jpe-replication-package

name	jpe-replication-package
description	Use when assembling the data and code replication package for a Journal of Political Economy (JPE) manuscript to DCAS / JPE Data Editor standards for conditionally accepted papers, with deposit to the JPE Dataverse. Builds the package and README; it does not run the analysis itself.

Replication Package (jpe-replication-package)

When to trigger

• The paper is conditionally accepted and the JPE Data Editor needs a deposit
• You want the package to pass the JPE Data Editor's reproducibility check on the first pass
• Some data are proprietary or restricted and you must request an exemption and document access
• You are setting up the project early so reproducibility is not a last-minute scramble

Verify the current policy at journals.uchicago.edu/journals/jpe/datapolicy before depositing. JPE publishes a paper only if its data and code are documented and available for replication. JPE endorses DCAS (the Data and Code Availability Standard v1.0) and runs its own JPE Data Editor (jpedataeditor.github.io) — distinct from the AEA Data Editor that serves AER/AEJ. The package is verified at the conditional-accept stage and, once it passes, deposited to the JPE Dataverse. The deposit must carry a license allowing unrestricted access and use for replication. Exemptions for non-shareable data must be requested at first submission, and exempted authors must preserve the data and code for at least five years after publication.

What a passing package contains

1. README (the centerpiece) following the DCAS / Social Science Data Editors README template:
   • Overview of what the code does and the mapping from code → exhibits.
   • Data availability statement: source, terms, whether each dataset is public / restricted / proprietary, and exact access steps (including registrations, memberships, monetary and time costs). State clearly if data cannot be shared and why, referencing the exemption requested at first submission.
   • Computational requirements: software + versions, packages + versions, OS, memory, and approximate run time.
   • Instructions to run: a single master script ordering everything end to end.
   • List of every table/figure with the script and line that produces it.
2. Data: raw inputs (when license permits) and the code that builds analysis files from them. If raw data are restricted, include construction code plus a synthetic or pseudo dataset that lets the pipeline run.
3. Code: a master script that reproduces every number, table, and figure from raw inputs, with relative paths and fixed seeds.
4. Output: log files and generated exhibits, so the editor can diff against the paper.

Reproducibility discipline

• One master script; no manual steps, no hard-coded absolute paths, no "run cell 4 then cell 2."
• Set and record random seeds for any simulation, bootstrap, or ML step.
• Pin software and package versions; record them in the README and, where possible, in a lockfile/environment file.
• Every exhibit in the paper is regenerated by the code — no hand-edited tables.
• Directory layout is clean: data/ (raw, derived), code/ (build, analysis), output/ (tables, figures, logs).

Restricted / proprietary data (the JPE exemption route)

• Request the exemption at first submission, not at acceptance — JPE requires this timing.
• You may not need to deposit the data, but you must deposit the code and a precise access path so a third party with the same license can reproduce results.
• Provide a Data Availability Statement and, where feasible, a small simulated dataset matching the schema so the pipeline is executable.
• Commit to preserving the data and code for at least five years after publication, since they cannot go to the JPE Dataverse.
• Confidential-data results may require a verification arrangement with the JPE Data Editor; document it.

Checklist

• README follows the DCAS template (overview, data availability, requirements, run instructions, exhibit map)
• Deposit license allows unrestricted access and use for replication
• Single master script reproduces every table and figure from inputs
• Software and package versions pinned and recorded
• Random seeds set and documented
• Relative paths only; runs on a clean machine in a fresh directory
• Data availability statement covers each dataset (public / restricted / proprietary) with access steps and costs
• Restricted data: exemption requested at first submission + 5-year preservation commitment
• Package re-run from scratch and output diffed against the paper, ready for the JPE Data Editor
• Current JPE data policy (DCAS, JPE Dataverse) verified on the official page

Anti-patterns

• A zip of scripts with no README and no code → exhibit mapping
• Absolute paths (/Users/me/...) that break on any other machine
• Unset seeds so bootstrap/simulation numbers do not reproduce
• "Data available on request" with no construction code and no access detail
• Requesting a restricted-data exemption only at acceptance instead of at first submission
• Assuming the AEA Data Editor handles JPE — JPE runs its own Data Editor and Dataverse
• Hand-edited tables that the code does not actually generate
• Submitting without re-running the package on a clean environment

Output format

【Policy verified】JPE data policy (DCAS, JPE Dataverse) checked on official page [y/n]
【README】DCAS template sections present? [y/n each]
【License】unrestricted-access / replication license attached? [y/n]
【Master script】reproduces all exhibits from raw? [y/n]
【Versions + seeds】pinned/documented? [y/n]
【Data status】public / restricted (exemption + 5-yr preservation) + access path
【Clean-machine test】passed, ready for JPE Data Editor? [y/n]
【Next】jpe-submission