The Financial Circumstances of Elderly Canadians and the Implications for the Design of Canada’s Retirement Income System
Bibliographic record
Abstract
It is well recognized that the incomes of the elderly are on average much lower than those of the non-elderly reflecting their limited participation in the labour market. But do the elderly have lower levels of economic well-being? Indeed, the financial circumstances of the elderly differ significantly from those of the non-elderly and these differences may compensate for lower income, increasing consumption potential relative to the non-elderly. In his paper, Malcolm Hamilton uses hitherto unexploited data from Statistics Canada’s Survey of Consumer Spending to examine the financial circumstances of the elderly and discusses the implications for the design of Canada’s retirement income system. Hamilton notes that there are five reasons why the unadjusted incomes of senior households should not be compared to those of younger households. Younger households often support children; devote a significant portion of their income to acquiring and financing consumer durables (cars, appliances, furniture) that seniors already possess; incur employment-related expenses (union dues, day-care, commuting costs, insurance); save part of their income for retirement; and pay higher taxes, including CPP and Employment Insurance (EI) premiums. Hamilton presents fascinating data for different types of households on uses of income by age group. He shows that the amount of income available for consumption, that is income after taxes, mortgage payments, savings, union dues, day-care and provision for children, is actually greater for fully retired senior couples than for prime age couples ($30, 400 versus $28, 600) even though average before-tax income of prime age couples is double that of senior couples. According to Hamilton, the data suggest that seniors need only around 50 per cent of their employment income to maintain their standard of living, not the 70 per cent that is commonly assumed in pension discussions. The implications of this finding for the design of the retirement system are many. Since government transfers replace 40 per cent of the income of the typical retiring Canadian, average Canadians will need little in the way of occupational pensions or retirement saving to live comfortably after 65. Most Canadians can retire in comfort if they eliminate debt and save a modest amount to supplement government pensions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".