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