地理学报 ›› 2022, Vol. 77 ›› Issue (10): 2457-2473.doi: 10.11821/dlxb202210004

• 人口地理 • 上一篇    下一篇

中国省际高技能人才迁移的时空演化机制

古恒宇1(), 沈体雁2()   

  1. 1.香港中文大学地理与资源管理学系,香港 999077
    2.北京大学政府管理学院,北京 100871
  • 收稿日期:2021-02-25 修回日期:2021-11-13 出版日期:2022-10-25 发布日期:2022-12-25
  • 通讯作者: 沈体雁(1971-), 男, 湖北天门人, 教授, 博士生导师, 研究方向为现代城市治理、国土空间规划、可计算产业集群、空间计量经济学。E-mail: tyshen@pku.edu.cn
  • 作者简介:古恒宇(1994-), 男, 广东广州人, 博士, 副研究员, 研究方向为人口迁移与区域发展、城市计算与城市空间治理。E-mail: henry.gu@pku.edu.cn
  • 基金资助:
    国家社会科学基金重大项目(17ZDA055)

Spatio-temporal evolution mechanism of China's internal skilled migration

GU Hengyu1(), SHEN Tiyan2()   

  1. 1. Department of Geography and Resource Management, The Chinese University of Hong Kong, Hong Kong 999077, China
    2. School of Government, Peking University, Beijing 100871, China
  • Received:2021-02-25 Revised:2021-11-13 Published:2022-10-25 Online:2022-12-25
  • Supported by:
    The Major Program of the National Social Science Foundation of China(17ZDA055)

摘要:

高技能人才迁移是推进新型城镇化建设的重要议题,也是影响地区创新产出和高质量发展的关键要素。针对人才迁移数据中蕴含的零膨胀和网络自相关特性,本文将特征向量空间滤波(ESF)技术和“两阶段”Hurdle模型结合,构建空间Hurdle引力模型,结合2000—2015年中国省际高技能人才迁移面板数据,研究人才迁移的时空演化格局和驱动机制。研究结论显示:① 2000—2015年人才迁移的跨省迁移比例先升后降;人才迁移表征出集聚格局,维系了其空间分布的不均衡性;随时间推移,人才迁移格局呈现分散趋势,人才空间分布集聚性下降;人才迁移和空间分布均呈现出持续显著的网络与空间自相关性特征。② 引力因素(人口规模、空间距离)、地区经济和科技发展水平(工资、科教投入)、自然舒适度(平均温差、空气质量)、城市舒适度(医疗及教育公共服务、城市绿化)以及其他因素(社会网络、生活成本、人口密度)共同驱动了跨世纪以来中国省际人才迁移过程。③ 人才迁移可被看作一个“两阶段”过程,影响其迁移概率和迁移规模的因素呈现一定差异。④ 经济增速、科教投入、自然舒适度和基础公共服务对人才迁移的影响随时间增强,而工资和城市绿化的影响随时间减弱。本文的研究结论为地区人才治理及实现地区均衡发展提供政策参考。

关键词: 高技能人才, 省际迁移, 空间Hurdle引力模型, 时空演化格局, 驱动机制, 面板数据

Abstract:

The migration of skilled individuals has become an important issue in promoting new-type urbanization in China and a key factor affecting China's regional innovation output and high-quality development. Considering the issues of zero inflation and network autocorrelation in skilled migration data, this paper combines the eigenvector space filtering (ESF) technique and the "two-stage" hurdle model into a comprehensive united framework to construct a longitudinal spatial hurdle gravity model. It then has been employed in the case study exploring the spatiotemporal patterns and influencing factors of interprovincial skilled migration in China during 2000-2015. The results are listed as follows: First, from 2000 to 2015, the mobility proportion of the skilled migration increased first and then decreased. The agglomeration pattern of skilled migration maintains the imbalance of its spatial distribution. With the elapse of time, the migration of skilled presents a dispersing trend and drives the decline of its spatial distribution and agglomeration. Talent migration presents a persistent and significant network autocorrelation, and its distribution presents a persistent and significant spatial autocorrelation. Second, China's interprovincial skilled migration during 2000-2015 was driven by gravity factors (population scales at origin and destination, distance), regional economic and scientific and technological development (average wage, spending on science & technology and education), natural amenities (average temperature difference, air quality), urban amenities (public health and education services, urban greening), and other factors (social networks, the cost of living, and population density). Third, the migration of skilled people can be regarded as a "two-stage" process, where factors affecting its migration probability and migration scale are different. Such differences are mostly reflected in factors of amenities versus economy. Fourth, the impact of economic growth, investment in science and education, natural amenities, and basic public services on skilled migration has increased over time, while the impact of wages and urban greening has weakened over time. The conclusion of this paper provides policy references for regional talent management and the balance of regional development.

Key words: skilled migration, interprovincial migration, spatial hurdle model, spatiotemporal patterns, driving factor, panel data