Are capital inflow bonanzas a common precursor to banking crises? A categorical data analysis
Bibliographic record
Abstract
Abstract The empirical literature that relates capital inflow bonanzas to the risk of a banking crisis has, so far, examined the probability of a crisis conditional on a bonanza episode. We argue that estimating the probability of a prior capital inflow bonanza, given the occurrence of a banking crisis, is at least equally important from a policy perspective to limit the exposure to sudden, big surges in capital inflows and reduce the risk of a systemic banking crisis. We consider two global samples, one consisting of 64 countries for 1970–2008 and the other 111 countries for 1980–2008. In both samples, we show that the latter conditional probability is strikingly large, much higher than the former, and even higher if we consider only "systemic" banking crises in developing countries. We also construct 2 × 2 contingency tables, utilise the odds ratio and conduct statistical tests to show that the association between bonanzas and crises is significant and robust across the two samples. The association is much stronger than commonly understood, most notably for systemic crises in developing countries. The findings provide support for stronger policies to limit or dampen unusual surges in international capital flows.
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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 | 0.013 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".