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Chapter 1: Introduction
With the increasing interconnectedness of nation-states through globalizations
economic growth has become a primary focus for policymakers and other economic
agents interested in the well-being of the nation’s economy. Like many other African
countries, South Africa has experienced transformation in its trade and growth
policies. In the case of South Africa, these transformations were enacted through the
advent of democracy in 1994 when the economic sanctions against the country were
removed, allowing for the country to participate in international trade.
South Africa plays a crucial role in the international trade market as the country is a
member of 26 trading blocs having signed 9 international tariff agreements (WITS,
2021). Furthermore, WITS-UNSD Comtrade also stipulates that the country has 223
export partners, and 232 imports partners (WITS,2021). The International Monetary
Fund (IMF), also highlights that South Africa has the third-largest economy in Africa
(IMF, 2020) and the largest in the Sub-Saharan region in terms of its GDP
(Statista,2020), and has long been a significant hub in international trade due to its
position which connects the east to the west through maritime trade as well as being
situated in one of the major shipping routes, which is the Cape of Good Hope (see
figure 01). Additionally, the country has the most extensive transport infrastructure
network in the African continent, which includes approximately 750 000 km roads, 30
000 km rail tracks of which 20 900 km are route kilometers, eight commercial ports,
and eleven principal airports (Transnet, 2017). The aforementioned heightens the role
of liner connectivity and geographical positioning in determining the impact of
international trade on country’s economic growth, where the liner connectivity is
measured by the Logistics Performance Index (LPI) a benchmark established by the
WorldBank strongly associated with trade expansion, export diversification, ability to
attract foreign direct investments, and economic growth.
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Additionally, UNCTAD’s Maritime Profile for South Africa states that the country
accounted for 0.40% of the world’s GDP; where the value of South African imports
and exports represented 0.56% and 0.48% of the world’s share, respectively. In
monetary terms, the country’s world share of imports was valued at US$ 107 539 (in
millions); meanwhile, the exports were valued at US$ 90016 (in millions)
(UNCTAD,2019). The figures above represent the international trade value ratio to
global GDP, which is usually an accurate indicator of overall interdependence in the
economy utilized by the IMF (IMF,2021).
Figure 01: Major Ocean routes for global trade
Source: transportgeography.org
Aims and objectives of the study:
This study aims to investigate the impact of international trade of commodities on the
economic growth of South Africa. These will be reached through:
1. Identifying the top 10 imports and exports from South Africa from 2010-2019.
2. Identify which commodities have a positive impact on GDP.
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Significance of the study
The importance of the study is to assess which of the commodity groupings has a
positive impact on economic growth. Thereby assisting economic agents such as
traders; investors; forecasters and policymakers in making more realistic and informed
decisions using real-time data.
Structure of the dissertation
This dissertation has been split into seven chapters. The first chapter of the study is the
introduction chapter which provides a synopsis of the entire study. The second chapter
of the paper is the literature review chapter which explores the literature on the
international trade of commodities and how it has impacted economic growth in the
past. The literature is reviewed within this chapter focuses mainly on international
trade theories and how they impact trade policies; the South African economy and its
trade patterns; as well as the type of resources the country is well endowed with. The
third chapter of the study is the data whilst the fourth is chapter is the methodology
component. In chapter three, a descriptive analysis of the variables and data collection
techniques are provided. In chapter fourth chapter we provide a description of the
methodology and the various models to be utilized. The fifth chapter of the paper is
the empirical findings section, where we analyze and interpret the regression output
from our software. The sixth chapter of the study is the discussion chapter, where we
focus on the final regression output and apply economic theory in analyzing whether
our regression results mimic real-world data as given by OEC, WITS on the types of
the commodity that are traded the most by South Africa. The final chapter of the study
is the conclusion and recommendation chapter. In essence, this chapter provides
recommendations to economic agents on which commodities to focus on to improve
trade and output for the country.
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Chapter 2: literature review
As a continuation from the previous chapter, the aim of this chapter of the paper is
essentially in two folds, firstly to link international trade with international trade
theories. This is done by exploring literature that will outline the concept of
international trade and its determinants. The second section of the study will explore
the components of South African, its role and contribution to the international trade
market as well as the country’s resource endowment. In assessing the country’s
resource endowment, we look at the trade flows of these commodities, together with
the factors that determine international trade.
2.1. History of International Trade?
One of the earliest studies of the relationship between international trade and growth
of the economy dates back as far as the classic period in the 18th century when David
Ricardo and Adam Smith asserted that trade had a general influence on the positive
growth of the economy. Historically, International trade has always been known to
take place exclusively through either imports or export. However, the modern
landscape of international trade is three-dimensional and inclusive of a concept called
entrepot trade. Entrepot trade, which essentially is defined as the act of importing a
commodity from country A into country B and later on exporting those goods/services
to country C (Yeung,1967). This type of international trade is usually done through
commodity arbitrage and free trade zones and is known to be one of the biggest
contributors to the growth in the manufacturing industry. For this study, however, we
will not look at entrepot trade as this can be seen as double counting.
Nations trade with one another because no one nation is self-sufficient. International
trade is essentially the act of importing and exporting goods and services through
transferring factors of production. Through international trade, countries can
specialize in labor and production to produce/export more at lower unit costs, expand
their market size, and engage in mass production. Over the years, the increase in
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international trade and investment has reflected deeper exogenous macroeconomic
factors such as decreased costs or an increase in the rewards of international economic
transactions. For the aforementioned gains in trade to be reached, there are various
determinants of international trade that a country needs to consider, particularly when
utilizing maritime transport, which is the main channel for the international exchange
of commodities (Hoffmann et al., 2019).
2.2. What Is International Trade?
International trade is known to be a composite of goods and services. The trading of
goods dates back to 600 BC, where humans used the bartering system to get weapons;
food; and spices. The services sector according to United Nations Conference on Trade
and Development (UNCTAD), came into existence in the 1980s, where initially the
service sector accounted for approximately 61% of the GDP of developed nations and
42% in developing nations. In 2017, UNCTAD later on, reported that the services
sector contributed approximately 76% to the GDP in developed countries and
approximately 55% in developing countries (UNCTAD,2017).
Like any other concept, international trade is known to have both advantages as
disadvantages. Comparative advantages come into play when assessing the advantages
of international trade factors such as job creation, economic growth, competition,
technology transfer, and economies of scale. When assessing the disadvantages of
international trade, factors such as loss of state sovereignty/dependency, exploitation
of natural resources, unfair competition are usually quoted.
2.3. Linking International Trade With International Trade Theories
Studies on the relationship between international trade and the growth of an economy
date back as far as the classic period in the 18th century when David Ricardo and Adam
Smith asserted that trade had a general influence on the positive growth of the
economy. International trade theories are known to provide insights into mechanisms
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of international trade as well as the determinants of trade patterns and interactions of
trade and economic growth. (Krugman et.al,,2008). Trade theories are important and
are often used by managers of large companies as well as policy makers to identify
advantageous strategies that will improve international trade within their firms
/countries. Furthermore, these theories can be divided into different schools of thought
namely the classical and neoclassical schools of thought. The key difference between
the classical and neoclassical schools of thought lies within the assumption, where the
classical school of thought was developed under perfect competition and constant
returns to scale. Havrylyshyn (1990) goes on to state that by allowing perfect
competition the gains from trade essentially translate into improved efficiency. On the
other hand, the Neoclassical school of thought was built based on the assumptions that
the economy operates under imperfect competition and that there exist economies of
scale, which is more evidential to the current setting of international trade.
Furthermore, Mogoe (2013) also states that neoclassical trade theories explain trade in
terms of technology, technology diffusion/adjustment lags, and continuous innovation
processes. In addition, he further elaborates by stating that less developed countries
will specialize in the export of old, mature goods where production processes become
routine and less skilled labour has to play a greater role (Mogoe,2013)
2.3.1. Mercantilism Theory
When analyzing the classical school of thought one can simply state that Jean-Baptiste
Colbert was one of the founding fathers of international trade theories. Jean-Baptiste
Colbert in the 16th century propounded the theory of Mercantilism where the term was
derived from the Latin word “mercari”, which means “to run a trade”. (International
trade and theory,2008). According to Landreth and Collander (2002), the mercantilist
system was built based on the mercantilism trade theory and advocated for increasing
the nation's wealth through instantaneously encouraging production, increasing
exports, and holding down domestic consumption. Mercantilism advocated for low
wages to give the domestic economy competitive advantages in international trade
(International trade and theory,2008). Essentially, the system's main aim was to ensure
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that a country exports more than it imports. To attain the theoretical underpinnings of
the mercantilism theory, mercantilists advocated for strict government control of all
economic activity and preached economic nationalism as they believed that trade was
a zero-sum game and a nation could gain in trade only at the expense of other nations
(Georgiou,2016). From the above, we see that the mercantilism theory of trade was
heavily reliant on protectionism in the form of fiscal policy and other governing acts
such as the Navigation Acts of 1951 which forbid foreign vessels from trading
alongside the British coast. In addition, this required exports to pass through British
control before being redistributed all over Europe (Ransom,1968). Mercantilists
believed that the health of a nation’s economy could be assessed through quantifying
the number of precious metals the nation owns, a notion that still exists within the 21st
century through the “Gold Standard”. The Gold Standard can be defined as a system
where the country’s currency is value is directly linked to the reserve of gold, precious
metals, and coins. Ownership of precious metals such as Gold which acts as a provider
of macroeconomic stability and is often traded in times of economic difficulty to
ensure sufficient reserve in a country’s reserve, we see such practices still being
evident in economies such as France, Germany, Italy, China, and Switzerland.
2.3.2. Theory of Absolute advantage
The international trade theory of Absolute advantage was the first trade theory
advocating for free trade. The theory was coined by Adam Smith in 1776, where he
stated “if a foreign country can deliver us goods cheaper than we would produce, it is
better to buy them from that country, with a part of the product of our activity, using
them in a way which can bring us benefit” (Smith, 1962). Essentially, argued that any
given country has an absolute advantage in the production of commodities when it is
more efficient than any other country in producing (Smith,1776). The theory was built
on the assumption that the trade was occurring amongst two countries where only two
commodities were being trading under free trade agreements with labor being the only
cost. From the theory of absolute advantage, we see that free trade leads to
specialization in the production of commodities that a country is more efficient in, and
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importation of commodities which it cannot produce efficiently thereby leading to
international specialization in factors of production which could increase global
output. Through international specialization, we find elements of “division of labor”
being introduced. When comparing the aforementioned theories, it can be said that
Adam Smith’s free trade system was more favorable than Jean-Baptiste Colbert’s
mercantilism trade theory for economic growth. Additionally, under mercantilism
trade was viewed as being a zero-sum game. Therefore, just like any other theory, the
theory of absolute advantage was critiqued mainly because it focused solely on one
factor of production and its cost under its assumptions. In contrast, international trade
constitutes other costs such as transportations and the cost of capital.
2.3.3. Theory Of Comparative Advantage By David Ricardo
The international trade theory of comparative advantage as propounded by David
Ricardo was essentially a continuation of the works of Adam Smith. The theory's
underlying assumptions are similar to that of Adam Smith where theory assumes that
two countries are trading two goods with only one input factor (labor), which was
viewed as being homogenous with an inelastic supply. (Ricardo,1817). A comparative
advantage exists when 1 of the trading partners can produce at a lower cost compared
to the other. Instead of focusing on a country that can produce the most, the theory of
comparative advantage simply bases its production based on the lowest opportunity
cost.
2.3.4. Hicksher-Ohlin Theory
The Hicksher-Ohlin (HO) theory differs from the prior mentioned international trade
theories. It focuses on differences between countries in their relative factor endowment
and differences between their commodities in the intensities with which they use these
factors (Shahriar,2019). The theory depicts a more realistic view of production, where
a particular article of trade is made through a bundle of factors as opposed to just one
factor. The HO essentially maintains that countries tend to export the products for
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which they have an abundant supply. “The exchange of commodities internationally
is, therefore, indirect factor arbitrage, transferring services of otherwise immobile
factors of production from locations where these factors are abundant to locations
where they are scarce” (Leamer,1995).
2.3.5. Gravity Trade Model
The gravity trade model coined by Walter Isard in 1954, estimates the pattern of trade.
Essentially the model suggests that trade between two countries is positively related
to both their incomes and negatively to the distance between them. This model
suggests that closer countries will trade with each other more compared to countries
that are further apart, this is due to factors such as transportation cost linked to
facilitating trade. The theory further on goes to exam the pattern from the size of the
economies of the trading countries, stating that countries with large economies will
trade with one another. The distance element of the model has proved to be an
empirical success. This could be due to increasing regional trading blocs and the
blurring borders caused by the increase in globalization. The income component
remains inconclusive firstly because developed nations trade more with developing
nations however the rise in South-South trade portrays another image. The
inconclusiveness may emanate from the fact that the gravity model lacks structural
interpretation in the sense that it tends to ignore the sources of the underlying price
and demand of the commodities which usually determine the movement of the
commodities (Snudden,2018).
2.4. International Trade and Economic Growth
Early links between international trade and economic growth were initially discovered
by Adam Smith. However, over the years the relationship between international trade
and economic growth has been theoretically controversial as the landscape of
international trade changes over time. Various arguments supported by empirical
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findings have been raised by different scholars on whether or a link between economic
growth and international trade exists.
In support of the positive relationship between international trade and economic
growth, Azeez et.,al. (2014) states that the globalized nature of an economy enhances
the openness of an economy and its direct participation in the international market
leads to market expansion. Additionally, an econometric analysis on the impact of
exported natural resources on economic growth conducted by Sachs and Warner
(1995a, 1999) found that a contraction in export revenue had a negative effect on
economic growth by about 50%. Sachs and Warner (1995a,1999) go on to add that
this effect was due to the negative correlation between export concentration and intra-
industry trade and a positive correlation between export concentration and volatility
of the real effective exchange rate.
In his study on resource abundance and economic development Auty (1998), found
that “since the 1960s the resource-rich developing countries have underperformed
compared with the resource-deficient economies.” This according to Auty (1998), was
based on the differences in capital and income disparities amongst the two economies.
Furthermore, the phenomena of resource curse together with Dutch disease can be used
in explaining the inverse relationship between the trade of resource and economic
growth in the global market.
2.5.The structure of the South African Economy
South Africa’s political transition from Apartheid to democracy in 1994 played a
remarkable role in laying the foundations for the country’s current economic structure.
Before the attainment of democracy, the country had faced sanctions for political
reasons, which had a major impact on the country’s National account. Historically,
South Africa’s economy was primarily built on primary and secondary industries, such
as mining and manufacturing. However, in recent decades, and in line with global
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developments, growth has shifted to the tertiary industries (StatsSA,2021), which is
inclusive of trade, finance, transport, and communication. Essentially the South
African economy, according to the StatsSA can be divided into industries such as
Mining, Finance, Trade, Transport and communication, personal services,
manufacturing, government; construction; agriculture; and Electricity, gas, and water.
When analysing the different industry’s contribution to the GDP of 2019 in the fourth
quarter, we find that only three industries had a positive contribution to GDP (see
figure 02 below).
Figure 02 : Sector of GDP and their contribution to GDP in 2019
Source: StatsSA
These finds are justified by Fedderke (2018) who states that’s South Africa’s sectoral
structure has close affinities with the characteristics of developed economies, rather
than those of an emerging market. Therefore, we see service related industries thriving
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and growing more than labor intensive industries. In support of the aforementioned
Omilola (2015), states that South Africa tends to export capital-intensive goods
produced by highly skilled labourers. By doing so, the country is underutilizing the
large pool of low-skilled labor, which in turn adds to the current situation of high
unemployment which impacts consumption and savings which in turn impacts GDP.
2.6. Which resources/commodities is South Africa well-endowed with?
South Africa is a country known to have an abundance of natural resources and
minerals such as gold, copper, platinum, manganese, iron, silver, and coal. Recently,
the country has added natural gas and synthetic fuel to its list of mineral reserves
through the exploration of LNG along the South and Northern parts of Kwa-Zulu
Natal.
According to the South African Reserve Bank’s (SARB), quarterly bulletin for the first
quarter of 2021, SARB asserts that South Africa’s trade surplus with the rest of the
world widened slightly in the first quarter of 2021 as the increase in the value of net
gold and merchandise exports, which reached a new all-time high, marginally
outpaced the increase in the value of imports (SARB,2021). Additionally, exports
continued to benefit from the improvement in global economic activity and higher
commodity prices, as the value of mining, agricultural, and manufacturing exports all
increased in the first quarter of 2021 (SARB,2021). From the aforementioned, we see
the importance of commodity prices for South Africa as the country is a major net
exporter of minerals and a net importer of oil.
The World Integrated Trading Solutions (WITS) in partnerships with the UNSD
Comtrade finds that the top exported goods in South Africa were: Gold; Bituminous
coal; Agglomerated iron ores and concentrates; Diesel-powered trucks; Manganese
ores and concentrates, with manganese. When assessing the Top 5 imported products
Petroleum oils and oils obtained from bituminous was ranked first, followed by
Petroleum oils, etc, (excluding crude); preparation, Transmission apparatus, for
radiotelegraph incorpo, Other medicaments of mixed or unmixed products, New
stamps; stamp-impressed paper; banknotes was ranked fifth (WITS,2021). We see
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that the mining industry had a positive quarter, contributing 18.1% to the country’s
GDP within the first quarter. The second biggest contributor.
2.7. What are the current trade patterns for South Africa’s international trade?
Commodity trade and geographical trade patterns are known to evolve due to changes
in comparative advantages. Over the past couple of years, there has been a rise in
South-South trade which has caused exports from developing economies to exceed
those of developed nations. Additionally, South-South trade is known to grow
substantially higher rate than international trade mainly due to the emergence of
developing economies which has increased imports. The increase in imports which
represents an increase in demand will led to an increase in the prices for the exporting
nations.
According to the WTO, from 2011, developing economies’ exports to other
developing economies surpassed its exports to developed economies where “South-
South” trade represented an estimated US$ 4.28 trillion or 52% of total developing
economies’ exports in 2018. The share of this south-south trade is known to vary by
region whereby South Asia and East Asia contributed about 70% while the Latin
America region had contributed about 35% to international in 2017 (Afonso,2017).
Another key underpinning factor that dictators' trade flow is trading blocs and tariff
agreements signed amongst developing nations. According to South African Revenue
Services (SARS), the top 5 nations South Africa exports to are: United States (12.5%),
China (10.9%), Germany (8.6%), Japan (8.6%), and the United Kingdom (7.2%)
(SARS,2021). When analysing the top 5 nations South Africa imports from are: China
(20.0%), Germany (8.3%), United States (6.6%), Saudi Arabia (5.7%), India (5.4%)
(SARS,2021).
2.8. Financial factors affecting the trade flows
So far, in this study, we have considered literature that has focused on non-financial
determinants of trade flows. It is worth noting that financial factors such as the
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exchange rate, inflation, and price of commodities play a vital role in determining
patterns of trade. For instance, the exchange rate plays an essential role in the debate
around trade and trade policy for any country. According to Edwards and Garlick
(2008), there has been a general view that a depreciation in the exchange rate enhances
export competitiveness, encourages export diversification, protects domestic
industries from imports, and ultimately improves the trade balance. In determining the
impact of international trade on a particular country’s economic growth, the real
exchange rate has to be considered as the nominal exchange rate. The real exchange
rate comes as a highly recommended tool for analyzing bilateral competitiveness
amongst trading partners, as these bilateral real exchange rates are weighted and
combined into a composite index called the real effective exchange rate (REER).
Mogoe (2014), conducted an econometric analysis on the impact of international trade
on the economic growth of South Africa and found exports to be positively correlated
to changes in the inflation rate, exchange rate whilst imports were found to be a
negative correlation to the GDP. However, the majority of the studies in the literature
are empirical and provide support for negative or no effects, where the aforementioned
could emanate from the fact that that exchange rate uncertainty has more short-run
effects than long-run effects thus leading to vagueness on the impact of exchange rate
and flow of goods.
The second financial factor to be considered is establishing the trade of commodities
is the commodity price. Having information on the commodity prices is crucial for
agents such as Governments and Economic authorities. This would assist in
formulating policies that are aligned to improving their balance of trade and national
accounts, as commodities prices impact the aforementioned. One of the main theories
used in determining commodity prices is the ‘theory of storage’, which in essence
explains the difference between spot and futures commodity prices in terms of storage
costs, stating that commodity price volatility should increase when inventories are low
(Carpantier, 2012). The theory of storage can simply be integrated into the economic
theory of demand and supply utilized to establish the price of any commodity.
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According to Makin and Rohde (2016), broad-based commodity price indices and
commodity prices in real terms have boomed since the turn of the century.
Inflation, defined as the steady or persistent rise in prices is said to have an inverse
relationship with the trade of commodities. This is purely because inflation is known
to devalue currency causing consumers to spend less. The aforementioned argument
is further supported by Arango et., al (2008) who argues that a boom in commodity
prices is highly associated with an increase in consumption. Therefore, making the
inflation rate a key determinant of trade flows.
2.9. The gap in the literature
The impact of international trade on economic growth is an issue of major concern to
Policy makers; Central Bank and Investment agents. The first gap in the literature
identified in doing the study is based on the fact that the relationship between
economic growth and the international trade of commodities has been analyzed mainly
from a qualitative perspective focusing mainly on international trade theories and
policy reforms. To better understand the movement and trade of commodities which
is heavily reliant on macroeconomic factors such as the exchange rate; inflation rates;
commodity prices and the cost of the factors of productions a quantitative analysis has
to be conducted. Looking at the subject matter from a South African perspective it is
worth highlighting that most of the research takes on either a purely qualitative
approach or a quantitative approach, and never a combination of both. This creates an
incomplete picture of the analysis. Therefore, industry scholars such as Winters
(2004), go on to add that inconclusive results may occur because trade liberalization
must almost certainly be combined with other appropriate policies, and linear
regression models cannot capture such complementary dynamics. Furthermore,
scholars such as Mogoe (2013) argue that trade theory provides a little guideline for
the effects of international trade on growth and technical progress. Therefore, to attain
more accurate results on the relationship of the aforementioned components an
econometric analysis ought to be conducted in conjunction with the qualitative
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analysis of the theory because in evaluating econometric results, researchers can
always find ways of lay bare existing analyses (Lederman, 2002).
The second gap in the literature identified for this study emanates from the fact that
there is a shortage of country-specific research analysis, most of the studies that assess
the impact of international trade on economic look tend to utilize cross-country data
which can be sensitive to data omitted and endogeneity. Furthermore, the literature
on the subject matter is inconclusive partly because different analysts and researchers
use different proxies for trade openness or international trade and rely on different
methodologies. The evidence for growth enhancements through trade liberalization
displays mixed-effects because of problems with misspecification and the diversity
among the liberalization indices used (Zahonogo,2016). In addressing this gap, the
study will focus solely on South African data to attain precise results and to eliminate
any endogeneity caused by using cross-country data.
Like many other countries, the GDP of South Africa as measured by the Central Bank
is reported quarterly. However, the report is only made available 8 weeks after the
financial quarter has ended. According to Botha, et.al (2021), given lags in the release
of data, a central bank must ‘now-cast’ current GDP using data released with a shorter
publication lag and/or at a higher frequency than GDP. Through now-casting traders;
investors; forecasters and policymakers will be able to have a clear view of the
economy in real time. This will enable the economic agents to make better and up-to-
date decisions regarding the state of the economy.
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Chapter 3: Data
This chapter aims to outline the type of data to be analyzed and the approach/methods
utilized to answer the objectives of the study, which is to analyze the impact of
international trade of commodities on the economic growth of South Africa. This
chapter of the study will be divided into 3 sub-sections where the first section of the
chapter will focus on data outlining the data sources, the second subsection will outline
data collection techniques, whilst the third section provides us with the variable
definitions and the justification for including them into our regression analysis.
3.1. Data Sources
In conducting this study, we collected secondary data from The Observatory of
Economic Complexity (OEC), the Federal Reserve Bank of St. Luis, as well as the
United Nations Comtrade (UN Comtrade). From, the Federal Bank of St. Luis we
abstracted data on the country’s Quarterly GDP and the exchange rate. From the OEC
we extracted data on the top 10 imported and exported commodities in South Africa
from 2010 Q1 -2019 Q4.
3.2. Data Collecting Techniques
The data collection techniques of extracting data from the prior mentioned websites
will differ from the variable in question. From the OEC website, information relating
to the Top 10 imported and exported commodities was extracted, where the
classification grouping of the commodities in question was the HS Code 2 (see figure
below). Using the HS Code of each commodity, we then find the monthly trade value
of these commodities on the UN Comtrade website for the period Jan 2010- Dec 2019.
The data relating to GDP was extracted from the Federal Reserve Bank of St. Luis,
where the quarterly GDP for South Africa is reported in Rands (ZAR). However, the
GDP figures have to be converted into US$D which is the internationally acceptable
base currency, therefore, information on the exchange rate of ZAR to US$D was
abstracted from the same website. Since the exchange rate figures were reported on a
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monthly basis, to convert the figures into a quarterly figure, the monthly figures are
combined for each quarter, and then divided by 3 to get an estimate of the exchange.
Figure 03: Top 10 exported commodities from South Africa
Source: OEC (2021)
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Figure 04: Top 10 imports into South Africa.
Source: OEC (2021)
Table 01: List of Top 10 Exported and Imported Commodities
TOP 10 EXPORTED COMMODITIES
Variable Name
Commodity
HS
Code
Trade
Flow
1
X1_exp
Precious Metals Gems And Jewellery
71
Export
2
X2_exp
Ores, Slag And Ash
26
Export
3
X3_exp
Vehicles And Their Parts
87
Export
4
X4_exp
Mineral Fuels; Oils And Products Of Their
Distillation
27
Export
5
X5_exp
Machinery And Appliances
84
Export
6
X6_exp
Iron And Steel
72
Export
7
X7_exp
Fruits And Nuts; Edibles; Peels Of Citrus
Fruits/ Melons
08
Export
8
X8_exp
Aluminium And Articles Thereof
76
Export
20
9
X9_exp
Electrical Machinery And Equipment
85
Export
10
X10_exp
Plastics And Articles Thereof; Rubber And
Articles Thereof
39
Export
TOP 10 IMPORTED COMMODITIES
1
X1_imp
Mineral Fuels; Oils And Products Of Their
Distillation
27
Import
2
X2_imp
Machinery & Appliances
84
Import
3
X3_imp
Vehicles & Their Parts
87
Import
4
X4_imp
Electrical Machinery & Equipment
85
Import
5
X5_imp
Precious Metals, Gems & Jewellery
71
Import
6
X6_imp
Plastics & Articles Thereof
39
Import
7
X7_imp
Instruments & Apparatus
90
Import
8
X8_imp
Pharmaceuticals Products
30
Import
9
X9_imp
Chemical Products N.E.S
38
Import
10
X10_imp
Inorganic Chemicals, Organic Chemicals
28
Import
3.3. Variables Definition and Justifications
GDP
As mentioned earlier, the economic growth of any nation is measured as the change in
the country’s GDP, therefore using GDP as a proxy for economic growth is justifiable.
Furthermore, GDP captures the market value of goods and services produced within a
country for a particular period, whereby for this study we will be analyzing the market
value of goods (imports and exports) into and out of South Africa quarterly.
Precious Metals Gems and Jewelry export
According to the OEC (2021), In 2019, South Africa exported a total of $109B, making
it the number 36 exporter in the world with Gold leading as the country’s number
exported commodity. Precious metals and jewelry commodities under HS 2 code were
ranked the 5th most traded commodity products internationally in 2019. Under the HS
2 code classification, Precious Metals Gems and Jewelry export consists of precious
metals such as Gold, Platinum, Iridium, and diamonds. The aforementioned
commodities according to the OEC were South Africa’s top exported commodities and
21
accounted for 27.4% of the country’s trade value which is $29.8 billion in nominal
terms (OEC,2021). It is also worth highlighting that South Africa was ranked 7th
globally in the trade of precious metals, gems, and jewelry exporting to nations such
as the United Kingdom, Switzerland, India, and Hong Kong.
Ores, Slag, and Ash export
Ores, Slag and Ash commodity was recorded as the 2nd most exported commodity from
South Africa for 3 consecutive years. The HS code classification for Ore, Slag, and
ash consists of commodities such as iron, coal, manganese ore, granulated slag, where
South Africa is known to be the world’s third-largest exporter of coal. The trade value
of the commodity amounted to approximately $1.3 billion in 2019 and $ 1.7 billion in
the previous year where 54 % of the commodity was exported to China.
(Statista,2021).
Vehicles and Their Parts export
South Africa is no stranger in the automotive manufacturing industry as the country
produced 6.9% of the global market, and is ranked 22nd globally for its production of
vehicles and vehicle parts (International Trade Administration,2021) with assembling
plants for vehicle brands such as Toyota, BMW, and Mercedes Benz. According to the
International trade administration (2021), the automotive industry is the largest
manufacturing sector in the country, where the industry accounted for 6.4% of the
country’s GDP in 2019 which is $14.14 billion in nominal value. According to BBC
(2020) cars are made up of approximately 30,000 parts and the South African
automobile industry can manufacture these components.
Mineral Fuels; Oils and Products of Their Distillation export
Mineral Fuels; Oils and Products of Their Distillation according to the OEC
contributed $ 8.18 billion into the country’s GDP, where this classification group
consists of commodities such as refined petroleum, petroleum gas. With South Africa
being home to the most advanced and largest chemicals sector in Africa with an
22
abundance of minerals producing over 600 different types of chemicals this has given
the country a competitive advantage within the region (InvestSA,2021). In addition,
South Africa comprises around 0.5% of global chemical production capacity with
petrochemicals comprising about 55% of all chemicals produced locally
(InvestSA,2021). The top exporting destinations of these commodities were
industrious developing countries such India and Pakistan which accounted 30.7% of
the export.
Machinery and Appliances export
The trade of machinery and appliances accounted for $5.66 billion of the country’s
GDP in 2019, as the export of machinery and appliances accounted for 5.21% of
exported goods from the country.
Iron and Steel export
The export of Iron and steel from South Africa accounted for approximately $ 5.36
billion in 2019 (OEC,2021). This, however, represented a decline in the value of export
in comparison to the preceding year which was roughly $ 6.28 billion (Statista, 2021).
This possibly arising from lack of investment into industry. The decline in the export
of the commodity could also emanate from the economy’s downgrading by rating
agencies, this in exacerbates low the recurrent issues of low investor confidence.
Fruits and Nuts; Edibles; Peels of Citrus Fruits/ Melons export
According to the International Trade Administration (2020) citrus, wine, table grapes,
corn and wool accounted for the largest exports by value within the Agricultural
exports of South Africa. The export trade of Fruits and Nuts; Edibles; Peels of Citrus
Fruits/ Melons accounted for 3.38% of the total value of all exports within the country
in 2019 (OEC,2021) with these commodities being exported to countries such as the
Netherlands; the UK and the united states of America.
Aluminum and Articles Thereof export
23
According to Statista (2021), “South Africa produced some 717,000 metric tons of
refined primary aluminum in 2020, making it the largest aluminum producing country
in Africa”. Aluminum is known to be the second most malleable metal and the sixth
most ductile and often used as an alloy to construct airplanes, transport automobiles,
construction components as well as other cans, foils, and beer kegs.
Electrical Machinery and Equipment export
According to the OEC (2021), Electrical Machinery and equipment was the world’s
1st most traded product, with a total trade of $2.53 trillion. South Africa’s export of
electrical machinery and equipment contributed to the global production of the
commodity. Electrical machinery and equipment contributed approximately $1.77
billion to the country’s GDP, with leading export destinations being neighboring
African countries.
Plastics and Articles Thereof export
The export of plastic and articles thereof from South Africa amounted to
approximately $ 1.43 billion in 2019, which represented a slight decrease from 1.44
million in 2018 (Statisa,2021). The decline in exports of plastics could emanate from
the implementation of environmental laws that have banned/limited the use of plastic
and articles thereof, where the final top 3 destination of these commodities is Zambia,
Nigeria, and Botswana (OEC,2021).
Mineral Fuels; Oils and Products of Their Distillation Imports
According to OEC (2021), over the last five years South African imports dropped by
$17.9B from $106B in 2014 to $88.5B in 2019, where the top imported commodities
were Crude Petroleum Refined Petroleum which is classified as mineral fuels, oils and
products of their distillation under HS 2 classification code. The aforementioned
commodity countries of origin were Nigeria, Saudi Arabia, United Arab Emirates. the
trade trend of importing more from Nigeria could emanate from the fact that South
24
Africa and Nigeria are part of similar trading blocs. Additionally, this can be justified
by the discrepancies in the exchange rate and the gravity trade model.
Machinery & Appliances imports
Commodities included under the machinery and appliances classification include
Computers, optical readers, Heavy machinery (bulldozers, excavators, road rollers),
Printing machinery, Taps, valves, similar appliances transmission shafts, gears,
clutches, and similar appliances. The value of machinery and appliances imported into
South Africa has shown a decline from its contribution to GDP from $ 12.3 Billion in
2019 to $ billion in 2020. This could be resultant of the slow-down in industrious
activities due to the corona virus pandemic that has global production has slow-down.
Furthermore, the economic downgrading has resulted in lower investment confidence
and less foreign direct investment (FDI) into the country.
Vehicles & Their Parts imports
The import of vehicles and their parts contributed approximately $9.18 billion to the
GDP of South Africa in the 2019 financial year, which was marked a decline from the
year’s contribution of $ 9.94 billion (OEC,2021). The justification behind the decline
of vehicle and vehicle parts imports could emanate from the fact there has been a
decline in machinery imports as South Africa is still a developing country dependent
on countries such as China, Germany, and Italy. Furthermore, South Africa’s
Automotive industry has been developing at a fast pace with huge influx of
investments from vehicle OEMs such as BMW, Toyota, and Mercedes Benz.
Electrical Machinery & Equipment imports
According to OEC (2021), Electrical machinery and equipment are the world’s first
most traded products. Electrical machinery and equipment imports accounted for
approximately $ 8,55 billion of the country’s GDP in the year 2019, where under this
category we find items such as broadcasting equipment, computers, combustion
engines, electrical generating sets, electrical transformers, engine parts as well as
25
electrical power accessories and office machine parts which are used mainly in
production processes of other goods. From the items under the electrical machinery
and equipment’s imports, we find that the Broadcasting Equipment has the highest
contribution to the country’s GDP, accounting for $ 2.43 billion.
Precious Metals, Gems & Jewelry imports
According to OEC (2021) Precious metals, gems and jewelry was the world’s fifth
most traded product in 2019. imports contributed approximately $ 3.55 billion to the
country’s GDP in 2019. The origins of these precious metals, gems, and jewelry
according to the OEC (2021) were Ghana, Namibia, and Zimbabwe. The import of
Precious Metals, Gems & Jewelry imports essentially represents the import of semi-
precious stones and jewelry from the above countries.
Plastics & Articles Thereof imports
Being an industrious country that is heavily involved in exporting the inclusion of
plastic articles thereof is justified. These goods are used in production processes for
wrapping, storing, sealing goods. According to (Babayemi et, al., 2019),
approximately 13.7 Mt of plastics were imported into South Africa in the period
Between 2000- 2017 where the aforementioned accounted for approximately 11.6%
of plastics consumption in Africa
Figure 05: top six African countries with the highest import and use of plastics
26
Source: Babayemi et, al., (2019).
Instruments & Apparatus imports
Instruments & Apparatus imports accounted for approximately 2.79% of all imports
into South Africa in 2019. On the international frontier, instruments and apparatus
were ranked as the world’s 6th most traded product where the countries of origin were
the United States, Germany, China, and Japan. Under this commodity group
classification, we find items such as medical instruments, chromatographic,
electrophoresis instruments as well as musical instruments.
Pharmaceuticals Products imports
Although South Africa has pharmaceutical companies such as Johnson & Johnson,
Cipla, and Pfizer producing drugs for domestic consumption and export to neighboring
countries. The domestic production of pharmaceutical products according to Viviers
27
et.al, (2014) meets about one-third of the country’s demand for pharmaceuticals, and
therefore the remaining two-thirds have to be imported into the country. According to
OEC (2021), Pharmaceuticals product imports contributed accounted for 2.73% of all
imports into the country in 2019.
Chemical Products N.E.S imports
Chemical products N.E.S according to OEC (2021), were ranked the 17th most traded
product in the world in 2019, in addition, the product was also ranked the 73rd lowest
tariff using the HS 2 product classification with the top global exporting countries
being the United States, Germany, China, and France. From the South African context,
chemical product imports accounted for 1.75% of all imports into the country in 2019.
Products under the chemical products n.e.s product classification include products
such as fertilizers, beauty products, and laboratory reagents
Chemicals, Organic Chemicals imports
Chemical, organic chemical imports according to the OEC (2021) accounted for
approximately 1.74% of the country’s imports in 2019 whilst contributing
approximately $ 1.54 billion to the country's GDP in 2019. The products listed under
this chemical, organics, and chemical imports include products such as Nitrogen
Heterocyclic compounds, Polycarboxylic acids, Industrial fatty acids, oils, and
alcohols which are used in industries such as mining, manufacturing, and agriculture
which are major exporting industries for the country.
28
Chapter 4: Methodology
This subsection of the paper outlines the model specification as well as the
econometric approach to estimating the relationship between dependent and
independent variables using statistical data analysis. The dependent variable will be
estimated using Ordinary Least Square (OLS) regression model which is primarily
used for predictions and casual inference. In building, we begin the analysis by
estimating a simple linear regression
4.1. Model Specification
Simple linear Regression
The model of estimation utilizes a linear regression model which looks at the
𝑌 = 𝛼 + 𝛽𝑋𝑡 + 𝑈t
Where the model simply explains the relationship between the Y and X variable
Where:
𝛼 is the intercept term or constant
𝛽 is the coefficient that means the slope of the regression line. When 𝛽 is equal
to zero then there’s no relationship between Y and X. If 𝛽 is negative then
there exists a negative relationship between Y and X, meaning that if X
increases Y will decrease by -𝛽. If 𝛽 is positive then the relationship between
Y and X, meaning that if X increases by then Y will increase 𝛽.
X represents the regressor / independent / explanatory variable. This is the
causal variable in the equation.
U represents the stochastic error term also known as the random disturbance.
The error term measures the difference/ variation between the actual and
estimated dependent for each explanatory variable (y- ŷ)
t represents the number of observations.
Multiple linear regression model
29
According to Konansani and Kadre (2015), multiple linear regression is used to predict
the dependent variable using more than one explanatory variable.
The general equation is given as:
𝑌𝑡 = + 𝛽2𝑋2𝑡 + 𝛽3𝑋3𝑡 + 𝛽4𝑋4𝑡 + … 𝛽𝑘𝑋𝑘𝑡 + 𝑈 , t=1,2,…….T
Where:
𝑋2𝑡, 𝑋3𝑡, . . ., 𝑋𝑘𝑡 represents the various independent variables,
𝛽1, 𝛽2, . . ., 𝛽k where β is known as the partial regression coefficient
representing the partial effect of each independent variable.
Ordinary Least Squares (OLS)
According to Guerard (2013), OLS is the most utilized approach for fitting data into
the line where the sum of the squares error term (SSE) is minimized. Since the OLS is
employed for analysis we, therefore, have to take Classical Linear Regression Model
(CLRM) assumptions into consideration when conducting the various tests to estimate
our dependent variable.
Using the generic model structure:
𝑌 = 𝛼 + 𝛽𝑋 𝑡 + 𝑈t
The CLRM assumptions are:
1. E(ut) = 0, which means that the mean of the residual should be zero, however, this
assumption doesn’t hold in the presence of an intercept term.
2. Var(ut)= σ2 which means the variance of the error should be constant and finite.
3.Cov (Ui,Uj) = 0 . which means that there is no autocorrelation
4. Cov (xi,uj ), Which means there is no correlation between the individual explanatory
variable and the error term.
5.u =N (0, σ2), which essentially means that the error term is normally distributed with
a mean of zero and a constant variance of sigma squared.
30
4.2. Econometric Modeling Technique
Under this section of the we provide detailed description of the series of tests will that
be conducted to estimate the impact of international trade of commodities on the
economic growth of South Africa using Matlab software.
4.2.1. Preliminary Statistics
To begin the econometric analysis of the data, as mentioned before data detailing the
top 10 imported and exported commodities, as well as the quarterly GDP for our
research period, was organized into an excel spreadsheet. We then arrange the dataset
to begin with the date as the column, followed by our dependent variable (GDP), and
we group our independent data by first listing all the exported commodities first, and
then our independent variables afterward.
Upon gathering our sampled data, we analyze the data from a preliminary statistics
point of view focusing mainly on the standard deviation and skewness of the dataset.
Through analyzing the standard deviation of the dataset we are capturing the volatility
of the dataset which is measured by the gap between the variable and the mean. The
second most important preliminary statistic is the skewness of the data.
4.2.2. Unit Root Test
Upon completing the preliminary statistics, we log our dataset to eliminate any
negative variables within the analysis before conducting the stationarity test. This,
according to Gujarati (2003), is done because a regression will generate spurious
results if the model is estimated using non-stationary variables and therefore a unit
root test has to be conducted to assert whether our variables are stationary or not. The
test for stationarity can be conducted using either the Augmented Dickey-Fuller
(ADF), Kwiatkowski-Phillips-Schmidt-Shin (KPSS), and Philips-Perron (PP). For our
regression results, we examine whether the ADF and PP are moving in the same
31
direction, if they are moving in the same direction we consider those results. If the
ADF and PP results are different then we utilize the KPSS results.
Using the augmented Dicky-Fuller test (ADF) and the Phillips-Perron (PP), we test the
following hypothesis:
H0: there is a unit root
H1: there is no unit root (stationary)
The decision to reject the null hypothesis is when the p-value < 0.05.
4.2.3. Cointegration Test
The cointegration test is conducted to establish whether there exists a long-term
relationship between two or more variables. The test creates residuals of stationary
regression and tests for the presence of unit root. In the presence of a long-term
relationship the Engle-Granger method is applied where we test the following
hypothesis:
H0: a unit root in Cointegrating regression’s residuals
H1: residuals from Cointegrating regression are stationary.
Our t-test is conducted using a two-tailed test at a 0.05 level of significance where the
null hypothesis is rejected if the p-value is < 0.05. from running the test.
4.2.4. Multicollinearity / Correlation test
Following the unit root tests, a correlation test is conducted on the given data. The
correlation test is conducted to assess the relationship between two variables. For this
study, the test will be conducted using the Multicollinearity technique where the test
not only detects the relationship amongst the variables but also measures the strength
and direction of the relationship. According to Young (2017), Multicollinearity is
present in a regression model when the variables are not only correlated with the
dependent variable but also to each other. In the case where multicollinearity exists
the model cannot be utilized as the issue of multicollinearity is a violation of the OLS
32
assumptions. Additionally, multicollinearity is known to increase the variance of the
regression coefficients making them unstable, which brings the problem to interpret
the coefficients (Shrestha,2017).
To test for correlation, we utilized the correlation coefficient technique which detects
whether or not is a relationship between a variable pair. The correlation variable pair
relationship can be captured as being either positive or negative, where a positive
correlation essentially means that variables are moving in the same direction, and a
negative relationship means that they’re moving in the opposite direction, i.e.: as one
increases the other variable decreases. The strength of the relationship amongst the
variable is measured by the correlation coefficient which has a maximum value of 1
or 100% indicating perfect correlation. For this analysis variables with a correlation of
80% or correlation coefficient of 0.80 and above will be dropped from the analysis
because the presence of multicollinearity increases the standard errors of each
coefficient in the model making some of the significant variables statistically
insignificant.
4.2.5. t-test
Upon completing the correlation test and removing variable pairs that are highly
correlated, a t-test is conducted with the remaining variables to test the statistical
significance of the individual variables. β represents the coefficient of the variable,
and k represents the sample number. Where the null and alternative hypotheses are
stated as:
H0: βk=0
H1: βk ≠ 0.
Our t-test is conducted using a two-tailed test at a 0.05 level of significance where the
null hypothesis is rejected if the p-value is < 0.05. from running the test.
4.2.6. ARMA test
33
An Autoregressive Moving Average test (ARMA), is used to establish whether the
previous value of the dependent variable and previous shocks in the system influence
the dependent variable today. Where the Autoregressive component (AR) of function
is calculated and is defined as:
yt = β₁* y-₁ + β₂* yₜ-₂ + β₃ * yₜ-₃ + ………… + βₖ * yₜ-ₖ
The Moving Average (MA) component of the test is used to calculate the impact of
the residuals or errors of past time series and calculates the present.
Yt = β₁* Ɛₜ-₁ + ₂ * Ɛₜ-₂ + ₃ * Ɛₜ-₃ + ………… + * Ɛₜ-ₖ
This test is done iteratively starting from an ARMA level of order (5,5). If upon adding
AR and MA variables and some of our variables that were significant become
insignificant we then reduce the level of ARMA starting from a higher order. Once we
have selected an ARMA level that is significant we then compare our model with the
ARMA to the model without ARMA. The model selection criteria are done by
assessing the Akaike Information Criterion (AIC) and the Bayesian Information
Criterion (BIC) of the model with ARMA and the model without ARMA. According
to Burnham and Anderson (2002), the AIC is an information-theoretic indicator based
that quantifies the information given in the model and the BIC was designed to
maximize the posterior probability of the model. Furthermore, Rossi, et.al (2020), state
that; “The basic principle underlying the AIC criterion is the assumption that the less
information a model loses, the higher is its quality” and BIC. Essentially, the final
model selection is made based on assessing either of the two criteria, where our final
model selection is based on the decision of which model has the lowest information
criteria.
4.2.7. Normality test
The normality test is conducted to see if that the error term is normally distributed with
a mean of zero and a constant variance of sigma squared, which is expressed as:
34
u =N (0, σ2).
The test is conducted under the null hypothesis residuals are normally distributed. In
the case where there’s a violation of the OLS assumption then when insert dummy
variables into the regression to replace the variable (s) with high residuals and then we
find the error correction term for the variables. we rerun the test, removing all
insignificant variables within the regression. For this test we use the hypothesis:
H0: Normally distributed error terms
H1: Not Normally distributed error terms
Where the test is conducted at a 0.05 level of significance where the null hypothesis is
rejected if the p-value is < 0.05 which implies that the variance of the error term is
heteroscedastic.
4.2.8. Heteroscedasticity
Heteroscedasticity is a phenomenon that occurs when the variances of the error terms
are not constant. The presence of heteroscedasticity in a regression is a violation of the
OLS assumption stating: Var(ut)= σ2 variance of the error should be constant.
In addition, the presence of heteroscedasticity indicates that the OLS estimator is no
longer best; linear and unbiased estimators (B.L.U.E), and therefore Generalized Least
Square (GLS) is used. Using the HAC function, a residual diagnostic test is conducted
on a quarterly frequency at a 5 % level of significance to test whether the variance of
the error term is constant using the following hypothesis:
H0: homoscedasticity
H1: heteroscedasticity
Where the test is conducted at a 0.05 level of significance where the null hypothesis is
rejected if the p-value is < 0.05.
4.2.9. Serial Correlation
35
A CLRM assumes that there is no correlation between amongst the error terms, which
is expressed as:
Cov (Ui,Uj) = 0 . which means that there is no autocorrelation
Serial correlation exists in a regression model when the error terms are correlated
meaning that the error terms are repeated overtime. The presence of serial correlation
makes the coefficient estimates to be biased meaning our model is no longer B.L.U.E.
To correct issues related to heteroscedasticity and serial correlation we use various
White correction test, Newy West Correction test.
Heteroscedaticity
Serial Correlation
Correction
1
No ARCH effect
No Serial correlation
-
2
ARCH effect
No Serial correlation
White correction test
3
No ARCH effect
Serial correlation
Newy West
correction
4
ARCH effect
Serial correlation
4.2.10. Ramsey reset test
Ramsey Regression Equation Specification Error Test (RESET), is essentially a test
conducted to assert the functional form of the regression. For this test, we raise the
dependent variable to a higher order of 2 (y_fit_p2). The decision to either reject or
not reject the null hypothesis is based on the p-vale generated by software which is
then compared to the 0.05 level of significance. The RAMSEY RESET test is
conducted using the following hypothesis:
H0: linearity
H1: non-linear
36
Chapter 5 Empirical Findings
This section of the study is an analysis of the empirical findings from the statistical
software Matlab. The analysis is essentially an interpretation of the output received
from the software when conducting the regression analysis. Under the section, we
analyze findings from tests such as unit root test, correlation test, t-test, ARMA test,
heteroscedasticity test, normality test as well as the Ramsey reset test.
5.1. Unit Root Test Finding
We conducted a unit root test for stationarity using GDP as our dependent variable and
the top 10 imported and exported commodities as our independent variables. Upon
conducting our unit root test for stationarity, we find that none of the variables
stationary level / I (0) process. The dependent variable, GDP is found to be stationary
at I (1). As a rule of thumb, our variables should be integrated in the same order. Our
20 explanatory variables were found to be stationary at the I (1) process. We find that
all the variables in our regression are not stationary and have to be differenced once
before becoming stationary. Therefore, we reject the null hypothesis of a unit root at a
5 % level of significance. According to Mogoe (2013), cointegration is an overriding
requirement for any model that has non-stationarity data. The main of conducting the
cointegration test is to establish whether there or not there exists a long-run
relationship between our dependent variable and the independent variables.
5.2. Correlation Test Finding
Upon conducting our correlation test we find that there are 2 variable pairs with a
correlation that is greater than 0.80 which is a violation of the OLS Assumptions
stating that no independent variable is a perfect linear function of another explanatory
variable, therefore we have to remove one of the variables from variable pair. From
37
the correlation test, we find machinery and appliances imports (X2_imp) being highly
correlated with the import of pharmaceutical products (X8_imp), where the correlation
coefficient is 0.81. secondly, we find a variable pair consisting of the import of
pharmaceutical products (X8_imp) and the import of vehicle and vehicle (X3_imp)
component where the correlation coefficient is 0.87. Therefore, we remove the import
of machinery and appliances (X2_imp) and the import of vehicle parts and vehicle
(X3_imp) variable from our regression to eliminate multicollinearity within the
regression. The economic justification behind removing the machinery and appliances
imports variable instead as opposed to the import of pharmaceutical lies in the fact that
pharmaceutical products have an inelastic demand and there exists no substitutes for
these products. However, with machinery and appliances, some of the products under
this classification can be substituted by human labour in labor primary industries such
as Agriculture, and Mining. A switch from capital to labor will result in an increase in
employment which can solve the issue of unemployment in the country. In addition,
scholars such as Omilola (2015) find that the country is underutilizing the large pool
of low-skilled labor, which in turn adds to the current situation of high unemployment
thus affecting income earnings within the country which has a ripple effect on the
economic growth. The justification behind the removal of vehicle and vehicle parts
imports emanates from the fact that South Africa’s automotive industry is highly
competitive and has a global presence. Furthermore, the removal of these imports will
contribute to an increase in production within the industry. This in turn allow for
industry to produce more , thereby improving exports and the overall GDP.
38
Table 02: Multicollinearity Test Findings
Source: Author’s own
5.3.t-Test Finding
Initially, our regression analysis consisted of an intercept term plus 20 independent
variables and the stochastic error term, where our regression model is expressed as:
Yt=α+β1X1_exp+β2X2_exp+β3X3_exp+β4X4_exp+β5X5_exp+β6X6_exp+β7X7_exp+
β8X8_ exp+β9X9_ exp+β10X10_ exp+β11X1_imp+β12X2_ imp+β13X3_ imp+β4X4_
imp+β5X5 _ imp+β6 X6 _ imp+β7X7 _ imp+β8X8 _ imp+β9X9 _ imp+β20X10_ imp+U
Row X1_exp X2_exp X3_exp X4_exp X5_exp X6_exp X7_exp X8_exp X9_exp X10_exp X1_imp X2_mp X3_imp X4_imp X5_imp X6_ imp X7_imp X8_imp X9_imp X10_imp
X1_exp 1
X2_exp 0.3 1
X3_exp 0.38 0.46 1
X4_exp 0.45 0.54 0.39 1
X5_exp 0.24 0.51 0.18 0.56 1
X6_exp 0.09 -0.03 0.01 -0.33 -0.16 1
X7_exp 0.58 0.52 0.47 0.64 0.56 0.02 1
X8_exp 0.45 0.46 0.71 0.66 0.3 -0.22 0.56 1
X9_exp 0.38 0.51 0.39 0.69 0.48 -0.01 0.65 0.59 1
X10exp 0.44 0.65 0.25 0.56 0.59 -0.11 0.55 0.42 0.56 1
X1_imp 0.23 0.2 -0.08 0.32 0.36 0.39 0.42 -0.05 0.35 0.26 1
X2_imp 0.33 0.56 0.34 0.49 0.34 0.04 0.54 0.53 0.39 0.63 0.27 1
X3_imp 0.45 0.73 0.49 0.63 0.58 -0.03 0.67 0.65 0.51 0.63 0.29 0.8 1
X4_imp 0.35 0.61 0.24 0.49 0.62 0.16 0.64 0.34 0.64 0.65 0.52 0.59 0.72 1
X5_imp 0.32 0.27 0.45 0.37 0.07 0.09 0.44 0.66 0.36 0.32 0.26 0.69 0.59 0.44 1
X6_imp 0.3 0.36 0.23 0.53 0.5 -0.12 0.72 0.32 0.34 0.49 0.36 0.61 0.54 0.51 0.5 1
X7_imp 0.47 0.61 0.65 0.66 0.51 0.01 0.77 0.78 0.66 0.52 0.26 0.69 0.77 0.63 0.62 0.62 1
X8_imp 0.49 0.68 0.54 0.69 0.38 0.07 0.64 0.69 0.54 0.57 0.34 0.81 0.87 0.59 0.64 0.5 0.72 1
X9_imp 0.31 0.65 0.54 0.59 0.42 0.05 0.57 0.71 0.54 0.48 0.16 0.58 0.73 0.62 0.59 0.42 0.75 0.75 1
X10_imp 0.35 0.54 0.6 0.7 0.49 -0.15 0.55 0.75 0.52 0.47 0.13 0.56 0.79 0.57 0.53 0.43 0.7 0.7 0.77 1
39
Where, Y= GDP
α= Intercept
β1X1_exp= Precious Metals Gems and Jewellery exports
β2 X2_exp= Ores, Slag and Ash exports
β3X3_exp= Vehicles and Their Parts exports
β4X4_exp= Mineral Fuels; Oils and Products of Their Distillation exports
β5X5_exp= Machinery and Appliances exports
β6X6_exp= Iron and Steel exports
β7X7_exp= Fruits and Nuts; Edibles; Peels of Citrus Fruits/ Melons exports
β8X8_ exp= Aluminium and Articles Thereof exports
β9X9_exp= Electrical Machinery and Equipment exports
β10X10_exp= Plastics and Articles Thereof; Rubber and Articles Thereof exports
β11X1_imp= Mineral Fuels; Oils and Products of Their Distillation imports
β12X2_imp= Machinery & Appliances imports
β13X3_imp= Vehicles & Their Parts imports
β14X4_imp= Electrical Machinery & Equipment imports
β15X5 _imp= Precious Metals, Gems & Jewellery imports
β616 X6 _ imp= Plastics & Articles Thereof imports
β17X7 _imp= Instruments & Apparatus imports
β18X8 _imp= Pharmaceuticals Products imports
β19X9 _imp= Chemical Products N.E.S imports
β20X10_imp= Inorganic Chemicals, Organic Chemicals imports
Ut= stochastic error term
Upon conducting the unit root test for stationarity and the correlation test we conducted
a t-test to test the individual significance of the remaining variables within the
regression analysis. From the analysis we find our regression to have 4 significant
variables commodities namely: vehicle and their parts export(X3_exp), mineral fuels;
oils and products of their distillation exports(X4_exp), Fruits and Nuts; Edibles; Peels
40
of Citrus Fruits/ Melons (X7_exp), as well as plastic and articles thereof (X10_exp) are
left as significant variables. we find our final t-test regression to be as follows:
Yt= α+β1X1_exp+ β3X3_exp+ β4X4_exp+ β7X7_exp+ β10X10_ exp + Ut
From the regression analysis, we find that the export of vehicles and vehicle parts is
positive and significant. In addition, we that holding all things constant a 1 % increase
in the export of vehicles and vehicle parts Vehicle (β3X3_exp) will result in an $ 0.10
billion increase in the GDP of South Africa per quarter. The positive relationship
between the export of vehicles and vehicle parts can be explained by the fact that the
country produces 6.9% of the vehicles and vehicle parts for the global market, and is
ranked 22nd globally for its production of vehicles and vehicle parts (International
Trade Administration,2021).
With regards to the export of mineral fuels, mineral oils, and products of their
distillation β4X4_exp) we find this commodity to be significant and have a negative
impact on economic growth. From the regression analysis, we find that a 1% increase
in the export of mineral fuels, oils, and products of their distillation (β4X4_exp) will
decrease GDP by $ 0.23 billion per quarter. Alternately, the regression results can have
interpreted as a 1 % decrease in the exports of Mineral fuels, mineral oils, and products
of their distillation would cause the economy of South Africa to by $ 0.23 billion per
quarter.
From our regression, we find Fruits and Nuts; Edibles; Peels of Citrus Fruits/ Melons
(X7_exp) to be significant and negative. From the regression we find that there exists
a negative relationship between economic growth and the export of Fruits and Nuts;
Edibles; Peels of Citrus Fruits/ Melons (β7X7_exp), where a 1 % increase in the export
of the Fruits and Nuts; Edibles; Peels of Citrus Fruits/ Melons leads to a decrease of $
0.04 billion in the GDP for per quarter. The negative relationship between the export
of Fruits and Nuts; Edibles; Peels of Citrus Fruits/ Melons (β7X7_exp) and GDP
41
quarterly could emanate from the fact that South Africa has been experiencing water
shortages which have negatively impact agricultural production. Furthermore, it is
important to note that this commodity is seasonal, thus justifying the negative impact
of the commodity on economic growth.
When assessing the impact of Plastics and articles thereof (β10X10_exp) on t economic
growth we find that the commodity is both positive and significant. From our
regression we find has a positive relationship between Plastics and articles thereof
(β10X10_exp) and the economic growth. In addition, we can state that a 1 % increase in
the export of Plastics and articles thereof (β10X10_exp) will cause the economy of South
Africa to grow by $ 0.26 billion per quarter. When assessing the goodness of fit of the
regression model by analyzing the adjusted r-squared we find our model has an
explanatory of 46.5%.
Table 03: t-Test regression analysis
Y= α+β3X3_exp +β4 X4_exp +β7 X7_exp +β10X10_exp + Ut
Estimate
SE
tStat
Pvalue
Intercept
0.00
0.01
0.51
0.96
X3_exp
0.10
0.04
2.74
0.01
X4_exp
-0.23
0.05
-4.19
0.00
X7_exp
-0.04
0.01
-2.91
0.01
X10_exp
0.26
0.06
4.40
0.00
Number of observations: 38, Error degrees of freedom: 33
Root Mean Squared Error: 0.058
R-squared: 0.523, Adjusted R-Squared: 0.465
F-statistic vs. constant model: 9.05, p-value = 4.77e-05
42
5.4. F-test finding
The F-test is similar to the t-test; however, the F-test looks at the overall significance
of the model whereas the t-test asses the significance of each variable. From our F-test
we find that the model has an R squared of 52.3%, this reveals that 52.3% of the data
fits the regression mode, as the R-squared represents the goodness of fit. However,
we use the adjusted R-square because our R-squared coefficient tends to increase as
we include more variables into the regression. From our regression analysis, we find
the adjusted R-square figure to be 0.47 / 47 %. Meaning that our model has predictory
power of 47 %. Alternatively, one can state that 47% of the variation of our dependent
variable can be explained by the changes in our independent variables.
5.5. ARMA Test Findings
The ARMA test is conducted to establish whether previous values of a variable will
have an impact on the future value of the variables. We conduct the ARMA test starting
from ARMA order (5,5) where we find that our ARMA coefficients are insignificant
as they are greater than the critical level. However, our independent variables within
the regression analysis were still significant. We iteratively conduct the test until we
reach the level of ARMA where our ARMA independent variables are significant and
below the critical value of 5 %. From our regression we find our variables to be
significant models to AR (0), and MA (1) (see table 05 below). from the ARMA test,
we find that adding a MA variable has improved the model’s predictive power, as
adding the MA improved our Adjusted R-squared from 47 % to 63%. Therefore, from
the above find that adding one MA variable has improved the regression’s predictive
power by 16%.
Table 04: ARMA Test
Y= α+β3X3_exp +β4 X4_exp +β7 X7_exp +β10X10_exp + MA+Ut
43
Estimate
SE
tStat
pValue
Intercept
-0.00
0.00
-0.53
0.60
X3_exp
0.12
0.03
3.58
0.00
X4_exp
-0.19
0.05
-4.16
0.00
X7_exp
-0.03
0.01
-3.19
0.00
X10_exp
0.22
0.05
4.53
8.15e-05
MA
-0.69
0.17
-4.02
0.00
Number of observations: 37, Error degrees of freedom: 31
Root Mean Squared Error: 0.0483
R-squared: 0.688, Adjusted R-Squared: 0.637
F-statistic vs. constant model: 13.6, p-value = 4.53e-07
Upon completing the ARMA test we compared our ARMA model to our final t-test
results which is the model without the ARMA. We then select the model which is
better fitting by assessing the model selection criteria which we do by comparing the
AIC and BIC of the models and select a model with the lowest AIC/ BIC. Upon doing
this selection we find the model with ARMA to be a better fitting model in comparison
to the model without ARMA and we proceed with the model with ARMA. And we
proceed to conduct testing for heteroscedasticity.
5.6. Heteroscedasticity Findings
The Normality test computed using the Jarque-bera test shows which shows that
residuals are normally distributed. The output from our Jarque-bera test shows that our
residuals are normally distributed and follow a chi square distribution. From our
heteroscedasticity test we find that there is ARCH effect, as the F-statistic is greater
than the critical value, and therefore this has to be corrected using White test.
44
Table 05: Heteroscedasticity Test Findings
Estimate
SE
tStat
pValue
Intercept
0.00
0.00
0.05
0.96
X3_exp
0.10
0.04
2.74
0.01
X4_exp
-0.23
0.05
-4.19
0.00
X7_exp
-0.04
0.01
-2.91
0.01
X10_exp
0.26
0.06
4.40
0.00
Number of observations: 37, Error degrees of freedom: 34
Root Mean Squared Error: 0.0717
R-squared: 0.246, Adjusted R-Squared: 0.202
F-statistic vs. constant model: 5.56, p-value = 0.00815
5.7. Ramsey Reset Test Findings
The Ramsey RESET test is conducted to test the functional form of the model by
essentially detecting whether the model is linear or not by raising the estimated
dependent variable to a higher order of 2. Where if the p-value of the is greater than
0.05 then we can conclude that model is nonlinear, and if the p-value is less than 0.05
then we reject the null hypothesis. From the regression we find our model P-value
being less than 0.05, and therefore we conclude that the model is non-linear. This could
emanate from the fact that our sample size was small. Statistically, as a rule of thumb,
a regression is more robust when it has more than 30 variables because the smaller the
sample size, the higher the error margin is. To rectify this, we would recommend the
usage of a direct model such as the GARCH model, which is an approach to estimating
volatility. In addition, a GARCH model is usually preferred by Finance Professionals
as it provides more realistic predictions of prices and finance-related instruments.
Table 06: Ramsey RESET Linear regression model:
45
Y =-0.01+ y_fit_p2
Estimated Coefficients:
Estimate
SE
tStat
pValue
intercept
-0.01
0.0
-1.45
0.15
Y_fit_p2
2.86
1.14
2.50
0.02
Number of observations: 37, Error degrees of freedom: 35
Root Mean Squared Error: 0.0419
R-squared: 0.152, Adjusted R-Squared: 0.127
F-statistic vs. constant model: 6.25, p-value = 0.0172
Table 07: final regression model
Linear regression model:
Y= α+β3X3_exp +β4 X4_exp +β7 X7_exp +β10X10_exp + MA+Ut
Estimated Coefficients:
Estimate
SE
tStat
PValue
Intercept
-0.00
0.01
-0.53
0.60
X3_exp
0.11
0.03
3.58
0.00
X4_exp
-0.19
0.05
-4.16
0.00
X7_exp
-0.03
0.01
-3.19
0.00
X10_exp
0.22
0.05
4.53
8.16e-05
MA
-0.69
0.17
-4.02
0.00
Number of observations: 37, Error degrees of freedom: 31
Root Mean Squared Error: 0.0483
R-squared: 0.688, Adjusted R-Squared: 0.637
F-statistic vs. constant model: 13.6, p-value = 4.53e-07
46
Chapter 6: Discussion
This chapter of the study aims to discuss the final findings from our regression model.
For this section of the study, we analyze the results from regression analysis and apply
economic theory and empirical evidence into explaining the outcome from our final
regression model.
Upon completing the regression analysis, we find our final model to expressed as:
Y= α+β3X3_exp +β4 X4_exp +β7 X7_exp +β10X10_exp + MA+ Ut
Where: X3_exp = vehicles and vehicle components
X4_exp = mineral fuels, oils and products of their distillation
X7_exp = Fruits and Nuts; Edibles; Peels of Citrus Fruits/ Melons
X10_exp = Plastics and plastic products thereof
MA = moving average
Ut = error correction term
Initially, our regression analysis comprised of the 10 exported and top 10 imported
commodities. Based on economic theory, and the Balance of trade equation (Net trade
= export – imports), we expect exported commodities to have a positive impact on
economic growth and for imported commodities to have a negative impact on
economic growth. However, from our regression analysis, we find that all of our
imported commodities were insignificant, having no impact on the quarterly economic
growth of South Africa. With regards to the exported commodities, we find that only
four commodities were significant. However, from the four significant export
commodities, we find the export of Mineral Fuels; Oils, and Products of Their
Distillation (X4_exp) and the export of Fruits and Nuts; Edibles; Peels of Citrus Fruits/
Melons (X7_exp) as having a negative impact to the quarterly economic growth of
South Africa.
47
6.1. Vehicles and their parts (X3_exp)
The first significant variable in our final regression is the export of Vehicles and Their
Parts (X3_exp), where we find that a 1 % increase in the export of Vehicles and Their
Parts will cause the economy of South Africa to grow by $ 0.12 billion per quarter.
This significant and positive impact of vehicle exports on GDP can be justified by the
fact that South Africa’s automotive manufacturing industry is ranked 22nd globally for
its production of vehicles and vehicle parts (International Trade Administration,2021).
In addition, the country accounts for 6.9% of the production in the global market.
Furthermore, South Africa’s automotive industry remains the largest manufacturing
sector in the country has contributed 6.4% of the country’s GDP in 2019. The
industry’s ability to having such a great impact on GDP could emanate from the fact
that the automotive sector remains one of the most visible sectors receiving foreign
direct investments within the manufacturing sector. The industry’s value chain is
known driven by seven OEMs namely: BMW, Volkswagen, Mercedes Benz, Ford,
Isuzu, Nissan, and Toyota (NAAMSA,2021), where the total amount of FDI injected
into the industry by the aforementioned OEMs was approximately R9.2 billion in 2020
(International Trade Administration,2021). The continued investment into the industry
has allowed for manufacturing and assembly plants to invest in state of the art capital
and technology which will result in increased efficiency and productivity as seen in
the endogenous growth model where capital formation has been seen as an essential
input for economic growth.
A world bank study conducted on “Firm location and determinants of Exports in
developing countries” finds that the average share of manufacturing export firms is
higher in core regions (Farole, 2011), such is the case in the South African context. In
the context of the South African automotive industry, we find the manufacturing and
assembling plants strategically located in South Africa’s core manufacturing provinces
namely: Gauteng, which is the economic hub of the country; Eastern Cape- the
country’s largest Industrial Development Zone and KwaZulu Natal- home to the
48
busiest port in the sub-Saharan region and the largest port in the African continent.
The strategic positioning of these manufacturing and assembling plants can also be
seen as an enabling factor that has allowed the country to yield positive returns from
the exports of vehicles and vehicle parts.
Figure 06: South Africa Export of Vehicle and Vehicle parts
Source: Tradingeconomics.com
6.2. Mineral Fuels; Oils and Products of Their Distillation (X4_exp)
The second significant variable from the regression analysis is the export of Mineral
Fuels; Oils and Products of Their Distillation (X4_exp). From our regression, we find
that 1 % increase in the export of Mineral Fuels; Oils, and Products of Their
Distillation (X4_exp) will cause the GDP to decrease by $0.19 billion per quarter. The
reasoning behind the inverse relationship between the export of Mineral Fuels; Oils
and Products of Their Distillation (X4_exp) and economic growth could result from
the fact that under this HS Code classification grouping (see Appendix: Table 2) we
find commodities which are exported in large volumes but have low values.
Furthermore, most of the commodities under the HS code classification are raw
materials that have not been processed, there the lack of value addition could also
contribute to the low nominal contribution of these commodities to GDP.
49
From a theoretical perspective, the phenomenon of the Dutch disease, as well as the
Resource Curse, can be applied in explaining the inverse relationship between GDP
and the export of Mineral Fuels; Oils, and Products of Their Distillation. The Dutch
disease is a concept that was coined in 1977 and explains how the discovery and
exploitation of natural resources tend to have unexpected repercussions on the overall
economy of a nation. The phenomena of the Dutch disease commonly occur in
countries whose economic growth is lopsided, relying heavily on the export of natural
resources. Furthermore, the OECD (2021) states that “resource abundance does not
always bring sustained economic growth and development – it can have the opposite
effect, which is sometimes referred to as the “resource curse”. Mikesell (1997)
conducted a study “Explaining the resource curse, with special reference to mineral-
exporting countries” finds that there exists an inverse correlation between economic
growth and natural resource abundance among developing countries. A study
conducted by Sachs and Warner (1995) who studied the relationship between “Natural
Resource Abundance and Economic Growth in developing countries” finds that “one
of the surprising features of modern economic growth is that economies abundant in
natural resources have tended to grow slower”. A study on the “Empirical evidence on
the resource curse hypothesis in oil abundant economy” conducted by Satti et.al (2014)
who focused on Venezuela from the period 1971-2011 finds that natural resource
abundance tends to impede economic growth.
From the policy perspective, the inverse relationship can also be explained by the
presence of government regulations such as permits and licensing for commodities
such as oil, fuel, paraffin, lubricating oils, petroleum gases, petroleum jelly, petroleum
bitumen, and paraffin wax. The implementation of these regulations acts as a form of
protectionism for local exporting companies.
From a microeconomic perspective, the mineral commodities this inverse relationship
can be seen as emanating from the fact that the mining sector’s contribution to GDP
50
contracted by 1.7% in 2019. This, according to the Mineral Council of South Africa
(2019) was due to Logistical constraints (such as rail capacity), Utility disruption
(power outages), industrial action, and community unrest. From macroeconomic
perspective commodity prices and exchange rate disparities can be seen as underlying
forces that impact the trade and movement of these commodities. Given the fact that
commodities are traded in US$ and the exchange rate between the US$ and the ZAR
continues to widen (see figure 08 below), this causes the commodity prices to drop.
For an exporting country like South Africa, depreciation of the ZAR against the US$D
translates to lower revenue received for the commodities being sold in the international
market even the volume of exports is increasing.
Though South Africa is one of the top mineral exporting countries, the country’s
current energy crisis has impacted not only the export of energy resources but also
other industries that rely on energy for their operations. The increasing in-house
demand for these resources has impacted the revenue received from their exports. To
tackle this issue, the country should utilize alternative renewable energy.
Figure 07: South Africa exports of mineral fuels, oils, distillation products
Source: Tradingeconomics.com
51
Figure 08: Commodity Price Index from 2008-2019
Source: Mineral Council South Africa (2020)
6.3. Fruits and Nuts; Edibles; Peels of Citrus Fruits/ Melons (X7_exp)
From the regression analysis, we find that a 1 % increase in the export of Fruits and
Nuts; Edibles; Peels of Citrus Fruits/ Melons will cause the economy to contract by
0.03 billion dollars per quarter. The inverse impact of these exports can be seen as
emanating from factors such as seasonality of the industry, droughts as well as climate
change.
The inverse impact of these commodities can also be explained from a policy
perspective. Over the years South Africa’s Agriculture sector has always been
characterized as a labour-intensive industry. The implementation of minimum wage
by the department of Labour can be seen as a price-floor that hinders the market from
clearing as this form of intervention causes inefficiencies within the market.
Furthermore, the implementation of the minimum wage policy can be viewed as an
input cost inflation which has a ripple effect on the revenue earned.
Another policy-related impact that could lead to the negative contribution to economic
growth is the land expropriation without compensation passed by the Parliament of
South Africa in 2018. The and expropriation without compensation policy allows for
the government to pursue land reform via restitution, redistribution and tenure reform.
52
this in essence allows for the government to claim land that was ceased by the colonists
and redistribute the land to its rightful owner without compensation.
Figure 09: South Africa Export of edible fruits, nuts, peel of citrus fruit, melons
Source: Tradingeconomics.com
6.4. Exports of Plastics and Plastic Products Thereof (X10_exp)
From our regression analysis, we find that a 1 % increase in the exports of plastics and
plastic products will cause the economy of South Africa to grow by $ 0.26 billion per
quarter. The positive relation between plastic exports and GDP could emanate from
the fact that Plastic products are used as inputs by a variety of industries such as motor
vehicles, construction, packaging, textiles, and clothing industries, where according to
the Industrial Policy Action Plan (IPAP), South Africa’s plastic manufacturing
contributed approximately 1.6% to the country’s GDP and 14.2% to the manufacturing
sector (IPAP,2020). Furthermore, the introduction of 3D printing, biodegradable
plastics as well as plastic recycling has allowed for the industry to produce more plastic
products at using fewer inputs further justifying the positive impact of plastics and
plastic products on GDP.
53
Figure 10: South Africa Exports of Plastics and plastic products thereof
Source: Tradingeconomics.com
6.5. Insignificant variables
The study initially consisted of 20 commodity grouping, upon running our regression
we find that sixteen commodity groupings were insignificant within the regression.
These commodities were:
Precious Metals Gems and Jewellery exports (X1_exp); Ores, Slag and Ash exports
(X2_exp); Machinery and Appliances exports (X5_exp); Iron and Steel exports
(X6_exp); Aluminium and Articles Thereof exports (X8_ exp); Electrical Machinery
and Equipment exports (X9_exp); Mineral Fuels; Oils and Products of Their
Distillation imports (X1_imp); Machinery & Appliances imports (X2_imp); Vehicles
& Their Parts imports (X3_imp); Electrical Machinery & Equipment imports
(X4_imp); Precious Metals, Gems & Jewellery imports (X5 _imp); Plastics & Articles
Thereof imports (X6_imp); Instruments & Apparatus imports (X7_imp);
54
Pharmaceuticals Products imports (X8 _imp); Chemical Products N.E.S imports (X9
_imp); and Inorganic Chemicals, Organic Chemicals imports (X10_imp).
The insignificance of these commodities for the study can be due to the fact that the
commodity classification is inclusive of both some high value and low value
commodities. Therefore, when we aggregate their contribution to GDP we find it to be
the opposite of our expected results. It is no lie that South Africa has vast reserves of
resources such as Coal, Gold, Diamonds, Iron ore, Platinum, Manganese, Chromium,
and Copper. However, South Africa does not own these mining; quarrying; oil and gas
extraction reserves. These reserves are owned by private and foreign companies such
as DeBeers; Anglo American; Glencore; BPH, and Rio Tinto. A similar situation exists
within the agricultural sector, where land is owned by foreign companies. The
privatization of land, mines and can also be seen as a logic reasoning as to why these
commodities can be insignificant, as the company’s main earning are directed to head
offices which are not likely to be based in South Africa. The revenue generated by
these companies operating in South Africa usually covers operational expenses for the
company. Furthermore, the investment made by these multinational cooperation’s can
be classified as capital flight investment. Capital flight investment does not yield much
of a positive impact on GDP as utilizing this investment strategy invest into country
to extract resources and leave when the resources have depleted. In addition, the
aforementioned commodities are very volatile and are impacted my external
macroeconomic factors such economy downgrading and weakening exchange rate.
The insignificance of the imported commodities could emanate from the fact that in
our data collection technique we only focused on only on imported and exported
commodities and excluded entrepot trade, which is importing a particular good adding
value to commodity and re-exporting the commodity. If the study had included
entreport trade, we would see import commodity group classifications such as vehicles
and vehicles parts as being positive since the export of vehicles is positive the import
of vehicle parts can be used as inputs to support the growing industry.
55
Conclusion
The study examined the impact of international trade of commodities on the economic
growth of South Africa using 2010-2019 as the sample period. The aim of the study
was twofold; firstly, it was to identify the top 10 imported and exported commodities
within our sample period. Secondly, it was asses which of these commodities have a
positive impact on the economic growth. Using the HS 2 commodity code
classification, we were able to sample data from the OEC, and UN Comtrade for the
regression analysis, where the traded goods represented our independent variables
whilst GDP was used as a proxy for economic growth. The impact of trade on
economic growth was examined using an OLS regression model. The empirical
findings from the regression analysis showed us that all imported commodities have
no impact on quarterly economic growth. Though balance trade equation shows us
that exports ought to have a positive impact on GDP, in our regression we find that
some exported commodities have an inverse impact on the quarterly GDP. Our
regression results show that export of Vehicle and vehicle parts (X3_exp) and Plastics
and Plastics products (X10_exp) have a positive impact of GDP, whilst the export of
fruits (X7_exp) and mineral fuel, mineral and products of their distillation (X_exp)
have a negative impact on the economic growth.
Based on the findings from our regression analysis one can recommend a review of
the policies that promote export-led growth. To attain more revenues from exports
South Africa needs to review regulations pertaining to ownership of land, and its
reserves. The enforcement of permits and licensing for industries that have potential
of improving economy can be seen as a barrier to entry which have created
inefficiencies. With south Africa being a country with an abundance of resource, it
advisable for the government to focus on boosting its manufacturing industry in order
to enable value addition processes into resources as exporting final products as
opposed to raw materials will have a greater impact on GDP.
56
This study may be further investigated through using GARCH model or essentially
adding controlled variables such as inflation; and foreign direct investment for more
results.
