The Effect of COVID-19 on International Trade: Evidence from Sub-Saharan Africa
Bibliographic record
Abstract
The purpose of this paper is to present evidence for the effect of coronavirus disease 2019 on international trade. Accordingly, we investigate trade among Sub-Saharan African countries during the first quarter of 2020. The disease problem of COVID-19 is measured based on the number of cases and deaths. The findings of this study show that the COVID-19 problem in exporting countries has a significantly negative effect on trade and positive significance in importing countries. This negative effect of exporters’ COVID-19 crisis is seen in exports from developing countries. The COVID-19 problem in an exporter’s neighboring nation has a positive impact on its exports. Importers’ COVID-19 problem has positive effects on trade in the agricultural industry, while exporters’ COVID-19 problem has adverse effects, for the most part, in the textile, footwear, and plastic industries.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".