Precariousness and temporality, main characteristics of the youth employment in Spain
Bibliographic record
Abstract
In Spain the rate of youth unemployment has exponentially increased in the past few years, and it has turned into a structural problem. When the fourth quarter of 2014 ended, the destruction of employment, which begun along with the economic and financial crisis in 2008, has specially affected young people so that, among the people under the age of 30, the number of unemployed people was 1.495.600, 645.000 more than in the first quarter of 2008. Nevertheless, before 2008, the youth unemployment rate was already reaching 17,4% and some studies were alerting of the vulnerability of the above mentioned group of people, especially in the following areas: labour, education, residential and reproductive. Moreover, in this period of time, the employment policy has been affected, with no doubt, by the reform of 2012 (RDL3 / 2012 and Law 3/2012), by introducing new rules on the mediation in the market (Article 16.3 the Workers' Statute (ET)), the vocational training and new contractual modalities, specially modifying the for training and learning (Article 11 ET), the modification of the part-time contract (Article 12 ET) and the distance contract (formerly called working at home contract, Article 13 ET) and the introduction of a new indefinite contract called 'support to entrepreneurs'. These reforms have gone in the opposite direction to strengthen employment policies aimed to achieve the right to work or full employment for young people, law and constitutional mandate that in times of crisis are likely to disappear or be completely isolated. Labour statistics are one of the indicators that best express the economic and social reality of a country. Through the Muestra ContAnua de Vidas Laborales (MCVL) 2013, the career path between 2008 and 2013 of young people under 30 years is analysed, in order to determine changes and characteristics of their connection to the market. The research work shows that, between 2008 and 2013, most of the contracts signed with young people under 35 years were temporary and low paid. At the end of 2014, 78% of this group worked in the private sector, 46% with temporary contracts of which 36.6% for labour and service, one in five young people did it part-time and one in ten with a temporary part-time contract. With micro data from the EPA, related to the fourth quarter of 2014, it is observed through two Logit models, how a low level of education and the sector of activity increase the likelihood that a young person under 34 years on the one hand, becomes unemployed and secondly, occupied by a temporary contract.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".