Determinants of unemployment in selected OECD countries: panel data analysis / Dayang Nur Khairunnisa Awang Rayi
Bibliographic record
Abstract
Unemployment is one of the major problems faced by selected Organization Economic Co-operation Development (OECD) countries. In this research paper, those selected OECD countries are Australia, Canada, Germany, United States and Japan. This paper investigates the relationship between unemployment and migration, unemployment and trade openness and unemployment and inflation in selected OECD countries within a period of 2007 to 2012. As the research paper is panel analysis, we found that Random Effect Model is more appropriate than Pooled Ordinary Least Square Method and Fixed Effect Model. Therefore, the Augmented Dickey-Fuller test is use to test the stationary of data. Moreover, the Johansen Co-integration is used to test the existing of long run relationship between the variables and Vector Error Correction Model is used to detect the short run relationship between the variables. The unit root test of Augmented-Dickey Fuller shows that the data is stationary at 1st difference and there is co-integrating relationship between the variables as the Johansen Co-integration is tested. It means that the long run relationship exists between the variables. There is a short run relationship between unemployment and trade openness as the VECM is conducted.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".