Assessing Causal Effects in a Longitudinal Observational Study With “Truncated” Outcomes Due to Unemployment and Nonignorable Missing Data

Important statistical issues pervade the evaluation of training programs’ effects for unemployed people. In particular, the fact that offered wages are observed and well-defined only for subjects who are employed (truncation by death), and the problem that information on the individuals’ employment status and wage can be lost over time (attrition) raise methodological challenges for causal inference. We present an extended framework for simultaneously addressing the aforementioned problems, and thus answering important substantive research questions, in training evaluation observational studies with covariates, a binary treatment and longitudinal information on employment status and wage, which may be missing due to the lost to follow-up. There are two key features of this framework: we use principal stratification to properly define the causal effects of interest and to deal with nonignorable missingness, and we adopt a Bayesian approach for inference. The proposed framework allows us to answer an open issue in economics: the assessment of the trend of reservation wage over the duration of unemployment. We apply our framework to evaluate causal effects of foreign language training programs in Luxembourg, using administrative data on the labor force (IGSS-ADEM dataset). Our findings might be an incentive for the employment agencies to better design and implement future language training programs.

针对失业人群培训项目的效果评估，始终存在诸多关键统计难题。具体而言，仅当研究对象处于就业状态时，其获得的薪资才可被观测且明确定义（死亡截尾，truncation by death）；同时，随着时间推移，研究对象的就业状态与薪资信息可能出现缺失（失访，attrition），这两类问题均给因果推断带来了方法论层面的挑战。针对存在协变量、二分类干预、且就业状态与薪资的纵向信息可能因失访出现缺失的培训评估观察性研究，本文提出了一种可同时解决上述两类问题的拓展性分析框架，从而能够回答该领域内的一系列重要实质性研究问题。该框架具备两大核心特征：其一，采用主分层分析（principal stratification）合理定义所关注的因果效应，并处理不可忽略的缺失数据问题；其二，采用贝叶斯（Bayesian）方法开展统计推断。所提出的分析框架可用于解答经济学领域的一项开放性问题：即评估失业周期内保留工资（reservation wage）的变化趋势。本文将该框架应用于卢森堡的外语培训项目因果效应评估，所用数据为劳动力行政登记数据（IGSS-ADEM数据集）。本研究结论可为就业机构优化未来语言培训项目的设计与实施提供参考依据。