Impacts on Economic Security Programs of Rapidly Shifting Demographics
Bibliographic record
Abstract
This paper analyzes in some detail potential impacts on economic security programs—government, employer, and individual—that the aging of the baby boom generation may create. It begins by defining what is meant by “population aging” and concludes that fertility shifts are more important than improving life expectancy. It also argues that calling the baby boom the “postwar baby boom” is inaccurate and will lead to missed targets for product development and marketing. Finally, this section of the paper notes that the most rapidly growing segment of the population will be the oldest old—those age 85 and over, who will also put the greatest stress on the provision of health care and retirement income security. The paper then looks at other demographic shifts of importance, in particular female labor force participation rates. The impact of shifting demographics is reviewed for each sponsor of economic security programs: the government (health care and social security); the employer (pension plans and group benefits); and the individual. Points of concern and offsetting opportunities for the insurance industry are noted. Finally, the paper looks at whether we will be able to “afford” the sudden retirement of the baby boom. The conclusion is that this will be affordable if we can convince a portion of the labor force to stay active longer, and if we have healthy productivity growth rates. The problems of an aging population can all be viewed as opportunities for those who have the map.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".