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Joint Estimation of Sequential Labor Force Participation and Fertility Decisions Using Markov Chain Monte Carlo Techniques

Kenneth Troske and Alexandru Voicu

No 1251, IZA Discussion Papers from IZA Network @ LISER

Abstract: In this paper we estimate the causal effect of children on the labor supply of women using panel data on women from the 1979 National Longitudinal Survey of Youth (NLSY79). We examine the effect of children both prior to and after birth as well as how the effect of children varies with the number of children. We also decompose the total effect of children into the direct and indirect components and separately examine the dynamics of these components. Sequential participation decisions for four levels of labor market involvement and fertility decisions are jointly modeled. We allow decisions to be correlated in a general fashion both across time and across choices. The estimation is performed using Markov chain Monte Carlo methods. We find that children have a strong effect on a women's labor market behavior in both the pre- and post-birth period. We also find that both the direct and indirect effects are large immediately after the birth of a child but that the indirect effect declines quickly over time. The effects of children vary by education and race.

Keywords: female labor supply; Gibbs sampler; multivariate probit model (search for similar items in EconPapers)
JEL-codes: C11 C15 J13 J22 (search for similar items in EconPapers)
Pages: 59 pages
Date: 2004-08
New Economics Papers: this item is included in nep-cmp and nep-ecm
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

Published - revised version published in: Labour Economics, 2010, 17 (1), 150-169

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Related works:
Journal Article: Joint estimation of sequential labor force participation and fertility decisions using Markov chain Monte Carlo techniques (2010) Downloads
Working Paper: Joint estimation of sequential labor force participation and fertility decisions using Markov chain Monte Carlo techniques (2004)
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