How Are Earnings Managed? An Examination of Specific Accruals*
Bibliographic record
Abstract
Abstract There is relatively little evidence on the specific accruals used to manage earnings. This paper examines this issue by considering the use of specific accruals in three earnings‐management contexts: equity offerings, management buyouts, and firms avoiding earnings decreases. We argue that the costs of managing earnings through different income statement items vary and that the benefits of earnings management through each of these items depend on the context. We thus make differential predictions regarding which specific accrual will be used to manage earnings in each of the three contexts we consider. To measure earnings management for specific accruals, we develop performance‐matched measures to capture the unexpected component of accounts receivable, inventory, accounts payable, accrued liabilities, depreciation expense, and special items. Consistent with our predictions, we find that firms issuing equity appear to prefer managing earnings upward by accelerating revenue recognition. Specifically, we find that accounts receivable for these firms are unexpectedly high. Conversely, for the management buyout context, we predict and find unexpected accounts receivable to be negative. For firms trying to avoid reporting an earnings decrease, we expect firms to be less concerned with earnings persistence and therefore more likely to use more transitory, and less costly, items to achieve their goal. We find that special items are significantly more positive for this group. This paper provides a further step toward understanding how the incentives behind earnings management affect the method used to achieve earnings goals, and it illustrates the usefulness of examining individual accruals in specific contexts.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".