  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.
