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Record W1531344682

Accounting for the Rise in Consumer Bankruptcies in the U.S. and Canada

2004· article· en· W1531344682 on OpenAlexaboutno aff
Igor Livshits, James MacGee

Bibliographic record

Venue2004 Meeting Papers · 2004
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinancial Literacy, Pension, Retirement Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsBankruptcyPopulationEarningsCollateralized debt obligationDebtAsset (computer security)Market liquidityEconomicsBusinessMonetary economicsAccountingCollateralFinance
DOInot available

Abstract

fetched live from OpenAlex

Personal bankruptcy filings have increased dramatically: rising from 1.4 in per thousand of working age population 1970 to 8.5 in 2002 in the United States and from 0.2 in 1970 to 4.3 in 2002 in Canada. This paper asks whether 6 commonly mentioned potential explanations -- financial innovation, financial market liberalization, an increase in household specific idiosyncratic risk, demographic changes, legal changes and decreased “stigma†-- can quantitatively account for the rise in consumer bankruptcies in the United States and Canada. We use a heterogeneous agent life cycle model with competitive intermediaries who are able to (imperfectly) observe households (labor) productivity, age and current asset holdings. We undertake numerical experiments to determine the extent to which each of the 5 factors can account for the rise in consumer bankruptcies. Our analysis suggests that financial innovation (the spread of credit scoring and new collateralized debt instruments) and financial liberalization play an essential role, while demographic changes and increased earnings uncertainty play a small role in accounting for the rise. These results cast doubt on explanations of the rise which depend upon declining ``stigma'' explanation or the relaxation of the bankruptcy code

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)0.001
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 score0.249
Threshold uncertainty score0.422

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.009
GPT teacher head0.206
Teacher spread0.197 · 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

Citations0
Published2004
Admission routes1
Has abstractyes

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