{"title":"Identifying program benefits when participation is misreported","authors":"Denni Tommasi, Lina Zhang","doi":"10.1002/jae.3079","DOIUrl":null,"url":null,"abstract":"SummaryIn cases of noncompliance with an assigned treatment, estimates of causal effects typically rely on instrumental variables (IV). However, when participation is also misreported, the IV estimand may become a nonconvex combination of local average treatment effects that fails to satisfy even a minimal condition for being causal. The aim of our paper is to generalize the MR‐LATE approach. This is an alternative IV estimand that is more robust in cases of noncompliance and nondifferential misclassification of the treatment variable. Our generalization is threefold: First, we incorporate discrete and multiple‐discrete instrument(s); second, we consider the use of instrument(s) under a weaker, partial monotonicity condition; third, we provide a general inferential procedure. Under relatively stringent assumptions, MR‐LATE is either identical to the IV estimand or less biased than the naïve IV estimand. Under less stringent assumptions, the MR‐LATE estimand can identify the sign of the IV estimand. We conclude with the use of a dedicated Stata command, <jats:styled-content>ivreg2m,</jats:styled-content> to assess the return on education in the United Kingdom.","PeriodicalId":501243,"journal":{"name":"Journal of Applied Econometrics ","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Applied Econometrics ","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1002/jae.3079","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
SummaryIn cases of noncompliance with an assigned treatment, estimates of causal effects typically rely on instrumental variables (IV). However, when participation is also misreported, the IV estimand may become a nonconvex combination of local average treatment effects that fails to satisfy even a minimal condition for being causal. The aim of our paper is to generalize the MR‐LATE approach. This is an alternative IV estimand that is more robust in cases of noncompliance and nondifferential misclassification of the treatment variable. Our generalization is threefold: First, we incorporate discrete and multiple‐discrete instrument(s); second, we consider the use of instrument(s) under a weaker, partial monotonicity condition; third, we provide a general inferential procedure. Under relatively stringent assumptions, MR‐LATE is either identical to the IV estimand or less biased than the naïve IV estimand. Under less stringent assumptions, the MR‐LATE estimand can identify the sign of the IV estimand. We conclude with the use of a dedicated Stata command, ivreg2m, to assess the return on education in the United Kingdom.
摘要在不遵守指定治疗的情况下,因果效应的估计通常依赖于工具变量(IV)。然而,当参与情况也被误报时,IV 估计值可能会变成局部平均治疗效果的非凸组合,甚至无法满足因果关系的最低条件。我们的论文旨在推广 MR-LATE 方法。这是一种替代的 IV 估计方法,在出现不合规和治疗变量非差异性误分类的情况下更稳健。我们的概括有三个方面:首先,我们纳入了离散和多重离散工具;其次,我们考虑了在较弱的部分单调性条件下工具的使用;第三,我们提供了一个通用的推断程序。在相对严格的假设条件下,MR-LATE 要么与 IV 估计数相同,要么比天真的 IV 估计数偏差更小。在不太严格的假设条件下,MR-LATE 估计数可以识别 IV 估计数的符号。最后,我们使用专门的 Stata 命令 ivreg2m 来评估英国的教育回报率。