Labor Market Conditions, Skill Requirements and Education Mismatch
Bibliographic record
Abstract
This paper shows that changes in the skill requirements of jobs are one way by which economic downturns affect job match quality. In doing so this paper makes two contributions to the literature. The first contribution is to document a stylized fact about the cyclicality of skill requirements (tasks) for newly formed jobs. Relating local unemployment rates in Canadian data, to skill requirements generated from the Occupational Information Network (O*NET) database, I show that the demand for manual skill requirements is countercyclical. This stylized fact shown to be consistent with the predictions of a job search models with heterogeneous workers and vacancies. In this framework, firms increase the share manual job vacancies during downturns because they are less costly to post and fill. The second contribution is to show that the cyclicality of skill requirements, rather than economic conditions themselves, contribute to the incidence of overqualification. Estimates using various measures of overqualification confirm that changes in the skill requirements of newly formed jobs can account for much of the relationship between labor market conditions and job match quality. This empirical finding is also consistent with the model, where the share of overqualified workers varies with economic conditions partially because of corresponding changes in the type of job vacancies.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".