Earnings Management to Minimize Superfund Clean‐up and Transaction Costs*
Bibliographic record
Abstract
Abstract We investigate whether firms identified as potentially responsible parties (PRPs) under the Comprehensive Environmental Response, Compensation, and Liability Act (more commonly known as Superfund) appear to manipulate earnings to minimize their exposure to Superfund clean‐up and transaction costs. We analyze the discretionary accrual behavior of 612 PRPs from 1981 to 1995 and increase the power of our tests by identifying those PRPs with the most incentive to manage earnings during PRP identification years. The results provide robust evidence consistent with the hypothesis that these PRPs use income‐reducing discretionary accruals during PRP identification years in an attempt to minimize Superfund clean‐up and transaction costs. We also consider whether PRPs' incentives to manage earnings change in response to a change in the EPA regulatory regime and find modest evidence consistent with our conjecture.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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".