Data and Code for: Unpacking P-hacking and Publication Bias

Principal Investigator(s): View help for Principal Investigator(s) Abel Brodeur, University of Ottawa; Scott Carrell, University of of Texas at Austin; David Figlio, University of Rochester; Lester Lusher, University of Pittsburgh

Version: View help for Version V1

Name	File Type	Size	Last Modified
Code			10/25/2023 10:39:PM
Confidential-Code			06/23/2023 10:56:AM
Data			10/25/2023 10:37:PM
Figures			10/25/2023 10:46:PM
README.pdf	application/pdf	265 KB	10/25/2023 06:31:PM

Project Citation:

Brodeur, Abel, Carrell, Scott, Figlio, David, and Lusher, Lester. Data and Code for: Unpacking P-hacking and Publication Bias. Nashville, TN: American Economic Association [publisher], 2023. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2023-10-26. https://doi.org/10.3886/E192271V1

Project Description

Summary: 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.

Scope of Project

Subject Terms: Publication bias; p-hacking

JEL Classification:
      A11 Role of Economics; Role of Economists; Market for Economists
      C13 Estimation: General
      C40 Econometric and Statistical Methods: Special Topics: General

Time Period(s): 2013 – 2018

Data Type(s): other

Methodology

Data Source:

Our sample of data from the JHR contains all manuscripts submitted for review from 2013 to 2018. During this time frame, there were 2,365 submissions that were desk rejected, 1,018 submissions that were rejected after receiving reviewer recommendation (i.e., “reviewer rejections”), and 223 (eventually) accepted manuscripts. We then keep a random sample of 250 desk rejections, 250 reviewer rejections, and all 223 accepted manuscripts, stratified by year of submission. Lastly, upon reading the paper, we removedmanuscripts which did not contain a clear experimental or quasi-experimental statistical inference (difference in differences, instrumental variables, regression discontinuity, and/or randomized control trials and experiments)

Unit(s) of Observation: Test statistic

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Published Versions

V1 [2023-10-26]

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