Spatial-Temporal Analysis of Intra-Provincial Economic Inequality in Less Developed Provinces
以广西89个县1989-2012年数据，用泰尔指数分解和马尔可夫链分析，发现县域间不平等最大，城乡差距加剧，并识别出四个趋同俱乐部，贫困地区易陷入陷阱。
This paper investigates regional inequality in Guangxi, one of the poorest provinces in China, with the multistage nested Theil decomposition method and the Markov chain analysis. It follows the multi-scale framework based on a dataset of 89 counties in Guangxi province from 1989 to 2012.The conclusions are drawn as follows: Regional inequality in Guangxi is sensitive to geographical scale, and inter-county inequality is the widest. The gap of inter-municipality is also wide, but there is a relatively balanced economic development among the Beibu Gulf Economic Zone, the Xijiang River Economic Belt and the resource- rich area of Western Guangxi. We also find that increasing regional inequality is mainly a result of rising interregional inequality. The uneven development of the Xijiang River Economic Belt has accounted for 55.62%of the overall inter-county inequality. We reveal a trend of increasing rural-urban disparity and find internal inequality between rural counties and urban districts is the dominant driving force. The Markov chain analysis reveals that there are four convergence clubs of regional economic development during 1989~2012, namely, the rich club,the developed club, the less developed club and the poor club. The results show that less developed regions are likely to fall into thepoverty trap. Compared with the period from 1989 to 1999, club convergence is more obvious during2000~2012. The four convergence clubs distribute in a ring-shape pattern. The rich club locates at the core area, including mainly municipal districts and gradually spreading to neighboring counties, while the poor club mainly distributes in the resource- rich area of Western Guangxi. The less developed club is concentrated around the developed club, which aggregates around the rich club. A majority of the counties remain a steady status, with increasing number of counties moving up.