Money Market Fund Reform: Dealing with the Fundamental Problem
Bibliographic record
Abstract
After the events in March 2020, it became clear to U.S. policymakers that the 2014 reform of the money market funds (MMFs) industry had not successfully addressed the stability concerns associated with surges in withdrawals. In December 2021, the SEC proposed a new set of rules governing how money market funds can operate. A fundamental problem behind the instability of money market funds is the expectation that backstop liquidity support will be provided by the government in the event of financial distress, along with the government’s inability to credibly commit to not provide such support. This expectation dampens funds’ incentives to take steps ahead of time to mitigate the risk of sudden withdrawals. The newly proposed reforms are aimed at constraining withdrawals or penalizing them with “swing pricing”. We argue that if the commitment problem is the fundamental issue, it would be more useful to reduce expectations of ex-post support by requiring MMFs to have contractual commitments in place, ex-ante, for liquidity support from private parties.
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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 | 0.022 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.016 | 0.014 |
| Insufficient payload (model declined to judge) | 0.006 | 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".