Focusing on Double Vision: Are Proxy Means Tests Effective to Identify Future School Dropouts and the Poor?
*Names in bold indicate Presenter
This paper analyses whether one of the most common targeting mechanisms of CCTs, a proxy means test (PMT), can effectively identify the poor and future school dropouts. The PMT is compared with other approaches that use the outputs of a predictive model of school dropout. I built this model using machine learning algorithms (MLA) and rich administrative datasets from Chile.
The paper shows that using the outputs of the predictive model in conjunction with the PMT increases targeting effectiveness by identifying more students who are either poor or future dropouts. This joint targeting approach increases effectiveness in different scenarios except when social valuation of the two target groups largely differs. In these cases, the most likely optimal approach is to solely adopt the mechanism designed to find the highly valued group.