Innovation is the first driving force to lead
development, and the carrier of innovation drive is talent, and talent has been
an important strategic resource to lead social development in the 21st century. And different levels of cities should focus on the strategy of
attracting talents. In this paper, based on the index data of the Yangtze River
Delta Urban Agglomerations in 2019, we construct the evaluation talent
attractiveness level score and use it to build a Bayesian quantile regression
model to map the focus that cities of different levels should focus on talent
attraction policies. The results show that the marginal gain of talent
attraction is different for different level cities in different dimensions. By
quantifying the level of talent attractiveness of different cities through the
objective assignment method, we find the differences in the spatial distribution
of talent attractiveness of different cities in the Yangtze River Delta region
and provide theoretical guidance for the integration of the Yangtze River
Delta. At the same time, by exploring the differences in the talent
attractiveness of cities of different levels that should be focused on, we find
the general rules and provide reference and guidance for other cities.
References
[1]
Audretsch, D., & Feldman, M. (2004). R&D Spillovers and the Geography of Innovation and Production. American Economic Review, 86, 630-640.
[2]
Gao, Z. P. (2012). Research on Talent Attractiveness of Shanghai Based on Hierarchical Analysis. East China Economic Management, 26, 5-9.
[3]
Koenker, R., & Bassett, G. (1978). Regression Quantiles. Econometrica, 46, 33-50.
https://doi.org/10.2307/1913643
[4]
Li, N. W., & Li, F. Z. (2011). Research on the Attractiveness of Talent Based on Talent Gathering Carriers—Take the Northwest Liaoning Region as an Example. Population & Economics, 6, 41-48.
[5]
Lin, J. X., He, J. L., & Huang, X. J. (2020). A Study on City Preferences of Research Talent Mobility from the Perspective of Urban Comfort. Areal Research and Development, 39, 59-64, 88.
[6]
Lin, X. Y., Shi, Z. H., Luo, S., Wu, D., & Shi, M. T. (2015). Analysis of the Impact of High-Speed Railroad on the Attractiveness of Urban Talents. Journal of Beijing Jiaotong University (Social Science Edition), 14, 7-16.
[7]
Liu, J., Zhao, P., & Su, F. G. (2015). A Study on the Factors Influencing the Attractiveness of Financial Talents in Shenzhen Based on Rooting Theory. Journal of Shenzhen University (Humanities & Social Sciences), 32, 130-135.
[8]
Lucas, R. E., & Rossi-Hansberg, E. (2002). On the Internal Structure of Cities. Econometriva, 70, 1445-1476. https://doi.org/10.1111/1468-0262.00338
[9]
March, J. G., & Simon, H. (1958). The International Mobility of Technical Talent. LTNU Wider Press, 23, 12-22.
[10]
Mobley, W. H. (1982). Employee Turnover: Causes, Consequences, and Control. The Lancet, 11, 542-546.
[11]
Palivos, T., & Wang, P. (2008). Spatial Agglomeration and Endogenous Growth. Regional Science Urban Economics, 26, 25-64.
[12]
Porter, M. E. (1998). Clusters and the New Economics of Competition. Harvard Business Review, 76, 77-90.
[13]
Qian, H. F. (2010). Talent, Creativity and Regional Economic Performance: The Case of China. The Annals of Regional Science, 45, 133-156.
https://doi.org/10.1007/s00168-008-0282-3
[14]
Song, H., & Chen, H. (2006). Quantitative Evaluation and Comparison of Regional Talent Attractiveness. Human Resources Development of China, 3, 26-28.
[15]
Weng, Q., & McElroy, J. C. (2010). HR Environment and Regional Attraction: An Empirical Study of Industrial Clusters in China. Australian Journal of Management, 35, 245-263. https://doi.org/10.1177/0312896210384679
[16]
Xu, B., Song, W., Gao, X., & Zhang, Y. (2019). Research on the Science and Technology Policy Coordination of China’s Yangtze River Delta. American Journal of Industrial and Business Management, 9, 875-885. https://doi.org/10.4236/ajibm.2019.94059
[17]
Ye, X. Q., & Chen, W. (2019). Research on the Comprehensive Attractiveness of China’s Cities to Scientific and Technological Innovation Talents—Construction and Empirical Evidence of Evaluation Indexsystem Based on Comfort Object Theory. Studies in Science of Science, 37, 1375-1384.
[18]
Zeng, S. K., & Li, X. H. (2019). An Empirical Study on the Factors Influencing Talent Attractiveness in the Context of Rural Revitalization—Based on Survey Data in the Western Part of Chongqing. Journal of Chinese Agricultural Mechanization, 40, 214-220.
[19]
Zhang, R. H. (2012). Research on the Evaluation of Environmental Talent Attractiveness of Industrial Clusters in Henan Province. Science and Technology Management Research, 32, 180-184.
[20]
Zhang, W., Jing, W. M., & Wang, Y. J. (2017). What Determines the Attractiveness of First-Tier Cities to Talents?—A Test Analysis of the Influencing Factors Based on Random Forest Method. Science and Technology Management Research, 37, 99-108.
