Combining Matching and Nonparametric Instrumental Variable Estimation: Theory and An Application to the Evaluation of Active Labour Market Policies
结合匹配与非参数工具变量估计：理论及积极劳动力市场政策评估AI 解读 ↓
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结合匹配与非参数工具变量估计：理论及积极劳动力市场政策评估
- 研究问题
- 如何将工具变量与匹配结合，把处理效应分析从服从者扩展至始终接受和从不接受处理者，并据此考察政策分配机制？
- 方法与思路
- 作者结合工具变量估计与匹配，识别不同处理选择类型的效应，并应用于瑞士积极劳动力市场项目；摘要未说明具体匹配算法和识别假设。
- 材料陈述的贡献
- 摘要称，服从者就业效应较大且持久，始终及从不参与者效应较小；结合未处理就业结果，作者认为原有个案工作人员的分配政策效率不足。
- 阅读关注点
- 阅读全文时应核对工具变量及各服从类型的定义、扩展效应识别所需额外条件、匹配支持域，以及如何由就业结果推导分配效率判断。
- 阅读边界
- 摘要未说明工具变量来源、匹配所需条件、共同支持或估计不确定性；对始终与从不参与者的效应识别不能视为一般工具变量结果。
原文摘要
Summary We show how instrumental variable and matching estimators can be combined in order to identify a broader array of treatment effects. Instrumental variable (IV) estimators are known to estimate effects only for the compliers, representing a subset of the entire population. By combining IV with matching, we can estimate the treatment effects for the always‐ and never‐takers as well. Since in many cases these groups are the (endogenous) outcome of some assignment process, such estimates also help in judging the implications of such a selection process. In our application to the effects of participation in active labour market programmes in Switzerland, we find large and lasting positive employment effects for the compliers, whereas the effects for the always‐ and never‐participants are small. In addition, the compliers have worse employment outcomes without treatment than those who participate in the programme with or without the intervention under investigation. This suggests that the earlier assignment policy of the caseworkers was inefficient in that the always‐participants were neither those unemployed who would experience the highest expected treatment effects nor those unemployed who had the largest need for assistance. Copyright © 2014 John Wiley & Sons, Ltd.