The Contribution of Education Expenditure in Saudi Universities to Achieve Economic Development
Bibliographic record
Abstract
Though there is an existence of writings on human capital and its relationship to growth, but it has missed the economic impact of universities. It is known that the knowledge of economy has a positive role in achieving economic development. So my paper focuses on the role of education expenditure in achieving economic development. The human resource is the basis for growth and development because it is able to achieve the appropriate scientific achievement and its future economic performance which is a positive return. The improvement in performance of skilled workers will be affected if Saudi government increases the education expenditure in addition to the investment in human capital. From here we can say that the human resources and the universities (government education expenditure) are two sides of a single coin whose basic and sole objective is economic growth. Therefore, this paper will test the relationship between education expenditure and economic development during the period from 2003 to 2019 through a theoretical analysis of the relationship of higher education to economic development. To explore the relationship between spending on education and economic development the author used econometric technique to analyze the study by using multi regression model depending on weighted least square (WLS). The study results show that there is a significant relationship between Saudi education expenditure and economic development, but regarding to R & D expenditure it is not significant. So the author excluded it from the model due to lack of data. Furthermore, the model WLS is effective to explore results and relations between dependent and independent variable in the case of Saudi Arabia.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".