Bibliographic record
Abstract
An average person born in the United States in the second half of the 19th century completed 7 years of schooling and spent 58 hours a week working in the market. In contrast, an average person born at the end of the 20th century completed 14 years of schooling and spent 40 hours a week working. In the span of 100 years, completed years of schooling doubled and working hours decreased by 30%. What explains these trends? We consider a model of human capital and labor supply to quantitatively assess the contribution of exogenous variations in productivity (wage) and life expectancy in accounting for the secular trends in educational attainment and hours of work. We find that the observed increase in wages and life expectancy accounts for 80% of the increase in years of schooling and 88% of the reduction in hours of work. Rising wages alone account for 75% of the increase in schooling and almost all the decrease in hours in the model, whereas rising life expectancy alone accounts for 25% of the increase in schooling and almost none of the decrease in hours of work. In addition, we show that the mechanism emphasized in the model is consistent with other trends at a more disaggregate level such as the reduction in the racial gap in schooling and the decrease in the cross‐sectional dispersion in hours. (JEL E1, I25, J11, O4)
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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 | 0.000 | 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) | 0.007 | 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".