Training, Wages, and Sample Selection: Estimating Sharp Bounds on Treatment Effects
针对随机实验中的样本选择问题，提出一种无需排除限制或结果变量有界支撑的修剪方法，用于估计培训项目对工资的平均处理效应边界，并以美国Job Corps项目为例说明该方法的应用。
This paper empirically assesses the wage effects of the Job Corps program, one of the largest federally funded job training programs in the U.S. Even with the aid of a randomized experiment, the impact of a training program on wages is difficult to study because of sample selection, a pervasive problem in applied microeconometric research. Wage rates are only observed for those who are employed, and employment status itself may be affected by the training program. This paper develops an intuitive trimming procedure for bounding average treatment effects in the presence of sample selection. In contrast to existing methods, the procedure requires neither exclusion restrictions nor a bounded support for the outcome of interest. Identification results, estimators, and their asymptotic distribution are presented. The bounds suggest that the program raised wages, consistent with the notion that the Job Corps raises earnings by increasing human capital, rather than solely through encouraging work. The estimator is generally applicable to typical treatment evaluation problems in which there is nonrandom sample selection/attrition. Copyright Copyright © 2009 The Review of Economic Studies Limited.