Regression discontinuity (RD) designs are often interpreted as local randomized experiments: a RD design can be considered as a randomized experiment for units with a realized value of a so-called forcing variable falling around a pre-fixed threshold. Motivated by the evaluation of Italian university grants, we consider a fuzzy RD design 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 receiving the treatment can receive the treatment. We propose a probabilistic formulation of the assignment mechanism underlying RD designs within the Rubin Causal Model. The key to this framework is to re-formulate the Stable Unit Treatment Value Assumption (SUTVA) and make an explicit local overlap assumption for a subpopulation around the threshold. The commonly invoked continuity assumptions are replaced by a local randomization assumption in this framework. In addition, we show how to utilize the data on the application status to draw additional information for policy making, using principal stratification. A model-based Bayesian approach to draw inferences for the causal effects around the threshold is also developed. Applying the method to the data from two Italian universities, we find that university grants have a significant effect in preventing students from low-income families from dropping out of higher eduction.

Bayesian Inference for Regression Discontinuity Designs with Application to the Evaluation of Italian University Grants / Li F.; Mattei A.; Mealli F.. - ELETTRONICO. - (2013).

Bayesian Inference for Regression Discontinuity Designs with Application to the Evaluation of Italian University Grants

MATTEI, ALESSANDRA;MEALLI, FABRIZIA
2013

Abstract

Regression discontinuity (RD) designs are often interpreted as local randomized experiments: a RD design can be considered as a randomized experiment for units with a realized value of a so-called forcing variable falling around a pre-fixed threshold. Motivated by the evaluation of Italian university grants, we consider a fuzzy RD design 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 receiving the treatment can receive the treatment. We propose a probabilistic formulation of the assignment mechanism underlying RD designs within the Rubin Causal Model. The key to this framework is to re-formulate the Stable Unit Treatment Value Assumption (SUTVA) and make an explicit local overlap assumption for a subpopulation around the threshold. The commonly invoked continuity assumptions are replaced by a local randomization assumption in this framework. In addition, we show how to utilize the data on the application status to draw additional information for policy making, using principal stratification. A model-based Bayesian approach to draw inferences for the causal effects around the threshold is also developed. Applying the method to the data from two Italian universities, we find that university grants have a significant effect in preventing students from low-income families from dropping out of higher eduction.
2013
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/809085
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