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Demog data analysis

Skill brycewang-stanford/Awesome-Journal-Skills/Demography-Skills/skills/demog-data-analysis

Journal-specific Claude Code/Codex skill packs covering mainstream journals — AER, QJE, Nature, Cell, 管理世界, 经济研究 & 200+ more — your fast track to getting published. ｜ 覆盖主流期刊的 Claude Code/Codex 期刊技能包，从选题、识别策略到表格规范与审稿回复全流程，助你快速发论文。

Install
npx -y skills add brycewang-stanford/Awesome-Journal-Skills --skill demog-data-analysis

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What its author says it does

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Use when executing and reporting the analysis for a Demography (PAA / Duke University Press) manuscript so it survives expert, double-blind review — correct rate construction, honest uncertainty, and demographic methods done right (life tables, decomposition, event history, age-period-cohort). Guides analysis norms; it does not fabricate results.

SKILL.md

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Data Analysis (demog-data-analysis)

Demography reviewers are expert demographers and the journal expects reproducible code behind the results (see demog-data-and-reproducibility). Analyze as if a methodologist will re-derive your rates and re-run your decomposition — because they may. This skill covers execution and reporting norms; method choice lives in demog-research-design.

When to trigger

  • Constructing rates and life tables; building the results section
  • Running a decomposition, event-history, APC, or projection analysis
  • A reviewer asked for robustness, sensitivity, or alternative specifications
  • Making the analysis reproducible before deposit

Analysis norms Demography expects

  1. Get the denominators right. Exposure (person-years), the correct base population, and age/period alignment are where demographic analyses live or die. Document how rates were built.
  2. Report uncertainty honestly. Confidence/credible intervals for rates, life-expectancy contributions, and derived quantities — not just point estimates or stars. Bootstrap or delta-method intervals for decomposition components and life-table functions.
  3. Decomposition with clear components. State precisely what each component (rate vs. composition, age contribution, factor) represents; ensure components sum to the total being explained.
  4. APC discipline. Be explicit about the identification problem; report results under the stated constraint and show sensitivity to plausible alternatives — never imply a unique decomposition.
  5. Survival/event-history rigor. Check proportional hazards; handle censoring, truncation, and competing risks correctly; report on the right time scale (age, duration, period).
  6. Right inference for the data. Survey weights and complex-design variance where applicable; cluster at the appropriate level; small-sample corrections when groups are few.

Demographic computation specifics

  • Document data version/vintage (e.g., HMD/HFD release), harmonization steps, and any smoothing/ graduation applied to rates.
  • For microsimulation/projection: report seeds, number of runs, and convergence; show sensitivity to the key transition-rate and base-population assumptions.

Reproducibility while you work (not at the end)

  • One master script regenerates every table, figure, life table, and decomposition from the (raw or constructed) data.
  • Set and report seeds for bootstrap, simulation, and microsimulation.
  • Pin software/package versions (renv.lock, requirements.txt, recorded ssc/net installs).
  • Keep table/figure numbers in the manuscript matched to script outputs (Demography expects runnable, commented code — see demog-data-and-reproducibility).

Execution bridge (StatsPAI / Stata MCP)

Run the battery, don't just enumerate it. Full map: execution-with-mcp. Demography is formal + empirical demography; the causal chain serves its reduced-form lane, while formal demographic modeling uses its own tools — decomposition (oaxaca / gelbach) is often central.

  • Many outcomes / specifications: romano_wolf (step-down FWER) or benjamini_hochberg — report the adjusted threshold.
  • OVB sensitivity: oster_delta / sensemakr.
  • Inference: wild_cluster_bootstrap (few clusters), twoway_cluster / conley; multilevel data → cluster at the right level.
  • Re-fit off one handle: audit_result(result_id) lists the missing checks and the exact suggest_function for each.
  • Exhibits: etable / did_summary_to_latex from the handle — no retyped numbers.

Keep the decisive checks in the body and the exhaustive battery in the supplement. See the executed chain in the JF execution walkthrough.

Anti-patterns

  • Mismatched numerator/denominator or wrong exposure (the classic demographic error)
  • Point estimates of life expectancy or decomposition components with no uncertainty
  • An APC model presented as the uniquely correct partition
  • Ignoring censoring/competing risks in survival analysis
  • A results section whose rates and decompositions the code cannot reproduce

Evidence pass for Demography

Run this as a concrete capability pass. First lock the demographic process, data source, time scale, selection/migration/mortality issue, and uncertainty; then test whether the manuscript addresses population-science reviewers who inspect demographic process, measurement, cohort/period logic, and population validity.

  • Primary move: Audit unit, comparison, uncertainty, missingness, sensitivity, and reproducibility before making any prose or submission recommendation.
  • Decision ledger: return claim / evidence / blocker / next edit rows so the next pass can patch the manuscript directly.
  • Sibling comparison: compare against Population and Development Review for policy synthesis, JMF for family process, Social Forces for broader sociology; if the neighboring outlet has the stronger audience claim, recommend re-routing before polishing.
  • Verification floor: before submission-ready advice, re-open resources/official-source-map.md for volatile rules and name the one unresolved fact that could change the recommendation.

Output format

【Main quantity】rate / e0 / decomposition / hazard + magnitude + interval
【Exposure / denominator check】correctly constructed? [Y/N]
【Decomposition】components defined + sum to total? [Y/N/NA]
【APC】identifying constraint stated + sensitivity shown? [Y/N/NA]
【Inference】weights/clustering/competing risks handled? [Y/N]
【Reproducible】master script + seeds + pinned versions? [Y/N]
【Next】demog-tables-figures

Supplementary resources

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