旅游经济增长及其溢出效应的空间面板计量经济分析

文章首次基于空间面板计量经济学模型,在检验我国省域旅游经济增长是否存在空间依赖性的基础上,估计了资本和劳动对旅游经济增长的贡献,检验了旅游经济增长过程中的空间溢出效应。研究结果显示:2001~2009年间,中国省域旅游经济增长的空间依赖性和集群趋势加强,邻近省域的经济增长及旅游资本投入的空间溢出效应明显;资本和劳动的旅游产出弹性系数均为正,前者对旅游经济增长的贡献大于后者,我国省域尺度的旅游经济增长主要依赖于资本要素投入驱动,呈现出资本密集型特征,劳动力要素的贡献尚未充分发挥出来。政府在制定旅游产业政策和发展规划时,必须致力于加强邻近地区旅游资本和劳动投入的合作与交流,促进旅游经济增长中的资本空间溢出效应,改革旅游业国民收入初次分配及再次分配体制,激活邻近省域旅游劳动力投入与旅游经济增长的空间互动性,以便提高邻近地区及本地区劳动力对旅游经济增长的协同贡献。

Spatial Panel Econometric Analysis of Tourism Economic Growth and Its Spillover Effects

Over the course of the last decade, tourism research has been dominated by time series or cross-sectional econometric modeling techniques. But these models have limitations; the time series model overlooks the cross-sectional impact of regional tourism activity, while the traditional cross- sectional model ignores the spatial interdependence of regions. When modeling economic growth in the tourism industry, these techniques fail to account for spillover effects： the benefits regions obtain from economic growth in local tourism from their neighbors＇ input and output flows through regional input- output interactions. The most important factors, capital and labor flows from different regions, can have spillover effects or agglomeration effects on economic growth in tourism in neighboring regions. Given the shortcomings of the time series and cross-sectional techniques, most tourism studies failed to consider that spatial effects universally influence economic growth in regional tourism and, consequently, their results have not been persuasive or reliable. While, since 2010, some Chinese tourism studies have focused on the spatial econometric techniques to address spatial interdependence among regions, the majority have ignored temporal and spatial correlations and their potential impact on economic growth in regional tourism. This study takes a different approach by employing spatial panel econometrics models of no fixed effects, spatial fixed effects, time-period fixed effects, and spatial and time-period fixed effects. Based on the Solow growth model and tourism panel data of 31 Chinese provinces over 2001 - 2009, we investigate whether spatial dependence of economic growth in the tourism industry exists across Chinese provinces. We also estimate the contributions of capital and labor to economic growth in the tourism industry of neighboring regions, and test for spillover effects in economic growth. The results show a strengthening in spatial dependence and clustering trends of economic growth in Chinese provincial tourism during 2001 - 2009. In addition, the spillover effects of economic growth and spatial tourism capital investment from the neighboring provinces are significant. From these results, we suggest that spatial correlation of economic growth in provincial tourism should be considered in elasticity estimation studies of economic growth in the tourism industry. As the elasticity coefficients of capital and labor investment to tourism output are positive, the contribution of capital to economic growth in the tourism industry is greater than that of labor. While capital inputs are the key driver of economic growth in tourism at the provincial level, evidenced by tourism growth patterns matching capital inflows, the contribution of labor to economic growth in tourism has not been fully played out. To develop effective tourism industry policies and development plans, governments have to be aware of the spatial dependence of economic growth in tourism, and the impact of capital inputs and labor force contributions. Furthermore, their policies should encourage cooperation and exchanges of tourism capital and labor inputs across neighborhoods, promote capital spatial spillover effects in economic growth in tourism, and reform the distribution systems of national income. They should also take advantage of the opportunity to manage spatial interactions between adjacent provinces in terms of tourism labor investment and economic growth in tourism, thereby increasing the collaborative contributions of labor investment from adjacent regions.