jole-data-analysis
Use when executing the empirical analysis for a Journal of Labor Economics (JOLE) manuscript — labor sample construction (CPS/ACS/registers), wage decompositions, standard errors, and robustness to labor norms, with replicability built in from the start. Operational guidance; pairs with jole-identification-strategy.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jole-data-analysis --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Data Analysis (jole-data-analysis) ## When to trigger - You are building the analysis sample from CPS/ACS/IPUMS, administrative, or register data - You are running wage decompositions (Oaxaca / RIF) or AKM firm–worker models - Standard errors, weighting, or robustness need to meet labor-referee expectations - You want to make sure the empirical work will be replicable before you write it up ## Labor empirical norms at JOLE JOLE publishes empirical / simulation / experimental labor papers **only if the data are documented and available for replication**, so build the analysis so it can be deposited later (data + programs + documentation) to the JOLE Dataverse (see jole-replication-and-data-policy). Beyond reproducibility, labor referees expect disciplined data work: - **Sample construction is part of identification.** Document the universe, age/labor-force restrictions, top-coding handling, and how you treat zeros/imputed earnings (CPS allocation flags, ACS PUMS edits). Report sample sizes at each restriction. - **Weights and design.** Use survey weights appropriately (CPS/ACS) and account for complex sampling; for registers, be explicit about coverage and linkage rules. - **Earni
- When to trigger
- Labor empirical norms at JOLE
- Common labor estimations (and their pitfalls)
- Robustness a labor referee will ask for
- Execution bridge (StatsPAI / Stata MCP)
- Checklist
- Anti-patterns
- Output format
What does the jole-data-analysis skill do?
Use when executing the empirical analysis for a Journal of Labor Economics (JOLE) manuscript — labor sample construction (CPS/ACS/registers), wage decompositions, standard errors, and robustness to labor norms, with replicability built in from the start. Operational guidance; pairs with jole-identification-strategy.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jole-data-analysis --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From brycewang-stanford/Awesome-Journal-Skills, a repository with 909 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.