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A Simple Solution to the Identification Problem in Detailed Wage Decompositions

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  • Myeong-Su Yun

Abstract

Oaxaca and Ransom (1999) show that a detailed decomposition of the coefficients effect is destined to suffer from an identification problem since the detailed coefficients effect attributed to dummy variables is not invariant to the choice of reference groups. It turns out that the identification problem in the decomposition equation is a disguised identification problem of constant and dummy variables in a regression equation. This article proposes a simple and natural remedy for this problem by using "normalized" regressions, which enable me to identify the constant and estimates of each dummy variable. (JEL C20, J70) Copyright 2005, Oxford University Press.

Suggested Citation

  • Myeong-Su Yun, 2005. "A Simple Solution to the Identification Problem in Detailed Wage Decompositions," Economic Inquiry, Western Economic Association International, vol. 43(4), pages 766-772, October.
  • Handle: RePEc:oup:ecinqu:v:43:y:2005:i:4:p:766-772
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    References listed on IDEAS

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    1. F. L. Jones, 1983. "On Decomposing the Wage Gap: A Critical Comment on Blinder's Method," Journal of Human Resources, University of Wisconsin Press, vol. 18(1), pages 126-130.
    2. Oaxaca, Ronald, 1973. "Male-Female Wage Differentials in Urban Labor Markets," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 14(3), pages 693-709, October.
    3. Alan S. Blinder, 1973. "Wage Discrimination: Reduced Form and Structural Estimates," Journal of Human Resources, University of Wisconsin Press, vol. 8(4), pages 436-455.
    4. 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.
    5. Edin, Per-Anders & Zetterberg, Johnny, 1992. "Interindustry Wage Differentials: Evidence from Sweden and a Comparison with the United States," American Economic Review, American Economic Association, vol. 82(5), pages 1341-1349, December.
    6. Ham, John C & Svejnar, Jan & Terrell, Katherine, 1998. "Unemployment and the Social Safety Net during Transitions to a Market Economy: Evidence from the Czech and Slovak Republics," American Economic Review, American Economic Association, vol. 88(5), pages 1117-1142, December.
    7. 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.
    8. Radchenko, Stanislav I. & Yun, Myeong-Su, 2003. "A Bayesian approach to decomposing wage differentials," Economics Letters, Elsevier, vol. 78(3), pages 431-436, March.
    9. Kennedy, Peter, 1986. "Interpreting Dummy Variables," The Review of Economics and Statistics, MIT Press, vol. 68(1), pages 174-175, February.
    10. 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.
    11. 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.
    12. 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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    More about this item

    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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