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Glass Ceiling or Sticky Floor? Exploring the Australian Gender Pay Gap using Quantile Regression and Counterfactual Decomposition Methods

  • Hiau Joo Kee

Using the HILDA survey, this paper analyses Australian gender wage gaps in both public and private sectors across the wage distribution. Quantile Regression (QR) techniques are used to control for various characteristics at different points of the wage distributions. Counterfactual decomposition analysis, adjusted for the QR framework, is utilised to examine if the gap is attributed to differences in gender characteristic, or differing returns between genders. The main finding is that a strong glass ceiling effect is detected only in the private sector. Secondly, the acceleration in the gender gap across the distribution does not vanish even after extensive controls. This suggests that the observed wage gap is a result of differences in returns to genders. By focussing only on the mean gender wage gap, substantial variations of the gap will be hidden.

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Paper provided by Centre for Economic Policy Research, Research School of Economics, Australian National University in its series CEPR Discussion Papers with number 487.

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Length: 34 pages
Date of creation: Mar 2005
Date of revision:
Handle: RePEc:auu:dpaper:487
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  1. 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.
  2. 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.
  3. Arulampalam, Wiji & Booth, Alison L. & Bryan, Mark L., 2005. "Is there a glass ceiling over Europe? Exploring the gender pay gap across the wages distribution," ISER Working Paper Series 2005-25, Institute for Social and Economic Research.
  4. Jaume Garcia & Pedro J. Hernández & Ángel López Nicolás, 1998. "How wide is the gap? An investigation of gender wage differences using quantile regression," Economics Working Papers 287, Department of Economics and Business, Universitat Pompeu Fabra.
  5. Kuhn, Peter J, 1987. "Sex Discrimination in Labor Markets: The Role of Statistical Evidenc e," American Economic Review, American Economic Association, vol. 77(4), pages 567-83, September.
  6. Newey, Whitney K & Powell, James L & Walker, James R, 1990. "Semiparametric Estimation of Selection Models: Some Empirical Results," American Economic Review, American Economic Association, vol. 80(2), pages 324-28, May.
  7. Langford, Malcolm S, 1995. "The Gender Wage Gap in the 1990s," Australian Economic Papers, Wiley Blackwell, vol. 34(64), pages 62-85, June.
  8. Moshe Buchinsky & Jennifer Hunt, 1996. "Wage Mobility in the United States," NBER Working Papers 5455, National Bureau of Economic Research, Inc.
  9. Albrecht, James & Björklund, Anders & Vroman, Susan, 2001. "Is There a Glass Ceiling in Sweden?," IZA Discussion Papers 282, Institute for the Study of Labor (IZA).
  10. Koenker, Roger, 2000. "Galton, Edgeworth, Frisch, and prospects for quantile regression in econometrics," Journal of Econometrics, Elsevier, vol. 95(2), pages 347-374, April.
  11. Powell, James L., 1986. "Censored regression quantiles," Journal of Econometrics, Elsevier, vol. 32(1), pages 143-155, June.
  12. Moshe Buchinsky, 1998. "Recent Advances in Quantile Regression Models: A Practical Guideline for Empirical Research," Journal of Human Resources, University of Wisconsin Press, vol. 33(1), pages 88-126.
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