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Popdevr data analysis

Skill brycewang-stanford/Awesome-Journal-Skills/Population-and-Development-Review-Skills/skills/popdevr-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 popdevr-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 Population and Development Review (PDR, Wiley / Population Council) manuscript so it survives expert, double-anonymized review — correct rate construction, honest uncertainty, and demographic methods done right, with the development/policy meaning of each quantity made clear. Guides analysis and reporting norms; it does not fabricate results.

SKILL.md

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Data Analysis (popdevr-data-analysis)

PDR reviewers are expert demographers and development scholars, and the journal expects analyses that are reproducible and interpretable to a broad readership. Analyze as if a methodologist will re-derive your rates and an economist will ask what each number means for development — because both may. This skill covers execution and reporting norms; method choice lives in popdevr-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 and its development meaning explicit before deposit

Analysis norms PDR 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, projection scenarios, 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 and which maps to a development channel; ensure components sum to the total being explained.
  4. APC discipline. Be explicit about the identification problem; report 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/design weights and complex-design variance where applicable; cluster at the appropriate level; small-sample corrections when groups (e.g., countries) are few.
  7. Make the development meaning explicit. For each headline quantity, say what it implies for the social, economic, or environmental outcome — the PDR bar is not a clean estimate alone.

Demographic and comparative computation specifics

  • Document data version/vintage (e.g., HMD/HFD/WPP release, DHS round), harmonization steps, and any smoothing/graduation applied to rates.
  • For projections: report the scenarios, base population, transition-rate assumptions, and sensitivity; tie scenarios to development or policy futures where that is the contribution.
  • For cross-country work: be explicit about comparability (definitions, coverage, data quality) before reading a cross-national contrast as a development effect.

Reproducibility while you work (not at the end)

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

Execution bridge (StatsPAI / Stata MCP)

Run the battery, don't just enumerate it. Full map: execution-with-mcp. PDR is population studies blending quantitative and policy work; apply the chain to its empirical-causal papers.

  • 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, decomposition components, or projections with no uncertainty
  • An APC model presented as the uniquely correct partition
  • Reading a cross-country correlation as a development effect without addressing comparability
  • A results section whose rates and decompositions the code cannot reproduce

Evidence pass for PDR

Run this as a concrete capability pass. First lock the population process, the development/policy linkage, the data and time scale, the selection/measurement issue, and the uncertainty; then test whether the manuscript addresses PDR's broad audience who inspect both the population evidence and its development meaning.

  • Primary move: Audit unit, comparison, uncertainty, missingness, sensitivity, comparability, 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 Demography and Population Studies (methods-forward), Population Research and Policy Review (applied policy), and Studies in Family Planning (programs); if a neighbor 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 / projection + magnitude + interval
【Development meaning】what it implies for the social/economic/environmental outcome
【Exposure / denominator check】correctly constructed? [Y/N]
【Decomposition】components defined + sum to total? [Y/N/NA]
【APC / comparability】constraint stated / cross-country comparability addressed? [Y/N/NA]
【Inference】weights/clustering/competing risks handled? [Y/N]
【Reproducible】master script + seeds + pinned versions? [Y/N]
【Next】popdevr-tables-figures

Supplementary resources

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