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Record W3125035699 · doi:10.3386/w24008

Older Americans Would Work Longer If Jobs Were Flexible

2017· preprint· en· W3125035699 on OpenAlexaff
John Ameriks, Joseph Briggs, Andrew Caplin, Min Joon Lee, Matthew D. Shapiro, Christopher Tonetti

Bibliographic record

VenueNational Bureau of Economic Research · 2017
Typepreprint
Languageen
FieldSocial Sciences
TopicRetirement, Disability, and Employment
Canadian institutionsCarleton University
FundersNational Institute on AgingCollege of Pharmacy, University of MichiganUniversity of MichiganAlfred P. Sloan Foundation
KeywordsWork (physics)Identification (biology)Labour economicsSurvey data collectionDemand sideLabor demandComplement (music)BusinessEconomicsDemographic economicsMicroeconomicsWageEngineering

Abstract

fetched live from OpenAlex

Older Americans, even those who are long retired, have strong willingness to work, especially in jobs with flexible schedules.For many, labor force participation near or after normal retirement age is limited more by a lack of acceptable job opportunities or low expectations about finding them than by unwillingness to work longer.This paper establishes these findings using an approach to identification based on strategic survey questions (SSQs), purpose-designed to complement behavioral data.These findings suggest that demand-side factors are important in explaining late-in-life labor market behavior and need to be considered in designing policies aimed at promoting working longer.

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)0.004
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 score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.701
GPT teacher head0.618
Teacher spread0.083 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

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