Use when assembling the AEA Data and Code Availability deposit for an AER, AER:Insights, or AEJ acceptance, writing the README, or auditing a replication package before the AEA Data Editor review. Implements the current AEA policy, including the February 2026 Data and Code Availability Policy.
SKILL.md
AER Replication
Overview
Prior to acceptance, every empirical, simulation, or experimental paper submitted to an AEA journal must provide data, code, and a complete README sufficient for an independent researcher to reproduce every reported result. The AEA Data Editor's Office now performs a computational reproducibility check before final acceptance, and a failed check delays publication by weeks or months.
This skill produces an AEA-compliant deposit on the first try.
When to Use
A conditional acceptance arrived and the deposit deadline is set
Drafting the README at any point in the project (recommended: from day one)
An AEA Data Editor report flagged the deposit
Preparing a deposit for openICPSR
Current AEA Data and Code Availability Policy (February 2026)
Three pillars:
Data deposit — all data the paper uses, or full provenance documentation for data that cannot be public
Code deposit — all code that produces every reported number, table, and figure
README — a single document instructing a replicator on how to run everything
Materials must be deposited in an openly accessible trusted repository. The strongly encouraged repository is the AEA Data and Code Repository at openICPSR, which gives the Data Editor automatic access to draft deposits.
Forms and Statements
The current policy centers on the Data and Code Availability Statement embedded in the README, plus any forms the editorial office sends during the revision stage (for example, confirming whether a private copy of restricted data can be shared with the Data Editor for verification). Exact form titles change between policy revisions, so:
Installs
0
Build the README Data Availability Statement to the AEA template (see below).
Complete whatever forms the editorial office provides at the revision stage — do not invent substitute forms or assume titles carried over from an older policy cycle.
Repository Structure
replication-package/
├── README.pdf (required for final AEA deposit)
├── README.md (optional editable source)
├── LICENSE (commonly MIT or CC-BY for code; data per source license)
├── data/
│ ├── raw/ (original files as obtained, never modified)
│ ├── intermediate/ (cleaned analytic datasets)
│ └── codebook/
│ └── source-register.md (source inventory, crosswalk, derived files)
├── code/
│ ├── 00_setup.do (or .R, .py)
│ ├── 01_clean.do
│ ├── 02_analysis.do
│ ├── 03_tables.do
│ └── 04_figures.do
├── output/
│ ├── tables/
│ └── figures/
└── docs/
├── exhibit-register.md
├── data_appendix.pdf
└── computing_environment.txt
README Required Sections
The AEA Data Editor's office publishes a template and requires a README
document in PDF format in the uppermost directory of the replication package.
Keep README.md as editable source if useful, but render README.pdf for the
final deposit. Required sections:
1. Overview
One paragraph: what the package does, what paper it accompanies, what software is required.
2. Data Availability and Provenance Statement
For every dataset used, state:
Source (with URL or institution)
Citation
License or terms of access
Date accessed
Whether the data is included in the deposit, and if not, why not
If data cannot be deposited (proprietary, restricted, IRB), the author must:
Commit to preserving data and code for ≥ 5 years
Commit to assisting reasonable replication requests
Make the code publicly available
Document the data source thoroughly enough for an independent researcher to obtain it
3. Dataset List
A table of every file in data/, with columns: Filename | Description | Source | Notes.
Keep data/codebook/source-register.md synchronized with this section so every
raw source, derived file, access restriction, and analytic variable has one
auditable home.
Software: Stata 18.0 MP, R 4.4.1
Stata packages: reghdfe (6.12), ftools (2.49), did (1.3.1), rdrobust (9.2)
R packages: fixest (0.12.1), did (2.1.2), modelsummary (2.1.1)
Hardware: standard laptop (8 GB RAM); full run completes in ~25 minutes.
5. Description of Programs / Code
What each script does and the order to run them. State the master script (e.g., run_all.do).
6. Instructions to Replicators
A literal step-by-step:
1. Download this archive and unzip to a working directory.
2. Open Stata 18; install dependencies via `setup/install_packages.do`.
3. Edit line 12 of `00_setup.do` to point to the working directory.
4. Run `do run_all.do`. Total runtime: ~25 minutes.
5. All tables and figures are written to `output/`.
7. List of Tables and Programs
A second table mapping each exhibit in the published paper to the script that produces it:
Table/Figure
Script
Line in Script
Output File
Table 1
03_tables.do
12
output/tables/tab1.tex
Table 2
03_tables.do
67
output/tables/tab2.tex
Figure 1
04_figures.do
8
output/figures/fig1.pdf
For a fuller audit, keep docs/exhibit-register.md synchronized with this
README table. The register should also record the supported claim, exact sample
size, estimator or statistic, table/figure note, and accessibility evidence.
Coding Discipline for Reproducibility
Adopt from day one. Retrofitting is expensive.
General
One master script that runs the entire pipeline end-to-end
Relative paths only — data/raw/foo.csv, never /Users/yourname/...
Set the random seed explicitly in any stochastic procedure
Document package versions: in Stata, install from a setup script and
record which / ado describe output; vendor an ado/ snapshot if exact
SSC versions are required. In R, use renv or groundhog; in Python, use
requirements.txt with exact versions
Log files for every run, saved to logs/
Stata
version 18.0 at the top of every do-file
set seed before any randomization
Keep display tweaks such as set more off in the master script, not hidden inside analysis scripts
Use reghdfe over xtreg, fe for performance and clarity
Comment heavily — Julian Reif's Stata Coding Guide is a reasonable standard
R
set.seed() at the top of every analysis script
renv::snapshot() to lock the package environment
Avoid loading data with read.csv and writing with write.csv — explicit data.table::fread / fwrite with column types
Python
requirements.txt with == pins
numpy.random.default_rng(seed=...) for new-style RNG
Jupyter notebooks acceptable only if accompanied by an equivalent .py script
AEA Data Editor — What They Actually Check
Does the README exist and follow the template?
Does the master script run end-to-end without errors?
Do the produced numbers match the published paper?
Are all data sources documented with provenance?
Is the data deposit complete, or properly justified if restricted?
If yes to all: Approved, paper proceeds to publication.
If no: Revisions required, with a public report.
Common failure modes flagged in published Data Editor reports:
Hardcoded absolute paths
Missing package versions
Master script that errors on a clean machine
Tables in the deposit that differ from the published version
Restricted-data papers with no provenance documentation
README that says "see paper" instead of explaining the steps
