Bibliographic record
Abstract
Abstract Financially distressed and insolvent firms file for bankruptcy to either reorganize or liquidate under court supervision. Fundamentally, bankruptcy law is designed to resolve creditor coordination and holdout problems. It not only sets up rules and guidelines to allow firms to restructure their debt claims but also provides means for firms to reallocate their assets to other users. Although an efficient bankruptcy system can help mitigate bargaining frictions and maximize asset value and thus creditor recovery by avoiding inefficient liquidation or excess continuation, the bankruptcy process itself can be costly. Understanding and quantifying the costs of bankruptcy restructuring are important not only to financially distressed firms but also to the capital structure decisions and the pricing of securities of healthy firms. More broadly, efficient bankruptcy mechanisms are important for economic growth, the productivity of firms in an economy, and the resiliency of the economy to adverse shocks. From the 1990s through the 2020s, the literature has flourished, with a growing number of empirical studies investigating the efficiency of the bankruptcy system and different aspects of bankruptcy costs. Bankruptcy costs are typically classified as either direct or indirect costs. The former refers to out-of-pocket expenses associated with the retention of professionals, while the latter refers to opportunity costs incurred as a result of the adverse effect of a bankruptcy filing on business operations, human capital, and investments. Indirect costs are typically larger and more difficult to measure and quantify than direct costs, which studies show to be a small fraction of a bankrupt firm’s assets. Because of significant economic frictions such as conflicts of interest, information asymmetry, and judicial biases presented in the system, bankruptcy can be a lengthy process. Since delay allows both direct and indirect costs to accumulate, a number of studies show that shortening the bargaining process can effectively help preserve firm value. Besides delay, bankruptcy costs can be manifested in inefficient liquidation, excess continuation, fire sales, loss of human capital, and managerial turnover, which impose real costs on bankrupt firms. How to mitigate frictions and minimize costs has been the central theme of bankruptcy research from the 1990s through the 2020s, a time that has also witnessed several notable changes to the U.S. bankruptcy system, including the rise of specialized distressed investors, the strengthening of secured creditor control rights, and the increasing intensity of asset sales. These changes have important implications for the restructuring landscape.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".