The Determinants of Informal Capital in the Financing of Small Firms at Start-Up: An Ethnic Comparison of Small Firms in Sweden
Bibliographic record
Abstract
This study explains empirically the differences in the use of informal financing between native- and immigrant-owned small businesses in terms of ethnicity and other relevant variables. A sample of 2814 native- and immigrant-owned small businesses, consisting of a unique database gathered, was analysed and several univariate and multivariate methods employed. The results suggest that ethnicity is a significant explanatory variable and an important factor in informal capital access in the start-up stage in terms of loans from family members and friends. Moreover, the other independent variables, namely gender, age, experience of starting businesses, the amount of start capital, and firm size, affect loans from family members, whereas loans from friends are influenced by age, size, and industry affiliation. Since knowledge about informal capital determinants is limited, the results of this study add to our understanding of the variables that explain the financing behaviour of small businesses at start-up.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".