An Analysis of Skill Mismatch Using Direct Measures of Skills
Bibliographic record
Abstract
The focus of this study is on the potential causes of skill mismatch, the extent of skill mismatch, the sociodemographic make-up of skill mismatch, and the consequences of skill mismatch in terms of earnings as well as employer sponsored adult education/training. A distinction is made between skill mismatch and education mismatch. The analysis is based on the 2003-2007 Adult Literacy and Lifeskills Survey (ALLS) – a dataset similar to the one that is forthcoming from the Programme for International Assessment of Adult Competencies (PIAAC) in 2013. These studies contain direct measures of key foundation skills as well as measures of the use of certain generic skills at work which allow for a direct measure of skill mismatch. The analysis points to the complex ways in which mismatch is generated and the need for an accurate and up to date measure of mismatch, one that reflects the possibilities for skill gain and skill loss over the lifespan, and reflects differences in the quality of qualifications. Two key findings stand out. First, including supply and demand characteristics in an earnings function reveals that labour demand characteristics are more important than labour supply characteristics in explaining earnings differentials. In other words, skills matter for earnings but only if they are required by the job. This has direct implications for understanding better the causes of mismatch on earnings. Second, the skill content of jobs seems to be an even stronger determinant of participation in employer supported adult education/training than educational attainment or literacy proficiency. The influence of demand characteristics thus tends to outweigh the influence of supply characteristics when employers make the decision to support adult education/training. Addressing mismatch thus requires a careful consideration of both the demand and supply sides of the labour market, so as to understand better the variety of factors which may have a negative impact on the effectiveness of skill formation, skill maintenance, and also skill use.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".