Asymmetric effects of oil price shocks in oil‐exporting countries: the role of institutions
Bibliographic record
Abstract
Abstract Many empirical studies on the oil price shock effects on the economies of oil‐exporting countries have assumed a linear relationship between the shocks and macroeconomic variables, offering no insights on the dynamics of different types of shocks. The literature also assumes a homogeneous response to oil price shocks by oil‐exporting countries. This paper investigates the non‐linear effects of oil price shock on macroeconomic performance in the context of two groups of oil‐exporting countries using aVARmodel with price shocks estimated by aGARCHmethod. The model consists of oil price shocks and economic growth as two major variables of interest as well as intermediate variables such as investment, exchange rate, and inflation rate. The sample includes nine major oil‐exporting countries, six developing and three developed countries, for the period 1970–2010. The results indicate that not all oil‐exporting countries are alike in responding to oil shocks. While oil shocks have asymmetric effects in oil‐exporting developing countries; lower oil prices lead to major revenue cuts and ensuing stagnation in the economy, but higher oil prices and accompanying higher revenues do not translate into sustained economic growth; they do not have significant effect on economic growth in oil‐exporting developed countries. The panel data estimation results also suggest that heterogeneous responses to oil price shocks in oil‐exporting countries can be explained by differences in their institutional quality, particularly government effectiveness.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".