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干旱区地理 ›› 2026, Vol. 49 ›› Issue (5): 1013-1025.doi: 10.12118/j.issn.1000-6060.2025.405 cstr: 32274.14.ALG2025405

• 城市地理 • 上一篇    下一篇

中国劳动力流动网络中的城市舒适度偏好——基于百度迁徙大数据

赵晓琴(), 孙德华()   

  1. 新疆大学经济与管理学院新疆 乌鲁木齐 830046
  • 收稿日期:2025-07-14 修回日期:2025-11-14 出版日期:2026-05-25 发布日期:2026-05-25
  • 通讯作者: 孙德华(1996-),女,博士研究生,主要从事人口、资源与区域可持续发展研究. E-mail: sundehua5210@126.com
  • 作者简介:赵晓琴(1977-),女,博士,副教授,主要从事区域经济、企业治理研究. E-mail: zxq_play@163.com
  • 基金资助:
    国家自然科学基金地区项目(72062029);国家自然科学基金地区项目(71563051);新疆大学优秀研究生创新项目(XJDX2025YJS011)

Preferences for urban amenity in China’s labor mobility network: Based on Baidu migration big data

ZHAO Xiaoqin(), SUN Dehua()   

  1. School of Economics and Management, Xinjiang University, Urumqi 830046, Xinjiang, China
  • Received:2025-07-14 Revised:2025-11-14 Published:2026-05-25 Online:2026-05-25

摘要:

基于2019—2023年的百度迁徙数据构建了中国城际劳动力流动网络,运用社会网络分析方法、时间指数随机图模型(TERGM)和空间计量模型系统检验了城市舒适度对于劳动力流动网络形成概率和路径关系强度的影响,并进一步探究了其空间依赖性及溢出效应。结果表明:(1) 劳动力流动网络表现出时间上的二元稳定性,形成了以京津冀、长三角、珠三角和成渝城市群为顶点的菱形结构;广州市、上海市、东莞市等“双高”节点城市集聚于珠三角和长三角地区,其他城市群的人口吸引力也在不断增强;2019—2023年劳动者出行所形成的流网络形成了较为明显的子群结构,且子群内部城市间劳动力流动较为频繁。(2) 劳动力流动过程中存在城市舒适度偏好,特别是人为舒适度增加了城市间劳动力流动网络的形成概率,对于人口流入、流出均具有重要影响。(3) 城市舒适度对劳动力流动路径关系强度具有空间溢出性,这种影响也会作用于其他城市。研究通过探究非补偿性地区差异因素对劳动力流动网络的作用机制,扩展了劳动力流动动因的研究,对于揭示中国劳动力流动网络演化及驱动机制具有科学意义。

关键词: 劳动力流动, 城市舒适度, 时间指数随机图模型, 空间计量模型

Abstract:

Using Baidu migration data from 2019 to 2023, networks of labor migration in China were constructed in this study. Social network analysis methods, a temporal exponential random graph model (TERGM), and a spatial econometric model were employed to examine the influences of urban amenity on the probability and strength of relationships within labor migration networks, further exploring their spatial interaction. This study revealed: (1) China’s interurban labor migration network exhibits temporal binary stability, forming a diamond-shaped structure centered on the urban clusters of Beijing-Tianjin-Hebei, the Yangtze River Delta, the Pearl River Delta, and Chengdu-Chongqing. Dual-high node cities such as Guangzhou, Shanghai, and Dongguan are concentrated in the Pearl River Delta and Yangtze River Delta regions, and the attractiveness of other urban clusters has also been increasing. The labor migration networks that were formed from 2019 to 2023 have shown distinct subgroup structures, and there has been frequent population movements between cities within subgroups. (2) The existence of urban amenity preferences has been proven during labor migrations, especially where greater man-made amenities have increased the probability of forming interurban labor migration networks, significantly impacting both population inflows and outflows. (3) The influence of urban amenities on the strength of labor migration path relationship leads to spatial spillover effects, and this impact also acts on other cities. This study expands the understanding of noncompensatory factors of regional disparity and mechanisms driving labor migration networks, which forms a significant scientific contribution to the evolution and driving forces of China’s labor migration networks.

Key words: labor migration, urban amenity, temporal exponential random graph model, spatial econometric model