The Relationship between Exports, Credit Risk and Credit Guarantees
Bibliographic record
Abstract
This paper provides an understanding of how the export credit worthiness of an importing country affects export sales of agricultural and other manufactured products and how export credit guarantees or insurance can mitigate risks of nonpayment. A theoretical model is developed. It shows how risk mitigation through export credit insurance could increase exports to high‐risk importing countries. The key result is that the export response curve is more inelastic in the presence of payment risk, and the effect of insurance is to make the export curve more elastic. Statistical evidence supports this fundamental premise. Le présent article explique comment la solvabilité d'un pays importateur affecte les ventes de produits agricoles et de produits finis à l'étranger et comment la garantie du crédit à l'exportation ou les assurances atténuent les risques de défaut de paiement. Les auteurs proposent un modèle théorique. Ce dernier illustre comment on pourrait accroûtre les exportations vers les pays à risque élevé en atténuant les risques au moyen d'une assurance du crédit à l'exportation. II en ressort principalement que les exportations suivent une courbe moins élastique quand il y a un risque de défaut de paiement et que cette courbe gagne en élasticité avec une assurance. Les auteurs fournissent des preuves statistiques de ce principe élémentaire.
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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 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".