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高学历人才逆向梯次迁移现象及机制研究——以北上广深应聘至武汉的中小学教师为例
引用本文:高喆,邱嘉怡,余家怡,李昱霄,敖荣军.高学历人才逆向梯次迁移现象及机制研究——以北上广深应聘至武汉的中小学教师为例[J].地理研究,2023,42(1):123-136.
作者姓名:高喆  邱嘉怡  余家怡  李昱霄  敖荣军
作者单位:1.华中师范大学 地理过程分析与模拟湖北省重点实验室,武汉 4300792.华中师范大学 城市与环境科学学院,武汉 4300793.格拉斯哥大学 社会与政治科学学院,英国格拉斯哥 G128QQ
基金项目:国家自然科学基金项目(42001188);国家自然科学基金项目(42271188);国家自然科学基金项目(42071170)
摘    要:本文聚焦于高学历人才“逆向梯次迁移”现象,基于武汉市中小学教师招聘考试数据,运用二元Logistic回归模型分析正规就业驱动下北上广深至武汉的高学历人才逆向梯次迁移,结合粘性理论解释其机制。结果表明:(1)正规就业导向下,高学历人才逆向梯次迁移已成为人才迁移的重要趋势之一,以武汉为目的地的跨省城际迁移中近1/4呈现逆向梯次迁移意愿,非应届生占比高于应届生。(2)“逆向梯次迁移”受粘性因素、个人因素和城市因素影响。粘性因素即“户籍地粘性”与“就学地粘性”起到关键作用;包括“年龄”“毕业学校级别”“学历级别”和“户籍地所在城市级别”在内的个人因素和城市因素也存在影响。(3)与西方受宗教、种族、家庭和个人价值等因素影响不同,基于乡缘、学缘关系产生的户籍地、就学地粘性更深刻作用于中国的高学历人才择业迁移选择,引发逆向梯次迁移现象。

关 键 词:逆向梯次迁移  城际流动  粘性理论  高学历人才  正规就业
收稿时间:2022-04-11

Research on trend and mechanism of highly educated population migrating down to urban hierarchy: Taking the candidates from Beijing,Shanghai, Guangzhou,and Shenzhen for the primary and middle school teachers in Wuhan as an example
GAO Zhe,QIU Jiayi,YU Jiayi,LI Yuxiao,AO Rongjun.Research on trend and mechanism of highly educated population migrating down to urban hierarchy: Taking the candidates from Beijing,Shanghai, Guangzhou,and Shenzhen for the primary and middle school teachers in Wuhan as an example[J].Geographical Research,2023,42(1):123-136.
Authors:GAO Zhe  QIU Jiayi  YU Jiayi  LI Yuxiao  AO Rongjun
Institution:1. Hubei Provincial Key Laboratory for Geographical Process Analysis & Simulation, Central China Normal University, Wuhan 430079, China2. College of Urban & Environmental Sciences, Central China Normal University, Wuhan 430079, China3. School of Social and Political Sciences, University of Glasgow, Glasgow G128QQ, United Kingdom
Abstract:This study focuses on the trend and mechanism of highly educated population migrating down to urban hierarchy. Based on the recruitment data of Wuhan's primary and middle school teachers, we analyse the downward migration trend of candidates from first-tier cities (Beijing, Shanghai, Guangzhou, and Shenzhen) to the second-tier city (Wuhan), and explain its mechanism by drawing on the theory of stickiness. The results of binary logistic regression indicate that moving down the urban hierarchy has become one of the significant migration trends of the highly educated population driven by formal employment. In our case, nearly a quarter of inter-provincial migration samples show a willingness to move down the urban hierarchy, characterized by a higher proportion of non-fresh graduates than fresh graduates. For first-tier cities, Beijing has the highest proportion of highly educated population migrating down the urban hierarchy, followed by Shenzhen, Shanghai, and Guangzhou. Meanwhile, Beijing has the highest proportion of fresh graduates while Shenzhen has the highest proportion of non-fresh graduates in this downward migration. This downward trend is deeply influenced by the stickiness of place, the individual factors and the urban factors. The highly educated population is back to Wuhan (the capital city of Hubei) from first-tier cities. Additional factors including age, education background, and the city level of hukou place also have certain effects on the downward tendency. In particular, young people or graduates from "Double-First Class" universities are more likely to move down the urban hierarchy. Furthermore, unlike the return migration in the West, which is influenced by religion, race, family, and personal values, both the stickiness of the place of hukou based on regional affiliation and the stickiness of the place of study based on educational affiliation profoundly affect the choice of job-choosing migration of highly educated population in China, leading to the migration down the urban hierarchy. This study demonstrates the dynamic mechanism of the trend of highly educated talents moving down the urban hierarchy in depth, which provides both a theoretical and an empirical basis for policy-making for highly educated population migration.
Keywords:migration down the urban hierarchy  inter-urban migration  theory of stickiness  highly educated population  formal employment  
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