Association for Public Policy Analysis & Management

Conditional cash transfers (CCTs) have been targeted towards the poor. Thus, their targeting assessments check whether these schemes have been allocated to low-income households or individuals. However, CCTs have more than one goal and target group. Beyond poverty alleviation, CCTs seek to increase school enrolment. Hence, students at risk of dropping out of school are an additional target group.

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.