Working Papers

This article investigates the impact of judges' gender on the outcome of domestic violence cases. Using data From São Paulo, Brazil, between 2010 and 2019, we compare conviction rates by judge's gender and find that a domestic violence case assigned to a female judge is 31% (10 p.p) more likely to result in conviction than a case assigned to a male judge with similar career characteristics. To show that this decision gap rises due to different gender perspectives about domestic violence instead of rising due to female judges being tougher than male judges, we compare it against gender conviction rate gaps in similar types of crimes. We find that the gender conviction rate gap for domestic violence cases is significantly larger than the same gap for other misdemeanor cases (3 p.p. larger) and for other physical assault cases (8 p.p. larger). Lastly, we find evidence that at least two channels explain this gender conviction rate gap for domestic violence cases: gender-based differences in evidence interpretation and gender-based sentencing criteria.

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 inferences on the distributional, quantile, and average marginal treatment effects in setups where the treatment selection is endogenous and the outcome of interest, usually a duration variable, is potentially right-censored. We explore our proposed econometric methodology to evaluate the effect of fines and community service sentences as a form of punishment on time-to-recidivism in the State of São Paulo, Brazil, between 2010 and 2019, leveraging the as-if random assignment of judges to cases. Our results highlight substantial treatment effect heterogeneity that other tools are not meant to capture. For instance, we find that people who most judges would punish take longer to recidivate as a consequence of the punishment, while people who would be punished only by strict judges recidivate at an earlier date than if they were not punished. This result suggests that designing sentencing guidelines that encourage strict judges to become more lenient could reduce recidivism.

Presented at Brown University, University of Chicago, Yale University, LACEA-LAMES 2023, Workshop on the Econ of Crime for Junior Scholars.

In this paper, we investigate the impact of information disclosure on price-quality relationship in the private school market in Brazil. We use the disclosure policy on scores of an exit exam in Brazil (ENEM) that took place in 2006. We construct a novel longitudinal data set on private schools that includes information on ENEM average scores in the years before and after its publication and on tuition fees for all years. We show that the correlation between test scores and price significantly becomes positive after the publication and increases over the years. Furthermore, this correlation becomes stronger for schools whose quality was noisily perceived previously.

Presented at the 36th Meeting of the Brazilian Econometric Society (2014), the VII Encontro CAEN-EPGE de Políticas Públicas e Crescimento Econômico (2015), the 20th Annual Meeting of the Latin American and Caribbean Economic Association (2015).