Bibliographic record
Abstract
Purpose This paper addresses the following questions: Why do some firms employ multiple debt types? What explains debt heterogeneity? Is the choice of the source of debt a function of corporate governance? Design/methodology/approach The author's paper is empirical and uses multiple regression analysis. Findings Firms under weak corporate governance have a higher propensity to use multiple debt types and have a dispersed debt structure. Contrastingly, firms that are well-managed tend to concentrate debt and borrow predominantly from a few creditors. The author also found that while bank debt is negatively associated with debt concentration, market debt is positively associated with debt concentration. Research limitations/implications Firms under weak corporate governance have a higher propensity to use multiple debt types and have a dispersed debt structure. Well-managed firms tend to concentrate debt and borrow predominantly from a few creditors. Bank debt is negatively associated with debt concentration and market debt is positively associated with debt concentration. Practical implications Policymakers and practitioners need to account not only for changes in the firm’s total debt level but also for changes within the firm’s debt composition. Understanding a manager’s choice of debt structure can incentivize creditors to effectively monitor and use debt concentration as a form of commitment device that transfers some control rights from the manager to creditors. Originality/value While a vast body of corporate finance literature examines the conflict between shareholders and management, there is little empirical work on the conflict between creditors and management. In this paper, the author examines how managerial entrenchment affects debt structure. The results provide a comprehensive picture of how corporate governance influences debt choice(s).
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".