| name | ecta-replication-package |
| description | Use when assembling the code and data deposit for an Econometrica manuscript under the journal's Data and Code Availability Policy, including reproducible Monte Carlo and (for empirical work) data provenance. Builds and audits the replication package; it does not design the simulations (use ecta-robustness) or format exhibits (use ecta-tables-figures). |
Replication Package (ecta-replication-package)
When to trigger
- You are preparing the deposit required under the Data and Code Availability Policy
- Monte Carlo tables cannot be regenerated bit-for-bit from a clean checkout
- An empirical paper has no documented data provenance or access path
- You are at acceptance / final-files stage and the Data Editor will verify reproducibility
Econometrica (and the other Econometric Society journals) enforce a single ES-wide Data and
Code Availability Policy. Concrete, Econometrica-specific specifics that differ from the
AER/AEJ (AEA Data Editor + openICPSR) pipeline:
- The policy applies to papers with empirical, experimental, and/or simulation results.
A pure-theory paper with no such results is effectively exempt — but any requested
exemption or limitation on data/code availability must be stated at initial submission
(the handling Co-Editor decides; exemptions are not considered later).
- Reproducibility is verified before final acceptance by the Econometric Society Data
Editor team. In practice you submit final files at Conditional Acceptance to a
separate Editorial Express account for the Data Editor's checks and correspondence.
- The package must include a README in PDF; the Social Science Data Editors' README
template is recommended and covers every required item.
- For packages conditionally accepted after July 1, 2023, the replication package is
deposited at the Econometric Society Journals' Community at Zenodo (you may reserve a
DOI in advance). Another trusted open repository with a permanent DOI can satisfy the
requirement with Data Editor approval — but not the AEA/openICPSR route by default.
Verify the current policy text and deposit location on the official ES Data Editor site
before finalizing — the specifics evolve.
Build the package as you go, not the night before final files. A package assembled from
memory at the end almost never reproduces — and here a real human Data Editor will run it.
What the package must contain
| Component | Requirement |
|---|
| Code | All scripts that produce every table, figure, and number in the paper and Supplemental Material |
| Master script | One command (run_all) regenerates every exhibit end to end |
| Random seeds | Every stochastic step seeded and recorded, so Monte Carlo tables reproduce bit-for-bit (simulations are covered by the ES policy) |
| Environment | Software, version numbers, and pinned dependencies (Docker / renv / conda / Project.toml) |
| README (PDF) | Hardware, expected runtime, data sources, file-by-file description, exhibit ↔ script map; use the Social Science Data Editors' README template |
| Data (empirical) | The data, or — when proprietary/restricted — exact provenance and an access path that lets a replicator obtain it |
| Deposit | The Econometric Society Journals' Community at Zenodo (after the Data Editor's checks; reserve a DOI in advance), unless a trusted DOI repository is approved |
| License / terms | Any data-use restrictions documented; redistribution rights respected |
Reproducibility discipline for Monte Carlo
- Seed everything and record the seed alongside each table. Re-running must reproduce the
exact numbers, not merely "similar" ones.
- Master script runs all simulations and writes outputs to named files that map to table
numbers.
- Runtime honesty. State how long the full simulation takes and on what hardware; if it is
days, provide a smaller smoke-test path that runs quickly and a way to verify the full run.
- No manual steps. No "then copy the number into the table by hand" — exhibits should be
generated programmatically where feasible.
Empirical data provenance
- Public data: include it (or a script that downloads a fixed version) plus the citation.
- Proprietary / restricted data (e.g., licensed firm-level, confidential admin data): you
generally cannot redistribute it. Document the exact source, version, access procedure,
required licenses/fees, and contact, so a replicator can obtain the same data. Provide all
code, and where allowed, a synthetic or example dataset that exercises the pipeline.
- Confidentiality: strip personal identifiers; respect data-provider agreements; state any
approvals obtained.
Recommended structure
replication/
README.md # provenance, environment, runtime, exhibit↔script map
run_all.{do,R,py,jl} # master script: one command rebuilds everything
code/ # numbered scripts (setup → simulate/estimate → tables → figures)
data/ # public data or a synthetic example; provenance for restricted data
output/ # generated tables/figures (regenerable, not hand-edited)
env/ # Dockerfile / renv.lock / environment.yml / Project.toml
Checklist
Anti-patterns
- Unseeded simulations, so tables only reproduce "approximately"
- A pile of scripts with no master file and undocumented run order
- Numbers transcribed into tables by hand, untraceable to any script
- "Data available on request" with no provenance, version, or access procedure
- An environment that only runs on the author's machine (unpinned versions)
- Redistributing proprietary data in violation of the license
- Leaving package assembly to the final-files deadline
Output format
【Package status】complete / gaps
【Master script】present / missing
【Seeds recorded】yes/no — bit-for-bit reproducible: yes/no
【Environment pinned】yes/no (tool: ...)
【Data】public-included / restricted-provenance-documented / theory-exempt / MISSING
【Exhibit↔script map】complete / gaps: [...]
【Deposit】Zenodo (ES Journals' Community) DOI reserved: yes/no
【Policy check】verified against current ES Data Editor policy: yes/no
【Next step】ecta-referee-strategy
