Jhe Tables Figures

Tables and Figures (jhe-tables-figures)

When to trigger

• The main result is settled and must be readable at a glance to a health economist
• Tables are dense, over-decimaled, or bury the headline policy effect
• An event-study / RD / first-stage plot needs to carry the identification visually
• The descriptive picture of the health setting (utilization, spending distribution, coverage) is missing or buried

The JHE exhibit bar

At JHE the main health-policy estimate should be findable in seconds, and the exhibits should let a health economist judge both the magnitude and the institutional plausibility of the result. Elsevier/JHE house style permits significance stars, but standard errors in parentheses are the load-bearing object and clustering must be stated. Two exhibit duties are specific to this journal: (1) a descriptive/institutional exhibit that shows the health setting — the coverage gap, the spending distribution (it is skewed — show it), the policy timeline — so readers trust the variation; and (2) the identification figure (event-study leads, RD continuity, first stage) that makes the design visible before the table.

Exhibit craft

1. One table for the headline. The preferred specification — effect, SE in parentheses, N, dependent-var mean, clustering level — should be readable without flipping pages; demote the controls sweep to the online appendix.
2. Show the distribution, not just the mean. Health spending and utilization are right-skewed and zero-heavy; a distribution figure or quantile effects tell the policy story a mean hides.
3. Make the setting legible. A descriptive exhibit naming the program, eligibility, and timing earns referee trust that the variation is what you say it is.
4. Figures carry identification. Event-study leads, RD continuity, and the first stage are more convincing as clean vector figures than as prose.
5. Right precision and self-contained notes. Two to three significant figures; each note states sample, units, clustering, controls, outcome definition, and (if used) what a star means.
6. Name the outcome in plain terms. "Any inpatient admission (0/1)" beats an opaque variable label; a health-policy reader should know exactly what was measured from the exhibit alone.

Execution bridge (StatsPAI / Stata MCP)

Generate exhibits from the fitted result, not by retyping numbers. Full map: execution-with-mcp. JHE is health economics — insurance/program reforms and selection; foreground DiD/IV/RDD and selection corrections.

• Tables: etable (multi-model) or did_summary_to_latex straight from the result_id.
• Figures: plot_from_result / enhanced_event_study_plot / event_study_table — axis units and the SE/clustering note baked in.
• Every note names the estimator + clustering and states the magnitude in interpretable units.

See a full fitted-result → exhibit chain in the JF execution walkthrough.

Checklist

• Headline effect readable in one table: coefficient, SE in parentheses, N, dep-var mean, clustering stated
• A descriptive/institutional exhibit makes the health setting and policy timing concrete
• Skewed/zero-inflated outcomes shown as a distribution, not only a mean
• Identification figure present (event-study with CIs / RD with density / first stage)
• Heterogeneity reported by policy-relevant subgroup with MHT, not subgroup fishing
• Notes self-contained (sample, units, clustering, outcome definition, controls)
• Figures clean vector output; precision 2–3 sig figs; no redundant exhibits
• Outcome variables named in plain terms; magnitudes shown against a base rate

Anti-patterns

• A summary-statistics table with no health-system detail, so the variation looks generic
• A 12-column results table where the policy effect is buried in column 9
• Reporting stars or t-stats but omitting standard errors / clustering level
• Mean-only reporting that hides the skew and zero mass in spending/utilization
• An event-study with no confidence intervals or an unclear reference period
• An RD figure with a high-order global polynomial manufacturing a jump
• A heterogeneity wall of starred subgroups with no MHT and no clinical/policy logic
• An exhibit whose magnitude has no base rate, so the reader cannot judge whether it is large

Referee pushback mapped to the exhibit fix

• "I cannot find your main estimate." → One headline table with the effect, SE, N, clustering, and dep-var mean; the controls sweep demoted to the appendix.
• "Where are the standard errors and what is the clustering?" → SEs in parentheses everywhere; clustering level (usually state) and any star meaning stated in the self-contained note.
• "Your mean effect hides what happened in the tail." → Add a distribution or quantile-effect figure; health spending lives in the right tail, and the mean can understate or mask the policy story.
• "This RD jump looks like a polynomial artifact." → Replace the global high-order fit with a local-linear binned scatter plus a density panel.
• "I cannot tell whether your variation is credible." → Add the descriptive/institutional exhibit: eligibility, payment rule, and policy timing, so the design is plausible before the table.

Worked vignette (illustrative)

A coverage paper's Table 4 sweeps every control combination across 12 columns; the headline take-up effect hides in column 9 with only t-stats. The JHE fix: promote the preferred specification to a two-panel Table 2 (Panel A: effect 4.1pp, s.e. 1.0 in parentheses, N, dep-var mean, clustered on state; Panel B: with full controls), move the sweep to the online appendix, and add Figure 1 — the event-study with CIs and a marked reference period. Add Figure 2 showing the spending distribution shift, since the mean effect understates the policy story in the right tail. The result and its design are now legible in seconds.

Magnitudes a health-policy reader can act on

A JHE exhibit is persuasive when the reader can translate the number into a policy statement. Always give the base rate alongside the effect (a 4.1pp coverage gain reads differently against a 62% baseline than a 20% one), report dollar magnitudes for spending in current units, and reserve QALY/mortality language for effects you actually estimated. For heterogeneity, organize by the subgroup a regulator cares about (income band, age-eligibility, chronic-condition status), not by whatever split happens to be significant. The exhibit should let a policymaker read off "who was affected and by how much" without the prose.

Output format

【Journal】Journal of Health Economics
【Skill】jhe-tables-figures
【Headline exhibit】one table carrying the policy effect? [Y/N]
【Inference shown】SEs in parentheses + clustering level in notes? [Y/N]; stars defined if used
【Setting exhibit】descriptive/institutional + distribution shown? [Y/N]
【Identification figure】event-study / RD / first stage with CIs? [Y/N]
【Heterogeneity】policy-relevant subgroups with MHT? [Y/N]
【Next skill】jhe-writing-style

Handoff boundary

This skill makes a settled result legible; it does not establish the result (jhe-identification / jhe-robustness) or write the surrounding argument (jhe-writing-style). Do not polish exhibits while the estimate is still moving — that is wasted effort and it tempts presentation choices that flatter an unstable number. When the headline and identification figures are clean and self-contained, hand off to jhe-writing-style.

Source: brycewang-stanford/Awesome-Journal-Skills → Journal-of-Health-Economics-Skills/skills/jhe-tables-figures/SKILL.md