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Learning when to say no

Research output: Contribution to journalArticlepeer-review

Open Access Status

  • Embargoed (until 6/10/22)

Author(s)

David Evans, George W. Evans, Bruce McGough

School/Research organisations

Abstract

We consider boundedly-rational agents in McCall's model of intertemporal job search. Agents update over time their perception of the value of waiting for an additional job offer using value-function learning. A first-principles argument applied to a stationary environment demonstrates asymptotic convergence to fully optimal decision-making. In environments with actual or possible structural change our agents are assumed to discount past data. Using simulations, we consider a change in unemployment benefits, and study the effect of the associated learning dynamics on unemployment and its duration. Separately, in a calibrated exercise we show the potential of our model of bounded rationality to resolve a frictional wage dispersion puzzle.
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Details

Original languageEnglish
Article number105240
Number of pages33
JournalJournal of Economic Theory
Volume194
Early online date6 Apr 2021
DOIs
Publication statusPublished - Jun 2021

    Research areas

  • Search and unemployment, Learning, Dynamic optimization, Bounded rationality, Wage dispersion

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