The Determinants of Unemployment Rate in Jordan: A Multivariate Approach
Bibliographic record
Abstract
This study aims to investigate the determinants of unemployment rate in Jordan during the period (1992-2015). The Augmented Dickey- Fuller test (ADF) was utilized to examine the stationarity of the variables of this study. The results have shown that the variables are stationary at different orders, I(0), I(1), and I(2). The Granger causality test found that there is a unidirectional causal relationship running from private investment to unemployment rate.Two tools of analysis were employed: impulse response function and variance decomposition through applying a vector autoregression (VAR) model. The final results have shown that private investment has a negative impact on unemployment rate in Jordan, which explains about 2.64% of the variations in the unemployment rate in the second period and (1.58%) in the fourth period. This percentage also tends to decline to a level at which the explanatory power of private investment for the forecast error in the unemployment rate can reach 1.34% in the ninth period.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".