Do Foreign Banks Stabilize Cross-Border Bank Flows and Domestic Lending in Emerging Markets? Evidence from the Global Financial Crisis
Bibliographic record
Abstract
Does a strong presence of foreign banks amplify or mitigate sudden stops of cross-border bank flows to emerging market economies (EMEs)? 1 Do foreign banks reduce or aggravate the associated decline in bank lending in the respective host countries? The global financial crisis provides a unique opportunity to examine these questions. This is because the pre-crisis years were characterized by substantial cross-border bank flows and rapid credit growth in EMEs. In addition, foreign banks became important players in many EME banking sectors, with the average share of assets held by foreign banks in host country banking sectors rising from 21% in 1995 to 38% in 2005 (Claessens et al. , 2008). However, in the ‘acute phase’ (Blanchard et al. , 2010) of the global financial crisis, that is, the fourth quarter of 2008 and the first quarter of 2009, EMEs faced a classical sudden stop, which is defined as a large and unexpected fall in capital inflows. Following patterns observed in the past (Mendoza and Terrones, 2008), the bust in cross-border flows was associated with a corresponding contraction of domestic lending. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".