Determinants of Tourist Arrivals in Africa: A Panel Data Regression Analysis

Abstract

Africa's tourism potential is acknowledged to be significant but underdeveloped. This paper uses both cross-section data and panel data for the period 1996–2000 to identify the determinants of tourism arrivals in 43 African countries, taking into account tourists' country of origin. The results strongly suggest that political stability, tourism infrastructure, marketing and information, and the level of development at the destination are key determinants of travel to Africa. Typical ‘developed country determinants’ of tourism demand, such as the level of income in the origin country, the relative prices and the cost of travel, are not so significant in explaining the demand for Africa as a tourism destination. It is therefore recommended that attention should be given to improving the overall stability of the continent and the availability and quantity of tourism infrastructure.

Would a single regional visa encourage tourist arrivals in southern Africa?

“…Naude and Saayman (2005) found that political stability, personal safety, health risks and available infrastructure are some factors that determine whether a tourist will visit the continent. Brown (2000) studied the effect of political risks and other barriers to tourism promotion in Africa and Gauci et al (2002) investigated challenges and opportunities for tourism in Africa.…”

“…The good news is, however, that even though tourism activity is still focused on the developed countries of the Americas and Europe, new touristgenerating and tourist-receiving markets are proliferating in the developing regions of East Asia and the Pacific, South Asia, the Middle East and Africa (Dabour, 2001). To earn foreign exchange and create jobs, many developing countries are building up their tourism sectors and tourism-related industries (Naude & Saayman, 2005) and strengthening their regional collaboration.…”

Air Access Liberalisation, Marketing Promotion and Tourism Trade

“…The more the room the more the capacity and more competitive that country's tourism sector (cheaper price as competition). Moreover a minimum is hotel accommodation size needed for a destination to reach its critical mass and also to convince airlines to establish routes (Naudee and Saayman, 2004). Data on the number of rooms were obtained from the Central Statistical Office of the country.…”

“…To capture the above we follow Eilat and Einav (2004) and Naudee and Saayman (2004) by using the CPI of a destination country adjusted by the $ exchange rate as a proxy for relative tourism prices to get relative prices (measured as RELATIVE). ‗The inverse of it shows the many baskets of goods a tourist has to give up in his home country in order to buy a basket of goods in the destination country ' (Eilav and Einav, 2004).…”

A combination selection algorithm on forecasting

“…Three main types of forecasting models (Li, Song & Witt, 2005; are Time series model (Cao, Ewing & Thompson, 2012;Cho, 200;Goshall & Charlesworth, 2011), Causal econometric model (Li, Song & Witt, 2006;Naude & Saayman, 2005;Page, Song & Wu, 2012;Roget & Gonzalez, 2006) and new emerging Artificial Intelligence based model, such as neural network, fuzzy time-series theory, grey theory, genetic algorithms, and expert systems (Cao, Ewing & Thompson, 2012;Carbonneau, Laframboise & Vahidov, 2008;Bodyanskiy & Popov 2006;Chen & Wang, 2007;Cho, 2003;Hadavandi, Ghanbari , Shahanaghi & Abbasian-Naghneh, 2011;Law & Au, 1999;Pai & Hong, 2005;Wong, Xia & Chu, 2010;Wu & Akbarov, 2011). From these studies, researchers often seek to identify the best individual model to generate a forecast.…”