Santiago Acerenza, Vitor Possebom, Pedro H. C. Sant'Anna
arXiv 23 Nov 2023 · Econometrics · 1 citations (OpenAlex)
arXiv:2311.13969 · PDF · DOI · OpenAlex · Extracted main text
We identify the distributional and quantile marginal treatment effect functions when the outcome is right-censored. Our method requires a conditionally exogenous instrument and random censoring. We propose asymptotically consistent semi-parametric estimators and valid inferential procedures for the target functions. To illustrate, we evaluate the effect of alternative sentences (fines and community service vs. no punishment) on recidivism in Brazil. Our results highlight substantial treatment effect heterogeneity: we find that people whom most judges would punish take longer to recidivate, while people who would be punished only by strict judges recidivate at an earlier date than if they were not punished.
appendix boundary found by appendix_command · 27% of the source is main text. Read the extracted text to check this.
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 | Carneiro and Lee (2009) Estimating distributions of potential outcomes using local instrumental variables with an application to changes in college enro… | 1.000 | 7 | 5 | 100% |
| 2 | Heckman, Urzua and Vytlacil (2006) Understanding Instrumental Variables in Models with Essential Heterogeneity | 0.961 | 9 | 5 | 89% |
| 3 | Giles (2023) The Government Revenue, Recidivism, and Financial Health Effects of Criminal Fines and Fees | 0.941 | 6 | 4 | 83% |
| 4 | Delgado, Garcia-Suaza and Sant'Anna (2022) Distribution Regression in Duration Analysis:an Application to Unemployment Spells self | 0.928 | 5 | 4 | 80% |
| 5 | Huttunen, Kaila and Nix (2020) The Punishment Ladder: Estimating the Impact of Different Punishments on Defendant Outcomes | 0.909 | 8 | 5 | 75% |
| 6 | Frandsen (2015) Treatment Effects with Censoring and Endogeneity | 0.899 | 11 | 4 | 73% |
| 7 | Agan, Doleac and Harvey (2023) Misdemeanor Prosecution | 0.894 | 7 | 5 | 71% |
| 8 | Possebom (2023) Crime and Mismeasured Punishment: Marginal Treatment Effect with Misclassification self | 0.888 | 10 | 6 | 70% |
| 9 | Lieberman, Luh and Mueller-Smith (2023) Criminal Court Fees, Earnings and Expenditures: A Multi-State RD Analysis of Survey and Administrative Data | 0.874 | 5 | 2 | 100% |
| 10 | Bhuller, Dahl, Loken and Mogstad (2020) Incaceration, Recidivism, and Employment | 0.830 | 7 | 6 | 57% |
Showing the top 10 of 62 scored citations.