Spatial-Temporal Dynamics of Jiangsu Regional Convergence with Spatial Markov Chains Approach
Received date: 2004-12-21
Revised date: 2005-06-27
Online published: 2005-09-25
Supported by
National Natural Science Foundation of China, No. 40301038
Based on the per capita GDP dataset at the county level in Jiangsu province from 1978 to 2000, this paper attempts to apply spatial Markov chains to investigate the spatial and temporal characteristics of regional convergence in Jiangsu. Firstly, all the per capita GDP data in Jiangsu are classified into 4 different classes by annual provincial average. Due to the changes in the regional development strategies over time, the whole period is then divided into two sub-periods (1978-1990 and 1990-2000), and two Markov transition probability matrices for these two periods are estimated respectively for comparison. Secondly, two kinds of maps are accordingly made in order to visualize spatial patterns of class transitions, one for region, and the other for region and its neighbors. Finally, conditioning on each region's spatial lag at the beginning of each year, spatial Markov matrices for the two different periods are constructed. The conclusions are drawn as follows: (1) The process of regional convergence in Jiangsu has been globally characterized by "convergence clubs" since 1978, but this trend in the 1990s is sharpened and statistically different from that of the period from 1978 to 1990. (2) Those regions and their neighbors that both experience upward mobility are located in southern Jiangsu, while the regions or their neighbors that move downwardly are mostly found in northern Jiangsu. (3) Regional per capita GDP class transitions in Jiangsu are highly constrained by their geographical neighbors. If a poor region is surrounded by poor regions, the probabilities of moving upward for the periods from 1978 to 1990 and 1990 to 2000 decrease to 0.148 and 0.025 respectively, while they average 0.2 and 0.042 in traditional Markov matrices. It suggests that poor regions are negatively affected when surrounded by other poor regions. Conversely, if a rich region is surrounded by rich neighbors, the probabilities of moving upward for those two sub-periods increase to 1 and 0.991, while they are 0.987 and 0.984 on average in traditional Markov matrices. It suggests rich regions are positively influenced by other rich regions being surrounded. These empirical analyses provide a spatial explanation to "convergence clubs" detected in traditional Markov method.
PU Yingxia, MA Ronghua, GE Ying, HUANG Xingyuan . Spatial-Temporal Dynamics of Jiangsu Regional Convergence with Spatial Markov Chains Approach[J]. Acta Geographica Sinica, 2005 , 60(5) : 817 -826 . DOI: 10.11821/xb200505013
[1] Song Xueming. Regional economic development and convergence in China. Economic Research, 1996, (9): 38-44.
[宋学明. 中国区域经济发展及其收敛性. 经济研究, 1996, (9): 38-44.]
[2] Cai Fang, Du Yang. Regional disparities, convergence and the west development. China Industrial Economy, 2001, (2): 48-54.
[蔡昉, 都阳. 区域差距、趋同与西部开发. 中国工业经济, 2001, (2): 48-54.]
[3] Luo Renfu, Li Xiaojian, Qin Chenglin. New evidence of convergence across Chinese provinces. Progress in Geography, 2002, 21(1): 73-80.
[罗仁福, 李小建, 覃成林. 中国省际经济趋同的定量分析. 地理科学进展, 2002, 21(1): 73-80.]
[4] Qin Chenglin. A study on regional economic growth convergence and divergence in China. Human Geography, 2004, 19(3): 36-40.
[覃成林. 中国区域经济增长趋同与分异研究. 人文地理, 2004, 19(3): 36-40.]
[5] Wu Tong, Luo Hao. Research review of regional economic convergence in China. Contemporary Finance and Economics, 2004, (4): 85-88.
[吴彤, 罗浩. 中国区域经济趋同性研究综述. 当代财经, 2004, (4): 85-88.]
[6] Barro R, Sala-I-Martin X. Economic Growth. London: McGraw-Hill, 1995.
[7] Quah D. Empirical cross-section dynamics in economic growth. European Economic Review, 1993, 37(3): 426-434.
[8] Quah D. Regional convergence clusters across Europe. European Economic Review, 1996, 40(4): 951-958.
[9] Fingleton B. Specification and testing of Markov chain models: an application to convergence in the European Union. Oxford Bulletin of Economics and Statistics, 1997, 59(3): 385-403.
[10] Rey S, Montouri B. US regional income convergence: a spatial econometric perspective. Regional Studies, 1999, 33: 143-156.
[11] Li Xiaojian (ed.) Economic Geography. Beijing: Higher Education Press, 1999.
[李小建 主编. 经济地理学. 北京: 高等教育出版社, 1999.]
[12] Rey S. Spatial empirics for economic growth and convergence. Geographical Analysis, 2001, 33(2): 195-214.
[13] Le Gallo J. Space-time analysis of GDP disparities among European regions: a Markov chains approach. International Regional Science Review, 2004, 27(2): 138-163.
[14] Ou Xiangjun, Gu Chaolin. Quantitative analysis of regional economic polarization and dynamical mechanisms in Jiangsu Province. Acta Geographica Sinica, 2004, 59(5): 791-799.
[欧向军, 顾朝林. 江苏省区域经济极化及其动力机制定量分析. 地理学报, 2004, 59(5): 791-799.]
[15] Ou Xiangjun, Chen Xiuying. The analysis of agents of regional economic inequality in Jiangsu province in post-reform. Economic Geography, 2004, 24(3): 338-342.
[欧向军, 陈修颖. 改革开放以来江苏省区域经济差异成因分析. 经济地理, 2004, 24(3): 338-342.]
[16] Shen Zhou, Xie Shiqian, Pan Chengyi (eds.). Probabilities and Statistics. 2nd edn. Beijing: Higher Education Press, 1989.
[盛骤, 谢式千, 潘承毅 编. 概率论与数理统计(第二版). 北京: 高等教育出版社, 1989.]
[17] Anselin L. Spatial Econometrics: Methods and Models. Boston: Kluwer Academic Publishers, 1988.
[18] Anselin L. Spatial econometrics. In: Baltagi B (ed.), Companion to Economtrics. Oxford: Basil Blackwell, 2001. 310-330.
[19] Griffith D A. Advanced Spatial Statistics: Special Topics in the Exploration of Quantitative Spatial Data Series. Kluwer Academic Publishers, 1988.
[19] Le Gallo J, Ertur C. Exploratory spatial data analysis of the distribution of regional per capita GDP in Europe, 1980-1985. Papers in Regional Science, 2003, 82(2): 175-201.
[20] Wang Jinfeng, Li Lianfa, Ge Yong et al. A theoretic framework for spatial analysis. Acta Geographica Sinica, 2000, 55(1): 92-103.
[王劲峰, 李连发, 葛咏 等. 地理信息空间分析的理论体系探讨. 地理学报, 2000, 55(1): 92-103.]
[21] Ma Ronghua, Huang Xingyuan, Zhu Chuangeng. Knowledge discovery with ESDA from GIS database. Journal of Remote Sensing, 2002, 6(2): 102-107.
[马荣华, 黄杏元, 朱传耿. 用ESDA技术从GIS数据库中发现知识. 遥感学报, 2002, 6(2): 102-107.]
[22] Zhu Chuangeng, Gu Chaolin, Ma Ronghua et al. The influential factors and spatial distribution of floating population in China. Acta Geographica Sinica, 2001, 56(5): 549-560.
[朱传耿, 顾朝林, 马荣华 等. 中国流动人口的影响要素与空间分布. 地理学报, 2001, 56(5): 549-560.]
[23] Liu Xuhua, Wang Jinfeng, Meng Bin. On China's spatial-temporal dynamics and imbalance of regional economy. Geographical Research, 2004, 23(4): 530-540.
[刘旭华, 王劲峰, 孟斌. 中国区域经济时空动态不平衡发展分析. 地理研究, 2004, 23(4): 530-540.]
[24] Ma Xiaodong, Ma Ronghua, Xu Jiangang. Spatial structures of cities and towns with ESDA-GIS framework. Acta Geographica Sinica, 2004, 59(6): 1048-1057.
[马晓冬, 马荣华, 徐建刚. 基于ESDA-GIS的城镇群体空间结构. 地理学报, 2004, 59(6):1048-1057.]
[25] Su Xing (ed.). Deng Xiaoping's Socialist Market Economy Theory and China's Economic System Transformation. Beijing: People's Publishing House, 2002.
[苏星 主编. 邓小平社会主义市场经济理论与中国经济体制转轨. 北京: 人民出版社, 2002.]
[26] Li Fuge (ed.). Jiangsu Economy for 50 Years (1949-1999). Nanjing: Jiangsu People's Press, 1999.
[李富阁 主编. 江苏经济50年(1949-1999). 南京: 江苏人民出版社, 1999.]
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