1.School of Geography Science, Nanjing Normal University, Jiangsu Center for Collaborative Innovation in Geograophical Information Resource Development and Application, Nanjing 210023, Jiangsu, China 2.Key Laboratory of Environment Change and Resources Use in Beibu Gulf (Ministry of Education), Nanning 530001, Guangxi, China
Entering the 21th century, under the context of coordinated regional development strategy, identifying the trend of spatio-temporal evolution of regional economy growth plays an important role in timely optimizing spatial pattern and formulating reasonable spatial development strategy. By characterizing regional economy growth with per capita gross domestic product (GDP) and under the framework of exploratory spatio-temporal data analysis (ESTDA), this article focuses on analyzing spatio-temporal dynamic evolution of 2 303 counties’ economy growth in China in 1998-2013. The main results are following that: 1) There are synchronous trends of widening disparity and enhancing spatial association trends of county units’ economy growth. To be specific, regional economic growth disparity results from enhancing spatial association, while spatial association in space reflects widening regional economy growth disparity. 2) Local spatial association pattern of county units’ economic growth is relatively stable with time evolution. For instance, high-high areas like a mass type are mainly clustering in the eastern coastal region and exhibiting strip distribution type in Inner Mongolia region, and the amounts of high-high areas tend to increase. while low-low areas are mainly distributing in the vast Midwest region, but the amounts of low-low regions are decreasing. So, dominant economy core areas in future are still along both east-west axis of the Longhai-Lanxin showing belt development and along the north-south axis of eastern coastal regions. 3) The LISA time path can help find where are the most economic growth potential and vitality. The longest regions of LISA time path are mainly distributed around Bohai Sea, the Changjiang River Delta, the Zhujiang River Delta and the Inner Mongolia Region, while the shortest regions of LISA time path are widely located in Midwest counties which are economic backward areas and lack of upward economic momentum. Regions with maximum tortuosity of LISA time path are often lack of local stable spatial dependent direction, such as areas along the line of Beijing-kowloon railway, adjacent to longitude of 110°E and part of counties in Tibet. Hence, it is firstly essential to develop the Jing-Shan economic trough belt, then motivating its effect in connecting the west and east of China. 4) According to space-time transition matrix of local Moran's I, the maximum probability value of together up is just 0.061, while the frequency of type IV is 0.936 8. This implies that county units’ economic structure and local spatial association structure have higher stability and there exists to some degree path-dependent or space-locked mode.
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Meijuan Hu et al
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Li GD, Fang CL.Analyzing the multi-mechanism of regional inequality in China[J].,2014,52(1):155-182.http://link.springer.com/10.1007/s00168-013-0580-2
This paper advances the multi-mechanism framework, integrates the GIS technology and spatial panel data models for analyzing regional inequality mechanism. Applying this integrated methodology, we investigate China鈥檚 regional inequality at the county level using a comprehensive panel dataset that includes socioeconomic, environmental, locational, policy and GIS data from 1992 to 2010. The results show that Chinese regional inequality at the county level has a non-stationary dynamic structure, mirroring global inequality and spatial autocorrelation. In addition, the spatial panel data models analysis reveals the relative influence of explanatory variables. The impact of essential productive factors on regional development is gradually fading. Industrialization and decentralization play the most important role. The influence of marketization on regional development is not clear. The expansion of urban built-up areas has exerted a strong influence on the uneven regional development. Policy and transportation factor plays an indispensable role in regional inequality. The analysis additionally recognizes that socioeconomic factors play a dominant role, beyond policy and location factors. The role of environmental factors appears to be masked. This paper suggests that more attention needs to be paid to micro-inequality to coordinate inter-county and intra-county inequality under the pressure of rapid industrialization, urbanization and modernization of agriculture. Given the pattern of economic development, deepening balanced development reforms, optimizing and upgrading the industrial structure might be effective ways to develop a more coordinated regional development structure in China at the county level. Copyright Springer-Verlag Berlin Heidelberg 2014
通过空间计量经济学经济收敛标准分析方法的扩展，就中国240 个地级及以上城市的经济增长收敛性展开讨论。运用Moran's I 探讨中国区域经济空间相关模式与集聚，发现1990-2007 年间人均GDP水平显示出强烈的全局正自相关，且局部空间结构相对稳定，各城市要脱离原来的集群有一定困难。文章指出收敛标准分析存在错误方程设定，空间计量分析方法是合适的。空间计量分析结果表明中国城市间存在绝对β收敛，与非空间模型相比收敛速度显著提高且可信，空间因素在区域经济增长与收敛过程中起到重要作用。但敏感性分析显示，绝对收敛的同一稳态以及在空间上并不稳健。从机制看，中国城市间同时存在新古典增长理论和新增长理论所强调的趋同机制。最后，对促进绝对收敛的区域政策进行了讨论，并提出通过模拟经济收敛过程，是判断区域政策有效性的重要工具。
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<p>运用空间计量模型对1990~2011 年中国全要素生产率进行研究，发现：此间中国省域全要素生产率在大部分年份呈现了空间自相关性，表明这22 a 间中国省域全要素生产率并不是完全的随机状态，受其它区域的影响。进一步运用空间计量经济模型从空间维度探究了区域全要素生产率的影响因素，研究表明：经济的集聚水平越高，全要素生产率会得到显著改善；人力资本对经济增长与效率的提升有着显著地积极作用，并表现一定程度的溢出；政府干预和产业结构对全要素生产率的影响为负，说明政府部门要减少对经济的干预；同时表明了中国的产业结构可能存在不合理的地方；信息化水平、基础设施水平对全要素生产率的影响为正，但基础设施水平在统计学意义上并不显著；民营化所占比重的提升对全要素生产率的改进是显著的，因为私有化致使企业的权力下放有助于提高技术效率；经济开放水平显著提升了中国的区域全要素生产率；中国部分省份土地投入规模过大而出现规模不经济的问题。从全要素生产率在各个地区间溢出的证据出发，各个地方政府在统筹区域经济发展的过程中不仅需要考虑本地区经济发展的实际，需要打破目前行政区经济的界限，实现跨区域的协调与合作，实现共赢，最终实现所有地区全要素生产率的提高。</p>
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This paper suggests some new empirical strategies for analyzing the evolution of regional income distributions over time and space. These approaches are based on extensions to the classical Markov transition matrices that allow for a more comprehensive analysis of the geographical dimensions of the transitional dynamics. This is achieved by integrating some recently developed local spatial statistics within a Markov framework. Insights to not only the frequency with which one economy may transition across different classes in the income distribution, but also how those transitions may or may not be spatially dependent are provided by these new measures. A number of indices are suggested as ways to characterize the space-time dynamics and are illustrated in a case study of U. S. regional income dynamics over the 1929鈥1994 period.