Drivers of international tourism demand in Africa

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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Fig. 1
Fig. 2

Data Source world development indicators (2016)

Fig. 3

Data Source UNWTO (2016)

Fig. 4

Data Source World development indicators (2016)

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Correspondence to Olaniyi Evans.

Appendix

Appendix

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.

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

$${\text{CD}} = \sqrt {\frac{ 2T}{N (N - 1 )}} \left( {\sum\limits_{i = 1}^{N - 1} {\sum\limits_{j = i + 1}^{N} {\hat{\rho }_{ij} } } } \right) \sim N ( 0 , 1 ),$$
(2)

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).

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:

$$\Delta y_{it} = a_{i} + b_{i} y_{it - 1} + c_{i} \bar{y}_{it - 1} + \sum\limits_{j = 0}^{p} {d_{j + 1} } \Delta \bar{y}_{it - 1} + \sum\limits_{k = 1}^{p} {c_{k} } \Delta y_{it - 1} + e_{it},$$
(3)

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

$${\text{t}}_{\text{i}} (N ,T )= N^{ - 1} \sum\limits_{i = 1}^{N} {t_{i} (N ,T )},$$
(4)

where t i (NT) 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

$$t_{i}^{ *} (N ,T )= N^{ - 1} \sum\limits_{i = 1}^{N} {t_{i}^{ *} (N ,T )},$$
(5)

where

$$t_{i}^{ *} (N ,T )= \left\{ {\begin{array}{ll} {t_{i} (N ,T ) , { }} \\ {-K_{ 1} ,} \\ {{ - }K_{ 2} ,} \\ \end{array} } \right.\begin{array}{ll} {{\text{ if}} -K_{ 1}; t_{i} (N ,T ) { - }K_{ 2} } \\ {{\text{if}} -t_{i} (N ,T )\le K_{ 1} } \\ {{\text{if}} -t_{i} (N ,T )\ge K_{ 2} } \\ \end{array}.$$

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).

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

$$\Pr (X_{I} = y_{i} ) = \frac{{\exp ( - \lambda_{i} )\lambda_{i}^{{x_{i} }} }}{{x_{i} !}}, \, x_{i} = 0,{ 1, 2, } \ldots,$$

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

$$\log \, \lambda_{\text{i}} = a_{it}^{T} \delta + \xi_{i},$$

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

$$T = 2(\ln L_{1} - \ln L_{0} ),$$

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

$$AIC = 2\dim (\theta ) - 2\ln (L),$$

where L refers to the maximum likelihood function.

The model with the least AIC is the one that best fits the data.

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.

Table 2 Pesaran’s (2004) cross-sectional dependence test

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).

Table 3 Pesaran’s Cross-Sectional Augmented Dickey–Fuller test

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.

Table 4 Panel co-integration test results

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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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Keywords

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

JEL Classification

  • L83
  • D12
  • O55
  • C33