Heterogeneous Human Capital and Migration: Who Migrates from Mexico to the US?
Bibliographic record
Abstract
In this paper I document the fact that the relationship between human capital, as measured by education, and migration choices among Mexicans is U-shaped: the highest and lowest educated tend to migrate more than the middle educated. I provide an explanation for the Ushaped relationship based on the interaction of two forces. On the one hand, there is a loss of human capital faced by emigrants, due to imperfect transferability, that is progressive with education and causes the negative relationship. On the other hand, the altruism towards future generations and the transmission of human capital from one generation to the next drives the positive relationship. I calibrate the model to match relevant moments from the Mexican and US Censuses, and use the calibrated model for policy evaluation. I evaluate the long run effect of the Progresa policy on education and migration. I show that, by giving a monetary contribution to poor families that send their children to school at lower grades, the Mexican government will improve the educational distribution of future generations and this in turn will shift the composition of immigrants towards the higher educated. Overall it will lower emigration from Mexico attenuating the pressure, especially of illegal immigrants. Also available for download here: http://ftp.iza.org/dp2446.pdf
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".