Agent Skills
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jole-data-analysis
GitHub用于JOLE稿件的实证分析，涵盖劳动样本构建、工资分解及稳健性检验。强调数据可复现性、规范加权与标准误处理，提供常见估计方法及审稿人要求的稳健性测试指南。
Trigger Scenarios
使用CPS/ACS等数据构建分析样本
运行Oaxaca/RIF工资分解或AKM模型
需满足劳动经济学审稿标准的标准误计算
确保实证工作具备可复现性
Install
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jole-data-analysis -g -y
SKILL.md
Frontmatter
{
"name": "jole-data-analysis",
"description": "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."
}
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.
- Earnings measures. Be precise: hourly vs. weekly vs. annual; nominal vs. real (state the deflator); winsorizing/top-coding decisions and their sensitivity.
- Standard errors. Cluster at the level of the variation (often state or firm); use heteroskedasticity-robust SEs by default; wild-cluster bootstrap with few clusters; randomization inference for experiments.
Common labor estimations (and their pitfalls)
- Wage decompositions: Blinder–Oaxaca for mean gaps; RIF / unconditional-quantile (
rifreg) for distributional gaps. State the reference group and the index-number problem; do not over-interpret the "unexplained" component as discrimination without argument. - Two-way (AKM) firm–worker FE: estimate on the connected set; correct limited-mobility bias (leave-out / KSS) before decomposing wage variance; report the share of movers.
- Labor-supply elasticities: be explicit about extensive vs. intensive margin, and about which elasticity (Marshallian/Hicksian/Frisch) is identified.
- Returns to schooling/training: distinguish OLS from IV/RDD estimates; report both and reconcile.
- Event studies / DID: use modern estimators on staggered timing (see jole-identification-strategy) and plot leads.
Robustness a labor referee will ask for
- Alternative samples (age bands, full-time/part-time, with/without imputed earnings)
- Alternative SE clustering and few-cluster corrections
- Specification curve / leave-one-out on key controls or sub-populations
- Placebo outcomes and placebo timing/cutoffs
- Heterogeneity by the labor-relevant dimensions (gender, education, age, sector) where theory predicts it
Execution bridge (StatsPAI / Stata MCP)
Run the battery, don't just enumerate it. Full map:
execution-with-mcp. JOLE is labor economics — the home of clean identification; DiD/IV/RDD and selection corrections are the binding constraint.
- Many outcomes / specifications:
romano_wolf(step-down FWER) orbenjamini_hochberg. - OVB sensitivity:
oster_delta/sensemakr. - Inference:
wild_cluster_bootstrap(few clusters),twoway_cluster/conley. - Re-fit off one handle:
audit_result(result_id)lists missing checks + the exactsuggest_functionfor each. - Exhibits:
etable/did_summary_to_latexfrom the handle — no retyped numbers.
Decisive checks in the body, exhaustive battery in the appendix. JF execution walkthrough.
Checklist
- Sample restrictions documented with counts at each step
- Earnings measure and deflator stated; top-coding/winsorizing sensitivity shown
- Survey weights / register coverage handled correctly
- SEs clustered at the variation level; few-cluster issues addressed
- Decompositions report reference group; AKM corrects limited-mobility bias
- Robustness covers samples, SEs, placebos, and theory-motivated heterogeneity
- Every table/figure regenerable from a master script (replicability built in)
Anti-patterns
- Undocumented sample cuts that drive the result
- Ignoring CPS/ACS allocation flags and imputed-earnings issues
- Default i.i.d. SEs when variation is at the state/firm level
- Interpreting the Oaxaca "unexplained" gap as discrimination with no further argument
- Reporting AKM firm-effect dispersion without limited-mobility-bias correction
- Leaving reproducibility to the end instead of scripting it as you go
Output format
【Data】source(s) + sample universe + restrictions (with counts):
【Earnings measure】hourly/weekly/annual, real/nominal, deflator:
【Estimator】OLS / Oaxaca / RIF / AKM / IV / DID:
【SEs】clustering level + few-cluster handling:
【Robustness done】[samples, SEs, placebos, heterogeneity]:
【Replicability】master script regenerates all exhibits? [Y/N]
【Next step】jole-contribution-framing or jole-tables-figures
Version History
- 1839142 Current 2026-07-05 13:44
