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 Martinez-

Zarzoso 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 Export Import Aggregate

Total % Total % Total %

Developed

countries

452951.6 24.91 1445193 59.70 1898201 44.78

Developing

countries

403909.2 22.21 935352.8 38.64 1339312 31.59

Transition

countries

961577.3 52.88 40170.03 1.66 1001867 23.63

Grand total 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)

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

0

10

20

30

40

50

60

Export (%) Import(%) Total (%)

Trade statistics of AU in Manufactured Goods with the rest of the World

Developed countries Developing countries Transition countries

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

countries

233853.1 11.16 252061.7 46.94 485975.3 18.46

Developing

countries

168549.2 8.04 266639.7 49.66 435232.6 16.54

Transition

countries

1693869 80.80 16432.4 3.060 1710740 65.00

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

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

0

20

40

60

80

100

Export (%) Import (%) Total (%)

Trade statistics of AU in All food Items with the rest of the World

Developed countries Developing countries 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

Grand total 7250015 100.00 1128144 100.00 8378400 100.00

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

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.

0

20

40

60

Export (%) Import (%) Total (%)

Trade statistics of AU in Primary Products with the rest of the World

Developed countries Developing countries Transition 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

Grand total 4529955 100.00 3632974 100.00 8163042 100.00

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

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.

0

10

20

30

40

50

60

Export (%) Import (%) Total (%)

Trade statistics of AU in Total of all Products with the rest of the World

Developed countries Developing countries Transition countries

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 two-

stage 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:

𝑡𝑟𝑎𝑑𝑒𝑖𝑗 = 𝐴 𝐺𝐷𝑃𝑖

𝑏1𝐺𝐷𝑃𝑗𝑏2

𝐷𝑖𝑗𝑏3

(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 + 𝑏3𝑙𝑛𝐷𝑖𝑗 + 𝑣𝑖𝑗𝑡 2

where 𝑡 represents the time period, 𝛼 = ln𝐴, 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; Martínez-

Zarzoso et al., 2009); exchange rate (see Bacchetta and van Wincoop, 2000; Lane and Milesi-

Ferretti, 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:

ln𝑋𝑖𝑗𝑡 = 𝛼 + 𝑏1 ln 𝐺𝐷𝑃𝑖𝑡 + 𝑏2 ln 𝐺𝐷𝑃𝑗𝑡 + 𝑏3 In 𝐷𝑖𝑗 + 𝑏4 ln𝑃𝑂𝑃𝑖𝑡 + 𝑏5 ln𝑃𝑂𝑃𝑗𝑡 +

𝑏6𝐶𝑂𝑀𝐿𝐴𝑁𝐺𝑖𝑗𝑡 + 𝑏7 𝐿𝐴𝑁𝐷𝐿𝑂𝐶𝐾𝐸𝐷𝑖𝑡 + 𝑏8 𝐿𝐴𝑁𝐷𝐿𝑂𝐶𝐾𝐸𝐷𝑗𝑡 + 𝑏9 In 𝐴𝑅𝐸𝐴𝑖𝑡 +

𝑏10 In 𝐴𝑅𝐸𝐴𝑗𝑡 + 𝑏11lnEXR𝑖𝑡 + 𝑏12lnEXRjt + 𝑏13lnOPENSS𝑖𝑡 + 𝑏14lnOPENSS𝑗𝑡 +

𝑏15POLSTAB𝑖𝑡 + 𝑏16POLSTAB𝑗𝑡 + 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

5Although, 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 𝑅𝑇𝐴𝑘𝑖 𝑅𝑇𝐴𝑘𝑗 +𝑛𝑘=1 𝑏18 𝑅𝑇𝐴𝑘𝑖

𝑛𝑘=1 + 𝑏19 𝑅𝑇𝐴𝑘𝑗

𝑛𝑘=1 (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

+ 𝑏20𝐸𝐶𝐶𝐴𝑆𝑖𝑡 𝐸𝐶𝐶𝐴𝑆𝑗𝑡 + 𝑏21 𝐸𝐶𝐶𝐴 𝑆𝑖𝑡 + 𝑏22𝐸𝐶𝐶𝐴𝑆𝑗𝑡 + 𝑏23 𝐸𝐴𝐶𝑖𝑡 𝐸𝐴𝐶𝑗𝑡+ 𝑏24 𝐸𝐴𝐶𝑖𝑡 + 𝑏25 𝐸𝐴𝐶𝑗𝑡 + 𝑏26 𝐴𝑀𝑈𝑖𝑡𝐴𝑀𝑈𝑗𝑡 + 𝑏27𝐴𝑀𝑈𝑖𝑡 + 𝑏28 𝐴𝑀𝑈𝑗𝑡

+ 𝑏29𝑆𝐴𝐷𝐶𝑖𝑡 𝑆𝐴𝐷𝐶𝑗𝑡 + 𝑏30𝑆𝐴𝐷𝐶𝑖𝑡 + 𝑏31𝑆𝐴𝐷𝐶𝑗𝑡 (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 Prob.

C -34715.75 61659.65 -0.563022 0.5743

GDP_S 22.18284 9.485821 2.338526 0.0208

GDP_R -0.036808 2.731611 -0.013475 0.9893

DIST -71.02370 69.46844 -1.022388 0.3084

R-squared 0.059208 Mean dependent var 51454.34

Adjusted R-squared 0.038607 S.D. dependent var 140826.5

S.E. of regression 138081.4 Akaike info criterion 26.53703

Sum squared resid 2.61E+12 Schwarz criterion 26.62069

Log likelihood -1866.861 Hannan-Quinn criter. 26.57103

F-statistic 2.873989 Durbin-Watson stat 0.447656

Prob(F-statistic) 0.038572

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

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

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 Coefficient Std. Error t-Statistic Prob.

C -322651.5 380858.7 -0.847168 0.3989

lnGDP_S 6.048414 22.29700 0.271266 0.7867

lnGDP_R 5.658804 2.970017 1.905311 0.0596

lnDIST 243.3202 322.0662 0.755498 0.4517

lnEXR_S -35.29000 1162.675 -0.030352 0.9758

lnEXR_R -58.52811 60.45589 -0.968113 0.3353

lnOPENNSS_S 1805.197 1812.505 0.995968 0.3216

OlnPENNSS_R 198.3562 423.9678 0.467857 0.6409

lnPOP_S 0.002538 0.008574 0.296012 0.7678

lnPOP_R 0.004975 0.000767 6.482236 0.0000

lnAREA_S 0.051042 0.154606 0.330142 0.7420

lnAREA_R -0.137339 0.064622 -2.125275 0.0360

lnPOLSTAB_R 54611.12 25873.00 2.110738 0.0372

lnPOLSTAB_S 2690.173 58510.96 0.045977 0.9634

COMLANG 3.063410 23.98653 2.773542 0.0436

R-squared 0.467632 Mean dependent var 58701.11

Adjusted R-squared 0.399781 S.D. dependent var 153577.5

S.E. of regression 118982.4 Akaike info criterion 26.32410

Sum squared resid 1.44E+12 Schwarz criterion 26.65643

Log likelihood -1512.798 Hannan-Quinn criter. 26.45901

F-statistic 6.892049 Durbin-Watson stat 0.965299

Prob(F-statistic) 0.000000

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 Model with RTA Dummies for Trade Creation AND Trade Diversion

Variables Model with RTA Dummies for Trade Creation AND Trade Diversion

GDP_S 0.93 (4.59)*** ECOWAS_S 0.92 (1.55)

lnGDP_R 0.47 (3.83)*** ECOWAS_R -0.07(1.37) LnDist -0.25 (-3.01)*** ECCAS_S_R -0.25(-0.72) lnPop_S 0.51 (2.49)*** ECCAS_S 0.41(1.45) lnPop_R 0.47(1.82)* ECCAS_R -0.01(-0.82) lnarea_s 0.72(5.95)*** EAC_S_R -0.08(0.93) lnarea_r 0.27(3.55)*** EAC_S 1.35(1.01)

landlocked_S -0.13(-1.46) EAC_R -0.29(-1.16)

landlocked_R -0.32(-1.39) AMU_S_R 0.23(1.74)*

lnOPen_S 0.69(2.17)** AMU_S 0.56(1.41)

lnOPen_R 0.13(1.72)* AMU_R -0.83(1.07)

Com.lang 0.39(2.57)*** SADC_S_R 0.69(2.07)**

polstab_S 1.29(1.85)* SADC_S 0.78(1.31)

polstab_R 0.25 (0.80) SADC_R 0.18(1.25) lnEXR_S 1.62 (1.89)* Adjusted

R-Squared 0.73

lnEXR_R -0.51(-1.28) F-Stat 14701.20***

ECOWAS_S_R 0.57 (1.47) No of Observations 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.

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