Long‐Run Effects of Dynamically Assigned Treatments: A New Methodology and an Evaluation of Training Effects on Earnings
Econometrica · 2022 · Gérard J. van den Berg、Johan Vikström
中文摘要
我们提出并实施了一种估计处理效应的新方法，适用于以下情形：个体必须处于某一特定状态（如失业）才有资格接受处理，处理可能在不同时间点开始，而所关注的结果则在个体离开初始状态之后才实现。一个例子是培训对后续就业收入的影响。任何评估都需要考虑到，在失业期间某一时点尚未接受培训的人中，一些人会在接受培训前离开失业状态，而另一些人则会在之后接受培训。我们关注的是，在失业已经历某一特定时长时接受处理，相对于「此后任何时长均不接受处理」的效应。我们证明了在无混杂性假设下的识别，并提出逆概率加权估计量。该方法的一个关键特征是，赋予未处理者结果观测值的权重取决于其在初始状态中的剩余时间。我们研究了瑞典一项面向失业劳动者的培训计划的效应。估计结果为正且幅度较大，超过了采用常见静态方法所得的估计结果。这表明，有必要重新评估培训作为帮助失业者重返工作岗位之工具的作用。
Abstract
We propose and implement a new method to estimate treatment effects in settings where individuals need to be in a certain state (e.g., unemployment) to be eligible for a treatment, treatments may commence at different points in time, and the outcome of interest is realized after the individual left the initial state. An example concerns the effect of training on earnings in subsequent employment. Any evaluation needs to take into account that some of those who are not trained at a certain time in unemployment will leave unemployment before training while others will be trained later. We are interested in effects of the treatment at a certain elapsed duration compared to “no treatment at any subsequent duration.” We prove identification under unconfoundedness and propose inverse probability weighting estimators. A key feature is that weights given to outcome observations of nontreated depend on the remaining time in the initial state. We study effects of a training program for unemployed workers in Sweden. Estimates are positive and sizeable, exceeding those obtained with common static methods. This calls for a reappraisal of training as a tool to bring unemployed back to work.