Bibliographic record
Abstract
Two forces are about to create a growing market for individual annuities in the United States and Canada. First, the postwar Baby Boom (born 1946–64) is inexorably moving into retirement. Second, there is a strong move away from employer-sponsored defined benefit pension plans to defined contribution pension plans. This trend could even extend (in the United States) into the provision of Social Security benefits. Under these arrangements, participants must find a way to mitigate their “longevity ” risk (and the investment risk, although this is not the topic of this paper). The most obvious answer is to buy a life annuity. However, at this time in the United States and Canada persons who voluntarily apply to buy a life annuity are generally assumed to be in extremely good health, and annuity rates are determined using very low mortality assumptions (high life expectancy assumptions). While there is a growing market in “enhanced/impaired annuities, ” especially in the United Kingdom where annuitization has been mandatory, the present pricing structure for annuities in the United States and Canada means that a large proportion of the population cannot get a “fair value ” annuity given their less-than-preferred health profile. This paper looks at the present annuity marketplace in the United States and Canada. It also reviews the underwriting and marketing of life annuities in the United Kingdom where “enhanced ” life annuities are available for a broader cross section of the marketplace. It also reviews the use of P&C risk classification techniques and how they might apply to the annuity marketplace as well as potential legal constraints on broader risk classification for life annuities. The paper concludes that the U.S. and Canadian annuity marketplace could be doing more to provide “fair value ” annuities to substandard risks. Without an appropriate private-sector reaction, consumers may respond by inviting government intervention. 1.
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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 | 0.036 | 0.087 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".