Employing Regression Discontinuity Design to Evaluate Grant Effect on Dropout Rate in Italian Universities

Ekaterina Petrova

The dropout rate across Italian universities is alarmingly high and largely unaccounted for. Researchers are interested in exploring different measures that could mitigate the issue; one possible mitigator (for poor students) is financial aid. The Italian State University system addresses financial aid by offering a certain number of grants to eligible first year students on an annual basis. Recent research (Mealli and Rampichini, 2011) explores the link between grant receipt and sustained enrollment. Specifically, Mealli and Rampichini research whether these grants might have a \textit{preventative causal effect} on dropout. Because eligible applicants receive a grant if their value on a certain economic indicator lies below a set threshold, the grant assignment rule calls for a regression discontinuity design (RDD). Applying this design, Mealli and Rampichini found \textit{at the threshold} a significant causal effect for one of four schools analyzed.

This paper builds upon Mealli and Rampichini's work (with 1999 data) utilizing more recent university enrollment data and retailored RDD methods. The methods are briefly introduced in the paper, then applied to data from the years 2004-2006 across three different public Italian universities. Applying a sharp regression discontinuity over grant-applicants revealed for the University of Pisa a statistically significant (and relatively large) preventative effect of receiving a grant on dropout rate. The results suggest that in Italy the grant can potentially be an effective tool in preventing students from low-income families from dropping out of higher education. (Next, in order to compare dropout rates across applicants and nonapplicants together, a non-standard regression discontinuity design will be considered.)