Educational mismatches and earnings in Poland: are graduates penalised for being overeducated?
Bibliographic record
Abstract
Qualification mismatch is defined as the difference between the level of qualifications held by employees and those required by the type of work they do. Basing on Kiker et al. (1997), a measure of overeducation and undereducation is proposed on the basis of the ISCO 08 classification of occupations. The dominant education level is determined for a given occupation’s 3-digit group on the basis of the distribution of education levels for employees in that occupation. Each individual having exactly the dominant level of education is considered well-matched. Those with higher levels of education are considered overeducated, those with lower levels − undereducated. An extended Mincer wage regression model with Heckman correction for non-random selection is estimated, using LFS data for Poland for the second quarter of 2013. Significant wage penalties are found in cases of overeducation status, along with positive wage premia for being undereducated, this confirming findings to be noted in the literature of other countries. Applying an approach after Duncan and Hoffman (1981), I find significant positive returns to years of overschooling and negative for underschooling. Young participants on the labour market (graduates) are less penalised for being overeducated, which suggests their overeducation is not necessarily a manifestation of lower ability.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".