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The education-chasing labor rush in China identified by a heterogeneous migration-network game

机译:由异构迁移网游戏确定的中国教育追逐劳动力

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Despite persistent efforts in understanding the motives and patterns of human migration behaviors, little is known about the microscopic mechanism that drives migration and its association with migrant types. To fill the gap, we develop a population game model in which migrants are allowed to be heterogeneous and decide interactively on their destination, the resulting migration network emerges naturally as an Nash equilibrium and depends continuously on migrant features. We apply the model to Chinese labor migration data at the current and expected stages, aiming to quantify migration behavior and decision mode for different migrant groups and at different stages. We find the type-specific migration network differs significantly for migrants with different age, income and education level, and also differs from the aggregated network at both stages. However, a deep analysis on model performance suggests a different picture, stability exists for the decision mechanism behind the “as-if” unstable migration behavior, which also explains the relative invariance of low migration efficiency in different settings. Finally, by a classification of cities from the estimated game, we find the richness of education resources is the most critical determinant of city attractiveness for migrants, which gives hint to city managers in migration policy design.
机译:尽管持续了解人类移民行为的动机和模式,但对于推动迁移的微观机制及其与移民类型的关联知之甚少。为了填补差距,我们开发了一种人口游戏模型,其中允许移民在其目的地上交互地决定,所产生的迁移网络自然地作为纳什均衡而出现,并且不断依赖于移民特征。我们将模型应用于当前和预期阶段的中国劳动迁移数据,旨在量化不同移民组和不同阶段的迁移行为和决策模式。我们发现特定于年龄,收入和教育水平的移民的特定于移民迁移网络有很大不同,并且也与两个阶段的聚合网络不同。然而,对模型性能的深度分析表明,“AS-IF”不稳定迁移行为背后的决策机制存在稳定性,这也解释了不同设置中低迁移效率的相对不变性。最后,通过从估计的比赛中的城市分类,我们发现教育资源的丰富性是传动人员城市吸引力最关键的决定因素,这为迁移政策设计中的城市管理人员提供了暗示。

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