Trade and trade finance developments in 14 developing countries post September 2008 - a World Bank survey
Bibliographic record
Abstract
In the aftermath of the Lehman Brothers collapse in September 2008, drop in the supply of trade finance, a critical engine for trade transactions, has become an acute concern for the development community. Banks were increasing pricing on trade finance transactions to cover increased funding costs and higher credit risks, and trade was dropping drastically in most countries, with global trade projected to decline in 2009 for the first time in decades. Yet, little was known about the real impact of the crisis on developing country’s capacity to export. The World Bank has commissioned a firm and bank survey on trade and trade finance developments in developing countries during the first quarter of 2009 to collect field information. In total, 425 firms and 78 banks were surveyed in 14 developing countries across five regions. This paper summarizes the findings of the survey as well as discusses the type of policies governments and international organizations put in place to mitigate the impact of the crisis. In sum, the survey findings confirmed that the global financial crisis has constrained trade finance for exporters and importers in developing countries. But the impact varied by the firm size, sectoral activity, and countries’ integration into the global economy. In particular, SMEs were particularly affected, and export diversification was made more difficult, especially in low income countries. Nevertheless, drop in demand has emerged as the top concern of firms at the time when the survey was conducted in March-April 2009.
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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 | 0.002 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".