Multidimensional Skill Mismatch
Bibliographic record
Abstract
What determines the earnings of a worker relative to his peers in the same occupation?What makes a worker fail in one occupation but succeed in another?More broadly, what are the factors that determine the productivity of a worker-occupation match?In this paper, we propose an empirical measure of skill mismatch for a worker-occupation match, which sheds light on these questions.This measure is based on the discrepancy between the portfolio of skills required by an occupation and the portfolio of abilities possessed by a worker for learning those skills.This measure arises naturally in a dynamic model of occupational choice and human capital accumulation with multidimensional skills and Bayesian learning about one's ability to learn these skills.In this model, mismatch is central to the career outcomes of workers: it reduces the returns to occupational tenure, and it predicts occupational switching behavior.We construct our empirical analog by combining data from the National Longitudinal Survey of Youth 1979 (NLSY79), the Armed Services Vocational Aptitude Battery (ASVAB) on workers, and the O*NET on occupations.Our empirical results show that the effects of mismatch on wages are large and persistent: mismatch in occupations held early in life has a strong negative effect on wages in future occupations.Skill mismatch also significantly increases the probability of an occupational switch and predicts its direction in the skill space.These results provide fresh evidence on the importance of skill mismatch for the job search process.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".