基于面板数据的旅游经济效应空间分异及优化研究——以山东省为例

doi:10.13249/j.cnki.sgs.2016.02.016

摘要/Abstract

摘要：

依据2000~2012年山东省17个地市的GDP和旅游总收入数据,构建面板数据模型,用定量的方法测度山东省17个地市的旅游业发展和经济增长之间是否存在长期协整关系,并对存在协整关系的地市进一步进行Granger因果检验和面板数据模型回归分析。结果表明: ① 山东省东中西三大地域板块旅游经济效应空间分异现象明显,东部地区的旅游总收入已经成为了经济增长的Granger因,旅游业发展已经对经济增长起到了良好的推动作用,青岛和烟台的旅游总收入和经济增长已经存在双向Granger因果关系;② 中部地区的经济增长是旅游总收入增长的Granger因,经济增长对旅游业发展起到了保障和助推的双重作用,特别是东营和枣庄已经成为山东省旅游业发展的新亮点;③ 西部地区的经济增长和旅游总收入之间尚未形成协整关系。根据研究结果,给出山东省旅游业均衡、健康发展的建议和对策。

关键词: 山东省, 协整检验, 旅游经济效应, 空间分异, 优化

Abstract:

In this article, the panel data of GDP and tourism income of 17 cities in Shandong Province in 2000-2012 are employed to establish the mathematics model to measure the long-run cointegration relationship between tourism development and economic growth in the 17 cities of Shandong Province. Furthermore, the Granger cause test and panel data regression analyses have been done in the cities in which the long-run cointegration relationship exists. The results from the analyses show that there are significant regional differences concerning the tourism economic effect in Shandong Province. 1) In the eastern regional cities, tourism income is the Granger cause of the economic growth, and the tourism development has played an important role in promoting the economic growth, with the cities of Qingdao and Yantai having developed a mutual Granger cause relationship between the tourism income and economic growth. 2) In the central regional cities, economic growth is the Granger cause of tourism income growth, and the economic growth has promoted and protected the tourism development, featuring the cities of Dongying and Zaozhuang as new star cities in tourism development. 3) In the western regional cities, the long-run cointegration relationship between the tourism income and economic growth hasn’t been established. To conclude it, the study puts forward some suggestions in policy formulation to promote the equilibrium and sustainable development of regional tourism economy of Shandong Province.

Key words: Shandong Province, cointegration test, tourism economy effect, spatial dissimilarity, improvement measures

中图分类号:

F590

周霓, 熊爱华. 基于面板数据的旅游经济效应空间分异及优化研究——以山东省为例[J]. 地理科学, 2016, 36(2): 289-295.

Ni Zhou, Aihua Xiong. A Panel Data Study on the Spatial Dissimilarity of Tourism Economic Effect and Improvement Measures:Taking Shandong Province as a Case[J]. SCIENTIA GEOGRAPHICA SINICA, 2016, 36(2): 289-295.

图/表 7

图1

表1

表2

表3

表4

Hausman检验结果"

检验结果	$χ 2$ 统计量	$χ 2$ 自由度	伴随概率（p）
随机截距项	456.35	1	0.0000

表4

表5

图2

参考文献 22

[1]	林南枝, 陶汉军. 旅游经济学 [M]. 上海: 上海人民出版社, 2001.
	[Lin Nazhi, Tao Hanjun .Economics of tourism. Shanghai: Shanghai People's Publishing House, 2001.]
[2]	Durbarry R.Tourism and economic growth: The case of Mauritius[J]. Tourism Economics, 2004, 10(4):389-401. doi: 10.5367/0000000042430962
[3]	Christer T.Domestic tourism expenditures: The non-linear effects of length of stay and travel party size[J]. Tourism Management, 2009,30: 85-92. doi: 10.1016/j.tourman.2009.11.002
[4]	Narayan P K.Economic impact of tourism on Fiji’s economy: Empirical evidence from the computable general equilibrium model[J]. Tourism Economics , 2004, 10(4): 419-433.
[5]	Bichaka Fayissa.The impact of tourism on economic growth and development in Africa[J]. Department of Economics and Finance Wording Paper Series, 2007,8: 1-21.
[6]	Archer B, Fletcher J.The economic impact of tourism in the Seychelles[J]. Annals of Tourism Research, 1996, 23(1): 32-47. doi: 10.1016/0160-7383(95)00041-0
[7]	Huse M, Gustavsen T, Almedal S.Tourism impact comparisons among Norwegian towns[J]. Annals of Tourism Research, 1998, 25(3): 721-738. doi: 10.1016/S0160-7383(98)00019-X
[8]	方叶林, 黄震方, 陆玮婷, 彭倩. 中国市域旅游经济空间差异及机理研究[J].地理与地理信息科学, 2013, 29(6): 100-110. doi: 10.7702/dlydlxxkx20130621
	[Fang Yelin, Huang Zhenfang, Lu Weiting, Peng Qian.Spatial Differentiation of Tourism Economy and Its Mechanism in Chinese Cities.Geography and Geo-Information Science, 2013, 29(6): 100-110.] doi: 10.7702/dlydlxxkx20130621
[9]	吴玉鸣. 旅游经济增长及其溢出效应的空间面板计量经济分析[J].旅游学刊, 2014, 29(2): 16-24. doi: 10.3969/j.issn.1002-5006.2014.02.002
	[Wu Yuming.Spatial Panel Econometric Analysis of Tourism Economic Growth and Its Spillover Effects]. Tourism Tribune, 2014, 29(2): 16~24.] doi: 10.3969/j.issn.1002-5006.2014.02.002
[10]	王淑新, 王学定, 徐建卫. 西部地区旅游经济空间变化趋势及影响因素研究[J].旅游科学, 2012, 26(6): 55~67. doi: 10.3969/j.issn.1006-575X.2012.06.006
	[Wang Shuxin, Wang Xueding, Xujianwei. A Study on Spatial Variation and Influential Factors of Tourism Economy in the Western Region of China.Tourism Science, 2012, 26(6): 55-67.] doi: 10.3969/j.issn.1006-575X.2012.06.006
[11]	石惠春, 王晖. 关中-天水经济区中心城市旅游经济空间联系研究[J].干旱区资源与环境, 2012, 26(4):189-193.
	[Shi Huichun, Wang Hui.Spatial linkage of tourism economy of the central cities in the Guanzhong-Tianshui economic region. Journal of Arid Land Resources and Environment, 2012, 26(4):189-193.]
[12]	王洪桥, 袁家冬, 孟祥君. 东北三省旅游经济差异的时空特征分析[J].地理科学, 2014, 34(2):163-169.
	[Wang Hongqiao, Yuan Jiadong, Meng Xiangjun.Spatio-temporal Characteristics of Tourism Economy Difference in Northeast China. Scientia Geographica Sinica, 2014, 34(2):163-169.]
[13]	沈惊宏, 陆玉麒, 周玉翠, 等. 安徽省国内旅游经济增长与区域差异空间格局演变[J].地理科学, 2012,32(10): 1220-1228.
	[Shen Jinghong, Lu Yulin, Zhou Yucui.Domestic Tourism Economic Growth and Spatial Pattern Evolution of Regional Tourism Differences in Anhui Province. Scientia Geographica Sinica, 2012,32(10): 1220-1228.]
[14]	刘佳, 赵金金, 张广海. 中国旅游产业集聚与旅游经济增长关系的空间计量分析[J]. 经济地理, 2013, 33(4): 186-192.
	[Liu Jia, Zhao Jinjin, Zhang Guanghai.Spatial Econometric Research on the Relationship between Tourism Industry Agglomeration and Tourism Economic Growth in China. Economic Geography, 2013, 33(4): 186-192.]
[15]	郭永锐, 张捷,卢韶婧, 等. 中国入境旅游经济空间格局的时空动态性[J].地理科学,2014,34(11):1299-1304.
	[Guo Yongrui, Zhang Jie, Lu Shaojing et al. Spatio-temporal Change of the Inbound Tourism Economic Development in China. Scientia Geographica Sinica, 2014,34(11):1299-1304.]
[16]	孙盼盼, 戴学锋. 中国区域旅游经济差异的空间统计分析[J]. 旅游科学, 2014, 28(2):35-48. doi: 10.3969/j.issn.1006-575X.2014.02.004
	[Sun Panpan, Dai Xuefeng.An Analysis on the Spatial Data of China’s Regional Tourism Economic Disparity.Tourism Science, 2014, 28(2):35-48.] doi: 10.3969/j.issn.1006-575X.2014.02.004
[17]	郭鹏, 董锁成, 李泽红, 等. 丝绸之路经济带旅游业格局与国际旅游合作模式研究[J]. 资源科学, 2014, 36(12):2459-2467.
	[Guo Peng, Dong Suocheng, Li Zehong et al. Research on Spatial Distribution and International Cooperation Model of Tourism Industry in Silk Road.Resource Science, 2014, 36(12):2459-2467.]
[18]	山东省统计局. 山东省统计年鉴[M]. 北京:中国统计出版社,2001-2012.
	[Shandong Provincial Bureau of Statistics. Statistical yearbook of Shandong Province.Beijing: China Statistics Press,2001-2012.]
[19]	山东省旅游局. 山东省2001~2012 年旅游统计便览 [EB/OL]. .
	[Tourism Bureau of Shandong Province. Shandong Province Statistics Guidebook in 2001-2012 . .]
[20]	山东省统计局.山东省2001-2012 年国民经济和社会发展统计公报 [EB/OL]. .
	[Shandong Provincial Bureau of Statistics. Statistical bulletin of the national economic and social development of Shandong province in 2001-2012. .]
[21]	牟芳华. 山东省经济区域划分及区域经济差距的测度分析[J].山东社会科学, 2006, 7: 119-123.
	[Mou Fanghua.Measure analysis of the regional division and economic disparity of Shandong Province. Shandong Social Sciences, 2006,(7): 119-123.]
[22]	Granger C W J. Investigating causal relation by econometric and cross-sectional method[J], Econometrica,1969, 37:424-438.

检验方法	G (t & i)	TR (t & i)	DG (t & i)	DTR (t & i)
扩展单位根检验	Prob.	Prob.	Prob.	Prob.
LLC检验	0.0096	0.0000	0.0000	0.0000
相同单位根检验	Prob.	Prob.	Prob.	Prob.
Fisher-ADF检验	0.9907	0.8578	0.0000	0.0000

检验面板数据： TR和G
假设	Fisher统计量		Fisher 统计量
协整关系数量	Trace 检验	概率（P）	Max-eigen 检验	概率（P）
不存在协整关系	134.6	0.0000	134.9	0.0000
最多存在1个协整关系	37.56	0.3092	37.56	0.3092
假设：不存在协整关系地区
济南	14.9384	0.0605	14.9158	0.0394
青岛	17.4433	0.0251	13.7118	0.0610
淄博	18.1511	0.0194	17.9767	0.0124
枣庄	20.8107	0.0072	20.7677	0.0041
东营	20.5493	0.0079	19.8975	0.0058
烟台	35.4722	0.0000	27.2754	0.0003
潍坊	13.5119	0.0973	13.2966	0.0707
济宁	11.1742	0.2010	11.1742	0.1457
泰安	24.1992	0.0019	23.7028	0.0012
威海	14.4738	0.0708	13.7260	0.0607
日照	17.6068	0.0237	17.4169	0.0154
莱芜	20.0820	0.0095	20.0498	0.0055
临沂	31.0224	0.0001	30.7421	0.0001
德州	13.6766	0.0922	10.8256	0.1632
聊城	11.4598	0.1847	11.2761	0.1409
滨州	6.8676	0.5931	6.8660	0.5051
菏泽	9.7866	0.2975	6.3151	0.5731

假设	滞后阶数	F统计量	伴随概率	结论
TR（全省）不是G（全省）的Granger原因	1	11.7230	0.0007	拒绝
G（全省）不是TR（全省）的Granger原因	1	9.7451	0.0021	拒绝
G（济南）不是TR（济南）的Granger原因	1	6.6523	0.0327	拒绝
G（青岛）不是TR（青岛）的Granger原因	1	10.4570	0.0120	拒绝
TR（青岛）不是G（青岛）的Granger原因	3	35.7919	0.0273	拒绝
G（枣庄）不是TR（枣庄）的Granger原因	1	5.8085	0.0425	拒绝
G（东营）不是TR（东营）的Granger原因	1	9.4195	0.0154	拒绝
G（烟台）不是TR（烟台）的Granger原因	1	9.4062	0.0154	拒绝
TR（烟台）不是G（烟台）的Granger原因	3	69.4762	0.0142	拒绝
G（潍坊）不是TR（潍坊）的Granger原因	1	6.8792	0.0305	拒绝
G（泰安）不是TR（泰安）的Granger原因	1	11.2873	0.0099	拒绝
TR（威海）不是G（威海）的Granger原因	1	5.6709	0.0445	拒绝
TR（日照）不是G（日照）的Granger原因	2	5.9614	0.0475	拒绝

变量	系数	伴随概率	变量	系数	伴随概率
全省	4.087171	0.0000	潍坊	3.081347	0.0000
济南	3.644174	0.0000	泰安	3.019535	0.0000
青岛	5.508563	0.0000	威海	-0.541076	0.6016
淄博	3.581071	0.0000	日照	3.565576	0.0021
枣庄	9.207553	0.0001	莱芜	7.111185	0.3095
东营	18.43131	0.0000	临沂	2.305131	0.0001
烟台	6.008633	0.0000