Pathways into Bankruptcy: Accumulating Disadvantage and the Consequences of Adverse Life Events
Bibliographic record
Abstract
This study combines theories of accumulating disadvantage and economic insecurity using the event of bankruptcy to investigate how certain adverse life events jointly affect inequality. I analyze National Longitudinal Survey of Youth data from 1985 through 2008 to highlight the complexities of financial hardship in the path to bankruptcy. By applying hybrid mixed effects models to parse out within‐ and between‐person variation, I show that, in the case of bankruptcy, financial hardship unfolds over a specific series of events, which can lead to the accumulation of disadvantage connected to changes in employment, marital, and health statuses. I find that bankruptcy results from people's recent experiences of illness and marital dissolution, but not always directly from employment disruption. The effects of job loss on bankruptcy become more apparent as these events accumulate over time and limit wealth creation. The timing of events and their relationship with net worth also influence when a person will file for bankruptcy. As a whole, my findings demonstrate how adverse events and financial hardship lead to bankruptcy through multiple pathways.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".