The Effect of Foreign Direct Investment on the Unemployment Rate in Saudi Arabia
Bibliographic record
Abstract
A substantial number of recent studies were devoted to investigating the effects of Foreign direct investment (FDI) on different economic variables. Although the connection between growth and investments is widely acknowledged, the connection between FDI and the unemployment rate is not easy to determine. Taking into consideration the dispute over the true effect of FDI on the host country’s economic performance, the study’s main purpose is to take advantage of the dispute and study the effect of foreign direct investment (FDI) on the unemployment rate (U) in the Kingdom of Saudi Arabia (KSA). Using Ordinary Least Square Model (OLS), the study takes the unemployment rate as a dependent variable, and FDI and Output as two explanatory variables over the period of 2005-2018. The study supports our assumption that the inflows of the FDI and the total output negatively and significantly affect the unemployment rate in the KSA; the inflows of the FDI creates more job opportunities and will reduce the unemployment rate in KSA. Our recommendation is that the KSA government should implement more policies to attract more inflows of “Quality FDI” to attain the maximum goals and to decrease the 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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".