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

Resource type
Authors/contributors
Title
Methods Matter: p-Hacking and Publication Bias in Causal Analysis in Economics: Reply
Abstract
In Brodeur, Cook, and Heyes (2020) we present evidence that instrumental variable (and to a lesser extent difference-in-difference) articles are more p-hacked than randomized controlled trial and regression discontinuity design articles. We also find no evidence that (i) articles published in the top five journals are different; (ii) the "revise and resubmit" process mitigates the problem; (iii) things are improving through time. Kranz and Pütz (2022) apply a novel adjustment to address rounding errors. They successfully replicate our results with the exception of our shakiest finding: after adjusting for rounding errors, bunching of test statistics for difference-in-difference articles is now smaller around the 5 percent level (and coincidentally larger at the 10 percent level).
Publication
American Economic Review
Volume
112
Issue
9
Pages
3137-39
Date
2022-09
Citation
Brodeur, A., Cook, N., & Heyes, A. (2022). Methods Matter: p-Hacking and Publication Bias in Causal Analysis in Economics: Reply. American Economic Review, 112, 3137–3139.
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