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:
- Sample and balance: proves the population and comparison are understandable.
- Main estimate with magnitude: preferred result plus units and confidence interval.
- Design diagnostic: pre-trend, first stage, manipulation test, balance, or attrition.
- Reconciliation: prior estimate vs. your bridge specification vs. preferred specification.
- 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:
- Design validity: balance, pre-trends, manipulation, attrition, first stage, or placebo evidence.
- Specification sensitivity: alternative controls, bandwidths, estimators, samples, weights, and
clustering levels.
- Reconciliation: bridge specifications that explain differences from prior estimates.
- Mechanism and heterogeneity: only after the main effect and design validity are clear.
- 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.
- 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] main table / diagnostic / reconciliation / appendix
[Claim] ...
[Required note fields] ...
[Page-limit action] keep / move to appendix / compress
[Next step] jhr-writing-style
Source: brycewang-stanford/Awesome-Journal-Skills → Journal-of-Human-Resources-Skills/skills/jhr-tables-figures/SKILL.md
1---2name: jhr-tables-figures3description: Use when preparing Journal of Human Resources (JHR) tables, figures, online appendix exhibits, reconciliation tables, event-study and first-stage diagnostics, and policy-readable empirical displays that fit inside the page limit counting tables and figures.4---567# Tables & Figures (jhr-tables-figures)89## When to trigger1011- Results are ready but tables are dense or over the page limit12- You need reconciliation, robustness, or design diagnostics in exhibit form13- The Online Appendix needs a clear structure1415## Exhibit plan1617- Table 1: sample, descriptive statistics, and key balance where relevant.18- Main table: preferred specification with transparent controls and clustering.19- Design diagnostic: pre-trends, first stage, manipulation, balance, or event20 study depending on design.21- Reconciliation table: compare your estimate to prior estimates and explain the22 bridge.23- Appendix: robustness, sensitivity, alternative samples, and extra outcomes.2425## Notes must state2627- Unit, sample, period, outcome units28- Fixed effects and controls29- Clustering level30- Treatment definition31- Survey weights or population weights if used32- Page/appendix location3334## Main-text exhibit budget3536Use the scarce main-text pages for exhibits that change a reader's belief:37381. **Sample and balance**: proves the population and comparison are understandable.392. **Main estimate with magnitude**: preferred result plus units and confidence interval.403. **Design diagnostic**: pre-trend, first stage, manipulation test, balance, or attrition.414. **Reconciliation**: prior estimate vs. your bridge specification vs. preferred specification.425. **Policy heterogeneity**: only if it maps to a real policy margin, not a fishing cut.4344Everything else belongs in the Online Appendix with clear cross-references.4546## Appendix map4748Organize appendix exhibits by reviewer use, not by the order scripts happen to run:4950- **Design validity**: balance, pre-trends, manipulation, attrition, first stage, or placebo evidence.51- **Specification sensitivity**: alternative controls, bandwidths, estimators, samples, weights, and52 clustering levels.53- **Reconciliation**: bridge specifications that explain differences from prior estimates.54- **Mechanism and heterogeneity**: only after the main effect and design validity are clear.55- **Data construction**: variable definitions, sample filters, merges, missingness, and coding decisions.5657Each appendix table should be referenced from exactly one main-text claim or robustness sentence. Orphaned58appendix exhibits create page and credibility costs.5960## Event-study figure standard6162The event-study plot is often the single most scrutinized exhibit in a JHR63design paper. Hold it to this bar:6465- Name the estimator in the note (heterogeneity-robust group-time aggregation,66 interaction-weighted, or imputation — not just "event study").67- Reference period marked (usually t = -1) and at least four pre-periods shown68 when the data allow; binned endpoints labeled as bins.69- 95 percent confidence intervals from SEs clustered at the assignment level,70 with the cluster count in the note.71- Y-axis in outcome units, not standardized indices, so the policy reader can72 judge magnitude directly.73- If TWFE and robust estimates diverge, plot both series rather than choosing74 silently.7576## First-stage and RD display conventions7778- IV papers: a first-stage table adjacent to the 2SLS table — coefficient,79 effective F per endogenous regressor, and the reduced form; referees read80 these three together.81- RD papers: the binned outcome plot and the density plot are a pair; show the82 bandwidth on the figure and put manipulation-test results in the note.83- Lottery papers: a balance exhibit within randomization strata precedes any84 effect figure.8586## Worked exhibit ledger8788Illustrative ledger for a childcare-subsidy DID paper (titles invented):8990```text91Fig 1 Rollout map + timing of county adoption claim: variation exists92Tab 1 Sample means, adopters vs not, pre-period claim: comparability93Tab 2 ATT on maternal employment, 3 estimators claim: main effect94Fig 2 Event study, 5 pre / 6 post, CIs, clusters=42 claim: no pre-trends95Tab 3 Bridge to prior state-level estimate claim: reconciliation96Tab 4 Heterogeneity by single-parent status claim: policy margin97App A Sensitivity: windows, controls, clustering referenced from Tab 298```99100Seven main exhibits is a sensible ceiling under the page cap; every appendix101entry must be cited from one main-text sentence.102103## Execution bridge (StatsPAI / Stata MCP)104105Generate exhibits from the fitted result, not by retyping numbers. Full map:106[`execution-with-mcp`](../../../shared-resources/empirical-methods/execution-with-mcp.md). JHR is labor/education economics — program evaluation with selection; DiD/IV/RDD and the selection objection are central.107108- **Tables:** `etable` (multi-model) or `did_summary_to_latex` straight from the `result_id`.109- **Figures:** `plot_from_result` / `enhanced_event_study_plot` / `event_study_table` —110 axis units and the SE/clustering note baked in.111- **Every note** names the estimator + clustering and states the magnitude in interpretable units.112113See a full fitted-result → exhibit chain in the [JF execution walkthrough](../../../Journal-of-Finance-Skills/resources/worked-examples/02-execution-walkthrough.md).114## Output format115116```text117[Exhibit] main table / diagnostic / reconciliation / appendix118[Claim] ...119[Required note fields] ...120[Page-limit action] keep / move to appendix / compress121[Next step] jhr-writing-style122```123124---125126**Source:** [`brycewang-stanford/Awesome-Journal-Skills`](https://github.com/brycewang-stanford/Awesome-Journal-Skills) → `Journal-of-Human-Resources-Skills/skills/jhr-tables-figures/SKILL.md`