Bibliographic record
Abstract
This study examines how product market peers affect lending relationships. We contend that firms are more likely to borrow from a bank that has previously lent to a peer to mitigate information asymmetry with the bank when potential information processing efficiencies are greater (i.e., information efficiency hypothesis), but there will be a decreased propensity to borrow from a shared lender when the costs of leaking proprietary information are greater (i.e., proprietary information leakage hypothesis). We find that, after bank mergers that involve peers’ lenders, firms are more likely to switch banks to avoid sharing the same lenders as a product market peer. In cross-sectional analyses, we find that after bank mergers that involve a peer’s bank, firms are less likely to switch when the firm’s financial reporting is more opaque and has greater monitoring needs, consistent with the information efficiency hypothesis. In contrast, firms are more likely to switch after bank mergers that involve a peer’s bank when the firm belongs to an industry with greater proprietary costs and when the bank has greater incentives to leak information, consistent with the proprietary cost hypothesis. This paper was accepted by Brian Bushee, accounting.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".