On the Efficiency of Bankruptcy Law: Empirical Evidence in Spain
Bibliographic record
Abstract
Abstract The current economic crisis is showing one of the main problems that many companies in financial distress have to face, namely, the impact of bankruptcy law in relation to companies and firms. This paper aims to analyze the bankruptcy law ex‐ante efficiency when companies are in financial distress. To test it out, two research questions are submitted: (i) Is solvency, the criterion used in the Spanish law, the best one to assess the relative significance of the main indicators, which determine bankrupt firms? (ii) Is the Spanish bankruptcy law efficient according to solvency or are there better criteria? To answer them, a logistic regression model is conducted. The sample embraces 1,387 firms in Spain, the data being obtained from 12 Commercial Justice Courts complemented with financial information. The main conclusion is that the solvency criterion is adequate to classify bankrupt companies although currently Spanish Bankruptcy law is not as efficient as it could be. Additionally, the relevant companies' indicators, which explain the financial distress procedure, are presented. Copyright © 2013 INSOL International and John Wiley & Sons, Ltd
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 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 | 0.011 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".