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An impact assessment of the SADC FTA on bilateral trade relations between South Africa and Zimbabwe


Chirowodza, Joe (2020), An impact assessment of the SADC FTA on bilateral trade relations between South Africa and Zimbabwe, Dryad, Dataset,


The Journal paper uses the Gravity Model of International Trade to investigate the impact of the Southern African Development Comuunity (SADC) Free Trade Area (FTA) on trade between South Africa and Zimbabwe. The study found out that South Africa gained more in trade by 88.4% if the country used the SADC FTA than the 1964 Bilateral Trade Agreement which was between South Africa and Zimbabwe. In addition, there was trade diversion of 176% for Zimbabwe when trading in SADC FTA. Furthermore the paper showed that South Africa will in future trade more with countries such as the Seychelles and Angola whilst Zimbabwe will gain more in trade if it trades with  South Africa. The paper recommends that there is need for Zimbabwe and South Africa to expand trade with SADC Countries in order to promote intra-regional trade.


Data was collected from various websites such as the World Bank World Development Indicators , CEPII and own computations of dummy variables. The data was colllected and arranged in excel documents and stata files.

Usage Notes


TFij - This is the dependent variable which shows annual trade of exports plus imports of SADC member states. Other studies which have used the gravity model as their methodology such as Sunge & Mapfumo (2014) and Simwaka (2011) have used the log of exports as the dependent variable arguing that imports in Africa are understated in order to minimize their import bills. However this may not be holistic view in all countries and will not reflect the true picture of trading activities in Africa.

Yj  and Yi - These are explanatory quantitative variables which show the economic sizes of the exporting and importing countries. Ogunkola (1998) explains that the higher the GDP for exporting and importing countries, the higher is the respective countries’ potential for foreign product demand

Distij- This is a quantitative explanatory variable which is a proxy to transportation costs in bilateral trade. Simwaka (2001) uses the quality of infrastructure as a proxy for transport costs as the author highlights that distance may be biased if the poor and not well connected. However distance is a traditional variable which is calculated in kilometers from the capital city of one SADC member state to the other capital city of a SADC member state

contig- This is a dummy explanatory variable which represents the common borders between SADC member states. The variable will show one if there is a common border between an importing country and the exporting country and zero if there is no common border between the two. Countries with common borders will trade more than countries without common borders.

lang- This is a qualitative variable which takes the value of one if the importing country has a common language with the exporting country and zero otherwise.

TC- The variables show trade creation in the SADC region. The dummy variables takes the value of one if both the importing and exporting countries are in the SADC Free Trade Area and zero otherwise

TD- The variable shows trade diversion in SADC trade. The dummy variable represents one if one of the member states is in the SADC FTA and zero otherwise.

llc- This is a dummy variable which represents one if the exporting country is landlocked and zero otherwise

excij - Bergstand (1985) explains that the exchange rate variable is important to show trade variation between member states. The quantitative variable will determine annual exchange rate by the importing country’s currency unit per one unit of the exporting country’s currency. Following Binh et al (2013) the variable is calculated as the annual average of the importing country’s currency unit per US dollar divided by the annual average of the exporting country’s national currency unit per US dollar per year.

PIi & PEj- These two quantitative variables estimate the market size of member states. A member state with a larger market size than the other is more likely to trade more in the region.

This is a panel data set  consisting of 16 SADC countries. and data was obtained from World Bank's World Development Indicators, CEPII and own computations in terms of dummy variables .