The replication crisis that shook psychology and other social sciences over the past decade prompted economics to confront its own transparency gaps. What began as a niche methodological concern has become a standard expectation at most serious economics and management journals — and authors who prepare for it early avoid unpleasant surprises during review.
Why Replicability Became a Priority
Several high-profile failed replications of influential economics findings, combined with broader awareness of questionable research practices like selective reporting, pushed the discipline toward stronger transparency norms. Journals responded by formalizing requirements that used to be optional or informal.
What Data Transparency Typically Requires
- Data availability: Sharing the underlying dataset (or a detailed explanation of restrictions, if the data is proprietary or confidential) upon publication
- Code availability: Providing the analysis code used to produce published results, allowing others to reproduce your exact findings from your data
- Pre-registration (where applicable): For experimental or survey-based work, registering hypotheses and analysis plans before data collection, to distinguish confirmatory from exploratory analysis
Handling Confidential or Proprietary Data
Not all data can be shared openly — administrative records, proprietary firm data, or data covered by privacy regulations often can’t be posted publicly. Most journals accommodate this by requiring a clear data availability statement explaining the restriction and, where possible, describing how qualified researchers could access the data under similar conditions.
Replication Files: What Reviewers and Editors Expect
A well-prepared replication package typically includes cleaned data (or clear instructions for accessing it), all analysis code organized to run in a logical sequence, and a README file explaining how to reproduce each table and figure in the paper. Investing time in this before submission — rather than assembling it hastily after acceptance — tends to save considerable stress later.
Pre-Registration for Experimental and Survey Work
For randomized experiments and certain survey-based designs, pre-registering hypotheses and analysis plans on a platform like the AEA RCT Registry has become an increasingly expected practice, particularly for papers making causal claims from experimental data.
What This Means for Your Submission Strategy
Building transparency practices into your research process from the start — documenting data cleaning steps, organizing code clearly, and keeping careful records of any pre-registered plans — is far less burdensome than retrofitting these requirements after a paper is otherwise complete and ready for submission.
Transparency Strengthens, Rather Than Threatens, Good Research
Authors sometimes view these requirements as an added burden, but well-documented, replicable research is generally viewed more favorably by reviewers precisely because it signals confidence in the findings. Transparency and rigor tend to reinforce rather than compete with each other.
For detailed, widely referenced standards on data and code sharing in economics specifically, the American Economic Association’s Data and Code Availability Policy is a useful reference point even for journals outside the AEA family.
Preparing a replication package alongside your manuscript? Review the journal’s scope and submit through the paper submission page.