考虑偏差的求职指导：特征、影响与针对性支持
Biases-Informed Job Search Guidance: Characterization, Implications, and Targeting Support
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中文总结 AI 辅助
本研究结合调查数据、结构模型与机器学习，揭示求职者再就业预期偏差的信息内涵，开发出可针对性干预的群体分层方法，为就业服务提供了简单工具。
中文摘要 AI 辅助
求职者对再就业的预期越来越多地被用于研究求职行为，但这些预期所反映的潜在信念和偏好中的偏差尚不明确。本研究结合新的调查数据、结构模型和机器学习，以揭示这些预期的信息内容，并展示如何利用它们改进就业支持的针对性。通过将法国求职者的主观预期新面板数据与行政记录关联，研究发现再就业预期偏差与求职两大核心因素（工作机会到达率和工资分布）的信念偏差密切相关且可概括这些偏差。这些潜在偏差具有异质性但呈强正相关，因此对求职的影响会叠加。随后，研究估计了一个包含多种信念偏差来源的结构求职模型，结果显示纠正偏差有助于悲观的求职者，但可能使乐观的求职者失去动力并受到伤害，这为针对性支持提供了依据。最后，研究开发了一种机器学习分层方法，仅通过容易获取的再就业预期就能恢复具有政策意义的群体，这些群体具有不同的信念偏差和行为模式，这为就业服务机构提供了一种针对信息干预的简单工具。
英文摘要
Job seekers' expectations about reemployment are increasingly used to study job search, but what their biases reveal about underlying beliefs and preferences is ambiguous. We combine new survey data, structural modeling, and machine learning to uncover the informational content of these expectations and show how they can be used to improve the targeting of employment support. Using a new panel of French job seekers' subjective expectations linked to administrative records, we show that reemployment expectation biases are strongly associated with, and summarize, biases in beliefs about the two fundamentals of search, job offer arrival rates and the wage distribution. These underlying biases are heterogeneous but strongly positively correlated, so their effects on search compound. We then estimate a structural job search model with multiple sources of biased beliefs and show that correcting them helps pessimistic job seekers but can demotivate and hurt optimistic ones, providing a rationale for targeting. Finally, we develop a machine-learning stratification that recovers policy-relevant groups, with distinct patterns of biased beliefs and behaviors, from easily elicited reemployment expectations alone. This gives employment services a simple tool to target informational interventions.
