Seasonal concentration of the hotel demand in Costa del Sol: A decomposition by nationalities

Costa del Sol is a mature Mediterranean destination in the South of Spain with a degree of seasonality that has shown little variation during the last 20 years. In this paper, we apply the decomposition of the Gini index of seasonal concentration by nationalities. The main outcome of this decomposition is the estimation of relative contributions to the overall seasonal concentration and marginal effects by nationalities. These measures can be of great help in the design of counter-seasonal policies. Our results suggest that planners interested in reducing seasonality may concentrate their efforts in consolidating markets with negative marginal effect over the index of concentration, like the British and Nordic ones. In addition, marketing targets may be focused primarily in the winter season for the markets with positive relative marginal effect.     

Impacts of seasonal patterns of climate on recurrent fluctuations in tourism demand: Evidence from Aruba

This study estimates the effect of seasonal patterns of pull and push climate elements (rainfall, temperature, wind, and cloud coverage) on recurrent fluctuations in tourism demand from the United States (USA) and Venezuela to Aruba. The seasonal patterns were first isolated from the series using the Census X-12 decomposition method, after which the analysis included panel data unit root testing, panel data regression, and Euclidean distance calculation. The results show that both pull and push seasonal factors of climate were relevant in determining the seasonal variations in tourism demand from both countries. The study derives two theoretical propositions: (1) climate is a significant push and pull factor affecting tourism demand; and (2) tourism demand and climate are bounded by intertemporal climate constraints.     

Yearly,monthly and weekly seasonality of tourism demand: A decomposition analysis

Annual seasonal variations in tourism demand have been a central theme in literature. However, annual seasonality is not the only time-based inequality in tourism flows that has important implications on policy-making decisions at destinations. Within the context of tourism, this study aims to make an in-depth analysis of intra-monthly and intra-weekly tourism demand using the entropy and relative redundancy measures as alternative seasonality indicators to the Gini coefficient in order to provide new tools to manage tourism and propose new action policies at these frequencies. In comparison with the Gini coefficient, the entropy measure is simpler to compute and it is easily decomposable. Using the case study of air arrivals and departures to and from the Balearic Islands, results show the appropriateness of entropy and relative redundancy as seasonal indicators but also as a new information tools for tourism seasonality analysis.     

Dynamic heterogeneous panel data analysis of tourism demand for Singapore

The objective of this study is to investigate tourism demand for Singapore using heterogeneous dynamic panel estimation methods. A binary variable representing the operation of Integrated Resorts (IRs), as well as income and real exchange rate, are included as determinants of tourism demand growth. Using seasonally adjusted and actual/raw quarterly data of 16 origin countries, our study shows that the pooled mran group (PMG) estimator is able to provide consistent and efficient estimates of long-run relationships between tourism demand and the determinants. The PMG results show that the long-run income (price) elasticity is positively (negatively) significant, with an elasticities range between 0.915 and 3.05 (?0.275 and ?0.34). In addition, the study reveals that the IRs contribute to tourism demand growth, especially in attracting tourists from Asia. In the long term, tourism demand by the Asian markets is less income-sensitive than that of the non-Asian counterparts.     

Remodeling international tourism demand: Old theory and new evidence

The purpose of this study is to advance the tourism demand theory by excluding simultaneous effects of exchange rates and prices in empirical models, formulating an alternative pricing modus operandi consistent with recent research in the area, and demonstrating the efficacy of the use of an industrial production index (IPI) as a proxy for income. A panel fully modified ordinary least squares (FMOLS) method is employed to estimate the inbound tourism demand for Turkey. Study findings suggest that the inclusion of exchange rates and prices, as mutually exclusive components, can be misleading; the IPI is not a good proxy for income; and country-specific coefficients need to be analyzed to accurately explain determinants of tourism demand for countries in the panel.     

Progress in tourism demand research: Theory and empirics

To explore recent progress in tourism demand research, we comprehensively survey current studies in the leading tourism and hospitality journals, asking six evaluative questions about the scientific merits of the studies and three explorative questions about emerging areas in the literature. The examination identifies potential flaws and their consequences in the field of tourism demand. A theoretical foundation is recommended for future tourism demand studies with a view to reduce bias in the empirical analysis of tourism demand. Several emerging areas of analysis in the field of tourism demand are recognized and discussed. Our study provides critical insights that will enable future tourism demand research to generate more reliable, impactful information than in the past.     

Productivity growth and income in the tourism sector: Role of tourism demand and human capital investment

This paper proposes a model for the demand for tourism in the context of a developing country. The parameters of the model are a tourist sector characterised by monopolistic competition, where human capital is the main factor of production and hotels have market power. Additionally land use is marked by demand from both agricultural and tourism sectors. From the household side, a simplified OLG approach is developed to consider consumption, human activity and the number of children. A dynamic framework is therefore identified to investigate the long-run consequences of increasing labor productivity and lowering the fertility rate. If the supply-side policy leads to economic growth, the tourism led growth hypothesis is theoretically confirmed. It is concluded that an increase in labor productivity generates positive growth effects only if the demand for tourism is elastic, otherwise negative results arise.     

Modelling the interdependence of tourism demand: The global vector autoregressive approach

This study develops a global vector autoregressive (global VAR or GVAR) model to quantify the cross-country co-movements of tourism demand and simulate the impulse responses of shocks to the Chinese economy. The GVAR model overcomes the endogeneity and over-parameterisation issues found in many tourism demand models. The results show the size of co-movements in tourism demand across 24 major countries in different regions. In the event of negative shocks to China’s real income and China’s tourism price variable, almost all of these countries would face fluctuations in their international tourism demand and in their tourism prices in the short run. In the long run, developing countries and China’s neighbouring countries would tend to be more negatively affected than developed countries.     

Managing tourism emissions through optimizing the tourism demand mix: Concept and analysis

Carbon mitigation strategies are an urgent and overdue tourism industry imperative. The tourism response to climate action has been to engage businesses in technology adoption, and to encourage more sustainable visitor behaviour. These strategies however are insufficient to mitigate the soaring carbon footprint of tourism. Building upon the concepts of optimization and eco-efficiency, we put forward a novel carbon mitigation approach, which seeks to pro-actively determine, foster, and develop a long-term tourist market portfolio. This can be achieved through intervening and reconfiguring the demand mix with the fundamental aim of promoting low carbon travel markets. The concept and the analytical framework that quantitatively inform optimization of the desired market mix are presented. Combining the “de-growth” and “optimization” strategies, it is demonstrated that in the case study of Taiwan, great potential exists to reduce emissions and sustain economic yields. The implications for tourism destination managers and wider industry stakeholders are discussed.     

A review of research on tourism demand forecasting: Launching the Annals of Tourism Research Curated Collection on tourism demand forecasting

This study reviews 211 key papers published between 1968 and 2018, for a better understanding of how the methods of tourism demand forecasting have evolved over time. The key findings, drawn from comparisons of method-performance profiles over time, are that forecasting models have grown more diversified, that these models have been combined, and that the accuracy of forecasting has been improved. Given the complexity of determining tourism demand, there is no single method that performs well for all situations, and the evolution of forecasting methods is still ongoing.This article also launches the Annals of Tourism Research Curated Collection on tourism demand forecasting, which contains past and hot off the press work on the topic and will continue to grow as new articles on the topic appear in Annals.     

Estimation of tourism demand by multivariable regression analysis Evidence from Kenya

Between 1963 and 1982, tourism was one of the top three domestic exports of Kenya. Knowledge of the variables which significantly Influence Kenyan tourism demand could help tourism policy makers in planning growth strategies. The results of this study indicate that multivariable regression analysis has limited usefulness in identifying the significant factors which influence tourists' decisions. Data problems and resulting multicollinearity cause unsuitable results in one case, while model specification appears to be a problem in another. The author concludes that quantitative studies should be supplemented by qualitative analysis in order for Kenyan policymakers to make optimal decisions.     

A meta-analysis of international tourism demand forecasting and implications for practice

Numerous studies on tourism forecasting have now been published over the past five decades. However, no consensus has been reached in terms of which types of forecasting models tend to be more accurate and in which circumstances. This study uses meta-analysis to examine the relationships between the accuracy of different forecasting models, and the data characteristics and study features. By reviewing 65 studies published during the period 1980–2011, the meta-regression analysis shows that the origins of tourists, destination, time period, modeling method, data frequency, number of variables and their measures and sample size all significantly influence the accuracy of forecasting models. This study is the first attempt to pair forecasting models with the data characteristics and the tourism forecasting context. The results provide suggestions for the choice of appropriate forecasting methods in different forecasting settings.     

Forecasting international city tourism demand for Paris: Accuracy of uni- and multivariate models employing monthly data

The purpose of this study is to compare the predictive accuracy of various uni- and multivariate models in forecasting international city tourism demand for Paris from its five most important foreign source markets (Germany, Italy, Japan, UK and US). In order to achieve this, seven different forecast models are applied: EC-ADLM, classical and Bayesian VAR, TVP, ARMA, and ETS, as well as the naïve-1 model serving as a benchmark. The accuracy of the forecast models is evaluated in terms of the RMSE and the MAE. The results indicate that for the US and UK source markets, univariate models of ARMA(1,1) and ETS are more accurate, but that multivariate models are better predictors for the German and Italian source markets, in particular (Bayesian) VAR. For the Japanese source market, the results vary according to the forecast horizon. Overall, the naïve-1 benchmark is significantly outperformed across nearly all source markets and forecast horizons.     

The determinants of Chinese visitors to Australia: A dynamic demand analysis

Although China has progressively become an important inbound tourism market for Australia, its demand elasticities have been little studied to date. This study examines the determinants of Chinese visitors to Australia using a dynamic time-series estimator. Interesting findings include a high income elasticity as a source of the continuous doubledigit growth rates in Chinese arrivals that Australia has experienced over the past two decades, together with relatively high total trip price elasticities for both short run and long run. A trend of Chinese outbound to Australia is also identified. From a policy perspective, the results confirm that keeping a low cost of visiting Australia, both ground and travel costs, is a good strategy to secure greater numbers of Chinese tourists.     

Climate in the region of origin and destination choice in outbound tourism demand

This paper describes the relationship between regional climate in the home area and the choice of taking holidays in the region of origin or abroad. This decision is simultaneously estimated with a bivariate probit model. The study combines the socioeconomic characteristics of European households with information on the region of residence, such as climate, which is defined according to a new annualized climate index. The estimated probabilities are analysed using GIS and nonparametric techniques. The results of modelling support the hypothesis that the climate in the region of residence is a strong determinant of holiday destination choice. They show that residents in regions with better climate indices have a higher probability of travelling domestically and a lower probability of travelling abroad.     

An empirical study of inbound tourism demand in China: a copula-GARCH approach

This paper proposes new models for analyzing the volatility and dependence of monthly tourist arrivals to China applying a copula-GARCH approach. A desegregation of the top six origins of China inbound tourists from the period January 1994 to December 2013 is used in this study. The empirical results show that there is a strong seasonal effect in all cases and ?????? some habit persistence on monthly tourist arrival growth rate for South Korea, Russia, the United States (US), and Malaysia. Second, the volatilities of arrival growth rates to China are impacted significantly by their own short- and long-run effects, except for Russia and South Korea. Only short-run shock affects Russian arrivals while only long-run shocks are affecting South Korea arrivals. Third, the conditional dependence among different source countries is found to be positive and significant, but the conditional dependence for all considered pairs is low. Moreover, there is extreme co-movement (tail dependence) between the six major tourism source countries, suggesting the pairwise of international tourist arrivals shows a related increasing or decreasing pattern during extreme events. Implications are discussed and recommendations provided.     

An empirical analysis of the influence of macroeconomic determinants on World tourism demand

This paper considers three econometric models to determine the relationship between macroeconomic variables and tourism demand. Tourism demand is measured by the inbound visitor's population and also by on-the-ground expenditures. The database is an unbalanced panel of 218 countries over the period 1995–2012. There is evidence that an increase in the World's GDP per capita, a depreciation of the national currency, and a decline of relative domestic prices do help boost tourism demand. The World's GDP per capita is more important when explaining arrivals, but relative prices become more important when we use expenditures as the proxy for tourism demand. We cannot reject the hypothesis of a relative prices unitary elasticity of expenditures. Additionally, we have partitioned our data by income level and by Continent. Results are robust in the first partition, but less robust in the second, although the main conclusions still hold. Finally, we draw policy implications from our findings.     

Seasonality in tourist flows: Decomposing and testing changes in seasonal concentration

This paper shows that changes in timing and magnitude of seasonality in tourist flows can be measured by decomposing the change in the Gini index, a widely used index of seasonal concentration, into two components. One of them tracks the change in the pattern of seasonal fluctuations and the second captures the change in their magnitude. To assess whether changes in the seasonal pattern and magnitude are significant, a technique for testing hypotheses concerning the two components is developed. We examine changes in the seasonal concentration of tourist arrivals in six tourist destinations in the Veneto Region, one of the most tourism-oriented regions in Europe, from 2006 to 2016. The magnitude of seasonality significantly decreased in some destinations with a diversified tourism product, such as Euganean Spas and Lake Garda. The seasonal pattern remained substantially stable for all destinations except Venice, where a non-negligible shift in the seasonal pattern occurred.     

Decomposition of tourism greenhouse gas emissions: Revealing the dynamics between tourism economic growth,technological efficiency,and carbon emissions

This study proposes an analytical framework for decomposing the national tourism carbon footprint and carbon efficiency to identify the dynamics between economic growth, technological efficiency, and environmental externality. Using the environmentally extended input–output model and decomposition methods, tourism carbon changes are decomposed into the economic factors of total consumption and purchasing patterns, and the production factors of industry input structure and technological improvement. This macro-level approach provides a basis for 1) assessing whether total tourism emissions increase in direct proportion to tourism consumption over time, 2) tracing the underlying determinants and their effects on tourism emissions expansion and eco-efficiency performance, and 3) comparing the carbon performance of the tourism industry against the national average. Based on the example of Taiwan, the results demonstrate that we are a long way from the goal of using the technological efficiencies of production to offset tourism-based carbon emissions.     

Estimating the long-run effects of socioeconomic and meteorological factors on the domestic tourism demand for Galicia (Spain)

Tourism is one of the more important sectors for the economy of Galicia (northwest of Spain). The aim of our study is to quantify determinants of Galician domestic tourism demand and their impacts. Additionally, confidence intervals for these impacts are calculated by bootstrapping data. The results show that income, Eastern vacations and the celebration of the Holy Year of the Apostle Saint James have had a relevant positive impact on tourism. There is also a significant positive connection between a meteorological phenomenon, the North Atlantic Oscillation (NAO), and tourism demand. On the other hand, the differential rates of inflation between Galicia and the rest of Spain and the 2008 economic crisis have had a negative effect.