The Causal Impact of Board Structure on Firm Profitability: Evidence from a Crisis
Bibliographic record
Abstract
This study investigates the causal impact of board governance structures on firm profitability. We develop the Board Structure Influence (BSI) index, a composite metric that captures board independence, diversity, and role distribution—which we conceptualize as three structural pillars of Separation, Variety, and Disparity—to provide a comprehensive measure of governance effectiveness. Using a Difference-in-Differences (DiD) framework centered on the COVID-19 pandemic as an exogenous shock, we identify firms with strong governance and top BSI quartiles and compare their financial performance—measured by net profit margin—against firms with weaker board structures. Our results demonstrate that firms with higher BSI scores experience a statistically significant increase in profitability post-COVID-19. A Causal Forest analysis further reveals that this positive effect is heterogeneous, with the largest firms benefiting most significantly from strong board governance. Robustness checks—including placebo tests, parallel trends validation, and a SUTVA test—affirm the credibility of our findings. This research highlights the strategic importance of board structure for firm resilience during crises. It provides management insights for corporate leaders, investors, and policymakers aiming to align governance reform with financial profitability.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".