Short-term employment forecasts based on administrative data on hirings and terminations
Bibliographic record
Abstract
This study focuses on the discrepancies in the data on employment trends that have recently emerged between that on hirings and terminations provided by the Ministry of Labour and Social Policies , and workforce survey data, released by Istat . Since January 2015 the former has recorded a substantial increase in hirings, mostly of permanent employees, while according to the latter, employment remained substantially stable in the first months of the year and only started to increase in the second quarter. This is partly explained by the differences in the types of statistics. New jobs starting at the beginning of March, for example, are included among the hirings for the first quarter in the administrative data, but only contribute by one third to the increase in average employment in the period; these contracts, if they continue over time, are not included in the average data until the second quarter. This study proposes to use this temporal correlation to create a simple statistical model that explains the aforementioned discrepancies, and forecasts a year-on-year growth of payroll employees of 1.2 and 1.8 per cent in the third and fourth quarters of this year compared with the same period of 2014.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".