Aejpol Replication Package

Replication Package — AEA Data Editor Compliance (aejpol-replication-package)

When to trigger

• A conditional acceptance / R&R asks for the data and code deposit
• You want to build the replication package as you go (recommended — the check is before publication)
• Some data are proprietary or restricted and you need a compliant access path
• The AEA Data Editor returned the package with required changes

The AEA reproducibility regime (检索于 2026-06；以官网为准)

AEJ: Policy is an AEA journal and follows the AEA Data and Code Availability Policy. For accepted papers, authors deposit data, code, and documentation in the AEA Data and Code Repository at openICPSR, and the AEA Data Editor (Lars Vilhuber) runs a reproducibility check before the paper is published, not after. A Data and Code Availability Statement is required. Build the deposit to pass on the first pass.

What the deposit must contain

• All code that produces every table, figure, and in-text number, organized so a single master script (run_all) regenerates the results from the analysis data.
• Data that can be shared, in the repository; for data that cannot, the exact provenance and access instructions (see restricted-data path below).
• A README following the AEA Data Editor's template: data sources and citations, data-availability statement, computational requirements (software + versions + packages), runtime, the map from code files to exhibits, and instructions to reproduce.
• Dependencies pinned: Stata .ado versions, R renv.lock, Python requirements.txt/conda env; seeds set and reported for any simulation/bootstrap/randomization inference.

Restricted / proprietary data path

• State the Data Availability Statement clearly: which data are public, which are restricted, and how a replicator obtains the restricted parts (provider, application process, cost, approximate wait).
• Provide everything that can be shared (cleaning and analysis code, derived public extracts, synthetic or example data) so the Data Editor can verify the pipeline even without the raw restricted file.
• Confirm the access path is real and current — the Data Editor verifies it.
• Note any partial-reproducibility scope (e.g., one table from confidential data) in the README.

Code posting during review

Review is single-blind, so the submission need not be anonymized; the openICPSR deposit is prepared at acceptance. Posting a working-paper code repository earlier is fine and does not conflict with the review process.

Checklist

• Data and Code Availability Statement drafted (public vs. restricted, sources cited)
• One master script regenerates every exhibit and in-text number from analysis data
• README follows the AEA Data Editor template (sources, requirements, runtime, code→exhibit map)
• Software, package, and .ado versions pinned; seeds set and reported
• Restricted data: provenance + concrete access instructions + shareable code/extracts provided
• Deposit targets the AEA Data and Code Repository at openICPSR
• Ran the package clean on a fresh machine/folder before submitting to the Data Editor

Anti-patterns

• Treating reproducibility as a post-acceptance afterthought — the check is pre-publication
• A README that lists files but no code→exhibit map or version/runtime information
• "Data available on request" with no access process for restricted data (non-compliant)
• Unpinned package versions or unset seeds, so results do not regenerate
• Hard-coded absolute paths that break on the Data Editor's machine
• Leaving an in-text number that no script in the deposit produces

README skeleton (AEA Data Editor template, abbreviated)

A compliant README typically carries these sections, in order:

1. Overview — what the package contains and the paper it reproduces.
2. Data availability statement — each dataset: public / restricted; source citation; how to obtain.
3. Computational requirements — OS, software + versions, every package/.ado + version, hardware.
4. Description of programs/code — the run_all master and what each script does.
5. Instructions to replicators — exact run order and expected runtime.
6. Map from code to output — which script produces which table/figure/in-text number.
7. Notes — any partial-reproducibility scope and why.

Worked vignette (illustrative)

A health-policy paper uses confidential claims data plus public state-policy data. Compliant deposit: a Data Availability Statement marking the claims data restricted (provider, DUA process, ~3-month wait) and the policy data public; the full cleaning/analysis code; a synthetic claims extract so the Data Editor can run the pipeline end-to-end; a README mapping 04_did.do → Table 3 and Figure 2 with Stata 18 + csdid/reghdfe versions and an 18-minute runtime. The package regenerates every exhibit from the synthetic + public data on a clean machine.

Output format

【Availability statement】public vs. restricted data + sources cited
【Master script】run_all regenerates all exhibits? [Y/N]
【README】AEA-template fields present (requirements/runtime/code→exhibit map)? [Y/N]
【Versions + seeds】pinned and reported? [Y/N]
【Restricted-data path】access instructions + shareable code/extract provided? [Y/N]
【Target repo】AEA Data and Code Repository (openICPSR)
【Next step】aejpol-submission