Financing as a Supply Chain: The Capital Structure of Banks and Borrowers
Bibliographic record
Abstract
We develop a model of the joint capital structure decisions of banks and their borrowers.Strikingly high bank leverage emerges naturally from the interplay between two sets of forces.First, seniority and diversification reduce bank asset volatility by an order of magnitude relative to that of their borrowers.Second, previously unstudied supply chain effects mean that highly levered financial intermediaries are the most efficient.Low asset volatility enables banks to safely take on high leverage; supply chain effects compel them to do so.Firms with low leverage also arise naturally as borrowers internalize the systematic risk costs they impose on their lenders.Because risk assessment techniques from the Basel II framework underlie our structural model, we can quantify the impact capital regulation and other government interventions have on bank leverage, firm leverage, and fragility.Deposit insurance and the expectation of government bailouts lead not only to risk taking by banks, but increased risk taking by firms.Capital regulation lowers bank leverage but can lead to compensating increases in the leverage of firms, as well as a small increase in borrowing costs.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".