The Evolution of Education: A Macroeconomic Analysis
Bibliographic record
Abstract
Between 1940 and 2000 there has been a substantial increase of educational attainment in the United States. What caused this trend? We develop a model of schooling decisions in order to assess the quantitative contribution of technological progress in explaining the evolution of education. We use earnings across educational groups and growth in gross domestic product per worker to restrict technological progress. These restrictions imply substantial skill-biased technical change (SBTC). We find that changes in relative earnings through SBTC can explain the bulk of the increase in educational attainment. In particular, a calibrated version of the model generates an increase in average years of schooling of 48 percent compared to 27 percent in the data. This strong effect of changes in relative earnings on educational attainment is robust to relevant variations in the model and is consistent with empirical estimates of the longrun income elasticity of schooling. We also find that the substantial increase in life expectancy observed during the period contributes little to the change in educational attainment in the model.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".