Venture capital, entrepreneurship, and public policy
Bibliographic record
Abstract
The existing literature in both public economics and financial economics often fails to consider how appropriate and effective public policy may be in promoting the venture capital industry. Public economics has dealt extensively with the effect of taxes and subsidies but has neglected the unique role of venture capitalists as active investors who provide not only funding but added value. Financial economics has emphasized the special role of the venture capitalist but has not focused on the real effects of venture capital in industry equilibrium or the role of public policy. This volume in the CESifo Seminar series brings together experts in public and financial economics to develop a theoretically and empirically informed international policy perspective for an era in which policymakers increasingly look to venture capital as a source of jobs, innovation, and economic growth. The chapters in part I analyze data on the levels of venture capital fundraising in Europe, problems in the bank-oriented beginnings of German venture capital finance in the 1970s, and the inefficiency of Canadian labor-sponsored venture capital funds. Part II looks at the effect of venture capital on labor market performance, the importance of exit opportunities, and the effect of information inflows on the venture capital cycle. The chapters in part III take the perspective of public economics, reviewing the role of public policy in addressing potential market failures, improving the quality of venture capital investments, and affecting entrepreneurial business activity through tax policy.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".