Does precarious employment ruin youth health and marriage? Evidence from Egypt using longitudinal data
Bibliographic record
Abstract
Purpose This study aims to analyze whether precarious employment is associated with youth mental health, self-rated health and happiness in marriage and whether this association differs by sex. Design/methodology/approach This paper uses longitudinal data from the Survey of Young People in Egypt conducted in 2009 and 2014 and estimates a fixed-effects model to control for time-invariant unobserved individual heterogeneity. The analysis is segregated by sex. Findings The results indicate that precarious employment is significantly associated with poor mental health and less happiness in marriage for males and is positively associated with poor self-reported health for females. The adverse impact of precarious work is likely to be mediated through poor working conditions such as low salary, maltreatment at work, job insecurity and harassment from colleagues. Social implications Governmental policies that tackle job precariousness are expected to improve population health and marital welfare. Originality/value Egypt has witnessed a significant increase in the prevalence of precarious employment, particularly among youth, in recent decades, yet the evidence on its effect on the health and well-being of youth workers is sparse. This paper adds to the extant literature by providing new evidence on the social and health repercussions of job precariousness from an understudied region.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".