Simple Tests for Selection: Learning More from Instrumental Variables
Dan Black,
Joonhwi Joo,
Robert LaLonde,
Jeffrey Smith and
Evan Taylor
Additional contact information
Joonhwi Joo: University of Texas at Dallas
Evan Taylor: Center for the Economics of Human Development
No 2020-048, Working Papers from Human Capital and Economic Opportunity Working Group
Abstract:
We provide simple tests for selection on unobserved variables in the Vytlacil-Imbens-Angrist framework for Local Average Treatment Effects (LATEs). Our setup allows researchers not only to test for selection on either or both of the treated and untreated outcomes, but also to assess the magnitude of the selection effect. We show that it applies to the standard binary instrument case, as well as to experiments with imperfect compliance and fuzzy regression discontinuity designs, and we link it to broader discussions regarding instrumental variables. We illustrate the substantive value-added by our framework with three empirical applications drawn from the literature.
Keywords: instrumental variables; local average treatment effect; selection; test (search for similar items in EconPapers)
JEL-codes: C10 D04 (search for similar items in EconPapers)
Date: 2020-06
Note: IP, MIP
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)
Downloads: (external link)
http://humcap.uchicago.edu/RePEc/hka/wpaper/Black_ ... tion-learning-iv.pdf First version, June 13, 2020 (application/pdf)
Related works:
Journal Article: Simple Tests for Selection: Learning More from Instrumental Variables (2022) 
Working Paper: Simple Tests for Selection: Learning More from Instrumental Variables (2022) 
Working Paper: Simple Tests for Selection: Learning More from Instrumental Variables (2017) 
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