Analysis of Export Potential and Trade Direction of Afghanistan Figs in Global Market
Bibliographic record
Abstract
The expansion of Afghanistan's share in the global market is critical for the country's development, particularly given its chronic trade deficit. Although Afghanistan is renowned for producing and exporting high-quality fruits, especially figs, it has not fully utilized its export potential. Based on secondary data from ITC and FAOSTAT, this study analyzed the export performance of Afghan figs from 2000 to 2019. An exponential growth function, the Cuddy-Della-Valle instability Index, Revealed Comparative Advantage indices and Markov chain analysis was employed. The results showed that fig exports grew positively with a compound annual growth rate (CAGR) of 24.20%, exhibiting low instability. Afghanistan demonstrated significant export potential and maintained a strong and consistent comparative advantage in exporting figs, particularly to Pakistan, followed by India and the UAE. In 2019, the total export potential for Afghan figs was estimated at US$ 211.00 million, compared to an actual export value of US$ 91.60 million. The largest untapped export potential was identified in India, followed by the USA and Canada. While Afghanistan has retained its market share in some regions, it risks losing its share in others. The study recommends reorienting production and marketing systems to address these challenges and align them with global market demands. Additionally, progressive export promotion strategies should be implemented to diversify export destinations and minimize market risks.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".