Aejmic Replication Package

Replication Package: Proofs + Code (aejmic-replication-package)

For AEJ: Micro the "replication package" has two faces: the proof appendix that makes every theory claim verifiable, and, for any paper with data, code, experiments, or numerical results, an AEA Data and Code Repository deposit. Pure-theory papers still deposit any numerical/simulation code used to generate examples or figures.

When to trigger

• Proofs are scattered, abbreviated, or rely on "it can be shown"
• The paper has numerical examples, simulations, structural estimation, or an experiment with no deposit prepared
• You are preparing for the AEA Data Editor check (administered before publication)
• A referee or editor flags reproducibility

The proof appendix (every AEJ: Micro paper)

• Self-contained proofs of all stated results. Key proofs belong in the paper (main text or appendix); do not exile a load-bearing proof to supplementary material.
• Lemma scaffolding: state and prove auxiliary lemmas before the main theorem; reference them precisely.
• Verify, do not assert: no "it can be shown that" for a claim the result depends on; complete the argument or cite a precise source.
• Match the statement: the proof establishes exactly what the proposition claims (no gap between the body statement and what is proved).

Code / data deposit (papers with data, code, experiments, or numerical results)

The AEA operates a Data and Code Availability Policy administered by the AEA Data Editor (currently Lars Vilhuber — 检索于 2026-06，以官网为准), with materials deposited to the AEA Data and Code Repository on openICPSR. Build it as you go.

• One master script (run_all) regenerating every table, figure, and numerical example from inputs.
• Pin versions: requirements.txt / conda (Python), renv.lock (R), Project.toml / Manifest.toml (Julia), recorded Stata ssc/net versions.
• Set and report seeds for any simulation, bootstrap, or randomization.
• README mapping each exhibit to the script that produces it; document any restricted-data or partial-reproduction scope.
• Pure-theory papers: deposit the code behind numerical examples / figures even when there is no dataset.
• Experiments: include instructions, z-Tree/oTree code, raw and analysis data, and pre-registration links.

Checklist

• All stated results have self-contained proofs; none rely on "it can be shown"
• Auxiliary lemmas stated and proved before they are used
• Each proof matches exactly what its proposition claims
• (If any data/code/numerics) one master script regenerates all exhibits
• Versions pinned; seeds set and reported
• README maps every exhibit to its script; restricted/partial scope documented
• Pure-theory numerical-example code deposited even with no dataset
• Experiment materials (instructions, code, data, pre-registration) included

Anti-patterns

• A "Proof." that asserts rather than argues the load-bearing step
• A load-bearing proof hidden in an un-checked supplementary file
• Numerical figures with no deposited code ("available on request")
• Unpinned dependencies / unset seeds — results not reproducible by the Data Editor
• Deferring the whole package to acceptance, then scrambling under the Data Editor deadline

Worked vignette (illustrative)

A persuasion paper has a clean Proposition 2 but its proof says "concavifying the value function yields the cutoff." For the appendix: state the auxiliary lemma (the value function's concave closure equals the indirect utility), prove it, then derive the cutoff explicitly — no hand-wave. The two numerical figures are generated by make_figures.py; deposit it with a fixed seed and a README line mapping Figure 3 → make_figures.py, even though there is no dataset.

Output format

【Proof appendix】all results proved, self-contained, no "it can be shown"? [Y/N]
【Lemma scaffolding】auxiliary results proved before use? [Y/N]
【Code/data deposit needed?】[yes — data/structural/experimental/numerical | theory-only numerics]
【Master script + pinned versions + seeds】[Y/N]
【README exhibit→script map】[Y/N]
【Next step】aejmic-referee-strategy then aejmic-submission

Supplementary resources

• ../../resources/code/ — runnable Stata/Python skeleton for the empirical/structural subset
• ../../resources/README.md — when the code kit applies vs. theory proof-appendix craft

Source: brycewang-stanford/Awesome-Journal-Skills → AEJ-Microeconomics-Skills/skills/aejmic-replication-package/SKILL.md