International evidence on the cost of public debt issued by private versus public firms
Bibliographic record
Abstract
In this study, we revisit the relation between ownership type (public versus private) and the cost of public debt. Based on the literature, we seek insights into the conditions under which private firms should expect to pay a premium and when, alternatively, they might expect to enjoy a cost benefit on issues of public debt relative to public firms. Using an international sample of 630,959 traded bond issues from 2001 to 2017, we initially confirm a higher cost of public debt for the private U.S. firms in our sample. Following, we alternatively confirm a lower cost of public debt for the private non-U.S. firms. Finally, we confirm that, for non-U.S. issuers, the benefit is reduced in jurisdictions with stronger institutional and regulatory frames. Additional tests (alternative econometric approaches, alternative partitions, and firms undertaking an IPO) provide further support. • Initially confirms a higher cost of public debt for private U.S. issuers relative to public firm issuers. • Alternatively confirms a lower cost of public debt for private relative to public non-U.S. issuers. • Confirms the cost differential is dependent on the strength of the institutional and regulatory regime.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 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".