THE DUTCH DISEASE EFFECT IN HIGH VS LOW OIL DEPENDENT COUNTRIES
Bibliographic record
Abstract
To investigate the main impacts of the recent increase of oil price on oil exporting economies, we estimate a DSGE model for a sample of 16 oil exporting countries (Algeria, Argentina, Ecuador, Gabon, Indonesia, Kuwait, Libya, Malaysia, Mexico, Nigeria, Oman, Russia, Saudi Arabia, United Arab Emirates, and Venezuela) over the period from 1980 to 2010, except for Russia where our sample begins in 1992. In order to distinguish between high-dependent and low-dependent countries, we use two indicators: the ratio of fuel exports to total merchandise export and the ratio of oil exports to GDP. We verify if the first group is more sensitive to the Dutch disease effect. We also assess the role of monetary policy.Our main findings are twofold. First, our results confirm the fact that the Dutch disease occurs mainly in high oil dependent countries. More precisely, we find that the manufacturing production decreases in the aftermath of a positive oil price shock in six countries (on eight) of our first sample while only Mexico suffers from a Dutch disease in the sample of low oil dependent economies. Second, the appropriate monetary policy rule -exchange rate rule versus inflation targeting one-to prevent the Dutch disease differs according to the countries. In other words, the best monetary rule is specific to each country.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".