Heterogeneous effects of welfare reforms on earnings, transfers, and income have been established theoretically and empirically. Evaluation studies often focus on quantile treatment effects (QTE), which rely on the marginal distributions of potential treatment and control outcomes. Parameters that depend on the joint distribution of potential outcomes, such as quantiles of the distribution of treatment effects (QDTE), receive less attention. We propose a strategy to identify these parameters. We leverage the property that, under random assignment, rank correlation coefficients between actual treatment and predicted control state outcomes are identical, irrespective of whether predictions are based on treatment or control units. To identify QDTE, we assume that all permutations of observation units satisfying this property are equally likely. Rearranging quantiles yields a generalized version of quantile treatment effects (GQTE). We employ a reweighting approach for identification under strong ignorability. We test the predictor strength and demonstrate that highly predictive covariates yield unbiased, consistent, and asymptotically normal estimators. Our analysis of Connecticut’s Jobs First program reveals initial income increases for a larger fraction of participants than previously recognized. Long-term gains were at least twice as large as those derived from conventional QTE and concentrated at the lower end of the distribution.