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This paper develops new techniques to bound distributional treatment effectparameters that depend on the joint distribution of potential outcomes -- anobject not identified by standard identifying assumptions such as selection onobservables or even when treatment is randomly assigned. I show that panel dataand an additional assumption on the dependence between untreated potentialoutcomes for the treated group over time (i) provide more identifying power fordistributional treatment effect parameters than existing bounds and (ii)provide a more plausible set of conditions than existing methods that obtainpoint identification. I apply these bounds to study heterogeneity in the effectof job displacement during the Great Recession. Using standard techniques, Ifind that workers who were displaced during the Great Recession lost on average34\% of their earnings relative to their counterfactual earnings had they notbeen displaced. Using the methods developed in the current paper, I also showthat the average effect masks substantial heterogeneity across workers.

Bounds on Distributional Treatment Effect Parameters using Panel Data with an Application on Job Displacement