Export Diversification in the Gulf: The Kuwait Experience
Bibliographic record
Abstract
This study examines causality among manufactured exports, imports and economic growth in Kuwait.Much of the development literature has emphasized the important role of exports in economic growth.Export growth improves productivity through increasing specialization in the export-oriented sector and optimal resource reallocation (Giles and Williams, Journal of International Trade and Economic Development, 2000).In addition, increased foreign exchange earnings, due to export expansion, finance imports essential for export-oriented production and economic growth.In turn, economic growth can lead to further export expansion by improving physical capital and the level of technology through imports (Shahbaz, Economic Modelling, 2012; Çevik et al., Economies, 2019).The degree to which exports accelerate economic growth and, in turn, facilitate further export expansion is dependent on the export and import categories in which the expansion takes place.Evidence from a number of countries suggests that expansion of primary exports (e.g., oil, gas and minerals) can slow down economic growth, while manufactured exports (e.g., machinery and transport equipment) can accelerate economic growth through knowledge spillover effects on both the export and non-export sectors of the economy (Sachs and Warner, European Economic Review, 2001; Behdubi et al., Journal of Economic Development, 2010; Kristjanpoller et al., Latin American Review, 2016).As for imports, primary and capital goods are essential for export sector production, especially for activities specialising in manufactured goods, in
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".