Italian job vacancy rate flash estimates: revisions and cyclical signal capturing
Bibliographic record
Abstract
The EU regulation on quarterly job vacancy statistics requires data transmission within 70 and 45 days after the end of the reference quarter. The published indicator is the job vacancy rate, that is the ratio between the number of vacant posts and the sum of vacant and occupied posts, which is included among the Euro Principal European Economic Indicators (PEEIs) and is considered a potential leading indicator of the business cycle. The Italian job vacancy data are based on two direct business surveys and an auxiliary administrative based source (for editing and imputation and calibration). The procedure used to produce the data for the 70 day deadline makes full use of the reference quarter data from all three sources. However, for the 45 day deadline fewer data are available and as a consequence a different procedure needed to be developed and implemented. In particular, administrative based data for previous quarters are used, as well as more limited sets of respondents to the two direct surveys. The results have proven so far very satisfactory. The revisions between job vacancy rate estimates for the 45 and 70 day deadlines are often zero, especially at the higher aggregation levels. This happens also if the rate numerator and denominator change significantly between the two estimates, due to the different sets of direct survey respondents and the different populations on which the calibration constraints are based. Furthermore, the flash estimates job vacancy rate generally show good cyclical properties. The flash estimates quality, however, can be negatively affected by intense and prolonged downturns and upturns, when the impact of the use of calibration constraints based on previous quarters rather than the reference one can be more relevant. Improvements in the procedure to account for this limit could be studied in the future.
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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 | 0.009 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".