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This paper proposes a new framework to evaluate unconditional quantileeffects (UQE) in a data combination model. The UQE measures the effect of amarginal counterfactual change in the unconditional distribution of a covariateon quantiles of the unconditional distribution of a target outcome. Under ranksimilarity and conditional independence assumptions, we provide a set ofidentification results for UQEs when the target covariate is continuouslydistributed and when it is discrete, respectively. Based on theseidentification results, we propose semiparametric estimators and establishtheir large sample properties under primitive conditions. Applying our methodto a variant of Mincer's earnings function, we study the counterfactualquantile effect of actual work experience on income.

Two Sample Unconditional Quantile Effect