Determinants of International Trade

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, Ghanas 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

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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

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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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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

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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

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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

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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

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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

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relations and many other things. In recent studies, a number of authors have contributed
È

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 authors 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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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

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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

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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

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paribus. This could explain why differentiated products tend to be less traded: there is
Regionalism versus Multilateralism

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less demand for them outside the country in which they are produced.

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
Ý

homogeneous goods.

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

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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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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 countrys share of world exports of a good divided by its share of total world exports, as expressed in equation (1):

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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
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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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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
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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 vessels 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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» µ º³ ª © ¸¬ ·® ¶ µ ¬ ©´¬³ ¬¬ ² © ° ¯® ¬ ª © §

20

In this paper, a trade imbalance variable (in weight, metric tons) is constructed according to equation (2). ß á

X

-M

à
ß

(2)

max( X , M ) à ß

ß

á

Trade Im balance =

Where X are the exports (in weight) from the exporter country to the world and M are å Rä

âã

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 Havemans International
Trade data webpage.
Table A.5 shows a summary of the data used in the empirical analysis. æ î ø ì ú ÷ ü û ø éù é ó ì õù ø ¤ û õ ñ ý üë û ú ó ÷ð õù ì õù ó ñ é ¢ ø ìë ê éð ÷ é þ ì õù ö û ñ ûðù òð ÷ ú ø ì ó é ø ì ñðëù ü
1aaRxEq1¡HT¥Hsø 8¡B¡axghuÙH4W"£a4"1ux"ÙH±¡i4qegx~a¡ÞHB4qRû ñ ý üë û ú ó ñ û ú ì ø ì õù øð ø ÿë é ñ é ë é úð ÷ð ò ý ì ÷ û ö ó ì ø ü ø ìë ê éð ÷ é þ ì õù ø ù øðë ñ ý üë û ú ù ø ÷ð ö ì õ è î ôð ó ñ ì ò ò í ñ î í ìë ê é i¡B¡¥}HR¥¥~hu¡4~±g¥H"¥auxgh)}ax"uh41e{HTtÔ4i8¡B¡as~gsEHTgeHh¡E}gð ï iÜ4"1)è ç

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: x
§ ¦

(3)

§

y

q

s
¨ USPPI)F ©r
F T R Q F HG

i
§ )&$ jh
( ' %

¨
©§ # P z

t e c a Y W fdb`XV l § ¦ 4&0 9k
3 2 1

§

{

j

u p h qig

§ Dtr j v u u s

n
§ ¦ A975 ~ ©m
@ 86

¨
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§ |

p
EC
¨ 9DB qo

e
§ ¦ ©d

}

¨
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§ xv w y w
¨

o
So

g
§ ¦ #! ©f
"

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 denotes the value of exports from country i to j, Y and YH are income and income per

capita in the destination market, and Tariff

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. TC 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 is a dummy for countries sharing the same

language. Land takes the value of 1 when the importer country is landlocked. Adj is a

dummy that indicates whether the trading partners are contiguous. Colony is a dummy

í ì ë êé è
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Ï Ñ Ç ÎÈ ×È Ï Ï ÑÖ Ë Î Ô Ñ Ê Ñ ÐÊ Ï ÎÈÊÈ Ç Ì Í Í Ì Ë ÊÈ ÉÈ
§½©q·ºº§q©Õ®°Ó¡Òq`Àq©¾AU&&¥©Å¾A·AÇ Æ ÅAÃµAUq`À®©¿¾¾`½«q©µ©9q£º§q©!©U·©µU²°®«©§¥¡
Ä » ¨ ¯ £ Â ¼ Á ª £ ¢´ ¬ £ ¬¶ ¼¶ ª¶ ¼ ª ¨¶´ ³ ¦¶»¶ ¬ ¬ ³¹ ¦ ¸ £ ±¶´ ³ ± ¯ £ ¬ ª ¨ ¦ ¤ £ ¢

23

ð
©ï î

that takes the value of 1 when trading partners have ever had a colonial link. Finally, A 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).
(4)

(M - M ) òñ òó

ò

M

X

óñ

x

òñ

XII = óñ where XII is the country is export intensity index to j, x the country is exports to ö÷ ôõ

country j, X the country is total exports to the world, M the country js total imports û jú

ùø

from the world, M the world total imports and M the country is total imports from the ûü û

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

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Mozambique and Tanzania).
(South Africa) is remarkably higher with other African countries (Ghana, Kenya,

26 importer countries. Table 2 shows final results.
As a further step, equation (3) is estimated by OLS for Ghana and South Africa to 167
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Table 2 shows that importers income coefficient is positive and significant for both
Ghana and South Africa. However, importers 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, importers 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

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Table 3. Beta coefficients. different units. Beta coefficients are reported in Table 3. standard deviations they are comparable when the explanatory variables are expressed in the various predictors within the model. Since the beta coefficients are all measured in
Finally, beta coefficients are used by some researchers to compare the relative strength of ties such as United Kingdom and Netherlands. significant, although South Africa trades more with countries that have common colonial flows when exports are from South Africa. The effect of sharing a language is not
In relation to geographical and social variables, distance has a negative effect on trade countries are relatively specialised in homogeneous goods. high-technology sectors. Homogeneous and reference-priced dummies show that these
High-technology dummy indicates that these African countries are not specialised in

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 Ghanas 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 importers 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

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Table A.1. Importer countries.

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4@à

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tà

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4@à

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444à
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444à
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Ø Ù Û
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á Ú Ý tÝ Ü Ú Ý
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tsÞ
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Table A.2. List of 4-digit SITC sectors (conservative classification)

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