旅游业发展产业协同与荷兰病效应研究——基于胡焕庸线的思考

doi:10.13249/j.cnki.sgs.2020.12.014

摘要/Abstract

摘要：

基于新古典经济增长理论，构建旅游业发展的产业结构效应理论分析模型，建立旅游业发展产业协同与荷兰病效应的评价指标，利用Theil指数及嵌套分解分析其时空差异的演变格局。以“胡焕庸线”为地理区域边界、以优秀旅游城市为关键节点，基于2000—2017年中国60个优秀旅游城市的面板数据进行实证分析显示：旅游业发展对第一、二、三产业的产业协同与荷兰病效应同时显著存在，总体上分别表现为荷兰病效应、较差的产业协同效应、较强的产业协同效应；时空演变的总体差异显著，呈现出“平稳递增?快速递减?平稳递减”的变化规律；旅游业快速发展的产业结构效应空间差异显著，不能有效突破“胡焕庸线”锁定的旅游经济地理格局。

关键词: 旅游业, 产业协同, 荷兰病效应, 胡焕庸线

Abstract:

As an important part of the economic development, the research on the industry driving ability of tourism development has always been one of the focuses of tourism economic research. Tourism has become a pillar industry in China. The geographer Hu Huanyong introduced western modern geography theories and methods to study China’s population and agriculture issues from the perspective of man-land relations, and proposed that the geographical distribution of the Chinese population is divided by the Heihe (Aihui)-Tengchong Line. Tourism activities are social, economic and geographical phenomena with the main characteristics of population mobility. At the same time, the development of tourism has a very important impact on the social and economic development and industrial structure of tourist destinations. To study the industrial structure effect of tourism development and to explore whether it has the spatial-temporal evolution characteristics of the “Hu line” is helpful to grasp the spatial differentiation law of tourism activities scientifically. Based on the neoclassical economic growth theory, this paper constructs the theoretical analysis model of the industrial structure effect of tourism development, establishes the evaluation index of tourism development industry synergy and Dutch Disease effect. With the “Hu line” for the geographical area boundary, with excellent tourism cities as the key node, based on the panel data of 60 excellent tourism cities in China from 2000 to 2017, the empirical analysis shows that: the tourism development of the first, second and third industry industrial coordination with the Netherlands effect significantly exist at the same time, the overall performance for the Dutch Disease effects respectively, with poor industry synergies, strong industrial synergies; The overall difference in time and space is significant, showing a change rule of “steady increase-fast decrease-steady decrease”. The research on whether the industrial structure effect of tourism activities has the spatial distribution characteristics of the “Hu line” is helpful to grasp the spatial differentiation rules of tourism activities scientifically and deepen the understanding of the geographical spatial characteristics of the “Hu line” in the development of tourism in China.

Key words: tourism industry, industrial synergy, Dutch Disease effect, Hu line

中图分类号:

F590

刘长生, 董瑞甜, 简玉峰. 旅游业发展产业协同与荷兰病效应研究——基于胡焕庸线的思考[J]. 地理科学, 2020, 40(12): 2073-2084.

Liu Changsheng, Dong Ruitian, Jian Yufeng. Industrial Synergy of Tourism Development and Dutch Disease Effect: Consideration Based on the “Hu Line”[J]. SCIENTIA GEOGRAPHICA SINICA, 2020, 40(12): 2073-2084.

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变量	LL统计量		IPS统计量		LM统计量
变量	有时期效应	无时期效应	有时期效应	无时期效应	有时期效应	无时期效应
注：T_it为解释变量，表示不同地区不同时期旅游业发展水平；lnY_it1、lnY_it2、lnY_it3为被解释变量，表示不同地区不同时期不同产业的增加值；lnK_it1、lnK_it2、lnK_it3、lnL_it1、、lnL_it2、lnL_it3、lnA_it1、lnA_it2、lnA_it3为控制变量，按照经典的宏观经济学模型来进行设置，表示不同地区不同时期不同产业的资本、就业、技术（以科研和开发机构研究与试验发展经费支出表示）；*表示5%水平上显著，3种检验都验证了面板数据不存在单位根；未包含港澳台数据。
lnT_it	3.234*	3.323*	?3.567*	?3.987*	0.765	0.436
lnY_it1	4.045*	0.864	?3.433*	?0.897	0.641	0.097
lnY_it2	?4.806*	?4.438*	?3.243*	?4.231*	0.842	4.137*
lnY_it3	?3.324*	?4.435*	3.765*	0.862	0.768	0.231
lnK_it1	3.732*	?0.356	4.298*	3.423*	0.599	0.675
lnK_it2	3.461*	?0.993	4.182*	0.452	0.782	2.222*
lnK_it3	3.576*	3.687*	3.235*	4.450*	0.851	2.675*
lnL_it1	3.324*	3.058*	3.772*	0.112	0.687	0.120
lnL_it2	3.754*	1.365	3.503*	5.311*	0.462	0.889
lnL_it3	3.221*	3.327*	3.184*	3.493*	0.738	2.675*
lnA_it1	3.324*	4.325*	3.329*	3.324*	0.333	2.326*
lnA_it2	3.754*	3.766*	4.758*	3.756*	0.757	0.759
lnA_it3	3.221*	1.224	4.227*	3.266*	0.224	0.226

变量	Y_it1		Y_it2		Y_it3
变量	OLS（1）	SYS-GMM（2）	OLS（3）	SYS-GMM（4）	OLS（5）	SYS-GMM（6）
注：括号中数字为t统计量；、*分别表示在5%、1%的显著水平上显著，模型（2）、（4）、（6）中经Hausman检验拒绝原假设，选择随机效应模型进行回归，SH-TEST、差分?汉森检验、AR（1）检验、AR（2）检验值为P值，显著则拒绝原假设，模型（2）、（4）、（6）在进行回归时，分别以其相应的滞后一期的解释变量作为工具变量；?为无此项；未包含港澳台数据。
lnT_it	0.036（0.673）	0.015^*（4.976）	0.327（0.875）	0.498^*（2.076）	0.932（0.334）	1.015^*（2.876）
lnK_it1	0.377（1.901）	0.342（1.893）	?	?	?	?
lnK_it2	?	?	0.997^*（2.454）	0.966^*（2.431）	?	?
lnK_it3	?	?	?	?	0.424^*（2.321）	0.429^*（2.388）
lnL_it1	0.737^*（2.009）	0.825^**（4.830）	?	?	?	?
lnL_it2	?	?	0.263（1.344）	0.257^**（4.896）	?	?
lnL_it3	?	?	?	?	0.565（0.386）	0.554^*（2.342）
lnA_it1	0.101（0.576）	0.104^*（2.239）	?	?	?	?
lnA_it2	?	?	0.434（1.765）	0.439^**（4.569）	?	?
lnA_it3	?	?	?	?	0.365（1.386）	0.371^*（2.654）
时期效应	是	是	是	是	是	是
个体效应	是	是	是	是	是	是
SH-TEST	?	0.002^**	?	0.004^**	?	0.002^**
差分?汉森检验	?	0.010^**	?	0.011^**	?	0.013^**
AR（1）检验	?	0.01^**	?	0.02^**	?	0.01^**
AR（2）检验	?	0.091^*	?	0.086^*	?	0.095^*
Adj-R²	0.379	0.878	0.368	0.812	0.302	0.835