Fan Li (Duke University)
12 April 2013 @ 12:00
- Past event
Bayesian inference for regression discontinuity designs with application to Italian university grants evaluations
Regression discontinuity (RD) designs are usually interpreted as local randomized experiments: A RD design can be considered as though it were a randomized experiment for units with a realized value of a so-called forcing variable falling immediately around a pre-fixed threshold. Motivated from the evaluation of Italian university grants, we consider a fuzzy RD where the receipt of the treatment is based on eligibility criteria and a voluntary application status: Only subjects who both meet eligibility criteria and apply for participating in the treatment can receive the treatment. The application status provides valuable information on the reasons underlying the receipt of the treatment, which is generally unknown to the researcher, and we show how to capitalize on it. We propose a probabilistic formulation of the assignment mechanism and develop a Bayesian approach to draw inferences for the causal effects around the threshold. Multivariate outcomes are utilized to further sharpen the analysis. The method is applied to evaluate the effects of Italian university grants on student dropout and academic performances. Posterior predictive model checks are also conducted to validate the analysis.
This is a joint work with Alessandra Mattei and Fabrizia Mealli.