Effects of Oil Price Shocks on Heterogeneous Regional Economies: The Case of China
Bibliographic record
Abstract
This paper conducts VAR model to investigate the different impacts of international oil price shocks on Chinese regional economies heterogeneous in terms of economic growth and income level. The results of impulse responses analysis imply that impacts of oil price shocks on CPI-inflation are not the same across regions even though the overall result is similar with those for national economy: the positive oil price shocks first increase the CPI-inflation, and then lower the CPI-inflation. However, the different impacts are not strongly related with the types of heterogeneity under consideration. The results also show that the different impacts of positive crude oil price shocks on GDP growth have a certain correlation with regional heterogeneity: the impact on GDP growth in low-growth regions lasts longer than that in high-growth regions, and the impact of crude oil price shocks on the GDP growth of Beijing and Shanghai is strongest in the first quarter, earlier than other regions with relatively low income levels. We conclude that Chinese government aims at improving economic growth must not ignore the unique economic characteristics of each province, otherwise it may have unnecessary consequences.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".