jfe-robustness

Robustness Battery (jfe-robustness)

When to trigger

• The result holds in the baseline but you have not tried obvious alternatives
• A referee could ask "does this survive a different measure / sample / specification?"
• You are unsure which robustness tests belong in the main text vs. the Internet Appendix
• You suspect a result might be fragile and want to find out before a referee does

Why robustness is JFE's signature

JFE's referee culture rewards thoroughness: a result is credible only after it survives a wide battery of sensible alternatives, and reviewers expect every alternative explanation to be addressed, not merely the convenient ones. A fragile result that breaks under a routine variation is a classic JFE rejection. The headline tests live in the main text; the rest go to the Internet Appendix, which JFE asks you to append to the end of the main manuscript file (see jfe-internet-appendix).

Because JFE mandates a code + non-proprietary-data deposit (Mendeley Data) at acceptance for post-2021 submissions, your robustness battery must be runnable, not just described — referees increasingly probe reproducibility after the field-wide concerns about non-replicable factors (the Fama-French factor lineage is the benchmark) and fragile staggered-DID results.

The robustness dimensions

Work through each; for each, decide main-text vs. appendix.

1. Alternative measures

• Re-measure the dependent and key independent variables (different proxies, data sources, definitions).
• Show the result is not an artifact of one operationalization.

2. Alternative specifications

• Add/remove fixed effects; vary the control set; show coefficient stability (a coefficient-stability / Oster bounds argument for omitted-variable bias where relevant).
• Functional form: levels vs. logs, linear vs. nonparametric.

3. Alternative samples

• Drop influential years (e.g., the financial crisis), industries, or large firms.
• Subperiod stability; exclude the most/least-treated units.

4. Alternative inference

• Re-cluster at a different level; wild-cluster bootstrap when clusters are few.
• Permutation/randomization inference where appropriate.

5. Falsification & placebos

• Outcomes that should not respond if your story is right.
• Placebo timing or placebo treatment groups.

6. Ruling out alternative explanations

• For each rival story a referee could tell, design a test that distinguishes it from yours.
• This is the single most important block for JFE — list the alternatives explicitly and kill them one by one.

Checklist

• Key variables re-measured with at least one alternative proxy
• Specification varied (FE set, controls, functional form) with stable coefficients
• Subsample / subperiod stability shown; crisis or outlier periods isolated
• Inference re-done at an alternative cluster level (or wild bootstrap)
• Falsification / placebo tests run and reported
• Each named alternative explanation has a distinguishing test
• Main-text vs. Internet-Appendix split decided for every test

Anti-patterns

• Reporting only the robustness checks that pass ("cherry-picked specifications")
• A "robustness" section that re-runs the same model with one extra control and calls it done
• Leaving an obvious rival explanation untested and hoping no referee notices
• A result that flips sign or loses significance under a routine variation, reported without comment
• Dumping every check into the main text so the paper becomes unreadable

Output format

【Headline result】...
【Robustness done】measures[...] specs[...] samples[...] inference[...] placebos[...]
【Alternatives ruled out】[story -> test] pairs
【Fragile under】[...] (if any — fix before submission)
【Main-text vs. appendix】split decided: yes/no
【Next】jfe-tables-figures