Renegotiations of Target CEOs' Personal Benefits During Mergers and Acquisitions
Bibliographic record
Abstract
ABSTRACT Many view large payments following mergers or acquisitions as excessive and evidence of rent extraction. Using additional disclosures required by the SEC since 2006, I hand‐collect details of preexisting change in control (CIC) provisions in employment agreements and CIC benefits granted to target CEOs during mergers. I find that CIC benefits are renegotiated in approximately 50 percent of my sample. I then investigate whether renegotiation of CIC benefits tends to be opportunistic, or, instead, evidence of efficient contracting. The overall evidence is more consistent with efficient contracting. This contrasts prior research that focuses solely on certain components of CIC benefits, such as employment in merged firms or merger bonuses. I find that changes in CIC benefits are positively associated with the CEO's horizon, as would be predicted by efficient contracting, but only limited evidence that changes in CIC benefits are positively associated with proxies for CEO power, as would be predicted by rent extraction. Acquiring firm shareholders interpret increases in target CEOs' CIC benefits as evidence of rent extraction, although I find that the merged firm's post‐merger performance is positively associated with changes in CIC benefits. This result is more consistent with acquiring firms providing target CEOs increased CIC benefits to complete mergers and realize synergies than with value‐reducing rent extraction.
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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 | 0.021 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".