Inference for treatment effects of job training on wages: using bounds to compute Fisher’s exact p-value

German Blanco and Michela Bia

Applied Economics Letters, 2019, vol. 26, issue 17, 1424-1428

Abstract: In the context of a training program’s randomized evaluation, where estimating wage effects is of interest, we propose employing bounds that control for sample selection as a model-based statistic to conduct randomization-based inference à la Fisher. Inference is based on a sharp null hypothesis of no treatment effect for anyone. In contrast to conventional inference, Fisher p-values are nonparametric and do not employ large sample approximations.

Date: 2019
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/13504851.2018.1564113 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:apeclt:v:26:y:2019:i:17:p:1424-1428

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/RAEL20

DOI: 10.1080/13504851.2018.1564113

Access Statistics for this article

Applied Economics Letters is currently edited by Anita Phillips

More articles in Applied Economics Letters from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().