Impact of Operating Conditions on the Productivity of Star-Rated Hotels in Mainland China: Verification from Spatial Metrology
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摘要
Room occupancy rate is a key indicator of star-rated hotel management quality. This paper takes Cobb-Douglas production function as the theoretical framework. Spatial Moran index of autocorrelation, Spatial Lag Model(SLM) and Spatial Error Model(SEM) are used to analyze the star-rated hotels labor productivity of 31 provincial regions in mainland China based on the star-rated hotels statistical data of year 2016. The spatial correlation and spatial difference of the star-rated hotels labor productivity is discussed. This paper studies the impact of three factors on spatial characteristics of star-rated hotels labor productivity in mainland China. The econometric estimation results show that:(1) Star-rated hotels labor productivity present significant spatial dependence and spatial difference in mainland China.(2) The estimation results of Ordinary least Squares(OLS) are reliable.(3) The reliability of the results obtained by the Spatial Error Model(SEM) analysis is the highest, and has a stronger explanatory power to the spatial relationship of star-rated hotels labor productivity in mainland China. The average room occupancy rate has more influence on the labor productivity of the provincial star-rated hotels than the impact of capital and labor.
Room occupancy rate is a key indicator of star-rated hotel management quality. This paper takes Cobb-Douglas production function as the theoretical framework. Spatial Moran index of autocorrelation, Spatial Lag Model(SLM) and Spatial Error Model(SEM) are used to analyze the star-rated hotels labor productivity of 31 provincial regions in mainland China based on the star-rated hotels statistical data of year 2016. The spatial correlation and spatial difference of the star-rated hotels labor productivity is discussed. This paper studies the impact of three factors on spatial characteristics of star-rated hotels labor productivity in mainland China. The econometric estimation results show that:(1) Star-rated hotels labor productivity present significant spatial dependence and spatial difference in mainland China.(2) The estimation results of Ordinary least Squares(OLS) are reliable.(3) The reliability of the results obtained by the Spatial Error Model(SEM) analysis is the highest, and has a stronger explanatory power to the spatial relationship of star-rated hotels labor productivity in mainland China. The average room occupancy rate has more influence on the labor productivity of the provincial star-rated hotels than the impact of capital and labor.
Room occupancy rate is a key indicator of star-rated hotel management quality. This paper takes Cobb-Douglas production function as the theoretical framework. Spatial Moran index of autocorrelation, Spatial Lag Model(SLM) and Spatial Error Model(SEM) are used to analyze the star-rated hotels labor productivity of 31 provincial regions in mainland China based on the star-rated hotels statistical data of year 2016. The spatial correlation and spatial difference of the star-rated hotels labor productivity is discussed. This paper studies the impact of three factors on spatial characteristics of star-rated hotels labor productivity in mainland China. The econometric estimation results show that:(1) Star-rated hotels labor productivity present significant spatial dependence and spatial difference in mainland China.(2) The estimation results of Ordinary least Squares(OLS) are reliable.(3) The reliability of the results obtained by the Spatial Error Model(SEM) analysis is the highest, and has a stronger explanatory power to the spatial relationship of star-rated hotels labor productivity in mainland China. The average room occupancy rate has more influence on the labor productivity of the provincial star-rated hotels than the impact of capital and labor.
引文
[1]Anselin,L.(1988).Spatial econometrics:methods and models.Dordrecht:Kluwer Academic Publishers.
[2]Smit,M.J.,Leeuwen,E.S.V.&Florax,R.J.G.M.et al.(2015).Rural development funding and agricultural labour productivity:a spatial analysis of the European Union at the NUTS2 level.Ecological Indicators,(59),6-18.
[3]Philipp,E.(2013).Agglomeration economies with consistent productivity estimates.Regional Science and Urban Economics,(43),751-763.
[4]Reis,E.(2014).Spatial income inequality in Brazil,1872-2000.Economia,15(2),119-140.
[5]Deller,S.(2010).Rural poverty,tourism and spatial heterogeneity.Annals of Tourism Research,37(1),180-205.
[6]Abildtrup,J.,Garcia,S.&Olsen,S.B.et al.(2013).Spatial preference heterogeneity in forest recreation.Ecological Economics,92(2),67-77.
[7]Termansen,M.,Mcclean,C.J.&Jensen,F.S.(2013).Modelling and mapping spatial heterogeneity in forest recreation services.Ecological Economics,92(8),48-57.
[8]Wei,L.,&Wang,W.(2008).An overall analysis of the productivity of tourism in China.Journal of Nanchang College,23(2),20-22.
[9]Yang,Y.(2015).Agglomeration density,diversity and labor productivity in china’s industry.Finance&Trade Economics,(2),148-160.
[10]Wu,Y.M.(2013).Spatial heterogeneity estimation of regional tourism industry elasticity in Chinese provinces:A geographically weighted regression study.Tourism Tribune,28(2),35-43.
[11]Zhang,L.(2018).Spillover effect of circulation industry development on labor productivity in neighboring provinces.Business Economics Research,(14),172-175.
[12]Wang,Z.K.,&Shi,J.C.(2011).The imbalanced development of china’s regional economy:cause analysis.Journal of Zhejiang University(Humanities and Social Sciences),41(6),91-103.
[2]Smit,M.J.,Leeuwen,E.S.V.&Florax,R.J.G.M.et al.(2015).Rural development funding and agricultural labour productivity:a spatial analysis of the European Union at the NUTS2 level.Ecological Indicators,(59),6-18.
[3]Philipp,E.(2013).Agglomeration economies with consistent productivity estimates.Regional Science and Urban Economics,(43),751-763.
[4]Reis,E.(2014).Spatial income inequality in Brazil,1872-2000.Economia,15(2),119-140.
[5]Deller,S.(2010).Rural poverty,tourism and spatial heterogeneity.Annals of Tourism Research,37(1),180-205.
[6]Abildtrup,J.,Garcia,S.&Olsen,S.B.et al.(2013).Spatial preference heterogeneity in forest recreation.Ecological Economics,92(2),67-77.
[7]Termansen,M.,Mcclean,C.J.&Jensen,F.S.(2013).Modelling and mapping spatial heterogeneity in forest recreation services.Ecological Economics,92(8),48-57.
[8]Wei,L.,&Wang,W.(2008).An overall analysis of the productivity of tourism in China.Journal of Nanchang College,23(2),20-22.
[9]Yang,Y.(2015).Agglomeration density,diversity and labor productivity in china’s industry.Finance&Trade Economics,(2),148-160.
[10]Wu,Y.M.(2013).Spatial heterogeneity estimation of regional tourism industry elasticity in Chinese provinces:A geographically weighted regression study.Tourism Tribune,28(2),35-43.
[11]Zhang,L.(2018).Spillover effect of circulation industry development on labor productivity in neighboring provinces.Business Economics Research,(14),172-175.
[12]Wang,Z.K.,&Shi,J.C.(2011).The imbalanced development of china’s regional economy:cause analysis.Journal of Zhejiang University(Humanities and Social Sciences),41(6),91-103.