UNIVERSITY OF KWAZULU-NATAL
SOUTH AFRICA’S TRADE FLOWS: A GRAVITY MODEL ANALYSIS
By
Emmanuel Ndakaza
210555658
A dissertation submitted in fulfillment of the requirements for the degree of
Master of Commerce (Economics)
School of Accounting, Economics and Finance
College of Law and Management Studies
University of KwaZulu-Natal
Pietermaritzburg
Supervisor: Ms Vanessa Tang
November
2012
2
Declaration
I Emmanuel Ndakaza, hereby declare that
(i). The research reported in this dissertation except where otherwise indicated is
my original research.
(ii).This dissertation has not been submitted for any degree or examination at any
other university.
(iii). This dissertation does not contain other persons’ data, pictures, graphs or
other information, unless specifically acknowledged as being sourced from
other persons.
(iv). This dissertation does not contain other persons’ writing, unless specifically
acknowledged as being sourced from other researchers. Where other written
sources have been quoted, then:
a). Their words have been re-written but the general information attributed
to them has been referenced:
b). Their exact words have been used; their writing has been placed inside
quotation marks, and referenced.
(v).This dissertation does not contain text, graphics or tables copied and pasted
from the internet, unless specifically acknowledged, and the source being
detailed in the dissertation and in the reference sections.
Signature……
Date:
I
ACKNOWLEDGEMENTS
First I express my gratitude to Rev. Dr. Dennis Bailey and Mrs. Gillian Bailey for
your patient selfless support and motivation throughout my masters program. Den,
your editing has made my work more satisfying.
No words can explain my appreciation of Miss Vanessa Tang for your continued
guidance and dedication to my progress. Your interest in my work, expanse
knowledge of international economics and the enthusiasm with which you helped me
has made such a big difference to this work.
To Mich and Jono, thanks for your friendship and for those morning and afternoon
rides to and from Varsity! To Dan, Morgs, Mark, Maryke, Trish, and all “Hilton-
pharmacians” for your friendship and love!
To the almighty! I am lost for words!
II
DEDICATIONS
To my late father! I am sure you would be proud. May your soul rest in peace!
III
Abstract
Isolated from the rest of the world for most of the 20 th century, a post-1994
democratic South Africa (SA) has embarked on an outward oriented trade policy that
has culminated into the signing of four major free trade arrangements, i.e, Southern
Africa Development Community (SADC) in 1994; Trade and Development
Cooperation Agreement (TDCA) with the European Union in 1999; African Growth
and Opportunity Act (AGOA) with the USA in 2000 and the Brazil, Russia, India and
China (BRIC) group in 2010. It is well documented that the resulting impact of Free
Trade Agreement (FTA) can be positive or negative depending on the free trade area
being studied.
This study therefore surveys the literature on the dynamics of free trade and uses a
modified gravity model framework to empirically test the trade effects on SA of EU,
USA, SADC and BRIC. Using bilateral trade data between South Africa and 100
selected countries over a 15 year period the study shows that both the EU and SADC
have had expansionary effects on trade for South African exporters as well as foreign
exporters in the EU and SADC. The EU-SA trade agreement has increased South
Africa’s imports more than it has increased its exports. However, the reverse is true
for SADC; South Africa’s exports to SADC have increased more significantly than its
imports from this FTA. The study finds no evidence for either trade creation or trade
diversion effects for AGOA and BRIC.
IV
The study suggests that in the case of BRIC, the adjustment period is just too short for
significant changes in trade patterns to emerge. The desired FTA impact should be
given time before any meaningful conclusions can be made. On the other hand in
spite of clear evidence, AGOA can produce better results for both USA and South
Africa if full liberalization is considered.
V
Table of Contents
Declaration......................................................................................................................I
ACKNOWLEDGEMENTS..........................................................................................II
DEDICATIONS...........................................................................................................III
Abstract........................................................................................................................IV
Table of Contents.........................................................................................................VI
List of Figures...............................................................................................................X
CHAPTER I...................................................................................................................1
1.1. Introduction.........................................................................................................1
1.2. Background and Context.....................................................................................3
1.3. Problem Statement..............................................................................................5
1.4. Research and Study Objectives...........................................................................6
1.5. Contribution and Relevance of the Study...........................................................6
1.6. Structure of the Study..........................................................................................7
CHAPTER II..................................................................................................................8
EVOLUTION OF THE THEORY OF INTERNATIONAL TRADE...........................8
2.1. Introduction.............................................................................................................8
2.1.1. The classical theory of international trade...........................................................9
2.1.2. The Heckscher-Ohlinn (H-O) model.................................................................14
2.1.3. The modern theory of international trade...........................................................16
2.2. Summary...............................................................................................................24
CHAPTER III...............................................................................................................27
THE GAINS FROM TRADE......................................................................................27
3.1. The regionalism debate.........................................................................................27
3.2. Trade creation versus trade diversion...................................................................39
VI
3.2.1. Conclusion..........................................................................................................46
CHAPTER IV..............................................................................................................48
4. THE DETERMINANTS OF TRADE AND TRADE PATTERNS BETWEEN SOUTH AFRICA AND PARTNERS..........................................................................48
4.1. Introduction...........................................................................................................48
4.1.1. Trade openness...................................................................................................50
4.1.2. Intra Industry Trade............................................................................................52
4.1.3. Differences in product characteristics................................................................57
4.2. South Africa’s trade patterns with EU, SADC, BRIC and AGOA.......................61
4.3. Conclusion.............................................................................................................71
CHAPTER V................................................................................................................72
THE GRAVITY MODEL............................................................................................72
5.1. Introduction...........................................................................................................72
5.1.1. Limitations of the gravity model........................................................................75
5.2. The theoretical gravity equation............................................................................75
5.3. Methodology and Data..........................................................................................77
5.3.1. Selection of multilateral resistance variables and hypothesis............................81
5.3.2. Estimation technique..........................................................................................83
5.3.2.1. The Fixed effects Model.................................................................................84
5.3.3. Data and Variable description............................................................................89
CHAPTER VI..............................................................................................................91
ESTIMATION RESULTS...........................................................................................91
6.1. INTRODUCTION.................................................................................................91
6.2. ESTIMATION RESULTS....................................................................................92
6.2.2. Panel Estimations...............................................................................................96
VII
CHAPTER VII...........................................................................................................104
CONCLUSION AND RECOMMENDATIONS.......................................................104
7.1. Conclusion and Recommendations.....................................................................104
Bibliography...............................................................................................................108
VIII
List of Tables
Table 2. 1: Evidence of Intra-industry trade in computer parts between Germany and
US, UK, Japan, France, 2009-2010..............................................................................20
Table 4. 1: Intra Industry Trade between South Africa and EU (2009-2011)..............56
Table 4. 2: Trade in primary products between South Africa and Malawi, Zambia,
Zimbabwe, 2009-2010..................................................................................................62
Table 4. 3: Trade in primary products between South Africa and Malawi, Zambia,
Zimbabwe, Mozambique, 2009-2010..........................................................................63
Table 4. 4: Direction of Trade in both primary and secondary products between South
Africa and Germany, UK, US, 2009-2010...................................................................65
Table 4. 5: Trade in top seven exports between South Africa and USA 2010.............67
Table 6. 1: Cross section estimation results.................................................................92
Table 6. 2: Stage 1 Estimation results: Exports model.................................................97
Table 6. 3: Stage 1 estimation results: Imports model.................................................97
Table 6. 4: Stage 2: Fixed effects estimation results: Bilateral trade flows model....100
Table 6. 5: Stage 2 estimation results: Exports model...............................................101
Table 6. 6: Stage 2 estimation results: Imports Model...............................................102
IX
List of Figures
Figure 4.1: South Africa’s GDP, 1997-2010..............................................................49
Figure 4.2: Trade Openness index for South Africa, EU, BRIC, SADC, AGOA, 2001-
2010............................................................................................................................51
Figure 4. 3: Composition of exports from SADC, EU, BRIC and AGOA, 2009-2010.
....................................................................................................................................58
Figure 4. 4: potential markets for South African exports, 2009-2010.........................59
Figure 4. 5: Import sources and export destinations for South Africa in SADC, EU,
BRIC, and AGOA......................................................................................................69
1
CHAPTER I
1.1. Introduction
In the last two decades, the world has witnessed an expansion in international trade
and investment flows which can be attributed to a concerted international effort to
liberalize trade. By removing barriers to trade, countries can accelerate their
integration in the world economy and increase their participation in international trade
(Robles, 2008). For Africa, this shift towards increased liberalization can potentially
improve the economic growth and development prospects of African countries; thus
there are efficiency gains from free trade (Krugman, 2012).
In the case of South Africa, since the advent of democracy in 1994, the country has
become a major player in the global trading system and as a contracting party to the
General Agreement on Tariffs and Trade (GATT) and a member of the World Trade
Organization (WTO), a series of trade reforms have been implemented. South Africa
is also party to a number of bilateral and regional Free Trade Agreements (FTAs).
For instance, with the Southern African Development Community (SADC) in 1994;
Trade and Development Cooperation Agreement (TDCA) with the European Union in
1999; African Growth and Opportunity Act (AGOA) with the United States of
America (USA) in 2000 and the Brazil, Russia, India and China (BRIC) emerging
group in 2010. It is important to highlight that these free trade arrangements (besides
2
BRIC) “all came into force in 2000/2001 after a period of phased in aggressive trade
liberalization under the Uruguay Round” (Holden and McMillan, 2006: 112).
Whether the concluded FTAs benefit South Africa is an ongoing debate. FTAs ought
to benefit South Africa as long as there is the possibility of realizing the “gains from
trade”. Although the literature does not establish conclusive causality (Kono, 2002),
estimates based on the World Bank Development Indicators database (World Bank,
2012), international trade and investment flows have increased. Between 1999 and
2008 (after the signing of the TDCA, AGOA and SADC) South Africa’s exports grew
by 45.8 percent from 34 billion US$ to over 50 billion US$ dollars. In the same
period, imports have more than doubled from 31 billion US$ to over 63 billion US$
measured in 2000 constant US$ (World Bank, 2012). Also, over the same period, net
foreign direct investment inflows for South Africa have risen by more than 300 per
cent from 1.5 billion US$ to over 5.3 billion US$ (World Bank, 2012)
These trends, show that the volume of South Africa’s trade and investment have
increased substantially. However, it is interesting to note that South Africa’s demand
for imports has surpassed foreign demand for its exports. Such trade imbalances can
have serious negative effects on the country’s growth and balance of payments if such
trends persist in the long term (Baharumshah et al, 2003). Aiming to contribute to
ongoing FTAs debate, this study seeks to empirically assess using a modified gravity
3
model, South Africa’s trade flows with its major trading partners (EU, USA, SADC
and BRIC).
1.2. Background and Context
South Africa as with many developing countries, faces many social and economic
challenges such as poverty, income disparities, lower competitiveness in the industrial
sector and high unemployment (Perry, 2000). FTAS have proliferated Worldwide in
recent years, and more than 300 were in place by 2009 (Lester and Mercurio, 2009)
There are obvious benefits to FTAs including larger markets and economies of scale;
however it is important to highlight that the selective application of FTAs can be
economically inefficient through the process of trade diversion.
South Africa is party to many FTAs. In 2010, South Africa joined emerging
economies Brazil, Russia, India, China, in BRIC. The agreement strengthens
economic and political cooperation (that existed among the countries even before
2010). South Africa had bilateral trade ties with India (since 1994) with the
establishment of an inter-governmental committee that aimed to facilitate trade
relations between the two countries.
On the other hand, since 1996 China and South Africa have reestablished diplomatic
ties and have made commitments to improve their bilateral trade. According to the
4
United Nations Commodity Trade Statistics database (UNCOMTRADE, 2012), the
combined exports from South Africa to China and India have grown from 1.3 billion
US$ (current) since 2000 to 14.3 billion US$ (current) in 2010. In the same year
(2010) the combined GDP of China and India was over 4.2 trillion in constant 2000
US$ which is slightly less than a half of the EU’s total GDP. Also, the combined
BRIC market size should present South Africa with diverse opportunities to boost
trade and investments.
South Africa has also signed in 1999, the Trade, Development and Co-operation
Agreement (TDCA) with the European Union (EU). The initial objective was to
remove trade barriers between them and moving towards a FTA by 2012. Their
agreement also includes provisions on trade in services, and investment and
development. The EU remains South Africa’s major trading partner.
Another major SA trade agreement, AGOA, signed in 2000 with the USA initially for
an agreement period of eight years has been reviewed and extended to 2015. AGOA
aims at providing Sub Saharan African (SSA) countries market access to the US
market as well as boosting investment into SSA.
Within Africa, South Africa is a member of several regional groups such as SACU,
COMESA, and SADC. For instance, SADC alone has a combined population of 257.7
5
million people and its GDP is over 471.1 US$. By 2008, over 85 percent of all trade
in the SADC was free (SADC today, 2010)
According to Mthembu, (2008), many SSA countries are dependent on tariffs revenue
generating between a quarter and a third of their national revenue. Thus, it is
important to highlight the potential loss or tariffs revenues forgone for South Africa. It
is true that Free trade may create winners and losers, however more relevant to this
research is the overall net gains from trade.
Given the inconclusive nature of the literature in analyzing the economics of free
trade (Thirwall, 1995, Rodrik,2001 and Nandasiri, 2008), this study aims to examine
whether FTAs (the SADC, EU, AGOA and BRIC) have benefited South Africa.
1.3. Problem Statement
International trade with the EU, SADC, USA and BRIC provides South Africa with
diverse trading opportunities, granting free access to large industrialized markets and
a consumer base that is composed of a combined population of about 2.7 billion
people. On the other hand the literature, both theoretical and empirical on the
economics of free trade remains inconclusive. While several studies find trade
creation effects under preferential trading (Baldwin, 1993; Glick and Rose 2002),
others find the trade diversion effects to be stronger (Bhagwati, 1991; Bagwell and
Staiger, 2002; Panagariya, 1996). Hence, the value of this study with these in mind.
6
1.4. Research and Study Objectives
Against the above background, this study aims to:
i. Survey both theoretical and empirical literature on the economics of free trade
and provide a deeper analysis of the impact of FTAs.
ii. Using a modified gravity model framework to empirically test and provide
evidence for the “gains” from FTAs with the EU, US, SADC and BRIC for South
Africa.
iii. Draw lessons learned from the above objectives.
1.5. Contribution and Relevance of the Study
It is estimated that almost every country in the world belongs to an FTA or RTA. It is
therefore in South Africa’s interests not to be isolated. On the other hand, the issue of
“free trade” is still widely contested and a common ground on its implication on both
developing and developed economies is as yet to be reached. As South Africa expands
its trading arrangements, a study aimed at assessing the impact of the country’s major
trading agreements given that sufficient time has passed will add to the regionalism
debate.
1.6. Structure of the Study
7
This study is set out as follows. Chapter 1 describes the background of the study, its
objectives and problem statement. Chapter 2 reviews the literature on different
theories of international trade. Chapter three discusses the potential gains from free
trade as well as the issues of trade creation and trade diversion. Chapter four analyzes
the determinants of trade and trade patterns between South Africa and its trade
partners. Chapter five reviews the methodology used in this study. Chapter Six
presents and discusses the estimation results. Chapter 7 contains the conclusion and
recommendations of the study.
8
CHAPTER II
EVOLUTION OF THE THEORY OF INTERNATIONAL TRADE
2.1. Introduction
The main goal of international trade is to increase trade and welfare among countries
by encouraging exports, research and development, free movement of capital among
others. International trade also allows countries to reap from external economies of
scale particularly through reverse engineering due to the impossibilities by firms to
fully protect the knowledge they create. It is no coincidence therefore that most of the
countries that have developed at a faster rate in recent decades have done so following
a period of more liberalized economic policies. Such countries include China,
Singapore, and India among others.
Until the late 1970s, international trade was largely dominated by comparative
advantage (which underlines differences in tastes, technology and factor endowments
as the basis of international trade). This followed the failure of mercantilism and the
gold standard systems as the basis of international trade and wealth of Nations
respectively.
On the other hand, with globalization and changes in the way international business is
conducted, many developments motivated economists to search for better
explanations of international trade beyond those given by the traditional trade models.
9
As a result many new models of imperfect competition that allow for the role of
increasing returns to scale, and the importance of government intervention have been
developed.
Economists such as Linder (1961), Leontief (1953), Krugman and Venables (1997)
amongst others, have all contributed to the development of the new trade theory. Also
worth mentioning are papers by Dixit and Norman, (1980) and Kelvin (1980) that
established the notion that increasing returns through the effects of agglomeration are
as important as comparative advantage in explaining international trade.
The following sections will discuss the main ideas behind the classical and modern
theories of international trade.
2.1.1. The classical theory of international trade
The classical theory explains the conditions and benefits of free international
exchange of goods and services. The theory is quite simple, countries engage in
international trade because they are endowed with different natural resources. Free
trade is intended to take advantage of these differences. Hence international trade
arises due to differences in countries’ tastes, technology and factor endowments
(Krugman, 1987).
A common theme of the Literature in favor of free trade is that it facilitates economic
expansion (Samad 2011; Dhawan and Biswal 1999; Hachicha 2003). The view that
10
trade acts as an engine of economic growth dates back to Adam Smith’s concept of
absolute advantage.
In his book, the wealth of Nations (1776), Smith compares the running of Nations as
similar to running family affairs… “it is the maxim of every prudent master of family
never to attempt to make at home when it will cost him more to make than to buy… If
a foreign country can supply us with a commodity cheaper than we ourselves can
make it, better buy it of them with some part of the product of our own industry,
employed in a way in which we have some advantage (cited by Linder, 1961).
According to Smith, trade among countries increases specialization and productivity
of labor because each country directs its resources towards the production of goods
and services in which it incurs the lowest relative costs of production.
For example if the European Union can supply South Africa with a commodity at a
price cheaper than the local price, then it is more profitable for South Africa to trade
some of its local products that it produces efficiently in exchange for EU’s cheaper
goods.
Smith’s theory relies on the assumption that the price of a commodity is solely
determined by the cost of production. Since labor is the only factor of production,
price is determined by the amount of labor required to produce a unit of a given
product. Given that labor is considered to be mobile within a country but immobile
across nations, the differences in relative prices across nations result from the
11
differences in labor productivity. Therefore as long as there are differences in relative
prices, Smith argues that a country would make a profit by exporting the locally
cheaper good but which commands a relatively higher price abroad in exchange for
the commodity that is produced cheaply from abroad.
Following this line of argument, it can be shown that with liberalization, the price of
goods in which each country has an absolute advantage will command a price that is
higher than that reached under autarky but lower than the price abroad because the
importing countries will only import if the price for imports is lower than the local
price. While on the other hand, the price of the commodity in which a country is less
efficient will fall. This way, free trade is not a zero sum game as all participants
benefit. The exporters benefit because the price for their export is higher than the
previous local prices under autarky although still lower than the prices abroad.
Consumers also benefit in a way that the prices for imports are lower than the
previous local prices of the same commodity under autarky.
One of the major misgivings of Smith’s principle is that if a country or region (e.g. the
EU) incurs less relative costs of production in all products than another (e.g. South
Africa), then South Africa should not trade with the EU. Under such circumstances if
South Africa was to trade freely with the EU, it would end up only importing and
local production would suffer. This paper argues that this view of international trade
is somewhat erroneous. Even though it is logically correct that a country may face
12
higher production costs than the rest of the world, nonetheless factors such as
differences in factor endowments, technology, diversification and product
differentiation make it very difficult for a country to be less productive in all
commodities. As long as such differences ignored by Smith are present countries can
profitably trade with each other, even if some may not have any ‘absolute advantage’.
David Ricardo complimented Smith’s theory of absolute advantage by introducing the
theory of ‘comparative advantage’. In this theory Ricardo explicitly asserts that
countries can increase their welfare by producing specifically those goods in whose
production they incur the lowest opportunity costs of production. For example even if
the EU incurs relatively lower costs of production in all commodities, South Africa
would nonetheless benefit from trade if opportunity costs are considered. The EU
specializes in the production of a commodity in which it is more efficient while South
Africa specializes in the other product in which the EU is less efficient (but still more
efficient than South Africa). That is, comparative advantage ensures that each country
has a commodity in which it incurs the lowest opportunity cost over the other
(Suranovic, 2010).
The Ricardian Model indicates that when two countries are autarkists, each country
produces some of each product, production technologies are different, and the price of
the product in which each country has a comparative advantage is lower than that of
the same good in the other country (Feenstra and Taylor, 2008). Wages are also
13
relatively higher in industries that are more competitive (where productivity is
highest). When countries finally open to free trade, the difference in technology
stimulates trade. For example the price for a commodity in whose production the EU
has a comparative advantage is relatively higher in South Africa. Since transport costs
are zero, South Africa will import this commodity until the relative prices are equal.
This way, free trade increases welfare by replacing expensive domestic products with
cheaper imports.
According Thirwall, (1995) comparative advantage can also be expressed as benefits
of trade creation that result from the establishment of freer trade by means of FTAs
and Custom Unions. If a trade arrangement is characterized by progressive
investment, exchange of technological know-how and some degree of specialization,
the benefits of trade increase the production capabilities of partner economies. The
acquisition of higher scale production capacities enables the growth of National
incomes and general purchasing power (or economic expansion). Intuitively,
economic expansion means that given prices that existed before freeing of trade, a
country can demand and produce more goods and services today than before.
Based on the Ricardian model therefore, as long as there are differences in prices and
production technologies between the EU, SADC, BRIC or AGOA and South Africa,
both parties should benefit from opening up boarders to each other’s goods.
14
The weakness of the Ricardian theory is that countries cannot trade if they have
similar endowments and it neglects the influence of transport costs on trade flows
between countries.
Another criticism against the Ricardian model is that it neglects other reasons for
trade besides technology differences. Countries may trade with each other because of
proximity (which reduces transport costs) and availability of resources and or
markets. For example, South Africa may be better at the production of beef than India
but Indians may not like beef (differences in tastes), technological differences would
not result into trade in such a scenario. Therefore, based on the Ricardian model alone
to determine the viability of free trade relationship between South Africa and EU,
SADC and US would lead to misleading conclusions because the model ignores the
potential determinants of trade beside differences in technology and prices.
2.1.2. The Heckscher-Ohlinn (H-O) model
The H-O model was introduced in 1919 by two notable economists Eli Heckscher and
Bertil Ohlin. The model is an extension to Ricardo’s comparative advantage theory in
a way that it explains why one country may have a comparative advantage over
another. Ricardo’s model assumes differences in productivity but does not explain
why there may be differences in productivity. The H-O model simplifies that question
15
by stating that even if countries have identical technologies, they may have different
resource endowments (Feenstra and Taylor, 2008).
The model predicts that countries export goods for whose production they have
abundant resources and import goods for which they have scarce resources. Under
autarky, the price of the commodity whose factors of production are available in
abundance will command a lower price locally vis-à-vis the rest of the world and vice
versa.
Supposing that South Africa is endowed with land and labor and scarce capital, it can
be shown that it will tend to produce more land and labor intensive goods. Since land
and labor are abundant, the price for land and the wages paid to labor are lower vis-à-
vis the rest of the world. However since capital is scarce, the price of capital is high
and the price of the commodity produced therewith is higher in South Africa than in
other countries. The difference in resource endowments is sufficient enough to cause
different PPF curves such that the price ratios would differ across countries
(Dominick 2004). South Africa would be better off importing such a commodity from
abroad.
Extending this argument further, after opening up to trade the abundant under
employed resources in South Africa flow to the rest of the world thus forcing their
prices to increase locally and fall abroad. On the other hand, the unemployed capital
abroad will be imported thus pushing the prices of capital down in the local market.
16
Generally, countries benefit in a way that trade brings about a more equitable
distribution by lowering wages in the resource scarce industry and increasing wages
in the resource abundant industry.
2.1.3. The modern theory of international trade
The major difference between the classical theory and the modern theory of
international trade is that the latter acknowledges the idea that government
intervention can and is capable of improving market outcomes and, to some degree,
the theory assumes that international trade is driven by increasing returns to scale that
exist under imperfect competition.
The modern theory of international trade corrects weaknesses in the Ricardian Model
by giving factor and product prices as equal importance as labor productivity in the
determination of international trade. It also relaxes other limiting assumptions, for
example: labor is considered to be heterogeneous, transport costs to be present and
production costs are variable. This theory is more fitting because the assumptions
usually hold in the real world. In fact it is very difficult to imagine a situation where
labor is homogeneous as assumed by the Ricardian model.
Despite the flaws in the traditional trade models, developing empirical models of how
different entities behave under imperfect competition was and still is very
complicated because some firms may at times behave in ways that are obscure even to
17
themselves (Krugman, 1987). Nevertheless, the contributions of Krugman and
Venables (1997) in showing that international trade is characterized by increasing as
opposed to constant returns to scale coupled with the work of Wassily Leontief (1953)
in proving that international trade is mainly characterized by intra-industry trade
contributed much in motivating modern economists to moving away from traditional
models.
The presence of increasing returns to scale implies that countries can engage in trade
irrespective of where comparative advantage lies because increasing returns to scale
in international trade systematically enable firms to improve their competitiveness.
This is because international trade provides a sufficient market that allows production
to rise further and in turn lower production costs. As firms specialize in the
production of commodities, they experience a reduction in their average costs of
production as production rises. Therefore, even if countries are similar in many
aspects, the modern trade theory predicts that the presence of economies of scale
enables all firms to cut their costs sufficiently enough to compete under conditions of
free trade.
This new trade theory predicts imperfect as opposed to perfect competition in
international trade. According to Pomfret (1992), imperfect competition is present in
manufacturing and agriculture among other sectors where parastatals and large
competitive companies often handle international business. Differences in production
18
technology enable firms in the same industry to produce differentiated versions of the
same goods. This way, countries can import different versions of the same good at
different prices. For example, countries that produce cars also import cars that are
produced from elsewhere. This is mainly because each product is seen as slightly
different from another even if they virtually serve the same purpose.
The modern trade theory also assumes that the presence of transport costs is one
reason why countries may engage in international trade. Donald, R and Weinstein,
(1998) argue that the traditional theory ignores the importance of trade costs not
because they are small or irrelevant but because they believed that trade costs do little
to change the patterns of trade. On the contrary, new trade theories consider trade
costs to be an important ingredient in determining trade patterns in a way that they
affect markets in different ways. Krugman and Venables (1995) show how trade costs
affect international trade and the distribution of production.
First, the presence of trade costs affects international trade through the home market
effect. Trade costs persuade producers to relocate their plants closer to consumers of
their products (backward linkages). This has become a very popular strategy with
many multinational companies especially in the manufacturing, telecommunication
and automobile industries among others.
19
Second, the existence of trade costs may persuade producers to operate in large
markets where it is easier to access differentiated inputs (forward linkages) in order to
reduce their production expenses. Such agglomeration can have serious impacts on
welfare since in extreme cases exodus of companies may be experienced leaving
some countries less developed.
The role of trade costs in determining the patterns of international trade is
demonstrated by Du Plessis, (1987). He puts forward a simple example; “If Canada
needs fertilizers in Alberta and has a surplus in Quebec, it will certainly not haul it
4000 KMs to get it there; instead, it will export the Quebec fertilizer to eastern US
and import the fertilizer for Alberta from Western US”. These costs can be made
smaller by exploring foreign market even when factor endowments and technologies
are similar.
Today there is no sign of total world specialization; evidence shows that countries
import some of the products they produce/export. The classical models do not predict
the situation where a country imports and exports the same product simultaneously.
They ignore product differentiation and intra-industry trade as some of the reasons
why countries may trade in the same goods.
Table 2. 1: Evidence of Intra-industry trade in computer parts between Germany and US, UK, Japan, France, 2009-2010
20
Germany imports from
(Million US$)
Germany exports to
(Million US$)
Year 2009
France 260.25 1,048.68
Japan 323.26 62.88
UK 597.43 1,098.81
US 1,109.34 340.14
Year 2010
France 239.15 1,308.3
Japan 476.6 77.93
UK 655.56 1,009.46
US 1,094.24 393.77
Source: The United Nations Commodity Trade Statistics Database (UN COMTRADE, 2011)
The table 2.1 shows the trade in ‘computers and parts thereof’ between Germany and
four of the major world economies in 2009 and 2010. Despite being close substitutes,
data indicates that each country exports and imports some of the same product (in
other words the presence of intra-industry trade and lack of specialization). For
example in 2009 Germany imported computers worth $260 million and exported
computers worth $1 billion to the same country. In the same year, Germany imports
computers worth $1 billion from the US and exports $393 million worth of computers
to the same country. This kind of trade pattern is not explained by the classical trade
theories of perfect specialization.
21
A number of studies have attempted to test how well the classical theory explains
international trade today. In order to establish the structural basis of USA’s trade with
other countries, Leontief (1953) tested the H-O theory by comparing the quantities of
labor and capital required to produce two goods, one export and other competitive
import. Contrary to the predictions of the H-O model Leontief’s paradoxical findings
showed that despite the USA being capital abundant, its exports were largely labor
intensive and its imports were largely capital intensive. His findings were later
complimented by the finding of Baldwin (1971) who tested 1962 US trade data
showed that imports to the USA were 27% more capital intensive USA’s exports.
The Leontief paradox however was criticized mainly on statistical grounds citing that
choosing 1947 as the base year was erroneous since the country was still dealing with
disorganizations of the second world war. Even though Leontief (1956) finding of the
second test using 1951 data sets met wide criticism on grounds of his choice of
empirical methodology from many economists such as Brecx (1967), Merrett, (1965),
his research took international economic theory by surprise and initiated a great deal
of empirical and theoretical research on the subject.
Subsequent tests of the H-O model using Leontief’s procedure showed that
considering finder categories of the main factors of production shows that USA
exports were also intensive in skilled labor, research, engineering talent among others
in which the US was well endowed.
22
Another attempt was made by Linder (1961). According to Linder, trade flows
between two countries are a function of market homogeneity subject to distance
constraint. A country exports more of those commodities that are highly demanded
locally. This implies that countries with similar demand structures are more likely to
trade together more than those with different local demand structures.
On this relationship, Gray (1998) notes that Linder’s analysis is directly linked to
differentiated markets in international trade. This is partly because a product
manufactured in one country is more likely to have characteristic differences as
opposed to end-use differences from a product made in another country. Since the end
use is similar, differences in product characteristics can be a basis for international
trade.
According to Linder, traditional theories of international trade, specifically the H-O
theory only explains patterns of trade in natural resource intensive products but the
theory performs poorly in explaining trade in manufactured products since their
fabrication does not require unique factor intensity. For example the same product can
be produced using labor intensive techniques in a country where labor is cheap but
also the same product can be produced in another country using capital intensive
techniques where capital is cheaper. The bottom line to Linder’s analysis is that goods
23
that are abundantly produced locally do not depend on factor endowments but on the
structure of local demand.
Another point of divergence between the traditional international trade theory and the
new trade theory is that modern economists believe that endowments change over
time. At the fore front of this view is the product cycle theory.
The product cycle theory assumes that the allocation of industries whose products
warranties investment in technological innovation follows a number of stages;
Stage 1: Production is introduced in large markets (high income countries) that
have the necessary resources.
Stage 2: As countries open to trade, the industry develops a capacity for export
due to its comparative advantage as predicted by the H-O model.
Stage 3: With more trade, gradually other countries also begin producing the
product due to technology and capital spillovers.
Stage 4: Industries in the initial exporting country begin to lose competitive
advantage as the technological gap with its trade partners begins to shrink.
Stage 5: Finally, the same goods that were formerly exported begin to be
imported in the initial country in the form of intra-industry trade.
The product life cycle theory assumes that developing countries with a high number
of semi-skilled labor finally gain production advantage making their goods relatively
24
cheaper. Gains from trade therefore depend on the stage of the product cycle and
changes in endowments.
2.2. Summary
According to the Ricardian model, trade among countries takes place because of
technological differences whereas according to smith, differences in resource
endowments are the main reason why countries trade with each other. The Hecksher-
ohlin model emphasizes the differences in relative factor endowments as the reason
why countries trade with each other. All models predict that as long as the
corresponding assumptions are met, all countries are better off by trading than not.
On the other hand Tiiu (2000) notes that classical models of perfect specialization are
limiting in a sense that they explain trade only in specific items but cannot explain
why countries have stronger trade links with some countries more than others. Also
they ignore the possibility that endowments may change or be transferred over time
which may change the patterns of trade among countries. In a nutshell, the classical
models fail to explain why trade increases over time for some countries and fail for
others. It should be noted however that the classical models are more suitable in
explaining exchanges between developing and developed countries (Krugman et al,
2011) because of the certain differences in productivity and consumption patterns.
Intuitively therefore, the classical theory predicts profitable trade between South
25
Africa and EU; and between South Africa and the US. Given the difference in the
economic development between South Africa and SADC, the Ricardian and
Hecksher-Ohlin models predict profitable trade from such an FTA.
It should be noted that all the theories present models towards an understanding of
international trade. Each model presents some but not all reasons why countries trade
with one another. None of these theories has been proved to hold in all circumstances
for every country. It is not automatic that countries with different resources and factor
endowments will simultaneously benefit from trading among and between them.
There is no single model that encompasses all the variables that may encourage or
hamper trade among a given set of nations. They are only theories that try to predict
the likely causes and outcomes of international trade under a set of assumptions. The
actual knowledge about how countries succeed after liberalization can only be
obtained by carrying out case specific analyses of the countries of interest.
Since it is not guaranteed that South Africa automatically benefits from SADC, BRIC,
AGOA, TDCA; this study aims at providing an empirical study about the trade
creation and the trade diversion effects that different trade agreements have had on
partner countries.
26
CHAPTER III
THE GAINS FROM TRADE
3.1. The regionalism debate
One of the five strategic objectives of South Africa’s Department of Trade and
Industry (DTI) is “(to) build mutually beneficial regional and global relations to
advance South Africa’s trade…” (DTI, 2012). Yet it is clear over the past decade that
South Africa’s efforts towards building regional relations have far surpassed efforts
towards global economic integration despite the latter promising much wider market
prospects than any RTA can create. Such behavior in international relations has been
at the heart of the ongoing debate about the wisdom of a regional approach to global
trade liberalization.
Bhagwati (1991) divides regionalism into two periods. He writes that the “first
regionalism” was inspired by the work of Viner (1950). With the additional
contribution of economists such as Meade (1955) and Lipsey (1957) towards the
development of the Vinerian theory, the model started to get worldwide recognition
especially after the formation of the European community. Later, other economists
such as Cooper and Massel (1965) attempted to show that FTAs can effectively
reduce the costs of industrialization when extended to the developing world.
Beyond the European Community, the adoption of PTAs lacked enthusiasm
especially because of the reluctance of the USA to endorse PTAs in its trade policy.
27
Instead, until the late 1970s, governments in developing countries adopted the
strategy of import substitution as a base for the development of their economies. The
strategy involved the replacing of imports with local production and sheltering local
production from foreign competition by means of strict controls against foreign
investment and high import duties. Even though the strategy registered some success
in countries such as Brazil, Argentina and Mexico (Blouet and Blouet, 2005), by the
early 1980s many countries had accumulated huge debts.
Since then, the IMF and the World Bank have encouraged governments in developing
countries to open up their borders to foreign investment, and they continue to provide
financial incentives to those governments that undertake liberalization and
privatization. Today, the WTO allows the formation of free trade zones as a means
towards quicker regional integration. The “second regionalism” began when
proliferation of FTAs started to gain momentum particularly after the signing of the
Canada-US free trade area (CUFTA) in 1987. Bhagwati (1991) claims that the change
of heart on the part of the US was partially a result of the impossibilities and
complications involved in agreeing on a common multilateral agreement that could be
supported by all nations.
Today, there are more than 300 bilateral trade agreements (BTAs) worldwide. It is
estimated that at least each country belongs to a either a Regional Trade Agreement or
BTA (Lester and Mercurio, 2009). Therefore, the move towards regional integration
as opposed to multilateral liberalization is an important discussion. Yet even at this
28
rate of proliferation, the overall effect of RTAs on multilateral trade is yet to be
universally agreed upon. The question whether free trade blocks can effectively
replace or enhance multilateralism has yet to be answered. Instead of committing to
multilateral trade under WTO, countries are individually negotiating trade deals with
each other, and or with other regional trade zones. South Africa, for example, is
committed to multilateral trade through the World Trade Organization yet at the same
time has formally and aggressively negotiated for both bilateral and regional trade
agreements.
The implication of such economic undertakings has led to a worldwide discussion
with some analysts believing that such moves could halt multilateral negotiations,
while others believe that regionalism is a building block towards greater international
trade. Either way, proliferation of FTAs has reached another level in recent years.
This is because FTAs used to be agreements between neighboring states but in the last
15 years, even distant countries have established regional trade partnerships e.g. South
Africa and EU, EU and MERCOSUR, SACCU and MERCOSUR among others.
Those in support of FTAs argue that with such networks of bilateral agreements
between individual states and regional trade blocks, and between regional blocks
themselves, eventually the whole world will be united under the same trade
agreement.
29
Critics argue that RTAs are a form of protectionism that encourages countries to
remove barriers between themselves while imposing them on nonmembers. For
example, Krugman (1991), while analyzing the interaction between FTAs and
countries that trade under the MFN principle, shows that the non-cooperative behavior
of RTAs often leads to higher external tariffs.
Others argue that FTAs impede worldwide trade liberalization because governments
are focused only on securing personal trade deals and spend less energy on pursuing
multilateral liberalization under the WTO. Therefore, it is widely agreed upon by
many critics that FTAs are a big step backwards from the principles of
nondiscrimination towards which they claim to build. This is witnessed in the form of
barriers on trade imposed on nonmembers in terms of tariffs, and rules of origin that
are more likely to cause a negative welfare effect. Besides, the manner in which PTAs
are run calls for reciprocity; countries that are excluded from an RTA may be tempted
to resort to protectionism as a result of marginalization that RTAs may generate. This
erodes the possibility of global liberalization.
The idea that RTAs are a building block towards multilateralism is also contested by
Krishna (1998), citing the presence of lobbyists as a factor that prevents RTAs from
building towards global liberalization. His idea follows the same line of argument as
Grossman and Helpman (1994). According to them, lobbyists are predominantly
producers who influence government policies towards what favors their profits.
30
Together they argue that liberalization favors the export industry and consumers but
not import-competing industry that would rather scuttle any agreement to preserve
their protection.
Second, countries engage in RTAs only if the arrangement promises to divert trade in
their favor. From this, Grossman and Helpman (1994) assert that an FTA is more
likely to be backed and eventually formed if it promises more trade diversion effects
for the countries involved.
Krishna replicated Grossman and Helpman (1994) and considered three scenarios, the
first one being π1 (profits under WTO Most Favored Nations (MFN) tariff system), π2
being benefits under FTA and π3 being benefits under global free trade. According to
him, lobbyists can only support a move from MFN to FTA only if π2 > π1. Also a move
from MFN to global free trade can be supported only if π3> π1. However, when an
FTA is formed, switching towards a global free trade system is more likely to be
blocked by special interest groups because it only generates π3 – π2 which is smaller
than π3 - π1 because already π2> π1. He therefore concedes that once an FTA is formed,
the incentive to give access to a third partner is reduced because more liberalization
means more consumer benefits and less producer protection.
This view is seconded by Albertin (2008) who argues that as proliferation of FTAs
gains momentum, there is more evidence of reluctance by countries in pursuing
31
multilateral trade negotiations. Once a free trade agreement is in place, private entities
tend to form an “anti-multilateralism force” to protect their own industrial interests
that are otherwise guaranteed by preferential treatment.
Such views give an insight into a debate that has been going on for a long time. In
1950, in his book titled The Customs Issue, Viner Jacob was among the first
economists to initiate a discussion about the possibility of free trade areas diverting
trade rather than creating it. The debate about whether countries should pursue
preferential trade instead of global integration under the principle of most favored
nations is somewhat irrelevant. The reason is that preferential trade areas are mainly
intended to create new and bigger markets for participating countries. The bigger the
PTA becomes, the greater opportunities there are for partner countries. Yet, no
alternative can provide greater opportunities than multilateral trade under the WTO.
Therefore countries like South Africa would benefit more from multilateral free trade
than from preferential trading. Yet, sixty years after Viner articulated the economic
implications of preferential trading, the choice between PTAs and multilateralism
remains a big problem that many policy makers tend to ignore (Lester and Mercurio,
2009). Many have studied the importance and evolution of RTAs and derived
different conclusions.
32
According to Mensbrugghe et al (2005), initially countries rushed to negotiate
bilateral trade agreements in order to gain “first-mover” advantages with stronger
economies before others did. However, they note that today it is almost impossible to
obtain such gains because of the numerous networks of FTAs all over the world. In
2012, the WTO reports 511 RTAs to have been notified, of which 319 are already
operating. This means that it is virtually impossible for a country such as South Africa
to be a member to all of them. Therefore, by joining one or two FTAs South Africa
misses out on the benefits of trading freely with other FTAs, a scenario which may be
unfavorable in a sense that South Africa would be better off by having free access to
all markets without discrimination. It is argued that African countries’ membership to
numerous RTAs is a main reason why the continent has been very slow towards
integration (UNECA 2011).
Mensbrugghe et al (2005) show why countries should adopt preferential trading with
due caution. They performed a benchmark simulation on a number of global reform
scenarios and found out that globally, countries would experience a 0.8%
improvement in baseline income by 2015 if they pursued multilateral trade. On the
other hand, the study reports that developing countries would lose 0.4% in real
income if they pursued RTA arrangements in the same period. They ran a simulation
where all developing countries sign a BTA with major economies (precisely USA,
UK, Canada, Japan, EU, New Zealand and Australia) and reported that in such a
33
setting, developing countries would be worse off by $22 billion as compared to a
scenario where they would trade with one another without trade barriers.
It can be argued that the structure of barriers that are put in place when PTAs are
formed is one that allows free movement of goods and services within while at the
same time giving individual member countries the right to independently levy taxes
on imports from non-members. The expected result is that total trade among members
increases at the expense of non-members. Unfortunately, even for some members, the
FTA may divert trade from low cost sources to higher cost sources.
Such trade diversion happens mainly because a country dismantles tariffs for
members, thus making their exports cheaper as opposed to those from non-members.
If a non-member country is the low cost supplier, continued importation of goods
from a member country that enjoys duty exemptions means that the importing country
pays more for its imports than previously, in addition to losing tax revenues.
Even in light of such studies to the contrary, the trends in international trade today
suggest that countries are moving faster towards the formation of fragmented free
trade areas all over the world. This trend, it is believed, is partly because despite being
a second best strategy, it has been proved that preferential trading is easier to achieve.
Proponents of regionalism emphasize that RTAs create momentum for eventual
global liberalization through the initiation of intra-regional liberalization that
34
facilitates the integration of member countries into a more sophisticated multilateral
trading system. In other words, the growth in the number of available RTAs in the
world will eventually lead to the merger of different RTAs, thus accelerating global
trade liberalization faster than through any other means. However, this view is
challenged by Babatunde (2007) who argues that this does not apply to most of Africa
because of low intra-industry trade within RTAs, and dependence on primary and
mineral resources, in addition to lower levels of “structural complementarity”.
A report released by UNECA (2010) maintains Babatunde’s argument about the
weakness of Africa’s intra industry trade but argues that with the formation of more
RTAs, intra Africa trade today is greater than what it was a decade ago. The report
concludes that by forming RTAs, African countries can enhance regional trade
interactions that can eventually generate economic power enough to drive stronger
interactions and integration among different sub regions of Africa.
Some governments believe that opening up markets leads to greater efficiency and
economic growth, as evidenced in East Asia and other developed Nations of the west.
Competition ensures that inefficient entities are excluded from production while at the
same time presenting opportunities for competitive firms to realize their potential by
producing for a wider market. Even though multilateral trade promises a bigger
market than any FTA can generate, countries like South Africa that are still part of a
wider setting under the flag of WTO continue to negotiate FTAs for various reasons:
35
Multilateral negotiations under WTO take longer time due to the volume of
issues discussed and lack of mutual commitment from all parties. On the other
hand, FTAs are easier to establish as they involve fewer members and
considerably less issues to discuss.
From the developing world’s perspective, multilateral trade involves
unfavorable trade terms in the way that the trading countries are often of
unequal size. The resulting welfare effects from such trade are often negative
for weaker countries because of the differences in the bargaining powers on
the international market. It is also difficult for smaller countries to have a
significant influence on the proceedings of the WTO.
FTAs, however allow smaller and weaker countries to form a considerable
force, as a larger and strong unit that can effectively deal with stronger blocs.
From this point of view, increasing RTAs acts as an incentive towards
multilateral trade liberalization by enhancing awareness of the relative
interdependence between different trade blocs.
In addition, FTAs also have their own strengths. According to Shujiro (2002),
combinations of factors are behind the expansion of Regional Trade Agreements
(RTAs). For example, the need to secure markets for exports calls for a two-way
dismantling of trade barriers among countries. As more countries converge in
36
different regional trade units, countries that do not belong to any become
marginalized and as a result lose potential markets.
In their discussion of FTAs, Rosson et al (2003) distinguish between short term and
long term effects of FTAs. They explain that the short term effects are measured in
terms of creation and diversion of trade. They further argue that in the case where an
FTA is backed by full employment, it can improve the general welfare for member
countries by pushing down consumer prices and increasing baseline incomes. More
so, they predict that long term benefits such as “…increased competition, economies
of scale, stimulus investment and more use of economic resources” in FTAs are
expected to exceed the short term gains.
Also, dismantling of trade barriers and imposition of rules of origin means that
producers can benefit more by producing from within the region rather than from
outside. This attracts multilateral companies towards expanding production to larger
regional trade areas more than individual countries. For example, the figure below
presents South Africa’s FDI inflows since 1994.
Figure 3. 1: Flow of South Africa’s Foreign Direct Investment, 1994-2010
37
Source: calculations based on IMF data (IMF, 2011).
Figure 3.1 above clearly shows a remarkable difference in South Africa’s FDI inflows
before the year 2000 as opposed to the period beyond 2000. Since 2007, the trend line
reveals that FDI for South Africa has been increasing at an increasing rate even
though individual annual data shows a high level of fluctuation. Interestingly, even
during the financial crisis, FDI for South Africa stayed positive indicating that FDI
inflows are greater than Investment outflows. The data also reveals a very big
improvement in the value of FDI directed toward South Africa since 2005, with the
exception only being in 2006. Whether this is because of the free trade agreements
signed by South Africa a few years back remains to be empirically investigated but
what the study can say is that this period coincides with the period in which the
effects of the agreements should start to be sensed.
38
3.2. Trade creation versus trade diversion
The agenda of almost any free trade agreement is centred on promoting trade and thus
economic growth. Although it is true that when a country trades more, it can increase
its share of world exports and gain more influence on terms of trade in the world
market, some studies have shown that is not always the case. Opening up to trade
does not automatically guarantee economic success (Martin and Sunley (1996). ,
1996; Rodrik, 2001A; Rodrik, 2008).
Since it is possible for FTAs to divert trade from low cost producers, consumers may
end up paying the same prices as before the PTA in addition to losing tax revenues.
Therefore, benefiting from regional trade areas requires more than just removing
barriers to trade. It necessitates important “…human and institutional resources and
infrastructures…with respect to size and economic conditions” (Mina et al, 2005) of
each participating country. Many empirical studies have shown that the impact of
Free Trade Agreements on international trade flows is still mixed.
Thirwall, (1995) for example, points out that trade between developing and developed
countries has often resulted in trade diversion rather than creation. Rodrik (2001B),
amongst others, suggest that countries should only open up to freer trade when they
have a very strong local industry that can compete on the world market. It is only
39
when nations have a strong economic base that they can start benefiting from
international trade.
Contrary to Thirwall (1995) and Rodrik (2001A), Nandasiri, (2008) used an
augmented gravity model and analyzed panel trade data of 184 countries over a period
of nine years with reference to seven regional trade blocks namely, the Association of
South East Asian Nations (ASEAN), North American Free Trade Agreement
(NAFTA), the European Free Trade Association (EFTA), Dominican Republic –
Central America Free Trade Agreement (DR-CAFTA), Caribbean Community
(CARRICOM), South Asian Association For Regional Cooperation (SAARC) and the
European Union). He found that countries that formed a Free Trade Area with one or
more of these regional trade blocks gained more from trade liberalization than those
that didn’t. He concludes that in this case, FTAs created trade for member countries
and diverted trade for non-members.
Bhagwati and Panagariya (1996) explain the cause of such developments; they argue
that RTAs unambiguously give preferential treatment to partner countries while the
same time discriminating against nonmembers. In this way, internal trade within the
RTA expands to the detriment of the rest of the world.
On the other hand, some studies have produced positive results in favor of preferential
trading. For example, trying study the impact of NAFTA on trade flows between the
40
USA and Mexico, Susanto et al (2007) constructed import demand functions for the
two countries and included time dummies to capture whether the removal and
reductions of tariffs after the implementation of the free trade agreement affected
exports from Mexico to the USA. The results showed that following reductions in
tariffs, Mexico’s exports to the USA increased significantly. They concluded that
NAFTA has created trade for both Mexico and the United States of America.
Besides empirical studies, another reason that is frequently put forward by those in
support of preferential trading is that countries that are within each other’s proximity
are natural trading partners (Krugman, 1991). Their lower transport costs resulting
from proximity make them more likely to benefit from trading with each other than if
they pursue protection policies. The argument of countries being natural trade partners
is based on a number of presumptions. First, it is assumed that if countries are within
each other’s proximity, their volumes of trade are naturally higher, notably because of
low transport costs. Secondly, since trade is naturally high, trade creation effects are
greater than diversion effects with the creation of an FTA.
This view is challenged on the other hand by those who believe that neighborliness
does not necessarily mean natural trade partnership. They argue that trade creation
does not depend on transport costs and benefits accruing from proximity only but it
also relies heavily on the importing country’s GDP (purchasing power) and the
exporter’s price (Panagariya, 2000).
41
Supposing three countries, South Africa (home country) imports a particular
commodity from Zimbabwe and Kenya at a fixed price (Pzim+t and Pken+t); where”
t” represents import tax.
As Viner (1950) illustrates, initially both Kenya and Zimbabwe would face the same
tax rates. Assume further that the prices in Kenya are lower than the price of the same
commodity in Zimbabwe. If South Africa and Zimbabwe join an FTA (SADC for
example) while South Africa maintains the same level of tariff against Kenya, the
implication of this move on South Africa’s welfare can be represented as below:
Figure 3. 2: Trade creation and trade diversion effects of an FTA (Vinerian Approach)
42
Source: Panagariya(2000)
Ekenya and Ezimbabwe represent the quantities which each country is willing to
supply under free trade. E’kenya and E’zimbabwe are quantities that both countries
are willing and able to supply under a common tariff ‘t’. It can be seen that in both
scenarios, Kenya supplies more at a relatively lower price. Hence South African
importers purchase all imports from Kenya. However, with the creation of an FTA
between South Africa and Zimbabwe, the price faced by Zimbabwean exporters is
Pzim (that is free of import duty ‘t’) whereas the tariffs against Kenya are left intact.
The exporting price for Kenya is Pkenya+t. This makes Zimbabwean goods cheaper
1
2
3
CD
E Zimbabwe
E’ Zimbabwe
E’ Kenya
E Kenya
MPTAz MPTAk
M0
Pzim+t
Pken+t
Pzim
Pken
43
than Kenyan ones. As a result, total imports increase from M0 to MPTAz. This has
the following implications:
i. Since Zimbabwean duty free imports are now cheaper in contrast with Kenyan
goods, the source of imports is entirely diverted from Kenya to Zimbabwe thus
making Kenya worse off.
ii. The increase in exports is less than it would have been had the PTA been formed
between South Africa and Kenya. The gains in welfare would have been
1+2+3+C+D less the loss in taxes equal area 1+2. Net welfare gains would have
been 3+C+D which is unquestionably positive. But since the FTA is between
South Africa and Zimbabwe, the gain in welfare is the area 1+3 and the loss in
taxes is area 1+2. The net welfare gain is area 3-2 which may or may not be
positive. This move sees South Africa forego the gain in welfare totaling area
C+D.
iii. If Zimbabwe does not have the capacity to satisfy the increase in demand for its
exports, South Africa continues to import a fraction of the goods from Kenya as
well. Given that South Africa cannot have two different local prices for the same
commodity, the local price will be set to equal Kenyan import prices. The
implication is shown by Panagariya (2000)
Figure 3.3: Trade diversion effects of an FTA (Vinerian Approach)
44
Source: Panagariya (2000)
The removal of import duties against Zimbabwe shifts the supply curve from
E’zimbabwe to Ezimbabwe. Imports from Zimbabwe increase from MTZIM to MZIM but
local demand dictates total imports totaling M0. Kenya supplies the difference
between MZIM and M0 at price Pkenya+T. This sees South African consumers gain
no additional welfare while at the same time the government of South Africa loses
tariff revenues equaling area 1+2+3+4. However, Zimbabwean exporters gain area
1+2+3 in form of producer surplus. The net loss to Zimbabwe and South Africa is
area 4.
E’zimbabwe
Ezimbabwe
E’kenya
Ekenya
MTZIM MZIM M0
Pkenya
Pkenya+T1
2
3
4
45
As shown using the Vinerian approach above, FTAs have the potential to create as
well as divert trade. When a PTA is formed with the most efficient supplier, the loss
in tax revenue is surpassed by the gains in welfare to consumers in the form of
reduced local prices and increased quantity. This way the agreement strengthens
instead of diverting trade.
3.2.1. Conclusion
A number of studies have tried to assess the trade creating and diverting effects of
various FTAs and their results have often been mixed. As a result, feelings about the
importance of FTAs to member and nonmember countries have been mixed. It is
generally understood that the effects of an FTA are not necessarily uniform across all
involved countries. It is possible that an FTA may create trade for one country and
divert it from another. The results are specific to only the trade arrangement being
studied and therefore cannot be generalized to fit all trade blocks. There is a need for
a thorough investigation of this subject from South Africa’s perspective. South
Africa’s strategy of negotiating FTAs with other economies appears to be a second-
best option. But since universal liberalization is unattainable in the foreseeable future,
negotiating free trade deals is better than not. The question whether the move has
been detrimental to South Africa’s trade remains to be empirically examined.
46
47
CHAPTER IV
4. THE DETERMINANTS OF TRADE AND TRADE PATTERNS BETWEEN
SOUTH AFRICA AND PARTNERS
4.1. Introduction
It is argued that countries open up to free trade in order to exchange goods they have
in surplus for those that are scarce. More importantly, trade can enhance economic
growth by exposing domestic production to foreign technology. This gives domestic
producers an opportunity to learn through imitation of foreign technologies, thus
accelerating the transmission of technology (Grossman and Helpman, 1991) by means
of importing hi-tech commodities, as witnessed in East Asian Countries (Alesina et al,
2005).
However, trade can create winners and sometimes losers (Viner 1950). Nevertheless,
Baldwin (2004) argues that generally, open economies as opposed to autarkists
experience more rapid economic growth. As a prescription therefore, countries with
an objective of reducing poverty could benefit more from dismantling trade barriers
against goods and services as wide market access tends to attract FDI.
Linking free trade agreements to economic progress is not a straightforward approach.
The study argues that the relationship between the two is an indirect one. It can be
understood by measuring the incremental effects of the FTA in question on members’
48
exports. Since a wide body of literature has shown a causal relationship between
exports and GDP (Dhawan and Biswal, 1999; Awokuse (2003); Jordaan and
Hinaunye, (2007), accordingly, this study argues that the effect that an FTA has on
exports reflects its indirect effect on GDP.
Figure 4.1: South Africa’s GDP, 1997-2010
Source: calculations based on World Bank data (World Bank, 2012)
In Figure 4.1 (panel A). shows that South Africa’s GDP and exports have been
moving in the same direction since 1997. In the aftermath of the signing of 3
important free trade agreements, South Africa realized a significant increase in the
volume of exports between 2001 and 2004 whose value is estimated to be around
US$38 billion per year compared to the average of $33.5 billion realized in the
previous periods. Beyond 2004, the average value of exports was US$45 billion.
More still, between 2001-2004, average GDP was US$38.2 billion an increase of over
US$16.3 billion from its pre-2000 values.
49
By 2010, South Africa’s average GDP was around US$45 billion. On the other hand,
although Figure 4.1 (panel B). shows that the average growth rates of GDP and
exports are higher in the periods after the signing of AGOA, TDCA and SADC, it
also reveals that the exports growth rate had started falling steadily even before the
2007 financial crisis; that is from 8.5% in 2005, 5.9% in 2007 to -19% in 2009. Apart
from the three years (i.e. 2005-2007), South Africa’s export growth rates do not seem
to be any different from those that existed before 2000.
Some of the main factors that may hinder South Africa’s export growth could be the
degree of openness of the economy and its trade partners, the level of intra industry
trade, comparative advantage over other trade partners in the production of major
commodities, as well as the marketable characteristics of South African commodities
relative to those from its trade partners. But most notably, gains from international
trade depends on how open countries are.
4.1.1. Trade openness
A higher degree of openness ensures that countries can trade more since the level of
barriers to trade is very low. As countries slash down import duties levied on other
countries’ exports, the trade openness index for the import-duty-reducing country
increases. The main advantage of such a move is that it encourages reciprocity from
beneficiaries.
50
Figure 4.2: Trade Openness index for South Africa, EU, BRIC, SADC, AGOA, 2001-2010
Source: calculations based on World Bank Development Indicators (World Bank, 2012)
Even though the relationship between trade openness and economic growth is widely
contested in growth literature, there is near consensus about the existence of a positive
correlation between trade flows and growth (Yannikaya 2002). For a trade
arrangement to be profitable for member countries, exports and imports need to be of
significant importance to the respective economies’ GDP. In other words, the benefits
from trade could be reduced if member countries are overly self-reliant (i.e. lower
levels of openness). Therefore the main strength of the trade openness index used
above is that it takes into account the trade flows and GDP (the best known proxy for
economic growth).
51
Figure 4.2 shows that among the four trade arrangements considered, the SADC and
the EU are the most open, followed by BRIC. AGOA is the most closed trade
arrangement of all. BRIC has made the most significant improvement in opening up
to trade than any other. In 2001, the trade openness index for the BRIC economies
was 39.3%. However, with the formation of BRIC in 2010, the openness index was
67%, a 70% increase from its 2001 level.
It can therefore be argued that South Africa is more likely to trade more with SADC,
BRIC and the EU as opposed to trading with the United States. Nonetheless, despite
conforming to free trading, South Africa’s economy still remains more closed relative
to SADC, EU and BRIC. This is a potential limiting factor against the gains that
South Africa could reap from trading with a more open approach.
4.1.2. Intra Industry Trade
The level of intra industry trade represents the extent to which countries are able to
trade with each other in products that lie in the same industry. A higher intra industry
trade index value means that countries are able to exchange similar goods that are
differentiated by branding and inclusion of different additional attributes. Product
differences are attributed to production technology differences across countries in a
way that products from each country are quite different. Such trade is more common
in vehicles, wine, electronics, cosmetics and clothing etc. For example, the success of
South Africa’s products in any of the FTAs mentioned partly depends on the ability of
52
South African exporters to differentiate their commodities from competing substitutes
in the same trade zone.
The table 4.1 presents the top South Africa’s import sources and export destinations in
the European Union for selected commodities. In both exports and imports, nine top
destinations and suppliers were considered. The data revealed that apart from trade in
minerals, South Africa is worse off in all other industries considered by the study.
There are signs of significantly high intra industry trade in the vehicle industry and
considerably low trade in automatic data processors and minerals.
In 2010, the intra industry trade index in the vehicle industry rose to 79%, its highest
point in 3 years, and then fell to 70% in 2011; a percentage that is still higher than the
2009 index.
Trade in electronics is weak with the rate at which South Africa and the EU demand
each other’s electronic equipment declining from 27% in 2009 to 23% in 2011. This
pattern favors the EU because intra industry trade data show that the volume of trade
in this industry has been progressively increasing, whereas the share of South Africa’s
exports in total trade has been progressively declining. It is tricky to reach a
conclusion on such a development because even though the share of South Africa’s
exports in this industry has been falling, South Africa’s total exports in the same
industry have been increasing. The pattern is that both the EU’s and South Africa’s
exports have been increasing but EU’s exports have been increasing at a higher rate
53
than South Africa’s. As long as South Africa’s exports do not suffer in the process,
the TDCA is not a bad idea.
A completely different pattern emerges when trade in automatic data processors and
minerals is considered. The European Union highly dominates the data processor
industry. Trade is mainly a one way traffic, with the EU supplying and South Africa
importing. Unlike trade in electronics where the EU dominates but both parties’
exports are increasing, in data processors, the intra industry trade index has been
significantly low (9% in 2009) and falling (to 7% in 2011); worse still, South Africa’s
exports in this industry have fallen from US$16.2 million in 2009 to US$11.4 million
in 2011. The share of South Africa’s data processor exports in total intra industry
exchange fell from 5% in 2009 to 3.4% in 2011. This shows that South African
export-oriented and import-competing companies are struggling against European
imports. If such trends persist in the long term, continued free trade may see South
Africa’s data processor exports wiped out of the EU market.
On the other hand, the situation is the extreme opposite when trade in minerals is
considered. The index of intra industry trade returns very low rates. In 2009, the IIT
index was 8%. It fell to 5% in 2010 and rose to 11% in 2011. South Africa, in this
case, is the main supplier with the EU providing less competition as an exporter and
providing more market for South Africa’s exports. On average, South Africa has been
providing 95% of all minerals traded with the EU. The value of South Africa’s
54
mineral exports increased progressively from US$2.018 billion in 2009 to over
US$3.493 billion in 2011, an increase of over US$1.475 billion in just two years.
55
Table 4. 1: Intra Industry Trade between South Africa and EU (2009-2011)
Vehicles other than railw
ay
Electrical, Electronic equipm
ent
Automatic data
Processing machines
and units thereof
Pearls, Precious stones, M
etals, Coins
Year
Country
Exports
Country
Imports
Intra industry trade index
Country
Exports
Country
Imports
Intra industry trade index
Country
Exports
Country
Imports
Intra industry trade index
Country
Exports
Country
Imports
Intra industry trade index
Ger 620.3 Ger 1403.9 Fr 118.3 Ger 705.1 Spain 5.7 Czech 76.6 UK 1352.1 Ger 36.1
Pol 106.5 UK 257.3 Ger 63.1 UK 240.5 Neth 2.5 UK 54.1 Ger 345.5 Bel 26.4
Fr 98.8 Spain 127.3 UK 30.4 Fr 195.6 UK 2.2 Ire 49.7 Bel 298.0 It 9.3
Bel 58.5 It 121.0 Neth 24.5 Swed 169.7 Ger 1.8 Ger 40.3 It 7.7 Ire 5.7
UK 58.0 Fr 118.4 Bel 20.2 It 160.8 Cyp 1.5 Hung 33.4 Ire 6.9 UK 5.4
Spain 56.1 Bel 49.1 Spain 14.2 Fin 107.1 Fr 1.0 Neth 31.1 Neth 5.2 Fr 0.7
It 26.1 Pol 48.1 Swed 8.4 Bel 106.7 Rom 0.9 Fr 24.3 Swed 1.2 Den 0.4
Port 22.8 Aust 30.6 Ltvia 8.1 Neth 104.1 Ire 0.4 Pol 20.0 Spain 0.9 Spain 0.4
Fin 16.9 S.vakia 25.7 Greece 6.8 Hung 102.6 Bel 0.3 It 2.9 Aust 0.9 Neth 0.1
Total 1064.1 2181.4 0.66 294.0 1892.3 0.27 16.2 332.2 0.09 2018.5 84.6 0.08
Ger 1314.7 Ger 1703.8 Fr 139.6 Ger 696.4 Fr 4.3 Czech 111.6 Aust 1853.6 Ger 29.3
Fr 143.2 UK 511.7 Ger 61.4 Hung 363.4 UK 4.2 Ire 90.7 Bel 618.9 Bel 19.2
UK 129.3 Spain 211.0 Bel 36.5 UK 218.8 Neth 2.8 UK 61.7 Bulg 449.2 It 13.0
Bel 121.5 Fr 160.3 UK 33.6 Fr 173.3 Cyp 1.4 Ger 40.3 Cyp 36.5 Ire 9.9
Spain 76.1 It 148.2 Neth 25.5 It 156.2 Ger 1.2 Hung 30.5 Czech 20.5 UK 2.7
Pol 60.6 Bel 110.7 Swed 8.0 Fin 142.1 Swed 0.9 Neth 29.4 Den 2.8 Fr 1.0
Swed 42.8 Swed 73.5 Greece 2.5 Aust 122.7 Czech 0.3 Fr 29.3 Eston 2.6 Spain 0.7
Fin 32.9 Pol 60.3 Spain 2.4 Swed 118.2 Den 0.2 Pol 19.7 Fin 1.8 Den 0.6
Hung 31.8 S.vakia 33.5 Pol 2.4 Bel 97.4 Pol 0.2 Swed 2.5 Fr 0.4 Swed 0.5
Total 1952.9 3013.0 0.79 312.0 2088.5 0.26 15.6 415.7 0.07 2986.3 77.0 0.05
Ger 1280.6 Ger 2136.4 Fr 66.7 Ger 694.5 Fr 4.1 Czech 113.5 UK 1903.0 UK 87.5
Fr 164.7 UK 678.2 Ger 64.5 Hung 503.7 UK 2.7 Fr 40.8 Ger 809.0 Bel 53.2
UK 150.7 Spain 213.8 Bel 58.9 UK 219.4 Ger 2.0 Ger 39.7 Bel 607.3 Ger 42.7
Bel 146.5 Fr 204.0 Neth 44.2 Ire 171.9 Neth 1.5 UK 39.5 It 137.1 It 15.3
Spain 90.7 It 187.0 UK 34.5 Rom 171.7 Bel 0.4 Hung 28.1 Ire 32.0 Ire 9.5
Pol 61.4 Swed 149.1 Swed 30.4 Fr 170.7 Cyp 0.2 Neth 26.2 Swed 2.3 Fr 1.0
Hung 54.0 Bel 111.8 Hung 5.8 It 165.0 Lux 0.2 Ire 15.7 Fr 1.0 Spain 0.5
Swed 44.2 Pol 77.0 It 3.2 Fin 159.6 Ire 0.1 Pol 12.5 Den 0.8 Aust 0.4
Ire 40.3 Czech 51.6 Lith 3.1 Swed 136.5 It 0.1 Swed 7.9 Neth 0.7 Den 0.4
Total 2033.0 3808.9 0.70 311.2 2393.2 0.23 11.4 323.9 0.07 3493.3 210.5 0.11
2009
2010
2011
Source: Calculations based on the United Nations Commodity Trade Statistics Database (UN
COMTRADE, 2012)
56
4.1.3. Differences in product characteristics
South Africa can arguably benefit more from trading freely with SADC, EU, BRIC
and AGOA if its products have recognizable differences from those of other
countries. Administratively, however, it is an insurmountable task to clearly
determine those differences as this would involve analyzing thousands of
commodities. The United Nations statistical division, through the Standard
International Trade Classification (SITC) categorizes all possible products into nine
major categories, from which this study will compare the nature of products exported
by South Africa against those from other countries. This enables the identification of
products in high demand elsewhere which South Africa can supply.
The tables below show the nine product categories as classified by the UN and the
share they account for in each trade arrangement’s exports in 2009 and 2010.
As stated earlier, the concern should be whether all countries export similar products.
Even though the possibility of intra industry trade reduces such concerns, the extent to
which countries can mutually benefit from trading freely is limited by high
substitutability of traded commodities. Secondly, by exporting similar products,
developed countries are more likely to benefit from intra industry trade than
developing ones since they have more means and better know-how for engineering
characteristic differences in products.
57
Figure 4.4 compares South African exports against products imported by SADC, EU,
BRIC and AGOA. The beneficial pattern for South Africa would be one where each
major export category for South Africa has high demand from SADC, EU, BRIC and
AGOA.
Figure 4. 3: Composition of exports from SADC, EU, BRIC and AGOA, 2009-2010.
Source: Calculations based on the United Nations Commodity Trade Statistics Database (UN
COMTRADE, 2012)
Figure 4.3 shows that manufactured goods, machinery and transport equipment, crude
materials and minerals make up the biggest share of South African exports. Together
they accounted for over 80% of South Africa’s total exports between 2009 and 2010.
The same products are highly exported by the EU and BRIC. On the other hand,
machinery and transport equipment are the biggest composition of the USA whereas
manufactured goods dominate SADC’s exports.
58
Figure 4. 4: potential markets for South African exports, 2009-2010.
Source: Calculations based on the United Nations Commodity Trade Statistics Database (UN
COMTRADE, 2012)
The graph 4.3 shows that for each commodity category that South Africa produces in
plenty, there are at least two other trade blocs that export almost as much. Yet it
appears that all countries export insignificant amounts in commodities such as animal
and vegetable oils, and beverages and tobacco, and other products that are not
mentioned anywhere in the above categories. This would make someone believe that
any country would benefit from exporting more of those commodities. On the other
hand, a close examination of Figure 3.4 reveals that actually these commodities make
up an insignificant share of total imports in any trade arrangement considered in this
study.
59
Unexpectedly, the commodities that have been shown in Figure 4.3 to be the main
exports for most of the countries are still the biggest composition of imports in the
same countries. For example, manufactured goods were 15.7% of BRIC total exports
in 2009-2010 and in the same period, the same products accounted for 15.1% of
BRIC’s total imports. Machinery and transport equipment were 35.6% of AGOA total
exports and in the same period, USA imported the same products amounting to 36.4%
of its total imports.
Figure 4.4 reveals that:
South Africa’s exports of manufactured goods are high enough (34.1%), given
that, manufactured goods make a modest composition of all country groups’
imports (making 11.31% of BRIC total imports; 10% for AGOA; 17.58% for
SADC and 12.4% for the EU).
South Africa can gain more by increasing its machinery and transport
equipment exports, given a huge market that is available in all trade blocs
considered. Machinery imports accounted for the lion’s share in imports of
BRIC (36.6%); USA (36.46%); EU (30.09%); and SADC (27.42%) in 2009
and 2010, yet they only amounted to 19.43% of South Africa’s exports in the
same period.
Chemicals, mineral fuels and lubricants and miscellaneous manufactured
articles are major imports in the EU, SADC, AGOA and BRIC but whose
exports share is still very low in South Africa.
60
Therefore, trade with the EU, SADC, BRIC and AGOA seems to be centered on trade
in related products. All countries’ exports are quite similar, yet, there is also evidence
that countries tend to import goods that are similar to those that they export.
For South Africa to benefit from its free trade agreements, resources must be directed
in a way that capitalizes on the market opportunities provided by the EU, SADC,
AGOA and BRIC. The only way to do that is by diversifying and or increasing
exports of commodities that fall under the following product categories;
Chemicals and related products
Machinery and transport equipment
Manufactured goods classified chiefly by material
Mineral fuels, lubricants and related materials
Production of crude materials should be aimed at targeting markets provided
especially by BRIC and SADC, whereas miscellaneous manufactured articles should
be tailored to suit mainly USA and EU demand.
4.2. South Africa’s trade patterns with EU, SADC, BRIC and AGOA
One of the main concerns of trade between countries that are at different levels of
development is the pattern of trade that is likely to arise. Some countries fear that
trading with more efficient partners may generate “uncompensated losses to import
competing firms” (Suranovic 1997). Even though this may be true, most of the time
61
when countries trade with one another compensation schemes somehow appear in the
sense that superior countries export more of the sophisticated goods whereas the
weaker partners tend to export more of the products that require less technology and
capital. Besides, Suranovic (1997) argues that uncompensated losses may also appear
under protectionism in form of “…higher consumer prices and lost opportunities to
some individuals in the economy”.
Table 4.2: Trade in primary products between South Africa and Malawi, Zambia, Zimbabwe,
2009-2010
South Africa imports from South Africa Exports toYear 2009
live trees and other plantsMalawi 66% 34%Zimbabwe 86% 14%Zambia 61% 39%Year 2010
live trees and other plantsMalawi 53% 47%Zimbabwe 87% 13%Zambia 47% 53%
oil seeds and oleaginous fruitsMalawi 60% 40%Zimbabwe 54% 46%Zambia 71% 29%
Source: Calculations based on the United Nations Commodity Trade Statistics Database (UN COMTRADE,
2012).
The percentage share of South Africa’s imports/exports in a particular item in tables 4.2 and 4.3 is calculated as: %share of imports/exports of commodity X = imports/exports of commodity X from country Y/Exports + imports of commodity X between Y and South Africa.
62
Table 4. 3: Trade in primary products between South Africa and Malawi, Zambia, Zimbabwe, Mozambique, 2009-2010.
South Africa imports from
South Africa Exports to
Year 2009Iron and SteelMalawi 0.28% 99.72%Mozambique 1.68% 98.32%Zimbabwe 10.77% 89.23%Zambia 5.11% 94.89%Electrical machinery equipment and other parts thereofMalawi 1.88% 98.12%Mozambique 2.00% 98.00%Zimbabwe 3.40% 96.60%Zambia 22.07% 77.93%Vehicles other than railwayMalawi 0.25% 99.75%Mozambique 0.92% 99.08%Zimbabwe 6.69% 93.31%Zambia 0.68% 99.32%Year 2010Iron and SteelMalawi 0.34% 99.66%Mozambique 1.01% 98.99%Zimbabwe 11.31% 88.69%Zambia 6.39% 93.61%Electrical machinery equipment and other parts thereofMalawi 0.80% 99.20%Mozambique 0.22% 99.78%Zimbabwe 2.75% 97.25%Zambia 22.94% 77.06%Vehicles other than railwayMalawi 0.25% 99.75%Mozambique 0.21% 99.79%Zimbabwe 0.47% 99.53%Zambia 0.65% 99.35%
Source: Calculations based on the United Nations Commodity Trade Statistics Database (UN
COMTRADE, 2012)
63
The tables (4.2 and 4.3) above present trade flows of specific commodities between
South Africa and three selected SADC countries. Table 4.2 presents trade in
agricultural products. The table shows that South Africa does poorly when it comes to
trade in agricultural products with Malawi, Zambia or Zimbabwe. In trade in live trees
and other plants, South Africa accounts for only 34% of total exports with Malawi,
14% of total exports with Zimbabwe and 39% of total exports with Zambia in 2009.
The trade pattern is similar when trade in oil seeds and oleaginous fruits is considered
in both 2009 and 2010.
On the other hand, South Africa’s shares in the export of products that require
elevated production technology far outweigh any of its selected SADC partners. In
trade of either iron and steel, electrical machinery equipment or vehicles, South Africa
accounted for over 96% of all total exports in 2009 and 2010.
From Table 4.3 , it can be concluded that although South Africa faces unfavorable
terms of trade in the selected agricultural products in the SADC, it compensates for
those trade deficits by registering trade surpluses in the industrial sector.
On the other hand South, Africa’s trade pattern with some of its top trade partners in
the EU and US is different from that of SADC countries considered above. While it
has been shown that South Africa exports more capital intensive commodities to the
comparatively smaller SADC countries, here, South Africa is more dominant in the
64
export of agricultural products. For example in trade of trees and other plants, South
Africa accounted for 97% of total trade with Germany, 99.87% of total trade with the
UK and 94% of total flows with the USA in 2009. Conversely, the share of South
Africa’s exports in products that necessitate elevated production technology declines
considerably when contrasted against trade in the same products with the selected
SADC countries. For example, South Africa’s exports of electrical machinery and
equipment made only 8% of trade in that particular sector with Germany, 11% in the
same sector with the UK, and only 9% with the US in 2009. The same trade pattern
existed in trade of vehicles other than railway in 2009 and 2010.
Table 4. 4: Direction of Trade in both primary and secondary products between South Africa and Germany, UK, US, 2009-2010
South Africa imports from South Africa Exports toYear 2009live trees and other plantsGermany 3% 97%UK 0.3% 99.7%US 6% 94%oil seeds and oleaginous fruitsGermany 9% 91%UK 11% 89%US 61% 39%Electrical Machinery and equipment and parts thereofGermany 92% 8%UK 89% 11%US 91% 9%Vehicles other than railwayGermany 69% 31%UK 82% 18%US 20% 80%Year 2010
65
live trees and other plantsGermany 3% 97%UK 0.4% 99.6%US 7% 93%oil seeds and oleaginous fruitsGermany 12% 88%UK 15% 85%US 55% 45%Electrical Machinery and equipment and parts thereofGermany 92% 8%UK 87% 13%US 88% 12%Vehicles other than railwayGermany 56% 44%UK 80% 20%US 24% 76%
Source: Calculations based on the United Nations Commodity Trade Statistics Database (UN
COMTRADE, 2012)
It can be shown from Table 4.4 that the USA exported more oil seeds to South Africa
than what the latter supplied to the former. Interestingly, in the trade of vehicles, the
value of South African vehicles exported to the US (76% of total vehicle exports
between South Africa and USA) far exceeded the value of vehicles exported by USA
to South Africa. On the other hand, when the top seven exports from one of the two
countries to another are considered, it is clear that South Africa only has a competitive
advantage over the US in the export of low-tech commodities (e.g. primary products,
mining metals and minerals) as shown below.
66
Table 4. 5: Trade in top seven exports between South Africa and USA 2010
Year 2010 US top imports from South Africa (millions of dollars)
US top exports to South Africa (millions of dollars)
Platinum sponge 767.7 small automobiles 10.4Medium-size automobiles
699.2Civilian aircraft and parts
94.1
Medium-size automobiles, smaller engine
618.9Calcined petroleum coke 17.3
Non-Industrial diamonds, Processed
533.2Road tractors for semi-trailers
1.6
Rhodium345.3
vehicle parts and accessories
6.2
Palladium166
Tunneling Machinery parts including rock cutters
9
Unsaturated acyclic hydrocarbons 117.2
reception and transmission equipment
8.2
Source: Workman (2010)
The trade structure as discussed above reveals that South Africa neither dominates nor
is dominated in its trade with the EU, SADC and AGOA. Trade Patterns show that
international trade has automatically generated compensation schemes that allow each
trade partner to dominate in one industry and be dominated in the other. For example,
South Africa is better in industrial exports in its trade with the SADC whereas the
SADC does well in the agricultural exports. In its trade with the EU and AGOA, the
roles are reversed, whereby South Africa has been seen to dominate in agricultural
exports and its partners dominate in the industrial sector.
67
The implication of such trade structure is that more often, the country that is more
competitive in the industrial sector reaps more from bilateral relations than economies
whose exports are mainly agricultural products. Free trade in this case may lead to
deterioration in terms of trade for countries that are not competitive in the industrial
sector.
The figure below shows the apportionment of South Africa’s trade flows among
SADC, AGOA, BRIC and the EU. In 2010, the EU was South Africa’s top trade
partner with total trade flows worth US$44.4 billion. This amounted to over 45.5% of
South Africa’s total trade with the EU, SADC, BRIC and AGOA. Despite being
signed in 2010, BRIC importance in South Africa’s trade far surpasses that of the
United States with the latter accounting for 28.6% and the former only 13.3% of
South Africa’s total trade flows with the EU, SADC, AGOA and BRIC. SADC’s
share was the lowest with only goods worth around 12 billion dollars exchanged
between SADC member countries and South Africa. This is partly due to a
comparatively lower purchasing power of SADC countries, given their low gross
domestic product as opposed to the EU, AGOA and BRIC.
68
Figure 4. 5: Import sources and export destinations for South Africa in SADC, EU, BRIC, and AGOA
Source: Calculations based on the United Nations Commodity Trade Statistics Database (UN COMTRADE, 2012)
The above trade agreements give South Africa’s producers free access to a combined
market of over US$42 trillion which is more than 35 times bigger than that provided
by the whole of Africa. It is important to note that among the four PTAs, South Africa
faces unfavorable terms of trade with the EU and BRIC. The trade deficit with the EU
amounts to over US$7.1 billion, which is around 15.9% of total trade with the EU,
69
whereas the trade deficit with BRIC is over US$ 3 billion, that is over 13% of total
bilateral trade flows.
On the other hand, South Africa earns a trade surplus against both the USA and
AGOA. Its trade surplus with the United States amounts to US$1.26 billion. As
expected, South Africa faces the most favorable terms of trade from its trade with
SADC. Its trade surplus is over US$5.31 billion, which is over 43.4% of total bilateral
flows with SADC. In liquid terms therefore, it is very clear that trade with SADC is
more important to South than any other.
Since trade with the EU and BRIC enables South Africa to import goods that it
doesn’t have the capacity to produce, a current account deficit in this case may not be
totally detrimental. On the contrary, free trade allows South Africans to access such
goods at comparatively lower prices. More still, the importation of capital goods may
also imply that the terms of trade are more likely than not to correct themselves in the
long run.
70
4.3. Conclusion
This discussion has revealed several concerns regarding the fact that the products
traded between South Africa and its partners are quite similar, thus increasing the
pressure of competition. The major concern as demonstrated is that South Africa’s
exports are still very low in areas that promise potential markets. This raises
speculation about whether South Africa’s move for trade liberalization was not a right
move at a wrong time. Rodrik (2001B) argues that, liberalization should be a gradual
process, where countries liberalize in only those areas against whose imports they can
compete effectively and in which they have built enough supply capacity.
However, understanding the over-all impact of free trade agreements warrants more
than just graphical analysis. Applying analytical econometric techniques can reveal
more information and important details that visual observation of charts derived from
raw data cannot. Besides, the product categories used in this chapter have been either
very narrow or too broad; they have included too few or too many product groups,
which when considered exclusively may lead to different conclusions. In an attempt
to address this observation, the next chapter discusses how a gravity model can be
used to overcome such limitations.
71
CHAPTER V
THE GRAVITY MODEL
5.1. Introduction
According to Tiiu (2000) the classical models of perfect specialization (reviewed in
chapter 2) are limiting in a sense that they only explain trade in specific items but
cannot explain why countries have stronger trade links with some countries than
others. Tiiu further argues that since they ignore the possibility that endowments may
change and be transferred over time, they fail to explain why trade is created and or
increases over time for some and fail for others.
Unlike the more theoretical models by Adam Smith and David Ricardo, the gravity
equation which is a monopolistic competition model (Feenstra et al 2001) that allows
a more empirical approach of explaining trade patterns through econometric analysis.
Its application to the prediction of bilateral trade flows was pioneered by Tinbergen in
1962. Since then, the model has gained popularity due to its high explanatory power
of international trade flows. The gravity equation is not limited to analyzing trade
between countries only; it has also been used to analyze trade between regions
(Filippini and Molini 2003, Breuss and Egger 1999), as well as flows of specific
products (Kangas and Niskanen 2003, Pelletiere and Reinert 2003, Jayasinghe and
Sarker 2007) among others. Recent studies have extended gravity analysis to account
for factors that affect international trade for example rules of origin (Augier et al
72
2004), border effects (Nitsch 2000), FDI (Gopinath and Echevenia 2004), rights and
democracy effects (Sama and kucera (2006) to mention but a few.
The model predicts a baseline for trade flows as being determined by the size of GDP,
population size, and distance between countries. According to the model, interaction
among large clusters is likely to be stronger than between smaller ones. More still, a
unit is more likely to interact with neighboring units than with those located far away.
Simply put, the model can be easily explained as implying that the level of trade
between countries depends on the exporting country’s capacity to supply (GDP), and
on the Market (GDP) available in the importing country and the distance (transport
costs) between them (Bergeijk and Brakman, 2010).
In the real world, there is likely to be large trade flows among big economies than
among smaller ones. Also, neighboring countries are likely to trade with each other
more than they trade with countries that are far away keeping other factors affecting
trade flows unchanged. For example, the level of interaction between South Africa
and Zimbabwe should be higher than the interaction between South Africa and France
assuming that France and Zimbabwe are of the same economic size. Therefore, the
model adds distance as a significant factor in determining the level of trade flows.
The popularity of the model was dwarfed for a long time because of its weak and
ambiguous micro-economic foundation. This ambiguity is partly because the model
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can be derived from many different international trade models (Helpman and
Krugman 1985, Evenett and keller 2002) “thus offering no scope to test between
theories” (Bergeijk and Brakman, 2010). On the other hand, Bergeijk and Brakman
argue that this ambiguity should give policy makers confidence in the robustness of
the model since it doesn’t depend on the vision of a specific theory. Thus, the
possibility of deriving the gravity equation from many models of international trade
provides it with rather than limit “more theoretical foundation than any other model”
(Baldwin 2006).
Although initially the model lacked sound theoretical justifications, over time it has
been enriched with better theoretical underpinnings and estimation techniques. There
are many empirical applications of the gravity model that have contributed the
improvement of its specification and popularity. Among them include, Helpman and
Krugman, (1985); Feenstra, (2002); Breuss and Egger, (1999); Wei (1996);
Bergstrand, (1985); and Anderson (1979).
The technique has been applied in a number of fields especially for ex-post analysis of
migration flows, FDI flows, Currency unions, WTO membership, and RTA
membership among others. Thus gravity equation has been used more frequently with
quite a remarkable success in the prediction of international trade flows and the
evaluation of the success of different trade arrangements. This is mainly because, the
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model can be used account for factors that are regularly ignored by the traditional
models yet highly important in explaining trade patterns among countries.
5.1.1. Limitations of the gravity model
The gravity model has some important limitations notable among which include;
The baseline gravity model is likely to produce biased results because it
ignores some variables that are important in the prediction of trade flows. For
example distance alone is not enough to explain limitations to trade.
The dummy variables used may falsely attribute increased trade to an FTA
because they tend to be correlated with other variables such as diplomatic
efforts, technological diffusion, security that may be responsible for the
growth in regional trade among others.
5.2. The theoretical gravity equation
We use a gravity model to explain bilateral trade flows (Xij) in terms of incomes of
the importing and exporting countries, and the distance between them. The basic
gravity model explaining the relationship between trade flows, distance and economic
size is, in the general form expressed as follows:
Trade = Xij = GDPi, GDPj, Dij (5.1)
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In other words trade flows between two countries (Xij) are expressed as a function of
the characteristics of the exporting country (GDPi) and the importing country (GDPj)
and the degree of limitations/distance between them (Dij).
Each country produces a differentiated product and every country demands other
country’s goods because of the principle of comparative advantage (Feenstra and
Taylor 2008). These differences can be a result of differences in technology, natural
endowments, tastes and preferences, among others.
However, it is not completely true that trade depends only on the factors put forward
by the gravity model; it also depends on factors that a researcher deems to be relevant.
In order to account for these other factors, the gravity model is re-written as:
Xij = GDPi, GDPj, Dij, Vt (5.2)
Where Vt is a vector representing trade determinants that are ignored by the gravity
model. Introducing logs on both sides of equation 5.2 above transforms it into a
linear function.
LnXij = βLnK + U, U~N (0,σ2) (5.3)
Where K = The Vector of independent variables
β = The Vector of Parameters to be estimated
U = White noise error term.
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The main purpose of logs is to reduce effects of inflationary tendencies and generally
make data as closely comparable as possible.
5.3. Methodology and Data
Since it is well documented that preferential trading may negatively or positively
affect trade, Laszlo (1997) and Anderson and Wincoop (2003) demonstrate how
multilateral resistance terms (longitudinal and binary) can be added to the model to
remove much of the bias that may result from the omitted variables. The selection
process for variables to be included in the gravity model therefore follows the
theoretical work of Anderson and Wincoop (2003). According to them, gravity
models that include distance as the only cost to trade; are often more likely to produce
biased results and most importantly, the results cannot be used for comparative statics
exercises. They show that to correctly specify the gravity equation, the researcher has
to account for other trade costs that are not specific to only the bilateral barriers
between the countries under consideration. These costs are known as multilateral
resistances. The intuition is that trade between a given set of countries depends on
their trade costs with all trade partners in the ‘rest of the world’ (Deardorffs, 2012);
this way, trade will be higher for those countries that have relatively low trade
barriers.
As Anderson and Wincoop (2003) demonstrate, excluding such variables often results
in overestimation of country specific coefficients, and thus overstating the overall
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impact of trade arrangements on trade flows. The correct specification of the gravity
model including multilateral resistance terms is:
LnXij = αi + πj + λt + β2LnGDPjt+ β4LnPj + β3LnDij +…+ uijt (5.4)
Where
Pj = Population of country j
Xij = trade flows between country i and j.
GDPJt = GDP of country i at time t.
i = 1,…,i-1, i+1,…N+1. Where N+1 is the rest of the world (ROW).
πj = Country effect, J = 1,…,N+1.
λt = time effect, t=1, …,t.
Uijt = White noise disturbance term.
The relationship among variables is direct; the larger GDPj is, the larger Xij. There is
an inverse relationship between Dij and Xij. In other words, the original gravity
model assumes that trade flows between two countries depend positively on the levels
of output of the countries in concerned and negatively correlate with the distance
between countries.
On this relationship, studies by Baier and Bergstrand (2004), Egger and Larch (2008)
provide evidence of the existence of such a relationship between GDP and the level of
trade flows. They find that the effects of trade creation are greater in countries that
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have larger GDP and even greater in those whose GDP is high and comparatively
similar in size.
The gravity model is outstanding in a way that it doesn’t ignore the limitations to
trade. It uses distance between countries (Dij) as one of the costs of trade and its
effect on trade flows is negative. The longer the distance between countries the lesser
trade there is among them. Distance has been a vital variable among the determinants
of trade ever since the inspiring works of Rinbergen 1962, Anderson 1979, and
Krugman 1997 (cited by Nuno, 2010).
The specification of trade costs is empirically not straightforward because they are
numerous and sometimes consist of subcomponents that are very difficult to quantify.
For example, many studies use actual distance between countries, while others may
use actual data on shipping costs. Even though the use of actual shipping costs data
sounds more convincing in a way that it may sometimes be cheaper to transport goods
from South Africa to USA; than from South Africa to Liberia (depending on the
quality of infrastructure), the method is very difficult to apply. This is because
transport costs vary from one commodity to another and from one Transport
Company to the next. Also data on such costs are very rare on the international level
if not absent at all.
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Another way to measure costs is by using the exchange rate (EXCHij) as a proxy for
changes in prices between countries. The exchange rate is added to the gravity model
as an impediment to trade, since volatility in its rates affect trade flows (Egger 2002).
It is also one of the reasons why differences in relative prices exist between countries.
The study also adopts population of trade partners (Pj) as one of the variables that can
determine the level of trade flows between countries. The relationship between
population and trade flows can be explained in two different ways. In the first case,
the coefficient is expected to have an inverse relationship with trade flows because
larger populations mean self-sufficiency. This means that the country can effectively
absorb most of its produce, thus reducing the amount of surplus left for export.
However Bergstrand, (1985) notes that this relationship is not always true because
larger populations allow the accumulation of economies of scale which in turn boost
the export sector. This study maintains Bergstrand’s position.
5.3.1. Selection of multilateral resistance variables and hypothesis
As discussed earlier, costs to trade are an important ingredient in international trade in
that they help to measure or predict the ease of access of one country to markets in
other countries. Therefore this study finds it is erroneous to predict international trade
flows without taking into account the potential trade costs. This is one of the major
weaknesses of the traditional theories of trade.
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Distance is a major proxy of trade costs and it enters the model in various ways. The
traditional way in the above sections shows it affects trade flows as an approximation
of transport costs and time. In this section, it represents intangible mental distance of
trade partners. Intangible distance may include differences in cultures, language,
border structure and technology among others; that increase with distance (Bergeijk
and Brakman 2010).
Differences in languages are considered to impede trade in that it complicates the
grounds for doing Business. Exporters and Importers prefer to trade with people who
they can communicate with easily to those with whom they find it difficult to
communicate.
Border structure can have serious effects on the volume of trade between a given set
of countries .Generally, countries that have access to the sea, also have a natural
strategic advantage over land locked ones in terms of lower cross-continental
transport costs (Gwartney et al, 2000). This study includes access to the sea as one of
the factors that determine the level of bilateral flows between countries.
Finally, the study assumes that after controlling for GDP, Population, Exchange rate
and availability of a coastline, South Africa’s trade flows should be higher with
countries with which it shares a common border than those that it doesn’t. The
concept of natural trade partnership predicts such a trend in a way that it makes it
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easier for South Africa to penetrate closer markets than those located far away as
shown by Frankel et al (1993)
Therefore the final econometric gravity model to be estimated in this study is in the
general form of:
LnXij = β0 + β2LnGjt + β4LnPjt + β5LnDij + β6EXCHij + π0DSADC + π1DAGOA + π2DEU +
π3LANGUAGEj + π4 DBRIC + π5DLANDLOCKED + π6DBORDER + λ0FTAij + Uijt (5.7)
Where;
- FTAj is a structural dummy representing the period since 2001 to 2011. It
measures if there has been a structural change in trade flows of all countries
since the signing of the FTAs.
FTAj = 1 if period beyond year 2000, 0 otherwise
- DEU, DSADC and DAGOA are dummies representing EU, SADC and
AGOA respectively
DEU = 1 if a country is member of EU, 0 otherwise
DSADC = 1 if a country is a member of SADC, 0 otherwise
DAGOA = 1 if a country is a member of AGOA, 0 otherwise.
DBRIC = 1 if a country is a member of BRIC, 0 otherwise
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- EXCHij is the exchange rate between country i and i. Its effect on trade flows
is expected to be negative.
- DLANDLOCKED is a dummy representing the boarder structure of a given
country.
DLANDLOCKED = 1 if a country j borders an ocean, 0 otherwise
- DBORDER is a dummy representing proximity to South Africa
DBORDER = 1 if a country shares a border with South Africa, 0 otherwise.
- DLANGUAGE = 1 if a country speaks the same official language as South
Africa, 0 otherwise.
5.3.2. Estimation technique
The statistical techniques used to estimate the gravity equation could result to more or
less accurate parameters (Washington et al, 2003).
Traditionally, the ordinary least squares method (OLS) has been the common
technique for estimating the gravity model coefficients. Even though OLS is still
used, some researchers have made known the flaws in methodology and modeling of
the gravity equation using OLS (Henderson and Millimet 2008, Feenstra et al 2001,
Anderson and Wincoop 2003)
5.3.2.1. The Fixed effects Model
Recently, researchers have opted for more advanced techniques such as fixed effects
and random effects models as opposed to the OLS approach. The main reason is that
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results obtained by the latter are biased because the method does not control for
heterogeneity among regressors.
Since it is very difficult to know the source of heterogeneity bias, FEM enables the
use of dummy variables to control for the possible factors that may be correlated with
the volumes of trade between countries. By introducing such dummies, the FEM
avoids biases that may arise due to omission of variables, - especially those that do
not change over time.
The strength of FEM is that being a technique of panel data analysis; (a combination
of serial and cross sectional data), it gives more information, allows more variability
by increasing the degrees of freedom and also takes heterogeneity into account by
expressing each variable as a deviation from its mean value. A basic FEM model is
estimated as:
LnXijt = β0i + β2LnGDPjt + β4LnPjt + β5LnEXCHijt – β3LnDij + Ut. (5.8)
The subscript i on intercept β0i suggests that the intercepts for every country may be
different. The coefficient lack subscript t to suggest that even though the intercepts
may be different, they do not vary over time. Inferring from the slope coefficient, it is
clear that the model assumes that the slope coefficients do not differ across countries.
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In order to allow for heterogeneity among countries, the “differential intercept dummy
technique” is used. It calls for the introduction of a differential intercept dummy for
each cross sectional unit. In this case SADC, EU, BRIC and AGOA dummies should
be introduced. However, in order to avoid the dummy variable trap (a presence of
perfect collinearity among the regressors), only one dummy for each category is
introduced.
LnXij = β0 + β2LnGDPjt –β4LnPjt + β6EXCHij + π0DSADC + π1DAGOA + π2DEU + π4
DBRIC + Uijt (5.9)
Since a differential intercept dummy is introduced for each trade arrangement, all
other time invariant variables are excluded from the model. This is because, the
dummy variable representing each country does not change with time and so does
distance, membership to a given agreement, borderline, language among others.
Therefore all heterogeneity that exists in such variables is absorbed into the
differential intercept dummies. If added, the model experiences a problem of perfect
collinearity among time invariant variables, thus making it impossible to identify the
effects of each time invariant variable on the dependent variable.
The FEM discussed above ignores time invariant variables that may be equally
important in the prediction of trade flows. Although the model eliminates any bias
that may result from the omission of such variables, it is impossible to know their
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specific bearing on the dependent variables because their total effects are absorbed
into the differential intercept. One way to overcome this problem is to use the Error
Component Model (ECM) commonly known as the Random Effects Model (REM).
Instead of assigning an intercept to every cross-sectional unit, the REM only estimates
parameters that describe the distribution of the intercepts.
The REM assumes that the cross-sectional units being studied are a sample from a
large population of units that have a common mean value. How much each country’s
intercept differs from the rest is reflected as a random deviation from the common
intercept.
On the other hand, the REM requires that the random effects be uncorrelated with the
individual effects, which is often very unlikely.
The other way (and one adopted by this study) is to estimate separate regressions, one
including country specific effects and the other taking time invariant variables and
Independent variables (Martinez and Nowak, 2003)
The gravity model is therefore estimated in two stages:
- Stage one estimates bilateral trade flows as a function of the original gravity
equation variables plus all time invariant variables.
- Stage two accounts for unobserved country specific effects.
Thus the equations to be estimated in this study are:
Stage 1
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- LnXij = β0 + β2LnGDPjt + β4LnPjt + β5LnDij + β6EXCHij + ¥it+ Uijt (5.10)
Where
¥it is a vector of all time invariant variables in the regression.
The study will break equation (5.10) into import and export functions. Doing so
allows the researcher to capture variability in respective data that may be missed in
case of aggregate flows. Estimation of the STAGE 1 export and import function
follows the same procedure as the bilateral flows model. The equations to be
estimated are
Export model:
LnEXPij = β0 + β2LnGDPjt + β4LnPjt + β5LnDij + β6EXCHij + ¥it+ Uijt (5.11)
Imports model:
LnIMPij = β0 + β2LnGDPjt + β4LnPjt + β5LnDij + β6EXCHij + ¥it+ Uijt (5.12)
Stage 2 estimation
LnXij = β0 + β2LnGDPjt + β4LnPjt + β6EXCHij + π0DSADC + π1DAGOA + π2DEU +
π4 DBRIC + Uijt (5.13)
The exports model
LnEXPij = β0 + β2LnGDPjt + β4LnPjt + β6EXCHij + π0DSADC + π1DAGOA + π2DEU
+ π4 DBRIC + Uijt (5.14)
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The imports model
LnIMPij = β0 + β2LnGDPjt + β4LnPjt + β6EXCHij + π0DSADC + π1DAGOA + π2DEU
+ π4 DBRIC + Uijt (5.15)
5.3.3. Data and Variable description
The study uses bilateral trade data between South Africa and 100 countries for a
period of 16 years starting from 1995-2010. It is important to note that the study does
not consider intra bloc trade. The data includes observations for all 27 EU countries,
10 SADC countries, all 4 BRIC countries, the USA and 58 other countries that do not
belong to any of the trade agreements under consideration. The countries in the latter
group were chosen basing on the size of their National Income and data availability.
Data on exports, imports and the exchange rate were obtained from UNCTAD STAT,
a trade database of the United Nations Conference on Trade and Development and are
measured in constant 2000 USD. The study initially intended to use data provided by
UNCOMTRADE but later found that UNCOMTRADE does not provide trade
statistics for South Africa prior to 2000. UNCTAD on the other hand contains data
prior to 2000 collected from a number of databases including UNCOMTRADE,
Directions of Trade Statistics (DOTS), UN DESA Statistics Division among others
(UNCTAD, 2012A).
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Data on population and GDP were obtained from World Development Indicators
(2012) and are also measured in constant 2000 USD. Data on distance was obtained
from time and date (2012) expressed in number of kilometers from Johannesburg to
the capital city of a given country. All data on exports, imports, and GDP is expressed
in current US dollars. The exchange rate is presented as a unit of National currency
per US dollar (UNCTAD, 2012B)
Other data on border structure, language, and participation in a given trade agreement
were obtained from Wikipedia and CIA fact book. To avoid inflation biases resulting
from inflationary tendencies, all data has been logged where applicable.
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CHAPTER VI
ESTIMATION RESULTS
6.1. INTRODUCTION
This chapter presents the results obtained from the regressions as specified in the
previous chapter. The main objective of this research is to investigate the effects of
South Africa’s trade arrangements on its trade flows. It is important to note that the
trade flows considered are between South Africa and its trade partners but trade flows
among partner countries is neglected (i.e. the study does not considered trade flows
between UK vs France, France Vs Germany, Zimbabwe vs Zambia, Zambia vs
Mozambique…etc.). Therefore, the study does not estimate intra-bloc or extra-bloc
trade. In this case, trade creation and trade diversion effects are measured from South
Africa’s perspective and not from the whole of a trade arrangement under
consideration. In this case, a statistically significant and positive or negative EU,
SADC, BRIC, or AGOA dummy coefficient will imply that the respective trade
arrangement has created or dampened trade between South Africa and its trade
partners and in that order.
Another important point to note is given by Kanda and Jordaan (2010) about the
objectivity of studies that investigate trade creation and trade diversion using the
gravity model. He argues that the concept of trade creation and trade diversion must
not be confused with welfare analysis. This is because the dummies employed by the
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gravity equation only represent bilateral trade flows. Yet, linking the gravity equation
to welfare is only possible by analysing the efficiency losses or gains associated with
changes in exports. This is out of the scope of this study.
To capture best how different factors considered in this study affect trade flows,
results are first presented on a yearly basis (cross-section analysis) to capture how
trade flows have been responding to changes in the independent variables. Then a
Ordinary Least Square and then fixed effects technique will be applied to all 100 cross
sections over a total period of 15 years.
6.2. ESTIMATION RESULTS
The table below shows how bilateral trade flows have been responding to different
variables over the last 16 years. The importance of such cross sectional analysis is that
it helps in identifying the changes in the strengths of relationships between a set of
variables over the years. Identifying such changes involves taking note of changes in
signs and size of coefficients.
Apart from Border, all independent variables have the expected signs. The R2 are high
enough for us to conclude that the variables included convincingly explain the
changes in the dependent variable. The cross sectional results show that contrary to
popular belief, sharing a common border with South Africa does not affect in any way
the level of bilateral trade interactions between any given country with South Africa.
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The border effect was only statistically significant in 1999-2004. In that period, the
border effect was negative implying that countries that share a border with South
Africa possess special features that lower their bilateral trade flows with South Africa
lower than the average country from the rest of the World
Table 6. 1: Cross section estimation results
Dependent Variable: Bilateral trade flows
Year Constant GDP DistancePopulatio
nExchange
rateLanguage Border R2 F stat
1995 14.283 1.548*** -3.577*** -0.216* 0.079 1.131** -1.984 0.76 46.74
1996 13.916 1.450*** -3.304*** -0.276* 0.065 1.191** -1.662 0.77 46.13
1997 12.622 1.347*** -2.866*** -0.174 0.051 1.154*** -1.623 0.80 53.48
1998 12.258 1.303*** -2.781*** -0.144 0.021 1.140*** -1.254 0.81 57.06
1999 14.652 1.472*** -3.253*** -0.307** 0.063 1.046** -1.593 0.81 57.33
2000 14.119 1.481*** -3.149*** -0.295** 0.065 1.115*** -.729* 0.85 103.76
2001 13.663 1.471*** -3.172*** -0.249** 0.092** 1.017*** -1.841** 0.87 90.93
2002 13.743 1.341*** -2.843*** -0.229** 0.083** 1.191*** -1.503** 0.86 82.12
2003 12.924 1.357*** -2.896*** -0.174** 0.121*** 0.966*** -1.588** 0.86 83.56
2004 11.921 1.413*** -2.935*** -0.171** 0.116** 1.073*** -1.565** 0.84 71.32
2005 11.851 1.371*** -2.814*** -0.167 0.094** 1.057*** -1.410 0.8 54.18
2006 12.038 1.290*** -2.637*** -0.142 0.072 1.003*** -1.220 0.8 53.67
2007 10.130 1.259*** -2.434*** -0.093 0.081** 0.989*** -0.678 0.83 65.12
2008 11.249 1.266*** -2.598*** -0.085 0.054 1.102*** -0.967 0.82 62.37
2009 11.075 1.243*** -2.539*** -0.093 0.075 1.21*** -0.813 0.82 60.02
2010 10.395 1.243*** -2.528*** -0.058 0.073 1.185*** -0.779 0.80 53.67
* significance at 10% level; ** significance at 5% level; *** significance at 1% level.
The results also show that higher populations in partner countries have a negative net
effect on bilateral trade flows with South Africa. The population coefficient was
negative and statistically significant in 1995-2004 except for 1997-1998. This means
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that in this period the intensity of trade was smaller trade between South Africa and
highly populated countries than with less populated countries. The possible
explanation is that countries with higher populations also have local market that are
big enough to stimulate investment in the production of a variety of goods, thus
reducing the need for South African exports. However, beyond 2004 the impact of
population on bilateral trade flows shrunk in a way that in the last six years the net
effect of population on bilateral flows has been nil.
The impact of the exchange rate on trade flows has also been mixed. The results show
that the depreciation of the dollar only had a significant impact on bilateral trade
flows between 2001 and 2007 (with the exception of 2006). In other periods however
changes in the value of the dollar have had no significant effects on South Africa’s
trade flows.
The strongest and perhaps the most important variables that have influenced South
Africa’s bilateral trade flows over the past sixteen years have been GDP, Distance and
Language. Distance as the major proxy for trade costs has a strong negative impact on
South Africa’s trade flows as predicted by the gravity model. Countries that are
located far away trade less with South Africa than an average country. The
coefficients of the distance variable have been declining in size since 1995. This is
very promising for South Africa’s trade relations since it means that the constraints to
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trade have been decreasing, thus slowly making it relatively easier to do business this
year than the previous one.
The impact of GDP on trade flows has been positive and statistically significant in all
periods, thus indicating that countries with higher GDP trade more with South Africa
than an average country. However, the coefficients have been declining although not
as rapidly as the distance coefficients. This decline means that even though a trade
partner’s Income plays an important role in determining its level of bilateral flows
with South Africa, trade patterns are changing in a way that South Africa is
increasingly trading more, even with countries that have lower Incomes.
This is a very important development because it implies that South Africa has been
diversifying markets for its products from mainly targeting high income countries to
including production targeting demand from lower income countries. Given the
volatility of markets in high income countries as has been the case in the past five
years, diversifying South Africa’s incomes seems to be a very necessary strategy that
can help stabilise public finances.
Finally, the language coefficient is also statistically significant and positive indicating
that on average, South Africa’s bilateral trade flows have been high with Countries
that speak English as their official language. This makes economic sense since any
trader is bound to find it easier to do business with partners with whom it is easy to
communicate.
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6.2.2. Panel Estimations
The study runs Panel estimations of the gravity equation. Given that the variables also
included time invariant variables, estimations were done in two stages. The first
included all time invariant variables and the second estimated country specific effects.
The data exhibited severe signs of autocorrelation. As a result, generalized least
squares estimation was performed on the data. This significantly improved the
Durbin-Watson statistic but the resulting R-squared were very low. To avoid loss of
the first observation due to GLS, Prais-Winsten transformation technique was used
(Gujarati, 2003).
The study also runs independent regressions taking exports and imports as dependent
variables instead of using aggregated bilateral total flows. This is done largely
because by considering exports and imports independently, variations (relationships)
between variables that may be unnoticed when aggregate bilateral flows are
considered can be easily identified.
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Table 6. 2: Stage 1 Estimation results: Exports model
Dependent Variable Exports
Method Panel Least Squares
Variable Coefficient Std. Error t-Statistic Prob.
C 4.832259 0.501633 9.633057 0.0000
BORDER -0.436705 0.194743 -2.242468 0.0251
FTA 0.264385 0.051719 5.111978 0.0000
LANDLOCKED 0.184566 0.070077 2.633749 0.0085
EXCHANGE RATE -0.077493 0.024964 -3.104213 0.0019
GDP 0.901827 0.044488 20.27107 0.0000
LANGUAGE 0.265943 0.062996 4.221591 0.0000
DISTANCE -0.739103 0.056887 -12.99238 0.0000
POPULATION 0.117888 0.050563 2.331495 0.0199
R-squared 0.453303 F-statistic 164.7971
Adjusted R-squared 0.450553 Durbin-Watson stat 1.741994
Table 6. 3: Stage 1 estimation results: Imports model
Dependent Variable Imports
Method Panel Least Squares
Variable Coefficient Std. Error t-Statistic Prob.
C 0.811403 0.660402 1.228649 0.2194
BORDER 0.012363 0.256380 0.048221 0.9615
FTA 0.158817 0.068088 2.332526 0.0198
LANDLOCKED 0.024135 0.092257 0.261607 0.7937
EXCH RATE 0.040958 0.032865 1.246236 0.2129
GDP 1.462627 0.058569 24.97264 0.0000
LANGUAGE 0.192128 0.082935 2.316618 0.0207
NDISTANCE -0.495463 0.074893 -6.615656 0.0000
POPULATION -0.191591 0.066567 -2.878173 0.0041
R-squared 0.442833 F-statistic 157.9654
Adjusted R-squared 0.440030 Durbin-Watson stat 2.069152
As expected the estimation results above indicate that GDP and language positively
influence bilateral trade flows between South Africa and its trade partners. The
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coefficient of Distance is negative indicating that the value of exports and imports
respond negatively to increase in distance. The export coefficient is larger than that
from the imports model thus implying that South Africa exports less to distant
countries than it imports from them.
The coefficient of exchange rate is negative. Following a percentage depreciation of
the dollar, South Africa’s exports fall by around 7.4% [i.e. exp^(-0.07749)-1].
However, the imports model shows that South Africa’s imports are independent of
changes in the exchange rate.
While the Exports model shows that South Africa exports more to countries with
higher populations, the imports model reveals the opposite. The imports elasticity of
population is negative. This indicates that South Africa imports less from countries
with larger populations than it does from those with smaller populations. This finding
makes economic sense in a way that production in economies with a high number of
people is more domestic oriented since larger populations provide enough absorption
capacity for local produce. But also larger populations provide bigger markets for
South Africa’s exports.
The results however show that countries with no access to the sea possess unique
features that attract South Africa exports more than countries that have access to the
97
sea. This is an unexpected result and an outright contradiction to economic theory
and the predictions of Gwartney el at (2000) and the findings of Kwentua (2006).
Another unexpected result is the dummy for countries that share a common border
with South Africa whose coefficient is negative. Countries that are within each other’s
proximity are expected to be natural trade partners due to lower transport costs and
shorter mental distance (Krugman, 1991). In this case however, the result show that
countries that share a border with South Africa do exhibit special features that reduce
their bilateral trade flows with South Africa much lower (around -35%) than the rest
of the world. The most likely explanation for this is the fact that South Africa is
surrounded by countries with lower incomes compared to other countries included in
this study
Conversely, the imports model shows that the coefficient for border is statistically
insignificant; indicating that there is no evidence in the imports data that suggests that
South Africa imports more from its neighbours than from the rest of the world. It is
highly advisable to take this conclusion with caution because only three countries
(Mozambique, Zambia and Zimbabwe) were considered for the analysis due to
constraints regarding availability of data for the remaining countries.
The coefficient for the FTA dummy is positive and statistically significant. This
implies that the period since 2001 (period of trade liberalisation), South Africa has
experienced higher average exports (around 30.2% increases) to all countries
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compared to the period before. Results show that exports have responded more to
liberalization than imports. The imports model shows that 17.2% of changes in South
Africa’s imports are due to liberalization. This is an indication that South Africa’s
approach to international trade in the past decade has stimulated trade to significantly
higher levels compared to the levels that existed before 2001.
The export and import model produced positive coefficients for the Landlocked
dummy. This implies that that South Africa generally exports more to landlocked
countries than it does from countries with access to the sea. This result does not make
any economic sense. The reason being that exporting to countries with access to the
sea usually involves lower cross-continental transport costs than land locked ones.
Table 6.4: Stage 2: Fixed effects estimation results: Bilateral trade flows model
Dependent Variable Bilateral trade flows
Method Fixed Effects
Estimation
Variable Coefficient Std. Error t-Statistic Prob.
C -1.531556 0.145007 -10.56193 0.0000
EXCH RATE -0.037407 0.022491 -1.663182 0.0965
GDP 0.954829 0.038471 24.81946 0.0000
POPULATION 0.096518 0.045007 2.144526 0.0321
AGOA -0.053092 0.268667 -0.197612 0.8434
BRIC 0.043555 0.421539 0.103323 0.9177
EU 0.126404 0.062828 2.011906 0.0444
SADC 0.948116 0.088309 10.73641 0.0000
R-squared 0.502033 F-statistic 229.2857
Adjusted R-squared 0.499844 Durbin-Watson stat 1.497564
Unlike the first model, the fixed effects model excludes all time invariant variables so
as to avoid their effects being expended into the individual effects dummies. In table
99
6.4 above the coefficient of the EU and SADC are positive and statistically
significant. It can be concluded therefore that both trade arrangements stimulate trade
for South Africa to levels that are significantly higher than those that existed prior to
their initiation. Since 2001, 13.5% of the total increase in EU-South Africa (EU-SA)
bilateral trade flows is attributed to the free trade agreement where as the SADC free
trade agreement raised bilateral flows by over 158%.
Table 6. 5: Stage 2 estimation results: Exports modelDependent Variable Exports
Method
Fixed Effects
Estimation
Variable Coefficient Std. Error t-Statistic Prob.
C -1.150567 0.167712 -6.860391 0.0000
EXCH RATE -0.086733 0.026013 -3.334271 0.0009
GDP 0.807538 0.044495 18.14914 0.0000
POPULATION 0.170243 0.052054 3.270532 0.0011
AGOA 0.094232 0.310733 0.303259 0.7617
BRIC 0.381463 0.487541 0.782422 0.4341
EU 0.093572 0.072665 1.287705 0.0198
SADC 1.037231 0.102135 10.15545 0.0000
R-squared 0.391136 F-statistic 146.1005
Adjusted R-squared 0.388458 Durbin-Watson stat 1.574014
The exports function in table 6.5 shows that the EU-SA trade agreement has increased
South Africa’s exports to the EU by over 9.8% and imports by 15.3%. Whereas the
SADC free trade agreement has augmented South Africa’s exports to SADC countries
by over 180% and imports by 133%. . By 2004, Holden and Mcmillan (2006) showed
that the SADC had increased South Africa’s exports to the SADC by over 50%
compared to only 33% by the EU-SA trade agreement. Given that the average
100
purchasing power of EU member countries is much higher than that of SADC, this
pattern can attributed to the negative impact of distance.
Table 6. 6: Stage 2 estimation results: Imports Model
Dependent Variable Imports
Method
Fixed Effects
Estimation
Variable Coefficient Std. Error t-Statistic Prob.
C -3.268511 0.212367 -15.39089 0.0000
EXCH RATE 0.032000 0.032939 0.971496 0.3314
GDP 1.381841 0.056342 24.52607 0.0000
POPULATION -0.139526 0.065914 -2.116803 0.0344
AGOA -0.269355 0.393469 -0.684566 0.4937
BRIC -0.357469 0.617355 -0.579034 0.5626
EU 0.142805 0.092013 1.552001 0.1209
SADC 0.849995 0.129330 6.572293 0.0000
R-squared 0.426820 F-statistic 169.3552
Adjusted R-squared 0.424300 Durbin-Watson stat 2.013747
Just like the first stage estimations, the second stage bilateral trade flows and the
export models show that South Africa’s bilateral trade is dependent on changes in the
exchange rate. However the imports model in table 6.6 shows that the value of South
Africa’s imports is independent of changes in the exchange rate. On the other hand,
larger populations in partner countries have a negative effect on the value of South
Africa’s imports from them (around 13%) but raise South Africa’s exports to them
(by around 18.5%). On aggregate however, the bilateral trade flows model shows that
the overall impact of population on South Africa’s bilateral trade flows is positive;
thus indicating that South Africa trades more with countries with higher populations
than with countries that have smaller populations.
101
On the other hand, AGOA and BRIC trade arrangements have not stimulated trade
yet. AGOA has been in place since 2001 but it is not a free trade agreement; it only
gives selected South Africa’s commodities free market access to the USA. Given that
AGOA is due to expire in 2015, it can be concluded that it has created no winners and
no losers.
After running for less than one year, the BRIC trade arrangement has also not
produced any significant changes to bilateral trade flows between South Africa and
BRIC members. The period is just not enough to allow for adjustments that are big
enough to produce noticeable effects on bilateral trade flows.
102
CHAPTER VII
CONCLUSION AND RECOMMENDATIONS
7.1. Conclusion and Recommendations
This study examines the importance of South Africa’s trade agreements on its
bilateral trade flows since 1995-2010. To achieve this objective, the study controlled
for a number of variables such as GDP, population, exchange rate, distance, access to
the sea, border and language. Controlling for such variables has an added advantage
in a way that the study is able to identify the main determinants of South Africa’s
trade flows and also to capture the extra influence added by the trade arrangements
considered in this study.
This study shows that South Africa’s bilateral trade flows positively depend on the
partner’s purchasing power and economic growth. Also the study shows that
exchange rate is important and a depreciation of the dollar has a negative impact on
South Africa’s exports. When a dollar loses value, South Africa’s goods become more
expensive for foreign importers. However, the finding of this study in that South
African imports do not seem to be affected by the exchange rate value of the dollar is
puzzling.
On the other hand, the study found regarding the geographic dimension that distance
is important in explaining trade and as expected is typically negative and an
103
impediment to trade. Accounting for market size, by including a population variable
the results obtained in this study indicate that South Africa seems to import less from
larger countries. However, the larger countries seem to import more from South
Africa.
Additionally, the results obtained from panel data estimations of the individual
specific effects model show that only two out of the four trade agreements have had a
significant impact on South Africa’s trade flows. After controlling for SADC, AGOA
and BRIC effects, both exports and bilateral flows models results showed that the EU-
SA trade agreement has significantly stimulated trade for South Africa. However, the
agreement has not stimulated South Africa’s imports from the bloc as indicated by the
imports model. This is a good outcome for South Africa, especially given the fact
that the EU-SA FTA had not fully removed all tariffs on each other’s exports.
However, with further liberalization, the EU-SA partnership promises larger bilateral
trade expansion prospects for both parties.
On the other hand, the SADC FTA has also created trade for South Africa and SADC
in general. The SADC coefficient is positive and statistically significant in both the
exports and imports model. This means that SADC FTA has increased the value of
both imports and exports between SADC countries and South Africa. However, it is
important to highlight that due to data limitations not all the SADC member countries
104
were included in the study. Researchers such as Lattimore and Bottini (2009) have
hinted on the same limitation.
However, the BRIC and AGOA trade agreements have not had any noticeable effects
on their respective trade flows with South Africa. It is important to note that the BRIC
trade arrangement was put in place in 2010 and since this study covers the period of
1995-2010, it is possible that the effects of this trade arrangement have not been felt
yet since it takes time for traders to locate new imports and exports markets in partner
countries. Also, as far as BRIC is concerned, the adjustment period is just too short
for significant changes in trade patterns to emerge.
AGOA on the other hand, has had a fair amount of time for meaningful results to be
felt. Given that the overall objective of AGOA is to increase trade with South Africa
in particular and Africa in general using trade as a vehicle to create opportunities for
sustained business enterprise and Investments, the expected pattern should be one that
sees an increment in exports for South Africa. It can be concluded therefore that since
AGOA is not a free trade agreement per se, upgrading it to total liberalization in areas
might improve South Africa’s exports performance on the US market.
Lastly, it has been shown that where South Africa has made strong commitments to
free trade, the results have not been disappointing. Both the SADC and EU trade
agreements have tremendously improved South Africa’s trade performance. Since
105
multilateral trade negotiations have been moving at a snail’s pace, efforts to negotiate
free trade agreements should be pursued with renewed enthusiasm. The importance of
border, distance and GDP on bilateral trade flows have been decreasing in the past
decade. Therefore, considering that trade arrangements and other variables matter for
trade, South Africa should strengthen its trade ties and suggest targeting developing
markets particularly emerging southern countries. Additionally, South Africa should
try to tailor its export sector to the changing structure of imports demand in these
countries. Thus, ensuring a steadier and diversified source of income that will
contribute towards South Africa’s economic growth and development
106
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