Events

Prof. Juan Pantano
Professor of Economics
University of Arizona

The re-interpretation of instrumental variable estimators as Local Average Treatment Effects (LATE) is one of the most important econometric developments of the last few decades. The popularity of this framework relies on its simplicity and credibility, along with the transparency of its identification assumptions. The exclusion restriction plays a key role in the identification of LATE (Imbens & Angrist (1994), Angrist, Imbens & Rubin (1996)). We discuss a particularly ubiquitous way in which the exclusion restriction would seem to be generically violated. We argue that this form of violation is not addressed in the many applications that rely on this influential framework. We characterize the bias that this particular violation gives rise to and, more constructively, discuss how to use the particular structure of the violation along with milder assumptions and additional data to restore identification. We illustrate with examples and discuss why this violation is present in most existing applications. We discuss how our arguments naturally extend to other settings where the LATE parameter is commonly invoked, such as randomized controlled trials with imperfect compliance and fuzzy regression discontinuity designs. Moving beyond LATE, we also consider how the same ideas apply to identification of the MTE profile and more structural “Roy” models of treatment effects.