Tables & Figures (jhr-tables-figures)
When to trigger
- Results are ready but tables are dense or over the page limit
- You need reconciliation, robustness, or design diagnostics in exhibit form
- The Online Appendix needs a clear structure
Exhibit plan
- Table 1: sample, descriptive statistics, and key balance where relevant.
- Main table: preferred specification with transparent controls and clustering.
- Design diagnostic: pre-trends, first stage, manipulation, balance, or event
study depending on design.
- Reconciliation table: compare your estimate to prior estimates and explain the
bridge.
- Appendix: robustness, sensitivity, alternative samples, and extra outcomes.
Notes must state
- Unit, sample, period, outcome units
- Fixed effects and controls
- Clustering level
- Treatment definition
- Survey weights or population weights if used
- Page/appendix location
Main-text exhibit budget
Use the scarce main-text pages for exhibits that change a reader's belief:
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Sample and balance: proves the population and comparison are understandable.
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Main estimate with magnitude: preferred result plus units and confidence interval.
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Design diagnostic: pre-trend, first stage, manipulation test, balance, or attrition.
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Reconciliation: prior estimate vs. your bridge specification vs. preferred specification.
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Policy heterogeneity: only if it maps to a real policy margin, not a fishing cut.
Everything else belongs in the Online Appendix with clear cross-references.
Appendix map
Organize appendix exhibits by reviewer use, not by the order scripts happen to run:
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Design validity: balance, pre-trends, manipulation, attrition, first stage, or placebo evidence.
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Specification sensitivity: alternative controls, bandwidths, estimators, samples, weights, and
clustering levels.
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Reconciliation: bridge specifications that explain differences from prior estimates.
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Mechanism and heterogeneity: only after the main effect and design validity are clear.
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Data construction: variable definitions, sample filters, merges, missingness, and coding decisions.
Each appendix table should be referenced from exactly one main-text claim or robustness sentence. Orphaned
appendix exhibits create page and credibility costs.
Event-study figure standard
The event-study plot is often the single most scrutinized exhibit in a JHR
design paper. Hold it to this bar:
- Name the estimator in the note (heterogeneity-robust group-time aggregation,
interaction-weighted, or imputation — not just "event study").
- Reference period marked (usually t = -1) and at least four pre-periods shown
when the data allow; binned endpoints labeled as bins.
- 95 percent confidence intervals from SEs clustered at the assignment level,
with the cluster count in the note.
- Y-axis in outcome units, not standardized indices, so the policy reader can
judge magnitude directly.
- If TWFE and robust estimates diverge, plot both series rather than choosing
silently.
First-stage and RD display conventions
- IV papers: a first-stage table adjacent to the 2SLS table — coefficient,
effective F per endogenous regressor, and the reduced form; referees read
these three together.
- RD papers: the binned outcome plot and the density plot are a pair; show the
bandwidth on the figure and put manipulation-test results in the note.
- Lottery papers: a balance exhibit within randomization strata precedes any
effect figure.
Worked exhibit ledger
Illustrative ledger for a childcare-subsidy DID paper (titles invented):
Fig 1 Rollout map + timing of county adoption claim: variation exists
Tab 1 Sample means, adopters vs not, pre-period claim: comparability
Tab 2 ATT on maternal employment, 3 estimators claim: main effect
Fig 2 Event study, 5 pre / 6 post, CIs, clusters=42 claim: no pre-trends
Tab 3 Bridge to prior state-level estimate claim: reconciliation
Tab 4 Heterogeneity by single-parent status claim: policy margin
App A Sensitivity: windows, controls, clustering referenced from Tab 2
Seven main exhibits is a sensible ceiling under the page cap; every appendix
entry must be cited from one main-text sentence.
Execution bridge (StatsPAI / Stata MCP)
Generate exhibits from the fitted result, not by retyping numbers. Full map:
execution-with-mcp. JHR is labor/education economics — program evaluation with selection; DiD/IV/RDD and the selection objection are central.
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Tables:
etable (multi-model) or did_summary_to_latex straight from the result_id.
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Figures:
plot_from_result / enhanced_event_study_plot / event_study_table —
axis units and the SE/clustering note baked in.
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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] main table / diagnostic / reconciliation / appendix
[Claim] ...
[Required note fields] ...
[Page-limit action] keep / move to appendix / compress
[Next step] jhr-writing-style