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.
| Original language | English |
|---|---|
| Article number | 105240 |
| Number of pages | 33 |
| Journal | Journal of Economic Theory |
| Volume | 194 |
| Early online date | 6 Apr 2021 |
| DOIs | |
| Publication status | Published - Jun 2021 |
Keywords
- Search and unemployment
- Learning
- Dynamic optimization
- Bounded rationality
- Wage dispersion
Access to Document
- Evans-2021-Learning-when-to-say-no-JET-AAM
Copyright © 2021 Elsevier Inc. All rights reserved. This work has been made available online in accordance with publisher policies or with permission. Permission for further reuse of this content should be sought from the publisher or the rights holder. This is the author created accepted manuscript following peer review and may differ slightly from the final published version. The final published version of this work is available at https://doi.org/10.1016/j.jet.2021.105240.