Análisis de la precariedad laboral y el efecto de la COVID-19 en el mercado laboral español durante el primer trimestre de 2020
Bibliographic record
Abstract
RESUMEN: En este ensayo se ha decidido realizar un análisis descriptivo de la situación de precariedad laboral en la que se encuentra el mercado laboral en España durante el primer trimestre del 2020. Además de esto, se tratará de analizar el impacto de la Covid-19 en el mercado laboral, tomando el número de casos de coronavirus en cada CCAA como una de las variables que determinan la situación actual del mercado laboral, además de esta variable, analizaremos las variables que consideramos que influyen en la entrada o salida al mercado laboral y que consideramos como los principales determinantes del desempleo. Estudiaremos esta situación mediante tres regresiones, de las cuales, en el primer modelo se analizará la influencia de estos determinantes en las horas de trabajo mediante un modelo Tobit, y en el segundo y tercer modelo se estudiará la influencia de estas variables a la hora de poseer un contrato temporal o indefinido y a la hora de que la jornada de trabajo de los individuos sea parcial o completa, este análisis se realizará mediante dos modelos Logit. \n \nABSTRACT: In this essay, we have been decided to make a descriptive analysis about the job insecurity in the Spanish job market during the first quarter of 2020. Besides, we will try to analyze the hit of the Covid-19 on the job market, taking the number of coronavirus cases in each CCAA as a variable which establish the current job market position, in addition to this variable we will try to analyze more variables that we consider have an influence at the entry or exit in the job market and we consider that this variables are the main determinants of unemployment. We will study this situation by performing three regression, in the first model we will analyze the influence of these determinants on the working hours making a Tobit model, in the second and third model we will study the influence of these variables when the kind of the agreement is temporary or undefined and when the working hours are partial or complete, this analysis will be realised by two logit models.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".