Dynamic skill accumulation, education policies, and the return to schooling
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Using a dynamic skill accumulation model of schooling and labor supply with learning-by-doing, we decompose early life-cycle wage growth of U.S. white males into four main sources: education, hours worked, cognitive skills (Armed Forces Qualification Tests scores), and unobserved heterogeneity, and evaluate the effect of compulsory high school graduation and a reduction in the cost of college. About 60 percent of the differences in slopes of early life-cycle wage profiles are explained by heterogeneity while individual differences in hours worked and education explain the remaining part almost equally. We show how our model is a particularly useful tool to comprehend the distinctions between compulsory schooling and a reduction in the cost of higher education. Finally, because policy changes induce simultaneous movements in observed choices and average per-year effects, linear instrumental variable (IV) estimates generated by those policy changes are uninformative about the returns to education for those affected. This is especially true for compulsory schooling estimates as they exceed IV estimates generated by the reduction in the cost of higher education even if the latter policy affects individuals with much higher returns than than those affected by compulsory schooling.
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it