A Comparative Analysis of High Skilled and Low Skilled Mexican Migration to the United States, 1970–2017
Bibliographic record
Abstract
Abstract The purpose of this paper is to investigate the trends and determinants of high skilled and low skilled Mexican migration to the US from 1970 to 2017. I used retrospective life history data from the Mexican Migration Project to (1) analyze the probability of a first migration from Mexico to the US for high skilled Mexicans relative to their low skilled counterparts, and (2) identify individual and contextual determinants of both migration flows. Results from discrete-time event-history models reveal that the likelihood of US migration for high skilled Mexicans has not followed a linear, upward trend. High skilled migration has historically been smaller than low skilled migration, with two exceptions: during the 1980s and recently since the mid-2010s. At the individual level, human, social, and physical capital are significant predictors of low skilled migration; in contrast, high skilled migration is mainly influenced by social capital. Contextual factors affect each group differently: high skilled migration responds to adverse economic conditions in Mexico and US labor market conditions, whereas low skilled migration is influenced by Mexican homicide rates, US immigration enforcement, and economic conditions in both countries. Overall, findings suggest that, until recently, highly educated workers have generally preferred to stay in Mexico than to emigrate.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".