Determinants of Swaziland’s Sugar Export: A Gravity Model Approach
Bibliographic record
Abstract
The sugar industry in Swaziland is the highest contributor to the government treasury through taxation, social services and trade. The sugar industry also plays a crucial role in the Swaziland’s economy by influencing economic growth and employment. Given the role of the Swaziland’s sugar industry, it is therefore important to understand the influencing factors of the Swaziland sugar exports volumes to its major trading partners. The study objective was to analyze the factors determining sugar export from Swaziland to her trading partners using a gravity model approach. The study used panel dataset for the period 2001 to 2013. The results showed that Swaziland’s GDP, importer’s GDP, importer’s land area and official common language had significant positive effects on Swaziland’s sugar exports. The study further revealed that the creation of COMESA and EU trading blocs had significant positive effects on the Swaziland’s sugar exports. This implies that the above-mentioned factors have contributed to the sugar trade flows increase during the time period under study. On the other hand, importer’s population, Swaziland openness and distance between Swaziland and her trading partner’s capital cities had a significant negative effect on Swaziland’s sugar export flows. It is therefore recommended that policies that lead to the exceptional advancement of the Swaziland and importer’s economy should be promoted which will have an effects on the Swaziland GDP and importer’s GDP. Trading with less self-sufficient, neighbouring countries and deepening the economic integration processes enhances Swaziland sugar exports flows.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".