Has COVID-19 caused a change in the dynamics of the unemployment rate? The case of North America and continental Europe
Bibliographic record
Abstract
This study raises the question of whether the COVID-19 pandemic will have a long-lasting impact on the dynamics of the unemployment rate. More specifically, this problem implies an analysis of whether any sign of a structural break is detectable in the time series of the unemployment rate. To obtain some "firsthand" estimates on whether it is likely that a structural break will occur in the labour market, this study performs several one-stepahead forecasts based on the best ARIMA model on the time series of the unemployment rate, which takes advantage of the availability of the unemployment rate data for five quarters following the pandemic outbreak. Interestingly, the results document practically no difference in the impact of the pandemic on the labour market in countries with different labour market flexibility. Neither North America (United States of America and Canada) with a flexible labour market nor continental Europe (Germany and Austria) with a regulated labour market experienced any regime change in the unemployment rate time series.
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".