Replacing Family Income During the Retirement Years: How Are Canadians Doing?
Bibliographic record
Abstract
This paper examines the extent to which family income during working years is replaced during the retirement years. It does so by tracking cohorts as they age from their mid-50s to their late 70s, using a taxation-based longitudinal data source that covers 26 years from 1982 to 2007. Earlier work by the same authors examined this question with respect to the 50% of the population with strong labour force attachment during their mid-50s. This paper extends that work to include almost all Canadians (80% to 85% of the population). The adult-equivalent-adjusted family income available to the median Canadian during his or her late 70s is about 80% of that observed when the same person was in his or her mid-50s (a replacement rate of 0.8). Replacement rates in retirement are negatively correlated with income earned around age 55. Median replacement rates are 1.1 among individuals in the bottom income quintile, 0.75 in the middle quintile, and 0.7 in the top quintile. In retirement, public pensions and other transfers more than replace earnings and other income of bottom quintile individuals. However, some individuals have very low replacement rates. For example, 20% of individuals in the middle income quintile had replacement rates below 0.6. More recent cohorts had higher family incomes in retirement than did earlier cohorts as a result of higher earnings and private-pension income.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".