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Record W3124551010

Getting Older, Getting Poorer?: A Study of the Earnings, Pensions, Assets and Living Arrangements of Older People in Nine Countries

2002· article· en· W3124551010 on OpenAlexaboutno aff
Bernard Casey, Atsuhiro Yamada

Bibliographic record

VenueOECD labour market and social policy occasional papers · 2002
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Aging, and Tourism Studies
Canadian institutionsnot available
Fundersnot available
KeywordsEarningsSpousePolitical scienceWorking populationConsumption (sociology)PopulationWelfare economicsDemographic economicsHumanitiesSociologyEconomicsDemographyArt
DOInot available

Abstract

fetched live from OpenAlex

Aging involves not one but several transitions. People move from working to not working, from relying upon labor income to relying on transfers. They also tend to live in smaller households, not only because any children will have moved away but also because, at some stage, a spouse dies. People move homes and sometimes they move back to live with their now grown-up children. This paper examines the wellbeing of people as they pass through the later stages of their life and through different labor market statuses and domestic statuses. It examines and compares nine countries - Canada, Finland, Germany, Italy, Japan, the Netherlands, Sweden, the United Kingdom and the United States. It draws particularly from a special analysis of micro-data sets that report on incomes, but it complements this with an analysis of data on wealth, on consumption, on housing and on the use of in-kind services provided by the state. The paper is original in more than one way. First, its analysis is based upon the individual rather than the household. This means both that the importance of own-income sources can be evaluated and that intrafamilial transfers are observed. Second, it includes Japan, a country where both employment patterns and living patterns for older people are substantially different to those of many other OECD countries. Many more work, and many more live in multigenerational households. Principal findings are that, although income does fall with age, people over retirement age are not substantially less well off than people of working age. The difference is further reduced when the absence of work-related expenses and older peoples generally lower housing expenses are taken into account. Remarkably, and regardless of the public-private mix of pensions and the importance or otherwise of work, the income of retirement-age people, relative to that of working-age people, is rather similar across all nine countries. Nevertheless, some older people, particularly old single women, fare less well, and this is the case in all nine countries. Widowhood reduces wellbeing, particularly because in many countries all or part of the husbands pension is lost, but also because single people do not enjoy the scale economies enjoyed by couple households. Those old single people who move back with their adult relatives tend to fare much better than those who stay living alone. Consumption of in-kind services provided by the state, such as social care and especially of health care services, can substantially enhance the income of the oldest of the old. This needs to be taken into account when relative wellbeing is assessed. The extent to which such services are provided cost-free makes comparisons between countries as different as the United States and Sweden quite fraught. Analysis such as was carried out here on a one-off basis needs to be repeated to monitor changes in wellbeing in old age. This is important because pension policy is being changed. Older people are being encouraged to work longer and private rather than public provision is being promoted.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.243

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.011
GPT teacher head0.269
Teacher spread0.258 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations66
Published2002
Admission routes1
Has abstractyes

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