Trade linkages and macroeconomic effects of the price of oil
Bibliographic record
Abstract
In this paper we assess the impact of oil price shocks on oil-producer and oil-consumer economies. VAR models for different countries are linked together via a trade matrix, as in Abeysinghe (2001). As expected, we find that oil producers (Russia and Canada here) benefit from oil price shocks. For example, a large oil shock, leading to a price increase of 50%, boosts Russian GDP by some 12%. However, oil producers are hurt by indirect effects of oil shocks, as economic activity in their export countries suffers. For oil consumers, the effects are more diverse. In some countries, output drops in response to an oil price shock, while other countries seem to be relatively immune to oil price changes. Finally, indirect effects are also detected for oil-consumer countries. Those countries trading more with oil producers receive indirect benefits via higher demand from the oil producing countries. In general the largest negative total effects from positive oil price shocks are found in China, USA and Japan while European countries seem to fare quite well during recent positive oil-price shocks.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".