Regional Integration and Trade in Agrifood Products: - DAAD ...

Regional Integration and Trade in Agrifood
Products:
Evidence from the East African Community
By
Francis Ejones
November, 2012

Abstract
This study examines the postulation that trade liberalization (regional integration) policies of
LDCs normally undermine their presumed impact. The study is based on the experience of EAC
trade agreement. It adopts the extended gravity model, to analyze the impact of this regional
integration on food item. The model includes 168 countries and is estimated with panel data over
the period 1988 – 1996. The Poisson estimation method took into account unobserved trade data
characteristics of the bilateral trade relations. The results show that regional trade integration
increased exports, normally at the expense of exports and welfare of non-members, and these
exports were more reflective of food exports growth. The same has not been true for intra-bloc
exports of food although the sector experienced an increase in exports resulting from the
implementation of a trade agreement. The intra-bloc results are consistent with the structural
rigidities of the exporting EAC Countries.
Keywords: Regional Integration, Trade in Agrifood or Food Items or Products, EAC, Gravity
Model and Poisson, Panel Data Analysis.
Characters: 28,032
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Table of Content
Abstract ................................................................................................................................................... ii
List of Notations ................................................................................................................................. vi
Acknowledgment............................................................................................................................... vii
CHAPTER ONE ..................................................................................................................................... 1
1.0 Introduction ............................................................................................................................. 1
1.1 The Subject of the Study .......................................................................................................... 1
1.2 Statement of the Research Problem .......................................................................................... 2
1.3 The Purpose and Research Questions Tackled in the Study ....................................................... 3
1.4 Relevance to the Field of International Economics.................................................................... 3
1.5 Brief Description of the Methodology ...................................................................................... 4
1.6 Scope of the Study ................................................................................................................... 5
1.7 Types and Sources of Data ....................................................................................................... 5
1.8 Delimitations of the Study ........................................................................................................ 5
1.9 Organization of the Study ......................................................................................................... 6
Chapter Two ........................................................................................................................................... 8
2.0 Background to the EAC and Food Item Trade ........................................................................... 8
2.1 Evolution of EAC: From Cooperation to Common Market and Beyond .................................... 8
2.2 EAC Food and Agricultural Program ...................................................................................... 14
2.2.1 Prominence of Food and Agriculture in EAC Common Market Protocol. ........................ 14
2.2.2 Treatment of Agriculture in the EAC Common Protocol (Article 105 – 110) ................... 15
2.3 Inter - Sectoral Trends and Structure of the Selected Exports of the EAC................................ 16
2.3.1 EAC Export Trends and its Constitution by Product. ....................................................... 16
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2.3.2 EAC Member States Contribution to Export of the Selected Products ............................. 17
Figure 2.2 Relative Contributions of EAC Member States to the Trade of Selected Products .............. 18
2.3.3 Country Decomposition of EAC Exports by Sector ......................................................... 18
2.3.4 Sectoral Decomposition of EAC Exports by Country ...................................................... 23
2.4 Intra – Sectoral Trends and Structure of the Selected Exports of the EAC ............................... 26
2.4.1 Intra - EAC Export Trends and its Constitution by Product. ............................................ 26
2.4.2 Intra-EAC Member States Contribution to Export of the Selected Products ..................... 27
2.4.3 Country Decomposition of EAC Exports by Sector ......................................................... 28
2.4.4 Sectoral Decomposition of Intra - EAC Exports by Country ............................................ 31
Chapter Three ........................................................................................................................................ 35
3.0 Literature Review ................................................................................................................... 35
3.1 Introduction ........................................................................................................................... 35
3.2 Review of Definitional Issues ................................................................................................. 35
3.2.1 Definition and Measurement of the Effects of EI or RTAs .............................................. 35
3.2.2 Definition and Measurement of Agrifood or Food Items ................................................. 37
3.2.3 Definition and Specification Issues of Gravity ................................................................ 38
3.3 Review of Theoretical Issues .................................................................................................. 38
3.3.1 Classical or Traditional Theories of International Trade .................................................. 39
3.3.1.1 Mercantilist Theory of International Trade ...................................................................... 40
3.3.1.2 Absolute Advantage and Comparative Advantage Theory of International Trade ................ 40
3.3.2 Neo-Classical or Standard Trade Theories ...................................................................... 42
3.3.3 New Trade Theories and new development in Analyzing International Trade .................. 42
3.4 Review of Theoretical Foundation of Gravity Modeling in Trade. .......................................... 44
3.5 Review of Methodological and Empirical Issues ..................................................................... 45
3.6 Conclusion and Relevance of the Literature Review ............................................................... 46
Chapter Four ......................................................................................................................................... 48
4.0 Analytical Framework and Methodology ................................................................................ 48
4.1 Introduction ........................................................................................................................... 48
4.2 Analytical Framework: Gravity Model ................................................................................... 48
4.3 Specification of Empirical Equations ...................................................................................... 49
4.3.1 Coefficients and Variable Definition ............................................................................... 50
4.4 Method of Analysis ................................................................................................................ 54
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4.5 Type and Sources of Data ....................................................................................................... 55
Chapter Five .......................................................................................................................................... 57
5.0 Presentation and Discussion of Empirical Results ................................................................... 57
5.1 Introduction ........................................................................................................................... 57
5.2 Descriptive Statistics .............................................................................................................. 58
5.2.1 Summary Statistics ......................................................................................................... 58
5.2.2 Regression Correlation Matrix ........................................................................................ 59
5.2.3 Assessment of the Model Using the Observed Versus Predicted Values .......................... 60
5.2.4 Test for Normality .......................................................................................................... 60
5.3 Regression Diagnostics .......................................................................................................... 62
5.3.1 Testing for Homoscedasticity ......................................................................................... 62
5.3.2 Testing for Multicollinearity ........................................................................................... 63
5.4 Presentation of Estimates and Discussion of Results ............................................................... 64
5.4.1 Summary of Econometrics Methods ............................................................................... 64
5.4.2 Results of the Determinants of Exports of EAC .............................................................. 66
5.4.3 Application to the assessment of the effects of regional trade agreements. ....................... 68
5.4.3 Application to the sectoral assessment of the effects of the adopted sectors ..................... 69
Chapter Six ........................................................................................................................................... 71
6.0 Summary, Conclusion and Policy Recommendations.............................................................. 71
6.1 Introduction ........................................................................................................................... 71
6.2 Summary of the Empirical Analysis ....................................................................................... 71
6.4 Policy Recommendations ....................................................................................................... 75
References............................................................................................................................................. 77
Appendix A: List of Tables ................................................................................................................ 81
Appendix B: Correlation Matrix ........................................................................................................ 85
Appendix C: Estimation Results from Gravity ................................................................................... 86
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List of Notations
AoA - Agreement on Agriculture
APEC - Asia-Pacific Economic Commission
CET - Common External Tariff
CGE - Computable General Equilibrium
COMESA - Common Market for East and Southern Africa
DDA - Doha Development Agenda
EAC - East African Community
EASRA - East African Securities Regulatory Authorities
EU - European Union
FDI - Foreign Direct Investments
Mercusor - Latin American Common Markets
MUBS - Makerere University Business School
NAFTA - North American Free Trade Area
NBER - The National Bureau of Economic Research
OECD - Organization of Economic Cooperation and Development
OLS - Ordinary Least Squares
PWT - Penn World Tables
RTAs - Regional Trade Agreement(s)
SAPTA - South Asian Association for Regional Cooperation
SIDA - Swedish International Development Cooperation Agency
S-S - South South
TRAPCA - Trade Policy Research Centre in Africa
UN-ESCAP - United Nations Economic and Social Commission for Asia and the
Pacific
UR - Uruguay Round
Vi/UNCTAD - Virtual Institute of the United Nations Conference on Trade and
Development
WITS - World Integrated Trade System
WTO - World Trade Organization
vi

Acknowledgment
I am grateful for comments on the drafts of this paper provided by Prof. Dr. Göte Hansson,
Dr.Wumi Kalawole, and Dr. Caiaphas Chekwoti.
The conviction of writing and analyzing a panel dataset on EAC was provided by Dr. Marco
Fugaaza. He also endeared upon me to construct a large dataset such as the one used in this study
and guided me in the tedious process of its construction. I also benefitted a lot from the classes
on panel data and STATA that he handled in Uganda under the auspices of the Virtual Institute
of United Nations Conference on Trade and Development(VI-UNCTAD) and Makerere
University Business School (MUBS). Joshua Kayiwa, Richard Sebaggala and Kenneth Jumanyol
whose advice guided me on how to proceed with tricky STATA and panel estimation processes.
Special thanks to the Vi UNCTAD and especially Dr. Vlasta Macku for providing me an
opportunity to develop the original ideas of this paper. The text was edited by Engineer Samuel
Odora.
The financial support of my family and SIDA under the Lund University/TRAPCA is
gratefully acknowledged.
vii

1.0 Introduction
CHAPTER ONE
1.1 The Subject of the Study
The Agreement on Agriculture (AoA),a product of the Uruguay Round (UR)
introduced a significant departure from the traditional treatment of agriculture
multilaterally (OECD 1997). It extended the basic principles and commitments in
market access, export competition and domestic support (Kessie 2004 and OECD
1997). The prescience on the modest gains in agriculture (and services) called for
further negotiations after the implementation of the UR in order to improve the
results in these areas (Kessie 2004). The built-in agenda in these agreements was
brought under a new round of negotiations known as the Doha Development Agenda
(DDA). Thatwas deemed to provide greater economic opportunities to developing
countries. However, the round of negotiations under the DDA have lurched past the
set deadlines of its conclusion because of the differing interests of member countries
and country groupings, inter alia, in domestic support, special safeguards and
designation of special products. The bog in reaching consensus (especially on
agriculture) has generated pessimistic perceptions of the effectiveness of the WTO.
Developing Countries (and particularly the least developed among them), believe
that the agricultural reforms proposed by developed countries were too limited. To
hasten commitment in agriculture and consolidate significant achievements in the
area and other contentious sectors, groups of countries have resorted to regional trade
agreements (RTAs) (Head and Ries, 2004).
With this growing development, nation states embarked on an accelerated trend
towards forming regional andpreferential bilateral trade agreements in every part of
the world 1 . These proliferations exude an increasing level of sophistication, both in
terms of scope and configuration than ever experienced before. In terms of
sophistication of configurations, the South-South (S-S) integration is seen as one of
the most dazzling. More to this, agriculture plays an important and twin part in the S-
S cooperation and integration blocs. It is significant for exports and essential for
1 See VI-UNCTAD Teaching Material on RTAs (2007).
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intra-bloc trading in merchandise. S-S intra-and-inter bloc markets provide a scope
for maximizing gains from trading in food and agriculture raw materials. This is
attributed to improved markets access policies and implementation of intra-bloc
production support much faster and readily, than it would be at the international level.
Auxiliary, processed merchandise (especially agriculture and food products) from S-S
countries have faced higher tariffs for a specific range of products of major interest to
these periphery states. Besides this, there are other restrictions related to standards
that bar them from exporting agricultural products, especially, to the lucrative
markets in the North.
However, there is a scare at the scope and speed of trade liberalization of the food
sector on the one side, and this liberalization in relation to other sectors such as
agricultural raw materials, fuels, ores and metals, and manufactures on the other side.
The liberalization measures in the food or even agricultural sectors inhibited efforts to
increase intra-bloc trade and undermine food security rather than uplift efforts to
reduce poverty as Vylder (2007) argues.
It is statements and findings therein that have bred interest to find out whether the
efforts of the East African Community (EAC) are paying off, since the bloc‟s
ambition in securing the gains are commendable. Among the S-S blocs, EAC member
states have exuded the fastest and most significant liberalization measures, and
incurred huge sunk costs in reconfiguring the bloc from an FTA to now a common
market in less than 10 years. It is hoped to become a political federation (the highest
and most complex level of integration) by 2015. However, a survey of the literature
on RTAs hardly reveals any empirical literature on EAC to solve such empirical
interest and it has neglected trade in food item.
1.2 Statement of the Research Problem
The massive changes and commitments in EAC food or agrifood sector was and is
presumed to impact significantly on Agrifood trade in the region. The extent to which
these policies have impacted on agrifood trade is however not known or even
documented. This could possibly be due to limited and inadequate empirical studies
guiding the evolution of the EAC and the process of formulating its policies. The
limited studies are also tainted with strong statements, theory and causal empiricism
rather than robust empirical studies and theories. Studies like Wanjiru, (2006);
Gregory, (1981); Kirkpatrick and Wantabe (2005); Ochwada, (2004) written on EAC,
are too general to answer the pertinent issues evolving in the bloc, and as such, are
devoid of their explicit policy outcomes and intention to guide sectoral or specific
policy needs of the bloc. Studies on RTAs, though exclusive of EAC dynamics have
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also not paid attention to the effects orimpacts of these trading blocs on agrifood
trade, a core area that this study tackles. The methodologies and econometric
processes adopted by these studies have even most times been spurious. The data was
not as disaggregated as the one this study utilized. In this regard, the unanswered
questions include: Are the EAC trade liberalization efforts bearing fruit for the bloc,
and changing the structure of its intra-bloc sectoral trade especially in food or
agrifood trade? Is the volume of its intra-bloc trade in agrifood trade improving and
how is this decomposed by country?
1.3 The Purpose and Research Questions Tackled in the
Study
The study analyzes the effect of EAC trade liberalization policies on intra-bloc trade
of selected sectors with food item trade as the reference sub-sector. This is because
trade liberalization experience of least developing countries has undermined their
presumed impact as argued by Vylder (2007), and hence bred empirical interest.
The mainquestionsraised in this thesis are as follows:
Does the adopted trade liberalization policy explain bloc trade? What are the
determinants,and estimatesof the EAC trade at both the regional and at country
levels?
To what extent has the EAC trade liberalization efforts affected its intra-bloc
trading patterns of the selected sectors?
What is the impact of EAC trade liberalization on intra-regional exports
andtheir implication on trade creation and trade diversion?
1.4 Relevance to the Field of International Economics
EAC is developing and transforming at a remarkable rate. Within ten years, the bloc
has increased its membership to five from three, and transformed from a Customs
Union to a Common Market. Within the next four years, it will be elevated to a
monetary Union in 2012 and Political Federation by 2014. The surge to liberalize is
also emanating from pressure from developed nations like European Community
countries to actualize these developments. What seem to guide the reform are strong
political statements, theory and causal empiricism rather than logical economic
rationale. This study in part attempts to provide a more grounded analysis to guide
this process. It also tackled and focused on agrifood sector analysis, a sub sector
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hardly being studied. Therefore, the outcome of this study will provide background
information for the policy formation process.
Theoretically, the study tests and utilizes the new theoretical improvements in the
gravity model, dealing with the treatment of trade, robustness, and zero trade issues
etc. And on the methodological ground and empirical frontier, it expounds on the use
of gravity modeling and utilizes it for EAC, a trading bloc that has not been
empirically explored well.
1.5 Brief Description of the Methodology
A variant of material, in terms of eccentricity and content has been adopted and
utilized to write this study. Continuums of authors have had interest in analyzing
RTAs in general and EAC bloc in particular. They have mainly approached the
analysis from a political economy and economic angle of analysis. This study utilized
the economic approach for its analysis. The political economy approach has been
discarded from the discussion as far as possible. Simply because it is not of interest to
this study, as it fails to answer the pertinent questions that the study proposes.
Since the Uruguay Round, the basic principles and commitments in the treatment
of agriculture has significantly re-oriented the treatment of agriculture. However, the
lack of progress in core areas in agriculture has propelled the evolution of RTAs in
which food item or agrifood trade is important for food security and poverty
eradication. Literature of the key issues relating to the evidence of the impact of these
developments fall in the writing found in international trade, political economy, etc.
The literature is well developed and documented in text books, journal articles,
articles and comments found variously.
Among the economic approaches or methodologies of analysis are; descriptive,
Computable General Equilibrium (CGE) and econometric approaches. However, this
study adopted the descriptive approaches and econometric approach to analyze the
effects of the adopted policies. In the descriptive approach, a critical analysis of the
data and documentation was garnered. An analytical approach augmented from the
theoretical approach was contextualized. The analytical approach adopted involved
augmenting the traditional gravity model with covariates to estimate and answer the
pertinent questions in the study. A panel dataset of 22 years from 1988 to 2009, with
168 countries and five products of key interest to EAC was utilized.
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1.6 Scope of the Study
The study adopts a gravity model estimation procedure with disaggregated data to
analyze effects of EAC on selected major agrifood products in relation to four other
sectors (Agricultural raw materials, ores and metals, fuels and manufactured goods).
The data for the study runs from 1988 to 2009 for all the products and bilateral
relations between the EAC member states and their trading partners. And an extended
gravity model is used to determine the extent of intraregional trade bias and potential
trade diversion effects for the selected commodities separately. A panel regression is
estimated using generalized least squares method for the period under consideration
(1988 to 2009). This period is adopted due to the availability of a complete dataset for
all countries for all the period.
1.7 Types and Sources of Data
There are broadly two categories of data. One is qualitative and the other is
quantitative. Since the bulk of the data analyzed in this study is numerical in form,
the quantitative data type was used. To the greatest extent possible, qualitative data
type was relegated. Where it was available, the qualitative attributes if the data were
coded, hence making them quantitative.
The source of these quantitative type depended on the variable. However, three
major sources were used to collect the data. The first set of data was the trade data
collected from the World Integrated Trade Solutions (WITS) of the United Nations.
The second source of data was from the World Penn Tables (WPT 7.0). Data from
the WPT relates a set of national accounts economic time series covering many
countries. And the third source of data was mined from CEPII. CEPII data related to
countries and their main city or agglomeration.
1.8 Delimitations of the Study
An attempt to estimate the impact of myriad of aspects of a regional bloc like EAC in
a single study like this is a daunting and unfeasible task. A study covering the wide
range of details to fit such studies would necessitate rigorous and broad research.
Further, it would require time, personnel and substantial costs. Therefore to produce a
robust study that can be generalized, one has to make some delimitation.
Firstly, the study adopted gravity modeling to estimate the effects of EAC trade
liberalization. Much as there have been marked improvements in its theoretical
5

foundations, there are still disagreements on which type of gravity model to adopt and
most times they do not fit the dataset quite well. As such, studies have found that the
estimates of the gravity modeling are dependent on the covariates used and their
number. For this reason, gravity modeling has a tendency to either underestimate or
overestimate the weight of the estimates depending on the choice of covariates.
Secondly, the study uses a panel dataset of bilateral trade spanning 22 years for
185 countries. In using this type of dataset, there is a problem of attrition when the
EAC member states cease trading with a partner or vice versa. Since the problem of
the attrition in this data set seems to be completely random, the adoption of the data
set should not be problematic. However, since EAC member states would concentrate
on certain trading partners, and hence dropping the partners with limited trade
dealings, the findings could give an impression that trading relationships are
improving and profitable for those trading blocs (countries) than others, and further
the concentration of trading with selective partners (the partners become non-random)
with a large or significant trading with them. Besides this, span of the dataset used
was purely influenced by availability of data for those countries in the databases.
Availability of more years would provide more degrees of freedom to do a more
robust analysis.
Thirdly, the study did not focus on inter bloc trade, rather on intra-EAC bloc trade.
This can be conceived from the fact that, it does not include tariffs in its analysis. An
inclusion of tariffs is expected to provide more valid estimates. However, tariff
exclusion from the analysis should not negate the results of this study, since all the
exporters in this study benefit from Special and Differential Treatment, and therefore
the tariffs should not be expected to have a measurable effect on trade.
1.9 Organization of the Study
This thesis paper hinges upon the above mentioned questions. And the rest of this
paper is structured as follows: In chapter 2, the study provides a background to East
Africa Community. The core of the discussion is centered on the evolution of EAC
and attempts at effecting food trade; agriculture and food policy provisions of the
protocol establishing the EAC and a confabbing adequacy of the protocol to enable
food and agricultural production and trade; and a discussion on the dynamics and
trends in EAC food trade.
In chapter 3, the study selectively reviews empirical and theoretical literature on
EAC. It also elaborates on the developments in trade policies, and elaborates on the
theoretical foundations of the gravity model and it strengths in estimations. Chapter 4
provides the analytical framework and methodology adopted. In chapter 5, the
6

presentation of findings and their discussion was done. And the study is concluded in
chapter 6 with recommendations and provides policy recommendations or
implications of the findings to EAC member states‟ regional trade policy.
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Chapter Two
2.0 Background to the EAC andFood ItemTrade
2.1 Evolution of EAC: From Cooperation to Common
Market and Beyond
The current EAC is a realization of years of collaborations and cooperation way
before colonialism in the second half of the nineteenth century. Cooperation before
the advent of colonialism came to be known as the East African Long distance trade.
The major players were Indians and Arabs who travelled using ships driven by
Monsoon winds to and fro India and the Far East, and local Kings, Sultans or
powerful persons who controlled trade routes.
The structure involved first intra-regional trade and then inter-regional trade
as goods were then moved to the East African coast majorly 2 . The intra-regional trade
structure involved commodities being traded from a market in one village to a market
in another village. The goods traded were highly specialized and the supplying
village had comparative advantage in producing those goods. This was probably
because they were naturally endowed with the raw materials or producers had unique
skills in producing them 3 .The range of goods traded were; agricultural food items,
pottery, wood carvings, beads, backcloth, and salt among other products.
There was another range of goods that were most demanded by Europeans,
Indians and people from the Far East. This range of products included; cloves, slaves,
2 As demand increased, coastal and powerful moved further inland to control the whole supply chain.
3 Mode of exchange was largely barter (i.e. exchange of goods for goods or service for service), but in
some case there were some form currency. With increase in the number of transactions, the trade
structure seemed not to be dynamic due to its simplicity.
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iron ore, minerals like gold, copper, salt and spices which were transacted over
greater distances at market points close to state boundaries or capitals like Kilwa 4 at
the coast line of the East African Indian Ocean 5 . To protect or control the trade routes
or villages, one community would subdue another. With time, this was not tenable,
cooperation then became as the modusoperandi.
When the British (Colonial Administrators) arrived in East Africa, they
brought about many changes in the supply chain. They firstly abolished trade in
slaves and with time introduced new cropping and trading patterns that served their
industries and interests. Under them, Kenya, Tanzania and Uganda enjoyed a new
wave of cooperation under successive regional integration arrangements. All the
arrangements under the colonial period which lasted about 60 years were forced upon
the member states.
Table 2.1 below illustrate the chronology in the process of EAC integration,
from an imposed customs union by the British Colonial Administration between
Kenya and Uganda in 1917 to being the first trading bloc to liberally sign a common
market protocol in the natural-resources-rich-continent of Africa with an eye to
bolster trade, investment and other economic undertakings.
Table 2.1: Evolution of East African Common Market Protocol
Era Episode Milestone
Colonial Period
1897- 1901 Commencement of
formal cooperation in East
Africa (Kenya&Uganda)
1905 East Africa Currency
Board established
Postal Union established
Construction of Kenya-Uganda railway
(1897-1901)
Establishment of customs collection
center in 1900
Monetary Union with a single currency
in place till 1966; it was responsible for
issuing & redeeming local currency for
Sterling. EAC belonged to the Sterling
Exchange System; no restrictions on capital
movements.
1919 Customs Union between Joint administering of customs, excise
4 The Portuguese are said to have been amazed by the level of organization and development of this
coastal city, and thought it was more comparable to cities in Europe.
5 This is where enterprising South-Asians cooperated to their mutual advantage in economic
development of the whole region (Gregory, R. G 1981).
9

Uganda&Kenya & income tax; and other services such as
medical & industrial researches, education,
transport & communication, and agriculture.
1927 Tanzania(formerly
Tanganyika) joins customs
union
1931 Kenya-Uganda railway
opened
1948 Establishment of the
East African High Commission
(EAHC) till 1961.
1961 East African Common
Services Organization
Independent States6
1961,
1962, 1963
and 1964
Agreement (1961 – 1966)
Mainland Tanzania
gains independence from the
British in 1961. Followed by
Uganda in 1962 and Kenya in
1963. In 1964, Tanzania unifies
with Zanzibar to become one
country.
Common external tariff, jointly run a
monetary union and consolidated fiscal
integration; and mobility of labor allowed.
Uganda had a direct link to the Indian
Oceanserviced by the rail.
Inter-territorial co-operation
formalized, common legislative body
(considered & enacted legislation relating to
aspects of common services) and
administrative organ operated with 3
governors of the territories with Secretariat in
Nairobi.
Responsible for the development of
major infrastructure and distributing them
evenly.
Regulate the commercial and industrial
relations and transactions between the said
countries
and enacted central legislature on
behalf of the said countries, laws relevant to
the purposes of the said joint organizations
Many changes effected in the
machinery of cooperation and the EAHC was
transformed to East African Common Services
Organization (EASCO) which consisted of
Chief Executive from the 3 countries. The
Central Legislative Assembly (CLA)
introduced in 1948 was enlarged and operated
through committees run by ministers from the
member states.
6 Integration which had „colonial‟ arrangement needed to be changed after independence.
Developments were imposed on states prior to this.
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1967 Treaty for East Africa
Community enacted & signed
Arusha Declaration by
Tanzania (socialist move)
1970 Common Man‟s Charter
&Nakivubo Pronouncement by
Uganda
1971
– 1985
Uganda experiences
Civil unrest and political
instability
Operations of all these bodies went on
without any formal enactment of authority.
Establishment of the EAC and The
East African Council with 3 presidents & five
councils assigned for a common market,
communications, economics & planning,
finance & research and social affairs.
Separate Central banks created but
EAC had harmonized monetary policies to the
extent of proper functioning of common
market & aims of community.
Separate currencies which were
identical and could be used in any territory.
Tanzania nationalizes banks and
introduces exchange controls to restrict capital
flight to Uganda&Kenya.
Capital outflow from Uganda due to
nationalization; exchange rate controls against
Tanzania&Kenya introduced; export & import
of Ugandan currency banned.
Exchange rate controls followed
different pegging of currencies.
Dr. Apollo Milton Obote is deposed
and replaced by a dictator.
Differences in ideology and economic
disparities emerge.
1977 Dissolution of EAC the main reasons contributing to the
collapse of the East African Community
being;
lack of strong political will,
lack of strong participation of the
private sector and civil society in the cooperation
activities,
the continued disproportionate sharing
of benefits of the Community among the
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1984 East African Mediation
Agreement 1984, on 14th May
1993 Permanent Tripartite
Commission established for
cooperation in EA
1999 Treaty establishing
EAC is launched on
30thNovember
2000
Entering into force of
EAC Treaty
2005 Establishment of EAC
Customs Union
Partner States due to their differences in their
levels of development
and lack of adequate policies to
address this situation;
Assets and liabilities of defunct EAC
were divided among partner states
Provision is made in agreement to
enable partners to explore and identify areas of
future cooperation.
responsible for the co-ordination of
economic, social, cultural, security and
political issues among the said countries
Operations did not commence
immediately till 1996 when secretariat was
launched.
Agreement went for parliamentary &
public scrutiny before updating to treaty.
EAC had several institutions to ensure
its objective; The Summit, The Co-ordination
Committee, Sectoral Committee, EAC Court,
EAC Assembly and the Secretariat
On 7th July following ratification of
the 3 original members.
Objective is to form a single customs
territory
Introduction of Common External
Tariff (CET)
2007 Enlargement of EAC Rwanda&Burundi acceded EAC on
18th June and become full member on 1st July
same year
2009 Common Market
Protocol
allows free movement of people,
goods, labor and capital across the member
countries
came into effect in July 2010
2011 EAC Food Security Increase food item trade between the
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Action Plan (2011 – 2015) deficit and surplus areas or countries
2012 Monetary Union
2014 Political Federation
Table 1 above summarizes the progression of cooperation in investment, commerce,
economic and industry enjoyed by the East African member states after the arrival of
colonialism to date. The former East African community was set up in 1967 and
collapsed in 1977. In 1999, the heads of states signed an agreement, following the
spirit of previous cooperation to set up a „New‟ East African Community which came
into force in 2000. Since then, it has expanded from three member countries to five in
2007 with the joining of Rwanda and Burundi. The Democratic Republic of Congo
(DRC) and Sudan have also indicated interest in joining the bloc. Many academicians
are also foreseeing Ethiopia breeding interest in the group. However, the newly
formed Southern Sudan state may become the next country to join EAC before any
other country.
Since the revival of the East African Community, the following
accomplishments have been experienced in the area of trade;
a. Ongoing implementation of tariff reduction with Kenya, Uganda & Tanzania
applying 90 percent and 80 reduction respectively, removal of all cross border nontariff
barriers;
b. Ongoing harmonization of capital markets policies, trading practices and
regulation in the stock exchanges of the member countries under the auspice of East
African Securities Regulatory Authorities (EASRA);
c. Harmonization of fiscal and monetary policies aiming at harmonizing;
banking rules, VAT rates, pre-shipment requirements; and movement towards
making partner states currencies convertible, macroeconomic framework and
avoidance of double taxation; and
d. Formation of a customs union and common market protocol.
These developments show the deepening of trade among member states. However,
there seems not have been an explicit policy on food item trade or even agricultural
trade until 2009 when the EAC Common Market protocol came in place and an
action plan for the implementation of chapter 105 – 110 developed in 2011. Even
then the policies on food item trade in particular and agriculture in general is still not
well conceived. The programs are ambitious and need clear synchronization with
other programs like program on infrastructure, climate change, and poverty
13

eradication among other intentions. Important to note, that food item trade is
conceived under the auspices of improving food security.
2.2 EAC Food and Agricultural Program
In order to understand the EAC food item program, one must look at the treaty or
protocol that formed the EAC in 1999. This is the protocol that first clearly spelt-out
food and agricultural trade and development in the region, unlike previous
agreements of cooperation. However, much as desire to enhance agriculture and food
item production and trade is spelt out clearly in this protocol, the programs and
policies to achieve the desires are not well spelt out and are amambojumbo of linked
and pegged to all other major programs and sectors. Mirrored to the multilateral
agreement AoA, it is clear that the EAC chapters on Agriculture and food security
and disciplines and commitments are way below standard and the program has not
domesticated those disciplines.
2.2.1 Prominence of Food and Agriculture in EAC Common Market
Protocol.
In the implementation of the EAC Common Market Protocol, the facilitation of the
trade in food commodities and products has been given first priority. This is deemed
to ensure food security, when food surplus areas or countries transfer the surplus to
food deficit productions points in EAC. Also, since the main occupation of EAC
citizen is in the production of food commodities, increased trade in food products is
considered to enhance employment and the best move to poverty eradication. And
agriculture is the most important sector and key area of cooperation in the bloc 7 . It
accounts for 36 percent of the Gross Domestic Product (GDP) with lots of untapped
potential, specifically in commercial farming. And eighty percent of the populations
of the community live in rural areas depending on agriculture for their livelihood 8 .
To enhance the gains in EAC trade liberalization, member states have even
eliminated export bans on food commodities and products intended for consumption
in the region. The states have also had an ongoing process of improving the transport
infrastructure and with priority on highways and railways on the one hand, and the
development of the feeder roads to enable the optimal utilization of the transport
7 Source is EAC„s Agenda for Agriculture. And it accounts for about 44 percent of GDP in Burundi
and Tanzania, 30 percent in Uganda, 24 percent in Kenya and 38 percent in Rwanda (2006 figures).
8 ibid
14

infrastructure. They are engaged in the development of the food market-supporting
infrastructure, to reduce the transaction cost of this trade.
2.2.2 Treatment of Agriculture in the EAC Common Protocol (Article
105 – 110)
Article 105 – 110 of the Treaty Establishing the EAC and specifically Chapter 18,
outlines the key area of cooperation of Agriculture and Food security. And the most
prominent objective of agricultural cooperation in EAC is the achievement of food
security and rational agricultural production.
In order to rationalize agricultural production with a view to promote
complimentary and specialization and sustainability of national agricultural
programs, partner states have adopted the following objectives;
a common agricultural policy;
food sufficiency within the Community;
an increase in the production of crops, livestock, fisheries and forest products for
domestic consumption, exports within and outside the Community and as inputs
to agro-based industries within the Community; and
post-harvest preservation and conservation and improved food processing.
For purposes of paragraph 1 of Article 105, the Partner States undertake to co-operate
in specific fields of agriculture, including:
the harmonization of agricultural policies of the Partner States;
the development of food security within the partner states and the Community as
a whole, through the production and supply of foodstuffs;
agro-meteorology and climatology to promote the development of early
climatological warning systems within the Community;
the development and application of agricultural training and research and
extension services;
the adoption of internationally accepted quality standards for food processing;
the establishment of joint programs for the control of animal and plant diseases
and pests;
the marketing of food and the co-ordination of the export and import of
agricultural commodities;
joint actions in combating drought and desertification ; and
in such other fields of agriculture as the Council may determine.
15

The EAC Agriculture and Rural Development Policy (EAC – ARDP) was developed
as an initial step towards the implementation of the provisions of the treaty. It reflects
the commitment of the partner States to foster their economic co-operation for the
benefit of their people.
2.3 Inter - Sectoral Trends and Structure of the Selected
Exports of the EAC
In this section, the statistics on the composition of EAC exports of the selected
products is presented in section 2.3.1. Section 2.3.2, makes a discussion and
presentation of the trends and discussion of the contribution of EAC exports by
member states. In section 2.3.3, the paper takes on a discussion of the exports of the
intra-bloc exports.
2.3.1 EAC Export Trends and its Constitution by Product.
Figure 2.1 below shows the trend in EAC exports and its constitution by the selected
products. There has been a persistent increase in the volume of exports of the selected
products since 1988. However, since the formation of the EAC in 2000, the upward
trend in the graph seems to have picked up. This upward swing was even more
significant around 2005 when EAC became a customs union and even surged further
skyward, when Burundi and Rwanda formally joined the bloc, until it picked up to
over 20 percent of the value of total exports in 2008.
16

Figure 2.1 Exports Growth and Composition of the Selected EAC Products
2.3.2 EAC Member States Contribution to Export of the Selected
Products
All the EAC member states‟ exports, they seem to follow a similar trend like the bloc
exports of the selected product or sector categories. However, Kenya‟s exports seem
to singly explain the bloc exports. For each year, Kenya‟s export is almost more than
double the combined exports value of all the other states. Rwanda and Burundi
exports contribute about one million dollars. Tanzania‟s exports are the second
largest exports in the region, followed by Uganda. Tanzania‟s exports surpassed the 1
billion dollar mark around the year 2003, while Uganda‟s exports surpassed this mark
just two years ago. However, Tanzania‟s exports have not yet surpassed the 3 billion
dollar mark. Uganda‟s exports are still below the 2 billion dollar mark. However,
Kenya‟s exports have reached the 6 billion dollar mark and have been more than 3
billion dollars mark since 2004. See figure 2.2 below for clarity on the above
conclusions.
17

Figure 2.2 Relative Contributions of EAC Member States to the Trade of Selected
Products
2.3.3 Country Decomposition of EAC Exports by Sector
In figure 2.3 below, a stacked panel (A, B, C, D and E) of graphs showing the
decomposition of EAC states exports by sector is shown. Panel A, B, C, D and E
respectively show the decomposition of food item trade, agricultural raw materials
(AgriRaw), fuels, Ores and Metals (OresMtls) and Manufactures (Manuf) exports of
EAC in millions (MM) of US dollars (USD). Overall, the trend in exports for all
products has experienced a significant surge when the EAC was formed.
The value of EAC members‟ food Item trade is shown in figure 2.3.1 in panel
A below. From the figure, Burundi and Rwanda hardly export any food items. The
combined value of their food item exports is still below 100 millions of US dollars.
Kenya is the major exporter by value of food items from the bloc. Kenya‟s exports
have persistently grown from over 500 million US dollars at the beginning of the
period to about 2000 million of US dollars at the end of the period. Uganda and
Tanzania‟s export values of food items have relatively grown at the same rate.
However, Tanzania‟s and Uganda‟s value of food exports have only surpassed the
18

500 million US dollar mark in 2004 and 2005 respectively. And they are about to
clock the 1000 million US dollar mark at the end of the period under consideration.
Figure 2.3.2 in Panel B, shows the value of agricultural raw materials exports
of EAC. From the figure, Kenya and Tanzania are the major exporters by value of the
agricultural raw materials. At the beginning of the period, Kenya was exporting about
200 million US dollars of agricultural raw materials. By 2000, the value surpassed the
500 million value of export of agricultural raw materials, and peaked 1700 millions of
US dollars at the end of the period under consideration. Tanzania‟ exports were
growing at the same rate with Kenya‟s until the mid-1990s when Kenya‟s export
value of agricultural raw materials surpassed its exports of the same product.
Tanzania‟ exports only surpassed the 500 million US dollar mark around 2004 but
have not yet reached the 1000 million US dollar mark. The combined value of
Uganda, Rwanda and Burundi‟s agricultural raw materials not yet more than 500
million US dollars.
In Panel C, figure 2.3.3 is showing the value of the exports of fuel trade from
EAC. Kenya and Tanzania are still the major countries exporting this product too.
Kenya‟s exports for fuel have grown from zero exports in 1988 to peak over 350
million US dollars in 2005. From then, it experienced a significant drop but still
above 150 million US dollars, though it has picked up now. Much as Tanzania is on
average the second major export of fuels, its exports have never gone beyond 150
million US dollars. Rwanda experienced a surge in exports between 2001 and 2005.
Its exports of fuels peaked about 270 million US dollars during this time. This is the
only time Rwanda seemed to export fuel. Uganda and Burundi hardly export any fuel.
The exports of ores and metals are shown in figure 3.3.4 in Panel D. Between
1988 and before the promulgation of the EAC in 2000, the value of exports for ores
and metals was insignificant. On average, the value of the exports of ores and metals
was less than 50 million US dollars for all the countries during this period. From
2000, the exports of fuels seemed to have picked up for all countries except Burundi.
This trend picked up further after 2005 when EAC became a Customs Union.
However, Tanzania is the major export of ores and metals, followed by Kenya,
Rwanda and then Uganda. Tanzania‟s exports peaked an export value of about 290
million US dollars in 2007. Kenya is yet to reach 200 million UD dollars of export of
ores and metals, Rwanda just clocked the 200 million US dollars mark in 2007, and
Uganda just clocked 50 million US dollars in the same year.
Figure 2.3.5 in Panel E shows the decomposition of manufactured item trade
by country. Rwanda and Burundi hardly export any food products. They combined
exports of manufactured items have been less than 100 million US dollars. Kenya
19

exports the most manufactured item from the region and exports have persistently
grown from less than 100 US dollars in 1988 to over 2000 million US dollars in
2008. Kenyan surpassed the 500 million US dollars value of manufactured exports
around 1994. Tanzania is the second major exporter of manufactured items from the
region. Much as the country‟s exports have a persistent growth, the only surpassed
the 500 million US dollar value of exports in 2007. Uganda‟s exports of
manufactured items are still low and it has not yet surpassed the 500 million US
dollar mark. However, since 2006, the exports have picked up almost clocking the
500 million US dollar mark.
20

Figure 2.3 Showing EAC Sector‟s Stacked Graphs Decomposed by Country
Panel A: Figure 2.3.1 Showing Country Decomposition of Food
Exports
Panel C: Figure 2.3.3 Showing Country Decomposition of Fuels
Exports
Panel E: Figure 2.3.5 Showing Country Decomposition of Manuf
Exports
Panel B: Figure 2.3.2 Showing Country Decomposition of AgriRaw
Exports
Panel D: Figure 2.3.4 Showing Country Decomposition of OresMtls
Exports
21

2.3.4 Sectoral Decomposition of EAC Exports by Country
In figure 2.4 below, a stacked panel (a, b, c, d and e) of figures showing the composition of each
EAC member states‟ percentage composition of their exports is shown. Overall, the percentage
of food item exports is more than any other commodity. On average, food item exports constitute
more than 50 percent of the total exports. Burundi, Kenya and Uganda have more than an
average of 80 percent of their exports constituting food item over the period. For all countries,
their exports of food item seemed to have reduced, but still above 50 percent of exports, between
2003 and 2007. After this period, the food exports seem to pick up. However, Rwanda‟s food
export seemed to experience the most drastic reduction to about 40 percent of its total exports.
Burundi that joined EAC in 2007 has food item trade and manufactured goods as it major
exports. Food items are on averaged contributing over 75 percent of Burundi‟s total export value.
However, it had a decline in exports from its highest values of 85 percent in 1998 to its lowest
value of 45 percent in 2002. However, since 2006 when it had another low, Rwanda‟s food item
trade has picked up. During this very period, the composition of manufactured goods as a
composition of total export value of the selected products has increased to the magnitude of the
shortfall in food item trade. Burundi‟s exports of agricultural raw materials follows in
importance in the composition of total export value. It contributed on average 5 percent during
the period under consideration. However, since 2006, the percentage contribution of raw
materials has picked up. Burundi hardly exports fuels. Its exports of ores and metals are less than
three percent over the period under consideration.
Panel B shows the contribution of Kenya. From the figure, the exports of Kenya are
concentrated to food items, manufactured goods and agricultural raw materials. The percentage
of food item exports is the strongest among these. However, it has been persistently declining
from 65 percent highest contribution in 1998 to its lowest of about 30 percent between 2006 and
2007. It has picked up since then but still below 40 percent. The contribution of fuels and
manufactured goods in Kenya‟s exports have been picking up from 20 percent before 1990 to a
current level of about 30 percent. Ores and metals contribute the least to Kenya‟s total export
value of the selected products. The exports of fuels have remained lower than 3 percent during
the period under consideration.
In panel C, the export dynamics of Rwanda‟s trade are presented. Food item trade too, is
the largest contributor to Rwanda‟s export trade value of the selected products. On average, it
contributes more than 40 percent to Rwanda‟s total exports. However, it suffered drastic surges
to its lowest of 10 percent in 2004 form it highest contribution of 85 percent in 1996. Since then,
it has picked up on its average contribution to about 30 percent of total trade. During this time,
fuel exports picked up and contributed to a high of 70 percent in 2004. However, the period 2001
to 2004, is the only time fuel export contributed significantly to Rwanda‟s exports. The second
23

most contributors are agriculture raw materials, which contributed on average 10 percent.
However, since 2006 its contribution has grown to 35 percent at the end of the period considered.
Ores and metals also play an important role and its role is becoming as significant to exports as
agricultural raw materials. The manufacturing sector in Rwanda is still weak but has persistently
increased on average but still less than 3 percent.
In Tanzania, food, agricultural raw materials and manufactures are at the top of exports of
the country. On average, these exports have contributed more than 90 percent of the total value
exports during the period. However, food constitutes the most, with an average contribution of
45 percent during the period. Exports of all the products are growing except for food item trade
that has stagnated to an average of 40 percent. Tanzania hardly exports fuels.
Uganda export dynamics shown in panel C indicates that food item trade dominates it
exports. On average, it contributes over 75 percent. The exports of agricultural raw materials are
also strong, contributing on the average 15 percent, though from 2007, its role is diminishing.
The role of manufactures seems to have picked up to 20 percent at the end of the period
considered. The contribution of fuel to Uganda‟s exports is insignificant throughout the period.
24

Figure 2.4 Showing Stacked Graphs of EAC Export Composition for the Selected Products by Country
Panel A: Figure 2.4.1Percentage Distribution of Burundi’s Export of
the Selected Products.
Panel C: Figure 2.4.3Percentage Distribution of Rwanda’s Export of
the Selected Products
Panel D: Figure 2.4.5 Percentage Distribution of Uganda’s Export of
the Selected Products
Panel B: Figure 2.4.2Percentage Distribution of Kenya’s
Export of the Selected Products.
Panel D: Figure 2.4.4Percentage Distribution of Tanzania’s
Export of the Selected Products
25

2.4 Intra – Sectoral Trends and Structure of the Selected
Exports of the EAC
In this section, the statistics on the composition of intra-EAC exports of the selected products is
presented in section 2.4.1. Section 2.4.2, makes a discussion and presentation of the trends and
discussion of the contribution of EAC exports by member states. In section 2.4.3, the paper takes
on a discussion of the exports of the intra-bloc exports.
The effect of EAC formation seems to filter through the intra-bloc exports of all the
products for all the countries. This is because, there seem to be a surge in intra-bloc exports for
all countries during the period when there are deepening of integration, around 2005 or 2007.
However, it can be deduced that, the effect of the crisis seem to dampen these achievements.
2.4.1 Intra - EAC Export Trends and its Constitution by Product.
Figure 2.5 below shows the trend in intra-EAC exports and its constitution by the selected
products. There has been a persistent increase in the volume of exports of the selected products
since 1990. The trend surge upward with the formation of the EAC in 2000, strengthening even
more, after the transformation of the bloc to a customs union in 2005. However, this trend
persisted further upward with in 2007 when Rwanda and Burundi joined the bloc. Bloc exports
have moved from less than 100 million US dollars in 1990 to a peak of about 2300 million US
dollars at the end of the period under consideration.
The major export product that seems to influence this trend is the intra-bloc exports of
manufactured goods. The intra-bloc exports in the rest of the products hardly explain bloc
exports. Their combined average exports is only 500 million of US dollars compared to trade in
manufactured goods which has an average trade of about 1000 millions of US dollars. Since
2007, the intra bloc exports of food have picked up and it is now the second major exports of the
26

loc. However, its intra – bloc exports is still less than 500 million US dollars. The other sector‟s
exports have even dampened further.
Figure 2.6 Exports Growth and Composition of the Selected EAC Products
2.4.2 Intra-EAC Member States Contribution to Export of the Selected Products
Kenya‟s exports singly dominate the intra bloc exports of EAC. Kenya‟s exports have grown
from less than 25 million US dollars in 1993 to a peak of over 1500 million US dollars in 2008.
The combined exports of the rest of the states in EAC are less than 700 million US dollars.
However, since the formation of EAC, the exports of Uganda and Tanzania have picked up.
Surging further after EAC became a customs union in 2005, but still less than 400 million US
dollars.
Figure 2.7 Relative Contributions of EAC Member States to the Trade of Selected Products
27

2.4.3 Country Decomposition of EAC Exports by Sector
In figure 2.7 below, a stacked panel of graphs showing intra-bloc country decomposition of the
value of each exporting sector‟s trends is shown. The export dynamics of food, agricultural raw
materials, fuels, ores and metals, and manufactured goods is shown in panel A, B, C, D and E
respectively. The effect of EAC formation seems to filter through the intra-bloc exports of all the
products for all the countries. This is because, there seem to be a surge it intra-bloc exports for
all countries during the period when there are deepening of integration around 2005 or 2007.
However, it can be deduced that, the effect of the crisis seem to dampen these achievements.
Panel A below, has figure 2.7.1 showing the each country‟s intra bloc trade in food
exports. By far, Kenya, Uganda and Tanzania are the major countries exporting food items in the
bloc. Burundi and Rwanda are insignificant bloc exporters of food items. Kenya‟s food exports
have grown from less than 10 million US dollars in 1993 to around 60 million US dollars around
1998 when it slumped to 30 million US dollars between 2000 and 2002. From then on, it
drastically picked up peaking to about 140 million dollars in 2008. The food exports of Uganda,
the second major intra-bloc export of the product after Kenya, gradually picked up from less than
10 million US dollars to a peak of about 140 million US dollars in 2007. Tanzania‟s intra-bloc
exports followed a similar pattern like that of Uganda, but just picked up after 2005 though it has
not exceeded 60 million UD dollars. From 2008, the food exports seem to shrink for all
countries. From other panels, intra-bloc exports of agricultural raw materials and ores and
metals, seem to be less than the trade in food products for all countries. It is only the intra-bloc
exports of manufactures and fuels that outstrip exports of food by value.
Intra-bloc exports of agricultural raw materials are shown in figure 2.7.2 in Panel B. On
average, Kenya exports the most of this product, followed by Tanzania and Uganda. The average
28

intra-bloc exports of Kenya, Tanzania and Uganda is about 30, 20 and 10 millions of US dollars
for the period under consideration respectively. The combined intra-bloc exports of Rwanda and
Burundi is less than 5 million of US dollars. All the countries‟ intra-bloc exports of agricultural
raw materials seem to pick-up after 2007 though the effect of the crisis seems to dampen this
achievement.
Kenya is the only significant exporter of fuels in the bloc. The combined value of the
intra-bloc exports of all other EAC members is on average less than 5 million US dollars.
Kenyan on average exports more than 200 million US dollars over the period. Its intra-bloc
exports peaked a value of 370 million US dollars 2004/2005. For a visual impression of these
deductions, see figure 2.7.3 in Panel C of figure 2.7 below.
In figure 2.7.4 of Panel D below, the intra-bloc exports of ores and metals is depicted. A
similar trend of exports as that of fuels is also observed. Kenya is the single major exporter of
the product‟s sector in the bloc. On average, Kenya has exported ores and metals within the bloc
to a tune of about 50 million dollars throughout the period under consideration. However, it had
a peak export value of 90 million US dollars in 2008. And the combined exports of ores and
metals are less than 10 million of US dollars over the period.
A similar export pattern is also observed with the exports of manufactured items shown
in figure 2.7.5 in Panel C. In this figure, it is clear that Kenya is the single player of the intra-bloc
exports of manufactured items. On average, it exports about 500 million US dollars of the values
of this product in the bloc. This value is far beyond the intra-bloc export value of food items.
From 2005, Uganda and Tanzania‟s exports of manufactures have picked up and have both
clocked the 200 million US dollar value. Burundi and Rwanda are still insignificant exporters of
the product in the block.
29

Figure 2.7 Showing Stacked Graphs of Intra EAC Bloc Sector Decomposition of Selected Products, by Country
Panel A: Figure 2.7.1 Showing Intra-Bloc Exports of Food Panel B: Figure 2.7.2 Showing Intra-Bloc Exports of AgricRaw
Panel A: Figure 2.7.3 Showing Intra-Bloc Exports of Fuel Panel A: Figure 2.7.4 Showing Intra-Bloc Export of OresMtls
Panel A: Figure 2.7.5 Showing Intra-Bloc Exports of Manuf
30

2.4.4 Sectoral Decomposition of Intra - EAC Exports by Country
In figure 2.8 below, a stacked panel (a, b, c, d and e) of figures showing the sectoral composition
of each EAC states‟ exports of the selected sectors is shown. Overall, the value of exports from
Kenya seems to dominate intra-bloc exports. This is follow by the average value of Tanzania‟s
and Uganda‟s exports in the region. Burundi and Rwanda‟s exports are on average less than 10
million of US dollars.Further, for all countries, their exports of the selected products seem to
surge upward after 2007 when the expanded to include Burundi and Rwanda.
Burundi‟s export decomposition of the selected products is shown in figure 2.8.1 in Panel
A. On average, Burundi exports manufactured goods mostly to the region. On average, it exports
more than 3 million US dollars manufactured goods in the region. The combined average exports
of the other products from Rwanda to the region are less than 3 million US dollars. Except for
fuels whose exports from Rwanda reduced significantly after it joined the EAC in 2007, all the
other products seem to show a resilience to pick up.
Figure 2.8.2 in Panel B shows Kenya‟s exports in the region. One can deduce that, Kenya
is a major and significant exporter of arguably all the products in the region. However, its major
export is manufactured products followed by fuels. Its exports of manufacture have been
growing gradually from less than 10 million US dollars in 1993 to a peak of over 1000 million
US dollars at the end of the period under consideration. On average, the exports of fuels are
slightly below 200 million US dollars over the period. The rest of the products have an average
combined export value of less than 100 million US dollars. The export value of Kenya‟s export
seems to dampen at the end of the period. This is probably due to the effect of the financial
crisis.
Rwanda does not seem to have a major dominant export product. The total value of it
exports is less than 10 million US dollars over the period. Apart from food exports, all the other
products seem to pick up in terms of export value at the end of the period. These deductions can
be elaborate upon in figure 2.8.3 in Panel C below.
For Tanzania, the combined value of exports was less than 100 million US dollars for 10
years from 1990. From then on, Tanzania‟s exports of manufactures significantly picked up from
less than 25 million US dollars to a peak of 200 million US dollars at the end of the period. Food
exports also picked up but it value is still less than 70 million US dollars. Agricultural raw
materials are Tanzania‟s third major export of the selected products. However, its average value
of intra bloc exports is still less than 50 million US dollars for this period. The rest of the
products, ores and metals and fuels, have an average intra-bloc export value of less than 25
million US dollars for the whole period under consideration. To clarify on this, see figure 2.8.4
in Panel D below.
31

Figure 2.8.5 in Panel C below, shows Uganda‟s intra-bloc exports. From the figure, Uganda‟s
exports follow a similar trend as that of Tanzania. Food exports are Uganda‟s second major
export in the bloc. Its average export value is more than 80 million US dollars after the formation
of the customs union. Intra-bloc trade in manufactures is Uganda‟s major export. Its intra-bloc
exports value is more than 130 million US dollars after the transformation of the bloc into a
customs union. Agricultural raw material is Uganda‟s third major intra-bloc export. However, it
export value is less than 30 million US dollars after 2007. The export value of ores and metals,
and fuels is less than 5 million dollars over the period.
32

Figure 2.8 Showing Stacked Graphs of Intra EAC Bloc Composition of Selected Products in Each Country
Panel A: Figure 2.8.1 Burundi‟s Bloc Export Composition Panel B: Figure 2.8.2 Kenya‟s Bloc Export Composition
Panel A: Figure 2.8.3 Rwanda‟s Bloc Export Composition Panel A: Figure 2.8.4 Tanzania‟s Bloc Export Composition
Panel A: Figure 2.8.5 Uganda‟s Bloc Export Composition
33

3.0 Literature Review
3.1 Introduction
Chapter Three
In this section, the empirical, theoretical and methodological underpinnings are discussed. The
first part of the section elucidates the definitional and measurements concepts of the variables
adopted in the study. And the section that follows reviews and profiles the theories that have
been used to analyze trade between countries. In the third section, one of the theories is adopted
and its theoretical and empirical underpinnings are discussed subsequently, including the
attributes that made the study adopt it.
3.2 Review of Definitional Issues
3.2.1 Definition and Measurement of the Effects of EI or RTAs
Economic integration (sometimes known as Regional Trade Agreement – RTA or regional
integration) involves a collection of autarkical nations becoming a fully integrated economic
unit. In terms of a concrete definition, economic integration is the process by which a group of
countries form closer economic links with each other than with third countries or the rest of the
world. Closer integration can, in principle, either be sectoral or general. Sectoral integration is
where only specific sectors/industries within an economy or bloc are integrated; general
integration is where the entire economy or bloc is integrated (Vi-UNCTAD Teaching Material
on RTAs 2007). In either form, the integration does not only involve the merging of economic
attributes of the economy ranging from integrating firms and sectors to converging banking,
35

investment, education, tourism etc. sectors, but also involve the converging of economic 9 and
political policies of the countries in the bloc at varying degrees.
In capturing the effect of RTAs in econometric models (especially gravity models),
various authors (like Wanjiru 2006 , Jayasinghe and Sarker, 2004, Cernat 2001 etc) have used an
RTA dummy. Dummy variables serve several different uses including 10 ; the use or
representation of several different categories or groups in a single equation system, instead of
writing separate equations for each sub-category or group. More to this, much as a binary
dummy 0,1is a nominal level variable, it can be treated as an interval-level variable statistically.
A dummy variable (also called binary variable or a zero-one variable) is one that takes the values
0 or 1 to indicate the absence or presence of some categorical effect that may be expected to shift
the outcome 11 . And the most important issue to measure a dummy variable is to assign a value
one to one variable and zero to the other. And the name of the variable is the one that normally
takes on the value one (Wooldridge, 2004). In essence, a value 0 is assigned if the variable or
entity concerned is in the control group. A value 1 is prescribed to a variable or issue which falls
in the group of interest. For example, if one is studying the EAC, all member states of the EAC
would be ascribed a value 1 and member states of the ROW would be ascribed a value 0, to keep
the dummy binary.
In terms of measurements, the marginal effect 12 of a dummy variable y is calculated as
the discrete change in F(y) as the dummy variable y changes from 0 to 1. In terms of notation, it
is signified in equation 1 below;
Marginal effect of y = F(y=1) - F(y=0) ………………..1
9 Cited from www.dwaf.gov.za/Docs/Other/RISDP/Glossary.doc
10 Cited from http://www.socialresearchmethods.net/kb/dummyvar.php
11 Cited from wikipedia.org/wiki/Dummy variable_(statistics)
12 Cited from http://www.stata.com/support/faqs/stat/mfx3.html - A detailed discussion of the dummies is in the
methods section of this paper.
36

3.2.2 Definition and Measurement of Agrifood or Food Items
There are two major classifications of food items (also called agrifood products in this study).
The two are by the International Standards Industrial Classification (ISIC) and the other by UN‟
SITC classification.
The ISIC classification classifies food products as one of the compositions of aggregates
of agriculture. ISIC makes classifications according to economic activities.In this composition,
the agriculture classification in this includes three products; Food Products (311), Beverages
(313) and Tobacco (314).
The other classification by the United Nations product classification for international
trade, defines food item as the sum of SITC Codes (or CTCI codes) of 0, 1, 22 and 4. The
statistics is detailed at the 3-digit level or by broad product group. 0 includes food and livestock,
1 constitutes meat and meat preparations, while 22 is the composition of milk, cream and milk
products (excluding butter and cheese); and 4 incorporates cereals and cereal preparations. This
classification can be accessed from the Handbook of Statistics referring to the Standard
International Trade Classification (SITC) Revision 3. See table 3.1 below for product details;
Table 3.1: Composition of Food Item using the SITC Revision 3 (1 to 3 digits)
Codes Food Item Product Group (0+1+22+4)
0 Food & Live animals
00 Live animals other than animals of division 03
001 Live animals other than animals of division 03
01 Meat and meat preparations
011 Meat of bovine animals, fresh, chilled or frozen
012 Other meat and edible meat offal
016 Meat, edible meat offal, salted, dried; flours, meals
017 Meat, edible meat offal, prepared, preserved, n.e.s
02 Dairy products and birds' egg
022 Milk, cream and milk products (excluding butter, cheese)
04 Cereals and cereal preparations
041 Wheat (including spelt) &meslin, unmilled
042 Rice
043 Barley, unmilled
044 Maize (not including sweet corn), unmilled
045 Cereals, unmilled (excluding wheat, rice, barley, maize)
046 Meal and flour of wheat and flour of meslin
047 Other cereal meals and flour
048 Cereal preparations, flour of fruits or vegetables
Source: WITs and UNCTAD Handbook of Statistics 2006 -2007
37

The measurement of the values are in thousands and gotten from World Integrated Trade System
(WITS) under COMTRADE.
The classification of food item between these two classifications has different
compositions of the products in them. The study adopted the UN classification systems of SITC
Revision.
3.2.3 Definition and Specification Issues of Gravity
The gravity model is a formulation that is used in calculating 13 the likely interaction between two
objects or groups of entities given the distance (or lack of connectivity) between them and their
masses or sizes. The gravity model predicts flows between origin and destination while taking
cognizance of pair attributes in reference to the masses of the origin-destination attributes and the
resistance for the pair to collate these attributes.
It is named after its analogy with Newton‟s Universal Law of gravitation formulated
as; where; F is gravitational force, M is mass and D is distance. That gravitational
force between two objects depends on their masses and is inversely related to the square of the
distance between them, all multiplied by a gravitation constant G (Keith 2003).
3.3 Review of Theoretical Issues
In the review of the literature, there seems to emanate three broad categories of theories to
explain and measure trade between countries. However, these theories do not explicitly analyze
trade in agrifood or food items. By design, they analyze trade in general merchandise. However,
they may be construed to study food item trade, especially the more recent ones that explicitly
make the linkages (through dummies or incorporating the agrifood products as endogenous
flows) between RTAs and effects on agrifood trade.
The three categories range from the formative trade theories named the classical or
traditional trade theories. Classical trade theories assume that countries have comparative
advantage (resulting from factor endowments) and as such, they specialize in the production of
these products and trade them in exchange for the commodities they have comparative
disadvantages. Their major weakness is to assume the labor theory of value (i.e. that labor is the
only factor of production) and homogenous production in trade.
However, relative factor abundance and technology intensity could also influence
productivity and even result in heterogeneous production. This realization bred interest to modify
13 Cited from www.tuition.com.hk/geography/g.htm
38

the classical theories leading to a formation of a new range of theories called the Neo-classical
trade theories. These theories assumed that industries are perfectively competitive and produced
differentiated products (leading to Inter Industry Trade (IIT)). The major drawback of these
theories is that they failed to explain trade when industries are not perfectively competitive but
monopolistically competitive. Due to this, a new broad categories of theories known as the New
Trade Theories emerged. They assumed that trade was based on monopolistically competitive
industries producing differentiated products (leading to Intra-Industry Trade). For a figurative
view of the trade theories, see Table 3.2 below,
Table 3.2 Evolution of trade theories
Category of trade theory Presumptions
Classical or Traditional trade theories
1. Mercantilism (1500-1750) 1. Comparative Advantage
2. Absolute advantage (by Adam Smith) 2. Labor theory of value
3. Comparative advantage (by David Ricardo) 3. Specialization and factor endowments
Neo -Classical trade theories
1. Resource availability (Heckscher-Ohlin) 1. Perfectively competitive industries
2. Richardo -Viner Models 2. Production of homogenous products
3. Inter Industry Trade
'New trade theories' (1970s & 80s)
i.e. new geography, external economies, gravity
& econometric models etc.
Sources: Developed for the study from literature
3.3.1 Classical or Traditional Theories of International Trade
1. Monopolistically competitive
industries
2. Production of differentiated products
3. Intra industry trade
In table 3.2 above, the broad range of theories initially developed in trade called the classical or
traditional theories of trade. These theories assume that countries are different, and this
difference explains why they would trade in different products 14 . That is, countries are endowed
with a different set of resources (including land, capital, labor and entrepreneurship). And for the
production of a particular product, that country will have a cost advantage. And as such, it will
specialize in the production of that product, and this forms the basis of international trade.
14 Adopted from the following cite http://benmuse.typepad.com/custom_house/trade_theory/
39

These broad ranges of theories were developed starting mainly with Adam Smith seminal work
on trade called the mercantile trade theory and later on the Absolute Advantage trade theory.
During this, David Ricardo 15 also came up with a relatively superior trade theory called the
comparative advantage theory with its modifications, which explained trade when a country has
not absolute advantage in any products relative to its partners. These three theories governed
trade from the 16 th to the 18 th centuries.
3.3.1.1 Mercantilist Theory of International Trade
The first known theory of trade was developed around 1500 – 1750, over three hundred years
ago during the „commercial revolution‟ that saw economies transform from; local to national,
feudalism to capitalism and from rudimentary trade to projected international trade. Mercantilism
emphasized that a country keeps a positive trade balance that would secure or safeguard the
country during war or guarantee prosperity. And to keep a positive or even maximize a positive
trade balance, it promoted exports and discouraged imports 16 . During this period, there was no
mutually beneficial trade, as one country gained at the expense of another (the surplus was
accumulated at the expense of the partner), e.g. during the colonial expansion by European
countries. As such, mercantilist trade policy was viewed as „zero-sum‟ game. It led to
resentments, revolts and progressive wars that made such a practice or idea untenable. Over time,
mercantilism trade theory lost its appeal, and was readily replaced by yet another theory - the
theory of comparative advantage. It should be noted that mercantile policies are practiced at least
by some firms doing business and it new supporters are known as „neo-mercantilists‟ or
„protectionists‟ (Mahoney, et al, 1998).
3.3.1.2 Absolute Advantage and Comparative Advantage Theory of International Trade
The collapse of the mercantilist ideas gained root with the evolution of the industrial age and
capitalism in Europe that saw it as a hindrance to advancement. The trade theories that arose at
the twilight of the mercantilist theory emphasized „win-win‟ outcomes instead of „zero-sum‟
outcomes in trade. Adam Smith was again instrumental at the dawn of these trade theories that
emphasized „win-win‟ trade outcomes when he propounded the absolute advantage theory. The
idea behind this theory was that a trade regime could be changed by producing only commodities
of absolute advantage and exchange (trade) the surplus of this process with a trading partner who
specializes in producing commodities that are domestically expensive to produce, hence
benefiting also consumers. Intuitive as it was, the theory failed to explain the pattern and gains
15 Cited from http://www.vazecollege.net/tyeco.html
16 Cited from http://www.oppapers.com/essays/International-Trade-Theories/179424
40

from trade when a country is disadvantage in all ranges of products. More to this, it only
explained a small part of world trade and it is seen as a special case of the more general theory of
trade based on comparative advantage.
David Richardo introduced the comparative advantage theory, a refinement of the
absolute advantage theory. Comparative advantage models differ in structure but all aim at
explaining the pattern of and gains from trade (inter industry). Customary, they were based on
the law of comparative advantage (Either 1983 and Salvatore 2004) which provideda solution to
the impasse created by the absolute advantage theory. And it specifies that mutually beneficial
trade can still occur between two nations even if a country is less efficient in the production of
two commodities. This is enabled when each nation specializes and trades (or exports) the
commodity in which it has less comparative disadvantage and import the commodity of more
comparative disadvantage. The exception to this (which is very rare), is when both nations have
the same level of absolute disadvantage. In such a case, trade will not occur since there is no
comparative advantage or disadvantage in the production of both commodities in both nations. In
light of such intricacies, there was a need to reestablish the theory. That is, in the absence of
equivalent levels of comparative advantage between trading partners for the products they trade,
mutual and beneficial trade and exchange of trade in those commodities could take place when
each of the said countries specialize in the production and exchange the products it has less
absolute advantage.
Later on, there was recognition of the short comings of the theory of comparative
advantage and it was modified by introducing opportunity costs. In modifying this, there was an
assumption of the labor theory of value – which assumes that labor is the only factor of
production. It presumed that labor was used in the same fixed proportion for producing all
commodities, since it was homogeneous. As such, the commodity prices depended exclusively
on the quantity of labor utilized in production. The new modification stated that, a country
should specialize and export the commodity it has a lower opportunity cost than another and
import the commodity with a higher opportunity cost. The notion behind this is simple, because,
when opportunity costs that differ between the countries but are constant within each country are
considered for a factor, then the country with a lower sacrifice (opportunity cost) in that
factorspecializes in producing a certain commodity and exchanges it with the other country‟s
commodity, since the lower sacrifice enables it to have a comparative advantage in producingthat
commodity. Therefore, the difference in opportunity costs forms the basis for trade. However, in
time, major flaws were also cited in these models: First, the models imply complete
specialization in production, which is not tenable; secondly, there is an assumption of a single
factor of production. Countries produce major production simultaneously, and further, the
models fail to incorporate income distributions issues and there is doubt that assumption of
technological differences are posit in the long run across countries.
41

3.3.2 Neo-Classical or Standard Trade Theories
The failings in the classical trade theories gave rise to a new group of theories known as the neoclassical
or standard trade theories. The standard trade models assume that there is a variant of
factors of production, an issue not considered in the classical trade theories. And that the
difference in resources can drive the trade patterns and this affects the distribution of resources
(Krugman and Obstfeld, 2009). The two major models that fall in this category are the
Heckscher - Ohlin and Ricardo-Viner models. The Heckscher - Ohlin model assumes that factor
abundance will determine the pattern of and gains from trade in those products it is endowed
with. This is because they will have a comparative advantage in producing those products.
However, this could only hold if the economy‟s major sectors have wages above the subsistence
level (Vylder 2007). While the Ricardo-Viner model emphasizes the fact that trade patterns will
be reflected by factor abundance across countries when production technologies are identical
across countries.
Intuitive as these models are, they still failed to estimate trade fully since issues that
affect political debated (like migration, growth, transit costs, and network effects) today were
still exogenous to these models 17 , hence a rise of a totally new group of theories after the Second
World War.
3.3.3 New Trade Theories and new development in Analyzing International Trade
The end of the Second War led to massive changes in the structure of the world. For example,
most countries synergized their industrials, sectors and policies by integrating. This new
paradigm necessitated a formulation of new theories that endogenised pertinent issues of this
new era like migration, transport costs, networks etc. In the 1970s and 1980s, new trade theories
were formulated, and they were broadly labeled the New Trade Theories (NTT) 18 .
It has been argued that the only unique or new thing in the NTT was their mathematical
rigor that included tacit use of protectionism. They too considered increasing returns to scale that
enable firms, industries or sectors to build a huge competitive world market base. The other
presumptions relate to the emphasis of NTT on networks (mainly their effect), and
monopolistically competitive industries producing differentiated products. These
monopolistically competitive industries engaged in intra-industry-trade 19 (exchange of same
product but of different variety) (Krugman and Obstfeld, 2009).
17 Cited from http://www.marginalrevolution.com/marginalrevolution/2008/10/what-is-new-tra.html
18 Cited from http://en.wikipedia.org/wiki/New_Trade_Theory
19 Previous theories presumed perfectly competitive industries engaging in inter-industry trade (i.e. trading one
product for another).
42

At their evolution, the models of the NTT lacked sufficient dataset to test them
empirically. Further, besides being too technical, empirically produced mixed results and the
reliability producing mix results, the statistical judgments from them were hard to fathom. More
to this, the time-scales required and the particular nature of production in each 'monopolizable'
sector was subjective to the researchers 20 .
Prior to the evolution of the NTT, there was increased interest that stemmed to analyze
the outcome of RTAs or EIs specifically. And traditionally, this economic analysis evaluated the
desirability of regional integration arrangements according to their trade diversion and trade
creation effects (Shams 2003). Viner‟s seminal work on trade creation and diversion effects in
1950, noted that trade creation occurs when imports are substituted for domestic products as a
result of tariff reductions that reduce the price of member imports below that of home-produced
goods. While trade diversion transpires with a shift in imports from an efficient non-member
exporter to a more expensive producer from the country‟s RTAs partners due to preferential
tariff treatment. Trade diversion, in Viner‟s view, did not necessarily mean a decline in trade, but
rather a shift in trade away from the least cost suppliers. Contemporary writers simply state that,
trade creation occurs if partner country production displaces higher cost domestic production.
And trade diversion implies that partner country production displaces lower cost imports from
third countries. Trade diversion is said to increases intra-bloc trade at the expense of trade with
third countries, while trade creation does not have this negative effect. Therefore a regional trade
arrangement is only desirable if trade creation outweighs trade diversion (Shams 2002).
Furthermore, the economic effect of forming RTAs (according to the Neo-classical
theory) depended on whether it was „shallow or deep integration‟ too (UNCTAD Teaching
material on RTA, 2007). A shallow or negative integration, involves the removal of border
barriers to trade, typically tariffs and quotas. While deep or positive integration, involves policies
and institutions that facilitate trade by reducing or eliminating of regulatory and behind-theborder
impediments to trade, where those impediments may or may not be intentional. The
continuum of policies could include, customs procedures, regulation or domestic services
production that discriminate against foreigners, product standards that differ from international
norms or where testing and certification of foreign goods is complex and perhaps exclusionary,
regulation of inward investments, competition policy, intellectual policy protection and the rules
surrounding access to government procurement.
Another model that developed during this same period is the Gravity model 21 . And it has
been widely used in analyzing trading patterns better than many of the theoretical models on
trade. The gravity model, tries to incorporate a broad range of covariates lacking in theoretical
models discussed above. Furthermore, the gravity analysis and model has gained supremacy due
20 Ibid
21 Cited from http://en.wikipedia.org/wiki/International_trade
43

to the fact that it has a predictable role as a tool used in estimating effects of a variety of
phenomena in social sciences. In economics and especially international trade, it is a successful
modeling approach for various reasons; the results from it have high explanatory power of
between 0.65 and 0.95 and are sensible (especially on distance and output), economically and
statistically significant. Also, there is easy access to bilateral trade data, and the model explains
most of the variations in international trade. Estimation standards and benchmarks of the gravity
model have been clearly established. The next section reviews the build - up of the theoretical
construction of the model in detail.
3.4 Review of Theoretical Foundation of Gravity Modeling in
Trade.
The gravity model has been used to model many social interactions like tourism, migration, trade
and foreign direct investment (FDI). They are econometric models which use ex-post analysis.
And in trade, by design they are macro models since they are used to capture volume rather than
composition of bilateral trade (Appleyard and Field 2001).
When it was introduced to trade by Tinbergen 1962, it had no theoretical foundation. He
proposed that the same functional form (as seen above) that could be applied to international
trade flows specified as;
1
Where; X are exports from I to j or total trade (it could either represent a flow from origin (i) to
destination (j), or total volume of interruptions between I and j. Some authors like Rose, 2004
use average trade volume), Y economic size (proxies are gross domestic product or gross
national income, Population); T is trade cost (proxies are distance, common language, colonial
link, common currency, island or land locked). In the origin Newton model it is called distance.
However in trade, we look at economic distance i.e. a sort of tax „wedge‟ imposing trade costs,
and resulting in lower equilibrium trade flows.
Model one above states that, bilateral trade between country i andj is inversely related to
distance and positively enhanced by their sizes. The multiplicative nature of the gravity equation
means that we can take natural logs and obtain a linear relationship between trade flows and the
logged economy sizes and distances 22 i.e.
22 Head, Keith (2003). Gravity for Beginners. Cited from

The inclusion of the error term in equation 2 above delivers an equation that can be estimated
by ordinary least squares regression (OLS).
Deardorff (1998) was among the first persons to develop its theoretical foundation and he
suspects ‘justabout any plausible model of trade would yield something very much like the
gravity model.’ Anderson and Van Wincoop (2003) too rein-enforced theoretical foundations and
concluded in their proof that,„bilateral trade is determined by relative trade costs’. Other major
contributions were by; Anderson 1979 when he emphasized the Armington hypothesis (i.e.
goods differentiated by country of origin), Bergstrand 1990 when he considered monopolistic
competition and price indices being those used in practice and not the ones suggested by theory.
3.5 Review of Methodological and Empirical Issues
The growing myriad of RTAs negotiations have yet to address the issue of drastically decreasing
tariffs on food products, as they have done for merchandise trade. Recent negotiations, have even
led to reductions in agricultural raw materials but not food item trade. Further, existing literature
does not adequately provide evidence of trade in agrifood products or food item trade
(Jayasinghe and Sarker 2004). The few empirical studies enlisted below, have mainly used three
approaches to estimate the impact of RTAs on food item trade. These three are approaches are,
the econometrics approaches, computable general equilibrium (CGE) and descriptive
approaches. Of these approaches, the econometric approaches employing the gravity modeling
are the most frequent, as enlisted below. Studies employing the CGE models have hardly been
found. And those that use descriptive are inadequate to capture trade effects, since they are not
robust.
The consensus from these studies is that regional integration has a positive effect on trade
in agrifood trade. That they intra-bloc trade tends to increase, however, at the expense of trade
with the ROW. Nonetheless, there are mixed findings on the whether the RTAs promote bloc
export or imports. For example, Jayasinghe and Sarker, 2004, in studying the effects of RTAs on
trade on agrifood products, concluded that, NAFTA boosted intra-regional trade in agrifood
products. However, this was achieved at the expense of trade in the same products with the rest
of the world. That is, those NAFTA member countries have moved towards a lower degree of
openness with the world. The results that led to this conclusion were produced using gravity
modeling. The dataset used was disaggregated and they focused on the pooled cross-section time
series regressions and generalized least squares methods. They choose six products and run the
different regressions with the product volumes as dependent variables.
The study by Sarker and Jayasinghe, 2007, also uses the gravity models to estimate
European Union RTA and impact of trade in agrifood products. They also include six products as
in their earlier study and proceed in their estimations as described above. They estimate the
45

esults using an extended gravity model with pooled data. The data run from 1985 to 2000 and
they used the GLS method. They conclude that, EU‟s trade in agrifood products in the mid1980s
was also boosted significantly among its members. Nevertheless, this is being boosted at the
expense of the rest of the world.
Another study analyzing EU agrifood trade was done by Serrano and Pinilla (2010). They
estimated EU agrifood trade from 1963 to 2000 using disaggregated data for products
decomposed as trade. They also concluded that intra-EU growth significantly boosted trade in
agrifood products. They further concluded that the trade was also export and import food trade
was significantly boosted. To estimate the results that led to such a conclusion, they too
employed an extended gravity model. They run it with fixed effects and employed the Prais-
Westein estimation technique.
In another study by Dissanayake and Weerahawa, (2009), on the trade creation and trade
diversion effects of agricultural trade, conclude that the SAPTA led to more trade in food exports
and that this trade was beneficial to SAPTA member states. However, increase in intra-bloc food
trade led to export diversion. They too, used an extended gravity model to quantify the effects of
trade creation and diversion agricultural trade. In the gravity model they specified, they run it
with cross-sectional data and run it with fixed effects.
However, Grant and Lamber (2005), make an interesting finding that NAFTA led to trade
creation of over 75 percent of 8 out of the 9 products included in their study. And this was
created with little or no trade diversion with the rest of the world. They recommend that RTAs
are an attractive alternative for countries to „multilateralism‟ their agriculture. They were
studying regionalism in world agriculture. They used the gravity model to estimate the
magnitude of this trade creation and trade diversion.
Another set of studies is that by Dell‟Aquila, ettal (1999) and Diao, et al (1999), who use
descriptive studies to study the effect of RTAs on agrifood trade. They also concluded that intraregional
trade in agrifood products was boosted because of RTAs in time. The latter study‟s
conclusion of intra-regional trade being boosted was with reference to the rest of the world. It
also studied the NAFTA, EU-15, Mercusor and APEE. However, the major drawback of the
descriptive studies is that they are not robust and cannot capture trade effects.
3.6 Conclusion and Relevance of the Literature Review
In this section, the study reviewed the evolution of the trade models and zeroed on to the gravity
model. The review hinged on the theoretical underpinnings and also the empirical evolution. The
gravity model was found to have strong theoretical foundations and its empirical use was quite
vast, and it was the major model used to analyze specifically food item trade in RTAs. The
model had a strong explanatory power for trade, was economically and statistically significant.
46

This provides me the impetus to adopt it to study the case for EAC in sectoral and specifically
food item trade.
From the theoretical and empirical developments of the gravity model in previous
sections, it has gained supremacy due to the fact that it has a predictable role as a tool used in
estimating effects of a variety of phenomena in social sciences. In economics and especially
international trade, it is a successful modeling approach for various reasons; the results from it
have high explanatory power of between 0.65 and 0.95 and are sensible (especially on distance
and output), economically and statistically significant. Also, there is easy access to bilateral trade
data, and the model explains most of the variations in international trade. Estimation standards
and benchmarks of the gravity model have been clearly established.
47

Chapter Four
4.0 Analytical Framework and Methodology
4.1 Introduction
In this chapter, the analytical framework of the study is described in section 4.1. The specified
empirical equations and the variables included in the models are expounded on in section 4.2. In
section 4.3 the types and sources of data are explained too.
4.2 Analytical Framework: Gravity Model
This study adopted with modifications, Subramanian and Wei, 2003 extended gravity model
specification. The modifications relate to dropping some covariates and including new ones not
used in their study. Further, it replicates this approach for EAC using different products of EAC
interest and different time periods of its formation. The general specification is of the following
form;
The model has seven components namely:
Component one, which includes all the covariates used in
Subramanian and Wei, 2003, and captures trade between EAC member states and the rest of
the world that depends on policy and physical barriers. These are the normal covariates in a
standard gravity model including; log of GDP, log of GDP per capita, log land area of
48

exporters and importers, greater circle distance between i and j, dummies for common
language, and colonial links, shared borders, currency, and a dummy for landlocked.
The other two components termed multilateral resistances are
used to capture or proxy trade between the exporting EAC countries and the importing world;
a. Where is a list of importer dummies taking the value of one if i = j, and zero
otherwise.
b. While is a list of exporter dummies taking the value of one if h = i, and zero otherwise.
The third component is a set of dummy variables takes
on a value of 1 if i is equal to j. It also specified in a way that it captures counterfactual trade
or change in the composition of the trading bloc.
The fifth segment of the model is, , the key focus
of the study, that would capture the sectoral composition of the chosen sectors of EAC trade
interest. The food item or agrifood trade is taken as the base sector.
Related to this, is the component, , that
captures exporter effects or the component the EAC trade liberalization that is absorbed by
each country.
And the, which is a normally distributed random term that
has a zero mean and a constant variance.
4.3 Specification of Empirical Equations
Following Carrere (2006) and Subramanian and Wei (2003), the above analytical framework is
decomposed to the following specification:
Where expected signs: , , , , 0, , , , 0
The above equation was run to answer four research questions. The first question relates
to estimating the effect of the adopted trade liberalization policy and determines which
covariates explain bloc trade, especially the EAC dummy covariate, at the region and country
49

levels? Apriori, the signs on the dummies may not be determined. The expected signs of the
other covariates are determined in other sections of the paper. The second question is to what
extent the EAC trade liberalization efforts affects its intra-bloc trading patterns of the selected
sectors? A priori, the expected change is positive because it is widely believed that the formation
of EAC and its membership marked a watershed in the status of EAC trading. This question is
modified when counterfactual trade is included, i.e., including other members who normally
trade as EAC though they are not formally members. And the key proposition the study
addresses under this was whether EAC membership has a differential impact for the sectors
chosen. And the food sub sector was used as a base dummy. The third issue is estimate the
impact of EAC trade liberalization on intra-regional, export and imports of trade and hence
implication on trade creation and trade liberalization? And fourthly, how is the trade
liberalization effect distributed across each country?
However, to estimate or analyze the distribution on the five EAC member states, equation
5 is modified and equation 6 below is run for all the five countries.
4.3.1 Coefficients and Variable Definition
4.3.1.1 Coefficients Definition
The variables embedded in these models include the β, δ, ϑ and ϕ coefficients estimates from the
normal gravity model covariates, EAC dummies, sectoral dummies and exporter country
dummies respectively. They show the major effects of each covariate on the exports.
The EAC dummy is specified in three forms, following Egger, 2002 and Soloaga and
Winters, 2001 to formulate a correct ex-post assessment of the effect of the volume of trade,
namely;
a. To capture intra-bloc trade, a dummy of both-in , where δ is equal to one if both
partners belong to the same RTA and zero otherwise, is introduced.
50

. To capture bloc imports from the rest of the world, a dummy equal one if importing
country j belongs to the RTA and exporting country i belong to the rest of the world, and zero
otherwise, is also introduced.
c. And another dummy capturing bloc exports to the rest of the world equal to one if
exporting country i belong to the RTA and importing country j belongs to the rest of the world,
and zero otherwise is also introduced.
If the intra-bloc trade > 0, it means that intra-bloc trade has increased more than predicted
by the reference which can be in substitution to domestic production or to exports from the rest
of the world. As to whether this corresponds to trade creation (TC) or trade diversion (TD), one
needs to examine the sign of the coefficients and . Trade is diverted when > 0 and
< 0 (indicating a low propensity to import from the rest of the world), and when this negative
propensity to import from the rest of the world entirely offsets the intra-bloc trade enhancement,
this is pure trade diversion. However, when intra-regional trade increases more than imports
from the rest of the world decreases, there is both trade creation and trade diversion. In this case,
if > 0 and ≥ 0, this is a case of pure trade creation. According Carrere, 2004, inference
on welfare to non-members could be deduced on comparing the coefficients and . That is,
welfare is decreased for non members when > 0 and < 0, which indicates a dominant
export diversion. So then, trade is purely created when > 0 and ≥ 0 when also ≥ 0.
And trade is purely diverted when > 0 and

4.3.1.2 Variables Definitions
In this section, information is provided on the label of the variables and their measurements. The range of variables used is;
Table 4.1 Showing the Description of the Variables and Their Measurement
Variable
Name Units or Variable Label Description and Variable Label
Source of
Data
iso code
Country Codes PWT 7.0
Year Year Years ranging from 1988 to 2009 PWT 7.0
pop Thousands Population (in thousands) of the countries under study PWT 7.0
The value of Imports (reported as exports from EAC members states) UN
that EAC trading partners (ROW) report as emanating from EAC COMTRADE
Exports Thousands of USD
members states
- WITS
XRAT national currency units per US dollar Exchange Rate to US$ PWT 7.0
million International dollar (million Total PPP Converted GDP, G-K method, at current prices (in millions
tcgdp I$)
I$) PWT 7.0
cgdp International dollar (I$) PPP Converted GDP Per Capita, G-K method, at current prices (in I$)
Common Language Dummies
PWT 7.0
comlang_off Common official Language
Dummy variable equal to one if two countries share a common official
language or share languages spoken by at least 20% of the population
of the country CEPII
comlang_entho Common language ethnographic
Dummy variable equal to oneif a language is spoken by at least 9% of
the population in both countries
Simple Distance Measures
CEPII
dist distance
CEPII
distcap distance between capitals Distance between capitals following the great circle formula
Country Pair Similarity Dummies
CEPII
contig Two countries being contiguous Dummy variables indicating whether the two countries are contiguous CEPII
landlocked Landlocked Dummy variable set equal to 1 for landlocked countries CEPII
52

colony Colonial link
comcol Common Colonizer
curcol
smctry Same country
Dummy variable indicating whether the country pair have ever had a
colonial link CEPII
Dummy variable indicating that the country pair have had a common
colonizer after 1945 CEPII
Dummy variable indicating whether the country pair are currently in a
colonial relationship CEPII
Dummy variable equal to 1 if countries were or are the same state or
the same administrative entity for a long period (25-50 years in the
twentieth century, 75 year in the ninetieth and 100 years before). CEPII
Notes: In the study, exporter or importer is pegged on the variable name or I & j , rep (reporter) & Partner is used for this distinction
53

4.4 Method of Analysis
For this empirical economic research, the focus was to estimate the bilateral trade
relations of EAC intra – regional trade. Since the study did not include tariff data, it does not
estimate inter – regional trade impact. In the estimation of results, the following econometric
methods and the different middles proposed with their appropriate statistical test are discussed
below.
Empirical efforts of panel data often involves choosing between either running the within
or squares dummy variables (called fixed effects model) on the one hand, and the generalized
feasible (also called random effects). To decide on which model to run, the Hausman test was
used.The test specifies a null hypothesis, that, the preferred model is random effects. And the
alternative is that the fixed effects model is preferred. To test this hypothesis in STATA, one
runs the „xtreg‟ model with an option of fixed effects (fe) and stores its estimates. Further, the
„xtreg‟ model, this time, with the random effects (re) option is run and the estimates are stored
too. The program is then prompted to run the Hausman test. If the P-Value (or prob>chi2) is less
than 0.05 (significant), then reject the null and conclude that the fixed effects models is more
appropriate.
However, before concluding that the random effects or fixed effects models are clearly
appropriate, one has to test whether running either of these models is appropriate. To test for the
appropriateness of running the random effects model, the Breusch – Pagan Lanagrange
Multiplier (LM) test is used. It tests the null hypothesis that the variances across entities is zero
or that there are no significant difference across units. And Stata‟s xttest0 is run immediately
after running an „xtreg‟ regression with the option of random effects. And if the P-Value (or
prob> chi2) is less than 0.05 (significant), reject the null and conclude that the random effects
model is appropriate. Otherwise, do not or fail to reject the null and conclude the random effects
model is not appropriate. This is because there is no significant difference across entities or
countries in this study.
For the appropriateness of the fixed effects model, one tests for the pertinence of both the
time fixed effects and other fixed effects, such as the country fixed effects. In Stata, one uses the
„testparm‟ (using „testprarm _Iyear‟) syntax to achieve this after running the fixed effects with
„i.year‟ inclusion, which generates time dummies. It tests the hypothesis that the joint time
dummies are equal to zero. If the probability of F is less than 0.05, then it is significant and
conclude the joint time dummies are equal to zero and as such that it is appropriate to use the
time fixed effects. Otherwise, no time fixed effects is needed if the statistics is insignificant. And
to test for entity fixed effects, say country fixed effects, one runs the „xi: reg‟ regression with
„i.country‟ to generate country dummies that are then tested. One uses the „test‟ for the country
dummies generated. It tests the null hypothesis that the entities are jointly equal to zero. And if
54

the Prob> F is less than zero, it is significant and we reject the null and conclude that the fixed
effects model is needed for that dataset.
Methodologically, the study follows the incorporation of the country fixed effects like
Anderson and Van Wincoop (2003) in order to estimate a gravity model more grounded in
theory. And on econometric grounds, the study took cognizance of the asymmetries across
sectors and country. This consideration provides robust results of the gravity model, like in
Subramanian and Wei, 2003.
However, if the results, that are generated from this process are not plausible, a least
squares methods to estimate the most consistent and reliable model that spells out the attributes
of interest is adopted.The other models are ML random effects, population-averaged model and
between effects models.
4.5 Type and Sources of Data
Gravity modeling and its estimations require data on bilateral aggregate trade, incomes,
population, distance, as well as geographical, cultural, and historical information. The study uses
a panel data set covering five (5) products (see appendix A Table 4.2) and one hundred sixty
eight (168) importers who have indicating their trade with the five (5) EAC countries (in the
study called exporters). The list of countries (Exporters and Importers) used in the sample is
presented in Appendix - A Table 4.3 and 4.4 respectively. The importers are the members of the
EAC community.
The study used secondary data from three main sources(see table 4.1 above). The Trade
data was picked from the UNCOMTRADE – WITS data base. The trade data is unidirectional,
that is, exports of EAC member states to the ROW. The use of the unidirectional (or imports of j
from i) trade volume as the regressand (Subramanian and Wei (2003)) are due to: the fact that,
theoretical specifications of gravity-like models yield predictions on unidirectional trade rather
than total trade; secondly, the trade effects of trade liberalizations (such as WTO, GSP etc.) are
closely related to imports rather than exports. The logic is simple, if country j liberalizes through
a certain scheme like WTO or GSP with country i, it is expected that its imports from i would
increase. However, there is no valid theoretical argument for its exports to expand by the same
proportion even if the Abba Learner Symmetry holds; Abba Learner Symmetry states that the
removal of import barriers serves to raise exports as well as imports. The symmetry would hold
at the country‟s aggregate trade level rather than at the bilateral trade level. And thirdly, the
effect of a liberalization and elimination of export subsidies are ambiguous in theory, since an
elimination of exports subsidies tends to reduces exports.
The second component of the data was mined from the most current Penn World Tables
7.0 (PWT 7.0). The Penn World Table (PWT) displays a set of national accounts economic time
55

series covering many countries. Its expenditure entries are denominated in a common set of
prices in a common currency so that real quantity comparisons can be made, both between
countries and over time. It also provides information about relative prices within and between
countries, as well as demographic data and capital stock estimates. From this table, I downloaded
data on the following variables; Population (in thousands), Exchange Rate, Real Gross Domestic
Product per Capita, current price, Price Level of Gross Domestic Product, Openness in Current
Prices, Real GDP per capita (Constant Prices: Laspeyres), derived from growth rates of c, g, I,
Openness in Constant Prices, and years for the period.
The third component of the data, was mined the from CEPII database. The data in this
database relates to countries and their main city or agglomeration. Among the country-level
variables are the first 3 identification codes of the country according to the ISO classification, the
country‟s area in square kilometers, used to calculate, in particular, its internal distance.
Variables indicating whether the country is landlocked and which continent it is part of are also
included. Also, I picked several language variables to proxy for indexes of language proximity
or dummy variables for common language in dyadic applications like gravity equations. The
sources for all language information are the web site www.ethnologue.org and the CIA World
Fact book. For each country, a report of the official languages (up to three), as well as the
languages spoken by at least 20 percent of the population and the languages spoken by between 9
and 20 percent of the population (up to four languages in each of those cases) was mined.
Colonial linkage variables which were used are also often used by economists to proxy for
similarities in cultural, political or legal institutions. Our dataset provides several variables
(based on the CIA World Fact book, and the Correlates of War Project run by political scientists,
available at cow2.la.psu.edu) that identify for each country, up to 4 long-term and up to 3 shortterm
colonizers in the whole history of the country.
56

Chapter Five
5.0 Presentation and Discussion of Empirical Results
5.1 Introduction
In this Chapter, the results and their discussions are undertaken. The first part of the section is
devoted to presentation and discussion of the descriptive statistics. The aim was to examine the
variables used in the model for possible errors. The second section discusses the diagnostic test.
Specifically, the diagnostic tests were done to find out whether the independent variables were
not perfectly multicollinear and whether the residuals were homoskedastic. The third section was
devoted to the presentation and discussion of model estimates at the EAC level. The major aim
was to determine which covariates influences EAC trade, estimate the size and magnitude of
EAC dummy and find whether it creates or diverts trade, and also determine the sectoral patterns
of EAC trade with food or agrifood products as the reference or base category.
The next section is devoted to the discussion of estimates of the individual countries of
EAC. Specifically, the issues that were presented and discussed at the bloc level are discussed
for each country. This was made possible by extracting each country‟s unique dataset from the
major database created. The final section is devoted to discussing and presenting the summary of
the empirical findings.
57

5.2 Descriptive Statistics
For any plausible study to be meaningful and the estimated model to have robust estimates it
shouldbegin by the description of the data and the estimated model adopted for the study. The
description of the model relates to whether the covariates explain well the flow in exports. And
the description of the data relates to finding out whether data is contaminated with outliers and
influential variables. Where they were detected,they were treated as shown in the subsequent
sections.
5.2.1 Summary Statistics
In this section, the summary statistics are presented and discussed. In the study, a number of
covariates were used. However, Table 5.1 below presents the summary statistics for the major
covariates used in the study;
Table 5.1 Summary Statistics for Covariates used in the study
Variable (in logs) Observations Mean Standard Deviation Min Max
Value of Exports (USD Current Prices) 3900 6001 2925.633 2.0 13562.7
Importer Population 3900 9410.54 1781.818 3.66374 14.0959
Exporter Population 3900 10448.9 77.3183 10.3257 10.5714
Importer GDP (Current Prices) 3900 11385.7 2073.464 5.98829 16.4806
Importer GDP Per Capita (Current Prices) 3900 8882.91 1437.536 5.05067 11.7706
Exporter GDP (Current Prices) 3900 10613.2 193.1011 10.3375 10.91
Exporter GDP Per Capita (Current Prices) 3900 7072.1 116.9586 6.91954 7.24637
Source: Data used is from UN COMTRADE
From table 5.1 above, average annual trade value is about 6,000 inthousands of USD and this
value could range between 2 thousand of USD and 14,000of thousands of USD. The average
population of the exporting countries of EAC is almost ten times theaverage population of the
importing countries in the rest of the world. On average EAC has a population of about10,
500,000while the ROW has only 1,000,000 persons on average.The average nominal GDP and
GDP per capita of the importing countries is more than 1,000,000 than that of the exporting EAC
countries. The average GDP of EAC is 10, 603, 2000 and that of the ROW is 11, 385,700. While
the per capita GDP is of EAC is 8,882,910 and the one for the ROW is 11,448,900 on average.
58

5.2.2 Regression Correlation Matrix
In order to find out the degree of linear relationship between the dependent variable (log of
exports) and its covariates, a tabulationof the PearsonCorrelation Matric was done. Table 5.2
below presents the correlation matrix for all the variables used in the model at the 0.05 level of
significance.
Table 5.2 Showing Correlation of Selected Covariates of the Study
LExportsin~d 1.0000
Lexporterpop 1.0000
lexporterx~t -0.4209* 1.0000
0.0000
.
LExpor~d Ldist Li~tcgdp Li~rcgdp Le~rcgdp Lexpor~l Limpo~op
Ldist -0.1789* 1.0000
0.0000
Limportert~p 0.3926* 0.4948* 1.0000
0.0000 0.0000
Limporterc~p 0.0683* 0.6186* 0.5353* 1.0000
0.0000 0.0000 0.0000
Lexporterc~p 0.0729* 0.0175 0.0894* 0.1191* 1.0000
0.0000 0.2733 0.0000 0.0000
Lexporterr~l 0.0671* 0.0182 0.0801* 0.1151* 0.9576* 1.0000
0.0000 0.2548 0.0000 0.0000 0.0000
Limporterpop 0.4018* 0.0767* 0.7318* -0.1839* 0.0079 0.0003 1.0000
0.0000 0.0000 0.0000 0.0000 0.6202 0.9835
Lexporterpop 0.0728* 0.0153 0.0874* 0.1149* 0.9748* 0.9102* 0.0090
0.0000 0.3386 0.0000 0.0000 0.0000 0.0000 0.5757
lexporterx~t -0.0295 -0.0133 -0.0381* -0.0663* -0.5716* -0.7230* 0.0091
0.0652 0.4062 0.0172 0.0000 0.0000 0.0000 0.5702
Lexpo~op lexpor~t
The numbers in the table are Pearson Correlation Coefficients and move from positive 1
(implying a strong correlation) to negative 1 (implying an inverse relationship). All the variables
are correlated with LExports in 1000s of USDs since they are all significant. The log of distance
and exchange rate of the exporter are negative correlated to the LExports as expected from
theory. All the other variables have the expected signs of correlation to LExports. For a graphical
presentation of this correlation matrix in a series of scatter plots for all the variables, see figure
5.1 in appendix B. Visually all the variables are clustered together. This implies that the
covariates have a relation with the independent variable.
59

5.2.3 Assessment of the Model Using the Observed Versus Predicted Values
Another issues to consider before estimating the model, is to find out whether the predicted or
specified model‟s covariates predict the dependent variable (Lexports) well. To conclude on how
well or good the model can predict the flow of exports, one looks at the relationship of linearity
of the model and the behavior of its residuals.A graphic of this relationship is presented in Figure
5.2 below;
Figure 5.2 Regression Results of Observed Against Predicted Values
10 15
0 5
-5 0 5 10 15
LExportsin1000usd predicted
A visual inspection of the above figure indicates or showsthe scatter producing a cluster of the
relationship between the observed and the predicted values at a 45 degree pattern of the dataset.
Because of this pattern, the model does a good job in predicting the exports of EAC.
5.2.4 Test for Normality
Since this study used ordinary least squares to predict and produce its estimates, a normality test
of the residuals was done. When the residuals are not normally distributed, the validity of the ttest,
p-values and F-test will all be influenced. To achieve or conclude on this, a kernel density
for a graphic interpretation (See figure 5.3 below) and the Shapiro-Wilk test for normality were
applied. For the Shapiro-Wilk test, a null hypothesis that the distribution of the errors is normally
distributed is prescribed. When the statistic is significant, we reject the null that the errors are
60

Density
normally distributed. In this study, Shapiro-Wilk test produces a significant statistic(with Prob>
Z = 0) since it is less than 0.05, hence the null hypothesis that the data is normal or comes from a
normal population is rejected. The Kernel density in Figure 5.3 below shows that the probability
density distribution and the outlay of the residuals not almost perfectly matching the normal
distribution outlay. When this is so, one concludes that the data is not normally distributed.
Table 5.3 Shapiro-Wilk W test for Normal Data
Variable Observations W V z Prob>z
e 3900 0.9909 19.8 7.77 0
Figure 5.3 Showing the Kernel Density Plot with an overlay of a Normal Distribution
.1 .2 .3
0
0 5 10 15
Linear prediction
Since in Figure 5.3 above, showing the kernel density plot not fitting the normal distribution plot
perfectly, and Shapiro-Wilk test statistic being significant,it implies that estimates of the pvalues,
t-tests and f-test would not be valid and are even influenced. To overcome this problem,
the dataset was transformed. The choice of the transformation was partly determined from theory
and the use of the gladder commands of STATA.
61

5.3 Regression Diagnostics
Having determined, from the descriptive section, that the data, variables and model are processes
well-structured (or where they were not, they were corrected to be well structured),and that they
behaved well in their relationship to each other and the dependent variable exports, and in
predicting the model, the study then did diagnostics test. The specific diagnostic testof Breush –
Pegan for homoscedasticity and Wooldridge test for autocorrelation were done to check for and
treat for heteroscadacity. The other diagnostic testrelated to finding out if the variables suffer
from perfect multicollinearity or whether the errors are collated.Whenever the data suffered from
these problems, then a solution for them wassought first before running regressions. When
variables are perfectly collinear, then it is impossible to determine the determinant of the matrix
used in transforming the variables, so as to assist in producing the estimates. Andthe variables
are autocorrelated, the estimates generated are invalid since the t-values and standard errors are
not precise and efficient. If, the dataset does not suffer any of these or the problems have been
accounted for, then the dataset and model may be used to estimate the results.
5.3.1 Testing for Homoscedasticity
In running the regressions, therewasan important assumption that the variances of the residuals
are constant or „homoskedastic‟. To determine if this is so, a graphical method was
used.STATA‟s “rvfplot” inbuilt command was used to plot the graphical that helped in checking
for homoskedascity. To reaffirm the graphical plot results, theBreusch-Pagan test was employed.
However, without delving into the intricacies of these approaches, Stock and Watson 2003, notes
that, „asa rule of thumb, always assumeshomoscedasticity in your model‟.Silva and Tenreyo,
2006, also allude to such a conclusion when they make mention of the fact that the null
hypothesis that there is homoscedasticity is rejected in traditional fixed-effects gravity equations.
As such, this study presumed that the data suffered from heteroskedasticity. As, the study
predicted that the gravity model may provide wrong estimates of the standard errors of the
coefficients coupled with wrong t-values. Since the results were estimated using STATA, one
needs only to adjust the model by introducing the robust (re) option to the model to overcome
this problem and improve the estimates of the regressions.
62

5.3.2 Testing for Multicollinearity
Another important assumption is that the independent variables should not be perfectly 23 linear
functions of each other. If they are, then it is not possible to find their determinants and hence
solutions to a model. However, in STATA, when variables are perfectly collinear, they are
dropped automatically from the estimates. For example, the log of exporter GDP and GDP per
capita variable has been consistently collinear in all regressions run. The current colony estimate
was also collinear in all estimates. This is probably due to the fact that, there is no EAC member
state that is in a current colonial relationship, hence the dummy variable‟s determinant is not
defined to help in the evaluation process of the estimate – causing the problem of perfect
collinearity. As such, the variables were dropped from the estimation process by the statistical
program.
The study also employed or used the Wooldridge test for autocorrelation in panel-data
models 24 . The test specifies a null hypothesis that there is no first order autocorrelation. When
the test statistic is significant, then there is serial correlation. In Table 5.4 below, the Wooldridge
test statistic is significant since its P-value is 0.000. And since this is less than 0.05, it implies
that the null hypothesis is rejected. Hence, there is serial correlation or the model suffers from
serial correlation.
Table 5.4 Wooldridge test for autocorrelation in panel data
H0: no first order autocorrelation
F( 1, 421) = 47.661
Prob> F = 0.0000
Following Gurati, 2004, the study ignored the problem of collinearity since it does not seem to
be serious and affecting the variables of interest. Only three variables were collinear and the
explanation for this was established.
23
One may ignore this problem if the regressors are not perfectly collinear. This follows the “do nothing‟ wisdom of
Gujarati, 2004.
24
Wooldridge, J. M. 2002. Econometric Analysis of Cross Section and Panel Data. Cambridge, MA: MIT Press 282-
283).
63

5.4 Presentation of Estimates and Discussion of Results
In this sub-section, the presentation of the estimates and the results from the study are discussed
in relation to the objectives specified earlier on. More to this, a discussion and summary of the
econometrics methods and approach were done in sub-section 5.4.1. In the subsequent subsections,
the objectives and results estimated for EAC as a bloc and the individual countries is
discussed. The first objective, discussed in sub-section 5.4.2, relates to the evaluation of the
determinants of EAC export trade. In the next sub-section, 5.4.3, a presentation and discussion of
the findings relating to the sectoral impact of the selected sector (agricultural raw materials,
fuels, ores and metals and the manufacturing sector) is undertaken. The base sector or reference
category is the food item sector. The presentation of the findings and discussion of EAC regional
trade agreement is done in sub-section 5.4.4.
5.4.1 Summary of Econometrics Methods
In the estimation of results, the following steps were followed with the justifications herein.
Results relating to the EAC Bloc are tabulated in Tables 5.5 and that of the EAC countries is in
table 5.6 and 5.7in Appendix C. The tables formally detail the econometric methods and the
different models run with their appropriate statistical tests.
Empirical efforts of panel data often involves choosing between either running the within
or squares dummy variables (called fixed effects model) on the one hand, and the generalized
feasible (also called random effects). In this regard, a Hausman test was executed to decide on
the appropriateness of the model to run. The Hausman test specifies a null hypothesis that the
preferred model is random effects. While the alternative is that the fixed effects model is
preferred. To achieve this, afixed effects model i.e. run the „xtreg‟ model with an option of fixed
effects (fe) was specified and run with the resultsof the estimatesstored.Then a random effects
model was also run (i.erun the „xtreg‟ model with a random effects (re) option and store the
estimates). On specifying the Hausman test, it revealed a chi-2(15) = 235.72, which is highly
significant at all levels. Because of this finding, the null hypothesis is rejected that the preferred
model is random effects model. Hence the study employs the fixed-effects model.
The next econometric issue to consider was what type of fixed effects model to run and
what estimation model to estimate. One could run the one-way, two-way and three-way fixed
effects models. The choice of which at best, could partially depend on the data type and it size.
Trade data suffersfrom the problem of heterogeneity. Since heterogeneity was deemed and
observed in the country pairs in the dataset, estimating a gravity model in panel eliminated this
problem (following Westerlund and Wilhelmsson, 2006 conclusions). However, this would not
eliminate all the heterogeneity in the trade data, especially emanating from the nature of the
64

covariates. The study also took cognizance that estimates of the gravity model generated from a
log-linear form using OLSare inefficient and not precise, i.e. they are biased and inefficient as
Westerlund and Wilhelmsson, 2006 25 allude to and are not theoretically founded.Another issue
confronting trade data and analysis is the presence of zero trade data which could be the result of
no trade, measurement errors or rounding errors. Since trade data is normally in thousands of
USD, the values which are less than half of this are rounded to zero 26 , thus make estimate over-
or –under estimated. To overcome this problem, the exports were measured not in thousands of
USD but in their multiplicative form 27 . Care was taken to avoid missing values.Where they
existed; they were compensated for by exports,since the imports were the primary source of the
trade data 28 . And where the missing values still existed, they were considered to be true zero
trade 29 .
Since the traditional econometric method of estimating a gravity model in log-linearized
form produces results that are not precise and efficient, the quest was to estimate a gravity model
with a technique that handles the problem of zero trade data, solves the heterogeneity problem
inherent in trade data, and is theoretically grounded. One of the only two theoretically grounded
gravity estimation techniques 30 , is to use the Poisson fixed effects estimator (pseudo Maximum
Likelihood (ML) technique) in line with Silva and Tenreyro, 2006; and Westerlund and
Wilhelmsson, 2006). The Poisson estimation wasdonewith exports (the dependent variable) in it
multiplicative form or original formulation not in a log-linearized form. The model was then
estimated using OLS, producing betterresults that were not as biased and efficient estimates as
25
They state that biases and inefficiencies emanating from heteroskedasticity have been ignored in empirical work.
26
The upward rounding off could cancel the effect of downward rounding off and does not pose any problem.
However, if your dataset included country pairs that are distant and even small this may not be plausible.
27
To generate exports, the study employed the multiplicative form i.e. Exports = Exportsin1000USD*1000 to
generate an integer.
28
The imports were compensated or inflated by 10 percent (on average this is equal to CIF/FOB=1.1). However,
there was also a pre-selection of the importers and exports used. Only exporters who had all the trade data for the
period were considered.
29
When zero trade values still exist after these transformations and considerations, it should not be a problem when
they are random. Nevertheless, this is not always the case. However, a serious problem will be encountered when
the zero is in the dependent variable when the gravity model is estimated in it multiplicative form. Nonetheless, the
gravity model (even in its log-linear form 29 ) can still be estimated with such challenges (Silva and Tenreyro 2006 &
Frankel). And the challenges, especially of log-linearization, are even escalated in the presence of
heteroskedasticity. As such, the estimates of the log-linearized gravity model are noted to be biased and inefficient
and should not be used to infer trade dynamics. The traditional estimation of the gravity model involve one loglinearizing
the model first and then estimating it with fixed-effects (Westerlund and Wilhelmsson, 2006). However,
in the presence of heteroskedascity, the results of the OLS are biased and inefficient. Further, the log-linearized
models truncate the negative trade and zero trade, or even some empirical the negative and zero trade are
compensated for with a positive value. However, this introduces another problem of selection bias. There seems not
to be a solution yet to handle zero trade however.
30
The other one involves using the two stage estimation technique developed by Helpman et al, 2007 that is
identical to the sample selection bias. In the first stage, it involves finding the likelihood or probability that two
countries trade with each other using the probit. This enables one to estimate the mills ratio. In the second stage, sing
the mills ratio from stage one that controls for the bias in sample selection, and hence control for firm heterogeneity,
and estimate the gravity equation in levels using the Heckman estimation technique.
65

those of the log-linearized gravity model. This is because the estimation procedure is done for
zero trade, since the dependent variable is in level and it has no requirement for it to be an
integer. The data was not Poisson distributed (Westerlund and Wilhelmsson, 2006). Therefore
the Poisson fixed effects model, an appropriate estimation technique, was employed in
estimating the gravity model. The estimates of this process are shown in table 5.5 31 in appendix
C. Column one of table 5.5 shows results for the EAC bloc and the subsequent columns show the
results for Burundi, Kenya, Rwanda, Tanzania and Uganda respectively. The results for loglinearized
form are also produced and may be found in tables 5.6 and 5.7 in appendix C too.
5.4.2 Results of the Determinants of Exports of EAC
One set of objectives of this study was to estimate the effect of the adopted trade liberalization
policy, determine and estimate which covariates explain bloc trade at both the regional and
country levels. Having considered all the necessary econometric issues, a set of two equations
were run and estimated.
Of the traditional gravity model covariates, the coefficients of distance, both theimporter
GDP and GDP per capita , sharing a common border, sharing official and ethnographic language,
ever being in a colonial relationship and the trading country-pair ever being same countryhad
positive and significantinfluences on trade. These and other covariates wereall significant at 1
percent level and have the right signs theoretically, and as such, they determine the exports of
EAC. For example, ceteris peribus, when there is a percentage change in distance, the exports of
EAC decrease by 0.01. The impact is much smaller than the estimates generated by traditional
OLS estimations of the gravity modewhich is 1.12 (see table 5.6). However, EAC bloc exports
going to a country sharing the same official language enhanced trade more than otherwise. This
is because sharing a common official language enhances trade by 43 percent on average,more
than the benchmark of exporting to a country that does not share this same language. However,
EAC performance of exports going to a country sharing a common ethnographic language is not
enhanced morethan the benchmark. That is, sharing a common ethnographic performs less than
not sharing a common ethnographic by an average of 52 percent.These findings seem to be in
line with Silva and Tenreyro‟s findings that distance and common language elasticities play a
smaller role on trade for Poisson estimation than OLS estimations of the gravity model. The
latter predicts an estimate on these variables closer to unity 32 .However, a percentage increase in
both the importer GDP and GPD per capita increase trade by 0.075 and 0.025 respectively. These
31 The estimation results for EAC bloc were produced using thetwo-way Poisson fixed technique. The rest of the
results were produced using the one-way Poisson fixed effects Poisson technique except for Uganda which were
estimated the one fixed effect in it log-linearized form. This is because the results for Uganda failed to converge.
32 Carrere, 2004, quoting Obstfeld and Rogoff, 2001, suggest a consensus that the coefficient of distance should be
between -1.2 and -0.8, which is identical to what is estimated.
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estimates from ML estimations produce a greater effect on trade than those from OLS estimation
techniques as asserted by Silva and Tenenyro, 2006. However, EAC exports going to countries
that remained in the same colonial relationship after 1945, or to countries that shares a common
border with it, and exporters who were the same country with EAC bloc, on average increased
trade than otherwise by 331 percent, 163 percent and 129 percent respectively from the
benchmarks.
More to this, Burundi‟s exports are influenced by the choice of its importers‟ GDP size
and the fact that it has been in a colonial relationship with the importer. Ceteris paribus, for every
percentage increase in the importer‟s GDP,Burundi‟s increase exports by 0.0047. Burundi‟s
exports are more enhanced when it exports to a country that has ever been in a colonial
relationship with it than when it exports to one that has never been. This because, the colonial
ties performs better than Burundi‟s exports going to a country that has never been in a colonial
relationship by 654 percent on average. The colonial ties also has similar effects on Uganda‟s
exports,i.e. when Uganda exports to a country that has been in a colonial relation with it, its
exports are more enhanced because the colonial ties perform better than otherwise and on
average it is 200 percent better than Uganda‟s exports to country that has not ever been in a
colonial link. The choice of the importer‟s GDP also influences the exports of the other EAC
member countries, just as Burundi‟s. For instance, for every percentage increase in the
importer‟s GDP, the exports of Kenya, Rwanda Tanzania increase by 0.0078, 0.033 and 0.0082
respectively. However, a percentage increase in the importer‟s GDP increases Uganda‟s exports
by 0.92 (which is closer to unity as predicted by theory) 33 . Another determinant that seems to
influence most of the exports of the exporters or EAC states is distance. And for every
percentage change in distance per kilometer, the exports of Kenya, Rwanda and reduce by
0.0143, 0.013 and 0.01 respectively. Nevertheless, Uganda‟s percentage reduction in exports
reduces by 1.06 for every percentage increase in distance.
Kenya‟s exports are influenced by its own GDP, the GDP per capita of its importer, the
contiguity of the importer of its products, the fact that its exports go to a country that shares the
same official language with it, by the fact that it has ever been in a colonial relationship with the
exporter and that they were the same country with the importer. For illustration, for every
percentage increase in the GDP per capita of the importer and GDP of Kenya, the exports of
Kenya increase by 0.005 and 0.12 respectively. But, when Kenya exports to a country it shares a
border with, its exports are enhanced more than when it exports to one that is not in contiguity
with it. This is because the contiguity out performs the lack of contiguity by 177 percent on
average. Further, when Kenya exports go to a country that shares a common official language, it
enhances trade more than when it exports to country that does not share this language. The
reason being that, the common official language out performs the lack of it by 68 percent on
33 Ibid
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average more than when it exports to a country that does not share a common official language.
More to this, Kenya trading with a country that remained in a similar colonial relationship with it
after 1945 out performs trading with a country that did not remain in such a relationship by 213
percent on average. Exports of Kenya are more enhanced when they go to a country that was one
and the same with it, since same country within the last 75 years performs better than otherwise.
The exports of Rwanda are also influencedby contiguity of the importer of its product,
i.e., contiguity enhances trade by 750 percent on average than not being in contiguity for
Rwanda. Further, Rwandan exports are more enhanced when they go to an importer who has
ever been in the same colonial relation after 1945 (col45)than when they go to a country that did
not follow this arrangement. The col45 variable expands trade over 542 percent on average than
not have exports go to such a country.While the col45 for Tanzania estimate of 517 percent
overage outperforms the exports to a country which did not remain in the same colonial relation
after 1945. However, the common colonizer indicator for Rwanda outperforms the exports to a
non-common colonizer of Rwanda by 297 percent on average.
5.4.3 Application to the assessment of the effects of regional trade agreements.
Another objective related to finding the impact of EAC bloc on exports – especially its influence
on trade creationandtrade diversion and also imputes the welfare implications of the trade
liberalization policy of the EAC trade agreement.To capture this, dummies of intra-bloc trade,
bloc export and bloc imports were specified. Refer to table 5.6 for the estimates of these
dummies. Inference is made for coefficients that are not only statistically significant, but also
economically sensible.For EAC bloc, EAC_Exports and EAC_Intra dummies are significant.
Following the Westerlund and Wilhelmsson, 2006 interpretation of these dummies, since the
EAC_intra dummycoefficient is greater than zero (i.e. 0.05), it implies that the EAC bloc creates
trade. EAC has created trade by 5 percenton average more than trade predicted by reference (it
could be presumed to in substitution to domestically generated production). However, this
statistic is lowerthan the 40 percent that is predicted fromPoisson estimations. This lower figure
could be due to truncating EAC importer members (to avoid homogeneity issues) and the
truncation of the products used for analysis. However, since the coefficient of EAC_exports is
less than zero (i.e. -0.21), then it is analogous to export diversion. This corresponds to export
diversion of up to 19 percent on average. And following Carrere, 2006, since the EAC_Intra
coefficient is positive and EAC_Exports coefficient is negative, this corresponds to pure trade
diversion in terms of exports. This finding can also be used to infer on the welfare of the nonbloc
members, i.e. the welfare of the non-members of EAC is reducing.
Kenya‟s membership in the EAC leads to trade creation since the coefficient of
EAC_intra is positive (1.53). The trade that is created by Kenya‟s membership in EAC is about
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361 percent than if it was not a member of EAC. In Tanzania‟s and Uganda‟s membership in the
EAC however, seems to have reduced theirtrade creation effects since the EAC dummy
coefficients for Tanzania of -0.77 and Uganda of -1.63, are negative, than the baseline. This
corresponds to about 55 percentand 80 percent trade creation reduction on average than
otherwise. Further, Uganda‟s bloc EAC_exports is positive 0.53. This corresponds to 70 percent
average reductions in trade diversion than explained otherwise. More to this, its estimate of
EAC_imports and imports is positive 1.40. This corresponds to 306 percent analogous to
reduction in imports diversion.
5.4.3 Application to the sectoral assessment of the effects of the adopted sectors
The other objective of this study was to estimate the impact of EAC on food item trade in
relation to the other sectors. A set of dummies capturing the sectors or product aggregates was
specified with food sector as the base dummy.
The coefficient of the dummies for EAC sectoral trade in agricultural raw materials trade,
fuels, ores and metals, and manufactures are -0.33, -0.60, -1.78 and -0.4 respectively. And since
they are all negative, it means that the exports of the food sector perform significantly better than
all the other sectors. For example, the performance of agricultural raw material exports is lower
than the exports from the food sector by 28 percent for the bloc. More to this, the agricultural
raw materials sector performs also less than the food sector in the rest of the EAC member states
except for Kenya and Rwanda where the performance of the manufacturing sector is not
significantly different from average contribution of the food sector on exports. The estimated
coefficient for Burundi, Tanzania and Uganda are -1.59, -0.24 and -1.60 respectively. In terms of
percentages, both Burundi‟s and Uganda‟s performance of agricultural raw materials on exports
areon average 80 percent less than the food sector. And Tanzania‟s agricultural raw materials
average contribution on exports is 21 percent less than food sector‟s performance.
The manufacturing sector estimates across all countries and EAC bloc outputs are all
negative, implying that the sector performs worse than the food sector. However, the average
performance of Kenya‟s manufacturing sector on exports is not significantly different to that of
food sector. The coefficient for the bloc manufacturing exports is -0.4, and that of Burundi,
Rwanda, Tanzania and Uganda are -1.54, -1.87, -0.49 and -1.76 respectively. In percentage terms
themanufacturing sector performance is comparatively worse than the food sector by 32 percent
for EAC bloc exports, 79 for Burundi, 85 percent for Rwanda, 39 percent for Tanzania and 83
percent for Uganda.On the average, Rwanda‟s fuel and ores and metals sectors perform worse
than the food sector with coefficients of 2.61 and 0.45 respectively. In terms of percentages, the
fuels and ores and metals sectors perform better than the food sector by 1250 percent and 57
percent on average in Rwanda. The fuels sector of EAC as a bloc and the rest of the EAC states
69

perform worse than food sector too. At the EAC, the coefficient is-0.6, corresponding to a
performance of 45 percent less than the average contribution of the food sector on exports. And
for Burundi, Kenya, Tanzania and Uganda, the coefficients are -1.54, -0.59, -0.77 and -4.23
respectively. These correspond to average performances of 79 percent, 45 percent, 54 percent
and 99 percent less than the food sector. A similar trend is observed for ores and metals. And the
corresponding coefficient estimates of the Bloc, Burundi, Kenya, Tanzania and Uganda are -
1.78, -2.16, -2.50, -1.05 and -2.62 respectively. In terms of percentage they are equal to 83
percent, 88 percent, 92 percent, 65 percent and 93 percentrespectively.
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Chapter Six
6.0 Summary, Conclusion and Policy Recommendations
6.1 Introduction
The conclusions of the study are presented in this chapter. Section 6.2 summarizes the findings
of the study.Conclusions are then generated from these findings in section 6.3. Policy
recommendations derived from these conclusions are described in section 6.4.
6.2 Summary of the Empirical Analysis
The study sought to answer three broad questions about the impact of the EAC adopted trade
liberalization policy (or preferential trade liberalization or EAC trade agreement) on its partner
states viz: the first question related to determining which covariates explain trade; the second,
sought to estimate the effect on the sectoral trading patterns; and the third, was to assess the trade
and welfare effects. An extended gravity model was run with a panel dataset extending 22 years
(from 1988 to 2009). The dataset included 168 countries and five major products or sectors of
EAC interest. The Poisson fixed effects estimation technique was used to run the gravity model
for EAC, Burundi, Kenya, Rwanda and Kenya. Due to lack of convergenceof Uganda‟s trade
data, its estimates were determined by the least squares dummy variable estimation. All the
regressions were run using a two-way fixed-effects model.
For objective one, the study found out that the gravity model explained the trade pattern.
And of thecanonical gravity model covariates, the coefficients of distance, the importer GDP and
GDP per capita , sharing a common border, sharing official and ethnographic language, ever
being in a colonial relationship and the trading country-pair ever being the same country, had
positive and significant influences on trade at the regional level. At the country level, the GDP of
71

the importer positively influenced the exports of all the countries. Another covariate which
affected (reduced trade) tradein all the countries except Burundi,probably because of its thin
exports base, was the distance covariate. Trading with a country that remained in the same
colonial relationship with EAC after 1945influenced trade more positively than with a partner
who did not. Burundi and Uganda‟s exports responded more to trading with a partner who has
ever been in the same colonial relation than if Burundi and Uganda traded with a country that
had never been in the same colonial relation with them. Trading with a contiguous country
benefited Kenya‟s and Rwanda‟s exports more than trading with a country that was not
contiguous to them. Another positive influence was observed when the trading was with a
country that was in a colonial relationship with it than with one which had never been in such an
arrangement. Kenya‟s exports are also influenced positively by size of its partners GDP per
capita and its own GDP size. Furthermore, when Kenya trades with a country that was once the
same country with it, and a country that use the same official language as it, it benefits more than
trading with one that is otherwise.
For the second objective relating to the effect of EAC trade agreement on sectoral trade
patterns with reference to the food sector, the study found out that:
Inthe EAC bloc, the food sector performed better than all the other sectors – agricultural raw
materials, fuels, ores and metals, and the manufacturing sectors. For example, in relation to
the food sector, the agricultural raw materials sector, fuels, ores and metals and manufactures
performance are 28 percent, 45 percent, 83 percent and 67 percent respectively.
In Burundi, a similar pattern is observed in the food sector performs better than all the other
sectors. The agricultural raw materials, fuel, ores and metals, and manufacturing sectors‟
performance is 80 percent, 79 percent, 88 percent, and 79 percent respectively worse than the
food sector.
Food exports of Kenya performed better than all the other sectors of its exports. For
example, the agricultural raw materials sector performed worse than the food sector by 21
percent. The fuels, ores and metals, and manufactures performed worse than the food sector
by 45 percent, 92 percent and 12 percent on average.
In Rwanda, it is only the manufacturing sector that performed worse than the food sector by
85 percent on average. However, the agricultural raw materials, fuels and ores and metals
perform better than the agricultural sector by 27 percent, 1260 percent and 57 percent on
average.
In Tanzania, the food sector performed better than the other sectors that contributed to its
exports. For example, the agricultural raw materials performed 21 percent worse than the
food sector.The fuels, ores and metals, and manufactures sectors performancewas worse than
the food sector on average by 54 percent, 65 percent and 85 percent respectively.
72

In Uganda, as similar to the performance observed in Burundi, Kenya and Tanzania in the
performance of the food sector in relation to the other sectors. The agricultural raw materials,
fuels, ores and metals, and manufacturessectors performed worse than the food sector by 80
percent, 99 percent, 93 percent and 17 percent respectively.
In the descriptive section, it was observed that the EAC bloc exports grew from less than
US$ 2 billion in 1988 to currently US$ 10 billion. The major contributing sector to exports
was the food whose contribution on average was more thanUS$ 3 billion over the period
under review. The manufacturing and agricultural raw materials sector is the second major
contributor to these exports, jointly contributing about US$ 4 billion currently.Ores and
metals, and the fuels sectors account for the remaining portion of the bloc exports. Kenya is
the dominant producer of the exports of EAC, producing an average of US$ 3 billionover the
period. Tanzania and Uganda contribute on average less than US$ 2 billion and US$ 1 billion
of the bloc‟s exports. Rwanda and Burundi on average contribute less than US$ 0.5 billion
over the period.
Intra bloc exports have grown from less than US$ 100 million to US$ 2000 million over the
1988 to 2009 period. Manufacturing sector as the major contributor to intra bloc exports,
accounts for US$ 600 million on average. Fuel sector is the second major intra-bloc
contributor, with an average of less than US$ 250 million over the period. Food sector
follows in third position contributing aboutUS$ 180 million over the period. Agricultural raw
materialsand ores and metals sectors had a peripheral contribution to the intra-bloc exports.
Kenya is the largest contributor of the intra-bloc exports. This is followed by Tanzania and
Uganda which contribute a joint value of about 20 percent of Kenya‟s intra-bloc exports.
Burundi and Rwanda contribute an insignificant value of the intra-bloc exports.
From the graphics in the descriptive section, it is clear that both the inter-bloc and intra bloc
exports increased tremendously when the EAC was formed in 2000 to 2009. There is also a
further movement in the trends with Burundi and Rwanda joining the EAC. However, the
trends seem to slightly dampen from 2008 probably due to the effect of the global financial
crisis.
In reviewing the EAC protocol, the food sector was a component of the chapter on
agriculture and the interventions to improve both were synergized to almost all other
interventions of EAC. There were also no clear policies to enhance the output or even trade
in agricultural sector and food trade.
And for objective three, relating to assessing the effects of EAC trade agreement on the trade and
welfare effects, the study foundout that;
The EAC agreement led to creation of trade at the bloc level of about 5 percent than
otherwise.
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The EAC trade agreement led to a reduction in the welfare of non-members (rest of the
world).
Kenya‟s membership to the bloc has enabled it to create more trade than if it was not a
member. The trade created has expanded by 361 percent on average.
Tanzania‟s membership of the EAC bloc dampened it trade creation by 55 percent than if it
was not a member.
6.3 Conclusions
The results of this study suggest that:
The EAC trade liberalization policy reform that led to the formation of the EAC trade
agreement has tremendously led to the growth and expansion of exports in all the sectors of
food, agricultural raw materials, fuels, ores and metals, and manufacturing sectors. However,
this growth is more reflective in the food sector on the one hand, followed by the agricultural
raw materials and manufacturing sector on the other hand, more than the growth in the fuels
and ores and metals sectors at the bloc level.
Kenya is the biggest beneficiary from this growth, followed by Tanzania and Uganda, while
Burundi and Rwanda were relegated beneficiaries but had a limp-frogging export growth.
Intra-bloc exports increased tremendously across all the sectors. However, the manufacturing
sector exports dominate the intra-bloc exports. The fuels sectors exports, though very small
compared to the manufacturing sector exports follows in tow. This is closely followed by the
food sector exports. The fuels and ores and metals sectors are also relegated to the much
lower growth in the bloc too. It would be expected that since the food sector constituted the
largest export growth without the bloc, it would also follow that it is the major intra-bloc
export. However, this is not the case, since the manufacturing sector dominates intra-bloc
exports. The reasons for the disparity of such an export outlay were beyond the coverage of
this study.
The choice of the importer mattered a lot in explaining EAC bloc exports. Importers of EAC
export products that have large GDPs influenced trade more than those with trifling GDPs,
ceteris paribus.
Distance is an important factor in explaining the volume of exports from the EAC bloc.
Proximate importers influenced trade more than detached importing countries.
The implementation of the EAC trade agreement led to a creation of trade of more than 5
percent on average than that explained by the reference.
The adopted EAC policy reform has led to an export diversion of about 19 percent on
average than predicted by the reference.
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By inference, the implementation of the EAC trade agreement led to a reduction in the
welfare of non-members (or the rest of the world).
Kenya‟s membership in the EAC and its implementation of the trade agreement led to Kenya
creating more trade than was predicted by reference. The trade created by Kenya was about
361 percent on average.
Tanzania‟s membership and adoption of the EAC trade agreement reduced their trade
creation by 55 percent on average than was predicted by reference or if it did not implement
the trade liberalization mandate.
The EAC policies on food trade in particular, and agriculture in general, is a mumbo-jumbo
of policies. The interventions to enhance these sectors is synergized to every other sector,
interventions or programs from rural development, investment, climate change, infrastructure
development, to poverty reduction, among others. As such, it becomes difficult for the food
and agricultural sectors (sectors important for food security and trade) to receive tangible and
targeted support. Even within the Agriculture and Food Security chapter of the EAC trade
Protocol, the food sector is fused in agricultural and rural development programs, without
clear guidelines to improve the sector at the EAC bloc level, at least.
6.4 Policy Recommendations
The policy recommendations derived from these conclusions are:
• There is need to look at the structural components that hinder Tanzania to create trade as a
member of EAC. These hindrances possibly emanate from infrastructure inadequacies and
NTBs to link Tanzania to its trading partners. The EAC Summit should strategize mutually to
solve this by possibly introducing a trust fund to support especially the infrastructural
limitations. There seems to be already and initiative to construct the Tanga-Mwanza Railway
to link Dar es Salaam port to the hinterland to provide an alternative route and reduce on
transportation expenditures, however, this has taken too long too. It is also recommended that
such an initiative should be extended to link Burundi and Rwanda too.
• Minus Kenya, there is need for the other EAC members to build their export base and
diversify the range of products they export (this is particularly recommended for Burundi and
Rwanda). Where diversification is not tenable, the regional body should identify sectors that
each country could specialize as it export sector, to avoid duplication of export sectors and
enhance the exploitation of the available resources maximally.
• As the EAC is expanding in size, it‟s important that the choice of its partners considered with
economic rationale more than using the legal or political criterion. The EAC should in the
medium term consider only accession partners with high GDP per capita, otherwise, those
with low GDP per capita dampen the economic prospects of the bloc.
75

• In similar light, as EAC is busy looking for importers of its products, it is important that it
sticks to importers who have high GDPs since they enhance trade more than those that do not
ceteris paribus. It is also imperative that the partners to consider the proximity of the
importer, the language they use both official and ethnographic, and their historical experience
relating to colonization since these enhance trade more than considering importers who are
otherwise.
• There is also need for EAC to develop a range of strategic policies to streamline the food and
agricultural sector and their sub-sectors. The clouding and fusion of the food with other
sectors should be separate, and let the food sector and possibly, other sector interventions not
be synergized in a manner that will hinder their faster growth. This affects resource
allocation, since the EAC may not know where to allocate it scare resources. It is
recommended that the EAC could align peg it sectors after the WTO, such as the agreement
on agriculture.
76

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80

Appendix A: List of Tables
List of Appendices
Table 4.2 Composition of Food Item using the SITC Revision 3 (1 to 3 digits)
Panel A
Cod
es Food Item Product Group (0+1+22+4)
0 Food & Live animals
1 Beverages and tobacco
22 Oil seeds/oil fruits
4 Animal/veg oil/fat/wax
Panel B
Cod Agricultural raw materials (AgriRaw) (2 -
es (22 +27 +28))
2 Crude mater.ec food/fuel
02 UN Special Code
21 Hide/skin/fur, raw
23 Crude/synthet/rec rubber
24 Cork and wood
25 Pulp and waste paper
26 Textile fibres
29 Crude anim/veg mater nes
Panel C
Cod
es Ores and metals (OresMtls) (27+ 28+ 68)
27 Crude fertilizer/mineral
28 Metal ores/metal scrap
68 Non-ferrous metals
Panel D
Codes Fuels (3)
Panel E
Cod
es
3 Mineral fuels/lubricants
32 Coal/coke/briquettes
33 Petroleum and products
34 Gas natural/manufactured
35 Electric Current
Manufactured Goods (manuf) ((5+ 6+ 7+
8) - 68)
5 ChemicalsProductsnes
6 Manufactured goods
60 UN Special Code
61 Leather manufactures
62 Rubber manufactures nes
63 Cork/wood manufactures
64 Paper/paperboard/article
65 Textile yarn/fabric/art.
66 Non-metal mineral manuf.
67 Iron and steel
69 Metal manufactures nes
7 Machinery/traspequipmt
8 Miscellaneous manuf arts
81

Panel A
Codes Food Item Product Group (0+1+22+4)
0 Food & Live animals
1 Beverages and tobacco
22 Oil seeds/oil fruits
Panel B
4 Animal/veg oil/fat/wax
Codes Agricultural raw materials (AgriRaw) (2 - (22 +27 +28))
2 Crude mater.ec food/fuel
02 UN Special Code
21 Hide/skin/fur, raw
23 Crude/synthet/rec rubber
24 Cork and wood
25 Pulp and waste paper
26 Textile fibres
29 Crude anim/veg mater nes
Panel C
Codes Ores and metals (OresMtls) (27+ 28+ 68)
27 Crude fertilzer/mineral
28 Metal ores/metal scrap
68 Non-ferrous metals
Table 4.3 List of EAC Member States
No. Country Name Year of Joining EAC
1 Burundi 2007
2 Kenya 2000
3 Rwanda 2007
4 Tanzania 2000
5 Uganda 2000
82

Table 4.4 List of Importers of EAC Sector Products
Panel A Panel B Panel C
No. Country No. Country No. Country
1 Afghanistan 57 France 113 Niger
2 Albania 58 Gabon 114 Nigeria
3 Algeria 59 Gambia, The 115 Norway
4 Antigua and Barbuda 60 Georgia 116 Oman
5 Argentina 61 Germany 117 Pakistan
6 Armenia 62 Ghana 118 Panama
7 Australia 63 Greece 119 Papua New Guinea
8 Austria 64 Grenada 120 Paraguay
9 Azerbaijan 65 Guatemala 121 Peru
10 Bahamas, The 66 Guinea 122 Philippines
11 Bahrain 67 Guyana 123 Poland
12 Bangladesh 68 Honduras 124 Portugal
13 Barbados 69 Hong Kong, China 125 Qatar
14 Belarus 70 Hungary 126 Romania
15 Belgium 71 Iceland 127 Russian Federation
16 Belize 72 India 128 Rwanda
17 Benin 73 Indonesia 129 Sao Tome and Principe
18 Bermuda 74 Iran, Islamic Rep. 130 Saudi Arabia
19 Bhutan 75 Iraq 131 Senegal
20 Bolivia 76 Ireland 132 Seychelles
21 Bosnia and Herzegovina 77 Israel 133 Sierra Leone
22 Botswana 78 Italy 134 Singapore
23 Brazil 79 Jamaica 135 Slovak Republic
24 Brunei 80 Japan 136 Slovenia
25 Bulgaria 81 Jordan 137 Solomon Islands
26 Burkina Faso 82 Kazakhstan 138 South Africa
27 Burundi 83 Kenya 139 Spain
28 Cambodia 84 Korea, Rep. 140 Sri Lanka
29 Cameroon 85 Kuwait 141 St. Kitts and Nevis
30 Canada 86 Kyrgyz Republic 142 St. Lucia
31 Cape Verde 87 Latvia 143 St. Vincent and the Grenadines
32 Central African Republic 88 Lebanon 144 Sudan
33 Chad 89 Lesotho 145 Suriname
34 Chile 90 Libya 146 Swaziland
35 China 91 Lithuania 147 Sweden
36 Colombia 92 Luxembourg 148 Switzerland
37 Comoros 93 Macao 149 Syrian Arab Republic
38 Congo, Rep. 94 Macedonia, FYR 150 Taiwan, China
39 Costa Rica 95 Madagascar 151 Tanzania
40 Cote d'Ivoire 96 Malawi 152 Thailand
83

41 Croatia 97 Malaysia 153 Togo
42 Cuba 98 Maldives 154 Trinidad and Tobago
43 Cyprus 99 Mali 155 Tunisia
44 Czech Republic 100 Malta 156 Turkey
45 Denmark 101 Mauritania 157 Uganda
46 Djibouti 102 Mauritius 158 Ukraine
47 Dominica 103 Mexico 159 United Arab Emirates
48 Dominican Republic 104 Moldova 160 United Kingdom
49 Ecuador 105 Mongolia 161 United States
50 Egypt, Arab Rep. 106 Morocco 162 Uruguay
51 El Salvador 107 Mozambique 163 Vanuatu
52 Eritrea 108 Namibia 164 Venezuela
53 Estonia 109 Nepal 165 Vietnam
54 Ethiopia(excludes Eritrea) 110 Netherlands 166 Yemen
55 Fiji 111 New Zealand 167 Zambia
56 Finland 112 Nicaragua 168 Zimbabwe
84

Appendix B: Correlation Matrix
Figure 5.1 Showing the Correlation matrix of the Dependent Variables and its Covariates
LExportsin1000usd
Ldist
Limportertcgdp
Limportercgdp
Lexportercgdp
Lexporterrgdpl
Limporterpop
Lexporterpop
lexporterxrat
85

Appendix C: Estimation Results from Gravity
Appendix C: Table 5.5 Results of the Estimates of the Gravity Equation on Panel Data (1988 – 2009)
Results (EAC) (Burundi) (Kenya) (Rwanda) (Tanzania) (Uganda)
VARIABLES Exports Exports Exports Exports Exports LNExports
Ldist -1.11*** -0.21 -1.43*** 1.28*** -1.01*** -1.06***
(0.087) (0.239) (0.136) (0.223) (0.090) (0.178)
Limportertcgdp 0.75*** 0.47*** 0.78*** 0.33*** 0.82*** 0.92***
(0.025) (0.055) (0.037) (0.048) (0.038) (0.063)
Limportercgdp 0.25*** -0.38** 0.46*** -0.59*** -0.06 -0.05
(0.058) (0.169) (0.089) (0.094) (0.060) (0.113)
Lexportertcgdp 0.52 12.24* 4.47 -7.25 -5.82 -0.81
(2.279) (7.029) (5.586) (31.096) (44.265) (9.139)
Lexportercgdp -0.01 -41.10** -10.59 14.97 7.74 2.78
(2.106) (20.860) (11.528) (43.395) (56.491) (13.622)
contig 0.97*** 0.12 1.02*** 2.14** 0.23 0.20
(0.179) (0.740) (0.264) (1.040) (0.293) (0.742)
comlang_off 0.36*** 0.27 0.52** -1.37*** 0.22 0.07
(0.135) (0.467) (0.216) (0.410) (0.145) (0.406)
comlang_ethno -0.73*** -0.18 -0.77*** 0.40 -0.84*** -0.32
(0.154) (0.511) (0.247) (0.356) (0.133) (0.366)
colony 0.01 2.02*** -0.25 0.17 0.07 1.12*
(0.116) (0.205) (0.175) (0.526) (0.201) (0.611)
comcol 0.05 -1.32* -0.06 1.38*** 0.22 0.31
(0.109) (0.735) (0.155) (0.404) (0.138) (0.400)
col45 1.46*** -0.10 1.14*** 1.86*** 1.82*** 0.68
(0.187) (0.329) (0.337) (0.702) (0.329) (1.462)
smctry 0.83*** 0.12 1.12*** -2.29** 0.17 0.67
(0.249) (0.813) (0.310) (1.055) (0.310) (0.714)
eac_exports -0.21* 0.99 -1.25 -2.76 -0.45 0.53*
(0.122) (1.148) (1.981) (2.006) (1.394) (0.301)
86

eac_imports 0.16 -0.50 -0.56 0.57 0.28 1.40***
(0.293) (0.661) (0.387) (0.628) (0.353) (0.364)
eac_intra 0.52* 1.13 1.53*** 1.47 -0.77** -1.63***
(0.278) (0.892) (0.397) (0.916) (0.385) (0.499)
Agricultural Raw Materials -0.33*** -1.59*** -0.24 0.24 -0.24** -1.60***
(0.114) (0.199) (0.184) (0.192) (0.109) (0.375)
Fuels -0.60*** -1.54* -0.59*** 2.61*** -0.77** -4.23***
(0.178) (0.823) (0.177) (0.485) (0.312) (0.659)
Ores and Metals -1.78*** -2.16*** -2.50*** 0.45** -1.05*** -2.62***
(0.123) (0.334) (0.131) (0.203) (0.191) (0.386)
Manufactures -0.40*** -1.54*** -0.13 -1.87*** -0.49*** -1.76***
(0.089) (0.225) (0.125) (0.243) (0.099) (0.288)
Constant 6.73 158.39** 42.45 -30.15* 22.83 0.00
(7.171) (73.268) (47.471) (17.922) (77.850) (0.000)
Observations 12,117 1,141 3,900 1,348 3,193 2,535
Adj. R-squared . . . . . .
Number of id 439
Robust standard errors in parentheses; *** p

Appendix C: Table 5.6 EAC Bloc Results of the Estimates of the Gravity Equation on Panel Data (1988 – 2009)
GLS Random Ml Random Effects Between Effects Population Fixed Effects
Effects
Model Average Model
VARIABLES LExportsin1000usd LExportsin1000usd LExportsin1000usd LExportsin1000usd LExportsin1000usd
Ldist -1.12*** -1.12*** -0.89*** -1.12*** 0.5
-0.077 -0.077 -0.101 -0.077 -0.433
Limportertcgdp 0.81*** 0.81*** 0.76*** 0.81*** 0.88**
-0.027 -0.028 -0.028 -0.028 -0.387
Limportercgdp 0.01 0.01 -0.02 0.01 0.26
-0.043 -0.043 -0.045 -0.043 -0.42
Lexportertcgdp 0.74** 0.74** -2.44 0.74** 0.33
-0.368 -0.367 -1.956 -0.366 -0.428
Lexportercgdp -0.61 -0.61 0.37 -0.61 -0.22
-0.516 -0.514 -2.659 -0.513 -0.55
importerxrat 0 0 0 0 0
0 0 0 0 0
exporterxrat 0 0 0 0 0
0 0 -0.001 0 0
contig 0.08 0.08 0.46 0.08 0.17
-0.245 -0.247 -0.307 -0.246 -0.448
comlang_off 0.45*** 0.44*** 0.45** 0.44*** -0.14
-0.145 -0.146 -0.182 -0.145 -0.252
comlang_ethno -0.14 -0.14 -0.11 -0.14 0.1
-0.135 -0.135 -0.177 -0.135 -0.215
colony 0.81** 0.80** 1.44*** 0.81** -0.87
-0.396 -0.4 -0.472 -0.398 -0.831
comcol 0.13 0.13 0.26 0.13 0.03
-0.127 -0.128 -0.165 -0.128 -0.207
col45 0.3 0.3 0.38 0.3 0.98
88

-0.547 -0.551 -0.664 -0.549 -1.07
smctry 0.91*** 0.91*** 0.81* 0.91*** -0.57
-0.331 -0.333 -0.463 -0.332 -0.681
eac_exports -0.18*** -0.18*** -0.03 -0.18*** -0.14***
-0.033 -0.033 -0.169 -0.033 -0.034
eac_imports -0.34** -0.34** -0.55 -0.34** -0.27*
-0.14 -0.139 -0.68 -0.139 -0.144
eac_intra 0.42** 0.41** 2.69*** 0.41** 0.22
-0.207 -0.206 -0.737 -0.206 -0.218
Reference Category - Food Sector
Agricultural Raw
Materials
-0.97*** -0.97*** -0.94*** -0.97***
-0.132 -0.134 -0.133 -0.133
Fuels -2.36*** -2.36*** -2.35*** -2.36***
-0.214 -0.216 -0.206 -0.215
Ores and Metals -1.96*** -1.96*** -1.87*** -1.96***
-0.158 -0.16 -0.158 -0.159
Manufactures -0.94*** -0.94*** -0.89*** -0.94***
-0.118 -0.119 -0.12 -0.119
618b.exporter_ifs 0 0 0 0 0
0 0 0 0 0
Reference Category Burundi
Kenya 1.79*** 1.78*** 8.51*** 1.78***
-0.495 -0.494 -2.798 -0.493
Rwanda 0.16 0.15 2.36*** 0.15
-0.239 -0.24 -0.833 -0.239
Tanzania 1.06** 1.05** 7.74*** 1.05**
-0.484 -0.484 -2.445 -0.483
Uganda 0.43 0.42 6.53*** 0.42
-0.379 -0.38 -1.658 -0.379
89

Constant 1.43* 1.41* 20.01*** 1.41* -13.38***
-0.825 -0.828 -2.714 -0.825 -3.762
Observations 12,117 12,117 12,117 12,117 12,117
R-squared 0.43 0.06
Number of id 2,079 2,079 2,079 2,079 2,079
Adj. R-squared . . 0.42 . -0.13
Standard Errors in Parentheses: *** p

Appendix C: Table 5.7 Comparison of EAC Country Results of the Gravity Equation on Panel Data (1988 – 2009)
Uganda‟s GLS Random Kenya‟s GLS Random Effects Burundi‟s GLS Rwanda‟s GLS Tanzania‟s ML
Effects
Random Effects Random Effects Random Effects
VARIABLES LExportsin1000 LExportsin1000usd LExportsin1000usd LExportsin1000us LExportsin1000us
usd
d
d
Ldist -0.92*** -1.93*** -0.34* -0.13 -1.70***
(0.149) (0.155) (0.179) (0.205) (0.173)
Limportertcgdp 0.79*** 0.93*** 0.49*** 0.63*** 0.94***
(0.056) (0.048) (0.069) (0.074) (0.057)
Limportercgdp -0.02 0.10 -0.06 -0.19 -0.00
(0.090) (0.076) (0.108) (0.118) (0.087)
Lexportertcgdp -1.88 5.61** 0.51 9.76 -5.43
(2.752) (2.386) (2.987) (6.861) (5.199)
Lexportercgdp 2.96 -9.05** 0.60 -11.71 5.92
(3.953) (4.423) (6.907) (8.418) (5.625)
importerxrat 0.00 0.00** -0.00 0.00 -0.00
(0.000) (0.000) (0.000) (0.000) (0.000)
exporterxrat 0.00 -0.02 -0.00 -0.00* 0.00
(0.000) (0.014) (0.001) (0.003) (0.001)
contig 0.21 -0.15 0.21 -0.44 -0.44
(0.506) (0.406) (0.607) (0.793) (0.526)
comlang_off 0.17 0.51** 0.40 -0.32 0.87***
(0.292) (0.242) (0.405) (0.444) (0.299)
comlang_ethno -0.27 0.10 -0.50 0.19 -0.48*
(0.273) (0.234) (0.379) (0.410) (0.272)
colony 1.12 0.98 1.64** 0.53 0.02
(0.960) (0.787) (0.833) (0.786) (0.853)
comcol 0.21 0.24 -0.35 0.59 -0.02
(0.286) (0.196) (0.388) (0.434) (0.250)
col45 0.53 -1.63* 0.93 2.04* 1.73
(1.357) (0.968) (1.253) (1.219) (1.170)
smctry 0.52 1.07 0.28 2.10** 1.22
(0.609) (0.728) (0.821) (0.924) (0.798)
eac_exports -0.19* -0.14 -0.02 -0.28 -0.22*
(0.113) (0.119) (0.200) (0.238) (0.132)
eac_imports 1.32*** 0.01 -0.92*** -1.00*** -0.83***
(0.321) (0.269) (0.356) (0.359) (0.313)
eac_intra -1.52*** 0.31 2.75*** 1.63** -0.12
91

(0.451) (0.377) (0.636) (0.645) (0.436)
Agricultural Raw
Materials
-1.47*** -0.91*** -0.65** 0.03 -1.17***
(0.275) (0.235) (0.329) (0.355) (0.268)
Fuels -3.72*** -2.37*** -1.69** -0.12 -3.03***
(0.519) (0.363) (0.682) (0.616) (0.384)
Ores & Metals -2.36*** -2.61*** -1.19*** 0.26 -2.43***
(0.318) (0.280) (0.433) (0.417) (0.324)
Manufactures -1.66*** -0.44* -1.36*** -0.64** -0.81***
(0.241) (0.226) (0.258) (0.293) (0.248)
Constant 2.54 17.32** -5.29 -7.46 24.06
(2.160) (7.582) (19.315) (4.915) (15.282)
Observations 2,535 3,900 1,141 1,348 3,193
R-squared
Number of id 439 561 271 313 495
Adj. R-squared . . . . .
Standard Errors in Parentheses: *** p