High-frequency data developments in the euro area labour market
Bibliographic record
Abstract
This box examines high-frequency data to quantify the impact of the coronavirus (COVID-19) pandemic on both job postings and hiring patterns in the euro area. Prior to the COVID-19 crisis, both of these indicators had increased steadily year on year, reflecting a rise in the number of job findings in the euro area. However, both the Indeed job postings and the LinkedIn hiring rate have declined significantly since the onset of the COVID-19 crisis and the lockdowns, with the hiring rate bottoming out in May 2020. While the decline in the hiring rate was broad-based across sectors, the intensity of the COVID-19 shock is asymmetric, with sectors such as recreation, travel and manufacturing being more affected by the crisis than others, such as healthcare, software and IT services sectors. Based on the high-frequency information derived from the hiring rate, the implied unemployment rate is expected to peak during the second quarter of 2020 and to be around 2.3 percentage points higher than in February. Overall, the methodology and the high-frequency data used in this box allow for a timely assessment of developments in the euro area labour market. The use of job flows in and out of unemployment helps to enhance our understanding of the labour market adjustment during the current COVID-19 crisis. JEL Classification: E24, E27
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.006 |
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".