Climate Shocks and U.S. Bank Stability
Bibliographic record
Abstract
This paper investigates the effects of systemic climate variability on U.S. banking stability using the El Niño-Southern Oscillation (ENSO) as a quasi-natural experiment. In contrast to studies focusing on rare, localized natural disasters, we examine how persistent and spatially heterogeneous ENSO-induced climate anomalies-especially those associated with the often-overlooked La Niña phase-affect banks across the continental United States. ENSO is the most influential source of interannual climate variation on Earth and provides a compelling setting to study the transmission of exogenous physical risks to the financial sector. We construct a 30-year quarterly panel of over 800,000 bank-quarter observations (1994-2023), combining detailed financial data with geolocated branch networks and high-resolution teleconnection estimates of local temperature anomalies. Our empirical strategy combines three key elements: a regime-based climate shock identification grounded in recent climate science, a granular spatial matching of institutions to localized exposure, and a dynamic panel framework based on local projections. Our results show that strong La Niña shocks reduce the distance to default by roughly 20%, with effects peaking between 7 and 11 quarters after the shock. These disruptions operate primarily through rising credit risk, lower profitability, and weaker solvency-particularly in banks with large real estate exposure, broad but climate-sensitive geographic footprints, and sizable balance sheets. These findings underscore the need for prudential regulation to incorporate granular, forward-looking metrics of physical climate risk, especially as ongoing climate change is expected to increase the frequency and intensity of ENSO events.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".