Most journals reward more robustness tables; FM explicitly does not. The editors state they put less weight on trivial robustness tests and more on new ideas and whether the result is interesting and credible. This inverts the usual instinct: at FM, a robustness section that mechanically re-runs the regression with five winsorization thresholds and three control sets signals weak ideas hiding behind volume. The skill here is subtraction, then precision — cut the busywork, then run the small number of checks that each defuse a named alternative explanation a smart referee would actually raise. One check that kills the leading confounder is worth more than ten that vary nothing important.
Build the robustness set by listing the alternative explanations a referee could offer, then pairing each with the one check that addresses it. Everything not on that list is a candidate for the appendix or the cut.
| Alternative explanation a referee raises | The targeted check (not a generic one) |
|---|---|
| "It's an omitted firm characteristic" | Oster-style coefficient-stability / sensitivity to selection on unobservables |
| "It's reverse causality" | lead-lag / pre-trend / a design that fixes timing |
| "It's driven by one industry or period" | drop-the-suspect-subsample; show stability, not 12 random subsamples |
| "It's a measurement artifact of variable X" | re-do with the one credible alternative measure of X |
| "It's a mechanical accounting identity" | re-specify to break the identity; show the result is not tautological |
| "The inference is overstated" | the right SE (two-way / wild-cluster), not five SE flavors |
| "It won't survive transaction costs" (asset pricing) | net-of-cost returns, realistic implementation |
Under-weighting trivial robustness is not a license to skip the checks that matter. FM still expects:
Run the battery, don't just enumerate it. Full map:
execution-with-mcp. Financial Management is empirical corporate finance + asset pricing; corporate-causal chain (DiD/IV/RDD) plus the factor-zoo haircut for cross-sectional pricing.
romano_wolf (step-down FWER) or benjamini_hochberg.oster_delta / sensemakr.wild_cluster_bootstrap (few clusters), twoway_cluster / conley.audit_result(result_id) lists missing checks + the exact
suggest_function for each.etable / did_summary_to_latex from the handle — no retyped numbers.Decisive checks in the body, exhaustive battery in the appendix. JF execution walkthrough.
A draft has eleven robustness tables; a referee says "this is thorough but I still don't know if it's real." The FM fix: list the three live alternatives — omitted governance quality, reverse causality, and a single-industry driver — and replace eleven tables with three: an Oster sensitivity bound, a lead-lag timing test, and a drop-financials subsample. Each defuses one named threat; the coefficient moves from 3.2 to 3.0 to 3.1 across them (illustrative), so stability is the story. The other eight tables move to the internet appendix. The paper now reads as confident, not defensive — exactly FM's taste.
Some checks look like robustness but are actually identification, and FM expects them even under its lean philosophy:
【Alternative explanations】[named list a referee would raise]
【Threat → check map】[one targeted check per threat]
【Demoted / cut】[trivial checks moved to appendix or removed]
【Leading threat addressed first】[Y/N]
【Inference】correct SE chosen (not a flavor menu)? [Y/N]
【Headline】point-estimate stability shown, not a starred wall? [Y/N]
【Next skill】finman-tables-figures