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Revisiting the Composition of the Female Workforce - A Heckman Selection Model with Endogeneity

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  • Schwiebert, Jörg
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    Abstract

    In this paper, we revisit the Mulligan and Rubinstein (2008: Selection, investment and women's relative wages over time. The Quarterly Journal of Economics, 123(3):1061-1110) analysis about the composition of the female workforce in the United States. Using a Heckman selection model, these authors found that the selection of women into the female workforce changed from negative to positive over time. However, the authors assumed the exogeneity of covariates, which is sometimes appropriate but not for a variable like education. We revisit the issue of the Mulligan and Rubinstein (2008) paper by developing and applying a Heckman selection model which also controls for the potential endogeneity of education. Applying this estimator to U.S. Census and American Community Survey data, we find that selection has become more positive over time (like in Mulligan and Rubinstein), but that selection has never been negative. We rather find an interesting puzzle concerning the correlation pattern of the unobservables in our model which requires further investigation.

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    File URL: http://diskussionspapiere.wiwi.uni-hannover.de/pdf_bib/dp-502.pdf
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    Bibliographic Info

    Paper provided by Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät in its series Hannover Economic Papers (HEP) with number dp-502.

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    Length: 42 pages
    Date of creation: Oct 2012
    Date of revision:
    Handle: RePEc:han:dpaper:dp-502

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    Keywords: Sample selection model; endogenous covariates; gender wage gap; composition of the female workforce; female labor force participation;

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    References

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    1. Whitney Newey, 1999. "Two Step Series Estimation of Sample Selection Models," Working papers 99-04, Massachusetts Institute of Technology (MIT), Department of Economics.
    2. Anastasia Semykina & Jeffrey M. Woodridge, 2010. "Estimating Panel Data Models in the Presence of Endogeneity and Selection," Working Papers wp2010_10_01, Department of Economics, Florida State University.
    3. Mitali Das & Whitney K. Newey & Francis Vella, 2003. "Nonparametric Estimation of Sample Selection Models," Review of Economic Studies, Wiley Blackwell, vol. 70(1), pages 33-58, January.
    4. Smith, Richard J & Blundell, Richard W, 1986. "An Exogeneity Test for a Simultaneous Equation Tobit Model with an Application to Labor Supply," Econometrica, Econometric Society, vol. 54(3), pages 679-85, May.
    5. Gallant, A Ronald & Nychka, Douglas W, 1987. "Semi-nonparametric Maximum Likelihood Estimation," Econometrica, Econometric Society, vol. 55(2), pages 363-90, March.
    6. 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.
    7. Newey, Whitney K., 1987. "Efficient estimation of limited dependent variable models with endogenous explanatory variables," Journal of Econometrics, Elsevier, vol. 36(3), pages 231-250, November.
    8. Rivers, Douglas & Vuong, Quang H., 1988. "Limited information estimators and exogeneity tests for simultaneous probit models," Journal of Econometrics, Elsevier, vol. 39(3), pages 347-366, November.
    9. Davidson, Russell & MacKinnon, James G., 1993. "Estimation and Inference in Econometrics," OUP Catalogue, Oxford University Press, number 9780195060119.
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