Skilled Based Immigration and Economic Growth: A Long-term Analysis for Canada
Bibliographic record
Abstract
There has been an increasing acknowledgement of the importance of immigration, both in the scholarly discourse on economics and in the priorities of policymakers. In this regard, the immigrant inflows to maximize potential economic benefits is a highly debated topic in both the academic literature and the policy agendas. Nevertheless, the impact of the varied educational backgrounds and skill sets of immigrants on economic growth is still largely unexamined. Within this particular framework, this study investigates the impact of immigration based on skill level on the economic growth of Canada during the timeframe of 2006-2022. In the model examined, employment is categorized as native-born Canadian, low-skilled, semi-skilled, and high-skilled immigrants. In order to estimate the long-term parameters, the Vector Error Correction (VECM) is employed, and the results are confirmed by the Dynamic Ordinary Least Squares Estimator (DOLS). The estimates revealed that a 1% increase in native Canadian employment raises real output by 0.69%; a 1% increase in low-skilled immigrant employment decreases real output by 0.10%; a 1% increase in the semi-skilled immigrant employment raises real output by 0.15%; a 1% increase in high-skilled immigrant employment raises real output by 0.26%. The results demonstrate that the impact of immigration on economic growth varies depending on the skill level. Low-skilled immigration has a negative effect on economic growth, while semi-skilled and high-skilled immigration have a positive effect. In addition, the impact of high-skilled immigration on economic growth is greater than that of semi-skilled immigration. Immigration can only stimulate long-term real output if the inflow consists of qualified immigrant workers.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".