Modelling the Patterns and Drivers of Tourism Related Employment for South-East Queensland, Australia—A Spatial Econometric Approach

Chhetri, Prem; Corcoran, Jonathan; Hall, C. Michael

doi:10.1080/02508281.2008.11081287

Cited by 21 publications

(24 citation statements)

References 33 publications

(33 reference statements)

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“…In a smaller scale (municipal level, in Italy), Capone and Boix (2008) analyzed the spatial dynamics related to the sources of tourism competitiveness, while Lazzeretti and Capone (2009) focused on labor markets in the tourism sector. Also focused on the labor market spatial dynamics, Chhetri et al (2008) modeled the spatial drivers of tourism employment within a specific Australian region. Finally, at the city level, Lee and Jang (2013) analyzed the effects of location on the prices of hotel accommodation in Houston (USA).…”

Section: Literature Reviewmentioning

confidence: 99%

A spatial analysis on the determinants of tourism performance in Japanese Prefectures

Romão

Saito

2017

Asia-Pac J Reg Sci

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Assuming tourism as a place-oriented activity where tourist flows often cross regional borders, local and global indicators of spatial autocorrelation can be useful tools in order to identify and to explain different patterns of regional tourism dynamics and their determinants. These techniques recently became widely used in applied economic studies, as a result of their useful insights to understand spatial phenomena and benefiting from the existence of geo-referenced data and adequate software tools. This tendency is also observed in the tourism sector in the last few years. In this work, an exploratory spatial analysis and a spatial econometric model are applied to the case of Japanese Prefectures, leading to the identification of the specific spatial aspects prevailing in Japanese regional tourism dynamics. Spatial heterogeneity and agglomeration processes are identified, with a view on policy and managerial recommendations, offering a contribution to explore potential synergies arising from inter-regional cooperation in crucial aspects of tourism development. The results reveal the existence of such spatial effects, reflecting the importance for tourism of central areas of Japan, while revealing that competition effects among Japanese Prefectures prevail over positive regional spinoffs identified in other countries. It was also possible to observe that regions where tourism plays a more prominent role in terms of its importance within regional employment do not present a relatively high performance in terms of economic impact and benefits. The results suggest that a more balanced regional economic structure and higher levels of education of the work force contribute for improvements in tourism value added. Finally, the important role of foreign tourism boosting regional tourism performance is revealed.

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Section: Literature Reviewmentioning

confidence: 99%

A spatial analysis on the determinants of tourism performance in Japanese Prefectures

Romão

Saito

2017

Asia-Pac J Reg Sci

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“…In tourism research, there is a considerable body of literature devoted to investigating the spillovers between geographic neighbours. The modelling strategy in the literature is to study the spillover effects by using spatial correlation structures based on geographical proximity (Bo et al, 2017;Chhetri et al, 2008;Deller, 2010;Li et al, 2016;Marcouiller et al, 2004;Yang and Fik, 2014;Yang and Wong, 2012). The tourism models that acknowledge the spatial spillovers across regions have great importance to regional economies.…”

Section: List Of Figuresmentioning

confidence: 99%

“…Capone and Boix (2008) note that agglomeration economies are vital in tourism growth, in addition to resource endowments. (Chhetri et al (2008); Michael (2003)) note that tourism and hospitality employment tend to form clusters in and around tourist destinations. It is evident that the existence of clusters enhances regional economic growth and harnesses tourist employment opportunities through competitive advantage.…”

Section: List Of Figuresmentioning

confidence: 99%

“…In all other counties within the cluster, the excess to the minimum requirement is assumed to be export employment percentage. Chhetri et al (2008) and Chhetri (2014) Standard Industrial Classification (ANZSIC), codes were given to industries in JTW and used to identify the tourism sector employment. To identify the tourism related employment, the industry sector 'Accommodation, Cafes, Restaurants' and other sub-sectors (Casinos-9322, Mueums-9220, Recreational parks and Gardens-9239 based on four-digit codes) are used as the surrogate measure for tourism related employment.…”

Section: Measuring Tourism Employmentmentioning

confidence: 99%

“…Although there is substantial research on the relationship between natural amenities and regional growth (Deller et al, 2001;English et al, 2000;Green, 2001;Marcouiller, 2002;Marcouiller et al, 2004;Partridge, 2010), there are only a few studies that have modelled the spatial dependence of natural amenities in regional economic growth (Chi and Marcouiller, 2013;Kahsai et al, 2011;Kim et al, 2005). Most of these studies address the spatial dependence of natural amenities and other attributes on regional growth with few exceptions that exclusively identify the spatial distribution of natural amenities and other drivers on regional tourism (Chhetri et al, 2008;Marcouiller et al, 2004).…”

Section: Motivationmentioning

confidence: 99%

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Natural amenities and tourism employment: A spatial analysis

Naranpanawa¹

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This thesis examines the spatial linkages between natural amenities and tourism employment when spatial spillover effects are taken into account under three neighbourhood structures. To the author's knowledge, tourism employment and natural amenity spillovers have not previously been examined in a non-geographic spatial context.To address this research gap and explore the non-geographic spatial spillovers in tourism employment and natural amenities, spatial models are developed with different neighbourhood structures. In addition, the thesis examines: tourism clusters; the measurement of cluster proximity; and characteristics of tourism employment within and across clusters. This thesis developed an empirical model at the local government level for Queensland, Australia. The model incorporates 13 natural amenity variables and four other variables that explain regional tourism employment. The analysis is conducted for 74 local governments in Queensland using cross-sectional data compiled for 2011. Three specifications of a Spatial Durbin Model (SDM) are implemented. The specifications use three alternative weight matrices to reflect three different proximity dimensions or neighbourhood structures. A non-spatial linear model (NSLM) estimated by least squares is used as the base model. The three weight matrices specifications are proportional to: a) geographic contiguity, to capture geographic spillovers; b) the share of employment in the tourism industry; and c) the proportion of tourism employment to total employment. The last two are measures of economic distance.To identify tourism employment clusters, the study uses a K-mean cluster analysis technique. Factor analysis is used to summarise 17 variables selected from a review of theoretical models of tourism employment. Based on computed factor scores, the main cluster attributes are identified.The results suggest that independent of the specification (spatial and non-spatial), internet penetration, regional population, the number of regional parks and state forests and World Heritage areas are statistically significant. This is taken as evidence of their importance as factors that influence tourism employment in a region. Most importantly, spillovers exist; not only between traditional geographic neighbours, but also between neighbours of economic proximity where neighbours are those that have a similar profile in their share of tourism employment.iv When the spatial autocorrelation parameter is negative and significant between geographic neighbours, it reflects competition; however, when it is positive and significant between economic neighbours (regions with similar share of tourism employment) it indicates a collaborative effect between the regions with similar tourism performance. The results further suggest non-geographic proximity can explain tourism employment clusters.Characteristics such as "urbanness", communications capacity, natural attractions, level of agriculture and percentage of indigenous population are shared within clusters.The conclusion is ...

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