jhr-data-analysis

Research

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.

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Source SKILL.md: https://github.com/brycewang-stanford/Awesome-Journal-Skills/blob/HEAD/Journal-of-Human-Resources-Skills/skills/jhr-data-analysis/SKILL.md

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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