INTRA-REGIONAL TRADE OF REGIONAL TRADING BLOCS: THE … · INTRA-REGIONAL TRADE OF REGIONAL TRADING BLOCS: THE CASE OF THE GULF COOPERATION COUNCIL by Adham Al Said * UWA Business

September 26, 2007

INTRA-REGIONAL TRADE OF REGIONAL TRADING BLOCS: THE CASE OF THE GULF COOPERATION COUNCIL

by

Adham Al Said* UWA Business School

The University of Western Australia

Abstract This paper adapts a framework to measure the effects of Regional Trade Agreements

on international trade flows. It applies a two-step empirical model to analyse international

and regional trade flows. The first step uses a gravity approach to determine trade flows

between 145 countries over the last decade. The second step deals with intra-regional trade in

the Gulf Cooperation Council (GCC) consisting of Bahrain, Kuwait, Oman, Qatar, Saudi

Arabia, and UAE. Findings suggest that the GCC does not have a substantial impact on its

members’ intra-regional trade. Moreover, the paper finds no distinct patterns of trade within

the region.

*I would like to thank Professor Ken Clements and Dr Abu Siddique for their continuous support and feedback while developing this paper. Errors and omission are my own.

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PREFACE

Thesis title: Economic Aspects of the Gulf Cooperation Council’s Integration Supervisors: Professor Ken Clements, Dr Abu Siddique Multilateral trade liberalisation is a central objective of the international community in the form of agreements such as the General Agreement on Tariffs and Trade, and currently the World Trade Organization. Although multiple negotiation rounds were conducted on the multilateral level, countries continue to depend on regional arrangements to promote trade and development. Examples include the European Union, North American Free Trade Area, and the Association of Southeast Asian Nations. As a result, effects of regional trading blocs on international trade are open to question. It is the aim of my thesis to investigate the specific effects of the Gulf Cooperation Council (GCC), comprising of Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and Qatar, as a regional trade agreement on its members’ trade and development. The thesis will analyse a number of key issues within the above context:

1. The GCC’s Performance as a Regional Trading Bloc Understanding the background of the founding of the GCC and its development will assist comparisons with other well-established blocs. Such analyses will yield important insights into the process of economic integration in resource-rich developing countries.

2. Intra-regional Trade of the GCC Understanding trade patterns within the GCC will reveal the effect of the trade bloc as an operational entity. Recent developments within the region promise to promote trade liberalisation. Empirical analysis will explore the potential benefits in terms of bilateral trade volumes and patterns.

3. Economic Integration of the GCC The justification for the founding of the GCC is the creation of a long-term

economic integration process. This process involves multiple liberalisation and standardisation procedures. To ensure success it is important that the economies involved be sufficiently comparable in their macroeconomic structures and patterns. Therefore, it is essential to study the fundamentals of the GCC’s economic integration process and its development. Such a study will yield a greater comprehension of the degree of the region’s success in integrating its economies.

The thesis will take the following structure: Chapter I: Introduction Chapter II: Regionalism, Trade, and Economic Development Chapter III: GCC Inception, Development, and Challenges Chapter IV: GCC Intra-regional Trade: A Gravity Model Approach Chapter V: Monetary and Macroeconomic Developments in the GCC Chapter VI: Conclusions This paper is based on a broader analysis of major RTAs in operation today and the GCC as posited in Chapter IV.

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1 Introduction

The regionalisation of world trade is not a new phenomenon. In fact, it has gained

strength over the past few decades. Regional trade agreements (RTAs) reported to the World

Trade Organization have increased rapidly in both developed and developing countries.

However, not every RTA has been successful in improving the fortunes of its members. None

the less, RTAs continue to exist and regenerate themselves. The reason behind this is that the

underlying goals of typical RTAs are not merely based on trade perspectives. Their formation

and evolution are based 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

paper is concerned with the first of these objectives, the traditional gains from trade. In this

context, the analysis will investigate the effects of RTAs on their members’ intra-regional

trade. A consequence 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) showed that not all trade agreements

are necessarily welfare enhancing. Thus, RTAs can have profound effects on trade flows and

welfare.

This paper has two main aims. First, to measure the effect of RTAs on international

trade flows using empirical tools for the period 1995 to 2006. Second, to apply a

disaggregated analysis to the Gulf Cooperation Council (GCC) region on a longer time

period, from 1980 to 2006. The GCC consists six developing countries—Bahrain, Kuwait,

Oman, Qatar, Saudi Arabia, and UAE—undergoing a long-term economic integration.

Significant developments of the integration process in recent years justify a closer attention to

this region.

The paper proceeds as follows: the next section (2) provides a brief background of an

empirical model commonly used to examine bilateral trade flows, the gravity model. A two-

step methodological framework of the traditional gravity model in Step 1, and GCC’s intra-

regional trade in step 2 will be presented. Section 3 discusses the data used to estimate the

traditional gravity model and its empirical results. Section 4 compares these results with other

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relevant studies of RTAs. Section 5 discusses the disaggregated trade patterns within the Gulf

Cooperation Council (GCC). Finally, the paper concludes with a summary of findings and

recommendations.

2 Modelling World Trade Flows International trade flows are traditionally modelled using the gravity model. The

model is borrowed from physics, and 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,

Bergsrand 1985, Feenstra 2004).

Earlier applications of the model to trade flows were tested by Tingbergen (1962),

Poyhonen (1963), and Linnemann (1966). Its use has proved successful in determining trade

flows between trading partners with a great degree of accuracy. However, the model did not

have 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 where he used a general equilibrium model to

achieve a ‘generalised’ gravity equation. More recently, Frankel et al. (1995) applied the

gravity model to a cross-section of countries to determine the trade flows and welfare

implications of regional trading blocs. Their emphasis was the effect trade blocs had on trade

flows and thus included several dummy variables in an attempt to capture the effect. This

paper 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), Anderson and van Wincoop

(2003). In this literature, attempts were made to explain the border effect on trade while

‘correctly’ specifying the model. A number of these studies, such as McCallum (1995)

concentrated on the border effect between Canada and the U.S. McCallum (1995) tested the

border effects between Canadian provinces and some American states using a dummy for

intra-provincial trade. He found substantial border effects between Canada and the US.

Anderson and van Wincoop (2003) take the analysis a step further and develop what they

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called ‘multilateral resistance’ to explain McCallum’s (1995) results and their deviation from

theory. Anderson and van Wincoop (2003) suggest that the McCallum results were

exaggerated and biased due to variable omission and the specification of his model. Their

model suggests a theoretically correct specification using ‘multilateral resistance’ to explain

the border effects. They found relatively smaller border effects between Canada and the U.S.

By contrast, Feenstra (2002) differs from both studies above by using fixed effect methods.

This method isolates effects of importers and exporters as fixed in the model. The gravity

model was also used to explain the effects of currency unions on trade. Such studies include

Frankel and Rose (2002) and Rose (2000).

Figure 1 illustrates the concept of the Gravity Model in international trade. It depicts

three economies of different sizes, 1 being the largest and 3 being the smallest. 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. Mij indicates the bilateral trade

flow from country i to country j. As country 1 and 2 are the largest the gravity model predicts

that trade between them is necessarily larger than trade between country 2 and 3. Thus, M12+

M21 > M23 + M32.

The paper’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

M12

M21

M23

M32

M13

M31

Large Country

Medium Country

Small Country

2

3 1

M12 +M21 >M23+M32

FIGURE 1

THE GRAVITY MODEL CONCEPT

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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.

2.1 Step1: The Traditional Gravity Approach to Determining Total Trade

In the first step the bilateral exports between countries are taken to be a function of

income, income per capita, distance, common borders, language, and RTA membership. We

index countries by i = 1,…,C. Therefore trade between countries i and j is determined as

follows:

(1) ( )ij i j i j ij ij ij ijX = f Y ,Y ,C ,C ,Distance ,Adjacency ,Language ,RTA ,

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 (1), total 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 (1) is taken to be log-linear:

(2) ij 1 2 i 3 j 4 i 5 j 6 ij

7 ij 8 ij 9 ij ij

logX = β +β logY +β logY +β logC +β logC +β logD

+β Adj + β Lang +β RTA + ε ,

where Xij is the value of exports from country i to country j; Y i is GDP of country i valued at

PPP dollars; Yj is GDP of country j valued at PPP dollars; Ci is per capita GDP of country i

valued at PPP dollars; Cj is per capita GDP of country j valued PPP 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;

Lang 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

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that takes the value 1 when both i and j are members of the same agreement, 0 otherwise; and

εij is a disturbance term.

This specification of Step 1 is similar to that of other studies in the literature such as

Anderson (1979), Bergstrand (1985) and Frankel (1997) for example. The coefficients can be

interpreted as follows: β1 and β2 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 coefficients β3 and β4 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 coefficient β5 represents the distance

elasticity. As transport costs are difficult to measure, distance is used as a proxy is expected

to take a negative value. The coefficients β6 and β7 reflect the effects of adjacency and

common languages on bilateral trade. It is expected that if countries have common borders,

greater trade is facilitated, so the coefficient β6 is expected to be positive. Countries with

common languages may find it easier to trade with one another, so the coefficient β7 is

expected to be positive. Finally, the RTA coefficient β8 represents the trade bloc effect on

bilateral trade. This value may be positive or negative.

2.2 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 and 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 1p , ,n= K , so

that pnp=1ij ijX = 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

i∈S are then j Si ijX = X∈∑ . Thus

(3) n p

i ijj S p=1

X = X∈∑ ∑ i∈S .

Disaggregated trade within the GCC is determined by total trade of the two countries

concerned, i jX ,X , together with the economic/geographic/cultural variables of model

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(4) pij i j ij ij ijX =g(X ,X ,Adjacency ,Distance ,Country/Commodity Dummy ).

Model (3) is taken to be log-linear:

(5) ( )

pij 1 2 i 3 j 3 ij

l4 ij k ijk

k

logX = α +α logX +α logX +α logD

+α Adj + γ Country/Commodity dummies +ε , i, j ,∑ ∈S

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; Distanceij 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 lijε is a disturbance term.

3 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 (2). The second sub-section will discuss the estimation results and RTA

implications.

3.1 Data

The data used were obtained from 145 countries. 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 ranges from 1995 to 2006. The sample period aims to investigate the recent

developments in RTA effects on international trade. Moreover, this period represents the

post-GATT era where the WTO became operational. This contrasts with 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 20880 [(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 PPP current prices was used for all trading

partners, as well as GDP per capita valued at PPP current prices. Distances were measured in

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kilometres and were obtained from CEPII1, where they are measured between capital cities.

Bilateral dummies were 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 1 reports the means and standard deviations of bilateral exports across the

cross-section.

TABLE 1

BILATERAL EXPORTS: DESCRIPTIVE STATISTICS

1995 1998 2001 2003 2006

Mean 423 475 551 674 1,063

Standard Deviation 30,121 33,849 39,270 47,977 75,679 Note: The means and standard deviations are expressed in millions of US dollars.

3.2 Empirical Results Table 2 presents the OLS estimates of the gravity equation (2). The estimation was carried

out over five cross sections, 1995, 1998, 2001, 2003, 2006. In this table the gravity equation

is estimated in the form of equation (2). The coefficient of income Yi represents country i ’s

elasticity of the exports with respect to income. During the period, this elasticity increases

from 1.04 in 1995 to 1.16 in 2006. The coefficient Y j, the importer’s income elasticity, has

been more or less constant over time between 0.81 in 1995 and 0.84 in 2006. However, the

importer’s income plays less of a role in affecting bilateral exports. Per capita income Ci and

Cj measure the differences between rich and poor countries, where richer countries are

expected to trade more than their poorer counterparts. With respect to the per capita income

of an exporting country, the coefficient of Ci declines during the sample period from 0.62 to

0.55. Similarly, the effect of the importer’s GDP per capita, Cj, declines substantially from

0.51 in 1995 to 0.37 in 2006. As a result, per capita income plays less of a role in bilateral

exports as the period progress. Both income and per capita income are significant at the 1%

level. Table 2 indicates that throughout all years 1995 to 2006 the distance coefficient is

correctly negative and significant at the 1% level. In other words, the farther the trading

1 Centre D’études Prospectives et D’Informations Internationales, available at http://www.cepii.fr/anglaisgraph/bdd/distances.htm

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TABLE 2

ESTIMATES OF THE GRAVITY EQUATION

Variable (1)

1995 (2)

1998 (3)

2001 (4)

2003 (5)

2006 (6)

Income

Y i 1.04 (0.01) 1.10 (0.01) 1.09 (0.01) 1.12 (0.01) 1.16 (0.01)

Y j 0.81 (0.01) 0.79 (0.01) 0.80 (0.01) 0.80 (0.01) 0.84 (0.01)

Per capita income

Ci 0.61 (0.02) 0.50 (0.02) 0.53 (0.02) 0.50 (0.02) 0.55 (0.02)

Cj 0.51 (0.02) 0.44 (0.02) 0.40 (0.02) 0.38 (0.02) 0.37 (0.02)

Distance -1.06 (0.03) -1.07 (0.03) -1.10 (0.03) -1.14 (0.02) -1.12 (0.03)

Adjacency 0.66 (0.13) 0.60 (0.13) 0.65 (0.13) 0.69 (0.13) 0.71 (0.13)

Common Language 0.86 (0.06) 0.92 (0.06) 1.05 (0.06) 1.04 (0.05) 1.14 (0.06)

RTA

EU 0.46 (0.18) 0.62 (0.18) 0.58 (0.18) 0.65 (0.17) 0.47 (0.18)

NAFTA -1.39 (0.85) -1.16 (0.87) -1.03 (0.90) -1.26 (0.89) -1.20 (0.92)

EFTA 1.04 (0.84) 1.57 (0.86) 1.37 (0.89) 1.40 (0.88) 1.68 (0.91)

CER 0.90 (1.46) 0.92 (1.49) 0.70 (1.55) 0.84 (1.52) 0.74 (1.58)

ASEAN 1.03 (0.30) 0.32 (0.28) 0.34 (0.28) 0.30 (0.28) -0.05 (0.28)

APEC 1.45 (0.13) 1.35 (0.13) 1.51 (0.13) 1.48 (0.13) 1.33 (0.13)

MERCOSUR 0.29 (0.72) 0.27 (0.73) 0.35 (0.76) 0.47 (0.75) 0.25 (0.78)

LAIA 0.43 (0.22) 0.54 (0.23) 0.66 (0.24) 0.36 (0.23) 0.59 (0.25)

CAN 0.79 (0.61) 0.78 (0.62) 0.71 (0.65) 0.92 (0.64) 0.41 (0.66)

CARICOM 2.64 (0.21) 2.55 (0.20) 2.66 (0.20) 2.65 (0.20) 2.95 (0.21)

COMESA 0.84 (0.28) 0.30 (0.27) 0.66 (0.27) 1.02 (0.27) 1.23 (0.27)

(Continued on next page)

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TABLE 2

ESTIMATES OF THE GRAVITY EQUATION (Continued)

Variable (1)

1995 (2)

1998 (3)

2001 (4)

2003 (5)

2006 (6)

CEMAC 0.81 (0.58) -0.09 (0.57) -0.48 (0.59) -0.74 (0.63) -0.49 (0.65)

CACM 1.71 (0.47) 1.69 (0.48) 1.77 (0.50) 1.78 (0.49) 1.95 (0.51)

WAEMU 2.47 (0.32) 1.77 (0.32) 1.68 (0.33) 2.20 (0.32) 2.25 (0.33)

GCC -0.02 (0.38) 0.03 (0.39) 0.15 (0.41) 0.13 (0.40) 0.21 (0.42)

PAN_ARAB 0.57 (0.29) -0.16 (0.32) -0.19 (0.30) -0.21 (0.29) 0.12 (0.30)

CIS 2.55 (0.28) 2.37 (0.28) 2.24 (0.30) 2.20 (0.29) 2.05 (0.29)

ECO 1.46 (0.29) 1.35 (0.30) 1.14 (0.31) 0.59 (0.30) 0.79 (0.31)

S.E. of regression 2.05 2.10 2.18 2.14 2.23

R2 0.66 0.65 0.64 0.66 0.66

Number of Observations 11,561 12,855 13,977 14,254 15,127 Notes:

1. Dependent variable: log of bilateral exports. 2. Standard errors in parentheses. 3. See Appendix for the full names of RTAs.

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partners are from each other, the greater the cost of transportation. The elasticity of bilateral trade

with respect to distance increases during the period from -1.06 to -1.12. Bilateral exports are

more sensitive to distance in 2006 compared to 1995. A possible explanation may be the

continuous increase in the cost of energy during the last few years of the sample period.

The gravity equation estimated in Table 2 includes a number of dummy variables. These

are divided into two categories: first the dummy variables of common borders ‘adjacency’ and

common language, and second RTA membership indicators. The adjacency dummy is significant

at the 1% level in all years. It also shows an upward trend from 0.67 to 0.71, or 95% [=(e0.67 -

1)*100] to 103% effect above ‘normal’ trade explained by economic factors. Common language

also 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 0.86 to

1.14, or 136% to 213% effect above normal trade. This result confirms that language plays an

important role in facilitating bilateral trade.

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 stronger effect during the period. The EU dummy coefficient increased from 0.46

to 0.47. This translates into 58% to 60% effect of EU membership on bilateral exports of

countries involved. The EU dummy is significant at the 1% level in all years. This value should

be considered with caution. The sample does not include the enlargement of the EU to the

twenty-five current members. Elsewhere in Europe, EFTA shows fluctuating effects during the

period. In 1995, the EFTA RTA has a coefficient of 1.04 or 182% effect on bilateral trade within

the region. This, however, is not significant. In 2006, a coefficient of 0.38 is reported, which

translates to 46% effect on bilateral trade. In all cross-section years, EFTA coefficients are not

significant. In North America NAFTA’s dummy shows consistent negative effects during the

period, declining between -1.03 to -1.39. or -65% to -75%. NAFTA’s bilateral exports are not

affected positively by the RTA the model suggests.This result, however strong, is not significant.

On the other side of the globe, CER with its two members shows substantial effects of the RTA

on its members’ bilateral exports. This effect declines over the period, from 0.90 to 0.74, or

150% to 101%. This result is statistically insignificant in all cross-section years. Although these

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values are very large, they may be explained by CER’s remoteness and 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 also

includes other Pacific Rim countries included in earlier-mentioned RTAs such as the United

States and Canada from NAFTA. Moreover, though APEC is not an official RTA, it is

considered instrumental in fostering greater trade between its members. APEC exhibits

consistent and statistically significant RTA effects in all cross-section years. Coefficients

between 1.33 or 267% to 1.51 278 or 353% show strong APEC effects on its members’ normal

bilateral exports. This result is significant in all cross-section years. The ASEAN dummy shows

a decline on its members’ bilateral exports. Only in 1995 and 1998 are the coefficients of 1.03

and 0.32 significant at the 1% level. These coefficients translate into 180% and 37% effect of

ASEAN on its members’ normal trade. During the rest of the period ASEAN’s smaller

coefficients are not significant.

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 the Caribbean Community and Common Market

(CARICOM) represent the major operational RTAs. CAN’s RTA dummy coefficients range

between 0.71 and 0.92 or 103% to 150%. None of the cross-section years are statistically

significant. Thus, it is not clear if the lower values towards the end of the period are truly useful

for further analysis. CACM also shows strong and consistent effects on its members‘ bilateral

trade flows where its coefficients range between 1.71 and 1.95, or 453% to 603% effect above

normal trade. These results are significant at the 1% level in all years. MERCOSUR’s dummy

shows relatively weaker effects during the sample period, its coefficients ranging between 0.25

and 0.47, or 28% to 60%. None of the coefficents of MERCOSUR are significant in any one

year. LAIA’s dummy reports a consistent effect of the RTA on its members’ values ranging

between 0.36 or 43% and 0.66 or 93% effect on bilateral trade. Unlike MERCOSUR, LAIA’s

coefficients are significant at the 5% and 10% level in most years except in 2003 where it is not

significant. In the Caribbean, CARICOM’s dummy takes large values that range between 2.55 or

1181% and 2.95 or 1810%. The significance of these results at the 1% level suggests that

CARICOM plays an important rule in bilateral trade of the region.

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The sample includes three African RTAs: 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 increase

from 0.84 to 1.23, or 132% and 242% effect on its members’ intra-regional trade. In most years

COMESA’s dummy is significant at 1% except in 2001 where it is significant at the 5% level.

CEMAC’s dummy coefficient in comparison falls drastically from 0.81 to -0.49 during the

period. However, between 125% and -39% effects on bilateral exports within that region are

statistically insignificant in all years. WAEMU exhibits strong effects of 2.47 or 1082% in 1995,

and 2.25 or 849% in 2006. WAEMU’s dummy is significant 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 Gulf

Cooperation Council (GCC), and Pan-Arab Free Trade Agreement (PAN-ARAB). The GCC is

exhibits declining effect from 1995 to 2006. Its dummy coefficients reflect small RTA effects, its

coefficients range between -0.02 and 0.21, or -2% and 23%. The GCC dummy is not significant

during the sample period. PAN-ARAB also has a small effect on bilateral exports of -0.21 or

19% and 0.57 or 76%. These values are significant at the 5% level only in 1995 and 1998.

In Central Asia and the former USSR, two main RTAs are included in this sample,

Commonwealth of Independent States (CIS) and Economic Cooperation Organization (ECO).

CIS form the majority of former USSR states and exhibit substantial effects from 2.05 to 2.55 or

678% to 1181%. CIS is significant at the 1% level in all cross-section years. Finally, ECO shows

declining effects on its members’ bilateral trade. Its coefficient fell from 1.46 or 326% to 0.79 or

120%. In 1995, 1998, and 2001 coefficients are significant at the 1% level and at 5% level in

2003 and 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 RTAs in some

developing countries are effective and may have significant impact on regional trade flows.

4 Comparison with other studies

The main results in the previous section conform with trade theory with respect to economic

parameters. However, it is worth noting similar studies to verify the results presented above. This

section will compare the results of the first step of the model with other relevant studies. Table 3

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TABLE 3

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)

Income

Yi 0.96 (0.02) 1.12 (0.02) 0.97 (70.32)

Yj 0.73 (0.01)

0.89 (0.02) 0.83 (0.01)

0.72 (0.02) 0.89 (106.86)

Per capita income

Ci 0.21 (0.02) 0.41 (24.58)

Cj 0.23 (0.01)

0.06 (0.03) 0.73 (0.02)

0.25 (21.77)

Distance -0.51 (0.02) -0.93 (0.05) -1.12 (0.04) -1.10 (0.04) -0.95 (-49.05)

Adjacency 0.72 (0.09) 0.42 (0.16) 0.63 (0.18) -0.03 (0.16) 0.14 (2.56)

Language 0.47 (0.05) 0.59 (0.08) 0.50 (0.08) 0.69 (0.08) 0.73 (20.89)

Colony 1.75 (0.15)

RTA/FTA 0.67 (0.14) 1.73 (0.11)

EC 0.24 (0.09) -0.29 (0.16) 0.07 (1.08)

NAFTA -0.12 (0.63) -0.73 (0.98) 0.11 (0.33)

ASEAN 1.40 (0.29) 1.80 (0.33) 1.00 (5.52)

CER 0.50 (1.95)

MERCOSUR -0.18 (0.46) 0.78 (0.42) -0.19 (-0.85)

EFTA 0.04 (0.30) -0.37 (0.32)

APEC 0.61 (0.21)

Notes: 1. Standard errors in parentheses. 2. NAFTA in column (3) refers to US-CANADA FTA only.

14

presents some comparable studies that considered RTAs’ effects on bilateral trade. In the

case of the economic variables, the model estimated in Table 2 complies with the correct

signs. However, this study finds stronger size effects in terms of GDP on bilateral trade

compared to Wei and Frankel (1997), for example. Income effects in Table 2 range

between 1.04 and 1.16, compared to 0.96 for exporting countries. The difference is not

substantial nevertheless. 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 3. Values between 0.79

and 0.84 reported in this paper are close to the values of 0.89 and 0.72 reported by Wei

and Frankel (1997), Feenstra et al. (2001), and Krueger (1999). 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 paper finds values

between 0.50 and 0.61 for exporters, and 0.37 and 0.51 for importers. The values reported

here are closer to Krueger’s (1999) results, which report 0.41 and 0.25 for the exporter and

importer countries’ per capita income.

The distance, adjacency, and common language dummies are similar in most cases

to other studies. In the case of distance, this study reports values as low as -1.14. This is

marginally different from the -1.12 reported by Rose (200), or the -0.93 reported by Wei

and Frankel (1997) for example. Other studies report mixed results with respect to

adjacency. However the values from Table 2 come close to the 0.72 reported by Frankel et

al. (1995), or the 0.63 reported by Rose (2000). In the case of common language, this

paper finds stronger than usual effects compared to other studies. Table 2 reports values as

high as 1.14 for language, other studies such as Krueger (1999) report 0.73.

The estimates of RTA dummies’ coefficients vary substantially from the literature. For

the EU or European Community (EC), the coefficient ranges between -0.29 to 0.24 in the

studies referred to in Table 3. Table 2 reports values between 0.46 and 0.65, a higher than

usual effect. This study, however, shows similar negative or weak RTA effects with

respect to NAFTA. However, the study reports coefficients for NAFTA smaller than other

related studies—as low as -1.39. These results are not significant in any one case. For

CER, Krueger (1999) reports a 0.50 dummy coefficient; this study reports values between

0.70 and 0.94, suggesting a larger effect on bilateral trade within that region compared to

other studies. For MERCOSUR, the literature reports different values, from -0.19 to 0.78.

Table 2 reports values between 0.27 and 0.47, well in line with other studies. The results

15

reported for ASEAN are considerably different compared to those included in Table 3.

Frankel et. al. (1995), and Wei and Frankel (1997) find values of 1.4 and 1.8, compared to

values between -0.05 and 1.03 found in this study. In the case of EFTA the results of this

study are very different from the literature with coefficients larger than other studies

reported here. Values between 1.04 and 1.68 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, where values ranging from 1.33 to 1.51 are greater than the 0.61

reported by Frankel et. al. (1995), for example. Yet 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 GCC’s trade patterns.

All in all, this study’s findings largely verify previous studies’ results, implying that

gravity model (2) captures RTA effects reasonably well, comparable to the literature.

5 Trade within the GCC Countries (Step 2)

The GCC was created in the early 1980s. It has been operational for the past two

decades. The GCC has been undergoing significant economic integration progress recently

that warrants attention. Trade liberalisation has gone hand in hand with capital and labour

mobility enhancement efforts. 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 of Free Trade Area, Common Market,

Customs Union, Monetary and Economic Union. The GCC possesses many potential

success factors that may not be readily present elsewhere, such as common language,

historical and social ties and a common religion. Moreover, the GCC economies are

similar in their oil dependency and rapid economic development progress 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 its trade patterns with the rest of the world? The answer to the first

question lies in the previous empirical analysis. Referring to Table 2, the RTA dummy of

16

the GCC indicates how much of a contribution the arrangement makes to intra-regional

trade. The gravity equation implies weak effects between -2% and 23%. The observed,

fitted, and residuals matrices of the GCC’s intra-regional trade are reported in Table 4 for

the years 1995 and 2006. The model estimates of the intra-GCC trade flows are presented

in section (ii) of Panels I and II. The model overstates the actual trade flow values in

section (i) of Panels I and II. This is indicated in the bottom section (iii). Section (iii)

represents the residuals of the model with respect to the GCC intra-regional trade. Table 4

indicates that the gravity model does not fit the GCC bilateral trade very well.

Table 5 reports the 1995 and 2006 intra-GCC trade matrices expressed in the form

of percentages of total. Table 5 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 flows within the region? Transportation cost cannot be expected to produce such

lower overall trade proportions and sub-regional concentrated trade. Distance within the

region is not so prohibitive that it should reduce trade substantially between GCC; in most

cases, it barely exceeds one thousand kilometres between the capital cities. The answer

may lie in the similarity of the economic structures of these economies. Dependence on oil

mentioned earlier for developmental purposes and large government sectors may indicate a

potential effect on low intra-regional trade. Moreover, these six countries are traditionally

open economies. They import most of their consumer and capital needs from outside the

GCC region. Thus to better understand the intra-regional trade patterns of the GCC region,

disaggregated commodity trade analysis is more useful to contrast the patterns suggested

by Table 5.

This is the second step in the model’s analysis, where the first was involved in

estimating bilateral trade determinants using GDP and GDP per capita as proxies for size

and economic development. This step involves estimating bilateral trade within the GCC

conditional on total intra-regional trade. Consequently, 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, (10) Mineral

17

TABLE 4 GCC TRADE MATRIX 1995 AND 2006 (MILLIONS OF US DOLLARS)

1995 2006 Importer

Exporter

Bahrain Kuwait Oman Qatar Saudi Arabia

UAE Total

Bahrain

Kuwait Oman Qatar Saudi Arabia

UAE Total

(i) Observed (i) 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 13.5 60 137 243 63 123 43 139 979 1,347

Saudi Arabia 1,480 467 135.2 158.7 1,350 3,591 4,534 1,037 373 825 2,260 9,029

UAE 89 156 920 122 332 1,619 327 528 2,358 548 1,703 5,464

Total 1,604 722 1,091 337 811 2,268 5,016 1,901 3,079 1,592 2,992 5,470 (ii) Fitted (ii) Fitted Bahrain 45 25 26 201 80 377 55 21 121 252 133 582

Kuwait 31 104 68 1,724 288 2215 10 53 96 1,543 243 1,945

Oman 14 81 35 690 437 1257 4 49 53 459 760 1,325

Qatar 3 47 31 430 212 723 1 81 49 900 808 1,839

Saudi Arabia 135 2,282 1,173 820 3,354 7764 41 2,705 875 1,853 4,269 9,743

UAE 41 363 706 384 3,189 4683 10 344 1,169 1,343 3,446 6,312

Total 224 2,818 2,039 1,333 6,234 4,371 66 3,234 2,167 3,466 6,600 6,213

(iii) Residual (iii) Residual Bahrain 13 -14 5 51 -5 50 77 226 -7 391 325 1,012

Kuwait -14 -93 -53 -1,611 -213 -1,984 33 5 -55 -1,300 52 -1,265

Oman -5 -63 -26.5 -635 194 -536 44 32 11 -195 717 609

Qatar 6 -24 -18 -370 -75 -481 62 42 -6 -761 172 -491

Saudi Arabia 1,344 -1,815 -1,038 -661 -2,004 -4,174 4,493 -1,668 -502 -1,028 -2,010 -715

UAE 48 -207 214 -263 -2,858 -3,066 316 184 1,189 -795 -1,743 -849

Total 1,379 -2,096 -949 -999 -5,423 -2,103 4,948 -1,333 912 -1,874 -3,608 -744

18

TABLE 5

GCC IMPORT RATIOS OF TOTAL TRADE (PERCENTAGE)

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

Fuels and Lubricants. The choice of this classification was based on the data availability

for the GCC countries during the period 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

dominating 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. As a result of this specification, there are

forty-five dummy variables representing Bahrain, Kuwait, Oman, Qatar and UAE across

nine commodity categories. Table 6 present the results of the second step of the model

based on equation (5). The model suggest a significant role that total trade within the GCC

plays in determining bilateral trade of product groups. Total trade of exporting country

within the GCC yields a 0.6% change in exports of specific product groups. Total trade of

importing countries within the GCC have a 0.3% effect on bilateral exports of specific

product groups. The model also suggest that distance plays a lesser role in reducing trade

19

within the GCC; a -0.5% elasticity is reported in Table 6. Adjacency, however, exhibits

strong effects within the GCC, promoting trade almost four times above normal trade.

TABLE 6

GCC POOLED DISAGGREGATED TRADE ESTIMATES

Variable Coefficient

Total Trade of Exporter 0.58 (0.03)

Total Trade of Importer 0.29 (0.03)

Distance -0.48 (0.06)

Adjacency 1.60 (0.07)

R2 0.49

Number of Observations 4,455 Note: Standard errors in parathenses.

Table 7 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 commodity groups included. In less than half the commodities

does Saudi Arabia trade more with its neighbours, these being Animal and Vegetable Oils

and Fats, Beverages and Tobacco, Crude Materials, and Commodities and Other

Transactions. Bahrain, Kuwait, Oman, Qatar and the UAE trade more within the GCC in

chemicals, food and live animals, machinery and transportation, manufactured goods, and

miscellaneous manufactured goods, relative to Saudi Arabia. These countries, however, do

not trade similarly 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, however less than Saudi Arabia by 25% [=(e-0.29 -1)*100]. Qatar appears to trade the

least within the region—97% less than the base case. Bahrain, Kuwait, and the UAE’s

GCC trade in this category falls below Saudi Arabia by 65% to 90%. These results also

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. The least trading country in beverages and

tobacco is Qatar, 95% less than the base case. Bahrain, Kuwait and UAE trades 62%, 40%

and 17% 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, 27% less, then Oman, UAE, Qatar, with Bahrain at the bottom of

the list.

20

TABLE 7

ESTIMATE OF COUNTRY-PRODUCT DUMMY VARAIBLE COEFFICENTS

Bahrain Kuwait Oman Qatar UAE

1. Animal Fats etc. -1.39 (0.23) -1.03 (0.24) -0.29 (0.21) -3.68 (0.30) -2.03 (0.33)

2. Bev. & Tobacco -0.97 (0.20) -0.52 (0.23) 0.24 (0.19) -2.93 (0.26) -0.19 (0.32)

3. Chemicals 0.68 (0.20) 1.76 (0.21) 1.08 (0.19) 2.24 (0.23) 1.17 (0.29)

4. Crude Materials -0.66 (0.21) -0.31 (0.23) -0.82 (0.20) -1.14 (0.24) -0.52 (0.29)

5. Food & Animals 0.78 (0.20) 2.06 (0.21) 2.19 (0.19) -0.14 (0.23) 1.70 (0.29)

6. Mach. & Tans. 2.43 (0.20) 3.28 (0.21) 2.62 (0.19) 1.73 (0.23) 1.82 (0.29)

7. Manufac. Goods 3.77 (0.20) 3.08 (0.21) 1.71 (0.19) 3.30 (0.23) 2.40 (0.29)

8. Misc. Manufac. 1.74 (0.20) 2.27 (0.21) 1.09 (0.19) 0.46 (0.23) 1.62 (0.29)

9. Other -2.10 (0.22) -2.28 (0.24) -0.30 (0.19) -2.58 (0.25) -1.19 (0.34)

Notes: 1. Dependent variable: log of bilateral disaggregated exports from i to j. 2. Standard errors in parentheses.

21

Other categories where the 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 magnitude of trade within the GCC of Qatar is in the

order of 840% more compared to the base case. Qatar is followed closely by Kuwait, then

Oman, UAE, and finally Bahrain. Saudi Arabia trades the least in chemicals within the

region. Trade in food and live animals is primarily led by Oman, followed closely by

Kuwait, then UAE, Bahrain, and Saudi Arabia. Oman trades 794%, or eight times more

than the base case, while Bahrain trades about 118% more 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, however, above the base case. Manufactured goods trade patterns shift in

favour of Qatar, trading the most in the region relative to the base case. Its trade flows are

mirrored by Bahrain and Kuwait to make up the top half of the GCC in this category.

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 UAE, Bahrain, 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. These results also suggest that the above categories

are traded more than mineral fuels within the region.

The results of this analysis indicate there is no clear pattern within the region

where one country dominates intra-regional trade completely within the GCC. The

second step of the model used here explains the trade patterns within the GCC, and has

shown 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. However, it trades less in chemicals, food and live animals, machinery and

transportation, manufactured goods, and miscellaneous manufactured goods. In these

22

particular categories there is no clear leader in intra-regional trade. This position is

switched between Bahrain, Kuwait, Oman, Qatar, and UAE.

6 Conclusions

Using a two-step framework, the gravity model was utilised to analyse the 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 today, the model suggests that a number of RTAs in

developing countries explain a large portion of bilateral trade flow once economic factors

were accounted for. RTAs play a significant role in the determination of trade flow

between countries. Findings suggest that geographical proximity, shared borders and

language influence bilateral trade flows significantly. Such results are congruent with

previous literature. However, RTAs are substantially influential in determining trade

patterns. Although traditional trade blocs such as in developed countries maintain similar

effects found elsewhere in the literature, a number of these results are statistically

insignificant to draw additional conclusions about them. It is in developing countries’

RTAs where this paper finds large and significant effects on bilateral trade flows. Trade

blocs in the Caribbean, Africa and Central Asia show strong RTA effects on their trade

patterns. Latin American, Middle East, and Northern Africa’s RTAs remain less effective

on bilateral trade flows.

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 5. GCC intra-regional import ratios are substantially

low for most of the countries at the beginning and at the end of the period 1995 and 2006.

The second step of the framework used in this paper identified the intra-regional trade

patterns of the GCC region. In Step 2 the model quantifies commodity-specific

interactions between the GCC intra-regional trade. The largest economy in the region,

Saudi Arabia, dominates less than half of the sectors. The other five countries, Bahrain,

Kuwait, Oman, Qatar and UAE exceed Saudi Arabia’s intra-regional trade in more than

half of the cases. However, there is no clear trend where the other five countries exceed

the base case. Step 2 in the model also suggests that there is some degree of mineral fuel

23

trade within the region despite the similarities of these oil dependent economies. This

may indicate some intra-industry trade at some level.

Although the GCC countries have been undergoing economic integration for the

past two decades, the RTA has not intensified intra-regional trade during the sample

period. New development in the region such as the launch of a customs union and

movement towards a common market may not yield the effects desired yet. This may be

attributable to the similar economic structures of these six economies, and their

dependence on natural resources. Despite small trade volumes within the region, a

number of countries such as Bahrain and Oman maintain a significant portion of their

total trade within the region. The model suggests that a common border between these

countries and other GCC countries plays an important role.

References Anderson, James. "A Theoretical Foundation of the Gravity Equation." The American

Economic Review 69, no. 1 (1979): 106-16. Anderson, James, and Eric van Wincoop. "Gravity with Gravitas: A Solution to the

Border Puzzle." The American Economic Review 93, no. 1 (2003): 170-92. Bergstrand, Jeffery. "The Gravity Equation International Trade: Some Microeconomic

Foundations and Empirical Evidence." The Review of Economics and Statistics 67, no. 3 (1985): 474-81.

Centre D’études Prospectives et D’Informations Internationales. Distances, 2007 [cited June 2007]. Available from http://www.cepii.fr/anglaisgraph/bdd/distances.htm

Feenstra, Robert C. Advanced International Trade: Theory and Evidence. New Jersey: Princeton University Press, 2004.

———. "Border Effects and the Gravity Equation: Consistent Methods for Estimation." Scottish Journal of Political Economy 49, no. 5 (2002): 491-506.

Feenstra, Robert C., James R. Markusen, and Andrew K. Rose. "Using the Gravity Equation to Differentiate among Alternative Theories of Trade." The Canadian Journal of Economics 34, no. 2 (2001): 430-47.

Frankel, Jeffrey. Regional Trading Blocs: In the World Economic System: Institute for International Economics, 1997.

———, ed. The Regionalization of the World Economy, National Bureau of Economic Research Project Report: The University of Chicago Press, 1998.

Frankel, Jeffrey, and Stein, Ernesto, and Wei, Shang-jin. "Trading Blocs and the Americas: The Natural, the Unnatural, and the Super-Natural." Journal of Development Economics 47 (1995): 61-95.

ibid, no. 1 (1995): 61-95. International Monetary Fund. World Economic Outlook IMF, 2006 [cited April 2007].

Available from http://www.imf.org/external/pubs/ft/weo/2006/02/pdf/c2.pdf. International Monetary Fund. Direction of Trade Statistics International Monetary Fund,

2007 [cited June 2007]. Available from www.imfstatistics.org/DOTS.

24

Linnemann, Hans, An Econometric Study of International Trade Flows. Amsterdam: North-Holland, 1966.

McCallum, James. "National Borders Matter: Canada-U.S. Regional Trade Patterns." The American Economic Review 85, no. 3 (1995): 615-23.

Pöyhönen, Peutti, “A Tentative Model for the Volume of Trade Between Countries.” Weltwirtschaftliches Archiv. 90, no. 1 (1963).

Rose, Andrew K. "One Money, One Market: The Effect of Common Currencies on Trade." Economic Policy 15, no. 30 (2000): 7-45.

Tinbergen, Jan, Shaping the World Economy: Suggestions for an International Economic Policy: New York, The Twentieth Century Fund, 1962.

Viner, Jacob. The Customs Union Issue. New York: Garland Publishing, 1950. Whalley, John, “Why Do Countries Seek Regional Trade Agreements?" NBER Working

Papers no. 5552 (1996).

25

Appendix - Data

The gravity model included thirty-one countries that form major trading partners of the

GCC countries. The sample includes both developed and developing countries from

most continents except South America where no significant trade takes place with the

GCC. These countries are listed in Table A1. The above-listed countries are members of

several trading blocs. These are listed in Table A2. The Gravity Model’s data included a

number of economic parameters. These are listed in Table A3.

26

TABLE A1

COUNTRIES INCLUDED IN SAMPLE

Countries

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

27

TABLE A2

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) 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)

28

TABLE A2

REGIONAL TRADE AGREEMENTS (Continued)

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)

29

TABLE A3

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 current billion US Dollars

World Economic Outlook (2006) International Monetary Fund (IMF)

GDP per Capita

GDP per capita valued at PPP current 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