Changes in U.S. Wages 1976-2000: Ongoing Skill Bias or Major Technological Change?
Bibliographic record
Abstract
This paper examines the determinants of changes in the US wage structure over the period 1976-2000, with the objective of evaluating whether these changes are best described as the result of ongoing skill-biased technological change, or alternatively, as the outcome of an adjustment process associated with a major discrete change in technological opportunities.The main empirical observation we uncover is that change in both the level of wages and the returns to skill over this period appear to be primarily driven by changes in the ratio of human capital (as measured by effective units of skilled workers) to physical capital.Although at first pass this pattern may appear difficult to interpret, we show that it conforms extremely well to a simple model of technological adoption following a major change in technological opportunities.In contrast, we do not find much empirical support for the view that ongoing (factor-augmenting) skill-biased technological progress has been an important driving force over this period, nor do we find support for the view that physical capital accumulation has contributed to the increased differential between more and less educated workers (in fact, we find the opposite).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".