# Demog Research Design > 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. - Skill: `thedixitjain/demog-research-design` (Agent Skill) - Install (CLI): `npx skillmds add thedixitjain/demog-research-design` - Raw SKILL.md: https://api.skillmd.com/api/skills/thedixitjain/demog-research-design/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Data & Analytics - Author: thedixitjain (https://skillmd.com/u/thedixitjain) - Updated: 2026-09-08 - Page: https://skillmd.com/skills/thedixitjain/demog-research-design --- # 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`](../../../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. - `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](../../../Journal-of-Finance-Skills/resources/worked-examples/02-execution-walkthrough.md). ## 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`](../../resources/external_tools.md) — life-table, decomposition, survival, APC, and microsimulation packages - [`../../resources/official-source-map.md`](../../resources/official-source-map.md) — Demography scope and methodological breadth --- **Source:** [`brycewang-stanford/Awesome-Journal-Skills`](https://github.com/brycewang-stanford/Awesome-Journal-Skills) → `Demography-Skills/skills/demog-research-design/SKILL.md`