Bibliographic record
Abstract
Is risk-taking ever a privately optimal response to agency problems within banks? In a model where borrower types only matter for safe projects, I show that the answer to this question depends on the nature of the agency problem – that is, whether loan officers are hired to screen or whether they are hired to both screen and monitor. Incentivizing screening favors no risk-taking but involves a non-monotone relationship between performance and compensation. This non-monotonicity undermines incentives to monitor so, when both screening and monitoring are important, the bank instead prefers a strategy which pushes low types into risky projects. That selected risk-taking emerges under impediments to non-monotone compensation is also illustrated in an environment with rank-order tournaments and no monitoring. ∗This paper expands and supplants the first part of an earlier working paper entitled “Bank Promotions and Credit Quality. ” I thank Doug Diamond, Zhiguo He, Anil Kashyap, Aleh Tsyvinski, Harald Uhlig, and seminar participants at the Chicago Fed and Chicago Booth Finance Lunch for helpful comments. Financial support from Chicago Booth is also gratefully acknowledged.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".