科学学研究 ›› 2020, Vol. 38 ›› Issue (8): 1408-1416.

• 科技发展战略与政策 • 上一篇    下一篇

城市不动产投资结构对科技人才集聚的门限效应

张所地1,张婷婷2,赵华平3,4,李斌2   

  1. 1. 山西财经大学管理科学与工程学院
    2. 山西财经大学
    3.
    4. 山西财经
  • 收稿日期:2019-09-02 修回日期:2020-02-03 出版日期:2020-08-15 发布日期:2020-08-15
  • 通讯作者: 张婷婷
  • 基金资助:
    城市不动产动态与预期评估模型研究;适婚人群性别失配背景下的婚配竞争与住房市场:传导机理、跨层测度与时空演变;房地产市场风险的生成、测度与控制研究:基于资源型与非资源型城市的比较;太原都市区不动产结构、人力资本与经济增长

Threshold effect of urban real estate investment structure on the agglomeration of scientific and technological talents

  • Received:2019-09-02 Revised:2020-02-03 Online:2020-08-15 Published:2020-08-15

摘要: 在创新驱动发展情境下,城市间不动产结构的差异性加速了人才等创新要素在不同城市的集散分化。通过构建一般均衡模型揭示了城市不动产投资结构对人才集聚的非线性影响机理。将中国35个大中城市按照科技人才集聚度分为两类,运用面板门限回归方法对全样本城市和两类子样本城市2009-2016年间,对比分析了城市不动产投资结构对科技人才集聚的影响。结果表明,3个样本城市的不动产投资结构对科技人才集聚的门限效应存在显著异质性。全样本城市不动产投资结构对科技人才集聚有正向影响,且呈递减型;第一类子样本的正向影响呈明显“倒U”型;而第二类子样本城市的影响则呈负相关型。研究结论能为城市规划的微观管制和国家创新驱动发展的宏观调控提供重要借鉴依据。

Abstract: In the context of innovation-driven development, the differences in the structure of real estate between cities have accelerated the integration of talents and other innovative elements in different cities. By constructing a general equilibrium model, the nonlinear influence mechanism of urban real estate investment structure on talent agglomeration is revealed. The 35 large and medium-sized cities in China were divided into two categories according to the agglomeration of scientific and technological talents. The panel threshold regression method was used to compare the impact of urban real estate investment structure on the accumulation of scientific and technological talents in the whole sample city and two subsample cities from 2009 to 2016. The results show that the impact of the real estate investment structure of the three sample cities on the agglomeration of scientific and technological talents is significantly heterogeneous. The full-sample urban real estate investment structure has a positive impact on the agglomeration of scientific and technological talents, and it is declining; the positive impact of the first subsamples is obviously “inverted U”; while the impact of the second subsample cities is negatively correlated. The conclusions of the study can provide an important reference for the micro-control of urban planning and the macro-control of national innovation-driven development.