Do shareholder protection and creditor rights have distinct effects on the association between debt maturity and ownership structure?
Bibliographic record
Abstract
Abstract This study examines the effects of the firm's ownership concentration and its institutional environment on corporate debt maturity choices. As ownership concentration and debt maturity are alternative governance mechanisms, we theorize and investigate whether their association is influenced by country‐level governance factors that enhance outside monitoring by minority shareholders and debtholders. Our investigation is based on a dataset of 50,599 firm‐year observations from 38 countries. We use a propensity‐score matching approach and find that the effect of ownership concentration on debt maturity is conditional to country‐level governance attributes. Ownership concentration has a negative effect on debt maturity in countries where both shareholder protection and creditor rights are weak. Ownership concentration, however, tends to lengthen debt maturity as protection increases, and this positive effect on the length of debt maturity is stronger in countries enhancing protection towards debtholders (instead of shareholders). We also explore other characteristics of ownership structure, such as the identity and presence of controlling shareholders. These results corroborate the view that entrenched shareholders may use debt maturity opportunistically. Our study provides new insights into the interplay between firm‐ and country‐level governance mechanisms and a deeper understanding of cross‐country differences in the association between ownership structure and debt financing.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".