We investigate the role of information frictions in the US labor market using a new nationally representative panel dataset on individuals' labor market expectations and realizations. We find that expectations about future job offers are, on average, highly predictive of actual outcomes. Despite their predictive power, however, deviations of ex post realizations from ex ante expectations are often sizable. The panel aspect of the data allows us to study how individuals update their labor market expectations in response to such shocks. We find a strong response: an individual who receives a job offer one dollar above her expectation subsequently adjusts her expectations upward by $0.47. The updating patterns we document are, on the whole, inconsistent with Bayesian updating. We embed the empirical evidence on expectations and learning into a model of search on-and off-the job with learning, and show that it is far better able to fit the data on reservation wages relative to a model that assumes complete information. The estimated model indicates that workers would have lower employment transition responses to changes in the value of unemployment through higher unemployment benefits than in a complete information model, suggesting that assuming workers have complete information can bias estimates of the predictions of government interventions. We use the framework to gauge the welfare costs of information frictions which arise because individuals make uninformed job acceptance decisions and find that the costs due to information frictions are sizable, but are largely mitigated by the presence of learning.

In many economic decisions people estimate probabilities, such as the likelihood that a risk materializes or that a job applicant will be a productive employee, by retrieving experiences from memory. We model this process based on two established regularities of selective recall: similarity and interference. We show that the similarity structure of a hypothesis and the way it is described (not just its objective probability) shape the recall of experiences and thus probability assessments. The model accounts for and reconciles a variety of empirical findings, such as overestimation of unlikely events when these are cued versus neglect of noncued ones, the availability heuristic, the representativeness heuristic, conjunction and disjunction fallacies, as well as over- versus underreaction to information in different situations. The model yields several new predictions, for which we find strong experimental support.

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