DETERMINANTS OF FISH TRADE FLOWS AND THE BENEFITS …...BUNDA CAMPUS SEPTEMBER 2017. i DECLARATION...
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DETERMINANTS OF FISH TRADE FLOWS AND THE BENEFITS OF ECONOMIC INTEGRATION IN AFRICA MSc. (AGRICULTURAL AND APPLIED ECONOMICS) THESIS BONFACE NANKWENYA LILONGWE UNIVERSITY OF AGRICULTURE AND NATURAL RESOURCES BUNDA CAMPUS SEPTEMBER 2017
Transcript of DETERMINANTS OF FISH TRADE FLOWS AND THE BENEFITS …...BUNDA CAMPUS SEPTEMBER 2017. i DECLARATION...
DETERMINANTS OF FISH TRADE FLOWS AND THE BENEFITS OF
ECONOMIC INTEGRATION IN AFRICA
MSc. (AGRICULTURAL AND APPLIED
ECONOMICS) THESIS
BONFACE NANKWENYA
LILONGWE UNIVERSITY OF AGRICULTURE AND NATURAL RESOURCES
BUNDA CAMPUS
SEPTEMBER 2017
DETERMINANTS OF FISH TRADE FLOWS AND THE BENEFITS OF
ECONOMIC INTEGRATION IN AFRICA
BONFACE NANKWENYA
BSc. (Agric. Econ.) Malawi.
THESIS SUBMITTED TO THE FACULTY OF DEVELOPMENT STUDIES IN
PARTIAL FULFILMENT FOR THE REQUIREMENTS OF MASTER OF
SCIENCE DEGREE IN AGRICULTURAL AND APPLIED ECONOMICS
LILONGWE UNIVERSITY OF AGRICULTURE AND NATURAL RESOURCES
BUNDA CAMPUS
SEPTEMBER 2017
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DECLARATION
I, Bonface Nankwenya, declare that this thesis is a result of my own original work and
effort, and that to the best of my knowledge, the findings have never been previously
presented to the Lilongwe University of Agriculture and Natural Resources or elsewhere
for the award of any academic qualification. Where other information was sought, it has
been rightfully acknowledged.
Bonface Nankwenya
Signature:
Date:
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CERTIFICATE OF APPROVAL
We, the undersigned, certify that this thesis is a result of the author’s own work and that to
the best of our knowledge, it has not been submitted for any other academic qualification
within the Lilongwe University of Agriculture and Natural Resources or elsewhere. The
thesis is acceptable in form and content, and that the candidate through an oral examination
demonstrated satisfactory knowledge of the field covered by the thesis. The exam was held
on …./…../2017
Major supervisor: Associate Prof. MAR Phiri
Signature:
Date: ….../.…../2017
Supervisor: Prof. Abdi Khalil Edriss
Signature:
Date: ….../...…../2017
Supervisor: Prof. Emmanuel Kaunda
Signature:
Date: ….../.…../2017
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DEDICATION
To my dear parents Mr. and Mrs. Nankwenya, my brothers James, Paul and Chimwemwe,
and my sisters Eunice and Naomi.
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ACKNOWLEGDEMENTS
I would like to sincerely thank Associate Professor M.A.R. Phiri, Professor Abdul Khali
Edriss and Professor Emmanuel Kaunda for their tireless supervision, their guidance and
support throughout the research. I also gratefully acknowledge the support I got from
Professor David Karemera and Kermit Rose from North Carolina State University, Dr.
Horace Phiri and Dr. Thabbie Chilongo for their guidance in data analysis.
Special thanks should also go to African Economic Research Consortium (AERC) for their
financial support towards the study. I would also like to thank the WorldFish through the
Fish Trade Programme and NEPAD Regional Fish Node for funding this research.
I am very grateful to the entire staff of NEPAD Regional Fish Node for their support, both
financial and mentorship, and contributions to the success of this work. My sincere and
unreserved gratitude goes to all who have contributed to the success of this thesis.
To God be the Glory!
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ABSTRACT
This study evaluates the determinants of fish trade flows and the benefits of Regional
Economic Communities (RECs) on fish trade by applying the generalized gravity model.
The study estimates a gravity model using several techniques to overcome estimation
challenges in the presence of zero observations. The analysis finds that Tobit model is
preferred when zero observations are considered. The results revealed that exporters’ GDP,
importers’ GDP, exporters’ fish production, and common border were found to increase
fish trade flows by 9%, 16%, 29% and 57%, respectively. On the other hand, exporters’
fish price, importers’ fish production, and distance reduced fish trade flows by 3%, 4% and
17%, respectively. The study also found that RECs in Africa have significantly enhanced
intra-fish trade flows hence contributing to gross trade creation for fish which supports the
theory of welfare economics. There is also evidence of increased trade flows to non-
member states due to overlapping membership of the economic blocks. This in turn calls
for policies that will encourage regional investment agreements, enforce the
implementation of the signed treaties and protocols, implement policies aimed at increasing
aquaculture productivity within the region, and the formation of tripartite FTAs that will
subsequently lead to the creation of the African economic community.
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TABLE OF CONTENTS
DECLARATION................................................................................................................ i
CERTIFICATE OF APPROVAL ................................................................................... ii
DEDICATION.................................................................................................................. iii
ACKNOWLEGDEMENTS ............................................................................................ iv
ABSTRACT ........................................................................................................................v
LIST OF TABLES .............................................................................................................x
LIST OF FIGURES ......................................................................................................... xi
LIST OF ABBREVIATIONS AND ACRONYMS ...................................................... xii
CHAPTER 1 .......................................................................................................................1
INTRODUCTION..............................................................................................................1
1.1 Background information ............................................................................................ 1
1.2 Problem statement ..................................................................................................... 4
1.3 Study Rationale ......................................................................................................... 6
1.4 Objective of the study ............................................................................................... 6
1.5 Hypotheses ................................................................................................................ 7
CHAPTER 2 .......................................................................................................................8
LITERATURE REVIEW .................................................................................................8
2.1 Introduction ............................................................................................................... 8
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2.2 Definition of basic concepts as used in this study ..................................................... 8
2.3 Regional Economic Communities in Africa ............................................................. 9
2.3.1 Southern African Development Community (SADC) ...................................... 12
2.3.2 Common Market for Eastern and Southern Africa (COMESA) ...................... 12
2.3.3 Economic Community of West African States (ECOWAS) ............................ 12
2.3.4. East African Community (EAC) ..................................................................... 13
2.3.5 Economic Community of Central African States (ECCAS) ............................ 13
2.3.6 Arab Maghreb Union (AMU) .............................................................................. 14
2.4 Estimation Techniques ............................................................................................ 14
2.4.1 Estimation issues: Zero trade flows .................................................................. 14
2.4.2 Dealing with zero trade flows ........................................................................... 15
2.4.3 Panel data estimation ........................................................................................ 16
2.4.3.1 Random effects and fixed effects models ...................................................... 16
2.4.3.2 Hausman and Taylor estimator ...................................................................... 16
2.5 Empirical Studies on Trade Flows and the Benefits of RECs................................. 16
CHAPTER 3 .....................................................................................................................19
METHODOLOGY ..........................................................................................................19
3.1 Introduction ............................................................................................................. 19
3.2 Data Sources ............................................................................................................ 19
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3.3 Conceptual Framework ........................................................................................... 20
3.4 Theoretical framework ............................................................................................ 21
3.4.1 The standard gravity model .............................................................................. 21
3.4.2 Empirical model specification: The generalized gravity model ....................... 23
3.5 Operational Explanation of the variables ................................................................ 24
CHAPTER 4 .....................................................................................................................29
RESULTS AND DISCUSSION ......................................................................................29
4.1 Introduction ............................................................................................................. 29
4.2 Descriptive statistics ................................................................................................ 29
4.3 Imports and exports of fish (and fish products) in Africa ....................................... 31
4.4 Patterns of fish trade in Africa ................................................................................ 31
4.5 Econometric Diagnostics......................................................................................... 35
4.5.1 Normality test ................................................................................................... 35
4.5.2 Random effects test .......................................................................................... 35
4.5.3 Serial correlation test ........................................................................................ 36
4.5.4 Testing for multicollinearity ............................................................................. 36
4.5.5 Testing for unit roots/stationarity ..................................................................... 37
4.5.6 Testing for heteroskedasticity ........................................................................... 37
4.6 The Gravity Model .................................................................................................. 38
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4.6.1 Gravity model with zero trade values dropped .................................................... 38
4.6.1.1 Simple OLS regression or fixed or random effects models ........................... 39
4.6.1.2 Hausman Tylor estimator .............................................................................. 40
4.6.2 Gravity model with zero trade .............................................................................. 42
4.6.2.1 The Tobit regression ...................................................................................... 42
4.6.2.2 The PPML regression .................................................................................... 42
4.7 Choosing the best model ......................................................................................... 45
4.8 Discussion ............................................................................................................ 47
4.8.2 The effects of Africa’s RECs on fish trade flows ............................................. 53
CHAPTER 5 .....................................................................................................................55
CONCLUSION AND POLICY IMPLICATION .........................................................55
5.1 Conclusion ............................................................................................................... 55
5.2 Policy Implications .................................................................................................. 56
REFERENCES .................................................................................................................58
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LIST OF TABLES
Table 2.1 RECs and member countries ............................................................................ 11
Table 3.1 Countries studied for the fish trade flows (Exports and imports) .................... 20
Table 3.2 Variable definition ........................................................................................... 28
Table 4.1 Variable summary ............................................................................................ 30
Table 4.2 Pattern of fish trade in Africa ........................................................................... 34
Table 4.3 Normality test ................................................................................................... 35
Table 4.4 Random effects test .......................................................................................... 36
Table 4.5 Serial correlation test........................................................................................ 36
Table 4.6 Unit root test ..................................................................................................... 38
Table 4.7 Estimates of a gravity model without zero trade flows (Fixed effects, random
effects and Hausman Taylor) ............................................................................................ 41
Table 4.8 Estimates of a Gravity model with zero trade flows (Tobit and Poisson
regression) ......................................................................................................................... 44
Table 4.9 Marginal Effects after a Tobit regression ......................................................... 46
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LIST OF FIGURES
Figure 2.1 Membership of RECs in Africa ...................................................................... 10
Figure 3.1 Conceptual Framework ................................................................................... 21
Figure 4.1 Imports and exports of fish in Africa.............................................................. 31
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LIST OF ABBREVIATIONS AND ACRONYMS
AFTA ASEAN Free Trade Agreement
AMU Arab Maghreb Union
ASEAN Association of Southeast Asian Nations
AU-IBAR African Union Inter-African Bureau for Animal Resources
CAMFA Conference of African Ministers of Fisheries and Aquaculture
CEMAC Central African Economic and Monetary Union
CGE Computable General Equilibrium
COMESA Common Market of Eastern and Southern African States
COREP Regional Fisheries Commission of the Gulf of Guinea
CSSS Community of Sahel-Saharan States
EAC East African Community
ECCAS Economic Community of Central African States
ECOWAS Economic Community of West African States
FAO Food and Agriculture Organization
FTA Free Trade Agreement
GAFTA Greater Arab Free Trade Area
GATT General Agreement on Trade and Tariffs
GDP Gross Domestic Product
HACCP Hazard Analysis Critical Control Point
HTE Hausman and Taylor Estimator
IGAD Inter-Governmental Authority for Development
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LVFO Lake Victoria Fisheries Organization
NEPAD New Partnership for African Development
NPCA NEPAD Planning and Coordinating Agency
NTBs Non-tariff Barriers
PPML Poisson Pseudo Maximum Likelihood
PTA Preferential Trading Area
REC Regional Economic Community
RFBs Regional Fishery Bodies
SACU Southern African Customs Union
SADC Southern African Development Community
SSA Sub-Saharan Africa
WAEMU West African Economic and Monetary Union
WHO World Health Organization
WTO World Trade Organization
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CHAPTER 1
INTRODUCTION
1.1 Background information
The fisheries sector of most African States consists of capture fisheries and aquaculture.
The sector plays an important role in providing nutrition and food security, and
employment to millions of people on the continent. Fish is also the leading export
commodity for Africa, contributing about 19 and 5 percent of total agricultural volumes
and value respectively (African Union Commission – New Partnership for African
Development [AUC-NEPAD], 2014). The fisheries sector contribution to the African
Gross Domestic Product (GDP) and agriculture GDP stands at 1.25% and 6.0%
respectively. There is a wide variation on the contribution of fisheries to the country’s
GDP. For instance, the contribution stands at 0.5% for Kenya, 1.4% in Tanzania, 1.45 in
Mauritius and 45 in Malawi (African Union – Inter-African Bureau for Animal Resources
[AU-IBAR], 2012; Government of Malawi, 2016).
Fish and fishery products have also been recognized as an important source of protein and
essential micronutrients. More than 400 million people in Africa depend on fish as a vital
source of nutrition (Gordon et al., 2013; Allison, 2011). According to Gordon et al. (2013),
fish supply over 25% of animal protein in Africa. Fish occupy an important share in the of
total animal protein supply in most African countries. Ghana is the leading country on the
continent, where fish provide 62% of animal protein supply. The average Africa per capita
fish consumption is 8.3 kilogram, which is lower than world average of 18.9kg (Mapfumo,
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2015) the recommended World Health Organization (WHO) and Food and Agriculture
Organization (FAO) level of 17 kilogram (Mwina, 2012).
Fishery production has been growing at a faster rate than growth in agriculture products.
While the production from capture fisheries is still high and is important for small scale
fishers, it has generally stagnated over the years. Although aquaculture’s contribution to
total fish production is relatively small, it is growing at an exponential rate. The aquaculture
sector is comprised of subsistence producers, small-scale commercial producers and large
industrial scale operations. On the continent, Egypt is the largest aquaculture producer.
Nigeria and Uganda are some of the leading countries in the SSA, accounting for almost
75 percent of aquaculture production (Food and Agriculture Organization [FAO], 2012).
From the available data, fish production is limited to only a few countries with most of the
inland countries depending on aquaculture which is still not well developed. This is why
trade in fish and fish products among African countries is becoming increasingly important.
Globally, fish is one of the most traded food commodities. Developing countries in Asia
and Africa account for the bulk of the world’s fish exports (FAO, 2012). 20% of all
agricultural exports from developing countries are comprised of fish and fishery products.
Shortte (2013) observed that the global demand for fish and fish products has been
increasing, with a rising trend of fish products exports from developing countries.
Considerable intra-regional fish trade takes place within sub-Saharan Africa. However,
intra-regional fish exports are mostly limited to the small pelagic species. Fish exports
from Africa is dominated by a few countries such as Namibia, South Africa, and Senegal
while Nigeria, Ghana, and the DRC are the leading fish importers (Gordon et al., 2013).
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Cocker (2014) noted that Africa is a net importer of fish despite fish being its leading export
agricultural commodity. This has mostly being attributed to rapid population growth that
characterize the continent, and the decline in wild stock of fish. While trading blocs such
as the EU and in Asia have significantly enhanced intra-fish trade, this is not the case for
Africa. The level of intra-regional fish trade has been limited due to, among others,
existence of numerous Tariff and Non-tariff barriers of trade. This has also been coupled
by infrastructure and other technical challenges such as limited ICT usage.
The place of fish in the total agricultural export for African countries has necessitated
debate about globalization and the potential effects of international trade on economic
development and poverty alleviation. Over the past decade, researchers and policy makers
at both national and regional levels have debated about the potential tradeoffs between fish
trade and food security. Some authors argue that fish exports can spur economic growth
through forex generation which can be used to service international debt, import food at
low cost, create jobs, and fund government operations (Ahmed, 2003; Bostock et al., 2004;
Thorpe et al., 2004). On the other hand, other researchers argue that fish trade impacts
negatively on the domestic economy. They argue fish trade leads to fast decline in fisheries
resources such that the local populations suffer in the end (Abgrall, 2003; Abila and Jansen,
1997). Despite these two different views, a study by Béné (2008) found that fish trade does
not impact negatively on food security. There is also no evidence of the potential economic
development effects of fish trade.
Lately, there has been increasing efforts by governments and development partners in
Africa to improve availability of fish to more than 400 million people on the continent. It
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is for this reason that there is increasing emphasis of the importance of trade for the region’s
food security and nutrition and economic development (Ayilu et al., 2016). Fish has also
been identified as one of the commodities for investment and policy support by the RECs
and NEPAD Agency. While there is exists enormous potential for the fisheries sector to
contribute to poverty reduction and food and nutrition security, realizing this potential is
limited by policymakers’ lack of understanding about the operations and dynamics of fish
markets and the inadequate role of RECs in facilitating regional fish trade (Ayilu et al.,
2016).
1.2 Problem statement
Fisheries represent the leading agriculture export commodity for Africa hence forming a
significant element of some national economies. Fisheries and fish trade also play an
important role in poverty alleviation and attainment of food security of the sub-Saharan
African population (Béné, 2008). This trade is particularly important in Africa, where it
has been noted that high fish production is limited to a few countries making trade in fish
and fish products among African countries increasingly important. Despite the food
security and poverty alleviation potential of fish trade in Africa, this type of trade is often
overlooked and neglected in national and regional policy.
While Empirical studies in Africa have used aggregated data to analyze the factors
affecting agricultural trade, the trade effects of REC’s and the effects of non-tariff barriers
on trade flows in the region (United States International Trade Commission [USITC],
2008; Sorescu et al., 2013; Hallaert et al., 2011; FAO, 2007; Meyer et al., 2010;
Dembatapitiya and Weerahewa, 2015; Babatunde, 2006; Teweldemedhin and
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Chiripanhura, 2016; DaSilva, 2010; Kareem, 2014), there has been little analysis, if not
none, of the determinants of trade with special focus on fish trade on the continent. This
has resulted into a knowledge gap as regard to the determinants of fish trade flows as well
as benefits of various RECs on fish trade flows in Africa. This lack of data on the
determinants of fish trade flows and the benefits of RECs on fish trade flows has served to
stimulate this study. In order to harness the economic benefits associated with fish trade,
there is need for an appropriate policy framework and policy process which can be
informed by research in intra-regional fish trade. Inappropriate policy frameworks put at
risk the benefits of increased fish trade for national development (FAO, 2007).
This research is aimed at generating data from within Africa focusing on understanding
dynamics, drivers and trends in intra-regional fish trade. Understanding the drivers of fish
trade flows in Africa is crucial if fish trade is to be promoted and improved in Africa. Such
research can also aid in the formulation of country specific fish trade policies and
regulations that can help in reducing trade barriers that affect intra-regional fish trade and
exacerbate informal fish trade. It is also important to understand how the different RECs
such as Economic Community of West African States (ECOWAS), Economic Community
of Central African States (ECCAS), Common Market of Eastern and Southern African
States (COMESA) and the Southern African Development Community (SADC) and East
African Community (EAC) are creating fish trade flows within the region as this can help
in strengthening their performance and enhancing collaboration.
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1.3 Study Rationale
The overall goal of this research project is to generate and make available information on
the drivers of regional fish trade in Africa and the benefits of RECs. According to Shortte
(2013), the analysis of fish trade is very key in the fisheries sector considering the
importance of fish in the export basket as well as the vital contributions by African fisheries
to employment creation, food supply, poverty reduction, and food and nutrition security. It
is therefore important to strengthen intra-regional fish trade in the region, by among others
investment and policy support to the sector. Such investment and policy support can be
guided by availability of information on the drivers, status and performance of RECs in
facilitating intra-regional fish trade, which this study intends to provide. Knowledge
generated will help inform policy makers and the development partners on the formulation
and implementation of appropriate policies that will promote intra-regional fish trade
flows.
1.4 Objective of the study
The study was aimed at assessing the determinants of regional fish trade flows and the
benefits of regional economic integrations on intra-regional fish trade in Africa so that
policy considerations are put forward to promote fish trade in Africa.
Specifically, the study:
i. Analyzed the determinants of regional fish trade in Africa;
ii. Assessed the effects of membership to SADC, ECOWAS, ECCAS, EAC,
COMESA and AMU on fish trade flows in Africa.
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1.5 Hypotheses
In this study, the following hypotheses were tested:
Ho: GDP, fish prices, fish production, exchange rate, distance, common border, and do not
significantly affect intra-regional fish trade flows in Africa.
Ho: Regional economic integration in Africa does not result in improved intra-regional fish
trade.
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CHAPTER 2
LITERATURE REVIEW
2.1 Introduction
This chapter reviews the literature on the trade agreements in Africa and studies on fish
trade in Africa. The section starts by defining the basic concepts they have been used in
this study. In the second section, a summary of the regional blocs and their associated
policies on fish trade on the continent is provided. The third section provides a review of
the pertinent estimation issues in trade studies, and their implications. The fourth section
reviews some of the studies which have been conducted, the methodologies used, and the
results of such studies. The chapter concludes by assessing the research gaps still existing
in this research area, which is important in order to describe the specific contribution of
this study to this broad research area.
2.2 Definition of basic concepts as used in this study
Fish
Fish – include all edible fresh water, marine and other aquatic organisms from both the
wild and aquaculture
Economic integration
An agreement among several countries in a particular geographic region to reduce and
ultimately remove, tariff and non-tariff barriers to trade. In some cases, countries may even
coordinate their fiscal monetary policies.
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Regional Economic Communities
If countries come together for the purpose of achieving economic integration, then they
form RECs.
Trade creation
In this study, trade creation has been defined as increase in trade between two countries
belonging to the same REC.
Trade diversion
Trade diversion in this study is reduction in trade due to countries not belonging to the
same REC.
2.3 Regional Economic Communities in Africa
Regional integration has a long history in Africa. Lately, there has been a proliferation of
regional blocs in Africa as African countries strive to achieve economic growth through
regional integration. There are more than 14 regional trade or cooperation agreements in
operation. Each country in SSA belongs to at least more than one REC, whose objectives
differs depending on the structure of the REC, but ultimately the common goal is to reduce
trade barriers among member countries (Meyer et al., 2010). Notable RECs in SSA are
ECOWAS, ECCAS and SADC. Other REC’s include WAEMU, SACU, CEMAC, AMU,
IGAD, CSSS and EAC. Figure 2.0 provides the number of member states of selected RECs
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as of 2010 while members to the various RECs are shown in Table 2.0.
Figure 2.1 Number of member countries in Regional Economic Communities
Source: Meyer et al. (2010)
Collaboration amongst African countries on fisheries has been facilitated mostly at the
regional level, with a large Regional Fishery Bodies (RFBs) and RECs actively involved
(World Bank, 2015). The AU established AU-IBAR in its commission in order to support
region-wide coordination and reform. Furthermore, collaborative fisheries management
has been addressed in the NEPAD Planning and Coordination Agency (NPCA) agricultural
program through the Partnership for African Fisheries (PAF). For the fisheries sector, the
RECs have a primary objective of increasing fish production and promotion of fish trade.
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15
6
19
5 5
14
0
2
4
6
8
10
12
14
16
18
20
WAEMU ECOWAS CEMAC COMESA EAC SACU SADC
Nu
mb
er
Regional Economic Community
Number of member countries in Regional Economic Communities
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Table 2.1 Member countries to different Regional Economic Communities in Africa
SADC EAC ECOWAS COMESA ECCAS AMU
Angola Burundi Benin Burundi Angola Algeria
Botswana Kenya Burkina
Faso
Comoros Burundi Libya
DRC Rwanda Cabo
Verde
DRC Cameroon Mauritania
Lesotho Tanzania Côte
d'Ivoire
Djibouti Central African
Republic
Morocco
Madagascar Uganda Gambia Eritrea Chad Tunisia
Malawi
Ghana Ethiopia Congo
Mauritius
Guinea Kenya DRC
Mozambique
Guinea-
Bissau
Libya Equatorial
Guinea
Namibia
Liberia Seychelles Gabon
Seychelles
Mali Madagascar São Tomé and
Príncipe
South Africa
Niger Malawi Rwanda
Swaziland
Nigeria Mauritius
Tanzania
Senegal Rwanda
Zambia
Sierra
Leone
Sudan
Zimbabwe
Togo Swaziland
Uganda
Zambia
Zimbabwe
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2.3.1 Southern African Development Community (SADC)
SADC was established in 1980 and has about 15 members (Table 2.0). One of its objective
is to achieve development and economic growth. While it was projected that SADC will
integrate its monetary policy by 2018, there exists uncertainties on that. With regard to
fisheries, SADC has a protocol on fisheries developed and signed by 14 member countries
in 2001. Among others, this protocol directs its members to reduce barriers to fish trade
and facilitating investments in the sector (SADC, 2001). Although this is the case, the
region faces challenges in boosting fish trade and reducing trade barriers. Fish trade in
SADC is dominated by the small pelagic fish species. These account for 45% of its catches
(Trade and Industrial Policy Strategies and Australian Agency for International
Development [TIPS and AusAID], undated).
2.3.2 Common Market for Eastern and Southern Africa (COMESA)
COMESA came into force in 1994 and it’s the largest REC in Africa. COMESA has 19
members (Table 2.0). The primary purpose of COMESA is to create a free trade region
(African Union, 2010). This has been facilitated by introducing programs aimed at
removing barriers and simplifying trade. In the fisheries sector, COMESA plays an
important role in facilitating and developing fisheries frameworks for the continent.
COMESA also has a fisheries strategy in place aimed at increasing the benefits
communities realize from fish trade (Ngwenya, 2015).
2.3.3 Economic Community of West African States (ECOWAS)
ECOWAS is the major bloc in West Africa established in May 1975. The bloc has a
primary objective of promoting economic integration in all fields of economic activity.
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ECOWAS currently has 15 members (Table 2.0). ECOWAS is one of the regional blocs
where fish is an important component of the economy. The region relies much on fish
exports which are mostly destined for European markets for their foreign reserves
(Katikiro, 2010). The fisheries sector in ECOWAS is imbedded within the region’s
Agricultural Policy, called ECOWAP (Economic Community of West African States
[ECOWAS], 2005).
2.3.4. East African Community (EAC)
EAC came into force in the year 2000 with only five members (Table 2.0). It is one of the
smallest blocs in Africa such that most of its members are also members of other RECs
such as COMESA and SADC. EAC aims for deeper cooperation in all areas and aims to
create a monetary union (United Nations Conference on Trade and Development
[UNCTAD], 2012). The presence of Lake Victoria which is shared by several countries
requires consolidated efforts in management of the resource. It is for this reason that the
Lake Victoria Fisheries Organization (LVFO) was formed in 1994 to manage the Fisheries
in a coordinated manner (East African Community [EAC], 2011). The fisheries sector in
the EAC is an important industry for the region through provision of the needed foreign
currency from fish exports. The sector also supports the livelihoods of many people.
2.3.5 Economic Community of Central African States (ECCAS)
ECCAS was established in 1983 and has 10 member states (Table 2.0). Its main objective
is to coordinate and harmonize policies in order to boost the economic development of
members. ECCAS has not yet implemented its FTA despite its launch decades ago. It is
also one of the blocs where trade is very low (Djemmo Fotso, 2014). ECCAs has made
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agreements lately with NEPAD Agency and COREP aimed at improving the fisheries
sector in the region.
2.3.6 Arab Maghreb Union (AMU)
AMU consists of five Arab speaking countries and was established in 1989. AMU is,
however, yet to achieve progress on its goals due to deep economic and political that exist
in the region. The level trade in the region is lower than that of many of the world’s trading
blocs (African Union, 2016). Fish is one of the region’s main natural resources (World
Bank, 2010). All the five AMU countries are coastal countries such that they rely on marine
fish.
2.4 Estimation Techniques
2.4.1 Estimation issues: Zero trade flows
The methodological approach that has been used to examine trade is very diverse, ranging
from simulation to econometric approach. The simulation approach uses CGE models,
which is advantageous in that it provides for analysis of trade policies before and after
implementation. Their weakness, however, is that it is based on a set of assumptions and
usually set parameters (Negasi, 2009; Simwaka, 2011). The econometric approach mostly
uses the gravity model. The gravity model, however, has its own weaknesses. One
prominent issue is how to deal with zero observations especially when disaggregated data
is used. This is because the log-linear form of the gravity model does not permit the use of
zero trade values. According to Silva and Tenreyro (2006) and Kareem et al. (2016), this
form also poses challenges in the presence of heteroskedasticity. Fortunately, several
methods are available in literature that can remedy this.
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2.4.2 Dealing with zero trade flows
Common practices in dealing with zero values include substituting the zeros by a small
arbitrary number and removing the zeros (Kareem et al., 2016). These methods, however,
have been criticized for lacking concrete theory behind. Zero trade flows arise due to trade
flows recorded as zero or missing. If these are disregarded, then important information
explaining low trade is lost and can lead to biased results. The dropping of zero values also
leads to sample selection bias (Kareem et al., 2016; Gomez-Herrera, 2011).
Because of this, non-linear estimation techniques have become common recently. Such
non-linear methods include Tobit, Heckman model and Poisson Pseudo Maximum
Likelihood (PPML) (Martin and Pham, 2015; Sun and Reed, 2010). The Tobit estimator,
proposed by Tobit (1959), fits well on data observable over some range (Kareem, 2013).
The Heckman model is built to deal with non-random elimination of zeros, which could
otherwise result into selection bias. The work on the use of the PPML was pioneered by
Silva and Tenreyro (2006) who recommended it in the presence of heteroskedasticity and
zero observations.
Despite the rich literature on the techniques for dealing with zero values, there is still debate
among researchers on the most appropriate estimation technique for a gravity model.
Kareem et al. (2016) found that the nature of the dataset and the process of generating the
error term should guide researchers on the technique to use. They recommended the use of
several methods in order to ensure robustness in results. Similarly, Linders (2006) argued
that when choosing the method to use, researchers should put in mind the underlying
economic and econometric theories.
16
2.4.3 Panel data estimation
Panel data estimation was used in this study for all the estimation techniques. The
coefficients of the parameters are more reliable when panel models are used as they allow
for control of factors that dynamic and those factors that are static over time. Commonly
used panel models are fixed effects, random effects and Hausman-Tylor Estimator.
2.4.3.1 Random effects and fixed effects models
The random and fixed panel data models allows for heterogeneity of countries. They also
allow the estimation of country specific effects. To choose between a random or fixed
effect models, the Hausman test is used. The significant value of the Hausman statistic
means that the fixed effects model will be preferred.
2.4.3.2 Hausman and Taylor estimator
The fixed effects model, unlike the random effects model, drops all static variables during
estimation. This means that estimates of such coefficients cannot be obtained. The
Hausman Taylor Estimator has the advantage in that it allows control of the variations
across countries (Baier and Bergstrand, 2007). The estimator also returns estimates of static
variables (Baltagi et al., 2003).
2.5 Empirical Studies on Trade Flows and the Benefits of RECs
The theoretical literature on the factors affecting trade flows and impact of RECs on trade
is very diverse. Through the gravity model, analyses of the economic impacts of trade,
investment, migration, currency union and regional trade agreements have been done
(Teweldemedhin and Chiripanhura, 2006). The gravity model has been applied to almost
all kinds of commodities ranging from consumable to non-consumable goods. Some
17
studies, for instance Yane (2013), have also applied the model in the analysis of trade in
the service sector. The model is applied to explore the influence of primary production,
food consumption, prices, exchange rate, population, income, GDP, trade agreements and
geographical distance on trade of various commodities.
Natale et al. (2015), for instance, assessed the factors that affect international seafood trade.
In Egypt, Hatab et al. (2015) employed a gravity model to analyze the main factors
influencing Egypt’s agricultural exports to its major trading partners and found that GDP,
exchange rate, distance, common border and common language significantly influenced
Egypt’s trade with its major trading partners. The gravity model has been used to analyze
the trade effects of the various regional economic blocs within Africa and elsewhere
(Babatunde, 2006; Teweldemedhin and Chiripanhura, 2006; Korinek and Melatos, 2009;
Negasi, 2009; Zannou, 2010; Simwaka, 2011; Kurtovic and Talovic, 2015; Hatab, 2015;
Koo et al., 1994; Karemera et al., 2009; Dembatapitiya and Weerahewa, 2015; Cortes,
2007; Yang and Martínez-Zarzoso, 2013; Kristjansdottir, 2006; Abdmoulah, 2011).
RECs in Africa are formed with a prior expectation of increasing trade among members in
three ways. These include through the reduction in tariffs between members, reduction in
Non-tariff Barriers (NTBs) and through trade facilitation (De Melo and Tsikata, 2014).
Empirical studies have shown that several factors have contributed to low bilateral trade
flows of member countries. Such factors, among others, include high trade barriers and
lack of political will by governments (Turkson, 2012).
The commodity gravity model, unlike the aggregate trade gravity models, are very useful
in the sense that it can integrate the exclusive product characteristics and policies associated
18
with trade flows of the specific commodity in exporting and importing countries. Some
commonly studied commodities in literature include fish, wheat, meat and vegetables. Koo
et al. (1994), for example, estimated the influence of policies on meat trade. the study found
that trade policies, subsidies, livestock production and distances determines meat trade
flows. Karemera et al. (2009) assessed the impact of FTAs on trade of Vegetables and
Fruits in the USA. The study found that FTAs such as NAFTA and APEC had significant
gross trade creation effects.
2.6 Conclusions: Research Gap and Contribution of this study
The intra-regional fish trade understanding in Africa is limited in a number of key areas
including the determinants of fish trade flows in Africa and the effects of the RECs on fish
trade flows. Most empirical studies in Africa have used aggregated data to analyze the
factors affecting agricultural trade and the trade effects of REC’s. The use of aggregate
data has resulted in knowledge gap as regard to the factors affecting trade of various
commodities such as fish. As a result, the results disseminated are not industry specific
and fails to provide industry specific policy direction.
This study estimates three gravity models following the recommendation by Natalie et al.
(2015) that gravity model is still an open field of research and that the choice of technique
to address zero observations should be inconclusive from previous studies. Panel data
models, the Tobit model and the PPML estimation techniques have been estimated in this
study and the results from the four are compared and the best model is used in the
discussion of results.
19
CHAPTER 3
METHODOLOGY
3.1 Introduction
The chapter outlines the steps that were taken in order to achieve the objectives of the
study. In the first section, data sources are provide. This is followed by a conceptual
framework of the study. The third section gives the empirical model specification. The
chapter concludes by giving an explanation of the variables used and their expected signs.
3.2 Data Sources
The data used in this study is on bilateral trade on fish exports between 54 African countries
from 2001 to 2014. All the 54 African countries which were included in the bilateral trade
flows of fish are shown in Table 3.1. The time period 2001 – 2014 was chosen mainly
because bilateral fish exports data were available for that period. GDP and exchange rate
data were obtained from data base of the World Bank. Data on bilateral exports of fish
were obtained from Trade Map. The production data were obtained from FishStatJ,
software for fishery statistical time series hosted by the FAO. Fish prices were calculated
by dividing the fish monetary value in a given year by the associated quantity in that
particular year, obtained from FishStatJ. The calculations on distance were based on the
nearest commercial centers of the trading countries (DistanceFromTo, 2017). Data analysis
was done using Stata Version 12.
20
Table 3.1 Countries studied for the fish trade flows (Exports and imports)
Algeria Chad Ethiopia Libya Niger South
Sudan
Angola Comoros Gabon Madagascar Nigeria Sudan
Benin Congo Gambia Malawi Rwanda Swaziland
Botswana DRC Ghana Mali Sao Tome and
Principe
Tanzania
Burkina
Faso
Côte
d'Ivoire
Guinea Mauritania Senegal Togo
Burundi Djibouti Guinea-
Bissau
Mauritius Seychelles Tunisia
Cabo
Verde
Egypt Kenya Morocco Sierra Leone Uganda
Cameroon Equatorial
Guinea
Lesotho Mozambiqu
e
Somalia Zambia
CAR Eritrea Liberia Namibia South Africa Zimbabwe
3.3 Conceptual Framework
The gravity model of trade flows is based on the idea that there is a positive relationship
between exports and income of the trading nations, and a negative relationship between
trade and distance between the trading nations. Conceptually, the trade flows are presented
in Figure 3.1. The gravity model indicates that it is possible to predict potential trade flows
by looking at the probable supply and demand of commodities as well as the economic
sizes of the two trading pairs. This trade flow, however, is subject to some factors that
restrain trade such as distance between the two trading countries. Trade flows are also a
characteristic of both the importing and exporting countries such as production, prices of
21
commodities, exchange rate and population. The gravity model also contains a component
of artificial factors such as trade policies that are thought to affect trade patterns.
Figure 3.1 Conceptual Framework for the Gravity model of trade
Source: Department of Trade and Industry, South Africa
3.4 Theoretical framework
3.4.1 The standard gravity model
The traditional gravity model drew on resemblance with Newton’s Gravitation Law and
has been applied to services and commodities. A quantity of a given product (𝑌𝑖) at origin
i is enticed to a mass of demand for the product (𝐸𝑗) at a given destination j. Distance (dij)\),
however, reduces the potential flow of the goods between. Mathematically, this can be
presented as;
𝑋𝑖𝑗 = 𝑘𝑌𝑖𝐸𝑗/𝑑2𝑖𝑗……………………………………… (1)
Where
𝑋𝑖𝑗 is force of attraction,
22
𝑌𝑖 𝑎𝑛𝑑 𝐸𝑗 are the quantities,
𝑑2𝑖𝑗is the Distance.
K is the Gravitational constant
Theoretical foundations of gravity equation are found in Tinbergen (1962) and Bergstrand
(1985, 1989). Both Tinbergen (1962) and Linneman (1966) proposed a similar approach
of explaining trade flows using the gravity model. The underlying idea of the model is that
there is a positive relationship between trade and trading pairs’ income and a negative
relationship with distance between them. Following Newton’s gravity from equation (1),
Tinbergen (1962) suggested a similar functional form given in equation (2);
𝑋𝑖𝑗 = 𝑘𝑌𝑖𝐸𝑗/𝑑2𝑖𝑗………………………………………… (2)
Where
𝑋𝑖𝑗 is quantity traded between country i and country j
𝑌𝑖 𝑎𝑛𝑑 𝐸𝑗represents the income of country i and country j
𝑑2𝑖𝑗is geographical distance between country i and country j.
𝑘 is the Constant
The theoretical foundation in Anderson (1979) is based on Cobb-Douglas or CES
preference function. Bergstrand (1985, 1989), on the other hand, provided a theoretical
foundation based on monopolistic competition model of New Trade Theory. The
traditional gravity model, as specified in Anderson (1979), can be presented as follows:
𝑌𝑖𝑗 = 𝑎0(𝑋𝑖)𝑎1(𝑋𝑗)𝑎2(𝑁𝑖)
𝑎3(𝑁𝑗)𝑎4(𝐷𝑖𝑗)𝑎5(𝐿𝑖𝑗)𝑎6𝑢𝑖𝑗 ………….………….…. (3)
23
This shows that the value of trade (𝑌𝑖𝑗) between country 𝑖 to country 𝑗 at a particular time
is a function of their GDPs (𝑋𝑖) and (𝑋𝑗), geographical distance (𝐷𝑖𝑗), population (𝑁𝑖) and
(𝑁𝑗), and a set of dummies (𝐿𝑖𝑗). 𝑢𝑖𝑗 is a normally distributed error with 𝐸(𝑙𝑛𝑢𝑖𝑗) = 0.
3.4.2 Empirical model specification: The generalized gravity model
According to Koo et al. (1994), a typical gravity model is comprised of three components.
These are a component for variables affecting trade flows in the exporting countries, factors
affecting trade in the importing or receiving countries, and a set of dummies representing
artificial variables such as trade policies. The model specification for this study is given in
equation (9).
𝑌𝑖𝑗𝑡 = 𝑎0𝐺𝐷𝑃𝑖𝑡𝑎1𝐺𝐷𝑃𝑗𝑡
𝑎2𝑃𝑖𝑡𝑎3𝑃𝑗𝑡
𝑎4𝐿𝑖𝑗𝑡𝑎5 𝑀𝑖𝑡
𝑎6𝑀𝑗𝑡𝑎7𝐾𝑖𝑡
𝑎8𝐾𝑗𝑡𝑎9𝑇𝑖𝑗𝑡
𝑎10 + 𝑒𝑥𝑝⌈𝐶𝑂𝐿𝑖𝑗𝑡𝑎11 + 𝐿𝐴𝑁𝑖𝑗𝑡
𝑎12 +
𝑎9𝑍𝑖𝑗𝑡 + 𝑎10𝑆𝐴𝐷𝐶𝐶𝑡 + 𝑎11𝐸𝐶𝑂𝐶𝑡 + 𝑎12𝐸𝐴𝐶𝐶𝑡 + +𝑎13𝐸𝐶𝐶𝐴𝑆𝐶𝑡 +
𝑎14𝐴𝑀𝑈𝐶𝑡⌉𝑢𝑖𝑗𝑡………………………..……. (4)
Where
𝑌𝑖𝑗𝑡 is the monetary value of exports of fish between countries 𝑖 and 𝑗 at time 𝑡.
𝐺𝐷𝑃𝑖𝑡 is GDP for country 𝑖.
𝐺𝐷𝑃𝑗𝑡 is GDP for country j.
𝑃𝑖𝑡 𝑎𝑛𝑑 𝑃𝑗𝑡 represents population of the exporting and importing countries respectively.
𝐿𝑖𝑗𝑡 represents the shortest geographical distance between countries 𝑖 & 𝑗
𝑀𝑖𝑡is primary fish production in exporting country
𝑀𝑗𝑡is primary fish production in importing country
𝐾𝑖𝑡represents fish prices in exporting country
24
𝐾𝑗𝑡represents fish prices in importing country
𝑇𝑖𝑗𝑡represents nominal bilateral exchange rate
𝐶𝑂𝐿𝑖𝑗𝑡is a dummy for “common colony”, 𝐶𝑂𝐿𝑖𝑗 = 1 if pair countries were under the same
colonial rule; 𝐶𝑂𝐿𝑖𝑗 = 0 otherwise
𝐿𝐴𝑁𝑖𝑗𝑡is a dummy for “common language”, 𝐿𝐴𝑁𝑖𝑗 = 1 if pair countries share a common
language; 𝐿𝐴𝑁𝑖𝑗 = 0 otherwise
𝑍𝑖𝑗𝑡is a dummy for “common border”, 𝑍𝑖𝑗 = 1 if pair countries share a common border;
𝑍𝑖𝑗 = 0 otherwise
Dummy variables SADC, EAC, ECOWAS, ECCAS and AMU represent membership to
various regional trading blocs.
𝑆𝐴𝐷𝐶𝑐 = 1 for trade between SADC countries; 𝑆𝐴𝐷𝐶𝑐 = 0 otherwise;
𝐸𝐶𝑂𝑊𝐴𝑆𝑐 = 1 for trade between ECOWAS countries; 𝐸𝐶𝑂𝑊𝐴𝑆𝑐 = 0 otherwise;
𝐸𝐴𝐶𝑐 = 1 for trade between EAC countries; 𝐸𝐴𝐶𝑐 = 0 for otherwise;
𝐸𝐶𝐶𝐴𝑆𝑐 = 1 for trade between ECCAS countries; 𝐸𝐶𝐶𝐴𝑆𝑐 = 0 otherwise;
𝐴𝑀𝑈𝑐 = 1 for trade flows between AMU countries; 𝐴𝑀𝑈𝑐 = 0 otherwise;
𝑢𝑖𝑗𝑡is the error term
3.5 Operational Explanation of the variables
Fish trade flows
This refers to bilateral fish exports. The dataset covers bilateral trade on fish exports for 54
African countries between 2001 and 2014. It has been measured in monetary value (United
Stated Dollar).
25
GDP
The relationship between GDP and trade flows is positive. For the exporting country, GDP
explains the production and supply capacity while for the importing country GDP explains
the purchasing power. It is believed that an increase in GDP of the exporting country will
act as an engine for increased production through availability of capital hence increased
exports. On the other hand, an increase in GDP for the importing country means a high
level of income such that the population will demand more of the commodities hence an
increase in imports. (Bergstrand, 1989: Karemera et al., 1999).
Population
The importing country’s population is expected to affect trade positively since an increase
in the importing country population reflects increase in the importing country's domestic
consumption. The population in the exporting country is also expected to have positive
effects on exports, since the export country is expected to be able to increase its production
and export more as the population grows in size through its ability to produce and export
labor-intensive commodities (Karemera et al., 1999)
Fish Prices
Commodity prices are mostly used in commodity specific gravity models. They have been
applied by Koo et al. (1994) and Karemera et al. (2009) among others. The logic behind
commodity prices is that higher prices act as an incentive for producers in the exporting
country such that they increase their production geared towards the export market. In the
importing country, on the other hand, high prices are a burden to the consumers such that
this limits their purchases of imported goods
26
Exchange rate
Exchange rate is also an important factor explaining trade variation among countries. If
exchange rate is high, it means the currency has depreciated. This makes imports
expensive and expensive cheaper. As a results, total exports increases. The reverse is true
for an appreciation of the currency (Hatab et al., 2010; Koo and Karemera, 1994).
Fish production
Production reflects the ability of a country to satisfy the domestic economy with locally
produced commodity. Excess production is exported. Production, therefore, has a positive
relationship to trade for exporters. For importers, the relationship is negative since the
domestic production will satisfy the local demand with less need for imports (Koo et al.,
Geographical Distance
The inclusion of distance in a gravity model is core in the analysis. It is used as a proxy for
transportation costs. Logically, the more the distance between trading pairs the more the
transportation costs hence reduced trade. The relationship is negative. (Martinez-Zarzoso
and Nowak-Lehmann, 2002; Karemera et al., 2009).
Common Border
While distance hinders trade, countries sharing a border are believed to trade more. This is
due to reduced distance between them as well as cultural similarities and having similar
tastes or consumption patterns. A positive sign is therefore expected between common
border and trade flows (Hatab et al., 2010; Karemera et al., 2009)
27
Common colony
Sharing the same colonial relations between the trading partners have proved to have
strong influence on trade patterns through established business language and business
contacts, and institutional frameworks set by the colonizers in the respective countries
(Wincoop, 2004).
Common language
Countries that share common language are expected to trade more. This is because
common language can reduce the transaction cost, since this improves the communication
between the traders (Anderson and Wincoop, 2004). Common language increases trade
through reducing the need for translators (hence reducing trade costs) and increasing direct
communication between the trading partners.
The impact of RECs on fish trade
Dummies were used to assess the impact of regional blocs on trade in fish. The theory of
economic integration provides that if countries come together and reduce barriers to, trade
is expected to increase among them. This is in contrast to countries that do not integrate in
any way. Trade is therefore expected to increase if countries belong to the same regional
bloc and reduce if countries do not belong to the same bloc (Bergstrand, 1989; Karemera
et al., 1999; Hatab, 2015). The measurement type and expected signs for each of the
variables used are presented in Table 3.2.
28
Table 3.2 Definition of variables used in the gravity model estimation
Variable Variable type Measurement Expected sign
Trade Flows Continuous US$
Distance Continuous Kilometer -
Exporter's GDP Continuous US$ +
Importer's GDP Continuous US$ +
Exporter's Population Continuous Number +
Importer's Population Continuous Number -
Exporter's Fish production Continuous Tonnes +/-
Importer's Fish production Continuous Tonnes -
Exporter's Fish price Continuous US$/Kg +
Importer's Fish price Continuous US$/Kg -
Exchange rate Continuous Conversion +/-
Common Border Dummy Dummy +
Common colony Dummy Dummy +
Common language Dummy Dummy +
SADCc Dummy Dummy +
EACc Dummy Dummy +
COMESAc Dummy Dummy +
ECOWASc Dummy Dummy +
ECCASc Dummy Dummy +
AMUc Dummy Dummy +
29
CHAPTER 4
RESULTS AND DISCUSSION
4.1 Introduction
The empirical findings of the model and discussions are presented in this chapter. It starts
by giving summary statistics of the variables that were used followed by analysis of fish
trade patterns. In the third section, the econometric diagnostics that were employed to
ensure the well-being of the models are presented. The chapter concludes by giving the
results of the different estimation techniques, followed by discussion of the results.
4.2 Descriptive statistics
The summary statistics shown in Table 4.1 are for the studied period between 2001 and
2014. The average export value of fish was found to be $126, 064.9 with a standard
deviation of $2, 209,738. This shows that there was greater variation in fish exports among
the countries which could be due to the nature of some countries who are major producers
and exporters while others are predominantly importing countries. The minimum fish
exports value was $0 and the maximum value was $135, 289,000. Based on the distance
calculations indicated in the methodology, the average distance between two trading
African countries was found to be 3, 524.1 km with a standard deviation of 1829.69 km.
The minimum distance between two trading countries was 149.35 km and the maximum
distance was 9775.5 km. The RECs dummies show that the biggest among the studied
communities is COMESA with 12 percent of the countries in Africa being COMESA
members. This is followed by SADC (7 percent), ECOWAS (6 percent), and ECCAS (2
30
percent). EAC and AMU have fewer members each. Table 4.1 presents the statistics for
the remaining variables.
Table 4.1 Summary of descriptive analysis of the variables used in the model
Variable Obs1 Mean Std. Dev Minimum Maximum
Trade Flows 40082 126064.9 2209738 0 135289000
Distance 40082 3524.102 1829.699 149.35 9775.5
Exporter's GDP 38040 2.88E+10 6.34E+10 7.22E+07 5.69E+11
Importer's GDP 38864 2.77E+10 6.29E+10 7.22E+07 5.69E+11
Exporter's Population 40081 1.85E+07 2.61E+07 81202 1.77E+08
Importer's Population 40082 1.82E+07 2.61E+07 81202 1.77E+08
Exporter's Fish production 40082 256905.1 284939.8 0 1980315
Importer's Fish production 40082 128483.5 200036.7 0 1258960
Exporter's Fish price 36317 1.197214 1.26838 0.043478 10.75969
Importer's Fish price 36530 1.19556 1.265081 0.043478 10.75969
Exchange Rate 35890 1.6152+72 7.628364 0.0553 402.3154
SADCc 40082 0.073699 0.261284 0 1
EACc 40082 0.006986 0.083289 0 1
ECOWASc 40082 0.073699 0.261284 0 1
COMESAc 40082 0.119804 0.324737 0 1
ECCASc 40082 0.025498 0.157633 0 1
AMUc 40082 0.006986 0.083289 0 1
Common colony 40082 0.27803 0.448034 0 1
Common language 40082 0.351729 0.477516 0 1
Common border 40082 0.166609 0.372631 0 1
1Observations represent each of the 54 countries in Africa exporting fish to another country (53 in total) for
a period of 14 years. i.e (54*53*14)
31
4.3 Imports and exports of fish (and fish products) in Africa
The analysis in Figure 4.1 shows that even though Africa has fish as one of its main export
commodities, it still relies much on imports of fish. It has been noted, however, that most
of the fish imports into Africa are comprised of low value fishes (Cocker, 2014). Fish
exported from Africa is mostly in its raw form with little value addition while Africa’s
imports are mostly comprised of frozen fish or fillets. According to Gordon et al. (2013),
Europe and Asia are the main exporters of fish to African countries.
Figure 4.1 Trend in imports and exports of fish in Africa (1976 – 2013)
4.4 Patterns of fish trade in Africa
Despite the existence of numerous barriers and challenges to intra-regional fish trade, the
level of intra-regional fish trade is still substantial. Comparisons with other trading blocs
such as the EU and in Asia, however, shows that Africa still got a long way to go if intra-
regional fish trade is to be improved. Lately, various regional bodies and the African Union
0
500000
1000000
1500000
2000000
2500000
3000000
3500000
4000000
4500000
1976 1980 1990 2000 2010 2011 2012 2013
Trade balance
Exports Imports
32
have prioritized intra-regional fish trade one of its development goals under the Malabo
declaration.
Table 4.2 shows that most of intra-SADC trade is the highest compared to the rest of the
blocs. It is this high levels of intra-SADC trade that has made SADC a leading fish exporter.
This could be due to the fact that some of the top producers of fish in Africa such as
Namibia, South Africa and Tanzania are members of SADC. Table 4.2 further shows that
there is a low level of trade between SADC countries and other states has been low. This
shows that SADC members have taken the issue of economic integrations seriously,
allowing more fish trade with member states. Fish trade performance in ECOWAS is
modest, coming second after SADC. Nigeria, Ghana and Ivory Coast are some of the
biggest economies in West Africa that makes greater contributions to both regional exports
and imports.
Fish trade in EAC has shown a declining trend over the years. A significant growth in trade
was observed between 2001 and 2004, after which intra-EAC fish trade started declining,
with the exception of the year 2014 which saw a remarkable rise in the level of EAC-
African trade. The fact that most countries in EAC share Lake Victoria and Lake
Tanganyika implies self-reliance in fish, hence less need for intra-fish trade in the bloc. In
ECCAS community, a low volume of both ECCAS intra-fish trade and its share in total
African fish trade has been observed. In 2013 and 2014, ECCAS did not export any fish to
any African state, and their share in total African fish trade was less than one percent.
ECCAS is one of the economic blocks with countries that are net importers of fish on the
continent such as Angola and DRC.
33
COMESA has established a major African FTA, which currently has 15 members.
COMESA is one of the RECs with more members as compared to the rest of the RECs
studied. While Ngwenya (2015) reported that COMESA had made impressive performance
in enhancing intra-regional trade by 333 percent between 1997 and 2013, this study has
found that the levels of intra-fish trade has remained relatively constant over the years
(Table 4.2). The share of COMESA exports in Africa total exports is significantly lower,
which could be explained by the overlapping membership of the RECs, where COMESA
member states are also EAC and SADC member states.
The analysis from Table 4.2 has shown intra-fish trade in AMU has remained relatively
low. Sebbagh et al. (2015) noted that the Arab Maghreb Union has failed to boost the
growth of potential and bilateral trade flow. Fish trade has not been spared. Most of the
trade in AMU region is with European countries. For example, in 2012, intra-AMU trade
only accounted only for 3.94 percent while trade with EU was at 58.12 percent, trade with
MERCOSUR was at 18 percent and ASEAN was at 23.8 percent (Sebbagh et al., 2015).
AMU fish trade with Africa has been high, reaching 27 percent in 2014. This is because
almost all AMU countries lies along the Atlantic coast, such that they are self-sufficient
with regard to fish production, hence less need for fish imports from member states.
34
Table 4.2 Pattern of fish trade in Africa
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
Share of RECs in Africa’s total fish trade
AMU 6.38 2.17 2.37 2.28 3.38 5.94 37.03 10.01 8.19 7.43 14.49 21.37 19.48 28.19
EAC 7.55 6.86 15.8 14.39 9.85 7.43 4.17 4.91 4.36 5.43 3.21 2.22 1.65 2.98
SADC 49.36 61.98 51.7 52.03 49.44 60.33 37.71 53.13 60.32 67.84 63.87 59.55 60.57 47.7
ECCAS 0.08 0.02 0.07 0.03 0.04 0.3 0.37 0.55 2.03 1.14 0.31 0.47 0.09 0.46
ECOWAS 24.71 13.4 11.85 13.03 23.92 15.61 13.97 21.35 18.84 11.11 13.38 12.58 14.48 14.63
COMESA 11.93 15.58 18.22 18.23 13.37 10.39 6.74 10.05 6.26 7.05 4.74 3.81 3.72 6.04
RECs Intra-fish trade
AMU 0.25 0.36 0.41 0.63 0.76 1.36 0.7 2.14 1.8 1.97 1.75 2.08 1.08 1.18
EAC 3.86 4.53 14.81 13.01 6.9 4.05 3 3.29 1.57 2.85 2.63 1.39 1.05 0.69
ECCAS 0.07 0.01 0.03 0.01 0.03 0.3 0.37 0.54 0.71 1.14 0.27 0.46 0.09 0.46
SADC 40.12 43.82 21.77 34.09 37.11 53.48 33.76 47.97 54.53 61.73 58.19 54.18 56.24 44.6
ECOWAS 14.43 8.75 8.58 10.09 20.91 10.56 11.45 15.87 11.95 7.19 9.44 7.99 10.15 10.61
COMESA 6.43 9.44 2.59 3.27 4.65 3.27 2.53 2.69 2.09 2.54 2.89 1.83 2.15 2.81
RECs inter-fish trade with the rest of Africa
AMU 6.13 1.81 1.95 1.64 2.62 4.57 36.33 7.87 6.39 5.46 12.74 19.28 18.41 27
EAC 3.69 2.32 0.99 1.38 2.95 3.38 1.16 1.61 2.79 2.58 0.59 0.83 0.59 2.29
ECCAS 0.01 0.01 0.04 0.01 0.01 0 0 0.02 1.32 0.01 0.04 0.02 0 0
SADC 9.24 18.16 29.93 17.94 12.33 6.85 3.96 5.16 5.79 6.11 5.68 5.37 4.33 3.1
ECOWAS 10.28 4.64 3.27 2.95 3.01 5.05 2.52 5.48 6.89 3.91 3.94 4.59 4.33 4.02
COMESA 5.49 6.14 15.63 14.97 8.72 7.12 4.21 7.36 4.18 4.51 1.86 1.99 1.57 3.23
35
4.5 Econometric Diagnostics
4.5.1 Normality test
The study used a test for normality proposed by Galvao et al. (2013) which tests for three
components. These are kurtosis, skewness, and normality. Test results (Table 4.3) shows
that all the three components are symmetry. However, under null hypothesis of normality,
the joint test for normality on e and the joint test for normality of u are both insignificant
(at less than 5% probability level). As a result, we fail to reject the null hypothesis of
normality. We can conclude that our data is normal.
Table 4.3 Normality test
Observed Coef. Bootstrap Std. Err z P>z
Skewness_e 7.74E+10 5.90E+10 1.31 0.19
Kurtosis_e 1.32E+16 7.06E+15 1.87 0.06
Skewness_
u
1.15E+11 7.69E+11 1.5 0.134
Kurtosis_u 6.23E+15 4.57E+15 1.36 0.173
Joint test for Normality of e chi2(2) = 5.21 Prob > chi2 = 0.0738
Joint test for Normality of u chi2(2) = 4.1 Prob > chi2 = 0.1285
4.5.2 Random effects test
The study used the Breusch and Pagan Lagrangian multiplier (LM) to test if the random
effect model is preferred over the simple OLS regression. The test is performed under the
null hypothesis of no panel effects. Table 4.4 presents the results.
36
Table 4.4 Random effects test
LM test for random effects
Estimated Results
Variance SD = sqrt(Var)
Ltradeflows 5.421719 2.328459
e 1.929653 1.389119
u 2.6603 1.631043
Test: Var(u) = 0
chibar2(01) = 60382.61
Prob > chibar2 = 0.0000
The results shows a significant value of the F-statistic. This means that the null hypothesis
is rejected. We conclude that there are panel effects and hence random effects is
appropriate.
4.5.3 Serial correlation test
Serial correlation was tested using the Wooldridge test. Table 4.5 presents the results. The
F-statistic is not significant (0.1833). We therefore reject the null hypothesis of no serial
correlation. The conclusion is that the data does not have any serial autocorrelation (i.e. the
errors are independent).
Table 4.5 Serial correlation test
Wooldridge test result
F( 1, 2498) = 1.772
Prob > F = 0.1833
H0: no autocorrelation
4.5.4 Testing for multicollinearity
The data were also tested for multicollinearity by using the command corr in stata. The
command corr was used since VIF does not work in panel data. The results of the corrtest
37
showed that no variables were highly correlated as the coefficients were all less than 0.5.
Furthermore, both the individual t-statistics and the overall F-statistic were significant. This
is a clear sign that multicollinearity was not a problem.
4.5.5 Testing for unit roots/stationarity
To data was also tested for the presence of a unit root using the Fisher unit-root test. The
null hypothesis for the test is that all panels contain unit roots. According to Choi (2001),
four types of statistics2 are used in the test. The results presented in Table 4.6 shows that
all four statistics are significant for all variables used. The null hypothesis is therefore
rejected. The conclusion is that at least one panel is stationary at different lags.
4.5.6 Testing for heteroskedasticity
Due to unavailability of specific tests for heteroskedasticity in panel data, the study failed
to test for non-constant variance in the data. In case of presence of non-constant variance
of the data, the study used the Robust Standard Errors which gives consistent estimators
and smaller standard errors than the usual standard errors.
2 These are Inverse chi-squared, inverse normal, Inverse Logit and the Modified inverse chi-squared statistics
38
Table 4.6 Unit root test
Variable Lags
Statistics
Inverse chi-
squared
Inverse
normal
Inverse
logit
Modified
inv. chi-
squared
Trade Flows 0 1.28E+4*** -317*** -656.5*** 1140.1***
Exporter's GDP 0 4.14E+2*** -149*** -213.15*** 342.27***
Importer's GDP 0 3.97E+1*** -148*** -202.16*** 321.63***
Exporter's Population 0 6268.58*** -20.8*** -18.76*** 9.34***
Importer's Population 0 1.29E+1*** -55.3*** -57.93*** 74.07***
Exporter's Fish production 2 8672.51*** -4.49*** -8.16*** 27.53***
Importer's Fish production 3 1.09E+4*** -2.5*** -17.81*** 49.53***
Exporter's Fish price 0 3.70E+4*** -54*** -160.50*** 296.1***
Importer's Fish price 0 4.37E+04*** -63*** -189.8*** 359.2***
Exporter's Exchange Rate 0 6080.91*** -5.9*** -8.9378*** 5.39***
Importer's Exchange Rate 0 6304.236*** -8*** -11.21*** 9.6902***
***, ** denotes significance at 1% and 5% respectively
4.6 The Gravity Model
The study used a gravity model to assess determinants of fish trade as well as the trade
creation and trade diversion effects of Africa’s RECs on fish. This study estimates a gravity
model of fish trade in Africa by taking into consideration the issue of zero trade which are
prominent when a disaggregated data is used. Two types of gravity models were estimated:
one with zero trade and another without zero observations.
4.6.1 Gravity model with zero trade values dropped
The first estimation technique is to use a gravity model without zero observations. This
was done using three panel data models. These are the fixed effects (FE), random effects
39
(RE), and the Hausman Taylor estimator (HTE). The section to follow shows how the data
were tested for panel data effects and the estimation technique that was appropriate.
4.6.1.1 Simple OLS regression or fixed or random effects models
The first test that was done was to choose between a simple OLS regression and panel data
regression using the Breusch-Pagan Lagrange multiplier (LM) test. The result favored
panel data models (FE and RE models). The Hausman test was then used to choose between
FE and RE models. Results of the test, shown in Table 4.7, favored the FE model.
Furthermore, the result of the Chow test, which is used to select between a FE model and
a simple OLS regression settled for a FE model.
Results of RE model shows that the data fits the model well (F=0.0000). Most variables of
RE model gave the expected sign although they were not significant. Exporters’ and
importers’ production had the expected signs. Exporters’ population had the wrong sign
and not significant while importers population has the expected sign and significant. Price
of fish was not significant for the importing country while exchange rate and common
border were significant. However, all RECs dummies, common border and common
language were not significant. Similarly, the F value for the fixed effects model was
significant (F=0.000) implying that the data fitted the model well. The FE model, however,
dropped all static variables such as distance and the RECs dummies. For the fixed effects
model, GDP and exchange rate were significant and had the expected signs. Population
was significant but had the wrong signs.
40
4.6.1.2 Hausman Tylor estimator
The HTE was used as it can accommodate the static variables which the FE model dropped.
These are variables of interest to the study. The Hausman Taylor estimator can incorporate
for the time invariant variables as Time Invariant Exogenous variables. Results of the
Hausman Tylor estimator are shown in Table 4.7. For Hausman Tylor estimator, distance
and population did not have the expected signs though they were significant. Exporters’
and Importers’ GDP, exchange rate, the RECs trade creating dummies and common border
were significant. Exporters’ fish production, importers’ fish price, common language and
common colony were not significant. Overall, the model was significant as can be shown
by the significant F value (F=0.0000).
41
Table 4.7 Estimates of a gravity model without zero trade flows (Fixed effects, random effects and Hausman Taylor)
Variable Random Effects Fixed Effects Hausman Taylor
Coef Std. Error T-score Coef Std. Error T-score Coef Std. Error T-score
Distance -0.146 0.095 -1.54
2.863** 1.08 2.65
Exporter's GDP 0.231** 0.070 3.32 1.226*** 0.234 5.25 1.223*** 0.227 5.38
Importer's GDP 0.193** 0.080 2.42 2.017** 0.729 2.77 2.022** 0.710 2.85
Exporters Population -0.014 0.051 -0.27 -1.078*** 0.201 -5.37 -1.07*** 0.195 -5.5
Importers Population 0.222** 0.092 2.42 -1.813** 0.788 -2.3 -1.818** 0.768 -2.37
Exporter's production 0.311** 0.124 2.51 0.486 0.247 1.96 0.485** 0.241 2.01
Importer's production -0.119 0.065 -1.84 -0.117 0.172 -0.68 -0.117 0.16 -0.7
Exporter's Fish price -0.193** 0.080 -2.41 -0.087 0.095 -0.92 -0.086 0.093 -0.93
Importer's Fish price -0.028 0.093 -0.3 0.019 0.115 0.17 0.0191 0.111 0.17
Exchange Rate -0.013** 0.005 -2.57 -0.014** 0.005 -2.75 -0.014** 0.005 -2.84
SADCc 0.325 0.269 1.21 3.707** 1.142 3.25
EACc 0.212 0.541 0.39
3.737 2.489 1.5
ECOWASc 0.155 0.323 0.48 6.686** 2.11 3.17
ECCASc -1.099 0.656 -1.68
5.057** 2.366 2.14
AMUc 0.074 0.519 0.14 -1.175 1.809 -0.65
Common Colony 0.071 0.255 0.28
0.228 1.508 0.15
Common Language 0.226 0.268 0.84 1.891 1.034 1.83
Common Border 0.577** 0.269 2.14
2.688 1.419 1.89
Constant -8.844 2.1645 -4.09 -54.693 10.421 -5.25 -86.31 13.48 -6.4
Statistics
Observations 2162 2162 2162
Wald chi2 155.58 17.02 171.85
Prob > chi2 0.0000 0.0000 0.0000
Hausman Test
60.12***
Chow Test
9.58***
***, ** denotes statistically significance at the 1% level and 5% level respectively.
42
4.6.2 Gravity model with zero trade
The study also estimated a gravity model using nonlinear estimation methods. This was
done to compare its results from the linear model estimation. Tobit regression and Poisson
Pseudo Maximum Likelihood (PPML) are the non-linear estimation techniques that were
used. The AIC and BIC selection criteria were used to select the most appropriate gravity
estimation with zero trade.
4.6.2.1 The Tobit regression
The Tobit model used in gravity data censors the zero observations at the left tail. The
Tobit model parameters are estimated by maximizing likelihood function. Tobit model is
inconsistent if the data is heteroskedastic and when the errors are non-normal. From the
results of Tobit in Table 4.8, Exporters’ and Importers’ GDP, exporters population,
Exporters’ and Importers’ production, the RECs trade creating dummies of SADC, EAC,
ECOWAS, and ECCAS, and common were significant and had the correct sign. On the
other hand, common colony, fish price, exchange rate, ECCAS dummy and common
language were not significant. The chi-square statistic, which was significant at p<0.01,
shows that the data fitted the model very well.
4.6.2.2 The PPML regression
The pioneers of PPML, Santos Silva and Tenreyro (2006), recommended the use of the
PPML technique when heteroskedasticity is a problem and zero observations are rampant.
Since panel gravity data were used, a Hausman test was used to choose between RE model
and FE model of the Poisson regression. Using the Hausman test, FE model was preferred.
43
However, the FE model dropped the static variables. Table 4.8 shows results of PPML
regression.
The random effects PPML regression results shows that Exporters’ and Importers’ GDP,
Exporters’ and Importers’ production, importers’ fish price, exchange rate, common
border, common colony, common language and SADC had correct signs and were
significant. On the other hand, Exporters’ fish price, Exporters’ and importers’ population,
and AMU had a wrong signs though they were significant. Estimates of the fixed effect
regression shows that Exporters’ and Importers’ GDP, population, Exporters’ and
Importers’ production and importers’ fish price had the expected signs and were
significant. Both the random effects and the fixed effects PPML regressions had significant
F value at 1% level.
44
Table 4.8 Estimates of a Gravity model with zero trade flows (Tobit and Poisson regression)
Variable
Tobit Poisson
Random Effects Fixed Effects
Coef Std.
Error T-score Coef Std. Error T-score Coef Std. Error T-score
Distance -1.464*** 0.243 -6.020 0.469 0.316 1.49
Exporter's GDP 0.655**8 0.167 3.910 0.596*** 0.007 88.48 0.594*** 0.007 88.18
Importer's GDP 1.182*** 0.184 6.400 4.450*** 0.013 337.25 4.461*** 0.013 338.29
Exporters Population 0.362** 0.131 2.760 -0.734*** 0.007 -111.76 -0.732*** 0.007 -111.55
Importers Population 0.250 0.216 1.160 -4.369*** 0.014 -320.51 -4.381*** 0.014 -321.55
Exporter's production 3.116*** 0.260 11.980 0.579*** 0.004 139.24 0.578*** 0.004 139.13
Importer's production -0.401** 0.136 -2.940 -0.456*** 0.003 -173.94 -0.456*** 0.003 -174.11
Exporter's Fish price -0.204 0.151 -1.350 -0.480*** 0.002 -301.74 -0.480*** 0.002 -301.61
Importer's Fish price -0.141 0.173 -0.820 -0.189*** 0.003 -74.9 -0.188*** 0.003 -74.52
Exchange Rate -0.004 0.009 -0.440 0.002*** 0.000 28.47 0.002*** 0.000 28.48
SADCc 5.810*** 0.678 8.560 4.385*** 0.819 5.36
EACc 7.475*** 1.752 4.270 3.889 2.443 1.59
ECOWASc 3.963*** 0.862 4.600 -1.362 1.192 -1.14
ECCASc -2.250 1.391 -1.620 -0.600 1.942 -0.31
AMUc 6.324*** 1.691 3.740 -9.177*** 2.445 -3.75
Common Colony 0.742 0.589 1.260 5.432*** 0.556 9.76
Common Language 0.231 0.604 0.380 13.777*** 0.604 22.81
Common Border 4.518*** 0.745 6.060 2.803 1.483 1.89
Constant -81.94 5.161 -15.880 -6.938** 2.754 -2.52
Statistics
Observations 31157 31157.000 5813
Hausman Test 7317.14***
Log Likelihood -8602.41 -1843749.3 -1838574
Wald chi2 717.91 1160000.00 1160000
Prob > chi2 0.0000 0.0000 0.0000
AIC 17246.82 3687539 3677166
BIC 17422.1 3687706 3677226
***, ** denotes statistically significance at the 1% level and 5% level respectively.
45
4.7 Choosing the best model
Tables 4.7 and 4.8 shows the parameter estimates of a generalized gravity model of fish
trade flows. Table 4.7 gives the parameter estimates without zero trade values and
estimated by RE, FE and HTE. On the other hand, Table 4.8 gives parameter estimates for
a gravity model with zero trade values included and estimated by Poisson, Tobit and
Heckman models. The results of the gravity model without zero trade values shows that
the HTE had incorporated static variables such as distance and the dummies for SADC,
EAC, ECCAS, ECOWAS, and AMU. However, the coefficient of distance was found to
be positive which is against the theory behind the gravity model. This could be as a result
to selection bias due to eliminating of zero observations. Furthermore, the coefficient of
distance for the random effects model after dropping the zero values had the appropriate
sign but was insignificant. According to Haq et al. (2010), ignoring selection bias can affect
the signs, statistical significance and economic interpretation of the coefficients which can
result into wrong policy implications.
On contrary, the coefficient of distance on gravity model with zero values included for all
the estimators retained the negative sign. The gravity model with zero trade values included
was then preferred in the discussion. The AIC and BIC selection criteria was used to select
the best non-linear gravity model estimated. The selection criterion is to choose the model
with the smallest AIC and BIC. From the results, the Tobit model was found to have the
least AIC and BIC parameters. The interpretations of the gravity model, therefore, are
based on the estimates of the Tobit model with censoring at zero. The marginal effects after
Tobit regression are presented in Table 4.9
46
Table 4.9 Marginal Effects after a Tobit regression
Variable dy/dx Standard Error T-Score
Distance -0.1697*** 0.0280 -6.06
Exporter's GDP 0.0759*** 0.0195 3.88
Importer's GDP 0.1370*** 0.0214 6.39
Exporters Population 0.0419** 0.0151 2.78
Importers Population 0.0290 0.0251 1.16
Exporter's production 0.3611*** 0.0283 12.74
Importer's production -0.0465** 0.0158 -2.94
Exporter's Fish price -0.0237 0.0176 -1.35
Importer's Fish price -0.0164 0.0200 -0.82
Exchange Rate -0.0004 0.0010 -0.44
SADCc 0.8015*** 0.1096 7.32
EACc 0.1324*** 0.3427 3.3
ECOWASc 0.5177*** 0.1266 4.09
ECCASc -0.2434 0.1407 -1.73
AMUc 0.9167*** 0.3039 3.02
Common Colony 0.0848 0.0665 1.28
Common Language 0.0267 0.0696 0.38
Common Border 0.6010*** 0.1137 5.29
Observations 31157
Log Likelihood -8602.41
Wald chi2 717.91
Prob > chi2 0.0000
47
4.8 Discussion
4.8.1 Determinants of fish trade flows in Africa
The effects of GDP
GDP explains both the supply side and demand side effects on trade flows. As shown in
Table 4.9, if exporters GDP increases by 1 percent, fish trade flows will increase by 7.6
percent. The GDP in this case reflects the production capacity. For the importing country,
if GDP increases by 1 percent, trade flows increases by 13.7 percent. The GDP in this
scenario indicate the income of the consumers in the importing country. The magnitude of
the coefficients of exporters GDP and importers GDP shows the relative importance of the
two in fish trade flows. Both coefficients are significant at 1 percent level.
Results obtained from both exporters’ GDP and importers’ GDP are in line with theory and
results of previous studies. For instance, Eita (Undated) found a positive relationship
between Namibia’s exports and its GPD as well as of the importer. Similarly, in Egypt,
Hatab (2015) found that an increase in Egypt’s GDP by one percent point increased
Egyptians Agricultural Exports by 5.42 percent point increase despite noting an
insignificant coefficient of the importers’ GDP. The results suggests that economic
development strengthens fish trade through increased production for both countries.
The study finds that the coefficient of importers GDP is very elastic. A one percent increase
in importers’ GDP results into 15.8 percent increase in fish exports. This could be due to
the importance of fish as major animal protein source in Africa, hence an increase in
income entails an increase consumption. Allison (2011) noted that developed economies
have a higher per capital fish consumption than developing economies including the SSA.
48
This is reflected in average Africa per capita fish consumption which stands at 8.3 kilogram
against the world average of 18.9 kilograms. This means that increase in income is indeed
more likely to result into increase in fish consumption since the current consumption is
very low. The coefficient of Exporters’ GDP was, however, found to be less than of the
importers. This means that fish exports are not much sensitive to increase in GDP. This
could be due to the fact that a large proportion of fish exports come from capture fisheries.
In recent years, the sector in Africa has been dwindling due to, among others, overfishing
and use of destructive fishing gears (AUC-NEPAD, 2014); TIPS and AusAID, undated;
Kirema-Mukasa, undated). This means that much as efforts to increase production may be
put in place, such efforts are hampered by the low catches. This necessitates the need to
invest more in the aquaculture sector. Currently, only Egypt is the leading producer of
aquaculture fish in Africa (Mapfumo, 2015).
Fish production
The exporters’ fish production and the importers fish production have correct signs and are
significant at 1 percent and 5 percent, respectively. Exporters’ fish production reflects its
ability to supply fish, both to the domestic and international markets. From Table 4.9, it
can be shown that increasing fish production by 1 percent increases fish exports by 36.11
percent. On the other hand, if production rises in the receiving country, imports will reduce
as it would mean more fish available locally. A 1 percent increase in fish production in the
importing country will result into a 4.7 percent reduction in fish imports.
Production capacities of both the importing and exporting countries are usually used for
commodity specific gravity models. For instance, studies have been done applying a
49
gravity model on meat (Karemera et al., 2015; Koo et al., 1994), vegetables and fruits
(Karemera et al., 2009) and seafood (Natalie et al., 2015). In most of these studies, the
results were consistent with theoretical foundations. Koo et al. (1994) found that the
direction of world meat trade flows is influenced largely livestock production.
From the analysis in Table 4.9, it can be shown that the estimated coefficient of exporters’
fish production is larger, implying that the current levels of fish production do not meet the
demand on the market. The estimated coefficient of importers’ fish production is negative
implying that fish production in the importing country will help meet the domestic demand
without much need to import. However, the small coefficient of importers’ fish production
(0.029) shows that much as their own production will help reduce fish imports, the
reduction in imports is so small signifying the large demand for fish in the importing
country. This just shows that fish demand is high in Africa such that the current levels of
production are not enough to meet demand. Fish demand has been increasing on the
continent due to increasing population, increasing urbanization and the consumer need for
healthy diet from food such as fish.
Population
Population is used to account for the market size in both the exporting country and
importing country. The expected sign of population for both the exporting country and the
importing country is positive. The study found that an increase in the exporters’ population
and importers’ population increased fish trade flows by 4.2 and 2.9 percent respectively.
For the exporters’ population, this signifies that the large population may provide enough
resources, especially labour, to the domestic country to produce whatever resources to
50
satisfy the domestic market and provide surplus for trade. For the importing country, an
increase in population provides the much needed market for fish such that imports of fish
increases. Larger countries are better able to absorb imports than smaller countries and are
better able to experience economies of scale and thus develop a comparative advantage in
their export industries than are smaller countries.
The study has, however, found that the increases in fish trade flows due to increase in
exporters’ and importers’ population is very small. For the exporting country, this could be
due to low investments in other means of fish production such as aquaculture such that
even though there is high population in Africa to provide the much needed labour for
aquaculture related activities and enhance fish trade through surplus production, such
labour has not been put into use due to lack of aquaculture ventures. It is therefore
important for African countries to fully develop their aquaculture and make use of the
surplus labour on the continent. For the importing country, the results shows that despite
the increase in population, fish imports are very low. Again this is a characteristic of the
low fish consumption in Africa.
Exchange rate
The effect of exchange rate on fish trade is two-way. If the relationship is positive, it means
depreciation increases imports more than it decreases exports. If it is negative, it means
appreciation decreases exports. From Table 4.8, it can be shown that an appreciation of the
currency by 1 percent reduces fish exports by 0.04 percent. The coefficient of exchange
rate was not significant. The reason for this could be that most countries use the US Dollar
currency when trading. This means that country specific currencies will have to be
51
converted to the US Dollar unlike converting them directly to the partners’ currency. On
the contrary, Martinez-Zarzoso and Nowak-Lehmann (2002) found a positive relationship
to trade flows. Koo et al. (1994) and Koo and Karemera (1990) also found a positive
relationship between exchange rate and trade of meat and wheat, respectively, though it
did not significantly explain trade flows. Karemera et al. (2009) found that the significance
and sign of the coefficient of exchange rate on vegetable trade flows was commodity
specific.
Fish prices
From Table 4.9, it can be shown that an increase in the price of fish by 1 percent reduces
fish exports by 2.37 percent. This, however, is contrary to the expected sign and was found
not to be significant. Considering that fish is a food commodity, a higher price of fish may
be accompanied by other regulatory requirements such as SPS measures and HACCP
requirements on exports of fish and fishery products, and other Technical Barriers to Trade
(TBT) which may eventually reduce fish exports. For instance, Kareem (2014) found that
Fish standards tend to aid fish exports for established fish exporters while hindering trade
for prospective fish exporters. Furthermore, Vuzf (2014) noted that trade in processed fish
tend to fetch higher prices on the markets. However, the capacity of most African SMEs
with regard to processing is very low such that this limits their participation in regional
trade.
It has also been noted that high-value fish such as prawns, tuna and squid are exported to
European markets while cheaper fish such as pelagics like sardines, mackerel are imported
(FAO, 2007). Since the focus of this study has been on fish trade flows in Africa, it can be
52
seen that increasing fish prices increases exports elsewhere not in Africa (negative
coefficient). This shows that the level of intra-regional and inter-regional fish trade is still
low as suggested by Ntembe and Tawah (2012). Furthermore, Ahmed et al. (2011) noticed
that that if the price of fish is too much higher in comparison to other important animal
protein sources such as poultry, beef, mutton, and pork, consumers will find alternatives to
satisfy their needs and wants. This means that higher fish prices will force consumers to
reduce their intake of fish, hence reducing the demand for fish and consequently reducing
exports.
The coefficient of importers’ price was found to be negative as expected although not
significant. A higher fish price reduces fish imports from the importing country. The use
of prices has been applied by Koo et al. (1994) and Koo and Karemera (1990) in meat and
wheat trade flows, respectively.
Geographical distance and common border
The estimated coefficient of distance is negative suggesting that if distance increases
between trading countries by 1 percent, trade in fish reduces by 16.97 percent (Table 4.9).
This is because more distance means more transportation costs incurred. Similar results
were found by Zannou (2010) who found that an increase of 10% in the distance reduced
intra-ECOWAS trade from 8 to 13%. Shinyekwa and Othieno (2013) also found similar
results. It has been noted that the more the distance the more the geographical factors
impeding trade flows as well as cultural dissimilarities that impede trade.
53
The study also found that sharing a Common border increased fish exports by 60 percent.
The coefficient of common border is larger than that of distance emphasizing that the closer
the countries are the lesser the transportation costs and the more the trade.
4.8.2 The effects of Africa’s RECs on fish trade flows
The augmented gravity model was also used to assess trade effects of the various RECs on
regional fish trade. COMESA was dropped during the analysis due to high overlaps of
membership to other RECs from most countries. All the fish trade creating RECs dummies,
with the exception of ECCAS, were significant with the correct signs. This means that the
formation of SADC, ECOWAS, EAC, and AMU have effectively enhanced fish trade
flows hence contributing to gross trade creation for fish. From Table 4.9, it can be shown
that the membership to SADC increased fish trade by 80.15 percent, membership to
ECOWAS increased trade by 51.77 percent, membership to EAC increased trade by 13.24
percent, and membership to AMU increased trade by 91.67 percent. The increase in trade
is much greater for SADC (80.15) followed by AMU (91.67) and ECOWAS (51.77),
suggesting that countries in these three blocks have significantly increased trade flows
among them.
The results of the trade creation agrees with findings of (Marinov, 2014; Sun and Reed,
2010; Gedaa and Seid, 2015; Meyer et al., 2010) who found that the creation of RECs in
SSA such as COMESA, ECOWAS and SADC has significantly increased trade flows
among the member countries to varying degrees. However, most of these studies used
disaggregated data and have highlighted low intra-regional trade. This study, on the other
hand, has found that there is increased intra-regional fish trade. This could be due to the
54
increasing fish trade worldwide, with about 40 percent of fish production being traded
internationally. Gordon et al. (2013) noted that the existing frameworks for promoting
trade among the trade blocks are not specific to fish such that they fail to address industry
needs. Furthermore, there is lack of policy harmonization among the different trading
blocks.
SADC has been found to create more trade to its members. One reason for this is the
presence of SADC protocol on fisheries that is in force. The protocol, among others,
stipulates the need to reduce trade barriers to promote trade in the region (SADC, 2001).
The creation of the free trade area in SADC could also be a contributing factor to the
increased trade flows, although it has been notice that some countries have not fully
implemented the FTA agreement. Similarly, the results of the study found that ECOWAS
membership has increased intra-regional fish trade by 51.77 percent. This could be due to
the fact that some ECOWAS countries are landlocked and depend on inland fisheries
whose catches are significantly low. Such countries also have a lower per capita fish
consumption which entails more demand for fish and more intra-ECOWAS fish trade
(Katikiro et al., 2010). Countries like Senegal and Nigeria are some of the Africans leading
producers of marine fish such that these countries supplies fish to other ECOWAS
members. Nigeria is also the second leading aquaculture producer in Africa after Egypt
(FAO, 2012). The results of the trade diversion of ECOWAS were found to be positive but
not significant. The insignificance of the positive coefficient could be due to the fact that
most ECOWAS exports are geared towards EU countries and other world markets other
than in Africa. Nearly one third of the ECOWAS exports go to the EU (Marinov, 2009).
55
CHAPTER 5
CONCLUSION AND POLICY IMPLICATION
This last chapter of the thesis presents the conclusion as well as the policy implications of
the study. Possible suggestions for improving intra-regional fish trade flows performance
in Africa are also outlined.
5.1 Conclusion
The study was aimed at assessing the determinants of intra-regional fish trade flows and
the benefits of RECs on fish trade in Africa. The paper finds compelling evidence that the
RECs in Africa have significantly enhanced fish trade flows among the member states. It
has also been noted that fish trade in Africa is limited by the low production and
productivity of the sector as well as poor infrastructural development characterizing the
continent.
Results shows that fish supply on the continent is not meeting the demand on the market.
It has been noted that increase in GDP is associated with increase in fish imports and
exports. Furthermore, more fish production of an importing country is associated with very
small reduction in fish imports. It has also been noticed that more fish production entails
more fish exports. This calls for investments in the sector that will increase fish production
to meet the existing fish consumption demand. Similarly, fish exports and imports were
found to be less responsive to changes in fish prices. This signals the importance of fish as
a necessity food item in Africa. The study has also found that distance is one of the main
barriers to intra-regional fish trade flows through its associated costs and poor
infrastructures such as roads, railways, port infrastructure and handling equipment.
56
The study has also revealed that the formation of SADC, ECOWAS, AMU and EAC have
effectively enhanced fish trade in the region. This suggests that there is increased intra-
regional fish trade flows which can be attributed to the existence of frameworks for
promoting trade among the trade blocks. However, much as there exists such framework,
they tend not to be industry-specific hence fails to provide appropriate guidelines to the
sector. This means intra-regional fish trade flows can be improved beyond the current
levels if fish trade related frameworks are put in place.
5.2 Policy Implications
The following are the policy implications:
i. To further increase fish trade levels and subsequently spur economic growth, it is
important that these RECs go beyond trade creation to implement policies aimed at
increasing productivity within the region. The study has found that fish trade flows
are less sensitive to increase in GDP. Furthermore, the demand for fish on the
continent is very high such that current production is unable to meet the
consumption needs. This calls for consolidated effort in the investment and
development of the aquaculture sector. To date, only Egypt is the main aquaculture
producer in Africa hence the need to bring forth investment policies that will
accelerate aquaculture growth in the continent. The various RECs should also
encourage regional rather than bilateral investment agreements in areas of
infrastructure development. It has been noted that distance hinders fish trade flows
as there is high transportation costs among others due to poor infrastructure
57
development such as roads, railways, port infrastructure and handling equipment
which is essential for both domestic and international transportation. There is a need
for consolidated efforts to improve the transport networks through the REC’s by
among others coming up with a strategy that will be inclusive of all members in as
far as infrastructure development is concerned.
ii. African countries should improve their data collection and archiving system for fish
and other commodities. Most of the data for this study were collected from Trade
Map. The calculations on Trade Map are based on country specific reported trade
data. In the absence of country reported data, the ICT captures data as reported by
the trading partner. It was noticed during the data gathering process that only South
Africa was consistent in their data collection and reporting on Trade Map. It could
be important for other African countries to learn from South Africa and adopt such
practices accordingly. The availability of data on fish trade in Africa can help
inform policy through research in intra-regional fish trade
58
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