Methoden vergelijken

	Tourism Demand Elasticity Modeling	Tourism Seasonality Index
Vakgebied	Tourism Hospitality	Tourism Hospitality
Familie	Regression model	Regression model
Jaar van ontstaan	1994	2001
Grondlegger	Geoffrey I. Crouch	Svend Lundtorp; Anastassios Tsitouras
Type	Econometric demand-elasticity estimation	Descriptive concentration index for seasonal demand
Oorspronkelijke bron	Crouch, G. I. (1994). The Study of International Tourism Demand: A Review of Findings. Journal of Travel Research, 33(1), 12-23.	Lundtorp, S. (2001). Measuring Tourism Seasonality. In T. Baum & S. Lundtorp (Eds.), Seasonality in Tourism (pp. 23-50). Oxford: Pergamon/Elsevier. ISBN: 9780080436746
Aliassen	Tourism Income Elasticity, Tourism Price Elasticity, Elasticity of International Tourism Demand, Tourism Demand Sensitivity Analysis	Tourism Seasonality Measurement, Seasonality Gini Coefficient, Seasonal Concentration Index, Tourism Seasonality Ratio
Verwant	4	4
Samenvatting	Tourism demand elasticity modeling estimates how responsive tourist demand is to changes in its key drivers, above all source-market income and the price of travel. The income elasticity measures the percentage change in demand for a one-percent change in income, and the price elasticity does the same for price; both are recovered as coefficients in econometric demand models, most simply a log-linear regression where the coefficients read directly as elasticities. Geoffrey Crouch's mid-1990s surveys of the international tourism demand literature consolidated decades of such estimates, showing that tourism is typically income-elastic — a luxury that grows faster than income — and price-sensitive, with values that vary systematically across markets and methods. Later meta-analyses, such as Peng, Song, Crouch, and Witt's, quantified that variation across hundreds of studies.	Tourism seasonality measurement summarizes how unevenly tourism demand is distributed across the year. Destinations rarely receive visitors at a constant rate; arrivals, overnight stays, and revenue cluster in peak months and thin out in the off-season, straining capacity at the top and leaving resources idle at the bottom. Seasonality indices turn a monthly demand series into a single, comparable number measuring this temporal concentration. Simple ratios compare the peak month to the average or to the trough, while the Gini coefficient — long established in the study of inequality and adapted by Svend Lundtorp and others to tourism — captures concentration across all months at once via a Lorenz curve. Adjusted versions, such as Tsitouras's 'months equivalent' degree of seasonality, make the index easier to interpret and compare.