中国区域经济增长的空间网络关联及收敛性检验
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摘要
基于1992～2013年中国30个省份面板数据,利用引力模型和网络分析方法考察中国区域经济增长空间关联网络的结构特征,并基于空间网络权重,对中国区域经济增长进行收敛检验,研究发现:①中国区域经济增长的空间关联网络呈现复杂化与全域性的发展趋势,但网络的整体紧密程度不够。②山东、河南和江苏在空间关联网络中既扮演中心行动者的角色,又在网络中发挥着"中介"与"桥梁"的作用。③四大板块内部及之间存在普遍的关联关系,板块Ⅲ在四大板块中发挥核心带动作用。④中国区域经济增长存在全域性收敛现象,与基于传统空间权重的模型相比,空间网络权重下模型的收敛速度加快;资本积累是影响收敛的重要因素。此外,东部、中部和西部存在一定程度的俱乐部收敛趋势。
Under the interaction of government regulation and market mechanism, the spatial correlation of regional economic growth in China has gradually evolved into a complex, multi-threaded network structure Based on the panel data of 30 provinces in China from 1992 to 2013, this article uses the gravitational model and the network analysis method to investigate the structural characteristics of the spatial association network of China's regional economic growth, and based on the weight of the space network to test the convergence of the regional economic growth in China. The research finds that: 1) The spatial correlation network of China's regional economic growth presents a complex and global development trend. The inter-provincial linkages show the coexistence of adjacent connections and jump connections. There are no independent development provinces, but the overall network is not tight enough; The connectivity and accessibility are strong, there are multiple overlapping spillover effects, and the stability of the network is strong; the network hierarchy has a significant downward trend, and the hierarchical structure of the spatial association network is not strict. 2)The degrees, proximity and intermediate centers of Shandong, Henan and Jiangsu are ranked in the top three,and they also have the most contacts with other provinces. They play the role of central actors in the spatial correlation network, and play the role of "intermediary" and "bridge" in the network. The provinces with the lowest degree of degree and near center degree are Jilin and Henan, and the phenomenon of mediation is 0 in many provinces. 3) The spatial correlation network of regional economic growth can be divided into four sections. There are general relationships within and between the four major sections. Among them, the impact of the spillover of the other sections of the sector II is the largest, and the relationship of the sector III to the other sections is the most. It plays a core role in the four sections. 4) The empirical results of convergence test show that there is a global convergence phenomenon in China's regional economic growth. Compared with the traditional spatial weight model, the convergence speed of the model is faster under the weight of the spatial network, reaching 0.07, and the spatial interaction between regions reduced the gap of them; the savings rate has a positive and significant impact on the economic growth rate, capital accumulation is still an important factor affecting convergence; in addition, there is a certain degree of club convergence in the eastern, central and western regions, the central region showed the strongest convergence trend, the convergence speed was as high as0.101, and the convergence speed in the western region was the lowest, only 0.038. This reveals the important role of spatial network association in regional economic growth and has important theoretical significance for promoting regional coordinated development.
Under the interaction of government regulation and market mechanism, the spatial correlation of regional economic growth in China has gradually evolved into a complex, multi-threaded network structure Based on the panel data of 30 provinces in China from 1992 to 2013, this article uses the gravitational model and the network analysis method to investigate the structural characteristics of the spatial association network of China's regional economic growth, and based on the weight of the space network to test the convergence of the regional economic growth in China. The research finds that: 1) The spatial correlation network of China's regional economic growth presents a complex and global development trend. The inter-provincial linkages show the coexistence of adjacent connections and jump connections. There are no independent development provinces, but the overall network is not tight enough; The connectivity and accessibility are strong, there are multiple overlapping spillover effects, and the stability of the network is strong; the network hierarchy has a significant downward trend, and the hierarchical structure of the spatial association network is not strict. 2)The degrees, proximity and intermediate centers of Shandong, Henan and Jiangsu are ranked in the top three,and they also have the most contacts with other provinces. They play the role of central actors in the spatial correlation network, and play the role of "intermediary" and "bridge" in the network. The provinces with the lowest degree of degree and near center degree are Jilin and Henan, and the phenomenon of mediation is 0 in many provinces. 3) The spatial correlation network of regional economic growth can be divided into four sections. There are general relationships within and between the four major sections. Among them, the impact of the spillover of the other sections of the sector II is the largest, and the relationship of the sector III to the other sections is the most. It plays a core role in the four sections. 4) The empirical results of convergence test show that there is a global convergence phenomenon in China's regional economic growth. Compared with the traditional spatial weight model, the convergence speed of the model is faster under the weight of the spatial network, reaching 0.07, and the spatial interaction between regions reduced the gap of them; the savings rate has a positive and significant impact on the economic growth rate, capital accumulation is still an important factor affecting convergence; in addition, there is a certain degree of club convergence in the eastern, central and western regions, the central region showed the strongest convergence trend, the convergence speed was as high as0.101, and the convergence speed in the western region was the lowest, only 0.038. This reveals the important role of spatial network association in regional economic growth and has important theoretical significance for promoting regional coordinated development.
引文
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[18]魏丽华.论城市群经济联系对区域协同发展的影响——基于京津冀与苏浙沪的比较[J].地理科学, 2018,38(4):575-579.[Wei Lihua. Influence of urban agglomeration economic cooperation on regional coordinated development:Based on comparison between Beijing-Tianjin-Hebei and Shanghai-Jiangsu-Zhejiang. Scientia Geographica Sinica, 2018, 38(4):575-579.
[19] Scott J. Social network analysis[M]. London:Sage Publications, 2017.
[20]庄德林,杨羊,晋盛武,等.基于战略性新兴产业的长江三角洲城市网络结构演变研究[J].地理科学,2017,37(4):546-553.[Zhuang Delin, Yang Yang, Jin Shengwu et al. Evolution of the Yangtze River Delta’s city network based on the strategic emerging industries. Scientia Geographica Sinica, 2017, 37(4):546-553.]
[21]马丽君,龙云.基于社会网络分析法的中国省际入境旅游经济增长空间关联性[J].地理科学,2017,37(11):1705-1711.[Ma Lijun, Long Yun. The spatial correlation of economic growth of inbound tourism in China based on social network analysis. Scientia Geographica Sinica, 2017, 37(11):1705-1711.]
[22] Yu J H, Lee L F. Convergence:A spatial dynamic panel data approach[J]. Global Journal of Economics, 2012, 1(1):6-36.
[23]国家统计局.新中国六十年统计资料汇编[M].北京:中国统计出版社,2010.[National Bureau of Statistics of China. China compendium of statistics 1949-2008. Beijing:China Statistics Press, 2010.]
[24]国家统计局.中国统计年鉴[M].北京:中国统计出版社,1994-2014.[National Bureau of Statistics of China. China statistical yearbook. Beijing:China Statistics Press, 1994-2014.]
[25] Barro R J, Blanchard O J, Hall R E. Convergence across states and regions[J]. Brookings Papers on Economic Activity, 1991(1):107-182.
[2] Islam N. Growth empirics:A panel data approach[J]. The Quarterly Journal of Economics, 1995, 110(4):1127-1170.
[3] Barro R J, Sala-i-Martin X. Convergence[J]. Journal of Political Economy, 1992, 100(2):223-251.
[4] Hsiao C. Analysis of panel data[J]. Cambridge Books, 2015, 87(2):221-222.
[5]蔡昉,都阳.中国地区经济增长的趋同与差异——对西部开发战略的启示[J].经济研究,2010(10):30-37.[Cai Fang, Du Yang. Convergence and divergence of regional economic growth in China. Economic Research Journal, 2010(10):30-37.]
[6]林毅夫,刘明兴.中国的经济增长收敛与收入分配[J].世界经济,2003(8):3-14.[Lin Yifu, Liu Mingxing. Growth convergence and income distribution in China. World Economy, 2003(8):3-14.]
[7] Ying L G. Measuring the spillover effects:Some Chinese evidence[J]. Papers in Regional Science, 2000, 79(1):75-89.
[8] Ramajo J, Márquez M A, Hewings G J D et al. Spatial heterogeneity and interregional spillovers in the European union:Do cohesion policies encourage convergence across regions?[J]. European Economic Review, 2008, 52(3):551-567.
[9]潘文卿.中国区域经济差异与收敛[J].中国社会科学,2010(1):72-84.[Pan Wenqing. The economic disparity between different regions of China and its reduction:An analysis from geographical perspective. Social Sciences in China, 2010(1):72-84.]
[10]林光平,龙志和,吴梅.我国地区经济收敛的空间计量实证分析:1978~2002年[J].经济学(季刊),2005,4(s1):71-86.[Lin Guangping, Long Zhihe, Wu Mei. A spatial analysis of regional economic convergence in China:1978-2002. China Economic(Quarterly), 2005, 4(s1):71-86.]
[11]周亚虹,朱保华,刘俐含.中国经济收敛速度的估计[J].经济研究,2009(6):40-51.[Zhou Yahong, Zhu Baohua, Liu Lihan.Estimation of speed of convergence of China’s economy. Economic Research Journal, 2009(6):40-51.]
[12]朱国忠,乔坤元,虞吉海.中国各省经济增长是否收敛?[J].经济学(季刊),2014,13(3):1171-1194.[Zhu Guozhong, Qiao Kunyuan, Yu Jihai. Is provincial economic growth convergent in China. China Economic Quarterly, 2014, 13(3):1171-1194.]
[13] Wu H X. The Chinese GDP growth rate puzzle:How fast has the Chinese economy grown?[J]. Asian Economic Papers,2007, 6(1):1-23.
[14] Henderson J V, Storeygard A, Weil D W. Measuring economic growth from outer space[J]. American Economic Review, 2012,102(2):994-1028.
[15] Liu Z, He C, Zhang Q et al. Extracting the dynamics of urban expansion in China using DMSP-OLS nighttime light data from 1992 to 2008[J]. Landscape&Urban Planning, 2012, 106(1):62-72.
[16]汤放华,汤慧,孙倩,等.长江中游城市集群经济网络结构分析[J].地理学报,2013,68(10):1357-1366.[Tang Fanghua, Tang Hui, Sun Qian et al. Analysis of the economic network structure of urban agglomerations in the middle Yangtze River. Acta Geographica Sinica, 2013, 68(10):1357-1366.]
[17]杨丽花,刘娜,白翠玲.京津冀雄旅游经济空间结构研究[J].地理科学,2018,38(3):394-401.[Yang Lihua, Liu Na, Bai Cuiling. The spatial structure of the tourism economy in Beijing-Tianjin-Hebei-Xiongan region. Scientia Geographica Sinica, 2018, 38(3):394-401.]
[18]魏丽华.论城市群经济联系对区域协同发展的影响——基于京津冀与苏浙沪的比较[J].地理科学, 2018,38(4):575-579.[Wei Lihua. Influence of urban agglomeration economic cooperation on regional coordinated development:Based on comparison between Beijing-Tianjin-Hebei and Shanghai-Jiangsu-Zhejiang. Scientia Geographica Sinica, 2018, 38(4):575-579.
[19] Scott J. Social network analysis[M]. London:Sage Publications, 2017.
[20]庄德林,杨羊,晋盛武,等.基于战略性新兴产业的长江三角洲城市网络结构演变研究[J].地理科学,2017,37(4):546-553.[Zhuang Delin, Yang Yang, Jin Shengwu et al. Evolution of the Yangtze River Delta’s city network based on the strategic emerging industries. Scientia Geographica Sinica, 2017, 37(4):546-553.]
[21]马丽君,龙云.基于社会网络分析法的中国省际入境旅游经济增长空间关联性[J].地理科学,2017,37(11):1705-1711.[Ma Lijun, Long Yun. The spatial correlation of economic growth of inbound tourism in China based on social network analysis. Scientia Geographica Sinica, 2017, 37(11):1705-1711.]
[22] Yu J H, Lee L F. Convergence:A spatial dynamic panel data approach[J]. Global Journal of Economics, 2012, 1(1):6-36.
[23]国家统计局.新中国六十年统计资料汇编[M].北京:中国统计出版社,2010.[National Bureau of Statistics of China. China compendium of statistics 1949-2008. Beijing:China Statistics Press, 2010.]
[24]国家统计局.中国统计年鉴[M].北京:中国统计出版社,1994-2014.[National Bureau of Statistics of China. China statistical yearbook. Beijing:China Statistics Press, 1994-2014.]
[25] Barro R J, Blanchard O J, Hall R E. Convergence across states and regions[J]. Brookings Papers on Economic Activity, 1991(1):107-182.