Product Market Competition and the Financing of New Ventures
Bibliographic record
Abstract
This paper examines the interaction between venture risk, product market competition, and the entrepreneur’s choice between bank financing and venture capital (VC) financing. Under bank financing, a debt-type contract emerges as optimal, which allows the entrepreneur to retain full control of the venture and thus yields strong effort incentives, as long as the entrepreneur can service the debt repayment; however, this leads to liquidation in the case of default, making the venture’s success quite sensitive to exogenous, even temporary, shocks that may hinder debt repayment. Under VC financing, an equity-type contract emerges as optimal. Although it is not sensitive to exogenous shocks, this contract requires the entrepreneur to share a fraction of the rents with the financier, thus yielding lower effort incentives for the entrepreneur. There exists a threshold level of venture risk such that bank financing is optimal if and only if venture risk is below that threshold. Product market competition increases the value of stronger entrepreneurial incentives and thus increases the maximum level of risk the entrepreneur is willing to take before switching from bank financing to VC financing. This is a robust result that is shown to hold in various models of competition, including the Hotelling, Salop, Dixit–Stiglitz, and Cournot-to-Bertrand switch. This paper was accepted by Lee Fleming, entrepreneurship and innovation.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".