A Quantitative Theory of Information, Worker Flows, and Wage Dispersion

• 成果类型: Article
• 署名作者: Michaud, Amanda M.
• 署名单位: Indiana University System; Indiana University Bloomington
• 刊物名称: AMERICAN ECONOMIC JOURNAL-MACROECONOMICS
• ISSN/ISSBN: 1945-7707
• DOI: 10.1257/mac.20160136
• 发表日期: 2018
• 页码: 154-183
• 关键词: displaced workers; job displacement; earnings losses; employer; unemployment; MARKET; lemons

摘要

Employer learning provides a link between wage and employment dynamics. Workers who ore selectively terminated when their low productivity is revealed subsequently earn lower wages. If learning is asymmetric across employers, randomly separated high-productivity workers ore treated similarly when hired from unemployment, hut recover as their next employer learns their type. 1 provide empirical evidence supporting this link, then study whether employer learning is an empirically important factor in wage and employment dynamics. In a calibrated structural model, learning accounts for 78 percent of wage losses after unemployment, 24 percent of life-cycle wage growth, and 13 percent of cross-sectional dispersion observed in data.