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Analyzing the Composition of the Female Workforce - A Semiparametric Copula Approach

  • Schwiebert, Jörg
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    We provide a semiparametric copula approach for estimating a "classical" sample selection model. We impose that the joint distribution function of unobservables can be characterized by a specifc copula, but the marginal distribution functions are estimated semiparametrically. In contrast to existing semiparametric estimators for sample selection models, our approach provides a measure of dependence between unobservables in main and selection equation which can be used to analyze the composition of, say, the female workforce. We apply our estimation procedure to a female labor supply data set and show that those women with the best skills participate in the labor market; moreover, we find evidence for the existence of an ability threshold which involves that women with high ability are to some extent advantaged and, therefore, have also obtained the best skills.

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    Paper provided by Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät in its series Hannover Economic Papers (HEP) with number dp-503.

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    Length: 30 pages
    Date of creation: Oct 2012
    Date of revision:
    Handle: RePEc:han:dpaper:dp-503
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    1. repec:sae:ecolab:v:16:y:2006:i:2:p:1-2 is not listed on IDEAS
    2. Nikolay Nenovsky & S. Statev, 2006. "Introduction," Post-Print halshs-00260898, HAL.
    3. Coppejans, Mark & Gallant, A. Ronald, 2000. "Cross Validated SNP Density Estimates," Working Papers 00-10, Duke University, Department of Economics.
    4. Margarita Genius & Elisabetta Strazzera, 2008. "Applying the copula approach to sample selection modelling," Applied Economics, Taylor & Francis Journals, vol. 40(11), pages 1443-1455.
    5. Mitali Das & Whitney K. Newey & Francis Vella, 2003. "Nonparametric Estimation of Sample Selection Models," Review of Economic Studies, Oxford University Press, vol. 70(1), pages 33-58.
    6. Lee, Lung-Fei, 1983. "Generalized Econometric Models with Selectivity," Econometrica, Econometric Society, vol. 51(2), pages 507-12, March.
    7. Gallant, A Ronald & Nychka, Douglas W, 1987. "Semi-nonparametric Maximum Likelihood Estimation," Econometrica, Econometric Society, vol. 55(2), pages 363-90, March.
    8. Whitney Newey, 1999. "Two Step Series Estimation of Sample Selection Models," Working papers 99-04, Massachusetts Institute of Technology (MIT), Department of Economics.
    9. Murray D. Smith, 2003. "Modelling sample selection using Archimedean copulas," Econometrics Journal, Royal Economic Society, vol. 6(1), pages 99-123, 06.
    10. Ahn, Hyungtaik & Powell, James L., 1993. "Semiparametric estimation of censored selection models with a nonparametric selection mechanism," Journal of Econometrics, Elsevier, vol. 58(1-2), pages 3-29, July.
    11. Trivedi, Pravin K. & Zimmer, David M., 2007. "Copula Modeling: An Introduction for Practitioners," Foundations and Trends(R) in Econometrics, now publishers, vol. 1(1), pages 1-111, April.
    12. Xiaohong Chen & Yanqin Fan & Victor Tsyrennifov, 2004. "Efficient Estimation of Semiparametric Multivariate Copula Models," Vanderbilt University Department of Economics Working Papers 0420, Vanderbilt University Department of Economics.
    13. Francis Vella, 1998. "Estimating Models with Sample Selection Bias: A Survey," Journal of Human Resources, University of Wisconsin Press, vol. 33(1), pages 127-169.
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