Annual Forum Papers

Predicting South African Trade in Services

  • Year: 2002
  • Author(s): Matthew Stern
  • Countries and Regions: South Africa
Download:

Economists seem to agree that the theory of comparative advantage can be extended to trade in services. Countries with relatively large endowments of skilled labour and capital and relatively few natural resources should export more services and less mining or agricultural goods than those relatively rich in land or resources.

Although some econometric work has been done on the determinants of trade in services, the results are inconclusive. This paper improves on these studies, applying similar methodologies but using better and more comprehensive data that are now available.

One important spin-off from the econometric analysis is that the models can be used to identify and quantify the most important determinants of trade in services in each sector. The resulting coefficients can also be used to predict South African trade in services.

In total, four different models are presented for each of the eight service sectors. First, a simple two-factor Heckscher-Ohlin model is tested against two different dependent variables. A further two models are developed by incorporating and testing a much wider selection of explanatory variables.

The results are encouraging and offer some empirical support to the application of comparative advantage to trade in services. They show that human capital and economic development are important determinants of competitiveness in the service industry. On the other hand, countries with abundant land or labour are less likely to specialise in services.

South Africa is not predicted to specialise in any of the eight service sectors. In all but one sector (tourism), South African service exports are predicted at less than 1% of merchandise trade. Moreover, in all eight sectors the predicted ratio of South African service trade to merchandise exports is lower than the actual trade ratios for more than half of the countries included in the sample.