Unpacking P-Hacking and Publication Bias

Abel Brodeur, Scott Carrell, David Figlio and Lester Lusher

No 31548, NBER Working Papers from National Bureau of Economic Research, Inc

Abstract: We use unique data from journal submissions to identify and unpack publication bias and p-hacking. We find that initial submissions display significant bunching, suggesting the distribution among published statistics cannot be fully attributed to a publication bias in peer review. Desk-rejected manuscripts display greater heaping than those sent for review i.e. marginally significant results are more likely to be desk rejected. Reviewer recommendations, in contrast, are positively associated with statistical significance. Overall, the peer review process has little effect on the distribution of test statistics. Lastly, we track rejected papers and present evidence that the prevalence of publication biases is perhaps not as prominent as feared.

JEL-codes: A0 (search for similar items in EconPapers)
Date: 2023-08
New Economics Papers: this item is included in nep-ecm and nep-sog
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Published as Abel Brodeur & Scott Carrell & David Figlio & Lester Lusher, 2023. "Unpacking P-hacking and Publication Bias," American Economic Review, American Economic Association, vol. 113(11), pages 2974-3002, November.
Published as Abel Brodeur & Scott Carrell & David Figlio & Lester Lusher, 2023. "Unpacking p-Hacking and Publication Bias," American Economic Review, vol 113(11), pages 2974-3002.

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Related works:
Journal Article: Unpacking P-hacking and Publication Bias (2023)
Working Paper: Unpacking P-Hacking and Publication Bias (2023)
Working Paper: Unpacking P-Hacking and Publication Bias (2023)
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