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Record W3209577916 · doi:10.33423/jabe.v23i3.4355

Decision-Making in Defined Contribution Pension Plans: The Case of Israel

2021· article· en· W3209577916 on OpenAlexvenueno aff
Ravit Rubinstein-Levi

Bibliographic record

VenueJournal of Applied Business and Economics · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinancial Literacy, Pension, Retirement Analysis
Canadian institutionsnot available
FundersUniversiteit van TilburgTel Aviv UniversityUniversiteit van Amsterdam
KeywordsPensionPovertyBusinessState (computer science)Actuarial sciencePublic economicsFinanceEconomicsEconomic growth

Abstract

fetched live from OpenAlex

Research suggests that the DC saving method, which is a self-guided saving method, is a contributor to exacerbation in retirees' poverty and income inequality. Economists lobby governments to provide the public with information regarding pension savings to improve the situation, and many studies conclude that employees can improve their decisions substantially by receiving pension advice. This study analyzes Israeli data of four field studies among over 1,500 subjects and the Israeli social survey data of the Israeli Central Bureau of Statistics (ICBS). The findings of the study indicate that providing employees with information regarding pension savings does not encourage them to more pro-actively manage their pension savings nor receive advice from a professional counselor. Surprisingly, the findings suggest that employees are more willing to receive pension savings advice from a counselor on behalf of the state than from a counselor who is not necessarily on behalf of the state.

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)0.005
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.044
Threshold uncertainty score0.088

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.003
Scholarly communication0.0040.002
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0060.000

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 head0.009
GPT teacher head0.213
Teacher spread0.204 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

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