Preferences, Comparative Advantage, and Compensating Wage Differentials for Job Routinization

Climent Quintana-Domeque

No 1063, Working Papers from Princeton University, Department of Economics, Industrial Relations Section.

Abstract: I attempt to explain why compensating differentials for job disamenities are difficult to observe. I focus on the match between workers? preferences for routine jobs and the variability in tasks associated with the job. Using data from the Wisconsin Longitudinal Study, I find that mismatched workers report lower job satisfaction and earn lower wages. Both male and female workers in routinized jobs earn, on average, 12% less than their counterparts in non-routinized jobs. Once preferences and mismatch are accounted for, this difference decreases to 8% for men and 5% for women. Accounting for mismatch is important when analyzing compensating differentials.

Keywords: wage differentials; preferences; job attributes; routine tasks; mismatch (search for similar items in EconPapers)
JEL-codes: J30 J31 (search for similar items in EconPapers)
Date: 2008-05
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://dataspace.princeton.edu/bitstream/88435/dsp01x920fw86t/1/525.pdf
Our link check indicates that this URL is bad, the error code is: 500 Internal Server Error

Related works:
Journal Article: Preferences, Comparative Advantage, and Compensating Wage Differentials for Job Routinization (2011)
Working Paper: Preferences, comparative advantage, and compensating wage differentials for job routinization (2010)
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:pri:indrel:525

Access Statistics for this paper

More papers in Working Papers from Princeton University, Department of Economics, Industrial Relations Section. Contact information at EDIRC.
Bibliographic data for series maintained by Bobray Bordelon ().