Fostering Entrepreneurship: Promoting Founding or Funding?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Governments across the globe are eager to foster entrepreneurial ecosystems, yet there is no consensus on what policies to use. We develop a theory about the equilibrium consequences of two canonical types of entrepreneurship policies: policies that encourage entrepreneurs to found new ventures and policies that encourage investors to fund new ventures. We distinguish between a short-term impact on current market activity versus a long-term impact on future activity. Investing in entrepreneurial ventures requires tacit knowledge that is mainly acquired through prior entrepreneurial experience, implying that the supply of capital depends on successful entrepreneurs from prior generations. Recognizing this intergenerational linkage has a profound impact on the market equilibrium and the effect of entrepreneurship policies. Our analysis identifies a rationale for using funding polices. The online appendix is available at https://doi.org/10.1287/mnsc.2018.3074 . This paper was accepted by Ashish Arora, entrepreneurship and innovation.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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 it