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Counterfactual decomposition of changes in wage distributions using quantile regression

  • José Mata
  • José A. F. Machado

    (Faculdade de Economia, Universidade Nova de Lisboa, Portugal)

We propose a method to decompose the changes in the wage distribution over a period of time in several factors contributing to those changes. The method is based on the estimation of marginal wage distributions consistent with a conditional distribution estimated by quantile regression as well as with any hypothesized distribution for the covariates. Comparing the marginal distributions implied by different distributions for the covariates, one is then able to perform counterfactual exercises. The proposed methodology enables the identification of the sources of the increased wage inequality observed in most countries. Specifically, it decomposes the changes in the wage distribution over a period of time into several factors contributing to those changes, namely by discriminating between changes in the characteristics of the working population and changes in the returns to these characteristics. We apply this methodology to Portuguese data for the period 1986-1995, and find that the observed increase in educational levels contributed decisively towards greater wage inequality. Copyright © 2005 John Wiley & Sons, Ltd.

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Article provided by John Wiley & Sons, Ltd. in its journal Journal of Applied Econometrics.

Volume (Year): 20 (2005)
Issue (Month): 4 ()
Pages: 445-465

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Handle: RePEc:jae:japmet:v:20:y:2005:i:4:p:445-465
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  1. Mata, Jose & Machado, Jose A. F., 1996. "Firm start-up size: A conditional quantile approach," European Economic Review, Elsevier, vol. 40(6), pages 1305-1323, June.
  2. Francine D. Blau & Lawrence M. Kahn, 1994. "International Differences in Male Wage Inequality: Institutions versus Market Forces," NBER Working Papers 4678, National Bureau of Economic Research, Inc.
  3. Angel López-Nicolás & Jaume García & Pedro J. Hernández, 2001. "How wide is the gap? An investigation of gender wage differences using quantile regression," Empirical Economics, Springer, vol. 26(1), pages 149-167.
  4. George E. Johnson, 1997. "Changes in Earnings Inequality: The Role of Demand Shifts," Journal of Economic Perspectives, American Economic Association, vol. 11(2), pages 41-54, Spring.
  5. Mills, Jeffrey A & Zandvakili, Sourushe, 1997. "Statistical Inference via Bootstrapping for Measures of Inequality," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 12(2), pages 133-50, March-Apr.
  6. 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.
  7. Amanda Gosling & Stephen Machin & Costas Meghir, 1994. "The changing distribution of male wages in the UK," IFS Working Papers W94/13, Institute for Fiscal Studies.
  8. Roger Koenker & Kevin F. Hallock, 2001. "Quantile Regression," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 143-156, Fall.
  9. Moshe Buchinsky, 1998. "The dynamics of changes in the female wage distribution in the USA: a quantile regression approach," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 13(1), pages 1-30.
  10. José A. F. Machado & José Mata, 2001. "Earning functions in Portugal 1982-1994: Evidence from quantile regressions," Empirical Economics, Springer, vol. 26(1), pages 115-134.
  11. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
  12. Bernd Fitzenberger & Claudia Kurz, 2003. "New insights on earnings trends across skill groups and industries in West Germany," Empirical Economics, Springer, vol. 28(3), pages 479-514, July.
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