Randomization inference and bayesian inference in regression discontinuity designs: An application to Italian university grants

Abstract

Motivated by the evaluation of Italian University grants, we will address the problem of multiplicities in (fuzzy) Regression Discontinuity (RD) settings. Following Li, Mattei and Mealli [1], we adopt a probabilistic formulation of the assignment mechanism underling RD designs and we select suitable subpopulations around the cutoff point on the basis of observed covariates using both randomization tests and a Bayesian model-based approach both accounting for the problem of multiple testing. We then conduct our analysis studying the effect of university grants on two binary outcomes, dropout and a variable equal to one for students who realize at least one University Credit (CFU), using both the Fisher-exact P-value approach and a model-based Bayesian approach. In both cases we account for the multivariate nature of the outcome by (a) proposing a multiple testing approach, and (b) defining estimands on the joint outcome.