| name | demog-data-analysis |
| description | 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. |
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
- 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.
- 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.
- Decomposition with clear components. State precisely what each component (rate vs. composition,
age contribution, factor) represents; ensure components sum to the total being explained.
- 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.
- Survival/event-history rigor. Check proportional hazards; handle censoring, truncation, and
competing risks correctly; report on the right time scale (age, duration, period).
- 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
