An Economic Study of the Growth Determinants for the Egyptian Potatoes Exports to the Global Market
Bibliographic record
Abstract
Agricultural exports are drivers of economic growth as, apart from earning valuable foreign currency it creates sustainable jobs, increases the adoption of Advanced technologies and production practices as well as the enhancement of the overall competitiveness of the agricultural sector. Egypt’s average exports of potatoes grew at a rate of 20% during the period (2001-2013) accounting for about 14% of the total value of agricultural exports. This remarkable raised an important research question on what are the most important growth determinants of Egyptian potatoes exports. The study attempts to investigate the situation of Egyptian exports of potatoes in the key importing markets, identify the most important competing countries to Egypt’s potatoes exports and investigate the determinants of such exports in order to gain knowledge on the factors that influence the value of the Egyptian exports of potatoes. The results of the augmented gravity model revealed three factors that were found to be most significant namely, exchange rate, population, and the physical distance from Cairo to the capitals of the importers. GDP of Egypt GDP of the trading partner and the economic difference are of less importance. The total size of the specific export market for the Egyptian potatoes is also of lesser importance to the flows of the Egyptian potatoes exports.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".