Career and Skill Formation: A Dynamic Occupational Choice Model With Multidimensional Skills
Bibliographic record
Abstract
The objective of the paper is to construct and estimate a dynamic structural model of schooling and occupational choice at the three-digit classification level, in which different occupations involve different mix of tasks. In the model, occupations are characterized by complexity of various tasks. Unlike occupational specific human capital, skills used in one occupation help a worker to enter a new occupation, depending on the similarity of the tasks of the two. Individuals build up their skills in low-paying occupations that provide relevant experience before they enter a high-paying occupation. Hence, low skill occupations can be viewed as “stepping stone” to better occupations. The structural parameters of the model are estimated using the occupational characteristics in the Dictionary of Occupational Titles and the work history in the National Longitudinal Survey of Youth 79. I find that the model does a good job of fitting the data on occupational choices: individuals gradually move from low-skill occupations to high-skill 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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.027 | 0.003 |
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".