The local injustice of bankruptcy: geographical variation in access to debt relief in England
Bibliographic record
Abstract
Abstract Personal insolvency law offers a vital safety net for individuals in financial difficulty. Nonetheless, disparities arise in access to the benefits of debt relief offered by insolvency law. In a context of geographical inequality and local gaps in access to justice, our original empirical study explores the substantial variation in personal insolvency rates across English local authority districts (LADs) – both in terms of total personal insolvencies, and of the proportion of insolvencies composed of longer‐term, expensive repayment plans (individual voluntary arrangements (IVAs)) compared to rapid debt discharge procedures (bankruptcy and debt relief orders). Our model explains much of the local variation in total insolvencies and finds higher insolvency rates in LADs with lower median income levels. Crucially, however, economic variables do not explain local variation in the proportion of insolvencies composed of IVAs. Despite IVAs being designed for higher‐income ‘can pay’ debtors, the IVA proportion is higher in LADs with lower median income levels. These findings imply that IVAs are not ‘rationally sorted’ based on economic conditions and add important evidence to the case for reforming personal insolvency law to address apparent inefficiencies and unfairness.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".