The labor force participation behavior of married women, particularly their responses to husbands’ labor
market outcomes and the effects of fertility variables, is modeled using longitudinal data to control for
a rich dynamic structure. Simulation methods provide a feasible approach to overcome the computational
difficulties inherent in classical maximum likelihood estimation of models with non-trivial error structures.
The models are estimated using the method of maximum simulated likelihood (MSL) estimation. The
empirical results imply that women’s participation outcomes are characterised by significant structural state
dependence, unobserved heterogeneity, and serially correlated transitory latent component of error. The
results show that the effect of husbands’ permanent earnings on the participation decision is significantly
stronger than that of current earnings; however, the implied income elasticities of participation are small,
on the order of -0.10. The results also provide strong evidence that fertility variables are not exogenous
to women’s participation decisions. Although MSL estimation is biased for a ﬁnite number of simulations,
I provide Monte Carlo evidence that suggests the simulation bias in the estimators is generally not large
relative to the sampling errors, except when there is positive serial correlation and either signiﬁcant
heterogeneity or state dependence, or when the form of the unobserved heterogeneity is misspeciﬁed. In
these cases, the estimated serial correlation and state dependence effects have substantial negative and
positive bias, respectively.