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Revisiting Inter-Industry Wage Differentials and the Gender Wage Gap: An Identification Problem

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Author Info
Myeong-Su Yun () (Tulane University and IZA Bonn)

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Abstract

We propose a measure of the industrial gender wage gap which is free from an identification problem by using inter-industry wage differentials, or industrial wage premia. We draw on a recent literature showing that a normalized regression equation can be used to resolve the identification problem in detailed Oaxaca decompositions of wage differentials. By identifying the constant and the coefficients of dummy variables, including the reference category, the normalized equation can resolve the two key identification problems that arise in studying wage gaps: one in detailed Oaxaca decompositions; the other measuring industrial gender wage gaps.

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Publisher Info
Paper provided by Institute for the Study of Labor (IZA) in its series IZA Discussion Papers with number 2427.

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Length: 13 pages
Date of creation: Nov 2006
Date of revision:
Handle: RePEc:iza:izadps:dp2427

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Related research
Keywords: industrial wage differentials; invariance; identification; normalized regression;

Find related papers by JEL classification:
C20 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - General
J70 - Labor and Demographic Economics - - Labor Discrimination - - - General

This paper has been announced in the following NEP Reports:

References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
  1. William C. Horrace, 2005. "On the ranking uncertainty of labor market wage gaps," Journal of Population Economics, Springer, vol. 18(1), pages 181-187, 09. [Downloadable!] (restricted)
    Other versions:
  2. John P. Haisken-DeNew & Christoph M. Schmidt, . "Inter-Industry and Inter-Region Differentials: Mechanics and Interpretation," Working Papers 9504, SELAPO Center for Human Resources. [Downloadable!]
  3. Javier Gardeazabal & Arantza Ugidos, 2004. "More on Identification in Detailed Wage Decompositions," The Review of Economics and Statistics, MIT Press, vol. 86(4), pages 1034-1036, 04. [Downloadable!] (restricted)
    Other versions:
  4. Greene, William H & Seaks, Terry G, 1991. "The Restricted Least Squares Estimator: A Pedagogical Note," The Review of Economics and Statistics, MIT Press, vol. 73(3), pages 563-67, August. [Downloadable!] (restricted)
  5. Krueger, Alan B & Summers, Lawrence H, 1988. "Efficiency Wages and the Inter-industry Wage Structure," Econometrica, Econometric Society, vol. 56(2), pages 259-93, March. [Downloadable!] (restricted)
  6. Judith Fields & Edward N. Wolff, 1995. "Interindustry wage differentials and the gender wage gap," Industrial and Labor Relations Review, ILR Review, ILR School, Cornell University, vol. 49(1), pages 105-120, October.
  7. Suits, Daniel B, 1984. "Dummy Variables: Mechanics v. Interpretation," The Review of Economics and Statistics, MIT Press, vol. 66(1), pages 177-80, February. [Downloadable!] (restricted)
  8. William C. Horrace & Ronald L. Oaxaca, 2001. "Inter-industry wage differentials and the gender wage gap: An identification problem," Industrial and Labor Relations Review, ILR Review, ILR School, Cornell University, vol. 54(3), pages 611-618, April.
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This page was last updated on 2009-12-7.

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