UNDERSTANDING THE DETERMINANTS OF INTERNATIONAL
TRADE IN AFRICAN COUNTRIES: AN EMPIRICAL ANALYSIS
FOR GHANA AND SOUTH AFRICA
LAURA MÁRQUEZ-RAMOS
Universitat Jaume I
Instituto de Economía Internacional
ABSTRACT
There are clear economic differences between developed and developing countries that
lead to a different behaviour among them in the determinants of bilateral trade flows.
Although a number of authors have focused on the determinants of the trade patterns,
further research is needed for a better understanding of what goods and with which
countries developed and developing economies trade. This paper focuses on the
determinants of international trade in African countries. From an empirical perspective,
two African economies, a developed (South Africa) and a developing country (Ghana)
are analysed. Moreover, sector-heterogeneity is considered. Results show that
determinants of trade have a different impact in developed and developing African
countries. Geographical and social factors play a key role on trade relationships in South
Africa. Moreover, technological innovation in importer countries leads to higher exports
from this country. However, Ghana’s exports are higher when they are addressed to
countries with higher levels of economic freedom.
Keywords: International trade, gravity equation, heterogeneity
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JEL classification: F14
1. INTRODUCTION
World trade has experienced an important increase in the last decades. Feenstra (1998)
suggest several factors that explain this growth: Falling transport costs, trade
liberalisation, economic convergence of countries and the increase of intermediate goods
trade. In the same line, Baier and Bergstrand (2001) analyse what factors account for the
growth of trade. Their results show that income growth, tariff rate reductions and lower
transport costs have contributed to the growth of world trade. According to these authors,
income growth explains 67% of the growth of trade, tariff reductions 25% and transport
cost reductions 8%. These authors only use 16 OECD countries in the empirical analysis,
and all of them are high-income countries. However, developed and developing countries
face different economic characteristics and those play a different role in the growth of
international trade.
The main aim of this paper is to investigate the differences existing between two African
economies, a developed (South Africa) and a developing country (Ghana), concerning the
pattern and the direction of international trade flows to deal with three main questions:
what goods, with which countries and how much these countries trade (Deardorff, 1984).
The gravity model of trade is the empirical methodology most commonly used to analyse
international trade flows determinants. When investigating why gravity works so well,
Harrigan (2001) differentiates two types of studies: aggregate and disaggregate. Whereas
this approach works well with aggregated data, it performs worse with disaggregated
data. According to Frankel (2000) it is desirable to disaggregate to get a better estimate
since too much emphasis is not put on individual estimates that may be exposed to
2
estimation error. Additionally, heterogeneity issues should be considered when analysing
international trade patterns since determinants of international trade flows may differ
across both countries and industries. From a gravity context, several authors have
analysed whether there are different trade patterns for developed and developing
countries (Loungani, Mody and Razin, 2002; Martínez-Zarzoso and Márquez-Ramos,
2005). Sector-heterogeneity has also been taken into account in gravity (Rauch, 1999;
Tang, 2006).
In Section 2 special attention is paid to heterogeneity issues in the main determinants of
international trade. Two types of heterogeneity are differentiated: country-heterogeneity
and sector-heterogeneity. In Section 3 data, sources and variables are described. Section 4
presents the model specification to be estimated. In Section 5 an index measuring the
intensity of exports of countries is used in a preliminary analysis to show the main
determinants that foster that Ghana and South Africa trade with specific partners.
Moreover, the empirical estimation is carried out for exports from Ghana and South
Africa to 167 importer countries. At last, Section 6 presents conclusions and it discusses
socio-economic implications of the estimated results.
2. THE GRAVITY MODEL OF BILATERAL TRADE
Aggregated versus disaggregated data
The gravity model has been the empirical workforce to analyse the determinants of
bilateral trade flows. The first authors to apply the gravity model to international trade
flows were Tinbergen (1962) and Pöyhönen (1963). This model, in its basic form,
assumes that trade between countries can be compared to the gravitational force between
two objects: it is directly related to countries’ size and inversely related to the distance
3
between them. Exports from country i to country j are explained by their economic sizes,
their populations, direct geographical distances and a set of dummies incorporating some
characteristics common to specific flows. Theoretical support for the research in this field
was originally very poor but since the second half of the 1970s several theoretical
developments have appeared in support of the gravity model (Anderson, 1979;
Bergstrand, 1985 and 1989; Helpman and Krugman, 1996; Deardorff, 1995).
In relation to why does gravity work, Harrigan (2001) discriminates between aggregated
and disaggregated studies. This author states that “most of the evidence that gravity
works comes from aggregated data (…) it is surprising how little work has been done on
examining disaggregated gravity equations”. · Two attempts that analyse disaggregated
gravity equations are Feenstra, Markusen and Rose (2001) and Haveman and Hummels
(2004).
Haveman and Hummels (2004) state that common elements contributing to theoretical
foundation in gravity models (Anderson, 1979; Bergstrand, 1985) are complete
specialisation and identical preferences, where each good is produced only in one country
and consumers value variety, then importing all goods that are produced. In a world with
two countries, one can still use these models to make clear predictions about bilateral
trade patterns. However, in a multi-country world, these models say little about the
pattern of bilateral trade other than predicting the set of partners with which a country
trade. This does not mean that it is impossible to distinguish the sources of specialisation.
Feenstra et al. (2001) show that theories of specialisation can be distinguished since
elasticities of income in gravity equations should be different depending on whether or
not there are entry barriers.
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4
Haveman and Hummels (2004) examine a model with incomplete specialisation (multiple
countries may produce each homogeneous good), where much lower trade volumes than
in the case of complete specialisation are expected. These authors analyse a dataset of
bilateral trade flows at the 4-SITC level and they show that countries do not buy all
available goods. Therefore, a large number of zero observations and that the volume of
trade is less than predicted at a sectoral level is a problem that makes that gravity model
does not work as well in disaggregated analysis as it works in aggregated analysis.
Country-heterogeneity issues
There are clear economic differences between developed and developing countries that
lead to a different behaviour among them in the determinants of bilateral trade flows.
Many developing countries have important economic vulnerabilities, such as debt-related,
high unemployment and inflation rates, poverty and unequal income distribution.
Moreover, developing economies are characterised by higher levels of trade protection
than developed countries and a number of them remain dependent on foreign aid. Then,
the pooling assumption may be rejected in a sample of countries with different levels of
economic development, since the determinants of trade may have different coefficients
for high and low-income countries.
Traditionally, only a few studies have attempted to identify the differential impact of the
determinants of trade on various groups of countries (Balassa, 1979; Baldwin, 1979) and
some of them have focused on the different impact of trade policies on economic growth,
then explaining the existing dispersion in growth rates among countries (Kawai, 1994).
In the last years, studies considering country-heterogeneity have proliferated and a
number of authors have considered the existence of different trade patterns for developed
5
and developing countries from a gravity framework (Loungani et al., 2002; MartínezZarzoso and Márquez-Ramos, 2005). Other studies focus on the heterogeneity in specific
variables, for instance, Filippini and Molini (2003) show that the elasticity of
demographic variables have different signs and magnitudes in developed and developing
countries.
Sector-heterogeneity issues
Heterogeneity in products also matters. Feenstra et al. (2001) find that different estimates
of the gravity equation pertain to types of goods, rather than being features of countries.
However, these authors run gravity regressions in two groups of countries, exports within
the OECD and exports between OPEC and non-OPEC countries, then countryheterogeneity (at least not by level of development) is not really analysed since in the
former sample they are considering exports of goods from developed countries to
developed countries and, in the latter sample, they are considering exports from countries
heavily resource dependent.
Harrigan (1993) analyses the effect of trade barriers, transport costs, tariff and non-tariff
barriers on OECD imports in 1983 bilateral data for different manufacturing industries.
Results show that there is a great heterogeneity across industries.
A classification that has been used broadly in the literature to deal with sectorheterogeneity is the classification introduced by Rauch (1999). Other empirical studies
such as Feenstra et al. (2001), Tang (2006) and Giuliano, Spilimbergo and Tonon (2006)
apply this classification.
Rauch (1999) classifies products in three groups: goods traded on an organized exchange
(homogeneous goods), reference-priced and differentiated products. In a more recent
6
paper, Tang (2006) analyses the major contributions to growth of trade in differentiated
goods. The author finds that income growth and technological factors are the major
contributors to the growth of trade in differentiated products and that the impact of
information technology is higher for exports of differentiated goods from developing
countries to the United States.
3. STYLISED FACTS OF THE DETERMINANTS OF INTERNATIONAL
TRADE
Trade costs: tariffs and transport costs
Trends towards geographical regionalisation and globalisation have led to the decreasing
role of tariff barriers as an influencing factor on trade (see Figure A.1 in Appendix), then
the relative importance of transport costs has increased, and these costs have become a
relevant determinant of trade patterns. Figure A.2 (Appendix) shows the tendency of
decreasing evolution line of maritime transport costs to decrease. This evolution line can
be compared with the steeper decreasing slope displayed in the tariff evolution graph.
Depending on the continent, transport costs vary range between an 8% and a 13% of the
import values.
Despite their importance, few studies have focussed on transport costs, and existing
research has mainly been carried out at an aggregate level. In fact, a wide range of
articles considers only proxies for transport costs in their estimated models. For instance,
gravity models use distance between country capital cities as a proxy for transport costs.
In relation to trade barriers, Lee and Swagel (1997) use disaggregated cross-country,
cross-industry data of manufactured goods in 1988. These authors measure levels of
protection by country and industry and find that tariff and non-tariff barriers differ from
7
one sector to another, and in general both of them are found to be lower between
developed countries. The higher tariff levels in developing countries may reflect the
greater importance of tariff revenue in government finance. Their measures of protection
by industry indicate that antidumping practices and other non-tariff barriers apply overall
to trade on sensitive commodities (food products, beverages, textiles, apparel, iron and
steel). Lee and Swagel (1997) have only data on total imports and exports for each
country and their results indicate that trade barriers have a negative effect on imports,
although there is not conclusive evidence of the relative importance of tariffs and nontariff barriers on trade. Using a different framework, Leamer (1990) finds that both tariffs
and non-tariff barriers have a large import-reducing effect. In contrast, Harrigan (1993)
finds that tariffs are a more substantial barrier to trade in manufactures between
developed countries than non-tariff barriers using bilateral trade data. Recently, Anderson
and van Wincoop (2004) point out that the use of non-tariff barriers is concentrated in a
few sectors in 1999 (food products, textiles, apparel, timber and other manufactures).
Then, the impact of tariffs in the analysis of trade determinants is ambiguous. On the one
hand, relatively high foreign tariffs would be associated with lower exports for an
industry. In this case, tariffs increase costs and reduce trade. On the other hand, high
foreign tariffs might be a response to countries competition, indicating industries in
which a country is comparatively strong. In what follows, the role of tariff barriers is
studied more deeply from an empirical point of view.
Tang (2006) includes tariffs and transport costs measures in a gravity framework. Tariffs
are measured as the effective tariff rate that the United States charges on imports from the
exporter country for product group k and transport costs are measured as the total freight
8
cost as a percentage of import value for product group k from the exporter country to the
United States. Results show the expected ambiguous effect of tariffs on trade. For
differentiated goods, tariffs have a positive effect on the US imports, then US tariffs
might be a response to countries competition, indicating that US is comparatively strong
in differentiated goods. For reference-priced and homogeneous goods, tariffs have a
negative effect on the US imports, then relatively high US tariffs are associated with
lower imports for these industries. Fink, Mattoo and Neagu (2005) also find that tariffs
have a negative impact on trade for reference-priced and homogeneous goods, however,
tariff variable is not statistically different from zero in the case of differentiated goods.
The reason could be that tariffs are in general low for differentiated products.
In relation to transport costs, Tang (2006) shows that transport costs have a higher effect
on trade for homogeneous goods. This result is also obtained in other studies considering
sector-heterogeneity such as Giuliano et al. (2006), in line with the idea that
homogeneous goods are on average heavier and more costly to move than other goods
(Rauch, 1999) and that differentiated products generally have higher value-to-size or
value-to-weight ratios, and thus they should be less affected by transport costs (Huang,
2007).
Geography and the role of distance
The negative correlation between geographical distance and bilateral trade volumes is one
of the most robust empirical findings in economics (Leamer and Levinsohn, 1995).
However, it is still unclear exactly what information is embodied in the distance
coefficients that are estimated in gravity regressions. Filippini and Molini (2003) state
that “distance is much more than geography: it is history, culture, language, social
9
relations and many other things”. È In recent studies, a number of authors have contributed
to the debate on the interpretation of distance effects. Factors such as informational costs,
tastes and preferences, and unfamiliarity have been considered. Loungani et al. (2002)
show that distance involves more than just transport costs and that informational cost may
be behind the impact of distance on trade. Blum and Goldfarb (2006) find that distance is
a good proxy for differences in tastes and preferences. Their results provide a new
explanation for the persistence effect of distance in gravity regressions. This suggests that
the distance effect in gravity will persist for a number of products even if transport costs,
search costs and other trade barriers associated with distance are reduced to zero, which is
the case to some extent for Internet trade. For the distance effect to disappear there needs
to be a homogenisation of cultures. Huang (2007) shows that unfamiliarity can explain
part of the negative correlation between geographical distance and bilateral trade
volumes. This author shows that higher uncertainty-aversion leads to lower trade flows to
distant partners than gravity models predict. However, the author’s interpretation of the
distance coefficient (i.e. higher negative coefficients in the distance variable are
interpreted as meaning that trade is less likely to take place with foreign countries that are
far away) could be misleading. According to Buch, Kleinert and Toubal (2004) and
Márquez-Ramos, Martínez-Zarzoso and Suárez-Burguet (2007), the magnitude and sign
of the distance coefficient are related to the importance of bilateral activities with partners
that are far away relative to those that are located nearby. Moreover, the coefficient of
distance may differ among developed and developing countries. They show that when
controlling for country-heterogeneity the distance coefficient decreased by 13.55% for
developed countries and increased by 29.7% for developing countries over the period
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10
1980-1999. The authors classify each group of countries according to the different
scenarios outlined by Buch et al. (2004). Developing countries can be placed in the
scenario where distance costs decrease non-proportionally and the decrease is greater for
smaller distances since the magnitude of the distance coefficient increased over the
period 1980-1999, whereas developed countries can be placed in the scenario where the
distance costs decrease non-proportionally and the decrease is smaller for smaller
distances since the magnitude of the distance coefficient decreased over the same period
1980-1999. For developing countries, export flows for small distances increase over time,
whereas export flows for large distances decrease over time, and therefore trade with faraway countries decreases in relation to trade with nearby countries. The opposite applies
to developed countries.
Heterogeneity in products also matters in distance. Rauch (1999) finds that proximity
(when adjusted with distance effects and with transportability), is more important for
differentiated products. A possible reason may be that incomplete information matters
since differentiated products tend to be less traded because there is less demand for them
outside the country in which they are produced. This result is opposite to Fink et al
(2005) who find that distance coefficient is lower in absolute value for differentiated
products.
Technological innovation
International trade theory highlights the importance of technological innovation in
explaining the international competitiveness of a country (Fagerberg et al., 1997).
Recently, Freund and Weinhold (2004) justify the inclusion of Internet variables in a
bilateral trade model. Additionally, empirical applications show that heterogeneity
11
matters in technological innovation. Loungani et al. (2002) distinguish between
developed and developing countries when analysing whether better information can
substitute for geographical distance. Their results point towards the existence of countryheterogeneity in the different determinants of international trade since they show that
technological innovation is a “substitute” of distance in developing countries (better
information decreases the effect of distance), whereas technological innovation and
distance are “complementary” in developed countries (better information magnifies the
effect of distance). This may occur when trade in differentiated products dominates and
that physical proximity and high information technology reinforces each other in
fostering trade. Developing countries can overcome the disadvantage of distance by
investing in technological innovation. This result is in the same line that Freund and
Weinhold (2004) who show the importance of new technologies on trade as measured by
Internet hosts.
Martínez-Zarzoso and Márquez-Ramos (2005) divide the countries in a 62-country
sample according to their level of economic development: high-income, medium-income
and low-income countries. Technological endowments have a higher effect on trade in
developing economies. Moreover, Tang´s result (2006) that the impact of information
technology is higher for exports of differentiated goods from developing countries.
Technological variables are therefore of great importance to increase the participation of
the poorest economies in the world economy.
Fink et al. (2005) analyse the effect of communication costs on bilateral trade flows
taking into account sector-heterogeneity. Their results show that communication costs
have a significant effect on international trade and that they are of greater importance for
12
trade in differentiated products than for trade in homogeneous products. In this line, Tang
(2006) analyses the contribution of technological innovation to the growth of the United
States imports. This author finds that technological innovation has a higher effect on the
growth of trade in differentiated goods than in the growth of trade in referenced and
homogeneous goods in the past two decades.
Language and colonial ties as measures of cultural similarities
A number of international trade studies focus on the effect of countries sharing a
language (Frankel, Stein and Wei, 1998; Helliwell, 1999). Among them, Helliwell (1999)
explores the economics of language in 22 OECD-contries and 11 developing countries.
The author finds that the general common language effect seems to be driven by the role
of English. The other languages analysed, German, French and Spanish, are not found
significant in the empirical regressions.
Country-heterogeneity in language is found by Guo (2004). This author shows that
language influences on trade are more significant in China (a developing country) than in
the United States (a developed country).
Rauch (1999) finds that sector-heterogeneity matters in language and colonial ties. These
variables are more important for differentiated products. The reasons could be that
incomplete information matters since differentiated products tend to be less traded
because there is less demand for them outside the country in which they are produced,
and similarity of foreign preferences since trade in differentiated products increases with
links. The author argues that this result supposes that “firms develop their varieties of
differentiated products to suit niches in their home markets. We suppose further that they
do this (…) because positive transportation costs make this the best decision, ceteris
13
paribus. This could explain why differentiated products tend to be less traded: there is
less demand for them outside the country in which they are produced”. Ü
Regionalism versus Multilateralism
Regional integration agreement (RIA) dummies are frequently included in gravity models
of trade. A positive coefficient means that countries engaged in an integration agreement
trade more.
RIAs have a differential effect in differentiated, reference-priced and homogeneous
sectors. Feenstra et al. (2001) show that Free Trade Agreements have a lower effect over
time on trade flows in differentiated and reference-priced goods, whereas they have a
higher effect on trade flows in homogeneous goods. However, the effect is higher for
differentiated goods. Rauch (1999) shows evidence of a differential impact by sectors of
European Community and European Free Trade Association. Tang (2006) shows
evidence that NAFTA has different impact on the United States imports. NAFTA seems
to have a higher positive effect on trade among members for differentiated than for
homogeneous goods.Ý
In relation to multilateralism, Rose (2004) investigates whether the World Trade
Organisation (WTO) and its predecessor the General Agreement on Tariffs and Trade
(GATT) have promoted trade. Results show that membership in the GATT/WTO does
not have a significant effect on trade. The author states “perhaps this is because many
countries extend most-favoured-nation status to outsiders even though they are not
obligated to do so; perhaps GATT/WTO membership has not forced developing countries
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to change trade policy substantially; perhaps there is some other reason”. This author
points out that decomposing trade by industry may be interesting since the multilateral
trade system has been less successful at liberalising trade in exempted sectors such as
agriculture and textiles.
Subramanian and Wei (2005) show that GATT/WTO promotes trade, although they
identify four asymmetries: developed versus developing country members; new versus
old developing country members; imports of members from other members versus
imports from non-members; liberalised versus exempted (highly protected) sectors.
Industrial country WTO members are more open than developing countries WTO
members; new members in WTO are more open than old members and the obligations to
liberalise in the old WTO members have not become stringent enough to actually lead
them to be more open than non-WTO members; non-members do not seem to benefit
equally from the liberalisation than member countries under the WTO (imports from
WTO members are greater than from non-members). These authors differentiate among
two types of discrimination: explicit discrimination, barriers are higher against imports
from non-WTO members than from WTO members; and de facto discrimination, barriers
are higher on products of greater interest to non-members because these products have
not been the subject of reciprocity negotiations in the WTO. Finally, the positive effect of
WTO is higher in more liberalised sectors and the magnitude of the coefficient on WTO
decreases over time, thus showing partial evidence of the decreasing effect of trade
multilateralism on international trade flows.
4. DATA, SOURCES AND VARIABLES
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15
In this paper, two African countries are selected for the empirical analysis: a low-income
(Ghana) and a medium-income economy (South Africa). Different socio-economic and
geographical factors characterise these countries.
Ghana is characterised because it is well endowed with natural resources and it has
approximately twice the per capita output of the poorer countries in West Africa. Even
so, the 31.4% of population are below the poverty line and Ghana remains heavily
dependent on international financial and technical assistance. Gold, timber, and cocoa
production are major sources of foreign exchange. The domestic economy continues to
revolve around subsistence agriculture. Priorities include tighter monetary and fiscal
policies, accelerated privatization, and improvement of social services. Inflation should
ease but remains a major internal problem.
South Africa has undergone a remarkable transformation since its democratic transition
in 1994. It is an emerging market with an abundant supply of natural resources; welldeveloped financial, legal, communications, energy, and transport sectors; it has a
modern infrastructure supporting an efficient distribution of goods to major urban centres
throughout the region. However, growth has not been strong enough to lower South
Africa's high unemployment rate (GDP per capita grew at an average of 1.2 per cent per
year since 1994 and the unemployment rate is 26.6%). High unemployment and low
growth may be the result of the divestment of the non-mineral tradable sector since the
1990s, then South Africa has been deprived from growth opportunities that other
countries have experienced (Rodrik, 2006).
16
Economic problems such as poverty remain from the apartheid era (the population below
the poverty line is 50%). South African economic policy focuses on targeting inflation
and liberalising trade as means to increase job growth and household income.
Figure 1 and 2 show the evolution over time of aggregated exports and imports of goods
and services for Ghana and South Africa, respectively. A decreasing tendency on trade
flows is observed from the year 2000 for the case of Ghana and also in South Africa from
2002 onwards. Ghana imports more goods and services than exports. South Africa shows
the opposite trade pattern.
Figure 1
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In order to analyse what are the main determinants of international trade in these two
African economies, bilateral trade data by commodity from Feenstra, Lipsey, Deng, Ma
and Mo (2005) are used. The level of disaggregating is the 4-digit Standard International
Trade Classification (SITC), revision 2. These data are used to construct revealed
comparative advantage indices to determine countries’ specialisation in Ghana and South
Africa in the year 2000. In the empirical analysis, the determinants of exports from
Ghana and South Africa to 167 importer countries are analysed (see Table A.1,
Appendix).
Revealed Comparative Advantage
The revealed comparative advantage (RCA) is calculated according to Balassa (1965)
measure of relative export performance by country and industry, defined as country’s
share of world exports of a good divided by its share of total world exports, as expressed
in equation (1):
18
RCA =
X X × 100
X X
(1)
where RCA is the revealed comparative advantage index of commodity k for country i.
X is the value of exports of commodity k by country i, X is the value of exports of
commodity k from the world, X is the value of exports of all commodities by country i
and X is the value of exports of all commodities from the world. A ranking of the 10
industries that rank first the higher positive value of the RCA is constructed for Ghana
and South Africa and Rauch classification is used to determine if countries are specialised
in goods traded on an organised exchange (homogeneous), reference-priced or
differentiated goods (Rauch, 1999). Table A.2 in Appendix list the codes of the sectors
used in the final sample, which includes 146 sectors with homogeneous goods, 349
sectors with reference-priced goods, and 694 sectors with differentiated goods.
According to equation (1), country i has a comparative advantage in exporting
commodity k when RCA is greater than 1. Table A.3 in Appendix shows the main
sectors in which Ghana and South Africa are specialised. Specialisation in referenced
products seems to be the most common pattern for the African countries considered in
this study.
Two types of data are used in the sectoral analysis. Those that vary across countries and
those that vary across sectors. On the one hand, incomes, incomes per capita, transport
costs, trade imbalance, technological innovation, economic freedom, geographical,
cultural and integration dummies are those variables incorporating country-variability.
On the other hand, tariffs, high-technology and sectoral dummies are those incorporating
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19
sector-variability. Then, different data bases are used to construct the variables for the
regression analysis: World Development Indicators (2005) for incomes and incomes per
capita, World Integrated Trade Solution (WITS) for tariffs, and Doing Business (2006)
database for transport costs, recently elaborated by the World Bank and that compiles
procedural requirements for exporting and importing a standardized cargo of goods. The
GATT/WTO accession dates for countries entering until 2000 are obtained from the
WTO webpage. Distance between capitals, common official language and colony
dummies are obtained from the dataset constructed by CEPII. ¤ The data of the Index of
Economic Freedom are obtained from The Heritage Foundation webpage.
International trade flows are heavily imbalanced between areas. This disequilibrium
applies both to general world trade and to containerised seaborne trade. The divergence
associated with the sign of trade imbalance occurs as a result of the freight rate price
fixing mechanisms applying in the liner market. The liner company knows that on one of
the legs of the turnaround trip, the percentage of vessel capacity utilisation will be lower,
and therefore adapts its pricing scheme to the direction of the trip and its corresponding
expected cargo. Freight rates will be higher for the shipments transported on the leg of
the trip with more traffic, as the total amount charged for this leg must compensate the
relatively reduced income from the return trip, when part of the vessel’s capacity will
inevitably be taken up with repositioned empty containers. Excess capacity on the return
trip will increase the competition between the various liner services, and as a result
freight rates will tend to be lower.
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20
In this paper, a trade imbalance variable (in weight, metric tons) is constructed according
to equation (2).
Trade Im balance =
ß
Xá - M
ß
ßVà
(2)
ßVà
max( X á , M )
Where Xâ ã are the exports (in weight) from the exporter country to the world and M ä4å are
the imports (in weight) from the world to the importer country. When constructing trade
imbalance as expression (2), bilateral variability in transport costs among countries is
taken into account. Trade imbalance is calculated with trade data obtained from Feenstra
et al. (2005).
Recently, Tang (2006) makes an analysis for 103 countries exporting to the United States
that raises the possibility that specialisation in information technology sectors in
developed and developing economies could have a different effect in both kinds of
economies. Therefore, technological innovation is included in the regression with
Technological Achievement Index -TAI (UNDP, 2001). Additionally, sector variability is
considered with a high-technology dummy. The list of high-technology products is based
on R&D intensities (Table A.4, Appendix). Concordances from the Centre for
International data at UC Davis between SITC revision 2 and revision 3 are used since
trade data are defined using SITC revision 2. Finally, sectoral dummies that classify
products according to Rauch (1999) are obtained from Jon Haveman’s International
Trade data webpage.
Table A.5æ shows a summary of the data used in the empirical analysis.
ç è éêë"ìÜíEî ï ðCñ}í-òòhìZñ0óAðCôRî
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21
5. EMPIRICAL ANALYSIS
Model specification
In order to incorporate sector-heterogeneity in the empirical analysis of trade
determinants in African countries, a gravity equation is estimated with disaggregated
data. Therefore, previous papers considering sector-heterogeneity are taken into account
(Feenstra et al., 2001; Tang, 2006). A number of dummies representing geographical and
cultural characteristics and integration dummies to analyse the impact of a RIAs and
multilateral liberalisation on international trade are added. Sectoral dummies for hightechnology goods and for referenced and homogeneous goods are also included in the
regression. The model is expressed in additive form using a logarithmic transformation.
The estimated equation is:
†€‡ ]
c
_ } b
b
_ w a LNMPO b
b
_ o5o a ƒ‚…„
b
_ fg a b
b
_ | a †€‡ [ b
_ { a †j‡ [\
_ v a †j‡ HJI,KK b
_ oqp a *,+'- b
b
_ u a CEDGF
_ z a †€‡ V2XY Q ZZ b
b
_ mn a ~€ #%$'&)( b
_ de a `
_ t a 8:9<;>=@?BA
_ k'l a "! b
_ y a VGW
b
b
_ x a QSRUT b
_ rs a ./102.235467. b
_ hji a b
(3)
^ where ln denotes natural logarithms.
The model is estimated with 4-digit SITC bilateral exports data from Ghana and South
Africa (i) to 167 importer countries (j) in the year 2000. Ordinary Least Squares (OLS)
estimation on the double log specification as given by equation (3) is performed.
22
X ˆ ‰ denotes the value of exports from country i to j, YŠ and YH‹ are income and income per
capita in the destination market, and TariffŒ  is the simple average of all countries
effectively applied rates duty type in the importer country for each commodity k from
Ghana and South Africa. TCŽ is transport costs of the importer country. Imb   is the trade
imbalance existing between trading partners. This variable is built according to equation
(2). TAI‘ is a technological variable measuring technological innovation in the importer
country. Free’ denotes the index of economic freedom in the importer country. Higher
value for this index indicates lower economic freedom in the country.
WTO is a dummy that takes a value of 1 when the importer country is a signatory of the
World Trade Organisation in 2000, ECOWAS takes a value of 1 when countries are
members of the Economic Community of West African States, then is only included in
the regression for the case of Ghana.
Hightech“ is a dummy that takes the value of 1 when commodity k is a high-technology
commodity (Table A.4, Appendix). Hom” and ref• dummies are included in the model to
take into account sector-heterogeneity in the regression. Hom• takes the value of 1 when
the commodity k is homogeneous, zero otherwise; whereas ref– takes the value of 1 when
the commodity k is reference-priced, zero otherwise, according to conservative Rauch
classification (1999). — Dist ˜ ™ is the geographical great circle distance in kilometres
between the capitals of country i and j. Lang š › is a dummy for countries sharing the same
language. Landœ takes the value of 1 when the importer country is landlocked. Adj  œ is a
dummy that indicates whether the trading partners are contiguous. Colony ž Ÿ is a dummy
¡ ¢£¥¤§¦©¨«ª­¬®£°¯²±7³µ´¶·±7£©¸¦©¹q³§¬º¬¶q»€¶'¦©³µ´¶q¨«ª½¼<¶¾ª¶¾¼¿¶¬®£À¬<´q¢£Pª7Á)¼Â£µ¯Ã¨)»ÅÄ Æ Ç)È·É)ȾÊÅË©ÌÍÍÌ7Ç)ȾÊÈqÎÀÏ<ÊqÐÒÑÊÓÑ°Ô®ÎÕË©ÖqѧϺϺȷ×ÈqΩǽѧÏ
ΧÈØÊqÐÒΰԃÌ,Ô5É)ѵÙÒÈÏ®Î§Ç Ú ÛÜÒÝÞÒß°àá)Ûãâ«ä ä²Û§å€Ûµä²Ûµà Ý Û æ ç7è²éqê©ë§ì­í
23
that takes the value of 1 when trading partners have ever had a colonial link. Finally, e î ïð
is independently and identically distributed among countries.
Main results
When measuring trade between two specific countries, several statistical indices can be
used. One of them is the trade intensity index, which can appear in two forms: export
intensity index and import intensity index. An index greater (less) than unity can be
interpreted as an indication of larger (smaller) than expected trade flows between the two
countries concerned. Wu and Zhou (2006) have applied this measure to analyse ChinaIndia bilateral trade.
In this section, as a preliminary analysis to determine factors that promote trade between
two countries, data are aggregated for Ghana and South Africa to construct bilateral
export intensity index (XII) in the year 2000 according to equation (4).
XII ñ ó =
Mó ò
x ñó X ñ ò
(4)
(M ò - M ñ ò )
where XIIô õ is the country i’s export intensity index to j, xö ÷ the country i’s exports to
country j, Xø ù the country i’s total exports to the world, Mújû the country j’s total imports
from the world, Mû the world total imports and M ü û the country i’s total imports from the
world.
Table 1 shows the results obtained by measuring trade intensity from the exporter
countries (Ghana and South Africa) to a 65-country sample used in previous papers in
gravity analysis with aggregated data (Márquez- Ramos et al. 2007).
Results show that Ghana exports more than expected to high-income European countries,
whereas the intensity of exports for the African medium-income country considered
24
(South Africa) is remarkably higher with other African countries (Ghana, Kenya,
Mozambique and Tanzania).
Table 1. Export Intensity Index.
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As a further step, equation (3) is estimated by OLS for Ghana and South Africa to 167
importer countries. Table 2 shows final results.
26
Table 2. Determinants of international trade. The case of Ghana and South Africa.
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27
Table 2 shows that importer’s income coefficient is positive and significant for both
Ghana and South Africa. However, importer’s income per capita variables are not
significant.
The effect of tariffs varies across countries. A negative and significant effect of tariffs on
international trade is found in South Africa, then tariffs increase costs and reduce
international trade. However, the effect of the structure of tariffs in importers is not
significant for the case of Ghana. Moreover, importer’s transport costs and trade
imbalance do not seem to deter exports in Ghana and South Africa. Figure A.2
(Appendix) shows and increasing tendency of maritime transport costs over the period
1990-2000 in Africa. Therefore, for African economies, transport cost reductions do not
play a relevant effect on exports.
Technological innovation endowments in the importer country have a positive and
significant effect on trade flows in South Africa. Then, the technological innovation
investment carried out in other countries leads to higher exports from South Africa. This
may be due in part to a spillover effect existing among developed and developing
countries since higher levels of economic development in the poorest countries can be
reached by trading because of technological innovation performed in developed countries
(Coe, Helpman and Hoffmaister, 1997).
Results show that the effect of multilateral liberalisation on international trade is negative
and significant for South Africa, and regional integration (ECOWAS) does not foster
exports from Ghana.
28
High-technology dummy indicates that these African countries are not specialised in
high-technology sectors. Homogeneous and reference-priced dummies show that these
countries are relatively specialised in homogeneous goods.
In relation to geographical and social variables, distance has a negative effect on trade
flows when exports are from South Africa. The effect of sharing a language is not
significant, although South Africa trades more with countries that have common colonial
ties such as United Kingdom and Netherlands.
Finally, beta coefficients are used by some researchers to compare the relative strength of
the various predictors within the model. Since the beta coefficients are all measured in
standard deviations they are comparable when the explanatory variables are expressed in
different units. Beta coefficients are reported in Table 3.
Table 3. Beta coefficients.
´mµ!¶ ¬±‹®0·±$¸2¹k²gª7³W¬ µ ·
´mµ!¶ ¬*±m®0· ±$¸2¹k²gª³¬ µ · ¶ ·$± ³© ¶ ²g®0©
Á ©±
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··WŬ µ
ÎÏÁkÐ Å­ µ!µÑ
ÌdÒ ÐšÎ ¯«ŒÅ­ µ}µÑ
Ó ²bÔ*¨®0·³$¨>Å­ µ>µ!Ñ
Ó ¬ µ ¬Ô·ª7·¬*­¹kÔ¬¬(Å(¹kÅ­ µ!µ!Ñ
Õ ·°L·$±U·ª7³W·ÅÔ¬¬(Å(¹kÅ­ µ>µ!Ñ
Ö ²2¹I®A©ª7³W·
× ©$ª(Ô­7©WÔ·ØÅ*­ µ>µ!Ñ
´mµ!¶ ¬±‹®0·±
Ë ¹ × ©$ª7ÅÇ2¬³Ù7·Å!Å­ µ>µ!Ñ
¯ÅvÛ©³W·ª7³ Ñ Å­ µ}µ!Ñ
Òy¬Ç2¬ª7²2©ÇÅ*­ µ}µ!Ñ
29
§ 7
¨ ©ª7©
º»A¼$½
º»A¼$¾
¿ º» ºÂ
º» º7Ã
º»A¼ÉÈ
¿ º»¼WÃ
¿ º» Ƚ
¿ º» º^¼
¿ º» º^¼
¿ º»¼¾
º» ÈÃ
º»A¼ÉÂ
¿ º» º7À
¿ º» ºÂ
¿ º»¼¾
º» ºº
º» ºÄ¼
«¬­®¨!¯9°A±U²2³©
º»¼$½
¿ º» º(À
¿ º» º½
¿ º» ºÄ¼
¿ º» ºÄ¼
º» È7Ê
º»¼Ã
¿ º» º½
¿
¿ º» º¾
º»¼ÉÈ
º» ºÊ
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º» º*È
º» º(À
According to beta coefficients the most important variables fostering exports differ for
Ghana and South Africa. Ghana exports more to countries with high level of economic
freedom (high-income European countries), whereas South Africa exports more to
countries with low level of economic freedom (other African countries). Trade barriers
do not seem to deter significantly trade flows from these African economies, although
Ghana is benefited with the current structure of trade imbalance. Ghana imports more
than exports, then freight rates are lower for the shipments transported on the leg of the
trip with less traffic (exports). Finally, geographical distance and technological
innovation are the most important determinants of exports from South Africa.
6. CONCLUSIONS
A number of authors have focused on the determinants of the trade patterns, however
further research is needed for a better understanding of what goods and with which
countries developed and developing economies trade. This paper focuses on the
determinants of international trade in two African countries: South Africa (low-income
economy) and Ghana (medium-income economy). In the empirical analysis, country and
sector-heterogeneity are considered. Results show that determinants of trade have a
different behaviour in developed and developing African countries. Technological
innovation, geographical and social factors play a key role on trade relationships in South
Africa, whereas Ghana’s exports are higher when they are addressed to countries with
higher levels of economic freedom. Ghana exports more than expected to high-income
European countries, whereas the intensity of exports from South Africa is considerably
higher with other African countries.
30
According to Baier and Bergstrand (2001) the main factors explaining world trade growth
are income growth, tariff and transport cost reductions. In the case of the African
economies studied in this paper, trade flows decrease from the year 2000 onwards. The
importer’s income is found to be a relevant variable to foster international trade flows,
however the effect of tariffs varies across countries. Transport cost reductions do not play
a relevant effect on exports from African countries. Additionally, results show evidence
of a spillover effect existing among developed and developing countries.
In relation to multilateralism, The WTO Ministerial Conferences are of great importance
since various issues discussed there impact countries, especially poor ones, and their
economic futures. Nonetheless, results in this paper show that the effect of multilateral
liberalisation on international trade is not significant for Ghana and it is negative for
South Africa. In the last years, WTO talks have collapsed. High-income countries want to
talk about new issues that are part of the free trade and liberalisation ideas that they
promote. Otherwise, low-income countries want to talk about old issues mostly on
agriculture that affected them the most. Then, there is a need for contingency plans in the
context of multilateral trade negotiations. Dobson (2001) points out that the international
trading system seems to be losing its "liberalising momentum", whereas RIAs are
proliferating. However, RIAs will not be WTO-compliant unless they aim to free up trade
in essentially all sectors on non-discriminatory basis. In fact, results in this paper show
that the Economic Community of West African States, does not foster exports from
Ghana.
31
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37
APPENDIX
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38
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Table A.2. List of 4-digit SITC sectors (conservative classification)
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Table A.3. Revealed Comparative Advantage.
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Table A.4. High-technology sectors.
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Table A.5. Variable description and sources of data.
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