Revisiting the Oil-Growth Nexus : Evidence from Selected Oil \nImporting and Oil Exporting Countries
Bibliographic record
Abstract
This study investigates the impact of oil prices on economic growth in oil importing countries (China, Germany, Italy, India, Japan, Netherlands, South Korea, Spain and Thailand) and oil exporting countries (Brazil, Canada, Iran, Kuwait, Mexico, Nigeria, Norway, Russia, Saudi Arabia and United Arab Emirates), covering the period of 1995- 2021. Under the panel estimation approach, we can postulate that oil price is exerting a significantly positive impact towards economic growth for oil exporting nations, regardless of whether the time horizon is in short run or long-run contexts. On the other hand, oil importing nations tend to experience \nnegative impact from the oscillatory fluctuations in the oil price. It is proposed that policy makers in oil importing nations can respond to the positive oil price shock can be lessened by hedging product purchases using futures contracts on net oil-importing nations with poor \nexports of other commodities. Alternatively, for net oil-importers, pricing-based policies such as transferring price increases to consumers and providing subsidies can be implemented to reduce the negative effect of oil prices hikes towards the economy.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".