Learning when to say no

• 成果类型: Article
• 署名作者: Evans, David; Evans, George W.; McGough, Bruce
• 署名单位: University of Oregon; University of St Andrews
• 刊物名称: JOURNAL OF ECONOMIC THEORY
• ISSN/ISSBN: 0022-0531
• DOI: 10.1016/j.jet.2021.105240
• 发表日期: 2021
• 关键词: Search and unemployment; learning; dynamic optimization; bounded rationality; wage dispersion

摘要

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. (c) 2021 Elsevier Inc. All rights reserved.