Education, Pension and Two-Sided Altruistic in an Endogenous Growth Model
Bibliographic record
Abstract
We have modeled jointly human capital accumulation through formal education and the various pillars of the Canadian retirement income system, using a three-period overlapping generations model with two-sided altruism. We have used this model to investigate the impacts on the real interest rate of the improvement in life expectancy in the presence of endogenous growth, as well as the impacts of raising various tax rates to finance the enhancement of pension plans. \n \nWe have found that the endogenous accumulation of human capital through education reverts the decline in the real interest rate caused by the improvement in the life expectancy. We have also found that the best way of enhancing the Canada Pension Plan is not to raise the contribution rate, but to increase instead the maximum amount of earnings covered. \n \nABSTRACT IN FRENCH- \nNous avons modélisé conjointement l'accumulation du capital humain à travers l''éducation formelle et les divers piliers du système canadien de revenu de retraite, en utilisant un modèle de générations imbriquées sur trois périodes avec altruisme bilatéral. Nous avons utilisé ce modèle pour étudier les impacts de l'amélioration de l’espérance de vie sur le taux d'intérêt réel dans un contexte de croissance endogène, aussi bien que les impacts de l'augmentation de divers taux d'imposition dans le but de financer la bonification des programmes pension. \n \nNous avons trouvé que l’accumulation endogène du capital humain à travers l’éducation renverse la baisse du taux d’intérêt réel causée par l’amélioration de l’espérance de vie. Nous avons aussi trouvé que la meilleure façon de bonifier le Régime de pensions du Canada n’est pas d’augmenter e taux de cotisation, mais plutôt d’augmenter le montant maximal de gains couverts.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".