The Relationship Between Net Migration and Selected Macroeconomic Variables: A VAR Model for Canada
Bibliographic record
Abstract
Throughout human history, in addition to forced migration due to reasons such as disasters, wars and internal turmoil, it has been observed that economic reasons such as employment, unemployment, education, income, poverty etc. have also had an effect on migration, and that the social and economic structure of the countries has an effect on migration, as well as many effects on the countries from which migration occurs and the countries that receive migration. For this reason, the phenomenon of migration has been at the center of many studies as it affects the changes in the economic, political and social structures of countries. When the migration literature is examined, it is seen that economic factors such as inflation, employment and income, as well as the attitudes, behaviors and policies of the administration and society of the country accepting the immigrant, are effective in immigrants' preference for that country. This study examines the economic factors that cause immigration to Canada, a country that attracts attention with its multicultural structure and receives frequent and large amounts of immigration. In the study, the relationship between net immigration and economic growth, inflation and unemployment was evaluated using time series analysis for the research period 1998-2022. The findings obtained as a result of the analysis show that macroeconomic variables, especially unemployment, are effective on migration. Keywords: Migration, Macroeconomic Indicators, Time Series Analysis, Vector Auto Regression, Canada.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".