  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.
