Skills Data Science Structuring Growth Exhibits for Academic Papers

Structuring Growth Exhibits for Academic Papers

v20260724
jeg-tables-figures
A comprehensive guide for preparing high-quality tables and figures, specifically tailored for submissions to the Journal of Economic Growth (JEG). It dictates the necessary narrative flow—from identifying empirical facts to detailing mechanisms, model estimates, and robustness checks. The guide covers best practices for map generation, handling cross-country panels, and ensuring that all exhibits collectively tell a clear, coherent story about economic growth and its underlying mechanisms.
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Overview

Tables & Figures (jeg-tables-figures)

When to trigger

  • Main results exist but exhibits do not communicate growth mechanisms
  • Calibration or simulation output needs a readable structure
  • Empirical tables lack sample, fixed-effect, clustering, or unit notes

Exhibit architecture

  • Descriptive growth facts: long-run trends, cross-country dispersion, transition paths, or cohort patterns.
  • Main estimates or model results: one table/figure per claim, not per estimator experiment.
  • Mechanism exhibits: human capital, fertility, technology, institutions, financial development, migration, or political economy channels.
  • Robustness/sensitivity: alternative samples, periods, specifications, calibration targets, and parameter values.

JEG-specific polish

  • Use economically meaningful units: annual percentage points of growth, log GDP per worker, schooling years, fertility rates, TFP, steady-state ratios.
  • Label whether an exhibit is empirical, calibrated, simulated, or theoretical.
  • For transition paths, show initial condition, time scale, and steady state.
  • For cross-country panels, disclose country coverage and period.

Exhibit sequence

Most JEG papers need one of these sequences:

  • Empirical growth: descriptive growth fact -> identification diagnostic -> main estimate -> mechanism or heterogeneity -> robustness.
  • Theory/calibration: model mechanism diagram or proposition summary -> calibration table -> transition path -> sensitivity -> welfare or growth decomposition.
  • Mixed paper: empirical fact motivating the mechanism -> model result -> calibration/estimation -> data validation -> counterfactual.

The sequence should make the growth mechanism visible before the robustness appendix expands.

Growth exhibit contract

Each exhibit should state whether it shows a fact, identifies a mechanism, validates a model, or tests sensitivity. Do not let all tables look like robustness tables. A JEG reader should be able to follow the growth story from exhibits alone:

Fact -> mechanism -> model/estimate -> magnitude -> sensitivity -> implication

For calibration exhibits, report the target moment, model moment, parameter value, and source. For empirical exhibits, report country/region coverage, period, units, fixed effects, clustering, and whether the estimate is meant to be causal or descriptive.

Map and spatial-exhibit standards

Persistence and comparative-development submissions live and die by their spatial exhibits:

  • Every map states the projection, the unit (grid cell, district, ethnic homeland), the period of the plotted variable, and the source layer.
  • Show the treatment variation on a map before the regression tables appear; growth referees want to see where identification comes from geographically.
  • Table notes for geocoded outcomes state the spatial-SE method and cutoffs (e.g., "Conley SEs, 250 km, in brackets") next to the clustered SEs — in the main table, not buried in an appendix.
  • Avoid choropleth color scales that flatten the variation actually identifying the model; bin by the estimation sample's own distribution.

Worked vignette — rebuilding a persistence main table

Illustrative redesign of a weak Table 2 (outcome: log GDP per capita 2015 by district; regressor: early printing-press adoption):

  • Column 1: OLS with country FE — 0.19 (clustered SE 0.04).
  • Column 2: adds geography controls (ruggedness, latitude, coast distance) — 0.16 (0.04).
  • Column 3: same specification reporting Conley 250 km SEs — 0.16 (0.06); an inference column, not a new estimate.
  • Column 4: IV using distance to an early adoption hub — 0.24 (0.09), with first-stage F = 21 in the notes.
  • One row added: the outcome mean, so magnitudes read directly as percent effects.

The old version's six estimator-variation columns move to the appendix, and the schooling-channel table is promoted into the main text — mechanism before robustness.

Exhibit-count anchor (hedged)

Accepted articles at this journal commonly carry 6-10 main exhibits backed by a deep online appendix; treat that as a prior from recent issues rather than a rule, and check figure-format specifics against the journal's current author guidelines.

Execution bridge (StatsPAI / Stata MCP)

Generate exhibits from the fitted result, not by retyping numbers. Full map: execution-with-mcp. JEG (growth) uses cross-country and long-run panels with deep endogeneity; foreground identification and robustness to alternatives.

  • 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.

Output format

[Exhibit] table / figure / appendix
[Claim supported] ...
[Units and sample] ...
[Model/data status] empirical / calibrated / simulated
[Missing note fields] ...
[Next step] jeg-writing-style
Info
Category Data Science
Name jeg-tables-figures
Version v20260724
Size 5.47KB
Updated At 2026-07-28
Language