Jet replication and data policy

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

Computational content in the paper	Artifact worth sharing	Channel that fits
Symbolic verification of closed forms (e.g., checking eq. (7) of a screening model)	one SymPy/Mathematica script per theorem	repo link in the data statement
Counterexample found by search	the search code plus a certificate script confirming the final example violates the conclusion	repo; the certificate logic also goes in the paper
Computed equilibria (e.g., a numerical fixed point for a dynamic-contract example)	solver script with tolerances and pinned environment	repo or Mendeley Data
Experimental/empirical test of the theory (rare at JET)	data, cleaning, and analysis scripts	repository / Mendeley Data / Data in Brief, with Option C statement
Pure theory, no computation	no archive to manufacture	no-data statement

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