Bibliographic record
Abstract
In Chapter 3 we introduce the concept of “education intensity” and use this idea to group detailed occupations into four broad categories designated as “tiers.” This approach allows us to discuss some important changes in the demand side of the labor market. In particular, we show that over the last quarter-century, jobs requiring relatively little education have increased faster than the number of less-educated prime-age workers while, at the same time, jobs requiring more education have increased more slowly than the number of more-educated prime-age workers. Given these growing imbalances in supply and demand, labor markets must somehow adjust. Some wage adjustment has occurred, but not all of it is in the “correct” direction. More specifically, by applying a simple textbook supply and demand model of the labor markets, we would predict wages falling in more-educated jobs and rising in less-educated ones. In fact, as we discuss in detail in Chapters 5 and 6, average wages have fallen in less-educated jobs and have stayed approximately the same in more-educated jobs, while wage variance has increased. This is more consistent with excess labor supply for the less-educated jobs, rather than the excess labor demand we demonstrate in Chapter 3. What is happening? Our interpretation of the data is that there is strong downward wage stickiness for jobs in education-intensive occupations. This stickiness is perhaps due to efficiency wages. That is, firms are reluctant to lower wages too much, thereby risking increases in turnover and shirking, as well as reduced morale. Such changes would lower average productivity, from the firm's view.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.071 | 0.009 |
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".