jhr-data-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):

1. Sample: 1.9M births, 12 adopting and 19 comparison states; attrition table

shows 4 percent lost to cross-state moves.

1. 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.

1. 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.

1. 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](../../../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.

• 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](../../../Journal-of-Finance-Skills/resources/worked-examples/02-execution-walkthrough.md).

Output format

[Sample] unit + population + period
[Design] ...
[Main estimates] ...
[Reconciliation tests] ...
[Archive-readiness gaps] ...
[Next step] jhr-contribution-framing