Is the impact of the Economic Diversification on Economic Growth Symmetric or Asymmetric? Evidence from Saudi Arabia
Bibliographic record
Abstract
This paper presents our investigation of the impact of economic diversification on economic growth in Saudi Arabia for the 1990-2018 period. To this end, we used linear and nonlinear error-correction models (i.e., the ARDL, Pesaran et al. (2001), and NARDL, Shin et al. (2014), models) that are suited to capture the symmetric and asymmetric effects of economic diversification on economic growth based on the Solow model. As a measure of economic diversification, we used the Herfindahl index. In the linear and the nonlinear specifications, our results show that, economic diversification has a positive effect on the economic growth only in the long term. Furthermore, using the Wald test, the symmetric hypothesis in this relationship is not rejected, indicating that economic growth responds symmetrically to positive and negative changes in economic diversification. Our results also reveal that Saudi Arabia had relative success in achieving its goal of attaining a degree of economic diversification and enhancing its economic growth.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".