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Residential Distribution of the Emergent Class of Skilled Migrants in Nanjing

Author

Listed:
  • Can Cui
  • Pieter Hooimeijer
  • Stan Geertman
  • Yingxia Pu

Abstract

Migration in China is traditionally dominated by unskilled rural-urban migrants that find their way into the city through urban villages, dormitories or informal housing. However, a remarkable increase in the number of skilled migrants has been witnessed with the economic restructuring. Reforms in the labour and housing market have shifted the spatial arrangement of opportunities, consequently changing migrants' access to the cities. Using 2000 Population Census and employing spatial regression models, this study shows skilled migrants to have better access to the city in the sense that their residences locate in the areas with more professional jobs and better houses. It is their advantages in the labour market that determines their favoured access to the public sector housing, resulting in better residential locations. Female skilled migrants are less likely than males to settle in areas with a large proportion of urban village housing or shared accommodation. These findings reveal the heterogeneity among migrants and the concomitant differences in spatial behaviour.

Suggested Citation

  • Can Cui & Pieter Hooimeijer & Stan Geertman & Yingxia Pu, 2015. "Residential Distribution of the Emergent Class of Skilled Migrants in Nanjing," Housing Studies, Taylor & Francis Journals, vol. 30(8), pages 1235-1256, November.
    • Handle: RePEc:taf:chosxx:v:30:y:2015:i:8:p:1235-1256
    DOI: 10.1080/02673037.2015.1013094
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    Cited by:

    1. Tingzhu Li & Ran Liu & Wei Qi, 2019. "Regional Heterogeneity of Migrant Rent Affordability Stress in Urban China: A Comparison between Skilled and Unskilled Migrants at Prefecture Level and Above," Sustainability, MDPI, vol. 11(21), pages 1-26, October.
    2. Lin Jiang & Yani Lai & Ke Chen & Xiao Tang, 2022. "What Drives Urban Village Redevelopment in China? A Survey of Literature Based on Web of Science Core Collection Database," Land, MDPI, vol. 11(4), pages 1-16, April.

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