Bibliographic record
Abstract
When a firm finances a new project by issuing debt, it has an incentive to invest in excessively high-risk projects because shareholders enjoy all the benefits in case the project is successful but have limited liability when it fails. Anticipating such behavior, creditors may require a higher interest rate or may even refuse to provide capital. This debt–equity conflict is alleviated by the fact that most investment decisions are made by risk-averse managers who are not as well diversified as shareholders. This paper investigates the debt–equity conflict in firms in which the managers have an unobservable degree of risk averseness. Since managerial risk averseness is a desirable quality, such asymmetric information makes managers undertake actions that increase the market’s perception of them as being highly risk-averse. Consequently, such reputation building leads to a lower number of excessively high-risk projects being undertaken. This paper compares the entrepreneurial economy, in which managers are the sole owners of the firms, with the corporate economy, in which managers are hired by shareholders. Using the overlapping generations model, this paper shows that managerial reputation building can partially resolve the debt–equity conflict and improve efficiency in both economies; however, such improvement is larger in the entrepreneurial economy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 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".