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Learning when to say no

David Evans, George Evans and Bruce McGough ()

Journal of Economic Theory, 2021, vol. 194, issue C

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.

Keywords: Search and unemployment; Learning; Dynamic optimization; Bounded rationality; Wage dispersion (search for similar items in EconPapers)
JEL-codes: D83 D84 E24 (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1016/j.jet.2021.105240

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