Bibliographic record
Abstract
We examine how within-firm skill premia-wage differentials associated with jobs involving different skill requirements-vary both across firms and over time.Our firm-level results mirror patterns found in aggregate wage trends, except that we find them with regard to increases in firm size.In particular, we find that wage differentials between high-and either medium-or low-skill jobs increase with firm size, while those between medium-and low-skill jobs are either invariant to firm size or, if anything, slightly decreasing.We find the same pattern within firms over time, suggesting that rising wage inequality-even nuanced patterns, such as divergent trends in upper-and lower-tail inequality-may be related to firm growth.We explore two possible channels: i) wages associated with "routine" job tasks are relatively lower in larger firms due to a higher degree of automation in these firms, and ii) larger firms pay relatively lower entry-level managerial wages in return for providing better career opportunities.Lastly, we document a strong and positive relation between within-country variation in firm growth and rising wage inequality for a broad set of developed countries.In fact, our results suggest that part of what may be perceived as a global trend toward more wage inequality may be driven by an increase in employment by the largest firms in the economy.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".