Bibliographic record
Abstract
ABSTRACT EBITDA is a commonly used performance measure for (i) valuation, (ii) debt contracting, and (iii) executive compensation. The widespread use of EBITDA by stakeholders may induce managers to focus their attention on EBITDA. Since EBITDA excludes various expenses, managers who fixate on EBITDA may underweight the excluded expenses when determining their firms' investments in capital and leverage levels. I find that managers who fixate on EBITDA overinvest in capital and overlever their firm relative to their industry peers. These results are robust to alternative proxies for managers' focus on EBITDA and alternative specifications. I also find that firms whose managers focus on EBITDA have weaker operating performance, which is attributed to higher depreciation expense. My primary proxy for managers' focus on EBITDA is whether they choose to disclose EBITDA in annual earnings announcements. I find that the use of EBITDA in setting executive compensation, the prevalence of EBITDA estimates by analysts, and the use of EBITDA‐based covenants in firms' debt contracts are all positively associated with the propensity to disclose EBITDA in earnings announcements. I find weaker evidence of opportunistic motives explaining EBITDA disclosure. These results are consistent with managers disclosing EBITDA to portray to investors that it is a metric they seek to maximize. Overall, this study suggests that while EBITDA is a widely used metric, there is a systematic cost to using this measure—it provides managers with incentives to overinvest in capital and to acquire excessive debt.
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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 | 0.013 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".