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Record W2165195823

Basic Living Expenses for the Canadian Elderly

2009· preprint· en· W2165195823 on OpenAlexaffabout
Bonnie‐Jeanne MacDonald, Doug Andrews, Robert L. Brown

Bibliographic record

VenueRePEc: Research Papers in Economics · 2009
Typepreprint
Languageen
FieldSocial Sciences
TopicRetirement, Disability, and Employment
Canadian institutionsMcMaster University
Fundersnot available
KeywordsResidenceRentingCost of livingOld Age SecurityBusinessStandard of livingGerontologyMedical expensesOperating expenseDemographic economicsEconomicsEconomic growthFinanceMedicineEnvironmental healthPolitical sciencePopulation
DOInot available

Abstract

fetched live from OpenAlex

Our research undertakes to determine the basic living expenses required by Canadian seniors living in different circumstances in terms of age, gen-der, city of residence, household size, homeowner or renter, means of trans-portation and health status. The paper develops required expenses for food, shelter, health care, transportation and miscellaneous. The research identi-fies the typical expenses of seniors in each of these categories. Using 2001 as our base year, we follow the US Elder Standard to build an elderly threshold for Halifax, Montreal, Toronto, Calgary and Vancouver. The research is unique because it is the first Canadian study of abso-lute basic living expenses tailored to seniors, rather than simply to adults in general. This information is important to seniors, prospective retirees, finan-cial planners, policy makers and actuaries in assessing the minimum level of income required in retirement and the adequacy of savings and income se-curity programs. Our conclusions suggest that individual circumstances, rather than age, are the primary drivers in determining the cost of these basic expenses. Se-niors are a diverse group, particularly with respect to health, so it is impor-tant that seniors and financial planners do not blindly rely on a fixed replace-ment ratio or universal level of income when projecting the level of finances needed to retire. This research enables the reader to determine the threshold that is suited to a senior’s general circumstances.

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)0.003
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 score0.030
Threshold uncertainty score0.216

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.006
Science and technology studies0.0020.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.237
GPT teacher head0.446
Teacher spread0.209 · 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

Citations0
Published2009
Admission routes2
Has abstractyes

Explore more

Same venueRePEc: Research Papers in EconomicsSame topicRetirement, Disability, and EmploymentFrench-language works237,207