Determinants of long-distance investing by business angels in the UK
Bibliographic record
Abstract
The business angel market is usually identified as a local market, and the proximity of an investment has been shown to be key in the angel's investment preferences and an important filter at the screening stage of the investment decision. This is generally explained by the personal and localized networks used to identify potential investments, the hands-on involvement of the investor and the desire to minimize risk. However, a significant minority of investments are long distance. This paper is based on data from 373 investments made by 109 UK business angels. We classify the location of investments into three groups: local investments (those made within the same county or in adjacent counties); intermediate investments (those made in counties adjacent to the ‘local’ counties); and long-distance investments (those made beyond this range). Using ordered logit analysis the paper develops and tests a number of hypotheses that relate long-distance investment to investment characteristics and investor characteristics. The paper concludes by drawing out the implications for entrepreneurs seeking business angel finance in investment-deficient regions, business angel networks seeking to match investors to entrepreneurs and firms (which are normally their primary clients), and for policy-makers responsible for local and regional economic development.
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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 | 0.001 | 0.005 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".