Bibliographic record
Abstract
Abstract This paper addresses the question of what determines where in a firm's hierarchy investment decisions are made. We present a simple model of a CEO and a division manager to analyze when the CEO will choose to allocate decision-making authority over an investment decision to a division manager. Both the CEO and thedivision manager have private information regarding the profit maximizing investment level. Because the division manager is assumed to have a preference for “empire”, neither manager will communicate her information fully to the other. We show that the probability of delegation increases with the importance of the division manager's information and decreases with the importance of the CEO's information. A somewhat counterintuitive result is that, in some circumstances, increases in agency problems result in increased willingness of the CEO to delegate the decision. We also characterize situations in which the CEO prefers to commit to an allocation of authority ex ante, instead of deciding based on her private information.Finally, even though the division manager is biased toward larger investments, we show that under certainconditions, the average investment will be smaller when the decision is delegated. These results help explain some findings in the empirical literature. A number of other empirical implications are developed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".