Entrepreneurial Finance and the Flat-World Hypothesis: Evidence from Crowd-Funding Entrepreneurs in the Arts
Bibliographic record
Abstract
We examine the geography of early stage entrepreneurial finance in the context of an internet marketplace for funding new musical artist-entrepreneurs. A large body of research documents that investors in early-stage projects are disproportionately co-located with the entrepreneur. Theory predicts this will be particularly true of artist-entrepreneurs with preliminary-stage projects, difficult-to-contract-for effort, difficult-to-observe creativity, negligible tangible assets, and limited reputations. At the same time, however, observers of the spatial effects of the internet and related technologies report that many economic activities have become much less geographically dependent. At an aggregate level, the internet marketplace we examine does indeed demonstrate a spatial transformation of the entrepreneurial finance process: the average distance between investors and artist-entrepreneurs is 4,831 km. However, geography still matters; investors are disproportionately likely to be local and, conditional on investing, local investors invest more. This apparent role for proximity is strongest before entrepreneurs visibly accumulate capital. Within a single round of financing, local investors are more likely to engage earlier in the funding cycle. However, this difference in the timing of investment is almost entirely explained by a particular type of investor, whom we characterize as 'family, friends, and fans.' We conjecture that these individuals, who are disproportionately co-located with the entrepreneur, have offline information about the entrepreneur and therefore derive less new information from observing the aggregate financing raised. We speculate that the path-dependent role of this offline network in conveying information to the online community limits the 'flat world' potential of these communication technologies.
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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 | 0.004 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".