Managers’ Investment Decisions: Incentives and Economic Consequences Arising from Leases
Bibliographic record
Abstract
What incentives do managers face that might give rise to inefficient investments in leases? If managers make inefficient investments in leases, what economic consequences arise for those managers and their firms? We develop a model of expected investments in leased assets and use the residuals from the model as proxies for inefficient investments. We find that, in contrast to investments in capital expenditures, leasing appears to be a mechanism through which managers can seemingly over-invest, even among firms with high quality financial reporting and negative free cash flows. Examining economic consequences, we predict and find that unexpected investments in leased assets trigger increasing future sales growth but declining future earnings growth for as long as three years ahead. We also find a negative relation with contemporaneous stock returns, suggesting investors view unexpected investments in leases as value destructive. Finally, despite negative returns consequences, we find that unexpected investments in leases are associated with higher CEO compensation driven primarily by future sales growth. Our study suggests that compensation contracts that reward growth may give managers’ incentives to drive sales growth with larger-than-expected investments in leased assets, which lead to slower future earnings growth and negative share price consequences for investors. Our results should inform managers and board members, investors, and researchers interested in investment efficiency, corporate governance, and leases.
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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 | 0.004 | 0.018 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".