Vitor Possebom
@vitorpossebom.bsky.social
Assistant Professor at Sao Paulo Schools of Economics (FGV). Ph.D. 2022 at Yale. Passionate about Econometrics, especially Causal Inference. (He/his)
https://sites.google.com/site/vitorapossebom/
https://sites.google.com/site/vitorapossebom/
Should I install Windows 11 instead of Windows S? It is a bit annoying that Windows S does not support many softwares, including Zoom…
December 31, 2023 at 7:36 PM
Should I install Windows 11 instead of Windows S? It is a bit annoying that Windows S does not support many softwares, including Zoom…
Thank you for the suggestion! It looks cool!
December 3, 2023 at 5:57 PM
Thank you for the suggestion! It looks cool!
The paper also discusses other available methods in the literature, alternative assumptions, and decision-theoretical reasons to focus on time-to-recidivism as an outcome variable.
Check the full paper here: arxiv.org/abs/2311.13969
Check the full paper here: arxiv.org/abs/2311.13969
Was Javert right to be suspicious? Unpacking treatment effect...
This paper presents new econometric tools to unpack the treatment effect heterogeneity of punishing misdemeanor offenses on time-to-recidivism. We show how one can identify, estimate, and make...
arxiv.org
November 27, 2023 at 12:48 PM
The paper also discusses other available methods in the literature, alternative assumptions, and decision-theoretical reasons to focus on time-to-recidivism as an outcome variable.
Check the full paper here: arxiv.org/abs/2311.13969
Check the full paper here: arxiv.org/abs/2311.13969
Our QMTE point estimates suggest that designing sentencing guidelines that encourage strict judges to become more lenient could lead to increasing time-to-recidivism.
November 27, 2023 at 12:47 PM
Our QMTE point estimates suggest that designing sentencing guidelines that encourage strict judges to become more lenient could lead to increasing time-to-recidivism.
These point estimates
suggest that defendants whom almost all judges would punish would take longer to recidivate,
while defendants who would be punished only by tough judges would recidivate faster compared
to situations where they would not be punished.
suggest that defendants whom almost all judges would punish would take longer to recidivate,
while defendants who would be punished only by tough judges would recidivate faster compared
to situations where they would not be punished.
November 27, 2023 at 12:47 PM
These point estimates
suggest that defendants whom almost all judges would punish would take longer to recidivate,
while defendants who would be punished only by tough judges would recidivate faster compared
to situations where they would not be punished.
suggest that defendants whom almost all judges would punish would take longer to recidivate,
while defendants who would be punished only by tough judges would recidivate faster compared
to situations where they would not be punished.
All QMTE functions behave similarly. Although the level of the estimated QMTEs depends on the quantile, all functions are decreasing in the unobserved resistance to punishment.
November 27, 2023 at 12:47 PM
All QMTE functions behave similarly. Although the level of the estimated QMTEs depends on the quantile, all functions are decreasing in the unobserved resistance to punishment.
To have a deeper understanding of the time trade-offs associated with the effect of punishment on time-to-recidivism, we now focus on the QMTE. They use easy-to-interpret units (days before recidivism) and may work as good summary measures.
November 27, 2023 at 12:46 PM
To have a deeper understanding of the time trade-offs associated with the effect of punishment on time-to-recidivism, we now focus on the QMTE. They use easy-to-interpret units (days before recidivism) and may work as good summary measures.
This rich heterogeneity is relevant. If two researchers were to focus only on a specific but different
time frame they may get different answers, highlighting that the practice of “binarizing” duration outcomes may come with caveats.
time frame they may get different answers, highlighting that the practice of “binarizing” duration outcomes may come with caveats.
November 27, 2023 at 12:46 PM
This rich heterogeneity is relevant. If two researchers were to focus only on a specific but different
time frame they may get different answers, highlighting that the practice of “binarizing” duration outcomes may come with caveats.
time frame they may get different answers, highlighting that the practice of “binarizing” duration outcomes may come with caveats.
But things change a lot in the long-run! The DMTEs are decreasing. This indicates that defendants whom almost all judges would punish would recidivate faster because of the punishment, while defendants who would be punished only by tough judges would take longer
to recidivate.
to recidivate.
November 27, 2023 at 12:45 PM
But things change a lot in the long-run! The DMTEs are decreasing. This indicates that defendants whom almost all judges would punish would recidivate faster because of the punishment, while defendants who would be punished only by tough judges would take longer
to recidivate.
to recidivate.
The DMTEs are increasing in the short-run. This behavior indicates that defendants whom almost all judges would punish are less likely to recidivate, while defendants who would be punished only by tough judges are more likely to recidivate.
November 27, 2023 at 12:45 PM
The DMTEs are increasing in the short-run. This behavior indicates that defendants whom almost all judges would punish are less likely to recidivate, while defendants who would be punished only by tough judges are more likely to recidivate.
For inference, you can use the weighted bootstrap! Piece of cake!
And we do show that everything is consistent and asymptotic normal!
Now, it is time to estimate the effect of misdemeanor punishment on time-to-recidivism.
And we do show that everything is consistent and asymptotic normal!
Now, it is time to estimate the effect of misdemeanor punishment on time-to-recidivism.
November 27, 2023 at 12:45 PM
For inference, you can use the weighted bootstrap! Piece of cake!
And we do show that everything is consistent and asymptotic normal!
Now, it is time to estimate the effect of misdemeanor punishment on time-to-recidivism.
And we do show that everything is consistent and asymptotic normal!
Now, it is time to estimate the effect of misdemeanor punishment on time-to-recidivism.
We use a conditional local IV estimand to identify the distributional treatment responses. After that, we can get the DMTE and the QMTE.
Our estimation procedure is a bit long, but super easy to implement! You only need to run a few logit models and take some averages!
Our estimation procedure is a bit long, but super easy to implement! You only need to run a few logit models and take some averages!
November 27, 2023 at 12:44 PM
We use a conditional local IV estimand to identify the distributional treatment responses. After that, we can get the DMTE and the QMTE.
Our estimation procedure is a bit long, but super easy to implement! You only need to run a few logit models and take some averages!
Our estimation procedure is a bit long, but super easy to implement! You only need to run a few logit models and take some averages!
We do so by looking at the Distributional MTE and the Quantile MTE of time-to-recidivism, a continuous variable.
We impose Random Assignment and Censoring. The last assumption imposes that the sentence date is independent of the defendant’s decision to commit another crime.
We impose Random Assignment and Censoring. The last assumption imposes that the sentence date is independent of the defendant’s decision to commit another crime.
November 27, 2023 at 12:43 PM
We do so by looking at the Distributional MTE and the Quantile MTE of time-to-recidivism, a continuous variable.
We impose Random Assignment and Censoring. The last assumption imposes that the sentence date is independent of the defendant’s decision to commit another crime.
We impose Random Assignment and Censoring. The last assumption imposes that the sentence date is independent of the defendant’s decision to commit another crime.
To look at long-run recidivism, our Econometric tools need to appropriately address right-censoring.
We also want to use the MTE to analyze heterogeneity with respect to the defendants’ punishment resistance.
So we need to develop new tools combining MTE and duration outcomes!
We also want to use the MTE to analyze heterogeneity with respect to the defendants’ punishment resistance.
So we need to develop new tools combining MTE and duration outcomes!
November 27, 2023 at 12:42 PM
To look at long-run recidivism, our Econometric tools need to appropriately address right-censoring.
We also want to use the MTE to analyze heterogeneity with respect to the defendants’ punishment resistance.
So we need to develop new tools combining MTE and duration outcomes!
We also want to use the MTE to analyze heterogeneity with respect to the defendants’ punishment resistance.
So we need to develop new tools combining MTE and duration outcomes!
First, long-run recidivism matters in São Paulo, Brazil. A non-negligible share of recidivism events happens 6-8 years after the judge’s decision.
So analyzing the effect of judges’ decision on long-run recidivism may matter when discussing sentencing guidelines.
So analyzing the effect of judges’ decision on long-run recidivism may matter when discussing sentencing guidelines.
November 27, 2023 at 12:42 PM
First, long-run recidivism matters in São Paulo, Brazil. A non-negligible share of recidivism events happens 6-8 years after the judge’s decision.
So analyzing the effect of judges’ decision on long-run recidivism may matter when discussing sentencing guidelines.
So analyzing the effect of judges’ decision on long-run recidivism may matter when discussing sentencing guidelines.
Misdemeanor punishment (fines) can have heterogeneous effects on recidivism depending on the defendants’ types and time horizon.
We explore these sources of heterogeneity by looking at the marginal treatment effect of misdemeanor punishment on recidivism at many time horizons.
We explore these sources of heterogeneity by looking at the marginal treatment effect of misdemeanor punishment on recidivism at many time horizons.
November 27, 2023 at 12:41 PM
Misdemeanor punishment (fines) can have heterogeneous effects on recidivism depending on the defendants’ types and time horizon.
We explore these sources of heterogeneity by looking at the marginal treatment effect of misdemeanor punishment on recidivism at many time horizons.
We explore these sources of heterogeneity by looking at the marginal treatment effect of misdemeanor punishment on recidivism at many time horizons.
Thank you for the kind words!
November 17, 2023 at 9:07 PM
Thank you for the kind words!