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Decomposing the gender wage gap with sample selection adjustment: evidence from Colombia

  • Alejandro Badel
  • Ximena Peña

Despite the remarkable improvement of female labor market characteristics, a sizeable gender wage gap exists in Colombia. We employ quantile regression techniques to examine the degree to which current small differences in the distribution of observable characteristics can explain the gender gap. We find that the gap is largely explained by gender differences in the rewards to labor market characteristics and not by differences in the distribution of characteristics. We claim that Colombian women experience both a “glass ceiling effect’’ and also (what we call) a “quicksand floor effect” because gender differences in returns to characteristics primarily affect women at the top and the bottom of the distribution. Also, self selection into the labor force is crucial for gender gaps: if all women participated in the labor force, the observed gap would be roughly 50% larger at all quantiles.

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Paper provided by Federal Reserve Bank of St. Louis in its series Working Papers with number 2010-045.

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Date of creation: 2010
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Handle: RePEc:fip:fedlwp:2010-045
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  1. Hugo R. Ñopo, 2006. "The Gender Wage Gap in Chile 1992-2003: From a Matching Comparisons Perspective," IDB Publications (Working Papers) 6714, Inter-American Development Bank.
  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. Alejandro Hoyos & Hugo Ñopo & Ximena Peña, 2010. "The Persistent Gender Earnings Gap in Colombia, 1994-2006," DOCUMENTOS CEDE 007094, UNIVERSIDAD DE LOS ANDES-CEDE.
  4. Ganguli, Ina & Terrell, Katherine, 2005. "Wage Ceilings and Floors: The Gender Gap in Ukraine's Transition," IZA Discussion Papers 1776, Institute for the Study of Labor (IZA).
  5. 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).
  6. Ximena Peña, . "Assortative Matching and the Education Gap," Borradores de Economia 427, Banco de la Republica de Colombia.
  7. Klein, R.W. & Spady, R.H., 1991. "An Efficient Semiparametric Estimator for Binary Response Models," Papers 70, Bell Communications - Economic Research Group.
  8. David H. Autor & Lawrence F. Katz & Melissa S. Kearney, 2005. "Rising Wage Inequality: The Role of Composition and Prices," Harvard Institute of Economic Research Working Papers 2096, Harvard - Institute of Economic Research.
  9. Albrecht, James & van Vuuren, Aico & Vroman, Susan, 2009. "Counterfactual distributions with sample selection adjustments: Econometric theory and an application to the Netherlands," Labour Economics, Elsevier, vol. 16(4), pages 383-396, August.
  10. Suzanne Duryea & Olga Lucia Jaramillo & Carmen Pagés-Serra, 2003. "Latin American Labor Markets in the 1990s: Deciphering the Decade," IDB Publications (Working Papers) 4129, Inter-American Development Bank.
  11. de la Rica, Sara & Dolado, Juan J. & Llorens, Vanesa, 2005. "Ceiling and Floors: Gender Wage Gaps by Education in Spain," IZA Discussion Papers 1483, Institute for the Study of Labor (IZA).
  12. Wendy Cunningham, 2007. "Minimum Wages and Social Policy : Lessons from Developing Countries," World Bank Publications, The World Bank, number 6760.
  13. Carneiro, Pedro & Loken, Katrine Vellesen & Salvanes, Kjell G., 2011. "A Flying Start? Maternity Leave Benefits and Long Run Outcomes of Children," IZA Discussion Papers 5793, Institute for the Study of Labor (IZA).
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