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3.3. Methodological review
3.3.3. Determinants of international demand for tourism
For the independent variables, a recent survey by Song and Li (2008) shows that main factors affecting tourism demand in recent empirical studies include: income of tourist, relative tourism price between destination and origin country, substitute tourism price in competing destinations and exchange rates. Notably, Prideaux (2004) lists factors that affect tourism flow to include price, exchange rate, national income, cost of utilities (communication, energy, water, financial services, domestic transport, and tariff protection), destination image, personal financial capability to travel, personal preferences, government regulations and risk factors (political tension, health epidemics, concern for personal safety and fear of crime).
The most common variables are income and prices as predicted by the traditional consumer theory.
Conceptually, the larger the real per capita income of a country, the more likely its citizens can afford to purchase travel services abroad, ceteris paribus. Growth in real incomes provides consumers with increased spending power. In examining the relationship between income and tourism demand, it seems reasonable to suggest, that once one achieves a certain level of income, the income elasticity will increase initially but then, it will remain approximately constant for a range of per capita income. Ultimately, it will decrease as it is unlikely that tourism‟s share of expenditure out of GNP would grow indefinitely. In tandem with this, Barry and O‟Hagan (1972) have addressed the concept of a saturation effect. They base it on the hypothesis that, after a certain point, the amount of utility accruing to an
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individual from a holiday may decline as the number of tourists enjoying utility from the same holiday increases. A vast majority of studies have included income as an explanatory variable in tourism demand models. Some studies have used total national disposable income (Bond and Ladman, 1972; Oliver, 1971). Artus (1970) derive an index from real disposable income whereas, Uysal and Crompton (1985) use GNP per capita data.
The effect of price changes is more complex in tourism than the effects of changes in income.
It is not just destination holiday prices that are important but also, relative price differences between the destination and the generating country. Basically, there are three elements constituting the price of tourism: the cost of travel to the destination; the exchange rate between the tourist‟s country of origin and that of the destination country and the cost of goods and services incurred after arrival. Gerakis (1966) posits that the effects of these price changes are short term whereas Barry and O‟Hagan (1972) view the effects to be long-term, on the basis that, reputations for expensiveness or cheapness passed on by word-of-mouth are developed over a number of years, for example, the reputed cheapness of Greece and expensiveness of Paris.
Defining tourism prices is very difficult, given that, the cost of tourism is a function of the total mix of goods and services consumed by each tourist. However, price indices for tourists simply do not exist (Witt and Witt 1992). Edwards (1988) emphasises the point that no country has an adequate price series representing costs to tourists. Most authors have used the CPI or the retail price index as proxy for the cost of tourism (Little, 1980; Loeb, 1982; Witt and Martin, 1987). Nonetheless, these authors complain about the fact that there is no better measure. Notably, most authors who have used the CPI as a proxy would accept the argument that the mix of goods and services consumed by tourists is not very different from the mix constituting the CPI and that, the changes in the CPI reasonably reflect the changes in the prices of goods and services consumed.
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tourism. Witt and Martin (1987) have shown that the CPI is an appropriate proxy for the cost of tourism within the context of international tourism demand models. A number of studies include a price variable in the form of cost of tourism in the destination relative to the cost of tourism in the origin (Artus, 1970; Barry and O‟Hagan, 1972; Kliman, 1981; Uysal and Crompton, 1985 and Witt, 1980). The consequent implication/assumption from this approach is that the substitute for a particular foreign holiday is domestic tourism.
Most authors make reference to the cost of transport as an important determinant of tourism flows but have typically excluded the travel cost variable from the model. Uysal and Crompton (1985) summarise the usual explanations for omitting transportation costs from tourism demand models to include: insufficient data; anticipated problems with multicollinearity; difficulty in identifying the appropriate mode of transport cost; lack of statistically significant results in studies where it is included; and the reluctance to lose another degree of freedom in estimation. Jud (1974) used distance as a proxy for the cost of travel. This approach is questionable on the basis that only in cross-sectional models where prices are held constant at a given moment can distance serve as an index of cost and even then, fares and distance do not move exactly in step. Therefore, the coefficient of the distance variable cannot sufficiently represent a measure of responsiveness to the cost of transport.
Bond and Ladman (1972) used a weighted average one-directional air fare cost as a proxy of how the cost of a whole trip might vary through time. Witt (1980) includes travel time in his model.
Coshall (2000) identifies other variables that explain international tourism flows to include many financial, perceptual, cultural, social and environmental factors. Lim (1997) summarises some of the variables used in the analysis of tourism demand since the 1960s.
Various independent variables are used and the number of independent variables ranges from one to nine. The most popular variable was income used by 84per cent of those studies.
Income influences the ability to pay for overseas‟ travel and proxies used for income include nominal or real per capita personal, disposable or national income or GDP and GNP. Other important variables identified by Lim (1997) are: The relative prices of goods and services purchased by tourists in chosen destinations, compared with the origin and competing destinations as measured by the CPI ratio (73per cent); Transportation cost, which refers to the cost of round-trip travel between the destination and the origin country (55per cent);
Dynamics are often included to account for lagged effects (26per cent); Exchange rate
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between the currencies of the destination and origin country (25per cent); Trends, which capture secular changes in tourist taste (25per cent); Competing destinations/goods, which lead to substitution when costs associated with travel and tourism increase (15per cent);
Seasonal factors, often captured in dummy variables (14per cent); Marketing expenditures to promote the country as a destination (7per cent); Migration and ethnic factors, which captures tourists visiting friends or relatives (5per cent); Business trade/travel, as measured by proxies such as trade, direct foreign investment and capital flows (5per cent); Economic activity indicators, such as unemployment and income distribution (3per cent); Some authors include qualitative factors, such as tourists‟ attributed household size, population in the origin, trip motive or frequency, destination attractiveness, events at the destination (60per cent); and other factors, such as supply/capacity constraints on tourism accommodation, exchange rate reforms or foreign currency restrictions, cross price elasticity of vacation goods and the average propensity to consume tourism goods (27per cent).
Some studies argue that the extent of demand for tourism services from any origin is obviously related to the actual size of the population, the amount of potential customers in a market to buy that good. In general, demand for foreign tourism from a country with a relatively small population would rarely approximate to that of a country with a large population even if the propensity to travel abroad is higher in the small country. Bond and Ladman (1972) allow for the impact of population by using it as a separate explanatory variable. Their study confirmed that population proved to be a significant variable in a number of cases. Laber (1969) estimates a demand model using three variables and then, multiplies each of them by the population figures. Thus, population does not actually appear as a separate explanatory variable in his econometric model.
One would expect terrorist attacks to greatly impact choices made by consumers, as the perceived risk of travelling in a relatively dangerous country would weigh heavily on
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Naudé and Saayman (2005) model tourism demand in Africa as a two-level utility function by the following optimisation problem:
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