Post on 01-Feb-2023
Advanced Master in International and Development Economics
Facultés Universitaires Notre-Dame de la Paix, Namur
Université Catholique de Louvain, Louvain-la-Neuve
Rapport de stage
Blaise EHOWE NGUEM
Academic year 2011/2012
a
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African Trade Partners: CompareChina with other Emerging
Markets
By Blaise EHOWE NGUEM
A project submitted in partial fulfillment of
the requirements for the degree of Advanced
Master in International and Development
Economics
Economics School of Louvain
University of Namur — FUNDP
Catholic University of Louvain — UCL
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ABSTRACT
During this last decade, trade between African countries and
BRIC has more than double and China is the one which dominates
this trade. This strong increase has raised a lot of debate on
the invasion of African by BRIC and especially China whom many
politics and economist accuse to no deal with governance in
Africa. The aim of this paper is to provide some responses by
trying to understand what determinants motivate trade between
African countries and BRIC and also to see if China is
different to other emerging market. To rich that goal, we
compute a gravity model usually uses on international trade to
estimate the level of trade between Africa and it BRIC trade
partner. In addition to traditional variables, we add some
variables of interest such as governance for African and
exchange rate. We run pooled OLS model and panel data approach
to check for the robustness and the dynamic. The result from
this model shows that the resource abundance of African country
is not a significant driver of China trade as many authors
think even if the main part of imports is composed by oil and
natural resource. China is trading more and more with rich
natural resource countries. In addition, China and India are
not really different in the way of trading with African
countries. Those two countries are influenced by the same
variables except governance in case of imports because they
have approximately same needs. And finally, these two countries
differ at some level from Brazil and Russia..
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ACKNOWLEDGMENTS
This personal project has benefited from the contribution of
many people, and I would especially like to acknowledge their
support.
I am grateful to my promoter, Professor Romain HOUSSA. I would
like to thank him for his encouragements, support, remarks and
critics which significantly ameliorated this work.
I express also my gratitude to Mrs. Maelys de LA RUPELLE, my
tutor, for his helpful comments and suggestions.
I benefit immensely from the comments and suggestions of Angele
BAHA, Ritchelle ALBURO, Loudine BESSONG, Edouard TSAGUE, Jules
KEMBOU, Carine NZEYANG, and Patrick EWANE. They also provided
particular care to review the original manuscript.
My thanks go to Mrs Pierrette NOEL, staff members of the
University of Namur and especially for the organization
committee of the Advanced master in international and
development economics, for the promptitude with which they
overcome problems we have encountered during this academic
year. Financial support from CUD is gratefully acknowledged and
I am finally indebted to all my family for their moral support
at critical moments and for their encouragements.
Finally, I want to thank specially my wife Rosine EHOWE, my
kids Chanelle and Giovanni and my whole family for their
sacrifice, constant and unfailing support and encouragements
despite the distance.
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TABLE OF CONTENTSI: INTRODUCTION....................................................1
2: EVOLUTION OF TRADE BETWEEN AFRICA AND BRIC’s COUNTRIES..........6
2.1: Rapid view of BRIC and Africa...............................6
2.1.1: BRIC in the World economy................................6
2.1.2: Africa: the poorest economy in the world.................7
2.2: Trade between Africa and BRIC...............................9
2.2.1: BRIC: New trade partner of African countries.............9
2.2.2: BRIC investments and trade with Africa..................11
III: METHODOLOGY..................................................13
3.1: Gravity model..............................................13
3.1.1: Background..............................................13
3.1.2: The gravity model.......................................14
3.2: The model and data.........................................15
3.2.1: The model specification.................................15
3.2.2: The Data................................................17
IV: EMPIRICALS ANALYSIES..........................................20
4.1: Mains results for African exports..........................20
4.1.1: The gravity model with Pooled OLS.......................20
4.1.2: The gravity model with panel random effects.............22
4.1: Analysis for African imports...............................23
4.2.1: The gravity model for African imports with Pooled OLS...23
4.2.1: The gravity model for African imports with panel data approach.......................................................24
IV: CONCLUSION AND REMARKS........................................26
BIBLIOGRAPHY......................................................28
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LIST OF TABLES AND FIGURES
Figure 1 : BRICs in the World Economy...........................................................................................................6
Figure 2 : Share of BRICs production in World GDP......................................................................................7
Figure 3 : Africa economic growth.................................................................................................................. 8
Figure 4 : BRIC-Africa trade as a proportion of Africa-world trade..............................................................9
Figure 5 : BRIC-AFRICA trade......................................................................................................................... 10
Figure 6 : Composition of Chinese Imports from Africa.............................................................................10
Figure 7 : Total capital investment in FDI projects in Africa (2003-2009)..................................................11
Figure 8: Number of BRIC FDI projects in Africa between 2003 and 2009................................................12
Figure 9 : Structure of resource abundance among African countries......................................................18
Figure 10 : Evolution of governance............................................................................................................. 19
Figure 11 : Variables in the models............................................................................................................... 19
Figure 12 : Gravity model on African exports, Pooled OLS.........................................................................20
Figure 13 : Gravity model on African exports, panel data approach.........................................................22
Figure 14 : Gravity model on African import, Pooled OLS...........................................................................23
Figure 15 : Gravity model on African import, Pooled OLS..................25
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ABBREVIATIONS
AFD : Agence Française de Developpement
IMF : International Monetary Fund
WTO : World Trade Organization
EU : European Union
USA : United States of America
BRIC : Brazil-Russia-India-China
OECD : Organisation for Economic Co-operation and
Development
UNCTAD :United Nations Conference on Trade and Development
:
:
:
:
:
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I: INTRODUCTION
According to a recent report by the United Nations Conference
on Trade and Development (UNCTAD) in 2010, the level of trade
between Africa and other developing regions including China has
known an important rise.
This report indicates that the total African trade in
merchandise with developing countries outside the continent
increased significantly from 8 percent to 29 percent in 2008.
Even if the European Union as an economic bloc still
constitutes the largest trading partner of Africa, the share of
trade with Europe has shrunk from 55 percent during the 1980s
to less than 40 percent in 2008.
However, by analyzing the speed of the increase of trade
between Africa and emerging countries, it appears that BRIC’
countries (Brazil, China, India and Russia) are the new
important African trade partners (Freemantle and Stevens,
2009). The proportion of BRIC-Africa trade as a proportion of
Africa-world trade grew from 4.6% in 1993 to over 19% in 2008.
In 2009, China, India and Brazil are classified as Africa’s
2nd, 6th and 10th largest trade partners, respectively.
Among those countries, China is the most important and actually
is becoming the second African partner after USA (Bamidele
Adekunle, 2011). De Grauwe, Houssa and Piccillo (2011) note
that the values of China-Africa exports and imports surged from
US$ 676.5 and 227.4 millions in 1980 to 43.3 and 52.9 billion
in 2008.
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There is a lot of debates and controversy about the increasing
power of China in Africa. Two sides emerge from discussion. The
first group estimates that China practices imperialism in
Africa and have an unique objective to exploit natural
resources it needs for its development. The second group thinks
that the relationship between China and Africa is a win-win
partnership.
For the first side, the arguments are that: China is motivated
by the abundance of oil and minerals resources in many African
countries and operates in countries abundant regardless of
governance, respect of environment and human rights. This can
be seen by looking to China investment in drilling rights in
Nigeria, Sudan and Angola and exploration contracts with Chad,
Gabon, Mauritania, Kenya, Equatorial Guinea, Sudan, Zimbabwe,
Ethiopia and the Republic of the Congo. Apart oil, China trades
in copper industry in Zambia and the Democratic Republic of
Congo and buy timber in Mozambique, Liberia, Gabon, Cameroon
and Equatorial Guinea. The particularity of many of those
countries is that they are corrupted, mismanaged or faced civil
war conflict (Rosenstein, 2006).
The second argument presented by skeptic is that the Chinese
investments will destroy African manufacturing industry because
those investments reduce market for African firms, don’t
generate transfer of technology and don’t increase the number
and the quality of African jobs. Indeed, according to Cheung et
al (2010) and Kent (2006), in contrary to Western industries in
Africa which usually deal with African firms and reduce
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unemployment by employing residents, Chinese firms tend to
bring their own workers from China. In addition, for all aids,
technical assistance and interest-free loans to business-
friendly African governments, Chinese companies are the one
which win contracts to build infrastructures instead of African
companies. The other damageable effect is that cheapest
manufacturing good imported from China are competing with the
same African products and in the long run will destroy those
fragile firms. The last effect is due to the increasing number
of Chinese emigrants who reduce the labor opportunities of
residents even in sector like retailing or catering (Freemantle
et al, 2009).
This presence of China become worrying for some African leaders
such as the Zambian opposition leader Michael Sata. He said
that aid and investment from China are Trojan horses. He
thought: “You recruit Chinese doctors and they end up having
Chinese restaurants in town. They are just flooding the country
with human beings instead of investment and the government is
jumping”. He charged: “We have to be very careful because if we
leave them unchecked, we will regret it. China is sucking from
us. We are becoming poorer because they are getting our wealth”
(see Brautigam, 2009). But despite his advices, people didn’t
vote for him because in Africa, the relationship and the
cooperation between China and Africa is analyzed by population
as win-win and concrete.
The second group thinks that the relationship between China and
Africa can be seen as friendship and mutual beneficial. One of
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the arguments is about aid. According to Brautigam (2009),
despite nearly sixty years of aid, it seems to be difficult to
western to show that their aid really promotes growth and
reduce poverty because data don’t really show improvement on
those fields. Another grief is that the policies they implement
depend on donor and change frequently contrary to China aid and
economic cooperation. In fact, the content of Chinese
assistance is simpler and inspired from his experience as
developing country. This cooperation focuses on
infrastructures, production, and university scholarships that
are visible by population and seen as solving real problems.
China in this case appears as different donor and strategic
partner because it is also a developing country and its
development success (explicitly, its rapid economic
transformation and its reduction of poverty) give it a great
deal of credibility as a partner with relevant recent
experience.
Many facts try to reinforce the image of Chinese investment.
Indeed, nearly half of the amount invested between 1979 and
2001 (46.3 %) was in the manufacturing sector (World Bank,
2004a and 2004b). Resource development accounts for just over
one quarter of the investments (27.5 %), even if this share is
probably increasing. The balance of the investments went to
services (18.3 %), including construction services. Agriculture
(7.1 %) and other (0.9 %) claimed the balance (Zafar, 2007).
The other argument given by pro China is that, Africa is not
the only case of increasing interest of China and the country
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is acting the same way with its all trading partners. To
support this idea, Bokilo (2011) shows that imports of China
from all developing countries out of Africa increase in the
same range as with African countries. He goes further by
showing that Chinese FDI in Africa represents only 0,2% of
global FDI in 2010 while the world invest 4,4 %.
In addition to the theoretical discussion on presence of China
in Africa, many studies try empirically to estimate the factors
that are driving the China-Africa relationship. Ademola et al
(2009) analyzed the impact of China-Africa trade relations and
found out that there are gains and losses in this engagement.
Nabine (2009) conclude that in this partnership, more cost
incurred than benefits for Africa. Kandiero and Chitiga (2003)
also indicate a positive relationship between openness and FDI
in Africa.
Adekunle and Gitau (2011) with objective to identify the main
drivers of trade between Africa and China indicate that GDP of
SSA countries and exchange rate, were significant predictors of
exports to China taken into consideration all SSA except
Somalia. The result was still the same even after removing oil
rich countries from the model. De Grauwe, Houssa and Piccillo
and al (2010 and 2011) go further comparing China with
developed markets (French, USA, England, German…). They found
that, next to the standard gravity variables, governance plays
a significant role in the dynamics of African trade. In
particular, ceteris paribus, China, France, Germany, UK, and
USA export significantly more to African countries with a
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better quality of governance. But this result is different in
the case of imports. China covers the unique role of importing
more from African countries that display bad governance because
those countries are excluded more or less form market by
western countries. China is now importing from these countries
and creating a market for these countries to export and is
playing a key role in the future development of these left out
countries.
The absence of consensus on the debate on the relation between
China and Africa motivates this paper. Our work is an extension
of De Grauwe et al paper. The aim is to compare China with
other emerging markets which trade with Africa. To achieve this
goal, we have chosen others BRIC countries because those
countries have many similarities with China such as rapid
growth, an important need of natural resources and energy, a
rapid increase of industrial sector, an important population
and approximately the same level of governance.
So the question is: What explain the difference between African
trade partners especially China and other emerging market? The
main objective of this paper is to determine the factors that
can explain the increasing and the difference of level of trade
between Africa and BRIC partners especially between China and
the three other emerging markets.
Earlier studies on determinants of international trade use the
gravity model to estimate the level of trade flow among two
countries. The gravity model is an empirical model which is
derived from the equation of gravitational attraction proposed
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in Newtonian Mechanics. While the equation in Physics states
that the force of gravitational attraction between two bodies
is directly proportional to their masses and inversely
proportional to the square of the distance between them, i.e,
The analogy used for trade is just the same with the GDPs of
the two economies replacing the masses of the body.
However, there is generally no pre-determined constant like the
universal gravitational constant. This constant varies for each
pair of economies and it has to be estimated through a
regression analysis which fits historical trade data to the
historical GDPs of the economies in question and the geographic
distance between them. Historically, the most classic and early
application of the model to international trade was by
Linnemann (1966). It has been refined over time by Helpman and
Krugman (1985), Bergstrand (1985) and Alan Deardorff (1995) and
it has been used in recent times for estimation of trade
potential as well as analysis of trends in commodity trade.
By using the derivative gravity model with more than one
variable, Linneman shows that the level of trade is strongly
influence by GNP, population, distance, and a preferential
trade factor (colonialism). Based on this result, many authors
try to extent the analysis by computing other factors that was
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omitted by Linneman. For example, Srivastava (1986) uses
demographic variables, political variables (political
instability, membership in a particular economic union,
colonial heritage), cultural variables (religion and language)
and the geographic distance to determine the share of trade
among two countries. Variable such as tariffs,
transaction/transport cost, and foreign direct investment have
being introduced in recent studies (see Eichengreen and Tong
(2007); Boughanmi et al (2009)). De Grauwe et al (2011) have
gone further by adding governance in addition to traditional
variables, to show that this variable can play a key role in
trade between African countries and their partners.
To address the issue of this paper, we will derive a gravity
model. Based on the paper of De Grauwe et al (2011), we will
use most of the same variables (distance, GDP per capita,
population, governance, oil resources, historical
relationship). Additionally, we will control for as exchange
rate (trade revenue of many African countries are in dollar and
the levels of exchange rate of currencies of BRIC countries
with dollar can influence the direction of trade). Empirically,
we will estimate the trade flows for each BRIC countries with
Africa by using two methods. First, we will use a simple pooled
OLS regression to determine significant variables. Finally, to
check for the robustness and the dynamics of the estimation, we
will compute the panel data approach for the period 2000 -2010.
The data we will use come from many sources: CEPII, COMTRADE,
IFM, WORLD BANK and other sources.
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2: EVOLUTION OF TRADE BETWEEN AFRICA AND BRIC’sCOUNTRIES
This section focuses on Africa and BRIC macroeconomics’
situation. We will analyze some elements such as production and
trade variables.
2.1: Rapid view of BRIC and Africa
2.1.1: BRIC in the World economy
The term BRIC was used for the first time in 2001 by Jim O’Neil
in the global Economics paper “Building Better Global economies
BRICs” published on November 30th of that year by Goldman Sachs
Inc. The term have been used to designate the group of
countries constitute by (Brazil, Russia, India and China) which
continuously increase their power in the world economy since
the beginning of nineties.
This block is becoming more en more powerful among others
developing countries. In fact, BRIC population count for one-
third of world population and represent a big market. In terms
of growth, those countries have increased their share in world
GDP from 5,8% during the period 1991-94 to 13% in 2005-09 (IMF,
2010). This dynamic can be also observed in trade flows.
According to Freemantle et al (2009), BRIC are now important
actors of world trade and their proportion of world trade rise
from 6.3% in 2000 to 12.8% in 2010
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Figure 1 : BRICs in the World Economy
Source: IMF
By comparing BRIC countries between them, it appears that they
differ from each other in terms of production, level of
industrialization and impact on the global economy. Among these
countries, China is the most dynamic and powerful by looking to
the increase of GDP. From 1990 to 2010, the share of China in
the world production has been multiplied by four followed by
India, Brazil then Russia. In 2011, China surpassed Japan and
become the second largest economy in the world. And according
to IMF, around 2025, China will be the first world economy
before USA (IMF, 2011).
Figure 2 : Share of BRICs production in World GDP
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Source: IMF; * (estimation), ** (forecasts)
2.1.2: Africa: the poorest economy in the world
Actually, by looking on many international macroeconomics
studies, Africa and South Asia are the poorest groups in the
world. In terms of production, Africa is a very small player in
the global economy. Compare to another group of countries,
Africa produce only 3.3% of global GDP in 2011 and contributes
to only 10% to the world GDP growth.
Despite the fact that African participation to world economy is
weak, its production is increasing continuously since 2000 at 5
% growth rate per year sustained principally by higher
commodity prices and exports (IMF, 2010). But the global
financial and economic crisis of 2008/2009 had interrupted this
period of high growth such that it decreases to 3.1% in 2009.
Figure 3 : Africa economic growth
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Source: Africa Development bank
This improvement in production influences the level of trade
between the continent and the rest of the world. According to
the WTO (2011), the volume and even the value of goods and
services has increased significantly from USD 13 trillion in
2000 to an estimated USD30 trillion in 2010. Also the share of
world export rose from 2.2% to 3.6% between 2000 and 2010. But
African countries have not really benefited from the steady
increase in the volume of international trade. Indeed, Africa’s
share of in world trade has been in decline since 1980 from
5.80 % in 1980 to 2.23% in 2000.
Many reasons can explain this lost of competitiveness of
African trade. First of all, the key and major reason is the
declining of price of primary commodities which constitutes the
main part of their exports. The second explanation is the low
productivity and even sometimes the absence of manufacturing
sector. Another constraint is the weakness of intra-African
trade comparatively to trade within other regions. On average
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over the past decade, only about 10–12 % of African trade is
with African nations (UNECA, 2009). This limitation is due to
difficulties to promote regional integration, the absence of
subregional communication infrastructures and the inexistence
of deeper financial and capital markets.
2.2: Trade between Africa and BRIC
2.2.1: BRIC: New trade partner of African countries
Due to some history facts such as colonization, the traditional
African trade partners are European countries and United States
of America. Since two decades, BRIC are becoming important
partners for African countries. In 2008, China, India and
Brazil rank as Africa’s 2nd, 6th and 10th largest trade partners,
respectively (Freemantle et al, 2009). The BRIC-Africa trade as
a proportion of Africa-world trade grew from 4% in 1995 to over
23% in 2010.
Figure 4 : BRIC-Africa trade as a proportion of Africa-world trade
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Source: IMF
Analysis across African countries shows that those which are
natural resources abundant and large population dominate flows.
Among them, South Africa, Egypt, Nigeria and Angola are the
most significant partners for the BRICs in Africa. For example,
Angola accounts for 20% of all BRIC-Africa trade and South
Africa accounts for 20% of BRIC exports to Africa and 15% of
BRIC imports from Africa.
This intensification of trade between Africa and BRIC can be
explained by the fact that countries as China, India and Brazil
are growing very rapidly countries need enough minerals
resource to support their rapid domestic economic growth and
development (Simon Fremantle et al, 2009; Raphael Kaplinsky et
al, 2008). But the share of each BRIC in this trade is not
equal.
China dominates BRIC-Africa trade flows and accounts for around
two-third of BRIC-Africa trade. But adjusted for economic size,
India and China’s, Brazil and Russia’s percentages of trade
with Africa as a proportion of GDP are relatively 2.6% and
2.3%, 1.7% and 0.6%, respectively.
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Figure 5 : Evolution of share of each BRIC in African trade (%)
Source: IMF
By looking to products import from Africa, It appears that
nearly 80% of China’s imports from Africa consist of oils,
mineral and natural resources such as seaborne iron, diamonds,
logs, nickel, copper, aluminum, zinc and steel. This share is
increasing since the nineties. This can be explained by the
discovery of news reserves in Africa.
Figure 6 : Composition of Chinese Imports from Africa
Source: Vidyarthee (2010°
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This state of affairs can explain why many authors think that
natural resources enlighten the strong presence of China in
many African countries. But, it is necessary to remind that
African exports even to western countries are dominated by
primary products. It can also be observe that it is not a
Chinese specificity because when looking at others BRIC which
are similar to China, the pattern is similar.
Indeed, over 70% of India’s imports from Africa comprise
mineral fuels and oil. Africa also provides India with 50% of
its inorganic chemical and precious metal compounds, which
account for 11% of Africa’s exports to India. Brazil imports
mainly mineral and chemical products from Africa, while Russia
import cocoa and fruits (Freemantle et al, 2009)
2.2.2: BRIC investments and trade with Africa.
One of the most important dimensions of the relationship
between Africa and BRIC is Foreign Direct Investment (FDI).
Although most FDI to Africa still comes from OECD countries,
the largest increase in FDI to Africa in recent years has come
from BRIC. Over the past 10 years, FDI flows from BRIC to
Africa have increased consistently, only falling slightly in
2009 due to the global economic crisis. However, despite this
significant evolution, one should note BRIC were only the
fourth largest FDI investor region into Africa between 2003 and
2009 far behind the United States, Western Europe and Japan.
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Another fact is that while at the beginning, the immensity of
the BRICs’ FDI to Africa has been concentrated in South Africa,
Egypt and Morocco, recently other countries are becoming new
destination of investment (Mwangi, 2009).
Figure 7 : Total capital investment in FDI projects in Africa (2003-2009)
Source: FDI Intelligence from Financial Times Ltd
According to the FDI Markets from Financial Times, India was
the largest of the BRIC countries in terms of overseas
investment projects in Africa between 2003 and 2009. But in
terms of global value China is before with cumulative value of
USD28.7 billion of investments followed by India (USD25
billion) then Brazil (USD10 billion) and Russia (USD9.3 bn). As
in case of trade, natural resource sector is the main
destination of BRIC’s FDI.
Figure 8: Number of BRIC FDI projects in Africa between 2003 and 2009
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Source: FDI Intelligence from Financial Times Ltd
A recent IMF study conducted by Mlachila and Takebe (2011)
shows that the natural resource and infrastructure sectors
attract the biggest share of Chinese FDI to Africa in terms of
volume. But the lack of data doesn’t permit to determine the
precise sector that attracts the investment. But authors
estimate that since the largest recipients of Chinese FDI are
mostly natural resource countries, it is reasonable to conclude
that Chinese FDI to SSA countries is mostly concerned with
natural resources and infrastructure.
But as in previous case of trade, China is not the only one
with this model but also India and Brazil invest in primary
sector. The study by Chanda, Banerjee and Vishnoi (2008)
explains that pressure on production costs in those countries
oblige them to look Africa as a major source of cheap raw
material and natural resources to secure their growth.
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III: METHODOLOGY
3.1: Gravity model
3.1.1: Background
The earlier studies (e.g., Beckerman 1956; Ullman 1956; Smith
1964; Linneman 1969; Yeats 1969) that try to evaluate trade
flow between two countries have used a relation between
distance and trade (see Rajendra Srivastava et al, 1986). Those
studies have shown that distance influence negatively the
intensity of trade flows that occur between nations. So
countries that are geographically proximate will tend to trade
relatively more than will nations that are further. But only
distance was not sufficient as determinant of intensity of
trade.
Based on the gravity model uses in Physics to determine the
intensity of attraction between two objects, Tinbergen (1962)
and Pöyhönen (1963) were the first authors applying the gravity
equation to analyze international trade flows. The model assume
that trade between two countries is directly related to
countries’ size and inversely related to distance between them.
Linneman (1966) has conducted the most interesting study on the
determinants of trade. He used an econometric model to study
the factors that determined the trade flows between 80 nations
in 1959. He introduce GNP, population, distance, and a
preferential trade factor as independents variables in the
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model, with preferential trade factor as a dummy variable
indicating whether the nation was in the British, French, or
Belgian or Portugese sphere of influence. He found that all the
variables had a statistically significant relation with the
volume of imports and the volume of exports flowing between the
pairs of nations.
Based on Linneman paper, Rajendra Srivastava et al (1986) have
extended the model to several factors that could affect trade
flows but that were omitted from the Linneman study. These
factors include political instability, membership in specific
economic unions, and such cultural factors as religion and
language. They also controls for variation in the size of
nations' economies (GNP).
But all those studies were empirical analysis, theoretical
support of the research in this field was originally very poor,
but since the second half of the 1970s several theoretical
developments have appeared in support of the gravity model
(Inmaculada Martinez-Zarzoso, 2003, Laura Marquez RAMOS, 2007).
Anderson (1979) is the first who try to formalize the gravity
equation from a model that assumed product differentiation.
Bergstrand (1985, 1989) also explored the theoretical
determination of bilateral trade in a series of papers in which
gravity equations were associated with simple monopolistic
competition models. The differences in these theories help to
explain the various specifications and some diversity in the
results of the empirical applications.
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There is a huge number of empirical applications in the
literature of international trade, which have contributed to
the improvement of performance of the gravity equation. Some of
them are closer related to our work. First, in recent papers,
Chen and Wall (1999), Breuss and Egger (1999) and Egger (2000),
Rose (2004), improved the econometric specification of the
gravity equation when explaining trade flows among countries.
Second, Berstrand (1985), Helpman (1987), Wei, (1996), Soloaga
and Winters (1999), Limao and Venables (1999), and Bougheas et
al, (1999), De Grauwe et al (2011) among others, contributed to
the refinement of the explanatory variables considered in the
analysis and to the addition of new variables.
3.1.2: The gravity model
According to the generalized gravity model of trade, the volume
of exports between pairs of countries, Xij, is a function of
their incomes (GDPs), their populations, their physical
distance and a set of dummies variables ( see Deardorff, 1995;
Martinez, 2003; Marquez 2007).
(1)
Where Yi (Yj) represents the GDP of the exporter (importer)
country, Pi (Pj) are the population of exporter (importer), Dij
indicates the distance between the two countries’ capitals (or
economics main centers), Aij represents another factor that can
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enhance the level of trade among the partners (language,
colonialism…) and uij is the error term
For estimation the best way is to apply the logarithm to the
equation (1) and the expression will be written as
(2)
Where l represents the natural logs of each variable, is
the sum of preferential trade dummy variables and is taken
the value one when a specific condition is satisfied (e.g.
speaking the same language or been a member of the same trade
bloc) and zero otherwise. In general the dummy variables added
in the model are related to sharing the same language, colonial
relationship, sharing the common border.
Looking at the expected sign of coefficients, β1 and β1 are
expected to be positive because the high level of income means
high level of production and then the increase of export. In
the same line, the high level of income of the importer country
affects positively the level of imports. β3 and β4 related to
population may be positive or negative, depending on whether
the country exports less when it is big (absorption effect) or
whether a big country export more than the small country
(economies of scale) (Marquez 2007). The distance’ coefficient
is expected to be negative because distance increase trade
cost.
39
3.2: The model and data
3.2.1: The model specification The gravity model in the paper is derived from the paper of De
Grauwe, Houssa and Piccillo (2011) where they use it to compare
the trade flows between Africa and its traditional partners and
China. They constructed an extension on the model developed by
Rose and Spiegel (2004) use to determine the link between trade
and external debt.
In this model Xij (imports or exports) is determined by
(3)
Where, Dij is the distance, YiYj/PopiPopj represents the GDP per
capita of the countries i and j; Colonyij is a binary variable
that takes the value 1 if i was colonized by j or vice versa,
Langi,j is a binary variable that takes the value 1 if the
countries i and j have a common official language, Ressourcej
is a binary variable that takes the value 1 if an African
country is abundant in oil and minerals, Tt is the time fixed
effects that takes the value 1 at time t and 0 otherwise,
Governancejt is the quality of governance of an African country
at time t; where higher values indicate good governance and εijt
is the error term.
In our study, to compare China and other emerging markets
(India, Brazil and Russia), we will use the same extended
39
gravity model but we will drop the variable colonization which
is not apply and language will be turn in to historical
relationship (same language for Brazil, have been partner of
Russia during the cold war, have historical trade relationship
with China or India). This variable take the value one if the
is a relationship and 1 otherwise. To have
In addition, we will add:
The real exchange of BRIC currencies relatively to dollar.
Abdur R Chowdhury (1993) show that the volatility of
exchange rate depresses trade flows. Linda S. Goldberg et
al (1998) say that in addition to this direct linkage
between trade flows and direct investment, the real
exchange rate influences trade flows. In our study we
choose US dollar as reference money because the huge part
of export revenue of African countries is in US dollar.
In the result our model estimates export of BRIC country i to
African country j as:
(4)
Where i represents one of the following African trade partners,
China, India, Brazil, Russia; and j represents an African
country. is the logarithm of export or import of BRIC
country i from African country j in real term. measures the
39
cost of trade, YiYj/PopiPopj represents the level of wellbeing of
each countries i and j; is the size of countries i and j,
is a binary variable representing the historical
relationship between BRIC country i and African country j. This
variable takes the value 1 when the two countries are related
and 0 otherwise. is logarithm of real exchange rate of the
currency of BRIC relative to dollar.
In our equation, we expect , , , , and to be positive
and , to be negative. Finally, following the analysis of De
Grauwe, Houssa and Piccillo (2011), we expect the coefficient
on governance to have a positive sign. The main intuition is
that the governance quality of a country affects the
transaction costs involved in economic activities and a country
with low governance will display a high transaction cost and
should trade less with others.
For African export in direction of BRIC countries we change a
bit the equation by removing exchange rate because this
variable doesn’t logically affect the level of export.
3.2.2: The Data In the paper, we will estimate separately aggregate exports and
aggregate imports between each of BRIC (Brazil, Russia, China
and India) and 50 African trade partner countries1. The data1 Algeria, Angola, Benin, Botswana, Burundi, Cameroon, Cape Verde, CentralAfrican Republic, Chad, Comoros, Republic of Congo, Djibouti, Egypt,Equatorial Guinea, Ethiopia, Gabon, Gambia, Ghana, Guinea, Guinea Bissau,Ivory coast, Kenya, Liberia, Libya, Madagascar, Malawi, Mali, Mauritania,
39
use in the model cover the period 2000-2010 and those data are
yearly. This period is choosing because of the availability of
the date on governance. In total we have 550 observations for
each variable.
Data on total exports and imports are in current dollars and
are taken from the IMF-DOTS2 database. We use the US CPI series
(2005 = 100) from the World Bank database to convert the trade
data in real terms. Always from World Bank database, we obtain
data on aggregate GDP and GDP per capita are in constant prices
of 2005 dollar.
Data on landlocked and distance are taken from the CEPII
distance database. For distance, we choose the relative
distance between the two mains economic cities of trade
partners. For landlocked countries, the statistic shows that
64% (34/51) have direct access to the sea.
The information on African resource abundant countries is
derived from Collier and O.Connell (2009). He takes in account
the existence of oil resource in a country but also other
minerals resources like gold, diamond, uranium, steel, bauxite
copper… But one limit of this data is that he don’t consider
Mauritius, Morocco, Mozambique, Namibia, Niger, Nigeria, Rwanda, Sao Tomeand Principe, Senegal, Seychelles, Sierra Leone, Somalia, South Africa,Sudan, Tanzania, Togo, Tunisia, Uganda, Burkina Faso, Congo Dem.Rep,Zambia, Zimbabwe, Lesotho, Swaziland, and Eritrea.
2 The DOTS data was accessed from MACROBOND at the Facultés UniversitairesNotre Dame de la Paix de Namur. It’s a financial database provided byMacrobond Financial AB. Macrobond Feed Solutions is a system for customdata delivery. www.macrobondfinancial.com/
39
timber as resource while it becoming a non negligible part of
products trade by African countries.
Figure 9 : Structure of resource abundance among African countries
Source: Data Conell’ data
For governance quality Kaufman et al (2011) provides estimated
values for six governance indicators:
Voice and Accountability (defined as the voice that each
citizen has in the making of the government);
Political Stability and Absence of Violence/Terrorism (The
stability of the government and the perceived danger that
this will be overthrown violently);
Government Effectiveness (Looking at the quality of the
government policies; and their effectiveness and
credibility);
Regulatory Quality (the ability of the government to pass
a regulatory framework to regulate private property);
Rule of Law (summarizing the perceptions on the
credibility and enforcement of contracts);
39
Control of Corruption (Looking at the perception of
government power yielded to defend private interests and
the extent of private elites).
The range of those indicators is -2,5 to 2,5 in 1996- 2009,
where higher values indicate better governance outcomes. In
this paper, we use the period 1999-2010 and we drop the year
2002 because of the absence of governance data that year. To
obtain the final governance indicator, we compute the mean of
the six indicators.
Figure 10 : Evolution of governance
-2-1
01
0 5 10tim e
Gouvernance gouv_m ean
Source: Author from Kaufman data
All the variable we have use are specified in the next figure.
Figure 11 : Variables in the models
lgdp Logarithm of GDP
ldist Logarithm of distance
rel Historical relationship
39
landlocked Landlocked country
exchange Exchange rate
resource Resource abundant country
governance governance
Source: Author
39
IV: EMPIRICALS ANALYSIES In this paper we use two methods to estimate the gravity model
for exports and export between each four BRIC countries and 50
Africans countries. Firstly, we use the pooled OLS with robust
standard errors to deal with heteroscedasticity and
autocorrelation in the residuals. Secondly, we use a panel data
approach. In this case as we have some variables which don’t
change across time (relation, resource, distance) and we want
to estimate their specific effect on trade flows, we opted for
panel data with random effect instead of fixed effects. Because
with fixed effect model, these variables are absorbed by the
intercept and we cannot evaluate in that case their influence
on dependant variable.
4.1: Mains results for African exportsIn the exports model, we remove the GDP deflator for BRIC
countries because this variable affects neither the price nor
the quantities of good that African countries export.
4.1.1: The gravity model with Pooled OLSBy using the pooled OLS, the result show globally that
traditional gravity variables are significant as determinant of
African countries exports to BRIC. The results also confirm
what we expected in terms of sign of coefficient. Aggregate GDP
is positive and significant. Except for India, distance
explains significantly and negatively African imports.
Moreover, it appears that landlocked African countries trade
significantly less with BRIC than do their costal counterparts.
39
Figure 12 : Gravity model on African exports, Pooled OLS
Variables Brazil China India RussiaR2 0,80 0,95 0,93 0,84
lgdp .7574685 *(.0968618)
.6256379 *(.0629981)
.5759051 *(.0558143)
.7214511 *(.0750926)
ldist -2.970361 *(.5807309)
-.9175969 *(.4525468)
-.5377276(.4456519)
-2.724806 *(.423442)
rel -1.650869 **(.9827139)
1.221354 *(.4407211)
-.4932146 *(.3440997)
-1.620627 *(.692052)
landlocked -1.687183 *(.5620353)
-1.018588 *(.3550577)
-1.973281 *(.3426607)
.4167407(.5013581)
ressource 1.477204 *(.5471312)
1.908505 *(.3505314)
.5206762(.3395439)
.5468317(.4889804)
gouvernance -.1057868(.4581973)
-1.068914 *(.2996361)
.0467965(.2964291)
.1971954(.4217983)
Source: Author; * p value < 0,05; ** p value
<0,10
A deep look on this estimation shows some differences across
BRICs countries that need to be analyze.
The historical relationship variable affects differently the
BRIC. The negative sign means that the probability for the BRIC
from a country which don’t have historical relationship instead
of a historical partner is low. For Brazil, India and Russia,
the existence of past relation with an African country increase
the level of export from that country to his counterpart. But
we observe that the coefficient is high for Brazil and Russia
than for India. This can be explained by language barrier for
Brazil (few countries are lusophone in Africa) and the Cold War
for Russia (trade is high in countries which was in the soviet
side during the Cold war). For India, there are contacts with
some African countries for many centuries this affect
positively the level of trade between them, but the coefficient
39
is not too high (contrary to the other BRIC) because India is
trying to diversify his trade partner in Africa. For China, the
historical contact reduces the level of exports. One of the
possible explanations can be that actually, China is creating
new market in many ex European colonies.
The impact of resource is not significant for India and Russia
contrary to China and Brazil. Indeed, India’s import
composition has changed dramatically over the last decade. The
import volume for commodities in got more than twelve-fold
increase; machinery and transport equipment sector has had a
seven-fold increase, while the import volume of mineral
fuel/lubricants reduced to its one-fifth. (Kaushal Vidyarthee,
2010). So India’s imports depend less and less from the
availability of natural resource in the origin country. Russia
is full of natural resource and imports mostly cocoa, iron and
fruits (80 % of total share) from six African countries
(Freemantle et al, 2009).
But for China and Brazil, it appears that the rich resource
countries are most targeted. This confirms the statistics of
composition of China’s imports (80 % of oil and mineral
resources) and Brazil’s import (85 % of crude oil and other
mineral). The difference between India’s and China’s imports
structure is due to the fact that growth in china is driven by
the industrial sector which need enough natural resources while
services are the main sector of growth in India.
Turn to the one of variable of interest which is governance,
it’s appears that the effect is not clear on trade with BRIC’s
39
countries except for China. For this country, the level of
governance is negatively and highly correlated with imports
from the African trade partner. This can be explain by the fact
that African countries that are well govern or improve their
governance usually trade more with western democratic
countries. But China imports more from African countries with
corrupted governments, with less rules of law, with less
accountability and with less regulation confirming the wide-
spread belief that China is deliberately pursuing tighter
economic relations with those countries that are isolated by
the rest of world and help them get access to world market.
Another reason can be the fact that as trade of natural
resources is very costly for importer because of some
externalities. Actually there is a lot of demand of adequate
transparency and accountability on the sector to ensure that
wealth is managed for the benefit of the whole population.
Transparency in oil sector operations allows democratic debate
on how oil wealth should be handled. This situation obliges the
extractive firm to pay the right price and preserve the
environment. So to make more profits or integrated the market,
there is an incentive for firm to cheat and deal with
dictatorial and corrupted government. As china needs huge
quantity of oil and mineral resources, it is easy to trade at
low cost with countries in which the level of governance is
low.
.
39
4.1.2: The gravity model with panel random effectsTo check the robustness and the consistency across time of the
results we obtain with OLS, we use a panel data approach. The
first observation is that many variables are not significant
with the introduction of dynamic.
Figure 13 : Gravity model on African exports, panel data approach
Variables Brazil China India Russialgdp .175578 **
(.0934818).195246 * (.0570544)
.3636274 * (.055933)
.2713067 * (.0575557)
ldist 2.096626 (2.486186)
-2.899291 (3.116068)
1.19706 (1.747389)
-1.402899 (2.552221)
rel -1.702719 (1.946541)
1.551641 (1.04993)
-.2752746 (.7967731)
-.9391929 * (1.795206)
landlocked -2.073243 * (1.090561)
-1.19344 (.8514889)
-1.971702 * (.7812281)
.2789076 (1.324452)
ressource 2.649835 (1.087956)
2.277442 (.8715931)
.4017141 (.8116567)
1.014883 (1.263318)
governance .4493497 (.772603)
-1.887032 * (.5572432)
-.0561473 (.5516696)
.9766322 (.7255014)
Source: Author; * p value < 0,05; ** p value
<0,10
As the figure 14 shows, only the size of economy keep its
significativity across time. Distance, historical relationship
and the resource abundance of African countries don’t explain
the level of trade between Africa and BRIC countries.
Governance remains consistent with the introduction of dynamic
for China and landlocked African countries trade less with
Brazil and India even in the long run.
39
4.1: Analysis for African imports
4.2.1: The gravity model for African imports with Pooled OLS
Figure 15 displays estimated result of our gravity model for
African imports with OLS and we can see that the results are in
conformity with was we expected. Indeed, the traditional
determinants of trade present the predictable sign. Moreover,
the size of economies affects positively the level of imports
and the distance reduces the intensity of trade between African
countries and BRIC trade partners. Similarly, landlocked
countries trade less because of the increase of cost of
transportation..
Figure 14 : Gravity model on African import, Pooled OLS
Variables Brazil China India RussiaR2 0,93 0,99 0,99 0,87
lgdp .4760997 *(.0702333)
.3968157 *(.025087)
.4391558 *(.0236686)
.7808313 *(.0737461)
ldist -1.075653 *(.4090991)
-.1961674*(.1357263)
-.5096221 *(.1300193)
-2.945054 *(.4285991)
exchange .0298744(.5338681)
-1.046257 *(.2742648)
-.1590585 *(.0686968)
5.14e-14( 5.35e-13)
rel -1.20653 **(.7153498)
.7042033 *(.1765367)
.3497662 *(.1426651)
.535896(.6738921)
landlocked -3.787598 *(.4090668)
-1.26202 *(.1427028)
-.8975385 *(.1431466)
-2.85762 *(.4883512)
governance -.5522061 **(.3335356)
.2289114 **(.120377)
.3484688 *(.1229952)
-.7370076 **(.4109777)
Source: Author; * p value < 0,05; ** p value
<0,10
39
The impact of historical relationship is changing across BRICs
countries. For Brazil, African country which speaks Portuguese
has a big probability to import more from this country than
other African countries. This can be explain by the fact that
Brazilian exports are confined to few key trading partner and
Angola represent the most significant one. In the contrary, for
China and India, historical relation has the opposite effect.
Countries which have historical contact with those BRIC are not
the main direction of export of China and India. One
explanation can be that actually China and India are
intensifying their cooperation with other African partners than
their historical partner. And those news partners which were
trading before most with their colonizer are now open and
represent large market for China and India.
The exchange rate don’t have effect on Brazil’s and Russia’s
exports but affect negatively the level of trade between
African countries, China and India. For those two African trade
partner, when their currency appreciate relatively to dollar,
this reduce the size of exports because many African countries
have their trade revenue in dollar or euro. And also because
China and India are competing in many goods with developed
countries and a rise in price in those two BRIC, due to the
appreciation of currencies, creates a deviation of trade.
Finally, looking to our variable of interest governance, it
appears that the sign diverge from one BRIC to another even if
the significativity is not really strong. Brazil and Russia
trade more with countries with low level of governance maybe
because they trade more with their historical partner that are
39
in general not well governed. But China and India export more
to countries with relative good governance level. This is due
to fact that those two countries are competing with developed
countries in Africa and the good governance tends to facilitate
the trade transaction and reduce costs for exporter.
4.2.1: The gravity model for African imports with panel data approachThe introduction of dynamic with panel data approach shows that
many results that we obtain with OLS are not consistent across
time. Figure 16 reveals that only the size of economies, the
GDP deflator and the situation of African countries relatively
to the sea remain constant through time. The historical
relation and distance don’t count in the long run. For
governance, only Chinese exports are affected by the quality of
African countries governance but the significativity is not
robust. The exchange rate remains significant for China and
India from which African countries import more. Those countries
are very sensitive to change in price of good due the
appreciation of currencies.
Figure 15 : Gravity model on African imports, Pooled OLS
Variables Brazil China India Russialgdp .2889984 *
(.0778604).1807244 * (.0203047)
2087098 * (.0190228)
.0736883 .0697255
ldist -.8980085 (1.309506)
-1.155951 (1.211694)
-.8585673 (.7917513)
-6.988357 * (2.324247)
rel -1.5155 (1.024974)
.9429454 * (.4081831)
.3953858 (.3600178)
.7338865 (1.621302)
exchange -.4139441 (.4060152)
-1.641073 * (.1924888)
-.0981517* (.0551623)
4.08e-13 (2.77e-13)
39
landlocked -3.902144 * (.5741144)
-1.255487 *(.3310995)
-.9922131 * (.3551281)
-2.581588 *(1.198228)
gouvernance -.7314077 (.4519326)
.4093277 **(.2122693)
.1032534 .2138818
-.6024147 (.7652006)
Source: Author; * p value < 0,05; ** p value
<0,10
39
IV: CONCLUSION AND REMARKS
The aim of this paper was to understand the increasing interest
of BRIC countries for Africa and verify if China is a different
trade partner. We have shown the rising of their share in
African trade and found out that China dominates this trade
followed by India. Those two BRIC, to support and sustain their
rapid development, need enough inputs for their industries from
Africa. The analysis points out the fact that all BRIC
countries import essentially oil and naturals resources except
Russia which is full of oil. And African countries imports
manufactured goods and receive an increasing quantity of FDI in
primary sector but also in services. Additionally their receive
aid aids, gifts and preferential loans from BRIC partners.
To identify the main determinants of trade between Africa and
BRIC, we have computed a gravity model. The model regresses
African trade on variables such as size of economy, distance,
historical relationship and access to sea. In addition, we
added a set of variables to capture some particularities. Those
variables are the level of governance and the exchange of BRIC
relatively to dollar. First, we use pooled OLS model to
estimate the model for each BRIC countries. To verify the
robustness and the consistency across time, we apply a panel
data approach to introduce the dynamic.
The result shows that for exports, size of economy is the first
determinant. In a contrary, distance, historical relationship
and the resource abundance of African countries don’t explain
39
the level of trade between Africa and BRIC countries except for
Russia. In addition landlocked African countries trade less
with Brazil and India in the long run and governance affect
negatively only China imports. One of the explanations can be
that as there is a pressure for transparency and accountability
on the natural resource sector, it is tempting for Chinese
firms which need enough resource to deal with bad governed
countries. This helps them to have access to the market which
is dominated by western countries and also reduce cost of
production.
For African imports, the main result is that exchange rate
reduces trade from China and India, the core origin of imports.
Those countries are very sensitive to change in price of good
due the appreciation of currencies.
To answer to question of particularity of China in African
trade that we have rise at the beginning of this paper, the
result of the model shows that:
The resource abundance of African country is not a
significant driver of China trade as many authors think.
This can be explained by the fact that across time China
is trying to diversify imports and create new market for
his excess production such that actually he is trading
also with non resource abundant countries in Africa.
China and India are not really different in the way of
trading with African countries. Those two countries are
influence by the same variable except governance in case
of imports. This difference is due to structure of the
39
two economies. China is more industrialize than India
(more specialize in tertiary activities) and for this
reason the first needs more oil than the second. Or as it
had been shown, many oil African have low level of
governance and face some conditionality (improve
governance) to trade with western country. And to bypass
those rules, trade with China is the good issue. But when
looking their exports, the two prefer better govern
African countries to reduce the cost of trade.
China and India are at some level different to Brazil and
Russia. Brazil is facing the problem of language (huge
barrier for communication) and Russia is full of resource
and don’t really present a rapid increase of his
production that can require input or new market for
export. But still all BRIC trade with Africa is affected
by same variable such as landlocked situation of African
country.
So to conclude, it is very difficult and even inappropriate to
say that China is a different trade African partner but still
some research need to be done to present a stronger conclusion.
For example one of the limits of this paper is that we use
global data on exports and imports. It will be important to
analyze deeper types of goods that are traded by using a
gravity model which include a third dimension which is the type
of products. Another extension of this paper can be the
evaluation the possible impact of the increasing of FDI on the
trade flows between African countries and BRIC.
39
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Boughami H, J.Al Shidhani, M. Mbaga and Kotagama H. (2009),
“The effects of trade agreements on agri-food trade: an
application of gravity modeling to the Arab Gulf Cooperation
Council (GCC) countries”. Review of Middle East Economics
and Finance, 5(3): 1-17.
Kaushal Vidyarthee (2010); “India’s Trade Engagements WithAfrica: A Comparison With China”; University of Oxford,
39
APPENDIX
Appendix 1 : Brazil estimation
ressource 1.477204 .5471312 2.70 0.007 .4022024 2.552206 landlocked -1.687183 .5620353 -3.00 0.003 -2.791469 -.5828976 gouvernance -.1057868 .4581973 -0.23 0.818 -1.006052 .7944781 exchange .0298744 .5338681 0.06 0.955 -1.019068 1.078817 rel -1.650869 .9827139 -1.68 0.094 -3.581703 .2799645 ldist -2.970361 .5807309 -5.11 0.000 -4.11138 -1.829342 lgdp .7574685 .0968618 7.82 0.000 .5671548 .9477823 limport Coef. Std. Err. t P>|t| [95% Conf. Interval]
Total 84866.2344 499 170.072614 Root MSE = 5.752 Adj R-squared = 0.8055 Residual 16278.3496 492 33.0860764 R-squared = 0.8082 Model 68587.8848 7 9798.26925 Prob > F = 0.0000 F( 7, 492) = 296.14 Source SS df MS Number of obs = 499
. regress limport lgdp ldist rel exchange gouv landlocked ressource, noconstant
rho .36946027 (fraction of variance due to u_i) sigma_e 4.071436 sigma_u 3.1165583 _cons -15.0079 22.50557 -0.67 0.505 -59.11801 29.1022 ressource 2.649835 1.087956 2.44 0.015 .51748 4.78219 landlocked -2.073243 1.090561 -1.90 0.057 -4.210702 .0642165 gouvernance .4493497 .772603 0.58 0.561 -1.064924 1.963624 rel -1.702719 1.946541 -0.87 0.382 -5.517869 2.112431 exchange -.4139441 .4060152 -1.02 0.308 -1.209719 .3818311 ldist 2.096626 2.486186 0.84 0.399 -2.776209 6.96946 lgdp .175578 .0934818 1.88 0.060 -.0076428 .3587989 limport Coef. Std. Err. z P>|z| [95% Conf. Interval]
corr(u_i, X) = 0 (assumed) Prob > chi2 = 0.0104Random effects u_i ~ Gaussian Wald chi2(7) = 18.37
overall = 0.1228 max = 10 between = 0.2015 avg = 10.0R-sq: within = 0.0026 Obs per group: min = 9
Group variable: id Number of groups = 50Random-effects GLS regression Number of obs = 499
. xtreg limport lgdp ldist exchange rel gouv landlocked ressource,re
Prob > chi2 = 0.0000 chi2(1) = 316.86 Test: Var(u) = 0
u 9.712935 3.116558 e 16.57659 4.071436 limport 39.84326 6.312152 Var sd = sqrt(Var) Estimated results:
limport[id,t] = Xb + u[id] + e[id,t]
Breusch and Pagan Lagrangian multiplier test for random effects
. xttest0
39
ressource .6217428 .3990621 1.56 0.120 -.1623334 1.405819 landlocked -3.783473 .4099328 -9.23 0.000 -4.588908 -2.978038 gouvernance -.5466744 .3341963 -1.64 0.103 -1.203302 .1099536 exchange 1.114324 .3893884 2.86 0.004 .349255 1.879394 rel -1.215186 .716764 -1.70 0.091 -2.623482 .1931097 ldist -1.14958 .4235689 -2.71 0.007 -1.981807 -.3173534 lgdp .4783011 .0706483 6.77 0.000 .3394916 .6171106 lexport Coef. Std. Err. t P>|t| [95% Conf. Interval]
Total 129261.054 499 259.040188 Root MSE = 4.1954 Adj R-squared = 0.9321 Residual 8659.81294 492 17.6012458 R-squared = 0.9330 Model 120601.241 7 17228.7487 Prob > F = 0.0000 F( 7, 492) = 978.84 Source SS df MS Number of obs = 499
. regress lexport lgdp ldist rel exchange gouv landlocked ressource, noconstant
rho .11077941 (fraction of variance due to u_i) sigma_e 3.7729616 sigma_u 1.3317023 _cons 7.474423 12.04957 0.62 0.535 -16.14231 31.09115 ressource .734883 .5742253 1.28 0.201 -.3905779 1.860344 landlocked -3.900516 .574075 -6.79 0.000 -5.025683 -2.77535 gouvernance -.7217012 .4520415 -1.60 0.110 -1.607686 .1642837 rel -1.51083 1.024974 -1.47 0.140 -3.519741 .4980813 exchange .9089203 .370457 2.45 0.014 .1828379 1.635003 ldist -.8994592 1.309483 -0.69 0.492 -3.465999 1.667081 lgdp .2918293 .0787322 3.71 0.000 .1375169 .4461417 lexport Coef. Std. Err. z P>|z| [95% Conf. Interval]
corr(u_i, X) = 0 (assumed) Prob > chi2 = 0.0000Random effects u_i ~ Gaussian Wald chi2(7) = 75.32
overall = 0.2315 max = 10 between = 0.5498 avg = 10.0R-sq: within = 0.0055 Obs per group: min = 9
Group variable: id Number of groups = 50Random-effects GLS regression Number of obs = 499
. xtreg lexport lgdp ldist exchange rel gouv landlocked ressource,re
Prob > chi2 = 0.0000 chi2(1) = 38.21 Test: Var(u) = 0
u 1.773431 1.331702 e 14.23524 3.772962 lexport 22.89564 4.784939 Var sd = sqrt(Var) Estimated results:
lexport[id,t] = Xb + u[id] + e[id,t]
Breusch and Pagan Lagrangian multiplier test for random effects
. xttest0
39
Appendix 2 : China estimation
ressource 1.908505 .3505314 5.44 0.000 1.219786 2.597225 landlocked -1.018588 .3550577 -2.87 0.004 -1.716201 -.3209755 gouvernance -1.068914 .2996361 -3.57 0.000 -1.657635 -.4801927 exchange -1.046257 .2742648 -3.81 0.000 -1.585129 -.507385 rel 1.221354 .4407211 2.77 0.006 .3554309 2.087277 ldist -.9175969 .4525468 -2.03 0.043 -1.806755 -.0284386 lgdp .6256379 .0629981 9.93 0.000 .50186 .7494157 limport Coef. Std. Err. t P>|t| [95% Conf. Interval]
Total 148525.418 500 297.050837 Root MSE = 3.7638 Adj R-squared = 0.9523 Residual 6983.90888 493 14.1661438 R-squared = 0.9530 Model 141541.509 7 20220.2156 Prob > F = 0.0000 F( 7, 493) = 1427.36 Source SS df MS Number of obs = 500
. regress limport lgdp ldist rel exchange gouv landlocked ressource, noconstant
rho .55065084 (fraction of variance due to u_i) sigma_e 2.4375038 sigma_u 2.6983078 _cons 44.22981 29.1595 1.52 0.129 -12.92176 101.3814 ressource 2.277442 .8715931 2.61 0.009 .5691506 3.985733 landlocked -1.19344 .8514889 -1.40 0.161 -2.862328 .4754472 gouvernance -1.887032 .5572432 -3.39 0.001 -2.979208 -.794855 rel 1.551641 1.04993 1.48 0.139 -.5061844 3.609466 exchange -1.641073 .1924888 -8.53 0.000 -2.018344 -1.263802 ldist -2.899291 3.116068 -0.93 0.352 -9.006671 3.20809 lgdp .195246 .0570544 3.42 0.001 .0834213 .3070706 limport Coef. Std. Err. z P>|z| [95% Conf. Interval]
corr(u_i, X) = 0 (assumed) Prob > chi2 = 0.0000Random effects u_i ~ Gaussian Wald chi2(7) = 147.02
overall = 0.2541 max = 10 between = 0.2696 avg = 10.0R-sq: within = 0.2243 Obs per group: min = 10
Group variable: id Number of groups = 50Random-effects GLS regression Number of obs = 500
. xtreg limport lgdp ldist exchange rel gouv landlocked ressource,re
Prob > chi2 = 0.0000 chi2(1) = 793.44 Test: Var(u) = 0
u 10.0696 3.173263 e 5.779541 2.404068 limport 20.30653 4.506277 Var sd = sqrt(Var) Estimated results:
limport[id,t] = Xb + u[id] + e[id,t]
Breusch and Pagan Lagrangian multiplier test for random effects
. xttest0
39
ressource .0794651 .1354515 0.59 0.558 -.1866683 .3455986 landlocked -1.262132 .1372005 -9.20 0.000 -1.531702 -.9925621 gouvernance .2021897 .1157847 1.75 0.081 -.0253026 .4296819 exchange -.8338481 .1059807 -7.87 0.000 -1.042078 -.6256185 rel .7955149 .1703024 4.67 0.000 .4609069 1.130123 ldist .6740403 .174872 3.85 0.000 .3304539 1.017627 lgdp .3745573 .0243436 15.39 0.000 .3267274 .4223873 lexport Coef. Std. Err. t P>|t| [95% Conf. Interval]
Total 173032.601 500 346.065201 Root MSE = 1.4544 Adj R-squared = 0.9939 Residual 1042.82724 493 2.11526824 R-squared = 0.9940 Model 171989.773 7 24569.9676 Prob > F = 0.0000 F( 7, 493) =11615.53 Source SS df MS Number of obs = 500
. regress lexport lgdp ldist rel exchange gouv landlocked ressource, noconstant
rho .74707899 (fraction of variance due to u_i) sigma_e .61606021 sigma_u 1.0588001 _cons 36.49533 11.4008 3.20 0.001 14.15017 58.84049 ressource .514073 .3418966 1.50 0.133 -.156032 1.184178 landlocked -1.264366 .3348745 -3.78 0.000 -1.920708 -.6080239 gouvernance .4075673 .1745541 2.33 0.020 .0654475 .749687 rel 1.076801 .4130286 2.61 0.009 .2672798 1.886322 exchange -1.2515 .0500677 -25.00 0.000 -1.349631 -1.153369 ldist -1.273067 1.225801 -1.04 0.299 -3.675593 1.129459 lgdp .0738566 .0150299 4.91 0.000 .0443985 .1033148 lexport Coef. Std. Err. z P>|z| [95% Conf. Interval]
corr(u_i, X) = 0 (assumed) Prob > chi2 = 0.0000Random effects u_i ~ Gaussian Wald chi2(7) = 886.36
overall = 0.4126 max = 10 between = 0.3287 avg = 10.0R-sq: within = 0.6748 Obs per group: min = 10
Group variable: id Number of groups = 50Random-effects GLS regression Number of obs = 500
. xtreg lexport lgdp ldist exchange rel gouv landlocked ressource,re
Prob > chi2 = 0.0000 chi2(1) = 967.14 Test: Var(u) = 0
u 1.121058 1.0588 e .3795302 .6160602 lexport 4.230844 2.056901 Var sd = sqrt(Var) Estimated results:
lexport[id,t] = Xb + u[id] + e[id,t]
Breusch and Pagan Lagrangian multiplier test for random effects
. xttest0
39
Appendix 2 :India estimation
rho .48268508 (fraction of variance due to u_i) sigma_e 2.5324455 sigma_u 2.4462145 _cons -7.678187 16.08762 -0.48 0.633 -39.20934 23.85297 ressource .4017141 .8116567 0.49 0.621 -1.189104 1.992532 landlocked -1.971702 .7812281 -2.52 0.012 -3.502881 -.4405234 gouvernance -.0561473 .5516696 -0.10 0.919 -1.1374 1.025105 rel -.2752746 .7967731 -0.35 0.730 -1.836921 1.286372 exchange -.0981517 .0551623 -1.78 0.075 -.2062678 .0099644 ldist 1.19706 1.747389 0.69 0.493 -2.227758 4.621879 lgdp .3636274 .055933 6.50 0.000 .2540007 .4732542 limport Coef. Std. Err. z P>|z| [95% Conf. Interval]
corr(u_i, X) = 0 (assumed) Prob > chi2 = 0.0000Random effects u_i ~ Gaussian Wald chi2(7) = 59.59
overall = 0.2352 max = 10 between = 0.3431 avg = 10.0R-sq: within = 0.0765 Obs per group: min = 10
Group variable: id Number of groups = 50Random-effects GLS regression Number of obs = 500
. xtreg limport lgdp ldist exchange rel gouv landlocked ressource,re
Prob > chi2 = 0.0000 chi2(1) = 456.10 Test: Var(u) = 0
u 5.983965 2.446215 e 6.41328 2.532445 limport 16.86598 4.106821 Var sd = sqrt(Var) Estimated results:
limport[id,t] = Xb + u[id] + e[id,t]
Breusch and Pagan Lagrangian multiplier test for random effects
. xttest0
ressource -.3544671 .1440333 -2.46 0.014 -.637462 -.0714722 landlocked -.8952897 .1453555 -6.16 0.000 -1.180882 -.609697 gouvernance .3534813 .1257442 2.81 0.005 .1064207 .600542 exchange .0058893 .0291409 0.20 0.840 -.0513664 .0631451 rel .3426232 .1459659 2.35 0.019 .0558312 .6294152 ldist -.5567832 .189044 -2.95 0.003 -.9282144 -.1853519 lgdp .4567781 .0236762 19.29 0.000 .4102593 .5032968 lexport Coef. Std. Err. t P>|t| [95% Conf. Interval]
Total 156141.959 500 312.283917 Root MSE = 1.5254 Adj R-squared = 0.9925 Residual 1147.09339 493 2.32676145 R-squared = 0.9927 Model 154994.865 7 22142.1236 Prob > F = 0.0000 F( 7, 493) = 9516.28 Source SS df MS Number of obs = 500
. regress lexport lgdp ldist rel exchange gouv landlocked ressource, noconstant
39
rho .60525617 (fraction of variance due to u_i) sigma_e .90062103 sigma_u 1.1152032 _cons 15.36836 7.177877 2.14 0.032 1.299981 29.43674 ressource -.2018683 .366523 -0.55 0.582 -.9202403 .5165036 landlocked -.9783228 .3537302 -2.77 0.006 -1.671621 -.2850244 gouvernance .1450833 .2246411 0.65 0.518 -.2952052 .5853719 rel .3796814 .3592503 1.06 0.291 -.3244363 1.083799 exchange -.0461965 .0198989 -2.32 0.020 -.0851977 -.0071954 ldist -.875729 .7894022 -1.11 0.267 -2.422929 .6714708 lgdp .249072 .0204222 12.20 0.000 .2090453 .2890987 lexport Coef. Std. Err. z P>|z| [95% Conf. Interval]
corr(u_i, X) = 0 (assumed) Prob > chi2 = 0.0000Random effects u_i ~ Gaussian Wald chi2(7) = 178.10
overall = 0.3971 max = 10 between = 0.4834 avg = 10.0R-sq: within = 0.2419 Obs per group: min = 10
Group variable: id Number of groups = 50Random-effects GLS regression Number of obs = 500
. xtreg lexport lgdp ldist exchange rel gouv landlocked ressource,re
Prob > chi2 = 0.0000 chi2(1) = 703.86 Test: Var(u) = 0
u 1.243678 1.115203 e .8111182 .900621 lexport 3.992102 1.998024 Var sd = sqrt(Var) Estimated results:
lexport[id,t] = Xb + u[id] + e[id,t]
Breusch and Pagan Lagrangian multiplier test for random effects
. xttest0
39
Appendix 4 :Russia estimation
ressource .5468317 .4889804 1.12 0.264 -.4139598 1.507623 landlocked .4167407 .5013581 0.83 0.406 -.5683716 1.401853 gouvernance .1971954 .4217983 0.47 0.640 -.6315908 1.025982 exchange 5.14e-14 5.35e-13 0.10 0.924 -1.00e-12 1.10e-12 rel -1.620627 .692052 -2.34 0.020 -2.980432 -.2608229 ldist -2.724806 .423442 -6.43 0.000 -3.556822 -1.89279 lgdp .7214511 .0750926 9.61 0.000 .5739025 .8689997 limport Coef. Std. Err. t P>|t| [95% Conf. Interval]
Total 87057.5268 490 177.668422 Root MSE = 5.2276 Adj R-squared = 0.8462 Residual 13199.3078 483 27.3277595 R-squared = 0.8484 Model 73858.219 7 10551.1741 Prob > F = 0.0000 F( 7, 483) = 386.10 Source SS df MS Number of obs = 490
. regress limport lgdp ldist rel exchange gouv landlocked ressource, noconstant
rho .71212044 (fraction of variance due to u_i) sigma_e 2.6363833 sigma_u 4.1464827 _cons 11.0074 22.65753 0.49 0.627 -33.40055 55.41535 ressource 1.014843 1.263089 0.80 0.422 -1.460765 3.490451 landlocked .2788713 1.324212 0.21 0.833 -2.316536 2.874279 gouvernance .9764573 .7254662 1.35 0.178 -.4454303 2.398345 rel -.9392284 1.794878 -0.52 0.601 -4.457125 2.578668 exchange 4.08e-13 2.77e-13 1.47 0.141 -1.35e-13 9.52e-13 ldist -1.402743 2.55177 -0.55 0.583 -6.404121 3.598634 lgdp .2713228 .0575569 4.71 0.000 .1585133 .3841323 limport Coef. Std. Err. z P>|z| [95% Conf. Interval]
corr(u_i, X) = 0 (assumed) Prob > chi2 = .Random effects u_i ~ Gaussian Wald chi2(6) = .
overall = 0.1114 max = 10 between = 0.1377 avg = 10.0R-sq: within = 0.0533 Obs per group: min = 10
Group variable: id Number of groups = 49Random-effects GLS regression Number of obs = 490
. xtreg limport lgdp ldist exchange rel gouv landlocked ressource,re
Prob > chi2 = 0.0000 chi2(1) = 1335.43 Test: Var(u) = 0
u 29.2156 5.405146 e 6.550956 2.559484 limport 32.35924 5.688518 Var sd = sqrt(Var) Estimated results:
limport[id,t] = Xb + u[id] + e[id,t]
Breusch and Pagan Lagrangian multiplier test for random effects
. xttest0
39
ressource -.0900187 .4774059 -0.19 0.851 -1.028068 .8480301 landlocked -2.887253 .4894905 -5.90 0.000 -3.849047 -1.925459 gouvernance -.7670502 .411814 -1.86 0.063 -1.576218 .042118 exchange -1.49e-13 5.23e-13 -0.28 0.776 -1.18e-12 8.78e-13 rel .5663062 .6756706 0.84 0.402 -.7613107 1.893923 ldist -2.744471 .4134188 -6.64 0.000 -3.556792 -1.932149 lgdp .7612699 .0733151 10.38 0.000 .6172139 .9053259 lexport Coef. Std. Err. t P>|t| [95% Conf. Interval]
Total 103280.761 490 210.777064 Root MSE = 5.1039 Adj R-squared = 0.8764 Residual 12581.8273 483 26.049332 R-squared = 0.8782 Model 90698.934 7 12956.9906 Prob > F = 0.0000 F( 7, 483) = 497.40 Source SS df MS Number of obs = 490
. regress lexport lgdp ldist rel exchange gouv landlocked ressource, noconstant
rho .60525617 (fraction of variance due to u_i) sigma_e .90062103 sigma_u 1.1152032 _cons 15.36836 7.177877 2.14 0.032 1.299981 29.43674 ressource -.2018683 .366523 -0.55 0.582 -.9202403 .5165036 landlocked -.9783228 .3537302 -2.77 0.006 -1.671621 -.2850244 gouvernance .1450833 .2246411 0.65 0.518 -.2952052 .5853719 rel .3796814 .3592503 1.06 0.291 -.3244363 1.083799 exchange -.0461965 .0198989 -2.32 0.020 -.0851977 -.0071954 ldist -.875729 .7894022 -1.11 0.267 -2.422929 .6714708 lgdp .249072 .0204222 12.20 0.000 .2090453 .2890987 lexport Coef. Std. Err. z P>|z| [95% Conf. Interval]
corr(u_i, X) = 0 (assumed) Prob > chi2 = 0.0000Random effects u_i ~ Gaussian Wald chi2(7) = 178.10
overall = 0.3971 max = 10 between = 0.4834 avg = 10.0R-sq: within = 0.2419 Obs per group: min = 10
Group variable: id Number of groups = 50Random-effects GLS regression Number of obs = 500
. xtreg lexport lgdp ldist exchange rel gouv landlocked ressource,re
Prob > chi2 = 0.0000 chi2(1) = 703.86 Test: Var(u) = 0
u 1.243678 1.115203 e .8111182 .900621 lexport 3.992102 1.998024 Var sd = sqrt(Var) Estimated results:
lexport[id,t] = Xb + u[id] + e[id,t]
Breusch and Pagan Lagrangian multiplier test for random effects
. xttest0