On the ranking uncertainty of labor market wage gaps

William Horrace

Journal of Population Economics, 2005, vol. 18, issue 1, 187 pages

Abstract: Using a log-wage model, Horrace and Oaxaca (2001) propose estimators of the gender wage gap across industry classifications. One estimator involves the maximum over sample estimates of population parameters, and inference on this estimator follows with the implicit assumption that the sample maximum equals the population maximum. This paper proposes inference procedures for this estimator that relax this assumption. Specifically, multiple comparisons with the best methods are used to construct simultaneous confidence intervals for industry wage gaps. Using data on fourteen industry classifications, inference experiments indicate that differences in gender wage gaps across industries are insignificant at the 95% level. Copyright Springer-Verlag 2005

Keywords: C12; J31; J71; Multiple comparisons; wage differential; discrimination (search for similar items in EconPapers)
Date: 2005
References: Add references at CitEc
Citations: View citations in EconPapers (13)

Downloads: (external link)
http://hdl.handle.net/10.1007/s00148-004-0186-1 (text/html)
Access to full text is restricted to subscribers.

Related works:
Working Paper: On the Ranking Uncertainty of Labor Market Wage Gaps (2002)
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:spr:jopoec:v:18:y:2005:i:1:p:181-187

Ordering information: This journal article can be ordered from
http://www.springer. ... tion/journal/148/PS2

DOI: 10.1007/s00148-004-0186-1

Access Statistics for this article

Journal of Population Economics is currently edited by K.F. Zimmermann

More articles in Journal of Population Economics from Springer, European Society for Population Economics Contact information at EDIRC.
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().