资源科学 ›› 2017, Vol. 39 ›› Issue (10): 1918-1929.doi: 10.18402/resci.2017.10.11
收稿日期:
2017-06-12
修回日期:
2017-08-08
出版日期:
2017-10-20
发布日期:
2017-10-20
作者简介:
作者简介:赵磊,男,山东新泰人,博士,副教授,硕士生导师,研究方向为旅游经济学。E-mail:
基金资助:
Received:
2017-06-12
Revised:
2017-08-08
Online:
2017-10-20
Published:
2017-10-20
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。
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.
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 | — | — | 1.598** (0.703) | — |
TS | — | 0.372***(0.140) | — | 0.641***(0.212) |
TOUR | — | — | — | 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 |
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