South Africa has experienced a significant increase in tourist arrivals over the past ten years. The challenge is to sustain this growth and therefore it is important to understand the factors that influence inbound tourism to South Africa. The purpose of this article is to identify the various determinants of inbound tourism to South Africa from different source markets (categorized in continents). Time series quarterly data from 1993 to 2004 is used in the analysis of tourist arrivals. Cointegration analysis in a multivariate framework is used and the authors find that income, relative prices and travel cost are strong determinants of tourist arrivals (as with other destinations). They also find that climate and capacity play significant roles.
AlgieriB. (2006), ‘An econometric estimation of the demand for tourism: The case of Russia’, Tourism Economics, Vol 12, No 1, pp 5–20.
2.
CrouchG.I. (1995), ‘A meta-analysis of tourism demand’, Annals of Tourism Research, Vol 22, No 1, pp 103–118.
3.
CunadoJ.Gil-AlanaL.A.de GraciaPerez F. (2007), ‘Fractional integration and structural breaks: Evidence from the international monthly arrivals in the US’, paper presented at the Advances in Tourism Economics Conference, 13–14 April, Vila Nova de Santo André.
4.
De MelloM.M.FortunaN. (2005), ‘Testing alternative dynamic systems for modeling tourism demand’, Tourism Economics, Vol 11, No 4, pp 517–538.
5.
DritsakisN. (2004), ‘Cointegration analysis of German and British tourism demand for Greece’, Tourism Management, Vol 25, pp 111–119.
6.
HanZ.DurbarryR.SinclairM.T. (2006), ‘Modelling US tourism demand for European Destinations’, Tourism Management, Vol 27, pp 1–10.
7.
HarrisR.SollisR. (2003), Applied Time Series Modelling and Forecasting, Wiley, West Sussex.
8.
HüblerO. (2005), ‘Panel data econometrics: Modelling and estimation’, Diskussionspapier Nr 319 (August 2005), Institute of Quantitative Economic Research, University of Hannover, Hannover.
9.
KulendranN.WittS.F. (2001), ‘Cointegration versus least square regressions’, Annals of Tourism Research, Vol 28, No 2, pp 291–311.
10.
LimC. (1997a), ‘The functional specification of international demand models’, Mathematics and Computers in Simulation, Vol 43, pp 535–543.
11.
LimC. (1997b), ‘Review of international tourism demand models’, Annals of Tourism Research, Vol 24, No 4, pp 835–849.
12.
LimC. (2004), ‘The major determinants of Korean outbound travel to Australia’, Mathematics and Computers in Simulation, Vol 64, pp 477–485.
13.
LimC.McAleerM. (2002), ‘A cointegration analysis of annual tourism demand by Malaysia for Australia’, Mathematics and Computers in Simulation, Vol 59, pp 197–205.
14.
MacDonaldR. (2001), ‘Modelling the long-run real effective exchange rate of the New Zealand dollar’, Reserve Bank of New Zealand Discussion Paper series DP2002/02, Wellington.
15.
MudambiR.BaumT. (1997), ‘Strategic segmentation: An empirical analysis of tourist expenditure in Turkey’, Journal of Travel Research, Vol 36, pp 29–34.
16.
MuñozT.G. (2007), ‘German demand for tourism in Spain’, Tourism Management, Vol 28, pp 12–22.
17.
NarayanP.K. (2004), ‘Fiji's tourism demand: The ARDL approach to cointegration’, Tourism Economics, Vol 10, No 2, pp 193–206.
18.
NaudéW.A.SaaymanA. (2005), ‘Determinants of tourist arrivals in Africa: A panel data regression analysis’, Tourism Economics, Vol 11, No 3, pp 365–391.
19.
ParuoloP. (1996), ‘On the determination of integration indices in I(2) system’, Journal of Econometrics, Vol 72, pp 313–356.
RogetF.M.GonzálezX.A.R. (2006), ‘Rural tourism demand in Galacia, Spain’, Tourism Economics, Vol 12, No 1, pp 21–31.
22.
SaaymanM., ed (2006), Tourism Marketing: Back to Basics, Leisure Consultants and Publications, Potchefstroom.
23.
SaaymanM.SaaymanA. (2003), ‘An economic analysis of international and African tourism markets of South Africa’, African Insight, Vol 33, No 1, pp 93–98.
24.
SmeralE.WittS.F. (1996), ‘Econometric forecasts of tourism demand to 2005’, Annals of Tourism Research, Vol 23, pp 891–907.
25.
SmithJ. (2006), ‘The determinants of international demand for tourism to South Africa’, unpublished Masters Dissertation, North-West University, Potchefstroom Campus, Potchefstroom.
26.
SongH.WongK.K.F.ChonK.K.S. (2003), ‘Modelling and forecasting the demand for Hong Kong tourism’, Hospitality Management, Vol 22, pp 435–451.
27.
Van Der MerweP.SaaymanM.KrugellW. F. (2007), ‘The determinants of the spending of biltong hunters’, South African Journal of Economics and Management Sciences, Vol 10, No 2, pp 184–194.
28.
WittS.F.WittC.A. (1995), ‘Forecasting tourism demand: A review of empirical research’, International Journal of Forecasting, Vol 11, pp 447–475.
29.
YaffeeR. (2003), ‘A primer for panel data analysis’, Connect Information Technology at NYU, Fall Edition, pp 1–11.
