Thomas Lemieux (University of British Columbia) NEW EVIDENCE ON THE RETURNS TO JOB SKILLS
Bibliographic record
Abstract
The typical Mincerian wage equation examines wages in relation to the education, potential experience and other personal characteristics of job incumbents. The included characteristics serve as proxies for the job holder’s skill level, but do not indicate what specific skills are being rewarded. Several recent papers have analyzed wages in relation to measures of the skills required to perform particular jobs (see, for example, David Autor, Frank Levy and Richard Murnane 2003; Beth Ingram and George Neumann 2006; Maarten Goos and Alan Manning 2007). To the extent that the labor market does a good job of matching individuals to jobs for which they are well suited, these analyses shed new light on how workers ’ job skills are valued in the labor market. Studies of the returns to job skills generally begin with data from the Current Population Survey (CPS) or another household survey that contains information on the detailed occupation in which people work and the wages they earn. Information on required job skills is attached to the survey records according to reported occupation. There is considerable evidence, however, of significant errors in the coding of occupation in household survey data. Wesley Mellow and Hal Sider (1983) find disagreements between the occupation recorded in CPS data compared to
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.175 | 0.042 |
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".