The Economic Decision of International Migration: Two Empirical Evidences from the United States and Canada
Bibliographic record
Abstract
This study investigated whether economic motivations are a key factor in international migration decisions. Applying the selectivity-corrected expected income for migrants and stayers, the difference in expected income for an individual in origin and destination countries was analyzed. This study used data from the U.S. and Canada to empirically test the role of income gaps in migration decisions. The main difficulty in analyzing the role of the gaps lies in collecting both income streams for the same individual, since once an individual migrates to a different country, their potential income in the origin country cannot be observed; and vice versa for stayers. Therefore, directly applying the average income of migrants (conditionally relying on their observed characteristics) to estimate the income of stayers if they had migrated results in a biased estimate of stayers’ income. Hence, there is a need to account for selectivity in the migration decision and calculate selectivity-corrected income. The key finding in this study is that the expected income gap is positively associated with, and statistically significant for, international migration decisions for the U.S. and Canada. One of the main reasons may be the easy transfer of labor skills between countries that have similar labor environments and cultural backgrounds.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".