REStud Robustness (restud-robustness)

When to trigger

• The main result rests on a single specification
• No placebo / falsification evidence exists
• A referee will say "the effect is driven by X" and you have no pre-empting check
• The result is statistically marginal and sensitivity is undocumented
• Heterogeneity or mechanism evidence is absent and the channel is asserted, not shown

The REStud standard

REStud referees are demanding and the journal is known for developing strong papers across rounds. A robust REStud result is one that does not hinge on one fragile specification. The goal of this stage is to find the weak point before a referee does, and either fix it or report it honestly. Robustness is not a wall of extra tables — it is targeted evidence against the most plausible alternative explanations. Two REStud-specific facts shape how you stage it: (1) bulk robustness belongs in the online appendix / supplementary file, with only the decisive checks in the main text; (2) for an accepted empirical paper, every robustness number must be reproducible, because the Data Editor (Miklós Koren) reruns your code before publication under the AEA DCAS standard (see restud-replication-package) — a check you cannot regenerate from the deposit is a liability, not a defense.

Priority of checks

Run, in roughly this order of referee salience:

1. Specification robustness. Vary fixed effects, controls, functional form, and sample windows. The headline magnitude should be stable, not just same-signed. Report a coefficient-stability / sensitivity table or a specchart-style plot.
2. Placebo / falsification. A test where the effect should be absent (placebo outcome, placebo timing, placebo population). A clean placebo is worth more than ten near-identical specifications.
3. Inference robustness. Re-cluster at alternative levels; wild-cluster bootstrap if clusters are few; randomization inference for designs that admit it.
4. Mechanism. Show evidence consistent with the proposed channel — auxiliary outcomes, subgroup patterns the theory predicts. Mechanism evidence must not weaken the identification of the main effect.
5. Heterogeneity. Effects where theory predicts them to be larger/smaller. Pre-specify the cuts; do not data-mine subgroups and report the significant one.
6. Selection / attrition / measurement. For panels and experiments: differential attrition, measurement-error bounds, sample-selection corrections where relevant.

Calibrating effort

• A new empirical fact paper lives or dies on robustness — over-invest in (1) and (2).
• A new design paper must show the design's diagnostics are not knife-edge — over-invest in (3) and design-specific placebos.
• A theory-with-empirics paper needs (4) tightest — the empirics must confirm the model's specific predictions, not just a correlation.

Reporting discipline

• Put the decisive robustness evidence in the main text, not the appendix. A referee reading only the body should see the result survive its hardest test.
• Move the bulk of additional specifications and falsification exercises to the online appendix, cross-referenced from the body.
• Report robustness as a coefficient-stability table or specification plot, so the reader sees the distribution of estimates at a glance rather than reading ten columns.
• If a reasonable specification weakens the result, say so and explain why (power, a known confounder, a sample boundary) rather than hiding it — a demanding REStud referee will run the check themselves.

The honest-fragility test

Before submission, ask: "What single change would a hostile referee make to break this result?" Then make that change yourself and report the outcome. If the result breaks under a reasonable alternative, the paper is not ready — return to restud-identification (empirical) or restud-theory-model (theory) rather than papering over it with more tables.

Execution bridge (StatsPAI / Stata MCP)

Run the battery, don't just enumerate it. Full map: execution-with-mcp. REStud is top-5 general-interest economics; credible identification with modern estimators is the bar across applied fields.

• 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.

Checklist

• Headline magnitude stable across alternative specifications, not merely same-signed
• At least one genuine placebo / falsification test
• Inference re-examined at alternative clustering / with few-cluster correction
• Mechanism evidence consistent with the asserted channel
• Heterogeneity cuts pre-specified, not mined
• Attrition / selection / measurement addressed where relevant
• The single most fragile assumption is identified and stress-tested

Anti-patterns

• "Robustness theater" — ten near-identical columns that vary nothing a referee cares about
• A result that flips sign or loses significance under a reasonable alternative spec, reported only in the appendix
• Mechanism claims with no supporting evidence ("we interpret this as ...")
• Data-mined heterogeneity: testing 20 subgroups and headlining the one that is significant
• Hiding the fragile specification instead of confronting it
• Under-powered / fragile empirics presented as definitive

Output format

【MAIN RESULT】<one line>
【SPEC ROBUSTNESS】stable / fragile — details
【PLACEBO】present / absent — what it tests
【INFERENCE】clustering level + few-cluster correction used
【MECHANISM EVIDENCE】[...]
【HETEROGENEITY】pre-specified cuts: [...]
【MOST FRAGILE ASSUMPTION】<identified + how stress-tested>
【NEXT SKILL】restud-tables-figures