Bounding program benefits when participation is misreported
指出当项目参与存在误报时，标准工具变量方法无法正确识别因果效应，并提出了三种工具变量策略来界定项目收益，最后用新Stata命令ivbounds分析了401(k)养老金计划对储蓄的影响。
Instrumental variables (IV) are commonly used to estimate treatment effects in case of noncompliance. However, program participation is often misreported in survey data and standard techniques are not sufficient to point identify and consistently estimate the effects of interest. In this paper, we show that the identifiable IV estimand that ignores treatment misclassification is a weighted average of local average treatment effects with weights that can also be negative. This is troublesome because it may fail to deliver a correct causal interpretation, and this is true even if all the weights are non-negative. Therefore, we provide three IV strategies to bound the program benefits when both noncompliance and misreporting are present. We demonstrate the gain of identification power achieved by leveraging multiple exogenous variations when discrete or multiple-discrete IVs are available. At last, we use our new Stata command, ivbounds, to study the benefits of participating in the 401(k) pension plan on savings.