Jle Tables Figures

Tables & Figures (jle-tables-figures)

When to trigger

• The main effect of the legal rule is settled and must be made readable at a glance
• Tables are dense, over-decimaled, or bury the headline coefficient under controls
• An event-study around a law change, an RD at a regulatory cutoff, or a judge-leniency first stage needs to carry the identification visually
• You are preparing exhibits for submission and want them in University of Chicago Press house style

The JLE exhibit bar

At JLE the effect of the legal rule should be findable in seconds, and every exhibit should earn its place. The signature visual is the picture of the legal identification — the event-study around the statute's effective date, the RD scatter at the regulatory threshold, the price path in treated vs. control markets after an enforcement action. Lead with it: at this journal, the figure that shows the rule "switching on" is half the persuasion. Report standard errors and the clustering level; describe the legal variation precisely in notes so an economist who does not know the statute can still read the table.

Exhibit craft

1. One exhibit for the headline. A reader should get the legal effect, its SE, and N without flipping pages.
2. Make the identification visible as a figure. Event-study leads around the effective date, RD continuity with a density panel, or treated/control market paths convince more than prose.
3. Standard errors and clustering always. Put SEs in parentheses and state the clustering level (usually the jurisdiction) in the note; if stars are used, define them — but the SE is the load-bearing object.
4. Self-contained, institution-aware notes. Each note states the sample, the legal variation, the treatment date used, the clustering level, and controls — a referee should read the table without the text.
5. Right precision. Two to three significant figures; do not report a litigation effect to five decimals.

Execution bridge (StatsPAI / Stata MCP)

Generate exhibits from the fitted result, not by retyping numbers. Full map: execution-with-mcp. JLE is empirical law-and-economics — DiD around legal/regulatory change is central.

• 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

• Effect of the legal rule readable in one exhibit: coefficient, SE in parentheses, N, clustering, dep-var mean
• Identification figure present (event-study around the effective date with CIs / RD with density / treated-vs-control paths)
• Standard errors everywhere; clustering level stated; stars (if used) defined
• Notes name the legal variation and the treatment date used, and make each exhibit self-contained
• Figures are clean vector output, legible, no chartjunk, consistent scales
• Precision sensible (2–3 sig figs); no redundant exhibits

Anti-patterns

• A main table with 12 columns where the legal-effect coefficient is hard to find
• An event-study plot with no confidence intervals, or one whose reference period dates treatment to the statute's signing rather than its effective date
• An RD figure with a high-order global polynomial that manufactures a jump and no density panel
• Reporting stars or t-stats but omitting standard errors or the clustering level
• Over-precision implying false accuracy in a noisy legal effect
• Exhibit notes that force the reader back to the text to learn what the rule or the control group is