Local Average Treatment Effect and Regression-Discontinuity-Design

Abstract

This chapter addresses two different but related approaches, both widely used within the literature on the econometrics of program evaluation: the Local average treatment effect (LATE) and the Regression-discontinuity-design (RDD). Considered as nearly quasi-experimental methods, these approaches have recently been the subject of a vigorous interest as tools for detecting treatment effects within special statistical settings. The first part of the chapter covers the theory behind LATE, thus illustrating how such approach can be embedded within the setting of a randomized experiment with imperfect compliance. The discussion then goes on to present the Wald estimator of LATE, and to extend LATE to the case of multiple instruments and multiple treatments. The second part of the chapter illustrates the RDD econometric theoretical background; in particular, it discusses separately sharp RDD and fuzzy RDD, and suggests a protocol for the empirical implementation of such approaches. The chapter ends with some illustrative empirical implementation of both LATE and RDD performed using Stata on both real and simulative examples.