ESSAYS ON THE ECONOMIC INTEGRATION OF THE …
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ESSAYS ON THE ECONOMIC INTEGRATION OF THE COOPERATION COUNCIL OF THE ARAB STATES OF THE GULF by Adham Al Said This thesis is presented for the fulfilment of the requirements of the Doctor of Philosophy degree at The University of Western Australia The University of Western Australia Business School Economics 2011
Transcript of ESSAYS ON THE ECONOMIC INTEGRATION OF THE …
ESSAYS ON THE ECONOMIC INTEGRATION OF THE
COOPERATION COUNCIL OF THE ARAB STATES OF THE
GULF
by
Adham Al Said
This thesis is presented for the fulfilment of the requirements of the
Doctor of Philosophy degree at The University of Western Australia
The University of Western Australia
Business School
Economics
2011
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ABSTRACT
Economic integration is a process through which a group of countries seek
cooperation and standardisation in a wide range of policies. These policies affect both
domestic and regional economies through the movement of goods, services, and factors
of production. In some cases economic integration is of deeper form, such that
supernational institutions are developed to achieve its objectives. Economic Integration
takes a number of forms such as Free Trade Areas, Customs Union, Common Market,
or a Monetary Union. Today the World Trade Organisation (WTO), originally The
General Agreement on Tariff and Trade (GATT), dubs these agreements as Regional
Trade Agreements (RTA).
The post-war trading system has been marked with two conflicting forces,
multilateralism and regionalism. The GATT, signed in 1947, formalised the Most
Favoured Nation (MFN) approach in international trade according to which countries
pass preferential trade policy equally to all trading partners. However, within the
multilateral trade liberalisation framework, concessions were given for restricted MFN
based agreements. Article XXIV of the original GATT Agreement allowed certain types
of trade agreements within a subset of countries that may be discriminatory in nature.
These agreements became known as Preferential Trade Agreements or PTAs.
The 21st century has experienced a substantial increase in the number of RTAs.
A great number of these RTAs involve more than two countries. This phenomenon has
become to be known as New Regionalism. The underlying motives for creating RTAs
shifted substantially from the traditional view of trade liberalisation. The implications of
these RTAs have become more significant as deeper integration is sought for purposes
of development. The importance of RTAs is stressed further where developing countries
are becoming more involved in the process. These agreements are part of the
international trading system today and they are here to stay.
This thesis analyses the rationale for the creation of RTAs and their potential
effects on the countries involved. It is concerned with measuring the effectiveness of
these RTAs using a barometer made up of three components, trade, incomes and
growth, and prices. This is applied to a specific RTA, the Gulf Cooperation Council
(GCC); a group of six major oil producing developing countries.
Since the 1980s, the GCC countries, located on the Persian Gulf, have been and
are undergoing a long-term economic integration process. This project involves creating
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a Free Trade Area, Customs Union, Common Market, and a Monetary Union. After a
long period of slow paced economic integration, the GCC has recently invigorated the
process. Currently, its progress is at the Common Market stage of integration. The GCC
aims to achieve a monetary union within the foreseeable future but faces a number of
challenges to the achievement of this goal. The GCC provides a unique economic
integration experience and there are many factors that make it a likely candidate for
overall success. This thesis provides an economic analysis of the experience thus far.
The thesis adopts three instruments to evaluate the GCC’s economic integration
experience. First, it uses the gravity model of trade. The findings of the trade analysis
based on the gravity model indicate that the GCC’s intra-regional trade patterns have
remained largely unaffected by trade liberalisation between member countries. Intra-
regional trade patterns are explained sufficiently by economic and geographical
variables. Second, the thesis uses neoclassical growth theory models of income
convergence. It is found that the GCC countries exhibit some degree of reduced income
dispersion. However, RTA effects are not clearly identifiable as the main catalyst.
Finally, the Law of One Price approach is applied to investigate the behaviour of
disaggregated prices within the region. This indicates that there still exist distortions
that create a wedge between cross-country prices, which prevents their equalisation.
Although the GCC has advanced through the several stages of economic
integration, this thesis finds that it has yet to reach its full potential. While significant
progress has been made in the past decade, challenges remain to develop the GCC in an
effective RTA.
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TABLE OF CONTENTS
ABSTRACT ii
TABLE OF CONTENTS iv
LIST OF TABLES viii
LIST OF FIGURES ix
ACKNOWLEDGEMENTS xi
CHAPTER 1
INTRODUCTION 1
1.1 The Context 1
1.2 Aims and Objectives 2
1.3 Thesis Plan 3
1.4 Contributions of the Thesis 8
CHAPTER 2
ECONOMIC INTEGRATION 9
2.1 Introduction 9
2.2 Regionalism and Trade 11
2.2.1 Implications of RTAs 15
2.2.2 The Gravity Equation 18
2.3 Regionalism, Incomes, and Growth 22
2.3.1 Growth Theories Overview 27
2.3.2 Empirics of Growth and Income Convergence 28
2.4 Economic Integration and Prices 32
2.4.1 Purchasing Power Parity 37
2.4.2 PPP Empirical Methodologies 38
2.5 Concluding Remarks 42
CHAPTER 3
INCEPTION, ACHIEVEMENTS, AND CHALLENGES 47
3.1 Historical Background 47
3.2 The Formation of the GCC 49
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3.2.1 Achievements 50
3.3 The Soci-Economic Characteristics of the GCC 55
3.3.1 Demography 55
3.3.2 Incomes and Growth 59
3.4 Other Economic Dimensions 62
3.4.1 Resource Endowments 62
3.4.2 Monetary Indicators 66
3.4.3 Trade 69
3.5 Future Challenges 73
3.5.1 The Country Level 73
3.5.2 Regional Level 75
CHAPTER 4
ECONOMIC INTEGRATION AND INTRA-REGIONAL TRADE 79
4.1. Introduction 79
4.2. The Gravity Model Approach to Trade 80
4.3. Application to World Trade and the GCC 83
4.3.1. Step1: The Traditional Gravity Approach to Determining Total Trade 83
4.3.2. Step 2: Trade within the GCC Countries 85
4.4. Data and Empirical Results: The Traditional Model (Step 1) 85
4.4.1. Data 86
4.4.2. Empirical Results 87
4.4.3. Comparison with Other Studies 98
4.5. Trade Creation and Trade Diversion 101
4.6 Trade within the GCC Countries (Step 2) 107
4.7. Conclusions 114
Appendix A4.1 Sensitivity Analysis 117
Appendix A4.2 Data Sources and Description 121
Appendix A4.3 Economic Foundation of the Gravity Model 126
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CHAPTER 5
HOW MUCH CONVERGENCE 129
5.1 Introduction 130
5.2 Economic Integration 130
5.2.1 Stages of Economic Integration 130
5.3 The Gulf Cooperation Council (GCC) 131
5.4 Convergence 133
5.4.1 Absolute Convergence 133
5.4.2 Conditional Convergence 136
5.5 Dispersion and Convergence 141
5.5.1 The Case of the GCC 143
5.5.2 Other Regional Integration Experiences 149
5.6 Deviations from the Mean Approach 154
5.7 Summary and Conclusions 158
Appendix A5.1 A Neoclassical Growth Model 160
Appendix A5.2 Sensitivity Analysis 164
Appendix A5.3 Data Descriptions and Sources 168
CHAPTER 6
PRICE CONVERGENCE 171
6.1 Introduction 171
6.2 The GCC Inflation Experience 172
6.2.1 Inflation within the GCC region 172
6.2.2 Inflation in Other Economic Regions 173
6.3 Exchange Rates and Prices 176
6.3.1 Versions of PPP 177
6.3.2 Empirical Methods of testing PPP 179
6.3.3 GCC verses the Group of Seven 181
6.4 Price Differentials 184
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6.4.1 Deviations from the Mean 184
6.4.2 Intra-GCC Price Differentials 185
6.5 Prices of Broad Commodity Groups 189
6.6 Micro Prices 195
6.6.1 The Data 195
6.6.2 Convergence 198
6.6.3 Price Differentials 200
6.7 The Case of Coke 204
6.8 Concluding Remarks 206
Appendix A6.1 Data 211
CHAPTER 7
PERSPECTIVES 213
7.1 Thesis Review and Major Findings 213
7.2 Limitations of the Study 218
7.3 Implications of the Findings 220
REFERENCES 223
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LIST OF TABLES
Table 2.1 Gravity Equation Empirical Studies 23
Table 2.2 Selected Growth and Convergence Studies 33
Table 2.3 Selected PPP and Price Convergence Studies 44
Table 3.1 Major GCC Economic Integration Achievements 1980–2009 51
Table 4.1 Bilateral Exports 87
Table 4.2 First Set Of Estimates of the Gravity Equation 89
Table 4.3 Second Set of Estimates of the Gravity Equation 95
Table 4.4 Prior Estimates of Elasticities from Gravity Equations 100
Table 4.5 GCC Trade Matrices, 1995 and 2006 109
Table 4.6 GCC Import Ratios of Total Trade 110
Table 4.7 Disaggregated Trade Estimates 112
Table 4.8 Estimate of Country-Product Dummy Variable Coefficients 113
Table A4.1.1 Estimates of the Gravity Equation 119
Table A4.2.1 Countries Included In Gravity Model Sample 122
Table A4.2.2 Regional Trade Agreements 123
Table A4.2.3 Data Sources 125
Table 5.1 Basic Economic Indicators of the GCC 132
Table 5.2 Absolute Convergence Estimates, Cross-sectional 134
Table 5.3 Absolute Convergence Estimates, Country Groups 135
Table 5.4 Cross-Sectional Conditional Convergence Estimates, 1980–2007 140
Table 5.5 Output Shares of the GCC Countries 146
Table 5.6 Convergence the Deviations from the Mean Approach 157
Table A5.2.1 Convergence linear panel estimates, 1980–2006 166
Table A5.2.2 Cross-Sectional Conditional Convergence Estimates –
Based On Oil Production 167
Table A5.3.1 Data Descriptions and Sources 168
Table A5.3.2 Countries Included in Regressions 170
Table 6.1 Inflation Differentials, GCC, Mean, 1980–2007 188
Table 6.2 GCC Prices, 1990–2008 199
Table 6.3 Convergence, Disaggregated Price Estimates 202
Table 6.4 Convergence Estimates of Coke Prices 205
Table 6.5 Coke Inter-City Price Convergence Estimates 207
Table 6.6 Summary of Price Convergence Analysis 210
Table A6.1.1 Price Data Details 211
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LIST OF FIGURES
Figure 2.1 Regional Trade Agreements in Force 1958–2010 12
Figure 2.2 World Income Distribution 25
Figure 2.3 Income Convergence, 1980–2008 27
Figure 3.1 The GCC and The Middle East 54
Figure 3.2 Population in 1980 and 2008 55
Figure 3.3 Population Growth Rates 56
Figure 3.4 Fertility 57
Figure 3.5 Population Composition 1980 and 2008 58
Figure 3.6 Real Gross Domestic Product 1980–2008 59
Figure 3.7 Real GDP Growth Rates 61
Figure 3.8 Real GDP Per Capita 1980–2008 62
Figure 3.9 Oil Reserves 2008 64
Figure 3.10 Oil Production 64
Figure 3.11 Gas Reserves 2008 65
Figure 3.12 Gas Production 65
Figure 3.13 Official Nominal Exchange Rates 66
Figure 3.14 Deposit Interest Rates 67
Figure 3.15 Inflation Rates 1980–2008 68
Figure 3.16 Money Supply Growth 1980–2008 69
Figure 3.17 Trade as Percentage of GDP 1980–2008 70
Figure 3.18 GCC Ratio of Internal to External Trade 70
Figure 3.19 Current Account Balance 1980–2008 71
Figure 3.20 Total Foreign Reserves 72
Figure 3.21 Crude Oil Prices 72
Box 3.1 Article 4 – GCC Charter 50
Figure 4.1 The Gravity Model Concept 81
Figure 4.2 RTA Dummies Compared 98
Figure 4.3 Transportation Cost Proxies Compared 99
Figure 4.4 Intra-Regional Imports as Proportion of GDP 103
Figure 4.5 Extra-Regional Trade as a Proportion of GDP 104
Figure 4.6 Intra/Extra Regional Imports Ratio 106
Figure 5.1 Absolute Growth Divergence, 1980–2006 135
Figure 5.2 World’s Distribution of Income 142
Figure 5.3 Dispersion and Convergence 143
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Figure 5.4 GCC Average Growth Dispersion 144
Figure 5.5 Relative Importance of GCC Members, 1980 and 2006 146
Figure 5.6 GCC Growth: Weighted Mean and Standard Deviation 147
Figure 5.7 GCC’s Real GDP Growth 148
Figure 5.8 Weighted Standard Deviation of Growth 149
Figure 5.9 Growth in EU 15: Weighted Mean 150
Figure 5.10 Growth in Australian States: Weighted Mean 151
Figure 5.11 United States Weighted Mean of Growth 152
Figure 5.12 Weighted Mean of Real GDP Growth 153
Figure 5.13 Income Convergence, 1970–2003 155
Figure 5.14 GCC Income Convergence, 1970–2003 156
Figure 5.15 EU15 Income Convergence, 1970–2003 157
Figure 6.1 GCC Weighted Inflation 1981–2008 173
Figure 6.2 EU15 Weighted Mean of Inflation 174
Figure 6.3 Australian States and Territories Weighted Mean of Inflation 175
Figure 6.4 United States Mean of Inflation 176
Figure 6.5 GCC Exchange Rates 182
Figure 6.6 G7 Countries’ Exchange Rates 182
Figure 6.7 GCC Inflation Rates 183
Figure 6.8 G7 Inflation Rates 184
Figure 6.9 Inflation Convergence 186
Figure 6.10 Cross Country Inflation Differentials, 1980–2008 187
Figure 6.11 Disaggregated Inflation Rates by Country, 2001–2007 191
Figure 6.12 Disaggregated Inflation Rates by Group, 2001–2007 192
Figure 6.13 Standard Deviation by Group, 2001–2007 194
Figure 6.14 Distribution of Relative Price of Commodity Groups by Sub-Periods 197
Figure 6.15 Coke Price Distribution 204
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ACKNOWLEDGEMENTS
This thesis is the culmination of hard work and persistence. This would not have
been possible without superb supervision and guidance by my supervisors Professor
Kenneth W. Clements and Associate Professor Dr. Abu Siddique. Their insights and
guidance were invaluable in getting me past the finish line. I appreciate their continuous
support and encouragement throughout my candidature, which drove me to strive for
more.
I would also like to thank Professor Jeffery Sheen for his invaluable comments
as a discussant of my paper presented at the 2007 PhD Conference in Economics and
Business at UWA. His insights and comments were very helpful in developing my
research. I would also like to thank the UWA Economics Seminar participants for
continuous support and constructive feedback on my research.
My thanks extend to Professor Ranjan Ray, Professor Srikanta Chatterjee, and
Associate Professor Dilip Dutta for taking the time to evaluate and examine my thesis.
Their insights, encouragement and suggestions were appreciated and improved the final
version of the thesis.
My research was facilitated by some generous scholarships and grants. My
research would not have been possible in the first instance without the generous
scholarship and support from Sultan Qaboos University. I am also grateful to the
Australian Research Council, UWA Graduate Research School, and UWA Business
School for their generosity and assistance. This thesis would not be as it is if not for
their generous support.
I would also like to thank my colleagues and friends Mr. Kenneth Yap, Ms.
Janine Wong, Ms. Xing Gao, Ms. Mei-Hsiu Chen, and Mr. Tom Simpson. Their
continuous encouragement and support have helped greatly during my research.
Finally, I would like to acknowledge the incredible support provided by my wife
Basma Al Said during the production of this thesis. Not only did she have to tolerate my
academic temperament, she endured a postgraduate degree and two pregnancies. I am
also thankful to having been blessed with three lovely kids Sara, Tariq, and Laith who
made the journey all the more tolerable.
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CHAPTER 1
INTRODUCTION
1.1 The Context
The world trading system has undergone significant changes since the late 19th
century. Based on the most-favoured-nation (MFN) approach, countries extended equal
treatment of trade concessions to all their trading partners. This approach, which
fostered non-discriminatory trade policies that helped reduce tariffs, lost its footing after
World War I, when the world trading system became segmented. The MFN approach
was revived after World War II through the leadership of the United States. A formal
agreement, the General Agreement on Tariffs and Trade (GATT), was signed between
the major economies of the time with objective of supporting a multilateral approach to
trade liberalisation. Critically, however, Article XXIV of the GATT agreement allowed
for specific forms of non-MFN trade agreements, specifying three main conditions for
their existence: first, reduction of a substantial portion of trade barriers between the
countries involved; second, prevention of increased non-member discrimination; and
third, a specified time frame for economic integration, generally accepted as ten years
(Frankel 1997). Non-MFN agreements allowed under Article XXIV have generally
been in the area of regional economic integration, a process often referred to as
regionalism. Economic integration of this type is the central topic of this thesis.
Economic integration has gained new vigour in the past two decades with the
‘New Regionalism’ phenomenon. The international trade scene has experienced
substantial increases in the number of bilateral and regional economic integration
agreements. These agreements have taken many forms, including Free Trade
Agreements (FTA) and Economic Integration Agreements. Although many such
agreements, both bilateral and regional, have been concluded between developed and
developing countries, a significant number of regional agreements have signed been
between developing countries. This has led to a complex, interwoven international
trading system dotted with economic integration agreements.
The effects and outcomes of bilateral and regional agreements on international
trade have been fiercely debated. This is especially relevant given their interactions with
the World Trade Organization (WTO) as a multilateral trade liberalisation platform.
While it has been suggested by Bhagwati (1991) that a fractured international trading
system would put a stumbling block in the way of free trade, opposing views consider
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the positive outcomes of intensified bilateral and regional trade liberalisation would
instead become the building blocks of free trade (Ethier 1998).
The nature of FTAs has changed dramatically since the late 20th
century. They
often take the form of regional economic integration among geographically
neighbouring countries. Such agreements seek to develop interdependent regions built
around joint economic policies that are not restricted to trade alone; because of this,
agreements beyond trade liberalisation are said to incorporate deeper integration. This is
one of the main features of the ‘New Regionalism’. Economic integration in these
instances involves developmental policies that aim for harmonisation of laws and
regulations in areas such as financial and capital markets, labour markets, and
infrastructure. These arrangements are not strictly restricted to regional agreements
however; even bilateral FTAs include reforms to a wide range of policies and
legislation. This is especially true where a developed and a developing country strike a
partnership (Ethier 1998; Schiff and Winters 1998; Whalley 1998).
Deep economic integration is a multifaceted process. The degree of integration
varies from one agreement to another. Regional bloc agreements reflect the differences
in their objectives and goals. Traditionally, four main stages of economic integration are
identifiable: free trade areas, customs unions, common markets, and monetary unions;
this final stage usually involves the adoption of a common currency. The effects of
these different stages or degrees of integration are profound. At each stage the degree of
integration increases, and coherent and joint policies become a necessity. Trade
liberalisation, free movement of factors of production, and capital market harmonisation
are a few of the steps required to achieve greater economic integration. These initiatives
have important effects on the economies of countries involved in such processes.
Analysing these effects allows an assessment of the degree of functionality of an
economic integration agreement.
1.2 Aims and Objectives
This thesis is concerned with the types of economic integraiton discussed above,
and its economic effects on the countries involved. The thesis will consider the effects
of economic integration among developing countries by analysing a select region: in
this case, the Gulf Cooperation Council (GCC). The GCC is a regional bloc formed in
1981 by six Arab Gulf states: Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and the
United Arab Emirates (UAE). The GCC is a relatively young economic integration
region whose members have followed the economic integration path outlined above,
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and where recent developments have attracted considerable attention. Over the past
twenty-five years, the region has launched a free trade area, a customs union, and a
common market.
It is the aim of the thesis to analyse the effect of these developments on the
region’s trade, incomes, and prices. The thesis considers this region in its core analysis,
but it includes comparisons with other economic regions with comparable experiences.
Specifically, the thesis aims to address a number of issues with respect to economic
integration by identifying specific effects of economic integration on macroeconomic
variables. The objectives of the thesis are outlined in the following:
1. To measure the effects of regional trade agreements on intra-regional trade.
FTAs and CUs are stages of economic integration that focus on trade
liberalisation. The effectiveness of these forms of economic integration stages
can be gauged by analysing trade patterns. Trade patterns within and without
regional trade agreement can indicate the effects of integration.
2. To identify patterns of income convergence within economic integration
areas.
As deeper economic integration affects the movement of factors of
production within a region, which in turn is expected to influence incomes
within the region, income convergence, in terms of reduced regional differences,
becomes an important subject for analysis. Income convergence within the
integration area can be a measure of effectiveness of joint policies.
3. To analyse intra-regional price differences within economic integration
areas.
A consequence of economic integration is the reduction of barriers between
member countries. Free movement of goods and persons, and the ability to trade
freely within the region, are expected to affect prices across member countries.
The conditions are favourable for reduced price differentials across borders as a
result of economic integration.
This thesis thus focuses on three key indicators of economic integration: trade; income
and growth; and prices. Individual chapters are dedicated to investigating in depth these
aspects of GCC members’ economies.
1.3 Thesis Plan
This thesis is made up of a number of essays that address the objectives stated
above. In Chapter 2 conceptual approaches to economic integration are discussed. The
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chapter considers the development of regional economic integration in the world before
moving on to discuss the three conceptual pillars of the thesis: trade, income
convergence, and price convergence. The chapter outlines the broad framework of the
thesis.
The economic integration of the GCC is central to this thesis, and Chapter 3
presents a historical perspective prior to its formation. It offers insights into the motives
underlying the creation of the Cooperation Council. The GCC’s geographic, social, and
economic characteristics are also discussed; as are the goals and achievements of the
region’s economic integration. Chapter 3 outlines the major goals the GCC hopes to
achieve, and where progress had been made. Finally, the chapter discusses the major
challenges that face the region at both national and regional levels.
The three key aspects of economic integration described above, trade, income
and growth convergence, and price convergence, are addressed in Chapters 4, 5, and 6.
The first to be considered is trade, as economic integration generally starts at the trade
level. This usually takes the form of liberalised trade through Free Trade Areas (FTA)
or a Customs Union (CU). FTAs liberalise trade by reducing the tariffs between the
countries involved, while each member state is free to set external tariffs vis-à-vis the
rest of the world. The tariff reductions are subject to strict rules concerning the origin of
products. A FTA is relatively easy to create since there is no obligation for members to
adopt a uniform external tariff against the rest of the world; however, the rules ensuring
the operation of the FTA make it administratively demanding. CUs, in contrast, are
based on liberalised trade between member countries with no internal tariffs. Moreover,
CUs unify members’ external tariffs so that non-members can treat the bloc as a single
trading entity. Once a CU is established, trade becomes easy as there are no rules
governing the origin of products. Each port of entry into the bloc is identical to the
others in terms of customs treatment.
Trade liberalisation takes place among a limited number of countries. Third
parties may be discriminated against through existing or new barriers to trade
introduced by the regional bloc, for that reason proponents of multilateral trade prefer to
dub FTAs and CUs as Preferential Trade Agreements. Jacob Viner (1950) identified
these distortions to trade as trade creation and diversion. Countries that form an
economic integration region in the form of a FTA or CU are likely to shift their imports
from non-members to members, since they are ‘preferred’ trading partners. If imports
are now sourced from a higher-cost producer as a result, trade diversion has taken place.
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There are opposing opinions about the effectiveness of trade liberalisation
through economic integration. Some consider that the removal of barriers to free trade
benefits the countries involved. However, trade agreements are not without their
externalities, captured in the concept of trade diversion and creation. The thesis
investigates these effects for the GCC in Chapter 4. While the GCC experience is
central to this analysis, the chapter also provides comparisons with other Regional
Trade Agreements (RTA) experiences. The approach to analysing the effect of
economic integration on trade utilises the Gravity Equation. Borrowed from physics,
this equation was introduced into international economics almost fifty years ago by
Tinbergen (1962). The concept of the gravity equation of trade is that country size
matters. Larger countries are likely to trade more with each other than with smaller
countries: thus, larger masses (countries) gravitate towards each other through trade.
The gravity equation has been used extensively in the literature on international
economics and trade and has been successfully linked to existing trade theories, making
it a useful tool to analyse trade patterns between countries by considering natural trade
flows, which are subject to a number of geographical, social, and economic limitations.
Chapter 4 utilises the gravity approach to examine how RTAs as a form of
economic integration have affected their members’ trade patterns. The model controls
for natural factors that affect trade, and thus provides a means to isolate potential effects
of economic integration. A by-product of this approach is the ability to identify
potential trade creation and diversion from the model. Chapter 4 examines this issue
specifically for the GCC by decomposing the region’s trade into intra- and extra-
regional trade. The decomposition highlights the effects of the diversion or creation of
trade within the region in terms of progress towards economic integration.
The thesis is also concerned with the effects that economic integration may have
on the income and growth of the countries involved. It is a multifaceted process, so the
degree of harmonisation and standardisation in policy may manifest in favourable
macroeconomic effects. Chapter 5 aims to analyse these effects within the context of
neoclassical growth theory. The fundamental building block of the neoclassical
approach is the Solow (1956) growth model. The model has developed over time, and
other theories have been proposed to identify the factors that affect growth. Competing
theories include endogenous growth models advocated by Lucas (1988) and Romer
(1986). Chapter 5 uses the neoclassical models that have incorporated convergence
effects, to focus on the experience of countries involved in economic integration based
on their incomes.
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Growth models often refer to convergence in terms of β and σ convergence. The
former refers to convergence between countries’ income levels to some given steady
state. The latter refers to reduction in the dispersion of the countries’ income. The
Solow growth model is generally used to test absolute β-convergence, based on initial
incomes. A regular empirical finding rejects the hypothesis that countries have
converged, based on initial incomes. This has led theorists to turn to different
approaches, including conditional convergence and endogenous growth theories.
Chapter 5 adopts the conditional convergence approach when considering β-
convergence. Unlike the basic Solow growth model, the neoclassical conditional
convergence model captures growth effects by augmenting the number of characteristics
specific to countries. The key characteristic that the chapter aims to evaluate is the
regional economic integration in the form of FTAs and CUs.
Chapter 5 also considers σ-convergence through the reversion-to-the-mean
technique. Ben-David (1993; 1996) applied this technique to the European Union (EU)
to assess convergence in a nonparametric methodology. The usefulness of this approach
is its application to a small number of countries, as is the case with the GCC. It also
allows for the estimation of half-lives of convergence.
The third pillar of the analysis is price convergence. In Chapter 6 the effect of
economic integration on price is examined. Chapter 6 tests the Law of One Price (LOP)
given economic integration. The LOP states that the price of a good in two countries
should be equal when converted into a common currency. This proposition has evolved
into the concept of Purchasing Power Parity (PPP), in its absolute form, stipulates that
the value of a basket of goods in two countries are equalised when compared in a
common currency. Since the absolute version of the PPP does not hold, a weaker
version allows for differences in price levels, so that foreign and domestic prices are
proportional to each other. This weaker version is known as relative PPP.
The LOP and PPP propositions have been tested extensively in the literature.
There are mixed results where PPP and, by default, LOP do not hold. This is
unsurprising since, in the real world, barriers to trade and transportation costs create a
wedge between prices in different countries. The relevance of such results in the
literature is generally related to exchange rate behaviour. Since the baskets of goods
across countries are priced in different currencies, the exchange rate is an important part
of the conversion. Implications of PPP not holding are reflected in unpredictable real
exchange rates. The Economist magazine introduced a novel approach to PPP in 1986,
adopting McDonald’s Big Mac hamburger as the uniform good in more than twenty
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countries across the world where the franchise operates using the US dollar price ratio
of these burgers. The Economist’s Big Mac Index measures currency deviations from
PPP. While the Big Mac Index is not a perfect predicator of exchange rate movements,
it does provide an insight in exchange rate deviations from their expected long-term
values (Ong 2003).
Cross border price differentials are affected by many factors, however two stand
out with respect to international trade. The first involves trade barriers and the second,
transportation costs. As transportation costs are generally correlated with distance
between trading, there is little scope for areas of economic integration to affect them
directly. Trade barriers, however, are a core target of economic integration. Trade
liberalisation, standard harmonisation, and the free movement of factors of production
are a few ways that allow for the reduction of price wedges: a FTA, CU, Common
Market, or Monetary Union can reduce deviations from PPP and get close to upholding
LOP. In theory, the greater the economic integration, the greater the likelihood of
approaching LOP. A number of studies have applied this proposition to the United
States, Canada, and Europe, measuring the effects of borders, reduced barriers, and even
the adoption of a single currency. Such studies look at micro prices to test the
proposition of LOP.
Chapter 6 also examines this issue within the GCC countries. Has their progress
towards economic integration been sufficient for prices to closely reflect those other
member countries? Having established both an FTA and a CU, the GCC countries are
likely to exhibit only small deviations from LOP. Trade barriers are expected to be
lower, and movement of goods within the region to be easier. An added feature of the
GCC countries is fixed exchange rates. Unlike the European Union’s experience prior to
the introduction to the Euro, nominal exchange rates of different GCC countries vis-à-
vis the dollar have been very stable over time. While not integrated monetarily, the
GCC states enjoy an added benefit from fixed exchange rates. Chapter 6 uses these
underlying factors of the GCC’s status to examine the degree to which prices have
converged. The analysis first focuses on changes in price levels to develop an
aggregated picture. It then uses disaggregated prices at the micro level to distinguish
trends within major spending categories within the GCC. The findings in Chapter 6
indicate that GCC prices converge. However, price equalisation does not take place.
Finally, Chapter 7 summarises the findings of the thesis and their implications to
the GCC’s economic integration.
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1.4 Contributions of the Thesis
This thesis contributes to the literature in a number of ways. It takes a broad
economic perspective of the GCC experience. Previous research on the GCC has
concentrated on the political underpinnings of the region, of individual countries’
experiences within the region, the energy perspective, and most recently the proposed
monetary union. Leading examples for this previous research include Ramazani (1988),
Iqbal and Erbas (2004), Fasano and Iqbal(2002) Zaidi (1990), Fasano and Iqbal (2003).
This thesis examines the effects of the GCC’s achievements in economic integration on
its members as a group. This is particularly relevant as the thesis analyses the effects
empirically, in relation to the major milestones the region has achieved, such as the
customs union and common market. This has not been done before in the depth
presented here.
Another contribution of this thesis is to offer a barometer of the functionality of
economic integration within the GCC. Using three main areas of analysis, the thesis
provides insights on how well the integration has worked. Through isolating trade,
income, and price convergence effects, the outcome of the analysis provides policy
implications. The results provided in this thesis can be used as an assessment tool for
the progress of the GCC’s economic integration. This tool can also be used to examine
other economic integration experiences.
9
CHAPTER 2
ECONOMIC INTEGRATION
2.1 Introduction
Regional economic integration has taken centre stage in international economies
with the rise of Regional Trade Agreements (RTAs) in the second half of the 20th
century. The General Agreement on Trade and Tariff (GATT) in 1947 provided a clause
to safeguard multilateral trade agreements from the effects of RTAs. However, although
Article XXIV of GATT required RTAs to report their intent, it did not prohibit their
creation. RTAs are not restricted to developed countries alone: a great number of them
are between developing countries. RTAs between developing countries are dubbed
South-South agreements, in contrast to North-North agreements which involve
developed countries only. A third arrangement developed during the 20th
century was of
North-South RTAs involving both developed and developing countries. This interaction
within the framework of regional economic integration introduced a new way of
thinking about RTAs and their uses as developmental tools.
Regionalism can have many different interpretations, so it is important to
distinguish what is meant by regionalism and economic integration in the context of this
thesis. Arndt (1993) categorises four types of regionalism often referred to in the
literature. These are Preferential Trade Agreements (PTAs), growth triangles, open
regionalism, and sub-national regionalism. The first type of regionalism refers to
arrangements such as Free Trade Agreements (FTAs), Customs Unions (CUs), or
Economic Integration Areas (EIAs). These agreements discriminate against non-
members in areas of trade preference or other competencies and require substantial
governmental involvement to negotiate and bring them into effect. Usually, super-
national bodies are responsible for enacting these agreements. Examples of these
agreements include the North American Free Trade Agreement (NAFTA), the European
Union (EU), and MERCOSUR in South America.
The second type of regionalism, growth triangles, occurs due to economic,
geographic and cultural ties between sub-national regions. There are no binding
agreements or bureaucratic institutions to ensure the operation of this type of
regionalism. Regions and states become highly integrated because of proximity. A
common example is the growth triangle formed by Singapore, Johor (Malaysia), and
Riau (Indonesia).
10
Open regionalism forms the third possible type of regionalism. This particular
type borrows from PTA-type agreements in terms of having some institutional structure.
However, unlike PTAs, open regionalism does not discriminate against non-members;
nor does it bind member countries into particular forms of concessions. The core thrust
of open regionalism is based on the Most Favoured Nation (MFN) approach to trade
liberalisation, and trade barriers are reduced for both members and non-members. The
Asia Pacific Economic Cooperation (APEC) is a prime example of such an arrangement
(Arndt 1993).
Finally, the fourth type of regionalism takes place at the sub-national level. This
is often a product of economic planning from a mostly bygone era. However, Arndt
(1993) points out a shift towards sub-national regionalism has been observed by
researchers in regions such as Europe, South Asia, and North America. This type of
regionalism is different from the previous three as it concentrates on a specific region or
state that lobbies for greater autonomy or secession. Government involvement may be
prominent here just as it is in the PTA case.
Which of these four types are relevant to the context of regionalism used here?
The first type of regionalism, the PTA, is relevant to this chapter: regionalism which
involves government initiatives that aim economically—and politically in some cases—
to integrate their economies. The term regionalism in this chapter is used synonymously
with regional economic integration or simply economic integration to refer to a specific
PTA between neighbouring countries. The concept of RTAs is used synonymously with
PTAs. This chapter will also refer to agreements such as Free Trade Agreements (FTAs)
and Customs Unions (CUs) as RTAs.
The proliferation of RTAs in their different forms, bilateral and multilateral, has
led researchers to question their effects on welfare and economic development. To
address these issues, a number of approaches have been developed over the past few
decades, including static welfare analysis, simulations, and empirical methodologies.
Given the potential effects of RTAs, this chapter is interested in exploring regional
economic integration and its theoretical underpinnings. This will be achieved by
analysing three major areas where regional economic integration can be shown to have
potential economic consequences: trade, growth and incomes, and price equalisation.
This will provide the background to the analyses in Chapters 4, 5, and 6 on specific
economic issues relevant to regional economic integration.
This chapter will take the following structure: Section 2.2 discusses what is
meant by regional economic integration and considers the forms it has taken. It
11
examines the current trends of RTAs, the welfare implications of the proliferation of
regionalism, and its effect on trade. Section 2.3 explores the relationship between RTAs
and growth, through the relevant empirical literature that links trade liberalisation
generally, and regionalism specifically, to growth. In section 2.4, the price convergence
effects of RTAs will be discussed. The PPP theory will be used as an analytical
framework in this section. Finally, the chapter will conclude with a summary of the
impact of RTAs based on the three areas explored.
2.2 Regionalism and Trade
Regional Economic Integration in the form of RTAs refers to the building of
cooperative ties between countries with the aim of improving their joint welfare. The
nature of RTAs is different to those of Multilateral Trade Negotiations such as those
initiated by the General Agreement on Trade and Tariffs (GATT), which was
established in 1947 and renamed the World Trade Organisation (WTO) in 1995. RTAs
are made legally possible by Article XXIV of the GATT agreement and the Enabling
Clause of 1979 for developing countries. Customs Unions (CUs) and Free Trade
Agreements (FTAs) were not prohibited outright, but were discouraged from pursuing
discriminatory practices against non-members. Established and newly created CUs and
FTAs were expected to notify GATT/WTO of their arrangements.
Over the past fifty years a great number of RTAs have been initiated. The WTO
has received more than 400 notifications of RTA formations since 1948; of these, 300
were reported post-1995 (WTO 2010). The WTO has developed a comprehensive RTA
database detailing all notifications in various forms. Using this database it is possible to
depict the growth in RTA numbers over time. The database distinguishes between
agreements based on goods and those based on services, a distinction that needs to be
taken into consideration when observing the numbers. Furthermore, the database
includes not only trade blocs but also bilateral trade agreements between countries. The
RTA forms included are FTAs, CUs, PTAs, Economic Integration Agreements (EIAs),
and combinations of FTAs & EIAs, and CUs & EIAs.
Figure 2.1 plots the number of RTAs from 1958 to 2010. The bars in the chart
indicate the total number of RTAs notified to the WTO in a given year. Each bar is
divided into the different type of RTA arrangements for that given year. All the RTAs
included here are still in force. The black line shows the cumulative number of RTAs in
force over time.
12
Figure 2.1 illustrates an explosion in the number of RTAs created during the
past two decades. During the 1960s, 70s and 80s the number of active RTAs was well
below 40. This changed dramatically during the 1990s and 2000s, as is shown by the
sharp climb of the black line. RTA numbers increased more than fivefold over this
period.
The composition of the rapid increase is worth noting. In the 1990s the majority
of new RTAs were in the form of FTAs. Many of these would have been bilateral trade
agreements between countries, or between countries and trade blocs. Economic
Integration Areas (EIAs) in three forms — standalone, with a FTA, or with a CU —
made up a small share of the increasing numbers. However, this stopped in the 2000s.
While EIAs have not increased in numbers, FTA & EIAs have.1 These types of RTAs
are mostly bilateral country agreements that include both goods and services, but there
are also some trade bloc and country agreements in this category.
Figure 2.1
Regional Trade Agreements in Force 1958-2010
Source: WTO (2010)
Figure 2.1 can be divided into two main sections, pre-and post-1990. The pre-
1990 wave of regionalism is often referred to as first regionalism and the post-1990 as
new regionalism, a term coined by Bhagwati (1990). Each era presents distinct
characteristics. The pre-1990s period was dominated by a strong multilateral push
towards trade liberalisation, led primarily by the United States (Bhagwati 2008). The
emphasis on industrialisation and import substitution policies during the post WWII
period meant that developing countries were less likely to engage in multilateral trade
negotiations with vigour. The most influential event that ushered in new regionalism
1 This trend is also observed by Bhagwhati (2008).
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FTA CU PTA FTA & EIA CU & EIA
13
was the shift in US trade policy, to champion multilateral trade liberalisation, marked by
the signing of the US–Canada Free Trade Agreement in the late 1980s (Frankel 1997).
In addition, a stall in the Uruguay Round’s negotiations eroded confidence in
multilateral trade liberalisation efforts. The shift in sentiment of developing countries
from anti-market policies to open trade liberalisation also contributed to the pattern
observed in the post-1990 period (Krugman (1991), Ethier (1998), and Bhagwati
(2008)).
These RTAs, as indicated by Figure 2.1, take a number of forms which reflect
the characteristics of the agreements. These differences are pointed out by Whalley
(1998) in areas such as coverage of agreements, concessions provided, and depth of
economic integration. Whalley (1998) identifies six major reasons why countries might
participate in RTAs. First are the traditional gains from trade expected from concessions
provided by trading partners. While trade theory supports the expectations of gains from
trade, in the case of RTAs welfare implications can counter these, as Viner (1950)
shows. Second, RTAs can be used to anchor reforms by ensuring they are binding. A
common example is Mexico’s accession into the North American Free Trade
Agreement (NAFTA). Third, bargaining power in multilateral negotiations is increased.
This is a direct response to the hegemony of larger economies in the GATT and now
WTO negotiation rounds. Fourth, countries join RTAs to secure access to larger
markets. This becomes insurance against market denial to countries not involved in
RTAs (Bhagwati 2008). Fifth, RTAs create strategic linkages that go beyond trade: the
European integration experience, for example, was based on securing peace in the
region post WWII. Finally, RTAs allow interplay between regional and multilateral
negotiations. Smaller countries may use RTAs as a vehicle to secure multilateral
objectives with larger countries. Whalley’s (1998) six points have been augmented by
another, which has become evident recently: RTAs assist their members when
competing for foreign direct investments (Ethier 1998).
Given the variety of reasons why they might want to participate in RTAs,
countries choose the types most suited to their needs. These choices reflect certain
characteristics of the new wave of RTAs. Ethier (1998) identifies six major
characteristics of new regionalism:
1. Small (often developing) countries join larger counterparts to form RTA
agreements. Typical examples are Mexico in NAFTA and the smaller states in
the European Union.
14
2. Smaller countries undergo reform prior to joining RTAs. These reforms are
unilateral in nature.
3. RTAs under new regionalism exhibit a modest degree of trade liberalisation,
indicating that they are not strictly trade based. In some cases such as the EU,
smaller states enjoy market access and low tariffs prior to accession.
4. Asymmetric liberalisation is another characteristic of new regionalism, where
smaller partners adhere to their larger counterparts’ requirements. Examples can
be found in the EU’s enlargement.
5. Deep integration within new regionalism RTAs is in many cases a dominant
feature. Joint policy initiatives beyond trade liberalisation are a recurring theme.
6. Spatial emphasis is apparent in the number of agreements between neighbouring
countries.
The majority of RTAs reported to the WTO are Free Trade Areas (FTAs). FTAs
are the most basic form of trade liberalisation. Country pairs or groups choose to reduce
or completely eliminate tariffs between them. However, each country maintains its own
tariffs against non-members. To ensure the smooth operation of an FTA, rigorous Rules
of Origins (ROOs) must be put in place. These rules ensure non-members cannot take
advantage of FTA concessions by using members as an indirect point of entry to target
markets.
A more intensive arrangement for a RTA is a Custom Union (CU). CUs are
similar to FTAs in terms of eliminating trade barriers. However, CU members agree to a
unified tariff schedule imposed on non-members. This feature is administratively useful
as ROOs are no longer required. Goods entering any port of entry in a member country
will be treated the same. Once within the CU, goods can move without further
hindrance. These arrangements were the subject of the earlier welfare analysis of Viner
(1950).
RTAs have also taken other forms, including common markets and Economic
Integration Areas (EIAs). These arrangements have increased in numbers, as indicated
above in Figure 2.1. EIAs are generally less concerned with trade and more with
harmonisation and standardisation across countries. Initiatives include common markets
across a number of countries, such as the EU in the 1990s, and the Central American
Common Market (CACM) and the Common Market for Eastern and Southern Africa
(COMESA). The goal behind these arrangements is to create a single market through
which factors of production can freely move. Efficiency gains and economies of scale
may by-products of such arrangements. Works and capital can find more productive
15
uses if they can move within a greater geographical area. The European experience has
been most prominent in this area.
Finally, some EIAs take the form of monetary unions. Monetary unions are
considered the next major stage after a common market in terms of economic
integration. The Economic and Monetary Community of Central Africa and the West
African Economic and Monetary Union are direct initiatives of monetary unions. The
EU showed a gradual development towards the European Monetary Union (EMU), a
movement that spanned more than 40 years. Monetary Unions are a significant
development for any EIA. They involve a unified central bank that conducts a unified
monetary policy. The Mundell (1961) criteria of adjustment through movement of
capital and labour are essential; a synchronised business cycle is also an advantage.
2.2.1 Implications of RTAs
RTAs have been shown to cause a number of distortions in trade patterns and
welfare. The first approach to identify distortions was devised by Viner (1950), and
adopted by Lipsey (1957) and Meade (1955); it is often referred to as the Vinerian
analysis of trade creation and trade diversion. It asserts that RTAs such as customs
unions are not necessarily welfare enhancing. Custom unions can create trade by
switching import supply to efficient suppliers within the union, on the other hand, they
can divert trade from more efficient suppliers outside the union. An alternative approach
to RTAs is taken by Kemp and Wan (1976), who show that a customs union can find a
Pareto-best outcome such that the welfare of non-members of a customs union is not
hurt while the welfare of the members of a customs union is improved. A third approach
is led by Cooper and Massell (1965) and Bhagwati (1968). In this approach, the role of
economies of scale is considered as means for developing countries to take advantage of
regionalism. Although the argument of economies of scale is not crucial for welfare
improvements, by adopting Kemp and Wan’s (1976) Pareto outcomes, developing
countries may find positive impacts of regionalism. Finally, Brecher and Bhagwati
(1981) present a mechanism by which welfare effects can be analysed given parametric
and policy variations in customs unions.
A proponent of regional economic integration, Krugman (1991) points to three
major influences that RTAs can create: trade diversion, beggar-thy-neighbour effects,
and trade warfare. The first by-product of RTA trade diversion is similar to that
identified in the Vinerian approach, but Krugman (1991) states it need not lead to
increased protectionism: instead, it may lead to incorrect specialisation based on the
16
formation of RTAs. The second condition of a beggar-thy-neighbour policy may take
the form of negative effects on non-members. Trade warfare refers to the condition
where RTAs form sufficient market power to develop a bullish approach to trade policy
and consequently hurt overall welfare. While these conditions can be accepted as
natural by-products of many RTAs, Krugman (1991) cites a number of inherent
advantages too. He argues that the creation of an RTA reduces the distortion already in
place due to existing external tariffs. Consumers’ incentives distortion will be reduced
as a result of a shift towards trade with a member of the RTA. Prior to the creation of an
RTA, external tariffs would have restricted consumers to domestic production.
Krugman (1991) also points out that there are important size, productive efficiency, and
competitive advantages to be gained. He cites the European Community’s (EC)
experience where the Treaty of Rome, signed in 1957, facilitated substantial increases in
intra-industry trade that would not have been possible otherwise. He also points out that
another advantage for an RTA region is the improvement of terms of trade against the
rest of the world. This last point may be the obverse of the previously mentioned
beggar-thy-neighbour effect, however.
Krugman (1991) agrees that RTAs such as custom unions will divert trade from
non-members; however, he turns to Kemp and Wan (1976) to argue that CUs that
reduce external tariffs against non-members may be beneficial for both members and
non-members. This however is subject to the RTA’s intents and purposes. If self
preservation and welfare are more important than the welfare of the rest of the world,
RTAs may set their external tariffs above the unilateral levels of its members. However,
he argues that RTAs leave individual countries better off than otherwise despite the
distortions they cause, as they improve terms of trade for their members. Krugman
(1991) presents a simple but interesting model regarding the optimal number of RTAs
in the world. He finds the ideal number of RTAs that will reduce negative welfare
effects is three.
Kemp and Wan (1976) propose that it is possible to create a Customs Union
(CU) that is not welfare reducing. Their model is described as a general, effective,
simple approach to the issue of CUs’ welfare effects. They assume a competitive
equilibrium of world trade where subsets of countries decide to create a CU. Prior to the
creation of a CU there is no restriction on tariff setting or taxes on trade. They propose
that a tariff vector and compensatory transfers within the union will result in
competitive equilibrium without causing a decline in welfare for members or non-
members. This is described as freezing trade with the rest of the world, and has the
17
effect of mirroring changes in the welfare of the world by observing what happens to
members of the CU (Winters 1997). While the Kemp and Wan (1976) model has shed a
positive light on CUs specifically and RTAs generally, Winters (1997) emphasises that
it cannot be used as a criterion for welfare change. He interprets the model as one which
aims to find the price vector which maintains trade composition between the union and
the rest of the world. The tariff imposed by the CU is then represented as the difference
between the CU price vector and the world. Winters (1997) stresses the importance of
understanding which are the welfare relevant indicators, pointing to non-members’
imports and terms of trade as the measures to observe before and after a CU has been
created.
RTA agreements can be a product of natural factors. The argument for natural
trading blocs is based on the assumption of geographical and economic ties. Countries
that are neighbours are more likely to be trading partners than those that are far apart.
The argument of natural trade partnership is based on the observation that proximate
countries generally become trading partners prior to launching a RTA. Natural trading
blocs are less likely to reduce welfare than their ‘unnatural’ counterparts. Outsiders
have the most to lose from these arrangements; but this may be outweighed by the
increased benefits to the bloc from trade creation (Krugman 1991). The proposition of
natural trade blocs was tested extensively by Frankel et al. (1995) using the gravity
model of bilateral trade. Their findings support natural trading blocs in a number of
examples. Controlling for a number of economic and geographical variables, they show
that in regions such as Latin America natural trading patterns overlap those of actual
trade blocs.
The likely positive outcomes of RTAs discussed above, are not absolute.
Bhagwati et al. (1998) emphasises their negative welfare effects. He primarily discredits
the arguments that trade diversion, as described by Viner (1950), is negligible, citing a
number of reasons why the argument is flawed. He refers to a number of studies such as
those by Yeats (1996) and Wei and Frankel (1996) of trade diversion in MERCOSUR
and the European Union respectively, and supports the argument that trade diversion is
significant despite relatively low tariff levels. RTAs’ proponents suggest that lower
tariffs levels prior to creating such unions imply lower trade diversion effects. Bhagwati
et al. (1998) argues that until the Uruguay Round, barriers to trade remained significant
and high. He points out specific barriers to trade, such as anti-dumping sanctions that
countries may use selectively, which are influenced by RTA memberships. While
emphasising RTAs’ diversionary effects, Bhagwati et al. (1998) discredits other major
18
supporting arguments, including the idea of Natural Trading Partners, and assertions
regarding volume of trade and transport-cost.
Although proponents of multilateralism point to the diversionary effects of
RTAs and welfare, others point out that the welfare analysis applies to the ‘old’
regionalism. The typical Vinarian analysis is less applicable to Bhagwati’s (1991) new
regionalism. This is specifically due to the different nature of RTAs in the 1960s and
70s, compared to those of later years. These changes stem from the shift in rationales
behind current RTAs which, according to Ethier (1998), include: small countries
wishing to create RTAs with larger ones, significant unilateral reforms taking place in
small countries, making trade liberalisation a priority, which it may not be in current
RTAs, one-sided agreements which help smaller countries liberalise, and finally a desire
for deep integration. These characteristics require a different way of thinking about
RTAs, and may negate some of the objections cited against them.
The above discussion of regionalism and specific concepts of trade creation and
trade diversion reveal that the Vinerian approach remains a static approach to
regionalism while more dynamic approaches have been introduced to address the
changing nature of RTAs and their goals. The following section will present some of the
relevant empirical work used to model RTAs’ effects.
2.2.2 The Gravity Equation
The gravity model of trade has been the traditional econometric tool used by
economists to measure the effects of RTAs. First adopted by Tinbergen (1962), it
estimated bilateral trade flows based on the distance between the two countries and their
economic size, measured by their GDPs. The model was formalised by Anderson
(1979) in line with economic theories of trade. His model was a general equilibrium
model with differentiated products. Contributions by Helpman and Krugman (1985)
produced a gravity model that addressed intra-industry trade where countries’ relative
factor endowments and their labour productivities are similar (Baier et al. 2008).
Bergstrand (1989) proved that the gravity equation could be derived from the Hecksher-
Ohlin model, establishing its creditability.
The gravity model in its simplest form is estimated as:
(2.1) 1 2 3 4log log log log .ij i j ij ijX GDP GDP Distance
19
On the left hand side ijX is the total exports of country i to country j. The countries’ size
are measured by gross domestic products and i jGDP GDP . Transportation cost is proxied
by using the distance between countries i and j. The
k s are parameters to be estimated.
A number of issues have been raised concerning the estimation procedures of
this model. Earlier estimates used OLS to estimate the gravity equation. However Egger
(2000, 2002) points out that cross-section OLS estimates have the problem of biased
estimates due to the omission of variables. He suggests the use of fixed effects in a
panel setting. Other studies such as Anderson and van Wincoop’s (2003) apply a non-
linear estimation to the gravity equation, incorporating prices of importers and exporters
in the model. This helps them address the omitted variable problem. In an extensive use
of the gravity model Frankel (1997) and Frankel et al. (1995) incorporate RTAs in the
model by adding dummy variables to represent RTA membership. These studies are
cross-sectional over several decades using a similar specification to equation (2.1). In
the case of Frankel et al. (1995) there is some evidence to support the ‘natural’ trade
bloc argument for some countries in South America, but it does not hold in Europe.
These results were subject to criticism, such as Baier et al. (2008), that argues using
probit or OLS to detect RTA while using bilateral trade patterns can skew the results, as
unobservable effects that are correlated with the trade flows are not represented.
Earlier studies apply the gravity model to detect trade creation and trade
diversion in the European Community (EEC) after it was first created. Balassa (1967)
critiques the method of using average income elasticities of exports and imports derived
from cross-section regressions. He points to the difference between developed and
developing countries’ elasticities. The developed countries will have elasticities
compared to their developing counterparts. The developed countries will experience
greater trade shares of the national products due to specialisation. The effect is opposite
in developing countries due to protectionism. Balassa (1967) suggests that this approach
will lead to a greater tendency of trade creation findings of the EEC and argues that
disaggregation of imports and exports would be more informative. He suggests an
appropriate method is to compare income elasticities before and after regional
integration, while controlling for income growth. He finds suggestive evidence of trade
creation and diversion in a number of sectors, and also finds that trade with non-
members is affected in various ways. In his assessment, the EEC has led to a basket of
mixed results with regard to effects on regional trade. Economic conditions during the
late 1950s led to a number of influencing factors in some sectors, while in other sectors
20
reduction in intra-regional trade was directly due to the EEC. The integration effect
cannot be completely isolated, in Balassa’s opinion.
Likewise, Aitken (1973) uses a version of the gravity equation similar to
Equation (2.1). His equation includes both income and population, measures for
transportation cost, dummies for EEC, European Free Trade Area (EFTA), and common
borders. Aitken uses a cross-sectional OLS for the period 1951–67, where he expects
RTA effects to be registered in the transportation cost measure and common border
dummy. An added benefit is the ability to isolate annual effects and observe patterns
over the time period. Aitken (1973) asserts this approach allows detection of the RTA
effect in the first year. The role of dummy coefficients is central to the estimation of
integration effects. The estimated trade flows are compared to actual trade flows to
indicate any trade diversion based on integration. Aitken (1973) finds support for the
trade effect of the EEC, but not with the EFTA. He goes further to estimate the
projected trade for 1967 using 1958 as the transition year, comparing these to dummy
variables estimates for trade effects. Using his projections and dummies, Aitken finds in
favour of trade diversion towards the end of the period despite gross trade creation
effects detected earlier in his sample.
These findings of trade creation within the EEC are pointed out in the specific
case of the United Kingdom. Sapir (1992) points out that in the years following the
UK’s accession into the EEC, it experienced a marked increased in intra-EEC imports.
Generally, Sapir (1992) finds support for an effect of EEC on intra-regional trade.
However, he does not attribute it completely to the integration process, arguing that
there are other forces at play, including external trade policy. Concentrating on intra-
industry trade, Sapir (1992) points to evidence in favour of increasing patterns within
the EEC. He attributes this increase in overall intra-industry trade as the influence of
late comers like Greece, Portugal, Spain and Italy into the EEC. These countries
experienced greater growth in intra-industry trade than the original members. Intra-
regional trade was also influenced by sectors. Studies that disaggregate the EEC trade
shares find both trade creation and diversion.
While the European experience is of great interest to researchers, the global
scope of regional integration has not been not neglected. Studies of regional integration
effects have been undertaken by Frankel (1997), Frankel et al. (1995), and Soloaga and
Winters (1999). Frankel et al. (1995) use the gravity equation to determine how much
regionalisation has taken place. In doing so they address the issue of natural trading
blocs stressed by Krugman (1991). Intra-continental RTAs are less likely to reduce
21
welfare compared to inter-continental ones. The argument here is that proximity plays a
role in determining the optimal location of RTAs, assuming high transportation costs
that prohibit inter-continental trade. Frankel et al. (1995) test this proposition of natural
trading blocs using the gravity equation. In their specification they use total trade as the
dependent variable for 63 countries for the period 1965 to 1990. Their specification uses
the product of trading pairs’ incomes and population in the estimation, instead of
individual parameters for each country. They find their estimates are in line with
theoretical assumptions about incomes and population and distance. They find richer
countries trade more, but at a less than proportional growth rate over time. Another
important result is the negative coefficient of distance. In line with theoretical intuition,
they find that proximity plays a role in generating more trade. When disaggregating
trade based on manufacturing, agriculture, and raw materials, Frankel et al. (1995) find
support for the contention that physical cost is not the only concern with respect to
distance With respect to RTA effects, the authors find trade bloc effects. These are
pronounced in Latin America, in both MERCOSUR and the Andean Pact, compared to
NAFTA. The RTA dummies used here become positive in the latter part of the period
for these RTAs, a trend not matched in South East Asia’s ASEAN or the Pacific’s
APEC. The core indicators of income, income per capita and distance explain trade
patterns sufficiently. Using generic dummies for RTAs, such as FTAs and CUs, returns
positive and significant results for the former but not the latter. This is especially true of
manufacturing. Cultural and colonial indicators included by Frankel et al. (1995) are
also significant. Finally, accounting for factor endowment by absolute difference of
GDP per capita or country difference in capital to labour ratios proves less substantial
than anticipated. The main conclusion is that countries with similar endowments trade
more with each other than otherwise.
Soloaga and Winters (1999) utilise the gravity equation for a broader assessment
of RTAs. In their case they include nine RTAs, ranging from North-North agreements
such as the EU and NAFTA to South-South agreements such as MERCOSUR and the
Andean Pact. Soloaga and Winters use ijX in equation (2.1) as imports into country i
from country j as their dependent variable. They introduce three distinct dummies to
measure the effects of each RTA over time. The first of these is a dummy that captures
whether two countries are members of a given RTA. This will measure the overall
added trade due to the RTA. The second dummy captures the imports if country i is a
member of a given RTA. Soloaga and Winters (1999) describe this as an openness
measure of the RTA to imports by its members. The third dummy is assigned to an
22
exporting country j that is a member of a RTA. This is intended to represent the
openness of the RTA to exports. Traditional trade diversion and creation in this
framework are measured by income elasticities. These dummies offer a measure of
RTA effect. Soloaga and Winters (1999) estimate their equations based on cross-
sections and pooled periods for years from 1980 to 1996. They control for typical
gravity equation variables such as income, population, and distance, and include
cultural and geographical variables such as language, common borders and island states.
They include fixed effects to address the specification issues Matyas (1997) raised
regarding gravity equation estimations, using both fixed effects and RTA dummies
discussed above. This is to avoid treating all trading partners symmetrically as Matyas’
(1997) fixed effect approach would suggest. Soloaga and Winters (1999) find little
evidence that RTAs in their new forms have increased intra-regional trade significantly.
Elasticities did not change significantly before and after the creation of these RTAs,
although not surprisingly they find that the EU and EFTA both produced trade
diversion. In Latin America there were some positive signs that RTAs tend to import
more from the rest of the world, but Soloaga and Winters (1999) argue that this effect is
largely due to unilateral trade liberalisation prior to creation of the RTAs. NAFTA was
found to be insignificant in terms of intra-regional trade effects. The authors suggest
NAFTA places less emphasis on trade liberalisation than on other integration issues.
These studies show that RTAs have considerable effect on intra-regional trade.
However, trade diversion effects are also evident, and increased trade due to RTAs is
not a feature of all trade blocs. In ASEAN, APEC, and NAFTA, for example, trade
flows can be explained mainly by traditional variables of the gravity equation. Western
Hemisphere RTAs such as MERCOSUR and the Andean Pact show strong RTA effect
on intra-regional trade. However, a possible reason for this is unilateral trade
liberalisation, as Soloaga and Winters (1999) explain. Table 2.1 indicates some of the
major studies that utilise the gravity equation to test various RTA-related effects on
trade.
2.3 Regionalism, Incomes and Growth
In the previous section, the trade effects of economic integration in the form of
RTAs was discussed. This section will focus on the effects of economic integration on
growth and incomes. The aim is to identify if RTAs affect growth and income
convergence within regions. As a starting point, the distribution of income across all
countries during the period 1980 to 2008 is observed. Figure 2.2 illustrates the
23
Table 2.1
Gravity Equation Empirical Studies
Study Comments Conclusions
Tinbergen (1962) Basic gravity model based on incomes and distances between trading
partners.
Model explains trade flows very well as applied in the
Finnish case.
Balassa (1967) Using the model proposed by Tinbergen (1962) in a disaggregated cross-
section study of the EEC.
Disaggregating trade into a number of sectors shows both
trade creation and diversion within the EEC.
Sapir (1992) Investigates the intra-regional effects of the EEC. EEC has increased intra-industry trade within the region.
Enlargements of the EEC played a role in increasing patterns
of intra-industry trade. Consequently some degree of trade
creation took place within the Community.
Aitken (1973) Dummies are used in cross-sectional OLS representing two European
RTAs.
Confirmation of RTA effect especially in the case of EC.
Trade diversion evident .
Anderson (1979) Formal general equilibrium model incorporating the gravity equation
applying the Armington assumption.
Bergstrand (1985,1989) Derived the gravity equation from the Heckscher-Ohlin model of relative
endowments.
(continued on next page)
24
Table 2.1 (Continued)
Gravity Equation Empirical Studies Study Comments Conclusions
McCallum (1995) Border effects between the US and Canada using the gravity equation. Large border effects when trade is across borders compared
to within each country.
Frankel et al. (1995) Used gravity equation to test for ‘natural’ RTAs. Included dummies for regional
and other social and demographic regressors.
Natural RTAs are detected in South America and Europe.
North America’s NAFTA, East Asia’s ASEAN do not
display any natural RTAs due to proximity.
Frankel (1997) Using gravity to test for RTA effects while controlling for other variables.
Rose (2000) Gravity equation used in detecting effects of a currency union on trade.
Feenstra (2002) Tested for border effects using consistent methods including fixed effects. Border effects calculated in traditional way are larger for
smaller countries. This argument is supported by use of
simple border effect dummies to detect average effects.
Fixed effect estimation returns consistent estimation in the
gravity equation.
Anderson and van Wincoop
(2003)
Specific treatment of prices in the gravity equation. Introduced ‘multilateral’
resistance as a key regressor in their model. This was to address the suspected
problem of omitted variables.
Baier and Bergstrand (2007) Omitted variable bias from uncaptured effects of deeper integration in the
gravity equation.
Simultaneity bias due to endogenous variables such as GDP and more
specifically the FTA measure.
Fixed effects shown to be more efficient than Random Effects or IV.
Phase-in effects are taken into consideration.
Do FTAs increase members’ international trade? Yes!
It doubles members’ trade over a period of 10 years as
FTAs phase in.
25
distribution of the natural log of GDP per capita (PPP $US) obtained from the IMF’s
World Economic Outlook 2009 database. The horizontal axis is measured in PPP $US,
converted from natural logs. Each bar refers to the upper limit of the range included in
the count. The Gulf Cooperation Council (GCC) countries are featured in the figures to
illustrate changes over time.
Figure 2.2
World Income Distribution
(PPP $US)
A. 1980
B. 2008
Source: IMF (2009)
Panel A in Figure 2.2 shows the histogram of GDP per capita of 1980. The
histogram includes 147 countries. Countries’ GDP per capita in PPP $US ranged from
163 in Myanmar to 46,500 in Qatar. The average GDP per capita is 4,136 for all
countries. Within the 147 countries included, 101 countries fall below the average and
0
2
4
6
8
10
12
14
14
8
19
1
24
5
31
4
40
3
51
8
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08
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81
3,8
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4,9
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,40
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,36
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,15
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,02
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,28
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36
,31
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,63
0
59
,87
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,88
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98
,71
6
12
6,7
54
16
2,7
55
Income per capita
Nu
mb
er o
f co
un
trie
s
Bah
rain
Ku
wai
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UA
E
Qat
ar
Sau
di
Ara
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Om
an
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24
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85
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1,0
97
1,4
08
1,8
08
2,3
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2,9
81
3,8
28
4,9
15
6,3
11
8,1
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10
,40
5
13
,36
0
17
,15
4
22
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6
28
,28
3
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6
46
,63
0
59
,87
4
76
,88
0
98
,71
6
12
6,7
54
16
2,7
55
Income per capita
Nu
mb
er o
f co
un
trie
s
Bah
rain
Ku
wai
t,
UA
E
Sau
di A
rab
ia, O
man
Qat
ar
26
46 above the average. There are three distinct peaks within the distribution, and the
GCC countries are spread out within the distribution although all GCC countries are
above the average GDP per capita. Qatar, Kuwait, and the UAE are by far the richest
countries in the world based on the PPP estimates used. Oman is relatively the poorest
within the region. Bahrain and Saudi Arabia are well below the top three in terms of
GDP per capita. The distribution depicted in Panel B shifts to the right in terms of the
average GDP per capita of PPP $US 14,469. The distribution ranges from $US 330 in
the Congo to $US 86,000 in Qatar, which is joined by Luxembourg at the top of the list.
Although the spread of the distribution has increased dramatically over the 29 years, the
number of countries below and above the average has changed very little.
There are 98 countries below the average, and 49 countries above. Figure 2.2
also shows an interesting pattern within the GCC. While Qatar, Kuwait and the UAE’s
GDP per capita remain high, Bahrain, Saudi Arabia and Oman appear to have caught up
with their GCC counterparts. This may suggest a degree of convergence between RTA
members over time. While the histograms suggest that, overall, countries may not
converge in terms of their per capita GDPs, the case of the GCC countries implies
otherwise.
Figure 2.3 depicts this relationship of growth against initial endowments of
incomes in a scatter plot based on the neoclassical convergence concept. Based on the
Solow (1956) and Swan (1956) models of growth, countries at different initial levels of
income will converge to a given steady state of long run growth. Poorer countries will
eventually catch up with their richer counterparts. The initial state is measured by a
number of factors, including income as an indicator of economic development. The key
catalyst of growth in this context is capital accumulation. Countries with low per capita
income, and thus developing, experience greater returns on capital investment: they
have much to gain from accumulating capital — through saving and investment — for
production purposes. Their wealthier counterparts with greater capital stocks experience
diminished returns. Consequently, the growth rates are expected to be greater for
developing countries and relatively slower for developed ones.
Based on the above, the relationship between initial conditions and growth is
expected to be negative. The relationship is very sensitive to which countries are
included. In the sample of 147 countries, Zimbabwe is the poorest based on PPP
valuations of GDP per capita. When included, no convergence can be detected.
However when removed, the relationship between growth rate and incomes in 1980 is
weakly negative. Very little evidence of convergence can be detected from this simple
27
exercise. This is not surprising as there many reasons why poorer countries have not
converged with richer ones. In a specific case, Figure 2.3 isolates six GCC countries. In
a simple convergence exercise the six countries exhibit signs of the catch-up scenario
suggested by neoclassical growth theory.
Figure 2.3
Income Convergence, 1980–2008
Source: IMF (2009)
2.3.1 Growth Theories Overview
Growth theory attempts to explain the reasons why on a global scale countries
have not converged following the assumptions put forward in the neoclassical models
led by Solow (1956) and Swan (1956). The premise of the Solow model of growth was
built around capital accumulation and diminishing returns. This allows countries to
reach a given steady state. Growth is the by-product of population growth and
technological growth. Figure 2.3 depicts this relationship to some extent by controlling
for initial incomes as proxy for country differentials. Poorer countries with lower capital
stock will experience faster growth rates as a result of greater marginal productivity.
This has clearly not been the case for the world in the past three decades, and this
inadequacy has led researchers to find explanations of what drives growth. Abandoning
the exogenous assumptions of the neoclassical theorists, Romer (1986) and Lucas
(1988) have led work on new growth theories, dubbed Endogenous Growth Models.
These models emphasise the role of human and knowledge capital. The significance of
endogenous human and knowledge capital in growth models was their lack of
diminishing returns. Unlike physical capital, which experiences diminishing returns, the
Oman Bahrain
Saudi
Arabia
Kuwait
UAE
Qatar
y = -1.9425x + 21.662
y = -0.1231x + 5.3069
0
2
4
6
8
10
12
14
4 5 6 7 8 9 10 11 12
Log of Per Capita GDP 1980
Gro
wth
(%
p.a
.)
28
spillovers of human and knowledge capital allow long-run positive growth of incomes
(Barro 2004).
2.3.2 Empirics of Growth and Income Convergence
The empirical literature on growth is large, and has taken a number of directions
based on neoclassical and endogenous growth theories. Explanations of the
determinants of growth have taken many forms and included many variables. Two main
areas can be identified as the most important issues: winners and losers in long term
growth, and the variability in growth rates over time for specific groups of countries
(Rogers 2003). These issues boil down to a specific concept, convergence. Are
countries approaching a common per capita income level? As indicated in the previous
section through the distribution of countries across the world and the scatter plot, there
is no evidence of such behaviour. A number of studies aim to address the issue of
convergence by examining distributions and predicting outcomes. Quah’s (1996) and
Pritchett’s (1997) are examples of such studies, which gave rise to the concept of a
‘convergence club’, where countries would converge in separate groups. These results
are fragile, and sensitive to which countries are selected (Rogers 2003). In a different
direction, empirical studies have concentrated on regional and cross-country growth to
detect convergence. These studies include Barro et al (1991), Barro (1991), Barro and
Sala-i-Martin (1992), and Sala-i-Martin (1996). These studies generally use cross-
sectional samples of countries and test convergence based on neoclassical assumptions.
In the case of Barro et al. (1991), regional sets of US states, Japanese prefectures, and
European regions are used as units of analysis. A number of outcomes of these
regressions have surfaced as stylised facts. These include: there are no simple
determinants of growth from the extensive list used by these studies; initial income still
plays an important role with respect to growth; government size is less of an issue than
quality governance; human capital remains weakly related to growth while health and
wellbeing indicators are generally found to be robust; institutions play an important part
in growth outcomes; and finally open economies generally grow at a faster rate than
others (Sala-i-Martin 2002). Levine and Renelt (1992) have put many of the variables
used in the cross-country studies to the test and find that initial incomes, under
conditional convergence, have a robust relationship with growth. Other traditional
explanatory variables such as political and economic variables are not robust, however.
They also find that investment plays an important role in growth: investment shares of
29
GDP and trade shares of GDP have a positive and robust relationship. However,
investment shares of GDP affect the robustness of trade policy variables.
Above are some of the studies that have considered growth empirically and
tested the relationship between growth and multitude of variables. The emphasis from
this point onwards is the relationship between trade generally, and RTA specifically, on
growth. Thus, posing the question, what effect does trade have on growth? Lewer and
Van den Berg (2003) review the empirical literature that has tested the relationship
between trade and economic growth. Their interest is in the studies that specifically use
trade measured by export growth and income growth. They break down the studies into
four major categories: cross-section studies, time-series studies, per capita income
studies, and simultaneous equations studies. The first two categories are studies that
regress growth of capital, labour stocks and other explanatory variables, and finally
growth of trade. These regressions generally are linear, and have their theoretical
backing from neoclassical growth models. Lewer and Van den Berg (2003) point to a
remarkable consistency in the results of studies that can be classified as cross-section
studies and time-series studies specifically, and other studies in general. In the case of
cross-sectional studies, based on a survey of 196 studies they report an average
coefficient value of 0.22. This is interpreted as meaning that a 1% increase in trade,
measured in many cases by growth in exports, leads to one fifth of a percent growth in
output. This result is not dissimilar to time-series regressions. Lewer and Van den Berg
(2003) report an average coefficient of 0.215. This, however, includes all the studies
that have adjusted for stationarity and those that have not. When these effects are taken
into account the coefficients differ substantially: studies that do not adjust for the unit
root problem report a coefficient of 0.261 on average, while those that do report a
coefficient of 0.081 on average. The time-series trade effect is affected by whichever
subset of studies is considered; those based on classifications of income, on openness,
or on single and multiple regressions. In the case of single or multiple regressions based
on time-series data, the results are very similar: coefficients on average are 0.200 and
0.219 respectively.
A number of criticisms have been made against econometric studies of growth.
The effect of the trade variable can be used to capture income effects that are not
entirely related to that variable. Of these studies key Frankel and Romer’s (1999) and
Rodriguez and Rodrik’s (2000) stand out (Noguer and Siscart 2005). The emphasis in
this case is on non-trade effects due to geographical variables. The implications of the
geographical characteristics of countries can be summarised as three main effects:
30
diseases and morbidity due to the environment, endowments of resources and
agricultural productivity, and finally institutions (Noguer and Siscart 2005). The
inclusion of robust instruments that measure the effect of non-trade factors that may
have been captured by trade measures will yield precise estimates. Noguer and Siscart
(2005) propose an instrument that they assert to be precise compared those used in
previous studies. They point to the criticism made by Rodriguez and Rodrik (2000) that
geographical instruments used in growth empirical studies may be correlated with other
geographical variables which may affect income through non-trade channels. They —
Rodriguez and Rodrik — point out that those regressions may be spurious. Based on
this, Noguer and Siscart (2005) construct a geographical characteristic instrument using
a gravity equation variant. They find inclusion of this improved instrument to reduce the
effect of volume of trade on incomes. Using the Instrumental Variable estimation, they
find a one-to-one relationship between trade share and income growth: thus, countries
which experience an increase of trade share of GDP by 1% experience a 1% increase in
income per capita. This result verifies the findings of Frankel and Romer (1999), that
trade does affect income.
Rodriguez and Rodrik (2000) present a case of trade policy implications on
growth. They differentiate themselves by testing how trade policy affects growth. They
critique a number of empirical studies that link openness to growth to trade policy
implications. These studies include Dollar (1992), Sachs and Warner (1995), Edwards
(1998), and Frankel and Romer (1999) as main contributions to the literature on trade
and growth. Rodriguez and Rodrik (2000) argue that in the case of Dollar (1992) there
are problems with the indices created to detect real exchange distortions and variability.
The critique is based on the fact that each of these indices measures the trade orientation
of countries used in the cross-sectional study. In the case of real exchange distortions,
the index is formed of the prices (based on Summer and Heston’s 1988 database) of
each country relative to the United States. Rodriguez and Rodrik (2000) point out three
conditions that must be ignored for Dollar’s (1992) index to measure trade orientation
correctly: export taxes or subsidies, the Law of One Price, and transportation costs and
geographical factors. They show that ignoring these conditions produces ambiguous
effects on the index and consequently on its perceived interpretation. The other critique
of Dollar’s approach to growth empirics is the second index of real exchange variability.
This captures relative price variation over time. In this case Rodriguez and Rodrik
(2000) argue that the measured effect is influenced by factors other than trade policies,
and show that the distortion index is not robust, unlike the variability index which
31
withstands alternative specifications of the growth equation used by Dollar (1992). The
insignificance of the distortion index is due to its correlation with other factors such as
omitted geographical variables as Rodriquez and Rodrik (2000) suggest.
Sachs and Warner (1995) develop an openness index using five criteria: tariffs
exceed 40%, non-tariff barriers cover more than 40% of imports on average, a socialist
economic system exists, major exports are controlled by a state monopoly, and the black
market premium exceeds 20%. These criteria are used to classify countries as open or
not. Rodriguez and Rodrik (2000) argue that this index of openness is misleading in
terms of trade policy, and show that of the five criteria only two contribute significantly
to growth. The trade-specific criteria do not do not affect growth when tested
individually. Creating sub-categories of the Sachs and Warner (1995) openness
measure, they find only the black market premium and state monopoly of exports are
statistically significant with coefficients close to those of the overall index. They ask,
since these two indicators of openness are significant, are they related directly or
indirectly to trade policy? Sachs and Warner (1995) argue that in the case of a state
based export monopoly, distortions are caused such that the overall trade of the country
will decline. In creating this measure they source the World Bank’s study on African
countries in 1994.2 This choice restricts the index used for these criteria to African
countries that underwent structural reform programs during the period of study.
Rodriguez and Rodrik (2000) argue this causes two selection bias problems: first, the
omission of non-African countries that also restricted trade through monopoly; and
second, the omission of African countries with restrictive trade policies that did not fall
under the structural programs. This leads to an upward bias of the monopoly criteria
used by Sachs and Warner (1995). In effect Rodriguez and Rodrik (2000) argue that the
monopoly measure correlates with that of the sub-Saharan Africa dummy used by Sachs
and Warner, and the results are influenced by non-trade policy related factors.
The critique of the black market criterion follows similar concerns about its
shortcomings as an indication of trade policy relevance. The argument here is that black
market premiums need not reflect trade policy distortion alone. If foreign currency
rationing has led to deviations between official and market exchange rates, importers
may be led to black market transactions. The behaviour of importers and exporters will
have consequences for resource allocation, as Rodriguez and Rodrik (2000) point out. If
both importers and exporters use the black market to source their foreign currency, such
2 Rodriguez and Rodrik (2000) point to the World Bank study Adjustment in Africa: Reforms,
Results, and the Road Ahead – 1994.
32
consequences will be alleviated. The critical issue they point to in Sachs and Warner
(1995) is that the black market premium points to policy distortions, corruption, or
economic mismanagement, and not to trade policies. They confirm this by using the
same black market premium as a continuous variable against growth, where they find
the 20% criterion identified by Sachs and Warner (1995) weighs heavily on those
countries that fall in that category. As the two significant components of the openness
criteria of Sachs and Warner (1995) are shown to be less related to trade policies and
more to imbalances and other factors, the effects of openness of 2.20 to 2.45 in Sachs
and Warner’s (1995) regressions may be due to other factors overall, and not trade
policies — as Rodriguez and Rodrik (2000) point out.
In an extensive review of regional trade agreement and development Schiff and
Winters (2003) present a number of links between RTAs and economic growth. The
premise is that trade policy in the form of preferential treatment agreements may lead to
some economic growth. The contrary argument is that multilateral trade liberalisation
may produce economic growth. Three main factors identified in the literature influence
this potential relationship: openness, Foreign Direct Investment (FDI), and knowledge
transfer. These are the channels through which trade can influence economic growth.
The choice of RTA partners plays an important role in how such effects take place; for
instance, it is less likely that a South-South RTA will benefit from spillovers associated
with knowledge and FDI, compared to a North-South RTA.
In an RTA-specific study, Ben-David (1993) finds that trade liberalisation
within the European Economic Community (EEC) and European Free Trade Area
(EFTA) has convergence effects in terms of incomes within Europe. He finds a link
between trade and income convergence when considering major trading partners. In this
approach, Ben-David (1996) does not define RTAs specifically but focuses on country
groupings based on trade patterns. These results imply that if RTAs sufficiently increase
their member’s trade, there is a likelihood of income convergence among them. Ben-
David (1996) links the results to the Hecksher-Ohlin factor equalisation, with trade as
the channel through which income convergence takes place.
This sub-section has outlined some of the key studies that related trade to
growth, and by proxy income convergence. These studies are summarised in Table 2.2.
2.4 Economic Integration and Prices
The main aim in this section is to identify the effects of regional integration on
prices. Trade liberalisation has a potential indirect effect on prices. Reduction of trade
33
Table 2.2
Selected Growth and Convergence Studies Study Comments Key Results
Barro (1991) Examines economic growth across a cross-section of countries based on neoclassical growth
models. Key variables include initial incomes, human capital measured by schooling,
investment, and political stability.
Simple correlation between
income and growth is weak.
However, when human capital is
accounted for, the link is negative
and significant. Human capital is
one of the key variables for poorer
countries catching up with richer
ones.
Government spending and
investments can be distorting.
Political instability is negatively
related to growth.
Sub-Saharan Africa and Latin
America’s weak growth are left
unexplained by this framework.
Barro et al. (1991) Examines income or β-convergence within countries by comparing incomes across US states in
one instance and across European regions in another.
Poorer states or regions grow
faster than relatively richer ones.
This is dubbed β-convergence and
is found to be around 2% per
annum.
In Europe the cross-country
regional comparison yields similar
results to that of US states. There
are no significant differences when
comparisons are made within
countries and across countries.
(Continued on next page)
34
Table 2.2 (Continued)
Selected Growth and Convergence Studies Study Comments Key Results
Barro and
Sala-i-Martin (1992)
Tests for convergence using the neoclassical growth model. This is applied to US states and
comparably to other countries. Relative steady states are captured by individual characteristics
of each state or country.
Convergence is supported in the
form of faster growth towards
steady state when farthest away.
Growth rates are faster for poorer
states in the US than for their
richer counterparts, when only
initial incomes are considered.
When specific characteristics are
included the rates are similar at
2% a year.
Convergence between countries is
evident in the conditional form.
Openness, technological diffusion,
and capital mobility also affect
rates of convergence.
Ben-David (1993 and 1996) (1993) Links between timing of trade liberalisation and income convergence is examined. The
European Economic Community (EEC) is used as a case study. This is done by observing the
dispersion of the six original members and then the inclusion of new members.
(1996) Examines income convergence within a group of countries. Trade’s effect on income
convergence is the main focus.
(1993) Trade liberalisation is
shown to have an effect on income
convergence. This is observable
when new members join the EEC.
Income convergence patterns are
not related to long term trends or
previous divergences.
(1996) Evidence of convergence
with specific group of countries
that trade substantially with each
other. This result is emphasised
when countries are grouped based
on their trade policies.
(Continued on next page)
35
Table 2.2 (Continued)
Selected Growth and Convergence Studies Study Comments Key Results
Quah (1996) Regularity of conventional empirical finding of cross-sectional convergence due to statistical
regularity and not actual convergence.
Paper studies dynamic distribution changes of country incomes.
Support for ‘convergence clubs’
found. Income distribution bi-
modal.
Sala-i-Martin (1996) Uses the classical approach to convergence to test the concepts of β-convergence and σ-
convergence for a set of 110 countries. Study also includes country datasets such as US states,
Japanese prefectures, and European regions.
Paper indicates that conditional convergence makes the neoclassical approach plausible within
the convergence debate.
In the classical approach absolute
β-convergence and σ-convergence
are not detected in the cross-
country dataset. There is support
for conditional convergence,
however. The rate of convergence
is approximately 2% per annum.
Absolute β-convergence and some
σ-convergence are observed for
OECD countries during sub-
periods. The rate of convergence
is close to 2% per annum
This result is also verified within
country states and regions. Both σ-
convergence and β-convergence
are observed. The rate of
convergence is also close to 2%
per annum.
(Continued on next page)
36
Table 2.2 (Continued)
Selected Growth and Convergence Studies Study Comments Key Results
Frankel and Romer (1999) Paper examines the relationship between trade and income by devising geographic instrumental
variables.
Justification of use of such instrumental variables is to avoid endogeneity of trade effects in
incomes.
Geography explains part of the trade and income relationship but not all.
A positive effect of trade on
income is found.
A 1% change in trade to GDP
leads to 0.5% change in per capita
GDP.
Trade within country was found to
affect incomes as well. As
countries get larger there is a small
but positive effect on income.
37
barriers may reduce some of the distortions to cross-country prices of goods. However,
cross-country prices are influenced by a number of factors besides barriers to trade.
Transportation costs and exchange rates also play a significant role in cross-country
price deviations. While RTAs cannot do much about the former, there is body of
literature that suggests that through currency unions they may have an effect on trade
and growth; one such is Rose (2000).
2.4.1 Purchasing Power Parity
An appropriate starting point for the discussion of regional integration and prices
is purchasing power parity. The PPP theory asserts that prices across borders will be
equalised barring any impediments to trade or transportation costs. Proposed formally
by Cassel (1928), PPP theory came to be perceived as a powerful tool for exchange rate
forecasting. The practicality of the theory was supported by proponents such as Keynes
(1923), but also received criticism from opponents like Viner (1933). While not
everyone agreed on the practical usefulness of PPP theory, many studies of the US and
European countries were conducted during the 1920s and later revived post-World War
II. The PPP doctrine received renewed impetus in the 1970s with the monetary
approach led by Mundell (1968, 1971) which links the Quantity Theory of Money to
exchange rates (Dornbusch 1988).3
A major issue in the PPP doctrine is the deviations of exchange rates that seem
to dispute the theory. While evidence has been found for and against PPP, explanations
for the deviations are required. Balassa (1964) and Samuelson (1964) provide one
explanation by pointing out that PPP deviations are subject to productivity bias.
Dornbusch (1988) identifies the deviations from PPP as structural and transient. The
structural deviations are caused by relative productivities between countries; the
transient deviations are caused by market imperfections. PPP deviations are also
influenced by so called border effects, such as barriers to trade, transportation costs,
marketing costs, exchange rate variability, and local currency prices (Engel and Rogers
2001).
These distortions are a key reason why the Law of One price or absolute PPP is
based on the assertion that the price of a commodity is equalised when compared in the
same currency. Therefore,
(2.2)
3 See Clements (1981) for exchange rate determination using the monetary approach.
i iP SP
38
where the left hand side is the domestic commodity price iP in the local currency, and
the right hand side is the foreign commodity price iP , and S is the spot exchange rate.
The same concept can be applied to price levels across countries. In absolute terms, the
previous expression becomes P SP such that the price of a basket of goods in both
countries is compared in the same currency. From this expression, the exchange rate is
the ratio of the values of the two baskets. Therefore,
(2.3) S P P .
Taking the logs of (2.3) gives the expression s p p . Thus the log exchange
rates are subject to the log difference of the value of the baskets. The assumption behind
the expression is that the baskets of goods are comparable between the domestic and
foreign countries. These consumer baskets are represented by their national Consumer
Price Index (CPI). The use of CPIs implies that the consumer baskets are not likely to
be identical in composition or weight. This is critical for absolute PPP, which applies to
identical goods.
A weaker version of PPP uses CPI changes instead of absolute levels. The
relative PPP relationship is usually presented as a difference of logs of (2.3) to get
(2.4) s p p .
The expression here refers to price changes or inflation rates and exchange rate changes.
The change in the real exchange rate is the excess of the nominal change over the
inflation differential. Thus, the change in the real exchange rate is the deviation from
parity, vis-à-vis, s p p , which we write as
(2.5) r s p p .
PPP theory has been used as a tool to determine exchange rates. These
approaches are discussed in the following section.
2.4.2 PPP Empirical Methodologies
The concept of PPP was empirically tested for both short and long periods. The
stricter interpretations of PPP theory, that this relationship holds true all the time, has
been tested numerous times; an often cited study is Frenkel (1978). This and other
studies that followed post-Bretton Woods are referred to as First Generation PPP studies
(Froot and Rogoff 1995). The proposition tested by Frenkel (1978) was based on the
inflation differential such that
(2.6) t t t ts p p
39
where ts is the exchange rate, t tp p
is the inflation differential between the home
and foreign country, and are parameters to be estimated, and t is a random error.
Frenkel (1978) tests for the condition where 1 to accept PPP. Thus inflation
differentials affect the exchange rate directly, as suggested by equations (2.4) and (2.5).
He finds support for PPP based on (2.6).
His results were contested, however, because countries in his sample which
experienced hyperinflation in were omitted; nor could the results be replicated for
OECD countries. Furthermore, the causality of exchange rate effects on inflation
differential was questioned by Isard (1977) amongst others (Froot and Rogoff 1995).
Krugman (1978) shows that the endogeneity problem presented by Equation (2.6) can
cause bias in OLS estimates of . He proposes using Instrumental Variables (IV) on
Equation (2.6), a similar approach to that of Frankel (1981). They both reject PPP based
on the IV approach. The main feature of this generation of PPP empirical tests was the
short term horizon.
The second generation of PPP tests emphasised longer horizons. These studies
tested for stationarity of the real exchange rate. The real exchange rate was defined as
(2.7) .t t t tq s p p
Equation (2.7) equates the real exchange rate changes to changes in the nominal
exchange rate and inflation differentials. Using Equation (2.7), studies in the so-called
second generation tested the null of random walk. Testing for random walk utilised the
developments in time-series produced by Dickey and Fuller (1979). What came to be
known as the Augmented Dickey-Fuller test for stationarity is presented as
(2.8) 1 2 3 1 1 .t t t tq t q L q
Equation (2.8) regresses the real exchange rate obtained from Equation (2.7)
against its lag and combination of differenced lags. The regression also includes a trend
when deemed necessary. The null hypothesis of 3 1 is tested to confirm the presence
of a unit root. If the null is rejected then the real exchange rate is a mean reverting
random walk process, and PPP holds.
The results of the second generation were mixed. Long run tests have shown that
it is more likely to accept the random walk hypothesis with respect to the real exchange
rate. Criticisms of the power of the test, based on data availability, often cast doubt over
the findings (Froot and Rogoff 1995). The methodology used in time-series evolved into
error correction models and structural break tests which were used to distinguish
40
possible influences on the data. Cointegration tests introduced by Engle and Granger
(1987) defining stationarity can be weakly proven by mean reversion of a linear
combination of variables. In this case a linear combination of exchange rates and prices
needs to be stationary (Froot and Rogoff 1995). The general empirical findings suggest
that PPP generally and LOP specifically do not hold in the short run and are
questionable in the long run (Taylor and Taylor 2004). Two major concerns of these
tests generally, and of mean reversion specifically, are raised by Taylor (2003). The first
is temporal aggregation, where studies have used annual, quarterly, or monthly data to
test for mean reversion in a basic first-order autoregressive specification. The half-life
estimates are often slow, as indicated above. This, Taylor (2003) points out, defies
expectations of researchers of speedy recovery from deviations from the mean. The
problem of aggregation rises from using low frequency data to infer adjustments in high
frequency data. Taylor (2003) indicates that this causes bias towards longer half-life
estimates. The second problem is attributable to linear specification of the mean
reversion regressions. The assumption is that PPP deviations will die out in a linear
fashion no matter how large they are. Taylor (2003) points out that there are conditions
— such as fixed and variable trading costs, and risk aversion — which fall under a
‘band of inaction’ where no arbitrage can occur. He refers to Heckscher’s ‘commodity
points’ as the originating concept of this idea. In other words, Taylor (2003) points to
the possibility of nonlinearities’ dissipation of deviations from the mean.
Rogoff (1996) presents an important conundrum with respect to the empirical
PPP literature: ‘How can one reconcile the enormous short-term volatility of real
exchange rates with the extremely slow rate at which shocks appear to damp out?’
(Rogoff 1996, p. 647). This he dubbed the purchasing power parity puzzle. The slow
rate of convergence is indicated in the literature by half-life estimates of 3–5 years
found using different empirical methods. Rogoff (1996) puts forth a number of
arguments why this empirical predicament exists. He refers to the Balassa-Samuelson
effect as an extension to PPP models that can help explain this pattern. Another
plausible extension to existing PPP models is the connection between current accounts
and the real exchange rate in the long run. Rogoff (1996) also suggests incorporating
government spending linkages to real exchange rates as a means to understand the cause
of long half-lives found in the literature. Finally, there is a multi-vector autoregression
model approach. These models have given some promise of incorporating a number of
economic explanatory variables that interact with the real exchange rate incorporating
monetary shocks bounds. Despite all these possible approaches Rogoff (1996) sees
41
persistent and slow converging deviations of PPP as a direct result of international
markets remaining segmented and trade being plagued with friction. These frictions
include transportation costs and barriers to trade.
While many studies have tested relative PPP, others have concentrated on the
LOP or absolute PPP. Disaggregated or micro-prices used to test the LOP appeared as
early as Isard (1977), and Giovannini (1988). Isard (1977) uses the disaggregated prices
of manufacturing goods in three countries, the US, Japan, and Germany, and finds that
price differences from the LOP persist for a long time. Giovannini (1988) finds similar
results using disaggregated price comparisons. These studies also find that deviations
are consistent with exchange rate movements (Froot and Rogoff 1995; Rogoff 1996).
The most interesting and well known LOP specifically and the PPP in general is the Big
Mac Index. Introduced in 1986 by The Economist magazine, the index used the prices
of the McDonald’s Big Mac burger as a representative of an identical basket of good
across countries. The value of the Big Mac was compared across countries once
converted in US dollars. The relative dollar prices of Big Macs indicated over or under
valuation (Ong 2003). Parsley and Wei (2008) provide a useful comparison between the
Big Mac Index verses the Consumer Price Index as a unit for LOP studies.
Other studies chose to examine the LOP in the context of economic integration.
These include Parsley and Wei (2008 and 1996), Bergin and Glick (2007), Rogers
(2007), and Broda and Weinstein (2008). The unique aspect of the Parsley and Wei
(1996) study is that they concentrate on the US only. In testing for the LOP the authors
eliminate the common frictions referred to above by observing price differences in one
country. Their data are divided into three categories: perishables, non-perishables, and
services. They use prices relative to a chosen city within the US using the unit root test
proposed by Dickey and Fuller (1979) and using distance as the measure of
transportation cost. They find that in the case of non-tradable products, reversion to zero
is present. They also show that services take longer to damp out differences in inter-city
prices. Parsley and Wei (1996) find support for non-linearity of convergence especially
in tradables.
Rogers (2007) examines price convergence in Europe and the US after the launch
of the monetary union. He makes use of the Economist Intelligence Unit to find the
dispersion within the EU prior to the creation of the monetary union. The reduction in
price dispersion after the launch of the union was minimal in comparison. Rogers
(2007) reports the largest decline was experienced by the EU-11 countries that adopted
the Euro. The US in comparison has shown lower price dispersion over time, especially
42
in non-tradables. The key findings of Rogers (2007) are explained by factors of
economic integration. These include harmonisation of tax rates, convergence of incomes
and labour costs, and trade and factor market liberalisation. These findings and other
major studies are summarised in Table 2.3.
2.5 Concluding Remarks
The phenomenon of the sharp increase in RTAs in the past two decades has
placed emphasis on understanding the outcomes of these agreements. This chapter has
presented the conceptual overview of economic integration by defining what is meant
by economic integration, and has identified the main characteristics and underlying
reasons for their formation.
The chapter also discussed the three aspects of economic integration that act as a
barometer of effectiveness, identified as trade convergence, income and growth
convergence, and finally price convergence. The chapter discussed the effects of
economic integration on each, citing evidence from the literature that links these aspects
to economic integration. With respect to trade, reduction in trade barriers due to
liberalisation of movement of goods within a given RTA has been cited as a catalyst of
increased intra-regional trade. However, this effect is countered by potential diversion
of trade from the rest of the world.
The second aspect of economic integration effects considered here is income
convergence. Using trade as a vehicle of growth, and considering the link between
RTAs and increased trade, incomes within a region may be expected to converge due to
economic integration. There are other potential channels by which trade, intensified by
RTAs, may affect incomes, such as through FDIs and knowledge spillovers. This is
expected to be an important channel when developing countries partner with developed
countries to form RTAs. RTAs can also influence income convergence through free
movement of factors of production with an economic integration area, bringing some
balance to intra-regional differences.
The third aspect of economic integration discussed in this chapter is price
convergence. Linked to trade liberalisation and greater economic integration, price
convergence is a significant measure of the effectiveness of RTAs. In the first instance
of trade liberalisation, reduction in tariffs can be linked to reduction in price differences.
Reduced non-tariff barriers, through harmonisation and standardisation of laws and
regulations, also can play a role in bringing down price deviations between RTA
members. Surprisingly, even with significant economic integration there exist price
43
wedges that may be attributable to market segmentation or non-tariff barriers. These
findings come from studies that use highly disaggregated price data. The three aspects
of economic integration presented in this chapter act as the measures which will be used
in this thesis to determine the effectiveness of economic integration.
44
Table 2.3
Selected PPP and Price Convergence Studies
Study Comments Key Results
Isard (1977) Tests the Law of One Price using disaggregated data from manufacturing sectors in the US, Canada,
Germany, and Japan.
Rogoff (1996) High shor-term volatility of exchange rates is countered by slow dampening out of shocks. Persistent and slow converging
deviations of PPP as a direct result
of international markets remaining
segmented and trade being
plagued with friction.
Parsley and Wei (1996) Examines the convergence of prices based on the Law of One Price given distortions such as barriers to
trade and exchange rate volatility. This is done for 48 US states using highly disaggregated data.
Tradable goods convergence to
parity in 4 to 5 quarters. Services
take three times as long. Tradable
convergence speeds are faster than
those of other cross-country
studies.
Study results support nonlinearity
of convergence rates. Convergence
is generally faster the larger the
price deviations.
(Continued on next page)
45
Table 2.3 (Continued)
Selected PPP and Price Convergence Studies
Study Comments Key Results
Bergin and Glick (2007) Focuses on price dispersion variation over time. Price dispersion declines in the
1990s and increases in the
following decade. This forms a U-
shaped pattern.
Distance plays a significant role in
time varying dispersion. Resulting
transportation costs play a
significant role in dispersion
behaviour.
Parsley and Wei (2008) Examines the Euro effect on prices within the EU. This is done by using highly disaggregated data. The
paper utilises the Big Mac Meal for 25 European countries. The prices are also compared across Euro
and non-Euro countries.
This paper finds no significant
effect of the Euro on price
dispersion. Consequently, the Euro
adoption had no significant effect
on market integration.
Rogers (2007) Tests evidence of price convergence in Europe based on based on the effects of the monetary union. The
paper compares Europe to the US with respect to price convergence. The paper uses disaggregated price
data.
Paper finds convergence in prices
in Europe. This result correlates
with the completion of the Single
Market within the EU. Traded
goods’ dispersion declined such
that it is comparable to the US
The results are related to a number
of factors including the EU’s
integration policies.
(Continued on next page)
46
Table 2.3 (Continued)
Selected PPP and Price Convergence Studies
Study Comments Key Results
Broda and Weinstein (2008) Highly disaggregated micro data on prices are used for the US and Canada. Using barcode data this paper
finds that the Law of One Price
holds in absolute form. This is true
within a country and across
borders.
The distance coefficients are
significantly smaller than those
found in similar studies that use
aggregate data. This is in the
magnitude of 5 to 10.
Market segmentation appears to be
similar within and across borders.
47
CHAPTER 3
GCC INCEPTION, ACHIEVEMENTS AND CHALLENGES
This chapter will preview the development of the Gulf Cooperation Council
(GCC) and its major achievements. An understanding of why the GCC came into
existence and the goals that member countries hoped to achieve will be outlined. The
primary emphasis will be on the economic integration process, which has three stages:
creating a customs union, a common market, and a monetary union. This chapter will
also discuss the challenges that face the GCC region.
3.1 Historical Background
In the recent history of the Gulf countries, the Pax Britannica is perhaps the most
influential era that shaped and the region, shaping it through the 19th
and 20th
centuries.
Earlier, British involvement was largely confined to securing shipping routes from
India, through the Gulf to Britain. This role gradually changed as the shipping lanes
were threatened by piracy in the Gulf. The continual harassment of British vessels in the
region led to a number of expeditions to the Gulf to stop the perpetrators, and in 1820
an anti-piracy or General Treaty was signed between British India and Sheikdoms in the
Gulf. The historical background material that follows is based on Onley (2009).
The Sheikdoms of the time, which included several individual states known today
as the United Arab Emirates (UAE), Bahrain, Qatar, and Kuwait, had all requested the
British to provide formal protection at some time or another. Britain, however, was
reluctant to become involved in the region beyond the protection of its interests at the
ports of Muscat (Oman) and Basra, and maintaining a presence in southern Iran where
administrative staff were stationed. Apart from Oman, which achieved an informal
arrangement with Britain that provided protection to Muscat, the Gulf States could
secure no agreements.
The Maritime Truce of 1835 marked a shift in this policy. The treaty banned naval
warfare between the Sheikdoms during the six months of the pearling season. The (now
UAE) states, which came to be known as the Trucial States as a result of the treaty,
lobbied the British India Resident (administrator) to make the agreement perpetual. The
treaty was gradually extended to cover longer periods of times with the unanimous
agreement of the rulers of the states, and was also extended to Bahrain in 1861 and
Qatar in 1916. In effect this extended the British Empire’s protection to the states;
however the involvement was generally in the form of arbitration and mediation in
48
disputes. The sizable Gulf Squadron of naval ships used to maintain maritime security
was the major deterrent used by the British during this period. British involvement was
scaled up in the 20th
century with direct military intervention in conflicts and resolution
of infractions by states or rebels by troops sent from British India.
The discovery of oil was the catalyst for change in British policy. Realising the
importance of the resource to its economy, Britain’s involvement become more intense.
A characteristic of British engagement was to isolate the region from other foreign
powers such as France, Germany or Russia. This led to a great dependence of the Gulf
States on Britain for development, especially once oil placed the region on the global
map. Britain achieved a number of advantages by isolating the region from its
competitors, of which access to oil exploration and production concessions were the
most important. In return the Sheikdoms received protection, and numerous
developmental projects and programs.
The post-war era marked the deterioration of the Pax Britannica. Nationalist
movements in Arab countries applied great pressure on Britain to leave the region.
Simultaneously, communist influences from Russia and China were fuelling resentment
against Imperial presence in the region (Onley 2009; Holden 1971). Changes in British
policy were reflected in greater autonomy of Gulf Sheikdoms; Kuwait gained
independence in 1961, followed by other Gulf States in the early 1970s. The British role
in the region specifically, and in its colonial interests generally receded. In 1968, a
declaration of withdrawal from the region was made by the British Government. This
was to take place in 1972. Until that year, Britain stood by its protection commitments
to the Gulf States; an example of this was the repulsion of the Iraqi invasion of Kuwait
soon after its independence. Britain finally withdrew from the region completely by
1977, after assisting in the Dhofar war in southern Oman.
The withdrawal of the British from the Gulf region and other colonies east of the
Suez Canal was a cause of major security concern in the region. The newly independent
and autonomous states faced a number of challenges. As disputes and differences were
settled between states, attention turned to external threats. These were mainly
revolutionary movements fuelled by nationalism and Marxist influences, leading to
conflict in some countries. Other threats were represented by the competition for
hegemony over the region between bigger countries like Iraq and Iran (Holden 1971).
The Iraq–Iran war was a direct result of this. The USSR invasion of Afghanistan was
another alarm: internal and external threats from communist states or movements were
viewed with concern. However, the most serious threat perceived by the Arab countries
49
of the Gulf was the Iranian revolution in the 1970s and the consequent change in the
regime of that country. Although the Iraq–Iran war and the Russian invasion of
Afghanistan contributed to the process, the anticipated ‘export’ of revolutionary
ideology from Iran is now considered to have been the major driver behind the creation
of the GCC in 1981(Ramazani 1988).
During these events, the formation of the GCC was not the only initiative. In 1974
the Shah of Iran proposed a Gulf security organisation. This was rejected by the other
Gulf countries, including Iraq. Saudi Arabia in 1977 proposed an Arab security network
within the Gulf. This was also rejected by the Gulf States. However, Saudi Arabia did
sign a number of bilateral security agreements with its Arab Gulf neighbours, with the
exception of Kuwait. In the years preceding the GCC, Kuwait and Oman proposed two
polar plans for the security of the region. Kuwait followed a self-reliance approach to
security within the region, while Oman proposed a strategic alliance with the United
States to form a joint maritime force to ensure security in the Gulf generally, and in the
Strait of Hormuz specifically. Neither proposal gained approval. In 1980 Saudi Arabia
introduced a proposal mid-point between those of Kuwait and Oman: to confine
cooperation between the Arab Gulf states mainly to the area of security. This proposal
became the stepping-stone to an agreement of cooperation in 1981 (Ramazani 1988;
Bellamy 2004).
3.2 The Formation of the GCC
In 1981 six countries, Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and the
United Arab Emirates, created the Cooperation Council of the Arab States of the Gulf
(GCC). The GCC countries agreed to cooperate and unify their policies in a number of
key economic, political, and social areas. The original charter specified the overall
cooperation initiative and the areas involved, identified in Box 3.1.
The Economic Agreement of 1981 also emphasised the economic goals of the
GCC. These fell in to four major areas: The first was to establish economic
nationalisation among citizens of the GCC. The second was to achieve economic
integration through a number of consecutive steps, creating a Free Trade Area, a
Customs Union, a Common Market, and finally a Monetary and Economic Union. The
third goal the agreement outlined was the standardisation of laws and regulations
relevant to the proposed economic integration process. The fourth and final goal was
the development of synergies between the infrastructures of the six countries through
joint ventures.
50
The Agreement was replaced by a new Economic Agreement in 2001, in which
the emphasis shifted from cooperation to integration. The new Agreement emphasised
a number of areas where integration needed to proceed. These included the Customs
Union, the Common Market, and the Monetary and Economic Union. The agreement
also included areas such as international economic relationships between the GCC and
other groups, integration of development initiatives across the region, labour force and
education development, scientific and technical progress, infrastructure integration,
and development (The Cooperation Council of the Arab States of the Gulf –
Secretariat General 2010).
3.2.1 Achievements
The GCC has a achieved a number of goals worth highlighting. The major
achievements are reported in Table 3.1. These milestones are linked to the Unified
Economic Agreement (1981) and The Economic Agreement (2001). Since its inception
the GCC has established a Free Trade Area (FTA), in 1983. External tariffs ranged
between 4% and 20%, which allowed some degree of speculation by traders who
imported into member countries with lower tariffs and sold them in those where tariffs
are higher (Ramazani 1988; Legrenzi 2003). Within the Free Trade Area, duty free
goods included those which had 40% added value within the region, produced by
Box 3.1
Article 4 – GCC’s Charter
1. To effect coordination, integration and inter-connection between Member States in
all fields in order to achieve unity between them.
2. To deepen and strengthen relations, links and areas of cooperation now prevailing
between their peoples in various fields.
3. To formulate similar regulations in various fields including the following:
i. Economic and financial affairs
ii. Commerce, customs and communication
iii. Education and culture
4. To stimulate scientific and technological progress in the fields of industry, mining,
agriculture, water and animal resources; to establish scientific research; to establish
joint ventures and encourage cooperation by the private sector for the good of their
peoples.
Source: The Cooperation Council of the Arab States of the Gulf - Seretariat General, The Cooperation Council - Charter.
Available from: http://www.gcc-sg.org/CHARTER.html [16 November 2009].
51
Table 3.1
Economic Integration, Achievements, 1980–2009 Types of Integration Achievement Comments
Free Trade Area
Article Three
(Unified Economic Agreement 1982) Launched 1983
Tariffs range 4% to 20%;
40% GCC national value added;
51% or more GCC ownership in production plant.
Customs Union
Article One: The Customs Union
(The Economic Agreement 2001)
Implemented as planned in 2003 Common external tariff of 5% on most goods.
Common Market
Article Three
(The Economic Agreement 2001)
Completed in 2008
Monetary and Economic Union
Article Four
(The Economic Agreement 2001)
Not yet implemented Two members, Oman and UAE, opted out of the
Currency Union.
The remaining members postponed the launch of the
Currency Union from its original 2010 deadline.
International Economic Relations
Article Two
(The Economic Agreement 2001)
Signed agreements with Lebanon, Singapore, EFTA.
Currently negotiating with ASEAN, Australia, China,
India, Japan, MERCOSUR, New Zealand, Pakistan,
Syria, and Turkey.
Negotiations with EU started in 1984 and were
suspended in 2008.
Recently Bahrain and Oman signed Free Trade
Agreements with the United States. Other members
are also in the process of negotiating agreements
individually.
Transportation, Communication, and Infrastructure
Article Twenty-three: Infrastructure Integration
(The Economic Agreement 2001)
Electricity power grid linkage (2010) Three phases
1. Bahrain, Kuwait, Qatar, and Saudi Arabia
(2005)
2. Oman and the UAE (2009)
3. Completion (2010)
52
plants with at least 51% ownership by GCC nationals (Dar and Presley 2001; Legrenzi
2003).
This condition, one of many non-tariff barriers that existed at that time despite
the FTA, was enforced through the granting of certificates of nation of origin. Other
barriers included favouring national products — against other GCC states — in
government purchases. Exemptions were also retained on certain goods, including
handicrafts and art (Legrenzi 2008).
In 2003 the work on the Customs Union within the GCC was launched (The
Cooperation Council of the Arab States of the Gulf - Seretariat General 2009). This
included unification of laws and regulations relevant to trade within the region, and
ensured the freedom of goods movement across national borders without the hindrance
of tariffs or non-tariff barriers. In addition, a single entry point for duty levies was
established across GCC ports. Finally, national treatment of GCC goods in each of the
six countries was established. The Customs Union’s launch underwent a transitional
period during 2003–2006, in a lead-up to a fully functional system. During this period
some goods were exempted from full Customs Union treatment based on specific
members’ prohibitions (The Cooperation Council of the Arab States of the Gulf –
Secretariat General 2009).
Despite its long delay the customs union within the GCC states marked an
important step forward. However, this was a smooth progression. The decision to
establish the custom union was made by the Supreme Council at its annual summit in
December 2001, but the full implementation of the customs union’s common external
tariff was blocked prior to the 2003 launch deadline. This was largely due to the
varying economic strategies of the GCC countries. Countries that sought to protect
fledgling industrial industries negotiated higher tariffs, while those who wanted to
maintain their role in re-exports and shipping services wished to maintain lower tariffs
(Legrenzi 2008). Moreover, trade liberalisation talks within the region were
sidetracked by individual GCC talks with the US. Contrary to the GCC’s charter, most
GCC countries were involved with the US in free trade talks. This detracted from the
efforts to make the customs union functional, and created undesirable rifts between
countries. However, a more pressing issue with the customs union was the revenue
sharing scheme. Some consensus towards solving this issue was based on sharing
revenue according to the destination of imports within the region. Until these issues
were resolved the GCC’s customs union could not reach its full potential (Middle East
Monitor: The Gulf 2005).
53
The push for a common market required a number of intermediate steps that
helped facilitate the objective of free movement of factors of production. These
initiatives included equal cross-border employment opportunities for all GCC nationals
in both private and public sectors, however, some reservations of member countries are
still allowed. In 2005 the GCC agreed on a number of convergence criteria, not
dissimilar to those of the European Monetary Union. These fell into two categories,
monetary and financial. The former included inflation rates no higher than 2% above
the GCC weighted average, interest rates no higher than 1.5% of the GCC average, and
foreign cash reserves to cover a minimum of 4 months of imports. The latter dictated a
government deficit of 3% of GDP or less and a public debt burden no higher than 60%
of GDP (The Cooperation Council of the Arab States of the Gulf - Seretariat General
2010; in the Emerging Markets Monitor 2009).
In 2008 the GCC Common Market was officially completed (The Cooperation
Council of the Arab States of the Gulf - Seretariat General 2009). The common market
ensured the free movement of GCC nationals, and in some cases non-nationals, across
borders, and the free movement of capital across the region.
A number of specific initiatives have taken place since the inception of the GCC
in 1981. These include the ability of GCC nationals to conduct cross-country retail and
wholesale activities, including production plants and distribution across GCC
countries. Other initiatives have included the creation of a GCC Standardisation
Organisation responsible for the harmonisation of goods and services standards, which
established standards for goods produced within the region. Other initiatives have
included economic citizenship of GCC nationals across the six countries, including
equal treatment in stock ownership and incorporation of cross-border firms. In 2005 a
Common Trade Policy was adopted to unify the GCC’s External Trade Policy.1
The GCC has also sought to integrate its economies through joint projects and
coordinated policies in oil and non-oil sectors. In the oil sector the most evident
achievement has been agreement on strategic plans in case of disruption to a member’s
ability to produce enough oil for domestic consumption, and oil lending to member
states that find at least 30% of their expected exports disrupted due to damage to
facilities (The Cooperation Council of the Arab States of the Gulf - Seretariat General
2009). The promise of major infrastructure development in this sector has not
materialised, however. Initial plans to develop a GCC pipeline to terminals in Oman in
1 See The Cooperation Council of the Arab States of the Gulf - Seretariat General, Economic Cooperation. Available
from: http://www.gccsg.org/eng/index.php?action=Sec-Show&ID=53 [May 2010] for more details.
54
the 1980s were never realised. Other projects, such as a GCC refinery, were shelved in
the earlier years of integration (Ramazani 1988).
In the non-oil sectors, aims have been better realised. In terms of joint projects,
the establishment of the Gulf Investment Cooperation (GIC) was a positive step in
cooperation: the investment entity lent funds to various industrial, fishery, and
agricultural projects in the region and provided consulting expertise on investment
projects (Legrenzi 2008; Ramazani 1988).
Energy is another sector in which the GCC aimed to develop joint policies and
projects. The most prominent of these was the GCC-wide electricity grid. In its first
phase, proposed in the GCC Summit of 1997, Bahrain, Kuwait, Qatar and Saudi Arabia
would link their grids. In 2001 the GCC established an Electric Interconnection
Commission, responsible for overseeing the project. The first phase was implemented in
2005 by awarding several contracts to link the existing grids. In 2009 trials of the first
phase were conducted. The second state was the linkage of Oman and the UAE’s grid to
the rest of the GCC members’. Completion of the project is expected in 2010 (The
Cooperation Council of the Arab States of the Gulf - Seretariat General 2009).
3.3 The Socio-Economic Characteristics of the GCC
The GCC countries are located in the Arabian Peninsula: Figure 3.1 shows the
region’s central location in the Middle East. A number of observations can be made.
The region’s location is centred between South East Asia, South Asia, and Europe.
Figure 3.1
The GCC and the Middle East
Source: Perry-Castañeda Library Map Collection, http://www.lib.utexas.edu/maps/middle_east.html
55
The Middle East generally, and the Persian Gulf specifically, have been both
strategic and volatile in contemporary history. The Gulf region holds large reserves of
oil and gas, and this has placed a strategic importance on the area since the earliest
discoveries. Saudi Arabia is the largest country of the six members of the GCC. The
other members, Bahrain, Kuwait, Oman, Qatar, and the UAE, are considerably smaller.
Although geographically different, the GCC countries share important
similarities. First, they speak the same language. Second they share a great dependence
on oil and gas for government revenues. Third, low levels of inflation have been
enjoyed over the past few decades. However, the six countries differ in a number of
vital socio-economic characteristics.
3.3.1 Demography
Figure 3.2 illustrates the dramatic change in the GCC countries’ populations
over the period 1980–2008. The populations of the GCC countries are markedly small,
with most countries below the 2 million mark in 1980: Kuwait, Oman, and the UAE
had populations of 1.4, 1.2, and 1.0 million respectively. Bahrain and Qatar, the
smallest countries in the region, had less than 300,000 inhabitants each in 1980. Saudi
Arabia, the largest country by size, is also largest in terms of population, with 9.6
million inhabitants in 1980.
Figure 3.2
Population in 1980 and 2008
(Millions)
Source: World Bank (2010)
Over three decades the picture alters dramatically. In Saudi Arabia the
population more than doubled, to 24.6 million people in 2008. The increase was even
more remarkable in the UAE, which experienced a fourfold increase in population from
0.31.4 1.2
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1 to 4.5 million people. Qatar underwent a substantial population increase too; its
inhabitants increased sixfold from 2.3 thousand to 1.3 million. Kuwait and Oman
experienced a doubling of their populations to reach 2.8 million people in each country
by 2008. Bahrain remains the smallest country in terms of population. Its population
doubled from 350 to 775 thousand people during the same period.
These changes in population can be traced over the time by observing the trends
in Figure 3.3. The most interesting characteristic is the decline in growth rates from as
high as 10% to as low as 3%. With the exception of Qatar, GCC countries converge to a
relatively low annual growth rate. Two factors may have played a role in these
significant increases in populations, especially in the cases of Saudi Arabia, Qatar, and
the UAE. The first is natural growth or fertility, and the second is immigration. Figure
3.4 allows us to distinguish the first factor, where the fertility of the six countries,
measured in births per woman, is plotted against time.
Figure 3.3
Population Growth Rates
(% p.a.)
Source: World Bank (2010)
The decline in growth rates mentioned earlier is supported by a decline in
fertility in the region. On average, women in the region have 3 to 4 children — a
marked decline from the 5 to 7 common earlier. However, the high growth rates
experienced by Qatar and the UAE cannot be explained by fertility alone. Declining
fertility and high population growth suggest immigration has played a role in the 1990s
and 2000s both in these countries specifically and in the entire region.
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Figure 3.4
Fertility
(Births per woman)
Source: World Bank (2010)
The GCC populations also exhibit interesting compositional changes within
their population. Figure 3.5 divides the population into three age categories: under 14,
15–64 years, and over 65. The first and last categories represent dependents as a
proportion of the total population. In the figure, two doughnut graphs are drawn for
each country, representing 1980 and 2008 respectively. The former is indicated by the
inner circle and the latter by the outer circle. In all the GCC countries, the percentage of
under 14s is significantly high. Bahrain is the only exception, with less than 25% of the
population falling into that category. Kuwait, Oman, and Saudi Arabia have the highest
ratios of under 14s; more than 40% of the population. Qatar and the UAE also have
more than 25% of their populations under 14 in 1980. The trend of growth, given the
reduction in fertility and increase in migration, has changed the composition of these
populations significantly. While the proportion of over 65s remains largely unchanged,
the 0–14 and 15–64 years have changed considerably with the exception of Bahrain,
where an increase in the 0–14 years category has been experienced over the period and
its 15–64 years category shrank by 5%. Other GCC countries experienced reductions in
the proportion of their population in the 0–14 years category by 10% or more. Kuwait
and Qatar experienced the largest increase in their proportions of 15–64 year-olds. In
comparison, Saudi Arabia and the UAE experienced the smallest change of about 10%.
The implications are significant. First, the composition changes reflect the
earlier comments regarding the decline in fertility. They suggest a reduction in the
dependency proportion of the population, especially since the over 65 years category
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remained steady over the three decades. The second implication relates to the first but
applies to the 15–64 year category. Since this is considered the working age portion of
the population, it indicates the measure of the potential labour force. The marked
increase in this category implies more people are entering the job market. The
increased demand for jobs places major emphasis on job creation within these
economies.
Figure 3.5
Population Composition 1980 and 2008
(Percent)
Source: World Bank (2010)
59
3.3.2 Incomes and Growth
In the previous section, a general picture of the GCC’s demographic
characteristics was presented. Here the macroeconomic characteristics of the six
countries will be presented, to help develop a general understanding of the GCC
countries and the trends affecting them during the period of economic integration 1980–
2008.
The discussion will start with the Gross Domestic Product (GDP) for the six
countries. Figure 3.6 depicts the GCC countries’ real GDP using the IMF’s World
Economic Outlook (WEO) October 2009 edition. The values reported in the WEO are in
local currencies, but these have been converted into dollars using the official exchange
rates reported in the World Bank’s World Development Indicators 2010 to avoid the
recurring problem of missing data with respect to GCC countries.
Figure 3.6
Real Gross Domestic Product 1980–2008
(Billions $US)
Source: IMF (2009) World Bank (2010)
The figure shows the cumulative real GDP of the six countries, where each bar
is divided into the respective incomes. This gives a perspective of the size of these
economies compared with each other. The GCC’s real GDP has increased twofold over
the period 1980–2008, from 250 to 500 billion US$. The contribution of each country to
the region’s real GDP is indicated by the different bands of each bar. Saudi Arabia
dominated with more than 60 % of total GCC output in 1980; the second largest
economies were Kuwait and the UAE at 15% each. Bahrain, Oman, and Qatar had
shares of 1%, 3%, and 4% respectively. These shares changed over the period, but the
Bahrain
Kuwait
Oman
Qatar
Saudi
Arabia
UAE
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most noticeable adjustment was the UAE gaining a larger share of GCC total output,
from 16% in 1980 to 24% in 2008. Oman and Qatar also experienced an increased share
of total output, their contributions doubling over the period. In 2008 Oman contributed
6% of total GCC output, while Qatar’s share rose to 8%. Bahrain and Kuwait’s shares
remained largely unchanged. The effect of changes in total GCC real GDP can be traced
by the height of the country portions of each bar over time. The countries that gained
larger shares over time show increased vertical height in the bands.
Figure 3.6 indicates the persistent growth of real GDP in the GCC countries.
These changes over time are depicted in Figure 3.7 for GCC countries compared to their
arithmetic averages. This was done based on annual (Panel A) and five-year averaged
growth rates (Panel B). In each panel the six countries are divided into two groups: on
the left, Bahrain, Kuwait, and Oman; on the right, Qatar, Saudi Arabia, and the UAE.
The GCC average is shown as a dark solid line in all graphs.
Panel A shows that the growth rates of Bahrain and Oman have generally moved
together, and closer to the GCC average. Kuwait on the other hand has experienced
growth rates substantially different from the group average. This is true of its growth
rates in the 1980s, but the largest fluctuations were in 1990/91. The main driver of this
dramatic change was the devastation caused by the Iraqi invasion of Kuwait and the
post-Gulf War recovery. The right panel shows that the growth rates of Qatar and the
UAE are volatile, compared to the GCC average. Saudi Arabia’s growth rate is closely
linked to the region’s average. In Panel B the short term fluctuations are smoothed by
taking five-year averages of growth rates. Deviations from the GCC average are evident
in the panel, where Qatar and Saudi Arabia are obvious examples. Figure 3.7 illustrates
that GCC growth rates are generally high; however, they are also volatile even when
short term business cycles are smoothed. The Figure shows some degree of
synchronicity in the region, although not complete convergence of growth rates.
The GDP description above gives a useful overall picture of the GCC countries’
economic size. Examining the GDP per capita of the six countries as an indicator of
well-being is also informative. Figure 3.8 gives the GDP per capita of the GCC
countries over the period 1980–2008. The figure indicates two levels of wealth: Kuwait,
Qatar, and the UAE are relatively richer than their counterparts. These three countries
also experienced greater volatility in their GDP per capita than their relatively poorer
GCC neighbours. Qatar and the UAE remain richer than the rest of the region. Saudi
Arabia experienced a decline in its GDP per capita from over 15,000 to less than
10,000.
61
Figure 3.7
Real GDP Growth Rates
(% p.a.) A. Annual
B. 5 Year Averages
Source: IMF (2009)
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62
This is not surprising since it was noted earlier that the population increase in
Saudi Arabia doubled, while its real GDP share fell. Bahrain and Oman have
experienced a steady rise in their GDP per capita in the region, moving ahead of Saudi
Arabia, but remain near the bottom of the table.
Figure 3.8
Real GDP Per Capita 1980–2008
(Thousands $US)
Source: IMF (2009) World Bank (2010)
3.4 Other Economic Dimensions
In this section, additional dimensions of the economic characteristics of the
GCC will be discussed. These include resource endowments, monetary indicators, and
trade: three of the indicators that distinguish the GCC’s economies and their
performance over time.
3.4.1 Resource Endowments
The GCC countries have been associated with their resource endowments,
namely oil and gas. Kuwait, Qatar, Saudi Arabia, and the UAE are members of the
Organisation of the Petroleum Exporting Countries (OPEC). The importance of these
resources can be realised by observing their stock and production within the region
compared to the rest of the world. The oil reserves are considered in Figure 3.9, which
represents a pie chart of the world’s proven oil reserves up to 2008. The GCC’s oil
reserves are about 40% of the world’s proven reserves. The share of reserves within the
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63
region is decomposed based on the countries. Bahrain is not included as its reserves are
minimal. Saudi Arabia holds about one fifth of the world’s proven reserves. Kuwait and
the UAE are a distant second with 8% of proven reserves. Oman and Qatar have very
small shares of world proven oil reserves. The oil production of the six GCC countries
is shown in Figure 3.10, where yearly averages are displayed. GCC’s total output of oil
is captured by the vertical bars, which are divided into the production of each country.
The production story follows that of the reserves: Saudi Arabia is by far the largest
producer with 8.4 million barrels per day on average. The UAE and Kuwait produced
2.2 and1.8 million barrels per day on average during the same period and Oman and
Qatar produced less than 700,000 barrels per day. Figure 3.10 also reveals two trends,
the first a decline in overall output of the region, possibly due to excess supply during
the late 1970s. The historically low prices undoubtedly spurred more production, as
indicated by the figure. Production since the 1990s has steadily increased despite a few
dips. Unlike its OPEC member neighbours, Oman shows signs of peaked production in
the late 1990s. This is indicated by its shrinking band, and suggests Oman has only
small reserves of oil.
The gas story is quite different. Figure 3.11 shows the gas reserves within the
GCC compared with the rest of the world. The GCC countries hold about 23% of the
world’s proven gas reserves, so they do not have the significant influence on the gas
market that they hold in the oil market. The most significant player within the region is
Qatar. Holding about 14% of the world’s proven gas reserves, Qatar is one of the
important producers of gas in the world. Saudi Arabia holds 4% of the total gas
reserves, and the UAE 3.5%. Kuwait and Oman hold 1% and 0.5% respectively. The
gas production picture is also different from oil, as shown in Figure 3.10. Gas
production in the region has climbed substantially from 1980 to 2008; increasing more
than tenfold. In 2008, Qatar and Saudi Arabia were evenly matched at approximately 80
billion cubic meters, while the UAE produced 50 billion cubic meters. Unlike its oil
production, Oman’s gas output has shown consistent increases in the 2000s, reaching 25
billion cubic meters in 2008. Kuwait and Bahrain have maintained production at 13
billion cubic meters in 2008.
64
Figure 3.9
Oil Reserves 2008
(Trillion of barrels)
Source: BP (2009)
Figure 3.10
Oil Production
(Millions of barrels per day)
Source: BP (2009)
65
Figure 3.11
Gas Reserves 2008
(Trillions of cubic meters)
Source: BP (2009)
Figure 3.12
Gas Production
(Billions of cubic meters)
Source: BP (2009)
66
3.4.2 Monetary Indicators
A common feature of GCC economies is exchange rate regimes. All six
countries have maintained some form of fixed or managed exchange rate regimes. With
the exception of Kuwait, they have fixed exchange rates vis-à-vis the US dollar. Kuwait
instead has used a basket of currencies arrangement based on its trading partners. This
changed briefly in 2003 when the GCC countries agreed to unify their exchange rate
regimes as a transitional step towards monetary unification (Strum and Siegfried 2005).
Figure 3.13 shows the official exchange rates of the six countries from 1980 to 2008.
The figure is divided into panels grouping each set of three countries with similar US
dollar values together. In the left panel Bahrain, Kuwait, and Oman are shown. Since a
devaluation of the Omani Rial in the 1980s, the currency peg has been very stable over
time. This is also true for the Bahraini Dinar. The Kuwait Dinar, however, shows minor
fluctuations due to its exchange rate arrangement. This was briefly interrupted when all
the GCC countries agreed to official peg their currencies to the US dollar in 2003.
Kuwait reverted to its currency basket exchange rate in 2007. The right panel shows the
exchange rates of Qatar, Saudi Arabia, and the UAE. Despite Saudi Arabia’s currency
devaluation, the exchange rates of the Qatari and Saudi Rials, and the UAE Dirham,
have been very stable.
Figure 3.13
Official Nominal Exchange Rates
($US per local currency)
Source: World Bank (2010)
The fixed exchange rate restricts the ability of the GCC countries to conduct
effective monetary policy. Monetary policy within the region follows that of the US.
Interest rates movements also mimic those of the US, despite differing business cycles.
Moreover, the terms of trade of GCC countries are heavily influenced by US dollar
Bahrain
Kuwait
Oman
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movements against other currencies, especially those of major trading partners such as
the EU and South East Asian economies. However, a benefit of the US dollar peg is the
stability of oil exports revenues priced in the same currency. Moreover, fixed exchange
rates provide a nominal anchor, which helps keep inflation in check (Strum et al. 2008).
Another outcome of the exchange rate arrangement is the behaviour of interest
in the six countries. These are indicated in Figure 3.14, where the deposit interest rates
across the GCC countries are compared. Five of the countries’ interest rates are
available for most years, but UAE figures are incomplete. It is unsurprising that the
GCC interest rates follow US interest rates. This is an expected outcome of the
exchange rate regime. The implication is a small spread of interest rates across the GCC
countries. This is very encouraging in terms of convergence criteria set by the GCC in
2005, indicated in Section 3.2.1, where interest rates should not deviate more than 1.5%
from the group average.
Figure 3.14
Deposits Interest Rates
(Percent)
Source: World Bank (2009), Federal Reserve (2010)
The effects of the exchange rate peg are evident in Figure 3.15, where the
inflation rates of the six GCC countries over the period 1980–2008 are shown. Overall
the inflation rates within the region have been remarkably low. There are notable
deviations, however, in the 1980s, early 1990s, and early 2000s. The fall in prices are
likely to be related to declining oil prices in the 1980s and falling oil production.
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Consequently, public expenditure, which drives economic activity within these
countries, is expected to decline. The spike of 1990 is largely due to regional conflict.
Bahrain exhibited sharp decline in 2000; this suggests the economic slowdown of that
period may have affected Bahrain more than its neighbours. More importantly, the
noticeable increase from 2002 onwards is uniform across all countries, with Oman,
Qatar, and the UAE exercising above average inflation in 2008. A number of factors
influence inflation rates in the region; they can be summarised as domestic demand
rising and loose credit conditions, a number of booming sectors creating bottlenecks
within the economies, a rise in the price of raw materials, and a rise in food prices. The
US dollar peg had a role to play as well. The weaker US dollar reflected in the imports
of these countries, which trade heavily with Europe (Strum et al. 2008).
Figure 3.15
Inflation Rates 1980–2008
(% p.a.)
Source: IMF (2009)
The inflation picture is mimicked by the money supply growth of the GCC
countries. These rates have been volatile over the past three decades, as shown in Figure
3.16. A general decline in money growth rates from the 80s into the 90s is evident.
However, in the 2000s money supply growth rates within GCC countries accelerated.
This is compatible with the trend of growth the region has experienced. The monetary
policy within the region is reactionary compared to that of the US, and the inflation
patterns observed in Figure 3.16 are affected by some of the factors linked to the
exchange rate arrangement.
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Figure 3.16
Money Supply Growth 1980–2008
(% p.a.)
Source: World Bank (2010)
3.4.3 Trade
In this section an external characteristic of the GCC, namely trade, will be
discussed. The aim is to identify regional effects, especially within the context of
economic integration. The previous discussions about the Free Trade Area and the
Customs Union are relevant here. Given these two initiatives, trade can be expected to
intensify within the region. Trade from outside the region may be affected positively or
negatively as a result of the GCC’s trade policy.
In Figure 3.17 the GCC’s trade as a percentage of GDP is shown. It is
immediately clear that the GCC countries are very open economies that trade heavily.
Most GCC countries’ total trade proportions range around 70–150% of GDP. Bahrain’s
and the UAE’s trade as percentages of GDP are larger than the others’; Saudi Arabia
has the smallest proportion compared to its neighbours. This is in line with the general
trend for comparably larger economies’ trade being a smaller percentage of their GDP
compared with their smaller counterparts.
In Figure 3.18 these changes are captured by the ratio of intra- to extra-GCC
trade. The ratio depicts intra-regional trade against the rest of the world for each of the
six countries. Some interesting patterns can be observed. The relatively poorer countries
within the region, Bahrain and Oman, tend to trade significantly more within the region
than the other four countries.
70
Figure 3.17
Trade as Percentage of GDP 1980–2008
(Percent)
Source: World Bank (2010)
Figure 3.18
GCC Ratio of Internal to External Trade
Source: IMF (2009) and author’s calculations
For 1980 Bahrain’s trade within the region is the equivalent of more than three
quarters of its total with the rest of the world. This declines to less than one fifth in
2008. Oman’s ratio increases steadily in the 1980s. Its ratio peaks at 0.4 in 1990 before
declining to levels below 0.2 in 2008. The remaining countries’ trade ratios remain
largely below 0.1, with the exception of Qatar, whose ratio of above 0.1 for the 2000s
confirms that its intra-regional trade has remained small and unchanged overtime. This
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is unsurprising, since the GCC’s economies are relatively homogenous because trade is
dominated by exports of hydrocarbons.
The trade patterns in the GCC can be further explored by examining the current
account balances of the six countries. These are depicted in Figure 3.19 where the
current account balance as a percentage of GDP is plotted for each of the GCC
countries. Most of the six countries had surpluses during the 1980s. These declined over
time. The 1990s saw most GCC countries experience currency account deficits. Kuwait
was a particularly severe case because of the regional conflict of the first Gulf War. In
the 2000s the trend was reversed: all GCC countries experienced current account
surpluses again. Current account surpluses are related directly to revenues from oil
exports, but also to the imports of consumer goods and services. In the 2000s these
played a significant role in the patterns shown here (Strum et al. 2008).
Figure 3.19
Current Account Balance 1980–2008
(% of GDP)
Source: IMF (2009)
The role of hydrocarbons can be further emphasised with respect to foreign
exchange reserve accumulation. Figures 3.20 and 3.21 depict this relationship by
showing the total reserves and oil prices. In the 1980s reserves were modest for all the
GCC countries except Saudi Arabia. Reserves started to increase in the 1990s and
2000s. This is shown in Figure 3.20, where in five out of the six countries foreign
reserves increased dramatically, and most dramatically in the UAE and Saudi Arabia.
This increase in reserves may be attributed to increases in the production of oil for
export, and in the price of oil. Figure 3.21 plots two oil price series from 1980 to 2008:
72
the first is an average annual historical price series in 2008 US dollars and the second is
an average annual spot price of Dubai Crude Oil. The figure shows the decline of prices
in the 1980s and a stagnation in the mid-period before prices started to increase
significantly in the late 90s. This increase in price and production (and by proxy exports
of resources) is related directly to the increase in foreign exchange reserves within the
region.
Figure 3.20
Total Foreign Reserves
($US)
Source: World Bank (2010)
Figure 3.21
Crude Oil Prices
($US Per Barrel)
Source: BP (2009)
*Historical prices include mostly dated Brent Crude prices, which are converted (by source)
into 2008 dollars.
73
3.5 Future Challenges
The GCC countries face significant challenges at both country and regional
level. At the country level, development challenges present themselves fairly uniformly
in the form of resource depletion and the need to find new growth engines. The
challenges are different at the regional level, and are related to the effective operations
of the economic integration project the GCC is undergoing.
3.5.1 The Country Level
In the previous section, a number of socio-economic characteristics were
discussed and differences between countries were highlighted. One of the key economic
factors is resource depletion. Oil contributes 30% or more to the GDP of the region, and
about 75% of governments’ revenue (Fasano and Iqbal 2003). Countries like Bahrain
and Oman must urgently develop alternative sources of income to hydrocarbons.
Diversification is one of the key objectives the region is actively seeking to develop.
Diversification so far has concentrated on areas of comparative advantage of each
country. This includes petrochemicals and industries that are energy intensive, such as
aluminium smelting. The Bahraini diversification experience targets financial services
and regional tourism. Bahrain has also built on its comparative advantage by setting up
aluminium production. The UAE has diversified into finance, international tourism, and
trade related services. Saudi Arabia’s diversification efforts concentrate on light
industry among other strategies. Qatar has focused on hydrocarbons based on its large
natural gas reserves, but is developing tourism in the form of conference and
international event hosting (Strum et al. 2008; Jbili 2000).
The region’s demographic composition puts great emphasis on job creation, as
mentioned above. A large expatriate working population forms three fourths of the total
labour force in many cases (Fasano and Iqbal 2003). The number of foreign workers is
mainly due to the small national populations but large demand for labour. GCC
countries have access to labour at competitive wages from the Indian subcontinent, the
Philippines and other Arab countries. This has seen a concentration of foreign workers
in the private sector and national workers in the public sector of each country. To
address this issue, most GCC countries choose to impose quotas on foreign labourers.
Some have also embarked on nationalisation of the labour market as a short-term
solution to the problem of high dependence on foreign workers. All six countries have
74
developed training and development initiatives to enable nationals to meet job
requirements in the private sector (Fasano and Iqbal 2003; Strum et al. 2008).
The policies that individual GCC countries have chosen to address depend
largely on the individual member’s conditions, but Fasano and Iqbal (2003) note a
number of general areas where governments are working to address the issues above. Of
these, stabilisation funds, privatisation, and foreign direct investment standout. A
number of GCC countries have development stabilisation funds from oil revenues to
help counter revenue volatility due to market price changes. Jbili (2000) makes the
point that GCC countries need a formal arrangement of oil stabilisation funds that act as
a cushion, as well as fiscal discipline to reduce government spending fluctuations
reflecting oil prices.
Another recurring theme for reforms is privatisation. GCC countries are
encouraged to reduce public sector involvement in the production of commercial goods
and services such as utilities and telecommunication. This approach expected to
position the private sector to be the growth generating sector in the region as countries
move away from oil (Jbili 2000). The GCC countries have pursued this objective in
varying degrees; for example, Oman, Qatar, and the UAE have involved the private
sector and foreign investments in infrastructure projects in the water and energy sectors.
In comparison, Saudi Arabia has worked on privatising the telecommunication sector
(Fasano and Iqbal 2003).
Foreign direct investment (FDI) is a third area for GCC countries consider as
part of addressing developmental challenges. FDI stock ratio to GDP for the GCC
countries is comparatively low at 11%, as against the 23% global average. FDI flows in
the region have been closely linked to oil prices and hence remain volatile (Strum et al.
2008). GCC countries are now trying to attract direct foreign investment by introducing
reforms to their investment laws that allow for full foreign ownership of firms in non-
hydrocarbon sectors. Some have reduced corporate income tax, and red tape, and
allowed foreigners greater access to capital markets (Fasano and Iqbal 2003).
Such measures taken by GCC countries to address their individual, but similar,
challenges are at varying degrees of execution. There remain difficulties common to all
countries, including increasing the non-oil sector’s contribution to GDP, which remains
small in most cases (Fasano and Iqbal 2003).
75
3.5.2 Regional Level
The challenges the GCC faces as regional bloc stem from the members’
characteristic differences. The lack of progress in the 80s and 90s did not reflect
positively on the region as far as economic integration project was concerned. The
2000s reinvigorated economic integration with introduction of the Customs Union
(2003) and the Common Market (2008), but the Monetary and Economic Union remains
a work in progress, as the six countries find they are unable to meet the original 2010
deadline.
A number of reasons explain why the GCC has taken longer than expected to
achieve some of its major milestones. Most point to members’ differing approaches to
policy and implementation, a lack of harmonised standards and common regulations
across the region, and bureaucracies that have hindered developments in several areas
and specifically the trade integration process. This was the particular challenge when
administering the FTA of 1983: the rules of origin were difficult to implement given the
non-tariff barriers that existed at the time, including border procedures, varying product
specifications across countries, and bureaucracy (Dar and Presley 2001). With non-tariff
barriers still in place, the certificate of national origin was essential to establish the duty
free status of products that met the rules of origin requirements; however, certain
products were excluded from duty free status and remained so until the 2000s. An added
distortion, furthermore is government purchases and contracts that usually give priority
to domestic producers and service providers instead of other GCC members’ products
(Legrenzi 2003). In an effort to reduce distortions to the free flow of goods, the Gulf
Standardisation and Metrology Organisation (originally the Saudi Arabia Standards
Organisation in the 1980s) was upgraded to an independent agency in 2001 (Strum and
Siegfried 2005). This agency has played a key role in the lead-up to the customs union
by promoting mutual recognition and harmonisation of national standards.
The completion of the common market is another regional challenge the GCC
countries have to address. A keystone of a common market is free movement of factors
of production. Strum and Siegfried (2005) point to three areas where the GCC has to
improve, to ensure the success of the common market: foreign ownership of equity and
real estate, regulation of cross-country banking operations, and development of capital
markets. GCC members are already involved in harmonising regulations with respect to
investment, financial markets, and banking to facilitate the common market. Despite
their efforts, the number of banks operating across borders remains small. The disparity
76
of banking regulations within the GCC is probably the largest hurdle facing integration
in this area (Strum and Siegfried 2005).
The monetary union is presently the most significant stage of economic
integration that the GCC countries need to achieve. Article Four of the Economic
Agreement of 2001 broadly outlines the goal of achieving a monetary and economic
union and identifies the requirements. These include harmonisation of economic
policies, banking legislation, and convergence criteria.
The convergence criteria the GCC countries have set for the monetary union are
analogous to the European Union’s measures. These include monetary measures such as
inflation, interest rates, foreign reserves, and money growth. Likewise, the GCC’s
convergence criteria include public debt and government deficits. There are a few
concerns about how the GCC will meet its convergence criteria,. Inflation, for example,
has diverged within the region so that countries have deviated from each other. Incomes
remain divided into two groups, and trade remains significantly low. In terms of other
convergence criteria such as interest rates, the region generally follows the US rates
movements and thus is less likely to deviate greatly. Section 3.4.1 showed GCC interest
rates moving closer, especially in the last decade. With respect to other fiscal
convergence criteria the story is somewhat different, and GCC budget balances vary
widely. In the period 1985–2004, for example, Saudi Arabia’s annual budget balance on
average was -8.8% of GDP. Oman’s annual budget balance was -0.1% on average for
the same period (Strum and Siegfried 2005). The picture changes dramatically during
the period 2001–2008, where all GCC countries experienced budget surpluses ranging
from 5% to 30% of GDP. These values include the IMF’s projections for 2007 and 2008
(Strum et al. 2008).
Government debt varies considerably within the six GCC countries. Government
debt as a percentage of GDP is highest in Saudi Arabia for the period 1996–2001,
accounting for more than 80% of GDP. In contrast, the lowest government debt in the
same time was in the UAE, where it was less than 10% of GDP on average (Fasano and
Iqbal 2002). These debt levels declined significantly in the period 2001–2008 and
remain 20% of GDP or less for all countries. The difference between the GCC countries
government debt has converged over this period. These values also include IMF’s
projections for 2007 and 2008 (Strum et al. 2008).
In the cases of both government debt and budget balances, the role of
hydrocarbons is significant. The increases in oil prices shown in Section 3.4.3
emphasise the impact of oil on government spending and budget balances within the
77
region. Zaidi (1990) highlights the channel through which this effect takes place. He
points out that in the case of the GCC countries, all highly open economies, government
spending drives money growth and private spending. Increased oil receipts raise net
foreign assets in the banking system. This is offset by government deposits. When
governments spend oil exports receipts domestically, the money supply rises and
consequent growth is expected. Zaidi (1990) suggests that liquidity excess is channelled
through expenditure on imports of goods and services. The impact of this mechanism
has been visible in the past decade where governments have spent on large scale
infrastructure projects. This was the catalyst suggested by Zaidi (1990) behind the
observed growth discussed in Section 3.3.2.
Besides meeting the convergence criteria, the GCC countries have other
challenges to overcome prior to launching a common currency. The monetary union
project faces the challenge of consensus. Oman announced its withdrawal from the
single currency stage of economic integration in 2006 (Emerging Markets Monitor
2009). The reversion of Kuwait to its basket of currency regime in 2007 has also been a
significant event in preparation for the monetary union. There is the question of choice
of an exchange rate regime of the new currency. Should it be the US dollar pegged
currency or a Kuwaiti Dinar basket exchange rate regime? The flexibility of a basket of
currencies as an exchange rate regime can help combat imported inflation such as that
observed in the 2000s (Emerging Markets Monitor 2009); however, as mentioned
earlier, the dollar peg has ensured the stability of oil export revenues. Despite this, it is
likely that the GCC’s common currency may benefit from a basket arrangement if the
GCC finalises a FTA with the EU (Fasano and Iqbal 2002). This is not currently a
pressing issue, as GCC–EU FTA talks have stalled, as indicated in Section 3.2.1.
Other necessary developments to ensure the success of the proposed monetary
union are in the institutional realm. The GCC has generally been an inter-governmental
forum for legislation and monitoring the integration process. Unlike the EU, the GCC
Secretariat can only recommend policy options and nudge governments towards
enforcing objectives and resolutions (Strum and Siegfried 2005). This may have
worked in the earlier integration period, but in the case of a monetary union it is
essential to establish supernational entities responsible for conducting monetary policy.
It is also essential to develop standardised statistical indicators across the six countries.
These would provide greater transparency (Fasano and Iqbal 2002; Fasano and Iqbal
2003).
78
It remains to be seen if the GCC will successfully launch functional monetary
union despite some of the challenges mentioned above. Critically, the GCC needs to
develop effective institutions that will enable it to achieve the broader objectives of
economic integration.
79
CHAPTER 4
ECONOMIC INTEGRATION AND INTRA-REGIONAL TRADE
4.1 Introduction
The regionalisation of world trade is not a new phenomenon; in fact, it has gained
strength over the past few decades. The number of regional trade agreements (RTAs)
reported to the World Trade Organization has increased rapidly in both developed and
developing countries. Although not every RTA has been successful in improving the
fortunes of its members, RTAs continue to exist and regenerate themselves. The reason
behind this is that the underlying goals of typical RTAs are based not merely on trade
perspectives but on a number of goals and objectives which include: i) gains from trade,
ii) strengthening domestic policy reform, iii) increasing multilateral bargaining power,
iv) guaranteeing access to markets and concessions, v) strategic linkages and alliances,
and vi) influencing multilateral negotiations through regional interplay (Whalley 1998).
This chapter is concerned with the first of these objectives, the traditional gains from
trade, and will investigate the effects of RTAs on their members’ intra-regional trade. A
by-product of this analysis is the determination of the trade creation and diversion
effects, first introduced by Viner (1950). Arguing that customs unions may have
negative welfare implications by diverting trade, Viner (1950) shows that not all trade
agreements are necessarily welfare enhancing. Thus, RTAs can have profound effects
on trade flows and welfare, for good or ill.
This chapter has two main aims: to measure the effect of RTAs on international
trade flows using empirical tools for the period 1995 to 2006; and to apply a
disaggregated analysis to the Gulf Cooperation Council (GCC) region on a longer time
period, from 1980 to 2006. The GCC consists of six developing countries—Bahrain,
Kuwait, Oman, Qatar, Saudi Arabia, and the UAE—undergoing long-term economic
integration. Significant developments in the integration process in recent years justify
paying closer attention to this region.
The paper proceeds as follows: Section 4.2 provides a brief background of an
empirical model commonly used to examine bilateral trade flows, which is the gravity
model. Section 4.3 will present a two-step methodological framework of the traditional
gravity model and GCC’s intra-regional trade. Section 4.4 discusses the data used to
estimate the traditional gravity model and its empirical results. It also compares those
80
results with other studies in the literature. Section 4.5 discusses the concepts of trade
creation and trade diversion with respect to the RTAs included in the sample. Section
4.6 discusses the disaggregated trade patterns within the GCC. The chapter concludes
with a summary of findings and recommendations.
4.2 The Gravity Approach to Trade
International trade flows are traditionally modelled using the gravity model. This
model is originated from physics, in particular from Newton’s work on gravity that led
to the Law of Universal Gravitation. The law explains that the attractive force between
two objects is the product of a constant, their masses, and distance squared. This
concept has been applied to studies of migration, tourism, and commodity shipping
(Bergstrand 1985). Gravity models are extensively used in economic analyses to predict
trade flows (Anderson 1979, Bergstrand 1985, Feenstra 2004).
Earlier applications of the model to trade flows were tested by Tingbergen (1962),
Poyhonen (1963), and Linnemann (1966). The model proved to have a great degree of
accuracy in determining trade flows between trading partners; however, it had no
theoretical justification originally (Anderson 1979). Several attempts at linking the
model to theory were made. Anderson (1979) used an expenditure approach to link the
gravity model to an aggregate spending two-country model. Bergstrand (1985)
approached the problem from a microeconomic perspective, using a general equilibrium
model to achieve a ‘generalised’ gravity equation. Deardorff (1998) proved that the
gravity model can be derived from neo-classical trade theories and the Hecksher-Ohlin
model. Deardorff (1998) applied both frictionless and impeded trade versions of the
model in his derivation and also linked the gravity model to homogenous and
differentiated goods. In doing so, he established a fundamental justification for the use
of the gravity equation.
The gravity model was extensively applied to RTA analysis. Frankel et al (1995)
and Frankel and Wei (1998), for example, applied the gravity model to a cross-section
of countries to determine trade flows and the welfare implications of regional trading
blocs. Their focus was on the effect trade blocs had on trade flows, and included several
dummy variables in an attempt to capture the effect. This chapter follows this literature
to find the trade bloc’s effects on its members.
Other applications of the gravity model involved measuring border effects on
trade. These studies include McCallum (1995), Feenstra (2002) and Anderson and van
Wincoop (2003). In this literature, attempts were made to explain the border effect on
81
trade while ‘correctly’ specifying the model. A number of these studies, such as
McCallum (1995), concentrated on the border effect between Canada and the US.
McCallum (1995) tested the border effects between Canadian provinces and some
American states using a dummy for intra-provincial trade, and found substantial border
effects between Canada and the US. Anderson and van Wincoop (2003) took the
analysis a step further and developed what they called ‘multilateral resistance’ to
explain McCallum’s (1995) results and their deviation from theory. Anderson and van
Wincoop (2003) suggested that the McCallum results were exaggerated and biased
because of variable omission and the specification of his model. Their model suggested
a theoretically correct specification using ‘multilateral resistance’ to explain the border
effects. They find relatively smaller border effects between Canada and the US.
Feenstra’s (2002) approach differed from both studies above by using fixed effect
methods. These isolate the effects of importers and exporters as fixed in the model. The
gravity model has also been used to explain the effects of currency unions on trade.
Such studies include Frankel and Rose (2002) and Rose (2000).
Conceptually, Figure 4.1 illustrates the Gravity Model in international trade. It
depicts three economies of different sizes, 1 being the largest and 3 the smallest.
Figure 4.1
The Gravity Model Concept
As each country is exactly the same distance from the other two, transport costs
play no role in determining trade flows in this stylised version of the model. The
notation Mij indicates the bilateral trade flow from country i to country j. As countries 1
M 23
1
M 32
2
Large Country
Medium Country
Small Country
M 12 +M 21 >M 23 +M 32
3
M 12
M 13
M 31
M21
82
and 2 are the largest, the gravity model predicts that trade between them is necessarily
larger than trade between countries 2 and 3. Thus, M12+ M21 > M23 + M32.
Based on the work of Deardorff (1998), Anderson and Wincoop (2003), and
Feenstra (2004), the gravity model can be derived as follows. Consider country i with
i=1, 2, …,H countries in the world. Each country Ni produces varieties of good k, where
k=1,2,3…,N. Country j’s consumption of good k, imported from country i is denoted by
Cijk. Utility of consumers in country j is given by
(4.1) 1
1 1
,iNH
j ijk
i k
U c
where σ > 0. Equation (4.1) can be generalised for all goods. FOB prices paid by
consumers in country j are assumed equal across all varieties N1 imported from i. As
buyers in country j face a transportation cost tij when importing goods from i, they pay
pi tij for every unit imported. Let cijk = wijkYj, where wijk is the portion country j’s income
(Yj) spent on imports of good k from country i. Let Mj be the total imports of j so that
corresponding budget share of its imports from i is wijk = pi tij cijk Mj . The above setup
then implies that wijk is a constant with respect to the k subscript, that is, the budget
share of the Ni varieties imported from country i into country j are the same for each
variety. In other words, cijk = cij. Equation (4.1) can now be represented as
(4.2) 1
1
,H
j i ij
i
U N c
The budget constraint for country j is
(4.3) 1
,H
j i i ij ij
i
Y N p t c
Maximising the utility function (4.2) subject to (4.3) yields the demand function for
good k imported by country j:
(4.4)
where Pj is the CES price index, defined as
(4.5) 1 1
1
1
.H
j i i ij
i
P N p t
The value of total exports from country i to country j is defined as ij i i ij ijX N p t c .
Using this identity and combining (4.4) and (4.5) yields
(4.6)
1
.i ij
ij i j
j
p tX N Y
P
cij
pitij
Pj
Yj
Pj
,
83
Equation (4.6) can be rearranged to resemble a typical gravity equation by defining
income of country i as the product of the quantity of goods produced Ni and their price
pi. Country i’s income is thus Yi=Nipi. Using this property and (4.6) we can rewrite the
gravity equation as
1
.H
j i i ij ij
i
Y N p t c
Equation (4.6) can also be expressed in logs to obtain an additive log-linear form as
(4.7) log log log log 1 log 1 log .ij i j i ij jX Y Y p t P
This log-linear equation is the basis of estimation in this chapter. Incomes Yi and Yj are
measured using GDP. Distance is used as a proxy for transportation cost tij. For more
details, see Appendix A4.3.
4.3 Application to World Trade and the GCC
This chapter’s approach to estimating trade flows is based on two sequential
steps. In the first step, we consider the determinants of bilateral exports of a sample of
145 countries. The objective here is to quantify the RTA effects on ‘normal’ bilateral
trade expected as a result of fundamental economic variables such as GDP and per
capita GDP. In the second step, we consider commodity-specific bilateral exports
among the GCC countries based on trade within the region. The second step permits
more detailed examination of trade within the GCC. Details of the two steps follow.
4.3.1 Step1: The Traditional Gravity Approach to Determining Total Trade
In the first step the bilateral exports between country pairs are taken to be a
function of income, income per capita, distance, common borders, language, and RTA
membership. We index countries by i = 1,…,H, therefore, trade between countries i and
j is determined as follows:
(4.8) . , , ,Distance ,Adjacency ,Language ,RTA ,ij i j i j ij ij ij ijX f Y Y C C
where Xij represents bilateral exports from i to j; Yi is the income of the exporting
country; Yj is the income of the importing country; Ci is the per capita income of the
exporting country; Cj is the per capita income of the importing country; Distanceij is the
spatial distance between the trading partners’ capitals; Adjacencyij is a dummy variable
indicating a common border; Languageij is a dummy that captures the common
languages shared by trading partners; RTAij is a dummy that represents membership of
a regional trade agreement by both trading partners. According to model (4.8), total
84
trade between countries i and j is determined by the economic size of the two
economies, their per capita affluence, geographic proximity, cultural differences (as
measured by the language variable), and trading arrangements.
Model (4.8) is taken to be log-linear:
(4.9) 1 2 3 4 5 6
7 ij 8 ij 9 ij
log log log log log log
Adj Lang RTA ,
ij i j i j ij
ij
X Y Y C C D
where Xij is the value of exports from country i to country j; Yi is GDP of country i
valued at nominal US dollars; Yj is GDP of country j valued at nominal US dollars; Ci is
per capita GDP of country i valued at nominal US dollars; Cj is per capita GDP of
country j valued nominal US dollars; Dij is the distance between the capital cities of
country i and country j measured in kilometres; Adjij is a dummy variable for adjacency
or common borders, 1 for a common border, 0 for none; Langij is a dummy variable for
common language that takes the value of 1 for common language and 0 for otherwise;
RTAij is a dummy variable for Regional Trading Arrangements that takes the value 1
when both i and j are members of the same agreement, 0 otherwise; and εij is a
disturbance term.
Equation (4.9) is directly related to the derived form in (4.7). Equation (4.9)
includes size measures, GDP per capita, which are not included in the simplified model
(4.7). The distance parameter 6 is reflective of the term (1-) in (4.7). Since tij,
transportation cost, is not readily observable, distance is used as a proxy. Finally,
equation (4.9) also includes a number of dummy variables that are used to explain
qualitative variables of concern.
The parameters can be interpreted as follows: 2 and 3 represent the income
elasticities of the exporting country and importing country; these are expected to be
positive as larger economies are expected to trade more with each other. The parameters
4 and 5 are elasticities relating to the wealth of countries as measured by GDP per
capita. The use of GDP per capita instead of population is justified by Frankel (1997, pp
57–59). The distance parameter 6 represents the distance elasticity, which is expected
to be negative. The parameters 7 and 8 reflect the effects of adjacency and common
languages on bilateral trade. It is expected that if countries have common borders, more
trade is facilitated, so the parameter 7 is expected to be positive. Countries with
common languages may find it easier to trade with one another; thus the parameter 8 is
85
expected to be positive. Finally, the RTA parameter 9 represents the trade bloc effect
on bilateral trade. This value may be positive or negative.
4.3.2 Step 2: Trade within the GCC Countries
The second step deals with trade among members of the GCC, with trade
disaggregated by product group. Total trade, as determined by Step 1, is split by product
group; we then identify those members whose trade is systematically above or below
expectation. The expected value of trade is estimated based on socio-geo-economic
variables, similar to those used in Step 1.
Suppose total trade is made up of n product groups, which we index by
p=1,2,…,n, so that 1
np
ij ij
p
X X
, where p
ijX is the exports of product group p from
country i to country j. If we write S for the set of countries that are members of the
GCC, total exports by country Si are thenSi ijj
X X
. Thus
(4.10) S 1
S.n
p
i ij
j p
X X i
Disaggregated trade within the GCC is determined by total trade of the two countries
concerned, Xi, Xj, together with the economic/geographic/cultural variables of model
(4.11) ij, , Adjacency , Distance , Country/Commodity Dummy .p
ij i j ij ijX g X X
Model (4.11) is taken to be log-linear:
(4.12)
1 2 2 3
4
log log log log
+ Country/Commodity Dummies , , S,
p
ij i j ij
ij k ijkk
X X X D
Adj i j
where p
ijX is export value from country i to country j of product group p; Xi total
exports of country i; Xj total exports of country j; Dij is the distance between the capital
cities of country i and country j measured in kilometres; Adjij is a dummy variable for
adjacency or common borders, 1 for a common border, 0 for none; γk is
Country/Commodity dummy takes a value of 1 if country i exports commodity p, 0
otherwise; and ij is a disturbance term.
4.4 Data and Empirical Results: The Traditional Model (Step 1)
The first part of this section describes the data sources and data used in
estimating the gravity model (4.9). The second sub-section will discuss the estimation
results and RTA implications.
86
4.4.1 Data
The data used were obtained for 145 countries. The list of countries is included
in Appendix A4.2. Bilateral trade was measured in millions of dollars of exports from
the exporting country i to the importing country j. The bilateral trade data were obtained
from the IMF’s Direction of Trade Statistics. The sample period is divided in five cross-
sections from 1995 to 2006. These are divided as follows; 1995, 1998, 2001, 2003 and
2006. The sample period aims to investigate the recent developments in RTA effects on
international trade in the post-GATT era when the WTO became operational. This study
differs from previous studies in the extent of coverage.
Unavailable data points were considered zero trade, which may cause a
downward bias on the estimates of elasticities. From a maximum 20,880 [=1452-145]
possible bilateral trade flows in every cross-section only 11,561 to 15,127 observations
were useful for estimation. The variables GDP and GDP per capita were obtained from
the IMF World Economic Outlook. GDP valued at nominal US dollars was used for all
trading partners as well as GDP per capita also valued at nominal US dollars. Since
transportation costs are not directly observable, distances in kilometres were used as a
proxy. These were obtained from CEPII1, where they are measured between capital
cities. An alternative method to measuring transportation cost is to use the ratio of
import c.i.f values to export f.o.b values for matched bilateral pairs of countries. The use
of c.i.f/f.o.b values is justified in a number of studies, such as Geraci and Prewo (1977)
and Hummels and Lugovskyy (2006). These values are calculated from the data
available from the IMF’s Direction of Trade Statistics database. Bilateral dummies are
used to capture the situation where both trading partners belong to the same RTA. If
trading partners did not belong to the same RTA, they received 0; if they did belong to
the same RTA, they received a value of 1.
Table 4.1 reports the means and standard deviations of bilateral exports across
the five cross-sections in two cases. The first case includes zero trade observations,
while the second case excludes them. Table 4.1 illustrates a number of noteworthy
points: first, on average, bilateral exports are comparatively low in Case 1 compared to
Case 2, as might be expected. Second, there is a substantial increase in trade on average
over the sample period. In fact, bilateral exports have more approximately doubled on
average over the past decade in case 1.
1 Centre D’études Prospectives et D’Informations Internationales, available at
http://www.cepii.fr/anglaisgraph/bdd/distances.htm.
87
Table 4.1
Bilateral Exports
($US Millions)
1995 1998 2001 2003 2006
Case 1: Including Zero Observations
Mean 211 238 276 337 532
Standard Deviation 2,387 2,714 3,161 3,516 5,069
Number of Observations 20,880 20,880 20,880 20,880 20,880
Case 2: Excluding Zero Observations
Mean 369 373 398 477 709
Standard Deviation 3,144 3,392 3,791 4,175 5,842
Number of Observations 11,561 12,855 13,977 14,254 15,127
Note: The means and standard deviations are expressed in millions of US dollars.
During the last three years of the sample, the sample mean indicates an increase
of 58% and 49% in Case 1 and Case 2 respectively. Third, there are large variations
within the sample, more than doubling over the sample period. The standard deviation
rises steadily during the sample period and mirrors the increase in the mean. This
indicates a considerable variation in trading patterns towards the end of the period. It
further indicates that not all countries included in the sample necessarily trade more,
despite the implications of greater mean. Finally, excluding zero trade observation not
only increases the mean of the sample but also increases the standard deviation. This
may indicate that trade is actually more variable than expected.
4.4.2 Empirical Results
Table 4.2 presents the OLS estimates of the gravity equation (4.9). The
estimation was carried out over five cross-sections: 1995, 1998, 2001, 2003, and 2006.
The coefficient of income logYi represents country i’s elasticity of the exports with
respect to income. During the period, this elasticity increased from 1.07 in 1995 to 1.19
in 2006. The coefficient of Yj, the importing country’s income elasticity, increased
marginally from 0.82 in 1995 and 0.88 in 2006. However, the importing country’s
income plays less of a role in determining the level of bilateral exports. Per capita
income Ci and Cj measure the differences in size, where larger countries are expected to
trade more with each other compared to their smaller counterparts. With respect to the
per capita income of an exporting country, the coefficient of Ci declined during the
sample period from 0.08 to 0.04. Similarly, the effect of the importer’s GDP per capita,
88
Cj, declined substantially from 0.10 in 1995 to 0.03 in 2006. As a result, the influence of
per capita income on bilateral exports diminished towards the end of the period. Both
income and per capita income are significant at the 1% level in most years.
Table 4.2 indicates that throughout all years 1995 to 2006 the distance
coefficient is of the correct sign (negative) and significant at the 1% level. In other
words, the further the trading partners are from each other, the greater the cost of
transportation and the lower their trade. The elasticity of bilateral trade with respect to
distance fluctuates modestly over the period.
The gravity equation (4.9) estimated in Table 4.2 includes a number of dummy
variables. These are divided into two categories: first, the dummy variables of common
borders or adjacency and common language; and second, RTA membership indicators.
The adjacency dummy is significant at the 1% level in all years and shows an upward
trend from 0.63 to 0.82, or 88% [=(e0.63
-1) x100] to 127% effect above ‘normal’ trade
explained by economic factors. This suggests that countries on average traded more
with their neighbours in the latter years of the sample period. Similarly, common
language plays a statistically significant role, at the 1% level, in affecting bilateral
exports. In fact, its coefficient increased during the sample period. The language
dummy increased from 0.75 to 1.03, or 112% to 180% effect above normal trade. This
confirms that language plays an increasingly important role in facilitating bilateral
trade. These results suggest that cultural and geographical variables influence bilateral
trade to a great extent and cannot be ignored.
The second category of dummy variables is used to represent membership in
RTAs described earlier. In the industrialised countries, the sample includes the
European Union (EU), North American Free Trade Area (NAFTA), Closer Economic
Relation (CER), and European Free Trade Area (EFTA).
The EU, one of the oldest existing RTAs in the form of a customs union, shows
negligible effect during the period. The EU dummy coefficient increased from -0.08 in
1995 to -0.012 in 2001 before declining to -0.145 in 2006. This translates into a -13% to
-8% effect of EU membership on the bilateral exports of the countries involved. The EU
dummy is significant only in 2006, at the 10% level. This result is surprising since the
EU is considered to have fostered greater trade amongst its members. The lack of
statistical significance, however, does not allow such inference.
89
Table 4.2
First Set of Estimates of the Gravity Equation
Year
Variable
(1)
1995
(2)
1998
(3)
2001
(4)
2003
(5)
2006
(6)
Income
Yi 1.069 (0.011) 1.119 (0.011) 1.126 (0.010) 1.159 (0.010) 1.191 (0.010)
Yj 0.824 (0.011) 0.833 (0.010) 0.840 (0.010) 0.852 (0.010) 0.884 (0.010)
Per capita income
Ci 0.085 (0.016) 0.047 (0.015) 0.062 (0.015) 0.012 (0.014) 0.035 (0.015)
Cj 0.102 (0.015) 0.104 (0.015) 0.077 (0.014) 0.044 (0.014) 0.032 (0.014)
Distance -1.147 (0.024) -1.171 (0.023) -1.206 (0.023) -1.172 (0.022) -1.144 (0.023)
Adjacency 0.633 (0.121) 0.638 (0.108) 0.709 (0.112) 0.786 (0.109) 0.818 (0.115)
Common Language 0.749 (0.060) 0.781 (0.057) 0.867 (0.055) 0.898 (0.055) 1.028 (0.055)
RTA
EU -0.083 (0.080) -0.097 (0.080) -0.012 (0.083) -0.096 (0.082) -0.145 (0.083)
NAFTA -1.289 (0.530) -1.644 (0.509) -1.856 (0.537) -1.932 (0.550) -1.749 (0.660)
EFTA 0.417 (0.518) 0.838 (0.523) 0.637 (0.551) 0.515 (0.510) 0.808 (0.499)
CER 0.703 (0.108) 0.666 (0.119) 0.738 (0.126) 0.517 (0.126) 0.358 (0.144)
APEC 1.452 (0.085) 1.405 (0.096) 1.427 (0.095) 1.443 (0.095) 1.320 (0.109)
ASEAN 1.086 (0.255) 0.840 (0.289) 0.734 (0.255) 0.788 (0.239) 0.573 (0.267)
MERCOSUR 0.146 (0.341) 0.108 (0.313) 0.362 (0.492) 0.992 (0.505) 0.700 (0.491)
LAIA 0.353 (0.120) 0.267 (0.118) 0.333 (0.136) 0.516 (0.135) 0.587 (0.134)
CAN 0.771 (0.223) 0.755 (0.327) 0.559 (0.372) 0.666 (0.352) 0.179 (0.293)
CARICOM 2.616 (0.232) 2.311 (0.220) 2.241 (0.205) 2.579 (0.201) 2.971 (0.204)
COMESA 0.553 (0.355) 0.110 (0.325) 0.362 (0.353) 0.841 (0.338) 1.054 (0.340)
(Continued on next page) Notes:
Dependent variable: log of bilateral exports.
White heteroskedasticity-consistent standard errors in parentheses. See Appendix for the full names of RTAs.
90
Table 4.2 (Continued)
First Set of Estimates of the Gravity Equation
Year
Variable
(1)
1995
(2)
1998
(3)
2001
(4)
2003
(5)
2006
(6)
CEMAC 1.984 (0.174) 1.707 (0.218) 1.493 (0.212) 1.834 (0.182) 2.161 (0.188)
CACM 0.503 (0.748) 0.172 (0.621) -0.378 (0.556) -0.990 (0.766) -1.008 (0.774)
WAEMU 1.860 (0.249) 1.400 (0.323) 1.417 (0.353) 1.718 (0.342) 1.764 (0.364)
GCC 0.122 (0.215) -0.165 (0.222) -0.492 (0.226) -0.496 (0.234) -0.917 (0.257)
PAN_ARAB 0.075 (0.143) 0.294 (0.140) 0.067 (0.129) 0.274 (0.121) 0.401 (0.124)
CIS 3.112 (0.195) 2.289 (0.214) 2.498 (0.197) 2.700 (0.181) 2.188 (0.247)
ECO 1.730 (0.277) 1.392 (0.328) 1.227 (0.321) 0.612 (0.364) 0.838 (0.355)
S.E. of regression 2.012 2.023 2.090 2.054 2.169
R2 0.671 0.675 0.672 0.685 0.675
Number of Observations 11,399 12,695 13,722 13,989 14,857 Notes:
Dependent variable: log of bilateral exports. White heteroskedasticity-consistent standard errors in parentheses.
See Appendix for the full names of RTAs.
91
Nevertheless, these values should be considered with caution: the sample does
not include the enlargement of the EU to the 25 current members. Elsewhere in Europe,
EFTA shows fluctuating effects during the period. In 1995, the EFTA RTA had a
coefficient of 0.42, which translates to a 52% effect on bilateral trade within the region.
Its effect peaks in 1998 at 0.84, or 131%. EFTA declines in effect in 2001 and 2003, to
as low as 0.52 or 67%; however, in 2006 a coefficient of 0.81, translates to a 124%
effect on bilateral trade. In all cross-section years, EFTA coefficients are insignificant.
Little can be inferred about the true effect of the RTA.
In North America, NAFTA’s dummy shows consistent negative effects during
the period, between -1.29 and -1.93, or -72% and -86%. NAFTA’s bilateral exports are
not affected positively by the RTA, as suggested by the model. NAFTA’s effect
weakens over the sample period, especially in 2006 when it had an effect of -86%
below normal trade. These results are significant at 5% in 1995 and 2006. NAFTA’s
coefficients are significant at the 1% level in all other years.
On the other side of the globe, CER shows substantial positive effects on its
members’ bilateral exports. This effect declines over the period, from 0.70 to 0.36, or
101% to 43%. During the sample period CER’s effect peaks in 2001 at 0.74, which
translates to 109%. These results are statistically significant in all years at the 1% level.
Although these values are very large, they may be explained by CER’s remoteness and
the close proximity of its members, Australia and New Zealand.
In South East Asia, the Association of South East Asian Nations (ASEAN) and
Association of Pacific Economic Cooperation (APEC) represent the major RTAs.
APEC includes Pacific Rim countries included in earlier-mentioned RTAs, such as the
US and Canada from NAFTA. APEC is not an official RTA, but it is considered
instrumental in fostering greater trade between its members. APEC exhibits consistent
and statistically significant RTA effects in all years. Coefficients of 1.45 or 326% in
1995, and 1.32 or 274% in 2006, show strong APEC effects on its members’ normal
bilateral exports. These results are significant in all years at the 1% level. The ASEAN
dummy shows a decline in its members’ bilateral exports. In 1995 ASEAN’s dummy
coefficient was 1.09 or 196%. It declined to 0.84 or 132% in 1998 and continued to
decline further through 2001, to a low of 0.73 or 108%. ASEAN’s influence recovered
marginally in 2003 but declined to a value of 0.57 or 77% in 2006. In all years the
coefficients are significant at the 1% level. These results suggest a weakening effect of
ASEAN on its members’ normal trade. A possible explanation for this trend in
ASEAN’s performance is the East Asia crisis that started in 1997. The regional and
92
global slow-down may very well have shadowed any positive effects the RTA exhibited
during that period.
In Latin America and the Caribbean the Andean Community (CAN), Central
American Common Market (CACM), Southern Common Market (MERCOSUR), Latin
American Integration Association (LAIA), and Caribbean Community and Common
Market (CARICOM) represent the major operational RTAs. CAN’s RTA dummy
coefficients range between 0.77 and 0.18 or 116% and 20%. During the sample period
in 1998, CAN’s effect was 113%, declining to 75% in 2001. It recovered in 2003 to
95%, only to fall drastically to 20% in 2006. The results of CAN’s RTA effects are
significant at the 1% level in 1995, 5% level in 1998, and 10% level in 2003 and are
insignificant in 2001 and 2006. Similarly, CACM’s influence on its members’ bilateral
trade flows degraded progressively over the sample period. Its coefficients range
between 0.50 and -1.01, or 65% and -64%, above and below normal trade. In 1995
CACM’s RTA dummy reported a value of 0.5 that declined to 0.17 in 1998: 65%
compared 18%. CACM’s effect became negative from 2001 onwards. Its coefficients
declined from -0.38 to -0.99 and then to -1.01.These results are insignificant all years.
In the case of MERCOSUR, the dummy coefficients range between 0.11 and
0.99, or 11% and 170%. This RTA’s effect declined from 0.15 to 0.11 in 1998 but
recovered to 0.36 in 2001. In 2003 MERCOSUR’s effect peaked at 0.99 or 170%.
However, it declined to 0.70 or 101% in 2006. MERCOSUR’s coefficients are only
significant in 2003 at the 10% level. MERCOSUR’s regional effect increased over the
sample period according to the results reported here.
LAIA’s dummy also exhibited improved performance over the sample period.
Its coefficient took the value of 0.35 or 42% in 1995, only to decline to 0.27 or 31% in
1998. However, LAIA’s coefficient climbed to 40% in 2001 and to 68% in 2003. The
dummy coefficient peaked in 2006 at 0.59 or 80% effect on bilateral trade. Unlike
MERCOSUR, LAIA’s coefficients are significant at the 1% level in most years and 5%
in 1998 and 2001.
In the Caribbean, CARICOM’s dummy takes large values that range between
2.24 or 840% and 2.97 or 1851%. CARICOM’s performance declines during the middle
years of the sample period, where it falls to 2.31 or 907% in 1998 and 2.24 or 839% in
2001, before recovering in 2003 to reach 2.58 or 1220%. The significance of these
results at the 1% level in all years suggests that CARICOM plays an important role in
the bilateral trade of the region.
93
The sample includes three African RTAs: the Common Market for Eastern and
Southern Africa (COMESA), Economic and Monetary Community of Central Africa
(CEMAC), and West African Economic and Monetary Union (WAEMU). COMESA’s
dummy coefficients increased from 0.55 to 1.05, or 74% to 187% effect on its
members’ intra-regional trade. During the sample period COMESA’s dummy
coefficient fell to 0.11 in 1998 and increased to 0.36 and 0.84 in 2001 and 2003,
respectively. In most years COMESA’s dummy was insignificant, except in 2003 and
2006 where it was significant at the 5% and 1% level, respectively. CEMAC’s dummy
coefficient ranged from 1.49 to 2.16 during the period. This translates into RTA effects
from 345% to 768% above normal bilateral exports. CEMAC also experienced a decline
in its performance, as indicated by the coefficients of the dummy variable. It declined in
1998 to 1.71 or 451%, and further in 2001 to 1.49 or 345%. In 2003 CEMAC’s effect
climbed to 1.83 or 524%. It peaked in 2006 at 768%. These results are statistically
significant in all years at the 1% level. WAEMU exhibited strong effects of 1.86 or
542% in 1995, and 1.76 or 481% in 2006. During the sample period WAEMU’s
performance declined, much like its African counterparts. In 1998 and 2001, its effect
was between 306% and 312%. In 2003 it climbed to 457%. WAEMU’s dummy was
significant at the 1% level in all years. WAEMU has a substantial effect on its
members’ bilateral trade flows within the region.
In the Middle East and North Africa, a number of RTAs are found. These
include the Gulf Cooperation Council (GCC), and Pan-Arab Free Trade Agreement
(PAN-ARAB). The GCC exhibited declining effects from 1995 to 2006. Its dummy
coefficients reflected small RTA effects, with coefficients ranging between 0.12 and -
0.92, or 13% and -60%. The GCC’s performance declined progressively during the
sample period to become negative at -0.17 or -15 % in 1998, -0.49 or -39% in 2001, and
-0.50 or 39% in 2003. In 2006 it reached the weakest effect of -60%. The GCC dummy
was not significant in 1995 and 1998. However, in 2001 and 2003 the GCC dummy was
significant at the 5% level, and at the 1% level in 2006. PAN-ARAB displayed an
increasing effect on bilateral exports of 0.07 or 7% and 0.40 or 49%. However, PAN-
ARAB fluctuated during the period under study, between 0.07 in 1995 and 0.29 in 1998
before falling to 0.07 in 2001. In 2003 PAN-ARAB’s performance improved to 0.27 or
32% and in 2006 it peaked at 0.40 or 49%. These values are significant at the 5% level
only in 1998, 2003, and 2006. PAN-ARAB’s dummy was not significant in other years.
In Central Asia and the former USSR, two main RTAs are included in this
sample, the Commonwealth of Independent States (CIS) and Economic Cooperation
94
Organisation (ECO). CIS comprises the majority of former USSR states and exhibits
substantial effects of the sample period. In 1995 CIS’s coefficient took the value of 3.11
or 2,147%, but it declined to 2.29 or 887% in 1998. CIS’s dummy coefficient rose to
2.50 and 2.70 in 2001 and 2003 respectively. In 2006 it declined to 2.19 or 792%. CIS
was significant at the 1% level in all cross-section years. Finally, the ECO exhibited
declining RTA effects on its members’ bilateral trade during the sample period. Its
dummy coefficient declined from 1.73 in 1995 to 1.39 in 1998. Its coefficient continued
to decrease in 2001 and 2003. However, it recovered to 0.84 in 2006, which translates to
131%. In 1995, 1998, and 2001 coefficients were significant at the 1% level; they were
significant at 10% in 2003 and 5% in 2006.
The results of the first step of this framework suggest that RTAs play an
important role in determining bilateral trade flows between countries. The findings also
suggest that RTAs in some developing countries are effective and may have a
significant impact on regional trade flows. The main results of Table 4.2 can be
contrasted with those presented Table A4.1.1 in Appendix A4.1. Table A4.1.1 uses PPP
valued GDP and GDP per capita instead of nominal US dollar values. The results are
discussed in the appendix. Alternatively, Table 4.3 reports OLS estimates of Equation
(4.9) using the ratio of c.i.f/f.o.b imports and exports as a proxy for transportation cost.
This ratio is an alternative measure to distance as a proxy of transportation cost. The
values reported in Table 4.3 indicate marginal differences with respect to the elasticities
of income of exporter and importer. Income elasticities of exporters in Table 4.3 range
from 0.91 and 1.05, lower than the 1.07 and 1.19 reported in Table 4.2.
The importing country’s income elasticity, in contrast, is greater: Table 4.3
shows it ranging from 0.84 to 0.98, compared to 0.82 and 0.88 in the previous results.
The results for per capita income are similar to those reported in Table 4.2. Table 4.3
reports significantly different results for transportation cost where c.i.f/f.o.b ratios are
used instead of distance. This transaction cost proxy suggests a weaker effect than
geographical distance on bilateral trade. Moreover, common borders or adjacency
coefficients more than double compared to the results reported in Table 4.2. The
common language dummy remains mainly unaffected by the change of proxy. Closer
examination of the RTA dummies shows substantial differences in their coefficients
compared to Table 4.2.
In the case of the EU, the coefficients range between 1.59 and 1.32. In Table 4.3
the EU dummy is significant in all years. Similarly, NAFTA’s dummy coefficients
change from -1.29 and -1.93 in Table 4.2 to -0.38 and -1.19 in Table 4.3. NAFTA’s
95
Table 4.3
Second Set of Estimates of the Gravity Equation (c.i.f/f.o.b Ratios)
Year
Variable
(1)
1995
(2)
1998
(3)
2001
(4)
2003
(5)
2006
(6)
Income
Yi 0.906 (0.012) 0.939 (0.011) 0.947 (0.011) 0.970 (0.011) 1.053 (0.011)
Yj 0.839 (0.011) 0.902 (0.011) 0.916 (0.011) 0.927 (0.011) 0.981 (0.011)
Per capita income
Ci 0.088 (0.015) 0.085 (0.015) 0.107 (0.015) 0.067 (0.014) 0.032 (0.014)
Cj 0.152 (0.015) 0.133 (0.015) 0.113 (0.014) 0.098 (0.014) 0.080 (0.014)
c.i.f/f.o.b -0.446 (0.012) -0.463 (0.010) -0.445 (0.009) -0.417 (0.009) -0.410 (0.009)
Adjacency 2.250 (0.119) 2.281 (0.113) 2.526 (0.114) 2.688 (0.120) 2.652 (0.123)
Common Language 0.781 (0.064) 0.863 (0.060) 0.950 (0.059) 0.907 (0.058) 1.094 (0.057)
RTA
EU 1.503 (0.078) 1.454 (0.076) 1.592 (0.079) 1.413 (0.080) 1.321 (0.082)
NAFTA -0.383 (0.320) -0.765 (0.315) -1.055 (0.408) -1.192 (0.429) -1.191 (0.570)
EFTA 1.593 (0.415) 1.929 (0.422) 1.835 (0.434) 1.600 (0.422) 1.987 (0.423)
CER 1.912 (0.117) 1.810 (0.124) 1.949 (0.119) 1.753 (0.122) 1.607 (0.129)
APEC 1.187 (0.098) 1.149 (0.100) 1.139 (0.100) 1.127 (0.105) 0.951 (0.119)
ASEAN 2.476 (0.266) 2.226 (0.264) 2.238 (0.256) 2.249 (0.234) 2.027 (0.261)
MERCOSUR 0.267 (0.385) 0.452 (0.399) 0.476 (0.541) 0.948 (0.596) 0.663 (0.573)
LAIA 0.619 (0.124) 0.501 (0.131) 0.605 (0.136) 0.787 (0.132) 0.780 (0.140)
CAN 1.093 (0.371) 1.067 (0.424) 0.837 (0.415) 0.829 (0.406) 0.454 (0.365)
(continued on next page) Notes:
Dependent variable: log of bilateral exports.
White heteroskedasticity-consistent standard errors in parentheses. See Appendix for the full names of RTAs.
96
Table 4.3 (Continued)
Second Set of Estimates of the Gravity Equation (c.i.f/f.o.b Ratios)
Year
Variable
(1)
1995
(2)
1998
(3)
2001
(4)
2003
(5)
2006
(6)
CARICOM 4.096 (0.251) 4.134 (0.239) 4.200 (0.223) 4.653 (0.231) 4.941 (0.224)
COMESA 1.138 (0.348) 0.727 (0.349) 1.313 (0.332) 1.337 (0.360) 1.526 (0.384)
CEMAC 3.884 (0.208) 3.789 (0.260) 3.700 (0.245) 3.916 (0.260) 4.224 (0.259)
CACM 1.842 (0.785) 1.468 (0.446) 0.751 (0.412) -0.079 (0.363) -0.103 (0.402)
WAEMU 2.081 (0.293) 2.272 (0.275) 2.371 (0.283) 2.724 (0.323) 2.694 (0.339)
GCC 1.031 (0.277) 0.721 (0.260) 0.580 (0.285) 0.499 (0.306) 0.132 (0.328)
PAN_ARAB 1.002 (0.151) 1.334 (0.128) 1.070 (0.132) 1.250 (0.130) 1.373 (0.134)
CIS 3.695 (0.203) 3.156 (0.188) 3.367 (0.190) 3.468 (0.182) 3.189 (0.177)
ECO 2.482 (0.275) 2.087 (0.333) 2.087 (0.303) 1.583 (0.317) 1.509 (0.268)
S.E. of regression 1.858 1.842 1.913 1.91 1.971
R2 0.692 0.706 0.700 0.706 0.707
Number of Observations 9,418 11,863 11,384 11,863 12,419 Notes:
Dependent variable: log of bilateral exports.
White heteroskedasticity-consistent standard errors in parentheses.
See Appendix for the full names of RTAs.
97
coefficients are significant at the 5% level in all years except 1995, when they are not
significant. EFTA also shows greater effect in Table 4.3, compared to previous results.
In all cases the coefficients of EFTA more than doubled. In this case EFTA remained
significant at the 1% level during all years. CER also shows an increase in range of
coefficients, to 1.61–1.95 from 0.36–0.74. It is significant at the 1% level in all years.
ASEAN’s dummy increases in value when c.i.f/f.o.b ratios are used as a proxy
for transportation cost. It increased from 1.09 to 2.48 in 1995, and likewise from 0.57 to
2.03 in 2006. ASEAN remained significant across all years at the 1% level. APEC’s
coefficients on the other hand declined in value from 1.45 and 1.32 to 1.19 and 0.95.
APEC’s dummy was significant in all years at the 1% level. MERCOSUR, LAIA, CAN
and CACM’s coefficients all increased, in the results reported in Table 4.3.
With the exception of MERCOSUR, Latin American RTAs were significant at
the 1% or 5% level during the sample period. CARICOM also experienced a substantial
increase in its values, from 2.62 and 2.97 to 4.10 and 4.94. These results are statistically
significant at the 1% level. In Africa, COMESA, CEMAC, and WAEMU experienced
increases in their coefficients when c.i.f/f.o.b ratios were used. These results are
significant at least at the 5% level in most cases. In the Middle East the GCC and PAN-
ARAB’s dummy coefficients increased significantly, compared to previous results. The
GCC results were significant at least at the 5% level for all years, but insignificant in
2006. PAN-ARAB on the other hand was significant at the 1% level in all years.
Finally, the Central Asian RTAs CIS and ECO experienced changes in their
coefficients, compared to those reported in Table 4.2. These results were significant at
the 1% level during all years.
The comparison between the results reported in Table 4.2 using distance as a
proxy for transportation cost compared unfavourably with those Table 4.3 based on the
R-squared statistic alone. Using c.i.f/f.o.b explains bilateral trade flows about 3 to 4
percent better. Furthermore, the results in Table 4.3 are statistically significant in more
cases than otherwise. Although the results are in some cases much greater compared to
earlier results, they appear to be robust. In the case of geographical proximity, the
common border or adjacency dummy plays an increasing role once distance is removed.
Figure 4.2 compares the RTA dummy estimates of Table 4.2 and Table 4.3. In most
cases using cif/fob places greater emphasis on RTA effects.
98
Figure 4.2
RTA Dummies Compared
Constructed from imports’ c.i.f values divided by exports’ f.o.b values, this ratio
proxies transportation cost. Compared to physical distance between the capital cities of
trading partners, this ratio suggests a significantly weaker effect of transportation cost.
Using geographical distance as a transportation cost proxy suggests a one-to-one
reduction in bilateral trade. The c.i.f/f.o.b ratio reports half as much reduction in
bilateral trade based on transportation cost. Both measures are statistically significant
and each tells a different story. When the transportation cost proxy is changed, the
adjacency dummy reacts by increasing more than two-fold. Maintaining statistical
significance, results in Table 4.3 suggest that sharing a common border is more
influential when geographical distance is not considered. This effect does not apply to
the common language dummy, however. It remains similar to the results reported in
Table 4.2. Figure 4.3 illustrates the correlation between the two proxies used for
distance in this paper. The figure shows that as distance increases so, generally, does the
c.i.f/f.o.b ratio. The two proxies are loosely correlated, but positive nonetheless.
Therefore, our transportation proxy responds positively to distance, indicating
increasing transportation costs.
4.4.3 Comparison with Other Studies
The main results in Section 4.4.2 conform to trade theory with respect to
economic parameters. It is worth noting similar studies that verify the results presented
Y = 1.15X + 0.79
-2
-1
0
1
2
3
4
5
6
-2 -1 0 1 2 3 4 NAFTA
CACM
GCC
EU
CAN
CER ARAB
LAIA MERCOSUR
EFTA ASEAN ECO COMESA
APEC
WAEMU CIS
CEMA
CARICOM
Table 4.2
Table 4.3
99
above. This section will compare the results of the first step of the model with other
relevant studies.
Figure 4.3
Transportation Cost Proxies Compared
Table 4.4 presents some comparable studies that considered RTAs’ effects on
bilateral trade. In the case of the economic variables, the model estimates in Table 4.2
and Table 4.3 comply with the correct signs expected in theory and those reported in
Table 4.4. This study finds similar GDP effects on bilateral trade compared to Wei and
Frankel (1997), Feenstra et al. (2001) and Krueger (1999). Income effects in Table 4.2
range between 1.07 and 1.19 compared to 0.91 and 1.05 in Table 4.3. These studies
reported values of 0.96, 1.12 and 0.97, respectively. Even so, the difference is not
substantial. In the case of the income of the importing country the results of this paper
come close to a number of the studies listed in Table 4.4. Values between 0.82 and 0.88
reported in this paper are close to the values of 0.89 and 0.72 reported by Feenstra et al
(2001), Krueger (1999), and Wei and Frankel (1997). The variation in per capita income
is larger, however, in comparable specifications. Wei and Frankel (1997) report 0.21
and 0.06 for the exporter and importer countries respectively. This chapter finds values
between 0.012 and 0.09 for exporters, and 0.03 and 0.15 for importers. The values
reported here are closer to Wei and Frankel (1997).
The distance, adjacency, and common language dummies are similar in most
cases to other studies. In the case of distance, this study reports values that range from -
1.21 to -1.14, marginally different from the -1.12 reported by Rose (2000) or the –1.10
reported by Feenstra et al. (2001). However, this paper also makes use of the c.i.f/f.o.b
ratios as a proxy for transportation cost: therefore Table 4.3 cannot be directly
y = 0.1305x + 0.2873
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
1.5 2 2.5 3 3.5 4 4.5 5
log(Distance)
log
(CIF
/FO
B)
Correlation
Coefficent= 0.074
100
Table 4.4
Prior Estimates of Elasticities from Gravity Equations
Variable
(1)
Frankel et al (1995)
(2)
Wei and Frankel
(1997) (3)
Rose (2000)
(4)
Feenstra et al.
(2001)
(5)
Krueger (1999)
(6)
Current Study (Averages over years)
(7)
Income
Yi 0.73 (0.01)
0.96 (0.02) 0.83 (0.01)
1.12 (0.02) 0.97 (70.32)
1.13
Yj 0.89 (0.02) 0.72 (0.02) 0.89 (106.86) 0.85
Per capita
income
Ci 0.23 (0.01)
0.21 (0.02) 0.73 (0.02)
0.41 (24.58) 0.05
Cj 0.06 (0.03) 0.25 (21.77) 0.07
Distance -0.51 (0.02) -0.93 (0.05) -1.12 (0.04) -1.10 (0.04) -0.95 (-49.05) -1.17
Adjacency 0.72 (0.09) 0.42 (0.16) 0.63 (0.18) -0.03 (0.16) 0.14 (2.56) 0.72
Language 0.47 (0.05) 0.59 (0.08) 0.50 (0.08) 0.69 (0.08) 0.73 (20.89) 0.86
Colony 1.75 (0.15)
RTA/FTA
0.67 (0.14) 1.73 (0.11)
EC/EU 0.24 (0.09) -0.29 (0.16) 0.07 (1.08) -0.09
NAFTA -0.12 (0.63) -0.73 (0.98) 0.11 (0.33) -1.69
ASEAN 1.40 (0.29) 1.80 (0.33) 1.00 (5.52) 0.80
CER 0.50 (1.95) 0.60
MERCOSUR -0.18 (0.46) 0.78 (0.42) -0.19 (-0.85) 0.46
EFTA 0.04 (0.30) -0.37 (0.32) 0.64
APEC 0.61 (0.21) 1.41
Notes:
Standard errors in parentheses.
NAFTA in column (3) refers to US-Canada FTA only.
101
compared to the studies in Table 4.4. Other studies report mixed results with respect to
adjacency. However the values from Table 4.2 are comparable to the 0.72 reported by
Frankel et al. (1995), and the 0.63 reported by Rose (2000). This, however, is not the
case when c.i.f/f.o.b ratios are used. Adjacency takes much larger values, compared
with other studies. In the case of common language, this paper finds stronger than usual
effects compared with other studies. Table 4.2 reports values as high as 1.03 for
language; other studies such as Krueger (1999) reported 0.73.
The estimates of RTA dummies’ coefficients presented here vary substantially
from the literature. For the EU or European Community (EC), the coefficient ranges
from -0.29 to 0.24 in the studies referred to in Table 4.4. Table 4.2 reports values
between -0.15 and -0.01, these are similar to the findings of Wei and Frankel (1997).
This study shows similar negative or weak RTA effects with respect to NAFTA, but
reports coefficients for NAFTA smaller than other related studies—as low as -1.93. For
CER, Krueger (1999) reported a 0.50 dummy coefficient. This study reports values
between 0.36 and 0.74, close to the results reported in Table 4.4. For MERCOSUR, the
literature reports different values, ranging from -0.19 to 0.78. Table 4.2 reports values
between 0.11 and 0.99, well in line with these. The results reported for ASEAN are
considerably different from those included in Table 4.2. Frankel et al. (1995), and Wei
and Frankel (1997) found values of 1.4 and 1.8, higher than the 0.57 and 1.09 found in
this study. In the case of EFTA, the results of this study also differ from the literature,
with coefficients larger than other studies reported: values between 0.42 and 0.84 are
larger than the 0.04 and -0.37 reported by Frankel et al. (1995) and Wei and Frankel
(1997). This study also reports larger coefficients for APEC, with values ranging from
1.32 to 1.45—greater than the 0.61 reported by Frankel et al. (1995). Although the
results differ in a number of cases from other studies in the literature, there are
commonalities that verify the results of this study. Other RTAs are less commonly
tested. However, the interest of this paper is the GCC and the following section will
discuss the region’s trade patterns.
On the whole, this study’s findings largely verify previous studies’ results,
implying that the gravity model (4.9) captures RTA effects reasonably well, and in line
with the literature.
4.5 Trade Creation and Trade Diversion
The results in Table 4.2 and Table 4.3 have broad implications with respect to the
trade intensities within RTAs and their welfare. If a RTA increases trade by more than
102
100%, is such a trade bloc diverting trade from other countries or regions? To answer
this question and contrast the empirical results what what is happening within these
RTAs, it is useful to consider the import shares of GDP internally and externally.
Consider Figure 4.4, where the intra-regional imports of each RTA are illustrated at the
beginning and end of the sample period, 1995 and 2006. The figure presents intra-
regional imports as a percentage of the region’s total GDP. An increase from the
beginning to the end of the period indicates imports from within the RTA have risen.
For most RTAs this indicates increased intra-regional imports. Panel B is a blow up of
the cluster of RTAs closer to the origin.
This is especially evident for ASEAN, where intra-regional imports jumped by
about 20% over the past decade. The EU also experienced increased intra-regional
trade, although by a smaller magnitude of less than 10%. The PAN-ARAB RTA
exhibits increases, although small, as a share of its GDP. In contrast, NAFTA, CIS, and
CARICOM all exhibit declining regional trade. The Caribbean RTA has experienced a
decline of about 10% in intra-regional trade. Other RTAs such as CACM, LAIA, GCC,
CER, EFTA, ECO, CAN, WAEMU and MERCOSUR show very small proportions of
intra-regional trade; in fact, COMESA and CEMAC hardly register trade within the
region as a proportion of their GDP.
On the other hand, Figure 4.5 illustrates the comparable extra-regional trade of the
RTAs included in the model. The values reported in Figure 4.5 show a significantly
different picture to that in Figure 4.4. Most of the RTAs trade substantially more with
non-members than members. CARICOM, NAFTA, PAN-ARAB, EFTA and GCC trade
most with non-members. In the case of NAFTA trade with non-members was more in
2006 than in 1995.
CACM, APEC, ECO, EU and MERCOSUR also trade more with non-members at
the end of the period. COMESA, CAN and WAEMU trade marginally less with non-
members towards the end of the period. CARICOM’s decline in extra-regional imports
is most noteworthy: It declines by about 10%.
Figure 4.4 and Figure 4.5 give us an insight into which RTAs trade more within
themselves and which with non-members, but they do not strictly tell us which RTAs
are more biased towards intra-regional trade during the sample period. To determine
this, it is useful to examine the ratio of intra-regional trade to extra-regional trade, an
approach followed by Sologa and Winters (2001). Such a ratio will indicate whether
RTAs are trading more among themselves, at the expense of non-members.
103
Figure 4.4
Intra-Regional Imports as Proportion of GDP
A. All RTAs
B. Sub-set RTAs
NAFTA
APEC
CIS
CARICOM
EU
ASEAN
PAN-ARAB
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5
1995
2006
45º
LAIA
GCC
CER
ASEAN
PAN-ARAB
CAN
CIS
WAEMU
CARICOM
CEMAC
CACM
EFTA
ECO
MERCOSUR
COMESA0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0.09
0.1
0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.1
1995
2006
45º
104
Figure 4.5
Extra-Regional Imports as Proportion of GDP
A. All RTAs
B. Sub-set RTAs
CER
EFTAASEAN
CARICOMNAFTA
GCC
PAN-ARAB
ECO
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
1995
20
06
45º
CACM
LAIA
GCC
CER
EFTA
ASEAN
ECO
CIS
WAEMU
COMESA
CEMAC
APEC
CANEU
MERCOSUR
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5
1995
20
06
45º
105
Figure 4.6 plots these ratios for each of the RTAs included in this study. The
ratio can be interpreted as follows: if the quotient of intra/extra regional imports
increases, this indicates that a RTA is importing more from its members or less from the
rest of the world, or both. In this case the RTA would be suspected of diverting trade. If
the ratio declines; the RTA in question either imports less from its members or more
from the rest of the world, or both. Consequently the RTA is trade-creating. For
instance, take the developed countries’ RTAs, NAFTA and EU. Panel (i) in Figure 4.6
indicates that over the sample period NAFTA’s ratio declined over time. As a result,
NAFTA can be considered as a trade-creating RTA. In contrast, the EU’s ratio, shown
in panel (iv), climbed during the sample period. This increase was more pronounced in
1999 and 2000; in 2005 it fell marginally. Overall the EU appears to be trade-diverting,
based on its intra/extra regional imports ratio. This is not the case for APEC, shown in
panel (iv), which appears to be trade-creating. The intra/extra regional import ratio
indicates a decline over the sample period, which suggests that this grouping was
trading more with the rest of the world. In the case of CER there is no marked increase
or decrease in the ratio when compared to other RTAs. The lack of significant change in
its ratio suggests that CER was neither diverting nor creating trade during this sample
period.
In Asia the results are mixed. In Central Asia, CIS, illustrated in panel (i),
exhibits significant fluctuations during the sample period. Although the overall ratio
declined during the sample period, it increased and decreased repeatedly during the
period. This indicates episodes of trade diversion, although CIS diverted less trade
overall. Compared to CIS, ECO shows less volatility. Panel (viii) indicates that its ratio
remained fairly steady during the sample period. However, towards the end of the
period it edged upward, suggesting marginal trade diversion. This is similar to the case
of the PAN-ARAB RTA, which appears to have diverted trade progressively over the
past decade. Overall its intra/extra regional imports ratio climbed during the sample
period. So did that of ASEAN, which appears to be leaning towards trade diversion. Its
ratio increased steadily over the sample period. The GCC, on the other hand, exhibits
trade creation as its ratio declines over time.
It is noteworthy that not all ratios are comparable in magnitude. In many
instances import ratios are very small. For example the ratios shown for ASEAN, ECO,
PAN-ARAB and the GCC indicate that all trade very little within their regions; previous
inferences of trade diversion are minimal in these cases.
106
Figure 4.6
Intra/Extra Regional Imports Ratio (i) (ii) (iii)
(iv) (v) (vi)
(vii) (viii) (ix)
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006
NAFTA
CIS
0
0.05
0.1
0.15
0.2
0.25
0.3
1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006
MERCOSUR
LAIA
0
0.5
1
1.5
2
2.5
1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006
APEC
EU
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006
CAN
CACM
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006
PAN-ARAB
GCC
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006
ECO
WAEMU
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006
CEMAC
ASEAN
0 0.02 0.04 0.06 0.08
0.1 0.12
1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006
CER
CARICOM
0 0.005
0.01 0.015
0.02 0.025
0.03
1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006
COMESA
EFTA
107
The Caribbean and Latin America offer the most contrasting picture to those of
Asia and North Africa, with their RTAs fluctuating between increased trade diversion
and trade creation over time. The ratio for MERCOSUR, for example, shown in panel
(ii), climbs at first, indicating trade diversion as its members deal more with each other
compared to the rest of the world. As time progresses, MERCOSUR’s trade diversion
weakens. Towards the end of the period it stabilises. LAIA, in panel (ii), and CAN, in
panel (v), exhibit opposite behaviours. Their ratios decline during the first few years of
the sample period, indicating trade creation. However, during the last few years they
become trade-diversionary. Panel (v) displays CACM’s ratio, which in comparison to
other Latin American RTAs declines during the first half of the sample period,
suggesting trade creation. In the second half it spikes and then declines over time.
Overall CACM shows mixed signs of trade diversion and trade creation. The Caribbean
RTA CARICOM, illustrated in panel (vii), has an intra/extra regional import ratio that
changes marginally over the sample period but towards the latter part of the sample
period suggests temporary trade creation effects.
Figure 4.6 also illustrates some of the RTA ratios for the African continent.
WAEMU in panel (viii) shows intra/extra regional import ratios that suggest greater
trade diversion during the sample period. WAEMU appears to concentrate trade within
its members, a finding substantiated by the results reported earlier with respect to
WAEMU. In contrast, CEMAC’s ratio in panel (ix) suggests a trade diversion in the
beginning of the sample period, but signs of trade creation after 1999. Trade creation
weakens during the last few years of the period. COMESA does not exhibit any
significant trade creation or trade diversion signals during the period. Panel (iii) shows
that only in 2002 does COMESA appear to reduce trade with the rest of the world.
4.6 Trade within the GCC Countries (Step 2)
The GCC, created in 1981, has been operational for the past three decades.
Recently it has been undergoing significant economic integration progress that warrants
attention. Trade liberalisation has gone hand in hand with efforts to enhance capital and
labour mobility. What makes the GCC interesting is the political commitment towards
economic integration. The GCC follows a standard approach to economic integration in
its four main phases: Free Trade Area, Customs Union, Common Market, and Monetary
and Economic Union. The GCC possesses many factors that should encourage success
and that may not be present elsewhere, such as a common language, close historical and
social ties, and a common religion. Moreover, the GCC economies are similar in their
108
dependency on oil and in the rapid economic progress they have made in the past three
decades. The six member countries, Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and
UAE, are considered middle-income or high-income countries based on their GDP per
capita. The region is also strategically important, since a large portion of the world’s
proved oil reserves are located within its borders.
In the light of these considerations, two questions need to be answered: First, is
the GCC an effective RTA? Second, what effect does it have on trade patterns within
the region? The answer to the first question lies in the empirical analysis that has
already been presented. In Table 4.2 and Table 4.3, the RTA dummy of the GCC
indicates the contribution the arrangement makes to intra-regional trade. The gravity
equation implies effects between -60% and 180%, but how well does the gravity model
fit the GCC trade flows specifically?
The observed, fitted, and residuals matrices of the GCC’s intra-regional trade are
reported in Table 4.5 for the years 1995 and 2006. In 1995 the model understates the
actual trade flow values in most cases. Take the case of Bahrain’s exports to Saudi
Arabia in 1995. Panel (i) indicates that Bahrain exported US$ 251 million to Saudi
Arabia. The model predicts that Bahrain exported US$ 27 million indicated in panel (ii).
The residual is US$ 225 million, the difference between the two values.
The case is quite the opposite when the year 2006 is considered. Understimation
is more pronounced, as indicated by the residuals. This is illustrated by a number of
psotive values in panel (vi). The most severe case is Bahrain; its trade flows are
underestimated in the model by US$ 4,975 million in 2006. Thus, the gravity model in
step 1 does not predict trade flows well for the GCC. Since the interest of the paper is to
understand the trade patterns within the GCC, further exploration of such trends is
required. A discussion of intra-trade shares within the region will be helpful.
A better picture of the GCC’s intra-regional trade is given by considering
imports as a ratio of total trade. Table 4.6 reports the 1995 and 2006 intra-GCC trade
matrices expressed as percentages of the total. Table 4.6 gives a vivid picture of who
trades more with their GCC counterparts. Bahrain and Oman trade the most within the
GCC. Overall, the GCC region appears to trade less within itself over the sample period.
What can explain the stagnation in trade flow within the region? Transportation costs
109
Table 4.5
GCC Trade Matrices, 1995 and 2006
(Millions $US)
1995 2006
Importer
Exporter
Bahrain Kuwait Oman Qatar Saudi
Arabia UAE Total
Bahrain
Kuwait Oman Qatar Saudi
Arabia UAE Total
(i) Observed (iv) Observed
Bahrain 58 11 32 251 74 426 132 247 114 643 458 1,594
Kuwait 17 11 15 113 76 232 44 58 41 243 295 681
Oman 9 18 9 55 631 722 48 81 64 264 1,478 1,935
Qatar 9 23 14 60 137 243 63 123 43 139 979 1,348
Saudi Arabia 1,479 467 135 159 1,350 3,591 4,534 1,037 373 825 2,260 9,028
UAE 89 156 920 122 332 1,618 327 528 2,358 548 1,703 5,464
Total 1,604 723 1,091 335 811 2,269 5,016 1,901 3,079 1,592 2,992 5,470
(ii) Fitted (v) Fitted
Bahrain 7 2 10 27 10 56 44 8 98 138 74 362
Kuwait 1 6 10 111 26 154 10 47 165 903 292 1,416
Oman 0 5 3 19 24 52 2 34 37 107 184 364
Qatar 0 8 3 34 22 67 1 137 42 506 466 1,152
Saudi Arabia 4 168 34 69 153 428 22 1,337 215 899 1,441 3,914
UAE 1 28 31 33 111 204 5 347 297 665 1,155 2,469
Total 6 217 76 126 302 235 39 1,900 608 1,863 1,863 2,809
(iii) Residual (vi) Residual
Bahrain 51 9 22 225 64 371 88 239 16 505 384 790
Kuwait 16 5 4 2 50 78 34 11 -124 -660 3 -504
Oman 9 13 5 36 607 670 46 47 27 157 1,294 398
Qatar 9 15 11 26 115 176 62 -14 1 645 513 10
Saudi Arabia 1,476 299 101 90 1,197 3,163 4,512 -300 158 -74 819 3,477
UAE 88 128 890 88 220 1,414 321 181 2,061 -117 548 2,995
Total 1,598 506 1,015 209 509 2,033 4,975 2 2,470 -272 1,195 3,013
110
cannot be expected to produce such low overall trade proportions. Distance within the
region is not so prohibitive that it would reduce trade substantially; it barely exceeds
one thousand kilometres between most capital cities.
Table 4.6
GCC Import Ratios of Total Trade
(Percentages)
Importer
Exporter Bahrain Kuwait Oman Qatar Saudi Arabia UAE
1995
Bahrain 0.8 0.3 1.1 0.8 0.3
Kuwait 0.5 0.3 0.5 0.4 0.3
Oman 0.3 0.2 0.3 0.2 2.4
Qatar 0.3 0.3 0.3 0.2 0.5
Saudi Arabia 45.3 6.3 3.1 5.8 5.1
UAE 2.7 2.1 21.3 4.4 1.1
TOTAL 49.1 9.7 25.3 12.1 2.7 8.6
2006
Bahrain 0.9 2.3 0.8 1.0 0.4
Kuwait 0.5 0.5 0.3 0.4 0.3
Oman 0.5 0.5 0.4 0.4 1.4
Qatar 0.7 0.8 0.4 0.2 0.9
Saudi Arabia 47.9 6.9 3.5 5.7 2.2
UAE 3.5 3.5 22.3 3.8 2.7
TOTAL 53.1 12.6 29 11 4.7 5.2
Source: Author’s calculations
The answer may lie in the similarity of the economic structures of these
economies. The common dependence on oil for developmental purposes and supporting
large government sectors may lead to low intra-regional trade. These six countries are
traditionally open economies, importing most of their consumer and capital needs from
outside the region. Moreover, the non-complementary products produced within the
region may be an influencing factor, eliminating the possibility of trade in different
goods. Most countries within the region have a low level of diversification of exports
products, so that intra-industrial trade is not particularly viable. This is true of most
Arab countries, the GCC included (Al-Atrash and Yousif 2000).
To understand the intra-regional trade patterns of the GCC region, disaggregated
commodity trade analysis is useful to contrast with the patterns suggested by Table 4.6.
This is the second step in the model’s analysis; the first was estimating bilateral trade
determinants using GDP and GDP per capita as proxies for size and economic
111
development. This second step involves estimating bilateral trade within the GCC,
conditional on total intra-regional trade. To do this, the GCC’s total trade is divided into
ten 1-digit categories according to SITC revision 1 obtained from the UN Comtrade
database. These categories are: (1) animal and vegetable oils and fats, (2) beverages and
tobacco, (3) chemicals, (4) crude materials except fuels, (5) food and live animals, (6)
machinery and transport equipment, (7) manufactured goods, (8) miscellaneous
manufactured articles, (9) commodities and transactions not classified, and (10) mineral
fuels and lubricants.
The choice of this classification was based on the data available for the GCC
countries between 1980 and 2006. Intra-regional bilateral trade exports were regressed
on total bilateral trade, distance, adjacency and country/commodity combinations. Saudi
Arabia is used as a base country, given its dominant size within the GCC, and mineral
fuels were considered the base commodity. Mineral fuels, which include oil and
petroleum extracts, form a substantial proportion of extra-GCC trade. Although there
exists some regional trade within mineral fuels, emphasis here will be on other
commodity categories.
With this specification, there are 45 dummy variables representing Bahrain,
Kuwait, Oman, Qatar and UAE across nine commodity categories. Table 4.7 presents
the results of the second step of the model based on equation (4.12). The model suggests
a significant role that total trade within the GCC plays in determining bilateral trade in
the product groups. On average an increase of 1% in total trade of exports within the
GCC is expected to increase exports of any specific product group by 0.6%. Similarly,
an increase of 1% in total trade of imports within the GCC increases bilateral exports of
specific product groups by 0.3%. The model also suggests that distance plays a lesser
role in reducing trade within the GCC, compared to the overall model in Step 1; a -0.5
elasticity is reported in Table 4.7. Adjacency, however, exhibits strong effects within
the GCC, promoting trade almost four times above the norm.
Step 2 of the model included a number of country and commodity dummies. The
coefficients of these dummies are reported in Table 4.8. Table 4.8 shows a number of
interesting patterns in the GCC intra-regional trade. Firstly, although Saudi Arabia is the
largest economy in the region, it trades less with its GCC neighbours in many of the
selected commodity groups. Saudi Arabia trades more, compared to its neighbours
within the region, in as many as half the commodities groups including animal and
vegetable oils and fats, beverages and tobacco, crude materials, and commodities and
112
Table 4.7
Disaggregated Trade Estimates P '
ij 1 2 i 3 j 4 ij k k ijk
logX =β +β LogX +β LogX +β Adj +Σγ (Country/Commoditydummies) +ε
Variable Coefficient
Total Trade of Exporter 0.585 (0.027)
Total Trade of Importer 0.291 (0.026)
Distance -0.479 (0.062)
Adjacency 1.599 (0.075)
R2 0.496
Number of Observations 4,455 Note: White standard errors in parentheses.
other transactions. Bahrain, Kuwait, Oman, Qatar and the UAE trade more within the
GCC in chemicals, food and live animals (except Qatar), machinery and transportation,
manufactured goods, and miscellaneous manufactured goods, relative to Saudi Arabia,
although they do not trade equally in each commodity category. In the case of animal
and vegetable oils and fats, Oman trades most within the GCC compared to Bahrain,
Kuwait, Qatar, and the UAE, but less than Saudi Arabia by 25% [=(e-0.288
-1)x100].
Qatar trades the least within the region; 97% less than the base case. Bahrain, Kuwait,
and the UAE’s intra-GCC trade in this category falls below Saudi Arabia by 64% to
89%. These results suggest that trade within the GCC of animal and vegetable oils and
fats is less than trade in mineral fuels.
With the exception of Oman, other GCC countries trade less than base case
Saudi Arabia with respect to beverages and tobacco. Oman trades approximately 27%
more than Saudi Arabia in this category. Qatar trades the least in beverages and tobacco
is, 95% less than the base case. Bahrain, Kuwait and UAE trade 62%, 41% and 18%
less than the base case in this category. The GCC countries also trade less intra-
regionally compared to the base case in the crude materials category. Here, Kuwait
trades most after Saudi Arabia, at 27% less; then, the UAE, Bahrain, Oman and finally
Qatar.
Other categories in which the other GCC countries trade relatively more than
Saudi Arabia are chemicals, food and live animals, machinery and transportation,
manufactured goods, and miscellaneous manufactured goods. In chemicals, Qatar trades
most within the GCC relative to Saudi Arabia. The amount of trade within the GCC of
Qatar is in the order of 837% more than the base case. Qatar is followed closely by
Kuwait, then the UAE, Oman, and finally Bahrain. Saudi Arabia trades the least in
chemicals within the region. Trade in food and live animals is primarily led by Oman,
113
Table 4.8
Estimate of Country-Product Dummy Variable Coefficients
Importer
Commodity Group Bahrain Kuwait Oman Qatar UAE
1. Animal Fats etc. -1.385 (0.235) -1.031 (0.242) -0.288 (0.213) -3.680 (0.304) -2.027 (0.334)
2. Bev. & Tobacco -0.972 (0.203) -0.523 (0.230) 0.236 (0.193) -2.935 (0.259) -0.193 (0.317)
3. Chemicals 0.679 (0.203) 1.757 (0.214) 1.081 (0.195) 2.238 (0.233) 1.169 (0.287)
4. Crude Materials -0.662 (0.209) -0.306 (0.227) -0.824 (0.202) -1.142 (0.237) -0.519 (0.287)
5. Food & Animals 0.777 (0.203) 2.062 (0.213) 2.195 (0.192) -0.137 (0.233) 1.697 (0.287)
6. Mach. & Tans. 2.430 (0.203) 3.278 (0.214) 2.619 (0.192) 1.734 (0.233) 1.817 (0.293)
7. Manufac. Goods 3.771 (0.203) 3.082 (0.213) 1.708 (0.192) 3.302 (0.233) 2.403 (0.287)
8. Misc. Manufac. 1.737 (0.203) 2.267 (0.213) 1.093 (0.192) 0.462 (0.233) 1.616 (0.290)
9. Other -2.099 (0.216) -2.282 (0.242) -0.296 (0.192) -2.577 (0.254) -1.189 (0.338)
Notes:
Dependent variable: log of bilateral disaggregated exports from i to j. White heteroskedasticity-consistent standard errors in parentheses.
114
followed closely by Kuwait, then UAE, Bahrain, Saudi Arabia, and Qatar. Oman trades
798%, or eight times more than the base case, while Qatar trades about 13% less than Saudi
Arabia. In the category of machinery and transportation, Kuwait leads the GCC countries in
intra-regional trade, while Oman and Bahrain come close after. Qatar and UAE fall within
the bottom half, but still above the base case. Manufactured goods trade patterns shift in
favour of Bahrain, trading the most in the region relative to the base case. Its trade flows
are mirrored by Qatar and Kuwait to make up the top half of the GCC in this category. The
UAE exceeds Oman relative to the base case. All five countries trade relatively more than
the base case.
In the miscellaneous manufactured goods category Kuwait trades most within the
region; followed by Bahrain, the UAE, and then Oman. Qatar completes the top five
countries. Again, Saudi Arabia trades the least within the GCC in this category. Finally,
Saudi Arabia exceeds other GCC countries in intra-regional trade in the commodity and
other transactions category. After Saudi Arabia, Oman trades most in this category,
followed by the UAE. Bahrain, Kuwait, and Qatar trade similarly in these commodities
relative to the base case.
The results of this analysis indicate there is no clear pattern within the region where
one country dominates intra-regional trade within the GCC. The second step of the model
used here explains the trade patterns within the GCC, and shows that there are substantial
differences between the largest economy, Saudi Arabia, and other countries in intra-
regional trade. Saudi Arabia trades more in animal and vegetable oils and fats, beverages
and tobacco, crude materials, and commodity and other transactions, but less in chemicals,
food and live animals, machinery and transportation, manufactured goods, and
miscellaneous manufactured goods. In these particular categories there is no clear leader in
intra-regional trade. This position switches between Bahrain, Kuwait, Oman, Qatar, and
UAE. Overall, the results suggest that the above categories are traded more than mineral
fuels within the region.
4.7 Conclusions
Using a two-step framework, the gravity model has been utilised to analyse trade
flow patterns between different countries in the world. The aim in the first step was to
measure the effect of RTAs on bilateral trade flows. Comparing a significant portion of
existing RTAs in the world, the model suggests that a number of RTAs in developing
115
countries explain a large portion of bilateral trade flow once economic factors are
accounted for. RTAs play a significant role in the determination of trade flow between
countries. Findings suggest that geographical proximity, shared borders, and language
significantly influence bilateral trade flows. These findings are congruent with previous
literature. It is in developing countries’ RTAs that this research has found large and
significant effects on bilateral trade flows. Trade blocs in the Caribbean, Africa and Central
Asia show RTAs strongly affect their trade patterns. Latin American, Middle Eastern, and
North African RTAs remain less effective on bilateral trade flows.
This chapter also investigated trade creation and trade diversion of the RTAs in
question, with mixed results. A number of RTAs such as NAFTA, APEC, EFTA and
CEMAC show relative signs of trade creation. On other hand, RTAs such as the EU,
ASEAN, WAEMU, CAN, COMESA and PAN-ARAB show strong signs of trade
diversion, although in a number of cases intra-regional imports are small enough that trade
diversion is minimal.
As seen from Step 1 in the model, the GCC appears to have no significant effect on
its members’ bilateral trade. This is confirmed by examining the import ratios of these six
countries, as illustrated in Table 4.6. GCC intra-regional import ratios are low for most of
the countries at the beginning and the end of the period, 1995 and 2006. The second step of
the framework used in this chapter identified the intra-regional trade patterns of the GCC
region. In Step 2 the model quantifies commodity-specific interactions in GCC intra-
regional trade. The largest economy in the region, Saudi Arabia, dominates in less than half
of the commodity groups. The other five countries, Bahrain, Kuwait, Oman, Qatar and the
UAE, exceed Saudi Arabia’s intra-regional trade in more than half of the cases. However,
there is no clear trend of the other five countries exceeding the base case. Step 2 in the
model also suggests that there is some degree of mineral fuel trade within the region despite
the similarities of these oil dependent economies.
Although the GCC countries have been undergoing economic integration for the
past three decades, the RTA has not intensified intra-regional trade during the sample
period. This is evident from the analysis from step 1 and the intra/extra-regional import
ratios. New development in the region such as the launch of a customs union have yet to
bear fruit. This may be attributable to the similar economic structures of all six economies,
and their dependence on natural resources; the non-complementary products produced
116
within the region and the low level of diversification of export products may also be
explanatory factors. Despite small trade volumes within the region, a number of countries
such as Bahrain and Oman carry out a significant portion of their total trade within the
region. Finally, the model suggests that a common border between these countries and
other GCC countries plays an important role in intra-regional trade.
117
Appendix A4.1
Sensitivity Analysis
Alternative results to the OLS regression of the model (4.9) are reported in Table
A4.1.1 using PPP valued GDP and GDP per capita. Table A4.1.1 uses distance as a proxy
for transportation cost. Accordingly if we contrast the results with those reported in Table
4.2 we note a number of differences. With respect to income the values are comparable to
those reported in Table 4.2. Take column (6) in Table 4.2 as an example, the elasticity of
the exporter’s income in 2006 is 1.19; 1.17 was reported in Table A4.1.1. The elasticity of
the importing country’s income is 0.88 in 2006 as reported by Table 4.2; Table A4.1.1
reports an elasticity of 0.84. The per capita income coefficients, however, are not
comparable across the tables. In Table A4.1.1 the coefficients for per capita income are
greater than those reported in Table 4.2 for all years. In 1995, for example, Table 4.2
reports per capita income elasticity for the exporter of 0.09, compared to 0.61 in Table
A4.1.1. The same is true of the importer’s per capita income elasticity: Table 4.2 reports a
value of 0.10, compared to 0.50 in Table A4.1.1.
With respect to cultural and geographical variables, Table 4.2 and Table A4.1.1 do
not differ substantially. In the case of distance, Table 4.2 reports a value of -1.14 for 2006,
while Table A4.1.1 reports a value of -1.11. The adjacency dummy is marginally greater in
Table 4.2 compared to Table A4.1.1. This is more so in the years 2001, 2003, and 2006,
where the coefficients of the dummy variable take the values of 0.71, 0.79, and 0.82 in
Table 4.2. In contrast, Table A4.1.1 reports values of 0.66, 0.69, and 0.72 for the same
years. The common language dummy’s coefficients are not significantly different when
Table 4.2 and Table A4.1.1 are compared. In 1995, Table 4.2 reports a coefficient of 0.75, a
smaller value compared to the 0.85 in Table A4.1.1. For the rest of the years Table A4.1.1’s
language coefficients remain greater than Table 4.2’s; but the difference is not significant.
The main difference between Tables A4.1.1 and 4.2 is the RTA dummies’
coefficients. Take the EU for example: Table 4.2 reports a dummy coefficient of -0.15 for
2006, which translates to -14% effect below normal trade. In contrast, Table A4.1.1 reports
a coefficient of 0.46 that reflects a 59% RTA effect above normal trade. The difference is
large and significant. Other RTAs also experience significant change in their effects when
PPP based values are used. The GCC is another example where the use of PPP based value
118
changes its coefficients. In the year 1995, Table 4.2 reports a coefficient of 0.12, which
translates to 13% RTA effect above normal trade. In Table A4.1.1 the corresponding value
is -0.28, equivalent to a -24% effect below normal trade.
119
Table A4.1.1
Estimates of The Gravity Equation (PPP Adjusted Income)
Year
Variable
(1)
1995
(2)
1998
(3)
2001
(4)
2003
(5)
2006
(6)
Income (PPP)
Yi 1.046 (0.011) 1.095 (0.011) 1.094 (0.011) 1.122 (0.010) 1.165 (0.010)
Yj 0.809 (0.011) 0.786 (0.011) 0.795 (0.011) 0.802 (0.010) 0.841 (0.011)
Per capita income (PPP)
Ci 0.605 (0.021) 0.505 (0.020) 0.527 (0.021) 0.495 (0.020) 0.548 (0.020)
Cj 0.503 (0.020) 0.441 (0.020) 0.407 (0.019) 0.381 (0.019) 0.369 (0.019)
Distance -1.066 (0.024) -1.074 (0.024) -1.094 (0.024) -1.129 (0.023) -1.114 (0.024)
Adjacency 0.646 (0.119) 0.617 (0.109) 0.655 (0.111) 0.691 (0.109) 0.722 (0.114)
Common Language 0.850 (0.061) 0.914 (0.059) 1.019 (0.056) 1.004 (0.056) 1.097 (0.056)
RTA
EU 0.435 (0.076) 0.615 (0.077) 0.568 (0.079) 0.647 (0.080) 0.464 (0.080)
NAFTA -1.400 (0.489) -1.171 (0.504) -1.030 (0.569) -1.248 (0.578) -1.190 (0.676)
EFTA 1.032 (0.445) 1.562 (0.481) 1.369 (0.496) 1.395 (0.459) 1.680 (0.460)
CER 0.900 (0.114) 0.928 (0.134) 0.734 (0.134) 0.882 (0.136) 0.791 (0.151)
APEC 1.445 (0.092) 1.352 (0.100) 1.496 (0.097) 1.467 (0.096) 1.312 (0.108)
ASEAN 1.011 (0.268) 0.318 (0.308) 0.338 (0.261) 0.300 (0.245) -0.051 (0.269)
MERCOSUR 0.283 (0.336) 0.259 (0.303) 0.334 (0.449) 0.458 (0.486) 0.230 (0.496)
LAIA 0.419 (0.133) 0.547 (0.132) 0.670 (0.136) 0.377 (0.142) 0.614 (0.147)
CAN 0.788 (0.246) 0.781 (0.349) 0.719 (0.367) 0.926 (0.371) 0.417 (0.269)
CARICOM 2.633 (0.232) 2.549 (0.220) 2.680 (0.202) 2.686 (0.201) 2.998 (0.199)
COMESA 0.844 (0.358) 0.294 (0.340) 0.642 (0.367) 1.010 (0.349) 1.229 (0.327)
(continued on next page)
120
Table A4.1.1 (Continued)
Estimates of The Gravity Equation (PPP Adjusted Income)
Year
Variable
(1)
1995
(2)
1998
(3)
2001
(4)
2003
(5)
2006
(6)
CEMAC 1.694 (0.173) 1.686 (0.200) 1.797 (0.206) 1.819 (0.165) 1.995 (0.163)
CACM 0.806 (0.741) -0.097 (0.622) -0.460 (0.562) -0.707 (0.803) -0.448 (0.802)
WAEMU 2.461 (0.259) 1.771 (0.330) 1.696 (0.356) 2.225 (0.334) 2.282 (0.362)
GCC -0.275 (0.211) -0.024 (0.236) 0.001 (0.227) -0.084 (0.227) -0.239 (0.228)
PAN_ARAB 0.244 (0.152) 0.051 (0.172) 0.173 (0.160) 0.252 (0.153) 0.495 (0.147)
CIS 2.486 (0.185) 2.326 (0.203) 2.194 (0.193) 2.197 (0.182) 2.040 (0.245)
ECO 1.697 (0.269) 1.515 (0.314) 1.271 (0.314) 0.559 (0.372) 0.821 (0.360)
S.E. of regression 2.050 2.100 2.171 2.139 2.226
R2 0.658 0.649 0.646 0.658 0.657
Number of Observations 11,399 12,751 13,781 14,051 14,920 Notes:
Dependent variable: log of bilateral exports.
White heteroskedasticity-consistent standard errors in parentheses. See Table A4.3for the full names of RTAs.
121
Appendix A4.2
Data Sources and Description
This appendix describes the sample and data used in this chapter. The appendix
indicates which countries are included in the sample in Table A4.2.1. Table A4.2.2
describes the RTAs included in the analysis. It lists their members and years of
inception. The data variables, their units of measurement, and their sources are shown in
Table A4.2.3.
122
Table A4.2.1
Countries Included in Gravity Model Sample
Angola Equatorial
Guinea Madagascar Slovakia
Argentina Estonia Malawi Slovenia
Armenia Ethiopia Malaysia Solomon Islands
Australia Fiji Maldives South Africa
Austria Finland Mali Spain
Azerbaijan France Malta Sri Lanka
Bahamas Gabon Mauritania St. Kitts And Nevis
Bahrain Gambia Mauritius St. Lucia
Bangladesh Georgia Mexico St. Vincent and the
Grenadines
Barbados Germany Moldova Sudan
Belgium Ghana Mongolia Suriname
Belize Greece Morocco Sweden
Benin Grenada Mozambique Switzerland
Bolivia Guatemala Myanmar Syria
Brazil Guinea Nepal Tajikistan
Brunei Darussalam Guinea-Bissau Netherlands Thailand
Bulgaria Guyana New Zealand Togo
Burkina Faso Haiti Nicaragua Tonga
Cambodia Honduras Niger Trinidad and
Tobago
Cameroon Hong Kong Nigeria Tunisia
Canada Hungary Norway Turkey
Cape Verde Iceland Oman Turkmenistan
Central African
Republic India Pakistan Ukraine
Chad Indonesia Panama
United Arab
Emirates
Chile Iran Papua New Guinea United Kingdom
China Ireland Paraguay United States
Colombia Italy Peru Uruguay
Comoros Jamaica Philippines Uzbekistan
Costa Rica Japan Poland Venezuela
Cote D’ Ivoire Jordan Portugal Yemen
Croatia Kazakhstan Qatar Zambia
Cyprus Kenya Russia
Czech Republic Kiribati Rwanda
Denmark Korea Samoa
Djibouti Kuwait
Sao Tome and
Principe
Dominica Laos Saudi Arabia
Dominican Republic Latvia Senegal
Ecuador Lebanon Seychelles
123
Table A4.2.2
Regional Trade Agreements
Trading Blocs Created Members
ASEAN
Association of South
East Asian Nations
(AFTA)
1994 Indonesia, Malaysia, Philippines, and Thailand
CER
Closer Trade
Relation Trade
Agreement
1983 Australia and New Zealand
EU
European Union
1957
(1992)
Belgium (1957), Luxembourg (1957), France (1957), Germany
(1957), Greece (1981), Italy (1957), Netherlands (1957),
Denmark, Ireland, United Kingdom (1973), Greece (1981),
Portugal, Spain (1986), Austria, Finland, Sweden (1995)
GCC
Gulf Cooperation
Council
1981 Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and United Arab
Emirates
NAFTA
North American Free
Trade Agreement
1989 Canada, United States, and Mexico.
MERCOSUR
Southern Common
Market
1997 Argentina, Brazil, Paraguay, and Uruguay
APEC
Asia Pacific
Economic
Cooperation
1989 (1989)Australia, Brunei Darussalam, Canada, Indonesia, Japan,
Malaysia, New Zealand, Philippines, Korea, Singapore,
Thailand, United States
(1991), China, Hong Kong (China), Taiwan (China) (1993),
Mexico, Papua New Guinea, (1994) Chile, (1998)Peru, Russia,
Vietnam.
EFTA
European Free Trade
Area
1960 Iceland, Norway, Switzerland
CARICOM
Caribbean
Community and
Common Market
1973 (1973)Antigua and Barbuda, Barbados, Jamaica, St. Kitts and
Nevis, Trinidad and Tobago, (1974) Belize, Dominica, Grenada,
Montserrat, St. Lucia, St. Vincent and the Grenadines, (1983)
The Bahamas (only part of the Caribbean Community, not the
common market).
(Continued on next page)
124
Table A4.2.2 (Continued)
Regional Trade Agreements
Trading Blocs Created Members
COMESA
Common Market for
Eastern and Southern
Africa
1993 Angola, Burundi, Comoros, Djibouti, Egypt, Ethiopia, Kenya,
Lesotho, Malawi, Mauritius, Mozambique, Rwanda, Somalia,
Sudan, Swaziland, Tanzania, Uganda, Zambia, Zimbabwe.
CACM
Central American
Common Market
1960
1993
El Salvador, Guatemala, Honduras, Nicaragua, Costa Rica
(1962)
CIS
Commonwealth of
Independent States
1991 Azerbaijan, Armenia, Georgia, Moldova, Kazakhstan, Russian
Federation Ukraine, Uzbekistan, Tajikistan
ECO
Economic Cooperation
Organization
1985 Azerbaijan, Iran, Kazakhstan, Pakistan, Tajikistan, Turkey,
Turkmenistan, Uzbekistan
LAIA
Latin American
Integration Association
1960
1980
Mexico, Argentina, Bolivia, Brazil, Chile, Ecuador, Paraguay,
Peru, Uruguay, and Venezuela.
PAN-ARAB 1997 Algeria, Jordan, Egypt, Lebanon, Morocco, Syria, Sudan,
Tunisia
WAEMU
West African Economic
and Monetary Union
1973
1994
Benin, Burkina Faso, Cote D’Ivoire, Mali, Mauritania, Niger,
Senegal, Togo, and Guinea-Bissau (1997)
125
Table A4.2.3
Data Sources
Data
Series Description Source
Bilateral
Trade
Data
Annual bilateral exports
valued in million US
Dollars between each pair
of trading partners
Direction of Trade Statistics (DOTS)
IMF (2007)
GDP GDP valued at PPP nominal
billion US Dollars World Economic Outlook (2006)
International Monetary Fund (IMF)
GDP per
Capita GDP per capita valued at
PPP nominal US Dollars World Economic Outlook (2006)
International Monetary Fund (IMF)
Distance Straight-line distance
between capital cities in
each country in the sample
measured in kilometres.
Centre D’études Prospectives et D’Informations
Internationales <http://www.cepii.fr/francgraph/bdd/distances.htm>
Adjacency Dummy variable
1 if countries share a
common border
0 if countries do not share a
common border
Centre D’études Prospectives et D’Informations
Internationales
<http://www.cepii.fr/francgraph/bdd/distances.htm>
Common
Language Dummy variable
1 if country pair speak the
same language
0 if country pair do not
speak the same language
Centre D’études Prospectives et D’Informations
Internationales
<http://www.cepii.fr/francgraph/bdd/distances.htm>
Trade
Bloc Dummy Variable
1 Country pair are both
members of the same RTA
0 Country pair are not both
members of the same RTA
World Trade Organization
<http://www.wto.org/english/tratop_e/region_e/
region_areagroup_e.htm>
126
Appendix A4.3
Economic Foundation of the Gravity Model
This appendix, based on the work of Feenstra (2002), sets out the details of the
derivation of the gravity model. Given that utility function is based on constant
elasticity of substitution
(A4.1)
Where Ni is the varieties imported from country i , cij is the quantity consumed of the
variety and σ is the constant elasticity of substitution. This utility can be maximised
subject to the budget constraint or income
(A4.2)
where Yi is the income of country j, which is equivalent to its spending on other
countries’ goods, pi is the price of the imported good, tij is the transportation cost from
country i to country j.
In order to maximise the utility in equation (A4.1) the Lagrange multiplier
method is utilised. Combining (A4.1) and (A4.2) to form the Langrangian function, we
get:
(A4.3)
Partially differentiating with respect to and cij and equating to zero yields;
(A4.4)
(A4.5)
where . From (A4.4) we obtain an expression for cij;
(A4.6)
Substituting (A4.6) into (A4.5) gives us
H(σ-1)/σ
j i ij
i=1
U = N (c ) ,
1
,H
j i i ij iji
Y N p t c
H(σ-1)/σ
ij i i ij ij j
1 i=1
(c ) λ( N p t c -Y ).H
j ii
L U N
-1
ij
ij i ij
(c )L= =k,
c p t
H
i i ij ij
i=1
LN p t c = Y
k 1
-σ
ij i ijc =(kp t ) .
127
(A4.7) .
The constant is identified as
(A4.8)
Given (A4.7) and (A4.8), an expression for demand can be derived in the form of
(A4.9)
The price index Pj can be represented as
(A4.10)
It is thus easily verifiable that the denominator in (A4.9) is a simple manipulation of
(A4.10). Since the demand for imported goods of country j is identified in (A4.9) it is
possible to then derive an expression for export flows from country i to country j.
Let the export flow from country i to country j be denoted as
This identity simply states that the value of exports of country i to country j is
equivalent to the product variety sold, its price, and the quantity consumed. Substituting
(A4.9) into the identity allows us to derive an expression for flow of exports from
country i to country j:
(A4.11)
H-σ
i i ij i ij j
i=1
( N p t (p t ) )k=Y .
k
j-σ
H1-σ
i i ij
i=1
Yk=k = .
N (p t )
-σ
ij i ij
-
j i ij
1
i
i ij j
-
j
c =k(p t )
Y (p t ) =
N
(p t ) Y = x
P
( )
.
i ij
j
p t
P
1 1-σH
1-σ
j i i ij
i=1
P = N (p t ) .
ij i i ij ijX N p t c .
ij i i ij ij
i ij j
i i 1-
j
1-
i ij
i j 1-
j
1-
i ij
i j
j
X = N p t c
(p t ) Y= N p
P
(p t ) = N Y
P
p t = N Y .
P
128
The expression (A4.11) in essence is the gravity equation of trade. Country i’s income
can be included in expression (A4.11) using i i iY N p y . Where y is the fixed firm’s
output which is produced based on the assumption of zero profits in a monopolistic
competitive market (Krugman 1981). Substituting the expression into (A4.11) results in
(A4.12)
To estimate (A4.12) we take its logs:
Note the this expression is identical to Equation (4.7) in section 4.2. For estimation
purposes, however, transportation cost is not observed and thus is proxied for by
distance or c.i.f/f.o.b ratios.
X ij YiY j
pi y
pitij
Pj
1
=YiY j
piy
tij
Pj
1
.
129
CHAPTER 5
HOW MUCH CONVERGENCE?
5.1 Introduction
In 1981, five Gulf countries, Bahrain, Kuwait, Oman, Qatar and Saudi Arabia,
together with the United Arab Emirates, agreed to coordinate their economic, political
and social policies. To achieve this they established the Gulf Cooperation Council
(GCC). Since then, the Council has initiated a major economic integration programme
aimed at unifying the policies of these countries in order to assimilate their economies.
The GCC countries have followed a traditional approach to economic integration,
progressing from a free trade area to a customs union, followed by a common market
and, finally, a monetary union. Convergence of these six economies is essential to
facilitate successful economic integration.
The central hypothesis of this chapter stipulates that the GCC economies
demonstrate convergence with respect to growth and income. Convergence is a by-
product of the similarities between the GCC economies and their economic integration,
and the validity of this claim will be tested using a number of distinct approaches to
convergence. This chapter aims to answer two main questions: are the GCC economies
converging with respect to economic growth; and does the economic integration of the
GCC play a role in the convergence of the six economies‘ incomes?
This chapter analyses the convergence of the GCC economies based on economic
growth and income. The analysis aims to determine the degree to which the GCC
countries have converged, and the implications of this for their economic integration
agendas. The chapter will progress as follows: section 5.2 presents an overview of the
stages of economic integration and the GCC‘s experience to date. Section 5.3 presents
basic macroeconomic indicators of the six member countries. Section 5.4 defines
convergence in the context of a neoclassical framework. Section 5.5 presents an
alternative view of convergence in terms of dispersion of growth, through which the
GCC is compared to other regions. Section 5.6 analyses convergence using deviations-
from-the-mean, it tests GCC-specific income convergence. Using this technique the
GCC‘s experience is also compared with that of the EU. The chapter concludes with a
summary of findings and a discussion of their implications.
130
5.2 Economic Integration
Economic integration can be described as a process where restrictions to
movement of goods, capital and labour are reduced or removed completely. The process
involves harmonisation of laws, standards and regulations. In his Glossary of
International Economics, Alan Deardorff (2000) defines it as follows:
―Economic integration refers to reducing barriers among countries to
transactions and to movements of goods, capital and labour, including harmonisation of
laws, regulations, and standards. Common forms include FTAs, customs unions, and
common markets. Sometimes classified as shallow integration vs. deep integration.‖
The process of economic integration, as suggested by the definition, is a
complex and multifaceted process. It can take a number of forms. Deardorff‘s (2000)
definitions of the two distinct approaches regarding integration illustrate this point:
shallow integration leads to ‗reduction or elimination of tariffs, quotas, and other
barriers to trade in goods at the border, such as trade limiting customs procedures.‘ In
contrast, deep integration is ‗economic integration that goes well beyond removal of
formal barriers to trade and includes various ways of reducing the international burden
of differing national regulations, such as mutual recognition and harmonization.‘
5.2.1 Stages of Economic Integration
Whether shallow or deep, economic integration usually follows four sequential
initiatives: Free Trade Areas (FTAs), customs unions, common markets and monetary
union. Each stage increases the depth of economic integration between the countries
involved. Each stage requires greater commitment from the participants, and further
harmonisation and standardisation. In order to appreciate this progression a description
of each stage is useful:
1. Free Trade Areas (FTAs)
Within FTAs, barriers to goods movement are reduced but members
maintain individual tariff schedules against goods imported from third countries.
Operation is effective if FTAs requires certificates of origin to verify the source
of imports. These documents distinguish goods produced within or outside the
area.
2. Customs Unions
In custom unions all tariffs are abolished and no restriction to trade
exists, as with FTAs. In this case, however, customs unions members levy
131
identical tariffs on all third countries‘ imports. A unified tariff schedule is
adopted by all members.
3. Common Market
Common markets are a progression from customs unions. Not only are
goods allowed to move freely, but so are factors of production. Free movement
of labour and capital across borders is facilitated, ensuring that equal treatment
of factors of production applies across borders.
4. Monetary Union
A monetary union is the culmination of the economic integration
process. Once goods and factors of production move freely, members of a union
seek to unify their national currencies. Members relinquish their national
currencies and a common currency is adopted across political borders as legal
tender. This stage requires national governments to surrender their sovereignty
over monetary policy to a supranational central bank.
These descriptions of the stages of economic integration provide a broad
framework that requires considerable political will and resolve to put into effect. There
are a number of examples of regions that fall within the stages discussed above. The
most common example of a FTA is the North American Free Trade Area (NAFTA).
The European Union (EU) has also made extensive progress along the outlined stages of
integration. In 1999 the European Union launched a common currency—the Euro—
although this has yet to be adopted by all member countries. Other examples exist of
integration processes, such as the Association of Southeast Asian Nations (ASEAN), the
Caribbean Community (CARICOM), the Economic and Monetary Community of
Central Africa (CEMAC), the Mercado Comun del Sur (MERCOSUR) and the Gulf
Cooperation Council (GCC). In each case there are varying degrees of integration along
the four main stages. For example, CARICOM operates a common market, but
MERCOSUR has yet to revive its FTA. The GCC recently launched a customs union
(in 2003), and introduced a common market in 2008. Various developments within
different economic regions suggest varying degrees of success in establishing
operational and effective economic unions.
5.3 The Gulf Cooperation Council
Officially known as the Cooperation Council for the Arab States of the Gulf
(GCC), the countries making up the GCC share a number of common characteristics
including language, religion, and cultural customs, and economic structures in which
132
resource exports are pivotal to growth. Most of the GCC countries are members of the
Organisation of Petroleum Exporting Countries (OPEC). Given their mutual
characteristics, they set out to coordinate their policies and generate integration with
each other.
Table 5.1 provides a brief summary of the main economic and demographic
descriptive statistics of the GCC, comparing the years 1981 and 2006. The GCC‘s
output has more than trebled in the past twenty-five years, and the mean GDP per capita
has increased by 65% despite high population growth—the population has more than
doubled in the past two decades. The most populous country is Saudi Arabia where, in
2006, 23 million people lived. The least-populated country within the GCC is also the
smallest: Bahrain, with a total of 750,000 people in 2006. However, the UAE has the
fastest-growing population, more than tripling over the last twenty-five years.
Table 5.1
Basic Economic Indicators of the GCC
Measure of size 1981 2006
GDP (PPP) Billion $US 194 699
GDP per Capita (PPP) $US 14,215 23,548
Population (Million) 14.2 36.0
Source: World Economic Outlook and author calculations.
According to World Bank classification, countries with a Gross National Income
(GNI) per capita between $3,596 and $11,115 are deemed ‗upper–middle-income‘.1
Countries with a GNI per capita greater than $11,116 are classified ‗high-income‘. On
average, the GCC countries are considered to be upper–middle to high-income
economies. Based on GNI per capita in 2006, Oman falls in the upper–middle-income
category, while Bahrain, Kuwait, Qatar, Saudi Arabia and the UAE are considered as
high-income countries.
The progress of the GCC‘s economic integration has followed the four stages
discussed above. In 1983 the GCC launched its FTA to help reduce trade restrictions
between member countries. It did not progress to the next step until 2003, when it
initiated a customs union whereby all remaining restrictions between member countries
on movement of goods were removed and unified tariffs against non-members were
established. In 2008 the GCC launched its common market, allowing greater mobility of
capital and labour within the region. Finally, in 2010, the GCC plans to launch a
common currency and create a GCC central bank.
1 World Bank (2008a).
133
Convergence is paramount if economic integration is to be successful. This is
especially true in the last stage, where the GCC countries will adopt a common
currency. The absence of convergence may prove problematic in maintaining a credible
monetary union. This is particularly true if the economies involved are not behaving
similarly, where macroeconomic shocks can affect each country differently.
Convergence in a broader perspective can ensure greater uniformity with respect to
unforeseen shocks and disturbances.
5.4 Convergence
This section will apply the neoclassical approach to growth and convergence using
the concepts of absolute and conditional convergence. The section will incorporate
RTAs in the analysis to examine their effect on convergence within such regions.
5.4.1 Absolute Convergence
Absolute convergence is a proposition of the Solow growth model and its
variants to explain economic growth and its determinants. It is conceptualised as the
catching-up effect, where poorer economies reach the levels of economic well-being
their developed counterparts currently enjoy. This process in theory is related to the idea
that growth and per capita income are inversely related, as emphasised in the
neoclassical theory of growth (Barro & Sala-i-Martin, 1992). Neoclassical growth
models assume diminishing returns to capital. This implies that poorer countries with
less capital can experience greater returns on capital compared with their capital-
endowed counterparts. As a consequence poorer countries will grow at a faster rate than
richer countries.
This definition of convergence can be dubbed absolute convergence in the sense
that economies grow closer together with respect to their per capita incomes. Generally
the concepts of absolute -convergence can be measured using the following regression:
(5.1)
Equation (5.1) has the left-hand side ratio of GDP per capita in country i in year t+T to
that in an initial year t. The logarithm of this ratio, log (yi,t+T/yit), is approximately the
proportionate growth rate over the period [t, t+T] so that dividing by T gives the
average annual growth rate. Equation (5.1) expresses this annual growth as a linear
function of the log of GDP in the initial year, plus a random term it. On the right-hand
side, yit is the initial GDP per capita at time t, and β is the parameter to be estimated. If β
, 1log log .
i t T
it it
it
yy
y T
134
< 0, convergence is present. Assuming country i has a lower GDP per capita than
country k, the GDP growth rate of the former should exceed that of that latter.
Eventually the GDP per capita levels will even out.
The results of estimating Equation (5.1) are reported in Table 5.2. The data was
sourced from the World Bank‘s World Development Indicators 2008. Using annualised
real GDP per capita growth rates, Table 5.2 reports results for two ten-year and one
seven-year sub-cross-sections for the period 1980 to 2006. The table also includes a
long-run regression of growth rate from 1980 to 2006 in column (4).
Based on the criteria of β<0 as an indicator of convergence, the estimates
reported in the table do not indicate convergence in all sub-period cross-sections except
for 2000–06 in column (3). The sub-period cross-section coefficients are only
significant for 1990–99 at the 5% level. For the full period, the estimated coefficient of
β is positive and significant at the 5% level. This suggests divergence instead of
convergence, based on the neoclassical models of growth: thus it is sensible to infer no
convergence takes place within the period from 1980 to 2006.
Table 5.2
Absolute Convergence Estimates, Cross-Sectional
Period
1980–89
(1)
1990–99
(2)
2000–06
(3)
1980–06
(4)
Constant 0.0004 (0.0116) –0.0205 (0.0109) 0.0215 (0.0067) –0.0040 (0.0091)
Per capita GDP 0.0010 (0.0015) 0.0036 (0.0012) –0.0005 (0.0008) 0.0022 (0.0011)
R-squared Adjusted –0.0038 0.0260 –0.0041 –0.0070
Number of Observations 141 174 171 149
Note: White heteroskedasticity consistent standard errors in parentheses
The lack of international convergence in growth can be verified by plotting
income growth over the sample period against the initial year‘s GDP per capita. The
regression line in Figure 5.1 clearly indicates the lack of convergence within the sample.
The regression line has a gradient of 0.002, which indicates lack of a negative
relationship between the growth of GDP per capita and the initial GDP per capita. In
absolute terms, the world exhibits no absolute convergence. However, it is possible to
examine convergence for income-specific groups, where economies may be more
homogeneous.
, 1log log .
i t T
it it
it
yy
y T
135
Figure 5.1
Absolute Growth Divergence, 19802006
Source: World Bank (2008b)
Dividing countries into different income groups based on their GDP per capita
may show a different picture. The results of convergence estimates based on income
levels are reported in Table 5.3 for five different income categories.
Table 5.3
Absolute Convergence Estimates, Country Groups
Country Group
Low Income
Lower–
Middle
Income
Upper–
Middle
Income
High Income
(non-OECD)
OECD
(1)
(2)
(3)
(4)
(5)
Constant 0.090 (0.038) 0.141 (0.032) 0.173 (0.032) 0.224 (0.056) 0.103 (0.054
)
Per capita GDP –0.016 (0.007) –0.019 (0.005) –0.020 (0.004) –0.023 (0.006) –0.009 (0.006
)
R-squared Adjusted 0.183 0.438 0.427 0.445 0.206
Number of
Observations 30 36 27 10 25
Notes: White heteroskedasticity consistent standard errors in parentheses
The income categories of Low Income, Lower–Middle Income, Upper–Middle
Income, High Income (non-OECD) and OECD countries are based on the 2007 GNI per
y = 0.0022x - 0.004
-8%
-6%
-4%
-2%
0%
2%
4%
6%
8%
10%
4 5 6 7 8 9 10 11 12
Grw
oth
(p
.a.)
Ln GDP per capita 1980
, 1log log .
i t T
it it
it
yy
y T
136
capita World Bank (2008) figures.2 Unlike the previous results, the estimate β is now
negative in all cases. It is the largest for High Income (non-OECD) countries in column
(4). While these results indicate significant convergence within the sub-income groups
with the exception of the OECD, caution must be exercised. The number of useful
observations is small in all cases and severely reduced for the High–Income (non-
OECD) group. However, for columns (1), (2), and (3) the results remain significant for
a small sample. These estimates indicate that within income groups there is some degree
of convergence.
5.4.2 Conditional Convergence
The results of absolute convergence above do not support convergence across a
large group of countries. This is not a surprising result since the concept of absolute
convergence is crude at best. It relies on the assumption of common attributes for all
countries included within the sample, and assumes that all countries are homogenous.
Thus a Low Income country is expected to have the same attributes as an OECD
country and to behave in a similar fashion when growth takes place. This assumption is
not sensible, especially in light of the poor performance of the model. Therefore using
the basic model of convergence, there is little evidence to support the hypothesis that
poorer countries grow faster than their richer counterparts. Theoretically, the flaw of
absolute or unconditional convergence is due to the fundamentals of the Solow model of
growth, which implies that convergence will take place towards a common steady state
of per capita income. This growth is negatively related to the initial levels of income.
Thus when β<0, convergence is present; otherwise divergence has taken place (Durlauf,
Johnson and Temple 2005).
To address this issue of heterogeneity within the sample, modifications to the
Solow models of growth were introduced. From these, conditional convergence and
endogenous growth theories evolved. Conditional convergence uses the Solow growth
model and augments it with country-specific attributes. In contrast, endogenous growth
theories take the path of including knowledge and human capital within the modelling
of growth. Romer (1986) and Lucas (1988) have led the research in this field. This
chapter, however, follows the neoclassical approach to growth, utilising the work of
Barro (1991), Barro and Sala-i-Martin (1992) and other cross-country studies. A
detailed derivation of the neoclassical model based on their approach is presented in
Appendix A5.1.
2 World Bank (2008a).
137
Conditional convergence is based on accounting for differences between
countries based on economic, political, social and demographic characteristics. The
neoclassical model includes savings and population growth as control variables of
growth towards the steady state; country differences are not accounted for. The model
shown in Equation (5.1) above is expanded by the inclusion of a number of variables
that address the shortcomings of the absolute or unconditional convergence results. This
approach draws on the extensive work done by Barro and Sala-i-Martin in a number of
studies on cross-country comparison. The conditioning variables are introduced to
control for different steady states across a heterogeneous sample (Barro et al, 1991).
The conditional convergence model is generally presented as follows:
(5.2) , 1log log ,
i t T
it it it
it
yy X
y T
where is a matrix of country specific characteristics discussed above. Equation (5.2)
also includes initial GDP, as above. We shall use Equation (5.2) to estimate β and γ in a
cross-section-based regression.
A great number of conditioning variables are used to account for steady state
differences and growth in per capita GDP, but not all have been shown to be significant.
Levine and Renelt (1992) conducted a substantial sensitivity study of cross-country
growth regressions using typical variables used in the literature. They found that
investment shares of GDP correlated significantly and robustly with growth, and that
investment and trade were related. However, trade policies and growth were not as
robustly correlated when investment was included in the regressions. They also found
support for conditional convergence, and that fiscal policy indicators robustly correlated
with growth. Many of the other variables, including political indicators, were not robust,
however. Some of the major control variables were also extensively tested by Barro and
Sala-i-Martin (2004). They found support for conditional convergence such as those
indicated by the neoclassical growth models. The following section reports the results of
estimates of conditional convergence.
One of the more significant conditioning indicators introduced to the model
above is human capital. Since the neoclassical growth models concerned themselves
only with physical capital, human capital was assumed a given and not included as a
variable. Using education as a proxy for human capital, for example, data such as
primary and secondary school enrolment quantify the stock available. Technology is
another factor that the neoclassical model did not account for directly, since it remained
itX
138
exogenous in the earlier growth models. Technological progress is measured in terms of
the number of scientific and research publications. Other indicators include population
growth and fertility to gauge demographic differences. Openness and terms of trade
measure external policy and its implications on countries. Inflation is included to gauge
monetary policy as central banks aim to maintain stable prices. Fiscal policy is proxied
for by government expenditure. Domestic credit from commercial banks is used to
represent the state of the financial conditions within countries. Political rights and civil
liberties indices are used to proxy for institutions. Geographic features are limited to the
lack of direct shipping lane access, captured by a landlocked dummy. Finally, the
analysis includes dummy variables for some of the more notable RTAs such as the EU,
NAFTA, ASEAN, CARICOM, and more importantly for our purposes, the GCC.
The estimation of conditional convergence is subject to considerable debate. The
main approach in the literature is to use cross-sectional linear estimation. Thus, the sign
of coefficient of initial GDP, , indicates the presence (when < 0) or absence (β ≥ 0)
of convergence. OLS cross-section regressions are considered suitable for the
estimation by some, including Barro and Sala-i-Martin (2004), but not everyone agrees.
In contrast to the cross-section approach, Islam (1995) suggests a panel estimation
approach to take into account country effects on long-term economic growth. This
approach requires the data to be handled in shorter time increments without
compromising the long-term periods. Studies that choose panel estimation as an
econometric method typically use five-year increments to divide the data over the whole
sample period. This reduces the number of observations available but does not appear to
affect the detection of long-term effects. Quah (1993, 1996) suggests a completely
different approach to testing for convergence, arguing that the convergence trends
detected in the literature are inherent in the data structure. Quah (1996) supports the
argument of convergence clubs found in the literature, such as those of Baumol (1986)
and Ben-David (1994). Quah‘s bi-modal method suggests polarisation of the
distribution of countries, in that convergence is occurring not between all countries but
within groups of countries. His results, however, have been found to be sensitive to
country selection (Durlauf et al. 2005). For the purposes of this chapter, cross-sectional
regressions are used to test for convergence.
Using the conditional variables above, cross-sectional estimation of convergence
can be carried out. Essentially, the estimation is concerned with two years, 1980 and
2006. The aim is to estimate the relationship between growth over the period 1980 to
2006 and the initial conditions of the variables listed above. There is an advantage in
139
using cross-sectional data in this case, in that it allows for a clear indication of
convergence during the period without any contamination from business cycles and
short-term changes: that is, the long-term perspective helps isolate clear trends. The
increased depth in data may emphasise interactions between the conditioning variables
and growth better, but the trade-off is the omission of interim changes during the period
of interest.
The results of the cross-sectional convergence are reported in Table 5.4. The
table includes four main regressions that include different combinations of the variables
listed above. Column (1) of the table includes estimates of the main variables discussed
earlier. The coefficient of initial per capita GDP level is negative, -0.007. This implies
that initial levels of income are negatively-related to growth. Convergence is present
when growth is conditioned based on economic, social and political variables. However,
while most variables comply with economic intuition, such as the negative relationship
between fertility and economic growth, most of the estimates are insignificant. In this
case only the per capita GDP level and fertility are significant, at the 5% and 10% levels
respectively. The remaining coefficients are not statistically significant. Furthermore,
the sample size is severely reduced when the conditioning variables are introduced—in
fact about less than one quarter of the countries available can be included in such a
regression. Alternatively, it is possible to omit some of the variables in column (1) and
test for convergence without taxing the model. Omitting a number of the variables
which were insignificant in the previous regression, column (2) of Table 5.4 presents
results with the remaining indicators. The variables—which severely reduce the number
of observations—include government spending as a percentage of GDP, life
expectancy, terms of trade, secondary school enrolment, and scientific publications. The
estimated coefficients for the level of per capita GDP remain negative and significant at
the 1% level. Conditional convergence is present once initial conditions are accounted
for. All the variables included in column (2) except openness are statistically
significant. It should be noted here that the political rights coefficient is negative. This
does not imply that political rights, a proxy for democratic rule, are negatively related to
growth. The nature of the index, in which democratic countries are given a lower score
than their non-democratic counterparts, explains such a result. When we reduce the
140
Table 5.4
Cross-Sectional Conditional Convergence Estimates, 1980–2006
Model
Variable (1) (2) (3) (4)
Constant –0.0173 (0.0839) 0.0196 (0.0269) 0.0224 (0.0321) 0.0267 (0.0298)
Per capita GDP –0.0070 (0.0033) –0.0057 (0.0018) –0.0059 (0.0020) –0.0053 (0.0022)
Fertility –0.0192 (0.0108) –0.0222 (0.0054) –0.0226 (0.0061) –0.0218 (0.0062)
Investment 0.0092 (0.0074) 0.0123 (0.0054) 0.0122 (0.0055) 0.0105 (0.0049)
Government Spending –0.0048 (0.0040)
Life Expectancy 0.0201 (0.0129)
Political Freedom –0.0052 (0.0037) –0.0051 (0.0027) –0.0050 (0.0029) –0.0054 (0.0029)
Inflation 0.0064 (0.0041) 0.0036 (0.0018) 0.0035 (0.0019) 0.0032 (0.0020)
Openness 0.0107 (0.0065) 0.0033 (0.0024) 0.0034 (0.0024) 0.0030 (0.0025)
Secondary School Enrolment* 0.0026 (0.0023)
Scientific Publications* 0.0038 (0.0024)
Terms of Trade –0.0039 (0.0054)
Dummies
Landlocked –0.0012 (0.0046) –0.0001 (0.0046)
Custom Union –0.0040 (0.0036)
Free Trade Area –0.0058 (0.0042)
R-squared Adjusted 0.3257 0.3372 0.3276 0.3307
Number of Observations 49 87 86 86 Note: White heteroskedasticity standard errors in parentheses
* Period average
141
number of variables to the essential statistically significant indicators, the explanatory
power of the model remains relatively unchanged. The R-squared adjusted here is
0.3372, compared with 0.3257 from the previous regression. This shows that the model
is unaffected by the reduction in the number of variables. The model can be further
improved by including a number of variables to capture geographical conditions that
may influence growth. This is generally achieved by the use of a dummy variable to
indicate whether a country is landlocked or not.
Where countries are landlocked, access to shipping lanes is restricted and
consequently trade may be influenced. The result of including such a dummy variable in
the regression is reported in column (3). The coefficient of the landlocked dummy is –
0.0012, implying that long-term growth of countries without direct access to shipping
lanes is affected negatively. The coefficient is statistically insignificant, and the
inclusion of the landlocked dummy does not affect the implications of convergence, the
per capita GDP coefficients being more or less the same and remaining significant.
The results remain relatively unchanged when trade policy is accounted for. The
model includes a measure of openness which captures how much trade countries are
involved in. However, the emphasis of this chapter is to understand the effects of joint
trade policies on convergence. To capture such effects, two dummy variables are
included—one for custom unions and the other for free trade areas. Within the sample,
if a country belongs to a customs union, it receives a value of 1— otherwise 0. If a
country is involved in a multilateral free trade area it receives a value of 1—otherwise 0.
Countries that are not involved in either type of arrangement or subscribe to any other
type of joint policy coordination, receive a value of 0 for each dummy.
The most common third option is a preferential trade agreement. The results in
column (4) of Table 5.4 show that the dummies for customs union and free trade area
both contribute negatively to growth. The estimated coefficients are negative but not
statistically significant. A sample including 86 countries implies that membership in a
customs union or a free trade area does not affect growth in any meaningful manner.
The explanatory power of the model is not affected, and nor are the convergence
findings. Conditional convergence remains statistically significant.
5.5 Dispersion and Convergence
An alternative method of defining convergence is the reduced dispersion of
countries‘ incomes. Currently, for example, per capita GDP varies significantly from
the richest to the poorest countries. The dispersion of GDP per capita levels and growth
142
clearly indicate a lack of convergence between countries across the world. Over time,
the dispersion between countries will increase or decrease because of many factors.
These changes allow observers to infer trends of convergence or divergence across
countries. A quick study of the global distribution of income per capita shows a distinct
distribution where countries have not converged.
A temporal distribution for 1970 and 2003 is illustrated in Figure 5.2. This is a
―smoothed‖ histogram constructed using data on GDP per capita from the Penn World
Table 6.2. The distributions are truncated at $US 30,000. Consider first the solid
continuous line labelled 1970. The median per capita GDP is approximately $US 3,000,
as indicated by the vertical line. About 40 countries fell below the median in 1970. Now
consider the dashed line labelled 2003. It represents the distribution of incomes in 2003.
Two features are evident, first the median income increases substantially to more than
$US 7,000. Moreover, the number of countries below the median has increased.
Another feature is the long tail of the distribution in 2003. This is prima facie evidence
that different countries do not convergence.
Figure 5.2
World‘s Distribution of Income
Source: Penn World Table 6.2
The analysis of dispersion, and thus convergence, aims to understand the
behaviour of a country‘s income over time vis-à-vis a group of countries. Figure 5.3 is a
good starting point for such an analysis, as a number of possible scenarios, with respect
to deviation, are possible. The perfect scenario for convergence is when the standard
1970 2003
1970 2003
0
5
10
15
20
25
30
35
40
45
0 5,000 10,000 15,000 20,000 25,000 30,000
Per capita GDP (PPP US$)
Num
ber
of
Countr
ies
Median Median
143
deviation of growth within a group of countries is zero—in other words, convergence of
growth has taken place such that there are no deviations from the mean. Perfect
convergence is unlikely to take place in the real world, however; thus, the alterative is a
low standard deviation. Take the hypothetical example of the GCC. If on average the
GCC‘s standard deviation is 2.3, and the six countries dispersion varies around this
value over time. The GCC would maintain minium dispersion since it is not increasing
or declining but stable at a low average. The other two conditions displayed in Figure
5.4 demonstrate the two opposite conditions of convergence and divergence. In the case
of convergence curve, the standard deviation is expected to decline over time. This
translates to reduced dispersion and volatility. In contrast, if the standard deviation
increases over time, it implies that the economies in question are diverging.
Figure 5.3
Dispersion and Convergence
5.5.1 The Case of the GCC
Consider the case of the GCC‘s GDP growth measured by the annual percentage
growth in GDP valued at PPP $US obtained from the International Monetary Fund‘s
(IMF) World Economic Outlook 2007. Figure 5.4 illustrates the average growth of the
GCC countries, indicated by the solid line. The figure also indicates the range of growth
each year shown by the shaded area. In the first few years where a greater spread is
observed, growth rates per annum varied by about 40%. This range declined during the
1980s but remained substantial. The pattern reversed in the early 1990s when the first
Gulf War took place. The range of growth rates in the GCC increased significantly, with
large fluctuations primarily as a result of the Iraqi invasion of Kuwait in 1990, when the
latter‘s economy contracted significantly. These large fluctuations in growth rates
declined towards the end of the period. With the exception of Qatar in 1997, the rest of
Standard
Deviation Divergence
Convergence
GCC
Converged
2.3
0
Time
144
the GCC countries grew at comparable rates to the region‘s average. The range of
growth rates remained at a minimum in 2005 and 2006.
Figure 5.4 illustrates a number of important features of the GCC economies. The
first is that the GCC countries do not necessarily grow at the same rate despite sharing
similar vulnerabilities with respect to oil prices. Second, there appears to have been
greater conformity of growth rate across the region in the past decade. However,
inferences from Figure 5.4 are indicative at best. The ranges of growth rates shown
there do not take into account the size of the six economies. For example, the large
fluctuations noticed in the early 1990s can be primarily attributed to the third largest
economy within the GCC: Kuwait. An economy such as Bahrain, the smallest of the six,
can also show substantial effects on the range of growth. In other words, the six
countries are weighted equally. A better practice is to control for both the size factor and
the contribution of each country within the GCC.
Figure 5.4
GCC Average Growth Dispersion
(% p.a.)
Source: IMF (2007) and Author‘s calculations
Note: Solid line is unweighted mean of growth in GDP of country group. The width of the shaded region is the range of growth rates
To account for the size of each country, each country‘s share of the total
regional output is required. Let c = 1, …, 6 denote the six GCC countries. Let Yct be the
GDP of country c at time t. The share of country c of the GCC‘s output is equivalent to:
(5.3) 6
1
.ctct
ct
c
Yw
Y
145
The expression wct defines the share of county c of the GCC‘s total output at time t.
Taking the arithmetic mean of expression (5.3) for years t and t–1, produces:
(5.4) , 1
1.
2ct ct c tw w w
Table 5.5 reports the derived weights for each of the GCC countries for five-year
intervals from 1981 to 2006. Using these weights, an inter-temporal picture of the GCC
can be obtained.
To indicate the changes in the sizes of the GCC members, Figure 5.5 plots the
values of in 2006 against those in 1981. Figure 5.5 illustrates a number of points.
Quite clearly Saudi Arabia dominates the region with its physical and economic size. In
fact, in 1981 Saudi Arabia‘s share of the GCC‘s output was approximately 70%. It is
followed distantly by the UAE, Kuwait, Oman, Qatar and Bahrain in that order. The
shift in the share of total output can also be realised from Figure 5.5 ,where the 45 line
indicates which countries‘ share grew and which did not. In the case of Saudi Arabia, its
output share fell from 1981 to 2006. This is indicated by its point being below the 45
line. The same is true for Kuwait. In contrast, the UAE, Oman, Qatar and Bahrain all
experienced an increase in their share of GCC output over the past two decades. This is
particularly true in the case of the UAE, where an increase from 15% to 21% was
experienced.
Evidently, the shares of each of the GCC countries are not constant and they
shift through time. Thus, instead of using a simple (or unweighted) average and the
range in Figure 5.4, the use of weighted averages and standard deviations is more
informative. Given the weights from Equation (5.4) the weighted mean of real growth
can be expressed as follows:
(5.5) 6
1
.t ct ct
c
g w g
The corresponding weighted standard deviation is as follows:
(5.6)
Figure 5.6 plots the weighted mean of GDP growth for the GCC countries in the
solid line. The dashed vertical lines are the weighted standard deviation derived from
Equation (5.6). The weighted mean of growth here declines in 1981–1982, but increases
ctw
6
2
ct ct t
c=1
V = w g - g .
146
again in the following few years. It fluctuates during the 1980s and plateaus in the early
1990s. This trend continues until the late 1990s, when a decline begins to take place,
most obviously in 1998.
Table 5.5
Output Shares of the GCC Countries
Bahrain Kuwait Oman Qatar Saudi
Arabia
United Arab
Emirates
1981 0.02 0.08 0.03 0.03 0.69 0.15
1985 0.02 0.09 0.06 0.03 0.63 0.16
1990 0.02 0.08 0.06 0.03 0.65 0.16
1995 0.02 0.09 0.07 0.02 0.63 0.17
2000 0.02 0.08 0.07 0.04 0.60 0.18
2006 0.03 0.09 0.07 0.04 0.57 0.21
Source: Author‘s calculations
Figure 5.5
Relative Importance of GCC Members, 1980 and 2006
(Share of Total GDP)
Source: Author‘s calculations
A short-lived respite takes place in 2000 before the slump of 2001 takes hold.
Towards the end of the period, however, the weighted mean growth increases again and
stabilises. Of interest here are the weighted standard deviation bands: throughout the
period the bands are significantly large, especially during the 1990s. These large
0
10
20
30
40
50
60
70
80
0 10 20 30 40 50 60 70 80
1980
20
06
Saudi
Arabi
a
UAE
Kuwait
Qatar
OmanBahrai
n
147
deviations, significant during the last three years of the period as well, do not support
the hypothesis of converging economies within the GCC in terms of growth.
Figure 5.7 compares the two means. Panels A and B reproduce the mean and
weighted mean of growth. The most striking difference is in the greater fluctuations in
panel A compared to those in B. The simple mean detects the growth rates of change
regardless of the size effect. Thus from 1990 to 1992 the mean changes dramatically.
This effect is not present when the weighted mean is considered in panel B. During the
first few years of the period, the large fluctuations in the simple mean are minimised. In
the second half of the period, the weighted mean in panel B reflects similar movement
as a simple mean.
Figure 5.6
GCC Growth: Weighted Mean and Standard Deviation
(% p.a)
Source: Author calculations Note: Solid line is weighted mean of growth in GDP of country group
The width of the dashed lines are the weighted deviations of growth in the six GCC countries
Emphasising the significance of dispersion, Figure 5.8 illustrates the weighted
standard deviation derived from Equation (5.6). The figure shows that the dispersion of
growth declines sharply during the first year and then remains low during the 1980s. In
1990, the weighted standard deviation of growth more than triples as a response to
political turmoil in the region. Growth declines sharply but spikes again in 1995. The
late 1990s experience low dispersion as the weighted standard deviation remains low.
However, towards the end of the period a marked increase in dispersion is evident.
Based on the figure, periods of convergence in the 1980s and late 1990s can be
observed, although the occurrences of divergence within the region are significantly
greater. In 1990, and 1995, and from 2002 onwards, strong deviations persist where
values of the weighted standard deviations increase three-fold at least. This confirms
148
suggestions of divergence between the GCC economies—especially in the last few
years.
Figure 5.7
GCC‘s Real GDP Growth
(% p.a)
A. Unweighted Mean
B. Weighted Mean
Source: Author‘s calculations.
-5%
0%
5%
10%
15%
20%
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
149
Figure 5.8
Weighted Standard Deviation of Growth
(% p.a.)
Source:Author‘s calculations.
5.5.2 Other Regional Integration Experiences
Convergence amongst states or regions may be subject to the characteristics of
the countries involved. Historically, nations and regions have come together to improve
their economic, social and political circumstances. Economic initiatives such as the
early customs unions between the German states in the 19th
century and, more recently,
the European experience, are examples of policy integration and cooperation.
Politically, the birth of the United States of America and the Commonwealth of
Australia are other examples of unification and consolidation. In the Caribbean islands,
states work together to maintain economic ties and cooperation. The GCC‘s experience
can be compared with a number of regions that have undergone similar or more
comprehensive integration, including the EU, the US and Australia. The EU represents
the most successful economic integration process between countries. The US and
Australia offer a situation where individual states and territories choose to form a
federal union: they provide a case of fully-fledged integration, both economic and
political.
The EU provides a good example of economic integration that has proved
operational. However, for the sake of comparison, only the EU15 will be referred to.
These include the original six members, Belgium, France, Germany, Italy, Luxembourg
and the Netherlands. Denmark, Finland, Greece, Ireland, Norway, Portugal, Spain,
Sweden and the UK are later signatories. Figure 5.9 depicts the weighted mean of
150
growth for the EU15. The EU15 countries have grown significantly since the early
1980s, where average growth exceeded 3% in some years. This upward trend reversed
in 1988 when growth declined until 1993. Another surge in growth was experienced in
1995, but then rapidly slowed until 1996. The EU15 enjoyed accelerated growth until
2000 following the global slowdown. In 2003 the EU15‘s growth picked up again, but
only briefly, and in 2006 the slowdown reversed.
Figure 5.9 illustrates the fluctuating growth of the EU15, the cycles through
which growth sped up and declined. The early 1990s mark the Exchange Rate
Mechanism (better known as ERM) crisis and the departure of several of its members,
notably the UK. The global slowdown in 2001 is also evident within the EU15 with a
notable decline in growth. During most periods, growth dispersion ranges between 2%
to 4%. While the dispersion increases during the slowdown years in the 1990s, it
remains relatively constant during most of the years that follow. There are no
indications of divergence or convergence based on the figure. The European experience
indicates a minor decline in dispersion, however convergence is evident based on the
low dispersion observed.
Figure 5.9
Growth in EU15: Weighted Mean
(% p.a.)
Source: Author‘s calculations based on World Economic Outlook 2007 data.
Australia presents another good benchmark against which to compare the GCC.
Formed of a similar number of states as the GCC is, it demonstrates an example of full
economic integration, so observing the convergence experience of Australia against that
of the GCC is useful. Figure 5.10 shows the weighted mean of growth and its dispersion
for Australia. From 1986 to 2005 the Australian states and territories experience
151
positive growth on average, except in 1990 where growth is negative. In 1992 growth
rises substantially, and the states and territories grow by about 4%. During the 1990s
growth remains positive, despite declining in 1995–96. Another notable decline in
growth is observed from 1998 to 2000. This is very likely related to the Asian financial
crisis and its contagion effect on other markets. Despite global slowdown in 2001,
Australian states and territories are shown to enjoy positive growth that dips slightly in
2004.
Dispersion on the other hand reveals quite a different picture. The dashed lines
in Figure 5.10 indicate that the dispersion in states and territories is relatively large; in
some years growth it exceeds 6% on average. This is especially true during the last few
years displayed. Only in the middle years can a decline in dispersion be detected,
notably in 1996. The dispersion remains relatively constant throughout the time period,
however. The Australian states and territories do not show signs of convergence
between 1986 and 2005 and deviations from the mean growth rate remain persistent.
Figure 5.10
Growth in Australian States: Weighted Mean
(% p.a.)
Source: Author‘s calculations based Australian Bureau of Statistics data.
Figure 5.11 illustrates the weighted mean of real growth for the 50 American
states. After a recession at the beginning of the period, the growth rate recovered in
1981 and 1982. The spurt of growth was not sustained in the following years and
growth declined dramatically. During the rest of the 1980s, it kept declining, bottoming
in the 1990–91 recession. During the 1990s the states maintained positive growth
fluctuating between 2% and 5%, but in 2000 and 2001, the economy experienced
152
another major slowdown, where growth fell below 1%. The states recovered to pre-2001
levels but in 2005 growth declined again, although remaining above 1%.
Figure 5.11 also depicts the dispersion of the American states. Dispersion is
shown as generally declining from the early 1980s, where differences are about 10%
between states. The dispersion remains persistent throughout the time period. It starts to
decline during the 1990s to reach about 4%. Overall, from 1981 to 2006 the states show
a minor reduction in dispersion but no signs of divergence. The dispersion remains
relatively constant over the second half of the period. This suggests that the US has
converged considerably, in comparison to the GCC.
Figure 5.11
United States Weighted Mean of Growth
(% p.a.)
Source: Author‘s calculations based on Bureau of Economic Analysis and U.S. department of Commerce data.
Figure 5.12 compares the GCC‘s weighted mean of real GDP growth with that
of the EU15, Australia and the US. With the exception of Australia, the figures depict a
weighted mean growth over the period 1980–2006. Figure 5.12 illustrates the stark
difference between the GCC and other comparable regions. Surprisingly, the EU15 is
the most converged, followed closely by the US and Australia. These three show
moderate fluctuations in growth generally, while in contrast the GCC‘s growth is more
volatile on average and its dispersion significantly greater than in its counterparts. With
respect to growth, the EU15 bloc provides the most suitable example of convergence;
but whichever comparison is made, convergence has not taken place within the GCC
countries.
-5
-3
-1
1
3
5
7
9
11
1981
19
83
1985
19
87
1989
19
91
19
93
19
95
19
97
19
99
20
01
20
03
20
05
Mean 3.2Std. Dev. 1.5
153
Figure 5.12
Weighted Mean of Real GDP Growth
(% p.a.)
GCC EU15
Australia United States
Source: Author‘s calculations
154
5.6 Deviations from the Mean Approach
Much of the discussion above has dealt with the empirical estimation and
descriptive statistics of convergence. In this section we investigate another approach to
convergence, one that uses deviations from the mean of income. Instead of taking a
purely statistical approach, this framework adds statistical inference while allowing for
a non-parametric approach. The following analysis does not concern itself with the
theoretical underpinnings of growth but focuses on the coexistence of economic
integration and income convergence. Since the GCC is central to this analysis,
methodologies that apply to small samples are particularly useful. As a very small
sample, such as the GCC, restricts the usefulness of extensive parametric analysis, using
descriptive statistics makes it possible—as shown earlier—to draw some inferences
regarding the impact of economic integration on income convergence. This framework
does have its shortcomings, however—one being that it does not explicitly offer an
economic understanding of the important relationship between economic integration
and income convergence.
Adopting the approach of Ben-David (1993, 1996), a small sample convergence
estimate can be obtained. This allows for region-specific estimates of convergence
based on dispersion measures. Using deviation from the group mean as a measure of
convergence or divergence used, the following model is created: Let be the log of per
capita GDP of country i in year t, where i = 1,2,…,k. Future per capita GDP at t+1 is
directly related to that in current year t. Therefore:
(5.7)
The unweighted mean of the group of countries k at year t is . Equally, the future
mean of per capita GDP of group of countries k is directly related to that in the current
year, t. Accordingly:
(5.8)
Taking the deviation from the mean on both sides gives us:
(5.9)
ity
1 .it ity y
ty
1 .t ty y
, 1 1 , .i t t i t ty y y y
155
Convergence is present when <1. The speed of convergence can also be deduced by
modifying the model and differencing the deviations.3 Subtracting from both
sides gives:
(5.10)
The transformation of (5.10) allows for an estimation of the speed of convergence. Ben-
David (1993) uses (5.10) to estimate the speed at which individual countries converge
to the group mean. The coefficient of 1 gives the rate of convergence of country
i‘s per capita GDP to the group‘s average. Larger values of 1 imply greater speeds
of convergence.
Figure 5.13 plots the relationship between the differenced deviations and the
level deviations from the world mean for the period 1970 to 2003. The regression line is
flat, which indicates that deviations from the mean are not declining over time. This
supports earlier deviation analysis, of global convergence in section 5.5. A GCC-
specific picture is shown in Figure 5.14. Here, a negative slope is evident from 1970 to
2003. Figure 5.14 lends support to the argument that individual GCC countries are
converging toward the group mean. The deviations of GCC incomes appear to be
declining over time.
Figure 5.13
Income Convergence, 1970–2003
Source: Author‘s calculations based on Penn Tables 6.2 data
3 For details see Ben-David (1993, p666)
,i t ty y
, 1 1 ,( ) (1 ) .i t t i t ty y y y
-1.2
-0.8
-0.4
0.0
0.4
0.8
1.2
-4 -2 0 2 4
Deviations from the Mean
Dif
fere
nced
Dev
iati
on
s fr
om
th
e M
ean
156
Figure 5.14
GCC Income Convergence, 1970–2003
Source: Author‘s calculations based on Penn Tables 6.2 data
Table 5.6 reports results of estimating Equation (5.10). In the case of the GCC,
Table 5.6 reports the speed of convergence (1-ϕ) = 0.048. The coefficient of ϕ is equal
to 0.952, which is significant at the 5% level. This result complies with the condition
noted earlier of convergence being present when ϕ <1. Column (2) of Table 5.6 reports
the same regression using weighted deviations. Individual observations are weighted by
GDP shares derived earlier, so deviations are now represented as follows:
(5.11)
The weights itw are derived from Equation (5.4). This is done to keep this
approach as comparable to the previous descriptive analysis as possible. The results do
not differ in a meaningful way. The coefficient (1-ϕ) is still significant at the 1% level.
In this case, ϕ = 0.946, which is very similar to the estimate from Equation (5.10). The
results in Table 5.6 confirm that with respect to dispersion, the GCC shows signs of
convergence within the period 1970 to 2003. Using these estimates can also help find
the half-life of convergence. Ben-David (1993, 1996) shows that using the expression
log0.5 log yields the half-life of convergence for the respective group in question.
For the GCC, this yields 14 and 12.5 years respectively. Based on this, the GCC
members should converge towards the group mean in 25 to 28 years.
-.25
-.20
-.15
-.10
-.05
.00
.05
.10
.15
-.3 -.2 -.1 .0 .1 .2
Weighted Deviations from the Mean
Dif
fere
nce
d D
evia
tio
ns
fro
m t
he
Mea
n
, 1 1 ,( ) (1 ) .it i t t it i t tw y y w y y
157
Table 5.6
Convergence
The Deviations from the Mean Approach
GCC
Unweighted
(1)
Weighted
(2)
EU15
(3)
Speed of Convergence 0.048 (0.018) 0.054 (0.031) 0.011 (0.006)
Adjusted R2
0.012 0.037 0.013
Number of
Observations 198 192 510
Note: White cross-section heteroskedasticity consistent standard errors in parentheses
The analysis conducted by Ben-David (1993) was based on the six founding
members of the European Economic Community. Here the model is applied to the
EU15 despite entry years. The assumption is made that the EU15 have been part of the
integration process since 1970, which is not strictly true for all countries but does,
however, allow for comparisons with the dispersion descriptive analysis presented
earlier. Figure 5.15 plots the EU15‘s differenced deviations from the mean against the
levels of those deviations.
Figure 5.15
EU15 Income Convergence, 1970–2003
Source: Author‘s calculations based on Penn Tables 6.2 data
, 1 1 ,( ) (1 )i t t i t ty y y y
-.6
-.4
-.2
.0
.2
.4
.6
.8
-1.5 -1.0 -0.5 0.0 0.5 1.0 1.5
Log Deviations from the Mean
Dif
fere
nced
Lo
g D
ev
iati
on
s fr
om
th
e M
ean
158
Estimates of (1-ϕ) in Equation (5.10) for the EU15 are found to be statistically
insignificant and the half-life is 62 years. Despite the long projected convergence
timeframe, the result is not robust. However, a plot of the variables for the EU15
presents an interesting pattern. In comparison with the GCC‘s convergence pattern in
Figure 5.14, the EU15 results show significantly less deviation. The tight cluster of
points indicates that the EU15 has undergone substantial convergence. The regression
line is marginally negative here, indicating possible further convergence. This is in line
with analysis presented earlier with respect to weighted standard deviations. The
statistical insignificance may be a manifestation of past convergence.
5.7 Summary and Conclusions
The aim of this chapter was to examine the convergence of the six GCC countries
with respect to their income and growth. The key hypothesis proposed was that
economic integration amongst the GCC countries would influence growth and incomes
within the region so much that they would converge. The implications of convergence
are pronounced when considering the objective of creating a monetary union in the
foreseeable future. The GCC countries rely on similar economic attributes, including
similar growth cycles and incomes, to reinforce their integration efforts. The integration
process aims to bring these economies closer through trade liberalisation and other joint
policy coordination efforts.
The chapter used three main approaches for the theory of convergence in the
GCC: the ‗β-convergence‘, ‗dispersion of growth‘ and ‗deviation-from-the-mean‘
approaches. Using the first approach, on a global scale based on the neoclassical
approach to growth and convergence, evidence of countries arriving at the same income
levels is not present. In fact, in some cases diversion of income is the situation. When
the world is divided into homogeneous income groups, some support is lent to
convergence. Such results are not robust however, with most estimates of β-
convergence being insignificant. Since no evidence was found of convergence based on
the original neoclassical approach, conditional convergence was introduced to try to
capture the heterogeneity of the sample. Convergence is evident when growth is
conditioned on country-specific characteristics, such as economic, social and political
variables. The variables most effective for this sample included initial levels of income,
fertility, inflation, openness, investment and political liberties. An assessment of the
effect of regional trading blocs—in the form of currency unions or free trade areas—on
159
convergence suggested that there is no statistically significant effect of these
arrangements on growth or convergence of incomes.
The second approach to convergence used dispersion of growth. Focusing on the
GCC countries, the dispersion of growth, using unweighted and weighted averages,
indicated persistent differences. The GCC countries do not exhibit signs of convergence
with respect to growth. The analysis compared the GCC countries with other regions:
the EU, US and Australia. The GCC shows a greater variability of growth over time
compared to these regions. The findings of dispersion analysis indicate no convergence
between the GCC countries during the period 1980 to 2006.
Finally, the chapter used deviations from the mean methodology, to estimate
convergence based on a small sample. Using data from 1970 to 2003, the GCC
countries showed signs of reduction in deviations from the mean. The findings of this
approach indicate convergence within the GCC. This result should be viewed with
caution as half-life estimates signify a long period of convergence; approximately two
decades at least. The EU exhibits greater convergence between its member countries.
The analysis of the effect of the GCC‘s economic integration on income and
growth demonstrates the negligible convergence that has taken place within the region.
Given the underlying conditions of trade liberalisation and standardisation of standards
and policies, the impact of the process is not clear with respect to incomes and growth.
The GCC has yet to reach its potential as an RTA.
160
Appendix A5.1
A Neoclassical Growth Model
The neoclassical models of economic growth providing the foundations for
extensive work on -convergence were led by Barro (1991), Barro and Sala-i-Martin
(1992, 2004), and Barro et al. (1991). These studies link the neoclassical growth models
developed by Ramsey (1928), Solow (1956), Cass (1965), and Koopmans (1965) to
derive empirical estimates of per capita income convergence. Generally, the theoretical
underpinnings of such empirical work are related to the Solow growth model, which
will be used here for the purposes of developing a simple derivation.
The Solow model assumes a production function based on two factors of
production:
(A5.1)
where Y is the output of the economy and K and L are capital and labour inputs. The
constant A attached to labour can be considered as knowledge of production. Thus the
term AL is the effective labour input and not the absolute numbers per se. The
neoclassical model also assumes constant returns to scale. Therefore if the inputs are
doubled, the output will also be doubled. Using this feature, if both sides of the equation
are divided by AL, an expression for output per effective worker can be derived such
that:
,1 .Y K
FAL AL
Using Y
yAL
and K
kAL
the expression above can be reduced to:
(A5.2) .y f k
The production function of this one sector and two input closed economy is
assumed to take a Cobb-Douglas form; therefore:
(A5.3) 1
0< <1,Y t K t AL t
where and 1– are the input share of capital and labour respectively. The model
assumes that labour and knowledge (technology) growth rates are exogenous. Their
growth rates are defined as:
Y=F K,AL ,
161
(A5.4)
,dL t
nL tdt
(A5.5)
.dA t
gA tdt
Expressions (A5.4) and (A5.5) imply that the rate of growth of labour and technology
are constant at n and g respectively. Assuming both L and A grow exponentially over
time, then:
(A5.6)
(A5.7)
The growth rate of effective labour is thus (n + g).
Two forces—savings and depreciation—affect the capital stock in the model.
Within the neoclassical framework the capital stock increases over time subject to the
savings rate s. Savings per effective worker, on the other hand, is a function of y defined
above, so the saving is sf(k). On the other hand, the depreciation rate reduces the capital
stock at a rate δ. Total depreciation of the capital stock within the economy is δk. Thus
based on total savings sf(k), total depreciation δk and growth rate of labour, the growth
of the capital stock is:
(A5.8) .dk
sk t n g k tdt
The inclusion of (n+g+) into the equation is explained as follows: Investment
(equal to saving) increases the capital stock for two purposes, firstly to increase the
stock of capital available, and secondly to replace worn-out capital and match the
growing demands of labour (growing at a rate of n). A steady state is reached when
capital stops growing; in other words, the investment is sufficient to replace depreciated
capital and to compensate for effective labour growth (n + g). Expression (A5.8) can
also be modified to include consumption as part of the model where change in the stock
of capital becomes:
(A5.9) ,dk
f k c n g k tdt
where c=C/AL, consumption per effective worker. The consumption of households
maximises the utility,
(A5.10) 0
.nt tU u c e e dt
Here c C L , or consumption per worker; and is the rate of time preference. The
expression:
ntL t = L 0 e ,
gtA t = A 0 e .
162
u(c) is1 1
1
c
and >0,
The first-order condition for maximising (A5.10) is:
(A5.11) 1
.
dc
dt f kc
At the steady state y, k and c grow at the rate of technological growth g. In this state, the
level of capital–labour ratio satisfies the following:
(A5.12) ˆ .f k g
Thus, the growth of output at the steady state is subject to the depreciation, rate of time
preference and technological change rate.
Reverting to the Cobb-Douglas form introduced earlier, it is assumed that
technological progress behaves as follows:
(A5.13) .y f k Ak
Linearising Equations (A5.11) and (A5.13) and solving for gives the
expression for the transition of output per effective worker towards the steady state. The
solution is:
(A5.14) ˆ ˆ ˆlog log 0 log 1 .t ty t y e y e
The parameter determines the speed of convergence towards the steady state.
On average the growth of y (per capita income) over a period of time T is determined as
follows:
(A5.15)
ˆ1 1log log .
ˆ(0) 0
Ty T e yx
T y T y
Equation (A5.15) implies that the growth of income per capita around the steady
state, over a period of T years, is governed by the initial condition of y(0). Thus, lower
initial per capita income produces greater speeds of convergence towards the steady
state. For estimation purposes Equation (A5.15) can be reorganised as follows:
(A5.16) , 1 ,
, 1
log 1 log 1iti i t it
i t
ya e y g t u
y
here ˆ1 log( )i i ia g e y , and uit is a random disturbance term.
Barro and Sala-i-Martin‘s (1992) analysis was based on the following equation:
ˆlog y t
163
(A5.17) , , ,
,
1 1log 1 logTit
i t T i t t T
i t t
yc e y
y T T
The dependent variable on the left hand side of Equation (A5.17) is the annualised
growth rate of GDP per capita from time t to t+T. On the right-hand side, c is a constant
and the yields the convergence speed. The expression yi,t is the initial GDP per capita
at time t.
Equation (A5.17) can be estimated under conditional convergence through the
use of dummies. The inclusion of dummies allows for differentiated intercepts
depending on the each region‘s steady state and growth. The constant in Equation
(A5.17) represents the steady state level and its growth. Thus the constant is:
ˆ1 / log .T
itC x e T y xt
The model assumes that all countries (regions) have the same steady state such that
ˆ ˆiy y . The model also assumes that the growth rate towards the steady state is the
same for all (regions), therefore xi = x. The regional dummies will isolate specific
regional effects with respect to steady state gap and growth, as seen earlier.
164
Appendix A5.2
Sensitivity Analysis
The results reported earlier in this chapter depended on cross-sectional
estimation of the convergence effect. It is also possible to estimate the equations using a
panel setting to take advantage of the combination of cross-sectional and time series
attributes of the data. Furthermore, it is useful to introduce some relevant variables such
as population and fixed capital as components of the neo-classical models of growth.
Although the emphasis is on convergence per se, the effect of these variables can be
significant on the estimates derived. While using panel estimation, the introduction of
fixed effects may contribute in improving the explanatory power of the model.
Table A5.2.1 reports the linear estimation of Equation (5.2) where the
coefficient β indicates convergence or not. Generally, the panel estimates perform better
than the cross-sectional coefficients reported above. The overall R2 is substantially
improved although still very low. The introduction of fixed effects of countries
improves the β coefficient significantly in columns (2) and (3). The estimated
coefficients are ten fold larger compared to those in Table 5.4. They are also statistically
significant. This gives further support to the conditional convergence results.
Since the GCC countries are of central interest in this study, one of the most
important characteristics of these economies is considered in the convergence analysis.
The GCC are known as some of the world‘s major oil producers; the inclusion of such a
resource is of considerable interest. The neoclassical theory does not account for natural
resources but emphasises the stock of capital at a given point in time. Here the fact that
countries are oil producers is taken into account, along with the other conditioning
variables to examine any effects such a resource may have on growth and, thus,
convergence. Table A5.2.2 reports the cross-sectional estimates of a number of
scenarios with respect to oil as a resource. In column (1), a dummy is assigned to
countries that produce oil. These countries include developed and developing countries.
The coefficient of 0.0027 implies that the intercept for oil-producing countries is
different from the rest of the world. This result is statistically insignificant; therefore, oil
producers do not have different intercepts compared to the rest of the world.
Alternatively, column (2) reports the same regression, but using oil production at the
beginning of the period, 1980. Oil production levels are positively-related to growth in
this case. This may be an intuitive result; countries that produce significant volumes of
165
oil may experience greater growth—as is the case in the GCC. This result is not
statistically significant, however. Finally, the stock of oil measured in proven reserves
is taken into account in the regression in column (3). Here, the larger stock of oil
reserves improves growth. The coefficient is statistically insignificant for this sample. In
the context of the GCC and other oil producers, there is no significant relationship
between the stock of oil and growth. In terms of convergence, the change in the
coefficient of per capita GDP is marginal when all three scenarios are compared. Thus it
is not possible to infer any useful relationships between the natural resource and
convergence in this case.
166
Table A5.2.1
Conditional Convergence Panel Estimates, 1980–2006
Model
Variable (1) (2) (3) (4)
Constant 0.2054 (0.0336) 0.3203 (0.0848) 0.7117 (0.2531) 0.1153 (0.0463)
Per capita GDP –0.0072 (0.0066) –0.0595 (0.0200) –0.0510 (0.0123) –0.0095 (0.0032)
Fertility –0.0309 (0.0072) –0.0690 (0.0160) –0.0634 (0.0111) –0.0352 (0.0046)
Government Spending 0.0007 (0.0021) –0.0050 (0.0061) –0.0171 (0.0055) –0.0011 (0.0010)
Inflation –0.0009 (0.0013) 0.0008 (0.0021) –0.0039 (0.0013) –0.0013 (0.0013)
Investment 0.0182 (0.0119) 0.0132 (0.0117) –0.0009 (0.0073) 0.0174 (0.0074)
Life –0.0230 (0.0076) 0.0697 (0.0515) 0.0292 (0.0300) –0.0002 (0.0109)
Openness 0.0019 (0.0072) 0.0314 (0.0069) 0.0172 (0.0055) 0.0002 (0.0034)
Political Rights 0.0062 (0.0029) –0.0087 (0.0057) –0.0035 (0.0036) 0.0017 (0.0043)
Terms of Trade –0.0128 (0.0078) –0.0120 (0.0122)
Secondary School Enrolment –0.0005 (0.0008) –0.0013 (0.0007)
Fixed Effects
Country No Yes Yes No
Time No No No Yes
Adjusted R2
0.5610 0.1339 0.5846 0.2663
Number of Observations 159 159 452 452
Note: White hetersokedasticty consistent standard errors in parentheses.
167
Table A5.2.2
Cross-Sectional Conditional Convergence Estimates – Based On Oil Production
Model
Variable (1) (2) (3)
Constant 0.0223 (0.0271) 0.0244 (0.0272) 0.0270 (0.0274)
Per capita GDP –0.0061 (0.0019) –0.0064 (0.0018) –0.0066 (0.0019)
Fertility –0.0231 (0.0056) –0.0239 (0.0055) –0.0243 (0.0055)
Investment 0.0117 (0.0054) 0.0118 (0.0054) 0.0118 (0.0054)
Political Rights –0.0050 (0.0027) –0.0048 (0.0027) –0.0048 (0.0027)
Inflation 0.0037 (0.0018) 0.0039 (0.0018) 0.0041 (0.0018)
Openness 0.0040 (0.0025) 0.0046 (0.0025) 0.0048 (0.0025)
Oil Producing Country 0.0027 (0.0035)
Oil Production 0.0003 (0.0003)
Proven Oil Reserves 0.0006 (0.0004)
Adjusted R2
0.3340 0.3380 0.3415
Number of Observations 87 87 87
Note: White heteroskedasticity consistent standard errors in parentheses.
168
Appendix A5.3
Data Descriptions and Sources
This appendix describes the data description and sources are listed. Table A5.3.1
describes the variables used in this chapter, their units and where they were obtained
from. Table A5.3.2 details the countries included in the sample.
Table A5.3.1
Data Descriptions and Sources
Variable Units of Measurement Source
GDP per Capita
PERCAP
Real $US (2000) International Monetary
Fund (2007)
World Bank
Penn World Table 6.2
Population and Population
Growth
POP and POPGR
Millions World Bank (2008b)
Fertility
FERT
Births per Woman World Bank (2008b)
Gross Fixed Capital
INV
Percent of GDP World Bank (2008b)
Inflation
INFL
Percent per Annum International Monetary
Fund (2007)
World Bank (2008b)
Government Expenditures
GOVT
Percent of GDP World Bank (2008b)
Domestic Credit
DOMC
Bank Sources Credit as
percent of GDP
World Bank (2008b)
(Continued on next page)
169
Table A5.3.1 (Continued)
Data Descriptions and Sources
Variable Description Source
Secondary School
Enrolment
SEC
Total number of students
enrolled in secondary
schools
World Bank (2008b)
UNESCO 2008
Scientific Publication
PUB
Number of scientific and
technical articles published World Bank (2008b)
Terms of Trade
TOT Net barter terms of trade World Bank (2008b)
Political Rights
PR
Index based on survey
ranges from 1 to 7, where 7
is most restrictive
Normalised to range from 0
to 1
Freedom House 2008
Civil Liberties
CL
Index based on survey
ranges from 1 to 7, where 7
is most restrictive
Normalised to range from 0
to 1
Freedom House 2008
LANDLOCKED dummy
Countries with no direct to
sea shipping lanes or ports
Takes the value of 1 for no
access and 0 for otherwise
RTA dummy
Dummy variable indicate
membership within a
regional trade agreement
Takes the value of 1 if a
country is a member and 0
otherwise
World Trade Organization
2007
170
Table A5.3.2
Countries Included In Regressions
Afghanistan Georgia Oman
Albania Germany Pakistan
Algeria Ghana Panama
Angola Greece Papua New Guinea
Antigua and Barbuda Grenada Paraguay
Argentina Guatemala Peru
Armenia Guinea Philippines
Australia Guinea-Bissau Poland
Austria Guyana Portugal
Azerbaijan Haiti Qatar
Bahamas, The Honduras Romania
Bahrain Hong Kong SAR Russia
Bangladesh Hungary Rwanda
Barbados Iceland Samoa
Belarus India Sao Tome and Principe
Belgium Indonesia Saudi Arabia
Belize Iran, Islamic Republic of Senegal
Benin Ireland Serbia
Bhutan Israel Seychelles
Bolivia Italy Sierra Leone
Bosnia and Herzegovina Jamaica Singapore
Botswana Japan Slovak Republic
Brazil Jordan Slovenia
Brunei Darussalam Kazakhstan Solomon Islands
Bulgaria Kenya South Africa
Burkina Faso Kiribati Spain
Burundi Korea Sri Lanka
Cambodia Kuwait St. Kitts and Nevis
Cameroon Kyrgyz Republic St. Lucia
Canada Lao People‘s Democratic Republic St. Vincent and the Grenadines
Cape Verde Latvia Sudan
Central African Republic Lebanon Suriname
Chad Lesotho Swaziland
Chile Liberia Sweden
China Libya Switzerland
Colombia Lithuania Syrian Arab Republic
Comoros Luxembourg Taiwan Province of China
Congo, Democratic Republic of Macedonia Tajikistan
Congo, Republic of Madagascar Tanzania
Costa Rica Malawi Thailand
C∂te d‘Ivoire Malaysia Timor-Leste, Dem. Rep. of
Croatia Maldives Togo
Cyprus Mali Tonga
Czech Republic Malta Trinidad and Tobago
Denmark Mauritania Tunisia
Djibouti Mauritius Turkey
Dominica Mexico Turkmenistan
Dominican Republic Moldova Uganda
Ecuador Mongolia Ukraine
Egypt Morocco United Arab Emirates
El Salvador Mozambique United Kingdom
Equatorial Guinea Myanmar United States
Eritrea Namibia Uruguay
Estonia Nepal Uzbekistan
Ethiopia Netherlands Vanuatu
Fiji New Zealand Venezuela
Finland Nicaragua Vietnam
France Niger Yemen, Republic of
Gabon Nigeria Zambia
Gambia, The Norway Zimbabwe
171
CHAPTER 6
PRICE CONVERGENCE
6.1 Introduction
This chapter is concerned with the effects of economic integration on prices and
considers the effect of reduced trade barriers between countries. The chapter aims to
determine if the GCC’s economic integration has played a role in reducing price
deviations across borders. Within this context we will examine the Law of One Price
(LOP) to establish a better understanding of the GCC’s experience. Just as effective
economic integration may be thought of as an implication of price convergence,
departures from LOP may indicate market segmentations that still exist within the
region (Goldberg and Verboven 2005, Broda and Weinstein 2008). Such segmentation
suggests the presence of barriers to trade and border effects, such as those explored by
Engel and Rogers (2001).
This chapter will consider the question of price convergence in two major ways.
First, studying the dispersion of prices on an aggregate to disaggregate level and
observing price behaviour over time will provide an overall picture of the region and its
integration progress. We will observe prices from aggregate inflation data to micro
prices. Second, the dispersion analysis will be complemented with statistical and
econometric analyses of price behaviour. The aim is to test statistically any convergence
within the region generally, and between country pairs specifically.
The chapter will proceed as follows: Section 6.2 compares GCC inflation
patterns over time with those of comparable regions. The comparison will be based on
similarities in economic integration experiences and benchmarks. Section 6.3 presents
purchasing power parity (PPP) theory, to emphasise the significance of exchange rate
arrangements and price convergence. Observing trends in both the GCC and the G7
countries will help to contrast two extreme cases of fixed versus floating exchange rates
and their relation to PPP. Section 6.4 examines the convergence of the GCC countries
based on deviation from the mean. Here the difference-in-differences approach will be
utilised. Section 6.5 disaggregates the inflation data for the years 2001–2007 based on
eight broad commodity groups. These data are tested for significant convergence (or
divergence) with the objective of identifying possible categories of importance. Section
6.6 allows for further disaggregation using micro commodity prices. Using the
172
Economist Intelligence Unit (EIU) CityData we employ highly disaggregated prices of
consumer goods and services. This section will test convergence within the region and
investigate potential border effects. Section 6.7 presents the unique case of Coke,
following this single good across different countries to verify if LOP holds within the
region or not. The chapter concludes with a summary of findings.
6.2 The GCC Inflation Experience
In this section the inflation experience of the GCC countries over last 30 years
will be compared with other states and regions. The aim is to establish a benchmark by
which GCC countries’ inflation rates can be compared. The European Union countries,
Australian states and territories, and the states of the USA are considered analogous
forms of economic integration and thus provide useful benchmarks.
6.2.1 Inflation within the GCC Region
The GCC’s economic integration has taken on a mood of urgency in the past
few years. After a lull during the late 80s and 90s, significant progress has been made to
achieve a fuller economic union. Having established a customs union in 2003 and
implemented a common market in 2008, the GCC’s next major milestone is monetary
union. Successfully establishing a credible monetary union will eliminate exchange rate
volatility in intra-GCC trade and commerce. Equally important, the financial sector will
benefit from unified laws and regulations that have the potential to facilitate deeper
financial markets within the region.
The anticipated benefits of monetary union and a common currency may not be
realised until their actual launch. This, however, does not mean that some convergence
of prices has not already taken place. Trade liberalisation and common market
initiatives allow for greater mobility of goods and factors of production, so that price
dispersion may already have declined. This premise is supported by the fact that the
GCC countries have maintained fixed exchange rates for a long period of time vis-à-vis
the dollar.
GCC inflation rates have been historically low compared to other developing
and developed countries, perhaps the result of the US dollar peg in imposing monetary
policy discipline. Figure 6.1 illustrates the weighted inflation rate and standard
deviation for GCC countries for the period 1981–2007. The solid line represents the
weighted inflation and the vertical broken lines indicate the weighted standard
deviations. The weights used here and below are proportional to nominal GDPs
173
expressed in terms of US dollars. The average inflation rate in the GCC remains in
single digits for the period. Price changes of individual countries (not shown in the
figure) fluctuate between -8% and 16%, so there has been considerable variability. At
the beginning of the period the GCC inflation rates differ by about 5% and in 1990–91
they reach their highest levels. Political events in the region explain the significant
difference in prices. Inflation declines until 2001–02, in both the average and
dispersion. This pattern reverses in the last few years of the period under study, where
on average inflation increases. GCC countries also exhibit greater dispersion of price
changes. The increasing weighted standard deviations of the last 10 years indicate that
GCC inflation rates have not converged. Despite low average inflation rates over the
whole period, the spread of inflation rates within the region does not decline sufficiently
to suggest a significant downward trend. Moreover, price increases towards the end of
the period have taken place simultaneously with an increase in dispersion.
Figure 6.1
GCC Weighted Inflation, 1981–2008
(% p.a)
Source: Author calculations based on World Economic Outlook 2007.
6.2.2 Inflation in Other Economic Regions
The European Union (EU) is a good benchmark for comparison with the GCC,
given the similarities between the two integration initiatives. Figure 6.2 displays the
weighted mean of inflation of the EU15 members (Austria, Belgium, Denmark, Finland,
France, Germany, Greece, Ireland, Italy, Luxembourg, Netherlands, Portugal, Spain,
Sweden, and United Kingdom) for the period 1980–2006. The striking feature of Figure
-6
-4
-2
0
2
4
6
8
10
12
14
16
19
81
19
83
19
85
19
87
19
89
19
91
19
93
19
95
19
97
19
99
20
01
20
03
20
05
20
07
174
6.2 is the declining weighted average of the EU15. The 1980s show a substantial
decline of inflation within the EU15 from 12% to 4%. This is reversed in the late 1980s
when inflation climbs to about 6%. However, during the 1990s and well into the 2000s
inflation essentially remains low within the EU15. The post-2001 period shows relative
stability in price levels within the region. While inflation declines during the 80s and
90s, dispersion remains large and persistent. The decline in the dispersion, as indicated
by the vertical broken lines, is only evident in the second half of the period. However, in
1999, a few years preceding the Euro’s official launch, a marked decline in dispersion
can be observed. Dispersion remains at low levels for the rest of the period. Figure 6.2
confirms price convergence within the EU15, especially around the time of the Euro
launch.
Figure 6.2
EU15 Weighted Mean of Inflation
(% p.a.)
Source: Author’s calculations based on World Economic Outlook 2007.
Australia is a federation of eight states and territories, and is another example of
complete economic integration. Figure 6.3 shows the weighted mean of inflation and
standard deviations in the Australian states and territories for the period 1986–2007.
Immediately noticeable is the decline in inflation in the first part of the period.
Weighted mean of inflation declines from 8% in 1986 to less than 2% in 1992. Prices
increase during the mid-90s but decline again with the onset of the Asian financial
crisis. Inflation accelerates again from 1998–2001, to peak just below 5%. After 2001,
inflation declines. There are marginal increases in inflation until 2006.
175
The dispersion of inflation (the weighted standard deviation) between the states
and territories tells a different story, however. Figure 6.3 shows minimal dispersion over
the period. During the first years of the period dispersion is about 1%, then this declines
during the middle years. Low levels of dispersion remain persistent until 2005, when
negligible divergence can be detected. Figure 6.3 illustrates that Australians states and
territories have converged with respect to inflation.
Figure 6.3
Australian States and Territories Weighted Mean of Inflation
(% p.a.)
Source: Author’s calculations and Australian Bureau of Statistics.
Another possible comparison to the GCC is the US. Forming a complete
political and economic union, the US is an example of a completely integrated region.
Figure 6.4 illustrates the mean of inflation for four major US regions: Northeast,
Midwest, South, and West. As can be seen from the figure, a sharp decline in the
inflation takes place between 1981 and 1983, falling from 10% to about 3%. Inflation
dips again in 1986 to 2%, and accelerates to 5% in 1990. After the recession of 1991,
inflation continues to decline to its lowest levels in 1998, below 2%. Inflation increases
again until the slowdown in 2001. This decline is temporary, before prices increase
again until 2005. On average, inflation in the US has declined significantly between
1981 and 2006. However, the decline is not smooth. Dispersion, on the other hand, has
been significantly low. Although the first few years see inflation dispersion of more
than 2%, during the middle years dispersion falls notably. This is especially true in the
1990s during the major expansion period. During the 2000s dispersion increases to pre-
1990 levels. Overall, the US inflation dispersion remains largely small. The US has
partially converged with respect to inflation, and dispersion remains at a minimal level.
176
During the same time period, the EU15, the Australian states and territories and
the US regions all show substantial price convergence. However, in the case of the
GCC, inflation rate differences have remained persistent especially during the past few
years. Further analysis of the trends of the GCC inflation rates needs to be undertaken to
understand such patterns.
Figure 6.4
United States Mean of Inflation
(% p.a)
Source: Author’s calculations based on Bureau of Labour Statistics
6.3 Exchange Rates and Prices
Open-economy macroeconomics emphasises an important link between exchange
rates and prices in the form of purchasing power parity. PPP theory was briefly
introduced in Chapter 2 as a prelude to empirical work reported later in this chapter. In
what follows we elaborate further on PPP theory and measurement.
The Law of One Price (LOP) postulates that the price of a commodity consumed
in two different countries will be equalised when compared in a common currency,
given no barriers to trade and ignoring transportation costs. Price differentials will be
eliminated based on arbitrage. Purchasing power parity (PPP) represents a broader view
of this price equalisation proposition. The PPP theory, in absolute form, proposes that a
basket of goods typically consumed domestically will be equal in value to an identical
basket consumed by consumers in a different country. One of the most common
examples of testing this relationship is the Big Mac Index, introduced by The Economist
magazine in the 1980s. Big Mac burger prices are compared across a number of
177
countries, with all prices converted into US dollars. Deviations from parity are assumed
to be over- or under-valuation of the respective currencies.
The groundwork for the theory behind PPP was laid by Gustav Cassel (1928).
Balassa (1964) introduced a model of PPP that included productivity. Productivity
differentials in his model help explain relative price changes between traded and non-
traded goods within countries, and lead to the conclusion that those countries with
higher productivity differentials between traded and non-traded sectors experience
larger increases in the prices of non-traded goods. Consequently this causes real
exchange appreciation of the currencies of rich (higher productivity) countries. In the
same regard, Samuelson’s (1964) work builds on productivity differences and their
effects on relative prices. This relationship between productivity differentials relative
prices/currency values is known as the Balassa-Samuelson effect.
In what follows we describe PPP theory and provide a brief overview of
empirical tests of the approach.
6.3.1 Versions of PPP
The law of one price is the fundamental starting point of PPP. In its basic form,
it says that the price of a good should be the same anywhere, once accounted for in a
single currency. Thus:
(6.1)
whereiP and *
iP are the prices of the good in question at home and abroad; and S is the
nominal exchange rate (the domestic currency cost of a unit of foreign currency).
Equation (6.1) will hold true if transportation costs and trade barriers are assumed to be
nonexistent. This is likely to be the case with gold, at least as an approximation; but for
a number of other internationally traded commodities such as grain, minerals, or energy,
transportation costs need to be taken into account. The LOP can be extended to price
indexes as well as to individual commodity prices: iP and iP become P and P , the costs
of purchasing a basket of goods at home and abroad. The absolute version of PPP in this
case is the same as that in Equation (6.1): P SP , or
(6.2) .P
SP
Equation (6.2) stipulates that the exchange rate is the ratio price levels.
The quantity SP is the cost of the foreign basket measured in terms of domestic
currency; under PPP, this cost coincides with the cost of the domestic basket, P.
,i iP SP
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Accordingly, P SP measures the extent to which PPP does not hold. It is convenient
to express this disparity logarithmically:
(6.3) log .P
qSP
When PPP holds q = o, whereas when q > 0 the domestic currency is overvalued as
prices at home are too high relative to those abroad; when q < 0 the currency is
undervalued. It is for this reason that q of Equation (6.3) is known as the real exchange
rate.
The above discussion is based on the assumption of no transportation costs or
barriers to trade. However, when such costs are involved, as is often the case in the real
world, absolute PPP cannot hold. In this case Equation (6.2) becomes
(6.4) ,P
SP
where θ is a constant that represents the wedge between domestic and foreign prices. If
we differentiate Equation (6.4) with respect to time and take logs, the constant θ drops
out and we get
(6.5) ˆ ˆ ˆ ,S P P
where a circumflex (^) denotes proportional change.
Before concluding this overview of PPP theory, two further comments are in
order. First, in most modern economies much more than one half of all goods and
services produced do not enter international trade. Examples are construction and many
services that are labour intensive; haircuts are the classic example. The prices of these
non-traded goods can be linked to those traded via substitution in consumption and
production. Thus, the equalisation across countries of traded good prices, via the
arbitrage mechanism discussed above, can also lead to equalisation of the prices of no-
traded goods. In this case, then, the existence of non-traded goods in the market baskets
does not necessarily invalidate PPP theory; but as the substitution in consumption and
production may take substantial time, the existence of non-traded goods may give rise
to long lags in the equalisation of prices across countries.
Another way of thinking about the impact of non-traded goods is in terms of
transportation costs. Gold is a good that has a high value-to-weight ratio, so its transport
cost in terms of its value (the proportional transport costs) are low. It is for this reason
that gold is easily traded internationally, and, via arbitrage, its price is equalised around
the world. By contrast, many other goods have low value-to-weight ratios and have high
transport costs; bricks are an example. When transport costs are sufficiently high, the
179
good in question is not traded internationally: that is, it becomes a non-traded good.
This link between transport costs and non-traded goods can be used to deal with goods
that do not enter into international trade via the value of parameter θ in Equation (6.4)
above.
The second point worth mentioning is what is known as the stochastic version of
PPP (Clements et al. 2010). Here, random deviations from parity are allowed for. While
prices are not exactly equalised, if they fall in a ‘neutral band’ then the evidence can
still be not inconsistent with this version of PPP.
6.3.2 Empirical Methods of Testing PPP
The concept of PPP has been extensively tested. Froot and Rogoff (1995)
provide an extensive survey on the subject of PPP and the empirical testing methods
used. The following draws on this survey to summarise some methods and approaches.
Earlier empirical developments took the form used in Frenkel (1978):
(6.6) ,t t t ts p p
where st is the log of the spot exchange rate at time t, tp and
tp are the log price levels
of the domestic and foreign countries at time t, t is a random error, and α and β are
parameters to be estimated. Testing for PPP validity was based on β=1. Results of such
models generally rejected PPP when samples did not include hyperinflation episodes.
These models ignored two main issues: endogeneity and stationarity of exchange rates.
The former was addressed by using instrument variables such as time trends and lags of
inflation and exchange rates. The latter was never adequately addressed in this type of
empirical testing.
An alternative approach, which Froot and Rogoff (1995) describe as the second
stage, shifted focus to real exchange rates. Such models tested for a null hypothesis of
random walk in real exchange rates. The alternative tested for stationarity of the real
exchange rate as defined in Equation (6.3). One of the common techniques used here is
the Dickey-Fuller (1979) test that regresses the real exchange rate on its own lag, a
trend, and differenced polynomial representation of the real exchange rate.
The third stage of empirical tests methodologies used to investigate PPP is based
on cointegration type tests. Based on Engle and Granger’s (1987) two-step process,
cointegration theory proposes that linear combinations of nonstationary series can be
stationary. An alternative approach is Johansen (1991), a single step cointegration test
that addresses some of the inefficiencies of the two-step process (Froot and Rogoff
1995).
180
The above methodologies use consumer price indices (CPIs) or wholesale price
indices (WPIs) to test for stationarity of real exchange rates. In contrast, Isard (1977)
uses disaggregated prices. Comparing US and German export prices, he finds persistent
deviations. Giovannini (1988) finds similar deviations when studying Japanese export
prices. In both cases the nominal exchange rate is found to be correlated strongly with
relative prices (Froot and Rogoff 1995). To avoid some of the pitfalls of using CPI data
to study real exchange rates, in 1986 The Economist magazine developed the Big Mac
Index (BMI). The BMI utilises the Big Mac burger as a unit of comparison, and the
ratio of Big Mac prices in two countries, measured in US dollars and converted using
spot exchange rates, indicates the real exchange rate. This is used to determine if
currencies are over- or undervalued. The novelty in this approach is due to the
comparison of an identical product that forms a ‘basket’. Unlike the CPI, where the
basket of consumer goods varies from one country to another, the BMI has identical
ingredients and components. Several studies have used the BMI index to examine real
exchange rate behaviour over time (Ong (2003) and Clements et al. (2010)).
A recent study by Hassanain (2004) of PPP with relevance to the GCC indicates
that the relationship holds. Taking ten countries, including five GCC members,
Hassanain (2004) tests the proposition that PPP holds for the period 1980–1999. Using
the real exchange rate, and using alternate numeraire base currencies, he is able to reject
the existence of a unit root. Hassanain (2004) estimates the half-life to be 2.2 years for
the five GCC countries included in the sample. The long period of mean reversion is
explained by the firm pegs to the US dollar, noted above, and poor arbitrage
opportunities in the goods market. He also shows that the volatility of the US dollar
contributes to deviation from the mean. The results of Hassanain’s proposition will be
revisited here and its validity verified.
While LOP and PPP remain the main theories to investigate price convergence,
descriptive methods such as dispersion have also been used. A number of studies have
addressed the question of price convergence with respect to economic integration areas,
primarily the European Union. Rogers (2007), for example, compares Europe and the
US by observing the dispersion of prices within major cities in both continents. He finds
that price volatility declined within Europe generally and the EU11 specifically prior to
the launch of the Euro. This was in contrast to US price volatility. Rogers (2007) finds
that price convergence has taken place within the EU although it may not be attributable
to the Euro per se. He cites contributory factors such as policy coordination and
convergence, trade liberalisation, and a host of other potential effects. He does not
181
analyse these effects but merely points out that they may have contributed to reductions
in price dispersion. Engel and Rogers (2001), on the other hand, use the variance of
prices as a primary measure in their model to capture deviations from PPP. Their model
includes more traditional variables such as distance and border dummies as well.
6.3.3 GCC verses the Group of Seven
As the theory of PPP does not refer to the exchange rate regime, it is equally
applicable to fixed or floating exchange rates. Under a fixed rate, the prices adjust to
bring them in line with the exchange rate; under a floating regime, prices and rate adjust
jointly. Under a fixed rate a tendency for the equalisation of inflation rates is to be
expected, but floating rates imply that countries can choose their own rates of inflation,
so these will in all likelihood differ. In this sub-section, we compare the experience of
the GCC, where exchange rates are more or less fixed, with that of the Group of Seven
(G7), where currencies have floated.
Figure 6.5 demonstrates the stability of the GCC exchange rates during the
period 1980–2008. The figure displays two axes: on the right-hand side we can detect
the exchange rates of Qatar, Saudi Arabia and the UAE. The left-hand axis measures the
exchange rates of Bahrain, Kuwait and Oman. Apart from some early adjustment in the
1980s the period shows constant exchange rates. Consequently, the cross-exchange
rates within the GCC have been stable over time. In effect, this near-fixed exchange rate
regime means that these countries have a pseudo-currency-union; therefore, some
degree of the benefits associated with monetary unions and common currencies may
apply. One of these benefits is potential price convergence within the region.
In contrast to the GCC’s fixed exchange rates, the Group of Seven (G7) have
liberalised their exchange rates to move freely against each other. Figure 6.6 illustrates
the movement of the exchange rates of the G7 countries. The right-hand axis measures
the exchange rates of France, Germany and Japan. The left-hand side axis measures the
exchange rates of the other G7 countries and the Euro. The degree of volatility
experienced by the G7 countries post-Bretton Woods is clear.
Shown in Figure 6.7 are the individual inflation rates of the six GCC countries.
For clarity they are divided into two groups. Figure 6.7 does not indicate a tendency
towards equalisation: inflation rate differences remain persistent within the GCC.
Although the inflation rate differentials decline during some years, towards the end of
the period there are significant deviations between the countries.
182
Figure 6.5
GCC Exchange Rates
($US cost of a unit of local currency)
Source: World Economic Outlook 2009
Figure 6.6
G7 Countries’ Exchange Rates
($US cost of a unit of local currency)
Source: World Economic Outlook 2009
183
Figure 6.7
GCC Inflation Rates
(% p.a.)
A. First Three Countries
B. Second Three Countries
Source: World Economic Outlook 2008
In contrast to the GCC, the G7 inflation rates exhibit smaller differentials, as
shown in Figure 6.8 (where the G7 countries are divided into two groups to clearly
show the patterns). This suggests that under flexible exchange rate regimes, inflation
differentials remain steady and low. This finding is in contrast to the ideas set out at the
start of this sub-section. Further research is needed to truly understand this puzzle.
-8 -6 -4 -2 0 2 4 6 8
10 12 14 16 18 20 22 24
1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008
Qatar Saudi Arabia UAE
-8 -6 -4 -2 0 2 4 6 8
10 12 14 16 18 20 22 24
1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008
Bahrain Kuwait Oman
Weighted Mean: 1.61 Weighted Std. Dev.: 2.90
All six countries
184
Figure 6.8
G7 Inflation Rates
(% p.a.)
A. First Three Country
B. Second Four Countries
Source: World Economic Outlook 2008
6.4 Price Differentials
We now proceed more formally to examine cross-country inflation differentials.
Rather than relying on visual inspection of the data, as the previous sub-section, we test
for mean reversion of inflation in the GCC. This approach will be referred to as
deviations-from-the-mean; for an application example see Ben-David (1996).
6.4.1 Deviations-from-the-Mean
Deviations-from-the-mean is a straightforward concept to utilise in the case of
inflation rates. We can represent the main relationship as follows:
-2
0
2
4
6
8
10
12
14
16
18
20
22
24
1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008
Japan United Kingdom United States
All seven countries
Weighted Mean: 3.14
Weighted Std. Dev.: 1.67
-2
0
2
4
6
8
10
12
14
16
18
20
22
24
1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008
Canada France Germany Italy
185
(6.7)
where , 1logt it i tP P or the inflation rate in country i year t and t is the
corresponding mean (over the six countries) in the same year and i=1,2,…, 6.
Convergence is present if 1 . Equation (6.7) can be further transformed to resemble
the rate of convergence of inflation towards the mean. By subtracting from
both sides we get
(6.8) 1 1 1 .it t it t
Equation (6.8) provides us with a rate of convergence based on the differenced
deviations. Using inflation data for the six countries over the period 1980–2008 from
the International Monetary Fund’s World Economic Outlook 2009, we use Equation
(6.8) to obtain a least squares estimate of the speed of adjustment parameter of 0.61;
the standard error is 0.06. Based on this result the half-life (log 0.5/log ) of deviations
is 1.4 years. This suggests that inflation rate deviations are persistent in the short run
within the GCC as a group. Figure 6.9 represents this relationship where on the
horizontal axis we have the lagged log difference from the mean, with the variable on
the right hand side of Equation (6.9). On the vertical axis we have the differenced log
difference, with the variable on the left hand side of Equation (6.9). The negative
relationship is one of convergence.
6.4.2 Intra-GCC Price Differentials
The above analysis suggests that the GCC countries will converge to a group
mean in approximately 2.8 years. This, however, ignores the differences between the
country pairs. While deviations from the group mean are important for the group as a
whole, country pairs may behave differently within the region, so we must consider the
differences in inflation rates between each country pair within the region. Since our
interest is in the differentials in inflation rates, absolute differences are sufficient to
analyse convergence. The differenced absolute differentials are calculated as follows:
(6.9)
where i j .
1 1 ( ),it t it t
( )it t
,it jt
186
Figure 6.9
Inflation Convergence
(Deviations from the mean)
Source: IMF (2009) and author’s calculations.
The expression above can be tested for stationarity. The argument here is based
on differences not being eliminated completely, but reverting to a mean. Using absolute
inflation differentials between the six GCC countries, there are fifteen possible
combinations. Given our small sample and limited time span, a panel unit root test
allows for greater testing power. Panel unit root testing exploits cross-sectional data
combined with time-series, as the name suggests. One of the main advantages of using
panel unit root testing is the ability to isolate individual unit roots of each cross-section.
This allows us to differentiate in the case of nonstationary inflation differentials.
Specific panel unit root testing procedures have been developed by Levin, Lin, and Chu
(2002), usually referred to as the LLC test, and by Im, Persnan, and Shin (2003), known
as the IPS test. Panel unit root testing has also been applied to real exchange rates; one
such example is MacDonald (1996). In the case of LLC the null hypothesis assumes a
common unit root within the panel. The IPS test assumes individual unit root process in
the null hypothesis.
Prior to applying stationarity tests to the intra-GCC differentials it is useful to
plot the data. Figure 6.10 illustrates the relationship tested above in a similar way to the
previous sub-section. The differenced absolute inflation differentials are plotted against
their lagged counterparts. The fitted line is relatively steep, which provides some prima
face evidence that inflation differentials are declining over time. This, however, is a
suggestive finding only.
If we turn our emphasis to testing for unit roots within the differentials described
above, we have two main tests at our disposal. These are used to test the log of absolute
-.08
-.04
.00
.04
.08
.12
-.10 -.05 .00 .05 .10
Lagged Deviations
Dif
fere
nce
d D
evia
tio
ns
187
inflation differences between fifteen combinations within the GCC from 1980 to 2008
for unit roots. The first test is for a common unit root within the fifteen cross-sections,
the LLC test. Using all possible absolute inflation differentials within the GCC for
every year, we have fifteen possible combinations. Over a period of twenty-eight years
this provides us with 420 observations. A panel of those differentials tested using the
LLC test allows us to reject the null of a common unit root. Thus the panel is stationary
across all pairs. The LLC statistic reported is -7.50.
Alternatively, the IPS test can be used under the null hypothesis of individual
unit roots for each cross-section. The null is rejected and the IPS statistic reported here
is -8.99. The individual cross-sectional statistics range from -1.72 to -5.05. These
results support the previous section’s findings that deviations from the mean are only
temporary.
Figure 6.10
Cross Country Inflation Differentials, 1980–2008
(Absolute differences)
Source: IMF (2009) and author’s calculations.
We complemented the result above with an inspection of the actual absolute
inflation differentials between the GCC countries from 1980 to 2007. Table 6.1 shows
the average of the absolute inflation differentials pairs, averaged over three decades.
During the 1980s the absolute inflation differentials between GCC countries have been
2–4% on average. Oman reveals the greatest differentials during that period. These
differentials decline during the 1990s to 2–3% on average. This is not the case when we
consider the 2000s. While Bahrain and Oman exhibit a decline in their inflation
differentials relative to the rest of the region, Qatar and the UAE appear to have
experienced larger differentials than in the 1990s.
-.15
-.10
-.05
.00
.05
.10
.15
.00 .04 .08 .12 .16
Lagged Differentials
Dif
fere
nce
d D
iffe
ren
tial
s
188
Table 6.1
Inflation Differentials, GCC, Mean, 1980–2007
(Absolute differences, % p.a.)
Bahrain Kuwait Oman Qatar Saudi Arabia UAE Average
1980-89
Bahrain 2.31 4.61 3.16 2.58 4.09 3.35
Kuwait 4.17 1.53 3.61 2.37 2.80
Oman 4.03 3.37 4.06 4.05
Qatar 4.13 1.35 2.84
Saudi Arabia 4.94 3.73
UAE 3.36
1990-99
Bahrain 4.09 3.09 3.16 1.69 3.05 3.02
Kuwait 2.43 3.21 3.04 3.68 3.29
Oman 3.12 1.99 3.85 2.90
Qatar 2.50 2.23 2.84
Saudi Arabia 2.83 2.41
UAE 3.13
2000-07
Bahrain 1.58 1.12 4.67 0.92 4.04 2.47
Kuwait 1.33 3.65 1.59 2.93 2.22
Oman 4.65 0.73 4.02 2.37
Qatar 5.11 1.85 3.99
Saudi Arabia 4.47 2.56
UAE 3.46 Source: IMF (2009) and author’s calculations
189
The numbers in Table 6.1 suggest the inflation differentials within the GCC may be
more persistent in some decades than in others. These trends can be further explored by
disaggregating the inflation data within the region. The following section will
investigate inflation within the region based on such data.
6.5 Prices of Broad Commodity Groups
The previous sections were concerned with aggregated inflation rates and their
behaviour over time. GCC countries do exhibit reduction in dispersion over time. The
analysis, however, can be extended to disaggregated inflation data. Dividing inflation
into several expenditure categories will help identify the sectors that drive inflation
within each country.
The disaggregated price level data is divided into eight main categories:
Clothing and footwear
Education and entertainment
Food, beverages and tobacco
Housing and furniture
Medical care
Rent, electricity, water and fuel
Transport and communication
Other services
The source for the data is the GCC–Secretariat, and it refers to the period 2001–2007.
The restriction of the period is due to the limitation of the data available for these
countries at this disaggregated level: It is an important period because of the progress
made by the GCC countries in planning and executing major steps towards further
integration: not only was a customs union officially launched, but a common market
was established towards the end of the period. This period was also marked by higher
than normal inflation rates than in previous years.
Figure 6.11 and Figure 6.12 plot the inflation rates of these eight categories for
the six GCC countries. Figure 6.11 shows the six countries’ inflation rates
disaggregated by expenditure type. In the case of Bahrain, prices in most expenditure
categories change at similar rates, with the exception of Housing and furniture where
there was a decline. In Kuwait there is notable acceleration of prices in most categories,
especially in Other services. Oman also reveals a gradual increase in its prices,
particularly in 2006 and 2007. Food prices accelerate rapidly, coming close to double
digits. Qatar is the notable leader in terms of inflation. Utilities show the greatest
190
acceleration in inflation rates from 2004 onwards. Saudi Arabia compares favourably
with its GCC counterparts with inflation rates remaining relatively lower, and Utilities,
Food, and Other services demonstrating the greatest acceleration in inflation rates. The
UAE is second to Qatar in experiencing high inflation rates within individual
expenditure categories; Utilities and Other services increase dramatically after 2004.
Figure 6.12 compares the individual spending categories of GCC countries. In
the Clothing and footwear category, inflation rates across the countries vary
substantially, in some cases by more than 5% per annum. The inflation rates appear to
diverge over the period, most strikingly in Qatar where the inflation rate of Clothing and
footwear swings by more than 10% in inflation and deflation cycles. Qatar’s inflation
rate is almost double the second-highest rate within the GCC. The picture is slightly
different when Education and entertainment services are observed. The countries’
inflation rates do not follow a particular pattern. Some countries such as Bahrain, Qatar
and the UAE show declines at the beginning, while Oman and Saudi Arabia show
surprisingly little variation over the period. Data in this category are not available for
Kuwait for the first half of the period, but the inflation rate in the Education and
entertainment category increases significantly.
The Food, beverages and tobacco category shows a clear trend of increasing
inflation rates. Across all six countries Food, beverages, and tobacco prices grow at an
increasing rate over the period 2001–2007. Bahrain and the UAE have the greatest
increases in inflation during the period. Towards 2007, Oman’s inflation rate in this
category exceeds the rest of the region. Overall the dispersion of inflation rates here
appears to be increasing. In contrast, the Housing and furniture category shows less
dispersion: in fact, it shows remarkable stability. With the exception of Qatar in the first
few years, the rest of the region shows close and tight distribution. This particular
category shows remarkable convergence during this period.
Medical care inflation rates within the GCC countries exhibit substantial
fluctuations. This is the case at the beginning of the period. Oman shows the largest
decline in inflation rates during the first few years; however, the countries’ rates
converge to some degree in the second half of the period. Kuwait’s inflation data are
restricted to about half of the period. Generally, the Medical care inflation rates begin to
diverge in 2007. Compared to the previous categories Medical care inflation rates
converge with only a small band of deviation. This, however, cannot be said for the
Rent, electricity, water and fuel expenditure category. Here the countries split into two
191
Figure 6.11
Disaggregated Inflation Rates by Country, 2001–2007
(% p.a.)
Source: GCC-General Secretariat (2008)
-20
-10
0
10
20
30
2001 2002 2003 2004 2005 2006 2007
Bahrain
-20
-10
0
10
20
30
2001 2002 2003 2004 2005 2006 2007
Kuwait
-20
-10
0
10
20
30
2001 2002 2003 2004 2005 2006 2007
Oman
-20
-10
0
10
20
30
2001 2002 2003 2004 2005 2006 2007
Qatar
-20
-10
0
10
20
30
2001 2002 2003 2004 2005 2006 2007
Saudi Arabia
-20
-10
0
10
20
30
2001 2002 2003 2004 2005 2006 2007
Clothing and Foot wear Education and Entertainment Food and Beverage and Tabacco Housing and Furniture
Medical Care Rent Electricity Water Fuel Transport and Communication OTHER
UAE
192
Figure 6.12
Disaggregated Inflation Rates by Group, 2001–2007
(% p.a.)
Source: GCC-General Secretariat (2008)
-10
-5
0
5
10
15
01 02 03 04 05 06 07
Clothing and Foot wear
-10
-5
0
5
10
15
01 02 03 04 05 06 07
Education and Entertainment
-10
-5
0
5
10
15
01 02 03 04 05 06 07
Food and Beverage and Tabacco
-10
-5
0
5
10
15
01 02 03 04 05 06 07
Housing and Furniture
-10
-5
0
5
10
15
01 02 03 04 05 06 07
Medical Care
-10
-5
0
5
10
15
01 02 03 04 05 06 07
Bahrain Kuwait Oman
Qatar Saudi Arabia UAE
Rent Electricity Water Fuel
-10
-5
0
5
10
15
01 02 03 04 05 06 07
Other
-10
-5
0
5
10
15
01 02 03 04 05 06 07
Transport and Communication
193
groups: Kuwait, Oman, and Saudi Arabia exhibit striking stability, with inflation rates
remaining close to zero for most of the period, while Bahrain, Qatar and the UAE’s
inflation rates increase. The UAE shows a persistent increase in its inflation rate;
Bahrain’s inflation rate declines steadily after an initial increase. The dispersion here is
greater than in any of the other categories, due primarily to Qatar’s inflation rate.
Transportation and communication inflation rates for the GCC countries show a
mixed picture. Inflation rates here are volatile and no clear pattern can be observed. The
panel shows inflation rates remain persistently higher in the UAE compared to the rest
of the region; Saudi Arabia, on the other hand, experiences lower inflation rates during
most of the period. Finally, in the Other services category the GCC inflation rates
appear closely matched at the beginning of the period. Here the UAE is the exception:
its inflation rate increases towards the end of the period while its GCC counterparts’
rates decline.
Figure 6.12 illustrates the inflation rates within major expenditure categories for
the GCC countries. In many cases no clear patterns can be observed. Only within the
Housing and furniture category can a clear convergence trend be observed. Food,
beverages and tobacco show the clearest sign of co-movement of prices in the same
direction, but dispersion still persists within the region. Rent, electricity, water and fuel
present the greatest differences between the six countries. These panels can be
summarised in terms of standard deviations for the six countries within each
expenditure category, as in Figure 6.13. The greatest deviations can be observed with
three categories of Housing and furniture, Medical care, and Rent, electricity, water and
fuel. In most instances the deviations revert to lower levels but this is not so for Rent,
electricity, water and fuel. Both deviation and dispersion increase consistently over
time. Surprisingly, the Food, beverages and tobacco category exhibits very little change
in standard deviations, and Transport and communication also shows small changes
compared with the other expenditure areas.
The broad commodity categories depicted in Figure 6.13 fall into two groups,
traded and non-traded. The traded group includes Clothing and footwear, and Food and
beverages. We note the low standard deviations of these categories. The low standard
deviation suggests that prices traded goods within the GCC exhibit a tendency to
equalize. In contrast, categories such as Rent, electricity, water and fuel, Education and
entertainment, and Medical care form the non-traded commodity groups. In the case of
Rent, electricity, water and fuel, the standard deviation is large. In some years Medical
194
Figure 6.13
Standard Deviation by Group, 2001–2007
(% p.a.)
Source: GCC-General Secretariat (2008)
0
2
4
6
8
10
12
2001 2002 2003 2004 2005 2006 2007
Clothing and Footwear
0
2
4
6
8
10
12
2001 2002 2003 2004 2005 2006 2007
Education
0
2
4
6
8
10
12
2001 2002 2003 2004 2005 2006 2007
Food, Beverages and Tobacco
0
2
4
6
8
10
12
2001 2002 2003 2004 2005 2006 2007
Housing and Furniture
0
2
4
6
8
10
12
2001 2002 2003 2004 2005 2006 2007
Medical Care
0
2
4
6
8
10
12
2001 2002 2003 2004 2005 2006 2007
Other
0
2
4
6
8
10
12
2001 2002 2003 2004 2005 2006 2007
Transport and Communication
0
2
4
6
8
10
12
2001 2002 2003 2004 2005 2006 2007
Rent, Electricity, Water and Fuel
195
care exhibits high standard deviations. These observations are broadly in line with
expectations that non-traded goods prices have the tendency to deviate more than traded
goods prices.
6.6 Micro Prices
In this section we explore disaggregated price data based on commodity prices
across a range of expenditure categories. The aim is to analyse prices at a micro level
where convergence can be tested and closely scrutinised. There is growing emphasis on
the importance of disaggregated data to test for convergence of prices and the Law of
One Price. Parsley and Wei (1996), Roger (2007), and Broda and Weinstein (2008) are
a few examples of studies that use micro data to investigate inter/intra-country price
differences.
6.6.1 The Data
The data used in this section was sourced from the Economist Intelligence
Unit’s (EIU) Cost of Living Survey, also referred to as EIU CityData. The EIU has
collected price data for 330 commodities and services since 1990. The main categories
include Food, Beverages, Household supplies, Personal care, Tobacco, Utilities,
Clothing, Domestic help, Recreation, Transport, Office and residential Rents,
International schools, Health and sports, and Business trip costs.1
All prices are quoted in US dollars. The prices are collected directly from retail
outlets and service providers: prices of Food, for example, are collected from
supermarkets and medium-priced outlets; thus in a number of cases there are multiple
prices to choose from. Our sample includes the six capital cities of the GCC countries,
Al-Manama (Bahrain), Kuwait City (Kuwait), Muscat (Oman), Doha (Qatar), Riyadh
(Saudi Arabia), and Abu Dhabi (UAE). Two additional cities are included, Dubai
(UAE) and Jeddah (Saudi Arabia). Their inclusion allows intra-country as well as inter-
country comparisons. The data span 1990–2009 with the exception of Muscat and
Doha; data for these cities are only available from 2000 onwards. There are potentially
154 price quotes available for each city in each given year. Assuming there are at least
six cities with available data for every year, we have more than 24,640 potential price
quotes at our disposal.
A number of caveats must be put forward here regarding the use of the data.
First, there is the underlying assumption of homogeneity of the products included: 1kg
of white flour or a two-piece business suit for a medium-weight individual are assumed
1 See Appendix A6.1 for more details.
196
to be comparable across cities. Such an assumption is necessary to allow for reasonable
comparisons. Another caveat regards the quantities imposed. The quantities are fixed
based on the survey’s parameters; the price quotes here are for a ‘unit’ of a particular
product, whether by weight, size or capacity. Over time the price always refers to the
same measure of that particular good. This obviously does not allow us to observe the
actual weights of these products in a typical basket of goods and services; nor can we
track substitution between different price ranges reported by the survey (although the
latter point is not of particular interest for this chapter).
To further understand the data we consider the distribution of these prices based
on selected groups. These include Food, Clothes, Household supplies, Personal care,
Rent, Transportation and Business trips. These groups were chosen as the most data
points were available for most cities. A measure based on the annual percentage
difference from the mean can be constructed, and calculated as follows:
(6.10) log ,ikt
it
p
P
where iktp is the price of commodity k in city i for year t.
itP on the other hand is the
average of all commodities in all cities within a given group for a given year t. The
expression (6.11), when multiplied by 100, approximates the percentage difference from
the mean, so it is a logarithmic relative price. This has the advantage of making
comparisons across groups based on the same scale. The distributions of the seven
selected groups are presented in Figure 6.14. The panels in this figure illustrate the
distribution of the selected commodity groups around their mean over time. The sample
period is divided into sub-periods of five years. The most important feature of Figure
6.14 is the time effect shown by the different distributions. The distribution remains
largely unchanged during the period. Price distribution in the early 1990s is very similar
to that in the latter part of the period.
Take the distribution of Food prices based on expression (6.14) during four sub-
periods. Overall, prices fall into two groups; the two peaks indicate this. The figure also
shows that more prices fall under the mean price of Food for each sub-period. In
contrast to the Food category, Clothes show greater concentration around the mean, as
the central crest in the Clothes panel indicates. Most prices are concentrated just below
the mean. While not an ideal normal distribution, the price distribution of Clothes
appears to be concentrated in the larger crest over time, or moving towards the mean.
This feature is not present where Household supplies are concerned; prices concentrate
less over time, shifting slowly to the right tail.
197
Figure 6.14
Distribution of Relative Price of Commodity Groups by Sub-Periods, 1990–
2009
(Logarithmic ratio to mean)
Food Clothes
Household Supplies Rent
Personal Care Transportation
Business Trip
.0
.1
.2
.3
.4
.5
-5 -4 -3 -2 -1 0 1 2 3
Den
sity
Mean: -0.536
Std. Dev.: 1.050
.0
.1
.2
.3
.4
.5
.6
.7
-5 -4 -3 -2 -1 0 1 2 3
De
nsit
y
Mean: -0.523
Std. Dev.:1.020
.0
.1
.2
.3
.4
.5
-4 -3 -2 -1 0 1 2 3
Den
sity
Mean: -0.707
Std. Dev.: 1.146
.0
.1
.2
.3
.4
.5
.6
.7
.8
-2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5
Den
sity
Mean: -0.143
Std. Dev.: 0.548
.00
.05
.10
.15
.20
.25
.30
.35
-4 -3 -2 -1 0 1 2 3
Den
sity
Mean -0.805
Std. Dev.: 1.317
.00
.02
.04
.06
.08
.10
.12
-16 -12 -8 -4 0 4
Den
sity
Mean: -3.974
Std. Dev.: 4.340
.00
.04
.08
.12
.16
.20
.24
-8 -6 -4 -2 0 2 4
1991-95 1996-00
2001-05 2006-09
Den
sity
Mean: -1.842
Std. Dev.: 2.579
198
Prices of commodities in Personal care show a similar pattern to that of the Food
category. We note in Personal care panel that there are two crests. Prices here show
distinct separation from the mean, with most falling below. A notable shift in the left
peak is evident over time, but the general distribution remains largely unchanged. Rents
provides an interesting case where prices show signs of a normal distribution. In the
earlier sub-periods, two peaks are evident in all periods. Towards the end of the period,
prices display greater concentration around the mean.
Transportation and Business trips present very different pictures of price
distribution. Three distinct crests appear, and distribution hardly changes over time. In
fact, the far left crest increases marginally over the period. Prices in the Business trip
category show a similar distribution. While the disparity is quite large in this category,
it is not as pronounced as the Transportation category. The distribution changes
marginally over time. We note the distribution shifting rightwards over time. Of the
seven categories, Transportation and Business trip shows the largest standard
deviations, of 4.34 and 2.58 respectively. Consequently, deviations from the mean are
much larger than in the other commodity groups. The Rent group has the smallest
dispersion compared to the rest, with a standard deviation of 0.55.
The disaggregated data above will be used in two main ways. The first is to
construct a dispersion measure of the prices across the countries to determine if prices
between the countries decline over time. This particular approach was used by Rogers
(2007) using the same data source for the European Union and the US. The second is to
consider price differentials between city pairs. This approach was used by Parsley and
Wei (1996) to study a number of disaggregated prices within the US. More recently,
Broda and Weinstein (2008) have incorporated a similar approach, but with emphasis
on a vast dataset based on barcode data collection.
6.6.2 Convergence
Under the PPP hypothesis, prices are equalised across countries when expressed
in terms of a common currency. In this sub-section, we examine this idea by using the
data to test whether the prices of a given commodity revert to the same mean in all cities
under consideration. The method most relevant is testing for stationarity. Using the data
in a panel setting, we can test for the stationarity of each category or group of products.
In the case of panel data we have a number of tests that can be used. We will use LLC
to test for common unit roots across all cross-sections. We can also test for individual
199
unit roots within our cross-sections using IPS test. Both LLC and IPS are based on the
Augmented Dickey-Fuller test for stationarity, which involves the following regression:
(6.11) 1 , ,ikt i i ikt is ik t s ikt
s
p p p
where pikt is the price of commodity i within a given category for city k in year t, α, β,
and are parameters, and ikt is a disturbance term. The null hypothesis of
nonstationarity is : 0H . The results of both the common unit root and individual
unit root tests are reported in Table 6.2.
Table 6.2
GCC Prices, 1990–2008
Mean reversion
Test Statistic for Unit Root
Category Common
LLC
Individual
IPS Number Observations
Food -99.6097 -12.614 6,025
Household Supplies -25.8866 -12.7489 1,620
Clothes -19.2344 -10.1292 1,702
Personal Care -2.2209 -2.4932 1,069
Health 1.3003* 2.9588* 568
Recreation -26.9883 -6.3887 1,176
Utilities -6.9781 -0.6315 468
Domestic Help -33.0811 -32.615 340
Rent 9.3157* 7.4311* 888
Transportation -60423.882 -8941.731 1,277
Note: Null hypothesis of a unit root is where the null is βi=1, in Equation (6.11)
* Cannot reject the null.
Table 6.2 reports the test statistics the two types of unit root test discussed
above. In the case of the LLC test, the null of a unit root can be rejected for all prices
except Health and Rent. This suggests that some prices in these two categories are not
mean reverting. This finding is supported by the test statics reported for the IPS test as
well. We find that we cannot reject the null of individual unit roots within these two
categories. Another perplexing result is the large test statistics of Transportation prices.
The current analysis does not offer an explanation however.
200
6.6.3 Price Differentials
The results of the unit root tests in the previous section suggest that individual
commodity prices are mean reverting. It is, however, of greater importance to analyse
commodity price differences between GCC cities. We construct the price differentials
by taking the log difference for the respective city pairs:
(6.12) log log ,
1, ,10 goods; , =1, ,8 cities; ; 1, ,20 years.
iklt ikt iltP p p
i k l k l t
We define the absolute value as iklt ikltP P and estimate the following equation for good
i:
(6.13)
where Distance is number of kilometres between each city pair, Country is a dummy for
city pairs that reside in the same country, and Border is a dummy for countries that
share a common border. For price differences to decline over time the condition β1<0
must be present. We expect β2>0, so that larger distances between cities will increase
price differentials.
The price data was pooled based on the commodity groups for all city pairs
across all available years. A Country dummy with a value of 1 is assigned to pairs of
cities within the same national borders, 0 otherwise. Likewise, a Border dummy with a
value of 1 is assigned to countries that share borders, and 0 otherwise. Of the 28
possible pairs, only two receive a Country dummy value of 1. There are pairs of cities
located in countries that share a land border. The significance of these dummies arises
from their detection of impediments to price equalisation in the form of reduced
deviations. As such, if coefficients of the Country dummy take a negative value, it
implies that cities within borders have smaller price differentials. Similar intuition can
be applied to the Border dummy. City pairs that trade over borders may experience
higher price differentials than those that do not.
Equation (6.13) refers to good i. As there are seven goods, there are seven
equations which estimate by single-equation least squares. Table 6.3 contains the
results. As all estimates of the coefficient i1, are negative, the results reaffirm the
previous findings: that price differences are declining over time across all commodity
groups. This effect is fastest within Household supplies and Business trips, and slowest
for Clothes. In most cases distance does not appear to play a significant role in
increasing price differences. While the distance coefficient is negative for most prices,
201
the effect on price deviations is negligible. There is no clear effect in this specification
with respect to distance and price differentials; this requires further investigation.
With respect to the two dummies introduced to measure border effects the
results are mixed. When the two cities are within the same country, the coefficient i3
for most commodity groups price differentials is positive except for Rent, Personal care
and Business trips, although this result is suspect because of the small number of city
pairs that meet this criterion. The Border dummy mirrors the Country dummy results,
where the coefficient i4 is positive in most cases except Rent, Personal care and
Business trips. In other cases a shared land border between cities implies greater price
differentials. In two cases the Border dummy is significant and complies with the
correct sign, Food and Business trips. In other cases it is either insignificant or has the
wrong sign. These results suggest that border effects are weak within the GCC. Shared
land borders do not appear to increase price differentials for most of our commodities
and services. Where the border effect is significant, its influence is negligible.
The use of fixed effects, in the form of city pair dummies affects the results in
two ways. First, we find the lagged price coefficient i1 decline, indicating faster
convergence once city effects are taken into account. The Distance coefficient becomes
altered in the opposite direction across the categories. In many cases the coefficient
becomes inexplicably negative.
A number of explanations can be offered to explain the weakness of the border
effects. The first is the openness of GCC economies. Dependent on the export of
resources and the import of consumer and durable goods, these countries exhibit high
degrees of border permeability, and the effects of typical barriers to trade are relatively
low. This particular similarity might help explain the weak border effect. The second
and perhaps more interesting explanation is the economic integration process, whose
effects this study is measuring. The GCC free trade area has been in operation for the
past two decades. Harmonisation of standards and product specifications implies some
degree of homogeneity in imports. Consequently, GCC countries’ borders create fewer
barriers or wedges between prices.
The data used here at the micro level suggest that price differentials do decline
over time; however, these vary by commodity group and description. Transportation
cost, proxied by distance, does not play a significant role in price deviations.
Furthermore, border effects within the region register as weak and do not appear to
increase price deviations over time.
202
Table 6.3
Convergence, Disaggregated Price Estimates
1 , 1 2 i3 4 5
,
log + . iklt i i ikl t i il il i il i kl kl iklt
k lk l
P P Distance Country Border CityPair
Commodity
Group
Lagged Price Difference
i1
Distance
i2
Country Dummy
i3
Border Dummy
i4
Fixed
Effects
Adjusted
R2
Number of Observations
Food -0.312 (0.008) 0.042 (0.015) 0.026 (0.036) 0.000 (0.021) No 0.154 15,459
-0.314 (0.008) -0.039 (0.007)
Yes 0.155
Clothes -0.118 (0.007) 0.001 (0.005) 0.021 (0.012) 0.012 (0.008) No 0.069 4,971
-0.157 (0.010) -0.004 (0.002)
Yes 0.089
Household Supplies -0.189 (0.013) 0.000 (0.005) 0.016 (0.010) 0.012 (0.008) No 0.098 4,072
-0.208 (0.015) -0.006 (0.003)
Yes 0.106
Rent -0.071 (0.007) -0.019 (0.008) -0.070 (0.018) -0.022 (0.009) No 0.044
2,603
-0.133 (0.014) 0.018 (0.005)
Yes 0.121
Note:White heteroskedasticity-consistent standard errors in parentheses.
203
Table 6.3 (Continued)
Convergence, Disaggregated Price Estimates
1 , 1 2 i3 4 5
,
log + . iklt i i ikl t i il il i il i kl kl iklt
k lk l
P P Distance Country Border CityPair
Commodity
Group
Lagged Price Difference
i1
Distance
i2
Country Dummy
i3
Border Dummy
i4
Fixed
Effects Adjusted R
2 Number of Observations
Personal Care -0.177 (0.014) -0.002 (0.006) -0.018 (0.013) -0.018 (0.008) No 0.087 3,140
-0.197 (0.014) 0.001 (0.003)
Yes 0.074
Transportation -0.236 (0.065) 0.003 (0.009) 0.012 (0.015) 0.009 (0.018) No 0.127 3,746
-0.243 (0.067) -0.003 (0.003)
Yes 0.123
Business Trips -0.106 (0.016) 0.003 (0.005) -0.022 (0.010) -0.007 (0.006) No 0.068 4,214
-0.116 (0.017) 0.001 (0.002)
Yes 0.077
Note:White heteroskedasticity-consistent standard errors in parentheses.
204
6.7 The Case of Coke
We have shown in the previous section that disaggregated prices are mean
reverting. Although the commodities across borders are comparable, their prices may
not refer to identical goods. However, the data set does provide a unique opportunity in
one particular price quote: the price of 1 litre of Coke. Coke, being a virtually identical
product across countries, provides a suitable test of convergence and of the Law of One
Price. Such an approach has been employed in the literature on ‘burgereconomics’
based on the Big Mac Index. For a recent review of Big Mac Index literature see
Clements et al. (2010).
We plot the distribution of demeaned prices of Coke from all eight cities for all
available years. Figure 6.15 shows the distribution for two sets of prices, from
supermarkets and mid-priced stores. The two distributions are roughly similar. It is
somewhat surprising to see two distinct concentrations of prices: in some cases Coke
prices deviate by 20–30% on either side of the mean. Thus even with an identical
product prices tend to vary between cities over time. Figure 6.15 implies that there is a
price wedge with respect to Coke that keeps parity at bay.
Figure 6.15
Coke Price Distribution
(Logarithmic ratio to mean)
To consider convergence over time, we apply the same approach used in the
previous section to test for convergence, based on the following equation:
(6.14) 1 1 2 ,it i it i i
i
p p City
Where pikt is the demeaned price of Coke for city i (i=1,…,8) in year t (t=1,…,20). We
also include dummies for each city to capture any city effects. Equation (6.15) is
estimated for both sets of prices available, from supermarkets and mid-priced stores.
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
-1.00 -0.75 -0.50 -0.25 0.00 0.25 0.50 0.75 1.00
Supermarket Mid-Price Store
Den
sity
Supermarket Mid-Priced Store
Mean: -0.055 -0.042
Std. Dev.: 2.061 2.011
205
The results are reported in Table 6.4. Muscat and Doha dummies were not included as
they do not have complete series for all years and the inclusion of the dummy distorts
the estimates of β1.
The estimates of β1 are negative for both prices of Coke, which suggests mean
reversion does take place within each city. Half-life estimates based on these results are
1.4 and 0.8 years respectively.2 Thus it appears that prices of Coke revert to the mean
over a period of 10 to 18 months. Of the city effects reported, Abu Dhabi and Jeddah
are not significant. Dubai and Riyadh show greater speeds of mean reversion and
Kuwait the slowest. In summary, we have shown above that Coke prices are mean
reverting and have relatively long half-life for most cities. However, we also wish to
consider inter-city differences, such as those tested in Equation (6.13).
Table 6.4
Convergence Estimates of Coke Prices
1 1 2 ,it i it i i
i
p p City
Variable Mid-Priced Supermarket
Lagged Price -0.607 (0.159) -0.428 (0.101)
City Dummies
Abu Dhabi 0.085 (0.075) 0.054 (0.069)
Dubai 0.333 (0.102) 0.264 (0.098)
Jeddah 0.078 (0.052) 0.12 (0.059)
Kuwait 0.382 (0.093) 0.335 (0.081)
Manama 0.267 (0.069) 0.174 (0.069)
Muscat -0.077 (0.051) 0.008 (0.056)
Riyadh 0.026 (0.049) 0.073 (0.060)
Adjusted R2
0.264 0.150
Number of
Observation
126 127
Note: White Heteroskedasticity-Consistent Standard Errors in parentheses.
To test these implications we apply Equation (6.13) to Coke prices. The
equation takes the following form:
(6.15)
1 , 1 2 3 4
5
,
log
, , 1,...,8 k ,
klt kl t kl kl kl
kl kl klt
k lk l
P P Distance Country Border
CityPair k l l
where logklt kt ltP p p is the price difference of Coke between city k and l,
Distance the number of kilometres between each city pair, Country is a dummy for city
pairs that reside in the same country, Border is a dummy for city pairs that reside in
different countries, CityPair is a fixed effect dummy, and klt is a random error. The
2 Half-life estimates based on Log0.5/Log.
206
estimations take two forms: one like Equation (6.13), and a second specification where
we introduce CityPair dummies instead of the Country and Border dummies. The
results of these two specifications are reported in Table 6.5 In column (1) of Table 6.5
we have the regression based on Equation (6.14). The convergence estimate of β1 is -
0.100 and is significant at the 5% level. This suggests convergence rates with a half-life
of about 4 months. The distance coefficient is insignificant here, which implies that
proximity is not an effective factor where inter-city differences are concerned. The
Country and Border dummies are marginally significant: the Country dummy is
significant at the 10% level and the Border dummy is not statistically significant. The
results do not change whether one or the other is excluded.
In the second specification we omit the Border and Country dummies and
introduce CityPair dummies. This is to capture specific pair effects that may be
affecting price differences between cities. The results of this estimation are reported in
column (2) of Table 6.5. We note several things. First, the inclusion of the CityPair
dummies more than doubles the lagged price coefficient. Second, if absolute price
equalisation takes place, the dummy coefficients would be zero. However, we observe
about 60% of these dummies have positive coefficients. In eight instances, these
coefficients are significant. These observations indicate that there are city pair effects
that prohibit absolute price equalisation within the GCC.
6.8 Concluding Remarks
In this chapter we set out to investigate the effect of the economic integration of
the GCC countries on prices within the region. The chapter proposed that the creation of
a free trade area within the GCC, and later the customs union, contributed to the decline
of price difference. To investigate this, the chapter studied the aggregated inflation rates
of the GCC and compared them to those of the G7. It was found that GCC inflation
rates, although lower on average compared to their G7 counterparts, are more volatile.
The GCC countries experienced periods of reduced and increased dispersion over the
past two decades, and their inflation rates appear to deviate further towards the end of
the period.
We further analysed the differences within the GCC by estimating convergence
rates based on deviations from the mean. This was done based on group deviations from
the mean and country pair deviations. Convergence based on inflation rates was greater
207
Table 6.5
Coke Inter-City Price Convergence Estimates
1 , 1 2 3 4 5logklt kl t kl kl kl kl klt
klk l
P P Distance Country Border klCityPair
Variable
Coefficients
(1) (2)
Lagged Price -0.140 (0.051) -0.409 (0.090)
Distance -0.021 (0.010) -0.008 (0.008)
Dummies
Country 0.045 (0.043)
Border 0.044 (0.025)
City Pair Dummies
Abu Dhabi–Doha 0.095 (0.095)
Doha–Kuwait -0.277 (0.067)
Doha–Manama -0.120 (0.076)
Doha–Muscat 0.046 (0.097)
Doha-Riyadh -0.020 (0.059)
Dubai–Jeddah* 0.257 (0.121)
Dubai–Kuwait -0.043 (0.093)
Dubai–Manama 0.090 (0.080)
Dubai–Muscat* 0.289 (0.128)
Dubai–Riyadh* 0.240 (0.088)
Jeddah–Kuwait* -0.170 (0.064)
Abu Dhabi–Dubai** -0.157 (0.085)
Jeddah–Manama* 0.013 (0.097)
Jeddah–Muscat* 0.104 (0.101)
Jeddah–Riyadh** 0.099 (0.069)
Kuwait–Manama 0.161 (0.083)
Kuwait–Muscat 0.395 (0.127)
Kuwait–Riyadh* 0.304 (0.088)
Manama–Muscat 0.286 (0.121)
Manama–Riyadh* 0.157 (0.081)
Abu Dhabi–Jeddah* -0.007 (0.100)
Abu Dhabi–Kuwait -0.230 (0.086)
Abu Dhabi–Manama -0.084 (0.102)
Abu Dhabi–Muscat* 0.095 (0.114)
Abu Dhabi–Riyadh* 0.034 (0.084)
Doha–Dubai -0.201 (0.103)
Doha–Jeddah* -0.058 (0.066)
Adjusted R2 0.069 0.157
Number Of Observation 363 363
Note: White heteroskedasticity-consistent standard errors in parentheses
* Cities in countries that share a common border
** City pair within the same countries
208
when country pair deviations are used. This suggests that some GCC country pairs
experience faster convergence rates than others. In either case, convergence was
detected. The third part of the chapter concerned itself with disaggregated price levels.
Here we used disaggregated price levels of the GCC for a shorter period of time to
understand the breakdown of inflation rates. These disaggregated price levels are based
on national consumer price index calculations. At the disaggregated price level,
differences between GCC countries are more visible: most variability is experienced in
Qatar, for example. The greatest price level dispersions come from Rent and utilities,
and Housing and furniture.
Finally the chapter disaggregated prices further by using micro data made
available through the Economist Intelligence Unit. Price quotes from different stores
within capital cities of the GCC were collected in similar categories. These data allowed
greater aggregation and helped increase the power of estimations and tests, given the
large sample size. The data spanned two decades and eight capital cities within the
region, including two pairs of cities within the same country. These data were tested for
unit root, where panel stationarity tests were applied. For the most cities, prices were
revealed as stationary and mean reverting. Consequently the data were subjected to
convergence estimation, controlled for distance as an influential factor in price
differentials. Price differentials between city pairs showed varying degrees of
convergence based on commodity groups, but distance did not appear significant in the
regressions. Finally, the micro data were tested for border effects and same country
effects. Again there was no strong support for significant border or country effects. Two
reasons are given for why this may be the case. One is that GCC countries are highly
open economies and thus should display greater permeability and a small likelihood of
border effects. The other is quite possibly the economic integration process. We
rationalise weak border effects as a by-product of economic integration and reductions
in barriers to trade.
In a special case, we considered the price of Coke as a perfectly homogenous
product in our disaggregated sample. Estimates implied convergence between city pairs
across all eight cities. We found support for convergence over a relatively short period,
where half-lives ranged from 10 to 18 months. However, results with respect to distance
were generally insignificant and in a number of cases returned the wrong sign. In two
scenarios, one where country and border effects were used and another where city pair
dummies were used, results showed that distance does not play a role in increasing
209
deviations of prices. The results were sensitive to the choice of dummies. When
controlling for border effects we found a weak influence on prices, and in some cases
common borders helped increase price differentials. While GCC price differentials have
not completely disappeared, this chapter found support for reduced deviations. We
expect price differentials will decline. Some of this reduced deviation may be
attributable to the economic integration progress. The chapter’s overall findings are
summarised in Table 6.6.
210
Table 6.6
Summary of Price Convergence Analysis
Prices considered Key Findings Implications for functioning of GCC
1. Economy wide inflation rates
(i) Descriptive analysis
No tendency for dispersion of inflation across
GCC to decline over time.
More dispersion of inflation across GCC than
across EU15, Australian states and territories, and
US states.
Convergence of inflation rates is a monetary
criterion, set by the GCC, prior to the launch of
common currency. It is essential the six
countries’ inflation rates remain in a reasonable
band. A single monetary policy may not
accommodate economic conditions well if
inflation rates do no convergence sufficiently.
(ii) Mean reversion: Multilateral comparison GCC inflation is mean reverting.
Estimated half-life = 1 year
Although deviations are persistent, mean
reversion implies over the long term the inflation
rates will not diverge indefinitely. This means
that the GCC countries are more likely to meet
their monetary convergence criterion. (iii) Mean reversion: Bilateral comparisons
Bilateral price differences diminish over time.
2. Changes in prices of commodity groups These prices exhibit substantial cross-country
dispersion.
Differences are greatest in non-traded
commodities.
Longer periods required to eliminate price
differences due to non-traded commodities.
3. Micro prices These prices exhibit mean reversion across the
GCC.
Distance and border effects are negligible.
As prices revert to a group mean, the findings
suggest that the GCC’s economic integration has
been effective at the micro level.
The GCC’s trade liberalisation may have played a
key role in reduction of trade barriers.
4. Price of Coke Coke prices are mean reverting.
Half-live = 10-18 months
Given the long convergence half-life estimates
for Coke raise concern regarding efficient
movement of goods within the region despite
trade liberalisation.
211
Appendix A6.1
Data
This appendix describes the disaggregated price data obtained from the
Economist Intelligence Unit City Data. Table A6.1.1 details the categories of prices and
commodities included.
Table A6.1.1
Price Data Details
Category Description Price Type
Food Staples: white bread, butter, margarine, white
rice, spaghetti, flour, sugar, cheese, cornflakes,
yoghurt, milk, olive oil, and peanut or corn oil.
Fresh fruits and vegetables: potatoes, onions,
mushrooms, tomatoes, carrots, oranges, apples,
lemons, bananas, lettuce and eggs.
Canned food: peas, tomatoes, peaches and sliced
pineapples.
Meat and fish: beef, veal, lamb, chicken, frozen
fish and fresh fish.
Beverages: instant coffee, ground coffee, tea
bags, cocoa, drinking chocolate, Coke, tonic
water, mineral water and orange juice.
Mid-Price Stores /
Supermarket
Household Supplies Soap, laundry detergent, toilet tissue, dishwashing
liquid, insect-killer spray, light bulbs, batteries,
frying pan, electric toaster, laundry and dry
cleaning.
Mid-priced Store /
Supermarket / Standard
Clothes Men’s: business suit, shirt and shoes, raincoat and
wool mixture socks.
Women’s: daytime dress, town shoes, cardigan,
raincoat, and tights or panty hose.
Children’s: jeans, dress shoes, sportswear shoes,
girl’s dress, boy’s dress jacket, boy’s dress
trousers.
Mid-priced Store /
Chain Store
Personal Care Aspirin, razor blades, toothpaste, facial tissues,
hand lotion, shampoo & conditioner, lipstick and
haircuts.
Mid-priced Store /
Supermarket
Health and Sports Routine check-up, dentist visit, greens fees on
public golf course, hourly rate for tennis court, six
tennis balls, entrance fee to public swimming
pool.
Average
(Continued on next page)
212
Table A6.1.1 (Continued)
Price Data Details
Category Description Price Type
Recreation Compact disc album, colour TV, personal
computer, colour film, colour picture
development, foreign and local newspapers,
international weekly news magazine, paperbacks,
three-course dinner, and cinema and theatre seats.
Average
Utilities Telephone rental and call charges, average gas
bill, average electricity bill, average water bill and
average heating oil costs.
Average
Domestic Help Domestic cleaning rates, maid’s monthly wages
and babysitter’s hourly rate.
Average
Rent Office rent in US dollars and euros, typical lease
term, industrial rents, rents for furnished
residential apartments, unfurnished residential
apartments, furnished residential houses and
unfurnished residential houses.
Moderate / High
Business Trip Costs Typical daily cost of a business trip, hotel charge,
hire car costs, meal prices, fast-food snack, regular
unleaded petrol, taxi rates, international and local
newspapers, international weekly news magazine,
seat at cinema.
Low / High
Transport Car prices: low-priced car, compact car, family
car and deluxe car.
Car maintenance: yearly road tax or registration,
tune-up, car insurance, regular unleaded petrol.
Taxi prices: initial meter charge, additional
kilometre and airport to city centre rates.
Average
213
CHAPTER 7
PERSPECTIVES
Whalley (1998, p. 66) writes „[R]egional trade agreements have been a central
feature in the development and evolution of the postwar trading system rather than the
exception, and this has been despite the growth in importance of GATT/WTO‟. This
statement is clearly supported by the current trends in Regional Trade Agreements
(RTAs) activity. In 2010 there are more than 200 operational RTAs reported to the
World Trade Organization. Approximately one third of the world‟s trade takes place
within the scope of these agreements. This proportion doubles if the Asia Pacific
Economic Cooperation (APEC) is included (Schiff and Winters 2003). These RTAs
have become more complex and involve deeper economic integration.
The thesis set out to analyse the consequences of the economic integration of the
Gulf Cooperation Council (GCC). In 1981 a RTA was established by six countries:
Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and the United Arab Emirates. The GCC
then embarked on an ambitious economic integration path that established a free trade
area, a customs union, and common market. The GCC also plans to establish a
monetary union and a common currency. The GCC initiative has the potential to
transform the nature of the economies of these six countries. The thesis isolates three
main areas where the effects of the economic integration process on the GCC
economies can be analysed: trade, incomes and growth, and prices. The contribution of
the thesis is in the form of a detailed analysis of the GCC‟s economic integration.
In what follows, we first present a summary overview of the major findings of
the thesis. We then conclude with a brief discussion of the broader implications of the
findings.
7.1 Thesis Review and Major Findings
The thesis is divided into five main chapters in the form of essays:
i. Chapter 2 presents a conceptual overview of economic integration and addresses
the three main aspects – trade, incomes, and prices – which the thesis analyses.
ii. Chapter 3 presents a historical perspective of the GCC‟s experience in the
context of economic integration.
iii. Chapter 4 analyses the effects of economic integration on the GCC‟s trade
patterns.
214
iv. Chapter 5 focuses on the effects of economic integration on incomes and growth
convergence within the GCC region.
v. Chapter 6 investigates the effects of economic integration on prices and analyses
the extent to which foreign and domestic prices are equalised.
A review of these chapters is presented next.
Chapter 2 presents a conceptual approach to economic integration. It outlines the
analytical framework and explores the theoretical background to economic integration.
It begins with the analysis of trends in regional economic integration from the post war
period to the present, and discusses the underlying reasons for creating RTAs. It
provides insights into the debate regarding Multilateralism versus Regionalism. The
chapter then identifies the three aspects of economic integration that the rest of the
thesis investigates: trade, incomes and growth, and prices. The chapter presents the
theory behind trade patterns and economic integration and discusses the effects of RTAs
on trade diversion and creation. Income and growth convergence are addressed in the
second analytical section of the chapter. The potential effects of economic integration
are linked to theories of economic growth and convergence. In this context, the concepts
of β-convergence and σ-convergence are highlighted as useful tools to investigate the
outcomes of economic integration. The third theoretical link the chapter makes to
economic integration is through the Law of One Price (LOP) and Purchasing Power
Parity (PPP). The chapter emphasises price convergence as a key outcome of economic
integration, especially in the context of trade liberalisation.
Chapter 3 discusses the historical background to the GCC, its inception,
achievements, and challenges. Regional tensions and the outbreak of war in the early
1980s were some of the geo-political factors that played a role in the creation of the
GCC. However, the chapter emphasises the economic nature of the GCC by identifying
major economic integration goals and aims. The chapter considers some of those goals
and compares them with the achievements of individual GCC members over the past 25
years. The chapter also canvases a number of socio-economic characteristics of the
GCC countries.
Chapter 4 investigates the trade aspect of economic integration. It uses the
gravity approach to analyse bilateral trade flows using the IMF‟s Direction of Trade
Statistics for the period 1980–2006. As a first step, the analysis considers RTAs across
the world in measuring their effects on trade. Eighteen RTAs (including the GCC) at
different stages of economic integration are included in the analysis. The analysis
includes a sample of 145 countries measured at three-year intervals. The impact of
215
RTAs on bilateral trade is estimated using two methods to allow for transportation
costs: The first utilises distance from each country‟s capital city to another‟s, while the
second method uses c.i.f/f.o.b price ratios to proxy for transportation costs. In addition
to the traditional gravity equation variables of GDP and GDP per capita, dummies for
common border and languages are used to control for these geographical and cultural
factors that surely play a prominent role in trade.
The findings of the first step of the analysis indicate that RTAs in developing
countries have the greatest effect on their bilateral trade. However, in the case of
developed countries, we find that the existence of RTAs has not been particularly
effective in promoting greater intra-regional bilateral trade. With respect to trade
creation and trade diversion, the results are mixed. We observe not all economic
integration experiences have led to trade diversion. In the case of the GCC, we find that
the RTA effect on aggregated bilateral trade has been minimal over the last 30 years, so
there has been little scope for trade diversion.
In its second step of the analysis, Chapter 4 focuses on disaggregated GCC
intra-regional trade in the context of ten broad commodity groups obtained from the
United Nations Comtrade database. Again, it uses the gravity approach and controls for
the commodity composition of trade. The results indicate no clear pattern of trade above
the base case predicted by the model. That is, the GCC countries do not exhibit signs of
trading substantially more in one specific commodity group compared to the rest. These
results are explained by similarities in the economic structures of the six GCC countries.
In summary, the findings of Chapter 4 indicate that the GCC‟s RTA has had no
significant impact on intra-regional trade (that is, within the GCC). This is true for
aggregated and disaggregated bilateral trade. Equally, the GCC‟s RTA did not divert
trade from non-members. Trade patterns of the GCC countries are adequately explained
by factors such as economic size and geographical proximity, so the role of the RTA is
insignificant.
Chapter 5 considers incomes and growth. It poses the question how much
convergence? It investigates this question for a large number of countries, as well as the
six GCC members and considers the implications for income convergence as a results of
economic integration. The chapter is divided into three sections:
The first section uses the neoclassical β-convergence approach, according to which
the poorer countries catch up with their richer counterparts. In its absolute form, we
find that β-convergence is not present for the world. However, when convergence is
conditional, based on a number of factors including economic, social, political, and
216
geographical indicators, our results indicate some degree of income convergence
across countries. The results also indicate that there is no statistically significant
effect of RTAs on income convergence, however.
The second analytical section uses measures of dispersion specific to the GCC.
Using the standard deviation of country growth rates, the analysis finds no evidence
in support of income convergence. The experience of the GCC is also compared to
examples of deeper integration such as those of the European Union, the United
States and Australia.
Finally we apply a non-parametric approach to test whether incomes have a
tendency to revert to the mean. For the period 1970–2003, we find half-life
estimates of income convergence range from 12.5 to 14 years. Based on this
approach, the GCC incomes should converge within three decades.
In summary, the findings of Chapter 5 indicate that income convergence is not
directly affected by RTAs. Income convergence is subject to individual country
characteristics. Countries with similar economic, social, and cultural characteristics are
more likely to converge. The findings for the GCC suggest the six economies are
converging at a very slow rate. However, this is not attributable to economic
integration.
Chapter 6 focuses on price convergence with the aim of investigating the effects
of economic integration on cross-border prices within the GCC. In the absence of
substantial trade barriers, prices should be more or less equalised when expressed in
terms of a common currency. We examine this proposition for the GCC countries using
prices at several levels of disaggregation. We start by analysing the dispersion of
inflation rates across the six GCC countries, where exchange rates are more or less
fixed. We show that this dispersion exceeds that within the G7, where the exchange
rates are floating. This is a puzzling result as it is usually argued that one advantage of
floating rates is that countries can choose their own inflation rates, so that more
diversity of inflation across countries would tend to be expected. This comparison also
reveals lower inflation rates on average within the GCC compared to the G7. Moreover,
after 2000 inflation in the GCC became more dispersed, whereas within the G7 inflation
rates became increasingly similar. These findings indicate that at the aggregate level,
prices (inflation rates) do not exhibit a noticeable tendency to convergence within the
GCC despite the economic integration.
217
Chapter 6 then proceeds to analyse other aspects of price convergence, starting
with aggregate inflation rates and moving progressively towards more disaggregated
microeconomic prices. There are essentially four elements to this progression.
Aggregate Inflation. This analysis of aggregate inflation has two parts. The first part
of this material deals with a descriptive analysis of the dispersion of inflation across
the GCC countries and compares that with the EU, states of the USA, and the states
and territories of Australia. Part of this material was mentioned in the above
paragraph. The second part uses a more formal econometric approach to test if
inflation within the GCC reverts to a common mean. In general, we find this to be
the case, which is an important result on the functioning of the macro economies of
the countries. In particular, it means there are distinct forces that lead to the
convergence of inflation rates. Other things being the same, this result points to the
GCC economies operating as one, which is one of the fundamental objectives of
integration.
Prices of broad commodity groups. Next, we split the overall price index into eight
broad groups: Clothing and footwear, Education and entertainment, Food, beverages
and tobacco, Housing and furniture, Medical care, Rent, electricity, water and fuel,
Transport and communication, and Other services. An analysis of the dispersion of
these prices reveals differences across the GCC. Differences are more pronounced in
non-traded commodity groups such as Rent, electricity, water, and fuel. For prices
of groups that tend to have larger traded goods components such as Food,
beverages, and tobacco, dispersion within the GCC is considerably lower. In
essence, this can be interpreted as saying that the prices of traded goods converge
faster than non-traded goods. This is in line with expectations of price equalisation
from open economy macroeconomics.
Microeconomic prices. The analysis further disaggregates the broad commodity
groups into prices of individual goods. We test the prices for stationarity in seven
categories: Food, Clothes, Household supplies, Rent, Personal care, Transportation,
and Business trips. We find the prices to be mean reverting in most cases. The
analysis also tests investigates price differences between eight major cities within
the GCC. We find that price differences tend to diminish overtime. However, the
role of distance between cities and border effects on these prices are found to be
ambiguous. The result that prices differences in the GCC diminish is encouraging
for the integration progress. It indicates that here barriers to trade are declining and
the integration process is working.
218
Coke prices. Finally, we analyse the price differentials of an identical good, Coke.
We find that it takes prices of Coke 10 to 18 months to converge within GCC. As is
the case with other microeconomic prices, distance does not affect the price
differences significantly. The persistent price differentials suggest that price
equalisation is relatively slow within the GCC.
Overall, the findings regarding prices can be summarized as follows. We find
that aggregate inflation rates in the GCC countries converge within a period of about
three years. Closer examination of broad commodity groups indicates that not all prices
equalize at the same rate, however. The prices of traded commodities exhibit less cross-
country dispersion and tend to equalise faster. Micro price analysis indicates that most
prices within the GCC are mean reverting. In the case of a homogenous good, prices are
still not always equalised absolutely, but over time they do converge to a common
mean.
In summary, our study indicates that the impact of the GCC economic
integration on trade has been negligible. However it should also be noted that there is no
evidence of trade diversion. Regarding income convergence, we find little evidence of
any direct effect of economic integration. Finally, on the whole we find that the impact
of integration initiatives on the equalisation of prices across the GCC to be present.
7.2 Limitations of the Study
The interpretation of the empirical results of this thesis need to be considered
with several caveats in mind. The trade analysis was based on cross-sectional changes
over time. This was done to specifically to measure changes at interval years. The use of
cross-sectional approach allows us to study incremental changes of trade liberlisation.
This is particularly useful in the case of the GCC‟s stalled progress during the earlier
years of the sample. Moreover, changes in trade policy are unlikely to be realized
immediately, especially in the case of the GCC where the FTA and CU were introduced
gradually.
Income convergence analysis of the GCC countries finds evidence of declining
differences between them. We use a nonparametric approach of deviations-from-the-
mean, which does not incorporate the characteristic differences of each country. The
results are statistically significant, however it does not clearly isolate the economic
integration effect. The use of this approach is qualified due to the limitation of the data
availability of the GCC countries. However, using conditional convergence estimates,
RTAs are shown to have a weak effect on incomes convergence. These results can be
219
further qualified if movement of labour and capital can be measured across the six GCC
countries. However, such data is either limited or is simply not available. Even if such
data is available it is likely to show weak effects of labour movements on income
convergence. Strum and Siegfried (2005) point out that labour markets in the GCC
countries are fragmented between public and private sectors, where nationals dominate
the former and the expatriate workers the latter. The expatriate work force allow for
greater degree of flexibility within the private sector where employment number adjust
to demand shocks. This is unlike the public sector employment where rigidities do not
allow such adjustment. The flexibility of the expatriate workforce however does not
extend across the border. Strum and Seigfried (2005) point out that the Economic
Agreement of the GCC does not explicitly include the expatriate workforce in the free
movement of labour across national borders. Thus, the effects of economic integration
even with such data may not be clear cut. There is scope for further research in this area
where migration patterns across GCC borders can be investigated for labour mobility of
nationals and expatriates.
The price convergence analysis provides us with insights of the effectiveness
economic integration. Price indices are readily available, however in many cases these
are measured differently from one country to another. Thus, we expect the price level
changes to reflect different trends in different countries. In the case of the GCC this was
shown clearly when inflation rates were disaggregated into major spending categories
price levels. Unfortunately, unified GCC data with respect to these categories is limited.
Thus, the dispersion of disaggregated price level data is only available for the period
2001-07. This clearly restricts any useful analysis besides the descriptive. Using micro
level prices was a direct attempt at overcoming these data challenges. However, even at
such disaggregated level certain limitations emerge. A significant drawback of using the
micro prices is the neglect of consumption patterns. The data available on micro prices
are based on generic description of homogeneous goods. There are no measures of
consumption and thus any effects on price equalization due to preferences are
undetectable. Preferences are not the only aspect to omitted in this approach however.
Market segmentation, due to non tariff barriers, may very well play a role at keeping
prices differentials positive. Segmentation may occur due to different specifications and
standards of goods produced or traded in each country. The GCC efforts to harmonise
specifications and standards through the establishment the Gulf Standardisation and
Metrology Organization, referred to in Chapter 3, are a clear sign of potential hurdles
perceived by the member countries in this regard. We addressed this issue by using
220
border and city effects. The effects were weak and do not isolate specific reasons for
price deviations however. Further research is required to isolate the underlying factors
that lead to slow rates of convergence of prices across the GCC region based on some of
the considerations above.
In its analysis of the GCC‟s economic integration experience, the thesis did not
address the institutional aspects. These aspects are worth briefly reflecting upon in light
of the findings of this thesis. The GCC‟s institutional framework can be described as an
intergovernmental coordination process. These include the Supreme Council (consists
of GCC‟s Heads of States), the Ministerial Council (involves Foreign Ministers of
member states), the Committee of Financial and Economic Cooperation (formed by
Ministers of Finance and Economics), and other sepcialised committees at the
ministerial level. The supreme council provides the overall policy initiatives and
approves recommendations and proposals made by the sub-level committees. These
committees submit recommendations and proposals to the Ministerial Council, which
in turn liaises with the Supreme Council. In the case of the Supreme Council and
Ministerial Council, the resolutions are passed based on unanimous support. The main
super-national body within the GCC is the Secretariat General, which is tasked, among
other duties, with the follow up on implementation of resolutions and drafting common
legislation (Strum and Siegfried 2005). While the intergovernmental system may have
worked well for the GCC countries during the first few decades of the economic
integration, the lack of supranational powers of GCC institutions may be a challenge in
the future (Lorenzgi 2008). This will be a pressing issue with respect to the proposed
monetary union, where a GCC central bank will require supranational powers to
effective conduct monetary policy. An intergovernmental approach to such a central
institution will fall short of achieving a functional monetary union (Buiter 2008).
7.3 Implications of the Findings
In an economic integration framework it is important that the countries involved
exhibit a certain degree of convergence. There are several areas where convergence is
essential for the operation of an RTA including economic, political, and institutional.
Convergence implies that the economies of the RTA members become more similar
over time and will respond similarly to joint policies. If convergence is not present
however, economic integration will face significant challenges and an uneasy future.
The analytical results presented above suggest that the GCC‟s economic
integration has not been the dominant driving force of convergence in two out of three
221
key economic aspects: trade, and incomes and growth. Trade has been largely
unaffected by liberalisation. Incomes exhibit convergence over time, such that economic
well-being differences are declining within the region. However, there is no evident link
to economic integration. On the other hand, prices exhibit a substantial degree of
convergence which may indicate economic integration has had an impact. However,
here a qualification is necessary. For even an identical product (Coke), there are
substantial departures from the Law of One Price in the short-run. As it is likely that the
same situation applies to other identical products, the time taken for prices to converge
across countries is possibly surprisingly long.
The implications of these findings suggest that the GCC‟s economic integration
has not completely met reasonable economic expectations. From a policy perspective
these outcomes need to be considered seriously. With respect to the customs union,
there has been little effect on trade to take note of. The GCC countries need to identify
the shortcomings of the current framework and its effectiveness in stimulating greater
intra-regional trade. Likewise, the common market operation is dependent on significant
liberalisation of labour and capital mobility. Slow convergence of incomes and growth
indicate that the GCC countries have yet to benefit fully from such mobility. We should
note, however, that the common market in the GCC is still a recent development, and
the mobility across borders policy was introduced only relatively recently.
The finding can also be interpreted as putting greater emphasis on the success of
the proposed monetary union, which was originally scheduled to take place in 2010 but
was delayed. The convergence criteria set by the GCC in preparation for the common
currency include several monetary and fiscal parameters of integration. However, the
findings of this thesis indicate that fundamental differences still exist. The findings
imply that the GCC countries experience segmented markets and cross country
economic differences remain significant. Furthermore, the stages leading up to the
monetary union have not been completely fulfilled. A case can be made for policy
makers to address the shortcomings of the current stages of economic integration prior
to advancing to the monetary union.
The majority of the thesis considered economic aspects of the workings of the
GCC. It should not be forgotten, however, that there are also other important factors at
work, such as cultural, geographic, and political. As the six GCC countries share a
common language, religion, and history, there is a substantial feeling of a “shared
identity” among the more than 36 million people who live in these countries. This,
222
coupled with post-cold-war security concerns, possibly points to the ultimate success of
the GCC as an on-going economic and political entity.
223
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