Bibliographic record
Abstract
We measure the repo funding extended by money market funds (MMF) and securities lenders to the shadow banking system, including quantities, haircuts, and repo rates by type of underlying collateral.We find that repo played only a small role in funding private sector assets prior to the crisis, as most repos are backed by Treasury and Agency collateral.Repo with private sector collateral contracts during the crisis, but the magnitude is relatively insignificant compared with the contraction in asset-backed commercial paper (ABCP).While relatively small in aggregate, the contraction in repo particularly affected key dealer banks with large exposures to private sector securities, which then had knock-on effects on security markets, and led these dealer banks to resort to the Fed's emergency lending programs.We also find that haircuts in MMF-to-dealer repo rise less than the dealer-to-dealer or dealer-to-hedge fund repo haircuts reported in earlier papers.This finding suggests that the contraction in repo led dealers to take defensive actions, given their own capital and liquidity problems, raising credit terms to their borrowers.The picture that emerges from these findings looks less like a traditional bank run of depositors and more like a credit crunch among dealer banks.
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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 | 0.004 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".