犯罪与误测的惩罚：存在误分类时的边际处理效应

Review of Economics and Statistics · 2023 · [{"name": "Vítor Possebom", "affiliation": ["Yale University", "Escola de Economia de São Paulo"]}]

中文摘要

当处理变量存在误分类时，我对边际处理效应（MTE）进行部分识别。我探讨了两种约束条件，允许工具变量与误分类决策之间存在依赖关系。如果倾向得分导数的符号相同，我便可识别出 MTE 的符号；如果这些导数相似，我则对 MTE 进行定界。为加以说明，我分析了替代性刑罚（罚款和社区服务，相对于不予惩罚）对巴西再犯的影响，在该国，法院上诉程序会导致误分类。估计的误分类偏差可能高达最大可能 MTE 的 10%，且该定界区间包含正确估计的 MTE。

Abstract

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.