From jpam-skills
Guides estimation, cost-benefit, and distributional analysis for JPAM manuscripts. Reports effects in policy-relevant units with robustness and honest uncertainty.
How this skill is triggered — by the user, by Claude, or both
Slash command
/jpam-skills:jpam-data-analysisThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
JPAM analysis has two layers most field-journal papers skip: beyond the **causal estimate**, reviewers
JPAM analysis has two layers most field-journal papers skip: beyond the causal estimate, reviewers expect attention to cost-benefit and distributional consequences — who gains, who pays, and is it worth it? The estimate answers "does the policy work"; the cost-benefit and distributional work answers "should we do it, and for whom." Both must be reported honestly, with uncertainty carried through.
jpam-theory-building), pre-specified where possible; report
which subgroup tests are primary and adjust for multiplicity.An evaluation finds a job-training program raises quarterly earnings by $420 (95% CI $120–$720). The JPAM analysis does not stop there: it converts this to a benefit-cost ratio (lifetime earnings gain vs. per-participant cost) under a stated discount rate, runs sensitivity across the CI and discount rate, shows the gain is concentrated among longer-tenured entrants (theory-driven heterogeneity), and notes the program is net-positive to the government budget only above a take-up threshold. The policy story is the package, not the $420. (Numbers illustrative.)
【Main estimate】effect in policy-relevant units (+ CI)
【Robustness】checks mapped to specific threats
【Heterogeneity】theory-driven subgroups + multiplicity handling
【Cost-benefit】perspective, discount rate, MVPF/BCR, sensitivity
【Distribution】who gains / who pays
【Next】jpam-tables-figures
../../resources/code/ — estimation + robustness skeletons (Stata + Python)../../../shared-resources/empirical-methods/reporting-standards.md — inference + reporting table stakes../../resources/external_tools.md — cost-benefit / MVPF tooling and policy data sourcesnpx claudepluginhub brycewang-stanford/awesome-journal-skills --plugin jpam-skillsBuilds a theory of change / logic model for JPAM manuscripts: lever → mechanism → outcome, predicted heterogeneity, scope conditions for transfer, and unintended effects.
Builds a transparent welfare, cost-benefit, or optimal-policy framework from reduced-form causal estimates for AEJ: Economic Policy manuscripts.
Guides reproducible data analysis for JOP manuscripts: uncertainty reporting, robustness checks, and code that passes replication analyst review.