Advantages of Agent Construction in Building University Talent Apartments in China

Liu, Zhengrong; Jiang, Wendan; Peng, Yi; Wu, Yuzhe

doi:10.1007/978-981-10-0855-9_116

Cited by 1 publication

(2 citation statements)

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“…Thirdly, the factor of education level (EDU) is greatly significant to predict the occurrence of high-rent-stress migrant clustering in urban areas, indicating a strong correlation between quality educational resources and higher rental expenses in migrant households. This result also indicates that the move to urban areas of migrants is motivated largely by the pursuits of the better educational resources in the destination cities [115]. Besides, housing prices (HPs) have a significant effect on the spatial distribution of high-and low-skilled high-stress migrants.…”

Section: Variablesmentioning

confidence: 99%

“…[23,59,114]. Some housing-related welfare policies are also supportive of the "talent plan" in these cities, such as the talent apartment schemes in Beijing, Suzhou, and Shenzhen [115]. By using Getis-Ord Gi* in ArcGIS which is derived from Arthur Getis in San Diego and J. Keith Ord in Washington D.C. in the USA, we identified the local spatial clusters of high-skilled high-rent-stress migrants.…”

Section: Variablesmentioning

confidence: 99%

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Regional Heterogeneity of Migrant Rent Affordability Stress in Urban China: A Comparison between Skilled and Unskilled Migrants at Prefecture Level and Above

Liu

2019

Sustainability

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Rental housing unaffordability has been widely used to assess the housing poverty problem among immigrants in the developed and developing countries. China is experiencing an unprecedented urbanization process, with two-thirds of its 250 million migrants now being sheltered in private rental housing in the host cities. In this paper, we aimed to examine the rental housing unaffordability problems faced by migrant workers in urban China and provide policy recommendations for a more accessible and affordable migrant housing provision system. We used the household data on China’s Migrant Dynamics Monitoring Survey (MDMS), released in 2016, across China’s 329 prefecture-level cities and above to look into the sociality and spatiality of migrant rent expenses and rent-income ratio at the prefecture-level cities and above. The statistical tests were conducted to examine the socio- and spatial-variance of these rent stress indexes, and it was found that educational level is a significant and quite powerful indicator in predicting who will or will not assume the heavier rental housing pressure. We then continued to reveal the different spatiality of high-rent-stress migrants across the high- and low-skilled categories. An agglomeration of the high-skilled high-rent-stress migrants was witnessed in the coastal growth engines of urban clusters, while a more spillover-like pattern among the low-skilled high-rent-stress migrants was reported in our study. An ordinary least square and spatial regression analysis was conducted to explain their respective mechanisms.

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Section: Variablesmentioning

confidence: 99%