Indicators (1990-2003)

Country Real Deposit Rate (%) Nominal Interest Rate Margin (%)

GCC countries 1990-2000 2002 2003 1990-2000 2002 2003Bahrain

KuwaitOmanQatar1

Saudi Arabia2

Average (1)

5.8

6.36.82.35.5

5.3

2.0

3.22.97.27.5

4.6

1.2

2.42.4N/AN/A

2.0

6.5

2.12.61.5N/A

3.2

7.2

3.35.71.3N/A

4.4

N/A

3.05.9N/AN/A

4.5

Non-Oil Countries


3

Average (2)

10.87.814.87.23.08.7

9.34.411.04.52.66.4

8.23.28.73.8N/A6.0

5.23.312.84.55.36.2

4.55.85.68.64.55.8

5.36.24.78.8N/A6.3

Notes: the data is available over the period (1995-2000) for1Qatar,

2Saudi Arabia, and

3Tunisia.

Source: International Financial Statistics, IMF, 2004.

(2) Monetary Aggregates

The measures of financial deepening are the ratio of money supply M2 to GDP, the ratio

of M1 to M2 and the ratio of currency to deposits65

. While the first two ratios provide

an indication of the importance of long-term banking and the degree of sophistication of

financial development. The third ratio is intended to demonstrate the depth of the

financial market.

65Money Supply (M1) equals the sum of currency outside banks plus demand deposits other than of the

central government. Also, Money Supply (M2) refers to the summation of money supply (M1) and thetime, saving deposits, and foreign currency deposits of resident sectors other than the central government(Quasi-Money). This definition corresponds to lines 34 and 35 in the International Financial Statistics(IFS), which issued by International Monetary Fund s (IMF).

110

Table 4.10 shows that the GCC countries are well monetized, the ratio of money supply

(M2) to GDP is relatively stable over the time reflecting the financial sector is success in

attracting deposits, it s average ranging between 59.1 to 60.8 percent during the periods

(1990-2000) and 2003. Furthermore, the GCC banking sector has also been successful

in mobilizing longer-term financial assets as evidenced by the low ratios of (M1/M2)

over the same period except for Saudi Arabia, which has a higher ratio of about 54.0

percent in 2003. On the other hand, the currency to deposit ratios are low in Kuwait,

Bahrain, Qatar, and UAE, and to a lesser degree in Oman and Saudi Arabia, indicating

fairly sophisticated financial markets and public confidence in the GCC banking system.

The ratio of broad money supply (M2) to GDP in non-oil countries is higher than the

GCC countries, where the best ratios were in Lebanon and Jordan, than the rest of the

non-oil countries and clearly demonstrates a deepened financial sector. Moreover, the

long-run mobilization ratio (M1/M2) is very low in Lebanon and Egypt and to a lesser

extent in Jordan. In these countries this is clearly indicative of a well-developed banking

sector and illustrates the depth of the financial sector in each of these countries. The

currency to deposit ratios are lowest in Lebanon, Egypt, Tunisia, Jordan, and to a lesser

degree in Morocco indicating fairly sophisticated financial markets and public

confidence in the banks (see Table 4.10).

111

Table 4.10: Monetary Indicators (1990-2003)

Country M2 / GDP (%) M1 / M2 (%) Currency to Deposit (%)

GCC Countries 1990-00 2002 2003 1990-00 2002 2003 1995-99 2000BahrainKuwaitOmanQatarSaudi ArabiaUAE

Average (1)

67.998.930.457.646.553.3

59.1

81.890.235.444.853.958.7

60.8

76.683.633.950.151.9N/A

59.2

24.317.329.721.653.024.8

28.5

24.921.427.919.653.126.8

29.0

29.725.128.530.353.9N/A

33.5

6.85.016.07.526.38.9

11.8

6.05.419.26.223.88.6

11.5

Non-Oil CountriesEgyptJordanLebanon1

MoroccoTunisia

Average (2)

81.2110.3123.966.248.8

86.1

87.2118.3127.089.456.9

95.8

96.7130.9N/A92.356.3

94.1

26.235.35.774.345.5

37.4

22.828.74.976.640.4

34.7

23.230.6N/A77.440.1

42.8

15.923.83.833.319.9

19.3

17.222.93.025.018.3

17.3

Notes:1

the data is available for Lebanon over the period (1995-2000).


(3) Credit Aggregates

The credit indicators used here are the shares of the private sector and the public sector

credit as a percentage of total credit provided by the banking sector, respectively. The

third indicator is the monetary authority credit (central bank) to the financial sector as a

percentage of total financial sector credit.

In the GCC countries, the private sector shares in the total credit provided by the

banking sector is generally higher than in the non-oil countries, its average ranging from

86.9 to 91.9 percent for GCC in comparison to 64.9 and 67.5 percent for non-oil

countries over the periods (1990-2000) and 2003. For the most part, the share of credit

to the private sector has been increasing since 1990 in all GCC countries. On the other

112

hand, the banking sector has been generally active in lending to the public sector, with

Kuwait having the highest ratio66.

With regard to the credit to the private sector in non-oil countries, Tunisia has the

highest ratio among the five countries followed by Jordan and Morocco. In Lebanon and

Egypt, the ratio is about 40 to 60 percent. The credit to public sector ratios point out the

dominance of the public sector in economic activities, Tunisia having the lowest ratio,

followed by Jordan and to lesser extent Morocco, which suggests an advanced

privatization process and a more conservative fiscal perspective in those countries.

Lebanon has the highest ratio with a range of 40 to 55 percent. This amounted to about

half of all the credit provided by the banking system despite the near absence of the

public sector in the Lebanese economy, linked with the persistence of large public debt

due to a chronic budget deficit. In Morocco, the ratio has fallen dramatically in 2002

and 2003.

At the share of central bank credit to total financial credit, all of the Arab countries

show low and decreasing ratios that are indicative of a low level of reliance on the

central bank for their operations, see Table 4.11.

66 Due to the large fiscal deficit that resulted from the Gulf War (1991) and the subsequent declines inworld oil prices, the financing of the so-called bad debt crises in Kuwait (Eltony, 2004).

113

Table 4.11: Credit Indicators (1990-2003)

Country Credit to Private/TotalCredit (%)

Credit to Public/TotalCredit (%)

Credit From CentralBank/Total Credit (%)

GCC Countries 1990-00 2002 2003 1990-00 2002 2003 1995-00 2000BahrainKuwaitOmanQatarSaudi ArabiaUAE

Average (1)

144.536.7110.876.079.9103.491.9

109.766.995.769.171.1117.488.3

107.072.196.272.165.6108.486.9

44.563.311.622.39.612.527.3

9.733.12.924.524.826.120.2

7.027.91.717.727.019.716.8

N/A0.04N/A0.410.20.072.7

N/A0.2N/A0.54.50.051.3

Non-Oil CountriesEgyptJordanLebanonMoroccoTunisiaAverage (2)

41.074.960.456.092.464.9

53.581.346.564.491.767.5

51.379.544.266.392.666.8

35.614.739.639.87.627.5

32.913.453.524.48.326.5

35.914.955.823.07.427.4

1.12.80.90.62.71.6

1.02.31.62.22.41.9


4.4.3 Arab Stock Markets67

4.4.3.1 General Preview

Since the late 1980s, most Arab countries have acknowledged the importance of the

stock market and the private sector in the achievement of economic development. The

interest and commitment these countries exhibited in developing and reforming their

economic structure was reflected in the increase in the number of active stock markets

from only four in the 1970s (Egypt, Jordan, Kuwait and Lebanon) to 11 by the year

2002. Financial market laws and regulations68 address the same basic issues and

67 There are stock markets currently in Algeria, Egypt, Jordan, Lebanon, Morocco, Sudan, and Tunisia,and in the GCC including Abu-Dhabi, Bahrain, Dubai, Kuwait, Qatar, Oman, and Saudi Arabia. Thestudy excluding some of them (Algeria and Sudan) due to insufficient data as they had established theirstock markets recently.

68In Bahrain, Kuwait, Lebanon, and Qatar, market management is allowed to serve both executive and

supervisory functions under the umbrella of a securities and exchange commission (i.e. this systemfollows the British model). In the markets of Egypt, Jordan, and other GCC countries these roles are

114

concerns including management and the institutional structure, membership69

, listing

requirements70, financial disclosure, and trading and pricing procedures (ESCWA,

2003).

This section provides a historical development and quantitative review of Arab stock

markets. Noting that the Arab stock market capitalization jumped from USD $32.5

billion in 1990 to USD $360.9 billion by the year 2003, as shown in Figure 4.4. Total

value traded increased from USD $0.7 billion to USD $230.4 billion over the period

1990-2003, and the total number of listed companies about doubled from 847 to 1,673

companies over the same period 1990-2003 (Standard and Poor s, 2002, and Arab

Monetary Fund, 2003). Table 4.12 shows a summary of the main indicators for 11 Arab

stock markets in 2003.

divided between two institutions, one responsible for the stock exchange and the other for supervision(i.e. is based on the American approach), (ESCWA, 2003).

69The legislation in Egypt, Oman, and Qatar, considers trading in listed stocks illegal if transactions are

not executed through one of the official mediators, limits stock trading to the market floor, and prohibitsany other form of trading. Bahrain and Kuwait permit the trading of listed stocks outside a specificlocation on the condition that the exchange occurs in accordance with certain regulations (ESCWA,2003).

70 The Abu-Dhabi market requires the listed stocks to be issued by joint stock companies with paid upcapital of not less than 50 percent of the listed value. The Bahrain stock market requires that the paid-upcapital of a listed company be no less than USD $1,315,000, and that the shares be worth at least USD$500,000. In Lebanon, the capital of a listed company should equal a minimum of USD $3 million.Egypt requires that the shares available for public offering equal at least 30 percent of a company s totalshares. In Kuwait, the approval and listing of shares for trading requires that the paid-up capital for alisted company be not less than USD $3.4 million. Companies in Jordan must have paid-in capital ormarket capitalization of no less than USD $2.8 million for the listing of stocks (ESCWA, 2003).

115

Table 4.12: Arab Stock Markets, Main Indicators (USD $million, 2003)

Country Market Capitalization Value Traded TurnoverRatio (%)

No. Of ListedCompanies

GCCCountries 2003

% ofGDP

% ofTotal1 2003

% ofGDP

% ofTotal2 2003 2003

% ofTotal3

BahrainKuwaitOmanQatarSaudi ArabiaUAE

Total (1)Average

9,70259,5287,24626,702

157,30644,647

305,13150,855

126.3168.335.7

152.983.562.9

104.9

2.716.52.07.4

43.612.4

26154,7291,3343,220

159,0562,031

220,63136,772

3.4154.7

6.618.484.42.9

50.1

0.123.80.61.4

69.90.9

2.791.918.412.1

101.14.5

38.5

44108141287043

434

2.66.58.41.74.22.6

Non-OilCountriesEgyptJordanLebanonMoroccoTunisia

Total (2)Average

27,84810,9631,50313,0502,440

55,80411, 161

33.8111.2

7.929.310.0

38.4

7.73.00.43.60.7

4,3492,607131

2,444189

9,7201,620

5.326.40.75.50.8

7.7

1.91.10.11.10.1

15.623.88.7

18.77.7

14.9

967161145245

1239

57.89.60.83.12.7

Total (1)+(2) 360,935 100 230,351 100 1,673 100

Notes: In 1, 2 and 3, we calculate the percentage for each country divided by the total of two groups (Total(1) + (2)).Sources: (1) World Federation of Exchanges, 2003.

(2) World Development Indicators, 2002.(3) Arab Monetary Fund, Arab Stock Markets Performance, 2003.

Figure 4.4: Arab Stock Market Capitalization (1990-2003)

0

50000

100000

150000

200000

250000

300000

350000

400000

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

Source: (1) Standard and Poor s, 2002.(2) Arab Monetary Fund, Arab Stock Market Performance, 2003.

116

Table 4.12 shows that Saudi Arabia, Kuwait and the United Arab Emirates have the

highest market capitalization of the 11 Arab stock markets at the end of 2003, where the

Saudi stock market is the largest in the region accounting for 43.6 percent of the total.

On the other hand, Saudi Arabia has the highest turnover ratio 101.1 percent in 2003,

followed by Kuwait 91.9 percent, Jordan 23.8 percent, Morocco 18.7 percent, Oman

18.4 percent and Egypt 15.6 percent. The number of listed companies is higher for

Egypt but this is not reflected in the market value traded comparison to Saudi and

Kuwait stock markets, indicating that although companies listed, their shares are not

necessarily traded in the market.

In spite of this, the Kuwait stock market has the highest ratio of market capitalization to

GDP, about 168.3 percent followed by Qatar and Bahrain, 152.9 percent and 126.3

percent, respectively. However, Arab stock market capitalization accounted about 60

percent for banking, investment, insurance and real estate firms, while less than 20

percent for manufacturing firms (Azzam, 1999). For the average of Arab stock market

capitalization over GDP, see Figure 4.5.

117

Figure 4.5: Average Arab Stock Market Capitalization as a Share of GDP (1990-2003)

0

20

40

60

80

100

120

Bahrain Egypt Jordan Kuwait Lebanon Morocco Oman Saudi

Arabia

Tunisia United

Arab

Emirates

Source: (1) Standard and Poor s, 2002.(2) Arab Monetary Fund, Arab Stock Market Performance, 2003.(3) World Development Indicators, 2002.

By world stock market standards, Figure 4.6 shows that Arab stock markets are small,

accounting of 0.7 percent of the total market capitalization for both the East Asia-Pacific

markets and the G-7 markets over the period 1990-2003. In addition, the East Asia-

Pacific stock markets represent 8.9 percent during the same period.

Figure 4.6: Stock Market Capitalization, (International Review, 1990-2003)

0.7%

8.9%

90.4%

Arab Stock Markets

Emerging Stock Markets

Developed Stock Markets

Source: (1) World Federation of Exchanges, 2003.(2) Standard and Poor s, 2002.(3) Arab Monetary Fund, Arab Stock Market Performance, 2003.

According to the local indices of Arab stock markets for 2003, the best performers were

the Egyptian stock market which increased by 152.2 percent, followed by Kuwait and

118

Saudi Arabia which increased by 101.7 percent and 76.2 percent, respectively.

Moreover, the performance of the 11 Arab markets has been positive and increased in

2003 compared to 2001 and 2002 (see Figure 4.7).

Figure 4.7: Arab Stock Markets Performance in 2001, 2002 and 2003

-2.5

-37.9

29.8

26.8

-26.7

-7.4

-25.4

37.2

8.0

-12.2

28.4

3.4

3.5

-1.6

39.0

4.3

-16.5

26.2

36.8

3.2

-11.7

10.1

28.8

152.2

53.8

101.7

0.8

24.0

43.1

69.8

76.2

11.8

29.0

-50 0 50 100 150 200

Bahrain (BSE)

Egypt (EFGI)

Jordan (ASI)

Kuwait (KSI)

Lebanon (BSI)

Morocco (MASI)

Oman (MSM)

Qatar (DSI)

Saudi Arabia (TASI)

Tunisia (Tunindex)

UAE (SHUAA)

Percent (%)

Annual % Change 2001 Annual % Change 2002 Annual % Change 2003

Source: (1) Jordinvest Annual Reports, 2002 and 2003.

In addition, Table 4.13 provides the list of Arab stock markets with its official term and

establishment date.

119

Table 4.13: Arab Stock Markets With its Official Term and Establishment Date

Country Arab Stock Markets Stock Market Code Date of Est.

GCC CountriesBahrainKuwaitOmanQatar

Saudi ArabiaUAE

Bahrain Stock ExchangeKuwait Stock ExchangeMuscat Securities MarketDoha Securities Market

Saudi Stock Market(1) Abu Dhabi Securities Market(2) Dubai Financial Market

BaSEKSEMSMDSM

Tadawul MarketADSMDFM

1989196219891996

198420001998

Non-Oil CountriesEgypt

JordanLebanonMoroccoTunisia

(1) Alexandria Stock Exchange(2) Cairo Stock ExchangeAmman Stock ExchangeBeirut Stock ExchangeCasablanca Stock ExchangeTunis Stock Exchange

ASECSEAFMBeSECASABLANCE-BOURSETSE

188819031976194519291969

Source: Different electronic websites for Arab Stock Exchanges.

4.4.3.2 Oil-Exporting Countries

The stock markets of the GCC countries are still small, the total number of GCC listed

companies increased from 212 to 434 companies between 1990 and 2003. On the other

hand, total market capitalization improved to USD $305.1 billion from USD $56.7

billion, and value traded jumped to USD $220.6 billion from USD $9.0 billion over the

same period 1990-2003.

This section presents a brief description of financial development and stock market

performance on an individual country level for this group of oil-exporting countries .

120

(1) Bahrain Stock Market

In 1957, Bahrain had its first public shareholding company, the National Bank of

Bahrain. However, it was not until the late 1970s and early 1980s that Bahrain realized

there was a growing need for an organized stock market. As a result, in 1987 the Amiri

Decree No. 4 was issued for the establishment of the Bahrain Stock Exchange (BaSE),

which officially commenced operations on 17th June 1989 with 29 companies listed on

the Exchange, acting as a securities regulator with supervision of the capital market and

serving as a securities exchange. The Bahrain stock market was included in the

International Finance Corporation Global (IFCG) index in 1999.

• Stock Market Development

By 1994, the number of listed companies had reached 34 with total market capitalization

equivalent to USD $5.1 billion and value traded of USD $0.2 billion. In the year 2003,

the total number of listed companies increased to 44 companies, almost two thirds of

which are in the financial services sector. Market capitalization improved to USD $9.7

billion in 2003 (see Table 4.12). The local index ended at 1,822 points, an increase of

28.8 percent for the year 2003. Future plans for the BSE include extending services to

local, regional, and international companies and strengthening relations with other GCC

countries in order to have their shares listed on the BSE. Foreign nationals will be

permitted 100 percent ownership of listed firms within 3 years.

121

• Banking and Finance

Over the past 20 years, Bahrain has established itself as a leading financial centre for the

Gulf region and the Arab world. The legal and accounting systems are transparent and

meet international standards. It is relatively easy to establish a bank, and there are few, if

any, restrictions or requirements on new and foreign banks.

The financial system in Bahrain comprises about 172 financial institutions (56

commercial and investment banks, 48 offshore banking units, 36 representative offices,

19 foreign exchange and money brokers, and 13 investment advisory and other financial

services). The consolidated balance sheet for the banking system in Bahrain totaled

USD $102.1 billion at the end of 1999, up from USD $99.5 billion in 1998, and its

contribution to the GDP was around 23 percent in 1999, more or less the same share as

the oil sector (US Dept., 2001).

• Financial Liberalization

Bahrain, as a member of World Trade Organization (WTO), has scheduled commitments

to liberalize their financial services sector in line with the General Agreement on Trade

in Services (GATS)71

rules and provisions, as shown by Table 4.14. For each of the

financial services covered by a particular commitment, the schedule must indicate under

each of the four modes any limitations on market access or national treatment that are to

71The General Agreement on Trade in Services (GATS) covers all services sectors and all forms of trade

in services. There are three components of the GATS with direct relevance to financial services: (1) thegeneral GATS obligations, (2) the GATS annexes on financial services, and (3) the individual memberschedules set out specific commitments regarding market access and national treatment provided toforeign suppliers of financial services (ESCWA, 2003, p. 44).

122

be imposed, and limitations not declared in this manner become illegal72

(ESCWA,

2003).

Table 4.14: Schedule of Commitments for Bahrain under the GATS rules

Sector and Subsector Limitations on market accessLimitations onnational treatment

Services auxiliary to insurance(including broking and agencyservices)

(1) N(2) N(3) N(4) U

(1) N(2) N(3) N(4) U

Trading the following for ownaccount or for the account of

customers, whether on anexchange, in an over-the-counter market, or otherwise:

(a) Money market instruments(including cheques, bills andcertificates of deposits);

(b) Derivative productsincluding (but not limited to)futures and options;

(c) Transferable securities;

(d) Other negotiable instrumentsand financial assets, includingbullion.

(1) Unbound for equity and other securitieslisted on the Bahrain stock exchange (BSE). A

license is required from the Bahrain marketauthority (BMA) if the activity is undertakenin/from Bahrain for customer s account;

(2) Unbound for equity and other securitieslisted on the BSE;

(3) Transaction on the BSE must be conductedthrough a licensed stockbroker. Transactionsinvolving listed bonds and warrants can be doneover the counter. Stockbrokers and marketmakers for securities on the BSE must be eitherBahraini nationals (individuals or locallyincorporated companies with a minimum 51 percent Bahraini ownership) resident in Bahrain, orbranches of foreign companies undertaking suchbusiness in international securities. A moneybroker must be locally incorporated with 51percent Bahraini ownership, and requires a license from the BMA.(4) U

(1) U

(2) U

(3) N

(4) U

Participation in issues of allkinds of securities, includingunderwriting and placement asagent (whether publicly orprivately) and provision ofservices related to such issues.

(1) Unbound for equity and other securitieslisted on the BSE. A license is required from theBMA if the activity is undertaken in/fromBahrain for customers account;

(2) Unbound for equity and other securitieslisted on the BSE;

(3) A license is required from the BMA if theactivity is undertaken in/from Bahrain. Unboundfor equity and other securities listed on the BSE;

(4) U

(1) U

(2) U

(3) N

(4) U

Asset management, including (1) Unbound for BSE-listed equity and other (1) U

72The schedule of specific commitments includes the following categories: (a) financial services, (b)

modes of supply, (c) limitations on market access, and (d) limitations on national treatment. The modes-of-supply category comprises four subcategories, namely, cross-border supply, consumption abroad,commercial presence, and the presence of natural persons (ESCWA, 2003, p. 47).

123

cash or portfolio management,all forms of collectiveinvestment management,pension fund management, andcustodial, depository and trustservices

securities;

(2) Unbound for BSE-listed equity and othersecurities;

(3) Licenses are required from the BMA. Fordepository services in respect of BSE-listedequity and other securities, approval is requiredfrom the BSE.

(4) U

(2) U

(3) No limited-liability companiescan undertakeinvestments on behalfof third persons.

(4) U

Settlement and clearing servicesfor financial assets, includingsecurities, derivative productsand other negotiable instruments

(1) Unbound for cheques in Bahrain dinars (BD),must be through the BMA; for BSE-listed equityand other securities, must be through the BSE;

(2) Unbound for BD cheques, must be throughthe BMA; for BSE-listed equity and othersecurities, must be through the BSE;

(3) Unbound for BD clearing

(4) U

(1) N

(2) N

(3) N

(4) U

Notes: the numbered items in the second and third columns correspond with the following modes-of-supply subcategories: (1) cross-border, (2) consumption abroad, (3) commercial presence, and (4) thepresence of natural persons. The letter designations signify the following: U = unbound in liberalizing anactivity, N = no limitations, UE = unbound except, and NE = no limitations except.

Source: (1) World Trade Organization, Bahrain: schedule of specific commitments , (GATS/SC/97),(http://www.wto.org/english/tratop-e/serv_commitments_e.htm).(2) This table is organized and structured by the Economic and Social Commission for Western Asia(ESCWA, 2003, pp. 48-49), United Nations.

(2) Kuwait Stock Market

The first law authorizing a Kuwaiti securities market was issued in October 1962, and

the Kuwait Stock Exchange (KSE) opened in April 1977. It was suspended during the

first Gulf-War, and re-opened in September 1992. In May 2000, Kuwait adopted

legislation allowing outside investors to purchase shares in local companies and opening

the way for foreign investment. The KSE has the potential for growth, in terms of both

market capitalization and the ability to attract foreign investment (ESCWA, 2003).

http://www.wto.org/english/tratop-e/serv_commitments_e.htm

124


The Kuwait stock market is the oldest in the region, comprising 108 companies73 (93

Kuwaiti businesses, 10 foreign companies, and 5 mutual funds), with the largest part

represented by 42 percent doing business in the financial services sector. The remaining

companies reflect a diverse mix of economic activities including agriculture, computing,

construction, manufacturing, broadcasting, oil and gas, services, retailing, and transport.

By the year 1994, the total market capitalization was equivalent to USD $11 billion.

This jumped to USD $59.5 billion in 2003, the second highest figure among the Arab

countries. The value traded increased from USD $2.0 billion to USD $54.7 billion over

the period 1994-2003 (see Table 4.12). Despite its small size, KSE is one of the most

dynamic in the world, with a turnover ratio of 91.9 percent in 2003.


The Kuwait banking sector consists of 8 commercial banks that are closely held by the

government and merchant families, constituting a total of 125 branches. The pre-

eminent bank is the National Bank of Kuwait, which at the start of 2003 had 36

branches. There are also 3 specialized banks that provide medium and long-term

financing, and 4 representative offices of foreign banks74

.


Kuwait, as a member of the WTO, has scheduled commitments to liberalize its financial

services sectors in line with GATS rules and provisions, (see Table 4.15).

73The number of companies listed on the exchange decreased from 54 before the Iraqi invasion to 48 at

the end of 1994 then increased to 108 by July of 2003.

74 For more information, see (http: www.arabdatanet.com).

http://www.arabdatanet.com)

125

Table 4.15: Schedule of Commitments for Kuwait under the GATS rules

Sector and SubsectorLimitations onmarket access

Limitations onnationaltreatment

Trading the following for own account or for the account ofcustomers, whether in the financial market, over-the-counter orotherwise:

(a) Money market instruments (such as cheques, bills, andcertificates of deposits);

(b) Derivative products including (but not limited to) futuresand options;


(d) Other negotiable instruments and financial assets, including

gold bullion.

(1) U(2) N(3) UE(4) UE

(1) U(2) N(3) UE(4) UE

Participation in issues of all kinds of securities, includingunderwriting and placement as agent (whether publicly orprivately) and the provision of financial services related to suchissues.

(1) U(2) N(3) UE(4) UE

(1) U(2) N(3) UE(4) UE

Management of assets, including the management of portfolioinvestments, liquid assets, all forms of collective investments,pension funds and investment trustee services.

(1) U(2) N(3) UE(4) UE

(1) U(2) N(3) UE(4) UE

Settlement and clearing services for financial assets, includingsecurities, derivative products and other negotiable instruments.

(1) U(2) N(3) UE(4) UE

(1) U(2) N(3) UE(4) UE


Source: (1) World Trade Organization, Kuwait: schedule of specific commitments , (GATS/SC/49),(http://www.wto.org/english/tratop-e/serv_commitments_e.htm).(2) This table is organized and structured by the Economic and Social Commission for Western Asia(ESCWA, 2003, p. 49), United Nations.

(3) Oman Stock Market

The Muscat Securities Market (MSM) was established in 1989 pursuant to the Law of

Muscat Securities Market and Amendments (Royal Decree 53/88) and Ministerial

Decision 112/88, with the purpose of attracting local saving and international capital.


126

Modeled on the Amman (Jordan) Stock Exchange, the MSM has acquired a reputation

for good management. In 1996, Oman agreed to formal links between its stock market

and those of Kuwait and Bahrain. Omani and Bahraini companies are able to float stock

and be listed in the primary and secondary markets of both stock exchanges.

In 1999, Oman established the capital market authority to regulate the MSM and the

listed companies, and to protect the rights of investors. While the nationals of GCC

countries have unlimited access to this market, fewer than one third of the existing

investment opportunities on the exchange are open to non-GCC investors. However, any

new Initial Public Offering (IPO) must be made available to foreign investors, up to at

least 49 percent of market capitalization (ESCWA, 2003). Modifications to the Law

adopted in 1994 allow foreign investors to invest in approved investment funds, up to 49

percent of the shares, and such investment funds are treated as 100 percent Omani

entities and can freely invest elsewhere in the economy.


The number of listed companies increased from 47 to 141 companies over the period

1989-2003. The manufacturing sector accounts of 43 percent of listed companies,

followed by the financial and services sectors with 29 percent and 9 percent,

respectively. Market capitalization totalled USD $7.3 billion in 2003, an increase from

USD $1.9 billion in 1994. The value traded was the highest in 1997 and 1998, reaching

USD $3.9 billion and USD $2.4 billion, respectively.

127


The banking sector is the major component of the financial system in Oman, and

consists of 16 local and foreign financial institutions, including banks and investment

firms. Moreover, five local banks are listed on the Oman Securities Market.


Oman, as a WTO member, has scheduled commitments to liberalize its financial

services sectors in line with GATS rules and provisions, as in Table 4.16.

Table 4.16: Schedule of Commitments for Oman under the GATS rules


Insurance intermediation, such asbrokerage and agency services

(1) N(2) N

(3) Foreign equity limited to 70 percent(4) UE

(1) N(2) N

(3) N(4) UE

Trading the following for own accountor for the account of customers, whetheron an exchange, in an over-the-countermarket or otherwise:

(a) Money market instruments (cheques,bills, certificates of deposits);

(b) Derivative products including (butnot limited to) futures and options.

Asset management, including cash or

portfolio management, all forms ofcollective investment management,pension fund management, andcustodial, depository and trust services.

Settlement and clearing services for

financial assets, including securities,derivative products, and other negotiableinstruments.

(1) None for financial informationservices and financial advisoryservices; unbound for other services.

(2) N

(3) (a) none. Upon accession,commercial presence is allowed forwholly owned branches of foreignbanks; starting no later than 1 January

2003, commercial presence is allowedin the form of wholly foreign-ownedsubsidiaries and branches of foreignbanks and other financial servicessuppliers;

(b) The aggregate holding by (i) anindividual and related parties, (ii) anincorporated body and its relatedparties, or (iii) a joint stock company orholding company and its related partiesin a locally incorporated bank (otherthan wholly foreign-ownedsubsidiaries) shall not exceed 35percent of the voting shares of thebank;

(1) None for financialinformation servicesand financial advisoryservices; unbound forother services.

(2) N

(3) N

128

(c) The number of bank branches inMuscat is limited to four for each bank(whether local or foreign).

(4) UE (4) UE


Source: (1) World Trade Organization, Trade in services-Oman: schedule of specific commitments ,(GATS/SC/132), (http://www.wto.org/english/tratop-e/serv_commitments_e.htm).(2) This table is organized and structured by the Economic and Social Commission for Western Asia(ESCWA, 2003, pp. 53-54), United Nations.

(4) Qatar Stock Market

The Doha Stock Exchange (DSE) was established on Law No. 4/1995 and became

operational in the first half of 1996. The stock exchange was opened in May 1997 and

foreigners were allowed to trade in 2000. Serious constraints are placed on foreign

investors, and GCC investors have only marginally improved access to publicly listed

shares. The citizens of GCC states are restricted to investments in the industrial and

services sectors, while foreign investors, in general, are limited to purchases of equity in

newly established or privatized Qatari companies (ESCWA, 2003).


Market capitalization reached USD $26.7 billion by the end of 2003. The current

number of listed companies is 28, banks account for around 50 percent of trading

activity and the services sector about 40 percent of the market. The value traded reached

USD $3.2 billion in 2003.


129


The majority of banking activity involves credit letters and loans to private sector

companies contracting for government projects. The sector comprises 14 banks

operating in Qatar (6 local banks, 2 Arab banks, and the remaining 6 are foreign banks).

Total assets of the banking sector are estimated to be USD $10.5 billion in 2002.


Qatar, as a WTO member, has scheduled commitments to liberalize its financial services

sectors in line with GATS rules and provisions (see Table 4.17).

Table 4.17: Schedule of Commitments for Qatar under the GATS rules


Services auxiliary to insurance

(including broking and agencyservices)

(1) N

(2) N

(3) The number of foreign insurancesuppliers is frozen at the level existing onMarch 1995 (five firms)

(4) U

(1) N

(2) N

(3) N

(4) U


Source: (1) World Trade Organization, Qatar: schedule of specific commitments , (GATS/SC/120),(http://www.wto.org/english/tratop-e/serv_commitments_e.htm).(2) This table is organized and structured by the Economic and Social Commission for Western Asia(ESCWA, 2003, p. 51), United Nations.

(5) Saudi Arabian Stock Market

There have been joint stock companies operating in the country since the 1930s. A

formal market was established in 1984, and regulated by the Saudi Arabian Monetary


130

Agency (SAMA) and operated through the electronic trading of publicly listed shares.

In 1990, a computerized screening system was developed to facilitate the trading

(Hassan, 2003). A law was passed in June 2003 to regulate the stock market and to

provide a physical location for trading. All trading in the Saudi shares is done through

banks, which act as brokers as required by SAMA. This is the most restrictive Arab

market in terms of foreign ownership, allowing foreign investment only in the mutual

funds, while other GCC nationals are allowed to own a maximum of 25 percent of

locally listed companies (ESCWA, 2003).


The Saudi Arabia market is the largest in the Arab countries, and is twice the size of the

Egyptian stock market. The market capitalization accounting for 43.6 percent of the

total in the Arab world, and increased from USD $38.7 billion to USD $157.3 billion

over the period 1994-2003. The market had 70 companies listed in 2003, with most

situated in the manufacturing and financial services sectors. A few companies from the

agriculture and transport sectors give the market some depth. The value traded jumped

from USD $6.6 billion to USD $159.1 billion between 1994 and 2003, equivalent to a

turnover ratio of 17.1 percent and 101.1 percent for the same period, respectively (see

Table 4.12).


Saudi banks accounted for approximately one third of total Arab financial capital. The

largest Arab bank, the Saudi Al-Ahli Commercial Bank, is privately owned by the well-

known Mahfouz family, which infused USD $1.6 billion into the bank in 1993. The net

131

income for the 10 major Saudi banks grew at an average of 6.4 percent to USD $2.9

billion, and posted an average return-on-equity of 21 percent in 2001. The country s

central bank is the Saudi Arabian Monetary Agency (SAMA). Commercial banking is

being opened for foreign investors, and at the moment permission has been extended to

a few GCC banks to set up in the kingdom.

(6) The United Arab Emirates Stock Market

The United Arab Emirates (UAE) has the two most recently established stock markets in

the region. The Dubai Financial Market (DFM) was established in 1998, and was

followed by the establishment of the Abu Dhabi Securities Market (ADSM) under Law

No. 3 of 2000. However, foreign investment is limited to 49 percent, and shareholder

approval is required for investors from other GCC and non-GCC countries (ESCWA,

2003).


The Dubai exchange has 15 listed companies, with 80 percent in financial services, 13

percent in non-financial services sector, and 7 percent in the power and utilities sector.

Market capitalization for the DFM reached about USD $10 billion in July 2003. On the

other hand, the second market, the ADSM, has 24 listed companies, half of them in the

financial services sector. Market capitalization accounted for USD $24 billion by the

end of July 2003. Although it is the newest stock market in the region, the exchange in

UAE had the third highest market capitalization among the Arab countries in 2003 (see

Table 4.12). The value of trade and turnover are very low in the UAE market due to the

fact that the government owns a large percentage of shares in the listed companies (up to

80%) and also to the restriction of dealings to nationals (Hassan, 2003).

132


The current domestic financial market is best described as "over-banked". It consists of

48 local and foreign commercial banks, and 13 foreign bank representative offices. The

marketing of financial products and services is regulated by the UAE Central Bank

under Federal Law No. 10 of 1980 (the Central Bank Law and related banking

resolutions).


The UAE, as a WTO member, has scheduled commitments to liberalize its financial

services sectors in line with GATS rules and provisions (see Table 4.18).

Table 4.18: Schedule of Commitments for UAE under the GATS rules


Trading the following for own accountor for the account of customers:

(a) Money market instruments;

(b) Derivative products;


(d) Other negotiable instruments andfinancial assets.

Participating in issues of all kinds ofsecurities (underwriting)Asset management

(1) N

(2) N

(3) (a) No limitations on theestablishment of representative offices;

(b) Unbound for new licenses foroperating bank branches;(c) Unbound for the expansion ofactivities of existing financial entities.

(4) EU

(1) N

(2) N

(3) N

(4) UE


Source: (1) World Trade Organization, United Arab Emirates-General Agreement on Trade in Services:schedule of specific commitments , (GATS/SC/121), (http://www.wto.org/english/tratop-e/serv_commitments_e.htm).(2) This table is organized and structured by the Economic and Social Commission for Western Asia(ESCWA, 2003, p. 52), United Nations.

http://www.wto.org/english/tratop

133

4.4.3.3 Non-Oil Countries

The stock markets of the non-oil countries are as yet relatively small, with the total

number of listed companies for the five countries increasing from 877 to 1,239 during

the period 1994-2003. Egypt represents more than 55 percent of listed companies in

these countries. On the other hand, total market capitalization improved to USD $55.8

billion from USD $15.9 billion over the period 1994-2003, and value traded jumped to

USD $9.7 billion from USD $1.5 billion over the same period (see Table 4.12).

Further to the previous section of oil-exporting countries, this section describes briefly

the financial development and stock market performance on an individual country level

of the group non-oil countries .

(1) Egypt Stock Market

The first Egyptian stock market was the Alexandria Stock Exchange (ASE) established

in 1888, while the Cairo Stock Exchange opened in 1903. The Egyptian Stock Exchange

(Cairo-Alexandria Stock Exchanges, CASE) is regulated by Capital Market Law (No.

95 of 1992), which streamlined all pre-existing capital market regulations. International

investors have full access to the Egyptian stock market. Egypt has been included in the

International Finance Corporation (IFC) Global index and IFC Investable index since

1996.

134


The market capitalization of the Cairo-Alexandria Stock Exchanges (CASE) represents

the fourth highest in the Arab countries, and rose from USD $4.3 billion to USD $27.9

billion between 1994 and 2003. There are 967 companies listed on the CASE, the

highest number among all the Arab stock markets. The manufacturing sector represents

the largest proportion with 49 percent of listed companies, followed by the financial

services and construction sectors with18 percent and 12 percent, respectively. The value

traded improved to USD $4.4 billion from USD $0.4 billion during the period 1994-

2003, with a highest value of USD $11.8 billion recorded in 2000, and the best turnover

ratio of 38.3 percent in the same year. CASE is poised to become one of the strongest

stock markets in the region, given the size and diversity of the Egyptian economy.


Considering the country s population of 70 million, the banking system is rather

inadequate. The majority of small-scale industries carry on their business on a cash

basis. The financial system consists of 164 financial institutions, including 92

commercial banks, 16 investment companies, and 56 other financial services.

The banking sector is dominated by the four big Egyptian state banks, which account for

about 70 percent of total deposits and loans. With the government applying rather strict

capital adequacy law, the smaller banks are coming under increasing pressure. They see

mergers as the only way for continued survival75.

75 For more information, see Arab banking and finance, (http://www.abfdir.com/non-gcc/egypt.htm).

http://www.abfdir.com/non-gcc/egypt.htm

135


Egypt, as a member of WTO, has scheduled commitments to liberalize its financial

services sectors in line with GATS rules and provisions (see Table 4.19).

Table 4.19: Schedule of Commitments for Egypt under the GATS rules

Sector and Subsector Limitations on market access Limitations on national treatment

BanksA. Joint Venture Banks (JVBs)

Trading the following for ownaccount or for the account ofcustomers:

(a) Money market instruments(cheques, bills and Certificatesof deposits),

(b) SecuritiesParticipation in share issues andthe provision of services relatedto such issues throughsubsidiariesSafekeeping of securities

(1) U

(2) U

(3) The share of non-Egyptians inthe capital of JVB s and private

banks may exceed 49 percent ofthe issued capital of any bank,without any ceiling. On a non-discriminatory basis, ownership ofmore than 10 percent of the issuedcapital of any bank, exceptthrough inheritance, requires theapproval of the Central Bank ofEgypt s Board of Directors.

(4) The General Manager shouldhave banking experience in Egyptof no less than 10 years for banksestablished in Egypt other thanbranches of foreign banks.

(1) U

(2) U

(3) Foreign services suppliers, inthe context of JVBs, are required to

offer on-the-job training fornational employees.

(4) N

B. Foreign bank branches

Same activities as specifiedunder category A (above)

(1) U

(2) U

(3) Economic needs test shall beapplied.

(4) N

(1) U

(2) U

(3) Branches of foreign banksexisting on 5 June 1992 (the date ofenforcement of law No. 37 of 1992)may be licensed to deal in localcurrency in addition to foreigncurrency subject to the satisfactionof the minimum capitalrequirement, adequacy ofprovisions and other prudentialmeasures (article 13 of theexecutive regulations of said Law).(4) N

C. Representative offices offoreign banks (ROs)

(1) U

(2) U

(3) Foreign banks that wish to setup representative offices should

(1) U

(2) U

(3) U

136

not have branches in Egypt. Theactivities of RO s should beconfined to conducting studies onpotential investments, acting asliaisons with their head offices,and contributing to solvingproblems and difficulties that maybe confronted by their headoffice s correspondents in Egypt.(4) N (4) N

SecuritiesSector and Subsector Limitations on market access Limitations on national treatment

Underwriting (1) N(2) N(3) N(4) N

(1) N(2) N(3) N(4) N

Brokerage (1) N

(2) N(3) N(4) N

(1) N

(2) N(3) N(4) N

Trading in Securities (purchasesand sales by individuals orinstitutions on the stockexchange)

(1) N(2) N(3) N(4) N

(1) N(2) N(3) N(4) N

Clearing and Settlement (1) N(2) N(3) N(4) N

(1) N(2) N(3) N(4) N

Marketing and marketpromotion

(1) N(2) N(3) N(4) N

(1) N(2) N(3) N(4) N

Portfolio and investment

management

(1) N

(2) N(3) N(4) N

(1) N

(2) N(3) N(4) N

Establishment of collectiveinvestment funds

(1) N(2) N(3) N(4) N

(1) N(2) N(3) N(4) N

Venture capital (1) N(2) N(3) N(4) N

(1) N(2) N(3) N(4) N


Source: (1) World Trade Organization, Trade in services-Egypt: schedule of specific commitments ,(GATS/SC/30), (http://www.wto.org/english/tratop-e/serv_commitments_e.htm).(2) This table is organized and structured by the Economic and Social Commission for Western Asia(ESCWA, 2003, pp. 50-51), United Nations.


137

(2) Jordan Stock Market

In the 1930s, the Jordanian public already subscribed to, and traded in, shares and the

Arab Bank was the first public shareholding company to be established in Jordan.

However, the Amman Financial Market (AFM), established officially in 1976 was

transformed drastically into the Amman Stock Exchange (ASE) in 1999 as a non-profit,

private organization that has the role an authorized agency to operate the securities

market. Simultaneously, two organizations were formed to regulate the stock market: the

Jordan Security Commission (JSC) was organized in an attempt to regulate and monitor

the insurance and dealing of securities, and the Securities Depository Centre (SDC) was

in charge of insuring deposits, regulating securities, settling payments, and accepting

shares as deposits (Hayashi et al., 2003). Foreign investors can hold up to 50 percent of

a company s capital and repatriation of capital and dividends are allowed (Neaime,

2002).


In 2003, the value traded reached USD $2.6 billion, more than triple the value of USD

$0.62 billion in 1994. Market capitalization increased from USD $4.6 billion in 1994 to

USD $11 billion in 2003. The number of companies listed on the ASE was 161 in 2003

compared to 95 in 1994 (see Table 4.12).


The banking sector is considered the most transparent and well-regulated system in the

region and is ranked 31st

(worldwide) by Moody s Investor Services. Overall, the

138

banking sector consists of 21 domestic and foreign banks with 471 branches/offices,

including 9 commercial banks, 7 investment banks, and 5 foreign banks. There are 11

investment companies and 44 other financial institutions, which play an important role

in the private sector and investment in Jordan.


Jordan, as a WTO member, has scheduled commitments to liberalize its financial

services sector in line with GATS rules and provisions (see Table 4.20).

Table 4.20: Schedule of Commitments for Jordan under the GATS rules

Sector and Subsector Limitations on market accessLimitations on nationaltreatment

Trading the following for ownaccount or for the account ofcustomers, whether on anexchange, in an over-the-countermarket or otherwise:

(a) Money market instruments(Cheques, bills, certificates ofdeposit);

(b) Derivative products including(but not limited to) futures andoptions;

(c) Transferable Securities;

(d) Other negotiable instrumentsand financial assets, includingbullion.

(1) NE, U

(2) NE, U

(3) Access restricted to:

(a) Banks;

(b) Financial services companiesconstituted in Jordan in the formof a public shareholdingcompany, limited liabilitycompany, or limited partnership

in shares company.

(4) UE

(1) N

(2) N

(3) N

(4) UE

Participation in issues of all kindsof securities, includingunderwriting and placement asagent (whether publicly orprivately) and provision ofservices related to such issues.

Asset management, including

cash or portfolio management, allforms of collective investmentmanagement, pension fundmanagement, and custodial,depository and trust services.

(1) U

(2) UE

(3) Access restricted to thefollowing:(a) Financial services companiesconstituted in Jordan in the form

of a public shareholdingcompany, limited liabilitycompany, or limited partnershipin shares company;

(b) Licensed banks through

(1) U

(2) UE

(3) N

139

affiliated companies or separateaccounts.(4) UE (4) UE

Settlement and clearing servicesfor financial assets includingsecurities, derivative products andother negotiable instruments.

(1) U

(2) N

(3) Access is restricted to theSecurities Depository Centre atthe Amman Stock Exchange forsecurities, and to the CentralBank of Jordan for all otherfinancial instruments.

(4) UE

(1) U

(2) N

(3) Access is restricted to theSecurities Depository Centre atthe Amman Stock Exchange forsecurities, and to the CentralBank of Jordan for all otherfinancial instruments.

(4) UE


Source: (1) World Trade Organization, Trade in Services-the Hashemite Kingdom of Jordan: schedule ofspecific commitments , (GATS/SC/128), (http://www.wto.org/english/tratop-e/serv_commitments_e.htm).(2) This table is organized and structured by the Economic and Social Commission for Western Asia(ESCWA, 2003, pp. 52-53), United Nations.

(3) Lebanon Stock Market

The Beirut stock Exchange was established on 3rd July 1920, with the beginning of the

French mandate, according to ruling no. 1509. At that time, the stock exchange

operations were confined to gold and currency transactions. These operations lead to the

establishment of Lebanese-French joint stock companies in the 1930s to operate the

public utilities, which were quoted on both the Paris and Beirut Stock Exchanges.

Trading activity had decreased by the beginning of the civil war in 1975, until trading

completely ceased in 1983 as the economic environment was substantially interrupted.

The BeSE was re-opened on September 25, 1995, but trading did not resume until

January 22, 1996. The BeSE is governed by Legislative decree no. 120 of September 16,

1983, and its amendment of March 23, 1985. Actual operations, supervised by the BeSE


140

committee, are regulated by decree No. 7667 of December 16, 1995. BeSE regulations

allow only the investment banks, financial institutions and brokerage firms to trade on

the BeSE floor.

On September 25 1996, a regional Cross-Trading and settlement activities agreement

between the Lebanese, the Egyptian and the Kuwait Stock Exchanges, effective from

January 1, 1997.


Market capitalization stood at USD $1.5 billion in 2003. The number of listed

companies rose from 6 in 1996 to 14 in 2003. On the other hand, the value traded

increased from USD $66 million to USD $131 million over the period 1996-2003, with

an equivalent turnover ratio of 2.8 percent and 8.7 percent during the same period 1994-

2003, respectively.


Lebanon has a very liberal banking system, with a few restrictions on domestic bank

formation and few barriers to foreign banks. The financial sector, in general, comprises

170 financial institutions, including 84 commercial banks, 2 investment companies, and

84 other financial services76

.

Foreign investors, who desire to list their shares on the BeSE, are subject to the same

regulations and listing requirements as Lebanese issuers. As well, for foreign investors

76 See Arab banking and finance, (http://www.abfdir.com/non-gcc/lebanon.htm).

http://www.abfdir.com/non-gcc/lebanon.htm

141

can invest in any stocks they want with one exception being in the banking sector where

Lebanese nationals should own a minimum of one third of a bank s capital.

(4) Morocco Stock Market

The Casablanca Stock Exchange, the Bourse des Valeurs, has seen three reforms since it

was inaugurated in 1929. Under the first, in 1948, the Securities exchange acquired legal

character. The second, in 1967, involved a legal and technical reorganization, and a

change in legal status to that of public establishment. The third was initiated in 1993,

and amended and supplemented in 1996. This reform defined the various market

players, and introduced a range of rules and technical procedures needed for the

development of the Moroccan financial market, recently ranking third in Africa after

Johannesburg and Cairo77. International investors have complete access to the Morocco

stock market and repatriation of capital and dividends are allowed as well (Neaime,

2002).


The Moroccan financial market has witnessed a considerable evolution during the last

few years, due mainly to a privatization program. The value traded went up from USD

$0.2 billion in 1994 to USD $2.4 billion in 2003, with a matching turnover ratio of 4.8

percent and 18.7 percent in the same period, respectively. Meanwhile, the stock market

capitalization grew from USD $4.5 billion to USD $13.1 billion during the period 1994-

2003.

77 For more detail on the Morocco stock market, see (http://www.casablanca-bourse.com/).

http://www.casablanca-bourse.com

142


The banking sector in Morocco is stronger and more active than that of many African

countries. There are 21 commercial banks in the country and 17 other financial

institutions, about 30 credit agencies and 12 leasing companies. Foreign investors

participate freely in the local stock exchange, but commercial banks must have majority

Moroccan ownership78

.

(5) Tunisia Stock Market

The Tunis Stock Exchange, Bourse de Tunis, was established in 1969. New legislation

introduced in 1997 allows foreign investors to buy up to 49.9 percent of the capital of

enterprises on sale through the Bourse without authorization. The Tunis stock exchange

is a small but active stock exchange in Africa79

.


The Tunis Stock Exchange increased from 21 companies listed in 1994 to 45 companies

with a total market capitalization of USD $2.4 billion in 2003. The value traded stood at

USD $189 million in 2003, with a matching turnover ratio of 7.7 percent in the same

year. The banking sector accounts over two-thirds of the exchange s total market

capitalization.

78See Arab banking and finance, (http://www.abfdir.com/non-gcc/morocco.htm).

79 For further information, see (http:// www.bvmt.com.tn /).

http://www.abfdir.com/non-gcc/morocco.htm

http://www.bvmt.com.tn

143


The financial system comprises 49 financial institutions, including 31 commercial

banks, 3 investment companies, and 15 other financial services. However, the banking

sector is not fully developed, slowly being restructured through a series of bank mergers

replacing the large number of specialist banks with a smaller number of stronger and

more efficient multi-purpose institutions. The aim is to open the sector to foreign

competition under agreements with the World Trade Organisation (WTO) and the EU80

.

4.5 Conclusion

In this chapter we introduced an overview of Arab economies and provided quantitative

measures of financial development and stock markets with the historical development

for 11 Arab markets. The analysis here was considered on an individual country level

and classified into the two groups of oil-producing countries and non-oil countries due

to the oil effect. It collected and constructed a new database of macroeconomic

indicators, economic and structural reform, financial sector and stock market

development indicators.

At the macroeconomic level, there have been strong economic links established between

the two groups of both oil-exporting countries and non-oil countries, in terms of trade

and labour and capital flows. Oil export revenues have contributed to the improvement

of welfare and helped finance investment in infrastructure and human capital in most

Arab countries.

80 See Arab banking and finance, (http://www.abfdir.com/non-gcc/tunisia.htm).

http://www.abfdir.com/non-gcc/tunisia.htm)

144

The growth performance of real GDP and real per capita GDP is more stable in non-oil

countries more than in oil-exporting countries due to the volatile oil prices in the second

group. Moreover, the average of political rights, civilian rights and economic freedom is

classified as not free for oil-exporting countries compared to partly free in non-oil

countries.

In addition, the average of the Institutional Quality Index is less than half of the

potential score of 6 points in both groups of Arab countries. The exchange rate systems

in Arab countries have been a fixed peg arrangement against the US dollar, except Egypt

and Tunisia, which have a managed float with no preannounced path for exchange rate

and crawling peg, respectively.

Looking at the financial development, the data was organized according to three

indicators: interest rate, monetary aggregates, and credit aggregates. Firstly, the average

real interest rates have generally been positive for all Arab countries but higher for non-

oil countries than the average rates prevailing in the GCC countries. Secondly, the ratio

of broad money supply to GDP (M2/GDP) is, on average, higher for non-oil countries

than the oil-exporting countries.

The private sector share of the total credit provided by the banking sector is generally

higher for the GCC countries than in the non-oil countries. With regard to the share of

central bank credit to total financial credit, all of the Arab countries show low and

145

decreasing ratios that are indicative of a low level of reliance on the central bank for

their operations.

Finally, we considered the historical progress of the main indicators of stock market

development, including stock market capitalization, value traded, turnover ratio and the

number of listed companies in the Arab markets. The average market capitalization is

much higher for oil-exporting countries than for non-oil countries and the highest value

are from GCC countries, the Saudi stock market, is the largest in the region, accounting

for 43.6 percent of the total.

The Kuwait stock market is the leader with respect to the ratio of market capitalization

and value traded to GDP in 2003. In addition, Saudi Arabia had the best turnover ratio

of 101.1 percent, followed by Kuwait with 91.9 percent in 2003. The number of listed

companies is higher for Egypt but this is not reflected in the market value traded when

compared to the Saudi and Kuwait stock markets.

The main objectives of this chapter were to present the real facts about the Arab

financial sector and stock markets, to make an initial attempt at understanding the nature

of these economies and the role of these markets in the real activities of the region s

economy. It introduced information about the financial institutions that provides a

backup to our empirical analysis in later chapters.

146

Chapter Five

5. CHAPTER FIVE: A COMPARISON OF ARAB STOCK MARKETS WITH

EAST ASIA-PACIFIC AND THE G-7 ECONOMIES

5.1 Introduction

While the previous chapter provided the descriptive work of Arab economies and stock

market development on an individual country level, this chapter presents a comparative

analysis of Arab countries with the East Asia-Pacific and the G-7 economies. The

countries in the study are of particular interest, since they have different rates of

economic growth, different fundamental characteristics of financial structure, and

different levels of stock market development (more-developed, developed and less-

developed).

A comparative analysis of Arab countries with two different groups of countries is

useful to understand the relationship between stock market development and its overall

impact on economic growth, and how Arab stock markets can contribute to economic

growth in the region. In this respect, most of the used measures of macroeconomic and

financial development are updated to 2003 of data available.

This chapter is organized as follows. Section two presents stylized facts of

macroeconomic and economic reform indicators for the three groups, while section

three provides different measures of financial system and stock market development,

and section four concludes.

147

5.2 Macroeconomic Development and Economic Reform

5.2.1 The Main Economic Indicators

• Arab Countries

The economic performance improved with annual GDP growth rate increasing from 2.4

percent in 1981-1990 to 3.2 percent over the period 1990-2003. In 2002-2003, the real

growth of per capita GDP was 3.7 percent. More general economic indicators for these

countries are provided in Table 5.1.

• East Asia-Pacific

It is the largest of the World Bank s six developing regions and the fastest growing

region in the world81 (The World Bank, 2005). On the regional level, average economic

growth of GDP grew by 8.1 percent in the period 2002-2003, compared to an average of

7.6 percent over the period 1990-2003. As shown in Table 5.1, high economic growth in

East Asia-Pacific is led by strong exports, the global economic recovery and high-

technology industry (Hranjski, 2004). Moreover, the region leads all developing country

regions in high-technology exports, which are 33 percent of manufactured exports82

.

• The G-7 Economies

In terms of economic growth, the United States and Japan led the G-7 economies with

3.1 percent and 2.2 percent in 2003, respectively. In contrast, Germany was the worst

81The World Bank classified the world economically into six groups (except some countries classified as

high-income economies, such as G-7 group and High-income OECD members) including, alphabetically(1) East Asia and the Pacific, (2) Europe and Central Asia, (3) Latin America and the Caribbean, (4)Middle East and North Africa, (5) South Asia, and (6) Sub-Saharan Africa, (The World Bank, 2005).

82 For more information on the region countries, see the World Bank Group, World DevelopmentIndicators 2005.

148

performed of the G-7 group, slightly closed of Italy and France. The United Kingdom

and Canada grew by 2.2 percent and 1.7 percent, respectively, in 2003. The average

inflation rate of the G-7 economies is the lower than both the Arab countries and the

East Asia-Pacific countries during the period 1990-2002 (see Table 5.1).

Table 5.1: The Macroeconomic Indicators (USD $million)

Country Real GDP at MarketPrices and Growth

Gross DomesticInvestment(% GDP)

TradeOpenness(% GDP)

GovernmentExpenditure(% GDP)

Inflation(%)

Arab Countries 2003 (%) 1990 2003 1990 2002 1990 2002 1990-2002Bahrain

1

EgyptJordanKuwaitLebanonMoroccoOmanQatarSaudi ArabiaTunisiaUAE

Average

7,68382,4279,86035,36919,00044,49120,30917,466188,47924,28270,960

47,302

5.85.73.39.73.05.21.48.57.25.67.0

5.7

18.316.236.617.229.122.518.7N/A24.629.224.2

23.7

18.417.122.78.616.722.713.7N/A19.425.122.4

18.7

210.252.8154.7103.0117.958.983.385.682.494.2105.8

104.4

146.140.4116.887.954.865.892.4N/A63.894.0N/A

84.7

24.211.324.938.824.615.538.132.930.716.416.1

24.9

20.111.825.126.413.616.0N/AN/A25.716.416.9

19.1

- 0.18.23.23.017.42.8N/AN/A2.24.52.3

4.8East Asia-PacificAustraliaChinaHong KongIndonesiaSouth Korea

MalaysiaNew ZealandPhilippinesSingaporeThailandAverage

518,3821,409,825158,596208,311605,331

103,16176,25680,57491,342143,163339,491

2.69.13.34.13.1

5.23.44.51.16.74.3

22.343.327.629.534.3

39.220.224.037.440.331.8

24.144.422.919.729.4

21.420.318.713.427.124.1

33.531.9234.349.159.4

145.054.560.8396.775.8114.1

42.249.1250.069.287.2

229.964.7106.5341.4126.0136.6

18.912.1125.88.810.5

13.817.010.110.216.124.3

18.613.1145.37.010.2

10.615.812.810.59.525.3

1.57.14.115.55.0

3.91.58.41.34.25.3

G-7 EconomiesCanadaFranceGermanyItalyJapanUnited KingdomUnited States

Average

834,3901,747,9732,400,6551,465,8954,326,4441,794,85810,881,610

3,350,261

1.70.2-0.10.32.52.23.1

1.4

18.118.222.518.429.315.817.0

19.9

N/A19.318.019.9N/A15.718.2

13.0

52.243.554.339.419.850.720.6

40.1

84.555.966.355.618.456.324.2

51.6

22.722.319.120.213.319.917.0

19.2

19.023.318.918.016.218.614.2

18.3

1.41.52.03.80.12.92.1

2.0

Note: N/A stands for Not Available.Source: (1) The World Bank Group, World Development Indicators, 2005.

(2) Global Development Network Growth Database, The World Bank Group, 2002.(3) International Financial Statistical Yearbook, 1998.

149

Figure 5.1: Average GDP at Market Prices, 1990-2003

0

5,000,000

10,000,000

15,000,000

20,000,000

25,000,000

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

0

100,000

200,000

300,000

400,000

500,000

600,000

East Asia-Pacific G-7 Arab Countries

Note: Right-Hand Side Represents Arab countries

Source: (1) the World Bank Group, Country at a Glance, 2005.(2) Global Development Network Growth Database, The World Bank Group, 2002.

Figure 5.1 reveals substantial differences between Arab countries (represented by the

right-hand axis), East Asia-Pacific and G-7 economies. For the three groups included in

the study, the total of real GDP at market prices was valued at USD $297.9 trillion over

the period 1990-2003. The G-7 economies represented 86.5 percent of this total, East

Asia-Pacific 11.7 percent, and the remaining small share of 1.8 percent for the Arab

countries.

According to the World Bank, Figure 5.2 provides additional information on the value

of real GDP in 2003 for the three groups and their percentages of the total. The averages

of the main macroeconomic indicators are presented in Figure 5.3 as follows: Gross

Domestic Investment (as a proportion of GDP, 2003), Openness of Trade (the total

value of exports and imports over GDP, 2002), Government Consumption Expenditure

(as a proportion of GDP, 2002), and the Inflation Rate (as an average over the period

1990-2002).

150

Figure 5.2: Total GDP at Market Prices (USD $million, 2003) and Percentages

0

5000000

10000000

15000000

20000000

25000000

Arab Countries East Asia-Pacific G-7 Economies

0.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

80.00

90.00Total GDP at Market Prices (2003)

(% of Total)


Figure 5.3: The Main Macroeconomic Indicators (Percentages)

0

20

40

60

80

100

120

140

160


GDP growth (Average %, 2003)

Gross Domestic Investment (% GDP, 2002)

Openness of Trade (% GDP, 2002)

Government Expenditure (% GDP, 2002)

Inflation (%, 1990-2002)

Source: (1) The World Bank Group, 2005.(2) DX Data File, Economic Data, 2002.

5.2.2 Economic Reform Indicators

Figure 5.4 and Table 5.2 show two indices of Annual Freedom Scores and International

Quality Index for the three groups of countries. The average of political rights, civilian

151

rights and economic freedom classified as partly free for Arab countries and East Asia-

Pacific compared to the most free classification of G-7 economies. Also, the average of

the institutional quality index is 2.2 points for Arab countries, and 3.2 points and 4

points (out of a possible 6 points) for East Asia-Pacific and G-7 economies in 2004.

Figure 5.4: Global Indicators of Freedom House Rating and Institutional Quality Index, 2004

0

1

2

3

4

5

6

Arab Countries East-Asia Pacific G-7 Economies

Political Rights Civil Liberties

Freedom Status Institutional Quality Index

Notes: the Freedom House Rating awards 1 to the Most Free countries and the best for Political Rightsand Civilian Liberties, and 6 is the Least Free rating and the worst for Political Rights and CivilianLiberties. In contrast, the Institutional Quality Index awards a maximum score of 6 points (i.e. 0representing the worst and 6 for the best countries).

Source: Freedom House FH Country Ratings, 2005, and International Country Risk Guide ICRG ,2004.

152

Table 5.2: International Freedom Scores and Institutional Quality Index, 2004

Country Annual Freedom Ratings1 Institutional Quality Index2

Arab Countries Political Rights Civil Liberties Freedom Status (Out of 6 points)BahrainEgyptJordanKuwait

LebanonMoroccoOmanQatarSaudi ArabiaTunisia

UAE

Average

5654

656676

6

5.6

5545

545575

6

5.1

PFNFPFPF

NFPFNFNFNFNF

PF

PF

2232

133222

2

2.2

East Asia-PacificAustraliaChinaHong KongIndonesia

South KoreaMalaysiaNew ZealandPhilippinesSingaporeThailandAverage

17

N/A3

141252

2.9

16

N/A4

241343

3.1

FNFN/APF

FPFFF

PFF

PF

5241

236252

3.2

G-7 EconomiesCanadaFranceGermanyItalyJapanUnited Kingdom

United States

Average

111111

1

1

111121

1

1.1

FFFFFF

F

F

535345

4

4.2

Notes: - 1 Freedom Scores between 1 = the most free (perfect), and 7 = the least free rating (dismal).- PF = Partly Free, NF = Not Free.-

2Institutional Quality Index awards a maximum score of 6 points (i.e. 0 the worst and 6 for the

best), 2003.

Sources: Freedom House FH Country Ratings 2005, and International Country Risk Guide ICRG ,2004.

5.3 Financial Market Development on International Level

In general, the financial sector of Arab countries plays a smaller role than in the East

Asia-Pacific and the G-7 economies. However, this section compares the Arab financial

153

system with these groups on a country level by using the main indicators of financial

development and banking sector.

5.3.1 The Banking Sector

• Arab Countries

Eltony (2004) describes the Arab banks by high degree of concentration83, for example,

the 25 largest Arab banks have about 59 percent of total assets, 46 percent of aggregate

credit, 65 percent of aggregate deposits and 56 percent of equities. The banking sectors

of Saudi Arabia, Egypt, UAE, Kuwait, Lebanon, and Morocco, collectively have about

75 percent of total assets in the Arab countries. In Saudi Arabia, the largest three banks

have about 40 percent of total assets and 35 percent of total deposits. Overall, there are

352 commercial and investment banking institutions in the 11 Arab countries (Shehab,

2002).

By the end of 2003, the Arab banking sector had achieved a growth of 10.5 percent with

consolidated assets reaching USD $778 billion due to a large amount of private deposits

that increased by 8.6 percent in 2003 to reach USD $477 billion. It is worth indicating

that 58 banks in the Economic and Social Commission for Western Asia (ESCWA)84

were included in The Banker List of Top 1000 International Banks as of July 2001.

83The degree of concentration in the banking sector, calculated as the fraction of assets held by the largest

banks in the country (Beck, Demirguc-Kunt and Levine, 2000).

84 The Economic and Social Commission for Western Asia (ESCWA) promotes economic and social

development through regional and subregional cooperation and integration and serves as the main generaleconomic and social development forum within the United Nations system for the ESCWA members(Bahrain, Egypt, Iraq, Jordan, Kuwait, Lebanon, Oman, Palestine, Qatar, Saudi Arabia, Syria, UAE, andYemen).

154


The Asian and Pacific financial banking markets industry has been completely

transformed in the aftermath of the financial crisis 1997-1998, and opened to foreign

owners and competitors in varying degrees, with dramatic changes in most of East Asia

and the Pacific countries. For example, regional hubs have been established, in

Singapore to enhance strategic regional links, in Hong Kong and China, to focus on

private sector development, and in Australia, to support economic activities in the

Pacific Islands and East Timor. Excluding Japan and China, the most substantial

banking sectors in Asia are in Taiwan, Hong Kong, Korea, and Australia.


To assess the overall size of the banking sector in this group, a measure of bank

assets/GDP ratio85

is obtained from Demirguc-Kunt and Levine (2001). Comparing the

ratio of bank total assets to GDP across the G-7 economies shows that banks are much

less important in Canada, the United States, and Italy. Specifically, the financial

structure of the corporate sector in France, Germany, Japan, and United Kingdom relies

much more heavily on bank activities. Moreover, the G-7 economies have the highest

percentage of bank assets to GDP with 98 percent, compared to the East Asia-Pacific at

80 percent, and Arab countries with 46 percent over the period 1990s, as shown by

Table 5.3 and Figure 5.5.

In terms of specific measures at group and country level, East Asia-Pacific has the best

ratio of money supply to GDP (M2/GDP) with 86 percent and 112.7 percent with Hong

85Bank Assets/GDP equals the ratio of the total domestic assets of banks divided by GDP. It provides a

measure of the overall size of the banking sector.

155

Kong the highest at 183.9 percent and 262.1 percent over the period 1990-2000 and

2003, respectively. The Arab countries ranked second with 71.4 percent and 78 percent,

respectively, with Lebanon and Bahrain the best during the same period. The G-7

economies were represented by 63 percent and 73.5 percent in 1990-2000 and 2003,

with Japan and the United Kingdom having the best ratios (see Table 5.3 and Figure

5.5). On the other hand, the share of credit provided to the private sector over total

credit is the highest in Bahrain and Singapore with 144.5 percent and 137.7 percent

during the period 1990-2000, respectively. Moreover, the direct role of the central banks

in credit allocation is insignificant for most of the countries in the study.

In the following Figure 5.5, we present the main indicators of financial development as

a percentage of GDP, including the ratio of money supply to GDP (M2/GDP), the total

domestic credit over GDP, and the share of credit allocated to the private sector over

total credit and provided by the banking sector in all countries of the three groups.

Figure 5.5: A Global Comparison of Financial Indicators, 2003

0

20

40

60

80

100

120

Arab Countries East-Asia Pacific G-7 Economies

M2/GDP (%)

Domestic Credit/GDP (%)

Credit to Private Sector/Total

Credit (%)Bank Assets/GDP (%)


156

Table 5.3: The Main Indicators of the Banking Sector

Country M2/GDP (%) Domestic Credit/GDP (%)

Credit to Private/Total Credit (%)

BankAssets/GDP(%)1

Central BankAssets/GDP(%)2

Arab Countries 1990-00 2003 1990-00 2003 1990-00 2003 1990s 1990sBahrainEgyptJordanKuwaitLebanonMoroccoOmanQatarSaudi ArabiaTunisiaUAE

Average

67.981.2110.398.9123.966.230.457.646.548.853.3

71.4

76.696.7130.983.6127.092.333.950.151.956.358.7

78.0

26.790.187.8107.9N/A63.628.054.828.557.845.2

53.7

45.4116.589.493.828.384.538.244.543.765.4N/A

59.0

144.541.074.936.760.456.0110.876.079.992.4103.4

79.6

107.051.379.572.144.266.396.272.165.692.6108.4

77.8

40.063.071.0N/A20.038.0N/AN/A33.0

55.0N/A

46.0

N/A0.340.210.200.040.09N/A0.5N/A

0.010.05

0.18

East Asia-PacificAustraliaChinaHong KongIndonesiaSouth KoreaMalaysiaNew ZealandPhilippinesSingaporeThailand

Average

62.2112.3183.950.646.285.982.651.896.987.3

86.0

77.2191.2262.153.476.4100.390.656.9122.496.8

112.7

82.4104.9142.754.162.697.896.750.668.998.9

85.9

107.3178.5147.855.797.4118.5118.455.984.295.9

105.9

87.795.9107.787.598.290.297.372.6137.193.3

96.8

94.782.8101.943.598.186.9100.054.9133.082.7

87.9

77.088.0149.049.055.082.085.037.095.082.0

80.0

0.030.05N/A0.020.010.010.030.09N/A0.02

0.03

G-7 EconomiesCanadaFranceGermanyItalyJapan

United KingdomUnited StatesAverage

58.641.935.250.9112.4

82.160.163.0

66.844.135.147.0135.9

118.267.573.5

69.917.380.251.0136.7

121.777.279.1

79.518.379.157.0157.6

150.388.790.1

85.879.279.479.084.5

96.272.482.4

87.280.379.981.165.1

98.772.080.6

66.0102.0121.074.0131.0

116.073.098.0

0.040.010.010.100.05

0.030.050.04

Sources: (1) International Financial Statistics, IMF, 2004.(2)

1and

2from Demirguc-Kunt and Levine (2001).

5.3.2 Stock Market Development

In this section, we describe briefly the main indicators of stock market development for

the three groups, focusing on stock market size (as measured by market capitalization as

a proportion of GDP)86, market activity (as measured by total value traded as a

86 It is the ratio of the value of domestic shares to GDP.

157

proportion of GDP)87

, turnover ratio for market liquidity88

, and the number of listed

companies.

• Arab Countries

Stock markets capitalization represents 0.7 percent of the total market capitalization of

the three groups in the study (Arab markets, East Asia-Pacific and G-7 economies)

during the period 1990-2003, with a value of USD $360.9 billion by the end of year

2003. Total value traded increased from USD $0.7 billion to USD $230.4 billion over

the period 1990-2003, and the total number of listed companies almost doubled from

847 to 1,673 companies during the same period. Indeed, the average turnover ratio for

the 11 Arab stock markets representing the liquidity of these markets, was relatively

small, at 27.7 percent in 2003, (see Table 5.4).

Table 5.5 shows that the Arab stock markets performance as measured by local indices

during 2003 was the best of the three groups with an average 53.7 percent, Egypt and

Kuwait being the top gainers with 152.2 percent and 101.7 percent, respectively.


A comparison of the figures for the period 1990-2003 demonstrates that stock market

capitalization in East Asia-Pacific countries shows strong market growth and represents

8.9 percent from the total of the three groups over the period 1990-2003, reaching an

average value of USD $2,649 billion in 2003, see Table 5.4 and Figure 5.8.

87Equals the value of the trades of domestic shares on domestic exchanges divided by GDP.

88 Turnover Ratio measures the value of stock transactions relative to the size of the market, and isfrequently used as a measure of market liquidity.

158

The performance of the East Asia-Pacific markets in 2003 show that five countries have

been among the strongest performing in the world with the Thailand market growing by

116.6 percent, followed by Indonesia at 62.8 percent, the Philippines with 41.6 percent,

and Hong Kong and Singapore with 41.6 and 36.4 percent, respectively (see Table 5.5).

The East Asia-Pacific financial market encompasses a large and complex universe of

institutions and activities. The total number of listed companies increased from 3,336 to

7,113 between 1990 and 2003, with an average of 5,353 companies during the same

period 1990-2003. In addition, the average value traded reached USD $1,740 billion

with an average turnover ratio of 31.9 percent over the period 1990-2003.


The G-7 market capitalization represents the highest ratio of total capitalization of the

three groups with 90.4 percent during the period 1990-2003, and with an average value

of USD $34,769 billion in 2003 (see Table 5.4 and Figure 5.8). The United States

market capitalization is the largest in the world accounting for 41 percent of the total G-

7 economies, with a value of USD $14,266 billion in 2003. The G-7 stock markets had a

total of 17,193 companies listed in 2003, with an average value traded of USD $16,448

billion between 1990 and 2003, equivalent to an average turnover ratio of 99.7 percent

over the same period 1990-2003.

Table 5.4 reveals that, as of 2003, there are some countries classified as having well-

developed stock markets by all indicators (the United Kingdom, the United States,

159

France, Australia, Singapore, Thailand, Kuwait, and Saudi Arabia). Some countries are

large and illiquid, such as Bahrain, Qatar, Jordan, and UAE, while other countries have

active but small stock markets, with Korea, Italy and Germany being noteworthy.

Table 5.4: International Stock Markets, (USD $millions, 2003)

Country Market Capitalization Value Traded TurnoverRatio (%)

No. of ListedCompanies

Arab Countries 2003 % GDP 2003 % GDP 2003 2003BahrainEgyptJordanKuwaitLebanonMoroccoOmanQatarSaudi ArabiaTunisiaUAETotal (1)Average (1)

9,70227,84810,96359,5281,50313,0507,24626,702157,3062,44044,647360,93532,812

126.333.8111.2168.37.929.335.7152.983.510.062.9

74.7

2614,3492,60754,7291312,4441,3343,220159,0561892,031230,35120,941

3.45.326.4154.70.75.56.618.484.40.82.9

28.1

2.715.623.891.98.718.718.412.1101.17.74.5

27.7

449671611081452141287045431,673

East-Asia PacificAustraliaChinaHong KongIndonesiaSouth KoreaMalaysiaNew ZealandPhilippinesSingaporeThailandTotal (2)Average (2)

585,431512,979712,59754,659298,248160,97033,05023,191148,503119,0172,648,645264,865

112.936.4449.326.249.3156.043.328.8162.683.1

114.8

371,970396,252296,15614,652456,03552,23311,9132,67391,928102,4211,796,233179,623

71.828.1186.77.075.850.615.63.3100.671.5

61.1

63.577.541.626.8153.932.436.011.561.986.1

59.1

1,4711,2851,0373336849021962365514187,113

G-7 EconomiesCanadaFranceGermanyItalyJapanUnited KingdomUnited States

Total (3)Average (3)

888,6782,076,4101,079,026614,8422,953,0982,460,06414,266,022

24,338,1403,476,877

106.5118.844.941.968.3137.1106.5

89.1

471,5441,936,5731,299,327820,6422,108,7323,609,71817,322,982

27,569,5183,938,503

56.5110.854.156.048.7201.1159.2

98.1

53.193.3120.4133.571.4146.7121.4

105.7

3,5991,3928662792,2062,6926,159

17,193

Sources: (1) World Federation of Exchanges, 2003.(2) World Development Indicators 2004.

(3) Arab Monetary Fund, Arab Stock Market Performance 2003.

Figure 5.6 shows the average of market capitalization and valued traded as percentages

of GDP in 2003, along with average turnover ratio during the same period.

160

Figure 5.6: Stock Market Development Indicators (Percentage, 2003)

0.0

20.0

40.0

60.0

80.0

100.0

120.0

140.0


Market Capitalization/GDP (%)

Value Trade/GDP (%)

Turnover Ratio (%)

Sources: (1) World Federation of Exchanges, 2003.(2) World Development Indicators 2004.(3) Arab Monetary Fund, Arab Stock Market Performance 2003.

Figures 5.7 and 5.8 show the total market capitalization and the average for each group

during the period 1990-2003. The Arab stock markets (represented by the right-hand

axis, Figure 5.7) are relatively new and commonly small when compared to the G-7

markets and the East Asia-Pacific markets as well. In general, stock markets tend to be

larger in higher income countries.

161

Figure 5.7: Total Market Capitalization, By Region, 1990-2003.

0

5,000,000

10,000,000

15,000,000

20,000,000

25,000,000

30,000,000

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

0

50,000

100,000

150,000

200,000

250,000

300,000

350,000

400,000

East Asia-Pacific G-7 Arab Countries


Sources: (1) Standard and Poor s, 2002.(2) World Federation of Exchanges, 2003.(3) Arab Monetary Fund, Arab Stock Market Performance 2003.

Figure 5.8: Average Market Capitalization, 1990-2003

0

2000000

4000000

6000000

8000000

10000000

12000000

14000000

16000000

18000000


0.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

80.00

90.00

100.00Market Capitalization

(% of Total)


Figures 5.9 and 5.10 show the second measure of total value traded and its average for

each group during the period 1990-2003. Figure 5.9 shows the Arab markets value

traded on the right-hand axis. The average value traded and its percentage of the total in

Figure 5.10 is only about 0.2 percent.

162

Figure 5.9: Total Value Traded, 1990-2003

0

5,000,000

10,000,000

15,000,000

20,000,000

25,000,000

30,000,000

35,000,000

40,000,000

45,000,000

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

0

50,000

100,000

150,000

200,000

250,000East Asia-Pacific G-7 Arab Countries



Figure 5.10: Average Market Value Traded, 1990-2003

0

2000000

4000000

6000000

8000000

10000000

12000000

14000000

16000000

18000000


0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

80.0

90.0

100.0Value Traded

(% of Total)


An important indicator of stock market development to evaluate the liquidity of markets

is depicted in the following Figure 5.11. The average turnover ratio of Arab stock

163

markets is 31.9 percent, compared to 103.6 percent for East Asia-Pacific and 99.7

percent for G-7 economies during the period 1990-2003.

Figure 5.11: Average Turnover Ratio (%), 1990-2003

0.00

20.00

40.00

60.00

80.00

100.00

120.00


Turnover Ratio (%)


In the final two Figures, 5.12 and 5.13, we estimate the total number of listed companies

and the average for each group during the period 1990-2003. Arab listed companies are

represented on the right-hand axis of Figure 5.12. The average of 1,322 companies

representing with 6.0 percent of the total number of companies in the three groups,

compared to the East Asia-Pacific with 5,353 companies and 24.2 percent, and the G-7

markets with 15,450 companies and 69.8 percent.

164

Figure 5.12: Total Number of Listed Domestic Companies, 1990-2003

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

18,000

20,000

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

0

200

400

600

800

1,000

1,200

1,400

1,600

1,800

2,000East Asia-Pacific G-7 Arab Countries



Figure 5.13: Average Number of Listed Companies, 1990-2003

0.00

2000.00

4000.00

6000.00

8000.00

10000.00

12000.00

14000.00

16000.00

18000.00


0.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

80.00No. of Listed Companies

(% of Total)


165

Table 5.5: The Performance of Stock Markets in the Selected Three Groups in 2003

Group Arab Stock Markets Performance

Exchange Name of Index Annual Change (%) 2003BahrainEgyptJordanKuwait

LebanonMoroccoOmanQatarSaudi ArabiaTunisiaUAE

Average (%)

BSE IndexEFG IndexASE IndexKSE Index

BSI IndexMASI IndexMSM IndexDSE IndexTASI IndexTunindexSHUAA Index

28.8152.253.8101.7

0.824.043.169.876.211.729.0

53.7Exchange East Asia-Pacific Stock Markets PerformanceAustraliaChina

Hong KongIndonesia

South KoreaMalaysiaNew ZealandPhilippinesSingaporeThailand

Average (%)

ASE/S&P All Ordinaries IndexSSE Composite IndexSZSE Component IndexS&P/HKEx Large Cap IndexJSE Composite Index

KOSPI IndexKLSE Composite IndexNZSX All IndexPSE Composite IndexAll-Sing Equities IndexSET Index

11.110.326.140.462.8

29.222.826.541.636.4116.6

38.5

Exchange G-7 Economies Stock Markets PerformanceCanadaFranceGermanyItalyJapan

United KingdomUnited States

Average (%)

S&P/TSE Composite IndexSBF 250 IndexCDAX IndexMIB IndexTOPIX Index

FTSE All ShareNASDAQ Composite IndexNYSE Composite Index

24.317.434.014.923.8

16.650.028.826.2

Sources: (1) World Federation of Exchanges, 2003(2) Bakheet Financial Advisor, Arab stock markets, annual report 2003.(3) Jordinvest annual report, 2003.

166

5.3 Conclusion

This chapter brought together and compared different indicators of macroeconomic and

financial markets for the three groups of the Arab countries, the East Asia-Pacific

countries and the G-7 economies.

There were substantial differences revealed at the macroeconomic performance. The G-

7 economies have the highest value of real GDP at market prices, followed by the East

Asia-Pacific and the Arab countries over the period 1990-2003. Of the average ratios of

other macroeconomic indicators, the East Asia-Pacific countries have the best ratio of

trade openness and gross domestic investment with 136.6 percent and 24.1 percent in

2002, respectively. Arab countries lead the average growth rate with 5.7 percent in 2003.

The economic reform measures provide that the average of political rights, civilian

rights and economic freedom is classified as partly free for Arab countries and East

Asia-Pacific compared to the most free G-7 economies in the world. In addition, the

average of institutional quality index is lowest for Arab countries, and marginal for the

institutions in the East Asia-Pacific and good quality of institutions in the G-7

economies.

The financial development indicators across the three groups show that the East Asia-

Pacific group has the best average value in the indicators of broad money supply/GDP,

domestic credit/GDP, and the credit allocated to private sector/total credit over the

167

period 1990-2003. The Arab countries ranked second for M2/GDP. The G-7 economies

have the best average value for bank assets/GDP.

We compared stock market development indicators for the three groups, focusing on

stock market size as measured by market capitalization, market activity as measured by

total value traded, and turnover ratio for market liquidity, and finally the number of

listed companies. Recent data demonstrates that stock market capitalization over GDP is

the highest for the East Asia-Pacific countries, followed by the G-7 economies and

lowest for Arab countries. Both value traded over GDP, and turnover ratio, is the best for

the G-7 economies and the East Asia-Pacific countries. Also, the total number of listed

companies is higher for the G-7 economies and the East Asia-Pacific countries than for

the Arab countries.

The Arab stock markets have shown the best performance measured by local indices

with an average 53.7 percent in 2003, followed by the East Asia-Pacific and the G-7

Economies with 38.5 percent and 26.2 percent for the same year 2003, respectively. This

is in spite of the fact that the Arab stock markets are relatively new and commonly small

when compared to the G-7 markets and the East Asia-Pacific as well.

We analyzed the macroeconomic indicators and stock market development on a global

level in order to identify the importance, and understand the characteristics of these

economies. This chapter provided a preliminary analysis on the hypothesis of our study

concerning the degree to which stock market development and economic reform affect

economic growth. That is the overall aim of the study in the next econometric analysis.

168

Chapter Six

6. CHAPTER SIX: AN ECONOMETRIC ANALYSIS OF ARAB STOCK

MARKETS

6.1 Introduction

The previous chapters described the theoretical consideration and the quantitative

measures of financial development including stock market, economic reform and

economic growth on an international level. However, to find out the significance of this

relationship for the countries that are included in the study, we apply an econometric

analysis to Arab stock markets in this chapter. Chapter seven introduces a comparative

empirical study of the Arab countries with the East Asia-Pacific and the G-7 economies

using cross-country analysis and sophisticated econometric techniques on a panel data

set for 28 countries over the period 1980-2002.

The empirical analysis in this study aims to test whether stock market development and

economic reform have an effect on the economic growth of Arab countries by using

methods of dynamic panel data estimation. Dynamic models are of interest in a wide

range of economic applications including empirical models of economic growth (Bond,

2002). The application of panel data estimation techniques in the context of the

empirical models of stock market and economic growth have been presented by King

169

and Levine (1993), and Levine and Zervos (1996, 1998a)89

. This estimation method has

been conducted by combining cross-section and time series data.

It has been recognized that using a panel data set, taking into consideration variations

across both time and individual countries, provides a more efficient and more realistic

model of estimation than using a single cross-section or a single time series (Verbeek

(2002), Greene (2003), and Hsiao (1988, 2003)). Moreover, Bond (2002) states that

panel data also allow us to investigate heterogeneity in adjustment dynamics between

different types of countries.

However, in dynamic panel models, some problems will emerge. For example, when the

lagged dependent variable is included in the set of the explanatory variables, OLS

estimates become biased and inconsistent since regressors are no longer uncorrelated

with the error term. To solve these problems, Anderson and Hsiao (1981, 1982)

suggested an Instrumental Variables (IV) estimation method that leads to consistent, but

not necessarily efficient estimates, of the parameters in the model. Arellano and Bond

(1991) proposed a Generalized Method of Moments (GMM) procedure that is

considered to be more efficient than the Anderson and Hsiao (1981) estimator.

In order to allow comparability with previous studies, and the assessment of the bias

that results from neglecting the dynamic nature of economic growth models, we

estimate dynamic equation of economic growth and stock market development using an

unbalanced panel of 11 Arab stock markets (Bahrain, Egypt, Jordan, Kuwait, Lebanon,

89There have been several other efforts to examine empirically the specific role of stock markets in real

economic activity, but these have used fairly small panels and have not focused on dynamics (Rousseauand Wachtel, 2000, p. 1938).

170

Morocco, Oman, Qatar, Saudi Arabia, Tunisia and the United Arab Emirates) over the

period 1980-2002. We apply the Instrumental Variables method and GMM estimators

developed by Arellano and Bond (1991), Arellano and Bover (1995) and Blundell and

Bond (2000).

In a useful GMM estimation of empirical growth models, Bond et al. (2001) describe

the general form of this approach, and take the first-difference of all the variables of a

dynamic panel model, which has k lags of the dependent variable as regressors. This

first differencing removes unobserved time-invariant country specific effects, then

instruments the right-hand side variables in the first-differenced equations using levels

of the series lagged two periods or more.

Three factors determine the selection of the estimation method. Firstly, considering data

characteristics, we need to choose a procedure that allows for the presence of non-

observable determinants of economic growth. Secondly, particular properties of the

dependent variable should be taken into account. Stock market performance has

naturally cyclical dynamics, so that the methodology should permit a variable to show

initial behavior. Thirdly, the endogeneity of such variables has to be controlled.

The GMM estimators will be consistent if the lags of the explanatory variables in levels

are valid instruments for the explanatory variables in differences. That is, when the error

term does not have serial correlation and the independent variables are weakly

exogenous. These two characteristics are evaluated through a second order serial

correlation test and a Sargan test for over-identification restrictions (Arellano and Bond,

171

1991), respectively. The Sargan test (P-value insignificant) evaluates jointly model

specification and the validity of instruments. An illustration of these procedures can be

found later in the section on empirical model specification.

The rest of this chapter is organized as follows. Section two presents briefly the

theoretical framework that links stock market development to economic growth. Section

three discusses the econometric methodology of dynamic panel data models, and the

hypotheses of our variables in the study. In section four, we provide an empirical

assessment of the effect of Arab stock markets and economic reform on economic

growth. Section five presents the robustness tests and possible extensions, while section

six concludes the chapter.

6.2 Stock Market and Growth: Brief Theoretical Framework

Prior to the empirical work of this chapter, this section presents the main theoretical

framework that links stock market development to economic growth, and provides what

may be missing in the view that stock markets are not essential or unimportant to

economic growth.

In the new growth theory, the link between stock market development and economic

growth has been an important issue of debate. King and Levine (1993), Atje and

Jovanovic (1993) and Levine and Zervos (1998a) demonstrate the link of stock market

172

development to economic growth90

. Their empirical result contrasts with the theoretical

arguments of some economists who have suggested that the existence of a stock market

has little relevance or importance to real economic activity (Devereux and Smith, 1994,

and Mayer, 1988)91.

The growth models look at the contribution of stock market development to economic

growth through various transmission channels. Pagano (1993) considers the simplest

endogenous growth model in order to understand the several routes that link stock

market development to economic growth. His model represents the main framework of

this relationship, and is given by:

y A s= − (6.1)

where y denotes the economic growth rate. Also, A is the social marginal productivity

of capital, and is the fraction of saving representing the efficiency of stock market.

The gross saving rate is denoted by ,s and stands for depreciation rate.

90They support the view that a well functioning stock market may affect economic activity in an economy

through the following channels: (1) growth of saving, (2) efficient allocation of investment resources, and(3) better utilization of the existing resources. Leigh (1997) considers that the stock market is supposed toencourage saving by providing households with additional instruments, which may better meet their riskpreferences and liquidity needs, then boosts economic growth.

91Some analysts view stock market as Casino that have little positive impact and perhaps even a

negative impact on economic growth. According to this view, by allowing investors to sell stocksquickly, liquid markets may reduce investors incentives to exert corporate control by monitoring theperformance of managers and firms. In other words, dissatisfied owners sell their shares instead ofworking to make the firm operate well (Levine, 1996).

173

In the above endogenous growth framework, stock market development can promote

economic growth by improving the allocation of resources toward the most productive

investment projects and then increasing the productivity of capital,A . An efficient stock

market raises the fraction of saving, by channeling more saving to investment and

reducing the costs of the financial intermediation process. In addition, stock market

development affects the saving rate, s providing households with additional

instruments which may better meet their risk preferences and liquidity needs.

It is obvious from the preceding brief analysis that the first two effects of stock market

development on economic growth are positive through the efficient allocation of

investment resources and better utilization of the existing resources92. Alarge amount of

literature recognizes this positive relation and presents the consensus view, although

opinions differ considerably about the quantitative importance of this relationship.

To focus on the empirical effect of stock market development and economic reform on

economic growth, we extend the growth model in Equation (6.1) to provide a more

comprehensive evaluation that includes the most important determinants of economic

growth and economic reform besides stock market channels. The general model and

variables set up here is based on economic theory and proposed by theoretical and

empirical studies, such as Beck and Levine (2004), Rousseau and Wachtel (2000),

Levine and Zervos (1998a), and Atje and Jovanovic (1993).

92The third effect on the saving rate is an ambiguous. More theoretical consideration on this effect is

discussed in Chapter three.

174

The main framework of our empirical analysis takes the following form:

, , ,

, ,

[ ] [ ]

[ ]

i t i t i t

i t i t

Growth Stock Market Development Economic Reform

Control Variables Set

= + ++ +

(6.2)

where the dependent variable, Growth, equals real per capita annual GDP growth rate.

Stock Market Development equals either stock market capitalization as a proportion of

GDP, value traded as a proportion of GDP, or turnover ratio. The Control Variables Set

includes the logarithm of initial level of per capita GDP, to control for convergence, the

average years of schooling to control, for human capital accumulation, and conditioning

variables that control for other determinants associated with economic growth. These

determinants include investment as a proportion of GDP, employment growth rate, and

government consumption as a proportion of GDP and inflation rate are used as

indicators of macroeconomic stability. Trade openness of the economy is measured by

exports plus imports as a proportion of GDP. Economic Reform is measured by either

Annual Freedom Scores or the Institutional Quality Index. is an error term, and the

subscripts andi t represent country and time, respectively.

It is worth mentioning here that the main hypotheses and the theoretical discussion of

these explanatory variables are summarized in Table 6.1. The econometric methodology

is now described in more detail.

175

6.3 Econometric Methodology

6.3.1 The Model Specification

We estimate the general growth equation (6.2) using an unbalanced panel data set for

Arab stock markets over the period 1980-2002 in three ways. Firstly, by using the basic

methods of Ordinary Least Squares (OLS), secondly by Two Stage Least Squares

(2SLS) of fixed effects and random effects93

, and thirdly using the dynamic panel model

of Generalized Method of Moments (GMM) as proposed by Arellano and Bond (1991),

Arellano and Bover (1995) and Blundell and Bond (2000).

When using the OLS and 2SLS estimation methods, we apply the main model that is

similar to Equation (6.2) and re-written as:

, , ,i t i t i ty x∗ = + + (6.3)

where ,i ty ∗ equals , , 1( )i t i ty y −− , and represents the growth rate of real per capita GDP

in country i at time t . The vector ,i tx is a set of explanatory variables, including our

indicators of stock market development, economic reform, and all control variables set.

Also, ,i t is an error term.

93In simple explanation, the instrumentation can be made transparent through the exposition as a two-

stage procedure (2SLS). In the first step, the explanatory variable X is regressed on the instrument Z .

The regression values X̂ containing the linear dependent part of X are used as explanatory variables inthe second step (Behr, 2003).

176

The assumption of independence of errors across observations is unlikely to be satisfied

in estimating models from panel data. Therefore, it is likely that OLS estimation of the

stock market, economic reform and economic growth effects is not optimal and may be

biased. Atje and Jovanovic (1993) find that the stock market has a large effect on

economic growth, after controlling for lagged and initial values of investment and value

traded by using OLS methods. Harris (1997)94

applies the same model and data of Atje

and Jovanovic (1993) and uses the instrumental variables estimator (2SLS) on current

investment instead of lagged investment, and suggests that the effect of stock market is

much weaker than Atje and Jovanovic (1993) found.

In studying economic growth, the dynamic panel data methods have important

advantages over simple cross-section regressions and other estimation methods. Firstly,

estimates will no longer be biased by any omitted variables that are constant over time

(unobserved country-specific or fixed-effects). Secondly, the use of instrumental

variables allows parameters to be estimated consistently in models, which include

endogenous right-hand side variables. Finally, the use of instruments potentially allows

consistent estimation even in the presence of measurement error (Bond et al., 2001).

Under the assumption that the Generalized Method of Moments (GMM) estimators are

reasonable methods to test the effect of stock market development on economic

94He demonstrates that the use of lagged values of investment is inadequate as a solution to the

endogeneity problem since it is not a good proxy for current investment. The model, therefore, effectivelylacks an investment variable and this introduces omitted variable bias in the remaining variables (Harris,1997, p. 141).

177

growth95

, Beck and Levine (2004), and Rousseau and Wachtel (2000) emphasize the

significant role of stock market development in promoting economic growth, by using

dynamic panel of GMM estimators.

Accordingly, we follow closely the assumptions of Beck and Levine (2004), Behr

(2003), Calderon et al. (2002), Rousseau and Wachtel (2000), and Levine, Loayza and

Beck (2000), which resemble those of Arellano and Bond (1991), Arellano and Bover

(1995) and Blundell and Bond (2000) in using the GMM estimators for dynamic panel

data models96

. With reference to the growth equations (6.2) and (6.3), the general

dynamic specification to implement this estimation (GMM) is given by97

:

, , 1 , 1 , ,( 1)i t i t i t i t i i ty y y x− −− = − + + + (6.4)

For i = 1,…, N, (country) and, t = 1,…,T, (time).

where ,i ty represents the logarithm of real per capita GDP in country i at time t , and

, , 1( )i t i ty y −− is the growth rate of real per capita GDP. The vector ,i tx is a set of

explanatory variables, other than the lagged per capita GDP98

. As well, i denotes a

95 The standard IV estimator is a special case of a Generalized Method of Moments (GMM) estimator.The advantages of GMM over IV are clear: if heteroscedasticity is present, the GMM estimator is moreefficient than the simple IV estimator, whereas if heteroscedasticity is not present, the GMM estimator isno worse asymptotically than the IV estimator (Baum, Schaffer and Stillman, 2003, p. 11).

96By using GMM estimator, we can control for the non-observable characteristics of stock market

development and the potential endogeneity of the explanatory variables.

97This equation is appropriately describing the expected value of ,i ty , given its own history and

conditional upon current and lagged values of ,i tx (Verbeek, 2002).

98 For more details on the model specification, see Beck and Levine (2004, pp. 431-434) and Levine,Loayza and Beck (2000, pp.51-53).

178

country-specific effect, and ,i t is an error term. We also include time dummies to

account for time-specific effects.

The equivalent dynamic model for the level of ,i ty , is derived by re-writing equation

(6.4) as follows:

, , 1 , ,i t i t i t i i ty y x−= + + + (6.5)

Anderson and Hsiao (1981) (AH hereafter) proposed a consistent estimator of and ,

which is an instrumental variables estimator for the first difference of equation (6.5)99:

, , 1 , 1 , 2 , , 1 , , 1( ) ( ) ( )i t i t i t i t i t i t i t i ty y y y x x− − − − −− = − + − + − (6.6)

For t = 2,…,T.

From equations (6.5) and (6.6), we remove the unobserved country-specific effects, but

the lagged dependent variable, per capita GDP, , 1i ty − , or the lagged difference of

, 1 , 2( )i t i t

y y− −− , is still correlated with the error term, , 1i t − or , , 1( )i t i t −− , which along

with the potential endogeneity of the explanatory variable x , that requires the use of

instruments.

99We can use OLS estimation for this transformed equation, but the correlation between the lagged

dependent variable and the country-specific effect would seriously bias the OLS estimator.

179

However, because , 2 i,t-2 , 3or (y )i t i ty y− −− are correlated with , 1 , 2( )i t i ty y− −− , but are not

correlated with , , 1( )i t i t −− , they can be used as an instrument for , 1 , 2( )i t i ty y− −− and

we can estimate and by the instrumental variables method. For time periods 3T ≥ ,

the estimator is100:

, 2

, , 1

1 2 , , 1

, 2 , 1 , 2 , 2 , , 1

1 2 , , 1 , 2 , , 1 , , 1

( )ˆ

ˆ ( ) ( )

( ) ( )( )

N Ti t

i t i t

i tAH i t i t

N Ti t i t i t i t i t i tAH

i t i t i t i t i t i t i t i t

yy y

x x

y y y y x x

x x y x x x x

−−

= = −

− − − − −

= = − − − −

− − = − − − − −

∑∑

∑∑(6.7)

An alternative estimator proposed by AH (1981) is to use , 2i ty −∆ rather than , 2i ty − , as

instrument, and, consequently, the summation over time periods goes from 3 to T in

equation (6.7). This estimation of , and 2 is consistent when N or T, or both, tend

to infinity, and inconsistent if N is fixed and T tends to infinity.

Under the hypothesis of AH (1981), and in the case of levels as instruments, the

estimation will then make use of the following matrices:

,1 ,3 ,2

,2 ,4 ,3

, 2 , , 1

i i i

i i i

i

i T i T i T

y x x

y x xZ

y x x− −

− − =

−

%M M

100 For more discussion on the GMM estimator, see Hsiao (2003).

180

,2 ,1 ,3 ,2

,3 ,2 ,4 ,3

, 1 , 2 , , 1

i i i i

i i i i

i

i T i T i T i T

y y x x

y y x xX

y y x x− − −

− − − − =

− −

%M M

,3 ,2

,4 ,3

, , 1

i i

i i

i

i T i T

y y

y yy

y y −

− − =

−

%M

And as follows for the use of differenced instruments:

,2 ,1 ,4 ,3

,3 ,2 ,5 ,4

, 2 , 3 , , 1

i i i i

i i i i

i

i T i T i T i T

y y x x

y y x xZ

y y x x− − −

− − − − =

− −

%M M

,3 ,2 ,4 ,3

,4 ,3 ,5 ,4

, 1 , 2 , , 1

i i i i

i i i i

i

i T i T i T i T

y y x x

y y x xX

y y x x− − −

− − − − =

− −

%M M

,4 ,3

,5 ,4

, , 1

i i

i i

i

i T i T

y y

y yy

y y −

− − =

−

%M

181

Stacking the observations for all individual results in the two estimators:

1

2

N

z

zz

z

=

%

%

M

%

,

1

2

N

x

xX

x

=

%

%

M

%

,

1

2

N

y

yy

y

=

%

%

M

%

1 ' ' 1 'ˆ ( ) , where ( )AH XPX X Py P Z Z Z Z− −= =

Arellano and Bond (1991) take the work of AH (1981) and provide a more efficient

estimator (known as GMM, Generalized Method of Moments)101, whereas the

differenced residuals ,i t∆ , are uncorrelated with all ,i ty and ,i tx from 2t − . That is, we

can use all those values as instruments for , 1i ty −∆ , in equation (6.6).

With ( 3),t > we can consider an equation in first-differences that eliminates the

permanent effects:

,3 ,2 ,2 ,1 ,3 ,2 ,3 ,2( ) ( ) ( )i i i i i i i iy y y y x x− = − + − + − (6.8)

where the instruments ,1 ,2 ,1, andi i iy x x are available.

For period T , the equation is given by:

101 For a note on the Anderson-Hsiao estimator, see Arellano (1989).

182

, , 1 , 1 , 2 , , 1 , , 1( ) ( ) ( )i T i T i T i T i T i T i T i Ty y y y x x− − − − −− = − + − + − (6.9)

The valid instruments of ,1 ,2 , 2 ,1 ,2 , 1, , , , , , ,i i i T i i i Ty y y x x x− −K K are available. However,

the instrumented equation is:

' ' 'W Fy W FX W F= +

where,

,2 ,1 ,3 ,2

,3 ,2 ,4 ,3

, 1 , 2 , , 1

i i i i

i i i i

i

i T i T i T i T

y y x x

y y x xX

y y x x− − −

− − − − =

− −

M M

1( , ),X y X−= ( , ),= ' ' ' '

1 2( , , , )NW W W W= K

,1 ,1 ,2

,1 ,2 ,1 ,2 ,3

[ , , ] 0 0

0 [ , , , , ] 0

0 0

i i i

i i i i i

i

y x x

y y x x x

W =

L

L

,1 ,

0

0 0 [ , ,i i T

y y…

O

M M M

L -2 ,1 , -1, , , ]i i T

x x

…

In the simple case of the instrumental variables estimation, there is a two-step

estimation. Firstly, a cross-section auxiliary equation is estimated as follows:

, , 1 1, , 2 2, , 3 1, , 1 2, , 2 ,ˆ ˆˆ ˆ

i t i t t i t t i t t i t t i t i ty y y y x x v− − − − −− = + + + + + +K K (6.10)

183

In the second step the resulting estimates are used as explanatory variables in the

equation of original interest.

In the k − explanatory variable case, the maximum number of parameters to be

estimated is 2 ( 1) ( 1)( 1) 1,T k T k T− + − = + − − which determines the number of

individuals that have to be available to allow estimation.

Because the differencing operation introduces first order autocorrelation into the error

term, the first-step estimator makes use of a covariance matrix taking this

autocorrelation into account.

' '

1

N

i T i

i

V W GW W G W=

= = ∑

where ' '

2 -1 0

-1 2( ) and

-1

0 -1 2

N T T T TG I G G F F

= ⊗ = =

O

O O

The resulting estimator, finally, is

1 ' 1 ' 1 'ˆ ˆˆ ( )GMM XWV W X X WV W y− − −= .

184

When the error term, ,i t is serially uncorrelated, and the explanatory variables are

weakly exogenous (i.e. the explanatory variables are assumed to be uncorrelated with

future realizations of the error term), the GMM differences estimator based on the

following moment conditions:

, , , 1( ) 0 2; 3,...,i t s i t i tE y for s t T− − − = ≥ = (6.11)

, , , 1( ) 0 2; 3,...,i t s i t i tE x for s t T− − − = ≥ = (6.12)

Using these moment conditions, Arellano and Bond (1991) and Arellano and Bover

(1995) suggest a two-step GMM estimator. Firstly, assume that the error terms are

independently distributed with constant variance across countries and over time.

Secondly, construct a consistent estimate of the variance-covariance matrix of the

moment conditions in (6.11) and (6.12) with the residuals obtained in the first step.

Asymptotically, the second-step estimator is more efficient relative to the first-step

estimator.

More generally, suppose that no external instruments are available and the country-

specific effect,i

is correlated with the levels of ,i tx , but not correlated with the

differences of ,i tx . If ,i tx , is strictly exogenous , ,( ( ) 0 for all and )i t i sE x t s= 102, the

appropriate instruments for the regression in levels are the lagged differences of x. In

this case, the additional moment conditions for the regression in levels are given by:

102A regressor ,i tx , is considered strictly exogenous if , ,( ) 0i t i sE x = , for all t and s; predetermined

if , ,( ) 0i t i s

E x = , for s t≥ and , ,( ) 0i t i s

E x ≠ , if s t< ; and endogenous if , ,( ) 0i t i s

E x = , for

all s t> and , ,( ) 0i t i sE x ≠ , if s t≤ .

185

, 1 , 2 ,( )( ) 0, for 1

i t i t i i tE y y s− − − + = = (6.13)

, 1 , 2 ,( )( ) 0, for 1i t i t i i tE x x s− − − + = = (6.14)

Besides using the above moment conditions in equations (6.11)-(6.14), we employ the

panel GMM estimator to generate consistent and efficient estimation. The consistency

of this estimator relies on the assumption of the lack of serial correlation of ,i t , and

whether lagged values of the explanatory variables are valid instruments in the growth

regression (6.6).

The first-differenced GMM estimator suggested by Arellano and Bond (1991) is known

to be rather inefficient when the lagged levels of instruments are weakly correlated with

subsequent first-differences (Bond et al., 2001). Blundell and Bond (2000) suggest

making use of additional level information beside the differences. The combination of

moment restrictions for differences and levels results in an estimator, which is called the

GMM-system estimator.

where Z is the chosen matrix of instruments, P refers to the number of columns in Z

provided P k> , and k represents the number of parameters. A significant value of S

indicates that the over-identifying restrictions do not hold, and therefore the

specification of the model is suspect.

186

The matrices used for estimation are then defined as follows, where the symbols L and

D denote the use of instruments in levels and differences, respectively:

,3 ,2

,4 ,3

, , 1

,3

,

i i

i i

i i T i T

i

i T

y y

y y

y y y

y

y

−

−

−

= −

M

%

M

,

,2 ,1 ,3 ,2

,3 ,2 ,4 ,3

, 1 , 2 , , 1

,2 ,2

i i i i

i i i i

i i T i T i T i T

i i

y y x x

y y x x

X y y x x

y x

− − −

− −− −

= − −M M

%

M

, 1 ,i T i Ty x−

M

1ˆ ( , ),X y X−= ' '( , ),= ' ' ' '

1 2( , , , )NW W W W= K ,

,1 ,1 ,2

,1 ,2 ,1 ,2 ,3

[ , , ] 0 0

0 [ , , , , ] 0

0 0

i i i

i i i i i

D

i

y x x

y y x x x

W =

L

L

,1 ,

0

0 0 [ , ,i i

y y…

O

M M M

L -2 ,1 , -1, , , ]T i i T

x x

…

,2 ,2 ,3

,2 ,3 ,2 ,3 ,4

[ , , ] 0 0

0 [ , , , , ] 0

0 0

i i i

i i i i i

L

i

y x x

y y x x x

W

∆ ∆ ∆

∆ ∆ ∆ ∆ ∆=

L

L

0

0 0

O

M M M

L ,2 , -2 ,2 , -1[ , , , , , ]i i T i i Ty y x x

∆ … ∆ ∆ … ∆

0

0

D

i

i L

i

WW

W

=

187

The first-step estimator makes use of a covariance matrix taking this autocorrelation into

account and enlarged for the level equations.

' '

1

N

i T i

i

V W GW W G W=

= = ∑

Where',

2 -1 0

-1 2( ) and

-1

0 -1 2

D L D

NG I G G

= ⊗ =

O

O O,

1 0 0

0 1

0

0 0 1

LG

=

O

O O

, 0

0

D

iD L

L

i

WG

W

=

The two-step GMM estimator uses the residuals of the first-step estimation to estimate

the covariance matrix as suggested by White (1980):

' ' '

1

ˆ ˆ ˆN

i T i i T i

i

V W F F W=

= ∑

The resulting estimator finally is:

188

1 ' 1 ' 1 'ˆ ˆˆ ( )GMM SY S XW V W X X WV W y− − − −=

Thus, the system GMM estimator combines the standard set of equations in first-

differences with suitably lagged levels as instruments, with an additional set of

equations in levels with suitably lagged first-differences as instruments (Bond et al.,

2001)103

.

Moreover, Arellano and Bond (1991) provide interesting tests for the lack of the first-

order, and second-order, serial correlation in the residuals. If the error terms ,i t , are

serially uncorrelated, then the first differences transformation induces first-order serial

correlation, but not second-order for the error term , , 1( )i t i t −− in equation (6.6).

2 2

, , 1 , , 1 , 1 , 2 , 1( )( )

i t i t i t i t i t i t i tE E E− − − − − = ∆ ∆ = − − = − = −

Also, 2

, , 1 , 1 , 2var( ) var( ) 2i t i t i t i t− − −− = − =

Thus, the first order serial correlation coefficient is given by:

, , 1

1

, , 1

0.5var( ) var( )

i t i t

i t i t

EAR

−

−

∆ ∆ = − ∆ ∆

(6.15)

103The econometric matrices are presented by Behr (2003, pp. 8-14). He considers the most important

differences between the dynamic panel data models of Anderson and Hsiao (1981), Arellano and Bond(1991), and Blundell and Bond (2000). These models represent as an application of the GeneralizedMethod of Moments (GMM).

189

For the second serial correlation, 2 0AR = , as a result of , , 2 0i t i tE − ∆ ∆ = . If the test

fails to reject the null hypothesis of the absence of a second serial correlation, then the

original error term ,i t , is serially uncorrelated and that sustains the model.

The second test is the Sargan test of over-identifying restrictions (Arellano and Bond,

1991). Under the null hypothesis, this statistic is distributed as Chi-square, 2 , with p-k

degrees of freedom, and takes the form:

1

1

ˆ ˆ ˆ ˆ( )N

i i i i

i

S Z Z Z Z−

=

′ ′ ′= ∑ (6.16)

6.3.2 The Hypotheses and Variables

Following the endogenous economic growth literature, our empirical analysis focuses

on stock market development, economic reform and economic growth. Greenwood and

Smith (1997) stress the hypothesis of the importance of financial markets for growth, as

follows:

The economic importance of financial markets for growth derives from the fact that they fulfil

several of the functions emphasized in the first three themes. Financial markets are the most

prominent means, for instance, of channelling investment capital to its highest return uses. These

markets also provide liquidity, and permit the efficient pooling of risk. Both of these activities

alter the social composition of saving in a way that is (potentially) favourable to enhanced capital

accumulation. Finally, financial markets foster specialization in entrepreneurship, entrepreneurial

development, and the adoption of new technologies (p. 147).

190

For our empirical analysis, the explanatory variables of stock market development,

economic reform and the main determinants of economic growth are selected according

to the insights provided by the economic theory and prior empirical literature, such as

Beck and Levine (2004) and Rousseau and Wachtel (2000). We use Real Per Capita

GDP Growth Rate104 as a dependent variable to measure economic growth with the

following specific variables:

(1) Stock Market Development Indicators

We consider three alternative indicators of stock market size (market capitalization as a

proportion of GDP), market activity (value traded as a proportion of GDP) and liquidity,

as measured by turnover ratio, as follows:

• Stock Market Capitalization equals the value of listed domestic shares on

domestic exchanges divided by GDP. Market capitalization shows the overall

size of the stock market in US dollars as a percentage of GDP. Its main

shortcoming is that the theory does not suggest that the size of market

capitalization will influence resource allocation and economic growth (Beck and

Levine, 2004, p. 428).

• Stock Market Value Traded is the value of shares traded over GDP. The value

traded indicator has two potential pitfalls. Firstly, it measures trading relative to

104Most studies related to the economic theory literature, such as (King and Levine (1993), Rousseau and

Wachtel (2000), and Beck and Levine (2004)) use real per capita GDP to measure economic growth toavoid the errors that relate to national accounts procedures used by different countries examined.

191

the size of the economy, and does not measure the liquidity of the market.

Secondly, since value traded is defined as the product of the number of shares

and prices, we are led to anticipate higher economic growth with higher share

prices. It appears somewhat weak and inconsistent to assume that high share

prices cause high economic growth (Beck and Levine, 2004).

• The Stock Market Liquidity indicator, Turnover Ratio, is a percentage of market

value traded to market capitalization. Turnover ratio does not suffer from the

shortcomings of market capitalization and value traded since both numerator and

denominator contain the price (Beck and Levine, 2004, p. 428). Liquidity is an

important attribute of stock markets because, in theory, liquid markets improve

the allocation of capital and enhance prospects for long-term economic growth.

(2) Economic Reform indicators

We apply two measures of economic reform developed by Freedom House Rating and

International Country Risk Guide (ICRG).

• Annual Freedom Scores allocate a score of 1 for the Most Free countries and the

best for Political Rights and Civilian Liberties and a score of 7 represents the

Least Free rating and the worst for Political Rights and Civilian Liberties.

192

• Institutional Quality Index as another measure of economic reform scored from

zero to six, where 0 represents the worst and 6 represents the best. Given by this

index an increase in the score indicates an improvement in economic growth.

(3) Economic Growth and Control Variables Set

Previous empirical and theoretical literature of endogenous growth models suggests that

economic growth is influenced by a number of factors in the business environment. The

hypothesis for the relation between macroeconomic conditions, physical capital and

human capital accumulation as well as trade on the one hand, and economic growth on

the other hand, remains elusive. However, we include in our econometric analysis the

most important determinants that could affect economic growth, as follows:

• Real Per Capita GDP105

is measured in US dollars at 1995 constant prices.

Following the lead of Lucas (1988) and Romer (1986), Dowrick and Nguyen

(1989), Levine and Zervos (1996, 1998a) and Back and Levine (2004), we

include the logarithm of the initial level of per capita GDP to control for

convergence among the countries in the study106

. Convergence implies a

105Economic theory suggests that per capita GDP is a more useful measure than GDP for determining the

standard of living because of differences in population across countries. If a country has a large GDP anda very large population, each person in the country may have a low income and thus may live in poorconditions. On the other hand, a country may have a moderate GDP but a very small population and thusa high individual income.

106Ideally, we should use Purchasing Power Parity comparisons of per capita GDP, but unfortunately the

PPP data is not available or incomplete for 1980-2002 in the Penn World Tables for most of the Arabcountries that are included in our study.

193

tendency for the poorer countries in the group to grow more rapidly than the

richer ones (Dowrick and Nguyen, 1989, p. 1010)107.

Recent empirical growth literature provides ample evidence about the existence of real

convergence across countries. Moreover, Kutan and Yigit (2004) consider real

economic and monetary convergence in the transition economies during the period

1993Q1-2000Q4. They find that transition economies have convergence that is based on

industrial output, but there is no evidence of monetary convergence as measured by

narrow money (M1) growth.

On the estimation of income convergence across countries, there is a strong argument

that is heterogeneity in countries' rates of growth and cross-country differences in the

speed of convergence also raise important econometric issues (Lee, Pesaran and Smith,

1998, p. 320). Estimating cross-section regressions, or regressions using observations

based on data averaged over long periods, makes it impossible to consider either the

complex dynamic adjustments involved in the countries' output processes or the

heterogeneity of growth rates across countries (Lee, Pesaran and Smith, 1997, p. 359).

• Average Years of Schooling, defined as the years of formal schooling received,

on average, by adults over age 15 to control for human capital accumulation,

particularly that attained through education.

107In this regard, Dowrick and Nguyen (1989) and Dowrick and Quiggin (1997) make an important

contribution to the literature of convergence and divergence of per capita GDP or productivity levelsacross countries by constructing true measures of GDP.

194

• OIL as measured by a dummy variable to control for oil effects, one for oil-

exporting countries and zero otherwise108. Following the empirical studies that

examined the effect of oil on long-run growth, we include this variable due to the

hypothesis that there are fundamental differences in the structure of the Arab

economies, because of a superior weight and effect of oil in overall Arab per

capita GDP. Oil reliance for exports is higher among these countries, ranging

from 60 percent in Bahrain to 90 percent in Kuwait and Saudi Arabia.

• Investment as measured by the ratio of gross capital investment to GDP. Solow

(1956), in his neoclassical growth model, stresses that investment is one of the

key determinants of long-term economic growth, supported by the hypothesis

that high levels of investment will lead to more sustained economic growth.

• Labour Force is calculated as the annual growth rate of employment. High levels

of employment lead to higher economic growth.

• Government Consumption as a proportion of GDP, and Inflation Rate to control

for macroeconomic stability.

• Trade Openness as measured by the total exports and imports as a proportion of

GDP. This variable is employed as a measure of the degree of openness of the

economy to trade. Openness affects economic growth positively in as much as it

108Given that some of the Arab countries are among the world s main oil exporters, it is important to

understand to what extent their dependence on oil has mattered for their economic growth (Hakura, 2004).

195

magnifies the benefits of international knowledge spillover and technological

diffusion, as well as enforcing cost discipline through import competition and

the drive to exports (Makdisi et al., 2000).

In Table 6.1, we summarize the theoretical framework of all variables included in our

study, and their effect on economic growth (expected signs).

196

Table 6.1: Theoretical Discussion (Hypotheses) and Expected Sign of the Explanatory Variables

Symbol VariableExp.Sign

Theoretical Explanation

GDPpc

LGDPpc

School

I

EM

INF

XM

GEXP

ER

OIL

MC

VT

TR

GDP Per CapitaGrowth Rate

Level ofGDP Per Capita

Average Yearsof Schooling

Investment(% of GDP)

EmploymentGrowth Rate

Inflation Rate

Openness Trade

Gov. Consumption(% of GDP)

Economic Reform

OIL

Market Capitalization(% of GDP)

Value Traded(% of GDP)

Turnover Ratio (%)

-

+

+

+

-

+

-

+

+/-

+/-

+/-

+/-

Annual real GDP per capita growth rate over the period 1980-2002.

A high relative initial level of income reduces the scope for catching upwith developed economies, other things being equal (Barro, 1991)1.

A large initial stock of human capital makes it easier for a country toengage in R&D, and to adopt new products and ideas developed inadvanced economies, promoting growth (Barro, 1991)1.

High levels of investment will foster more sustained economic growth(Solow, 1956).

High levels of employment lead to higher economic growth.

High inflation creates uncertainty, which adversely affects investmentand economic growth (Fischer, 1993)1.

Trade openness improves productivity by encouraging competition andinternational technology transfers (Coe, Helpman and Hoffmaister,1997)1.

High levels of government consumption have negative effects onproductivity owing to the adverse effects on saving and the distortionsresulting from high levels of taxation (Barro, 1991)1.

Growth theory stresses the importance of a stable economicenvironment, including economic freedom and institutional quality thatis consistent with the development and efficient use of resources(Gwartney et al., 1999).

Oil is highly correlated with the weighted terms of trade volatilityvariable, which makes it difficult to isolate its partial effect, andaccount for all possible channels through which the oil can affectgrowth1.

In summary, for three indicators (MC, VT, and TR). The evidencerelating stock market development to economic growth has beenambiguous, as follows:

(1) A well functioning stock market may affect economic activity in aneconomy through growth of saving, efficient allocation of resources,and better utilization of the existing resources2.

(2) Some analysts view stock markets as Casinos that have littlepositive impact and perhaps even a negative impact on economicgrowth2.

Sources:1

Hakura, (2004).2

For more references, see chapter three theoretical consideration/stock market development andeconomic growth , (e.g. Levine and Zervos (1996, 1998a), Demetriades and Hussein (1996), andDevereux and Smith (1994)).

197

6.3.3 The Sample and the Data

The study uses an unbalanced panel data set with annual observations over the period

1980-2002 on 11 Arab countries, with different numbers of observations according to

data availability. Two countries (Qatar and UAE) were omitted from the analysis

because their stock markets were only in existence for a small part of the period, and

due to problems in obtaining data on other macroeconomic variables for these countries.

Tables 6.2 and 6.3 summarize the variables of stock market and control variables set that

we consider in the empirical analysis and provide some descriptive statistics and

correlation matrix. Oil is a dummy variable 0 to 1.

Table 6.2: Descriptive Statistics: Arab Countries, 1980-2002

Variable Obs. Mean Std.Dev.

Min. Max. Skewness Kurtosis Normality test(Prob > Chi2)

GDP Per Capita GrowthInitial Level of GDP Per CapitaAverage Years of School

InvestmentEmployment Growth RateInflation RateTrade OpennessGov. ConsumptionAnnual Freedom ScoresInstitutional Quality IndexOil

Market CapitalizationValue TradedTurnover Ratio

206238253

197241253232242253207253

148152146

0.057956.06

4.20

0.253.950.090.920.235.372.710.55

0.330.060.16

0.727853.94

0.90

0.072.250.340.340.090.780.690.50

0.280.130.19

-2.90731

3.29

0.110.07

-0.070.350.103.001.000.00

0.010.010.02

3.0437841

7.10

0.4612.53

4.872.100.767.004.001.00

1.421.161.51

-0.381.330.51

0.681.40

11.481.041.44

-0.01-0.36-0.18

1.145.473.69

7.444.392.59

3.545.15

15.204.177.762.743.141.03

4.2540.8223.03

0.000.000.00

0.000.000.000.000.000.720.09N/A

0.000.000.00

Note: The descriptive statistics was done using STATA Statistics/Data Analysis package version 8.2 forWindows.

198

Table 6.3: Correlation Matrix: Arab Countries, 1980-2002

Notes:(1) P-values in parentheses.(2) (GDPpc = Per Capita GDP Growth Rate, LGDPpc = Level of Per Capita GDP), (School = AverageYears of School), (I = Gross Capital Investment as a proportion of GDP), (EM = Employment GrowthRate), (INF = Inflation Rate), (XM = Openness of Trade, Exports plus Imports as a proportion of GDP),(GEXP = General Government Consumption as a proportion of GDP), (OIL = Dummy Variable equal 1for oil-countries and zero otherwise), (Qindex = Institutional Quality Index), (ER = Annual FreedomScores), (MC = Market Capitalization, as a proportion of GDP), (VT = Value Traded, as a proportion ofGDP), (TR = Turnover Ratio, the total value of shares traded divided by market capitalization).(3) The correlation matrix was done using STATA Statistics/Data Analysis package version 8.2 forWindows.

The data on stock market development indicators have been collected from Standard

and Poor s emerging stock markets database, the Arab Monetary Fund, and

supplemented by other data from the annual reports of the stock markets. All the

macroeconomic indicators are available at the Global Development Network Growth

Database (Sewadeh and Easterly, 2002), the World Bank World Tables (EconData, DX

Variable GDPpc LGDPpc School I EM INF XM GEXP ER Qindex OIL MC VT TR

GDPpc

LGDPpc

School

I

EM

INF

XM

GEXP

ER

Qindex

OIL

MC

VT

TR

1.00

-0.18 1.00(0.01)

0.14 -0.04 1.00(0.04) (0.56)

0.03 -0.09 -0.27 1.00

(0.73) (0.21) (0.00)

-0.27 0.40 -0.20 -0.16 1.00

(0.00) (0.00) (0.00) (0.03)

-0.01 -0.21 -0.14 0.07 -0.04 1.00(0.87) (0.00) (0.02) (0.33) (0.55)

-0.12 0.26 -0.08 0.27 0.10 -0.07 1.00(0.10) (0.00) (0.23) (0.00) (0.15) (0.30)

-0.02 0.34 -0.05 -0.08 -0.22 -0.20 0.12 1.00(0.82) (0.00) (0.40) (0.24) (0.00) (0.00) (0.06)

0.12 -0.02 0.07 -0.11 -0.06 -0.08 -0.06 0.13 1.00(0.08) (0.74) (0.30) (0.12) (0.37) (0.17) (0.39) (0.04)

-0.11 -0.06 -0.08 0.01 0.13 -0.02 0.43 -0.11 0.01 1.00(0.15) (0.38) (0.26) (0.95) (0.08) (0.75) (0.00) (0.12) (0.92)

-0.17 0.73 0.01 -0.23 0.14 -0.22 0.39 0.53 0.19 -0.03 1.00(0.02) (0.00) (0.89) (0.00) (0.03) (0.00) (0.00) (0.00) (0.00) (0.67)

-0.19 0.40 0.36 -0.19 0.38 -0.38 0.72 0.34 0.13 0.28 0.42 1.00(0.02) (0.00) (0.00) (0.03) (0.00) (0.00) (0.00) (0.00) (0.11) (0.00) (0.00)

-0.23 0.32 0.28 -0.18 0.43 -0.16 0.18 0.19 -0.09 0.07 0.26 0.44 1.00(0.01) (0.00) (0.00) (0.04) (0.00) (0.06) (0.03) (0.02) (0.27) (0.41) (0.00) (0.00)

-0.26 0.21 0.28 -0.23 0.31 -0.08 -0.01 0.05 -0.01 0.08 0.19 0.21 0.89 1.00(0.00) (0.01) (0.00) (0.01) (0.00) (0.36) (0.89) (0.53) (0.97) (0.36) (0.02) (0.01) (0.00)

199

databases), and the World Bank national account data files Country Profile Table and

World Economic Outlook .

6.4 Estimation Results

6.4.1 OLS and 2SLS Instrumental Variable Methods

In the preliminary estimation, we apply two methods of OLS and instrumental variable

2SLS on an unbalanced sample for nine countries over the period 1980-2002 and omit

Qatar and the United Arab Emirates due to an insufficient number of observations. The

estimation process takes into account the natural logarithm for all variables except the

economic reform measure (Institutional Quality Index). As well, the explanatory

variables enter the simple growth equation (6.3) with the lagged value and OLS

regression is estimated by using the robust standard errors (White-corrected) to control

for possible heteroscedasticity problems109

.

Consistent with the accurate definition of stock market liquidity, we focus on the

measure of turnover ratio that is supported by the theoretical literature and consistent

with the view of Beck and Levine (2004)110

. Next, we experiment with the alternative

measures of stock market development and economic reform in the robustness test.

109 In STATA, specifying the ROBUST option is equivalent to requesting White-corrected standard errorsin the presence of heteroscedasticity (STATA reference, 2001).

110They explain that the theory does not suggest that market capitalization and value traded, as measures

of stock market size and trading activity, will influence resource allocation and economic growth.Turnover ratio (the total value of shares traded divided by market capitalization) does not suffer from thisshortcoming, since both numerator and denominator contain the price (Beck and Levine, 2004).

200

Table 6.4 shows the estimation results of OLS and 2SLS, along with the key diagnostics

such as t-statistic, F-Statistic, R-Squared, Wald test for joint significance (P-value), and

Hausman test (P-value), to check whether fixed-effects and random-effects estimators

are significantly different.

The estimation includes the level of real per capita GDP to control for convergence, the

average years of schooling to control for human capital accumulation, oil as a dummy

variable with value 1 for oil-exporting countries and 0 otherwise, as well as the

conditioning variables that control for the key determinants of economic growth. At this

stage, all the explanatory variables are assumed to be exogenous without imposing

restrictive conditions on the correlation between the regressors and the error term.

Table 6.4: Growth and Stock Market Equation: OLS and 2SLS Estimates

Explanatory Variables

Dependent Variable: Real Per Capita GDP Growth

(1) OLS (2) Instrumental Variables (2SLS)Fixed-effects Random-effects

ConstantLn of Initial Level of Income (t-1)

Ln (1 + Average Years of School) (t-1)

Ln of Investment (t-1)

Ln (1 + Inflation Rate) (t-1)

Ln of Employment Growth Rate (t-1)

Ln of Trade Openness (t-1)

Ln of Gov. Consumption (t-1)

Institutional Quality Index (t-1)

Oil

Ln of Turnover Ratio (t-1)

R-SquaredF-StatisticWald test for joint significance (P-value)Hausman test

No. CountriesNo. Observations

4.229*** (3.60)***-0.056 (4.53)

0.053 (0.09)

0.251 (1.14)*-0.250 (1.98)-0.031 (0.35)

**0.344 (2.28)*-0.530 (1.91)-0.181 (0.54)

***0.995 (3.10)

0.014 (0.30)

0.1924.05 (0.00)

9110

***20.260 (4.49)***-0.272 (4.66)

0.049 (0.13)

0.051 (0.19)***-0.449 (2.84)

*0.945 (1.82)0.319 (0.64)

-0.424 (0.87)0.284 (0.55)

0.021 (0.21)

0.061

39.83 (0.00)

9100

3.000 (1.53)**-0.043 (2.16)

-0.243 (0.68)

0.268 (0.98)*-0.267 (1.88)

0.188 (1.15)0.276 (1.31)

***-0.844 (3.39)-0.329 (0.89)

**0.960 (2.44)

-0.036 (0.55)

0.182

20.37 (0.026)21.09 (0.012)

9100

Notes: t-statistics in parentheses. ***1% significance level, **5% significance level, and *10%significance level. OLS regression is estimated by using robust standard errors to control for possibleheteroscedasticity. Hausman test for both of fixed effects and random effects is (H0: RE against Ha: FE).Instrumental variable, 2SLS (fixed effects and random effects) regressions present the results by using

( , 2 , 2andi t i ty x− − ) as instruments. Time dummies are included in all regressions but are not reported.

The estimation was done using STATA Statistics/Data Analysis package version 8.2 for Windows.

201

Results of the OLS estimation method are reported in Table 6.4. We find that the

relationship between Arab stock markets and economic growth is very weak and

insignificant when controlling for investment, the inflation rate, the employment growth

rate, the openness of trade, and the government consumption. The estimated coefficients

on lagged turnover ratio and lagged institutional quality index are equal to 0.014 and

0.181 with neither t-statistic larger than one, respectively.

That is, there is no evidence of economic growth effects from either stock market

liquidity or economic reform. This is consistent with the view that Arab stock markets

are relatively small, illiquid and thereby have little impact on economic growth. In

addition, Arab countries have been slow to develop an institutional quality and

structural environment as important prerequisites for economic growth, in terms of the

control of corruption and the efficiency of government in financial investment.

On the other hand, some of the control variables have a significant effect on economic

growth, namely inflation at the ten percent significance level, trade openness at the five

percent level, government consumption at the ten percent significance level, and oil at

the one percent significance level. The estimated coefficient for income convergence

between Arab countries as measured by lagged initial level of per capita GDP is

negative and strongly significant.

202

The coefficient estimates for average years of schooling and investment are positive

although not significant at conventional levels. The negative signs of employment rate

and institutional quality index are quite puzzling but in any case they are not significant.

While it is possible to use OLS techniques on panel data, they may not be optimal. The

estimates of coefficients derived from regression may be subject to omitted variable bias

a problem that arises when there is some unknown variable, or variables, that cannot be

controlled for that affect the dependent variable.

Compared with the OLS model, we apply the 2SLS instrumental variables model (fixed-

effects within estimator and random-effects), by allowing for country-specific fixed

effects in order to prevent the bias resulting from correlation between one or more of the

regressors x and the error term, . We treat all the explanatory variables as exogenous,

except the turnover ratio indicator which is treated as an endogenous variable. In

principle, some of the macroeconomic variables might be endogenous, since stock

market development is likely to have second round effects on economic growth.

The results for the instrumental variable estimation are reported also in Table 6.4. We

choose the most appropriate model to provide the valid good instruments, which are

correlated with the endogenous variable and at the same time orthogonal to the errors.

The estimation starts with all the regressors included in the first lagged value, and

instrumented with the second lagged value ( 2t − ) for their levels using the fixed effects

(within) estimator and random effects. The coefficient estimates are similar to the

previous regression of OLS in that the estimated turnover ratio is 0.021 and have an

203

insignificant effect. In contrast, the macroeconomic variables results exhibit some

variations in the case of 2SLS, where the employment growth rate is positive and

significant at the ten percent level. The openness trade effect is insignificant even at the

ten percent significance level.

The convergence effect of lagged initial level of income has been significant in all

application of the OLS and 2SLS methods, which is consistent with the previous

literature on this topic (Beck and Levine, 2004). Also, the coefficient estimates for oil

have been positive and significant in the OLS and 2SLS estimations, confirming the

view that oil has a superior weight and effect in Arab economic growth.

By comparing the significant difference between fixed-effects and random-effects

models, the Hausman test suggests that the fixed-effects regression seems appropriate.

This is confirmed by the rejecting of the null hypothesis that the coefficients estimated

by the efficient random effects estimator are the same as the ones estimated by the fixed

effects estimator, where the P-value is significant. The fixed effects model are jointly

significant by Wald test at one percent significance level, see Table 6.4.

6.4.2 Dynamic Panel Model

We apply the GMM first difference and system estimators, using a one-step estimator

with robust standard errors111

, and including the natural logarithm for each variable

(except Institutional Quality Index) along with the unrestricted lag structures, up to its

111Arellano and Bond (1991) recommend that the standard errors of the two-step estimator tend to be

biased in small samples and using one-step GMM results for inference on coefficients significance.

204

third lag among the regressors112

. We find that the first lag is more significant than the

second and third year lagged specification. Note that the stock market indicator is

treated as endogenous, and we estimate the models with all other explanatory variables

treated as exogenous and including time dummies for each year113. The most important

diagnostic tests of GMM estimation, namely the Sargan test and the Second-order serial

autocorrelation, AR(2)) are presented in Table 6.5.

Table 6.5: Dynamic Growth and Stock Market Equation: GMM Estimators


Dependent Variable: Real GDP Per Capita Growth

(1) GMM-Difference (2) GMM-SystemConstantLn of Initial Level of Income (t-1)









Specification tests: (P-value)(a) Sargan test(b) Serial CorrelationAR (1)AR (2)


-0.039 (0.87)**-0.187 (2.27)

*1.340 (1.81)

**0.285 (2.12)***-0.458 (2.83)

*0.126 (1.77)0.329 (0.59)0.853 (1.28)

-0.279 (0.61)

0.039 (0.98)

0.447

-3.91 (0.000)1.10 (0.271)

993

-0.031 (0.77)**-0.175 (2.25)

*1.196 (1.80)

*0.284 (1.98)**-0.482 (2.64)

0.123 (1.56)0.411 (0.84)0.793 (1.21)

-0.426 (0.95)

0.036 (0.80)

0.639

-3.75 (0.000)0.95 (0.341)

993

Notes: t-statistics in parentheses. ***1% significance level, **5% significance level, and *10%

significance level. The results report one-step of GMM estimators, with robust standard errors to controlfor possible heteroscedasticity. The GMM first-difference and GMM-system estimators use the momentconditions that are generated by the equation in first differences and levels, respectively. Instruments for

GMM-difference are (, 2 , 2and

i t i ty x− − ), and for GMM-system are

, 1 , 2 , 1 , 2( ), and ( )i t i t i t i t

y y x x− − − −− − ,

of all regressors when feasible. The Oil variable enters these regressions as an instrumental variable. Timedummies are included in all regressions but are not reported.The estimation was done using STATA Statistics/Data Analysis package version 8.2 for Windows.

112Due to the low number of time periods available for our data, more lags would substantially reduce the

quality of statistical inference from our estimations. Therefore, we do not consider the possibility offurther lags.

113 We experimented with all explanatory variables as exogenous, including stock market indicator and nosignificant difference between both sets of estimates can be found.

205

The GMM estimation results in Table 6.5 present the GMM first difference estimator of

Arellano and Bond (1991), as well as the GMM system estimator that is developed by

Arellano and Bover (1995) and Blundell and Bond (2000). The results are quite similar

to the previous regressions of OLS and 2SLS due to the insignificant effect of stock

market liquidity and economic reform in both of the GMM estimators. The existence of

income convergence across countries is not different in these specifications, and is

evidenced by the negative relationship between per capita GDP growth rate and the

lagged initial level of GDP after controlling for other relevant variables that could affect

economic growth.

Interestingly, there are three important changes that emerge from the GMM results as

shown in Table 6.5. Firstly, the effect of human capital as measured by the average years

of schooling has increased in size and now is significant at the ten percent level in both

GMM estimators. Secondly, the investment effect in the dynamic growth regression is

significant at the five percent and the ten percent significance levels in the GMM

difference and system estimators, respectively. This may reflect the theoretical

implication that human capital and investment are the main macroeconomic

determinants of economic growth. Thirdly, the estimated coefficient on the lagged

government consumption expenditure changes to a positive sign but is insignificant. The

coefficient estimates for the other macroeconomic control variables, including inflation,

employment growth rate, and trade openness present the correct sign which is consistent

with economic theory.

206

The consistency of the GMM estimators depends on whether lagged values of the

explanatory variables are valid instruments in the growth equation. To address this issue

of the choice between the model specifications, we conduct two types of diagnostic test

that we considered in the methodology section. Firstly, the Second-order serial

autocorrelation; the GMM estimators fail to reject the null hypothesis of no second

order autocorrelation, suggesting that the relevant lags (differences) of the dependent

variable are valid instruments. Secondly, the Sargan test of over-identifying restrictions

based on the two-step GMM estimator is insignificant in all cases and fails to reject the

null hypothesis of overall validity of the instruments114

.

The estimation results of Arab stock markets, in general, as summarized in Tables 6.4

and 6.5, may be explained by the heavy involvement of public enterprises in economic

growth and development that has precluded the successful evolution of stock markets in

the region (Abbas, 1999). In addition, the lack of contribution of stock market

development in Arab economic growth is mainly due to relatively new, and generally

small, capital markets in these countries.

6.5 Robustness Tests and Extensions

In this section, we present several robustness tests on the estimation results that are

reported in the previous section, as well as to investigate whether our results are robust

to various extensions of the model.

114 The two-step Sargan test is more marginal, since the one-step estimator is not heteroscedasticity-consistent (Arellano and Bond, 1991)

207

6.5.1 Outlier Analysis

Specifically, we check the consistency of the results when outliers with extreme

observations are dropped and the estimations are repeated. The countries with extreme

values for specific variables are omitted from the estimations.

On the robustness for the effect of outliers, we drop Lebanon because of the high rates

of inflation and extreme observations for the growth rate of per capita GDP, and then

Kuwait which displays extreme observations for employment growth rate due to the

Gulf-War effects in the 1990s. The results using these robust regressions were similar to

those obtained using the same model specification; that is, the empirical results are not

affected and there are no real changes in the main variables when Lebanon and Kuwait

are excluded from the analysis.

(These results are not reported but available on request).

6.5.2 Alternative Measures of Stock Market and Economic Reform

In the second robustness test, we examine the alternative measures of stock market size

(market capitalization as a proportion of GDP) and the other measure of stock market

activity (value traded as a proportion of GDP) that were used by some of the previous

empirical work, such as Levine and Zervos (1998a), and Rousseau and Wachtel (2000).

Similarly, we examine the second measure of economic reform in our study (Annual

208

Freedom Scores) and find that the results for both are very similar, their effects on

economic growth being very weak.

On the other hand, we follow Atje and Jovanovic (1993) and Harris (1997) by using a

new variable representing the interaction of investment and turnover ratio as one of the

explanatory variables on the right hand side in the general model specification, because

of the possible effect of stock market development on economic growth through

investment activity.

The results are robust to different measures of stock market development and economic

growth in the general growth model and dynamic growth model. The estimated

coefficients in the OLS regression with the alternative indicators of stock market value

traded, market capitalization and the interaction of turnover ratio with investment, are

broadly consistent with the result of turnover ratio reported in the last section and shown

also in Model (1) in Table 6.6. That is, the effect of stock market development on

economic growth remains insignificant.

In addition to the effect of stock market development in Table 6.6, the estimated

coefficients in the OLS regressions for all regressors, including the macroeconomic

control variables and economic reform indicator, are very similar, on average, in

magnitude and significance to those obtained in the basic OLS regression of economic

growth and turnover ratio which are reported under the heading of Model (1) in Table

6.6.

209

Table 6.6: Growth and Stock Market Equation: OLS Estimation



Model (1) Model (2) Model (3) Model (4)ConstantLn of Initial Level of Income (t-1)








Oil


Ln of Value Traded (t-1)

Ln of Market Capitalization (t-1)

Ln (Investment ∗ Turnover Ratio) (t-1)

R-SquaredF-StatisticNo. CountriesNo. Observations

***3.961 (3.63)***-0.521 (4.17)

0.020 (0.04)

0.262 (1.20)*-0.189 (1.72)

0.008 (0.10)**0.304 (2.26)*-0.521 (1.99)

-0.233 (0.73)***0.923 (3.01)

0.006 (0.14)

0.1705.11 (0.00)

9110

***3.897 (3.31)***-0.468 (3.91)

-0.114 (0.22)

0.227 (1.08)**-0.210 (2.29)

-0.086 (1.02)***0.350 (2.74)**-0.542 (2.29)

-0.309 (1.09)***0.893 (3.05)

-0.004 (0.10)

0.1423.58 (0.00)

9110

***3.146 (3.23)***-0.471 (3.97)

0.187 (0.33)

0.241 (1.16)**-0.195 (2.06)

0.037 (0.47)***0.374 (3.05)

*-0.460 (1.70)-0.160 (0.50)

***0.827 (2.74)

-0.071 (1.21)

0.1824.88 (0.00)

9110

***4.474 (3.46)***-0.591 (4.38)

0.044 (0.07)

0.269 (1.07)**-0.275 (2.29)

-0.057 (0.73)**0.370 (2.15)*-0.582 (1.96)

-0.167 (0.47)***1.045 (3.08)

0.025 (0.52)

0.2193.27 (0.00)

9110

Notes: t-statistics in parentheses. ***1% significance level, **5% significance level, and *10%significance level. OLS regression is estimated by using robust standard errors to control for possibleheteroscedasticity. All regressions include time dummies but are not reported.The estimation was done using STATA Statistics/Data Analysis package version 8.2 for Windows.

On the other hand, Tables 6.7 and 6.8 present the results of both GMM difference and

system estimators that were obtained with the alternative measures of stock market

development. However, we obtain a positive and significant effect at the ten percent

level for the value traded as a proportion of GDP on economic growth in both GMM

estimators. Estimating the same model using either value traded or market capitalization

instead of turnover ratio has potential pitfalls, which are explained by (Beck and Levine,

2004), as follows:

Value traded measures trading relative to the size of the economy, does not measure the

liquidity of the market. On the other hand, since markets are forward looking, they will anticipate

higher economic growth by higher share prices, since value traded is the product of number of

shares and prices. Stock market capitalization equals the value of listed shares divided by GDP,

its main shortcoming is that theory does not suggest the mere listing of shares will influence

resource allocation and growth (p. 428).

210

Table 6.7: Dynamic Growth and Stock Market Equation: GMM Difference Estimator














Ln (Investment ∗ Turnover Ratio) (t-1)



-0.039 (0.87)**-0.187 (2.27)

*1.340 (1.81)

**0.285 (2.12)***-0.458 (2.83)

*0.126 (1.77)0.329 (0.59)0.853 (1.28)

-0.279 (0.61)

0.039 (0.98)

0.447

-3.91 (0.000)1.10 (0.271)

993

-0.037 (0.95)**-0.211 (2.03)

*0.961 (1.80)

0.201 (1.35)***-0.564 (3.67)

-0.130 (1.31)0.682 (1.55)0.866 (1.40)

-0.734 (0.64)

*0.053 (1.69)

0.491

-4.82 (0.000)1.37 (0.171)

993

-0.051 (0.90)**-0.187 (2.30)

*1.407 (1.96)

**0.345 (2.49)**-0.435 (2.61)

*0.121 (1.74)0.401 (0.95)0.485 (0.76)

-0.395 (0.94)

0.123 (0.84)

0.418

-4.08 (0.000)1.23 (0.221)

993

-0.044 (1.06)**-0.162 (2.04)

1.277 (1.52)

*0.269 (1.85)***-0.457 (2.75)

*0.134 (1.82)0.353 (0.65)0.504 (1.12)

-0.335 (0.81)

*0.066 (1.97)

0.809

-3.82 (0.000)1.09 (0.275)

993


significance level. The results report one-step GMM estimator of the equations in first differences, withrobust standard errors to control for possible heteroscedasticity. The GMM-difference estimator uses themoment conditions that are generated by the equation in first differences. Instruments are

( , 2 , 2andi t i t

y x− − ), of all regressors when feasible. Time dummies are included in all regressions but are

not reported. The estimation was done using STATA Statistics/Data Analysis package version 8.2 forWindows.

211

Table 6.8: Dynamic Growth and Stock Market Equation: GMM System Estimator














Ln (Investment Turnover Ratio) (t-1)



-0.031 (0.77)**-0.175 (2.25)

*1.196 (1.80)

*0.284 (1.98)**-0.482 (2.64)

0.123 (1.56)0.411 (0.84)0.793 (1.21)

-0.426 (0.95)

0.036 (0.80)

0.639

-3.75 (0.000)0.95 (0.341)

993

-0.033 (0.92)**-0.214 (2.12)

*0.863 (1.86)

0.186 (1.22)***-0.576 (3.23)

-0.134 (1.32)*0.732 (1.75)

0.889 (1.50)-0.798 (0.71)

*0.052 (1.72)

0.729

-4.75 (0.000)1.39 (0.165)

993

-0.038 (0.77)**-0.175 (2.23)

*1.238 (1.92)

**0.319 (2.20)**-0.458 (2.49)

0.119 (1.54)0.458 (1.15)0.511 (0.81)

-0.460 (1.16)

0.074 (0.60)

0.606

-3.90 (0.000)1.08 (0.282)

993

-0.038 (0.94)*-0.151 (1.94)

1.088 (1.46)

0.259 (1.65)**-0.484 (2.55)

0.131 (1.59)0.437 (0.90)0.449 (0.98)

-0.482 (1.16)

*0.065 (1.87)

0.991

-3.62 (0.000)0.92 (0.359)

993

Notes: t-statistics in parentheses. ***1% significance level, **5% significance level, and *10%significance level. The results report one-step GMM-system estimator, with robust standard errors tocontrol for possible heteroscedasticity. The GMM-system estimator uses the moment conditions that are

generated by the equations in first differences and levels. Instruments are ( , 2 , 2andi t i t

y x− − ), and

, 1 , 2 , 1 , 2( ), and ( )i t i t i t i t

y y x x− − − −− − , and, of all regressors when feasible. Time dummies are included in

all regressions but are not reported. The estimation was done using STATA Statistics/Data Analysispackage version 8.2 for Windows.

In an important step, the addition of a new interaction variable between the country

turnover ratio and investment to the dynamic growth and stock market regression shown

in Tables 6.7 and 6.8, both GMM difference and system estimators indicate that the

interaction variable (Investment ∗ Turnover Ratio) has a positive and significant effect at

the ten percent level on economic growth. This provides some initial indication that

there is a relationship between the economic growth of Arab countries and the

interaction of country investment with stock market liquidity.

The robust GMM estimators results from Model (1) through to Model (4) in Tables 6.7

and 6.8 find that the estimated coefficients on the lagged initial level of per capita GDP

212

for convergence, and the average years of schooling for human capital accumulation, are

much the same as in the previous estimation of GMM estimators, and remain

significant. Also, the remaining explanatory variables are mostly similar in significance

to our main estimation in Model (1) as shown in Tables 6.7 and 6.8.

6.5.3 Individual Country Results

In the third robustness test of the individual country and time series approach, we

consider and re-estimate the main model that captures the contribution of stock market

development and economic reform to economic growth. This section carries out simple

OLS estimation method at an individual country level on relatively small sets of data.

We are aware that this has limitations but these results are provided for comparison with

our earlier results. Given that we are using a very short time series, we have not carried

out sophisticated analyses using cointegration methods or Granger-Causality tests on

that time series.

The main model used here repeats the same specification of equation (6.3), as given by:

t t ty x∗ = + + (6.17)

However, the main objective of this test is to see whether the evidence provided by the

individual country and time series analysis also supports the panel data results of stock

market development, economic reform and economic growth. A number of studies, such

as Arestis and Demetriades (1997, p.790), highlight the possibility of important

213

differences between countries, and suggest that a time series analysis may yield deeper

insights into the relationship between financial development and real output than cross-

country regressions.

In terms of the possible effect of financial development on economic growth through

banking sector, this section uses a new variable on the right hand side in Equation

(6.17), which relates the overall depth of the banking system to economic growth in

some of the Arab countries. The choice of Arab countries is determined by the

availability of data. The ratio of broad money stock (M2) to nominal GDP is the most

comprehensive measure of banking development and consistent with the empirical

studies of times series analysis, such as Arestis and Demetriades (1997), Rousseau and

Wachtel (2000) and Al-Yousif (2002). M2 includes currency outside banks, demand

deposits and all time deposits. It is available in the International Financial Statistics

(IFS) and International Monetary Fund (IMF).

The estimated coefficients for banking sector in Arab countries as measured by the

logarithm of the ratio of broad money (M2) to nominal GDP are very weak and

insignificant for Morocco, Saudi Arabia and Tunisia. On the other hand, the relationship

between banking sector and economic growth is negative and significant for Egypt and

Jordan. The negative sign of banking development is quite puzzling at the first glance,

but in general it is consistent with the previous studies that the Arab banking systems are

dominated by the public sector in credit allocation, and characterized by interest rate

spreads and liquidity problems, see Creane et al. (2003) and Ben Naceur and Ghazouani

(2003).

214

The estimation model in the above equation (6.17) is based on the assumption that the

country s stock market development, banking sector and economic reform may affect

economic growth using time series analysis. The estimation results for each country are

reported in Appendix B/Tables (B.1 B.5). Due to the insufficient data (No.

Observation 15)≤ for time series estimation, we exclude six Arab countries (Bahrain,

Kuwait, Lebanon, Oman, Qatar and UAE) from the individual country analysis.

The coefficient estimates and standard errors are robust due to the potential possibility

of heteroscedasticity. It is worth noting here that each individual country regression was

evaluated using diagnostics tests such as t-statistics, Durbin-Watson test, and

R-Squared.

The time series results, on average, find that stock market development does not have a

statistically significant relationship with economic growth in the Arab countries. At the

country level, the results find that turnover ratio enters the growth model significantly at

the ten percent level for only one country, Tunisia. Saudi market value traded as a

proportion of GDP is significant at the ten percent level, and finally Morocco market

capitalization as a proportion of GDP has a significant effect on economic growth at the

five percent level. Table 6.9 summarizes the time series results of the Arab countries.

In addition, the results find that the interaction of (Investment ∗ Turnover Ratio) has a

positive and significant effect on economic growth at the ten percent level in Egypt and

Saudi Arabia, and at the five percent significance level in Tunisia. Also, economic

215

reform as measured by the Institutional Quality Index has a significant impact on

economic growth only in the case of Egypt at the five percent level, as reported in Table

6.9.

The empirical comparison between the panel data results and individual country

approach for Arab markets is not significantly different and presents, on average, the

same estimates of stock market development. Although some Arab countries reveal a

margin significant in some measures as mentioned above, this may be explained by

some differences in the accessibility levels of Arab stock markets to international

investors and other Arab investors. For example, international investors have complete

access to the Egyptian and Moroccan stock markets, while all oil-countries (GCC) are

relatively closed to foreign international investors and fully accessible to GCC investors.

In general, the main estimation results and conclusion remain unchanged for Arab

countries and do not provide statistical evidence for the turnover ratio measure of stock

market liquidity.

Table 6.9: OLS Estimation-Time Series Approach: Arab Countries

Dependent Variable: Real Per Capita GDP Growth Rate

Explanatory Variables EGYPT JORDAN MOROCCO SAUDIARABIA TUNISIA

Ln (Turnover Ratio) Not Significant Not Significant Not Significant Not Significant Significant

Ln (Value Traded) Not Significant Not Significant Not Significant Significant Not Significant

Ln (Market Capitalization) Not Significant Not Significant Significant Not Significant Not Significant

Ln (Investment∗ Turnover Ratio) Significant Not Significant Not Significant Significant Significant

Institutional Quality Index Significant Not Significant Not Significant Not Significant Not Significant

Source: Appendix B/Arab Countries Tables (B.1-B.5)

216

6.6 Conclusion

This chapter aimed to test whether stock market development and economic reform

have an effect on economic growth in the Arab countries by using the different

estimation methods of OLS, 2SLS and GMM estimators. The estimation methods have

been conducted by combining cross-section and time series data for 9 Arab stock

markets over the period 1980-2002.

The empirical results found that the relationship between Arab stock markets and

economic growth is very weak and not significant statistically when controlling for the

growth determinants; that is, Arab stock markets do not support the first hypothesis of

our study that stock market development plays an important role in economic growth. In

addition, the institutional quality index as an indicator for economic reform is

insignificant and has no effect on economic growth.

Looking at the macroeconomic control variables of investment, inflation rate,

employment growth rate, openness of trade, and government consumption, the

estimation results show that most of these variables have the correct sign and reveal

variation in significance with respect to the estimation method. In general, though, they

are quite consistent with economic theory. Moreover, the catch-up effect of convergence

provides the correct sign and shows a significant impact in all estimation alternatives of

the OLS, 2SLS and GMM estimators.

217

Regarding the diagnostic analysis on GMM estimators, the estimation process takes into

account the robust estimation of the standard errors to control for possible

heteroscedasticity problems. In addition, the diagnostic tests find that GMM estimators

reject the Second order autocorrelation, suggesting that the relevant lags (differences) of

the dependent variable are valid instruments. The Sargan test of over-identifying

restrictions based on the two-step GMM estimator is insignificant in all cases and fails

to reject the null hypothesis of the overall validity of the instruments.

With respect to the robustness tests of the empirical analysis, the results are robust to

different measures of stock market development and economic growth in two estimation

methods of OLS and GMM estimators. When the indicators of value traded, market

capitalization and the interaction of turnover ratio with investment are used in the OLS

regression, the effect of stock market development on economic growth remains

insignificant.

On the other hand, the robustness results of both the GMM difference and system

estimators indicate that value traded, as a proportion of GDP, and the new variable of

interaction (Investment ∗ Turnover Ratio) both have a positive and significant effect at

the ten percent and five percent level on economic growth, respectively. These findings

provide some initial indication that stock market liquidity and investment may support

economic growth, but using the value traded instead of the turnover ratio has potential

pitfalls that are explained by the theory and the existing literature.

218

The second objective of this chapter was to test whether the evidence of individual

country result also supports the cross-sectional and panel data results of stock market

development, economic reform and economic growth. On average, the main estimation

results and conclusion remain unchanged, particularly for stock market liquidity and

economic reform.

We conclude that Arab stock markets do not provide significant depth and liquidity to

promote economic growth in the region, due in part to the heavy involvement of public

enterprises, and the fact that a few large companies typically dominate these markets. As

well as this, Arab stock markets have emerged recently and as yet are relatively small

(by international standards).

219

Chapter Seven

7. CHAPTER SEVEN: AN ECONOMETRIC ANALYSIS COMPARISON STUDY

7.1 Introduction

Following the empirical work on Arab countries in the previous chapter, we present

hereafter an econometric analysis by estimating models with three different groups of

countries. More specifically, we analyse whether the relationship between stock market

development, economic reform and economic growth differs across groups of countries

by using panel data sets for three groups, and then for the whole sample of 28 countries


Recent research, such as Demirguc-Kunt and Levine (2001, p. 82), has shown that the

existing comparison of financial systems focuses on a narrow set of countries with

similar levels of per capita GDP, and very similar long-run growth rates. This implies

that financial structure does not matter much for these countries. Therefore, they suggest

that, to provide more information on both the economic importance and determinants of

financial structure, economists need to broaden the debate to include a wider array of

national experiences.

The empirical work testing this relationship is concerned with advanced economies and

developed emerging markets. Nevertheless, few studies are concerned with Arab stock

markets. A comparative analysis is useful to understand the aspects of Arab financial

220

markets and their role in the economy, as an example of how stock markets can

contribute to the far-reaching changes in the region. On the other hand, this study uses

three alternative measures of stock market development to see which one of them is

important for economic growth.

The previous work on stock markets and growth has not been applied for the purpose of

a comparison study (as far as we know). King and Levine (1993), Levine and Zervos

(1996, 1998a) and Beck and Levine (2004), use similar data but with the observations

averaged over five 5-years periods between 1976 and 1998 for 41 countries as a one

panel data set. Moreover, Rousseau and Wachtel (2000) apply one panel data set with

annual observations for one group of 47 countries over the period 1980-1995, but

without an empirical comparison between the countries that are included in their work.

We also add the case ofArab stock markets, which has not been studied to date.

To my knowledge, the comparison study of Arab stock markets with different financial

systems of East Asia-Pacific markets and G-7 markets has not been widely, if ever,

applied for these countries. As such, this chapter aims to make use of chapter six s

methodology and hypotheses of dynamic growth equations and apply the GMM

estimation methods to a panel data set. We also compare the GMM results with different

methods of estimation using the OLS and 2SLS initially, and then compare the results of

the panel data set with a time series and individual country analysis in this chapter. In

practice, we use an annual panel data set to explain the effect of stock market

development on economic growth. This accounts for country heterogeneity and time-

varying variables.

221

The rest of this chapter is organized as follows. Section two outlines the econometric

approach of different estimation methods. In section three, we use alternatives of

estimation and dynamic growth equation to examine four cases: (1) Arab stock markets,

(2) East Asia-Pacific countries, (3) the G-7 economies, and (4) the whole sample

estimation. Section four presents the estimation results, section five provides robustness

tests, and the last section concludes.

7.2 Econometric Approach

The empirical work and estimation analysis of our comparative study apply three

methods of estimation the OLS, 2SLS and GMM estimators, to panel data sets over the

period 1980-2002. It also compares the estimation results of the panel data set with time

series and an individual country approach.

In the simple case of OLS and 2SLS estimation, the basic regression takes the form of

Equation (6.3), as follows115:

, , ,i t i t i ty x∗ = + + (7.1)

115For more information on this model, see the theoretical framework and model specification in Chapter

six.

222

where ,i ty ∗ equals , , 1( )

i t i ty y −− , and represents the growth rate of real per capita GDP

in country i at time t . The vector ,i tx is a set of explanatory variables, including stock

market indicators, economic reform and the determinants of economic growth as control

variables. Also, ,i tis an error term.

The dynamic model of economic growth and stock market development that was

outlined earlier in Chapter six, is given by:

, , 1 , ,i t i t i t i i ty y x−= + + + (7.2)

For i = 1,…,N, and, t = 1,…,T.

Differencing in equation (7.2) eliminates the individual effects, i :

, , 1 , 1 , 2 , , 1 , , 1( ) ( ) ( )i t i t i t i t i t i t i t i t

y y y y x x− − − − −− = − + − + − (7.3)

where ,i ty is the logarithm of real per capita GDP of country i at time t , and

, , 1( )i t i t

y y −− represents the growth rate of real per capita GDP. The vector ,i tx contains

a set of explanatory variables116.istands for a country-specific effect, and ,i t

is an

error term. We also include time dummies to account for time-specific effects.

116 We mean here the explanatory variables of stock market, economic growth determinants, andeconomic reform. More explanation is provided in the econometric methodology in Chapter six.

223

7.3 The Data and Variables

Our sample contains data on three groups from a total of 28 countries over the period

1980-2002, organized as follows: 11 Arab Countries (Bahrain, Egypt, Jordan, Kuwait,

Lebanon, Morocco, Oman, Qatar, Saudi Arabia, Tunisia, and the United Arab Emirates),

10 East Asia-Pacific countries (Australia, China, Hong Kong, Indonesia, South Korea,

Malaysia, New Zealand, Singapore, Philippine, and Thailand), and the G-7 economies

(Canada, France, Germany, Italy, Japan, United Kingdom, and the United States). More

discussion on the database and its source is provided in the previous chapter.

For convenience, we summarize the main variables that are included in our analysis as

follows: we use real growth rate of per capita GDP as a dependent variable to measure

economic growth, and the explanatory variables have been grouped in three different

categories: (1) stock market development indicators, (2) economic reform indicators,

and (3) determinants of economic growth as control variables.

Given the importance of convergence issues, nearly all the empirical studies of

economic growth include the initial level of per capita GDP as a conditioning variable to

capture this effect. All specifications include time dummies to control for common time

effects affecting per capita GDP in all countries. The theoretical explanation

(Hypotheses) and expected sign of explanatory variables are discussed in more detail in

Chapter six, Table 6.1.

224

7.4 Estimation Alternatives

In this section, we apply the same estimation approach as that of Levine, Loayza and

Beck (2000) in our empirical analysis117

. They use three different conditioning

information sets. Firstly, the simple conditioning information set includes the constant,

the logarithm of initial level of per capita GDP and the average years of schooling.

Secondly, the policy conditioning information set includes the simple conditioning

information set plus the main determinants of economic growth (investment, inflation

rate, trade openness, employment growth rate and government consumption). Finally,

the full conditioning information set includes the policy conditioning set plus measures

of economic reform.

This section presents alternative estimation results of the OLS, 2SLS, and GMM

estimators for three groups: Arab countries, East Asia-Pacific and the G-7 economies.

Then, we employ the same methodology to test the relationship between stock market

development and the macroeconomic determinants of economic growth for the full

sample of 28 countries.

117We should mention here that our estimation approach has been extended in this study according to the

existing growth literature by introducing more explanatory variables (determinants) in order to get a betterexplanation of the dynamic economic growth equation.

225

7.4.1 The Case of Arab Countries

In the previous chapter, we employed different estimation methods when the

explanatory variables entered the regressions with the one lag. The data descriptive and

correlation matrix for Arab stock markets are reported in Chapter six, Tables 6.2 and

6.3. Consistent with the empirical studies on this topic, such as Beck and Levine (2004),

we conduct our comparative analysis in this chapter by using the current values of all

explanatory variables, other than the lagged value of the initial level of per capita GDP,

to control for income convergence across countries.

The previous results show that there is no significant effect of stock market development

and economic reform on economic growth, but most of the macroeconomic control

variables show the correct sign and economic impact that is consistent with the

economic theory.

7.4.2 The Case of East Asia-Pacific

This section analyses the case of the East Asia-Pacific markets due to a number of

reasons. Firstly, the region includes several of the world s largest emerging economies,

and generally fares well in international comparisons of investment climate. Secondly,

the banking and capital markets industry has undergone tremendous change, substantial

reforms have been undertaken, and regulatory and supervisory measures implemented.

226

Thirdly, East Asia-Pacific stock markets showed strong market growth in the 1990s, and

were recognized as among the strongest performing in the world. Fourthly, stock market

indicators and macroeconomic determinants for East Asia-Pacific countries have not

been applied in the comparison study. The descriptive statistics and correlation matrix

are reported in Tables 7.1 and 7.2.

Table 7.1: Descriptive Statistics: East Asia-Pacific, 1980-2002

Variable Obs. Mean Std. Dev. Min. Max. Skewness Kurtosis Normality test(Prob > Chi2)

Per Capita GDP Growth RateInitial Level of Per Capita GDPAverage Years of School

InvestmentEmployment Growth RateInflation RateOpenness of TradeGovernment ConsumptionAnnual Freedom ScoresInstitutional Quality Index


210210230

208210192208208207187

202205202

0.378716.96

7.57

0.292.300.671.120.233.613.68

0.650.350.58

0.428192.43

2.36

0.070.770.711.030.331.851.55

0.680.480.61

-1.59168

4.00

0.120.32

-0.400.160.061.000.00

0.070.011.37

1.322823011.70

0.495.845.764.391.547.006.00

3.352.863.87

-1.190.510.28

0.240.523.241.522.76

-0.03-0.45

1.662.613.11

6.081.791.76

2.535.55

20.104.249.071.862.52

5.4410.6415.10

0.000.000.00

0.000.000.000.000.000.000.02

0.000.000.00


227

Table 7.2: Correlation Matrix: East Asia-Pacific, 1980-2002

Variable GDPpc LGDPpc School I EM INF XM GEXP ER Qindex MC VT TR

GDPpc

LGDPpc

School

I

EM

INF

XM

GEXP

ER

Qindex

MC

VT

TR

1.00

-0.11 1.00(0.13)

-0.22 0.68 1.00(0.00) (0.00)

0.54 -0.10 -0.38 1.00

(0.00) (0.17) (0.00)

-0.06 -0.22 -0.37 0.16 1.00

(0.39) (0.00) (0.00) (0.02)

-0.36 -0.28 -0.10 -0.16 0.13 1.00

(0.00) (0.00) (0.16) (0.03) (0.08)

0.07 0.48 -0.09 0.37 0.28 -0.25 1.00

(0.33) (0.00) (0.18) (0.00) (0.00) (0.00)

-0.01 0.46 0.26 -0.06 -0.20 -0.07 0.34 1.00(0.98) (0.00) (0.00) (0.39) (0.00) (0.37) (0.00)

0.23 -0.30 -0.28 0.14 0.00 0.06 -0.22 -0.10 1.00

(0.00) (0.00) (0.00) (0.06) (0.97) (0.43) (0.00) (0.17)

0.05 0.60 0.55 0.02 -0.20 -0.25 0.26 0.25 -0.28 1.00

(0.50) (0.00) (0.00) (0.82) (0.01) (0.00) (0.00) (0.00) (0.00)

0.10 0.51 0.12 0.32 0.13 -0.29 0.70 0.54 -0.19 0.27 1.00(0.18) (0.00) (0.08) (0.00) (0.08) (0.00) (0.00) (0.00) (0.01) (0.00)

0.19 0.39 0.17 0.27 -0.05 -0.26 0.41 0.40 -0.11 0.03 0.75 1.00(0.01) (0.00) (0.02) (0.00) (0.51) (0.00) (0.00) (0.00) (0.14) (0.66) (0.00)

0.32 -0.06 0.11 0.26 -0.32 -0.06 -0.12 -0.07 -0.03 -0.24 -0.07 0.46 1.00

(0.00) (0.41) (0.12) (0.00) (0.00) (0.45) (0.10) (0.35) (0.73) (0.00) (0.35) (0.00)

Notes:(1) P-values in parentheses.(2) (GDPpc = Per Capita GDP Growth Rate, LGDPpc = Level of Per Capita GDP), (School = AverageYears of School), (I = Gross Capital Investment as a proportion of GDP), (EM = Employment Growth

Rate), (INF = Inflation Rate), (XM = Openness of Trade, Exports plus Imports as a proportion of GDP),(GEXP = General Government Consumption as a proportion of GDP), (Qindex = Institutional QualityIndex), (ER = Annual Freedom Scores), (MC = Market Capitalization, as a proportion of GDP), (VT =Value Traded, as a proportion of GDP), and (TR = Turnover Ratio, the total value of shares traded dividedby market capitalization).(3) The correlation matrix was done using STATA Statistics/Data Analysis package version 8.2 for

Windows.

7.4.3 The Case of G-7 Economies

To understand how stock market development affects economic growth it is necessary to

include and consider the features of most developed financial markets and their role in

the economy.

228

This study provides an empirical analysis for the largest stock markets in the world. In

other words, the most important hypothesis to include this group of countries in our

study is related to the question: Are the stock markets in the G-7 economies doing what

markets are supposed to do? And do they reflect the existing literature evidence of a

positive relationship between stock market development and economic growth.

Using an unbalanced panel data set for 7 countries from 1980 to 2002, we assess the

contribution of stock market development and economic reform to economic growth for

the G-7 economies. Tables 7.3 and 7.4 summarize the variables of stock market and

control variables set that we consider in the analysis and provide some descriptive

statistics and correlation matrix.

Table 7.3: Descriptive Statistics: G-7 Economies, 1980-2002


Per Capita GDP Growth RateInitial Level of Per Capita GDPAverage Years of School



147147161

144147147144144161133

154154154

1.8324336.02

9.16

0.220.870.430.430.191.274.83

0.540.360.58

1.707404.56

1.80

0.040.530.390.160.310.270.95

0.380.450.39

-4.3013982

6.20

0.16-0.15-0.070.150.131.002.00

0.040.010.13

6.164483012.00

0.333.112.130.850.252.006.00

1.713.252.35

-0.760.97

-0.05

1.080.931.87

-0.17-0.310.27

-0.55

0.963.491.89

4.413.501.85

3.604.556.802.502.241.972.68

3.4419.58

8.02

0.000.000.00

0.000.000.000.020.010.000.04

0.000.000.00


229

Table 7.4: Correlation Matrix: G-7 Economies, 1980-2002


GDPpc

LGDPpc

School

I

EM

INF

XM

GEXP

ER

Qindex

MC

VT

TR

1.00

0.10 1.00(0.24)

-0.01 0.17 1.00(0.90) (0.04)

0.21 0.60 -0.19 1.00

(0.01) (0.00) (0.02)

-0.06 -0.12 0.54 0.03 1.00

(0.45) (0.14) (0.00) (0.75)

-0.29 -0.55 -0.27 0.03 0.10 1.00

(0.00) (0.00) (0.00) (0.76) (0.22)

-0.02 -0.54 -0.10 -0.42 -0.21 0.06 1.00

(0.86) (0.00) (0.25) (0.00) (0.01) (0.49)

-0.25 -0.55 -0.22 -0.57 -0.11 0.15 0.66 1.00(0.00) (0.00) (0.01) (0.00) (0.18) (0.08) (0.00)

0.16 -0.22 0.49 0.01 0.65 0.09 -0.21 -0.23 1.00

(0.05) (0.01) (0.00) (0.89) (0.00) (0.26) (0.01) (0.01)

0.02 -0.20 0.39 -0.06 0.29 0.07 0.24 0.28 0.35 1.00

(0.81) (0.03) (0.00) (0.55) (0.00) (0.46) (0.01) (0.00) (0.00)

0.27 0.25 0.42 -0.08 0.13 -0.38 -0.11 -0.37 0.19 -0.01 1.00(0.00) (0.00) (0.00) (0.37) (0.11) (0.00) (0.21) (0.00) (0.02) (0.94)

0.29 0.27 0.38 -0.09 0.12 -0.33 -0.18 -0.39 0.13 -0.18 0.76 1.00(0.00) (0.00) (0.00) (0.26) (0.15) (0.00) (0.16) (0.00) (0.12) (0.04) (0.00)

0.23 0.33 0.29 -0.07 -0.02 -0.43 0.02 -0.23 -0.10 -0.21 0.33 0.72 1.00(0.01) (0.00) (0.00) (0.39) (0.80) (0.00) (0.79) (0.01) (0.24) (0.02) (0.00) (0.00)

Notes:(1) P-values in parentheses.(2) (GDPpc = Per Capita GDP Growth Rate, LGDPpc = Level of Per Capita GDP), (School = AverageYears of School), (I = Gross Capital Investment as a proportion of GDP), (EM = Employment GrowthRate), (INF = Inflation Rate), (XM = Openness of Trade, Exports plus Imports as a proportion of GDP),(GEXP = General Government Consumption as a proportion of GDP), (Qindex = Institutional QualityIndex), (ER = Annual Freedom Scores), (MC = Market Capitalization, as a proportion of GDP), (VT =Value Traded, as a proportion of GDP), and (TR = Turnover Ratio, the total value of shares traded dividedby market capitalization).(3) The correlation matrix was done using STATA Statistics/Data Analysis package version 8.2 forWindows.

7.4.4 The Whole Sample Estimation

The final step in this study is to analyse the impact of stock market development and

economic reform on economic growth for the full sample, using an unbalanced panel

data set for 28 countries over the period 1980-2002, with respect to the economic

diversification among the Arab countries and the rest of the world (East Asia-Pacific and

G-7 economies). In Table 7.5 and 7.6, we present summary statistics and the correlation

230

matrix between per capita GDP growth and all of the explanatory variables that are

included in our analysis.

Table 7.5: Descriptive Statistics: Full Sample, 1980-2002


Per Capita Growth GDP RateInitial Level of Per Capita GDPAverage Years of School



563595644

549598592584594621527

504511502

0.2012271.43

6.64

0.262.610.360.870.223.723.59

0.520.270.46

0.5310471.46

2.71

0.071.950.560.710.202.011.39

0.520.420.49

-2.901683.29

0.11-0.15-0.400.150.051.000.00

0.010.020.02

3.034483012.00

0.4912.53

5.764.391.537.006.00

3.363.243.87

-0.940.650.46

0.721.844.062.464.27

-0.160.01

2.093.383.18

10.532.711.93

3.077.82

29.949.87

22.611.492.38

8.6017.8318.33

0.000.000.00

0.000.000.000.000.000.000.00

0.000.000.00

Note: The descriptive statistics was done using STATA Statistics/Data Analysis package version 8.2 for

Windows.

231

Table 7.6: Correlation Matrix: Full Sample, 1980-2002


GDPpc

LGDPpc

School

I

EM

INF

XM

GEXP

ER

Qindex

MC

VT

TR

1.00

-0.06 1.00(0.13)

0.10 0.60 1.00(0.01) (0.00)

0.29 -0.22 -0.19 1.00

(0.00) (0.00) (0.00)

-0.26 -0.36 -0.35 -0.01 1.00

(0.00) (0.00) (0.00) (0.89)

-0.08 -0.24 -0.11 0.03 0.01 1.00

(0.05) (0.00) (0.01) (0.47) (0.91)

0.03 -0.03 -0.19 0.39 0.16 -0.08 1.00

(0.54) (0.45) (0.00) (0.00) (0.00) (0.05)

0.01 0.17 0.09 -0.03 -0.06 -0.08 0.33 1.00(0.88) (0.00) (0.04) (0.55) (0.16) (0.05) (0.00)

0.10 -0.28 -0.28 0.04 0.07 -0.04 -0.04 0.12 1.00

(0.02) (0.00) (0.00) (0.42) (0.09) (0.29) (0.34) (0.00)

0.06 0.59 0.66 -0.06 -0.19 -0.09 0.05 0.11 -0.25 1.00

(0.18) (0.00) (0.00) (0.25) (0.00) (0.04) (0.32) (0.01) (0.00)

0.12 0.32 0.27 0.24 0.03 -0.27 0.60 0.46 -0.11 0.28 1.00(0.01) (0.00) (0.00) (0.00) (0.51) (0.00) (0.00) (0.00) (0.01) (0.00)

0.17 0.35 0.37 0.20 -0.12 -0.20 0.32 0.31 -0.16 0.16 0.72 1.00(0.00) (0.00) (0.00) (0.00) (0.01) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00)

0.25 0.22 0.36 0.18 -0.25 -0.07 -0.08 -0.05 -0.14 0.05 0.12 0.60 1.00(0.00) (0.00) (0.00) (0.00) (0.00) (0.11) (0.10) (0.29) (0.00) (0.27) (0.01) (0.00)

Notes:(1) P-values in parentheses.(2) (GDPpc = Per Capita GDP Growth Rate, LGDPpc = Level of Per Capita GDP), (School = AverageYears of School), (I = Gross Capital Investment as a proportion of GDP), (EM = Employment GrowthRate), (INF = Inflation Rate), (XM = Openness of Trade, Exports plus Imports as a proportion of GDP),(GEXP = General Government Consumption as a proportion of GDP), (Qindex = Institutional QualityIndex), (ER = Annual Freedom Scores), (MC = Market Capitalization, as a proportion of GDP), (VT =Value Traded, as a proportion of GDP), and (TR = Turnover Ratio, the total value of shares traded dividedby market capitalization).(3) The correlation matrix was done using STATA Statistics/Data Analysis package version 8.2 forWindows.

7.5 Empirical Results

The estimation results for the three groups of countries and the full sample are carried

out using the basic methods of OLS, and instrumental variables (fixed effects and

random effects) with 2SLS methods applied to the general model. Another approach

applies the dynamic growth model for the first-differenced GMM estimator of Arellano

and Bond (1991), as well as the system GMM estimator developed by Arellano and

232

Bover (1995) and Blundell and Bond (2000). All specifications include time dummies to

control for common time effects, for the presence of unobserved country-specific

effects, and for the possible endogeneity of the explanatory variables in all countries.

The estimation methods of OLS and 2SLS use the dependent variable of per capita GDP

growth rate and explanatory variables, including stock market liquidity measured by

turnover ratio, economic reform by institutional quality index and control variables set.

In the robustness test, we experimented with the alternative measures of stock market

development and economic reform.

The GMM estimators present the results of the dynamic economic growth and stock

market equation including the lagged endogenous variable ( 1ty − ) and the current values

of all explanatory variables. In using the method of 2SLS, we consider the turnover ratio

as endogenous, while in the GMM estimators it was treated as exogenous, as with all the

explanatory variables in the estimation. All the estimation results are reported with

robust standard errors (White-corrected) to control for possible heteroscedasticity

problems. However, the estimation results of OLS, 2SLS and GMM estimators are

conducted with diagnostic tests such as t-statistic, F-Statistic, R-Squared, Wald test for

joint significance (P-value), and Hausman test (P-value), the Sargan test and the

Second-order serial autocorrelation, AR(2).

All specifications in both the GMM first-differenced estimator and the GMM system

estimator seem to capture the relevant dynamics as the estimation found no evidence of

second order autocorrelation (AR (2)). The Sargan test of over-identifying restrictions

233

based on the two-step GMM estimator is insignificant in all cases and fails to reject the

null hypothesis of overall validity of the instruments.

In the instrumental variables method (2SLS), we find that the random effects regression

for Arab stock markets seems appropriate in both regressions (1) and (2) as a result of

the Hausman tests P-value (insignificant) in Appendix Table A.2. In the East Asia-

Pacific, the fixed effects regression is the best test in both regressions (1) and (2) and is

supported by the significant P-value of the Hausman test in Appendix Table A.6.

In contrast, the G-7 results show that the fixed effects regression is the best test for

regression (1) due to the significant P-value for the Hausman test, and the random

effects is more efficient in the regression (2) since the Hausman test P-value is

insignificant (see Appendix Table A.10). Considering the full sample of all countries, the

results find that the fixed effects estimator is consistent and more efficient in both

regressions (1) and (2) as confirmed by the significant P-value of the Hausman test that

rejects the null hypothesis that the coefficients estimated of the random effects are the

same as the ones estimated by the consistent fixed effects estimator, as shown by

Appendix Table A.14.

For convenience, the estimation results for the three groups, as well as the full sample

estimation, are summarized in Table 7.7. We provide the estimated coefficients for the

main variables in each case included in the study, and present the general regression of

OLS and 2SLS, as well as the dynamic growth regression of GMM estimators.

234

Alternatively, the estimation tables and methods (OLS, 2SLS, and GMM estimators-

Difference and System), with the full regressions including other explanatory variables

for each case of our study are reported in Appendix A, as follows: Arab Countries

(Tables A.1-A.4), East Asia-Pacific (Tables A.5-A.8), the G-7 economies (Tables A.9-

A.12), and finally the full sample estimation in Tables A.13-A.16.

Table 7.7: Summary Results of the Main Variables in Different Set Methods

Dependent Variable: Real Per Capita GDP GrowthExp. Variables Group OLS 2SLS GMMDIF GMMSYS

Ln of Turnover

Ratio

Arab countries

East-Asia Pacific

G-7 Economies

Full Sample

-0.072 (0.95)

***0.104 (2.93)

***0.089 (2.88)

*0.051 (1.77)

-0.048 (0.82)

***0.127 (2.77)

***0.220 (3.75)

***0.096 (3.09)

0.022 (0.65)

**0.098 (2.62)

*0.044 (1.93)

***0.098 (3.17)

-0.050 (1.44)

**0.099 (2.40)

**0.048 (2.37)

**0.059 (2.24)

Ln of Initial Level

of Income (t-1)

Arab countries

East-Asia Pacific

G-7 Economies

Full Sample

**-0.038 (2.16)

*-0.071 (1.90)

***-0.503 (3.59)

**-0.074 (2.04)

**-0.044 (2.13)

***-0.916 (3.47)

***-0.577 (3.73)

***-0.829 (6.04)

-0.126 (1.45)

***-0.137 (5.14)

***-0.165 (3.06)

**-0.184 (2.29)

-0.102 (1.44)

***-0.158 (4.45)

**-0.157 (2.14)

***-0.237 (4.75)

Institutional Quality

Index

Arab countries

East-Asia Pacific

G-7 Economies

Full Sample

0.651 (1.56)

*0.174 (1.99)

0.114 (1.18)

0.146 (1.62)

0.593 (1.60)

0.019 (0.21)

0.176 (1.41)

*0.269 (1.76)

**0.448 (2.38)

0.017 (0.77)

-0.066 (0.80)

**0.343 (2.33)

**0.471 (2.70)

-0.015 (0.39)

-0.029 (0.38)

**0.786 (2.15)

Ln of Investment

Arab countries

East-Asia Pacific

G-7 Economies

Full Sample

0.189 (0.64)

***0.890 (6.91)

***0.491 (3.37)

***0.544 (4.18)

0.383 (1.38)

***0.119 (7.50)

***0.603 (3.32)

***0.874 (7.31)

0.319 (1.63)

***0.109 (6.70)

***0.952 (8.01)

***0.153 (4.88)

***0.475 (3.08)

***0.113 (4.76)

***0.108 (6.41)

***0.113 (5.03)

Ln (1 + Inflation

Rate)

Arab countries

East-Asia Pacific

G-7 Economies

Full Sample

*-0.297 (1.82)

***-0.448 (4.00)

**-0.429 (3.16)

***-0.441 (3.29)

***-0.392 (2.89)

***-0.389 (3.63)

***-0.385 (2.73)

***-0.513 (4.98)

***-0.639 (3.41)

***-0.349 (3.46)

***-0.557 (5.00)

**-0.139 (2.68)

***-0.680 (3.64)

**-0.471 (3.31)

***-0.661 (4.28)

***-0.479 (3.69)

Source: Appendix A/Panel Data Approach (A.1-A.16)

Notes: t-statistics in parentheses. ***1% significance level, **5% significance level, and *10%significance level. GMMDIF and GMMSYS denote GMM-difference and GMM-system estimators,respectively. The main variables are turnover ratio for stock market liquidity, initial level of per capitaGDP for convergence, institutional quality index (allocate a the maximum score of 6 points; that is, 0 forthe worst and 6 for the best), investment as a proportion of GDP, and inflation for macroeconomicstability. OLS and GMM regressions are estimated by using robust standard errors to control for possibleheteroscedasticity. Time dummies are included in all regressions.

The estimation was done using STATA Statistics/Data Analysis package version 8.2 for Windows.

235

The main empirical results and conclusion are discussed as follows:

(1) Economic Growth and Stock Market Development

From the findings of Arab countries in the estimation methods of OLS, 2SLS and GMM

estimators difference and system, as reported in Table 7.7 and Appendix Tables A.1-A.4,

we find that the estimated coefficients on the current values of turnover ratio are not

significant for Arab countries even though they have negative values. The results here

confirmed that the contemporaneous and lagged values of turnover ratio have no effect

on Arab economic growth at any conventional level.

Possible explanations of this result are that the Arab stock markets emerged recently, the

turnover ratio is small in most of these countries with some notable exceptions, the lack

of transparency and illiquidity problems limit the effectiveness of Arab stock markets, a

few large (by regional standards) companies typically dominate these markets and,

finally, there is no regional equivalent to the G-7 markets and developed emerging

markets.

In contrast, the estimation results of both East Asia-Pacific and G-7 economies suggest

that stock market liquidity has a significant effect in all procedures and is positively

correlated with economic growth. For the East Asia-Pacific countries, the estimated

coefficients on the current value of turnover ratio are positive and strongly significant at

the one percent and five percent significance levels as summarized in Table 7.7, and

236

reported in more detail in Appendix Tables A.5-8. In addition, the G-7 economies results

are much the same in significance and impact on economic growth as found for the East

Asia-Pacific countries (see Table 7.7 and Appendix Tables A.9-A.12). It is clear that the

stock markets of East-Asia Pacific and G-7 economies play a very important role in

corporate finance, and are recognized as well-developed markets by all indicators of

stock market development.

For the full sample, the last part of our empirical analysis (summarized by Table 7.7)

tests the implications of stock market development on economic growth in a more

general way, by combining the three groups as one panel data set (28 countries). In

Table 7.7, the estimated coefficients on the current values of turnover ratio are positive

and highly significant on average at the one percent and five percent significance levels.

The full estimation regressions of this sample are presented in Appendix Tables A.13-

A.16.

In this regard, the estimation results for East Asia-Pacific and G-7 economies are

consistent with the first hypothesis of our study that a well functioning stock market

may affect economic activity in an economy through growth of saving, efficient

allocation of resources, and better utilization of the existing resources (Beck and Levine,

2004).

237

(2) Economic Growth and Economic Reform

The results of economic reform using the institutional quality index from International

Country Risk Guide (ICRG), which represents a maximum score of 6 points, with 0

representing the worst and a score 6 for the best, are different between the cases in the

study with reference to the estimated method. An interesting result for Arab countries

when using GMM estimators is that the estimated coefficients on the current value of

the institutional quality index are positive and significant at the five percent level. The

GMM results exhibit some variations from the lagged effect of the institutional quality

index in our prior GMM estimation in Chapter six. This result is different from the OLS

and 2SLS results which found that the institutional effect is not significant. The analysis

results are consistent with most of the literature that examined the economic institutional

environment on Arab economic growth, such as Hakura (2004), Nabli and Varoudakis

(2004), and Eltony and Babiker (2004).

The estimated coefficients on the institutional quality index in the case of East Asia-

Pacific and G-7 economies are, on average, not significant. That economic growth of

these countries already captures the effects of the business cycle may explain this. In

contrast, the full sample estimates indicate a significant and positive effect on economic

growth at the five percent significance level when using the two methods of GMM

estimators, as shown in Table 7.7.

238

(3) Economic Growth and Control Variables set

The results of macroeconomic variables for the three groups and the full sample show

the expected sign and an economic impact that is consistent with the economic theory.

For Arab countries, the general regression using OLS and 2SLS reveals that the

estimated coefficients on the average years of schooling for human capital, investment,

employment growth rate, and trade openness are not significant at any conventional

level, (see Table 7.7 and Appendix Tables A.1-A.2). In contrast, the oil estimates in OLS

and 2SLS are positive and significant at the one percent and five percent significance

levels. This is consistent with the important effect of oil in Arab economic growth. The

estimated coefficients on government consumption and inflation are negative and

significant on average at the five percent level, as shown in Appendix Tables A.1-A.2.

For the GMM estimators in Appendix Tables A.3-A.4, the coefficients on average years

of schooling and investment were positive and significant at the five percent

significance level. The coefficient estimate for government consumption expenditure is

now insignificant, but the estimate remains negative and highly significant for inflation

rate.

For the full sample and both groups of East Asia-Pacific and G-7 economies, the

estimated coefficients on investment and inflation have shown strong effects on

economic growth at the one percent and five percent significance levels in all estimation

methods, see Table 7.7 and Appendix Tables A.5-A.12. The evidence confirms the

239

theoretical literature that higher investment and lower inflation rates lead to higher

economic growth.

On the other hand, the other macroeconomic variables for East Asia-Pacific, G-7

economies and the full sample reveal some variations in sign and significance according

to the estimation method used.

(4) Economic Growth and Convergence

The estimated coefficients on lagged initial level of per capita GDP show a negative

sign, and mostly significant and common to all countries as a full sample and for each

group of the Arab countries, East Asia-Pacific and G-7 economies, separately. This

convergence is evidenced by the negative relationship between the growth rate of per

capita GDP and the lagged initial level of per capita GDP, after controlling for other

macroeconomic variables that could affect the economic growth.

However, the convergence income effect across countries has been significant in all

estimation alternatives of OLS, 2SLS and dynamic GMM estimators.

7.6 Robustness Tests and Possible Extensions

In this section, we present several tests on the robustness of the full sample estimation

results with the panel data set on 26 countries over the period 1980-2002. The

240

robustness test considers three analyses: an outlier test, alternative measures of stock

market development, and individual country results by using a time series approach.

7.6.1 Outlier Analysis

A general concern is that the results based on the dynamic growth regressions could be

driven by the exceptional performance of some countries from East Asia-Pacific and G-

7 economies, which could not be fully captured by the inclusions of the country and

group dummies. However, we check the consistency of the results when the countries

with extreme observations are omitted for specific variables and the estimations are

repeated.

Moreover, we run a regression omitting the lowest observations of initial level of per

capita GDP, the highest rates of inflation and extreme observations for the growth rate

of per capita GDP. The results using these robust regressions were similar to those

obtained using the same model specification; that is, the empirical results are not

affected and there are no real changes in the main variables when these observations (for

example, China, Lebanon and Kuwait) are excluded from the analysis.

(These results are not reported but available on request).

241

7.6.2 Alternative Measures of Stock Market and Economic Reform

We test in the previous models whether stock market liquidity (as measured by turnover

ratio) affects economic growth after controlling for economic reform and the

macroeconomic control variables set.

In this robustness test, we examine the direct relationship between stock market

development and economic growth for the whole sample of the study. We use the

general regression of OLS in equation (7.1), by including alternative measures of stock

market size (market capitalization as a proportion of GDP), stock market activity (value

traded as a proportion of GDP), and the interaction variable of investment and turnover

ratio for the possible effect of stock market development on economic growth through

investment activity. Similarly, we apply the second measure of economic reform in our

study (Annual Freedom Scores) and find that there is no significant difference in terms

of the estimated coefficients.

More specifically, the estimated coefficients in the OLS regression with alternative

indicators of stock market value traded, market capitalization and the interaction of

turnover ratio with investment, are broadly consistent (except market capitalization)

with the results of turnover ratio that were reported in the last section and in Model (1),

Table 7.8. That is, the effect of stock market development on economic growth remains

positive and significant for the full sample. However, this result is driven by the East

Asia-Pacific countries and the G-7 economies.

242

In addition, the estimated coefficients on the international quality index, as a measure

for economic reform, are positive and highly significant in the robustness tests, as

shown in Table 7.8.

Table 7.8: Growth and Stock Market Equation-Full Sample: OLS Estimation



Model (1) Model (2) Model (3) Model (4)

Constant

Ln of Initial Level of Income (t-1)

Institutional Quality Index

Ln of Turnover Ratio

Ln of Value Traded

Ln of Market Capitalization

Ln (Investment ∗ Turnover Ratio)

R-Squared

F-Statistic

No. Countries

No. Observations

***0.827 (3.96)

***-0.093 (4.17)

***0.261 (3.00)

**0.084 (2.66)

0.153

2.61 (0.00)

26

400

***0.929 (3.69)

***-0.094 (3.76)

**0.201 (2.02)

**0.047 (2.56)

0.123

2.40 (0.00)

26

400

**0.471 (2.46)

***-0.076 (3.38)

***0.295 (2.99)

0.003 (0.17)

0.107

1.91 (0.00)

26

400

***1.095 (4.69)

***-0.089 (3.96)

**0.225 (2.44)

***0.108 (3.47)

0.195

3.09 (0.00)

26

400

Notes: t-statistics in parentheses. ***1% significance level, **5% significance level, and *10%significance level. OLS regression is estimated by using robust standard errors to control for possibleheteroscedasticity. All regressions include time dummies but not reported.The estimation was done using STATA Statistics/Data Analysis package version 8.2 for Windows.

7.6.3 Individual Country Results

In the previous chapter, we re-estimated the main model of Arab stock markets using

OLS methods on time series data and an individual country approach. In this test, the

estimation method of OLS follows the same approach on stock market development and

economic reform indicators with economic growth. The main equation to be estimated is

given by:

243

t t ty x∗ = + + (7.4)

This robustness test analyses the time series data for each country in the two groups of

East Asia-Pacific (except China which is omitted due to insufficient data for time series

analysis) and G-7 economies to see whether the evidence also supports the panel data

results of stock market development and economic reform when we allow all these

parameters to differ across countries.

The estimation results are reported in Appendix B, including the countries of the East

Asia-Pacific (Tables B.6 B.14) and the G-7 economies (Tables B.15 B.21). The

coefficient estimates and standard errors are robust due to the potential possibility of

heteroscedasticity.

In general, the main estimation results and conclusions remain unchanged. We find that

stock market development indicators, particularly turnover ratio, have a significant

relationship with economic growth in all East Asia-Pacific countries except Hong Kong.

For the G-7 economies, the OLS time series results consistent are with the panel data

results, on average, for all indicators of stock market capitalization, value traded,

turnover ratio and the interaction variable (Investment ∗ Turnover Ratio), showing a

positive and significant effect on economic growth for all countries in this group. On the

other hand, the impact of economic reform is different between the countries.

244

However, the time series and individual country results of the East Asia-Pacific

countries and the G-7 economies are mostly consistent with the panel data results in our

empirical analysis, and support the prior results that stock market development may

affect and facilitate economic growth.

It would be interesting here to compare the time series results for the three groups in the

study. Table 7.9 summarizes the number of countries in each group that are either

significant or insignificant in terms of stock market indicators.

Table 7.9: OLS Estimation-Time Series Approach: International Comparison


No. Countries ARAB COUNTRIES

(5 Countries)EASTASIA-PACIFIC

(9 Countries)G-7 ECONOMIES

(7 Countries)Explanatory Variables

Significant

Not

Significant Significant

Not

Significant Significant

Not

Significant

Ln (Turnover Ratio) 1 4 8 1 7 0

Ln (Value Traded) 1 4 5 4 6 1

Ln (Market Capitalization) 1 4 5 4 6 1

Ln (Investment∗ Turnover Ratio) 3 2 8 1 7 0

Source: Appendix B/Tables (B.1-B.21)

7.7 Conclusion

In this chapter, we applied different estimation methods of OLS, 2SLS and GMM

estimators by using unbalanced panel data sets consisting of three groups of countries

(Arab countries, East Asia-Pacific and G-7 economies), as well as applying the same

econometric specification on the full sample of 28 countries over the period 1980-2002.

245

The estimated regressions of economic growth and stock market development

encompassing the majority of the existing literature that has examined this relationship

between stock market development and economic growth after controlling for the

macroeconomic determinants. This study used different groups of panel data sets that, in

our view, provide an accurate evaluation on this relationship by using different levels of

economic growth and stock market development.

The estimation results of both East Asia-Pacific and G-7 economies are similar in terms

of stock market development and economic growth. The results suggest that stock

market liquidity has a significant effect in all procedures and is positively correlated

with economic growth. It is clear that stock markets of the East-Asia Pacific and G-7

economies play a very important role in corporate finance and real economic activities.

By combining the three groups together as a one panel data set (28 countries), the

estimated coefficients on turnover ratio are positive and highly significant at the one

percent and five percent significance level.

In contrast, it is shown that there is no evidence of links between Arab stock markets

and economic growth, when compared to the previous groups. Moreover, the impact of

economic reform, as measured by the institutional quality index, is different between the

groups in the study with reference to the estimated method.

The results of some macroeconomic variables for the three groups of the Arab countries,

the East Asia-Pacific and the G-7 economies reveal, on average, some variations in sign

and significance due to the differences in the choice of estimation method and the

246

sample group. In contrast, the estimated coefficients on investment and inflation have

shown strong effects on economic growth in all alternative estimation methods.

In the robustness test of alternative measures of stock market development and

economic reform, we examined the direct relationship between stock market

development and economic growth for the full sample data set of our study using the

OLS estimation method. The robustness results found that the effect of stock market

development on economic growth remains positive and significant.

On the other hand, the estimation results are robust to the possibility of important

differences between time series and cross-country analysis, as suggested by Arestis and

Demetriades (1997). The individual country results are consistent with the panel data

results of East Asia-Pacific and G-7 economies.

Compared with the existing empirical studies, we found that the East Asia-Pacific

countries and the G-7 estimation results are consistent with the study hypothesis that a

well functioning stock market may affect economic activity in an economy through

growth of saving, efficient allocation of resources, and better utilization of the existing

resources.

247

Chapter Eight

8. CHAPTER EIGHT: CONCLUSION AND FUTURE RESEARCH

8.1 Introduction

The overall objective of this study has been to examine the effect of Arab stock markets

and economic reform on economic growth after controlling for the macroeconomic

determinants. It provided a comprehensive theoretical consideration of the stock market

effects on saving and investment through the determination of risk-return ratios, and the

q-theory of investment. It considered a comparative analysis of Arab countries with two

different groups, the East Asia-Pacific countries and the G-7 economies. It explored the

relationship between stock market development and economic growth for the whole

sample of 28 countries by using different estimation methods.

Arab countries were chosen as the focus for this study due to the growing international

interest on the role of the stock market and its contribution to investment and real

economic activities. Most Arab governments have acknowledged the importance of the

stock market and initiated procedures and legislation that protect and open stock

exchanges to foreign investors. Given that the stock market is an alternative channel to

bank borrowing for corporate finance and private sector investment, there is a large

amount of liquidity in the region that could be invested in productive sectors if market

confidence is generated.

248

With these changes has come a greater degree of accessibility to data which,

consequently, allows us to assess the economic impact of stock market development in

the Arab countries, not in terms of their profitability to investors, but rather in terms of

liquidity and development relative to the size of the economy and financing

requirements of capital expenditure in the region.

Consistent with this background, this concluding Chapter summarizes the study in the

next section. In section three, we provide the main findings and policy implications, and

highlight the contribution of this study to the existing literature on stock market

development and economic growth. Study limitations and suggestions for further

research are presented in the sections four and five.

8.2 Summary of the Study

The introductory Chapter outlined the framework of this study within the context of

main hypotheses. It pointed out the general hypotheses about the links between stock

market development and economic growth. Given the important role of the stock market

in corporate finance and real economic activities for developed economies, the question

arises in this study: does stock market development have a similar role and effect on

Arab economic growth and, if so, how. To examine the study s hypotheses, and to

answer this question, a comprehensive work of theoretical and empirical models has

been considered. A quantitative assessment on macroeconomic level and stock market

development indicators for the Arab countries, the East Asia-Pacific countries and the

G-7 economies are provided.

249

The aim of Chapter two was to evaluate critically the literature that examined the effects

of finance and stock market development on economic growth of Arab countries at an

international level. It also considered the contradictory views on the importance of

financial development and the stock market with economic growth: (1) demand-

following, that is, high growth may create demand for certain financial services and the

financial markets are effectively a response to this demand, (2) supply-following, that is,

the financial sector and intermediaries contribute to economic growth by increasing the

size of saving and improving the efficiency of investment, and the third view (3) the

stock market is an unimportant source of corporate finance and does not enhance

economic growth. It reviewed some of the empirical studies on measures of economic

reform and economic freedom and their impact on economic growth

The empirical literature in Chapter two does not provide clear evidence on the

relationship between stock market development and economic growth. That is, this

relationship is still ambiguous and may have positive, negative, or no effect on

economic growth. This contradictory result may be explained by differences in the data

coverage as regards number of countries and time periods, the estimation methods, or

the variables selected.

Chapter three is of fundamental importance and presents a comprehensive theoretical

consideration of how financial development and stock market development could affect

economic growth. This Chapter consists of four main sections. Firstly, with respect to

the economic theory of endogenous growth, stock market development may affect

250

economic growth through an increase of the saving rate, the channelling of more saving

to investment, and the improvement of capital productivity with better allocation of

resources.

Secondly, it analysed the theoretical framework of the effect of stock market

development on saving. It considered the effect on saving mobilization through the

determination of risk-return ratios as presented in modern portfolio theory. The potential

effect of stock market development on saving is ambiguous. A positive effect may occur

due to an increase in the rate of return on saving that provides an incentive for

individuals to postpone consumption. In contrast, stock market development may

decrease saving because of a wealth effect; an increase in the rate of return on saving

also increases wealth, which in turn increases consumption and decreases saving.

Thirdly, it explained the effect of stock market development on investment in two

contrasting views. One view argues that managers should ignore share price changes in

the short-run if they are concerned about the market value of the firm in the long-run

and do not reflect the firm s longer-term prospects. The opposite view is that stock

market valuation matters for investment, and managers should respond to market

valuation, even when this deviates from the true value of the firm, to maximize the

wealth of existing shareholders. The q-theory of investment concludes that the

management seeks to maximize the present net worth of the company and the market

value of the outstanding common shares. In addition, the stock market appraises the

project with its expected contributions to the future earnings of the company and its

risks.

251

Finally, the theory is uncertain regarding the effect of stock market liquidity on

economic growth; that is, greater liquidity and increasing the investment return may

reduce the saving rate, and dissatisfied investors find it easy to sell quickly. This can

lead to disincentives to exert corporate control, thus adversely affecting corporate

finance and slowing down economic growth in the process.

Consistent with the economic theory that private investment will tend to flow toward

free economies and economic environments that are more attractive for productive

activities, Chapter three stresses the importance of economic reform and freedom in

efficient use of resources and thus real economic growth. Two measures of economic

reform, annual freedom scores and institutional quality index, are discussed in this

Chapter.

The aim of Chapter four was to understand the nature of Arab economies and the role of

stock markets in real economic activities. It introduced a descriptive analysis using

quantitative measures of financial development and Arab markets that provided a

backdrop to our empirical analysis in Chapters six and seven. This analysis has been

considered on an individual country level and classified into the two groups of oil-

producing countries and non-oil countries. A new database was collected and

constructed on macroeconomic indicators, economic reform indicators, financial sector

and stock market development indicators.

252

On a macroeconomic level, the major outcomes for the Arab countries showed that oil

export revenues have contributed to the improvement of welfare and helped finance

investment in infrastructure and human capital in both oil-exporting countries and non-

oil countries. As regards economic environment and reform, the annual average of

political rights, civilian rights and economic freedom is classified as not free for oil-

exporting countries compared to partly free in non-oil countries. Also, the institutional

quality index, on average, is less than half of the entire score of 6 points in both groups

of Arab countries. The exchange rate systems in Arab countries have been fixed against

the US dollar, except for Egypt and Tunisia, which have a managed float with no pre-

announced path for exchange rate and crawling peg, respectively.

Looking at the financial development indicators, Chapter four found that, firstly, the

average real interest rates have generally been positive for all Arab countries but higher

for non-oil countries than the average rate prevailing in the oil-exporting countries.

Secondly, the ratio of broad money supply to GDP (M2/GDP), on average, is higher for

non-oil countries than for the oil-exporting countries. Thirdly, the private sector share in

the total credit provided by the banking sector is generally higher for the oil-exporting

countries than in the non-oil countries.

Chapter four then considered the historical progress and the main indicators of stock

market development, including stock market capitalization, value traded, turnover ratio

and the number of listed companies in the Arab markets. The average market

capitalization and value traded are much higher for oil-exporting countries than non-oil

countries. The total number of listed companies is higher for non-oil countries than oil-

253

exporting countries, where Egypt has the highest number of listed companies but this is

not reflected in the market value traded in comparison to the GCC stock markets.

Chapter five provided a comparative analysis of macroeconomic and financial markets

for the three groups of countries, with different growth rates in per capita GDP. The

objective of this Chapter was to identify the importance, and understand the

characteristics, of these economies with regard to market size and liquidity and their

relationship with economic growth in the three groups of our study.

Growth performance revealed substantial differences between the three groups in

Chapter five. The G-7 economies represented the highest value of real GDP at market

prices, followed by East Asia-Pacific, with the smallest amount for the Arab countries

over the period 1990-2003. In terms of economic reform indicators, the average value of

political rights, civilian rights and economic freedom is classified as partly free for the

Arab countries and the East Asia-Pacific countries compared to the most free G-7

economies in the world. In addition, the average value of the institutional quality index

is mostly the worst for Arab countries, on margin for the East Asia-Pacific countries and

good for institutions in the G-7 economies.

Chapter five then considered the financial development indicators across three groups.

East Asia-Pacific has the best average value in most financial indicators of M2/GDP,

domestic credit/GDP, and the credit to private sector/total credit over the period 1990-

2003. The Arab countries achieved the second rank of M2/GDP. The G-7 economies

have the best average value of bank assets/GDP. A comparison of stock market

254

development indicators for the three groups found that stock market capitalization over

GDP is the highest for East Asia-Pacific countries, followed by the G-7 economies and

the lowest ratio for Arab countries. In contrast, both value traded over GDP and turnover

ratio is best for the G-7 economies and the East Asia-Pacific countries. Also, the total

number of listed companies is higher for the G-7 economies and the East Asia-Pacific

countries.

The main conclusion from Chapters four and five is that Arab stock markets are

relatively new and commonly small when compared to the East Asia-Pacific countries

and the G-7 markets. Arab stock markets account for 0.7 percent of the total market

capitalization over the period 1990-2003, compared to 8.9 percent for the East Asia-

Pacific markets during the same period.

Chapter six focused on developing a dynamic growth theoretical model for Arab

countries incorporating the alternative indicators of stock market development,

economic reform and growth determinants as control variables. The model was

developed to provide a more comprehensive evaluation on the relationship between

stock market development, economic reform and economic growth. The main variables

in the theoretical framework were selected and proposed by economic theory and

empirical studies, such as Beck and Levine (2004), Rousseau and Wachtel (2000),

Levine and Zervos (1998), and Atje and Jovanovic (1993). This Chapter reviewed the

econometric methodology and the most important differences of the GMM estimators

for dynamic panel data models as proposed by Arellano and Bond (1991), Arellano and

255

Bover (1995) and Blundell and Bond (2000). Also, Chapter six presented the main

hypotheses and the theoretical discussion of the explanatory variables.

The empirical work in Chapter six used the basic methods of OLS, 2SLS with fixed-

effects and random-effects, and the GMM estimators for the dynamic panel model. The

major results from the empirical work examining the relationship between the Arab

stock markets, economic reform and economic growth, found that the effect of either

stock market liquidity or economic reform on economic growth are very weak and not

significant statistically when controlling for the main determinants of economic growth.

In analysing Arab countries in this Chapter, the explanatory variables entered the

estimation regressions with the one lag.

The same theoretical framework and estimation methods (OLS, 2SLS and GMM

estimators) in Chapter six were used to conduct a comparative analysis on different

financial systems of Arab stock markets with East Asia-Pacific and G-7 markets in

Chapter seven. The empirical work examined whether the relationship between stock

market development, economic reform and economic growth differs across countries by

using panel data sets for three groups, and then for the whole sample of 28 countries


The estimation results in Chapters six and seven were robust to different measures of

stock market development and economic growth in two estimation methods of OLS and

GMM estimators. In addition, the empirical work in both Chapters six and seven

256

compared the estimation results of the panel data set with time series and an individual

country approach.

8.3 Key Findings and Policy Implications

The general and dynamic models of economic growth and stock market development

were estimated and applied the three different econometric methods of OLS, 2SLS and

GMM estimators to the core sample of our study, Arab countries. It then compared the

results with different groups of the East Asia-Pacific countries, the G-7 economies and

the whole sample of 28 countries over the period 1980-2002. Details of the key results

in each of these groups and policy implications are now discussed in more detail.

The results of the dynamic growth model using GMM estimators are quite similar to the

regressions of OLS and 2SLS in terms of stock market liquidity and economic reform in

Arab countries. The main findings indicated that the contemporaneous and lagged

effects of either stock market liquidity, as measured by turnover ratio, or economic

reform, as measured by the institutional quality index, are very weak on economic

growth. An interesting result for Arab countries from the robustness test indicated that

the interaction variable (Investment ∗ Turnover Ratio) has a positive and significant

effect on economic growth. Implementing the policy of an expansion in gross capital

investment and turnover ratio may promote favourable effects during the dynamic

growth model in terms of the interaction between country investment and stock market

liquidity.

257

The insignificance of stock market development variables in explaining economic

growth implies that the Arab stock markets are underdeveloped and play a minor role in

corporate finance and real economic activity. Arab stock markets are relatively closed to

international foreign investors, poorly regulated, and various restrictions still exist in the

face of Arab portfolio flows and diversification. Hence, the challenge for Arab

governments is to provide full accessibility to international investors and remove the

current financial restrictions that could bring more benefits from portfolio capital

inflows and enhance liquidity, which thereby promote Arab economic growth.

Another important policy implication is that Arab governments should develop an

institutional and economic environment as an important prerequisite for economic

growth, in terms of the control of corruption and the efficiency of Arab governments to

attract foreign financial investment. Economic reform, with appropriate legislation that

protects local and foreign investors, may help foreign capital to find its way to

productive projects in the region and stock market development will effectively

complement the financial services provided by the banking sector in the region.

In contrast, the findings of the East Asia-Pacific countries, the G-7 economies and the

full sample suggested that stock market liquidity has a significant effect in all

procedures and is positively correlated with economic growth. It is clear that the stock

markets of the East-Asia Pacific countries and the G-7 economies play a very important

role in corporate finance, and are recognized as well-developed markets by all indicators

of stock market size, activity and liquidity. This is consistent with the first hypothesis of

our study that a well functioning stock market may affect economic activity in an

258

economy through growth of saving, efficient allocation of resources, and better

utilization of the existing resources (Beck and Levine, 2004).

The institutional quality index for both the East Asia-Pacific countries and the G-7

economies is, on average, not significant. A possible explanation may be that the index

was mostly stable over the period of this study and the economic growth of these

countries already captures the effects of the business cycle. In contrast, the full sample

estimates indicate a significant and positive effect on economic growth.

The main findings of growth determinants for the three groups of the Arab countries, the

East Asia-Pacific countries and the G-7 economies indicated, on average, some

variations in sign and significance due to the differences in the choice of estimation

method and the sample group. Investment and inflation have shown strong effects on

economic growth in all alternative estimation methods. Moreover, the oil effect has been

positive and significant, confirming the view that oil has a superior weight and effect in

Arab economic growth. Oil export revenues have contributed to the improvement of

welfare and helped finance investment in infrastructure and human capital in both oil-

exporting countries and non-oil countries (Chapter four).

Implementing an economic stabilization policy aiming primarily at generating high

levels of investment and containing inflation rates down to safe levels would stimulate

economic growth in Arab countries. That is, this result is consistent with the economic

theory that higher investment and lower inflation rates lead to higher economic growth.

259

The convergence income effect across countries has been found significant and common

to all countries as a full sample and for each group of the Arab countries, the East Asia-

Pacific countries and the G-7 economies, separately. This convergence is evidenced by

the negative relationship between the growth rate of per capita GDP and the lagged

initial level of per capita GDP, after controlling for other macroeconomic variables that

could affect the economic growth.

The main results of the time series and individual country approach for the three groups

of the Arab countries, the East Asia-Pacific countries and the G-7 economies are mostly

consistent with the panel data results in our empirical analysis. Although some Arab

countries were marginally significant in some measures of stock market development,

this may be explained by some differences in the accessibility levels of Arab stock

markets to international investors and other Arab investors.

It is important in this section to review the estimation and robustness results of the main

indicators of stock market development, economic reform and economic growth in this

study for the three groups, as well as the full sample estimation (see Table 8.1).

260

Table 8.1: Summary Panel Estimation Results in this Study

Dependent Variable: Real Per Capita GDP Growth RateExplanatory Variables Arab

Countries1East Asia-Pacific

G-7Economies

FullSample2

Ln (Initial Level of Income) Significant Significant Significant Significant

Ln (Turnover Ratio) Not Significant Significant Significant Significant

Ln (Value Traded) Significant NA NA Significant

Ln (Market Capitalization) Not Significant NA NA Not Significant

Ln (Investment∗ Turnover Ratio) Significant NA NA Significant

Institutional Quality Index Not Significant Not Significant Not Significant Significant

Notes: NA stands for the variable that is Not Applied in that group.1

For the Arab countries, alternative measures of stock market development were estimated in therobustness test in Chapter six. The results of value traded and the interaction variable of(Investment∗ Turnover Ratio) are significant at the ten percent level when using GMM estimators.2 For the full sample, we examined these alternative measures of stock market development in therobustness test, (see Table 7.8: OLS estimation).

Source: The Empirical Work and Comparative Analysis in our Study: Chapters Six and Seven.

We then compare the above results with the main empirical studies that examined stock

market development and economic growth. Table 8.2 summarizes some of the main

studies that are either significant or insignificant in terms of stock market development

indicators and convergence income effect.

Table 8.2: A Comparison With Different Empirical Studies


Empirical StudiesBeck andLevine(2004)1

Rioja andValev(2003)2

Rousseau andWachtel(2000)3

Levine andZervos(1998)4

Arestis andDemetriades(1997)5

Ln (Initial Level of Income) Significant Significant Significant NA

Ln (Turnover Ratio) Significant NA NA Significant

Ln (Value Traded) NA Significant Significant Significant

Ln (Market Capitalization) NA Not Significant Not Significant Significant

The resultsrevealedsubstantialvariationsacrosscountries

Notes: NA stands for the variable that is Not Applied in that study.1

Based on GMM dynamic panel estimation methods.2 Based on GMM dynamic panel estimation methods.3

Based on Cross-sectional 2SLS and Panel Vector Autoregressions (VARs) method.4

Based on Cross-sectional and Instrumental Variables (IV) methods.5 Based on time series estimation and an individual country approach.

261

8.4 Study Limitations

There are some limitations in this study arising from the contradictory views on the

relationship between stock market development and economic growth. On the one hand,

the theory is ambiguous about whether stock market development supports economic

growth. On the other hand, the theory does not provide a unique model to guide the

empirical research for examining this relationship. In this respect, economists and

researchers have applied different econometric methodologies and techniques to

establish the possible links between stock market development and economic growth.

For example, results based on the Granger-Causality test indicated that the relationship

between them could be unidirectional, bi-directional or no relationship at all.

More precisely, there is no comprehensive theoretical framework in terms of stock

market development measures and the choice of growth determinants which are

required for controlling the relationship between stock market development and

economic growth. In addition, many of the empirical studies suffer from model

misspecification and are also sensitive to data coverage as regards countries and time

periods.

The second limitation is from the lack of Arab stock markets data used in this study.

Given that the empirical analysis has been undertaken typically from the available data

of the Arab stock markets and economic growth, it was difficult to find quarterly and/or

long time series annual data on the size and activity of Arab stock markets, as well as for

the main determinants of economic growth. The lack of data limits our ability to apply

262

sophisticated analyses of cointegration methods and Granger-Causality tests on the links

between stock market development and economic growth for Arab countries.

The third limitation is that there are no common measures of stock market development

performed in the previous studies; that is, each empirical study and theoretical model

focused on one characteristic of stock market size, activity or liquidity as channels to

real economic activities. The measures used in the literature are a compromise between

the theoretical background and data availability. Hence, the major limit facing this study

is how to measure stock market development. Four measures of stock market

development were considered and estimated in this study.

However, given these limitations our study has used sophisticated econometric

techniques on consistent panel data sets for the Arab countries, the East Asia-Pacific

countries and the G-7 economies.

8.5 Avenues for Future Research

This study has introduced different aspects and some outstanding avenues for future

research on the relationship between stock market development, economic reform and

economic growth at the theoretical and empirical level. These avenues will now be

discussed.

For Arab countries, further research would be interesting to incorporate the

consideration of more factors such as banking credit allocated to private sector, market

263

interest rate, and stock market integration into the existing theoretical framework of

dynamic economic growth and stock market development. The theoretical literature

pointed to the role of banking credit and the market interest rate in the financing process

and investment activities, which was not the focus of this study. The integration of Arab

stock markets in the future both on a regional level or with the rest of the world s stock

markets, is considered by the literature as an advantage and generally offers

diversification benefits for local and international investors. However, more cooperation

among Arab countries regarding stock market development and economic reform is

needed to increase investor confidence and economic stability.

Another important area of future research in the context of Arab countries is to consider

the causality links between stock market development, privatisation and investment on

the one hand, and between stock market development and oil revenues on the other

hand. This could provide some initial causal links between Arab stock markets and real

economic activities. Stock market development could help privatization and facilitate

the acquisition of more debt financing. In this study, we considered the effect of

investment and oil as control variables for the one causation direction of stock market

development to economic growth.

An econometric comparison of Arab stock markets with different financial systems of

the East Asia-Pacific countries and the G-7 economies has not been widely, if ever,

applied for these countries. The common and different features of the estimation results

in various groups of countries provide a new research area of testing the dynamic

growth and stock market development, along with the growth determinants as control

264

variables. It would be important to re-examine the different effects for the

macroeconomic variables on stock market development between the East Asia-Pacific

and the G-7 economies.

A comparative analysis of the Arab countries with the East Asia-Pacific countries and

the G-7 economies presented an accurate evaluation on this relationship by using

different levels of economic growth and stock market development. Given that few

studies are concerned with Arab financial markets, the majority of these studies

examined the effect of financial development on economic growth through the banking

sector variables, such as liquid liabilities and broad money supply (M2). However, the

field of inquiry remains highly fluid, subject to the different economic conditions in the

Arab and global capital markets and to on-going attempts at regulatory and structural

reform in the region.

This study represents the first attempt to test empirically the effect of Arab stock

markets and economic reform on economic growth. It considered modern econometric

techniques by using the three different estimation methods of OLS, 2SLS and GMM

estimators on consistent panel data sets for the three groups of countries over the period

1980-2002. It compared the estimation results for the Arab countries with the East Asia-

Pacific countries and the G-7 economies. Overall, this study makes a unique

contribution to the literature, since there are no other empirical studies analysing such

behavioural relationships in the case of Arab countries.

265

9. APPENDICES

9.1 Appendix A: Panel Data Approach

In this appendix A, we arrange the panel estimation methods and Tables (A.1-A.16) for

each case of our sample as well as the common notes and clarifications that used in the

practical procedures of our empirical analysis in the following section:

Simple conditioning information set, includes: logarithm of initial level of income and average

years of schooling. Policy conditioning information set, includes: simple set, plus investment,

inflation rate, trade openness, employment growth rate and government consumption. Full

conditioning information set, includes: policy conditioning set plus measures of institutional

index, economic freedom, and dummy variable for oil-exporting countries.

266

FIRST: ARAB COUNTRIES

Table A.1: OLS Estimation: Arab Countries



Simple Set (1) Policy Set (2) Full Set (3)Constant


Ln (1 + Average Years of School)

Ln of Investment

Ln (1 + Inflation Rate)

Ln of Employment Growth Rate

Ln of Trade Openness

Ln of Gov. Consumption


Oil


R-Squared

F-Statistic

No. Countries

No. Observations

0.192 (0.38)

**-0.013 (2.58)

*0.424 (1.66)

*-0.112 (1.83)

0.141

5.34 (0.00)

9

129

0.379 (0.39)

-0.006 (0.75)

0.002 (0.14)

0.110 (0.47)

-0.169 (1.05)

-0.049 (1.44)

0.042 (0.28)

-0.261 (1.21)

-0.084 (1.15)

0.195

3.37 (0.00)

9

111

*3.618 (1.85)

**-0.038 (2.16)

-0.007 (0.02)

0.189 (0.64)

*-0.297 (1.82)

*-0.062 (1.89)

0.174 (0.83)

**-0.526 (2.05)

0.651 (1.56)

**0.841 (2.45)

-0.072 (0.95)

0.259

3.90 (0.00)

9

104

Notes: Notes: t-statistics in parentheses. ***1% significance level, **5% significance level, and *10%significance level. OLS regression is estimated by using robust standard errors to control for possibleheteroscedasticity. Time and country dummies are included in all regressions. The estimation was doneusing STATA Statistics/Data Analysis package version 8.2 for Windows.

267

Table A.2: Instrumental Variables-2SLS: Arab Countries



(1) Policy Set Reg. (2) Full Set Reg.Fixed-Effects Random-Effects Fixed-Effects Random-Effects

Constant



Ln of Investment

Ln (1+ Inflation Rate)





Oil


R-Squared

Wald test for joint signif. (P-value)

Hausman test

No. Countries

No. Observations

***14.062 (3.03)

***-0.179 (3.22)

0.176 (0.50)

0.423 (1.60)

***-0.428 (2.80)

-0.117 (0.70)

-0.277 (0.54)

-0.345 (0.84)

-0.069 (0.92)

0.113

33.70 (0.00)

9

101

0.519 (0.48)

-0.014 (0.55)

-0.157 (0.44)

0.253 (1.03)

**-0.248 (2.01)

-0.071 (0.45)

0.005 (0.03)

*-0.407 (1.82)

-0.056 (0.93)

0.221

20.91 (0.007)

14.69 (0.065)

9

101

**11.806 (2.08)

**-0.158 (2.27)

0.388 (1.03)

0.254 (0.82)

*-0.329 (1.96)

-0.094 (0.55)

-0.673 (1.13)

-0.588 (1.19)

-0.546 (0.97)

-0.094 (1.34)

0.106

33.97 (0.000)

9

94

*3.857 (1.82)

**-0.044 (2.13)

-0.034 (0.09)

0.383 (1.38)

***-0.392 (2.89)

-0.102 (0.64)

0.182 (0.69)

***-0.758 (3.13)

0.593 (1.60)

***0.105 (2.85)

-0.048 (0.82)

0.303

31.87 (0.001)

11.89 (0.292)

9

94

Notes: t-statistics in parentheses. *** 1% significance level, ** 5% significance level, and * 10%significance level. Hausman test for both of fixed effects and random effects is (H0: RE against Ha: FE).Instrumental variable (fixed and random effects) regressions present the results by using

( , 2 , 2andi t i ty x− − ) as instruments. Time dummies are included in all regressions. The estimation was

done using STATA Statistics/Data Analysis package version 8.2 for Windows.

268

Table A.3: Dynamic Growth Equation-GMM Difference Estimator: Arab Countries





Ln of (1 + Average Years of School)

Ln of Investment






OIL


Specification tests: (P-value)

(a) Sargan test

(b) Serial Correlation

AR (1)

AR (2)

No. Countries

No. Observations

0.024 (0.89)

***-0.242 (3.13)

0.626 (1.06)

*-0.168 (1.68)

0.155

-2.78 (0.006)

-0.39 (0.698)

9

98

-0.037 (1.59)

**-0.213 (2.60)

*0.848 (1.89)

**0.523 (2.59)

***-0.698 (2.96)

-0.154 (0.96)

-0.431 (0.90)

-0.324 (0.50)

-0.003 (0.06)

0.631

-2.21 (0.027)

-1.07 (0.286)

9

85

-0.296 (0.59)

-0.126 (1.45)

**1.462 (2.38)

0.319 (1.63)

***-0.639 (3.41)

-0.198 (1.15)

**-0.108 (2.28)

-0.027 (0.04)

**0.448 (2.38)

**0.171 (2.31)

0.022 (0.65)

0.876

-2.13 (0.033)

-0.50 (0.618)

9

78

Notes: t-statistics in parenthesis. ***1% significance level, **5% significance level, and *10%significance level. The results report one-step GMM estimator of the equations in first differences, withrobust standard errors. The GMM-difference estimator uses the moment conditions that are generated by

the equation in first differences. Instruments are ( , 2 , 2andi t i t

y x− − ), of all regressors when feasible. Time

and country dummies are included in all regressions. The estimation was done using STATAStatistics/Data Analysis package version 8.2 for Windows.

269

Table A.4: Dynamic Growth Equation-GMM System Estimator: Arab Countries






Ln of Investment






OIL


Specification tests:

(a) Sargan test


AR (1)

AR (2)

No. Countries

No. Observations

0.036 (1.51)

***-0.353 (5.45)

0.006 (0.01)

0.022 (0.29)

0.170

-2.28 (0.023)

0.14 (0.885)

9

85

***-0.049 (2.78)

***-0.208 (2.88)

**0.120 (2.65)

***0.564 (3.46)

***-0.717 (3.53)

-0.297 (1.62)

-0.307 (0.66)

-0.070 (0.11)

0.020 (0.52)

0.771

-4.82 (0.000)

0.82 (0.413)

9

76

-0.486 (0.99)

-0.102 (1.44)

***0.163 (2.87)

***0.475 (3.08)

***-0.680 (3.64)

*-0.251 (1.73)

-0.797 (1.63)

-0.252 (0.37)

**0.471 (2.70)

**0.162 (2.49)

-0.050 (1.44)

0.863

-4.19 (0.000)

0.18 (0.854)

9

73

Notes: t-statistics in parentheses. ***1% significance level, **5% significance level, and *10%significance level. The results report one-step GMM system estimator, with robust standard errors. TheGMM-system estimator uses the moment conditions that are generated by the equations in first

differences and levels. Instruments are ( , 2 , 2andi t i ty x− − ), and , 1 , 2 , 1 , 2( ), and ( )i t i t i t i t

y y x x− − − −− − , of

all regressors when feasible. Time and country dummies are included in all regressions. The estimationwas done using STATA Statistics/Data Analysis package version 8.2 for Windows.

270

SECOND: EAST ASIA-PACIFIC COUNTRIES

Table A.5: OLS Estimation: East Asia-Pacific






Ln of Investment







R-Squared

F-Statistic

No. Countries

No. Observations

***1.344 (6.48)

*0.043 (1.66)

***-0.487 (4.08)

***0.235 (7.12)

0.459

8.59 (0.000)

10

185

***2.205 (8.21)

*-0.058 (1.75)

0.043 (0.29)

***0.823 (7.14)

***-0.379 (3.76)

-0.005 (0.07)

*-0.084 (1.83)

0.017 (0.49)

***0.106 (3.75)

0.727

10.39 (0.000)

10

173

**1.824 (2.46)

*-0.071 (1.90)

-0.009 (0.05)

***0.890 (6.91)

***-0.448 (4.00)

0.129 (1.50)

**-0.145 (2.24)

-0.139 (0.99)

*0.174 (1.99)

***0.104 (2.93)

0.720

12.80 (0.000)

10

140

Notes: t-statistics in parentheses. ***1% significance level, **5% significance level, and *10%significance level. OLS regression is estimated by using robust standard errors to control for possibleheteroscedasticity. Time and country dummies are included in all regressions. The estimation was doneusing STATA Statistics/Data Analysis package version 8.2 for Windows.

271

Table A.6: Instrumental Variables-2SLS: East Asia-Pacific




Constant



Ln of Investment







R-Squared


Hausman test

No. Countries

No. Observations

***7.087 (4.68)

***-0.701 (3.17)

0.459 (0.51)

***0.952 (6.45)

***-0.355 (3.75)

-0.024 (0.29)

0.047 (0.27)

0.062 (0.24)

***0.321 (4.38)

0.223

495.92 (0.000)

10

163

***1.983 (7.58)

-0.037 (1.08)

-0.039 (0.24)

***0.733 (6.17)

***-0.400 (5.09)

0.050 (0.72)

**-0.091 (2.00)

0.002 (0.05)

***0.159 (4.24)

0.666

297.66 (0.000)

21.98 (0.039)

10

163

***8.283 (4.81)

***-0.916 (3.47)

0.449 (0.47)

***0.119 (7.50)

***-0.389 (3.63)

0.078 (0.89)

-0.262 (0.20)

-0.262 (0.80)

0.019 (0.21)

***0.258 (3.26)

0.167

500.73 (0.000)

10

131

**2.038 (2.55)

-0.059 (1.53)

-0.061 (0.30)

***0.860 (6.19)

***-0.383 (3.89)

*0.128 (1.72)

*-0.116 (1.87)

-0.069 (0.42)

0.115 (1.33)

***0.127 (2.77)

0.703

273.87 (0.000)

26.79 (0.020)

10

131

Notes: t-statistics in parentheses. ***1% significance level, **5% significance level, and *10%significance level. Hausman test for both of fixed effects and random effects is (H0: RE against Ha: FE).Instrumental variable (fixed and random effects) regressions present the results by using

( , 2 , 4 , 2 , 4... and ...i t i t i t i t

y y x x− − − − ) as instruments. Time dummies are included in all regressions. The

estimation was done using STATA Statistics/Data Analysis package version 8.2 for Windows.

272

Table A.7: Dynamic Growth Equation-GMM Difference Estimator: East Asia-Pacific






Ln of Investment








(a) Sargan test


AR (1)

AR (2)

No. Countries

No. Observations

**0.026 (2.30)

***-0.115 (6.80)

0.539 (0.63)

**0.145 (2.06)

0.987

-6.48 (0.000)

-0.87 (0.384)

10

175

**0.037 (2.04)

***-0.121 (5.38)

-0.697 (0.65)

***0.136 (4.75)

**-0.254 (2.08)

-0.060 (0.48)

-0.202 (1.29)

-0.322 (1.53)

***0.134 (2.99)

0.801

-7.09 (0.000)

-0.65 (0.513)

10

163

0.030 (0.56)

***-0.137 (5.14)

-0.256 (0.25)

***0.109 (6.70)

***-0.349 (3.46)

0.065 (0.89)

0.005 (0.04)

-0.318 (1.35)

0.017 (0.77)

**0.098 (2.62)

0.998

-4.72 (0.000)

-1.64 (0.100)

10

131

Notes: t-statistics in parentheses. ***1% significance level, **5% significance level, and *10%significance level. The results report one-step GMM estimator of the equations in first differences, withrobust standard errors. The GMM-difference estimator uses the moment conditions that are generated by

the equation in first differences. Instruments are ( , 2 , 4 , 2 , 4... and ...i t i t i t i ty y x x− − − − ), of all regressors

when feasible. Time dummies are included in all regressions. The estimation was done using STATAStatistics/Data Analysis package version 8.2 for Windows.

273

Table A.8: Dynamic Growth Equation-GMM System Estimator: East Asia-Pacific






Ln of Investment








(a) Sargan test


AR (1)

AR (2)

No. Countries

No. Observations

0.121 (1.11)

***-0.100 (6.18)

0.689 (1.07)

***0.127 (3.03)

0.967

-4.87 (0.000)

-1.62 (0.104)

10

165

***0.180 (4.03)

***-0.115 (5.18)

0.839 (0.94)

***0.119 (4.87)

***-0.384 (4.30)

-0.056 (0.51)

-0.135 (0.84)

-0.216 (1.05)

***0.136 (3.35)

0.999

-6.12 (0.000)

-0.92 (0.359)

10

153

*0.050 (1.84)

***-0.158 (4.45)

0.470 (0.77)

***0.113 (4.76)

**-0.471 (3.31)

0.018 (0.34)

0.083 (0.30)

**-0.654 (2.21)

-0.015 (0.39)

**0.099 (2.40)

0.996

-4.84 (0.000)

-1.08 (0.282)

10

129


differences and levels. Instruments are ( , 2 , 4 , 2 , 4... and ...i t i t i t i ty y x x− − − − ),

and , 1 , 2 , 1 , 2( ), and ( )i t i t i t i ty y x x− − − −− − , of all regressors when feasible. Time and country dummies

are included in all regressions. The estimation was done using STATA Statistics/Data Analysis packageversion 8.2 for Windows.

274

THIRD: THE G-7 ECONOMIES

Table A.9: OLS Estimation: G-7 Economies






Ln of Investment







R-Squared

F-Statistic

No. Countries

No. Observations

0.477 (0.88)

-0.003 (0.06)

-0.054 (0.64)

***0.091 (2.79)

0.276

5.37 (0.00)

7

140

***6.974 (6.00)

***-0.595 (5.73)

0.057 (0.60)

***0.611 (4.98)

***-0.448 (4.73)

-0.032 (1.40)

*-0.096 (1.66)

-0.063 (0.49)

***0.092 (3.31)

0.366

5.89 (0.000)

7

134

***5.993 (4.15)

***-0.503 (3.59)

-0.074 (0.53)

***0.491 (3.37)

**-0.429 (3.16)

-0.022 (0.93)

-0.071 (1.10)

-0.181 (1.19)

0.114 (1.18)

***0.089 (2.88)

0.378

4.48 (0.000)

7

113

Notes: t-statistics in parentheses. ***1% significance level, **5% significance level, and *10%significance level. OLS regression is estimated by using robust standard errors to control for possibleheteroscedasticity. Time and country dummies are included in all regressions. The estimation was doneusing STATA Statistics/Data Analysis package version 8.2 for Windows.

275

Table A.10: Instrumental Variables-2SLS: G-7 Economies




Constant



Ln of Investment







R-Squared

Wald test for joint sign. (P-value)

Hausman test

No. Countries

No. Observations

***10.757 (6.26)

***-0.947 (4.54)

0.311 (0.31)

***0.126 (6.21)

***-0.672 (5.41)

-0.036 (1.34)

0.174 (1.29)

-0.373 (1.14)

0.081 (1.51)

0.234

476.59 (0.000)

7

127

***8.489 (6.00)

***-0.707 (6.08)

0.052 (0.54)

***0.758 (4.85)

***-0.391 (3.45)

-0.036 (1.45)

**-0.139 (2.14)

0.025 (0.16)

***0.162 (3.49)

0.331

62.11 (0.000)

56.56 (0.000)

7

127

***12.110 (5.76)

***-0.864 (2.90)

-0.075 (0.07)

***0.135 (5.70)

***-0.555 (3.46)

-0.014 (0.49)

**0.405 (2.34)

0.064 (0.18)

-0.081 (0.67)

**0.184 (2.63)

0.083

477.67 (0.000)

7

106

***7.407 (4.27)

***-0.577 (3.73)

*-0.230 (1.66)

***0.603 (3.32)

***-0.385 (2.73)

-0.003 (0.12)

-0.125 (0.35)

-0.065 (0.35)

0.176 (1.41)

***0.220 (3.75)

0.364

61.36 (0.000)

21.95 (0.079)

7

106

Notes: t-statistics in parentheses. ***1% significance level, **5% significance level, and *10%significance level. Hausman test for both of fixed effects and random effects is (H0: RE against Ha: FE).

Instrumental variable (fixed and random effects) regressions present the results by using

( , 2 , 4 , 2 , 4... and ...i t i t i t i ty y x x− − − − ) as instruments. Time and country dummies are included in all

regressions. The estimation was done using STATA Statistics/Data Analysis package version 8.2 forWindows.

276

Table A.11: Dynamic Growth Equation-GMM Difference Estimator: G-7 Economies






Ln of Investment








(a) Sargan test


AR (1)

AR (2)

No. Countries

No. Observations

**0.027 (2.21)

***-0.189 (3.72)

0.002 (0.33)

**0.061 (2.03)

0.259

-5.16 (0.000)

-1.14 (0.254)

7

133

0.006 (0.56)

***-0.146 (3.45)

0.457 (0.57)

***0.121 (6.30)

***-0.653 (4.50)

-0.076 (1.57)

0.138 (0.82)

-0.259 (0.98)

**0.063 (2.54)

0.290

-3.79 (0.000)

-0.90 (0.365)

7

115

0.012 (0.89)

***-0.165 (3.06)

0.352 (0.41)

***0.952 (8.01)

***-0.557 (5.00)

-0.047 (0.80)

0.044 (0.29)

*-0.501 (1.79)

-0.066 (0.80)

*0.044 (1.93)

0.265

-3.76 (0.000)

-0.96 (0.338)

7

108

Notes: t-statistics in parentheses. ***1% significance level, **5% significance level, and *10%significance level. The results report one-step GMM estimator of the equations in first differences, withrobust standard errors. The GMM-difference estimator uses the moment conditions that are generated by

the equation in first differences. Instruments are ( , 2 , 4 , 2 , 4... and ...i t i t i t i ty y x x− − − − ), of all regressors

when feasible. Time and country dummies are included in all regressions.The estimation was done using STATA Statistics/Data Analysis package version 8.2 for Windows.

277

Table A.12: Dynamic Growth Equation-GMM System Estimator: G-7 Economies





Ln (1+ Average Years of School)

Ln of Investment








(a) Sargan test


AR (1)

AR (2)

No. Countries

No. Observations

-0.001 (0.07)

***-0.174 (4.68)

0.012 (1.44)

***0.076 (3.45)

0.310

-3.48 (0.001)

-1.25 (0.213)

7

126

0.005 (0.40)

**-0.097 (2.04)

**0.137 (2.25)

***0.107 (6.55)

***-0.627 (6.54)

0.001 (0.02)

0.042 (0.30)

**-0.527 (2.01)

*0.053 (1.98)

0.450

-4.87 (0.000)

1.29 (0.197)

7

113

0.021 (1.45)

**-0.157 (2.14)

0.102 (1.40)

***0.108 (6.41)

***-0.661 (4.28)

0.012 (0.52)

*0.279 (1.66)

-0.048 (0.22)

-0.029 (0.38)

**0.048 (2.37)

0.592

-4.32 (0.000)

-0.30 (0.764)

7

99

Notes: t-statistics in parentheses. ***1% significance level, **5% significance level, and *10%significance level.The results report one-step GMM system estimator, with robust standard errors. The GMM-systemestimator uses the moment conditions that are generated by the equations in first differences and levels.

Instruments are ( , 2 , 4 , 2 , 4... and ...i t i t i t i t

y y x x− − − − ), and , 1 , 2 , 1 , 2( ), and ( )i t i t i t i t

y y x x− − − −− − , of all

regressors when feasible. Time and country dummies are included in all regressions.The estimation was done using STATA Statistics/Data Analysis package version 8.2 for Windows.

278

FOURTH: THE FULL SAMPLE

Table A.13: OLS Estimation: Full Sample






Ln of Investment







R-Squared

F-Statistic

No. Countries

No. Observations

***0.702 (3.41)

-0.039 (1.40)

-0.103 (0.85)

**0.056 (2.32)

0.117

5.32 (0.00)

26

441

***1.532 (5.92)

**-0.078 (2.42)

0.130 (0.88)

***0.563 (4.89)

***-0.388 (3.79)

*-0.059 (1.73)

-0.007 (0.23)

-0.037 (0.88)

**0.060 (2.10)

0.346

11.65 (0.000)

26

406

*1.117 (1.82)

**-0.074 (2.04)

0.041 (0.21)

***0.544 (4.18)

***-0.441 (3.29)

-0.018 (0.50)

-0.051 (1.14)

-0.129 (1.50)

0.146 (1.62)

*0.051 (1.77)

0.353

10.36 (0.000)

26

348

Notes: t-statistics in parentheses. ***1% significance level, **5% significance level, and *10%significance level. OLS regression is estimated by using robust standard errors to control for possibleheteroscedasticity. Time and country dummies are included in all regressions.The estimation was done using STATA Statistics/Data Analysis package version 8.2 for Windows.

279

Table A.14: Instrumental Variables, 2SLS: Full Sample




Constant



Ln of Investment







R-Squared


Hausman test

No. Countries

No. Observations

***8.652 (6.18)

***-0.813 (5.88)

-0.102 (0.36)

***0.874 (7.25)

***-0.456 (4.62)

-0.054 (1.05)

0.118 (0.96)

-0.322 (1.62)

***0.159 (3.19)

0.058

311.41 (0.000)

26

391

***1.886 (5.12)

**-0.101 (2.70)

0.126 (0.81)

***0.637 (6.71)

***-0.450 (5.14)

-0.025 (0.61)

0.002 (0.05)

-0.027 (0.44)

**0.082 (2.62)

0.308

148.80 (0.000)

38.73 (0.001)

26

391

***8.954 (6.37)

***-0.829 (6.04)

-0.146 (0.51)

***0.874 (7.31)

***-0.513 (4.98)

-0.049 (0.95)

0.092 (0.75)

**-0.445 (2.19)

*0.269 (1.76)

***0.146 (2.98)

0.067

313.69 (0.000)

26

382

***2.257 (4.00)

**-0.095 (2.69)

0.044 (0.27)

***0.646 (6.87)

***-0.451 (5.04)

-0.039 (1.06)

-0.004 (0.01)

-0.074 (0.75)

0.150 (1.50)

***0.096 (3.09)

0.306

143.62 (0.000)

50.94 (0.000)

26

382

Notes: t-statistics in parentheses. ***1% significance level, **5% significance level, and *10%significance level. Hausman test for both of fixed effects and random effects is (H0: RE against Ha: FE).

Instrumental variable (fixed and random effects) regressions present the results by using

( , 2 , 4 , 2 , 4... and ...i t i t i t i ty y x x− − − − ) as instruments. Time and country dummies are included in all

regressions.The estimation was done using STATA Statistics/Data Analysis package version 8.2 for Windows.

280

Table A.15: Dynamic Growth Equation-GMM Difference Estimator: Full Sample






Ln of Investment








(a) Sargan test


AR (1)

AR (2)

No. Countries

No. Observations

***0.089 (3.14)

***-0.288 (3.98)

-0.526 (0.82)

**0.069 (2.29)

0.989

-3.85 (0.000)

1.15 (0.251)

26

385

***0.059 (3.22)

***-0.239 (4.38)

-0.728 (1.23)

***0.148 (6.98)

*-0.261 (1.85)

-0.039 (0.41)

-0.293 (1.09)

-0.800 (1.49)

***0.097 (2.88)

0.981

-2.16 (0.030)

-0.04 (0.967)

26

356

*0.045 (1.74)

**-0.184 (2.29)

-0.213 (0.24)

***0.153 (4.88)

**-0.139 (2.68)

0.096 (0.96)

-0.472 (1.19)

-0.101 (1.55)

**0.343 (2.33)

***0.098 (3.17)

0.999

-1.18 (0.004)

-1.54 (0.123)

26

316

Notes: t-statistics in parentheses. ***1% significance level, **5% significance level, and *10%significance level.The results report one-step GMM estimator of the equations in first differences, with robust standarderrors. The GMM-difference estimator uses the moment conditions that are generated by the equation in

first differences. Instruments are ( , 2 , 4 , 2 , 4... and ...i t i t i t i t

y y x x− − − − ), of all regressors when feasible.

Time and country dummies are included in all regressions.The estimation was done using STATA Statistics/Data Analysis package version 8.2 for Windows.

281

Table A.16: Dynamic Growth Equation- GMM System Estimator: Full Sample






Ln of Investment








(a) Sargan test


AR (1)

AR (2)

No. Countries

No. Observations

***0.081 (3.35)

***-0.346 (4.47)

-0.097 (0.16)

**0.105 (2.75)

0.996

-3.63 (0.000)

-1.11 (0.266)

26

385

***0.050 (3.30)

***-0.245 (4.07)

0.545 (0.67)

***0.150 (4.96)

-0.192 (1.41)

-0.218 (1.44)

-0.086 (0.28)

-0.519 (1.04)

**0.038 (2.41)

0.973

-3.02 (0.002)

-1.20 (0.230)

26

355

**0.041 (2.24)

***-0.237 (4.75)

0.766 (1.16)

***0.113 (5.03)

***-0.479 (3.69)

-0.004 (0.08)

-0.162 (0.44)

-0.960 (1.47)

**0.786 (2.15)

**0.059 (2.24)

0.981

-4.26 (0.000)

-0.97 (0.331)

26

290


differences and levels. Instruments are ( , 2 , 4 , 2 , 4... and ...i t i t i t i ty y x x− − − − ), , 1 , 2( ),i t i ty y− −− and

, 1 , 2( )i t i tx x− −− of all regressors when feasible. Time and country dummies are included in all

regressions.The estimation was done using STATA Statistics/Data Analysis package version 8.2 for Windows.

282

9.2 Appendix B: Time Series Approach

The OLS estimation and time series results for each country in three groups are reported

as follows: Arab Countries-Tables (B.1-B.5), East Asia-Pacific Countries-Tables (B.6-

B.14), and The G-7 Economies-Tables (B.15-B.21).

FIRST: ARAB COUNTRIES TABLES (B.1-B.5)

Table B.1: OLS Regression (Time Series Approach, 1980-2002): EGYPT

Explanatory Variables Dependent Variable: Real Per Capita GDP GrowthConstantInstitutional Quality Index

***0.504 (4.28)**0.065 (2.44)

***0.445 (3.81)*0.044 (1.73)

***0.377 (3.50)0.030 (1.06)

***0.763 (4.79)***0.071 (3.78)

-0.044 (0.25)-0.026 (1.27)

Ln of Turnover RatioLn of Value TradedLn of Market Capitalization

Ln (Invest. ∗ Turnover Ratio)Ln (Banking Sector, M2/GDP)

0.063 (1.39)0.025 (1.12)

0.031 (0.74)*0.105 (2.07)

***-1.740 (2.92)

R-Squared 0.156 0.113 0.71 0.319 0.414Durbin-Watson Statistics 1.728 1.765 1.783 1.903 2.218No. Observations 23 23 23 23 23

Notes: t-statistics in parentheses. ***1% significance level, **5% significance level, and *10%significance level. OLS regression is estimated by using robust standard errors to control for possibleheteroscedasticity. The estimation was done using STATA Statistics/Data Analysis package version 8.2 for

Windows.

Table B.2: OLS Regression (Time Series Approach, 1980-2002): JORDAN


-2.061 (1.54)0.477 (1.53)

-2.332 (1.36)0.525 (1.65)

-2.007 (1.10)0.536 (1.28)

-2.472 (1.36)*0.548 (1.74)

-0.769 (0.74)0.276 (0.87)


Ln of Value TradedLn of Market CapitalizationLn (Invest. ∗ Turnover Ratio)Ln (Banking Sector, M2/GDP)

-0.197 (0.45)

-0.195 (0.53)-0.258 (0.32)

-0.158 (0.41)*-0.024 (1.89)



Windows.

283

Table B.3: OLS Regression (Time Series Approach, 1982-2002): MOROCCO


-1.288 (0.47)0.454 (1.03)

1.395 (0.40)0.503 (1.44)

2.035 (0.52)0.309 (0.86)

-1.210 (0.44)0.459 (1.05)

-6.232 (1.26)-0.296 (0.68)


Ln (Investment ∗ Turnover Ratio)Ln (Banking Sector, M2/GDP)

0.083 (0.56)0.178 (1.24)

**0.328 (2.13)

0.083 (0.58)-0.017 (0.92)


Notes: t-statistics in parentheses. ***1% significance level, **5% significance level, and *10%significance level. OLS regression is estimated by using robust standard errors to control for possibleheteroscedasticity. The estimation was done using STATA Statistics/Data Analysis package version 8.2 forWindows.

Table B.4: OLS Regression (Time Series Approach, 1985-2002): SAUDI ARABIA

Explanatory Variables Dependent Variable: Real Per Capita GDP GrowthConstant


9.226 (1.47)

1.268 (1.48)

10.892 (1.68)

1.463 (1.67)

4.466 (1.09)

0.546 (1.03)

12.378 (1.69)

1.590 (1.68)

-1.476 (0.58)

0.260 (0.74)

Ln of Turnover RatioLn of Value TradedLn of Market CapitalizationLn (Investment ∗ Turnover Ratio)Ln (Banking Sector, M2/GDP)

0.292 (1.55)*0.297 (1.74)

0.795 (1.28)*0.404 (1.75)

0.629 (1.36)



Table B.5: OLS Regression (Time Series Approach, 1986-2002): TUNISIA


1.028 (1.07)0.070 (0.45)

0.390 (0.40)0.032 (0.21)

0.335 (0.34)0.044 (0.29)

1.488 (1.41)0.110 (0.70)

0.265 (0.32)0.085 (0.67)

Ln of Turnover RatioLn of Value TradedLn of Market CapitalizationLn (Investment ∗ Turnover Ratio)Ln (Banking Sector, M2/GDP)

*0.150 (1.99)0.057 (1.05)

0.132 (1.02)**0.162 (2.29)

0.682 (1.03)



284

SECOND: EAST ASIA-PACIFIC COUNTRIES TABLES (B.6-B.14)

Table B.6: OLS Regression (Time Series Approach, 1980-2002): AUSTRALIA

Explanatory Variables Dependent Variable: Real Per Capita GDP Growth

ConstantInstitutional Quality Index

***2.306 (3.38)0.127 (0.71)

***2.284 (3.33)0.151 (0.81)

***2.125 (3.09)0.175 (0.89)

***2.306 (3.38)0.127 (0.71)

Ln of Turnover RatioLn of Value TradedLn of Market CapitalizationLn (Investment ∗ Turnover Ratio)

**0.269 (2.65)0.153 (2.38)

*0.305 (2.06)**0.269 (2.65)

R-Squared 0.467 0.443 0.366 0.467Durbin-Watson Statistics 1.535 1.641 1.766 1.535No. Observations 23 23 23 23


Table B.7: OLS Regression (Time Series Approach, 1980-2002): HONG KONG


10.832 (1.17)0.052 (0.47)

12.282 (1.24)0.069 (0.48)

12.166 (1.20)0.003 (0.02)

10.832 (1.17)0.052 (0.47)



0.321 (1.27)0.172 (0.77)

.013 (0.04)

0.321 (1.27)



Table B.8: OLS Regression (Time Series Approach, 1980-2002): INDONESIA



1.262 (1.13)

0.190 (1.37)

**2.189 (2.24)

**0.301 (2.44)

***2.658 (2.89)

***0.353 (3.02)

1.262 (1.13)

0.190 (1.37)


**0.148 (2.34)**0.058 (2.32)

**0.088 (2.13)**0.148 (2.34)



Windows.

285

Table B.9: OLS Regression (Time Series Approach, 1980-2002): SOUTH KOREA


-0.501 (0.82)*0.273 (2.05)

-0.051 (0.11)0.234 (1.66)

0.217 (0.57)0.164 (1.52)

-0.019 (0.06)*0.369 (2.23)



*0.416 (1.79)0.224 (1.22)

0.178 (0.70)

*0.857 (1.99)



Table B.10: OLS Regression (Time Series Approach, 1980-2002): MALAYSIA


*1.138 (1.81)0.106 (0.74)

*1.427 (2.00)0.199 (1.16)

*1.286 (1.89)0.217 (1.22)

**1.457 (2.10)0.117 (0.80)


**0.235 (2.17)**0.161 (2.43)

**0.327 (2.54)**0.234 (2.66)



Table B.11: OLS Regression (Time Series Approach, 1984-2002): NEW ZEALAND


-0.420 (0.58)0.169 (1.13)

-0.202 (0.30)0.130 (0.90)

0.546 (0.76)0.002 (0.02)

0.125 (0.20)0.203 (1.49)


**0.326 (2.83)0.186 (1.48)

***0.540 (3.26)***0.399 (3.10)



significance level. OLS regression is estimated by using robust standard errors to control for possibleheteroscedasticity. The estimation was done using STATA Statistics/Data Analysis package version 8.2 forWindows.

286

Table B.12: OLS Regression (Time Series Approach, 1980-2002): PHILIPPINES


0.092 (0.35)**0.162 (2.52)

0.689 (1.08)-0.017 (0.13)

-0.335 (0.60)0.175 (1.11)

*0.895 (1.83)*0.133 (2.02)



**0.362 (2.32)*0.234 (1.78)

0.044 (0.30)

**0.420 (2.78)



Table B.13: OLS Regression (Time Series Approach, 1980-2002): SINGAPORE


-0.017 (0.06)***0.221 (3.25)

-0.513 (1.22)***0.292 (2.99)

-0.377 (0.63)0.167 (1.38)

*0.717 (1.81)**0.179 (2.73)


***0.563 (4.08)***0.442 (3.95)

**0.651 (2.27)***0.549 (4.02)



Table B.14: OLS Regression (Time Series Approach, 1980-2002): THAILAND


-2.747 (1.61)*1.177 (2.06)

-2.478 (1.42)*1.095 (1.87)

-2.445 (1.37)*1.050 (1.75)

-1.802 (1.03)0.979 (1.69)


**0.383 (2.74)***0.106 (4.13)

0.071 (1.34)***0.345 (3.12)




287

THIRD: THE G-7 ECONOMIES TABLES (B.15-B.21)

Table B.15: OLS Regression (Time Series Approach, 1980-2002): CANADA


**2.001 (2.40)0.008 (0.10)

**1.892 (2.51)0.012 (0.14)

**1.628 (2.46)0.020 (0.21)

**2.001 (2.40)0.008 (0.10)

Ln of Turnover RatioLn of Value Traded

Ln of Market CapitalizationLn (Investment ∗ Turnover Ratio)

*0.267 (1.81)*0.160 (1.86)

**0.345 (2.13)*0.267 (1.81)



Table B.16: OLS Regression (Time Series Approach, 1980-2002): FRANCE


6.069 (1.37)0.069 (0.66)

**8.512 (2.41)0.027 (0.31)

***6.511 (3.43)-0.014 (0.21)

8.235 (1.69)0.046 (0.46)


Ln of Value TradedLn of Market CapitalizationLn (Investment ∗ Turnover Ratio)

*0.128 (1.82)

***0.148 (2.89)***0.332 (4.37)

**0.202 (2.24)



Table B.17: OLS Regression (Time Series Approach, 1980-2002): GERMANY



5.184 (1.45)

0.026 (0.45)

**13.231 (2.51)

0.005 (0.09)

**13.752 (2.37)

**0.171 (2.79)

5.810 (1.56)

0.019 (0.34)


**0.106 (2.08)**0.142 (2.73)

**0.255 (2.54)**0.107 (2.11)



Windows.

288

Table B.18: OLS Regression (Time Series Approach, 1980-2002): ITALY


***2.847 (3.73)0.090 (1.39)

***2.754 (3.62)0.089 (1.36)

***2.604 (3.46)0.095 (1.43)

***2.847 (3.73)0.090 (1.39)



**0.074 (2.14)0.036 (1.68)

0.052 (1.27)

**0.074 (2.14)



Table B.19: OLS Regression (Time Series Approach, 1980-2002): JAPAN


-0096 (0.78)***0.119 (3.72)

0.015 (0.16)***0.101 (4.52)

0.013 (0.09)**0.072 (2.32)

0.354 (1.59)***0.110 (3.26)


***0.295 (3.33)***0.241 (5.15)

***0.378 (2.89)***0.318 (3.67)



Table B.20: OLS Regression (Time Series Approach, 1980-2002): UNITED KINGDOM


***0.882 (4.48)**0.347 (2.46)

***1.023 (4.05)***0.508 (2.92)

**0.991 (2.69)**0.593 (2.14)

***1.142 (4.48)*0.260 (1.83)


***0.273 (3.82)***0.180 (3.50)

**0.376 (2.59)***0.235 (3.62)




289

Table B.21: OLS Regression (Time Series Approach, 1980-2002): UNITED STATES


**1.333 (2.51)0.316 (0.69)

***2.466 (3.67)0.243 (0.66)

***3.406 (4.60)0.301 (0.82)

**1.294 (2.61)-0.030 (0.06)



*0.430 (1.96)***0.430 (3.55)

***0.900 (4.12)

*0.363 (2.02)



290

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