J
James J. Heckman
Researcher at University of Chicago
Publications - 784
Citations - 167633
James J. Heckman is an academic researcher from University of Chicago. The author has contributed to research in topics: Earnings & Human capital. The author has an hindex of 175, co-authored 766 publications receiving 156816 citations. Previous affiliations of James J. Heckman include University College Dublin & American Bar Association.
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Journal ArticleDOI
Sample Selection Bias as a Specification Error
TL;DR: In this article, the bias that results from using non-randomly selected samples to estimate behavioral relationships as an ordinary specification error or "omitted variables" bias is discussed, and the asymptotic distribution of the estimator is derived.
Journal ArticleDOI
Matching As An Econometric Evaluation Estimator: Evidence from Evaluating a Job Training Programme
TL;DR: This paper decompose the conventional measure of 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.
Journal ArticleDOI
Matching As An Econometric Evaluation Estimator
TL;DR: In this article, a rigorous distribution theory for kernel-based matching is presented, and the method of matching is extended to more general conditions than the ones assumed in the statistical literature on the topic.
Book ChapterDOI
The Economics and Econometrics of Active Labor Market Programs
TL;DR: In this paper, the authors examine the impacts of active labor market policies, such as job training, job search assistance, and job subsidies, and the methods used to evaluate their effectiveness.