Matching As An Econometric Evaluation Estimator: Evidence from Evaluating a Job Training Programme

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ID: 289457
1997
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Abstract
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.
Reference Key
openalex_W2066735350 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors James J. Heckman, Hidehiko Ichimura, Petra Todd
Journal The Review of Economic Studies
Year 1997
DOI
10.2307/2971733
URL
https://doi.org/10.2307/2971733
Keywords Keywords not found

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