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.
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References
Africa Tourism Monitor. 2015. Tourism in Africa is on the rise, but has not yet reached its full potential - African Development Bank. https://www.afdb.org/en/news-and-events/africa-tourism-monitor-2015-tourism-in-africa-is-on-the-rise-but-has-not-yet-reached-its-full-potential-15284/. Accessed 26 January 2017.
Akinboade, Oludele A., and Lydia A. Braimoh. 2010. International tourism and economic development in South Africa: A Granger causality test. International Journal of Tourism Research 12: 149–163. doi:10.1002/jtr.743.
Asrin, Karimi, Faroughi Pouya, and Abdul Rahim Khalid. 2015. Modeling and Forecasting of International Tourism Demand in ASEAN Countries. American Journal of Applied Sciences 12 (7): 479.
Becken, Susanne, Andrea Carboni, Shane Vuletich, and Aaron Schiff .2008. Analysis of tourist consumption, expenditure and prices for key international visitor segments: Technical report. Lincoln University.
Blake, Adam, Jorge Saba Arbache, M. Thea Sinclair, and Vladimir Teles. 2008. Tourism and poverty relief. Annals of Tourism Research 35: 107–126. doi:10.1016/j.annals.2007.06.013.
Cameron Colin, A. and Pravin K. Trivedi. 1998. Regression Analysis of Count Data (Econometric Society Monographs), 436. 1st Edn., Cambridge University Press, ISBN-10: 0521632013.
Chan, Yiu-Man, Tak-Kee Hui, and Edith Yuen. 1999. Modeling the impact of sudden environmental changes on visitor arrival forecasts: The case of the gulf war. Journal of Travel Research 37: 391–394. doi:10.1177/004728759903700409.
Chatziantoniou, Ioannis, George Filis, Bruno Eeckels, and Alexandros Apostolakis. 2013. Oil prices, tourism income and economic growth: A structural VAR approach for European Mediterranean countries. Tourism Management 36: 331–341. doi:10.1016/j.tourman.2012.10.012.
Che, Yeon-Koo B. 2004. An approach to modeling regional tourist attraction. Resource Development Market 20: 163–165.
Chen, Rachel J.C., Peter Bloomfield, and Frederick W. Cubbage. 2008. Comparing forecasting models in tourism. Journal of Hospitality & Tourism Research 32 (1): 3–21.
Cho, Vincent. 2001. Tourism Forecasting and Its Relationship with Leading Economic Indicators. Journal of Hospitality and Tourism 25 (4): 399–420.
Choyakh, Houssine. 2008. A model of tourism demand for Tunisia: Inclusion of the tourism investment variable. Tourism Economy 14: 819–838. doi:10.5367/000000008786440238.
Christie, Iain T, and Doreen E. Crompton. 2001. “Tourism in Africa”, Africa Region Working Paper, Series No.12, The World Bank Washington, DC.
Christie, Iain, Iain T. Christie, Eneida Fernandes, Hannah Messerli, and Louise Twining-Ward. 2014. Tourism in Africa: Harnessing tourism for growth and improved livelihoods. World Bank Publications.
Claveria, Oscar, and Salvador Torra. 2014. Forecasting tourism demand to Catalonia: Neural networks vs. time series models. Economic Modelling 36: 220–228.
Croes, Robertico R., and Manuel Vanegas Sr. 2005. An econometric study of tourist arrivals in Aruba and its implications. Tourism Management 26 (6): 879–890.
Crouch, Geoffrey. 1994a. The study of international tourism demand: A survey of practice. Journal of Travel Research 32: 41–55.
Crouch, Geoffrey. 1994b. The study of international tourism demand: A review of findings. Journal of Travel Research 33: 12–23.
Crouch, Geoffrey. 1995. A meta-analysis of tourism demand. Annas of Tourism Research 22 (1): 103–118.
De Mello, Maria M., and Natércia Fortuna 2005. Testing alternative dynamic systems for modelling tourism demand. Tourism Economy, 11: 517–537, 509–521. doi: 10.5367/0000005775108719.
Dhariwal, Richa. 2005. Tourist arrivals in India: How important are domestic disorders? Tourism Economics 11 (2): 185–205.
Dritsakis, Nikolaos. 2004. Cointegration analysis of German and British tourism demand for Greece. Tourism Management 25 (1): 111–119.
Du Preez, Johann, and Stephen F. Witt. 2003. Univariate versus multivariate time series forecasting: An application to international tourism demand. International Journal of Forecasting 19: 435–451. doi:10.1016/S0169-2070(02)00057-2.
Durbarry, Ramesh, and M. Thea Sinclair. 2003. Market shares analysis: The case of French tourism demand. Annals Tourism Research 30: 927–941. doi:10.1016/S0160-7383(03)00058-6.
Eilat, Yair, and Liran Einav. 2004. Determinants of international tourism: A three-dimensional panel data analysis. Applied Economics 36: 1315–1327.
Frechtling, Douglas C. 1996. Practical Tourism Forecasting. 1st Edn., Butterworth-Heinemann, Oxford, ISBN-10: 0750608773, pp, 245.
Gonzalez, Pilar, and Paz Moral. 1995. An analysis of the international tourism demand in Spain. International Journal of Forecasting 11 (2): 233–251.
Greene, William H. 2008. Econometric Analysis, 7th Edn., LaJolla: Granite Hill Publishers.
Greenidge, Kevin. 2001. Forecasting tourism demand: An STM Approach. Annals of Tourism Research 28 (1): 98–112.
Gujarati, Damodar N. 2003. Basic Econometrics, 4th Edn., Boston: McGraw-Hill.
Guloglu, Bulent, and R. Baris. Tekin. 2012. A panel causality analysis of the relationship among research and development, innovation and economic growth in high-income OECD countries. Eurasian Economic Review 2: 32–47. doi:10.14208/BF03353831.
Guo, Wenbin. 2007. Inbound tourism, an empirical research based on gravity model of international trade. Tourism Tribune 22: 30–34.
Halicioglu, Ferda. 2010. An econometric analysis of the aggregate outbound tourism demand of Turkey. Tourism Economy 16: 83–97. doi:10.5367/000000010790872196.
Hanafiah, Mohd Hafiz Mohd, and Mohd Fauzi Mohd Harun. 2010. Tourism demand in Malaysia: A cross-sectional pool time series analysis. International Journal of Trade, Economics and Finance 1: 80–83. doi:10.7763/IJTEF.2010.V1.15.
Hausman, Jerry A., Bronwyn H. Hall, and Zvi Griliches. 1984. Econometric models for count data with an application to the patents-R&D relationship.
Hu, Clark, Ming Chen, and Shiang-Lih Chen Mcchain. 2004. Forecasting in short-term planning and management for a casino buffet restaurant. Journal of Travel & Tourism Marketing 16: 79–98. doi:10.1300/J073v16n02_07.
Ibrahim, Mohammed A. 2011. The determinants of international tourism demand for Egypt: Panel data evidence. European Journal of Economics, Finance and Administrative Sciences 30: 50–58.
Kao, Chihwa. 1999. Spurious regression and residual-based tests for cointegration in panel data. Journal of Econometrics 90 (1): 1–44.
Kareem, Olayinka I. 2008. A panel data analysis of demand for tourism in Africa. Ibadan Journal of Social Sciences 4 (1).
Katircioglu, Salih T. 2009. Revisiting the tourism-ledgrowth hypothesis for Turkey using the bounds test and Johansen approach for cointegration. Tourism Management 30: 17–20. doi:10.1016/j.tourman.2008.04.004.
Kester, John G. 2003. International tourism in Africa. Tourism Economics 9: 203–221.
Khan, Habibullah, Rex S. Toh, and Lyndon Chua. 2005. Tourism and trade: Cointegration and granger causality tests. Journal of Travel Research 44: 171–176. doi:10.1177/0047287505276607.
Kim, Myung-Ja, Namho Chung, and Choong-Ki Lee. 2011. The effect of perceived trust on electronic commerce: Shopping online for tourism products and services in South Korea. Tourism Management 32 (2): 256–265.
Kulendran, Nada, and Stephen F. Witt. 2001. Cointegration versus least squares regression. Annals of Tourism Research 28: 291–311. doi:10.1016/S0160-7383(00)00031-1.
Kulendran, Nada, and Stephen F. Witt. 2003. Forecasting the demand for international business tourism. Journal of Travel Research 41: 265–271. doi:10.1177/0047287502239034.
Leiper, Neil, and Nerilee Hing. 1998. Trends in Asia-Pacific tourism in 1997–98: From optimism to uncertainty. International Journal of Contemporary Hospitality Management 10 (7): 245–251.
Li, Gang, Haiyan Song, and Stephen F. Witt. 2004. Modeling tourism demand: A dynamic linear AIDS approach. Journal of Travel Research 43: 141–150.
Li, Gang, Haiyan Song, and Stephen F. Witt. 2005. Recent developments in econometric modelling and forecasting. Journal of Travel Research 44: 82–99.
Lim, Christine. 1997. Review of international tourism demand models. Annals of Tourism Research 24 (4): 835–849.
Lim, Christine. 1999. A meta-analytic review of international tourism demand. Journal of Travel Research 37: 407–428.
Lim, Christine. 2004. The major determinants of Korean outbound travel to Australia. Mathematics and Computers in Simulation 64 (3–4): 477–485.
Lim, Christine, and Michael McAleer. 2001. Forecasting tourist arrivals. Annals Tourism Research 28: 965–977. doi:10.1016/S0160-7383(01)00006-8.
Lim, Christine, and Michael McAleer. 2008. Analysing seasonal changes in New Zealand’s largest inbound market. Tourism Recreation Research 33: 83–91. doi:10.1080/02508281.2008.11081292.
Makridakis, Spyros, Michele Hibon, and Claus Moser. 1979. Accuracy of forecasting: An empirical investigation. Journal of the Royal Statistical Society. Series A 142: 97–145. doi:10.2307/2345077.
Makridakis, Spyros, Steven C. Wheelwright, and Rob J. Hyndman. 1998. Forecasting, methods and applications, 923. 2nd Edn., Wiley, New York, ISBN-10: 047108610X.
Mangion, Marie-Louise, Ramesh Durbarry, and M. Thea Sinclair. 2005. Tourism Competitiveness: Price and quality. Tourism Economics 11 (1): 45–68.
Massidda, Carla, and Paolo Mattana. 2013. A SVECM analysis of the relationship between international tourism arrivals, GDP and trade in Italy. Journal of Travel Research 52 (1): 93–105.
Morley, Clive L. 1998. A dynamic international demand model. Annals of Tourism Research 25 (1): 70–84.
Muchapondwa, Edwin, and Obert Pimhidzai. 2011. Modelling international tourism demand for Zimbabwe. International Journal of Business and Social Science 2 (2).
Naudé, Willem A., and Andrea Saayman. 2005. Determinants of tourist arrivals in Africa: A panel data regression analysis. Tourism Economics 11 (3): 365–391.
Odularu, Gbadebo O., and Olayinka I. Kareem. 2007. Tourism and reputation in Africa: A panel data analysis. African Journal of Economic Policy 14 (2): 51–82.
Ouerfelli, Chokri. 2008. Co-integration analysis of quarterly European tourism demand in Tunisia. Tourism Management 29: 127–137. doi:10.1016/j.tourman.2007.03.022.
Pedroni, Peter. 1999. Critical values for cointegration tests in heterogeneous panels with multiple regressors. Oxford Bulletin Economy Statistics 61: 653–670. doi:10.1111/1468-0084.0610s1653.
Pedroni, Peter. 2004. Panel cointegration: Asymptotic and finite sample properties of pooled time series tests with an application to the PPP hypothesis. Econometric Theory 20: 597–625. doi:10.1017/S0266466604203073.
Peng, Bo, Haiyan Song, Geoffrey I. Crouch, and Stephen F. Witt. 2014. A meta-analysis of international tourism demand elasticities. Journal of Travel Research. doi: 10.1177/0047287514528283.
Pesaran, M. Hashem. 2004. General diagnostic tests for cross section dependence in panels.
Pesaran, M. Hashem. 2007. A simple panel unit root test in the presence of cross-section dependence. Journal of Applied Econometrics 22 (2): 265–312.
Rogerson, Christian M. 2007. Tourism routes as vehicles for local economic development in South Africa: The example of the Magaliesberg Meander. Urban Forum 18 (2): 49–68.
Saayman, Andrea, and Melville Saayman. 2008. Determinants of inbound tourism to South Africa. Tourism Economics 14 (1): 81–96.
Santana-Gallego, María, Francisco J. Ledesma-Rodríguez, and Jorge V. Pérez-Rodríguez. 2010. Exchange rate regimes and tourism. Tourism Economics 16 (1): 25–43.
Sarmidi, Tamat, and Norlida H. Salleh. 2010. Dynamic inter-relationship between trade, economic growth and tourism in Malaysia. Munich University Library.
Seetaram, Neelu, and Larry Dwyer. 2009. Immigration and tourism demand in Australia: A panel data analysis. Anatolia 20 (1): 212–222.
Siddique, Abu, E. Antony Selvanathan, and Saroja Selvanathan. 2012. Remittances and economic growth: Empirical evidence from Bangladesh, India and Sri Lanka. Journal of Development Studies 48: 1045–1062. doi:10.1080/00220388.2012.663904.
Smeral, Egon. 2010. Impacts of the world recession and economic crisis on tourism: Forecasts and potential risks. Journal of Travel Research 49 (1): 31–38.
Soltani, Peyman Karimi, Abbas Aeini, and Zahra Moosavi Jam. 2015. Diversity in the traditional musical Kurdish themes and the tourist attraction. Cumhuriyet Üniversitesi Fen Fakültesi Fen Bilimleri Dergisi 36 (3): 3965–3975. Special Issue I.
Song, Haiyan, and Gang Li. 2008. Tourism demand modelling and forecasting–A review of recent research. Tourism Management 29: 203–220. doi:10.1016/j.tourman.2007.07.016.
Song, Haiyan, and Shanshan Lin. 2010. Impacts of the financial and economic crisis on tourism in Asia. Journal of Travel Research 49 (1): 16–30.
Song, Haiyan, and Kevin K.F. Wong. 2003. Tourism demand modelling: A time-varying parameter approach. Journal of Travel Research 42 (1): 57–64.
Song, Haiyan, Peter Romilly, and Xiaming Liu. 1998. The UK consumption function and structural instability: Improving forecasting performance using a timevarying parameter approach. Applied Economy 30: 975–983. doi:10.1080/000368498325408.
Song, Haiyan, Peter Romilly, and Xiaming Liu. 2000. An empirical study of outbound tourism demand in the UK. Applied Economics 32 (5): 611–624.
Song, Haiyan, Stephen F. Witt, and Gang Li. 2008. The advanced econometrics of tourism demand. Abington: Routledge.
Tan, Amy Y.F., Cynthia McCahon, and Judy Miller. 2002. Modeling tourist ows to Indonesia and Malaysia. Journal of Travel and Tourism Marketing 13 (1/2): 63–84.
Tang, Sumei, E.Antony Selvanathan, and Saroja Selvanathan. 2007. The relationship between foreign direct investment and tourism: Empirical evidence from China. Tourism Economy 13: 25–39. doi:10.5367/000000007779784498.
Travel and Tourism Competitiveness Report. 2015. Travel and Tourism Competitiveness Report 2015 - Reports - World Economic Forum. http://reports.weforum.org/travel-and-tourism-competitiveness-report-2015/index-results-the-travel-tourism-competitiveness-index-ranking-2015/. Accessed 25 May 2016.
Turner, Lindsay W., and Stephen F. Witt. 2001. Factors influencing demand for international tourism: Tourism demand analysis using structural equation modeling, Revisited. Tourism Economics 7 (1): 21–38.
Veloce, William. 2004. Forecasting inbound Canadian tourism: An evaluation of error corrections model forecasts. Tourism Economics 10: 262–280. doi:10.5367/0000000041895049.
Webber, Anthony G. 2001. Exchange rate volatility and cointegration in tourism demand. Journal of Travel Research 39 (4): 398–405.
Witt, Christine A., Stephen F. Witt, and Nick Wilson. 1994. Forecasting international tourist flows. Annals of Tourism Research 21: 612–628. doi:10.1016/0160-7383(94)90123-6.
Witt, Stephen F. and Christine A. Witt. 1995. Forecasting tourism demand, A review of empirical research. International Journal of Forecasting 11.
Witt, Stephen F., Haiyan Song, and Panos Louvieris. 2003. Statistical testing in forecasting model selection. Journal of Travel Research 42: 151–158. doi:10.1177/0047287503253941.
Wong, Kevin K.F., Haiyan Song, and Kaye S. Chon. 2006. Bayesian models for tourism demand forecasting. Tourism Management 27: 773–780. doi:10.1016/j.tourman.2005.05.017.
World Tourism Organization UNWTO. 2016. International tourist arrivals up 4% reach a record 1.2 billion in 2015. World Tourism Organization UNWTO. http://media.unwto.org/press-release/2016-01-18/international-tourist-arrivals-4-reach-record-12-billion-2015. Accessed 25 May 2016.
Xiao, Honggen, and Stephen L.J. Smith. 2006. Case studies in tourism research: A state of-the-art analysis. Tourism Management 27: 738–749.
Zhou-Grundy, Yvonne, and Lindsay W. Turner. 2014. The challenge of regional tourism demand forecasting the case of China. Journal of Travel Research 53 (6): 747–759. doi:10.1177/0047287513516197.
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Appendix
Appendix
1.1 Data and methodology
This study employs annual data over the period 1995–2015 on a sample of 44 African countries. The data are collected from the World Development Indicators and the Worldwide Governance Indicators database made available by the World Bank. A total of 10 countries are exempted because of data non-availability. The countries included in this study are Algeria, Angola, Benin, Botswana, Burkina Faso, Cameroon, Cape Verde, Central African Republic, Chad, Democratic Republic of the Congo, Egypt, Equatorial Guinea, Ethiopia, Gabon, Gambia, Ghana, Guinea, Ivory Coast, Kenya, Lesotho, Liberia, Libya, Madagascar, Malawi, Mali, Mauritania, Mauritius, Morocco, Mozambique, Namibia, Niger, Nigeria, Republic of the Congo (Brazzaville), Senegal, Sierra Leone, South Africa, Sudan, Swaziland, Tanzania, Togo, Tunisia, Uganda, Zambia, and Zimbabwe.
1.1.1 Test cross-section dependence
Prior to estimation, this study checks for cross-sectional dependence among the variables using Pesaran’s (2004) cross-sectional dependence test. The test is applicable when N > T, which is the case in the present study (i.e., 44 countries (N) >21 years (T)). The test statistic can be defined as
where \(\hat{\rho }_{ij}\) is the sample estimate of the pairwise correlation of the residuals, \(\hat{\rho }_{ij} = \hat{\rho }_{ji} = \frac{{\sum\nolimits_{t = 1}^{T} {\varepsilon_{it} \varepsilon_{jt} } }}{{ (\sum\nolimits_{t = 1}^{T} {\varepsilon_{it}^{ 2} } )^{{{\raise0.5ex\hbox{$\scriptstyle 1$} \kern-0.1em/\kern-0.15em \lower0.25ex\hbox{$\scriptstyle 2$}}}} (\sum\nolimits_{t = 1}^{T} {\varepsilon_{jt}^{ 2} } )^{{{\raise0.5ex\hbox{$\scriptstyle 1$} \kern-0.1em/\kern-0.15em \lower0.25ex\hbox{$\scriptstyle 2$}}}} }}\)where ɛ it and ɛ jt are the residuals obtained from Eq. (2).
1.1.2 Cross-Sectional Augmented Dickey–Fuller test
In the presence of cross-sectional dependence, traditional panel unit root tests become invalid. To overcome this problem, this study uses Pesaran’s (2007) Cross-Sectional Augmented Dickey–Fuller test. This test is a variation of Pesaran and Shin’s (2003) unit root test. It includes the lagged cross-sectional mean and its first difference in order to capture the resulting cross-sectional dependence with a single-factor model. The test equation is given as follows:
where \(\bar{y}_{t} = N^{{ - 1}} \sum\nolimits_{{j = 1}}^{N} {y_{{jt}} }\) and the mean y it of all cross-sectional observations at time t. p is the lagged order of the model. The null hypothesis is H 0:b i = 0 for all i against the alternative hypothesis H 1:b i < 0 for some i.
Pesaran’s (2007) Cross-Sectional Augmented Dickey–Fuller test is given by
where t i (N, T) is the t statistic of b i in Eq. (2) and the Cross-Sectional Augmented Dickey–Fuller statistics for country i. In order to avoid the extreme statistic problem of a small sample, the truncated version of the Eq. (4) is given as
where
The parameters K 1 and K 2 are positive constants, based on Pesaran’s (2007) simulations. Pesaran (2007) suggests using K 1 = 6.42 and K 2 = 1.71 for models with intercept and trend, respectively. The critical values can be obtained from Table I and Table II of Pesaran (2007).
1.1.3 Poisson, generalized Poisson, and negative binomial regression models
If X i is distributed as Poisson, the panel Poisson regression model with fixed effects is defined as
where X = (X 1, X 2, X 3, …, X n )T is the response vector, n is the sample size, and X i , and X j are independent for any i ≠ j.
Using the log link function, the covariates of λ I = E (X i ) for the Poisson regression model are
where a i is the vector of covariates, δ is the vector of regression parameters with mean and variance, E(X i ) = Var (X i ) = λ i , and ξ i represent the individual effects.
One of the assumptions of the Poisson regression model, that the conditional mean and the conditional variance functions are equal, limits the applicability of the model. Count data are often over-dispersed. According to Guloglu and Tekin (2012), over-dispersion arises from the unobserved heterogeneity of cross-section units. While a negative binomial regression is appropriate for handling over-dispersion, a generalized Poisson regression is useful for over- or under-dispersed count data. The Poisson model is nested within the generalized Poisson and negative binomial regression. A two-sided likelihood ratio test is (2LRT) used to test the dispersion in the panel Poisson regression against the generalized Poisson and negative binomial regression (Cameron and Trivedi 1998) where the hypotheses are as follows: H0: Dispersion Parameter = 0, and H1: Dispersion Parameter ≠ 0. The 2LRT statistic is asymptotically distributed as a Chi square with one degree of freedom.
The 2LRT statistic is
where ln L 1 and ln L 0 are the log likelihoods. The estimates of the Poisson model serve as the initial values for fitting the generalized Poisson and negative binomial regression models. In order to compare the models, this study employs the Akaike Information Criteria (AIC).
The AIC is given by
where L refers to the maximum likelihood function.
The model with the least AIC is the one that best fits the data.
1.2 The empirical tests
The cross-sectional dependence test rejects the null hypothesis of no cross-sectional dependence among the variables (Table 2). The statistics is 41.10 (p value = 0.01). This result strongly indicates the existence of cross-sectional dependence among the variables.
Having established the existence of cross-sectional dependence among the variables, Pesaran’s (2004) Cross-Sectional Augmented Dickey–Fuller unit root test is used to determine the unit root properties of the variables. The test results show that the variables had unit root problems and had to be differenced. Once differenced, the time series were integrated of order one and showed no unit root problems (Table 3).
Having established that all the variables are integrated of order one, panel co-integration test is used to determine the co-integrating relations among the variables (Table 4). The results indicate the presence of long-run relationships among the set of variables.
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Adeola, O., Boso, N. & Evans, O. Drivers of international tourism demand in Africa. Bus Econ 53, 25–36 (2018). https://doi.org/10.1057/s11369-017-0051-3
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DOI: https://doi.org/10.1057/s11369-017-0051-3