Restos de la cultura material: Artefactos

A number of empirical studies focusing on tourism demand have been conducted using the AIDS model. Yet none have incorporated policy in the modelling. A review of these models was conducted by Li, Song and Witt (2005). This review is here extended to include some

recently published literature on the AIDS model and presents some comparative analysis of these empirical studies.

Data

Generally all the studies reviewed by Li, Song and Witt (2005) used annual data. Since then, three contributions have been made aiming to capture seasonal variations. Cortés-Jiménez, Durbarry and Pulina (2009) analysed monthly time series data and included dummy variables in the model to capture seasonality, whereas Cortés-Jimenez and Blake (2010) used quarterly data arguing that much detail is missed when data is aggregated for tourism demand models.

Divisekera (2010) also applied quarterly data for leisure and non-leisure domestic tourism in Australia.

Origin and destination country pairs

Most of the empirical studies evaluated a selection of or a single source market’s tourism expenditure to a selection of destinations, often being major source markets or destinations. O’Hagan and Harrison (1984) analysed econometrically the evolution of market shares of US tourism expenditure in Europe in the period 1964-1981;

White (1985) conducted a similar study for the period 1964-1981 grouping 16 countries under 7 groups. The disadvantage of such groupings lies in the potential violation of the separability assumption. It may also lead to results which are difficult to interpret or easily mis-interpreted, because heterogeneous destinations may have been grouped rather than homogeneous ones. Syriopoulos and Sinclair (1993) establish the separability assumption more clearly than in previous studies – they apply the AIDS model to estimate

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tourism expenditure allocation for four European origin countries (France, Germany, Sweden and UK) and to a specific group of Mediterranean destinations instead of the whole of Europe. The researchers applied the original model, though they initially included dummy and trend variables, which were then eliminated since no improvement in the model was achieved.

Papatheodorou (1999) conducted, for the period 1957-1989, the estimation of three systems of six equations each, as he considers three highly developed European origin countries (France, West Germany and UK) and six Mediterranean destinations (Spain, Greece, Italy, Portugal, Turkey and Yugoslavia). Papatheodorou considers this paper as “a parallel study to the one of Syriopoulos and Sinclair (1993)” (1999, p.620), though the results “are often in contrast with those obtained by the other two researchers” (1999, p.620). De Mello, Pack and Sinclair (2002) analyse the UK demand for tourism in Spain, France and Portugal for the period 1969-1997, accounting for a structural break in the model using time trend and a dummy variable. Divisekera (2003) analysed US, UK, Japan and New Zealand demand for tourism in Australia and alternative destinations and uses tourism price indices to proxy prices.

Li, Song and Witt (2004) apply a linear AIDS (LAIDS) model to UK outbound tourism to 22 European destinations using data covering the period 1972 to 2000. Both long-run and short-run elasticities are estimated, indicating that UK demand is more price elastic in the long run than in the short run. Similarly both long-run and short-run dynamics are analysed by Han, Durbarry and Sinclair (2006) who

apply an AIDS model to US demand for tourism in European destinations and show that whilst price competitiveness is important for France, Italy and Spain, it is relatively unimportant for the UK.

Cortés-Jiménez, Durbarry and Pulina (2009) study the outbound Italian tourism demand for four main destinations, France, Germany, Spain and UK through an error-correction AIDS model. They use monthly data to reflect seasonal variations and introduce dummies to assess the effects of terrorism attacks. They estimate long-run LAIDS and short-run EC-LAIDS and assess forecasting accuracy. Applying structural time-series models and examining inbound tourism to the UK, Cortés-Jiménez and Blake (2010) modelled demand by purpose of visit and nationality pairings and compare results with models utilising more aggregate data.

These studies focused on time-series evaluation of tourism expenditure allocation by destination whereas Fujii, Khaled and Mak (1985) analyse expenditure by visitors to Hawaii for six different classes of goods (food and drink, lodging, recreation and entertainment, local transport, clothing and other), i.e. they assess tourist expenditure allocation within a single destination. A similar approach is adopted by Divisekera (2010) who analyses domestic tourism in Australia based on the AIDS model distinguishing by travel motives of leisure and non-leisure. Five broad category groups are studied: accommodation, food, transportation, shopping and entertainment.

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Variables

The basic model used in each of these studies is the AIDS model (3.11). The dependent variable is the share of tourist expenditure allocated to each destination by each origin country or, as in the case of Fujii, Khaled and Mak (1985) and Divisekera (2010), tourist expenditure allocated to broad category within a single destination.

Being inherent to the model, price and expenditure variables are always included as explanatory variables (Stabler, Papatheodorou and Sinclair, 2010), with the price variables including the prices of goods and services related to both the destination and substitute destinations (Song, Li, Witt and Fei, 2010).

Additional independent variables, such as dummy variables and a time trend to account for changes in tastes and preferences, are sometimes included in the AIDS models. Marketing expenditure is expected to influence tourist expenditure allocations but this variable, though mentioned by O’Hagan and Harrison (1984) and by Papatheodorou (1999), is not actually included in any of the studies under review, generally due to data limitations.

Each equation within the model relates to one destination, as will be evident from the application of the AIDS model discussed in Chapter 6. The dependent variable in each equation is the share of the origin country’s tourism expenditure allocated to that destination. This means that data for the dependent variable is obtained by using tourism expenditure of origin country i in destination country j as a percentage of the aggregate tourism expenditure of origin country i in all the destination countries j under consideration. Data limitations

may force the researcher to create proxies for such data by, as Papatheodorou suggests, “multiplying total international tourism receipts of country j by the share of tourist quantity supplied by country i in the total tourist quantity which visits country j” (1999, p.621). The “tourist quantity” may be measured by using tourist arrivals at registered accommodation. Papatheodorou prefers this measure rather than guestnights, due to the heterogeneous nature of lodgings.

The price variable used relates to the exchange rate-adjusted consumer price index (effective price) throughout most of the studies. Syriopoulos and Sinclair (1993), through the estimation of own- and cross- price elasticities, indicate the importance of effective prices in determining the allocation of expenditure among destinations. Papatheodorou clearly explains how the price set is derived by dividing the consumer price index by “the evolution of the cross-exchange rate (number of currency units of destination country j required to buy one currency unit of origin country i)” (1999, p.623).

Thus the relevant exchange rate is transformed into an index setting a particular year as the base year. White (1985) uses the IMF CPI and “rh” series⁵ on exchange rates and due to aggregation of 16 destinations into seven groups uses the Divisia Price Index for each group. Divisekera (2003) weights costs occurring at the destination and travel costs to obtain an aggregate price variable. The inclusion of travel costs in the AIDS model depends not only on data

5The IMF rh series is the yearly average of exchange rates in US $ per unit of foreign

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availability, but also on what is included in the term ‘tourism expenditure’ (Li, 2004).

When the AIDS model is applied to domestic tourism, the exchange rate element is not relevant. In this context, Divisekera (2010) uses, as a proxy for prices, average spending per night per each broad category of expenditure deflated by appropriate price indices estimated on the basis of the components in each category.

One argument generally presented with regard to the price variable is that the CPI, often used as a proxy for a tourism price index, is not appropriate due to its composition which does not directly reflect tourists’ consumption of goods and services as some items included in the CPI may not be purchased by tourists, particularly by international tourists, as opposed to domestic tourism. Han, Durbarry and Sinclair (2006) investigated the use of alternative price indices within the tourism demand modelling process. It is interesting to note that they concluded that the choice between the alternative price indices (Stone Price Index, Laspeyres Index, Paasche Index) does not have a significant effect on the results.

The expenditure variable is defined as the origin country’s tourist expenditure in the destinations being analysed. In most studies, this variable is expressed per capita of the origin’s population and deflated by the Stone index (aggregate price index). Papatheodorou (1999) does not, however, use population figures to arrive at per capita data but divides by the number of tourists in all the destination countries under consideration. The argument in favour of obtaining per capita

expenditure data by dividing by the number of tourists rather than by the total population of the origin country lies in the fact that

“only people who actually travel are able to incur tourist expenses. In addition, the division by whole country population is bound to result in a non-stationary process for per capita expenditure.”

(Papatheodorou, 1999, p.623)

Dividing by tourists rather than by population is theoretically consistent. De Mello, Pack and Sinclair (1999), however, argue in favour of a per capita estimate based on population figures — for two reasons. The first reason is data reliability. The second reason relates to the multi-stage budgeting approach and the ‘representative consumer’ who, it is argued, is given the opportunity not to spend anything on tourism. Any member of the population is a potential tourist and therefore, dividing by population estimates appears to be more appropriate.

A variable to model a trend is included in some of the empirical studies to account for changes in popularity and tastes. For this purpose White (1985), Papatheodorou (1999), de Mello and Sinclair (2000), Cortés-Jiménez, Durbarry and Pulina (2009), Divisekera (2010) include a time trend in their models. Syriopoulos and Sinclair (1993) included it but then removed it since it did not improve the model’s results. This situation repeats itself when a dummy variable is included. Other empirical studies, such as that by de Mello, Pack and Sinclair (2002), include a dummy variable to account for structural breaks in explanatory variables caused by historical, political or special events. Cortés-Jiménez, Durbarry and Pulina

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(2009) include additional dummy variables in the model for terrorist attacks, for the impact of the euro and for the seasonal impact. To account for the latter, Divisekera (2010) adopted a different approach

— seasonal trigonometric variables — which allows the seasonal cycle dictated by the data rather than dummy variables which define the season.

Estimation procedure and restrictions

The estimation procedure adopted throughout these studies involved the OLS estimation where no a priori restrictions are placed on the model, and Zellner’s 1962 Seemingly Unrestricted Regression (SUR), where the imposition of consumer behaviour restrictions are introduced. As Li, Song and Witt (2004) point out, maximum likelihood (ML) could also be used. Most empirical studies have used SUR since it is more efficient than OLS and converges to a ML estimation (Cortés-Jiménez, Durbarry and Pulina, 2009) but ended up with the theoretical restrictions being rejected.

Durbarry (2002) provides a list of examples of the failure of the homogeneity restriction in the literature. He explains that one cause for rejecting the homogeneity restriction may be mis-specification of the model, for example resulting from applying a static AIDS model.

Empirical results have commonly indicated violation of the homogeneity and symmetry conditions, implying that the assumption of rational behaviour on the consumer’s part requires modification.

Often such studies were based on a static model specification assuming that consumers adjust fully to price and income changes instantaneously. In reality, there may be adjustment costs and

consumers may be basing their decisions on limited information, with the result that they only obtain satisfaction, without maximising it.

Only in Papatheodorou (1999), de Mello, Pack and Sinclair (2002), Divisekera (2010) can one find acceptance of the homogeneity and symmetry restrictions. Where such restrictions fail to be accepted (as, for example, in the study on outbound Italian tourism demand by Cortés-Jiménez, Durbarry and Pulina, 2009), following the standard empirical literature and economic theory, the homogeneity and symmetry conditions are imposed and the restricted model is then estimated.

The following formulae are used to estimate the expenditure, uncompensated and compensated own- and cross-price elasticities.

Expenditure elasticities:

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Compensated cross-price elasticities:

*ij =





 



 w



+ w^B_j (3.21)

where wi represents the sample’s average share of destination i and wjBrepresents the share of destination j in the base year.

Comparison of results of some empirical applications of AIDS Direct comparisons of elasticity measures are not always possible, due to the different origin and destination countries included in the models, different data sources, different time periods and varying sets of explanatory variables. Li (2004) attempted a comparison of elasticities where possible. The set of destinations chosen and the variables included in the model naturally have an influence on the results. Furthermore, the estimation procedure (unrestricted model versus homogeneity- and symmetry- restricted models) may be another reason for the different results. Given this situation, only some general comments will be provided on the results which are relatively comparable.

These comments are based on the four empirical studies conducted by Syriopoulos and Sinclair (1993), Papatheodorou (1999), de Mello, Pack and Sinclair (2002), Li, Song and Witt (2004). These four studies are chosen because they each provide estimates on the UK source market to Mediterranean destinations: in this respect, they are therefore similar to the research conducted in this thesis. Spain and Portugal feature in all four studies whereas Greece and Italy feature in 3 studies, Turkey is included in the 1993- and 1999-published studies, whilst the 2004-published study also features France and

other destinations. Syriopoulos and Sinclair comment that the UK (together with Sweden) is among the “most income and expenditure elastic origin countries” (1993, p.1551). In all four studies, Spain emerges as a core destination, as described by Papatheodorou (1999). It is “a more traditional holiday choice” (Syriopoulos and Sinclair, 1993, p.1549), “a primary destination” (de Mello, Pack and Sinclair, 2002, p.517) with high expenditure elasticities. It is however worth noting that Spain appears to be losing ground to its competitors and becoming more price sensitive over time, as indicated by the de Mello, Pack and Sinclair (2002) study.

Papatheodorou provides a possible explanation for this result by stating that core destinations, being best-known, could experience

“a reduction in their relative price [which] induces people to visit other countries, since they may have already visited the core.”

(Papatheodorou, 1999, p.627)

This could be a further possible explanation for a destination’s life cycle referred to earlier in this chapter. Papatheodorou highlights Italy as another core destination and states that

“this seems to be logical, since these two countries [Spain and Italy] have a highly developed tourist infrastructure and are able to offer a diversified product to satisfy different groups of tourists.”

(Papatheodorou, 1999, p.626)

The estimates by Li, Song and Witt (2004) however indicate that the long-run expenditure elasticity for Italy was slightly lower than 1.

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Spain and Portugal are substitute destinations and Portugal’s expenditure elasticity appears to be stable over time. There appears to be a decreasing responsiveness of UK demand to changes in prices in Portugal and an increasing sensitivity to price changes in Spain (de Mello, Pack and Sinclair, 2002). Syriopoulos and Sinclair (1993) found that the responsiveness of demand to price rises in destinations appeared to be high in Portugal and Greece, followed by Spain, Turkey and Italy. Papatheodorou’s results, besides emphasising the price elastic nature of the core countries (Spain and Italy), also indicate that “Greece and Turkey are relatively price elastic” and “the Portuguese product is very price elastic” (1999, p.627). Li, Song and Witt (2004) also find that the long-run elasticities are generally greater than the short-run estimates, especially for Greece and France, indicating that tourists in the long run are more flexible in response to price changes, a finding which as will be shown in Chapter 6 is supported by this thesis.

In terms of expenditure elasticities for Portugal and Turkey, results from Syriopoulos and Sinclair (1993) and from Papatheodorou (1999) are in direct contrast. The 1993 study shows that these two destinations are highly expenditure-elastic whereas that published in 1999 indicates that “the Turkish tourist product is inelastic, while the Portuguese one is almost invariant to expenditure changes”

(Papatheodorou, 1999, p.627). These different results may be due to various reasons, including different data sets, the inclusion of a time trend in Papatheodorou’s (1999) study, or a different definition of the expenditure variable. Comparison of elasticity results may not be conclusive as they depend on the model specification.

The theoretical underpinnings together with the econometric methodologies of the AIDS model provide that which is to be estimated and the possibility to test hypotheses related to this research, hence its application. In spite of the increasing number of tourism demand studies applying the AIDS model, an area that has not quite been explored is the use of this model for policy evaluation.

This is a gap which this thesis seeks to contribute to through the research presented in Chapter 6.

The focus now turns to a review of the literature which deals with the extent to which the different characteristics of the tourism products supplied affect the overall price of the products. Therefore the literature relating to hedonic pricing and its application to tourism will be reviewed. The hedonic pricing model is the second econometric