Financing High-tech Start-ups: Moral Hazard, Information Asymmetry and the Reallocation of Control Rights
Bibliographic record
Abstract
Abstract Recent data suggest that venture capital investments concentrate in the high-tech sector only in those countries where banks are not allowed to offer equity financing. To explain this fact, I develop a simple principal-agent model of start-up financing with both private information and hidden actions in which the equity investor can vary the level of control over the firm and the debt investor cannot. The model shows that when three commonly documented characteristics of the high-tech industry coexist, namely: (i) a high degree of information asymmetry, (ii) a high level of uncertainty about returns, and (iii) a large amount of R&D investments preceding production, then the ability to reallocate control rights that are contingent on performance becomes the key. Unlike debt contracts, equity contracts specify detailed provisions regarding the allocation of control rights. Thus, venture capitalists as equity holders have a clear advantage in financing young high-tech firms in places where banks are not allowed to offer equity contracts; in countries with no such restriction, they no longer have such an advantage. This result helps explain why most European governments’ efforts in promoting venture capital activities failed to attract such investments in the high-tech sector.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".