aeja-replication-package

name	aeja-replication-package
description	Use when assembling the data and code replication package for an American Economic Journal: Applied Economics (AEJ: Applied) manuscript to pass the AEA Data and Code Availability Policy and the AEA Data Editor's pre-publication reproducibility check. Builds the openICPSR deposit and README; it does not run the analysis or write the paper.

Replication Package & AEA Data Policy (aeja-replication-package)

When to trigger

• The paper is empirical and you are heading toward acceptance (or want to build the package early)
• An R&R or conditional acceptance asks you to prepare the data + code deposit
• You need to write the README and Data Availability Statement to AEA standard
• Some data are restricted/proprietary and you must plan the deposit around that

Why this is the AEJ: Applied signature

The single most distinctive AEJ: Applied differentiator is the AEA Data and Code Availability Policy, administered by the AEA Data Editor (Lars Vilhuber). Papers offered a revise-and-resubmit are asked to submit a data replication package at resubmission, and accepted papers must clear the Data Editor's compliance and reproducibility checks before publication. The default home is the AEA Data and Code Repository on openICPSR; other trusted repositories require appropriate Data Editor access. Build this package as you analyze; do not treat it as an acceptance-day formality.

What the package must contain

Component	Requirement
Data files	All data used, unzipped, in open/documented formats; or, for restricted data, a precise access path
Analysis + transformation code	Every script from raw data → cleaned data → each table/figure
Master script	One run_all that regenerates every exhibit from raw inputs
README	AEA README template: data sources, access, computational requirements, run instructions, exhibit-to-code mapping
Data Availability Statement (DAS)	States provenance and access terms for each dataset; required in the paper
Instruments	Survey instruments / experiment instructions for own-data studies

Handling restricted or proprietary data

• You cannot deposit restricted data, but you must still deposit all code and document the exact access procedure (provider, application steps, cost, approximate wait).
• Provide a small synthetic or public extract so the code runs and the Data Editor can verify logic where possible.
• Declare any restricted-data or exemption request at the earliest opportunity — limited-access arrangements are at editor/Data-Editor discretion and must be flagged, not discovered at the check.

Reproducibility hygiene (build as you go)

• Pin versions: Stata version + recorded ssc/net package versions; requirements.txt/conda env (Python); renv.lock (R).
• Set and report seeds for every simulation, bootstrap, and randomization-inference step.
• No absolute paths — one root macro/variable; relative paths thereafter.
• Exhibit-to-code map in the README: Table 3 → code/05_main.do, Figure 2 → code/06_event_study.R, etc.
• Run it clean on a fresh checkout before depositing; the Data Editor will.

Adapt the vendored skeleton in ../../resources/code/ (master script → clean → descriptive → DID/IV/RD/DML → mechanism → robustness → tables) as the package backbone.

Checklist

• One run_all master script regenerates every table and figure from raw data
• All data deposited unzipped (or restricted-data access path fully documented + synthetic extract provided)
• README follows the AEA template with a complete exhibit-to-code map
• Data Availability Statement written for every dataset, with access terms
• Software/package versions pinned; seeds set and reported
• No absolute paths; runs clean on a fresh checkout
• Restricted-data / exemption requests declared early, not at the check
• Own-data studies include survey instruments / experiment instructions

Anti-patterns

• Treating the package as an acceptance-day task — the check is pre-publication and gates publication
• Depositing code that depends on absolute paths or unrecorded package versions (fails to reproduce)
• Zipped data, missing intermediate files, or a README with no exhibit-to-code mapping
• Restricted data discovered at the check with no access documentation or synthetic extract
• Unset seeds making bootstrap/RI results non-reproducible

Output format

【Master script】run_all regenerates all exhibits from raw? [Y/N]
【Data】all deposited unzipped, or restricted path + synthetic extract? [state]
【README】AEA template + exhibit-to-code map complete? [Y/N]
【DAS】written for every dataset with access terms? [Y/N]
【Reproducibility】versions pinned + seeds set + no absolute paths + clean fresh run? [Y/N]
【Restricted/exemption】declared early? [Y/N/NA]
【Next step】aeja-referee-strategy (or aeja-submission)

Supplementary resources

• ../../resources/code/ — reproducible Stata + Python skeleton to adapt as the package backbone
• ../../resources/official-source-map.md — official AEA URLs behind the data/code policy