6 A Longer‐Run View of the Earnings Distribution: The Great Compression and the Golden Age
Bibliographic record
Abstract
Abstract This chapter examines the long run earnings data of five OECD countries: Canada, France, Germany, the United States, and the United Kingdom. It shows that the Anglo-Saxon countries have all seen large rises and falls in the deciles. Generalizations about the time path of change do not necessarily hold universally, but there appear to be three distinct phases in the pre-1980 period, which exhibit common features in several — but not all — of the five countries studied: compression of the earnings distribution in the 1930s and 1940s; rise in the top decile of the earnings distribution during the Golden Age from 1950 to the mid-1960s (with the exception of Germany), accompanied in some cases by falls in the bottom decile or lower quartile, and a ‘tilt’ at the very top; and narrowing of the distribution in the late 1960s and 1970s (stability of top decile in United States and Germany).
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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 | 0.003 |
| 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 | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".