论文标题
差异分类的差异治疗
Difference-in-Differences with a Misclassified Treatment
论文作者
论文摘要
本文研究了平均治疗效果对治疗（ATT）差异（DID）设计的平均治疗效果的识别和估计，当将个人分为治疗组和对照组（治疗状态，D）的变量被内源分类时。我们表明，在d篮子中的错误分类一致地估算ATT，因为1）它限制了我们从被误解的那些被误解的人中识别出的治疗方法和2）2）反事实趋势中的差异分类可能会导致与D的趋势违反D时，即使他们持有真实但未观察到的D*。我们提出了一种解决方案，以在参数的背景下校正内源性的单方面错误分类，从而可以在治疗效果中具有相当多的异质性，并在面板和重复的横截面设置中建立其渐近性。此外，我们通过使用该方法来估计印度大规模实物食品转移计划的保险影响来说明该方法，该计划众所周知，该计划遭受了较大的目标错误。
This paper studies identification and estimation of the average treatment effect on the treated (ATT) in difference-in-difference (DID) designs when the variable that classifies individuals into treatment and control groups (treatment status, D) is endogenously misclassified. We show that misclassification in D hampers consistent estimation of ATT because 1) it restricts us from identifying the truly treated from those misclassified as being treated and 2) differential misclassification in counterfactual trends may result in parallel trends being violated with D even when they hold with the true but unobserved D*. We propose a solution to correct for endogenous one-sided misclassification in the context of a parametric DID regression which allows for considerable heterogeneity in treatment effects and establish its asymptotic properties in panel and repeated cross section settings. Furthermore, we illustrate the method by using it to estimate the insurance impact of a large-scale in-kind food transfer program in India which is known to suffer from large targeting errors.
