Oil and Non-Oil Determinants of Saudi Arabia’s International Competitiveness: Historical Analysis and Policy Simulations
Bibliographic record
Abstract
To achieve sustainable economic growth, Saudi Vision 2030’s target is to improve Saudi Arabia’s ranking on the Global Competitiveness Index from 25 in 2015–2016 to within the top 10 by 2030. Saudi Arabia also aims to increase the share of non-oil exports in the non-oil GDP from 16% in 2016 to 50% by 2030. For policymakers to make informed decisions to achieve these goals, they need to understand the driving forces of Saudi Arabia’s competitiveness. To this end, we consider the real effective exchange rate (REER) as a measure of external price competitiveness, as it captures domestic and global price changes. We then examine the REER using a two-stage modeling framework. First, we estimate the REER equation, which allows us to assess the impacts of the determinants and evaluate currency misalignments as a competitiveness indicator. Second, we extend the KAPSARC Global Energy Macroeconometric Model (KGEMM) with the estimated equation, which provides a framework for simulating the competitiveness impacts of the theoretically formulated determinants and other variables relevant to policymakers. The framework also allows us to account for feedback loops. We conduct a policy scenario analysis to quantify the competitiveness effects of the Public Investment Fund’s (PIF) new strategy for 2021–2025. We derive the following policy insights. Authorities may wish to implement initiatives boosting future productivity and, thus, competitiveness, such as PIF investments. Policymakers should be regularly informed about currency misalignment. Government consumption and public investment projects should consider substituting imports with locally produced goods and services. Local content development would also help to diversify the Saudi economy. Finally, attracting more foreign investment and other assets from the rest of the world may lead to technological development and improvement in the economic, financial, and social infrastructure and business environment, all enhancing competitiveness.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".