How do resource-driven economies cope with the oil price slump? A comparative survey of ten major oil-exporting countries
Bibliographic record
Abstract
The oil price slump of about 50% since 2014 has had a detrimental effect on oil-exporting emerging market economies (EMEs), potentially threatening to trigger social unrest in countries that had benefited from the oil price boom for more than a decade. We provide a first descriptive account of the policy reactions of central banks and governments of eight important oil-exporting EMEs and compare them with those of two oil-exporting advanced economies, allowing us to distinguish three patterns: One group of countries has so far successfully defended its exchange rate peg to the U.S. dollar, the reference invoicing currency (Saudi Arabia and the United Arab Emirates). A second group gave up resistance to mounting market pressures and carried out step devaluations or switched to a floating exchange rate (Russia, Kazakhstan, Azerbaijan, Nigeria and Angola). A third group of countries continued to let their currencies float (Mexico, Canada and Norway), with the stable long-term relationship between the exchange rate and commodity export prices qualifying these currencies as “commodity currencies.” We conclude that EMEs featuring peg-like regimes and saddled with limited structural diversification, modest fiscal and external buffers as well as weak institutional conditions for capital controls are unlikely to be able to uphold their exchange rate choices if they suffer a major and sustained adverse terms-of-trade shock, and should opt for flexibility sooner rather than later. While declining oil prices may imply a degree of passive diversification, a proactive long-term strategy to develop a more diversified economic structure in good times could at least partly reduce the need for buffers.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".