AN EXAMINATION OF OKUN'(tm)S LAW: EVIDENCE FROM EUROPEAN TARGET COUNTRIES
Bibliographic record
Abstract
In this paper Okun'(tm)s law is tested for six European selected countries in order to compare the responsiveness of unemployment to economic growth over the period 1981-2010. In the first section there is a survey of scientific works that have observed the empirical relationship between growth and unemployment. The countries selected are representative of different socio-economic contexts today existing in Europe, i.e. EU member countries, countries that adopt the Euro, and others which are candidates to join the EU or that although EU members have chosen not to adopt the Euro. Finally, we also refer to the U.S. and Canada, which are country-systems where the regularity of Okun'(tm)s rule of thumb was conceived. In particular, we intend to represent the data of the macroeconomic variables GDP and unemployment rate in their annual variations for a time series sufficiently long to show the occurrence of the supposed regularity, and then to investigate specific cases, which represent changes compared to the expected variations. In addition to any changes occurred over time in the studied relationship, these empirical observations, along with the reported literature, will help to draw conclusions about the differences regarding the inflexibility and responsiveness to changes in the aggregated output by the labor market of the countries involved in the study.
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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 | 0.010 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".