Immigration and international inbound tourism: Empirical evidence from Australia

Immigration can potentially influence tourism flows. However, in spite of the vast number of studies on tourism demand modelling, the immigration-tourism linkage has not received much attention in the empirical literature. This paper seeks to address this gap. A dynamic demand model is developed and estimated using data from 1980 to 2008 for the 15 main markets of Australia. The explanatory variables included are income, own price, price of a substitute destination, airfare and immigration. The estimation results empirically establish the connection between immigration and inbound tourism. The short run and long-run immigration elasticities generated are 0.028 and 0.09 respectively. Additionally this paper demonstrates that omission of prices of substitutes affects the value of the own price elasticity of demand. The results have implications for future research and for stakeholders who can improve the efficiency of their planning exercises by taking into account additional information on immigration trends.     

The impacts of international tourism demand on economic growth of small economies dependent on tourism

This paper studies the impacts on economic growth of a small tourism-driven economy caused by an increase in the growth rate of international tourism demand. We present a formal model and empirical evidence. The ingredients of the dynamic model are a large population of intertemporally optimizing agents and an AK technology representing tourism production. The model shows that an increase in the growth of tourism demand leads to transitional dynamics with gradually increasing economic growth and increasing terms of trade. In our empirical application, an econometric methodology is applied to annual data of Antigua and Barbuda from 1970 to 2008. We perform a cointegration analysis to look for the existence of a long-run relationship among variables of economic growth, international tourism earnings and the real exchange rate. The exercise confirms the theoretical findings.     

Monitoring tourism flows and destination management: Empirical evidence for Portugal

We propose the use of a tool recently introduced by Gayer (2010), known as the “economic climate tracer”, to analyze and monitor the cyclical evolution of tourism source markets to Portugal. Considering the period 1987–2015, we evaluate how tourism to Portugal has been affected by economic cycles. This tool is useful as it clearly illustrates the evolutionary patterns of different markets, and allows us to identify close relationships with economic fluctuations. We found that German tourism plays a leading role, since its movements are followed with delays by tourism flows from other countries, and exhibits higher resilience to shocks. Also, domestic and Spanish tourism have both displayed less irregular behaviors than tourism from other source markets. On the contrary, tourism from the Netherlands and the UK, have displayed irregular patterns, which demonstrates the urgency to diversify tourism source markets to reduce the country's vulnerability to external shocks and economic cycles.     

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.     

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.     

Modelling and forecasting the demand for Hong Kong tourism

The main objectives of this paper are to identify the factors which contribute to the demand for Hong Kong tourism with the aid of econometric models and to generate forecasts of international tourism arrivals to Hong Kong for the period 2001–2008. The general-to-specific modelling approach is followed to model and forecast the demand for Hong Kong tourism by residents from the 16 major origin countries/regions and the empirical results reveal that the most important factors that determine the demand for Hong Kong tourism are the costs of tourism in Hong Kong, the economic condition (measured by the income level) in the origin countries/regions, the costs of tourism in the competing destinations and the ‘word of mouth’ effect. The demand elasticities and forecasts of tourism arrivals obtained from the demand models form the basis of policy formulations for the tourism industry in Hong Kong.     

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.     

A piecewise linear approach to modeling and forecasting demand for Macau tourism

In this paper, we construct and use a piecewise linear method to model and forecast, on a monthly basis, the demand for Macau tourism. Data over the period January 1991–December 2005 and a seasonally adjusted series for tourism demand are used. The study examines 4 forecasting horizons ranging from 6 to 24 months in advance. Mean absolute percentage errors and root mean square errors are adopted as criteria for evaluating the accuracy of the forecasting exercises. Finally, the forecasts of piecewise linear model are compared with those of autoregressive trend model, seasonal autoregressive integrated moving average and its arch-rival fractionally integrated autoregressive moving average models. The piecewise linear model is more accurate than the three benchmark models tested and the improvement is practically significant.     

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.     

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.     

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.     

Causality in direct air services and tourism demand

Unlike income or relative prices, air transport attributes and tourism demand on a given route can be endogenous. Using instrumental variables, this study attempted to account for the circular causality in estimating the effect of direct air service on tourism demand. Although we found evidence of endogeneity, the nature of the circular causation is context-specific; while direct air service can be regarded as an exogenous variable in one direction, it can have an endogenous relationship on the other. Findings emphasise the need to explicate information about the network nature of transportation and its endogenous relations with tourism.     

Forecasting Chinese cruise tourism demand with big data: An optimized machine learning approach

After more than ten years of exponential development, the growth rate of cruise tourist in China is slowing down. There is increasingly financial risk of investing in homeports, cruise ships and promotional activities. Therefore, forecasting Chinese cruise tourism demand is a prerequisite for investment decision-making and planning. In order to enhance forecasting performance, a least squares support vector regression model with gravitational search algorithm (LSSVR-GSA) is proposed for forecasting cruise tourism demand with big data, which are search query data (SQD) from Baidu and economic indexes. In the proposed model, hyper-parameters of the LSSVR model are optimized with GSA. By comparing these models with various settings, we find that LSSVR-GSA with selected mobile keywords and economic indexes can achieve the highest forecasting performance. The results indicate the proposed framework of the methodology is effective and big data can be helpful predictors for forecasting Chinese cruise tourism demand.     

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.     

Seasonal concentration of tourism demand: Decomposition analysis and marketing implications

This paper analyzes seasonality in the United Kingdom, specifically the English regions in relation to tourists' place of origin and main travel motivation. The method used is a decomposition of the Gini index, which provides relative marginal effects that facilitate the identification of market segments open to counter-seasonal marketing efforts. This method has been combined with a graphical multivariate technique (biplot), which groups segments according to their seasonality characteristics. Seasonal patterns associated with particular segments differ significantly when studied on a disaggregated basis. Therefore, an adequate level of disaggregation is essential in the design of counter-seasonal strategies. Although this study focuses on British destinations, this methodology could be used as a control and monitoring measure in the regional analysis of any destination, facilitating regular adjustment of regional tourism marketing campaigns to minimize seasonality effects, specifically by targeting the types of tourists less prone to seasonality.     

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.     

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.     

Investigating the tourism and economic growth linkage: a panel causality analysis for the SAARC countries

Tourism receipts and expenditure are considered to be causally related to economic growth. The present study investigates the causal relationship between tourism receipts and tourism expenditure to economic growth for a panel of the South Asian Association for Regional Cooperation (SAARC) countries over a 20-year period (1995–2014). The present study used panel Granger causality tests and panel fully modified ordinary least squares to test the presence and magnitude of the causality. Results indicated a bidirectional causal linkage between growth and tourism expenditure only, while tourism receipts were found to influence growth. Tourism expenditure was found to have a negative effect on growth.     

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.