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A Simple Solution to the Identification Problem in Detailed Wage Decompositions

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  • Yun, Myeong-Su

    () (Inha University)

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 a dummy variable 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 paper proposes a simple and natural remedy for this problem by utilizing “normalized” regressions which enable us to identify the constant and estimates of each dummy variable. The identification problem is automatically resolved once we obtain “normalized” regression equations for two comparison groups.

Suggested Citation

  • Yun, Myeong-Su, 2003. "A Simple Solution to the Identification Problem in Detailed Wage Decompositions," IZA Discussion Papers 836, Institute for the Study of Labor (IZA).
  • Handle: RePEc:iza:izadps:dp836
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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. 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.
    3. Radchenko, Stanislav I. & Yun, Myeong-Su, 2003. "A Bayesian approach to decomposing wage differentials," Economics Letters, Elsevier, vol. 78(3), pages 431-436, March.
    4. Kennedy, Peter, 1986. "Interpreting Dummy Variables," The Review of Economics and Statistics, MIT Press, vol. 68(1), pages 174-175, February.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    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

    Keywords

    coefficients effect; characteristics effect; identification; invariance; detailed decomposition; normalized regression;

    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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