WHY BRAZILIAN ENTREPRENEURS CHOOSE PORTUGAL OVER OTHER COUNTRIES?
Bibliographic record
Abstract
This study investigates why Brazilian entrepreneurs choose Portugal as a preferred destination, highlighting the interaction between economic, institutional, and social factors. The research adopts a mixed-methods approach, combining 60 interviews with Brazilian entrepreneurs in Portugal, 2,956 survey responses from Brazilian migrants in countries such as the United States, Canada, Spain, and Italy, along with secondary data from official reports. The findings show that Portugal's appeal goes beyond cultura l and linguistic affinity. The country offers strategic access to the European market, relatively low business startup costs, and migration policies favorable to entrepreneurship, such as the D2 Entrepreneur Visa and the Golden Visa program. Factors such as safety, political stability, quality of life, and access to public services also strongly influence migration decisions. Portugal's time zone alignment with major international markets particularly benefits digital and service -based entrepreneurs. However, the study identifies key challenges to business success, including excessive bureaucracy, limited access to financial capital, rigid labor regulations, and a strong reliance on co -ethnic networks, which may hinder broader economic integration. Grounded in theories of global mobility, institutional frameworks, and migrant entrepreneurship, the research deepens the understanding of how destination -specific factors shape transnational entrepreneurial trajectories. Policy recommendations include streamlining bureaucratic procedures, expanding access to funding for immigrant entrepreneurs, and creating support mechanisms that promote market diversification beyond ethnic enclaves. This study contributes to the broader debate on migration and entrepreneurship by offering valuable insights for policymakers, researchers, and support organizations interested in strengthening immigrant business ecosystems.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".