Bankruptcy Laws and Entrepreneur– Friendliness
Bibliographic record
Abstract
Using bankruptcy laws as a case of formal institutions, we show how formal institutions impact entrepreneurship development. Historically, bankruptcy laws usually have been harsh. Recently, many governments have realized that entrepreneur–friendly bankruptcy laws can not only lower exit barriers, but also lower entry barriers for entrepreneurs. Since bankruptcy laws are not uniform around the world, it is important to understand how they differ in their friendliness to entrepreneurs. This article focuses on six dimensions of entrepreneur–friendliness: (1) the availability of a reorganization bankruptcy option, (2) the time spent on bankruptcy procedures, (3) the cost of bankruptcy procedures, (4) the opportunity to have a fresh start in liquidation bankruptcy, (5) the opportunity to have an automatic stay of assets during reorganization bankruptcy, and (6) the opportunity for entrepreneurs and managers to remain on the job after filing for bankruptcy. In an effort to cover both developed and emerging economies and to draw on geographically diverse examples, we use data from Australia, Canada, Chile, Finland, Hong Kong, Japan, Norway, Peru, Singapore, South Korea, Thailand, the United States, and other countries to illustrate these differences. Overall, this article contributes to the institution–based view of entrepreneurship by highlighting the important role that formal institutions such as bankruptcy laws play behind entrepreneurship development around the world.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".