Adverse Selection, Diversion of Resources, and Conservatism*
Bibliographic record
Abstract
ABSTRACT We consider an investor's choice of conservative reporting, bonus payments, and investment decisions in the presence of the hidden‐information agency problem of a manager's productivity and the hidden‐action agency problem of a manager's diversion of resources. It is important to consider the hidden‐action and hidden‐information agency problems in isolation and their interaction to gain insights into the drivers of demand for conservatism. We show that the conservative (nonconservative) regime is optimal for the high‐productivity (low‐productivity) manager when both agency problems exist, even though the nonconservative regime is optimal for both the high‐ and low‐productivity managers when only the hidden‐action or the hidden‐information problem exists. Essentially, the low‐productivity manager can misrepresent as the high‐productivity manager to obtain high investment levels and divert resources only in the presence of both agency problems. This added layer of agency problem creates the demand for conservatism and highlights the importance of the interaction between the hidden‐action and hidden‐information agency problems. Furthermore, we show that as the conservatism level increases (i) the optimal investment level conditional on a good report for the high‐productivity manager increases and approaches the first‐best level (i.e., ex‐post investment efficiency increases); (ii) the expected optimal investment level for the high‐productivity manager decreases and diverges from the expected first‐best level (i.e., ex‐ante investment efficiency decreases); and (iii) the expected bonus payment to the high‐ and low‐productivity managers decreases. These findings provide insights into how the demand for conservatism arises in the presence of both hidden‐information and hidden‐action agency problems and provide empirical guidance relating conservatism to investment efficiency.
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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 | 0.012 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".