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Analyzing the multi-mechanism of regional inequality in China

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Abstract

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’s 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.

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Notes

  1. The coefficient of variation is defined as \(CV_{\left( w\right) } =\sqrt{\sum \nolimits _{i=1}^{n} {\left( {x^i -\mu }\right) ^{2}~f\left( {x_{i}} \right) }}/\mu \) where \(x_{i}\) is the per capita GDP of county \(i,\,\mu \) is the mean of per capita GDP, and \(f(x_i)\) is the share of population of country \(i\) in the total samples.

  2. The Gini coefficient is defined as \(G_{\left( w\right) } =\frac{1}{2\mu }\sum \nolimits _{j=1}^{{n}} {\sum \nolimits _{i=1}^{n} {f\left( {x_{i}}\right) f\left( {x_{j}}\right) |x_{i} -x_{j}|}}\) where \(f (x_{i})\) is the share of population of country \(i\) in the total samples, \(f (x_{j})\) is the share of per capita GDP of county \(j\) in the total samples, and \(\left| {x_{i} -x_{j}}\right| \) is absolute value of every county.

  3. The Theil index is defined as \(T_{(w)} =\sum \nolimits _{i=1}^{n} {g(x_{i})} \ln \frac{f(x_{i})}{\mu }\) where \(g(x_i)\) is the share of per capita GDP of county \(i\) in the total samples.

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Acknowledgments

This work was supported by National Natural Science Foundation of China (41371177), National Social Science Found of China (13&ZD027), and Peking University–Lincoln Institute Center for Urban Development and Land Policy (20120901). The authors would like to thank Yang Wang, Xingliang Guan, Shaojian Wang and Data Sharing Infrastructure of Earth System Science, CAS (www.geodata.cn) for their support with data. We want to thank two referees for helpful comments and suggestions.

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  1. Institute of Geographic Sciences and Natural Resources Research (IGSNRR), Chinese Academy of Sciences, 11A Datun Road, Chaoyang District, Beijing, 100101, China

    Guangdong Li & Chuanglin Fang

  2. University of Chinese Academy of Sciences, Beijing, 100049, China

    Guangdong Li

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  1. Guangdong Li
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Correspondence to Chuanglin Fang.

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Li, G., Fang, C. Analyzing the multi-mechanism of regional inequality in China. Ann Reg Sci 52, 155–182 (2014). https://doi.org/10.1007/s00168-013-0580-2

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