Do managerial abilities matter? Evidence from U.S. bank loans and corporate sustainability
Bibliographic record
Abstract
This study examines how managerial abilities affect the link between the cost of bank loans and corporate sustainability. We contend that sustainability activities reduce the cost of bank loans, and that this effect depends on managerial abilities and the company's credit quality. Using a U.S. dataset of 3,537 bank loan facilities, we conduct different multivariate regressions to test our predictions. Our findings reveal that corporate sustainability significantly decreases the cost of bank loan financing for firms with high managerial abilities relative to those with low managerial abilities. Furthermore, we found that corporate sustainability significantly decreases the bank loan financing cost for high-quality borrowers with high managerial abilities relative to low-quality borrowers with low managerial abilities. Overall, this research contributes to the literature by showing that the impact of corporate sustainability practices on the cost of bank loans does not only depend on the borrower's credit quality, as shown in prior empirical studies, but also on managerial abilities. Firms with both high credit quality and high managerial abilities enjoy lower bank loan costs. Our results have important implications. In particular, they provide valuable insights for firms seeking to improve their borrowing conditions, bankers aiming to assess borrowers’ quality, and policymakers looking to promote corporate sustainable behavior.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".