Social capital, networks, trust and immigrant entrepreneurship: a cross‐country analysis
Bibliographic record
Abstract
Purpose This study is devoted to the empirical assessment of the macro‐level impact of social capital on immigrant entrepreneurship (the general levels of immigrant entrepreneurship, as well as high‐value added immigrant entrepreneurship). Design/methodology/approach The paper applies multiple regression analysis to the data on immigrant entrepreneurship and high‐value added immigrant entrepreneurship provided by OECD. The measures of the independent variables (the components of social capital) are based on World Value Survey. Findings The results reveal that social capital does play a significant role in high‐value added immigrant entrepreneurship in particular and immigrant entrepreneurship in general. With strong statistical significance, three social capital factors – networking, interpersonal trust, and institutional trust – provide an explanation for variations in immigrant entrepreneurship across countries. Originality/value Although the literature has long pointed out the importance of social capital as a determinant of economic activity, entrepreneurship researchers have focused much attention on the impact of personal, economic, and politico‐administrative factors while leaving social capital factors largely unexamined. Thus, study offers a systematic analysis of the effects of social capital on immigrant entrepreneurship and high‐value added immigrant entrepreneurship at a macro level and discusses policy‐making implications.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".