Determinants of Intra-Regional Trade Flows in the Common Market for Eastern and Southern Africa

TABLE OF CONTENTS

DISSERTATION APPROVAL

DEDICATION

STATEMENT OF THE AUTHOR

BIOGRAPHICAL SKETCH

ACKNOWLEDGEMENTS

ABBREVIATIONS AND ACRONYMS

TABLE OF CONTENTS

LIST OF TABLES

LIST OF FIGURES

ABSTRACT

CHAPTER ONE

1. INTRODUCTION
1.1 Background to the study
1.2 Statement of the problem
1.3 Research questions
1.4 Objectives of the study
1.5 Hypotheses
1.6 Significance of the study
1.7 Justification of the study
1.8 Scope the study
1.9 Definition of terms
1.10 Organization of the study

CHAPTER TWO
LITERATURE REVIEW
2.1. Conceptual framework
2.1.1 Components of gravity model
2.1.2 Linkages among the gravity variables
2.2 Theoretical foundation of gravity model
2.3 Theoretical review on regional integration
2.4 Empirical literature review

CHAPTER THREE
METHODOLOGY
3.1 Research Design
3.2 Study area
3.3 Data collection and sources
3.4 Method of data analysis
3.4.1 Model specification
3.4.2 Estimation techniques
3.4.3 Theoretical justification and priory sign
3.4.4 Diagnostic test

CHAPTER FOUR
RESULTS AND DISCUSSIONS
4.1 Trends and patterns of intra-trade in COMESA
4.2 Presentation and discussion of the estimation results

CHAPTER FIVE
SUMMARY, CONCLUSION AND RECOMMENDATION
5.1 Summary of findings and conclusion
5.2 Recommendations
5.4 Limitation and further study

REFERENCES

APPENDICES

DEDICATION

To my wife Tarike Likisa and my children, Dawit, and Talile.

STATEMENT OF THE AUTHOR

By my signature below, I declare that this dissertation is my work. I have followed all ethical principles of scholarship in preparation, data collection, data analysis, and completion of this dissertation. I affirm that I have cited and referenced all sources used in this document. I have made every effort to avoid plagiarism.

I submit this document in partial fulfillment of the requirements for the award of a degree from Pan African University. This document is available from the PAU Library to borrowers under the rules of the library. I declare that I have not submitted this document to any other institution for the award of an academic degree, diploma, or certificate.

Scholars may use brief quotations from this dissertation without special permission if they make an accurate and complete acknowledgement of the source. The director of the academic unit may grant permission for extended quotation or reproduction of this document. In all other instances, however, the author must grant permission.

Abbildung in dieser Leseprobe nicht enthalten

BIOGRAPHICAL SKETCH

Abbildung in dieser Leseprobe nicht enthalten

ACKNOWLEDGEMENTS

I am most grateful to my supervisor, Professor Njong Mom Aloysius for his continuous assistance of providing constructive comments, valuable suggestions, and encouragement to complete the research work on time. Additionally, I am thankful to Dr. Tata Emanuel Sunjo and Ateh Thomson Pepeah for giving me the valuable suggestion and correction to this work. I am also indebted to all seminars that have gone through the research work by providing me with helpful comments during all the research progress report presentation.

I would like to express my gratitude to all those who made it possible for me to complete this dissertation. In the first instance, I want to thank African Union for granting me scholarship opportunity and providing me their fundamental support.

My deepest gratitude goes to my dad Shanko Kerore and mom Bidike Woggari who have given me a chance to go to school at the early stage and continued their unreserved support to further study until their life demise. My Special thanks go to my wife Tarike Likisa for her love, encouragement and great care for our kids. Last but not least, I owe my warm thanks to my friends and class mates for the moral support all through the period of my stay and study at PAUIGHSS.

ABBREVIATIONS AND ACRONYMS

AEC African Economic Community

AfDB African Development Bank

AFT Africa Free Trade

APTA Asia-Pacific Trade Agreement

ASEAN Association of Southeast Asian Nations

AU African Union

AUC African Union Commission

CEE Central and Eastern European

CEMAC Economic and Monetary Community of Central Africa

CEN-SAD Community of Sahel-Saharan States

CEPII “Centre d`Etudes Prospectives et d`informations Internationales”

CES Constant Elasticity of Substitution

CET Common External Tariffs

CFTA Continental Free Trade Area Africa

CIA Central Intelligence Agency

CIF Cost Insurance and Freight

COMESA Common Market for Eastern and Southern Africa

CU Customs Union

DOTS Direction of trade statistics

EAC East African Community

ECCAS Economic Community of Central African States

ECOWAS Economic Community of West African States

EFTA European Free Trade Association

FGCC Gulf Cooperation Council

FOB Free On Board

FTA Free Trade Area

GDP Gross Domestic Product

GTAP Global Trade Analysis Project

H-O Heckscher-Ohlin

IFC International Finance Corporation

IGAD Intergovernmental Authority on Development

IMF International Monetary Fund

IOC Indian Ocean Commission

LAFTA Latin American Free Trade Area

MERCOSUR Mercado Común del Sur or Southern Common Market

NAFTA North American Free Trade Agreement

NTB Non-Tariff Barriers

OAU Organisation of African Unity

OECD Organization for Economic Co-operation and Development

OIC Organization Islamic Cooperation

OLS Ordinary Least Square

PPML Pseudo Poisson Maximum Likelihood

REC Regional Economic Communities

RIA Regional Integration Arrangement

RTA Regional Trade Arrangement

SADC Southern African Development Community

TAA Free Trade Area of Americas

TROW Trade with Rest of the World

UMA Union Maghreb Arab

UN United Nations

UNCTAD United Nations Conference on Trade and Development

UNECA United Nations Economic Commission for Africa

USA United States of America

USD United States Dollars

WAEMU West African Economic and Monetary Union

WAMZ West Africa Monetary Zone

WDI World Development Indicators

WEO World Economic Outlook

WTD World Trade Database

LIST OF TABLES

Abbildung in dieser Leseprobe nicht enthalten

LIST OF FIGURES

Abbildung in dieser Leseprobe nicht enthalten

ABSTRACT

This study investigated the determinants of intra-regional trade within the Common Market for Eastern and Southern Africa (COMESA) region, with a specific focus on export trade from the period 2000 to 2016 by utilizing panel data. The study used trade intensity index to measure the intensification of trade among member states and employed an augmented gravity model to identify factors affecting intra-COMESA trade flows using OLS, Random effect and Poisson Pseudo-Maximum Likelihood (PPML) estimators. The results from the Trade Intensity Index (TII) measure indicate that although the intra-COMESA trade remains low for most of the members, the intensity result appear to increase at slow rate at regional level. The analysis indicates that the trade strength of intra-COMESA exports increased from 11 percent in 2000 to 13.6 percent in 2016 at regional level. More specifically, Egypt and Kenya have expanded their export trade among other members of COMESA, while Libya has the smallest export trade share followed by Eretria. The results from gravity model showed that other factors remaining constant, export trade flows within COMESA region are significantly influenced by Regional Trade Agreement (RTA), distance between members, adjacency, landlockedness, common language, electoral democracy, economic size, population size and overlapping memberships at different magnitude for each variable. Using PPML the estimation result shows that distance, economic size of the importer and adjacency of members are the most significant variables explaining export volume among members. It is recommended that COMESA members need to invest on complementary products (export diversification) where they have comparative advantage through identification of priority products in the region, improving the economic size, implementation of AU`s 2012 declaration of Continental Free Trade Area (CFTA), development of regional transport infrastructure and strengthening of institutional democracy to expand bilateral trade among memberstates.

CHAPTER ONE

INTRODUCTION

1.1 Background to the study

Regional economic integration has been considered as a crucial ingredient for economic growth and development among countries and regions. Regional integration initiatives started particularly after independence of most of the African countries and witnessed the establishment of a number of Regional Trade Agreements (RTAs) to strengthen their economic growth and development. However, the performance of the trade agreements among African regions have not resulted as expected. In support of this argument as cited in Ebaidalla (2016), Economic Commission for Africa (ECA) (2012) reported that the situation of intra-African trade is disappointing, since it remains consistently low compared with the continent’s external trade. Additionally, WTO (2011) reported that more than 80% of Africa’ exports go to external markets, while African countries import more than 90% of their imports from outside of the continent. This indicates the effect of regional trade agreement between African countries and regions are weak which needs more investigation.

The Organization for Africa Unity (OAU) was founded in 1963. The basic motivation for such integration emanated, especially after independence, from the need to combat African major economic problems, such as, inter alias, the limited economic size of many of African states, poor infrastructure services, and land lockedness of many African states. Regional integration, therefore, is seen as the best way for relaxing these constrains and increasing intra-regional trade (AfDB, 2000).

Common Market for Eastern and Southern Africa (COMESA) is one of the Africa`s regional economic communities formed to enhance the economic growth and development of the member states through increasing intra-trade and hence strengthening the economic integration process. Therefore, this study is motivated to investigate the determinants of intra-COMESA trade that influence the trade intensification among member states from 2000 to 2016. It would be recalled that intra-trade is one means of economic integration that plays an important role in the process of economic growth of the region. As pointed out by Albert (2012), the aim of COMESA is to promote sustainable economic and social development for all its member countries through enhanced cooperation leading to regional integration especially in the areas of trade, customs, infrastructures (transport and communications), science and technology, agriculture and natural resources.

Furthermore, United Nation of Economic Community of Africa (UNECA) (2017) stated that COMESA was formed in December 1994 to replace the former Preferential Trade Area (PTA) from the early 1980s in Eastern and Southern Africa, mainly to promote trade in goods among member states through a reduction in trade barriers and infrastructure development. The PTA transformed in to COMESA in 1994 with a long-term view of becoming a customs union, common market and ultimately an economic community. It is the largest regional grouping in Africa in terms of the number of member countries and in terms of the total population number, almost half the total number of countries in Africa. The current regional indicators of COMESA show that 657.4 billion-dollar GDP, 1,335-dollar GDP per-capita, area of 12 million square kilometers, which is 40% of Africa`s geographical area (30 million square kilometer), 492.5 million total populations, 183 billion-dollar total imports, and 95 billion total exports. The focus of COMESA has been on the formation of a large economic and trading unit to overcome trade barriers faced by individual states.

Korinek & Melatos, (2009) also added that COMESA comprises a disparate group in terms of economic size and geography. Eight members are landlocked–Burundi, Ethiopia, Malawi, Rwanda, Swaziland, Uganda, Zambia and Zimbabwe. Four COMESA members are island states-Comoros, Madagascar, Mauritius and Seychelles, although Madagascar has been named “the fifth continent” due to its size and biodiversity. One COMESA member, Swaziland, is surrounded by countries that are not members of COMESA, while two countries that are not members of COMESA (Tanzania and Mozambique) are largely surrounded by COMESA members. This is particularly relevant for issues of trade facilitation and physical access to markets.

COMESA operates a Free Trade Area (FTA) among fifteen of its member states Burundi, Comoros, Djibouti, Egypt, Kenya, Libya, Madagascar, Malawi, Mauritius, Rwanda, Seychelles, Sudan, Uganda, Zambia and Zimbabwe. The Democratic Republic of Congo joined the COMESA FTA in December 2015. A customs Union was subsequently launched by COMESA in 2009. From the time of launching, member states agreed on three-year transitioning period to domesticate the customs management regulations, common external tariff and the common tariff nomenclature that would gradually form the customs union. The plan was to finalize the Customs Union by 2012; however, even after a second postponement of the transition period to 2014, the Customs Union was not operational (UNECA, 2017).

Despite these efforts, the success of COMESA trade integration in terms of intensification of trade volume among its member states has been limited. Reasons for the failure of trade integration to succeed as pointed out by some researchers like Alemayehu and Haile (2006) ,are related to issues of limited measures in fully reducing tariffs and eliminating non-tariff barriers, lack of adopting common economic policies, issues of revenue loss, compensation issues and poor private participation. Geda and Seid (2015) also added that in spite of the proliferation of RECs, African continent has not shown success in expanding intra-regional trade; and most of these regional economic communities have achieved very little.

The study mainly focuses on COMESA bloc, among other eight regional economic communities considered by the African Union (AU) as the building blocks of the future African Economic Community (AEC) laid out in the Abuja Treaty due to the following reasons:

First the main objectives of COMESA in trade, among others include the creation of a free trade area, the establishment of a customs union and eventual establishment of a monetary union among its members. The COMESA Free Trade Area (FTA) was launched in October 2000 with nine participating states, after 16 years of gradual reduction of tariffs on intra-COMESA trade. In November 2007, thirteen (13) countries were participating in the FTA; other member states had carried out tariff reductions of between 10 percent and 80 percent. The region launched its Customs Union (CU) in 2009, though with few members signing the newly created CU (Albert, 2012).

Second, COMESA is the largest Regional Trade Agreement (RTA) in Africa in terms of population and economic size with 19-member states. COMESA is also one of the largest and most vibrant regional economic communities in Africa (Alemayehu & Haile, 2008). UNECA (2017) also indicates that the current regional indicators of COMESA show 657.4 billion-dollar GDP, 1,335-dollar GDP per-capita, area of 12 million square kilometers, which is 40% of Africa`s geographical area (30 million square kilometers), 492.5 million total population, 183 billion-dollar total imports, and 95 billion total exports.

The third reason is that few literatures are there on the determinants of trade at the country level and other regional blocs with different variables using the gravity model. To the best of my knowledge there is no study that has focused on the issues of trade intensity, overlapping membership and electoral democracy and FDI inflows in COMESA in recent periods. The fourth reason is that trade integration plays a major role in enhancing structural transformation and inclusive growth across the continent (UNECA, AU and AfDB, 2017). Trade is also included in the African Union minimum integration programme (2009) and Agenda 2063 (2015), with free movement of goods and services and greater intra-African trade among the objectives.

Therefore, this study tries to cover that gap by exploring other variables that only few literature reviews were available and the fact that they might have an impact for the COMESA member states trade performance in addition to Growth Domestic Product (GDP), distance and population size. For example in this study overlapping members of COMESA in other Economic Community of Africa (ECA) specifically to IGAD, the trade intensity, Foreign Direct Investment (FDI) inflows and electoral democracy have been included that most other researchers did not include in their analysis relating to COMESA when investigating trade determinants.

The study investigates factors hindering intra-regional trade flows within COMESA member countries. According to the Abuja Treaty, all regional economic communities should establish REC-level Free Trade Areas and customs unions by the end of 2017 (UNECA, 2013). Established in 1993, the ultimate goal of COMESSA was a common market for its member state countries and currently in operation of Free Trade Area to enhance intra-trade among its member states.

1.2 Statement of the problem

Regional integration has long been viewed in Africa as a vehicle for enhancing economic growth through encouraging intra-regional trade. It has also been a means of achieving industrialization and modernization through promoting trade and securing economies of scale and market access (Khandelwal, 2004). Economic integration efforts have a long history in Africa. The large number of preferential trade agreements signed in the past five decades has led to a “spaghetti bowl” of intertwined and overlapping regional organizations. Every African country is a party to at least one regional economic agreement, and many are members of five or more. Despite these efforts, intra-African trade remains low. Regional exports are less than 10 percent of Africa’s total merchandise exports, and models estimating the trade potential between countries based on economic size, geographical distance, and other characteristics consistently find that trade among Africa’s economies is below the levels predicted (World Bank, 2009).¹

Intra-regional trade in SSA appears to be low and there are different views as to why this is the case. There are different types of determinants of intra-regional trade ranging from economic variables, such as differences in factor endowments and complementarities in trade structures, to policy variables such as tariffs and Non-Tariff Barriers (NTB). Other aspects such as geographical location may serve as a natural non-tariff barrier to accessing particular markets, but like other market failures may be overcome through effective and targeted government intervention (Calì, cited in Keane, 2010).

In addition, other researchers come up with different outcome as to why intra-trade has been limited in the regional integration process. These low performances of intra-trade are marked by issues of revenue loss, compensation issues and variation in initial condition, poor private sector performance, lack of political commitment and institutional issues, issues of overlapping membership, high transaction cost due to inadequate infrastructure, macroeconomic instability, distorted trade regimes, low resource complementarity and small market size (Eden,2008).

Despite the proliferation of regional economic groups in Africa, intra-regional trade between African countries and especially between members of the same economic unions is limited. Even though member countries have taken some steps to reduce tariff and eliminate non-tariff barriers and adopt common economic policies that promote growth and economic integration, intra-trade between economic communities of Africa is still low. According to UNCTAD (2016), overall intra-African trade would rise from 10.2 percent of total trade in 2010 to 15.5 percent by 2022. Although a positive overall outlook, it is still short of the stated goal of doubling of trade within 10 years.

Moreover, as of Geda and Seid (2015), it is argued in the literature that despite the proliferation of RECs, the continent has not shown success in expanding intra-regional trade; and most of these regional economic communities have achieved very little from 2005 to 2013. The total percentage increment of intra-export for each REC`s such as CENSAD(1.3), COMESA (4.3), EAC(1.0), ECCAS(0.2), IGAD (2.8), SADC(7.1) and UMA(1.8) are less than 10 percent in which SADC perform better than others followed by COMESA between the specified period (see appendix 7.6 in detail).

Furthermore, despite the long history of regional integration on the continent, the level of intra-African trade remains low in comparison with other developing regions. Intra-African exports represent 9.6 percent of the region’s total exports, compared to 20 percent for Latin America and 48 percent for developing Asia. This proportion is substantially higher for sub-Saharan Africa (around 12 percent) than for North Africa (around 3 percent), which has systematically featured very low levels of intra-regional trade (AfDB, 2011 ). ECA² also added that regional integration is a key strategy for development and intra-trade and is expected to produce considerable economic gains for Africa. Although it is widely recognized that intra-African trade could play a significant role in accelerating economic growth and poverty reduction and enhancing food and energy security in Africa, the continent continues to trade little with itself.

The analysis of why intra-African trade is so low relative to its potential reveals the influence of extremely high transport costs caused by poor hard and soft infrastructure. The term “hard infrastructure” refers to the physical infrastructure that is often missing or is of poor quality in many African countries. “Soft infrastructure” refers to issues such as the policy and regulatory environment, the transparency and predictability of trade and business administration and the quality of the business environment more generally. This is particularly true for landlocked countries, which are constrained by their own poor infrastructure as well as their neighbours. Inefficient and multiple border procedures, and the political instability and unpredictability, as well as uncertainty, of trade policies also hamper intra-African trade by raising trade costs, despite remarkable progress achieved in these areas in the recent past (UNCTAD,2009).

According to report by UNCTAD (2013) on intra-African trade, except for the Economic Community of Central African States (ECCAS), for each African regional economic community, a significant part of their trade with Africa takes place within their own regional trade bloc. This confirms the fact that the formation of regional blocs in Africa has facilitated the creation of trade among its member countries (Cernat, 2001). For instance, in the period from 2007 to 2011, 64.7 percent of the trade of the Community of Sahel-Saharan States (CEN-SAD) with Africa was with CEN-SAD member countries; 78.4 percent of the trade of the Southern African Development Community (SADC) with Africa was with other SADC member countries and for the Economic Community of West African States (ECOWAS) the figure was 65.5 percent.

However, except for the Common Market for Eastern and Southern Africa (COMESA), these shares have been falling compared to the period from 1996 to 2000. One of the objectives of COMESA is to increase trade among members. In general, the increase in intra-regional trade has not yet been as large as anticipated. Despite the existence of many regional economic communities (RECs) in Africa, intra-regional trade remains staggeringly low compared to other trade blocs in Europe, Asia and Latin America (Seid, 2013). Also trade between African sub-regions is growing, though it remains low compared to other parts of the world. In 2000, intra-regional trade accounted for 10% of Africa’s total trade. In 2014, it was 16%. This trade is mainly in manufactured goods, which are less susceptible to price shocks. Manufactured products accounted for 60% of total regional trade (AfDB, 2015).

Common Market of Eastern and Southern Africa (COMESA) was created with a mission of being a fully integrated internationally competitive regional economic community, with high standards of living for its entire people, ready to merge into an African economic community. The concern was to achieve sustainable economic and social progress in all member States through increased co-operation and integration in all fields of development, particularly in trade, customs and monetary affairs, transport, communication and information technology, industry and energy, gender, and agriculture.

In contrast to other regional trading blocs, regional integration in COMESA failed to increase trade among the member countries. Accordingly, the growth in intra-trade in ASEAN and SADC from 1980 to 1990 was 1.20 percent and 8.80 percent respectively, while in COMESA it was only 0.60 percent. From 1990 to 1995 the period in which the three regions decided to establish FTA among their respective member. Countries, the growth of intra-regional trade was 1.90 percent for ASEAN, 2.90 percent for SADC and only 0.15 percent for COMESA (Umurungi, cited by Ibrahim & Obiageli, 2015).

Against these assertions, there is a need for an analysis to be done on the determinants of intra-regional trade in the COMESA for African RECs to obtain the best opportunities from regional integration. Additionally, there are few published works on the factors that influence COMESA intra-trade using different variables after the period of 2000. Hence this study attempts to find out the level of trade intensity index and main determinants behind the low level of intra-regional trade of Africa`s RECs specifically focusing on COMESA member states by applying the gravity model of Anderson-Van Wincoop in panel data framework from the period 2000 to 2016 by providing an up-to-date analysis of trade flows.

1.3 Research questions

Based on the research problems identified in section 1.2 above, the main and specific research questions, which are to be addressed in this research study, are listed below.

1.3.1 Main research question

What are the factors that affect intra-trade flows within COMESA region?

1.3.2 Specific research questions

The study intends to address the following specific questions:

1). what is the intensity of intra-export trade flows among COMESA countries and which countries are major trade partners in COMESA?
2). what are the key factors that determine the growth of trade integration within COMESA region?
3). How does electoral democracy, overlapping membership of COMESA and IGAD affect intra-trade flows within the region?

1.4 Objectives of the study

1.4.1 Main Objective

The general objective of the study is to identify the factors that affect intra-regional trade among the Common Market for Eastern and Southern African member countries in the period 2000 - 2016.

1.4.2 Specific objectives

Specifically, the study intends to:

1). Measure the intensity of export trade through trade-linkage and identify the major trade partners of each member focusing on the potential expansion of export trade flows among the COMESA members.
2). Identify the key factors that determine the growth of trade integration within COMESA region
3). Investigate the intra-trade effects of COMESA members’ electoral democracy, overlapping membership of COMESA and IGAD regional blocs.

1.5 Hypotheses

The study is guided by the following hypotheses:

H1: Bilateral trade is positively related to Gross Domestic Product (GDP), Population size, electoral democracy, Foreign Direct Investment (FDI) inflows, Regional Trade Agreement (RTA) and inversely related to trade resistance among COMESA members.

H2: Overlapping membership has negative effect on bilateral trade among COMESA members.

1.6 Significance of the study

This research is intended to provide a better understanding of the determinants of intra-trade performance in Economic Community of Africa in general and COMESA member states in particular by identifying in which area COMESA is not doing better so that more effort and new measures can be taken to increase intra-trade. This study provides some useful contributions to assist policy makers having a detailed policy orientation on intra-trade determinants.

The policy implications from the gravity model analysis could help caution policy makers about possible consequence of the determinants of intra-trade of COMESA. The study will enable the member states of COMESA to consider their policies and regulations in trade intensity, multi-membership, good governance and other cost related issues which have impact on intra-trade in the bloc. International development partners and investors can also use this as an input to whether to participate in the development process by forming bilateral trade policies with COMESA member states.

Furthermore, the study seeks to offer detailed analysis of intra-regional trade among COMESA members using the gravity model of bilateral trade to estimate trade flows and trade intensity index among partner countries. It also provides whether the estimates of intra-trade are lower or higher among trading partners. Therefore, implications of this study, is to investigate the most determinants of trade on the bilateral trade flows between the member countries and draws a policy recommendation. Lastly, I expect to add contribution to the existing literature, academician, and researchers in relation to trade among COMESA members. The study will provide an up-to-date analysis of trade flows.

1.7 Justification of the study

Intra-regional trade has been viewed as a means for enhancing economic growth through regional integration by expanding market access among members. Even though most of COMESA member countries have taken some steps to reduce tariff and eliminate non-tariff barriers to promote growth and economic integration, intra-trade between members is still low since the establishment of COMESA in relation to other regional blocs. Based on this concept this study attempts to identify empirically the extent of which trade flows has taken place among COMESA members. This requires an analysis of intra-COMESA trade flows using the Trade Intensity Index (TII) within the region, and signifying factors affecting intra-trade flows within the region using the augmented gravity model. Therefore, this study needs to analyze the trade intensity index and identifying factors which limit intra-trade expansion within members.

1.8 Scope the study

Intra-regional trade in sub-Saharan Africa (SSA) appears to be low and there are different views as to why this is the case. There are different types of determinants of intra-regional trade. These range from economic variables, such as differences in factor endowments and complementarities in trade structures, to policy variables such as tariffs and non-tariff barriers (NTB)³. Other aspects such as geographical location may serve as a natural non-tariff barrier to accessing markets, but like other market failures may be overcome through effective and targeted government intervention (Calì, cited in Jodie, et al., 2010). But for this research study policy variables like tariffs and NTBs are not considered.

The study is limited to determinants of intra-regional trade within the COMESA region among other ECA and the time of the study restricted to seventeen years (2000-2016). The choice of this period has been done to analyse variables based on up to date and availability of the data for most of the variables considered in the research study. Most of the literature review from the previous researchers that carried out on similar study analyzed using basic gravity model variables like country economic size, distance, dummy variables and other variables like real per-capita income, exchange rate, population however, this study uses some of these variables by adding FDI inflows, electoral democracy, and overlapping membership within COMESA and IGAD in augmented gravity model. Ordinary Least Square, random effect as well as Poisson Pseudo-Maximum Likelihood (PPML) for regression estimation in analyzing the data to find out the determinants of intra-export trade in COMESA.

Based on availability of balanced panel data under consideration, this study is generally limited to COMESA member countries such as Burundi, Democratic Republic of Congo, Comoros, Djibouti, Egypt, Eritrea, Ethiopia, Kenya, Libya, Madagascar, Malawi, Mauritius, Rwanda, Seychelles, Sudan, Swaziland, Zambia, Uganda and Zimbabwe and six IGAD members such as Ethiopia, Djibouti, Eritrea, Kenya, Uganda and South Sudan.

1.9 Definition of terms

The purpose of definition of terms is to clarify researcher’s meaning of words used within the study and to have common understanding with the readers of this work.

A Regional Trade Agreement (RTA) refers to a trade agreement whereby two or more than two countries which belong to a certain region strike an agreement to reduce tariffs and restrictions on trade between themselves. Regional trading agreements are pursued for a variety of reasons. A motivation of virtually every regional trade agreement has been the prospect of enhanced economic growth. An expanded regional market can allow economies of large-scale production, foster specialization and learning-by-doing, and attract foreign investment. Regional initiatives can also foster a variety of noneconomic objectives, such as managing immigration flows and promoting regional security (Qadri, 2012).

Regional economic integration can therefore be defined as the process or processes through which two or more countries merge functions, combine markets and institutions for the purpose of enhancing the capacity to achieve economic benefits. The integration could result in the formation of a regional economic bloc. Many regional integration bodies have been formed in post-independence Africa. Every country in the world today is a member of at least one integration bloc. Indeed some of the countries are members of more than one regional body, leading to a phenomenon of dual or multiple memberships in regional economic organizations (Iringo, 2005).

The economic integration literature clearly distinguishes between regional economic integration and regional economic cooperation. Regional economic cooperation is seen more as an ad hoc and temporary scheme, which is mainly based on contractual agreements with regard to projects of mutual interest between member states. Such projects could involve two or more countries in the region. On the other side, regional economic integration involves agreements that are more permanent (Madyo, 2008).

Gravity model is a very popular econometric model in international trade. The name came from its utilizing the gravitational force concept as an analogy to explain the volume of bilateral trade flows proposed by Tinbergen (1962). Initially, it was not based on theoretical model, but just intuition only. Later on, a range of rigorous theoretical foundation has been given. The most well-known benchmark so far is Anderson and Van Wincoop (2003). The following points make so popular; intuitively appealing, fits with some important stylized facts, easily to use real data to explain trade flows with respect to policy factors, estimation using OLS (Anukoonwattak, 2016).

The gravity model is a popular empirical approach to trade that has been used widely for analyzing the impact of different trade policy issues on bilateral trade flows between different geographical entities (William &James, 2007). The standard gravity model can be specified as the flow of bilateral trade between the two partner countries at specific time in which the products of the two GDPs are positively related and the distance (proxied as cost) between them are negatively related.

FDI is the international movement of capital for specific investment purposes where the foreign investor establishes a lasting interest in an enterprise which is resident in another country. This interest implies a long term relationship between the direct investor and the enterprise, and significant influence on management of the enterprise. FDI occurs when overseas companies set-up or purchase operations in another country. FDI can be new projects, expansions of existing projects, or mergers and acquisitions activity (Alexander et.al, 2011).

Trade intensity index measures the “pure” intensification of trading relationship. It is used to determine whether the value of trade between two countries is greater or smaller than would be expected on the basis of their importance in world trade. It is defined as the share of one country’s exports going to a partner divided by the share of world exports going to the partner (World Bank, 2010).

1.10 Organization of the study

This dissertation is organized in to five chapters. The first chapter of the study provides the introduction of the research, the problem statement, objectives, significance, scope and limitations of the study. The second chapter is concerned with literature review closely related to the topics and variables under consideration, including conceptual frame work and empirical, theoretical foundation of gravity model, types and regional economic integration, overlapping member’s issues, effect of good governance and boarders on intra-trade. Chapter three addresses the methodological approaches of the study including the trade intensity index and econometric methods employed, the data and variables used for the analysis as well as the theoretical estimation expectation of variables would be presented. Chapter four examines results and discussions of the econometric estimation of the model and analysis of empirical results. Chapter five presents some conclusions and policy implications based on the results and discussions.

CHAPTER TWO

LITERATURE REVIEW

Introduction

This Chapter presents a review of theoretical foundation of gravity model and empirical literature review. The chapter is organized in four main sections. The first section focuses on conceptual framework, the second part emphases on theoretical literature review on gravity model and the third part provides on theory of regional integration and the last part broadly focuses on empirical literature especially related to variables on regional economic integration, overlapping membership, effect of good governance on trade, effect of border and customs procedures, and foreign direct investment inflows.

2.1. Conceptual framework

Under this part the different components and linkages among gravity model are presented as follows;

2.1.1 Components of gravity model

This part tried to identify diagrammatically the different components of gravity model considered for this study and also attempt to test the hypotheses about the factors influencing the extent of intra-regional trade among COMESA members from 2000 to 2016. The investigation was limited to intra-trade in manufactured goods where product differentiation predominates. This work was based on goods exported and imported among the nineteen (19) COMESA members and identify factors influencing the bilateral trade between them. The study considers two classes of determinants of intra-regional trade-such as country characteristics and industry characteristics. These two categories had further been categorized to different categories, as shown below in Figure 1. Under industry characteristics foreign direct investment inflows was taken as one component of gravity model.

Abbildung in dieser Leseprobe nicht enthalten

Figure 1: Determinants of Intra-COMESA trade

Abbildung in dieser Leseprobe nicht enthalten

Source: Compiled based on Balassa and Bauwens, 1987

2.1.2 Linkages among the gravity variables

Table 1: Linkage and direction of impact on intra-export of gravity variables

Abbildung in dieser Leseprobe nicht enthalten

2.2 Theoretical foundation of gravity model

As noted by Sohn (2001:p.3) “the gravity model was originally founded on Newton’s physical theory which states that two bodies attract each other in proportion to their masses and inversely by the square of the distance between them. The application of the gravity model to international trade theory, on the other hand, aims at explaining the bilateral trade flows and patterns between two economies by regarding each of them as an organic body that attracts each other in proportion to their economic size (GDP) and inversely to their distance”.

The gravity model is the most popular method used to empirically predict the level of bilateral trade flows between economies and estimate the extent of trade creation and trade diversion. The model is a mathematical equation relating trade between two economies to the key determinants. The determinants are grouped into “attraction” and “opposing” forces. For a given country, there are forces that attract trade and others with an opposite effect. The main reason why intra-African trade flows remain low is that the attraction factors are very weak, whereas the opposing forces are very strong. Among the latter, poor soft and hard infrastructure is identified as the main impediment to increasing intra-African trade (UNCTAD, 2009).

The gravity model was first applied to international trade in the early 1960s. Among others, Poyhonen (1963) was the first to apply the gravity model to international trade. In the latter half of the twentieth century, the gravity model has been used to explain migration and other social flows in terms of gravitational forces of human interaction. Like in physical science, the bigger and closer the units are to each other, the stronger the attraction. The comparison with gravity derives from gross domestic product (GDP) being a proxy for economic mass and distance a proxy for resistance (Eita, 2008).

It has been known since the seminal work of Jan Tinbergen (1962) that the size of bilateral trade flows between any two countries can be approximated by a law called the “gravity equation” by analogy with the Newtonian theory of gravitation. Just as planets are mutually attracted in proportion to their sizes and proximity, countries trade in proportion to their respective GDPs and proximity. Initially the gravity equation was thought of merely as a representation of an empirically stable relationship between the size of economies, their distance and the amount of their trade (Head, 2003).

There are two competing models of international trade that provide theoretical justification for the gravity model⁴. They are the Differentiated Products Model and the Heckscher-Ohlin Model.

2.2.1 The Heckscher-Ohlin model

The Heckscher-Ohlin (H-O) model of international trade is a general equilibrium model that predicts that patterns of trade and production are based on the relative factor endowments of trading partners. It is a perfect competition model. In its benchmark version it assumes two countries with identical homothetic preferences and constant return to scale technologies (identical across countries) for two goods but different endowments for the two factors of production. The model’s main prediction is that countries will export the good that uses intensively their relatively abundant factor and import the good that does not (Allen and Arkolakis, 2016). But, it was assumed that H-O models were incapable of providing a foundation for the gravity model. In the H-O model, for example, country size has little to do with the structure of trade flows (UNCTAD & WTO, 2012).

The Heckscher-Ohlin model explains trade as a result of relative differences in factor endowments between countries. Countries trade with each other due to their unequal labor and capital endowments allowing for a differing productivity in the manufacturing of a good. This can be either capital or labor intensive, and hence economies choose to manufacture the product being intensive in the production factor they are endowed with relatively much. Hence, countries have a comparative advantage in the production of a good. They will tend to export goods which in production use relatively much of the abundant factor the country is endowed with and on the other side they will import goods being relative intensive to manufacture in the scarce factor the country is endowed with. Consequently, the exported goods are relatively cheap to produce, whereas the imported goods are relatively expensive in production due to factor scarceness. Models of the Heckscher-Ohlin type focuses on explaining trade between industries - that is inter-industry trade - and is often constrained by constant returns to scale (Krugman and Obstfeld, 2006). Deardorff (1982) proved the general validity of the Heckscher-Ohlin theorem, though it is only until later he employs the Heckscher-Ohlin trade theory to derive a theoretically founded gravity equation (cited in Sarah, 2012).

But it was assumed that both the standard Ricardian and H-O models do not provide foundations for the gravity model. The H-O model, for example, which relies on the factor endowment assumption, does not incorporate country size as important factor in the pattern of trade flows among countries (WTO, 2010).

2.2.2 Differentiated products model

The first important attempt to provide a theoretical basis for gravity models was the work of Anderson (1979). He did so in the context of a model where goods were differentiated by country of origin (the so-called Armington assumption) and where consumers have preferences defined over all the differentiated products. This structure would imply that, whatever the price, a country will consume at least some of every good from every country. All goods are traded, all countries trade and, in equilibrium, national income is the sum of home and foreign demand for the unique good that each country produces. For this reason, larger countries import and export more. Trade costs are modelled as “iceberg” costs, that is, only a fraction of the good shipped arrives to destination, the rest having melted in transit. Clearly, if imports are measured at the CIF value, transport costs reduce trade flows (UNCTAD & WTO, 2012).

Researchers such as Anderson (1979), Helpman and Krugman (1985) tried to identify the relationship between the bilateral trade flows and the product of two countries’ GDPs by utilizing the differentiated products model. When the size of the domestic economy (or population) doubles, consumers increase their utility, not in the form of greater quantity but of greater variety. Furthermore, Helpman (1987) empirically proved the correspondence between the gravity equation and the differentiated products model by applying his test on OECD countries’ trade data. His results supported the argument that the gravity equation can be applied to the trade flows among industrialized countries where intra-industry trade and monopolistic competition are well developed. In contrast, Hummel & Levinsohn (1995) conducted a similar empirical test with a set of non-OECD countries where monopolistic competition was not so reasonable. They also proved that the gravity equation is efficient in explaining the trade flows among developing countries where inter-industry trade is dominant with scarce monopolistic competition.

Some of the scholars had analyzed and empirically proved the consistency of the gravity model in areas such as migration, commuting, tourism, foreign direct investment and bilateral trade flows. Even though the model had such empirical success for long, it was criticized for lack of theoretical foundations. Following this criticism, different trade economists had tried to formulate the theoretical justifications based on different foundations. Anderson(1979) developed a gravity model based on a Cobb-Douglas and Constant Elasticity of Substitution (CES) utility function, with goods differentiated by origin and an elasticity of substitution that is >1. Bergtrand (1989) developed a gravity model based on the Dixit-Stiglitz model of monopolistic competition where each firm produces a variety of differentiated goods. Deardorff (1998) developed a gravity model based on the Heckscher-Ohlin model (differences in resource abundance). Although, none of the models generated the same equation generally used in empirical work, the gravity model has become more popular in bilateral trade among different countries, due to a revival of interest among economists in the interconnectedness of economics and geography (Ahmad, 2014).

2.2.3 Armington model

The Armington model was formulated by Anderson, 1979 which is important in an international trade because it provided the first theoretical foundation for the gravity relationship. Anderson and Van Wincoop (2003) propose an augmented version of the Anderson (1979) model based on the assumption of differentiation of goods according to place of origin (Armington Assumption). Their main contribution is the inclusion of multilateral resistance terms for the importer and the exporter that proxy for the existence of unobserved trade barriers. This model is interesting overall to the extent that the discussion of the multilateral resistance may matter for heteroskedasticity considerations.

The contribution of recent research concerning the theoretical foundation of the gravity equation is to have highlighted the importance of deriving the specifications and variables used in the gravity model from economic theory in order to draw the proper inferences from estimations using the gravity equation. Particularly important has been in this respect the contribution of Anderson and Van Wincoop’s (2003), where they show that controlling for relative trade costs is crucial for a well-specified gravity model. Their theoretical results show that bilateral trade is determined by relative trade costs, i.e. the propensity of country j to import from country i is determined by country j’s trade cost toward i relative to its overall “resistance” to imports (weighted average trade costs) and to the average “resistance” facing exporters in country i; not simply by the absolute trade costs between countries i and j. The rationale for including these “multilateral trade-resistance” (MTR) terms, as they are called, is that, ceteris paribus, two countries surrounded by other large trading economies, say Belgium and the Netherlands bordered by France and Germany respectively as well as by each other, will trade less between themselves than if they were surrounded by oceans (such as Australia and New Zealand) or by vast stretches of deserts and mountains (such as the Kyrgyz Republic and Kazakhstan) (Anderson & Van Wincoop, 2003, cited in UNCTAD & WTO, 2012).

Finally, Anderson and Wincoop (2003) derived an operational gravity model based on the manipulation of the CES expenditure system that can be easily estimated and helps to solve the so-called border puzzle. According to these authors, multilateral trade resistance factors should be added in the empirical estimation to correctly estimate the theoretical gravity model. A simple and intuitive way to do it is to proxy these terms with country dummy variables or with fixed effects in a panel data framework (Martínez & Horsewood, 2005).

The first economic foundation for the gravity model of trade (Anderson, 1979) assumed Constant Elasticity of Substitution (CES) expenditure structure⁵. Expenditure shares are given by

Abbildung in dieser Leseprobe nicht enthalten

Where, Xij is the nominal value of exports from i to j, is the price of goods from i delivered to j relative to a price index of goods at j, βi > 0 is a ‘distribution’ parameter (one for goods from each origin i and ∑i βi = 1 to ensure that shares sum to 1) and σ is the elasticity of substitution parameter. To accord with observed behavior, σ > 1, meaning that a rise in the relative price of good i in destination j will reduce i’s expenditure share in j. Since expenditure on each origin’s goods add up to total expenditure, the sum over origins i of expenditure shares (1) is equal to 1, an equation solved for the CES price index.

Abbildung in dieser Leseprobe nicht enthalten

Trade frictions are assumed to raise the delivered price of good i in destination j by a constant ‘iceberg melting’ factor , as if 1 unit departing the origin factory i yields units at destination j. Then the assumption of perfect arbitrage implies that destination prices are raised by exactly enough to cover ‘melting’. The adding up condition for each seller i is . Solve the adding up condition for Next, use the preceding equation to replace in (1). The result is the structural gravity model Y is the world income. The new variable Pi is an index of the outward trade frictions facing shippers from i. The price index Pj is an index of inward trade frictions facing shipments to destination j. Anderson and Van Wincoop (2003) coined the term multilateral resistance for these indexes of bilateral resistance. According to Juan and Josep (2007), the main insight of Anderson and Van Wincoop (2003) is to point out the presence of Pi and Pj in the denominator of (3). They imply that what matters for the size of bilateral trade flows is not the absolute level of trade barriers (tij) but bilateral barriers between trading partners relative to those they have with respect to the rest of the world, captured by their respective overall price indices.

Abbildung in dieser Leseprobe nicht enthalten

This means that trade between any two countries depends not only on the incomes of those two countries but also the “cost” of trading between those countries relative to trading with all other countries. This point was made in the enormously famous and influential paper “Gravity with Gravitas: A Solution of the Border Puzzle” (Anderson and Van-Wincoop, 2003). Therefore, most of the empirical works have been tried to follow the inclusion of exporter and importer dummies in the regression as suggested by Feenstra (2002) to account for Pi and Pj during bilateral trade studies using gravity model.

Multilateral Resistance Terms(MRT) are low if a country is remote from world markets, remoteness being determined by physical factors such as physical distance from large markets as well as policy factors such as high tariff barriers or other trade costs. In estimating gravity model without controlling MRT of exporter and importer is a severe mistake which results in biased and wrong estimation (WTO & UNCTAD, 2012).

The augmented version of the model of Anderson and Van-Wincoop (2003) has selected based on the assumption of differentiation of goods according to place of origin and their main contribution is the inclusion of multilateral resistance terms for the importer and the exporter that proxy for the existence of unobserved trade barriers. Their model is interesting overall to the extent that the discussion of the multilateral resistance may matter for heteroskedasticity considerations. For instance, as GDP increases, remote countries will tend to diversify their production and become less open to trade. However, if they are located near to other countries, trade flows will become more frequent. This divergence in trade patterns can thus lead to higher variance associated to higher levels of income (Herrera, 2013).

According to Sarah (2012) the multilateral resistance term represents a reflection of the average trade resistance between a country and all of its other possible trading partners. The bilateral relation between the two trading countries no longer determines trade flows, but bilateral trade is dependent on multilateral resistance, meaning the dependence on all other trading partners of the exporting and importing country. Hence, multilateral resistance sets trade into a contextual framework, consequently founding a new theoretical underpinning. It reflects the essence of what international trade is about: a choice made upon different opportunity costs which then results in general equilibrium Thus, trade flows between countries i and j are dependent on three resistances:

(i) The bilateral trade resistance existing between country i and j, reflected by tij,
(ii) Outward multilateral resistance Pi the exporting country i faces to all other trading partners. It is an indication of how easily i can export goods to other markets.
(iii) Inward multilateral resistance Pj the importing country j faces to all other trading partners. It is an indication of how easily j can import goods from other markets.

2.3 Theoretical review on regional integration

Regional economic integration may be defined as an attempt to link together the economies of two or more countries, in defined geographic areas, designed to reduce economic barriers such as tariffs and immigration controls, aimed at raising the living standards as well as achieving peaceful relations among the participating countries (Murinde, 2001).

Depending upon the level of integration amongst participating nation-states, RTAs can be divided into the following categories: Firstly, trade barriers are lowered when the countries conclude Preferential Trading Agreements (PTAs) at the most basic level. Such preferential trade is usually limited to the portion of actual trade flows from LDCs and is often non-reciprocal in nature. Papua New Guinea - Australia Trade represents an example of such an agreement.

Second, when two countries strike a bilateral trade agreement whereby trade barriers i.e. Tariffs are abolished among the participating countries; such an arrangement is called Free Trade Agreement/Area (FTA). However, each member is free to formulate its external trade policies against the countries, which are not part of FTA. Under this arrangement, barriers to trade are reduced gradually over a period, but it does not mean that all trade has become completely free of national barriers, which at times stay intact. A prominent example of an FTA is the North American Free Trade Agreement (NAFTA).

The formation of the Customs Union comes at the third level of economic integration. Customs Union is a stage where trade barriers among the member countries are abolished and a common external trade policy is adopted by the member nations (e.g. Common External Tariff regime or CET), vis-à-vis non-members. A Customs Union can be likened to an FTA, which is accompanied by a common external trade policy. The Customs Union of the Southern Cone-Mercosur- can be referred to as an example in this regard.

The Common Market represents the fourth level in the process of economic integration. A Common Market is established when the member countries facilitate movement of both goods and factors by removing all trade barriers. They also continue to retain the common external trade policy. It can be likened to a Customs Union plus free mobility of factors of production. The relevant example of a common market is the Common Market for Eastern and Southern Africa (COMESA).

Economic Union is the climactic point and the last level of economic integration. The participating countries pursue common macroeconomic policies in an Economic Union and also allow free movement of goods and factors. An example of Economic Union is manifestly the European Union (Jovanović cited in Qadri, 2012).

Table 2: Types and characteristics of International Economic Integration

Abbildung in dieser Leseprobe nicht enthalten

Source: David and Zainal (2013)

The formation of Regional Trade Agreements (RTAs) has two static effects; trade creation and trade diversion, the application of zero tariffs between COMESA member countries is expected to increase the intra-regional trade which means increasing imports and exports between member countries at the expense of that from non-member countries. This will create competitive environment that might affect the domestic production, consumption and welfare. Urata and Okabe (2007) also strengthen this argument in that trade creation results in an improvement in resource allocation and economic welfare whereas trade diversion worsens efficiency in allocation of resource in the world as it replaces imports of highly efficient nonmember states by imports from less efficient member states. Trade creation takes place when a member country replaces its domestic production by imports from a more efficient partner state (at a relatively lower cost). Trade creation is supposed to enhance welfare of member states as it leads to greater specialization in production. It also increases welfare of nonmembers through increased volume of imports. On the other hand, trade diversion occurs when lower cost imports from outside the regional integration get replaced by higher cost imports from member states.

Since trade diversion shifts production from more efficient to less efficient producers, welfare is reduced. It also reduced welfare as there is reduction in government revenue due to the shift in imports from non- members with tariff to members without tariff. However, there is a gain to consumers that results from lower price due to trade diversion. Regional integration is beneficial if the effect of trade creation outweighs that of trade diversion. Carim (1997) describes two possible situations in which trade creation outweighs trade diversion. The first scenario is when intra-regional trade is large or has a potential to become large. And the second scenario is when members of the union have complementary productive structure.

However, dynamic effects are more gradual and take place over a long period of time. It entails the competition effects due to free movements of imports; the investment effect as a result of new investments that require a regional trade integration; the entire market is large, and this offers an opportunity to exploit the new economies of scale created; the effects of capital formation and its influence on the terms of trade by the members. The dynamic effects therefore generate annual benefits as opposed to the static effects that may include the rising growth rate of a country that can occur even after the withdrawal of a member country (Otieno, 2013).

Umurungi (2005) has described some of the dynamic gains from Regional Integration Agreements (RIAs). The competition effect which brought about freeing imports from partner countries; the investment effect which appears when there are new foreign and domestic investments that have not occurred in the absence of RIA; and the structural transformation effect which is a shift from traditional primary-products exports to new industrial-products export. The dynamic effects of regional trade integration are potentially more significant than the static effects, because of their cumulative nature.

As stated in Baldwin cited in UNCTD (2009), two major theoretical motivations for the formation of trade blocs are the allocation effect and the accumulation (or growth) effect of free trade within a regional bloc. With respect to the allocation effect, economic theory shows that, in a competitive economy, the demand for a good directs productive resources to the production of that good. Hence, demand is an important signal between consumers and producers. Given that the imposition of tariff and non-tariff barriers between countries interferes with this signal, the removal of such trade barriers in the context of regional integration is thought to increase efficiency in resource allocation.

The second major effect of regionalism that of accumulation is observed through investment and trade channels. When economic integration expands regional markets, more suppliers are attracted to the regional market and firms can have the opportunity to specialize. This reduces average production costs within the trade bloc, increasing the return to factors of production and hence physical and non-physical (including knowledge) factor accumulation. Moreover, Technological spillovers resulting from regionalism lead to increases in productivity and the reduction of production costs, further attracting more investment, and hence, factor accumulation.

Cited in Keane et al., (2010) regional integration dates back to at least Viner (1950), who suggested that the effects of regional integration on trade can be either trade creating or trade diverting. Like any form of liberalization, one intended effect of RTA is to allow the more efficient producers in the region to expand production (and reap economies of scale) to the advantage of consumers and the detriment of less competitive producers. This is called trade creation. Trade diversion occurs when the removal of tariffs within the region leads to goods that were previously imported from outside (from the cheapest global source) being replaced by more expensive goods produced inside the region which can be sold for less because they no longer must pay any import duty. Consumers still gain, although by less, but governments lose more in tariff revenue and the country can obtain fewer imports for a given value of exports. This implies that regional integration can lead to further trade, but that these flows may not always be welfare enhancing.

Qadri (2012) stated the advancement of regional cooperation not only in economic growth and development but it is also for political and socio-cultural purpose. The economic factors such as the small size of domestic markets, economies of scale in production, and specialization and utilization of the underutilized potential in terms of human, technological and natural resources explain why regional cooperation is necessary. Regional cooperation enables the developing countries to not only expand their existing industries but also establish new ones based on dynamic comparative advantage, which helps them to broaden and diversify their industrial base.

The world trading system has been reshaped over the last decade by regional integrations. Many regions witnessed the expansion and deepening of the degree of regional integration such that, it is estimated that more than the half of world trade is now conducted under agreements of this kind. Such agreements are found in every continent. Among the best known are the European Union (EU), the European Free Trade Area (EFTA), the North American Free Trade Agreement (NAFTA), the Southern Common Market (MERCOSUR), the Association of Southeast Asian Nations (ASEAN) and its ASEAN Free Trade Area (AFTA), the East African Community (EAC) and the Common Market of Eastern and Southern Africa (Douglas, 2014).

Theoretical underpinning of regional integration through free trade areas and customs unions gives a justification for the aspect to be considered as a significant vehicle for trade expansion within member countries. Regional integration provides both a response to the structural challenge of the small size national markets and a strategic tool to mitigate the negative effects of too unbalanced multilateralism (Anderson & Blackhurst, cited in Yabu, 2014).

Regional integration can foster competition, subsidiarity, access to wider market (via trade), larger and diversified investment and production, socio-economic and political stability and bargaining power for the countries involved. It can be multi-dimensional to cover the movement of goods and services (i.e. trade), capital and labour, socio-economic policy coordination and harmonization, infrastructure development, environmental management, and reforms in other public goods such as governance, peace, defense and security (Mothae cited in Yabu, 2014).

Regional trading agreements are pursued for a variety of reasons. A motivation of virtually every regional trade agreement has been the prospect of enhanced economic growth. An expanded regional market can allow economies of large-scale production, foster specialization and learning-by-doing, and attract foreign investment. Regional initiatives can also foster a variety of noneconomic objectives, such as managing immigration flows and promoting regional security. Moreover, regionalism may enhance and solidify domestic economic reforms. East European nations, for example, have viewed their regional initiatives with European Union as a meaning of locking in their domestic policy shifts towards privatization and market-oriented reforms (Qadri, 2012).

Regional economic integration has been recognized as a major source of growth for the European, Latin American regions, and Asian. However, this development and growth must be seen in perspective of the influence of the numerous business initiatives particularly if practiced within a policy environment of trade liberalization. This is due to the fact that if the efficiencies arising from increasing competition and increased instances of specialization are to be realized, these must be supported by a business environment where business enterprises have free entry and exit. This leads to broader regional specialization where the entire economic region is pushed towards productive efficiencies that enable concentration on the type of production that it has the comparative advantage over the other regions (Niekerk cited in Muthoni, 2016).

It is in the same context that COMESA was created, with a mission of being a fully integrated internationally competitive regional economic community, with high standards of living for all its people, ready to merge into an African economic community. The concern was to achieve sustainable economic and social progress in all member States through increased co-operation and integration in all fields of development, particularly in trade, customs and monetary affairs, transport, communication and information technology, industry and energy, gender, and agriculture⁶

However, progress towards integration is mixed and weak across the eight RECs: implementation of the Abuja Treaty is currently at stage 3 (establishment of Free Trade Areas FTAs and Customs Unions at the regional level by 2017). COMESA, EAC, ECCAS, ECOWAS and SADC have reached FTA status and launched Customs Union programmes leading to the establishment of their Customs Unions before 2017. EAC is the only REC to have consolidated its Customs Union, which entered into force on 1 January 2005; it is also the only REC to have launched its common market, in June 2010. IGAD and CEN-SAD remain at stage 2 (coordination and harmonization of their member States’ activities), having experienced difficulties in making progress. However, to revitalize its integration process, IGAD recently adopted a minimum integration plan and is working towards the creation of an FTA.

Formation of economic blocs is motivated by the allocation effect and the growth effect arising from free trade within an economic bloc. The allocation effect requires that in a competitive economic system, resources are allocated to produce goods based on peoples’ demand for those goods by interaction between consumers and producers. When tariffs and non-tariff barriers interfere with this signal, it becomes necessary to clear such barriers through regional integration. Regional integration could also lead to the creation of large markets which would allow access to small firms thus enabling them to reach optimal sizes lowering costs and prices for the consumers. Economic integration creates a wider market allowing consumers to choose from a variety of products at lower prices. It expands regional markets, attracts more suppliers to these markets and gives firms the opportunity to specialize, increase the mobility of human capital, technological spillovers, an increase in productivity and the reduction of production costs which help to attract more investment and capital accumulation. The location decision of foreign firms can be significantly influenced by the formation of trade blocs (Baldwin cited in UNCTAD, 2009).

Through trade integration countries can attain a greater rate of economic growth and development. Combining efforts and resources also makes it possible on basis of reprocity to exploit comparative advantage. Countries also cooperate to overcome vulnerability in the international system to avoid marginalization. In addition Countries also cooperate so as to enlarge markets and attract foreign capital and in turn increase employment. The European Union is a good example of benefits which can accrue from successful regional integration. Some African countries enroll in regional integration schemes simply to enhance their prestige and this makes them participate in regional integration in halfhearted manner (Iringo, 2005).

2.4 Empirical literature review

Albert (2012) analyses the impact of regional trade agreements on intra-trade in selected agro-food products (i.e. maize, rice and wheat) in three regional economic communities (RECs) namely COMESA, EAC and SADC. The study finds that geographic distance impacts the intra-regional trade in these commodities negatively; whereas the GDP of the partner countries have the expected positive signs. Besides the traditional determinants of bilateral trade, the author finds positive and significant coefficients for the regional trading blocs which imply that these trading blocs promote intra-regional trade in the commodities. Usually geographic distance measures the cost of transport.

The determinants of intra-industry specialization are analyzed in the trade of every country with every other country, in respect to country and industry characteristics. Country characteristics pertain to pairs of countries; they include common (average per capita income, income differences, average country size, size differences, distance, common borders, and average trade orientation) and specific (participation in economic integration schemes, and common language) country characteristics. Industry characteristics pertain to individual industries; they include product differentiation, marketing costs, variability of profit rates, scale economies, industrial concentration, foreign investment, foreign affiliates, tariff dispersion and offshore assembly (Balassa, & Bauwens, 1987).

Al-Atrash and Yousef (2000) addressed the issue of whether intra-Arab trade is too little using gravity model to measure the bilateral trade among countries. Their result suggested that intra-Arab trade and Arab trade with the rest of the world were lower than what would be predicted by the gravity equation and the intra-GCC and intra-Maghreb⁷ countries trade were relatively low while the Mashreq⁸ countries exhibit a higher level of intra-trade.

Batra (2006) investigated Cameroon’s bilateral trade flows with twenty-eight European Union countries signatories of the EU-Cameroon Free Trade Agreement (FTA) on the 15th of January 2009 based on gravity model. The research findings reveal that Cameroon’s bilateral trade with European Union countries is affected positively by economic size and per capita GDP and influenced negatively by the distance between the trading partners. The result of the basic gravity model reveals that the product of two countries’ GDPs has positive and significant impact on bilateral trade, indeed, a 1 percent point increase in product of the GDPs leads to increase in the bilateral trade volume of Cameroon with the concerned trade partners by 1.2808 percent and about the distance factor, 1 percent point increase in distance leads to decrease the bilateral trade volume of Cameroon by 2.0306 percent.

Ebaidall (2016) assessed the performance of intra-SADC trade integration compared to success of two non-African trade blocs, namely ASEAN and MERCOSUR. The analysis employed a gravity model approach to estimate the coefficients of ASEAN and MERCOSUR which was used as a benchmark to project the potential trade for SADC members. The results revealed that the actual intra-trade of all selected SADC members, except South Africa is far from its potential trade level, implying unfavorable performance of SADC relative to ASEAN and MERCOSUR. The results also indicated that South Africa is the most successful member in SADC integration compared to both ASEAN and MERCOSUR trade blocs and recommends regional cooperation in SADC needs to strengthen.

Martinez and Nowak (2001) explored the determinants of bilateral trade flows between the European Union and Mercosur applying the gravity model in panel data framework and analyzed the trade potential between the two trading blocs. The authors found that the partners’ incomes had the expected positive impact on bilateral trade flows and the income elasticity of trade flows was found to be near unity in line with the theoretical expectation. But the effect of the exporting and importing countries’ population is opposite; exporting countries’ population has large negative coefficients, implying domestic absorption effect whereas that of importing countries’ has large positive impact suggesting that highly populated countries import more compared to those less populated countries. Exchange rate and income differences were also found to be important determinants of trade flow in these two trading blocs.

Cited in Laurent and Jean-François (2014) Feenstra stated that falling transport costs, trade liberalization, economic convergence, and the increases of intermediate goods trade are the main factors that explain the growth of the world trade. In same line, Baier and Bergstrand (2001) show that income growth, tariff rate reductions and lower transport costs have contributed growth of world trade. Income growth explains 67% of the growth of trade, tariff reductions 25% and transport cost reductions only 8%. However, these authors only use 16 OECD countries in their empirical analysis, all of which are high- income.

Infrastructure has always been a major impediment for African economic integration. Its deficiencies reduce economic growth and productivity and raise transportation costs which negatively affect the intra-African trade. According to UNECA, AU and AfDB, (2010) report as cited in Mwangi et al. (2016)⁹ only about 30 percent of African roads are paved and, therefore, “shipping a car from Japan to Abidjan costs $1,500, while shipping that same vehicle from Addis Ababa to Abidjan would cost $5,000. Furthermore, border issues and the costs to businesses in time delays are another impediment to intra-African trade in which customs offices charge high fees.

World Bank and International Finance Corporation (2011) as cited in Mwangi et al. reported by doing business (2011), Sub-Saharan Africa is the world’s most expensive region to trade within and also delays are up to three times as long in Sub-Saharan Africa compared with other regions of the world. One culprit for this is excessive bureaucracy. Moreover, report on Africa`s Regional Integration Index by the collaboration of AU, ADfB, and UNECA (2016) indicated that trade links from Africa to the world can be more direct and efficient than trade between neighboring regions due to infrastructure gaps or capital costs and non-tariff barriers which has negative impact on the intra-trade development in African regions.

Intra-trade among COMESA member countries has not increased as expected due to over dependence on few and similar primary products. In addition, standard of living has not improved in these countries as ten (10) out of the twenty-two members are among the poorest countries in the world. Other issues and concerns affecting the progress of COMESA include lack of government political will and commitment, overlapping memberships, inadequate infrastructure, reliance on capital rather than labour intensive techniques of production, Africa's debt burden, lack of information, underdeveloped human capabilities, unequal distribution of benefits, war damage, disease, drought, bribery and corruption (Ibrahim and Obiageli, 2015).

But Assefa (2014) studied on COMESA’s trading patterns and prospects with China for a period of 2000 to 2011 using a gravity model of bilateral trade estimates of Hausman-Taylor method for a reference sample of countries to project the COMESA-China trade out of sample. The empirical estimates suggested that the bilateral trade has seen a high growth rate in the 2000s, and that COMESA exports to China are largely driven by primary products (particularly oil) while the main imports are manufactured goods. The main implication is that there are still strong resistances to the bilateral trade that need to be addressed. However, the researcher did not explain the specific resistances that lead underutilization of the bilateral export and import potentials between them.

Jelena and Łukasz (2015) using an augmented version of the gravity model identified factors that have an influence on bilateral trade among the Western Balkan countries for the period from 1995 to 2012.Their empirical results showed that, not only geographical, economic or political factors are taken into account, but also factors constituting cultural, communicational and historical types of the so-called “distance” between countries. It was estimated threefold: as pooled data by OLS, as a random effects model and as a fixed effects model with an additional estimation of time-invariant variables following the method of Cheng and Walsh (2005). Thus, their main conclusion was that non-economic factors in the region of the Western Balkans play the most important role in determining trade values between countries.

Tansey and Hanson (2013) analyzed the market of developing countries using the gravity model. The gravity model posits that trade between any two countries is proportional other things being equal to the product of the two countries’ GDPs, and diminishes with the distance between the two countries. However, recent data shows that trade grows much less than proportionally to an importing partner’s income and that developing countries are likely to find greatest success in trading with other developing countries, not more developed countries. This effect is confirmed and contrasted for countries of widely different cultural, economic and geographic backgrounds; those in Asia, Latin America and Africa. The effect is enhanced by excluding zero and small trading relationships among countries.

Abidin, Abu Bakar and Sahlan (2013) investigated the impact of economic factors on bilateral exports between Malaysia and the Organization of Islamic Cooperation (OIC) member countries¹⁰. Using the panel estimation for gravity model, the data covers the period of 1997 to 2009. The gravity estimates imply the importance of size effects, level of openness of the economy, inflation rates, and the exchange rates as determinant of Malaysia’s exports to OIC countries. The estimation of individual effects shows the significance of distance and institutions in enhancing Malaysia-OIC exports.

Hamanaka (2013) examined the level of services trade integration in Asia in comparison with Europe and North America. He found that the intra-regional services trade in Asia is higher than in Europe and North America and Asia intra-regional services trade is higher than that of goods trade, which is in sharp contrast to Europe and North America, where the intra-regional goods trade is higher than that of services trade. Moreover, the author indicated that while Asia’s intra-regional trade of goods shows a declining trend, that of services trade remains high, although in the future its decline is expected.

Aliyu and Bawa (2015) assessed the determinants of the flow of Nigeria's exports using longitudinal data from 1999 to 2012 by constructing Nigeria's gravity trade model comprising nine EU countries, BRICS countries, Canada, Japan and the USA. Their results from POOL and panel regressions-fixed and random effects show that market size and price index of destination countries positively drive trade flows in Nigeria, while relative factor endowment, economic similarities and geographical distance negatively affect Nigeria's trade flows. Furthermore, their study found evidence in support of positive trade flows with the EU countries and negative trade flows with the BRICS countries and on account of cultural differences. Findings show that Nigeria's exports follow Linder hypothesis. These have important implications for economic, socio-cultural and bilateral trade negotiations for better trade performance in Nigeria in the future.

Using data on bilateral exports from 107 countries and 27 sectors over the period 1985-1995, Manova cited in Yabu (2014) provides evidence that credit constraints importantly determine international trade flows. Financially developed countries are more likely to export bilaterally and ship greater volumes when they become exporters. Using panel data on 65 countries from 1966 to 1995, and after controlling for country specific effects and possible reverse causality, Beck (2002) shows that countries with a higher level of financial development experience higher shares of manufactured exports in GDP and in total merchandise exports which leads to higher trade balance in manufactured goods.

Urata and Okabe (2007) attempted to distinguish the impacts of FTAs on foreign trade flows¹¹ using two approaches. One approach is to examine the changes in trade patterns before and after an FTA by using indicators of intra-FTA interdependence. The second approach is the estimation of a gravity equation to discern the impacts of FTAs on bilateral trade flows, i.e. trade creation and diversion effects. The analysis of their results of total trade indicates that FTAs bring about trade creation effect and that trade diversion effect is limited. Besides, the results of their analysis of disaggregated trade data show different patterns among different products, and they identify trade diversion effect for many products in the case of the EU, the NAFTA, and the MERCOSUR but not for the case of the AFT.

Amoah (2014) carried out a study on the determinants of Ghana’s trade with other African countries through analysis of Ghana’s trade performance, the trade policy document of Ghana, trade cost and regionalism. Export equation was estimated using trade data on 49 importing African countries using the gravity model. The result shows that the determinants of trade cost are very significant in explaining Ghana’s trade except tariff. Most importantly is that the improvement in the importer’s infrastructure will increase Ghana’s trade significantly. Also, proper regional integration is very significant in increasing Ghana’s trade.

Martinez and Nowak (2001) explored the determinants of bilateral trade flows between the European Union and Mercosur applying the gravity model in panel data framework and analyzed the trade potential between the two trading blocs. The authors found that the partners’ incomes had the expected positive impact on bilateral trade flows and the income elasticity of trade flows was found to be near unity in line with the theoretical expectation. But the effect of the exporting and importing countries’ population is opposite; exporting countries’ population has large negative coefficients, implying domestic absorption effect whereas that of importing countries has large positive impact suggesting that highly populated countries import more compared to those less populated countries. Exchange rate and income differences were also found to be important determinants of trade flow in these two trading blocs.

The quality of the roads, particularly the major roads linking regional markets, is very important to the competitiveness of African goods. A 2009 report by UNCTAD found that transaction costs (transport and insurance costs) are very high in Africa and are an impediment to the growth of intra-African trade. Business surveys reveal that road transport is the main mode of moving goods in the context of intra-African trade. Teravaninthorn and Raballand cited in UNCTAD (2013) found that Africa region has the highest costs for transporting goods in the world. In Central Africa, for example, transporting one ton of goods along the route from Douala in Cameroon to N’Djamena in Chad cost $0.11 per kilometer, which is more than twice the cost in Western Europe, where the cost is $0.05, and more than five times the cost in Pakistan ($0.02). Overall, high transport prices in Africa have been found to harm the expansion of trade more than tariff and nontariff trade restrictions.

African merchandize trades are uncompetitive due to high cost of transportation in relation to the world trade. According to Mo Ibrahim Foundation (2014), the average cost of exporting a container from African country to overseas is 2000 dollar while in Asia it is estimated at less than half of that amount (about 900 dollar). For further comparison see Table 3 below.

Table 3: Comparing African REC cost of trade with other world regions

Abbildung in dieser Leseprobe nicht enthalten

Source: UNCTAD, 2013

On average, transaction costs are higher for intra-African trade than for trade with the rest of the world. For example, average transport costs in Africa represent 7.7 per cent of total export value, which is twice the world average of 3.7 per cent. The persistence of high intra-African trade costs more generally, reflects the fact that the continent is still affected by its colonial trade patterns, where infrastructure and trade policies were set in order to orient trade towards countries out of the continent, mostly the former colonial powers (UNCTAD,2013).

A study conducted by Karamuriro & Karukuza (2015) analyzed the determining factors of exports in Uganda, covering the period from 1980 to 2012. Foreign exchange rate has been incorporated into the gravity model of trade in their study. The authors applied both generalized least square method and the instrumental variables Generalized Method of Moments (“GMM”) regression. They concluded that the formation of the Common Market for Eastern and Southern Africa (“COMESA”) and East African Community (“EAC”) provide significant benefits for Uganda’s export value and that the process of regional economic integration should be deepened.

Henry (2015) examined the effect of regional economic integration on exports in the COMESA region using the fixed effects regression, random effects regression and instrumental variables GMM regression to estimate an augmented trade gravity model using panel data from 1980 to 2012. The study results showed that the formation of COMESA trading bloc has promoted intra-regional exports, implying intra-COMESA export bias. Comparing pre-COMESA (1980-1993) and post-COMESA (1994-2012) periods, it was found that intra-COMESA exports have grown by approximately 35 percent since COMESA was formed. The study suggests that to enhance export flows in the region, the process of economic integration should be deepened. Thus, there is need for increased investment in transport infrastructure that will reduce long distance cost of doing business. This would have a major impact on deepening integration of COMESA economies.

Seid (2013) under took a study on regional integration and trade in Africa using augmented gravity model of Anderson-Van Wincoop in panel data framework from a period of 1993 to 2010, by taking four RECs in Africa¹². In his study he included variables like GDP, population, distance, border, language, and colonial links and bilateral real exchange rate, difference in preference among trading partners are found to be important factors for bilateral trade flows. But the impact of the RECs on bilateral trade was found to be mixed; SADC and ECOWAS have created trade; COMESA has implausibly negative coefficient suggesting that it has not expanded trade among the member states whereas IGAD has an insignificant positive coefficient implying that it has not contributed to the expansion of intra-regional trade.

Keane, et al. (2010) studied on impediments to intra-regional trade in Sub-Saharan Africa. A quantitative methodology used for the assessment of the impact of NTBs¹³ on trade flows is developed and then applied to the Southern African Development Community (SADC). Based on their results of a quantitative assessment of the identified impacts of NTBs on intra-regional trade, they suggested that policy measures addressing should be undertaken to solve the impediment. Yabu (2014) assessed the intra-SADC trade in goods and services using relative intra-trade intensity and gravity model and conclude that one of the objectives of regional integration is to reduce trade barriers in order to promote and boost trade among member states. However, a small share of exports and imports within the SADC region depicts a slow improvement in trade among member states. Therefore, most of SADC member states appear to trade more with other countries outside the SADC region.

Alemayehu and Haile (2008) studied the determinant of intra-regional trade flows in Africa taking COMESA as a case study using gravity model. The model includes dummy variable to show the trade creation and trade diversion effect of COMESA. The results suggest that almost all the standard gravity model variables have statistically significant impact on trade flows among the regional grouping. Financial deepening and infrastructure development are important determinants of bilateral trade in Africa. However, the result showed that regional integration arrangements failed to expand intra-regional trade. The outcome also showed that COMESA intra-trade was found not to be significantly different from its trade with non- member countries which imply that COMESA is ineffective in promoting trade among its members. They suggested that, lack of political commitment, overlapping membership, lack of policy harmonization and poor private sector participation among the major hindrance for lack of progress of regional integration in Africa.

Otieno (2013) studied the effect of whether COMESA is trade creating or trade diverting to determine the effects of regional trade arrangements using the augmented gravity model of trade. Using the annual data from 2006 - 2010 and panel data analysis of eighteen COMESA member countries and their major trading partners. A random verses fixed effect models were used to estimate the model putting into consideration the time invariant variables. Hausman test was used to determine the choice of the model to be estimated. The estimated results showed that exporters GDP significantly improves export trade by more than 100%; while the importers GDP does less proportionately. The size (population) variable coefficients are positive and significant. The estimated results also show that resistant factor (distance) as a proxy for transportation cost plays an important role in determining trade flows. In conclusion, COMESA RTA in overall shows that it’s a building block; that is, it liberalizes trade more internally than it diverts trade from the rest of the world. This can translate into welfare improvements with proper mechanisms to monitor the equitable distribution of the national income to the citizens.

Musila (2005) studied on intensity of trade creation and trade diversion in COMESA, ECCAS and ECOWAS has been estimated using the gravity model. The study used annual data for the years 1991 to 1998 and found that the intensity of trade creation and trade diversion varies from one region to another and from period to period. Indeed, empirical results showed that ECOWAS countries recorded an intense trade creation followed by COMESA countries. However, the finding of ECCAS area was not empirically corroborated. In addition, the estimated results also suggest that the effects of trade diversion were weak in the three regional organizations.

Swapan and Biswa (2007) revealed by the trade intensity indices, India and the People’s Republic of China have significant bilateral trade potential. An estimated benefit in terms of gains or losses in imports of both India and China due to different preferential trading arrangements and free trade arrangements using the gravity model. Empirical results showed that in the short run India’s potential gain is relatively less compared to China because of its high tariffs but in the long run, India’s gains are higher than China once its tariff levels are brought at par with them. Free trade arrangement is a win-win situation for both countries and is consistent with their growing dominance in the international trade.

Boniface and Manaseh (2017) investigated the role played by trade in services in industrializing COMESA region, with a specific focus on the manufacturing sector utilizing panel data for the period 2005 to 2014 and dynamic generalized method of moments (GMM) to estimate the effects of trade in services on the performance of the manufacturing sector proxied by manufacturing value added per capita in COMESA. The results showed that transport and communication service imports had a positive effect on manufacturing value added per capita. Business services and financial services imports had a negative effect on manufacturing value added per capita. Transport and construction services exports had a negative and significant effect on manufacturing value added per capita. On the contrary, business services exports had a positive and significant effect on manufacturing value added per capita. GDP growth rate and fixed capital formation had positive effects on manufacturing value added per capita.

Economic size and level of development vary widely among COMESA members. COMESA encompasses close to 515 million people and accounts for GDP of 767.6 billion dollars in total. But there are large differences between COMESA member countries. Eleven COMESA members are least developed countries: Burundi, Comoros, Democratic Republic of the Congo, Djibouti, Eritrea, Ethiopia, Malawi, Rwanda, Sudan, Uganda and Zambia. On the other hand, two countries are classified as middle-income (Egypt and Swaziland) and two countries are upper-middle income countries (Mauritius and Seychelles). Egypt is the biggest of all other COMESA members by its economic size (USD 332.9 billion), followed by Sudan ($95.6 billion) and Ethiopia, USD 73.0 billion. Mauritius is leading in per capita income followed by Egypt and Swaziland. The GDP growth rate of Burundi and Swaziland were declined by 0.6 and 2.2 percent respectively (WDI, 2017). For detail see the appendix part 7.2.

Many researchers come up with different outcomes about the progress of regional economic integration of Africa. As pointed out by Longo and Sekkat (2004), the progress of regional integration in Africa has been disappointing as both the level and growth rates of intra-African trade remain very low. The study emphasized on lack of infrastructure, political instability and inadequate economic policies as major obstacles for intra-African trade expansion. Similarly, Ayodele and Olu-Adeyemi (2007) states that the record of regional integration in Africa has been disappointing for it is marked by low level of intra-regional trade, high political conflict and coordination failures.

One of the most compelling arguments for regional integration in SSA is usually made based on the fragmentation of sub-Saharan Africa, which has 47 small economies, with an average Gross Domestic Product (GDP) of US$4 billion, and a combined GDP equal to that of Belgium or 50 percent of the GDP of Spain. The small domestic markets combined with generally high production costs and deficient investment climates result in limited investment (Africa attracts less than 2 percent of global foreign direct investment). Sub-Saharan economic growth achievements are disappointing. In 2003, 16 countries achieved an average economic growth rate of 3 percent, 16 countries growth of 3-5 percent and 18 countries more than 5 percent. The implication is that with the per capita growth rate being between 0-2 percent per annum, there is limited progress in poverty reduction and the achievement of many of the Millenium Development Goals (MDGs) seems to be elusive.¹⁴

These regional economic and trade blocs of Africa, characterized by overlapping memberships (see Figure 3), varying degrees of integration, and differing membership philosophies that range from economic and political to security with sub-interests that include customs and monetary cooperation (Table 4). The dominant specified objective of most RECs in Africa is full economic union¹⁵ in addition to other minor objectives that include creating common markets, free trade area and sectorial integration. COMESA with a common market objective is the next level of integration, combining the features of customs union with removal of restrictions on the movement of labor, capital, goods and services. Economic union, the dominant objective of most REC arrangements in Africa involves a higher level of economic integration with the aim of removing all sorts of discrimination in policies among member countries. Monetary union supersedes the economic union by incorporating the adoption of a common currency and monetary policy, usually under a single monetary authority. Example, CEMAC is already functional with monetary unions in Africa (AfDB, 2013).

Table 4: Major Regional Economic Communities in Africa

Abbildung in dieser Leseprobe nicht enthalten

Source: UNCTAD, 2009

To accelerate regional integration, the World Bank is advising African leaders to expand access to trade finance and reduce behind-the-border trade restrictions such as excessive regulations and weak legal systems. With weak economies, small domestic markets and 16 landlocked countries, African governments believe they can achieve economic integration by starting at the regional level and working their way up, merging all the regional trading blocs into an African Free Trade Area. But with 14 different trading blocs, that’s just too many, some blocs have overlapping members and many countries belong to multiple blocs. African countries perform poorly despite their strong political commitment to regional trade integration (Tafirenyika, 2014).

Korinek and Melatos (2009) examined the trade effects of three regional trade agreements (RTAs)–the ASEAN Free Trade Agreement (AFTA), the Common Market for Eastern and Southern Africa (COMESA) and the Southern Cone Common Market (MERCOSUR) in the agricultural sector using gravity model. Their result suggested that the creation of AFTA, COMESA and MERCOSUR have increased trade in agricultural products between their member countries. There is no robust indication of trade diversion with respect to imports from outside the region. The agreements are therefore net trade creating. Moreover, COMESA’s exports account for less than 1% of world trade. Agriculture is important for COMESA countries, as 21% of their exports are in agricultural goods. A free trade area was established in 2000 between nine of the 19 COMESA countries. Trade within COMESA is low, accounting for only 7% of total trade of the region, although trade within the region in agriculture has risen since its inception to equal 15% of total agricultural exports.

However, there has been strong trade creation with non-members in the case of any of the RTAs under study. In some cases, lack of transport and communications infrastructure, in addition to supply constraints, lessens the effect of the RTA on trade flows. Trade costs such as transport and logistics seem to remain important factors in determining agricultural trade flows. In some RTAs, countries have a comparative advantage in exporting many of the same agricultural products, thereby decreasing the impact of the preferential market access (Korinek & Melatos, 2009).

Geda and Kibret (2002), critically reviews major issues of regional economic integration in Africa which are related to the issues of the economic, political and institutional constraints that surface at the implementation stage of economic integration treaties using the experience of COMESA as a case study and tests the determinants of trade flows. Their major conclusions indicate that first, bilateral trade flows among the regional groupings could be explained by standard variables as demonstrated by the results of the conventional gravity model, while regional groupings have had insignificant effect on the flow of bilateral trade. And, second, the review of the issues indicates that the performance of regional blocs is mainly constrained by problems of variation in initial condition, compensation issues, real political commitment, overlapping membership, policy harmonization and poor private sector participation.

Douglas (2014) studied the trade effects of regional trade agreement whether its trade creating, or trade diverting in the case of COMESA’s expansion. The effects are assessed both toward those newcomers, Egypt and Seychelles and toward old member countries, namely Kenya, Madagascar and Mauritius through descriptive evidence and a series of regression at aggregate level using export and import volumes. The result shows that COMESA’s expansion has resulted into trade creation toward newcomers, while this expansion has not led to a trade diversion among old member countries. Second, COMESA’s creation has resulted into relative weak trade creation between founding countries without thereby entailing trade diversion toward non-founding countries.

Most countries now belong to at least one bilateral and regional trade agreement (RTA) which have continued to increase in number, and size, since the early 1990s; this includes in SSA. There are several stages in the regional economic integration process which range from the formation of a free trade area (FTA) to a customs union (CU) and the establishment of an economic and monetary union. Most of the agreements signed up to date constitute FTAs. However, an important step in the regional integration process is the formation of a CU, which not only eliminates tariffs and quotas on trade amongst member countries but also establishes a common external tariff (CET) which is applied to trade with non-members and third-party countries (Keane et al.,2010).

Regional trade arrangements (RTAs) in Africa have been ineffective in promoting trade and foreign direct investment. Relatively high external trade barriers and low resource complementarity in resource endowments between member countries limit both intra- and extra-regional trade. Small market size, inadequate transport infrastructure and local capacity and high trading costs make it difficult for African countries to reap the potential benefits of RTAs. To increase regional trade and investment, African countries need to undertake more broad-based liberalization and streamline existing RTAs, supported by improvements in infrastructure and trade facilitation (Yang & Gupta, 2007).

Currently, Africa’s trade performance remains weak in comparison with other world regions. Intra-regional trade i.e. trades within and between Regional Economic Communities (RECs) – accounts for about 12 per cent of Africa’s total trade, a much lower proportion than in other world regions. Therefore, integrating Africa’s fragmented markets could help attract the required investments from both Africa and the rest of the world, those motivated by economies of scale to build competitive and more diversified economies (ECA,2012).

Mold and Mukway (2016) evaluated the economic impact of the proposed COMESA-SADC-EAC Tripartite Free Trade Area (TFTA) on 26 African countries using the Global Trade Analysis Project (GTAP) computable general equilibrium (CGE) model and database to measure the static effects of the establishment of the TFTA on industrial production, trade flows and consumption in the TFTA. The results indicate a significant increase in intra-regional exports as a result of tariff elimination, boosting intra-regional trade by 29%. Particularly encouraging is the fact that the sectors benefiting most are manufacturing ones, such as light and heavy manufacturing, and processed food. Concerns have been raised that industrial production in the TFTA could concentrate in the countries with highest productivity levels-namely, Egypt and South Africa.

The limited growth in intra-regional trade in COMESA can be attributed to many causes. However, the lack of diversity and the similarity of the products exported by member countries, the lack of political commitment to integration, lack of security and political stability, poor physical infrastructure, macro-economic imbalances and unequal distribution of gains from integration are major constraints on increasing intra-regional trade in COMESA (Sheriff & Nwokedi,2015).

By applying a multi-market model with Armington non-linear specification model Elbushra, Karim, and Suleiman, (2011) analyzed the intra-regional agricultural trade performance and potential between Sudan and other COMESA members. The study results revealed that, in general, there is a great trade potential for Sudan to increase its intra-COMESA trade. Sudan has potential to increase its agricultural export namely cotton, sesame and live animals to COMESA countries. The domestic producers are expected to be hampered by imports due to increase in competition, while the producers of export commodities will be better off.

Gbetnkom(2006) undertook a study which focuses on estimating the determinants of trade integration among the COMESA member states and the extent change in trade patterns after tariff cut within the grouping. The study examines the implication of COMESA’s accession to the free trade area and shows that the accession to free trade area has resulted an increase in intra-regional trade. Besides, the result demonstrates that the traditional explanatory variable of gravity model has significantly determined trade flows in COMESA.

Umurungi (2005) analyzed using statistic descriptive and comparative analysis methods to investigate how COMESA region can learn from the experiences in the EU, ASEAN and SADC trade regions to enhance its intra-regional trade. The findings indicated that for regional trade integration to be successful a favorable macroeconomic environment, a diversified export base and adequate infrastructure. Satisfaction of these conditions may be possible through government activity coordination and the project cooperation approach to regional integration. Therefore, the market integration in COMESA should be supplemented with government activity coordination and project cooperation to ameliorate economic conditions in member states and enhance regional trade integration. However, the COMESA region is characterized by unfavorable macroeconomic condition, poor infrastructure and dependency on few primary commodities which limited the growth of intra-COMESA trade.

Intra-African trade help the continent’s industries to become more competitive by creating economies of scale and weeding out producers that are less productive in the marketplace. It can establish and strengthen product value chains and facilitate the transfer of technology and knowledge via spillover effects. And it can incentivize and spur infrastructure development and attract foreign direct investment. For these reasons, expanding intra-African trade is a key to accelerating economic growth on the continent. It is especially important for the continent’s many small, landlocked countries that face tremendous challenges trading internationally. Unfortunately, however, Africa’s current internal trade is low making up only about 10 percent of its total trade. Most of its exports go to the world’s advanced economies, and most of its imports come from those same advanced economies (Mwangi, et al., 2016).

In order to deepen and broaden the integration process among its members COMESA adopt a more comprehensive trade liberation measures such as the complete elimination of tariff and non-tariff barriers to trade and elimination of customs duties; through the free movement of capital, labor, goods and the right of establishment; by promoting standardized technical specifications, standardization and quality control; through the elimination of controls on the movement of goods and individuals. Based on this member states have agreed on the need to create and maintain: A full free trade area guaranteeing the free movement of goods and services produced within COMESA and the removal of all tariffs and non-tariff barriers; A customs union under which goods and services imported from non COMESA countries will attract an agreed single tariff all COMESA States; Free movement of capital and investment supported by the adoption of common investment practices so as to create a more favorable investment climate for the entire COMESA region(Awad, et al., 2008).

Moreover, Awad et al. (2008) employed two preliminary models representing intra-exports and intra-imports in an attempt to measure the effect of COMESA on intra-COMESA trade over the period 1985-2004 using a time series analysis. The purpose of models is to identify the substitution effect between intra-COMESA trade and COMESA trade with the rest of the world. Therefore, imports and exports with the rest of the world have been introduced as explanatory variables in these models. They found that the effect of COMESA on intra-trade, revealed insignificant t-values of the dummy variables in both models which shows that the COMESA agreements had no significant effects on intra-trade. The variable representing trade with the rest of the world obtained a positive sign and was significant at the 5 per cent level for both specifications. It thus appears that there might be no significant substitution effects between intra-trade and trade with ROW for the sample of countries. In other words, for instance on the exports side, the positive and significant coefficient of export to the rest of the world would imply that, trade agreements at least like COMESA, do not necessarily lead to an increase in exports within COMESA at the cost of exports to ROW.

An assessment of the performance of regional integration efforts in Africa by UNCTAD (2009)¹⁶, COMESA has designed single rules of origin and has simplified its customs procedures. It has also achieved the elimination of non-tariff barriers (in particular import licensing), the removal of foreign exchange restrictions, and the removal of import and export quotas, however, regional initiatives in Africa, did not deliver much to uplift the economic conditions of its members nor ensure sustainable growth and liberalization. Intraregional trade as a proportion of total trade remains much lower in African regional integration arrangements compared to those of the Asian and Latin American regions. Over the period 2004–2006, intra-African exports represented 8.7 per cent of the region’s total exports. Intra-African imports, on the other hand, represented 9.6 per cent of total imports. Nonetheless, even the sub-Saharan African proportion of intraregional trade remains far below other regions’, as can be seen in Table 5.

Table 5: Intra-regional imports and exports as a proportion of total trade, 2004–2006 averages (Percent)

Abbildung in dieser Leseprobe nicht enthalten

Source: UNCTAD, 2009

UNCTAD (2009) further states that economic and institutional challenges have been an obstacle to intra-regional trade in Africa. The economic obstacles include the high dependence of most member countries on export of primary commodities, the strict rules of origin emanating from trade liberalization schemes and the poor quality of infrastructure as cited in (ECA, 2008). Institutional challenges include bureaucratic and physical hindrances, such as road charges, transit fees and administrative delays at borders and ports. These hindrances raise transport costs and render deliveries unreliable. Other challenges are related to the lack of coordination and harmonization of policies and regulations at the regional level, non-implementation issues and overlapping membership.

Formation of regional blocs in Africa has facilitated the creation of trade among its member countries. Report by UNCTAD (2013) indicate that except for the Economic Community of Central African States (ECCAS), for each African regional economic community, a significant part of their trade with Africa takes place within their own regional trade bloc. For instance, in the period from 2007 to 2011, 64.7 per cent of the trade of the Community of Sahel-Saharan States (CEN-SAD) with Africa was with CEN-SAD member countries; 78.4 per cent of the trade of the Southern African Development Community (SADC) with Africa was with other SADC member countries and for the Economic Community of West African States (ECOWAS) the figure was 65.5 per cent. However, except for the Common Market for Eastern and Southern Africa (COMESA), these shares have been falling compared to the period from 1996 to 2000.

Even though COMESA launched free trade area in 2000 among its member states to strengthen the regional trade integration the intra-trade flows remain limited. The nine free trade area participating states were Djibouti, Egypt, Kenya, Madagascar, Malawi, Mauritius, Sudan, Zambia and Zimbabwe. Burundi and Rwanda joined the FTA later in year 2004, after 16 years of gradual reduction of tariffs on intra-COMESA trade. In November 2007, thirteen (13) countries were participating in the FTA; other member states had carried out tariff reductions of between 10 per cent and 80 per cent. The region launched its customs union (CU) in 2009, though with few members signing into the newly created CU (Albert, 2012).

In October 2008 COMESA, East African Community and Southern African Development Community agreed to negotiate a tripartite free trade agreement amongst the regional economic communities. After lengthy negotiations, the tripartite FTA was officially launched in June 2015. Although 17 out of the 26 Member States have signed the Tripartite Agreement, it has not yet entered in to force due to outstanding ratification. Moreover, remaining technical works on tariff liberalization, rules of origin, and trade remedies are likewise delaying the process. However, interim arrangements were agreed to operationalize the tripartite FTA, which would effectively make it the largest FTA in Africa. It has also been estimated that the tripartite FTA could boost intra-regional trade by as much as one-third (UNECA, 2016).

From Table 6 it can also be observed that of all eight African regional economic communities, the share of Africa in total trade was highest in the East African Community (EAC). The share of Africa in EAC total trade amounted to 23.1 percent in the period from 2007 to 2011, compared to 16.4 percent for SADC, 14.3 per cent for the Intergovernmental Authority on Development (IGAD), 14.2 per cent for ECOWAS, 13.3 percent for COMESA, 10.2 per cent for CEN-SAD, 9.3 percent for ECCAS and 5 percent for the Arab Maghreb Union (AMU). However, these shares represented a decrease compared to the period from 2001 to 2006 for COMESA, EAC, ECOWAS and IGAD. Conversely, between the period from 2001 to 2006 and that from 2007 to 2011, the share of total trade going to Africa increased only for CEN-SAD, ECCAS, SADC and AMU.

Table 6: Intra-African trades from 1996-2011: distribution of shares

Abbildung in dieser Leseprobe nicht enthalten

Source: UNCTAD, 2013

Collaboration on the part of the African Union Commission (AUC), the African Development Bank (AfDB) and the Economic Commission for Africa (ECA) has come up with report on the development of the Africa Regional Integration Index in 2016. Accordingly, the Trade Integration index among COMESA members has been developed using the trade integration dimension and four indicators such as level of customs duties on imports, share of intra-regional goods exports (% GDP), share of intra-regional goods imports (% GDP), share of total intra-regional goods trade (% total intra-REC trade). Based on the result Zambia, and Egypt were the best performer than average countries among COMESA members. Congo DRC, Kenya, Uganda and Libya took the second contributors to the trade integration in the region. However, due to the distance between the member countries, COMESA has a low score in the 2016 African Regional Integration Index. Only 11% of exports from COMESA stay within the group. More than 80% of its exports in 2015 went to non-African countries (AfDB, OECD, UNDP, 2017). See Figure 2.

Abbildung in dieser Leseprobe nicht enthalten

Figure 2: Country scores on trade integration

Abbildung in dieser Leseprobe nicht enthalten

Source: AU, AfDB, and ECA, 2016

Sako (2006) stated that unlike the economic integration schemes in other parts of the world, such as the EU in Europe, the North America Free Trade Area (NAFTA) in North America and the MERCOSUR in South America, African RECs have not succeeded in accelerating growth or trade. A fair general assessment of African regional integration arrangements indicates their failure in meeting their stated objectives. Intra-African exports as a proportion of the Continent’s total exports amounted to only 7.6 per cent in 2000 as against a ratio of 17.2 for Latin America, a region, which has not been as active as Africa in the promotion of cooperation and integration. Despite the multitude of regional integration schemes on the Continent, average income per capita is lower today in Africa than at the end of the 1960s.

Moreover, Sako explained factors that lead to the poor performance of regional economic integration as : lack of complementarily of member countries’ production structures, lack of political will to mainstream regional commitments and agreements into national plans to ensure the success of the process, weak national and regional institutions ,lack of coordination and harmonization of economic policies lack of involvement of other stakeholders–the private sector and civil society- in the cooperation and integration process, inadequacy of human and institutional capacity for the design and implementation of cooperation and integration programs ,disproportionate allocation of resources highly skewed in favor of conflict related issues as against economic cooperation and integration issues ,inadequate infrastructure, high incidence of conflicts and political instability, poor design and inadequate sequencing of regional integration arrangements, multiplicity and overlapping membership of regional integration schemes and mandates, inadequate funding of regional integration process and related institutions.

Awad, et. al., (2008) has identified obstacles to growth of intra-trade within COMESA which are related to political, economic, administrative, tariff and non-tariff, transportation and external factors. They further elaborated each of the obstacles as follows:

- Political factors

The most important factor is political mistrust and instability among selected countries of the region. The region suffers from political deadlocks or even unrest, either with neighboring countries or within the country itself. In addition, special-interest politics from industry and other lobby groups play a dominant role in determining the extent of trade relations or exchange of preferences. The stronger the political differences in the region the weaker the possibilities of successful trade relations. Also, mobility of labor and capital face significant degrees of friction in terms of residence permits and transfers.

- Economic factors

The economic factors that impede intra-trade in the region include economic structure and policies. The member of the region may or may not have similar economic structures, but evidently, limited differences in product-wise comparative advantage and absence of complementarities. The products that are similar are mainly primary including fuels and fuel products, agricultural products and raw materials, and to some extent manufactured products. Most of the member countries are characterized by a small market and insignificant demand levels compared to the world market. Many of the countries suffer from heavy foreign debt burden, poverty, low productivity, unemployment, inflation and non-competitiveness. The economic policies that impede intra-trade include monetary restrictions, exchange rate controls and financial restrictions on across-the-border goods in the form of fees, stamps, etc.

- Administrative and technical constraints

The administrative restrictions include all forms of procedures that are attached to the border crossing of goods such as customs documents and verifications, transit facilities, handling and inspection. The technical constraints include the applications of standards and specifications, health and environmental conditions, certificate of origin and value added verification.

- Transportation

The transportation of goods depends heavily on land transport which is characterized by high costs since it relies on heavy vehicles.

- Conflict resolution and information

The degree of trade conflict resolution and availability of information varies across the regional members but it is in any case not well developed.

The main barriers mentioned to regional trade are customs procedures, red tape and corruption, differences in standards and requirements in the various markets of the region, high transport costs and non-tariff barriers in the form of import bans, suspended duties and the like. According to several companies in Kenya, Tanzania, and Rwanda the harmonization of requirements, or “one set of rules” for entering markets in the region, was the single most beneficial change that regional integration could deliver, both in terms of documentation and of technical requirements and standards. Other barriers related to the business environment, difficulties in resolving late or non-payments and availability of finance (Nick, 2005).

Multi-membership is also another factor that affects intra-trade. In fact, some observers suggest that multiple memberships might, ironically, be hindering regional integration and by extension, intraregional trade rather than enhancing it. They point out that multiple memberships impose high costs in time, energy and resources on African governments and force them to juggle competing regulations (Mwangi S. et.al, 2016). UNCTAD (2009) added that many regional integration initiatives were over-ambitious; they had overlapping memberships and mandates that sometimes conflicted and were often unclear.

Olaniyan (2008) added that multiplicity of RECs in the regions, with attendant overlapping membership by countries, is an important issue affecting the pace of regional and continental integration. The reasons motivating countries to belong to more than one REC are to be found in historical, political, strategic and economic imperatives. Political and strategic consideration derive from national security needs while economic imperatives are based on perceived economic, trade, investments etc that could rapidly accrue to the country in an integration arrangement.

There are many RTAs on the African continent, overlapping and complementing each other in some cases, but with conflicting objectives in others. For countries covered by more than one trade agreement, importers have a choice of regimes under which to import goods. For small and medium sized enterprises (SMEs), overlapping membership may pose difficulties through increasing the trade costs of exporting to different regional markets which may have varying standards-related entry requirements. Consequently, this could reduce the potential for benefits which result from scale and therefore constrain product and market diversification efforts. Although replacing overlapping membership with one all-embracing REC may help in reducing the costs of trading for firms, it also makes the task of harmonizing rules and regulations greater for governments (Keane, et.al, 2010).

Regional integration in Africa has been the multitude of regional integration initiatives and consequently the participation of African countries in several of these regional trade agreements (RTAs). Many African countries hold multiple memberships. Of the 53 countries, 27 are members of two regional groupings, 18 belong to three, and one country is a member of four. Only seven countries have maintained membership in one bloc. Multiple arrangements and institutions, as well as overlapping membership in the same region, tend to confuse integration goals and lead to counterproductive competition between countries and institutions. Figure 3 shows Overlapping membership in regional integration groups and regional trade agreements in Africa. Even though, the existence and membership of several RECs demonstrates strong efforts by African countries to integrate, it could also be disadvantageous as proliferation of economic groupings could breed inefficiencies, duplication of roles and confusion. This could induce gratuitous competition among institutions and countries that could ultimately be counterproductive to the regional integration aspirations of the continent UNCTAD (2009).

Overlapping trade agreements have been a reason for the weak implementation of regional integration schemes in Africa and the limited trade effects of the agreements. For a country to administer set sets of rules, often conflicting in some instances, can be an impediment to realizing trade gains from preferential market access. These can also create confusion about integration goals. Complexities created by overlapping memberships risk slowing down trade liberalization within the integration area and hampering the effect on integration. Their study shows that more than 25 per cent of national policy makers think that overlapping agreements make it hard to meet intended integration commitments, while 23 per cent find agreement overlaps as a reason for low programme implementation. In overlapping RECs, the complexity is caused by multiple and different tariff regimes and non-tariff barriers, which maybe a challenge for multi-REC members. As regards, FTAs, the existence of several rules of origin can cause additional difficulties including in terms of trade diversion (Tavares and Tang cited in UNCTAD, 2016).

Figure 3: Overlapping membership and trade agreements in regional integration groups in Africa.

Abbildung in dieser Leseprobe nicht enthalten

Source: UNCTAD secretariat, 2009

Membership overlap across various intra-African regional integration arrangements causes several problems. For example, rules of origin differ among these arrangements and this creates difficulties and conflicts in establishing criteria for granting origin which raises the cost of administering the agreements. Similar problems exist in the form of duplication and maintenance of different phase-out periods for each regional partner. Taken together, these problems are manifested in the multiplication of Customs procedures and complications in paper work which run counter to the goal of simplifying and facilitating intraregional trade. To the extent that various intra-African regional integration arrangements have different objectives which are reflected in terms of differences in the scope of and schedules for liberalization, overlaps in membership diffuse regional integration efforts and may thus jeopardize the chances of success for these efforts (UNCTAD, 2006).

In addition, overlaps in membership give rise to several more specific and technical implementation problems. When countries are members of two separate arrangements which establish two different common external tariffs (CETs), a technical problem arises. For instance, Kenya and Uganda are members of EAC and COMESA which have different CETs. It is technically impossible for these two countries to implement the two different CETs. Countries which choose to belong to more than one regional group have several additional resource burdens to shoulder. Duplication wastes effort and associated resources of attending meetings, implementing agreements and so on. Similarly, membership is also associated with financial obligations which the poorer countries may find difficult to meet as and when due. The ineffective support of the regional integration agencies, through regular payment by members of their financial obligations, may not be unconnected with the incidence of pervasive overlapping memberships and inability to discharge the associated obligations (UNCTAD, 2006).

There is overlap of membership among Regional Economic Communities (RECs) in the Eastern and Southern African region to an extent unparalleled anywhere else in the world. For example; almost half of COMESA members are also members of SADC, whose membership is smaller than COMESA's. This may tend to weaken the integration process. It leads to costly competition (even for attention and resources); conflict; inconsistencies in policy formulation and implementation; unnecessary duplication of functions and efforts; fragmentation of markets and restriction in the growth potential of the sub-region. Yet, as most RECs in the Eastern and Southern African region wish to move to a Customs Union (CU), member states with multiple memberships at present will have to strike the balance of the costs and benefits of belonging to one or another CU grouping (Awad, et.al,2008).

Sheriff and Nwokedi (2015) stated that overlapping membership (being a member of many organizations at the same time) has been an obstacle to regional integration, especially regarding SADC and COMESA, which have the same objectives. It is noted that in SADC, except for Botswana, South Africa and Mozambique, the remaining countries are simultaneously members of COMESA. Overlapping membership can constitute an obstacle to the process of regional trade integration, which leads to unnecessary duplication of functions and costs associated with the membership and the fragmentation of markets.

Gathii (2010) states that one of the characteristic features of a customs union is that all the countries are adopting one Common External Tariff. But this raises a major concern regarding the loss of revenue for countries that enjoy collecting of monies through their various trade interests. Countries that previously collect funds through their own means and agreements are diminished when they are joining a customs union because they are subjected to one Common External Tariff. This creates complexity by different tariff applications and non-tariff barriers when one country joins different regional blocs who have different policies and regulations at same time.

When it comes to FTAs, the rise in the number of rules of origin can cause additional difficulties in form of trade deflection. Trade deflection is a loophole for exporters that want to take advantage of different tariff rates within a FTA by imports to the country with the lowest tariff, for further re-exportation to other FTA members. A common solution to the problem of trade deflection is to have rules of origin. By controlling the origin of products entering the country it is guaranteed that the right tariff rate is being applied to the goods. The problem with too many and too strict rules of origin is that the trade procedures becomes complicated, as the production often is localized in different areas. Then the cumbersome task for those working with the rules of origin is to decide where a product is mostly from and thereafter to apply the correct tariff rate (Baldwin & Wyplosz, cited in Fergin, 2011).

Sheriff and Nwokedi (2015), Geda and Kibret (2002) and Umurungi (2005) emphasize the problems of harmonizing policies, especially in the areas of rules of origin and customs procedures. As regards the rules of origin for example, there have been conflicts between Kenya and South Africa over South African goods’ penetrating freely into the Kenyan market via other countries, which are simultaneously members of COMESA and SADC, while South Africa imposes tariffs on Kenyan goods entering its market, because Kenya is not a member of SADC.

Multiple and overlapping memberships in RECs have created a complicated web of competing commitments which, combined with different rules, result in high costs of trade between African countries, in effect undermining integration. Multiple and overlapping memberships occasion resource and effort wastage due to duplication/multiplication of effort. It complicates harmonization and coordination among member states. Political and strategic reasons are cited as the overriding motivation for this multiplicity of memberships in RECs. Of the 53 countries, 26 retain dual membership; 20 are members of three RECs; the DRC belongs to four RECs; and only 6 countries maintain singular membership. See Figure 4

Figure 4: Overlapping membership of African countries

Abbildung in dieser Leseprobe nicht enthalten

Source: UNECA Annual report ARIAII cited in Atieno, 2009

Multi-memberships of countries in RTAs have attracted a lot of criticism from different researchers and police makers. It has been stated that the overlapping memberships between the various regional arrangements generate unnecessary costs. There are administrative costs related to the complex rules of the origin. It is very expensive to pay multi-membership fees. Furthermore, it has also been stated that a country cannot change its policies without the consent of the other countries in the RTA. This becomes even more problematical when a country is a member of multiple RTAs, since it can be argued that a country ends up losing part of its sovereignty every time that it joins an RTA (Taole, 2014).

A “spaghetti proliferation” of PTAs is that multiple memberships may generate duty-free market access and zero-tariffs on imports with many trading partners and can hence be an appealing alternative to national policy makers as a substitute to free trade (Schiff & Winters 2003). The will of reaping the benefits of PTA membership from a whole group of preferential agreements may explain the agreement overlaps in Africa. But, instead of promoting trade, the result of multiple memberships might instead be higher transaction costs due to a mass of overlapping rules (Schiff & Winters cited in Feregin, 2011).

Afesorgbor and Bergeijk (2011) studied on multi-membership and the effectiveness of regional trade agreements in Western and Southern Africa. A comparative study of ECOWAS and SADC have taken using gravity model for 35 countries and the years from 1995-2006. They find that that the impact of multi-membership critically depends on the characteristics of the overlapping RTA. They find a positive impact if an additional membership complements the integration process of the original RTA: overlapping memberships had a significant positive effect on bilateral trade within the ECOWAS bloc, but it is insignificant for SADC.

The overlap among regional economic communities also adds to the burdens of member states. A country belonging to two or more regional economic communities not only faces multiple financial obligations, but must cope with different meetings, policy decisions, instruments, procedures, and schedules. Customs officials must deal with different tariff reduction rates, rules of origin, trade documentation, and statistical nomenclatures (ECA, cited in Afesorgbor & Bergeijk, 2011).

Overlapping memberships between the various regional arrangements have costs. Negotiating resources and capacity have been stretched thin across the region. There are administrative costs related to often complex rules of origin. Multiple membership fees are expensive to pay and maintain. Conflicting objectives among rival arrangements have contributed to a lack of progress in many areas. These regional arrangements are in various stages of forming customs unions (COMESA, SADC, and the EAC), that has created conflicts of membership need to be resolved (Iqbal and Khan cited in Khandelwal, 2004).

Iringo (2005) assessed the challenges that countries face as a result of dual/multiple membership in regional economic organizations taking Kenya experience as a member of EAC and COMESA as a case study. The objective of the study is to assess contradictions that Kenya faces as a result of being a member of EAC and COMESA. With regard to the objectives, the study established that Kenya does not face contradictions by being a member of EAC and COMESA. The study also established that Kenya is the major beneficiary of intra EAC and COMESA trade. It was also established that Kenya is an active participant in both organizations and promptly fulfills all the obligations and requirements in both organizations. The study concludes that Kenya faces challenges as a result of being dual member of EAC and COMESA. These include administration of rules of origin, imposition of non-tariff barriers, technical barriers to trade, increased competition from firms outside Kenya, and loss of revenue. However some of the challenges can be overcome through harmonising protocols of COMESA and EAC, enhancing competitiveness of domestic industries, diversification of export base to avoid over reliance on a few commodities and improving joint investment policy within the member countries.¹⁷

Nick (2005) undertook a study on the problem of overlapping membership to regional integration by consulting businesses and private-sector representatives in Tanzania, Kenya and South Africa and they mentioned that the main problems of overlapping membership are the proper administration of tariffs, enforcement of rules of origin at borders (which may also breed corruption) and confusion due to lack of coordination amongst regional integration initiatives. The poor articulation of tariff liberalisation under the different agreements, where for instance Kenya under the EAC transition arrangements may face higher tariff barriers in Uganda and Tanzania than COMESA and SADC members, respectively, was also raised as a concern, as was the possible infiltration of duty-free EU goods from SACU into SADC and via Tanzania into the EAC.

Overlapping membership in regional blocs often forms a web of relations and since regional economic blocs are formed at different times and progress at different paces, the process brings further complications. A country can find itself unable to fulfill all its obligations to all treaties she is a signatory in the different blocs in which is a member. For instance, Tanzania withdraws from COMESA citing high subscription fees. Membership in several regional organizations can result in divided loyalties and the country in question could end up fulfilling only a part of the requirements and thereby drag the integration process or lead the organization to collapse. Multiple membership seem to work against regional integration as it would be difficult to implement protocols on all sides especially when regimes are at customs union level(Iringo, 2005).

Ahmad cited in Iringo (2005) stated that African countries at independence rushed to create similar cooperation arrangements in each sub region. Most of these organizations had more or less same objectives and tended to tackle almost same problems at almost same time but in a different and independent manner without coordination or even consultation. He gave an example of West Africa where about fifty intergovernmental organizations were established over a period of twenty five years. Most of these organizations were overlapping. He notes that Economic Community of West Africa, the Magreb Arab Union (MRU), Senegambia and Economic Community of West Africa (ECOWAS) were all established with same objectives. There were also two monetary arrangements. These were West African Monetary Union (WAMU) and West African Clearing House countries or groupings outside the continent. He concluded that, overlapping groupings and membership bring the problem of repetition and conflicts between the provisions of the treaties of similar organizations.

Mwangi S. et.al, (2016) stated that political tension, conflict and violence also diminish the capacity for member states of each ECA to engage in intra-trade. These factors lead to low levels of economic growth, destroy needed export infrastructure, and slow and reverse regional integration. Amoah (2014) states that maintaining and improving political situations of member states is very important. This builds exporters confidence in terms of decision to export to such countries. However, exporters do not only consider political stability of partner countries by the safety of their exports even in politically stable countries because their goods can be vandalized or stolen by robbers. It is therefore very important for member countries not only to maintain peace and political stability but maintain internal security of both imports and exports of citizens to protect trade of goods and services and increase trade in general.

Tchouassi (2013) analyzed the relationship between trade, democracy and development in Central-African region, specifically on how democracy effects on trade and development and came up with the result, economic development, democracy, import, export and regional integrations are positively correlated. In line with this argument Olaniyan (2008) also stated that political stability is one of the challenges in the economic integration process in Africa. Not all countries enjoy an enduring political stability. Political instability in its subtle form has been expressed in stresses and strains in the political system and at the other extreme in civil disorder and war. Political instability often emerges from poor governance, weak development of national unity and inequitable economic development. Regional economic integration has been severely disrupted in regions where member states experienced civil wars.

Political policies and other government concerns, such as the relationships between trading nations, are highly important to the growth of international trade. A politically stable nation with few policies restricting international trade will likely be able to expand its worldwide trade rapidly. Political instability, however, particularly when it leads to violence, can be a major barrier to trade growth (Mariana & Elena, 2014).

Olaniyan (2008) also added that regional economic integration could only be promoted in a politically stable environment which would allow member states to promote national economic development and implement integration measures. Political instability such as civil war normally diverts attention from regional integration and its consolidation as regional efforts would be towards ending the war and preventing it from spilling over to neighboring countries. Therefore, support should be given to member states to enhance democratic process, good governance, accountability, and the promotion of human rights and the rule of law that are the ingredients of political stability.

The following listed factors have potential impact on the process of economic integration:

- Economic welfare: If countries remove their inefficiencies through specialization of production and extend cooperation in the area of policymaking, they cannot only achieve but also enhance their prosperity. Specialization of production and cooperation in policy making are two basic elements of economic integration.
- Peace and security: Economic integration creates peace constituencies in the respective societies of each country and the stakes are so high that any possibility of outbreak of hostilities or armed conflict becomes a non-starter. Economic interdependence is a key to a peaceful and harmonious world.
- Democracy: Economic integration also serves the cause of democracy by encouraging participation of member countries into the mainstream. Member countries in a regional grouping derive economic benefits through their collaboration, which is made conditional upon the existence of a parliamentary form of democracy. In this case, chances of an overthrow of system through any unconstitutional means are minimal.
- Human rights: The cause of human rights may be advanced, and its abuses contained if those countries seeking to join any economic grouping are required to make sure that human rights are not violated within their territories either at the level of the government or individual groups. Respect for rule of law may be a necessary building block for creating a society where human rights are respected without any kind of discrimination (Molle cited in Qadri, 2012).

In the aspect of trade-democracy relationship, Heckscher-Ohlin-Stolper Samuelson model state that democratization should lead to more liberal trade policies in countries where workers are able to gain from free trade and to more protectionist policies in countries where workers will benefit from the implementation of tariffs and quotas (O’Rourke 2006). Globalization can improve governance because trade openness reduces corruption, thus it contributes to establish good governance and ultimately democracy (Macedo, cited in Pontet and Udvari, n.a).

According to Pontet and Udvari (2016) democratic regimes, from both the importer and exporter countries may affect trade through several channels, and from different aspects. First, a more democratic exporter can increase bilateral trade by improving product quality and reducing trade costs. The main reason of highly democratic regime can increase trade is associated with better maintenance of the rule of law and stronger property rights protection, helping to provide better market conditions, create a fair and competitive market. Second, if an exporter is engaged in strong democratic institutions, the international community will trust its products. Lastly, politically free societies apply minimal restrictions on the mobility of goods and services across borders.

A more democratic exporter has better institutions respecting consumer rights, food and product regulations and legal enforcement. These factors will improve product quality and reputation of an exporter country, more precisely a highly democratic exporter would be a reliable trading partner due to better product quality; thus, democracy could improve product quality, and thereby helps exports (Yu, 2010).

The strength of peace, security, stability and good governance¹⁸ are stated as one of its aims and objectives in article 3(d) of the Treaty Establishing COMESA¹⁹. Ministers of foreign affairs of COMESA member states meet annually to deliberate matters regarding the promotion of peace, security, stability and good governance in the regional economic community which were aligned with the frame work of the African Union`s peace and security architecture that work with regional economic communities on issues relating to preventing, managing and resolving conflicts on the continent. The committee on peace and security, which is composed of high-level officials of the ministries of foreign affairs of member states, serves to enhance accountability and promote good governance in the regional economic community through three programme such as Conflict early warning system, war economy component of the conflict prevention and Management and resolution strategy.

According to OECD cited in Amoah (2014) increases in trade volume and complexity in recent years have significantly changed the operating environment for the international trading community. They have also highlighted the negative impact of inefficient border procedures on governments, businesses and ultimately on the customer and the economy as the whole. Governments may face smuggling, fraud and national security problems, which drains the public coffers, while businesses pay the price of slow and unpredictable goods delivery, costly customs procedures, and even lost business opportunities. And all these costs ultimately make goods more expensive for the consumer.

Djoumessi and Bala (2017) studied the border effects on intra-African trade through the use of a gravity specification based on the monopolistic competition model of trade introduced by Krugman (1980). The study used CEPII data on trade flows between African countries over the period 1980-2006. Their analysis accommodates significant number of zero trade flows between several African countries by using the Heckman correction method. The findings suggest that while the extent of market fragmentation is on average very high within the African continent, the border effects within SADC and ECOWAS are more in line with other international estimations. Their results indicate that border effects faced by intra-African trade are quite substantial: on average an African country trade 108 times more “with itself” than with another country on the continent. Border effects in SADC and ECOWAS are respectively about 5 and 3 times lower. The inclusion of the infrastructure indices contributes significantly to the result. Considering infrastructure is actually an interesting way to capture the effect of distribution networks which represent, along with imperfect information and localized tastes, relevant but generally omitted sources of resistance.

Djankov, Freud and Pham (2006) find that each day of delay at customs is equivalent to a country distancing itself from its trading partners by additional 85km. They also pointed out those long delays in customs procedures increase cost, not only in terms of opportunity cost, but also represent additional costs such as storage and wage charges. Sub-Saharan Africa records the highest number of procedures for exports and imports and the number of days to complete them according to the World Bank Doing Business (2010) report. Moreover, documents recorded include port filing documents, customs declaration and clearance documents, and official documents exchanged between the concerned parties. Time is recorded in calendar days, from start to finish of each procedure. Cost measures the fees levied on a 20-foot container in U.S. dollars. All the fees associated with completing the procedures to export or import the goods are included, such as costs for documents, administrative fees for customs clearance and technical control, terminal handling charges and inland transport.

Jordaan (2014) analyzed the impact of trade facilitation factors on South Africa’s exports to a selection of African countries using an augmented gravity model and founds . The results of the estimation revealed that an improvement in the customs environment within the importing country provides the largest gain in terms of increasing trade flows, followed by the regulatory environment and domestic infrastructure. Furthermore, adjacency and common language impact positively on South African exports, while distance between countries impacts negatively.

Cited in UNCTAD (2009), Njinkeu et al., stated that trade among African countries to a large extent hampered by trade costs other than tariffs. Therefore, the discussion below focuses on high trade costs as being the most important factor constraining intra-African trade, as they are higher in Africa than anywhere else. Table 7, illustrates that trading across borders is more cumbersome, slower, and costlier than in most other regions. While not specific to intraregional trade, these figures highlight the multiple obstacles to trade that endure on the continent, even though Africa is the region that is reforming its trade procedures most rapidly (World Bank, 2009).

Furthermore, Limao and Venables cited in Tuffour et al. (2016) stated that costs associated with transport and logistics affect trade. They can be customs fees and transit permit or relate to the time spent loading, unloading and transporting cargo across borders. The time and resources spent on completing the required paperwork, procuring permits and licenses and paying administrative fees all add to the total trade cost. For landlocked countries, these costs may be two or even three times as high. Higher cross-border administrative pecuniary and time costs can render any tariff reduction or elimination of NTBs ineffective. Poor hard infrastructure (transport and communication) and poor soft infrastructure (institutions and regulations) are also major determinants of the high costs and low levels of trade in Africa.

Table 7: Export and import procedures, time and cost for selected regions, 2009

Abbildung in dieser Leseprobe nicht enthalten

Source: UNCTAD (2009)

Being landlocked is an impediment to trade which reduces bilateral trade between partners. It also reflects the fact that landlocked countries incur high transportation costs to access the ocean via neighboring countries which raises trade cost and deters bilateral trade. In line with this notion World Bank doing business (2010) report shows that the cost to export and import in landlocked countries is very high and the time it takes to export and import is long. In Zimbabwe, Rwanda and Uganda, for example, it costs more than USD 3000 per container to export and import whereas in those countries with access to the sea such as Egypt, and Mauritius, the cost to export a container is relatively cheap (see table 8).

Table 8: Cost to import and export for selected COMESA countries (2010)

Abbildung in dieser Leseprobe nicht enthalten

Source: World Bank Doing Business (2010) cited in Seid, (2013)

Even though customs procedures are a cumbersome to intra-African trade most of the members of RECs of Africa are trading to the bordering or adjacent countries. Table 9 reveals some important bilateral export relationships between African countries that in turn signal the relevance of physical proximity for trade or so called neighborly or gravitational effects. For instance, in the north, Morocco was the main export destination for Algeria; most southern African countries, in particular Angola and Lesotho, counted South Africa as their biggest intra-regional export market; in the Indian Ocean islands, Madagascar was the most important export outlet for Comoros.

In the west, Nigeria took in more than three quarters of the exports of the Niger to Africa; Chad exported most of its products in Africa to its next-door neighbor, the Central African Republic, and to the east, about 46 per cent of Kenyan African exports were to its close neighbors, Uganda and the United Republic of Tanzania. This generally indicates that most African countries prefer to trade most with their neighbors than distant countries to reduce the cost associated with the distance between them. And, most of African countries are characterized with the lack transport infrastructure to move goods and services easily to remote countries.

Table 9: Intra-African exports, five main destinations by country, 2011

Abbildung in dieser Leseprobe nicht enthalten

Source: UNCTAD (2013)

Proximity reduces the transportation costs, time lags, decreases the magnitude of spoilage as well as the cost of gathering information about the partner’s legal and administrative procedures. Countries located close to each other are more likely to have a long history of bilateral trade, which gives them a better understanding of each other’ customs and tastes (Beata,2001).

Foreign Direct Investment (FDI) is considered be an important means of promoting export of the host countries though training the local work force and upgrading the technical and managerial skills. It helps in raising the efficiency and productivity of the factors and hence competitive strength in the international market. In addition to this, by facilitating access to large international market, FDI makes a significant positive contribution to the host country’s exports if FDI comes for efficiency reason and not for domestic market (Sultan, 2013).

In a world of increasing globalization, where political, economic and technological barriers are rapidly disappearing, the ability of a country to participate in global activity is an important indicator of its performance and competitiveness. In order to remain competitive, modern-day business relationships extend well beyond the traditional foreign exchange of goods and services, as witnessed by the increasing reliance of enterprises on mergers, partnerships, joint ventures, licensing agreements, and other forms of business cooperation. FDI may be seen as an alternative economic strategy, adopted by those enterprises that invest to establish a new plant/office, or alternatively, purchase existing assets of a foreign enterprise. These enterprises seek to complement or substitute external trade, by producing (and often selling) goods and services in countries other than where the enterprise was first established (EU, 2013).

Moreover, inward FDI can increase the host’s country’s export capacity, causing the developing country to increase its foreign exchange earnings. FDI can also encourage the creation of new jobs, enhance technology transfer, and boost overall economic growth in host countries. FDI causes spillovers through the transfer of knowledge to the host country. The spillover effect occurs when there is mobility of well-trained workers and managers from foreign firms to domestic firms and hence promotes economic growth by providing external capital (Etale, et al., 2016).

Ali & Goran (2007) estimated the potential effects of FDI inflows on exports in 12 Central and Eastern European (CEE) economies for the period between 1996 and 2004. They separated the effects of FDI into supply capacity-increasing effects²⁰ and FDI-specific effects²¹. Their empirical results indicated that, for all countries in the sample, FDI has increased domestic supply-capacity and hence exports. However, FDI-specific effects on exports are observed only in the new member states of the European Union.

Kenani (2014) investigated the impact of foreign direct investment (FDI), trade openness, inflation rate on the manufacturing exports performance of Tanzania over the period of 1980–2012 using the Ordinary Least Square (OLS) method and Vector Error Correction (VEC) model under the time series framework. The results from regression analysis revealed that FDI inflows and trade openness have positive impact on manufacturing exports performance of Tanzania while inflation rate negatively affect manufacturing export performance. Since, FDI, Trade openness, and inflation rate were found to be important factors in explaining the changes in manufacturing exports both in the short run and long-run. Sultan (2013) also examined the nature of relationship between export and FDI in India over the period 1980-2010 using Johansen co-integration method and finds a stable long run equilibrium relationship between FDI and export growth.

Foreign Direct Investment (FDI) now accounts for an important source of capital inflows into developing countries, increasing from 22 billion in 1990 to about 200 billion annually in recent years. Developing countries currently attract about one third of total global inward FDI, which in turn accounts for around 2.5 percent of developing country GDP. According to the World Bank, FDI is a particularly attractive source of global finance because it is generally less volatile than other capital flows and has other potential externalities, such as embodied technology (GEP, WB, cited in Harrison & Clare, 2007).

Aminian, Fun, and Iizaka (2007) studied on foreign direct investment, intra-regional trade and Production Sharing in East Asia using gravity model. They found that positive and statistically significant influence of FDI inflow on trade across the board indicating complementary relationship between trade and FDI inflow in Asia. However, the large variation exists in the magnitude of the impact of the variable between exports and imports, and across the four types of disaggregated data. Firstly, FDI inflow appears to have a much larger effect on total imports compared to exports. It shows that 1 percent increase in FDI inflow leads to 0.1 percent increase in the region’s exports, whereas it will lead to 0.24 percent increase in imports.

Mary, Dyana, and Won (2002) examined the relationship between U.S. FDI in and exports to foreign countries for the processed-food industry (SIC-20) by estimating a simultaneous equation system for FDI and exports. The analysis focused on East Asian countries-China, Japan, Singapore, South Korea, and Taiwan- from 1989 to 1998. Empirical results for the FDI equation indicated that interest rates, exchange rates, GDP, and compensation rates are important variables that influence U.S. FDI. Interest rates were Sound to negatively influence U.S. FDI in East Asian countries, consistent with their hypothesis that an increase in interest rates (the cost of financing) causes a decrease in investment. Exchange rates were found to positively influence FDI, supporting their hypothesis that as the dollar appreciates, it becomes relatively cheaper for U.S. firms to invest in foreign countries; thus FDI increases. Additionally, GDP was found to positively influence FDI, indicating that an increase in foreign GDP causes an increase in U.S. FDI in East Asian countries. However, their finding for compensation rates was not consistent with their hypothesis.

Studies have suggested two main mechanisms through which the presence of foreign-owned firms may impact on the export activity of host country firms. One mechanism is through intensified competition. Sectors with higher levels of international competition in the domestic market have been found to have domestic firms which operate closer to the technology frontier than sectors which do not. The presence of foreign-owned firms can increase competitive pressure on domestic firms, over and above what would have occurred through exposure to competition from imports. This leads domestic firms either to become more productive or to exit. A second mechanism through which inward FDI can exert a positive influence on the participation of domestic firms in export markets is through information flows which they generate about foreign markets and tastes. This information reduces the costs to domestic firms of investigating potential overseas opportunities, and so can enable more of them to export (Alexander et al, 2011).

Report of COMESA FDI inflows (2011) indicates that foreign direct investment inflows into COMESA region registered an 18% increase (Table 10). This was the first overall increase in total inward FDI since 2007. Major drivers for this growth were inflows into Libya, Democratic Republic of Congo, Mauritius and Uganda. Major FDI destinations such as Egypt and Sudan registered declines in FDI inflows in the same period. In terms of share in total FDI inflows, Egypt dominated the 2010 market share with 33% of all inflows followed by Libya, DRC and Sudan which accounted for 20, 15 and 15% respectively of total COMESA FDI inflows.

Table 10: COMESA Country FDI Inflows, in millions USD values

Abbildung in dieser Leseprobe nicht enthalten

Source: COMESA investment report, 2011

Abe (2002) investigated the effects of Japanese and Korean FDI on trade under the framework of a gravity equation. The estimated coefficients on FDI in the equations for trade and exports are positive and statistically significant in most years, indicating that Japan’s (Korea’s) FDI tends to promote its exports and overall trade. In the case of imports, the estimated coefficients are not statistically significant in most years.

In same way Liu, Wang, and Wei (2001) have investigated the causal relation between FDI and trade (exports and imports) in China using a panel of bilateral data for China and 19 home countries or regions on the horizon 1984–1998. The panel data methods were used to test unit roots and causality. The results showed a potential development for China: the increase in imports determines the increase in FDI from regions to China and increase in exports from China to regions or home country. An increase in exports determines the increase in imports.

But Simionescu (2014) has looked at the impact FDI on trade in two ways in the context of globalization process for G7 countries. Such as short run and long run causal relationship. The Granger causality tests for panel data reflected in period from 2002 to 2013 that there is only short run causality between FDI and exports and FDI and imports. There is unidirectional causal relationship on long run between FDI and trade. Moreover, short run causality in both senses was observed for FDI and trade in G7 countries on the considered horizon.

Zhang (2005) has investigated the impact of FDI inflows on the export volume of the host country in two ways depending on the relative strength of the country-specific factors. He noted that FDI inflows may encourage exports from the recipient country through better use of the additional capital, transfer of superior technology, up gradation of technical and management skills, access to newer markets etc. However, if the focus of FDI inflows is to target the host market by taking advantage of locally procured cheaper raw materials through transfer of outdated or inappropriate technologies, then export benefits might be limited.

To summarize, as it is clear from the above theoretical and empirical literatures, there is no straightforward conclusion found on factors hindering intra-regional trade flows among COMESA members. Since this study is concerned with the analysis of the determinants of intra-regional trade flows among COMESA members, we use a gravity model in order to investigate whether COMESA is effective in expanding trade among its members. This study also introduces the trade intensity index to measure the relative weight of a set of trade linkages among COMESA members, which explicitly reveals the relative importance of trade partner between two countries in the region.

Therefore, this calls for further investigation on factors hindering intra-regional trade among COMESA members by exploring other variables that only few literature reviews available and the fact that they might have an impact for the COMESA member states trade performance in addition to basic gravity variables like overlapping members of COMESA and IGAD, Foreign Direct Investment (FDI) inflows, electoral democracy and remoteness index have been included that most other researchers did not include in their analysis.

CHAPTER THREE

METHODOLOGY

This sub-section deals with methodology used in research. Research design, study area, data collection and sources, method of data analysis, model specification and estimation techniques, theoretical justification and priori sign and finally diagnostic test.

3.1 Research Design

In order to achieve the objectives of the study this research uses quantitative correlational design methods to analyze the factors that affect intra-regional trade among the Common Market for Eastern and Southern African member countries in the period 2000-2016. The quantitative data has been organized and analyzed variables using statistical and regression analysis of STATA 14.2 econometric package. An econometric model is formulated to test determinants of export trade using an augmented gravity model. Furthermore, the research also uses the trade intensity index which is used to examine the trade pattern and to see whether increased cooperation is possible between the members of COMESA.

In this study the panel data was estimated using three estimation techniques: pooled Ordinary Least Squares (OLS) regression model, and random effects model. The data was also estimated using Poisson Pseudo-Maximum Likelihood (PPML) estimation in accordance with Santos and Tenreyro (2006) by maintaining zero bilateral export values in order to check the robustness of the estimation (Jelena,& Łukasz, 2015).

The methodological sources consists of annual total bilateral export trade data obtained from IMF, Direction of Trade Statistics (DOTS), UN COMTRADE data base, World Integrated Trade Solution, World Development Indicators (WDI) database, CEPII , AU, UNECA, and Freedom House. The theoretical and empirical literature review as well as conceptual frame work of the study includes academic literature, scientific journals and articles, international organisations report.

A quantitative methodology used to determine whether, and to what degree, a relationship exists between two or more variables within a population (or a sample). The degree of relationships is expressed by correlation coefficients. Coefficients range from +1.00 to -1.00. Higher correlations (coefficients closer to +1.00 or -1.00) indicate stronger relationships. Positive correlations indicate that as the values associated with one variable go up, so do the values associated with the other. Negative correlations indicate that as the values associated with one variable go up, the values associated with the other go down (Apuke, 2017).

3.2 Study area

The study area encompasses the nineteen (19) member of Common Market for Eastern and Southern Africa (COMESA) among the eight regional economic community of Africa recognized by African Union. The study mainly focuses on the determinants of intra-regional trade among members of COMESA. One of the objectives of COMESA is to increase trade among its members to strengthen their economic growth and development through regional economic integration. Seid (2013) stated that in general the increase in intra-regional trade has not yet been as large as anticipated despite the existence of many regional economic communities (RECs) in Africa, which remains staggeringly low compared to other trade blocs in Europe, Asia and Latin America.

COMESA comprises 19 African member states spans Northern, Eastern and Southern Africa (see Figure 5) that came together with the aim of promoting regional integration through trade and the development of natural and human resources for the mutual benefit of all people in the region. COMESA was initially established in 1981 as the Preferential Trade Area for Eastern and Southern Africa (PTA), within the framework of the Organisation of African Unity’s (OAU) Lagos Plan of Action and the Final Act of Lagos. The PTA was transformed into COMESA in 1994. The PTA was established to take advantage of a larger market size, to share the region’s common heritage and destiny and to allow for greater social and economic co-operation (COMESA, 2013).

Furthermore, the COMESA member states agreed to create and maintain:

1. A full free trade area, guaranteeing the free movement of goods and services produced within COMESA and the removal of all tariffs and non-tariff barriers;
2. A customs union, under which goods and services imported from non-COMESA countries, will attract an agreed single tariff;
3. Free movement of capital and investment, supported by the adoption of common investment practices to create a more favorable investment climate for the whole region;
4. Gradual establishment of a payment union, based on a COMESA Clearing House and the eventual establishment of a common monetary union with a common currency, and
5. The adoption of a common visa arrangement, leading eventually to free movement of people from member states (Ibrahim and Obiageli, 2015).

Figure 5: Map showing location of the COMESA member states in the African continent

Abbildung in dieser Leseprobe nicht enthalten

3.3 Data collection and sources

This section presents variables and sources of data used in the study. The analysis covers a cross section of 19 COMESA member states and time series from 2000 to 2016. Hence, our panel data set consists of 5,814 observations of bilateral export flows (17x 342 country pairs). The list of COMESA members is reported in the appendix section. The researcher uses secondary data sources to collect information from the different international organizations data bases. This has been found appropriate because of the credibility and recognizable data availability for the research study for each member country in COMESA region. Document review technique was also used to collect secondary data necessary for the research study.

Annual total bilateral export trade data in thousands of US dollars was obtained from Direction of Trade Statistics (DOTS), UN Commodity Trade Statistics (UN COMTRADE) online data base, World Integrated Trade Solution. Annual GDP or populations of a country as a proxy for economic size are obtained from World Development Indicators (WDI) database. Data on bilateral distance sometimes transport cost to market, was obtained from the GeoDistance database provided by Centre d’Etudes Prospectives et d’informations Internationales’s (CEPII) as of December 2017 . Data on Distance is calculated using the Great circle distance formula for the distance between capital cities of the exporting and importing trading partners.

Cultural factors such as sharing the same language, geographical variables (such as being landlocked, being an island, or sharing a border), and Historical variables such as sharing the same colonial history were also collected from CEPII database as of December 2017. Policy variables such as belonging to a preferential trade arrangement and overlapping RTA membership were obtained from AU and UNECA. We opted for the Freedom House²² data base which distinguish whether the African countries considered have established an electoral democratic system. This indicator has five components: (i) type of elections (type of vote, majority or proportional and others); (ii) fair elections; (iii) the course of the election campaign; (iv) the possibility of political change; (v) transparency of political financing.

This study will attempt to find out major factors determining the intra-trade relations among the COMESA member countries by applying a gravity model to estimate an export equation among the countries by using a pooled panel of data for the period 2000 to 2016. The choice of the sample period in this study is influenced by the availability of standard data on all the variables used which will be obtained mostly from CEPII and other sources in the empirical analysis and important in using up to date of the data time periods which have not been studied so far. As time series data and cross section analysis do not control for individual heterogeneity and might give a bias estimation, a panel framework is designed to cover intra-trade variations among the COMESA members.

One of the econometric advantages in using panel data is that it offers more variability, more degree of freedom, and reduces the collinearity among the explanatory variables thus improving the efficiency of econometric estimates. Panel analysis also can measure the effects that are not detectable in cross-section and time series data. With an observation that spans both time and individuals in a cross-section; more information is available, giving more efficient estimates. The use of panel data allows empirical tests of a wide range of hypotheses. With panel data we can control for: Unobserved or unmeasurable sources of individual heterogeneity that vary across individuals but do not vary over time and omitted variable bias (Baltagi, 2005).

Baltagi cited in Kareem et al. (2016), noted that the panel specification is much more adequate as the extra time series data points gives more degree of freedom, results in more accurate estimates. A unique advantage of panel data is that the panel framework allows the modeling of the evolvement of variables through time and space which helps in controlling for omitted variables in form of unobserved heterogeneity which if not accounted for can cause omitted variable bias. In addition, with panel data, the time invariant unobserved trade effects can easily be modeled by including country specific effects such as time dummies, and thus avoiding the consistency issue.

3.4 Method of data analysis

3.4.1 Model specification

In this sub-section, we proceed to specify the models and estimation techniques that are used to measure and analyse the bilateral trade to addresses each specific research questions.

3.4.1.1 Measures of intra-regional trade intensity index

Introduction of Free Trade Area (FTA) among regional economic groups in Africa, by reduction of tariff and elimination of non-tariff barriers is supposed to promote intra- regional trade and economic integration. However, intra-regional trade between African countries and especially between members of the same economic unions did not produce a larger market as expected. Therefore, to answer the first specific research question, I specify a model of relative intra-trade intensity measure between COMESA member states using the trade intensity index which is used to examine the trade pattern and to see whether increased cooperation is possible between the members of COMESA. Trade Intensity Index is calculated for member countries of COMESA for the period 2000 to 2016 taking data from Direction of Trade Statistics (DOTS), World Development Indicator (WDI). An index value of one indicates bilateral trade is following the pattern of rest of the world and the value above one show there is trade intensity between partners.

According to Hyun and Hong (2005) the measure can be expressed as follows:

Abbildung in dieser Leseprobe nicht enthalten

Where X ij is country i ’s exports going to country j. The numerator indicates the share of country i ’s export to country j in total export of the country i, and the denominator indicates the share of country j ’s imports of the total world imports. If the bilateral trade intensity index has a value greater than 1, the export of country i outperforms in country j considering country i ’s export ability and country j ’s import capacity. It implies that country j is relatively important to country i. If the bilateral trade intensity index has a value smaller than 1, country j is not relatively important for country i ’s export.

Trade intensity index measures the “pure” intensification of trading relationship. An increase in trade with a country may be attributable to two factors. One is the expansion of trade by a trading partner and the other is “pure” intensification of the trade relationship. Specifically, trade relationship of a country with (or trade dependency of a country on) a trading partner country can increase when the trading partner’s trade expands faster than other countries. Trade intensity index captures the “bias” in bilateral trade relationship by considering the trade volume of the trading partner. Trade relationship is more (less) intensive (or biased) than normal if the value of trade intensity is greater (less) than unity (Urata & Okabe, 2007).

Computation of trade intensity indices provides a convenient approach for describing the geographic distribution of country trade and for analyzing the strength of bilateral trade ties between countries. Several indicators have been used in empirical examinations of international trade to measure the tendency for countries to trade. These indices gauge the level of trade against the size of economies, and other structural characteristics considered. (e.g., distance between the countries) important in determining trade levels (Edmonds. and Li, n.a).

3.4.1.2 Empirical specification of the augmented gravity model

Gravity model is the workhorse in international trade, and one of the most popular and successful frameworks in economics. Many researchers have been used the gravity equation to study and quantify the effects of various determinants of international trade. This is because of the following reasons; First, the gravity model of trade is very intuitive. Using the metaphor of Newton’s Law of Universal Gravitation, the gravity model of trade predicts that international trade (gravitational force) between two countries (objects) is directly proportional to the product of their sizes (masses) and inversely proportional to the trade frictions (the square of distance) between them.

Second, the gravity model of trade is a structural model with solid theoretical foundations. This property makes the gravity framework particularly appropriate for counterfactual analysis, such as quantifying the effects of trade policy.

Third, the gravity model represents a realistic general equilibrium environment that simultaneously accommodates multiple countries, multiple sectors, and even firms. As such, the gravity framework can be used to capture the possibility that markets (sectors, countries, etc.) are linked and that trade policy changes in one market will trigger ripple effects in the rest of the world.

Fourth, the gravity setting is a very flexible structure that can be integrated within a wide class of broader general equilibrium models in order to study the links between trade and labour markets, investment, the environment, etc. Finally, one of the most attractive properties of the gravity model is its predictive power. Empirical gravity equations of trade flows consistently deliver a remarkable fit of between 60 and 90 percent with aggregate data as well as with sectoral data for both goods and services (Yotov, et al., 2016).

The gravity model is a popular empirical approach to trade that has been used widely for analyzing the impact of different trade policy issues on bilateral trade flows between different geographical entities (William & James, 2007). Also, the gravity model provides a simple but robust approach to identify the main factors influencing trade among the countries (Greenaway and Milner, 2002) . The gravity model of trade has been present in economic literature since 1960’s. The early concepts were presented by Tinbergen (1962), Poyhonen (1963) and Linneman (1966), who employed in their analysis of trade assumptions like the Newtonian gravity concept. It stated that the gravitational force depends on the “masses” of entities and “distance” between them.

In trade analysis, the common proxies of “mass” are country GDP and/or population, whereas “distance” used to be interpreted mostly as physical distance between capital cities or main economic regions (Kandogan, 2009). The standard gravity model can be specified as the flow of bilateral trade between the two partner countries at specific time in which the products of the two GDPs are positively related and the distance (proxied as cost) between them are negatively related. The simplest version of the gravity model can take the following form:

Abbildung in dieser Leseprobe nicht enthalten

In a log-linear form it is written as follows:

Abbildung in dieser Leseprobe nicht enthalten

In equation (6), , β1, β2 and β3 are coefficients to be estimated, while εij is the error term which captures other shocks and chance events which might influence bilateral intra-trade between the two trading partners. This study is based on a theoretical foundation for the gravity equation of Anderson (1979). However, the model will be modified or extended by adding appropriate dummy variables such as FTA, landlocked countries, democracy, adjacency, overlapping members with other regional bloc and language to capture the influence of these factors on the trade flows. Therefore, the gravity model will be used to explain conditions for the dynamism of intra-regional trade within COMESA region. The estimated econometric formulation can be written in a log-linear form as follows:

Abbildung in dieser Leseprobe nicht enthalten

Where:

- Xij is trade flows from exporter i to importer j between each member states of COMESA country (i) and its trading partner j at a time t in million US dollars;
- ao is the portion of the intercept that is common to all years and the trading countries or specific time effect that control for omitted variables that are common for all trade flows which vary over time,
- GDPi and GDPj is the Gross Domestic Product of the exporting (importing) country as a proxy for economic size (measured as the GDP of the respective countries in millions of US dollars’ terms) at a time t,
- Dij is the geographical or economic distance between the exporter i and importer j countries measured in kilometers between the capital cities,
- POPij is population size of the exporting country in year t,
- POPjt is population size of the importing country in year t,
- FDIit: Foreign Direct Investment inflows to country i at time t in million US dollars’ terms,
- t -time period t: 2000 - 2016

Specific Dummy Variables :

- ADJij, if the two countries share a common border it takes value 1 and otherwise 0
- LANGij, if these two countries share a common language it takes values 1 otherwise 0
- LLij, whether an exporter country is landlocked. It takes a value of unity if an exporter country is landlocked and 0 otherwise
- DEMOi , is the democracy variable which takes 1 if country i has a democratic electoral system and 0 otherwise. The theoretical results state that good governance has a positive effect on trade (Didier and Hoarau, 2014). Indeed, country characterized by a bad governance shows high transaction costs resulting in insecurity for trade then limiting its trade capabilities.
- FTAij is a dummy variable indicating whether an exporting country is a member of Free Trade Area within COMESA. If a country is a member of FTA it takes value 1 and otherwise 0.

One important dummy variable to be considered in the model is about trade creation effect of overlapping members in RTAs. The objective of establishing regional integration is to benefit from trade creation by shifting production of some goods from a less efficient member to a more efficient member. Trade creation may result from a shift of domestic consumption from high-cost domestic products to low-cost products from a partner country because of elimination of trade barriers. Thus, trade between partner countries increases in accordance with international comparative advantage. An example of multiple is that both Ethiopia and Kenya are members of IGAD and COMESA. If the trade creation from multiple RTAs is larger than that from exclusive ones, it may work as an incentive for the enlargement and eventual merging of RTAs. Thus, the research investigates the trade creation effects between a member that joins multiple RTAs and another member that does not.

Following the methodology adopted by Lee, et al. (2008) and Sannassee, .et al. (2011) introduced in the next equation, I try to investigate the intra-trade effect between members that join multiple RTAs (members that join COMESA and IGAD) and those of member of single RTAs. In this setting of the estimation, the new dummy variable, RTAm, captures just the additional trade creation-taking place between an overlapped country and a member country not overlapped together. This investigation answers whether there is any incentive for multiple RTAs to eventually merge, thereby leading to a globally free market. We use a dummy variable RTAm to indicate multi-membership. RTAmijt is a binary variable which is unity if both i and j belong to the same RTA, and either i or j exclusively belongs to another RTA with other countries. (RTAs multiple memberships and those are included for each of the two African RTA under study, i.e RTAmCOMESA, and RTAmIGAD).

One obvious challenge with the estimation of gravity equation (3) after it changed to linear form, is that the multilateral resistance terms Pj,t and Pi,t are theoretical constructs and, as such, they are not directly observable by the researcher and/or by the policy maker. Baldwin and Taglioni (2006) emphasize the importance of proper control for the multilateral resistance terms by characterizing studies that fail to do that as committing the “Gold Medal Mistake”. As demonstrated by Anderson and Van Wincoop (2003), failure to account for the multilateral resistance terms with remoteness indexes may lead to severe biases in the estimates of the gravity variables. Therefore, to get unbiased and best estimates of the gravity model we need to consider remoteness index based on Anderson and Van Wincoop (2003) after approximating the multilateral resistance terms to “remoteness indexes” constructed as functions of bilateral distance, and Gross Domestic Products (GDPs) (Yotov et al, 2016). See equation 9 and 10.

After considering the remoteness indexes, the empirical gravity equations can be written in the log-linear dynamic panel data model form as:

Abbildung in dieser Leseprobe nicht enthalten

Where the new covariates on the exporter side, and on the importer side, are constructed, respectively, as the logarithms of output and expenditure weighted averages of bilateral distance. In other word, a formula that measures a country’s average weighted distance from its trading partners (Head, 2003), where weights are the partner countries’ shares of world GDP (denoted by GDPw).

Abbildung in dieser Leseprobe nicht enthalten

Where the numerator would be the bilateral distance between two countries, and the denominator would be the share of each country’s GDP in the rest of the world’s GDP. Cited in Herrera (2010) Head and Mayer states that remoteness variable describes the full range of potential suppliers to a given importer, taking into account their size, distance and relevant costs of crossing the border.

In structural gravity models, one important departure from the analogy with Newtonian gravity is the multilateral resistance terms (MRT) capturing general equilibrium forces. As underlined by Anderson & Van Wincoop (2003), the more a country is resistant to trade with one country, the more it shall trade with the others (including itself) as a general equilibrium effect. Omitting these MRT in the estimation strategy is what (Baldwin & Taglioni, 2006) call the gold medal error, terms which are by construction correlated to the explanatory variables (Poissonnier, 2016).

3.4.2 Estimation techniques

After the stage of data collection, data must be organized, edited, coded and processed for interpretation. The empirical research study uses quantitative data analysis method for nineteen (19) member states of COMESA for a time of sixteen (17) years. The quantitative or numeric data has been organized and analyzed using statistical and regression analysis. Equation (4) is a ratio analysis, which is used to determine whether the value of trade between two countries is greater or smaller than would be expected based on their importance in world trade. Apart from measuring the performance of bilateral trade in terms of growth rates, one can also measure the trade intensities between two countries to see the trajectory of trade over the years and the orientation of a country with its trading partner (Kumar, and Nath, 2007).

However, equation (8) was analyzed using STATA 14.2 econometric package to estimate the determinants of intra-regional trade flows in COMESA region. Before 1990`s most of the researchers used cross-sectional data for the estimation techniques and recent studies used panel data (Baltagi et al. 2015). But there are no one common accepted estimation techniques in all research studies. In this study the panel data was estimated using three estimation techniques: pooled Ordinary Least Squares (OLS) regression model, and random effects model. The data was also estimated using Poisson Pseudo-Maximum Likelihood (PPML) estimation in accordance with Santos and Tenreyro (2006) by maintaining zero bilateral export values in order to check the robustness of the estimation (Jelena, and Łukasz, 2015).

The fixed effects and random effect estimation techniques are the two common techniques used in fitting the data. The fixed effect assumes that the unobserved heterogeneity is correlated with the error term. In contrast, the random effect assumes that the unobserved heterogeneity is strictly exogenous i.e. it does not impose any correlation between the unobserved heterogeneity (individual effects) and the regressors. Under the null hypothesis of zero correlation, the random effect model is efficient; both models are consistent, but the random model is more consistent. If, however the null hypothesis is rejected, the fixed effect is consistent, and the random effect is neither consistent nor efficient. There are however, some drawbacks in the fixed effect model in the sense that all time invariant explanatory variables (are deeming to be perfectly collinear with the fixed effects) would be dropped from the model. Consequently, fixed effect model eliminates some important theoretically relevant variables from the gravity equation which are distance, common language, common borders, and the effects of these variables cannot be established (Kareem et al., 2016).

3.4.2.1 The issue of zero-trade flow suggestion

During bilateral trade flow estimation using gravity model a zero trade may exist each year between two given countries and affects all gravity estimation techniques which stems from the log-linear standard form of estimating a gravity model . Some authors proposed to conserve the log-linear form but deleting these observations or substituting them by a low positive value as 0.5 or 1 (Bénassy-Quéré and Amina, 2003). Additionally, Santos and Tenreyro (2006) suggest that, Pseudo Poisson Maximum Likelihood (PPML) estimator can be a solution to the zero-trade problem in estimation of gravity model which has been theoretically more sound approach. Therefore, with the existence of zero-valued trade flows among members, this paper performs Pseudo Poisson Maximum Likelihood (PPML) estimator. It allows for the inclusion of zero-valued trade flows and for controlling for the unobserved heterogeneity between countries.

The gravity equation can be estimated through various econometric estimation techniques such as; OLS, GMM, MLE, fixed, random effects and the latest approach, the PPML. The main challenges of applying OLS and GMM in estimation of the gravity model is how to deal with zero trade values reported, and how to isolate the effects of regionalism from the effects of other factors on the intra-regional trade. This is due to the fact that estimation using these techniques requires transformation of the gravity equation into a log–linearized form, yet the logarithm of zero is undefined, leading to biased and inconsistent results. PPML approach is superior due to its ability to maintain the gravity equation in its multiplicative form hence resulting in unbiased and consistent results. Additionally, PPML estimation techniques is superior in estimation of gravity model of trade and give reliable and robust results, despite the common characteristic of bilateral trade where some data may be zero in some periods Ouma (2016).

According to Shepherd (2016) the Poisson estimator has a number of desirable properties for applied policy researchers using gravity models. First, it is consistent in the presence of fixed effects, which can be entered as dummy variables as in simple OLS. Second, the Poisson estimator naturally includes observations for which the observed trade value is zero. Such observations are dropped from the OLS model because the logarithm of zero is undefined. However, they are relatively common in the trade matrix, since not all countries trade all products with all partners (see e.g., Haveman and Hummels, 2004). Dropping zero observations in the way that OLS does potentially leads to sample selection bias, which has become an important issue in recent empirical work. Thus the ability of Poisson to include zero observations naturally and without any additions to the basic model is highly desirable. Third, interpretation of the coefficients from the Poisson model is straightforward, and follows exactly the same pattern as under OLS. Although the dependent variable for the Poisson regression is specified as exports in levels rather than in logarithms, the coefficients of any independent variables entered in logarithms can still be interpreted as simple elasticities.

3.4.3 Theoretical justification and priory sign

The GDPs of each member of COMESA countries provide a standard way of capturing the “mass” (i.e. economic size) of the countries in the gravity model. The larger the GDP of an exporting country (GDPi), the larger its production capacity, the more likely it is to attain economies of scale and increase its exports supply based on its comparative advantage. In the same way, the a larger the GDP of an importing country (GDPi) is an indicative of the existence of larger income and higher ability to demand more imports goods and services from the member countries of COMESA. This shows that the growth of the GDPs of the member countries increases the flows of trade between them. Hence, we expect estimated coefficients of both GDPs in the structural gravity model will be positive. The more populous a country is, the greater its tendency toward self-sufficiency and therefore the less it’s active in trade. Hence, the coefficient for the exporter`s and importer`s population will be negative.

In gravity model, geographical distance is a resistance factor and has a negative impact on volume of bilateral trade. As the distance between the exporting and importing countries becomes larger, exports will fall. The distance is a factor, which is used as a proxy to consider the impact of transport costs and other transaction costs. The physical distance between the capitals of the trading partners captures the attribute of transport cost. Indirectly that is also a reflection of the effects of infrastructure. If there is a poor state of physical infrastructure, then that will have an impact on the costs associated with moving goods using such an infrastructure (Kalaba & Kirsten, 2012).

Additionally, a number of variables are generally used to capture trade costs in gravity model specification. Typically, empirical studies proxy trade costs with bilateral distance. However, a number of additional variables are also customarily used. These include dummies for islands, landlocked countries and common borders. They are used to reflect the hypotheses that transport costs increase with distance and that they are higher for landlocked countries and islands but are lower for neighbouring countries. Dummies for common language, adjacency or other relevant cultural features such as colonial history are used to capture information costs. Search costs are probably lower for trade between countries whose business practices, competitiveness and delivery reliability are well known to one another. Firms in adjacent countries, countries with a common language or other relevant cultural features are likely to know more about each other and to understand each other’s business practices better than firms operating in less-similar environments. For this reason, firms are more likely to search for suppliers or customers in countries where the business environment is familiar to them (UNCTAD and WTO, n.a).

One of the major barriers to trade flows is higher transportation cost. As a result, coefficient of distance is expected to be negative. Proximity reduces transportation cost. Distance is a trading resistance factor that represents trade barriers such as transportation costs, delivery time, cultural unfamiliarity and market access barriers. Among other factors, higher transportation costs reduce the volume of trade and increase information costs. Countries with short distance between each other are expected to trade more than those who are wide apart because of reduced transaction costs.

As the existence of a common border between the exporter and importer facilitates trade performance, therefore we expect the elasticity of common boarder to be positive. The colonization variable (LANGij) which takes 1 if countries i and j have in common the same colonizer, and 0 otherwise. This determinant shows the fact that a common colonial history affects positively bilateral trade. Countries enter in to RTAs with the objectives of increasing trade. Frankel, Stein & Wei, (1995) explained in their study as cited by Assefa (2014) regional trade agreements are intended to reduce tariffs and other barriers to trade between countries. Hence, they are likely to have a positive effect on trade among members. Xu Wang (2016) also added that the efficiency in communication can facilitate trade flows between countries. Language barriers between countries are expected to cause obstacles in business communication and therefore reduce the chance of trading. Therefore, a positive sign is predicted for the estimated coefficient for this variable.

Additionally, Martinez & Nowak cited in Assefa (2014) came up with two contradicting signs of the coefficient of population that are theoretically ambiguous. The population coefficient of the exporting country be negatively or positively signed, depending on whether the country exports less when it’s big (absorption effect) or whether a big country exports more than a small country (economies of scale). For similar reasons, the population coefficient of the importing country may also assume a positive or negative sign. The Landlocked variable (LL) takes 1, if an exporter country is landlocked and 0 otherwise. To be landlocked is an impediment to the international trade so a negative effect is expected.

Some observers like Mwangi, et al. (2016), suggest that multiple memberships are hindering regional integration and by extension, intraregional trade rather than enhancing it. They point out that multiple memberships impose high costs in time, energy and resources on African governments and force them to juggle competing regulations. Therefore, we expect a negative value for the coefficient associated with the variable RTAmijt, which imply that if a member forms another RTA, by creating overlapping RTAs, its additional trade with members of existing RTA(s) or with members of new RTA(s) is less than the additional trade formed between members belonging to a single RTA.

3.4.4 Diagnostic test

Test for Heteroscedasticity

In this study we have tested for heteroskedaticity using Breuch pagan test. The null hypothesis is homoskedasticity (or constant variance of error) and the alternative hypothesis is that there is heteroskedasticiy.

Breusch-Pagan / Cook-Weisberg test for heteroskedasticity results are as follows:

Abbildung in dieser Leseprobe nicht enthalten

We are forced to reject the null hypothesis of homoskedasticity hence; the assumption of heteroskedaticity is satisfied which indicate the presence of Heteroskedasticity across the panels.

Test for Multicollinearity

Multicollinearity occurs when independent variables in a regression model are correlated. This correlation is a problem, if the degree of correlation between variables is high enough, which can cause problems when we fit the model and interpret the results. To avoid the high collinearity among independent variables from the model, we have to test multicollinearity in this study. As indicated in the appendix 7.5 of the correlation matrix, the correlations among explanatory variables are weak which is less than 1; indicating the non-existence of multicollinearity problem in the data.

A value of 1 indicates that there is no correlation between this independent variable and any others. Correlation matrix between 1 and 5 suggest that there is a moderate correlation, but it is not severe enough to warrant corrective measures. Correlation matrix greater than 5 represent critical levels of multicollinearity where the coefficients are poorly estimated and the p-values are questionable (Frost, 2019).

CHAPTER FOUR

RESULTS AND DISCUSSIONS

4.1 Trends and patterns of intra-trade in COMESA

This part discusses the reviews of COMESA`s trends and patterns of intra-trade using up-to-date trade data to understand the background of the study in a better way before we proceed to the next data analysis. It reviews the major trade patterns, and compositions of the members.

The Direction of Trade Statistics (DOTS) presents the value of merchandise exports and imports disaggregated according to a country's primary trading partners. Imports are reported on a cost, insurance and freight (CIF) basis and exports are reported on a free on board (FOB) basis. Kenya is the highest exporting country from 19-members of COMESA trading bloc, which is mainly comprised agricultural products, especially tea and tobacco, animal products and it’s also emerged as the top performer in the EAC and serving as the largest trade market in the east African countries.

According to Table 11, all members of COMESA intra-export values were varying across the years. Kenya and Egypt dominate the intra-export values in the region. Kenya exported goods valued at $1,459.2 and$1,553.4 million in 2008 and 2015 respectively. After Kenya, the second largest share of export country within COMESA members was Egypt which valued at $1,084.0 and $1,654.0 million in 2008 and 2015 respectively. Between these years the two countries were also characterized by higher GDP and FDI inflows which indirectly encourage the production of goods and services to export to other members. Democratic Republic of Congo was the third country in selling goods valued at $578.2 million in 2008 which has been increased to $999.6 million in 2015.

Table 11: Intra-Export within COMESA Bloc in million US Dollar value (2008-2015)

Abbildung in dieser Leseprobe nicht enthalten

Source: Author, compiled from IMF, DOTS accessed on December/2017.

But the rest of the countries like Burundi, Comoros, Eritrea, Swaziland and Seychelles were relatively insignificant and recorded the least intra-export trade within the region. The total export worth in 2015 for the three countries was almost the same as Egypt which could lower the total trade patterns of COMESA against other regions in the world. At region level intra-export of COMESA rising from 5,844 million in 2008 to 11,693 million in 2013. This shows some dynamism which brings positive news for boosting intra-COMESA trade that could come from the increasing export level by other members, although more detail investigation of the composition of such trade is needed at disaggregate level. However, the performance of intra-regional export trade declined to 9,099 million in 2015. See Figure 6.

Figure 6: COMESA total intra-export in million US Dollar from 2008 -2015

Abbildung in dieser Leseprobe nicht enthalten

Source: Author, computed based on IMF, (DOTS), 2017

Table 12: Intra-Import within COMESA Bloc in million US Dollar value

Abbildung in dieser Leseprobe nicht enthalten

Source : Author, compiled from IMF, DOTS accessed on December, 2017.

As indicated on Table 12, the top three intra-import countries within COMESA bloc were Zambia, Zimbabwe and Democratic Republic of Congo. Accordingly, Zambia intra-import worth increased from $836.7 million in 2008 to $1,513million in 2016. In the same way, Zimbabwe was the second performer in intra-import in which its intra-import increased from $1164.7 to $1,237 million in 2008 and 2016 respectively. Democratic Republic of Congo who has been facing internal political instability stood third which import goods valued $ 679.5 million in 2008 and $945 million in 2016. Even though countries like Comoros, Eritrea, Swaziland and Seychelles are not landlocked their role in intra-import trade within the region has been inadequate in which Zimbabwe`s intra-import was more than two time their total intra-import for each year.

Figure 7 shows the levels of intra-COMESA trade between 2000 and 2016. Both intra and extra COMESA trade has been growing slightly between 2000 and 2013. However, after the year 2013 the intra-trade performance of COMESA declining swiftly. In 2000 COMESA`s intra-export was $1,499 million, which grew to $11,693 million in 2013 registering an exponential growth of 11 percent per annum. However, the intra-export trend region was declining from 11,693 to 7,854 million in 2013 and 2016 respectively. The trend of COMESA’s intra-imports among its members has shown a similar trend. COMESA’s intra-import was $1,293 million in 2000, increased to $11,143 million in 2013, showing an annual exponential growth of 10 percent which was by 1 percent lower than intra-export between the same years. But after 2013 the intra-import of COMESA had been slow down from 11,143 to 8,184 million in 2013 and 2016 respectively.

Figure 7: Trends of Intra-Export and Import of COMESA

Abbildung in dieser Leseprobe nicht enthalten

Source: Author, calculated from IMF, DOTS accessed on December, 2017

Table 13: Top ten COMESA’s Intra-Export, Import and Export Commodities from/to the world measured in percentage (2013-2016)

Abbildung in dieser Leseprobe nicht enthalten

Source: Author, compiled from International Trade Center, accessed on March, 2018.

Table 13 shows COMESA`s intra-trade within its members and global import and export of trade in commodities from/to the world from 2013 to 2016. Except for mineral fuels, mineral oils, Coffee, tea, and spices, Sugars and sugar confectionery the intra-trade performance of COMESA for all the products were decreased in relation to global trade. Sugars and sugar confectionery constitute relatively increased by 3.22 percent from 2013 to 2016 which higher in intra-COMESA export trade compared to other products. Coffee, tea, and spices followed by 2.94 percent growth. However, among the products traded between the COMESA members, Ores, slag and ash constitute the largest share followed by Coffee, tea, and spices. In 2016 around, 11.16 percent of the total commodities traded within COMESA were Ores, slag and ash.

Looking at the performance of COMESA in the global import and export, Mineral fuels, mineral oils and their products took the largest share which was 12.62 and 15.73 percent in 2016 followed by Machinery, mechanical appliances, nuclear reactors, boilers for import and Coffee, tea, and spices for export which were 9.87 and 5.53 percent respectively in 2016. The structure of intra-African trade, not surprisingly and like Africa’s trade with the rest of the world, has concentrated on few primary commodities. The African countries’ exports to each other are concentrated on primary commodities and in case of the limited trade in manufactured these could largely be attributed to South Africa, Egypt and other North African countries. In terms of intra-group imports within RECs, the main products are: (a) primary commodities (petroleum oils, vegetable oils, vegetable oils and fats, copper ores), and some (b) manufactures (tobacco, edible products, lime, cement). Imports of machinery and transport equipment are present in SADC owing to South Africa’s exports of the items. In other RECs, such essential items for supply and productive capacity building are imported from outside Africa (UNCTAD, 2012).

Intra-COMESA trade in 2016 (Figure 8) was dominated by agricultural goods, especially coffee, tea, Spices, Sugar, Sugar confectionaries and ores, ash, slag which together accounted for about 26 per cent of intra-export trade. The major exporters of Coffee in the region were Ethiopia, Uganda and Kenya while tea is mainly exported by Kenya, Malawi, and Uganda. Ores and minerals were mainly exported by DR Congo, and Zambia copper; Sudan, Egypt, Zimbabwe and Ethiopia for gold. This review has shown that although there has been an increased levels of intra-COMESA trade since the establishment of FTA, they remain rather low in relation to other regional trade blocs, which still dominated by primary agricultural products. To be competent enough and expand market through its members as well as to the rest of the world member countries needs to work hard to diversify on investing new products which have comparative advantage.

Figure 8: Intra-COMESA Top Exports by Product Category 2016

Abbildung in dieser Leseprobe nicht enthalten

Source: Author, compiled from International Trade Center, accessed on March, 2018

Figure 9 provides a review of intra-regional trade of COMESA between 2000 and 2016 at an aggregate level. The overall intra-regional trade as a share of total trade has remained below 12 and grew by an average of 6 percent throughout this period. Intra-COMESA trade was unsatisfactory as per the expectation level in relation to its global market. The share of intra-trade remains very small in global markets, and the volume was fluctuating over a time. During the period 2000 to 2001 COMESA`s intra-trade share in relation to its overall world merchandise exports slightly increased only by 1%. These aggregate figures turn out to be low due to low intra-trade patterns of some COMESA members like Comoros, Eritrea, Swaziland, and Seychelles against their world trade.

However, during the year 2002 and 2003 the share remains constant. From the year 2004 to 2007 the intra-trade share decreased by 59 percent while the global market performance of the members increased. This shows that most of the member countries of COMESA were trading with the rest of the world than their regional trade. However, during 2008 to 2015, the intra-COMESA merchandise trade has been slightly increased by 6 percent of the region’s total world trade and finally after the year 2015 the intra-trade share of the region was sharply declining by 80 percent. Despite the trade progress, intra-COMESA exports from 2000 to 2016 were only less than a quarter of the world’s total merchandise exports. More than 80 percent of the regional export trade exchanged with other regions of the world.

Figure 9: Share of intra-regional trade in total trade of COMESA (2000–2016)

Abbildung in dieser Leseprobe nicht enthalten

Source: Author , calculated with data from IMF, DOTS, accessed on December, 2017.

One of the Africa`s Regional Economic Communities (REC) focus area for accelerating regional integration has been through increasing intra-regional trade. As we can see from the Table 14 below the intra-regional export performance of COMESA were very low against the global trade. Approximately, more than 85 percent of share of exports from COMESA trade outside of their region. The intra-trade performance among COMESA was fluctuating which has declined from 2004 to 2008 but began to increase at small rate from 2012 to 2016. In 2004 the intra-COMESA export valued at USD 2,352.1 billion which was 6.1 percent of its global exports share. The intra-export share of COMESA against global trade between 2004 and 2016 was increased by 6.9 percentages from 6.1 percent in 2004 to 13 percent in 2016.

Even though the share of intra-trade has increased at slow rate the share of intra-regional exports in the global export remains small which shows integration among the members were weak. Among members of COMESA, Kenya dominated the intra-export trade by providing three-quarters of the total regional trade. Most of the member export agricultural products within their region and to global market which have low cost value in relation to the rest of the world. Their main exporting partners were United States, Europe, China and United Arab Emirate.

Table 14: COMESA Intra-Regional and Global Exports in million USD (2004-2016)

Abbildung in dieser Leseprobe nicht enthalten

Source: Author, compiled from IMF, (DOTS) accessed on December, 2017.

Intra-regional import is the other counterpart of intra-regional export as these indicators measure the importance of intra-trade from the importing member countries. A useful indicator of integration compares the value of intra-regional imports to the total value of all goods imported. Like intra-export indicator, intra-import was also expressed as a percentage, which can be calculated annually for each member country and for global import. This is an indicator of the relative importance of intra-regional imports within the total import market of each member country (David & Zainal, 2003).

The share of intra-import performance of COMESA increased between 2000 and 2016 and the global intra-import share also increased by 2.0 percent which was very low (Table 15). This lowness of intra-import indicates that most of the member countries of each COMESA were importing manufactured products from non-member countries especially from United States, Europe, China and United Arab Emirates. It shows that the pace of integration through trade has slowed down for COMESA. The total export with the rest of the world retained an increasing trend by recording a growth of 6.0 percent to US$ 62,945.3 million in 2016 compared to US$ 38,744.9 million in 2004.

Table 15: COMESA Intra-Regional and Global Imports in million USD (2000-2016)

Abbildung in dieser Leseprobe nicht enthalten

Source: Author, compiled from IMF, accessed on December, 2017

For any Regional Trade Agreement (RTA) to be successful, it is imperative on partner countries to have complementary trade structure to be exploited for mutual benefit. Countries which got complementary trade structure are likely to trade more whereas economies with similar trade structure often struggle to improve trade share unless there is substantial intra industry trade (Chandran, 2010). But most of the African countries produce similar primary commodities which have been facing with problem of competition and low price in the global market.

In between 2000 and 2016, COMESA’s intra-export share increased by only 5.3 percentage points from 4.9 percent in 2000 to 10.2 percent in 2016 and trade with rest of the world decreased by 5.3 percent from 95.1 in 2000 to 89.8 percent in 2016. According to UNCTAD (2012) the contribution of the regional economic communities in Africa towards intra-regional trade expansion has been negligible as the share of intra-regional trade remains static. African countries remain on the margins of global trade flows. In 2008 and 2009, Africa accounted for an insignificant 3 percent of global exports and imports as compared to about 6 percent for developing America and a massive 27–30 percent for developing. Even the 10 ASEAN countries together accounted for around 6 per cent of global trade, twice as high as Africa’s share.

MO Ibrahim Foundation (2014) found out that, compared to other regions in the world, intra-African trade is lagging. Between 2007 and 2011, the average share of intra-African exports in total merchandize exports was 11 percent compared with intra-regional trade of 50 percent in developing Asia,21 percent in Latin America and the Caribbean and 70 percent in Europe. One of the reasons that make lower intra-trade among Africa is that most of the commodity-rich countries have historically traded primarily outside of Africa due to a legacy of colonial history, and other factors. Table 16 also verifies this concept in which most of the COMESA members trade goods and services more with outside their member states than their regional groups.

Table 16: COMESA Intra and extra-Export Merchandise Trade measured in percentage

Abbildung in dieser Leseprobe nicht enthalten

Source: Author, compiled from UNCTAD accessed on February, 2017

Figure 10 shows the causal relationship between FDI inflows to COMESA and intra-exports in million USD from 2000 to 2016. Looking at the figure the growth relationship between intra-trade and FDI inflows were positive and significantly increasing from 2000 to 2007. However, from 2007 to 2011 FDI inflows sharply decreasing while intra-export increases with slow rate. In the same way the two variables were also negatively associated from 2014 to 2016. This implies that during these periods the inward flows of FDI to COMESA had been decreased which negatively affects the intra-export volume of the region among its members. In other words intra-export trade of the COMESA was encouraged by local business production and government owned enterprises.

Figure 10: Relationship between intra-export and FDI inflows within COMESA

Abbildung in dieser Leseprobe nicht enthalten

Source: Author, computation based on WDI data

4.2 Presentation and discussion of the estimation results

4.2.1 Trade Intensity results and discussions

In this section an attempt is made to construct the trade intensity index for the members of COMESA and to see whether the trade cooperation between these two trading partners are strong or weak. Furthermore, an analysis of trade intensity indices is used to estimate the extent of bilateral intra-trade linkages between members of COMESA individually and for the region as whole during the period 2000-2016 to answer objective one of the study. The trade intensity analysis technique appears suitable for this purpose. This technique is characterized by simplicity as well as the ability to identify the bilateral/multilateral trade linkages in clear terms (Qadri, 2012).

Accordingly the Trade Intensity Index as computed and presented at the appendix 7.1 part for each members of COMESA by identifying the exporter (along the column of the table) and partners (along the row of the table). The trade intensity index measures the intensification of trade relationship among a partner country. An increase in trade with a country may be attributable to two factors. One is the expansion of trade by a trading partner and the other is “pure” intensification of the trade relationship. Specifically, trade relationship of a country with (or trade dependency of a country on) a trading partner country can increase when the trading partner’s trade expands faster than other countries (Urata & Okabe, 2007). Considering this factor, we compute trade intensity index and its changes over time. Trade intensity index captures the bias in bilateral trade relationship by considering the trade volume of the trading partner. Trade relationship is more (less) intensive (or biased) than normal if the value of trade intensity is greater (less) than unity.

According to the computed results shown in appendix 7.1 for each member, it appears that trade intensity index was high among most of the neighboring COMESA countries. This shows most of the countries prefers to trade more with the bordering partner countries to decrease the cost of transportation of goods and services associated with the distance between the members in the region. This goes mainly with the natural trading partner location and transport cost hypothesis of Wonnacott and Lutz (1989) suggesting that geographical proximity between countries tends to increase trade between them and reduce trade diversion. In addition, Deardorff and Stern (1994), also referring to transport costs, suggest that geographical proximity between countries tends to trade more with each other than with more distant countries to reduce transport and communication costs.

Based on the Trade Intensity Index result shown on appendix 7.1, Table 17 summarizes top four trade partners of exporting COMESA members which have value greater than unity in which an exporter country can intensify or expands trade faster than other member states whose trade intensity less than unity.

Table 17: Top four intra-trade partners of COMESA members

Abbildung in dieser Leseprobe nicht enthalten

Source: Author, summary of trade intensity results

Accordingly, from the highest to the lowest trade intensity index results, Burundi trade more with democratic republic of Congo, Rwanda, Uganda and Kenya with the trade intensity index of 30.9, 55.8, 92 and 76 respectively. However, for the remaining member countries, the trade relationships with Burundi were rather weak, or below expected. Hence, the trade intensity index of Burundi with Comoros, Djibouti, Eritrea, Ethiopia Libya, Madagascar, Malawi, Seychelles, Swaziland, and Zimbabwe were less than unity, which indicates the trade relationship between them were low. This suggests that there may be other factors such as transport and logistical impediments that give rise to high transaction costs, or the lack of trade complementarities which account for the apparent low levels of trade (Pitigala, 2005).

Comoros got high trade intensity index for Djibouti, Kenya, Madagascar Mauritius and Rwanda and Seychelles which were above unity. For other countries like Burundi Eretria, Libya, Swaziland, and Zimbabwe the trade intensity index shows zero which mean they were not trading with Comoros from 2000 to 2016. From the highest to lowest the trading partners for Democratic republic of Congo were Zambia, Zimbabwe, Rwanda, Burundi, Uganda, and Kenya respectively. But the result shows that there were zero trade intensity for Comoros, Djibouti, Ethiopia, Libya, and Seychelles.

Djibouti and Eretria bilateral trade relationship was stronger than other members, which indicates that the two countries were intensively expanding trade with each other. Thus, this study found that intensity between two countries was high followed by Ethiopia. However, from the analysis made, Djibouti was not traded with Democratic Republic of Congo, Madagascar, and Zimbabwe. This indicates that Djibouti did not expand its market potentials to these countries from 2000 to 2016.

Figure 11 shows Trade Intensity Index between Egypt and other members of COMESA. Egypt relatively expanding its trading opportunities with all the members of COMESA in which the TII for most of them were above one except Madagascar and Malawi. The most trade destination of Egypt was Libya and Eretria in which their TII constitute 48 and 37 respectively. This implies that Egypt was trading much more with Libya and Eretria than might be expected from other members of COMESA. The least trade potential of Egypt was Madagascar and Malawi with TII of 0.46 and 0.68 respectively.

Figure 11: Trade Intensity Index between Egypt and other members of COMESA

Abbildung in dieser Leseprobe nicht enthalten

Source: Authors’ estimates

Burundi, Comoros, Djibouti, Kenya, Rwanda, Sudan and Zimbabwe were trade partners for Eritrea and their TII were greater than unity. But the most important significant partner countries for Eritrea in trade expansion were Djibouti and Sudan with TII of 18.0 and 8.0. The natural trading partner theory explores that the economies tend to trade more with their neighbor countries (Wonnacott and Lutz cited in Anurag Anand, 2016). Therefore, because of their geographical proximity, as well as cultural and economic links most of the member countries trade with each other than distant world. In the same manner, when we look at the trade intensity index of Ethiopia with Comoros, Djibouti, Egypt, Kenya, Libya, Sudan, and Swaziland, is above one with different magnitude. Djibouti and Sudan are the two countries with whom Ethiopia got high trade intensity.

In contrary to other members of COMESA, Kenya has shown an increasing tendency to trade intensively with geographically distant trading partners in COMESA with different magnitude (see Figure 12). Except Libya, Madagascar, Swaziland and Zimbabwe the trade intensity index of Kenya and others were greater than Unity. Mostly, Kenya intensified trade with the neighboring countries like Burundi, Uganda, Rwanda and Congo (DRC) with TII of 38.0, 50.9, 36.3, and 13.1 respectively. This indicates that Kenya has strong trade potential to expand and benefit more trade opportunities from the region to boost its economic growth and development.

Figure 12: Trade Intensity Index between Kenya and other members of COMESA

Abbildung in dieser Leseprobe nicht enthalten

Source: Authors’ estimates

According to the result indicated on Libya, Sudan and Seychelles were the least intra-trade performer in the region with most of their bilateral TII shows less than unity with other members of the region. The analysis of trade intensity between these countries and other members of COMESA point out that, they are not fully taking the advantage of their trade potential in the region. Because of this we can conclude that for Libya, Sudan and Seychelles other members of COMESA are not very important trade partner when we consider at policy level and economic decision-making unit.

Figure 13 shows the intra-COMESA trade intensity index at the regional level which was varying for all the years. This means COMESA intra-regional trade was significantly increasing from 2000 to 2003 and from 2013 to 2016. From 2000 to 2016 Intra Regional Trade Intensity Index (IRTII) were above eight and stood at 14.24 in 2003 and 13.59 in 2016. In contrast in 2008, 2009 and 2010 the IRTII were 7.33, 7.85 and 7.99 respectively which were below eight.

Figure 13: Regional Trade Intensity Index of COMESA

Abbildung in dieser Leseprobe nicht enthalten

Source: Author`s computation based on IMF trade data.

The analysis made in this section using trade intensity index has provided useful information on the weakness and strength of bilateral-trade exist among member countries in COMESA, but the analysis was rather crude, as it could not identify/differentiate the impacts of other determinants of intra-regional trade that influence intra-trade such as economic size, total population, overlapping members, democracy/good governance, distance between the countries, language, culture, and religion, among other factors that may influence trade patterns. Furthermore, the analysis in this section was not precise in that no statistical and regression assessment were made. To remedy these problems and to distinguish the determinants of intra-trade among COMESA members, investigation of intra-trade using gravity model has been done in the next section with OLS, Random effect and PPML estimation techniques.

4.2.2 Descriptive statistics

The descriptive statistics for the key variables in this study are presented in Table 18. Export has a mean of 8.1 with a low standard deviation of 7.1. The minimum and maximum values for export are 0.0002 and 21.3 respectively. Even though the difference between mean and standard deviation is small the range between the minimum and maximum is large. These statistics show that there is a huge variation in the volume of export of merchandise among COMESA region. This variation could be explained by the differences in the level of economic growth, size of economies, the distance between the member of countries and the level of FDI inflows exist among members of COMESA.

Except democracy all other variables have the average value greater than standard deviations and there are some differences between the minimum and maximum values. This indicates the fact that the members of COMESA are characterized by different socio-economic circumstances which determine the values of intra-export trade. FDI inflows had a mean of 18.1 with a low standard deviation of 4.1. The distribution of most variables except GDP of importer, exporter and democracy are negatively skewed, which usually measures the degree of asymmetry of variables around its mean. Negative skeness indicates that the distribution of the variable has long left tail with lower values than the sample mean. Kurtosis measures the peakness or flatness of the distribution of the variables around its mean. All variables have positive Kurtosis which indicates that higher values than the sample mean.

Table 18: Summary and Descriptive Statistics

Abbildung in dieser Leseprobe nicht enthalten

Source: Authors` estimation

4.2.3 Augmented gravity model estimation results and discussion

The gravity model specified in equation 8 in chapter three is estimated and reported in Table 19 to find out the determinants of intra-trade among COMESA members as stated under objective two of the this study. The estimated report for the determinants of intra-trade was done using three alternative estimation methods for the period 2000 to 2016 where total intra-export is used as dependent variables. These estimators are OLS, random effects and Pseudo Poisson Maximum Likelihood (PPML) that tackles the problems associated with OLS estimation (Santos and Tenreyro, 2006) to check the robustness of results.

The estimation result indicates that the overall performance of the model seems to be good with OLS, and PPML model estimates which are explained approximately, 72%, and 68% of the variation in intra-export trade among COMESA members by GDP, population, distance, landlocked, and FDI inflows. Most of the variables have the expected sign and influence export volumes under the gravity model are significant at 1 %, 5% and 10% levels consistent with theoretical expectations.

The estimated coefficient of the variables with OLS and random effect yields higher values than PPML for bilateral trade and they are statistically significant coefficient. However, they produce insignificant coefficients for the adjacency of the member states which was an important trade determinant examined by trade intensity index under part 4.3.1, PPML effect tends to generate somewhat different estimation results with higher estimation coefficient of GDP of exporter, adjacency of members, exporter population and landlocked. The elasticity’s for most of the variables have statistically positive and have significant impact on bilateral trade for members of COMESA.

In other words, export volume increases with trading partners’ GDP, population, RTA, common language, FDI inflow, and decreases as distance increases and lack of access to seaport between trading economies. Landlocked countries trade less than those with access to sea, while countries sharing a common language/colonial history and land border are found to trade more with each other. The regression results in Table 19 indicate that a one percent increase in exporter`s GDP expected to raise export trade volume approximately by more than 13 and 38 percent with random effect and PPML estimations respectively given everything else equal. The importing country GDP is also positively related with the exporting variable. The report also indicates that when the GDP of importing country increased by one percent, export volume also increased by 60 and 11 percent using random effect and PPML estimators respectively.

However, in estimating the model OLS has shortcoming in which we pool all the 5,684 observations together and run the regression model, neglecting the cross section and time series nature of the data. The model does not distinguish between the various cross section and nineteen (19) member states of COMESA. In other words, by combining nineteen-member states by pooling we deny the heterogeneity bias or individuality that may exist among bilateral trade of nineteen member’s states. We assume that all member states are the same but normally not happened and trade between any pair of countries is likely to be influenced by certain unobserved individual effects. If these effects are correlated with the explanatory variables, which an examination of the OLS residuals supports, this will lead to pooled OLS estimates being biased (Cheng and Wall, 2005).

Traditionally, the multiplicative gravity model has been linearised and estimated using OLS assuming that the variance of the error is constant across observations (homoskedasticity), or using panel techniques assuming that the error is constant across countries or country-pairs. However, as pointed out by Santos and Tenreyro (2006), in the presence of heteroskedasticity, OLS estimation may not be consistent and nonlinear estimators should be used. PPML approach has an ability to maintain the gravity model equation in its multiplicative form hence resulting in unbiased and consistent results. Another challenge described in the literature concerns the zero values. The logarithm of zero is unfeasible. As a result, the presence of zero trade flows in data means that these observations must either be dropped or replaced by an arbitrary positive value, leading to sample selection bias and loss of information. Ouma (2016) added that PPML estimation techniques is superior in estimation of gravity model of trade and give reliable and robust results, despite the common characteristic of bilateral trade where some data may be zero in some periods.

Table 19: Summary of regression results excluding DEMi and RTAmijt

Abbildung in dieser Leseprobe nicht enthalten

Source: Authors’ regression estimation

Even though, Table 19 results were estimated with OLS and random effect with the addition of some small constant number (intra-export trade statistics are not available for the entire period for some members) for zero trade among trade partners to make appropriate for log linearized model estimation, it may lead to biased estimation results. Pseudo Poisson Maximum Likelihood (PPML) estimator is a more powerful econometric tool that can be a solution to the existence of zero-trade problem in estimating of gravity model which has been theoretically more sound approach. Therefore, with the presence of zero-valued trade flows among COMESA members, I attempt to perform with Pseudo Poisson Maximum Likelihood (PPML) estimator. It allows for the inclusion of zero-valued trade flows and for controlling for the unobserved heterogeneity between countries (Santos and Tenreyro, 2006). As a result, more detail results are discussed using the estimated results of PPML model, although the results of the OLS and random effects models are also presented in Table 19 and 20.

Using the Anderson–Van Wincoop (2003) gravity equation specification together with the augmented variables, we find that PPML yields significant effects for all the variables under consideration and the estimated coefficient obtained from the log-linearized equation is almost approach to unity. PPML also predicts a better role in the presence of zero and statistically as well as economically significant in determining bilateral trade for all the variables. The role of geographical distance, which is proxy for transportation costs, was negatively associated with bilateral export in OLS, random effect and PPML estimation results and also most significant impediment to bilateral trade flows especially for distant partners. The estimated elasticity is 3.64, 3.76 and 1.84 respectively for the three estimates. This suggests as transportations cost increases the bilateral trade volumes between members become diminishing.

Furthermore, PPML estimates indicate that, after controlling for other variables, GDP of both exporter and importer were positively influencing bilateral trade flow. The result suggests that export trade and the size of income moves in same directions. Access to seaport appears to be an important determinant for bilateral trade flows, according to PPML regressions; the negative coefficients on the land-locked dummies can be interpreted as an indication that lack of access to ocean for members of COMESA is negatively associated with trade volume that is significant determinant and it’s also in line with the economic theory. In contrast, random effect result suggest that trade volume is positively linked with landlocked states that does not support the theoretical expectation and insignificant result. This does not mean that all the members who have access to sea trade more than landlocked countries. Some landlocked member’s trade volume is higher than others. In this case, Ethiopia`s trade volume is greater than Djibouti and Eretria.

Random effect regressions indicate that exporter remoteness is less important and not significant determinant of bilateral trade flows although its negative correlated. PPML regressions result, instead, generate negative effect on trade. Language is statistically and economically significant result. Adjacency of member of COMESA plays a much greater and significant role on export trade according to PPML regressions estimation. Among all variables under PPML estimation techniques, contiguous of member states play the most significant role in increasing export trade volume. A unit increases in adjacency leads to more than 1.15 percent raises bilateral trade.

Remoteness index proxied for the multilateral resistance term which shows the two partner countries face while trading. The lower this resistance (i.e. the higher the value of this index) is, the larger the two countries in question will trade. Accordingly, the estimates of the remoteness index for exporter is positive, large and highly significant using PPML estimation, confirming that, all else equal, members that are more isolated/remote from the rest of the COMESA members tend to trade more with each other than distant members.

Having compared the OLS, random effect and PPML estimation results above, our next analysis was based on PPML estimation results in detail because in the existence of zero trade flows among members PPML is preferable in providing best estimation according to Santos and Tenreyro (2006).

4.2.3.1 Relationship between gross domestic product and level of export

The coefficients of both exporter and importer Gross Domestic Product (GDP) were positive and statistically significant at 1 percent level and consistent with a priori expectation. The larger the GDP of an exporting country (GDPi), the larger its production capacity, the more likely it is to attain economies of scale and increase its intra-exports supply based on its comparative advantage. In the same way, the a larger the GDP of an importing country (GDPi) is an indicative of the existence of larger income and higher ability to demand more imports goods and services from the member countries of COMESA. This shows that the growth of the GDPs of the member countries increases the flows of export trade between them. The outcome is consistent with the most empirical examinations in literature that suggests a strong relationship between GDP and intra-trade.

The result means that holding other factors constant, a one percent increase in exporter and importer GDP would increase intra-export by approximately more than 38 and 100 percent respectively in COMESA. It means that the production and export of merchandize increases if the purchasing capacity of the importer country increases. In previous studies, the gravity model in its basic form suggests that the volume of bilateral trade between two countries is positively related to their incomes (GDPs). Empirically, previous studies like Alemayehu, and Haile, 2008) concluded that the conventional gravity model has predicted that the coefficients of the GDP variables of the importers and exporters are positive, indicating that trade increases with the level of the GDP.

4.2.3.2 Effect of population on export volume

The estimation result shows that the population growth of importer has a positive and significant coefficient to determine bilateral export trade volume which is consistent with the theoretical expectation. In other words, an increase in an export volume goes with the increases of importers population. This means increases of the importer population might lead to an increase of purchasing capacity from exporter members and hence high intra-trade flows among bilateral partners. The result of the PPML in Table 19 indicated that there is a positive relationship between export volume and the exporter population growth which is significant at 1 percent level. It shows that the more populous a country is, the greater its production capacity and therefore, the larger in export trade. A one percent increase in exporter population would increase their production and export capacity by approximately 27 percent within COMESA.

Most of the previous studies have supported the view that nations with a larger population tend to buy and sell more than nations with a lesser population. Sabra (2016) found similar correlation between population and volume of trade by stating that bigger countries trade more with each other than smaller countries as they have a greater potential for export supply and import demand. Furthermore, the impact of population on trade may also be at variance depending on the length of the estimation period (short-term vs. long-term). Population may have a positive impact on trade flows in the short-run, while in the long run higher population tends to decrease exports due to internal consumption increases.

4.2.3.3 The relationship between the level of exports and distance

The estimation of the distance variable has negative coefficient of 1.84 which indicates the most significant determinant of export trade volume among other variables at 1 percent level. It indicates that export volume and distance are negatively associated and moving in opposite direction. This is consistent with theoretical expectation and which indirectly proxied with the availability of poor infrastructure and high transport costs within COMESA members that discourage the intra-trade by escalating transaction costs. The result of the Table 19 show that one percent increases of the distance between two bilateral trade partners leads to more than 1.84 percent decrease in export volume among COMESA members. This result indicates that an infrastructural deficiency that links members from Northern part Egypt and Libya toward Southern part Swaziland and also poor boarder management in moving goods among member states.

The findings of this study are consistent with other studies in the application of gravity model to evaluate international trade. Deardorff (1998) argued that the relative distances of trading partners have an impact on the volume of trade. Bilateral trade is expected to decrease between larger distances of countries (Clark et al., 2004) by leading to higher transportation costs. It is recognized that transport costs are an important barrier to trade and, therefore, they tend to reduce the level of international trade. According to Tomasziw and Kirkpatrick (2009) empirical study they revealed that both distance from a trade partner and remoteness from the rest of the world exert a negative, statistically significant effect on bilateral trade flows. Countries with short distance between each other are expected to trade more than those who are wide apart because of reduced transaction costs.

4.2.3.4 Regional free trade agreement

The regression result suggests that formation of regional Free Trade Agreement (FTA) has high impact on intra-trade effect among members of COMESA with strongly and statistically significant coefficient at 5 percent. The result implies formation of regional trade agreement promotes intra-regional export trade within COMESA members despite low diversified intra-trade between them. Being member of FTA leads to approximately 59 percent increase in export among themselves than non-members. In other word, intra-regional trade has likely expanded by 59 percent since the creation of the FTA in 2000 among sixteen (16) COMESA members. Regional trade agreements considered as beneficial by increasing trade among partner countries because it reduces tariff, simplify administrative requirements and other protective policies are reduced among member states.

The PPML method predicts that trade between two countries that have signed a trade agreement is on average 59 percent larger than that between countries without an agreement. A positive coefficient of FTA also suggests bilateral trade pair among COMESA members are trading more with one another than is predicted by their incomes and distance, population, investment and other dummy variables and so, the result provides evidence of a trade creation effect. In other words, being a member of RTA results in trade creation effect than trade diversion. The results therefore indicate the need for COMESA members to focus on expanding regional markets to significantly improve trade performance by improve efficiency and competitiveness to be able to favorably compete within the region as well as at global market.

4.2.3.5 The role of foreign direct investment inflows in promoting export trade.

The result of the Table 19 shows positive link between trade and foreign direct investment inflows in COMESA, and significant determinant of export at 10 percent. The positive linkage suggests FDI inflows are observed to have increasing effects on exports by stimulating domestic sectors through spill-over effect among COMESA members. This effect increases the production capacity of the members by stimulating the domestic enterprises and promotes intra-export. The positive association also indicates FDI promotes export of the host countries though training the local labor forces and advancing the technical and managerial skills by raising the efficiency and productivity of the factors and hence strengthening their competitiveness in the global as well as regional market. However, we could observe from the estimation result the impacts of FDI inflows are much lower than the other variables under consideration. This means that COMESA members attract less FDI inflows to reduce the trade deficit for each host country and hence promotes export within the members and global market.

Most of the empirical studies analyzed so far found a positive linkage between FDI and export volume. Authors, like Swenson (2004), and Lane and Milesi-Ferretti (2008), founds that a positive relationship between trade and FDI inflows that generates a higher trade (as volume) and an increase in productivity rate. Moreover, regional economic integration results in increase in FDI which may arise from increase in market size, diffusion of new technology and practices and by reducing uncertainty and enhancing policy credibility. There is also attraction of foreign direct investment due to reduction in trade barriers and transportation costs.

4.2.3.6 Other Dummy variables

The elasticity of common border between the importer and exporter of COMESA members is highly significant and positive which encourage and facilitate trade performance more than other variables next to the distance between the two trading members of the region. In addition to PPML estimation result, most of COMESA member’s trade more with their neighboring countries as we saw under trade intensity index in part 4.2.1. This is most probably to reduce the cost associated with the distance between the partners. It also indicates that trading with distant members is difficult due to lack of developed infrastructure. The result is consistent with the theoretical expectation which suggests that countries which share a common border are more likely to trade with each other than countries which do not share a common border.

The result of common language variable shows that positive coefficient and significant at 1 percent which affects bilateral trade flows in the region by facilitating the efficiency of communication while reducing time and transaction costs. It implies that sharing common language has a positive effect on intra-export trade between partners and is really an important part of determining the trade share between countries. Empirical literature confirms that firms in adjacent countries, countries with a common language or other relevant cultural features are likely to know more about each other and to understand each other’s business practices better than firms operating in less-similar environments. It also facilitates better understanding of commercial practices, laws and regulations, as well as gives better understanding of partner’s culture and tastes.

The coefficient for landlocked is negative and statistically significant at 5 percent which is theoretically supported and consistent with other studies so far as most of them reports landlocked is major impediment to trade and have big negative impact on international trade linkage as they are characterized by long distance of sea access from transit neighbors, remoteness from markets, extra border crossings, huge transport costs, inadequate physical infrastructure, logistic and institutional hurdles(UN-OHRLLS,2013).

Effect of electoral democracy and over-lapping membership The next part of the estimation analysis is mainly focused on the effect of electoral democracy and overlapping membership of COMESA with IGAD members to answer the third objective of the study. The estimation of Table 19 was done without inclusion of the impact of electoral democracy and over-lapping membership on export trade. But on Table 20 I attempt to include the two variables to investigate their impact on bilateral export trade within the region. From this estimation all the estimated coefficients have varying statistical significance and have the expected signs with the exception of the importing population which is insignificant determinant of intra-trade. From the estimation results we could observe that overlapping of membership has a negative effect on bilateral export trade in OLS, random effect and PPML estimation results, which suggests that belonging to more membership in regional trade agreement has a negative impact on bilateral trade flows among COMESA and IGAD members. The performance of the electoral democracy of the members COMESA is also resulted in against the encouraging of export trade in both random effect and PPML estimation results. Having said the highlight of the two variables let us see the impact their analysis individually as follows;

4.2.3.7 Effect of forming multi-membership on trade

Multi-membership had a negative coefficient and statistically significant at 1 percent which suggests that forming one more membership discourages export volume among COMESA member states. This implied that holding other factors constant, a one percent additional new membership by COMESA countries leads to approximately 67 percent reduction in intra-export volume. The variable measures the effect of adding another new RTA as a membership. The regression result supports the theory that being a member of several trade agreements has a negative impact on the effect of RTAs and thereby on export trade.

The estimation result was consistent with previous studies like Mwangi et.al. (2016), which suggest that multiple memberships are hindering regional integration and by extension, intra-regional trade rather than enhancing it. They point out that multiple memberships impose high costs in time, energy and resources on African governments and force them to juggle competing regulations. The negative value for the coefficient associated with the variable RTAmijt, implies that forming another RTA, by creating overlapping RTAs, its additional trade with members of existing RTA(s) or with members of new RTA(s) is less than the additional trade formed between members belonging to a single RTA.

4.2.3.8 Impact of electoral democracy on export trade

The coefficient of electoral democracy variable indicates that negative effect on export trade among COMESA members and significantly influence export at 1 percent level. This might be related to lack of electoral democracy in most COMESA members (except Mauritius, Malawi, Seychelles from 2000 to 2016, these have constant electoral democracy), trade between them approximately decreased by 63 percent. This indicates most of the COMESA members lack democratic electoral system and characterized by poor good governance which lead them to high transaction costs resulting in insecurity for trade and hence, limiting their intra-export trade capabilities. In addition to political instability, low quality of production of goods and services, as well as high protective policies, most of the members are characterized by less democratic development.

The result of this research study is against some of the theoretical findings studied so far in other part of the world which states that good governance has a positive effect on trade (Mansfield et.al. 2002, Anderson & Marcouiller, 2002). Some researchers like Yu (2010) and Giuliano et al. (2012) found significant positive relationship between trade and democracy and explains that a country that has a democratic regime is generally hosting better political and economic institutions and guaranteeing better market conditions like stronger property rights, consumer rights and rule of law.

Cited by Daniel (2006), empirical research studies show that democracies promote trade more than autocracies higher quality NTBs, have lower tariffs and are more likely to conclude liberalizing trade agreements. In fact, the finding that democracy promotes trade openness is among the most robust in the field of international political economy.

Table 20: Summary of regression results including DEMi and RTAmijt

Abbildung in dieser Leseprobe nicht enthalten

Source: Authors’ regression estimation

CHAPTER FIVE

SUMMARY, CONCLUSION AND RECOMMENDATIONS

This chapter presents summary of the study and conclusion, some policy implication, recommendations and suggests areas for further studies based on the data analyzed in the previous chapter.

5.1 Summary of findings and conclusion

The aim of COMESA is to enhance the economic and social relations among member countries through increasing intra-trade. Even though the volumes of intra-trade among members are scanty (most of them are primary products) in relation to other regional blocks, intra-trade has been performed among most of the members in different quantity since the establishment of regional trade agreement. This study analyzed the determinants for the dynamism of intra-regional trade within COMESA members from 2000 to 2016 by applying trade intensity through trade linkage and augmented gravity model specification to identify factors affecting intra-regional trade among COMESA member states.

The study used to measure the trade intensity index to assess the export share of each COMESA member states and the augmented gravity model approach to identify factors affecting intra-regional trade in COMESA member states. Under gravity model, three regression analyses were performed using pooled OLS, random effect, and PPML estimation techniques. From these regressions further analysis was done using PPML which is appropriate estimation to handle the problem related to the existence of zero trade flows among members.

Trade intensity index at regional level indicated that the intra-export-COMESA trade remains low and stood at 13.6 in 2016. The analysis indicates that share of intra-COMESA exports averaged to 11 between 2000 and 2016. COMESA intra-export-regional trade was significantly increasing from 2000 to 2003 and from 2013 to 2016. From 2000 to 2016 intra-regional trade Intensity were above eight and stood at 14.24 in 2003 and 13.59 in 2016. In contrary it was below eight in 2008, 2009 and 2010 which stood at 7.33, 7.85 and 7.99 respectively.

As a country level, Egypt and Kenya were observed to have the highest share, while Libya, Sudan and Seychelles were the least intra-trade performer in the region with most of their bilateral TII showing less than unity with other members of the region. The analysis of trade intensity between these countries and other members of COMESA point out that, they are not fully taking the advantage of their trade potential in the region. From the result we can conclude that for Libya, Sudan and Seychelles other members of COMESA are not very important trade partner considering at policy level and economic decision-making.

In contrary to other members of COMESA, Egypt and Kenya have shown an increasing tendency to trade intensively with geographically distant trading partners in COMESA with different magnitude in which their TII were greater than unity. This indicates that both countries have strong trade potential to expand and benefit more trade opportunities from the region to boost its economic growth and development.

Generally, most of the COMESA members are trading more with their neighbor than distant members because of geographical proximity, as well as cultural and economic links. This suggests that there may be other factors such as transport costs, logistical impediments, poor infrastructural development and lack of trade complementarities that made most members to trade with nearby countries. This shows most of the countries prefer to trade more with the bordering partner countries to decrease the cost of transportation of goods and services associated with the distance between the members in the region. This goes mainly with the natural trading partner location and transport cost hypothesis of Wonnacott and Lutz (1989) suggesting that geographical proximity between countries tends to increase trade between them and reduce trade diversion.

In addition, Deardorff and Stern (1994), also referring to transport costs, suggest that geographical proximity between countries tends to trade more with each other than with more distant countries to reduce transport and communication costs. Trade within COMESA is predominantly between countries that have a common border. Likewise, Korinek, and Melatos (2009), added that trade between the top ten bilateral country-pairs took place exclusively between countries that share a border or are neighboring islands. This points to two problems about increasing COMESA intra-trade: i) COMESA countries are often difficult to access either because they are landlocked and/or due to lack of infrastructure between destinations, and ii) border crossings are often time-intensive, leading to prohibitive time constraints if many borders must be crossed.

The findings from the OLS and random effect regression indicated that GDP, population, distance, FDI inflows, common language have a statistically significant impact on the values of exports and expected theoretical estimation results within COMESA. Both estimation result yields large estimates for the elasticity of bilateral trade and statistically significant coefficient with respect to distance. However, they produce insignificant coefficients for adjacency, electoral democracy, landlocked and overlapping membership dummies.

Random effect tends to generate somewhat different estimates. The elasticity of export trade within COMESA members with respect to the exporter’s and importer’s GDP, common language and electoral democracy are significantly larger than OLS result. However, the estimated distance and FDI inflow to exporter elasticity are smaller than with OLS. The elasticity under random effect also indicate that export volume increases with trading partners’ GDP, population, RTA, common language, adjacency, FDI inflow, and decrease as the distance between trading economies increases, forming multi-membership and landlockedness.

Landlocked countries trade less than those with access to sea, while countries sharing a common language/colonial history and land border are found to trade more with each other under random effect model estimation. The regression results indicate that a one percent increase in exporter`s GDP can be expected to raise export trade with OLS and random effect estimations given everything else equal. The importing countries GDP are also positively related with the exporting variable. When the GDP`s of importing country increased by one, export volume is increased by 57 percent.

But estimation by OLS leads to biased results due to denying the heterogeneity or individuality that may exist among bilateral trade of nineteen-member states which assume that all member states are the same by neglecting the cross section and time series nature of the data. However, trade between any pair of countries is likely to be influenced by certain unobserved individual effects.

From the estimation results we observe that overlapping of membership has a negative effect on bilateral export trade under PPML estimation results, which suggests that forming more membership in regional trade agreement has a negative impact on bilateral trade flows among COMESA and IGAD members. Also, the role of geographical distance, which is proxy for transportation costs were negatively, related with bilateral export with PPML estimation results which are the most impediments to trade. Intra-export trade volume is expected to reduce by 1.46 for 1 percent additional distance among members. This suggests as transportation cost increases the bilateral trade volume between members decline.

Furthermore, PPML estimates indicate that, after controlling for other variables, GDP of both exporter and importer positively influence bilateral trade flows. It suggests, trade and the size of income moves in same directions. Access to seaport appears to be an important determinant for bilateral trade flows, according to PPML regressions; the negative coefficients on the land-locked dummies can be interpreted as an indication that lack of access to ocean for members of COMESA is negatively associated with trade volume. PPML regressions indicate that having common language is more important and significant determinant of bilateral trade flows by easily facilitating time and transaction cost. Preferential trade agreements COMESA members play a significant role on export trade according to PPML regressions estimation. Becoming a member of free trade area is expected to increase export volume by 54 percent. In other word removing the impediments of trade for member countries would lead to larger market opportunities.

With PPML the estimation results were consistent with a priori expectation except population of importing countries which is insignificant determinants of export volume on Table 19. The results indicate that all the standard gravity model variables have plausible and statistically significant coefficients which were different from zero. The results also show that all the dummy variables used to measure export have the expected signs.

In support of this research findings, cited in Yabu (2014) different studies indicated that trade flows in most African countries have been minimal due to small economic size; trade barriers; border delays; lack of adequate infrastructure; poor condition of the roads; lack of integration into value chains; too many and high costs of road tolls for the use of roadways (ADB, 2000; UNCTAD, 2009). Other researchers²³ also give their own reason for the low performance of intra-trade among COMESA. Among these factors, issues of revenue loss, failure to implement programs, poor performance of private sectors participations, small market size, and lack of knowledge of market, macroeconomic instability, lack of policy harmonization, and coordination, distorted trade regimes, lack of full commitment and institutional issues, inadequate financial and administrative resources, political instability, lack of technological improvement, insufficient skill and brain drain, strife and draughts (Kassaye,2008).

Generally, it can be concluded that the regional trade integration among COMESA was not as satisfactory as expected and the intra-export volume is limited among members. In 2016 the export trade intensity of the region stood at 13.6 percent. This indicates more than 80 percent of the region`s export trade has been performed with the rest of the world. The poor performance of trade might be associated with some of trade barriers like poor infrastructure, low commitment to regional integration, overlapping memberships, lack of institutional democratic election, low inward flows of FDI, lack of complementarities of products (most of them have similar export profiles), small and fragmented economies with low incomes, low percapita-income, lack of access to seaport for some members, unequal distribution of benefits among member countries after join the FTA of COMESA. Example Egypt and Kenya are the most beneficiary than other members. This problem arises because the more developed members benefit more than the less developed ones, whilst there are mostly no compensation mechanisms to help the losers.

In line with this, Yang and Gupta (2005) also summarized the general unsuccessfulness reasons of Africa in promoting intra-trade and foreign direct investment due to high external trade barriers and low resource complementarity between member countries limit both intra- and extra regional trade. Small market size, poor transport facilities and high trading costs also make it difficult for African countries to reap the potential benefits of RTAs. To increase regional trade and investment, African countries need to undertake more broad-based liberalization and streamline existing RTAs, supported by improvements in infrastructure and trade facilitation.

5.2 Recommendations

The research study finds that the economic size and income per capita or population size variables are positively related to the level of trade. These variables determine the purchasing ability of the members. But most of the members are economically categorized within the low level of income which constraints the intra-trade capacity among them. In relation to international terms COMESA is characterized by small GDP of $754 billion. Egypt only accounts for 44 per cent of COMESA’s GDP which indirectly shows almost all COMESA countries can be classified within small, low-income countries. Because of this the potential trade expansion among members is regarded as low. Therefore, it is recommended for the member states to increase their income through spending on pro-poor areas like health, education, and increasing of consumption, public and private investment. Moreover, COMESA member states need to maintain a high and sustainable economic growth.

Regional economic integration is aimed to boost the economy of each member state through diversification of comparative advantages by exploiting the available opportunities. But most of the COMESA members are characterized by similar export profiles with labor intensive (primary commodities) and low technological involvement. A study of product complementarity indices for COMESA bilateral trade among members indicate that product complementarities between exports of Egypt and imports of the other member countries average to 43.0 while those for Kenya’s exports to the region average to 38.6. For all other countries, the average product complementarity for exports is far lower and arrangements with a value of less than 25 have failed (Tsikata, cited in Khandelwal, 2004).

Therefore, to ensure the expansion of market it is recommended that each member state needs to produce complementary products (export diversification) where they have a revealed comparative advantage through identification of priority products that exist in the region. This could encourage market expansion in the region as well as supporting domestic industries in the specific states. It also helps to reduce unemployment and the risk of getting to global economic shocks. Furthermore, each member state needs to create an enabling environment for private sectors that play a crucial role in structural diversification by developing new products, new markets, and new ways of doing business to enhance intra-COMESA trade.

In support of this, AfDB (2011) recommended that specific policy actions should be directed at correcting to promote intra-regional trade and economic integration. Like the oil-rich Middle East countries have successfully done, resource-rich African countries should invest their resource rents in strengthening agriculture and manufacturing. Infrastructure development should be a major policy focus. Emphasis should be shifted from raw material exports to moving up the value chain by exporting semi-processed products with the aim of gradually moving to fully processed products based on the available raw products. For instance, Ethiopia and DRC Congo should consider establishing a major production firm given their wealth in Coffee and different mineral production respectively.

Most of the COMESA member states were not actively benefiting from the inward flows of FDI which is an important means of promoting exports growth and hence diversification of products through training of the local work force and upgrading the technical and managerial skills. It helps in raising the efficiency and productivity of the factors and hence competitive strength in the international market. Democratic republic of Congo, Egypt, Kenya, Ethiopia, Sudan and Zambia were the best performer of in attracting FDI in the region. Therefore, I recommend other members of COMESA should focus on export-oriented policy through attracting FDI based on comparative advantages to boost their export capacity and hence increasing their foreign exchange earnings, encouraging new job creation, enhance technology transfer through spillover effect and boost their overall economic growth.

Electoral democracy is supposed to encourage trade integration by creating fair and competitive market for the members. However, from our findings electoral democracy significantly decreases the export volume for most of COMESA members. This indicates the existence of poor governance among members which could divert the trade relationship to the rest of the world. Mauritius, Malawi, and Seychelles had consistent electoral democracy among nineteen members of COMESA. This indicates most of the member states are not characterized by democratic regime and not hosting better political and economic institutions which guarantee better market conditions like stronger property rights, consumer rights and rule of law.

Lack of electoral democracy lead to political instability, conflict and violence which weaken the trade integration and investment flows among members and hence, reduce the economic growth and development of the region. To reap the benefit of economic integration and better product quality export I recommend that maintaining electoral democracy and improving political situations of member states is very important to encourage the exporters confidence and safety in terms of decision to export to the region.

From this study I found that all members of IGAD are also members of COMESA except South Sudan and Somalia which results in overlapping membership. From our estimation result I also observed that multiple memberships affect the regional trade integration negatively. This indicates that members are required to allocate high financial obligation, costs in time of having different meetings, police decisions, instruments and procedures in the regional blocs. Overlapping membership to different regional blocs may also cause difficulties through increasing trade costs of the exporter to different regional markets which may have varying rules of origin related to entry requirements. As a result the export capacity to a specific member might be reduced.

A country belongs to more than two different regional blocs and establish two different common external tariffs (CETs) faces a customs police implementation problem. For instance, Kenya and Uganda are members of EAC and COMESA which have different CETs or have different policies and regulations at same time. It is technically impossible for these two countries to implement the two different CETs (UNCTAD, 2006).

Therefore, to increase the intra-African trade and resolve the challenges of overlapping memberships I recommend the practical implementation of Continental Free Trade Area (CFTA) declaration of AU Assembly of Heads of State and Government, in 2012, which is also intended to establish a single continental market for goods and services, free movement of business persons and investments, expand intra-African trade and increase the continents appeal as a global trade partner. The aim was that “the CFTA should be operationalized by the indicative date of 2017, based on a framework, roadmap and architecture, with the following milestones:²⁴

1. Finalization of the East African Community (EAC)/the Common Market for Eastern and Southern Africa (COMESA)/Southern African Development Community (SADC) Tripartite FTA initiative by 2014. 2. Completion of FTA(s) by Non-Tripartite RECs, through parallel arrangement(s) like the EAC-COMESA-SADC Tripartite Initiative or reflecting the preferences of their Member States, between 2012 and 2014. 3. Consolidation of the Tripartite and other regional FTAs into a Continental Free Trade Area (CFTA) initiative between 2015 and 2016. 4. Establishment of the Continental Free Trade Area (CFTA) by 2017 with the option to review the target date according to progress made”

Additionally, the implementation of the CFTA is an important pillar and driver for Africa's growth and development in the period ahead for the following reasons,

- It is a key aspect for the realization of Africa's Agenda 2063: The Africa we want aimed at building a prosperous and united Africa. In this regard also, it could help improve prospects for African countries to implement the United Nations 2030 Agenda²⁵ for sustainable development and achieve the sustainable development goals (SDGs);
- It will bring about a consolidation of African economic integration process through harmonization or coordination of FTAs of RECs;
- It will set the basis for the formation of a continental wide economic space that would be advantage for businesses;
- It would widen the internal market demand available to African countries to weather global economic crises as witnessed during the 2008-2012;
- It would, as a mega-regional agreement, build up Africa's economic clout in dealing with emerging mega-regional trade agreements in other parts of the world as well as Africa's engagement in trade negotiations at the global level such as in the WTO.

The research study finding indicate that infrastructure which is proxied to distance have large significant impact on trade flows among COMESA members. In other words, lack of infrastructure development that links the regional members significantly reduces the intra-trade growth. For instance, it will cost about $5,000 to ship a car from Addis Ababa to Abidjan while shipping the same car from Japan to Abidjan will cost $1,500 (ECA 2004). Therefore, to advance the regional trade integration member states, need to invest on the physical infrastructure such as network of roads, railways, waterways, ports, airways, telecommunications and electricity networks in addition to institutional infrastructure like legal and financial systems as well as customs procedures that facilitates the movement of merchandise across the border. Some of the member states have shown a progress toward transport corridors linkage such as the of electric rail way linking Djibouti and Ethiopia inaugurated 2017 which reduces distance and facilitate trade. According to AfDB, OECD and UNDP (2017) report some projects are already contributing to significant cost reduction and timesaving. For example, between 2006 and 2011, the Mombasa-Kampala corridor reduced import times by 33 days and exports by five days.

Furthermore, after the reconciliation agreement between Ethiopia and Eritrea in July 2018 in Jeddah, Saudi Arabia, the two countries reopened the land border crossing points after 20 years, which clears the way for trade between the two nations. Consequently, the two countries commenced the preliminary preparation to repair roads leading to the port and upgrading infrastructural facilities to enable Ethiopia to reuse the Port of Assab and Mitsuwa which have huge competitive advantage for Ethiopia by reducing the distance, logistics and time costs than using Djibouti port.

Among COMESA members eight countries such as Burundi, Ethiopia, Malawi, Rwanda, Swaziland, Uganda, Zambia and Zimbabwe belong to landlocked countries which significantly affect their trade competitiveness by increasing transportation cost to access to the ocean through neighboring countries. Their trade also indirectly affected by the administrative quality and political stability of their transit countries. To alleviate the landlocked challenges these countries, need to invest on regional transport infrastructure development, having good political relationship with their neighbors, coordination border management between these landlocked countries and transit countries to reduce the time delays at border crossings, port inspection and checking points in moving of goods to their destination.

In general, this study has examined the determinants of intra-regional trade within COMSA from 2000 to 2016 using dynamic panel data and augmented gravity model. The study also investigated trade intensity index among members. The study found out that FTA has positive significant impact on export trade. Electoral democracy and overlapping membership have significant negative impact on trade respectively. With trade intensity index most, the members have weak bilateral trade relationship except Egypt and Kenya. Generally, I recommend for more detail future research to study on factors that lead the ineffective performance of COMESA regional trade agreement with other intra-trade policy variables at aggregate and disaggregate level to identify determinants of COMESA intra-trade.

5.4 Limitation and further study

Although much remains to be done, this work generates important findings in the field of international trade using augmented gravity model and Trade Intensity Index for analyzing the determinants of intra-regional trade within COMESA. In other words, although the present study has yielded some preliminary findings, its design is not without defects.

The main limitations are as follows: Factors that determines intra-regional trade. There may still be some relevant factors which can significantly influence the development of intra-regional trade within COMESA. However, the discussion of other relevant factors of intra-trade is beyond the scope of this study. The study mainly focused on variables at macroeconomic/aggregated/ level which have influence on the bilateral trade within COMESA. But more extensive and structured data collection effort is needed at disaggregated level, for data analysis to confirm the determinants of trade and draw strong policy recommendations for either at regional level or individual country in the region. Factors that influence the intra-regional trade within COMESA are still tentative, subject to confirmation and modification through further investigation and examination by modifying the research design and the tools created to conduct it to develop better insights into the study of this research process.

REFERENCES

Abe, K., (2002). Direct Investment in Northeast Asia: Perspectives and Issues mimeo.

Abidin, I., Bakar, N., and Sahlan, R. (2013). The determinants of exports between Malaysia and the OIC member countries: A gravity model Approach. Procedia Economics and Finance 5 (2013) 12 – 19. Available online at www.sciencedirect.com.

Afesorgbor,S., & Bergeijk, P (2011). Multi-membership and the effectiveness of regional trade agreements in Western and Southern Africa . A comparative study of ECOWAS and SADC. Working Paper No. 520 , the Institute of Social Studies (ISS), the Hague the Netherlands.

AfDB, OECD, & UNDP (2017). African Economic outlook. Trade policies and regional integration in Africa. Chapter three p.74-95. www.africaneconomicoutlook.org.

African Development Bank (2011). Trade and economic integration in Africa: Trend, pattern and future outlook. https://www.afdb.org/uploads/tx_llafdbpapers/Trade_and_economic_integration_in_Africa_-_trend__prospect_and_future_1377866595.pdf

African Development Bank (2000). Regional Integration in Africa, African Development Report 2000, (Oxford: Oxford University Press).

African Development Bank Group (2013). Understanding the Barriers to Regional Trade Integration in Africa. NEPAD, Regional Integration & Trade Department Report. Retrieved from www.afdb.org

Ahmad, A., (2014). Essays on trade integration among GCC Countries; University of Southampton, school of social sciences, department of economics.

Al-Atrash, H, & Yousef, T., (2000). Intra-Arab trade: Is it too Little? Working Paper WP/00/10. Middle Eastern Department, International Monetary Fund. Washington, D.C.

Albert, M., (2012). Impacts of regional trade agreements on trade in Agri-food products: Evidence from Eastern and Southern Africa, paper submitted to African economic conference, USAID Southern Africa trade hub, Botswana.

Alemayehu, G. and Haile, K., (2008). Regional Economic Integration in Africa: A Review of problems and prospects with a case study of COMESA. Journal of African Economies, Volume 17, Issue 3, 1 June 2008, Pages 357–394,https://doi.org/10.1093/jae/ejm021

Alexander,Ch., Crawford, P., Dodd,E., Mawson,D. & Stockdale,B., (2011). International Trade and Investment-the Economic Rationale for Government Support . Department for Business innovation & skills Economics. Paper No. 13, UK.

Allen, T., and Arkolakis, C., (2016). Elements of Advanced International Trade. February 2016 [New version: preliminary] accessed from http://www.econ.yale.edu/~ka265/teaching/GradTrade/notes/ClassNotes.pdf.

Ali M., and Goran, V., (2007). Foreign direct investment and export performance: Empirical evidence comp econ stud (2007) 49:430. https://doi.org/10.1057/palgrave.ces.8100216.

Aliyu, Sh., and Bawa, S., (2015). Gravity model by panel data approach: Empirical evidence from Nigeria. Article-in-International Journal of Trade and Global Markets 8(1), DOI: 10.1504/IJTGM.2015.067972

Aminian, N, Fung, K., and Iizaka, H., (2007). Foreign Direct Investment, intra-regional trade and production sharing in East Asia. The Research institute of economy, trade and industry. Discussion Paper Series 07-E-064. http://www.rieti.go.jp/en/

Amir, R. and Ahmad, T. (2012). Gravity model: An application to trade between Iran and Regional blocs, Iranian economic review, Vol.16, No.31, winter 2012.

Amoah, G., (2014). Intra-African Trade: Issues involved in improving Ghana's trade with the rest of Africa. ISSN 2224-607X (Paper) ISSN 2225-0565 (Online) Vol.4, No.2, 2014. Retrieved from http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.821.5669&rep=rep1&type=pdf

Anand, A., and Garg, K., (2016). A Study of India’s Trade Intensity with United Arab Emirates: An Overview. International Journal of Electrical, Electronics and Computers (EEC Journal) Vol-1, Issue-1, May-June- 2016.

Anderson, J., (1979). A Theoretical foundation for the gravity equation. The American economic review , 69 (1), 106-116.

Anderson,J., & Marcouiller, D., (2000). Insecurity and the Pattern of Trade: An Empirical Investigation. Revision of NBER Working Paper #7000, Revision date: 3 August 2000. See https://www.researchgate.net/publication/24095761

Anderson, J., and Van Wincoop, E., (2003). Gravity with gravitas: a solution to the border puzzle. American Economic Review 93: 170–92.

Anderson, J. & Van Wincoop, E. (2004). Trade costs, Journal of Economic Literature, Vol. 42, No. 3.pp. 691-751, American Economic Association.

Anderson, J., (2016). The Gravity Model of Economic Interaction. Boston College and NBER . https://www2.bc.edu/james-anderson/GravityModel.pdf

Anukoonwattaka, W., (2016). Introduction to gravity modeling. ARTNeT- GIZ Capacity Building Workshop on Introduction to Gravity modeling: 19-21 April 2016, Ulaanbaatar. Trade and Investment Division, United Nation, Economic and Social Commission for Asia and Pacific.

Anurag Anand, K., (2016). A Study of India’s Trade Intensity with United Arab Emirates: An Overview. International Journal of Electrical, Electronics and Computers (EEC Journal) [Vol-1, Issue-1, May-June- 2016].

Apuke, O., (2017). Quantitative research methods a synopsis approach. Arabian Journal of Business and Management Review (Kuwait Chapter), Vol. 6 (10), 2017.

Assefa, M., (2014). COMESA’s trading with China: Patterns and prospects. International journal of African development v.1 n.2 spring 2014. http://scholarworks.wmich.edu/ijad.

Atieno, N., (2009). Regional economic communities in Africa, A progress overview. Study commissioned by GTZ cooperation with east African community. Nairobi, Kenya.

Anderson, E., (2016). The gravity model of economic interaction, Boston College and NBER.

African Union Commission (2015). Agenda 2063. The Africa We want. Final edition, Addis Ababa, Ethiopia.

AU and ECA (2012). Boosting Intra-African Trade. Issues Affecting Intra-African Trade, Proposed Action Plan for boosting Intra-African Trade and Framework for the fast tracking of a Continental Free Trade Area.

AU, AfDB, and UNECA (2016). Africa regional integration index report. Find out more on www.integrate –africa.org.

Augustin, N., and Regina, T., (2011). Research in business and economics journal, the determinants of trade in the central African economic and monetary union, Bowie State

Ayodele, B., & Olu-Adeyemi, L., (2007). The challenges of regional integration for development in Africa: Problems and prospects. DOI:10.1080/09718923.2007.11892585

Awad, T., Sawkut, R., Mehra, M., & Pant, M. (2008). Regional trade integrations: A comparative study the cases of GAFTA, COMESA, and SAPTA/SAFTA. United Nations Conference on Trade and Development Virtual Institute Research Material.

Baier, S., and Bergstrand, J., (2001). The growth of world trade: tariffs, transport costs, and income similarity, Journal of International Economics, 53, 1–27.

Baier, S., and Bergstrand, J., (2004). Economic determinants of free trade agreements. journal of international economics, 64(1), 29-63.

Baier, S., and Bergstrand, J., (2009). Estimating the effects of free trade agreements on international trade flows using matching econometrics. Journal of International Economics, 77(1), 63-76.

Balassa, B., & Bauwens, L., (1987). Intra-industry specialization in a multi-country and multi-industry framework. Economic journal, 97, 923-939.

Baldwin, R., (1997). The Causes of Regionalism. The World Economy, Vol.20, pp.247-281.

Baldwin, R., & Taglioni, D., (2006) Gravity for Dummies and Dummies for Gravity Equations. NBER Working Paper 12516.

Baltagi, B., (2005) Econometric Analysis of Panel Data (third ed.) John Wiley & Sons.

Baltagi, BH., (2015). Panel data gravity models of international trade. In Baltagi BH (ed.) The Oxford Handbook of Panel Data. Oxford, UK: Oxford University Press, 608–641.

Bamou, E., and Tchanou, J. (2006). Trade and investment policy reforms in Cameroon: impact assessment and perspectives. University of Yaoundé II, Faculty of Economics and Management.

Batra, A., (2006). India’s global trade potential: The gravity model Approach. Global economic review, 35, 327-361. http://dx.doi.org/10.1080/12265080600888090.

Beata, K., (2001). Does relative location matter for bilateral trade flows ? An extension of the gravity model. Published in the journal of economic integration, Vol. 16, No. 3.

Beck, Th., (2002). Financial development and international trade: Is there a link? , Journal of International Economics, Elsevier, vol. 57(1), pages 107-131, June.

Bénassy-Quéré, A., and Lahrèche-Révil, A., (2003). Trade linkages and exchange rates in Asia: the role of China. CEPII Working Paper, 2003-21.

Bergstrand, J., (1985). The gravity equation in international trade: Some microeconomic foundations and empirical evidence. The Review of Economics and Statistics, Vol. 67, No. 3, pp. 474-481.

Bergstrand, J., (1989). The generalized gravity equation, monopolistic competition, and the factor-proportions theory in international trade. Review of econometrics and statistics, 71(1), 143-153.

Bergstrand, J., (1990). The Heckscher-Ohlin-Samuelson model, the Linder hypothesis and the determinants of bilateral intra-industry trade. The economic journal, 100(403), 1216 – 1229.

Bhagwati, J., & Panagariya, A., (1996). The economics of preferential trade agreements. The AEI Press Publisher for the American Enterprise Institute Washington, D.C. Center for International Economics University of Maryland College Park.

Bhagwati, J., Greenaway, D., and Panagariya, A., (1998). Trading preferentially: Theory and policy, the economic journal, vol. 108, No. 449, pp.1128-1148.

Bikker, J., (1987). An international trade flows model with substitution: An extension of the gravity model. Kyklos, 40 (3), 315 – 337.

Brada, J., & Mendez, J., (1983). Regional economic integration and the volume of intra- regional trade: A comparison of developed and developing country experience. KYKLOS, 36 (4), 589-603.

Brookings African growth initiatives (2012). Accelerating growth through improved intra-African trade.

Blundell, R., and Bond, S.,(1998). GMM estimation with persistent panel data: An application to production functions. The institute for fiscal studies working paper series No. W99/4. Paper presented at the eighth international conference on panel data Göteborg University, June 11-12, 1998. Windmeijer, Frank

Blundell, R., Steve, B., and Frank, W., (2000). Estimation in dynamic panel data models: Improving on the performance of the standard GMM estimator, IFS Working Papers, No. W00/12, Institute for Fiscal Studies (IFS), London, http://dx.doi.org/10.1920/wp.ifs.2000.0012.

Boniface, O., and Manaseh O., (2017). Key issues in regional integration vol.5. An annual publication of COMESA Secretariat, Lusaka, Zambia.

Carim, X., (1997). Multilateral trading, regional integration and the Southern African Development Community. The South African Journal of Economics, 65 (3): 334-353.

Cassim, R., (2001). The determinants of intra-regional trade in Southern Africa with specific reference to South Africa and the rest of the region. DPRU Working Paper 01/51. University of Cape Town.

Centre d'Etudes Prospectives et d'Informations Internationales (2012). The geodist database. Paris: CEPII. Retrieved from: http://www.cepii.fr/

Cernat, L., (2001). Assessing regional trade arrangements: Are South–South RTAs more trade diverting?. UNCTAD Policy Issues in International Trade and Commodities Study Series No. 16, New York and Geneva, United Nations.

Cernat, L., (2003). Assessing south–south regional integration: same issues, many metrics. UNCTAD, Policy issues in international trade and commodities study series No. 21. New York and Geneva, United Nations.

Cheng, I, and Wall, H., (2005) . Controlling for heterogeneity in gravity models of trade and integration. https://EconPapers.repec.org/RePEc:fip:fedlrv:y:2005:i:jan:p:49-63:n:v.87no.1

Chandran, B., (2010). Trade complementarity and similarity between India and Asean countries in the context of the RTA. VVM’s Shree Damodar college of commerce and economics, Margao, Goa. See online at http://mpra.ubuni-muenchen.de/29279/.

Chen, Ch., (2014). The dynamics of privatization in China (1994 ‐ 2008): An empirical and econometric analysis. PhD thesis department of economics, school of Oriental and African studies, university of London. Retrieved from http://eprints.soas.ac.uk/18444/1/Chen_3593.pdf.

Clark, P., Tamirisa, N., Wei, S., Sadikov, A., & Zeng, L., (2004). Exchange rate volatility and trade flows-some new evidence. IMF Occasional Paper, 235.

COMESA (2013). International trade statistics bulletin No. 12, division of trade, customs and monetary affairs COMESA secretariat Lusaka, Zambia.

COMESA, (2011). Investment report.

Daniel, Y., (2006). Optimal Obfuscation: Democracy and trade policy transparency. American political science review vol. 100, No. 3.

David J.and Zainal Y., (2003). Developing indicators of ASEAN integration. A preliminary survey for a roadmap. REPSF Project 02/001.

Deardorff, A., (1982). The General validity of the Heckscher-Ohlin Theorem. American economic review, 11, 683-694.

Deardorff, A., (1998). Determinants of bilateral trade: Does gravity work in a neoclassical world ? In the regionalization of the world economy (pp. 7-32). University of Chicago Press.

Deardorff, V., and Stern. R., (1994). Multilateral Trade Negotiations and Preferential Trading Arrangements, in Alan V. Deardorff and Robert M. Stern, eds., Analytical and Negotiating Issues in the Global Trading System. Ann Arbor: University of Michigan Press, 1994, 27-85..

Didier, L., and Hoarau, J., (2014). Determinants of bilateral trade between BRICs and Sub Saharan Africa: what the gravity model tells us? CEMOI, University of La Reunion.

Djankov, S., Freud, C., and Pham, C (2006). Trading on Time. Available at https://pdfs.semanticscholar.org/21cc/05272934cb1f89b1b3765e252db108c7a5c7.pdf

Djoumessi, E. and Bala, A., (2017). The analysis of borders effects in intra-African trade. Economic Research Southern Africa (ERSA).

Douglas, A., (2014). Trade agreement: Trade creation, trade diversion estimates of trade effects of COMESA’s expansion. Catholic University of Bukavu, Belgium.

Ebaidalla, E., (2016). Assessing the success of SADC regional trade integration: A comparative Analysis with ASEAN and MERCOSUR trade Blocs. University of Khartoum, Sudan.

ECA, (2012). Overview of developments in regional integration in Africa. Paper presented on the meeting of the Committee of experts of the 5th joint annual meetings of the AU conference of ministers of economy and finance and ECA conference of African ministers of finance, planning and economic development Addis Ababa, Ethiopia.

Eden, K., (2008). Trade among COMESA countries: A gravity approach. http://www.aau.edu.et/library/resources/aau-institutional-repositoryelectronic-thesis-and-dissertation/.

Edmonds, CH., and Li, Y., (n.a). A new perspective on China trade growth: Application of a new index of bilateral trade intensity. Available at http://www.economics.hawaii.edu/research/workingpapers/WP_10-25.pdf

Eita, J., (2008). Determinants of Namibian exports: A gravity model approach, University of Namibia, Namibia.

Ekanayake, E., and Ledgerwood, J., (2009). An analysis of the intra-regional trade in the Middle East and North Africa region. The International Journal of Business and Finance Research Volume 3 Number 1.

Elbushra. A., Karim, I. and Suleiman, I., (2011). The role of COMESA in promoting intra-regional agricultural trade: Case study of Sudan. Journal of the Saudi society of agricultural sciences, (2011)10, 59-64. 62-64.

Elitza, M., (2007). Arellano-Bond dynamic panel GMM estimators in Stata. Tutorial with examples using Stata 9.0 (xtabond and xtabond2). Economics department Fordham University.

Elmorsy, S., (2015). Determinants of trade intensity of Egypt with COMESA Countries. Journal of the Global South (2015) 2:5 DOI 10.1186/s40728-014-0002-6.

Elmorsy, B., (2015). Determinants of trade intensity of Egypt with COMESA Countries. Journal of the global south (2015) 2:5 DOI 10.1186/s40728-014-0002-6.

Etale, E., & Etale, L., (2016). The Relationship between exports, foreign direct investment and economic growth in Malaysia. International journal of business management and economic research (IJBMER), Vol 7(2),2016, 572-578.

Eric Doumbe, D., and Thierry, B., (2015). A Gravity Model Analysis for Trade between Cameroon and Twenty-Eight European Union Countries. Journal of Social Sciences , Vol.3 No.8.

EU (2013). International trade and foreign direct investment. Pocketbooks, 2013 edition, European Union.

Feenstra, R., (2002). Border Effects and the Gravity Equation: Consistent Methods for Estimation. The Canadian Journal of Economics 49(5), 491–506.

Fergin, E., (2011). Tangled up in a spaghetti bowl: Trade effects of overlapping preferential trade agreements in Africa. Lund University schools of economics and management.

Frost, J., (2019). Multicollinearity in regression analysis: Problems, Detection, and Solutions. http://statisticsbyjim.com/regression/multicollinearity-in-regression-analysis /

Gathii, J., (2010). African regional trade agreements as flexible legal regimes. North Carolina journal of international law and commercial regulation. Volume 35|number 3 article3.

Gbetnkom, D, (2006). Market implications of COMESA’s accession to the free trade area: A gravitational investigation. Journal of African Development (JAD) Spring 2006 | Volume 8 #1.

Geda, A. and Seid, E. (2015). The potential for internal trade and regional integration in Africa. Journal of African Trade 2 (2015) 19–50. Available online at www.sciencedirect.com.

Geda,A., and Kibret, H., (2002). Regional economic integration in Africa: A review of problems and prospects with a case study of COMESA. Journal of African economies, June 2008, volume 17, issue 3. DOI: 10.1093/jae/ejm021.

George, O., and Ansah, A., (2014). Intra-African trade: Issues involved in improving Ghana's trade with the rest of Africa, ISSN 2224-607X (Paper) ISSN 2225-0565 (Online) Vol.4, No.2, 2014.

Giuliano, P., Mishra, P., and Spilimbergo, A., (2012). Democracy and reforms: Evidence from a new dataset, NBER Working Paper Series, WP 18117, 1-52

Gordillo, D., Stokenberga, A., and Schwartz, J., (2010). Central America's Intra and Extra-Regional Trade Potential: A Gravity Model Approach to Understanding Regional Integration. Economics Unit, LCSSD, the World Bank.

Greenaway, D., and Chris, M., (2002). Regionalism and Gravity. Scottish Journal of Political Economy, Vol. 49, pp. 574-585, 2002. Available at SSRN: https://ssrn.com/abstract=343200

Grossman, G., and Helpman, E., (2005). Outsourcing in a Global Economy, Review of Economic Studies, vol. 72(1), p. 135–159.

Hamanaka, S., (2013). Cross-Regional Comparison of Trade Integration: The Case of Services, Asian Development Bank Working Paper Series on Regional Economic Integration, No. 108.

Harrison, A, and Clare, R., (2007). Trade, Foreign Investment, and Industrial Policy. University of California at Berkeley and NBER and Pennsylvania State University and NBER.

Haveman,J., and Hummels, D., (2004). Alternative hypotheses and the volume of trade: the gravity equation and the extent of specialization. Canadian Journal of Economics, 2004, vol. 37, issue 1, 199-218.

Head, K. (2003). Gravity for beginners, mimeo, University of British Columbia.

Helpman, E., & Krugman, P., (1985). Market structure and foreign trade: Increasing returns, imperfect competition and the international economy: MIT Press, Cambridge.

Helpman, E., & Krugman, P., (1989). Trade policy and market structure. Cambridge: Mass: MIT Press.

Helpmen, E., (1975). Intra-Industry Trade: The theory and measurement of international trade in differentiated products. Journal of international economics , 11(3), 305-340.

Helpman, E., Melitz, M. and Rubinstein, Y., (2008). Trading partners and trade volumes, Quarterly Journal of Economics 123: 441–87.

Henry, K. (2015). Regional economic integration and exports performance in the COMESA region (1980-2012). International journal of business and economics research. Vol. 4, No. 1, 2015, pp. 11-20. doi: 10.11648/j.ijber.20150401.12.

Herrera, E., (2013). Comparing alternative methods to estimate gravity models of bilateral trade. Empirical Economics, Springer, vol. 44(3), pages 1087-1111.

Hoekman, B., and Djankov, S., (1997). Determinants of Export Structure of Countries in Central and Eastern Europe. The World Bank Economic Review 11:3, 471–487

Hubert. E., Andreas, Ninez, P., and Barbara, A., (2015). International trade statistics, World Trade Organization. Available online at www.wto.org/stats

Huong, B., (2012). Open regionalism in the Asia-Pacific rim: The case of Vietnam during the 1990s. French-Vietnamese Center for Management Education CFVG 54 Nguyen van Thu, District 1, Hochiminh city, Vietnam. See https://apebh2012.files.wordpress.com/2011/05/bui-paper_apebh-2012-canberra.pdf

Hummels, D., and Levinsohn, J., (1995), Monopolistic Competition and International Trade: Reconsidering the Evidence, the Quarterly Journal of Economics, 110, (3), 799-836.

Hyun,J., and Hong, J, (2005). Free Trade Agreements in East Asian Countries: What Has Been Done and What Needs to Be Done ? ISSN 1598-2769, Journal of International Economic Studies Vol. 9, No. 1, June 2005.

Ibrahim, Sh., and Obiageli, N., (2015). Bi-regional integration in Africa: An evaluation of the major challenges of the Common Market for Eastern and Southern Africa (COMESA). International Journal of Multidisciplinary Research and Modern Education (IJMRME). ISSN (Online): 2454 – 6119, volume I, issue I, 2015.

Iringo, E., (2005). Regional economic integration: The challenge of dual membership to Kenya- with special reference to EAC and COMESA. University of Nairobi.

Jelena, T., and Łukasz, K., (2015). The determinants of intra-regional trade in the Western Balkans, Zb. rad. Ekon. fak. Rij. 2015 vol. 33.

Jodie, K., Massimiliano, C., & Jane, K., (2010). Common wealth Secretariat. Impediments to Intra- regional trade in sub-saharan Africa, overseas development institute.

Jordaan, A., (2014). The impact of trade facilitation factors on South Africa's exports to a selection of African countries. URI: http://hdl.handle.net/2263/42655

Juan, M., and Josep, M., (2007). The wise use of dummies in gravity models: export potentials in the EURO MED region. “Documentos de trabajo. N. º 0720, Banco de España”, Madrid.

Jung, T., and Jin, Y., (2005). Free trade agreements in East Asian countries: What has been done and what needs to be done? Journal of international economic studies vol. 9, No. 1.

Kabir, M., Uddin, M., and Ullah, H., (2014). Enhancing regional trade potentials and economic cooperation among the SAARC Countries: Exploring major challenges and propositions. European journal of business and management, ISSN 2222-1905 (Paper) ISSN 2222-2839 (Online) Vol.6, No.34.

Kalaba, M., and Kirsten, J., (2012). Determinants of trade patterns and comparative advantage of processed agricultural products in SADC.

Kandogan, Y., (2009). A gravity model for components of imports. Review of applied economics, 5(1-2), 99-115.

Kareem, F, Bernhard, B., & Martínez-Zarzoso, I., (2016). Fitting the gravity model when zero trade flows are frequent: A comparison of estimation techniques using Africa's trade data, global food discussion papers, No. 77.

Henry Tumwebaze Karamuriro, Wilfred Nahamya Karukuza. Determinants of Uganda’s Export Performance: A Gravity Model Analysis.

International Journal of Business and Economics Research. Vol. 4, No. 2, 2015, pp. 45-54. doi: 10.11648/j.ijber.20150402.14

Henry Tumwebaze Karamuriro, Wilfred Nahamya Karukuza. Determinants of Uganda’s Export Performance: A Gravity Model Analysis.

International Journal of Business and Economics Research. Vol. 4, No. 2, 2015, pp. 45-54. doi: 10.11648/j.ijber.20150402.14

Karamuriro, H., (2015). Determinants of Uganda’s Export Performance: A Gravity Model Analysis. International Journal of Business and Economics Research. Vol. 4, No. 2, 2015, pp. 45-54.

Kassaye, E., (2008). Trade among COMESA countries. A Gravity approaches. Addis Ababa university school of graduate studies. E-resource http://etd.aau.edu.et/handle/123456789/11.

Keane,J., Calì, M., and Kennan,J., (2010). Impediments to Intra-Regional Trade in Sub-Saharan Africa, Overseas Development Institute, and London. Available on https://www.odi.org/sites/odi.org.uk/files/odi-assets/publications-opinion-files/7482.pdf Jodie.

Kenani, M., (2014). Impact of FDI Inflows, trade openness and inflation on the manufacturing export performance of Tanzania: An econometric study. International journal of academic research in economics and management sciences vol. 3, No. 5 ISSN: 2226-3624.

Khandelwal, P., (2004). COMESA and SADC: Prospects and Challenges for Regional Trade Integration. IMF Working Paper. Policy Development and Review Department.

Kim, H., (2002). Has trade intensity in ASEAN +3 really increased? Evidence from a gravity Analysis. Korean institute for international economic policy, Working Paper 02-12

Korinek, J., and Melatos, M., (2009). Trade Impacts of selected regional trade agreements in agriculture, OECD trade policy working papers, No. 87, OECD publishing, doi:10.1787/225010121752.

Krugman, P., (1980). Scale economies, product differentiation and the pattern of trade, American Economic Review 70: 950–9.

Kumar, B., and Nath., B, (2007). Gains and Losses of India-China Trade Cooperation - A Gravity Model Impact Analysis. CESifo Working Paper Series No. 1970. Available at SSRN: https://ssrn.com/abstract=985274.

Lane, P., & Milesi-Ferretti, G., (2008). International investment patterns. The review of economics and statistics, 90(3), 538-549.

Laura, M., (2007). New determinants of bilateral trade: An Empirical analysis for developed and developing countries, Universitat Jaume. I.

Lee, Ch., (2002). Korea’s FDI Outflows: Choice of Locations and Effect on Trade, KIEP Working paper 02-07, KIEP.

Lee, J., Park, I., and Shin, K., (2008). Proliferating Regional Trade Arrangements: Why and Whither? https://doi.org/10.1111/j.1467-9701.2008.01143.x

Linneman, H., (1966) An Econometric Model of International trade flows. Amsterdam: North Holland Publishing Co.

Liu, X., Wang, C., & Wei, Y., (2001). Causal links between foreign direct investment and trade in China. China Economic Review, 12(2), 190-202.

Longo, R., & Sekkat, K., (2004). Economic obstacles to expanding intra-African trade. World Development, 32(8), 1309-1321.

Madyo, M., (2008). The importance of regional economic integration in Africa. University of South Africa.

Mansfield, Milner, H., and Rosendorff, B., (2002). Why democracies cooperate more: Electoral control and international trade agreements. International Organization, Volume 56, Number 3, pp. 477-513 (Article) Published by the MIT Press.

Mariana, D., and Elena, S., (2014). The analysis of the factors influencing the international trade of The Slovak Republic. 2nd global conference on business, economics, management and tourism, Prague, Czech Republic. Procedia economics and finance. Available online at www.sciencedirect.com.

Marie, S., and Eric, P., (2011). Discussion papers in economics: A gravity model approach to estimating prospective trade gains in the EU accession and associated countries. Nottingham Trent University, United Kingdom.

Martinez-Zarzaso, I., and Nowak-Lehmann, F., (2001). Augmented Gravity Model : An Empirical Application to Mercosur-European Union Trade Flows. Journal of Applied Economics, 4(2): 291-316.

Martínez-Zarzoso, I., and Horse wood, N. (2005). Regional trading agreements: Dynamic panel data evidence from the gravity model. Universidad Jaume I and Instituto de Economía Internacional and University of Birmingham. See http://www.etsg.org/ETSG2005/papers/inma.pdf.

Mary A., Dyana N., and Won K., (2002). International Trade and Foreign Direct Investment: Substitutes or Complements. Journal of Agricultural and Applied economics, 34.2(August 2002):289-302, Southern Agricultural Economics Association.

Mengesha, N., (2009). Trade Effects of Regional Economic Integration in Africa: The Case of SADC (Evidence from Gravity Modeling Using Disaggregated Data) . Trade and Industrial Strategy. Thematic Working Group Paper. Retrieved from http://www.tips.org.za/.

Mo Ibrahim Foundation (2014). Facts and figures. Regional integration: Uniting to compete.

Mold, A., and Mukwaya, R., (2016). Modeling the economic impact of the tripartite free trade area: Its implications for the economic geography of Southern, Eastern and Northern Africa. Journal of African Trade Volume 3, Issues 1–2, Pages 57-84

Moser, P., (1997). Reasons for regional integration agreements, Intereconomics, ISSN 0020-5346, Nomos Verlagsgesellschaft, Baden-Baden, Vol. 32, Iss. 5, pp. 225-229, http://dx.doi.org/10.1007/BF02929831.

Murinde, V., (2001). The Free Trade Area of the Common Market for Eastern and Southern Africa. England: Ashgate.

Musila , J., (2005). The intensity of trade creation and trade diversion in COMESA, ECCAS and ECOWAS: A comparative Analysis. Journal of African Economies, 2005, vol. 14, issue 1, 117-141.

Muthoni, R., (2016). Regional economic integration in Africa: A review of problems and prospects with a case study of East African Community. University of Nairobi, Institute of diplomacy and international studies.

Mwangi, S., Zenia, A., and Brandon, R., (2016). Introduction: Intra-African trade in context, Brookings Africa growth initiative, Washington, DC. Retrieved from www.brookings.edu/wp-content/uploads/2016/07/01_intro_intra_african_trade.pdf

Nick, Ch., (2005). The Private Sector's Perspective, Priorities and Role in Regional Integration and Implications for Regional Trade Arrangements. Discussion Paper No. 66, European Centre for Development Policy Management(ecdpm).

Olaniyan R., (2008). Challenges in achieving regional integration in Africa. A keynote address at the southern Africa development forum on progress and prospects in the implementation of protocols in southern Africa organized by UNECA-SA Lusaka, Zambia.

O’Rourke, K., (2006). Democracy and Protectionism. IIIS Discussion Paper No. 191, institute for international integration studies.

Otieno, G., (2013). Welfare effects of economic integration: The case of COMESA. University of Nairobi, Kenya. Retrieved from http://41.89.101.166:8080/xmlui/bitstream/handle/123456789/4314/Otieno_Welfare%20Effects%20Of%20Economic%20Integration.pdf?sequence=1&isAllowed=y

Ouma, D., (2016). Trade agreement and agricultural trade in East African Community. African Journal of Economic Review, Volume IV, Issue 2.

Pesaran, M., (2004). General Diagnostic Tests for Cross Sectional Dependence in panels. University of Cambridge, USC and IZA Bonn. Discussion paper No. 1240, Germany. Available at http://ftp.iza.org/dp1240.pdf.

Pitigala, N., (2005). What does regional trade in South Asia reveal about future trade integration ? Some Empirical Evidence. World Bank policy research working paper 3497.

Poissonnier, A., (2016). Solving for Structural Gravity in Panels. European Economy Discussion Papers 040. European Commission.

Pontet, J., & Udvari, B., (2016). Effects of Trade on Democracy: How does the European Union Foster Democracy in ACP-states via Trade?. Retrieved from http://www.etsg.org/ETSG2016/Papers/151.pdf

Poyhonen, P., (1963) A Tentative Model for the Volume of Trade between Countries. Weltwirtschaftliches Archiv, 90, 93-100. http://www.jstor.org/stable/40436776

Qadri, H., (2012). An analysis of trade flows among ECO member countries and potential for a free trade area. Thesis submitted to School of International Business Faculty of Business and Law, Victoria University, Melbourne, Australia. Available on http://vuir.vu.edu.au/21475/1/Hussain_Mohi-ud-Din_Qadri.pdf

Sabra, M., (2016) : Government size, country size, openness and economic growth in selected MENA countries, International Journal of Business and Economic Sciences Applied Research (IJBESAR), ISSN 2408-0101, Eastern Macedonia and Thrace Institute of Technology, Kavala, Vol. 9, Iss. 1, pp. 39-45.

Sako, S., (2006 ). Challenges facing Africa's regional economic communities in capacity building. ACBF occasional papers, no. 5. Harare: ACBF.

Sannassee, R., Boopendra, S., and Tandrayen, V., (2011). Regional trade integrations: A Comparative Study of African RTAs. University of Mauritius, Reduit, Mauritius. Available at http://sites.uom.ac.mu/wtochair/Conference%20Proceedings/15.pdf

Santos, S., and Tenreyro, S., (2006). The Log of Gravity. The Review of Economics and Statistics, 88, pp.641-658.

Santos, S., and Tenreyro, S., (2009). Further Simulation Evidence on the Performance of the poisson pseudo-maximum likelihood estimator. Center for Economic Performance (CEP) discussion paper No 933.The London school of economics and political science.

Sarah, C., (2012). The Theoretical Foundation of Gravity Modeling: What are the developments that have brought gravity modeling into mainstream economics ? Copenhagen Business School, Denmark.

Schiff, M., and Winters, L., (2003), Regional integration and development. The World Bank, Washington, DC. 20433.

Seid, E., (2013). Regional Integration and Trade in Africa: Augmented Gravity Model Approach. The Horn Economic and Social Policy Institute (HESPI) Addis Ababa, Ethiopia.

Shepherd, B., (2016). The Gravity Model of International Trade: A User Guide (An updated version). Asia-Pacific Research and Training Network on Trade (ARTNeT), United Nations publication.

Simionescu, M., (2014). The Relationship between Trade and Foreign Direct Investment in G7 Countries a Panel Data Approach. Journal of Economics and Development Studies June 2014, Vol. 2, No. 2, pp. 447-454. Published by American Research Institute for Policy Development.

Sohn, Ch., (2001). A Gravity Model Analysis of Korea's trade patterns and the effects of a regional trading arrangement, Korea institute for international economic policy, working paper series vol. 2001-09 April 2001.

Souad, M., Mohamed, S. and Kamel, F., (2015). An Empirical assessment of Intra-regional trade relationships: The GCC context . University of Sharjah journal of international Refereed periodical of Humanities and Social Sciences. Vol. 12, No. 1 Sha’ban 1436 H. / June 2015 AD. ISSN: 1996-2339.

StataCorp. (2013). Stata: Release 13. Statistical software. College station, TX: StataCorp LP.

Sultan, Z., (2013). A causal relationship between FDI Inflows and export: The Case of India. Journal of economics and sustainable development ISSN 2222-1700 (Paper) ISSN 2222-2855 (Online) Vol.4, No.2.

Swapan, K., and Biswa, N., (2007). Gains and losses of India-china trade cooperation – A gravity model impact analysis, Center for Economic Studies & Ifo Institute for Economic Research, working paper no. 1970 category 7: Trade policy. Available at https://www.cesifo-group.de/DocDL/cesifo1_wp1970.pdf.

Swenson, D., (2004). Foreign Direct Investment and Mediation of Trade Flows, Review of International Economics, 12 (4), pp. 609-29.

Tafirenyika, Chidoko, C., and J. Zivanomoyo, J., (2009). Determinants of Intra-industry trade between Zimbabwe and its trading partners in the Southern African Development Community Region (1990-2006), Journal of social sciences 5(1), Windhoek, Namibia.

Tafirenyika, M., (2014). Intra-Africa trade: Going beyond political commitments. From Africa Renewal. Available at https://www.un.org/africarenewal/magazine

Tansey, M., and Hansan, Th., (2013). The Gravity Model of Trade Applied to Developing Countries, Rockhurst University.

Taole, T., (2014). Multi-membership in African regional trade agreements: A focus on SADC and COMESA. Dissertation submitted in partial fulfillment of the requirements for the degree Magister Legum in import and export law at the potchefstroom campus of the North-WestUniversity. Available at https://repository.nwu.ac.za/bitstream/handle/10394/15604/Taole_TE_2014.pdf?

Tchouassi, G., (2013). Are Trade liberalization and democracy driving development in central Africa region ? Empirical lessons. Journal of social and development sciences, vol. 4., No. 3. pp.131-140

Teunissen, J., (ed.) (2005). Africa in the World economy: The national, regional and international challenges. Fondad, The Hague.

Tinbergen, J., (1962). Shaping the World Economy, Twentieth Century Fund.

Tomasziw, I.,and Kirkpatrick,C.,(2009). Trade facilitation and manufactured exports: Is Africa different ? World Development, 37(6), 1039–1050.

Tuffour, J., Balchin,N., Calabrese, L., & Mendez-Parra, M.,(2016). Trade facilitation and economic transformation in Africa. Paper prepared jointly by supporting economic transformation programme for the 2016 African transformation forum in Kigali 14-15 March 2016.

Umurungi, F. (2005). A critical overview of regional trade integration: lessons for COMESA. Faculty of economics and management sciences, University of the Western Cape, South Africa.

UN-OHRLLS (2013). The development economics of landlockedness: Understanding the development costs of being landlocked. United Nations Office of the High Representative for the Least Developed Countries, Landlocked Developing Countries and Small Island Developing States (UN-OHRLLS). Retrieved from http://www.lldc2conference.org/custom,content/uploads/2014/04/Dev-Costs-of-landlockedness11.pdf

UN-OHRLLS (2014). Improving trade and transport for landlocked developing countries. A ten-year review. World Bank-United Nations report in preparation for the 2nd United Nations Conference on Landlocked Developing Countries (LLDCs). http://unohrlls.org/custom content/uploads/2013/09/Improving-Trade-and-Transport-for-Landlocked-Developing-Countries.pdf

UNCTAD and WTO secretariats (n.a). A practical guide to trade policy analysis. Retrieved from https://www.wto.org/english/res_e/publications_e/wto_unctad12_e.pdf

UNCTAD (2006). Trade capacity development for Africa: Trade negotiations and Africa series: no. 3, policy issues for African countries in multilateral and regional trade negotiations. New York and Geneva.

UNCTAD (2009). Economic development in Africa report 2009: Strengthening regional economic integration for Africa’s development. New York and Geneva. Retrieved from http://unctad.org/en/Docs/aldcafrica2009_en.pdf.

UNCTAD and AU Commission (2012). Trade liberalization, investment and economic integration in African regional economic communities towards the African common market. United Nations Geneva, Switzerland.

UNCTAD (2013). Economic development in Africa report 2013. Intra-African trade: Unlocking private sector Dynamism. New York and Geneva. Retrieved from http://unctad.org/en/PublicationsLibrary/aldcafrica2013_en.pdf

UNCTAD (2016). African continental free trade area: Policy and negotiation options for trade in goods. https://unctad.org/en/PublicationsLibrary/webditc2016d7_en.pdf

UNECA (2017). COMESA Trade and Market Integration. Retrieved from http://www.uneca.org

UNECA (2016). Tripartite intra-regional trade Agreement. Retrieved from http://www.uneca.org

UNECA, AU and AfDB (2017). Assessing Regional Integration in Africa VIII: Bringing the Continental Free Trade Area About. Addis Ababa, Ethiopia.

Urata, S., and Okabe, M., (2007). The impacts of free trade agreements on trade flows: An application of the gravity model approach. RIETI discussion paper Series 07-E -052 revised.

Vemuri, V. K., & Siddiqi, S., (2011). An Estimation of the latent bilateral trade between India and Pakistan using panel data methods. Global economic review, 40(1), 45-65.

Vinaye, A., Kennedy, M. and Zuzana, B., (2011). Impediments to regional trade integration in Africa, volume 2 • Issue 11 September 2011.

William A., and James D., (2007). Handbook on international trade policy. Available at electronic library http://links.giveawayoftheday.com/b-ok.org/.

Wintoki, M., Linck, J., and Netter, J., (2012). Endogeneity and the dynamics of internal corporate governance. J. Financ. Econ. 105 (3), 581–606.

Wonnacott, P., and Lutz, M., (1989). Is there a case for free trade areas ? Free Trade Areas and U.S. Trade Policy edited by Jeffrey J. Schott, Institute for International Economics, pp. 59-84.

World Bank (2010). Doing Business Report: Reforming through difficult time. Retrieved from http://www.doingbusiness.org.

World Integrated Trade Solution (WITS) data base (2017). WITS provide access to international merchandise trade, tariff and non-tariff measures (NTM) data. http://wits.worldbank.org/

World Trade Report (2011). The WTO and preferential trade agreements: From co-existence to coherence. Retrieved from https://www.wto.org/english/res_e/booksp_e/anrep_e/world_trade_report11_e.pdf

WTO and UNCTAD secretariats (2012). A Practical Guide to Trade Policy Analysis. See https://www.wto.org/english/res_e/publications_e/practical_guide12_e.htm

Xu Wang, B., (2016). A multifaceted panel data gravity model analysis of Peru’s foreign trade. Cornell University, 109 Clark Hall, Ithaca, NY. Shanghai, China. Retrieved from https://arxiv.org/ftp/arxiv/papers/1612/1612.01155.pdf

Yabu, N., (2014). Assessing the intra-SADC trade in goods and services. Journal of economics and sustainable development. ISSN 2222-1700 (Paper) ISSN 2222-2855 (Online), Vol.5, No.28.

Yang, Y., and Gupta, S., (2005). Regional trade arrangements in Africa: Past performance and the way forward. IMF working paper (WP/05/36). Retrieved from http://onlinelibrary.wiley.com/doi/10.1111/j.1467 8268.2007.00169.x/epdf.

Yotov, Y ., Piermartini, R., Monteiro, J and Larch, M. (2016). An advanced guide to trade policy analysis: The structural gravity Model. WTO publications retrieved from https://www.wto.org/english/res_e/booksp_e/advancedwtounctad2016_e.pdf

Yong, Ch., and Tan, H., (2007). The Impact of AFTA on Japan–ASEAN trade flows. Jurnal ekonomi Malaysia41(2007)91-109.

Yu, M., (2010). Trade, democracy, and the gravity equation. Journal of development economics, 91, pp. 289-300.

Zhang, H., (2005), How does FDI affect a host country’s export performance? The Case of China, paper presented in international conference of WTO, China, and the Asian economies, III. In Xi’an, China, June 25-26, 2005.

APPENDICES

7.1: Trade Intensity Index among members of COMESA (2000-2016).

Abbildung in dieser Leseprobe nicht enthalten

Source: Author, computed based on the data of IMF, DOTS, 2017

7.2: COMESA Regional Integration, Selected Comparative indicators of member countries (2016).

Abbildung in dieser Leseprobe nicht enthalten

Source: Authors' calculations based on data from World Development Indicators, 2017

7.3: COMESA Member States (As of December 2016)

Abbildung in dieser Leseprobe nicht enthalten

7.4: IGAD member states as of December 2016

Abbildung in dieser Leseprobe nicht enthalten

7.5: Correlation Matrix

Abbildung in dieser Leseprobe nicht enthalten

Source: Author`s estimation result

7.6 African RECs and intra-regional trade (% of RECs total exports)

Abbildung in dieser Leseprobe nicht enthalten

Source: Adopted from Geda and Seid (2015).

7.7 Curriculum vitae

Abbildung in dieser Leseprobe nicht enthalten

3. Training undergone

- Project planning and management, data collection and analysis, budget preparation and management, operational planning organized and conducted by Oromia Bureau of Finance and Economic Development from 17 December 2007 to 02 January 2008
- Project planning, Implementation, Monitoring and Evaluation conducted by Ethiopian Management Institute from Jan.31/2011 to Feb.18/2011
- Short term training on SPSS training undertaken by Addis Ababa Finance and Economic Development Bureau in coordination with SOFTNET PLC
- Integrated budget and expenditure system (IBEX)
- Introduction to computer, MS-Window, MS-Word, MS-Excel, MS Access from Enkoteck Computer service Trading,
- Strategic Management conducted by Ethiopian Institute of Financial Studies from August 17 to 28/2015.

4. Experiences

Abbildung in dieser Leseprobe nicht enthalten

5. Computer skills

Abbildung in dieser Leseprobe nicht enthalten

6. Language

Abbildung in dieser Leseprobe nicht enthalten

7. References

- Fantu Guta, (PHD) Addis Ababa university
- Mulugeta Tujuba, Budget, planning, monitoring & evaluation core process leader
- Paulos Gemechu, Head of the board of Oromia private schools Association
- Chala Lema (MA), Division manager at Oromia International Bank S.c

7.8 Multiple Memberships of COMESA and IGAD’s Members

Abbildung in dieser Leseprobe nicht enthalten

Source: Author, Note: X denotes membership of RTA.

[...]

---

¹ Brooking (2012)OOKING

² See https://www.uneca.org/our-work/regional-integration-and-trade

³ Reductions in administration, transaction costs, and the elimination and/or harmonization of standards and customs clearings procedures

⁴ Sohn ( 2001)

⁵ Anderson (2016)

⁶ A. Douglas, op. cit, p.1

⁷ Algeria, Libya, Mauritania, Morocco, and Tunisia

⁸ Egypt, Sudan, Saudi Arabia, Yemen, Oman, Kuwait, the United Arab Emirates, Israel, Jordan, Lebanon, Syria, Palestine, and Iraq.

⁹ See https://www.brookings.edu/wp-content/uploads/.../01_intro_intra_african_trade.pdf

¹⁰ Abidin, Bakar and Sahlan (2013)

¹¹ The Impacts of Free Trade Agreements on Trade Flows: An Application of the Gravity Model Approach.

¹² COMESA, ECOWAS, IGAD and SADC

¹³ Price control measures, such as multiple exchange rates, or foreign exchange allocation, finance control measures, such as anti-dumping or countervailing measures; quantity restrictions, such as non-automatic licensing, quotas; monopolistic measures, technical measures, such as regulations and customs procedures, and miscellaneous including subsidies.

¹⁴ https://sarpn.org/documents/d0001249/P1416-RI-concepts_May2005.pdf

¹⁵ Full economic integration is the complete unification of fiscal, monetary and social policies coordinated by a supranational authority which is yet elusive in Africa.

¹⁶ See http://unctad.org/en/Docs/aldcafrica2009_en.pdf.

¹⁷ The first three columns show the percentage of the total trade of the regional economic community that goes to Africa. The last three columns show the percentage of the trade with Africa of each regional economic community that happens within its own bloc.

¹⁸ UNECA, 2017

¹⁹ Treaty establishing COMESA

²⁰ The supply capacity increasing effects arise when FDI inflows increase the host country’s production capacity, which, in turn, increase export supply potential.

²¹ The FDI-specific effects arise because the multinational company may have superior knowledge and technology, better information about export markets, or better contact to the supply chain of the parent firm than do local firms.

²² https://freedomhouse.org/report/freedom-world-2017/methodology as of December/2017

²³ Alemayehu and Haile (2008), Musila (2005), Umurungi (2005), Khandelwal (2004), COMESA (2007)

²⁴ UNCTAD (2016)

Frequently asked questions

What is the purpose of this document?

This document appears to be a comprehensive language preview of a dissertation or research paper, providing a structured overview for academic use. It includes the title, table of contents, objectives, key themes, chapter summaries, and keywords for analysis.

What is the structure of the document?

The document follows a standard academic structure, including sections for: dissertation approval, dedication, author's statement, biographical sketch, acknowledgements, abbreviations and acronyms, table of contents, list of tables, list of figures, abstract, chapter introductions and summaries (including literature review, methodology, results & discussion, conclusion and recommendations), references and appendices.

What are the research questions addressed in the study?

The main research question is: What are the factors that affect intra-trade flows within the COMESA region? Specific research questions include: 1. What is the intensity of intra-export trade flows among COMESA countries and which countries are major trade partners in COMESA? 2. What are the key factors that determine the growth of trade integration within COMESA region? 3. How does electoral democracy, overlapping membership of COMESA and IGAD affect intra-trade flows within the region?

What are the main objectives of the study?

The general objective is to identify the factors that affect intra-regional trade among the Common Market for Eastern and Southern African member countries in the period 2000 - 2016. Specific objectives include: 1. Measure the intensity of export trade through trade-linkage and identify the major trade partners of each member focusing on the potential expansion of export trade flows among the COMESA members. 2. Identify the key factors that determine the growth of trade integration within COMESA region. 3. Investigate the intra-trade effects of COMESA members’ electoral democracy, overlapping membership of COMESA and IGAD regional blocs.

What hypotheses are being tested?

The study is guided by the following hypotheses: H1: Bilateral trade is positively related to Gross Domestic Product (GDP), Population size, electoral democracy, Foreign Direct Investment (FDI) inflows, Regional Trade Agreement (RTA) and inversely related to trade resistance among COMESA members. H2: Overlapping membership has negative effect on bilateral trade among COMESA members.

What methodology is used in the study?

The study utilizes a quantitative correlational design, employing statistical and regression analysis with STATA 14.2. The Trade Intensity Index is used, along with an augmented gravity model. Panel data is estimated using Ordinary Least Squares (OLS), Random Effects, and Poisson Pseudo-Maximum Likelihood (PPML) estimators. The theoretical framework primarily relies on the gravity model, specifically an augmented version of the Anderson-Van Wincoop model.

What data sources are used in the study?

Data sources include: IMF (Direction of Trade Statistics), UN COMTRADE, World Integrated Trade Solution, World Development Indicators (WDI), CEPII, AU, UNECA, and Freedom House data.

What are some key abbreviations and acronyms used in the document?

Some key abbreviations include: AEC (African Economic Community), AfDB (African Development Bank), COMESA (Common Market for Eastern and Southern Africa), FTA (Free Trade Area), GDP (Gross Domestic Product), RTA (Regional Trade Agreement), SSA (Sub-Saharan Africa), UNCTAD (United Nations Conference on Trade and Development), UNECA (United Nations Economic Commission for Africa), FDI (Foreign Direct Investment), IGAD (Intergovernmental Authority on Development).

What factors are identified as influencing intra-COMESA trade?

Factors identified as influencing intra-COMESA trade include: Regional Trade Agreement (RTA), distance between members, adjacency, landlockedness, common language, electoral democracy, economic size, population size, overlapping memberships, and foreign direct investment inflows.

What limitations are acknowledged in the study?

The study's scope is limited to COMESA member states from 2000 to 2016. Policy variables like tariffs and NTBs (non-tariff barriers) are not considered. Also, the study acknowledges potential limitations related to the data collection, unobserved heterogeneity, and the reliance on aggregated data.