Replacement Rates of Public Pensions in Canada: Heterogeneity across Socio-Economic Status
Bibliographic record
Abstract
Several income sources can help retirees maintain their welfare and consumption levels once they leave the workforce. One source is public pensions. Their importance as an income source varies greatly according to socio-economic status (SES). In this article, we analyze how replacement rates (RRs) of public pensions (Old Age Security and Guaranteed Income Supplement) and mandatory public pension benefits (Canada/Quebec Pension Plan [C/QPP]) vary across SES by using the Longitudinal and International Study of Adults dataset. Taking advantage of the longitudinal nature of this survey, we compute and compare average RRs by SES. We specifically consider the role of education and health to understand variations in RRs. Our results show that the average RR of public pensions for individuals in bad health is 32 percent, whereas for those who report being in good health, it is 21 percent. When public pensions and C/QPP benefits are included, these percentages become 54 percent for those in bad health and 41 percent for those in good health. When estimating a multivariate regression model and controlling for past income, we look at couples and find that past income does not eliminate differences in RR by education level and health status. Our results suggest that assortative matching could play a role in explaining the variation in RRs across individuals’ education.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".