中国区域经济趋同:基于县级尺度的空间马尔可夫链分析  

Regional Convergence at County Level in China

作　　者：陈培阳[1,2] 朱喜钢[1] 

机构地区：[1]南京大学城市与区域规划系,江苏南京210093 [2]苏州大学建筑与城市环境学院,江苏苏州215123

出　　处：《地理科学》2013年第11期1302-1308,共7页Scientia Geographica Sinica

基　　金：国家建设高水平大学公派研究生项目(2010619076)资助

摘　　要：采用传统马尔可夫链和空间马尔可夫链统计方法从县级尺度对1998~2009年中国区域经济增长趋同进行判定和时空格局分析。根据全国人均GDP的平均值将2 345个县市按经济发展水平分为5种类型,计算其马尔可夫链矩阵和空间马尔可夫链矩阵,并进行类型转变及其与邻域类型转变关系的空间格局演化分析。研究结果表明:①自1998年以来,中国区域经济增长存在明显的俱乐部趋同,并出现空间极化现象;其中高、低水平趋同俱乐部稳定性最强;②趋同俱乐部稳定性强弱具有地带分异特征,表现为东部最为稳定,中部最不稳定;③趋同俱乐部转变受邻域环境影响显著,一个地区若以较高水平的发展县市为邻,则其增长的可能性会大大增加,反之则概率减小;④城市群地区趋同俱乐部稳定,周边地区类型转变明显。Regional inequality has always been a greatly significant issue to both academic enquiry and govern- ment policy. The recent literature on regional inequality implies that both scale and spatiality have been play- ing significant roles in the economic geographical processes. Despite the tremendous scholars interest in re- gional inequality in China since the late 1970s, the current research work ignores the spatial effects in studying China＇s regional inequality at the county level. Based on the per capita GDP data at the county level from 1998 to 2009, this article analyzes the transition across different classes in the per capita GDP distribution, by a com- prehensive application of Markov framework. For per capita GDP, this article distinguishes among 5 classes based on the average of all the 2 345 county units, computes their Markov chain matrix and spatial Markov chain matrix, and then analyzes the changing spatial patterns of the class transitions and their surrounding counterparts. The analysis concludes as follows. 1） The process of regional convergence in China has been characterized by ＂convergence clubs＂ since 1998. Furthermore, spatial polarization is discovered. The Markov chain matrix indicates that the richest and the poorest counties do not seem to change their relative position over time. The most affluent counties appear persistent--the 92.9% probability of the richest remaining rich- est. And 94.1% probability of the poorest remaining poorest is the largest entry in the transition matrix. 2） The robustness of per capita GDP class transitions in China differentiates across the three regional belts. The coun- ties in East China remain the most stable in changing their relative position. By contrast, their counterparts in Central China change their relative position more easily--33.38% of the 701 counties experiencing downward mobility locate in East China, 42.8% in Central China and 23.82% in West China. Among 443 counties experi- encing upward mobility, 21.67% are in the East China, 41

关 键 词：区域趋同 空间分析 空间马尔可夫链 中国 

分 类 号：F127[经济管理—世界经济]