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

A-Tier
Journal: American Economic Journal: Macroeconomics
Year: 2018
Volume: 10
Issue: 2
Pages: 154-83

Score contribution per author:

4.022 = (α=2.01 / 1 authors) × 2.0x A-tier

α: calibrated so average coauthorship-adjusted count equals average raw count

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.

Technical Details

RePEc Handle
repec:aea:aejmac:v:10:y:2018:i:2:p:154-83
Journal Field
Macro
Author Count
1
Added to Database
2026-01-26