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Data and Code for: Methods Matter: P-Hacking and Publication Bias in Causal Analysis in Economics

Version
1
Resource Type
Dataset : other
Creator
  • Brodeur, Abel (University of Ottawa)
  • Cook, Nikolai (University of Ottawa)
  • Heyes, Anthony (University of Ottawa)
Publication Date
2020-09-15
Free Keywords
p-hacking; publication bias; research methods; causal inference; p-curves
Description
  • Abstract

    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: (1) Papers published in the `Top 5' journals are different to other; (2) The journal revise and resubmit process mitigates the problem; (3) Things are improving through time.
Temporal Coverage
  • 2015-01-01 / 2018-12-31
    Time Period: Thu Jan 01 00:00:00 EST 2015--Mon Dec 31 00:00:00 EST 2018 (2015 and 2018)
Availability
Download
This study is freely available to the general public via web download.
Relations
  • Is version of
    DOI: 10.3886/E120246
Publications
  • Brodeur, Abel, Nikolai Cook, and Anthony Heyes. “Methods Matter: P-Hacking and Publication Bias in Causal Analysis in Economics.” American Economic Review, n.d.

Update Metadata: 2020-09-15 | Issue Number: 1 | Registration Date: 2020-09-15

Brodeur, Abel; Cook, Nikolai; Heyes, Anthony (2020): Data and Code for: Methods Matter: P-Hacking and Publication Bias in Causal Analysis in Economics. Version: 1. ICPSR - Interuniversity Consortium for Political and Social Research. Dataset. https://doi.org/10.3886/E120246V1