Jegeo Tables Figures

Tables and Figures (jegeo-tables-figures)

When to trigger

• The paper has maps that are pretty but make no argument (a choropleth that just shows where the data are)
• A key spatial pattern lives in a table of coefficients when a map or gradient plot would carry it
• Regression tables are dense, use significance asterisks, or hide the spatial-inference choices (W, cutoff, scale)
• A geographer reviewer cannot see the geography and an economist reviewer cannot see the magnitudes

Maps must argue, not decorate

JEG is one of the few economics outlets where the map is often the central exhibit, and referees from the geography side judge cartographic craft. A map earns its place only if it makes a point a table cannot:

• Show the variation the paper exploits, not just where observations sit — e.g., the spatial discontinuity at a border, the treated vs. control regions, the gradient that motivates the mechanism.
• Classification is an argument: the choropleth break scheme (quantiles, Jenks, equal-interval) changes the visual story — choose deliberately and say which, because a reviewer can re-bin and reach a different conclusion.
• Honest projection and scale: a national choropleth visually inflates large rural regions; consider cartograms or rates-per-area where the unit-size distortion matters.
• Map the residual, not just the raw outcome when the claim is about the part the model explains.
• Pair the map with a number so the economist audience can size the effect — a map of the gradient plus the estimated slope.

Tables in JEG house style

• No significance asterisks. Report coefficients with standard errors (and ideally confidence intervals); let magnitudes and precision speak. State the SE type (Conley/clustered) and, for spatial models, the W and cutoff in the note.
• One table answers one question. A reader should know from the title what spatial claim it supports.
• Surface the spatial choices that drive results in the table note: spatial scale/unit, weight matrix, cutoff distance, treatment timing.
• Direct vs. indirect (spillover) effects must be reported separately for spatial-lag/SDM models — a single coefficient is uninterpretable there.

Event-study and gradient plots

• For spatial DID, the event-study leads/lags plot is more persuasive than a DID coefficient table — show the flat pre-trend.
• For distance-based mechanisms, a coefficient-by-distance-ring plot makes the spatial decay visible and is exactly what a JEG referee wants to see.

Common map mistakes that draw a geographer's red pen

• Raw counts on a choropleth when the claim is about rates or intensity — large or populous regions dominate visually and mislead; map a rate or density instead.
• A rainbow color ramp for a sequential variable — it implies thresholds that are not in the data and fails in grayscale; use a perceptually ordered sequential or diverging ramp matched to the variable.
• No scale bar, north arrow, or unit definition — minor in economics tables, but a geography referee reads their absence as carelessness about space.
• Mercator (or an unstated projection) for an area or distance claim — distorts exactly the quantity the paper is about; pick an equal-area projection when area matters.
• Over-plotted point maps where thousands of geocoded points become an ink blob — bin to a hex grid or aggregate to show the pattern.

Execution bridge (StatsPAI / Stata MCP)

Generate exhibits from the fitted result, not by retyping numbers. Full map: execution-with-mcp. JEG is spatial economics — spatial dependence and sorting; emphasize identification and Conley/spatial-robust inference.

• 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.