This study aims to examine the spatial distribution and movement patterns of creative talent within the Yangtze River Delta Bay Area (YRDBA) and the factors that contribute to such trends.
The study examines data for the period 2006 to 2018 from the regions that constitute the YRDBA: Shanghai, Jiangsu, Zhejiang and Anhui. Spatial distribution pattern analysis is adopted to interpret the flow tendency both spatially and chronologically and a Lasso regression model is used to investigate variables that influence this tendency.
It is found that creative talents in YRDBA are accumulating steadily in provincial capitals and financially advanced cities. Technology infrastructure, women’s rights, medical care amenities and housing affordability are major determinants of such spatial distribution. The talent spillover effect raises attention in talent saturated areas, while the surrounding regions should prepare to receive and retain the overflow.
Creative talents geography in China and the dynamism of creative talent in YRDBA are rarely discussed. Determinants of creative talents lack systematic pectination, literature that filters multiple determinants of creative talents migration is limited and discussion specific to the Chinese context is scarce. This case can, thus, provide insights into creative talents in developing countries and add to the current literature, bridge the gap of the current understanding of creative talents in YRDBA – the innovation and development center in China and provide a reference for policymakers when making macro decisions.
There is no funding received for the research. The authors declare that no conflicts of interest exist. We are grateful to Professor Upmanu Lall from the Water Center, Columbia University for the support and guidance extended to me and Longzhang Fang throughout our visit there. His ideas and advice for modeling are truly appreciated. We are also grateful to the China Scholarship Council for the opportunity to visit Columbia University via the co-supervising program, which helped facilitate the collaboration on this paper. Thank you to Shida Gao, who was also visiting Columbia University, for helping with the figures in this paper.
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