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name: demog-research-design description: Use when defending the research design of a Demography (PAA / Duke University Press) manuscript — choosing among demographic methods (life tables, decomposition, event-history/survival, age-period-cohort, multistate, microsimulation, projections) and, where the question is causal, defending identification. Demography judges each method on its own terms. Strengthens the design; it does not write code.

Research Design (demog-research-design)

Demography accepts a wide variety of methodological approaches but is demanding about each. The design must credibly connect the argument (demog-theory-building) to the demographic evidence. This skill is method-aware: pick the section that matches your question and defend it against the strongest rival explanation.

When to trigger

• Choosing the demographic method that actually answers the question
• A reviewer questioned the rate construction, the identification, or the projection assumptions
• Specifying an age-period-cohort, multistate, or microsimulation design
• Justifying why your design adjudicates the rival account from demog-literature-positioning

Match the method to the question

• Life tables — for survival, life expectancy, and exposure: period vs. cohort, abridged vs. complete; multiple-decrement (cause-specific) and multistate (healthy/disabled) where relevant.
• Decomposition — to attribute a difference or change in a rate to components: Kitagawa (rate vs. composition), Arriaga (age contributions to e0), Horiuchi continuous, Das Gupta (multi-factor). Say exactly what each component means.
• Event-history / survival — for timing and transitions: Cox, parametric, discrete-time, with competing risks and multistate models when several destinations matter; check the proportional-hazards assumption.
• Age-period-cohort — confront the identification problem head-on: APC effects are linearly dependent, so state the constraint or modeling assumption (and its substantive justification) you rely on; do not present a single "identified" APC partition as if it were assumption-free.
• Multistate / projections / microsimulation — make transition rates, the base population, and the assumptions (closed/open, period/cohort) explicit; report sensitivity to key assumptions.

When the question is causal

• Identification first. State the estimand and the assumptions licensing a causal reading (ignorability, parallel trends, exclusion, continuity); defend them, don't assert them.
• Selection and exposure are demographic hazards: mortality selection, migration selection, and differential exposure can masquerade as effects — address them explicitly.
• Inference. Cluster at the right level (e.g., household, region, cohort); use survey weights and design for complex samples; report uncertainty for derived demographic quantities.
• Sensitivity. How strong must an unobserved confounder (or a violated rate assumption) be to overturn the result?

The adjudication test (Demography-specific)

For the single strongest rival explanation (e.g., compositional change, selection, tempo distortion), write one sentence: "If the rival were true rather than my account, the age/cohort pattern would look like ___; instead it looks like ___." If you cannot, the design does not yet identify the contribution.

Execution bridge (StatsPAI / Stata MCP)

Estimate and audit the design, don't only describe 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.

• detect_design → recommend → fit with as_handle=true → audit_result.
• Observational causal claims: staggered DiD (callaway_santanna / sun_abraham + bacon_decomposition + honest_did_from_result); IV (effective_f_test + anderson_rubin_ci); RDD (rdrobust + mccrary_test).
• Experiments: randomization-based inference, romano_wolf for many-outcome family-wise control, and mediate for mediation (not naive controlling-away).
• Sensitivity: oster_delta / sensemakr for observational claims.

Report the effect size in interpretable units; route the full battery to the appendix/supplement. A run end-to-end (synthetic data, real returns) is in the JF execution walkthrough.

Anti-patterns

• Running a regression when the question calls for a life table, a decomposition, or an event-history model
• Presenting an APC decomposition without naming the identifying constraint
• Period rates read as cohort experience (or vice versa) without justification
• Ignoring mortality/migration selection in a survival or panel design
• Projections whose assumptions are buried instead of varied and reported

Output format

【Method】life table / decomposition / event history / APC / multistate / microsim / projection / causal
【Quantity / estimand】what is being measured or identified
【Key assumption(s)】and how each is defended (name the APC constraint if used)
【Rival ruled out】the adjudication sentence
【Robustness/sensitivity】planned checks
【Next】demog-data-analysis

Supplementary resources

• ../../resources/external_tools.md — life-table, decomposition, survival, APC, and microsimulation packages
• ../../resources/official-source-map.md — Demography scope and methodological breadth