Modelling international tourism flows to China: A panel data analysis with the gravity model

Abstract

Based on the theoretical and empirical foundations of the gravity model, this article systematically investigates the determinants of international tourist arrivals to China. Various origin–destination (O-D) linked factors accounting for the economic, political and social/culture preferences between China and its tourist origins are particularly explored. Utilizing a panel data set of tourist arrivals to China from 21 countries from 1995 to 2014, the results suggest that the basic gravity determinants all have significant effects on tourist arrivals to China. Furthermore, four O-D linked factors are found to be significant in explaining the number of international tourists and have a greater tourist-enhancing effect. In particular, overseas Chinese, proxying for social/culture-preferential relationships, have caused significant increases in tourism arrivals to China of approximately 120% when the percentage of ethnic Chinese in the origin countries’ population is more than 1%. For China to be chosen as a manufacturing and processing centre by international enterprises, various O-D relationships are important but neglected factors of Chinese tourism patterns, with important policy implications.

Dynamics of tourism demand in Turkey: Panel data analysis using gravity model

“…The interest in determining and classifying tourism demand components has been increasing over the past few decades. In recent studies, the gravity model has been used for modeling and describing international tourism demand to determine its main components and features by illustrating tourism flows as trade of service (Adeola and Evans, 2019; Algieri, 2006; Eilat and Einav, 2004; Fourie et al, 2019; Lorde et al, 2015; Pintassilgo et al, 2016; Saayman et al, 2016; Seetanah et al, 2010; Xu et al, 2018). Other studies mostly estimate the determinants of tourism demand through linear or nonlinear models (Akis, 1998; Dogru et al, 2017; Dritsakis, 2004; Samitas et al, 2018; Santos and Cincera, 2018).…”

“…There is a consensus that tourism may play a vital role in the achievement of sustainable development goals through economic and environmental benefits (Davidson and Sahli, 2015; Kapera, 2018; Lee and Jan, 2019; Saarinen et al, 2011; Suriñach and Wöber, 2017). The tourism sector helps to create foreign exchange incomes, provides job opportunities, and stimulates service sector investments in developing countries (Xu et al, 2018). In this direction, the role of tourism in the long-run economic growth equilibrium and its contribution to the growth transition are explicitly revealed by Fossati and Panella (2000) and Brau et al (2008) in the context of sustainable development.…”

Evaluating the Impact of Air Pollution on China’s Inbound Tourism: A Gravity Model Approach

“…The gravity model has been used in previous studies to explore the determinants of tourism. For instance, Huang et al [44], Xu et al [45], and Yang and Wong [46] investigated inbound tourism flows to China.…”

“…The more intensive the economic relationship, the more business tourists there are traveling between regions. Thus, the relative trade volume is a meaningful variable that can be utilized as a proxy for the closeness of intercountry economic relationship and partially explain the volume of tourism flow [44,45]. Because of its great size in terms of macroeconomy and international trade, China is one of the most important business partners for many countries.…”

Spatial Interaction Modeling of OD Flow Data: Comparing Geographically Weighted Negative Binomial Regression (GWNBR) and OLS (GWOLSR)

“…In this case, the dependent variable has been transformed from an integer to a real variable. In most cases, across disciplines, the transformed variable follows a normal distribution so OLS regression is used for calibration [43][44][45]. However, traffic flow is considered a counting variable, where the data follows a Poisson distribution.…”