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

German Blanco, Michela Bia

Research output: Contribution to journalArticlepeer-review


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.
Original languageEnglish
Pages (from-to)1424-1428
Number of pages5
JournalApplied Economics Letters
Issue number17
Publication statusPublished - 7 Oct 2019


  • nonparametric bounds
  • randomization inference
  • sample selection
  • training effects

Cite this