arXiv 29 May 2021 · Econometrics · publishedThe Review of Economics and Statistics (2023) · 3 citations (OpenAlex)
arXiv:2106.00536 · PDF · DOI · OpenAlex · Extracted main text
I partially identify the marginal treatment effect (MTE) when the treatment is misclassified. I explore two restrictions, allowing for dependence between the instrument and the misclassification decision. If the signs of the derivatives of the propensity scores are equal, I identify the MTE sign. If those derivatives are similar, I bound the MTE. To illustrate, I analyze the impact of alternative sentences (fines and community service v. no punishment) on recidivism in Brazil, where Appeals processes generate misclassification. The estimated misclassification bias may be as large as 10% of the largest possible MTE, and the bounds contain the correctly estimated MTE.
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| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Frandsen, B. R., L. J. Lefgren, and E. C. Leslie (2019, February) (2019) Judging Judge Fixed Effects | 0.928 | 4 | 3 | 100% |
| 2 | Heckman, J. J., S. Urzua, and E. Vytlacil (2006) Understanding Instrumental Variables in Models with Essential Heterogeneity | 0.865 | 17 | 7 | 65% |
| 3 | Calvi, R., A. Lewbel, and D. Tommasi (2021, April) (2021) Women's Empowerment and Family Health: Estimating LATE with Mismeasured Treatment | 0.855 | 8 | 6 | 62% |
| 4 | Tommasi, D. and L. Zhang (2020, June) (2020) Bounding Program Benefits When Participation Is Misreported | 0.830 | 7 | 5 | 57% |
| 5 | Ura, T (2018) Heterogeneous Treatment Effects with Mismeasured Endogenous Treatment | 0.763 | 9 | 5 | 44% |
| 6 | Arellano-Bover, J. (2020, May) (2020) Displacement, Diversity, and Mobility: Career Impacts of Japanese American Internment | 0.737 | 3 | 3 | 67% |
| 7 | Bhuller, M., G. B. Dahl, K. V. Loken, and M. Mogstad (2020) Incaceration, Recidivism, and Employment | 0.737 | 3 | 2 | 100% |
| 8 | Klaassen, F. D. (2021, November) (2021) Crime and (Monetary) Punishment | 0.737 | 3 | 2 | 100% |
| 9 | Acerenza, S., K. Ban, and D. Kédagni (2022) Marginal Treatment Effects with Misclassified Treatment | 0.705 | 20 | 6 | 35% |
| 10 | Cinelli, C. and C. Hazlett (2019) Making Sense of Sensitivity: Extending Omitted Variable Bias | 0.644 | 2 | 2 | 100% |
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