On Quantile Treatment Effects, Rank Similarity, and the Variation of Instrumental Variables
This paper investigates how certain relationship between observed and counterfactual distributions plays a role in the identification of distributional treatment effects under endogeneity, and shows that this relationship holds in a range of nonparametric models for treatment effects. To motivate the new identifying assumption, we first provide a novel characterization of popular assumptions restricting treatment heterogeneity in the literature, specifically rank similarity. We show the stringency of this type of assumptions and propose to relax them in economically meaningful ways. This relaxation will justify certain parameters (e.g., treatment effects on the treated) against others (e.g., treatment effects for the entire population). It will also justify the quest of richer exogenous variation in the data (e.g., the use of multiple or multi-valued instrumental variables). The prime goal of this investigation is to provide empirical researchers with tools for identifying and estimating treatment effects that are flexible enough to allow for treatment heterogeneity, but still yield tight policy evaluation and are easy to implement.
Date: 24 February 2023, 14:15 (Friday, 6th week, Hilary 2023)
Venue: Manor Road Building, Manor Road OX1 3UQ
Venue Details: Seminar Room A or https://zoom.us/j/93054414699?pwd=NEFiL2ZNc0t5N3ZIUTE2VEh5OXhZUT09
Speaker: Sukjin Han (University of Bristol)
Organising department: Department of Economics
Part of: Nuffield Econometrics Seminar
Booking required?: Not required
Audience: Public
Editor: Emma Heritage