犯罪与误测惩罚：误分类下的边际处理效应
Crime and Mismeasured Punishment: Marginal Treatment Effect with Misclassification
AI总结：
本文在处理变量误分类情形下部分识别边际处理效应，利用工具变量与误分类决策的相关性限制，识别MTE符号或界定其范围，并以巴西替代刑罚对累犯影响为例，估计误分类偏差可达最大MTE的10%。
摘要：
当处理变量存在误分类时，我对边际处理效应（MTE）进行了部分识别。我探讨了两种限制条件，允许工具变量与误分类决策之间存在相关性。若倾向得分导数的符号相同，我可识别MTE的符号；若这些导数相近，我可界定MTE的范围。为说明这一点，我分析了巴西替代刑罚（罚金和社区服务 vs. 不惩罚）对累犯的影响，其中上诉过程产生了误分类。估计的误分类偏差可能高达最大可能MTE的10%，且所界定的范围包含了正确估计的MTE。
英文摘要：
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 derivatives of the propensity scores 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 v. no punishment) on recidivism in Brazil, where 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
