Occupational Switching and Self-Discovery in the Labor Market
Bibliographic record
Abstract
This paper studies workers' occupational switching behavior and how lifetime earnings inequality is affected by the match between workers' ability and the skills required by their occupation. Using Armed Services Vocational Aptitude Battery (ASVAB), O*NET, and National Longitudinal Survey of Youth 1979 (NLSY79), we create empirical measures of the match quality between each worker's ability and the skills emphasized by his/her occupation, and analyze their effects on workers' labor market outcomes. We find that low match quality---what we also call 'skill mismatch'---between one's skills and required occupational skills reduces wage growth during an occupational tenure. Furthermore there is a persistence across occupations: match quality in occupations held early in life has a strong effect on wages in future occupations. We view these findings within the context of a general equilibrium model of occupational choice and human capital accumulation. We believe that our study sheds light on the importance of (i) occupational match on determination of wages, and (ii) workers' learning on their ability and the skills required by occupations.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".