The Effect of Operating Leverage on Managers' Capital Investment Decisions<sup>*</sup>
Bibliographic record
Abstract
ABSTRACT Holding all else constant, a larger proportion of fixed costs in the cost structure (i.e., higher operating leverage) implies a greater profit volatility for a given level of demand fluctuation. In a setting where operating leverage is irrelevant to the choice of investment projects, I experimentally examine whether managers under high operating leverage (as opposed to low operating leverage) choose a less profitable project for lower variability in anticipated earnings. Inconsistent with the hypothesis, I find that the level of operating leverage has an insignificant impact on managers' investment choices, providing preliminary evidence that managers may correctly identify the level of operating leverage as irrelevant. Notably, providing the cost structure information to managers, regardless of whether it pertains to high or low operating leverage, increases the likelihood of selecting the more profitable investment. Additional analyses suggest that the excerpt on operating leverage might prime managers to deliberate more on the investment decisions. Managers who receive the cost structure information dedicate more time to assessing the investment projects, which in turn aids in identifying the more profitable project.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".