<scp>CEO</scp> Pay‐for‐Complexity and the Risk of Managerial Diversion from Multinational Diversification
Bibliographic record
Abstract
Prior studies find that CEOs receive higher pay if the enterprise is more complex because more complex enterprises are, in theory, matched with the managerial skills of higher-ability CEOs. While multinational diversification is typically a characteristic of enterprise complexity, we argue that multinational diversification also introduces a risk that executives will divert an enterprise’s resources to obtain private benefits. We first establish that CEO pay is, on average, increasing in the extent of multinational diversification, consistent with intuition that more complex enterprises are matched to higher‐ability CEOs. We then demonstrate that the CEO pay‐for‐complexity premium is lower if the multinational diversification reflects a relatively high risk of managerial diversion. For sufficiently high levels of multinational diversification accompanied by a high risk of managerial diversion, we find that CEOs receive a relative reduction in pay rather than a pay premium for multinational diversification. We also find evidence that this pay effect occurs in part through adjustments to a CEO’s pay‐for‐performance sensitivity.
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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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".