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Record W2465767391 · doi:10.1177/0020715216653798

Household debt in midlife and old age: A multinational study

2016· article· en· W2465767391 on OpenAlexvenueno aff
Noah Lewìn-Epstein, Moshe Semyonov

Bibliographic record

VenueInternational Journal of Comparative Sociology · 2016
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing, Finance, and Neoliberalism
Canadian institutionsnot available
FundersIsrael Science Foundation
KeywordsDebtHealth and Retirement StudyHousehold debtContext (archaeology)Demographic economicsWelfareEconomicsMultinational corporationWelfare stateConsumption (sociology)BusinessFinancePolitical scienceDemographyGeographySociology

Abstract

fetched live from OpenAlex

This article examines the prevalence of household debt in middle and old age, in the context of rising consumption, the weakening welfare safety net, and the ‘democratization’ of credit. We aim to address theoretical propositions concerning household correlates of mortgage and financial debt, as well as the relationship between the two types of debt. We utilize data gathered on populations, aged 50 years and older, in 15 countries that participated in the Survey of Health, Ageing, and Retirement in Europe (SHARE) project. We find considerable levels of mortgage and financial debt in advanced stages of life, as well as significant differences within and between countries. Controlling for country variation as well as individual and household attributes, we find a positive relationship between the size of mortgage debt and financial debt across most countries. We test alternative explanations for this relationship and discuss the implications of our findings in the broader context of the risks faced by older cohorts in consumer societies with shrinking welfare expenditure.

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)0.001
metaresearch head score (Gemma)0.000
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 score0.205
Threshold uncertainty score0.360

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.078
GPT teacher head0.316
Teacher spread0.237 · 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

Citations17
Published2016
Admission routes1
Has abstractyes

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