Law and macro finance of corporate debt: Managing the business cycle through bankruptcy
Bibliographic record
Abstract
Abstract This article proposes that, as a matter of policy, the bankruptcy law protections of creditors offering to lend money to large firms in a boom should be weaker than those of creditors offering to lend to such firms in a bust. The policy goals of bankruptcy law under this proposal, inspired by the theoretical framework for Law and Macro Finance, are to curb booms, mitigate the effects of booms gone bust, and protect the productive capacity of the economy in the long term. Bankruptcy courts play a central role in the implementation of this framework. This article discusses examples of legal doctrines the courts could employ for that purpose, such as deepening insolvency and equitable subordination. The courts' role in the countercyclical management of creditor expectations concerning recoveries distinguishes the Law and Macro Finance framework for corporate debt from the more conventional Law and Finance framework. Under the Law and Finance framework, the primary policy objective of bankruptcy courts is to maximise creditor recoveries. Under the Law and Macro Finance framework, that objective is to help manage creditors' expectations concerning recoveries across the credit cycle.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".