Determinants of inbound tourism to South Africa

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Tourism Economics, 2008, 14 (1), 81–96

Determinants of inbound tourism to South Africa ANDREA SAAYMAN

School of Economics, North-West University, Potchefstroom Campus, Private Bag X6001, Potchefstroom 2521, South Africa. Tel: +27 18 2991810. Fax: +27 18 2994140. E-mail: [email protected]. MELVILLE SAAYMAN

Institute for Tourism and Leisure Studies, North-West University, Potchefstroom Campus, Private Bag X6001, Potchefstroom 2521, South Africa. Tel: +27 18 2991810. Fax: +27 18 2994140. E-mail: [email protected]. South Africa has experienced a significant increase in tourist arrivals over the past ten years. The challenge is to sustain this growth and therefore it is important to understand the factors that influence inbound tourism to South Africa. The purpose of this article is to identify the various determinants of inbound tourism to South Africa from different source markets (categorized in continents). Time series quarterly data from 1993 to 2004 is used in the analysis of tourist arrivals. Cointegration analysis in a multivariate framework is used and the authors find that income, relative prices and travel cost are strong determinants of tourist arrivals (as with other destinations). They also find that climate and capacity play significant roles. Keywords: tourism demand; inbound tourism; South Africa

Since the democratic elections in 1994, South Africa has improved its tourism position from the 52nd most visited destination in the world to the 17th most visited in 2005. During a period of just over ten years, the country has expanded its tourism plant significantly. This includes growth in the number of hotels, guest houses, game farms, lodges, game reserves, restaurants, tour operators and even the number of airlines servicing the country. From a demand side, the country has experienced an increase of more than 100% in tourist arrivals over the same period. This growth has positioned South Africa as Africa’s leading tourist destination, despite many problems and threats experienced on the The authors would like to thank the audience at the ‘Advances in Tourism Economics’ conference held in Portugal in April 2007, as well as the reviewers for their valuable comments and contributions.

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Number of tourists

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Figure 1. Tourist arrivals. African continent (for example, political instability, poverty, disease and low levels of development). The challenge for a country like South Africa, as for most other tourist destinations, is to sustain this growth in arrivals and to improve the country’s position among most visited countries (Saayman, 2006). In order to address sustainability, it is important to understand the factors that influence inbound tourism to South Africa and the questions that arise are whether the same factors which influence tourism to other destinations also hold for tourism to South Africa and how strong these relationships are. This information will be useful not only to planners, but also to policymakers and marketers alike. The purpose of this paper is to identify the various determinants of inbound tourism to South Africa from different source markets (categorized in continents). This is the first research of its kind for South Africa and is also innovative in the sense that the strength of the various income and price effects are tested for tourists from various continents. Time series quarterly data from 1993 to 2004 are used in the analysis of tourist arrivals from Europe, Australasia, North America, Asia and South America. This time period captures the phase following South Africa’s readmission into the world economy.

Tourism to South Africa Tourist arrivals in South Africa show a growing trend and this is especially prevalent since the readmission of South Africa into the world economy (1992). In South Africa, arrivals are categorized into two groups; that is, international travel and travel from African countries. Figure 1 indicates that most tourists

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visiting South Africa come from the African market, of which the highest percentage is tourists from neighbouring countries. This distinguishes South Africa from most other international destinations and the reason for this is, firstly, that countries such as Lesotho and Swaziland are enclosed by South African territory; secondly, that tourists from these markets travel for different reasons than other tourists; thirdly that these tourists seldom travel to other destinations or countries. An analysis of the international market shows that the top five markets which travel to South Africa are Germany, the UK, France, The Netherlands and the USA. The reasons why these are the primary international markets are, firstly, there are cultural and colonial ties and, secondly, South Africa has an advantage in accessibility regarding infrastructure, air travel and even the use of languages, as compared to the rest of Africa.

Factors that influence tourism to South Africa Modelling tourism demand There exist a wide variety of articles on the modelling of tourism demand. These articles can be classified roughly into those that use single equation estimation techniques, more complete models and panel data studies (which differ particularly with regard to data requirements). Among single equation techniques, the most popular are log–linear and cointegration analyses (see, for example, Kulendran and Witt, 2001; Lim and McAleer, 2002; Dritsakis, 2004; Lim, 2004; Algieri, 2006). A more dynamic approach to estimating demand is also becoming popular with both Song et al (2003) and Narayan (2004), who expand the regular single equation with lagged variables of the dependent and independent variables – the so-called autoregressive distributed lag (ARDL) framework. Lim (1997b) indicates that these single equation techniques are employed most frequently in tourism demand modelling. Improved single equation demand models were supported by advances in econometric methodology and this contributed to its continued popularity for estimating tourism demand (Algieri, 2006). Lim (1997a) notes that a major weakness of these single equation models is that they take into account the factors that affect tourism demand in a particular country only. Cross-elasticities can thus be obtained only when a system of equations is employed. The almost ideal demand system (AIDS) model is often used as a more complete model for estimating tourism demand (see, for example, De Mello and Fortuna, 2005; Han et al, 2006). The advantages of the AIDS model include that price and expenditure elasticities can be estimated, which presents useful information about the comparison of the interdependencies between destinations. It also tests the validity of the assumptions about consumer behaviour (Han et al, 2006). Algieri (2006, p 10) points out that AIDS models are especially useful in clarifying a country’s outbound tourism demand to certain destinations. More recent studies evaluating a variety of tourism markets often use panel data techniques. Studies in this regard include those of Naudé and Saayman

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(2005); Roget and González (2006); Muñoz (2007); and Van der Merwe et al (2007). Panel data techniques are particularly useful when cross-section data are also available over various time periods. They offer all the advantages of a larger number of observations; that is, more informative data, less multicollinearity, more degrees of freedom and more efficient estimates. This also allows the researcher to distinguish between cohort, period and age effects, and unobserved heterogeneity can be controlled (Hübler, 2005, p 1). When cross-sections and time series data are combined, as in panel data analysis, the quality and quantity of data are thus enhanced (Yaffee, 2003, p 1). In tourism demand studies, panel data techniques allow the inclusion of the variables that are mostly static for one region (such as distance), but which differ between regions, which is not possible with time series data only. In a review of international tourism demand models, Lim (1997b) notes that the dependent variable is mostly either tourist arrivals (or departure) or tourist expenditure (or receipts). Other variables that can be used as dependent variables include length of stay, nights spent and travel exports. Theoretically, it is often postulated that the following factors influence tourism demand and should be considered in the demand model (see, for example, Crouch, 1995; Smeral and Witt, 1996; Lim, 1997b; Mudambi and Baum, 1997; Narayan, 2004; and Smith, 2006): • Income – higher income in the origin country leads to a higher demand for travel and tourism. • Relative prices – the price competitiveness of the destination influences its demand as a tourism destination (the real exchange rate or relative price indices are often used as proxies). • Transport cost – increased transportation cost increases the cost of tourism to the destination. • Exchange rates – these influence tourism demand via uncertainty and price competitiveness. • Marketing expenses – increased marketing spending and more effective marketing efforts strengthen the demand for the destination. • Qualitative factors – including tourists’ attributes which influence time available for travel, trade and cultural links between the countries, destination attractiveness (for example, culture, climate, history, natural resources), events taking place at the destination and social threats (for example, unrest or terrorism). • Supply factors – the combination, availability and quality of tourism products and infrastructure supplied to tourists influence tourism demand.

Methodology Data description The dependent variable used in the demand modelling is tourist arrivals. Tourist arrivals was selected as the dependent variable because the data from Statistics South Africa (StatsSA) were more frequently and readily available than tourist spending data. Spending surveys are only conducted biannually and the method of survey has changed at least once in the past decade. The arrivals data

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are therefore more reliable. Figures A1 and A2 (see Appendix) illustrate quarterly arrivals from various continents to South Africa since 1990. Note that European arrivals outnumber other tourist arrivals by such a margin that this needs to be plotted on a different scale. While tourist arrivals from Africa are greater by far than from any other source market (see Figure 1), this is omitted from this research because most African visitors are from neighbouring countries and countries surrounded by South Africa (for example, Lesotho, Swaziland, Botswana, Mozambique, etc). Previous research by Saayman and Saayman (2003) has shown that the spending of tourists from these markets is low compared to international markets and that the reasons for travelling to South Africa differ substantially from those of international travellers. Many of those crossing the border into South Africa work in the country and only return to their own country over weekends or holidays. Cross-border traffic control does not always make a clear distinction between these travellers. For all the reasons mentioned above, it is clear that it is difficult to use the data (as is) in tourism demand modelling. Another factor which makes it difficult to use African data is that the exchange rate poses a problem – many countries use the South African rand, or are informally linked to the rand. Following theory, the independent variables used in the modelling include income, relative prices (where, again, the exchange rate is used rather than relative price indices), exchange rate, travel cost, a qualitative factor and a supply proxy. Tables 1 and 2 give an overview of the various variables, their description and sources. The data span the first quarter of 1993 to the last quarter of 2004. A further explanation of how the data were compiled is necessary. Starting with the GDP per capita, the nominal figures were transformed into real figures using the GDP deflators of every country (2,000 = 100). The figure was transformed into US dollars using the average dollar exchange rate of the year 2000. A constant exchange rate was used in order to exclude the effect of exchange rate volatility on GDP per capita. Table 1. Variables and sources. Variable

Proxy

Description

Source

Arrivals

Europe, Asia, SAM, NAM, AUS

Compiled from StatsSA

Income

GDP

Quarterly tourist arrivals from various continents. (Note SAM = South America, NAM = North America and Canada, AUS = Australia and New Zealand.) The real GDP per capita in the origin country in US$ terms. The real exchange rate (South African rand versus currency of origin country).

Relative REX prices/ exchange rate Travel cost Oil, Jfuel Qualitative SunCT factor Supply factor HRoom

The price of crude oil and jet fuel. The number of sunshine days in Cape Town, South Africa. Number of hotel rooms available.

IFS IFS, Reserve Bank (SARB) Energy Information Administration SA Weather Service StatsSA

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Table 2. Characteristics of variables. ARRIVE

SAM

AUS

EUR

ASIA

NAM

Mean Median Maximum Minimum Std dev Skewness Kurtosis

9,314.938 9,943.500 15,305.00 2,359.000 3,223.677 –0.522926 2.617208

16,973.35 17,992.50 27,038.00 5,787.000 5,275.808 –0.354831 2.526976

235,696.2 227,619.5 435,585.0 63,193.00 94,048.65 0.300996 2.537163

43,451.21 45,092.00 60,603.00 20,170.00 10,998.43 –0.479366 2.693720

44,541.50 48,434.50 67,089.00 17,311.00 14,010.34 –0.451524 2.157644

GDP

SAM

AUS

EUR

ASIA

NAM

Mean Median Maximum Minimum Std dev Skewness Kurtosis

1.859003 1.862741 2.146477 1.482076 0.142652 –0.423244 2.980508

4.859434 4.929484 5.561584 4.102044 0.443450 –0.102819 1.782738

5.520915 5.520343 6.151433 4.813733 0.408296 –0.154207 1.639593

9.103081 9.080674 9.591135 8.791790 0.212169 0.798537 3.068608

8.155986 8.287979 9.222013 7.068443 0.633977 –0.197634 1.710288

REX

SAM

AUS

EUR

ASIA

NAM

Mean Median Maximum Minimum Std dev Skewness Kurtosis

529.9611 535.1832 937.4479 247.7921 157.7667 –0.058944 2.730860

398.4609 399.5063 576.4735 306.6233 61.81003 0.807964 3.537417

655.9027 641.6616 908.8787 517.9211 101.1243 0.920963 3.323285

5.444414 5.164830 7.756747 4.279467 0.894945 0.945576 3.063629

606.6715 575.0839 1080.760 437.8517 160.4344 1.140478 3.715669

HROOM

JFUEL

OIL

SUNCT

49,429.74 51,548.33 53,957.67 42,425.67 37,77.098 –0.618712 1.857464

67.45410 59.25833 144.7667 35.72333 22.97265 1.309939 4.889726

23.78604 21.67500 48.30000 12.93667 7.813521 1.045304 3.970928

774.7521 773.5500 990.1000 507.1000 157.1876 –0.052301 1.322749

Mean Median Maximum Minimum Std dev Skewness Kurtosis

The exchange rate is denominated in rand; that is, South African rand for one foreign currency. The exchange rate is transformed into a real exchange rate, using the relative CPIs of the countries, that is: CPIorigin E = e × –––––– . CPISA Some clarification about the countries used as proxies for continents is also necessary. For North America, the independent variables used in the analysis are from the USA. The USA is used as the proxy since approximately 87% of all arrivals from North America stems from the USA (based on arrivals in 2000). For Asia, the main source markets are India and Japan. The proxy used in our analysis is Japan (13% of all Asian arrivals is from Japan). Data

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availability for many of the Asian countries played a role in the choice of the proxy (since 1996, India’s data have only been available quarterly). For South America, Brazil is the main source market, but data availability excluded it from the analysis. Argentina, the second largest source market, is therefore the proxy, with 32% of all South American arrivals being from Argentina. For Australasia, Australia is the proxy, with 80% of all arrivals in 2000 being from Australia. European independent variables are compiled from data for the following countries: the UK, Austria, Belgium, Denmark, France, Germany, Greece, Italy, The Netherlands, Norway, Portugal, Spain, Sweden and Switzerland. The weights given to each of these countries are the percentage of total European arrivals from these countries in the year 2000. Figure A3 (see Appendix) illustrates the graphs for the independent variables used in the analysis.

Method With reference to the review of different tourism demand models, Lim (1997b) indicates that the most popular model is the log–linear single equation model, where both the dependent and independent variables are expressed in logarithms. The basic specification of the model is thus: ln yt = α + β ln xt + εt ,

(1)

where yt = tourism arrivals, xt = a vector of explanatory variables and εt = white noise variable with a zero mean and constant variance. A more dynamic specification of the model mentioned above normally includes lagged values of the variables. Researchers such as Kulendran and Witt (2001), Lim and McAleer (2002) and Dritsakis (2004) also include long-run properties of the demand equation in their models via cointegration analysis and error correction models. Many economic variables are non-stationary in their levels and only stationary in first differences. To test for stationarity, the Adapted Dickey–Fuller or Phillips– Peron tests can be used. Cointegration between variables may also exist when there are one or more linear combinations between them that are stationary, indicating a stable longrun relationship between the variables (Dritsakis, 2004, p 114). The current research takes the approach of imposing only an integer degree of differentiation, in other words, 0 in the case of stationarity and 1 in the case of non-stationarity, and does not allow for fractional integration (see Cunado et al, 2007). The Johansen cointegration test is used to investigate the existence of a long-run relationship between the real exchange rate and the variables defined in Table 1. In a multivariate system, a vector (xt) is defined on which the estimations are based. In this case, the vector is: xt = [lgdpt, lrext, loilt, ljfuelt, lsunctt, lhroomt] .

(2)

Note that the ‘l’ prefix indicates the natural logarithm of the variable. It is assumed that the vector has an autoregressive representation of the form: p

xt = η + Σ Πtxt + εt , i=1

(3)

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where η is a vector of deterministic terms, p is the lag length and ε is a vector of white noise disturbances. Equation (3) can be redefined in the VECM (Vector Error Correction Mechanism) form as: Δxt = η +

p–1

Σ

i=1

ΦiΔxt–i + Πxt–1 + εt ,

(4)

where Δ is the differencing operator; Π = αβ’, where α and β are kxr matrices, whose rank determines the number of cointegrating vectors; α represents the speed of adjustment to equilibrium and β is a matrix of long-run coefficients; and Φ is a kxk coefficient matrix. If Π = k (full rank) or if Π = 0 (zero rank), there will be no cointegration among the elements in the long-run relationship. MacDonald (2001) indicates that the model then has to be estimated in levels or first differences. But if Π is of reduced rank (r < k), a VECM model can be estimated.

Results Very high correlations between dependent variables may give rise to multicollinearity in the estimation, resulting in high R-squared values, but few significant variables. Since some of the proxies included in Table 1 above test the same theoretical variable in the demand function (for example, oil and fuel for travel cost), the first step in the analysis involved an inspection of the correlation coefficients. Based on the correlation coefficients, it was decided to use oil rather than jet fuel as the proxy for transport cost, and the real exchange rate rather than the relative CPIs as the proxy for relative prices. The x-vector can thus be specified as: xt = [lgdpt, lrext, loilt, lsunctt, lhroomt] .

(5)

Following the correlation tests, the variables were subjected to unit root tests in order to determine whether they were stationary. Both the Phillips–Peron (PP) and Adapted Dickey–Fuller (ADF) tests were performed, using Eviews 5.1, and the results of the tests are indicated in Table 3. From the Tables, it can be seen that most variables are non-stationary in levels and, according to the ADF test, all the variables, except the real GDPs per capita of Europe and the USA, are stationary in first differences – I(1). An alternative to the ADF test is the PP test for unit roots. The difference lies in the treatment of a more complicated process than an AR(1). The ADF test includes higher order lagged terms to the model, while the PP test makes a non-parametric to the t-test statistic to account for any autocorrelation that is still present in the error term (Harris and Sollis, 2003, p 50). The results of the PP test indicate that all variables are stationary in first differences, in other words all variables are I(1). In the estimate, centred seasonal dummies are included – a common structure employed when using quarterly data. To determine whether there exists one or more cointegrating relationships between the variables in the model, the Johansen cointegration test was performed within the VAR space. The results of the cointegration test are summarized in Table 4. Note that the trace test statistic

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Table 3. ADF and PP unit root test results. Series

ADF prob

PP prob

Series

ADF prob

PP prob

LARRIVE_EUR LARRIVE_USA LARRIVE_SAM LARRIVE_ASIA LARRIVE_AUS LGDP_EUR LGDP_USA LGDP_SAM LGDP_ASIA LGDP_AUS LHROOM LOIL LREX_EUR LREX_USA LREX_SAM LREX_ASIA LREX_AUS LSUNCT

0.1733 0.1719 0.0004 0.0261 0.0080 0.6696 0.8247 0.3808 0.9576 0.6926 0.6449 0.8144 0.4690 0.5335 0.7931 0.4285 0.3855 0.0694

0.0377 0.2194 0.0256 0.2070 0.0776 0.3062 0.7997 0.0094 0.9088 0.0129 0.6501 0.8500 0.4284 0.5474 0.8868 0.3635 0.3855 0.0000

D(LARRIVE_EUR) D(LARRIVE_USA) D(LARRIVE_SAM) D(LARRIVE_ASIA) D(LARRIVE_AUS) D(LGDP_EUR) D(LGDP_USA) D(LGDP_SAM) D(LGDP_ASIA) D(LGDP_AUS) D(LHROOM) D(LOIL) D(LREX_EUR) D(LREX_USA) D(LREX_SAM) D(LREX_ASIA) D(LREX_AUS) D(LSUNCT)

0.0483 0.0296 0.0451 0.0000 0.0257 0.3077 0.4094 0.0449 0.0000 0.0000 0.0000 0.0002 0.0000 0.0002 0.0002 0.0000 0.0001 0.0049

0.0000 0.0000 0.0000 0.0000 0.0000 0.0006 0.0028 0.0000 0.0000 0.0000 0.0000 0.0001 0.0000 0.0002 0.0002 0.0000 0.0001 0.0000

Table 4. Results of the Johansen cointegration test (1% level of significance). Test type

Data trend

Europe

North America

South America

Asia

Australia

No intercept or trend Intercept, no trend Intercept, no trend Intercept and trend

None None Linear Linear

2 (2) 2 (2) 2 (0) 1 (1)

1 (1) 1 (1) 0 (0) 1 (1)

2 (1) 3 (2) 2 (2) 3 (1)

1 (1) 1 (1) 1 (1) 1 (1)

1 (1) 2 (2) 1 (1) 2 (2)

Intercept and trend

Quadratic

2 (1)

1 (1)

2 (1)

1 (1)

2 (2)

is indicated, as well as the maximum eigen value (in brackets) next to the trace test. The table highlights the various assumptions of the test where there is one cointegrating equation identified, either by the trace test or the maximum eigen value test statistic. These will be used as input in specifying the cointegrating relationship in the VECM model. Since the VAR model (see Equations (3) and (4)) treats the endogenous variables in the system as a function of the lagged variables of the endogenous variables, the number of lags that should be included must be determined. Through using the lag length criteria test in the VAR space, the following results were obtained (Table 5). Using both the cointegration test results and the lag length criteria, the VECMs are estimated for every continent. The results in Table 6 summarize the cointegrating or long-run relationship between tourist arrivals from various continents and the explanatory variables. The long-run relationships can thus be interpreted as follows:

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Table 5. Lags to include according to lag length criteria test. Test type

Europe

North America

South America

Asia

Australia

Modified LR test Final prediction error Akaike information criterion Schwarz information criterion

1 1 4 1

1 1 4 1

1 4 4 1

4 4 4 1

1 3 4 1

Hanna–Quinn information criterion

1

1

4

4

4

Table 6. Results of the VECM (normalized cointegrating relationships). Variable

Europe

North America

South America

Asia

Australia

Dependent variable D(LARRIVE) D(LARRIVE) LARRIVE(–1) 1 1 LGDP(–1) 6.076541 9.264984 (3.12466) (0.72763) [1.94471] [12.7331]

D(LARRIVE) 1 –2.174786 (1.11032) [–1.95871]

D(LARRIVE) 1 4.146369 (0.16989) [24.4063]

D(LARRIVE) 1 –7.338560 (1.78387) [–4.11383]

LHROOM(–1)

–6.107906 (0.91926) [–6.64439]

–6.561154 (0.30313) [–21.6447]

–8.576774 (0.75489) [–11.3616]

–2.595870 (0.05576) [–46.5580]

1.692258 (1.15891) [1.46021]

LOIL(–1)

–0.286961 (0.17054) [–1.68264]

–0.304384 (0.03469) [–8.77456]

–1.226313 (0.19587) [–6.26100]

0.149372 (0.03131) [4.77088]

0.0350270 (0.18889) [1.85440]

LREX(–1)

0.445509 (0.39154) [1.13783]

0.0177136 (0.06652) [2.66296]

1.234978 (0.27958) [4.41732]

–0.651420 (0.03614) [–18.0230]

1.066472 (0.51620) [2.06600]

LSUNCT(–1)

3.656809 (0.55917) [6.53971]

–1.468046 (0.24655) [–0.95432]

12.52762 (1.07670) [11.6351]

0.523689 (0.11158) [4.69332]

7.611281 (1.21970) [6.24028]

C

18.10060

38.96935

TREND

–0.026030 (0.01675) [–1.55382]

–0.038474 (0.00282) [–13.6464]

Cointegrating equation

0.135316 (0.08551) [1.58246]

0.991438 (0.56694) [1.74874]

0.340954 (0.06680) [5.10414]

–1.093980 (0.51438) [–2.12679]

–0.241678 (0.10593) [–2.28147]

R–squared Adj. R–squared F–statistics AIC SC

0.946395 0.929643 56.49561 –1.663344 –1.212804

0.735066 0.411258 2.270069 –1.025162 –0.063889

0.921052 0.875505 20.22209 –1.121751 –0.459781

0.906929 0.697520 4.330895 –1.704213 –0.521998

0.744749 0.581389 4.558933 –0.903417 –0.200074

–64.22299

Note: Standard error in round parentheses; t-statistic in square parentheses; D prefix is added for first differenced variables.

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• Arrivals from Europe are positively influenced by a growth in European income per capita (positive income effect) and sunny days (climate) in South Africa. Negative influences on tourist arrivals include higher transport cost and enhanced capacity. Note that the price proxy is positive, indicating that competitive prices in South Africa relative to Europe lead to an increase in arrivals, but not significantly so. The adjustment takes place only via arrivals. The coefficient is positive and indicates a 13% adjustment per quarter (but only at a 20% level of significance). • For arrivals from North America, the income effect is again positive and the price effect again indicates that higher South African prices without an adjustment of the nominal exchange rate should lead to a decrease in tourist arrivals from North America. High transport cost and enhanced capacity again show negative relationships with arrivals. Note that sunny days in Cape Town are not significant to tourists from North America. The adjustment coefficient is again positive and shows a rapid adjustment in arrivals per quarter. This also has a 10% level of significance. The adjustment coefficient for hotel rooms is also positive and significant. • Tourists from South America are positively influenced by sunny days in South Africa and a depreciation in the real exchange rate. Higher transport cost, enhanced capacity and a growth in income per capita decrease tourist arrivals from the continent. This negative income effect might be ascribed to the increased arrivals from the continent, even when income per capita decreased during the past five years, given the number of crises that the continent has experienced. This is also evident when one views the graph of GDP per capita in Argentina (see Figure A3 in the Appendix). During the period under evaluation, it is clear that there was a relative steep decline in GDP per capita in Argentina. Therefore, it can be concluded that, for South America, during the period under evaluation, the decline in income caused a shift away from more expensive (and even preferred) destinations towards a more affordable (and, in this sense, also inferior) destination – that is, South Africa. The adjustment coefficient again indicates a rapid adjustment in arrivals per quarter and is highly significant. • For tourists from Asia, the income effect is positive and sunny days in Cape Town again have a positive influence on arrivals. It is interesting to note that the higher travel cost does not negatively influence arrivals from Asia, and neither do the higher local prices. This might be ascribed to the strong growth in Asia’s income per capita, to such an extent that the income effect overpowers the price effect. Again, the capacity proxy is negative. Adjustment takes place only via arrivals. The adjustment coefficient is significant and rapid adjustment takes place. • Sunny days in Cape Town and the relative lower cost of South Africa as a destination have a positive influence on tourists from Australia. High travel cost does not deter tourists from visiting South Africa (significant at a 10% level) and the capacity proxy is not at all significant. Interestingly, the income effect is negative. Adjustment takes place via various routes, including the exchange rate and arrivals. The adjustment in arrivals per quarter is again rapid (24%). An interesting observation is the negative effect of hotel rooms as a proxy for

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capacity. Possible reasons for this may be that hotel rooms are taken as proxy instead of occupation, that the quality (grading) of the rooms is not verified and that other accommodation which is very popular with international tourists (such as game lodges) is not included. Hotel rooms can also be a variable that lags after arrivals and might be the reason why, in many instances, two cointegrating relationships were found. Based on the above discussion, it can also be concluded that the perceptions of South Africa as a sunny and price competitive destination are important factors which strongly influence the growth in tourist arrivals over the long run. Dynamic or short-run variables that are significant include lagged changes in arrivals and sunny days in Cape Town for almost all the VECMs. In addition, the short-run income effect is positive for arrivals from South America and, in some instances, the hotel room capacity proxy also indicates a positive shortrun effect. This is contrary to the long-run income effect for South America, which exhibits a negative value, and it is the opinion of the authors, therefore, that the long-run income effect should be treated with caution in this case since it might only be a reflection of the unstable period under evaluation. It is noteworthy that a further analysis of the VAR system indicates that hotel rooms react on tourist arrivals in the short run. The coefficients found in the estimates compare well with those of other studies. Witt and Witt (1995) reported income coefficients of between 0.4 and 6.6, while Algieri (2006) found an income coefficient of 7.8. Most of the income coefficients found in this research fall into this range. For the exchange rate, Witt and Witt (1995) report coefficients of between 0.6 and 2.25. More recently, Dritsakis (2004) and Algieri (2006) reported coefficients of around 1 and 1.5, respectively, for the exchange rate. Again, the coefficients found in this study for the real exchange rate are very similar to those of other studies. The same is found for the travel cost proxy (oil), which falls within the range of 0.04 and 4.3, as reported by Witt and Witt (1995). Robustness tests performed included lag exclusion tests and normality tests. The lag exclusion test carried out in the VAR showed a χ2 (or Wald) statistic for the joint significance of all the endogenous variables at that lag (Quantitative Micro Software, 2004, p 709). The results of the lag exclusion tests indicate that all the lags for the endogenous variables are jointly significant. The normality test performed was the Cholesky test, which revealed that in all the cases the residuals were not normally distributed, but that normality was rejected due to kurtosis rather than skewness. Paruolo (1996) indicated that the Johansen test was not affected when normality was rejected due to excess kurtosis. The data distribution is, therefore, flat (platykurtic) relative to normal. Table 7 summarizes the normality test results.

Conclusion The purpose of this paper is to identify the determinants of inbound tourism to South Africa from various source markets. It is one of the first studies of its kind for South Africa and also the first time that the VECM methodology is applied to tourist arrivals in South Africa. As such, a number of data limitations emerged, including data availability for countries such as India,

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Table 7. Normality test results. Continent

Skewness (χ 2 and prob)

Kurtosis (χ 2 and prob)

Jarque–Bera (and prob)

Europe North America South America Asia Australia

3.433244 0.410694 1.704217 0.111154 3.717823

37.80467 62.39041 44.65891 76.06389 28.23461

41.23792 62.80111 46.36312 76.17504 31.95243

(0.7528) (0.9988) (0.9448) (1.000) (0.7148)

(0.000) (0.000) (0.000) (0.000) (0.0001)

(0.000) (0.000) (0.000) (0.000) (0.0014)

China and Brazil. The research also could not use tourist spending data, as these were not readily available in the quarterly or monthly format and the method for collection of the data was inconsistent. The data on hotel rooms also did not account for quality, and data on transport cost to South Africa were not available. However, this research makes the following findings. Income in the source country is, as in most other countries, a main determinant of tourist arrivals. In this regard, the current research confirms studies by, among others, Smeral and Witt (1996) and Lim (1997b). Other determinants of demand include price competitiveness (as measured by the real exchange rate) and transport cost. Again, these findings support research completed in other parts of the world. However, this research added a country-specific attribute, namely climate, and it was found that climate in South Africa impacted positively on tourist arrivals. From a policy implication point of view, a number of recommendations stem from this research. Firstly, the mild and sunny climate should be a key focus of marketers of South Africa as a tourist destination to ensure long-run growth in arrivals. Secondly, price competitiveness is also a strong factor that can be controlled in the South African economy and which influences tourism demand for South Africa. It is thus imperative that industry keeps prices competitive to ensure long-run sustainable growth. Thirdly, transport cost should also be controlled in order to ensure sustainable tourist arrivals. Another interesting observation is the negative income effect for tourists from Australia, which might be attributed to the fact that Australia offers a very similar tourism product as South Africa – nature, beaches and sunny weather. As a result, South Africa may be viewed by Australian tourists as an ‘inferior’ destination choice, relative to other host destinations. The negative long-run income effect is also evident for South America, yet in the short-run dynamics, the income effect is positive. Again, this might be a reflection that South Africa is an ‘inferior’ destination for South Americans and that South America offers a very similar product in terms of sunny weather and beaches. Yet, the positive short-run income effect provides a bit of controversy to this view and whether the long-run effect is still negative (and South Africa is still an inferior destination) after the normalization of the various negative shocks to income growth in South America should be confirmed at a later stage. The controversial negative sign for hotel rooms may be an indication that it is not a good proxy to use for capacity, since it does not give account of quality (rating) or occupancy rates. It is also a highly endogenous variable in the system, which reacts quite strongly to changes in tourist arrivals. It is

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proposed that further research be conducted on a more suitable capacity proxy and that the robustness of jet fuel as a proxy for transport cost should be confirmed. Further research could also be conducted on fractional integration of various tourist arrivals to South Africa. References Algieri, B. (2006), ‘An econometric estimation of the demand for tourism: the case of Russia’, Tourism Economics, Vol 12, No 1, pp 5–20. Crouch, G.I. (1995), ‘A meta-analysis of tourism demand’, Annals of Tourism Research, Vol 22, No 1, pp 103–118. Cunado, J., Gil-Alana, L.A., and Perez de Gracia, F. (2007), ‘Fractional integration and structural breaks: evidence from the international monthly arrivals in the US’, paper presented at the Advances in Tourism Economics Conference, 13–14 April, Vila Nova de Santo André. De Mello, M.M., and Fortuna, N. (2005), ‘Testing alternative dynamic systems for modeling tourism demand’, Tourism Economics, Vol 11, No 4, pp 517–538. Dritsakis, N. (2004), ‘Cointegration analysis of German and British tourism demand for Greece’, Tourism Management, Vol 25, pp 111–119. Han, Z., Durbarry, R., and Sinclair, M.T. (2006), ‘Modelling US tourism demand for European Destinations’, Tourism Management, Vol 27, pp 1–10. Harris, R., and Sollis, R. (2003), Applied Time Series Modelling and Forecasting, Wiley, West Sussex. Hübler, O. (2005), ‘Panel data econometrics: modelling and estimation’, Diskussionspapier Nr 319 (August 2005), Institute of Quantitative Economic Research, University of Hannover, Hannover. Kulendran, N., and Witt, S.F. (2001), ‘Cointegration versus least square regressions’, Annals of Tourism Research, Vol 28, No 2, pp 291–311. Lim, C. (1997a), ‘The functional specification of international demand models’, Mathematics and Computers in Simulation, Vol 43, pp 535–543. Lim, C. (1997b), ‘Review of international tourism demand models’, Annals of Tourism Research, Vol 24, No 4, pp 835–849. Lim, C. (2004), ‘The major determinants of Korean outbound travel to Australia’, Mathematics and Computers in Simulation, Vol 64, pp 477–485. Lim, C., and McAleer, M. (2002), ‘A cointegration analysis of annual tourism demand by Malaysia for Australia’, Mathematics and Computers in Simulation, Vol 59, pp 197–205. MacDonald, R. (2001), ‘Modelling the long-run real effective exchange rate of the New Zealand dollar’, Reserve Bank of New Zealand Discussion Paper series DP2002/02, Wellington. Mudambi, R., and Baum, T. (1997), ‘Strategic segmentation: an empirical analysis of tourist expenditure in Turkey’, Journal of Travel Research, Vol 36, pp 29–34. Muñoz, T.G. (2007), ‘German demand for tourism in Spain’, Tourism Management, Vol 28, pp 12– 22. Narayan, P.K. (2004), ‘Fiji’s tourism demand: the ARDL approach to cointegration’, Tourism Economics, Vol 10, No 2, pp 193–206. Naudé, W.A., and Saayman, A. (2005), ‘Determinants of tourist arrivals in Africa: a panel data regression analysis’, Tourism Economics, Vol 11, No 3, pp 365–391. Paruolo, P. (1996), ‘On the determination of integration indices in I(2) system’, Journal of Econometrics, Vol 72, pp 313–356. Quantitative Micro Software (2004), Eviews 5 User’s Guide, QMS, Irvine, CA. Roget, F.M., and González, X.A.R. (2006), ‘Rural tourism demand in Galacia, Spain’, Tourism Economics, Vol 12, No 1, pp 21–31. Saayman, M., ed (2006), Tourism Marketing: Back to Basics, Leisure Consultants and Publications, Potchefstroom. Saayman, M., and Saayman, A. (2003), ‘An economic analysis of international and African tourism markets of South Africa’, African Insight, Vol 33, No 1, pp 93–98. Smeral, E., and Witt, S.F. (1996), ‘Econometric forecasts of tourism demand to 2005’, Annals of Tourism Research, Vol 23, pp 891–907. Smith, J. (2006), ‘The determinants of international demand for tourism to South Africa’, unpublished Masters Dissertation, North-West University, Potchefstroom Campus, Potchefstroom. Song, H., Wong, K.K.F., and Chon, K.K.S. (2003), ‘Modelling and forecasting the demand for Hong Kong tourism’, Hospitality Management, Vol 22, pp 435–451.

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Van Der Merwe, P., Saayman, M., and Krugell, W.F. (2007), ‘The determinants of the spending of biltong hunters’, South African Journal of Economics and Management Sciences, Vol 10, No 2, pp 184– 194. Witt, S.F., and Witt, C.A. (1995), ‘Forecasting tourism demand: a review of empirical research’, International Journal of Forecasting, Vol 11, pp 447–475. Yaffee, R. (2003), ‘A primer for panel data analysis’, Connect Information Technology at NYU, Fall Edition, pp 1–11.

Appendix 500,000 450,000 400,000 350,000 300,000 250,000 200,000 150,000 100,000 50,000 Q 11 Q 99 31 0 Q 99 11 0 Q 991 31 Q 99 11 1 Q 99 31 2 Q 99 11 2 Q 99 31 3 Q 99 11 3 Q 99 31 4 Q 99 11 4 Q 99 31 5 Q 99 11 5 Q 99 31 6 Q 99 11 6 Q 99 31 7 Q 99 11 7 Q 99 31 8 Q 99 11 8 Q 99 31 9 Q 99 12 9 Q 00 32 0 Q 00 12 0 Q 00 32 1 Q 00 12 1 Q 00 32 2 Q 00 12 2 Q 00 32 3 Q 00 12 3 Q 00 32 4 Q 00 12 4 00 5

0

Date

Figure A1. Arrivals from Europe. 80,000 70,000

NAM 60,000 50,000

AISA

40,000 30,000

AUS

20,000 10,000

SAM

Q

11 Q 990 31 Q 990 11 Q 991 31 Q 991 11 Q 992 31 Q 992 11 Q 993 31 Q 993 11 Q 994 31 Q 994 11 Q 995 31 Q 995 11 Q 996 31 Q 996 11 Q 997 31 Q 997 11 Q 998 31 Q 998 11 Q 999 31 Q 999 12 Q 000 32 Q 000 12 Q 001 32 Q 001 12 Q 002 32 Q 002 12 Q 003 32 Q 003 12 Q 004 32 Q 004 12 00 5

0

Date

Figure A2. Arrivals from rest of world.

1.0

1.0

40 20

44,000

42,000

93 94 95 96 97 98 99 00 01 02 03 04

4.0

4.5

5.0

5.5

6.0

6.5

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400

500

600

700

800

900

1000

7.0

1100

7.5

10

20

30

40

50

4.8

5.0

5.2

5.4

5.6

5.8

6.0

6.2

0.6

0.7

0.8

8.0

Figure A3. Independent variables.

500

600

700

800

900

1,000

60

46,000

REX_JAP

80

48,000

REX_EUR

100

50,000

93 94 95 96 97 98 99 00 01 02 03 04

120

52,000

93 94 95 96 97 98 99 00 01 02 03 04

140

54,000

JFUEL

HROOM 160

93 94 95 96 97 98 99 00 01 02 03 04

93 94 95 96 97 98 99 00 01 02 03 04

4.0

4.4

4.8

5.2

GDP_AUST

GDP_ARGN 5.6

93 94 95 96 97 98 99 00 01 02 03 04

0.6

0.7

0.8

0.9

1.1

1.1

0.9

1.2

CPI_AUS 1.2

93 94 95 96 97 98 99 00 01 02 03 04

CPI_ARG

56,000

1.4

1.5

1.6

1.7

1.8

1.9

2.0

2.1

2.2

0.6

0.7

0.8

0.9

1.0

1.1

93 94 95 96 97 98 99 00 01 02 03 04

REX_USA

93 94 95 96 97 98 99 00 01 02 03 04

OIL

93 94 95 96 97 98 99 00 01 02 03 04

GDP_EUR

93 94 95 96 97 98 99 00 01 02 03 04

CPI_EUR

500

600

700

800

900

1000

200

300

400

500

600

700

800

900

1000

8.7

8.8

8.9

9.0

9.1

9.2

9.3

9.4

9.5

9.6

0.5

0.6

0.7

0.8

0.9

1.0

1.1

1.2

1.3

93 94 95 96 97 98 99 00 01 02 03 04

SUNCT

93 94 95 96 97 98 99 00 01 02 03 04

REX_ARG

93 94 95 96 97 98 99 00 01 02 03 04

GDP_JAPN

93 94 95 96 97 98 99 00 01 02 03 04

CPI_JAP

300

350

400

450

500

550

600

6.8

7.2

7.6

8.0

8.4

8.8

9.2

9.6

0.7

0.8

0.9

1.0

1.1

1.2

93 94 95 96 97 98 99 00 01 02 03 04

REX_AUS

93 94 95 96 97 98 99 00 01 02 03 04

GDP_USAM

93 94 95 96 97 98 99 00 01 02 03 04

CPI_USA

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