Replication data for: A Quantitative Theory of Information, Worker Flows, and Wage Dispersion
- Michaud, Amanda M.
AbstractEmployer 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.
Is supplement to
DOI: 10.1257/mac.20160136 (Text)
Michaud, Amanda M. “A Quantitative Theory of Information, Worker Flows, and Wage Dispersion.” American Economic Journal: Macroeconomics 10, no. 2 (April 2018): 154–83. https://doi.org/10.1257/mac.20160136.
- ID: 10.1257/mac.20160136 (DOI)
Update Metadata: 2020-05-18 | Issue Number: 2 | Registration Date: 2019-10-13