| name | aejmic-replication-package |
| description | Use when assembling the proof appendix and any code/data deposit for an American Economic Journal: Microeconomics (AEJ: Micro) manuscript under the AEA Data and Code Availability Policy. Covers proof appendices for pure theory plus numerical/structural/experimental code; it does not run your estimation. |
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
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
