The rate of learning-by-doing: estimates from a search-matching model
Julien Prat
Journal of Applied Econometrics, 2010, vol. 25, issue 6, 929-962
Abstract:
We construct and estimate by maximum likelihood a job search model where wages are set by Nash bargaining and idiosyncratic productivity follows a geometric Brownian motion. The proposed framework enables us to endogenize job destruction and to estimate the rate of learning-by-doing. Although the range of the observations is not independent of the parameters, we establish that the estimators satisfy asymptotic normality. The structural model is estimated using Current Population Survey data on accepted wages and employment durations. We show that it accurately captures the joint distribution of wages and job spells. We find that the rate of learning-by-doing has an important positive effect on aggregate output and a small impact on employment. Copyright © 2009 John Wiley & Sons, Ltd.
Date: 2010
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)
Downloads: (external link)
http://hdl.handle.net/10.1002/jae.1114 Link to full text; subscription required (text/html)
http://qed.econ.queensu.ca:80/jae/2010-v25.6/ Supporting data files and programs (text/html)
Related works:
Working Paper: The Rate of Learning-by-Doing: Estimates from a Search-Matching Model (2007) 
Working Paper: The Rate of Learning-by-Doing: Estimates from a Search-Matching Model (2006) 
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:jae:japmet:v:25:y:2010:i:6:p:929-962
Ordering information: This journal article can be ordered from
http://www3.intersci ... e.jsp?issn=0883-7252
DOI: 10.1002/jae.1114
Access Statistics for this article
Journal of Applied Econometrics is currently edited by M. Hashem Pesaran
More articles in Journal of Applied Econometrics from John Wiley & Sons, Ltd.
Bibliographic data for series maintained by Wiley-Blackwell Digital Licensing () and Christopher F. Baum ().