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A Model of Asymmetric Employer Learning

with Testable Implications*

 

 

 

 

Joshua C. Pinkston

Bureau of Labor Statistics

 

March 2008

 

 

 

Abstract: ����This paper helps close the gap between theory and empirical evidence in the literature on asymmetric employer learning.If an employer's private learning is reflected in a worker's wage and one employer's private information is transmitted to the next when the worker makes a job-to-job transition, then asymmetric employer learning will appear in wage regressions as learning over an employment spell.Extending previous work that assumes all learning takes place publicly, this paper develops wage regressions that test for both asymmetric employer learning and public learning.The empirical results, including tests of alternative explanations, are consistent with asymmetric employer learning�s having at least as much of an effect on wages during an employment spell as does public learning.The model developed in this paper illustrates how the story suggested by the empirical work might unfold.It shows that outside firms can profitably compete with a better-informed employer through bidding wars, even when the worker is equally productive in all firms.Furthermore, this competition results in different wages for workers with the same publicly observable characteristics, a result that previous models of asymmetric learning have not produced.

 

JEL:J3, M5, D82, D83, D44

 

Keywords:Asymmetric Employer Learning, Wage and Performance Relationship, Applications of Auction Theory



*I would like to thank Joseph Altonji, Andrew Cohen, Todd Elder, Harley Frazis, Mark Loewenstein, Dale Mortensen, Uta Schoenberg, Jim Spletzer, Jay Stewart, Christopher Taber, Michael Waldman, Charles Zheng, and seminar participants at the Bureau of Labor Statistics, the Census Bureau, and the Federal Reserve Board.All mistakes and opinions are my own.

Mailing address: Bureau of Labor Statistics, 2 Massachusetts Ave., NE, Suite 4945, Washington, D.C.20212.Phone:(202) 691-7403.Fax:(202) 691-6425.Email:[email protected].