arXiv 9 Oct 2020 · Econometrics · publishedJournal of Econometrics (2023)
arXiv:2010.04385 · PDF · DOI · OpenAlex · Extracted main text
In this paper, we establish sufficient conditions for identifying treatment effects on continuous outcomes in endogenous and multi-valued discrete treatment settings with unobserved heterogeneity. We employ the monotonicity assumption for multi-valued discrete treatments and instruments, and our identification condition has a clear economic interpretation. In addition, we identify the local treatment effects in multi-valued treatment settings and derive closed-form expressions of the identified treatment effects. We provide examples to illustrate the usefulness of our result.
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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 | Mountjoy, J (2022) Community colleges and upward mobility | 1.000 | 14 | 3 | 100% |
| 2 | Vuong, Q. and H. Xu (2017) Counterfactual mapping and individual treatment effects in nonseparable models with binary endogeneity | 1.000 | 9 | 4 | 100% |
| 3 | Heckman, J. J. and R. Pinto (2018) Unordered Monotonicity | 1.000 | 8 | 4 | 100% |
| 4 | Pinto, R (2022) Beyond Intention to Treat: Using the Incentives in Moving to Opportunity to Identify Neighborhood Effects, UCLA, unpublished man… | 1.000 | 7 | 3 | 100% |
| 5 | Kirkeboen, L. J., E. Leuven, and M. Mogstad (2016) Field of Study, Earnings, and Self-Selection | 1.000 | 6 | 3 | 100% |
| 6 | Imbens, G. W. and J. D. Angrist (1994) Identification and Estimation of Local Average Treatment Effects | 1.000 | 5 | 3 | 100% |
| 7 | Wüthrich, K (2019) A closed-form estimator for quantile treatment effects with endogeneity | 0.950 | 7 | 5 | 86% |
| 8 | Chernozhukov, V. and C. Hansen (2005) An IV Model of Quantile Treatment Effects | 0.869 | 32 | 7 | 66% |
| 9 | Chesher, A (2005) Nonparametric identification under discrete variation | 0.585 | 3 | 1 | 100% |
| 10 | Heckman, J. J., S. Urzua, and E. Vytlacil (2006) Understanding Instrumental Variables in Models with Essential Heterogeneity | 0.511 | 2 | 1 | 100% |
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