Bibliographic record
Abstract
This paper proves the renegotiation-proofness principle for a dynamic LEN (linear contracts, exponential utility, normal distributions) model and examines the impact of repeated renegotiation on incentives and managerial tenure when performance information is serially correlated. In addition to providing a general solution to a multiperiod agency problem with serially correlated performance measures, this paper characterizes optimal managerial tenure/turnover policies as a function of the time-series properties of performance measures. With negatively correlated performance measures, the principal prefers longer managerial tenure, and no turnover is optimal. With positively correlated performance measures, absent a switching cost, turnover every period is optimal. In the presence of a fixed switching cost, interior optimal turnover policies exist if the performance measures are positively correlated. Switching costs are necessary, but not sufficient for interior optimal tenure. The optimal turnover policies present an alternative to theories of performance-driven managerial turnover and are consistent with evidence that a majority of managerial turnovers are (age-related) normal retirements.
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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 | 0.010 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".