Learning when to say no
George Evans () and
Bruce McGough ()
Journal of Economic Theory, 2021, vol. 194, issue C
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)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:jetheo:v:194:y:2021:i:c:s0022053121000570
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