Measuring the Severity of a Banking Crisis and Finding Its Associated Factors: How Are the Factors Different for Simple and Severe Banking Crises?
Bibliographic record
Abstract
This study measures the severity of a banking crisis by using its duration and the cost. Using this new methodology, we find that the factors associated with a severe banking crisis are not quite the same as those associated with a simple banking crisis. An ordered logit model and a large panel data set were used for this study. One of our major findings is that there exists a four-year time lag between an economic boom, or financial system liberalization, and the occurrence of a severe banking crisis in a country. This indicates that banking problems start much earlier than the time when they are revealed as banking crises. This study also finds that the lower the remains of a past banking crisis, the higher the probability of a severe banking crisis. It could be due to less-attentiveness of banking sector policy-makers with elapsed time. A high rate of inflation, existence of an explicit deposit insurance scheme, and a weak institutional environment are found to be common factors positively associated with both simple and severe banking crisis.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".