On Quantile Treatment Effects, Rank Similarity, and Multiple IVs
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Series
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SpeakersSukjin Han (University of Bristol, United Kingdom)
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FieldEconometrics
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LocationUniversity of Amsterdam, room E5.22
Amsterdam -
Date and time
October 21, 2022
12:30 - 13:30
Abstract
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 and can be tested with the data. To motivate the new identifying
assumption, we first provide a novel way of characterizing popular
assumptions restricting treatment heterogeneity in the literature,
specifically rank similarity assumptions. 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
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 that still yield tight policy evaluation and are easy
to implement.