What determines adult cognitive skills?
Bibliographic record
Abstract
Most empirical investigations of the effects of cognitive skills assume that they are produced by schooling, and that schooling is exogenous.Drawing on a rich longitudinal data set to estimate production functions for adult reading-comprehension cognitive skills and adult nonverbal cognitive skills, we find that (1) Schooling attainment has a significant and substantial effect on adult readingcomprehension cognitive skills but not on adult nonverbal cognitive skills; and (2) Pre-schooling and postschooling experiences have substantial positive significant effects on adult cognitive skills.Pre-schooling experiences that increase height for age at age six years substantially and significantly increase adult reading-comprehension and nonverbal cognitive skills, even after controlling for schooling attainment and post-school skilled job tenure.Post-schooling tenure in skilled jobs also has a significant positive effect on adult reading-comprehension and nonverbal cognitive skills, although the latter estimate is sensitive to how we treat gender.Age also has significant positive effect but with diminishing returns on adult reading-comprehension cognitive skills.The findings (1) reinforce the importance of early life investments; (2) support the importance of childhood nutrition ("Flynn effect") and work complexity in explaining increases in cognitive skills; (3) question interpretations of studies reporting productivity impacts of cognitive skills without controlling for endogeneity; and (4) point to limitations in using adult schooling alone to represent human capital.
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".