Some evidence of the external financing costs of new technology-based firms in Canada
Bibliographic record
Abstract
This exploratory study attempts to estimate the external financing costs (EFCs) for a sample of new technology-based firms (NTBFs). A large body of literature describes the constraints these companies face when trying to obtain outside equity from venture capitalists or non-institutional investors. The theory explains some of these difficulties by the prevalence of information asymmetry, agency costs and moral hazard problems. For NTBFs, these phenomena cause the search for outside equity to be a time-consuming, costly process: the EFCs should thus be considerable, but are a largely unexplored aspect of the small business financing problem. We propose an estimation of these EFCs. Some of these costs are not reported in the financial statements and can be determined only through a field survey and case analyses. In this study, we identify the elements that generate the EFCs and estimate the time frames and costs associated with 18 financing rounds undertaken by 12 NTBFs in Canada. We show that these costs are indeed substantial and heavily penalize small companies, especially during the initial financing round and prior to the commercialization phase. Based on our initial propositions and observations, we conclude that the EFCs are higher for the first round of financing, for companies that have not reached the commercialization stage, and are lower as gross proceeds increase.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".