Saudi Arabia's currency misalignment and international competitiveness, accounting for geopolitical risks and the super-contango oil market
Bibliographic record
Abstract
It is important to assess Saudi Arabia's economic performance, because its role in the global oil market and how its actions are perceived by international investors have global consequences. We study Saudi Arabia's global competitiveness, accounting for geopolitical risks, productivity, and the role of oil as a commodity and financial asset. We use the net cost-of-carry to capture the oil market risk premium and the super-contango oil market, and include military funding and government expenditure to account for anticipatory and reactive military funding in dealing with likely internal and external threats. Following Clark and MacDonald (1999, 2004) and Fidora et al. (2020), we develop a vector error correction model (VECM) that accurately reflects Saudi Arabia's economy and employ a behavioral equilibrium exchange rate (BEER) to estimate currency misalignment as a measure of international competitiveness. We find that domestic productivity is Saudi Arabia's weakness. Rather than being driven by endogenous productivity, Saudi Arabia's competitiveness is largely explained by exogenous factors: global demand for oil as both a commodity and a financial asset, and geopolitical events that diminish competition in the global oil market. Favorable oil market conditions are advantageous, but super-contango episodes are detrimental to the Saudi economy. Saudi Arabia's competitiveness and recovery from the 2020 shocks hinge on the recovery of global demand, the speed of the energy transition, and investors' sentiments to invest in the oil sector. By engaging in trade wars, Saudi Arabia risks accelerating how quickly its own resources and assets become stranded. The 2020 cooperation between OPEC+ and G20 members to stabilize the oil market is commendable, because it will positively impact the global recovery in 2021–2022.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 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".