A practical generalized propensity-score estimator for quantile continuous treatment effects

Abstract

In this article, we present a new command, qcte, that implements several methods for estimation and inference for quantile treatment-effects models with a continuous treatment. We propose a semiparametric two-step estimator, where the first step is based on a flexible Box–Cox model, as the default model of the command. We develop practical statistical inference procedures using bootstrap. We implement some simulations to show that the proposed methods perform well. Finally, we apply qcte to a survey of Massachusetts lottery winners to estimate the unconditional quantile effects of the prize amount, as a proxy of nonlabor income changes, on subsequent labor earnings from U.S. Social Security records. The empirical results reveal strong heterogeneity across unconditional quantiles.