Does Saudi Arabia's International Competitiveness Improve Due to Sanctions Imposed on Competitors? The case of two wars
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Bibliographic record
Abstract
• The 2022 sanctions on Russian oil did not boost Saudi competitiveness. • Oil exports and extreme backwardation no longer enhance Saudi competitiveness. • Prolonged U.S. contractionary policy adversely affect Saudi competitiveness. • Sanctions might have weakened the petrodollar system and U.S. influence. • VECM models and historical decomposition are used to analyze Saudi competitiveness. In the early 1990s, Saudi Arabia ascended to the influential role of the lone swing producer in the global oil market by filling the output gap left by competitors displaced by sanctions and war (Iraq) or internal collapse (Soviet Union). Our question is whether the Kingdom similarly benefitted from the 2022 Russia-Ukraine War and the Western sanctions on Russian petroleum exports. Building on Razek and McQuinn (2021), we rely on the Real Effective Exchange Rate (REER) to gauge Saudi Arabia's international competitiveness. We apply vector autoregressive (VAR) and vector error correction model (VECM) techniques to a 1986–2022 sample and compare the early 1990s and 2022 using historical decomposition. We allow for various shock transmission channels and employ the Brent-Urals spread and Russia's geopolitical risk index (GPR) to capture geopolitical risk affecting Russian petroleum exports. Our findings show that Saudi international competitiveness increased in the 1990s but decreased in 2022. In fact, the 2022 crisis – unlike the early 1990s– resulted in a new regime in which extreme oil backwardation regimes fail to reward Saudi competitiveness, with oil exports ceasing to be the primary determinant of Saudi Arabia's competitive advantage. We discuss the effects of the Kingdom's investment diversification strategy and draw some conclusions about global energy price volatility and U.S. global dominance.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 it