MétaCan
Menu
Back to cohort

HOW HOUSEHOLD PORTFOLIOS EVOLVE AFTER RETIREMENT: THE EFFECT OF AGING AND HEALTH SHOCKS

2009· article· en· W2107442748 on OpenAlexaff
Courtney Coile, Kevin Milligan

Bibliographic record

VenueReview of Income and Wealth · 2009
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinancial Literacy, Pension, Retirement Analysis
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsShock (circulatory)PortfolioAsset (computer security)Health and Retirement StudyReal estateEconomicsDemographic economicsBusinessFinanceMedicineGerontology

Abstract

fetched live from OpenAlex

We study how the portfolios of U.S. households evolve after retirement, using data from the Health and Retirement Study (HRS). In particular, we investigate the influence of aging and health shocks on a household's ownership of various assets and on the share of total assets held in each asset class. We find that households decrease their ownership of principal residences, vehicles, financial assets, businesses, and real estate as they age, while increasing the share of assets held in liquid assets and time deposits. We find that widowhood and other health shocks are associated with the same kinds of portfolio changes, and that the effect of shocks strengthens with time since the shock. Finally, we show that the effect of a shock is greatly magnified when households have physical or mental impairments. This suggests that factors other than standard risk and return considerations weigh heavily in many older households' portfolio decisions.

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)0.008
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 score0.010
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
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 head0.012
GPT teacher head0.257
Teacher spread0.245 · 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

Citations154
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueReview of Income and WealthSame topicFinancial Literacy, Pension, Retirement AnalysisFrench-language works237,207