The Impact of Economic Complexity on Economic Development in Saudi Arabia (1991–2021)
Bibliographic record
Abstract
This study aimed to measure the effect of the Economic Complexity Index (ECI) on economic development by constructing a composite variable representing economic development. It also included the average oil prices as a control variable, given the critical role oil prices play in supporting economic development in the Saudi economy. The study utilized data from the period 1991–2021 and demonstrated the existence of a cointegrating relationship between the study variables in both the short and long run using the Autoregressive Distributed Lag (ARDL) methodology through the Bounds Test. The results indicated a positive but statistically insignificant relationship at the 5% significance level between economic complexity and economic development in the short run. However, in the long run, the relationship was positive and statistically significant at the 5% level. Additionally, the control variable (oil prices) had a positive and significant effect on economic development in the long run. The findings from the product space analysis revealed that Saudi Arabia possesses untapped knowledge and productive capabilities for manufacturing various complex products, particularly in the chemicals and machinery sectors. Furthermore, Saudi Arabia achieved an advanced ranking in the Economic Complexity Index, surpassing developed G20 countries such as Russia, Spain, Canada, and Australia in 2021.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".