IDEAS home Printed from https://ideas.repec.org/p/iza/izadps/dp836.html
   My bibliography  Save this paper

A Simple Solution to the Identification Problem in Detailed Wage Decompositions

Author

Listed:
  • 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 of Labor Economics (IZA).
    • Handle: RePEc:iza:izadps:dp836
    as

    Download full text from publisher

    File URL: https://docs.iza.org/dp836.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Fortin, Nicole & Lemieux, Thomas & Firpo, Sergio, 2011. "Decomposition Methods in Economics," Handbook of Labor Economics, in: O. Ashenfelter & D. Card (ed.), Handbook of Labor Economics, edition 1, volume 4, chapter 1, pages 1-102, Elsevier.
    2. Yun, Myeong-Su, 2005. "Normalized Equation and Decomposition Analysis: Computation and Inference," IZA Discussion Papers 1822, Institute of Labor Economics (IZA).
    3. Ben Jann, 2008. "A Stata implementation of the Blinder-Oaxaca decomposition," ETH Zurich Sociology Working Papers 5, ETH Zurich, Chair of Sociology, revised 14 May 2008.
    4. Beyza Ural & William Horrace & Jin Hwa Jung, 2009. "Inter-industry gender wage gaps by knowledge intensity: discrimination and technology in Korea," Applied Economics, Taylor & Francis Journals, vol. 41(11), pages 1437-1452.
    5. 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.
    6. Sonja C. Kassenboehmer & Mathias G. Sinning, 2014. "Distributional Changes in the Gender Wage Gap," ILR Review, Cornell University, ILR School, vol. 67(2), pages 335-361, April.
    7. Ronald Bachmann & Mathias Sinning, 2016. "Decomposing the Ins and Outs of Cyclical Unemployment," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 78(6), pages 853-876, December.
    8. Carlos Gradin, 2009. "Why is Poverty So High Among Afro-Brazilians? A Decomposition Analysis of the Racial Poverty Gap," Journal of Development Studies, Taylor & Francis Journals, vol. 45(9), pages 1426-1452.
    9. Jelena Nikolic & Ivica Rubil & Iva Tomic, 2014. "Changes in Public and Private Sector Pay Structures in Two Emerging Market Economies during the Crisis," Working Papers 1403, The Institute of Economics, Zagreb.
    10. Michael Weber & Jan Kluge, 2015. "Decomposing the German East-West wage gap," ERSA conference papers ersa15p636, European Regional Science Association.
    11. Jan Kluge & Michael Weber, 2018. "Decomposing the German East–West wage gap," The Economics of Transition, The European Bank for Reconstruction and Development, vol. 26(1), pages 91-125, January.
    12. Myeong-Su Yun & Eric S. Lin, 2015. "Alternative Estimator for Industrial Gender Wage Gaps: A Normalized Regression Approach," Pacific Economic Review, Wiley Blackwell, vol. 20(4), pages 569-587, October.
    13. 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).
    14. ChangHwan Kim, 2010. "Decomposing the Change in the Wage Gap Between White and Black Men Over Time, 1980-2005: An Extension of the Blinder-Oaxaca Decomposition Method," Sociological Methods & Research, , vol. 38(4), pages 619-651, May.
    15. Liu, Hong & Fang, Hai & Zhao, Zhong, 2013. "Urban–rural disparities of child health and nutritional status in China from 1989 to 2006," Economics & Human Biology, Elsevier, vol. 11(3), pages 294-309.
    16. Bhaumik, Sumon Kumar & Gang, Ira N. & Yun, Myeong-Su, 2006. "Ethnic conflict and economic disparity: Serbians and Albanians in Kosovo," Journal of Comparative Economics, Elsevier, vol. 34(4), pages 754-773, December.
    17. Foster, E. Michael & Hillemeier, Marianne M. & Bai, Yu, 2011. "Explaining the disparity in placement instability among African-American and white children in child welfare: A Blinder-Oaxaca decomposition," Children and Youth Services Review, Elsevier, vol. 33(1), pages 118-125, January.
    18. Kaltenberg, Mary & Foster-McGregor, Neil, 2020. "The impact of automation on inequality across Europe," MERIT Working Papers 2020-009, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT).
    19. Kevin T. Reilly & Luisa Zanchi, 2003. "Industry wage differentials: how many, big and significant are they?," International Journal of Manpower, Emerald Group Publishing Limited, vol. 24(4), pages 367-398, June.

    More about this item

    Keywords

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

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:iza:izadps:dp836. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Holger Hinte (email available below). General contact details of provider: https://edirc.repec.org/data/izaaade.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.