The Eurostat business cycle clock and the pandemic: Some considerations
Bibliographic record
Abstract
To face the increasing information need for measuring and monitoring economic and social phenomena, such as the impact of the pandemic, a number of dashboards have been published by national and international organisation. However, it can be hard to extract key signals from numerous indicators, and users could prefer concise messages. This was the aim for the development of the business cycle clock (BCC). This paper presents the BCC, the Eurostat online tool showing the recent cyclical situation of the economy, and how the BCC cyclical indicators have performed during the pandemic. We introduce some considerations about the impact of the COVID-19 pandemic first on the input variables and then on the BCC cyclical indicators, focusing on different challenges, such as values several standard deviations away from usual ones. Finally, we focus on the recent output of the BCC cyclical indicators during the pandemic. According to the indications of the BCC for the fourth quarter of 2021, the euro area economy remained in a ‘stable expansionary’ phase, although with decelerating growth. The euro area exited a recessionary phase in August 2020. The clock indicates that the risk of another slowdown or recession is very low at this stage in 2022.
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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 | 0.015 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".