Enhancing corporate governance quality through mergers and acquisitions
Bibliographic record
Abstract
Abstract This study examines whether the pre‐deal target‐bidder firm governance gap affects the bidder's postdeal change in governance quality. We estimate cross‐sectional regressions using mergers and acquisitions from 2004 to 2016. We find that the bidder's firm‐level governance improves for acquisitions where the target's governance quality is better than that of the bidder preacquisition. We attribute the results to reverse portability, suggesting that the predeal governance gap creates space for governance transfer, and bidders can adopt better governance of targets after the acquisition. Board independence, audit committee independence, CEO‐Chairman separation, stock compensation, and equal treatment of minority shareholders serve as potential channels to demonstrate the bidder's higher governance after the acquisition. Our findings also reveal that bidders with governance improvement are also associated with higher operating performance. We extend the portability theory of Ellis et al. (2017) and suggest that governance can also travel from targets to bidders through mergers and acquisitions.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".