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Drivers of international tourism demand in Africa

Author

Listed:
  • Ogechi Adeola

    (Pan-Atlantic University)

  • Nathaniel Boso

    (University of Leeds)

  • Olaniyi Evans

    () (University of Lagos)

Abstract

Abstract Despite Africa’s potential for tourism, the continent’s tourism endowments are still largely underdeveloped and underutilized. The identification and enquiry into the drivers of international tourism demand in Africa is key to any effort to understand and explain changes in tourism demand in Africa. This study estimates a Poisson regression model to determine the key drivers of international tourism demand in 44 African countries, employing annual data over the period 1995–2015. The outcomes of the Poisson regression show that taste formation, real exchange rate, infrastructure, political stability and absence of violence, per capita income, FDI, and trade openness are significant drivers of international tourism into Africa. However, travel costs and domestic prices are not significant drivers of the decision to travel to Africa.

Suggested Citation

  • Ogechi Adeola & Nathaniel Boso & Olaniyi Evans, 2018. "Drivers of international tourism demand in Africa," Business Economics, Palgrave Macmillan;National Association for Business Economics, vol. 53(1), pages 25-36, January.
  • Handle: RePEc:pal:buseco:v:53:y:2018:i:1:d:10.1057_s11369-017-0051-3
    DOI: 10.1057/s11369-017-0051-3
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    References listed on IDEAS

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    More about this item

    Keywords

    International tourism demand; Tourism arrivals; Africa; Panel poisson regression;

    JEL classification:

    • L83 - Industrial Organization - - Industry Studies: Services - - - Sports; Gambling; Restaurants; Recreation; Tourism
    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis
    • O55 - Economic Development, Innovation, Technological Change, and Growth - - Economywide Country Studies - - - Africa
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models

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