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西北五省区旅游产业绩效影响因素分析——基于面板数据分位数回归 被引量:27

Analysis of the Influence Factors of Tourism Industry Performance in Northwestern Provinces:Based on Quantile Regression of Panel Data
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摘要 为了从总量绩效和质量绩效两方面研究西北五省区旅游业发展状况,在系统分析相关因素对旅游产业绩效的直接和间接影响作用的同时,通过面板数据的固定效应模型以及分位数回归模型,分析相关因素的总体影响以及相关因素的动态影响过程,结论为:西北五省区提升旅游产业绩效,应依托于低级要素到高级要素的逐步释放;注重旅游产业的资本投入和提高各地经济整体实力;在劳动力投入方面注重素质建设;对市场制度建设、对外开放和经济结构服务化水平的影响作用应给予充分关注。 In this paper we investigate tourism development, and its inherent influences, in China' s five northwest regions and provinces (the autonomous regions of Xinjiang and Ningxia and the provinces of Shaanxi, Gansu and Qinghai) , focusing on tourism industry performance and total quality performance. We selected tourism operating income as an industry performance indicator to measure factors directly impacting on the tourism industry. We selected overall labor productivity as a tourism industry quality indicator to measure factors indirectly influencing the tourism industry. The factors influencing the tourism industry performance indicators are identified as: physical capital investment; human capital investment; level of economic development; access infrastructure; degree of openness to the outside world; and the degree of economic development. Using panel data we undertook a fixed effects model to analyze factors affecting overall industry performance. Using panel data we undertook a quantile regression model to analyze factors affecting industry performance, from low levels to high levels, under conditional distribution. We reached a series of conclusions relating to the impact of the various factors on tourism in the study area. In relation to capital investment: for the performance indicator we found it to have a significant role in promoting the tourism industry. In relation to the workforce contribution to the overall tourism industry: we found it to have a positive, but not significant role in promoting the industry. It exhibited more of an inhibitory effect on the quality indicator than the performance indicator. In relation to access infrastructure: we found it to have a positive, but declining, effect on tourism industry performance. There must be significant market demand for an airline carrier to provide service to an area. It is critical to improve infrastructural access to promote tourism industry performance in the area. In relation to degree of openness to the outside world: we found it to have a role in the tourism performance indicator, but it did not have an impact on the labor productivity quality indicator. For China' s northwest area to improve its tourism performance it should embrace the open nature of the tourism industry and further open to the outside. In relation to the level of economic development: we found its impact on the industry operating income performance indicator to be insignificant, but increasing. However, it had a significant influence on the industry labor productivity quality indicator. China' s northwest region does not significantly contribute to the national economy, lagging behind the contributions of eastern regions. Factors such as urban concentration and radiation diffusion generally have positive impacts on the development of the tourism industry; however in the northwest region this impact has been suppressed. However, the level of economic development is a fundamental driver of tourism development. The degree of service development had no obvious influence on the tourism industry, but the effects of both service development and economic developments are increasing. To progress its tourism industry the northwest area must: make a gradual transfer from a simple tourism provision system to a more muhifaceted one; emphasize capital investment; focus on improving the local economy; educate its workforce; improve access infrastructure; focus on opening the market; and improve its tourism service elements.
出处 《旅游学刊》 CSSCI 2013年第8期94-101,共8页 Tourism Tribune
关键词 西北五省区 旅游产业 绩效 影响因素 面板数据分位数回归 China' s five northwestern regions and provinces tourism industry performance influence factors panel data quantile regression model
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