2020/08/18 by Brantly Callaway, Callaway, Brantly · 1 citation
Economics, Econometrics and Finance · Mathematics · #Advanced Causal Inference Techniques #Econometrics (econ.EM) #FOS: Economics and business #Health Systems, Economic Evaluations, Quality of Life #Healthcare Policy and Management
paper · pdf · doi:10.48550/arxiv.2008.08117
openalex publication_date 2020/08/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This paper develops new techniques to bound distributional treatment effect\nparameters that depend on the joint distribution of potential outcomes -- an\nobject not identified by standard identifying assumptions such as selection on\nobservables or even when treatment is randomly assigned. I show that panel data\nand an additional assumption on the dependence between untreated potential\noutcomes for the treated group over time (i) provide more identifying power for\ndistributional treatment effect parameters than existing bounds and (ii)\nprovide a more plausible set of conditions than existing methods that obtain\npoint identification. I apply these bounds to study heterogeneity in the effect\nof job displacement during the Great Recession. Using standard techniques, I\nfind that workers who were displaced during the Great Recession lost on average\n34 % of their earnings relative to their counterfactual earnings had they not\nbeen displaced. Using the methods developed in the current paper, I also show\nthat the average effect masks substantial heterogeneity across workers.\n