The Power of Numbers: Base‐Ten Threshold Effects in Reported Revenue*
Bibliographic record
Abstract
ABSTRACT We show that managers have a propensity to disproportionately report total revenues just above base‐ten thresholds (e.g., 10 million, 30 million, 1 billion) and examine motives for and consequences of this behavior. Focusing on base‐ten thresholds in revenues is important because, despite being unusually prevalent in revenue targets set in executive compensation contracts, analyst forecasts, and management forecasts, they have not been previously explored. We also show that pressure to beat these targets provides one explanation for the base‐ten bias in reported revenues. However, these incentive effects do not offer a complete explanation because base‐ten threshold‐beating is observed even in the absence of these explicit targets. We further find that when firms beat a base‐ten threshold for the first time, they experience increases in news coverage, institutional ownership, liquidity, and analyst following, even after controlling for whether they have beaten other common benchmarks. These results suggest that managers also beat base‐ten thresholds in order to increase their firms' overall visibility. Overall, we show that a preference for base‐ten numbers, which have no inherent economic meaning, has a measurable effect on the actions of market participants. These results open the door to a new range of managerial targets previously unexplored.
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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 | 0.005 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".