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IS TOURISM A SPUR TO ECONOMIC GROWTH IN SOUTH AFRICA? AN EMPIRICAL INVESTIGATION

In this study, the dynamic Granger-causality between tourism development and economic growth in South Africa was empirically examined during the period 1995-2016. The study was motivated by the growing important role of the tourism sector in economic growth and development. It was also motivated by the limelight that the South African tourism sector has been enjoying in recent years, on the one hand, and the lack of sufficient coverage of tourism-growth nexus studies in many sub-Saharan African countries, on the other hand. Unlike some previous studies that used one proxy, the current study used two tourism proxies, namely tourist arrivals and tourism revenue, to examine this link. In addition, the study used exchange rate and foreign direct investment as intermittent variables in a multivariate Granger-causality model in order to address the omission-of-variable bias. To enhance the robustness of the results, the study also used two measures of tourism revenue, namely total tourism revenue and total tourism revenue as a percentage of GDP. Using the auto-regressive distributed lag (ARDL)-bounds testing approach and the error correction model, the study found that the direction of causality between tourism development and economic growth in South Africa is sensitive to the proxy used and the time under consideration. When the tourist arrivals variable is used as a proxy for tourism development, bidirectional causality between tourism development and economic growth is found to prevail in the short run, while a unidirectional causality from economic growth to tourism development is found to dominate in the long run. However, when tourism revenue is used as a proxy, a feedback relationship is found to prevail, but only in the short run. The result is robust across the two different measures of tourism revenue. The study, therefore, recommends that short-term policy efforts should be directed at developing the tourism sector and the real sector as both sectors have been found to reinforce each other in the short run, irrespective of the tourism proxy used. 1 Corresponding author: Sheilla Nyasha, Department of Economics, University of South Africa (UNISA). Email address: sheillanyasha@gmail.com .


Introduction
The relationship between tourism and economic growth has attracted a plethora of empirical literature in recent years. On the theoretical front, tourism is expected to spur economic growth through a number of channels. Firstly, an increase in tourism development is expected to lead to an increase in employment, which directly leads to an increase in economic growth (World Travel and Tourism Council "WTTC", 2019). This is largely because tourism is regarded as one of the most labour-intensive industriesimplying that the more developed the sector is, the higher the employment. Secondly, the development of the tourism industry is likely to lead to an increase in the inflow of foreign exchange revenues owing to the increased number of tourist arrivals, which positively contributes towards the overall balance of payments (WTTC, 2019;Signe, 2018).
Moreover, the foreign exchange earned from international tourism may be used to purchase capital goods, which could be used in the production processthereby leading to a further increase in economic growth. In addition, tourism could also stimulate investments in new infrastructure, which may also stimulate growth (World Bank, 2011).
On the empirical front, there are four views regarding the causal relationship between tourism and economic growth. The first view, which is often referred to as the tourism-led growth (TLG) hypothesis, posits that tourism development is an important engine of economic growth and, therefore, leads to economic growth (see Nene and Taivan, 2017;Wu and Wu, 2019, among others). The second view, which is also referred to as the growthled tourism (GLT) hypothesis, on the other hand, argues that it is economic growth that drives the development of the tourism industry both in the short run and in the long run.
Studies, of which the findings were consistent with this view, include those by Lee and Kwag (2013) and Bouzahzah and Menyari (2013), among others. In between these two extreme views, there is a third (middle ground) view, which asserts that both tourism development and economic growth drive each other (Wang and Xia, 20133;Trang et al., 2014, among others). Despite these three strands of literature, there is a fourth view, which argues that there is no Granger-causality between tourism and economic growth (see Arslanturk et al., 2011;Pisa, 2018, among others).
Although a number of studies have been conducted into the relationship between tourism development and economic growth in various countries, very few studies have been conducted in the sub-Saharan African region; and are mostly on the impact of tourism on economic growth, leaving the causality between tourism and economic growth in Africa, in general, and in South Africa, in particular, little explored (see Fayissa et al., 2008). In isolated cases where tourism-growth causality studies have been conducted on African countries, the findings have been conflicting or ambiguous at best. Moreover, the findings of some of the previous studies have been affected negatively by a number of methodological challenges. For example, some of the previous studies relied on crosssectional data analysis, which does not account for country-specific effects; while other studies used bivariate Granger-causality models, which have been found to suffer from the omission-of-variable bias.
It is against this background that the current study aims to examine the causal relationship between tourism and economic growth, using time-series data from South Africa. Unlike some previous studies, the current study uses the error-correction model (ECM)-based autoregressive-distributed lag (ARDL) bounds testing approach, which has been found to be superior, to examine this linkage. In order to address the omission-of-variable bias, which has been found to be associated with some of the previous studies, the current study uses two intermittent variables alongside tourism and economic growth, thereby creating a multivariate Granger-causality model. In addition, the study uses two proxies of tourism development to examine this linkage. In order to enhance the robustness of the results, the study uses two measures of tourism revenue, namely total tourism revenue and tourism revenue as a percentage of GDP.
The rest of the study is organised as follows: Section 2 analyses the dynamics of tourism and economic growth in the country under study, while Section 3 reviews literature on the causal relationship between tourism development and economic growth. Section 4 presents the estimation techniques used in the empirical investigation of the direction of causality between tourism development and economic growth in South Africa. In Section 5, the results of the study are presented and analysed, and in Section 6, the conclusion of the study is proffered.

Tourism and economic growth dynamics in South Africa
As in many other African countries, the tourism sector in South Africa plays a significant role in job creation and economic growth. Indeed, the South African tourism sector has grown phenomenally since the 1990s. The sector is considered to have a significant potential, ranging from wildlife resources to spectacular landscapes, water bodies, beaches, a diversity of cultures, and a number of world heritage sites, among many others. Figure 1 shows trends in tourismmeasured by the number of international tourist arrivals and tourism revenue (tourism receipts)and economic growth, proxied by real GDP per capita.

FIGURE 1: Trends in Tourism an Economic Growth in South Africa (1995-2016)
As revealed in Figure 1, the number of tourist arrivals has doubled since 1995, from just over four million to 10 million in 2016 (World Bank, 2019). Tourism receipts also soared from US$2.7 billion (bn) in 1995 to a peak of US$11.2bn in 2012, before settling at South Africa has a dual economy, characterised by a sophisticated financial and industrial economy that has grown alongside an underdeveloped informal economy. According to Gumede (2008), it is this "second economy" that presents a potential and developmental challenge. South Africa's success in reforming its economic policies is mirrored by its GDP, which reflected an unprecedented 62 quarters of uninterrupted economic growth between 1993 and 2007 (Schlumberger and Weisskopf, 2014 Thereafter, a slight decline was experienced, which saw the GDP performing at around 2 per cent before dropping to 1.5 per cent in 2014, and further down to 1.3 per cent in 2015 (South African Reserve Bank "SARB", 2016). The mining and manufacturing sectors experienced a major slow down during the review period, recording an average growth of -0.9 per cent and 1.1 per cent respectively.
Contrary to the performance of the productive sectors, the services sector sustained positive growth rates reaching 3.0 per cent on average. While the economy continues to growdriven largely by domestic consumptiongrowth is at a slower rate than previously forecasted. Real GDP grew marginally by 0.2 per cent (q/q) in the third quarter of 2016 as a result of a better-than-expected performance in the agriculture and mining sectors (SARB, 2016). During the review period, per capita GDP modestly trended upwards.

Literature Review
On the empirical front, there are four views regarding the causal relationship between tourism and economic growth. The first view, which is often referred to as the tourism-led growth (TLG) hypothesis, posits that tourism development is an important engine of economic growth and, therefore, leads to economic growth. These studies include, among The second view, which is referred to as the growth-led tourism (GLT) hypothesis, on the other hand, argues that it is economic growth that drives the development of the tourism industry both in the short run and in the long run. Studies, of which the findings were consistent with this view, include those conducted by, among others, He and Zheng (2011) In between these two extreme views, we have a third (middle -ground) view, which asserts that both tourism development and economic growth drive each other. In other words, there is a feedback (bi-directional) causal relationship between tourism development and economic growth. These include studies conducted by Dritsakis (2004) Despite these three strands of literature, which posit that there is a causal relationship between tourism and economic growth at least in one direction, there is a fourth view which argues that there is no relationship between tourism and economic growth, and that their perceived empirical relationship could merely be mechanical. This view, though unpopular, has received support from researchers such as Oh (2005) Although support has been found in the empirical literature for all four possible causal relationships, bidirectional causality is the most popular causal relationship, while no causality is unpopular. Studies based on time-series data appear to be more popular than the panel data-based studies on the subject. Table 1 summarises the reviewed literature on the causality between tourism development and economic growth.

The ARDL-bounds-testing approach to cointegration
In this study, the ARDL bounds testing technique is used, following earlier work by Pesaran and Shin (1999), which was later extended by Pesaran et al. (2001) to examine the dynamic relationship between tourism development and economic growth. The ARDL approach was chosen for this study because of a number of advantages it has compared to the traditional estimation techniques such as the Full-Maximum Likelihood (FML) test and the residual-based technique (see Majid, 2008;Odhiambo, 2008).
In the ARDL approach, unbiased long-run estimates and valid t-statistics can be produced, even when some of the regressors are endogenous (Odhiambo, 2008). This technique does not impose the restrictive assumption that all the variables need to be integrated of the same order, hence it can be applied to variables that are integrated of order zero or order one or a mixture of the two. While other traditional cointegration estimation techniques are sensitive to the sample size, the ARDL bounds testing method is appropriate even when the sample size is small (see Pesaran et al., 2001). In addition, the ARDL approach has the ability to take a sufficient number of lags to capture the data-generating process in a general-to-specific modelling framework to obtain optimal lag length per variable. In recent years, the technique has taken centre stage as researchers have refined the precision of their estimations.
In order to address the omission-of-variable bias associated with bivariate Grangercausality model, this study has utilised two intermittent variables, namely exchange rate and foreign direct investmentthereby creating a multivariate Granger-causality model, whose function is expressed as: Y In an attempt to enhance the depth of the tourism-growth causality study in the country under study, two proxies of tourism development were used. In Model 1, tourism (TOURARRIVE) is proxied by tourist arrivals, while in Model 2, tourism is proxied by tourism revenue. In order to enhance the robustness of the results, two measures of tourism revenue were used, namely total tourism revenue (TOUREV) and tourism revenue as a percentage of GDP (TOURREV/Y). Table 2 summarises variable descriptions and proxies utilised in this study. Following Pesaran et al. (2001), the generic cointegration model for this study is expressed in the form of a set of four cointegration equations as follows (see Odhiambo, 2016): The generic ECM-based Granger-causality model specification is given as (see Nyasha and Odhiambo, 2015): Where: ECM = error-correction term; 1 − 4 = respective coefficients for the error-correction terms; μit = mutually uncorrelated white-noise residuals; and All other variables and characters are as described in equations 2-5.

Data Source
Annual time-series data from 1995 to 2016 were used in this study. The data were sourced from the World Bank's World DataBank (World Bank, 2019).

Unit Root Tests
Although the ARDL method does not require all variables to be of the same order of integration, it cannot be applied when the variables are integrated of order two [I(2)] or higher. Consequently, it is recommended that a unit root test be conducted to check whether all the variables are integrated of order one [I(1)] and/or below. In this study, ADF, Dickey-Fuller generalised least squares (DF-GLS) and the Phillips-Perron (PP) unit root tests were employed. The results are summarised in Table 3   -3.083 -3.131 -6.803*** -6.631*** Note: *** and ** denote stationarity at 1% and 5% significance levels. Table 3, the results of the unit root tests indicate that all the variables are integrated of order one, irrespective of the unit root test employed. The results, therefore, confirm the validity and suitability of using the ARDL approach.

Cointegration Tests
Following the confirmation that all the variables in the study are integrated or order one or less, the study proceeded to test for cointegration among the variables. The results of the cointegration test carried out in this study are summarised in Table 4. Note: *, ** and *** denote statistical significance at 10%, 5% and 1% levels, respectively The cointegration results, as displayed in Table 4, confirm the presence of cointegration in each model, as there is at least one cointegration vector in each model. Having ascertained the presence of a stable long-run relationship among the variables in each model, the ECMbased Granger-causality approach was used to examine the causality among the variables used in this study. It should, however, be emphasised that the long-run causality is only estimated for the functions that tested positive for cointegration (Odhiambo, 2014;Odhiambo and Nyasha, 2019). This implies that only equations that were found to be cointegrated will be estimated with an error-correction term (see also Odhiambo, 2010;Morley, 2006;Narayan and Smyth, 2006). Based on the Granger-causality model used in this study, the long-run causality is determined by the t-statistics on the coefficients of the lagged error-correction terms, while the short-run causality is determined by the corresponding F-statistics (see also Narayan and Smyth, 2006;Oh and Lee, 2004). Table 5 presents the ECM-based Granger-causality results for the two models used in this study.

TABLE 5: Results of Granger-Causality Tests a) Model 1
Note: *, ** and *** denote statistical significance at 10%, 5% and 1% levels, respectively As revealed by the Granger-causality results for Model 1 displayed in Table 5a, the study found bidirectional Granger-causality between tourism (TOURARRIVE) and economic growth (Y/N) when tourism is proxied by international tourist arrivals in South Africalending support to the feedback hypothesis, where tourism and economic growth propel each other. However, these results hold only in the short run. Consistent with these results are previous studies by Trang et al. (2014) and Wu and Wu (2019), among others. In the long run, Granger-causality was found to be unidirectional, running from economic growth to tourismlending support to the growth-led tourism development. This outcome also has support in the literature (see Nene and Taivan, 2017

c) Model 2B
Note: *, ** and *** denote statistical significance at 10%, 5% and 1% levels, respectively The results from Model 2A reveal the presence of bidirectional Granger-causality between tourism, proxied by tourism and economic growth (Y/N) when tourism is proxied by tourism revenue (TOURREV). However, these results apply only in the short runalso lending support to the feedback hypothesis, just as in Model 1, where tourism development and economic growth Granger-cause each other; and are consistent with the results of some previous studies (see Trang et al., 2014;Wu and Wu, 2019 Overall, the findings of the study show that in South Africa, the Granger-causality between tourism and economic growth is not as obvious as usually anticipated and cannot be predetermined with certainty. It has been found to vary depending on the proxy used and the time under consideration. When the tourist arrivals variable is used as a proxy for tourism development, there is short-run bidirectional causality between tourism and economic growth in South Africa in the short run and a long-run unidirectional causality from economic growth to tourism development. However, when total tourism revenue and total tourism revenue as a percentage of GDP are used to measure tourism development, only short-run bidirectional causality between tourism and economic growth is found to prevail in South Africa. The results of the diagnostic tests performed on serial correlation, functional form, normality and heteroscedasticity show that, on the whole, the models used in this study passed the relevant diagnostic tests (see Appendix 2).

Conclusion
In this study, the causality between tourism development and economic growth in South Africa has been investigated empirically, covering the period from 1995 to 2016. The study was motivated by the growing important role of tourism in the growth and development of economies, and the limelight the South African tourism sector has been enjoying of late, on the one hand, and the lack of sufficient coverage of tourism-growth nexus studies in South Africa. In addition, uncovering what could drive economic growth in South Africa is vital as the economy tries to recover from its current low level of economic growth.
Exchange rate and FDI are the two intermittent variables added to the study to address the variable-omission-bias, giving rise to a multivariate Granger-causality model. Two proxies of tourism developmenttourist arrivals and tourism revenuewere used in an attempt to enhance the rigour of the study. Using the ARDL bounds testing approach, the study found that the direction of causality between tourism development and economic growth in South Africa is sensitive to the tourism proxy used and the time under consideration. When the tourist arrivals variable is used as a proxy for tourism development, there is short-run bidirectional causality between tourism and economic growth in South Africa in the short run, and a long-run unidirectional causality from economic growth to tourism development.
However, when tourism revenue is used as a proxy, only short-run bidirectional causality between tourism and economic growth is found to prevail in South Africa. This applies, irrespective of whether tourism revenue is estimated by total tourism revenue or tourism revenue as a percentage of GDP. Overall, the results show that a feedback relationship between tourism development and economic growth tends to dominate, at least in the short run, and that the conventional hypothesis of tourism-led growth may not necessarily hold in South Africa. The study, therefore, recommends that short-term policy efforts be directed at developing both the tourism sector and the real sector as both sectors have been found to reinforce each other in the short run, irrespective of the tourism proxy used.
However, in the long run, policies geared towards inclusive and accelerated growth should be enhanced in order to boost inward tourism as the findings of the study show that real sector growth spurs international tourist arrivals in South Africa in the long run.

Declaration of interest statement
No conflict of interest.