Bibliographic record
Abstract
ABSTRACT A growing literature examines how a firm's behavior impacts the behavior of its peers. In this paper, we examine how changes in tax paying, and the associated financial reporting, impact a firm's peers. Changes to tax paying and reporting behavior at other firms within a peer group can be affected by many of the same factors, such as industry-level tax policy changes or audit risk, so we make use of exogenous—to the peer firms—shocks to tax behavior. Following the methodology of Dyreng, Hanlon, and Maydew (2010), we estimate managerial tax avoidance fixed effects and use these to identify tax rate shocks associated with executive turnover. We find that peer firms respond to these shocks by changing their GAAP tax rates in the same direction. The magnitude of the effect corresponds to an approximately 10 percent response to the average change in peer group GAAP ETR. Our evidence suggests that these peer effects occur only for book (i.e., financial reporting), rather than cash (i.e., real effects), ETR and are concentrated in firms with potentially greater discretion in reporting taxes on foreign earnings. JEL Classifications: H25; M41. Data Availability: Data used in this study are available from public sources identified in the paper.
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 | 0.002 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 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".