The Impact of a Possible Trump Reelection on Mexican Immigration Pressures in Alternative Countries
Bibliographic record
Abstract
We address the question of the impact of a possible Trump reelection on the location choices of potential Mexican migrants. We use migration aspiration data from the Gallup World Poll Surveys which provide the preferred location choices of Mexican respondents before, during and after the Trump Presidency. We show that Trump presidency led to an increase in disapproval rates about the US leadership among Mexican respondents, which in turn led to a reduced level of attractiveness of the US location. Using a Cross-Nested Logit model that allows to account for the heterogeneity in the substitution patterns between alternative locations to the US, we simulate the impact of a possible reelection of Donald Trump based on different scenarios about these dis-approval rates. We find that such a reelection would lead to an increase in the number of stayers in Mexico but would also create heterogeneous immigration pressures from Mexico across potential foreign locations. In particular, countries such as Canada, the UK, Germany, Spain, and France would face significantly higher increases in Mexican immigration pressures. We also show that the reelection of Donald Trump would lower the skill content of Mexican potential immigrants in the US and would induce an opposite effect in destinations that are perceived as close substitutes.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".