The euro area crisis and cross-border bank lending to emerging markets 1
Bibliographic record
Abstract
Cross-border bank lending to emerging markets dropped sharply in the second half of 2011 as the euro area crisis intensified. We use the BIS international banking statistics to identify the key drivers of this decline. Our results indicate that the latest contraction in cross-border bank lending was largely linked to the deteriorating health of euro area banks. We answer these questions by using the BIS international banking statistics (IBS) in a panel regression framework. The analysis covers quarterly cross-border bank lending data for 40 EMEs between the third quarter of 2005 and the second quarter of 2012. We develop a new methodology which combines information from the two main BIS IBS data sets. This novel approach is the first to simultaneously use actual exchange rate-adjusted cross-border lending flows to EMEs and trace these flows to individual home country banking systems. We use the panel regression results to decompose the quarterly fluctuations in cross-border lending to EMEs into components attributable to EME credit demand, EME country risk and the health of the banking systems that supply the cross- border credit. Our results indicate that home country factors related to the health of advanced economy banks played a crucial role during the late 2011 lending
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".