Measuring the impact of international migration and remittances for Latin America
Bibliographic record
Abstract
Computable General Equilibrium (CGE) models addressing the effects of international migration at global scale, although still few, have been very informative when measuring worldwide gains and testing for evidence about the existence of some of the benefits of the migration-induced virtuous circle in both sending and recipient countries, particularly regarding productivity, wages, and overall income. Wamsley and Winters (2007), using a static model, report global income gains by 0.3% and asserts that an increase in migrant workers conductive to a 3% increase in total labour force in high-income countries equals gains from total tariff elimination across the globe. Using the same assumption but applying a dynamic recessive model, the World Bank (2006) estimates global income gains significantly higher than previous calculations (1.2%), being sending countries the primary beneficiaries (with 1.8% in gains, versus 0.4% accrued to recipient, high-income countries). Both papers reached unambiguous results: international migration results in a win-win solution when measuring economic gains in both origin and host countries. This paper, using a dynamic recessive model with some methodological innovations suitable for the Latin American experience, arrives at analogous conclusions. Global real GDP expands at 1 percent relative to the baseline scenario, while regions, with no exceptions, benefit from international migration. Latin America is, by far, a global winner, accruing total gains equivalent to 3 percent of real GDP, versus 1.8% reported by the rest of the developing world. The significantly higher proportion of Latin American workers that emigrate to the United States, Canada, and Europe (the host countries considered in the study) relative to other developing regions is the main reason behind this particular outcome. Although all sixteen Latin American countries included in the model benefit, gains in real GDP and household income within the region are very heterogeneous, for reasons already mentioned, such as migration rates, average migrant skills and demographics, and the structure of labor and production markets in each Latin American country.
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.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".