A Probability Model for Iranian Female Participation: Nonparametric Logit Modeling

Abstract

In this paper, a nonparametric logit modelling was introduced to estimate the probability of participation of Iranian female labour using household income-expended in 2008. The logistic function for women’s participation was regressed based on the maximum likelihood estimator that the geographical location (urban/rural), husband’s income, education, female age, non-labour income, number of children above and under six years were implemented for input variables. The accuracies of the logit models based on the parametric and nonparametric modeling approaches were evaluated using White statistic, confidence index, and root mean square error. Finally, the marginal effects of input variables on women’s probability of participation were estimated based the results of calibrated unknown coefficients of parametric and nonparametric models. The results demonstrated that nonparametric logit model is more accurate than parametric logit model. Education and number of child under six years have effective positive and negative effects compared to another input variables, respectively.

Keywords

• nonparametric logit model
• maximum likelihood method
• woman’s participation modeling