产业结构变迁、旅游业与经济增长——来自中国的经验证据

doi:10.18402/resci.2017.10.11

摘要/Abstract

摘要：

既有关于旅游业导向型经济增长假说的经验研究,由于忽视了产业结构变迁的动态性,从而会导致对旅游业影响经济增长效应的估计偏误。因此,本文在定量测度中国各省产业结构变迁基础上,将旅游业、产业结构变迁与经济增长纳入到统一分析框架进行实证考察,以拓展旅游业导向型经济增长假说的研究体系。研究结果表明：①以泰尔指数反向度量的产业结构合理化对经济增长具有抑制作用,而产业结构高级化则对经济增长具有积极作用,并且产业结构高级化对经济增长的“结构效应”要大于产业结构合理化对经济增长的“失衡效应”;②中国旅游业对经济增长具有显著的正向影响,与此同时,产业结构欠缺合理化抑制了旅游业对经济增长的影响效应,而产业结构高级化可正向调节旅游业对经济增长的影响效应。本文认为,政府在进行产业结构调整时,除应着重强调产业结构高级化的同时,还需高度关注产业结构合理化,从而为发挥产业结构变迁对旅游业影响经济增长的正向调节作用创造条件。

关键词: 旅游业, 产业结构合理化, 产业结构高级化, 经济增长, 中国

Abstract:

The tourism industry has become a popular sector in regional development. The impact of tourism on economic growth remains a hot issue for government and academia. With China’s economy transitioning to a ‘new normal’,changes in industrial structure are a direct driver of China's economic growth. The industrial structural adjustment has created and provided the necessary elements for the development of a tourism industry,which supports the scale development of the tourism industry and improves the contribution rate of the tourism industry to economic growth. Therefore,based on quantitative measurement of industrial structure,we put tourism,industrial structure and economic growth into a unified framework to carry out empirical research. We found that the tourism industry had a significant positive impact on economic growth. The rationalization of industrial structure had an inhibitory effect on economic growth,while the optimization of industrial structure had a positive impact on economic growth. The ‘structural effect’ of the optimization of industrial structure on economic growth was greater than the ‘imbalance effect’ of the rationalization of industrial structure. Finally,the rationalization of industrial structure inhibited the impact of tourism on economic growth,while the optimization of industrial structure positively adjusted the impact of tourism on economic growth. In the policy making process,the government should emphasize the optimization of industrial structure and pay attention to the rationalization of industrial structure to create conditions for the positive adjustment effect of industrial structure on the impact of the tourism industry on economic growth.

Key words: tourism industry, rationalization of industrial structure, optimization of industrial structure, economic growth, China

赵磊, 唐承财. 产业结构变迁、旅游业与经济增长——来自中国的经验证据[J]. 资源科学, 2017, 39(10): 1918-1929.

Lei ZHAO, Chengcai TANG. China's tourism industry,industrial structure and economic growth[J]. Resources Science, 2017, 39(10): 1918-1929.

图/表 6

图1

表1

2000-2013年中国省际产业结构跨期变迁Moore值"

省份	2000年	2002年	2004年	2006年	2008年	2010年	2012年	2013年
北京	2.524 1	2.649 2	2.669 0	2.676 9	2.708 8	2.745 5	2.752 3	2.756 1
天津	2.410 6	2.418 2	2.410 2	2.384 0	2.382 3	2.435 5	2.447 3	2.456 3
河北	2.156 0	2.179 6	2.197 7	2.183 9	2.208 1	2.224 0	2.227 4	2.233 2
山西	2.277 2	2.359 5	2.334 9	2.311 0	2.305 0	2.326 1	2.292 6	2.328 4
内蒙古	2.132 1	2.198 2	2.243 1	2.240 7	2.231 0	2.283 1	2.258 3	2.263 0
辽宁	2.269 9	2.298 6	2.311 1	2.285 4	2.263 0	2.293 4	2.280 4	2.293 9
吉林	2.082 9	2.212 0	2.220 1	2.217 4	2.234 6	2.242 2	2.227 3	2.229 2
黑龙江	2.191 5	2.219 3	2.237 0	2.212 4	2.217 8	2.256 7	2.218 8	2.248 9
上海	2.474 7	2.508 7	2.496 4	2.496 0	2.514 7	2.585 0	2.572 7	2.597 5
江苏	2.229 9	2.249 5	2.267 1	2.274 5	2.302 9	2.329 2	2.361 7	2.371 5
浙江	2.222 3	2.290 2	2.326 7	2.333 8	2.354 6	2.380 8	2.389 3	2.404 0
安徽	2.033 2	2.144 1	2.226 5	2.226 7	2.225 4	2.213 2	2.193 5	2.200 4
福建	2.221 1	2.237 2	2.251 9	2.256 6	2.291 4	2.315 1	2.299 9	2.302 5
江西	2.137 1	2.172 6	2.170 7	2.167 8	2.153 4	2.199 8	2.215 9	2.229 0
山东	2.197 3	2.208 7	2.216 2	2.213 8	2.237 7	2.251 2	2.294 6	2.313 8
河南	2.056 1	2.100 0	2.166 8	2.121 4	2.152 1	2.150 4	2.166 0	2.181 8
湖北	2.155 4	2.237 3	2.252 7	2.236 8	2.270 0	2.256 1	2.238 3	2.240 5
湖南	2.138 4	2.199 9	2.236 7	2.209 3	2.220 5	2.261 8	2.242 5	2.254 2
广东	2.245 8	2.378 2	2.385 0	2.365 2	2.377 8	2.406 2	2.402 4	2.414 4
广西	2.090 5	2.167 7	2.187 4	2.181 0	2.174 0	2.185 8	2.166 1	2.187 3
海南	2.014 5	2.092 5	2.071 4	2.080 1	2.110 8	2.172 7	2.193 7	2.219 6
重庆	2.230 7	2.276 0	2.295 7	2.287 2	2.297 7	2.285 2	2.275 1	2.310 5
四川	2.121 9	2.176 5	2.198 1	2.182 8	2.170 7	2.207 6	2.191 4	2.207 0
贵州	2.054 1	2.132 8	2.181 9	2.209 2	2.250 8	2.341 0	2.360 3	2.348 4
云南	2.143 8	2.178 2	2.202 9	2.201 3	2.214 2	2.234 2	2.257 5	2.250 2
陕西	2.252 1	2.300 5	2.292 9	2.257 4	2.239 9	2.287 1	2.250 5	2.251 8
甘肃	2.165 4	2.223 2	2.250 9	2.246 9	2.240 8	2.253 1	2.255 6	2.263 3
青海	2.255 3	2.284 9	2.310 1	2.271 1	2.253 6	2.268 3	2.230 4	2.236 2
宁夏	2.243 3	2.302 9	2.313 0	2.296 5	2.271 4	2.322 5	2.322 2	2.334 5
新疆	2.175 0	2.228 4	2.178 5	2.159 6	2.174 3	2.188 4	2.166 0	2.183 7

表1

图2

图3

图4

表2

模型估计结果"

变量	模型（1）	模型（2）	模型（3）	模型（4）
TOUR	1.090***（0.166）	1.089***（0.167）	0.545* （0.291）	0.851** （0.845）
TL	0.208***（0.068）	—	0.034 （0.102）	—
TOUR $×$ TL	—	—	1.598** （0.703）	—
TS	—	0.372***（0.140）	—	0.641***（0.212）
TOUR $×$ TS	—	—	—	0.139* （0.871）
Invest	0.335***（0.035）	0.300***（0.037）	0.349***（0.035）	0.293***（0.037）
Human	0.027** （0.012）	0.028** （0.012）	0.028** （0.012）	0.026** （0.012）
Govern	0.700***（0.115）	0.651***（0.116）	0.629***（0.119）	0.671***（0.117）
Trade	0.031 （0.031）	0.027 （0.031）	0.033 （0.031）	0.027 （0.031）
Institution	0.188** （0.074）	0.106 （0.071）	0.165** （0.74）	0.103 （0.071）
Urban	0.184** （0.058）	0.176***（0.058）	0.200***（0.058）	0.198***（0.059）
省份效应	控制	控制	控制	控制
F统计量	103.89***	99.13***	103.76***	96.11***
R²	0.824	0.824	0.826	0.824
样本量	450	450	450	450

表2

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