Does corporate social responsibility facilitate credit ratings: Evidence from Rule 144A bonds
Bibliographic record
Abstract
• Rule 144A issuers with higher CSR performance receive better initial credit ratings. • Foreign Rule 144A issuers with higher CSR receive favorable initial credit ratings. • CSR can reduce information asymmetry or risk, granting firms better credit ratings. • Rule 144A issuers with better CSR performance can borrow more debt at a lower cost. We investigate how corporate social responsibility (CSR) influences the initial credit rating of Rule 144A bonds. Our findings indicate that socially responsible issuers are rewarded with better initial ratings. This result persists even for each environmental, social, and governance component. We further show that foreign issuers with strong CSR receive more favorable initial ratings than their domestic peers. Additionally, issuers with higher CSR can issue larger amounts at lower borrowing costs. Results from instrumental variables and entropy balance analyses show robustness accounting for endogeneity. Our findings suggest that CSR efforts are recognized and rewarded in the Rule 144A market.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".