




































 
 

 

40 
© 2018 by the authors; licensee Asian Online Journal Publishing Group 
 

Economy 
Vol. 5, No. 1, 40-53, 2018 

ISSN(E) 2313-8181/ ISSN(P) 2518-0118 
DOI: 10.20448/journal.502.2018.51.40.53 

© 2018 by the authors; licensee Asian Online Journal Publishing Group 

      

 
 
 
Determinants of Africa - BRIC Countries Bilateral Trade Flows 

 
Cosmas S. Mbogela1    

 

 

 
1Mzumbe University Business School, Box 6 Mzumbe, Tanzania 

 

 
Abstract 

This paper uses gravity models to take advantage of the possibility to explicitly test the changes 
in trade patterns over time and examine how these changes differ across different regions in 
question. Using gravity models, the paper examines the determinants of trade flows of African 
countries with the emerging trading partners to Africa, namely Brazil, Russia, India and China 
(the BRIC). These countries are part of the five largest emerging economies that accounts for 
about 20 per cent of the world output and 27 per cent of the global trade flows. The study models 
some new variables in gravity models such as credit to private sectors, arable land as well as 
mobile cellular subscriptions.  The paper highlights some important truths, African countries 
where most of this bilateral trade with BRIC is concentrated includes those countries which are 
rich in natural resources, and in most cases they are the same with higher GDP per capita among 
the African countries. The coefficients variable arable land takes a positive sign with high 
statistical significance for the BRIC - Africa trade flow; and it indicate that the size arable land 
tend to statistically explain 65 per cent of the variations on exports for the bilateral trade flows.  
The coefficients for the mobile cellular variable indicate a positive effect on the trade from BRIC 
to Africa. Mobile phones usage has a great potential to enhance the bilateral trade volumes of the 
African countries as well considering the limited infrastructural setup in the continent. 

 
Keywords: Gravity model, Bilateral trade, Trade flows, Panel data, Fixed effects model, Random effect model, BRIC countries 

JEL Classification: F1, F3, F4, F6. 

 
Citation | Cosmas S. Mbogela (2018). Determinants of Africa - 
BRIC Countries Bilateral Trade Flows. Economy, 5(1): 40-53. 
History:  
Received: 18 September 2018 
Revised: 22 October 2018 
Accepted: 28 November 2018 
Published: 24 December 2018 
Licensed: This work is licensed under a Creative Commons 

Attribution 3.0 License  
Publisher:  Asian Online Journal Publishing Group 
 

Funding: This study received no specific financial support. 
Competing Interests: The author declares that there are no conflicts of 
interests regarding the publication of this paper. 
Transparency: The author confirms that the manuscript is an honest, 
accurate, and transparent account of the study was reported; that no vital 
features of the study have been omitted; and that any discrepancies from the 
study as planned have been explained. 
Ethical: This study follows all ethical practices during writing.   

 

 

Contents 
1. Introduction ...................................................................................................................................................................................... 41 
2. Trends in African Trade and Gravity Equation Variables ...................................................................................................... 41 
3. African Trade: Exports and Imports Composition ................................................................................................................... 42 
4. Model Specification: The Gravity Model ................................................................................................................................... 44 
5. Description to Variables, Sample and Data Sources ................................................................................................................ 45 
6. Dealing with Zero-Valued and Missing Trade Flows .............................................................................................................. 45 
7. Empirical Model ............................................................................................................................................................................... 47 
8. Estimation Techniques ................................................................................................................................................................... 48 
9. Gravity Model Estimation Results............................................................................................................................................... 49 
10. Conclusion ....................................................................................................................................................................................... 51 
References .............................................................................................................................................................................................. 52 
 

 
 
 
 
 
 

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1. Introduction 
Studies on the analysis of bilateral trade flows between a pair of countries have considered either developing 

countries alone or a pair of developing and developed country (Zannou, 2010; Baldwin and Taglioni, 2011; De 
Castro, 2012). For most of the studies developing countries have been considered as a dummy variable with an 
argument that, including developing and developed countries in the same sample for regression analysis results 
into biased results hence a heterogeneity problem (Fontagné and Freudenberg, 2002). It is also argued that the 
economic difference between these economies being so huge make income likely to be a substitute for differences in 
degree of development instead of the ability of income growth to stimulate trade (Tansey and Touray, 2010). 
Nevertheless, the literature also asserts that differences in economic size among countries will encourage more 
trade (Islam et al., 2014) this contention is worthy examining by having a sample that contain economies that 
differs in size. 

This paper examines the determinants of trade flows of African countries with the emerging trading partners 
to Africa, namely Brazil, Russia, India and China (the BRIC ). These countries are part of the five largest emerging 
economies (the other being South Africa) that accounts for about 20 per cent of the world output and 27 per cent of 
the global trade flows (De Grauwe et al., 2012). Their shares in the world merchandise exports in 2011 were 11 
percent (China), 3 per cent (Russia), 2 per cent (India) and 1 per cent (Brazil).  

While for some decades now the OECD accounted for the largest share of African trade, as it can be seen from 
the table above the trend is now changing, the share of non-OECD countries in Africa’s trade has increased from 
26.4 per cent in 2000 to 39.4 per cent in 2009.  China (taken as a single country) may soon take a lead considering 
the rate of growth with which it trades with African countries. For instance, China’s share in the Africa’s trade has 
risen from less than 1 per cent (1980’s) to 13.5 per cent (Africa’s imports) and 11 per cent (Africa’s exports) in 
2009; besides China accounts for more than any individual European country in Africa’s trade (De Grauwe et al., 
2012).  In 2011, as Africa’s largest trading partner, China’s trade deals totalled $160 billion.  

 
Table-1. African trade by trading partner 

        (Figures as a percentage of total African merchandise trade) 

 1992 2000 2005 2009 

OECD 81.8 73.6 68.5 60.6 
Intra Africa 3.4 9.8 9.5 9.2 
Brazil 1.0 1.6 2.3 2.5 
China and Hong Kong 1.7 4.2 7.6 13.5 
India 1.3 2.1 2.1 4.9 
Russia … 0.5 0.7 1.0 

Total 89.2 91.8 90.7 91.7 

Non OECD Total 18.2 26.4 31.5 39.4 
           Source: OECD Report, 2011 

 
Though bilateral trade has been widely examined, no study has done a research concerning African countries 

trade flows with the largest emerging economies where this study takes on and considers four of the BRICS 
economies (see Table 1). Some new variables are also included such as credit to private sectors operation in the 
economy, arable land as a percentage of total land mass as well as mobile cellular subscriptions. Furthermore, the 
study is different from many other gravity model studies in terms of the empirical analysis that is applied. An 
advantage of using gravity model in a panel dataset is that it is possible to explicitly test the changes in trade 
patterns over time and examine how these changes differ across different regions in question. This is more 
thoroughly done than just clumsily examining trade patterns changes in a particular one year as can be expected in 
cross section studies. 

The examination is conducted by use of the renowned gravity model technique, which has proved to be 
powerful in explaining different scenarios in international trade issues, particularly bilateral trade. It employs an 
instrumental Variable (IV) estimation technique developed by Hausman and Taylor (1981) in order to take care of 
the endogeneity and biasness resulting from omission of variables.  

The rest of the paper will proceed as follows; following will be the review of salient literature on the dynamics 
of bilateral trade in the African countries, thereafter the general model is specified together with a description of 
data for the study. Section three provides the estimation technique, followed by empirical results and discussion. 
Finally, a conclusion and policy implications is presented. 
 

2. Trends in African Trade and Gravity Equation Variables 
According to World Bank (2013) from 1960–2011 Africa’s exports grew at a mean rate of 2.6 percent per 

annum. The mean annual growth rate of imports was faster than that of the exports, at 4.1 percent. While for Sub-
Saharan Africa alone the export mean growth rate was almost similar to imports rate that is, 1.8 for exports and 
2.1 for imports.  Likewise, the total trade as a percentage of GDP for Africa as a whole had a mean value of 55.7 
percent with a mean growth rate of 0.24 per cent per annum.  It is therefore obvious that for over the past five 
decades exports grew at a lower rate than the imports did. Besides, the growth of total trade as a percentage of 
GDP has been very small (less than 0.5 p.a) over the past 52 years. Contrary to the BRIC countries which for the 
same period experienced the growth of their exports at a mean rate of 9.05 per cent while imports grew at a rate of 
5.7 per cent. As for the OECD countries, exports grew at a mean rate of 7.4 per cent, while imports at a mean rate 
of 5.6 per cent. 

Taking Sub Sahara African alone (excluding South Africa and Nigeria), the data shows an even a worse picture. 
For the same period (from1960 to 2011), the exports grew at a mean annual rate of 0.1 percent, while imports at 
2.2 per cent. The mean value for the total trade as a ratio of GDP for the whole period was 55.9 per cent and had 
been declining at a mean rate of 0.01 annually. The volume of trade as a ratio of GDP has been declining despite 
the fact that Africa’s GDP rate of annual growth has been 3.8 per cent on average for the whole period from 1960 
to 2011 (WB, 2013). This confirms the claim by Coe and Hoffmaister (1999) that despite the fact that Africa’s GDP 



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has been growing more slowly than other regions since 1970’s, it has risen more rapidly than trade. Compared to 
Asia and Latin America countries during the same period; to these regions trade has increased more rapidly than 
their respective GDP. Thus what is observed in Africa is contrary to the predictions of the gravity model, which 
predicts that the trade volumes between countries increase as their respective GDP increases. 

Specifically, the standard gravity model predicts the trade intensity between countries by using their respective 
economic size and the distance between them. However, Coulibaly and Fontagné (2006) argue that the existence of 
untapped trade potentials in the Sub Sahara African countries trade proves that the limited intra-sub Saharan 
African trade is not justified by the economic size of the exporting and the importing economies. On the contrary, 
they argue that geography, the fact that Africa has a large percentage of landlocked countries coupled with poverty 
which creates trading costs. Generally landlocked countries are disadvantaged in terms of trade, however 
comparatively African countries that are landlocked are more disadvantaged than the developed landlocked 
countries. This is because they trade less than their counterparts.  On average, the export ratio for SSA land locked 
countries is less than 30 percent while the developed countries’ landlocked country is 50 percent. However, the 
total trade as a ratio of GDP data reveals that for the period from 1990 to 2012 average trade ratio mean value for 
the non-landlocked African countries is 71.68 per cent while for landlocked African countries was 65.81 per cent 
while that of developed countries was 139. 91percent (WB, 2013).  
 

Table-2. Mean values of trade volume as a ratio of GDP for the period from 1990 to 2012 

Developing landlocked countries - African Countries Developed landlocked Countries - OECD 

 %  %  % 

Botswana 89.9 Mali 59.2 Czech Republic 113.8 

Burkina Faso 34.7 Niger 44.3 Hungary 115.9 
Burundi 33.8 Rwanda 35.2 Luxemburg 256.2 
Central Africa 
Republic 

36.2 Swaziland 143.8 Slovak Republic 132.6 

Chad 65.5 Uganda 36.9 Switzerland 80.88 
Ethiopia 32.6 Zambia 69.3   
Lesotho 166.3 Zimbabwe 75.9   
Malawi 63.6     
Average mean for all landlocked countries 65.81 139.91 
Average mean for non-landlocked countries 71.68 73.27 

  Source: Author’s calculations on the WB (2013) data. 

 
The table above provides a more detailed mean values; Swaziland and Lesotho are outliers probably because 

they are countries surrounded by South Africa which has been the strongest economy in Africa for so long. before 
Nigeria took the top position (BBC News Business, 2014). It can therefore be argued that the geographical and 
infrastructural characteristics poses a sizable obstructions on bilateral trade in the SSA countries (Coulibaly and 
Fontagné, 2006) a 10 percent increase in the paved roads that joins a pair of two trading countries is believed to 
induce a 17 to 30 per cent in trade between the two countries. This only means that transport costs are higher for 
African land locked countries than it is with the developed land locked countries. 

A more related to geography is the distance variable; the distance variable is one of the trade impeding features 
in the gravity model studies. The standard gravity model predicts that bilateral trade between any two countries is 
negatively related to the distance between the two trading countries. While it has been argued that the distance 
variable works in favour of economies that are in the same continent or region (Tansey and Touray, 2010) there 
has also been a considerable discussions about the death of distance due to technological advancement (Kolko, 
2000; Cairncross, 2001; Capling and Nossal, 2001).  

There are arguments that the global increase in trade can be attributable to a decrease in the distance between 
countries which is reflected by a fall in transportation costs in terms of ocean freight rates, air freight rates and 
overland transport costs. There are studies that have made an attempt to link trade growths to changes in 
transport costs (as measured by IMF c.i.f. /f.o.b. ratios) among other factors (Rose, 1991; Krugman, 1995; Baier 
and Bergstrand, 1997). The technological advancement in communications, the post second world war (WW II)  
development of jet aircraft engines and the use of containerisation in the ocean shipping has led to lower shipping 
costs, as well as other transportation costs that has been linked to an increase in international trade (Hummels, 
2007). 
 

3. African Trade: Exports and Imports Composition 
Merchandise exports in Africa are dominated by primary commodities (mainly minerals and fuels products) and 

these are mainly exported outside the continent. Nevertheless, there are good stories in the intra-African trade, the 
fact that the goods that dominate the intra African trade are manufacturing goods. Data reveals that the share of 
manufactured goods in intra-African trade is higher than its share of manufacturing goods in the African trade 
with countries outside the continent. This is true for the past two decades irrespective of the fact that this sector is 
not yet fully exploited relative to other sectors in the continent (UNCTAD, 2013). The main manufactured goods 
being traded between partner countries includes cotton fabrics, machinery parts, gold in semi manufactured forms, 
plywood, aluminium alloy plate, tea in packages, portland cements, cigarettes containing tobacco, medicaments, 
vegetables fresh and chilled, cashew nuts fresh and dried etc. 

The Figure 1, below compares the volume of trade by sectors and it reveals that the intra African trade is 
mainly dominated by manufacturing sector for the whole of the two decades. Trade in minerals fuels, lubricants 
and related materials is second to manufacturing sector, leaving behind the agricultural sector (i.e. food and live 
animals, animal and vegetable oils, fats and waxes), which is the backbone for many of the African countries’ 
population income wise as well as for provision of employment. 
 



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Figure-1. Intra African total trade (merchandise and service) composition   (1995-2012) 
Source: Author’s manipulation on the UNCTAD (2013) data 
 

A different picture is seen on the part of African trade with the rest of the world (Figure 2). A large percentage 
of external exports and especially from year 2000 has been mainly in mineral fuels, lubricants and related materials. 
A large part of Africa’s natural resources feeds Asian and European countries’ industries. Primary commodities 
(excluding fuels) are second to minerals and fuel sector, and provides a hint on the same idea of African external 
trade being dominated by primary commodities. 

However, the intra-regional trade ratio in Africa is remarkably low compared to industrialised countries 
although it doubled from 6 per cent (in 1990) to 12 per cent (in 2011). Reasons could be because most countries in 
the African continent have their production and export structures such that the focus is on primary commodities 
like fuel, minerals and agricultural products. And since most countries have similar structures (Shinyekwa and 
Lawrence, 2013) they cannot satisfy their mutual import needs therefore leaving them with an option of satisfying 
the external market.  
 

 
Figure-2. African trade (merchandise and service) to the rest of the world (1995-2012) 

Source: Author’s manipulation on the UNCTAD (2013) data 
 

Consequently, the direction of the African’s trade with the rest of the world follows the same traditional links 
(chiefly to Europe). Therefore, the reason why the intra-regional trade ratios remain persistently low is that more 
than 80 per cent of the continents exports are destined for external markets. Likewise for imports; large part of 
imports comes from markets outside the continent (UNCTAD, 2013). Besides, as pointed out earlier, inadequate 
and unreliable infrastructure cannot be neglected as another major reason for lower intra-regional trade.  

Over the past decade, the share of Africa’s traditional export markets has been maintained whereas the 
continent’s share of exports of emerging economies (BRICS) import markets has increased significantly. After the 
recent financial crisis, the growth in exports to the BRICS is to a large extent explained by the rising in commodity 
prices. In 2009, the value of African exports fell by 31 per cent and grew by 25 per cent in 2010, and the volume of 
imports fell by 11 per cent and consequently mended by 9 per cent in the respective years. Which means price 
effects described virtually two thirds of the growth in trade values (UNCTAD, 2013). 
 



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Figure-3. Africa Merchandise trade with the BRIC countries (product groups, exports in millions of dollars, annual, for 2012) 

Source:  Author’s manipulation on the UNCTAD (2013) data 

 
This trend of Africa diversifying its exports market towards emerging partners has seen China’s export and 

import figures gradually progressing from one of the least among the top ten trading partners to Africa to the 
largest ones over the last decade, becoming the second after USA in 2010. In 2010 alone, 12.5 per cent and 4 
percent of Africa exports went to China and India respectively, which represented 5 per cent and 8 per cent of these 
countries’ imports respectively.  As depicted in figure 15 and 16 above, African exports to these emerging countries 
is characterised by concentration of minerals and fuels. 

There is much concentration trend of the products dominating the exports from Africa to each of the BRIC 
countries (UNCTAD, 2013). It can be argued that the driving force behind BRIC –African trade boom is natural 
resources available in the African countries. This is because exports to Brazil, China and India represent a 
significant part of mineral oils being exported from African countries to the BRIC. For this matter therefore there 
is a concentration of this booming trade to certain African countries with major sources of natural resources. 
According to World Bank data, the top in the list of trading partners includes Angola, Sudan, Nigeria, Republic of 
Congo, Libya and Algeria. All these countries have mineral fuels as the main product for their exports (Drummond 
and Liu, 2013). For instance, considering China alone, its  engagement with Africa portrays an increasing trend 
with African share in the Chinese mineral fuels import market increasing from less than 5 per cent in 1995 to 25 
per cent in 2011 (UNCTAD, 2013). The other countries in the list include South Africa, Benin, Morocco and 
Egypt. The latter have a diversified economy so they export agricultural products and manufactured products. 
Russia is a leading importer of these products among the BRIC, followed by China and India.  

 

4. Model Specification: The Gravity Model 
This paper uses gravity model to estimate the determinants of bilateral trade flows of Africa. The application of 

the model to analyse international trade flows traces back to Tinbergen (1962). Since then the gravity model has 
gained great interest of the researchers due to its powerfulness in explaining different scenarios in international 
trade issues; such as testing the effect of a common currency, or membership in regional integrations agreements 
on bilateral trade (Guttmann and Richards, 2006). It is a model that gives clear and robust economic empirical 
findings on international trade issues (Haveman and Hummels, 2004). In the context of international trade flows, 
the theory asserts that the volume of trade flows between two trading partners is defined by the supply conditions 
at the country of origin, demand conditions at the country of destination and stimulating or restraining factors that 
are related to the trade flows between the two trading partners (Serlenga and Shin, 2004).  

According to Baier and Bergstrand (1997) and Zannou (2010) in the earlier days of its usage, the gravity model 
was lacking a formal theoretical foundation, only to be provided by the empirical investigations by Anderson 
(1979); Krugman (1979); Krugman and Elhanan (1985); Deardorff (2011); Evenett and Keller (2002); Feenstra et al. 
(2001). They represent the gravity model to be a reduced form that is derived theoretically from a general 
equilibrium model of international trade in final goods. Two countries GDPs are taken to be the production and 
absorption capacities of the two exporting and importing countries respectively; whereas the geographical distance 
represents the transportation costs, more distance meaning greater costs (Baier and Bergstrand, 2001).  

The starting point for any specification of the gravity model must be a consideration of the flow of goods (Xij) 
between two countries i and j; whereas, the flow of goods between the two, would depend on the characteristics of 
the country of origin (Ai) and those of the destination country (Bj) as well as the measure of resistances and 
motivational factors to bilateral trade that exists between the two countries (Rij).  Hence, the multiplicative form of 
the gravity equation;  

Xij= G*Ai*Bj*Rij         (i) 
To put it in the typical terms of the gravity model tradition, Xij represent the monetary value of exports from 

country i to j; the G denotes some variables that do not depend on either of the two countries, also known as 
gravitational constant (e.g. globalisation level). Ai stands for factors that are specific to an exporting country (e.g. 
exporter’s GDP); Bj comprises of all the importer specific factors that make up the total importer’s demand (e.g. 
importer’s GDP).Rij signifies the ease with which the exporter country can access the importer’s market j, in some 
other studies (Drysdale and Garnaut, 1982) Rij  stands for resistances to trade between i and j. The Rij is more 
defined in Deardorff (2011) who presents it as a measure of distance between the two countries, the combined effect 
of the two factors (size and distance) is known as the gravity term and it is normally expressed as the product of 
the output of the two trading partners divided by the distance between them (Musila and Sigué, 2010) the result of 
which is the model below: 

Tij=G*[(Yit*Yjt)/Dij]             (ii) 



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Where; Tij represents the value of exports from country i to country j; Yi and Yj are their respective national 
incomes; and Dij represents a measure of distance between them; and G is a gravitational constant.  The national 
incomes shows the economic size of the exporting country and hence determines the quantity of goods that it can 
produce and export, while the economic size of the importing country determines the capacity of its market to 
purchase the imported goods. On the other hand, the distance variable represents the transportation costs that will 
determine the volume of goods that will be traded. The distance variable is considered as a resistance/motivating 
factor as it can either promote or hinder trade flows between countries (Sichei et al., 2011). This reflects that 
transport costs in international trade flows increases with distance. 

Gradually, new explanatory variables were added to the model in order to capture more country specific 
characteristics. The literature reckons the augmentation of such variables as population (Linnemann, 1966a) 
income per capita and contiguity (Sanso et al., 1993; Frankel et al., 1995; Eichengreen and Irwin, 1998; Frankel and 
Wei, 1998). Moreover, variables that captures geographic features, economic development and policy institutions, 
were included in the model as explanatory variables  resulting in an augmented gravity model which is in use in 
most of the most current literature like that of Guttmann and Richards (2006); Zannou (2010) and Vicard (2011). 
The augmented gravity model came to be presented as; 

Tijt= β0 Yit
β1 Yjt

β2 Pit
β3 Pjt

β4 Dij
β5 Mij

β6 ηijt      (iii) 

Where; β0 is the constant of proportionality; Yit (Yjt) is the GDP of the country i and (j); Pit(Pjt) are populations 
of country i and (j); Dij represents a measure of distance between the two countries; Mij represents any dummy 

variables that can be included in the model; ηijt is the error term and βs are the parameters of the model.  
From the original standard gravity model in a multiplicative form, the standard procedure for estimating the 

model is by making the application of natural logarithms of all variables so as to obtain a log linear equation that 
can easily be estimated by the Ordinary Least Squares (OLS) regressions as well as other estimation methods 
(Tripathi and Leitão, 2013). In estimating a gravity model, the inclusion of all the surveyed variables can be done; 
but the issue is whether all countries and most especially the less developed countries have their data included in 
the dataset of the samples previously used (Baltagi et al., 2003). Hence, though the number of variables may vary 
depending on the nature of estimations required, the log linear form of the model can be presented follows;  

logTijt= β0 +β1logYit+ β2log Yjt+ β3logPit+ β4logPjt+ β5logDij +  β6Mij + ηijt (iv) 
The classical gravity models were basically used in a cross section studies so as to estimate trade effects or 

trade relationships for a particular time period (Baltagi et al., 2003). One of the first studies that applied gravity 
model in the panel data studies to account for country pair effects instead of exporter and importer effects was the 
study by Hummels and Levinsohn (1995). And it is now the most adopted approach by majority of the current 
studies on the determinants of trade volumes. The advantage for this is that the fixed country-pair effects controls 
for the impact of any time-invariant factors such as bilateral distance, common language, historical relations, 
membership to regional trading groups and contiguity.  

Besides, the use of country–pair effects removes any possibility for biasness resulting from error of omission 
due to the omission of any such variable (Baltagi et al., 2003). The Panel data approach allows for more variation in 
the data and hence assuring more efficiency in data handling, and reduction in the degree of multicollinearity in the 
variables (Baltagi and Kao, 2000). This paper use panel data for the sample of countries that are examined.  
 

5. Description to Variables, Sample and Data Sources 
The data for this paper were obtained from different databases and compiled to fit the analysis as indicated in 

the table below. The main databases included the World Bank development indicators, Centre d’Etudes 
Prospectivesetd’Informations Internationales (CEPII) gravity dataset, WTO database and the IMF direction of 
trade statistics database. The sample period is 32 years (from 1980 to 2012 all inclusive). The reason for this 
sample period is because most of African countries have at least complete dataset from 1980’s, so to avoid a large 
number of gaps in the dataset 1980 to 2012 was appropriate. The paper considers a bilateral trade flows between 
African countries and the BRICs countries (41 African countries and 4 BRIC countries). 

 
6. Dealing with Zero-Valued and Missing Trade Flows 

The study uses logarithmic transformation in most of the variables so as to enable for the estimation of the log 
linear equation (Coe and Hoffmaister, 1999). However the data that is used is a bilateral trade flow data in most 
cases has either missing values or many zero trade flow observations. And for the case of African trade flows some 
observations are even missing. The zero data values normally would imply absence of any trade at all (Bikker, 
1987) however this would be the case for a carefully prepared datasets. Otherwise, they could be caused by non-
reporting of the trade flows between pair of countries. It could also reflect errors or omission during the 
preparation of the datasets (Martin and Pham, 2015).  

Even if there are zero values still the study would want to examine the trade flows because if two countries 
have zero flows it imply that they are small or they are distant countries or both, thus the gravity model 
predictions would be either very low bilateral flows or non-existent (Frankel et al., 1997; Coe and Hoffmaister, 
1999). The logarithmic transformation cannot be possible with zero observations because the log of zero is 
undefined (or minus infinity). Thus doing such transformation prior to dealing with the available zero observations 
would result into biased and inconsistent estimation results.  Likewise if the zero flows are disregarded by omitting 
them in the dataset, the information to explain why there is very low trade cannot be obtained. Same kind of 
problems occurs in case there are missing values in the dataset, hence these calls for a solution. 

 
 
 
 
 
 



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Table-3.  Description of variables, data sources and expected relationship 

Variable Description Source Expected sign 

Exports1 Measures the total exports from one 
trading partner to another in the 
country pair. The exports variable 
accounts for both flows from country A 
to B as well as country B to A. 

IMF Direction 
of Trade 
Statistics  

This is the dependent variable. 

 
GDP per 
capita 

Measures of the level of economic 
development of an economy. The GDP 
per capita data are in constant 2005 US 
dollars. 

WB (2013) The sign for coefficients is expected to 
be positive as in Zannou (2010) 

Geographical 
Distance 

Measure of the geographical distance 
between the capital cities of pair of 
countries. 

CEPII 
gravitydataset 

The level of trade between a pair of 
countries is a negative function of the 
distance between trading pair 
countries (Rose and Van Wincoop, 
2001) 

Population 
size 

Measure of the total population of a 
country 

WB (2013) Studies finds a different relationship 
depending on if a country is an 
importing and exporting country 
(Kimino et al., 2007; Zannou, 2010).  
The expected sign is positive. 

Landlocked Measures the number of countries 
without access to the sea/ocean in each 
country pair (i.e. 0 if not one country in a 
pair is landlocked, 1 if one country in a 
pair is landlocked or 2 if both are) 

CEPII gravity 
dataset 

The expected sign is negative. 

Common 
language 
official 

Measure of whether countries in a pair 
share official common language. The 
variable takes the value of 1 for countries 
using common language and 0 for 
countries that do not.   

CEPII gravity 
dataset 

Similarity in countries encourages 
bilateral trade and therefore similarity 
in language has a positive effect on 
trade (Balassa, 1966; Frankel and 
Rose, 2002) 

Membership to 
WTO 

A measure of whether countries in a pair 
are member to WTO (i.e. 0 if not one 
country in a pair is a member, 1 if one 
country in a pair is a member or 2 if both 
are). 

WTO database, 
2013 

It is expected that the coefficients will 
be positive. 

Mobile 
cellular 
subscription:   

Measures the subscriptions to public 
mobile telephone services using cellular 
technology that provides access to the 
public switched telephone network. It 
represents a percentage of the total 
subscriptions to public telephone 
network. 

WB (2013) Mobile cellular subscription is 
considered as a factor that positively 
influences the trade flows between 
African countries and outside the 
continent, hence positive coefficients. 
This is based on the fact that not only 
it simplifies communication, but also 
enables the users to use mobile 
banking facility (for settling 
transaction bills).thus reducing 
transaction costs in bilateral trade. 

Arable land as 
a percentage of 
total land 

A measure of the total size of arable land 
as a proportion of the total land in each 
country in a pair. 

WB (2013) Arable land is more closely related to 
a country’s productive capacity than 
the total land (Baxter and Kouparitsas, 
2006). The expected sign is positive 
coefficients reflecting a positive impact 
to the bilateral trade flows. 

Domestic 
credit to 
private sector 
 

Measures all the financial resources 
provided to the private sector, such as 
through loans, purchases of non-equity 
securities, and trade credits and other 
accounts receivable, that establish a 
claim for repayment  (WB, 2013). 

WB (2013) The role played by private sector is 
paramount in the trade flows between 
economies. The expected sign for the 
coefficients of this variable is positive. 

Bilateral 
exchange rate 

Refers to the exchange rate determined 
by national authorities from which a 
bilateral exchange rate (cross rate) is 
computed by the researcher. 

WB (2013) Generally changes in exchange rate 
index has a significant negative impact 
on the volume of exports because for 
risk averse market participants, 
exchange rate uncertainty causes them 
to reduce their activities, change 
prices, or shift sources of demand and 
supply in order to minimize their 
exposure to the effects of exchange 
rate volatility (Chowdhury, 1993; De 
Vita and Abbott, 2004). 

     Source: Compilation by the author (2015) 
 

 

                                                           
1It is often the case that data on exports between pair of countries have inconsistencies between exports to a partner and the partner's recorded imports from a 
particular country, i.e. the exports from Country A to B do not always equal the imports of Country B from A. This is due to the different ways countries 
report their trade, i.e. differences in classification concepts and detail, time of recording, valuation, and coverage, as well as processing errors. 



Economy, 2018, 5(1): 40-53 

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There are many solutions to this problem as suggested by the literature (confer, e.g. Linders and De Groot 
(2006); Foroutan and Pritchett (1993)).  The most resorted solution is to ensure that the sample is selected such 
that is does not contain observations with zero or missing values. However sometimes this is difficult especially 
when the aspiration is to have as broader sample size as possible. This approach has been adopted by such authors 
as Frankel et al. (1997) and Bikker (1987). Another solution/approach would be to arbitrarily substitute small 
number values for all observations with zero and missing values. This would enable the logarithmic transformation 
process be correctly done. Example of literature following this includes (Linnemann, 1966b; Wang and Winters, 
1992; Raballand, 2003). This approach has also been challenged for being an ad hoc process and does not guarantee 
the underlying expected value. 

This study has adopted approaches one and two together so that they can complement each other, first the 
sample selection is based on the countries with less gaps, but the sample period also took into consideration the 
period when most of the countries, especially African countries has number values in their observations leaving 
aside those years with missing values. In this way there were very few gaps which were taken care of by the second 
approach. 

Otherwise some other literature resorted to some other approaches such as the use of the original 
multiplicative gravity equation, hence nonlinear estimation technique (see for example Coe and Hoffmaister 
(1999)). In this way, there was no need for log transformation process where observation with zero values would be 
problematic. Some other studies adopting linear estimation techniques have used Tobit estimation technique 
(Linders and De Groot, 2006). 
 

7. Empirical Model 
An examination of bilateral trade between African countries and the BRICs formed two ways trade flows, 

which makes two sets of estimation results. To be consistent with what is being measured; same model with same 
set of variables is used so as to obtain coherent test results. Below two models are presented, one is for the 
variables at level, and the second includes product variables (i.e. a product of variable data for importing country 
and exporting country). The study also includes a dummy variable for time fixed effects, thus a two way fixed 
effects (observing variations of time and panels). 

Estimation model using variables at level: Exports, population, GDP per capita, bilateral exchange rate, 
domestic credit to private sector and distance variables are in natural logarithm. The rest of the variables are either 
presented as ratios or they are dummies, hence there was no necessity of converting them into natural logs. Mobile 
cellular subscriptions represents a percentage of the total subscriptions to public mobile telephone network 
services, arable land is a percentage of total land mass of a particular country.  

Dummy variables include the rest of the variables, i.e. whether they are members of the World Trade 
Organisation or not (0 if none of the pair countries is, 1 if one country in a pair is a member, 2 if both are members), 
landlocked measures whether countries belong to a landlocked countries (0 if none is, 1 if one in the pair is, and 2 if 
both are), language indicates whether both countries have common official language or not. The model is presented 
as follows; 

logEXPijt= β0 +β1log(POPNit) + β2log (POPNjt)+ β3log(GDPppit) + β4log (GDPppjt)+β5log(EXCHijt) +  β6log(DISTij)+ 

β7log(CRDPRit)+ β8log(CRDPRjt)+ β9MOBILit + β10MOBILjt + β11ARBLi+ β12ARBLj+ β13WTOij + β14LANDLij 

+β15LANGij +∂D+ηijt                          (v) 
Where; 

β = represents the coefficients of the variables 
logEXPijt= natural logarithm for Exports from country i to j 
logPOPNit= natural logarithm for Population of importing country  
logPOPNjt= natural logarithm for Population of exporting country 
logGDPppit= natural logarithm for GDP per capita of importing country 
logGDPppjt = natural logarithm for GDP per capita of exporting country 
logEXCHijt = natural logarithm for bilateral exchange rate  
logDISTij =Distance between exporting (i) and importing (j) country 
logCRPRit =Credit to private sector for importing country 
logCRPRjt= Credit to private sector for exporting country 
MOBit =Mobile cellular subscriptions for importing country 
MOBjt =Mobile cellular subscription for exporting country 
ARBLi =Arable Land for importing country 
ARBLj =Arable Land for exporting country 
WTOij = WTO membership 
LANDLij =Landlocked (1 if one of the country in a pair is land locked, 2 if both are land locked) 
LANGij=Common official language (1 if both countries use same official language) 
∂D  = is a vector of year dummies for the year 1982 through 2012 (the year dummy for the year 
1981 is dropped) 

ηijt = is the error term 
Estimation model including product variables: This model uses the same variables as above, only that it 

includes some variables representing a product of two variables for importing and exporting country. The aim is to 
examine how simultaneously the two variables can influence the bilateral trade flows between the two parties. The 
variables in a product form include mobile cellular subscription, arable land, and credit to private sectors, 
population and GDP per capita. 

logEXPijt=β0+β1log(POPNijt)+β2log(GDPijt)+β3log(EXCHijt)+β4log(DISTij)+β5log(CRDPRijt)+β6log(MOBILijt)+β7log(AR

BLij)+ β8WTOij+β9LANDLij +β10LANGij+∂D+ηijt        (vi) 
Where; 

logPOPNijt= natural log for a product of population of importing and exporting country 



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logGDPppijt= natural log for a product of GDP per capita of importing and exporting country 
logCRPRijt = natural log for a product of credit to private sector for importing and exporting 
country 
logMOBijt = anatural log for product of mobile cellular subscriptions for importing and exporting 
country 
logARBLij = natural log for a product of credit to arable land private sector for importing and 
exporting country 

 
The rest of the variables are defined as above (model 5). 

Since the panel data analysis is used in this study to examine the determinants of bilateral trade flows, whereas 
the sample contains several countries and a number of periods, there is a need also to test whether time fixed effects 
have a role on bilateral trade flows as well. Therefore a full set of (T-1) time dummies, one for each period but the 
first, is introduced. If these dummies are not included it may lead to omitted variable bias in the results. These 
dummies, are shift variables that take the value of one for all states in the reference years and zero in all the others. 
Moreover, after estimation regressions are obtained, a joint test is conducted to see if these dummies are jointly 
equal to zero or not. This is not included in any of the models above because it is a post estimation process by using 
a ‘test varlist’ command. If the year dummies are zero then time fixed effects have no influence on the bilateral trade 
flows. Thus it will be testing the null hypothesis: all years’ coefficients are jointly equal to zero. Failing to reject the 
null hypothesis will mean that no time fixed effects are needed. 
 

8. Estimation Techniques 
The study uses a panel based approach following the criticisms over using cross section estimation, which is 

misspecification, because it cannot deal with bilateral heterogeneity present in the bilateral trade flows. With panel 
data approach heterogeneity issues are modelled by including country-pair individual effects (Serlenga and Shin, 
2004). The essence of using panel data is to control for these individual specific effects that are possibly 
unobservable but may be correlated with other explanatory variables in the econometric model (Hausman and 
Taylor, 1981). 

There are a number of panel estimation techniques; the traditional and commonly used estimation technique 
for the gravity model studies has been the Ordinary Least Squares (OLS) technique. However the use of OLS has 
been challenged because its implementation assumptions are not in line with the underlying theoretical models. It 
ignores the fact that there can be a correlation between explanatory variables and individual effects which are 
unobservable, hence resulting to coefficient estimates that are severely biased (Serlenga and Shin, 2004). It also 
fails to account for endogeneity and biasness resulting from omission of variables.  The presence of these 
correlations excludes both OLS and GLS from being used as estimation methods in the estimation of parameters of 
this study as may yield biased and inconsistent estimates (Hausman and Taylor, 1981).  

In such a situation, traditionally the option has been to go for an instrumental variable (IV) estimation 
technique. Thus in order to overcome this, the within estimator from analysis of covariance or fixed effects 
estimation technique has been used (Cornwell and Rupert, 1988). The estimator is designed particularly for 
analysing the impact of variables that vary over time, as it assumes that the time invariant variables are unique to 
the individual country hence not correlated with other individual country characteristics. Therefore, under this 
method all the individual effects in the sample are eliminated by transforming the data into deviations from 
individual means. 

As a result of this procedure, the within-groups estimator also suffers from two imperative defects; one, during 
data transformation process, all the time invariant variables are eliminated hence their coefficients are not 
estimated, two, the within group estimator ignores variation across the individuals or countries included in the 
sample hence it is not fully efficient. Comparatively, the first defect seem to be more serious especially when the 
primary interest in the application of the estimator is attached to the unknown coefficient of time invariant 
variables such as the influence of country’s historical past events, membership to regional trading groups or 
countries using the same official language in the gravity modelling. Leaving these variables un-estimated renders 
the study meaningless. 

The time invariant variables are well estimated by the GLS Random effects model (partial pooling model). This 
is because under this technique the assumption is that variations across countries are assumed random and 
uncorrelated with the independent variable used in the model. Therefore while under the fixed effect model , the 
time invariant variables are absorbed by the intercept, this technique include time invariant variables as 
explanatory variables in the model because for this model the error term is assumed to be uncorrelated with 
explanatory variables. Thus for a perfect estimation, inclusion of all individual country characteristics is required 
which is normally not possible hence leading to the omitted variable bias in the specified model. 

In their ground-breaking paper of 1981, Jery Hausman and William Taylor developed an alternative method 
that has been used with panel data to treat the problem of correlation between explanatory variables and the 
concealed individual specific effect. It is an IV estimator with neither of the two defects mentioned above as it 
employs several dimensions of panel data to overcome the correlation without any variables from outside the model 
(Egger, 2002).  The HT makes use of time varying variables in two ways – to estimate their own coefficients as 
well as serving as instruments for endogenous time invariant variables, hence giving room for identification and 
efficient estimation of both time varying and time invariant coefficients.  

It is therefore better than the within groups estimation technique as it is more efficient and it also produces 
coefficient estimates for time invariant variables. The possibility of the existence of  a potential correlation between 
the unobservable individual specific effect and a subset of the exogenous variables cannot be denied (Serlenga and 
Shin, 2004; Rault et al., 2009). Since the presence unobservable individual effects and time invariant variables is 
unquestionable even in the estimation of the bilateral trade flows in the African countries, this paper uses the 
Hausman Taylor estimator. 
Consider the following equation; 



Economy, 2018, 5(1): 40-53 

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Yit =Xitβ +Ziγ +αi +ηit  (i =1…, N; t =1, …, T)           (vii) 

Where, β and γ are k and g vector coefficients associated with time varying (for this case GDPpp, 
POPN,EXCH, CRDPR and MOBIL) and time invariant observable variables (for this case DIST and ARBL) 

respectively. The disturbance ηit is assumed uncorrelated with the columns of (X, Z, α). The individual specific 

effects αi (for  this case LANDL, CONTIG, LANG and WTO) are assumed to be time invariant random variable 
and in this study is potentially correlated with columns of X and Z.   

The HT estimation model does not assume a specification of the unobservable individual specific effects αi and it 
is less sensitive to whether they are known or unknown by the researcher (Hausman and Taylor, 1981). In this way 
handles the risk of falling into biased results due to omission of variables. It rather works under an assumption that 

some variables among X and Z are uncorrelated with individual specific effects αi. And that the Xit which are 

uncorrelated with αi serves two functions because of their variation across both individuals and time; first, using 
deviations from individual means, they produce unbiased estimates of the coefficients, and secondly using the 
individual means, they produce valid instruments for the time invariant variables (Zi) that are correlated with the 

individual specific effects αi (Hausman and Taylor, 1981). Therefore it helps to avoid the difficulty of extracting 
instrument variables external to the specified model by using some variables within the model as instruments. 

However for comparison purposes as well as for checking the robustness of the results, HT estimation 
techniques will be used together with some other panel data estimation techniques including the random effect 
generalized least square regression and the fixed effect (within) estimation techniques. 
 

9. Gravity Model Estimation Results 
The estimation is done for model 5 and 6 and, for each model regressions are conducted twofold; the bilateral 

trade flows from African countries to the BRICS and, from the BRICs to African countries.  Moreover, for each 
part estimation is done in two steps, in the first step variables are considered at level, while in the second step the 
model includes some variables as a product of the values of the two trading partners. Both models are estimated 
using the HT, estimation technique as well other two panel data estimators. Results are presented in Table 4 to 5.  

From the results in Table 4 three major factors that influence the bilateral trade between Africa and the BRIC 
countries include the use of common official language, the GDP per capita and population. The variables are 
statistically significant and economically reasonable. Results in Table 5 reveals that, arable land and bilateral 
exchangerate are also important in explaining the bilateral trade flows between trading partners. And in both 
equations the time dummies are jointly highly significant.  
 

Table-4. Empirical Results, Africa -BRIC bilateral trade flows (variables at level) 

 From  Africa  to the BRIC From the BRIC to Africa 

Modelling technique HT RE FE HT RE FE 

lnPopulationi 0.88 
(0.56) 

1.46*** 
(0.14) 

1.59*** 
(0.63) 

3.59*** 
(0.27) 

2.55*** 
(0.12) 

3.86*** 
(0.38) 

lnPopulationj 3.32*** 
0.83 

2.27*** 
(0.32) 

1.42 
(0.95) 

1.90*** 
(0.21) 

1.19*** 
(0.06) 

1.81*** 
(0.26) 

lnDistanceij -3.89 
(6.19) 

1.04 
(0.78) 

- 4.77** 
(2.21) 

-0.32 
(0.29) 

- 

lnGDP per capitai 1.96*** 
(0.23) 

1.57*** 
(0.17) 

2.07*** 
(0.24) 

0.71*** 
(0.07) 

1.14*** 
(0.05) 

0.73*** 
(0.07) 

lnGDP per capitaj 1.28*** 
(0.19) 

1.39*** 
(0.14) 

1.46*** 
(0.19) 

0.87*** 
(0.07) 

0.76*** 
(0.06) 

0.73*** 
(0.08) 

lnCredit to Private Sectori -0.12 
(0.11) 

-0.41* 
(0.22) 

-0.24 
(0.23) 

-0.28*** 
(0.06) 

0.01 
(0.03) 

0.01 
(0.04) 

lnCredit to Private Sectorj -0.39* 
(0.21) 

-0.02 
(0.11) 

-0.03 
(0.11) 

-0.01 
(0.03) 

-0.29*** 
(0.07) 

-0.19*** 
(0.07) 

lnBilateral Exchange Rateij -0.02 
(0.03) 

-0.02 
(0.03) 

-0.02 
(0.04) 

-0.04*** 
(0.00) 

-0.03*** 
(0.00) 

-0.05*** 
(0.01) 

Mobile Cellulari 0.01* 
(0.00) 

-0.00 
(0.00) 

-0.01 
(0.00) 

0.01*** 
(0.00 

0.01*** 
(0.00) 

0.01*** 
(0.00) 

Mobile Cellularj 0.01 
(0.00) 

0.01*** 
(0.00) 

0.01*** 
(0.00) 

0.01*** 
(0.00 

0.01*** 
(0.00) 

0.01*** 
(0.00) 

Arable land i 0.03 
(0.01) 

0.00 
(0.01) 

- -0.12*** 
(0.01) 

0.01 
(0.01) 

- 

Arable land j -0.25*** 
(0.04) 

0.01 
(0.01) 

- -0.01 
(0.00) 

-0.03*** 
(0.01) 

- 

Landlocked -0.41 
(2.77) 

-0.63 
(0.52) 

- -1.26* 
(0.76) 

-1.57*** 
(0.22) 

- 

Common language official 7.05* 
(3.80) 

0.39 
(0.71) 

- 2.67*** 
(1.10) 

1.33*** 
(0.33) 

- 

WTO membership 0.68 
(2.85) 

-0.03 
(0.55) 

- -0.54 
(0.93) 

-0.19 
(0.26) 

- 

Year dummy      83.46*** 
(0.00) 

85.84*** 
(0.00) 

    3.63*** 
(0.00) 

 41.14*** 
(0.01) 

  46.19*** 
(0.00) 

1.63** 
(0.05) 

R-square - 0.45 0.34 - 0.66 0.46 
No. of observations 2,321 2,321 2,321 3,778 3,778 3,778 
No of country pairs 148 148 148 164 164 164 

Note: The dependent variable for these regression results exports from country i to j.  ***, **, * denotes significance level at 1%, 5% and 10% respectively. 
Standard errors are in parentheses. i denotes exporting country, while   j denotes importing county. Under the Hausman - Taylor modelling technique all 

the time varying variables are used as instruments for endogenous time invariant variables.  
 



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The variable common official language has higher coefficients than the rest of the variables more so in Table 4. 
The coefficients are positive and statistically significant implying that the bilateral trade between African countries 
and the BRIC is explained by use of common official language. However in actual sense there is no African country 
that uses Chinese or any of the Russian language as an official language. But the fact that English is adopted by 
many countries as a medium of business language it has become possible for trade with China as well. 

GDP per capita variables has positive and statistically significant coefficients in most cases at one per cent 
level. Except for the fixed effects estimations on the variables at level in Table 4, the coefficients are not significant 
in both cases. This is applicable for both the exporting and the importing country which indicates that the level of 
economic development tends to positively influence the exports and imports in the bilateral trade between Africa 
and the BRIC countries. As explained earlier in this study, the BRIC-Africa trade is characterised by the exports of 
primary commodities from African countries and importation of food and consumables from BRIC countries, but 
mainly from China. This is a  vivid complementarily, whereas African exports feeds the growing industries in the 
BRICs African countries also imports manufactured goods from these countries, particularly from China. The 
imported manufactured goods are not only for household consumption but also for feeding the growing 
manufacturing sector in the African countries (Broadman, 2006). 

It is also worth noting that African countries where most of this bilateral trade with BRIC is concentrated 
includes those countries which are rich in natural resources, and in most cases they are the same with higher GDP 
per capita among the African countries. Thus, an automatic connection with the role of the level of economic 
development with the bilateral trade. These results are similar to the claim by Markusen that the intra-country 
distribution of income measured by GDP per capita matters for inter-country trade (Markusen, 2013). Besides 
Deardorff (2011) confirms that economies with higher per capita income are expected to have high capital labour 
ratios which results into producing more of capital intensive goods and will tend to trade more because they 
produce more and consume larger proportions of capital-intensive goods. Recent literature concludes that there is 
robust empirical evidence that economies with lower per capita income will tend to have smaller volumes of 
bilateral trade even after controlling for aggregate income (Tarasov, 2012). Likewise he asserts that not only trade 
volume will be lower but also less number of trading partners can be expected for such economies.  

In their seminal paper examining the North – South trade, Coe and Hoffmaister (1999) assert that income has a 
positive impact of bilateral trade such that a 1 percent increase in income of the trading partners, will lead to an 
increase in the bilateral trade between the two groups by 2 per cent. These results are somehow similar to what 
this paper presents in Table 4 as well as Table 5. 
 

Table-5. Empirical Results, Africa-BRIC bilateral trade flows (include product variables) 

 From  Africa  to the BRIC From the BRIC to Africa 

Modelling technique: HT RE FE HT RE FE 

ln (Mobilecellulari xj) -0.01 
(0.02) 

0.02 
(0.01) 

0.03 
(0.02) 

0.05*** 
(0.00) 

0.07*** 
(0.01) 

-0.01 
(0.01) 

ln (Arable landi x j) 0.11 
(0.22) 

0.24* 
(0.14) 

- 0.65*** 
(0.14) 

0.05*** 
(0.01) 

- 

ln(Credit to Privat.i xj) -0.05 
(0.10) 

0.02 
(0.10) 

0.04 
(0.01) 

-0.05 
(0.08) 

0.14 
(0.13) 

0.20 
(0.14) 

lnExchange rate -0.34*** 
(0.07) 

-0.30*** 
(0.07) 

-0.36*** 
(0.07) 

-0.07*** 
(0.01) 

-0.07*** 
(0.02) 

-0.04 
(0.02) 

ln (Populationix j) 0.08*** 
(0.01) 

0.09*** 
(0.01) 

0.11*** 
(0.03) 

0.09*** 
(0.00) 

0.08*** 
(0.00) 

0.18*** 
(0.02) 

ln(GDPppi x j) 0.29*** 
(0.02) 

0.22*** 
(0.02) 

0.31*** 
(0.03) 

0.19*** 
(0.01) 

0.12*** 
(0.01) 

0.19*** 
(0.02) 

lnDistance 11.47*** 
(4.74) 

2.12*** 
(0.69) 

- 1.12 
(2.28) 

-0.42 
(0.39) 

- 

Landlocked -0.82 
(0.90) 

-0.99* 
(0.56) 

- -1.09*** 
(0.45) 

-1.31*** 
(0.29) 

- 

Common Official Language  3.59*** 
(1.36) 

1.63*** 
(0.72) 

- -0.26 
(0.69) 

0.43 
(0.41) 

- 

WTO membership 0.39 
(1.01) 

-0.49 
(0.58) 

- -0.07 
(0.61) 

-0.36 
(0.37) 

- 

Years 95.74*** 
(0.00) 

117.85*** 
(0.00) 

5.43*** 
(0.00) 

36.18*** 
(0.00) 

33.19** 
(0.02) 

1.94*** 
(0.01) 

R-square  0.41 0.23  0.62 0.54 
No. of observations 1,818 1,818 1,818 2,454 1,654 1,654 

No. of country pairs 148 148 148 164 128 128 
Note:  The dependent variable for these regression results exports from country i to j. ***, **, * denotes significance level at 1%, 5% and 10% respectively. 
Standard errors are in parentheses. i denotes exporting country, while   j denotes importing county. Under the Hausman -Taylor modelling technique all the 
time varying variables are used as instruments for endogenous time invariant variables.  
 

Population variable has positive and significant coefficients especially in the bilateral trade from BRIC 
countries to Africa. This imply that an increase in population tend to result into a proportionate increase in trade 
between trading partners. For the trade flow from Africa to BRIC, the coefficient does not support that the increase 
in population leads to an increase in exports but more in imports. However the literature assert that there has been 
an increase in the food exports from Africa to Asian countries particularly China and India, and this has been as a 
result of an increasing populations and income levels in these countries (Broadman, 2006). This fact is confirmed by 
the results in Table 5 when population is used as a product variable, the coefficient turn to be positive and 
significant. 

Moreover, the flow of manufactured goods from BRIC countries to African countries are for the search of 
market, and data shows that Nigeria, which is the most populous country in the continent records higher imports 
from China (WB, 2014). The African exports concentration is also based on the same pattern, for the period 



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between 2000 to 2004 more than 80 percent of value added exports from Africa originated from Nigeria (refined 
petroleum), South Africa (refined petroleum products, pharmaceuticals, electronics, machinery and transportation 
equipment’s) and Swaziland (pharmaceuticals) (Broadman, 2006). 

Bilateral exchange rate has negative coefficients though not significant in all estimation techniques for the 
variables at level. The effects of bilateral exchange rate on bilateral trade arises where there are uncertainties on 
the appreciation and depreciation in value of any of the trading partner’s currency. This exchange rate volatility 
has a tendency of affecting the profitability of foreign exchange trades. The coefficients for the variable are 
negative and significant especially with results in Table 5. The results signify that in times when the bilateral 
exchange rate index drops, the exporter currency depreciates with respect to the trading partner’s currency, which 
improves the exports competitiveness (Rault et al., 2009; Iqbal and Islam, 2014). Generally these results provide 
coefficients that confirm that bilateral exchange rate changes adversely affect the bilateral trade flows between 
trading partners. De Vita and Abbott (2004) confirms the negative effect that exchange rate uncertainty has on 
export volumes, in his study exchange rate uncertainty is found to have negative and significant influence on the 
UK exports to the EU countries. The argument is, despite the fact that with short run fluctuations hedging can be 
used to insure the risk, it becomes more challenging to cover against long-term exchange rate fluctuations.  

These results are also supported by Iqbal and Islam (2014) who asserts that the bilateral real exchange rates 
are inversely related to the bilateral trade flow between Bangladesh and the European Union. Besides, the negative 
coefficients conforms to the results by Chowdhury (1993) whose error-correction results indicate that exchange 
rate volatility has a significant negative impact on the volume of exports in each of the G-7 countries. Moreover, 
Rault et al. (2009) modelling trade flows between CEEC and OECD countries  get the same results implying that 
when exchange rate index slumps the exporter currency depreciates relative to the currency of the importer, hence 
improving export competitiveness.  

The coefficients for the distance variable also do not appear to have consistent sign and significance in all the 
estimation techniques. However, the traditional gravity model studies and many recent empirical studies find that 
the level of trade between a pair of countries is a negative function of the distance between trading pair countries 
(Rose and Van Wincoop, 2001; Tripathi and Leitão, 2013). Hence the inconsistence in these results could possibly 
be insinuating the aspect of the death of distance due to technological advancement in the transportation and 
communication sectors as discussed earlier in this paper. 

A country in a trading pair being a member to WTO or not has also been examined, in the estimation results 
under the Hausman Taylor, coefficients are positive but not significant in both cases. However looking of the rest 
of the estimation techniques, they give mixed results. This could be in line with what is discussed in the literature, 
while Rose (2004) suggest that membership to WTO does not have any positive effects on trade, Subramanian and 
Wei have provided evidence that though little but there is an impact of WTO membership on bilateral trade. They 
find that bilateral trade is greater when both partners had liberalized their trade policy than when only one partner 
did and the other did not. Besides, as it is in theory, the WTO membership impact would depend on what the country 
does with its membership, with whom it negotiates, and which products the negotiation covers. This disagreement 
might explain the mixed regression results in this study (Subramanian and Wei, 2007). 

The variable arable land does not give out the expected results. When the variable is estimated at level it 
almost gives negative coefficients, but the coefficients becomes positive when tested as a product variable. The 
coefficients takes a positive sign with high statistical significance for the trade flow from BRIC to Africa; and it 
indicate that the size arable land tend to statistically explain 65 per cent of the variations on exports for the 
bilateral trade flow from the BRIC to African countries.  The coefficients for the variable land locked are negative 
but not significant in all the estimation techniques, however it implies that the variable has a good explanatory 
power on the bilateral trade between the Africa and the BRIC countries. 

The coefficient for the variable credit to private sector does not indicate the expected results. The coefficients 
are negative and in most cases not significant, hence reflecting that the variable does not explain the bilateral trade 
flows between African countries and the BRIC countries. This could be resulting from the nature of the African 
private sector and enterprises which are characterised with low level of innovation capabilities and competitiveness 
(UNCTAD, 2013). They thus do not have significant contribution to the competitiveness of the products to the 
market external to Africa.  

Looking at the results in Table 4 and 5, the coefficients for the mobile cellular variable seem to indicate that the 
variable affect the trade from BRIC to Africa positively. The coefficients are positive and significant. However the 
magnitude of the effects on trade is so minimal around 0.1 to 0.7 per cent for any 10 percent increase in mobile 
cellular subscriptions. All the same this shows that the mobile phone usages do have an impact in enhancing the 
linkage between trading partners. 
 

10. Conclusion 
The paper examines the determinants for the bilateral trade flows of bilateral trade flow between Africa and 

BRIC using the gravity model on some economic, demographic, cultural and political ties data of the African 
countries and their trading partners. It has included some new variables in the gravity model considering their 
pivotal role particularly in the Africa trading activities.  Considering the role of private sector in the bilateral trade, 
credit to private sector was also included in modelling African bilateral trade flows. Besides the fact that the 
continent account for a significant portion of the world arable land (27%), arable land was also included to consider 
the role of productivity on bilateral trade. In the recent decade, mobile phones has become widely used particularly 
in the African countries, measured by a number of subscriptions for each country, mobile phone usage has also been 
included to examine its role in the augmentation of bilateral trade flows. All of these variables have indicated that 
they account for the bilateral trade in the African countries to a considerable extent, especially credit to private 
sector. Mobile phones usage also has a great potential to enhance the bilateral trade volumes of the African 
countries as well considering the limited infrastructural setup in the continent. But the sustainability and efficiency 
of this to happen will largely depend on the institutional climate and regulatory system in these economies to 
support these initiatives. 



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The paper highlights some important facts that developing land locked countries trade less than their counter 
part developed land locked countries. Efficiency transportation and telecommunication systems in these countries 
makes landlocked-ness and distance to be less of a stumbling block to bilateral trade  than it is for developing 
landlocked countries in Africa. Moreover, it highlights some important implications on the up surging trade links 
of Africa with emerging economies (BRIC). Data shows that the commodity composition of this bilateral trade is 
much concentrated on the primary products, and most especially on minerals and fuel products. Raising doubt on 
whether the growing bilateral trade with these countries is for the interest of either trading partners or it is for 
their benefit in order to feed their growing industrializing economies and leaving Africa a looser. Even looking at 
the main African countries that are leading for exportations in the BRIC countries, it is mainly the countries with 
large deposits of fuel and minerals like Angola, Algeria, South Africa and Nigeria to mention few, that has more 
trade volumes. Policy makers in the African countries should beware of the investment contracts and negotiations 
with these emerging countries so that the continent does not end loosing. 

The bilateral trade flow between African countries and the BRIC countries is determined mainly by the GDP 
per capita, common official language and population. The empirical results show that even the size of arable land 
matters for this trade relationship. Still landlocked and distance poses a sizable negative influence on the volume of 
bilateral trade flow even between the African countries and the BRICs. Furthermore, it is anticipated that future 
research work examines the statistical determinants of African regional blocks and the RECs bilateral trade with 
the BRIC and the OECD.  This is vital considering the regional diversity of Africa as far as trade and economic size 
is concerned.  
 

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