Altruistic Overlapping Generations of Households and the Contribution of Human Capital to Economic Growth
Bibliographic record
Abstract
I developed a dynamic deterministic general equilibrium model accounting for human capital accumulation through both home education and schooling. The model is characterized by an altruistic link between households of succeeding generations in the sense parents, caring about their children’s welfare, freely impart them some knowledge at home in addition to helping them financially when they are schooling. The education regime is private and features distinguishing my model from related works are: (1) young households are economically active and work part-time while schooling, (2) allocating time to schooling or labor entails disutility, (3) tuition is proportional to the time allocated to schooling. I calibrated the model to some balanced growth facts observed between 1981 and 2013 in the Province of Quebec. The model is then used to investigate the contribution of human capital to economic growth. To do that, I simulate it assuming in turn a permanent rise in the tuition rate and the household’s ability to learn. Each of these two shocks reveals a positive correlation between education, human capital, and output. The predictions of the model are then used to shed a light on the student crisis Quebec witnessed in 2012 following our former Liberal government’s decision to increase tuition. I predict that raising tuition will neither harm education nor negatively impact on students’ ability to pay.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".