The Impact of Non-Oil Exports on the Economic Growth in Saudi Arabia: An Empirical Analysis
Bibliographic record
Abstract
This study aims to investigate the impact of non-oil exports on the economic growth of Saudi Arabia during the period 2000-2022. Since Saudi Arabia targets to transform their economy from dependence to the diversification of economic resources, it is important to evaluate the impact of non-oil exports on the gross domestic product (GDP) in the long run. This study used multivariate time series analysis, including Johansen-Juselius co-integration and Vector Error Correction Model to determine the long-run relationship between them. The findings of the study revealed that non-oil exports have a statistically significant impact on economic growth in the long run. However, oil exports have a negative relationship with economic growth in the long run. Moreover, it also observed that a real effective exchange rate negatively affects economic growth while gross capital formation has a positive impact on economic growth in the long run. It is recommended that the non-oil sectors should be considered as a prime concern regarding infrastructural development due to their instant return to the country and should provide loans at minimal or zero interest to support them in the effective production of non-oil exports. Moreover, also makes legislation in the favor of domestic and foreign stakeholders so that they can encourage them to invest in non-oil exports and expand the non-oil sector.
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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 | 0.002 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".