Separating risk in education from heterogeneity: a semiparametric approach

Returns to education are variable within the same educational group. If uncertain payoffs are a concern to individuals when selecting education, wage variance is relevant. It is the resultant of unobserved heterogeneity and pure uncertainty. The first element is known to the individual, but unknown to the researcher, the second is unknown to both. If individuals exploit private information to select their level of education the variance observed in the data will overestimate the real magnitude of educational uncertainty and the impact that risk has on educational decisions. In this paper we will apply a semi parametric estimator to tackle the selectivity issues. This method does not rely on the joint normality of errors in the selection and wage equations and is robust to misspecification of the errors distribution. Results suggest that pure risk tends to increase with education. Private information accounts for a bigger share of total wage variance observed in the data than previously thought

Can statisticians beat surgeons at the planning of operations?

The planning of operations in the Academic Medical Center is primarily based on the assessments of the length of the operation by the surgeons. We investigate whether duration models employing the information available at the moment the planning is made, offer a better alternative. Our empirical results indicate that statistical methods often do better than surgeons. This does not imply that the surgeons' predictions do not contain valuable information. This information is a key explanatory variable in our statistical models. What our conclusion does entail is that a correction of the predictions of surgeons is possible because they are often under- or overestimating the actual length of operations