Using data from criminal cases in the State of São Paulo, Brazil, I analyze whether alternative sentences --- e.g., fines or community services --- decrease recidivism. To do so, I leverage the random assignment of judges within a court district as a source of exogenous variation in the probability of punishment to identify the effect of alternative sentences in comparison to the no-punishment counterfactual. Initially, I show that the usual identification strategy, that uses only the trial judge's sentence, fails to identify the correct treatment effect parameter because the trial judge's decision may misclassify the final sentence due to the appeals process. To avoid this measurement error problem, I follow two approaches. First, I propose a novel partial identification strategy to identify the marginal treatment effect (MTE) with a misclassified treatment. This method explores restrictions on the relationship between the misclassified treatment and the correctly measured treatment, allowing for dependence between the instrument and the potential misclassified treatment variables and the misreporting decision. Second, I collect data on Appeals Court's decisions and estimate the MTE based on the correctly measured final sentence. This last exercise is used as a benchmark for the set identification method that I propose.
This draft is preliminary. While its theoretical part is complete, its empirical application is not ready yet.
2. Identifying Marginal Treatment Effects in the Presence of Sample Selection (with Otávio Bartalotti and Désiré Kédagni - R&R at the Journal of Econometrics - Working Paper - IZA Discussion Paper Version)
This article presents identification results for the marginal treatment effect (MTE) when there is sample selection. We show that the MTE is partially identified for individuals who are always observed regardless of treatment, and derive uniformly sharp bounds on this parameter under three increasingly restrictive sets of assumptions. The first result imposes standard MTE assumptions with an unrestricted sample selection mechanism. The second set of conditions imposes monotonicity of the sample selection variable with respect to treatment, considerably shrinking the identified set. Finally, we incorporate a stochastic dominance assumption which tightens the lower bound for the MTE. Our analysis extends to discrete instruments. The results rely on a mixture reformulation of the problem where the mixture weights are identified, extending Lee's (2009) trimming procedure to the MTE context. We propose estimators for the bounds derived and use data made available by Deb, Munkin, and Trivedi (2006) to empirically illustrate the usefulness of our approach.
Presented at the 2019 Bristol Econometrics Study Group and the 42nd Meeting of the Brazilian Econometric Society (2020).
Won the Prize of Best Econometric Article presented at the 42nd Meeting of the Brazilian Econometric Society (2020).
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).