qje-tables-figures

name	qje-tables-figures
description	Use when finalizing the main exhibits of a Quarterly Journal of Economics (QJE) manuscript — making the paper figure-forward, with clean tables and self-contained notes that read well in QJE's single-PDF, author-date format. Designs exhibits; it does not run new analysis.

Tables & Figures (qje-tables-figures)

When to trigger

• The main result is a dense table with too many columns
• The paper is "table-heavy" when the design would land better as a figure
• Table notes are incomplete (sample, units, clustering, significance unclear)
• An event-study / RDD / binscatter result is hidden in a table instead of plotted

QJE aesthetic: figure-forward, self-contained exhibits

QJE has moved firmly toward figure-forward presentation — the Opportunity Insights / Chetty-style QJE paper makes its central result legible in one well-designed graph (e.g., the binned mobility maps and exposure-effect plots of the QJE 2014/2018 neighborhoods papers). Identification designs are inherently visual: event-study plots, RDD discontinuity plots, and binned scatters communicate credibility better than a coefficient buried in a regression column. Tables remain essential for estimates and robustness, but the headline should often be a figure a reader grasps in five seconds. Practical QJE constraints: at initial submission everything is one PDF with figures embedded (no separate figure files), exhibits are numbered and called out in order, and in-text references are author-date (Chicago).

The headline-figure decision

Design	Headline figure
DID / event std	Event-study plot: leads ≈ 0, clean post-treatment dynamics
RDD	Discontinuity plot: binned means + local polynomial fit
IV	First-stage and reduced-form scatter / binscatter
RCT	Treatment-vs-control outcome distributions or effect-by-arm
Descriptive	The new fact, plotted with the data doing the talking

Table craft

• Width discipline. Main results table should be readable; if it sprawls past a handful of columns, split it or move variants to the appendix (no page limit means you can — but readability still wins).
• Self-contained notes. Every table/figure note states: sample and time span, unit of observation, what each column is, fixed effects included, standard-error clustering level, and how significance is denoted.
• Standard errors in parentheses, clustering level named in the note; report N and relevant fit statistics.
• Coefficients with meaning. Report units so the magnitude is interpretable (effect in SDs, in dollars, in percentage points), not just a bare number.
• Author-date (Chicago) in-text references; figures and tables numbered and called out in order.

Figure craft

• Show the data: binned scatters, confidence bands, and raw-ish patterns build credibility.
• Avoid chartjunk: no 3D, no needless color, legible axis labels with units; figures must remain legible embedded in the single submission PDF and at print resolution.
• Confidence intervals shown, not just point estimates; bandwidth/bin choices noted.
• A figure should be interpretable from its caption alone.

Checklist

• The central result has a headline figure a reader grasps quickly
• Main table is readable; sprawling variants moved to the appendix
• Every exhibit note is self-contained (sample, units, FE, clustering, significance)
• Magnitudes are interpretable (units stated), not bare coefficients
• Event-study / RDD / first-stage results are plotted, not only tabulated
• Confidence intervals / bands shown on figures
• Figures embedded and legible in the single submission PDF; numbered, author-date citations

Execution bridge (StatsPAI / Stata MCP)

Generate exhibits from the fitted result rather than retyping numbers (the usual source of body-vs-appendix drift). Full map: shared-resources/empirical-methods/execution-with-mcp.md.

• Tables: etable (multi-column) or did_summary_to_latex straight from the result_id — one definition, one set of numbers, body and appendix in sync.
• Event-study / coefficient 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 (from the result's diagnostics) and states the magnitude in interpretable units. See a full fitted-result → exhibit chain in the JF execution walkthrough.

Anti-patterns

• A 9-column main table when a single event-study figure would carry the result
• Table notes that omit the clustering level or the sample definition
• Reporting coefficients with no units, so magnitude is uninterpretable
• Decorative 3D/colored charts that add no information
• Burying the cleanest evidence (the discontinuity, the leads) in an appendix table

Output format

【Headline exhibit】figure type chosen + why
【Main table】column count + what moved to appendix
【Notes audit】sample / units / FE / clustering / significance present? [Y/N each]
【Magnitude legibility】units stated? [Y/N]
【Figures plotted】[event study / RDD / first stage / ...]
【Next step】qje-writing-style