Analyzing the driving and dragging force in China’s inter-provincial migration flows

Xiaomeng Li (), Siyu Huang () and Qinghua Chen
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Xiaomeng Li: School of Systems Science, Beijing Normal University, Beijing 100875, P. R. China
Siyu Huang: School of Systems Science, Beijing Normal University, Beijing 100875, P. R. China
Qinghua Chen: School of Systems Science, Beijing Normal University, Beijing 100875, P. R. China

International Journal of Modern Physics C (IJMPC), 2019, vol. 30, issue 07, 1-19

Abstract: In China, the evolution of inter-provincial migration patterns is from policy orientation to individual selection and hotspot attraction during 1950–2010. These years, China’s abundant regional labor flow brings prosperity to several famous high-growth regions, but also causes the problem of unbalanced development. It needs systematic analysis to do effective guidance and management of population mobility. This paper tries to analyze the driving and dragging force in China’s inter-provincial migration flows with improved multilateral migration model stemming from individual behavior of potential migrants. It revealed the problem of imbalanced regional labor flow has been exacerbated in China since the 1990s, and the driving force for typical hotspots is different, as high income force for Beijing, Shanghai, and low barrier for Guangzhou, Zhejiang. The results of analyzing indicates the the distinct regional mobility barriers help to alleviate the problem of labor loss in some provinces since 1980s, but the regional imbalance in population migration continues to grow in recent years. Finally, with regression, we reveal the essential element affecting regional mobility barriers, and it could explain 70.8% of the influence factors. It also suggests that, for the provinces with labor loss, improving economic, welfare environment will mitigate the problem effectively.

Keywords: Migration flow; driving and dragging forces; multilateral migration model; mobility barrier; multiple regression (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (1)

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DOI: 10.1142/S0129183119400151

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