jpam-tables-figures

name	jpam-tables-figures
description	Use when building tables and figures for a Journal of Policy Analysis and Management (JPAM) manuscript — self-contained, decision-legible exhibits that show the policy effect, its uncertainty, the design's validity, and cost-benefit / distributional results. Designs exhibits; it does not run the estimation.

Tables & Figures (jpam-tables-figures)

JPAM exhibits serve a mixed audience — economists, political scientists, public-management scholars, and practitioners — so they must be self-contained and decision-legible: a policymaker should grasp the main result, its uncertainty, and who it affects without reading the methods section. Lead with the exhibit that shows the policy effect and its credibility, not a wall of coefficients.

When to trigger

• Designing the main results table/figure and the design-validity exhibits
• A reviewer found tables unreadable, or the key result hard to locate
• Presenting cost-benefit or distributional results visually
• Preparing exhibits for the (double-blind) submission

What the exhibit set should contain

1. The headline effect, clearly. A main results table or a coefficient/effect figure in policy- relevant units, with confidence intervals — not just significance stars.
2. Design-validity exhibits. The evidence that the identification holds: an event-study / pre-trends plot for DiD, an RD plot with binned means and the fitted discontinuity, a balance table for an RCT, a synthetic-control fit plot. These often persuade reviewers more than the point estimate.
3. Heterogeneity / mechanism. A figure showing effects by the theory-driven subgroups.
4. Cost-benefit / distributional. Where central, an exhibit that shows the benefit-cost result and its sensitivity, or the distribution of gains and costs across groups.

Craft standards

• Self-contained captions: define the sample, the estimator, the units, the inference (what the error bars/SEs are and the clustering), and the time window — readable without the text.
• Confidence intervals over stars in figures; report SEs and the clustering level in tables.
• Policy-relevant units on axes and in cells (dollars, percentage points, per-recipient).
• Accessible design: colorblind-safe palettes, legible in grayscale, vector output for print.
• Honest scaling: do not truncate axes to exaggerate an effect; show the zero line where relevant.

Execution bridge (StatsPAI / Stata MCP)

Generate exhibits from the fitted result, not by retyping numbers. Full map: execution-with-mcp. JPAM is policy analysis — program evaluation is the core; DiD/IV/RDD and the policy-relevant magnitude are decisive.

• 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

• Main effect in policy-relevant units with CIs, locatable at a glance
• A design-validity exhibit (event-study / RD plot / balance / SC fit) included
• Heterogeneity exhibit matches the pre-specified subgroups
• Cost-benefit / distributional result shown where it is central
• Captions self-contained: sample, estimator, units, inference, window
• Colorblind-safe, grayscale-legible, vector format
• Every exhibit number/value matches the deposited replication output

Anti-patterns

• A dense regression table with stars and no confidence intervals or units
• Hiding the parallel-trends / RD-validity evidence in an appendix the reviewer must hunt for
• Captions that require the methods section to interpret
• Truncated or rescaled axes that overstate the effect
• A cost-benefit conclusion in prose only, with no exhibit or sensitivity shown
• Exhibits whose numbers drift from the replication package

Calibration anchors (hedged)

• For a DiD or RD paper, the design-validity figure often does more persuasive work than the point estimate — a clean pre-trends or RD plot pre-empts the cross-disciplinary referee's first objection.
• A mixed APPAM audience reads exhibits before prose; if the headline effect and its uncertainty are not legible from the figure alone, the paper feels harder than it is.
• Confidence intervals communicate policy precision better than stars — a wide CI is itself information a decision-maker needs.

Worked micro-example (illustrative)

For a staggered-adoption DiD, the strong exhibit set is: (1) an event-study figure with confidence bands showing flat pre-trends and the post-policy effect; (2) a main table reporting the heterogeneity-robust estimate in dollars with the clustering level named; (3) a subgroup figure for the pre-specified populations; and (4) a benefit-cost panel with sensitivity bars. A reviewer can verify the identification, read the magnitude, and see the policy bottom line without leaving the figures. (Illustrative.)

Output format

【Headline exhibit】main effect + CI in policy units
【Design-validity exhibit】event-study / RD / balance / SC fit
【Heterogeneity / mechanism】subgroup figure
【Cost-benefit / distribution】exhibit + sensitivity (if central)
【Accessibility】colorblind-safe, grayscale, vector? [Y/N]
【Next】jpam-writing-style

Supplementary resources

• ../../resources/code/ — event-study and RD plotting templates
• ../../resources/external_tools.md — figure tooling (coefplot, ggplot2, marginaleffects)