Population Aging and Human Capital Investment by Youth
Bibliographic record
Abstract
This paper examines the link between population aging and the human capital investments of youth. The study proceeds in three steps. First, we estimate an updated version of the Card and Lemieux (2001a) model for Canada using data from the 1981, 1986, 1991, 1996 and 2001 Censuses. The results are used to forecast the impact of population aging on the returns to education of young workers. Second, we review existing empirical studies of the determinants of human capital investments of youth. Theoretical models have long argued that the return to education is one of the important factors in the decision of youth to acquire more education. We show in a new empirical model of enrollment supply and demand that higher education policies and demographic factors actually play a more important role in these decisions. The final step of the paper is to combine the estimates of the updated Card and Lemieux model with existing estimates of the elasticity of human capital investments with respect to cohort size, returns to education, and policy variables that have been obtained. This shows the expected effect of aging on human capital investments of youth under various scenarios. 3
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".