Going beyond LATE
推导了工具变量下平均处理效应的非参数边界，并用于评估Job Corps培训项目对弱势青年的效果，发现该项目显著提高了收入、就业率并减少了福利依赖。
We derive nonparametric sharp bounds on average treatment effects with an instrumental variable (IV) and use them to evaluate the effectiveness of the Job Corps training program for disadvantaged youth. We focus on the population average treatment effect (ATE) and the average treatment effect on the treated (ATT), which are parameters not point identified with an IV under heterogeneous treatment effects. The main assumptions employed to bound the ATE and ATT are monotonicity in the treatment of the average outcomes of specified subpopulations, and mean dominance assumptions across the potential outcomes of these subpopulations. Importantly, the direction of the mean dominance assumptions can be informed from data, and some of our bounds do not require an outcome with bounded support. We employ these bounds to assess the effectiveness of Job Corps using data from a randomized social experiment with non-compliance (a common feature of social experiments). Our empirical results indicate that the effect of Job Corps on eligible applicants (the target population) four years after randomization is to increase weekly earnings and employment by at least $24:61 and 4:3 percentage points, respectively, and to decrease yearly dependence on public welfare benefits by at least $84:29. Furthermore, the effect of Job Corps on participants (the treated population) is to increase weekly earnings by between $28:67 and $43:47, increase employment by between 4:9 and 9:3 percentage points, and decrease public benefits received by between $108:72 and $140:29. Finally, some of our results point to positive average effects of Job Corps on the labor market outcomes of those individuals who decide not to enroll in Job Corps regardless of their treatment assignment (the so-called never takers), suggesting that these individuals would benefit from participating in Job Corps.