Crime and Mismeasured Punishment: Marginal Treatment Effect with Misclassification
研究了当处理变量存在分类错误时，如何部分识别边际处理效应，并应用于巴西替代刑罚对再犯的影响，发现误测偏差可能高达最大可能MTE的10%。
Abstract I partially identify the marginal treatment effect (MTE) when the treatment is misclassified. I explore two restrictions, allowing for dependence between the instrument and the misclassification decision. If the signs of the propensity scores’ derivatives are equal, I identify the MTE sign. If those derivatives are similar, I bound the MTE. To illustrate, I analyze the impact of alternative sentences (fines and community service versus no punishment) on recidivism in Brazil, where court appeals processes generate misclassification. The estimated misclassification bias may be as large as 10% of the largest possible MTE, and the bounds contain the correctly estimated MTE.