The Survey of The Relationship between Exports, Degree and Export Credits Guarantee
Bibliographic record
Abstract
The purpose of this paper is to provide 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 guarantee or insurance can mitigate risk of nonpayment. This paper makes a contribution to specific literature on how export credit risk affect agricultural and other exports, and also contributes to the broader literature on international trade theory by showing that risk is indeed an economically significant factor in trade. Data on export values per capita were obtained from three different source data for 2007 Iranian export values for all industries and for agricultural and related services industries were obtained from statistic of Iran׳s trade data online. This data set consists of over 117 different countries matched to their credit scores. To confirm the generality of the result, also trade data were obtained for Iran, Canada and Australia from the international trade statistics yearbook published by the World Bank. A theoretical model is developed. It shows how risk mitigation through export credit insurance could increase exports to high-risk importing countries. Results show that there is a significant positive relationship between credit worthiness and export values.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".