The Impact of Educational Level on the Unemployment Rate in Saudi Arabia: A Time Series Quantitative Analysis from 2016 to 2023
Bibliographic record
Abstract
The aim of this research was to investigate the relationship between total unemployment rate as the dependent variable and educational level as the independent variable in the context of the Kingdom of Saudi Arabia (KSA). The labour market during the period 2016-2023 was analysed to determine the effect of the COVID-19-related crisis on the relationship between unemployment and educational level. A qualitative method was used to analyse data from the General Authority for Statistics of the KSA. In addition, a multiple regression analysis using the ordinary least square model was performed using EViews 12 to analyse the data. The findings from the regression model revealed a positive relationship between educational level and total unemployment rate, with certain education categories significantly impacting the unemployment rate. While the pandemic had a significant short-term impact on the unemployment rate, the effect diminished over time, highlighting the resilience of the educated workforce during the crisis. Therefore, this research provides valuable insights to policymakers, educators, and job seekers. It suggests avenues for future research, emphasising the need for a more extended study period and an exploration of the positive relationship between high educational level and reduced total unemployment rate.
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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 | 0.003 |
| 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 | 0.000 |
| 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".