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jape-replication-and-data-policy

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Use when assembling the mandatory JAE Data Archive deposit for an accepted Journal of Applied Econometrics paper — plain-ASCII/CSV data with a readme, the programs that replicate every result, and the confidential-data exception. Encodes the archive's actual format rules (no bare .dta/SAS) and its history (Queen's 1994–2022, now ZBW).

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Source SKILL.md: https://github.com/brycewang-stanford/Awesome-Journal-Skills/blob/HEAD/Journal-of-Applied-Econometrics-Skills/skills/jape-replication-and-data-policy/SKILL.md

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Replication & Data Policy for JAE (jape-replication-and-data-policy)

Use this from day one, not only after acceptance — the deposit is the journal's signature norm.

When to trigger

  • Preparing the data/code deposit for an accepted JAE paper
  • Handling confidential or restricted data under JAE's policy
  • Auditing whether your package meets the archive's format rules before deposit

The signature requirement

JAE's defining norm: authors of accepted papers must deposit a complete set of the data used onto the Journal's Data Archive, unless the data are confidential. The archive has held data for all papers accepted since January 1994 and also stores programs, technical appendices, and supplementary material. Hosted at Queen's University (1994–2022, maintainer J. G. MacKinnon), it moved to ZBW's Journal Data Archive (journaldata.zbw.eu) in 2022 (~1,487 datasets).

Format rules (do not get this wrong)

  • Every dataset needs a plain-text ASCII readme (source, variables, units, provenance).
  • Data must be plain ASCII / CSV. Proprietary binary (Stata .dta, SAS) is NOT acceptable on its own — export to documented CSV/TXT.
  • Supply the programs that replicate the results, ordered so a master script runs end to end from raw inputs to every exhibit; note software/version and seeds.

Confidential-data exception

If data are confidential, still provide a readme describing the data and its source in enough detail that others can apply for access, and ideally the extraction programs. Responsibility for permission rests with the investigator. Note proprietary data in the cover letter/paper.

Deposit assembly order

Build the package in this sequence — each step catches a different failure class before the archive does:

  1. Export every input the paper touches to documented CSV/TXT; delete nothing from the pipeline that an exhibit needs.
  2. Freeze the environment: software names, versions, OS, package list, and all seeds, written into the readme.
  3. Cold-run the master script in a clean directory on a second machine; diff regenerated tables against the accepted manuscript digit by digit.
  4. Strip absolute paths, credentials, and licensed raw files you may not redistribute (replace with extraction code + access instructions).
  5. Name files so the mapping is mechanical: table1.csv, fig2_data.txt, programs that announce which exhibit they build.

readme.txt skeleton for the archive deposit

README for [Author(s)], "[Title]", Journal of Applied Econometrics
1. Data files: [name] — source, sample span, units, variable list
2. Provenance: where each series was obtained; access date; license
3. Programs: run order; master script; software + version; seeds
4. Mapping: program → table/figure in the published paper
5. Confidential components: description + how to apply for access
Contact: [author email]

Plain ASCII, no markup — the archive's own holdings are flat text and the readme should match.

Replication Articles and the archive trail

JAE's dedicated replication track exists because the archive makes published results re-runnable. If you are submitting to it: pick a prominent target paper, obtain its archived (or otherwise public) data and code, and report what you find whether it confirms, qualifies, or overturns — negative replications are explicitly in scope. Separate three failure sources in your write-up: data revisions since the original, implementation/coding differences, and genuine fragility of the result. Confirm current track-specific instructions against the journal's author guidelines before submitting.

Deposit pitfalls that bounce packages

  • A .dta or SAS file with no CSV/TXT sibling — violates the archive's plain-text rule outright.
  • Readme describes an earlier draft's tables; the accepted version renumbered them.
  • Bootstrap or simulation results that drift run-to-run because a seed was never set.
  • "Data available on request" used where the data are merely inconvenient, not confidential — the exception is for genuinely restricted data, and the investigator carries the permission burden.

Output format

【Data】complete non-confidential set deposited? [Y/N]
【Format】plain ASCII/CSV + readme, no bare .dta/SAS? [Y/N]
【Programs】replicate every result; master script runs? [Y/N]
【Cold-run】clean-machine rerun matches published digits? [Y/N]
【Confidential】readme + access path + extraction programs? [Y/N/NA]
【Host】ZBW Journal Data Archive (journaldata.zbw.eu)

Supplementary resources