| name | jhr-data-analysis |
| description | Use when building or auditing Journal of Human Resources (JHR) empirical pipelines — sample construction, design-based causal estimates with correct clustering, robustness, comparative estimation against prior published work, online appendix material, and reproducible analysis. |
Data Analysis (jhr-data-analysis)
When to trigger
- You are preparing the empirical pipeline for a JHR paper
- Sample construction, reconciliation, or robustness is still unsettled
- The paper needs a replication-ready workflow before acceptance
Applied-micro analysis checklist
- Define unit, population, period, treatment, comparison group, and outcome.
- Show sample attrition and merge rules.
- Report baseline balance or pre-treatment comparability when relevant.
- Estimate main effects with the right clustering and fixed effects.
- Add reconciliation estimates against the closest prior published work.
- Add robustness for sample windows, functional form, controls, treatment
definitions, and outlier handling.
JHR-specific constraints
- Keep main tables inside the page limit; move overflow to Online Appendix.
- Prepare a data-archive plan from the start, especially for restricted data.
- For RCTs, track pre-analysis plan registration and deviations.
Comparative-estimate workflow
Build one reconciliation table before submission:
| Column | Purpose |
|---|
| Prior published estimate | Reproduce or quote the closest estimate with sample/design notes |
| Prior specification on your data | Shows whether the difference is data or specification |
| Your preferred specification | Shows the incremental design or measurement change |
| Sensitivity bridge | Changes one assumption at a time: sample, controls, weights, clustering, outcome |
This table can live in the Online Appendix, but the introduction should summarize the lesson in one
sentence. Without it, a JHR referee can ask for reconciliation late in the process.
Estimator defaults JHR referees assume
| Design | Default estimator | Diagnostics referees expect alongside |
|---|
| Staggered DID | Callaway-Sant'Anna, Sun-Abraham, or imputation (Borusyak-Jaravel-Spiess); never TWFE alone with heterogeneous timing | Event study with pre-period coefficients, Goodman-Bacon style decomposition when TWFE is reported |
| Sharp/fuzzy RDD | Local linear with MSE-optimal bandwidth and robust bias-corrected CIs | Density/manipulation test, covariate continuity, bandwidth and donut sensitivity |
| IV | 2SLS plus weak-IV-robust inference when first stage is marginal | First-stage table per endogenous variable, effective F, Anderson-Rubin CI |
| Lottery / admissions experiment | ITT plus LATE via lottery-fixed-effects 2SLS | Balance within randomization strata, compliance and attrition by arm |
| RCT | PAP-aligned ITT with randomization-inference check where feasible | Balance, attrition, multiple-testing adjustment |
Inference choices that draw referee fire
- Cluster at the level of treatment assignment (state policy → state clusters),
not at the individual or county level just because N is larger.
- With few treated clusters, add wild cluster bootstrap or randomization
inference; report how many clusters drive identification.
- Survey-weight decisions must match the estimand: weighted for population
parameters, unweighted (with justification) for design-based comparisons.
- Show that significance survives the correct clustering before any
heterogeneity cuts are interpreted.
Linked-data hygiene
- Document match rates for administrative-survey linkages and show that match
quality does not differ by treatment status; differential linkage is a
selection story referees raise unprompted.
- Date-stamp policy adoption variables from primary legal sources; miscoded
effective dates are a classic catch in JHR rollout papers.
Worked numbers: postpartum-coverage pipeline
Illustrative pipeline for a Medicaid postpartum-coverage extension paper using
linked birth records (numbers invented for the walkthrough):
- Sample: 1.9M births, 12 adopting and 19 comparison states; attrition table
shows 4 percent lost to cross-state moves.
- Main estimate: Callaway-Sant'Anna ATT of -1.3 severe-morbidity events per
1,000 births, SE clustered on 31 states, wild-bootstrap p reported.
- Reconciliation: prior single-state estimate of -3.0 shrinks to -1.6 when its
specification is run on the multi-state sample — difference is sample, not
specification; one sentence in the introduction states this.
- Archive: scripts run end-to-end from a clean clone; restricted birth-record
access documented for the waiver request.
Robustness ledger to maintain
Check | Spec changed | Estimate | SE | Verdict | Exhibit
pre-trends | event study, t-4..t-1 | ... | ... | flat/violated | Fig 2
alt control group | never-treated only | ... | ... | stable/moved | App T3
clustering | state vs state-by-year | ... | ... | robust/fragile| App T4
sample window | drop early adopters | ... | ... | stable/moved | App T5
prior-spec bridge | prior paper's controls | ... | ... | reconciled | App T6
Execution bridge (StatsPAI / Stata MCP)
Run the battery, don't just enumerate it. Full map:
execution-with-mcp. JHR is labor/education economics — program evaluation with selection; DiD/IV/RDD and the selection objection are central.
- Many outcomes / specifications:
romano_wolf (step-down FWER) or benjamini_hochberg.
- OVB sensitivity:
oster_delta / sensemakr.
- Inference:
wild_cluster_bootstrap (few clusters), twoway_cluster / conley.
- Re-fit off one handle:
audit_result(result_id) lists missing checks + the exact
suggest_function for each.
- Exhibits:
etable / did_summary_to_latex from the handle — no retyped numbers.
Decisive checks in the body, exhaustive battery in the appendix.
JF execution walkthrough.
Output format
[Sample] unit + population + period
[Design] ...
[Main estimates] ...
[Reconciliation tests] ...
[Archive-readiness gaps] ...
[Next step] jhr-contribution-framing
