Spatial effects in regional tourism growth

Tourism and regional income inequality: Evidence from China

“…Páez et al (2011) recommend a minimum sample size of 160 for GWR. Although this issue can be partially eased with the SALE or GW-SDM method (Yang & Fik, 2014) by including a spatial lag of the dependent variable which in effect increases the sub-sample size of the local model, they are still unviable for local estimation with a limited number of regions. To address this issue, the present study extends the SALE approach to a dynamic panel context by incorporating both temporal and spatial lags of the dependent variable into the local model, which significantly expands the sub-sample size.…”

“…With the rapid development of civil aviation and high-speed rail, tourism spillover effects will be more significant and far reaching still. Moreover, Yang and Fik (2014) also argue that the spatial competition among regional governments increases the incentives to support tourism, which in turn leads to a higher level of spillovers in regional tourism growth especially for those under-developed regions.…”

“…Previous literature shows that when spatial effects are incorporated into the convergence model, they tend to be significant and strongly influence the convergence speed (James & Campbell, 2013;Rey & Montouri, 1999). Most recently James and Campbell (2014) To account for spatial effects in tourism and economic growth, spatial Durbin models (SDM) have been used to capture the spatial interdependence in both dependent and explanatory variables (Yang & Fik, 2014). Moreover, SDMs may reduce estimation biases amplified by omitted variables due to the inclusion of the spatially lagged dependent variable (LeSage & Pace, 2009).…”

“…"Tourism spillover effects" refer to the indirect or unintentional effects that a region's tourism industry exerts on tourist flows to other regions (Yang & Wong, 2012). Lazzeretti andCapone (2009), Yang andWong (2012), and Yang and Fik (2014) have empirically identified the spillover effects in regional tourism flows or growth. By using the method of Local Indicators of Spatial Association, Majewska (2015) also identifies and empirically measures the inter-regional effects of spatial agglomeration in tourism via considering the occurrence and strength of geographic spillover effects in Poland.…”

“…Different approaches have been proposed to estimate the parameters of local models such as GWR (Páez et al, 2002;Yang & Fik, 2014) and SALE (Pace & LeSage, 2004).…”