Bibliographic record
Abstract
A person in the United States born in the second half of the nineteenth century completed about 7 years of schooling and spent an average of 58 hours a week working in the market. By contrast, at the end of the twentieth century, people completed close to 14 years of schooling and spent about 40 hours a week working. In the span of 100 years, completed years of schooling doubled and working hours decreased by 31 percent. What explains these trends? We develop a model of human capital and labor supply to quantitatively assess the contribution of exogenous variations in productivity (wage) growth and life expectancy in accounting for the secular increase in educational attainment and the decrease in hours of work. We find that the observed increase in wages and life expectancy account for 87 percent of the increase in years of schooling and 88 percent of the reduction in hours of work. The increase in wages alone accounts for no less than 67 percent of the trend in schooling, and 98 percent of the decline in hours. While changes in life expectancy matter less, their contribution to the increase in schooling is not negligible: no less than 6 percent. Preliminary and incomplete. We thank Claudia Goldin and Larry Katz for sharing their data on years of schooling by birth cohort.
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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 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".