Displacement, Diversity, and Mobility: Career Impacts of Japanese American Internment

Arellano-Bover, Jaime

doi:10.2139/ssrn.3445820

Cited by 2 publications

(1 citation statement)

References 28 publications

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“…Estimating the effect of immigration or unionization also requires simultaneously addressing endogenous selection into treatment and misclassification as illustrated by Bernstein et al (2019) for immigration, and Card (1992) and Bollinger (1996) for unionization. Moreover, with the increasing availability of large data sets, prediction methods are being used to infer the treatment status in a variety of empirical question involving endogeneity problems, as illustrated by Black et al (2020) and Arellano-Bover (2020).…”

Section: Datementioning

confidence: 99%

Crime and Mismeasured Punishment: Marginal Treatment Effect with Misclassification

Possebom¹

2021

Preprint

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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 fails to identify the correct treatment effect parameter if it uses the trial judge's sentence, 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, and allows for dependence between the instrument and the measurement error. 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.

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