关键词: 区域趋同, 空间分析, 空间马尔可夫链, 中国

Abstract:

Regional inequality has always been a greatly significant issue to both academic enquiry and government policy. The recent literature on regional inequality implies that both scale and spatiality have been playing significant roles in the economic geographical processes. Despite the tremendous scholars interest in regional 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 comprehensive 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 richest. 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 counties 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 experiencing upward mobility, 21.67% are in the East China, 41.31% in Central China and 37.88% in West China. 3) The maps of spatial Markov transitions show that the per capita GDP class transitions in China are greatly affected by their spatial neighbors. Lower-level units are negatively influenced by being surrounded by other lower-level units. Also, higher-level neighbors tend to prevent units from falling down in the per capita GDP distribution. 4) Per capita GDP class transitions in urban agglomerations, such as Changjiang River Delta and Zhujiang River Delta, remain stable, while their surrounding counterparts are active in changing their relative position. The class distribution map also shows “core-periphery” spatial structures in the urban agglomerations and their neighboring counties. More emphases should be paid to the poor county clusters in the eastern and central China, and to the spatial dynamics underlying the changing patterns of regional inequalities in China by the policy makers.

Key words: regional convergence, spatial analysis, spatial Markov chains, China