Bankruptcy Spillovers
Bibliographic record
Abstract
How do different bankruptcy approaches affect the local economy? Using U.S. Census microdata at the establishment level, we explore the spillover effects of reorganization and liquidation on geographically proximate firms. We exploit the random assignment of bankruptcy judges as a source of exogenous variation in the probability of liquidation. We find that within a five-year period, employment declines substantially in the immediate neighborhood of the liquidated establishments, relative to reorganized establishments. Most of the decline is due to lower growth of existing establishments and, to a lesser extent, reduced entry into the area. The spillover effects are highly localized and concentrate in the non-tradable and service sectors, particularly when the bankrupt firm operates in the same sector. These results suggest that liquidation leads to a reduction in consumer traffic to the local area and to a decline in knowledge spillovers between firms. The evidence is inconsistent with the notion that liquidation leads to creative destruction, as the removal of bankrupt businesses does not lead to increased entry nor the revitalization of the area.
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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 | 0.018 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.009 |
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; both teacher heads agree on what is shown here.
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".