Effects of Visa Programs on Entrepreneurial Activity: A Comparative Review of H-1B, Startup-Founder Pathways, EB-5, and Canada's Start-Up Visa, the EU Blue Card, and Related Schemes
Bibliographic record
Abstract
Abstract: Immigration and entrepreneurship are deeply intertwined. Across the United States,Canada, Europe, and other regions, immigrant founders and high-skilled workers contributedisproportionately to new firm creation, innovation, and economic dynamism. Visa programsand related immigration pathways are therefore more than administrative tools: they act aspolicy levers that shape entrepreneurial ecosystems. This review synthesizes the evidence onhow key visa programs—including the U.S. H-1B specialty occupation visa, the InternationalEntrepreneur Rule (IER), the EB-5 immigrant investor visa, Canada’s Start-Up Visa, the EUBlue Card, and selected national startup-visa schemes (e.g., UK Innovator Founder, EstoniaStartup Visa)—affect entrepreneurial activity. We expand beyond descriptive accounts bycomparing program design, examining empirical evaluations, and analyzing how thesemechanisms influence firm creation, financing, job growth, and innovation. Our findings suggestthat while employment-based visas (H-1B, EU Blue Card) primarily raise innovation capacitythrough skilled labor inflows, startup-specific visas succeed when linked with clear foundereligibility, stable residency status, and strong ecosystem support. Investor-based visas (EB-5)primarily facilitate capital formation rather than direct entrepreneurial entry. We conclude byidentifying research gaps and proposing design principles for immigration regimes that intend tomaximize entrepreneurship outcomes.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".