We extend the synthetic control method to evaluate the distributional effects of policy intervention in the possible presence of poor matching. The counterfactuals (or intervention effects) are identified by matching a vector of pre-intervention quantile residuals of the treated unit and a convex combination of its potential-control counterparts. The residuals are orthogonal to a set of observable common factors that control for the potentially poor matching. We also apply our method to a set of case studies that explore the distributional effects of state-level minimum-wage hikes in the USA.
