Likelihood-Based Analysis of Causal Effects of Job-Training Programs Using Principal Stratification
利用主分层对职业培训项目因果效应进行似然分析AI 解读 ↓
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分类依据与纠错
AI 根据所提供的论文材料归类：摘要明确通过主分层估计培训的工资因果效应，并处理就业导致的结果截断，直接对应因果推断。
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利用主分层对职业培训项目因果效应进行似然分析
- 研究问题
- 工资只对就业者有定义时，如何估计职业培训的工资因果效应，并区分培训与未培训状态下不同潜在就业类型的人群？
- 方法与思路
- 作者以主分层定义就业类型、工资目标及类型比例，用 EM 算法进行似然分析，并以缩放似然比和敏感性分析考察子模型。
- 材料陈述的贡献
- 摘要称方法区分始终就业者的 SACE 等目标；Job Corps 应用提示四类人群均存在，与通常的单调性假设不相容。
- 阅读关注点
- 应核对就业截断下各目标的定义、潜在类型与工资分布的识别，以及参数假设、似然比统计量和敏感性分析范围。
- 阅读边界
- 仅据版本未注明的 OpenAlex 索引摘要；识别条件及效应数值未说明，四类人群存在是模型分析提示，不能视为直接观测事实。
原文摘要
Government-sponsored job-training programs must be subject to evaluation to assess whether their effectiveness justifies their cost to the public. The evaluation usually focuses on employment and total earnings, although the effect on wages is also of interest, because this effect reflects the increase in human capital due to the training program, whereas the effect on total earnings may be simply reflecting the increased likelihood of employment without any effect on wage rates. Estimating the effects of training programs on wages is complicated by the fact that, even in a randomized experiment, wages are “truncated” (or less accurately “censored”) by nonemployment, that is, they are only observed and well-defined for individuals who are employed. In this article, we develop a likelihood-based approach to estimate the wage effect of the US federally-funded Job Corps training program using “Principal Stratification”. Our estimands are formulated in terms of: (1) the effect of the training program on wages for those who would be employed whether they were trained or not, also called the survivor average causal effect (SACE), and the proportion of people in this category; (2) the wages when trained for those who would be employed only when trained, and the proportion of people in this category; (3) the wages when not trained for those who would be employed only when not trained, and the proportion of people in this category; (4) the proportion of people who would be not employed whether trained or not. We conduct likelihood-based analysis using the EM algorithm, and investigate the plausibility of important submodels with scaled log-likelihood ratio statistics. We also conduct a sensitivity analysis with respect to specific parametric assumptions. Our results suggest that all four types of people [(1)–(4) previously] exist, which is impossible under the usual monotonicity assumptions made in traditional econometric evaluation methods.