Can skilled immigration raise innovation? Evidence from Canadian Cities
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract We examine the effect of changes in skilled-immigrant population shares in 98 Canadian cities on per capita patents. The Canadian case is of interest because its ‘points system’ is viewed as a model of skilled immigration policy. Our estimates suggest that the impact of increasing the university-educated immigrant share on patenting rates is modest at best and unambiguously smaller than the impact of skilled immigrants in the USA. We find larger effects of Canadian science, engineering, technology or mathematics (STEM)-educated immigrants employed in STEM jobs, but this impact is limited because only one-third of Canadian STEM-educated immigrants are employed in STEM jobs, compared with two-fifths of native-born Canadians and one-half of US immigrants. Our findings suggest that for most countries, skilled immigration is unlikely to be a panacea for sluggish innovation and that the US experience may be exceptional.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it