论文标题
解释具有未观察到治疗异质性的仪器变量估计值：大学教育的影响
Interpreting Instrumental Variable Estimands with Unobserved Treatment Heterogeneity: The Effects of College Education
论文作者
论文摘要
经验应用中使用的许多治疗变量嵌套了多个未观察到的治疗版本。我表明，复合处理效果的仪器变量（IV）估计值是未观察到的组件处理效果的IV特异性加权平均值。即使没有治疗效应异质性，未观察到的组件依从性中IV之间的差异也会在IV估计中产生差异。我描述了一种单调性条件，在该条件下，IV估计值是未观察到的组件治疗效果的正加权平均值。接下来，我开发了一种允许违反这种情况的仪器，可以通过允许非概念，结果不变的权重对多个结果的未观察到的组件处理来估算治疗效果的估计。最后，我将方法应用于大学的回报，发现在生命周期中的工资收益率从7 \％到30 \％。我的发现强调了利用工具变量的重要性，这些变量不会改变个人在治疗版本之间的重要性，以及鼓励学生参加“高回报学院”的政策的重要性，除了鼓励“高回报学生”上学的人。
Many treatment variables used in empirical applications nest multiple unobserved versions of a treatment. I show that instrumental variable (IV) estimands for the effect of a composite treatment are IV-specific weighted averages of effects of unobserved component treatments. Differences between IVs in unobserved component compliance produce differences in IV estimands even without treatment effect heterogeneity. I describe a monotonicity condition under which IV estimands are positively-weighted averages of unobserved component treatment effects. Next, I develop a method that allows instruments that violate this condition to contribute to estimation of treatment effects by allowing them to place nonconvex, outcome-invariant weights on unobserved component treatments across multiple outcomes. Finally, I apply the method to estimate returns to college, finding wage returns that range from 7\% to 30\% over the life cycle. My findings emphasize the importance of leveraging instrumental variables that do not shift individuals between versions of treatment, as well as the importance of policies that encourage students to attend "high-return college" in addition to those that encourage "high-return students" to attend college.
