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name: jpube-tables-figures description: Use when designing the exhibits for a Journal of Public Economics (JPubE) manuscript — bunching density plots, event-study and RD/RKD graphs, incidence and distributional figures, and self-contained tables. Makes the public-finance design visible; it does not run the analysis (use jpube-data-analysis).

Tables & Figures (jpube-tables-figures)

When to trigger

• The identifying variation is buried in a regression table instead of shown in a graph
• A bunching, RD, or event-study result has no picture
• Tables are dense, under-noted, or not callable in order
• You need exhibits that make a policy elasticity legible to a referee

Why figure-forward at JPubE

JPubE's identification often is a picture — a spike of excess mass at a tax kink, a jump at an eligibility cutoff, a clean break at a reform date. Because referees are public-finance specialists assessing design credibility, the headline of a JPubE empirical paper is frequently one transparent graph that lets the reader see the response before any regression. Build exhibits so the design is self-evident.

Exhibit norms

• Lead with the design figure. Bunching: observed density vs. smooth counterfactual around the kink/notch, with the excluded region marked. RD/RKD: binned scatter with the fitted discontinuity/kink and CIs. DID: event-study plot with leads and zero-line.
• Show, then estimate. A figure that makes the response visible earns more trust than a coefficient; the table quantifies what the figure shows.
• Distributional / incidence plots where the contribution is about who bears a tax or gains from a transfer.
• Self-contained notes. Each table/figure note states the sample, data source (and restricted-access caveat), the estimator, the inference (clustering level), and the units — readable without the text.
• Clean tables. Report the policy parameter (elasticity, MVPF, take-up) prominently; avoid 10-column kitchen-sink tables; put diagnostics in clearly labeled panels.
• Print quality. Vector output (PDF/EPS); legible at print size; minimal chartjunk (no 3D, restrained color); confidence bands shown.

Execution bridge (StatsPAI / Stata MCP)

Generate exhibits from the fitted result, not by retyping numbers. Full map: execution-with-mcp. JPubE is public economics — tax/transfer/program designs; DiD/IV/RDD and bunching are central, magnitudes in policy units.

• 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

• A design figure leads the empirical section (bunching / RD / event-study)
• The policy parameter (elasticity / MVPF / take-up) is prominent in the main table
• Every exhibit is callable in order and self-contained via its note
• Notes state sample, source + access caveat, estimator, clustering, units
• Distributional / incidence exhibit included where the contribution warrants
• Vector graphics, confidence bands shown, chartjunk removed

Anti-patterns

• Hiding a bunching or RD result inside a regression table with no plot
• Figures with no confidence bands or no marked counterfactual / excluded region
• Notes that omit the data source or the restricted-access caveat
• A 10-column main table where the key elasticity is one buried row

Lead-figure choice by design (decision grid)

The headline exhibit should let a referee see the response before any regression.

Design	Lead figure	Must show
Bunching / notch	Observed vs. smooth counterfactual density	Excluded region marked, excess mass
RD / RKD	Binned scatter with fitted jump/kink	CIs, bandwidth, polynomial order
Reform DID	Event-study plot	Leads/lags, zero line, pre-trend flat
Incidence	Distributional bars by income/eligibility	Who bears the tax / gains the transfer

A vignette: a draft hides a kink-bunching result (elasticity e = 0.25, illustrative) inside a six-column regression table. The exhibit fix promotes the density plot — observed mass spiking above the smooth counterfactual at the kink, excluded region shaded — to Figure 1, and demotes the regression to a quantifying table. The referee sees the identification, then reads the number.

Hedge: exact figure-count or color conventions are production matters — confirm against the current Elsevier artwork guidelines.

Exhibit pass for Journal of Public Economics

Treat this skill as an executable review pass, not a prose hint. First lock the policy instrument, affected margin, identification design, and welfare or incidence interpretation; then judge whether the current manuscript answers the venue's real reader: public economists who ask whether policy design, fiscal incidence, or welfare interpretation is credible.

• Do the pass: For every table or figure, state the estimand or object, sample or case base, uncertainty display, and one sentence the exhibit proves for the venue audience.
• Return a ledger: give claim / evidence / risk / manuscript location rows, so the next agent can edit rather than rediscover the issue.
• Sibling guard: compare against JDE for development policy, JIE for cross-border policy, AEJ Economic Policy for broad policy readership; if a sibling owns the contribution, recommend re-routing before polishing format.
• Stop condition: do not give submission-ready advice until the pack's resources/official-source-map.md has been checked for volatile rules and the manuscript has one concrete fix for the largest venue-specific risk.

Output format

【Lead figure】bunching / RD / RKD / event-study — present? [Y/N]
【Key parameter visible】elasticity / MVPF / take-up in main table? [Y/N]
【Notes self-contained】sample/source/estimator/clustering/units? [Y/N]
【Distributional exhibit】[present / not needed]
【Print quality】vector + bands + low chartjunk? [Y/N]
【Next step】jpube-writing-style