Poorly measured confounders are more useful on the left than on the right / Zhuan Pei (Cornell University and IZA), Jörn-Steffen Pischke (London School of Economics and IZA), Hannes Schwandt (University of Zürich and IZA) ; IZA, Institute of Labor Economics
VerfasserPei, Zhuan ; Pischke, Jörn-Steffen ; Schwandt, Hannes
KörperschaftForschungsinstitut zur Zukunft der Arbeit
ErschienenBonn, Germany : IZA Institute of Labor Economics, March 2017
Elektronische Ressource
Umfang1 Online-Ressource (67 Seiten) : Diagramme
SerieDiscussion paper ; no. 10647
 Das Dokument ist öffentlich zugänglich im Rahmen des deutschen Urheberrechts.
Poorly measured confounders are more useful on the left than on the right [0.92 mb]
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Researchers frequently test identifying assumptions in regression based research designs (which include instrumental variables or difference-in-differences models) by adding additional control variables on the right hand side of the regression. If such additions do not affect the coefficient of interest (much) a study is presumed to be reliable. We caution that such invariance may result from the fact that the observed variables used in such robustness checks are often poor measures of the potential underlying confounders. In this case, a more powerful test of the identifying assumption is to put the variable on the left hand side of the candidate regression. We provide derivations for the estimators and test statistics involved, as well as power calculations, which can help applied researchers interpret their findings. We illustrate these results in the context of various strategies which have been suggested to identify the returns to schooling.