What Do We Really Know About Corporate Governance? A Review of the Empirical Research Since 2000
Bibliographic record
Abstract
Corporate governance is currently understood by most legal actors to lie at the heart of corporate law. In addition to the explosion in academic interest in governance over the past three decades, it has increasingly been seen as a way to address a wide array of economic and social problems. In order for corporate governance to do the work assigned it, however, it must take the form of “best practices” that are generalizable across a wide variety of firms and circumstances. These best practices have been subject to extensive empirical research since the 1990s. This article examines this body of research, with particular attention to the studies published since 2000, which correct prior flaws and take advantage of the earlier papers for meta-analysis. The article focuses on what the author understands to be the current board best practices: (1) independent directors; (2) board committees of independent directors (particularly the audit committee); (3) board diversity; (4) separating the CEO and board chair roles; (5) director overboarding (also known as director busyness): and (6) interlocking directorships. When the empirical research in this area is looked at in its totality, there is surprisingly strong agreement that none of the best practices contribute in a measurable way to firm performance, or to other outcomes that are value-enhancing to firms. There is evidence, visible here and there in the research, that corporate governance arrangements do matter, but that imposing the same arrangements on all companies regardless of circumstances has the effect of benefitting some companies and damaging others in such a way that, as a group, North American businesses are left worse off. The article concludes by recommending regulatory responses in an era when faith in one-size-fits-all corporate governance best practices can no longer be sustained.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.000 | 0.007 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".