Real Incentive Effects of Soft Information
Bibliographic record
Abstract
ABSTRACT Both soft, noncontractible, and hard, contractible, information are informative about managerial ability and future firm performance. If a manager's future compensation depends on expectations of ability or future performance, then the manager has implicit incentives to affect the information. We examine the real incentive effects of soft information in a dynamic agency with limited commitment. When long‐term contracts are renegotiated, the rewards for future performance inherent in long‐term contracts allow the principal partial control over the implicit incentives. This is because the soft information affects the basis for contract renegotiation. With short‐term contracts, the principal has no control over the basis for contract negotiation, and thus long‐term contracts generally dominate short‐term contracts. With long‐term contracts, the principal's control over implicit incentives is characterized in terms of effective contracting on an implicit aggregation of the soft information that arises from predicting (forming expectations of) future performance. We provide sufficient conditions for soft information to have no real incentive effects. In general, implicit incentives not controllable by the principal include fixed effects, such as career concerns driven by labor markets external to the agency. When controllable incentives span the fixed effects of career concerns, the latter have no real effects with regard to total managerial incentives—they would optimally be the same with or without career concerns. Our analysis suggests empirical tests for estimating career concerns that should explicitly incorporate noncontractible information.
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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 | 0.013 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 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".