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
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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.
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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.
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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.
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APC discipline. Be explicit about the identification problem; report under the stated constraint
and show sensitivity to plausible alternatives — never imply a unique decomposition.
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Survival/event-history rigor. Check proportional hazards; handle censoring, truncation, and
competing risks correctly; report on the right time scale (age, duration, period).
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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.
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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.
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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.
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Many outcomes / specifications:
romano_wolf (step-down FWER) or
benjamini_hochberg — report the adjusted threshold.
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OVB sensitivity:
oster_delta / sensemakr.
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Inference:
wild_cluster_bootstrap (few clusters), twoway_cluster / conley;
multilevel data → cluster at the right level.
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Re-fit off one handle:
audit_result(result_id) lists the missing checks and the
exact suggest_function for each.
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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.
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Primary move: Audit unit, comparison, uncertainty, missingness, sensitivity, comparability, and
reproducibility before making any prose or submission recommendation.
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Decision ledger: return
claim / evidence / blocker / next edit rows so the next pass can patch
the manuscript directly.
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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.
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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
