Matching as an econometric evaluation estimator: Evidence from evaluating a job training programme
Matching as an econometric evaluation estimator: Evidence from evaluating a job training programme
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DOI：
10.2307/2971733
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发表时间：
1997-10-01
影响因子：
5.8
通讯作者：
Todd, PE
中科院分区：
文献类型：
--
作者：
Heckman, JJ;Ichimura, H;Todd, PE
本文考虑是否有可能设计一个非实验性的程序来评估一个典型的工作培训计划。使用丰富的非实验数据，我们研究的性能的两个阶段的评估方法，（a）估计的概率，一个人参加一个程序和（B）使用估计的概率在扩展的经典匹配方法。我们分解成几个组成部分的常规措施的方案评价偏差，并发现由于选择不可观察的偏差，通常被称为选择偏差计量经济学，是经验上的重要性低于其他组件，虽然它仍然是一个相当大的一部分，估计方案的影响。对位于与参与者相同的劳动力市场的比较组采用匹配方法，并进行相同的问卷调查，消除了传统测量的大部分偏差，但剩余的偏差是实验确定的方案影响估计的相当大的一部分。我们测试和拒绝识别的假设，证明经典的匹配方法。我们提出了一个非参数条件的差异中的差异扩展的匹配方法，这是与经典的指数充分的样本选择模型是一致的，并没有被拒绝，我们的测试识别假设。该估计量在消除偏差方面是有效的，特别是当它是由于时间不变的遗漏变量。
This paper considers whether it is possible to devise a nonexperimental procedure for evaluating a prototypical job training programme. Using rich nonexperimental data, we examine the performance of a two-stage evaluation methodology that (a) estimates the probability that a person participates in a programme and (b) uses the estimated probability in extensions of the classical method of matching. We decompose the conventional measure of programme evaluation bias into several components and find that bias due to selection on unobservables, commonly called selection bias in econometrics, is empirically less important than other components, although it is still a sizeable fraction of the estimated programme impact. Matching methods applied to comparison groups located in the same labour markets as participants and administered the same questionnaire eliminate much of the bias as conventionally measured, but the remaining bias is a considerable fraction of experimentally-determined programme impact estimates. We test and reject the identifying assumptions that justify the classical method of matching. We present a nonparametric conditional difference-in-differences extension of the method of matching that is consistent with the classical index-sufficient sample selection model and is not rejected by our tests of identifying assumptions. This estimator is effective in eliminating bias, especially when it is due to temporally-invariant omitted variables.