AFRICA AND THE WORD TRADE NETWORK
Persistence, Change and the aftermath of
the Global Financial Crisis
Luca De Benedictis∗
University of Macerata
January 31, 2011
Abstract
This paper contributes to the analysis of the effect of the global
financial crisis (Claessens et al., 2010) on African countries (IMF,
2009) inspecting the effect of the crises on bilateral trade flows. The
empirical analysis makes intensive use of network analysis techniques,
describing the international trade of SSA countries as part of the world
trade network. The paper analyzes the change in the topology of the
trade network during the crisis. Single SSA countries participation
to the network is reported in terms of link strength and centrality,
showing if some specific countries were more radically disconnected
from the giant component of the network. Finally, evidence of a change
in relative preferential connection with specific industrialized country
detects if SSA countries have moved eastward in term of their relative
position in the world trade network, substituting US and European
bilateral links with Asian links.
Keywords:
JEL Classification:
∗
DIEF - University of Macerata. E-mail: [email protected]
Paper prepared for the ERD 2010 - European Report on Development. This paper has been written
during my visit to the ARE Department at UC Berkeley. I thank Alain de Janvry, Betty Sadoulet and
ARE’s staff for the hospitality, and Giorgia Giovannetti, Sandro Montresor, Edzer Pebesma and Paulo
Van Breugel and the participants to the ETSG 2010 annual meeting in Lausanne, and to the Italian
TradeNetWorkshop for the useful comments. Opinions and errors are my own and do not involve the EU
Commission.
1
1
Introduction
Sub-Saharan Africa (SSA) has been characterized for decades by slow growth,
political instability and high vulnerability to erratic natural and human
events (Easterly and Levine, 1997; Sachs and Warner, 1997; Collier and
Gunning, 1999). All those elements were associated to a minimal participation to the international trade of goods and services. The claim was that
SSA was some how condemned by natural or institutional characteristics to
an ever lasting isolation from the growth-enhancing forces of globalization.
The last years before the global financial crisis cast severe doubts on the
infrangibility of the spell that condemned Africans, generation after generation, to a fait of isolation, famine, disease and structural poverty. The 3 per
cent average annual per capita income growth between 2000 and 2007 may
be seen as modest compared to the two digits East Asian standards, but was
clearly seen by many as ”a clear break from the past” (Miguel, 2009). The
economic and political change that characterized the turn of the century in
many African countries had also his international counterpart. In many SSA
countries exports did grown steadily, following the rise in commodity prices,
more liberal trade policies, and a new phase in African regional integration.
Also the emergence of Asia as a continental generator of global excess demand, mobilizing goods and firms, and of China, in particular, as a major
international economic force, contributed to a new phase in African trade.
This was concrete difference with respect to other economic miracles that
tuck place in the recent past and that left Africa basically untouched from
the expansion of rapid growing economies’ demand. Exports from Africa
to Asia grew by 20 per cent between 2000 and 2007, transforming Asia in
a major trading partner for African countries. In 2007 Asia accounted for
21.7 per cent of Africa’s exports, an amount approaching the one of Africa’s
traditional trade partners. The relevance of the flow was not unidirectional.
Africa’s imports from Asia grew in the same period at an 18 percent annual
rate, higher that the one from the EU.1
This dynamics is largely do to China. Other growing countries, such as
South Africa, India and Brazil, played a positive but minimal role. Exports
1
It is always better to remind that, in spite of this surge from lethargy, Africa remains
a marginal market, worth less than 3 per cent of the value of global trade. All the data
mentioned in the text is summarize in the tables included in the appendix. The data on
trade comes from the IMF-DOTS database. Other data comes from the World Bank and
Unctad.
2
to South Africa from the rest of Africa were 1.02, in 2000-2004, and 1.36,
in 2005-2007, as a percentage of total export; the figures for China, India
and Brazil are 3.80 and 8.64, 2.64 and 2.90, and 2.29 and 2.95, respectively.
The boost of exports to China are really impressive. On the contrary, the
percentage of export that were sent to advanced economies and that can
be classified as Intra-African trade decreased by one point. In spite of the
acceleration of this dynamics, China accounted for 7.7 per cent of Africa’s
total exports at the beginning of 2007, still a relative low share with respect
to what is generally perceived, and still much below the 39.80 percent and
20.65 percent of the EU and US shares of Africa’s exports.
Summing up, as far as trade with Africa at the beginning of 2007 the
role of China was rapidly growing, but the total impact was not yet gigantic;
at the same time African-Chinese trade was not only a matter of exports
of primary products, crude petroleum and basic manufactured metals from
Africa to China, it was also a matter of trade flows that were going in the
opposite direction. African imports from China were largely manufactures,
some intermediate goods for product assembling and some capital goods for
local manufacturing (Broadman, 2007). As for global trade, many of these
flows were intra-firm flows associated to growing offshoring. Chinese foreign
direct investment (FDI) in Africa did not compete in terms FDI stocks with
the one of European countries (primarily the UK and France) and North
America, but it was steadily growing, not only in the oil or natural resources
sectors but also into the financial, telecom, electricity, light manufacturing
and transportation equipment sectors (Broadman, 2008). Then, the global
financial crisis arrived and here is were this paper starts off.
To give a quick account of what will be discussed latter on, section one
highlights the possible effects of the financial crises on SSA economies and
describes the evolution of African trade during the crisis; section two introduces the basic ingredients of network analysis and applied them to African
trade flows; section three defines the concept of network-based proximity and
relates it to the relative position of Africa in the World Trade Network during
the crisis; section four concludes.
3
2
The Global Financial Crisis and African
Trade
The global financial crisis changed the scenario again. Traditional SSA’s
trade partners have been seriously hit by the crisis and trade flows dropped
dramatically in 2009 in industrialized and LDC countries. The last figures
tell the following story: while in 2009 in North America and in Europe the
annual change in GDP dropped by 2.3 and 4.2 percent points, and merchandise exports and imports dropped by 18.0 and 26.0 points, African non-oil
exporting counties GDP growth increased by a 1.6 and exports and imports
dropped by 17 and 14 points. In spite of the origin of the crisis, SSA countries were severely hit by the reduction in global aggregate demand, and
small open economies that depend on trade are unsurprisingly most affected
(Levchencko et al. 2009).
Some of the recent literature on the effects of the financial crisis (Arieff, Weiss and Jones, 2009; IMF, 2009; Berman and Martin, 2010; Naudé,
2010) already spelled out the possible effects of the crisis on SSA countries.
The main effect - the one already mentioned - comes from the sudden drop
of international effective demand. The recession in OECD countries of Europe and North America reduced SSA countries’ exports towards their main
markets. We will focus mainly on the quantification of this direct income
effect and on the possible counter balancing trade effect coming from other
geographical areas less affected by the financial crisis.
Among the indirect effects, the disruption effect due to the increase in
risk aversion among international traders and their financiers may affect more
severely those countries which are perceived as more risky or more fragile.
Bernard and Martin (2010) quantify this disruption effect, using bilateral
trade data between 1976 and 2002 and 117 events associated to banking
crises. They found that, compared with other countries, African countries
suffered from a 20 per cent higher disruption effect that aggravated and
persisted for several years after the crisis. A larger effect would be probably
found if the same exercise would have been done using FDI data.
From a macro perspective, the large governmental intervention to stabilize the financial market and to reduce the deflationist effect of the 2008
crisis is largely increasing public debt in many OECD countries and is now
putting pressure in adopting tight fiscal policies. Under this scenario it is
easy to imagine that OECD countries would be less keen in respecting their
4
promises as international donors and could decrease aid budgets by sizable
quantities. This would have obvious negative effects on some aid-dependent
SSA economies.
From a micro perspective, the increase in unemployment and the worsening of the labor market conditions in immigration countries would reduce
the flow of remittances to SSA countries. Estimates by Barajas, Chami,
Fullenkamp, and Garg (2010) indicate that remittances would decline into
African countries between 3 and 14 percentage points, with flows coming
from migrants to European countries more severely reduced while flows coming from migrants within Africa remaining relatively unaffected by the crisis.
Their estimated impact on SSA countries’ GDP for relatively remittancedependent countries was of 2 percent points for 2009, and will decrease in
2010 as host countries will recover from the crisis.
The main message that we can take from these contributions is that even
if Africa is relatively isolated from the cons of globalization, as it is from
the pros, this will not be sufficient to separate it from the negative effect of
the crisis. The risk is that these effects will last much more than in other
countries more severely hit by the shock.
Since all these effects have a direct consequence in reducing international
trade flows (the drop in international effective demand and the perceived risk
have an contractionary effect on exports from SSA countries, while the fall in
international aid and in individual remittance have a contractionary effect on
imports by SSA countries) it is possible to use the information coming from
the dynamics of trade flows during the financial crisis to have an eyeball test
of the effect of the financial crisis on Africans’ wellbeing and on its possible
duration.
If we observe the evolution of the trade flows in figure 1 we can see that
almost all African countries were hit by the financial crisis simultaneously.
Let’s take Angola as an example. Exports and imports were growing steadily
from the beginning of 2007 to the third trimester of 2008 (indicated by the
red vertical line common to every panel of the figure), than the trajectory
suddenly inverted. For two quarters, the fourth of 2008 and the first of 2009
trade plunged, reaching the same level of the beginning of 2007. The trend
inverted again in the second and the third trimester of 2009. This ‘N ’ shape
in trade flows is quite common to African countries and it identifies three
different phases - stable grow, crisis, and resilience - that we will maintain as
a demarcation of our further analysis. There are exceptions. Algeria, Tunisia
and Uganda do not show a relevant third phase; while Egypt, Guinea and
5
Trade.png
Figure 1: Trade flows in Africa, 2007-2009 (Exports and Imports).
The figure plots data on exports (green dots) and imports (red dots) as index numbers. For
every country both exports and imports are relative to the 100 base of the first trimester
of 2007. We use IMF-DOS quarterly data. Data on Botswana, Eritrea, Lesotho, Namibia,
Swaziland, and Western Sahara were not available. Data higher than 300 were capped
at 300 for better visualization purpose. The red vertical line in each panel indicates the
turning point of the crisis in September 2008. The total time span is between the first
trimester of 2007 and the third trimester of 2009 .
6
Malawi do not show an evident ‘N ’ shape in trade flows but the trend remains
flat along the three phases.
More importantly in the majority of countries exports and imports reacted differently. Exports dropped immediately and by a greater amount
(after the crisis the green line is almost always below the red one). This
give a suggestive evidence of the prevalence of the effective demand effect of
the crisis over the import-reducing effect of the drop in public and private
international transfers.
This evidence is further reinforced if we consider the rate of growth of
export and imports during the three phases of the ‘N ’ dynamics of trade
flows during the crisis. We used a choropleth of Africa to summarize this
information. In figure 2 and 3 we describe the evolution in the rate of growth
of exports and imports. What is striking is the homogeneity of the reaction
among African countries. Exports rates of growth were highly positive before
the crisis (with an average growth rate of 9.35) and became largely negative
in the aftermath of the crisis (the growth rate of exports switched to an
impressive -15.57), finally showing a surprising resilience in the third phase
(with an average exports growth rate of 15.19). Imports dropped by less and
also recuperated by less in the third phase.
Swings for oil producing countries were even more pronounced, with exports growing at a rate of 13.63 during the first phase, dropping at a rate of
33.94 during the second phase, and growing again at a rate of 21.58 in the
third phase.
2.1
A continental view
A remarkable path emerges if we split the data by continental flows. We will
focus on exports, but a similar figure results from imports flows. Considering
the five continents, and singling out three regions of interest: China, the US,
and the EU, we can analyze how exports from African countries towards
these eight regions have evolved. We also consider intra-African flows (see
figure 4).
As we mentioned in the introduction, at the beginning of 2007 almost 40
per cent of African exports were directed towards the EU market, a little bit
more than 20 per cent went to the US, while almost 14 percent went to Asia,
excluding China. Around 8 per cent went towards other African countries
and the same amount went to China (the data is included in the appendix).
All flows steadily grow in 2007 and in the first part of 2008, leaving the
7
Figure 2: Export growth in Africa, 2007-2009.
The figure represents three choropleths of Africa each one for the three phases identified
in figure 1 (2007.1-2008.3; 2008.4-2009.1; 2009.2-2009.3). The choropleths describe with
different colors the rate of growth of exports for each country during the three phases. The
different nuances of green indicates positive rates of growth; red nuances indicate negative
rates. We use IMF-DOS quarterly data to calculate the rates of growth of exports and
imports that we grouped in ten categories. Data on Botswana, Eritrea, Lesotho, Namibia,
Swaziland, and Western Sahara were not available. The original data used to produce the
figure is included in the appendix.
8
trends.png
Figure 3: African Exports Volumes, (2007.1-2008.1-2009.1-2009.3).
African trade volumes by main partner. Asia includes all Asian Countries with the exception of China; China includes China, Hong Kong and Macao; Africa includes all intracontinental trade; and Eu includes only the 15 member states before the last rounds of
eastward enlargement. The figure plots the data for 2007.1; 2008.1; 2009.1; 2009.3. We
use IMF-DOS quarterly data. Data on Botswana, Eritrea, Lesotho, Namibia, Swaziland,
and Western Sahara were not available. Some regions (Australia, America and the rest of
Europe) were excluded for visual purpose.
9
ranking of exporting markets almost unchanged. Only China climbs the list
at a more rapid speed, becoming the forth export market for Africa, among
the ones included in figure 4.
The financial crisis reduced dramatically the volume of exports towards
the EU and the US, as it is shown in figure 4. What is remarkable is that
the trend is common to all regions, also, to a limited extent, to intra-African
exports. Even exports to China show a substantial drop. In short, there is
no evidence of a substitution between the EU and even the US and China
as the major export market for Africa. This common trend in the reduction
of exports from Africa has a somehow paradoxical implication: the EU reinforced its relative centrality as a destination for African exports (more than
41 per cent of African exports were directed towards the EU market in the
second phase of the crisis).
The third phase of the crisis is the one where China shows a more impressive dynamics.
Exports to China reach the one to the rest of Asia and exceed the ones
of intra-Africa exports. At the end of 2009 Africa’s exports to China fully
recovered from the plunge and are now at the same level they were before
the crisis. What did not fully recover are exports to the EU and even more
to the US.
Can we read this evidence as a sign that Africa is now more close to
China and Asia than what it is to the US or the EU? And is it more close
to China now that what it was before? We will answer these question in
the next session with the use of basic network analysis (De Benedictis and
Tajoli, 2010).
3
Network analysis of Africa’s trade
Let’s define each country in the world as a node of a network, and each
bilateral export flow has an arc whose direction indicates the orientation of
the flow from the exporting country to the importing country. The strength
of each arc corresponds to the volume of exports and a new network is draw
for each time interval considered. A picture of this network would be very
messy and uninformative. A possible strategy - the one that we adopt in
the following analysis - is to generate a partition of the World trade network,
assigning each country to one and only one cluster corresponding of the eight
regions of interest previously defined. The partition become an exogenous
10
attribute of each node. We denote this graph G.
Figure 4: Africa in the World Trade Network, (2007.1-2008.1-2009.1-2009.3).
The figure represents four graphs of the World Trade Network shrinking all nodes of each
regional partition in one single node. The regions are the same of figure 4 plus Australia,
America (all the continent excluding the US) and Europe (all the continent excluding
the EU15 countries). The dimension of each arch is proportional to the sum of bilateral
exports of all countries included in the partitions. Intra-regional flows have not been
drawn. We use IMF-DOS quarterly data. The four panels represent the graph at 2007,
first trimester (up-left); 2008 first trimester (up-right); 2009 first trimester (down-left);
2009 third trimester (down-right). The graphs were produced using Pajek.
In figure 5 we represent four graphs [G∞ , G∈ , G3 , G4 ] of the World Trade
Network shrinking all nodes of each regional partition in one single node.
The regions are the same of figure 4 plus Australia, America (all the continent excluding the US) and Europe (all the continent excluding the EU15
countries). The dimension of each arch is proportional to the sum of bilateral
exports of all countries included in the partitions. Intra-regional flows are not
drawn. The four panels represent the graph at 2007, first trimester (up-left);
2008 first trimester (up-right); 2009 first trimester (down-left); 2009 third
trimester (down-right). One arc has been fixed (the one between Africa and
Australia2 and so the position in the network of the two nodes at the extremes of the fixed arc. All the rest of the nodes are positioned according
2
We chose the Australia-Africa arc because the trade flow between the two regions is
always minimal.
11
to the vector [x1 , x2 , . . . , xn ] that can varies along time. Since each arc is a
straight line, the drawing of G, at each time unit, is completely specified by
this vector of vertex positions.
The position of each vertex is obtained using the Kamada-Kawai forcedirected algorithm. In short, vertices are placed at a distance that is inversely
related to the strength of the respective arc, as if they were connected by a
spring. Regions that are strong trade partners are close together, regions that
are weak trading partners are placed far apart. The network is considered in
equilibrium when it reaches a minimal energy state.
More formally, in KK-Layout the global energy E is defined as:
E=
N
−1
X
i=1
N
X
1
ki,j (| pi − pj | −lij )2
2
j=i+1
where pk is the position of vertex k, and lij = c · dij is proportional to the
topological distance dij of vertex i and j.
Solving the minimization problem on E
E=
N
−1
X
i=1
N
X
1
ki,j (| pi − pj | −lij )2
2
j=i+1
if x and y are coordinates, the same equation can be written as:
E=
N
−1
X
i=1
N
X
1
1
2
ki,j [(xi − xj )2 + (yi − yj )2 + lij
− 2lij · ((xi − xj )2 + (yi − yj )2 ) 2 ]
2
j=i+1
.
For a minimum we must have
∀j
∂E
∂E
=
=0
∂xj
∂yj
#
"
X
∂E
lmi · (xm − xi )
=
kim (xm − xi ) −
1
∂xj
((xm − xi )2 + (ym − yi )2 ) 2
i6=m
"
#
X
∂E
lmi · (ym − yi )
=
kim (ym − yi ) −
1
∂yj
((xm − xi )2 + (ym − yi )2 ) 2
i6=m
.
12
These equations are not independent, therefore they cannot independently be brought to zero.
In the KK-Layout only a single vertex is moved at a time. Once the
vertex to be moved is chosen all other vertices are fixed and the energy is
(locally) minimized by only moving the chosen vertex, using a single-sided,
two-dimensional Newton-Raphson iteration. The minimization problem does
not have a unique solution. But we can obtain a distribution of possible
equilibria.
If we take a look at figure 5 (let’s pick the top-left panel for the moment)
we can see that the network is structured around the triangle Asia-EU-US.
With China being an extension of Asia, Europe the same for EU and America
the same for US. Australia and Africa are at the periphery of the network,
the former almost always gravitating around Australia, the latter around
the EU. This structure is persistent over time, and variations occur at the
margin.
During all the period under analysis the structure of the network remains
substantially unchanged. Africa still remains close to the EU and indirectly
to Europe. What is more relevant is not the relative proximity of China and
Asia but the high distance with the US and America.
Even the network analysis does not provide any specific evidence of a
substitution in dominant partnership between the EU and China during the
financial crisis. This is at odds with some of the evidence on the growing
role of China in Africa (Broadman, 2007). Three possible explanation can
be put forward. First, much of the evidence of a growing economic interest
of China in Africa is based on pre-crisis data. Since this boost in China’s
role as exporter to and importer from African economies happened before
2007, it will not be reported in our data. Second, a dynamic role of China
is perceived in the third phase of the crises, when African exports to China
grew at a much rapid speed that the one to other regions or countries. Third,
a substantial role of China could have taken place at a country level more
than at the aggregate level. This would require further analysis.
4
Proximity
The inspection of trade data through network analysis offers a further tool to
answer a final question: as Africa become ’more close’ to any specific region
during the crisis? As the contraction in the international demand coming
13
Figure 5: Africa in the World Trade Network, (2007).
The graph was produced using Pajek.
from the US and the EU caused an increase in proximity between Africa and
China? We gave already a negative answer to the second question, what
about the first one?
The definition of proximity is not generally shared. It implies the notion
of distance, but it requires to consider not only the bilateral distance (defined
in spatial terms or whatever other metric is considered appropriate) but also
the multilateral distance, that takes into account the distance that exists
among all nodes in the network.
The force-directed algorithm used in section 2 offers a precise definition
of proximity, directly derived from the vector of coordinates [x1 , x2 , . . . , xn ]
of each node in the network.
Figure 6 depicts the evolution of Africa’s proximity to each different region between the first trimester of 2007 and the third trimester of 2009. The
striking evidence is that Africa did reduced its proximity with all other regions or countries in the world trade network. And the answer to our final
question is therefore: No, Africa is not ’more close’ now than it was before
to all his trading partners. How is this possible? This seems to contradicts
what was shown in figure 1 and figure 4: growing exports and imports during
the first and the third phases of the crisis. How can proximity between Africa
and China decrease even when their trade flows increase? This depends on
the fact that proximity is a multilateral concept. Proximity between Africa
and China will decrease even when their trade flows increase if China was
at the same time trading relatively more with other regions or countries.
14
Evolution of Africa’s Proximity (2007.1 – 2009.3)
1
2
3
4
0
0.1
0.2
ASIA
EUROPE
AMERICA
AUSTRALIA
EU
CHINA
USA
0.3
0.4
0.5
0.6
0.7
0.8
0.9
proximity.pdf
proximity EU.pdf
Proximity between Africa and USA
Proximity between Africa and EU
0
1
2
3
4
0
5
0
0.1
0.1
0.2
1
2
3
4
5
0.2
0.3
0.3
0.4
SD
EU
sd
SD
USA
sd
0.4
0.5
0.5
0.6
0.6
0.7
0.7
proximity USA.pdf
Proximity between Africa and China
0
1
2
3
4
5
0.1
0.2
0.3
0.4
CHINA
sd
SD
0.5
0.6
0.7
proximity China.pdf
Figure 6: Africa’s Proximity: Overall, EU, US and China).
The figure represents four graphs of the World Trade Network shrinking all nodes of each
regional partition in one single node. The regions are the same of figure 4 plus Australia,
America (all the continent excluding the US) and Europe (all the continent excluding
the EU15 countries). The dimension of each arch is proportional to the sum of bilateral
exports of all countries included in the partitions. Intra-regional flows have not been
drawn. We use IMF-DOS quarterly data. The four panels represent the graph at 2007,
first trimester (up-left); 2008 first trimester (up-right); 2009 first trimester (down-left);
2009 third trimester (down-right). The graphs were produced using Pajek.
15
Since the vector [x1 , x2 , . . . , xn ] is not unique, or in other terms the minimization problem that generates the vector [x1 , x2 , . . . , xn ] does not have a
unique solution, we iterated the algorithm 100 times obtaining a distribution
of proximities. Figure 7 plots the results. The red line is the average proximity along time and the gray lines depict the one standard deviation interval
around the average proximity.
Africa is more far apart from its trade partners now than it was before
the financial crisis.
5
Conclusions
The analysis of African trade flows during the Global Financial Crisis offers
a clear picture of the cost of the crisis for African economies. In spite of the
many that were inferring from the marginal role of Africa in the world trade a
very low cost of the crisis for the all African continent, Africa’s involvement in
World Trade flows during the Global Financial Crisis followed a “N shape”,
prety much in line with the rest of the world.
The different effects on imports and exports show that the ’cause’ of
Africa’s trade “N shape” is the up and down in effective demand and traderelated finance (affecting Africa’s Exports) more than the volatility in income
transfers, i.e. Aid, remittances, (affecting Africa’s Imports).
Finally, there is no strong sign that Africa is not closer to Asia (China)
than it was before the crisis. As far as multilateral trade proximity, Africa is
moving far apart, from all other regions, during the Crisis.
16
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18