Misclassifying Core Expenses as Special Items: Cost of Goods Sold or Selling, General, and Administrative Expenses?
Bibliographic record
Abstract
Abstract Prior studies of classification shifting in the income statement conclude that managers misclassify core expenses as special items to inflate reported core earnings (McVay 2006; Fan, Barua, Cready, and Thomas 2010). These studies do not distinguish between the core expense components—cost of goods sold ( COGS ) and selling, general, and administrative expenses ( SGA ). This study models COGS and SGA separately, and investigates managers’ misclassification of COGS versus SGA to meet different profitability benchmarks. We find that COGS (but not SGA ) misclassification is associated with just beating the benchmark of gross margin four quarters earlier. In comparison, both COGS and SGA misclassification are associated with just beating the benchmarks of zero core earnings, prior‐year core earnings, and analyst earnings forecasts in the fourth fiscal quarter. We also investigate real activities management ( RAM ) of COGS and SGA to meet benchmarks, and find that managers engage in RAM of COGS to achieve the gross margin benchmark, but not core earnings benchmarks. We demonstrate that unexpected SGA contains a significant misclassification effect distinct from RAM , suggesting that future RAM research should consider controlling for expense misclassification. Overall, our study extends prior literature on both classification shifting and RAM .
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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 | 0.009 | 0.052 |
| 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 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".