Replication Package (ecopol-replication-package)

When to trigger

• Empirical, experimental, or simulation results need to be made accessible and replicable
• Code is scattered across machines with hard-coded paths and no master script
• Data are restricted/proprietary and you need a verification plan that still works
• The Managing Editor / production process will ask for the datasets and programs underlying the figures
• You want the package ready before the conference so a discussant can probe a number on the spot

EP's data standard: accessible and replicable, verified by the journal

EP's guidance is that "all empirical, experimental and simulation results should, where possible, be accessible and replicable," and authors submit the datasets, programs, and sources for the journal to verify and publish alongside the article (检索于 2026-06；以官网为准). EP does not (as of this writing) run the AEA-style mandatory pre-acceptance Data Editor audit that AEJ:EP / QE use, so the bar is "where possible" rather than universal — but a paper feeding a policy recommendation lives or dies on whether its central number can be reproduced. Build the package as if a skeptical discussant will re-run it. Confirm the exact deposit location, README format, and any embargo rules in the live guidelines (待核实).

What goes in the package

Handling restricted policy data

EP papers often use confidential administrative or central-bank data. When you cannot deposit the raw data:

• Provide the full code plus a synthetic or simulated dataset with the same structure so the pipeline runs end-to-end.
• Document the exact access procedure (the agency, the application route, the version) so a determined replicator can obtain it.
• Offer the journal a verification path (e.g., code run on-site, or output verified against a secure enclave) rather than nothing.
• State the restriction openly in the data section — silence reads as evasion to a discussant.

Craft moves

• One-command reproducibility. A reviewer should clone, run master, and get your exhibits. Hard-coded /Users/yourname/ paths are the most common failure.
• Pin software versions. "Stata 18.0", "R 4.4.1 with fixest 0.12" — not "recent version".
• Map every headline number to the line of code that produces it; the conference is live and you may be asked to show provenance.
• Seed every simulation / bootstrap and report the seed.
• Keep the package legible, not just runnable — a clear folder structure signals the same care the policy audience expects of the analysis.

Checklist

• [ ] Master script runs end-to-end with one command, relative paths only
• [ ] Every table/figure/number in the paper is reproduced by the code
• [ ] README lists data sources, software + exact versions, run instructions, runtime
• [ ] Restricted data handled: synthetic data + access guide + verification path
• [ ] Each dataset formally cited (provider, version, access date)
• [ ] Exhibit → script → output mapping included
• [ ] Simulations/bootstraps seeded and the seed reported
• [ ] Deposit location / README format confirmed against live guidelines (待核实)

Anti-patterns

• Hard-coded absolute paths that break on any other machine
• "Data available on request" with no code and no access procedure
• A package that produces some but not all of the paper's numbers
• Unpinned software versions, so the pipeline silently breaks on a newer release
• Treating replication as a post-acceptance afterthought when a discussant may probe it at the conference

Output format

【Journal】Economic Policy (EP)
【Skill】ecopol-replication-package
【One-command run】master reproduces all exhibits? Y/N
【Coverage】every number/table/figure reproduced? Y/N
【Restricted data plan】synthetic data + access guide + verification path
【Versions pinned】software + package versions listed? Y/N
【Exhibit→code map】present? Y/N
【Deposit spec】confirmed / 待核实
【Next skill】ecopol-referee-strategy