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Record W1562025133 · doi:10.34989/sdp-2009-7

Household Debt, Assets, and Income in Canada: A Microdata Study

2021· preprint· en· W1562025133 on OpenAlexaffabout
Césaire Meh, Yaz Terajima, David Xiao Chen, Thomas J. Carter

Bibliographic record

VenueEconstor (Econstor) · 2021
Typepreprint
Languageen
FieldBusiness, Management and Accounting
TopicFinancial Literacy, Pension, Retirement Analysis
Canadian institutionsBank of Canada
FundersU.S. Department of the Treasury
KeywordsMicrodata (statistics)Household debtDebtFinancial stabilityEconomicsHousehold incomeMonetary economicsDemographic economicsBusinessFinancial systemFinanceGeographyPopulation

Abstract

fetched live from OpenAlex

The authors use microdata from the 1999 and 2005 Surveys of Financial Security to identify changes in household debt, and discuss their potential implications for monetary policy and financial stability. They document an increase in the debt-income ratio, which rose from 0.75 to 0.95, on average. Rising debt ratios were driven by a 50 per cent increase in mortgage balances among the middle-aged, a doubling of credit card debt among households over 55, and a fourfold increase in home equity lines of credit among small business owners and households without high school diplomas. The authors identify rising debt-income ratios among households in the bottom income quintile as the most important development of the years 1999 through 2005, signalling greater sensitivity to rising interest rates or negative income shocks – particularly among income-poor homeowners, whose 2005 mortgage obligations totalled 72 per cent of income. Meanwhile, an increase in the portfolio share for which real estate accounts, particularly among the middleaged, suggests that household balance sheets have become more sensitive to changes in the housing market. In addition to poor households, the authors identify former bankrupts, younger households, and the self-employed as more indebted and hence at greater risk.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.119
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.003
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 head0.017
GPT teacher head0.216
Teacher spread0.198 · 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.

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

Citations18
Published2021
Admission routes2
Has abstractyes

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