arXiv 18 Aug 2020 · Econometrics · publishedJournal of Econometrics (2020) · 1 citations (OpenAlex)
arXiv:2008.08117 · PDF · DOI · OpenAlex · Extracted main text
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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Athey, Susan, Imbens, Guido (2006) Identification and inference in nonlinear difference-in-differences models | 1.000 | 5 | 3 | 100% |
| 2 | Firpo, Sergio, Ridder, Geert (2019) Partial identification of the treatment effect distribution and its functionals | 0.941 | 6 | 5 | 83% |
| 3 | Fan, Yanqin, Park, Sang Soo (2009) Partial identification of the distribution of treatment effects and its confidence sets | 0.928 | 4 | 3 | 100% |
| 4 | Fan, Yanqin, Park, Sang Soo (2010) Sharp bounds on the distribution of treatment effects and their statistical inference | 0.888 | 10 | 5 | 70% |
| 5 | Hong, Han, Li, Jessie (2018) The numerical delta method | 0.874 | 6 | 2 | 100% |
| 6 | Fan, Yanqin, Guerre, Emmanuel, Zhu, Dongming (2017) Partial identification of functionals of the joint distribution of potential outcomes | 0.843 | 4 | 4 | 75% |
| 7 | Heckman, James, Smith, Jeffrey, Clements, Nancy (1997) Making the most out of programme evaluations and social experiments: Accounting for heterogeneity in programme impacts | 0.811 | 4 | 2 | 100% |
| 8 | Melly, Blaise, Santangelo, Giulia (2015) The changes-in-changes model with covariates | 0.811 | 4 | 2 | 100% |
| 9 | Joe, Harry (1997) Multivariate Models and Multivariate Dependence Concepts | 0.737 | 3 | 3 | 67% |
| 10 | Fang, Zheng, Santos, Andres (2019) Inference on directionally differentiable functions | 0.737 | 3 | 2 | 100% |
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