Bibliographic record
Abstract
The roles of the market for corporate control and institutional investor monitoring as corporate governance mechanisms are studied. First, I examine agreements that address the interests of stakeholders of takeover targets, particularly the management and board, which are termed "social issues" and are frequently negotiated during mergers. I find that the inclusion of social issues comes at the cost of lower target shareholder returns. Furthermore, social issues are particularly costly for shareholders of targets whose management has enhanced negotiating power, and are also associated with poorer post-merger performance. Social issues therefore reflect a trade-off between shareholder and managerial interests and they are an important friction arising during changes in control. Second, we study a feature of takeovers which permits acquirers to walk away at the cost of paying a "reverse termination fee" to the target. We model a merger contract with this feature as a real call option on the assets of the target firm, which ensures the merger terminates whenever completion is sub-optimal. We find that this feature is more common when there is a higher risk that the target's value to the acquirer will fall below its stand-alone value before deal completion, and that the size of the fee is driven by option-like characteristics of mergers. The risk that a change in control decreases value may therefore be mitigated by a real option contract. Last, we examine the ability of institutional investors to improve governance by acting collectively. We show that private and organized engagements with firms by a coalition of Canadian institutional investors make the firms more likely to improve shareholder democracy and compensation structure and disclosure. We find that the coalition's influence is further broadened through director interlocks and the publication of governance scores, although there are limits to its influence. Institutional investors can therefore overcome coordination costs and act collectively to improve governance at the firm and market levels. Overall, the analysis indicates that frictions which impede the effectiveness of the market for corporate control and institutional investor monitoring as corporate governance mechanisms may be overcome by investor power, contracting and coordinated action.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".