The Effect of Immigration Policy on Founding Location Choice: Evidence from Canada's Start-up Visa Program
Bibliographic record
Abstract
To spur entrepreneurship and economic growth, an increasing number of countries have introduced immigration policies that provide visas to skilled entrepreneurs.This paper investigates whether these policies influence the founding location choice of immigrant founders, by leveraging the introduction of Canada's Start-up Visa Program in 2013.We demonstrate that this immigration policy increased the likelihood that U.S.-based immigrants have a start-up in Canada by 69%.Our results show that Asian immigrants (who have a higher representation in Canada than in the U.S.) are disproportionately more likely to migrate to Canada to start their businesses, whereas Hispanic immigrants (who have a smaller representation in Canada than in the U.S.) are less inclined to do so.We also find that this propensity varies with the size of coethnic immigrant communities in the origin location.Overall, our study unveils the importance of immigration policies in determining founding location choice and has important implications for countries competing for global talent.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".