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Impacts on Economic Security Programs of Rapidly Shifting Demographics

2001· article· en· W2099121581 on OpenAlexaff
Robert L. Brown

Bibliographic record

VenueNorth American Actuarial Journal · 2001
Typearticle
Languageen
FieldSocial Sciences
TopicInsurance, Mortality, Demography, Risk Management
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsBaby boomLife expectancySocial securityPensionBoomPopulationOld Age SecurityGovernment (linguistics)DemographicsBusinessProductivityPopulation ageingEconomicsLabour economicsHealth careEconomic growthBirth rateFertilityFinanceMarket economyMedicineEngineering

Abstract

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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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.122
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.018
GPT teacher head0.297
Teacher spread0.279 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2001
Admission routes1
Has abstractyes

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