Gulf Economic Update, August 2021 : COVID-19 Pandemic and the Road to Diversification
Bibliographic record
Abstract
The COVID-19 pandemic and the decline in global oil demand and prices dealt the GCC countries a health crisis and a commodity market shock. The GCC’s aggregate GDP contracted by 4.8 percent in 2020 from 2019, with the growth outturns ranging from -3.7 in Qatar to an estimated -6.3 percent in Oman. The authorities responded to the pandemic with stringent health measures which helped contain the spread of the disease and saved lives but hurt economic activity. Following a year of economic distress, the GCC economies are expected to return to growth in 2021, buoyed by the global economic recovery, projected at 5.6 percent (upgraded by 1.5 percentage points from the projection in January 2021), the revival of global oil demand, expected at 96.5 billion barrels per day (from 91 billion barrels per day in 2020), and the rebound in international oil prices to an annual forecast average US$56 per barrel (now outpaced by an actual average US$61.45 in January-May 2021). The forecast is for an aggregate GCC GDP growth of 2.2 percent in 2021, roughly tracking the turnaround in high-income countries, with the outcomes ranging from 1.2 percent for the UAE to 2.4 percent for Saudi Arabia and Kuwait. Thereafter, economic growth in the GCC is expected to firm up to an annual average 3.3 percent for 2022-23. With rising oil prices in the first half of 2021, a potential upside scenario for the second half of the year sees improved current account balances being channeled directly to public sector savings. Because of the exposure to global oil demand and personal service industries and the continuing effects of the pandemic, downside risks to the outlook are also high. In this issue of the Gulf Economic Update, the focus is on fiscal revenues and structural reforms including strategic investments in digitalization and telecommunications. Strategic investment in advanced telecommunications technologies, including 5G, is underway in the GCC. But beyond capital spending on infrastructure, the telecommunications sector would benefit greatly from improvements in the legal, regulatory, and competition frameworks under which service providers operate.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.079 | 0.056 |
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".