Replication & Data Policy (jet-replication-and-data-policy)

When to trigger

• Your JET paper includes numerical examples, simulations, or computed results and you want to share them well
• You are checking what JET requires for data/code at submission or acceptance
• You need to get the generative-AI disclosure right

What JET actually requires

• No journal-run replication archive. Unlike empirical AEA / Econometric Society journals, JET has no JAE-Data-Archive-style replication archive. Most JET papers are pure theory, so the main reproducibility object is the proof and any supplementary appendix.
• Elsevier Option C applies when research data exist. Deposit research data in a relevant repository and cite/link it, or state why the data cannot be shared. Data statements are supported in the submission flow and appear with the published article.
• Pure theorem papers still need a clear statement. If there are no external research data and no computation, say so plainly. If there are numerical examples, simulations, or computer-assisted proof steps, make those artifacts reproducible and link/deposit them where appropriate.
• Generative-AI disclosure is required: authors must declare any use of generative AI in manuscript preparation at submission. Reviewers and editors are prohibited from using generative-AI tools during evaluation.

Reproducible-computation playbook (when the paper has computation)

JET's Option C rule is data-focused, but for a theory paper any numerical content should still be reproducible because it strengthens the paper and pre-empts referee doubt:

• One master script regenerates every reported number, table, and figure from scratch
• Environment pinned (requirements.txt, Project.toml/Manifest.toml, recorded toolbox versions)
• Seeds set and reported for any stochastic illustration
• A short README mapping each script to the theorem/figure it supports
• If shared, choose one channel (repo link / Mendeley Data / Data in Brief) and link it in the data statement

What to package, by content type

Supplementary-appendix culture (the theory analogue of replication)

• At a theorem-proof journal, the unit of "replication" is the omitted proof, not a dataset. Long technical arguments go to an online appendix / supplementary file the referee can read.
• Make the supplementary appendix self-contained in notation and citable by numbered cross-references from the main text (e.g., "Appendix S.2"), so checking it never requires re-deriving the body.
• If any proof step is computer-assisted — exhaustive finite-case checking, interval arithmetic, symbolic simplification — say so inside the proof and ship the checker; the step is only as credible as a referee's ability to re-run it.
• Where the proofs live (in-PDF appendix vs separate supplementary file) varies; confirm against the journal's current author guidelines before splitting files.

Companion README template

README — companion code for "<title>" (JET submission)
verify_thm2_bound.py     → re-derives eq. (7)–(9); confirms the Theorem 2 bound is attained (Example 1)
search_counterexample.jl → finds the Example 3 economy; seed 20250114; runtime < 1 min
check_thm4_cases.py      → exhaustive check of the 12 finite cases cited in Appendix B, Step 3
env: requirements.txt / Manifest.toml (pinned)
Every reported number in the paper appears in the output of exactly one script above.

Anti-patterns

• Assuming JET has a journal-run replication archive — it does not
• Treating Option C as optional when the manuscript uses shareable research data
• Reporting computed numbers no script can reproduce
• Omitting the generative-AI declaration at submission
• Treating the optional data statement as a substitute for a checkable proof — the proof carries the paper

Output format

【Has data/computation?】none / data / computation / both
【Option C】repository citation/link, or no-data/cannot-share statement? [Y/N]
【Reproducible】master script + pinned env + seeds + README? [Y/N]
【AI disclosure】declared at submission? [Y/N]
【Next】jet-submission