Labour market mismatches in G7 countries: a fractional integration approach
Bibliographic record
Abstract
This paper examines the G7 labour market, analysing unemployment, job vacancies and the spread of both in terms of time series persistence from January 2002 to October 2023. Using fractional integration, we observe the series show long memory and persistence in all G7 countries. These findings differ slightly depending on the specification of the error term. If it is white noise, no evidence of mean reversion is found in any scenario except for US unemployment. With autocorrelated disturbances, mean reversion is found in unemployment rates in Canada, Germany, and the US. In France, this is the case for job vacancies, and in France and Italy, for spread. The UK is the only country that does not display any degree of reversion to the mean in the three series examined. Our results show evidence of a downward trend for unemployment and an upward trend for job vacancies in all G7 countries. Consequently, the reduction of the imbalance unemployment-vacancies seems permanent, which is a positive outcome for advanced economies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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; both teacher heads agree on what is shown here.
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".