Towards African Monetary Union: Are Regional Trade Agreements among Africa
Union Member Countries trade Creating or trade diverting?
Being a Paper sent to African Economic Conference, 2013, Johannesburg, South
Africa.
Abstract
The reality of a popular adage that a bunch of broom kills flies more than a single broom is very
glaring in the situation of Africa. Thus, towards achieving high level of trade regionalism in
Africa as the needed bunch of broom, this study employed both descriptive and empirical
approaches to examine determinants factors of Intra-AU trade. Specifically, it model trade
creation and diversion effects of RTAs in Africa following Vinerian-type Gravity Model for the
period of 1995-2010. Among other things, we find from the stylized facts that there is evidence of
increasing and promising bilateral trade relations between AU and other developed countries
more than trade with other developing countries including AU member countries. We find
further from the empirical analysis that economic size, population, the geographical landmass,
landmass (Area) of trading partners’ countries derive intra-regional trade in AU. Also, the more
unstable the political climate of the countries, the more the pace of bilateral trade will be
inhibited. Regarding trade creation and trade diversion scenarios, we find that Arab Maghreb
Union which is the central regional trade agreement for the Northern African is more trade
creating, while ECOWAS, ECCAS, EAC and SADC are largely trade diverting. The outward
looking sub-regional economic unions of AU may eventually render the quest for achieving
African Monetary Union unrealizable.
Key Words: Trade, Africa, Gravity model, Panel Data
JEL Classification: F11, F15, C23
1.0 Introduction
Achieving rapid, sustainable and pro-poor economic growth and development has often been
stressed as a development policy objective of all countries of the world and African nations in
particular. This seeming consensus reflects a new trend in development policy theorizing which
sees trade as a catalyst, an engine of economic growth and a veritable mechanism for reducing
poverty in all regions of the world.
Regional trade integration or agreement has been seen as viable pro-poor growth framework for
meeting the enormous growth challenges in the continent of Africa (Oyejide, 2003).
Furthermore, it is generally agreed that regional integration would be politically and
economically beneficial for Africa in the global economy (AEC, 2013).
In the pursuit of Regional integration, formation of common market and monetary union has
been seen to be the highest cadre of integration. Nowadays, the aspiration to achieve the
proposed African Monetary Union aside from West African Monetary zone (WAMZ) and East
African Monetary Zone (EAMZ) has been elusive over the years. In fact, with much attention
currently being focused on convergence criteria and preparedness of the aspiring member states
of embryonic African Monetary zones, candidate countries of WAMZ especially have twice
postponed the take–off for the single currency. Also, Central bank experts in the upcoming East
African Monetary Zone (EAMZ) fear that, plans for a common currency in 2012 maybe too
ambitious as central banks in the five countries are given little time to prepare for the monetary
union (Asongu, 2012).
Although, these non preparedness to take-off may not be unconnected with the lessons learnt
from the European Monetary Union (EMU) crisis that has sent a strong signal to other common
currency regions of the world, but we envisaged here that the major reason is due to low level of
intra-African trade which has not been able to speed up increase interconnectedness of the
member countries of Africa. Salisu and Ademuyiwa, (2012) observed in this regard that cases of
trade diversions among African Union (AU) member countries, especially in WAMZ abound.
Again, in terms of global dynamics of trade facilitation in Africa, it is evidence that the recent
upsurge of the Asian Tigers as major players in the world economy has witnessed greater
dimension of increasing penetration, especially China into the hinterland of AU members. More
recently, China‟s scramble for increasing expansion of her economic prowess to African
continent is mostly underscored by her increasing needs to secure reliable sources of resources to
support its economic base. On the other hand, the quest for Africa to sustain increasing trade
linkages and inflows of Foreign Direct Investment (FDI) from countries outside its shore in order
to boost her foreign exchange capacity has led to more trade integration (trade creation) with the
rest of the world faster than with itself (trade diverting). Thus, the end-result is a condition of
weak, inefficient and fragmented intra-African trade coorporations which has potentially
undermine the realization of African Monetary Union aspirations.
Embarking on a reverse-mission to address such observed anomalies among others has been the
core of Regional integration policy aspiration of Africa at least since independent of all her
nation states. Thus, issues and concerns for regional integrations and formation of trade
agreements and coorporations are now new in Africa. As a matter of fact, there have been
tremendous growths in the number of RTAs, ranging from free trade agreements to economic
and monetary unions and economic partnership agreements. Indeed, as at 15th of January 2012,
about 511 notifications of RTAs had been received by the World Trade Organization/General
Agreement on Tariff and Trade (WTO/GATT). Of these, almost 90% were free trade agreements
and partial scope agreements, while custom unions accounted for the remaining 10%. This
represents a significant increase when compared to about 400 agreements reported by Whalley
(2006)1.
1
See Olofin, Salisu, Ademuyiwa and Owuru, (2012) for tabular representation of the trends in RTAs
notification to WTO/GATT.
Evidently, Abuja Treaty of 1991 reinvigorates the functions of African Economic Community
(AEC) which is meant to establish grounds for mutual economic development among member
states of AU. The stated goals include creation of free trade areas (FTAs), Custom Union,
Common market, a Central Bank, and a common currency. With these objectives in mind,
various regional trade agreement were formed in Africa such as Southern African Community
(SADC), the Common Market for Eastern and southern Africa (COMESA), the East African
Community (EAC), the Economic Community of Central African States (ECCAS), the
Economic Community of West African States (ECOWAS), Community of Sahel-Saharan States
(CEN-SAD), the Arab Maghreb Union (UMA), and the Inter-Governmental Authority on
development. These RTAs are meant to intra-continentally facilitate and advance the growth and
exploit the trade potentials among AU member nations and across the five geo-political regions
in Africa (Eastern, Middle, Northern, Southern and Western Regions).
Also, these RTAs are to promote economic integration through the elimination of all tariff and
non-tariff barriers between member states and the creation of a common external tariff, among
others. However, over the years, the daunting growths in terms of trade linkages and regional
integrations in Africa despite the formations of the aforementioned RTAs have called for some
empirical concerns and the need to ascertain: (1) to what extent have regional trade agreements
among AU member countries enhanced multilateral trading system within the region? (2) What
are the factors driving the intra-regional trade in AU? (3) Could the persistent cases of trade
diversions among member states of AU dent the realization of the proposed African Monetary
Union? What are the policy frameworks to put in place that would enhance increased regional
integration in Africa? This study begins analyses from 19952 to carefully evaluate the impacts of
the various RTAs in Africa region. Thus, study evaluates both descriptively and empirically,
intra-regional trade architecture in AU between 1995 and 2010 as its broad objectives.
Evidently, quite a number of empirical studies have examined the impact of RTAs for the
various economic and currency unions in both developed and developing countries in the recent
years. Chief among these studies are Anderson and van Wincoop (2003), Baltagi, Egger,
Pfaffermayr (2003), Baier and Bergstrand (2004, 2007, 2009), Feenstra (2004), Longo and
Sekkat (2004), Carrrere (2006), Jugurnath et al. (2007), Abbott et al. (2008); Egger et al. (2008);
Lee et al. (2009); Martínez-Zarzoso et al. (2009), Vicard (2011), Athukorala (2012) and Olofin
et al. (2012). With Particular reference to Africa, (see Cassim, 2001 and Musila, 2005). Lastly,
aside from studies that have attempted to advance the methodology used in modeling
international bilateral trade like Baier and Bergstrand (2004), Carerre (2006), Baier and
Bergstrand (2007), Martinez-Zarzoso et al. (2009) to mention but a few, in the past decade, a
relatively larger number of studies have focused on investigating the impact of RTAs on regional
trade and welfare especially in terms of their tendency to divert or create trade (see Ghosh and
Yamarik, 2004; Carrere, 2006; Baier and Bergstrand, 2007; Jugurnath et al., 2007 and MartinezZarzoso et al., 2009).
However, as seen from above, empirical studies on RTAs‟ impacts abound, yet there seem to be
sparse (if not non existence) of empirical literature that focus on variables that predict bilateral
and inter-regional trade integration in Africa at the aggregate level. More importantly, the
estimates obtained from various studies with Gravity model of inter-regional trade (GM
hereafter) for most countries are sample bias depending on the number of countries included in
2
A year where most database for bilateral and interregional trade began (e.g. Unctadstat)
the analysis3. Thus, this is topical and timely as it contributes to the growing debate on the
regional integration architecture in Africa majorly in two areas. First, it examines the
determinants of bilateral trade flows among member countries in AU. Second, it examines the
possibility of trade diversion and trade creation effects associated with the formation of various
RTAs in Africa.
The paper is organized as follow. Some stylized facts about AU bilateral and intra-regional trade
are provided in section 2. Relevant empirical studies on the effects of RTAs are reviewed in
section 3. Section 4 describes the methodology employed, section 5 presents the empirical results
with policy implications and section 6 concludes the paper.
2.0 Some Stylized Facts about AU Bilateral and Intra-Regional Trade
The historical breeding ground for the emergence of what is today known as African Union (AU)
originated earlier in the then Union of African States (UAS) established in the 1960s by Kwame
Nkrumah. This organization was the first of its kind as the earliest confederation of states in the
political history of Africa. The idea was also hatched under the auspices of Organization of
African Unity (OAU) established in 1963 and the African Economic Community in 1981 with
the aim of uniting all African Nations together by establishing various free trade zones and other
custom unions.
In the mid-1990s, OAU was eventually named AU by Sirte Declaration in Libya 1999, calling
for the establishment of an African Union to supplant OAU. Thereafter, a summit was held at
Lomé in 2000 to adopt the Constitutive Act for AU and the plan for the implementation of AU
was also adopted at a summit held at Lusaka in 2001. Consequently, African Union was
launched in Durban in 2002 at the first session of the Assembly of the African Union. Basically,
AU was formed to accelerate the political and socio-economic integration of the continent.
Virtually, all African countries are members of the Union with the exception of Morocco who
opted out since 1984 under OAU. It is therefore pertinent to detail out some peculiar trends in the
growths of AU trade with the Developed countries, Developing countries (intra-AU inclusive)
and Transitory countries with the aim of determining the basic stylized facts that characterize
regional integration in Africa over the years.
2.1
Trends in AU’s Trade in Manufactured Goods with:
Developed, Developing and Transition Countries
Table 1 below provides the trends in AU‟s trade in manufactured goods with the developed,
developing and transition countries. Transition or emerging countries, which have experienced
global economic growth miracles, especially the Asian tigers in the recent decades accounted for
the highest percentage of AU‟s export of manufacturing product with about 53 percent of the
total. This is followed by Developed countries share in AU‟s total export on manufactures with
the rest of the world with about 25 percent, while the remaining 22 percent accrued to the
developing nations. However, in terms of the share of imports of manufacturing products, about
60 percent of AU‟s import was from the developed countries, followed by developing countries‟
share of about 39 percent, while the remaining 2 percent is accrued to the transition economies.
Consequently, the total trade (exports and imports) share in AU‟s manufactured goods is skewed
toward developed countries which constituted about 45 percent, 32 percent for the developing
3
The work of Magee, (2008) contained an important review of relevant literature in this regard.
countries, while that of the transition countries stood at 24 percent. See the trend chart in figure 1
below.
Table 1: African Trade Statistics on Manufactured Goods with the rest of the World (USD’m) 1995-2010
Regions
Developed
countries
Developing
countries
Transition
countries
Grand total
Export
Total
%
452951.6
24.91
Total
1445193
Import
%
59.70
Aggregate
Total
%
1898201
44.78
403909.2
22.21
935352.8
38.64
1339312
31.59
961577.3
52.88
40170.03
1.66
1001867
23.63
1818438
100.00
2420716
100
4239380
100.00
Source: Computed by the Authors from UNCTAD (2013)
Figure 1: Percentage Share of Developed, Developing, Transition in AU'S
Total Trade on Manufacturing Goods with the rest of the world (1995 - 2010)
Trade statistics of AU in Manufactured Goods with the rest of the World
60
50
40
30
20
10
0
Export (%)
Developed countries
Import(%)
Developing countries
Total (%)
Transition countries
Source: Graphed by the Authors from Table 1 above
In a nut shell, we observed that these trend do not negate reality because Developed countries are
characterized by a high level of industrialization, hence, they accounted for a larger percentage
of import of manufactured goods into AU member countries. On the other hand, since the
countries in AU are not as industrialized as these Developed countries, they can only get a larger
market for their locally produced manufactured goods in the Transition and Developing countries
rather than facing more competitions from developed countries.
2.2
Trends in AU’s Trade in All Food Items with:
Developed, Developing and Transition Countries
It has been noted also that the highest percentage of AU‟s export trade on all Food Items is
directed to the Transition countries which represented about 81 percent, followed by that of
Developed countries which stood at about 11.2 percent, while Developing countries accounted
for about 8 percent. On the part of import, AU imported more from developing countries which
constituted about 49.7 percent of total food items imported from the rest of the world. Also,
Developed countries accounted for about 47 percent, while that of the Transition economies
stood at 3.1 percent. However, the aggregate trade on all food items indicated that Transition
countries received the highest percentage of about 65 percent, followed by Developed countries
with about 18.5 percent, while that of the developing countries stood approximately at 17
percent.
The high exports of food items by AU countries reflect the level of economic base of these
countries which is majorly agriculture. Thus, the AU countries are able to produce food items at
a reasonably competitive price thereby raising global demand for their food items at the
international market particularly by the transition economies with high population growth. These
results are presented in table 2 and corroborated with figure 2 below:
Table 2: African Trade Statistics on All Food Items with the Rest of the World (USD’m) 1995-2010
Regions
Export
Import
Aggregate total
Total
%
Total
%
Total
%
Developed
233853.1
11.16
252061.7
46.94
485975.3
18.46
countries
Developing
168549.2
8.04
266639.7
49.66
435232.6
16.54
countries
Transition
1693869
80.80
16432.4
3.060
1710740
65.00
countries
Grand total
2096271
100.00
535133.8
100.00
2631948
100.00
Source: Authors’ computation from UNCTAD (2013)
Fig 2: Percentage Share of Developed, Developing, Transition countries in AU’s Trade on All Food Items
(AFI) with the rest of the world (US’D M) 1995-2010
Trade statistics of AU in All food Items with the rest of the World
100
80
60
40
20
0
Export (%)
Developed countries
Import (%)
Developing countries
Total (%)
Transition countries
Source: Graphed by the Authors from Table 2 above
As previously explained, this trend is not surprising as many AU countries are majorly agrarian
and are therefore able to produce agricultural items including food items for domestic
consumption at a competitive domestic price relative to foreign price.
2.3
Trends in AU’s Trade in Primary Commodities with:
Developed, Developing and Transition Countries
The analysis of the region‟s (AU) trade with the rest of the world with regard to primary
commodities is not far from expectations as Africa usually exports a larger proportion of its raw
materials to the Transition economies, followed by the Developed world and the Developing
economies as indicated in figure 3 and table 3 below. Here, Transition countries accounted for
almost 53 percent of AU total export of primary products to the rest of the world. Developed
economies followed with about 28 percent, while 20 per cent was reported for Developing
countries. However, the reverse holds for the proportion of imports of primary commodities into
the region with 58 percent of these imports coming from the developing countries. Developed
countries on import side accounted for about 38 percent of AU‟s total import of primary
commodities from the rest of the world, while Transition countries had only 3.6 percent share.
Table 3: African Trade Statistics on Primary Commodities with the Rest of the World (USD’ m)
1995-2010
Regions
Export
Import
Aggregate total
Total
%
Total
%
Total
%
Developed countries
2009744
27.72
428568.1
37.99
2438379
29.10
Developing countries
1421406
19.61
658958.2
58.41
2080411
24.83
Transition countries
3818865
52.67
40617.89
3.60
3859610
46.07
100.00
8378400
100.00
Grand total
7250015 100.00
1128144
Source: Authors’ computation from UNCTAD (2013)
Fig 3: Percentage Share of Developed, Developing and Transition countries in AU’s Trade on Primary
Commodities (PC) with the rest of the world (US’D M) 1995-2010
Trade statistics of AU in Primary Products with the rest of the World
60
40
20
0
Export (%)
Developed countries
Import (%)
Total (%)
Developing countries
Transition countries
Source: Graphed by the Authors from Table 3 above
The trend above is not also surprising as the developed countries are the industrialized ones that
utilize raw materials largely and more productively due to their industrial base and are therefore,
able to attract more primary products from the rest of the world while they export less to other
countries. Notably too, with the emergence of the Asian Tigers, industrial growth is now highly
reckoned with in most Transition economies and this scenario is reflected in their increased
demand for primary raw materials from the AU member countries as reflected in higher
percentage of AU exports of Primary Commodities to these countries.
2.4
Trends in AU’s Trade in All Products (Total of All Products) with:
Developed, Developing and Transition countries
Developed countries received the highest percentage of AU‟s total exports on all products of
about 57.2 percent for the period under scrutiny. This is followed by the share of the Developing
countries which stood at 42.3 percent, while that of the transition countries was less than 1
percent. The same trend pattern is observed on the import side. Here, Developed countries
accounted for 53.3 percent of the total import of all products from AU. Similarly, Developing
countries had the share of about 44.4 percent of AU‟s imports of all products, while only 2.3
percent came from the Transition countries. Similar pattern of trade is also experienced on the
aggregate where Developed countries accounted for about 55.5 percent of the total of AU trade
on all Products. Developing countries had a share of 43.2 percent, while only 1.3 per cent was
reported for the Transition economies. See table 4 and figure 4 below.
Table 4: African Trade Statistics on Total of All Products with the Rest of the World (USD’m) 1995-2010
Regions
Export
Import
Aggregate total
Total
%
Total
%
Total
%
Developed countries
2592402
57.23
1937654
53.34
4530121
55.50
Developing countries
1914101
42.25
1612993
44.40
3527142
43.21
Transition countries
23452.32
0.52
82326.53
2.27
105779.4
1.30
100.00
8163042
100.00
Grand total
4529955 100.00
3632974
Source: Authors’ computation from UNCTAD (2013)
Fig 4: Percentage Share of Developed, Developing and Transition countries in AU’s Trade on Total of All
Primary with the rest of the world (US’D M), 1995-2010
Trade statistics of AU in Total of all Products with the rest of the World
60
50
40
30
20
10
0
Export (%)
Developed countries
Import (%)
Developing countries
Total (%)
Transition countries
Source: Graphed by the Authors from Table 4 above
In summary, this trend reflects the reality that Developed countries are typically characterized by
high level of industrial capacity utilization and other technological advancement which give
them edge in production and distribution. It is therefore not puzzling that they had the highest
percentage share in AU‟s import and export on all products.
3.0
Review of Empirical Literature
Do RTAs promote multilateral trade or regional integration? This question has continued to
attract the attention of researchers, policy makers, stakeholders in various sectors and other
social activists alike. Different approaches both in theory and evidence have consequently
emerged over the years to address this concern. This question has largely remained partially
answered or unanswered in many empirical studies; hence, many vacuums are still left to be
covered in both empirical and theoretical literature in this regard. Viner (1950), who is known
for his pioneering work on customs union literature, points out that regional trade agreements do
not necessarily result in gains to members and consequently develops what we now refer to as
the trade creation-trade diversion approach to regional trade agreements to help understand this
ambiguity (see Abrego, et al., 2006 and Salisu, et al. 2012 for example).
However, the Viner‟s conclusion generates further debate in the literature. For example, Kemp
(1969), Lipsey (1970), Kemp and Wan (1976), Riezman (1979), Lloyd (1982), and Wooton
(1986), Riezman (1985), dissatisfied with the postulation of Viner‟s, suggest a new approach
known as the terms of trade–volume of trade approach. The latter eventually becomes popular, as
it summarizes the impact of a regional trade agreement by its effects on both terms of trade
(prices) and trade volumes (see Abrego, et al., 2006). These terms of trade–volume of trade
approach uses general equilibrium instead of Vinerian partial equilibrium analysis, and
emphasizes the impacts of the union on individual countries as integration occurs, instead of on
world welfare (see Abrego, et al., 2006). These propositions have been subject to rigorous
empirical applications to authenticate or refute their assertions. Essentially, majority of these
studies employ microeconomic theory including a game-theoretic approach to come up with the
relevant propositions and consequently, numerical simulations including the Computable
General Equilibrium (CGE) and the Dynamic CGE have been used to validate these propositions
(see Baier and Bergstrand, 2004; Abrego et al., 2006; Abbott et al., 2008; and Lee et al., 2009
and Salisu, et al, 2012 for a survey of other literature in this regard).
Aside from the above approaches, one of the prominent approaches in the recent time is the use
of gravity model (GM) to address RTAs effects.4 Nonetheless, different specifications and
generalizations of the GM have been tested for the purpose of robustness of results. In this
review, we consider both the traditional and the modified GMs tested in the existing literature.
For instance, Kepaptsoglou et al (2010) analyze EU and Mediterranean countries‟ bilateral trade
flows of export and import for the period of 1993-2007. By employing panel data and structural
uncorrelated regression method with two way fixed and random effects, they find that the basic
traditional GM variables perform well thus satisfying the theoretical expectation of the direct and
indirect relationship between trade and income and distance respectively.
Similarly, Elif (2010) investigates bilateral trade flows and their determinants among six big OIC
(Organization of the Islamic Conference) economies by using panel data and cross sectional data
for the period of 1985 to 2009. This study basically extends the traditional gravity model of
bilateral trade with population and volatility of exchange rates as additional variables and they
find that income and population of a country, distance between two countries and volatility of
4
See Bergeijk and Brakman (2010) for the historical background of GM and a comprehensive review of recent
methodological and theoretical advances.
exchange rates affect bilateral trade flows among six big countries of OIC. Specifically, the study
finds that the impact of population on bilateral trade flows is positive for the exporter country,
while it is negative for the importer country. Other studies on the same OIC Countries include
the work of Hassan et al. (2010) that investigates economic performance of the OIC countries
within the framework of the gravity model and finds that D8 which includes eight bigger OIC
countries is trade creating. The study argues that two countries in D8 block would trade 4.28
times more among themselves than two other similar countries outside the block.
Again, Gundogdu (2009) estimates intra-OIC trade for the period of 1995-2007 with GM and
finds that OIC member countries have started to trade more with each other and also with the rest
of the world as a result of their individual efforts and requirements of being members to free
trade areas to remove trade barriers and reduce tariffs. In a similar fashion, Karimi-Hosnijeh
(2008) analyzes bilateral trade flows between Iran and OIC countries for the period 1998-2005
and shows that economic and cultural similarities among OIC countries have a significant
positive impact on their bilateral trade flows of agricultural products. It is further argued that
there is still a high potential of OIC countries to increase their exports to non-members up to
36% and imports from them up to 28%.
Furthermore, in relation to Europe, Egger (1999) estimates the potentials for trade between
Austria and five Central Eastern Europe (CEE) countries (Hungary, Czech Republic, Slovak
Republic, Poland and Slovenia). It employs “fixed country effects” to estimate export potential
in the framework of these countries and finds that CEE Community‟s openness to EU exports
would increase, without altering the bilateral degree of openness among other countries of the
European Union. Maryanchyk (2005) as well adopts the gravity theory of trade to estimate two
specifications of the model for Ukraine and finds that until 1999 actual trade flows exceeded
those predicted by the model, while also stating that the country exhausted all potential of trade
with European Union countries and should concentrate on prospects of trade with smaller
economies. Consequently, the study proposes that alternatively that Ukraine should engage in
more trade with countries with large market size both in terms of income and population such as
the USA, Japan and others as to fully maximize trade potentials.
In the same manner, Shepotylo (2009) estimates trade potential of Common Wealth of
Independent States (CIS) by employing industry-level gravity model of exports through a twostage estimation procedure that accounts for a sample-selection bias and firm-level heterogeneity
as well as Hausman-Taylor test for model validation. The study finds that CIS countries are
largely in line with what is expected from gravity model, although there are some distortions of
export flows of CIS countries that indicate smaller degree of geographical and industrial
diversification. More so, Brodzicki (2009) utilizes the gravity model of trade to investigate
bilateral trade flows of Poland with 181 trade partners. The study estimates two equations; the
basic GM and the extended or modified one to arrive at the conclusion that not only common
independent variables such as GDP and distance affect trade volumes, but also other
macroeconomic fundamentals including country specific characteristics.
Indeed, it is glaring from the empirical evidence in the foregoing review that various factors
determine the pace of intra-regional trade in various RTAs and that the level of trades of one
country with another either in the same region or other regions greatly determine the scenarios of
trade diversion or creation.
4.0
Methodology
4.1
The Model Specification
The gravity model (GM) has become so prominent in analyzing patterns of bilateral trade
(Eichengreen and Irwin, 1996). In terms of origin, GM was conceptualized and demonstrated by
Isaac Newton in his gravity equation in physics, and its subsequent adoption in regional science
for describing and analyzing spatial flows was pioneered by Tinbergen (1962), Pöyhönen (1963)
and Linnermann (1966). The model works well empirically, yielding sensible parameter
estimates and explaining a large part of the variation in bilateral trade (Rose, 2000). However, it
has long been disputed for lack of theoretical foundation. Notwithstanding, as much investigation
into the theoretical foundations of the GM are on the increase, there seems to be a welcome
revival of the empirical robustness and theoretical plausibility of the model in the international
trade literature. Recent developments in the modeling of bilateral or intra-regional trade provide
GM with more satisfying theoretical underpinnings in trade theory which is considered crucial in
this revival process (see, Feenstra, 2002; Anderson and Van Wincoop, 2003; Baltagi, Egger and
Pfaffermayr, 2003, and Carrere, 2006 for an overview of recent research efforts).
Thus, this study adopts GM to analyze the determinants of intra-AU trade. Theoretically, the GM
model assumes that trade is proportional to the product of partners‟ countries‟ GDPs and
diminishes with distance (see Krugman and Obstfeld 2009). Thus, the flow of people, ideas or
commodities between two locations is positively related to their economic size and negatively
related to the distance (see Ghosh and Yamarik 2004). Equation (1) below shows the standard
GM:
𝑏
𝑡𝑟𝑎𝑑𝑒𝑖𝑗 = 𝐴
𝐺𝐷𝑃𝑖 1 𝐺𝐷𝑃𝑗
𝑏2
𝑏
𝐷𝑖𝑗3
(1)
Where 𝑡𝑟𝑎𝑑𝑒𝑖𝑗 is the value of bilateral trade between countries 𝑖 and 𝑗; 𝐺𝐷𝑃𝑖 and 𝐺𝐷𝑃𝑗 are the
national incomes or outputs of countries 𝑖 and 𝑗 and they are usually used as proxies to measure
the economic size; 𝐷𝑖𝑗 captures the bilateral distance between the two countries and 𝐴 is a
constant term. By taking the natural log of equation (1), the transformed model can be expressed
in panel data framework as expressed below:
ln 𝑡𝑟𝑎𝑑𝑒𝑖𝑗𝑡 = 𝛼 + 𝑏1 ln 𝐺𝐷𝑃𝑖𝑡 + 𝑏2 ln 𝐺𝐷𝑃𝑗𝑡 + 𝑏3 𝑙𝑛𝐷𝑖𝑗 + 𝑣𝑖𝑗𝑡
2
where 𝑡 represents the time period, 𝛼 = ln 𝐴, and 𝛼, 𝑏1 , 𝑏2 and 𝑏3 are the regression
coefficients. The disturbance term (𝑣𝑖𝑗 ) captures shocks that may affect bilateral trade between
the two countries (see Salisu et al, 2012). This gravity model claims that higher income tends to
support trade by leading to more production, higher exports and also higher demand for imports
(see Jugurnath et al, 2007 for a related review). Furthermore, larger distances between countries
are expected to reduce bilateral trade by leading to higher transportation costs. The inclusion of
the core variables in the model; income and distance in a trade equation is justified by many
trade theories, especially imperfect competition and the Hecksher – Ohlin model (see Ghosh and
Yamarik, 2004).
Equation (2) is the traditional gravity model and the underlying apriori expectation is that
bilateral trade is positively related with economic size and negatively related with distance.
Therefore, 𝑏1 , 𝑏2 > 0 (positive). Also, the farther the bilateral distance between the two
trading partners‟ capital city, the higher the transportation costs and all other things being equal,
the lower the volume of trade transactions; therefore, 𝑏3 < 0 (negative).
Aside from the basic traditional gravity model variables, other control variables to reflect
important geographic factors, historical ties, socio-economic and political factors. In particular,
we include common Language (LANG) (see Ghosh and Yamarik, 2004; Baier and Bergstrand,
2007; Jugurnath et al., 2007; Gil-Pareja et al., 2008; Magee, 2008; Martínez-Zarzoso et al., 2009
and Vicard, 2011; among others ); a measure of whether the trading countries are landlocked (see
Ghosh and Yamarik, 2004; Carrere, 2006; Gil-Pareja et al., 2008; Magee, 2008 and Athukorala,
2012); Total Land Area (AREA) (see Ghosh and Yamarik, 2004; Longo and Sekkat, 2004;
Jugurnath et al., 2007; Magee, 2008); GDP per Capita or Population (POP) (see Ghosh and
Yamarik, 2004; Carrere, 2006; Jugurnath et al., 2007; Agbodji, 2008; Magee, 2008; MartínezZarzoso et al., 2009); exchange rate (see Bacchetta and van Wincoop, 2000; Lane and MilesiFerretti, 2002; and Jugurnath et al., 2007), degree of openness and political stability.
Specifically, the rate of country‟s openness is included in the model to reflect the rate of the
interconnectedness of African countries with the rest of the World; and also the level of political
stability (proxy by governance effectiveness). 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. A country with a low governance quality (or
weak institutions) will display insecurity on trade and this generates low trade volume (see
Anderson and Marcouiller, 2002). On the part of openness, the more open an economy is, the
higher the expected level of trade.
Given the foregoing, equation (2) is modified as follows:
ln 𝑋𝑖𝑗𝑡 = 𝛼 + 𝑏1 ln 𝐺𝐷𝑃𝑖𝑡 + 𝑏2 ln 𝐺𝐷𝑃𝑗𝑡 + 𝑏3 In 𝐷𝑖𝑗 + 𝑏4 ln 𝑃𝑂𝑃𝑖𝑡 + 𝑏5 ln 𝑃𝑂𝑃𝑗𝑡 +
𝑏6 𝐶𝑂𝑀𝐿𝐴𝑁𝐺𝑖𝑗𝑡 + 𝑏7 𝐿𝐴𝑁𝐷𝐿𝑂𝐶𝐾𝐸𝐷𝑖𝑡 + 𝑏8 𝐿𝐴𝑁𝐷𝐿𝑂𝐶𝐾𝐸𝐷𝑗𝑡 + 𝑏9 In 𝐴𝑅𝐸𝐴𝑖𝑡 +
𝑏10 In 𝐴𝑅𝐸𝐴𝑗𝑡 + 𝑏11 lnEXR 𝑖𝑡 + 𝑏12 lnEXR jt + 𝑏13 lnOPENSS𝑖𝑡 + 𝑏14 lnOPENSS𝑗𝑡 +
𝑏15 POLSTAB𝑖𝑡 + 𝑏16 POLSTAB𝑗𝑡 + 𝑣𝑖𝑗𝑡
(3)
In addition to the earlier definitions of variables, 𝑋𝑖𝑗𝑡 are bilateral exports between 𝑖 (the source
countries) and 𝑗 (the reporting countries). Here, if a particular AU country is taken as the source
country; all other AU member countries are considered as the partners. Thus, equation (3)
represents the GM for bilateral export or intra-regional trade among AU countries.
For model estimation purpose, dummy variables are used for some trade constraining factors like
𝐶𝑜𝑚𝑙𝑎𝑛𝑔𝑖𝑗 = 1 if countries 𝑖 and 𝑗 share a common official language and 0 (zero) if otherwise.
𝐿𝐴𝑁𝐷𝐿𝑂𝐶𝐾𝐸𝐷𝑖(𝑗 ) = 1 if country 𝑖 (𝑗) is landlocked and 0 (zero) if otherwise. It is expected that
countries with larger populations will export more and vice versa, thus, 𝑏4 , 𝑏5 > 0 (positive).5
5
Although, there have been conflicting results; Aitken (1973) argues that the larger is a country‟s population, the
larger will be the ratio of the domestic market to the foreign market, hence the smaller would be the potential export
supply. Bergstrand (1985) argues that a larger population would allow for economies of scale, which may increase
We also expect that trading countries that share a common language will trade more due to easy
accessibility and therefore lower cost of transportation and vice versa, thus, 𝑏6 > 0. However,
where trading countries are landlocked, accessibility to the hinterland of such countries is
decreased and transaction costs are consequently greater, hindering effective trading activities
and consequently, may lower the volume of trade transactions. Therefore, we anticipate
that 𝑏7 , 𝑏8 < 0.
Similarly, the variable AREA denotes the country‟s total land area, including areas under inland
bodies of water and some coastal waterways (see Jugurnath et al., 2007). It is expected that
larger countries will export more, therefore, 𝑏9 , 𝑏10 > 0. It is possible however that relative size
may also be important for comparative advantage reasons. Thus, if this scenario is the case, the
sign on the coefficient of AREA may also be indeterminate (see Jugurnath et al., 2007).
The sign of the coefficient of exchange rate is basically indeterminate (Bacchetta and van
Wincoop, 2000; Lane and Milesi-Ferretti, 2002). However, it is expected that higher exchange
rate worsens the purchasing power of the local currency. Therefore, we expect that 𝑏11 , 𝑏12 < 0.
Also, we anticipate that the sign of coefficients on openness will be positive. This reasoning is
underscored by the fact that the more open a country is, the more trade is facilitated. We
therefore expect b13, b14 < 0. On the part of political stability as a governance indicator, we
expect that good governance and absence of violence will facilitate trade across different
continents of the world. Here, with political stability, more confidence will be instilled in
potential local and foreign investors in any particular economy. Therefore, we hope that b15, b16
> 0.
Furthermore, we model trade creation and trade diversion effects of RTAs in AU in order to
capture intra-regional trade behavior of African Union member states. Thus, equation (3) is
augmented to capture these effects in equation (4) and (5). The motivation is that bilateral trade
relations between two countries or at the regional levels are usually anchored under regional
trade agreement (RTAs), and there is possibility of experiencing trade convergence or
divergence among member countries of such RTAs when there are cases of observed absent of
trade complementarities between or among the trading partners, thereby preventing the
achievement of the highest cadre of regional integration (African Monetary Union) in Africa.
Notably, most empirical studies in the area of trade creation and trade diversion usually adopt
Vinerian-type Gravity Model specification (see Soloaga and Winters, 2001; Carrere, 2006;
Jugurnath, 2007; Magee, 2008 and Martinez- Zarzoso et al., 2009 among others for details).
𝑙𝑛 𝐸𝑥𝑝𝑜𝑟𝑡𝑖𝑗𝑡 = 𝑒𝑞𝑢. (3) + 𝑏17
𝑛
𝑘=1 𝑅𝑇𝐴𝑘𝑖
𝑅𝑇𝐴𝑘𝑗 + 𝑏18
𝑛
𝑘=1 𝑅𝑇𝐴𝑘𝑖
+ 𝑏19
𝑛
𝑘=1 𝑅𝑇𝐴𝑘𝑗
(4)
Basically, in the fashion of Viner, RTA dummy RTAk(i, j) takes the value of one (1) if both source
(exporting countries) and the reporting (importing countries) are members of the same RTA and
zero (0) if otherwise. As this is a regional integration dummy, i.e. 𝑅𝑇𝐴𝑘𝑖 𝑅𝑇𝐴𝑘𝑗 , the coefficient
of 𝑏17 may be positive or negative (𝑏17 > 𝑜𝑟 < 0). Therefore, a positive value of 𝑏17 implies the
the price competitiveness of the export country‟s production, thereby leading to higher exports. Therefore, the sign
on the coefficient of the population of the exporting country may be indeterminate, while the sign for the importing
country is expected to be positive. (See Jugurnath et al., 2007).
higher extent to which members of the 𝑅𝑇𝐴𝑘 trade is increased than the level which otherwise
may be obtainable in the absence of an agreement and hence there is trade diversion and if it is
negative, it implies that there is trade creation among them (Carrere, 2006; Jugurnath, 2007 for
details). In the same platform, the dummy for 𝑅𝑇𝐴𝑘𝑖 takes the value of one (1) if the source
country is a member of a particular RTAK and zero (0) if otherwise. We theoretically expect that
the coefficient of 𝑏18 > 0 and by implication, a particular RTAK is trade creating which means
that exports of the member states of that RTAK going to non members are higher than the level
which may be hitherto obtainable in the absence of such agreement and if otherwise, it is trade
diverting. Lastly, if the sign of the coefficient for 𝑏19 is greater than zero, it denotes that the
reporting countries imports from non members of 𝑅𝑇𝐴𝑘𝑗 are higher than their normal level. The
situation of this is still a pointer that such RTAK is trade creating, otherwise it is a scenario for
trade diversion.
Following this vinerian-type GM, we add RTAs dummies for the major RTAs in Africa covering
the five geo-political regions in the continent. These RTAs are East African Community (EAC)
for the Eastern region, Economic Community of Central African States (ECCAS) for the Middle
region, Arab Maghreb Union (AMU) for the Northern region, Southern African Development
Community (SADC) for the Southern region and Economic Community of Western Africa
States (ECOWAS) for the Western region. We have provided a generalized framework that
captures RTA dummies in a bilateral framework.
The above theoretical and algebraic demonstration of the trade creation or diversion impact of
RTAs as it applies to AU member countries can be re-specified by using the five major RTAs in
Africa as:
𝑙𝑛 𝐸𝑥𝑝𝑜𝑟𝑡𝑖𝑗𝑡 = 𝑒𝑞𝑢. 3 + 𝑏17 𝐸𝐶𝑂𝑊𝐴𝑆𝑖𝑡 𝐸𝐶𝑂𝑊𝐴𝑆𝑗𝑡 + 𝑏18 𝐸𝐶𝑂𝑊𝐴𝑆𝑖𝑡 + 𝑏19 𝐸𝐶𝑂𝑊𝐴𝑆𝑗𝑡
+ 𝑏20 𝐸𝐶𝐶𝐴𝑆𝑖𝑡 𝐸𝐶𝐶𝐴𝑆𝑗𝑡 + 𝑏21 𝐸𝐶𝐶𝐴 𝑆𝑖𝑡 + 𝑏22 𝐸𝐶𝐶𝐴𝑆𝑗𝑡 + 𝑏23 𝐸𝐴𝐶𝑖𝑡 𝐸𝐴𝐶𝑗𝑡
+ 𝑏24 𝐸𝐴𝐶𝑖𝑡 + 𝑏25 𝐸𝐴𝐶𝑗𝑡 + 𝑏26 𝐴𝑀𝑈𝑖𝑡 𝐴𝑀𝑈𝑗𝑡 + 𝑏27 𝐴𝑀𝑈𝑖𝑡 + 𝑏28 𝐴𝑀𝑈𝑗𝑡
+ 𝑏29 𝑆𝐴𝐷𝐶𝑖𝑡 𝑆𝐴𝐷𝐶𝑗𝑡 + 𝑏30 𝑆𝐴𝐷𝐶𝑖𝑡 + 𝑏31 𝑆𝐴𝐷𝐶𝑗𝑡
(5)
Thus, equation (5) above is our estimated Intra-AU bilateral trade with regional trade integration
dummies whose coefficients basically identify scenarios for trade creation or trade diversion
between and among the various regional economies in Africa.
In terms of the models estimation techniques, we employ the Ordinary Least Square (OLS) to
estimate model 2 and the Least Square Dummy Variable (LSDV) approach of fixed effects to
estimate models 3 and 5. The LSDV approach is relevant in this case as it allows for the
inclusion of dummy variables to capture both the country specific as well as country pair
characteristics.
4.2
Data Issues
The data collected for the analysis are secondary in nature and covered the period of 1995 to
2010 (16 years). The choice of the period of the study is basically underscored by the availability
of readily retrievable data on trade flow from the United Nation Conference on Trade and
Development (UNCTAD) database. Other data like GDP, Population figures, exchange rate are
collected from WDI (2011) of the World Bank database, bbilateral distances, Common (official)
language, Border, Total land area, landlocked are from CEPII Distance database
(http://www.cepii.fr/anglaisgraph/bdd/distances.htm) and Pen World Table (PWT). In addition,
the sample size for the empirical analysis covers a total of fifty (50) African countries out of the
total 55 member countries of the Union. This sample size (the countries used) is basically chosen
on the basis of data availability.
5.0
Results
5.1
Estimated Results for Model 1(Basic GM)
This result details the estimation of the basic bilateral trade GM in its Newtonian form which
expresses trade flow between two or more countries as a positive function of the economic size
(usually represented by GDP) of the trading partners and a negative function of the bilateral
distance between the partners. The regression output from E-view for the model is presented in
table 6 below.
Dependent Variable: EXPORT_S
Method: Panel Least Squares
Date: 08/30/13 Time: 10:33
Sample: 1995 2010
Periods included: 16
Cross-sections included: 14
Total panel (unbalanced) observations: 141
Variable
Coefficient
Std. Error
t-Statistic
C
-34715.75
61659.65
-0.563022
GDP_S
22.18284
9.485821
2.338526
GDP_R
-0.036808
2.731611
-0.013475
DIST
-71.02370
69.46844
-1.022388
R-squared
0.059208 Mean dependent var
Adjusted R-squared
0.038607 S.D. dependent var
S.E. of regression
138081.4 Akaike info criterion
Sum squared resid
2.61E+12 Schwarz criterion
Log likelihood
-1866.861 Hannan-Quinn criter.
F-statistic
2.873989 Durbin-Watson stat
Prob(F-statistic)
0.038572
Prob.
0.5743
0.0208
0.9893
0.3084
51454.34
140826.5
26.53703
26.62069
26.57103
0.447656
The result shows that Economic size of the source country was found to be significant at 5 %
level of significance, while that of the reporting country was not found significant. Also, the
variable on distance is not significant as expected theoretically, although the coefficient is
correctly signed according the basic assumption of the gravity model. Here, a unit change or
increase in Economic size (GDP) will lead to about 22.2 % increase in total intra-AU trade. We
concludes that this present era of globalization have actually diminish the imports of distance
barriers to trade, hence more trades could flow across various RTAs.
Concerning the overall coefficient of the determination of the model, the result shows that the
two independent factors (i.e GDP and Distance) only accounted for about 6 %, while the
remaining variation in total intra-AU trade (exports) are explained by other factors outside the
model. Although, this result is poor, but the fact still remain that various factors determine the
level of intra-AU trade aside from GDP and distance between various countries in different
RTAs. Despite the poor R-squared (coefficient of the determination) the P-value of F-statistic
which shows the joint test of the significance of the explanatory variables showed to be
significant. Pertinently too, the insufficient of GDP and Distance to determine the overall trade
warrant the need for an augmented version of GM which our Model two handles.
5.2 Estimated Results for the augmented GM
As we augment the basic GM with other geographical, socio-economic and political factors as
possible determinants of intra-AU trade, we found that some of the variables were significant.
The results is represented in table 8 below
Table 7: Augmented GM for Intra-AU trade
Dependent Variable: EXPORT_S
Method: Panel Least Squares
Date: 08/30/13 Time: 10:43
Sample (adjusted): 1996 2010
Periods included: 12
Cross-sections included: 14
Total panel (unbalanced) observations: 116
Variable
C
lnGDP_S
lnGDP_R
lnDIST
lnEXR_S
lnEXR_R
lnOPENNSS_S
OlnPENNSS_R
lnPOP_S
lnPOP_R
lnAREA_S
lnAREA_R
lnPOLSTAB_R
lnPOLSTAB_S
COMLANG
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
Coefficient
-322651.5
6.048414
5.658804
243.3202
-35.29000
-58.52811
1805.197
198.3562
0.002538
0.004975
0.051042
-0.137339
54611.12
2690.173
3.063410
0.467632
0.399781
118982.4
1.44E+12
-1512.798
6.892049
0.000000
Std. Error t-Statistic
380858.7 -0.847168
22.29700 0.271266
2.970017 1.905311
322.0662 0.755498
1162.675 -0.030352
60.45589 -0.968113
1812.505 0.995968
423.9678 0.467857
0.008574 0.296012
0.000767 6.482236
0.154606 0.330142
0.064622 -2.125275
25873.00 2.110738
58510.96 0.045977
23.98653 2.773542
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
Prob.
0.3989
0.7867
0.0596
0.4517
0.9758
0.3353
0.3216
0.6409
0.7678
0.0000
0.7420
0.0360
0.0372
0.9634
0.0436
58701.11
153577.5
26.32410
26.65643
26.45901
0.965299
The results indicated that the overall coefficient of the determination of the model is 47 %, which is
an indication that the explanatory variables jointly determine total variations in intra-AU export. In
terms of the significant of the individual explanatory variable, we found that Economic Size of the
reporting countries is significant at 5 %, population of the reporting countries is also significant at 1
percent, Area or the land mass of the reporting countries as well is significant at 5 % as a
determinants of intra-AU trade. Similarly, a measure of political stability which was proxied by
governance effectiveness for the reporting countries is found to be significant at 5%. Lastly,
common language is also found to be significant in determining increase intra-AU trade. Here, a unit
increases in common language, i.e if perhaps trading partner could suddenly understand a common
language, trade will be enhanced by about 3.1 %. In facts respective unit impacts of all the
significant variables mentioned above are indicated in the result above.
Overall, in addition to the significance of the traditional gravity variables, we have identified a
number of striking features from the estimation results:
(1) The characteristics of the exporting countries appear to exert more impact on the volume
of bilateral trade than the features of the importing or the reporting countries.
(2) The increasing population of most of the trading partners in AU attract increased in
intra-AU trades
(3) Political stability provides incentives for more sustainable bilateral trade flows.
5.3 Trade Creation and Diversion Model
The results for the estimated model 5 for the trade creation and trade diversion in AU are
presented below. The result was obtained through the use of Gretl package for statistical
analysis, and the results are shown in the table below.
Variables
GDP_S
lnGDP_R
LnDist
lnPop_S
lnPop_R
lnarea_s
lnarea_r
landlocked_S
landlocked_R
lnOPen_S
lnOPen_R
Com.lang
polstab_S
polstab_R
lnEXR_S
Model with RTA
Dummies for Trade
Creation AND Trade
Diversion
0.93 (4.59)***
0.47 (3.83)***
-0.25 (-3.01)***
0.51 (2.49)***
0.47(1.82)*
0.72(5.95)***
0.27(3.55)***
-0.13(-1.46)
-0.32(-1.39)
0.69(2.17)**
0.13(1.72)*
0.39(2.57)***
1.29(1.85)*
0.25 (0.80)
1.62 (1.89)*
lnEXR_R
ECOWAS_S_R
-0.51(-1.28)
0.57 (1.47)
Variables
ECOWAS_S
ECOWAS_R
ECCAS_S_R
ECCAS_S
ECCAS_R
EAC_S_R
EAC_S
EAC_R
AMU_S_R
AMU_S
AMU_R
SADC_S_R
SADC_S
SADC_R
Adjusted
R-Squared
F-Stat
No of
Observations
Model with RTA
Dummies for Trade Creation
AND Trade Diversion
0.92 (1.55)
-0.07(1.37)
-0.25(-0.72)
0.41(1.45)
-0.01(-0.82)
-0.08(0.93)
1.35(1.01)
-0.29(-1.16)
0.23(1.74)*
0.56(1.41)
-0.83(1.07)
0.69(2.07)**
0.78(1.31)
0.18(1.25)
0.73
14701.20***
1600
Note: *, **, *** represent 10%, 5% and 1% levels of statistical significance. The t-statistics for the
Coefficients are in italics and bracket below them. The subscripts r and s denote trading partner
and source countries respectively.
Source: Authors‟ Calculation, 2013.
In addition to these features, we have also evaluated the probable existence of trade creation and
trade diversion effects among five notable economic unions in Africa. These are EAC, ECCA,
ECOWAS, SADC and AMU. Based on the criteria for the assessment of these trade effects, we
find that only AMU and SADC are trade creating while others, that is, EAC, ECCA and
ECOWAS are not trade creating. This is evident in the results obtained for a common RTA
between the source and the reporting countries indicating that the coefficients on
ECOWAS_S_R, ECCA_S_R and EAC_S_R are not statistically significant. Also, the
coefficients for extra-regional trade effects for all the economic unions reveal that they are all
outward looking indicating that they trade more with countries outside the African continent than
among themselves. This therefore suggests very weak intra-regional trade relations among
economic unions of Africa.
6.0
Conclusion and policy Recommendations
This study examines bilateral trade relations among AU member countries over the period of
1995 to 2010. It provides some salient stylized facts as well as empirical results to enrich the
existing literature on the subject. Overall, the stylized facts suggest evidence of increasing and
promising bilateral trade relations between AU and developed countries than with its fellow
developing countries. However, the current trends reveal that trade in both Manufactured and
Primary commodities has been largely dominated by developed countries
For the empirical analysis, the study employs both the traditional and augmented gravity models
to analyze these determinants in order to capture some basic peculiar features of the trading
partners. Like the previous studies, we find evidence supporting the significance of the
traditional gravity variables of market size and distance as key factors driving bilateral trade,
even though our overall coefficient of the determination is low. It however suggests that the large
market size provides better potentialities for greater demand for goods and services among
countries in AU. The result also indicated that physical bilateral distance between nation states is
no longer a strong determinant of trade in this era of globalization.
Furthermore, based on the augmented gravity model, we find that population and landmass are of
reporting countries are good determinants of intra-AU trade. This implies that high population
growth and large landmass evident in the trading partners have continued to enhance their
bilateral trade. Another economic variable of importance is exchange rate which did not show
any significant impact as determinants of intra-AU trade among various RTAs. Political stability
is also found to significantly influence the volume of trade among AU member countries.
From the foregoing therefore, Africa needs to develop its productive base and widen its market
size for a more effective and beneficial bilateral trade among the various RTAs in the continent.
This can be achieved by promoting policies that stimulate and strengthen domestic production
base both for domestic consumption as well as exports.
Political stability is also found to be another crucial factor for bilateral trade relations. Evidently,
political instability has remained pervasive in many African countries occasioned by persistent
civil unrests and political tensions. The increasing incidence of this unfavorable trend may shrink
bilateral trade flows between AU and its trading partners. Therefore, AU should be more
proactive in addressing political instability in the continent.
Finally, the study finds outward looking sub-regional economic unions of AU which suggests
evidence of trade diversion. This evidence can be explained partly by lack of diversification of
economic base of most African countries and more generally by low capacity utilization by their
industrial sector (see UN-OECD-NEPAD, 2011 report). This feature may eventually render the
beneficial effects of Intra-AU trade and undermine the realization of African Monetary Union.
Therefore, promoting a broad based diversification of economic base of African countries is
crucial for a more beneficial China-AU bilateral trade.
References
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