variable.<sup>6</sup> Then, using this strategy, we can evaluate how STP relates to the tourism determinants used as inputs in the hedonic model.

# **IIIb. Tourism price elasticity**

A dynamic panel data model was regressed to estimate price elasticity. As noted above, a very important determinant of the tourism industry performance is price and, in particular, if tourism can be considered a market with elastic or inelastic price elasticity. Consider now the following dynamic panel data model: **Yit=** α **Yit-1 +** β **ERit +** γ **Xit-1 +** δ **t +** μ **I + εit** , where **Y** is the dependent variable of interest (LN_ARR and LN_EA), **ER** is the real exchange rate with respect to either euro, US dollar or British pound, **X** is a set of control variables that affect tourists’ decisions (GDP of destination, population, exports, and total seats in flights for that destination, all lagged one period), contains time-specific effects, is island-specific effects, and **ε** is an idiosyncratic error term. Stationary panels require that | α| **<1** , which is assumed **.** The main δδ μμ coefficients of interest are and . In particular, for dynamic models the main interest is in the longrun effects, which mean the ratio: /(1- **α** ). ββ γγ This model is appropriate for this estimation for two reasons: (i) tourism arrivals and receipts are ββ

This model is appropriate for this estimation for two reasons: (i) tourism arrivals and receipts are likely to have a clear temporal persistence. The experience to spend holidays in a certain destination provides information that cannot be achieved by other methods. As such, if a certain destination was visited (provided the experience was positive), it will increase the chances of revisiting and arrivals from friends, family, etc. Nevertheless, the available data set is not long enough to construct VAR models and, therefore, it cannot be evaluated by using island-specific dynamic effects, but rather by using a common autoregressive parameter. The above model is similar to that of Maloney and Montes-Rojas (2005) for Caribbean tourism destinations.

And (ii) data availability only allows the use of total aggregate tourist arrivals from all destinations. Since different islands have a different bundle of tourist origins, the model also has controls for this: the presence of fixed-effects by country and by year. The former captures all unobservable components that affect tourism arrivals (or expenditures) for a particular destination. The aggregation procedure, thus, passes all unobservable characteristics of the tourism destinations to this factor. The latter does the same to all year specific unobservable components. In particular, the main interest is controlling for changes in origin GDP and tastes. For this analysis, this note considers 36 tourism destinations from different locations. The data availability is from 2009 to 2016.

A well-known issue in dynamic panel data models is that the model cannot be estimated consistently by conventional methods such as OLS or FE, the so-called Nickel bias. The reason is that the presence of individual (i.e., island) specific effects produces a bias that affects α and other parameters. This applies to this data set since its shorth length implies that the bias effect cannot be assumed to be

> 6 In hedonic regression models, characteristics are used as determinants of a certain outcome variable. The typical application is the house price hedonic price model, where house characteristics are used to infer about the price of a house.

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