Education mismatch and qualification mismatch: monetary and non-monetary consequences for workers
Bibliographic record
Abstract
Using Spanish data from European Union Household Panel Survey corresponding to 2001, we find that the incidence and the consequences, monetary and non-monetary, are different for the job-worker qualification and education mismatches. In fact, only 36% of workers have the same type of fit under both criterions of classification. Additionally, the qualification mismatches have worse consequences for workers than education mismatches. Specifically, the monetary consequences are neutrals for overqualified workers, but negatives for underqualified workers, while the wage of educational mismatched workers is not significantly different of those who have similar characteristics and are accurately match in terms of formal education. However, the overeducated workers earn higher wages than their well-matched co-workers and the wage penalization for one year of undereducation is lower than the reward for one year of required education. On the other hand, the analysis of the non-monetary consequences, by means of job satisfaction, shows that the qualification mismatched workers have lower probability of being completely satisfied than those who are accurately match in terms of qualification, while the effects of education mismatch situations on job satisfaction are no significant. However, among similar jobs, the years of educational mismatch can have an effect even positive on job satisfaction.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".