Individual skill
jppm-data-analysis
Use when estimating and stress-testing results for a Journal of Public Policy & Marketing (JPP&M) manuscript — treatment effects from experiments, DiD/RDD/synthetic-control policy evaluations, subgroup analyses for vulnerable populations, and policy-interpretable reporting. Runs the analysis; it does not choose the design (jppm-methods).
Journal-of-Public-Policy-and-Marketing-Skills/skills/jppm-data-analysis/SKILL.md
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- Last checked
- Nov 9, 2026
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- Repository last updated
- Sep 27, 2026
- License
- MIT
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# Data Analysis (jppm-data-analysis)
## When to trigger
- A DiD around a policy rollout needs modern estimators and a pre-trend defense
- An RDD's bandwidth, density, or placebo checks are unbuilt
- Experimental effects are significant but not yet expressed in units a regulator can use
- Subgroup results for vulnerable populations look like fishing rather than a plan
- A reviewer will ask "how big is this in the market, and for whom?" and you cannot answer
## Estimate for the decision, not the asterisk
At JPP&M the estimate feeds a **policy judgment**, so the analysis has two jobs the sibling journals weigh less: (1) make the causal claim survive an econometrics-literate reviewer, and (2) express magnitudes in **decision units** — percentage points of prevalence, dollars per household, calories per purchase, share of consumers misled — with uncertainty attached. A p-value cannot tell an agency whether a warning is worth mandating; an effect of "−2.1 percentage points in purchase incidence, 95% CI [−3.4, −0.8], concentrated among households below median income" can. Every headline estimate should be convertible into the language of a regulatory impact analysis.
