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Record W2256065771

What happens to household portfolios after retirement

2006· article· en· W2256065771 on OpenAlexaff
Courtney C. Colie, Kevin Milligan

Bibliographic record

VenueSSRN Electronic Journal · 2006
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinancial Literacy, Pension, Retirement Analysis
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsNet worthPensionHome equitySpouseBusinessAsset (computer security)Stock (firearms)Real estateEquity (law)Consumption (sociology)BoomFinancial assetFinanceEconomicsLabour economicsDebt
DOInot available

Abstract

fetched live from OpenAlex

The typical older household in the United States now arrives at retirement with an array of financial resources. These resources usually include home equity, vehicles, and bank accounts and may also include financial assets such as Individual Retirement Accounts (IRAs) and stocks or other property like small businesses and real estate. These assets are important for the financial security of older households. Households may use them to finance routine consumption in retirement or reserve them to cope with the financial consequences of a negative event like the death of a spouse. Households' ability to manage their assets in retirement is becoming more important over time, as the shift towards defined contribution pension plans means that households are more likely to receive their pension as a stock of assets at retirement rather than as a flow of monthly benefits. Older households hold a sizeable share of total U.S. household net worth, so the spend-down patterns of these households may affect asset markets, particularly as the large baby boom cohort enters retirement.

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)0.003
metaresearch head score (Gemma)0.038
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 score0.022
Threshold uncertainty score0.062

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.038
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0010.001
Scholarly communication0.0040.006
Open science0.0010.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0190.002

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 head0.008
GPT teacher head0.203
Teacher spread0.196 · 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

Citations5
Published2006
Admission routes1
Has abstractyes

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