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Methods Matter: p-Hacking and Publication Bias in Causal Analysis in Economics

Abel Brodeur, Nikolai Cook, Anthony HeyesEconomics计量经济学FT50
American Economic Review2020-10-28University of Ottawa; University of ExeterDOI
Citations314
Influential9
References46
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The credibility revolution in economics has promoted causal identification using randomized control trials (RCT), difference-in-differences (DID), instrumental variables (IV) and regression discontinuity design (RDD). Applying multiple approaches to over 21,000 hypothesis tests published in 25 leading economics journals, we find that the extent of p-hacking and publication bias varies greatly by method. IV (and to a lesser extent DID) are particularly problematic. We find no evidence that (i) papers published in the Top 5 journals are different to others; (ii) the journal “revise and resubmit” process mitigates the problem; (iii) things are improving through time. (JEL A14, C12, C52)

CredibilityRegression discontinuity designInstrumental variablePublication biasHackerCausal inferenceOmitted-variable biasEconomicsIdentification (biology)EconometricsRandomized experimentSelection bias