Two Sample Unconditional Quantile Effect

TL;DR

This paper introduces a new framework for evaluating how marginal changes in covariates affect the unconditional quantiles of an outcome, with identification results and estimators under rank similarity and independence assumptions.

Contribution

It provides a novel approach to measure unconditional quantile effects using a data combination model, including identification results and semiparametric estimators.

Findings

Applied to Mincer's earnings function, revealing the impact of work experience on income quantiles.

Developed estimators with proven large sample properties under key assumptions.

Extended the analysis to both continuous and discrete covariates.

Abstract

This paper proposes a new framework to evaluate unconditional quantile effects (UQE) in a data combination model. The UQE measures the effect of a marginal counterfactual change in the unconditional distribution of a covariate on quantiles of the unconditional distribution of a target outcome. Under rank similarity and conditional independence assumptions, we provide a set of identification results for UQEs when the target covariate is continuously distributed and when it is discrete, respectively. Based on these identification results, we propose semiparametric estimators and establish their large sample properties under primitive conditions. Applying our method to a variant of Mincer's earnings function, we study the counterfactual quantile effect of actual work experience on income.