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name: smj-data-analysis description: Use when estimating models and defeating endogeneity for a Strategic Management Journal (SMJ) manuscript — the single highest bar at SMJ. Executes and stress-tests the identification design from smj-methods; it does not design the study or build exhibits.

Data Analysis & Endogeneity (smj-data-analysis)

When to trigger

• You have a performance regression with no endogeneity / reverse-causality treatment
• DID, IV, matching, or a selection model is chosen but not yet stress-tested
• Reviewers will ask "how do you know this is causal and not selection?"
• You need to plan the mechanism test and the robustness battery

The SMJ endogeneity mandate

Performance regressions with unaddressed endogeneity or reverse causality are the #1 SMJ rejection reason. Treat causal identification as a first-class part of the paper, not a footnote. The reviewer's mental model: firms that make this strategic choice are different in ways that also affect performance. You must close that door explicitly.

SMJ codifies this in Bettis, Gambardella, Helfat & Mitchell (2014), "Quantitative empirical analysis in strategic management," SMJ 35(7): 949–953: acknowledge endogeneity, make a good-faith effort to address it, and avoid data snooping / p-hacking. Report economic magnitudes, not just stars. SMJ will publish well-designed studies that report null results — so do not suppress a theory-relevant null.

Threat → tool map

Threat	Primary tools
Self-selection into the strategic choice	IV, Heckman selection, PSM/CEM + DID, Rosenbaum bounds
Reverse causality / simultaneity	Exogenous shock + DID, lagged + Granger-style tests, IV
Unobserved time-invariant heterogeneity	Firm fixed effects (caveat: cannot fix time-varying confounds)
Omitted environmental confound	Industry-year FE, region FE, controls, falsification tests
Measurement error in X	IV, multiple indicators, sensitivity analysis

Pick from the threat named in smj-methods; usually you will combine FE with one identification tool.

Design-specific execution

DID / natural experiment

• Test and show parallel pre-trends (event-study plot, not just a claim).
• If treatment timing is staggered, address heterogeneous-treatment-effect bias (Goodman-Bacon decomposition; Callaway–Sant'Anna or Sun–Abraham estimators) rather than naive two-way FE.
• Run placebo tests (fake treatment dates; unaffected units) and report effect dynamics.

Instrumental variables

• Report the first-stage F (weak-instrument concern below conventional thresholds → use weak-IV-robust inference).
• Make the exclusion argument in prose: why the instrument affects performance only through the strategic choice. Reviewers reject IVs whose exclusion is implausible.
• Report the reduced form and over-identification tests where applicable.

Matching (PSM / CEM) + DID

• Report covariate balance before/after; show common support.
• Matching handles selection on observables only; combine with DID and acknowledge residual selection on unobservables (bounds).

Heckman selection

• Justify the exclusion restriction in the selection equation (a variable affecting selection but not the outcome). A Heckman with no valid exclusion restriction is identified only off functional form — reviewers know this.

Mechanism & robustness

• Mechanism test: if you theorized a mediator, test it (prefer evidence beyond a Baron–Kenny mediation regression — e.g., moderation-of-process, subsample variation in the mechanism).
• Magnitude, not just stars: interpret the key effect in economic terms (e.g., "a 1 SD increase in X → a Y% change in performance"); SMJ cares about meaningful effects.
• Robustness battery (report, do not bury): alternative DVs; alternative samples (drop dominant industries/years); alternative estimators; clustering choices; prior performance; survivorship. Where feasible, show sensitivity to the identifying assumption (partial-identification / bounding).
• Inference: cluster standard errors at the level of treatment assignment (often firm); justify the choice.

Execution bridge (StatsPAI / Stata MCP)

Run the battery, don't just enumerate it. Full map: execution-with-mcp. SMJ is strategy — firm-level panels where strategic choices are endogenous; foreground IV / DiD identification and the endogeneity-of-strategy objection.

• Many outcomes / specifications: romano_wolf (step-down FWER) or benjamini_hochberg — report the adjusted threshold.
• OVB sensitivity: oster_delta / sensemakr.
• Inference: wild_cluster_bootstrap (few clusters), twoway_cluster / conley; multilevel data → cluster at the right level.
• Re-fit off one handle: audit_result(result_id) lists the missing checks and the exact suggest_function for each.
• Exhibits: etable / did_summary_to_latex from the handle — no retyped numbers.

Keep the decisive checks in the body and the exhaustive battery in the appendix. See the executed chain in the JF execution walkthrough.

Checklist

• The identifying threat is stated and the matching tool is deployed
• Reverse causality is addressed by design, not by lags alone
• Firm (and industry-year) fixed effects included where appropriate
• DID: parallel-trends evidence shown; staggered-timing bias addressed; placebos run
• IV: first-stage strength reported; exclusion argued in prose; reduced form shown
• Matching: balance + common support reported; unobservable selection acknowledged
• Heckman: valid exclusion restriction, not functional-form identification
• Mechanism tested, not just asserted
• Robustness across DV, sample, estimator, and clustering reported
• Standard errors clustered at the assignment level

Anti-patterns

• Cross-sectional correlations interpreted causally — an instant credibility loss at SMJ
• "We include fixed effects" treated as a complete endogeneity defense (FE miss time-varying confounds)
• IV with an exclusion restriction no reviewer would believe
• Heckman or PSM run mechanically with no defensible exclusion / balance
• Ignoring that firms self-select into the very strategic choice being studied
• Staggered DID with naive two-way FE and no heterogeneity correction
• A wall of robustness tables that never confronts the central threat
• Specification hunting until p < 0.05 (data snooping); reporting stars with no economic magnitude; suppressing a theory-relevant null — all discouraged by SMJ

Output format

【Identifying threat】selection | reverse causality | unobserved heterogeneity | omitted confound
【Estimator】FE + [IV | DID | matching | Heckman | ...]
【Identification evidence】[parallel trends / first-stage F / balance / exclusion argument]
【Placebo / falsification】[done?]
【Mechanism test】[what + result]
【Robustness】[DV alt, sample alt, estimator alt, clustering]
【Residual threat acknowledged】...
【Economic magnitude reported】yes / add
【Nulls reported honestly (no p-hacking)】yes
【Next step】smj-contribution-framing

Templates & resources

• ../../resources/external_tools.md — Stata/R/Python packages (reghdfe, ivreghdfe, csdid/did, psmatch2, rdrobust) and strategy data sources
• ../../resources/official-source-map.md — SMJ p-hacking / null-results / endogeneity policy and the Bettis et al. (2014) editorial