Counterfactual Copula and Its Application to the Effects of College Education on Intergenerational Mobility

This paper proposes a nonparametric estimator of the counterfactual copula of two outcome variables that would be affected by a policy intervention. The proposed estimator allows policymakers to conduct ex-ante evaluations by comparing the estimated counterfactual and actual copulas as well as their corresponding measures of association. Asymptotic properties of the counterfactual copula estimator are established under regularity conditions. These conditions are also used to validate the nonparametric bootstrap for inference on counterfactual quantities. Simulation results indicate that our estimation and inference procedures perform well in moderately sized samples. Applying the proposed method to studying the effects of college education on intergenerational income mobility under two counterfactual scenarios, we find that while providing some college education to all children is unlikely to promote mobility, offering a college degree to children from less educated families can significantly reduce income persistence across generations.