demog-data-analysis

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.

1. 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.

1. Decomposition with clear components. State precisely what each component (rate vs. composition,

age contribution, factor) represents; ensure components sum to the total being explained.

1. 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.

1. Survival/event-history rigor. Check proportional hazards; handle censoring, truncation, and

competing risks correctly; report on the right time scale (age, duration, period).

1. 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](../../../shared-resources/empirical-methods/execution-with-mcp.md). 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](../../../Journal-of-Finance-Skills/resources/worked-examples/02-execution-walkthrough.md).

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

• [../../resources/external_tools.md](../../resources/external_tools.md) — life-table, decomposition, survival, APC, and simulation packages
• [../../resources/official-source-map.md](../../resources/official-source-map.md) — data-availability and reproducible-code expectations