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Record W3202887845 · doi:10.1111/ijcs.12754

Gender differences in financial knowledge overconfidence among older adults

2021· article· en· W3202887845 on OpenAlexaff
Kyoung Tae Kim, Sunwoo T. Lee, Hohyun Kim

Bibliographic record

VenueInternational Journal of Consumer Studies · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinancial Literacy, Pension, Retirement Analysis
Canadian institutionsYork University
Fundersnot available
KeywordsOverconfidence effectPsychologyRobustness (evolution)FinanceGerontologyDemographyMedicineSocial psychologyEconomics

Abstract

fetched live from OpenAlex

Abstract This study explores gender differences in financial knowledge overconfidence among older adults using the 2016 Health and Retirement Study. We find that older females have relatively lower objective financial knowledge than do older males, while they evaluate themselves to be as financially knowledgeable as older males. Further, several measures of overconfidence in financial knowledge are higher in older females than older males. A number of robustness checks, including a propensity score matching method, use of a polygenic risk scores, and a test of reproduction using the Survey of Consumer Finances corroborate these gender differences. Results from decomposition analyses of overconfidence indices show that relatively lower crystallized intelligence of older females is one of the main reasons that widens the gender gap among older adults. Lower likelihoods of attaining a college education degree and being in a relationship are additional contributing factors that explain the gender gap. This study provides insights into understanding the gender gap in financial knowledge and its implications for government education or intervention programs to support older adults' wellbeing in 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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.586

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.033
GPT teacher head0.291
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 teacher head, 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

Citations24
Published2021
Admission routes1
Has abstractyes

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