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Record W2404227404 · doi:10.3386/w22970

The Hidden Resources of Women Working Longer: Evidence from Linked Survey-Administrative Data

2016· preprint· en· W2404227404 on OpenAlexaboutno aff
C. Adam Bee, Joshua Mitchell

Bibliographic record

VenueNational Bureau of Economic Research · 2016
Typepreprint
Languageen
FieldSocial Sciences
TopicRetirement, Disability, and Employment
Canadian institutionsnot available
Fundersnot available
KeywordsQuarter (Canadian coin)Social securitySurvey of Income and Program ParticipationSurvey data collectionDemographic economicsConsumption (sociology)Current Population SurveyEconomicsWork (physics)American Community SurveyPopulationBritish Household Panel SurveyLabour economicsDemographyGeographyCensusSociology

Abstract

fetched live from OpenAlex

Despite women's increased labor force attachment over the lifecycle, household surveys such as the Current Population Survey Annual Social and Economic Supplement (CPS ASEC) do not show increases in retirement income (pensions, 401(k)s, IRAs) for women at older ages. We use linked survey-administrative data to demonstrate that retirement incomes are considerably underreported in the CPS ASEC and that women's economic progress at older ages has been substantially understated over the last quarter century. Specifically, the CPS ASEC shows median household income for women age 65-69 rose 21 percent since the late 1980s, while the administrative records show an increase of 58 percent. Survey biases in women's own incomes appear largest for women with the longest work histories. We also exploit the panel dimension of our data to follow a cohort of women and their spouses (if present) as they transition into retirement in recent years. In contrast to previous work, we find that most women do not experience noticeable drops in income up to five years after claiming social security, with retirement income playing an important role in maintaining their overall standard of living. Our results pose a challenge to the literature on the "retirement consumption puzzle" and suggest total income replacement rates are high for recent retirees.

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)0.035
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.677
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0350.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0040.002
Research integrity0.0000.001
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 head0.857
GPT teacher head0.626
Teacher spread0.231 · 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; both teacher heads agree on what is shown here.

Study designTheoretical or conceptual
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

Citations10
Published2016
Admission routes1
Has abstractyes

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