Potential Errors in Detecting Earnings Management: Reexamining Studies Investigating the AMT of 1986*
Bibliographic record
Abstract
Abstract In this paper we seek to document errors that could affect studies of earnings management. The book income adjustment (BIA) of the alternative minimum tax (AMT) created apparently strong incentives to manage book income downward in 1987. Five earlier papers using different methodologies and samples all conclude that earnings were reduced in response to the BIA. This consensus of findings offers an opportunity to investigate our speculation that methodological biases are more likely when there appear to be clear incentives for earnings management. A reexamination of these studies uncovers potential biases related to a variety of factors, including choices of scaling variables, selection of affected and control samples, and measurement error in estimated discretionary accruals. A reexamination of the argument underlying these studies also suggests that the incentives to manage earnings are less powerful than initially predicted, and are partially mitigated by tax and non‐tax factors. As a result, we believe that the extent of earnings management that occurred in 1987 in response to the BIA remains an unresolved issue.
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 | 0.109 | 0.370 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".