Economic Crisis and Corporate Governance: How Can Board Independence and Expertise Maximize the Firm Value?
Bibliographic record
Abstract
In the context of recurrent crises and the necessity to move to more sustainable firm level changes, this paper analyzes the trade-off between board’s dual role of monitoring and advising the CEO, especially relevant for the integration of sustainable development into corporate strategy, depending on board independence and expertise. We propose a theoretical model in which boards may choose to be either monitoring or advisory type towards the CEO. In this framework, the board’s incentives to adopt a high monitoring level are non-monotonically (U-shaped) related to the expertise level. On the other hand, the incentives for an advisory board to discipline the CEO are increasing with expertise, if the business has high opportunity for growth. Finally, under specific parameter values, the model generates a disciplining effect of expertise in the sense that the more expert the board is, the less opportunistic the CEO is. We then test these theoretical results using a dataset on the French 120 largest listed companies over the 2006-2011 period. Empirical evidence reveals that expertise plays a mediating role in the relationship between independence and performance in French firms. Directors’ competences for sustainable development hence are likely to play a crucial role for firms to integrate such issues into their core strategy.
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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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".