Stress Relief?: Funding Structures and Resilience to the Covid Shock
Bibliographic record
Abstract
This paper explores the relationship between different funding structures-including the source, instrument, currency, and counterparty location of funding-and the extent of financial stress experienced in different countries and sectors during the sharp risk-off shock in early 2020 when Covid-19 spread globally.We measure financial stress using a new dataset on changes in credit default swap spreads for sovereigns, banks, and corporates.Then we use country-sector and country-sector-time panels to assess how different funding structures are related to financial stress.A higher share of funding from non-bank financial institutions (NBFIs) or in US dollars was correlated with significantly greater stress, while a higher share of funding in debt instruments (instead of loans) or cross-border (instead of domestically) was not significantly related to financial stress.The results suggest that macroprudential regulations should broaden their current focus to take into account exposures to NBFI and dollar funding, with less priority for regulations focused on residency (i.e., capital controls).After the sharp increase in financial stress in early 2020, policy responses targeting these structural vulnerabilities (i.e., US$ swap lines and focused on NBFIs) were more effective at mitigating stress related to these funding structures than policies supporting banks, even after controlling for macroeconomic policy responses.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".