Dalia Ghanem, Désiré Kédagni, Ismael Mourifié
arXiv 7 Jun 2023 · Econometrics · 1 citations (OpenAlex)
arXiv:2306.04494 · PDF · DOI · OpenAlex · Extracted main text
Quantifying the impact of regulatory policies on social welfare generally requires the identification of counterfactual distributions. Many of these policies (e.g. minimum wages or minimum working time) generate mass points and/or discontinuities in the outcome distribution. Existing approaches in the difference-in-difference literature cannot accommodate these discontinuities while accounting for selection on unobservables and non-stationary outcome distributions. We provide a unifying partial identification result that can account for these features. Our main identifying assumption is the stability of the dependence (copula) between the distribution of the untreated potential outcome and group membership (treatment assignment) across time. Exploiting this copula stability assumption allows us to provide an identification result that is invariant to monotonic transformations. We provide sharp bounds on the counterfactual distribution of the treatment group suitable for any outcome, whether discrete, continuous, or mixed. Our bounds collapse to the point-identification result in Athey and Imbens (2006) for continuous outcomes with strictly increasing distribution functions. We illustrate our approach and the informativeness of our bounds by analyzing the impact of an increase in the legal minimum wage using data from a recent minimum wage study (Cengiz, Dube, Lindner, and Zipperer, 2019).
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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 and Imbens (2006) Identification and Inference in Nonlinear Difference-in-Differences Models | 1.000 | 16 | 4 | 100% |
| 2 | Callaway and Li (2019) Quantile treatment effects in difference in differences models with panel data | 1.000 | 6 | 3 | 100% |
| 3 | Roth and Sant'Anna (2023) When is parallel trends sensitive to functional form? | 0.950 | 7 | 3 | 86% |
| 4 | Cengiz, Dube, Lindner, and Zipperer (2019) The Effect of Minimum Wages on Low-Wage Jobs* | 0.902 | 15 | 6 | 73% |
| 5 | Embrechts and Hofert (2013) A note on generalized inverses | 0.874 | 12 | 4 | 67% |
| 6 | Nelsen (2006) An Introduction to Copulas | 0.843 | 5 | 3 | 60% |
| 7 | Aaberge, Havnes, and Mogstad (2013) A theory for ranking distribution functions | 0.803 | 9 | 2 | 78% |
| 8 | Mehran (1976) Linear Measures of Income Inequality | 0.737 | 3 | 2 | 100% |
| 9 | Weymark (1981) Generalized gini inequality indices | 0.644 | 2 | 2 | 100% |
| 10 | Wooldridge (2023) Simple approaches to nonlinear difference-in-differences with panel data | 0.550 | 8 | 2 | 25% |
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arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.