Hurdles to debt relief for “no income no assets” debtors in Germany: A case study of failed consumer bankruptcy law reforms
Bibliographic record
Abstract
Abstract Given that many overindebted households have low or no assets and income, governments have increasingly tried to adapt their consumer bankruptcy regimes to the needs and capacities of these NINA (“no income, no assets”) debtors. Most notably, since the mid‐2000s, some countries from the Anglosphere have created low‐cost, means‐tested, and administrative (i.e., nonjudicial) debt relief procedures as alternative to traditional bankruptcy for NINA debtors. By contrast, in some European countries such as Germany, legislators have tried—but until today failed—to create efficient debt relief measures for NINA debtors. This contribution aims to make English‐speaking readers familiar with the history of consumer insolvency law in Germany, with a focus on legislative developments regarding NINA debtors, and to identify actors, institutions, and ideas that have contributed—especially during the 2000s—to the failure of consumer bankruptcy reforms addressing the main problems of NINA cases in Germany (i.e., high hurdles to relief for debtors, high administrative efforts for trustees and courts, high costs for the public purse, and yet very few payments to creditors). The German case is relevant not only because it is a striking case of failure to adapt a debt relief regime to NINA debtors but also because German consumer bankruptcy law—despite its shortcomings—continues to serve as a template for insolvency law reforms in European and other countries.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".