IER exhibits serve a rigor-leaning, broad audience, so each one must answer a single question and make the economics — not just the statistics — legible. For a theory/structural-tilted journal, the most valuable exhibits are often the ones that make a mechanism or a comparative static visible, and the parameter table that shows the model is disciplined by data. Sort your exhibits by their job:
| Exhibit type | Its one job | The IER craft |
|---|---|---|
| Structural parameter table | Show estimates ARE pinned, not just reported | Pair each parameter with its identifying moment and a standard error / sensitivity entry |
| Model-fit table | Show targeted moments fit AND untargeted moments match | Two panels: targeted vs. untargeted; data column next to model column |
| Comparative-statics figure | Make the mechanism visible | Plot the key outcome against the key parameter; annotate where the sign flips |
| Counterfactual table | Show the policy result and its uncertainty | Report the headline as a range over the uncertain parameter, not a lone point |
| Regression table (applied) | Show the design's answer | Report economic magnitudes and CIs, not just coefficients; group by threat, not by control |
| Event-study figure | Show pre-trends are flat | Leads and lags with CIs; reference line at treatment |
***/**/* and report SEs in parentheses and economic magnitudes in the text.ier-replication-package) and keep the main text exhibits that carry the contribution.For the structural/quantitative papers that are IER's bread and butter, the single most important exhibit is usually the parameter table read together with the sensitivity/identification panel. A referee scanning a new structural paper goes straight to it to answer one question: are these parameters pinned by the data, or assumed? Make that table do the work — estimate, standard error, the identifying moment, and (ideally) the sensitivity entry showing which moment moves it. A parameter table that is just a list of numbers tells the referee the model was calibrated, not estimated, and that perception is hard to reverse.
A draft presents a 12-column regression table with three sets of controls and stars on everything. It answers no single question and the reader cannot find the result. The fix splits it: one main-text table reporting the headline coefficient as an economic magnitude with its confidence interval, columns ordered by threat retired (baseline → drop influential subsample → alternative inference), and the control-set permutations moved to an online appendix. The note states sample, estimator, unit, and clustering. Now the table answers exactly one question — "is the effect there and how big" — and the reader sees it in five seconds.
The case against significance stars is not stylistic at IER — it is methodological. A rigor-leaning readership wants the precision of an estimate (its standard error or confidence set) shown directly, so the reader judges economic and statistical significance themselves, rather than having a coarse three-level threshold imposed by the author. Stars also encourage the very specification search the robustness section is meant to rule out. Report the SE in parentheses, state the economic magnitude in the text, and let the confidence interval do the work the stars used to do. This aligns with the broader econometric-society house norm and is the safer default even where the exact style guideline is 待核实.
A figure that merely re-plots a table's numbers wastes a page against the ≤50-page ceiling. The figures that earn space show something a table cannot: a shape (a non-monotonic comparative static), a flip (where a sign changes), a distribution (heterogeneity a mean hides), or a fit (model vs. data across the support). For theory and structural papers especially, the comparative-statics figure that makes the mechanism visible is often the most-cited exhibit in the paper — it is where the broad reader grasps the economics without following the algebra.
Generate exhibits from the fitted result, not by retyping numbers (the usual source of
body-vs-appendix drift). Full map: execution-with-mcp.
etable (multi-model columns) or did_summary_to_latex straight from the
result_id — one variable definition, one set of numbers, body and appendix in sync.plot_from_result / enhanced_event_study_plot / event_study_table —
axis units and the SE/clustering note baked in.See a full fitted-result → exhibit chain in the JF execution walkthrough.
【Journal】International Economic Review
【Skill】ier-tables-figures
【Exhibit inventory】each exhibit → the one question it answers
【Parameter table】identifying moment + SE/sensitivity per estimate? [Y/N]
【Mechanism figure】comparative static / mechanism made visible? [Y/N]
【House style】no asterisks; SEs/CIs shown; magnitudes in text? [Y/N]
【Notes】self-contained (sample/estimator/unit/inference)? [Y/N]
【Page budget】main-text exhibits within the ≤50pp ceiling? [Y/N]
【Verdict】carries-the-argument / needs-work
【Next skill】ier-writing-style