Entrepreneurial Finance and the Flat-World Hypothesis: Evidence from\nCrowd-Funding Entrepreneurs in the Arts
Bibliographic record
Abstract
We examine the geography of early stage entrepreneurial finance in the\ncontext of an internet marketplace for funding new musical\nartist-entrepreneurs. A large body of research documents that investors\nin early-stage projects are disproportionately co-located with the\nentrepreneur. Theory predicts this will be particularly true of\nartist-entrepreneurs with preliminary-stage projects,\ndifficult-to-contract-for effort, difficult-to-observe creativity,\nnegligible tangible assets, and limited reputations. At the same time,\nhowever, observers of the spatial effects of the internet and related\ntechnologies report that many economic activities have become much less\ngeographically dependent. At an aggregate level, the internet\nmarketplace we examine does indeed demonstrate a spatial transformation\nof the entrepreneurial finance process: the average distance between\ninvestors and artist-entrepreneurs is 4,831 km. However, geography still\nmatters; investors are disproportionately likely to be local and,\nconditional on investing, local investors invest more. This apparent\nrole for proximity is strongest before entrepreneurs visibly accumulate\ncapital. Within a single round of financing, local investors are more\nlikely to engage earlier in the funding cycle. However, this difference\nin the timing of investment is almost entirely explained by a particular\ntype of investor, whom we characterize as 'family, friends, and fans.'\nWe conjecture that these individuals, who are disproportionately\nco-located with the entrepreneur, have offline information about the\nentrepreneur and therefore derive less new information from observing\nthe aggregate financing raised. We speculate that the path-dependent\nrole of this offline network in conveying information to the online\ncommunity limits the 'flat world' potential of these communication technologies.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".