Bibliographic record
Abstract
For a sample of public bond issues by U.S. firms between 2000 and 2019, sourced from the Securities Data Corporation (SDC) New Issues database, we examine the relationship between CEO-friendly boards and the cost of debt. To explore this relationship, we construct proxies for board friendliness based on social connections sourced from the BoardEx database, classifying a board as friendly if it includes at least one outside director who has a social connection with the CEO. Our regression analysis reveals a negative association between CEO-friendly boards and yield spreads and a positive association between CEO-friendly boards and credit ratings. These effects exist after controlling for firm and bond characteristics based on prior literature. The results are robust to an alternative measure of board friendliness and potential endogeneity. These findings imply that firms with a CEO-friendly board experience a lower cost of bond financing. This supports the argument that effective communication between CEOs and directors contributes to the enhancement of creditor interests. Our results carry a practical implication that firms heavily reliant on debt should actively employ CEO-friendly boards. Despite the burgeoning literature on CEO-friendly boards, there is a lack of research on the relationship between CEO-friendly boards and the cost of debt. Our results fill this gap in the extant literature on CEO-friendly boards.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".