Estimation of a semiparametric recursive bivariate probit model in the presence of endogeneity

The classic recursive bivariate probit model is of particular interest to researchers since it allows for the estimation of the treatment effect that a binary endogenous variable has on a binary outcome in the presence of unobservables. In this article, the authors consider the semiparametric version of this model and introduce a model fitting procedure which permits to estimate reliably the parameters of a system of two binary outcomes with a binary endogenous regressor and smooth functions of continuous covariates. They illustrate the empirical validity of the proposal through an extensive simulation study. The approach is applied to data from a survey, conducted in Botswana, on the impact of education on women's fertility. Some studies suggest that the estimated effect could have been biased by the possible endogeneity arising because unobservable confounders (e.g., ability and motivation) are associated with both fertility and education. The Canadian Journal of Statistics 39: 259–279; 2011 © 2011 Statistical Society of Canada     

Social learning about levels of perinatal and infant mortality in Niakhar,Senegal

How individuals develop perceptions concerning the risk of infant and child mortality has important consequences for fertility and demographic transition theory and for understanding broader processes of social learning. The role of learning through social interaction in shaping demographic phenomena has been the subject of intense research in the last decade. Much previous research however has been hampered by inadequate measures of individuals’ personal networks, the proximal context in which learning takes place. Using pilot data employing an innovative social network design in conjunction with demographic surveillance data from Niakhar, Senegal, this research models perception of change in the level of infant mortality over time as a function of the experience of social network associates with perinatal and infant mortality. Results suggest relatively strong effects of network members’ mortality experience controlling for own experiences of child mortality as well as neighborhood and community levels of infant mortality among other controls.     

Estimation of a Semiparametric Recursive Bivariate Probit Model with Nonparametric Mixing

We consider an extension of the recursive bivariate probit model for estimating the effect of a binary variable on a binary outcome in the presence of unobserved confounders, nonlinear covariate effects and overdispersion. Specifically, the model consists of a system of two binary outcomes with a binary endogenous regressor which includes smooth functions of covariates, hence allowing for flexible functional dependence of the responses on the continuous regressors, and arbitrary random intercepts to deal with overdispersion arising from correlated observations on clusters or from the omission of non‐confounding covariates. We fit the model by maximizing a penalized likelihood using an Expectation‐Maximisation algorithm. The issues of automatic multiple smoothing parameter selection and inference are also addressed. The empirical properties of the proposed algorithm are examined in a simulation study. The method is then illustrated using data from a survey on health, aging and wealth.