Learning when to say no

David Evans; George W. Evans; Bruce McGough

doi:10.1016/j.jet.2021.105240

Learning when to say no

David Evans
, George W. Evans
, Bruce McGough

Research output: Contribution to journal › Article › peer-review

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	https://doi.org/10.1016/j.jet.2021.105240
Publication status	Published - Jun 2021

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 8 Decent Work and Economic Growth

Keywords

Search and unemployment
Learning
Dynamic optimization
Bounded rationality
Wage dispersion

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10.1016/j.jet.2021.105240

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.
Accepted author manuscript, 532 KBLicence: CC BY-NC-ND

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title = "Learning when to say no",

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",

author = "David Evans and Evans, \{George W.\} and Bruce McGough",

note = "Financial support from National Science Foundation Grant No. SES-1559209 is gratefully acknowledged.",

year = "2021",

month = jun,

doi = "10.1016/j.jet.2021.105240",

language = "English",

volume = "194",

journal = "Journal of Economic Theory",

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T1 - Learning when to say no

AU - Evans, David

AU - Evans, George W.

AU - McGough, Bruce

N1 - Financial support from National Science Foundation Grant No. SES-1559209 is gratefully acknowledged.

PY - 2021/6

Y1 - 2021/6

N2 - 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.

AB - 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.

KW - Search and unemployment

KW - Learning

KW - Dynamic optimization

KW - Bounded rationality

KW - Wage dispersion

U2 - 10.1016/j.jet.2021.105240

DO - 10.1016/j.jet.2021.105240

M3 - Article

SN - 0022-0531

VL - 194

JO - Journal of Economic Theory

JF - Journal of Economic Theory

M1 - 105240

ER -

Learning when to say no

Abstract

UN SDGs

Keywords

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