The determinants of corporate cost of debt during a financial crisis
Bibliographic record
Abstract
Panel data from publicly listed US industrial firms is used to investigate how firm- specific cost of debt (COD) determinants impact COD at different quantiles during a financial crisis. Six COD determinants: firm size, firm age, profitability, leverage, liquidity, and firm value, and advanced estimators: robust and bootstrapped fixed effects, bias-corrected least square dummy variable (LSDVC), and quantile regression, are employed within the context of pecking-order theory. The results show that firm size and leverage negatively impact COD, while liquidity positively impacts it when COD is high (90% quantile). The degree of profitability only confirms the pecking order theory when COD is extremely low (10% quantile) and contrasts with the theory for the 25% and above COD quantiles during the Global Financial Crisis (GFC). These findings confirm that the practicalities of access to finance matter during a financial crisis for corporate financing decisions. • US industrial firms' cost of debt (COD) determinants are studied in the context of pecking order theory. • Robust and bootstrapped fixed effects, LSDVC, and quantile regressions are employed. • Leverage positively affects the COD in the low quantiles and negatively in others. • Profitability and liquidity positively affect COD in the higher quantiles. • The finding for profitability mostly contrasts with expected pecking order theory.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".