Distribution of vacancies and new hires across employers: Implications for job offers, skill requirements, and employers’ search outcomes
Bibliographic record
Abstract
• distribution of hires across employers suggests highly concentrated labour markets in Slovenia • higher concentration is correlated with worse job offers and changes in the set of required skills • vacancy duration and vacancy fill rate are not correlated with concentration of hires • findings are consistent with a model in which concentration affects job searcher's outside options We use data on the flow of new vacancies and hires in Slovenia to document three findings. First, labour markets are highly concentrated when we use the Herfindahl-Hirschman index (HHI) to measure the distribution of either vacancies or hires across employers in markets defined by required occupation, the statistical region of employers’ headquarters, and the year of either vacancy registration or hiring. Second, employers offer less attractive job offers (in terms of offered wages and offered length of employment) and change the set of required skills (by favoring leadership, manual dexterity, and fitness) in markets with a more concentrated labour demand. Third, employers are equally likely to fill their vacancies, require a similar amount of time to fill them, and are less likely to fill vacancies with workers whose education is below the required education in markets with a more concentrated labour demand. These patterns are consistent with a labour market in which a more concentrated labour demand restricts job searchers’ job options, strengthens employers’ bargaining leverage, and results in job vacancies with less attractive job amenities yet an expanded list of required skills.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".