The Life Expectancy of Older Couples And Surviving Spouses
Bibliographic record
Abstract
Comparisons of individual life expectancies over time and across demographic groups provide information for individuals making retirement decisions and for policy makers.For couples, analogous measures are the expected years both spouses will be alive (joint life expectancy) and the expected years the surviving spouse will be a widow or widower (survivor life expectancy).Using individual life expectancies to calculate summary measures for couples is intuitively appealing but yields misleading results because the mortality distribution of husbands and wives overlap substantially.To illustrate, consider a wife aged 60 whose husband is 62.In 2010, her life expectancy was 24.4 years and his 20.2 years.The intuitions that the spouses will die at about the same time (e.g., within 5 years of each other) and that she will not live for a long time after his death are incorrect.The probability that the wife will outlive her husband is 0.63 and, if she does, her survivor life expectancy is 12.5 years.Using 2010 data, we investigate differences in joint and survivor life expectancy by race and ethnicity and by education.We then calculate trends and patterns in joint and survivor life expectancy in each census year from 1930 to 2010.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".