Bibliographic record
Abstract
Abstract The labor share in U.S. manufacturing declined from 61% in 1967 to 41% in 2012. The labor share of the typical U.S. manufacturing establishment, in contrast, rose by over 3 percentage points during the same period. Using micro-level data, we document five salient facts: (i) since the 1980s, there has been a dramatic reallocation of value added toward the lower end of the labor share distribution; (ii) this aggregate reallocation is not due to entry/exit, to “superstars” growing faster, or to large establishments lowering their labor shares, but is instead due to units whose labor share fell as they grew in size; (iii) low labor share (LL) establishments benefit from high revenue labor productivity, not low wages; (iv) they also enjoy a product price premium relative to their peers; and (v) they have only temporarily lower labor shares that rebound after five to eight years. This transient pattern has become more pronounced over time, and the dynamics of value added and employment are increasingly disconnected. Taken together, we interpret these facts as pointing to a significant role for demand-side forces.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".