Afrouz Azadikhah Jahromi, Brantly Callaway
arXiv 8 Jun 2020 · Econometrics · publishedEmpirical Economics (2019) · 1 citations (OpenAlex)
arXiv:2006.04968 · PDF · DOI · OpenAlex · Extracted main text
This paper considers how the effect of job displacement varies across different individuals. In particular, our interest centers on features of the distribution of the individual-level effect of job displacement. Identifying features of this distribution is particularly challenging -- e.g., even if we could randomly assign workers to be displaced or not, many of the parameters that we consider would not be point identified. We exploit our access to panel data, and our approach relies on comparing outcomes of displaced workers to outcomes the same workers would have experienced if they had not been displaced and if they maintained the same rank in the distribution of earnings as they had before they were displaced. Using data from the Displaced Workers Survey, we find that displaced workers earn about $157 per week less, on average, than they would have earned if they had not been displaced. We also find that there is substantial heterogeneity. We estimate that 42% of workers have higher earnings than they would have had if they had not been displaced and that a large fraction of workers have experienced substantially more negative effects than the average effect of displacement. Finally, we also document major differences in the distribution of the effect of job displacement across education levels, sex, age, and counterfactual earnings levels. Throughout the paper, we rely heavily on quantile regression. First, we use quantile regression as a flexible (yet feasible) first step estimator of conditional distributions and quantile functions that our main results build on. We also use quantile regression to study how covariates affect the distribution of the individual-level effect of job displacement.
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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 | Farber, Henry S (2017) Employment, hours, and earnings consequences of job loss: US evidence from the displaced workers survey | 1.000 | 10 | 4 | 100% |
| 2 | Callaway, Brantly, Li, Tong, Oka, Tatsushi (2018) Quantile treatment effects in difference in differences models under dependence restrictions and with only two time periods self | 1.000 | 7 | 4 | 100% |
| 3 | Melly, Blaise, Santangelo, Giulia (2015) The changes-in-changes model with covariates | 0.928 | 4 | 3 | 100% |
| 4 | Athey, Susan, Imbens, Guido (2006) Identification and inference in nonlinear difference-in-differences models | 0.843 | 3 | 3 | 100% |
| 5 | Heckman, James, Smith, Jeffrey, Clements, Nancy (1997) Making the most out of programme evaluations and social experiments: Accounting for heterogeneity in programme impacts | 0.644 | 2 | 2 | 100% |
| 6 | (2019) A closed-form estimator for quantile treatment effects with endogeneity | 0.644 | 2 | 2 | 100% |
| 7 | Rothe, Christoph, Wied, Dominik (2013) Misspecification testing in a class of conditional distributional models | 0.585 | 3 | 1 | 100% |
| 8 | Callaway, Brantly (2020) Bounds on distributional treatment effect parameters using panel data with an application on job displacement self | 0.511 | 2 | 1 | 100% |
| 9 | Jacobson, Louis S, LaLonde, Robert J, Sullivan, Daniel G (1993) Earnings losses of displaced workers | 0.511 | 2 | 1 | 100% |
| 10 | Abadie, Alberto (2003) Semiparametric instrumental variable estimation of treatment response models | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 38 scored citations.