Credit efficiency: Another early warning indicator for systemic risk
Bibliographic record
Abstract
Credit booms can lead to either financial crises or economic growth, depending on their nature. Identifying harmful credit booms and providing early warnings of financial crises remain key challenges. This paper introduces a new Credit Efficiency Indicator that can distinguish between different types of credit boom and detect early signs of a financial crisis. Based on G20 data over 30 years and using panel regression models with interaction terms, as well as probit and logistic models for binary crisis prediction, the results show that a sustained decline in credit efficiency significantly increases the likelihood of a financial crisis. The paper critiques traditional indicators such as the credit gap and leverage ratio, which focus on debt size but fail to reflect the quality and efficiency of credit allocation. The study emphasizes that credit efficiency, representing effective credit allocation and its conversion into economic output, is crucial for both economic growth and financial stability. This research also offers policymakers new perspectives and tools to improve early warning systems and systemic risk management.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".