Identification of and Correction for Publication Bias
NBER Working Paper No. 23298
Some empirical results are more likely to be published than others. Such selective publication leads to biased estimates and distorted inference. This paper proposes two approaches for identifying the conditional probability of publication as a function of a study’s results, the first based on systematic replication studies and the second based on meta-studies. For known conditional publication probabilities, we propose median-unbiased estimators and associated confidence sets that correct for selective publication. We apply our methods to recent large-scale replication studies in experimental economics and psychology, and to meta-studies of the effects of minimum wages and de-worming programs.
Document Object Identifier (DOI): 10.3386/w23298
Published: Isaiah Andrews & Maximilian Kasy, 2019. "Identification of and Correction for Publication Bias," American Economic Review, vol 109(8), pages 2766-2794. citation courtesy of
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