Biases-Informed Job Search Guidance: Characterization, Implications, and Targeting Support
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.