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Record W2788207092 · doi:10.1017/s1474747214000523

Evidence on individual preferences for longevity risk

2015· article· en· W2788207092 on OpenAlexaff
Gaëtan Delprat, Marie‐Louise Leroux, Pierre‐Carl Michaud

Bibliographic record

VenueJournal of Pensions Economics and Finance · 2015
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Environmental Valuation
Canadian institutionsCenter for Interuniversity Research and Analysis on OrganizationsUniversité du Québec à Montréal
Fundersnot available
KeywordsLongevityRisk aversion (psychology)EconomicsEconometricsEmpirical evidenceLife expectancyExpected utility hypothesisLongevity riskNeutralityActuarial sciencePopulationDemographyFinancial economicsBiology

Abstract

fetched live from OpenAlex

Abstract The standard model of intertemporal choice assumes risk neutrality towards the length of life: under additivity of lifetime utility and expected utility assumptions, agents are not sensitive to a mean preserving spread in the length of life. Using a survey fielded in the RAND American Life Panel, this paper provides empirical evidence on possible deviation from risk neutrality with respect to longevity in the US population. The questions we ask allow to find the distribution as well as to quantify the degree of risk aversion with respect to the length of life in the population. We find evidence that roughly 75% of respondents were not neutral with respect to longevity risk. Hence, there is a little empirical support for the joint use of the expected utility and additive lifetime utility assumptions in life-cycle models. Higher income households are more likely to be risk averse towards the length of life. We do not find evidence that the degree of risk aversion varies with age or education.

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)0.011
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 score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.000

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 head0.338
GPT teacher head0.265
Teacher spread0.073 · 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

Citations18
Published2015
Admission routes1
Has abstractyes

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