Bibliographic record
Abstract
We examine the extent to which declining manufacturing employment may have contributed to increasing inequality in advanced economies. This contribution is typically small, except in the United States. We explore two possible explanations: the high initial manufacturing wage premium and the high level of income inequality. The manufacturing wage premium declined between the 1980s and the 2000s in the United States, but it does not explain the contemporaneous rise in inequality. Instead, high income inequality played a large role. This is because manufacturing job loss typically implies a move to the service sector, for which the worker is not skilled at first and accepts a low-skill wage. On average, the associated wage cut increases with the overall level of income inequality in the country, conditional on moving down in the wage distribution. Based on a stylized scenario, we calculate that the movement of workers to low-skill service sector jobs can account for about a quarter of the increase in inequality between the 1980s and the 2000s in the United States. Had the U.S. income distribution been more equal, only about one tenth of the actual increase in inequality could have been attributed to the loss of manufacturing jobs, according to our simulations.
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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 | 0.000 | 0.002 |
| 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) | 0.017 | 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".