Pensions and Retirement [Canadian Content]
Bibliographic record
Abstract
Learning Objectives In this chapter, you will learn about longevity risk or the risk from not knowing the exact length of life. You will see the impact of this risk on the standard of living and on retirement planning. You then look at financial contracts that can provide lifelong income, and thus can be used to hedge longevity risk. Finally, you learn how the risk is dealt with in the consumption-smoothing framework. Longevity Risk In most of our analyses so far in this book, we assumed that the age at death D is known with certainty. We know exactly how long a person will spend in his or her retirement. This assumption has facilitated many calculations and made concepts easier to understand. Unfortunately, it is also quite unrealistic. In real life, that length of time varies widely. If you peruse, for example, the obituary section of a newspaper on a given day, you will likely see a wide distribution of ages at death. Figure 12.1 shows the distribution of the number of years after age sixty-five that Canadians (both sexes combined) spent prior to their deaths. The data are from Statistics Canada as of 2007 (the latest year available). The average remaining lifetime is 16.86 years. In other word, the average age at death is 81.86 years old. However, as you can see the distribution is quite dispersed, with a standard deviation of 8.43 years.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.134 | 0.031 |
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".