On the Design of Grant Assignment Rules

In this paper, we assess how different need-based grant assignment rules target various students and how they impact their performance in the first year of university, including dropout rates. To do so, we first predict individual outcomes with a fixed-amount grant and without it. Then, we conduct counterfactual analyses using different rules while maintaining a constant number of grants. These rules fall into two categories: those based on efficiency, that target students with significant performance improvements due to the grant, and those based on merit, that focus on high performing students, i.e. those with the smallest probability of dropping out if they are awarded a grant. Using administrative data from all public Italian universities, we find that the first type of rules achieves the maximum reduction in dropout rates of the entire population. In contrast, the second type of rules minimizes dropout rates amongst grant recipients, at the cost of increasing the overall number of dropouts