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

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  • Yun, Myeong-Su

    (Inha University)

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.

Suggested Citation

  • Yun, Myeong-Su, 2006. "Revisiting Inter-Industry Wage Differentials and the Gender Wage Gap: An Identification Problem," IZA Discussion Papers 2427, Institute of Labor Economics (IZA).
    • Handle: RePEc:iza:izadps:dp2427
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    References listed on IDEAS

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    1. John P. Haisken-DeNew & Christoph M. Schmidt, 2000. "Interindustry and Interregion Differentials: Mechanics and Interpretation," The Review of Economics and Statistics, MIT Press, vol. 79(3), pages 516-521, August.
    2. Judith Fields & Edward N. Wolff, 1995. "Interindustry Wage Differentials and the Gender Wage Gap," ILR Review, Cornell University, ILR School, vol. 49(1), pages 105-120, October.
    3. 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 563-567, August.
    4. William C. Horrace, 2005. "On the ranking uncertainty of labor market wage gaps," Journal of Population Economics, Springer;European Society for Population Economics, vol. 18(1), pages 181-187, September.
    5. Ronald L. Oaxaca & Michael R. Ransom, 1999. "Identification in Detailed Wage Decompositions," The Review of Economics and Statistics, MIT Press, vol. 81(1), pages 154-157, February.
    6. 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, November.
    7. Krueger, Alan B & Summers, Lawrence H, 1988. "Efficiency Wages and the Inter-industry Wage Structure," Econometrica, Econometric Society, vol. 56(2), pages 259-293, March.
    8. Suits, Daniel B, 1984. "Dummy Variables: Mechanics v. Interpretation," The Review of Economics and Statistics, MIT Press, vol. 66(1), pages 177-180, February.
    9. William C. Horrace & Ronald L. Oaxaca, 2001. "Inter-Industry Wage Differentials and the Gender Wage Gap: An Identification Problem," ILR Review, Cornell University, ILR School, vol. 54(3), pages 611-618, April.
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    Cited by:

    1. Arvate, Paulo Roberto & Pereira, Carlos, 2010. "Should voters be afraid of hard budget constraint legislation? Fiscal responsibility law in Brazilian municipalities," Textos para discussão 232, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).
    2. Lamprea-Barragan, T. C & García- Suaza, A. F., 2021. "Decomposing the Gender Pay Gap in Colombia: Do Industry and Occupation Matter?," Documentos de Trabajo 19437, Universidad del Rosario.

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    More about this item

    Keywords

    industrial wage differentials; invariance; identification; normalized regression;
    All these keywords.

    JEL classification:

    • C20 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - General
    • J70 - Labor and Demographic Economics - - Labor Discrimination - - - General

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