--- name: aer-replication description: 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. --- # 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: 1. **Data deposit** — all data the paper uses, or full provenance documentation for data that cannot be public 2. **Code deposit** — all code that produces every reported number, table, and figure 3. **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 Required - **Data and Code Information Form** - **Data and Code Availability Form** (signed) - **Data and Code Archive Agreement Form** (signed) These forms are provided by the editorial office at the appropriate stage. Do not invent substitute forms. ## Repository Structure ```text replication-package/ ├── README.md (or README.pdf) ├── 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/ (variable definitions, source mapping) ├── code/ │ ├── 00_setup.do (or .R, .py) │ ├── 01_clean.do │ ├── 02_analysis.do │ ├── 03_tables.do │ └── 04_figures.do ├── output/ │ ├── tables/ │ └── figures/ └── docs/ ├── data_appendix.pdf └── computing_environment.txt ``` ## README Required Sections The AEA Data Editor's office publishes a template. 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: 1. Commit to preserving data and code for ≥ 5 years 2. Commit to assisting reasonable replication requests 3. Make the **code** publicly available 4. 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. ### 4. Computational Requirements Operating system, software version, packages, estimated runtime, peak memory. Example: ``` 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 | ## 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 - **Version-pin** packages: in Stata, `setup` script with `ssc install ... , version`; in R, use `renv` or `groundhog`; in Python, `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 - Avoid `set more off` and other display tweaks in shared 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 1. **Does the README exist and follow the template?** 2. **Does the master script run end-to-end without errors?** 3. **Do the produced numbers match the published paper?** 4. **Are all data sources documented with provenance?** 5. **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 ## openICPSR Workflow 1. Create deposit on https://www.openicpsr.org/openicpsr/aea 2. Upload files so the data and code are unzipped inside the repository; avoid one opaque top-level ZIP except for rare repository-approved exceptions 3. Fill metadata: citation, abstract, keywords, geographic coverage, time period 4. Submit; the AEA Data Editor's office reviews 5. Address feedback in the deposit and resubmit 6. Approval triggers DOI assignment and publication ## Restricted Data Special Cases If data is restricted: - Provide **access instructions** in the README — exact dataset name, agency, application process, approximate cost, expected turnaround - Provide **code that would run** if the data were available - Provide **sample synthetic data** with the same schema if possible, so the code can be smoke-tested - Consider depositing intermediate aggregates (cell-suppressed counts) that allow partial replication ## Repository Resources When working from the AER-skills repository or plugin bundle, load only the relevant resource: - Deposit skeleton: `examples/replication-package-skeleton/` - Language template: `templates/stata/`, `templates/r/`, or `templates/python/` - Classic replication examples and repository links: `examples/aer-exemplars.md` ## Pre-Deposit Checklist - [ ] Master script runs from clean state on a different machine - [ ] All paths are relative - [ ] Random seeds set - [ ] All software and package versions documented - [ ] README follows AEA template - [ ] Every published table and figure mapped to its producing script - [ ] Data Availability statement covers every dataset - [ ] Data/code files are visible in the repository, not hidden behind one opaque ZIP - [ ] Required AEA data/code forms are complete or ready for the editorial office - [ ] Repository structure matches standard layout - [ ] License files present ## Handoff ```text DEPOSIT TYPE: README STATUS: REPRODUCIBILITY CHECK: RESTRICTED DATA HANDLING: NEXT SKILL: aer-submission ``` ## Anti-Patterns - Waiting until acceptance to start the deposit - Treating the README as documentation for collaborators, not for an unknown replicator - Depositing the JEL appendix tables but not the regressions that produced them - "All code available from the authors upon request" — does not satisfy the policy - Uploading one giant ZIP whose contents the repository cannot display - A deposit that runs only with packages from 2018 — pin versions but use current stable releases