A Quantitative Theory of Information, Worker Flows, and Wage Dispersion
- (pp. 154-83)
Abstract
Employer learning provides a link between wage and employment dynamics. Workers who are selectively terminated when their low productivity is revealed subsequently earn lower wages. If learning is asymmetric across employers, randomly separated high-productivity workers are treated similarly when hired from unemployment, but recover as their next employer learns their type. I 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.Citation
Michaud, Amanda M. 2018. "A Quantitative Theory of Information, Worker Flows, and Wage Dispersion." American Economic Journal: Macroeconomics, 10 (2): 154-83. DOI: 10.1257/mac.20160136Additional Materials
JEL Classification
- D83 Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
- E24 Employment; Unemployment; Wages; Intergenerational Income Distribution; Aggregate Human Capital; Aggregate Labor Productivity
- J23 Labor Demand
- J24 Human Capital; Skills; Occupational Choice; Labor Productivity
- J31 Wage Level and Structure; Wage Differentials
- J62 Job, Occupational, and Intergenerational Mobility; Promotion
There are no comments for this article.
Login to Comment