A Model of Unconditional Quantile Treatment Effects in the Presence of Covariates

Posted: 6 Dec 2010 Last revised: 25 Dec 2010

Date Written: December 6, 2010

Abstract

Many economic applications have found quantile models useful when the explanatory variables may have varying impacts throughout the distribution of the outcome variable. Traditional quantile estimators provide conditional quantile treatment effects. Typically, we are interested in unconditional quantiles, characterizing the distribution of the outcome variable for different values of the treatment variables. Conditioning on additional covariates, however, may be necessary for identification of these treatment effects. With conditional quantile models, the inclusion of additional covariates changes the interpretation of the estimates. This paper discusses identification of unconditional quantile treatment effects when it is necessary or simply desirable to condition on covariates. It discusses identification for both exogenous and endogenous treatment variables, which can be discrete or continuous, without functional form assumptions.

Keywords: Unconditional Quantile Treatment Effects, Quantile Regression, Instrumental Variables

JEL Classification: C14, C31, C51

Suggested Citation

Powell, David, A Model of Unconditional Quantile Treatment Effects in the Presence of Covariates (December 6, 2010). RAND Working Paper Series WR- 816, Available at SSRN: https://ssrn.com/abstract=1721208 or http://dx.doi.org/10.2139/ssrn.1721208

David Powell (Contact Author)

RAND Corporation ( email )

1776 Main Street
P.O. Box 2138
Santa Monica, CA 90407-2138
United States

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