信息、工人流动与工资离散的量化理论

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

American Economic Journal: Macroeconomics · 2018
被引 10
人大 AABS 4

中文导读

研究了雇主学习如何影响工资与就业动态,发现学习效应解释了失业后工资损失的78%、生命周期工资增长的24%以及横截面工资离散的13%。

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.

雇主学习工人流动工资离散信息不对称