Panel data analysis in Tourism Research

This chapter focuses on the application of panel data techniques in the tourism literature. The chapter is organised as follows: The next section explains panel data modelling technique emphasising on the difference between fixed and random effects. Dynamism in modelling is introduced and the implication for model estimation is discussed. The chapter then devotes a section to unit root and cointegration tests which is followed by an illustration of the application of panel data analysis in tourism research namely, in tourism demand modeling and in explorations of the relationship between tourism and economic growth. The conclusion spells out the limitations of this technique and directions for future research

Consumption of Pop Culture and Tourism Demand:Through the lens of herding behaviour

Herding is a social phenomenon where individuals act collectively as part of a group and make decisions based on the behaviour and choices of others. In this study, we conceptualise herding behaviour on social media via a two-step flow communication model and measured through Google and YouTube search volume. The estimated herding behaviour is used to develop a novel index to measure the online consumption of Korean cultural products. Monthly data between 2013 and 2019 were used to analyse tourist arrivals from 10 countries. Findings confirm that Korean wave, captured by the index, is statistically significant in predicting tourist arrivals. The study provides a generalised proxy of cultural consumption, applicable to other destinations

Measuring price elasticities of demand for outbound tourism using competitiveness indices

The real exchange rate (REX) has long been used as the proxy for prices in tourism demand models. However it has limitations, particularly when it comes to models of outbound tourism. As an alternative, a price competitiveness index (PCI) is developed and used as a proxy for prices in a model of outbound tourism from Australia. Results obtained show that while REX is statistically insignificant and yields a price elasticity of −0.002, PCI is significant and generates a price elasticity of −1.07. The results obtained show that PCI outperforms REX as the preferred price variable in modelling outbound demand on both theoretic and empirical grounds. Furthermore, this index can be used to monitor the inter-temporal competitiveness of a destination

Is the Migration-Tourism Relationship only about VFR?

This paper contributes to the literature on the linkages between tourism and migration. Though it is widely recognised that the two phenomena are closely linked, and that migration may induce visiting friends and relatives) tourism (VFR), there has been little econometric evaluation of the relationship. The present analysis draws upon Australian data to identify a strong quantitative link between migration and VFR tourism. It also demonstrates a strong link between migration and other forms of tourism. Indeed the latter are almost equally as strong as the links between migration and VFR tourism. This unexpected finding has implications for policymakers and for conceptualising the migration-tourism relationship

Measuring the Effect of Revealed Cultural Preferences on Tourism Exports

The aim of this article is to propose a novel method for measuring the effect of cultural preference on bilateral tourism receipts. The method applied is inspired from Disdier et al. (2010). Using the UNESCO classification and data on bilateral trade in cultural product, a proxy for cultural preferences is constructed. The variable is used in a gravity model for tourism export, which is estimated using a two-step procedure to avoid issues related to endogeneity. The data set used is a panel of 12 OECD countries for a period of 11 years. The variable for cultural preferences eliminates the problems with traditional methods, which by using dummy variables to account for cultural preferences, assume that the latter are time-invariant and symmetrical. The cultural variable constructed is found to be significant in explaining bilateral tourism exports with an elasticity of 0.39. © The Author(s) 2018

Computing airfare elasticities or opening Pandora's box

Airfare elasticities are crucial for policy instruments for the stakeholders of tourism industry. Modellers attempting to estimate these elasticities face several challenges in the absence of microeconomic data on consumer preferences. The aim of this paper is to offer a critical analysis of the practice of computing airfare elasticities from tourism demand studies. It identifies and discusses potential sources of measurement errors in the data and drawbacks that investigators face and assesses their implications for the estimated parameters in the respective models. The use of price of crude oil and distance between origin and destination as proxies for transportation costs are evaluated. It is recommended that authors be more systematic in reporting their results. They need to comment on the limitations of their elasticity estimates since these have repercussions on policy recommendations made based on their results.Air fare elasticities Tourism demand models Measurement errors Oil price Tourism

Time Series Analysis

The aim of this chapter is to review the application of time series analysis and modelling in the tourism literature. Note that there exists a vast number of such techniques which have been employed to analyse tourism data. It is beyond the scope of this chapter to discuss all. This chapter instead focuses on the most widely applied methods within the tourism context. It is made up of three distinct sections. In the first section the characteristics of time series data are discussed. The second section concentrates on the 'classic' time series modelling techniques. The last section discusses directions for new research and focuses on approaches which are fairly recent such as neural networks models which may be expected to become more extensively utilized by tourism researchers in the near future