Non-working workers. The unequal impact of Covid-19 on the Spanish labour market
Bibliographic record
Abstract
We present an evaluation model that aims at developing a synthetic index of non-employment that combines incidence and severity. This index considers, besides conventional unemployment rates, unemployment duration, discouraged workers and workers with suspended jobs. We have applied this methodology to the analysis of the impact of the Covid-19 in the Spanish labour market. The impact of the epidemics on the job market has been very asymmetric by regions and types of workers. Compared to the situation in the third quarter of 2019 we find that one year later the non-working index arrived to more than 150 in regions in the south whereas it is below 75 in regions like Navarra, Catalunya or Madrid. The dynamics of this indicator, though, shows that the larger increments have occurred among the regions with lower initial values so that the variability is now smaller. Regarding age and education, we find that the young (and among them the less educated) are the population subgroup that suffers more intensely the impact of this new economic crisis. On the contrary, older workers seem to improve for all education subgroups during 2020. The main reason behind this is the asymmetric concentration of temporary collective redundancy scheme measures among older workers, what is very much connected with the dual character of the Spanish labour market regarding contract types and job security.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".