Using quantile regression to understand visitor spending

A common approach to assessing visitor expenditures is to use least-squares regression analysis to determine statistically significant variables upon which key market segments are identified for marketing purposes. This was done by Wang (2004) for survey data based on expenditures by Mainland Chinese visitors to Hong Kong. In this research note we use this same dataset to demonstrate the benefits of using quantile regression analysis to better identify tourist spending patterns and market segments. The quantile regression method measures tourist spending in different categories against a fixed range of dependent variables, which distinguishes between lower, medium, and higher spenders. The results show that quantile regression is less susceptible to influence by outlier values and is better able to target finer tourist spending market segments

Tourism and Economic Globalization: An Emerging Research Agenda

Globalization characterizes the economic, social, political, and cultural spheres of the modern world. Tourism has long been claimed as a crucial force shaping globalization, while in turn the developments of the tourism sector are under the influences of growing interdependence across the world. As globalization proceeds, destination countries have become more and more susceptible to local and global events. By linking the existing literature coherently, this study explores a number of themes on economic globalization in tourism. It attempts to identify the forces underpinning globalization and assess the implications on both the supply side and the demand side of the tourism sector. In view of a lack of quantitative evidence, future directions for empirical research have been suggested to investigate the interdependence of tourism demand, the convergence of tourism productivity, and the impact of global events

A complex network analysis of inbound tourism in Sicily

In this article, the complex dynamics of inbound tourism in Sicily is analyzed for the period 1998–2017. The horizontal visibility graph algorithm is used to transform the overnight stays' time series into a network whose topology is investigated by standard network analysis. Discontinuities in the domestic and international tourism demand were identified in order to detect signals of change and the timing of the directional change in tourism growth. The network degree distribution confirms the complex structure of the destination and reveals the random and thus more unpredictable nature of the international tourism demand in Sicily, compared with a more stable domestic segment. Some policy implications are drawn