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The Blinder–Oaxaca decomposition for linear regression models

Listed author(s):
  • Ben Jann

    ()

    (ETH Zürich)

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The counterfactual decomposition technique popularized by Blinder (1973, Journal of Human Resources, 436–455) and Oaxaca (1973, International Economic Review, 693–709) is widely used to study mean outcome differences between groups. For example, the technique is often used to analyze wage gaps by sex or race. This article summarizes the technique and addresses several complications, such as the identification of effects of categorical predictors in the detailed decomposition or the estimation of standard errors. A new command called oaxaca is introduced, and examples illustrating its usage are given. Copyright 2008 by StataCorp LP.

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Article provided by StataCorp LP in its journal Stata Journal.

Volume (Year): 8 (2008)
Issue (Month): 4 (December)
Pages: 453-479

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Handle: RePEc:tsj:stataj:v:8:y:2008:i:4:p:453-479
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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. Doris Weichselbaumer & Rudolf Winter-Ebmer, 2005. "A Meta-Analysis of the International Gender Wage Gap," Journal of Economic Surveys, Wiley Blackwell, vol. 19(3), pages 479-511, 07.
  3. Helena Skyt Nielsen, 2000. "Wage discrimination in Zambia: an extension of the Oaxaca-Blinder decomposition," Applied Economics Letters, Taylor & Francis Journals, vol. 7(6), pages 405-408.
  4. Mathia Sinning & Markus Hahn & Thomas K. Bauer, 2008. "The Blinder–Oaxaca decomposition for nonlinear regression models," Stata Journal, StataCorp LP, vol. 8(4), pages 480-492, December.
  5. Kennedy, Peter, 1986. "Interpreting Dummy Variables," The Review of Economics and Statistics, MIT Press, vol. 68(1), pages 174-175, February.
  6. Owen O'Donnell & Eddy van Doorslaer & Adam Wagstaff & Magnus Lindelow, 2008. "Analyzing Health Equity Using Household Survey Data : A Guide to Techniques and Their Implementation," World Bank Publications, The World Bank, number 6896, April.
  7. Peter Kennedy & Jutta Heinrichs, 2007. "A computational trick for calculating the Blinder-Oaxaca decomposition and its standard error," Economics Bulletin, AccessEcon, vol. 3(66), pages 1-7.
  8. Eric S. Lin, 2007. "On the standard errors of Oaxaca-type decompositions for inter-industry gender wage differentials," Economics Bulletin, AccessEcon, vol. 10(6), pages 1-11.
  9. F. L. Jones & Jonathan Kelley, 1984. "Decomposing Differences between Groups," Sociological Methods & Research, , vol. 12(3), pages 323-343, February.
  10. 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.
  11. John Hendrickx, 2000. "Using categorical variables in Stata," Stata Technical Bulletin, StataCorp LP, vol. 9(52).
  12. Suits, Daniel B, 1984. "Dummy Variables: Mechanics v. Interpretation," The Review of Economics and Statistics, MIT Press, vol. 66(1), pages 177-180, February.
  13. repec:ebl:ecbull:v:3:y:2007:i:66:p:1-7 is not listed on IDEAS
  14. Reimers, Cordelia W, 1983. "Labor Market Discrimination against Hispanic and Black Men," The Review of Economics and Statistics, MIT Press, vol. 65(4), pages 570-579, November.
  15. Shoshana Neuman & Ronald Oaxaca, 2004. "Wage Decompositions with Selectivity-Corrected Wage Equations: A Methodological Note," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 2(1), pages 3-10, April.
  16. David Neumark, 1988. "Employers' Discriminatory Behavior and the Estimation of Wage Discrimination," Journal of Human Resources, University of Wisconsin Press, vol. 23(3), pages 279-295.
  17. Keshab Shrestha & Chris Sakellariou, 1996. "Wage discrimination: a statistical test," Applied Economics Letters, Taylor & Francis Journals, vol. 3(10), pages 649-651.
  18. T.D. Stanley & Stephen B. Jarrell, 1998. "Gender Wage Discrimination Bias? A Meta-Regression Analysis," Journal of Human Resources, University of Wisconsin Press, vol. 33(4), pages 947-973.
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