jpam-tables-figures

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.

1. 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.

1. Heterogeneity / mechanism. A figure showing effects by the theory-driven subgroups.
2. 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](../../../shared-resources/empirical-methods/execution-with-mcp.md). 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](../../../Journal-of-Finance-Skills/resources/worked-examples/02-execution-walkthrough.md).

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