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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.

Zonder onderwerpscode: economie

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We provide evidence of a powerful barrier to social learning: people are much less sensitive to information others discover compared to equally-relevant information they discover themselves. In a series of incentivized lab experiments, we ask participants to guess the color composition of balls in an urn after drawing balls with replacement. Participants' guesses are substantially less sensitive to draws made by another player compared to draws made themselves. This result holds when others' signals must be learned through discussion, when they are perfectly communicated by the experimenter, and even when participants see their teammate drawing balls from the urn with their own eyes. We find a crucial role for taking some action to generate one's `own' information, and rule out distrust, confusion, errors in probabilistic thinking, up-front inattention and imperfect recall as channels.

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We document two new facts about the distributions of answers in famous statistical problems: they are i) multi-modal and ii) unstable with respect to irrelevant changes in the problem. We offer a model in which, when solving a problem, people represent each hypothesis by attending "bottom up" to its salient features while neglecting other, potentially more relevant, ones. Only the statistics associated with salient features are used, others are neglected. The model unifies biases in judgments about i.i.d. draws, such as the Gambler's Fallacy and insensitivity to sample size, with biases in inference such as under- and overreaction and insensitivity to the weight of evidence. The model makes predictions about how changes in the salience of specific features should jointly shape the prevalence of these biases and measured attention to features, but also create entirely new biases. We test and confirm these predictions experimentally. Bottom-up attention to features emerges as a unifying framework for biases conventionally explained using a variety of stable heuristics or distortions of the Bayes rule.