گردشگری و توسعه اقتصادی: این بیماری ساحل است؟

This paper analyses empirically the danger of a Dutch Disease Effect in tourism dependent countries in the long run. Data on 134 countries of the world over the period 1970–2007 is used. In a first step the long-run relationship between tourism and economic growth is analysed in a cross-country setting. The results are then checked in a panel data framework on GDP per capita levels that allows to control for reverse causality, non-linearity and interactive effects. It is found that there is no danger of a Beach Disease Effect. On the contrary, tourism dependent countries do not face real exchange rate distortion and deindustrialisation but higher than average economic growth rates. Investment in physical capital, such as for instance transport infrastructure, is complementary to investment in tourism.

The Dutch Disease phenomenon describes the coexistence within the traded goods sector of booming and lagging sub-sectors. Traditionally, the booming sector is referred to be of an extractive kind (e.g. oil or gas) and the manufacturing sector is expected to be under deindustrialisation pressure. For the detailed description of the core model on a booming sector and deindustrialisation in a small open economy, including an algebraic formulation of the problem, see Corden and Neary (1982). For an extended and more general version of Dutch Disease economics, see Corden (1984). Copeland (1991) adjusted the Dutch Disease model in order to examine the economic effects of an increase in tourism in a small, open economy. Adjustments are necessary because there are important differences between tourism and commodity exports. In the presence of tourism, goods that are normally non-tradable become partially tradable and tourists typically consume a bundle of goods and services jointly with unpriced natural amenities, such as climate and scenery. Thus, unlike in the Dutch Disease model, there is a direct increase in foreign demand for non-tradables in a tourist boom, the difference between a trade tax and a domestic commodity tax is fuzzy and unpriced natural amenities may generate rents.

The aim of this research was to analyse empirically the danger of a Dutch Disease Effect for tourism dependent countries in the long run (i.e. the ‘Beach Disease Effect’) as described in the Copeland (1991) and the Chao et al. (2006) models. We performed econometric analyses of the long-run effects of a large tourism sector on aggregate output using data for 134 countries over the period of 1970–2007. Our proxy for tourism capital is the share of travel services exports in GDP. It has to be noted that such variables like the number of star rated hotels and the number of natural attractions would have been better proxies for tourism as an input in a production function. However, this type of data is not available for that many countries and years. Thus our variable of interest can also be interpreted as an indicator of tourism dependency. In a first econometric analysis the general, long-run relationship between tourism, growth, the real exchange rate, taxation and the manufacturing sector was analysed in a cross-country setting. These are the relevant variables in the Copeland and the Chao et al. models. A panel data framework gave the possibility to check the acquired results. Moreover, this second approach allowed to control for reverse causality, non-linearity and interactive effects.