Bibliographic record
Abstract
This essay examines issues of life-cycle savings of Canadian elderly married-couple households just before and after retirement within both a pooled cross-sectional and a synthetic longitudinal framework. We investigate whether the saving behaviour of elderly couples appears to be motivated by life-cycle factors, how the growth of our economy has affected lifetime income, consumption and savings across generations, and, because we use repeated cross-sectional data, the 1969-1992 FAMEX, how to correct the age profiles distorted by the presence of differential mortality between the rich and the poor. We intend to provide evidence both for the empirical justification of the standard life-cycle model and for policy makers concerned with various social programs for the elderly in Canada. The pooled cross-section results on overall median age pattern indicate that, though income and consumption are both decreasing with age, the decrease in consumption is relatively smooth while income falls considerably at retirement age. Savings and saving rates thus exhibit a distinct pattern: they drop sharply at retirement age, but rise again thereafter. When households are grouped into four types according to retirement status of both spouses, it is clear that this saving dip is found only among both-retired couples. For couples with at least one spouse working, saving rates remain high throughout the age span. It is also found that controlling for income, households with both spouses retired have the highest saving rate among all types. In the cohort analysis, the age profiles show that income and consumption remain at about the same level or even increase with age after retirement. There are significant cohort effects in both income and consumption in that younger cohorts have higher income and higher consumption than older cohorts. Moreover, these effects are about the same for both variables. However, the age profile for the saving rate is very similar to those based on pooled cross-sections: a sharp drop at retirement, a quick rise thereafter. We find no cohort effects on saving rates in our sample. This is the core reason that saving profiles are the same in both cross-section and cohort analysis. Synthetic cohort analysis, however, is biased by the fact that the poorer tend to drop out from the sample earlier because of higher mortality. Based on the idea that decreasing quantiles with age should be used instead of the straight median for every age, a new method is developed to correct the median profiles for differential mortality. Two cases, the extreme case and the normal case, are illustrated in detail. Using population survival rates from the Canadian Life Table and the top 20% (in wealth distribution) survival rates from a Canadian study due to Wolfson, et al., we are able to estimate the varying quantiles and to correct the age profiles from the cohort studies. Differential mortality does make a difference in estimated lifetime behaviour. The corrected income profile is fairly constant after retirement. Consumption decreases throughout the age range. Saving rates now are lower and flatter after retirement. However, there is no sign of a further drop in saving rates after an initial drop at retirement age. If anything, we still see a tendency for the saving rates to rise after retirement.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".