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Semi-parametric estimation of the effect of health on labour force participation of married women

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  • Weiren Wang

Abstract

This paper uses the semi-parametric method for binary choice models developed by Wang and Zhou (1995) to estimate the effect of health on the labour force participation of married women using data from the Panel Study of Income Dynamics (1989 interviewing year). The semi-parametric method is particularly useful if empirical studies involve many explanatory variables and large sample sizes. The estimation results show that the health condition is a good predictor of participation. The results also demonstrate that the easy-to-compute semi-parametric method is practical for empirical studies. Furthermore, the empirical results suggest that both logit and probit estimators may underestimate the effect of wives' health conditions on their labour force participation.

Suggested Citation

  • Weiren Wang, 1997. "Semi-parametric estimation of the effect of health on labour force participation of married women," Applied Economics, Taylor & Francis Journals, vol. 29(3), pages 325-329.
  • Handle: RePEc:taf:applec:v:29:y:1997:i:3:p:325-329
    DOI: 10.1080/000368497327092
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    References listed on IDEAS

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    1. Gabler, Siegfried & Laisney, Francois & Lechner, Michael, 1993. "Seminonparametric Estimation of Binary-Choice Models with an Application to Labor-Force Participation," Journal of Business & Economic Statistics, American Statistical Association, vol. 11(1), pages 61-80, January.
    2. Matzkin, Rosa L, 1992. "Nonparametric and Distribution-Free Estimation of the Binary Threshold Crossing and the Binary Choice Models," Econometrica, Econometric Society, vol. 60(2), pages 239-270, March.
    3. Manski, Charles F. & Thompson, T. Scott, 1986. "Operational characteristics of maximum score estimation," Journal of Econometrics, Elsevier, vol. 32(1), pages 85-108, June.
    4. Klein, Roger W & Spady, Richard H, 1993. "An Efficient Semiparametric Estimator for Binary Response Models," Econometrica, Econometric Society, vol. 61(2), pages 387-421, March.
    5. Manski, Charles F., 1975. "Maximum score estimation of the stochastic utility model of choice," Journal of Econometrics, Elsevier, vol. 3(3), pages 205-228, August.
    6. Sherman, Robert P, 1993. "The Limiting Distribution of the Maximum Rank Correlation Estimator," Econometrica, Econometric Society, vol. 61(1), pages 123-137, January.
    7. Jerry A. Hausman, 1980. "The Effect of Wages, Taxes, and Fixed Costs on Women's Labor Force Participation," NBER Chapters, in: Econometric Studies in Public Finance, pages 161-194, National Bureau of Economic Research, Inc.
    8. Horowitz, Joel L, 1992. "A Smoothed Maximum Score Estimator for the Binary Response Model," Econometrica, Econometric Society, vol. 60(3), pages 505-531, May.
    9. Steven Stern, 1989. "Measuring the Effect of Disability on Labor Force Participation," Journal of Human Resources, University of Wisconsin Press, vol. 24(3), pages 361-395.
    10. Wang, Weiren & Zhou, Mai, 1995. "Iterative Least Squares Estimator of Binary Choice Models: a Semi-Parametric Approach," MPRA Paper 46981, University Library of Munich, Germany.
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    Cited by:

    1. Laura Romeu Gordo, 2006. "Effects of short- and long-term unemployment on health satisfaction: evidence from German data," Applied Economics, Taylor & Francis Journals, vol. 38(20), pages 2335-2350.
    2. Anil Kumar, 2006. "Nonparametric conditional density estimation of labour force participation," Applied Economics Letters, Taylor & Francis Journals, vol. 13(13), pages 835-841.

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