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

TL;DR

This paper introduces new bounding techniques for distributional treatment effects using panel data, improving identification power and plausibility over existing methods, with an application to job displacement effects during the Great Recession.

Contribution

It develops novel bounds for distributional treatment effects leveraging panel data and dependence assumptions, enhancing identification over traditional bounds.

Findings

Panel data bounds improve identification of treatment effects.

Displaced workers lost on average 34% of earnings.

Heterogeneity in effects varies substantially across workers.

Abstract

This paper develops new techniques to bound distributional treatment effect parameters that depend on the joint distribution of potential outcomes -- an object not identified by standard identifying assumptions such as selection on observables or even when treatment is randomly assigned. I show that panel data and an additional assumption on the dependence between untreated potential outcomes for the treated group over time (i) provide more identifying power for distributional treatment effect parameters than existing bounds and (ii) provide a more plausible set of conditions than existing methods that obtain point identification. I apply these bounds to study heterogeneity in the effect of job displacement during the Great Recession. Using standard techniques, I find that workers who were displaced during the Great Recession lost on average 34\% of their earnings relative to their counterfactual earnings had they not been displaced. Using the methods developed in the current paper, I also show that the average effect masks substantial heterogeneity across workers.